But 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 hunger in Africa. So I was sort of expected to know a little about the world and I started in our Medical University currently in ski 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 the pretest 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 the 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 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 the confidence interval, which was pretty narrow, and I got happy, of course, at 1.8, right and zero 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 chimpanzee would score half right? If I gave him two bananas with Sri Lankan Turkey, they would be right Half of the cases, but the students are not that the problem for me was not ignorance. It was preconceived ideas. I did also an unfair, unethical study of the professors of the current institute that hands out the Nobel Prize in Medicine and they are on par with the chimpanzee there. So this is where I realized that there was really a need to communicate because the data or what's happening in the world and the child health, obviously, 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 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 1234 up to about eight children per woman. We have very good data since 1960 to 1960, about on the size of families in all countries, the error 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 of 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 stopped the world. And this is all un statistic that has been available. Here we go, can you see that it's China, they're moving up against better health care improving their or the green Latin American countries, they are moving towards smaller families. Your yellow ones here are the Arabic countries and they get larger families, but they no longer live but not larger families. The Africans are the green down here, but still remain here. This is India, Indonesia is moving on picket fence.

And in the 80s. Here you have Bangladesh, still among the African countries there, but not Bangladesh. It's a miracle that happens in the 80s. The moms start to promote family planning, 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 mall moves up into the corner where we have long lives and small family and we have a completely new world. definitely 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, that 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 live 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 do Look in the data, we under estimate 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 that distribution in the world of the income. This is the world distribution of income of people. $1 $10 or $100 per day. That's no gap between rich and poor any longer. This is a myth.

That's a little hump here. But there are people all the way. And if we look where the income ends up the income This is 100% of world's annual income, and the richest 20% they take out of that about 74%. And the poor is 20%. They take about 2%. And this shows that the concept of developing countries is extremely doubtful.

We sort of think about that. aid like these people here giving aid to these people here, but in the middle, we have most of the world population. And they have now 24% of 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 always seedy, the rich country, the Country Club of the UN, and they are over here on this side and quite an overlap between Africa and oacd. 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 could South Asia, and how did it look like if we go back in time to about 1970.

Then there was more of a hump. And we have most to live in absolute poverty. We're Asians. The problem in the world was the poverty In Asia, and if I now let the world move forwards, you will seen that wild 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 person, we look upon it as a possibility of percentage increase if I change this, and I take GDP per capita instead of family income, and I turn these 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 Sub Saharan Africa there, and we take off the Arab states, they're coming both from Africa and from Asia. And we put them separately and we can expand these Access. And I can give it a new dimension here.

By adding the social values, they shine survive, and I have money on that axis. And I have the possibility of children to survive there in some countries, 99.7% of children survived to five years of age, others, only 70. And here it seems that this is a gap between oacd Latin America, Eastern Europe, East Asia, Arab states, South Asia, and Sub Saharan Africa. The linearity is very strong between child survival and money, but let me split Sub Saharan Africa. Who else is there and better health is up there. I can go here.

And I can split Sub Saharan Africa into its Congress. And when it bursts, the size of his country bubble is the size of the population. Sierra Leone, the down there, ma reaches up there. My reaches was the first country to get away with trade barriers and they could sell a sugar they could sell their textile on equal terms as the people in Europe and North America, there's a huge difference between Africa and Ghana's here in the middle. in Sierra Leone, the 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 rarely often make that it's equal everything.

I can split South Asia here. India has the big bubble in the middle, but huge difference between Afghanistan and Sri Lanka. And I can speak Arab states, how are they same climate, same culture, same religion, huge difference, even between neighbors Yemen Civil War, United Arab Emirates money, which was quite equally and well used not as the method 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 their data is bad. There's an uncertainty March, 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 of the 10 years very, very differently. And there is Latin America.

Today, we don't have to go to Cuba to find the healthy country in Latin America, Chile will have a lower child mortality and 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 Meltzer tomb, he brought health to China, and then he died and then then shopping 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 counter which shows the pattern of the world. But I would like to bring you back to about here at 1960.

And I would like to compare 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're healthy first, then if you are wealthy first.

And to show that you can put on the way of United Arab Emirates, they came from here, a mineral country, they catch 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 I did that in a fairly good way. And in spite of falling oil prices, he brought this country up here. So we got 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 the 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 Today is where South Korea was 1965 split Uganda, there's quite a difference within Uganda. These are the quintiles of Uganda, the richest 20% of Ugandan saw 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, last Lee is like this, the 20% poorest of Nigeria 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 detail. We find that students get very excited when they can use this and even more policymakers.

On 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 that 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 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, if not the new database you need. We have wonderful design tools and more and more I ended up here. So we started a nonprofit venture, which we call linking data. To design we call it gapminder, from London Underground, where they warn your mind the gap.

So we thought gapminder 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 organization, some companies accept that the 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 unit. Everyone says it's impossible. This can't be done. Our information is so peculiar in detail.

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. This is what we would like to see, isn't it? The public Funded data is down here. And we would like flowers to grow up on the net. And one of the crucial points 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 President new head of UN statistics, he doesn't say it's impossible. He only says we can't do it. And that's a quite clever guy. 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.

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. The China's growing it's not so equal any longer and it's appearing here.

Overlooking the United States, almost like a ghost, isn't it? 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 instead of looking at this, I would like to end up by showing the internet users per 1000. And this software, we access about 500 variables from all the countries quite easily. It takes some time 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 secondly, 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 Show the reality we have to have statistical analytical methods. But this is hypothesis generating I end now with a world where the internet accounting, 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 the 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 is as if the world is flattening off it