Based on Benjamin F. Jones and Benjamin A. Olken. 2009. Hit or Miss? The Effect of Assassinations on Institutions and War. American Economic Journal: Macroeconomics, 1(2): 55–87.
All materials presented here build on the resources for instructors designed by Elena Llaudet and Kosuke Imai in Data Analysis for Social Science: A Friendly and Practical Introduction (Princeton University Press).
There is a longstanding debate in the study of international relations on whether individual political leaders make a difference. To explore this issue, let’s estimate the causal effect of the death of the leader on the level of democracy of a country. For this purpose, we will analyse data on assassinations and assassination attempts against political leaders from 1875 to 2004.
Whether an assassination attempt occurs or not is not a random process. However, once an assassination attempt has occurred, one could argue that whether the assassination attempt is successful or not is the result of small elements of randomness, such as the timing and path of the weapon. As a result, we can consider (at least for now) that, after an assassination attempt, the death a leader is close to random and, thus, the assassination attempts where the leader ended up dying should be, on average, comparable to the assassination attempts where the leader ended up surviving. If this is true, then we can estimate the average causal effect of the death of the leader by computing the difference-in-means estimator.
To measure the level of democracy of the country, we will use polity scores. Polity scores categorize the regime of a country on a 21-point scale ranging from -10 (hereditary monarchy) to +10 (consolidated democracy). The Polity Project has produced polity scores for all countries from 1800 and on. For example, here are the 2018 polity scores.
The dataset is stored in a file called “leaders.csv”. Table 1 shows the names and descriptions of the variables in this dataset, where the unit of observation is assassination attempts.
Table 1: Variables in “leaders.csv”
| Variable | Description |
|---|---|
| year | year of the assassination attempt |
| country | country name |
| leadername | name of the leader |
| died | whether leader died: 1=yes, 0=no |
| politybefore | polity scores before the assassination attempt (in points) |
| polityafter | polity scores after the assassination attempt (in points) |
In this problem set, we practice fitting a linear model to estimate average causal effects.
As always, we start by loading and looking at the data (remember to set your working directory first!):
year country leadername died politybefore polityafter
1 1929 Afghanistan Habibullah Ghazi 0 -6 -6.000000
2 1933 Afghanistan Nadir Shah 1 -6 -7.333333
3 1934 Afghanistan Hashim Khan 0 -6 -8.000000
4 1924 Albania Zogu 0 0 -9.000000
5 1931 Albania Zogu 0 -9 -9.000000
6 1968 Algeria Boumedienne 0 -9 -9.000000First, let’s identify our Y and X variables. Given that we are interested in estimating the average causal effect of the death of a leader on the polity scores of a country:
Compute the difference-in-means estimator directly (just as we did in problem set 3) and report its value. Please use the summarise() function, combined with either filter() or group_by(). Remember these come from the tidyverse.
Now, let’s use the base R lm() function to fit a line to the data and summarise the relationship between X and Y. Remember that the lm() function requires an argument of the form Y~X.
What is the fitted line? In other words, provide the formula \(\widehat{Y} = \widehat{\alpha} + \widehat{\beta} X\) where you specify each term, i.e., substitute \(Y\) for the name of the outcome variable, substitute \(\widehat{\alpha}\) for the estimated value of the intercept coefficient, substitute \(\widehat{\beta}\) for the estimated value of the slope coefficient, and substitute \(X\) for the name of the treatment variable.
Is the estimated slope coefficient (\(\widehat{\beta}\)) equivalent to the value of the difference-in-means estimator in this case? A yes or no answer will suffice.
Please provide a full substantive interpretation of the estimated slope coefficient (including the unit of measurement).
What is the average causal effect of the death of a leader on the polity scores of a country? Please write a full sentence answering the question, including the assumption, why the assumption might be reasonable, the treatment, the outcome, as well as the direction, size, and unit of measurement of the average treatment effect.