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.
Cao: Department of Political Science, Penn State University (email:
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13 Unlike the vehicle emissions standards discussed in Vogel's study, the human rights practices of exporting countries would be an example of a process, rather than product, standard.
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16 Greenhill, Brian, Mosley, Layna and Prakash, Aseem, ‘Trade-based Diffusion of Labor Rights: A Panel Study, 1986–2002’, American Political Science Review, 103 (2009), 669–690
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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.
23 Risse, Thomas, Ropp, Stephen C. and Sikkink, Kathryn, eds, The Power of Human Rights: International Norms and Domestic Change (Cambridge: Cambridge University Press, 1999)
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.
26 Cingranelli, David L. and Richards, David L., ‘The Cingranelli and Richards (CIRI) Human Rights Data Project’, Human Rights Quarterly, 32 (2010), 401–424
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27 Cingranelli, David L. and Richards, David L., ‘Measuring the Level, Pattern, and Sequences of Government Respect for Physical Integrity Rights’, International Studies Quarterly, 43 (1999), 407–417
28 Data on bilateral trade were obtained from the IMF's Direction of Trade Statistics database.
29 Mitchell, Ronald B. and McCormick, James, ‘Economic and Political Explanations of Human Rights Violations’, World Politics, 40 (1988), 476–498
30 Our results hold even when we replace Total Trade by exports as proportion of GDP. Because most work in this area tends to employ total trade dependence as a covariate, to maintain consistency, we employ it as well.
31 Hymer, Stephen H., The International Operations of National Firms: A Study of Direct Foreign Investment (Cambridge, Mass.: MIT Press, 1976)
32 David L. Richards, Ronald D. Gelleny and David H. Sacko, ‘Money with a Mean Streak? Foreign Economic Penetration and Government Respect for Human Rights in Developing Countries’; Cingranelli and Richards, ‘Respect for Human Rights after the End of the Cold War’.
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.
36 Keck, Margaret E. and Sikkink, Kathryn, Activists beyond Borders: Advocacy Networks in International Politics (Ithaca, N.Y.: Cornell University Press, 1998)
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38 Bruce Bueno De Mesquita, Feryal Marie Cherif, George W. Downs and Alastair Smith, ‘Thinking Inside the Box: A Closer Look at Democracy and Human Rights’, International Studies Quarterly, 49 (2005), 439–458
39 Keith, Linda Camp, ‘The United Nations International Covenant on Civil and Political Rights: Does It Make a Difference in Human Rights Behavior?’, Journal of Peace Research, 36 (1999), 95–118
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40 Stephen C Poe, Neal Tate and Linda Camp Keith, ‘Repression of the Human Right to Personal Integrity Revisited: A Global Cross-National Study Covering the Years 1976–1993’, International Studies Quarterly, 43 (1999), 291–313
Hafner-Burton, Emilie M. and Tsutsui, Kiyoteru, ‘Justice Lost! The Failure of Human Rights Law to Matter where Needed Most’, Journal of Peace Research, 44 (2007), 407–425
41 Hafner-Burton and Tsutsui, ‘Justice Lost!’ Data were downloaded from http://www.princeton.edu/~ehafner/downloads/justice_lost.zip (accessed 7 September 2007). Hafner-Burton and Tsutsui had obtained raw data on civil wars from the Correlates of War dataset. Including a dummy variable for interstate war (also obtained from the replication data set of Hafner-Burton and Tsutsui) in addition to civil war did not lead to a significant change in our estimate of the effect of Bilateral Trade Context.
42 Hafner-Burton, Emilie M., ‘Trading Human Rights: How Preferential Trade Agreements Influence Government Repression’, International Organization, 59 (2005), 593–629
43 Using the natural log of Population Density, Total Trade and Inward FDI did not substantially affect the results.
44 Ellen Lutz and Kathryn Sikkink, ‘International Human Rights Law and Practice in Latin America’, International Organization, 54 (2000), 633–659
45 Keohane, Robert, ‘After Hegemony’ (Princeton, N.J.: Princeton University Press, 1984)
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46 Hafner-Burton, ‘Trading Human Rights’.
47 Following Hafner-Burton, we used the legal text of the agreements themselves to determine whether each PTA should be classified as ‘hard’ or ‘soft’. Of the 2,528 total country-years included in our model, around 11 per cent were considered to belong to PTAs with ‘hard’ conditions, and around 21 per cent belonged to PTAs with ‘soft’ conditions.
48 Simmons, Beth A., Mobilizing for Human Rights: International Law in Domestic Politics (New York: Cambridge University Press, 2009)
49 Gleditsch, Kristian Skrede and Ward, Michael D., ‘Diffusion and the International Context of Democratization’, International Organization, 60 (2006), 911–933
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50 O'Loughlin, John, Ward, Michael D., Lofdahl, Corey L., Cohen, Jordin S., Brown, David S., Reilly, David, Gleditsch, Kristian S. and Shin, Michael, ‘The Diffusion of Democracy, 1946–1994’, Annals, Association of American Geographers, 88 (1998), 545–574
51 We also tested whether the results were affected by the inclusion of a dummy variable indicating whether the state had previously been a colony of another state. Data on colonial histories were obtained from the CIA World Factbook. The inclusion of this variable did not significantly affect our estimate of the effect of Bilateral Trade Context.
52 Rosenbaum, Paul R. and Rubin, Donald B., ‘The Central Role of the Propensity Score in Observational Studies for Causal Effects’, Biometrika, 70 (1983), 41–55
Rubin, Donald B., ‘Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies’, Journal of Educational Psychology, 66 (1974), 688–701
53 Kosuke Imai and David A. van Dyk, ‘Causal Inference With General Treatment Regimes: Generalizing the Propensity Score’, Journal of the American Statistical Association, 99 (2004), 854–866
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’.
56 Mansfield, Edward, Milner, Helen and Rosendorff, Peter, ‘Free to Trade: Democracies, Autocracies and International Trade Negotiations’, American Political Science Review, 94, (2000), 305–321
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.
* Cao: Department of Political Science, Penn State University (email: firstname.lastname@example.org); 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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