2016 ipri full report

IPRI - 2016 Levy Carciente, Sary

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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.


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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.


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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


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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


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.

(www3.weforum.org/docs/gcr/2015-2016/GCI_Dataset_2006-2015.xlsx).

Rule of Law

This item measures the extent to which agents have confidence in and abide by the rules of

society. In particular, it measures the quality of contract enforcement, property rights, police, and

courts, as well as the likelihood of crime and violence. The item combines several indicators

including: fairness, honesty, enforcement, speed, affordability of the court system, protection of

private property rights, and judicial and executive accountability. This item complements the

judicial Independence variable. For this item the chosen data source was the World Bank

Worldwide Governance Indicators, 2015

(http://info.worldbank.org/governance/wgi/index.aspx#homeDimension: Rule of Law).

Political Stability

The degree of political stability influences incentives to obtain or to extend ownership and/or

management of property. The higher the likelihood of government instability, the less likely


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people will be to obtain property and to develop trust in the validity of the rights attached. For

this item the chosen data source was the World Bank Worldwide Governance Indicators, 2015

(http://info.worldbank.org/governance/wgi/index.aspx#home Dimension: Political Stability and

Absence of Violence).

Control of Corruption

This item combines several indicators that measure the extent to which public power is exercised

for private gain. This includes petty and grand forms of corruption, as well as ‘capture’ of the

state by elites and private interests. Similarly to the other items in the LP component, corruption

influences people’s confidence in the existence of sound implementation and enforcement of

property rights. Corruption reflects the degree of informality in the economy, which is a

distracting factor to the expansion of respect for legal private property. A support for these ideas

is the research by Dong and Torgler (2011) in which they give theoretical and empirical evidence

of 108 countries from 1995-2006, showing that the effects of democratization on control of

corruption depend on the protection of property rights and income equality, creating a virtuous

circle.

The data chosen for this item was World Bank Worldwide Governance Indicators, 2015

(http://info.worldbank.org/governance/wgi/index.aspx#homeDimension: Control of Corruption)

II.2. Physical Property Rights (PPR)

A strong property rights regime commands the confidence of people in its effectiveness to

protect private property rights. It also provides for unified transactions related to registering

property and allows access to credit necessary to convert property into capital. For these reasons,

the following items are used to measure private physical property rights protection (PPR).

Protection of Physical Property Rights

Many scientific research efforts have attempted to explain countries’ prosperity: Talbott and Roll

(2001) found that enforcing strong property rights is among the main factors to the promotion of

growth of GDP per capita. Meinzen-Dick, R., 2009 and Meinzen-Dick, Kameri-Mbote, and

Markelova (2007) focus on the importance of property rights for poverty reduction.

The Protection of Physical Property Rights directly relates to the strength of a country’s property

rights system based on experts’ views on the quality of judicial protection of private property,

including financial assets. Additionally, it encompasses professionals’ opinions on the clarity of

the legal definition of property rights. The data used to measure this sub-component was the

Global Competitiveness Index of the World Economic Forum’s 2015-2016



This item reflects businesses’ point of view on the complexity of registering property in terms of

the number of days and procedures necessary. The data chosen for measuring this item was The

World Bank Group’s 2015 Doing Business Report (http://www.doingbusiness.org/custom-

query).This item records the full sequence of procedures necessary to transfer the property title

from seller to buyer when a business purchases land and a building. This information is critical


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because the more difficult property registration is, the more likely it is that assets stay in the

informal sector, thus restricting the development of the broader public’s understanding and

support for a strong legal and sound property rights system. Moreover, registration barriers

discourage the movement of assets from lower to higher valued uses. This item reflects one of

the main economic arguments set forth by Hernando de Soto: “what the poor lack is easy access

to the property mechanisms that could legally fix the economic potential of their assets so they

could be used to produce, secure or guarantee greater value in the extended market” (2000:48).

This item is calculated as:

Registering Property = 0.7 ∗ #days + 0.3 ∗ #procedures

Ease of Access to Loans

Access to a bank loan without collateral serves as a proxy for the level of development of

financial institutions in a country. Financial institutions play a complementary role, along with a

strong property rights system, to bring economic assets into the formal economy. An important

channel trying to alleviate poverty had been credit facilities. Singh and Huang (2011) in a

research of 37 countries in Sub-Saharan Arica from 1992-2006 conclude that not only do

property rights reinforce the effect of narrowing inequalities with financial deepening, but that its

absence could be in detrimental to the poor.

The data chosen for this item was The Global Competitiveness Index World Economic Forum’s

2015-2016

(www3.weforum.org/docs/gcr/2015-2016/GCI_Dataset_2006-2015.xlsx)

II.3. - Intellectual Property Rights (IPR)

The Intellectual Property Rights component evaluates the protection of intellectual property. In

addition to an opinion-based measure of the protection of intellectual property, it assesses

protection of two major forms of intellectual property rights (patents and copyrights) from de

jure and de facto perspectives, respectively.

A number of empirical studies exist on the relationship among IPRs, R&D, productivity and

economic performance: Diwan and Rodrik (1991) and Taylor (1994) find that stronger IPRs may

enhance global welfare, innovation, and productivity. Korenko (1999) found that, for the Italian

pharmaceutical industry, a strengthening of local intellectual property rights helped expand

domestic R&D and market share. And as confirmed in a recent paper by Zhang, Du and Park

(2015) there is a positive relationship between IPRs and economic growth.

Protection of Intellectual Property Rights

This item contains opinion survey outcomes reflecting a nation’s protection of intellectual

property; therefore, it is a crucial aspect of the IPR component. Expert participants in each

country were asked to rate their nation’s IP protection, scoring it from “weak and not enforced”

to “strong and enforced.” The source of the data chosen to measure this item was the Global

Competitiveness Index from The World Economic Forum’s 2015-2016


Patent Protection


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This item reflects the strength of a country’s patent laws based on five extensive criteria:

coverage, membership in international treaties, restrictions on patent rights, enforcement, and

duration of protection. The data used for this item was from Ginarte-Park Patent Protection

(1960-2010, International Patent Protection: 1960-2005, Research Policy, 2008, Vol. 37(4):761-

766. Specific Source: http://nw08.american.edu/~wgp/#PR Data: 2010). This data source is

updated each five years and data 2015 will be released ending 2016.

Copyright Piracy

The level of piracy in the IP sector is an important indicator of the effectiveness of intellectual

property rights enforcement in a country. The data source chosen for this item was the BSA

Global Software Survey; The Compliance Gap (June 2014 edition,

http://globalstudy.bsa.org/2013/downloads/studies/2013GlobalSurvey_Study_en.pdf), which

estimates the volume and value of unlicensed software installed on personal computers, and also

reveals attitudes and behaviors related to software licensing, intellectual property and emerging

technologies.

http://globalstudy.bsa.org/2013/downloads/studies/2013GlobalSurvey_Study_en.pdf)


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III. Methodology

The IPRI’s 2016 scores and rankings are based on data obtained from official sources made

publicly available by established international organizations (see Appendix I).This means that

most data is provided in different styles and on different scales. Consequently, most of the data is

rescaled in order to accurately compare among countries and within IPRI’s individual

components and overall score.

The overall grading scale of the IPRI ranges from 0 to 10, where 10 is the highest value for a

property rights system and 0 is the lowest value (i.e. most negative) for a property rights system

within a country. The same interpretative logic is applied to the three components and the ten

items. While the average mechanisms applied assumes equal importance of each component for

the final IPRI score (and also of each item for each component), some weights could be applied

to evaluate relative importance of the different aspects of a property rights system of a country.

The IPRI for 2016 uses data from period 2010 - 2016. The 10 Items are collected from different

sources, which imply that they have different accessibility times for the most updated data

available. The applied logic in the analysis has been to include the latest available data sets for

the 2016 IPRI. Most of the items present a lag of 1 year (see Appendix I), so the time difference

among data, should not affect our analysis. Almost all the items needed to be rescaled to the IPRI

range. The rescaling process was done as follow:

1. For bounded data series with same direction:

[(Country Value – MIN Original Scale

MAX Original Scale − MIN Original Scale) ∗ (MAX New Scale – MIN New Scale)] + MIN New Scale

2. For unbounded data series with same direction:

(MAX Value of data serie − Country Value)

(MAX Value of data serie − MIN Value of data serie)∗ 10

3. For bounded data series with inverse direction:

10 − [(Country Value – MIN Original Scale

MAX Original Scale − MIN Original Scale) ∗ (MAX New Scale – MIN New Scale)] + MIN New Scale

IPRI Calculations:

𝐿𝑃 =Judicial independence + Rule of Law + Political Stability + Control of Corruption

# Items

𝑃𝑃𝑅 =Property Rights + Registering Property + Ease Access Loans

#Items


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𝐼𝑃𝑅 =Intellectual Property Protection + Patent Protection + Copyright Piracy Level

#Items

𝑰𝑷𝑹𝑰 =𝑳𝑷 + 𝑷𝑷𝑹 + 𝑰𝑷𝑹

𝟑

Besides calculating the score of the IPRI and its components, countries were ranked according to

their scores. With some frequency, a few countries can exhibit almost the same score and they

will be placed in the same rank. This way, i.e., Country A could be ranked #1, while Country B

and Country C #2, and Country X, Country Y and Country Z are #3. To minimize this situation

and a diffusion bias, ranking calculations were made using IPRI scores with all their decimals,

this way the final scores were differentiated, and such were the ranking positions.

III.1 Countries and Groups

The 2016 IPRI ranks a total of 128 countries from around the world. This year we have a

reduction in one country as in 2015 they were 129. More specifically this year there are five (5)

countries that are not included in the index: Angola, Burkina Faso, Libya, Puerto Rico and Rep.

of Yemen, while four (4) were added: Benin, Bosnia-Herzegovina, Ecuador and Liberia.

The selection of countries was determined only by the availability of sufficient data. In order to

keep the meaningfulness of the data and analysis, only country-year combinations respecting

specific rules have been considered.

Since the IPRI 2013, such a rule is to have at least 2/3 of the data required for each component,

or more specifically, if a country does not have data available for at least 3 items for LP, 2 items

for PPR and 2 items for IPR, it has been excluded from the analysis.

All countries were grouped following different criteria (Appendix II):

1. Geographical regions: Latin America and Caribbean, Western Europe, Central/Eastern

Europe and Central Asia, Middle East/North Africa, Africa, Asia and Oceania, and North

America

2. Income classification (World Bank, July, 2015): High income, Upper middle income,

Lower middle income and Low income. This year the sub-classification for High Income

(OECD and non-OECD) is not included by the World Bank, however we kept track of it.

3. Regional and Development classification (International Monetary Fund, April, 2015):

Advanced Economies; Emerging & Developing Asia; Emerging and Developing Europe;

Middle East, North Africa & Pakistan; Latin America & the Caribbean; Commonwealth

of Independent States; and Sub-Saharan Africa


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4. Economic and Regional Integration Agreements: European Union, Southern African

Development Community, Economic Community of Western African States, Association

of Southeast Asian Nations, Central American Parliament, Gulf Cooperation Council,

Pacific Alliance, southern Common Market, South Asian Association for Regional

Cooperation, Central African Economic and Monetary Community, Central American

Common Market, Commonwealth of Independent States, Arab Maghreb Union,

Caribbean Community, Andean Community, European Free Trade Association,

Intergovernmental Authority on Development, North American Free Trade Agreement,

Organization of the Petroleum Exporting Countries, Economic Community of Central

African States and Trans-Pacific Partnership.


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IV. IPRI 2016 Country Results

This chapter presents the results of the 2016 International Property Rights Index. Starting from

the scores of the overall IPRI and its three (3) components, we follow showing countries’

ranking of the IPRI and its components. Variations between 2015 and 2016 of both individual

IPRI components and of the overall IPRI score were considered. This chapter also includes an

analysis of the IPRI for countries aggrupation.

