TED2007
Hans Rosling: New insights on poverty
Filmed:
Readability: 3.1
3,769,031 views
Researcher Hans Rosling uses his cool data tools to show how countries are pulling themselves out of poverty. He demos Dollar Street, comparing households of varying income levels worldwide. Then he does something really amazing.
Hans Rosling - Global health expert; data visionary
In Hans Rosling’s hands, data sings. Global trends in health and economics come to vivid life. And the big picture of global development—with some surprisingly good news—snaps into sharp focus. Full bio
In Hans Rosling’s hands, data sings. Global trends in health and economics come to vivid life. And the big picture of global development—with some surprisingly good news—snaps into sharp focus. Full bio
Double-click the English transcript below to play the video.
00:25
I told you three things last year.
0
0
2000
00:27
I told you that the statistics of the world
1
2000
3000
00:30
have not been made properly available.
2
5000
3000
00:33
Because of that, we still have the old mindset
3
8000
2000
00:35
of developing in industrialized countries, which is wrong.
4
10000
3000
00:39
And that animated graphics can make a difference.
5
14000
4000
00:44
Things are changing
6
19000
2000
00:46
and today, on the United Nations Statistic Division Home Page,
7
21000
4000
00:50
it says, by first of May, full access to the databases.
8
25000
3000
00:55
(Applause)
9
30000
3000
00:58
And if I could share the image with you on the screen.
10
33000
4000
01:03
So three things have happened.
11
38000
1000
01:04
U.N. opened their statistic databases,
12
39000
3000
01:07
and we have a new version of the software
13
42000
4000
01:11
up working as a beta on the net,
14
46000
2000
01:13
so you don't have to download it any longer.
15
48000
2000
01:16
And let me repeat what you saw last year.
16
51000
2000
01:18
The bubbles are the countries.
17
53000
1000
01:19
Here you have the fertility rate -- the number of children per woman --
18
54000
4000
01:23
and there you have the length of life in years.
19
58000
3000
01:27
This is 1950 -- those were the industrialized countries,
20
62000
3000
01:30
those were developing countries.
21
65000
1000
01:31
At that time there was a "we" and "them."
22
66000
2000
01:33
There was a huge difference in the world.
23
68000
2000
01:35
But then it changed, and it went on quite well.
24
70000
4000
01:39
And this is what happens.
25
74000
1000
01:41
You can see how China is the red, big bubble.
26
76000
3000
01:44
The blue there is India.
27
79000
1000
01:45
And they go over all this -- I'm going to try to be
28
80000
3000
01:48
a little more serious this year in showing you
29
83000
2000
01:50
how things really changed.
30
85000
2000
01:53
And it's Africa that stands out as the problem down here, doesn't it?
31
88000
3000
01:56
Large families still, and the HIV epidemic
32
91000
3000
01:59
brought down the countries like this.
33
94000
2000
02:01
This is more or less what we saw last year,
34
96000
3000
02:04
and this is how it will go on into the future.
35
99000
2000
02:07
And I will talk on, is this possible?
36
102000
2000
02:09
Because you see now, I presented statistics that don't exist.
37
104000
3000
02:13
Because this is where we are.
38
108000
2000
02:15
Will it be possible that this will happen?
39
110000
3000
02:19
I cover my lifetime here, you know?
40
114000
2000
02:21
I expect to live 100 years.
41
116000
2000
02:23
And this is where we are today.
42
118000
2000
02:25
Now could we look here instead at the economic situation in the world?
43
120000
7000
02:33
And I would like to show that against child survival.
44
128000
5000
02:38
We'll swap the axis.
45
133000
1000
02:40
Here you have child mortality -- that is, survival --
46
135000
4000
02:44
four kids dying there, 200 dying there.
47
139000
2000
02:47
And this is GDP per capita on this axis.
48
142000
2000
02:50
And this was 2007.
49
145000
3000
02:53
And if I go back in time, I've added some historical statistics --
50
148000
4000
02:57
here we go, here we go, here we go -- not so much statistics 100 years ago.
