The Pinkerton Risk Index

The Pinkerton Risk Index 

An Operationalization of the Pinkerton 

Approach to Corporate Risk Management

Executive Summary 

The Pinkerton Risk Wheel (PRW) provides a mutually-exclusive categorization of the 

most important business risks facing companies today. In order to highlight Pinkerton’s 

extensive expertise in understanding and managing business risk, this report and the 

accompanying database build on the PRW to provide a comprehensive survey of business 

risks at the country level, for all countries in the world. 

The report divides risks into two main categories, corresponding to the top and bottom 

halves of the PRW: 1) hazard, event, physical and operation risks; and 2) technology, 

informational, market and economic risks. 

The analysis draws on data from 17 distinct information sources, including both proprietary 

and publicly available sources from international organizations. Examples include the 

Centre for Research on the Epidemiology of Disasters, the Wall Street Journal and 

Heritage Foundation, Yale Center for Environmental Law and Policy, the United Nations 

Office on Drugs and Crime, World Economic Forum, the World Health Organization, 

the World Bank, the Center for Systemic Peace, the U.S. Committee for Refugees and 

Immigrants, the World Refugee Survey, the World Bank/International Finance Corporation, 

the International Telecommunication Union, the International Monetary Fund, UNESCO’s 

Institute for Statistics, the Organization for Economic Co-operation and Development 

(OECD), the World Trade Organization’s International Trade Centre, and the Insurance 

Information Institute. 

To aggregate this information into combined risk indices, we need both a theoretical 

foundation regarding risk and statistical methods for combining diverse variables 

which can be applied in situations in which variable measures appear in uncommon (or 

incommensurable) units. Our theoretical foundation accords with the standard definition 

of risk in Risk Management—namely, probability times consequence. We describe our 

statistical methods in Section 1. 

Section 2 presents detailed summary information regarding risks in the top half of the 

PRW—namely, those associated with hazard, event, physical, and operation risks. In 

addition to describing the data sources, we map the spatial distribution of the categoryspecific 

risk index at the country level. In addition, we use the index to provide detailed 

rankings of countries by riskiness. We do this for five categories of countries, segmented 

by United Nations (UN) country groups. 

A SPATIAL ANALYSIS OF RISK AROUND THE WORLD 

2

Section 3 replicates the analysis in Section 2, focusing on risks in the bottom half of the 

PRW—technology, informational, market and economic risks. In addition to describing 

the data for this portion of the report, we also provide a detailed map showing the spatial 

distribution of this category of risk across the world, and we rank countries for each of the 

five UN country groupings. 

Finally, Section 4 combines information from Sections 2 and 3 to provide a summary view 

of overall business risk around the world. This section includes a detailed “correlational 

analysis” showing the extent to which risks from the top and bottom halves of the risk 

wheel move together. It also maps the overall risk index at the country level, and ranks 

countries on overall riskiness by five UN country groupings. 


3

Table of Contents 

Executive Summary 2 

CONDENSED SUMMARY 7 

Overview 8 

Summary 8 

Creation of the Pinkerton Risk Index 8 

Results 9 

Spatial Analysis of Hazard, Event, Operational and Physical Risks 9 

Classification of Technology, Information, Market and Economic Risks 11 

Overall Risk 14 

FULL REPORT 16 

1 Introduction 17 

2 Measuring Risk 18 

2.1 Risk Index Components 18 

2.2 Index Construction 19 

3 Classification of Hazard, Event, Operational and Physical Risks 20 

3.1 Data Sources and Measurement 20 

3.1.1 Natural Disasters Risk 20 

3.1.2 Infectious Disease Risk 21 

3.1.3 Population Health Risk 21 

3.1.4 Violent Crime, Property Crime, and Terrorism Risk 21 

3.1.5 Business Operations Risk 22 

3.1.6 Supply Chain Risk 22 

3.1.7 Employee Risk 22 

3.2 Results 23 

3.2.1 Spatial Analysis of Hazard, Event, Operational, and Physical Risks 23 

3.2.2 Ranking Countries by Hazard, Event, Operational, and Physical Risks 23 

4 Classification of Technology, Information, Market and Economic Risks 28 

4.1 Data Sources and Measurement 28 

4.1.1 Economic Structure Risk 28 

4.1.2 Human Capital Risk 29 

4.1.3 Social and Institutional Structures Risk 30 

4.1.4 Societal Upheaval Risk 31 

4.1.5 Information and Technology Risk 31 


4

4.2 Results 32 

4.2.1 Spatial Analysis of Technology, Information, Market and Economic Risks 32 

4.2.2 Ranking Countries on Technology, Information, Market and 

Economic Risks 33 

5 Overall Risk 39 

5.1 Correlational Analysis of Risk Dimensions 39 

5.1.1 All Countries Correlation 40 

5.1.2 African Countries Correlation 41 

5.1.3 Asia-Pacific Countries Correlation 42 

5.1.4 Eastern European Countries Correlation 43 

5.1.5 Latin American and Caribbean Countries Correlation 44 

5.1.6 Western European and Other Developed Countries Correlation 45 

5.2 Spatial Analysis of Overall Pinkerton Risk 46 

5.3 Ranking Countries on Overall Risk 47 

Appendix 1: Imputation 50 

Appendix 2: Min-Max Standardization 51 

Appendix 3: Sample Standard Deviation and Z-Score 52 


5

Tables and Figures 

All Country Ranking, Hazard, Event, Operational, and Physical Risks 11 

All Country Ranking, Technology, Information, Market and Economic Risks 13 

All Country Ranking 15 

Table 3.2.1 All Country Ranking, Hazard, Event, Operational, and Physical Risks 24 

Table 3.2.2 African Country Ranking, Hazard, Event, Operational, and Physical Risks 25 

Table 3.2.3 Asia-Pacific Country Ranking, Hazard, Event, Operational, 

and Physical Risks 26 

Table 3.2.4 Eastern Europe Country Ranking, Hazard, Event, Operational, 

and Physical Risks 26 

Table 3.2.5 Latin America and Caribbean Country Ranking, Hazard, Event, 

Operational, and Physical Risks 27 

Table 3.2.6 Western Europe and Other Country Ranking, Hazard, Event, 

Operational, and Physical Risks 27 

Table 4.2.1 All Country Ranking, Technology, Information, Market and Economic Risks 33 

Table 4.2.2 African Country Ranking, Technology, Information, Market and 

Economic Risks 34 

Table 4.2.3 Asia-Pacific Country Ranking, Technology, Information, Market 

and Economic Risks 35 

Table 4.2.4 Eastern Europe Country Ranking, Technology, Information, Market 

and Economic Risks 36 

Table 4.2.5 Latin America and Caribbean Country Ranking, Technology, 

Information, Market and Economic Risks 37 

Table 4.2.6 Western Europe and Other Country Ranking, Technology, 

Information, Market and Economic Risks 38 

Figure 5.1.1 All Countries Correlation 40 

Figure 5.1.2 African Countries Correlation 41 

Figure 5.1.3 Asia-Pacific Countries 42 

Figure 5.1.4 Eastern European Countries 43 

Figure 5.1.5 Latin American and Caribbean Countries 44 

Figure 5.1.6 Western European and Other Developed Countries 45 

5.3.1 Table of All Country Ranking 47 

5.3.2 Table of African Country Ranking 48 

5.3.3 Table of Asia-Pacific Country Ranking 48 

5.3.4 Table of Eastern Europe Country Ranking 48 

5.3.5 Table of Latin America and Caribbean Country Ranking 49 

5.3.6 Table of Western Europe and Other Country Ranking 49 


6

CONDENSED SUMMARY

CONDENSED SUMMARY 

Overview 

SUMMARY 

In a world where new and emerging threats pose a significant danger to businesses and 

their bottom lines, understanding and responding to that changing threat landscape is 

a challenging proposition. Recognizing risks, accurately assessing vulnerabilities and 

prioritizing protective resources requires a deep and nuanced appreciation for marketand 

industry-specific factors, and the corresponding ability to evaluate real-world perils in 

a real-world context. 

As the threats facing organizations globally continue to change and evolve, it is important 

for business leaders to take a holistic approach to risk management—a concept illustrated 

by Pinkerton’s Risk Wheel, a tool designed to provide bigpicture 

perspective by categorizing the most important 

business risks facing companies today. The PRW is 

divided into four distinct quadrants that summarize 

the different types of risks faced by businesses 

around the world. 

Hazard & 

Event Risk 

Operational & 

Physical Risk 

Those four primary categories include: 

• Hazard & Event Risk 

• Operational & Physical Risk 

• Technology & Information Risk 

• Market & Economic Risk 

Technology & 

Informational 

Risk 

Market & 

Economic 


The Pinkerton Risk Index takes the concept laid out by the 

Risk Wheel to a whole new level by clarifying to businesses exactly where risk occurs 

around the world, and what the precise nature of that risk is most likely to be. If the Risk 

Wheel accurately reflects the general threat landscape, the Risk Index brings the features 

on that landscape into sharp relief, describing not only what could happen, but what is 

happening with unprecedented clarity and specificity. 

CREATION OF 

THE PINKERTON 

RISK INDEX 

The Risk Index was developed over years of work, with analyses and information gathered 

from 17 distinct sources, including both proprietary and publicly available data acquired 

from a range of international organizations. The finished product is the result of innovative 

work by scholars and security professionals alike, a collaborative endeavor that contains 

inputs and insights from experts around the world, including professors and experienced 

specialists in their respective fields. 


8

The country-specific and regional considerations that the Risk Index is based on range 

from complex geopolitical calculations to the state of the legal landscape in different 

nations. Rigorous scientific theory and statistical analysis connect inherent risks to specific 

business indices, creating an overall threat analysis tailored for businesses. The Risk Index 

incorporates 83 separate risk factors, such as hazard, event, operational and physical 

risks that include threats like natural disasters, infectious disease, population health, 

violent crime, property crime, terrorism, business operations, supply chain, and employee 

negligence. It also covers technology, information, market and economic risks, including 

risk factors like economic structure, human capital, social and political institutions, societal 

upheaval, and information and technology. The final Risk Index report is generated in part 

from sophisticated econometric and spatial analyses of those risks, and is converted into 

an overall risk profile that is available on a country level internationally, and, remarkably, 

on a county level in the United States. 