As an average, the sample of the 128 countries yielded this year an IPRI score of 5.45, with the

Legal and Political Environment (LP) being the weakest component with a score of 5.13,

followed by the Intellectual Property Rights (IPR) component with a score of 5.33 and Physical

Property Rights (PPR) as the strongest component with a score of 5.87. This year we found an

overall improvement of the IPRI score compared to 2015, and also of all of its components.

Using SPSS® a normality test was run for IPRI as for its components, showing a Gaussian

behavior. The IPRI, LP and IPR showed multimodal distributions, while PPR a unimodal one

(see Table 1, Table 2 and Figure 2).

Table 1.Statistics: IPRI and its Components

IPRI LP PPR IPR

N Valid 128 128 128 128

Missing 0 0 0 0

Mean 5.445881 5.130273 5.874578 5.332734

Std. Error of Mean .1263714 .1610364 .0985457 .1470181

Median 5.091350 4.648500 5.789500 5.085500

Mode 2.7297(a) 3.5820(a) 4.9100 4.3200(a)

Std. Deviation 1.4297296 1.8219192 1.1149174 1.6633194

Variance 2.044 3.319 1.243 2.767

Range 5.6471 7.2530 6.6120 6.9520

Minimum 2.7297 1.7550 1.6000 1.6800

Maximum 8.3768 9.0080 8.2120 8.6320

Percentiles 25 4.472375 3.670000 5.138500 4.209250

50 5.091350 4.648500 5.789500 5.085500

75 6.356600 6.450750 6.759250 6.437000

a Multiple modes exist. The smallest value is shown

Table 2.Tests of Normality: One-Sample Kolmogorov-Smirnov Test

IPRI LP PPR IPR

N 128 128 128 128

Normal Parameters(a,b)

Mean 5.445881 5.130273 5.874578 5.332734 Std. Deviation 1.429729

6 1.821919

2 1.114917

4 1.663319

4 Most Extreme Differences

Absolute .112 .135 .074 .087 Positive .112 .135 .074 .083 Negative -.073 -.072 -.047 -.087

Kolmogorov-Smirnov Z 1.271 1.522 .835 .983 Asymp. Sig. (2-tailed) .079 .019 .488 .289

a Test distribution is Normal.b Calculated from data.


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Figure 2. IPRI Histogram

Table 3 shows -alphabetically ordered- the score value of the 128 countries included in the IPRI

2016, as the scores of its components: Legal and Political Environment (LP), Physical Property

Rights (PPR) and Intellectual Property Rights (IPR). Figure 3 presents countries organized by its

IPRI ranking from top to bottom, showing simultaneously their IPRI scores.

Table 4 shows the IPRI 2016 rankings by quintile for all the 128 countries in our sample. In

general, the number of countries belonging to each quintile increases from the top 20% to the

bottom 20% (1st quintile 17 countries, 2nd quintile 21 countries, 3rd quintile 25 countries, 4rd

quintile 29 countries and 5th quintile 36 countries). Hence, the forth and the fifth quintiles

include 65 countries which is a 50.7% of our sample, while the first three quintiles includes

almost the same amount countries, 63, being the 49.2% of the sample.

9.008.007.006.005.004.003.002.00

VAR00007

30

25

20

15

10

5

0

Freq

uenc

y

Mean = 5.4459

Std. Dev. = 1.42973

N = 128


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Table 3. IPRI 2016. IPRI and its Components Scores by Country

COUNTRY IPRI LP PPR IPR COUNTRY IPRI LP PPR IPR COUNTRY IPRI LP PPR IPR

ALBANIA 4.00 4.21 4.71 3.10 GUYANA 4.28 3.97 5.32 3.54 NORWAY 8.25 8.75 7.92 8.09

ALGERIA 4.11 3.46 5.25 3.61 HAITI 2.84 2.91 1.60 4.02 OMAN 5.99 5.96 7.16 4.86

ARGENTINA 4.12 3.64 4.21 4.51 HONDURAS 4.71 3.59 5.89 4.64 PAKISTAN 3.68 2.80 5.04 3.21

ARMENIA 4.20 4.08 5.73 2.80 HONG KONG (SAR of China) 7.78 8.28 7.86 7.20 PANAMA 5.38 4.26 6.75 5.13

AUSTRALIA 7.93 8.34 7.27 8.18 HUNGARY 5.66 5.50 5.07 6.42 PARAGUAY 4.06 3.20 5.37 3.59

AUSTRIA 7.59 7.83 6.86 8.08 ICELAND 7.21 8.11 6.79 6.73 PERU 4.80 3.67 6.02 4.72

AZERBAIJAN 4.08 3.64 5.76 2.85 INDIA 5.22 4.25 5.78 5.62 PHILIPPINES 5.15 4.15 6.07 5.23

BAHREIN 5.97 5.21 7.21 5.49 INDONESIA 5.02 4.37 6.50 4.19 POLAND 5.95 6.22 5.68 5.94

BANGLADESH 2.78 3.07 2.87 2.39 IRAN 4.24 3.58 5.15 3.99 PORTUGAL 6.60 6.67 6.16 6.96

BELGIUM 7.45 7.65 6.47 8.25 IRELAND 7.58 8.18 6.48 8.07 QATAR 7.38 7.33 8.21 6.60

BENIN 4.51 4.13 4.32 5.07 ISRAEL 6.41 6.20 5.81 7.22 ROMANIA 5.45 5.03 5.79 5.54

BOLIVIA 4.12 3.36 5.14 3.84 ITALY 5.66 5.18 5.19 6.59 RUSSIA 4.58 3.33 5.57 4.84

BOSNIA AND HERZEGOVINA 4.12 4.25 4.80 3.32 JAMAICA 5.58 5.01 5.73 5.99 RWANDA 6.19 5.91 6.78 5.89

BOTSWANA 5.93 6.58 6.47 4.75 JAPAN 8.10 8.09 7.58 8.63 SAUDI ARABIA 6.10 5.60 7.12 5.59

BRAZIL 5.14 4.50 5.49 5.44 JORDAN 5.86 5.29 6.66 5.62 SENEGAL 4.80 4.79 5.38 4.24

BULGARIA 5.02 4.31 5.74 5.00 KAZAKHSTAN 4.76 4.28 6.30 3.71 SERBIA 4.02 4.34 4.56 3.18

BURUNDI 3.44 2.50 4.48 3.34 KENYA 4.61 3.70 5.76 4.37 SIERRA LEONE 4.22 3.40 4.46 4.82

CAMEROON 4.05 3.12 4.88 4.14 KOREA, REP 6.12 5.76 5.89 6.71 SINGAPORE 8.13 8.26 8.16 7.96

CANADA 8.01 8.36 7.46 8.22 KUWAIT 5.16 5.34 5.82 4.32 SLOVAKIA 6.02 5.22 6.10 6.73

CHAD 3.74 2.38 4.53 4.31 LATVIA 5.70 5.94 6.05 5.10 SLOVENIA 5.54 6.02 4.97 5.63

CHILE 6.72 7.13 6.76 6.28 LEBANON 3.83 2.69 5.64 3.17 SOUTH AFRICA 6.59 5.59 6.93 7.23

CHINA 5.41 4.39 6.51 5.32 LIBERIA 4.83 3.97 5.58 4.96 SPAIN 5.85 5.70 5.46 6.39

COLOMBIA 4.92 3.53 5.83 5.41 LITHUANIA 5.97 6.03 6.00 5.90 SRI. LANKA 5.00 4.73 5.64 4.61

COSTA RICA 5.82 6.38 5.79 5.28 LUXEMBURG 8.26 8.61 7.81 8.34 SWAZILAND 4.80 3.95 5.98 4.49

CôTE D'IVOIRE 4.73 3.99 5.99 4.22 MACEDONIA, FYR 5.01 4.85 5.96 4.21 SWEDEN 8.10 8.41 7.65 8.24

CROATIA 4.94 5.22 5.01 4.59 MADAGASCAR 3.84 3.35 4.36 3.83 SWITZERLAND 8.16 8.67 7.56 8.25

CYPRUS 6.12 6.71 5.91 5.74 MALAWI 4.61 4.56 5.05 4.21 TAIWAN (China) 6.93 6.57 7.34 6.87

CZECH REPUBLIC 6.53 6.33 6.19 7.08 MALAYSIA 6.75 6.13 7.69 6.44 TANZANIA, UNITED REP. OF 4.58 3.89 4.91 4.94

DENMARK 7.94 8.60 6.99 8.23 MALI 4.57 3.35 5.47 4.90 THAILAND 5.04 4.30 6.47 4.34

DOMINICAN REPUBLIC 4.55 3.92 5.51 4.22 MALTA 6.73 6.94 6.90 6.34 TRINIDAD AND TOBAGO 5.21 4.89 4.97 5.76

ECUADOR 4.75 3.27 5.88 5.11 MAURITANIA 3.73 3.02 4.19 3.98 TUNISIA 4.85 4.33 5.79 4.41

EGYPT 4.34 3.84 4.75 4.44 MAURITIUS 6.14 6.47 6.85 5.11 TURKEY 5.19 3.99 6.19 5.40

EL SALVADOR 4.79 4.23 5.61 4.51 MEXICO 4.79 3.69 5.09 5.59 UGANDA 4.63 3.55 5.31 5.04

ESTONIA 6.80 7.40 6.84 6.18 MOLDOVA 3.72 3.58 5.31 2.28 UKRAINE 3.93 2.43 5.05 4.32

ETHIOPIA 4.21 3.69 4.91 4.03 MONTENEGRO 4.55 4.91 5.42 3.32 UNITED ARAB EMIRATES 7.29 7.05 7.88 6.93

FINLAND 8.38 8.87 7.66 8.60 MOROCCO 5.29 4.45 6.22 5.18 UNITED KINGDOM (UK) 7.76 7.95 6.97 8.35

FRANCE 7.26 7.01 6.86 7.90 MOZAMBIQUE 4.31 3.48 4.79 4.67 UNITED STATES (USA) 7.74 7.26 7.32 8.63

GABON 4.66 4.11 4.92 4.95 MYANMAR 2.76 2.85 3.75 1.68 URUGUAY 6.10 7.22 5.92 5.17

GEORGIA 4.60 5.41 5.94 2.45 NEPAL 4.46 3.92 5.50 3.97 VENEZUELA, BOL. REP. OF 2.73 1.75 3.81 2.63

GERMANY 7.72 8.05 6.89 8.23 NETHERLANDS 8.03 8.46 7.22 8.40 VIETNAM 4.66 4.38 5.22 4.37

GHANA 5.46 4.96 5.68 5.74 NEW ZEALAND 8.27 9.01 7.85 7.94 ZAMBIA 4.79 4.79 5.64 3.95

GREECE 5.36 5.00 5.14 5.94 NICARAGUA 3.98 3.23 4.96 3.75 ZIMBABWE 3.40 2.72 4.12 3.36

GUATEMALA 4.63 3.41 5.99 4.47 NIGERIA 3.56 2.48 4.47 3.73 ALL COUNTRIES 5.45 5.13 5.87 5.33


16

Figure 3. IPRI 2016: Scores and Rankings


17

Table 4. IPRI 2016. Rankings by Quintiles

Top 20 Percent 2nd Quintile 3rd Quintile 4th Quintile Bottom 20 Percent

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

TRINIDAD & TOBAGO VIETNAM NICARAGUATURKEY UGANDA UKRAINEKUWAIT GUATEMALA MADAGASCARPHILIPPINES KENYA LEBANON

MALAWI CHADGEORGIA MAURITANIARUSSIA MOLDOVATANZANIA, UNITED REP PAKISTAN

NIGERIABURUNDIZIMBABWEHAITIBANGLADESHMYANMARVENEZUELA, BOLIVARIAN REP


18

Figure 4 shows the top 15 countries in this IPRI issue. Finland is #1 in the IPRI overall ranking

(8.38), followed by New Zealand (8.27), Luxemburg (8.26), Norway (8.25) and Switzerland

(8.16). Interestingly, Scandinavian countries report high IPRI score rankings (Norway #4,

Sweden #7 and Denmark #11). Sweden and Japan scores show a difference of just seven ten

thousandths (Sweden: 8.0985 Japan: 8.0978) while the difference between Denmark and

Australia is of seven thousandths. At the end of this top list we find Hong Kong (7.78), United

Kingdom (7.76) and the USA (7.74).