51
152000
6000
03:03
Some countries still had statistics.
52
158000
2000
03:05
We are looking down in the archive,
53
160000
2000
03:07
and when we are down into 1820,
54
162000
4000
03:11
there is only Austria and Sweden that can produce numbers.
55
166000
4000
03:15
(Laughter)
56
170000
3000
03:18
But they were down here. They had 1,000 dollars per person per year.
57
173000
4000
03:22
And they lost one-fifth of their kids before their first birthday.
58
177000
3000
03:26
So this is what happens in the world, if we play the entire world.
59
181000
3000
03:29
How they got slowly richer and richer,
60
184000
3000
03:32
and they add statistics.
61
187000
1000
03:33
Isn't it beautiful when they get statistics?
62
188000
2000
03:35
You see the importance of that?
63
190000
2000
03:37
And here, children don't live longer.
64
192000
2000
03:39
The last century, 1870, was bad for the kids in Europe,
65
194000
4000
03:43
because most of this statistics is Europe.
66
198000
2000
03:45
It was only by the turn of the century
67
200000
3000
03:48
that more than 90 percent of the children survived their first year.
68
203000
3000
03:51
This is India coming up, with the first data from India.
69
206000
3000
03:54
And this is the United States moving away here, earning more money.
70
209000
5000
03:59
And we will soon see China coming up in the very far end corner here.
71
214000
5000
04:04
And it moves up with Mao Tse-Tung getting health,
72
219000
2000
04:06
not getting so rich.
73
221000
1000
04:07
There he died, then Deng Xiaoping brings money.
74
222000
3000
04:10
It moves this way over here.
75
225000
1000
04:12
And the bubbles keep moving up there,
76
227000
2000
04:14
and this is what the world looks like today.
77
229000
2000
04:16
(Applause)
78
231000
6000
04:22
Let us have a look at the United States.
79
237000
3000
04:25
We have a function here -- I can tell the world, "Stay where you are."
80
240000
3000
04:29
And I take the United States -- we still want to see the background --
81
244000
3000
04:32
I put them up like this, and now we go backwards.
82
247000
3000
04:35
And we can see that the United States
83
250000
3000
04:38
goes to the right of the mainstream.
84
253000
3000
04:41
They are on the money side all the time.
85
256000
2000
04:44
And down in 1915, the United States was a neighbor of India --
86
259000
5000
04:50
present, contemporary India.
87
265000
2000
04:52
And that means United States was richer,
88
267000
2000
04:54
but lost more kids than India is doing today, proportionally.
89
269000
4000
04:59
And look here -- compare to the Philippines of today.
90
274000
3000
05:02
The Philippines of today has almost the same economy
91
277000
3000
05:06
as the United States during the First World War.
92
281000
2000
05:08
But we have to bring United States forward quite a while
93
283000
4000
05:12
to find the same health of the United States
94
287000
3000
05:15
as we have in the Philippines.
95
290000
1000
05:17
About 1957 here, the health of the United States
96
292000
3000
05:20
is the same as the Philippines.
97
295000
2000
05:22
And this is the drama of this world which many call globalized,
98
297000
3000
05:25
is that Asia, Arabic countries, Latin America,
99
300000
3000
05:28
are much more ahead in being healthy, educated,
100
303000
5000
05:33
having human resources than they are economically.
101
308000
3000
05:36
There's a discrepancy in what's happening today
102
311000
2000
05:38
in the emerging economies.
103
313000
2000
05:40
There now, social benefits, social progress,
104
315000
4000
05:44
are going ahead of economical progress.
105
319000
3000
05:47
And 1957 -- the United States had the same economy as Chile has today.
106
322000
6000
05:54
And how long do we have to bring United States
107
329000
3000
05:57
to get the same health as Chile has today?
108
332000
2000
06:00
I think we have to go, there -- we have 2001, or 2002 --
109
335000
5000
06:05
the United States has the same health as Chile.
