The Fund for Peace is a non-profit organization dedicated to preventing violence worldwide. One of their initiatives is the Fragile States Index (F.S.I), a yearly ranking of the stability of countries around the world. Each ranking is the sum of twelve factors rated on a scale of one (the best) to ten (the worst) that contribute to a country’s standing. These factors include Human Flight and Brain Drain, Poverty and Economic Decline, and State Legitimacy. A score between 90.0 and 120.0 corresponds to the “Alert” category. “Warning” to those between 60.0 and 89.9. “Stable” for the 30.0 – 59.9 group and “Sustainable” for the rest. The ratings are the product of the analysis by domain experts on raw data from a number of sources. The final results aim to provide a window into the state of a wide swath of the world since 2005.
In my analysis I excluded data from the Index’s first year due to its minuscule nature compared to later years. The following year, 2006, brought an increase from 78 countries to 147. In 2007 this number went up to 178, and has not deviated much since then. The appearance of countries isn’t consistent, a fact most easily attributable to the criteria for inclusion the Fund for Peace uses. Along with the obvious need for the pertinent data, one necessity is that each country be affiliated with the United Nations. Only 93 countries appear in all eleven years.
This subset would be the ideal group to consider, but 93 is too low of a number. A threshold of at least eight years of data is much better as it increases the coverage to 170 countries.
My first foray led me to look at the highest and lowest mean scores. At the wrong end of the spectrum are countries like Somalia, Sudan, and Afghanistan with totals of 113, 111, and 106 respectively. European countries such as Finland (18), Norway (20.17 )and Sweden (20.86) dominate the top 10 at the other end. This hegemony is only disturbed by by Australia (25.35) and New Zealand (23.03).
Another point of interest was year on year F.S.I changes. The top ten list is an exercise in contrast. Libya’s 2012 rating (85) was the highest increase (16 points) and it is the only country to appear twice. (The other year is 2016 with a score of 94.) This is quite unsurprisingly taking into consideration that 2012 marked the first calendar year after the beginning of the Libyan crisis with started with the Arab Spring and led to civil war.
Iceland’s rating in 2008 was another in the top ten that stuck out. An eight point jump to a total rating of 29 immediately points to the Financial Crisis of that year. The data supports this theory as “Poverty and Economic Decline” was the major driving force of the increase.
Right behind Libya’s increase in 2012 is Japan from the same year. This was a direct consequence of the devastating 2011 Tohoku earthquake, the strongest to ever hit Japan. It’s is a measure of Japan’s remarkable recovery that the country tops the list for decrease in total S.F.I going from the rating in 2012 to that in 2013.
I also looked at countries which experienced great ratings shifts. This was calculated using the sum of the absolute values of the year on year differences. Thus, countries with small sums mean that little deviation in conditions (bad or good) occured and vice versa. The plot below contains the top 10 countries in decreasing order. Dashed lines – except the one at 0 – represent transitions into different categorical bins.
Unfortunately, none of these ten had great sustained dips in their rating. Iceland and Japan are the only Stable/Sustainable states represented. The other countries are firmly in the yelllow-red zone of categorization. Libya pops up again, along with Syria, Turkey, and Yemen, a graphical mirroring of the turmoil brought to that region of the world over the past decade.
This is just a snapshot of the incredible work done by the Fund for Peace, and a small glimpse into why Data Science for Good is perhaps the most important facet of the field’s application. After all, it can a bridge to help lessen the travails of the world as a whole.
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Gordon studied Math before immersing himself in Data Science. Originally a die-hard Python user, R's tidyverse ecosystem gradually subsumed his workflow until only scikit-learn remained untouched. He is fascinated by the elegance of robust data-driven decision making in all areas of life, and is currently involved in applying these techniques to the EdTech space.