Week 2 of Data Management and Visualization

As previously explained I am working through the Data Management and Visualization course.  This is week 2 and I had the opportunity to use Python to explore some data.

I put together a Jupyter (formerly IPython) notebook and have uploaded it all to my GitHub repository.  To view the notebook visit https://github.com/mikeasilva/democracy-and-economic-well-being/blob/master/Data Managment and Visualization/Week 2 Assignment.ipynb.  It is an example of literate programming so it mixes narrative content with machine readable code.  If you want to view the Python script sans narration it is available too.

Project Overview

In this analysis I would like to examine the relationship between the economic well-being of a society and the level of democratization.  The data for this analysis comes from a subset of the GapMider project data.

The level of democratization is measured using the Polity IV democracy score.  It is a summary measure of a country’s democratic and free nature or lack thereof.  It ranges from -10 (an autocracy) to 10 (full democracy).

I did get some useful feedback on needing to clarify what is meant by “economic well-being.”  Economist frequently use GDP per capita as a measure of economic well-being.  Loosely GDP is a measure of how much stuff is produced by an economy.  So per capita GDP would be how much stuff everyone would have on average.  The higher the amount of stuff a person has the better off they are.  I personally don’t like this measure but I am using it since it is the only thing available.

Exploratory Analysis

There are 213 observations and 16 variables in the GapMinder data set.  161 out of 213 countries had a democracy score (52 missing).  190 out of 213 had per capita GDP figures (23 missing).  I subset this data selecting only those that had both a democracy score and per capita GDP figures which left me with 155 observations.

Democracy Score

There are 155 countries in the data set I am analyzing.  The breakout of these countries by their democracy score in 2009 is:

Democracy Score Count Percent
-10 2  1.3
-9 3  1.9
-8 2  1.3
-7 11  7.1
-6 2  1.3
-5 2  1.3
-4 6  3.9
-3 6  3.9
-2 5  3.2
-1 4  2.6
0 4  2.6
1 3  1.9
2 3  1.9
3 2  1.3
4 4  2.6
5 7  4.5
6 10  6.5
7 13  8.4
8 19  12.3
9 15  9.7
10 32  20.6

There are 32 countries that are full democracies (have a polity score of 10). This is roughly 21% of all the data. There are 2 observations that are autocracies.  We see that most of the countries are greater than zero.  I tabulated it and 69% of the countries (or 108) are “open” in one form or another.

Economic Well-Being

One challenge that I faced was GDP per capita is a continuous variable.  I broke the data up into quintiles.  The following table summarizes the frequencies:

GDP Per Capita Quintile Count Percent
Lowest 119  77.8
Second 14  9.0
Middle 5  3.2
Fourth 10  6.5
Highest 7  4.5

119 countries (roughly 78%) are in the lowest quintile which is a rather large proportion!  I will have to decide if there is a better way to break the data into groups.

Democracy Groups

The final variable that I examined in this was a summary measure of the level of democracy.  Using the 21 Polity IV scores is a little unruly.  So I aggregated the data into five categories identified by the Polity IV project authors.  I think I will use this in my full analysis:

Democracy Group Count Percent
Full Democracy 32  20.6
Democracy 57  37.8
Open Anocracy 19  12.3
Closed Anocracy 27  17.4
Autocracy 20  12.9

Most of the countries in the data set are either democracies (38%) or full democracies (21%).   47 of the 155 countries could be considered as closed.

I like this measure better because it provides more delineation than the full polity score variable does.  I do find it interesting that under this convention 20 of the 155 countries in the data set are autocracies as opposed to the 2 that I identified earlier.  I find the democracy groups easier to believe.


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