Country-Risk Measurement and Analysis:
A New Conceptualization and Managerial Tool
The Robinson Country Risk Index is the product of a partnership between Georgia State University’s World Affairs
Council of Atlanta (WACATL) and Center for International Business Education and Research (CIBER), both
affiliated with the GSU Robinson College of Business. Dr. Christopher L. Brown serves as Research Director and is
the leader and catalyst for the conceptualization and development of the index. Dr. S. Tamer Cavusgil provided
important foundational work and leadership, including developing the Opportunity Index at Michigan State
University and in creating the original Emerging Markets Risk Index within the GSU Institute of International
Business. Dr. A. Wayne Lord has been pivotal in bringing the project together and offering key insights across its
entire breadth. Additional critical contributions have been made by Dr. Cedric Suzman, Executive Vice President
and Director of Programs at WACATL; Ms. Paula Reyes del Toro, Mr. Ricardo Orjuela, and Mr. T.J. Ertley,
Graduate Research Assistants assigned to the project by Dean Fenwick Huss of the Robinson College; Ms. Alyssa
Smith and Ms. Joanne Essenwein, Senior Research Associates with WACATL at various points during the project’s
development; and Assistant Dean Dave Forquer and Mr. Jacobus Boers of the Robinson College of Business.
Numerous other wonderful colleagues and research assistants have contributed to the project.
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Country-Risk Measurement and Analysis:
A New Conceptualization and Managerial Tool
Abstract
Country risk has been a topic of investigation for decades, often focused on the risks to business
profits and assets when investing in a country. While there have been gradual improvements in
the analytic techniques and overall breadth of the research, many researchers and practitioners
continue to focus on limited conceptualizations of risk and a relatively small number of political
and economic variables. Researchers such as Nath (2008) have urged scholars to expand the
inquiry, produce better models, and tackle new puzzles. They have also pointed to the changing
global environment, greater availability of data, and enhanced computing techniques.
Advancing earlier works, we make the case for a new conceptualization and measurement of
country level risk. This tool, the Robinson Country Risk Index (RCRI), incorporates four broad
dimensions—Governance, Economics, Operations, and Society—across 67 sub-dimensions, 122
countries, and, at present, 6 years of data. Through the RCRI’s integrated and interactive
conceptualization, countries are not only ranked according to their overall aggregate level of risk
across a wide range of factors, but the investigator is also able to “drill down” and focus on any
of the 268 variables. Countries are clustered by region and perceived level of development and
time-series variable cross referencing and weighting manipulation are put at the user’s fingertips.
Finally, a number of embedded or otherwise available modeling and statistical techniques allow
the user to transform the index given a specific strategic vision, or initiate investigations into key
political-economic puzzles. We argue that the RCRI offers a dynamic new tool for researchers,
practitioners, and educators, as well as a robust, alternative measure of country development.
Key words: risk; country risk; index; ranking; management; tool; operations; development
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Country-Risk Measurement and Analysis:
A New Conceptualization and Managerial Tool
“Keynes… used to say that his best ideas came to him from ‘messing about with figures
and seeing what they must mean.’ He could be as excited as any economist at discovering
correlations in the data. Yet he was famously skeptical about econometrics—the use of
statistical methods for forecasting the future. He championed the cause of better statistics
not to provide material for the regression coefficient, but for the intuition of the
economist to play on. He believed that statistical information in the hands of the
philosophically untrained was a dangerous and misleading toy.”
Robert Skidelsky, “The Return of the Master”
It is now commonplace to theorize and examine how 21st century globalization is changing the
playing field for businesses, governments, and non-governmental organizations (see, for
example, Cusimano, 2007; Reich, 2007; Isdell, 2009; Friedman, 2009; Porter and Kramer, 2011).
Strategic thinkers can be overwhelmed by the many different types of state-level and other
challenges they face as they try to plan, carry out operations, invest, or achieve a wide range of
other goals. Intertwined with the multitude of state-level risks leaders must consider are vast
differences among countries in such areas as size, geography, culture, language, diversity and
other contextual dynamics. To be sure, assessing country risk has been a topic of academic
investigation for several decades, often focused on the risks to business profits and assets when
investing in a country. Yet, while there have been incremental improvements in the analytic
techniques and overall breadth of the research, many researchers continue to focus on limited
conceptualizations of risk and a relatively small number of political and economic variables
(Oetzel, Bettis, and Zenner, 2001; Cruces, Buscaglia, and Alonso, 2002; Bouchet, Clark, and
Groslambert, 2003; Coccia, 2007; Nath, 2008; Funston and Wagner, 2010).
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More recently, Nath (2008) has focused on the urgency and opportunities to expand the inquiry,
produce better models, and tackle new puzzles, pointing specifically to the changing global
environment, greater availability of data, and enhanced computing techniques. Oetzel, Bettis, and
Zenner (2001) analyze and question the usefulness of existing risk measures and point to the
need to construct indices that help pinpoint key risk variables and better forecast instability.
Cavusgil, Kiyak, and Yeniyurt (2004) and Coccia (2007) address the challenges, and yet utility,
in building taxonomic schemes for countries and variables. Ravallion (2012) assesses existing
composite or “mash-up” indices and argues for stronger theoretical clarity and recognition of the
“tradeoffs” conceptual foundations embody. He points to the sensitivity of indices to weighting
and structural changes, as well as data quality, and the ongoing importance of country-specific
contextual factors.
Drawing on such works, we introduce the Robinson Country Risk Index (RCRI). The RCRI
incorporates four broad dimensions—Governance, Economics, Operations, and Society
(GEOS)—across 67 sub-dimensions, 122 countries, and, at present, 6 years of data.1 Through the
RCRI’s integrated and interactive conceptualization, countries are not only ranked according to
their overall aggregate level of risk across a wide range of factors, but the investigator is also
able to “drill down” and focus on any of the 268 variables. Countries are clustered by region
(Africa, East Asia, Europe, Former Soviet Union, Latin America, Middle East, North America,
Oceania, and South Asia) and perceived level of development (Advanced, Developed, Emerging,
Frontier, and Least Developed); time-series variable cross referencing and weighting
manipulation are put at the user’s fingertips. Finally, a number of embedded or otherwise
1
This paper reports results of the 2010 RCRI. The data for the 2011 RCRI, with the best data available in 2011, has
been retrieved and the new version of the index, with 126 countries and 7 years of data, is being tested and cross
checked.
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available modeling and statistical techniques allow the user to transform the index given a
specific strategic construct or risk profile, as well as initiate investigations into key political
economic puzzles.
The RCRI offers a dynamic new analytical tool for researchers, educators, and practitioners
focused on country risk, as well as a robust, alternative measure of country development. As
noted in the introductory quote about John Maynard Keynes above, this tool is best used in
conjunction with the more intuitive, skeptical, comparative-historical, and qualitative
approaches, such as those found throughout the comparative political economy literature and
elsewhere (see, for example, Moore, 1966; Gourevitch, 1986; Hall, 1989; Haggard, 1990; Kholi,
2004; Katzenstein, 2005). In the remainder of this paper, we first address the relevant
background and literature on country risk. We then turn to the logic and limitations of the RCRI,
examining issues surrounding the integrative approach, variable and country clustering, rank
ordering, dynamic interactivity, and parsimony. We conclude by addressing implications and
future avenues for investigation.
1.1
Defining Country Risk and Examining the Literature.
Country risk can be broadly defined as the probability of particular future events within a state
that could have an adverse effect on the functioning of a given organization (or, for that matter,
an individual), whether that organization be a business, government agency, non-governmental
organization (NGO), or other type of body (see, for example, Fitzpatrick, 1983; Harland,
Brenchley, and Walker, 2003; Bouchet, Clark, and Groslambert, 2003; Jensen and Young, 2008).
