assessing risk and opportunity in conflict studies: a human rights analysis

Assessing Risk and Opportunity in Conflict Studies: A Human Rights AnalysisAuthor(s): Steven C. Poe, Nicolas Rost and Sabine C. CareySource: The Journal of Conflict Resolution, Vol. 50, No. 4 (Aug., 2006), pp. 484-507Published by: Sage Publications, Inc.Stable URL: http://www.jstor.org/stable/27638503 .

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Assessing Risk and Opportunity in Conflict Studies

A HUMAN RIGHTS ANALYSIS

STEVEN C. POE

NICOLAS ROST Political Science Department

University of North Texas

SABINE C. CAREY

School of Politics and International Relations

University of Nottingham

Over the past two decades, substantial progress has been made toward a theoretical understanding of

why physical integrity abuses are committed. Unfortunately, these theoretical developments have been

devoid of much practical application. In this article, the authors explore the feasibility of risk assessment

in the study of these human rights. Borrowing an approach by Gurr and Moore, they construct a risk assess

ment vehicle that uses existing models and data to develop expectations about future increases and

decreases in human rights abuses. Their results indicate that we can isolate a set of cases that are at a higher risk of experiencing increased human rights abuse in the following year, as well as those that are ripe for

better protection of human rights. The authors expect these risk and opportunity assessments to be of inter est to students of conflict and peace studies, as well as to human rights activists and policy makers.

Keywords: human rights; life integrity violations; early warning; risk assessment; conflict

\?uantitative research on the determinants of human rights violations and states' use of repression has attracted great interest in recent years. Studies published on

this topic have quickly proliferated. Scholars can now identify several factors that

explain why governments choose to imprison, torture, "disappear," or execute

people, either arbitrarily or for their involvement in nonviolent activities.

AUTHORS' NOTE: An earlier version of this article was presented at the annual convention of the

International Studies Association, in Honolulu, Hawaii, March 1-6, 2005. We thank George Lopez and

faculty and graduate students at the University of North Texas and the University of Iowa for their help ful comments. Data files and supplementary results are available at http://jcr.sagepub. com/cgi/content/ full/50/4/484/DCl/.

JOURNAL OF CONFLICT RESOLUTION, Vol. 50 No. 4, August 2006 484-507

DOI: 10.1177/0022002706289181

? 2006 Sage Publications

484



Poe et al. / RISK AND OPPORTUNITY IN CONFLICT STUDIES 485

Unfortunately, a rather gaping hole of this research results from the fact that there

has been little effort to put these successes to practical use by developing an early

warning or risk assessment system. This weakness is particularly notable because of

the professed desire of human rights scholars to "make a difference" (e.g., Carey and

Poe 2004). Scholars interested in foreseeing humanitarian problems such as refugee flows, ethnic conflicts, and international and civil wars have been active and at least

somewhat successful in building forecasting systems. Why haven't students of

human rights? We address this weakness in the human rights literature by building and testing a

model that identifies a set of countries that experience a greater than normal risk of

increased physical integrity abuses and a second set of countries whose characteris

tics indicate that there is an increased probability for improvement. Our study is

based on the assumption that human rights activists and policy makers have an inter

est in both supporting and encouraging countries with a potential for improvements and in taking preventive measures in countries with a risk of higher repression. We

hope that a risk and opportunity assessment system such as the one we test here will

someday assist governments, nongovernmental organizations (NGOs), and intergov

ernmental organizations (IGOs) in their efforts to better allocate their attention and

funds to cases where they are most likely to make a difference.

This study does not add directly to existing theory, but uses already developed

theoretically based models to build a risk assessment vehicle. Although we are more

concerned with prediction than explanation in this study, our results should still be

of theoretical interest. As Bueno de Mesquita, Newman, and Rabushka (1985, 6;

1989, 163); Choucri and Robinson (1978, 15); and Beck, King, and Zeng (2000) have emphasized, one valid factor for evaluating social science theory is whether it

has the capacity to predict. Ray and Russett (1996, 441) note the importance of pre dictions for testing theories, pointing out that they "cannot be modified in order to

accommodate the events upon which they focus, since the outcomes to be accounted

for are unknown." Hence, risk assessment models can be a particularly difficult and

discriminating test of social science theory. In this study, we first briefly discuss the literature that seeks to explain human

rights abuses, as well as the numerous studies that have sought to forecast or assess

the risk of other, oftentimes related, humanitarian issues. Second, we present the

model we use for the risk assessment, which is based on the work of Poe and T?te

(1994) and Poe, T?te, and Keith (1999). Third, using this model together with an

approach adapted from Gurr and Moore (1997), we divide countries into three

groups: one that the model judges to be at risk of declining respect for personal

integrity rights, one that it categorizes as having an increased potential for improve ment, and a third, larger group that has no special predisposition to either kind of

change. Tests using data from 1977 to 2002 show that countries placed in these three

groupings do indeed have substantially different propensities for increased and

decreased human rights abuses. In an effort to locate room for improvement, we

identify and consider the cases in which the model's performance was disappointing and the reasons behind this. Finally, we conclude by considering ways in which the



486 JOURNAL OF CONFLICT RESOLUTION

system can be improved prior to implementation in "real time." We hope that by the

end of this study, the reader will agree that our largely positive results bode well for

the possibility of future efforts to improve and institutionalize a meaningful risk assessment device for physical integrity abuse.

EARLY WARNING, RISK ASSESSMENT, AND THE HUMAN RIGHTS LITERATURE

THE QUANTITATIVE LITERATURE ON PHYSICAL INTEGRITY ABUSES

Since the early 1980s, a large body of research has been developing theoretical

models and testing them with empirical data to better understand why and under

what circumstances governments repress, torture, and kill their citizens. A relatively

standard set of indicators has evolved from these quantitative studies that are now

commonly associated with these kinds of human right violations.1 Certain state char

acteristics such as democracy, economic development, and population size, as well

as certain state behaviors such as past repression and involvement in international

and civil war, have a strong effect on governments' use of repression.2

When governments show that they are both able and willing to repress their citi

zens, they are likely to choose such violent measures again in the future. This argu

ment is strongly supported by multivariate research conducted with both events- and

standards-based approaches (e.g., Poe and T?te 1994; Davenport 1995, 1996; Poe,

T?te, and Keith 1999; Richards, Gelleny, and Sacko 2001). Similarly, governments that are fighting in international or civil wars have widely been found to have worse

human rights records than governments not participating in such large-scale violence

(e.g., Rasier 1986; Poe and T?te 1994; Krain 1997; Poe, T?te, and Keith 1999;

Zanger 2000; Sherborne 2003). Both civil and international wars present a threat to

governments, which makes them more likely to resort to repression (Poe 2004).

