Dangerous liaisons: An endogenous model of international trade and human rights. (May 2016)

• Record Type: Journal Article
• Authors: Chyzh, Olga
• Other Names: Dorussen Han guest-editor.; Gartzke Erik guest-editor.; Westerwinter Oliver guest-editor.
• Is Part Of: Journal of peace research. Volume 53:Number 3(2016:May)
• Page Start: 409
• Page End: 423
• Publication Date: 2016-05
• Subjects: human rights -- indirect trade -- network analysis -- network game -- repression; Peace -- Periodicals; Conflict management -- Periodicals; International relations -- Periodicals; 327.17205
• DOI: 10.1177/0022343316629605
• Languages: English
• ISSNs: 0022-3433

Abstract

This article applies recent advances in network analysis to highlight a central tension faced by policymakers – balancing the benefits of engaging with the international system and the associated domestic policy costs. International trade rewards certain domestic practices, such as respect for human rights. Enforcing such practices, however, is politically costly and sometimes prohibitive to state leaders who rely on political repression to stay in power. In such cases, domestic elites often resort to an alternative strategy of securing the benefits of international trade – setting up indirect trade channels through intermediary states. These competing incentives are modeled within a single framework using a formal network game in which states form trade-links (direct or indirect) with other states, while simultaneously choosing their optimal level of domestic human rights protections. The model suggests that there may be an inverse relationship between a state's embeddedness within a network of indirect trade and respect for human rights: indirect trade channels serve as loopholes that allow domestically troubled states to enjoy the benefits of trade without pressure for domestic improvement. The predictions are supported by the results of the empirical analyses of the international trade and repression data (1987–2000), conducted using a coevolutionary actor-oriented longitudinal-network model – a statistical estimator that closely mimics the theoretical model.