Series of tweets which explains what Baumol’s Cost Disease is and then applies it to countries instead of people, and then finally makes predictions about which developing countries will make it and why.

Here we go:
“Baumol's cost disease (or the Baumol effect) is the rise of salaries in jobs that have experienced no or low increase of labor productivity, in response to rising salaries in other jobs that have experienced higher labor productivity growth.” en.wikipedia.org/wiki/Baumol%27…
Examples:

Goldman Sachs must raise their starting wage for college grads because if they don’t, they will work at higher salaries for Facebook.

US manufacturers must raise their wages to compete with Amazon’s logistics and warehousing wages to get labor.
Countries can also be “diseased”.

“The Dutch disease is the apparent causal relationship between the increase in the economic development of a specific sector (for example natural resources) and a decline in other sectors (like the manufacturing sector or agriculture).”
Examples:

“The term was coined in 1977 by The Economist to describe the decline of the manufacturing sector in the Netherlands after the discovery of the large Groningen natural gas field in 1959.” en.wikipedia.org/wiki/Dutch_dis…
The Philippines has solid education and outstanding English but a weak manufacturing sector. The manufacturing sector struggles to get off the ground because the most talented labor can earn more doing customer service. The demand for customer service from the Philippines also
increases the demand for Philippines pesos which makes the manufacturing sector of the Philippines less competitive than it would be otherwise were there no demand for Philippines’ services.

That’s Baumol’s cost disease but for countries.

Let’s use this to make predictions!
Which developing countries are going to become middle income (China, Brazil)? Which will become high income (Japan, Korea)? And which will stay developing (Nigeria, Myanmar)?
We can look at 3 things to figure that out:

1. Minerals
2. English education
3. Science and math education

And then think about these 3 in the context of three industries:

A. Resource extraction
B. Service sector
C. Manufacturing sector
If a country has lots of high value mineral deposits, it usually develops a resource extraction industry. This is really bad for the country for two reasons: 1) it crowds our manufacturing and services 2) resource extraction doesn’t employ many people. Ouch.
What happens if they have strong English education? They will develop an export service sector be it via classic BPO with Accenture or SMB va’s via upwork, freelancer, fiverr. This is way better than being a resource extraction country but it still leaves a lot of people out.
If you can only do manual labor or if you’re smart but have bad English skills you’re going to get stuck as a driver/cook for those with the skills. Or if you’re lucky, you’ll be their math guy or something. Good way to go from developing to middle income, but that’s it.
Sadly, the service sector leaves a huge chunk of the population without good jobs and because of demand for the local service labor, it crowds out the manufacturing sector that would otherwise provide employment to the people who aren’t good at language.
Countries with good science and math education but poor English are paradoxically those most likely to make it to high income.
Manufacturing employs a country’s lower skill labor while also providing service sector jobs for the administrative, marketing and sales people needed to support the manufacturing.
Korea, Japan, and Taiwan are advanced high income countries despite weak English skills, while India and the Philippines have so far struggled to make the jump despite having strong service sectors.
Meanwhile, Nigeria with its 200 million plus population of smart English speakers can’t get their service nor manufacturing sectors off the ground because of crowding out (and the corruption) which comes from resource extraction.

Let’s do predictions:
Nigeria: will be a mess unless demand for oil drops or India Philippines gets too expensive

India: I’m not sure. I see services crowding out the manufacturing they need but with being so big, maybe they can swing both. So far I am not optimistic. Big quest for 21st century.
Vietnam: very much on the path to join Korea, Japan, Taiwan

China: to my mind, it’s a foregone conclusion. They’re going high income.

Indonesia: afaik they have bad English so it’s manufacturing for them but they have a lot of oil and coal which can be problematic. Not sure.
Mexico: what a sad situation. It seems like their whole economy gets crowding out by a 4th sector…drugs. They gotta get that under control.

Kenya, Rwanda: can get to middle income on the back of services
Ethiopia: obviously if you have unrest you’re screwed but afaik Ethiopia has bad English but solid science and math education. Assuming country doesn’t disintegrate i could see them slowly climbing up the value chain.
Russia, Brazil: have resource issues (and crime and corruption afaict) that will prevent them from going to where their population’s brain power should take them sadly.
Anyways, you get the pattern of the prediction.

Where do you think the countries that are split between two sectors like India, Brazil, Russia, and Indonesia will end up?

/end thread

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More from @Molson_Hart

7 Jan
When Bill Gates or Warren Buffet answer the question "Why are you so rich and successful?" with "I've been enormously lucky." they're doing society a disservice in the hope that more people will like them.

Wealth and success are a product of talent, drive, the ability to

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learn from your mistakes and then change your behavior— not luck.

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🧵👇
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1. Houston
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More people moved to Florida and Texas than any other states in 2021.

I’ve lived in Texas since 2021 and I like thinking about cities so I thought I’d make a quick guide of the big 5 Texas cities for people thinking about moving.
Here the 5 cities I decided to focus on:

1. Dallas
2. Dallas’ lesser known twin city Fort Worth
3. Houston
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5. Austin
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Without further ado let’s get started:
Read 22 tweets
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5. Messages: concentrated (Facebook, WhatsApp)
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