-3

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0

1

2

3

4

0 20 40 60 80 100 120 140 

Countries by Descending Average of Standard Deviations Across Indices

State Fragility Index,

George Mason University

Index of State Weakness

in the Developing World,

Brookings

Failed States Index,

Fund for Peace

Conflict Indicators for

Foreign Policy Fragile

States Index, 

Carleton University

A

Standard Deviations for Means of Individual Indices

PEACEW RKS

Vulnerability to intrastate

conflict

Evaluating Quantitative Measures

Barry B. Hughes

Jonathan D. Moyer

Timothy D. Sisk

[ [
The views expressed in this report are 

those of the authors alone. They do 

not necessarily reflect the views of the 

United States Institute of Peace, and 

do not represent official positions of 

the United States Government.

United States Institute of Peace

2301 Constitution Ave., NW

Washington, DC 20037

Phone: 202.457.1700

Fax: 202.429.6063

E-mail: usip_requests@usip.org

Web: www.usip.org

Peaceworks No. 72 

First published 2011

© 2011 by the United States Institute of Peace

About the Report

This report, which draws on the International Futures 

modeling system for its analysis, focuses on vulnerability to conflict. This meta-analysis approach seeks 

to help those in scholarly and policy environments 

understand more fully the various quantitative 

measures on conflict vulnerabilities. The analysis 

grew out of work done for the USIP. Nonetheless, 

the report was written by the authors in their 

personal capacities, and the views are theirs alone. 

The authors would like to thank Lawrence Woocher, 

Jonas Claes, and Abiodun Williams for their helpful 

comments and other assistance in preparing this 

report. Any errors or factual inaccuracies are solely 

the responsibility of the authors.

About the Authors

Barry B. Hughes is Johns Evans Professor at the 

Josef Korbel School of International Studies at the 

University of Denver and director of the Frederick S. 

Pardee Center for International Futures. Jonathan 

Moyer is a dissertation level PhD candidate at the 

Josef Korbel School of International Studies at the 

University of Denver, whose research focus is on the 

future of international conflict, both domestic and 

interstate. Timothy D. Sisk is professor of international 

and comparative politics at the Josef Korbel School 

of International Studies, University of Denver.
Peaceworks • JUNE 2011 • no. 72

CONTENTS

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-1

0

1

2

3

4

0 20 40 60 80 100 120 140 160 180 200

Countries by Descending Average of Standard Deviations Across Indices

State Fragility Index,

George Mason University

Index of State Weakness

in the Developing World,

Brookings

Failed States Index,

Fund for Peace

Conflict Indicators for

Foreign Policy Fragile

States Index, 

Carleton University

Average

Standard Deviations for Means of Individual Indices

[ How do the principal 

quantitative measures 

of conflict vulnerability 

compare to one another? ]

Introduction ... 5

Conceptual Orientations and Approach ... 7

What Is Vulnerability? ... 7 

Vulnerability as a Spectrum ... 7

Drivers of Vulnerability ... 9

Measuring Vulnerability ... 11

Approach 1: Qualitative Consideration ... 13

Approach 2: Quantitative Analysis ... 18

Approach 3: Country-Specific Analysis ... 24

Approach 4: Exploring Connections ... 31

Conclusions ... 35

3

Vulnerability to Intrastate Conflict

Summary

■ Efforts by researchers to quantitatively measure vulnerabilities of countries to conflict 

tend to focus on four major categories: social, economic, governance, and security.

Specific variables drawn on in each category vary widely.

■ Six leading studies evaluated in this report see vulnerability to conflict as a spectrum 

from failed or failing states to consolidated ones.

■ Four measures—emanating from the Brookings Institution, Carleton University, the 

Fund for Peace, and the Center for Global Policy at George Mason University—have the 

most similar conceptual frameworks and measurement approaches, and these studies 

routinely measure countries similarly. Even these four, however, differ in the variables 

they emphasize and the identification and ranking of states.

■ Two other measures, emanating from the Center for International Development and 

Conflict Management at the University of Maryland and from the Economist 

Intelligence Unit, differ more from the first four and each other in what conceptually 

constitutes vulnerability, in their variables, correlations, and assessments. They focus 

more heavily on states most likely to fail.

■ It is not possible to argue that any one research effort or any combination is clearly 

superior in predicting vulnerability to conflict. These measures have not been systematically compared to actual outbreaks of violence, and many failed to indicate that seemingly 

stable authoritarian countries such as Libya were particularly vulnerable. Whether there 

is a relationship between rankings of states and actual subsequent conflict onsets remains 

to be analyzed.

5

Vulnerability to Intrastate Conflict

Introduction

Surveys and indices suggest that as many as 20 to 30 percent of states today are deeply vulnerable to armed domestic conflict.1

 Such countries are a constant threat to international peace 

and security—a reality recognized in the top-level priorities at the United Nations, by the 

National Security Strategy of the United States, and around the world.2

Responsive to this threat,3 the number and quality of broadly comparative, quantitative approaches to understanding—and possibly anticipating—such conflict have expanded. Quantitative approaches have been used to track the frequency of armed conflict, its intensity, patterns 

of termination, and consequences. With respect to anticipating conflict, quantitative research 

that compares countries globally or regionally on a dizzying array of hypothesized conflictrelated variables allows us to evaluate the association of possible underlying vulnerabilities to 

violence. This research has been particularly important in understanding that conflict propensity is deeply and directly associated with state fragility and that weak states are promising 

settings for insurgent and nonstate militias to emerge.4

Uncertainty about future conflict remains high. Studies show that the overall frequency 

of armed conflict is down since 1992, following an initial spike at the end of the Cold War. 

Paradoxically, however, the underlying vulnerabilities may be worsening globally, particularly 

in the context of repeated economic shocks and climate change.5 The past thus may not predict 

the future in terms of trends in armed conflict.

This paper provides a bird’s eye view—a meta-analysis—that compares various quantitative measures of vulnerability to conflict. We seek to answer a question that has not been well 

addressed in this field: How do the principal quantitative measures of conflict vulnerability 

compare to one another? Other, related questions follow:

■ How do these projects suggest that their indices are similar to or different from others?

■ How do the measures actually correlate with each other?

■ What countries do the measures evaluate differently and why?

■ To what degree do the indices provide the same information as would one or a small set 

of key variables?

Comparing Approaches to Conflict Vulnerability

We are not the first to undertake such comparison. In a conceptually groundbreaking review 

for the Council on Foreign Relations, Monty Marshall found that models for anticipating 

conflict generally fall into three categories.6 First, the academic community has since the 1960s 

developed conditional and causal factor models, looking theoretically or with qualitative analysis 

to a focused driver or a small set of drivers and attentive to civil war (often associated with 

poverty and inequality), greed and grievance models of rebellion, and ethnonational and revolutionary civil war. Second are predictive models, in which theoretical foundation and significant 

empirical research is directed at developing parsimonious explanatory systems. The work of 

the Political Instability Task Force (PITF) is prominent here. Finally, general risk and capacity 

models look to drivers that indicate ability to manage conflict over a relatively long period, and 

seek to identify the likelihood that a state will experience conflict, rather than to predict when 

instability might appear.

Marshall identified four measures in particular in this third category: the Failed and Fragile States Index of the Country Indicators for Foreign Policy (CIPF) system at Carleton 

University, the Failed States Index (FSI) developed by Pauline Baker and used by the Fund 
6

PEACEWORKS 72

for Peace, the State Fragility Index (SFI) originally developed by the IRIS Center at the 

University of Maryland and carried by Marshall to George Mason, and the Index of State 

Weakness (ISW) project by Susan Rice and Stewart Patrick for the Brookings Institution.7

Marshall found high correlations across these measures, suggesting that analysts mostly agree 

about what they are measuring and how to measure it.

In a report for the German Development Institute and the United Nations Development 

Program, Javier Mata and Sebastian Ziaja conducted an extensive and more technical analysis 

of eleven measures of fragility.8 These included the four measures that Marshall identified 

as general risk and capacity models, and that are central also to our analysis in this report. 

Of their remaining seven measures, we also look here at two: the University of Maryland’s 

Peace and Conflict Instability Ledger (CIL) and the Economist Intelligence Unit’s Political 

Instability Index.

We considered three others Mata and Ziaja examined, but do not include them here because they focus on either issue or geography: the World Bank’s International Development 

Association Resource Allocation Index (IRAI), the Global Peace Index, and the World Peace 

Foundation Index of African Governance, which was initially sponsored by the Ibrahim Foundation. Mata and Ziaja also reviewed the Bertelsmann Transformation Index of State Weakness Index and the World Bank’s World Governance Indicator measure of political stability 

and the absence of violence.

The Mata and Ziaja analysis provides a useful review of the features of each measure. It 

also covers a great deal of ground: a set of standard descriptions (relevancy, quantification, 

accessibility, transparency, multicountry coverage, and information updating); validity and reliability issues; source of data (including the degree to which projects use standard quantitative 

measures or expert assessments and the extent of data overlap across projects); the institutional 

home (academic, think tank, media); the variable and mixed use of index values, rankings, and 

categorizations; and approaches to visualization. The report looked at the intercorrelations of 

measures as well as at the degree of similarity in the ranking of the ten most fragile states. As 

our analysis does, it noted that most indices look at fragility in four dimensions: social, political, 

economic, and security.9

In a volume devoted primarily to considerations of their own CIPF measure, David Carment, Stewart Prest, and Yiagadeesen Samy reviewed the relationships between it and the 

George Mason State Fragility Index, the Fund for Peace’s Failed State Index, the Brookings 

Index of State Weakness, and the World Bank’s low-income country under stress (LICUS) 

analysis measure—the Country Policy and International Assessment (CPIA) index.10 The review also found high correlations across the measures11 and close relationships between them 

and several measures of development, including the log of GDP per capita and an inverted-U 

relationship with democracy level. The volume includes a useful literature review situating the 

study of fragility within a variety of social-science traditions.

The IFs System

Building on these earlier efforts, the International Futures (IFs) modeling system provides 

a unique platform for extending such comparison.12 The IFs project provides the capability 

to collect, scale, and compare multiple quantitative studies on various dimensions of international peace and conflict and to analyze their similarities and differences statistically. We 

undertake the analysis, which focuses on vulnerability to conflict, in two steps, qualitative and 
7

Vulnerability to Intrastate Conflict

7

quantitative. First, we consider the various series and indicators available on their own terms. 

Second, we explore the relationships among the various series and indicators.

We situate our study in the context of the literature on drivers of conflict and theoretical approaches to vulnerability. This gives us a typology of vulnerable states to help guide 

subsequent analysis and interpret the results in relation to other findings. We then compare 

measures, using qualitative analysis, regression analysis, radial diagram presentation, and more 

general analysis to show how well, or how differently, some of the larger quantitative research 

projects compare. 

Overall, our hope is that this meta-analysis approach will help those in scholarly and policy 

environments understand and evaluate more fully the various quantitative series on conflict 

vulnerabilities. We view this project as an important step toward the IFs project’s goal of better 

forecasting the potential for conflict, thereby ameliorating or even avoiding it.

Conceptual Orientations and Approach

Before comparing measures of vulnerability to conflict, it is critical to define key terms 

and ideas. 

What Is Vulnerability?

The CIFP project focuses on failed and fragile states, presenting a fragility index. The Fund for 

Peace measures state failure. The Brookings Institution project looks at state weakness. In our view, 

a concept that links each of these studies, and lends them to comparative analysis, is vulnerability 

to conflict. This in turn relates to broader approaches to understanding causal drivers, risk, crisis, 

and the outbreak of armed conflict or other consequences of failure, fragility, or weakness.13

Alternative theoretical orientations derived from the causes of conflict literature directly 

inform the content and measures of conflict vulnerability frameworks. The literature on underlying causes of conflict typically explores two central questions: What are the underlying 

causes of conflict in a society? What are the dynamics, and therefore also the agency, that 

precipitate or accelerate the escalation of violence? Such causal analysis is an essential first step 

in preventing conflict, for example, as the backdrop to the more contingent stories about why 

and how a conflict escalates in any particular setting.14

The focus on the first question is critical. Most conflict assessment models first posit background conditions (drivers) of internal conflict as a set of preconditions that fuel escalation 

when a moment of crisis occurs.15 In our analysis here, we are interested in evaluating the 

research on conflict vulnerability in terms of leading theoretical orientations in the field of 

causes-of-conflict analysis. William Zartman, for example, argues that any single factor or 

exclusive theory of internal conflict, such as isolating greed (economics) or grievance (social 

psychological) determinants is “profoundly uninteresting”; instead, the intersection of need, 

creed, and greed offers the best hope for uncovering causal relationships.16 We need, in fact, to 

look still more broadly and to the interactions among putative root causes.

Vulnerability as a Spectrum

Conceptualizations of vulnerability may directly affect how the research projects we are comparing see any given country.17 Vulnerability to conflict is a spectrum, from fully collapsed or 

mostly failed states to stable ones.18 India, for example, does not routinely appear on the global 

lists of fragile states, and certainly not on those of failed states, but certain regions of India—
8

PEACEWORKS 72

such as Kashmir and some northeastern areas—do experience insurgencies, displacement, and 

high levels of militarization. Thus, it does appear in common measures of “conflict-affected 

states,” for example in the work of the Uppsala Conflict Data Program.19

Three states appear routinely at the top of fragility lists: Afghanistan, Democratic Republic 

of Congo (DRC), and Somalia. These countries are characterized by chronic, decades-long 

conflict during which the state has either collapsed or failed and the international community’s state-building efforts have yielded little return. They typify conflict-induced humanitarian emergency. Although all have a putative central government (even Somalia), most do not 

demonstrate enough authority, capacity, or legitimacy to minimally control their own territory 

or to provide basic services.

In a second tier are the commonly listed fragile or conflict-affected states, but identifying 

and ranking them along the spectrum gives rise to considerable dissonance among analysts 

and observers.20 For example, a theoretical perspective that emphasizes regime repression may 

rank a country like North Korea as particularly vulnerable to violence, whereas one that emphasizes ethnic differences may see it as less so. Likewise, Colombia sometimes makes this list 

because of the degree of recent and ongoing conflict in that country, whereas measures that 

rely on either deep-driver socioeconomic indicators, such as GDP per capita, or proneness to 

state failure may exclude it. In our analysis, it does not make the list (see appendix 3). Because 

of both their high vulnerability and the differences in their treatment by various measures, 

countries on this range of the spectrum animate our analysis.

A typology of countries with red-level vulnerability may help delineate subcategories. For 

instance, within the annual World Bank list of fragile states, four categories of vulnerability to 

conflict are distinguishable:21

■ autocratic regimes characterized by repression and misrule—for example, North Korea, 

Myanmar, or Zimbabwe

■ weak states unable to address widespread or acute poverty, suffering, or social 

grievances—for example, Haiti or Chad

■ states with deep internal ethnic and sectarian differences—for example, BosniaHerzegovina or Iraq

■ states still in transition from previous conflicts or peacebuilding efforts—for example, 

Angola, Bosnia-Herzegovina, Liberia, Rwanda, or Sierra Leone22

In our assessment of how projects differ in the treatment of outlier countries (those for 

which vulnerability assessments differ), such a typology, might be useful. Other categories, 

however, would bring particular theoretical orientations—such as anocracies (mixed regimes) 

or recently changed governmental systems within a perspective focusing on regime type—to 

our attention. Like unhappy families for Tolstoy, fragile states tend to be fragile in unique 

ways,23 and typologies of subcategories can quickly become overwhelming.

The picture does not necessarily clarify when one considers the next tier, approximately 

seventy countries that show some but more limited vulnerability to conflict. These include, 

as mentioned, India and Colombia—middle-income countries that are generally highercapacity states but have acute subregional or protracted, localized conflicts. Additional examples include regions of China, Indonesia, Russia, and the Philippines, in which conflict 

is for the most part geographically isolated, and does not affect central state power or overall stability. Another example is Lebanon, where overall levels of economic development are 

middle income, with pockets of high levels of both wealth and poverty, and where internal 

dynamics remain tense and an escalation of violence is an ever-present possibility.

Three states appear 

routinely at the top 

of fragility lists. . . . In 

a second tier are the 

commonly listed fragile or 

conflict-affected states, 

but identifying and 

ranking them along the 

spectrum gives rise to 

considerable dissonance.
9

Vulnerability to Intrastate Conflict

Also included in this category are other subcategories: states with problematic societal conditions (such as ethnic fragmentation, as in Ghana or Mexico, which often interacts with other 

drivers of conflict vulnerability such as competition for scarce productive land and pockets of 

extreme poverty); countries that may appear to have strong state capacity, but in which certain 

elements of vulnerability are significant (such as extreme income inequality in oil-exporting 

states); and autocratic regimes that may be vulnerable during moments of inevitable regime 

change (as in Belarus or—to take a recent example—Egypt). The imbroglio in Libya underscores that even these middle tier vulnerable countries are potentially vulnerable to state failure, 

given the right mix of external and internal precipitating conditions including factors such as 

primary commodity dependency.

Last is a group of about sixty countries—mostly the higher-income OECD states—in 

which vulnerability to internal armed conflict seems minimal. Even these states can be vulnerable to episodes of social violence, however, such as riots or violent protest or even moments 

of subnational state failure (as some have suggested occurred following Hurricane Katrina in 

the United States). And these countries may not be considered very peaceful when measured 

against a criterion such as participation in wars abroad.24 However, high levels of socioeconomic development, paired with significant state authority, capacity, and legitimacy, suggest 

that these countries are not at significant risk of internal armed conflict.