The multidimensionality of this understanding suggests that the specific factors underlying risk
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change with the context of the organization involved and the specific operationalization of the
dependent variable. What is a significant risk factor to one business or agency, for example
logistics bottlenecks for an NGO such as CARE, might be an opportunity for another, for
example a logistics supplier such as UPS. A certain type of risk, such as a prevalence of malaria
or water scarcity, may be much more important to one organization than it is to another, and
possibly an opportunity for yet another. A risk might be a concern because of potential shortterm profit losses for one organization, because of human suffering for another, and because of
national security threats to yet another. This lack of specificity has lead some researchers and
services to focus on specific, micro-level environmental changes that affect only selected
industries or types of organizations. Others lump key political, economic, and social variables
together into macro-level assessments of country risk that affect all industries, but leave the
determination of what specific variables are causing this risk difficult to ascertain (Oetzel, Bettis,
and Zenner, 2001).
Funston and Wagner (2010) point out that broad-based risk intelligence and management are key
means to the end of not just organizational survival, but also organizational value creation. As
such, researchers and risk services have worked to tackle the conceptual issues surrounding risk,
many centered on risk factors that may lead to losses to business investors. Broadly, these efforts
can be categorized as qualitative and quantitative (Bouchet, Clark, and Groslambert, 2003;
Coccia, 2007; Nath 2008). Qualitative assessments attempt to tackle head on the complexity of
the political, economic, and social aspects of risk without sacrificing granularity and context,
often weaving key statistics into their analysis. They generally rely on the perceptions of expert
analysts and can sometimes lack a structured format, making it difficult for users to compare
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countries. Still, whether the format is structured or unstructured, the uniqueness of each country
can be examined in extensive detail. The Economist Intelligence Unit (EIU), Stratfor, and
Business Environment Risk Intelligence (BERI) offer risk analyses which fit in this category.
Also in the qualitative grouping are services, such as the World Economic Forum’s Global
Competitiveness Report (GCR), which ask for expert, Likert-scale, perceptive scoring from
lowest to highest across a menu of variables. The benefit here is that the final average scores lend
themselves to quantitative use.
Some quantitative measures and services, such as those provided by Political Risk Services’
(PRS), Maplecroft, the Caux Roundtable, and Euromoney and Institutional Investor magazines,
attempt to rank order countries relative to each other or otherwise give them a risk rating. Rank
ordering can be broad across all countries, with respect to clusters of countries (such as “Latin
America” or “Emerging Markets”), or at the micro-risk level (such as “infectious diseases” or
“transportation infrastructure”). Risk Ratings can be through qualitative expert perceptions
(noted above) or based on hard data (or both). This field of inquiry includes a variety of
methodologies; indeed, relative rank methodologies vary considerably given the different goals
and conceptualizations of the investigation involved. Some rank orderings are not necessarily
focused on risk as specifically defined, but their subject matter is often tied to risk nonetheless,
such as the World Economic Forum’s Global Gender Gap Report (GGR), which is focused on
gender equality.
Quantitative approaches to risk analysis and forecasting sometimes incorporate econometric and
statistical modeling techniques, such as principal component analysis or regression analysis, for
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such reasons as finding relationships between variables endogenous or exogenous to the equation
at hand, moving toward parsimony in explanation, or taking the first steps toward forecasting
(Bouchet, Clark, and Groslambert, 2003; Coccia, 2007; Nath, 2008). Others have looked to
insurance markets to see if they offer better estimators of political violence risk (Clark, 1997;
Jansen and Young, 2008). Nath (2008) argues that while all such efforts have shown gradual
improvements, none is adequate in terms of scope and coverage. Hayes (1998) echoes this in
arguing that the enhanced speed of financial contagion, and hence business loss, augurs the need
for expanded scope in country risk analysis. In the end, whether initially more quantitative or
qualitative, most country risk efforts work to combine qualitative and quantitative approaches
and move on to forecasting the probability of adverse effects on business. However, Oetzel,
Bettis, and Zenner (2001) find that while country risk services sometimes do well in projecting
standard trends, they are not able to forecast major crises, the “black swan” rare events that lead
to substantial loss, disruption, or even a “punctuated equilibrium” that results in substantial
change. Funston and Wagner (2010) focus specifically on the organic risk intelligence and
management strategies needed to best identify the range of risks an organization faces (including
black swan events), match available risk management resources to the priority of the risk, and
allow the organization to survive and thrive. As discussed below, one avenue for future research
is to project trends using the RCRI to see if it can be a tool to add to this conversation.
In line with this literature, we conceptualize a country risk index based on a broad set of data,
some newly available, and a more integrated, dynamic, and ecological conceptualization of
country risk that we believe lends itself to better modeling and statistical analysis. The RCRI
provides a wider lens than previous quantitative measures, while at the same time allowing the
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researcher or practitioner to calibrate or refine the focus, depending on the specific area of
interest or organizational activity. It is a broad tool that can be sharpened, through weighting
changes, the addition or subtraction of data, econometric techniques, or even complete
reconceptualization, given the purposes of the organization or researcher. We argue that, within
its limits, the index can help tackle broad and narrow puzzles, some organic to its endogenous
variables and conceptualization, others with the index or its component parts serving as
independent or dependent variables, and others after the index has been reconceived. Of course,
we also stress the need for broader qualitative analysis; intuitive approaches are still needed. We
now turn to a more specific discussion of the construction of the index, focusing on variable and
group taxonomy, country ranking, the index’s dynamic interactivity, and moving toward more
parsimonious equations.
1.2
RCRI Variable Taxonomy and Data.
As noted by Coccia (2007), building a taxonomy or classification scheme is useful when it
maximizes the differences among groups but reduces the complexity of a population of variables
or countries into easily recallable macro-classes. For example, Political Risk Services’
“International Country Risk Guide” is based on 22 variables grouped into political, economic,
and financial risk categories, with the 12 variables that make up the political risk category
incorporating 15 additional variables or “sub-components.” Maplecroft uses a wide variety of
variables to construct some 500 indices tied to global, political, legal and regulatory, human
rights, and climate risks. The Caux Round Table incorporates 14 indicators across three main
categories—economic activity, social/cultural variables, and legal and political institutions—to
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measure the “social capital achievement” which “structures… risk/return functions” (Young and
Lindstrom, 2011).
Similar to variable taxonomies, Cavusgil, Kiyak, and Yeniyurt (2004) review the rich literature
on country clustering, substantiating the need to cluster different types of countries which offer
different opportunities. Grouping countries by “region” or “level of business opportunity” can be
helpful; however, they also note that country clustering is generally tied to aggregate macroindicators or assumptions and can fall short when the analyst wants to move on to specific sector
or otherwise more micro-level country analyses which belie the clusters chosen. They discuss
how country clustering assumes that countries are homogenous units, and how within country
heterogeneity is generally ignored. Another significant problem is the comparability, currency,
and reliability of data across countries. Despite such challenges, building taxonomic schemes for
countries and variables is an important first step in comparative, quantitative, country risk
analysis. An important goal must be that the tool developed can then be dynamically widened or
focused as the specific strategy, sector, or area of interest is made clearer.
In looking at “mash-up indices of development,” Ravallion (2012) echoes some of the concerns
outlined above, and adds clarity to the “warning signs” needed when constructing composite
indices. Specifically, he notes the need to be clear on the conceptual foundations of any “mashup” index and the tradeoffs they embody. Our focus here is on country risk, as defined and with
the multidimensionality outlined above. Ravallion also points to the explicit theoretical
challenges encountered when deciding on such things as weights, the data to use, and index
structure. He calls for transparency and an understanding of the risks or costs, not just benefits,
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potentially involved for organizational decisional making. How we tackle these challenges and
seek to put a tool in the hands of educators, practitioners, and researchers is discussed in the
pages below.
In this paper we make the case for a broad based country risk index based on an integrated
taxonomic scheme for endogenous variables and country clusters that if necessary can be
customized by users given their specific questions, risk profiles, and strategic priorities.
Customization can be done by adjusting weights, adding new data, subtracting unnecessary data,
re-conceptualization, or a combination of these steps. The RCRI draws Governance, Economic,
Operations, and Societal—GEOS—variables into a broader whole, yet embeds or otherwise
lends itself to modeling and statistical techniques which make the index dynamic and useful to a
wide variety of audiences. The GEOS taxonomy draws on the social science development
literature which calls for holistic, human centered views of country development and integrated,
clinical, country diagnoses (see, for example, Park, 1984; Sen, 1999; Sachs, 2005; Rapley,
2007). In a sense, a goal of the RCRI is to allow the user to run diagnostics on a country (or
region, cluster, variable, or set of variables) within a specified strategic vision or research
question. By being interactive and broadly based, the RCRI not only seeks to help the user get to
parsimony given a specific dependent variable, but also to spark that intuition and probing which
leads to broader understanding and effect decision making.
