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Defence and Peace Economics

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Economic sanctions and official ethnic discrimination in target countries, 1950–2003

Dursun Peksen

To cite this article: Dursun Peksen (2016) Economic sanctions and official ethnic discrimination in target countries, 1950–2003, Defence and Peace Economics, 27:4, 480-502, DOI: 10.1080/10242694.2014.920219

To link to this article: https://doi.org/10.1080/10242694.2014.920219

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ECONOMIC SANCTIONS AND OFFICIAL ETHNIC DISCRIMINATION IN TARGET COUNTRIES, 1950–2003

DURSUN PEKSEN*

Department of Political Science, University of Memphis, Memphis, TN, USA

(Received 10 December 2013; in final form 17 April 2014)

Conventional studies on the consequences of sanctions tend to focus on the target society as a whole without speci- fying how foreign economic pressures might affect the well-being of vulnerable groups within target countries – the same groups who often disproportionately bear the burden of sanctions. This study explores the extent to which sanctions increase the likelihood of discriminatory government practices against one of the globally most vulnera- ble groups, ethnic groups. It is argued that sanctions contribute to the rise of official ethnic-based economic and political discrimination through contracting the economy and creating incentives for the target government to employ ethnic-based discriminatory policies. Using data on over 900 ethnic groups from 1950 to 2003, the results lend support for the theoretical claim that sanctions prompt the government to pursue ethnic-based discriminatory economic and political practices in multiethnic countries. The findings also indicate that multilateral sanctions are likely to be more harmful to the well-being of ethnic groups than sanctions levied by individual countries. Further, the negative effect of comprehensive sanctions appears to be greater than that of sanctions with moderate and lim- ited impact on the target economy. The regime type of the target state, on the other hand, appears to have a signifi- cant role only in conditioning the hypothesized effect of sanctions on economic discrimination. Overall, this study’s focus on a vulnerable segment of the target society – ethnic groups – offers a greater understanding of the consequences of sanctions. It also provides additional insight as to how, in multiethnic countries, political elites might domestically respond to external pressures to retain power.

Keywords: Economic sanctions; Coercive diplomacy; Ethnic discrimination; Minority groups; Repression

JEL Codes: F13, F51, F59

1. INTRODUCTION

With growing international awareness of the humanitarian effects of sanctions in Iraq, Haiti, and the former Yugoslavia in the 1990s, more research has been devoted to the possible consequences of economic coercion (e.g. Cortright and Lopez 1995; Weiss et al. 1997; Gibbons 1999; Marinov 2005; Peksen 2009; Escribà-Folch 2010; Peterson and Drury 2011). This body of scholarship has been instrumental in demonstrating that the impact of sanctions on the target state extends well beyond the initial intended goal(s) of their use. One major shortcoming of the literature has been its focus on the target society as a whole. By doing so, studies overlook how sanctions affect the well-being of disadvantaged groups – the same groups who often disproportionately bear the burden of sanctions. This

*Department of Political Science, University of Memphis, Clement Hall 437, University of Memphis, Memphis, TN 38152, USA. Email: [email protected]

© 2014 Taylor & Francis

Defence and Peace Economics, 2016 Vol. 27, No. 4, 480–502, http://dx.doi.org/10.1080/10242694.2014.920219

study provides a systematic examination of the possible impact that sanctions have on one of the globally most vulnerable groups, ethnic groups. Specifically, it examines the extent to which sanctions contribute to the rise of official economic and political discrimination against ethnic groups in multiethnic target countries.1

Why is it useful to investigate the possible effect that sanctions have on official ethnic discrimination? Foreign economic restrictions are likely to have some significant effects on the target society in general. Yet, it is unlikely that every segment of the society equally bears the cost of the coercion. Groups with privileged access to political and economic resources might face minimum or no cost from external economic shocks by unevenly using the scarce resources in their favor. Vulnerable groups, on the other hand, might signif- icantly suffer from any major political and economic upheavals due to their disadvantaged position in society. Thus, this study’s focus on how sanctions contribute to the rise of offi- cial discrimination against a less privileged group extends our understanding of the conse- quences of economic coercion. It also provides additional insight as to how targeted political elites domestically respond to external pressures to retain power.

Another value of this study is to offer a greater understanding of the determinants of sys- tematic discriminatory policies against ethnic groups. A significant portion of the over 190 independent states today are considered multiethnic countries comprising more than one ethnic group. Fearon (2003, 204), for instance, notes that countries on average have about five ethnic groups that are at least 1% of the population, and half of the countries have between three and six such groups. Several of those multiethnic countries have faced or are still under interna- tional sanctions. Some well-known examples include the sanctions against Iran, Iraq, Liberia, Myanmar, Nigeria, the former Yugoslavia, and Somalia. These countries were targeted with sanctions for various reasons, such as drug trafficking, human rights violations, civil wars, nuclear proliferation, and trade disputes. Yet, there has not been any research to explore whether economic coercion has any inadvertent impact on the well-being of ethnic groups in the targeted countries. Researchers demonstrate that ethnic discrimination increases the likeli- hood of internal violent conflicts (Gurr 2000), interstate militarized disputes (Caprioli and Trumbore 2003), and domestic terrorism (Piazza 2011). Given the significant adverse effects of ethnic discrimination, it is important to understand the factors that trigger systematic ethnic discrimination. A greater understanding of what causes ethnic discrimination would, in turn, help local and international actors develop policies to avert future discriminatory practices.

The next section provides a brief overview of the relevant literature, followed by the sec- tion that presents the theoretical argument. The research design section deals with the data and model specifications, and the subsequent section reports the findings from the data anal- ysis. The conclusion section discusses the implications of the findings for foreign policy- making and scholarly research.

2. THE RELEVANT LITERATURE

A large body of scholarly research has addressed the determinants of ‘political gains’ – whether and when economic coercion achieves its stated goals – from sanctions.2 Most studies conclude that economic sanctions are generally ineffective in inducing the target country to comply with the sanctioning country’s demands. With the growing global

1Throughout the manuscript, I refer to an ethnic group as a group of ‘people who share a distinctive and enduring collective identity based on common descent, shared experiences, and cultural traits’ (Gurr 2000, 5). 2See Pape (1997), Drury (1998), Drezner (1999), Lektzian and Souva (2007), Hufbauer et al. (2007), Ang and Peksen (2007), Bapat and Morgan (2009), Mclean and Whang (2010), Early (2011).

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awareness of the humanitarian effects of sanctions in Iraq, Haiti, and the former Yugoslavia in the 1990s, scholars have devoted more attention to the possible inadvertent consequences of economic coercion. Studies show that external economic pressures might worsen public health conditions, economic well-being, and physical security of the populace in target countries (Cortright and Lopez 1995; Weiss et al. 1997; Gibbons 1999; Weiss 1999; Heine-Ellison 2001; Lektzian 2003; Peksen 2011). Some researchers find that sanctions induce the target government to commit more political repression to consolidate its authority (Peksen 2009; Peksen and Drury 2010).

Economic coercion is likely to escalate the level of anti-government protests in more dem- ocratic target countries (Allen 2008). There is also evidence suggesting that foreign eco- nomic pressures might shorten the leadership’s tenure, especially when the target regime suffers severely from the coercion by losing external rents (e.g. financial aid) and trade taxes (Marinov 2005). Escribà-Folch and Wright (2010), however, show that sanctions appear to speed up the removal of the incumbent only in personalistic dictatorial regimes, while having no destabilizing impact on other mixed or authoritarian regimes. Others find that sanctions are considerably effective in shortening the duration of civil wars (Escribà-Folch 2010) and reducing the severity of ongoing instances of genocide or politicide (Krain 2011). Sanctions also tend to improve the targeted state’s economic ties with third-party countries (Early 2009). Peterson and Drury (2011), on the other hand, show that sanctioned countries become more frequent targets of military aggression by states not involved in the sanctions.

This body of scholarship has been instrumental in advancing the cumulative knowledge on the consequences of sanctions. Yet, with the exception of gender-specific studies that show how sanctions disproportionately worsen women’s well-being as a vulnerable group in society (Buck, Gallant, and Nossal 1998; Drury and Peksen 2014), researchers mostly overlooked the effect that sanctions have on disadvantaged groups in society. I utilize the existing research on sanctions as a basis for the theoretical arguments concerning how economic coercion deterio- rates the economic and political status of a vulnerable segment of society, ethnic groups.