110
340000
2000
06:07
Chile's catching up!
111
342000
1000
06:09
Within some years Chile may have better child survival
112
344000
2000
06:11
than the United States.
113
346000
2000
06:13
This is really a change, that you have this lag
114
348000
3000
06:16
of more or less 30, 40 years' difference on the health.
115
351000
5000
06:21
And behind the health is the educational level.
116
356000
2000
06:23
And there's a lot of infrastructure things,
117
358000
2000
06:25
and general human resources are there.
118
360000
3000
06:28
Now we can take away this --
119
363000
3000
06:31
and I would like to show you the rate of speed,
120
366000
4000
06:35
the rate of change, how fast they have gone.
121
370000
3000
06:38
And we go back to 1920, and I want to look at Japan.
122
373000
7000
06:46
And I want to look at Sweden and the United States.
123
381000
3000
06:49
And I'm going to stage a race here
124
384000
2000
06:51
between this sort of yellowish Ford here
125
386000
3000
06:54
and the red Toyota down there,
126
389000
2000
06:56
and the brownish Volvo.
127
391000
2000
06:58
(Laughter)
128
393000
2000
07:00
And here we go. Here we go.
129
395000
2000
07:02
The Toyota has a very bad start down here, you can see,
130
397000
3000
07:05
and the United States Ford is going off-road there.
131
400000
3000
07:08
And the Volvo is doing quite fine.
132
403000
1000
07:09
This is the war. The Toyota got off track, and now
133
404000
2000
07:11
the Toyota is coming on the healthier side of Sweden --
134
406000
3000
07:14
can you see that?
135
409000
1000
07:15
And they are taking over Sweden,
136
410000
1000
07:16
and they are now healthier than Sweden.
137
411000
2000
07:18
That's the part where I sold the Volvo and bought the Toyota.
138
413000
2000
07:20
(Laughter)
139
415000
3000
07:23
And now we can see that the rate of change was enormous in Japan.
140
418000
4000
07:27
They really caught up.
141
422000
2000
07:29
And this changes gradually.
142
424000
2000
07:31
We have to look over generations to understand it.
143
426000
3000
07:34
And let me show you my own sort of family history --
144
429000
5000
07:39
we made these graphs here.
145
434000
2000
07:41
And this is the same thing, money down there, and health, you know?
146
436000
4000
07:45
And this is my family.
147
440000
2000
07:48
This is Sweden, 1830, when my great-great-grandma was born.
148
443000
4000
07:53
Sweden was like Sierra Leone today.
149
448000
2000
07:56
And this is when great-grandma was born, 1863.
150
451000
3000
08:00
And Sweden was like Mozambique.
151
455000
2000
08:02
And this is when my grandma was born, 1891.
152
457000
2000
08:04
She took care of me as a child,
153
459000
2000
08:06
so I'm not talking about statistic now --
154
461000
2000
08:08
now it's oral history in my family.
155
463000
2000
08:11
That's when I believe statistics,
156
466000
1000
08:12
when it's grandma-verified statistics.
157
467000
3000
08:15
(Laughter)
158
470000
3000
08:18
I think it's the best way of verifying historical statistics.
159
473000
3000
08:21
Sweden was like Ghana.
160
476000
1000
08:22
It's interesting to see the enormous diversity
161
477000
3000
08:25
within sub-Saharan Africa.
162
480000
2000
08:28
I told you last year, I'll tell you again,
163
483000
2000
08:30
my mother was born in Egypt, and I -- who am I?
164
485000
3000
08:33
I'm the Mexican in the family.
165
488000
1000
08:35
And my daughter, she was born in Chile,
166
490000
2000
08:37
and the grand-daughter was born in Singapore,
167
492000
2000
08:39
now the healthiest country on this Earth.
168
494000
2000
08:41
It bypassed Sweden about two to three years ago,
169
496000
2000
08:43
with better child survival.