We are, of course, not the first to suggest a spectrum of classifications.25 In fact, the Brookings analysis similarly identifies the bottom three countries, labeling them failed states; it refers 

to the bottom quintile as critically weak states and to the second quintile as weak states. The 

Bertelsmann project names failed states, very fragile states, fragile states, and remaining countries 

not classified. Although the Fund for Peace uses quartiles, Foreign Policy assigns names to its 

ranking: critical (1–20); in danger (21–40), and borderline (41–60). As Mata and Ziaja point out, 

such fixed category borders can be problematic in longitudinal analysis: for example, a country 

can move across category boundaries without a change in its index value, simply because of 

changes in other countries. For this reason we prefer the notion of a spectrum.26

Drivers of Vulnerability

Research on the underlying vulnerabilities tends to isolate the central factors of social, economic, political, and security conditions that are common in countries affected by armed conflict and armed violence.27 Although attention to multiple and interactive effects makes much 

sense in the analysis of conflict, some literature has tended to try to identify leading or most 

salient variables. For instance, the work of Collier and his colleagues has emphasized economic variables, particularly the importance of natural resources that allow access to “lootable 

goods.”28 Issues of natural resource management, especially of high-value commodities such as 

oil, access to employment, scarcity of water, food insecurity, lack of affordable and decent housing, or systematic economic discrimination—have all been seen as strong underlying drivers 

that have over time erupted into violent conflict.29

The social dimensions of vulnerability reflect a concern with the structure of society and 

the ways in which imbalances, discrimination, and the relationship between groups, economic 

opportunity, and the states interact.30 Leading theories today identify the overlap between 

social class deprivation and identity (particularly of marginalized minorities) as a critical and 

enduring cause of conflict. The state often exacerbates economic and social divisions, yielding 

political economy relationships of what are called horizontal inequalities. Thus ethnic fragmentation alone does not suggest vulnerability to conflict. Rather, such vulnerability normally 

Leading theories 

today identify the 

overlap between social 

class deprivation and 

identity (particularly of 

marginalized minorities) 

as a critical and enduring 

cause of conflict. 
10

PEACEWORKS 72

reflects a history of ethnic enmity, transboundary ethnic ties, and ethnic discrimination or 

marginalization.

Several studies have evaluated the importance of demographic factors on conflict 

vulnerability,31 as has research that looks at migration as both a cause and consequence 

of conflict. Some social instability may also arise from rapid urbanization, in which 

deprivation in and around burgeoning developing world cities can create conditions of 

human insecurity.32

Denial of essential human needs is a critical, micro-level category of vulnerability. Countries at the bottom of the global rankings on human development also tend to be susceptible 

to conflict; thus a vicious cycle between underdevelopment and conflict is hypothesized. For 

example, today’s most severe food security crises are in conflict-affected countries.33 Typically, 

measures of essential human needs and human development, such as the UNDP Human Development Index, indicate vulnerability to conflict, particularly components of these indices 

that most clearly suggest serious human suffering, such as infant or child mortality.

Governance matters for conflict vulnerability in two ways: state strength and regime type 

(or the character of governance). Indeed, it is the axis around which the vulnerabilities to 

conflict interact with the official and unofficial institutions and processes through which social 

conflict is to be managed. The type of regime is seen as a critical factor—the more repressive 

a regime, such as in authoritarian settings, the greater the likelihood that it will engage in 

conflict internally (for purposes of repression) or with other democratic states.34 Thus measures 

of vulnerability to conflict should incorporate variables such as the level of militarization and 

repression by a state. Critical in this regard is the analysis of the human rights performance 

of a state, in particular human rights violations: some indices, for example, include levels of 

extrajudicial killings as an indicator.

The relationships between governance, especially regime type, and vulnerability to conflict 

are not, however, monotonic. That is, mixed regimes or partial democracies tend to be more 

vulnerable than either democracies or autocracies.35 One of the weaknesses of most of the 

measures that we review in the next section is that they tend to build in variables as if their 

contributions to vulnerability are always consistently increasing or decreasing.

Looking more broadly at the security environment, criminality, lawlessness, and threats 

of armed violence are also closely related to conflict vulnerability. Indeed, interest in evaluating the interaction among social violence, vulnerability to armed conflict, and actual occurrence of armed violence is increasing.36 For example, some researchers emphasize analysis of 

violence against women as an overall indicator of the state’s propensity for involvement in 

violent conflict.37

Vulnerability to conflict can also arise from regional effects, such as tangible spillovers 

including rebel incursions, transborder migration, external minorities, or a phalange of conditions summarized as “regional conflict complexes.”38 Transborder flows of resources, recruits, 

and small arms are also factors in regional vulnerabilities. Likewise, cross-border support for 

rebel forces by hostile neighboring governments has also been seen as a component of regional 

instabilities that affect vulnerability to violence, especially in southwestern Asia and in several 

subregions in Africa. One can divide the security-condition variables into human security 

from regime repression, security-related vulnerabilities from intrastate conditions, and regional 

and global security effects.

Many vulnerable countries are conflict recidivists. The risk of new conflict is very much 

a function of experience of internal conflict or civil war.39 These fragile and conflict affected 

Vulnerability to conflict 

can also arise from 

regional effects, such 

as tangible spillovers 

including rebel incursions, 

transborder migration, 

external minorities, or a 

phalange of conditions 

summarized as “regional 

conflict complexes.”
11

states appear chronically (at least for some) stuck in a vicious cycle of governance failures, 

development catastrophes, and self-perpetuating conflict dynamics.

Given this richness of potential causality concerning vulnerability to conflict, it is 

not surprising that measures will vary in the assessment of countries. We turn now to an 

analysis of that variation.

Measuring Vulnerability

This project is interested in quantitative, macro-level indexes that explore state vulnerability 

to domestic conflict. Regardless of the specific terminology of the measure, we have chosen 

indices to compare that meet the criteria that the measures clearly focus on vulnerability in a 

broad sense and are publicly available, current, and global in scope. Those criteria generated 

six indices. Table 1 lists the names of the measures along with their institutional affiliation, 

associated scholars, and dates of publications.

A variety of sources describe each of these projects, the most useful generally being their 

websites. The following publications directly address the indices:

■ Pauline Baker, Failed States Index (Washington, DC: Fund for Peace, 2009), www.

fundforpeace.org

■ David Carment, “Country Ranking Table 2007,” Country Indicators for Foreign Policy, 

www.careleton.ca/cifp/app/ffs_ranking.php

■ Joseph Hewitt, Jonathan Wilkenfeld, and Ted Robert Gurr, Peace and Conflict 2010 

(College Park: Center for International Development and Conflict Management, 

University of Maryland)

■ Monty G. Marshall and Benjamin R. Cole, Global Report 2009: Conflict, Governance, and 

State Fragility (Vienna, VA: Center for Systemic Peace, George Mason University), 

www.systemicpeace.org/Global%20Report%202009%20Executive%20Summary.pdf

■ Susan Rice and Stewart Patrick, Index of State Weakness in the Developing World 

(Washington, DC: Brookings Institution, 2008), www.brookings.edu/reports/2008/02_

weak_states_index.aspx

■ Economist Intelligence Unit, “Political Instability Index,” ViewsWire, http://viewswire.

eiu.com/site_info.asp?info_name=social_unreast_table&page=noads&rf=0

Measure Institution Project Leader Coverage

Index of State Weakness in the 

Developing World

Brookings Susan Rice and 

Stewart Patrick

2008

Conflict Indicators for 

Foreign Policy Fragile States 

Index

Carleton David B. Carment 2007

Political Instability Index EIU 2007, 2010

Failed States Index Fund for Peace Pauline Baker 2005–10

State Fragility Index George Mason Monty Marshall 1995, 2001, 2007, 

2008, 2009

Conflict Instability Ledger Maryland Joseph Hewitt, 

Jonathan Wilkenfeld, Ted Gurr

2008, 2010

Table 1. Measures of Vulnerability to Domestic Conflict: Origins and Basic Information

Source: Authors’ compilation
12

PEACEWORKS 72

Most of the indices we considered but did not include had distinctly different conceptualizations of state vulnerability to conflict. For example, we omitted the Global Peace Index 

because it measured the presence or absence of domestic and international conflict associated 

with one country. Although external conflict sometimes does have a strong association with 

state weakness and failure, it is a different phenomenon. Because their focus is narrower, we 

did not include other measures, including some more closely related to governance quality; 

examples include the IDA Resource Allocation Index, the Freedom House Countries at the 

Crossroads measure, and the Bertelsmann Transformation Index. We also omitted the World 

Governance Indicator on Political Stability and the Absence of Violence, which reflects the 

level of instability more than the vulnerability to it. Finally, the Ibrahim Index of governance, 

which we also looked at in preliminary analysis, is both more specialized in issue focus and its 

geographic focus on Africa. In preliminary analysis, we explored correlations of such measures 

with each other and with the six measures examined here, finding that each evaluates or ranks 

countries quite differently from the ones that we chose and from each other.

We have chosen to compare six measures with four general methodological approaches. 

Our first approach is to look at them qualitatively. We begin the qualitative consideration 

by asking how the projects themselves define what they are measuring, their objectives, 

and their basic approach. We continue by identifying in four standard categories (social, 

economic, political, and security) the key variables, or at least groupings of them, that the 

measures tap. This should tell us much about what the projects believe they do similarly 

and differently.

Our second approach involves quantitative comparison of the measures across all 183 

countries of the IFs system that provide values or ranks (using values whenever possible). That 

comparison has two elements. The first is simply intercorrelation of all measures with one 

another. The second is more sophisticated: we rescale all the measures to a common range and 

valence—higher positive values then indicating more vulnerability—and compare the generated values in both graphical and tabular forms. This approach provides considerable initial 

insight into how comparable the measures are. In this process, we also compare the measures 

to the average value across the indices for each of the 183 countries, fully understanding, of 

course, that the average is not necessarily better than any or all of the individual measures. The 

computation of that average, however, also allows us to say something about the spectrum of 

vulnerability to conflict we discussed earlier. We use the average evaluations to divide countries 

into four categories of vulnerability more systematically than the common arbitrary divisions 

by percentiles or even-numbered sets (such as the top twenty or top forty).

The third approach turns to specific countries. We identify countries about which the measures most agree and, especially, disagree. We use radial diagram presentation to help us explore 

the differences across measures for these countries. Although it is an interesting digression 

rather than a continued comparison across measures, in the subsection on specific countries we 

also look at the way in which the values defined by the spectrum analysis (associated with approach 2) compares to a series of important development indicators, including GDP per capita 

and the human development index (HDI). We also add a temporal element to that analysis, 

looking at how the evolution of those key variables relates to the contemporary categories of 

the spectrum.

The fourth approach turns back to correlation analysis across all countries, but moves to 

an exploration of the relationship between the indices and discrete development variables. 

Specifically, we take advantage of the very large database in IFs to correlate each of the 

We identify countries 

about which the measures 

most agree and, 

especially, disagree.
13

Vulnerability to Intrastate Conflict

measures with multiple variables in the social, economic, political, and security categories. 

Identifying the highest relationships for each measure helps us identify the relative emphases 

of the projects.

Collectively, these four approaches allow us to better understand (considerably) the similarities and differences between the measures.

Approach 1: Qualitative Consideration

We first look at the measures on their own terms, and then consider the variables they tap.

Self-Description

How do the measures identify and define the variable of interest to them, what are the objectives of the projects, and what is the core of their methodology? Again, we invite the reader to 

consider the review of these and other measures by Mata and Ziaja, who consider these issues 

at greater length than we can here.40 Table 2 summarizes that information for the six indices.

We do not devote particular attention to their use of terminology because it is not at 

all consistent across analyses. For instance, the Failed States Index of the Fund for Peace 

(also published in summary by Foreign Policy) is clearly not a measurement of failed states 

against any common definition of that term. It is instead a broader measure of vulnerability 

to conflict. The attention of other projects to fragile states (Carleton and George Mason), 

state weakness (Brookings), and political instability (the EIU) convey more clearly a focus on 

vulnerability to conflict.

Yet the terminology of Maryland, conflict instability, begins to suggest a sharper focus 

on anticipating the escalation of conflict, not just on assessing the relative probability of it, as 

might be implicit in measuring the extent of state fragility and therefore of a country’s ability 

to avoid it. Mata and Ziaja point out also how Maryland’s focus shapes the distribution of their 

characterization of states, with a relatively few countries given high values and most assigned 

low values, a skewed distribution of values relative to the more normal distributions assigned 

by other projects.41

One might therefore conclude, and the definition of project purpose to some degree suggests, that the focus is to a greater degree on prediction, not predilection.42 Yet this is fuzzy 

territory across the various projects, because all certainly recognize that the escalation of conflict in any period is probabilistic and that point predictions (conflict or the lack of it) are not 

meaningful. And that is borne out again by the Fund for Peace, which, though it identifies 

early warning of state failure as a project objective, highly correlates with the measures of 

fragility in projects that talk less about forecasting. A third project that explicitly addresses predicting conflict is the Economist Intelligence Unit’s. The EIU measure, however, has 

many hybrid characteristics that partly link it to the Maryland measure, but also tie it fairly 

closely to the measures that explicitly target fragility (and risk) and more often forgo language 

around prediction.

In turning to the methodology of the projects, we see three key distinctions emerge 

rather quickly. First, most measures explicitly aggregate many variables or measurements 

across the four issue areas—social, economic, political, and security. The major exception 

is again the Maryland CIL measure. It builds explicitly on the analysis of the Political 

Instability Task Force, earlier the State Failure Task Force. It defines its dependent variable 

in terms of that project’s notion of state failure, namely Gurr’s conceptualization and data 
14

PEACEWORKS 72

Sponsor Fragility Index Definition Objectives Method

Carleton Conflict Indicators for 

Foreign Policy Fragile States Index

“State fragility is defined as the extent to which a state can or cannot provide the basic functions of governance to its population.”1

To create a Canadian-based measure of state fragility similar to the PITF. Has constructed a measure that takes structural data and “reprocesses” it into an authority, legitimacy, and capacity framework.

Constructed from more than seventy indicators chosen by their “relevance and coverage.” Each composite variable of each subdimension placed on a 9-point scale.2

Fund for Peace Failed States Index Focuses on sustainable security, defined 

as “capacity of a society to solve its own problems peacefully without an external administrative or military presence.”3

To provide an early warning model for state failure.

Conflict Assessment System Tool used to create forecasts by doing two things: aggregating content analysis from news feeds and analyzing structural data. Does so for twelve subindices organized by social, economic, and political characteristics.

George Mason State Fragility Index Explains fragility in terms of legitimacy and 

effectiveness with respect to four dimensions: security, governance, economic development, and social development.

To create a new index of state vulnerability that incorporates explicit measures of legitimacy and effectiveness.

Gleans macro-level data in four dimensions: security, governance, economic development, and social development. Each then broken down into effectiveness or legitimacy.

Brookings Index of State Weakness in the Developing 

World

Defines weak states as follows: “countries that lack the essential capacity and/or will to fulfill four sets of critical government responsibilities: fostering an environment conducive to sustainable and equitable economic growth; establishing and maintaining legitimate, transparent, and accountable political institutions; securing their populations from violent conflict and controlling their territory; and meeting the basic human needs of their population.”4

Concerned with correcting perceived weaknesses in other indices. Wanted to avoid a measure of state weakness that didn’t consider historic trends and developments. Strived to be transparent in its construction. Concerned with measuring the full range of instability and not just focusing on state collapse

Macro-level quantitative ranking that uses four subareas (economic, political, security, and social welfare) and ranks twenty variables.

Table 2. Measures of Vulnerability to Domestic Conflict: Definition, Objectives, and Method
15

Vulnerability to Intrastate Conflict

Maryland Conflict Instability 

Ledger

Measures future risk of state instability.5

Based on the PITF work. Definition of instability focuses on the onset of state fragility in terms of the traditional definition of revolutions, ethnic conflict, abrupt regime change and politicides/genocides. Dependent variable may be more diverse than other measures of instability that focus solely on the onset of armed civil conflict or political instability.6

Mostly concerned with forecasting in the medium term (three years) the likelihood of the onset of domestic conflict. 

Uses a statistical model fit to data for the last sixty years of domestic conflict. Relies on key variables PITF found to be related to state failure.

Economist Intelligence Unit

Political Instability Index

Vulnerability to social and political unrest. An attempt to quantify and predict 

political instability.

Uses insights taken from the PITF to create an index based on two subindices: underlying vulnerability to instability and economic distress. Uses fifteen indicators, and pays attention to social, economic, and political factors and traits.

Source: Authors’ compilationNotes:1. David Carment, Souleima El-Achkar, Yiagadeesen Samy, and Stewart Prest, “The 2006 Country Indicators for Foreign Policy: Opportunities and Challenges for Canada,” Canadian Foreign Policy 13, no. 1 (2006): 5.2. Ibid.3. Pauline H. Baker, The Conflict Assessment System Tool (CAST): An Analytical Model for Early Warning and Risk Assessment of Weak and Failing States (Washington, DC: The Fund for Peace, 2006), 4, http://www.fundforpeace.org/cast/pdf_downloads/castmanual2007.pdf.4. Susan Rice and Stewart Patrick, Index of State Weakness in the Developing World (Washington, DC: Brookings Institution, 2008), 3, http://www.brookings.edu/reports/2008/02_weak_states_index.aspx.5. Ibid., 5.6. Joseph Hewitt, Jonathan Wilkenfeld, and Ted Robert Gurr, Peace and Conflict 2008 (Boulder, CO: Paradigm, 2007), 8.
16

PEACEWORKS 72

of revolutionary wars, ethnic wars, adverse regime changes, and genocides or politicides. 

