TECHNICAL AND INSTITUTIONAL STATES: Loose Coupling in the Human Rights Sector of the World Polity

TECHNICAL AND INSTITUTIONAL STATES:Loose Coupling in the Human Rights Sectorof the World Politytsq_1163 65..95

Rob Clark*University of Oklahoma

While human rights treaties have become increasingly popular over the past quarter century, there

has not been a corresponding improvement in human rights practices. This discrepancy implies

that a country’s formal pledge to uphold human rights principles is “loosely coupled” from its

actual performance. In this study, I develop a model of loose coupling based on organizational

research and apply it to the human rights sector of the world polity. Empirically, I identify a set of

institutional states whose human rights practices fall short of their treaty commitments, as well as

a set of technical states whose practices exceed their commitments. Analyzing an unbalanced data

set with a maximum of 755 observations across 167 countries during the 1975 to 2000 period, I

use random effects models to predict a state’s location on the Human Rights Decoupling Index

(HRDI). The findings illustrate the importance of several organizational concepts for predicting

a state’s HRDI score. In particular, the analyses reveal the countervailing effects of globalization.

While economic globalization (i.e., trade and foreign investment) is associated with the technical

(positive) end of the HRDI, cultural globalization (i.e., memberships in international organiza-

tions) is associated with the institutional (negative) end.

INTRODUCTION

While human rights instruments have become increasingly popular over the pastquarter century, there has not been a corresponding improvement in human rightspractices (Hafner-Burton and Tsutsui 2005) (see also Figure 1). In fact, the human rightssector exhibits signs of “radical decoupling” (Hafner-Burton and Tsutsui 2005:1383),where ratification of human rights treaties not only lacks efficacy in improving behavior(Hathaway 2002), but it may have the opposite effect, negatively affecting a state’s humanrights record by providing a shield for states that wish to become more repressive(Hathaway 2002; Hafner-Burton and Tsutsui 2005). Several studies suggest that ratifyinghuman rights instruments may contribute to better human rights practices but that therelationship is contingent on a state being democratic or having a strong civil society(Hathaway 2002). Moreover, this conditional relationship may not even extend torepressor states where reform is needed most (Hafner-Burton and Tsutsui 2007).

In addition, studies that examine treaty ratifications as an outcome suggest thatparticipation may be largely ceremonial to begin with. Democratic states with fewerhuman rights violations ratify treaties significantly more quickly than more repressive

*Direct all correspondence to Rob Clark, Department of Sociology, University of Oklahoma, Kaufman Hall

331, 780 Van Vleet Oval, Norman, OK 73019; e-mail: [email protected]

The Sociological Quarterly ISSN 0038-0253

The Sociological Quarterly 51 (2010) 65–95 © 2010 Midwest Sociological Society 65

democracies, suggesting that states are more likely to participate in human rights instru-ments when they incur fewer costs (Hathaway 2003). Among nondemocracies or acrossa full sample of countries, repressive states appear to ratify human rights treaties at ratesthat are as high as other nations (Hathaway 2003; Cole 2005; Hafner-Burton and Tsutsui2007; Hafner-Burton, Tsutsui, and Meyer 2008) and may even ratify them more quicklywhen they possess substantial autonomy (Hafner-Burton et al. 2008). In sum, thehuman rights sector of the world polity appears to be highly ceremonial, as states ratifytreaties with which they already comply or ratify treaties with which they have nointention of complying.

More generally, this past research suggests that a country’s formal pledge to upholdhuman rights principles may, in some cases, be “loosely coupled” from its actual per-formance. Several studies have begun to draw empirical attention to human rightsdecoupling, examining the length of time between a state’s formal adoption of a humanrights principle and its subsequent implementation (Swiss 2009), as well as investigatingthe “joint probability” of ratifying a human rights treaty and complying with it (Powelland Staton 2009). In this study, I build on this recent turn in the literature by construct-ing a theoretical model of loose coupling based on organizational research and empiri-cally applying it to the human rights sector of the world polity.

23

45

6

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45

6

1975-80 1980-85 1985-90 1990-95 1995-00

Treaty Ratifications Amnesty Rating

FIGURE 1. Loose Coupling in the Human Rights Sector of the World Polity, N = 137.

Note: Scores represent the average number of treaty ratifications (0–8) and the average Amnesty

rating (1–5) for 137 states across five waves during the 1975 to 2000 period.

Technical and Institutional States Rob Clark

66 The Sociological Quarterly 51 (2010) 65–95 © 2010 Midwest Sociological Society

I begin with a theoretical discussion of loose coupling, followed by an introductionof the study’s dependent variable, the Human Rights Decoupling Index (HRDI), calcu-lated as the difference between a state’s human rights rating and its number of humanrights treaty ratifications. I develop a typology that places technical and institutionalstates at opposite ends of the HRDI spectrum, whereby the human rights practices oftechnical states exceed that of their treaty commitments, while the human rights prac-tices of institutional states fall short of what they formally promise. I then construct atheoretical model of loose coupling, importing five sets of explanations traditionallyused in organizational studies (resource capacity/strain, evaluation/monitoring, cogni-tion, conflict, and isomorphism), that I hypothesize will help predict variation in loosecoupling at the nation-state level. Finally, analyzing an unbalanced data set with amaximum of 755 observations across 167 states over five waves during the 1975 to 2000period, I use random effects models to predict a state’s location on the technical/institutional continuum of the HRDI. The analyses provide broad support for thespecified hypotheses. In particular, the results highlight the countervailing effects ofglobalization. Economic globalization (as measured by trade openness and foreigninvestment) is positively associated with HRDI scores via its positive effect on a state’shuman rights performance. Conversely, cultural globalization (as measured by interna-tional organization memberships) is negatively associated with HRDI scores via itspositive effect on a state’s number of human rights treaty ratifications.

LOOSE COUPLING

Organizations frequently adopt new policies in order to express their commitment tochange and gain legitimacy from the wider community (Meyer and Rowan 1977). Policedepartments may revise their code of conduct in order to abolish racial profiling;universities may announce new punitive measures that are intended to stop fraternityhazing; companies may promote sensitivity training seminars designed to eliminatesexual harassment. However, traditional practices often persist in the face of reform.Under these circumstances, an organization’s formal structure is said to be decoupled(or loosely coupled) from its ongoing activities. Theoretically, this occurs for a variety ofreasons. First and foremost, new procedures routinely conflict with an organization’sexisting behavior. And because strong enforcement mechanisms are frequently absent,local implementation is oftentimes a matter of discretion. Furthermore, new programscan be inefficient to implement, or organizations may simply lack the resources neces-sary to carry them out. And, as is often the case, policies may express vague principlesand/or reach diverse audiences, inviting a wide range of interpretations and hinderingexecution.

One indicator of an organization’s propensity to become decoupled is the type ofenvironment in which it is situated. Organizations embedded within institutional envi-ronments are thought to be more loosely coupled than organizations embedded withintechnical environments (Meyer, Scott, and Deal 1983). Technical organizations (e.g.,manufacturers, retailers) are held captive by performance indicators and are evaluated

Rob Clark Technical and Institutional States


by their output. Because survivability in technical environments is contingent uponmarket success, technical organizations closely monitor their activities in order to bettercontrol production. Consequently, technical environments tend to host tightly coupledorganizations. In contrast, performance indicators are often unavailable for institutionalorganizations (e.g., courtrooms, schools), so they tend to be judged by their formalstructure. Because survivability in institutional environments is contingent upon publiclegitimacy, institutional organizations adopt prestigious models in order to enhancetheir reputation. However, these models frequently conflict with ongoing activities.Consequently, loose coupling becomes endemic within institutional environments.

Interestingly, technical and institutional environments are not mutually exclusivebut may coexist in the same sector (Scott and Meyer 1991). Such “dual-environment”sectors feature both technical and institutional pressures and host organizations withadministrative structures that are thought to be larger and more complex. “In general,organizations of this type carry out tasks that combine complex technical requirementswith a strong ‘public good’ component” (Scott and Meyer 1991:123). I argue thatnation-states fit this organizational description fairly well and that the human rightssector of the world polity constitutes a “dual environment,” exerting both technical andinstitutional pressures on embedded actors. Not only do states actively participate inritual and ceremony, adopting celebrated models diffused by the world polity, but theyalso face very real technical demands from constituencies that regularly evaluate theirperformance. However, as I illustrate below, technical and institutional pressures do notact evenly across all countries. Some states focus on ceremonial participation in humanrights treaties, while others attend more heavily to the actual implementation of theseprinciples.