3. SANCTIONS AND OFFICIAL ECONOMIC AND POLITICAL DISCRIMINATION IN THE TARGET COUNTRIES

In this section, I develop a theoretical framework explaining how economic coercion increases the likelihood of official ethnic discrimination through its adverse effects on the economy and the government’s repressive policies. First, I explain why sanctions might induce the target government to commit economic discrimination against ethnic groups, fol- lowed by the section where I discuss the impact that sanctions have on ethnic-based politi- cal discrimination. Before turning to the argument in full, it is important to note that the hypothesized effect of sanctions on ethnic groups’ economic and political status does not occur in isolation from one another, on the contrary, they reinforce one another. For exam- ple, the contraction of the target economy as a result of sanctions not only harms ethnic groups’ economic well-being but also affects their political status by weakening their mate- rial capacity to protect their political and economic interests against discriminatory govern- ment policies. Similarly, the growing ethnic-based political repression following the sanctions not only restricts ethnic groups’ political freedoms but also affects their ability to have access to public goods and economic resources redistributed by the government. How- ever, to better illustrate the causal chain between sanctions and ethnic discrimination, I dis- cuss the effect of sanctions in two parts.

482 D. PEKSEN

3.1. Sanctions and official economic discrimination of ethnic groups

The economic cost of sanctions on target countries is likely to increase the extent of official economic discrimination against ethnic groups. Sanctions might disrupt the target economy in a few different ways. Trade sanctions (boycotts and embargoes) could be costly by reduc- ing foreign trade earnings and limiting the access to certain goods and products critical for production processes. They might also force the target economy to buy and sell goods through non-sanctioning third party countries, transnational smuggling networks, and other clandestine economic activities. Thus, trade might continue but often at worsened prices and circumstances. Similarly, financial and investment sanctions deprive the economy by limiting capital inflows into and out of the target economy. Further, foreign economic coer- cion creates uncertainty about the future economic and political stability of the target that discourages private investment. Once sanctions are in place, target countries on average experiences a 3.3% decline of GNP, while the average inflation is about 37% during sanc- tion years, excluding those countries experiencing hyperinflation (Hufbauer et al. 2007).

In multiethnic target countries, the contraction of the economy as a result of sanctions will likely prompt official economic discrimination against ethnic groups outside the sup- port base of the government. This will mostly occur through reducing minority groups’ access to the public resources and services made scarce by the sanctions. The distribution of scarce resources such as government contracts, special tax and tariff favors, educational opportunities, and access to welfare services often lies at the heart of ethnic discriminatory policies (Gurr 1993; Esman 1994; Hardin 1995). Because the state controls access to resources, the dominant groups who control the government determine the supply and redis- tribution of public goods and services.

Ethnic groups outside the support base of the government will have less access to public resources because the leadership will divert shrinking essential public resources to them- selves and their supporters. Leaders attempt to redirect resources in their favor to survive the economic hardship and minimize the damage caused by the sanctions to their capacity to rule (Cortright and Lopez 1995; Weiss et al. 1997; Gibbons 1999). The target regime also makes certain that their key supporters such as those in police, military, and civil services have sufficient access to the resources and services made scarce by sanctions. The diversion of scarce resources to their supporters is vital for the leaders to maintain their loyalty and support (Wintrobe 1998; Peksen 2009; Peksen and Drury 2010). As a result, in multiethnic societies, the uneven use of the goods and services by the state and its close allies increases the extent of economic discrimination against ethnic groups along with other disadvantaged groups in society.

For instance, in the case of the UN sanctions against Iraq in the 1990s, scholars show that economic sanctions did not cause any major damage to the regime controlled by Saddam Hussein, who was himself a Sunni and ruled through the Sunni minority (Reuther 1995; Hoskins 1997). On the contrary, foreign pressures boosted the allegiance of the Sunni minority to the regime as Hussein granted more economic rents and secured access to scarce resources in return for this group’s loyalty. Thus, while the sanctions against Hussein consolidated his repressive authoritarian rule by enhancing the relationship between his rule and the Sunni minority, the ethnic groups outside the support base of the government – the Iraqi Kurds, Shi’a Arabs and Turkomens – were denied access to the resources unevenly used by the Sunni-dominated Iraqi government. Consequently, this process helped Hussein and his supporters escape the costs of the sanctions, while deteriorating the economic well- being of the less powerful ethnic groups.

ECONOMIC SANCTIONS AND OFFICIAL ETHNIC DISCRIMINATION 483

Furthermore, the disruption of the regular functioning of the target market and industries will severely affect ethnic groups by increasing ethnic-based discrimination in the work- place and causing an overall decline in their economic wellbeing. Ethnic groups are among the most vulnerable social groups in the formal economy due to the existence of structural inequality and traditional discriminatory practices (Riach and Rich 2002; Bertrand and Mullainathan 2004; Carlsson and Rooth 2007). While ethnic-based economic discrimination may not always be overt or particularly evident, the economic hardships caused by sanc- tions will lead to significant manifestations of discriminatory practices. The violation of eth- nic groups’ economic rights following an economic downturn could occur in many different ways, such as lower wages, arbitrary firing or lay-offs, and unequal hiring and pro- motion practices as a result of their vulnerable status in society. For instance, international sanctions against the apartheid regime in South Africa were detrimental to the economic well-being of the black population in the 1980s and 1990s. The growing economic burden of sanctions on the South African economy coupled with systematic government discrimi- nation against the black population led to a significant decline in black employment and real wages (Lowenberg and Kaempfer 1998).

Based on the discussion above, I postulate that sanctions increase the likelihood of systematic economic discrimination against ethnic groups outside the support base of the government.

3.2. Sanctions and official political discrimination of ethnic groups

Leaders targeted by sanctions might also be more inclined to pursue political discriminatory policies against ethnic groups outside their support base. Political movements are consid- ered the primary link between the state and society. In multiethnic societies, ethnic cleav- ages and collective social interests are likely to generate political organizations and parties along ethnic lines (Horowitz 1985, 291–293; Hutchinson and Smith 1996; Chandra 2004). Ethnic groups form political parties and coalitions to protect their interests and effectively secure their demands from the state. Ethnic political actors are therefore involved in the competition over the control of the state and redistribution of public resources.

Scholars show that the target regime becomes more repressive in domestic politics fol- lowing the imposition of sanctions (Peksen 2009, 2010; Peksen and Drury 2010). The gov- ernment intentionally opts for repressive strategies as a calculated move to weaken the opposition. The repressive tools used by the government could range from restricting the conduct of free elections and functioning of political parties to committing torture and polit- ical imprisonment. Because ethnic political parties and organizations are major political actors in heterogeneous societies, they will be among the groups who face state repression. The use of discriminatory policies, such as banning ethnic political parties, will deny minor- ity groups a strong voice in the political arena and limit their access to government resources. Repressive measures against the political groups representing ethnic groups con- sequently allow the regime to consolidate its authority and maintain the status quo in the face of sanctions.3

3The targeted regime may be weakened in some sanction cases, especially when sanctions damage the economic and coercive capacity of the political elites and their supporters. This would, in turn, reduce the government’s abil- ity to use repressive measures against the rival ethnic political groups. However, the extant literature finds very lim- ited evidence to suggest that sanctions often weaken the target government and thus reduce its ability to commit political repression. On the contrary, studies show that sanctions are likely to (1) increase the government’s ability and willingness to use repression (Drury and Li 2006; Peksen 2009); (2) weaken the democratic opposition (Peksen and Drury 2010); and (3) restrict the freedom of expression and press freedom (Peksen 2010).

484 D. PEKSEN

Further, the target regime is also more inclined to commit ethnic-based political discrimination as it fears that economic coercion as a sign of the regime’s disapproval in the international community might encourage rival groups to mobilize against the govern- ment. This is because of the expectation that sanctions as an indication of international disapproval of the regime might give the opposition more leverage in domestic politics to rally support from citizens against the government (Drury and Li 2006; Peksen and Drury 2010).

For example, in the case of international sanctions against the apartheid regime in South Africa, Lowenberg and Kaempfer (1998) find that although imposed sanctions initially helped anti-apartheid black groups mobilize collectively against the regime, in the longer term, they inadvertently weakened black political activism by causing negative income effects on blacks through a reduction in black employment and real wages. The inadvertent negative effect of sanctions on black mobilization consequently allowed the apartheid regime to pursue its discriminatory policies against blacks to maintain the status quo until the last few years of sanctions.

Based on the argument laid out above, I hypothesize that sanctions increase the likeli- hood of systematic political discrimination against ethnic groups outside the support base of the government.

