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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.

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That was after having spent about 20 years

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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.

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But when you get that opportunity,
you get a little nervous.

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I thought, these students coming to us

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actually have the highest grade
you can get in Swedish college systems --

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so I thought, maybe they know everything
I'm going to teach them about.

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

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one has twice the child
mortality of the other.

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And this means that
it's much bigger a difference

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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.

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I did it so I got 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.

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That means that there was a place
for a professor of international health

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and for my course.

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(Laughter)

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But one late night,
when I was compiling the report,

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I really realized my discovery.

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I have shown that Swedish top students

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know statistically significantly less
about the world than the chimpanzees.

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(Laughter)

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Because the chimpanzee
would score half right

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if I gave them two bananas
with Sri Lanka and Turkey.

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They would be right half of the cases.
But the students are not there.

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The problem for me was not ignorance;
it was preconceived ideas.

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I did also an unethical study

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of the professors
of the Karolinska Institute,

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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:

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every bubble here is a country.

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This country over here is China.

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This is India.

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The size of the bubble is the population,

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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,

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and Third World is short life
and large family."

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So this is what I could display here.

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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 --

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on the size of families in all countries.

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The error margin is narrow.

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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,

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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.

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This is all U.N. statistics
that have been available.

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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 longer life,
but not larger families.

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

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This is India; Indonesia
is moving on pretty fast.

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(Laughter)

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In the '80s here, you have Bangladesh
still among the African countries.

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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.

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And in the '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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(Applause ends)

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Let me make a comparison directly

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between the United States
of America and Vietnam.

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1964.

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America had small families and long life;

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Vietnam had large families
and short lives.

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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.

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By the end of the year,
the family planning started in Vietnam;

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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.

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And in the '80s now,
they give up Communist planning

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and they go for market economy,

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and it moves faster even than social life.

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And today, we have in Vietnam

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the same life expectancy
and the same family size

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here in Vietnam, 2003,

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

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If we don't look in the data,

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I think we all underestimate
the tremendous change in Asia,

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which was 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.

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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.

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But there are people all the way.

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And if we look where the income ends up,

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this is 100 percent
the world's annual income.

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And the richest 20 percent,

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they take out of that about 74 percent.

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And the poorest 20 percent,
they take about two percent.

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And this shows that the concept
of developing countries

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is extremely doubtful.

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We think about aid,

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like these people here giving aid
to these people here.

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But in the middle, we have
most of the world population,

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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.

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10% the world population, most in poverty.

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This is OECD.

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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.

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It has everything on this Earth,
from the poorest to the richest

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in Latin America.

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

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we can put East Asia,
and we put South Asia.

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And how did it look like
if we go back in time,

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to about 1970?

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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.

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And if I now let the world move forward,

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you will see that
while population increases,

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there are hundreds of millions
in Asia getting out of poverty

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and some others getting into poverty,

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and this is the pattern we have today.

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And the best projection
from the World Bank

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is that this will happen,

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and we will not have a divided world.

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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.

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We look upon it as a possibility
of percentile increase.

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If I change this, and take GDP per capita
instead of family income,

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and I turn these individual data

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into regional data
of gross domestic product,

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and I take the regions down here,

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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,

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and I have the possibility
of children to survive there.

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

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others, only 70.

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And here, it seems,
there is a gap between OECD,

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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.

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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,

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the size of its country bubble
is the size of the population.

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Sierra Leone down there.
Mauritius is up there.

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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 --

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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.

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Here, time to invest;
there, you can go for a holiday.

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It's a tremendous variation within Africa

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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?

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Same climate, same culture,
same religion -- huge difference.

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Even between neighbors.

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Yemen, civil war.

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United Arab Emirates, money,
which was quite equally and well used.

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Not as the myth is.

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And that includes all the children
of the foreign workers

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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.

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The differences are much bigger
than the weakness of the data.

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East Europe: Soviet economy
for a long time,

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but they come out after 10 years
very, very differently.

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And there is Latin America.

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Today, we don't have to go to Cuba

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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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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.

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And if we look at it, how the world looks,

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in 1960, it starts to move.

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This is Mao Tse-tung.
He brought health to China.