THE HUMAN RIGHTS DECOUPLING INDEX (HRDI)

In this study, I refer to the lack of correspondence between a state’s treaty commitmentsand its actual human rights record as an indication of “loose coupling.” I operationalizethis concept through the Human Rights Decoupling Index (HRDI), which I constructedby differencing a state’s number of human rights treaty ratifications from its humanrights rating. Larger values indicate a high human rights rating relative to level of treatyparticipation, while smaller values indicate a low human rights rating relative to level oftreaty participation. I measure each state’s human rights practices with ratings based onAmnesty International reports (data come from Hafner-Burton and Tsutsui 2005),constructed from the standards-based ordinal scale of repression (see Table 1). Imeasure each state’s level of participation in human rights treaties with the number of“Core International Human Rights Instruments” that a state has ratified (see Table 2).Both scores (Amnesty rating and treaty ratifications) are standardized (mean 0; standarddeviation 1) prior to differencing.

Intuitively, a state’s level of treaty participation should map on fairly well to itshuman rights practices (e.g., high levels of participation should correspond with a highhuman rights rating). However, the two measures do not correlate very highly with one



another during the 1975 to 2000 period (r = .089). Conventionally, students of organi-zational behavior explain this lack of correspondence as a function of the model diffus-ing system-wide, but for its implementation to be uneven. This appears to explain thehuman rights sector somewhat accurately. Sixty-seven percent of the states in my samplehad ratified six or more of the eight human rights instruments under investigation by

TABLE 1. The Standards-Based Ordinal Scale of Repression

Level of repression Definition Rating

Rare Where states are under secure rule of law, political

imprisonment and torture are rare, and political murder is

extremely rare.

5

Limited Where imprisonment for nonviolent political activities is

limited, torture and beating are exceptional, and political

murder is rare.

4

Widespread Where political imprisonment is extensive, execution and

political murder may be common, and detention (with or

without trial) for political views is acceptable.

3

Extensive Where the practices of level 3 are expanded to a large segment

of the population, murders and disappearances are

common, but terror affects primarily those who interest

themselves in political practice or ideas.

2

Systematic Where levels of terror are population-wide and decision

makers do not limit the means by which they pursue

private or ideological goals.

1

Source: Hafner-Burton and Tsutsui (2005:1392).

TABLE 2. Core International Human Rights Instruments

Instrument Year

International Convention on the Elimination of All Forms of Racial Discrimination 1965

International Covenant on Civil and Political Rights 1966

International Covenant on Economic, Social, and Cultural Rights 1966

Convention on the Elimination of All Forms of Discrimination against Women 1979

Convention against Torture and Other Cruel, Inhuman, or Degrading Treatment or

Punishment

1984

Convention on the Rights of the Child 1989

First Optional Protocol to the International Covenant on Civil and Political Rights 1966

Second Optional Protocol to the International Covenant on Civil and Political Rights 1989

Source: The Office of the United Nations High Commissioner for Human Rights (http://

www.ohchr.org).

Notes: Another core treaty (regarding the rights of migrant workers) currently exists, along with

four other optional protocols to the above treaties. However, most countries began ratifying these

instruments during the post-2000 period.



the year 2000. Conversely, only 39 percent of the states in my sample had a human rightsrating of four or higher (on a scale of 1–5) during the 1995 to 2000 period. Classicexamples of uneven implementation abound. By the year 2000, Denmark had ratified alleight human rights instruments while also receiving the highest human rights ratingpossible (5.00) during the 1995 to 2000 period. By contrast, Colombia had likewiseratified all eight human rights instruments by 2000 but received one of the lowest humanrights scores possible (1.17) during the 1995 to 2000 period.

Alternatively, there are instances where states have implemented progressive humanrights practices without fully participating in adopting the models that espouse theseprinciples. The United States proves illustrative here, having earned the highest humanrights rating possible during the 1995 to 2000 period yet having ratified only three of theeight human rights instruments by 2000. Consequently, while some states are concernedwith public displays of embracing human rights norms (i.e., ratifying treaties), othersattend heavily to implementation (i.e., actual performance). I refer to those countriescomprising the former group as institutional states and those countries in the lattergroup as technical states. In sum, those states with high human rights ratings (relative totheir number of treaty ratifications) occupy the technical (positive) end of the HRDIspectrum (e.g., the United States), while those states with low human rights ratings(relative to their number of ratifications) occupy the institutional (negative) end (e.g.,Colombia). The goal for this study, then, is to identify which factors will accurately locateindividual countries along this continuum.1

HYPOTHESES

In this section, I develop a model of loose coupling, importing five sets of explanationstraditionally used in organizational studies (resource capacity/strain, evaluation/monitoring, cognition, conflict, and isomorphism) and applying them to the humanrights sector of the world polity. It is important to note at the outset that the model isbased heavily on predicted variation in human rights practices (rather than treatyparticipation) because of the theoretical attention (in organizational studies) andempirical attention (in human rights studies) given to technical performance. As thefollowing review suggests, the bulk of previous studies in the human rights literaturefocuses on state practices rather than ceremonial participation in human rights instru-ments. “With a few important exceptions, political scientists and legal scholars havelargely ignored the questions of when and why countries join international treaties”(Hathaway 2003:1825). This empirical focus on performance corresponds with organi-zational theory on decoupling. From an organizational perspective, the diffusion ofcelebrated models is thought to be fairly universal, such that their uneven implementa-tion is what accounts for differences between actors. Nevertheless, while the followinghypotheses are based primarily on a variable’s predicted impact on human rights prac-tices, some of the hypotheses (e.g., evaluation/monitoring, conflict, isomorphism) maybe influenced by a variable’s potential impact on treaty ratifications.



Resource Capacity and StrainLoose coupling is thought to be more severe among resource-poor organizations thatmust constantly behave within a context of scarcity (Weick 1976:13–14). Moreover,when bureaucracies are faced with high client ratios and/or caseloads, as is often the case(Lipsky 1980:29), strict adherence to formal policies may be costly, if not impossible, interms of time and resource. Likewise, wealthy nations that are plentiful in resource andorganizational capacity are thought to be tightly coupled, while poorer states are morelikely to emphasize formal structuration (Meyer et al. 1997:155). For example, decou-pling in the science sector is exaggerated among less developed countries (Drori et al.2003). In the human rights sector, past studies have shown that underdevelopment isempirically linked to poor human rights practices and repression (Poe and Tate 1994;Hafner-Burton and Tsutsui 2005, 2007), suggesting that resource capacity may influencea state’s location on the HRDI. Moreover, just as large caseloads and client ratios mayplace a great deal of strain on organizations, a country’s population size and/or densitycan also create a heavy burden for the state. Population measures are fairly common inmodels predicting human rights outcomes (Mitchell and McCormick 1988; Poe andTate 1994; Hafner-Burton and Tsutsui 2005, 2007; Cole 2006; Powell and Staton 2009).However, past studies suggest that population size most consistently affects humanrights practices. With respect to participation in human rights treaties, previous studiesgenerally find that a state’s wealth and population characteristics do not highly impactratification (Cole 2005; Hafner-Burton et al. 2008), suggesting that the main effects ofresource capacity and strain on a state’s HRDI score should be based primarily on theireffects on human rights performance.

Hypothesis 1a: GDP PC (resource capacity) positively affects a state’s HRDI score.Hypothesis 1b: Population size (resource strain) negatively affects a state’s HRDIscore.

Evaluation and MonitoringDecoupling is also widely present in systems that feature weak enforcement mecha-nisms. This may involve infrequent inspections or the delegation of discretion (Weick1976:5). “Street-level bureaucrats” (Lipsky 1980) enjoy a great deal of autonomy andperform their work unsupervised, thereby making it difficult to sanction or control theirbehavior. As a result, formal rules are routinely violated, policies remain unimple-mented, and traditional practices persist (Meyer and Rowan 1977). While organizationalscholars typically characterize such autonomy as highly functional, discretion also neu-tralizes the ability to reform behavior. For example, at the nation-state level, the inabilityto critically assess political regimes allows state actors to operate with few checks on theirconduct. Without competitive elections, authoritarian governments can buffer them-selves from constituent evaluation. Tyrannical states may also control media outlets,thereby thwarting any independent monitoring of how the state conducts its affairs.Conversely, democratic states are routinely subjected to evaluation through elections, aswell as monitoring from an independent press. Thus, human rights models are morelikely to be implemented in democratic states (Poe and Tate 1994; Powell and Staton



2009). Moreover, human rights abuse claims are less common among democratic stateswith progressive human rights practices (Cole 2006). However, several studies have alsoshown that democracies are more active ratifiers of human rights instruments thannondemocracies (Hathaway 2003; Cole 2005; Powell and Staton 2009). To the extent thatdemocracies commit fewer human rights violations, this finding may reflect the reduced“cost of commitment” for democratic states to ratify human rights treaties. As a conse-quence, democracy’s net effect on the HRDI may be substantially reduced, which wouldbe consistent with previous research showing that democracy has no significant impacton decoupling in the women’s rights sector (Swiss 2009).