4. DATA AND MEASURES

To substantiate the theoretical claims outlined above, I use time-series cross-section data for the 1950–2003 period. The ethnic group-year is the unit of analysis. That is, each datum represents an ethnic group i in a given year t. Below, I offer a detailed account for the oper- ationalization of the outcome and independent variables, and the methodological approach.

The data for ethnic groups are from the Minorities at Risk (MAR) data-set and Sorens (2010). The MAR is the most comprehensive time-series cross-section data-set on ethnic politics, which provides data for 361 groups for the 1950–2003 period. The ethnic groups included in the data-set meet one or both of two general criteria: (1) ‘the group collectively suffers, or benefits from, systematic discriminatory treatment vis-à-vis other groups in society’; and (2) ‘the group is the basis of political mobilization and action in defense or promotion of its self-defined interests’ (Gurr 2000, 7). The data-set includes groups that have more than 100,000 people or are at least 1% of the country population. Ethnic groups living in countries with populations less than 500,000 were excluded from the MAR project.

One main concern scholars (e.g. Fearon 2003; Sorens 2010) raise about the MAR project is its potentially biased selection method of ethnic groups: an ethnic group must collectively suffer or benefit from systematic discrimination to be included in the data-set. One way to minimize the sample selection problem is to model selection into the MAR by gathering additional data on a randomly selected sample of ethnic groups that are not included in the data-set. Fortunately, these additional data are available from Sorens (2010) who recently gathered original data for 620 additional ethnic groups for the 1950–2003 period. Sorens does not annually code the discrimination variables for these ethnic groups. He offers data only on various demographic, institutional, and historical characteristics of the ethnic groups. Hence, I use the data from Sorens only in the selection stage of the Heckman mod- els discussed below.

To model selection into the MAR data-set, I estimate a two-stage econometric model using data from the MAR project and Sorens (2010). Specifically, I run a dynamic

ECONOMIC SANCTIONS AND OFFICIAL ETHNIC DISCRIMINATION 485

Heckman selection model, in which the estimated mean function in the second stage (out- come equation) is conditioned on the selection process of the first stage (Heckman 1979). In the first stage, I analyze the data for all ethnic groups that could possibly face official dis- crimination. In the second stage (outcome equation), I consider the selected sample to esti- mate the determinants of ethnic discrimination. Since the outcome variables that I use in the second stage are dichotomous, a standard Heckman model would be inconsistent and biased: the standard Heckman selection model requires the use of a probit estimator in the selection stage and an ordinary least squares estimator in the outcome stage. Instead, I employ a modified Heckman selection procedure (Heckman probit selection model), which allows me to run a probit estimator in both stages.

One major restriction that I apply to ethnic group data gathered from MAR and Sorens (2010) is to exclude politically dominant ethnic groups from the data analysis. I do so because politically dominant groups often control the government and benefit from the poor treatment of other ethnic groups. Hence, they are the least likely victims of official ethnic discrimination. An ethnic group is considered politically dominant if it satisfies one of the following criteria: ‘(a) the country has a Polity IV regime (‘democracy’ minus ‘autocracy’) score of at least 6, and the group is a majority within the country, unless the country’s law has explicit provisions requiring interethnic governments, such as Belgium and Lebanon; (b) the country has a single-party dominant system, whether democracy or autocracy, and the group controls that dominant party; (c) the country has a regime score below 6, and the leader of the country and most members of the government belong to the group. When dominant status changes during a year, the group is coded as not dominant for that year’ (Sorens 2010, 549).

In the outcome equation of the Heckman selection model, I use two different outcome variables: Ethnic Discrimination and Political Discrimination.4 The Economic Discrimina- tion variable is coded 1 for the years that an ethnic group faces systematic discrimination by government actors and 0 otherwise. Similarly, the Political Discrimination variable was coded 1 for the years an ethnic group faced political discriminatory practices by the govern- ment and 0 otherwise. An ethnic group is considered facing official economic discrimina- tion if ‘public policies (formal exclusion and/or recurring repression) substantially restrict the group’s opportunity by contrast with others’ (Gurr 2000, 110). An ethnic group is con- sidered contending with official political discrimination if ‘public policies substantially restrict the group’s political participation relative to other groups’ (Gurr 2000, 114). During the time period of the analysis, the data-set includes 1621 group years (6% of the total groups years) in which an ethnic group faced official economic discrimination. The number of group years in which an ethnic group faced political discrimination is 2694 (10% of the total group years).

To test the hypotheses on the effect that economic sanctions have on official ethnic dis- crimination, I use the economic sanctions data from Hufbauer et al. (2007). Economic sanc- tions refer to the deliberate, government-led restrictions of exports, imports, and the flow of finance (commercial finance, bilateral aid, or the International Monetary Fund or the Word Bank funds) that includes such specific measures as tariffs, import duties, investment banks, asset freezes, reduction or suspension of military aid, restrictions on limited dual-use tech- nologies, and travel bans on target countries’ officials.

The first sanctions variable, Economic Sanctions, takes the value of 1 for all ethnic-group years in a target country and 0 otherwise. In addition, I use an Economic Sanctions*

4It is worth noting that the dichotomous discrimination variables account for the presence of discrimination and hence do not capture a possible increase or decrease in the extent of ethnic discrimination.

486 D. PEKSEN

Democracy interaction term to account for the possible conditioning effect of the regime type of the target state. Even when faced with foreign economic pressures, democratic regimes might be more restricted in their ability to pursue discriminatory policies against ethnic groups. This is because the elected leaders are constrained by several institutional mechanisms such as the rule of law and an efficient checks-and-balances system (Mitchel and McCormick 1988; Poe, Tate, and Keith 1999; Zanger 2000). Autocratic target regimes, on the other hand, are more likely to disregard rules and norms protecting minority rights along with other basic human rights since they are less accountable and not constrained by strong institutional mechanisms. Thus, I expect that sanctions are likely to cause a higher likelihood of ethnic discrimination in non-democratic target countries.

The suggested impact of sanctions on ethnic discrimination might also be conditioned by the economic impact of sanctions. Following the earlier, similar practice (Hufbauer et al. 1997; Hufbauer and Oegg 2003; Peksen 2009) to account for the severity of the coercion, I created three additional sanction variables: Extensive Sanctions, Moderate Sanctions, and Limited Sanctions. The extensive sanctions category includes comprehensive sanctions that cut any economic and financial transactions between the sender and target countries such as those against Cuba, North Korea, and the former Yugoslavia. Due to the complete or near complete cut of economic ties between the sender and target countries, comprehensive sanc- tion regimes are expected to be more detrimental to the target’s domestic stability than mod- erate (partial financial and trade sanctions) or limited sanctions (denial of military and economic assistance, asset freezes, and travel restrictions).

To explore whether sanctions collectively imposed by multiple countries are different from unilateral sanctions, I created the Multilateral Sanctions and Unilateral Sanctions dummy variables. The Multilateral Sanctions variable takes the value of 1 for sanctions imposed by multiple countries with or without the involvement of international organiza- tions and 0 otherwise. The Unilateral Sanctions variable is coded 1 for sanctions levied by individual countries and 0 otherwise. It is likely that multilateral sanctions are more harmful to the well-being of ethnic groups since multiple countries are putting pressure on target countries. The involvement of multiple countries in a sanction regime could potentially lead to more economic damage on target countries by reducing the extent of sanctions-busting by third party countries.5 Multilateral sanctions that considerably isolate the target regime from global economic and socio-political forces might also create more incentive for the government to become repressive to survive the sanctions.

To explore whether the stated goal of sanctions have any particular effect on the probabil- ity of ethnic discrimination, I created two dichotomous variables. The Human Rights Sanc- tions variable is coded 1 if a target country is facing sanctions because of its repressive policies and 0 otherwise. The Non-human Rights Sanctions variable takes the value of 1 when sanctions are initiated with non-human rights policy objectives, and 0 otherwise. The human rights sanctions variable accounts for sanction episodes in which the primary goal of sanctioning countries is at least one of the following: to prevent the use of repressive measures by target regimes against ethnic and other disadvantaged groups, to encourage better functioning of democratic procedures such as the conduct of free and fair elections or functioning of political parties and other grassroots organizations, and to restore failed democracies.

5Some sanction regimes imposed by multiple countries might not cause significant damage on the target economy because of sanctions-busting through overt or illicit trade between the target and third-party actors (Early 2009, 2011).