RESULTS 

The Risk Index essentially measures the impact of the unique types of risk outlined above 

that impact organizations globally, and then evaluates them in the context of: 

• Importance 

• Insurability 

• Information Reliability 

These three facets of risk measurement are combined into an aggregate index for every 

country in the world. Each country is given an “index score” and is then placed into one 

of five quintiles, which are represented by five different shades of blue—with dark blue 

shades corresponding to high-risk countries and light blue shades indicating low-risk 

countries. 

Spatial Analysis of Hazard, Event, Operational and 

Physical Risks 

The top half of the Risk Wheel encompasses 

Hazard & Event Risk and Operational & Physical 

Risk—areas that have the potential to create a 

meaningful interruption in business continuity. 

Quantifiable risks constituting the top half of 

the Risk Wheel include: 

• Natural Disasters 

• Infectious Disease 

• Population Health 

• Property Crime, Violent Crime and Terrorism 

• Business Operations 

• Supply Chain 

• Employee Risk 

Hazard & 


Technology & 

Informational 


Operational & 


Market & 

Economic 



9

Taking just the factors impacting the top half of the PRW into account, the map below 

shows a spatial analysis of risk on a global scale: 

Map 1: Spatial Distribution of Pinkerton Hazard, Event, Operational and Physical Risk Index 


Top Half Pinkerton Risk 

Insufficient Data 

Quintile 1 (High Risk) 

Quintile 2 

Quintile 3 

Quintile 4 

Quintile 5 (Low Risk) 

0 1,400 2,800 5,600 8,400 11,200 

Kilometers 


10

With the exception of Japan, Australia and New Zealand, all countries in the top 20 are 

in North America or Europe. Here is a breakdown of the top 20 countries with the lowest 

levels of risk: 

All Country Ranking, Hazard, Event, 

Operational, and Physical Risks 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

COUNTRY 

Japan 

Switzerland 

Germany 

United Kingdom 

Denmark 

Canada 

United States 

Austria 

Norway 

Netherlands 

Finland 

France 

New Zealand 

Belgium 

Sweden 

Luxembourg 

Ireland 

Australia 

Malta 

Spain 

SCORE 

1.0000 

0.9914 

0.9624 

0.9369 

0.9261 

0.9241 

0.9201 

0.9189 

0.9164 

0.9057 

0.9043 

0.8971 

0.8944 

0.8927 

0.8925 

0.8845 

0.8802 

0.8488 

0.8379 

0.8369 

Hazard & Operational & 

Event Risk Physical Risk 

Classification of Technology, Information, Market and Economic Risks 

Businesses are vulnerable to various sources 

of economic uncertainty—factors that are 

captured in the lower half of the Pinkerton 

Risk Wheel, which examines technology and 

information, and market and economic risk. 

Quantifiable risks constituting the lower half of the 

Risk Wheel include: 

• Economic Structure 

• Human Capital 

• Social and Political Institutions 

• Societal Upheaval 

• Information and Technology 

Technology & 

Informational 


Market & 

Economic 



11

Taking only the factors impacting the bottom half of the PRW into account, the map below 

shows a spatial analysis of risk on a global scale: 

Map 2: Spatial Distribution of Pinkerton Technology, Information, Market and Economic Risk Index 


Bottom Half Pinkerton Risk 



Quintile 2 

Quintile 3 

Quintile 4 


0 1,400 2,800 5,600 8,400 11,200 

Kilometers 

When comparing the results for the bottom half of the Risk Wheel to the top half, we see 

more regional global diversity. Countries that perform well in this area have established 

and well-developed economies, strong and stable institutions, are generally free from 

significant social upheaval, and benefit from a significant stock of human capital. 


12

Here are the top 20 countries with the lowest technology, information, market and 

economic risks: 

All Country Ranking, Technology, Information, 

Market and Economic Risks 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

COUNTRY 

Singapore 

Finland 

Switzerland 

Netherlands 

Denmark 


United States 

New Zealand 

Canada 

Luxembourg 

Australia 

Japan 

Ireland 

Germany 

Sweden 

Norway 

Austria 

Hong Kong 

Belgium 

Taiwan 

SCORE 

1.0000 

0.9530 

0.9423 

0.9306 

0.9294 

0.9184 

0.9176 

0.9174 

0.9135 

0.8977 

0.8964 

0.8923 

0.8902 

0.8894 

0.8856 

0.8784 

0.8704 

0.8619 

0.8453 

0.8291 


13

Overall Risk 

While each quadrant of the Risk Wheel brings attention to different types of risk, viewing 

all four quadrants together helps organizations begin to look at risk management in a 

more holistic manner. Failing to adopt such a comprehensive perspective may leave 

those organizations susceptible to unforeseen and unplanned-for hazards. Taking all of 

the factors from both the upper and lower halves of the PRW into account, the map below 

shows a spatial analysis of overall risk on a global scale: 

Map 3: Spatial Distribution of Pinkerton Overall Risk Index 


Overall Pinkerton Risk 



Quintile 2 

Quintile 3 

Quintile 4 


0 1,400 2,800 5,600 8,400 11,200 

Kilometers 

Not surprisingly, the countries with the lowest levels of overall risk are dominated by many 

of the world’s most sophisticated economies. The most important attributes differentiating 

these countries are their strong institutions, the quality of their supply chains, the relatively 

low risk of damage and disruption from terrorism, the low prevalence of organized crime, 

the commitment to property rights, and a relative freedom from endemic corruption. 


14

Here are the top 20 countries with the lowest overall risk: 

All Country Ranking 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

COUNTRY 

Singapore 

Switzerland 

Finland 

Denmark 

Netherlands 


United States 

Canada 

Japan 

New Zealand 

Germany 

Luxembourg 

Ireland 

Sweden 

Norway 

Australia 

Austria 

Belgium 

Hong Kong 

France 

SCORE 

1.0000 

0.9888 

0.9780 

0.9631 

0.9594 

0.9562 

0.9517 

0.9492 

0.9486 

0.9457 

0.9376 

0.9270 

0.9196 

0.9185 

0.9179 

0.9178 

0.9117 

0.8847 

0.8647 

0.8450 

With the Risk Index, Pinkerton has created a tool that effectively analyzes and 

summarizes risk in a way that not only demonstrates the value of a holistic approach to 

risk management, but also offers businesses and communities the detailed guidance 

they need to make informed decisions on how best to protect their organizational value. 

For Pinkerton and its clients, such a powerful tool—combined with Pinkerton’s suite of 

comprehensive security solutions, unmatched expertise and global network of resources— 

provides the ideal way to optimize resources and manage and mitigate business risks in a 

complex global marketplace. 


15

FULL REPORT

FULL REPORT 

1 Introduction 

Businesses are exposed to myriad risks that vary in 

time and space. Pinkerton has unique and nuanced 

expertise helping clients understand and manage 

the temporal and geographic distribution of risks. 

Building on the PRW, our analysis uses various 

spatial data sources and analytical methods 

to develop three different sets of spatiallyreferenced 

risk measures. 

To ensure a comprehensive survey of relevant 

risks, the first step is identifying the landscape of 

potential hazards. The PRW provides analytic direction, 

identifying four primary risk categories: 

• Hazard and Event Risks 

• Operational and Physical Risks 

• Technology and Informational Risks 

• Market and Economic Risks 

Hazard & 


Technology & 

Informational 


Operational & 


Market & 

Economic 


Conceptually, we view the first and second categories as focusing on one broader class of 

risk, and the third and fourth categories as focusing on a second broader class of risk. 

The risk assessment techniques employed in this report are informed by the ISO 31000 

Technical Management Board Working Group on Risk Management (the Working Group). 

The ISO 31000 Working Group identified a number of risk assessment techniques, 

including brainstorming, scenario analysis, business impact analysis, layers of protection 

analysis, bow-tie analysis, risk indices, and consequence/likelihood matrix analysis. Our 

global risk analysis is a hybrid of the risk indices and consequence/likelihood matrix 

methodologies, regarded by the Working Group as the strongest methods available. 

In the next section, we describe the basic analytical methodology. In the third and fourth 

sections, we discuss the data and results for the top and bottom halves of the PRW, 

respectively. Results for overall risk are presented in Section 5. 


17

2 Measuring Risk 

To combine different risk dimensions into an overall risk index, we devise an aggregation 

strategy that is both consistent with economic theory and empirically tractable. In this 

section, we provide a general overview of our approach to quantifying myriad risks from 

multiple dimensions into representative measures of the top and bottom halves of the 

PRW, and the overall risk posed in each country. We first describe the general approach, 

and then elaborate on specific aspects that are important in creating the index. 

2.1 RISK INDEX 

COMPONENTS 

The standard economic definition of risk is probability × consequence. We adopt this 

definition throughout the report. When possible, probabilities are estimated using a 

combination of historical data and forward-looking expert judgment. Consequence 

is the impact on post-hedged multinational firm operating profit. To combine risk 

dimensions into a single index of risk, we devise an aggregation strategy that accounts 

for the most important considerations of multi-national firms engaged in business 

operations internationally. 

In constructing an aggregate risk index, a critical challenge is reasonably combining 

measured quantities that are naturally expressed in distinct units. For example, data 

showing the dollar value of profit (loss) from theft are naturally expressed in monetary 

units, while data showing the number of deaths from infectious diseases are not. To 

combine such disparate categories, we employ an approach called the sum of min-max 

standardization 1 , an information compression method commonly employed in statistical 

applications. This method aggregates outcomes from disparate categories by first 

measuring the outcome in each category in terms of a standardized score, which is a 

unitless measure ranging from 0 to 1. In this way, all outcomes are given a unitless 

ranking that reflects their standing within the category relative to all other observed 

units. Expressing outcomes in this way makes it possible to add variables measured in 

different units. 