Using a range of traditional and innovative statistical methods, the task force found the 

strongest predictive model to include infant mortality, trade openness, regime consistency, 

and neighborhood effects.43 In rather sharp contrast to other projects, the CIL measure 

largely restricts itself to this rather parsimonious set of variables, along with militarization. 

We shall see how this orientation and approach also leads to a relatively lower correlation 

between the CIL measure and other measures. The EIU project indicates its attention to 

the PITF findings and approach, but in reality draws on a much wider set of variables 

across the four categories and is therefore a kind of hybrid approach.

A second key methodological difference is the degree to which data sources are standard, 

widely available, and transparent. Although we indicated earlier that we tried to select methods 

for review that drew on available data sources, the Fund for Peace uses a proprietary contentanalysis system called Conflict Assessment System Tool (CAST) that is not available to and 

therefore replicable by others. With respect to Carleton University’s CIFP, the structural components of the index are fully transparent and created from a comprehensive list of variables 

available on their website and in other documentation. However, taking these structural indicators of stability and translating them into the Authority-Legitimacy-Capacity framework—

the underlying components of the broader index—is not made fully transparent.

A third distinction across the six indices is their treatment of issues of governance. State 

fragility and failure is ultimately a condition that arises from inadequate governance. Some 

indicators reduce governance to a series of descriptive variables, such as the Brookings measure, which uses World Bank, Transparency International, and Freedom House measures. Two 

indicators stand out, however, in their more explicit and extensive treatment of the issue of 

governance across subindices. First is the work of George Mason University, which separates 

the four categories for indicators into two subcategories: effectiveness and legitimacy. The work 

of Carleton University takes this further, and reprocesses the subindices into three governance 

characteristics: authority, legitimacy, and capacity.

The many other methodological differences we leave for others to discuss.44

Issue Coverage

As discussed earlier, the components of the measures of state fragility typically fall in the following categories: social, political, economic, or security. Table 3 highlights the treatment of 

the six indices of variables within each category. We do not include an environmental variables 

category and, in general, the indices do not build on them; the CIPF Fragile States Index of 

Carleton is an exception.

What conclusions emerge from a cursory review? All projects use variables in each of the 

categories to some degree. Based solely on the extent of the listings in each category, it might 

appear that Carleton’s index coverage is among the most exhaustive with respect to variables in 

each category, perhaps especially with respect to human development. We need to recognize, 

of course, that extensive coverage of all the categories might not lead to significantly different 

results than moderate or even fairly light coverage, given the tendency for variables within 

categories to cluster and for original data sources to be similar.

The University of Maryland’s Conflict Instability Ledger is at the opposite extreme, with 

only one or two variables in each category. Whereas a subindex with three to four variables in 

a category might well correlate fairly highly with one based on ten variables, it is less likely that 

a single variable will correlate as strongly with that same subindex based on ten. This is one of 

The components of 

the measures of state 

fragility typically fall in 

the following categories: 

social, political, economic, 

or security.
17

Vulnerability to Intrastate Conflict

Sponsor Fragility Index Social Variables Economic Variables Political Variables Security VariablesCarleton Conflict Indicators for 

Foreign Policy Fragile States Index

Human Development category: many indicators; Demography category: many indicators; Gender 

Economics category: many indicators

Governance category: many indicators

Security and Crime 

category: many indicators

Fund for Peace Failed States Index Demographic Pressures; 

IDP; Group Grievance; Human Flight

Uneven Economic Development along Group Lines; Sharp Economic Decline

Criminalization of the State; Deterioration of Public Services; Suspension of Rule of Law

Security operates as state within a state (no civilian control)

George Mason State Fragility Index Effectiveness: HDI; Legitimacy: human capital 

care comparing GNP per capita and HDI

Effectiveness: GDP per capita $2,000 over 5-year average and most recent value; Legitimacy: share of export trade in manufactured goods over 12 years

Effectiveness: Regime Stability from Polity IV; Legitimacy: Polity IV and MAR

Effectiveness: MEPV; Legitimacy: Political Terror Scales

Brookings Index of State Weakness 

in the Developing World

Child Mortality; Primary School Completion; Undernourishment; Percent with Improved Water and Sanitation; Life Expectancy

GNI per capita; GDP growth 2002–2006; Income Inequality 2006; Inflation 2002–2006; Regulatory Quality (World Bank) 2006

Government Effectiveness; Rule of Law; Voice and Accountability; Corruption; Freedom

Conflict Intensity 1992–2006; Political Stability (Gov Matters); Coups 1992–2006 (EIU); Human Rights Abuses (Political Terror Scale); Territory Affected by Conflict 

1991–2005 (PITF)

Maryland Conflict Instability Ledger Infant Mortality Openness Regime Consistency Militarization; 

Neighborhood War

Economist 

Intelligence Unit

Political Instability Index Inequality; Ethnic Fragmentation; Trust in Institutions; Status of Minorities; 

Proclivity to Labor Unrest; Level of Social Provision

Growth in Income; Unemployment; Income per Capita

State History; Corruption; Regime Type; Factionalism

History of Political Instability Neighborhood

Table 3. Measures of Vulnerability to Domestic Conflict: Subcategories and VariablesSource: Authors’ compilation
18

PEACEWORKS 72

the reasons we see a result in using quantitative comparisons of measures (approach 2)—most 

indices do correlate highly with each other, even though the extent and specifics of variable 

treatment within categories differs significantly. But the CIL measure, with its parsimony, 

tends to be an outlier.

The other outlier in terms of correlations is the EIU’s Political Instability Index. Table 3 

suggests that one reason lies in the specifics of the variables it taps. It draws on many other indices, rather than tapping standard discrete variables, as other indices tend to do. It also directs 

more attention, within the human development category, to variables and indices that reflect 

dimensions of social inequality and division than to those that reflect average deprivation, 

though it does pick up infant mortality.

More generally, the full specification of the variables (the table sample is not exhaustive), 

the relationships among them (including the extent of intercorrelation across variables), and 

the explicit weighting systems of the projects all affect the degree to which indices represent 

variable groupings differentially. Approach 4, examining the actual correlations between the 

indices and key underlying variables, returns our attention to this topic.

Approach 2: Quantitative Analysis

We now build on our first approach to comparison, which was entirely qualitative and descriptive, and explore the intercorrelation among the measures. First, we look at the strength of 

linear relationships between measures by studying the R-squared values among the six indices. 

We then take each index, standardize it, compute an average across all indices, and compare 

the indices visually and numerically with each other and the average. We conclude that the 

indices fall clearly into three categories. Four of the indices most clearly focus on state fragility and relate highly to each other: the Brookings, Carleton University, Fund for Peace, and 

George Mason University measures. The University of Maryland measure and the Economist 

Intelligence Unit indices stand somewhat apart from the other four and do not appear to be 

measuring the same things, hence three categories rather than two.

Intercorrelation

Table 4 displays the covariation between each of the six indicators, as measured by the square 

of the Pearson product-moment correlation—without transformation of the indices, thereby 

picking up the linear relationship. As indicated, four of these measures correlate quite highly 

with each other, most near or above R-squared values of 0.8.45 The one that fits least well in 

that group of four is the Failed States Index of the Fund for Peace. Nonetheless, its R-squared 

relationships with the other three in the cluster are just below 0.8 and the measure clearly is 

more one of fragility than, say the Maryland measure, which more self-consciously provides 

alerts of possible failure.

The R-squared values linking both the Conflict Instability Ledger of the University of 

Maryland and the Political Instability Index of the Economist Intelligence Unit with the four 

fragility measures are still almost all at or above 0.4. This remains a decidedly high relationship, 

a covariation of 40 percent or more. Thus overlap between the enterprises of searching for high 

probabilities of state failure or conflict onset and mapping levels of fragility would appear to 

be considerable. The lowest relationship is that between the EIU and Brookings indices (0.27). 

Whereas the EIU looks to social divisions and poor governance, Brookings is more attuned to 

human development.

Overlap between the 

enterprises of searching 

for high probabilities of 

state failure or conflict 

onset and mapping levels 

of fragility would appear 

to be considerable. 
19

The two outliers, the Maryland and Economist indices, link with a relatively modest Rsquared (0.3). The Maryland index is, as it claims to be, a scion of the Political Instability Task 

Force model for forecasting episodes of state failure, paying much less attention to differentiating the level of fragility among countries not most subject to such failure. Predicting state 

failure requires a model to be tied to the historic onset of failed states. The gold standard for 

such a model is that of the Political Instability Task Force. The only index that rigorously adheres to the findings of the PITF is the Conflict Instability Ledger. We should therefore not be 

surprised that the CIL produces results different than shown by measures of state weakness.

The Political Instability Index of the Economist Intelligence Unit is considerably different 

in devoting much attention to social divisiveness and weakness in governance. Although the 

EIU methodology claims to adhere to the PITF model, its selection of variables is notably 

different from that of the CIL. A thread of work in the PITF focuses on fractionalization,46

and though the Maryland measure does not pick that up, the Economist measure does. The 

Economist measure blends that emphasis, however, with variables that owe more to the governance emphasis of work in the World Bank. Moreover, it also gives attention to past instability, 

which the CIL and, somewhat surprisingly, the PITF model itself does not. We see the Political Instability Index as a hybrid in a class of its own.

Index Values and Distribution

Although the correlations tell us much about the relationships of the indices, they cannot help 

us determine the similarities and differences, especially with respect to the distributional characteristics across the vulnerability spectrum. To help with that, the first step required was to 

rescale the measures for comparability, because they tend to have different numerical systems 

and even valences, whether higher is better or worse.

We adjusted the valences so that on each adjusted index higher positive numbers convey 

more fragility. We then computed the mean and standard deviation of each measure across all 

countries for which the projects provide assessments, using most recent evaluations in each 

George Mason: 

State Fragility

Brookings: State 

Weakness in Developing World

Fund for Peace:

Failed States

Carleton: 

Foreign Policy 

Fragile States

EIU: Political 

Instability

Maryland: Conflict Instability

George Mason: State 

Fragility

— 0.82 0.78 0.85 0.43 .055

Brookings: State 

Weakness in 

Developing World

— — 0.77 0.84 0.27 .042

Fund for Peace: 

Failed States

— — — 0.89 0.57 0.37

Carleton: 

Foreign Policy Fragile 

States

— — — — 0.52 0.46

EIU: Political 

Instability

— — — — — 0.3

Maryland: Conflict 

Instability

— — — — — —

Table 4. Correlation Matrix of Measures of Vulnerability to Domestic Conflict

Source: Authors’ compilation
20

PEACEWORKS 72

case—and fully recognizing that the distributions are not always normal, in fact wanting to see 

the character of distributions. We then computed derivative indices that recoded each countryindex value by the number of standard deviations above or below the index mean.

Although it would be possible to compare these rescaled indices directly, we took an additional step to create a common basis for comparison of the indices. We computed a simple 

average of the six indices and rank ordered all countries from highest to lowest average values. 

This has the added value of giving us a sense of how the indices collectively see fragility or state 

failure potential across countries. Figure 1 shows the average (the orange line) which is the collective judgment of the six index projects on country rankings. The Y-axis indicates the number 

of standard deviations above or below the mean of all countries and the countries are arrayed 

(but not named) in descending order of likelihood of fragility or state failure. Interestingly, the 

three countries at the high end of the average are Afghanistan (2.23 above the mean), Somalia 

(2.15), and the Democratic Republic of the Congo (2.08). There is a gap between these three, 

arguably the most nearly failed or failing states in the system, and Burundi (1.62). Appendix 1 

lists all countries in the descending order and provides the standard deviations on each index.

In addition to the ranked averages across indices, figure 1 also includes the recoded values 

for each country in the most recent year from the six indices. Thus, for instance, the value in 

the extreme upper left is the value of the Conflict Instability Ledger score for Somalia, 4.0 

standard deviations above the average value assigned to countries by the CIL.

Remarkably, all six indices show a rather tight distribution around the average. Despite using different input variables and combinatorial methods for exploring the issue of state fragility 

or failure, and using data from somewhat different years, these approaches have come to something of a consensus on the states that are failed or especially fragile and those that are not. 

This could indicate a kind of group think, whereby assessments of vulnerability don’t actually 

measure but instead simply reflect the generally accepted paradigm, or it could be a remarkable 

convergence of insight on what constitutes vulnerability.

The patterns for the indices relative to each other and to the average values are important. 

For instance, the Maryland CIL and the Economist’s Political Instability Index appear to provide the greatest spread around the average, which is consistent with their lower correlations 

with other measures and with direct analysis of variation around the average we provide.

But there is more. The Maryland measure has an interesting pattern in which a small 

number of states appear well above the average values (in the upper left of the figure) but the 

larger number appears below the average (except for the far right of the figure, where the measure does not distinguish among the least vulnerable states). This pattern is consistent with a 

measure trying to identify countries most at risk of failure and less interested in distinguishing 

them. What are the countries it so identifies? Those placed two or more standard deviations 

above the average are Afghanistan (4.00), Niger (3.27), Burundi (2.92), the DRC (2.77), Djibouti (2.66), Ethiopia (2.49), Mali (2.37), Nigeria (2.34), Tanzania (2.30),47 Zambia (2.16) 

and Sierra Leone (2.05). Interestingly, the CIL places Somalia considerably lower (1.25). One 

might disagree with this set, but the measure clearly differentiates its assessments of states at 

the very most vulnerable end of the spectrum from the rest.

Interestingly, with respect to its self-definition, the other index aimed at so distinguishing 

those most likely to experience conflict—the EIU measure—demonstrates a different pattern. It ranks only two countries even 1.5 standard deviations above its mean value, namely 

Chad (1.84) and the DRC (1.62). The next highest is Cambodia (1.48). It places Afghanistan 

lower (1.34) and does not assign a value to Somalia, perhaps because many of its series are not 

Countries are arrayed . . . 

in descending order of 

likelihood of fragility or 

state failure. The three 

countries at the high 

end of the average are 

Afghanistan, Somalia, and 

the Democratic Republic 

of the Congo. 
21

Figure 1. Conflict around Adjusted Average

-1-2-3-4

Standard Deviations for Means of Individual Indices

43210

Countries by Descending Average of Standard Deviations Across Indices

0 20 40 60 80 100 120 140 160 180 200

Political InstabilityIndex, EconomistIntelligence Unit

Conflict InstabilityLedger, University ofMaryland

Average State Fragility Index,

George MasonUniversity

Index of State Weaknessin the DevelopingWorld, Brookings

Failed States Index,Fund for Peace

Conflict Indicators for

Foreign Policy FragileStates Index, CarletonUniversity Source: Authors’ calculation
22

PEACEWORKS 72

available for that country. More generally, the visual image of the EIU’s valuations for countries is of a considerable scatter well above and below the average valuations, with relatively 

high values (0.5 to 1.0 standard deviations) assigned to a substantial number of countries 

that others consider not to be very fragile. It could be that focus on social fragmentation and 

socioeconomic inequality contributes significantly to this pattern.

The Brookings Index of State Weakness provides another pattern. Almost in the character 

of a measure of state failure, it places three countries much higher than all others: Somalia 

(3.41), Afghanistan (2.73), and the DRC (2.72). No other country has a transformed value 

on the index of more than 2.0 standard deviations. At the same time, however, and unlike the 

Maryland index, it quite clearly distinguishes among remaining countries, and its ranking is 

quite similar to that of the average across indices. Otherwise, it and the other three measures 

are hard visually to break out of the overall pattern. We will focus on these four most similar 

measures shortly, looking at them by themselves to see their patterns more clearly.

Supplementing the earlier correlation analysis, table 5 shows the variation of the six 

indices relative to the average values across indices. The statistical measure in the table is the 

sum of squared errors, which does not mean that larger values are inherently in error, only 

that the sum of squared values relative to the average line is larger. Not surprisingly, we once 

again clearly see that the two outlier indices remain as such. The other four measures fall 

into a relatively narrow band, but the Carleton and George Mason measures are closest to 

the overall average.

This clustering of the four measures that appear most sharply focused on state fragility 

suggests the value of looking at them separately from the other two. Figure 2 does this. We 

recompute the average values (the orange line again) only from the four measures, rerank 

all countries, and display the average value with the specific ones for the fragility indices. 

Appendix 2 includes the four measures and the new average computation.

A much tighter fit around the average is clear in figure 2. Two indices stand out somewhat in terms of relatively higher discrimination. As discussed earlier, the Brookings measure sharply differentiates Somalia, Afghanistan, and the Democratic Republic of the Congo 

from other countries. Less sharply, but fairly clearly, George Mason’s State Fragility Index 

also distinguishes a small set of countries from the others, namely Somalia (2.48), the DRC 

(2.17), Sudan (also 2.17), and Afghanistan (2.04). After that the values fall to Chad and 

Myanmar (both at 1.86).

