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PROFESSOR: What I want to do
today is to build on the movie

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and the discussion
we had last time.

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So I think in the movie, a lot
of themes appear that are also

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00:00:40,970 --> 00:00:44,030
in the chapter on health,
if you've read it.

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A lot of the themes are kind
there, but in sort of some

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random way.

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And in the discussion we have
had, we also have elaborated

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on the themes, but now what I
want to do is to try and put

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00:00:56,050 --> 00:00:59,330
them all together in a coherent
frame, give you a bit

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more of specific examples that
are in the book specifically.

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And we're, of course, going to
be talking about health in

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particular with the angle of how
people choose which health

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care to access, how people
choose what doctors to see for

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what, why people are not doing
more preventive care in

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investments, and things
like that.

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Obviously, we are not doctors,
so we are not talking about

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health from the point of view
of what could treat people.

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We are taking this as given, and
then wonder how do we get

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00:01:36,310 --> 00:01:38,960
this stuff that can
treat people out

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there in the landscape.

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So we start with these things.

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They are some technologies that
are known, that have been

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demonstrated in [INAUDIBLE]

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trials to be effective
and cheap ways to

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promote good health.

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Some examples of that include
bed nets to prevent malaria,

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would include immunization,
which costs a maximum of maybe

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$15 to $20 per child, and it's
one of the cheapest ways to

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prevent child death.

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00:02:15,730 --> 00:02:19,730
Breast feeding, which of
course is free and is

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recommended by WHO to be done
from one hour after birth

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00:02:24,510 --> 00:02:28,350
until six months, at least in
places where the water is not

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very clean.

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Oral rehydration solution, which
is basically a mix of

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sugar and salt that you put in
water when they kid comes with

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acute diarrhea.

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That's not going to cure
whatever caused the diarrhea,

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but it's going to prevent
dehydration, which is the main

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reason why people
die of diarrhea.

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And bleach, chlorine, that
you put in your water.

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These are just a few examples
of things that have been

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demonstrated to be effective,
and cost-effective, and cheap,

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and accessible.

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And one of the major puzzle and
frustration that we see in

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world health is that these
investments just don't really

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reach people, are not really
undertaken by people.

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And so the question is, why?

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It's certainly not because
they are not useful.

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They save lives.

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So you could argue what's
the value of a life?

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Maybe it's not so
much worth it.

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But the value of a life would
have been very, very low for

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these things not
to be worth it.

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But even if you leave that
behind, conditional on

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surviving, you're going to do
much better if you have not

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been very sick all through
your childhood.

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We've seen that for deworming.

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We've seen that if kids were
dewormed when they were

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children, they were not sick
with worms, they are making

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23% more every year, which
sounds to be like a

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fantastically good
rate of return.

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We're talking about the maybe
$1,400 in current dollars over

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a child's lifetime for an
investment of less than $1.

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And the same argument that
malaria also makes country's

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poor and people poor has been
made for malaria by Jeff

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Sachs, and Gallup who was also a
researcher at Harvard at the

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time when Sachs was there at
the time, found that if you

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control for other factors,
malarial countries, the

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countries where malaria is
prevalent, have a GDP that is

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30% lower than non-malarial
countries.

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And what are the
other factors?

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They are geography, latitude,
the climate, things like that.

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You can actually see it on a
map from Gallup and Sachs

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where it shows where malaria is
prevalent, at least where

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it was in 1965.

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And you can see that there is a
fair amount of bad luck that

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is involved with malaria.

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If you're in between the topics,
you're just much more

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likely to be infected with
malaria simply because this is

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environment where the mosquitoes
that carry the

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malaria thrive.

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So there used to be a lot of
malaria in Latin America.

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There used to be malaria in the
American South once upon a

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time, which is somewhat
above the tropic.

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But otherwise, most of the
malaria is in Africa, Latin

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America, India, and
Southeast Asia.

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Keep this figure in
mind, that this is

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where malaria is important.

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And now if we look
at GDP in 1994--

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oh no, that's malaria
in 1994-- it got

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better in Latin America.

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It didn't get much
better in Africa.

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It got worse in India.

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And this is GDP per
capita in 1995.

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And you can see that you have
a striking reversal of the

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colors, which is the countries
that are very dark in the

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malaria picture are now
very light in the

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GDP per capita picture.

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So the results that he found in
the regression, [INAUDIBLE]

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statistical bells and whistles,
is just translation

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of that picture that basically
the dark countries in the

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malaria picture are the light
countries in the GDP picture.

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So that is no doubt
about this fact.

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So on the [INAUDIBLE], this
article was very influential.

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It was published in 2001.

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Was very influential to bring
a push for the fight against

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malaria as an economic type of
intervention that has a decent

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rate of return.

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Naturally you should do not for
compassion value, but for

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economic purpose, fight malaria,
use bed nets and

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things like that.

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Now some people obviously
objected to that, saying,

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well, that's not necessarily
a proof that malaria

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causes the low GDP.

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And what else could
be going on?

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What else did they argue
was going on?

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AUDIENCE: Is it possible that
is poor so it isn't able to

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fight malaria?

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So it has malaria because it has
[INAUDIBLE] below GDP, not

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the other way around?

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PROFESSOR: Exactly.

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It cold be that it takes some
money to fight malaria.

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In fact, that's exactly what
Sachs is arguing, that it

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takes some money to fight
malaria, so we need to help

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the poor country.

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And in fact, when you look at
the countries that got malaria

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in 1965 and don't get it in
'94, or if you look at the

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country that had little of
malaria in '65 and have a lot

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in '94, this is some kind
of tracking pattern.

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Basically, it mostly
disappeared--

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not entirely, but mostly
disappeared--

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in Latin America between 1965
and 1994, and nothing happened

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00:08:04,160 --> 00:08:06,520
in most of Africa.

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00:08:06,520 --> 00:08:10,130
In India, it actually increased
between 1965 and

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00:08:10,130 --> 00:08:15,050
1994, but the same increase
didn't happen in Sri Lanka,

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which is the little dot
that is next to

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India on the map here.

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So why is it the case that given
the similar geographic

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circumstances, in a sense, Latin
America managed to get

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rid of malaria, but
not Africa?

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Why is it the case that malaria
increased in India in

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the same time it reduced
in Sri Lanka?

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That's not entirely explained
by geography.

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00:08:36,419 --> 00:08:39,435
In fact, for the most part, it's
not explain by geography.

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It is explained by the fact that
in Latin America, there

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have been very sustained, large
effort to fight malaria

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that we are going to talk
about in a moment.

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They just basically
sprayed extremely

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aggressively with DDT.

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They drained the swamps.

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They did all sorts of things
like that which managed to

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control malaria, and they
managed to do it.

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And the same thing happened
in Sri Lanka.

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So if you compare Sri Lanka, for
example, and Tamil Nadu,

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which is the part of India that
just faces Sri Lanka, for

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00:09:11,180 --> 00:09:13,670
the most part has the
same people--

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at least part of Sri Lanka is
Tamil, even though they are

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00:09:15,945 --> 00:09:19,870
not very pleased to be there--

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00:09:19,870 --> 00:09:23,940
in Sri Lanka you had very
aggressive control of malaria

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and pretty much the
disappearance of malaria.

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In the meantime, in Tamil Nadu,
right next door with a

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00:09:28,620 --> 00:09:31,060
similar climate, your
get if anything

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00:09:31,060 --> 00:09:32,910
an increase of malaria.

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00:09:32,910 --> 00:09:35,240
And so this is not due to
geography or anything.

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00:09:35,240 --> 00:09:39,020
This is due to politics, and
your ability to organize your

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people, and to organize
your country, and to

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00:09:41,140 --> 00:09:43,120
get something done.

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And so it is likely to be the
case that if you're able to

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get something done to control
malaria, you're just able to

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get something done in general.

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And if you're not able to get
something done with malaria,

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00:09:55,380 --> 00:09:58,110
you're not able to get something
done in general.

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So maybe the same countries--
and this is not only true for

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00:10:02,020 --> 00:10:02,700
Sri Lanka--

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00:10:02,700 --> 00:10:05,510
that at the same time they
managed to control malaria,

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they were also extremely
effective in getting out

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00:10:09,520 --> 00:10:12,050
preventive care for their
people, immunizations for

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their people, preschools,
and things like that.

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00:10:14,980 --> 00:10:19,185
So Sri Lanka has a lot of
political problems, but from

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the point of view of a country
that delivers social services

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00:10:23,610 --> 00:10:26,690
to their country, it's actually
quite effective.

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So it's certainly the same
countries that have managed to

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00:10:29,770 --> 00:10:31,940
control malaria have managed to
do other good things, and

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00:10:31,940 --> 00:10:35,030
maybe this is why they have
[INAUDIBLE] in 1995.

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00:10:35,030 --> 00:10:37,910
So on its own, this correlation
is certainly not

193
00:10:37,910 --> 00:10:41,360
sufficient to tell us that
there is a cause.

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So before moving further, I want
to be able to answer this

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question, which is we know that
the malarial countries

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are 30% poorer than the
non-malarial countries.

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To what extent can we say it is
due to malaria, and to what

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00:10:58,340 --> 00:11:03,660
extent is this the reverse
causality that the countries

199
00:11:03,660 --> 00:11:07,810
that were good at controlling
malaria also managed to do

200
00:11:07,810 --> 00:11:09,060
other good things?

201
00:11:11,230 --> 00:11:18,080
And we can answer this question
precisely by looking

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00:11:18,080 --> 00:11:21,730
at those episodes where my
malaria was eradicated,

203
00:11:21,730 --> 00:11:26,830
because malaria was eradicated
by very clear, specific action

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00:11:26,830 --> 00:11:29,810
that was taken at some point
in those countries.

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And there are a series of
papers, one on the Americas,

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00:11:34,020 --> 00:11:40,730
which is the one we are going
to study now, one on what is

207
00:11:40,730 --> 00:11:42,340
called malarial peripheries--

208
00:11:42,340 --> 00:11:46,650
so that's Paraguay, Sri Lanka,
and one in India--

209
00:11:46,650 --> 00:11:50,120
that looks at those eradication
campaigns and

210
00:11:50,120 --> 00:11:53,950
tries to look at what is the
impact for a child of having

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00:11:53,950 --> 00:11:58,780
been born in a place that used
to be malarial after the

212
00:11:58,780 --> 00:12:00,890
eradication campaign
rather than before.

213
00:12:03,570 --> 00:12:10,320
So let's look at a very nice
study that is the study about

214
00:12:10,320 --> 00:12:11,660
Latin America.

215
00:12:11,660 --> 00:12:14,240
It's a study by a researcher
in Chicago called Hoyt

216
00:12:14,240 --> 00:12:20,710
Bleakley, and what he looks
at is DDT spraying.

217
00:12:20,710 --> 00:12:23,570
This is also interesting because
actually the question

218
00:12:23,570 --> 00:12:28,030
of DDT spraying is a pretty
controversial one today,

219
00:12:28,030 --> 00:12:31,080
because it's now pretty much
forbidden anywhere to spray

220
00:12:31,080 --> 00:12:35,470
anything with DDT, because it's
not very good for you to

221
00:12:35,470 --> 00:12:37,480
eat food that has been
sprayed with DDT.

222
00:12:37,480 --> 00:12:40,440
On the other hand, maybe it's
also not very good for you to

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00:12:40,440 --> 00:12:41,230
get malaria.

224
00:12:41,230 --> 00:12:41,500
[LAUGHTER]

225
00:12:41,500 --> 00:12:44,220
PROFESSOR: So there is a little
bit of a conflict

226
00:12:44,220 --> 00:12:47,530
between those two objectives
with a lot of people are

227
00:12:47,530 --> 00:12:50,430
saying we should go back
to DDT and a lot of

228
00:12:50,430 --> 00:12:51,930
people saying no.

229
00:12:51,930 --> 00:12:55,370
For example, there is a huge
political fight in Uganda

230
00:12:55,370 --> 00:12:58,610
between the organic farmers and
the rest of the country,

231
00:12:58,610 --> 00:12:59,420
essentially.

232
00:12:59,420 --> 00:13:03,130
The organic farmers don't want
any DDT anywhere near their

233
00:13:03,130 --> 00:13:06,660
crop, obviously, because in
that case the European

234
00:13:06,660 --> 00:13:12,360
community would put a stop
on their export to

235
00:13:12,360 --> 00:13:14,160
other European Union.

236
00:13:14,160 --> 00:13:17,580
And so any effort of eradicating
malaria with

237
00:13:17,580 --> 00:13:20,990
spraying of DDT has been
stopped in Uganda.

238
00:13:20,990 --> 00:13:24,850
But at the time, they were not
so worried about that, so

239
00:13:24,850 --> 00:13:28,570
there was a big eradication
campaign in Latin America that

240
00:13:28,570 --> 00:13:33,070
started around 1955 partly with
international funding.

241
00:13:33,070 --> 00:13:35,860
And so they sprayed everywhere,
and they made sure

242
00:13:35,860 --> 00:13:37,660
to try and get rid of
malaria everywhere.

243
00:13:37,660 --> 00:13:43,010
They even sprayed under people's
roofs, like in the

244
00:13:43,010 --> 00:13:45,100
eaves of a house,
the mosquito's

245
00:13:45,100 --> 00:13:46,980
nest under the roof.

246
00:13:46,980 --> 00:13:50,670
So they went there and put the
DDT there, which is probably

247
00:13:50,670 --> 00:13:53,590
not excellent for people's
health directly, but very bad

248
00:13:53,590 --> 00:13:54,840
for the mosquitoes for sure.

249
00:13:58,280 --> 00:14:02,980
What the Bleakley study does is
to exploit the fact that if

250
00:14:02,980 --> 00:14:05,880
you started in a region where
there was not so malaria to

251
00:14:05,880 --> 00:14:09,330
start with, then the decline
in malaria was lower.

252
00:14:09,330 --> 00:14:13,850
So here is one example
for Columbia.

253
00:14:13,850 --> 00:14:18,330
This is cases of malaria
in Colombia by year.

254
00:14:18,330 --> 00:14:21,400
You can see that in 1950, you
had a lot of malaria cases

255
00:14:21,400 --> 00:14:22,630
every year.

256
00:14:22,630 --> 00:14:26,820
The campaign started
roughly in 1955.

257
00:14:26,820 --> 00:14:31,900
Intensive spraying started in
1958, and you start getting a

258
00:14:31,900 --> 00:14:35,680
huge drop in the cases of
malaria country-wide.

259
00:14:35,680 --> 00:14:36,800
So it's pretty effective.

260
00:14:36,800 --> 00:14:42,200
Basically you go from 600 cases,
I think it's a month,

261
00:14:42,200 --> 00:14:46,060
to about nothing, to close to
nothing, and that happened in

262
00:14:46,060 --> 00:14:49,100
a very short period of time.

263
00:14:49,100 --> 00:14:52,660
You could look at people born on
those times, but of course,

264
00:14:52,660 --> 00:14:54,870
other things happen over time,
so we don't really

265
00:14:54,870 --> 00:14:56,710
want to do that only.

266
00:14:56,710 --> 00:15:00,790
But what you can do then is to
say, well, now let's look at

267
00:15:00,790 --> 00:15:06,050
regions that got more malaria
before the campaign, and this

268
00:15:06,050 --> 00:15:08,400
is the reduction in the
number of malaria

269
00:15:08,400 --> 00:15:10,710
cases in those regions.

270
00:15:10,710 --> 00:15:13,650
And of course, the more malaria
you got, the bigger

271
00:15:13,650 --> 00:15:16,010
the reduction, because
the reduction was

272
00:15:16,010 --> 00:15:17,880
pretty much to zero.