Democracy has commonly been identified as a characteristic of countries that

inhibits, or at least limits, governments' use of violence against their own people

(e.g., Henderson 1991, 1993; Poe and T?te 1994; Davenport 1995, 2004; Richards

1999; Zanger 2000; Davenport and Armstrong 2004). Democratic institutions set

certain parameters for state behavior that are conducive to choosing nonviolent ways

such as compromise and participation over violent means to interact with their citi

zens (Gurr 1986; Rummel 1997; Poe 2004). However, some studies posit and find

support for a curvilinear impact, consistent with the "more murder in the middle"

hypothesis (Fein 1995; Regan and Henderson 2002).

1. Since we focus on assessing the risk of the violation of personal integrity rights (i.e., imprison ment, torture, "disappearance," and extralegal execution), either arbitrarily or for involvement in nonvio

lent activities, we do not include a discussion of the numerous studies that analyze the violation of other

types of human rights. 2. For a more detailed discussion of quantitative human rights research, see Poe (2004).




Many studies have found economic development to be linked to human rights abuse (e.g., Mitchell and McCormick 1988; Poe and T?te 1994; Davenport 1995;

Poe, T?te, and Keith 1999; Carey 2004). Economic scarcity is associated with

increased tension, which is likely to lead to repression. Numerous studies have also

identified population size as a characteristic that helps to distinguish more repressive countries from less repressive ones (e.g., Henderson 1993; Poe and T?te 1994; Poe,

T?te, and Keith 1999; Carey 2004). Larger populations are thought to put additional

strain on governments, which increases the risk of repression.

What the growing body of quantitative human rights research has in common is

its focus on the question of why human rights violations occur. Considerable

progress has been made toward identification of the variables that have a high prob

ability of affecting the levels of human rights abuse. Statistical models on this topic are able to explain more than 50 percent and as much as 75 percent of the variance

in repression, but they have not yet been used to explicitly highlight which countries are at risk of experiencing increased repression in the future.

PREVIOUS EFFORTS AT EARLY WARNING AND RISK ASSESSMENT

The absence of forecasting and early warning research on physical integrity rights is notable, as scholars and practitioners have attempted to forecast the probability of a number of other human-made catastrophes.3 During the cold war, most attention

was devoted to constructing early warning or risk assessment (EW/RA) models for

international conflict (Choucri and Robinson 1978; Singer and Wallace 1979;

Hopple, Andriole, and Freedy 1984; Singer and Stoll 1984). Bueno de Mesquita's (1981) expected utility model was created to identify necessary conditions for inter

state war, although it was later also applied to predict political outcomes in domes

tic situations (Bueno de Mesquita, Newman, and Rabushka 1985, 1996).

Partly due to specific needs of governmental and nongovernmental international relief

organizations, EW/RA systems for refugee movements have been developed (Gordenker

1986, 1992). Most prominent is the work of Susanne Schmeidl and Craig Jenkins (e.g., 1998b; Schmeidl 1995). Refugee movements have also been used as an indicator of

complex humanitarian emergencies (Auvinen and Nafziger 1999) and in EW/RA mod

els to anticipate these (Harff and Gurr 1998; see also Schmeidl and Jenkins 1998a). Barbara Harff (1998, 2003) has attempted to create EW/RA models for genocides

and politicides based on procedural and structural models (see also Harff and Gurr

1998). With a structural model, Harff (2003, 57) is able to "distinguish with 74%

accuracy between internal wars and regime collapses that do and those that do not

lead to geno-/politicide." She uses this model to identify the risk factors for twenty five countries that experienced armed conflict circa 2001, to assess their risk of

genocide or politicide in the following years. Similarly, Fein (1992) provides a list

of governments at high risk of committing acts of genocide.

3. For a general overview of the array of early warning and risk assessment systems, see both Davies

and Gurr (1998, 267-80) and Carment (2003, 419).




Attempts to predict ethnic conflict are different from efforts mentioned above in

that they often do not only analyze the phenomenon of interest at the state level but

at the level of the ethnic group (Gurr and Moore 1997; Gurr 1998; Moore and Gurr

1998; see also Tellis, Szayna, and Winnefeld 1997). Most important to our own study is the approach of Gurr and Moore (1997), who construct a regression model and use

the resulting residuals to create a list of high-risk ethnic groups. Groups were char

acterized as being at a high risk when their level of conflict was at least one standard

deviation below what one would expect from the regression model. Risk assessments

were generated for subsequent years by developing and testing models using cur

rently available data. In this study, we adapt some of the methods used in Gurr and

Moore's study to investigate personal integrity abuses.

In addition, state failures, which include genocides/politicides, civil wars, and

adverse regime changes, have been the subject of EW/RA models (Goldstone et al.

2000; King and Zeng 2001; Carment 2003). Finally, EW/RA models have been

constructed with regard to political instability in a more general sense (e.g., Bond

et al. 1997; Jenkins and Bond 2001; O'Brien 2002). Events data-based approaches, analyzing the presence of conflict in short time

intervals as measured by articles published in newspapers or issued over news ser

vices, offer yet another quantitative way of observing early warning signals on a

weekly or even daily basis. Early projects, such as the Conflict and Peace Data Bank

(COPDAB), the World Event Interaction Survey (WEIS), or the Global Events-Data

System (GEDS), have largely relied on human coded data and have generated some

useful predictions (see Davies and Gurr 1998). Today, events data approaches use

machine-coded data and are therefore in a position to analyze much more informa

tion (Gerner et al. 1994; Bond et al. 1997; Bond et al. 2003). Such approaches have

been applied to examine the political situation in the Middle East (Schrodt and

Gerner 1997, 1998, 2000; Gerner and Schrodt 1998) and in Kosovo (Pevehouse and

Goldstein 1999). The FAST early warning system of the Swiss Peace Institute com

bines qualitative and quantitative events data approaches and monitors a number of

countries in Africa, Asia, Europe, and the Middle East.4

Unfortunately, existing events data are currently insufficient to allow us to adopt

that approach to assess risk and opportunities for human rights on a global basis.