While a simple sum of standardized score measure is sometimes used, it is common to 

modify the approach by weighting the standardized scores of variables to accommodate 

additional information about the importance of the variable to the index of interest. In 

our case, we include weights that capture three additional features of the problem: 1) the 

relative importance of each risk category for the overall business risk faced by a typical 

firm; 2) the role of insurability; and 3) the reliability of data for a given risk category. 

1 

See Appendix 2 for definition. 


18

approach, we split the sample into two groups and test the accuracy of the imputed 

procedure for instances in which the true value is actually observed. This allows us to 

identify risk categories for which imputation error is relatively high, as well as those for 

which imputation error is relatively low. This information is used to construct a risk-specific 

deflation factor, DD ii , that we use to shift weight from variables prone to significant 

imputation error to more reliable variables in the same region of the PRW. 

2.2 Index Construction 

The three facets of risk measurement above are combined into an aggregate index for 

2.2 INDEX The three facets of risk measurement above are combined into an aggregate index for 

country jj using the following mathematical formula: 

country j using the following mathematical formula: 

CONSTRUCTION 

IIIIIIIIII jj = 

NN 

ii!11 

(11 − II ii,jj )WW ii ZZ ii,jj DD ii . 

For each country, the index sums For each across country, all risk the variables index sums (ii = across 1, … , NN). all risk Within variables the sum, (i = 1, the … , N). Within the sum, 

country and risk specific standardized score, ZZ ii,jj , is weighted first by the weight, WW ii , then 

the country and risk specific standardized score, Z 

by the insurability factor, (11 − II ii,jj ). Finally, the deflation factor is used to 

i,j 

, is weighted first by the weight, W 

shift variable 

i 

, 

then by the insurability factor, (1 – I 

weights from variables with relatively low accuracy to those with i,j 

). Finally, the deflation factor is used to shift variable 

relatively high accuracy, 

weights from variables with relatively low accuracy to those with relatively high accuracy, 

doing so in a way that preserves the original weighting of variable inputs within each 

broader risk category. 

doing so in a way that preserves the original weighting of variable inputs within each 

broader risk category. 

This procedure can be used to construct an aggregate risk index for all measures of risk, 

and it can be used to construct This a risk procedure index can for different be used to regions construct of the an aggregate PRW, including risk index the for all measures of risk, 

top and bottom halves of the wheel. and it can be used to construct a risk index for different regions of the PRW, including the 

top and bottom halves of the wheel. 

4 


19

3 Classification of Hazard, Event, Operational 

and Physical Risks 

The top half of the PRW includes hazard, event, 

operational and physical risks that have the potential 

to interrupt business continuity meaningfully. 

Quantifiable risks constituting the top half of the 

PRW include: 

• Natural Disasters 

• Infectious Disease 

• Population Health 

• Property Crime, Violent Crime and Terrorism 

• Business Operations 

• Supply Chain 

• Employee Risk 

Hazard & 


Technology & 

Informational 


Operational & 


Market & 

Economic 


3.1 DATA SOURCES 

AND MEASUREMENT 

To operationalize hazard, event, operational, and physical risks, we collected data from a 

number of sources, including: the Centre for Research on the Epidemiology of Disasters, 

the Yale Center for Environmental Law and Policy, the United Nations Office on Drugs 

and Crime, the World Economic Forum, the World Health Organization, the World Bank, 

the Center for Systemic Peace, the World Bank/International Finance Corporation, the 

International Monetary Fund, UNESCO’s Institute for Statistics, the Organization for 

Economic Co-operation and Development (OECD), the World Trade Organization’s 

International Trade Centre and the Insurance Information Institute. 2 

3.1.1 Natural Disasters Risk 

To quantify the panoply of natural disaster related risks that vary importantly across 

countries, we draw on three data sources. First, the Emergency Events Database (EM-DAT) 

provides a comprehensive measure of historical disaster events. It contains data on the 

occurrence and effects of over 18,000 mass disasters in the world from 1900 to present. 

The database is compiled from various sources, including UN agencies, non-governmental 

organizations, insurance companies, research institutes and press agencies. The main 

objective of the database is to help rationalize decision making for disaster preparedness. 

The dataset includes, among other things, evidence on fire related deaths and damages. 

Second, to consider forward-looking risks associated with potential climate change and 

sea-level rise, we collected World Bank data on the fraction of a country’s land area at 

or below 5 meters above sea level, and we consider a recent forward-looking estimate 

2 

Missing values for variables of interest are imputed by the regression method described in Appendix 1. 


20

of GDP impacts from climate change projections reported in the most recent Intergovernmental 

Panel on Climate Change Assessment Report. The estimate of GDP impacts 

is based on a recent study by Burke et al (2015) published in the journal Nature. The 

authors use historical cross-country data linking average annual temperature and GDP 

growth to quantify the potential GDP effects of climate change projections through 2100 

as reported in the most recent Inter-governmental Panel on Climate Change Assessment 

Report. The study finds dramatic cross-country variation in impact from climate change. 

Finally, to consider additional environmental risks, we use the Environmental Health 

Index, which is constructed jointly by The Yale Center for Environmental Law and Policy, 

The Earth Science Information Network at Columbia University, and The World Economic 

Forum. The index reflects several variables that relate to the protection of human health 

from environmental harm. These include water sanitation, air quality, and health impact, 

among others. 

3.1.2 Infectious Disease Risk 

To assess the burden of infectious disease, we focus on tuberculosis, a leading indicator 

of country-level susceptibility to various forms of infectious disease. An advantage 

of focusing on tuberculosis is that available datasets have very broad cross-national 

coverage. The Global Competitiveness Index from the World Economic Forum also 

provides country-level estimates of the Business Impact of Tuberculosis. In addition, we 

use direct data on the incidence of tuberculosis from the World Development Indicators 

published by the World Bank. 

3.1.3 Population Health Risk 

Three distinct variables regarding population health risk are employed. The World Health 

Organization uses mortality rates indicated by current country-specific life tables to 

estimate the fraction of 30-year-old-people expected to die before their 70th birthday 

from cardiovascular disease, cancer, diabetes, or chronic respiratory disease. This gives a 

comprehensive measure of mortality risks from non-communicable diseases among adults 

during the most economically productive age span. In addition, we use data from the 

World Bank World Development Indicators on infant mortality and life expectancy at birth. 

The measure of life expectancy indicates the number of years a newborn infant would live 

if prevailing patterns of mortality at the time of its birth were to stay the same throughout 

its life course. 

3.1.4 Violent Crime, Property Crime, and Terrorism Risk 

The index includes two broad categories of crime measures. The first tracks crime 

incidents by country. The second looks at large-scale events with more significant 

reverberations through society and the local economy. 


21

For the first category, we use data from the United Nations Office on Drugs and Crime. 

They assemble international data on the incidence of a wide variety of crimes, including 

intentional homicide, assault, motor vehicle theft, and robbery. We supplement this 

with data from the World Economic Forum’s Global Competitiveness Index on the 

business costs of crime and violence and the reliability of police services. These data are 

constructed from an annual survey of over 14,000 executives in 145 countries. 

For the second category, we use data from the U.S. Committee for Refugees and 

Immigrants (USCRI) World Refugee Survey. This reports data on the Total Number of 

Forcibly Displaced Persons. In addition, we track data from The Center for Systemic 

Peace, a research institute in Austria that monitors key global trends in armed conflict and 

political governance. Their annual global report includes data on the incidence and impact 

of High Casualty Terrorist Bombings, among other things. 

3.1.5 Business Operations Risk 

The World Economic Forum’s Executive Opinion Survey includes several variables 

relevant to the Business Operations and Change Readiness portion of the PRW. It is 

administered to over 14,000 executives in 145 countries. We use three variables: Business 

Sophistication, Quality of the Electricity Supply, and Efficiency of the Legal Framework in 

Settling Disputes. The question on electricity supply asks executive to evaluate the extent 

to which electricity supply in the country exhibits interruptions or voltage fluctuations. This 

variable is plausibly correlated with other infrastructure variables important for maintaining 

business operations in a reliable way. The legal framework question asks executives to 

evaluate the efficiency of legal institutions for settling disputes on a scale from “extremely 

inefficient” to “extremely efficient.” 

3.1.6 Supply Chain Risk 

To quantify supply chain risks, we use two additional variables from The World Economic 

Forum’s Executive Opinion Survey that reflect the robustness of local supply networks. 

These reflect both the quantity and quality of local suppliers. The survey asks “In your 

country, how numerous are local suppliers?” This variable measures the quantity of local 

suppliers in a country. It also asks “In your country, how would you assess the quality of 

local suppliers?” This variable measures the quality of local suppliers. 

3.1.7 Employee Risk 

To quantify employee risks, we use two variables from the World Economic Forum’s 

Executive Opinion Survey. First, the variable “Cooperation in Labor-Employer 

Relations,” which asks executive to rank local labor relations on a scale from “generally 

confrontational” to “generally cooperative.” Second, the variable “Country Capacity to 

Retain Talent,” which asks executives to rate the country’s capacity to retain talent on a 

scale from “the best and brightest leave to pursue opportunities in other countries” to “the 

best and brightest stay and pursue opportunities in the country.” This variable is a measure 

of domestic human capital. 


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3.2 RESULTS 

3.2.1 Spatial Analysis of Hazard, Event, Operational, and Physical Risks 

Map 1 presents the spatial distribution of hazard, event, operational and physical risks 

that impinge on firm operating profits. The distribution of index scores for countries 

around the world is divided into quantiles, with dark blue shades corresponding to highrisk 

countries and light blue shades indicating low-risk countries. More detail is provided in 

the next section. 



Top Half Pinkerton Risk 



Quintile 2 

Quintile 3 

Quintile 4 


0 1,400 2,800 5,600 8,400 11,200 

Kilometers 

3.2.2 Ranking Countries by Hazard, Event, Operational, and Physical Risks 

Next, we rank countries in terms of hazard, event, physical and operational risks. Table 

3.2.1 ranks all countries, while tables 3.2.2 through 3.2.6 provide regional rankings for 

the five regions in the UN classification of countries. These are 1) Africa, 2) Asia-Pacific, 

3) Eastern Europe, 4) Latin America and Caribbean, and 5) Western Europe and 

Developed Others. 


