Spaces of Terror: Modeling Spatial Non-Stationarity in State-Level ...

Appl. Spatial Analysis (2014) 7:245–258 DOI 10.1007/s12061-014-9106-2

Spaces of Terror: Modeling Spatial Non-Stationarity in State-Level Repression Joel A. Capellan & Jeremy R. Porter

Received: 26 August 2013 / Accepted: 27 March 2014 / Published online: 16 April 2014 # Springer Science+Business Media Dordrecht 2014

Abstract Over the years, social scientists have amassed an impressive body of knowledge on state repression. Despite our improved understanding, this body of research implicitly assumes that the relationships between the independent variables and state sponsored repression are stationary over space. Current approaches, like OLS regression, cannot capture the spatial heterogeneity of the relationships and may lead to public policy inferences that are poorly specified or completely wrong for some countries. The purpose of the paper is to reexamine the repression model using geographicallyweighted regression. We found significant variation in the goodness of fit of the model and strength of coefficients. A closer evaluation of the geographically varying results reveals that these variations have a distinct spatial trend, suggesting processes that might otherwise have gone undetected due to traditional ‘global’ results. Keywords Repression . Geographically-weighted regression . Democracy . Human rights

Introduction For the past 30 years, social scientists have paid considerable attention to government’s sponsored repression (Davenport 1996, 1997, 1999; Davenport and Armstrong 2004; Henderson 1991, 1993; Poe and Tate 1994; Poe et al. 1999; Richards 1999; Richards and Gelleny 2007; and Zanger 2000). This body of research has attempted to explain the types of repressive policies adopted, the frequency of its use, and the scope of its application (Davenport 2007). Given the findings of the existing research, we have J. A. Capellan (*) Criminal Justice Department, CUNY Graduate Center and John Jay College, 524 West 59th St. Room 636t, New York, NY 10019, USA e-mail: [email protected] J. R. Porter Sociology and Criminal Justice Programs, City University of New York, 2900 Bedford Ave., New York, NY 11220, USA e-mail: [email protected]

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J.A. Capellan, J.R. Porter

significantly improved our understanding of the causes of government sponsored human rights violations. The ‘repression model’, developed by Poe and Tate (1994) and refined by Davenport (1996), has become the standard in the repression literature. In this model, repression is a function of a country’s population, economic development, level of democracy and civil strife. This is a very robust and powerful model; some of its most sophisticated variations explain more than 70 % in the use of repression (Davenport 1999). This level of predictive power is rarely matched in the social sciences. Despite our improved understanding, this body of research implicitly assumes that the relationships between the independent variables and state sponsored repression are stationary over space. In other words, they assume that the direction and strength of the regression (or ‘global’) coefficients are the same for every country. This assumption is questionable at best. Regional scientists have long noted that spatial location matters and in reality these relationships might vary significantly over space, perhaps reflecting unique local processes that otherwise would go unnoticed (Ali et al. 2007). Unfortunately, ordinary least squares (OLS) linear regression cannot detect nonstationarity and thus the ‘average global’ results may lead to public policy inferences that are poorly specified or completely wrong for some regions (Matthews and Yang 2011). The goal of this paper is to explore the potential for spatial nonstationarity associated with the relationships between the key variables in the repression model and the use of repression throughout the world using Geographically Weighted Regression (GWR). GWR is a fixed-effect statistical technique that allows relationships between predictors and outcome variables to vary over space, producing a separate set of regression parameters for every observation based on a maximally efficient spatial regime that is iteratively identified. Our results show that the GWR approach proves to be a worthwhile endeavor. We found significant variation in the goodness of fit of the model and strength of coefficients. A closer evaluation of the geographically varying results reveals that these variations have a distinct spatial trend, suggesting processes that might otherwise have gone undetected due to traditional ‘global’ results. It should be noted early in the article that we build directly on the standard repression models (Poe and Tate 1994; Davenport 1996 for a review) and are only introducing a methodological variant to the prediction of repression. The previous models require little in the way of refinement concerning the identification of variables and theoretical development. Instead, our hope is to make a contribution by testing for non-stationary patterns associated with the strength of the model geographically as implied by the research of Fein (1995). We present the research in the following five sections. Immediately following, we review the empirical repression literature. Then we introduce the methods, data collection, operationalization, and statistical technique. We present the results of the OLS regression as well as the GWR model. Finally, we draw to a close with our concluding remarks, suggestions for future research and policy implications.