ECONOMIC SANCTIONS AND OFFICIAL ETHNIC DISCRIMINATION 487

To account for the effect of democratic freedoms and accountability on the likelihood of ethnic discrimination, I include a Democracy variable. The Democracy variable is the polity score derived from the Polity IV data-set (Marshall and Jaggers 2002) and each country’s democracy score ranges from −10 to 10, where 10 represents the highest level of democ- racy. As suggested, democratic states are less likely to carry out discriminatory government practices against ethnic groups. This is because democratically elected governments are more constrained through numerous institutional mechanisms such as the checks and bal- ances system and the removal of the government through the popular vote (Mitchell and McCormick 1988; Henderson 1991; Poe, Tate, and Keith 1999; Zanger 2000).

To control for the impact of national income, I include the natural log of GDP per Capita. Economic development might reduce the likelihood of ethnic discrimination since the benefits of economic prosperity would be diffused across different segments of society. Poverty and economic underdevelopment, on the other hand, might induce the government to resort to ethnic discrimination by limiting less privileged groups’ access to the scarce public resources and services. The data for economic development are from Gleditisch (2002).

In order to account for the level of Economic Openness, I include the natural log of total trade flows as a percentage of GDP. Economic globalization might promote more respect for ethnic minority rights, along with other human rights, by spreading the ideas and norms of human rights via transnational economic exchanges, communication, and participation in international movements and organizations in the global economy (Brunn and Leinback 1991; Keck and Sikkink 1998). Further, as economic openness improves living standards via achieving higher economic growth, disadvantaged groups might have greater access to economic opportunities and public resources.

Following Sorens (2010), I also include four group-specific variables that might affect the probability of ethnic discrimination against a particular group. These binary measures account for whether an ethnic group enjoys autonomous powers (Autonomous Power); dominates a particular region (Regional Base); immigrated after 1800 (Immigration since 1800); and is a minority in an autonomous region (Minority in Autonomous Region). Sorens (2010) hypothesizes that an ethnic group that enjoys autonomous powers or dominates a particular region might suffer less from systematic discrimination. Similarly, an ethnic group that has a long historical presence in a country might be less susceptible to discrimi- nation than a more recent immigrant group. If an ethnic group is a minority in an autono- mous region, the group members might become more frequent targets of economic and political discrimination. I use the Time Trend variable, which simply measures the calendar year, to examine whether official ethnic discrimination has become more or less likely over time.

Diagnostic tests reveal that both dependent variables have a significant, strong autore- gressive process, a common issue when time-series cross-sectional data are utilized (Beck and Katz 1995). To correct for the autoregressive process (temporal dependence), I include the Non-discrimination Years (i.e. number of years since the last discrimination incidence occurred) variable and three cubic splines as suggested by Beck, Katz, and Tucker (1998). To control for panel heterosdeskasticity present in the model, I use the Huber-White cor- rected robust standard errors. To control for possible unobserved country-specific effects resulting from unique historical and institutional factors, I include dichotomous country variables (i.e. country dummies). Finally, I use a one-year lag (t − 1) of the time-variant variables to mitigate any concerns with simultaneity bias. Specifically, lagged-IVs allow me to make sure that the independent variables temporally precede the dependent variables and thus eliminate any incorrect direction of inference.

488 D. PEKSEN

To model selection into the MAR project, following Sorens (2010), I include several group and country-specific variables in the selection equation. To examine the role of primary group identity in predicting selection, I include five mutually exclusive dichoto- mous variables: ancestral language (reference category), religion, race or national origin, past autonomy or independence, and ‘other’ (geographic, socioeconomic, ideological, etc.). To account for the effect of group-specific demographic factors, I control for the following four variables: group population (logged); group percentage of the country’s population; a dummy variable coded one if an ethnic group is at least 50% of the coun- try population; and a dummy variable to control for the MAR’s group population crite- rion for inclusion (coded one for group-years with group populations more than 100,000 or 1% of the country’s population). The selection equation also includes the natural log of country population and a dichotomous variable to control for the MAR’s country pop- ulation criterion for inclusion (coded one for group-years with country populations greater than 500,000).

To account for the role of possible unobserved region-specific effects in predicting selec- tion, I control for five world region dummies: Asia-Pacific, Former Soviet Union-Eastern Europe, Middle East-North Africa, Latin America-Caribbean, Sub-Saharan Africa, and Western Democracies-Japan (reference category). The selection equation also includes a dummy variable for groups that immigrated after 1945 and a time variable that counts the number of years since 1945. I also include the following variables from the outcome equa- tion: GDP per capita, Democracy, Economic Openness, and Immigration since 1800.

5. FINDINGS

Table I shows the frequencies of economic and political discrimination in the sanctioned and non-sanctioned countries. The number of sanction years with economic discrimination is 576. This accounts for 28% of all sanction years. The number of years with economic discrimination in non-target countries is 1055, which accounts for only 13% of all non- sanction years. The number of sanction years with political discrimination is 797, which is 38% of all sanction cases. The number of non-sanction years with political discrimination is 1908, which accounts for 23% of all non-sanction years. The difference between sanctioned and non-sanctioned cases for both economic and political discrimination is statistically sig- nificant (p-value = 0.001). Hence, according to this preliminary analysis, there is a strong possibility that ethnic-based discrimination is on average higher in sanctioned countries than non-sanctioned countries.

Table II reports the data findings from the models using the economic discrimination var- iable as the outcome measure.6 Models 1–5 show the results from the outcome equation of the selection-corrected models estimated with the Heckman procedure.7 In Models 6–10, I run independent probit models without the selection-correction procedure to check the robustness of the findings to the choice of the estimation technique. I show the results from individual probit models also because the rho coefficients reported at the bottom of the Heckman models in Tables II and IV are not statistically insignificant. This indicates a weak possibility that the errors in the selection and outcome equations are correlated (i.e. selection bias). That is, it is unlikely that there are significant unobserved factors affecting

6Diagnostic tests reveal that there was no issue with multicolinearity in any of the estimations. 7The results from the selection equation of the first models from Tables II and IV appear in Table VI below.

ECONOMIC SANCTIONS AND OFFICIAL ETHNIC DISCRIMINATION 489

selection that also influence ethnic discrimination. Hence, there appears to be no strong sample selection problem in the original MAR data-set.

The sanctions variable in the first and sixth models in Table II is statistically significant, indicating higher likelihood of ethnic-based economic discrimination in the targeted countries. The sanctions-democracy interaction term in Models 2 and 7 is statistically significant. This suggests that the regime type of the target is likely to condition the nega- tive impact of sanctions on ethnic groups’ economic well-being. That is, ethnic groups in non-democratic regimes are likely to suffer more from the sanctions than the groups in democratic politics. According to the estimates shown in Models 3 and 8, sanctions increase the probability of economic discrimination when they are levied unilaterally by individual countries or collectively through the involvement of multiple sender countries.

The results for the sanctions variable in Models 4 and 9 reveal that foreign economic punishments are detrimental to the economic status of ethnic groups, especially when they are extensive or moderate sanctions. Limited sanctions with trivial costs to the target’s econ- omy, on the other hand, appear to have no statistically significant effect on the probability of economic discrimination against ethnic minorities. Thus, it appears that the comprehen- siveness of economic coercion is important in determining when sanctions become more harmful to ethnic groups. The results in Models 5 and 10 suggest that both human rights and non-human rights sanctions are likely to increase the possibility of economic discrimi- nation. Overall, the results reported in Table I show robust statistical support for the hypoth- esized relationship between economic sanctions and the likelihood of official economic discrimination.

Figure 1 and Table III report the substantive impact of the sanctions variables and the other significant covariates of economic discrimination using the estimates in Models 6–10 in Table II. Specifically, I examine the change in the predicted probability of ethnic-based economic discrimination once I increase the dichotomous variables from 0 to 1 and the average value of the continuous variables by one standard deviation, holding all other continuous variables at their means and the binary variables at their medians.8 Figure 1 uses

TABLE I Economic Sanctions and Frequencies of Ethnic Discrimination

Economic discrimination

Non-sanction years Sanction years

No discrimination 7222 (87%) 1503 (72%)

Discrimination 1055 (13%) 576 (28%)

Total 8277 2079

Pearson chi2(1) = 280.2413, Pr = 0.000

Political discrimination

Non-sanction years Sanction years

No discrimination 6387 (77%) 1282 (62%)

Discrimination 1908 (23%) 797 (38%)

Total 8295 2079

Pearson chi2(1) = 202.7767, Pr = 0.000

8SPost Stata ado files by Long and Freese (2004) are used for the post-estimation interpretation of regression models for categorical outcomes.