Figure 4. IPRI 2016. Top 15 Countries

Considering the IPRI components we find that: New Zealand shows the highest LP score (9.01),

followed by Finland (8.87) and Norway (8.75); while Qatar (8.21), Singapore (8.16) and Norway

heads the PPR scores and USA (8.63), Japan (8.62) and Finland (8.59) the IPR ones.

Most of the top countries show as the stronger IPRI component the LP (though not the case for

the UK and USA) while the PPR is less relevant.

Top countries’ positions vary just a little from the previous IPRI edition, but the group of

countries remains the same and countries’ scores differ slightly (see Figure 5).


19

Figure 5.IPRI 2016 vs IPRI 2015. Top Countries Ranking Change

The bottom 15 countries are shown in Figure 6. The Bolivarian Rep. of Venezuela is #128 in the

IPRI overall ranking (2.73) followed by Myanmar (2.76), Bangladesh (2.77), Haiti (2.84),

Zimbabwe (3.40), Burundi (3.44), Nigeria (3.56), Pakistan (3.68), Moldova (3.72) Mauritania

(3.73), Chad (3.74), Lebanon (3.83) Madagascar (3.84), Ukraine (3.93) and Nicaragua (3.98).

Considering the IPRI components we find the following: LP bottom countries are: Bolivarian

Rep. of Venezuela is #128 (1.76), Chad (2.38), Ukraine (2.43), Nigeria (2.49) and Burundi

(2.50). PPR bottom countries are: Haiti (1.6) Bangladesh (2.87), Myanmar (3.752), the

Bolivarian Rep. of Venezuela (3.81) and Zimbabwe. And IPR bottom countries are: Myanmar

(1.68), Moldova (2.27), Bangladesh (2.39), Georgia (2.45) and the Bolivarian Rep. of Venezuela

(2.63).

Most of the bottom countries show PPR as the stronger IPRI (though not the case for Haiti and

Bangladesh) with the weakest being LP. This situation is just the opposite of top countries and

seems to hint at the ability of LP to pull the rest of components.


20

Figure 6. IPRI 2016. Bottom 15 Countries

A comparison between the IPRI scores in 2016 and 2015 reveals an improvement, not only in the

averages of the IPRI scores and of its components but also in the maximum and minimum level

showed by the sample of countries (see Figure 7). In 2015 the lowest score was 2.5 (Myanmar),

while this year it is 2.73 (Bolivarian Rep. of Venezuela). IPRI 2015 the highest score was 8.32

and this year is 8.38 (in both cases held by Finland). This reveals an improvement of the average

IPRI score from 5.3 in 2015 to 5.45 in 2016.

Countries that show the highest improvement in the IPRI are: Cote D’Ivoire (0.509), Lebanon

(0.361), Slovenia (0.357), Georgia (0.352) and Honduras (0.337). While the ones with highest

decreases in the IPRI scores 2016 are: Oman (-0,172), Hungary (-0.161), Ghana (-0.155),

Swaziland (-0.141) and Bolivia (-0.129). It is important to note that the main positive and

negative changes were in Europe, Latin America, Africa and the Middle East.

These evaluations were also made of IPRI components:

We found an improvement of the average score of the LP component from 4.99 in 2015 to

5.13 in 2016. Changes in the LP component score 2015-2016 are shown in Figure 8. The LP

component shows an improvement in most of the countries, with the most significant

increases in Cote d’Ivoire (0.695), Georgia (0.547), Kazakhstan (0.4856), Nepal (0.484) and

Bangladesh. Most of these countries do not show high levels of the LP component. However,

this improvement is encouraging. On the other hand, Ukraine (-0.408), Bolivia (-0.339),

Hungary (-0.330), Qatar (-0.2378) and Swaziland (-0.237) show the highest decreases in the

LP component.

Changes in PPR component score from 2015-2016 are shown in Figure 9. PPR also showed

an average improvement rising from 5.77 to 5.87 in 2016. The most significant increases in

the PPR component are reported by Slovenia (0.718), Myanmar (0.491, Cote d’Ivoire

(0.394), Iran (0.304) and Uganda (0.285), while the highest decreases are shown by

Madagascar (-0.279), India (-0,168), Hungary (-0.159), Jordan (-0.145) and Armenia (-

0.109).


21

Changes in the IPR component score from 2015-2016 are shown in Figure 10. The IPR

component average rose from 5.14 in 2015 to 5.33 for 2016. The most significant increases

in the IPR component are reported by Lebanon (0.686), Gabon (0.585), Mali (0.502), Uganda

(0.494) and Cote d’Ivoire (0.436); while the countries that showed the most relevant

decreases are Oman (-0.369) Swaziland (-0.307), Ghana (-0.1815), Sierra Leone (-0.149) and

Mauritania (-0.124).