The Systematic Study of Repression Governments coerce. Much of this coercion is legal and justified as well as supported by the citizenry, but let us not forget that governments, whether authoritarian or

Modeling Spatial Non-Stationarity in State-Level Repression

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democratic, modern or “primitive,” rule through coercion. In this light, state repression should be seen as an extrajudicial type of coercion for the purpose of deterring specific activities and/or beliefs perceived to be challenging to government’s legitimacy (Davenport 2007; Goldstein 1978). Repression policies may range from non-lethal sanctions (i.e. First Amendment-type violations and the erosion of due process) to the violation of physical integrity rights (i.e. torture, extrajudicial killing, arbitrary imprisonment, and disappearance). Repression is so widely applied that it has reached endemic proportions. In the last 100 years, more people have been killed by their own governments than by all wars combined—225 million more people (Rummel, 1997). In the last 30 years, researchers have paid considerable attention to government’s sponsored repression (Davenport 1996, 1997; Davenport and Armstrong 2004; Henderson 1991, 1993; Poe and Tate 1994; Poe et al. 1999; Richards 1999; Richards and Gelleny 2007; and Zanger 2000). During this time, they have developed vibrant research agenda that attempts to explain the types of repressive policies employed, frequency as well as the scope of it use (Davenport 2007). This literature has made three significant contributions to our understanding of repression. First, researchers have settled on a theoretical perspective from which to study the phenomenon. This framework (heavily influenced by rational choice theory) is rather straightforward: after considering all relevant factors (e.g. economic, contextual, and political) repression will be used (1) when the costs are low, (2) the benefits are high, (3) the likelihood of success is high, and (4) there are no acceptable alternatives (see Davenport 2007). The second contribution to come out of this literature is “the domestic democratic peace theory.” The theory argues that democracy pacifies (i.e. reduces) repression. Under the rational choice perspective outlined above, democratic institutions increase the cost of repressive behavior because authorities can be voted out of office. The democratic pacification thesis has been supported by every study that includes such variable—even when operationalized differently (Davenport 1996, 1997; Davenport and Armstrong 2004; Henderson 1991, 1993; Poe and Tate 1994; Poe et al. 1999; Richards 1999; Richards and Gelleny 2007; and Zanger 2000). Finally, researchers have noted that repression is almost always found in countries experiencing civil turmoil. Governments consistently engage in repressive behavior when their authority and legitimacy are challenged by the citizenry. This finding is so consistent and robust that some have named it the “law of coercive responsiveness” (Davenport 2007). Despite the significant advances, the literature assumes that these findings are stationary over space. Theory and empirical evidence, however, would suggest otherwise. Take for instance the effect of democracy on repression; the level of democracy has been consistently found to have a negative relationship with repression. However, this is only the global or average effect of democracy. Fein (1995) argues that the relationship between democracy and repression is not linear at all; rather it follows an inverted “U,” with repression increasing in the middle of the consolidation process (i.e. semi-democracies). In an empirical evaluation of the inverted “U” thesis, Regan and Henderson (2002) found that everything else equal, intermediate levels of democracy have the highest levels of repression—even more their authoritarian counterparts. Thus, the claim that democracy is the solution to state repression might not be true, at least not for every country. The goal of this paper is to test this implied assumption of stationary

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and reexamine the repression model and the relationships that have been taken from granted for over 30 years.

