Replication of Previous Studies with the CIRI Human Rights DatasetRights Dataset

I am conducting analyses on a dataset that, in comparison to the Political Terror Scales, so far has not been as excessively used in empirical analyses of the eectiveness of and compliance with IHRT. The present analysis therefore aims rst at replicating previous

5 Strongly autocratic = zero, strongly democratic = 20. In Appendix B the same relationship, but expressed in total numbers of country-years, is summarised in Table B.3.

6 Please note that at times of interregnum (Polity code = -77) HR records are not given by the CIRI Project due to an entire collapse of the central political authority in a given country. In Polity IV, countries of interregnum are coded as `neutral' (here `10') (Marshall and Jaggers 2005b, p. 16).

This explains the high percentage of missing records at Polity2-value 10, because in reality there is no government that could violate or respect human rights.

020406080100Percent of country−years

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Source: CIRI Human Rights Database 2007 and Polity IV Project 2004.

Frequent torture Occasional torture

No torture Missing records

Figure 6.4: Level of torture by level of democracy (Polity2).

studies on IHRL using the Cingranelli and Richards (2007) dataset in order to compare the results to the Political Terror Scales. This test aims at investigating, whether using an identical research sample and identical explanatory variables, but dependent variables taken from two dierent datasets, yields comparable results.

For simplicity, I rst determine a simple theoretical model that includes the main ex-planatory variables identied as theoretically and statistically signicant. This decision was based on research ndings on the eect of international human rights law in partic-ular and repression in general (Hafner-Burton and Tsutsui 2005: 2007; Hathaway 2002;

Keith 1999; Neumayer 2005; Poe and Tate 1994; Poe et al. 1999: 2006). I conduct both ordered logit and ordered probit analyses on the aggregate indices of physical-integrity-rights violations from the CIRI and PTS datasets respectively. Results are reported in Table 6.1. Please note that the dummy explanatory variables for the level of physical-integrity-rights violations in the previous year dier across the two datasets contingent on the respective dependent variable of each model⁷.

7 The dummy indicators are to be read as follows: lphysint2-1 PHYSINT2 was `1' in the previous year etc. ptsai2-2 PTSAI2 was `2' in the previous year etc.

Table 6.1: Replication of previous studies with CIRI compared to PTS

CIRI ologit PTS ologit CIRI oprobit PTS oprobit

(1) (2) (3) (4)

Intnat. civil society -.097 .076 -.064 .037

(.115) (.113) (.063) (.065)

... table 6.1 continued

CIRI ologit PTS ologit CIRI oprobit PTS oprobit

(1) (2) (3) (4)

Trade (ln) -.041 -.045 -.020 -.044

(.109) (.122) (.060) (.070)

Aid dependence -.014 -.009 -.009 -.006

(.007)^∗∗ (.008) (.004)^∗∗ (.004)

Time trend (YEAR) .023 .006 .011 .003

(.008)^∗∗∗ (.008) (.004)^∗∗ (.005)

N observations 2223 2223 2223 2223

N clusters 141 141 141 141

Wald chi² 1235.4 958.055 1346.411 869.431

df 18 14 18 14

Log pseudo-likelihood -3491.271 -2072.752 -3490.421 -2088.229

McK&ZR² 0.672 0.682 0.684 0.684

Adj CountR² 0.223 0.389 0.224 0.388

Note: Method ordered logit and ordered probit in comparison.

Estimations with robust standard errors. Standard errors in parentheses.

∗p<0.05. ^∗∗p<0.01. ^∗∗∗p<0.001. McK&ZR²... McKelvey and Zavoina'sR².

First, the results of the two datasets are compared with each other followed by a com-parison with other research ndings.

With regard to goodness of t, several measures are appropriate to evaluate, in how far the estimated models are able to explain the observed outcome (see Long and Freese 2006, pp. 104113). Goodness of t is better, the closer the Log likelihood score is to zero. The PTS models t by far better and indicate substantial dierence between the two datasets. Other appropriate measures of t are McKelvey and Zavoina's pseudo-R² (hereafter McK&ZR²) and the Adjusted Count R². The rst indicates the proportion of explained variance. The latter reports the proportion of correct predictions in com-parison to the observed values but adjusted for the largest row marginal (Long 1997, p. 106 et sqq.; Long and Freese 2006, p. 111). The proportions of explained variance are comparable over both datasets. With regard to the Adj. CountR² however, PTS clearly outperforms the CIRI dataset with regard to correct predictions⁸. Still, both models equally only achieve a very low proportion of correct predictions of under 40%.

With regard to coecients, there is some considerable dierence between the oprobit and ologit models, because estimated coecients naturally dier from logit to probit

8 I suspect that the predictive inaccuracy of the CIRI Physical-Integrity-Rights-Index stems largely from its many, nely tuned categories. These are, on the one hand, a lot more dierentiating than the PTS and thus oer a better picture on a country's PIR situation. But, on the other hand, this descriptive quality also hampers any precise predictions.

by a factor of about 1.7 due to the diering scaling of the two models (Long and Freese 2006, p. 192)⁹.

Yet, we observe very substantive dierences between the two datasets. Results from the CIRI dataset show that external war does not achieve statistical signicance, while dependency on foreign aid and the time trend (YEAR) do. Furthermore, population size is statistically less signicant in the CIRI dataset. These ndings are completely contrary to the estimated results gained by using the PTS. Since we have utilised the same empirical estimation methods on entirely equal research samples, we have to con-clude that there are substantial dierences in the operationalisations of the dependent variables. Results are not robust across the two datasets with regard to these variables.

Theoretical and statistical results, obtained from further analyses with the CIRI dataset below, will hence not be entirely comparable to previous studies on government respect for personal-integrity rights.

Overall, my analysis replicates the results obtained in earlier empirical studies on the im-pact of IHRT on national personal-integrity-rights records. Independent of other factors and all else equal, CAT does not alter state practice with regard to personal-integrity violations. Across both datasets the ratication of CAT does not reach statistical signi-cance. The expected causal relations of democracy, internal war, economic development, population size and past practice to the use of torture were conrmed in my replication analysis, while trade proves statistically not signicant (please cf. Hafner-Burton and Tsutsui 2005: 2007; Hathaway 2002; Keith 1999; Neumayer 2005; Poe and Tate 1994;

Poe et al. 1999: 2006)¹⁰. Contrary to Hafner-Burton and Tsutsui (2005), I cannot de-tect an impact of linkage to international civil society on the level of PIR violations in either of the two datasets. A reason for this contradiction may be that in the present study the variable lniINGOpc was updated for ve subsequent years, which provided a stronger empirical test for the hypothesis that INGOs further government respect for HR.

As we are interested in disaggregating physical-integrity violations into dierent subsets, let us now proceed to the following section on the impact of CAT on torture.

9 I hence conne myself to only estimating ologit models in the following.

10 With regard to the estimated coecients of external war and trade, I explicitly conducted likeli-hood ratio tests on the signicance of these variables in the disaggregate models of PIR violations.

Due to repeated low signicance I decided to skip both variables from further disaggregate analyses (Hosmer and Lemeshow 2000, p. 93).

6.3 The Eect of a Ratication of CAT on the Member