273
00:15:17,880 --> 00:15:21,220
So if you started with 100, you
get about 100% reduction,

274
00:15:21,220 --> 00:15:22,770
like in this place, Choco.

275
00:15:22,770 --> 00:15:25,530
If you started with no malaria
at all, t then you get no

276
00:15:25,530 --> 00:15:28,300
reduction because there
was nowhere to go.

277
00:15:28,300 --> 00:15:31,370
It's a little bit
like the anemia

278
00:15:31,370 --> 00:15:32,290
paper that we saw before.

279
00:15:32,290 --> 00:15:35,380
If you started anemic, then
getting the pill makes you

280
00:15:35,380 --> 00:15:37,720
non-anemic, but if you're
non-anemic, you don't benefit.

281
00:15:37,720 --> 00:15:39,220
Same thing here.

282
00:15:39,220 --> 00:15:41,860
This graph is by region,
so these are

283
00:15:41,860 --> 00:15:42,520
all different regions--

284
00:15:42,520 --> 00:15:45,300
Choco, Cauca, Narino,
Santander--

285
00:15:45,300 --> 00:15:47,240
these are all regions.

286
00:15:47,240 --> 00:15:52,860
And you can see that the regions
that had a lot of

287
00:15:52,860 --> 00:15:56,310
malaria before the eradication
got the biggest reduction

288
00:15:56,310 --> 00:15:59,180
between the post-campaign
to the pre-campaign.

289
00:15:59,180 --> 00:16:02,760
So this is the reduction in
cases, and this is where you

290
00:16:02,760 --> 00:16:04,650
started from.

291
00:16:04,650 --> 00:16:09,050
So now what it's going to look
at is it the case-- take a

292
00:16:09,050 --> 00:16:14,580
child who was born and who was
still a child before the

293
00:16:14,580 --> 00:16:18,890
eradication campaign started,
and take a child who was born

294
00:16:18,890 --> 00:16:21,560
after the eradication
campaign.

295
00:16:21,560 --> 00:16:25,690
So take a child who was, let's
say, 10 by 1960 and a child

296
00:16:25,690 --> 00:16:27,980
who was born in 1962.

297
00:16:27,980 --> 00:16:31,730
And the child who is 10 by 1960
doesn't benefit from the

298
00:16:31,730 --> 00:16:35,580
campaign whatsoever, but the
child who was born in 1962, by

299
00:16:35,580 --> 00:16:38,090
the time he's born, malaria
is history.

300
00:16:38,090 --> 00:16:43,230
So the young child relative to
the old child would benefit

301
00:16:43,230 --> 00:16:46,810
more in a region where malaria
was a big problem than in a

302
00:16:46,810 --> 00:16:49,390
region when it was
a small problem.

303
00:16:49,390 --> 00:16:53,560
So what he is going to do next
is to put on the y-axis here

304
00:16:53,560 --> 00:16:56,940
not the reduction in malaria
cases, but how much these

305
00:16:56,940 --> 00:17:01,630
people make as adult, how much
a child born after the

306
00:17:01,630 --> 00:17:04,290
campaign makes relative
to a child

307
00:17:04,290 --> 00:17:05,330
born before the campaign.

308
00:17:05,330 --> 00:17:08,424
What is this difference in
income between this, and is

309
00:17:08,424 --> 00:17:13,500
the difference in income
related to the

310
00:17:13,500 --> 00:17:15,230
malaria at the beginning.

311
00:17:15,230 --> 00:17:18,660
This is called a difference in
difference, because you're

312
00:17:18,660 --> 00:17:21,869
looking at whether the
difference in earning between

313
00:17:21,869 --> 00:17:26,339
a young and an old cohort is
different in places that start

314
00:17:26,339 --> 00:17:28,500
from a higher level.

315
00:17:28,500 --> 00:17:31,060
What you are assuming when
you're doing that is that

316
00:17:31,060 --> 00:17:33,780
there are no other factors that
are changing exactly at

317
00:17:33,780 --> 00:17:36,650
the same time in the same way,
and we're going to see what we

318
00:17:36,650 --> 00:17:39,330
can say about that.

319
00:17:39,330 --> 00:17:43,500
Here is this graph that I was
talking about for Brazil now.

320
00:17:43,500 --> 00:17:46,480
This is the pre-campaign
malaria intensity.

321
00:17:46,480 --> 00:17:47,920
Don't worry about the axis.

322
00:17:47,920 --> 00:17:52,530
It's sort of standardized at
zero, so for zero being the

323
00:17:52,530 --> 00:17:53,790
median case.

324
00:17:53,790 --> 00:17:59,030
And this is the income change of
those born in 1960, that is

325
00:17:59,030 --> 00:18:02,530
those who were born after the
campaign, minus those who were

326
00:18:02,530 --> 00:18:03,626
born in 1953.

327
00:18:03,626 --> 00:18:05,680
It is their income later.

328
00:18:05,680 --> 00:18:09,720
We measure the income much
later, in 1980, for example.

329
00:18:09,720 --> 00:18:13,980
So in 1980, we measure the
income of those born in 1960

330
00:18:13,980 --> 00:18:17,620
minus the income of
those in 1953.

331
00:18:17,620 --> 00:18:22,050
This is all in log, so it's log
income in 1960 minus log

332
00:18:22,050 --> 00:18:24,060
income in 1953.

333
00:18:24,060 --> 00:18:26,700
And what we see is exactly
what we would expect if

334
00:18:26,700 --> 00:18:30,670
malaria does make you poor,
which is the people who were

335
00:18:30,670 --> 00:18:34,930
born in places like Mato Grosso
here where malaria had

336
00:18:34,930 --> 00:18:39,300
been a huge deal beforehand, the
young people experience a

337
00:18:39,300 --> 00:18:42,580
bigger increase in earnings
relative to the old people

338
00:18:42,580 --> 00:18:46,240
than people, say, in Bahia where
malaria was not a big

339
00:18:46,240 --> 00:18:47,810
deal to start with.

340
00:18:47,810 --> 00:18:49,060
Do you understand this graph?

341
00:18:53,070 --> 00:18:56,190
So I'm arguing that nothing
else changed between this

342
00:18:56,190 --> 00:19:00,340
cohort in a way that's related
with malaria intensity, and

343
00:19:00,340 --> 00:19:01,750
what could you argue
back to me?

344
00:19:07,320 --> 00:19:10,757
What is the worry with
that assumption?

345
00:19:10,757 --> 00:19:14,499
AUDIENCE: There might be a lot
of other factors that have

346
00:19:14,499 --> 00:19:16,246
changed over time.

347
00:19:16,246 --> 00:19:22,234
I mean, if you're born in the
'60s, then [INAUDIBLE]

348
00:19:31,096 --> 00:19:33,606
PROFESSOR: Yes. so the worry
is something else

349
00:19:33,606 --> 00:19:34,836
might happen over time.

350
00:19:34,836 --> 00:19:38,280
AUDIENCE: I was going to say,
sort of similarly that there's

351
00:19:38,280 --> 00:19:41,232
probably a third, external
factor that's both raising

352
00:19:41,232 --> 00:19:43,692
income and decreasing malaria
at the same time.

353
00:19:43,692 --> 00:19:46,152
Since they have the same
effects, it's not necessarily

354
00:19:46,152 --> 00:19:47,628
that they're affecting
one another.

355
00:19:47,628 --> 00:19:49,550
There's a third thing that's
affecting both of them.

356
00:19:49,550 --> 00:19:50,030
PROFESSOR: Right.

357
00:19:50,030 --> 00:19:52,480
So it could be something
affecting both of them, so

358
00:19:52,480 --> 00:19:55,510
Brazil is just becoming
generally richer over time.

359
00:19:55,510 --> 00:19:59,470
But this should be something
that is affecting

360
00:19:59,470 --> 00:20:03,370
disproportionally Mato Grosso
than Bahia, right?

361
00:20:03,370 --> 00:20:07,220
Because here, I'm not only
telling you that income

362
00:20:07,220 --> 00:20:08,960
increases between these
two cohorts.

363
00:20:08,960 --> 00:20:13,100
I'm also telling you that
increases faster in the region

364
00:20:13,100 --> 00:20:15,520
that had more malaria
to start with.

365
00:20:15,520 --> 00:20:18,870
But you could ask me, for
example, is it the case that

366
00:20:18,870 --> 00:20:21,520
this place was poorer in the
beginning, so they had more

367
00:20:21,520 --> 00:20:22,960
places to go?

368
00:20:22,960 --> 00:20:25,780
As Brazil was becoming richer,
the poorer regions were

369
00:20:25,780 --> 00:20:28,350
catching up with the older
regions, and this is what I

370
00:20:28,350 --> 00:20:30,670
see here, just to catch up.

371
00:20:30,670 --> 00:20:31,740
So that could be.

372
00:20:31,740 --> 00:20:34,450
It's kind of one twist to the
point that both of you made,

373
00:20:34,450 --> 00:20:38,100
which is as Brazil was growing,
maybe it is possible

374
00:20:38,100 --> 00:20:41,330
that this region would have
been growing more anyway.

375
00:20:41,330 --> 00:20:43,960
Now of course, I'm looking at
the income in 1980s, but these

376
00:20:43,960 --> 00:20:48,130
are different cohorts, so maybe
the same places that had

377
00:20:48,130 --> 00:20:50,140
a lot of malaria had
people who were

378
00:20:50,140 --> 00:20:51,890
not very well educated.

379
00:20:51,890 --> 00:20:54,250
And at the same times that I
took care of malaria, I also

380
00:20:54,250 --> 00:20:57,177
built a lot of schools for them,
and I'm seeing these

381
00:20:57,177 --> 00:21:01,150
guys are income increased
relative to the older ones.

382
00:21:01,150 --> 00:21:02,820
It's just because I also
built a lot of

383
00:21:02,820 --> 00:21:03,850
schools in Mato Grosso.

384
00:21:03,850 --> 00:21:05,680
So that would be your third
factor that would be

385
00:21:05,680 --> 00:21:08,200
differentially important
in the region.

386
00:21:08,200 --> 00:21:10,870
So that is obviously
a real concern.

387
00:21:10,870 --> 00:21:14,520
By looking at changes in income
over the importance of

388
00:21:14,520 --> 00:21:17,370
malaria at the beginning, we've
gone one step towards

389
00:21:17,370 --> 00:21:18,780
some credibility.

390
00:21:18,780 --> 00:21:22,310
We're still very far from our
randomized trial, which would

391
00:21:22,310 --> 00:21:25,530
involve randomly treating some
people for malaria, waiting 20

392
00:21:25,530 --> 00:21:27,900
years, and seeing how much more
money they make like they

393
00:21:27,900 --> 00:21:29,490
did with the deworming.

394
00:21:29,490 --> 00:21:33,460
Such that it doesn't exist, so
we need to try to do the best

395
00:21:33,460 --> 00:21:35,120
with what we have.

396
00:21:35,120 --> 00:21:41,200
One way to verify this is that
we actually know exactly when

397
00:21:41,200 --> 00:21:45,160
people start getting treated
for malaria, because the

398
00:21:45,160 --> 00:21:50,620
campaign, if you go back to this
graph, was quite sudden.

399
00:21:50,620 --> 00:21:53,880
In fact, we know the date at
which they started spraying,

400
00:21:53,880 --> 00:21:58,540
and so we should have a pretty
clear idea of which cohort get

401
00:21:58,540 --> 00:22:02,090
exposed and which cohort
are not exposed.

402
00:22:02,090 --> 00:22:06,930
So instead of doing this kind
of graph for a broad cohort

403
00:22:06,930 --> 00:22:11,080
and looking at the slope here, I
could say, well, let me do a

404
00:22:11,080 --> 00:22:14,730
test, for example, if I did the
same graph for those born

405
00:22:14,730 --> 00:22:19,990
in 1953 versus those
born in 1950.

406
00:22:19,990 --> 00:22:26,050
If this graph was due to the
decline of malaria, what would

407
00:22:26,050 --> 00:22:30,180
I expect if I, instead of doing
these differences, I did

408
00:22:30,180 --> 00:22:34,120
1953 minus 1950?

409
00:22:34,120 --> 00:22:37,580
What should I expect for my line
if the only reason why I

410
00:22:37,580 --> 00:22:40,240
have an increasing line
here is due to the

411
00:22:40,240 --> 00:22:43,560
reduction in malaria?

412
00:22:43,560 --> 00:22:44,946
AUDIENCE: [INAUDIBLE]

413
00:22:44,946 --> 00:22:46,310
PROFESSOR: Yeah.

414
00:22:46,310 --> 00:22:48,870
I should expect a flat
line, exactly.

415
00:22:48,870 --> 00:22:52,370
Because the 1953 kids I
exposed to malaria.

416
00:22:52,370 --> 00:22:54,130
So are the 1950.

417
00:22:54,130 --> 00:22:57,270
So the differences between their
income should not be

418
00:22:57,270 --> 00:22:58,810
related to how much
malaria there is

419
00:22:58,810 --> 00:23:00,345
because no one benefited.

420
00:23:00,345 --> 00:23:08,070
Now if instead I'm taking kids
who were born in 1950, so in

421
00:23:08,070 --> 00:23:13,090
1970, and I'm comparing them
to the wages of kid born in

422
00:23:13,090 --> 00:23:19,760
1965, what should I expect
for this line?

423
00:23:19,760 --> 00:23:22,390
Now I'm looking at very
young kids, kids born

424
00:23:22,390 --> 00:23:36,170
in 1970 versus 1965.

425
00:23:36,170 --> 00:23:38,454
So we can go back to
this graph here.

426
00:23:41,590 --> 00:23:46,450
What's the pattern in malaria
cases after the 1970?

427
00:23:46,450 --> 00:23:47,665
AUDIENCE: [INAUDIBLE]

428
00:23:47,665 --> 00:23:49,865
PROFESSOR: Yeah, there is no
further decline because there

429
00:23:49,865 --> 00:23:50,330
is no malaria left.

430
00:23:50,330 --> 00:23:52,410
AUDIENCE: It should be
flat as well, right?

431
00:23:52,410 --> 00:23:52,920
PROFESSOR: Exactly.

432
00:23:52,920 --> 00:23:56,030
It should be flat as well, not
because everybody has malaria,

433
00:23:56,030 --> 00:23:58,370
but because no one
has malaria.

434
00:23:58,370 --> 00:24:02,250
So with malaria, we have a
pretty specific pattern as to

435
00:24:02,250 --> 00:24:04,260
when I should start
seeing an effect.

436
00:24:04,260 --> 00:24:07,460
If I'm thinking that the big
problem of malaria is when you

437
00:24:07,460 --> 00:24:10,190
are very small kids, then
I should start seeing a

438
00:24:10,190 --> 00:24:16,340
difference between the children
who were born just

439
00:24:16,340 --> 00:24:20,470
before and just after
the campaign.

440
00:24:20,470 --> 00:24:22,720
During that time of the campaign
scale up, I should

441
00:24:22,720 --> 00:24:26,225
this effect being the larger and
larger, but for the young

442
00:24:26,225 --> 00:24:29,540
cohort, it should flatten out if
I compare very young cohort

443
00:24:29,540 --> 00:24:32,650
to somewhat younger cohort but
all of them got exposed.

444
00:24:32,650 --> 00:24:35,400
And for the old cohort, it
should again flatten out,

445
00:24:35,400 --> 00:24:38,320
because the old cohort has
not yet been exposed.

446
00:24:38,320 --> 00:24:41,700
So I can now say something
more specific than why

447
00:24:41,700 --> 00:24:45,740
generally the very youngest
versus the young are the

448
00:24:45,740 --> 00:24:48,020
difference increase in
pre-malaria intensity, which

449
00:24:48,020 --> 00:24:49,770
could well be correlated
with other things

450
00:24:49,770 --> 00:24:52,720
happening at the same time.