Methods A Geographically-Weighted Model of Repression This study employs a repression model developed by Poe and Tate (1994) and refined by researchers such as Davenport (2007) and Richards and Gelleny (2007). This model has become the standard in the repression literature. The repression model consists of types of covariates: political, economic, and contextual factors. These factors have consistently been shown to explain a majority of the variation in repression at the national level. However, the accepted model makes assumptions inherent to the use of global regression models. The assumption of most interest to us is that of stationarity. The global regression model assumes that the effect of the contextual, political, and economic factors are consistent across space. Thus, the global model would predict that increases in levels of democracy are likely to decrease repression at the same rate for all nations in the sample. While this is certainly useful information on average effects, it is likely not plausible. For instance, movement on the democracy indicator in Northern Europe is likely to be qualitatively different than movement on the democracy indicator in Southeast Asia. Given that qualitative difference, one might expect that the effect of democracy on repression is likely to vary across nations and regions of the world. The same can be said for all of the determinants of repression and the overall fit of the referenced repression model itself. In order to examine this expected nonstationarity associated with the model fit and the effects of each determinant individually, we employ a geographically weighted regression (GWR) model. GWR models allow for local models to be estimated for each nation given the most maximally efficient set of “neighboring” countries to be included. The adaptive model setting performs and iterative estimation of the regression model specified by including nations within an increasing distance based buffer until the most efficient model for that specific nation is estimated. At that point, the effect of each of the determinants and the model fit statistic can be mapped in order to identify patterns associated with each. The GWR model itself takes the same form as the global regression model with its statistical variation being only the indicator of the model being estimated local, in fixed form, for each ith case (or each nation). Global Regression Model: Y ¼ α þ b1 x 1 þ … þ bn x n þ e Geographically Weighted Regression (GWR) Model: Y i ¼ αi þ b1i x1i þ … þ bni xni þ ei where, the repression of each nation (Yi) is equal to a unit specific constant (αi) a set of independent variables (b1ix1i + … + bnixni) and a unit specific residual (ei). This estimation approach yields further insight to the appropriateness of the model for each


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individual nation in local form and can be performed in a series of GIS and statistical analysis packages. We use ArcGIS’s ArcToolbox to perform the analyses here. Data This study employs a cross national time series design for the data collection. This dataset contains information on 158 countries from 2000 to 2008. The criteria for country selection are straightforward: (1) countries must be sovereign. All dependencies (e.g. Puerto Rico, Saint Matin etc.) could not be included because they are independent nations. (2) The last criterion is the availability of data; countries like Belize were not included simply because there was no data on the dependent variable (i.e. repression). Because GRW does not lend itself to time series design and we wanted to retain as much information as possible, we decided to aggregate all the variables, with the exception of the time lag, for the 2004–2008 time period. The Previous Repression (t-2) is aggregated average of repression for the 2000–2003 time period for each country.

Measures The Dependent Variable: Measuring Repression We use physical integrity rights violations as our measure of repression. Physical integrity rights (PIR) include the rights against torture, extrajudicial killing, arbitrary imprisonment, and disappearance. We employ the Cingranelli and Richards (1999) scale of physical integrity rights (known as CIRI). CIRI is an ordinal scale based on four physical integrity rights (the rights against torture, extrajudicial killing, arbitrary imprisonment, and disappearance). CIRI uses a content analysis from the annual State Department Country Report on Human Rights Practices and the annual Amnesty International reports to derive its data. The level of government respect for each physical integrity right is coded using an ordinal coding scheme—ranging from 0 (frequent violation of the right), 1(some violation of the right), to 2 (no violation of the right). The resulting cumulative 9-point scale is derived from Mokken scale analysis—a technique that produces a unidimensional scale of the government’s respect for physical integrity rights (Cingranelli and Richards 1999). This nine-point scale ranges from “0” representing no government respect for PIR to “8” representing full respect for PIR. This scale was inverted this scale so that larger values equal higher levels of repression. Political Factors Level of Democracy Democracy has been proclaimed, both by theorists and policymakers alike, to be the solution to state-sponsored repression. This assertion has found much support from the empirical literature. There is a consensus that democracy is reliably associated with lower levels of human rights violations (Davenport 1996, 1997; Davenport and Armstrong 2004; Henderson 1991, 1993; Poe and Tate 1994; Poe et al. 1999;