23

Table 3.2.1 shows the lowest risk countries in the world according to risks in the top half 

of the PRW. With the exception of Japan, Australia and New Zealand, all countries in the 

top 20 are located in North America or Europe. The United States ranks seventh, while 

Japan, Switzerland and Germany occupy the top three. The top three countries typify 

countries on the list. They are all healthy, innovative, low-crime societies with advancedfirst-world 

infrastructures. For example, Japan ranks first and Switzerland third in terms 

of having the lowest probability of dying between ages 30 and 70 from cardiovascular 

disease, cancer, diabetes, and/or chronic respiratory illness. In addition, Japan, Germany, 

and Switzerland rank first, third and fourth respectively in terms of patent applications per 

capita, while Japan has the lowest incidence of homicide and Switzerland is near the top 

in terms of the reliability of police services. 

Table 3.2.1 All Country Ranking, Hazard, Event, 

Operational, and Physical Risks 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

COUNTRY 

Japan 

Switzerland 

Germany 


Denmark 

Canada 

United States 

Austria 

Norway 

Netherlands 

Finland 

France 

New Zealand 

Belgium 

Sweden 

Luxembourg 

Ireland 

Australia 

Malta 

Spain 

SCORE 

1.0000 

0.9914 

0.9624 

0.9369 

0.9261 

0.9241 

0.9201 

0.9189 

0.9164 

0.9057 

0.9043 

0.8971 

0.8944 

0.8927 

0.8925 

0.8845 

0.8802 

0.8488 

0.8379 

0.8369 

The ranking of African countries begins with a top score by Mauritius of 0.62, compared 

to a score of 1.0 for Japan. Mauritius itself is an island country with a well-developed 

banking sector that has attracted a large number of offshore entities, primarily focused 

on supporting commerce in India and South Africa. Other high-ranking African countries 

include Tunisia, Egypt and Morocco. Morocco is one of the fastest growing African 

economies and was the only major Arab country in the region to escape institutional 

turmoil in the aftermath of the Arab Spring. 


24

Among African countries, Mauritius and Tunisia rank first and third for life expectancy. 

Mauritius and Morocco rank first and second in terms of the reliability of the electricity 

sector, and second and third in terms of the extent to which organized crime imposes 

costs on business. Mauritius also ranks first for business sophistication and second for 

the lowest business costs of terrorism. Tunisia has the lowest incidence of Tuberculosis 

in Africa, while Morocco has the third lowest. Mauritius and Egypt have the lowest per 

capita natural disaster deaths. While Egypt performs well in other dimensions, it is one of 

the worst performing countries in the entire world for business costs of terrorism. Despite 

the outlier countries near the top of the African rankings, the scores for African countries 

are systematically lower than those in other regions of the world, and African countries fall 

near the bottom in most categories of the PRW. 

Table 3.2.2 African Country Ranking, Hazard, 

Event, Operational, and Physical Risks 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Mauritius 

Tunisia 

Egypt 

Morocco 

Eritrea 

Sudan 

Seychelles 

Algeria 

Libya 

Kenya 

SCORE 

0.6272 

0.5996 

0.5794 

0.5689 

0.5041 

0.4824 

0.4796 

0.4731 

0.4728 

0.4714 

Among Asia-Pacific countries, Japan is a clear outlier with a score of 1 compared to .82 

in the next best country of Singapore. South Korea, the United Arab Emirates (UAE), 

and Qatar are grouped closely together just below Singapore. Countries in this list are 

innovative, healthy and safe. Japan and Korea rank first and second in the entire world for 

patent applications per capita, while Japan, Hong Kong and Korea are in the top five in 

the world for having the lowest annual homicide rate. UAE is near the top for the region in 

terms of having the lowest business costs of terrorism. In addition, Korea and Singapore 

are near the top in the region for life expectancy, while UAE has the lowest incidence 

of tuberculosis. In addition, Singapore, Japan, and the UAE, and South Korea rank first, 

second, and fourth in terms of protection of human health from environmental risks, and 

UAE has the lowest per capita natural disaster deaths. 


25

Table 3.2.3 Asia-Pacific Country Ranking, Hazard, 


RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Japan 

Singapore 

Korea Republic 

United Arab Emirates 

Qatar 

Taiwan 

Hong Kong 

Malaysia 

Jordan 

Lebanon 

SCORE 

1.0000 

0.8269 

0.8017 

0.7998 

0.7910 

0.7567 

0.7432 

0.7320 

0.6703 

0.6661 

Among the Eastern European countries, Slovenia stands out as being very safe and 

business-friendly. It is first in the region for reliability of police services, followed by Bosnia 

and Herzegovina, then Macedonia and Croatia. Slovenia is first in the region for having 

low business costs of terrorism—indeed, both Slovenia and Croatia are in the top five in 

the entire world for this variable. Slovenia also has the lowest homicide rate, followed by 

the Czech Republic and Poland. And Slovia has the highest life expectancy in the region, 

followed by the Czech Republic and Croatia. In addition, Hungary and Poland are among 

the top countries in the region for patent applications per capita, while Lithuania, Estonia 

and Latvia have the lowest per capita rates of natural disaster deaths. 

Table 3.2.4 Eastern Europe Country Ranking, Hazard, 


RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Slovenia 

Czech Republic 

Estonia 

Poland 

Slovak Republic 

Croatia 

Lithuania 

Belarus 

Hungary 

Bosnia and Herzegovina 

SCORE 

0.7423 

0.7188 

0.6638 

0.6591 

0.6524 

0.6433 

0.6334 

0.6288 

0.6215 

0.6164 

Crime is a major concern in the Latin America and Caribbean countries, with only two 

countries in the region, Chile and Uruguay, making the top 100 global list for lowest rate 

of intentional homicide. Nevertheless, Uruguay and Costa Rica are among the bestperforming 

countries in the world for business costs of terrorism. Countries in this region 

also perform among the worst in the world for Reliability of Police Services. Even so, Chile 

is among the five best countries in the world for this variable. Among Latin American and 

Caribbean countries, Chile, Costa Rica and Panama have the highest life expectancy, while 


26

Brazil, Uruguay, and Argentina have the lowest per capita rate of natural disaster deaths. 

Patent applications in the region are low by global standards, though the per capita rate 

of patent applications is significantly higher in Argentina, Uruguay, Chile and Brazil than 

elsewhere in the region. 

Table 3.2.5 Latin America and Caribbean Country Ranking, 

Hazard, Event, Operational, and Physical Risks 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Chile 

Costa Rica 

Barbados 

Panama 

Colombia 

Mexico 

Brazil 

Uruguay 

Ecuador 

Argentina 

SCORE 

0.7476 

0.6826 

0.6679 

0.6164 

0.6110 

0.6028 

0.5984 

0.5945 

0.5801 

0.5470 

Western Europe and Other Developed Countries include many of the most-developed 

nations in the world. So the ranking looks very similar to the top list for the entire world. 

The main exception is the absence here of Japan. Switzerland and Germany are first, while 

Canada and the United States are fifth and sixth. The countries on this list perform well 

on all dimensions of the PRW. For example, Switzerland has the highest life expectancy 

in the world, while Germany, Switzerland, the United Kingdom and Finland are in the top 

ten in the world for patent applications per capita. Finland is number one in the world for 

Reliability of Police Services. In addition, Switzerland, Germany and Denmark are among 

the safest countries in Western Europe in terms of homicide rate, while Finland and Austria 

are among the safest in Europe for business costs of terrorism. 

Table 3.2.6 Western Europe and Other Country Ranking, 

Hazard, Event, Operational, and Physical Risks 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Switzerland 

Germany 


Denmark 

Canada 

United States 

Austria 

Norway 

Netherlands 

Finland 

SCORE 

0.9914 

0.9624 

0.9369 

0.9261 

0.9241 

0.9201 

0.9189 

0.9164 

0.9057 

0.9043 


27

4 Classification of Technology, Information, 

Market and Economic Risks 

Businesses and their host communities 

are vulnerable to a variety of sources of 

Technology & 

economic uncertainty. Global challenges 

Informational 

include changing international competition, oil 


price shocks and international financial crises. 

The PRW’s lower half examines technology and 

information and market and economic risk. For our 

purposes, quantifiable risks constituting the lower half of the 

PRW include: 

• Economic Structure 

• Human Capital 

• Social and Institutional Structures 

• Societal Upheaval 

• Information and Technology 

Hazard & 


Operational & 


Market & 

Economic 


Each of these five categories is represented by variables that were identified through 

appeals to both economic theory and published research. The variables capture an array 

of threats to the overall health of a country’s economy and its stability rather than any 

particular business within it. The variables consider not only supply and demand factors 

affecting a country’s economy but also the social and institutional factors that affect a 

country’s stability. 

4.1 DATA 

SOURCES AND 

MEASUREMENT 

The variables in each of the five categories of risk were obtained from various data 

sources. The sources included World Development Indicators from the World Bank (WB), 

economic and institutional variables from the World Economic Forum (WEF), human 

capital variables from the UNESCO Institute for Statistics, state fragility measures from the 

Center for Systemic Peace, macroeconomic measures from the International Monetary 

Fund (IMF), trade statistics from the World Trade Organization (WTO), and the internet and 

technological adoption data from the International Telecommunications Union (ITU). 

4.1.1 Economic Structure Risk 

Economic risks facing countries are largely determined by (1) the country’s economic base, 

(2) its business environment, and (3) its current economic conditions and the dynamism of 

the economy. Each is considered in turn. 


28

Economic Base 

In an international context, a country’s economic base depends crucially on the 

importance of trade (exports and imports) and its ability to attract Foreign Direct 

Investment (FDI). Countries that are more integrated in the global economy tend to 

understand what is necessary to sustain relationships with businesses from abroad. 

Further, countries with more developed economies tend to have institutions more aligned 

with promoting stability. 