Hypothesis 2a: Democratization (evaluation) positively affects a state’s HRDI score.Hypothesis 2b: Newspaper circulation (monitoring) positively affects a state’s HRDIscore.

CognitionInstitutional actors are plagued with incomplete knowledge, unstable preferences, andlimited computational abilities when making decisions (Simon 1955). This “boundedrationality” with which actors must cope has important implications for loose couplingwithin organizations. Imported models are oftentimes unfamiliar and are met withsubjective interpretation and flawed implementation. Similarly, world polity scholarsanticipate limited results when broad, global prescriptions are adapted to specific, localcontexts (Drori et al. 2003:157). Nevertheless, some nations may be more adept atimplementing human rights models than others. In particular, recognition of humanrights requires a culture that views individuals as separate and autonomous from society,which is broadly considered to be a modern Western/European conception (Cole 2006).Thus, Western nations may have an interpretative advantage with human rights modelsover other nations. In addition, non-Western states inheriting a British political culturethrough colonial rule are commonly thought to respect human rights more so thanelsewhere (Mitchell and McCormick 1988:480). Organizationally, this common Britishheritage is reflected by membership in the Commonwealth, a voluntary association of 53independent states formerly part of the British Empire. Previous studies have examinedwhether former British colonies are less repressive (Mitchell and McCormick 1988; Poeand Tate 1994), generally yielding results that confirm a significant association. Thus, Iexpect both Western nations and members of the Commonwealth to have significantlygreater HRDI scores than other states due primarily to their more positive Amnestyratings and, in particular, because Western states do not appear to ratify human rightstreaties at significantly different rates than other nations (Cole 2005).

Hypothesis 3a: Western status (cognition) positively affects a state’s HRDI score.Hypothesis 3b: Commonwealth membership (cognition) positively affects a state’sHRDI score.

ConflictNot only are actors handicapped in their ability to interpret imported models, but theytend to feature a high level of abstraction to begin with (Strang and Meyer 1993). Models



that promote broad, abstract principles make compliance difficult to assess or achieve(Seidman 1983), providing organizations with the opportunity to construct visiblesymbols of compliance with great latitude while actual practices remain unaffected(Edelman 1992). Ambiguity is especially valuable as a response to interorganizationalconflict. Institutional actors work in “fragmented environments” (Orton and Weick1990:207), featuring conflicting goals and competing factions. Vague models allow inter-nal inconsistencies to persist without committing the organization to pursue a specificpath or goal (Lipsky 1980). Likewise, human rights treaties that express abstract prin-ciples may be especially attractive to states with fragmented populations, where ethnic orreligious cleavages can hinder compliance with human rights norms and produce loosecoupling (March and Olsen 1998:946). In its most extreme manifestation, intergroupconflicts have been shown to spark human rights abuses during periods of civil war (Poeand Tate 1994; Hafner-Burton and Tsutsui 2005, 2007). In sum, fragmented statesshould not only feature significantly more repressive human rights practices, but theyshould also be more likely to embrace the vague principles espoused in human rightstreaties. The overall effect, then, of intrastate conflict on a state’s HRDI score should benegative.

Hypothesis 4a: Civil war (conflict) negatively affects a state’s HRDI score.Hypothesis 4b: Ethnic fractionalization (conflict) negatively affects a state’s HRDIscore.

IsomorphismA major consequence stemming from the widespread diffusion of models is isomor-phism, as organizations come to resemble one another in their formal structure. “Insti-tutionalization tends to reduce variety, operating across organizations to overridediversity in local environments” (DiMaggio and Powell 1991:14). Organizational schol-ars typically cite three types of isomorphism: (1) coercive, where models spread as theresult of direct pressure or force; (2) mimetic, where uncertain actors imitate successfulor prestigious peers; and (3) normative, where actors are socialized to adopt legitimatemodels (DiMaggio and Powell 1983). As I hypothesize below, coercive isomorphismforces weaker states to adopt human rights treaties that they may not be able or willingto implement. In contrast, I hypothesize that mimetic and normative processes produceisomorphism in progressive human rights practices, as developing states imitate and/orlearn from their advanced peers via their integration in the global economy, as well astheir membership ties in IOs.

Coercive IsomorphismOrganizational studies focused on the wider environment have found that institutionalbehavior can be shaped externally by powerful actors. Pfeffer and Salancik (1978)emphasize the “external control” of resource-dependent organizations. Resourcedependency is an important source of coercive isomorphism, whereby the sector-widediffusion of prestigious models is enforced by powerful actors. At the cross-nationallevel, the diffusion of models may be similarly structured by dominant members of the



international community. World-system scholars refer to countries with the greatestamount of power in international relations as members of the “core,” with weak statescomprising the system’s “periphery.” Nations in the periphery are not self-sufficientand experience a variety of resource dependencies in the form of investment, aid,and/or debt, which subjects them to “external control” from the international commu-nity. On the one hand, peripheral states must rely on attracting foreign investment bysupplying cheap, docile labor to core investors who are seeking to reduce their pro-duction costs (London and Ross 1995), which may require significant repression ofpolitical rights and civil liberties. On the other hand, peripheral nations may feel com-pelled to embrace popular world models (e.g., human rights instruments) because theygenerate legitimacy and support. Thus, loose coupling in the human rights sector maybe more extreme within the periphery where socially progressive models clash withlocal practices.

Moreover, in contrast to the core, many peripheral nations are saddled with debtservicing and are often required to implement austerity programs to satisfy Interna-tional Monetary Fund (IMF) and World Bank lenders. Austerity measures typicallyinvolve significant reductions in social spending (e.g., health and welfare programs),which cause hardships for vulnerable portions of the population, leading to heightenedlevels of domestic conflict. Such conflict can be met with state repression, and theimposition of debt servicing and austerity programs has been found to worsen humanrights practices (Abouharb and Cingranelli 2006) and undermine unionization withinless developed countries (Martin and Brady 2007).

Previous studies have not examined the impact of world-system position on humanrights per se, but the periphery does exhibit significantly lower levels of democratizationthan the core (Bollen 1983), making noncore citizens more vulnerable to repressivehuman rights practices. And, in related research, Swiss (2009) finds that the length oftime between the adoption of women’s political rights and the implementation of theserights is significantly greater within the periphery, suggesting that peripheral states aremore highly decoupled than the core or semiperiphery.

Hypothesis 5a: Core status (coercive isomorphism) positively affects a state’s HRDIscore.

Mimetic IsomorphismImitation is the mechanism most commonly thought to produce isomorphism acrossorganizations (Mizruchi and Fein 1999). When uncertain or weak actors are relativelynew to an organizational sector, a natural source of information comes from well-established peers that boast successful or prestigious models. These models serve asguides to action and are copied by newer cohorts just entering the field. Similarly, theimitation of prominent organizational forms by new or uncertain actors can be seen atthe nation-state level, as with the spread of formal education models from establishedcountries to “candidate” nation-states (i.e., colonies and dependencies) (Meyer,Ramirez, and Soysal 1992). Many developing states have also come to imitate thosecountries in the advanced world by embracing global capitalism and opening their



borders to trade and foreign investment. Recent decades during the postcolonial erahave witnessed an increase in North–South trade and investment flows (Alderson andNielsen 2002).

These economic bridges may also serve as conduits through which human rightsstandards diffuse to developing nations. Previous studies suggest that cross-nationaleconomic exchange diffuses progressive human rights standards, subtly transformingtraditional societies by eroding restrictive communal roles, bonds, and obligations andinstilling a liberal market consciousness. Economic exchange legitimates individualism,along with the rights and liberties that are consistent with this form of actorhood.Moreover, integration in the global economy may contribute to gender equity by pro-viding new employment opportunities for women, thereby encouraging female schoolattendance and ultimately lowering fertility rates. Economic globalization can alsotransform society by modernizing class relations. Countries competing in a globaleconomy may feature improved labor standards in order to raise efficiency and workerproductivity.

Past research has shown that foreign investment positively affects political, economic,and social rights among developing states (Meyer 1996). Foreign direct investment (FDI)and trade significantly improve conditions for women (Gray, Kittilson, and Sandholtz2006; Richards and Gelleny 2007) while reducing child labor (Neumayer and de Soysa2005) and violations to free association and collective bargaining rights (Neumayer andde Soysa 2006). Trade flows and preferential trade agreements with “hard” human rightsstandards also negatively affect state repression (Hafner-Burton and Tsutsui 2007). Inshort, developing countries frequently look to resemble their more established peers in arange of manners, and economic linkages may serve as one way in which progressivehuman rights practices diffuse across nations. In contrast, very little is known about theeffect of economic globalization on participation in human rights instruments, althoughone study finds that trade flows have little impact (Hafner-Burton et al. 2008). Assumingthat FDI and trade do not significantly affect treaty ratifications, the overall effect ofeconomic globalization on a state’s HRDI score should be positive.

