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Annotated captions of Hans Rosling shows the best stats you've ever seen in English

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About 10 years ago, I took on the task to teach global development

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to Swedish undergraduate students. That was after having spent

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about 20 years together with African institutions studying hunger in Africa,

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so I was sort of expected to know a little about the world.

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And I started in our medical university, Karolinska Institute,

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an undergraduate course called Global Health. But when you get

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that opportunity, you get a little nervous. I thought, these students

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coming to us actually have the highest grade you can get

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in Swedish college systems -- so, I thought, maybe they know everything

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I'm going to teach them about. So I did a pre-test when they came.

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And one of the questions from which I learned a lot was this one:

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"Which country has the highest child mortality of these five pairs?"

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And I put them together, so that in each pair of country,

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one has twice the child mortality of the other. And this means that

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it's much bigger a difference than the uncertainty of the data.

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I won't put you at a test here, but it's Turkey,

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which is highest there, Poland, Russia, Pakistan and South Africa.

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And these were the results of the Swedish students. I did it so I got

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the confidence interval, which is pretty narrow, and I got happy,

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of course: a 1.8 right answer out of five possible. That means that

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there was a place for a professor of international health --

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(Laughter) and for my course.

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But one late night, when I was compiling the report

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I really realized my discovery. I have shown

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that Swedish top students know statistically significantly less

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about the world than the chimpanzees.

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(Laughter)

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Because the chimpanzee would score half right if I gave them

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two bananas with Sri Lanka and Turkey. They would be right half of the cases.

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But the students are not there. The problem for me was not ignorance;

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it was preconceived ideas.

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I did also an unethical study of the professors of the Karolinska Institute

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(Laughter)

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-- that hands out the Nobel Prize in Medicine,

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and they are on par with the chimpanzee there.

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(Laughter)

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This is where I realized that there was really a need to communicate,

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because the data of what's happening in the world

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and the child health of every country is very well aware.

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We did this software which displays it like this: every bubble here is a country.

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This country over here is China. This is India.

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The size of the bubble is the population, and on this axis here I put fertility rate.

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Because my students, what they said

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when they looked upon the world, and I asked them,

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"What do you really think about the world?"

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Well, I first discovered that the textbook was Tintin, mainly.

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(Laughter)

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And they said, "The world is still 'we' and 'them.'

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And we is Western world and them is Third World."

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"And what do you mean with Western world?" I said.

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"Well, that's long life and small family, and Third World is short life and large family."

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So this is what I could display here. I put fertility rate here: number of children per woman:

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one, two, three, four, up to about eight children per woman.

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We have very good data since 1962 -- 1960 about -- on the size of families in all countries.

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The error margin is narrow. Here I put life expectancy at birth,

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from 30 years in some countries up to about 70 years.

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And 1962, there was really a group of countries here

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that was industrialized countries, and they had small families and long lives.

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And these were the developing countries:

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they had large families and they had relatively short lives.

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Now what has happened since 1962? We want to see the change.

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Are the students right? Is it still two types of countries?

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Or have these developing countries got smaller families and they live here?

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Or have they got longer lives and live up there?

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Let's see. We stopped the world then. This is all U.N. statistics

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that have been available. Here we go. Can you see there?

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It's China there, moving against better health there, improving there.

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All the green Latin American countries are moving towards smaller families.

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Your yellow ones here are the Arabic countries,

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and they get larger families, but they -- no, longer life, but not larger families.

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The Africans are the green down here. They still remain here.

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This is India. Indonesia's moving on pretty fast.

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(Laughter)

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And in the '80s here, you have Bangladesh still among the African countries there.

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But now, Bangladesh -- it's a miracle that happens in the '80s:

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the imams start to promote family planning.

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They move up into that corner. And in '90s, we have the terrible HIV epidemic

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that takes down the life expectancy of the African countries

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and all the rest of them move up into the corner,

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where we have long lives and small family, and we have a completely new world.

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(Applause)

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Let me make a comparison directly between the United States of America and Vietnam.

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1964: America had small families and long life;

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Vietnam had large families and short lives. And this is what happens:

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the data during the war indicate that even with all the death,

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there was an improvement of life expectancy. By the end of the year,

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the family planning started in Vietnam and they went for smaller families.

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And the United States up there is getting for longer life,

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keeping family size. And in the '80s now,

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they give up communist planning and they go for market economy,

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and it moves faster even than social life. And today, we have

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in Vietnam the same life expectancy and the same family size

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here in Vietnam, 2003, as in United States, 1974, by the end of the war.

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I think we all -- if we don't look in the data --

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we underestimate the tremendous change in Asia, which was

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in social change before we saw the economical change.

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Let's move over to another way here in which we could display

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the distribution in the world of the income. This is the world distribution of income of people.

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One dollar, 10 dollars or 100 dollars per day.

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There's no gap between rich and poor any longer. This is a myth.

