Teaching with TwoRavens:
Replicating Fearon and Laitin (2003)
Fearon and Laitin (2003) is a landmark study on civil wars. It shows that many of the post-Cold War
civil conflicts were not due to ethnic differences, but rather to factors that enabled insurgencies to thrive.
These factors include mountainous terrain, low GDP, high population, and political instability.
This document will guide you through a replication of the statistical results in Fearon and Laitin (2003)
using TwoRavens.
Follow the link to the Teaching with TwoRavens dataverse. Click on the Fearon and Laitin replication dataset. Next to the “Download” button for the file repdata2.tab, click “Explore.” This should open a page, as shown in figure 1.1
In this module we will replicate the statistical analyses presented in Fearon and Laitin (2003). Their replication file, retrieved from http://web.stanford.edu/group/ethnic/publicdata/publicdata.html, reads as follows:
This is a replication script for Stata, so some knowledge of Stata syntax is required. logit means that we are using the logistic regression. The first variable name after logit is the dependent variable, which in this case is either onset, ethonset, emponset, or cowonset. In Stata syntax, the variable names following the dependent variable are the independent variables.
All of these variables are listed in the Data Selection panel of TwoRavens, under the Variables tab. If you mouseover these variable names, or click on them and mouseover the pebble in the center panel, you can view additional summary statistics, a brief description of the variable, and see its distribution.
Note that for Model #2, Fearon and Laitin subset the data for all observations where second > 0.49999, and estimates the model on that subset. Finally, the no log at the end of each line suppresses some of the information that Stata automatically prints to the console, and is not relevant for us here.
Begin with a clear modeling space. This means no variables are selected and thus no pebbles are in the center panel.
The resulting image in the center panel should appear like figure 2. Now that the relationships among variables have been specified, we now select the appropriate statistical model:
At this point, the variables have been selected, the relationships among them specified, and statistical model has been selected. Now, estimate the model:
Once the model is estimated, the results will appear in the right panel under the Results tab. Click the Models tab to return the right panel to the model list.
Notice that we need to estimate this model on a subset of the data. Specifically, on the subset where second > .049999.
Once the model is estimated, the results will appear in the right panel under the Results tab. You may toggle between Model 1 and Model 2 to see different sets of results. Click the Models tab to return the right panel to the model list.
Once the model is estimated, the results will appear in the right panel under the Results tab. Click the Models tab to return the right panel to the model list.
Once the model is estimated, the results will appear in the right panel under the Results tab. Click the Models tab to return the right panel to the model list.
Once the model is estimated, the results will appear in the right panel under the Results tab.
In the Results tab, all five models will be listed, as seen in figure 3. Click on the models to toggle through the results. For a complete replication script in R, including all system information necessary for a complete replication of your results, click on the Replication link in the lower left.
These results should be identical to the results in Fearon and Laitin’s Table 1, which appears below:
At this point, it is straightforward to perform additional robustness checks or extensions on any of these five models using variables in the data.
For example, notice that there is a dummy variable in the data called colfra. This variable indicates whether the state is a former French colony. On a different sample, Collier and Hoeffler (2002) investigate whether being a former French colony makes one less likely to experience a civil war. They find that the coefficient is negative, but not statistically significant at 0.05.
Using the Fearon and Laitin data, we can test the hypothesis that being a former French colony makes one less likely to experience a civil war by including the colfra variable in Fearon and Laitin’s model.
As we can see, the coefficient is negative, but not statistically significant at 0.05. This is consistent with the findings in Collier and Hoeffler (2002).