451
00:24:52,720 --> 00:24:56,960
He's doing that, so let me
guide you to this graph.

452
00:24:56,960 --> 00:24:58,190
What is this graph?

453
00:24:58,190 --> 00:25:01,030
It has the shape that I talked
to you about, right?

454
00:25:01,030 --> 00:25:03,490
It has the shape of being flat,
and then increasing, and

455
00:25:03,490 --> 00:25:05,190
then being flat again.

456
00:25:05,190 --> 00:25:07,470
So what is this graph?

457
00:25:07,470 --> 00:25:13,010
Each of these points is the
slope of a regression of this

458
00:25:13,010 --> 00:25:20,640
kind, except that instead of
being 1960 versus 1953, it is

459
00:25:20,640 --> 00:25:22,960
one year versus the other.

460
00:25:22,960 --> 00:25:36,810
For example, this is everyone
relative to the oldest cohort.

461
00:25:36,810 --> 00:25:39,540
Each dot indicates the strength
of the relationship

462
00:25:39,540 --> 00:25:43,530
between pre-malaria index
and the index for

463
00:25:43,530 --> 00:25:45,320
these particular cohorts.

464
00:25:45,320 --> 00:25:53,150
So for example, 1900 versus
1910, 1911, 1912, et cetera.

465
00:25:53,150 --> 00:25:55,830
I'm taking the difference
between the income of the

466
00:25:55,830 --> 00:26:02,900
cohort born, say, 1910 minus
1900, and I'm plotting this

467
00:26:02,900 --> 00:26:06,100
graph that is here
as a function of

468
00:26:06,100 --> 00:26:09,490
the pre-malaria intensity.

469
00:26:09,490 --> 00:26:15,700
So each of these points here is
a slope of a graph that is

470
00:26:15,700 --> 00:26:22,230
this graph with instead of 1960
minus 1953 is, say, 1901

471
00:26:22,230 --> 00:26:28,930
versus 1900, 1902 versus 1900,
1903 versus 1900, et cetera.

472
00:26:28,930 --> 00:26:35,580
And this is plotted as a
function of the 1901, 1902,

473
00:26:35,580 --> 00:26:37,770
1903, et cetera, the
cohort of both.

474
00:26:37,770 --> 00:26:42,850
So all of these cohorts were
cohorts that were not exposed,

475
00:26:42,850 --> 00:26:50,640
and we basically we see
not much of a line.

476
00:26:50,640 --> 00:26:55,420
This line is superimposed, but
if you see this cloud of dots,

477
00:26:55,420 --> 00:26:57,940
the cloud of dots is
not increasing.

478
00:26:57,940 --> 00:27:01,510
So the difference is not
related to the malaria

479
00:27:01,510 --> 00:27:03,250
intensity in those places.

480
00:27:03,250 --> 00:27:06,530
Those places are generally poor,
but it's not correlated

481
00:27:06,530 --> 00:27:08,340
with the malaria intensity.

482
00:27:08,340 --> 00:27:13,090
For the younger cohort, we see
the slope keeps increasing.

483
00:27:13,090 --> 00:27:14,890
The slope keeps increasing,
keep increasing, keeps

484
00:27:14,890 --> 00:27:18,370
increasing until there is
basically no difference

485
00:27:18,370 --> 00:27:23,275
between a region that initially
had a lot of malaria

486
00:27:23,275 --> 00:27:26,500
and a region that didn't have a
lot of malaria, and then it

487
00:27:26,500 --> 00:27:28,420
flattens again.

488
00:27:28,420 --> 00:27:35,190
So these are the patterns that
we were expecting to see where

489
00:27:35,190 --> 00:27:38,220
between the old cohort and the
slightly less old cohort we

490
00:27:38,220 --> 00:27:41,760
see no difference until they
get exposed to the malaria,

491
00:27:41,760 --> 00:27:45,290
and then it increases, and
then it flattens again.

492
00:27:45,290 --> 00:27:49,650
So you could do still have the
type of factors that you get

493
00:27:49,650 --> 00:27:51,590
we were talking about, that
maybe they were building

494
00:27:51,590 --> 00:27:54,260
schools exactly at the
same time, et cetera.

495
00:27:54,260 --> 00:27:57,250
But it would have to be relative
tricky to follow

496
00:27:57,250 --> 00:28:03,370
exactly that pattern where this
is the pattern of the

497
00:28:03,370 --> 00:28:07,080
malaria campaign going and going
and going, and we have

498
00:28:07,080 --> 00:28:09,920
the points pretty much following
the expansion of the

499
00:28:09,920 --> 00:28:13,140
malaria campaign.

500
00:28:13,140 --> 00:28:14,890
This is not complete.

501
00:28:14,890 --> 00:28:16,550
You still have to believe
that this is the

502
00:28:16,550 --> 00:28:18,300
only thing that happens.

503
00:28:18,300 --> 00:28:20,840
But it gives you a pretty good
sense that it must have been

504
00:28:20,840 --> 00:28:23,430
the only thing that really
happened for this, because

505
00:28:23,430 --> 00:28:26,130
otherwise why would
it have this

506
00:28:26,130 --> 00:28:27,490
bizarre-looking snake shape?

507
00:28:31,450 --> 00:28:34,630
Are you all OK with
this graph?

508
00:28:34,630 --> 00:28:39,040
And do find it reasonably
convincing?

509
00:28:39,040 --> 00:28:41,080
Yes, no?

510
00:28:41,080 --> 00:28:42,304
No vote.

511
00:28:42,304 --> 00:28:44,400
I find it reasonably convincing,
and I'm in the

512
00:28:44,400 --> 00:28:47,400
business of doing [INAUDIBLE],
so I'm very skeptical of

513
00:28:47,400 --> 00:28:50,780
anything that might be a
substitute to what I do.

514
00:28:50,780 --> 00:28:54,760
But it's hard to imagine another
factor that would

515
00:28:54,760 --> 00:28:58,480
follow so nicely the pattern
of the campaign.

516
00:29:01,320 --> 00:29:05,010
What he does, then, is to run a
regression which is based on

517
00:29:05,010 --> 00:29:09,360
this idea, and he concludes
that a child exposed to

518
00:29:09,360 --> 00:29:12,770
malaria in childhood would
have an income that's 50%

519
00:29:12,770 --> 00:29:17,530
lower than a child who had not
been exposed in childhood over

520
00:29:17,530 --> 00:29:18,150
their lifetime.

521
00:29:18,150 --> 00:29:20,630
So it's even better than
deworming, which is not

522
00:29:20,630 --> 00:29:22,110
surprising because
malaria makes you

523
00:29:22,110 --> 00:29:23,650
sicker than the worms.

524
00:29:23,650 --> 00:29:29,140
But this is a very large effect,
so it's high, but it's

525
00:29:29,140 --> 00:29:30,030
not absurdly high.

526
00:29:30,030 --> 00:29:33,620
If you consider that deworming
is 23%, we are still in kind

527
00:29:33,620 --> 00:29:35,760
of the ballpark of where
it makes sense.

528
00:29:35,760 --> 00:29:39,680
So it suggests that childhood
malaria actually makes you

529
00:29:39,680 --> 00:29:42,740
weak for the rest of your
life, and again we could

530
00:29:42,740 --> 00:29:45,070
calculate what it means
for the lifetime of

531
00:29:45,070 --> 00:29:46,720
someone, of an income.

532
00:29:46,720 --> 00:29:51,700
So if an increase of 23% of
income with deworming make you

533
00:29:51,700 --> 00:29:56,570
about, I think we had found,
$1,100 richer or $1,300

534
00:29:56,570 --> 00:30:00,060
richer, this is about twice
that over your lifetime.

535
00:30:00,060 --> 00:30:03,130
So if these effects are the same
in Kenya, it would mean

536
00:30:03,130 --> 00:30:06,230
that avoiding malaria in
childhood would make you

537
00:30:06,230 --> 00:30:10,970
$2,600 richer for
your lifetime.

538
00:30:10,970 --> 00:30:16,020
Which brings the question that
Zachary has asked at the end

539
00:30:16,020 --> 00:30:18,750
of the deworming lecture, which
is if that's so great,

540
00:30:18,750 --> 00:30:21,970
why aren't people
are doing it?

541
00:30:21,970 --> 00:30:25,250
So the question here becomes why
aren't countries doing it,

542
00:30:25,250 --> 00:30:27,740
and why are people
not doing it?

543
00:30:27,740 --> 00:30:31,390
So why aren't countries all
doing things like Latin

544
00:30:31,390 --> 00:30:34,670
America did of intensive
campaign to try and get rid of

545
00:30:34,670 --> 00:30:37,450
the affliction, so public
health kind of measure?

546
00:30:37,450 --> 00:30:39,600
And if they're not going to do
it because they all have

547
00:30:39,600 --> 00:30:42,570
difference or because there
is political, economic

548
00:30:42,570 --> 00:30:45,360
consideration that prevents
DDT from being used or

549
00:30:45,360 --> 00:30:47,880
anything like that, then why
aren't people at least buying

550
00:30:47,880 --> 00:30:53,130
a bed net which costs $7 or
maybe $10 for a family?

551
00:30:53,130 --> 00:30:55,950
It would mean the child would
have a pretty good chance of

552
00:30:55,950 --> 00:30:59,580
being substantially richer
over the lifetime.

553
00:30:59,580 --> 00:31:01,600
So that's kind of the mystery.

554
00:31:01,600 --> 00:31:06,610
One thing just to close the loop
on Sachs is now that you

555
00:31:06,610 --> 00:31:11,350
have this estimate, you can
calculate what is malaria

556
00:31:11,350 --> 00:31:13,480
prevalence, in the countries
that have a lot of malaria,

557
00:31:13,480 --> 00:31:16,340
how much more these people would
make if they didn't have

558
00:31:16,340 --> 00:31:20,450
malaria and obtain a number that
is comparable to the 30%

559
00:31:20,450 --> 00:31:23,280
differences that is in the
Sachs and Gallup article.

560
00:31:23,280 --> 00:31:26,290
And what you find is a number
that is much, much smaller.

561
00:31:26,290 --> 00:31:30,070
So you find that Sachs and
Gallup completely overestimate

562
00:31:30,070 --> 00:31:34,410
the impact of malaria on GDP
even though the impact on

563
00:31:34,410 --> 00:31:37,000
income is still positive and
still quite serious.

564
00:31:37,000 --> 00:31:40,220
So you do find it's not 30%.

565
00:31:40,220 --> 00:31:42,850
I don't want to give you the
number and then it happens not

566
00:31:42,850 --> 00:31:44,270
to be the right one,
but the order of

567
00:31:44,270 --> 00:31:46,070
magnitude is maybe a fourth.

568
00:31:46,070 --> 00:31:49,170
But it's still pretty
significant and important.

569
00:31:49,170 --> 00:31:53,850
So that gives us this idea of
why aren't countries spraying

570
00:31:53,850 --> 00:31:57,550
and why aren't people
buying bed nets?

571
00:31:57,550 --> 00:32:01,240
This question we could ask over
and over and over again.

572
00:32:01,240 --> 00:32:06,410
I haven't seen a cross benefit
analysis of not getting

573
00:32:06,410 --> 00:32:11,320
diarrhea all the time when
you're a child, but presumably

574
00:32:11,320 --> 00:32:14,600
that's something probably
similar to the deworming

575
00:32:14,600 --> 00:32:18,800
effect because worms are little
bit equivalent of

576
00:32:18,800 --> 00:32:22,680
diarrhea in terms of getting
rid of your nutrition.

577
00:32:22,680 --> 00:32:25,915
So why aren't people putting
bleach in their water?

578
00:32:25,915 --> 00:32:31,800
Why aren't people buying
[INAUDIBLE] when they're sick,

579
00:32:31,800 --> 00:32:32,820
and things like that.

580
00:32:32,820 --> 00:32:37,700
So this is the mystery
we have to ask.

581
00:32:37,700 --> 00:32:40,850
What we have already seen in the
previous lecture, and also

582
00:32:40,850 --> 00:32:44,160
a little bit with nutrition,
us that preventive care is

583
00:32:44,160 --> 00:32:46,010
characterized by two things.

584
00:32:46,010 --> 00:32:53,110
One is it is very low demand,
and the second is it is a high

585
00:32:53,110 --> 00:32:55,970
sensitivity to prices.

586
00:32:55,970 --> 00:32:59,330
Let me show a graph.

587
00:32:59,330 --> 00:33:02,610
This is the highest sensitivity
to positive

588
00:33:02,610 --> 00:33:05,930
prices, and I'm going to show
you in a moment the high

589
00:33:05,930 --> 00:33:10,180
sensitivity to negative prices,
which are incentives.

590
00:33:10,180 --> 00:33:12,700
You've already seen some
of these numbers.

591
00:33:12,700 --> 00:33:20,620
You've already seen the red
dots, which are coming from

592
00:33:20,620 --> 00:33:23,200
the Dupas experiment in Kenya,
so this is this one.

593
00:33:26,480 --> 00:33:29,760
This is the graph for bed nets,
so when bed nets are

594
00:33:29,760 --> 00:33:34,760
free, you pretty much get them,
but when you need to pay

595
00:33:34,760 --> 00:33:40,150
for them, a little bit of money
like $0.60, you're less

596
00:33:40,150 --> 00:33:43,106
likely to get and use
them, et cetera.

597
00:33:46,204 --> 00:33:49,790
What is interesting is that we
find the same kind of slope,

598
00:33:49,790 --> 00:33:54,800
even steeper, for other goods
that are completely different.

599
00:33:54,800 --> 00:33:58,390
So you find the same kind of
slope for clothing in Zambia

600
00:33:58,390 --> 00:34:02,010
where if you ask people to pay
a little bit for clothing,

601
00:34:02,010 --> 00:34:03,540
they're much less likely
to get to it.

602
00:34:03,540 --> 00:34:08,620
So when they start to have to
pay $0.10, $0.20, $0.30, the

603
00:34:08,620 --> 00:34:11,630
take up reduces a lot.

604
00:34:11,630 --> 00:34:15,820
You're finding the same thing
for deworming in Kenya.

605
00:34:15,820 --> 00:34:18,960
We actually briefly evoked
this when we were talking

606
00:34:18,960 --> 00:34:24,350
about deworming when we saw
that people in the school

607
00:34:24,350 --> 00:34:27,380
where they did the experiment,
at some point the NGO who was

608
00:34:27,380 --> 00:34:34,500
in this sustainability kind of
mood, I guess, decided it

609
00:34:34,500 --> 00:34:36,120
makes sense to ask
people to pay a

610
00:34:36,120 --> 00:34:39,250
little bit do for deworming.

611
00:34:39,250 --> 00:34:42,690
So they asked people to pay a
little bit, and basically what

612
00:34:42,690 --> 00:34:46,469
happened is that the take up
went essentially to zero.

613
00:34:46,469 --> 00:34:48,600
Let me try and get
the right point.

614
00:34:48,600 --> 00:34:50,730
I think this is this one.

615
00:34:50,730 --> 00:34:53,860
The take up went essentially to
zero when people had to pay

616
00:34:53,860 --> 00:34:56,010
just a little bit
for deworming.

617
00:34:56,010 --> 00:34:57,320
So you're looking at bed nets.

618
00:34:57,320 --> 00:34:58,420
You're looking at clothing.

619
00:34:58,420 --> 00:35:01,250
You're looking at deworming.

620
00:35:01,250 --> 00:35:03,900
They're all very different
products presumably with very

621
00:35:03,900 --> 00:35:06,560
different life time benefit, and
they pretty much all seem

622
00:35:06,560 --> 00:35:08,900
to be on the same slope, which
is as soon as people have to

623
00:35:08,900 --> 00:35:12,590
pay just a little bit, they
don't do it anymore.