490 D. PEKSEN

the estimates in the seventh model in Table II. According to the figure, the effect of sanctions on the predicted probability of economic discrimination is significantly condi- tioned by the level of democracy in target countries. Ethnic groups in autocratic target countries (the regimes with a polity score of −6 or lower) appear to suffer more from exter- nal sanctions than the groups in the regimes with a polity score between −6 and 6 (mixed regimes) or a polity score of 6 or higher (democracies). Further, when we compare a tar- geted country with a non-targeted country that has the same level of democracy, the figure shows that the predicted probability of economic discrimination is considerably higher in the target than the non-targeted country.

According to Table III, the predicted probability of ethnic-based economic discrimination increases by 94% when a country becomes a target of economic sanctions, holding all other continuous variables at their means and the binary variables at their medians. Unilateral and multilateral sanctions variables increase the predicted probability of economic discrimina- tion by 144 and 211%, respectively. When a country is targeted with extensive or moderate sanctions, the predicted probability of economic discrimination goes up by 205 and 105%, respectively. Human rights and non-human rights sanctions variables increase the predicted probability of economic discrimination by 100 and 94%, respectively.

A one standard deviation increase in the average value of the democracy measure decreases the predicted probability of economic discrimination by 44%. When the value of the GDP variable is increased by one standard deviation and the value of the immi- gration variable is shifted from 0 to 1, the predicted probability of economic discrimina- tion increases by 44 and 66%, respectively. When the autonomous power is changed from 0 to 1, the predicted probability of economic discrimination goes down by 22%. The result on the GDP variable appears to be counterintuitive. It is possible that more economic wealth in multiethnic countries might create new economic opportunities and incentives among ethnic groups to compete over the economic resources. This might, in

0 .0

1 .0

2 .0

3 .0

4 .0

5

P re

di ct

ed P

ro ba

bi lit

y

Autocracy Mixed Regimes Democracy

No Sanctions Sanctions 95% Confidence Interval

FIGURE 1 Predicted probability of official economic discrimination

ECONOMIC SANCTIONS AND OFFICIAL ETHNIC DISCRIMINATION 491

turn, prompt the dominant ethnic group to use repressive measures against the rival groups in society.

Table IV reports the findings of the data analysis using the political discrimination variable as the outcome measure. According to the results in Models 1 and 6, economic sanctions in general are significantly correlated with the risk of political discrimination against ethnic groups in target countries. The results in Models 2 and 7 indicate that the hypothesized relationship between sanctions and political discrimination is not condi- tioned by the regime type of the target state. The findings reported in Models 3 and 8 suggest that sanctions are likely to deteriorate the political status of ethnic groups whether they are imposed by multiple sender countries or unilaterally initiated by indi- vidual countries. In Models 4 and 8, I find that all three types of sanctions – extensive, moderate, and limited sanctions – that control for the comprehensiveness of sanctions are statistically significant in predicting the likelihood of official political discrimination. The results in Models 5 and 10 show that both human rights and non-human rights sanctions are detrimental to political rights of ethnic groups. Hence, the findings in Table IV provide strong support for the hypothesized effect of sanctions on the political status of ethnic groups.

In Table V, I report the substantive impact of the sanctions variables and the other signif- icant covariates of ethnic-based political discrimination using the estimates in Models 6–10 in Table IV. When I altered the economic sanctions (all) variable from 0 to 1, I find a 53% increase in the predicted probability of ethnic-based political discrimination, holding all other continuous variables at their means and the binary variables at their medians. The imposition of unilateral sanctions increases the predicted probability of political discrimina- tion by 65%, while the presence of multilateral sanctions results in a 136% increase in the initial predicted probability of the outcome variable.

The predicted probability of political discrimination goes up considerably when the impo- ser state(s) employs extensive or moderate sanctions (90% for extensive sanctions and 38% for moderate sanctions). Limited sanctions, on the other hand, raise the probability of politi- cal discrimination by 66%. Limited sanctions appear to have a substantively more signifi- cant impact on political discrimination than moderate sanctions. This could be because limited sanctions such as asset freezes and travel bans might be the types of sanctions that directly target the political leadership, which might create more political incentives for lead- ers to commit repression to show their resolve against foreign pressure. Human rights sanc- tions and non-human rights sanctions increase the predicted probability of political discrimination by 43 and 64%, respectively.

Among the other significant covariates of political discrimination reported in Table IV, a one standard deviation increase in the democracy and economic openness variables decrease the predicted probability of political discrimination by 46 and 23%, respectively. A change in the value of the immigration variable form 0 to 1 increases the predicted value of the out- come variable by 53%, while the move from 0 to 1 for the autonomous power and regional base variables decrease the predicted value of political discrimination by 69 and 17%, respectively. Similar to the results in Table III, the GDP variable in Table IV has a positive impact on the likelihood of political discrimination: a one standard change increase in the average value of the GDP measure increases the probability of political discrimination by 22%.

Finally, Table VI shows the results from the selection equations of the Heckman mod- els. I report only the selection equations for the first models in Tables II and IV since the results from each of the selection models are very similar to one another. The find- ings suggest that the variables that control for the country-specific demographic, political,

492 D. PEKSEN

T A B L E II

E co n o m ic S an ct io n s an d E co n o m ic D is cr im

in at io n o f E th n ic

G ro u p s, 1 9 5 0 – 2 0 0 3

S el ec ti o n -c o rr ec te d p ro b it m o d el s

In d iv id u al

p ro b it m o d el s

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6 M o d el

7 M o d el

8 M o d el

9 M o d el

1 0

S an ct io n s (a ll )

0 .2 7 2 * *

0 .2 4 5 * *

0 .2 8 1 * *

0 .2 5 9 * *

(0 .0 8 9 )

(0 .0 9 1 )

(0 .0 8 8 )

(0 .0 8 9 )

S an ct io n s × d em

o cr ac y

− 0 .0 3 0 *

− 0 .0 2 7 *

(0 .0 1 2 )

(0 .0 1 2 )

U n il at er al

sa n ct io n s

0 .3 8 5 * *

0 .3 9 2 * * *

(0 .1 1 9 )

(0 .1 1 8 )

M u lt il at er al

sa n ct io n s

0 .5 0 2 * *

0 .5 0 8 * *

(0 .1 9 5 )

(0 .1 9 2 )

E x te n si v e sa n ct io n s

0 .4 9 1 *

0 .4 8 9 *

(0 .2 0 9 )

(0 .2 0 5 )

M o d er at e sa n ct io n s

0 .2 9 0 *

0 .3 0 2 *

(0 .1 4 0 )

(0 .1 4 0 )

L im

it ed

sa n ct io n s

0 .1 4 8

0 .1 6 4

(0 .1 3 1 )

(0 .1 2 9 )

H u m an

ri g h ts sa n ct io n s

0 .2 6 7 +

0 .2 8 9 +

(0 .1 5 7 )

(0 .1 5 5 )

N o n -h u m an

ri g h ts sa n ct io n s

0 .2 7 5 * *

0 .2 7 6 * *

(0 .0 9 8 )

(0 .0 9 7 )

D em

o cr ac y

− 0 .0 3 1 * * *

− 0 .0 2 6 * * *

− 0 .0 3 1 * * *

− 0 .0 3 0 * * *

− 0 .0 3 1 * * *

− 0 .0 3 3 * * *

− 0 .0 2 9 * * *

− 0 .0 3 3 * * *

− 0 .0 3 3 * * *

− 0 .0 3 3 * * *

(0 .0 0 6 )

(0 .0 0 6 )

(0 .0 0 6 )

(0 .0 0 6 )

(0 .0 0 6 )

(0 .0 0 6 )

(0 .0 0 6 )

(0 .0 0 6 )

(0 .0 0 6 )

(0 .0 0 6 )

E co n o m ic

o p en n es s

− 0 .0 5 0

− 0 .0 4 5

− 0 .0 5 0

− 0 .0 4 3

− 0 .0 5 0

− 0 .0 6 4

− 0 .0 6 1

− 0 .0 6 4

− 0 .0 5 8

− 0 .0 6 5

(0 .0 6 3 )

(0 .0 6 3 )

(0 .0 6 3 )

(0 .0 6 2 )

(0 .0 6 3 )

(0 .0 6 3 )

(0 .0 6 3 )

(0 .0 6 3 )

