The best stats you've ever seen | Hans Rosling
TED
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1.Students' Knowledge of Global Development
0:00 / 1:40The speaker describes his experience teaching global development to Swedish undergraduate students. He pre-tested them on child mortality rates, expecting them to be knowledgeable, but found their understanding was surprisingly low.
- Global Development
- Child Mortality
- Pre-test
What's inside this course
- 0:00
1. Students' Knowledge of Global Development
The speaker describes his experience teaching global development to Swedish undergraduate students. He pre-tested them on child mortality rates, expecting them to be knowledgeable, but found their understanding was surprisingly low.
- 1:40
2. Chimpanzees vs. Students' Knowledge
The speaker humorously compares the students' performance on the child mortality test to that of chimpanzees, noting that the students scored worse than random chance. He concludes that the problem isn't ignorance, but rather preconceived ideas about the world.
- 3:50
3. Visualizing Global Data with Bubbles
To address the misconceptions, the speaker introduces a software that visualizes global data using animated bubbles. Each bubble represents a country, with its size indicating population, and its position on the axes showing fertility rate and life expectancy.
- 5:00
4. Challenging 'We and Them' Worldview
The speaker discusses the common 'we and them' worldview, where 'we' (Western world) have long lives and small families, and 'them' (third world) have short lives and large families. He uses the visualization to show how this simplistic view is outdated.
- 6:40
5. Global Progress Since 1962
The animation shows how countries have evolved since 1962, challenging the idea of a static 'developing world'. Countries like China, Latin American nations, and Bangladesh demonstrate significant improvements in health and family size, while Africa faces challenges like the HIV epidemic.
- 8:30
6. Vietnam's Rapid Development
The speaker highlights Vietnam's remarkable progress, comparing its development trajectory to the United States. Despite the Vietnam War, the country saw improvements in life expectancy and family planning, eventually achieving similar social indicators to the US in a relatively short period.
- 10:18
7. Income Distribution: No Gap Between Rich and Poor
The speaker shifts to income distribution, demonstrating that there is no longer a clear gap between rich and poor. He shows that while the richest 20% still hold a large share of the world's income, a significant portion of the global population now resides in the middle-income bracket.
- 12:00
8. Regional Income Overlaps and Changes
He illustrates the income distribution across different regions, showing significant overlaps between Africa, OECD countries, and Latin America. The animation then demonstrates how hundreds of millions in Asia have moved out of poverty since 1970, leading to a more 'middle' world.
- 13:50
9. Child Survival and Money Correlation
The speaker introduces a new dimension to the visualization: child survival rates against GDP per capita. He shows a strong linear correlation between a country's wealth and its child survival rate, highlighting the importance of economic development for social well-being.
- 15:40
10. Diversity Within Continents
He emphasizes the vast diversity within continents by splitting regions like sub-Saharan Africa, South Asia, and Arab states into individual countries. This reveals significant differences in health and wealth even among neighboring nations, challenging generalized perceptions.
- 18:50
11. Health First, Wealth Second
The speaker compares the development paths of South Korea, Brazil, and Uganda, demonstrating that investing in health first can lead to faster overall advancement than prioritizing wealth. He uses the example of the United Arab Emirates to show that wealth alone doesn't guarantee health without proper investment.
