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The best stats you've ever seen - Hans Rosling — Transcript · YTtoTranscript

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The best stats you've ever seen - Hans Rosling

TED-Ed · 3,218 words · 16 min read · EN

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Below is the complete, readable transcript of The best stats you've ever seen - Hans Rosling by TED-Ed on YouTube. Read the full text, copy any part you need, or generate a transcript for any video with our free tool.

[Music] [Music] [Applause] About 10 years ago, I took on the task to teach global development to Swedish undergraduate students. That was after having spent about 20 years together with African institutions studying Hungary in Africa. So I was sort of expected to know a little about the world and I started in our medical

university Karolinska Institute an undergraduate course called global health. But when you get that opportunity you get a little nervous. I thought these students coming to us actually have the highest grade you can get in Swedish college system. So I thought maybe they know everything I'm going to teach them about. So I did a

pre-est when they came and one of the question from which I learned a lot was this one. which country has the highest child mortality of these five pairs and I put them together so that in each pair of country one has twice the child mortality of the other and this means that um it's much bigger the difference

than the uncertainty of the data I won't put you at a test here but it's Turkey which is highest there Poland Russia Pakistan and South Africa and these were the results of the Swedish students I did it so I got a confidence interval which was pretty narrow And I got happy of course at 1.8 right answer out of

five possible. That means that there was a place for a professor of international health and for my course. But one light late night when I was compiling the report I really realized my discovery. I have shown that Swedish top students know statistically significantly less about the world than the chimpanzees. Because the chimpanzeee would score half

right. If I gave them two bananas with Sri Lanka and Turkey, they would be right half of the cases. But the students are not there. The problem for me was not ignorance. It was preconceived ideas. I did also an unethical study of the professors of the Carolinsky Institute that hands out the Nobel Prize in medicine and they are on

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par with the chimpanzeee there. So this is where I realized that there was really a need to communicate because the data of what's happening in the world and the child health of every country is very well aware. So we did this software which displays it like this. Every bubble here is a country.

This country over here is um this is China. This is India. The size of the bubble is the population. And on this axis here I put fertility rate because my students what they said when they looked upon the world and I asked them what do you really think about the world? Well I first discovered that the

textbook was tintin mainly and they said the world is still we and them and we is western world and them is third world and what do you mean with western world? I said well that's long life in small family and third world is short life in large family. So this is what I could display here. I put

fertility rate here. Number of children per woman 1 2 3 4 up to about eight children per woman. We have very good data since 1962 1960 about on the size of families in all countries. The arrow margin is narrow. Here I put life expectancy at birth from 30 years in some countries up to about 70 years. And

1962 there was really a group of countries here that was industrialized countries and they had small families and long lives. And these were the developing countries. They had large families and they had relatively short lives. Now what has happened since 1962? We want to see the change. Are the students right? It's still two types of

countries. Or have these developing countries got smaller families and they live here. Or have they got longer lives and live up there? Let's see. We start the world. And this is all UN statistic that has been available. Here we go. Can you see there? It's China. They're moving against better health. They are

improving there. All the green Latin American countries. They are moving towards smaller families. The yellow ones here are the Arabic countries and they get larger families but they no longer lives but not larger families. The Africans are the green down here. They still remain here. This is India. Indonesia is moving on pretty fast and

in the 80s here you have Bangladesh still among the African countries there but now Bangladesh it's a miracle that happens in the 80s the imams start to promote family planning and they move up into that corner and in '90s we have the terrible HIV epidemic that takes down the life expectancy of the African

countries and all the rest of the world moves up into the corner where we have long lives and small family and we have a completely new world.