(0 .0 6 3 )

(0 .0 6 3 )

G D P p er

C ap it a

0 .1 7 9 * * *

0 .1 6 4 * * *

0 .1 7 2 * * *

0 .1 8 4 * * *

0 .1 7 9 * * *

0 .1 3 2 * *

0 .1 2 0 * *

0 .1 2 3 * *

0 .1 3 7 * *

0 .1 3 2 * *

(0 .0 4 5 )

(0 .0 4 5 )

(0 .0 4 6 )

(0 .0 4 6 )

(0 .0 4 5 )

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

M in o ri ty

in au to n . re g io n

− 0 .0 6 7

− 0 .0 5 6

− 0 .0 6 0

− 0 .0 6 1

− 0 .0 6 7

− 0 .0 4 2

− 0 .0 3 1

− 0 .0 3 6

− 0 .0 3 7

− 0 .0 4 2

(0 .0 9 2 )

(0 .0 9 1 )

(0 .0 9 1 )

(0 .0 9 1 )

(0 .0 9 1 )

(0 .0 9 1 )

(0 .0 9 1 )

(0 .0 9 1 )

(0 .0 9 1 )

(0 .0 9 1 )

A u to n o m o u s p o w er

− 0 .0 9 0 +

− 0 .0 8 7 +

− 0 .0 8 4 +

− 0 .0 8 9 +

− 0 .0 9 0 +

− 0 .1 2 2 *

− 0 .1 2 0 *

− 0 .1 1 6 *

− 0 .1 2 2 *

− 0 .1 2 2 *

(0 .0 4 9 )

(0 .0 4 8 )

(0 .0 4 8 )

(0 .0 4 9 )

(0 .0 4 9 )

(0 .0 4 8 )

(0 .0 4 7 )

(0 .0 4 7 )

(0 .0 4 8 )

(0 .0 4 8 )

(C o n ti n u ed )

ECONOMIC SANCTIONS AND OFFICIAL ETHNIC DISCRIMINATION 493

T A B L E II

(C o n ti n u ed

)

S el ec ti o n -c o rr ec te d p ro b it m o d el s

In d iv id u al

p ro b it m o d el s

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6 M o d el

7 M o d el

8 M o d el

9 M o d el

1 0

R eg io n al

b as e

0 .0 0 9

0 .0 1 4

0 .0 1 0

0 .0 1 2

0 .0 0 9

− 0 .0 2 3

− 0 .0 1 8

− 0 .0 2 4

− 0 .0 2 0

− 0 .0 2 3

(0 .0 6 8 )

(0 .0 6 8 )

(0 .0 6 8 )

(0 .0 6 8 )

(0 .0 6 8 )

(0 .0 6 7 )

(0 .0 6 7 )

(0 .0 6 7 )

(0 .0 6 7 )

(0 .0 6 7 )

Im m ig ra ti o n si n ce

1 8 0 0

0 .1 9 2 *

0 .1 9 0 *

0 .1 9 3 *

0 .1 9 4 *

0 .1 9 2 *

0 .2 0 2 * *

0 .2 0 0 *

0 .2 0 4 * *

0 .2 0 5 * *

0 .2 0 2 *

(0 .0 7 8 )

(0 .0 7 8 )

(0 .0 7 8 )

(0 .0 7 8 )

(0 .0 7 8 )

(0 .0 7 8 )

(0 .0 7 8 )

(0 .0 7 9 )

(0 .0 7 8 )

(0 .0 7 8 )

T im

e tr en d

− 0 .0 0 2

− 0 .0 0 1

− 0 .0 0 2

− 0 .0 0 2

− 0 .0 0 2

− 0 .0 0 3

− 0 .0 0 2

− 0 .0 0 3

− 0 .0 0 3

− 0 .0 0 3

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

N o n -d is cr im

in at io n y ea rs

− 0 .8 4 3 * * *

− 0 .8 4 2 * * *

− 0 .8 4 3 * * *

− 0 .8 4 2 * * *

− 0 .8 4 3 * * *

− 0 .8 4 5 * * *

− 0 .8 4 4 * * *

− 0 .8 4 4 * * *

− 0 .8 4 4 * * *

− 0 .8 4 6 * * *

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

(0 .0 4 4 )

L o g -p se u d o li k el ih o o d

− 1 5 2 7 6 .8 8

− 1 5 2 7 3 .7 7

− 1 5 2 7 2 .9 0

− 1 5 2 7 6 .1 3

− 1 5 2 7 6 .8 8

− 1 2 6 2 .1 9

− 1 2 5 9 .5 3

− 1 2 5 8 .1 5

− 1 2 6 1 .5 2

− 1 2 6 2 .1 9

C h i- sq u ar e

3 9 5 9 .3 9

4 5 0 2 .3 5

3 9 1 6 .8 8

3 9 5 9 .2 9

3 9 7 5 .1 3

2 8 3 0 .0 9

2 7 9 4 .8 7

2 7 9 1 .2 0

2 8 3 2 .2 3

2 8 4 7 .1 3

N (t o ta l)

2 6 ,5 9 3

2 6 ,5 9 3

2 6 ,5 9 3

2 6 ,5 9 3

2 6 ,5 9 3

1 0 ,3 5 6

1 0 ,3 5 6

1 0 ,3 5 6

1 0 ,3 5 6

1 0 ,3 5 6

N (s el ec te d )

1 0 ,0 5 9

1 0 ,0 5 9

1 0 ,0 5 9

1 0 ,0 5 9

1 0 ,0 5 9

ρ − 0 .0 2 9

− 0 .0 3 6

− 0 .0 1 9

− 0 .0 2 7

− 0 .0 2 9

(0 .0 7 8 )

(0 .0 7 6 )

(0 .0 7 9 )

(0 .0 7 9 )

(0 .0 7 8 )

N o te s: R o b u st st an d ar d er ro rs

ap p ea r in

p ar en th es es . C o u n tr y d u m m ie s, co n st an ts an d cu b ic

sp li n es

fo r te m p o ra l d ep en d en ce

n o t sh o w n . D o m in an t m in o ri ti es

ex cl u d ed . A ll ti m e- v ar ia n t in d ep en d en t v ar ia b le s

la g g ed

o n e y ea r.

* * * p < 0 .0 0 1 .

* * p < 0 .0 1 .

* p < 0 .0 5 .

+ p < 0 .1 .

494 D. PEKSEN

and economic factors are statistically significant in predicting selection. Only one coun- try-level variable – economic openness – is not statistically significant in the selection equations. The variables that account for group-specific factors are also statistically significant in predicting selection. The only group-specific factor that is not statistically significant is the race variable.

5.1. Additional analyses and robustness checks

The data analysis might suffer from possible selection effects resulting from the sanctions sample. Since it is not possible to check the robustness of the findings to possible selection effects resulting from the sanctions sample (i.e. target countries) with the group-level data, I gathered country-level time-series cross-section data. I used the new data to run two-stage error correction models (Heckman-selection models). The rho coefficients in the country- level Heckman models are mostly insignificant indicating that it is unlikely that unobserved factors that affect the sanctions sample also influence ethnic discrimination (i.e. no selection bias). The equation in the first stage of the Heckman models uses a sanctions dummy as an outcome variable and includes several covariates of sanctions (democracy, GDP per capita, economic openness, civil wars, time trend, and past sanction dummy) on the right-hand side of the equation. The second stage (outcome equation) uses two different dichotomous dis- crimination variables – economic discrimination and political discrimination – as the out- come variables. These variables are coded 1 if one or multiple ethnic groups face economic or political discrimination in a country in a given year and 0 otherwise. The equation in the

TABLE III Predicted Probabilities of Official Economic Discrimination

Pr (economic discrimination = 1) Baseline (Initial value) Unit change New value (%Δ)

Sanctions (all) 0.018 0 → 1 0.035 (94%)

Unilateral sanctions 0.018 0 → 1 0.044 (144%)

Multilateral sanctions 0.018 0 → 1 0.056 (211%)

Extensive sanctions 0.018 0 → 1 0.055 (205%)

Moderate sanctions 0.018 0 → 1 0.037 (105%)

Hum. rights sanctions 0.018 0 → 1 0.036 (100%)

Non-hum. rights sanctions 0.018 0 → 1 0.035 (94%)

Democracy 0.018 Mean + 1σ 0.010 (-44%)

GDP per Capita 0.018 Mean + 1σ 0.026 (44%)

Autonomous power 0.018 0 → 1 0.014 (−22%)