Every chapter ends with a checkpoint (quiz, flashcards, retell, diagram, or prediction) and the course closes with a final boss-fight. More courses →
Transcript (458 segments)
0:06[Music]
0:25but ten years ago I took on the task to
0:28teach global development to Swedish
0:30undergraduate students that was after
0:33having spent about 20 years together
0:35with African institutions studying
0:37hunger in Africa so I was sort of
0:39expected to know a little about the
0:41world and I started in our medical
0:44university Karolinska Institute an
0:46undergraduate course called global
0:48health but when you get that opportunity
0:50you get a little nervous I thought these
0:53students coming to us actually have the
0:55highest grade you can get in Swedish
0:56college system so I thought maybe they
0:58know everything I'm going to teach them
1:00about so I did a pretest when they came
1:03and one of the question from which I
1:05learned a lot was this one which country
1:07has the highest child mortality of these
1:09five pairs and I put them together so
1:13that in each pair of country one has
1:15twice the child mortality of the other
1:17and this means that it's much bigger the
1:22difference than the uncertainty of the
1:23data I won't put you to test here but
1:26it's Turkey which is high as there
1:27Poland Russia Pakistan and South Africa
1:31and these were the results of the
1:32Swedish students I did that so I got the
1:34confidence interval which was pretty
1:35narrow and I got happy of course at one
1:38point eight right answer out of five
1:40possible that means that there was a
1:42place for a professor of international
1:43health and for my course but one life
1:47late night when I was compiling the
1:49report I really realized my discovery I
1:53have shown that Swedish top students
1:56know statistically significantly less
1:59about the world than the chimpanzees
2:02because the chimpanzee would score half
2:06right if I gave him two bananas with Sri
2:08Lanka and Turkey they would be right
2:09half of the cases but the students are
2:11not there the problem for me was not
2:14ignorant it was preconceived ideas I did
2:17also an unfair unethical study of the
2:19professors of the Karolinska Institute
2:21that hands out the Nobel Prize in
2:23medicine and they are on par with the
2:26chimpanzee there so this is where I
2:30realized that there was really a need to
2:33communicate because the data or what's
2:35happening in the world and the child
2:37health obviously every country is very
2:39well aware so we did this software which
2:41displays it like this every bubble here
2:43is a country this country over here is
2:47this is China and this is India the size
2:51of the bubble is the population and on
2:53this axis here I put fertility rate
2:56because my students what they said when
2:59they looked upon the world and I asked
3:01them what do you really think about the
3:03world huh well I first discovered that
3:06the textbook was Tintin mainly and they
3:09said the world is still we and them and
3:11we is Western world and them is third
3:14world and what do you mean with Western
3:17world I said well that's long life in
3:19small family and third world is short
3:21life in large family so this is what I
3:24could display here I put fertility rate
3:26here number of children per woman 1 2 3
3:294 up to about eight children per woman
3:32we have very good data since 1960 to
3:351968 on the size of families in all
3:37countries the error margin is narrow
3:39here I put life expectancy at birth from
3:4230 years in some countries up to about
3:4470 years and 1962 that was really a
3:47group of countries here that was
3:49industrialized countries and they had
3:51small families and long lives and these
3:54were the developing countries they had
3:55large families and they had relatively
3:58short lives now what has happened since
4:001962 we want to see the change or the
4:03students right it's still two types of
4:05countries or have these developing
4:06countries got smaller families and they
4:08live here or have they got longer lives
4:10and live up there let's see we stopped
4:12the world and this is all UN statistic
4:15that has been a
4:16here we go can you see that it's China
4:18they're moving them against better
4:19health they are improving there or the
4:21green latin-american countries they are
4:23moving towards smaller families your
4:25yellow ones here or the Arabic countries
4:26and they get larger families but they no
4:29longer life but not larger families the
4:31Africans are the green down here they
4:32still remain here this is India
4:34Indonesia is moving on pretty fast and
4:36in the 80s here you have Bangladesh
4:38still among the African countries there
4:40but now Bangladesh it's a miracle that
4:42happens in the 80s the Imams start to