While it would probably be possible to extract information on human rights viola

tions specifically, most existing events data sets concentrate on cooperation and

either violent or, like the Protocol for the Assessment of Nonviolent Direct Action

(PANDA), nonviolent conflict and not on human rights violations per se. Some mea

sure inter- and intrastate conflicts that are relevant to the study of human rights,

yet they unfortunately do not provide coverage nearly as complete as the standards

based data we use here. Because our purpose is to provide a global risk analysis,

we undertake this study with a widely available and frequently used human rights measure?the Political Terror Scale (PTS)?instead of events data.5

4. Accessed February 18, 2005, from http://www.swisspeace.org/fast/default.htm. 5. Studies using more discriminating events data measures of human rights, across time in particu

lar countries or perhaps in particular regions of the world, would, however, be a useful future augmenta tion to the work we are conducting here. We hold this open as an avenue for future research.




There are some differences in the approaches employed by EW/RA studies that are

worthy of discussion. One method proposed for the crafting of early warning models is

pattern recognition (Brecke 1998). O'Brien (2002) develops such a system by using a

pattern classification algorithm (fuzzy analysis of statistical evidence or FASE) to fore

cast political instability over fifteen years. His aim is to identify structural factors that

have been associated with political instability and conflict in the past, so that these fac

tors can be used to assess future risks. He is able to correctly predict between 66 and

90 percent of all conflicts that occurred, depending on the analyzed time period. More

commonly, however, recent structural risk assessment models have adopted the method

ological approach that is conventional in quantitative studies in the social sciences,

using ordinary least squares (OLS) regression (e.g., Gurr and Moore 1997) or logit

analysis (e.g., Harff 2003) to test theory-based models that generate expectations of

future events. These approaches identify relatively long-term structural factors that

would lead one to expect a country to have a greater than normal risk of ethnic conflict,

genocide/politicide, or, in our case, government repression. As such, they are probably

more accurately called risk assessment than early warning models.

We adopt the latter approach, building a risk assessment system based on the results

from previous OLS regression models. We wish to emphasize that we aim at risk and

opportunity assessment, not at early warning or prediction per se. We do not maintain

that we can ex ante identify every case in danger of increased human rights abuses, as

one might expect of an early warning system. Instead, we conceive of our enterprise

being similar to many medical studies, in which researchers identify groups that are at

a heightened risk associated with a specific factor (e.g., the relationship between smok

ing and lung cancer) without pinpointing what will happen in a particular case. What we can contribute are "watch lists" of countries deserving of greater attention in sub

sequent years, based on a set of risk factors that have been identified by social science

studies as determinants of repression. Countries on these lists might be given greater

attention in the form of more focused quantitative inquiry and qualitative analyses con

ducted by area experts and policy analysts. Our analyses serve as an example of how

empirical tests conducted by conflict researchers based on existing social science

theory can be helpful in practice.

BUILDING A RISK ASSESSMENT MODEL

A THEORY-BASED MODEL

We note above that the purpose of this study is not to gain additional theoretical

insights into the causal mechanisms that lead governments to repress basic human

rights to personal integrity. Rather, we are concerned with using existing theories to

generate a risk assessment model that classifies countries into three groups: one that

can expect to experience an increase in repression, one that is likely to see an improve

ment in their human rights records, and one that will likely not see any changes.

Although our purpose is mainly practical, if our predictions prove to be successful, it

would further support existing theories on which our risk assessment model is based.




As described above, quantitative human rights research has identified a number

of factors that exercise important influence on the prevalence and level of govern

ment repression. Six factors found to have the strongest impacts are past repression,

democracy, the level of economic development, population size, and the occurrence

of international and civil war. We adopt the Poe and T?te (1994) model, incorporat

ing these factors, as well as annual gross domestic product (GDP) and population

growth, and dichotomous variables for military regimes and former British colonies.

The model, together with events-based approaches, has been called "the standard by which most analyses in this area are currently judged" (Davenport and Armstrong

2004, 546). An examination of its utility as a risk assessment device will be an inter

esting test of the literature on human rights abuse.

The theoretical foundations of our model were laid in Poe and T?te (1994) and

Poe, T?te, and Keith (1999), where a detailed description of the theories, the

expected causal relationships, the measurement of the variables, and methodological

issues can be found. Table 1 summarizes the hypotheses tested by the model, mea

sures of key concepts, and data sources.

We use the Poe and T?te (1994) model with minor modifications. We exclude the

variable capturing the presence of leftist regimes, as this measure carries little utility for our forecasting purposes. For practical reasons, we use Freedom House data on

political rights to tap democracy because they are better suited for the purpose of risk

assessment, since they are compiled in a more timely manner than Polity data and for

a greater subset of countries. These data were used by Poe and T?te, instead of the

Polity measure used in the more recent Poe, T?te, and Keith (1999) study. Another

minor difference is that GDP per capita replaces gross national product (GNP) per

capita as a measure of economic development because we found these data to be more

easily available. The two are highly correlated, and we do not expect this difference to

be of importance. Finally, data on interstate and civil conflict now come from the

Uppsala Armed Conflict Dataset (Gleditsch et al. 2002), mainly because they are avail

able through 2003, whereas the Correlates of War data (Sarkees 2000) at the time of

writing were only available up to 1997.

Our dependent variable is the frequently used PTS generated by Michael Stohl, Mark Gibney, and their colleagues (e.g., Stohl and Carleton 1985; Poe and T?te

1994; Gibney and Dalton 1996).6 Those scholars analyze the content of State

Department and Amnesty International country reports and assign values on a five

point ordinal scale according to the following coding rules:

1. "Countries . . . under a secure rule of law, people are not imprisoned for their views,

and torture is rare or exceptional . . .

political murders are extremely rare."

2. "There is a limited amount of imprisonment for nonviolent political activity. However,

few persons are affected, torture and beating are exceptional... political murder is rare."

3. "There is extensive political imprisonment, or a recent history of such imprisonment. Execution or other political murders and brutality may be common. Unlimited deten

tion, with or without trial, for political views is accepted."

6. The data are currently kept by Mark Gibney, University of North Carolina-Asheville, at

http://www.unca.edu/politicalscience/faculty-staff/gibney.html.