We operationalize this measure by using data from several sources. To consider 

trade relations, we use the WEF's measures of Foreign Market Size and the Nature of 

Competitive Advantage, two variables that consider the importance and nature of trade 

relationships. With regard to openness to FDI, we consider the WEF's assessment of the 

Business Impact of Rules on FDI and the 2015 FDI Inflow and Trade Freedom Scores, as 

reported in the 2015 Index of Economic Freedom (IEF). Finally, to examine the importance 

of tariffs, we look at the Trade Tariffs, percent duty (which captures the percentage of 

imports subject to duties) reported by the World Trade Organization (WTO) for 2013. 

Business Environment 

Business climates vary significantly across countries. Some nations have rules and laws 

that are designed to facilitate private business activity, while others are more restrictive. 

Examples include taxation and labor relations policies, as well as environmental 

regulations. In operationalizing business risk, we consider the 2014 Economic 

Effectiveness and Economic Legitimacy Scores from the Center for Systemic Peace, the 

IEF’s 2015 Financial Freedom, Investment Freedom and Economic Freedom Scores. The 

IEF’s reporting of 2015 Income and Corporate Tax Rates, and the overall 2015 Tax Burden 

(%). From the WEF, we use variables capturing Cooperation in Labor-Employer Relations, 

Capacity for Innovation, and Country Capacity to Retain Talent. 

Current Economic Conditions and Economic Dynamics 

A country’s current economic conditions affect the economic risk associated with locating 

in that country. Those conditions are captured best with macroeconomic variables, 

including the country’s Gross Domestic Product (GDP), GDP per capita, inflation, 

government debt, exchange rate, and capital formation. If a country’s economy is strong, 

in terms of GDP, it is more able to withstand economic shocks and, thus, is associated 

with lower economic risk. On the other hand, a country with higher inflation rates and 

more government debt represents greater economic risk. Our economic dynamics index 

includes GDP (levels and growth), and inflation rates obtained from the World Bank. 

4.1.2 Human Capital Risk 

The demographic characteristics of a county’s population are one aspect of the 

fundamental base upon which an economy functions. A country with an educated, welltrained, 

and younger population will have greater potential for economic growth and 

development and, thus, represent a lower economic risk. 


29

We included several measures of human capital in a country. First, we obtained 

information on the percentage of the population enrolled in i) primary, ii) secondary, and iii) 

tertiary education from UNESCO Institute of Statistics. This measure provides some insight 

into the overall level of human capital in the country. Second, we included a variable 

representing the perceived quality of the education system from the WEF. 

4.1.3 Social and Institutional Structures Risk 

A country’s social stability is essential to the functioning of its economy and, consequently, 

to the level of economic risk associated with doing business in the country. Its stability 

depends on a variety of factors, including its laws, governmental institutions, and the social 

and ethnic relationships among its populous. A country’s laws are crucial to effective 

functioning of business. Property rights ensure that businesses may capture their returns 

from production and investment. Effective contract laws permit parties involved in an 

exchange of goods and/or services to identify mutual obligations and to assign risks which 

might arise out of performance of contractual obligations. Of course, criminal laws ensure 

peace and order within the country. A country’s governmental institutions are important 

to the enforcement of its laws and they serve to ensure stability within society. Finally, 

harmonious relationships between different ethnic and cultural groups within a country 

reduce the likelihood of social discord and strife. 

We included a variety of variables designed to capture countries’ social and institutional 

structures, focusing on the effectiveness and legitimacy of countries’ security systems, 

political institutions, economies, and social structures. We obtained measures of the 

efficacy of the legal and institutional framework of a country using results from the WEF 

Executive Opinion Survey. Specifically, we use variables measuring (1) the Extent to which 

Property Rights are Protected, and (2) the Extent to which Intellectual Property Rights are 

Protected. From the World Bank, we include the Legal Rights Index. 

Endemic corruption can also lead to instability. From the IEF we use a country’s 2015 

Freedom from Corruption Score, and from the WEF we use the Irregular Payments and 

Bribes Index. For more general government efficiency and stability measures, we use the 

IEF’s Fiscal Freedom Score, Business Freedom Score, Labor Freedom Score, Monetary 

Freedom Score, and the Public Debt as a share of GDP. From the WEF we use the Political 

Institutions Security Score. From the Center for Systemic Peace, we use the 2014 Political 

Effectiveness and Political Efficiency Scores, and the State Fragility Index. 

A country’s “institutions” also include its physical infrastructure. Because an effective 

infrastructure is important to the functioning of business, it affects the risks associated 

with doing business in a country. We include several measures designed to represent 

the efficacy of a country’s infrastructure. We include measures of a country’s (1) Quality of 

Electricity Supply and (2) Quality of Overall Infrastructure obtained from the WEF Executive 

Opinion Survey. 


30

4.1.4 Societal Upheaval Risk 

As we noted in the discussion of a country’s Social and Institutional factors, social stability 

is a predicate to functional business activity. Social upheaval produces prohibitive risks 

with regard to economic activity. We include several measures of societal upheaval. We 

incorporate six components of the 2014 State Fragility Index compiled by the Center for 

Systemic Peace; specifically, (1) State Effectiveness Score, (2) State Legitimacy Score, (3) 

Security Effectiveness Score, (4) Security Legitimacy Score, (5) Social Effectiveness Score 

and (6) Social Legitimacy Score. 

4.1.5 Information and Technology Risk 

The risks associated with use of information technology (i.e., cybercrime) will depend on 

the penetration of that technology into a country’s infrastructure. Greater penetration will 

expose a business to greater risks of cybercrime. We capture this risk by incorporating 

several measures of use of information technology in a country. From the WB World 

Development Indicators database, we use variables representing Internet Users (per 

100 people). From the ITU World Telecommunications/ICT Indicators Database, we use 

variables representing (1) Mobile-Cellular telephone subscriptions, and Fixed-broadband 

Internet Subscriptions, both per 100 people. From the WEF, we use expert assessment on 

Firm-level Technology Absorption. 


31

4.2 RESULTS 

4.2.1 Spatial Analysis of Technology, Information, Market and Economic Risks 

Map 2 presents the spatial distribution of technology, information, market and 

economic risk across countries. Once again, we divide index scores into quantiles, 

with dark blue shades corresponding to high-risk countries and light blue shades 

indicating low-risk countries. 



Bottom Half Pinkerton Risk 



Quintile 2 

Quintile 3 

Quintile 4 


0 1,400 2,800 5,600 8,400 11,200 

Kilometers 


32

4.2.2 Ranking Countries on Technology, Information, Market and Economic Risks 

Next, we rank countries in terms of technology, information, market and economic risks. 

As with the top half of the PRW, we present overall rankings and then analyze rankings 

within the five UN regions. 

Table 4.2.1 shows the lowest risk countries in the world according for risks in the bottom 

half of the PRW. There is more global diversity than in the upper half, with Singapore 

topping the list, joined by Japan, Taiwan and Hong Kong. Australia and New Zealand are 

the other countries in the top 20 that are not in either North America or Europe. Once 

again, the United States ranks seventh. The top 20 countries perform relatively well 

on essentially all risk measures we examine. They all have stable and well developed 

economies, strong and stable institutions, are free from significant social upheaval, and 

possess significant human capital stock. 

Table 4.2.1 All Country Ranking, Technology, 

Information, Market and Economic Risks 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

COUNTRY 

Singapore 

Finland 

Switzerland 

Netherlands 

Denmark 


United States 

New Zealand 

Canada 

Luxembourg 

Australia 

Japan 

Ireland 

Germany 

Sweden 

Norway 

Austria 

Hong Kong 

Belgium 

Taiwan 

SCORE 

1.0000 

0.9530 

0.9423 

0.9306 

0.9294 

0.9184 

0.9176 

0.9174 

0.9135 

0.8977 

0.8964 

0.8923 

0.8902 

0.8894 

0.8856 

0.8784 

0.8704 

0.8619 

0.8453 

0.8291 


33

Table 4.2.2 includes the top 10 African countries with respect to the bottom half of the 

PRW. Mauritius is, again, the highest-ranked African country. Overall, the continent is vastly 

hindered by weak institutions and social instability. Of the continental nations examined, 

all are members of the bottom two quintiles. Botswana has been a strong economic 

performer since its independence, largely fueled by diamond exports, and its per capita 

GDP of $16,600 puts it in line with other middle-income countries. The country has also 

enjoyed high political stability. South Africa is sub-Saharan Africa’s most developed 

economy, with relatively high levels of education and income, and a relatively good 

business environment. Morocco is a north-African country that has avoided much of the 

tumult that has affected its neighbors. Other variables from the bottom half of the PRW 

that differentiate the top 10 African nations from the others are the Security Effectiveness 

Score, Overall Tax Burdens, and Public Debt as a Percent of GDP. 

Table 4.2.2 African Country Ranking, Technology, 


RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Mauritius 

Botswana 

South Africa 

Morocco 

Namibia 

Cabo Verde 

Swaziland 

Tunisia 

Seychelles 

Djibouti 

SCORE 

0.7497 

0.5564 

0.5245 

0.5044 

0.4955 

0.4725 

0.4066 

0.3964 

0.3872 

0.3694 


34

The top 10 Asia-Pacific countries with respect to technology, information, market and 

economic risks are nearly the same as those on the top half of the PRW, and four of 

them are in the top 20 overall. The UAE, Qatar, Bahrain, and Oman are middle-eastern 

countries that have long enjoyed prosperity due to tremendous oil reserves, but they are 

dwindling for the latter two nations. Accordingly, those nations have worked to diversify 

their economies. In 2006, Bahrain was the first Gulf state to enter a free trade agreement 

with the US, but the country has experienced recent political instability in wake of the 

Arab Spring. Malaysia has grown significantly since the 1970s, largely due to its export 

economy, which accounts for 80 percent of GDP. The country is currently adversely 

affected by falling oil prices, putting financial strain on the government. Additional 

variables from the lower half of the PRW that distinguish the top 10 from the lower 

performing countries include security effectiveness, effectiveness of the legal system and 

public debt as a percent of GDP. 