4:44promote Family Planning and they move up
4:46into that corner and in 90s we have the
4:49terrible HIV epidemic that takes down
4:52the life expectancy of the African
4:54countries and all the rest of the world
4:56moves up into the corner where we have
4:58long lives and small family and we have
5:01a completely new world
5:02[Applause]
5:14let me make a comparison directly
5:17between United States of America and
5:19Vietnam 1964 America had small families
5:24and long life Vietnam had large families
5:27and short lives and this is what happens
5:29the data during the war indicate that
5:33even with all the death there was an
5:35improvement of life expectancy by the
5:37end of the year the Family Planning
5:39started in Vietnam and they went for
5:40smaller families and the United States
5:42up there is getting for a longer life
5:44keeping family size and in the 80s now
5:46they give up communist planning and they
5:49go for market economy and it moves
5:51faster even in social life and today we
5:54have in Vietnam the same life expectancy
5:57and the same family size here in Vietnam
6:0119 2003 as in United States 1974 by the
6:06end of the war I think we all if we
6:09don't look in the data we underestimate
6:12the tremendous change in Asia which was
6:14in social change before we saw the
6:17economical change so let's move over to
6:20another way here in which we could
6:22display the distribution in the world of
6:26the income this is the world
6:28distribution of income of people $1 $10
6:33or $100 per day there's no gap between
6:37rich and poor any longer this is a myth
6:39there's a little hump here but there are
6:42people all the way and if we look where
6:45the income ends up the income this is
6:49100 percent of world's annual income and
6:52the rich is 20% they take out of that
6:56about 74 percent and the poor is 20%
7:00they take about 2% and this shows that
7:04the concept developing countries is
7:06extremely doubtful we sort of think
7:09about aid like these people here giving
7:12aid to these people here but in the
7:14middle we have most a world population
7:16and they have now 24 percent of the
7:19income we heard it in other forms and
7:21who are who are these these where are
7:24the different countries
7:26I can show you Africa this is Africa
7:2910% of world population most
7:31impoverished this is oacd
7:34the rich country the country club of the
7:36UN and they are over here on this side
7:39and quite an overlap between Africa and
7:41oacd and this is Latin America it has
7:44everything on this earth from the
7:46poorest to the richest in Latin America
7:48and on top of that we can put East
7:51Europe we can put East Asia and we could
7:54South Asia and how did it look like if
7:57we go back in time to about 1970 then
8:00there was more of a hump and we have
8:04most who lived in absolute poverty were
8:06Asians the problem in the world was the
8:09poverty in Asia and if I now let the
8:12world move forward you will seen that
8:14wild populations increase there are
8:17hundreds of millions in Asia are getting
8:19out of poverty and some others get into
8:21poverty and this is the pattern we have
8:23today and the best projection from the
8:25World Bank is that this will happen and
8:27we will not have a divided world we have
8:30most people in the middle of course it's
8:32a logarithmic scale here but our concept
8:34of economy is growth with percent we
8:37look upon it as a possibility of percent
8:42increase if I change this and I take GDP
8:45per capita instead of family income and
8:47I turn these individual data into
8:51regional data of gross domestic products
8:54and I take the regions down here the
8:56size of the bubble distill the
8:57population and you have the OECD there
9:00and you have sub-saharan Africa there
9:01and we take off the Arab states they're
9:04coming both from Africa and from Asia
9:06and we put them separately and we can
9:08expand this axis and I can give it a new
9:12dimension here by adding the social
9:14values their child survival now I have
9:17money on that axis and I have the
9:19possibility of children to survive there
9:20in some countries ninety-nine point
9:22seven percent of children survive to
9:24five years of age others only seventy
9:27and here it seems that this a gap
9:29between oacd
9:30Latin America East Europe East Asia Arab
9:34states South Asia and sub-saharan Africa
9:37the linearity is very
9:39strong between child survival and money
9:42but let me split sub-saharan Africa
9:45health is there and better help is up
9:50there I can go here and I can split
9:52sub-saharan Africa into its countries
9:55and when it bursts the size of East
9:57country bubble it's the size of the
9:59population Sierra Leone the down there
10:01more reaches up there
