When people think about advanced economies they will think of places like, Germany,
Switzerland, Norway, Sweden all countries with a very high GDP Per Capita.
On the opposite end of the spectrum there are underdeveloped nations,
like those in sub-Saharan Africa, Central America, the middle east, and areas of southeast Asia.
There are all manner of factors that make poor countries poor and rich countries rich.
Many of these factors we have explored in depth when we have looked at economies like
the democratic republic of the Congo all the way up to places like Switzerland and Norway.
Political Stability, industry, natural resource wealth are all incredibly important variables
but there is something extra here that just doesn’t seem to make sense.
Cold countries are richer than hot countries. This is a bizarre correlation but it’s clear as
day to see. This is a map of the world with color-coding for countries’ GDP per Capita.
The poorest countries are centralized in the hottest parts of the world,
what’s more, is that apart from a few outliers almost all of the
developed countries in the world exist outside of the tropics.
So what is going on here? Is there actually a statistical
correlation between cold and prosperity? Could this just be a co-incidence?
And if this relationship is real, what could possibly be causing it?
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The phenomenon of rich area’s congregating to the cold even looks like it takes place
within individual countries. Take Australia for example,
it’s two largest cities are Melbourne, and Sydney, located here and here.
Logically speaking the most prosperous city in Australia should be Darwin. It is much closer to
significant natural resource wealth and it is significantly closer to trading partners like
China, Malaysia and Indonesia. But it’s not!
In fact Darwin is actually the poorest capital city in Australia while also being the hottest.
So since theory anecdotally holds true at all levels it is time to
Look at the data No conclusive studies have
ever been done on this subject, and in fact the only peer-reviewed paper we were able to find
on the subject did not look at raw data. This is always a really important step to
take because sometimes things sound true when presented, but the results do not match up.
For example, it sounds like the average amount of arms for an American Male to have is 2. This
theory would likely by supported by the anecdotal evidence that every American Male someone would
think of off the top of their head has… 2 arms! But in reality, the average amount of arms
an American male has is 1.998 because 2.2 people in every thousand have lost an arm.
Put another way, most American Men have an above-average amount of arms.
So statistics is fun and the results are sometimes surprising.
Fortunately for the sake of this theory though the data is very simple.
We want to see the correlation between the average temprature of a country and
the GDP per capita of that country. If we do a simple linear regression on
this we find a few really interesting results. As predicted, there is a negative relationship
between temperature and GDP per capita. For every extra degree Celsius in average
national temperature, the expected value of GDP per capita falls by $762 per year.
This means that if country A is 10 degrees colder than country B, it is expected to have
a GDP per Capita $7,620 a year larger. When we do statistical analysis we also
look at a value called R Squared. Statisticians have a knack for making
everything sound more complicated than it actually is so what the R Squared value means in plain
English is how much of the result is determined by the variables in the model compared to other
factors that haven’t been considered. For example, if we were looking at the
correlation between height and weight we would expect a positive relationship. The taller
someone is the heavier they tend to be. Using this example we may find an R squared
value of 0.5, meaning that 50% of someones weight is determined by their height.
This would mean that another 50% is explained by another factor,
(or factors) not in our model. In this example, it would probably be how uhh, round they are.
The relationship between temperature and GDP per capita gave us an R squared value
of 0.09. This means that 9% of a countries prosperity is determined by its temperature.
This sound pretty insignificant, but it is actually huge
That other 91% would then be made up of things like credit rating, natural resource wealth,
infrastructure, government stability and whole range of other factors that people
would expect to be far more important to an economy than the weather.
So the relationship is here! And it’s pretty significant. But before this devolves into a
statistics lesson it is still important to address potential problems. Namely
The outliers, and the cause-effect relationship.
There are some significant outliers in this set of data.
Bahrain, Qatar, the UAE and Singapore are all very hot and very rich. For the first three,
natural resource wealth has overcome any inherent burden of hot weather.
Singapore is just a genuine outlier that flies in the face of this theory.