624
00:35:12,590 --> 00:35:13,890
So that's on the
negative price.

625
00:35:13,890 --> 00:35:14,330
Yeah?

626
00:35:14,330 --> 00:35:16,214
AUDIENCE: Are some lines steeper
because people don't

627
00:35:16,214 --> 00:35:20,086
see the value as much in paying
for the other things

628
00:35:20,086 --> 00:35:20,570
[INAUDIBLE]

629
00:35:20,570 --> 00:35:22,870
PROFESSOR: So what is
interesting is that except for

630
00:35:22,870 --> 00:35:25,390
this one, all of the lines
are pretty steep.

631
00:35:28,660 --> 00:35:31,860
So the question that we have
to ask here is why is that?

632
00:35:31,860 --> 00:35:35,150
And one possible reason would
clearly be that people don't

633
00:35:35,150 --> 00:35:35,620
see the value.

634
00:35:35,620 --> 00:35:38,140
I'm going to get to
that in a moment.

635
00:35:38,140 --> 00:35:41,390
Before we go to that, let's
see the elasticity with

636
00:35:41,390 --> 00:35:45,170
respect to negative price, which
are small incentives.

637
00:35:45,170 --> 00:35:50,250
So one thing that you already
noticed is that the rate of

638
00:35:50,250 --> 00:35:53,230
immunization is very, very
low in some places.

639
00:35:53,230 --> 00:35:54,870
It's higher in some others.

640
00:35:54,870 --> 00:35:56,510
This is [INAUDIBLE], the places

641
00:35:56,510 --> 00:35:58,630
where you saw the movie.

642
00:35:58,630 --> 00:36:01,900
After the movie was finished,
one thing we noticed is that a

643
00:36:01,900 --> 00:36:05,170
big problem seems to be that
kids are not immunized.

644
00:36:05,170 --> 00:36:07,150
The immunization rate
was very, very, very

645
00:36:07,150 --> 00:36:09,660
low, less than 5%.

646
00:36:09,660 --> 00:36:12,620
So we decided the
try two things.

647
00:36:12,620 --> 00:36:15,920
The first thing was that the
NGOs [INAUDIBLE] would work

648
00:36:15,920 --> 00:36:20,470
with the government to do a
monthly camp for immunization.

649
00:36:20,470 --> 00:36:23,080
So every month, they would take
the vaccines from the

650
00:36:23,080 --> 00:36:26,370
government, go to the village,
and immunize whoever wanted to

651
00:36:26,370 --> 00:36:28,200
come to be immunized.

652
00:36:28,200 --> 00:36:33,200
And on top of this,
[INAUDIBLE]

653
00:36:33,200 --> 00:36:36,120
also instituted a small
incentive to get immunized, so

654
00:36:36,120 --> 00:36:39,190
that's a kilo of lentil.

655
00:36:39,190 --> 00:36:42,180
Lentil is a [INAUDIBLE].

656
00:36:42,180 --> 00:36:47,280
It's something people eat as a
source of protein, and a kilo

657
00:36:47,280 --> 00:36:50,960
of lentil is about half
the minimum wage.

658
00:36:50,960 --> 00:36:55,350
So this is a small, small
gift to go with it.

659
00:36:55,350 --> 00:36:57,280
It's not like a large inducement
that if it's

660
00:36:57,280 --> 00:36:58,730
something you don't want
to do it would

661
00:36:58,730 --> 00:37:00,570
convince you to do it.

662
00:37:00,570 --> 00:37:03,220
And these were the results.

663
00:37:03,220 --> 00:37:05,470
There was also a set of control
villages that I'm not

664
00:37:05,470 --> 00:37:06,190
showing to you.

665
00:37:06,190 --> 00:37:08,560
I'm just showing to you the
effect of the incentive.

666
00:37:08,560 --> 00:37:17,790
So comparing the camp
without incentive to

667
00:37:17,790 --> 00:37:20,350
the camp with incentive.

668
00:37:20,350 --> 00:37:24,710
Intervention B are the villages
where the lentils

669
00:37:24,710 --> 00:37:28,310
were put in place, and you can
see that pretty much everyone

670
00:37:28,310 --> 00:37:30,170
gets the first shot.

671
00:37:30,170 --> 00:37:32,310
That's BCG.

672
00:37:32,310 --> 00:37:35,930
And the second shot is
also pretty similar.

673
00:37:35,930 --> 00:37:38,720
From the third, we start seeing
a difference, and from

674
00:37:38,720 --> 00:37:41,370
the fourth, an even bigger
difference, and from the

675
00:37:41,370 --> 00:37:44,010
fifth, the biggest difference.

676
00:37:44,010 --> 00:37:49,810
The biggest difference is
between the more immunization

677
00:37:49,810 --> 00:37:53,500
you need to get, the more the
small incentive matter.

678
00:37:53,500 --> 00:37:55,580
So it seems that people are
not adverse to getting

679
00:37:55,580 --> 00:37:58,320
immunized, but they sort of
lose interest or something

680
00:37:58,320 --> 00:38:01,740
such that the more you get into
trying to get them to

681
00:38:01,740 --> 00:38:07,350
complete the course, the more
the incentive is needed.

682
00:38:07,350 --> 00:38:09,710
And this is a reasonably
large effect.

683
00:38:09,710 --> 00:38:13,380
Overall if you look at the
effect on immunization, about

684
00:38:13,380 --> 00:38:19,320
12% of kids who are in one
intervention received all the

685
00:38:19,320 --> 00:38:25,400
immunization they should get, so
18% versus 38% if they get

686
00:38:25,400 --> 00:38:26,650
the incentive.

687
00:38:29,880 --> 00:38:35,490
We have three things that we
need to try and square.

688
00:38:35,490 --> 00:38:39,530
One is the benefits are very
high, two is the demand is

689
00:38:39,530 --> 00:38:43,900
very low, And three is the price
elasticity is very high,

690
00:38:43,900 --> 00:38:47,300
both from the positive side
and the negative side.

691
00:38:47,300 --> 00:38:49,360
And the question is, how
do we put all of

692
00:38:49,360 --> 00:38:51,540
these elements together?

693
00:38:51,540 --> 00:38:54,770
And the reason it is surprising
is that suppose we

694
00:38:54,770 --> 00:38:58,950
take as given that the benefits
are indeed very high.

695
00:38:58,950 --> 00:39:05,000
In that case, if people knew
it, if people don't do

696
00:39:05,000 --> 00:39:07,740
something, it must mean that
they think that the cost is

697
00:39:07,740 --> 00:39:09,520
also very high.

698
00:39:09,520 --> 00:39:11,550
Why would they think the
cost is very high?

699
00:39:11,550 --> 00:39:13,380
Maybe they think it's not
culturally appropriate.

700
00:39:13,380 --> 00:39:17,830
Maybe they hate to get
their kids immunized.

701
00:39:17,830 --> 00:39:20,550
Maybe sleeping under the bad
nets is extremely unpleasant,

702
00:39:20,550 --> 00:39:23,340
or maybe they need the
money so much.

703
00:39:23,340 --> 00:39:27,040
So if people are very sensitive
to the benefits, are

704
00:39:27,040 --> 00:39:29,650
very aware of the benefits,
the only reason why they

705
00:39:29,650 --> 00:39:32,730
wouldn't do it is because they
think the costs are huge.

706
00:39:32,730 --> 00:39:37,280
For example, another fact that
we see both in rich and poor

707
00:39:37,280 --> 00:39:43,040
countries, people are not very
likely to get a test for HIV

708
00:39:43,040 --> 00:39:46,380
even and maybe particularly if
they have put themselves at

709
00:39:46,380 --> 00:39:47,960
risk of HIV.

710
00:39:47,960 --> 00:39:50,380
And one of the reasons that
people say is that people

711
00:39:50,380 --> 00:39:53,550
realize that it would be good to
know your status, but they

712
00:39:53,550 --> 00:39:55,320
are worried.

713
00:39:55,320 --> 00:39:57,940
There is a fear of what will
you do if you find out that

714
00:39:57,940 --> 00:39:58,790
you're positive.

715
00:39:58,790 --> 00:40:03,520
Or there is also the social
factor of people knowing that

716
00:40:03,520 --> 00:40:08,600
if you go to get tested, it
means you've done something

717
00:40:08,600 --> 00:40:11,660
not right, otherwise why would
you have HIV if you are only

718
00:40:11,660 --> 00:40:14,890
faithful to your husband
who is faithful to you?

719
00:40:14,890 --> 00:40:18,010
So this is an example where
the benefits are high and

720
00:40:18,010 --> 00:40:21,150
maybe the costs are also high.

721
00:40:21,150 --> 00:40:23,960
But what is surprising is that
if people don't do these

722
00:40:23,960 --> 00:40:27,560
things because the costs are
high, then I should not able

723
00:40:27,560 --> 00:40:30,780
to bribe them to do these things
with a small incentive

724
00:40:30,780 --> 00:40:33,040
like the kilo of lentil.

725
00:40:33,040 --> 00:40:36,020
If people know the benefits of
immunization but don't do it

726
00:40:36,020 --> 00:40:38,270
because they think that the evil
eye is going to be on the

727
00:40:38,270 --> 00:40:41,780
kid, then a kilo of lentils
shouldn't convince them that,

728
00:40:41,780 --> 00:40:43,845
no, the evil eye is going
to be good after all.

729
00:40:43,845 --> 00:40:47,020
It's something that just doesn't
square together.

730
00:40:47,020 --> 00:40:50,780
Or if people were so much aware
of the benefit, they

731
00:40:50,780 --> 00:40:54,030
shouldn't take a bed net when
it's free but refuse to spend

732
00:40:54,030 --> 00:40:55,920
a few cents for it.

733
00:40:55,920 --> 00:41:00,740
So we can't have these three
things be true at the same

734
00:41:00,740 --> 00:41:05,210
time, that people realize that
the benefits are very high,

735
00:41:05,210 --> 00:41:06,850
the demand is very low,
and the price

736
00:41:06,850 --> 00:41:09,030
elasticity is very high.

737
00:41:09,030 --> 00:41:12,940
So it has to be that the
benefits viewed from the

738
00:41:12,940 --> 00:41:16,130
people is actually not as high
as we think they are.

739
00:41:16,130 --> 00:41:20,240
And there could be three
reasons for that.

740
00:41:20,240 --> 00:41:22,975
One is that maybe people don't
care about their health or

741
00:41:22,975 --> 00:41:26,025
don't care about the health of
their children, and that we

742
00:41:26,025 --> 00:41:29,460
already sort of ruled out
because we've seen in the

743
00:41:29,460 --> 00:41:31,690
movie that people are extremely
concerned about

744
00:41:31,690 --> 00:41:32,240
their health.

745
00:41:32,240 --> 00:41:34,460
They spend a lot
of money on it.

746
00:41:34,460 --> 00:41:37,640
For example, they spend
7% of their

747
00:41:37,640 --> 00:41:38,950
monthly budget on health.

748
00:41:38,950 --> 00:41:42,050
That's a lot, and that is
something that we've seen in

749
00:41:42,050 --> 00:41:43,510
the first lecture.

750
00:41:43,510 --> 00:41:49,570
People do spend up to 5% to 7%
of their budget on health, the

751
00:41:49,570 --> 00:41:52,480
very poor, except in countries
like Mexico where there's a

752
00:41:52,480 --> 00:41:54,440
good public health system.

753
00:41:54,440 --> 00:41:57,650
So it is not that people don't
care about health.

754
00:41:57,650 --> 00:42:00,470
We have seen the example
of measles.

755
00:42:00,470 --> 00:42:02,950
They don't vaccinate their kids
against measles, but if

756
00:42:02,950 --> 00:42:05,100
the kids do get measles, they
spend a lot of money on the

757
00:42:05,100 --> 00:42:07,530
hospital and on treatment.

758
00:42:07,530 --> 00:42:10,440
So that's not because they
don't care about health.

759
00:42:10,440 --> 00:42:15,690
Also when you ask them about
what stresses them out, they

760
00:42:15,690 --> 00:42:18,530
tend to say that health is the
one thing that stresses them

761
00:42:18,530 --> 00:42:19,640
out the most.

762
00:42:19,640 --> 00:42:22,770
What makes them worried, tense
and anxious in the last month

763
00:42:22,770 --> 00:42:26,040
is their health, or the health
of their kids, or the health

764
00:42:26,040 --> 00:42:27,010
of their relatives.

765
00:42:27,010 --> 00:42:29,390
So people do care about their
health, and they spend money,

766
00:42:29,390 --> 00:42:32,010
but they don't spend it
on curative care.

767
00:42:32,010 --> 00:42:34,560
They do spend it on directive
care, not preventive.

768
00:42:34,560 --> 00:42:37,180
And they not only spend it
on curative care, but

769
00:42:37,180 --> 00:42:38,200
on pretty bad ones.

770
00:42:38,200 --> 00:42:39,960
We've seen these shots
and [INAUDIBLE]

771
00:42:39,960 --> 00:42:41,880
and stuff like that.

772
00:42:41,880 --> 00:42:43,680
So we can remove this.

773
00:42:43,680 --> 00:42:46,650
So now why do people don't
use preventive care?

774
00:42:46,650 --> 00:42:48,670
It's not because they don't care
about health, but it's

775
00:42:48,670 --> 00:42:51,900
because there's something about
preventive care, that

776
00:42:51,900 --> 00:42:56,100
either they don't really believe
that it works or

777
00:42:56,100 --> 00:42:58,480
there's something else, that the
perception of the benefits

778
00:42:58,480 --> 00:43:00,422
from today is relatively low.

779
00:43:03,220 --> 00:43:04,880
Are government to
blame for that?

780
00:43:04,880 --> 00:43:07,680
Another possible interpretation
is that people

781
00:43:07,680 --> 00:43:11,790
are not getting those services
because they are

782
00:43:11,790 --> 00:43:12,920
not generally available.

783
00:43:12,920 --> 00:43:16,410
People don't even know
that they exist.

784
00:43:16,410 --> 00:43:21,160
And we've seen in the movie that
to a certain extent, we

785
00:43:21,160 --> 00:43:24,950
have reasons to blame
the governments.

786
00:43:24,950 --> 00:43:28,160
Nurses are often absent, not
only in India, but everywhere.

787
00:43:28,160 --> 00:43:31,450
There was a World Bank survey
conducted in several

788
00:43:31,450 --> 00:43:34,520
countries, going to the little
hospitals where the nurses

789
00:43:34,520 --> 00:43:37,880
are, found 35% of them absent.

790
00:43:37,880 --> 00:43:40,620
And even when they're there,
they don't really spend a lot

791
00:43:40,620 --> 00:43:43,370
of time on people, they don't
treat them very well.

792
00:43:43,370 --> 00:43:50,340
There is this interesting 3-3-3
rule that Jishnu Das and

793
00:43:50,340 --> 00:43:52,990
Jeff Hammer found in India.

794
00:43:52,990 --> 00:43:57,000
That's three minutes, three
questions, three drugs.

795
00:43:57,000 --> 00:44:00,780
That's what you get when you go
to see a doctor in a public

796
00:44:00,780 --> 00:44:02,370
health facility.

797
00:44:02,370 --> 00:44:03,830
They interviewed
three minutes.

798
00:44:03,830 --> 00:44:05,380
They ask three questions.

799
00:44:05,380 --> 00:44:06,726
They usually don't touch you.

800
00:44:06,726 --> 00:44:09,540
They ask you what do you have?

801
00:44:09,540 --> 00:44:11,210
And then they give you
three medicines

802
00:44:11,210 --> 00:44:12,840
that you go away with.

803
00:44:12,840 --> 00:44:15,740
So that's not excellent care.