Hypothesis 5b: FDI and trade (mimetic isomorphism) positively affect a state’sHRDI score.

Normative IsomorphismWhile DiMaggio and Powell (1983) cite three routes to institutional isomorphism, worldpolity scholars have largely emphasized the normative mechanism to explain cross-national harmonization (Drori et al. 2003). Coercion and imitation imply instrumentaldiffusion through the intentions of powerful actors seeking to control the behaviorof others, or weaker actors seeking to replicate successful models found elsewhere.Normative diffusion, conversely, suggests a more cultural approach, where actors aresocialized to accept certain “taken-for-granted” models as appropriate without anyspecific goals or output in mind.

World polity scholars suggest that normative diffusion at the cross-national leveloccurs primarily through international organizations (IOs). IOs, both governmental



(international governmental organizations [IGOs]) and nongovernmental (interna-tional nongovernmental organizations [INGOs]), are widely considered to be theprimary carriers of world culture, setting global standards and principles (Boli andThomas 1997; Boli, Loya, and Loftin 1999; Beckfield 2003). IOs engage in “mass social-ization,” penetrating national boundaries and diffusing cultural models that are widelyadopted by member states. Consequently, countries that are highly embedded in IOstend to adopt models more quickly than those that are less centrally integrated. Pastresearch suggests that IOs help spread progressive human rights principles. IOs have apositive effect on democracy (Paxton 2002) and human rights performance (Hafner-Burton and Tsutsui 2005; Powell and Staton 2009) and have been found to reducedecoupling in the area of women’s rights (Swiss 2009).

Moreover, several studies suggest that IOs may have a negative impact on the rati-fication of specific human rights instruments (Cole 2005; Powell and Staton 2009).Theoretically, however, there is reason to believe that IOs may positively affect a state’soverall participation in human rights treaties. In fact, IOs may spread human rightsmodels faster (or more effectively) than they influence practices. IOs are primarilyinvolved in spreading world polity structuration and secondarily with transformingactivity. Under globalization, a greater number of cultural models become legitimated,forcing states to quickly adopt highly celebrated models without regard to local imple-mentation. “Well-intentioned international organizations, in their attempt to learnlessons from their success stories, transfer their programs from one context to anotherwithout adapting it to its new environment” (Drori et al. 2003:161). Moreover, differentversions of the same model are likely to diffuse. “World culture contains a good manyvariants of the dominant models, which leads to the eclectic adoption of conflictingprinciples” (Meyer et al. 1997:154). In short, IOs may hypersocialize countries to adopta variety of world cultural models and participate in human rights instruments whilefocusing less on implementation. Thus, when considering their overall impact on theHRDI, the positive effect of IOs on a state’s human rights performance may be muted bytheir similarly positive effect on treaty participation.

Hypothesis 5c: IOs (normative isomorphism) positively affect a state’s HRDI score.

METHODS

SampleMy sample includes a maximum of 755 observations across 167 countries overfive waves during the 1975 to 2000 period.2 The pooled data are unbalanced, withsome states contributing more observations than others. However, almost all states(166) are present across two or more waves, and 137 states (82 percent of the total) arepresent in all five waves. Each wave represents a six-year period (1975–1980, 1980–1985, 1985–1990, 1990–1995, and 1995–2000), where each data point for eachmeasure represents a state’s average value across the entire wave (except whereotherwise noted below).



Dependent VariableThe HRDII constructed the HRDI by differencing a state’s total number of human rights treatyratifications from its average human rights rating for each wave during the sampleperiod (1975–2000). Both scores (Amnesty rating and treaty ratifications) are standard-ized (mean 0; standard deviation 1) prior to differencing. As noted above, larger valuesindicate a high human rights rating relative to level of treaty participation, while smallervalues indicate a low human rights rating relative to level of treaty participation. HRDIscores range from -3.83 to 2.98 across the 1975 to 2000 period, with a falling meanacross time to reflect the rise in treaty ratifications coupled with the relative stability ofAmnesty ratings (see Figure 1).

Data on human rights practices come from Hafner-Burton and Tsutsui (2005), whoupdate existing data based on their content analysis of human rights reports using aninverted standards-based ordinal Political Terror Scale. As Table 1 indicates, scores rangefrom 1 to 5, with a score of 1 indicating “systematic repression,” 2 indicating “extensiverepression,” 3 indicating “widespread repression,” 4 indicating “limited repression,” and5 indicating “rare repression.” Separate scores based on annual reports from AmnestyInternational (AI) and the U.S. State Department (USSD) are available. The USSD tendsto rate its allies, trading partners, and foreign aid recipients more favorably than AI andrates leftist regimes relatively more harshly (Poe, Carey, and Vazquez 2001). Therefore, Irely primarily on the AI version. However, I follow previous studies that “blend” the two(e.g., Poe and Tate 1994), primarily using AI and relying on the USSD when AI ratingsare not available. Of the 755 cases in my sample, I rely on USSD ratings to construct only15 HRDI scores (about 2 percent of the sample). Despite potential bias in the USSDratings, the AI and USSD scores that come from Hafner-Burton and Tsutsui (2005) arehighly correlated (r = .840) and have become increasingly similar over time (Poe et al.2001). In most cases, there is no difference between the two scores, and in only 4 percentof cases do the scores differ by more than one point (Poe et al. 2001:659).

Data on human rights treaty ratifications come from the Office of the UnitedNations High Commissioner for Human Rights (2006). I consider a state’s ratification ofthe eight “Core International Human Rights Instruments,” which includes six humanrights treaties (racial discrimination, civil/political rights, economic/social/culturalrights, gender discrimination, torture, and child rights), as well as two optional protocolsfor the civil/political rights treaty that are designed to enhance the monitoring andenforcement of the treaty (First Optional Protocol to the International Covenant onCivil and Political Rights) and eliminate the death penalty (Second Optional Protocol tothe International Covenant on Civil and Political Rights). As Table 2 indicates, half ofthese instruments were created during the sample period (1975–2000). Thus, whenconstructing the HRDI for each wave, I am essentially considering each state’s treatyparticipation relative to the maximum number of instruments available for ratificationduring that period. Specifically, I measure a state’s treaty participation as the number ofhuman rights instruments a state has ratified by the end of each wave, with scoresranging from 0 to 8 across the sample period.



Independent Variables3

Time PeriodBecause of the substantial rise in treaty ratifications across the sample period, coupledwith the fairly stable (if not slightly falling) Amnesty ratings (see Figure 1), HRDI scorestend to drop with time. Thus, in order to control for the declining HRDI scores, I includetime period as a predictor in the subsequent analyses.

Resource CapacityGDP PC (Purchasing Power Parity [PPP]) (Log)Gross domestic product per capita is based on PPP. Data are in 1995 internationaldollars. An international dollar has the same purchasing power over GDP as the U.S.dollar has in the United States.

Resource StrainPopulation Size (Log)Population size counts all residents regardless of legal status or citizenship (except forrefugees not permanently settled in the country of asylum).

EvaluationDemocratizationDemocratization scores come from Marshall and Jaggers’s (2005) Polity IV project thatprovides longitudinal political regime characteristics for countries. I use their combinedpolity score, which is computed by subtracting a state’s autocracy score from its democ-racy score. Scores range from -10 to 10.

MonitoringNewspaper CirculationNewspaper circulation refers to newspapers published at least four times a week and iscalculated as average circulation (or copies printed) per 1,000 people.

CognitionWestern StatusWestern status refers to the 23 Western nations in my sample: Australia, Austria,Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy,Luxembourg, Malta, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden,Switzerland, United Kingdom, and United States.

Commonwealth MembershipCommonwealth membership refers to the 39 states in my sample that are formermembers of the British Empire and current members of the Commonwealth: Australia,Bahamas, Bangladesh, Barbados, Botswana, Brunei, Cameroon, Canada, Cyprus, Fiji,Gambia, Ghana, Guyana, India, Jamaica, Kenya, Lesotho, Malawi, Malaysia, Maldives,Malta, Mauritius, Mozambique, Namibia, New Zealand, Nigeria, Pakistan, Papua New



Guinea, Sierra Leone, Singapore, Solomon Islands, South Africa, Sri Lanka, Swaziland,Tanzania, Trinidad-Tobago, Uganda, UK, and Zambia.

ConflictCivil WarCivil war is a dummy variable, referring to the presence or absence of “intrastate war” ineach state during each wave of the sample period. Data come from Sarkees’s (2000)Correlates of War project (version 3.0).

Ethnic-Linguistic FractionalizationEthnic-linguistic fractionalization is a continuous time-invariant measure referring tothe degree of ethnic and linguistic diversity in each state. Scores range from 0 to 1, with0 indicating complete homogeneity and 1 indicating maximum fractionalization. Datacome from Taylor and Hudson’s (1972) classic measure.