Somewhat surprisingly, given its self-defined emphasis on state failure, the Fund for 

Peace assessments are considerably flatter across the entire range of countries. Still, the top 

Index SSE

Conflict Indicators for Foreign Policy Fragile States Index, Carleton 15.5

State Fragility Index, George Mason 15.8

Failed States Index, Fund for Peace 21.8

Index of State Weakness in the Developing World, Brookings 29.3

Political Instability Index, EIU 59.1

Conflict Instability Ledger, Maryland 60.9

Table 5. Sum of Squared Errors around Adjusted Mean: Domestic Vulnerability to Conflict

Source: Authors’ compilation
23

-1-2-3

43210

0 20 40 60 80 100 120 140 160 180 200

Countries by Descending Average of Standard Deviations Across Indices

State Fragility Index,George Mason University

Index of State Weaknessin the Developing World,

Brookings

Failed States Index,Fund for Peace

Conflict Indicators forForeign Policy FragileStates Index, Carleton University

Average

Figure 2. Domestic Conflict around Adjusted Mean

Standard Deviations for Means of Individual Indices

Source: Authors’ calculation
24

PEACEWORKS 72

end shows discrimination. The index places Somalia at the top (1.81), followed by Chad 

(1.77), Sudan (1.71), Zimbabwe (1.64), the DRC (1.63), Afghanistan (1.60), and Iraq (1.51). 

The Carleton rankings are similarly flatter than those of Brookings or George Mason. Its 

ranking is Sudan (1.83), Somalia (1.81), Afghanistan (1.73), Iraq (1.59), the DRC (1.55) 

and Yemen (1.51).

Using Average Values

Earlier we laid out the concept of a spectrum of countries across different levels of vulnerability 

to conflict. We identified four categories: failing or failed states, fragile or conflict-affected 

states, states with more limited vulnerability to conflict, and states with no significant vulnerability. We can use the average values across the transformed indices to help define and refine 

such a spectrum. The average standard deviations of countries above and below the means of 

their respective indices are a useful set of cut-offs. With either six indices (appendix 1) or four 

indices (appendix 2), only three countries are more than 2.0 standard deviations above the 

means; with four indices (the benchmark we will use here), their ordering is Somalia, Afghanistan, and the Democratic Republic of the Congo. Below them, the average value drops to 1.86 

for Sudan and 1.63 for Iraq.

Between 0.5 and 2.0 standard deviations are forty-eight states we can call fragile. The break 

between those states and the next tier is admittedly arbitrary. Gambia, Sri Lanka, and Algeria 

close out the listing of fragile states and Mali, Papua New Guinea, and Lesotho begin the 

ranking of another seventy-two in the category of more limited vulnerability to conflict. Although the borderline is fuzzy, most of the states in the two categories are more clearly distinct. 

In this third category, states are likely to have one or more elements of weakness in the social, 

economic, political, or security categories, but generally considered by consensus to be stable.

The remaining fifty-nine countries (more than 0.5 standard deviations on average below 

their index means) are primarily OECD and upper middle-income countries and for the most 

part have very limited vulnerability to conflict. Again, the border is fuzzy and therefore somewhat arbitrary. The states at the bottom of the third tier are Albania, Qatar, and Mexico, and 

those at the top of the fourth are Botswana, Samoa, and Bahrain. Members of this fourth tier 

generally enjoy high income, human development, and stable governance.

Although somewhat arbitrary, the approach 3 analysis shows that there are clear and important divisions across these spectrum categories. The measures, individually and collectively, 

identify significant differences across them.

Approach 3: Country-Specific Analysis

The third approach in comparing the indices is to explore more deeply the way in which they 

treat individual countries. We first consider those the indices treat most similarly and most 

differently, with particular attention to the latter. We find that discerning any clear patterns in 

the differential representation of countries is difficult.

We also look across individual countries with respect to the spectrum of vulnerability to 

conflict discussed throughout this paper. We find that the failing and fragile countries, those in 

which we are inherently most interested, have some important special characteristics.

Here we also present what was earlier called a digression. We look at how the spectrum 

frames not only the current position of countries on a number of important variables, but also 

over time. Although a digression, this discussion does reinforce the utility of looking at each 
25

Vulnerability to Intrastate Conflict

category of countries individually, and thus reinforces our decision with approach 4 to focus on 

the countries in the fragile category.

Interrelationships

Figure 3 presents a radial diagram of the transformed (and therefore comparable) evaluations 

by the six indices of the ten countries they treat most similarly. Values near the rim of the 

diagram indicate greater fragility and those near the center indicate less (or more stability). 

It is interesting that these ten countries range from high (Iraq) to low (Brazil) fragility and, 

although African countries predominate, they are scattered across the world. There is no obvious pattern.

It is, of course, the countries with the most difference across indices that particularly interest us and also where we might expect to find patterns. Figure 4 shows the ten states with the 

least overall agreement, as measured by the standard deviation of the six standardized measures. Only countries with measurements from all six indices were used for this analysis. We 

have countries that are previously failed (Afghanistan), fragile (Burundi, also previously failed), 

and not as vulnerable (Argentina and Latvia). Again, geographic variation is considerable and 

patterns are difficult to discern.

The two outlier indices do, however, once again stand out somewhat. The Conflict Instability Ledger, as we have seen, generates relatively higher values for a select few countries that it 

identifies as particularly prone to internal conflict or state failure, specifically, Afghanistan, Niger, Burundi, Mali, and Tanzania. In contrast, we have seen that the CIL tends to provide lower 

values, relative to its own mean, than other indices for more stable countries such as Argentina 

do. Somewhat surprisingly, however, it provides a lower value for the Sudan, probably because 

the index includes no variable explicitly representing recent conflict such as that in Darfur; this 

is a somewhat curious element of its formulation given the degree to which past conflict is a 

strong predictor of future conflict, and reflects its deep roots in PITF work, which similarly 

does not connect prediction of conflict to past levels.

Interestingly, the EIU Political Instability value for Argentina exceeds that of Ethiopia, and 

the value for Latvia exceeds that of either Tanzania or Rwanda. Most indices rank these in the 

other order. This reversal suggests that somehow—and this was not so clear from our earlier 

self-characterizations-based discussion of variables tapped—the measure may be weighting 

economic fragility (vulnerability to economic shocks and contagions) rather higher than political fragility.

We can reinforce this comparison of the two outlier measures with the four relatively more 

similar fragility measures by doing so more directly. Figures 5 and 6 compare both measures 

separately with the other four in combination. Figure 5 looks at the ten countries for which the 

valuation by the Conflict Instability Ledger most exceeds the average value of the four fragility 

measures. These states appear to include some of the worst performers in terms of development 

indicators—with the obvious exception of Finland and the surprising inclusion of Botswana, 

one of the best performing countries of Africa. Overall, the diagram shows again the influence 

of both the measure’s PITF roots, including the power of infant mortality, and the degree to 

which the measure purposefully emphasizes the least stable countries. The measure also identifies both Tanzania and Botswana as more vulnerable to conflict than the average of other measures does, which is somewhat surprising; because AIDS prevalence increases infant mortality, 

it is quite possible that the high CIL weight on that measure explains the result.
26

PEACEWORKS 72

26

Côte d’Ivoire

Nepal

Iraq

Guinea-Bissau

Russian Federa�on

Brazil

Lesotho

Cameroon

Madagascar

Central African Republic

State Fragility Index, George Mason University

Poli�cal Instability Index, Economist Intelligence Unit

Index of State Weakness in the Developing World,

Brookings

Failed States Index, Fund for Peace

Conflict Instability Ledger, University of Maryland

Conflict Indicators for Foreign Policy Fragile States Index,

Carleton University

Figure 3. Ten Countries, Measures of Vulnerability to Domestic Conflict Most Similar

Source: Authors’ calculation

The countries the EIU measure ranks most vulnerable relative to the four most similar 

fragility indicators (see figure 6) are a very different set. Note the inclusion of all three Baltic 

republics, as well as other states greatly affected by the global financial crisis, such as Greece 

and Iceland. We might almost expect to see Spain, Portugal, and Ireland here—countries that 

would round out the list of the so-called PIGS (Portugal, Ireland, Greece, and Spain). Clearly, 

this measure is more responsive to economic risk than one might expect from a measure called 

a political instability index.

Based on our approach 1 analysis, we labeled the EIU measure something of a hybrid for 

two reasons. First is its seeming attention in part (at least in its self-definition) to the approach 

of the PITF in combination with the selection of variables across social, economic, political 

and security categories more like the fragility measures. Second is its seemingly high attention 

to governance variables like those of the World Bank. Those characterizations may be true, 

but this more empirical analysis does suggest a different kind of hybrid character, perhaps 

more deeply rooted in the institutional home of the index, namely, a blending of political and 

economic risk.

Table 6 provides another and quite useful way of considering the groupings of states to 

which indices draw differential attention, and therefore helps identify the differences inher-
27

Vulnerability to Intrastate Conflict

27

Niger

Afghanistan

Djibou�

Mali

Tanzania

Burundi

Zambia

Botswana

Ethiopia

Nigeria

Average

Conflict Instability Ledger, University of

Maryland

Figure 4. Ten Countries, Measures of Vulnerability to Domestic Conflict, Most Different

Source: Authors’ calculation

ent in conceptualizations of vulnerability. Note, for instance, that the Fund for Peace index 

places Israel, Cuba, and Belarus relatively higher than other indices do; these three states have 

strong (some might say repressive) governments with respect to at least some of their population.48 Although the list of countries is quite different, several in the George Mason set (save 

Norway)49 are similarly repressive, but more often with some overt violence in recent history. 

We can see, in contrast, the EIU attentiveness to states that may be experiencing immediate 

economic weakness, such as slow growth or unemployment. The Maryland measure appears 

once again to emphasize states in the most fragile category, especially in human development. 

Even more, Brookings gives especially high values to the failing or failed states.

On the whole, it is difficult to see clear patterns in the differences among these indices. The 

measures are all quite rich in variables and quite different in their selections of concepts and 

indicators. If vulnerability truly is a multifaceted phenomenon, with vulnerable states being 

often nearly unique in the patterns of variables that underlie their particular vulnerability, this 

should perhaps not surprise us.
28

PEACEWORKS 72

28

Niger

Afghanistan

Djibou�

Mali

Tanzania

Burundi

Zambia

Botswana

Ethiopia

Nigeria

Average

Conflict Instability Ledger, University of

Maryland

Figure 5. Ten Countries, Values on Conflict Instability Ledger

Source: Authors’ calculation

Note: Most exceed average score

Spectrum Analysis

Grouping all countries in the international system into a single analysis lumps together societies with very different characteristics and levels of development. Comparing index treatment of 

Sweden and Somalia may not be theoretically sound. We therefore want to focus on the nearly 

fifty states of the system most vulnerable to conflict. Approach 4 does this. Before turning to 

that discussion, however, some analysis of the spectrum of vulnerability is merited—not on 

an index-specific basis to help us differentiate indices, but simply to understand the spectrum 

itself more fully.

Earlier in this analysis, we divided countries into four categories: states widely acknowledged to be somewhere in the process of failure; forty-eight fragile states; seventy-two states 

with some but limited vulnerability to conflict; and a remaining category of fifty-nine fundamentally stable countries (see appendix 3 for the full lists).

Figures 7 and 8 help us understand better the countries in the four categories of the spectrum. They each spread the countries across GDP per capita (at purchasing power parity) and 
29

Vulnerability to Intrastate Conflict

29

Argen�na

Latvia

Estonia

Iceland

Panama

Ukraine

Lithuania

Hungary

Greece

Dominican Republic

Average

Poli�cal Instability Index, Economist Intelligence Unit

Figure 6. Ten Countries, Political Instability Index

Source: Authors’ calculation

Note: Most exceed average score

show the countries, by category, against two other key development variables, life expectancy 

(figure 7) and fertility rate (figure 8).

Both figures offer useful insights. First is the fairly clear separation of countries with respect 

to GDP per capita. Almost all of the most fundamentally conflict-free countries have GDP 

per capita of $10,000 or above.50 The figure for Brazil is now about $8,000, sometimes said to 

be the lower boundary of middle-class income. Few countries in the least vulnerable category 

have incomes either at or even just below that level. Almost all of the countries in the other 

three categories do, however. Further, another obvious division is around $2,500 (about $7 per 

day). Above that level, most countries are in the limited vulnerability category (green dots) 

and, below it, most are in the fragile or failing state categories (yellow or red dots): in fact, all 

three of the failing states are just above or below $1,000 (Afghanistan). Clearly, income matters 

in shaping these categories of vulnerability.51 This finding is not, of course, greatly surprising. 

Almost all the indices include GDP per capita or highly related variables.

A second insight is that both life expectancy and fertility rates vary widely across both limited vulnerability states (although these are clustered at the more developed ends) and fragile 

Almost all of the most 

fundamentally conflictfree countries have GDP 

per capita of $10,000 

or above. Both life 

expectancy and fertility 

rates vary widely across 

both limited vulnerability 

states and fragile states.
30

PEACEWORKS 72

states (less clustered). That is, on these two variables the fragile states, though consistently 

low on income, are all over the map. This is more surprising than the relationships with GDP 

per capita, especially perhaps with respect to life expectancy, because most of the indices also 

include human development variables. Although countries with incomes above $10,000 per 

capita never appear in the fragile category and seldom in the somewhat vulnerable one, a number of states with life expectancies above 60 years or fertility rates below three (relatively high 

levels of human development) do appear in the fragile categories. In fact, many countries in the 

somewhat vulnerable category have life expectancies of above 70, and most such countries have 

fertility rates below three. This suggests, perhaps, that changes in non-income components of 

human development may often precede reduction in vulnerability to conflict, at least insofar as 

the measures tend to capture it.

We explore the fragile state category in approach 4.

Spectrum Analysis across Time

The longitudinal graphs in figure 9 compare the four groups further. Although they again do 

not help us compare the indices, they do help us understand better the differences across the 

countries that the indices collectively capture.52 In fact, this kind of analysis, pursued more 

extensively, could support theoretical considerations of the drivers of conflict vulnerability.

Figure 9 measures the Human Development Index across time for the four categories 

of the spectrum. The Stable 59 has enjoyed historically high levels of HDI, but the other 

three groups have experienced much lower overall levels. The Vulnerable 72 has seen a fairly 

rapid increase in HDI across time, though it has still not reached levels that the Stable 59 

achieved in 1975. The Fragile 48 is just now reaching HDI levels commensurate with those 

the Vulnerable 72 achieved in 1975. Members of our final group, the Failing 3, have seen their 

HDI levels decrease over time, which could obviously either be a cause, a result, or potentially 

independent of the failure.

Not all trends, though, are entirely negative, even for the Failing 3. Declining GDP per 

capita is what has, for the most part, held HDI down for each. For example, infant mortality 

rates declined for all four groups from 1960 to 2011, and the Failing 3 saw a reduction of about 

fifty deaths per thousand, from more than 200 in 1960 to around 150 today. (In comparison, 

the Stable 59 has an infant mortality rate of less than six per thousand in 2005.)

Differences in economic structure are notable across groups. For example, consistent with 

the analysis of Paul Collier and his colleagues, analysis with IFs found that members of the 

State Fragility, 

George Mason

Political 

Instability, 

EIU

State 

Weakness, 

Brookings

Failed 

States, Fund 

for Peace

Conflict 

Instability, 

Maryland

Conflict 

Indicators, 

Carleton

1 Rwanda Argentina Somalia Israel Niger Swaziland

2 Norway Latvia DRC Cuba Afghanistan São Tomé 

and Príncipe

3 Myanmar Estonia Eritrea Belarus Tanzania Equatorial 

Guinea

4 Sudan Panama North Korea Bhutan Djibouti Togo

5 Algeria Ukraine Afghanistan Cyprus Mali Yemen

Table 6. Top Five Countries Ranked More Unstable than Average

Source: Authors’ compilation
31

Vulnerability to Intrastate Conflict

Fragile 48 are considerably more dependent on energy exports than even the Vulnerable 72 

(where Saudi Arabia, Kuwait, and a number of other major exporters fall).53 Dependence of 

these countries on energy and other raw material exports has presumably increased instability 

for many reasons, including the corruption it can engender, the pain associated with the Dutch 

disease, and the disruptions associated with price volatility (dependence on other raw materials 

would have similar effects, of course).

Political structures are also different. Figure 10 shows the Polity measure of executive constraint, one significant regime-type indicator. The stable group has exhibited relatively high 

levels of executive constraint since the end of World War II. The fragile and vulnerable groups 

have notably different trajectories. The Fragile group emerged from World War II with comparable and for a time even higher levels of constraint than did the Vulnerable group. That 

changed rather rapidly, though, as the fragile group began to experience more authoritarian 

governments (many of the fragile group members are the African states that moved rather 

quickly away from democracy after independence).54 The vulnerable group did not experience 

the dip, and constraint grew steadily through the 1980s and thereafter.

Approach 4: Exploring Connections

This country-specific analysis and the enterprise of understanding vulnerability to conflict both 

suggest that we should devote more direct attention to the forty-eight fragile states. It is variability in vulnerability to conflict of those states that we most want to understand. How do the 

different indices perceive that vulnerability? That is, with what variables do they associate it?

Here we compare the six indices for the Fragile 48 to standard global measures of development in the four categories that so regularly characterize the study of vulnerability to conflict: 

social, economic, political, and security. Table 7 shows the results of this comparison. It includes 

the R-squared in the correlation of each index with each individual variable as well as the 

simple average of R-squared values for each index across the variables of each category.55 For 

the correlations, we used (unless otherwise noted) the most recent data available for all variables and the most recent assessments available for all indices (a cross-sectional analysis). All 

shaded measures have a relationship stronger than 0.15, an arbitrary threshold. The reason for 

looking at these correlations is that they tell us immediately the degree to which indices reflect 

the content of important individual variables; in a sense we are with this analysis decomposing 

the indices into their driving variables.