22

Figure 7. IPRI Score 2016-2015 and variation

0.1640.056

0.0000.165

0.0310.131

0.1930.007

0.291-0.035

0.051-0.067

0.202-0.012

0.1150.176

-0.1410.104

0.3570.142

0.110-0.027

-0.114

0.0100.238

0.124

0.2970.031

0.160-0.111

0.3170.044

0.0430.097

0.2000.114

0.128-0.172

0.0580.292

0.0250.123

-0.0710.184

0.1510.078

0.019-0.037

0.0960.009

0.226

0.0070.336

0.054

0.0380.024

0.1050.133

0.1630.109

0.1110.235

0.361

-0.0380.191

0.0770.275

0.1200.011

0.2220.067

0.168

0.1690.275

0.1480.065

0.118-0.161

0.1720.065

0.3370.180

-0.0070.162

0.082-0.155

0.3520.065

0.2270.049

0.0500.205

0.1820.119

0.2070.030

0.1850.044

0.1150.258

0.073

0.0830.167

0.1420.509

0.0190.078

0.110

0.0850.006

0.046-0.129

0.1540.084

0.2130.090

0.121-0.007

0.037

0.217-0.057

0.124-0.014

0.291

ZWEZMBZAF

VNM

VENUSAURYUKRUGA

TZATWNTURTUNTTO

THATCDSWZSWESVNSVKSRBSLVSLESGPSENSAU

RWARUSROUQATPRYPRTPOLPHLPERPANPAK

OMN

NZLNPLNORNLDNIC

NGAMYSMWIMUSMRT

MOZMNEMMRMLTMLI

MKDMEX

MDGMDAMARLVALUXLTULKALBN

KWTKORKENKAZJPNJORJAM

ITAISR

ISLIRNIRL

INDIDN

HUNHTI

HRVHNDHKG

GUYGTMGRCGHAGEOGBRGABFRAFINETHESTESPEGYDZA

DOMDNKDEUCZECYPCRI

COLCMR

CIVCHN

CHLCHECANBWABRA

BOLBHRBGRBGDBEL

BDIAZEAUTAUS

ARMARGAREALB

IPRI 2016

IPRI 2015

0.1640.056

0.0000.165

0.0310.131

0.1930.007

0.291-0.035

0.051-0.067

0.202-0.012

0.1150.176

-0.1410.104

0.3570.142

0.110-0.027

-0.114

0.0100.238

0.124

0.2970.031

0.160-0.111

0.3170.044

0.0430.097

0.2000.114

0.128-0.172

0.0580.292

0.0250.123

-0.0710.184

0.1510.078

0.019-0.037

0.0960.009

0.2260.007

0.3360.054

0.0380.024

0.1050.133

0.1630.109

0.1110.235

0.361-0.038

0.1910.077

0.2750.120

0.0110.222

0.0670.168

0.1690.275

0.1480.065

0.118-0.161

0.1720.065

0.3370.180

-0.0070.162

0.082-0.155

0.3520.065

0.2270.049

0.0500.205

0.1820.119

0.2070.030

0.1850.044

0.1150.258

0.0730.083

0.1670.142

0.5090.019

0.0780.110

0.0850.006

0.046-0.129

0.1540.084

0.2130.090

0.121-0.007

0.037

0.217-0.057

0.124-0.014

0.291

-1.000 0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 9.000

ZWEZMBZAF

VNMVENUSAURYUKRUGATZA

TWNTURTUNTTOTHATCDSWZSWESVNSVKSRBSLVSLESGPSENSAU


OMNNZLNPLNORNLDNIC

NGAMYSMWIMUSMRTMOZMNEMMRMLTMLI

MKDMEX



ITAISRISL

IRNIRL

INDIDN

HUNHTI

HRVHNDHKGGUYGTMGRCGHAGEOGBRGABFRAFINETHESTESPEGYDZA

DOMDNKDEUCZECYPCRI

COLCMR

CIVCHNCHLCHECANBWABRABOLBHRBGRBGDBELBDIAZEAUTAUS

ARMARGAREALB

Variación

IPRI 2016

IPRI 2015


23

Figure 8. LP Score 2015-2016 and variation

0.159

0.058

0.0120.023

0.0280.087

0.223-0.408

0.094

-0.0420.175

-0.1190.150

-0.1220.278

0.012-0.237

0.0150.021

0.1400.294-0.076

-0.142-0.013

0.314

0.1690.334

0.0520.223

-0.2380.449

0.123

0.0320.283

0.2120.170

0.082-0.058

0.0340.484

-0.0240.102

-0.2270.088

0.2970.088

-0.0380.040

-0.0060.044

0.1520.026

0.315

0.324-0.085

0.2640.026

0.2050.150

0.1640.139

0.4390.194

-0.206

0.1090.124

0.4860.166

0.1730.074

-0.0420.109

0.0740.303

0.1690.087

0.263-0.330

0.2430.083

0.4010.279

0.0170.023

0.079-0.224

0.5470.111

-0.0430.049

0.0640.415

0.2020.117

0.247-0.146

0.147-0.063

0.1040.260

0.1400.073

0.087-0.011

0.6960.073

0.009

0.1640.141

-0.074-0.053

-0.340

0.3110.136

0.475-0.025

0.363-0.022

-0.001

0.283-0.111

-0.079-0.048

0.456

ZWEZMBZAF


TWNTURTUNTTOTHATCDSWZSWESVNSVKSRBSLVSLE

SGPSENSAU


OMNNZLNPLNORNLDNIC

NGAMYS

MWIMUSMRTMOZMNE

MMRMLTMLI

MKDMEXMDGMDAMARLVALUXLTULKALBN

KWTKORKENKAZJPNJORJAMITAISRISL

IRNIRL

INDIDN

HUNHTI


DOMDNKDEUCZECYPCRI

COLCMR

CIVCHNCHLCHECAN

BWABRABOLBHRBGRBGDBELBDIAZEAUTAUS

ARMARGAREALB

LP 2016

LP 2015

0.1590.058

0.0120.023

0.0280.087

0.223-0.408

0.094-0.042

0.175-0.119

0.150-0.122

0.2780.012

-0.2370.015

0.0210.140

0.294-0.076

-0.142-0.013

0.3140.169

0.3340.052

0.223-0.238

0.4490.123

0.0320.283

0.2120.170

0.082-0.058

0.0340.484

-0.0240.102

-0.2270.088

0.2970.088

-0.0380.040

-0.0060.044

0.1520.026

0.3150.324

-0.0850.264

0.0260.205

0.1500.164

0.1390.439

0.194-0.206

0.1090.124

0.4860.166

0.1730.074

-0.0420.109

0.0740.303

0.1690.087

0.263-0.330

0.2430.083

0.4010.279

0.0170.023

0.079-0.224

0.5470.111

-0.0430.049

0.0640.415

0.2020.117

0.247-0.146

0.147-0.063

0.1040.260

0.1400.073

0.087-0.011

0.6960.073

0.009

0.1640.141

-0.074-0.053

-0.3400.311

0.1360.475

-0.0250.363

-0.022-0.001

0.283-0.111

-0.079-0.048

0.456

-2.000 0.000 2.000 4.000 6.000 8.000 10.000

ZWEZMBZAF



SGPSENSAU


OMNNZLNPLNORNLDNIC

NGAMYS

MWIMUSMRTMOZMNE

MMRMLTMLI



IRNIRL

INDIDN

HUNHTI


DOMDNKDEUCZECYPCRI

COLCMR

CIVCHNCHLCHECAN


ARMARGAREALB

LP 2016

LP 2015


24

Figure 9. PPR Score 2015-2016 and variation

0.014-0.037

-0.0720.157

0.0380.108

0.1230.180

0.285

-0.026-0.024

-0.0890.196

-0.060-0.010

0.2340.120

0.1310.718

0.087-0.024

0.064-0.050

0.0170.112

0.151

0.1690.042

-0.039-0.006

0.223-0.083

-0.016

-0.0920.120

0.0850.105

-0.0880.077

0.217-0.002

0.0900.080

0.2290.035

0.154-0.008

-0.0290.089

-0.0240.491

-0.0630.192

-0.0990.017

-0.2790.039

0.0280.091

0.0670.021

0.0070.201

-0.0750.211

0.1080.143

0.131

-0.1450.255

0.0240.133

0.1160.304

0.108-0.168

0.016-0.159

0.1220.104

0.2510.161

-0.026

0.2030.057

-0.0600.267

0.0370.138

0.0050.019

0.0790.104

0.0030.211

0.012

0.1650.021

0.1050.147

0.0600.0030.105

0.1600.394

-0.0240.051

0.054

0.0080.027

-0.045-0.010

0.0540.056

0.1050.073

-0.001

0.0000.022

0.204-0.109

0.1380.005

0.152

ZWEZMBZAF




OMNNZLNPLNORNLDNIC


MKDMEX



ITAISRISL

IRNIRL

INDIDN

HUNHTI


DOMDNKDEUCZECYPCRI

COLCMR


ARMARGAREALB

PPR 2016

PPR 2015

0.014-0.037

-0.0720.157

0.0380.108

0.1230.180

0.285-0.026

-0.024-0.089

0.196-0.060

-0.0100.234

0.1200.131

0.7180.087

-0.0240.064

-0.0500.017

0.1120.151

0.1690.042

-0.039-0.006

0.223-0.083

-0.016-0.092

0.1200.085

0.105-0.088

0.0770.217

-0.0020.090

0.0800.229

0.0350.154

-0.008-0.029

0.089

-0.0240.491

-0.0630.192

-0.0990.017

-0.2790.039

0.0280.091

0.0670.021

0.0070.201

-0.0750.211

0.1080.143

0.131-0.145

0.2550.024

0.1330.116

0.3040.108

-0.1680.016

-0.1590.122

0.1040.251

0.161-0.026

0.2030.057

-0.0600.267

0.0370.138

0.0050.019

0.0790.104

0.0030.211

0.0120.165

0.0210.105

0.1470.060

0.0030.105

0.1600.394

-0.0240.051

0.0540.008

0.027-0.045

-0.0100.054

0.0560.105

0.073-0.001

0.0000.022

0.204-0.109

0.1380.005

0.152

-1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0

ZWEZMBZAF




OMNNZLNPLNORNLDNIC


MKDMEX



ITAISRISL

IRNIRL

INDIDN

HUNHTI


DOMDNKDEUCZECYPCRI

COLCMR


ARMARGAREALB

PPR 2016

PPR 2015


25

Figure 10. IPR Score 2015-2016 and variation

0.3190.148

0.0610.315

0.0280.196

0.2330.250

0.494-0.036

0.0040.008

0.2600.146

0.0770.283

-0.307

0.1680.332

0.1980.059

-0.069-0.149

0.0260.289

0.0530.386

0.0000.295

-0.0900.280

0.0920.114

0.0990.267

0.0880.196

-0.3690.063

0.1730.103

0.176-0.0670.236

0.120-0.009

0.102-0.124

0.2050.009

0.035

0.0590.502

-0.0610.182

0.0870.251

0.1660.247

0.095

0.1730.259

0.6860.168

0.2530.000

0.198

0.0640.006

0.3380.220

0.2630.316

0.2190.168

0.2750.076

0.0070.152

0.0080.360

0.099-0.011

0.2610.109-0.182

0.2420.048

0.5850.095

0.0660.120

0.2390.238

0.1610.224

0.2430.175

0.1360.368

0.0190.171

0.3090.277

0.4360.007

0.175

0.113

0.1070.066

0.236-0.036

0.0970.060

0.0600.221

0.0000.000

0.090

0.1640.047

0.3130.000

0.265

ZWEZMBZAF



SGPSENSAU


OMNNZLNPLNORNLDNIC

NGAMYS

MWIMUSMRTMOZMNE

MMRMLTMLI



IRNIRL

INDIDN

HUNHTI


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COLCMR

CIVCHNCHLCHECAN


ARMARGAREALB

IPR 2016

IPR 2015

0.3190.148

0.0610.315

0.0280.196

0.2330.250

0.494-0.036

0.0040.008

0.2600.146

0.0770.283

-0.3070.168

0.332

0.1980.059

-0.069-0.149

0.0260.289

0.0530.386

0.0000.295

-0.0900.280

0.0920.114

0.0990.267

0.0880.196

-0.3690.063

0.1730.103

0.176-0.0670.236

0.120-0.009

0.102-0.124

0.2050.009

0.0350.059

0.502-0.061

0.1820.087

0.2510.166

0.2470.095

0.1730.259

0.6860.168

0.2530.000

0.1980.064

0.0060.338

0.2200.263

0.3160.219

0.1680.275

0.0760.007

0.1520.0080.360

0.099-0.011

0.2610.109-0.182

0.2420.048

0.5850.095

0.0660.120

0.2390.238

0.1610.224

0.2430.175

0.1360.368

0.0190.171

0.3090.277

0.4360.007

0.1750.1130.107

0.0660.236

-0.0360.097

0.0600.060

0.2210.000

0.0000.090

0.1640.047

0.3130.000

0.265

-1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

ZWEZMBZAF



SGPSENSAU


OMNNZLNPLNORNLDNIC

NGAMYS

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MMRMLTMLI



IRNIRL

INDIDN

HUNHTI


DOMDNKDEUCZECYPCRI

COLCMR

CIVCHNCHLCHECAN


ARMARGAREALB

IPR 2016

IPR 2015


26

IV.1 IPRI 2016 Groups Results

Countries were grouped following different criteria: geographical regions, income level, degree

of development and participation in economic and regional integration agreements. For each

group the IPRI score and its components were calculated. Also former years’ classifications were

retained for comparison purposes (see Table 5).

We can see that all the regions improved their scores compared to those of 2015. North America

and Western Europe keep the top positions with scores of 7.88 and 7.36 respectively. The highest

improvement was shown by the Middle East, North Africa and Pakistan group, rising 0.341 (7%)

to 5.32 in its IPRI score (See Figure 11).

Following a geographical classification as defined by the World Bank (see Figure 12), Oceania

heads the groups with an IPRI score of 8.098, followed by North America (6.846). All the

regions but Central America and the Caribbean improved their scores, which remained the same.

Africa was the group that increased the most its IPRI, from 4.39 to 4.63 (6%).

The Regional and Development classification of the International Monetary Fund shows that the

top IPRI-2016 scores (Figure 13) are held by Advanced Economies (7.166) followed by the

Middle East, North Africa & Pakistan (5.188), which was the group with the highest IPRI score

improvement (7%). The Emerging and Developing Europe stepped back to 4.902 (-0.105,

equivalents to 2%) followed closely by Emerging and Developing Asia (4.748), Latin America

and the Caribbean (4.728) and the Commonwealth of Independent States (4.269). At the bottom

is Sub-Saharan Africa (4.663), showing an important improvement (0.206, equivalent to 5%).

This year the criteria of the World Bank does not include the traditional sub-classification for

High Income, belonging or not to the OECD, though we maintained these classifications for

comparison purposes. The income classification gathers countries directly according to the

results of the IPRI-2016, the bottom group being Low income (4.275), then Lower Middle

Income (4.393), Upper Middle Income (4.981), High Income (6.704). It is important to highlight

that High income OECD countries decreased their score by 16% (from 7.09 to 5.944) while High

Income non-OECD increased their scores by 14% (from 6.15 to 7.027). All of the rest of the

groups showed better scores (see Figure 14).

Considering economic integration agreements, we found that the Pacific agreement between

Australia and New Zealand keeps its top score of the groups with an IPRI-2016 score of 8.098,

followed by the European Free Trade Association (7.787), the Trans Pacific Partnership

Agreement2 (6.9), the North American Free Trade Agreement (6.84), the European Union

(6.641) and the Gulf Cooperation Council (6.316). Then we find Pacific Alliance (5.406),

Association of Southeast Asian Nations (5.357), the Organization of the Petroleum Exporting

Countries (5.034), Southern African Development Community (4.9), Central American Common

Market (4.784), Central American Parliament (4.672), the Andean Community (4.648) and the

Economic Community of West African States (4.587) (See Figure 15).

It should be noted that some groups are in different classifications and they report different score

values. That is the case of Commonwealth of Independent States or Latin America and the

2This group was included for the first time in this edition


27

Caribbean. This is because in some of the classifications they include or exclude a particular

country.

Table 5. IPRI 2016. Groups Score

Group IPRI LP PPR IPR

IPRI Groups

A 4.628 4.016 5.267 4.602

AO 5.697 5.396 6.266 5.428

CEECA 5.136 4.955 5.549 4.903

LAC 4.728 4.126 5.348 4.709

MENA 5.324 5.011 6.224 4.737

NA 7.876 7.810 7.391 8.425

WE 7.362 7.613 6.789 7.683

Geographical Regions

European Union 6.641 6.752 6.288 6.884

Rest Of Europe 5.112 5.065 5.822 4.448

Africa 4.630 4.017 5.297 4.578

North America 6.846 6.435 6.625 7.479

Central America & Caribe 4.747 4.184 5.281 4.777

South America 4.704 4.114 5.432 4.568

Asia 5.519 5.119 6.297 5.141

Oceania 8.098 8.672 7.563 8.060

Region & Development Classification

Advanced economies 7.166 7.345 6.767 7.386

Commonwealth of Ind. States 4.269 3.822 5.665 3.320

Emerging and Developing Asia 4.748 4.231 5.637 4.378

Emerging and Developing Europe 4.902 4.803 5.357 4.546

Latin America and the Caribbean 4.728 4.126 5.348 4.709

Middle East North Africa &Pakistan 5.188 4.665 6.140 4.760

Sub-Saharan Africa 4.663 4.054 5.308 4.627

Income Classification

High income 6.704 6.781 6.549 6.781

Upper-middle income 4.981 4.443 5.844 4.657

Lower-middle income 4.393 3.828 5.337 4.013

Low income 4.275 3.606 4.760 4.459

Income Classification (w/OECD & nonOECD)

High income: OECD 7.027 7.124 6.593 7.366

High income: nonOECD 5.944 5.857 6.374 5.599

Upper-middle income 4.981 4.483 5.858 4.602

Lower-middle income 4.393 3.828 5.337 4.013

Low income 4.275 3.606 4.760 4.459

Regional& Economic

Integration Agreements

EU 6.641 6.752 6.288 6.884

SADC 4.900 4.537 5.510 4.653

ECOWAS 4.587 3.884 5.167 4.710

ASEAN 5.357 4.919 6.266 4.886

PARLACEN 4.672 3.775 5.786 4.454

GCC 6.316 6.083 7.234 5.631

AP 5.406 4.776 6.041 5.401

MERCOSUR 4.431 4.065 4.960 4.267

SAARC 4.227 3.755 4.966 3.961

CEMAC 4.149 3.203 4.780 4.464

MCCA 4.784 4.170 5.650 4.532

CIS 4.214 3.557 5.619 3.465

ARAB M UNION 4.493 3.819 5.364 4.296

CARICOM 4.476 4.195 4.405 4.827

CAN 4.648 3.457 5.717 4.771

EFTA 7.874 8.508 7.425 7.688

IGAD 4.484 3.645 5.326 4.481

NAFTA 6.846 6.435 6.625 7.479

PACIFIC 8.098 8.672 7.563 8.060

CEEAC 3.972 3.028 4.705 4.183

TPP 6.900 6.754 6.949 6.996

OPEC 5.034 4.425 6.008 4.670


28

Figure 11. IPRI 2016 and Components. Groups Score

Figure 12. IPRI 2016 and Components. Regional Groups Score


29

Figure 13. IPRI 2016 and Components. Development Groups Score

Figure 14. IPRI 2016 and Components. Income Groups Score


30

Figure 15. IPRI 2016 and Components.

