Civil war and the square of ethnic fractionalization

Steve Saideman wonders if the relationship between ethnic fractionalization and civil war is curvilinear, where increasing fractionalization increases the probability of civil war up to a certain point but then increasing fractionalization decreases the probability of civil war. Since I have my nose in this data right now, I’ve given this conjecture a quick probe. I find no evidence prima facie.

Below is a replication of Sambanis 2004 where I simply add the square of ethnic fractionalization. If Steve’s conjecture were true, we’d expect ef1 to be positive, ef1sq to be negative, and both of them to be significant. They are signed as expected but not significant given the controls recommended by Sambanis. I then try alternative codings of civil war and a simple equation with no controls. If Steve’s conjecture is true, it’s not obvious.

Everything below is reproducible in R-just download the replication data, easy to find with a quick search, and set your working directory to where the data is.

``` r Replicate Sambanis 2004 (Table 6 in paper, column 8, pp.845) library(foreign) sambanis<-read.dta(“SambanisJCR2004_replicationdataset.dta”) sambanis$ef1sq<-sambanis$ef1 * sambanis$ef1

model <- glm(warstnsb gdpl1 + grol1 + inst3l1 + anoc2l1 + oil2l1 + ef1 + lpopnsl1 + mtnl1 + warnsl1, data = sambanis, family = binomial(link = “probit”))
summary(model) ```

## Call:
## glm(formula = warstnsb ~ gdpl1 + grol1 + inst3l1 + anoc2l1 + 
##     oil2l1 + ef1 + lpopnsl1 + mtnl1 + warnsl1, family = binomial(link = "probit"), 
##     data = sambanis)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -0.604  -0.231  -0.173  -0.106   3.560  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -3.90226    0.46234   -8.44  < 2e-16 ***
## gdpl1       -0.09271    0.02295   -4.04  5.3e-05 ***
## grol1       -0.51380    0.49750   -1.03  0.30172    
## inst3l1      0.23713    0.09634    2.46  0.01384 *  
## anoc2l1      0.23792    0.08807    2.70  0.00690 ** 
## oil2l1       0.29680    0.11541    2.57  0.01012 *  
## ef1          0.35605    0.16455    2.16  0.03049 *  
## lpopnsl1     0.10503    0.02743    3.83  0.00013 ***
## mtnl1        0.00199    0.00182    1.09  0.27466    
## warnsl1     -0.06609    0.10492   -0.63  0.52873    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1133.2  on 5892  degrees of freedom
## Residual deviance: 1038.8  on 5883  degrees of freedom
##   (3567 observations deleted due to missingness)
## AIC: 1059
## 
## Number of Fisher Scoring iterations: 8

r Adding the square of ethnic fractionalization model <- glm(warstnsb gdpl1 + grol1 + inst3l1 + anoc2l1 + oil2l1 + ef1 + ef1sq + lpopnsl1 + mtnl1 + muslim + warnsl1, data = sambanis, family = binomial(link = "probit")) summary(model)

## Call:
## glm(formula = warstnsb ~ gdpl1 + grol1 + inst3l1 + anoc2l1 + 
##     oil2l1 + ef1 + ef1sq + lpopnsl1 + mtnl1 + muslim + warnsl1, 
##     family = binomial(link = "probit"), data = sambanis)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -0.595  -0.230  -0.172  -0.103   3.547  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -3.94606    0.48647   -8.11    5e-16 ***
## gdpl1       -0.09092    0.02370   -3.84  0.00012 ***
## grol1       -0.50874    0.49343   -1.03  0.30253    
## inst3l1      0.23723    0.09641    2.46  0.01386 *  
## anoc2l1      0.24253    0.08838    2.74  0.00607 ** 
## oil2l1       0.26000    0.12119    2.15  0.03192 *  
## ef1          0.51893    0.72622    0.71  0.47488    
## ef1sq       -0.17995    0.71442   -0.25  0.80113    
## lpopnsl1     0.10452    0.02775    3.77  0.00017 ***
## mtnl1        0.00184    0.00188    0.98  0.32529    
## muslim       0.00104    0.00111    0.93  0.35169    
## warnsl1     -0.06888    0.10501   -0.66  0.51185    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1133.2  on 5892  degrees of freedom
## Residual deviance: 1037.8  on 5881  degrees of freedom
##   (3567 observations deleted due to missingness)
## AIC: 1062
## 
## Number of Fisher Scoring iterations: 8

r Using Sambanis alternative coding of civil war model <- glm(warstns gdpl1 + grol1 + inst3l1 + anoc2l1 + oil2l1 + ef1 + ef1sq + lpopnsl1 + mtnl1 + muslim + warnsl1, data = sambanis, family = binomial(link = "probit")) summary(model)