1.2.1
Why “GEOS”?
Building an organic, statistical ecology for a country is in one sense an impossible proposition,
given the inability to measure certain factors, the limits of data availability and reliability, and
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the difficulty of understanding something so complex and contextualized as a “country ecology.”
Yet we argue that it is worthwhile to strive to think ecologically, use the significant sources of
data now available at the country level, and build a broad-based and dynamic index with distinct
macro-dimensions that cover major areas of country risk. Indeed, it is through building the index
that we seek to address some of the methodological concerns above.
Most existing country risk indices incorporate political and economic data into their analysis, and
some draw on societal variables. With excellent scope and coverage, Maplecroft possibly comes
the closest to the ideas offered here, but is markedly different in overall conceptualization and
variable taxonomy, not as focused on interwoven longitudinal dynamics, and often tied to its
own perceptive scoring. By weaving GEOS together we argue that the RCRI offers distinct
taxonomic categories which crudely approximate a country’s ecology more fully than other risk
indices. Indeed, a central and evolving criteria in all the conceptualizations surrounding the
RCRI’s dimensions and sub-dimensions is the rudimentary approximation of an integrated view
of these levels within the larger country whole. The RCRI is particularly distinguished from
other risk indexes and services by its extensive and holistically conceived operational and
societal macro-dimensions. Figure 1 graphically depicts the RCRI taxonomy and weighting.
Figure 1
The RCRI Taxonomy
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Governance
(25%)
Voice and
Accountability
(16.67%)
Political Stability and
Absence of
Violence/Terrorism
(16.67%)
Government
Effectivness
(16.67%)
Economics
(25%)
Macro-Economic
Indicators (55%)
• Broad Economy (GDP
Growth, GDP per Capita,
Current Acct., Inflation Rate,
Unemployment Rate)
• Finance (Public Debt, Budget
Balance, Interest Rate
Spread, Savings Rate,
Reserves/Short Term Debt)
Business Transaction
(37.5%)
(Starting/Closing a Business,
Getting Credit, Paying Taxes,
Enforcing Contracts, etc.)
Market Access (35%)
Regulatory Quality
(16.67%)
Health (16.67%)
• Major Indicators (Life Expectancy, Infant
Mortality, Clid Malnutrition, Doctor
Availability/10,000 hospital beds/10,000)
• Infectious Diseases (AIDS, Malaria, and TB
Prevalence Rates)
• Substance Abuse (Cannibis, Amphetamines,
Cocaine, Opiates, Alcohol, Tobacco)
Logistics (15%)
(Shipping Infrastructure,
Timliness, Tracking Ability,
Customs, etc.)
• Trade Profile (Trade Index,
Barriers to Trade)
• Investment Profile (FDI Flows,
FDI Stocks)
Society
(25%)
Operations
(25%)
Operational
Landscape (42.5%)
• Innovation and
Sophistication
• Infrastructure
• Tech Readiness
• Business Environment
• Market Efficiency
Education (16.67%)
• Basic Education Indicators and Primary
Education
• Higher Education and Training
Demographic Dynamic (16.67%)
(Population, Pop. Over 65, Birth/Death Rates,
Population Growth, Population Density,
Urbanization)
Gender Gap (16.67%)
Rule of Law (16.67%)
Currency Over/Under
Valuation (10%)
Short Term Currency
Fluctuation (5%)
•
•
•
•
Female/Male Econ. Part. and Op.
Femal/Male Ed. Attainment
Female/Male Health and Survival
Female/Male Pol. Empowerment
(1-Year Measure)
Control of Corruption
(16.67%)
Inequality/GINI (16.67%)
Environmental Sustainability
(16.67%)
(Enviromental Public Health, Ecosystem
Vitality)
The GEOS taxonomy and weighting scheme was conceived between the spring of 2009 and the
summer of 2011 with extensive input from researchers across a variety of academic disciplines
and practitioners from the business, government, and civil society sectors. Some RCRI
dimensions and sub-dimensions borrow from the structural conceptualizations and weightings
found in its more than 20 main sources, though most are uniquely conceptualized. We emphasize
that the RCRI’s overall conceptualization is exploratory and open to further analysis, critique,
and development. For example, we continue to work to strengthen the RCRI’s Health subdimension by finding longitudinal data on diseases associated more with the developed world
(heart disease, diabetes, cancer, etc.) to add to those associated more with the developing world
(HIV/AIDS, tuberculosis, malaria, etc.). Similarly, while the index’s Society dimension in
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general, and its’ Inequality sub-dimension more specifically, give insight into a country’s
propensity toward a strengthening middle class, we plan to add a Middle Class Propensity subdimension to the Society dimension in subsequent iterations of the index. The base weighting
schema might be refined through further perceptive surveying or statistical analysis. The RCRI
conceptualization is tied to the country risk definition stated above—namely, the probability of
particular future events within a state that could have an adverse effect on the functioning of a
given organization—but given the multidimensionality tied to this definition, the dynamic ability
to adapt the tool must be part of the core conceptualization of the project. The RCRI must be
able to have “offspring,” solving for different strategies, risk profiles, or puzzles tied to the
specific interests of the user and requiring the user to be responsible for the importance of
weighting, construction, and data quality and availability (despite the Keynesian concerns noted
in this paper’s introductory quote). As a secondary function, we argue the index offers a robust,
exploratory measure of development, given its crude but holistic, ecological conceptualization.
Here, we look at each dimension and its sub-dimensions in turn.
1.2.1.1
Governance.
As the sovereign actor within a given country, governments play the central thinking role for the
state. Hence, “governance” is central to any risk analysis, with much of the risk analysis field
focused on “political risk.” The RCRI’s Governance dimension is drawn from the World Bank’s
Worldwide Governance Indicators (WGI; Kaufmann, Kraay, Mastruzzi, 2010). Since 1996, the
WGI has examined more than 300 variables to construct six broad governance categories: Voice
and Accountability, Political Stability and Absence of Violence/Terrorism, Government
Effectiveness, Regularity Quality, Rule of Law, and Control of Corruption. It uses more than 30
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perceptive data sources, including surveys of households and firms, NGOs, public sector
organizations, and commercial business information providers, as well as its own unique
methodology, to compile these six macro-variables. We choose to rely on WGI because of its
integrative breadth across six main governance sub-dimensions; however, the complexity and
country-level uniqueness of the WGI methodology limits the RCRI users’ ability to focus the
RCRI Governance lens past the six main macro-variables. As such, Governance represents only
seven of the RCRI’s 268 variables (an overall Governance score and the six WGI scores), despite
being composed of a robust number of indicators. This limitation offers avenues for possible
future improvement of the RCRI. However, another advantage of the WGI is that, even though
its six macro-variables are composites of many variables, there is only minor overlap with the
numerous RCRI variables in the other three macro-dimensions.
It should also be noted that there are a number of other respected data sources for governance
indicators which were considered, such as those offered by Freedom House and the Polity IV
project, but these were seen as not directly amenable to the RCRI conceptualization or were
already included amongst the 300-plus WGI perceptive variables. It should also be noted that
some risk services, including Maplecroft and PRS, offer their own rich, alternative
conceptualizations of political risk. In PRS’s case, this data is also included in the WGI scores.
These alternative conceptualizations again point to possible future avenues of research.
1.2.1.2
Economics.
As noted by Rapley (2007):
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“… a theory that separates the state from society is rather like a medical lecturer who
treats the human head and body as distinct. The head may govern the body, but that does
not make it independent of the body.”
In line with this type of thinking, most risk researchers and services incorporate macro-economic
variables into their analysis, representing the musculoskeletal structure within which
organizations operate. The RCRI’s Economics macro-dimension includes three main subdimensions: Macro-Economic Indicators, Market Access, and Currency Over/Undervaluation.