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There's a little hump here. But there are people all the way.

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And if we look where the income ends up -- the income --

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this is 100 percent the world's annual income. And the richest 20 percent,

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they take out of that about 74 percent. And the poorest 20 percent,

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they take about two percent. And this shows that the concept

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of developing countries is extremely doubtful. We think about aid, like

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these people here giving aid to these people here. But in the middle,

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we have most the world population, and they have now 24 percent of the income.

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We heard it in other forms. And who are these?

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Where are the different countries? I can show you Africa.

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This is Africa. 10 percent the world population, most in poverty.

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This is OECD. The rich country. The country club of the U.N.

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And they are over here on this side. Quite an overlap between Africa and OECD.

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And this is Latin America. It has everything on this Earth,

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from the poorest to the richest, in Latin America.

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And on top of that, we can put East Europe, we can put East Asia,

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and we put South Asia. And how did it look like if we go back in time,

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to about 1970? Then there was more of a hump.

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And we have most who lived in absolute poverty were Asians.

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The problem in the world was the poverty in Asia. And if I now let the world move forward,

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you will see that while population increase, there are

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hundreds of millions in Asia getting out of poverty and some others

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getting into poverty, and this is the pattern we have today.

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And the best projection from the World Bank is that this will happen,

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and we will not have a divided world. We'll have most people in the middle.

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Of course it's a logarithmic scale here,

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but our concept of economy is growth with percent. We look upon it

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as a possibility of percentile increase. If I change this, and I take

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GDP per capita instead of family income, and I turn these

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individual data into regional data of gross domestic product,

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and I take the regions down here, the size of the bubble is still the population.

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And you have the OECD there, and you have sub-Saharan Africa there,

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and we take off the Arab states there,

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coming both from Africa and from Asia, and we put them separately,

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and we can expand this axis, and I can give it a new dimension here,

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by adding the social values there, child survival.

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Now I have money on that axis, and I have the possibility of children to survive there.

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In some countries, 99.7 percent of children survive to five years of age;

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others, only 70. And here it seems there is a gap

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between OECD, Latin America, East Europe, East Asia,

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Arab states, South Asia and sub-Saharan Africa.

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The linearity is very strong between child survival and money.

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But let me split sub-Saharan Africa. Health is there and better health is up there.

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I can go here and I can split sub-Saharan Africa into its countries.

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And when it burst, the size of its country bubble is the size of the population.

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Sierra Leone down there. Mauritius is up there. Mauritius was the first country

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to get away with trade barriers, and they could sell their sugar --

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they could sell their textiles -- on equal terms as the people in Europe and North America.

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There's a huge difference between Africa. And Ghana is here in the middle.

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In Sierra Leone, humanitarian aid.

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Here in Uganda, development aid. Here, time to invest; there,

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you can go for a holiday. It's a tremendous variation

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within Africa which we rarely often make -- that it's equal everything.

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I can split South Asia here. India's the big bubble in the middle.

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But a huge difference between Afghanistan and Sri Lanka.

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I can split Arab states. How are they? Same climate, same culture,

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same religion -- huge difference. Even between neighbors.

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Yemen, civil war. United Arab Emirate, money which was quite equally and well used.

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Not as the myth is. And that includes all the children of the foreign workers who are in the country.

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Data is often better than you think. Many people say data is bad.

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There is an uncertainty margin, but we can see the difference here:

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Cambodia, Singapore. The differences are much bigger

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than the weakness of the data. East Europe:

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Soviet economy for a long time, but they come out after 10 years

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very, very differently. And there is Latin America.

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Today, we don't have to go to Cuba to find a healthy country in Latin America.

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Chile will have a lower child mortality than Cuba within some few years from now.

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And here we have high-income countries in the OECD.

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And we get the whole pattern here of the world,

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which is more or less like this. And if we look at it,

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how it looks -- the world, in 1960, it starts to move. 1960.

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This is Mao Tse-tung. He brought health to China. And then he died.

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And then Deng Xiaoping came and brought money to China, and brought them into the mainstream again.

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And we have seen how countries move in different directions like this,

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so it's sort of difficult to get

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an example country which shows the pattern of the world.

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But I would like to bring you back to about here at 1960.

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I would like to compare South Korea, which is this one, with Brazil,

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which is this one. The label went away for me here. And I would like to compare Uganda,

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which is there. And I can run it forward, like this.

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And you can see how South Korea is making a very, very fast advancement,

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whereas Brazil is much slower.

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And if we move back again, here, and we put on trails on them, like this,

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you can see again that the speed of development

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is very, very different, and the countries are moving more or less

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in the same rate as money and health, but it seems you can move

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much faster if you are healthy first than if you are wealthy first.

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And to show that, you can put on the way of United Arab Emirate.

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They came from here, a mineral country. They cached all the oil;

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they got all the money; but health cannot be bought at the supermarket.