804
00:44:15,740 --> 00:44:21,950
And the government doctors
usually know more than the

805
00:44:21,950 --> 00:44:24,475
private doctors who are
not really qualified.

806
00:44:24,475 --> 00:44:26,160
They're all much
more qualified.

807
00:44:26,160 --> 00:44:31,040
If you ask them to rank
vignettes, so you show them a

808
00:44:31,040 --> 00:44:36,110
scenario of a child comes to the
clinic with diarrhea, and

809
00:44:36,110 --> 00:44:40,780
you ask the right question to
evaluate what is this kid

810
00:44:40,780 --> 00:44:44,440
suffering from, what should I
do, the public doctors do much

811
00:44:44,440 --> 00:44:47,970
better than the private doctors
on those tests.

812
00:44:47,970 --> 00:44:50,360
For example, when a pregnant
woman arrives with

813
00:44:50,360 --> 00:44:52,700
preeclampsia, which is a
potentially fatal condition

814
00:44:52,700 --> 00:44:56,890
that happens during pregnancy,
they are much more likely to

815
00:44:56,890 --> 00:44:59,335
diagnose at its start and to say
that the right course of

816
00:44:59,335 --> 00:45:00,960
action is to send them
to hospital.

817
00:45:00,960 --> 00:45:03,710
So in principle, they know more,
the public doctors, but

818
00:45:03,710 --> 00:45:06,300
in practice, they
do much less.

819
00:45:06,300 --> 00:45:08,210
So even though they know what
they are supposed to do, when

820
00:45:08,210 --> 00:45:11,460
you actually look at what they
do by putting someone to

821
00:45:11,460 --> 00:45:14,400
observe their behavior, you
realize that they don't really

822
00:45:14,400 --> 00:45:18,990
use their knowledge.

823
00:45:18,990 --> 00:45:20,990
So that's a part
of the problem.

824
00:45:20,990 --> 00:45:23,210
The nurses are not here, they
are desultory, they don't

825
00:45:23,210 --> 00:45:26,050
really care, but it's not
the entire problem.

826
00:45:26,050 --> 00:45:31,010
Because if you go back to the
immunization experiment that I

827
00:45:31,010 --> 00:45:34,520
just showed you where the first
treatment was to have

828
00:45:34,520 --> 00:45:37,050
perfect immunization camps--

829
00:45:37,050 --> 00:45:40,510
every month, in your own
village, with a paraworker

830
00:45:40,510 --> 00:45:43,570
that used to go to people and
try to remind them that the

831
00:45:43,570 --> 00:45:48,240
camp is coming, and you should
bring your kids, and the

832
00:45:48,240 --> 00:45:55,850
nurses who were vaccinating the
kids were good, caring, et

833
00:45:55,850 --> 00:45:56,600
cetera people--

834
00:45:56,600 --> 00:46:01,430
even when you do all that, only
18% of people or 12% of

835
00:46:01,430 --> 00:46:03,540
people got immunized.

836
00:46:03,540 --> 00:46:04,890
I think that's actually 18.

837
00:46:04,890 --> 00:46:06,500
It's 6 plus 12.

838
00:46:06,500 --> 00:46:10,140
18% of people got immunized.

839
00:46:10,140 --> 00:46:13,550
So the bad services by the
government doctor is not the

840
00:46:13,550 --> 00:46:15,590
only thing that there is to
blame, because even when you

841
00:46:15,590 --> 00:46:18,940
provide good services,
you still don't get

842
00:46:18,940 --> 00:46:21,480
a lot of them in.

843
00:46:21,480 --> 00:46:22,980
So supply is not the
only reason.

844
00:46:22,980 --> 00:46:27,380
Governments are not
only to blame.

845
00:46:27,380 --> 00:46:29,410
What are we left with?

846
00:46:29,410 --> 00:46:31,960
People don't demand preventive
care not because they don't

847
00:46:31,960 --> 00:46:34,460
care about health.

848
00:46:34,460 --> 00:46:37,380
Not because it's just not
available, because they don't

849
00:46:37,380 --> 00:46:40,750
demand it even when
it's available.

850
00:46:40,750 --> 00:46:44,540
So we are left with two
possible explanations.

851
00:46:44,540 --> 00:46:49,940
One is that they understand the
benefits, but the benefits

852
00:46:49,940 --> 00:46:52,880
are far away in the future,
and they discount those

853
00:46:52,880 --> 00:46:55,810
benefits that are far away
in the future a lot.

854
00:46:55,810 --> 00:46:59,870
And the second is that they
don't know the effectiveness

855
00:46:59,870 --> 00:47:03,730
of preventive care just because
it's very difficult to

856
00:47:03,730 --> 00:47:05,705
understand what works,
and why it works, and

857
00:47:05,705 --> 00:47:07,222
why it doesn't work.

858
00:47:07,222 --> 00:47:10,010
AUDIENCE: So in India we saw
that part of the reason is

859
00:47:10,010 --> 00:47:13,530
also because they believed
in religious cures more.

860
00:47:13,530 --> 00:47:15,855
Is that true in other countries
in the developing

861
00:47:15,855 --> 00:47:16,602
world as well?

862
00:47:16,602 --> 00:47:18,096
Is that predominantly
[INAUDIBLE]?

863
00:47:18,096 --> 00:47:20,110
PROFESSOR: No, I think it's true
in general that people

864
00:47:20,110 --> 00:47:22,140
construct all sorts of
interesting beliefs.

865
00:47:22,140 --> 00:47:25,580
What is important is that
you have those beliefs.

866
00:47:25,580 --> 00:47:27,760
On the other hand, if I give you
a kilo of lentils, those

867
00:47:27,760 --> 00:47:31,640
beliefs are not strong enough
for you not to sell them for a

868
00:47:31,640 --> 00:47:32,680
kilo of lentils.

869
00:47:32,680 --> 00:47:35,700
So I think what is really
important is that it's not

870
00:47:35,700 --> 00:47:39,035
that people think that
immunization is something bad,

871
00:47:39,035 --> 00:47:41,610
because otherwise it would be
very hard to convince them

872
00:47:41,610 --> 00:47:43,000
with small things.

873
00:47:43,000 --> 00:47:46,600
It has to be that they just
don't think much about it one

874
00:47:46,600 --> 00:47:49,720
way or the other.

875
00:47:49,720 --> 00:47:53,496
AUDIENCE: I was wondering
is there an example of a

876
00:47:53,496 --> 00:47:55,725
[INAUDIBLE]

877
00:47:55,725 --> 00:47:58,870
if people are embarrassed
[INAUDIBLE]

878
00:47:58,870 --> 00:48:01,750
assume something that
they [INAUDIBLE]

879
00:48:05,110 --> 00:48:06,070
PROFESSOR: Yes.

880
00:48:06,070 --> 00:48:08,090
That is a very interesting
question.

881
00:48:08,090 --> 00:48:16,910
For example, you could say if
you have a cultural reason not

882
00:48:16,910 --> 00:48:19,690
to do something, if you have
this social reason that you

883
00:48:19,690 --> 00:48:22,740
are embarrassed, if I give you
a small incentive, you can

884
00:48:22,740 --> 00:48:25,390
just say, oh, I've gone for
this small incentive.

885
00:48:25,390 --> 00:48:27,620
And in fact, there was a very
interesting study that was

886
00:48:27,620 --> 00:48:30,280
done which was trying to
shed some light on

887
00:48:30,280 --> 00:48:32,090
exactly this issue.

888
00:48:32,090 --> 00:48:36,520
It's a study where as part of a
survey, everyone got tested

889
00:48:36,520 --> 00:48:39,500
for HIV, everyone who agreed.

890
00:48:39,500 --> 00:48:41,710
But you didn't need to
get your results.

891
00:48:41,710 --> 00:48:44,730
If you wanted your result, you
had to go pick them up three

892
00:48:44,730 --> 00:48:48,880
weeks later at camp.

893
00:48:48,880 --> 00:48:54,600
And so, at the end, people
[INAUDIBLE] a bottle cap where

894
00:48:54,600 --> 00:48:59,610
they got a reward for picking
up their tests.

895
00:48:59,610 --> 00:49:03,060
It could go from zero to
something like $1 in small

896
00:49:03,060 --> 00:49:04,330
increments.

897
00:49:04,330 --> 00:49:07,680
And in addition, the researcher
also randomized

898
00:49:07,680 --> 00:49:12,230
where they put the tent where
you would get your results.

899
00:49:12,230 --> 00:49:14,740
In some cases, it was put right
in the middle of the

900
00:49:14,740 --> 00:49:20,610
village, so very convenient, but
very visible so everybody

901
00:49:20,610 --> 00:49:22,500
would know that you're
going there.

902
00:49:22,500 --> 00:49:25,340
In some places, it was put--
because it was random, so it

903
00:49:25,340 --> 00:49:28,000
was like throwing a dart on the
village and saying we are

904
00:49:28,000 --> 00:49:30,970
going to put it there-- it was
like in the middle of a field.

905
00:49:30,970 --> 00:49:34,510
So a little bit far away, not
very convenient, but much more

906
00:49:34,510 --> 00:49:37,870
discrete because no one would
see you go there.

907
00:49:37,870 --> 00:49:41,230
And what they found is that,
number one, people were

908
00:49:41,230 --> 00:49:46,980
extremely sensitive to the
reward, suggesting that it is

909
00:49:46,980 --> 00:49:50,420
not some deep psychological
fear that were making them

910
00:49:50,420 --> 00:49:53,770
worried to pick up their test,
because whatever this fear was

911
00:49:53,770 --> 00:49:56,310
could be overcome by $0.10.

912
00:49:56,310 --> 00:49:58,750
Now it could still be
the social thing.

913
00:49:58,750 --> 00:50:03,280
So here, what do you think
they found with the tent?

914
00:50:03,280 --> 00:50:06,530
Do you think more people got
their result when it was far

915
00:50:06,530 --> 00:50:09,520
away or when it was close by?

916
00:50:09,520 --> 00:50:12,610
First, what would we expect
under both, and what would we

917
00:50:12,610 --> 00:50:16,722
expect under the social
stigma story?

918
00:50:16,722 --> 00:50:19,082
AUDIENCE: The far away tent--

919
00:50:19,082 --> 00:50:20,380
PROFESSOR: That the far
away tent would

920
00:50:20,380 --> 00:50:21,442
have been more popular.

921
00:50:21,442 --> 00:50:24,350
But in fact what they found is
the exact opposite, that the

922
00:50:24,350 --> 00:50:25,990
far away tent was not
popular at all.

923
00:50:25,990 --> 00:50:28,270
Nobody wanted to walk more
than a kilometer

924
00:50:28,270 --> 00:50:29,390
to get their test.

925
00:50:29,390 --> 00:50:33,080
And the close by tent
was very popular.

926
00:50:33,080 --> 00:50:43,050
And furthermore, they also found
that the elasticity with

927
00:50:43,050 --> 00:50:47,200
respect to this tent was much
smaller when there was a gift.

928
00:50:47,200 --> 00:50:49,550
So when there was a gift, people
did walk a kilometer to

929
00:50:49,550 --> 00:50:56,260
get their results, but when
there was no gift, people were

930
00:50:56,260 --> 00:50:58,550
very sensitive to
the distance.

931
00:50:58,550 --> 00:51:05,260
So those suggest that in this
case, in Malawi, [INAUDIBLE],

932
00:51:05,260 --> 00:51:09,130
neither the social background,
not the psychological barrier,

933
00:51:09,130 --> 00:51:13,050
were so big, and that if people
didn't get their HIV

934
00:51:13,050 --> 00:51:17,000
result, it was more due to some
form of procrastination

935
00:51:17,000 --> 00:51:19,490
or inertia or something
like that.

936
00:51:19,490 --> 00:51:23,420
And that is something a little
bit surprising because

937
00:51:23,420 --> 00:51:26,610
anti-retrovirals are available
in Malawi, so in principal if

938
00:51:26,610 --> 00:51:29,440
you find that you're positive,
you can do something.

939
00:51:29,440 --> 00:51:34,600
And if you find out that you're
negative, then you can

940
00:51:34,600 --> 00:51:38,542
try and take some steps
to remain that way.

941
00:51:38,542 --> 00:51:43,100
AUDIENCE: Is it possible they
just value [INAUDIBLE]

942
00:51:43,100 --> 00:51:45,980
PROFESSOR: So in this case, it
shows that-- so in the case of

943
00:51:45,980 --> 00:51:48,380
the HIV, [INAUDIBLE]
all this money.

944
00:51:48,380 --> 00:51:51,280
Exactly what this means is that
it has to be that they

945
00:51:51,280 --> 00:51:56,100
value the small gains more than
the results of the exam.

946
00:51:56,100 --> 00:51:58,120
And this is what is surprising,
which is you have

947
00:51:58,120 --> 00:52:00,780
to say why is that the case when
it is something that it

948
00:52:00,780 --> 00:52:03,230
should be so helpful to
you to know whether

949
00:52:03,230 --> 00:52:04,620
or not you're positive?

950
00:52:04,620 --> 00:52:07,650
Or with the lentils, it should
be so helpful to you that your

951
00:52:07,650 --> 00:52:08,782
kid is immunized.

952
00:52:08,782 --> 00:52:11,334
AUDIENCE: Can I ask a
followup question?

953
00:52:11,334 --> 00:52:17,298
Does that mean that we should
reevaluate how we [INAUDIBLE]

954
00:52:17,298 --> 00:52:23,759
so you're saying HIV tests
could potentially be more

955
00:52:23,759 --> 00:52:25,747
helpful because they could
be cured [INAUDIBLE].

956
00:52:29,226 --> 00:52:32,208
Shouldn't we be assigning more
weight to [INAUDIBLE]

957
00:52:36,220 --> 00:52:40,190
PROFESSOR: What we have seen
is it doesn't mean that we

958
00:52:40,190 --> 00:52:43,790
should put more weight on
instant gratification things

959
00:52:43,790 --> 00:52:45,050
than we do.

960
00:52:45,050 --> 00:52:47,630
What this means, in my view, is
that we should understand

961
00:52:47,630 --> 00:52:51,630
why there is this disconnect
between what doctors know, for

962
00:52:51,630 --> 00:52:54,110
example, and what people feel.

963
00:52:54,110 --> 00:52:55,930
Because that's kind of the
source of the mystery, that

964
00:52:55,930 --> 00:52:58,950
there is this big tension, and
it has to be about the

965
00:52:58,950 --> 00:53:00,990
perception of the benefits.

966
00:53:00,990 --> 00:53:05,340
And one aspect of this is this
future versus present, and one

967
00:53:05,340 --> 00:53:07,300
aspect of this is whether
you understand what

968
00:53:07,300 --> 00:53:10,295
the benefits are.

969
00:53:10,295 --> 00:53:12,259
AUDIENCE: [INAUDIBLE]

970
00:53:12,259 --> 00:53:15,205
HIV testing, I think the
results make sense just

971
00:53:15,205 --> 00:53:17,169
because if your test
comes out HIV

972
00:53:17,169 --> 00:53:18,642
negative, well then, great.

973
00:53:18,642 --> 00:53:20,115
You're just going to go
on with your life.

974
00:53:20,115 --> 00:53:22,733
And if it comes out positive, is
there really that much you

975
00:53:22,733 --> 00:53:25,025
can do about it if you're
[INAUDIBLE]--

976
00:53:25,025 --> 00:53:26,365
I thought those [INAUDIBLE]
were very expensive.

977
00:53:26,365 --> 00:53:31,050
PROFESSOR: So, in Malawi and
in several other African

978
00:53:31,050 --> 00:53:35,510
countries, it's actually
available for free.

979
00:53:35,510 --> 00:53:37,050
So you can do something.

980
00:53:40,110 --> 00:53:43,340
It's not going to cure you,
but it's going to greatly

981
00:53:43,340 --> 00:53:45,500
improve your life for
the time to come.