The first two of these sub-dimensions have extensive “drill down,” with Macro-Economic
Indicators being broken into “Broad Economy” and “Finance” sub-dimensions before lending
themselves to further focusing across 14 variables. Market Access is sub-divided into “Trade
Profile” and “Investment Profile,” which have 4 and 6 sub-dimensions, respectively, and focus
the lens inside such areas as Barriers to Trade and Foreign Direct Investment Flows. In total,
Market Access includes 31 variables.
The Currency Over/Undervaluation variable is seen as a central barometer for a country’s overall
macro-economic health and is weighted at 10 percent of the Economics macro-dimension (2.5%
of the index). Data here was derived from an ongoing annual assessment by the Peterson Institute
(Cline and Williamson, 2008-2011), which we believe gives the best estimates of currency
over/undervaluation. However, the Institute’s work only includes 48 of the RCRI’s 122
countries, again pointing to a possible future area of improvement of the RCRI. The
methodology for addressing missing country data is covered below. Currency
Over/Undervaluation is the only variable where we do not treat the data in a linear fashion, using
the absolute value of the scores, because neither a high positive score nor a high negative score is
desirable (other issues surrounding the treatment of data are discussed in the “Data Challenges”
16
section below). Overall, the Economics macro-dimension includes 47 variables within its own
integrative structure. In addition to the Peterson Institute, data is retrieved from the U.S. Central
Intelligence Agency’s World Factbook, the International Monetary Fund’s World Economic
Outlook, the World Trade Organization’s World Trade Organization Statistics, the United
Nations Conference on Trade and Development’s World Investment Report, the World Bank’s
Joint External Debt Hub and World Trade Indicators, and the World Economic Forum’s Global
Competitiveness Report (GCR) and Enabling Trade Report (ETR).
1.2.1.3
Operations.
The RCRI offers a robust and unique Operations dimension in the belief that this dimension is
important not just because it is closely tied to the success of a particular organization in a
country, but also because it is analogous to a state’s circulatory system. The RCRI’s Operations
dimension has 4 main sub-dimensions, Business Transactions, Logistics, Operational Landscape,
and Short Term Currency Fluctuation, each of which provides a different element within a
country’s overall operational dynamic. The first three of these dimensions have additional subdimensions, allowing the user to “drill down” as many as five levels into the data. The
construction of a unique Operational dimension is possible because of data newly available over
the last several years. Overall, Operations includes 112 of the RCRI’s variables.
1.2.1.3.1
“Business Transactions”.
The Business Transactions data is drawn from The World Bank’s Doing Business project, which
since 2003 has examined and measured the regulations applying to domestic small and mediumsize companies, with the goal of providing an objective basis for understanding and improving
17
the regulatory environment for business. The RCRI incorporates each of Doing Business 9
quantitative measures: Starting a Business, Construction Permits, Registering Property, Getting
Credit, Protecting Investors, Paying Taxes, Trading Across Borders, Enforcing Contracts, and
Closing a Business. Each of these measures includes further sub-dimensions. The driving
premise of Doing Business is that economic activity requires good rules, and the overall
objective is that regulations are efficient, accessible, and simple to implement. To this end,
Doing Business includes perceptive and hard data. The perceptive data comes from assessing
laws and regulations; the hard data are “time and motion indicators that measure the efficiency in
achieving a regulatory goal (such as granting the legal identity of a business).” Doing Business
describes itself as a “kind of cholesterol test for the regulatory environment for domestic
businesses” (Doing Business, 2010).
1.2.1.3.2
“Logistics”.
If Doing Business is a cholesterol test for the regulatory environment, then the World’ Bank’s
Logistics Performance Index (LPI), published bi-annually starting in 2007, is a cholesterol test
for a country’s logistics infrastructure. The RCRI includes each of the LPI’s six indicators:
Customs Logistics, Logistics Infrastructure, International Shipments Logistics, Logistics
Competence, Tracking and Tracing Logistics, and Logistics Timeliness. The LPI is based on
perceptive surveys of operators on the ground (global freight forwarders and express carriers).
These operators provide feedback on the logistics “friendliness” of the countries in which they
operate, and on the logistics “friendliness” in those countries with which they trade. Every
country is ranked on a scale of one to five on each indicator, with one being the worst
performance and five being the best performance.
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1.2.1.3.3
“Operational Landscape”.
The RCRI’s Operational Landscape sub-dimension is a composite of data drawn selectively from
the World Economic Forum’s GCR, published since 1979, and ETR, published since 2008, with
one additional variable, “Labor Force Participation,” drawn from the World Bank’s World
Development Indicators. We include five main sub-dimensions under Operational Landscape—
Innovation and Sophistication, Infrastructure, Technological Readiness, Business Environment,
and Market Efficiency—each of which is distinct from other variables in the Operations macrodimension, but helps give a more nuanced view of the arteries, veins, and capillaries in a
country’s cardiovascular system. For example, drilling into Innovation and Sophistication one
finds such variables as “Local Supplier Quality” and “Availability of Scientists and Engineers”
amongst the 19 variables; Technological Readiness includes “Availability of Latest
Technologies” and “Internet Bandwidth” amongst its 9 variables; Market Efficiency includes
“Effectiveness of Anti-Monopoly Policy” and “Reliance on Professional Management” in its 12
variables. It should be noted that the World Economic Forum’s GCR and ETR report some of the
same variables, and sometimes move the variables within their various “pillars.” However,
because of the RCRI’s conceptualization, longitudinal focus, and overall purposes, we
selectively draw data from these sources and keep related but stable structures. We revisit this
issue as necessary.
1.2.1.3.4
“Short-Term Currency Fluctuation.”
Currency is the best approximation of the blood running through the circulatory system of a
country, and the daily fluctuation in the value of the currency measures the pulse. As such, we
19
included a single variable sub-dimension in the Operations macro-dimension, Short-term
Currency Fluctuation, defined as the standard deviation of the daily fluctuation of exchanges
rates vis-à-vis the US Dollar, with the United States and Ecuador expressed vis-à-vis a basket of
currencies. This variable has a 5 percent weight within the Operations macro-dimension (1.25
percent of the overall index; see Figure 1 above), with the data coming from Oanda.com and the
International Monetary Fund. Of course, countries with controlled or manipulated exchange rates
can score well on this indicator, just as a pulse may not help in uncovering a disease, or may
even mask the disease itself. This issue is somewhat counter set against the Currency
Over/Undervaluation variable discussed above. As noted above and below, the ability to
recalibrate the index is an important part of its interactivity.
1.2.1.4
Society.
Societal variables pump life into a country, and the RCRI includes 101 variables in its Society
macro-dimension, which is sub-divided into 6 main sub-dimensions: Health, Education,
Demographic Dynamic, Gender Gap, Inequality, and Environmental Sustainability. Each of
these sub-dimensions outside Inequality, which is based on Gini scores, has multiple additional
sub-dimensions, drilling as many six levels into the data. All but Gender Gap and Environmental
Sustainability have uniquely derived conceptualizations, with Gender Gap borrowing its
structure from the World Economic Forum’s Global Gender Gap Report (GGR), and
Environmental Sustainability borrowing its structure from the Environmental Performance Index
(EPI), jointly produced by the Yale Center for Environmental Law and Policy and the Center for
International Earth Science Information Network at Columbia University. The Gender Gap
Report was first produced in 2006 and is based on hard data, with sources including the United
20
Nations Development Programme, the International Labour Organization, the World Bank,
UNESCO, the World Health Organization, the Central Intelligence Agency, and the InterParliamentary Union. While the GGR only dates to 2006, we pulled much of the data back to
2005, to fit the RCRI’s time dimension. Still, we incorporate the World Economic Forum’s
structure, which fits the RCRI’s overall purposes. As for the Environmental Performance Index,
it had a first iteration in 2005, but the variables and methodology have changed considerably
over time. Because of this variation, the RCRI incorporates EPI data starting in 2008. We
emphasize that wherever the RCRI borrows structure from another source, we believe the given
source has the best available data, often available nowhere else, and a conceptualization which
fits the criterion of crudely approximating that sub-dimension’s dynamics within the overall
RCRI ecology.