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And then he died.

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And then Deng Xiaoping came
and brought money to China,

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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
an example country

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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

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South Korea, which is this one,
with Brazil, which is this one.

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The label went away for me here.

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And I would like to compare Uganda,
which is there.

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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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00:12:30,358 --> 00:12:33,976
you can see again
that the speed of development

242
00:12:34,000 --> 00:12:36,814
is very, very different,

243
00:12:36,838 --> 00:12:43,539
and the countries are moving more or less
in the same rate as money and health,

244
00:12:43,563 --> 00:12:45,437
but it seems you can move much faster

245
00:12:45,461 --> 00:12:48,120
if you are healthy first
than if you are wealthy first.

246
00:12:49,000 --> 00:12:52,976
And to show that, you can put
on the way of United Arab Emirates.

247
00:12:53,000 --> 00:12:55,856
They came from here, a mineral country.

248
00:12:55,880 --> 00:12:58,205
They cached all the oil;
they got all the money;

249
00:12:58,229 --> 00:13:00,468
but health cannot be bought
at the supermarket.

250
00:13:01,516 --> 00:13:04,612
You have to invest in health.
You have to get kids into schooling.

251
00:13:04,636 --> 00:13:07,779
You have to train health staff.
You have to educate the population.

252
00:13:07,803 --> 00:13:10,257
And Sheikh Zayed did that
in a fairly good way.

253
00:13:10,281 --> 00:13:13,976
In spite of falling oil prices,
he brought this country up here.

254
00:13:14,000 --> 00:13:17,976
So we've got a much more mainstream
appearance of the world,

255
00:13:18,000 --> 00:13:20,527
where all countries tend
to use their money

256
00:13:20,551 --> 00:13:22,662
better than they used in the past.

257
00:13:23,755 --> 00:13:30,727
Now, this is, more or less, if you look
at the average data of the countries --

258
00:13:30,751 --> 00:13:32,399
they are like this.

259
00:13:32,423 --> 00:13:35,765
Now that's dangerous, to use average data,

260
00:13:35,789 --> 00:13:39,560
because there is such
a lot of difference within countries.

261
00:13:39,584 --> 00:13:46,003
So if I go and look here,
we can see that Uganda today

262
00:13:46,027 --> 00:13:48,976
is where South Korea was in 1960.

263
00:13:49,000 --> 00:13:52,666
If I split Uganda, there's quite
a difference within Uganda.

264
00:13:52,690 --> 00:13:54,868
These are the quintiles of Uganda.

265
00:13:54,892 --> 00:13:57,076
The richest 20 percent
of Ugandans are there.

266
00:13:57,100 --> 00:13:58,485
The poorest are down there.

267
00:13:58,509 --> 00:14:01,385
If I split South Africa, it's like this.

268
00:14:01,409 --> 00:14:04,152
And if I go down and look at Niger,

269
00:14:04,176 --> 00:14:08,820
where there was such
a terrible famine, lastly, it's like this.

270
00:14:08,844 --> 00:14:11,738
The 20 percent poorest
of Niger is out here,

271
00:14:11,762 --> 00:14:14,531
and the 20 percent richest
of South Africa is there,

272
00:14:14,555 --> 00:14:16,573
and yet we tend to discuss

273
00:14:16,597 --> 00:14:18,976
on what solutions
there should be in Africa.

274
00:14:19,000 --> 00:14:21,567
Everything in this world exists in Africa.

275
00:14:21,591 --> 00:14:24,866
And you can't discuss
universal access to HIV [medicine]

276
00:14:24,890 --> 00:14:29,262
for that quintile up here
with the same strategy as down here.

277
00:14:29,286 --> 00:14:32,824
The improvement of the world
must be highly contextualized,

278
00:14:32,848 --> 00:14:36,778
and it's not relevant to have it
on regional level.

279
00:14:36,802 --> 00:14:38,332
We must be much more detailed.

280
00:14:39,070 --> 00:14:42,396
We find that students get
very excited when they can use this.

281
00:14:42,420 --> 00:14:46,038
And even more, policy makers
and the corporate sectors

282
00:14:46,062 --> 00:14:49,723
would like to see
how the world is changing.