982
00:53:45,500 --> 00:53:47,040
People might not fully
realize that.

983
00:53:47,040 --> 00:53:49,491
AUDIENCE: I have
two questions.

984
00:53:49,491 --> 00:53:51,946
[INAUDIBLE] certainty
of [INAUDIBLE]

985
00:53:51,946 --> 00:53:54,401
if you're getting [INAUDIBLE]

986
00:53:54,401 --> 00:53:57,347
a certain cost [INAUDIBLE]

987
00:53:57,347 --> 00:53:58,329
really uncertain.

988
00:53:58,329 --> 00:54:02,257
And the second question was
about with the lentil

989
00:54:02,257 --> 00:54:05,694
distribution, can you play
with short term kind of

990
00:54:05,694 --> 00:54:06,676
[INAUDIBLE] cost?

991
00:54:06,676 --> 00:54:10,113
So people can procrastinate
indefinitely to always get a

992
00:54:10,113 --> 00:54:13,385
pack of lentils when you get a
vaccine, but if you only get

993
00:54:13,385 --> 00:54:17,195
lentils on the first day of the
month, then wouldn't that

994
00:54:17,195 --> 00:54:20,202
really lower the cost today
relative to tomorrow?

995
00:54:20,202 --> 00:54:22,330
PROFESSOR: That's a
very good point.

996
00:54:22,330 --> 00:54:26,750
Why don't I table the questions
until we give a bit

997
00:54:26,750 --> 00:54:28,000
more context, and
we'll go back to

998
00:54:28,000 --> 00:54:29,250
exactly these questions.

999
00:54:31,920 --> 00:54:33,360
They really have two problems.

1000
00:54:33,360 --> 00:54:38,470
One is that you might not know
that it's worthwhile getting

1001
00:54:38,470 --> 00:54:41,730
your HIV test because you might
not know that you are

1002
00:54:41,730 --> 00:54:43,800
entitled to the drugs or that
the drugs will really help

1003
00:54:43,800 --> 00:54:48,720
you, or you might not know that
getting immunized is so

1004
00:54:48,720 --> 00:54:50,310
beneficial.

1005
00:54:50,310 --> 00:54:54,930
And the other is the benefits
are in the future, the cost is

1006
00:54:54,930 --> 00:54:56,480
now, and that leads people
to procrastinate.

1007
00:54:58,980 --> 00:55:02,010
Let's go over both things.

1008
00:55:02,010 --> 00:55:05,010
The first thing is learning
about health care, and we had

1009
00:55:05,010 --> 00:55:08,080
a little bit of that discussion
already last time.

1010
00:55:08,080 --> 00:55:13,540
But most diseases are
self-limiting, that is, they

1011
00:55:13,540 --> 00:55:15,395
just go away by themselves.

1012
00:55:20,790 --> 00:55:24,340
You don't know that because
how would you know?

1013
00:55:24,340 --> 00:55:27,850
You start with a theory that
someone has told you that as

1014
00:55:27,850 --> 00:55:30,590
soon as you're sick, you should
get a shot to put the

1015
00:55:30,590 --> 00:55:32,180
medicine right into
your blood.

1016
00:55:32,180 --> 00:55:35,440
And if you don't do that,
you won't get better.

1017
00:55:35,440 --> 00:55:40,000
And then if the market is
unregulated, like it is in a

1018
00:55:40,000 --> 00:55:42,160
lot of developing countries
where anybody can establish

1019
00:55:42,160 --> 00:55:43,590
themself as a doctor--
for example,

1020
00:55:43,590 --> 00:55:45,130
we saw that in India--

1021
00:55:45,130 --> 00:55:49,120
then you have a very strong
demand for shots and someone

1022
00:55:49,120 --> 00:55:50,720
who's willing to supply it.

1023
00:55:50,720 --> 00:55:52,150
You're never going
to experiment

1024
00:55:52,150 --> 00:55:53,990
away from the shorts.

1025
00:55:53,990 --> 00:55:57,430
So every time you're sick,
you're going to get a shot,

1026
00:55:57,430 --> 00:55:59,490
and you're going
to get better.

1027
00:55:59,490 --> 00:56:01,900
And so you're going to think
that your theory was, once

1028
00:56:01,900 --> 00:56:05,260
again, vindicated because you
were sick, you got a shot, now

1029
00:56:05,260 --> 00:56:06,420
you get better.

1030
00:56:06,420 --> 00:56:08,770
That is going to further
reinforce your belief that

1031
00:56:08,770 --> 00:56:11,000
this was a good theory and
further make you very

1032
00:56:11,000 --> 00:56:14,460
suspicious of not
getting a shot.

1033
00:56:14,460 --> 00:56:18,110
Now of course, if you
experimented once of not

1034
00:56:18,110 --> 00:56:20,140
getting a shot, you would
see, oh, I have

1035
00:56:20,140 --> 00:56:21,520
gotten better as well.

1036
00:56:21,520 --> 00:56:25,190
Progressively, your [INAUDIBLE]
would move towards

1037
00:56:25,190 --> 00:56:28,730
something closer to the truth,
which is, let's say, 95% of

1038
00:56:28,730 --> 00:56:31,650
the time you get better with a
shot, and 90% of the time you

1039
00:56:31,650 --> 00:56:33,910
get better without the shot.

1040
00:56:33,910 --> 00:56:36,760
But if your beliefs were 95%
of the time you get better

1041
00:56:36,760 --> 00:56:39,950
with a shot and 10% you get
without a shot, then mostly

1042
00:56:39,950 --> 00:56:41,240
you're never going
to experiment.

1043
00:56:41,240 --> 00:56:43,470
You're never going to know,
and you're continuing with

1044
00:56:43,470 --> 00:56:45,000
this very strong belief.

1045
00:56:47,630 --> 00:56:49,690
So this is for self-limiting
diseases.

1046
00:56:49,690 --> 00:56:54,170
And now if you take a disease
like diabetes, or chest pains,

1047
00:56:54,170 --> 00:56:57,290
or something like that that
doesn't go away with the shot,

1048
00:56:57,290 --> 00:57:00,700
when you go to a doctor and get
a shot, it doesn't go, so

1049
00:57:00,700 --> 00:57:02,060
progressively your [INAUDIBLE]

1050
00:57:02,060 --> 00:57:05,930
are going to be that everything
is useless, at

1051
00:57:05,930 --> 00:57:07,926
which point you might as
well go to [INAUDIBLE].

1052
00:57:11,770 --> 00:57:16,020
It might be why we see this
paradoxical result of people

1053
00:57:16,020 --> 00:57:18,910
spending a lot of money on
diseases that would cure

1054
00:57:18,910 --> 00:57:22,770
themselves anyway and not doing
really any attempt to

1055
00:57:22,770 --> 00:57:24,880
treat seriously the
diseases that they

1056
00:57:24,880 --> 00:57:26,600
should treat seriously.

1057
00:57:26,600 --> 00:57:29,330
Because the disease that they
should treat seriously are

1058
00:57:29,330 --> 00:57:32,660
beyond the ability of the
Bengali doctor to handle,

1059
00:57:32,660 --> 00:57:35,050
because first order,
the Bengali doctor

1060
00:57:35,050 --> 00:57:37,380
can't handle nothing.

1061
00:57:37,380 --> 00:57:39,940
So the Bengali doctor end
up spending [INAUDIBLE]

1062
00:57:39,940 --> 00:57:42,960
distributed this antibiotics
for diseases that fixes

1063
00:57:42,960 --> 00:57:44,560
themselves anyway.

1064
00:57:44,560 --> 00:57:46,700
People don't get any form of
treatment for things that they

1065
00:57:46,700 --> 00:57:48,910
should really try and treat,
which would be much more

1066
00:57:48,910 --> 00:57:52,310
complicated to treat, and
you get this no learning

1067
00:57:52,310 --> 00:57:56,990
equilibrium that is very
difficult to get away from.

1068
00:57:56,990 --> 00:58:00,660
And it will be harder to
attribute it to nothing, so

1069
00:58:00,660 --> 00:58:04,380
the tendency to over medicate
is a very [INAUDIBLE]

1070
00:58:04,380 --> 00:58:04,950
tendency.

1071
00:58:04,950 --> 00:58:09,350
And in fact, this tendency to
over medicate is not something

1072
00:58:09,350 --> 00:58:11,560
that you only have in
poor countries.

1073
00:58:11,560 --> 00:58:13,410
In rich countries, when
you go to the doctor

1074
00:58:13,410 --> 00:58:14,930
and they tell you--

1075
00:58:14,930 --> 00:58:17,770
like when I arrived in the US,
my first doctor visit was you

1076
00:58:17,770 --> 00:58:20,175
should breathe under the shower,
and I was like, what

1077
00:58:20,175 --> 00:58:21,130
are you talking about?

1078
00:58:21,130 --> 00:58:24,010
Like in my country, I would
have gotten an antibiotic

1079
00:58:24,010 --> 00:58:26,150
immediately.

1080
00:58:26,150 --> 00:58:29,640
So our own tendency is always
you must do something about

1081
00:58:29,640 --> 00:58:32,090
me, like you should be
able to do something,

1082
00:58:32,090 --> 00:58:34,150
not just let it go.

1083
00:58:34,150 --> 00:58:36,510
And the only reason why doctors
don't do it here is

1084
00:58:36,510 --> 00:58:39,020
because they are under
guidelines, and they have

1085
00:58:39,020 --> 00:58:40,830
regulations from their
hospital, from their

1086
00:58:40,830 --> 00:58:42,810
association, from what
they have learned to

1087
00:58:42,810 --> 00:58:44,770
not do these things.

1088
00:58:44,770 --> 00:58:49,603
Now preventive care is even
worse because with preventive

1089
00:58:49,603 --> 00:58:53,780
care you are taking an action
today to prevent something to

1090
00:58:53,780 --> 00:58:56,725
happen in the future, but
far away in the future.

1091
00:58:59,450 --> 00:59:05,500
For example, you breast feed
your child so that the child

1092
00:59:05,500 --> 00:59:07,580
gets stronger in the future and
doesn't get sick in the

1093
00:59:07,580 --> 00:59:11,280
future, or I guess with
breastfeeding it's also avoid

1094
00:59:11,280 --> 00:59:13,650
diarrhea in the meantime.

1095
00:59:13,650 --> 00:59:17,680
But for immunization, you get
immunized, and then you don't

1096
00:59:17,680 --> 00:59:19,540
get measles at some point.

1097
00:59:19,540 --> 00:59:22,630
So linking the two is
very difficult.

1098
00:59:22,630 --> 00:59:26,970
And I think Noah had made this
point with respect to

1099
00:59:26,970 --> 00:59:31,390
deworming, it's even harder when
you're immunized against

1100
00:59:31,390 --> 00:59:38,310
communicable diseases, because
even if you do get immunized

1101
00:59:38,310 --> 00:59:40,770
and people around you don't get
immunized, if there are

1102
00:59:40,770 --> 00:59:42,270
enough people who
get immunized,

1103
00:59:42,270 --> 00:59:44,920
then no one gets measles.

1104
00:59:44,920 --> 00:59:47,650
So the fact that you got
immunized protects other

1105
00:59:47,650 --> 00:59:50,600
people around you as well, so
what you're seeing is that,

1106
00:59:50,600 --> 00:59:55,880
oh, this whole immunization
thing, now I got it, and now,

1107
00:59:55,880 --> 00:59:58,420
yes, it's true my kid doesn't
have measles, but no one has

1108
00:59:58,420 --> 01:00:02,910
measles, so clearly it is not
because I got immunized that I

1109
01:00:02,910 --> 01:00:03,900
didn't get it.

1110
01:00:03,900 --> 01:00:06,400
Now of course, it is because
collectively there was

1111
01:00:06,400 --> 01:00:11,190
immunization around, but it's
very difficult to infer.

1112
01:00:11,190 --> 01:00:15,820
If you remember the results of
the deworming study, when

1113
01:00:15,820 --> 01:00:18,950
people had more friends around
them who got the deworming,

1114
01:00:18,950 --> 01:00:21,500
they were actually less likely
to get dewormed.

1115
01:00:21,500 --> 01:00:25,700
And one possible explanation
is that initially they were

1116
01:00:25,700 --> 01:00:28,740
convinced by the people that
deworming would make their

1117
01:00:28,740 --> 01:00:30,780
kids less sick, but then they
saw all of these kids around

1118
01:00:30,780 --> 01:00:35,137
them who don't get dewormed and
don't get sick either, and

1119
01:00:35,137 --> 01:00:36,230
so they're saying what
is the point?

1120
01:00:36,230 --> 01:00:37,480
I don't need to do it.

1121
01:00:40,690 --> 01:00:43,130
So this is basically
not something that

1122
01:00:43,130 --> 01:00:46,780
we can learn ourselves.

1123
01:00:46,780 --> 01:00:51,740
In a lot of our own lives, for
example if you try to go

1124
01:00:51,740 --> 01:00:57,240
afield, and you do something,
like, for example, plant in

1125
01:00:57,240 --> 01:01:00,740
rows instead of scatter plant,
you see immediately that your

1126
01:01:00,740 --> 01:01:03,000
plant is doing better.

1127
01:01:03,000 --> 01:01:05,950
So you can progressively
experiment with better

1128
01:01:05,950 --> 01:01:09,630
techniques, and you will
get better at it.

1129
01:01:09,630 --> 01:01:13,835
So in most of our lives, we
experiment things, and we kind

1130
01:01:13,835 --> 01:01:16,180
of see the results, or at least
we have a sense of what

1131
01:01:16,180 --> 01:01:19,850
the results are, and we can
adjust our behavior.

1132
01:01:19,850 --> 01:01:22,950
With health, our own
experience, or own

1133
01:01:22,950 --> 01:01:26,320
observations, is very misleading
most of the time.

1134
01:01:26,320 --> 01:01:29,160
We think we can infer from our
actions something that we

1135
01:01:29,160 --> 01:01:30,730
cannot really infer.

1136
01:01:30,730 --> 01:01:33,170
It's not because of the medicine
that we got better.

1137
01:01:33,170 --> 01:01:38,410
We think we can infer from
not seeing a result from

1138
01:01:38,410 --> 01:01:41,430
immunization that immunization
was not working, et cetera.

1139
01:01:41,430 --> 01:01:44,060
So it is just not possible to
learn, because the object is

1140
01:01:44,060 --> 01:01:45,810
too complicated.

1141
01:01:45,810 --> 01:01:47,840
So how do we learn
about health?

1142
01:01:47,840 --> 01:01:49,280
The answer is we don't.

1143
01:01:49,280 --> 01:01:52,240
Well, you guys might because
you've first taken biology, so

1144
01:01:52,240 --> 01:01:54,460
you have some sense of it.

1145
01:01:54,460 --> 01:01:59,670
But most of us just don't have
anything to do, no real

1146
01:01:59,670 --> 01:02:05,190
understanding of why medicines
are working unless we have red

1147
01:02:05,190 --> 01:02:06,640
stuff on the side.

1148
01:02:06,640 --> 01:02:08,730
But still, when our doctor
says you don't need an

1149
01:02:08,730 --> 01:02:10,970
antibiotic to cure that,
we trust them.

1150
01:02:10,970 --> 01:02:13,180
We trust them because they have
spent a lot of money and

1151
01:02:13,180 --> 01:02:16,850
a lot of time getting a health
care education, and we believe

1152
01:02:16,850 --> 01:02:19,090
that there is something
into it.

1153
01:02:19,090 --> 01:02:21,730
And that trust, same thing
with immunization.

1154
01:02:21,730 --> 01:02:25,620
We get immunized because we are
told to get immunized and

1155
01:02:25,620 --> 01:02:27,140
not because we understand
how it works.

