Civil War Intervention and the Problem of Iraq

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* Stephen Biddle is Senior Fellow for Defense Policy at the Council on Foreign Relations. Jeffrey Friedman is a doctoral student at the Harvard Kennedy School of Government. Stephen Long is Assistant Professor of Political Science at Kansas State University. Authors are listed in alphabetical order. Correspondence should be addressed to Stephen Biddle, Council on Foreign Relations, 1779 Massachusetts Avenue NW, Washington DC 20036,, 202-518-3476.

1 Both groups see an important role for regime type (democracies are seen as less prone to intervene), Cold War dynamics (Cold War cases are seen as more prone to intervention), and African experience (African cases are generally seen as more prone to conflict) (Kathmann np, Khosla 1999).

2 This coding rule implies that dyad-years for ongoing interventions would be coded as zeros even though the case involved a continuing intervention. To avoid bias from this effect, dyad-years for states that intervene are dropped for years subsequent to the first cross-border troop movement.

3 The authors thank Kimuli Kasara at Columbia University for making these data available to us.

4 Sources for information on rebel groups include Clodfelter (2002), Minorities at Risk “Minority Group Assessments” (Minorities at Risk 2002), Library of Congress country studies, Encyclopedia Britannica, as well as individual sources particular to each conflict. We only included information on rebel ethnicity that could be confirmed across multiple sources. Specific citations for each coding are provided in the data set.

5 We thank Patrick Regan for his assistance in identifying and correcting several errors in the 2004 dataset. Examples of our corrections include the elimination of the double counting of the Biafran civil war and the replacement of the civil war state in four conflicts from Congo-Brazzaville to Congo-Kinshasa. All changes and additions to preexisting data are documented in the data manual, which is available from the authors.

6 There are two exceptions to this coding rule. Conflict #922 (Iran) was coded from Leitenburg (2003), since the PRIO figure did not include battle-related civilian deaths for that conflict; conflict #971 (Iraq) was coded from Clodfelter, since PRIO did not record any value for battle deaths in that conflict. We also produced robustness checks using an intensity variable based solely on PRIO codings, but this resulted in the loss of more than 65% of our observations used in the main analysis due to missing data.

7 As a check on the robustness of this definition of the Middle East region, we examined another coding of this variable that included North Africa and the Horn of Africa, the results of which are reported below.

8 UNHCR refugee data for years prior to 1965 contain a very high fraction of missing values; inclusion of this variable thus effectively drops 25 of the 142 civil wars from the master dataset. The best fit model reported in Table 1 below nevertheless includes refugees, but dropping this variable yields statistical results very similar to Model 1 (the only notable changes are that CWSPowerShare becomes insignificant, while PotIntAlliance moves to the 0.1 threshold of significance). Given that dropping refugees adds almost 2,000 observations, these are fairly minor changes. Where the UNHCR reported no refugees in a dyad, we changed the value to 1, making the natural logarithm of the entry zero.

9 Cf. Regan (2004) which, contrary to most literature, finds Africa less prone to intervention than other regions.

10 All statistical analyses are conducted in Stata 10 using the probit command. All analyses have been checked for specification error (using the “linktest” command) and multicollinearity (using the “collin” command). The AIC and BIC measures of model fit show that Model 1 is superior in fit to alternatives that change control variable operationalizations individually and in various combinations, such as using Rebel link v2 and State link v2 instead of v1; using intensity numbers based only on PRIO instead of Regan et al and PRIO; and including North Africa and the Horn of Africa in the Middle East. While the pseudo-r2 measures did not agree with the AIC and BIC on the best-fitting model, when selecting a primary model, theoretical decisions should be weighed along with model fit statistics, and we believe that Model 1 represents the best combination of theoretically sound variables. We also performed negative binomial and Poisson analyses on a version of the data in which civil war years, rather than civil war year dyads, made up the observations, adjusting the variables to reflect that data structure, but the reduced number of observations had severe effects on the statistical significance that we were able to observe in the models. Details of the results, model fit, and regression diagnostics for each of these individual changes and each of the combinations of these changes tested are available upon request from the authors. In all, the robustness checks make up more than 50 pages of regression output, so it is impractical to attempt to summarize the results of every alternative model here.

11 Refugees, fatalities, and intensity use the natural log of each, using a minimum value of 1 for refugees to undefined expressions.