The unique Health (18 variables across 3 sub-dimensions), Education (16 variables across 3 subdimensions), and Demographic Dynamic (8 variables) sub-dimensions follow the same logic and
are drawn from a wide variety of data sources. Overall, the Society macro dimension draws from
8 sources thought to be the best available for that data. In addition to EPI and GGR, these
sources include the CIA’s World Factbook, United Nations Children’s Fund’s State of the
World’s Children, the World Bank’s World Development Indicators, the United Nations Office
on Drugs and Crime’s World Drug Report, the World Economic Forum’s GCR, and the World
Health Organization’s World Health Statistics.
1.2.2
Data Challenges.
21
Challenges exist with some of the RCRI data, particularly in three of the Society sub-dimensions,
because the risks presented by these variables are not necessarily linear, the data is previously
transformed by the data source, the data is truncated or targeted by the data source, or because
the RCRI borrows structure from a source, but uniquely and generally transforms the data in its
own way (see the RCRI Data Transformation and Country Ranking section below). For example,
in a much explored and debated hypothesis, Kuznets (1955) suggests that GINI scores represent
an inverted-“U” shape with respect to economic growth over time (income inequality first
increases and then declines as liberal economic development continues); however, because of the
breadth of the countries covered in the RCRI, the RCRI’s broad-based data transformation, and
the strains on the working and middle classes often presented by high inequality, this variable is
treated as a linear risk within the index, with higher GINI scores translating to higher risk. As
noted above, we plan to add a more robust Middle Class Propensity sub-dimension to the Society
dimension in subsequent iterations of the index.
Similarly, high population or population growth can be a risk; however, the RCRI uses a “people
are good” principle when measuring demographic variables because for many organizations
(particularly businesses), as well as for the countries themselves, the “people are good” principle
generally holds. Also, it is difficult to treat the demographic data any differently, yet necessary to
include demographics within the broadly conceived index.
Two of the RCRI’s sources for the Society dimension, the Gender Gap Index and the
Environmental Performance Index, uniquely treat their data because of their focus on gender
equality and environmental policy targets, respectively. The Gender Gap Index truncates its
22
measures at equality, not giving benefit or penalty to countries for variables where female values
exceed male values. We have adopted this truncation in the RCRI, but then transform the data in
a method unique to the RCRI, leading to somewhat different, but generally very small,
differences in the aggregated country rankings for the Gender Gap sub-dimensions. Still, there
are three countries which shift more than 30 ranks given the RCRI methodology. Importantly,
throughout the index there are no ranking differences at the raw data/base variable level, only in
aggregation to higher dimensions because of the RCRI aggregation methodology (see below).
The Environmental Performance Index data is uniquely and selectively transformed (i.e.,
selective use of varying logarithmic equations, winsorization, and imputation) by its authors
before being targeted toward meeting a percentage of specific policy goals. However, because
we do not generally want to transform already transformed data, as well as the fact that the
RCRI’s focus is on overall risk, we choose to use the raw data available from the EPI and
transform the data using the RCRI methodology discussed below. However, we do adopt the EPI
selective imputation method for missing variables in their data set, as this is a choice of
internally addressing missing raw values (also see below for a discussion of the RCRI general
methodology for missing data). Because of the decision to use the EPI raw data and the RCRI’s
own unique model of transformation, the rankings in the RCRI also show mostly minor
differences from those in the EPI. As with other sources, there are no ranking changes at the
base, raw data level. Still, a few cases rose or fell significantly, including four by more than 30
spots. We tested for ranking changes using the EPI’s selectively transformed and targeted data,
but they were much more significant than going with the raw data given the confluence of
different methodologies.
23
Outside the Society dimension, the data challenges are less significant. Indeed, because the
rankings only change in aggregation and not at the raw data level, similar dimensional ranking
changes do not occur with Worldwide Governance Indicators, which does not aggregate its
variables, and Logistics Performance Index, which only aggregates up one level. World
Economic Forum data only shows very minor variation because of a difference in how ranks are
assigned in countries with equivalent scores. Doing Business data, which does aggregate
upwards several dimensions through a simple averaging approach, does show some minor
ranking changes given the use of RCRI’s aggregation method; 6 countries change more than 10
places. Finally, while we treat the Short-Term Currency Valuation linearly and use a zero target
for Currency Over/Undervaluation, arguments can be made that a country can at times want
some short term currency fluctuation or over/under currency valuation. Each of these challenges
points to ongoing areas of investigation as we continuously build the dynamic, interactive, and
holistic country risk index.
1.2.3
Data Collection.
The “Golden Rule” for pulling RCRI data is that the data used for each year’s index is the best
available data released or available during that year. In other words, the data for the 20XX RCRI
is the best data released or available during 20XX, regardless of the name of the source (it might
be titled with a previous or following year) or if the data is known to have been gathered in the
previous year or years. However, because of the numerous sources used to construct the RCRI
and their individual dynamics, some caveats are still necessary. For example, some sources
revise their data for previous years in their current releases. In this case, the previous years of the
24
RCRI are revised to reflect these source revisions. Also, while pulling data forward from
previous years, if it is the best data available, is used for several Society variables, the Logistics
variables in Operations, and the Barriers to Trade sub-dimension in Economics, owing to the
more slow-moving nature of these variables and the fact that they are not updated annually (often
both), this principle is not used for other data points, such as in carrying forward GDP Growth
from a previous year. Another issue is when RCRI sources change their data collection
techniques or types of data reported. Each of these technical details is tracked and regularly
reviewed.
The RCRI reports 122 countries because of the limits of data availability across a wide variety of
sources. We could not take the index prior to 2005 for similar reasons. Specifically, we used a 15
percent threshold in 2010 for missing data in the RCRI. Notably, a small subset of countries had
significantly higher levels of missing data in years prior to 2010. For example, Cote D'Ivoire had
40.76 percent missing data in 2005, but dropped to only 4.41 percent missing data in 2010. The
15 percent threshold in 2010 was a natural break in the percentage of missing data, with the
countries not meeting this threshold having more than 30% of missing data. Eighty-six percent
(105/122) of countries had less than 5% of missing data; 98.36 percent (120/122) had less than
10 percent missing data; average missing data was 2.61 percent and missing data is reported
within the interactive index. Finally, it should be noted that in addition to the more than 20 main
sources used to construct the RCRI, additional sources are used to fill data holes; for example,
much of the Taiwan and Hong Kong data has to be retrieved directly from the Taiwan and Hong
Kong government websites.
25
1.2.4
Country Clusters.
As noted, in addition to a variable taxonomy, the RCRI includes country clustering. This
clustering is done by world region and perceived level of development. To determine regions we
used standard classifications done by a variety of organizations, such as the World Bank.
Sometimes countries are cross-listed in more than one region, such as Turkey and Mexico. For
development clusters, standard services such as MCSI Barra and FTSE offer delineations of
advanced, emerging, and frontier markets, and we adapted these classifications, recognizing they
are dynamic and change over time. We argue that in the future the RCRI will be useful in
constructing developmental clusters, but for present purposes we consulted how other
organizations built their country taxonomies and adapted them as we built the RCRI country
clusters.
1.3
RCRI Data Transformation and Country Ranking.
Data transformation and aggregation, as well as country rank-ordering, are well worn
methodological endeavors, but the specific steps used vary considerably and are not without
controversy. In developing our methodology, the RCRI team drew on methods cited by other
indexes, including several noted above, as well as the previous work of Cavusgil (1997) and
Cavusgil, Kiyak, and Yeniyurt (2004); however, given its broad focus, diversity of data, and
multileveled, dynamic conceptualization, the RCRI’s techniques are unique. Here, we discuss
how we transform and aggregate the data; address data directionality, missing values, and
extreme outliers; and compute the ranks and scores for countries as well as regions and clusters.
1.3.1
26
RCRI Data Transformation and Aggregation.
Figure 2
RCRI Data Transformation
Raw Data Matrix
Aggregate next level,
standardize ... and so on
Standardize Log(raw)
i.e. (x-miu)/sigma
Take Log(Raw)
Standardize next level i.e.