Table 4.2.3 Asia-Pacific Country Ranking, Technology, 


RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Singapore 

Japan 

Hong Kong 

Taiwan 


Qatar 


Bahrain 

Malaysia 

Oman 

SCORE 

1.0000 

0.8923 

0.8619 

0.8291 

0.8138 

0.8130 

0.7447 

0.7349 

0.6862 

0.6824 


35

Eastern European countries tend to be in the middle of the pack with respect to 

technology, information and market economic risks. Estonia, Latvia and Lithuania make up 

three of the top four, and the Baltic States were among the first to be dissolved from the 

former Soviet Union. All three nations are now members of NATO, but there is still concern 

in some quarters that Russia may act to destabilize the region. 3 Hungary has a fairly 

prosperous and developed economy, but several years ago the nation was in significant 

financial trouble, and its bonds were relegated to junk status. Recent efforts by the country 

to rein in the deficit have allowed the nation to exit the EUs Excessive Deficit Procedure 

for the first time since it joined the EU in 2004. Overall, the top 10 Eastern European 

nations tend to have stable political institutions and strong human capital attainment. The 

top nations score relatively high on security effectiveness, FDI inflows, and the general 

measure of fiscal freedom. Lower rankings tend to be due to middling scores on the 

dimensions of business and economic freedom, and concerns about financial stability due 

to fiscal policy challenges. 

Table 4.2.4 Eastern Europe Country Ranking, Technology, 


RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Estonia 


Lithuania 

Latvia 

Hungary 

Poland 


Slovenia 

Montenegro 

Macedonia 

SCORE 

0.8094 

0.7777 

0.7401 

0.7205 

0.7143 

0.7088 

0.6800 

0.6734 

0.6183 

0.6025 

3 

http://www.bbc.com/news/uk-31528981 


36

Latin American and Caribbean nations tend to be in the third and fourth quintiles of the 

lower half of the PRW. In general, the overall rankings are due to middle of the pack GDP 

performance, and fairly average performance on measures of social and institutional risk. 

Over the past decade, political institutions throughout the region have become more 

stable, even though some countries have been buffeted by economic shocks. In particular, 

Argentina was long-affected by its fiscal crises in 2001, which caused substantial political 

instability for much of the time since. Brazil is adversely impacted by corruption and high 

income inequality. Within the region, differences in fiscal policy account for the largest 

differences between the top 10 countries and the rest of the region. Especially important 

are differences in public debt, tax burdens and the income tax rate. 

Table 4.2.5 Latin America and Caribbean Country Ranking, 

Technology, Information, Market and Economic Risks 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Chile 

Costa Rica 

Uruguay 

Panama 

Jamaica 

Mexico 

Trinidad and Tobago 

Colombia 

Barbados 

Peru 

SCORE 

0.7592 

0.6854 

0.6743 

0.6010 

0.5853 

0.5765 

0.5653 

0.5535 

0.5504 

0.5183 


37

Western Europe and Other Countries tend to be the least risky nations according to the 

measures in the bottom half of the PRW. The nations listed in Table 4.2.6 are among the 

richest in the world, and have a long history of political stability that has allowed them 

to establish institutions and social agendas that are critical to security. The nations are 

among the world leaders in human capital attainment and retention. Although the nations 

have high penetration rates of information and technology, the overall wealth of these 

countries makes them the most obvious targets for cyberattacks. Weak fiscal performance 

is, perhaps the most important factor differentiating the bottom countries and the rest 

of the region; the lowest ranked nations in the region include Italy, Cyprus, Greece and 

Turkey (2001). 

Table 4.2.6 Western Europe and Other Country Ranking, 

Technology, Information, Market and Economic Risks 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Finland 

Switzerland 

Netherlands 

Denmark 


United States 

New Zealand 

Canada 

Luxembourg 

Australia 

SCORE 

0.9530 

0.9423 

0.9306 

0.9294 

0.9184 

0.9176 

0.9174 

0.9135 

0.8977 

0.8964 


38

5 Overall Risk 

5.1 CORRELATIONAL 

ANALYSIS OF RISK 

DIMENSIONS 

In this section we analyze the overall measure of risk, 

which combines 

5 

the 

Overall 

two indices 


described 

 

in Sections 

3 and 4. We start, in Section 5.1, by investigating the 

In this section we analyze the overall measure of risk, which 

extent to which combines top and bottom the two half indices indices described are in sections Hazard 3 & and 4. We Operational & 

statistically correlated. start, in In section Section 5.1, 5.2, by we investigating present the extent Event to Risk which top Physical Risk 

a spatial picture and of the bottom incidence half indices of overall are risk statistically correlated. In section 

for countries across 5.2, we world, present as well a spatial as rankings picture of of the incidence of overall 

risk for countries across world, as well as rankings of 

countries by UN regions. 

Technology & Market & 

countries by United Nations regions. 

Informational Economic 



5.1 Correlational Analysis of Risk Dimensions 

Pearson’s correlation coefficient is a statistical 

Pearson’s correlation coefficient is a statistical summary of the linear association between 

summary of the linear association between two 

two variables, having a value between +1 and -‐1, where +1 is a perfect positive association, 

variables, having a value between +1 and -1, where 

0 is no correlation (or independence), and -‐1 is a perfect negative association. 4 We can 

+1 is a perfect positive association, 0 is no correlation (or 

visualize this by plotting one variable against another. In particular, we plot for each 

independence), and -1 is a perfect negative association. 4 We can visualize this by plotting 

country the Z-‐score of the resulting weighted index obtained for both halves of the risk 

wheel (see Appendix 

one variable 

2 for 

against 

description 

another. 

of 

In 

z-‐scoring). 

particular, 

 

we 

The 

plot 

Z-‐score 

for each 

is meaningful 

country the 

in 

Z-score 

this 

of the 

context because resulting it produces weighted an index average obtained of 0 and for a both standard halves deviation of the risk of wheel 1, allowing (see Appendix us 3 for 

identify countries description that are of above Z-scoring). below The Z-score average is on meaningful Pinkerton in dimensions this context of because Hazard, it produces an 

Event, Operational average and of 0 Physical and a standard Risks deviation versus Technology, of 1, allowing Information, us to identify Market countries and that are above 

Economics Risks. or below Our average correlational on Pinkerton analysis dimensions begins with of all Hazard, countries Event, and Operational then countries and Physical 

by the five United Risks Nations versus regions. Technology, Information, Market and Economics Risks. Our correlational 

analysis begins with all countries and then countries by the five UN regions. 

5.1.1 All Countries Correlation 

Figure 3.1.2 is a scatterplot of all countries in two dimensions of Technology, Information, 

Market and Economic Risks (Y) and Hazard, Event, Operational and Physical Risks (X). We 

divide the space into quadrants: 1) Low X, Low Y; 2) High X, High Y; 3) Low X, High Y; and 

4) High X, Low Y. High and Low are defined as above or below the mean on each dimension, 

respectively. For all countries, the correlation (rr !" ) between the top (YY) and bottom (XX) 

halves of the PRW is 0.901, a very high positive correlation. 

4 The formula for 4 the The formula correlation for the correlation between between XX (Hazard, X (Hazard, Event, Event, Operational and Physical Risk Index) and 

Operational and Physical Risk Index) and 

n 

YY (Technology, Information, Y (Technology, Information, Market and Market Economic and Economic Risk Risk Index) is: 

∑ x y −∑x 

∑ y 

1 1 

1 1 

r xy 

= 

 

2 

2 

2 

2 

n x − ( x ) × n y − ( y ) 

∑ 

1 

∑ 

1 

∑ 

1 

∑ 

1 

26 


39

5.1.1 All Countries Correlation 

Figure 3.1.2 is a scatterplot of all countries in two dimensions of Technology, Information, 

Market and Economic Risks (Y) and Hazard, Event, Operational and Physical Risks (X). 

We divide the space into quadrants: 1) Low X, Low Y; 2) High X, High Y; 3) Low X, High 

Y; and 4) High X, Low Y. High and Low are defined as above or below the mean on each 

dimension, respectively. For all countries, the correlation (r xy 

) between the top (Y ) and 

bottom (X) halves of the PRW is 0.901, a very high positive correlation. 

Figure 5.1.1 All Countries Correlation 

Technology, Information, Market and Economic Risks (Y) 

Hazard, Event, Operational and Physical Risks (X) 

Of the 70 countries in the Low X, Low Y quadrant, 26 are from the UN Region of Western 

Europe and Other Developed countries, 10 are from the Latin America and Caribbean 

country grouping, 16 are from Eastern Europe, 16 are from the Asia-Pacific grouping, 

and two are from Africa. A Western European country typifying a Low X, Low Y nation 

is Switzerland, finishing first among Western European countries on the Pinkerton Risk 

dimension of Hazard, Event, Operational and Physical Risks, and second on the Pinkerton 

Risk dimension of Technology, Information, Market and Economic Risks. 


40

5.1.2 African Countries Correlation 

Figure 5.1.2 is a scatterplot of African countries in two dimensions of Technology, 

Information, Market and Economic Risks (Y) and Hazard, Event, Operational and Physical 

Risks (X). For African countries, the correlation between the top and bottom halves of the 

PRW is 0.598, a moderately high correlation. This correlation is substantially lower in Africa 

as compared to other UN Regions. 

Figure 5.1.2 African Countries Correlation 



The majority of African countries observed appear in the High X, High Y quadrant, 

indicating that countries in Africa are more risky places to conduct business than 

others regions of the world. The two countries in Africa that appear in the Low X, Low Y 

quadrant are Morocco and Mauritius. As compared with other North African countries, 

Morocco has escaped violent political and social upheaval. In terms of specific variables, 

Morocco performs significantly better than average on risk attenuating factors like tertiary 

educational enrollment, fixed broadband internet subscriptions, labor freedoms, and the 

business costs of terrorism, and scores lower than average on risk enhancing factors like 

death or injury from natural disasters, internal displacement, and inflation. 