Variable

TABLE 1

Operationalization and Measurement of Variables

Effect on

Repressiont Measurement Data Source

Repressiont_, Increase

Level of

democracy

Decrease

Gross domestic Decrease

product (GDP)

per capita

GDP per capita Decrease

growth

Population size Increase

Five-point Political Terror

Scale from 1 (almost no

abuses) to 5 (widespread

abuses)

Political rights, seven-point

scale, from 1 (most rights

oppressed) to 7 (most

rights respected)

In 1995 constant U.S

dollars, logged

Yearly change in

percentages

Logged

Gibney (2004); Poe, T?te, and

Keith (1999)

Freedom House Political Rightsa

(we reversed the original scale)

World development indicators

(World Bank 2004); some

missing values come from the

U.S. Energy Information

Administration (n.d.)

World development indicators; some missing values are

calculated from the GDP per

capita

World development indicators; some missing values from

Fearon and Laitin (2003)

Population

growth

Increase Yearly change in

percentages

World development indicators; some missing values interpolated based on population size

Civil war Increase

International war Increase

Military regime Increase

Coded 1 for civil war or

intermediate conflict, 0

otherwise

Coded 1 if participation in interstate war or

intervention in civil war,

0 otherwise

Coded 1 from the moment

of a military coup until the

military regime ceded

government power, 0

otherwise

Uppsala Armed Conflict Dataset

(Gleditsch et al. 2002)

Uppsala Armed Conflict Dataset

(Gleditsch et al. 2002)

Madani (1992); Political

Handbook of the World (various

years); CIA World Factbook

(Central Intelligence Agency

[CIA] 2004); BBC (2004)

British colonial Decrease

legacy

Coded 1 if country was a

British colony, 0 otherwise

Poe, T?te, and Keith (1999); CIA

World Factbook (CIA 2004)

a. Accessed October 2004, from http://www.freedomhouse.org/index.htm/.




4. "The practices of (Level 3) are expanded to larger numbers. Murders, disappearances are a common part of life. ... In spite of its generality, on this level terror affects

primarily those who interest themselves in politics or ideas."

5. "The terrors of (Level 4) have been expanded to the whole population.. . . The leaders

of these societies place no limits on the means or thoroughness with which they

pursue personal or ideological goals." (Gastil 1980, as quoted in Stohl and Carleton

1985,212-13)

Two separate scales have been generated, based on country reports from Amnesty

International and the U.S. State Department since 1976. We mainly use the State

Department scale, saving the Amnesty International data for supplementary analy

ses. Values of the two scales have been found to closely parallel one another.

Although there is some evidence of slight biases in the State Department reports that

affect a few cases in earlier years, the biases seem to have disappeared, with the

reports improving as a result of their institutionalization and in response to their crit

ics (Poe, Carey, and Vazquez 2001). We believe the State Department measure to be

superior for our purposes because they are more consistent (and thus probably more

reliable) and sometimes more extensive in their coverage of different types of human

rights abuses than Amnesty International's reports. The State Department data also

cover more countries around the world than the Amnesty International reports,

which is important if one is seeking to build a risk assessment system that is truly

global. In the few cases not covered by the State Department, we follow previous research and use the Amnesty International reports where available. Thus, we ensure

that our risk assessment is as global in its coverage as possible.

RISK ASSESSMENTS WITH A MODEL OF PERSONAL INTEGRITY ABUSE

THE BASE MODEL

As a first step, we run our basic regression model for the time period from 1977

to 2002. The results are shown in the appendix and are similar to the results reported in Poe and T?te (1994); Poe, T?te, and Keith (1999); and Davenport and Armstrong (2004). The most notable difference is that international war is no longer statistically

significant as a determinant of physical integrity abuse, possibly due to the different

data source used to identify wars or perhaps the additional years included in the sam

ple. This model is the basis for the findings that follow. Its explanatory power is

about the same as the models employed in earlier work, explaining about three

quarters of the variance in the PTS.

IN-SAMPLE TEST OF A RISK ASSESSMENT DEVICE

To develop a forward-looking risk assessment device, following Gurr and Moore

(1997), we identify cases with a negative residual falling at least one standard




deviation below the prediction line as being at risk for future violent conflict. The

underlying assumption is that if the model is valid, conditions in our "risk group" are

such that there should be a stronger probability of an increase in human rights abuse

in this group than for other cases. For example, it might be the case in a particular

country that an economic downturn or a turn away from democracy has occurred but

that these negative events have not yet resulted in greater repression, resulting in the

comparatively large negative residual. In that event, we might reasonably expect that

an increase in human rights abuses would be more likely to occur than in cases

where the residual is near zero or positive.

Conversely, cases that have positive residuals greater than one standard deviation

are identified as members of the "opportunity group," expected to have a greater

probability of human rights improvement in the next year. For example, government structures or the economy may have recently changed in such a way that less repres

sion would be the norm, but a strong personalist leader has not yet reacted to those

changes by loosening the government's grip on the population. In such instances, we

hypothesize that the political opportunity structure (e.g., Tarrow 1994; McAdam

1996) presents heightened possibilities for improvements in human rights conditions

in the near future.

Finally, in a third group, we include the relatively large number of cases within one standard deviation of the predicted values. We assume that these cases will not

be especially prone to change in either direction. Admittedly, it is probably the case

that risk and opportunity increase incrementally as countries approach the one

standard deviation cutoff and, for that matter, thereafter. But to consider this would

complicate the analysis. The analyses using this cutoff will suffice to show the utility of our model.

In our first test of a risk assessment model, we separate countries into these three

categories and compare our groupings to what actually happened in the next year:

did physical integrity abuses increase, decrease, or stay about the same? Any countries that moved upward on the PTS from one year to the next will be consid

ered to have experienced an increase in human rights abuse, and any country mov

ing downward will have experienced an improvement in the realization of physical

integrity rights. To gauge the utility of our risk assessment model, we first test the model in

sample. The residuals in 1977 are compared to the historical record for 1978, the

1978 residuals to 1979, and so on. All years included in the construction of our orig inal model, 1977 to 2002, are included in the analysis.