Coercive IsomorphismCore StatusCore status refers to Snyder and Kick’s (1979) trichotomous network classification ofstates (with revisions from Bollen 1983). According to world-system theory, statesbelong to one of three world-system positions: core, semiperiphery, or periphery. In thisnetwork measure of world-system position, core economies are considered the mostintegrated and powerful states in the capitalist world economy. The measure used hereis a dummy variable indicating whether or not a country is located in the core. The 17core states in my sample are Australia, Austria, Belgium, Canada, Denmark, France,Germany, Greece, Italy, Japan, Luxembourg, the Netherlands, Norway, Sweden,Switzerland, United Kingdom, and United States.

Mimetic IsomorphismFDI InflowsFDI inflows refer to net inflows of foreign investment to acquire a lasting managementinterest (10 percent or more of voting stock) in a domestic enterprise, measured as ashare of gross domestic product. Inflows represent the sum of equity capital, reinvest-ment of earnings, other long-term capital, and short-term capital as shown in thebalance of payments.

Trade Openness (Log)Trade openness refers to the sum of exports and imports of goods and services,measured as a share of gross domestic product.



Normative IsomorphismIOs (Log)IO memberships refer to the sum of conventional IGOs and INGOs to which states aremembers, listed in sections A to D of the Yearbook of International Organizations (Unionof International Associations). Data for each wave come from single years: 1977, 1981,1985, 1990, and 1995.

AnalysisMy data set contains multiple observations for different countries across time. Such apanel structure allows me to use estimation techniques that deal with potential hetero-geneity bias (the confounding effect of unmeasured time-invariant variables), which canseriously affect ordinary least squares (OLS) coefficient estimates. Random effects andfixed-effects models represent two estimation strategies designed to correct for theproblem of heterogeneity bias. Both procedures “simulate” the unmeasured time-invariant factors as country-specific intercepts (Nielsen and Alderson 1995:685).

I present results using a generalized least squares estimator of random effects modelsthat includes a first-order autocorrelation correction. Random effects models representthe matrix weighted average of the estimates produced by fixed-effects models (focusingon variation within cases) and between effects models (focusing on variation betweencases). By themselves, fixed-effects models ignore all cross-sectional variation in itsestimates by subtracting all variables from their case-specific means, while between-effects models ignore all temporal variation by using the case-specific means as predic-tors. Thus, by incorporating both the fixed- and between-effects components, randomeffects models are advantageous for capturing both cross-sectional and longitudinalvariation. In addition, several of my predictors (Western status, Commonwealth mem-bership, ethnic-linguistic fractionalization, and core status) are time invariant andwould be dropped from fixed-effects models.

My analysis proceeds as follows. In models 1 to 12 (Tables 3–5), I regress the HRDI oneach of the 12 predictors, controlling for time period. In the process, I decompose theHRDI into its constituent parts, regressing a state’s Amnesty rating, and then its numberof treaty ratifications, on each of the 12 predictors. In model 13 (Table 6), I estimate a fullmodel of the HRDI, retaining all significant predictors of the HRDI from models 1 to 12.In model 14 (Table 6), I estimate a final model consisting of all significant predictors frommodel 13. All analyses were performed using Stata 10.0 (Stata Corporation 2007).

I am sensitive to potential collinearity in models 13 and 14. Therefore, I reportmaximum and mean variance inflation factor (VIF) scores for these models generatedthrough OLS estimation. Across both models, the maximum VIF score never exceeds 10(the maximum VIF score is 5.73 in model 13), suggesting that collinearity is never severe(Chatterjee, Hadi, and Price 2000:240). In model 13, the mean VIF score rises notablyabove 2 (2.73), suggesting the presence of mild collinearity. However, once all nonsig-nificant predictors are dropped in model 14, the mean VIF score falls to 2.01. Thus,removing nonsignificant predictors appears to be a sufficient remedy to the mild



TAB

LE3.

Ran

dom

Eff

ects

Mod

els

ofth

eH

RD

I,A

mn

esty

Rat

ing

(AR

),an

dTr

eaty

Rat

ifica

tion

s(T

R),

1975

–200

0

HR

DI

AR

TR

HR

DI

AR

TR

HR

DI

AR

TR

HR

DI

AR

TR

(1a)

(1b)

(1c)

(2a)

(2b)

(2c)

(3a)

(3b)

(3c)

(4a)

(4b)

(4c)

Tim

ep

erio

d-.

503*

**

(.02

0)

-.12

6***

(.02

5)

.455

***

(.01

4)

-.45

2***

(.01

9)

-.09

7***

(.02

4)

.440

***

(.01

3)

-.48

8***

(.02

1)

-.13

3***

(.02

5)

.400

***

(.01

4)

-.47

8***

(.01

9)

-.10

3***

(.02

5)

.450

***

(.01

3)

Trea

ty

rati

fica

tion

s

.156

***

(.04

2)

.183

***

(.04

1)

.097

*

(.04

5)

.155

***

(.04

4)

Am

nes

tyra

tin

g.1

21**

*

(.03

4)

.144

***

(.03

1)

.061

*

(.03

1)

.111

***

(.03

1)

GD

PP

C(P

PP

).2

63**

*

(.06

4)

.355

***

(.05

1)

.074

(.04

5)

Popu

lati

onsi

ze-.

276*

**

(.04

2)

-.24

8***

(.03

6)

.068

*

(.03

0)

Dem

ocra

tiza

tion

.004

(.00

7)

.038

***

(.00

6)

.035

***

(.00

5)

New

spap

ers

¥10

2

.135

*

(.05

4)

.240

***

(.04

4)

.096

**

(.03

6)

Obs

erva

tion

s66

766

766

775

575

575

570

470

470

470

270

270

2

Stat

es15

215

215

216

716

716

715

815

815

815

915

915

9

R2

wit

hin

.631

.058

.741

.618

.052

.740

.620

.084

.754

.614

.010

.746

R2

betw

een

.117

.229

.232

.257

.228

.227

.058

.176

.339

.147

.249

.298

R2

over

all

.324

.210

.457

.388

.195

.448

.270

.159

.508

.305

.226

.473

†p<

.1,*

p<

.05,

**p

<.0

1,**

*p<

.001

(tw

o-ta

iled

test

s).

Not

es:

All

mod

els

incl

ude

afi

rst-

orde

rau

toco

rrel

atio

nco

rrec

tion

.Eac

hce

llre

port

sth

eu

nst

anda

rdiz

edco

effi

cien

tw

ith

the

stan

dard

erro

rin

pare

nth

eses

.



TAB

LE4.

Ran

dom

Eff

ects

Mod

els

ofth

eH

RD

I,A

mn

esty

Rat

ing

(AR

),an

dTr

eaty

Rat

ifica

tion

s(T

R),

1975

–200

0

HR

DI

AR

TR

HR

DI

AR

TR

HR

DI

AR

TR

HR

DI

AR

TR

(5a)

(5b)

(5c)

(6a)

(6b)

(6c)

(7a)

(7b)

(7c)

(8a)

(8b)

(8c)

Tim

epe

riod

-.47

8***

(.01

8)

-.09

4***

(.02

4)

.446

***

(.01

2)

-.47

8***

(.01

8)

-.11

7***

(.02

4)

.445

***

(.01

2)

-.48

4***

(.01

8)

-.12

1***

(.02

3)

.447

***

(.01

2)

-.49

1***

(.02

0)

-.11

7***

(.02

7)

.458

***

(.01

3)

Trea

tise

rati

fica

tion

s.1

28**

(.04

1)

.176

***

(.04

2)

.174

***

(.04

1)

.170

***

(.04

6)

Am

nes

tyra

tin

g.0

96**

(.03

1)

.133

***

(.03

0)

.138

***

(.03

2)

.134

***

(.03

2)

Wes

tern

stat

us

.697

**

(.22

0)

1.24

5***

(.17

1)

.501

***

(.14

4)

Com

mon

wea

lth

.570

**

(.17

9)

.365

*

(.15

8)

-.29

0*

(.11

7)

Civ

ilw

ar-.

589*

**

(.08

5)

-.57

4***

(.06

9)

.070

(.06

3)

Eth

nic

-lin

guis

tic

-.33

9

(.28

4)

-.71

6**

(.26

5)

-.35

3*

(.17

9)

Obs

erva

tion

s75

575

575

575

575

575

575

575

575

561

161

161

1

Stat

es16

716

716

716

716

716

716

716

716

712

412

412

4

R2

wit

hin

.619

.042

.741

.619

.043

.741

.637

.114

.742

.641

.037

.767

R2

betw

een

.109

.242

.242

.117

.061

.215

.226

.332

.190

.029

.113

.085

R2

over

all

.295

.230

.465

.300

.058

.445

.359

.264

.430

.307

.101

.494

†p<

.1,*

p<

.05,

**p

<.0

1,**

*p<

.001

(tw

o-ta

iled

test

s).