Economic & Regional Integration Agreement Groups Score


31

V. IPRI-Population

As in the last edition, we computed a population incidence to the index. In this regard we note

that although the IPRI-2016 average score is 5.45, when it is weighted by population it is

reduced to 5.28. However, it is higher than in 2015 (5.176) by 2%.

Our sample of 128 countries has a population of 6.83 billion (thousand million) people and it

showed that 63% of the world population lives in 44 countries with an IPRI between [4.5-5.4],

while 19% of the population enjoys higher levels of property rights protection in the other 53

countries [5.5-7.8]. Even though this is an improvement from the previous year, there is still

much room for upgrading the property rights systems in highly populated countries.

Table 6. IPRI 2016 and Population

IPRI 2016

(Score

Range)

Number of

Countries

Population

(thousands)

Population

(%)

IPRI

Incidence

in Total

Score (%)

IPRI-

Population

Incidence

in total

Score (%)

2,5 a 3,4 6

283,493.62 4.15 2.575 2.226

3,5 a 4,4 25

931,619.97 13.64 14.420 10.176

4,5 a 5,4 44

4,316,612.40 63.21 30.771 61.745

5,5 a 6,4 22

324,842.36 4.76 18.671 5.302

6,5 a 7,4 12

224,695.11 3.29 11.876 4.290

7,5 a 8,4 19

747,566.49 10.95 21.686 16.260

128

6,828,829.95 100 100 100

Taking into account a demographic perspective is very important for an index such as the IPRI,

which aims to assess the level of property rights that people have, regardless of whether

measurements are taken by countries. With this approach, the IPRI becomes an even more

powerful tool for policy makers.

Figure 16 shows a combination of elements while analysing changes in IPRI scores: country,

population and belonging to particular group. It is positive news to see that most of the countries

have improved their scores. However, there would be a huge impact if those densely populated

countries are able to foster their property rights system.


32

Figure 16. IPRI 2016. Country score changes (population and groups)


33

VI. IPRI and Gender

Gender refers to the social attributes and opportunities associated with being male and female

and the relationships between women and men, which are socially constructed and learned

through the socialization processes. Gender Equality refers to the equal rights, responsibilities

and opportunities of women and men and girls and boys; this means that the interests, needs and

priorities of both female and male are taken into consideration, recognizing the diversity of these

different groups. This is an issue of human rights and social justice, so it is a goal in itself.

Simultaneously, it has been demonstrated its relevance fostering development, being especially

evident in some areas like health, education, agriculture and equitable access to credit for

reducing poverty. This means that gender equality plays a crucial role for less developed and

developing countries.

We used the Social Institutions and Gender Index, SIGI (by OECD), to calculate the Gender

component for the IPRI, giving mayor relevance to those items more closely related to property

rights and its impact on economic development.3

To account for gender equality, this chapter extends the standard IPRI measure to include a

measure of gender equality (GE) concerning property rights. The IPRI formula was modified to

incorporate gender equality as following:

IPRI-GE = IPRI + 0.2*GE

A weight of 0.2 for the gender equality measure is arbitrary. We varied the weight to 0.5 or

according to the female/male population in each country, but scores were highly correlated. We

decided to keep the weight of 0.2 for comparison purposes with previous data series.

VI.1 Data & Methodology

The construction of the GE measure is based on the following five indicators (Source: OECD

Gender, Institutions, and Development Database 2014 (GID-DB) details in Appendix III):

1. Women’s Access to Land: Measures whether women and men have equal and secure access

to land use, control and ownership.

2. Women’s Access to Credit: Measures whether women and men have equal access to

financial services

3The SIGI is composed of 5 sub-indexes, each representing a distinct dimension of discrimination: Discriminatory

Family Code, Restricted Physical Integrity, Son Bias, Restricted Resources and Assets and Restricted Civil

Liberties.


34

3. Women’s Access to Property Other than Land: Measures whether women and men have

equal and secure access to non-land asset use, control and ownership

4. Inheritance Practices combines two elements:

a. Inheritance Practice to Daughters: Measures whether daughters and sons have equal

inheritance rights

b. Inheritance Practice to Widows: Measures whether widows and widowers have equal

inheritance rights

5. Women’s Social Rights, covers broader aspects of women’s equality and it is a composite of

four other items crucial to equal standing in society:

a. Parental authority

i. In marriage: Measures whether women and men have the same right to be the

legal guardian of a child during marriage

ii. After divorce: Measures whether women and men have the same right to be

the legal guardian of and have custody rights over a child after divorce

b. Female genital mutilation: Measures the prevalence of female genital mutilation

c. Access to public space: Measures whether women face restrictions on their freedom

of movement and access to public space

d. Son preference in education: Percentage of people agreeing that university is more

important for boys than for girls.

The original data has three levels: 0 (Best), 0.5 (Average) and 1 (Worst). All data series were

rescaled to IPRI scale (0-10). The final GE score is an index based on the average of the five

equally weighted variables. Those variable with more than one item where calculated also as

equally weighted.

A minimum score (0) means complete discrimination against women, while maximum score (10)

is given to countries with gender equality. Consequently, the IPRI-GE scale is (0-12).

As the GE data source is discrete, equal outcomes are likely to be found. That will be reduced in

the IPRI-GE due to the variability of the IPRI scores.

VI.2. IPRI-GE and GE. Country Results

The IPRI-GE shows results for 124 from 128 countries included in the IPRI-2016, as there was

no information available for Guyana, Malta, Montenegro and Taiwan. On average, the 124

countries show a GE of 7.466 and an IPRI-GE of 6.933. This is an improvement from 2015

which yielded a GE of 7.39 and an IPRI-GE of 6.76. The scores and ranking of IPRI-GE 2015

and GE-2015 can be seen in Figures 17a and 17b.

There are 14 countries with a maximum score of GE=10: Austria, Belgium, Croatia, Czech Rep.,

Denmark, Dominican Rep. Iceland Ireland, Latvia, Lithuania, Luxemburg, Panama, Portugal and

Slovakia, and there are 30 other countries in the range of 9-10. The bottom scores of GE are held


35

by Nigeria (3.12), Zambia (3.25), Egypt (3.365), United Arab Emirates (3.666), Oman (3.666),

Saudi Arabia (3.7), Chad (3.706), Iran (3.725) Mauritania (3.853), Qatar (3.86) and Bangladesh

(3.94).

Finland tops the IPRI-GE (10.371), followed by New Zealand (10.261), Luxemburg (10.256),

Norway (10.248), Sweden (10.001), Japan (10.082), Switzerland (10.051), Netherlands (10.022),

Canada (10.009)). All of them very close in their score values, and over 10. In the score range 9-

10 we find Denmark, Australia, USA, Germany, Singapore, Austria, Ireland, Belgium, UK,

Hong Kong, France and Iceland.

On the other extreme of the IPRI-GE we find Bangladesh (3.565), Myanmar (3.693), Nigeria

(4.186), Haiti (4.409), Chad (4.48) Mauritania (4.501), Burundi (4.64), Pakistan (4.683) and

Lebanon (4.7) and the Bolivarian Rep. of Venezuela (4.715). The lattermost not because of its

gender component (which is high: 9.93) but because of its low IPRI score (2.7). The same

applies for Haiti (GE=7.83, IPRI=2.8)

Analyzing the IPRI-GE by groups of countries we found very interesting results (see Figure 18):

• By Region: the three top groups are Oceania, North America, and Europe Union while at the

bottom we find Africa and MENA countries. In these two groups the GE component is

particularly low, pushing down the IPRI-GE score; just the opposite what happens to Latin

America & the Caribbean and part of Europe, high GE scores pull up the IPRI-GE.

• By Regional and Development criteria: The top group is Advanced Economies (9.11)

followed by Emerging and Developing Europe (6.75) and Latin America and the Caribbean

(6.41). Again these two last groups show much better behavior in their GE scores (9.04 and

8.31 respectively) than in the IPRI. The bottom groups are Sub-Saharan Africa (5.77)

Commonwealth of Independent States (5.95) and Emerging and Developing Asia (5.92). The

Middle East, North Africa and Pakistan show the lowest GE score (4.43) followed by the

Sub-Saharan Africa (5.52).

• By Income classification: the GE and the IPRI-GE, follow the same pattern as the IPRI. On

top we find High Income OECD countries (GE=9.63 IPRI-GE=8.95), followed High Income

non-OECD (GE=7.36 IPRI-GE=7.58), Upper Middle Income (GE=6.45 IPRI-GE=7.25),

Low Middle Income (GE=5.58 IPRI-GE=5.93) and Low Income countries (GE=5.44 IPRI-

GE=5.82).

• By Economic and Regional Integration Agreements: As with last year, the top five groups

are Pacific (10.09), European Free Trade Association (9.84), NAFTA (8.64), European

Union (8.60), TPP (8.57), GCC (7.25) and AP (7.06). However the Gulf Cooperation Council

shows a low GE score (4.66) just following the bottom of the list which is held by CEMAC

(4.45) Arab M. Union (4.49). The bottom three groups for the IPRI-GE are the Economic

Community of Central African States (4.94) Central African Economic and Monetary

Community (5.04), South Asian Association for Regional Cooperation (5.29), Arab

Monetary Union (5.39) and Intergovernmental Authority on Development (5.58). It should

be highlighted that all the Latin American agreements (PARLACEN, CAN, CARICOM,

MCCA, MERCOSUR) and the Commonwealth of Independent States show medium IPRI-

GE scores, while showing high levels in GE values.


36

Fig. 17a. IPRI-GE 2016. Scores & Rankings Fig. 17b. GE-2016 Scores & Rankings


37

Figure18. GE and IPRI-GE. Groups of countries


38

Table 7 shows the IPRI-GE 2016 rankings by quintile for the 124 countries in our sample. As in

the IPRI, the number of countries belonging to each quintile increases from the top 20% to the

bottom 20% (1st quintile 17 countries, 2nd quintile 20 countries, 3rd quintile 25 countries, 4rd

quintile 28 countries and 5th quintile 34 countries). Hence, the forth and the fifth quintiles

include 50% of the countries (62 countries) in the sample.

Table 7. IPRI-GE Ranking by quintiles

Top 20 Percent 2nd Quintile 3rd Quintile 4th Quintile Bottom 20 Percent

FINLAND UNITED KINGDOM (UK) KOREA, REP DOMINICAN REP. BOLIVIA

NEW ZEALAND HONG KONG (SAR of China) URUGUAY ECUADOR KENYA

LUXEMBURG FRANCE ITALY INDONESIA UGANDA

NORWAY ICELAND HUNGARY MOROCCO BOSNIA AND HERZEGOVINA

SWEDEN ESTONIA SLOVENIA GHANA AZERBAIJAN

JAPAN PORTUGAL JAMAICA MACEDONIA, FYR TUNISIA

SWITZERLAND CZECH REPUBLIC ROMANIA THAILAND MALI

NETHERLANDS CHILE COSTA RICA EL SALVADOR MOZAMBIQUE

CANADA ISRAEL PANAMA GUATEMALA BENIN

DENMARK SOUTH AFRICA RWANDA INDIA GABON

AUSTRALIA QATAR BAHREIN KAZAKHSTAN PARAGUAY

UNITED STATES (USA) CYPRUS TURKEY MEXICO TANZANIA, UNITED REP.

