To:
President Elect Donald Trump
From:
Isaac Haberman
Date:
12/14/2016
RE:
Modeling the onset of Coups D'etat
Executive Summary
Through
my statistical analysis, I have built a model for predicting the onset of coups
D'etat. The State Department and the CIA
will be able to practice better statecraft, better diplomacy, and better
overall planning if they rely more heavily on statistical modeling and
predictions. Using statistical
analysis and insight garnered my review of academic papers, I tested a variety of
logistic regression models and identified the best model. My chosen model has higher statistical
accuracy, relies on fewer assumptions, and is easier to understand and recreate
than the Ulfelder model. The State Department and
The CIA should adopt my chosen model as their statistical model for predicting
the onset of Coups D'etat.
Importance of Coup Prediction
I have iterated below the
importance of powerful statistical tools, including modeling and predictions,
for the CIA and State Department:
á
Better statecraft;
with a greater understanding of events and actions that cause preferred
results, the CIA and State Department can engage with other parties with greater
certainty of how situations and conflicts will end. For example, the State Department can
predict within a conflict which side will win and therefore which side the US
should work with.
á
Better
diplomacy; with a powerful understanding of how parties will react to State
Department actions, the State Department and CIA can commit to certain actions
knowing full well what events will unfold.
For example, the State Department could offer aid to a political leader,
knowing whether or not they would take the aid and how it will affect their
standing.
á
Better
planning; with more accurate predictions, the CIA and State Department can
better predict how interventions and aid will affect situations. For instance, the CIA will know with
more certainty whether arming select rebel groups in certain Middle Eastern
countries allow for a preferred outcome or a faster end to the conflict.
Notes
á
The
original data set and model are from Jay Ulfelder, former research director of
the Political Instability Task Force, a task force commissioned by the CIA.
á
The word
significant refers to statistical significance, a measure of the importance of
a variable within a model.
á
Probabilities
range from 0 to 1, with 0 as an absolute non occurrence and 1 an absolute
occurrence.
á
Predictions
represent chances of coups. Simply
said, the countries I have identified most likely to have a coup does not mean
they will absolutely have a coup, rather they are most likely to have a coup.
á
I will
refer to the data set, including my additions, as the Ulfelder data set. I split the data set into two:
o CW: full data set with
observations from 1960 to 2014
o Post: data set with observations
after the end of the Cold War, 1991 to 2014
á
For the
purpose of this paper, I will refer to four models:
o Ulfelder Model (CW): The original
model published by Jay Ulfelder
o Ulfelder Model (Post): The Ulfelder
model run on postCold War data
o Habermodel (CW): My chosen model
for the full data set
o Habermodel (Post): My chosen model
for the postCold War data
á
The Ulfelder
data set extends only to 2014, therefore predictions for 2016 could not be
generated.
á
All models
are predicting coups, the dependent variable, as defined by Jay Ulfelder in his
original report[1].
Data
For my
modeling, I used the aforementioned Ulfelder data set with the inclusion of the
data set from Determinants of the
Attempting and Outcome of Coups d'Žtat.
After reasoning through the data, I separated the data into two data
sets, the full data set, CW, and a data set with only observations after the
end of the Cold War, Post. I chose
to use two data sets and create two models, as my Cold War indicator proved to
be a significant predictor in all the models I tested. I was unable to identify what the
significant difference between preand postCold War coups, so I made the two data
sets and models to allow those interested to choose whichever they prefer.