Let me make a comparison directly between United States of America and Vietnam. 1964, America had small families and long life. Vietnam had large families and short lives. And this is what happens. The data during the war indicate that even with all the death there was an improvement of life expectancy. By the end of the year, the family planning

started in Vietnam and they went for smaller families and the United States up there is getting for longer life, keeping family size and in the 80s now they give up communist planning and they go for market economy and it moves faster even in social life. And today we have in Vietnam the same life expectancy

and the same family size here in Vietnam 19 2003 as in United States 1974 by the end of the war. I think we all if we don't look in the data we underestimate the tremendous change in Asia which was in social change before we saw the economical change. So let's move over to

another way here in which we could display the distribution in the world of the income. This is the world distribution of income of people. $1, $10 or $100 per day. There's no gap between rich and poor any longer. This is a myth. There's a little hump here. But there are people all the way. And if

we look where the income ends up uh the income this is 100% of world's annual income and the richest 20% they take out of that about 74% and the poorest 20% they take about 2%. And this shows that the concept developing countries is extremely doubtful. We sort of think about aid like these people here giving aid to

these people here but in the middle we have most of world population uh and they have now 24% of the income we heard it in other forms and who are who are these these where are the different countries I can show you Africa this is Africa 10% of world population most in poverty this is OECD

the rich country the country club of the UN and they are over here on this side and quite an overlap between Africa and OECD and this is Latin America. It has everything on this earth from the poorest to the richest in Latin America. And on top of that, we can put East Europe, we can put East Asia, and we put

South Asia. And how did it look like? If we go back in time to about 1970, then there was more of a hump. Uh, and we have most who lived in absolute poverty were Asians. The problem in the world was the poverty in Asia. And if I now let the world move forward, you will see

that while population increase, there are hundreds of millions in Asia getting out of poverty and some others get into poverty. And this is the pattern we have today. And the best projection from the World Bank is that this will happen and we will not have a divided world. We will have most people in the middle. Of

course, it's a logarithmic scale here. But our concept of economy is growth with percent. We look upon it as a possibility of percental increase. If I change this and I take GDP per capita instead of family income and I turn these uh individual data into regional data of gross domestic product and I

take the regions down here, the size of the bubble is still the population. And you have the OECD there and you have subs and Africa there. and we take off the Arab states there coming both from Africa and from Asia and we put them separately and we can expand this axis and I can give it a new dimension here

by adding uh the social values there child survival now I have money on that axis and I have the possibility of children to survive there in some countries 99.7% of children survive to five years of age others only 70 and here it seems that there is a gap between OECD Latin America America, East

Europe, East Asia, Arab States, South Asia and subseran Africa. The linearity is very strong between child survival and money. But let me split subseran Africa. Health is there and better health is up there. I can go here and I can split subsaran Africa into its countries. And when it burst the size of

east country bubble is the size of the population. and Sierra Leon down there, Ma Riches up there. Ma Riches was the first country to get away with trade barriers and they could sell their sugar, they could sell their textiles on equal terms as the people in Europe and North America. There's a huge difference

between Africa and Ghana is here in the middle in Sierra Leona, humanitarian aid. Here in Uganda, development aid here, time to invest, there you can go for holiday. It's a tremendous variation within Africa which we very often make that it's equal everything. I can split South Asia here. India is the big bubble

in the middle but huge difference between Afghanistan and Sri Lanka and I can split Arab states. How are they? Same climate, same culture, same religion, huge difference even between neighbors. Yemen civil war, United Arab Emirate money which was quite equally and well used not as the myth is and that includes all the children of the

foreign workers who are in the country. Data is often better than you think. Many people say data is bad. There is an uncertainty margin but we can see the difference here. Cambodia, Singapore, the differences are much bigger than the weakness of the data. East Europe, Soviet economy for a long time, but they

come out after 10 years very very differently. And there is Latin America. Today we don't have to go to Cuba to find a healthy country in Latin America. Chile will have a lower child mortality than Cuba within some few years from now. And here we have high income countries in OECD. And we get the whole

pattern here of the world which is more or less like like this. And if we look at it how it looks the world in 1960 it starts to move 1960 this is Mount Satung he brought health to China and then he died and then Deng Xiaing came and brought money to China and

brought them into the mainstream again. And we have seen how countries move in different directions like this. So it's sort of sort of difficult to get an example country which shows the pattern of the world. But I would like to bring you back to about here at uh 1960. And I would like to compare uh um South