41

5.1.3 Asia-Pacific Countries Correlation 

Figure 3.1.4 is a scatterplot of Asia-Pacific countries in two dimensions of Technology, 


Risks (X). For Asia-Pacific countries, the correlation between the top and bottom halves of 

the PRW is 0.886. 

Figure 5.1.3 Asia-Pacific Countries 



The Asia-Pacific region is near equally divided between Low X, Low Y countries and High 

X, High Y countries. Countries appearing in the Low X, Low Y quadrant include the highly 

economically developed countries of Japan, Singapore, South Korea, Hong Kong and 

Taiwan, as well as the rapidly developing Gulf countries of Qatar and the United Arab 

Emirates. Countries appear in the High X, High Y quadrant include subcontinent nations of 

Pakistan and Bangladesh, and Transcaucasian countries of Tajikistan and Kyrgyz Republic. 


42

5.1.4 Eastern European Countries Correlation 

Figure 5.1.4 is a scatterplot of Eastern European countries in two dimensions of 

Technology, Information, Market and Economic Risks (Y) and Hazard, Event, Operational 

and Physical Risks (X). For medium-small countries, the correlation between the top and 

bottom halves of the PRW is 0.793. 

The majority of Eastern European countries are located in the Low X, Low Y quadrant. 

Only four of the 23 countries in this UN Region are in the High X, High Y quadrant, 

including the former Soviet Republics of Moldova and Azerbaijan. Moldova is 

characterized by high than average scores on risk amplifying measures of corporate 

taxation, public debt as % of GDP, property rights, political legitimacy, and capacity to 

retain talent. 

Figure 5.1.4 Eastern European Countries 




43

5.1.5 Latin American and Caribbean Countries Correlation 

Figure 5.1.5 is a scatterplot of small countries in two dimensions of Technology, 


Risks (X). For Latin American and Caribbean countries, the correlation between the top 

and bottom halves of the PRW is 0.871. 

Figure 5.1.5 Latin American and Caribbean Countries 



The Latin American and Caribbean region is divided equally among high and low risk 

countries. Countries appearing in the Low X, Low Y quadrant include emerging economies 

of Brazil, Chile, Mexico and Argentina, and nations appearing in the High X, High Y 

quadrant include the nations of Haiti, Venezuela, and Nicaragua. Haiti is a country in the 

throes of social, economic and political disorder caused by the destructive earthquake 

of 2010. With respect to the risk of death by natural disaster, Haiti is the most risky nation 

on Earth. Haiti also scores higher than average on risk amplifying factors like tax burden, 

public debt, and various measures of institutional fragility. 


44

5.1.6 Western European and Other Developed Countries Correlation 

Figure 5.1.6 is a scatterplot of small countries in two dimensions of Technology, 

Information, Market and Economic Risks (Y) and Hazard, Event, Operational and 

Physical Risks (X). For Western European and Other Developed countries, the correlation 

between the top and bottom halves of the PRW is 0.850. All countries are low risk areas 

to conduct business. 

Figure 5.1.6 Western European and Other Developed Countries 




45



Overall Pinkerton Risk 



Quintile 2 

Quintile 3 

Quintile 4 


0 1,400 2,800 5,600 8,400 11,200 

Kilometers 

5.2 SPATIAL 

ANALYSIS 

OF OVERALL 

PINKERTON RISK 

Map 3 presents the spatial distribution of overall Pinkerton Risk around the world. Once 

again, the distribution of index scores is divided into quintiles, with dark blue shades 

corresponding to high-risk countries and light blue shades indicating low-risk countries. 

Map 3 represents a statistical summary of 83 risk attenuating and amplifying factors, 

including measures of institutional legitimacy and property rights, corporate taxation and 

tariffs, inflation risk, the prevalence of infectious diseases, supply chain and infrastructure 

quality, natural disasters and fire-related deaths and injuries, corruption, organized crime 

and the reliability of police services, as well as the risk terrorism and political violence. The 

map intuitively captures low business risk regions of world, like Western Europe and Other 

Developed economies, that are home to strong political, social, and economic institutions. 

The map also intuitively captures developing regions of the world like sub-Saharan Africa 

and the Asian sub-continent. 


46

5.3 RANKING 

COUNTRIES ON 

OVERALL RISK 

Next, we rank the top performing countries in terms of hazard, event, operational, physical, 

technology, information, market and economic risks. Again, we then divide countries into 

the five UN regions. Because the rankings represent an amalgam of the country-level 

attributes described in the prior two sections, we merely report the rankings here with 

discussion only of the overall risk assessment. 

Not surprisingly, the overall least risk countries are dominated by many of the world’s most 

sophisticated economies. The most important attributes differentiating these countries 

are their strong institutions, the quality of their supply chains, the relatively low costs of 

terrorism, the low prevalence of organized crime, the commitment to property rights, and 

freedom from corruption. 

5.3.1 Table of All Country Ranking 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

COUNTRY 

Singapore 

Switzerland 

Finland 

Denmark 

Netherlands 


United States 

Canada 

Japan 

New Zealand 

Germany 

Luxembourg 

Ireland 

Sweden 

Norway 

Australia 

Austria 

Belgium 

Hong Kong 

France 

SCORE 

1.0000 

0.9888 

0.9780 

0.9631 

0.9594 

0.9562 

0.9517 

0.9492 

0.9486 

0.9457 

0.9376 

0.9270 

0.9196 

0.9185 

0.9179 

0.9178 

0.9117 

0.8847 

0.8647 

0.8450 


47

5.3.2 Table of African Country Ranking 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Mauritius 

Morocco 

Botswana 

South Africa 

Cabo Verde 

Namibia 

Tunisia 

Seychelles 

Comoros 

Rwanda 

SCORE 

0.7438 

0.5240 

0.5074 

0.4863 

0.4697 

0.4642 

0.4401 

0.4051 

0.3723 

0.3706 

5.3.3 Table of Asia-Pacific Country Ranking 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Singapore 

Japan 

Hong Kong 

Taiwan 


Qatar 


Bahrain 

Malaysia 

Oman 

SCORE 

1.0000 

0.9486 

0.8647 

0.8402 

0.8370 

0.8344 

0.7793 

0.7398 

0.7142 

0.6917 

5.3.4 Table of Eastern Europe Country Ranking 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Estonia 


Lithuania 

Poland 

Hungary 

Latvia 

Slovenia 


Montenegro 

Macedonia 

SCORE 

0.8025 

0.7882 

0.7372 

0.7167 

0.7128 

0.7105 

0.7057 

0.6908 

0.6163 

0.6067 


48

5.3.5 Table of Latin America and Caribbean Country Ranking 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Chile 

Costa Rica 

Uruguay 

Panama 

Mexico 

Jamaica 

Barbados 

Colombia 

Trinidad and Tobago 

Brazil 

SCORE 

0.7792 

0.7022 

0.6730 

0.6161 

0.5924 

0.5868 

0.5853 

0.5749 

0.5589 

0.5375 

5.3.6 Table of Western Europe and Other Country Ranking 

RANK 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

COUNTRY 

Switzerland 

Finland 

Denmark 

Netherlands 


United States 

Canada 

New Zealand 

Germany 

Luxembourg 

SCORE 

0.9888 

0.9780 

0.9631 

0.9594 

0.9562 

0.9517 

0.9492 

0.9457 

0.9376 

0.9270 


49

Appendix 1: Imputation 

To deal with missing values, we used a regression-based method called multiple 

imputation through chained equations (MICE). The MICE method is a state-of-thescience 

strategy for addressing missing values (Rubin 1987). A regression model 

is fitted for each variable. For variable Y in countries j with a missing value, a model 

Y j 

= β 0 

+ β 1 

X 1 

+ β 2 

X 2 

+ ... + β k 

X k 

is fitted using cases with observed values on Y j 

and 

predictors/covariates X 1 

,X 2 

,...,X k 

. The fitted model includes parameter estimates 

β̂ = (β̂0 ,β̂1 ,...,β̂k, ) and the variance-covariance matrix (σ ĵ 2 

V j 

) of the estimates, where V j 

is the inverse matrix derived from the intercept and covariates X 1 

,X 2 

,...,X k 

. 


50

Appendix 2: Min-Max Standardization 

Min-max standardization is a method for rescaling the unit of measurement for given 

variable such that the minimum score for variable is equal to zero and the maximum 

score is equal to one. Min-max standardization is computed by: (V – max V)/(max V – min 

V), where V represents the value on a variable for a given country. This method allows 

variables to have differing means and standard deviations but equal ranges. 


51

Appendix 3: Sample Standard Deviation and Z-Score 

Appendix Appendix 3: 3: Min-‐Max 3: Min-‐Max Standardization Standardization 

 

Min-‐max Min-‐max standardization standardization is is a is a method a method for for for rescaling rescaling the the the unit unit unit of of measurement of measurement for for for given given given 

Appendix 3: Min-‐Max Standardization variable variable such such such that that that the the minimum the minimum score score score for for variable for variable is is equal is equal equal to to zero to zero zero and and and the the maximum the maximum score score score 

Min-‐max standardization is a method for rescaling Appendix 

the unit of 3: 

measurement Min-‐Max Standardization 

for given 

 

is is equal is equal equal to to one. to one. one. Min-‐max 

Min-‐max standardization standardization is is computed is computed by: by: by: (VV (VV − (VV − mmmmmm − mmmmmm mmmmmm VV)/(mmmmmm VV)/(mmmmmm −VV −− 

variable such that the minimum score for variable is equal 

Min-‐max to zero 

standardization and the maximum is a 

 

method 

score for rescaling the unit of measurement for given 

mmmmmm mmmmmm mmmmmm VV), VV), where VV), where where VV VV represents VV represents the the value the value value on on a on variable a a variable for for a for given a given a given country. country. This This This method method allows allows allows 

variable such that the minimum score for variable is equal to zero and the maximum score 

is equal to one. Min-‐max standardization is computed by: (VV − mmmmmm VV)/(mmmmmm VV − 

variables variables to to have to have have differing differing means means and and standard standard deviations deviations but but equal but equal equal ranges. ranges. 

is equal to one. Min-‐max standardization is computed by: (VV − mmmmmm VV)/(mmmmmm VV − 

mmmmmm VV), where VV represents the value on a variable To understand for a given the country. sample This standard method deviation allows (s) and Z-score we detail steps and provide 

variables to have differing means and standard deviations 

mmmmmm 

but 

VV), 

equal 

where 

ranges. 

