WEBVTT

00:00:00.040 --> 00:00:02.460
The following content is
provided under a Creative

00:00:02.460 --> 00:00:03.970
Commons license.

00:00:03.970 --> 00:00:06.910
Your support will help MIT
OpenCourseWare continue to

00:00:06.910 --> 00:00:10.660
offer high-quality educational
resources for free.

00:00:10.660 --> 00:00:13.460
To make a donation or view
additional materials from

00:00:13.460 --> 00:00:17.390
hundreds of MIT courses, visit
MIT OpenCourseWare at

00:00:17.390 --> 00:00:18.640
ocw.mit.edu.

00:00:28.190 --> 00:00:35.200
PROFESSOR: Where we left it at
the end of last time was the

00:00:35.200 --> 00:00:38.080
mechanism for poverty trap
that [INAUDIBLE]

00:00:38.080 --> 00:00:42.530
explained, and that was kind of
a workhorse of development

00:00:42.530 --> 00:00:45.540
economics for many years,
since the 1950s, might

00:00:45.540 --> 00:00:48.650
actually, surprisingly,
not be at play.

00:00:48.650 --> 00:00:54.510
In that, number one, the
effect of your calorie

00:00:54.510 --> 00:00:59.320
consumption on your productivity
in the immediate

00:00:59.320 --> 00:01:02.650
next few days is probably
not large enough.

00:01:02.650 --> 00:01:06.490
And, perhaps as a consequence,
or perhaps just because they

00:01:06.490 --> 00:01:09.140
have other things to do with
their money, we don't see the

00:01:09.140 --> 00:01:14.160
poor also consuming as
much as they can.

00:01:14.160 --> 00:01:18.790
And therefore, we don't see a
very high elasticity of food

00:01:18.790 --> 00:01:21.790
consumption with respect
to wages.

00:01:21.790 --> 00:01:25.140
So if we don't have a very high
elasticity of wages with

00:01:25.140 --> 00:01:28.560
respect to consumption, and
we don't have a very high

00:01:28.560 --> 00:01:32.020
elasticity of consumption with
respect to wages, then we are

00:01:32.020 --> 00:01:35.360
not going to get a very highest
elasticity of wages

00:01:35.360 --> 00:01:37.780
tomorrow with respect
to wages yesterday.

00:01:37.780 --> 00:01:40.990
And therefore, the whole thing
of I am poor because I am

00:01:40.990 --> 00:01:45.900
poor, based on how much
food I can consume

00:01:45.900 --> 00:01:47.730
is not really there.

00:01:47.730 --> 00:01:51.060
So on the one hand, you could
say fine, that great.

00:01:51.060 --> 00:01:53.470
It means we can start focusing
on other programs, and

00:01:53.470 --> 00:01:55.890
nutrition is not really
an issue.

00:01:55.890 --> 00:01:58.320
And for some people, that
has been the conclusion.

00:01:58.320 --> 00:02:04.360
For example, on Tuesday, we
showed some graphs coming from

00:02:04.360 --> 00:02:09.270
the paper by Angus Deaton and
Jean Dreze about the fact that

00:02:09.270 --> 00:02:13.130
people are consuming less and
less calories in India.

00:02:13.130 --> 00:02:16.060
So they are becoming richer, so
they are moving along the

00:02:16.060 --> 00:02:17.760
angle curve.

00:02:17.760 --> 00:02:20.680
And that would make
them consume more,

00:02:20.680 --> 00:02:22.450
everything else equal.

00:02:22.450 --> 00:02:24.920
But the thing is, not everything
else is equal.

00:02:24.920 --> 00:02:27.590
And at the same time, we
have the angle of curve

00:02:27.590 --> 00:02:29.370
shifting to the right.