The risk and opportunity categories are compared to the actual changes in future

repression levels in Table 2. Our predictions are shown in the three rows, followed

by row and column percentages. Real changes are shown in the columns. Of the

cases, 2,322 out of 3,446 (67.38 percent) lie on the diagonal and are predicted cor

rectly. About 42 percent, or 190 of the 454 countries identified as being at risk for

increased repression, actually experienced such an increase. This risk of there being an increase in repression at time t + 1 (41.85 percent) was almost nine times greater in our risk group than it was when our model indicated that a decrease in repression




TABLE 2

In-Sample Risk Assessment for Increases and Decreases in Repression at Time t + 1, Based on Residuals from Time t

Prediction

Change in Repression

Decrease No Change Increase Total

Decrease in repression

No change

Increase in repression

Total

229 43.95

47.71

238 9.63

49.58

13

2.86

2.71

480 13.93

100

267 51.25

11.03

1,903

77.01

78.6

251 55.29

10.37

2,421

70.26

100

25 4.80

4.59

330 13.35

60.55

190 41.85

34.86

545 15.82

100

521 100

15.12

2,471

100 71.71

454 100

13.17

3,446

100 100

NOTE: The number of cases is on top of each cell, followed by row percentages and column percentages at the bottom.

was likely: out of 521 cases that were classified as the "opportunity group," only 25 cases (4.80 percent) saw an increase in repression at time t + 1 (upper right-hand cell). And only 13.35 percent of all countries with residuals falling within one stan

dard deviation from the mean (2,471 cases) experienced an increase in repression. Thus, cases we identified as being at risk for increases in repression were more than

three times more likely to actually experience an increase compared to the group of

countries not expected to change. Our findings regarding the opportunity group are

also quite solid; 43.95 percent of cases placed in that group saw an improvement of

physical integrity, compared to 8.58 percent for those that were not (including both

the risk and no-change groups). Thus, cases in the opportunity group were about five

times more likely to experience improvements than those placed in the two other

groups, combined.

These in-sample tests clearly indicate that our model is identifying subsets of

countries that are more likely to experience increases or decreases in physical

integrity abuse in the subsequent year. The cases it did not identify correctly are

grouped into two categories, Type I and Type II errors. There are 556 cases of Type I errors (about 16.1 percent of the total AO in which countries were identified by the

model as having characteristics that predisposed them toward changing in a particu lar direction, but that change did not occur in the subsequent year. Clearly, the spec ifications we use in our model are not sufficient to explain all violations of physical

integrity rights. Just as cigarette smoking does not guarantee lung cancer, the pres

ence of conditions monitored by our model fails to guarantee that a country's human

rights practices will change in the subsequent year. Some Type I errors are probably




TABLE 3

Out-of-Sample Predictions for 2003, Based on the Model from 1977 to 2002

Observation

Prediction Decrease No Change Increase Total

Decrease in repression

No change

Increase in repression

Total

22

46.81

84.62

4

4.17

15.38

0

0

0

26

16.35

100

25 53.19

21.74

86

89.58

74.78

4

25

3.48

115 72.33

100

0 0 0 6 6.25

33.33

12

75

66.67

18

11.32

100

47 100 29.56

96

100 60.38

16

100 10.06

159 100 100

NOTE: The number of cases is on top of each cell, with row percentages in the middle and column per

centages beneath.

due to our predictions focusing on the following year and our use of annual data.

Many countries that were predicted to change at time t + 1 do see changes in their

respect for human rights in later years. In 355, or 10.3 percent, of cases, we failed to identify countries at risk of

increases in repression that did in fact occur in the next year. In an additional 251

cases, no improvements were foreseen, but the human rights situation did get better.

Some of these Type II errors are also no doubt the result of our (necessary) choice

of a design that compiles data on an annual basis. Most notably, in some of these

cases, changes in human rights abuse may have occurred due to events early in year

t + 1, which would not have been captured by our data in year t. A military coup or

the start of a civil war at the beginning of year t + 1, for example, could have led to

increased repression that was missed by our model, which depends on current

data to assess the probability of increases or decreases in human rights abuses next

year. In a later section of the article, we examine the set of cases in which our model

errs in an effort to identify possible areas for improvement. Still, overall, these find

ings are strong and quite supportive of the idea that meaningful risk assessment is

possible in the study of human rights.

OUT-OF-SAMPLE RISK ASSESSMENTS: A TEST

As encouraging as the results above are, a useful risk assessment tool must

demonstrate an ability to work in out-of-sample assessments. Our ultimate aim is to

assess the risk for changes in a year t + 1 for which data are not yet available. In

sample risk assessments are arguably easier because the years being "predicted" are




included in the calculation of the model. Out-of-sample risk assessments pose a

much more stringent test. To generate such assessments, we follow procedures sim

ilar to those we used above. We performed a risk assessment for 2003, based on a

model and data covering the years 1977 to 2002, as this was the last period for which

complete data were available at the time of the study.

The findings are similar to those above and even slightly stronger in some

aspects. Of the countries, 120 of 159 (75.47 percent) are predicted correctly. Again, there were some errors. Type II errors occurred in 10 countries (6.29 percent), in

which the model did not predict change, but an increase or decrease in repression took place in 2003. Here the risk group included 16 countries, 12 (or 75 percent) of

which actually experienced increases in physical integrity abuse in 2003 according to the State Department PTS. The model misses identifying 6 cases for which

increases in human rights abuses occurred in 2003 (El Salvador, Guyana, Niger, Sierra Leone, Zimbabwe, Laos). Still, only 6.25 percent of cases for which we

thought no change was likely saw such increases (twelve times less likely than the

at-risk group). There were also twenty-nine Type I errors, discussed in further detail

below.

Conversely, the model correctly identifies 22 of the 26 cases (84.62 percent) that

experienced improved human rights conditions in 2003. This number represents 46.81 percent of the 47 cases our model put in the opportunity group. Only 4 of the

112 cases, or 3.57 percent of those not in the opportunity group, experienced

improvements (Suriname, Italy, Burundi, and Turkey).7 Overall, countries placed in

the opportunity group were about thirteen times more likely to improve their human

rights scores than those that were not. Overall, the model did not predict an improve ment in any instances in which the human rights condition deteriorated in 2003.

Similarly, the model did not predict increased state repression in any cases where the

respect for life integrity rights actually improved.

A CLOSER EXAMINATION OF ERRORS

As noted above, the cases on which our model erred are clearly worthy of more

careful inspection, for this might allow us to discern patterns that would suggest additional factors that could be included to improve our future efforts at risk assess

ment. We could identify no geographical pattern in the ten Type II errors discussed

above, nor does it appear that the cases are grouped at a certain score on the PTS.