VV represents the value a variable for a given country. This method allows 

Appendix an 

Appendix example. 4: 4: 

variables The Sample 4: calculation 

Sample Standard to have differing of 

Standard the means sample Deviation and Deviation standard deviations and and and Z-‐Score but involves 

Z-‐Score equal six 

ranges. steps: 

 

 

Appendix 4: Sample Standard Deviation 1) Calculate Z-‐Score mean, X̅, for a given variable across all countries; 

To To understand To understand the the sample the sample standard standard deviation deviation (s) (s) and (s) and and z-‐score z-‐score we we detail we detail detail steps steps steps and and and provide provide 

Appendix 4: Sample Standard Deviation Z-‐Score 

To understand the sample standard deviation 2) (s) Subtract and z-‐score the we detail from steps the and observed provide value, 

an an example. an example. The The The calculation calculation of of the of the the sample sample standard standard 

X i 

, for each 

deviation deviation 

country (X 

involves involves 

i 

– X̅ ); 

six six six steps: steps: steps: 1) 1) 1) 

an example. The calculation of the sample standard 

To understand 

deviation involves the sample six standard 

steps: 1) 

deviation 

3) Square each mean subtraction (X i 

– X̅ ) 2 

(s) and z-‐score we detail steps and provide 

calculate calculate the the mean, the mean, mean, XX, XX, for XX, for a for given a given a given variable variable across across across all all counties; all counties; 2) 2) subtract 2) subtract the the mean the mean mean from from from the the 

the 

; 

calculate the mean, XX, for a given variable across all counties; 

an example. 2) subtract The the 

calculation 

mean from 

of the 

the 

 

sample standard deviation involves six steps: 1) 

observed observed value, value, value, XX XX ! XX , for each (XX ! − XX); 3) each mean XX ! − XX ! ! , for each county (XX ! − XX); 3) square each mean subtraction XX ! − XX ; ! 

! , for each county (XX ! − XX); 3) square each mean subtraction XX 

observed value, XX Sum ∑(X i 

– X̅ ) 2 ; 

! , for each county (XX ! − XX); 3) square 

calculate 

each mean 

the mean, subtraction XX, for a given 

XX ! − 

 

XX 

variable ! ; across all counties; 2) subtract the mean from the 4) sum the squared mean subtractions XX ! 5) − Divide XX ! ; 

observed 

5) the divide 

 

sum value, 

the of sum 

XX 

squared ! , of 

for squared 

each county 

mean subtractions mean 

(XX 

! − XX); ! − XX ! ; ; 

4) 4) sum 4) sum sum the the the squared squared mean mean mean subtractions subtractions XX XX ! XX − XX ! ! − XX ; ! 5) divide the sum of mean 

! − XX 

3) ! ; 5) ; divide 5) divide the the sum sum of squared of squared mean mean 

square each mean subtraction XX 

by the sample size (n) minus 1 to get ! XX 

; 

4) sum squared mean 

subtractions by the sample size (nn) minus 1 to get the the sample sample XX 

variance ! − XX subtractions ! XX 

nn − 1 ; and 

! − XX ! ; 5) divide the sum of XX 

squared mean by the size (nn) minus 1 to get the ! − XX ! 

subtractions by the sample size (nn) minus 1 to get the sample variance XX XX ! − XX ! 

subtractions by the sample size (nn) minus 1 to get the sample variance 

nn − 1 ; 

! − XX ! 

and XX 

subtractions by the sample size (nn) minus 1 get the sample variance ! − XX 6) take the root of the to arrive at the ! nn − nn1 ; − 1 ; 

and 6) take the square root of the variance to arrive at the standard deviation: 

and 6) take the square root of the variance 6) Take to arrive the square at the standard root of the deviation: variance ss = to arrive at the standard deviation: nn − 1 ; ss = ss = 

and 6) take the square root of the variance to arrive at the standard deviation: ss = 

XX and 6) take the square root of the variance to arrive at the standard deviation: ss = 

XX ! − XX ! nn − 1 . 

XX ! XX − XX ! ! − XX ! 

s = XX nn 

! −nn XX − 1 . 

! 1 . 

! − XX ! nn − 1 . 

nn − 1 . 

The table below is subsample of 10 countries The and The table their table life below below expectancies is is subsample at birth of for of 10 2013. 10 countries and and their their life life expectancies at at birth birth for for 2013. 2013. 

The table below is subsample of 10 countries and their life expectancies at birth for 2013. 

The table below is subsample of 10 countries and their life expectancies at birth for 2013. 

Country Country 

Country The table below Life is subsample Expectancy of 10 countries Step 2: and their life expectancies at birth for 2013. 

Life Life Expectancy Step Step 2: XX 2: XX Step 3: XX XX ! −XX XX Step 3: XX ! − XX ! 

Country Life Expectancy Step 2: XX ! − XX Step 3: XX ! − XX ! 

! − XX Step 3: XX ! − XX ! 

ountry Life Expectancy Step 2: XX ! − XX Step 3: XX ! − XX ! 

Country Life Expectancy Step 2: XX ! − XX Step 3: XX ! − XX ! 

anada 81.40 6.87 COUNTRY 47.21 LIFE EXPECTANCY STEP 2: X i 

– X̅ STEP 3: (X i 

– X̅) 2 

hile 81.20 6.67 Canada 

Canada 44.46 

81.40 6.87 

Canada Canada 81.40 81.40 81.40 6.87 6.87 6.87 47.21 47.21 47.21 

iji 69.92 -‐4.61 Chile 21.27 81.20 Chile 

6.67 

Chile Chile Chile 81.20 81.20 81.20 6.67 6.67 6.67 44.46 44.46 44.46 

reece 80.63 6.10 Fiji 37.26 69.92 Fiji 

-4.61 

Fiji Fiji Fiji 

ongolia 69.06 -‐5.47 Greece 29.90 69.92 69.92 69.92 

80.63 -‐4.61 -‐4.61 -‐4.61 

6.10 21.27 21.27 21.27 

37.26 

Greece 

etherlands 81.10 6.57 Mongolia 43.23 69.06 -‐5.47 6.10 

29.90 Greece Greece 80.63 80.63 80.63 6.10 6.10 6.10 37.26 37.26 37.26 

ew Zealand 81.41 6.88 Netherlands 47.30 81.10 6.57 -5.47 

43.23 Mongolia Mongolia 69.06 69.06 69.06 -‐5.47 -‐5.47 -‐5.47 29.90 29.90 29.90 

omania 74.46 -‐0.07 New Zealand 0.00 

81.41 6.88 6.57 

47.30 Netherlands Netherlands 81.10 81.10 81.10 6.57 6.57 6.57 43.23 43.23 43.23 

outh Africa 56.74 -‐17.79 New Romania 316.61 

74.46 -‐0.07 6.88 

0.00 

New New New Zealand Zealand 81.41 81.41 81.41 6.88 6.88 6.88 47.30 47.30 47.30 

ajikistan 69.40 -‐5.13 South 26.31 Africa 56.74 -‐17.79 -0.07 

316.61 

Romania Romania 74.46 74.46 74.46 -‐0.07 -‐0.07 -‐0.07 0.00 0.00 0.00 

nn = 10 Step 1: XX = 74.53 Tajikistan Step 4: XX ! − XX 69.40 ! = 613.56 -‐5.13 26.31 

South Africa 

-17.79 

nn = 10 Step 1: XX = 74.53 Step 4: XX ! South South South Africa Africa Africa 56.74 56.74 56.74 -‐17.79 -‐17.79 -‐17.79 316.61 316.61 

− XX ! = 613.56 

XX 

Step 5: ! − XX ! -5.13 

nn − 1 

Tajikistan Tajikistan 69.40 69.40 69.40 -‐5.13 -‐5.13 -‐5.13 26.31 26.31 26.31 

nn nn 10 Step 

= 613.56 1: XX 74.53 

10 − 1 = 68.17 

XX Step 5: 4: ! XX XX ! 

n=10 = Step 1: Step XX = 1: 74.53 X̅ =74.53 Step 4: 4: 

XX XX nn − ! − XX ! = 613.56 nn = nn = 10 1613.56 

Step 1: Step XX = 1: 74.53 XX = Step 4: Step XX 4: XX ! − XX ! = 

! − XX 

Step 6: ss = ! − XX ! = 

Step 5: 

613.56 XX ! = 

10 − 1 = 68.17 

nn − 1 

Step 5: XX XX 

XX 

= 68.17 = 8.26 

Step 6: ss = 

! − XX Step 5: ! −XX XX ! 

Step XX 5: ! − XX ! 

Step 5: ! − XX ! ! nn nn 613.56 nn − 1 

= 613.56 nn − 1 

= 613.56 nn − 1 

= 613.56 68.17 10 = 10 

10 – −8.26 − = 1 68.17 = 68.17 68.17 

10 − 1 = 1 68.17 

Step 6: 

6: ss XX XX Step 6: ss = ! −XX XX ! 

Step 6: ss = XX ! − XX ! 

Step 6: ss = ! − XX ! nn −nn 1 

nn − 1 − 1 

= = 68.17 8.26 68.17 68.17 = 8.26 = 8.26 

= 68.17 = 8.26 

42 

42 

42 42 

42 


52

With our standard deviation (s = 8.26) calculated, we can calculate a Z-score for each 

country, z = (X i – X̅ ) 

⁄ S , allowing one to express in standardized terms how much and in what 

direction a country’s life expectancy deviates from central tendency. Canada has a life 

expectancy at birth of 81.4 years. Expressed as a Z-score, Canada’s life expectancy is 

(81.4 – 74.53) 

⁄ 8.26 = 0.83 , meaning that Canada’s life expectancy is almost a full standard 

deviation above average. 


53

The Pinkerton Risk Index

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