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You have to invest in health. You have to get kids into schooling.

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You have to train health staff. You have to educate the population.

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And Sheikh Sayed did that in a fairly good way.

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In spite of falling oil prices, he brought this country up here.

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So we've got a much more mainstream appearance of the world,

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where all countries tend to use their money

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better than they used in the past. Now, this is, more or less,

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if you look at the average data of the countries -- they are like this.

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Now that's dangerous, to use average data, because there is such a lot

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of difference within countries. So if I go and look here, we can see

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that Uganda today is where South Korea was 1960. If I split Uganda,

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there's quite a difference within Uganda. These are the quintiles of Uganda.

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The richest 20 percent of Ugandans are there.

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The poorest are down there. If I split South Africa, it's like this.

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And if I go down and look at Niger, where there was such a terrible famine,

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lastly, it's like this. The 20 percent poorest of Niger is out here,

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and the 20 percent richest of South Africa is there,

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and yet we tend to discuss on what solutions there should be in Africa.

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Everything in this world exists in Africa. And you can't

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discuss universal access to HIV [medicine] for that quintile up here

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with the same strategy as down here. The improvement of the world

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must be highly contextualized, and it's not relevant to have it

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on regional level. We must be much more detailed.

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We find that students get very excited when they can use this.

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And even more policy makers and the corporate sectors would like to see

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how the world is changing. Now, why doesn't this take place?

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Why are we not using the data we have? We have data in the United Nations,

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in the national statistical agencies

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and in universities and other non-governmental organizations.

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Because the data is hidden down in the databases.

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And the public is there, and the Internet is there, but we have still not used it effectively.

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All that information we saw changing in the world

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does not include publicly-funded statistics. There are some web pages

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like this, you know, but they take some nourishment down from the databases,

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but people put prices on them, stupid passwords and boring statistics.

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(Laughter) (Applause)

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And this won't work. So what is needed? We have the databases.

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It's not the new database you need. We have wonderful design tools,

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and more and more are added up here. So we started

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a nonprofit venture which we called -- linking data to design --

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we call it Gapminder, from the London underground, where they warn you,

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"mind the gap." So we thought Gapminder was appropriate.

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And we started to write software which could link the data like this.

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And it wasn't that difficult. It took some person years, and we have produced animations.

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You can take a data set and put it there.

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We are liberating U.N. data, some few U.N. organization.

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Some countries accept that their databases can go out on the world,

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but what we really need is, of course, a search function.

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A search function where we can copy the data up to a searchable format

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and get it out in the world. And what do we hear when we go around?

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I've done anthropology on the main statistical units. Everyone says,

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"It's impossible. This can't be done. Our information is so peculiar

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in detail, so that cannot be searched as others can be searched.

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We cannot give the data free to the students, free to the entrepreneurs of the world."

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But this is what we would like to see, isn't it?

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The publicly-funded data is down here.

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And we would like flowers to grow out on the Net.

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And one of the crucial points is to make them searchable, and then people can use

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the different design tool to animate it there.

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And I have a pretty good news for you. I have a good news that the present,

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new Head of U.N. Statistics, he doesn't say it's impossible.

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He only says, "We can't do it."

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(Laughter)

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And that's a quite clever guy, huh?

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(Laughter)

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So we can see a lot happening in data in the coming years.

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We will be able to look at income distributions in completely new ways.

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This is the income distribution of China, 1970.

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the income distribution of the United States, 1970.

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Almost no overlap. Almost no overlap. And what has happened?

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What has happened is this: that China is growing, it's not so equal any longer,

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and it's appearing here, overlooking the United States.

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Almost like a ghost, isn't it, huh?

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(Laughter)

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It's pretty scary. But I think it's very important to have all this information.

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We need really to see it. And instead of looking at this,

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I would like to end up by showing the Internet users per 1,000.

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In this software, we access about 500 variables from all the countries quite easily.

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It takes some time to change for this,

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but on the axises, you can quite easily get any variable you would like to have.

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And the thing would be to get up the databases free,

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to get them searchable, and with a second click, to get them

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into the graphic formats, where you can instantly understand them.

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Now, statisticians doesn't like it, because they say that this

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will not show the reality; we have to have statistical, analytical methods.

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But this is hypothesis-generating.

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I end now with the world. There, the Internet is coming.

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The number of Internet users are going up like this. This is the GDP per capita.

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And it's a new technology coming in, but then amazingly, how well

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it fits to the economy of the countries. That's why the 100 dollar

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computer will be so important. But it's a nice tendency.

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It's as if the world is flattening off, isn't it? These countries

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are lifting more than the economy and will be very interesting

tedtalks 19:21
19:25

to follow this over the year, as I would like you to be able to do

tedtalks 19:25
19:27

with all the publicly funded data. Thank you very much.

tedtalks 19:28
19:31

(Applause)