A few weeks ago, I was irked when someone retweeted an old blog post that suggested that the difference between the age of the Kenyan Leader and the median age was 60. This was based on a brilliant 2012 article by Tedd Moss and Stephanie Majerowicz on the Centre for global development blog under the title The Generation Chasm: Do Young Populations Have Elderly Leaders?. If you have a spare 3 minutes then go ahead and read it because while in Kenya’s case it is no longer true, it still holds for a number of countries around the world. Additionally, it is a brilliant piece that you can chew on for a while.
In Kenya’s case the difference between the president’s age (54) and the median age (19) is 35 which is not such a high figure more so because Uhuru Kenyatta is among the top 10 youngest presidents in Africa. However, the presidents is not the only leader in Kenya and therefore we can’t have a discussion about age of leaders without mentioning the 47 governors and senators.
Gubernatorial Age gap
The counties with the top 3 oldest governors are Nyamira (70), Turkana(66) and Nandi(66) while those with the youngest governors are Nyandarua(32) Elgeyo Marakwet(36) and Samburu(40). Ages of 4 governors are not listed online thus the average age of other governors(53) was used as proxies. These four governors are Mwangi Wa Iria, Tuneya Hussein Dado, Zacharia Okoth Obado and Isaac Kiprono Rutto.
On average the 47 governors are 34 years older than the median age of the counties they lead with certain counties such as Nyamira, Turkana and Nandi standing out for having very big gaps at 52,49 and 48 respectively. On the other hand Nyandarua, Elgeyo Marakwet and Mombasa at 13, 19 and 20 are the counties with the smallest age gap.
Knowing the age difference between the leader and the median age can help one understand the level of sync between the policy makers and the generation for whom the policies are being made. Obviously, these differences are alarming but several studies have confirmed that millennials generally don’t vote regardless of the voting method employed. A good case study is Estonia which has allowed E-voting since 2005 and yet only 10% of their voters are millennials.
This article in no way suggests that the elected leaders would be younger were millennials to turn out in large numbers.
The next post under this study will focus on the proportion of aspirants who can be categorized as millennials.
Please skip the next chapter if you’re not interested in finding out about the data collection and analysis.
Data Collection and Analysis
I wrote a simple script to search the governors wikipedia pages and pick their ages whereby it was immediately clear that some ages were missing. I then went through Mzalendo and Utawala Africa to fill the gaps in the ages i was yet to find. Finally, I took the averages for the ages I found as ages for the governors whose ages i wasn’t successful to find. These governors include: Mwangi Wa Iria, Tuneya Hussein Dado, Zacharia Okoth Obado and Isaac Kiprono Rutto.
Next, I wrote a simple script that would pick out the data on population pyramids from opendata portal and calculate the median. After which i calculated the difference in ages and plotted them in the map shown.
Tool: Microsoft R Open 3.2.3 for data collection and Analysis, Arc GIS and CartoDB for visualization