(x-miu)/sigma
Aggregate Variables
(Weighted average)
The last step is the
overall standard score:
weighted avg. of GEOS
standardized
As depicted in Figure 2, we take the natural log of the RCRI raw data matrix, changing the scale
from the decimal scale to logarithmic scale, which shifts the data toward a natural distribution or
otherwise corrects for highly skewed distributions. However, given that the logarithmic function
works only for values greater than 0 (you cannot take the log of a negative number or zero), and
the fact that the log of numbers between 0 and 1 is negative, we took two additional steps. First,
we shifted the logarithmic curve by 1 so as to retain the zero values and make values between 0
and 1 positive after transformation. Second, for negative raw values, we took the log of the
absolute value of the variable plus 1, and then made that value negative, or a mirror of the
positive function.
Mathematically:
f(x) = -Ln(│x│+ 1)
if
x<0
f(x) =
if
x≥0
Ln(x + 1)
27
After logarithmically transforming the data, each sub-dimension, for each year, needs to be
standardized by using the formula (x-)/where represents the annual sub-dimensional mean
and  represents the annual sub-dimensional standard deviation. This step makes all variables
comparable within a given annual sub-dimension, with a mean 0 and standard deviation 1. Using
the RCRI weights, we then aggregated upwards. After each level of aggregation the resulting
variables are standardized again, so that they can be aggregated upward again, using the
determined weights, until all the aggregations are complete for a given year.
1.3.2
Directionality, Missing Values, and Extreme Outliers.
To address the directionality of the data (i.e. whether a high score is good or bad), the standard
scores are multiplied by 1 or -1. In general, high standard scores are coded as bad and low scores
are good, but if the high score is good for a particular variable, then if it is multiplied by -1, the
country’s high score becomes a low score and the variable is “reversed.” As noted above, for
Currency Over/Undervaluation we use the absolute value, because neither a high positive score
nor a high negative score is desirable.
For missing values, because our goal is accuracy as well as to have a flexible index, we decided
not to use the other standardized values within a level of aggregation to get a weighted average
to impute for missing values. We instead leave missing values as “N.A.”, and then use the
weighted average of the remaining values within the level of aggregation to aggregate upwards.
When all values are missing for a given level of aggregation, then the higher level of aggregation
is left as an “N.A.”. This methodology influences the ranks/scores in comparison to using
imputed data for missing values as done by some researchers, but we believe it is a more
28
accurate reflection of reality and allows us to construct a more dynamic index, where users can
“Build Your Own Index,” with their own unique conceptualization and weighting schema (see
below).
While we use a logarithmic transformation to push the data toward more normal distributions,
some variables still have significant outliers. Only in one case, however, did we decide to further
treat the data. Zimbabwe had a reported inflation rate of 14.9 billion percent in 2009, and
extremely high scores in other years 2005-2008. These reported rates so skew the inflation
distributions that we considered winsorizing at 99 percent. In other words, if 14.9 billion percent
is the worst rate, then countries such as Venezuela with 30 or 40 percent inflation get extremely
good scores because the worst score is so extreme. If you winsorize you bring your extreme
outliers back to the edges of the distribution. However, since we have 122 countries, a 99
percentile winsorization is close to the next worst country’s raw value, so instead of winsorizing
at 99 percent we manually replaced Zimbabwe’s inflation with the next worst value for every
year 2005–2009 (but we kept the actual raw data for reportage in the index). This manual
winsorization means that we generally have more representative RCRI scores, and Zimbabwe
still gets the worst score. An example of the drawback is that Ethiopia, second worst in 2009, is
seen as equally bad as Zimbabwe for that particular year. Starting in 2010, Zimbabwe’s inflation
was no longer extreme and the index was computed without any winsorization.
1.3.3
Ranks and Scores.
After computing all the standard scores we rescale the data 1 to a Maximum Score (1000 by
default, although this can be changed within the interactive index) using the formula:
29
(ScoreCountry i – Sample Minimumall countries)/(Sample Maximumall countries – Sample Minimumall
countries)*999+1
After deriving the scores, we rank them from 1 to 122, the number of RCRI countries in 2010.
Note that within the RCRI, high scores are desirable, with the lowest rank (#1) receiving the
highest score (1000).
To determine a cluster or regional score for any variable, either a simple average can be used
within the index, or, by clicking a button, country scores within a region/cluster can be weighted
by population. This is important because you may want to know what the simple average is for
Emerging Markets with respect to “Business Transactions”; alternatively, you may want to know
what the European average is of “GDP per capita,” but rich Germany, with a much larger
population, should count proportionally more than poor Macedonia, with a much smaller
population. Whichever method is used, regions are ranked against regions (9 total) and clusters
are ranked against clusters (5 total), again with low ranks and high scores designating lower risk.
1.4
Overall Results, Dynamic Interactivity, and Dashboarding.
Appendix A reports the overall 2010 RCRI rankings and the rankings across each macrodimension. Longitudinally, these results show some similarities with the 2005-2009 overall
RCRI rankings. However, 26 of the 122 countries show a +/- movement of 10 or more places in
the rankings over the 6 years (2005-2010); 12 of the 122 show a +/- movement of 15 or more
places; and 7—Albania (+26), Bosnia and Herzegovina (-29), Botswana (-20), Georgia (+29),
Mauritania (-30), Nepal (-23), and Venezuela (-26)—show a movement of 20 or more places.
30
Each of the significant movers rose or fell for different reasons, and, as noted, the index allows
the user to drill down to the major factors driving the change. For example, India fell 12 places
between 2006 and 2009, from 62nd to 78th. Societal factors lead the downward movement (see
Appendix B), with a fall of 40 places between 2006 and 2009 sparked by notable declines in the
Inequality, Education, and Gender Gap sub-dimensions, although the country also faces
significant Health and Environmental challenges. Interestingly, India gained 22 places on the
Economics dimension between 2005 and 2010, with a 14 rank change between 2009 and 2010
alone. The most significant driver in this rise was a marked jump in the country’s Investment
Profile.
On the other hand, Venezuela’s plummet of 26 places from 2005 to 2010 (from 84th to 110th) was
driven by sharp a fall of 63 places in the Economics dimension, as well as a 13 spot fall in
Operations, with the Governance rankings remaining poor throughout the time period. However,
the country improved from 78th to 53rd in Society, led by rises in rank in five of the six main
societal sub-dimensions (Health, Demographics, Gender Gap, Inequality, and Environmental
Sustainability; the country dropped 13 spots in Education). Two points noted above need
emphasis here. First, one of the advantages of the RCRI, but beyond the scope of this paper, is
that the user can further sharpen the focus down to the specific variables within the subdimensions driving the longitudinal ranking changes for these countries. Second, the broader
political economic analyses underlying cases such as India and Venezuela, analyses that tie in
comparative historical distinctions and dynamics such as state-level macro-economic strategy,
remain central to the index’s interpretation.
31
Appendix C provides three web-shot examples from the 2010 RCRI of the user’s general ability
to “drill down” and focus the index, from the Operation, Economics, and Society macrodimensions. Another important facet of the RCRI is time-series variable cross referencing, in
which the user can access a country’s RCRI rank, RCRI score, and, when available, raw data
value over, at present, six years, and then interactively compare this longitudinal information
with other variables or with other countries (see Appendix D). A number of interactive charts are
also put at the user’s fingertips (see the Society drill down in Appendix C). As noted above,
countries are clustered by region (Latin America, Europe, East Asia, etc.) and perceived level of
development (Advanced, Emerging, Frontier, etc.), and regional and cluster profiles offer similar
dynamic interactivity as the country profiles. Users can also access how the 122 countries rank
on each variable, and pull up the decimal and logarithmically scaled histograms to better see how
the countries distribute for any particular variable (see Appendix E).
Two other embedded features include the ability to manipulate weights (see Appendix F) and the
ability to “Build Your Own Index” (see Appendix G). As noted above, risk is a multidimensional
concept, with the specific strategic definition of risk or risk profile changing given the context of
the organization, organizational activity, or research involved. Also, as pointed out by the World
Economic Forum’s Global Competitiveness Report, data weighting at times should be different
for different groups of countries (e.g. Africa versus Europe or Advanced versus Frontier). By
allowing the user to change the weights assigned to each macro-dimension, sub-dimension, or
variable, the RCRI puts an important modeling technique at the user’s fingertip. Similarly, while
we believe the above defined RCRI GEOS structure provides a robust alternative measure of
32
country development, by allowing the user to construct a customized index—by selecting
dimensions and variables for a new conceptualization and weighting schema, adding
organization specific variables into a new structure, etc.—we allow for the adaption of the index
to solve for new strategic visions and risk puzzles. If desired, this new structure can be a more
limited, parsimonious tool with respect to countries and variables chosen. Finally, as touched on
above, the use of a variety of statistical techniques allows the researcher or practitioner to initiate
investigations into political economic puzzles of interest, whether using the RCRI data set and
GEOS structure, or simply drawing on the RCRI data set and using a newly conceived model.