We would also be reminisce if we didn’t mention North Korea,
it is very cold and very poor. Which is an example of political instability being a heavier burden
than the apparent benefit of a colder climate. The other thing that is really important to look
at when exploring economic relationships is cause and effect. Or rather that
correlation does not always mean Causation. The Classic example of this is that the sale
of icecream is very strongly correlated with drownings. Of course, ice creams don’t cause
drownings and drownings don’t increase the sale of ice cream’s but both of these events are
more likely to take effect in summer, meaning that their nominal peaks strongly coincide.
This might be a little bit of a dark example but it shows that just because something lines up,
doesn’t mean that there is anything useful to be discerned from the relationship.
This is called Spurious Correlation. Is it possible that the relationship
between temperature and economic prosperity is just another example of a spurious relationship?
Well no.. If this were the
case there would need to be an unknown third variable linking both of these factors.
In the ice cream example it was summer. People are more likely to go swimming and eat ice cream
during summer months, hence to correlated peaks. Given that there is no hidden variable between
temperature and GDP its safe to conclude that this correlation is not spurious.
ok , so now that the statistics are sorted, we have accounted for outliers, and addressed
potential flaws in this hypothesis. Why on earth is this happening?
Why do economies... Not like it hot?
There are actually a range of theories on this issue. But the reality is there is not
yet a conclusive answer. The most prominent theory
is that of economic selection. Cold climates are very harsh during winter months.
In a country like norway, people would not survive through these winters unless they planned ahead to
stockpile food, build good shelters, and reserved fuels like oil, coal, or wood to keep warm.
From the outset, this gave these economies a headstart. It wasn’t so much that people
were more industrious because of the cold, it was more so that societies that weren’t
industrious just froze and died off. A basic mud hut is a sufficient shelter
in tropical regions and food is available to hunt and gather year-round so there was
not the same impetus to build larger more engineered dwellings and become
a society that valued storing resources. This kind of forced industriousness would
compound on itself over many generations to produce a society that values capital goods
moreso than a society that has had it easy throughout its existence.
A critique of this theory is that a majority particularly prosperous ancient civilizations
were centered in warm regions. Ancient egypt, Mesopotamia, the mayans,
the persians, all called very hot regions home. This should discredit this theory but instead,
it actually offers even more insight. If we were to look at the correlation of
temperature and wealth 2,000 years ago you would actually get the opposite result.
Hotter nations were richer. So why did this change?
It changed because global industries changed. 2,000 years ago the wealth of a country was
effectively determined by how much food it could produce. More food meant it could feed more people
to toil more field to produce more food. Modern farming techniques did not exist
and even the most powerful empires would constantly struggle through famines.
Warmer climates could grow more food, and hence harbor more wealth.
In the modern world, wealth is no longer determined by how
much a country can farm. Instead, it is determined by Industry and innovation.
The societies which had been forced to adapt and innovate their way around a hostile environment
for centuries could now come into their own and lead the world into the age of industry.
Other theories also postulate that in colder environments people are
forced to be more tolerant of one another. If someone is going to be locked inside all
winter with their friends and family they are going to learn to get along moreso than
people who are free to frolic about year round. Ontop of this, hotter temperatures naturally lead
to more aggressive behaviours in human beings, cold temperatures specifically do the opposite.
Back in the age of conquest, heightened aggression was no problem at all. But in the modern world,
dictated by business deals and global negotiation the advantage definitely goes to cooler heads.
The good news amongst all of this is that cold ountries got a bit of a headstart in the age of
innovation but places like Singapore show that this is not a headstart that will last forever.
Every country has potential regardless of the weather outside.
Final Thoughts Economic curiosities like the correlation
between wealth and climate are truly fascinating. It might not show any information that can turned
into useful policy but it is a great case study that forces economists to truly grapple with what
it is that makes successful economies successful. Cause and effect is not only a driving force
in economics, it’s a driving force in the world around us.
Being able to understand if something causes something else, determining to what degree
it causes that something else and then being able to explain why it causes that something
else is the most important skill people in the modern professional world can posses.
Maybe it is a skill that can be honed, while stuck inside for the winter….