1156
01:02:27,140 --> 01:02:28,030
Not at all.

1157
01:02:28,030 --> 01:02:31,390
So it's got nothing to do with
our education or our superior

1158
01:02:31,390 --> 01:02:32,590
intelligence.

1159
01:02:32,590 --> 01:02:36,410
And in face, this trust
is quite fragile.

1160
01:02:36,410 --> 01:02:41,560
You see it eroding reasonably
easily when something happens.

1161
01:02:41,560 --> 01:02:49,520
so for example, there has been a
few well-publicized articles

1162
01:02:49,520 --> 01:02:54,530
linking the measles vaccine,
which is MMR--

1163
01:02:54,530 --> 01:02:57,610
Measles something Rubella,
MMR vaccine--

1164
01:02:57,610 --> 01:02:58,830
and autism.

1165
01:02:58,830 --> 01:03:04,620
There's been some court cases,
et cetera, which have been

1166
01:03:04,620 --> 01:03:11,070
actually the people who were
suing against the vaccine have

1167
01:03:11,070 --> 01:03:11,740
generally lost.

1168
01:03:11,740 --> 01:03:15,270
But despite the fact, there
is pretty much ingrained

1169
01:03:15,270 --> 01:03:18,500
somewhere in the collective
mentality the idea that, in

1170
01:03:18,500 --> 01:03:21,830
fact, MMR vaccines might
cause autism.

1171
01:03:21,830 --> 01:03:25,900
And as result, there is
kind of an epidemic of

1172
01:03:25,900 --> 01:03:31,130
non-vaccination for measles
which has led in some places

1173
01:03:31,130 --> 01:03:34,780
to measles outbreaks that you
didn't used to have before.

1174
01:03:34,780 --> 01:03:38,050
So these things are actually
reasonably fragile.

1175
01:03:38,050 --> 01:03:40,200
If you're interested in that,
there is a book by a New

1176
01:03:40,200 --> 01:03:43,830
Yorker journalist called
Michael Specter called

1177
01:03:43,830 --> 01:03:47,780
Denialism which has a very
interesting chapter on this

1178
01:03:47,780 --> 01:03:49,930
vaccination in the US.

1179
01:03:49,930 --> 01:03:55,130
And this vaccination in the US
story reminded me of polio

1180
01:03:55,130 --> 01:03:56,460
vaccine in India.

1181
01:03:56,460 --> 01:03:58,920
Polio vaccine is one thing
that for some reason the

1182
01:03:58,920 --> 01:04:01,000
government of India has decided
that they are going to

1183
01:04:01,000 --> 01:04:05,400
really do, so most kids do
get the polio vaccine.

1184
01:04:05,400 --> 01:04:09,730
But there are pockets where it's
not being done, and they

1185
01:04:09,730 --> 01:04:13,960
tend to be mostly in villages
which are refusing the polio

1186
01:04:13,960 --> 01:04:18,610
drugs, and the reason is that
they say it's an attempt to

1187
01:04:18,610 --> 01:04:20,300
sterilize us.

1188
01:04:20,300 --> 01:04:23,480
And why would they have this
idea that might sound a little

1189
01:04:23,480 --> 01:04:25,750
bit bizarre?

1190
01:04:25,750 --> 01:04:30,130
It is linked to the fact that
long time ago, during the

1191
01:04:30,130 --> 01:04:33,390
emergency period which is a when
Indira Ghandi suspended

1192
01:04:33,390 --> 01:04:37,770
the civil liberty, there was
a big drive to encourage

1193
01:04:37,770 --> 01:04:41,370
sterilization of people who
had at least two children.

1194
01:04:41,370 --> 01:04:45,270
And this big drive took a shape
that with sometimes

1195
01:04:45,270 --> 01:04:49,640
quite unacceptable, including
rounding up people who had no

1196
01:04:49,640 --> 01:04:52,310
desire being sterilized,
including lying to people

1197
01:04:52,310 --> 01:04:55,320
about what was being done
to them, et cetera.

1198
01:04:55,320 --> 01:04:58,530
And this has created this huge
mistrust, in particular in the

1199
01:04:58,530 --> 01:05:02,960
Muslim population, about what
government's trying to do to

1200
01:05:02,960 --> 01:05:06,680
them under the guise of doing
something good for them.

1201
01:05:06,680 --> 01:05:10,410
And so there are regions where
people will simply not accept

1202
01:05:10,410 --> 01:05:12,500
to be immunized.

1203
01:05:12,500 --> 01:05:13,370
This is an extreme.

1204
01:05:13,370 --> 01:05:15,760
Those are two extreme cases
where people have a strong

1205
01:05:15,760 --> 01:05:16,900
belief against.

1206
01:05:16,900 --> 01:05:20,110
If you do that here in this
region, giving people lentils

1207
01:05:20,110 --> 01:05:21,480
to immunize them
will not work.

1208
01:05:21,480 --> 01:05:23,930
In fact, it might be
counterproductive because they

1209
01:05:23,930 --> 01:05:26,790
might think that you're trying
to fool them like they did

1210
01:05:26,790 --> 01:05:28,930
with the sterilization
already.

1211
01:05:28,930 --> 01:05:32,730
But more generally, the fact
that people are generally

1212
01:05:32,730 --> 01:05:36,800
indifferent about preventive
care might be related to the

1213
01:05:36,800 --> 01:05:38,470
fact that they don't think
there is some grand

1214
01:05:38,470 --> 01:05:39,120
conspiration.

1215
01:05:39,120 --> 01:05:40,960
They just think you're
bullshitting them like you

1216
01:05:40,960 --> 01:05:42,210
always are.

1217
01:05:44,610 --> 01:05:46,850
And once this trust is eroded,
then it's very

1218
01:05:46,850 --> 01:05:48,100
difficult to go back.

1219
01:05:50,310 --> 01:05:53,820
AUDIENCE: I think in America,
don't most people figure out

1220
01:05:53,820 --> 01:05:55,700
all these things about
vaccination and

1221
01:05:55,700 --> 01:05:56,925
preventive care work?

1222
01:05:56,925 --> 01:06:01,020
There's a reason because they
have a family care doctor who

1223
01:06:01,020 --> 01:06:01,440
tells them?

1224
01:06:01,440 --> 01:06:03,818
There's some figure of authority
that you can trust

1225
01:06:03,818 --> 01:06:06,248
that tells them that
these things work.

1226
01:06:06,248 --> 01:06:09,650
I'm guessing most Americans
don't know how vaccines work.

1227
01:06:09,650 --> 01:06:11,108
PROFESSOR: That's
exactly right.

1228
01:06:11,108 --> 01:06:14,510
AUDIENCE: So if you have the
same thing in India or in

1229
01:06:14,510 --> 01:06:19,895
Africa, figures of authority
that have been mandated by

1230
01:06:19,895 --> 01:06:23,099
some government agency or some
school that tells them that

1231
01:06:23,099 --> 01:06:25,811
they know about this stuff,
telling them that, wouldn't

1232
01:06:25,811 --> 01:06:27,290
that [INAUDIBLE]

1233
01:06:27,290 --> 01:06:28,769
PROFESSOR: That's
exactly right.

1234
01:06:28,769 --> 01:06:31,330
The question is, who is this
figure of authority?

1235
01:06:31,330 --> 01:06:37,100
In the US, except for examples
like this autism in the US

1236
01:06:37,100 --> 01:06:40,640
where there was some idea of
conspiracy theory, where big

1237
01:06:40,640 --> 01:06:43,920
pharma is trying to make our
children autistic and things

1238
01:06:43,920 --> 01:06:50,050
like that, except in those
cases, we have a

1239
01:06:50,050 --> 01:06:51,490
basic sense of trust.

1240
01:06:51,490 --> 01:06:54,320
That if your doctor tells you
to do something, you think

1241
01:06:54,320 --> 01:06:55,600
they must know what it
is they're doing.

1242
01:06:55,600 --> 01:06:58,060
You don't understand what
is going on, but

1243
01:06:58,060 --> 01:07:00,200
you still trust them.

1244
01:07:00,200 --> 01:07:03,520
But the problem with a place
like India, for example, is

1245
01:07:03,520 --> 01:07:08,450
whether this authority figure
exists or whether they are

1246
01:07:08,450 --> 01:07:10,310
interested in preventive care.

1247
01:07:10,310 --> 01:07:13,435
So in India, this sterilization
campaign that I

1248
01:07:13,435 --> 01:07:18,520
was talking about had very, very
long-lasting damage of

1249
01:07:18,520 --> 01:07:22,160
convincing people that the
government was quite liable to

1250
01:07:22,160 --> 01:07:25,660
lie to them on any matter
involving health.

1251
01:07:25,660 --> 01:07:28,800
So people are quite suspicious,
and if enough tell

1252
01:07:28,800 --> 01:07:31,500
them you should get polio drugs,
they're thinking he's

1253
01:07:31,500 --> 01:07:33,120
trying to do something
else to me.

1254
01:07:33,120 --> 01:07:35,060
She's trying to get
me sterilized.

1255
01:07:35,060 --> 01:07:39,480
So once the trust is eroded,
someone who is mandated by the

1256
01:07:39,480 --> 01:07:46,350
government is someone who a
priori you should not believe,

1257
01:07:46,350 --> 01:07:50,080
so that the government becomes
a negative, not a positive.

1258
01:07:50,080 --> 01:07:53,380
AUDIENCE: So [INAUDIBLE] like
a Muslim doctor [INAUDIBLE]

1259
01:07:53,380 --> 01:07:57,077
saying [INAUDIBLE] the fact that
we've been mandated by

1260
01:07:57,077 --> 01:07:58,705
the government or [INAUDIBLE].

1261
01:07:58,705 --> 01:07:59,800
PROFESSOR: Exactly.

1262
01:07:59,800 --> 01:08:00,686
[INAUDIBLE]

1263
01:08:00,686 --> 01:08:02,768
so you would need also
authority figure once

1264
01:08:02,768 --> 01:08:03,750
you've eroded it.

1265
01:08:03,750 --> 01:08:06,400
And once you've eroded the
trust, it takes a lot of time

1266
01:08:06,400 --> 01:08:09,060
to rebuild it again,
but you can have

1267
01:08:09,060 --> 01:08:10,090
other authority figures.

1268
01:08:10,090 --> 01:08:14,270
So in Phuket, for example, in
the movie, you remember, there

1269
01:08:14,270 --> 01:08:18,760
is a doctor from an NGO who is
talking about the Bengali

1270
01:08:18,760 --> 01:08:21,609
doctors sometimes being good,
sometimes being bad, and about

1271
01:08:21,609 --> 01:08:23,710
the kids with the long hair.

1272
01:08:23,710 --> 01:08:28,850
So this doctor used to work in
a small hospital run by a

1273
01:08:28,850 --> 01:08:33,439
couple doctors.

1274
01:08:33,439 --> 01:08:37,250
In this area, everybody was
immunized, everybody was

1275
01:08:37,250 --> 01:08:39,080
always going to them, everybody
was getting

1276
01:08:39,080 --> 01:08:42,350
preventive care because that
have established that trust.

1277
01:08:42,350 --> 01:08:47,029
But the point of it is how do
you establish the trust?

1278
01:08:47,029 --> 01:08:52,960
They were on a very small area,
the government has a lot

1279
01:08:52,960 --> 01:08:55,800
of power to reach a lot of
people, but once they have

1280
01:08:55,800 --> 01:09:00,750
misused it once, or twice, or
three times, the temptation to

1281
01:09:00,750 --> 01:09:03,960
use the trust that you have from
the people to get them to

1282
01:09:03,960 --> 01:09:06,590
do things that are not
necessarily in their interest

1283
01:09:06,590 --> 01:09:07,939
is very strong.

1284
01:09:07,939 --> 01:09:09,700
And once you have done
that, then it's

1285
01:09:09,700 --> 01:09:11,461
difficult to get back.

1286
01:09:11,461 --> 01:09:14,239
AUDIENCE: [INAUDIBLE]

1287
01:09:14,239 --> 01:09:18,521
For example, in the movie that
we watched, there were a lot

1288
01:09:18,521 --> 01:09:22,473
of people who would choose
to go to the [INAUDIBLE]

1289
01:09:22,473 --> 01:09:25,439
instead of going to
the actual doctor.

1290
01:09:25,439 --> 01:09:29,420
Is there any initiative where
people actually use those

1291
01:09:29,420 --> 01:09:39,842
entities to actually provide the
population with medicine?

1292
01:09:39,842 --> 01:09:45,166
I know in Brazil, there's parts
of it where for a lot of

1293
01:09:45,166 --> 01:09:52,790
people who were dying with
diarrhea, people use like

1294
01:09:52,790 --> 01:09:59,490
these women that were supposed
to be known to help them

1295
01:09:59,490 --> 01:10:03,541
somehow, and they were more not
really like the typical

1296
01:10:03,541 --> 01:10:06,240
doctor, but they were more
like the cultural thing.

1297
01:10:06,240 --> 01:10:07,845
And [INAUDIBLE]

1298
01:10:07,845 --> 01:10:15,220
those persons to deliver
the [INAUDIBLE]

1299
01:10:15,220 --> 01:10:20,200
or dehydration solution to
actually help those people who

1300
01:10:20,200 --> 01:10:21,196
were dying.

1301
01:10:21,196 --> 01:10:22,200
PROFESSOR: Yeah.

1302
01:10:22,200 --> 01:10:23,510
I think that's a great idea.

1303
01:10:23,510 --> 01:10:24,650
I think it's been tried.

1304
01:10:24,650 --> 01:10:27,240
The problem is that then you
need to be able to control

1305
01:10:27,240 --> 01:10:29,870
these people, because they
already have some authority,

1306
01:10:29,870 --> 01:10:33,750
and then you give them some more
authority, and then you

1307
01:10:33,750 --> 01:10:37,060
really are wary of what
they start to say.

1308
01:10:37,060 --> 01:10:39,690
So you don't want to transform
them into a collection of

1309
01:10:39,690 --> 01:10:42,160
Bengali doctors that are going
to give them-- if the

1310
01:10:42,160 --> 01:10:43,640
[INAUDIBLE] start giving
antibiotics,

1311
01:10:43,640 --> 01:10:44,890
then you're in trouble.

1312
01:10:47,310 --> 01:10:52,640
But your idea is exactly right,
is that basically once

1313
01:10:52,640 --> 01:10:55,400
you've shut down the traditional
channels, which

1314
01:10:55,400 --> 01:10:58,320
would be your family doctor
that you trust, how do you

1315
01:10:58,320 --> 01:11:01,280
reconstruct some measure of
communication that comes from

1316
01:11:01,280 --> 01:11:02,700
other channels?

1317
01:11:02,700 --> 01:11:04,110
And these could be
the [INAUDIBLE].

1318
01:11:04,110 --> 01:11:07,310
These could be television that
a lot of people watch.

1319
01:11:12,230 --> 01:11:15,030
In Brazil, actually, it's
not about health,

1320
01:11:15,030 --> 01:11:17,422
but it's about fertility.

1321
01:11:17,422 --> 01:11:20,560
People were all watching the
soap opera, and in the soap

1322
01:11:20,560 --> 01:11:23,720
opera, the cool people have
very few children.

1323
01:11:23,720 --> 01:11:27,680
And there was a study that
was done looking at the

1324
01:11:27,680 --> 01:11:29,740
penetration of the soap opera.

1325
01:11:29,740 --> 01:11:33,120
It was on network TV, so it's
not always available, but it

1326
01:11:33,120 --> 01:11:36,060
became progressively available
in part of Brazil.

1327
01:11:36,060 --> 01:11:39,150
So you can follow the fertility
as it becomes

1328
01:11:39,150 --> 01:11:41,370
available in the different
part of Brazil.

