R-bloggers

The Forgotten Factor in the Middle East Conflict | Youth Bulge Theory

[This article was first published on R-Bloggers – Learning Machines, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.


The geopolitics of the Middle East has always been complex, with its share of conflicts and political unrest. Numerous theories have been proposed to dissect the underpinnings of the region’s political instability.

One such theory, known as the Youth Bulge Theory, asserts that a high proportion of young people within a population can lead to political instability and even violence. This theory could provide an illuminating perspective on the dynamics of the Middle East conflicts.

If you want to understand this most important ingredient of the ongoing conflict, read on!


You can also watch the following analysis as a video (in German):

< !-- iframe plugin v.4.7 wordpress.org/plugins/iframe/ -->

The Youth Bulge Theory posits that countries with a high percentage of young individuals are more susceptible to political instability and violence. The rationale behind this theory is that a large cohort of young people can create increased competition for limited resources like jobs and housing. If these needs remain unmet, the youth may become a fertile ground for political agitation, or worse, recruitment into extremist factions.


The Youth Bulge Theory was already extensively discussed on Learning Machines and used to analyse Afghanistan, Russia, and China.


To validate the Youth Bulge Theory, we can turn to data. By analyzing the proportion of young people against a political stability indicator across different countries, we might discern a pattern to substantiate this theory.

We’ve employed data from the World Development Indicators database, specifically focusing on the proportion of young people and a political stability indicator:

library(WDI) # install from CRAN

age0_14 <- WDI(indicator = "SP.POP.0014.TO.ZS", start = 2021, end = 2021) # proportion of 0-14 year olds 
pol_stab <- WDI(indicator = "PV.EST", start = 2021, end = 2021) # political stability indicator

data <- merge(age0_14, pol_stab)[c(1, 5, 6)] |> na.omit()
colnames(data) <- c("country", "age0_14", "pol_stab")

plot(data$age0_14, data$pol_stab, col = "darkgrey", xlab = "Proportion of young people (in %)", ylab = "Political stability", main = "Youth Bulge Theory")
abline(v = 23.2, col = "red") # cut-off value
abline(h = 0, col = "red")
lm_out <- lm(pol_stab ~ age0_14, data = data) 
abline(lm_out, col = "black", lwd = 3)

country <- "West Bank and Gaza"
points(data$age0_14[data$country == country], data$pol_stab[data$country == country], col = "darkgreen", lwd = 8)
text(data$age0_14[data$country == country], data$pol_stab[data$country == country], labels = country, pos = 4)

country <- "Israel"
points(data$age0_14[data$country == country], data$pol_stab[data$country == country], col = "blue", lwd = 8)
text(data$age0_14[data$country == country], data$pol_stab[data$country == country], labels = country, pos = 4)

country <- "Germany"
points(data$age0_14[data$country == country], data$pol_stab[data$country == country], col = "orange", lwd = 8)
text(data$age0_14[data$country == country], data$pol_stab[data$country == country], labels = country, pos = 4)

In the plot, each point represents a country, with its position determined by the proportion of young people and its political stability score. The red lines indicate the threshold where the situation toggles between stability and instability. A black line, derived from a linear regression analysis, depicts the statistically highly significant strong negative correlation of over 60% between the proportion of young people and political stability.


While the original Youth Bulge Theory uses the age bracket of 15 to 24, we use the age bracket betweenn 0 and 14 as a proxy. The reason is lack of data availability but both brackets are highly correlated and demographic structures are stable over longer periods of time.


A closer look at three distinct data points – West Bank and Gaza, Israel, and Germany – reveals an intriguing narrative. West Bank and Gaza, with 39% of its population being young, shows a political stability score of -1.84, indicating a highly unstable political environment. Israel, with a slightly lesser youth proportion of 28%, has a political stability score of -1.06, still signifying instability but to a lesser degree than the West Bank and Gaza. On the other end of the spectrum, Germany, with a youth proportion of 14%, exhibits a political stability score of 0.76, indicating a stable political environment.

The data-driven analysis aligns with the Youth Bulge Theory, suggesting that the high proportion of young individuals could be contributing to the political instability witnessed in the Middle East. However, it’s crucial to acknowledge that this theory doesn’t operate in a vacuum. The region’s political dynamics are influenced by a myriad of factors including historical grievances, religious tensions, and geopolitical interests.

Moreover, comparing the Middle East situation to historical European conflicts reveals both similarities and disparities. For instance, the long-standing rivalry between Germany and France, which waned as the demographic structure evolved, hints at a hopeful prospect for the Middle East. Yet, the unique intricacies of the Middle East conflicts necessitate a nuanced approach in drawing parallels and deriving solutions.

In summary, the Youth Bulge Theory provides a compelling lens through which to analyze the ongoing turmoil in the region. While it’s crucial to recognize that the youth bulge is not the sole driver of conflict, its impact on political stability cannot be underestimated.


DISCLAIMER
The views and opinions expressed in this blog post are those of the author and do not necessarily reflect the official policy or position of any organization. This blog post is for informational purposes and is an analysis based on available data, it is not intended to be an exhaustive explanation. The subject matter is highly sensitive and complex, and readers are encouraged to consider multiple perspectives when evaluating the issues.

To leave a comment for the author, please follow the link and comment on their blog: R-Bloggers – Learning Machines.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Exit mobile version