1.5
Implications and Future Research
Country-level risks and their cumulative impacts can pose vexing challenges for businesses,
governmental bodies, NGOs, and other types of organizations, as these risks are often complex,
integrated, and fast changing. Organizations need dynamic, interactive, and broadly
conceptualized tools to help identify, assess, and monitor challenges which can impede the
achievement of their specific strategic goals within a country. These tools need to be combined
with perceptive and intuitive insights. Undertaking broad-based risk management and
intelligence strategies is central not just for organizational survival, but also for organizational
value creation.
Drawing on greatly improved data availability and computing power, we argue the RCRI takes a
step in the right direction and assists organizational leaders as they seek to design and adopt
mitigation strategies and build resilient, adaptive, and thriving organizations. We also believe the
ecologically conceived RCRI offers a robust alternative measure of country development (albeit
33
in the Western liberal tradition), provides a dynamic classroom teaching tool, and can serve as
the basis for addressing a variety of political-economic puzzles. Still, one must be clear on the
embedded conceptual foundations and the potential costs, as well as benefits, associated with the
choices surrounding such areas as weights, structures, and data.
Avenues for further research include a richer examination of the modeling and statistical
techniques which might prove most useful in solving for alternative strategic conceptualizations,
tackling key puzzles, and moving toward parsimonious explanations. For example, a researcher
might be interested in constructing an index around the strategic orientation of a beverage
company, or in testing hypotheses surrounding increased trade and investment and improved
“Operations,” or in teasing out the key drivers of social wellbeing, foreign direct investment or
political stability across 122 embedded country ecologies (i.e., a “neural network”).
Another interesting avenue of research, in line with much of the development and political
economy literature discussed above, is in using the index as a dependent variable and asking the
question “why?” with respect to country or regional longitudinal profiles. What is the connection
between oil exportation, economic nationalism, or IMF-style liberalization and country
vulnerability, broadly conceived? How does democracy play in? The index offers an ecological
view into countries and regions which possibly exposes, in the aggregate as well as in detail, the
results of developmental efforts, adding clarity and specificity to existing development strategies
and literature. Here, the research efforts turn to the broader comparative historical analyses
underlying critical cases, such as the India and Venezuela cases discussed above, using a tool
that allows for a data-centered, ecological focus.
34
A third direction might be in contributing to traditional conceptualizations of “levels of
development.” Given a full picture, is the U.S., which ranks 22nd and scores 797 overall, still a
“most developed country”? What meat/detail can we put on those bones? A fourth may be to test
the RCRI’s forecasting and prescriptive capacity. Nevertheless, it must be emphasized that the
RCRI remains exploratory and limited by such factors as weighting, data availability, data
reliability, and human imagination. In the end, it is a useful place to start an investigation and
spark intuition, and needs to be combined with all techniques at the strategic thinker, decision
maker, researcher, or educator’s disposal.
35
Appendix A
ROBINSON COUNTRY RISK INDEX 2010 - RANKINGS
Year
Maximum Score
2010
1000
OVERALL
COUNTRY
Singapore
Norway
Hong Kong
Sweden
Switzerland
Netherlands
Denmark
Finland
Germany
Luxembourg
United Kingdom
Canada
Ireland
Austria
France
Belgium
Australia
Iceland
South Korea
New Zealand
Japan
United States
Qatar
United Arab Emirates
Israel
Taiwan
Czech Republic
Chile
Estonia
Kuwait
Saudi Arabia
Spain
Malaysia
Slovakia
Portugal
Cyprus
Oman
Slovenia
Lithuania
Poland
Hungary
Thailand
Italy
Costa Rica
Bahrain
Latvia
Mauritius
China
Mexico
Tunisia
Panama
Jordan
Croatia
Bulgaria
Kazakhstan
Romania
Turkey
Uruguay
Azerbaijan
Peru
GOVERNANCE
ECONOMICS
OPERATIONS
SOCIETY
Rank
Score
Rank
Score
Rank
Score
Rank
Score
Rank
Score
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
1000
991
980
974
924
919
911
904
897
889
865
860
859
846
845
839
830
825
811
810
807
797
795
790
757
751
741
740
730
722
721
719
710
703
697
696
692
682
661
659
652
639
638
628
628
628
624
620
606
601
590
582
573
559
555
551
547
540
539
535
18
10
16
3
5
7
1
2
13
6
14
8
15
11
19
17
9
12
31
4
22
20
39
44
30
29
28
21
24
54
70
26
47
34
25
23
48
27
37
36
32
63
43
42
49
38
33
77
62
61
53
55
45
51
91
56
52
35
106
72
890
950
913
982
965
959
1000
990
920
960
916
958
915
934
888
897
956
923
780
980
857
881
719
662
789
795
797
866
850
558
428
825
619
759
840
854
618
824
735
743
762
483
690
704
614
732
760
358
494
500
563
544
653
570
295
535
563
753
227
410
2
6
1
18
13
9
20
31
19
10
33
25
38
26
29
30
37
102
11
91
32
47
3
7
24
14
35
17
23
4
5
57
21
43
70
65
8
55
53
41
66
22
45
73
61
69
90
15
28
56
40
50
71
48
16
81
78
82
12
42
816
754
1000
643
674
715
629
592
642
703
580
614
564
612
595
595
569
352
695
410
591
525
807
750
618
666
577
659
619
786
757
493
629
532
455
469
743
494
499
547
461
628
531
453
490
456
417
665
598
494
560
505
454
515
661
437
445
434
694
533
1
8
2
3
11
16
4
10
13
19
6
9
12
24
20
18
17
21
23
5
15
7
26
14
31
25
34
36
28
48
22
33
27
38
30
35
45
43
37
51
47
32
52
60
29
40
44
42
55
41
50
58
71
64
70
56
49
72
53
54
1000
833
922
856
800
777
854
811
790
744
847
822
796
718
738
753
770
734
721
853
782
840
679
787
632
709
606
598
663
534
722
615
678
579
649
599
555
563
592
505
542
623
504
446
661
571
560
565
486
565
514
463
419
426
419
466
523
406
498
487
15
3
73
2
5
9
10
4
6
16
14
20
7
11
8
13
18
1
27
12
23
26
60
58
24
69
21
61
68
44
55
19
45
22
29
25
75
31
38
36
28
67
40
17
63
47
34
54
50
33
71
39
35
62
64
41
70
79
89
72
727
879
495
927
805
762
754
823
805
719
742
681
790
747
777
744
696
1000
642
747
657
645
535
539
653
507
675
534
509
582
543
688
579
669
634
647
491
626
594
598
638
512
594
700
532
572
599
544
565
607
497
594
599
533
530
593
505
474
420
495
© Georgia State University, 2011
36
Appendix A (cont.)