12 While this change in predicted probability seems remarkably high, it is important to remember that the predicted probability of intervention when all variables are at their means (continuous variables) or modes (dummy variables), the probability of intervention in a given civil war state-third party dyad year is less than one fifth of one percent. Percentage changes in such a low probability of intervention can appear large, while still reflecting a relatively low chance of intervention (a 383% increase over the base level holding all variables at their means or modes, for instance, still results in only a 0.87% predicted probability of intervention by a given politically relevant potential intervener in a given year of a given civil war).

13 There is no consensus on a single best measure of fit quality for probit. We considered eight different approaches, six variants on pseudo-r2 measures (McKelvey and Zavoina, McFadden, McFadden adjusted, Cox-Snell, Cragg-Uhler and Efron), and two information criterion measures (Akaike, or AIC, and Bayesian, or BIC). All pseudo-r2 measures show a loss of fit quality for Models II and III, with a degree ranging from 7% (0.132 to 0.123) for McFadden’s adjusted r2 in Model II to 33% (0.03 to 0.02) for Efron’s r2 in Model III. AIC and BIC show less difference and the BIC actually implies a slight improvement in fit quality for the partial models (the BIC score for Model I, for example, is -77092.333; the score for Model II is -77293.963 and that for Model III is -85531.857), though this is unrepresentative of the range of measures overall. For the Model I-II comparison specifically, loss of fit quality by pseudo-r2 approaches varied between 7% for McFadden’s adjusted r2 (0.132 to 0.123) and 19% for McKelvey and Zavoina’s r2 (0.249 to 0.202); the loss of fit quality between Model I and Model III varied between 9% for McKelvey and Zavoina’s r2 (0.249 to 0.226) and 33% for Efron’s r2 (0.03 to 0.02).

14 While it may seem odd for intensity and refugees to have negative minimum values, remember that these are the natural logs of the actual values. In some cases, the actual value of these per-month averages are between 0 and 1, leading to a negative result after logging. This does not mean that we are assuming or imputing the possibility of negative casualties or refugees, and the logic of using and interpreting natural logs here still holds.

15 In these simulations, Bahrain and Iran are coded as having ethnic links to the civil war state; Jordan, Kuwait, Qatar, and Saudi Arabia have links to the rebels; Syria is majority Sunni but is ruled by Alawites, who are a Shi'a offshoot, so we ran the simulation twice, once with Syria coded as linked to the rebels and once with it linked to the state. The results presented in Table 3 are based on coding Syria as linked to the rebels (Syria’s Sunni majority to Iraq’s Sunni rebels). The alternative coding (linking Syria’s Alawite leaders to Iraq’s government) yields different, but still high, estimates for at least one intervention within 5 years (63%), 10 years (87%), and 15 years (96%), for example. Dummies for alliances, civil war democracy, joint democracy, the Cold War, and Africa are all set to zero. Turkey is coded as a former colonizer of Iraq. CINC scores for Iraq and its neighbors were calculated for 2008 using the most up-to-date information possible (CIA 2008, International Energy Agency 2008, International Institute for Strategic Studies 2008, International Iron and Steel Institute 2008; documentation available from the authors on request). We assume that current trends in regional arms acquisition will continue, and that Iraq's neighbors' CINC scores will grow by roughly 10 percent per year (though the results are largely insensitive to this assumption: if CINC scores are held constant over time the probability of greater than zero interventions falls from 0.73 in 5 years to 0.70; from 0.93 in 10 years to 0.91; and from 0.98 in 15 years to 0.97). Refugee counts were drawn from the UNHCR's 2007 tabulation. The intensity of the Iraq civil war was calculated by taking the Iraq Body Count's estimate of total violent deaths among the Iraq Security Forces and Iraqi civilians from the beginning of 2004 through the end of 2007 (roughly 95,000), adding 4,000 for the number of American troops killed in Iraq, and adding twice that number (8,000) as an estimate of the number of insurgents killed in Iraq. For Iraq's ratio of primary commodity exports to total GDP, we used the figure reported by Collier and Hoeffler for 1999.

16 Values in Table 4 are the fraction of all (10,000) simulation replications in which the given state intervened in an Iraqi civil war assumed to be ongoing as of the given time.

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