## Call:
## glm(formula = warstns ~ gdpl1 + grol1 + inst3l1 + anoc2l1 + oil2l1 + 
##     ef1 + ef1sq + lpopnsl1 + mtnl1 + muslim + warnsl1, family = binomial(link = "probit"), 
##     data = sambanis)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -0.532  -0.217  -0.165  -0.102   3.499  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -3.73245    0.52338   -7.13  9.9e-13 ***
## gdpl1       -0.08649    0.02469   -3.50  0.00046 ***
## grol1       -0.09783    0.48861   -0.20  0.84131    
## inst3l1      0.23635    0.10911    2.17  0.03030 *  
## anoc2l1      0.28191    0.09773    2.88  0.00392 ** 
## oil2l1       0.16875    0.14066    1.20  0.23027    
## ef1          0.65104    0.77115    0.84  0.39853    
## ef1sq       -0.38362    0.77341   -0.50  0.61989    
## lpopnsl1     0.08969    0.03039    2.95  0.00316 ** 
## mtnl1        0.00209    0.00205    1.02  0.30854    
## muslim       0.00118    0.00124    0.95  0.34246    
## warnsl1     -0.21649    0.31589   -0.69  0.49313    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 915.34  on 5161  degrees of freedom
## Residual deviance: 842.13  on 5150  degrees of freedom
##   (4298 observations deleted due to missingness)
## AIC: 866.1
## 
## Number of Fisher Scoring iterations: 8

r Using Fearon and Laitin 2003 coding of civil war model <- glm(warst7b gdpl1 + grol1 + inst3l1 + anoc2l1 + oil2l1 + ef1 + ef1sq + lpopnsl1 + mtnl1 + muslim + warnsl1, data = sambanis, family = binomial(link = "probit")) summary(model)

## Call:
## glm(formula = warst7b ~ gdpl1 + grol1 + inst3l1 + anoc2l1 + oil2l1 + 
##     ef1 + ef1sq + lpopnsl1 + mtnl1 + muslim + warnsl1, family = binomial(link = "probit"), 
##     data = sambanis)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -0.501  -0.200  -0.145  -0.086   3.590  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -4.28989    0.54194   -7.92  2.5e-15 ***
## gdpl1       -0.08807    0.02665   -3.30  0.00095 ***
## grol1       -0.27141    0.52732   -0.51  0.60677    
## inst3l1      0.19536    0.10692    1.83  0.06768 .  
## anoc2l1      0.25845    0.09748    2.65  0.00802 ** 
## oil2l1       0.13327    0.14174    0.94  0.34707    
## ef1          0.44027    0.80427    0.55  0.58409    
## ef1sq       -0.22642    0.79784   -0.28  0.77657    
## lpopnsl1     0.11930    0.03065    3.89  9.9e-05 ***
## mtnl1        0.00255    0.00204    1.25  0.21124    
## muslim       0.00155    0.00122    1.26  0.20659    
## warnsl1      0.01688    0.11169    0.15  0.87987    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 897.81  on 5892  degrees of freedom
## Residual deviance: 822.48  on 5881  degrees of freedom
##   (3567 observations deleted due to missingness)
## AIC: 846.5
## 
## Number of Fisher Scoring iterations: 9

r Only ethnic fractionalization model <- glm(warstnsb ef1 + ef1sq, data = sambanis, family = binomial(link = "probit")) summary(model)

## Call:
## glm(formula = warstnsb ~ ef1 + ef1sq, family = binomial(link = "probit"), 
##     data = sambanis)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -0.303  -0.227  -0.196  -0.161   2.990  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -2.302      0.131  -17.63   <2e-16 ***
## ef1            0.323      0.589    0.55     0.58    
## ef1sq          0.283      0.580    0.49     0.63    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 1249.6  on 6268  degrees of freedom
## Residual deviance: 1230.5  on 6266  degrees of freedom
##   (3191 observations deleted due to missingness)
## AIC: 1237
## 
## Number of Fisher Scoring iterations: 7

Cite this post: RIS Citation BibTeX Entry

Murphy, Justin. 2013. "Civil war and the square of ethnic fractionalization," http://jmrphy.net/blog/2013/05/22/civil-war-and-the-square-of-ethnic-fractionalization-4/ (June 20, 2017).