GERMANY UNITED ARAB EMIRATES GREECE PERU ZAMBIA

SINGAPORE SLOVAKIA BULGARIA PHILIPPINES MADAGASCAR

AUSTRIA LITHUANIA TRINIDAD & TOBAGO SRI. LANKA ALBANIA

IRELAND MAURITIUS BOTSWANA LIBERIA ETHIOPIA

BELGIUM POLAND CROATIA HONDURAS MOLDOVA

MALAYSIA COLOMBIA SENEGAL NICARAGUA

SPAIN SAUDI ARABIA VIETNAM CAMEROON

LATVIA CHINA ARMENIA SIERRA LEONE

BRAZIL GEORGIA EGYPT

OMAN UKRAINE IRAN

JORDAN ARGENTINA ALGERIA

KUWAIT SERBIA ZIMBABWE

RUSSIA CôTE D'IVOIRE VENEZUELA, BOLIVARIAN REP.

NEPAL LEBANON

MALAWI PAKISTAN

SWAZILAND BURUNDI

MAURITANIA

CHAD

HAITI

NIGERIA

MYANMAR

BANGLADESH


39

VII. IPRI and Development

Since those early stages of modernity when the idea of progress became a goal, nations began to

promote economic growth to achieve development. But development is a much wider concept, a

multidimensional one, addressing economic, political, social, cultural, technological and

ecological spheres, looking for the well-being of present and future generations. Having

examined the important interactions between property rights and development, we analyzed in

this edition some different dimensions of development with the IPRI and its components, as

follows:

Economic Outcomes

Human Capabilities

Social Capital

Research and Innovation

Ecological Performance

VII.1. Economic Outcomes

Trying to grasp development, economic outcomes obviously do not capture everything and many

other factors are likely to influence it, however it is a first approach to it. Four (4) economic

elements were evaluated with the IPRI and its components:

Production: using the Gross Domestic Product (GDP) in constant USD in per capita terms,

and also adjusted by the GINI coefficient. GDP is the sum of gross value added by all

resident producers in the economy plus any product taxes and minus any subsidies not

included in the value of the products. It is calculated without making deductions for

depreciation of fabricated assets or for depletion and degradation of natural resources.

(Source: World Bank data, http://wdi.worldbank.org)

Domestic Investment: using the Gross Capital Formation in current per capita terms, which

consists of outlays on additions to the fixed assets of the economy plus net changes in the

level of inventories (Source: World Bank data, http://wdi.worldbank.org)

The composition of production: using the Index by the Atlas of Economic Complexity. The

complexity of an economy is related to the multiplicity of useful knowledge embedded in it.

We can measure economic complexity by the mix of products that countries are able to

make. (http://atlas.media.mit.edu/en/resources/economic_complexity/).

The entrepreneurship ecosystem: using the Global Entrepreneurship Index of the GEDI that

measures the health of the entrepreneurship ecosystems in countries. It then ranks the

performance of these against each other; providing a picture of how each country performs in

both the domestic and international context. (Source: http://thegedi.org/global-

entrepreneurship-and-development-index/)


40

Most of the correlations4 found were significant and positively strong. We considered: medium

correlation if Pearson ranges [0.5-0.6), high correlation if it ranges [0.6-0.8) and strong

correlation if it ranges [0.8-1). See Table 8.

It is worth noting that GDP per capita correlations increased when it was adjusted by the GINI

Coefficient, which is a measure of dispersion or inequality, giving to the GDP per capita a more

adjusted measure in each country. The highest correlation was found for the IPRI and the

adjusted GDP per capita (0.851). For the LP and PPR component the correlation was higher not

adjusting the GDP per capita by the GINI coefficient, while IPR component followed the same

behavior as that of the IPRI. Figures 19a and 19b show the best fit curve for the IPRI and its

components with each economic variable and the coefficients of determination5 (R2).

Table 8. Pearson Correlation Indexes

GDP per capita

(constant 2005

USD)

GDP per capita

(constant 2005

USD) * GINI

Gross capital

formation

(current USD

per capita)

Economic

Complexity

Global

Entrepreneurship

IPRI 0.836 0.851 0.778 0.722 0.855

LP 0.829 0.834 0.761 0.691 0.845

PPR 0.653 0.619 0.667 0.558 0.725

IPR 0.807 0.844 0.722 0.737 0.800

The relationship with domestic investments, showed for the IPRI a Pearson correlation of 0.778

followed by the LP (0.761), the IPR (0.722) and the PPR (0.667) component.

The characteristics or composition of the domestic production exhibited also a high correlation

with the IPRI, being the strongest the correlation with IPR (0.737), followed by the IPRI (0.722),

the LP (0.691) and the PPR (0.558) component.

Of all the items, the entrepreneurial environment presented the stronger correlation with the IPRI

(0.855), followed by the LP (0.845), the IPR (0.845) and the PPR (0.725) component. This is a

very important finding, as entrepreneurship is the building block of innovation, investment,

production and economic growth.

Figure 20 shows that, on average, countries in the top quintile of IPRI scores (i.e. top 20%) show

a per capita income almost 21 times that of the countries in the bottom quintile (in 2015 that

disparity was almost 24 times). Statistics are based on the averages of IPRI-2016 scores and

corresponding data on average GDP per capita in USD constant terms (2005=100, source: World

Bank data) for the last available year.

4Correlation theory is aimed to show the possible relationship, association or dependence between two or more

observed variables. Besides it allows analyze the type of association (direct or indirect) and the level or degree of

intensity between them. 5The coefficient of determination (R2) is a key output of the regression analysis. It is interpreted as the proportion of

the variance in the dependent variable that is predictable from the independent variable. It ranges from 0 to 1.


41

Figure 19a. IPRI Correlations with economic variables

Figure 19b. IPRI components correlations with economic variables

IPRI Components vs Economic Complexity (EC)

IPRI Components vs Global Entrepreneurship Index (GEI)

IPRI Components vs GDP per capita (Gp)

IPRI Components vs GDP per capita * Gini (GDPpc-Gini)

IPRI Components vs Gross capital formation per Capita (GKFpc)

IPRPPRLP

GSp

Gp-

GG

pG

EICE

1,5 9,5 1,5 9,5 1,5 9,5

R² = 0,484R² = 0,315

R² = 0,551

R² = 0,720

R² = 0,533 R² = 0,684

R² = 0,771 R² = 0,537R² = 0,752

R² = 0,784R² = 0,511 R² = 0,837

R² = 0,669R² = 0,604

R² = 0,603

0

3,5E+10

0

20000

0

90000

0

100

-2

2,5


42

Figure 20: Average per capita Income by IPRI Quintiles

These results insist in the significant and positive relationship between prosperity and a property

rights system, measured at an individual level. The statistical dispersion of the GDP distribution

in each country was considered in this analysis using the GINI coefficient, which improved the

correlations.

Figure 19a displays the relationship IPRI-economic outcomes showing countries with a

demographic perspective. This insists in the huge proportion of population (represented by the

radio of each circle) living in countries of middle level of IPRI and low to mid economic

outcomes.

VII.2. Human Capabilities

The focal element of the development equation is the people, and therefore their capabilities. In

this area, two (2) elements were evaluated:

Human Development Index (UNDP, http://hdr.undp.org/en/data) which has three

dimensions: long and healthy life, being knowledgeable and a decent standard of living.

Global Index on Freedom of Education, which includes a set of data on an international

scale, analyzing the protection and promotion of this fundamental human right, as well as

policies in support of freedom of education in the national context and in other countries. The

indicators will focus on: freedom of choice for children's education (constitutional and

legislative previsions, public schools, home schooling); public support for freedom of

education (family vouchers, direct support for schools, teachers' wages, costs of structures

and buildings etc.); NET (Net Enrolment Rate): the participation rate in a certain stage of

children's and young people's education; Rate of students' participation in comprehensive

schools (http://www.novaeterrae.eu/en/).


43

The correlations found were significant and positive. The HDI showed higher correlations than

the GIFE; and while the first is higher for LP (0.734) and followed by the IPRI (0.720), the GIFE

is higher for IPR (0.605), as creative capabilities will be enhanced by the enjoyment of freedoms

and for guarantees on intellectual property rights. See Table 9. The best fit curve for the indices

and the coefficient of determinations are shown in Figure 22.


Freedom of Education Human Development Index

IPRI 0.591 0.720

LP 0.579 0.734

PPR 0.424 0.616

IPR 0.605 0.638

Figure 22. IPRI Correlations with human capabilities variables

VII.3. Social Capital

Social capital is understood as the group of norms and bonds that allow collective social action.

Social capital is built upon trust, reciprocity, cooperation, assistance, support, interdependence,

interaction, dialogue, involvement and participation (Jaffé, Levy-Carciente & Zanoni, 2007).

Given the importance of having people as the axis around which the development concept and

policies should rotate, we tried to grasp the social capital of the countries using a group of

variables from the International Institute of Social Studies (http://www.indsocdev.org) and the

IPRI vs Freedom of Education Index (FEI)IPRI vs Human Development Index (HDI)

1

120

0,3

0

IPRI

HD

IFE

I

2 9 2 9

R² = 0,518

R² = 0,349


44

Social Capital sub-index of the Prosperity Index by Legatum (http://www.li.com). We evaluated

their correlation with the IPRI and its components (see Table 10 and Figure 23):

Inclusion of minorities: measures levels of discrimination against vulnerable groups such as

indigenous peoples, migrants, refugees, or lower caste groups. This measure focuses upon

whether there is systemic bias among managers, administrators, and members of the

community in the allocation of jobs, benefits, and other social and economic resources

regarding particular social groups.

Civic activism: refers to the social norms, organizations, and practices which facilitate

greater citizen involvement in public policies and decisions. These include access to civic

associations, participation in the media, and the means to participate in civic activities such

as nonviolent demonstration or petition.

Intergroup cohesion: refers to relations of cooperation and respect between identity groups in

a society. Where this cooperation breaks down, there is the potential for conflictual acts such

as ethnically or religiously motivated killing, targeted assassination and kidnapping, acts of

terror such as public bombings or shootings, or riots involving grievous bodily harm to

citizens, with concomitant effects upon growth and development.

Interpersonal safety and trust: Interpersonal norms of trust and security exist to the extent that

individuals in a society feel they can rely on those whom they have not met before. Where

this is the case, the costs of social organization and collective action are reduced. Where

these norms do not exist or have been eroded over time, it becomes more difficult for

individuals to form group associations, undertake an enterprise, and live safely and securely

Social Capital component of the Prosperity Index by Legatum: this sub-index measures

countries’ performance in two areas: social cohesion and engagement, and community and

family networks. Variables: perceptions of social support, volunteering rates, helping,

strangers, charitable donations, social trust, marriage and religious attendance.

The strongest correlations were found between Civic Activism and the IPRI (0.824) followed by

the IPR (0.813) and the LP (0.801). Inclusion, Intergroup Cohesion and Interpersonal Safety &

Trust were highly correlated, especially with IPRI and LP. The Social Capital component of the

Prosperity Index by Legatum showed high correlations with the IPRI (0.770), the IPR (0.738),

the LP (0.729) and the PPR (0.675) component.


Inclusion Civic Activism Intergroup

Cohesion

Interpersonal

Safety & Trust

Social Capital

Comp.

(Legatum)

IPRI 0.698 0.824 0.608 0.667 0.770

LP 0.732 0.801 0.649 0.702 0.729

PPR 0.507 0.656 0.493 0.579 0.675

IPR 0.664 0.813 0.527 0.570 0.738


45

Figure 23. IPRI Correlations with social capital variables

VII.4. Research and Innovation

Recognizing the importance of innovation in a knowledge society, using World Bank data for

research and innovation (http://wdi.worldbank.org/) we run correlations of the IPRI and its

component with three (3) items:

Full time research projects per million people: Reflects the professionals engaged in the

conception or creation of new knowledge, products, processes, methods, or systems and in

the management of the projects concerned. Postgraduate PhD students (ISCED97 level 6)

engaged in R&D are included (http://data.worldbank.org/indicator/SP.POP.SCIE.RD.P6).

Research and development expenditure as a percentage of GDP: Expenditures for R&D are

current and capital expenditures (both public and private) on creative work undertaken

systematically to increase knowledge, including knowledge of humanity, culture, and society,

and the use of knowledge for new applications. R&D covers basic research, applied research,

and experimental development (http://data.worldbank.org/indicator/GB.XPD.RSDV.GD.ZS).