10:02now reaches was the first country to get
10:05away with trade barriers and they could
10:07sell those sugar they could sell their
10:09textiles on equal terms as the people in
10:12Europe and North America there's a huge
10:14difference between Africa and Ghana is
10:16here in the middle in Sierra Leone a
10:18humanitarian aid here in Uganda
10:22development aid here time to invest
10:24there you can go for holiday it's a
10:27tremendous variation within Africa which
10:30we very often make that it's equal
10:32everything I can split South Asia here
10:35India's the big bubble in the middle but
10:37huge difference between Afghanistan and
10:40Sri Lanka and I can speed Arab states
10:43how are they same climate same culture
10:45same religion huge difference even
10:48between neighbors Yemen Civil War United
10:51Arab Emirates money which was quite
10:53equally and well used not as the methods
10:56and that includes all the children of
10:59the foreign workers who are in the
11:01country data is often better than you
11:03think
11:04many people say data is bad there is an
11:06uncertainty merge but we can see the
11:08difference here Cambodia Singapore the
11:10differences are much bigger than the
11:12weakness of the data East Europe Soviet
11:16economy for a long time but they come
11:18out of the ten years very very
11:20differently and there is Latin America
11:23today we don't have to go to Cuba to
11:26find a healthy country in Latin America
11:27Chile will have a lower child mortality
11:29thank you but within some few years from
11:32now and here we have high-income
11:34countries in OECD and we get the whole
11:37pattern here of the world which is more
11:40or less like like this and if we look at
11:43it how it looks the world in 1960 it
11:48starts to move 1960 this is mouths a
11:50tomb he brought health to China
11:52and then he died and then thanks your
11:54ping came and brought money to China and
11:56brought them into the mainstream again
11:58and we have seen how countries move in
12:00different directions like this so it's
12:03sort of sort of difficult to get an
12:07example country which shows the pattern
12:09of the world but I would like to bring
12:12you back to about here at 1960 and I
12:18would like to compare South Korea which
12:23is this one with with Brazil which is
12:27this one the label went away for me here
12:30and I would like to compare Uganda which
12:33is there and I can run it forward like
12:37this and you can see how South Korea is
12:43making a very very fast advancement
12:46whereas Brazil is much slower and if we
12:50move back again here and we put on
12:53trails on them like this you can see
12:56again that the speed of development is
12:59very very different and the countries
13:03are moving more or less in the same rate
13:06as money and health but it seems you can
13:09move much faster if you're healthy first
13:11than if you are wealthy first and to
13:15show that you can put on the way of
13:16united arab emirate they came from here
13:19a mineral country they catch all the oil
13:22they got all the money but health cannot
13:24be bought at the supermarket you have to
13:27invest in health you have to get kids
13:28into schooling you have to Train health
13:30staff you have to educate the population
13:32and sheikh zayed did that in a fairly
13:34good way and in spite of falling oil
13:36prices he brought this country up here
13:38so we got a much more mainstream
13:41appearance of the world where all
13:43countries tend to use their money better
13:46than they used in the past now this is
13:49more or less if you look at if you look
13:52at the average data of the countries
13:55they are like this now that's dangerous
13:59to use average data because there's such
14:02a lot of difference within countries so
14:05if I go
14:06look here we can see that Uganda that
14:09today is where South Korea was 1960 if I
14:14split Uganda there's quite a difference
14:15within Uganda these are the quintiles of
14:19Uganda the richest 20% of Uganda's are
14:21there the poorest are down there if I
14:23split South Africa it's like this and if
14:26I go down and look at Nigeria where
14:29there was such a terrible famine lost
14:31Lee it's like this the 20% poorest of
14:35Nigeria is out here and the 20% richest
14:38of South Africa is there and yet we tend
14:41to discuss on what solutions there
14:43should be in Africa everything in this
14:45world exists in Africa and you can't
14:47discuss universal access to HIV for that
14:50quintile up here with the same strategy
14:52as down here the improvement of the
14:55world must be highly contextualized and
14:58it's not relevant to have it on regional
15:01level we must be much more detailed we
15:04find that students get very excited when
15:06they can use this and even more
