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Where Is the Tipping Point? Bilateral Trade and the Diffusion of Human Rights

Published online by Cambridge University Press:  09 July 2012

Abstract

Drawing on a panel of 136 countries over the period 1982–2004, we study a tipping point version of Vogel's ‘California Effect’ in the context of the diffusion of human rights practices. Because human rights practices are often deeply embedded in a society's customs and political institutions, we expect that a high level of pressure from the importing countries is needed to bring about changes in an exporting country's human rights records. We find strong empirical support for this threshold effect; provided that the average level of respect for human rights in importing countries is sufficiently high, trading relationships can operate as transmission belts for the diffusion of human rights practices from importing to exporting countries.

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Articles
Copyright
Copyright © Cambridge University Press 2012

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Footnotes

*

Cao: Department of Political Science, Penn State University (email: xuc11@psu.edu); Greenhill: Department of Government, Dartmouth College; Prakash: Department of Political Science, University of Washington, Seattle. Previous versions of the article were presented at the annual conferences of the International Studies Association and the American Political Science Association. The authors thank Sarah Birch, Hugh Ward and the three reviewers for their comments. Replication data and R code as well as an online appendix containing more robustness checks are posted at: http://www.personal.psu.edu/xuc11/blogs/x/home/research/research.html. An appendix containing additional information is available online at: http://dx.doi.org/10.1017/S000712341200018X.

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35 Like many studies in the literature, we assume a linear relationship between wealth and human rights. However, as a robustness check we also tried testing for non-linearities in the relationship between GDP per capita and human rights by replacing the GDP per capita variable with a dummy variable indicating whether the country's GDP per capita exceeds $1,000. This alternative specification did not lead to significant change in the estimated effect of our key independent variable, Bilateral Trade Context. As a separate robustness test, we also decided to check whether a country's human rights performance may be affected by both the mean income of the country and also its income distribution. We experimented with the inclusion of the Gini coefficient variable to control for this potential distribution effect of income (data are from the World Income Inequality Database: http://www.wider.unu.edu/wiid/wiid.htm). The Gini coefficient varies theoretically from 0 (perfectly equal distribution of income) to 100 (the society's total income accrues to only one person/household unit). We find that in some model specifications, the Gini coefficient has a statistically significant and negative relationship to human rights, suggesting that countries with higher income inequality tend to have worse human rights practices. However, this relationship is not robust across all model specifications. Moreover, a large number of missing observations are introduced by including the Gini coefficient: for the model before matching, the number of observations is reduced by 1,433 – more than 50 per cent of observations. For models after matching, this scale of loss in the number of observations makes model estimation difficult. Therefore, we choose not to include this inequality variable for models reported in this article.

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58 ‘Jittering’ is a procedure for improving the display of bivariate data. This involves introducing a trivial amount of random variation in the position of overlapping points on a scatterplot in order to make it easier for the reader to get a sense of the distribution of the data. (This is especially useful when one of the variables is categorical and where multiple data points may otherwise be represented by a single overlapping point on a scatterplot).

59 We estimate ordered probit models because the dependent variable, PIR Score, takes on categorical values of 0 to 8. Moreover, because the independent variables cannot be expected to produce instantaneous changes in human rights practices, we lagged the independent variables by one year.

60 Note that in Figure 5, when the threshold to dichotomize the Bilateral Trade Context variable is larger than 7, even though the mean estimates of the treatment effect (black dots in the middle of the confidence intervals) are all above zero, the 95% confidence intervals become so large that the treatment effect becomes insignificant. This is largely a function of small sample sizes after matching when the threshold to dichotomize is too high. For example, when we use 7.1 as the threshold, there are only 388 observations left (from both the treatment and the control group); when we use 7.2 as the threshold, the number of observations is further reduced to 274.

61 Apodaca, ‘Global Economic Patterns and Personal Integrity Rights after the Cold War’; Hafner-Burton and Tsutsui, ‘Human Rights in a Globalizing World’; Poe, Tate and Camp Keith, ‘Repression of the Human Right to Personal Integrity Revisited’.

62 Hafner-Burton, ‘Trading Human Rights’. We also tested for potential interaction effects between PTA membership and Bilateral Trade Context, but did not find evidence of a significant effect of PTA membership. The fact that we found no significant effect of PTA membership in any of these models raises important questions about the efficacy of including human rights conditions in PTAs. We had coded our PTA variables using the system described by Hafner-Burton. However, we realize that this coding method has two important limitations. First, there arguably is a selection bias because countries that already have good human rights practices might self-select into PTAs with more demanding human rights standards. Second, the membership variable is simply a binary indicator of PTA membership and does not distinguish between countries that are members of multiple PTAs and countries that only participate in a single PTA. We believe that further work needs to address the question of how PTA membership might or might not affect human rights practices more carefully. We want to thank one anonymous reviewer for suggesting the idea of a potential PTA–Bilateral Trade Context interaction effect.

63 The correlation coefficients for Bilateral Trade Context with Hard PTA Membership and Soft PTA Membership are only 0.05 and −0.06, respectively, in the unmatched data.

64 Finnemore and Sikkink, ‘International Norm Dynamics and Political Change’.

65 We thank one of the anonymous reviewers for bringing this to our attention.

66 For a recent study of the conditional effects of domestic institutions on diffusion mechanisms, see Xun Cao and Aseem Prakash, ‘Trade Competition and Environmental Regulations: Domestic Political Constraints and Issue Visibility’, Journal of Politics, forthcoming.

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