Scientific and technical journal articles: Number of scientific and engineering articles

published in the following fields: physics, biology, chemistry, mathematics, clinical

medicine, biomedical research, engineering and technology, and earth and space sciences

(http://data.worldbank.org/indicator/IP.JRN.ARTC.SC).

0,7

0,9

0

0,8

0,1

0,70

6

0

0,35

-5

IPRI

SC

-LC

AIS

TIM

IC

2 9 2 9 2 9 2 9 2 9

R² = 0,617

R² = 0,699

R² = 0,450

R² = 0,494

R² = 0,384

IPRI vs Social Capital-Legatum (SC-L)

IPRI vs Civic Activism (CA)IPRI vs Interpersonal Safety and Trust (IST)IPRI vs Inclusion of Minorities (IM) IPRI vs Intergroup Cohesion (IC)


46

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.


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)


47

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.


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)


48

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.


49

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


50

Table 14. Clusters’ Members (Countries ordered alphabetically)


51

Figure 25. Clusters’ Members and Centroids. Factor 1 and Factor 2


52

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


Emerging and Developing Europe


Sub-SaharanAfricaEmerging and

Developing Asia

Commonwealth of Independent States

Sub-SaharanAfrica



Commonwealth of Independent States

Emerging and Developing Asia

Emerging and Developing Europe


1.474.552972.262

4.382.016


53

Fig. 26d. Clusters composition by Economic and Regional Integration Agreements

Table 15. Cluster statistics

Statistically significant only if Value-Test ≥ ∣1.96∣


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


Characteristic


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



54

Table 16. Illustrative variables. Averages by Clusters

Table 17. Regional Integration Agreements and Cluster


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

TPP Trans-Pacific Partnership 11 1 9,09 2 18,18 8 72,73

Regional Integration Agreements


55

VIII.1. Cluster Description

Cluster 1

Cluster 1 is composed of 41 countries with a population of more than 1.4 billion people. The

country closest to its centroid is Ethiopia, followed by Nicaragua, Bolivia, Iran and Cameroon.

Haiti is by far the most remote country of the cluster, followed by Bangladesh, Georgia,

Myanmar and Venezuela.

A close look at Cluster 1 countries’ coordinates reveals that Vietnam, Malawi and Uganda are

the closest to the Cluster 2 Centroid. Looking simultaneously to Cluster 1 and Cluster 2, the

closest countries are Dominican Republic (Cluster 1) and Russia (Cluster 2), which also means

similarity in conditions (see Fig. 25).

Countries in Cluster 1 are statistically significant for LP, PPR and IPR components with low

scores in each category. The same is true for the Gender component and the IPRI-GE. Cluster 1

countries also show low levels in all the dimensions we analyzed, that is, they show poor

performances in Economic outcomes, Human Capabilities, Social Capital, Research and

Innovation, and Ecological Performance. This is the result of lack of policies or inappropriate

ones to improve key elements as entrepreneurship, opportunities and freedom of education,

health and environment, social capital, or research and development.

Using the regional and development criteria of the IMF and the Income criteria of the World

Bank, the Sub-Saharan Africa group and the Low Income group are highly represented in this

cluster.

The Southern African Development Community (5/10 members) is the most common economic

and regional integration agreement in this cluster; followed by Commonwealth of Independent

States (4/6 members); Organization of the Petroleum Exporting (4/10 members) and the

Economic Community of Central African States (all members).

Cluster 2

Cluster 2 is composed of 56 countries with a population of more than 4.38 billion people. The

country closest to its centroid is Romania, followed by Morocco, Ghana, Bulgaria and

Philippines. Israel is the farthest country from the centroid, followed by Oman, Uruguay,

Mauritius and Bahrein. It is important to note that the most populous countries in the world,

China and India, are included in this cluster. India is very close to the centroids’ cluster

(d=0.167639) and China is mid-distance (d=0.342222). While Figure 25 illustrates that Russia,

Mali, El Salvador and Senegal are close to the centroid of Cluster 1; the countries closest to

Cluster 3 are Mauritius, Saudi Arabia, Rwanda and Israel; the last one is the closest to Czech

Republic that belongs to Cluster 3 and is placed in the direction of vectors LP and IPR.

As Cluster 2 is very near to the origin of the factors axes (the distance of the first factor to the

centroid is -0.10188), its results are close to the averages of the different variables used in the

analysis. This gives rise to non-significant results for most of the variables, while some turn out

to be significant in moderately low values. That is the case for Expenditure in R & D, numbers of

researchers, inclusion of minorities, GDP per capita, GDP per capita adjusted by GINI and

Gross Capital Formation per capita.

Using the regional and development criteria of the IMF, Latin America and the Caribbean are

highly represented in this cluster; whereas by the income criteria of the World Bank, the Upper


56

Middle Income countries exhibit the highest frequency in the cluster. Following the perspective

that focuses on economic and regional integration agreements, we can see that the European

Union has the highest frequency in Cluster 2 (with 13/28 members). At a lesser frequency we

find countries of the Pacific Alliance (5/6) and the Economic Community of West African States

(with 5/8 members).

Cluster 3

Cluster 3 is composed of 31 countries showing a population of more than 972 million people.

The country closest to its centroid is Austria, followed by Australia, Germany, United Kingdom

and France. The farthest country of the group is Czech Republic, followed by South Africa,

Malaysia, Portugal and Qatar. Czech Republic and Portugal are the closest countries to Cluster 2.

It’s worth noting that in 2015 Czech Republic belonged to Cluster 2, very close to Portugal

which already was in Cluster 3. In this edition, Czech Republic belongs to Cluster 3. A third

country very close to Portugal and Czech Republic is Israel, but still belongs to Cluster 2. While

Israel improved in LP and IPR it receded a little in PPR.

Compared to Cluster 1, countries belonging to Cluster 3 exhibit the opposite results: all the

variables are significant, but with positive and high values, showing good performances at

Economic outcomes, Human Capabilities, Social Capital, Research and Innovation, and

Ecological Performance, with positive results in human development, liberties and opportunities

for their citizens.

Using the regional and development criteria of the IMF, the Advanced Economies group is

highly represented in this cluster. By the Income criteria of the World Bank, the OECD-High

Income group shows the highest frequency in the cluster. Looking at economic and regional

integration agreements, the European Union is highly represented in Cluster 3 (15/28 members),

particularly those that belong to the OECD (only Malta is a non-OECD EU member), followed

by the Trans-Pacific Partnership (with 8/11 members).

When speaking of economic and regional integration agreements, the following should be noted:

Of the 128 countries included in the IPRI-2016 selection, there are 16 that do not belong to any

of the agreements chosen, 27 countries that are members of two of them, and there are 2

countries that are members of three integration agreements ( Mexico and Peru). Also, there are

some agreements with many members (EU has 28 members) and others with just a few (Pacific

encompasses only Australia and New Zealand).

The Southern African Development Community, Association of Southeast Asian Nations,

Organization of the Petroleum Exporting Countries and Trans-Pacific Partnership have members

in the three clusters. The Economic Community of Central African States and the Central

African Economic and Monetary Community only have members in Cluster 1. The European

Free Trade Association and Pacific only have members in Cluster 3. The rest of the agreements

have members in two clusters in different proportions.

The data suggests that most of the chosen integration agreements demonstrate some level of

heterogeneity in terms of the strength of the property rights systems among their members. In

presence of homogeneity it would be easier for an integration agreement to promote common

policies to enhance the strength of property rights. Heterogeneity could also be seen as an

advantage, as the policies could be targeted to specific members of the agreement.


57

On the other hand, the integration agreements showing members in just one cluster reveal

homogeneity amongst their countries’ property rights systems. Even those agreements

participating in two clusters show members in cluster boundaries and could be seen as a possible

transition from one cluster to the other.

In conclusion, in the cluster analysis we find that:

Each cluster represents more than a grouping by variables directly associated with property

rights; they are groups with common characteristics within them and with different features

among clusters, which confirms the consistency of the IPRI, and the relevance of property

rights systems influencing societies.

Cluster 1 and Cluster 3 are two extreme poles in terms of the performance of their

economies, human capabilities, social capital, research and innovation, ecological

performance, their institutional stability, as well as their IPRI scores.

Cluster 2 statistical values reflected intermediate positions and, depending on the decisions

taken in the present and near future of each country, will be inclined to one of the two polar

classes. Those countries that whose position remains very close to Cluster 1 should reread

their policies regarding property rights, but, as has been shown, also in other dimensions to

improve their performance and the well-being of their citizens.

Countries in Cluster 1 should make particular efforts to strengthen their legal and political

environment to protect physical and intellectual properties, which are still weak, in order to

improve the quality of life in their societies.

Countries in the boundaries between two clusters have to make special efforts to bridge the

gap, which will place them in a higher level.

Specific analyses of countries and of groups of them related to their cluster are a rich open

vein for future investigations.


58

IX. Final Remarks

The 10th edition of the International Property Rights Index – IPRI 2016 - shows consistency with

previous ones, arguing that the index is properly structured. In this sense, its follow-up in years

ahead is a key to monitoring the performance of property rights systems and their relationships to

societies’ prosperity; globally, regionally and by country.

Results suggest that countries with high IPRI scores and their components also show high

income and high development levels, pointing to a positive relationship between a property

rights regime and wellbeing.

In this edition we included a range of dimensions to be examined in conjunction with property

rights. Our results show that the IPRI is strongly associated with economic and political

opportunities within countries, as well as their social cohesion, human capabilities, innovative

research and the ecosystem.

Each of these dimensions was evaluated using different items: production (per capita level and

composition), investment, entrepreneurship ecosystem, human development, freedom of

education, minorities’ inclusion, civic activism, intergroup cohesion, interpersonal safety and

trust, social capital, number of researchers, number of papers, expenses in R&D and

environmental performance. All the items showed a strongly positive association with the IPRI

and its components.

This way, IPRI results can be used as guidelines for policy makers in different countries - as in

multilateral or integration agreements to which they belong - to enhance their policies aimed at

fostering development, defined as a multidimensional and synergic term.

IPRI-2016 includes 128 countries with an average score of 5.45, showing an increase of 0.1

points compared to 2015. This edition includes four countries (Benin, Ecuador, Bosnia &

Herzegovina and Liberia) that were not in the IPRI-2015, although five countries had to be

excluded (Puerto Rico, Angola, Burkina Faso, Libya and Yemen) due to the absence of enough

information. We urge these and other countries not included in the index to increase their efforts

in the availability of information so that in future editions they may be included.

Countries’ performances are quite dissimilar: we find countries with very high scores while

others have very low ones. Once a country grasps a top position it mostly keeps it. However, as

some countries improve, other may show a setback. We are glad to highlight the improvement of

Cote d’Ivore (Ivory Coast). Even though its IPRI score is still low; it showed an increase of

0.509 points, an exceptionally positive change.

IPRI-2016 keeps the calculations of IPRI-GE and IPRI-POP given the importance of showing

the impact of gender equality and countries’ demographic weight in analyzing property rights

systems.

IPRI-GE was calculated for a total of 124 countries and the 2016 average score is 6.93/12 which

results in an increase of 0.17 points compared to 2015.


59

IPRI-POP was calculated for the 128 countries, giving rise to a score of 5.28. This is due to the

fact that 63% of world population lives in 44 countries with an IPRI between 4.5 and 5.4,

insisting on the importance of fostering property rights systems in densely populated countries.

IPRI-2016 also included a cluster analysis, in order to gather countries into groups by their

homogeneity. Therefore, the 128 countries were classified according to their values in the IPRI

and its three components in three clusters. The analysis of clusters’ centroids and the countries

by the boundaries between groups, yields important information about their characteristics and

challenges. Cluster analysis also confirmed the consistency of the IPRI, since the assembled

countries exhibited a high degree of homogeneity, showing the relevance of property rights

systems shaping societies.

The regional and economic integration agreements included in the analysis showed heterogeneity

concerning to property rights systems, as their country members belongs to more than one

cluster. This presents special difficulties and challenges when coordinating or overtaking

multilateral policies on the issue of property rights.


