Homework 3

Replicating Fearon and Laitin (2003)

Author

Your Name

In this homework assignment, we are going to be exploring the hugely influential article by Fearon and Laitin (2003) on civil war onset. Click here to view the original article and see Table 1 for the model that we will be replicating. Click here to access the assignment Dropbox folder that includes the data we will be using for this exercise.

Load the Data

Load the data which is included in the “fl_data_model1.csv” file in the Homework 3 assignment folder.

The unit of analysis is the country-year.

Outcome Variable

  • onset - Whether a country-year experienced civil war onset (1 = yes, 0 = no)

Main Predictors

  • warl- Whether had a war ongoing in the previous year
  • gdpenl - GDP per capita (not logged)
  • lpopl1 - The log of population lagged by one year
  • lmtnest - The log of the percentage of country that has mountainous terrain
  • ncontig - An indicator for noncontiguous state
  • Oil - An indicator for whether the country is an oil exporter
  • nwstate - An indicator for whether a country was created in the last two years
  • instab - An indicator for whether the Polity score changes by 3 or more in a given year
  • polity2l - The Polity2 democracy score
  • ethfrac - Ethno-linguistic fractionalization index
  • relfrac - Religious fractionalization index

Replicate Model 1 from Fearon and Laitin (20%)

Run the first model (Model 1) from Table 1 of the Fearon and Laitin article. Use summary() to view the results. Interpret the coefficient on the Oil exporter variable. How much does being an oil exporter increase the risk of conflict onset?

Display the Results as a Coefficient Plot (20%)

Now use the modelplot() function from the modelsummary package to display the results as a coefficient plot.

When making your coefficient plot, start with the coefficient map. You might want to write it in the order that you’d like to see the coefficients displayed and then reverse it so that you can use the same map for your regression table below.

Be sure to load the modelsummary and ggplot2 packages before you get started with the rest of your code.

Interpret the results. Which predictor has largest positive effect on the probability of conflict onset? Which one has the strongest negative effect?

Create a Regression Table (20%)

Do stepwise regressions with different categories of regressors based on the same model to see how things change as you put different sets of variables into the model. Show the results of your models in separate columns in a regression table.

Step one: Run your glm() models and store them as objects. Here you might want to summarize them with summary() or tidy() from the broom package as you go along.

Step two: Save your models as a list and set up your caption and reference note.

Step three: Call modelsummary to display the table as a gt object.

Step four: Briefly interpret your results. What changes as you add different categories of variables to the model? Do any results become significant that were not significant before?

Show Odds Ratios and Interpret (20%)

Now make the table display odds ratios instead of coefficients and interpret one or two of the coefficients that you find interesting. (Note: you have already talked about Oil, so choose a different one for this part.)

Display Predicted Probabilities (20%)

Now display predicted probabilities for a handful of country-years. Filter the data for a particular year and the country cases that you are interested in. Find the predicted probabilities using the precitions() function from the marginaleffects package. Interpret the results. Why do some of the countries you chose have a higher probability of conflict onset than the others?

Extra Credit (4 pts)

If you would like to do some extra credit, you can try to recreate and extend the F&L analysis with data from the peacesciencer package. Display your results in a regression table or coefficient plot.

Here are some questions to answer about your analysis:

  1. Which variables were you able to include from peacesciencer? What variables that were in F&L were you not able to find? What are you missing? (1 pt.)

  2. Do you get results similar to F&L with the peacesciencer data if you take a similar timeframe? (1 pt.)

  3. If you rerun the F&L model with the same variables that you have available for peacesciencer, do you get similar results? If there are differences, why would this be? (1 pt.)

  4. What happens if you extend the analysis from 1945-1999 to 1945-present using the peacesciencer package? What changes? (1 pt.)

Submission Instructions

Head over to Blackboard and go to the Homework 2 assignment. There you will find a link that you can use to upload your project folder.

If you worked with a partner, only one of you should submit the project folder but both of you should still make a submission to Blackboard so that we have a submission to grade. If you have worked with someoone but still have a substantially different project, you can submit separately.

The person submitting the project folder should take a screen shot of the confirmation message (“Finished Uploading”) and share it with the other group members. This screen shot will be what you submit in Blackboard.

Next, click “Create Submission” and write a statement stating whether you worked on the project independently or whether you are submitting jointly with someone else. Also note whether you used a large language model (LLM) like ChatGPT to help you with the work and which parts of the assignment you used the LLM for. It is fine to use an LLM to help with your code but you must acknowledge the use of it in your submission.

Then, upload the screenshot you saved in the first step. Now submit the assignment.

Please remember to upload your entire project folder and not just the Quarto document.