# How Stable is China?

The current protests in China make many people wonder whether the situation will escalate further.

For a demographics-based analysis (youth bulge theory), read on!

I also created a video for this post (in German):

A series of protests against COVID-19 lockdowns began in mainland China middle of November 2022. The protests started in response to measures taken by the Chinese government to prevent the spread of COVID-19, including implementing a zero-COVID policy, which confines many people to their homes without work and left some even unable to purchase daily necessities.

Protesters demanded the end of the government’s zero-COVID policy and lockdowns, and some extended their protest towards the leadership of Xi Jinping and the Chinese Communist Party.

The question now is, will those protests spread and spark something bigger, perhaps even a revolution? Or will they just subside again? In this post, we will use the powerful youth bulge theory to analyze the situation. The basis of this theory is the demographics of a country, especially the form of the population pyramid.

The gist of the theory is that the larger the proportion of younger people compared to older people, the more violent political and social conflicts there will be. We showed a highly significant correlation between those two based on World Bank data. On top of that, we found a tipping point at about 24% for the age group 0-14.

More details can be found here: The “Youth Bulge” of Afghanistan: The Hidden Force behind Political Instability and here: Youth Bulge Theory: Why there won’t be an Uprising in Russia.

Now, let us dive into the situation in China and compare it with the US, Germany, and Afghanistan to provide some context:

```library(WDI)
## Warning: package 'WDI' was built under R version 4.2.1
age0_14 <- WDI(indicator = "SP.POP.0014.TO.ZS", start = 2020, end = 2020) # proportion of 0-14 year olds
pol_stab <- WDI(indicator = "PV.EST", start = 2020, end = 2020) # political stability indicator

data <- merge(age0_14, pol_stab)[c(2, 4, 5)] |> 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")
lm.out <- lm(pol_stab ~ age0_14, data = data)
abline(lm.out, col = "black", lwd = 3)
abline(v = 23.9, col = "red", lwd = 2)

country <- "China"
points(data\$age0_14[data\$country == country], data\$pol_stab[data\$country == country], col = "red", 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)

country <- "United States"
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 = "USA", pos = 4)

country <- "Afghanistan"
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)
```

As we can see the proportion of 0–14-year-olds is way below the critical tipping point (= the narrow red line). That alone of course doesn’t give a “guarantee” that there won’t be any serious uprising or revolution but it can provide a good heuristic with about 80% accuracy.

Comparing this with other countries shows that, with Germany and Afghanistan on different ends of the spectrum, the US and China are not too far apart, which is another indicator that the situation will probably not escalate quickly but calm down again after some time. The proportion of young people is even lower in China (17.7%) than in the US (18.4%).

What do you think will happen in China? And what do you think about the youth bulge theory in this context? Please let us know in the comments!

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.

## 9 thoughts on “How Stable is China?”

1. Interesting post, and nice to read as a (non-practicing) Sociologist. The theory, while temptingly simple, seems to have some predictive power. But let’s see, there are plenty factors playing into this. I’m curious to see how the future evolves, not only in China … “The times they are a’changing …”

1. Thank you for your comment, yes, the ancient Chinese curse: “May you live in interesting times” 😉

2. ZZT says:

I am curious about why 0-14 year olds were chosen, 14-year-olds in China are

1. Thank you for your question, ZZT.

As I have written above you can find the details here: The “Youth Bulge” of Afghanistan: The Hidden Force behind Political Instability.

There I write: “There are several definitions for “youth bulge” out there, we will use the following: “Proportion of age group 0-14 (% of total population)” whose data can directly be downloaded from the World Bank via the excellent WDI package (on CRAN) which makes use of their official API. Heinsohn uses the age group 15-24 (and especially males) but both proportions are highly correlated and those numbers are much harder to come by.”

3. Albert Lee says:

I have difficult time to replicate your entire code. I keep getting error message

Error in int_abline(a = a, b = b, h = h, v = v, untf = untf, …) :
‘a’ and ‘b’ must be finite

I am also interested in the stability in Taiwan. I don’t think the data for Taiwan is available.