ROBINSON COUNTRY RISK INDEX 2010 - RANKINGS
Year
Maximum Score
2010
1000
OVERALL
COUNTRY
GOVERNANCE
ECONOMICS
OPERATIONS
SOCIETY
Rank
Score
Rank
Score
Rank
Score
Rank
Score
Rank
Score
Colombia
61
529
67
449
52
500
63
432
59
537
Macedonia
62
523
64
481
72
454
59
454
57
539
Greece
63
522
41
705
107
330
66
424
48
567
Vietnam
64
517
94
283
51
503
57
464
43
584
Philippines
65
517
74
386
58
492
87
343
30
632
Albania
66
512
69
433
80
444
81
374
32
618
Indonesia
67
506
76
358
39
562
62
434
83
455
Georgia
68
491
59
517
98
382
39
575
93
409
Brazil
69
488
58
526
87
422
65
425
81
473
India
70
486
65
476
63
473
68
420
86
442
Mongolia
71
486
89
297
36
573
85
353
74
494
Argentina
72
483
87
327
59
492
79
382
56
541
Morocco
73
480
73
397
60
492
80
377
76
489
South Africa
74
479
46
626
79
445
46
543
111
252
Botswana
75
471
40
712
54
496
67
422
116
212
Russia
76
468
98
264
27
605
89
338
87
439
El Salvador
77
462
66
458
86
423
73
403
82
461
Egypt
78
460
81
347
99
375
69
419
46
572
Algeria
79
458
103
231
34
577
108
245
52
545
Armenia
80
458
71
415
85
426
61
437
85
446
Sri Lanka
81
452
75
381
101
356
74
403
49
566
Ghana
82
447
57
534
97
386
75
399
90
415
Serbia
83
441
68
438
108
329
92
329
42
591
Dominican Republic
84
435
82
346
93
407
76
399
77
480
Ukraine
85
435
97
266
62
487
97
304
66
515
Ecuador
86
420
114
189
49
509
103
279
65
519
Jamaica
87
420
60
504
115
294
77
397
80
474
Namibia
88
408
50
574
77
447
88
340
110
253
Syria
89
391
111
196
89
418
106
258
51
561
Nicaragua
90
389
105
227
83
432
98
302
78
478
Guatemala
91
388
93
285
68
460
78
391
100
327
Paraguay
92
386
101
239
74
452
99
301
88
437
Kyrgyzstan
93
376
115
179
116
293
83
361
37
595
Honduras
94
372
100
260
67
460
95
316
96
364
Bangladesh
95
355
107
214
84
428
101
286
92
409
Uganda
96
350
92
291
94
398
93
326
99
345
Guyana
97
341
78
358
111
312
91
333
95
375
Senegal
98
339
85
340
105
339
90
335
98
346
Kenya
99
333
99
261
114
300
84
355
91
411
Zambia
100
329
90
295
88
419
82
364
114
225
Bolivia
101
327
102
238
46
526
115
186
107
277
Tanzania
102
325
84
340
109
328
100
300
97
350
Cambodia
103
299
108
211
75
448
110
231
108
273
Nigeria
104
297
116
153
44
532
104
278
118
162
Benin
105
297
83
343
103
345
107
250
106
289
Pakistan
106
291
113
190
110
320
86
352
101
319
Gambia
107
290
96
269
119
232
94
321
94
397
Tajikistan
108
290
118
126
112
311
105
258
84
448
Ethiopia
109
289
104
228
104
341
102
285
102
317
Venezuela
110
283
119
123
100
369
120
41
53
544
Mozambique
111
282
80
351
106
333
112
230
109
273
Bosnia and Herzegovina
112
270
79
355
95
396
96
313
121
88
Cameroon
113
263
112
191
96
395
109
231
112
247
Cote D'Ivoire
114
263
120
123
76
447
113
213
113
242
Madagascar
115
256
95
281
117
291
111
230
105
295
Mali
116
251
88
312
92
408
114
191
120
151
Burkina Faso
117
233
86
334
113
308
117
178
115
220
Nepal
118
218
109
205
118
263
116
185
103
309
Mauritania
119
139
110
204
120
221
118
154
119
160
Chad
120
121
121
79
64
470
122
1
122
1
Burundi
121
120
117
147
121
187
121
11
104
309
Zimbabwe
122
1
122
1
122
1
119
131
117
199
© Georgia State University, 2011
37
Appendix B: RCRI Trends
38
Appendix B (cont.)
39
Appendix B (cont.)
40
Appendix C:
Example of RCRI Operations “Drill Down”
41
Appendix C (cont.)
Example of RCRI Economics “Drill Down” and Longitudinal Analysis
42
Appendix C (cont.)
Example of RCRI Society “Drill Down”
43
Appendix D
Examples of RCRI Longitudinal Data Analysis
44
Appendix E
Examples of RCRI Rank By Variable and Histograms
45
Appendix F
RCRI Flexibility: Adjusting Weights
46
Appendix G
Build Your Own Index: Hypothetical Example, “Acme Beverage Company”
47
Appendix G (cont.)
Build Your Own Index: Hypothetical Example, “Acme Beverage Company”
Acme Beverage Company COUNTRY RISK INDEX 2010 - RANKINGS
Year
Maximum Score
2010
1000
OVERALL
COUNTRY
Resources
Distribution and
Partners
Government
Profits
Rank
Score
Rank
Score
Rank
Score
Rank
Score
Rank
Singapore
1
1000
3
995
1
1000
18
890
2
969
Hong Kong
2
966
12
905
12
898
16
913
1
1000
Switzerland
3
913
1
1000
3
989
5
965
16
712
Finland
4
895
5
973
6
941
2
990
23
701
Luxembourg
5
891
21
807
14
873
6
960
4
875
Sweden
6
877
4
990
4
975
3
982
39
627
Norway
7
873
7
952
20
813
10
950
9
769
New Zealand
8
865
10
929
22
803
4
980
11
755
Denmark
9
864
13
890
8
931
1
1000
28
685
Ireland
10
859
11
909
21
811
15
915
6
789
Iceland
11
854
6
966
24
778
12
923
12
751
Canada
12
850
8
940
11
918
8
958
34
648
Austria
13
835
9
937
17
854
11
934
29
671
United Kingdom
14
835
20
807
10
925
14
916
14
729
Netherlands
15
833
23
792
5
958
7
959
27
688
Germany
16
820
25
777
2
996
13
920
32
666
United States
17
810
27
775
9
925
20
881
17
711
Belgium
18
808
29
755
7
936
17
897
21
704
France
19
805
17
833
15
866
19
888
25
692
Australia
20
775
37
709
16
863
9
956
30
668
Japan
21
773
19
814
13
881
22
857
37
635
Taiwan
22
755
2
995
18
854
29
795
73
516
United Arab Emirates
23
748
52
640
23
792
44
662
3
879
South Korea
24
744
31
742
19
823
31
780
22
702
Qatar
25
714
35
719
31
682
39
719
7
773
Cyprus
26
705
15
849
34
643
23
854
49
599
Portugal
27
702
41
683
29
723
25
840
31
667
Slovenia
28
682
14
882
43
584
27
824
56
575
Czech Republic
29
680
22
794
28
735
28
797
64
553
Estonia
30
675
16
842
35
640
24
850
68
534
Latvia
31
667
18
815
46
551
38
732
33
663
Spain
32
666
49
645
27
751
26
825
50
597
Lithuania
33
663
36
718
38
598
37
735
26
692
Malaysia
34
659
30
746
25
772
47
619
41
621
Bahrain
35
653
24
785
39
598
49
614
24
693
Israel
36
639
67
561
26
772
30
789
48
600
Chile
37
612
66
562
37
615
21
866
52
588
Slovakia
38
603
26
775
55
468
34
759
57
572
Croatia
39
594
28
772
53
472
45
653
44
616
Hungary
40
588
48
645
40
594
32
762
65
545
South Africa
41
578
75
506
30
723
46
626
40
622
China
42
567
60
589
32
673
77
358
13
734
Saudi Arabia
43
567
79
479
33
667
70
428
8
773
Thailand
44
563
42
675
36
636
63
483
45
607
Mauritius
45
553
51
642
65
411
33
760
55
580
Greece
46
541
50
644
61
433
41
705
59
571
Poland
47
537
40
701
52
481
36
743
85
464
Kuwait
48
526
85
426
48
531
54
558
15
717
Oman
49
524
83
430
44
575
48
618
35
643
Uruguay
50
522
32
739
60
443
35
753
95
419
Macedonia
51
522
44
668
79
332
64
481
19
709
Tunisia
52
511
53
637
41
591
61
500
70
526
Turkey
53
511
73
524
49
514
52
563
42
618
Italy
54
508
54
630
42
587
43
690
97
408
Botswana
55
497
95
395
83
317
40
712
18
711
Georgia
56
478
89
420
94
282
59
517
5
790
Costa Rica
57
476
33
735
58
453
42
704
111
331
Bulgaria
58
452
69
552
78
341
51
570
63
559
Mexico
59
452
80
473
51
482
62
494
58
572
Ghana
60
451
62
578
88
302
57
534
53
587
Score
48
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