170
498000
2000
08:45
But they're very small, you know?
171
500000
1000
08:46
They're so close to the hospital we can never
172
501000
2000
08:48
beat them out in these forests.
173
503000
1000
08:49
(Laughter)
174
504000
3000
08:52
But homage to Singapore.
175
507000
1000
08:53
Singapore is the best one.
176
508000
2000
08:55
Now this looks also like a very good story.
177
510000
4000
08:59
But it's not really that easy, that it's all a good story.
178
514000
4000
09:03
Because I have to show you one of the other facilities.
179
518000
3000
09:06
We can also make the color here represent the variable --
180
521000
5000
09:11
and what am I choosing here?
181
526000
1000
09:12
Carbon-dioxide emission, metric ton per capita.
182
527000
4000
09:17
This is 1962, and United States was emitting 16 tons per person.
183
532000
5000
09:22
And China was emitting 0.6,
184
537000
2000
09:24
and India was emitting 0.32 tons per capita.
185
539000
4000
09:28
And what happens when we moved on?
186
543000
3000
09:31
Well, you see the nice story of getting richer
187
546000
2000
09:33
and getting healthier --
188
548000
1000
09:34
everyone did it at the cost of emission of carbon dioxide.
189
549000
5000
09:39
There is no one who has done it so far.
190
554000
3000
09:42
And we don't have all the updated data
191
557000
3000
09:45
any longer, because this is really hot data today.
192
560000
3000
09:48
And there we are, 2001.
193
563000
2000
09:51
And in the discussion I attended with global leaders, you know,
194
566000
4000
09:55
many say now the problem is that the emerging economies,
195
570000
4000
09:59
they are getting out too much carbon dioxide.
196
574000
3000
10:02
The Minister of the Environment of India said,
197
577000
2000
10:04
"Well, you were the one who caused the problem."
198
579000
3000
10:07
The OECD countries -- the high-income countries --
199
582000
3000
10:10
they were the ones who caused the climate change.
200
585000
2000
10:13
"But we forgive you, because you didn't know it.
201
588000
2000
10:15
But from now on, we count per capita.
202
590000
3000
10:18
From now on we count per capita.
203
593000
2000
10:20
And everyone is responsible for the per capita emission."
204
595000
3000
10:23
This really shows you, we have not seen good economic
205
598000
3000
10:26
and health progress anywhere in the world
206
601000
2000
10:28
without destroying the climate.
207
603000
4000
10:33
And this is really what has to be changed.
208
608000
2000
10:36
I've been criticized for showing you a too positive image of the world,
209
611000
3000
10:39
but I don't think it's like this.
210
614000
2000
10:41
The world is quite a messy place.
211
616000
2000
10:43
This we can call Dollar Street.
212
618000
2000
10:45
Everyone lives on this street here.
213
620000
2000
10:47
What they earn here -- what number they live on --
214
622000
3000
10:50
is how much they earn per day.
215
625000
1000
10:51
This family earns about one dollar per day.
216
626000
3000
10:55
We drive up the street here,
217
630000
1000
10:56
we find a family here which earns about two to three dollars a day.
218
631000
4000
11:00
And we drive away here -- we find the first garden in the street,
219
635000
3000
11:03
and they earn 10 to 50 dollars a day.
220
638000
2000
11:05
And how do they live?
221
640000
2000
11:07
If we look at the bed here, we can see
222
642000
3000
11:10
that they sleep on a rug on the floor.
223
645000
3000
11:13
This is what poverty line is --
224
648000
2000
11:15
80 percent of the family income is just to cover the energy needs,
225
650000
3000
11:18
the food for the day.
226
653000
2000
11:20
This is two to five dollars. You have a bed.
227
655000
3000
11:23
And here it's a much nicer bedroom, you can see.
228
658000
2000
11:26
I lectured on this for Ikea, and they wanted to see
229
661000
2000
11:28
the sofa immediately here.