Italy, for example, reached a score of 1 in 2003, while the situation in Burundi rela

tively improved from 5 to 4. However, within the group of six unpredicted increases

in human rights abuse, countries concentrate in the middle of the scale, as all of the

countries either went from 2 to 3 or from 3 to 4. Looking at the countries that unex

pectedly underwent an improvement, Suriname went from being classified as 2 on

7. Our "predictions" for 2003 and the actual outcomes for each country in the sample are available

at http://jcr.sagepub.eom/cgi/content/full/50/4/484/DCl/.



Poe ?tal./ RISK AND OPPORTUNITY IN CONFLICT STUDIES 497

the PTS in 2002 to 1 in 2003. According to the U.S. State Department country

reports, Suriname underwent democratic consolidation during 2002 and 2003.

Human rights abuses in that country mainly consisted of police mistreatments and

beatings in custody, and these violations were apparently reduced in 2003 relative to

the previous year, although they did not disappear completely. Similarly, Italy had

PTS scores of 2 in 2002 and 1 in 2003. There were reports of police mistreatments

of detainees, particularly Africans and Roma, with fewer cases in 2003. Turkey, which moved from 4 to 3, reportedly saw arbitrary arrests in 2002, as well as regu

lar cases of torture and beatings by some members of the security forces, and sev

eral killings, particularly in the southeast. The State Department registered some

improvements for Turkey in 2003, some of which seem to have resulted from human

rights reforms passed by the parliament in January and July in an effort to meet the

requirements for European Union membership, a factor not captured by our model.

The last country in this group, Burundi, moved from 5 to 4. From November

2001 until August 2005, Burundi was governed by a transition government, marking a process of political transition from an authoritarian military regime and civil war

to the elections in 2005. In 2002, the human rights record of the new transition gov ernment was still poor, and in 2003, some slight improvements seem to have

occurred. Thus, a pattern of sorts does emerge. All countries except for Italy, where

we failed to "predict" improvement, underwent some form of political transition or

consolidation. In retrospect, the variables in our model might not be able to capture

the complexity of such processes, as Suriname and Turkey were both classified in

the same category by the Freedom House Political Rights Index (with which we

measured the concept of democracy) for 2002 and 2003. Furthermore, in all

countries apart from Burundi, human rights violations were committed by security

forces, mainly as a result of insufficient control by the government, another factor

that is not directly captured by the Poe and T?te (1994) model.

Turning to the group of countries where human rights violations unexpectedly increased in 2003, four countries moved from a PTS score 2 in 2002 to 3 in 2003:

El Salvador, Guyana, Niger, and Laos. In addition to some police killings and mis

treatments in 2002, El Salvador experienced politically motivated killings in 2003, with at least one during the campaign for the elections in March. In Guyana, secu

rity forces committed torture, beatings, and mistreatments and killed thirty-nine

people in 2003, in comparison with twenty-eight in 2002. Although no civil war is

coded for Laos in either of the two years, there were some low-level insurgency

activities, which increased in 2003. Together with the government's repressive

response, they led to a substantial number of civilian casualties. For Niger, we were

unable to pinpoint exactly the developments that led to the increase on the State

Department PTS. According to the Amnesty International reports, no such deterio

ration took place, as Niger was coded as 2 in both 2002 and 2003.

Of the two countries that moved from 3 to 4, Zimbabwe, according to the State

Department report, experienced a "government-sanctioned campaign of violence"

following the March 2002 presidential elections. During the local and legislative elections in 2003, there were additional reports of violence. Sierra Leone was coded




3 in 2002 as, unlike in previous years and in 2003, there were no reports that forces

operating in support of the government and rebels connected with the Revolutionary United Front committed unlawful killings. Still, people died in custody and during demonstrations.

The picture for countries that experienced an unexpected deterioration of the human

rights situation is thus less clear than for countries that made unexpected improvements. In two cases (El Salvador and Zimbabwe), additional violations seem to be related to

election processes. In Laos, more abuses occurred as a result of insurgent activity below

the level of civil war. For all these cases, it seems the variables in our model were not

sensitive to key events in the political realm. For future research, it might be interest

ing to explore whether elections in authoritarian or politically unstable countries are

generally related to more human rights violations. Finally, we could not identify the reasons for changes in the PTS in Guyana and Sierra Leone from the descriptions of

those countries in the U.S. State Department reports.

Type I errors tended to occur more in our efforts to identify opportunities for

improvement than in our attempt to identify countries at risk for human rights degrada tion. In twenty-five, or 53.2 percent, of the forty-seven cases included in our opportu

nity group, predicted improvements in the human rights situation did not occur. By contrast, only four of the sixteen cases that were placed in the risk group did not expe rience increases in repression in 2003. We could see no obvious patterns in these four, as two of the countries, Argentina and Gambia, stayed steady at 2 on the State

Department PTS, while Guinea was at level 3 and the Czech Republic stayed at level 1.

The Amnesty PTS scored the Czech Republic as holding steady in the 2 category for both 2002 and 2003, as that source seems to have placed a greater emphasis on

the issue of police mistreatment of the Romani minority than did the State

Department reports. The difficulties that led to Argentina being assigned a 2 seem to

have been the mistreatment of prisoners and instances of police brutality, perhaps tied to corruption. Similarly, according to the State Department report covering 2003, in Gambia, torture and mistreatment occurred, and security personnel (some

times off-duty) were reported to mistreat civilians. In both years, Guinea experi

enced extensive arbitrary imprisonment, as well as some killings and torture of

persons in police custody, but no increase in the incidence of these practices was evi

dent in the sources we read.

There are too many instances of countries not achieving the predicted improve

ments in human rights practices to go into much depth here. However, our inspec

tion of cases did yield some insights that we can briefly summarize. There were five

relatively developed, democratic countries that had scored 2 on the PTS but that

failed to move to level 1 in 2003 as predicted, indicating that these cases might have

posed a problem for our model. There were also a seemingly disproportionate number of peaceful and relatively democratic but poor countries that were at a rela

tively high level of repression (e.g., Nicaragua, Peru, Brazil, Kenya, Malawi,

Bangladesh, and Thailand). It does appear that in some instances, particular leaders

posed obstacles to efforts to improve human rights conditions, which the structural

factors in our model would indicate could be in the offing. In Mauritania, for




example, the rule of President Taya, who had engineered a cruel policy of removal

of black Mauritanians, was in its eighteenth year in 2003. In other cases, such as

Congo (Brazzaville), Brazil, and Botswana, human rights difficulties appear to be at

least partly due to lack of sufficient government control of police or security forces.

