The best stats you've ever seen

You've never seen data presented like this. With the drama and urgency of a sportscaster, statistics guru Hans Rosling debunks myths about the so-called "developing world."

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 hunger 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 the Swedish college system,

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

So I did a pretest when they came.

And one of the questions
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 countries,

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 is pretty narrow.

And I got happy, of course -- a 1.8
right answer out of five possible.

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

and for my course.

(Laughter)

But one 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.

(Laughter)

Because the chimpanzee
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 Karolinska Institute,

which hands out
the Nobel Prize in Medicine,

and they are on par
with the chimpanzee there.

(Laughter)

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 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.

(Laughter)

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

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

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

"Well, that's long life and small family.

And 'Third World' is short life
and large family."

So this is what I could display here.

I put fertility rate here --

number of children per woman:
one, two, three, four,

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 error margin is narrow.

Here, I put life expectancy at birth,

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

And in 1962, there was really
a group of countries here

that were 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, eh?

This is all UN statistics
that have been available.

Here we go. Can you see there?

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

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

Your yellow ones here
are the Arabic countries,

and they get longer life,
but not larger families.

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

This is India; Indonesia
is moving on pretty fast.

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

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 the '90s, we have
the terrible HIV epidemic

that takes down the life expectancy
of the African countries.

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

where we have long lives and small family,

and we have a completely new world.

(Applause)

(Applause ends)

Let me make a comparison directly

between the 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,
family planning started in Vietnam,

and they went for smaller families.

And the United States up there
is getting 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 than social life.

And today, we have in Vietnam

the same life expectancy
and the same family size

here in Vietnam, 2003,

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

I think we all, if we don't
look at the data,

we underestimate
the tremendous change in Asia,

which was in social change
before we saw the economic change.

So let's move over to another way here

in which we could display
the distribution in the world

of income.

This is the world distribution
of income of people.

One dollar, 10 dollars
or 100 dollars 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,

this is 100 percent
of the world's annual income.

And the richest 20 percent,

they take out of that about 74 percent.

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

And this shows that the concept
of developing countries

is extremely doubtful.

We think about 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 percent of the income.

We heard it in other forms.

And who are these?

Where are the different countries?

I can show you Africa.

This is Africa.

Ten percent of the world population,

most in poverty.

This is OECD -- the rich countries,
the country club of the UN.

And they are over here on this side.
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 what did it look like
if we go back in time,

to about 1970?

Then, there was more of a hump.

And 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 increases,

there are hundreds of millions
in Asia getting out of poverty,

and some others getting 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'll 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 percentile increase.

If I change this and 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 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 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,
there is a gap between OECD,

Latin America, East 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.

Health is there
and better health is up there.

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

And when it bursts,

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

Sierra Leone down there,
Mauritius is up there.

Mauritius 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 [within] Africa.

And Ghana is here in the middle.

In Sierra Leone, humanitarian aid.

Here in Uganda, development aid.

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

There's tremendous variation
within Africa,

which we very often make
that it's equal everything.

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

But there's a huge difference
between Afghanistan and Sri Lanka.

I can split 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 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.

Here, we have high-income
countries in the OECD.

And we get the whole pattern
here of the world,

which is more or less like this.

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

in 1960, it starts to move.

This is Mao Zedong.
He brought health to China.

And then he died.

And then Deng Xiaoping 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 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 1960.

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

with Brazil, which is this one.

The label went away for me here.

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

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 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 at 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 Emirates.

They came from here, a mineral country.

They cached 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 Sheikh Zayed did that
in a fairly good way.

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

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

where all countries tend
to use their money

better than they used it in the past.

Now, this is, more or less, if you look
at the average data of the countries --

they are like this.

That's dangerous, to use average data,

because there is 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 in 1960.

If I split Uganda, there's quite
a difference within Uganda.

These are the quintiles of Uganda.

The richest 20 percent
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 Niger,

where there was such
a terrible famine [recently],

it's like this.

The 20 percent poorest
of Niger is out here,

and the 20 percent richest
of South Africa is there,

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

Everything in this world exists in Africa.

And you can't discuss
universal access to HIV [treatment]

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 a 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
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 Nations,
in the national statistical agencies

and in universities and other
nongovernmental organizations.

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.

(Laughter)

And this won't work.

(Applause)

So what is needed? We have the databases.

It's not a new database that you need.

We have wonderful design tools
and more and more are added up here.

So we started a nonprofit venture
linking data to design,

we called "Gapminder,"

from the London Underground,
where they warn you, "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 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 in detail,

so that cannot be searched
as others 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.

One of the crucial points
is to make them searchable,

and then people can use the different
design tools to animate it there.

And I have pretty good news for you.

I have good news that the [current],

new head of UN statistics
doesn't say it's impossible.

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

(Laughter)

And that's a quite clever guy, huh?

(Laughter)

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:

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?

(Laughter)

It's pretty scary.

(Laughter)

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

We need really to see it.

And instead of looking at this,

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

In this software, we access
about 500 variables

from all the countries quite easily.

It takes some time to change for this,

but on the axes, 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, statisticians don't like it,
because they say

that this will not show the reality;

we have to have statistical,
analytical methods.

But this is hypothesis-generating.

I end now with the world.

There, the internet is 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 then 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 it 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.

(Applause)