230
663000
2000
11:30
(Laughter)
231
665000
2000
11:32
And this is the sofa, how it will emerge from there.
232
667000
4000
11:36
And the interesting thing, when you go around here in the photo panorama,
233
671000
3000
11:39
you see the family still sitting on the floor there.
234
674000
2000
11:41
Although there is a sofa,
235
676000
2000
11:43
if you watch in the kitchen, you can see that
236
678000
2000
11:45
the great difference for women does not come between one to 10 dollars.
237
680000
5000
11:50
It comes beyond here, when you really can get
238
685000
2000
11:52
good working conditions in the family.
239
687000
3000
11:55
And if you really want to see the difference,
240
690000
2000
11:57
you look at the toilet over here.
241
692000
2000
11:59
This can change. This can change.
242
694000
2000
12:01
These are all pictures and images from Africa,
243
696000
3000
12:04
and it can become much better.
244
699000
2000
12:07
We can get out of poverty.
245
702000
2000
12:09
My own research has not been in IT or anything like this.
246
704000
3000
12:12
I spent 20 years in interviews with African farmers
247
707000
3000
12:15
who were on the verge of famine.
248
710000
3000
12:18
And this is the result of the farmers-needs research.
249
713000
2000
12:20
The nice thing here is that you can't see
250
715000
2000
12:22
who are the researchers in this picture.
251
717000
2000
12:24
That's when research functions in poor societies --
252
719000
3000
12:27
you must really live with the people.
253
722000
2000
12:31
When you're in poverty, everything is about survival.
254
726000
4000
12:35
It's about having food.
255
730000
2000
12:37
And these two young farmers, they are girls now --
256
732000
2000
12:39
because the parents are dead from HIV and AIDS --
257
734000
4000
12:43
they discuss with a trained agronomist.
258
738000
2000
12:45
This is one of the best agronomists in Malawi, Junatambe Kumbira,
259
740000
4000
12:49
and he's discussing what sort of cassava they will plant --
260
744000
2000
12:51
the best converter of sunshine to food that man has found.
261
746000
4000
12:55
And they are very, very eagerly interested to get advice,
262
750000
3000
12:58
and that's to survive in poverty.
263
753000
3000
13:01
That's one context.
264
756000
1000
13:02
Getting out of poverty.
265
757000
2000
13:04
The women told us one thing. "Get us technology.
266
759000
3000
13:07
We hate this mortar, to stand hours and hours.
267
762000
3000
13:10
Get us a mill so that we can mill our flour,
268
765000
3000
13:13
then we will be able to pay for the rest ourselves."
269
768000
3000
13:16
Technology will bring you out of poverty,
270
771000
3000
13:19
but there's a need for a market to get away from poverty.
271
774000
4000
13:23
And this woman is very happy now, bringing her products to the market.
272
778000
3000
13:26
But she's very thankful for the public investment in schooling
273
781000
2000
13:28
so she can count, and won't be cheated when she reaches the market.
274
783000
3000
13:31
She wants her kid to be healthy, so she can go to the market
275
786000
3000
13:34
and doesn't have to stay home.
276
789000
2000
13:36
And she wants the infrastructure -- it is nice with a paved road.
277
791000
3000
13:39
It's also good with credit.
278
794000
2000
13:41
Micro-credits gave her the bicycle, you know.
279
796000
3000
13:44
And information will tell her when to go to market with which product.
280
799000
3000
13:47
You can do this.
281
802000
2000
13:49
I find my experience from 20 years of Africa is that
282
804000
3000
13:52
the seemingly impossible is possible.
283
807000
3000
13:55
Africa has not done bad.
284
810000
2000
13:57
In 50 years they've gone from a pre-Medieval situation
285
812000
3000
14:00
to a very decent 100-year-ago Europe,
286
815000
3000
14:03
with a functioning nation and state.
287
818000
3000
14:06
I would say that sub-Saharan Africa has done best in the world
288
821000
3000
14:09
during the last 50 years.