Factors relating to government ability to control such forces might be included to

improve future modeling efforts. Some other countries (e.g., Azerbaijan and

Nicaragua) appear to have made some movements toward human rights improve

ments in the period subsequent to our analysis, indicating again that the changes that our risk assessment device identifies as being probable may not occur in the imme

diately following year but in a later period.

ROBUSTNESS TESTS

We conducted several robustness tests over different time periods, with alterna

tive model specifications and data sources.8 In one such analysis, we replaced the

Freedom House Political Rights scale with the polity2 variable from the Polity IV

data set (Marshall and Jaggers 2002), which measures institutional democracy on a

scale from -10 to +10. We also experimented with a squared term of the respective

democracy variable, as suggested by research that would lead us to expect a curvi

linear relationship between democracy and human rights violations. The results of

the above robustness checks for out-of-sample risk assessment in 2003 were identi

cal, performed slightly worse, or contained fewer countries due to data availability than those that we presented earlier. However, our major conclusion?that useful

risk assessments can be made?was supported by each set of results.

To examine whether 2003 was special in any regards, we conducted out-of

sample assessments for 1996 (based on a model from 1977-1995) and 2001 (based on 1977-2000). The risk assessment device does not perform quite as well for these

years, but the results are still respectable. Poorer performance for 1996 may be due

to the short time period on which the model is based. Although we do not present the

results in detail here, we give summary statistics for these analyses in the next

section (see Table 4). We also examined whether the results change when we replace the State

Department with the Amnesty International scale (substituting the State Department values where Amnesty scores were missing) and found our examination of risk asso

ciated with the Amnesty scale to perform somewhat worse in assessing risk and

opportunity for 2003. This was to be expected since the State Department reports are

more systematic in their coverage within the reports, and (particularly in later years)

they cover a larger section of the world's countries. For 2003, we also generated a

risk assessment device that groups countries in the risk (opportunity) group only when they were more than one standard deviation below (above) the predicted value

in both the State Department and the Amnesty International models. Similarly, we

8. The results from these supplementary analyses are available at http://jcr.sagepub.com/cgi/ content/full/50/4/484/DC II.




only coded a country to experience an increase (decrease) in repression when both

scales coded a higher (lower) score. Thus, a country was expected to experience a

change only when the State Department and the Amnesty International models

would have predicted that it would do so. But a country was also only registered as

experiencing deterioration if both scales converged. Not surprisingly, in this analy sis, many more countries fall into the "no-change" category, both in terms of risk

assessments and observed changes, because a deflection on the two scales was

needed to classify a country in either the risk or the opportunity group.

Although no findings in the human rights literature support this claim so far, it

seemed to us that one might reasonably hypothesize that more recent years would be

more effective in generating useful risk assessments than those in the more distant

past, as patterns of causation of human rights abuses might have changed over time.

We therefore experimented with restricting the model generating assessments for

2003 to the post-cold war period, 1990 to 2002, and found it to perform slightly better in some regards but worse in others. Overall, the findings were not apprecia

bly different. Finally, we assessed the risk over three two-year periods: 1996/1997,

2001/2002, and 2002/2003. In these models, we counted a change in either year,

except for cases that first saw an improvement and then a worsening in their human

rights situation (or vice versa), only to end up at the same level after two years. The

two-year assessments performed relatively well but not quite as well as the one-year

assessments. In general, however, these tests show that our results are quite robust.

They lead us to conclude that it is possible to use this model to identify countries that

are at a heightened risk of increased state terror or present realistic opportunities for

improvement.

ASSESSING THE ACCURACY OF OUR ASSESSMENTS

A last matter of concern is whether our risk assessments perform well according

to standard benchmarks. O'Brien (2002, 803) lists three "standard forecasting

performance metrics": overall accuracy, recall, and precision. While we only claim

to build a risk assessment device, it will be informative to see how well our model

performs against these established benchmarks for assessing forecasting tools. The

three metrics, adapted to our purposes, are as follows:

Number of correctly classified country year observations

Number of country year observations classified

Number of correctly predicted changes

Number of changes that occurred

Number of correctly predicted changes

Number of changes predicted to occur

Most risk assessment and early warning models, such as Gurr and Moore (1997), Harff (2003), and O'Brien (2002), aim at predicting the occurrence of one specific

Overall accuracy =

Recall =

Precision =




TABLE 4

Forecasting Performance Metrics (in Percentages)

Opportunity Risk

Assessment Assessment Total

2003 Overall accuracy ? ? 75.47

Recall 66.67 84.62 77.27

Precision 46.81 75.00 53.97


Recall 46.67 56.25 53.19

Precision 41.18 50.00 47.17


Recall 35.00 25.93 37.50

Precision 66.67 43.75 43.75

event, such as ethnic conflict, genocide, or conflict in general. In contrast, we try to

assess the likelihood of positive and negative changes on the PTS (i.e., of both the

risk of increased repression and the opportunity of an improvement in the respect for

human rights). We therefore compute recall and precision statistics for both our risk

and opportunity groups. For an overall evaluation of our risk assessment system, we

compute the average of the two. The results for the simulated risk assessment for

1996, 2001, and 2003 are presented in Table 4.

Considering the mainly structural character of the model and the relatively short

term period for which risk assessments are produced, we believe the model performs

surprisingly well. In 2001 and 2003, the model performs better in the recall than the

precision metric, with the opposite being true for 1996. The overall accuracy of the

model improves over the years, which could partly be due to the increased sample size in later years. Apart from 1996, the risk assessment outperforms the opportunity assessment. One possible, admittedly ad hoc explanation for the better performance

in assessing risk as opposed to opportunities could be that many factors in our

model, such as war or military coups d'?tat, might serve as shocks that quickly dete

riorate the human rights situation in a country. In the aftermath of a war, however,

situations might improve only gradually, as conditions improve, meaning that

improvements in human rights conditions are not as immediate or sudden but can

take years to develop. Increases in democracy and a higher GDP per capita, for

example, do not seem to lead to sudden improvements in human rights records in the same way that the initiation of a coup or a civil war leads to their degradation.