Immigration since 1800 0.018 0 → 1 0.030 (66%)

ECONOMIC SANCTIONS AND OFFICIAL ETHNIC DISCRIMINATION 495

T A B L E IV

E co n o m ic

S an ct io n s an d P o li ti ca l D is cr im

in at io n o f E th n ic

G ro u p s, 1 9 5 0 – 2 0 0 3

S el ec ti o n -c o rr ec te d p ro b it m o d el s

In d iv id u al

p ro b it m o d el s

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6 M o d el

7 M o d el

8 M o d el

9 M o d el

1 0

S an ct io n s (a ll )

0 .2 5 4 * * *

0 .2 5 3 * * *

0 .2 6 6 * * *

0 .2 6 6 * * *

(0 .0 6 1 )

(0 .0 6 1 )

(0 .0 6 0 )

(0 .0 6 0 )

S an ct io n s × d em

o cr ac y

− 0 .0 0 1

− 0 .0 0 0 1

(0 .0 0 8 )

(0 .0 0 8 )

U n il at er al

sa n ct io n s

0 .3 0 2 * * *

0 .3 0 7 * * *

(0 .0 7 4 )

(0 .0 7 4 )

M u lt il at er al

sa n ct io n s

0 .5 5 2 * * *

0 .5 5 8 * * *

(0 .1 6 2 )

(0 .1 6 2 )

E x te n si v e sa n ct io n s

0 .3 7 4 * *

0 .3 9 9 * *

(0 .1 3 6 )

(0 .1 3 7 )

M o d er at e sa n ct io n s

0 .1 8 5 *

0 .1 9 4 *

(0 .0 8 9 )

(0 .0 8 9 )

L im

it ed

sa n ct io n s

0 .3 0 8 * * *

0 .3 1 7 * * *

(0 .0 9 1 )

(0 .0 9 0 )

H u m an

ri g h ts sa n ct io n s

0 .1 9 7 *

0 .2 1 5 *

(0 .0 9 2 )

(0 .0 9 1 )

N o n -h u m an

ri g h ts sa n ct io n s

0 .2 9 6 * * *

0 .3 0 3 * * *

(0 .0 7 4 )

(0 .0 7 3 )

D em

o cr ac y

− 0 .0 4 2 * * *

− 0 .0 4 2 * * *

− 0 .0 4 2 * * *

− 0 .0 4 2 * * *

− 0 .0 4 2 * * *

− 0 .0 4 5 * * *

− 0 .0 4 4 * * *

− 0 .0 4 5 * * *

− 0 .0 4 5 * * *

− 0 .0 4 4 * * *

(0 .0 0 4 )

(0 .0 0 4 )

(0 .0 0 4 )

(0 .0 0 4 )

(0 .0 0 4 )

(0 .0 0 4 )

(0 .0 0 4 )

(0 .0 0 4 )

(0 .0 0 4 )

(0 .0 0 4 )

E co n o m ic

o p en n es s

− 0 .3 3 8 * * *

− 0 .3 3 8 * * *

− 0 .3 4 8 * * *

− 0 .3 3 4 * * *

− 0 .3 3 7 * * *

− 0 .3 3 1 * * *

− 0 .3 3 1 * * *

− 0 .3 4 0 * * *

− 0 .3 2 6 * * *

− 0 .3 3 0 * * *

(0 .0 5 6 )

(0 .0 5 6 )

(0 .0 5 7 )

(0 .0 5 5 )

(0 .0 5 6 )

(0 .0 5 4 )

(0 .0 5 4 )

(0 .0 5 5 )

(0 .0 5 3 )

(0 .0 5 4 )

G D P p er

C ap it a

0 .1 4 1 * * *

0 .1 4 1 * * *

0 .1 3 7 * * *

0 .1 3 9 * * *

0 .1 3 9 * * *

0 .1 1 0 * * *

0 .1 1 0 * * *

0 .1 0 5 * * *

0 .1 0 7 * * *

0 .1 0 7 * * *

(0 .0 3 2 )

(0 .0 3 2 )

(0 .0 3 2 )

(0 .0 3 2 )

(0 .0 3 2 )

(0 .0 3 0 )

(0 .0 3 0 )

(0 .0 3 1 )

(0 .0 3 1 )

(0 .0 3 1 )

M in o ri ty

in au to n . re g io n

0 .0 0 9

0 .0 0 9

0 .0 1 0

0 .0 1 6

0 .0 11

0 .0 3 4

0 .0 3 4

0 .0 3 5

0 .0 4 0

0 .0 3 5

(0 .0 6 9 )

(0 .0 6 9 )

(0 .0 6 9 )

(0 .0 6 9 )

(0 .0 6 9 )

(0 .0 6 7 )

(0 .0 6 7 )

(0 .0 6 7 )

(0 .0 6 7 )

(0 .0 6 7 )

A u to n o m o u s p o w er

− 0 .2 4 6 * * *

− 0 .2 4 6 * * *

− 0 .2 3 7 * * *

− 0 .2 5 0 * * *

− 0 .2 4 8 * * *

− 0 .2 7 4 * * *

− 0 .2 7 4 * * *

− 0 .2 6 6 * * *

− 0 .2 7 8 * * *

− 0 .2 7 6 * * *

(0 .0 3 8 )

(0 .0 3 8 )

(0 .0 3 7 )

(0 .0 3 8 )

(0 .0 3 8 )

(0 .0 3 7 )

(0 .0 3 7 )

(0 .0 3 7 )

(0 .0 3 8 )

(0 .0 3 8 )

R eg io n al

b as e

− 0 .0 6 5

− 0 .0 6 5

− 0 .0 6 4

− 0 .0 6 6

− 0 .0 6 5

− 0 .1 0 4 *

− 0 .1 0 4 *

− 0 .1 0 3 *

− 0 .1 0 5 *

− 0 .1 0 4 *

(0 .0 5 2 )

(0 .0 5 2 )

(0 .0 5 2 )

(0 .0 5 2 )

(0 .0 5 2 )

(0 .0 5 0 )

(0 .0 5 0 )

(0 .0 5 0 )

(0 .0 5 0 )

(0 .0 5 0 )

Im m ig ra ti o n si n ce

1 8 0 0

0 .2 4 4 * * *

0 .2 4 4 * * *

0 .2 3 8 * * *

0 .2 4 4 * * *

0 .2 4 5 * * *

0 .2 5 6 * * *

0 .2 5 6 * * *

0 .2 4 9 * * *

0 .2 5 6 * * *

0 .2 5 7 * * *

(0 .0 5 8 )

(0 .0 5 8 )

(0 .0 5 8 )

(0 .0 5 8 )

(0 .0 5 8 )

(0 .0 5 8 )

(0 .0 5 8 )

(0 .0 5 8 )

(0 .0 5 8 )

(0 .0 5 8 )

496 D. PEKSEN

T im

e tr en d

− 0 .0 0 0 3

− 0 .0 0 0 3

− 0 .0 0 0 4

− 0 .0 0 0 4

− 0 .0 0 0 1

− 0 .0 0 1

− 0 .0 0 1

− 0 .0 0 1

− 0 .0 0 1

− 0 .0 0 0 5

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

(0 .0 0 2 )

N o n -d is cr im

in at io n y ea rs

− 0 .5 0 3 * * *

− 0 .5 0 3 * * *

− 0 .5 0 1 * * *

− 0 .5 0 3 * * *

− 0 .5 0 4 * * *

− 0 .5 0 5 * * *

− 0 .5 0 5 * * *

− 0 .5 0 4 * * *

− 0 .5 0 5 * * *

− 0 .5 0 6 * * *

(0 .0 1 7 )

(0 .0 1 7 )

(0 .0 1 7 )

(0 .0 1 7 )

(0 .0 1 8 )

(0 .0 1 8 )

(0 .0 1 8 )

(0 .0 1 7 )

(0 .0 1 8 )

(0 .0 1 8 )

L o g -p se u d o li k el ih o o d

− 1 6 2 8 6 .6 1

− 1 6 2 8 6 .6 0

− 1 6 2 8 0 .4 7

− 1 6 2 8 4 .9 0

− 1 6 2 8 6 .1 8

− 2 2 9 2 .7 4

− 2 2 9 2 .7 4

− 2 2 8 7 .0 6

− 2 2 9 0 .8 8

− 2 2 9 2 .4 0

χ2 4 1 9 5 .6 4

4 1 9 8 .0 5

4 2 0 8 .9 1

4 2 2 4 .1 1

4 1 9 5 .6 2

2 7 9 9 .6 1

2 8 0 0 .6 7

2 8 1 0 .4 6

2 8 1 0 .4 8

2 7 9 9 .2 0

N (t o ta l)