Three of the indices have fairly strong relationships with variables in the social category. 

Most notably, the Maryland Conflict Instability Ledger produces a 0.396 value with the Human Development Index, presumably related to its emphasis on infant mortality. Interestingly, this analysis pairs the Conflict Instability Ledger with the George Mason State Fragility 

Index and the Brookings Index of Weakness in the Developing World. It is not surprising 

that relationships of the EIU’s Political Instability Index with social variables are quite weak, 

but it is surprising that neither the Fund for Peace nor the Carleton University measures relate 

strongly to these variables. In brief, social variables differentiate two of our four more generally 

correlated state fragility measures from the two others and associate them instead with what 

we characterized as an indicator more sharply focused on state failure.

Within the social category, we also see that the George Mason measure correlates quite 

highly with a measure of Ethno Linguistic Fractionalization (0.211), something that none of 

the other measures do, as we might have expected for the EIU measure. All three of the measures quite strongly link, however, to the portion of population living on less than $1 per day. In 
Source: IFs version 6.39 

85807570656055504540

0 10,000 20,000 30,000 40,000 50,000 60,000

FragileStableVulnerableFailing

Life Expectancy

GDP per capita

Figure 7. Spectrum Analysis, GDP per capita at Purchasing Power Parity and Life Expectancy
Source: IFs version 6.39

876543210

0 10,000 20,000 30,000 40,000 50,000 60,000

Fertility Rate

GDP per capita

FailingFragileVulnerableStable

Figure 8. Spectrum Analysis, GDP per Capita at Purchasing Power Parity and Total Fertility RateSource: IFs version 6.39 
34

PEACEWORKS 72

Source: IFs version 6.39

0.32

0.42

0.52

0.62

0.72

0.82

0.92

19751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003200420052006

Vuln Spec Failing

Vuln Spec Fragile

Vuln Spec Some

Vuln Spec Stable

Figure 9. Spectrum Analysis, Human Development Index, 1975–2006

fact, that poverty measure and the HDI seem to best define the social orientation of the three 

measures most closely linked to social variables.

Somewhat surprisingly, the measures in general do not correlate highly with the economic variables we explored. This may be most surprising with respect to the EIU measure, 

though it suggests perhaps its attention to immediate economic condition (unemployment 

and growth) as well as economic development level. The Maryland Conflict Instability Ledger does pick up GDP per capita at a reasonably high level and the Brookings index correlates with income inequality (Gini). The latter is expected, because income inequality is a 

Brookings economic measure.

Turning to the political category of variables, the indices in general have higher correlations 

than they do with economic variables; this is true except for the George Mason measure, which 

has relatively low average correlations with both sets (as does the EIU measure). The political 

variables tapped most clearly vary across the other four indices. The Maryland measure picks up 

the Polity Democracy and Autocracy variables, consistent with its focus on regime consistency. 

The EIU and especially the Fund for Peace measure pick up the Political Terror scale. The 0.41 

R-squared relating the Political Terror variable and the Fund for Peace measure is, in fact, quite 

striking: the Fund for Peace attentiveness to elements of repression helps us understand why it 

rates Israel, Cuba, and Belarus much higher on vulnerability than other indices do.

In the third category—measures of political development—relationships between the six 

indices and our development measures are a strange mix. First, two policy measures—democracy and autocracy—have decidedly weak relationships with our four measures of state weakness and the EIU measure, which we consider a hybrid. It strongly correlates with the Conflict 

Instability Measure, in measures of democracy and autocracy rather than regime type, and this 

would be expected, because regime consistency is important in their formulation. Similarly, 

the Brookings Index of State Weakness in the Developing World has a strong relationship 

The Fund for Peace 

attentiveness to elements 

of repression helps 

us understand why it 

rates Israel, Cuba, and 

Belarus much higher on 

vulnerability than other 

indices do.
35

Vulnerability to Intrastate Conflict

with the World Bank measure of governance effectiveness, producing a 0.400 R-squared. The 

Brookings measure was also relatively highly correlated with the World Bank’s measure of 

governance regulatory quality. This is also what we would expect, because the Brookings index 

considers World Bank measures in creating its index. Three indices show relationships with 

corruption above our threshold.

In the security category we heavily relied on the data from the multiple Major Episodes of 

Political Violence (MEPV) series of the George Mason Center for Systemic Peace,56 supplementing those with the Uppsala measure of internal violence and the Global Peace Index, 

which picks up both internal and international violence. It may therefore not be surprising that 

the George Mason State Fragility Index correlates most highly its own measures of violence, 

but it more generally has high correlations with internal violence, including that indicated 

by the Uppsala measure. All indices except the Maryland Conflict Instability Ledger, which 

unsurprisingly does not much connect with anything in this category, correlate quite strongly 

with the Global Peace Index. In particular, the Fund for Peace’s Failed States Index shows an 

R-squared of 0.594 with that measure. This suggests the importance of attention for many 

measures to recent or ongoing conflict.

We summarize some of these findings, by index, in the next, and concluding, discussion.

Conclusions

We identified six measures of vulnerability to conflict (self-labeled and targeted in different 

ways) for detailed examination. These come from the Brookings Institution (Index of State 

Weakness in the Developing World), Carleton University (the Country Indicators for Foreign 

Policy Fragile States Index), the Fund for Peace (Failed States Index), the Center for Global 

Policy at George Mason University (the Goldstone and Marshall State Fragility Index), the 

Center for International Development and Conflict Management at the University of Maryland (the Conflict Instability Ledger measure), and the Economist Intelligence Unit (the Political Instability Index). As background for the analysis reported here, we also explored several 

other measures we found to be narrower in focus—not as sharply attentive to the concept 

of conflict vulnerability, but sometimes looking at governance, repression, violence, or other 

related or partial measures.57

To overcome the proliferation of terminology the measures use, but—more important—

to help us understand the underlying concept of vulnerability to conflict and how assorted 

concepts might relate to it, we mapped out a spectrum of vulnerability. We identified failing 

or failed states (three in particular), fragile ones (forty-eight in our analysis), those with some 

vulnerability (another seventy-two), and the residual set of those consistently and widely stable. 

We used an average analysis of consistently transformed and compared indices to rank all 

states and specify the spectrum. We also relied on a widely used division of drivers of vulnerability into four categories: social, economic, political, and security.

With such foundational tools, we used four approaches in the effort to understand the 

similarities and differences of the six indices. First, we looked at them qualitatively and on their 

own terms. This involved considering their self-definitions and the variables they include, as 

well as looking at some of the other key methodological approaches and despite being hindered by a considerable lack of transparency in how some indices were prepared. This approach 

to comparison clearly identified the CIL measure as different from others in terms of its close 

connection to the model and variables of the Political Instability Task Force, its therefore parsimonious use of input variables, and its leaning towards prediction of failure by a few states 
36

PEACEWORKS 72

Source: IFs version 6.39

1.9

2.4

2.9

3.4

3.9

4.4

4.9

5.4

5.9

6.4

6.9

Vuln Spec Fragile

Vuln Spec Some

Vuln Spec Stable

1946

1948

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

Figure 10. Polity Measure, Executive Constraint, 1946–2008

as opposed to assessment of relative fragility across many. We did, however, conclude that this 

distinction was perhaps not very strong across projects. Similarly, the EIU measure also began 

to stand out because that project, too, indicates more relative attention to prediction of conflict, 

but even more clearly because of its somewhat less typical use of variables across the four categories, including more attention to fractionalization and governance variables; its use of other 

indices and expert judgment analyses in contrast to off-the-shelf data; and its stated connection to the PITF state failure model—despite its obviously more hybrid character.

With approach 2, we compared all measures across all countries they assess. Using correlations of each with all others, as well as the transformation of indices to standardized derivative 

forms and analysis of each against each other and relative to the average index values, we found 

that, in fact, the Carleton, George Mason, Fund for Peace, and Brookings measures were most 

closely related to each other and the average, more or less in that sequence (that is, Carleton 

and George Mason most characterizing the central tendency), and we characterized these 

four as most sharply focusing on understanding variations within the fragile-state set. Yet we 

also found that the Brookings index, to some degree like that of Maryland, more sharply distinguishes a small set of most fragile or failing states from others. The EIU measure, like that 

of Maryland, sets off a number of countries from the average pattern, but whereas Maryland 

tends to do so by emphasizing the most fragile or failing, the EIU measure does so for the 

less fragile, which began to suggest that it is tapping something quite different—something 

perhaps closer to political risk.

Approach 3 took us to a consideration of how the measures differ with respect to specific 

countries. Our hope was that we could identify some groupings of countries that specific measures were most likely to assess as being more or less vulnerable than other indices did, but such 

identification proved at best difficult. There was no difficulty in once again seeing the degree 
37

Vulnerability to Intrastate Conflict

State 

Fragility

State 

Weakness

Failed 

States

Conflict 

Indicators

Political 

Instability

Conflict 

Instability

Social

Education 

secondary gross 

enrollment

0.21 0.254 0.04 0.157 0.012 0.303

Infant mortality 0.177 0.125 0.002 0.075 0.002 0.294

Ethno linguistic 

fractionalization

0.211 0 0 0.007 0.002 0.102

Human development 

index

0.257 0.264 0.076 0.082 0.031 0.396

Total fertility rate 0.204 0.032 0.006 0.063 0.001 0.293

Percent living on less 

than US$1/day

0.305 0.36 0.071 0.084 0.126 0.309

Average R-squared 0.227 0.173 0.033 0.078 0.029 0.283

Economic

GDP per capita at 

PPP

0.119 0.142 0.058 0.081 0.026 0.218

Energy exports oil 0.1 0.007 0.001 0.028 0.006 0.023

Energy exports as a 

% GDP

0.004 0.023 0.03 0 0.014 0.024

Gini for income 0.008 0.242 0.018 0.009 0.075 0.032

Average R-squared 0.058 0.104 0.027 0.03 0.03 0.074

Political

Political terror scale 0.043 0.101 0.41 0.091 0.218 0.002

Governance 

effectiveness

0.007 0.4 0.147 0.13 0.197 0.001

Governance regulatory quality0 0.22 0.078 0.033 0.036 0.081

Corruption 0.059 0.176 0.238 0.153 0.168 0.011

Polity democracy 0.005 0 0.003 0.008 0.058 0.302

Polity autocracy 0.075 0.011 0 0.001 0.047 0.286

Regime durability 0.151 0.014 0.01 0.101 0.064 0.146

Average R-squared 0.049 0.132 0.127 0.074 0.113 0.118

Security

Uppsala intensity of 

internal conflict, average all years1

0.204 0.051 0.093 0.043 0.011 0.001

MEPV ethnic violence 

magnitude, average 

all years

0.099 0.071 0.177 0.103 0.04 0.051

Table 7. Fragile States and Development Indicators

continued on page 38
38

PEACEWORKS 72

State 

Fragility

State 

Weakness

Failed 

States

Conflict 

Indicators

Political 

Instability

Conflict 

Instability

MEPV total neighborhood violence 

magnitude, average 

all years

0.003 0.024 0.002 0.001 0.006 0.04

MEPV total neighborhood violence 

episodes, average all 

years

0.081 0 0.057 0.008 0.001 0.008

MEPV social violence 

magnitude (average 

1994–2004)

0.18 0.082 0.006 0.048 0.031 0.001

MEPV total violence 

magnitude (average 

1994–2004)

0.198 0.12 0.014 0.082 0.026 0.004

MEPV social violence 

magnitude

0.165 0.082 0.029 0.065 0.015 0

MEPV total violence 

magnitude

0.12 0.075 0.03 0.064 0.02 0

Global peace index 0.154 0.359 0.594 0.279 0.298 0.038

Average R-squared 0.134 0.096 0.111 0.077 0.05 0.016

to which the Maryland and EIU measures stand out from the other four and each other. The 

Maryland CIL clearly emphasizes a smaller subset of the very most vulnerable, and the EIU 

Political Instability Index somewhat surprised us with the extent to which the countries it rates 

considerably more vulnerable than other indices rate them exhibit economic and especially 

financial vulnerability—not what we would consider vulnerability to destabilizing conflict. The 

Fund for Peace measure emerges in this analysis to be more attentive than others to strong and 

potentially repressive states, or at least to those exhibiting conflict ostensibly by news feed (this 

measure is the least transparent of all).

We took a bit of a digression with respect to comparison across countries and considered 

what the spectrum of vulnerability might show us in relationship to other variables and 

across time. We found a strong, curvilinear relationship with GDP per capita and an interesting connection between dependence on energy exports and membership in the fragile set 

of the spectrum.

The fourth and final approach to comparison moved us into a much more extensive consideration of specific variables related to vulnerability and their correlations with the six indices. 

Source: Authors’ compilation

1

 The onset of conflict is a discrete event; we averaged this variable across time to produce a longer-term 

conflict propensity, rather than simply relying on the most recent year. Theoretically, a country could have 

had conflict for many years excepting the most recent one. In other instances we looked, somewhat arbitrarily, at a block of eleven years (1994–2004).

Note: MEPF security measures are the Major Episodes of Political Violence series of the George Mason 

Center for Systemic Peace.

Note: Shared entries in the table represent adjusted R-squared values of 0.15 or higher. 

Table 7. Fragile States and Development Indicators (continued from page 37)
39

Vulnerability to Intrastate Conflict

We looked at the variables once again in the four standard categories. This analysis was productive and yielded these findings:

■ The George Mason State Fragility Index relates most strongly to social variables, 

especially poverty and human development, and to internal conflict. It gives some 

attention to regime durability but in general does not relate strongly to economic or 

political variables.

■ Although the overall highest correlations are with social and mostly human development 

variables, and not with social divisions, the Brookings Index of State Weakness has 

probably the most balanced coverage across all categories, consistent with its transparent 

identification with standard variables in each.

■ The Fund for Peace’s Failed State Index, the least transparent, clearly taps political terror 

and the content of the Global Peace Index—domestic and international violence—very 

strongly. It also picks up corruption fairly strongly. But, contrary to some measures of 

fragility, it does not relate to social variables. Because it uses news feeds, generally sensitive to events rather than levels of variables, this should perhaps not surprise us. Overall, 

the measure may be influenced fairly heavily by state delegitimization.

■ The Carleton University Fragile States Index is generally balanced, like Brookings, but 

rather surprisingly its correlations with specific measures are not very strong, the Global 

Peace Index being an exception.

■ The Economist Intelligence Unit’s Political Instability Index reflects primarily political variables and the Global Peace Index in the security category. We have not included 

the immediate economic condition variables, such as unemployment or the most recent 

year’s GDP growth, that might tie it to the economic category; these are important to 

its structure.

■ The University of Maryland’s Conflict Instability Ledger is most strongly related to the 

social variables: its average correlation with the variables in that category is the highest 

average correlation of all indices across all categories. Secondarily, it relates to the political 

category, notably to the Polity variables of democracy and autocracy. As noted in other 

analysis, it does not link directly to security variables, either existing or recent conflict.

One of the somewhat surprising overall conclusions of analysis with this fourth approach is 

the differentiation between the George Mason and Brookings measures of fragility from those 

of the Fund for Peace and even the Carleton measure.

Although this analysis can considerably help us in understanding the similarities and 

differences of conflict vulnerability indices, it cannot and does not attempt to identify the 

intrinsically or methodologically ‘best’ measure or measures. It does, however, allow those who 

use these findings to better understand the ways in which the principal validity constructs of 

different projects affect the ways in which lists of vulnerable states are generated and how some 

apparent discrepancies, such as the ranking of Tanzania in the CIL project, might occur. 

One might argue that measures producing country-specific assessments and overall 

rankings closest to the average across multiple measures have an inherent advantage. One 

foundation for such argument could be that such measures are likely to have more comprehensively included variables across the four categories of analysis. Yet it is also possible, even 

probable, that some variables and some categories are more important than others, so that 

universal inclusion (generally with equal weighting) is not necessarily superior to greater 

The Brookings Index 

of State Weakness 

has probably the most 

balanced coverage across 

all categories, consistent 

with its transparent 

identification with 

standard variables 

in each.
40

PEACEWORKS 72

selectivity. Moreover, some variables could find their way into multiple measures simply 

because of their ready availability, such as life expectancy or current conflict, rather than 

demonstrated forecasting power.

Another argument for looking at the central tendency of multiple measures could be that 

such measures may be capturing the collective wisdom of common-thinking analysts. We argue, however, that it would better serve users to be very aware and appreciative of the underlying concepts of vulnerability that these works seek to evaluate. Whether the underlying 

concern is state failure, fragility, political risk, or some other variant makes a very considerable 

difference in how these projects determine indicators, score cases, and report results. Thus the 

use of them in practical applications such as in early warning should be closely attentive to the 

underlying conceptualization.