283
00:14:49,747 --> 00:14:51,435
Now, why doesn't this take place?

284
00:14:51,459 --> 00:14:53,949
Why are we not using the data we have?

285
00:14:53,973 --> 00:14:57,783
We have data in the United Nations,
in the national statistical agencies

286
00:14:57,807 --> 00:15:00,976
and in universities and other
non-governmental organizations.

287
00:15:01,000 --> 00:15:03,334
Because the data is hidden
down in the databases.

288
00:15:03,358 --> 00:15:06,290
And the public is there,
and the Internet is there,

289
00:15:06,315 --> 00:15:08,476
but we have still not used it effectively.

290
00:15:08,499 --> 00:15:10,976
All that information
we saw changing in the world

291
00:15:11,000 --> 00:15:14,139
does not include
publicly-funded statistics.

292
00:15:14,163 --> 00:15:16,317
There are some web pages
like this, you know,

293
00:15:16,341 --> 00:15:20,976
but they take some nourishment
down from the databases,

294
00:15:21,000 --> 00:15:25,976
but people put prices on them,
stupid passwords and boring statistics.

295
00:15:26,000 --> 00:15:27,389
(Laughter)

296
00:15:27,413 --> 00:15:28,588
And this won't work.

297
00:15:28,612 --> 00:15:31,415
(Applause)

298
00:15:31,439 --> 00:15:33,861
So what is needed? We have the databases.

299
00:15:33,885 --> 00:15:35,662
It's not the new database you need.

300
00:15:35,686 --> 00:15:39,412
We have wonderful design tools,
and more and more are added up here.

301
00:15:39,436 --> 00:15:42,307
So we started a nonprofit venture

302
00:15:42,331 --> 00:15:46,702
which, linking data to design,
we called Gapminder,

303
00:15:46,726 --> 00:15:48,079
from the London Underground,

304
00:15:48,103 --> 00:15:49,880
where they warn you, "mind the gap."

305
00:15:49,904 --> 00:15:51,844
So we thought Gapminder was appropriate.

306
00:15:51,868 --> 00:15:56,224
And we started to write software
which could link the data like this.

307
00:15:56,248 --> 00:15:57,795
And it wasn't that difficult.

308
00:15:57,819 --> 00:16:01,370
It took some person years,
and we have produced animations.

309
00:16:01,394 --> 00:16:03,727
You can take a data set and put it there.

310
00:16:03,751 --> 00:16:07,976
We are liberating U.N. data,
some few U.N. organization.

311
00:16:08,000 --> 00:16:12,500
Some countries accept that
their databases can go out on the world,

312
00:16:12,524 --> 00:16:15,595
but what we really need is,
of course, a search function.

313
00:16:15,619 --> 00:16:19,976
A search function where we can copy
the data up to a searchable format

314
00:16:20,000 --> 00:16:21,732
and get it out in the world.

315
00:16:21,756 --> 00:16:24,127
And what do we hear when we go around?

316
00:16:24,151 --> 00:16:27,158
I've done anthropology
on the main statistical units.

317
00:16:27,182 --> 00:16:30,302
Everyone says, "It's impossible.
This can't be done.

318
00:16:30,326 --> 00:16:32,836
Our information is so peculiar in detail,

319
00:16:32,860 --> 00:16:35,964
so that cannot be searched
as others can be searched.

320
00:16:35,988 --> 00:16:38,343
We cannot give the data
free to the students,

321
00:16:38,367 --> 00:16:40,272
free to the entrepreneurs of the world."

322
00:16:41,256 --> 00:16:44,151
But this is what we would
like to see, isn't it?

323
00:16:44,175 --> 00:16:46,599
The publicly-funded data is down here.

324
00:16:46,623 --> 00:16:49,658
And we would like flowers
to grow out on the Net.

325
00:16:49,682 --> 00:16:52,849
And one of the crucial points
is to make them searchable,

326
00:16:52,873 --> 00:16:57,117
and then people can use the different
design tool to animate it there.

327
00:16:57,141 --> 00:16:59,581
And I have pretty good news for you.