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Richards 1999; Richards and Gelleny 2007; and Zanger 2000). The consistency of this finding is not surprising. Democracies are said to create an atmosphere of accountability, restriction, and representation that naturally hinders repressive behavior. For instance, the repressive capability of a political actor is greatly diminished when: (1) they are dependent on the people to stay in office and (2) when their power is limited by a number of constitutional constraints and veto player in other political institutions. Our measure of democracy is based on Gurr’s Polity IV measure of democracy (For a more detailed account see Marshall and Jaggers 2002). This is a composite index on many several important components for democracy such as political participation, constraints on the executive, and competitiveness of executive recruitment and regulation of political participation. This measure ranges from −10 for fully authoritarian systems to +10 for fully consolidated democracies. Economic Factors Although many different variables have been used in the Literature—some with more success than others—only a few have been consistently shown to affect the use of repression: GDP per capita (PPP) and population size. Wealth The health of the economy is viewed as a primary responsibility of the government. Thus governments are certain to feel the pressure from economic dissatisfaction in population (Henderson 1991). The job of dividing limited resources is a difficult and often governments resort to repression to control dissatisfied populations. Take for example refugee camps. These populations also strain finite resources, often sparking conflict with other dissatisfied groups in society. In addition, economic dissatisfaction within the camp may lead governments to repress in order to control the population. Thus, we can expect that countries with more resources are less likely to resort to lethal forms of repression. Wealth is a dummy variable with “1” equals countries on the top quartile of the distribution of Gross Domestic Product per Capita. Population Size Although the size of the population is hardly an economic variable, its impact on the economy and economic growth are very significant. Larger populations are a burden on the government. They are said to absorb any economic growth rate that may occur; this frustrates the state, making repression an appealing choice (Henderson 1993). Thus we can expect that larger population will increase will engage in higher levels of repressive behavior. We used the natural log of population size measured in millions. Contextual Factors Civil Unrest Governments are greatly influenced by context. For instance, in times of war or civil unrest, governments are more repressive than in times of relative peace. Violent


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political dissent—violent strikes, guerrilla warfare, domestic terrorism, and civil war— has been consistently identified with increase repressive behavior and (Davenport 2007). Conflict (domestic or international) makes repression an effective choice to maintain peace and stability. To measure civil unrest, we use the civtot variable from the Major Episodes of Political Violence (MEPV) dataset. Civiltot is the total summed magnitudes of all societal MEVP, that is civil violence, civil war, ethnic violence and ethic war. See Marshall (2005) for a complete discussion of this variable. Previous Repression Perhaps the most powerful predictor of state repression is “past repression” (Davenport 1996; Poe and Tate 1994; Poe et al. 1999). As conceived in the decision calculus, past repression lowers the cost because the mechanisms of repression are already in place. It also increases the probability of success by decreasing uncertainty among the actors. In other words, political actors know how to repress efficiently since they have used these mechanisms of repression in the past. In general, governments are more likely to repress when they have already established” repression mechanism. We use a 2-year lag on the dependent variable to create this variable. Although traditionally operationalized as continuous, we decided, because of extreme local multicollinearity, to make a dichotomous variable with “1” equals countries that were engaged in high levels of repression 2 years in the past.

Results Global Prediction of Repression Levels Table 1 presents the descriptive statistics associated with all variables included in the analysis for the 2004–2008 time period. During this time, the average level of repression among all nations in the sample is 3.45 (on a 1–8 scale with higher number indicating higher repression). States like Peru, Jamaica and Ecuador are fall around this mean. Based on this scale, the most repressive countries are Pakistan, North Korea and Myanmar each scoring an eight on the repressive scale. Conversely, the Netherlands, Norway and Belgium have the lowest scores (i.e. zero out of eight) in the dataset. Table 2 presents the correlation matrix. The first column shows that all of the Table 1 Descriptive statistics N

Mean

Std. deviation

Level of repression

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3.45

2.09

Level of democracy

158

3.65

6.29

Minimum 0.2 −10 0

Maximum 8 10

Civil unrest

158

0.40

1.14

ln(population)

158

16.18

1.51

Wealthy

158

0.25

0.44

0

1

Repression lag (t-2)

158

0.23

0.42

0

1

13.09

6 20.99

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Table 2 Correlation matrix 1

2

3

4

5

1) Level of repression 2) Level of democracy

−0.469a

3) Civil unrest

0.552a

−0.056

4) ln(population)

0.475a

0.009

5) Wealthy 6) Repression lag (t-2)

−0.501

0.726a

a

Correlation is significant at the 0.01 level

b

Correlation is significant at the 0.05 level

0.354a

a

0.249

−0.171b

−0.238a

0.560a

a

−0.070 0.453a

−0.253a

independent variables are moderately associated with the level of repression. Consistent with the literature, we find that the level of repression is negatively associated with level of democracy (r=−0.469), positively associated with civil unrest (r=0.552), positively associated with population size (r=0.475), negatively associated with wealth (r=−0.501), and positively auto-correlated with the previous use of repression (t-2) (0.726). These correlations are all significant at the p