The better overall performance of the model in 2003 than in earlier years seems

to be related to some differences in possible systemic- or regional-level trends that were occurring in those years. There were a relatively greater number of countries in

which respect for human rights improved from 1995 to 1996, while from 2000 to

2001, the changes were slanted more toward a deterioration. In 2002-2003, the

period in which our model performs best, there was a greater balance in the number

of increases and decreases. Because our risk assessment device used the distance




from the regression line as a criterion to identify countries that are predisposed toward change, the model assumes that change will be balanced. Thus, it is no sur

prise that its performance is less impressive if there is an overall movement in a par ticular direction in the year we are predicting. This points to another possible direction for improvement in our efforts to assess risk: greater attention to account

ing for diffusion processes over time and space that might help us to explain such

year-to-year variations at the system level.

The critical analysis above suggests many areas for improvement, but this dis

cussion should not obscure the larger finding that the overall performance of our

model in its current form is quite solid. The overall accuracy of our model was

between 61 and 75 percent. As a baseline for comparison, O'Brien's (2002) study

(which claimed to have built a forecasting as opposed to a risk assessment system) achieved overall accuracy of 75 and 81 percent for the longest time period examined.

SUMMARY AND CONCLUSIONS

Our results show that with a regression-based model using mainly structural vari

ables, we are able to identify groups of cases that have a substantially higher proba

bility of experiencing changes in their levels of repression than the average country. As a result, we are optimistic about the possibilities of an operational risk assess

ment vehicle for physical integrity rights in the not too distant future. Furthermore,

relating to theory and hearkening back to the literature that indicates success at

forecasting is one characteristic of strong theory, our findings have been supportive of the cumulation of theory and findings that have arisen in the study of physical

integrity abuse.

Some caveats are in order, however. Readers should keep in mind that risk assess

ments are a useful tool that should be supplemented with other approaches. Like

Schrodt and Gerner (1998, 106) we think of risk assessment and early warning mod

els as complementary. Events data-based early warning models, for example, might

be most constructive and meaningful when applied to a watch list identified by a risk

assessment model. Political decisions should clearly not be based on statistical analy ses alone. Large-n quantitative efforts at risk assessment or early warning should be

enhanced by qualitative analyses from area experts and policy makers and by more

focused and nuanced statistical analyses focusing on single cases across time.

In the course of conducting this study, several areas for future research and ideas

for possible improvements to our models occurred to us. Our models use relatively

crude, aggregate data, such as the overall level of democracy, gross domestic product

per capita, and population size, to explain variations in human rights abuses. That the

data are readily available in a usable format is a strength from the perspective of

practicality because they can be compiled quickly and without a great expenditure of research hours. However, a first avenue for improving our assessments of risk and

opportunity would be to tackle the data problems inherent in this type of study in

such a way as to allow us to aggregate our data over shorter periods of time, for both




independent and dependent variables, or to change key variables in real time. We

believe, for example, that our assessments would be improved if we were able to

change our assessment of a country's probability of experiencing an increase in

human rights abuse for a particular year, if we could change our evaluations in

instances when a military coup occurs in January or a civil war begins in February.

Second, although we experimented with modifications to the Poe and T?te (1994) and Poe, T?te, and Keith (1999) models, we found that there are probably still vari ables that could be added to improve the predictions?for example, those pertaining to domestic threats that are less serious than civil war. Those data are not included in the Poe and T?te model, which has been used as the baseline for this study and for a number of other studies in the literature on physical integrity abuses. Their addi tion to the model can only help future efforts to assess risk and opportunities in the

study of human rights. A third avenue is to improve the analysis by adopting a more discriminating

measure of the dependent variable. Despite its proven utility as a measure of human

rights abuse and state terror, the PTS is still a rather rough, ordinal scale. Clearly, it does not capture all improvement and decay in respect to physical integrity. It is

possible, for example, for a government in category 3 of the PTS to take hundreds more political prisoners than it had in the immediately previous year and still be iden tified by that scale as belonging in the same category. Similarly, more killing may

not be enough to move a country from category 4 to 5, unless the state terrorism

is expanded from those involved politically to the entire population. We believe that some of our Type I errors, in which changes were predicted but did not occur,

may be a result of the inability of the PTS to capture all of the improvement and deterioration that do in fact occur. We believe that the development of more

fine-grained measures would prove helpful in building an improved model and per

haps in separating cases into risk and opportunity groups more effectively. The obstacle at present is that existing events-based data sets do not yet provide the

global coverage and the specific focus on personal integrity violations that available standards-based measures do. In addition, but more easily achievable in the short

term, conducting similar tests on measures that separate the different dimensions of

human rights, such as those distinguishing between imprisonment, torture, and

killing (e.g., McCormick and Mitchell 1997; Cingranelli and Richards 1999), might yield additional insights not gained from the tests we conducted here.

Despite the caveats noted above, we believe this study has convincingly demon strated that useful risk and opportunity assessments of human rights abuses can be

made with available data. With a modest level of support from a granting agency and some elbow grease on the part of researchers, they could be generated in real time.

We will close by emphasizing the need for human rights scholars to follow their

colleagues who have conducted research on other humanitarian issues to search for

ways to make their research useful. Risk assessment and early warning of human

rights abuses is a direction that has not yet been fully explored and deserves much

greater attention. We hope that other researchers will soon join us in this endeavor.

Indeed, if this study turns out to be the last word on the topic, we would be both

surprised and disappointed.




APPENDIX

Regression Model 1977-2002

(Panel-Corrected

Coefficient Standard Error)

Constant 0.751** (0.141)

Repression, _, 0.651** (0.036)

Democracy -0.055** (0.008) Gross domestic product

(GDP) per capita3 -0.067** (0.010) GDP per capita growth -0.007** (0.002)

Population sizea 0.049** (0.008)

Population growth 0.003 (0.007) Civil war 0.453** (0.053) International war 0.056 (0.043)

Military regime 0.016 (0.030) British colonial legacy -0.045* (0.020)

R2 0.761

Waldx2(10) 7,579.62 Prob > x2 0.0000

N 3,464

a. Natural log taken.

*/?<.05. **/?<.001.

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