00:02:29.370 --> 00:02:31.840
So that it's a swimming upstream
movement, where

00:02:31.840 --> 00:02:34.590
you're trying to go up the angle
curve, but the angle

00:02:34.590 --> 00:02:37.050
curve is shifting right, so you
end up actually consuming

00:02:37.050 --> 00:02:43.050
less, fewer calories than you
would otherwise consume,

00:02:43.050 --> 00:02:47.090
So for some people in India,
this is a sign that there's

00:02:47.090 --> 00:02:50.790
much more poverty than
the official

00:02:50.790 --> 00:02:52.610
statistics are saying.

00:02:52.610 --> 00:02:55.890
Because if we define poverty as
not having enough to eat,

00:02:55.890 --> 00:02:58.400
then we have more and more
people who in fact don't have

00:02:58.400 --> 00:03:00.380
enough to eat.

00:03:00.380 --> 00:03:03.030
But what is strange is that if
we look at the other things

00:03:03.030 --> 00:03:06.070
that people consume, and we
measure poverty in this way,

00:03:06.070 --> 00:03:09.360
which is, if you look at the
entire budget, are you below--

00:03:09.360 --> 00:03:11.830
are you someone who consumes
less than a dollar a day per

00:03:11.830 --> 00:03:15.180
capita, of 16 rupees a day,
because it's India?

00:03:15.180 --> 00:03:16.210
And you don't find that.

00:03:16.210 --> 00:03:21.400
You find that actually there are
fewer and fewer people who

00:03:21.400 --> 00:03:22.830
are below a dollar a day.

00:03:22.830 --> 00:03:23.940
There is still a number.

00:03:23.940 --> 00:03:25.190
It's about 13%.

00:03:25.190 --> 00:03:27.330
but it's certainly going down.

00:03:27.330 --> 00:03:30.340
So it has to be that people
exercise a choice

00:03:30.340 --> 00:03:32.810
not to eat as much.

00:03:32.810 --> 00:03:38.856
So Deaton and Dreze who wrote
this paper and documented the

00:03:38.856 --> 00:03:42.680
decline in calorie consumption
in India, have one

00:03:42.680 --> 00:03:43.850
explanation.

00:03:43.850 --> 00:03:47.540
And their explanation is that
people's need for calories has

00:03:47.540 --> 00:03:51.950
gone down because they are
less ill, they have fewer

00:03:51.950 --> 00:03:54.865
children, they are doing less
intense physical work.

00:03:54.865 --> 00:03:57.560
A lot of people have moved
to the urban areas.

00:03:57.560 --> 00:03:59.900
So it's just they eat
less because they

00:03:59.900 --> 00:04:01.780
need less of the strength.

00:04:01.780 --> 00:04:05.370
And therefore we have nothing
to worry about, in a sense.

00:04:05.370 --> 00:04:08.380
The fact that people are eating
less is, in a sense, a

00:04:08.380 --> 00:04:15.840
sign of success of India's
economic growth.

00:04:15.840 --> 00:04:19.380
But if it were the case, then
we should find that the

00:04:19.380 --> 00:04:23.080
nutritional status of people
would be adequate.

00:04:23.080 --> 00:04:24.920
Defined in more objective
terms.

00:04:24.920 --> 00:04:28.190
Not the calories you're
consuming, but what is your

00:04:28.190 --> 00:04:30.660
weight, what is your height.

00:04:30.660 --> 00:04:32.580
Whether you're anemic or not.

00:04:32.580 --> 00:04:35.400
We should find an improvement
in that.

00:04:35.400 --> 00:04:38.100
Because, to the extent that
people are getting richer,

00:04:38.100 --> 00:04:40.240
they should want a little bit
of improvement in their

00:04:40.240 --> 00:04:41.790
nutritional status.

00:04:41.790 --> 00:04:44.700
And what is striking and
surprising, which is why they

00:04:44.700 --> 00:04:50.240
might be hidden traps is that
by all accounts, in India in

00:04:50.240 --> 00:04:53.710
particular but in other places
as well, people are still not

00:04:53.710 --> 00:04:55.440
very well-nourished.