289
824000
1000
14:10
Because we don't consider where they came from.
290
825000
2000
14:12
It's this stupid concept of developing countries
291
827000
3000
14:15
that puts us, Argentina and Mozambique together 50 years ago,
292
830000
3000
14:18
and says that Mozambique did worse.
293
833000
2000
14:21
We have to know a little more about the world.
294
836000
2000
14:23
I have a neighbor who knows 200 types of wine.
295
838000
3000
14:26
He knows everything.
296
841000
1000
14:27
He knows the name of the grape, the temperature and everything.
297
842000
2000
14:29
I only know two types of wine -- red and white.
298
844000
3000
14:32
(Laughter)
299
847000
2000
14:34
But my neighbor only knows two types of countries --
300
849000
2000
14:36
industrialized and developing.
301
851000
2000
14:38
And I know 200, I know about the small data.
302
853000
3000
14:41
But you can do that.
303
856000
1000
14:42
(Applause)
304
857000
5000
14:47
But I have to get serious. And how do you get serious?
305
862000
2000
14:49
You make a PowerPoint, you know?
306
864000
2000
14:51
(Laughter)
307
866000
5000
14:56
Homage to the Office package, no?
308
871000
2000
15:00
What is this, what is this, what am I telling?
309
875000
2000
15:02
I'm telling you that there are many dimensions of development.
310
877000
3000
15:05
Everyone wants your pet thing.
311
880000
2000
15:07
If you are in the corporate sector, you love micro-credit.
312
882000
3000
15:10
If you are fighting in a non-governmental organization,
313
885000
2000
15:12
you love equity between gender.
314
887000
3000
15:15
Or if you are a teacher, you'll love UNESCO, and so on.
315
890000
2000
15:17
On the global level, we have to have more than our own thing.
316
892000
2000
15:19
We need everything.
317
894000
2000
15:21
All these things are important for development,
318
896000
2000
15:23
especially when you just get out of poverty
319
898000
2000
15:25
and you should go towards welfare.
320
900000
3000
15:28
Now, what we need to think about
321
903000
2000
15:30
is, what is a goal for development,
322
905000
3000
15:33
and what are the means for development?
323
908000
1000
15:34
Let me first grade what are the most important means.
324
909000
3000
15:38
Economic growth to me, as a public-health professor,
325
913000
2000
15:40
is the most important thing for development
326
915000
4000
15:44
because it explains 80 percent of survival.
327
919000
2000
15:47
Governance. To have a government which functions --
328
922000
3000
15:50
that's what brought California out of the misery of 1850.
329
925000
4000
15:54
It was the government that made law function finally.
330
929000
3000
15:58
Education, human resources are important.
331
933000
2000
16:00
Health is also important, but not that much as a mean.
332
935000
4000
16:04
Environment is important.
333
939000
2000
16:06
Human rights is also important, but it just gets one cross.
334
941000
2000
16:08
Now what about goals? Where are we going toward?
335
943000
3000
16:11
We are not interested in money.
336
946000
2000
16:13
Money is not a goal.
337
948000
1000
16:14
It's the best mean, but I give it zero as a goal.
338
949000
3000
16:18
Governance, well it's fun to vote in a little thing,
339
953000
3000
16:21
but it's not a goal.
340
956000
2000
16:23
And going to school, that's not a goal, it's a mean.
341
958000
4000
16:27
Health I give two points. I mean it's nice to be healthy
342
962000
2000
16:29
-- at my age especially -- you can stand here, you're healthy.
343
964000
2000
16:31
And that's good, it gets two plusses.
344
966000
2000
16:33
Environment is very, very crucial.
345
968000
2000
16:35
There's nothing for the grandkid if you don't save up.
346
970000
2000
16:37
But where are the important goals?
347
972000
2000
16:39
Of course, it's human rights.
348
974000
2000
16:41
Human rights is the goal,
349
976000
2000
16:43
but it's not that strong of a mean for achieving development.
