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²² There are other examples to support the claim that consumers use a strategy of boycotts in an attempt to change the human rights practices of a country as a whole, e.g. the consumer boycott of South Africa during the apartheid regime, the boycott of Israeli goods regarding the Palestinian issue, and the boycott of Chinese products in response to China's Tibet policies. For a listing of some consumer boycotts being practised in Britain, see http://www.ethicalconsumer.org/Boycotts/currentboycottslist.aspx. While effective labelling of the product or the process helps consumers and activist groups to target specific countries and firms, even in the absence of labelling, countries come under pressure to check human rights violations. This can be found in the case of petroleum, mining (see the recent initiatives on banning the import of materials from conflict zones), and even oil (Nigeria). By looking at total trade and not distinguishing between industries which may or may not have labelled products, this article sets a relatively hard test of the role of bilateral trade pressure on exporting countries’ human rights standards.

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²⁴ Our measure of the average human rights practices of each country's export destinations (Bilateral Trade Context) weights the human rights practices of each destination country by the proportion of total exports of the exporting country in each period. Thus a high value of Bilateral Trade Context reflects the fact that the most salient countries in a given country's export basket have a high human rights score.

²⁵ The Cingranelli–Richards (CIRI) Human Rights Dataset, www.humanrightsdata.org. Dataset Version 2007.04.12, accessed June 2007.

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³³ Apodaca, ‘Global Economic Patterns and Personal Integrity Rights after the Cold War’; Mark Gibney, Linda Cornett and Reed Wood, Political Terror Scale. (available at http://www.politicalterrorscale.org. (2008)).

³⁴ Richards, Gelleny and Sacko, ‘Money with a Mean Streak?’

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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⁵⁴ Ho et al., ‘Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference’. They argue that parametric analysis (such as the ordered probit regression that we use in the following analysis) with control variables is a better choice than simply taking the difference in means ((E(Y∣T = 1) − E(Y∣T = 0)) in the final matched sample. Indeed, standard parametric data analysis procedures only need to be changed when using subclassification, full matching, or matching with replacement. We use neareast neighbour matching without replacement in this study.

⁵⁵ Ho et al., ‘Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference’.

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⁵⁷ Some examples of country-years that have a mean value for the Bilateral Trade Context variable include Bosnia and Herzegovenia 1995 and 1999, Colombia 1999, Cote d'Ivoire 1989, Qatar 1994 and 1995, and Sri Lanka 1990. Examples of country-years that have a modal value for the Bilateral Trade Context variable include Algeria 1998, Chile 1988, India 1990, Malaysia 1981, and Tunisia 1988. Moreover, for most of the countries in our sample, there is enough temporal variation in the Bilateral Trade Context variable. To provide an illustration of how this variable changes over time, Table 2 includes a series of plots showing the levels of the variable over the 1981–2004 period for a randomly selected sample of eight countries.

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.