328
00:16:59,605 --> 00:17:01,799
I have good news that the present,

329
00:17:01,823 --> 00:17:04,976
new Head of U.N. Statistics,
he doesn't say it's impossible.

330
00:17:05,000 --> 00:17:06,772
He only says, "We can't do it."

331
00:17:07,772 --> 00:17:10,976
(Laughter)

332
00:17:11,000 --> 00:17:12,976
And that's a quite clever guy, huh?

333
00:17:13,000 --> 00:17:14,976
(Laughter)

334
00:17:15,000 --> 00:17:18,976
So we can see a lot happening
in data in the coming years.

335
00:17:19,000 --> 00:17:23,660
We will be able to look at income
distributions in completely new ways.

336
00:17:23,684 --> 00:17:28,976
This is the income distribution
of China, 1970.

337
00:17:29,000 --> 00:17:33,796
This is the income distribution
of the United States, 1970.

338
00:17:33,820 --> 00:17:36,869
Almost no overlap.

339
00:17:36,893 --> 00:17:38,343
And what has happened?

340
00:17:38,673 --> 00:17:40,351
What has happened is this:

341
00:17:40,375 --> 00:17:43,347
that China is growing,
it's not so equal any longer,

342
00:17:43,371 --> 00:17:46,976
and it's appearing here,
overlooking the United States.

343
00:17:47,000 --> 00:17:49,658
Almost like a ghost, isn't it?

344
00:17:49,682 --> 00:17:50,976
(Laughter)

345
00:17:51,000 --> 00:17:52,587
It's pretty scary.

346
00:17:52,611 --> 00:17:54,872
(Laughter)

347
00:17:57,762 --> 00:18:01,572
But I think it's very important
to have all this information.

348
00:18:01,596 --> 00:18:04,209
We need really to see it.

349
00:18:04,233 --> 00:18:06,976
And instead of looking at this,

350
00:18:07,000 --> 00:18:12,648
I would like to end up by showing
the Internet users per 1,000.

351
00:18:12,672 --> 00:18:15,554
In this software, we access
about 500 variables

352
00:18:15,578 --> 00:18:17,979
from all the countries quite easily.

353
00:18:18,003 --> 00:18:20,976
It takes some time to change for this,

354
00:18:21,000 --> 00:18:26,855
but on the axises, you can quite easily
get any variable you would like to have.

355
00:18:26,879 --> 00:18:31,386
And the thing would be
to get up the databases free,

356
00:18:31,410 --> 00:18:33,976
to get them searchable,
and with a second click,

357
00:18:34,000 --> 00:18:38,976
to get them into the graphic formats,
where you can instantly understand them.

358
00:18:39,000 --> 00:18:41,102
Now, statisticians don't like it,

359
00:18:41,126 --> 00:18:48,094
because they say
that this will not show the reality;

360
00:18:48,118 --> 00:18:51,839
we have to have statistical,
analytical methods.

361
00:18:51,863 --> 00:18:53,976
But this is hypothesis-generating.

362
00:18:54,000 --> 00:18:55,905
I end now with the world.

363
00:18:57,021 --> 00:18:58,506
There, the Internet is coming.

364
00:18:58,530 --> 00:19:01,013
The number of Internet users
are going up like this.

365
00:19:01,037 --> 00:19:03,006
This is the GDP per capita.

366
00:19:03,030 --> 00:19:06,515
And it's a new technology coming in,
but then amazingly,

367
00:19:06,539 --> 00:19:10,258
how well it fits to the economy
of the countries.

368
00:19:10,282 --> 00:19:13,739
That's why the $100 computer
will be so important.

369
00:19:13,763 --> 00:19:15,168
But it's a nice tendency.

370
00:19:15,192 --> 00:19:18,096
It's as if the world
is flattening off, isn't it?

371
00:19:18,120 --> 00:19:20,525
These countries are lifting more
than the economy

372
00:19:20,549 --> 00:19:23,334
and will be very interesting
to follow this over the year,

373
00:19:23,358 --> 00:19:26,719
as I would like you to be able to do
with all the publicly funded data.

374
00:19:26,743 --> 00:19:27,976
Thank you very much.

375
00:19:28,000 --> 00:19:31,000
(Applause)