As a result, this study, like any other that has broken new ground, also sets the stage for 

future analysis, including exploration of the relationship between vulnerability measures 

and subsequent materialization or onset of conflict. Such understanding of predictive power 

would obviously be of great value.
41

Vulnerability to Intrastate Conflict

Appendix 1. Country Rankings across All Six Indices

State 

Fragility

Political 

Instability

State 

Weakness

Failed 

States

Conflict 

Instability

Conflict 

Indicators Average

Average 8.89 5.9 3.8 71.98 6.94 4.91 —

SD 6.51 1.42 1.66 23.33 7.99 1.03 —

Afghanistan 2.01 1.34 2.73 1.6 4 1.73 2.24

Somalia 2.48 — 3.41 1.81 1.25 1.81 2.15

Congo 2.17 1.62 2.72 1.63 2.77 1.55 2.08

Burundi 1.55 0.71 1.8 1.06 2.92 1.71 1.62

Iraq 1.55 1.41 1.86 1.51 1.6 1.59 1.59

Chad 1.86 1.84 1.38 1.77 1.21 1.32 1.56

Niger 1.4 1.13 0.96 1.11 3.28 0.98 1.48

Sudan 2.17 1.48 1.75 1.71 -0.29 1.83 1.44

Central African Republic1.55 1.34 1.72 1.48 1.33 1.18 1.43

Liberia 1.55 1.06 1.54 0.85 1.97 1.44 1.4

Palestine — — — — — 1.4 1.4

Sierra Leone 1.71 0.92 1.46 0.93 2.05 1.3 1.39

Côte d’Ivoire 1.25 1.34 1.52 1.25 1.57 1.4 1.39

Nigeria 1.71 0.78 0.79 1.21 2.34 1.36 1.36

Angola 1.4 1.2 1.49 0.5 1.64 1.4 1.27

GuineaBissau1.25 1.13 1.22 1.08 1.66 1.2 1.26

Zimbabwe 1.09 2.05 1.66 1.64 -0.09 0.98 1.22

Ethiopia 1.55 -0.57 1.04 1.15 2.49 1.43 1.18

Haiti 0.63 1.34 1.46 1.27 0.77 1.5 1.16

Djibouti 0.94 0.53 0.42 2.66 1.09 1.13

Zambia 1.4 1.34 0.58 0.51 2.16 0.65 1.11

Pakistan 1.09 1.34 0.58 1.31 0.96 1.23 1.09

Guinea 1.25 1.13 0.92 1.42 0.37 1.23 1.05

Kenya 0.94 1.13 0.33 1.23 1.38 1.12 1.02

Nepal 0.79 1.13 0.95 1 1.03 1.21 1.02

Mauritania 1.09 0.71 0.54 0.73 1.81 1.2 1.01

Myanmar 1.86 0.85 1.22 1.18 -0.12 0.96 0.99

Uganda 1.4 0.42 0.8 1.09 0.75 1.17 0.94

Cameroon 1.25 0.71 0.65 1 0.7 0.91 0.87

Mali 0.79 0.78 0.21 0.31 2.37 0.68 0.86

Yemen 1.09 0.14 0.61 1.2 0.58 1.51 0.86

North Korea 0.17 1.27 1.4 1.11 0.22 0.94 0.85

Eritrea 0.79 0.56 1.42 0.91 0.03 1.37 0.85

Burkina Faso 1.25 0.71 0.41 0.8 0.9 0.83 0.81
42

PEACEWORKS 72

State 

Fragility

Political 

Instability

State 

Weakness

Failed 

States

Conflict 

Instability

Conflict 

Indicators Average

Timor-Leste 0.94 0.99 0.41 1.12 0.92 0.41 0.8

Cambodia 0.63 1.48 0.56 0.72 0.11 0.73 0.7

Congo 1.09 0.28 0.98 0.88 -0.33 1.11 0.67

Tanzania 0.48 0 0.16 0.39 2.2 0.71 0.66

Malawi 0.94 -0.14 0.36 0.93 1.07 0.72 0.65

Tajikistan 0.63 0.85 0.51 0.74 0.17 0.85 0.62

Rwanda 1.71 -0.71 0.91 0.72 0.07 1.01 0.62

Comoros 0.79 — 0.6 0.56 0.12 0.9 0.59

Mozambique 0.94 -0.14 0.53 0.42 1.11 0.5 0.56

Bangladesh 0.48 1.13 0.34 1.03 -0.49 0.86 0.56

Lesotho 0.79 0.78 0.19 0.44 0.62 0.43 0.54

Togo 0.79 -0.43 0.84 0.69 -0.04 1.16 0.5

Equatorial 

Guinea

0.48 0.14 0.86 0.71 -0.39 1.16 0.49

Bolivia 0.17 1.27 0 0.55 0.73 0.21 0.49

Madagascar 0.17 0.85 0.33 0.45 0.63 0.5 0.49

Sri Lanka 0.48 0.99 0.16 1.02 -0.26 0.37 0.46

Kyrgyzstan 0.48 0.85 -0.12 0.7 0.23 0.5 0.44

Benin 0.63 0 -0.1 0.21 1.13 0.75 0.44

Colombia 0.32 0.78 0.34 0.7 -0.04 0.32 0.4

Senegal 0.17 1.13 -0.05 0.11 0.33 0.67 0.39

Solomon 

Islands

0.17 — 0.17 0.71 — 0.51 0.39

Gambia 0.94 0.56 0.25 0.35 -0.52 0.75 0.39

Iran 0.94 0.21 -0.03 0.87 -0.61 0.91 0.38

Algeria 1.09 0.49 0.08 0.4 -0.24 0.45 0.38

Uzbekistan 0.63 0.28 0.54 0.79 -0.67 0.66 0.37

Lebanon 0.17 0.78 -0.49 0.81 0.45 0.52 0.37

Papua New 

Guinea

0.32 0.71 0.53 0.51 -0.57 0.62 0.35

Ecuador 0.32 1.27 -0.35 0.42 0.11 0.26 0.34

Laos 0.79 -0.57 0.4 0.72 -0.33 0.94 0.32

Guatemala 0.48 0.49 0.03 0.39 -0.17 0.67 0.32

Philippines 0.48 0.63 0.07 0.65 -0.33 0.18 0.28

Venezuela 0.02 0.99 -0.08 0.29 0.03 0.21 0.24

Georgia 0.02 0.28 -0.48 0.79 0.26 0.55 0.24

Turkmenistan 0.32 0.21 0.56 0.45 -0.67 0.39 0.21

India 0.48 -0.99 -0.05 0.31 0.63 0.78 0.19

Indonesia -0.14 0.63 -0.18 0.48 -0.22 0.53 0.19
43

Vulnerability to Intrastate Conflict

State 

Fragility

Political 

Instability

State 

Weakness

Failed 

States

Conflict 

Instability

Conflict 

Indicators Average

Honduras 0.02 0.63 -0.08 0.34 -0.34 0.49 0.18

Egypt 0.63 -0.35 -0.18 0.67 -0.12 0.4 0.17

Ghana 0.63 0 -0.31 -0.21 0.42 0.51 0.17

Moldova 0.17 1.13 -0.42 0.51 -0.53 0.15 0.17

Turkey 0.17 0.63 -0.59 0.22 0.17 0.28 0.15

Azerbaijan 0.79 -0.5 -0.21 0.53 -0.38 0.56 0.13

Russian 

Federation

-0.14 0.42 0 0.3 -0.03 0.13 0.11

Guyana 0.17 0.56 -0.38 0.04 0.09 0.17 0.11

Swaziland — -0.85 0.52 0.46 -0.57 0.9 0.09

Nicaragua 0.02 0 -0.1 0.45 -0.36 0.44 0.07

Syria 0.02 -0.07 0.03 0.68 -0.73 0.43 0.06

Dominican 

Republic

-0.44 1.2 -0.49 0.21 -0.26 0.11 0.05

Saudi Arabia 0.17 0.14 — 0.24 -0.77 0.43 0.04

Peru -0.14 0.78 -0.49 0.21 -0.13 -0.01 0.04

Bosnia and 

Herzegovina

-0.6 1.13 -0.86 0.49 -0.34 0.22 0.01

South Africa 0.02 0.78 -0.78 -0.18 0.14 -0.07 -0.01

Ukraine -0.44 1.2 -0.71 -0.11 -0.14 0.06 -0.02

Bhutan 0.17 -0.43 -0.53 0.66 -0.49 0.4 -0.04

Thailand -0.29 0.78 -0.18 0.29 -0.67 -0.3 -0.06

Namibia -0.29 -0.07 -0.28 0.11 0.32 -0.17 -0.06

Armenia -0.29 -0.07 -0.69 0.09 0.48 0.08 -0.07

Paraguay -0.14 0.35 -0.15 0 -0.44 -0.09 -0.08

São Tomé 

and Príncipe

— -1.13 0.02 0.16 — 0.6 -0.09

Gabon 0.17 -0.57 0.01 0.14 -0.53 0.21 -0.09

Maldives — — -0.63 0.27 — -0.02 -0.13

Mongolia -0.14 0.14 0.02 -0.51 -0.54 0.05 -0.16

China 0.02 -0.78 -0.13 0.47 -0.72 0.15 -0.17

Serbia -0.6 0.35 -0.74 0.25 -0.66 0.38 -0.17

Morocco -0.44 -0.21 -0.55 0.21 -0.59 0.2 -0.23

Fiji -0.29 -0.16 0.36 -0.77 -0.31 -0.23

El Salvador -0.44 -0.5 -0.54 0.26 -0.27 0.01 -0.25

Macedonia -0.6 0.49 -0.88 0.03 -0.56 -0.07 -0.26

Israel -0.14 -0.28 0.54 -0.79 -0.64 -0.26

Kazakhstan 0.17 -0.78 -0.43 0.03 -0.67 0.09 -0.27

Belize — 0.21 -0.91 -0.14 — -0.24 -0.27
44

PEACEWORKS 72

State 

Fragility

Political 

Instability

State 

Weakness

Failed 

States

Conflict 

Instability

Conflict 

Indicators Average

Micronesia — — -0.65 -0.06 — -0.14 -0.28

Mexico -0.9 0.14 -0.98 0.18 0.04 -0.22 -0.29

Jordan -0.44 -0.35 -0.93 0.21 -0.28 -0.05 -0.31

Suriname -0.14 — -0.78 0.02 — -0.44 -0.33

Vanuatu — — -0.9 — — 0.23 -0.33

Brazil -0.6 -0.35 -0.61 -0.2 -0.04 -0.27 -0.35

Vietnam -0.29 -1.13 -0.28 0.2 -0.79 0.19 -0.35

Jamaica -0.9 0.07 -0.64 -0.2 -0.42 -0.07 -0.36

Botswana -0.75 -0.85 -0.64 -0.15 0.71 -0.5 -0.36

Albania -0.6 0.21 -0.84 -0.21 -0.54 -0.2 -0.36

Malaysia -0.44 0.42 -1.2 -0.12 -0.41 -0.49 -0.37

Montenegro -0.75 0.35 — -0.63 -0.51 -0.38

St. Vincent 

and the 

Grenadines

— — — — — -0.39 -0.39

Tonga — -0.67 -0.11 -0.39

Cape Verde — -0.28 -1.06 0.22 -0.68 -0.18 -0.4

Panama -0.6 0.85 -1.05 -0.54 -0.66 -0.43 -0.4

Cuba -0.6 -1.2 0.02 0.32 -0.81 -0.16 -0.4

Belarus -0.6 -0.78 -0.26 0.29 -0.76 -0.4 -0.42

Libya -0.29 -1.13 -0.36 -0.12 -0.72 0.08 -0.42

Romania -0.6 0.35 -1.03 -0.51 -0.48 -0.39 -0.44

Tunisia -0.29 -0.92 -0.85 -0.19 -0.47 -0.29 -0.5

Samoa — — -1.21 -0.04 — -0.33 -0.53

Bahrain -0.75 -0.28 — -0.57 -0.79 -0.32 -0.54

Trinidad and 

Tobago

-0.75 -0.85 — -0.25 -0.64 -0.33 -0.57

Kuwait -0.9 -0.28 — -0.45 -0.78 -0.46 -0.58

Bulgaria -0.75 0.07 -1.31 -0.46 -0.52 -0.54 -0.58

Argentina -1.21 0.85 -0.88 -1.12 -0.46 -0.8 -0.6

Estonia -1.37 0.56 — -0.91 -0.63 -1.09 -0.69

Qatar -0.44 -1.27 — -0.87 -0.79 -0.11 -0.7

Croatia -0.9 0.14 -1.49 -0.56 -0.73 -0.76 -0.72

Grenada — — -1.37 -0.21 — -0.58 -0.72

Brunei — — — — — -0.76 -0.76

Greece -1.21 0.28 — -1.12 -0.79 -0.95 -0.76

Bahamas — — — -0.56 -1 -0.78

Latvia -1.37 0.56 -1.73 -0.71 -0.57 -1.03 -0.81
45

Vulnerability to Intrastate Conflict

State 

Fragility

Political 

Instability

State 

Weakness

Failed 

States

Conflict 

Instability

Conflict 

Indicators Average

United Arab 

Emirates

-0.9 -1.27 — -0.84 -0.83 -0.28 -0.83

Cyprus -0.9 -1.27 — -0.17 -0.79 -1.12 -0.85

Oman -0.6 -1.41 -1.36 -1 -0.81 -0.33 -0.92

Spain -1.21 -0.28 — -1.22 -0.83 -1.21 -0.95

South Korea -1.37 -0.57 — -1.32 -0.69 -1.06 -1

Hungary -1.37 0.14 -1.93 -0.94 -0.82 -1.14 -1.01

Lithuania -1.21 0.14 -1.85 -1.04 -0.78 -1.33 -1.01

United States -1.06 -0.43 — -1.57 -0.76 -1.26 -1.02

Italy -1.37 -0.64 — -1.13 -0.82 -1.15 -1.02

Slovakia -1.21 -0.28 -1.93 -0.99 -0.82 -1.07 -1.05

Malta — -0.85 — -1.02 — -1.33 -1.07

Uruguay -1.21 -0.5 -1.54 -1.32 -0.76 -1.21 -1.09

France -1.37 -0.43 — -1.59 -0.82 -1.26 -1.09

Taiwan, 

China

-1.37 -1.13 — — -0.78 — -1.09

Barbados — — — -0.71 — -1.52 -1.11

Singapore -1.21 -0.85 — -1.59 -0.78 -1.2 -1.13

Poland -1.37 -0.99 -1.69 -0.99 -0.79 -0.97 -1.13

Chile -1.06 -0.57 -1.89 -1.46 -0.76 -1.15 -1.15

Portugal -1.37 -0.78 — -1.67 -0.83 -1.34 -1.2

Costa Rica -1.37 -1.7 -1.47 -0.86 -0.78 -1.01 -1.2

United 

Kingdom

-1.37 -0.92 — -1.63 -0.82 -1.32 -1.21

Czech 

Republic

-1.21 -1.56 — -1.31 -0.78 -1.26 -1.22

St. Lucia — — -1.75 — — -0.73 -1.24

Mauritius -1.21 -1.7 -1.56 -1.18 -0.78 -1.01 -1.24

Belgium -1.06 -1.34 — -1.71 -0.77 -1.49 -1.27

Slovenia -1.37 -1.48 — -1.54 -0.84 -1.54 -1.35

Germany -1.37 -1.48 — -1.57 -0.83 -1.53 -1.36

Netherlands -1.37 -1.34 — -1.89 -0.83 -1.46 -1.38

Japan -1.37 -1.48 — -1.74 -0.82 -1.52 -1.39

Ireland -1.37 -0.92 — -2.13 -0.83 -1.69 -1.39

Australia -1.06 -1.63 — -1.92 -0.82 -1.7 -1.42

New Zealand -1.21 -1.63 — -2.06 -0.82 -1.79 -1.5

Austria -1.37 -1.63 — -1.92 -0.83 -1.77 -1.5

Iceland — -0.43 — -1.81 — -2.29 -1.51

Switzerland -1.21 -1.77 — -2.15 -0.83 -1.83 -1.56
46

PEACEWORKS 72

State 

Fragility

Political 

Instability

State 

Weakness

Failed 

States

Conflict 

Instability

Conflict 

Indicators Average

Canada -1.37 -2.19 — -1.89 -0.81 -1.84 -1.62

Sweden -1.37 -1.91 — -2.19 -0.84 -2.17 -1.7

Finland -1.37 -1.91 — -2.26 -0.82 -2.16 -1.7

Luxembourg — -1.63 — -1.92 — -1.73 -1.76

Hong Kong — -1.34 — — — -2.19 -1.77

Denmark -1.37 -2.61 — -2.1 -0.83 -2.04 -1.79

Norway -1.06 -3.32 — -2.28 -0.82 -2.22 -1.94

Puerto Rico — — — — — — —

Source: Authors’ compilation
47

Vulnerability to Intrastate Conflict

State 

Fragility

State 

Weakness

Failed 

States

Conflict 

Indicators Average

Average 8.89 3.80 71.98 4.91

SD 6.51 1.66 23.33 1.03

Somalia 2.48 3.41 1.81 1.81 2.38

Afghanistan 2.01 2.73 1.60 1.73 2.02

Congo 2.17 2.72 1.63 1.55 2.01

Sudan 2.17 1.75 1.71 1.83 1.86

Iraq 1.55 1.86 1.51 1.59 1.63

Chad 1.86 1.38 1.77 1.32 1.58

Burundi 1.55 1.80 1.06 1.71 1.53

Central African 

Republic

1.55 1.72 1.48 1.18 1.48

Palestine — — — 1.40 1.40

Côte d’Ivoire 1.25 1.52 1.25 1.40 1.36

Sierra Leone 1.71 1.46 0.93 1.30 1.35

Liberia 1.55 1.54 0.85 1.44 1.34

Zimbabwe 1.09 1.66 1.64 0.98 1.34

Myanmar 1.86 1.22 1.18 0.96 1.31

Ethiopia 1.55 1.04 1.15 1.43 1.29

Nigeria 1.71 0.79 1.21 1.36 1.27

Haiti 0.63 1.46 1.27 1.50 1.22

Guinea 1.25 0.92 1.42 1.23 1.20

Angola 1.40 1.49 0.50 1.40 1.20

Guinea-Bissau 1.25 1.22 1.08 1.20 1.19

Eritrea 0.79 1.42 0.91 1.37 1.12

Uganda 1.40 0.80 1.09 1.17 1.12

Niger 1.40 0.96 1.11 0.98 1.11

Yemen 1.09 0.61 1.20 1.51 1.10

Rwanda 1.71 0.91 0.72 1.01 1.09

Pakistan 1.09 0.58 1.31 1.23 1.05

Congo 1.09 0.98 0.88 1.11 1.02

Nepal 0.79 0.95 1.00 1.21 0.99

Cameroon 1.25 0.65 1.00 0.91 0.95

North Korea 0.17 1.40 1.11 0.94 0.90

Kenya 0.94 0.33 1.23 1.12 0.90

Mauritania 1.09 0.54 0.73 1.20 0.89

Togo 0.79 0.84 0.69 1.16 0.87

Burkina Faso 1.25 0.41 0.80 0.83 0.82