2 6 ,5 9 3

2 6 ,5 9 3

2 6 ,5 9 3

2 6 ,5 9 3

2 6 ,5 9 3

1 0 ,3 7 4

1 0 ,3 7 4

1 0 ,3 7 4

1 0 ,3 7 4

1 0 ,3 7 4

N (s el ec te d )

1 0 ,0 5 9

1 0 ,0 5 9

1 0 ,0 5 9

1 0 ,0 5 9

1 0 ,0 5 9

ρ − 0 .0 0 2

− 0 .0 0 2

− 0 .0 0 3

− 0 .0 0 0 1

− 0 .0 0 2

(0 .0 6 2 )

(0 .0 6 2 )

(0 .0 6 1 )

(0 .0 6 2 )

(0 .0 6 2 )

N o te s: R o b u st st an d ar d er ro rs

ap p ea r in

p ar en th es es . C o u n tr y d u m m ie s, co n st an ts an d cu b ic

sp li n es

fo r te m p o ra l d ep en d en ce

n o t sh o w n . D o m in an t m in o ri ti es

ex cl u d ed . A ll ti m e- v ar ia n t in d ep en d en t v ar ia b le s

la g g ed

o n e y ea r.

* * * p < 0 .0 0 1 .

* * p < 0 .0 1 .

* p < 0 .0 5 .

ECONOMIC SANCTIONS AND OFFICIAL ETHNIC DISCRIMINATION 497

second stage also includes the sanction variables and the other country-level control vari- ables used in the manuscript.9

I also tested the hypotheses using the list of imposed sanction cases from the Threat and Imposition of Sanctions (TIES) data-set (Morgan, Bapat, and Krustev 2009) for the 1971– 2000 period. The results from the analysis using the TIES data are very similar. In the mod- els where I used the TIES data, the sanction cases over environmental policy and trade practice disputes are excluded from the analysis since such non-traditional sanction regimes do not cause any major damage on the domestic stability of target countries.

Finally, I ran additional models to explore whether the suggested impact of sanctions is conditioned by the level of economic development in a country, the population size of an ethnic group, the number of ethnic groups in a country, and the population size of the ruling ethnic group. The results showed no statistically significant support for these conditional hypotheses. I also ran partial models for the ethnic groups in autocratic and non-autocratic regimes. The sanctions variables were statistically significant for both samples. This sug- gests that sanctions against both democracy and non-democratic regimes could be detrimen- tal to the political and economic rights of ethnic groups.

TABLE V Predicted Probabilities of Official Political Discrimination

Pr (political discrimination = 1) Baseline (Initial value) Unit change New value (%Δ)

Sanctions (all) 0.098 0 → 1 0.152 (53%)

Unilateral sanctions 0.098 0 → 1 0.162 (65%)

Multilateral sanctions 0.098 0 → 1 0.231 (136%)

Extensive sanctions 0.098 0 → 1 0.185 (89%)

Moderate sanctions 0.098 0 → 1 0.135 (38%)

Limited sanctions 0.098 0 → 1 0.163 (66%)

Hum. rights sanctions 0.097 0 → 1 0.139 (43%)

Non-hum. rights sanctions 0.097 0 → 1 0.160 (64%)

Democracy 0.098 Mean + 1σ 0.053 (−46%)

Economic openness 0.098 Mean + 1σ 0.075 (−23%)

Autonomous power 0.098 0 → 1 0.058 (−69%)

Regional base 0.098 0 → 1 0.081 (−17%)

GDP per Capita 0.098 Mean + 1σ 0.120 (22%)

Immigration since 1800 0.098 0 → 1 0.150 (53%)

9The full tables reporting the results from the additional analyses are available from the author upon request.

498 D. PEKSEN

TABLE VI Selection Equation

Economic discrimination Political discrimination

Identity: religious 0.615*** 0.616***

(0.040) (0.041)

Identity: race 0.008 0.011

(0.037) (0.037)

Identity: past autonomy 1.222*** 1.224***

(0.028) (0.028)

Identity: other 0.786*** 0.789***

(0.034) (0.034)

Group population 0.084*** 0.083***

(0.012) (0.012)

MAR group pop. criterion 0.809*** 0.807***

(0.087) (0.087)

Country population 0.086*** 0.087***

(0.012) (0.012)

MAR country pop. criterion 0.346*** 0.342***

(0.096) (0.096)

Majority −0.273** −0.277**

(0.101) (0.101)

% of population 1.353*** 1.365***

(0.160) (0.160)

Country GDP per Capita 0.160*** 0.159***

(0.014) (0.014)

Economic growth 0.055 0.062

(0.113) (0.113)

Democracy 0.002 0.002

(0.002) (0.002)

Regional base 0.482*** 0.483***

(0.022) (0.022)

Immigration since 1800 0.790*** 0.790***

(0.035) (0.035)

Immigration since 1945 −0.716*** −0.717***

(0.047) (0.048)

Minority in aut. region 0.256*** 0.255***

(0.027) (0.027)

Autonomy 0.030* 0.027

(0.015) (0.015)

F. Soviet U. and E. Europe 1.088*** 1.101***

(0.050) (0.051)

Asia-Pacific 0.242*** 0.233***

(0.041) (0.041)

Middle East-N. Africa 0.821*** 0.822***

(0.048) (0.048)

Sub. S. Africa 0.465*** 0.465***

(0.050) (0.050)

L. America–Caribbean 0.623*** 0.622***

(0.044) (0.044)

Number of years −0.007*** −0.007***

(0.0007) (0.0007)

(Continued)

ECONOMIC SANCTIONS AND OFFICIAL ETHNIC DISCRIMINATION 499

6. CONCLUSION

This study has examined the extent to which sanctions affect the likelihood of official eth- nic discrimination in target countries. It is argued that economic coercion plays an inadver- tent role in the poor treatment of ethnic groups by contracting the economy and creating incentives for the target government to employ ethnic-based discriminatory policies. The results from the quantitative data analysis offer strong support for the assertion that sanc- tions contribute to the rise of official discriminatory practices against ethnic groups outside the support base of the government.

Extant literature on sanctions has mainly focused on the societal level effects of sanctions without detailing the inadvertent effects of the coercion on disadvantaged segments of soci- ety. This study’s focus on a globally vulnerable segment of society – ethnic groups – offers a greater understanding of the consequences of sanctions. It also improves our understand- ing of how political elites in multiethnic countries might domestically respond to external pressures to hold on to power. The findings also speak to the ethnic politics literature, dem- onstrating that economic coercion as an oft-used policy tool increases the possibility of more discriminatory policies against ethnic groups.

Economic coercion against multiethnic societies is likely to become a counterproductive policy tool prompting the target government to pursue ethnic-based discriminatory policies. More discriminatory government practices against ethnic groups might destabilize the target country given the significant link between ethnic discrimination and the emergence of vio- lent conflicts such as civil wars and domestic terrorism. This would, in turn, directly hurt the interests of sanctioning countries and their regional allies. More unrest and instability in the targeted country would pose threats to the stability of neighboring countries, causing more inter-state and civil wars. Problems posed by growing domestic instabilities in the tar- get could also lead to more engagement and intervention of sender countries and non-state international actors that might undermine regional and international security. Thus, policy- makers should take into account the possible inadvertent consequences of sanctions in weighing the costs and benefits of the decision to sanction.

This article has only focused on ethnic-based political and economic discrimination. Using the data from the MAR and other sources, future research could follow the lead of this study and examine to what extent sanctions increase ethnic-based grievances and vio- lence in the sanctioned countries. Finally, future studies could consider using dyadic data to account for the major economic and political characteristics of sender countries as well as the diplomatic and economic ties between sender and target countries.

TABLE VI (Continued )

Economic discrimination Political discrimination

Constant −5.135*** −5.138***

(0.213) (0.213)

Observations 26,593 26,593

Notes: Robust standard errors appear in parentheses. ***p < 0.001. **p < 0.01. *p < 0.05.

500 D. PEKSEN

ACKNOWLEDGMENTS

I would like to thank Yasemin Akbaba, Nicole Detraz, Patrick James, and Jason Sorens, and the anonymous reviewers for helpful comments and suggestions. I am also grateful to Jason Sorens for sharing his data on ethnic groups.

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