15:08policymakers and the corporate sectors
15:11would like to see see how the world is
15:13changing now why doesn't this take place
15:16why are we not using the data we have we
15:19have data in the United Nation in the
15:21National Statistical agencies and in
15:23universities another non-governmental
15:25organization because the data is hidden
15:27down in the databases and the public is
15:29there and the internet is there but we
15:31have still not used it effectively all
15:33that information was so changing in the
15:35world does not include publicly funded
15:38statistics there are some webpages like
15:40this you know but they take some
15:43nourishment down from the databases but
15:46people put prices on them stupid
15:48passwords and boring statistics and this
15:52won't work
15:55so what is needed we have the databases
15:58it's not a new database you need we have
16:00wonderful design tools and more and more
16:03I added up here so we started a
16:05non-profit venture which we called
16:08linking data to design we call it
16:11Gapminder from London Underground where
16:13they warn you mind the gap so we thought
16:15gap mind was appropriate and we started
16:17to write software which could link the
16:19data like this and it wasn't that
16:22difficult
16:22it took some person years and we have
16:24produced animations you can take a data
16:27set and put it there we are liberating
16:30you and data some few UN organizations
16:33some countries accept that their
16:35databases can go out on the world but
16:37what we really need is of course a
16:39search function a search function where
16:42we can copy the data up to a searchable
16:44format and get it out in the world and
16:46what do we hear when we go around I've
16:49done anthropology on the main
16:50statistical units everyone says it's
16:53impossible this can't be done our
16:55information is so peculiar in detail so
16:58that cannot be searched as other can be
17:00searched we cannot give the data free to
17:02the students free to the entrepreneurs
17:04of the world but this is what we would
17:07like to see isn't it the publicly funded
17:10data is down here and we would like
17:12flowers to grow out on the net and one
17:15of the crucial point is to make them
17:17searchable and then people can use the
17:19different design tool to animate it
17:21there and I have a pretty good news for
17:24you I have a good news that the present
17:26new head of UN statistic he doesn't say
17:29it's impossible he only says we can't do
17:32it
17:35and that's a quite clever guy so we can
17:41see a lot happening in data in the
17:43coming years we will be able to look at
17:45income distributions in completely new
17:48ways this is the income distribution of
17:52China 1970 this is the income
17:55distribution of the United States 1970
17:58almost no overlap almost no overlap and
18:02what has happened what has happened is
18:04this the China is growing it's not so
18:07equal any longer and it's appearing here
18:09overlooking the United States almost
18:13like a ghost isn't it it's pretty scary
18:22but I think it's very important to have
18:24have all this information we need we
18:27need really to see it and instead of
18:31looking at this I would like to end up
18:33by showing the Internet users per 1000
18:37and this software we access about 500
18:40variables from all the countries quite
18:42easily it takes some time to change for
18:46this but on the accesses you can quite
18:48easily get any variable you would like
18:51to have and the thing would be to get up
18:55the database is free to get them
18:57searchable and with a secondly to get
18:59them into the graphic formats where you
19:02can instantly understand them now the
19:04statisticians doesn't like it because
19:06they say that this will not this will
19:08not show the the reality we have to have
19:15statistical analytical methods but this
19:17is hypothesis-generating
19:19I end now with a world where the
19:22internet are coming the number of
19:24Internet users are going up like this
19:25this is the GDP per capita and it's a
19:28new technology coming in but in
19:30amazingly how well it fits to the
19:33economy of the countries that's why the
19:36$100 computer will be so important but
19:39the nice tenders it's as if the world is
19:41flattening off isn't it these countries
19:43are lifting more than the economy
19:45and will be very interesting to fall of
19:47this over the year as I would like you
19:49to be able to do with all the publicly
19:51funded data thank you very much what if
20:04great ideas weren't cherished what if
20:09they carried no importance or held no
20:14value there is a place where artistic
20:23vision is protected where inspired
20:25design ideas live on to become ultimate
20:28driving machines
20:34you