Not

es:

All

mod

els

incl

ude

afi

rst-

orde

rau

toco

rrel

atio

nco

rrec

tion

.Eac

hce

llre

port

sth

eu

nst

anda

rdiz

edco

effi

cien

tw

ith

the

stan

dard

erro

rin

pare

nth

eses

.



TAB

LE5.

Ran

dom

Eff

ects

Mod

els

ofth

eH

RD

I,A

mn

esty

Rat

ing

(AR

),an

dTr

eaty

Rat

ifica

tion

s(T

R),

1975

–200

0

HR

DI

AR

TR

HR

DI

AR

TR

HR

DI

AR

TR

HR

DI

AR

TR

(9a)

(9b)

(9c)

(10a

)(1

0b)

(10c

)(1

1a)

(11b

)(1

1c)

(12a

)(1

2b)

(12c

)

Tim

epe

riod

-.47

8***

(.01

8)

-.10

3***

(.02

4)

.446

***

(.01

2)

-.50

6***

(.02

2)

-.13

1***

(.02

8)

.451

***

(.01

4)

-.51

0***

(.02

0)

-.13

3***

(.02

5)

.459

***

(.01

4)

-.42

9***

(.02

4)

-.11

0***

(.02

6)

.394

***

(.01

6)

Trea

tise

rati

fica

tion

s.1

47**

*

(.04

2)

.153

**

(.04

7)

.162

***

(.04

2)

.188

***

(.04

3)

Am

nes

tyra

tin

g.1

10**

*

(.03

1)

.105

***

(.03

2)

.133

***

(.03

3)

.131

***

(.03

0)

Cor

est

atu

s.7

18**

(.25

2)

1.17

3***

(.20

5)

.394

*

(.16

6)

FDI

infl

ows

.018

†

(.01

0)

.016

*

(.00

8)

-.00

3

(.00

7)

Trad

eop

enn

ess

.455

***

(.09

0)

.498

***

(.07

4)

.002

(.06

6)

IOs

-.23

1***

(.06

8)

-.07

0

(.06

0)

.237

***

(.04

1)

Obs

erva

tion

s75

575

575

564

164

164

169

869

869

872

472

472

4

Stat

es16

716

716

715

015

015

016

216

216

216

716

716

7

R2

wit

hin

.619

.043

.741

.610

.045

.742

.629

.059

.742

.630

.053

.744

R2

betw

een

.101

.174

.213

.097

.041

.224

.205

.260

.224

.123

.018

.408

R2

over

all

.290

.165

.446

.280

.036

.434

.363

.210

.448

.277

.015

.509

†p<

.1,*

p<

.05,

**p

<.0

1,**

*p<

.001

(tw

o-ta

iled

test

s).

Not

es:

All

mod

els

incl

ude

afi

rst-

orde

rau

toco

rrel

atio

nco

rrec

tion

.Eac

hce

llre

port

sth

eu

nst

anda

rdiz

edco

effi

cien

tw

ith

the

stan

dard

erro

rin

pare

nth

eses

.



collinearity present in model 13. Interested readers may also turn to Appendix A, whichreports the correlation matrix for all variables.

Appendix B presents a set of sensitivity analyses that investigate alternative specifi-cations of the final random effects model (Table 6, model 14). The appendix presentsresults from 10 models that feature (1) an alternative GDP PC measure based on foreignexchange rates rather than PPP; (2–4) a set of alternative Commonwealth measures thatincludes former members, excludes newer members, and excludes current memberswho have previously withdrawn or been suspended; (5) an alternative measure of corestatus based on Snyder and Kick’s (1979) original version; (6) a sample that excludesHRDI scores based on USSD ratings of human rights; (7) a sample restricted to those130 states present in at least two waves; (8) a balanced sample of 106 states present in allfive waves; (9) a sample that excludes outliers; and (10) a sample that excludes Westernand core nations (see the notes below Appendix B for more details). As the resultsindicate, these alternative specifications produce results that are quite similar to thosereported in the main analyses below.

TABLE 6. Random Effects Models of the HRDI, 1975–2000

Summary of effects

(Tables 3–5)

HRDI

(13)

HRDI

(14)

Time period Negative -.377*** (.030) -.394*** (.026)

GDP PC (PPP) Positive .330*** (.092) .265*** (.070)

Population size Negative .070 (.057)

Democratization Nonsignificant

Newspapers ¥ 102 Positive .051 (.068)

Western status Positive .731** (.280) .591* (.257)

Commonwealth Positive .606*** (.136) .500*** (.129)

Civil war Negative -.577*** (.093) -.560*** (.090)

Ethnic-linguistic Nonsignificant

Core status Positive .536† (.297) .641* (.274)

FDI inflows Positive -.012 (.010)

Trade openness Positive .343** (.113) .237* (.097)

IOs Negative -.733*** (.118) -.554*** (.077)

Max VIF 5.73 3.13

Mean VIF 2.73 2.01

Observations 549 633

States 136 151

R2 within .678 .669

R2 between .551 .555

R2 overall .598 .590

†p < .1, *p < .05, **p < .01, ***p < .001 (two-tailed tests).

Notes: All models include a first-order autocorrelation correction. Each cell reports the unstand-

ardized coefficient with the standard error in parentheses.



RESULTS

Tables 3 to 5 present results from random effects models of the HRDI regressed on thefeatured predictors over five waves during the 1975 to 2000 period. The 12 “a” modelsacross Tables 3 to 5 regress the HRDI on each of the 12 predictors, while the 12 “b” and12 “c” models decompose the HRDI to regress the Amnesty rating and treaty ratifica-tions, respectively, on the predictors. As the models collectively reveal, the HRDI is aunique product of its two constituent parts, and it is equally correlated with Amnestyrating (r = .675) and treaty ratifications (r = -.675). All models include the time periodcontrol, which is highly significant as a negative predictor of the HRDI across all models.The negative effect of time period reflects stable Amnesty ratings coupled with increasesin the number of treaty ratifications. Thus, any relationship revealed in these modelsbetween the independent variables and the HRDI is net of this period effect.

Table 3 features models 1 to 4, which provide preliminary tests for the resourcecapacity/strain and evaluation/monitoring hypotheses. Models 1a and 2a providesupport for the resource capacity/strain hypotheses, as GDP PC (+) and population size(-) are both significantly associated with the HRDI in the expected directions. Wealthierstates fall on the technical end of the HRDI, while more populous states reside on theinstitutional end of the HRDI. In models 1b and 1c, I decompose the HRDI into itsconstituent parts to examine whether GDP PC positively affects a state’s Amnesty rating,negatively affects a state’s treaty ratifications, or both. Model 1b shows that GDP PCpositively affects human rights practices (b = .355; p < .001), while model 1c shows thatwealthier states do not ratify human rights instruments significantly more so thanpoorer states. However, the coefficient in model 1c is positive (b = .074), which deflatesthe positive effect of GDP PC on the HRDI in model 1a (b = .263; p < .001). As expected,then, the gap between rich and poor countries in treaty ratifications is considerablysmaller than the gap between rich and poor countries in their Amnesty ratings. Inmodels 2b and 2c, I decompose the HRDI and find that population size negatively affectsa state’s Amnesty rating (b = -.248; p < .001), as expected, while positively affectingtreaty ratifications (b = .068; p < .05), which works to inflate the negative effect ofpopulation size on the HRDI in model 2a (b = -.276; p < .001). Thus, large countries aremore likely to ratify treaties and less likely to implement them. The negative effect onAmnesty rating is consistent with previous studies. However, the positive effect onratification is new (but consistent with theoretical expectations) and may be a functionof the higher profile that large countries occupy.

Models 3a and 4a test the evaluation/monitoring hypotheses. In model 3a, democ-ratization is not a significant predictor of the HRDI. As expected, democratizationpositively affects a state’s Amnesty rating in model 3b (b = .038, p < .001) but alsopositively affects a state’s treaty ratifications in model 3c (b = .035, p < .001), therebyproducing no net effect on the HRDI (b = .004). In models 4b and 4c, newspapercirculation similarly exerts positive effects on both Amnesty rating (b = .240; p < .001)and treaty ratifications (b = .096; p < .01), but the impact on the former is considerablystronger, thereby producing a positive net effect on the HRDI (b = .135; p < .05).



Table 4 shows results for models 5 to 8, which provide preliminary tests for thecognition and conflict hypotheses. Models 5 and 6 reveal that both Western societies andmembers of the British Commonwealth reside near the technical end of the HRDIcontinuum. Both Western status and Commonwealth membership exert positive effectson a state’s Amnesty rating in models 5b (b = 1.245; p < .001) and 6b (b = .365; p < .05),respectively. However, Western nations are also significantly more likely to ratify humanrights instruments (b = .501, p < .001), thereby deflating the positive effect of Westernstatus on the HRDI in model 5a (b = .697, p < .01). In contrast, Commonwealth mem-bership negatively affects treaty ratifications (b = -.290; p < .05), thereby inflating itspositive effect on the HRDI in model 6a (b = .570; p < .01). In short, both Western andCommonwealth nations have human rights records that outpace their treaty commit-ments, but Western nations have given themselves a much higher bar to hop over.