Ulfelder Model
Below is the summary of the
original Ulfelder model. The
Ulfelder model is a logistic regression model run on the CW data set that
produces an average accuracy of 78.82%.
As can be seen below, Ulfelder has measures of government, poverty,
country age, country makeup and world stability. The largest coefficients, excluding the
intercept, were infant mortality rate and coup attempts in previous five years,
both of which were predictors in my later models.
Variable 
Coefficient 
Std. Error 
PValue 
Intercept 
3.08 
0.68 
5.4e6 
Colonized
by Britain 
0.10 
0.21 
0.65 
Colonized
by France 
0.28 
0.21 
0.19 
Colonized
by Spain 
0.41 
0.26 
0.11 
Logged
age of country 
0.09 
0.08 
0.27 
Infant mortality
rate 
0.74 
0.15 
1.3e6 
Coup
attempts previous 5 years 
0.94 
0.17 
3.3e8 
Coups
globally 
0.07 
0.23 
0.77 
Coups
Regionally 
0.10 
0.14 
0.47 
Previous
annual GDP growth rate 
0.41 
0.16 
0.01 
Anocracy 
0.01 
0.23 
0.95 
Autocracy 
0.07 
0.24 
0.77 
Democracy 
0.41 
0.36 
0.26 
Durability
of regime 
0.21 
0.08 
0.01 
Ethnic
Elitism 
0.19 
0.16 
0.23 
Election
year 
0.18 
0.17 
0.28 
Violent
civil conflict 
0.25 
0.18 
0.16 
Cold War
indicator 
0.71 
0.22 
0.00 
Below are the Ulfelder coups
predictions for 2015. The black dot
represents the probability of a coup occurring, while the grey dots represent
the 95% confidence interval. As can
be seen below, none of UlfelderÕs predictions are greater than 20%, indicating
that there are very few coups predicted for 2015. Later in this memo, we will compare his
predictions with the predictions of the Habermodel (Post).
Variables to Test
I reviewed four academic papers on coups d'Žtat modeling and summarized the significant predictors the authors had used, highlighting their respective theories:
á
Toward a Structural
Understanding of Coup Risk[2];
Aaron Belkin and Evan Schofer
Belkin and SchoferÕs article, models coup
risk based on government structures, society, political culture, and
statesociety relations. Belkin and
SchoferÕs final model has the following significant predictors:
o
Coup
Risk measured by Bueno de Mesquita
o
Ln(GDP
per capita)
o
South
America, dummy
o
Central
America, dummy
o
Regime
type, binary
á
Revisiting Economic
Shocks and Coups[3]; Nam Kyu Kim
KimÕs article analyzes the relationship
between coup risk and economic shocks, both permanent and transitory. I have listed the significant variables
from KimÕs model below:
o
GDP
growth previous year
o
Ln(GDP
per capita) previous year
o
Interstate
War previous year, binary
o
Coup
previous year, binary
o
Rainfall
deviation previous year x Agriculture
o
Temperature
deviation previous year
o
Oil
price shock previous year
á
Determinants of the
Attempting and Outcome of Coups d'Žtat[4];
Jonathan Powell
Powell investigates coupproofing of
militaries to lower the chance of structural coups. The significant variables
from PowellÕs model are listed below:
o
Expenditure
per soldier
o
Military
size in personnel
o
Paramilitary
binary variable
o
Counterbalancing
measured by Belkin and Schofer
o
Domestic
Instability
o
Regime
type measured by Polity score
o
Military
regime binary variable
á
The Predictability of
coups d'Žtat: A model with African data[5];
Robert W. Jackman
JackmanÕs paper explores the structural
determinants of coups in Africa.
JackmanÕs final model includes:
o
Social
Mobilization
o
Size
of largest ethnic group
o
Winning
party percentage
o
Electoral
turnout
o
Winning
party percentage * turnout
o
Ethnic
group size * winning party percentage
o
Ethnic
group size * turnout
o
Ethnic
group size * winning party percentage * turnout
I was able
to substitute for most of the significant variables from these papers in my
modeling.
Model Selection
Using both
data sets, I tested models until I reached a model that had both a higher
average accuracy than the corresponding Ulfelder model and had predictors that
fit within the academic papers I had read.
Below I have listed all the variables I tested and the variables I used
in my final model, the Habermodel.
Variables 
Ulfelder 
Intermediate
Models 
Habermodel 
Colonized
by Britain 
* 
* 