350
978000
3000
16:47
And culture. Culture is the most important thing, I would say,
351
982000
4000
16:51
because that's what brings joy to life.
352
986000
2000
16:53
That's the value of living.
353
988000
2000
16:55
So the seemingly impossible is possible.
354
990000
3000
16:58
Even African countries can achieve this.
355
993000
2000
17:01
And I've shown you the shot where the seemingly impossible is possible.
356
996000
6000
17:07
And remember, please remember my main message,
357
1002000
4000
17:11
which is this: the seemingly impossible is possible.
358
1006000
3000
17:14
We can have a good world.
359
1009000
2000
17:16
I showed you the shots, I proved it in the PowerPoint,
360
1011000
3000
17:19
and I think I will convince you also by culture.
361
1014000
6000
17:25
(Laughter)
362
1020000
4000
17:29
(Applause)
363
1024000
1000
17:30
Bring me my sword!
364
1025000
2000
17:36
Sword swallowing is from ancient India.
365
1031000
5000
17:41
It's a cultural expression that for thousands of years
366
1036000
5000
17:46
has inspired human beings to think beyond the obvious.
367
1041000
6000
17:52
(Laughter)
368
1047000
2000
17:54
And I will now prove to you that the seemingly impossible is possible
369
1049000
5000
17:59
by taking this piece of steel -- solid steel --
370
1054000
3000
18:03
this is the army bayonet from the Swedish Army, 1850,
371
1058000
3000
18:06
in the last year we had war.
372
1061000
2000
18:09
And it's all solid steel -- you can hear here.
373
1064000
3000
18:12
And I'm going to take this blade of steel,
374
1067000
6000
18:18
and push it down through my body of blood and flesh,
375
1073000
5000
18:23
and prove to you that the seemingly impossible is possible.
376
1078000
4000
18:28
Can I request a moment of absolute silence?
377
1083000
4000
18:43
(Applause)
378
1098000
22000
ABOUT THE SPEAKER
Hans Rosling - Global health expert; data visionaryIn Hans Rosling’s hands, data sings. Global trends in health and economics come to vivid life. And the big picture of global development—with some surprisingly good news—snaps into sharp focus.
Why you should listen
Even the most worldly and well-traveled among us have had their perspectives shifted by Hans Rosling. A professor of global health at Sweden's Karolinska Institute, his work focused on dispelling common myths about the so-called developing world, which (as he pointed out) is no longer worlds away from the West. In fact, most of the Third World is on the same trajectory toward health and prosperity, and many countries are moving twice as fast as the west did.
What set Rosling apart wasn't just his apt observations of broad social and economic trends, but the stunning way he presented them. Guaranteed: You've never seen data presented like this. A presentation that tracks global health and poverty trends should be, in a word: boring. But in Rosling's hands, data sings. Trends come to life. And the big picture — usually hazy at best — snaps into sharp focus.
Rosling's presentations were grounded in solid statistics (often drawn from United Nations and World Bank data), illustrated by the visualization software he developed. The animations transform development statistics into moving bubbles and flowing curves that make global trends clear, intuitive and even playful. During his legendary presentations, Rosling took this one step farther, narrating the animations with a sportscaster's flair.
Rosling developed the breakthrough software behind his visualizations through his nonprofit Gapminder, founded with his son and daughter-in-law. The free software — which can be loaded with any data — was purchased by Google in March 2007. (Rosling met the Google founders at TED.)
Rosling began his wide-ranging career as a physician, spending many years in rural Africa tracking a rare paralytic disease (which he named konzo) and discovering its cause: hunger and badly processed cassava. He co-founded Médecins sans Frontièrs (Doctors without Borders) Sweden, wrote a textbook on global health, and as a professor at the Karolinska Institut in Stockholm initiated key international research collaborations. He's also personally argued with many heads of state, including Fidel Castro.
Hans Rosling passed away in February 2017. He is greatly missed.
Hans Rosling | Speaker | TED.com