Models 7 and 8 test the conflict hypotheses. As expected, civil war negatively affectsa state’s HRDI score (b = -.589; p < .001) via its negative impact on Amnesty rating(b = -.574; p < .001) and nonsignificant effect on treaty ratifications (b = .070). In con-trast, the negative effect of ethnic-linguistic fractionalization on both Amnesty rating(b = -.716; p < .01) and treaty ratifications (b = -.353; p < .05) produces a nonsignifi-cant net effect on the HRDI (b = -.339). Thus, while fragmented societies are signifi-cantly more repressive than homogenous ones, they are also significantly less likely toparticipate in human rights instruments. Theoretically, the latter finding is somewhatsurprising, as I expected fragmented states to embrace the abstract principles featured inhuman rights instruments. It is possible that this result reflects my inclusion of the firstoption protocol (designed to enhance monitoring and enforcement) in the treaty rati-fications measure.

Table 5 shows results for models 9 to 12, which test the isomorphism hypotheses.Models 9a to 9c show that core status positively affects a state’s HRDI score via its strong,positive association with Amnesty rating (b = 1.173; p < .001). However, core states alsoratify human rights treaties significantly more than noncore states (b = .394; p < .05),which reduces the positive effect of core status on the HRDI (b = .718; p < .01). Thus, thegap between core and noncore states regarding the Amnesty rating is notably larger thanthe gap between core and noncore states regarding treaty ratifications.

Models 10 and 11 reveal that FDI and trade openness are associated with thetechnical end of the HRDI, while model 12 surprisingly shows that IOs push statestoward the institutional end. Thus, economic and cultural globalization appear to exertcountervailing effects on loose coupling in the human rights sector. In models 10a to10c, foreign investment positively affects the HRDI (b = .018; p < .10) via its positiveeffect on a state’s human rights performance (b = .016; p < .05) and no impact on treatyratifications (b = -.003). Similarly, trade openness positively affects the HRDI, as shownin model 11a (b = .455; p < .001), via its positive impact on the Amnesty rating in model11b (b = .498; p < .001) and no impact on treaty ratifications in model 11c (b = .002).Thus, economically integrated countries do not ratify human rights instruments signifi-cantly more than isolated economies but have significantly higher Amnesty ratings,thereby producing significantly higher HRDI scores. In stark contrast, models 12a to 12c



show that cultural globalization is positively associated with treaty ratifications(b = .237; p < .001), but not human rights performance (b = -.070), thereby producinga significant negative effect on the HRDI (b = -.231; p < .001). Thus, membership in IOsappears to oversocialize states to adopt human rights models but has no efficacy inactually improving human rights practices.

Table 6 presents results when regressing the HRDI on the significant predictors frommodels 1 to 12 simultaneously (the first column of Table 6 summarizes the results fromTables 3–5). Model 13 represents the full model (minus the nonsignificant predictorsfrom the previous analyses: democratization and ethnic-linguistic fractionalization),controlling for time period. Population size, newspaper circulation, and FDI inflowsbecome nonsignificant in the full model. The mean VIF score in model 13 is 2.73, whichsuggests potential collinearity among the predictors that dropped out of significance.The correlation matrix in Appendix A indicates that newspaper circulation is highlycorrelated with GDP PC (r = .742) and that population size is highly correlated withtrade openness (r = -.636) (these represent the highest correlations in the matrix).

In model 14, I drop the nonsignificant predictors from model 13. The maximum VIFscore is 3.13, the mean VIF score drops to 2.01, and the remaining predictors all retaintheir significance. GDP PC, Western status, Commonwealth membership, core status,and trade openness are positive predictors of a state’s HRDI score, while civil war andIOs remain powerful negative predictors. Thus, with the exception of the monitoring/evaluation hypotheses, the loose coupling model performs fairly well in explainingvariation in the HRDI. In addition, globalization continues to exert countervailingeffects, with trade openness positively associated with the HRDI (b = .237, p < .05) andIO memberships exerting a highly significant negative effect (b = -.554, p < .001). Model14 represents the core model of the study and explains almost 60 percent of the variationin HRDI scores (R2 overall = .590) in a sample of 633 observations across 151 states overfive waves during the 1975 to 2000 time period.

DISCUSSION

This study addresses a set of research puzzles that have increasingly occupied theattention of human rights scholars. Why do some states ratify human rights treatiesbut fail to implement them? Why do other states participate less actively in humanrights instruments but nevertheless exceed their commitments? By comparing formalpledges to actual performance (rather than examining either outcome in isolation),this study is able to identify and explain gaps in the implementation of human rightsstandards. While activists are typically most concerned with human rights “underper-formers,” this project also identifies a set of nations that are less interested in publicrituals than with their actual practices. Thus, although students of decoupling typicallyassume that organizational models diffuse sector-wide, my data reveal considerablevariation in this regard, indicating that treaty participation is far from automatic. Insum, the findings from this study contribute to the existing literature that examines the



function and efficacy of multilateral agreements (or lack thereof) in reforming nation-state activity, as well as what factors push countries closer to (or further from) theirformal commitments.

In this study, I develop and test a model of loose coupling in the human rights sectorof the world polity. The findings provide broad support for most of the hypotheses setforth in this investigation. Resource capacity, cognition, conflict, and isomorphismappear to play important roles in predicting a state’s location on the HRDI. A state’s levelof wealth is a significant, positive predictor of a state’s HRDI score, while Western andCommonwealth nations achieve significantly higher HRDI scores than other countries.Also, as expected, the presence of civil war is a significant, negative predictor of theHRDI.

The isomorphism hypotheses receive some support as well. Noncore states havesignificantly lower HRDI scores than the core, as weaker countries in the semiperipheryand periphery are subjected to a variety of external pressures to ratify but not necessarilyimplement human rights treaties (coercive isomorphism). To be sure, core states stillratify treaties at a significantly higher rate than the noncore, but the discrepancy inratification is far lower than that of performance. Also, nations integrated in the worldeconomy via trade feature significantly higher HRDI scores than more isolated states(FDI is also a positive predictor in its initial model but drops out of significance in thefull model). Moreover, the success of trade openness for predicting a state’s location onthe HRDI does not appear to be driven by developed states in the West and/or the core.Rather, the positive effect of trade openness is enhanced when Western and core statesare excluded from the sample (see Appendix B, model 10). This implies that developingstates that follow the advanced world into the global economy may also look to imitatewealthy nations when adopting progressive human rights practices (mimetic isomor-phism). In contrast, I did not find support for the hypothesis that IOs socialize states toimplement human rights models (normative isomorphism). In fact, IOs are much moreeffective at socializing states to ratify treaties, leading to the overall negative impact ofIOs on a state’s HRDI score. In this way, economic (+) and cultural (-) globalizationexert countervailing effects on a state’s location on the HRDI continuum. More gener-ally, it appears that coercive and normative isomorphism produce “ceremonial conver-gence,” as they work to push states toward treaty ratification through force andsocialization, respectively, while mimetic isomorphism produces a more “material con-vergence,” leading states to imitate actual practices.

Other measures in this study were less successful in predicting a state’s location onthe HRDI. First and foremost, the evaluation/monitoring hypotheses did not receivemuch support. Democratization (evaluation) has no net effect on the HRDI via itsequally strong positive effects on a state’s Amnesty rating and its treaty ratifications.Newspaper circulation (monitoring) was positively associated with the HRDI in pre-liminary models but dropped out of significance in the full model because of collinearitywith GDP PC. Similarly, population size became nonsignificant when controlling for theother predictors in the full model. Perhaps the most interesting nonfinding was ethnic-linguistic fractionalization’s nonsignificance in the preliminary models because of its



negative effect on both human rights performance and treaty ratifications, therebyproducing no net effect on a state’s HRDI score. While I hypothesized that fragmentedstates featured significantly lower Amnesty ratings because of the repressive manage-ment of internal conflict, I also suspected that these societies would act as institutionalstates by embracing the vague principles promoted within human rights treaties.Instead, fragmented states appear to eschew human rights standards altogether.