1329
01:11:41,370 --> 01:11:43,620
And you observe two interesting
things.

1330
01:11:43,620 --> 01:11:46,090
One is that as it became
available, people had fewer

1331
01:11:46,090 --> 01:11:49,010
kids, and the second is those
kids tended to be poor.

1332
01:11:49,010 --> 01:11:53,890
Like the name of the soap opera
heroine, the kids had

1333
01:11:53,890 --> 01:11:56,750
started to have those names,
which is like the other.

1334
01:11:56,750 --> 01:12:02,280
So other things like that you
could try and view imbue.

1335
01:12:02,280 --> 01:12:05,390
I haven't seen evolutions of
trying to get health messages

1336
01:12:05,390 --> 01:12:07,490
to go through the television,
but these are things that you

1337
01:12:07,490 --> 01:12:10,786
could also try, so use
those other channels.

1338
01:12:10,786 --> 01:12:13,960
In the last five minutes, I want
to say something about

1339
01:12:13,960 --> 01:12:15,240
the present and the future.

1340
01:12:15,240 --> 01:12:20,160
That's something we are going
to get back again, which is

1341
01:12:20,160 --> 01:12:24,700
kind of elaborating on the point
you were making before.

1342
01:12:24,700 --> 01:12:27,490
Another problem is that the
preventive health cost are

1343
01:12:27,490 --> 01:12:30,330
incurred today, but the benefits
are in the future,

1344
01:12:30,330 --> 01:12:32,350
and furthermore, as you
pointed out, in

1345
01:12:32,350 --> 01:12:34,160
the uncertain future.

1346
01:12:34,160 --> 01:12:37,280
Like, it is going
to happen later.

1347
01:12:37,280 --> 01:12:40,860
So even if you know that it
prevents getting measles, you

1348
01:12:40,860 --> 01:12:44,990
don't know whether you would
really have gotten measles,

1349
01:12:44,990 --> 01:12:48,530
and it's in the future,
sometime later.

1350
01:12:48,530 --> 01:12:51,720
And it turns out, which you
can easily verify from

1351
01:12:51,720 --> 01:12:54,440
introspection, is that human
beings-- not only the poor,

1352
01:12:54,440 --> 01:12:56,220
but the poor, the
rich, everyone--

1353
01:12:56,220 --> 01:12:59,270
tends to put much more weight
on the present than in the

1354
01:12:59,270 --> 01:13:00,980
entire future.

1355
01:13:00,980 --> 01:13:03,590
This is something different than
your regular discounting

1356
01:13:03,590 --> 01:13:06,900
that today is more important
than tomorrow, and tomorrow is

1357
01:13:06,900 --> 01:13:09,840
more important than day after
tomorrow, et cetera.

1358
01:13:09,840 --> 01:13:13,370
This is something that today is
much, much more important

1359
01:13:13,370 --> 01:13:16,920
than tomorrow, and then tomorrow
and day after,

1360
01:13:16,920 --> 01:13:19,162
tomorrow is little bit more
important than day after, and

1361
01:13:19,162 --> 01:13:21,140
day after a little bit more
important than the following

1362
01:13:21,140 --> 01:13:22,090
day, et cetera.

1363
01:13:22,090 --> 01:13:25,680
But today is much, much more
important compared to the

1364
01:13:25,680 --> 01:13:28,860
entire future in what we're
thinking about.

1365
01:13:28,860 --> 01:13:33,130
That's true for consumption, so
you would like to save for

1366
01:13:33,130 --> 01:13:34,520
your retirement, but
not starting

1367
01:13:34,520 --> 01:13:36,620
today, starting tomorrow.

1368
01:13:36,620 --> 01:13:39,640
It's the same with time.

1369
01:13:39,640 --> 01:13:44,050
I gave you the flexibility of
when you're doing essays for

1370
01:13:44,050 --> 01:13:48,280
this class, but I did warn you
that in my experience, a lot

1371
01:13:48,280 --> 01:13:51,990
of students will decide to do
the five last essays because

1372
01:13:51,990 --> 01:13:54,450
they think from today that
it's the absolute optimal

1373
01:13:54,450 --> 01:13:56,280
thing to do because now
you're very busy.

1374
01:13:56,280 --> 01:13:58,320
But of course at the end of
the semester when all the

1375
01:13:58,320 --> 01:14:00,820
projects are due, and the
exams too, you'll

1376
01:14:00,820 --> 01:14:02,660
have much more time.

1377
01:14:02,660 --> 01:14:07,220
So this is something that it
doesn't take much to realize

1378
01:14:07,220 --> 01:14:09,330
that there is this problem.

1379
01:14:09,330 --> 01:14:11,590
And not only we have this
problem, but we are not fully

1380
01:14:11,590 --> 01:14:12,870
aware of it.

1381
01:14:12,870 --> 01:14:15,880
Because if we were fully aware
of it, you would write me an

1382
01:14:15,880 --> 01:14:21,990
email and say can I please
commit to a schedule of essays

1383
01:14:21,990 --> 01:14:26,980
and ask Laura or Millicent
to enforce them.

1384
01:14:26,980 --> 01:14:31,560
We realize to some extent,
but we overestimate.

1385
01:14:31,560 --> 01:14:35,090
We're thinking that today, the
present is very important, but

1386
01:14:35,090 --> 01:14:38,250
that tomorrow we will start
being reasonable people again.

1387
01:14:38,250 --> 01:14:39,140
[LAUGHTER]

1388
01:14:39,140 --> 01:14:41,210
PROFESSOR: And now tomorrow
comes, and tomorrow becomes

1389
01:14:41,210 --> 01:14:47,550
today, and again today is so
important, and we get fooled

1390
01:14:47,550 --> 01:14:49,790
by ourselves like
that repeatedly.

1391
01:14:49,790 --> 01:14:52,530
So with the immunization, you
can think this can be

1392
01:14:52,530 --> 01:14:54,750
available every month.

1393
01:14:54,750 --> 01:14:59,070
So you're today I'm just so
busy, I can't go, but I will

1394
01:14:59,070 --> 01:15:00,130
go next month.

1395
01:15:00,130 --> 01:15:02,990
Then next month comes, and
then it's next month.

1396
01:15:02,990 --> 01:15:05,610
And next month is now today,
and you're so busy

1397
01:15:05,610 --> 01:15:06,810
that you can't go.

1398
01:15:06,810 --> 01:15:09,526
And that's way, you could
procrastinate.

1399
01:15:09,526 --> 01:15:11,438
AUDIENCE: After [INAUDIBLE]

1400
01:15:11,438 --> 01:15:13,828
an experiment with decreasing
incentives?

1401
01:15:13,828 --> 01:15:17,891
Like if you said people who turn
in essays, like the first

1402
01:15:17,891 --> 01:15:20,495
five would get 10 extra points,
and the next five

1403
01:15:20,495 --> 01:15:23,405
wouldn't get 10 extra points,
most people would do the first

1404
01:15:23,405 --> 01:15:24,860
five essays just
to [INAUDIBLE].

1405
01:15:24,860 --> 01:15:25,345
PROFESSOR: Yeah.

1406
01:15:25,345 --> 01:15:26,315
You're exactly right.

1407
01:15:26,315 --> 01:15:29,540
The intuition is exactly right,
which is if we suffer

1408
01:15:29,540 --> 01:15:34,250
from things like that, giving
us small incentive to act

1409
01:15:34,250 --> 01:15:39,510
today rather than tomorrow
will help.

1410
01:15:39,510 --> 01:15:42,100
For example, this idea of saying
if you're doing the

1411
01:15:42,100 --> 01:15:45,570
first five, you're getting 10
extra points for the first

1412
01:15:45,570 --> 01:15:46,820
five, that helps.

1413
01:15:50,110 --> 01:15:52,400
I could also have a
disincentive, which is to say

1414
01:15:52,400 --> 01:15:55,730
you get 10 negative points
if you give them later.

1415
01:15:55,730 --> 01:15:58,910
In principle, if you have some
awareness of this problem, you

1416
01:15:58,910 --> 01:16:01,910
should like this program,
because you should like the

1417
01:16:01,910 --> 01:16:05,670
idea of putting some incentive
on yourself to act today

1418
01:16:05,670 --> 01:16:07,320
rather than tomorrow.

1419
01:16:07,320 --> 01:16:10,520
And so with preventive care,
the problem is that in the

1420
01:16:10,520 --> 01:16:16,470
developing world, the costs tend
to be higher than for us.

1421
01:16:16,470 --> 01:16:19,140
With immunization, like not only
you have to go, but half

1422
01:16:19,140 --> 01:16:21,660
of the time she's not there,
and all of that.

1423
01:16:21,660 --> 01:16:24,510
So it's constructed exactly
the other way, which is a

1424
01:16:24,510 --> 01:16:28,010
small cost, everything becomes a
little bit more complicated.

1425
01:16:28,010 --> 01:16:32,010
In our lives, everything is
structured to make the small

1426
01:16:32,010 --> 01:16:36,200
cost less costly, and also
to impose schedule on us.

1427
01:16:36,200 --> 01:16:39,640
For example, for immunization,
you have a calendar that is

1428
01:16:39,640 --> 01:16:42,090
given by the government that
you have to follow.

1429
01:16:42,090 --> 01:16:44,460
Otherwise the kids can't go to
school, but since kids have to

1430
01:16:44,460 --> 01:16:46,480
be in school, you have
to follow that.

1431
01:16:46,480 --> 01:16:49,410
So it's a form of incentive,
very strong incentive, to make

1432
01:16:49,410 --> 01:16:50,650
it compulsory.

1433
01:16:50,650 --> 01:16:54,360
And the lentils is saying
there is a small cost of

1434
01:16:54,360 --> 01:16:57,860
going, but in extent of this
small cost, you get a small

1435
01:16:57,860 --> 01:17:00,740
benefit, which is the lentils.

1436
01:17:00,740 --> 01:17:05,030
And so that can help
people to go.

1437
01:17:05,030 --> 01:17:07,230
And you're right that combining
your two ideas, I

1438
01:17:07,230 --> 01:17:10,320
could say you're going to get
a bigger incentive if you

1439
01:17:10,320 --> 01:17:12,520
follow the schedule than
if you go at any point.

1440
01:17:12,520 --> 01:17:15,130
And that way, that gives
us a strong sense

1441
01:17:15,130 --> 01:17:16,630
of doing it on time.

1442
01:17:19,950 --> 01:17:24,640
So these procrastination issues,
combining with the

1443
01:17:24,640 --> 01:17:30,050
fact that people have probably
not a full understanding of

1444
01:17:30,050 --> 01:17:36,160
the benefits, could explain why
we see this huge waste.

1445
01:17:36,160 --> 01:17:39,330
Because we really have to call
this as a waste, all of this.

1446
01:17:39,330 --> 01:17:40,790
Kids who are not immunized.

1447
01:17:40,790 --> 01:17:42,570
Kids who are not dewormed.

1448
01:17:42,570 --> 01:17:46,500
Kids who drink dirty water,
and adults also.

1449
01:17:46,500 --> 01:17:50,620
And that could come from this
combination of not fully

1450
01:17:50,620 --> 01:17:55,060
understand the benefits, not
trusting what you are told,

1451
01:17:55,060 --> 01:17:58,710
and the disproportionate
importance of small cost.

1452
01:17:58,710 --> 01:18:00,420
How can we solve it?

1453
01:18:00,420 --> 01:18:02,100
Well, we can solve it by making

1454
01:18:02,100 --> 01:18:04,020
things as easy as possible.

1455
01:18:04,020 --> 01:18:06,990
This is what we benefit from.

1456
01:18:06,990 --> 01:18:10,330
When you open the tap in your
water, there is chlorine that

1457
01:18:10,330 --> 01:18:11,040
comes, right?

1458
01:18:11,040 --> 01:18:13,630
You don't have to remember
to add the tablet.

1459
01:18:13,630 --> 01:18:16,330
So making things automatic
and defaulted.

1460
01:18:16,330 --> 01:18:19,210
And when it's not possible, like
you're not a very well

1461
01:18:19,210 --> 01:18:22,090
organized country like India,
giving people small rewards

1462
01:18:22,090 --> 01:18:27,210
that are offsetting the small
cost, if possible exactly in

1463
01:18:27,210 --> 01:18:30,050
the way that you're talking
about it, which is [INAUDIBLE]

1464
01:18:30,050 --> 01:18:32,720
people to do it later
rather than earlier.

1465
01:18:35,330 --> 01:18:38,230
That means that charging a small
amount for goods may be

1466
01:18:38,230 --> 01:18:40,740
totally counterproductive
because you might lose a lot

1467
01:18:40,740 --> 01:18:43,320
of people when you have your
entire infrastructure to

1468
01:18:43,320 --> 01:18:44,860
deliver the goods.

1469
01:18:44,860 --> 01:18:48,235
And giving small incentive might
actually be productive.

1470
01:18:52,760 --> 01:18:54,010
The last word would be--

1471
01:18:58,170 --> 01:19:00,630
and that's the question of the
bed net that we spent a whole

1472
01:19:00,630 --> 01:19:04,080
lecture on-- is it going to have
some bad effect in the

1473
01:19:04,080 --> 01:19:07,590
future if people are used to
be helped in this way?

1474
01:19:07,590 --> 01:19:09,260
And I think the answer
is two prongs.

1475
01:19:09,260 --> 01:19:11,150
Some of these problems
are here to stay.

1476
01:19:11,150 --> 01:19:13,220
It's not that we have them
today, and it will be better

1477
01:19:13,220 --> 01:19:14,100
in the future.

1478
01:19:14,100 --> 01:19:15,410
We always have those problems.

1479
01:19:15,410 --> 01:19:19,280
That's why immunization in the
US is free and compulsory, and

1480
01:19:19,280 --> 01:19:21,080
it's going to be forever.

1481
01:19:21,080 --> 01:19:24,440
We're not expecting that one day
people will now understand

1482
01:19:24,440 --> 01:19:27,570
the value and will start
doing it on their own.

1483
01:19:27,570 --> 01:19:31,310
Secondly is when we think about
the dynamic effect, we

1484
01:19:31,310 --> 01:19:33,490
also have to include learning.

1485
01:19:33,490 --> 01:19:37,840
And because of the lack of trust
and people don't just

1486
01:19:37,840 --> 01:19:41,900
believe you because you say
something, the fact of giving

1487
01:19:41,900 --> 01:19:44,900
people an occasion to try for
themselves by making things

1488
01:19:44,900 --> 01:19:48,060
very easy and cheap may actually
have those dynamic

1489
01:19:48,060 --> 01:19:50,830
effects that are positive
because of the learning.

1490
01:19:50,830 --> 01:19:53,850
Which is exactly what we
saw with the bed nets.

1491
01:19:53,850 --> 01:19:57,730
We don't need to go over it
again, but with the bed net,

1492
01:19:57,730 --> 01:19:59,450
people were probably relatively
suspicious about

1493
01:19:59,450 --> 01:20:01,510
the effectiveness of
those bed nets.

1494
01:20:01,510 --> 01:20:02,730
You give them one--

1495
01:20:02,730 --> 01:20:04,670
[INAUDIBLE] not willing
to pay the cost.

1496
01:20:04,670 --> 01:20:06,730
You give them one for free,
and then they realize the

1497
01:20:06,730 --> 01:20:09,850
benefits are bigger than what
they were, and that can

1498
01:20:09,850 --> 01:20:12,690
overcome the small cost to get
the benefits in the future now

1499
01:20:12,690 --> 01:20:16,940
that they're convinced that
those benefits actually exist.

1500
01:20:16,940 --> 01:20:19,670
So we're done with health, and
we are going to start with

1501
01:20:19,670 --> 01:20:20,920
education next time.