60

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63

XI. Appendages

XI.1. Appendix I. Data Source


64

XI.1. Appendix II. Groups conformation

Class Group # Countries

A 27

BENIN,BOTSWANA,BURUNDI,CAMEROON,CHAD,CôTE

D'IVOIRE,ETHIOPIA,GABON,GHANA,KENYA,LIBERIA,MADAGASCAR,MALAWI,MALI,MAURITANIA,MAURITIUS,MOZAMBIQUE,NIGERIA,RWANDA,SENEGAL,SIERRA LEONE,SOUTH

AFRICA,SWAZILAND,TANZANIA UNITED REPUBLIC OF, UGANDA,ZAMBIA,ZIMBABWE

AO 20AUSTRALIA,BANGLADESH,CHINA,HONG KONG (SAR of China),INDIA,INDONESIA,JAPAN,KAZAKHSTAN,KOREA REP,MALAYSIA,MYANMAR,NEPAL,NEW

ZEALAND,PAKISTAN,PHILIPPINES,SINGAPORE,SRI. LANKA,TAIWAN (China),THAILAND,VIETNAM

CEECA 20ALBANIA,BOSNIA AND HERZEGOVINA,BULGARIA,CROATIA,CZECH REPUBLIC,ESTONIA,HUNGARY,LATVIA,LITHUANIA,MACEDONIA

FYR,MOLDOVA,MONTENEGRO,POLAND,ROMANIA,RUSSIA,SERBIA,SLOVAKIA,SLOVENIA,TURKEY,UKRAINE

LAC 22

ARGENTINA,BOLIVIA,BRAZIL,CHILE,COLOMBIA,COSTA RICA,DOMINICAN REPUBLIC,ECUADOR,EL

SALVADOR,GUATEMALA,GUYANA,HAITI,HONDURAS,JAMAICA,MEXICO,NICARAGUA,PANAMA,PARAGUAY,PERU,TRINIDAD AND TOBAGO,URUGUAY,VENEZUELA BOLIVARIAN

REPUBLIC OF

MENA 18ALGERIA,ARMENIA,AZERBAIJAN,BAHREIN,CYPRUS,EGYPT,GEORGIA,IRAN,ISRAEL,JORDAN,KUWAIT,LEBANON,MOROCCO,OMAN,QATAR,SAUDI ARABIA,TUNISIA,UNITED ARAB

EMIRATES

NA 2 CANADA,UNITED STATES (USA)

WE 19AUSTRIA,BELGIUM,DENMARK,FINLAND,FRANCE,GERMANY,GREECE,ICELAND,IRELAND,ITALY,LUXEMBURG,MALTA,NETHERLANDS,NORWAY,PORTUGAL,SPAIN,SWEDEN,SWITZE

RLAND,UNITED KINGDOM (UK)

EUROPEAN UNION 28

AUSTRIA,BELGIUM,BULGARIA,CROATIA,CYPRUS,CZECH

REPUBLIC,DENMARK,ESTONIA,FINLAND,FRANCE,GERMANY,GREECE,HUNGARY,IRELAND,ITALY,LATVIA,LITHUANIA,LUXEMBURG,MALTA,NETHERLANDS,POLAND,PORTUGAL,RO

MANIA,SLOVAKIA,SLOVENIA,SPAIN,SWEDEN,UNITED KINGDOM (UK)

REST OF EUROPE 14 ALBANIA,ARMENIA,BOSNIA AND HERZEGOVINA,GEORGIA,ICELAND,MACEDONIA FYR,MOLDOVA,MONTENEGRO,NORWAY,RUSSIA,SERBIA,SWITZERLAND,TURKEY,UKRAINE

AFRICA 31

ALGERIA,BENIN,BOTSWANA,BURUNDI,CAMEROON,CHAD,CôTE

D'IVOIRE,EGYPT,ETHIOPIA,GABON,GHANA,KENYA,LIBERIA,MADAGASCAR,MALAWI,MALI,MAURITANIA,MAURITIUS,MOROCCO,MOZAMBIQUE,NIGERIA,RWANDA,SENEGAL,SIE

RRA LEONE,SOUTH AFRICA,SWAZILAND,TANZANIA UNITED REPUBLIC OF,TUNISIA,UGANDA,ZAMBIA,ZIMBABWE

NORTH AMERICA 3 CANADA,MEXICO,UNITED STATES (USA)

CENTRAL

AMERICA&CARIBE10 COSTA RICA,DOMINICAN REPUBLIC,EL SALVADOR,GUATEMALA,HAITI,HONDURAS,JAMAICA,NICARAGUA,PANAMA,TRINIDAD AND TOBAGO

SOUTH AMERICA 11 ARGENTINA,BOLIVIA,BRAZIL,CHILE,COLOMBIA,ECUADOR,GUYANA,PARAGUAY,PERU,URUGUAY,VENEZUELA BOLIVARIAN REPUBLIC OF

ASIA 29

AZERBAIJAN,BAHREIN,BANGLADESH,CHINA,HONG KONG (SAR of China),INDIA,INDONESIA,IRAN,ISRAEL,JAPAN,JORDAN,KAZAKHSTAN,KOREA

REP,KUWAIT,LEBANON,MALAYSIA,MYANMAR,NEPAL,OMAN,PAKISTAN,PHILIPPINES,QATAR,SAUDI ARABIA,SINGAPORE,SRI. LANKA,TAIWAN (China),THAILAND,UNITED ARAB

EMIRATES,VIETNAM

OCEANIA 2 AUSTRALIA,NEW ZEALAND

EU 28

AUSTRIA,BELGIUM,BULGARIA,CROATIA,CYPRUS,CZECH

REPUBLIC,DENMARK,ESTONIA,FINLAND,FRANCE,GERMANY,GREECE,HUNGARY,IRELAND,ITALY,LATVIA,LITHUANIA,LUXEMBURG,MALTA,NETHERLANDS,POLAND,PORTUGAL,RO

MANIA,SLOVAKIA,SLOVENIA,SPAIN,SWEDEN,UNITED KINGDOM (UK)

SADC 10 BOTSWANA,MADAGASCAR,MALAWI,MAURITIUS,MOZAMBIQUE,SOUTH AFRICA,SWAZILAND,TANZANIA UNITED REPUBLIC OF,ZAMBIA,ZIMBABWE

ECOWAS 8 BENIN,CôTE D'IVOIRE,GHANA,LIBERIA,MALI,NIGERIA,SENEGAL,SIERRA LEONE

ASEAN 7 INDONESIA,MALAYSIA,MYANMAR,PHILIPPINES,SINGAPORE,THAILAND,VIETNAM

PARLACEN 6 DOMINICAN REPUBLIC,EL SALVADOR,GUATEMALA,HONDURAS,NICARAGUA,PANAMA

GCC 6 BAHREIN,KUWAIT,OMAN,QATAR,SAUDI ARABIA,UNITED ARAB EMIRATES

AP 6 CHILE,COLOMBIA,COSTA RICA,MEXICO,PANAMA,PERU

MERCOSUR 5 ARGENTINA,BRAZIL,PARAGUAY,URUGUAY,VENEZUELA BOLIVARIAN REPUBLIC OF

SAARC 5 BANGLADESH,INDIA,NEPAL,PAKISTAN,SRI. LANKA

CEMAC 3 CAMEROON,CHAD,GABON

MCCA 5 COSTA RICA,EL SALVADOR,GUATEMALA,HONDURAS,NICARAGUA

CIS 6 ARMENIA,AZERBAIJAN,KAZAKHSTAN,MOLDOVA,RUSSIA,UKRAINE

ARAB M UNION 4 ALGERIA,MAURITANIA,MOROCCO,TUNISIA

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

High income: OECD 34

AUSTRALIA,AUSTRIA,BELGIUM,CANADA,CHILE,CZECH

REPUBLIC,DENMARK,ESTONIA,FINLAND,FRANCE,GERMANY,GREECE,HUNGARY,ICELAND,IRELAND,ISRAEL,ITALY,JAPAN,KOREA REP,LUXEMBURG,MEXICO,NETHERLANDS,NEW

ZEALAND,NORWAY,POLAND,PORTUGAL,SLOVAKIA,SLOVENIA,SPAIN,SWEDEN,SWITZERLAND,TURKEY,UNITED KINGDOM (UK),UNITED STATES (USA)

Low income 16BENIN,BURUNDI,CHAD,ETHIOPIA,HAITI,LIBERIA,MADAGASCAR,MALAWI,MALI,MOZAMBIQUE,NEPAL,RWANDA,SIERRA LEONE,TANZANIA UNITED REPUBLIC

OF,UGANDA,ZIMBABWE

Lower-middle income 29

ARMENIA,BANGLADESH,BOLIVIA,CAMEROON,CôTE D'IVOIRE,EGYPT,EL

SALVADOR,GEORGIA,GHANA,GUATEMALA,GUYANA,HONDURAS,INDIA,INDONESIA,KENYA,MAURITANIA,MOLDOVA,MOROCCO,MYANMAR,NICARAGUA,NIGERIA,PAKISTAN,P

HILIPPINES,SENEGAL,SRI. LANKA,SWAZILAND,UKRAINE,VIETNAM,ZAMBIA

Upper-middle income 30

ALBANIA,ALGERIA,AZERBAIJAN,BOSNIA AND HERZEGOVINA,BOTSWANA,BRAZIL,BULGARIA,CHINA,COLOMBIA,COSTA RICA,DOMINICAN

REPUBLIC,ECUADOR,GABON,IRAN,JAMAICA,JORDAN,KAZAKHSTAN,LEBANON,MACEDONIA

FYR,MALAYSIA,MAURITIUS,MONTENEGRO,PANAMA,PARAGUAY,PERU,ROMANIA,SERBIA,SOUTH AFRICA,THAILAND,TUNISIA

Advanced economies 36

AUSTRALIA,AUSTRIA,BELGIUM,CANADA,CYPRUS,CZECH REPUBLIC,DENMARK,ESTONIA,FINLAND,FRANCE,GERMANY,GREECE,HONG KONG (SAR of

China),ICELAND,IRELAND,ISRAEL,ITALY,JAPAN,KOREA REP,LATVIA,LITHUANIA,LUXEMBURG,MALTA,NETHERLANDS,NEW

ZEALAND,NORWAY,PORTUGAL,SINGAPORE,SLOVAKIA,SLOVENIA,SPAIN,SWEDEN,SWITZERLAND,TAIWAN (China),UNITED KINGDOM (UK),UNITED STATES (USA)

Commonwealth of

Independent States7 ARMENIA,AZERBAIJAN,GEORGIA,KAZAKHSTAN,MOLDOVA,RUSSIA,UKRAINE

Emerging and

Developing Asia11 BANGLADESH,CHINA,INDIA,INDONESIA,MALAYSIA,MYANMAR,NEPAL,PHILIPPINES,SRI. LANKA,THAILAND,VIETNAM

Emerging and

Developing Europe11 ALBANIA,BOSNIA AND HERZEGOVINA,BULGARIA,CROATIA,HUNGARY,MACEDONIA FYR,MONTENEGRO,POLAND,ROMANIA,SERBIA,TURKEY

Latin America and the

Caribbean22

ARGENTINA,BOLIVIA,BRAZIL,CHILE,COLOMBIA,COSTA RICA,DOMINICAN REPUBLIC,ECUADOR,EL

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

Sub-Saharan Africa 26

BENIN,BOTSWANA,BURUNDI,CAMEROON,CHAD,CôTE

D'IVOIRE,ETHIOPIA,GABON,GHANA,KENYA,LIBERIA,MADAGASCAR,MALAWI,MALI,MAURITIUS,MOZAMBIQUE,NIGERIA,RWANDA,SENEGAL,SIERRA LEONE,SOUTH

AFRICA,SWAZILAND,TANZANIA UNITED REPUBLIC OF,UGANDA,ZAMBIA,ZIMBABWE

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65

XI.1. Appendix III. GE Data Source


66

XI.1. Appendix IV. Cluster Information

2016 ipri full report

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