Conceptually, I refer to nations with high HRDI scores as technical states and thosewith low HRDI scores as institutional states. Wealthy, core nations that are Western (orpart of the British Commonwealth) sit toward the technical end of the HRDI con-tinuum. These states are internally responsive, having the material and cognitive capac-ity to implement models that are highly valued by their citizenry. While Western andcore nations are also more likely to ratify human rights instruments, the difference inratification rates between the Western core and the non-Western/noncore is noticeablysmaller than the difference in implementation. Thus, non-Western nations in theperiphery acquiesce at least somewhat to the pressure to symbolically participate ininternational ceremony even though they are unwilling and/or unable to fully imple-ment human rights models. In contrast to the technical end of the HRDI, institutionalstates are weak and resource dependent upon the international community for survival.Thus, institutional states tend to be externally responsive, adopting (but not necessarilyimplementing) human rights models. Institutional states are poorer, ratifying humanrights treaties at approximately the same rate as wealthier nations but lacking theresources to fully implement them. Cognitively, they are relatively unfamiliar with theindividualist human rights models they import, as identities in these societies tend to beestablished at more communal levels.

CONCLUSION

Past studies investigating human rights have failed to simultaneously address the wide-spread diffusion of human rights principles and their uneven implementation as out-comes that require empirical explanation. Consequently, human rights outcomes areseldom interpreted from a loose coupling perspective. In this study, my adoption of theloose coupling perspective allows me to identify factors that not only predict variation ina country’s Amnesty rating and its number of treaty ratifications, but factors that predictgaps in implementation as well as gaps in formal participation. Instead of treating allnation-state entities as fundamentally identical in character, the current approach treatseach state’s level of formal commitment to human rights as a baseline by which it can beevaluated. In this way, critics of the human rights agenda are in a weaker position toclaim that this study evaluates states by subjective standards that are imposed uponthem.

To close, scholars interested in decoupling processes within organizational settingshave yet to apply these principles to the world polity in a holistic fashion. This studyintroduces an empirical measure of loose coupling in the human rights sector andadopts an institutional perspective to help explain this social problem. Future studies



should investigate loose coupling in other sectors of the world polity and continue toincorporate a wide range of explanations to address institutional behavior rather thanpursue more restricted narratives.

ACKNOWLEDGMENTS

I thank Clem Brooks and Amy Kroska for their generous assistance. I thank EmilieHafner-Burton for kindly sharing her data.

NOTES

1Because organizational scholars assume that models diffuse globally and that performance is the

only source of variation, past research on decoupling has typically focused on organizations that

fail to implement formally adopted models. However, cases such as the United States illustrate

that (1) there is considerable variation in both human rights practices and treaty participation,

and that (2) nations may disproportionately focus their attention on either dimension. Thus,

“top-coding” my data to prevent variation on the positive end of the HRDI or restricting my

analysis to only those countries that occupy the negative end of the HRDI would remove an

interesting and important source of variation in my data.2The following list includes all 167 states in the sample (all states appear in all five waves, unless

otherwise noted): Afghanistan, Albania, Algeria, Angola, Argentina, Armenia (2 waves), Australia,

Austria, Azerbaijan (2), Bahamas, Bahrain, Bangladesh, Barbados, Belarus (2), Belgium, Benin,

Bhutan (4), Bolivia, Bosnia-Herzegovina (2), Botswana, Brazil, Brunei (4), Bulgaria, Burkina

Faso, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Central African Republic, Chad,

Chile, China, Colombia, Comoros, Congo-R, Congo-DR, Costa Rica, Croatia (2), Cuba, Cyprus,

Czech Republic (2), Denmark, Djibouti, Dominican Republic, Ecuador, Egypt, El Salvador,

Equatorial Guinea, Eritrea (2), Estonia (2), Ethiopia, Fiji, Finland, France, Gabon, Gambia,

Georgia (2), Germany (4), Ghana, Greece, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti,

Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Ivory Coast,

Jamaica, Japan, Jordan, Kazakhstan (2), Kenya, Kuwait, Kyrgyzstan (2), Laos, Latvia (2), Lebanon

(4), Lesotho, Liberia, Libya, Lithuania (2), Luxembourg, Macedonia (2), Madagascar, Malawi,

Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius (4), Mexico, Moldova (2), Mongolia (3),

Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger,

Nigeria, North Korea (1), Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru,

Philippines, Poland, Portugal, Qatar, Romania, Russia (2), Rwanda, Saudi Arabia, Senegal, Serbia-

Montenegro (2), Sierra Leone, Singapore, Slovakia (2), Solomon Islands, Somalia, South Africa,

South Korea, Spain, Sri Lanka, Sudan, Suriname, Swaziland, Sweden, Switzerland, Syria, Tajiki-

stan (2), Tanzania, Thailand, Togo, Trinidad-Tobago, Tunisia, Turkey, Turkmenistan (2), Uganda,

Ukraine (2), United Arab Emirates, United Kingdom, United States, Uruguay, Uzbekistan (2),

Venezuela, Vietnam, Yemen (2), Zambia, and Zimbabwe.3Unless otherwise noted, the following measures come from the World Development Indicators

(International Bank for Reconstruction and Development 2004).

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(.09

6)

.254

**

(.09

8)

.241

*

(.09

7)

.240

*

(.09

7)

.228

*

(.09

9)

.200

†

(.11

0)

.237

*

(.09

7)

.297

**

(.10

9)

IOs

-.59

8***

(.07

8)

-.56

4***

(.07

6)

-.57

6***

(.07

7)

-.53

5***

(.07

8)

-.55

5***

(.07

8)

-.55

8***

(.07

9)

-.60

7***

(.08

4)

-.66

4***

(.11

3)

-.55

6***

(.07

7)

-.57

5***

(.08

0)

Obs

erva

tion

s63

363

363

363

363

362

161

253

062

551

4

Stat

es15

115

115

115

115

115

013

010

615

112

7

R2

wit

hin

.670

.669

.669

.669

.669

.669

.668

.677

.664

.634

R2

betw

een

.567

.570

.562

.536

.551

.551

.535

.538

.567

.557

R2

over

all

.601

.601

.596

.576

.587

.587

.590

.600

.594

.584

†p<

.1,*

p<

.05,

**p

<.0

1,**

*p<

.001

(tw

o-ta

iled

test

s).

Not

es:

All

mod

els

incl

ude

afi

rst-

orde

rau

toco

rrel

atio

nco

rrec

tion

.Eac

hce

llre

port

sth

eu

nst

anda

rdiz

edco

effi

cien

tw

ith

the

stan

dard

erro

rin

pare

nth

eses

.a T

he

follo

win

gn

otes

desc

ribe

each

ofth

e10

mod

els

show

nab

ove:

(1)

GD

PP

Cm

easu

reba

sed

onfo

reig

nex

chan

gera

tes

(FX

),ra

ther

than

purc

has

ing

pow

erpa

rity

(PP

P);

(2)

Com

mon

wea

lth

mea

sure

alte

red

byin

clu

din

gfo

rmer

mem

bers

:Ire

lan

dan

dZ

imba

bwe;

(3)

Com

mon

wea

lth

mea

sure

alte

red

byex

clu

din

gn

ewer

mem

bers

(pos

t-19

75m

embe

rsh

ip):

Cam

eroo

n,M

ozam

biqu

e,N

amib

ia,a

nd

the

Solo

mon

Isla

nds

;(4)

Com

mon

wea

lth

mea

sure

alte

red

byex

clu

din

gcu

rren

tmem

bers

wh

oh

ave

prev

iou

sly

wit

hdr

awn

orbe

ensu

spen

ded:

Fiji,

Nig

eria

,Pak

ista

n,a

nd

Sou

thA

fric

a;(5

)C

ore

stat

us

alte

red

byin

clu

din

gPo

rtu

gal,

Sou

thA

fric

a,an

dSp

ain

,refl

ecti

ng

Snyd

eran

dK

ick’

s(1

979)

orig

inal

vers

ion

ofth

em

easu

re;(

6)Sa

mpl

e

rest

rict

edby

drop

pin

gH

RD

Isc

ores

base

don

U.S

.Sta

teD

epar

tmen

trat

ings

ofhu

man

righ

ts;(

7)Sa

mpl

ere

stri

cted

toth

ose

130

stat

espr

esen

tin

atle

astt

wo

wav

es;(

8)Sa

mpl

eis

bala

nce

d,

rest

rict

edto

thos

e10

6st

ates

pres

ent

inal

lfive

wav

es;(

9)Sa

mpl

ere

stri

cted

bydr

oppi

ng

outl

iers

form

ally

iden

tifi

edu

sin

gth

eH

adi

proc

edu

reav

aila

ble

inSt

ata

10.0

(Sta

taC

orpo

rati

on

2007

).T

he

proc

edu

reid

enti

fies

mu

ltip

leou

tlie

rsin

mu

ltiv

aria

teda

tau

sin

gO

LS(I

use

the

p<

.05

sign

ifica

nce

leve

lfor

my

outl

ier

cuto

ff);

(10)

Sam

ple

rest

rict

edby

rem

ovin

gW

este

rnan

d

core

stat

esfr

oman

alys

is.



Documents

TECHNICAL AND INSTITUTIONAL STATES: Loose Coupling in the Human Rights Sector of the World Polity