Appendix 2. Country Rankings across Four Indices
48

PEACEWORKS 72

State 

Fragility

State 

Weakness

Failed 

States

Conflict 

Indicators Average

Equatorial Guinea 0.48 0.86 0.71 1.16 0.80

Zambia 1.40 0.58 0.51 0.65 0.79

Djibouti 0.94 0.53 0.42 1.09 0.75

Malawi 0.94 0.36 0.93 0.72 0.74

Timor-Leste 0.94 0.41 1.12 0.41 0.72

Comoros 0.79 0.60 0.56 0.90 0.71

Laos 0.79 0.40 0.72 0.94 0.71

Tajikistan 0.63 0.51 0.74 0.85 0.68

Bangladesh 0.48 0.34 1.03 0.86 0.68

Iran 0.94 -0.03 0.87 0.91 0.67

Cambodia 0.63 0.56 0.72 0.73 0.66

Uzbekistan 0.63 0.54 0.79 0.66 0.66

Swaziland — 0.52 0.46 0.90 0.63

Mozambique 0.94 0.53 0.42 0.50 0.59

Gambia 0.94 0.25 0.35 0.75 0.57

Sri Lanka 0.48 0.16 1.02 0.37 0.50

Algeria 1.09 0.08 0.40 0.45 0.50

Mali 0.79 0.21 0.31 0.68 0.50

Papua New Guinea 0.32 0.53 0.51 0.62 0.50

Lesotho 0.79 0.19 0.44 0.43 0.46

Tanzania 0.48 0.16 0.39 0.71 0.43

Turkmenistan 0.32 0.56 0.45 0.39 0.43

Colombia 0.32 0.34 0.70 0.32 0.42

Azerbaijan 0.79 -0.21 0.53 0.56 0.42

Guatemala 0.48 0.03 0.39 0.67 0.39

Kyrgyz Republic 0.48 -0.12 0.70 0.50 0.39

Solomon Islands 0.17 0.17 0.71 0.51 0.39

Egypt 0.63 -0.18 0.67 0.40 0.38

India 0.48 -0.05 0.31 0.78 0.38

Benin 0.63 -0.10 0.21 0.75 0.37

Madagascar 0.17 0.33 0.45 0.50 0.36

Philippines 0.48 0.07 0.65 0.18 0.35

Syria 0.02 0.03 0.68 0.43 0.29

Saudi Arabia 0.17 — 0.24 0.43 0.28

São Tomé and 

Príncipe

— 0.02 0.16 0.60 0.26

Lebanon 0.17 -0.49 0.81 0.52 0.25

Bolivia 0.17 0.00 0.55 0.21 0.24

Senegal 0.17 -0.05 0.11 0.67 0.23
49

Vulnerability to Intrastate Conflict

State 

Fragility

State 

Weakness

Failed 

States

Conflict 

Indicators Average

Georgia 0.02 -0.48 0.79 0.55 0.22

Nicaragua 0.02 -0.10 0.45 0.44 0.20

Honduras 0.02 -0.08 0.34 0.49 0.19

Indonesia -0.14 -0.18 0.48 0.53 0.17

Bhutan 0.17 -0.53 0.66 0.40 0.17

Ecuador 0.32 -0.35 0.42 0.26 0.16

Ghana 0.63 -0.31 -0.21 0.51 0.16

Gabon 0.17 0.01 0.14 0.21 0.13

China 0.02 -0.13 0.47 0.15 0.13

Venezuela 0.02 -0.08 0.29 0.21 0.11

Moldova 0.17 -0.42 0.51 0.15 0.10

Russian Federation -0.14 0.00 0.30 0.13 0.07

Turkey 0.17 -0.59 0.22 0.28 0.02

Guyana 0.17 -0.38 0.04 0.17 0.00

Kazakhstan 0.17 -0.43 0.03 0.09 -0.04

Vietnam -0.29 -0.28 0.20 0.19 -0.04

Israel -0.14 — 0.54 -0.64 -0.08

Paraguay -0.14 -0.15 0.00 -0.09 -0.09

Fiji -0.29 -0.16 0.36 -0.31 -0.10

Cuba -0.60 0.02 0.32 -0.16 -0.10

Peru -0.14 -0.49 0.21 -0.01 -0.11

Thailand -0.29 -0.18 0.29 -0.30 -0.12

Maldives — -0.63 0.27 -0.02 -0.13

Morocco -0.44 -0.55 0.21 0.20 -0.14

Mongolia -0.14 0.02 -0.51 0.05 -0.14

Dominican Republic -0.44 -0.49 0.21 0.11 -0.15

Namibia -0.29 -0.28 0.11 -0.17 -0.16

Libya -0.29 -0.36 -0.12 0.08 -0.17

Serbia -0.60 -0.74 0.25 0.38 -0.18

El Salvador -0.44 -0.54 0.26 0.01 -0.18

Bosnia and 

Herzegovina

-0.60 -0.86 0.49 0.22 -0.19

Armenia -0.29 -0.69 0.09 0.08 -0.20

Belarus -0.60 -0.26 0.29 -0.40 -0.24

South Africa 0.02 -0.78 -0.18 -0.07 -0.25

Micronesia — -0.65 -0.06 -0.14 -0.28

Ukraine -0.44 -0.71 -0.11 0.06 -0.30

Jordan -0.44 -0.93 0.21 -0.05 -0.30

Suriname -0.14 -0.78 0.02 -0.44 -0.33
50

PEACEWORKS 72

State 

Fragility

State 

Weakness

Failed 

States

Conflict 

Indicators Average

Vanuatu — -0.90 0.23 -0.33

Cape Verde — -1.06 0.22 -0.18 -0.34

Macedonia -0.60 -0.88 0.03 -0.07 -0.38

St. Vincent and the 

Grenadines

— — — -0.39 -0.39

Tonga — -0.67 — -0.11 -0.39

Tunisia -0.29 -0.85 -0.19 -0.29 -0.41

Brazil -0.60 -0.61 -0.20 -0.27 -0.42

Belize — -0.91 -0.14 -0.24 -0.43

Trinidad and 

Tobago

-0.75 — -0.25 -0.33 -0.44

Jamaica -0.90 -0.64 -0.20 -0.07 -0.45

Albania -0.60 -0.84 -0.21 -0.20 -0.46

Qatar -0.44 — -0.87 -0.11 -0.47

Mexico -0.90 -0.98 0.18 -0.22 -0.48

Botswana -0.75 -0.64 -0.15 -0.50 -0.51

Samoa — -1.21 -0.04 -0.33 -0.53

Bahrain -0.75 — -0.57 -0.32 -0.55

Malaysia -0.44 -1.20 -0.12 -0.49 -0.56

Kuwait -0.90 — -0.45 -0.46 -0.60

Romania -0.60 -1.03 -0.51 -0.39 -0.63

Panama -0.60 -1.05 -0.54 -0.43 -0.65

United Arab 

Emirates

-0.90 — -0.84 -0.28 -0.68

Montenegro -0.75 — -0.63 — -0.69

Grenada — -1.37 -0.21 -0.58 -0.72

Cyprus -0.90 — -0.17 -1.12 -0.73

Brunei — — — -0.76 -0.76

Bulgaria -0.75 -1.31 -0.46 -0.54 -0.76

Bahamas — — -0.56 -1.00 -0.78

Oman -0.60 -1.36 -1.00 -0.33 -0.82

Croatia -0.90 -1.49 -0.56 -0.76 -0.93

Argentina -1.21 -0.88 -1.12 -0.80 -1.00

Greece -1.21 — -1.12 -0.95 -1.09

Barbados — — -0.71 -1.52 -1.11

Estonia -1.37 — -0.91 -1.09 -1.12

Malta — — -1.02 -1.33 -1.18

Costa Rica -1.37 -1.47 -0.86 -1.01 -1.18

Latvia -1.37 -1.73 -0.71 -1.03 -1.21

Spain -1.21 — -1.22 -1.21 -1.21
51

Vulnerability to Intrastate Conflict

State 

Fragility

State 

Weakness

Failed 

States

Conflict 

Indicators Average

Italy -1.37 — -1.13 -1.15 -1.21

St. Lucia — -1.75 — -0.73 -1.24

Mauritius -1.21 -1.56 -1.18 -1.01 -1.24

South Korea -1.37 — -1.32 -1.06 -1.25

Poland -1.37 -1.69 -0.99 -0.97 -1.25

Czech Republic -1.21 — -1.31 -1.26 -1.26

United States -1.06 — -1.57 -1.26 -1.30

Slovak Republic -1.21 -1.93 -0.99 -1.07 -1.30

Uruguay -1.21 -1.54 -1.32 -1.21 -1.32

Singapore -1.21 — -1.59 -1.20 -1.33

Hungary -1.37 -1.93 -0.94 -1.14 -1.34

Lithuania -1.21 -1.85 -1.04 -1.33 -1.36

Taiwan, China -1.37 — — — -1.37

Chile -1.06 -1.89 -1.46 -1.15 -1.39

France -1.37 — -1.59 -1.26 -1.40

Belgium -1.06 — -1.71 -1.49 -1.42

United Kingdom -1.37 — -1.63 -1.32 -1.44

Portugal -1.37 — -1.67 -1.34 -1.46

Slovenia -1.37 — -1.54 -1.54 -1.48

Germany -1.37 — -1.57 -1.53 -1.49

Japan -1.37 — -1.74 -1.52 -1.54

Australia -1.06 — -1.92 -1.70 -1.56

Netherlands -1.37 — -1.89 -1.46 -1.57

Austria -1.37 — -1.92 -1.77 -1.69

New Zealand -1.21 — -2.06 -1.79 -1.69

Canada -1.37 — -1.89 -1.84 -1.70

Ireland -1.37 — -2.13 -1.69 -1.73

Switzerland -1.21 — -2.15 -1.83 -1.73

Luxembourg — — -1.92 -1.73 -1.82

Denmark -1.37 — -2.10 -2.04 -1.84

Norway -1.06 — -2.28 -2.22 -1.85

Sweden -1.37 — -2.19 -2.17 -1.91

Finland -1.37 — -2.26 -2.16 -1.93

Iceland — — -1.81 -2.29 -2.05

Hong Kong — — — -2.19 -2.19

Puerto Rico — — — — —

Source: Authors’ compilation
52

PEACEWORKS 72

Failing 3 Fragile 48 Vulnerable 72 Stable 59

Afghanistan Algeria Albania Argentina

DRC Angola Armenia Australia

Somalia Bangladesh Azerbaijan Austria

Burkina Faso Belarus Bahamas

Burundi Belize Bahrain

Cambodia Benin Barbados

Cameroon Bhutan Belgium

Central African 

Republic

Bolivia Botswana

Chad Bosnia and 

Herzegovina

Brunei

Comoros Brazil Bulgaria

Congo, Republic Cape Verde Canada

Côte d’Ivoire China Chile

Djibouti Colombia Costa Rica

Equatorial Guinea Cuba Croatia

Eritrea Dominican Republic Cyprus

Ethiopia Ecuador Czech Republic

Gambia Egypt Denmark

Guinea El Salvador Estonia

Guinea-Bissau Fiji Finland

Haiti Gabon France

Iran Georgia Germany

Iraq Ghana Greece

Kenya Guatemala Grenada

North Korea Guyana Hong Kong

Laos Honduras Hungary

Liberia India Iceland

Malawi Indonesia Ireland

Mauritania Israel Italy

Mozambique Jamaica Japan

Myanmar Jordan South Korea

Nepal Kazakhstan Kuwait

Niger Kyrgyzstan Latvia

Nigeria Lebanon Lithuania

Pakistan Lesotho Luxembourg

Palestine Libya Malaysia

Rwanda Macedonia Malta

Sierra Leone Madagascar Mauritius

Appendix 3. Spectrum Analysis Group Members
53

Vulnerability to Intrastate Conflict

Failing 3 Fragile 48 Vulnerable 72 Stable 59

Sri Lanka Maldives Montenegro

Sudan Mali Netherlands

Swaziland Mexico New Zealand

Tajikistan Micronesia Norway

Timor-Leste Moldova Oman

Togo Mongolia Panama

Uganda Morocco Poland

Uzbekistan Namibia Portugal

Yemen Nicaragua Romania

Zambia Papua New Guinea Samoa

Zimbabwe Paraguay Singapore

Peru Slovak Republic

Philippines Slovenia

Qatar Spain

Russian Federation St. Lucia

São Tomé and 

Príncipe

Sweden

Saudi Arabia Switzerland

Senegal Taiwan, China

Serbia United Arab Emirates

Solomon Islands United Kingdom

South Africa United States

St. Vincent and the 

Grenadines

Uruguay

Suriname

Syrian Arab Republic

Tanzania

Thailand

Tonga

Trinidad and Tobago

Tunisia

Turkey

Turkmenistan

Ukraine

Vanuatu

Venezuela

Vietnam

Source: Authors’ compilation
54

PEACEWORKS 72

Notes

1. Lists of fragile states vary, but the World Bank’s consolidated list of fragile and conflict-affected countries—

wracked by repeated cycles of violence with deleterious development consequences—is considered 

the authoritative guide for international financial institutions and OECD donors. The 2011 list names thirtytwo states and territories. World Bank, “Harmonized List of Fragile Situations FY11” (presentation, World 

Bank, New York, 2010), http://siteresources.worldbank.org/EXTLICUS/Resources/511777-1269623894864/

FS_List_FY11_(August_8_2010).pdf. Using a slightly different methodology that draws on a consolidation 

of three of the projects surveyed in this paper, a 2010 OECD Development Assistance Committee report lists 

forty-three countries and territories. OECD-DAC, “Resource Flows to Fragile States” (Paris: Organisation for 

Economic Co-operation and Development, 2010), 156. 

2. See General Assembly Security Council, “Report of the Secretary General on Peacebuilding in the Immediate 

Aftermath of Conflict,” No. A/63/881–S/2009/304 (New York: United Nations, July 2009); The White 

House, National Security Strategy of the United States (Washington, DC: Government Printing Office, May 

2010), www.whitehouse.gov/sites/default/files/rss_viewer/national_security_strategy.pdf. 

3. See, for example, United Nations, A More Secure World: Our Shared Responsibility (New York: United 

Nations, 2004), http://www.un.org/secure%20world; James Fearon and David Laitin, “Neotrusteeship and 

the Problem of Weak States,” International Security 28, no. 4 (2004): 5–23.

4. Frances Stewart and Graham Brown et al., “Fragile States” (working paper 51, Centre for Research on 

Inequality, Human Security and Ethnicity, Oxford, 2009); see also Fearon and Laitin, “Neotrusteeship and 

the Problem of Weak States.”

5. Halvard Buhaug, Nils Petter Gleditsch, and Ole Magnus Theisen, “Implications of Climate Change for 

Armed Conflict” (Washington, DC: World Bank Group, 2008); see also United Nations, “Understanding 

Environment, Conflict and Cooperation” (Nairobi: United Nations Environment Programme, 2004), www.

wilsoncenter.org/topics/pubs/unep.pdf. 

6. Monty G. Marshall, “Fragility, Instability, and the Failure of States: Assessing Sources of Systemic Risk” 

(working paper, Center for Preventative Action, Council on Foreign Relations, New York, October 2008), 

www.cfr.org/conflict-prevention/fragility-instability-failure-states-assessing-sources-systemic-risk/p17638. 

7. He also identified the World Bank’s World Governance Indicator project measures to be in this category 

(Marshall, “Fragility, Instability, and the Failure of States,” 16). See also Susan Rice and Stewart Patrick, 

“Index of State Weakness in the Developing World” (Washington, DC: Brookings Institution, 2008), 

www.brookings.edu/reports/2008/02_weak_states_index.aspx. 

8. Javier Fabra Mata and Sebastian Ziaja, Users’ Guide on Measuring Fragility (Bonn: German Development 

Institute and the United Nations Development Programme, 2009), www.undp.org/oslocentre/docs09/

Fragility_Users_Guide_(web).pdf. 