Colonized
by France 
* 
* 

Colonized
by Spain 
* 
* 

PostCold
War (Included in CW data set) 
* 
* 
* 
Logged
age of country 
* 
* 

Infant
mortality rate 
* 
* 
* 
Coup
attempts previous 5 years 
* 
* 
* 
Coups
globally 
* 
* 

Coups
Regionally 
* 
* 

Previous
annual GDP growth rate 
* 
* 

Anocracy

* 
* 

Autocracy

* 
* 

Durability of regime 
* 
* 

Ethnic
Elitism 
* 
* 

Election
year 
* 
* 

Violent
civil conflict 
* 
* 

Authoritarian
Type: Party 

* 
* 
Slow
Growth 

* 
* 
Central
America Dummy 

* 

South
America Dummy 

* 

Military
size in personnel 

* 

Expenditure
per soldier 

* 

Paramilitary
binary variable 

* 

Counterbalancing
measured by Belkin and Schofer 

* 

Authoritarian
Type: Personal 

* 

Authoritarian
Type: Monarchy 

* 

Authoritarian
Type: Military 

* 

Leaders
Tenure 

* 

Authoritarian
Type: Party * Leaders Tenure 

* 

Authoritarian
Type: Party * Durability of Regime 

* 

Authoritarian
Type: Party * Slow Growth 

* 

Authoritarian
Type: Party * Country Age 

* 

Model 
Accuracy 
Ulfelder (CW) 
78.82% 
Habermodel (CW) 
81.31% 
Ulfleder (Post) 
78.00% 
Habermodel (Post) 
83.25% 
To the left is a table
of accuracies summarizing the average accuracy, over the entire data sets, of
the four final models, the two Ulfelder models and the two Habermodels. The higher the accuracy, the better the
model is at predicting coups.
Chosen Model Summary
Below and to the right is the
summary of the Habermodel (Post).
The Habermodel (Post) is a logistic regression model run on the Post
data set that produces an average accuracy of 83.25%. As can be seen below, the Habermodel is
a much simpler model than the Ulfleder models as well as some of my
intermediate models. There are
similarities between this model and the Ulfelder models; the two largest
coefficients are infant mortality rate and coups attempts in the past five
years. There is a measure of
economic stability, and there is a measure of government type, echoing many of
the predictors from the Ulfelder models.
Variable 
Coefficient 
Std. Error 
Pvalue 
Intercept 
3.82 
0.21 
<2e16 
Infant Mortality Rate 
1.02 
0.19 
1.9e10 
Coup attempts in
previous 5 years 
1.00 
0.25 
8.4e5 
Authoritarian Party 
0.84 
0.34 
0.03 
Slow Growth 
0.20 
0.06 
0.00 
Below, are the Habermodel coups
predictions for 2015. Like the
previous dot plot, the black dot represents the probability of a coup
occurring, while the grey dots represent the 95% confidence interval. Again, like the Ulfelder predictions,
most of the Habermodel predictions are rather small, none greater than
30%. Interestingly, while there is
some variation between the predictions, most of the countries on the dot plot
are in similar positions to those of Ulfelder predictions. However, the Habermodel confidence
intervals appears smaller the Ulfleder ones, especially as the probabilities
approach 0.
Recommendations
Based on
my findings, I recommend the following:
The Habermodel, either
the Post or the CW, should be used as the standard model for predicting the
onset of coups for the relevant government agencies. I have shown above that the Habermodels
are the most accurate and simpler than the Ulfelder models, and should,
therefore, be used where appropriate.
Modeling and predicting
begin to be used regarding other forms of governmental failure. Coups are a specific form of
governmental failure; however, analysis should be done to model and predict
other forms of governmental failure, such as revolutions or civil wars.
Government agencies
begin standardized data collection, as the work of analysists can be simplified
with standardized data sets. Almost
all the data used for this work was created and managed by private citizens
through academia.
Future Work
While I was able to develop a model that
performed better than the Ulfelder Model (CW), the model can be improved. I believe further effort can be placed on
developing better measures for predicting coups as well as developing a more
complete data set that does not rely on incomplete, private data sets. For example, there may well be better
measures of a countries economic stability and healthcare than the ones I
chose. However, I used what was available to me in my data set. Additionally, I used UlfelderÕs
definition of coups; itÕs possible that a more stringent or a laxer definition
yields different results.
[1] Ulfelder, Jay. "Coup Forecasts for 2014." DartThrowing
Chimp. N.p., 2014. Web. 15 Dec. 2016.
[2] Belkin, A., and E. Schofer. "Toward
a Structural Understanding of Coup Risk." Journal of Conflict Resolution
47.5 (2003): 594620. Web.
[3] Kim, N. K. "Revisiting Economic
Shocks and Coups." Journal of Conflict Resolution 60.1 (2014): 331. Web.
[4] Powell, J. "Determinants of the
Attempting and Outcome of Coups D'etat." Journal of Conflict Resolution
56.6 (2012): 1017040. Web.
[5] Jackman, Robert W. "The
Predictability of Coups D'Žtat: A Model with African Data." Am
Polit Sci Rev American Political Science Review 72.04 (1978):
1262275. Web.