Korea, which is this one, with with Brazil, which is this one. The label went away for me here. And I would like to compare Uganda, which is there. And I can run it forward like this. And you can see how South Korea is making a very very fast advancement whereas Brazil is much

slower. And if we move back again here and we put on trails on them like this, you can see again that the speed of development is very very different and the countries are moving more or less in the same rate as money and health. But it seems you can move much faster if you

are healthy first than if you are wealthy first. And to show that you can put on the way of United Arab Emirate. They came from here a mineral country. They catched all the oil. They got all the money. But health cannot be bought at the supermarket. You have to invest in health. You have to get kids into

schooling. You have to train health staff. You have to educate the population. And Shakes say did that in a fairly good way. And in spite of falling oil prices, he brought this country up here. So we got a much more mainstream appearance of the world where all countries tend to use their money better

than they used in the past. Now this is more or less if you look at if you look at average data of the countries, they are like this. Now that's dangerous to use average data because there's such a lot of difference within countries. So if I go and look here, we can see that Uganda that today

is where South Korea was 1960. If I split Uganda, there's quite a difference within Uganda. These are the quintiles of Uganda. The richest 20% of Ugandans are there. The poorest are down there. If I split South Africa, it's like this. And if I go down and look at Nigeria where there was such a terrible famine

lastly, it's like this. The 20% poorest of Niger is out here and the 20% richest of South Africa is there. And yet we tend to discuss on what solutions there should be in Africa. Everything in this world exists in Africa. And you can't discuss universal access to HIV for that quintile up here with the same strategy

as down here. The improvement of the world must be highly contextualized and it's not relevant to have it on regional level. We must be much more detailed. We find that students get very excited when they can use this and even more policy makers and the corporate sectors would like to see see how the world is

changing. Now why doesn't this take place? Why are we not using the data we have? We have data in the United Nation in the national statistical agencies and in universities in other non-governmental organization because the data is hidden down in the databases and the public is there and the internet is there but we have still not used it

effectively. All that information we saw changing in the world does not include publicly funded statistics. There are some web pages like this you know but they take some uh nourishment down from the databases but people put prices on them. stupid passwords and boring statistics and this won't work. So what is needed? We have the

databases. It's not a new database you need. We have wonderful design tools and more and more are added up here. So we started a nonprofit venture uh which we called linking data to design. We call it gap minder from London underground where they warn you mine the gap. So we thought gap mind was appropriate and we

started to write software which could link the data like this and it wasn't that difficult. It took some person years and we have produced animations. You can take a data set and put it there. We are liberating UN data. Some few UN organizations. Some countries accept that their databases can go out on the world. But what we really need is

of course a search function. a search function where we can copy the data up to a searchable format and get it out in the world. And what do we hear when we go around? I've done anthropology on the main statistical units. Everyone says it's impossible. This can't be done. Our information is so peculiar and detailed.

So that cannot be searched as other can be searched. We cannot give the data free to the students, free to the entrepreneurs of the world. But this is what we would like to see, isn't it? The publicly funded data is down here and we would like flowers to grow out on the net and one of the crucial point is to

make them searchable and then people can use the different design tool to animate it there. And I have a pretty good news for you. I have a good news that the present new head of UN statistic he doesn't say it's impossible. He only says we can't do it. And that's a quite clever guy. Huh.

So we can see a lot happening in data in the coming years. We will be able to look at income distributions in completely new ways. Uh this is the income distribution of China 1970. This is the income distribution of the United States 1970. Almost no overlap. Almost no overlap. And what has happened? What

has happened is this that China is growing. It's not so equal any longer. And it's appearing here overlooking the United States almost like a ghost, isn't it? Huh? It's pretty scary.

But I think it's very important to have have all this information. We need we need really to see it. And uh instead of looking at this, I would like to end up by showing the internet users per 10,000. In this software, we access about 500 variables from all the countries quite easily. It takes some

time to to to change for this, but on the accesses you can quite easily get any variable you would like to have. And the thing would be to get up the databases free to get them searchable and with a second click to get them into the graphic formats where you can instantly understand them. Now the

statisticians doesn't like it because they say that this will not this will not um show the the reality. We have to have statistical analytical methods but this is hypothesis generating. I end now with a world there the internet are coming the number of internet users are going up like this this is the GDP per capita and it's a

new technology coming in but in amazingly how well it fits to the economy of the countries that's why the $100 computer will be so important but it's a nice tendency it's as if the world is flattening off isn't it these countries are lifting more than the economy and will be very interesting to

follow this over the year as I would like you to be able to do with all the publicly funded data. Thank you very much. [Music]