9. Mata and Ziaja, User’s Guide, 25. A draft chapter from the United States Institute for Peace provided another 

useful review, surveying a range of measures. It identified four types of analytic models: early warning 

watchlists (in which category they place the University of Maryland’s Peace and Conflict Instability Ledger 

and the CrisisWatch bulletin), conflict metrics instruments (including the Brookings Institution’s Index of 

State Weakness in the Developing World and the Global Peace Index as well as the World Bank’s Country 

Policy and Institutional Assessment), conflict assessment frameworks (more detailed accounts of particular 

conflicts), and conflict mapping tools (exploring actor networks).

10. David Carment, Stewart Prest, and Yiagadeesen Samy, Security, Development and the Fragile State: Bridging 

the Gap between Theory and Policy (Hoboken, NJ: Routledge, 2010), see especially chapter 4.

11. Carment, Prest, and Samy, Security, Development and the Fragile State, 120.

12. Barry B. Hughes and Evan E. Hillebrand, Exploring and Shaping International Futures (Boulder, CO: 

Paradigm Publishers, 2006); Barry B. Hughes, “Forecasting Long-Term Global Change: Introduction to 

International Futures (IFs)” (working paper, Frederick S. Pardee Center for International Futures, University 

of Denver, December 2009).

13. The concept of vulnerability is also used in a variety of other areas of global concern, such as vulnerability to 

disaster from climate change and its social effects. For an effort to develop a conceptual basis for the vulnerability concept, see Nick Brooks, “Vulnerability, Risk, and Adaptation: A Conceptual Framework” (working paper 

no. 38, Tyndall Center for Climate Research, University of East Anglia, Norwich, 2003), www.eird.org/cd/

on-better-terms/docs/Brooks-N-Vulnerability-risk-and-adaptation-a-conceptual-framework.pdf.

14. Barnett Rubin, Blood on the Doorstep: The Politics of Preventive Action (New York: Council on Foreign 

Relations, 2003).

15. Practitioner toolkits are one place to find theoretical and indicator-related systematic approaches to conflict 

vulnerability analysis. See, for example, UNDP Oslo Governance Center and German Center for 

International Development (2009). See also World Bank, “Conflict Analysis Framework” (draft document, 

Conflict Prevention and Reconstruction Team, Social Development Department, World Bank, April 

2005), http://siteresources.worldbank.org/INTCPR/214574-1112883508044/20657757/CAFApril2005.

pdf; USAID, “Conflict Vulnerability Assessment: A Framework for Strategy and Program Development” 
55

Vulnerability to Intrastate Conflict

(Washington, DC: U.S. Agency for International Development, April 2005), www.usaid.gov/our_work/

cross-cutting_programs/conflict/publications/docs/CMM_ConflAssessFrmwrk_May_05.pdf.

16. I. William Zartman, “Need, Greed, and Creed in Intrastate Conflicts,” in Rethinking the Economics of War: 

The Intersection of Need, Creed, and Greed, edited by Cynthia J. Amson and I. William Zartman (Washington, 

DC: Woodrow Wilson Center Press, 2005), 275.

17. For an overview of various definitions of fragility and state weakness in development literature and in 

comparative politics literature, see Claire Mcloughlin, Topic Guide on Fragile States (Birmingham, UK: 

Governance and Social Development Resource Centre, October 2010), www.gsdrc.org/go/fragile-states/

chapter-1--understanding-fragile-states/definitions-and-typologies-of-fragile-states#donor. 

18. Indeed, it may be appropriate to expand the spectrum metaphor to a kaleidoscope, in that in addition to an 

overall measure of fragility on a linear scale it may also be useful to consider various facets of fragility 

emanating from the salience of different drivers in different moments of conflict or in subnational regions. 

The kaleidoscope approach may be useful in considering detailed conflict vulnerability assessments but is 

perhaps too complicated a metaphor to be reflected in aggregate quantitative measures such as summary 

indices. 

19. See www.pcr.uu.se/research/UCDP.

20. “States are fragile when state structures lack political will and/or capacity to provide the basic functions 

needed for poverty reduction, development and to safeguard the security and human rights of their populations.” OECD-DAC, Principles for Good International Engagement with Fragile States (Paris: Organisation for 

Economic Development and Co-operation, 2007), 2. Rice and Patrick identify weak or fragile states as those 

“that lack the essential capacity and/or will to fulfill four sets of critical government responsibilities: fostering an environment conducive to sustainable and equitable economic growth; establishing and maintaining 

legitimate, transparent, and accountable political institutions; securing their populations from violent conflict and controlling their territory; and meeting the basic human needs of their population” (Index of State 

Weakness, 3).

21. Marshall identified as categories socialist and former socialist countries, obscure ones, and complex circumstances (“Fragility, Instability, and the Failure of States,” 17). That set indicates how difficult it is to clearly 

distinguish countries across measurement projects. We have found it nearly impossible to be systematic in 

doing so.

22. Among these, one might differentiate between those emerging from conflict as a consequence of negotiated 

settlement and those for which military victory was unilateral. This distinction may be important for 

understanding future vulnerability with respect to the debate in the literature over whether negotiated settlements are more vulnerable to conflict recurrence than military victories. For recent work in this genre (in 

this case, arguing that military victories are less prone to recurrence over time), see Monica Toft, Securing 

the Peace: Durable Settlement of Civil Wars (Princeton, NJ: Princeton University Press, 2009).

23. Carment, Prest, and Samy, Security, Development and the Fragile State, 4.

24. For example, in the 2010 rankings of the Global Peace Index, the United States is ranked 85 of 149. See 

Vision of Humanity, “GPI Map 2010,” www.visionofhumanity.org/gpi-data/#/2010/scor. 

25. For categorizations used by eleven measures, most of which do not use a spectrum, see Mata and Ziaja, 

User’s Guide.

26. Mata and Ziaja, User’s Guide.

27. Monty G. Marshall and Benjamin R. Cole, Global Report 2009: Conflict, Governance, and State Fragility 

(Vienna, VA: Center for Systemic Peace, George Mason University, 2009), 40, www.systemicpeace.org/

Global%20Report%202009.pdf. 

28. Paul Collier, V. L. Elliot, Havard Hegre, Anke Hoeffler, Marta Reynal-Querol, and Nicholas Sambanis, 

Breaking the Conflict Trap: Civil War and Development Policy (Washington, DC: World Bank and Oxford 

University Press, 2003).

29. For an evaluation, see Amnesty International, “Conflict Diamonds,” www.amnestyusa.org/diamonds/

index.do. 

30. “Every state . . . tends to support particular groups, to distribute privileges unequally, and to differentiate 

among various categories in the population. . . . The state itself is the greatest prize and resource over which 

groups engage in a continuing struggle in societies that have not developed stable relationships among the 

main institutions and centrally organized social forces.” Paul R. Brass, Ethnic Groups and the State 

(Philadelphia, PA: Taylor and Francis, 1985), 9, 29.

31. Richard P. Cincotta, Robert Engelman, and Daniele Anastasion, The Security Demographic: Population and 

Civil Conflict after the Cold War (Washington, DC: Population Action International, 2003); Richard 

Cincotta, “The New Fifteen Years, According to Demography” (unpublished working paper, U.S. National 

Intelligence Council, 2010).
56

PEACEWORKS 72

32. See, for example, CCHS, “Human Security in an Urban Century: Local Challenges, Global Perspectives” 

(Canadian Consortium on Human Security, Ottawa, 2007), www.humansecurity.info. 

33. Luca Alinova, Günter Hemrich, and Luca Russo, “Addressing Food Insecurity in Fragile States: Case 

Studies from the Democratic Republic of the Congo, Somalia and Sudan” (ESA working paper 07-21, Food 

and Agricultural Organization of the United Nations, Rome, 2007).

34. Margaret Hermann and Charles Kegley, “Democracies and Intervention: Is there a Danger Zone in the 

Democratic Peace?” Journal of Peace Research 38, no. 2 (2001): 237–45.

35. Jack A. Goldstone, Robert H. Bates, David L. Epstein, Ted Robert Gurr, Michael B. Lustik, Monty G. 

Marshall, Jay Ulfelder, and Mark Woodward, “A Global Model for Forecasting Political Instability,” 

American Journal of Political Science 54, no. 1 (January 2010): 195–96.

36. Armed conflict involves the state versus one or more organized and politically motivated armed groups where 

there is direct competition for the state or for territory. Armed violence is used to refer to situations of high 

social violence (such as criminal or gender-based violence). See the Geneva Declaration, The Global Burden 

of Armed Violence (Geneva Declaration Secretariat, 2008), www.genevadeclaration.org/fileadmin/docs/

Global-Burden-of-Armed-Violence-full-report.pdf. See also Charles Tilly, Securing the Peace: Durable 

Settlement of Civil Wars (Princeton, NJ: Princeton University Press, 2002).

37. Valerie M. Hudson, Mary Caprioli, Bonnie Ballif-Spanvill, Rose McDermott, and Chad F. Emmett, “The 

Heart of the Matter: The Security of Women and the Security of States,” International Security 3, no. 33 

(2008): 7–45.

38. David Lake and Donald Rothchild, eds., The International Spread of Ethnic Conflict: Fear, Diffusion, and 

Escalation (Princeton, NJ: Princeton University Press, 1998).

39. See Astri Suhrke and Ingrid Samset, “What’s In a Figure? Estimating the Recurrence of Civil War,” 

International Peacekeeping 14, no. 2 (2007): 195–203. Joseph Hewitt, Jonathan Wilkenfeld, and Ted Robert 

Gurr found that “in the past 10 years, 39 different conflicts that became active. . . . Of these, 31 were conflict 

recurrences—instances of resurgent, armed violence in societies where conflict had largely been dormant for at 

least a year. Only eight were entirely new conflicts between new antagonists involving new issues and interests.” 

Peace and Conflict 2010 (College Park, MD: Center for International Development and Conflict Management, 

University of Maryland, 2010), 1. See also Lotta Harbom, Stina Högbladh, and Peter Wallensteen, “Armed 

Conflict and Peace Agreements 1946–2008,” Journal of Peace Research 46, no. 5 (2009): 577. 

40. Mata and Ziaja, User’s Guide.

41. Ibid., 19.

42. Monty Marshall consciously takes a probabilistic approach, noting that extreme fragility (SFI values of 

20–24) suggest an annual probability of 0.0884 for problem onset, compared with a probability of 0.68 for 

high fragility (SFI values of 16–19), and so on down through serious fragility, low fragility, and little or no 

fragility. “Fragility, Instability, and the Failure of States,” 16.

43. Marshall also notes the task force attention to fractionalization (“Fragility Instability, and the Failure 

of States”).

44. See especially Mata and Ziaja, User’s Guide.

45. Carment, Prest, and Samy also show the Carleton and Brookings indices correlated at 0.84 but the Fund for 

Peace and George Mason indices at only 0.64 (compared with our 0.78). They correlated indices only across 

sixty-one developing countries so we would expect somewhat different values, but they also found most measures correlated at the 0.7 and 0.8 levels or higher. The exception for them was the World Bank low-income 

country under stress (LICUS) analysis measure called the Country Policy and International Assessment 

(CIPA) index, which correlated with most measures at about the 0.5 to 0.6 level. They note that the LICUS 

measure is heavily oriented to economic variables. Security, Development and the Fragile State, 120.

46. Marshall, “Fragility, Instability, and the Failure of States.”

47. This is an anomaly to which we will return.

48. This could, however, be the wrong interpretation for the measure’s attention to these states. It might be 

simply that, relying on news feeds, these states (and perhaps Bhutan and Cyprus also) happened to generate 

some particularly unhappy news during the period of the most recent analysis of them.

49. The difference in treatment of Norway in the George Mason measure is simply an artifact of that project’s 

decision not to differentiate among the most stable countries; it should be ignored.

50. Compare the GDP per capita breaks here with the World Bank’s classification system: low income is GNI 

per capita below $975; lower middle is $976 to $3,855; upper middle is $3,856 to $11,905; high income is 

$11,906 or more. See World Bank, “How We Classify Countries,” http://data.worldbank.org/about/countryclassifications. 

51. In 2005, the Stable 59 averaged over $26,000 (US) per capita at purchasing power parity. The Vulnerable 72 

averaged just over $4,000 of the same measure, the Fragile 48 was still well below $2,000, and the Failing 

3 averaged $373.

52. Longitudinal analysis of the measures relative to each other and relative to actual conflict levels would be 

ideal. The IFs project has only been able to obtain significant longitudinal data for the George Mason State 
57

Vulnerability to Intrastate Conflict

Fragility Index (1995–2008). Carment, Prest, and Samy report on longitudinal analysis (1980–2006) of their 

Carleton measure but do not make the index available over time to others. “The general trend that can be 

observed is that the developing world has become more fragile over time. . . . For all developing countries, 

increasing fragility and ALC [authority-legitimacy-capacity] scores were at their highest point in 1989, 

coinciding with the end of the Cold War, and after a brief decline in the early 1990s have been on the rise 

again” (Security, Development and the Fragile State, 124). What is curious is that Marshall and Cole report 

that actual domestic conflict declined quite steadily after 1989 rather than reversing (Global Report 2009). 

Hence this suggests a negative correlation between the Carleton index and the conflict trend. But Carment, 

Prest, and Samy do not analyze their measure against conflict, but instead against potential drivers of it. 

Again their conclusion is surprising, because a main driver, GDP per capita, has trended positively.

53. Collier et al., Breaking the Conflict Trap.

54. Analysis with the Polity measure of democracy does not, however, show the same crossover of fragile and 

more limited vulnerability states.

55. The R-squared values in the table explore only linear relationships, and some relationships are almost certainly nonlinear. The focus on only the forty-eight fragile states reduces the impact of that simplification, 

however, because we have seen that those states cluster into a fairly narrow range of GDP per capita values, 

a key variable with which many social variables have curvilinear relationship over a wider range. We looked 

at the possibility that nonlinear (specifically logarithmic) relationships with GDP per capita might generate 

higher correlations with the various indices and they generally did not.

56. See Integrated Network for Societal Conflict Research (INSCR), “Data Page,” www.systemicpeace.org/

inscr/inscr.htm. 

57. We added these measures to the IFs database and considered them in preliminary analysis but have not 

discussed our analysis of them here. Institutions and their indices include Freedom House (the Countries at 

the Crossroads measure), the World Bank (the Country Policy and Institutional Assessment IDA Resource 

Allocation Index, abbreviated as the IDA-IRAI measure), Vision of Humanity (the Global Peace Index), 

the University of North Carolina (Political Terror Scale), and the Mo Ibrahim Foundation (African governance measure).



About the Institute

The United States Institute of Peace is an independent, nonpartisan 

institution established and funded by Congress. Its goals are to 

help prevent and resolve violent conflicts, promote postconflict 

peacebuilding, and increase conflict management tools, capacity, and 

intellectual capital worldwide. The Institute does this by empowering 

others with knowledge, skills, and resources, as well as by its direct 

involvement in conflict zones around the globe.

Board of Directors

J. Robinson West (Chair), Chairman, PFC Energy, Washington, DC • 

George E. Moose (Vice Chairman), Adjunct Professor of Practice, The 

George Washington University, Washington, DC • Judy Ansley, Former 

Assistant to the President and Deputy National Security Advisor under 

George W. Bush, Washington, DC • Anne H. Cahn, Former Scholar in 

Residence, American University, Washington, DC • Chester A. Crocker, 

James R. Schlesinger Professor of Strategic Studies, School of Foreign 

Service, Georgetown University, Washington, DC • Kerry Kennedy, 

President, Robert F. Kennedy Center for Justice and Human Rights, 

Washington, DC • Ikram U. Khan, President, Quality Care Consultants, 

LLC., Las Vegas, NV • Stephen D. Krasner, Graham H. Stuart Professor 

of International Relations at Stanford University, Palo Alto, CA • 

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Independent Living, Potsdam, NY • Jeremy A. Rabkin, Professor of Law, 

George Mason University, Fairfax, VA • Judy Van Rest, Executive Vice 

President, International Republican Institute, Washington, DC • 

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Civil Rights, Washington, DC

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and Labor • James N. Miller, Principle Deputy Under Secretary of 

Defense for Policy • Ann E. Rondeau, Vice Admiral, U.S. Navy; President, 

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United States Institute of Peace (nonvoting)
As many as one-quarter to one-half of the countries in the world 

are, to varying extents, vulnerable to armed domestic conflict. This 

poses, as is widely recognized, an immediate and ongoing threat 

to international peace and security. Quantitative studies track the 

frequency of armed conflict, its intensity, patterns of termination, 

and consequences. The ultimate goal is to better understand—and 

thus better anticipate or avoid—such conflict. The authors of this 

report pursue the answer to a question not well addressed in the 

field: How do the principal quantitative measures of conflict vulnerability compare to one another? 

Peaceworks • JUNE 2011 • no. 72

Related Links 

■ Using Quantitative and Qualitative Models to Forecast Instability by Jack A. 

Goldstone (Special Report, March 2008)

■ Preventing Violent Conflict: Assessing Progress, Meeting Challenges by Lawrence 

Woocher (Special Report, September 2009)

■ Leashing the Dogs of War: Conflict Management in a Divided World edited by 

Chester A. Crocker, Fen Osler Hampson, and Pamela Aall (2007)

■ Scenarios for Sudan: Avoiding Political Violence Through 2011 by Alan 

Schwartz (Special Report, August 2009)

■ The Limits of Institutional Engineering: Lessons from Iraq by David Waldner 

(Special Report, May 2009)

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