00:04:55.440 --> 00:04:58.880
And it is more a matter
of there is some

00:04:58.880 --> 00:05:02.380
undernourishment, which is
people are not eating that

00:05:02.380 --> 00:05:03.790
many calories.

00:05:03.790 --> 00:05:08.470
And also, maybe something that
people referred to as hidden

00:05:08.470 --> 00:05:12.110
hunger, and you can think
about as malnutrition.

00:05:12.110 --> 00:05:15.270
Which is even the condition of
having enough calories if

00:05:15.270 --> 00:05:19.540
people are not getting enough
of the other micronutrients

00:05:19.540 --> 00:05:22.770
that they need-- for
example, anemia.

00:05:22.770 --> 00:05:26.980
So here is a number for India.

00:05:26.980 --> 00:05:35.440
33% of men and 36% of women
have a BMI below 18.5.

00:05:35.440 --> 00:05:40.750
And meanwhile, iron deficiency
anemia affects maybe something

00:05:40.750 --> 00:05:44.230
like a billion people
worldwide.

00:05:44.230 --> 00:05:48.590
And iron deficiency anemia means
that people are in fact

00:05:48.590 --> 00:05:52.840
less strong, because the ability
of their body or their

00:05:52.840 --> 00:05:56.940
blood to process the
oxygen is limited.

00:05:56.940 --> 00:06:00.700
Because we process the oxygen
with other blood cells in our

00:06:00.700 --> 00:06:02.710
body, the hemoglobin
in our blood.

00:06:02.710 --> 00:06:04.620
And if we don't have enough of
that, we're not very good at

00:06:04.620 --> 00:06:06.440
processing the oxygen.

00:06:06.440 --> 00:06:08.760
So you put people on the
treadmill that are anemic, and

00:06:08.760 --> 00:06:11.680
they are not able to
make it go as far.

00:06:14.740 --> 00:06:18.370
So we have here a puzzle that,
on the one hand, we don't see

00:06:18.370 --> 00:06:21.160
people appearing to be
hungry for calories.

00:06:21.160 --> 00:06:29.750
In fact, in China we see this
Jensen, Miller evidence, which

00:06:29.750 --> 00:06:30.760
goes the opposite way.

00:06:30.760 --> 00:06:33.030
Which is, you make the cheaper
source of calories cheaper,

00:06:33.030 --> 00:06:36.670
and people eat fewer calories,
at least in one region.

00:06:36.670 --> 00:06:41.620
And yet, they seem to be not
very well-nourished.

00:06:41.620 --> 00:06:44.410
so what could be going on?

00:06:47.620 --> 00:06:49.620
Let me start with you,
your reason.

00:06:49.620 --> 00:06:50.610
And I will present it.

00:06:50.610 --> 00:06:53.340
AUDIENCE: Their diets are
very narrow and the

00:06:53.340 --> 00:06:54.280
same all the time.

00:06:54.280 --> 00:06:58.192
So they don't really correlate
what they eat to--

00:06:58.192 --> 00:07:00.148
It's just kind of an
informational thing.

00:07:00.148 --> 00:07:02.430
They don't realize what kinds
of nutrients they actually

00:07:02.430 --> 00:07:04.304
need, so they just have what
tastes good or what they're

00:07:04.304 --> 00:07:05.530
used to eating.

00:07:05.530 --> 00:07:07.150
PROFESSOR: So it could
be, for example--

00:07:07.150 --> 00:07:09.380
I'm just rephrasing for
everyone, because you speak in

00:07:09.380 --> 00:07:11.980
this very nice and soft tone--

00:07:11.980 --> 00:07:16.210
It could be that they don't
have the information that

00:07:16.210 --> 00:07:18.560
nutrition affects
your strength.

00:07:18.560 --> 00:07:21.630
In fact, you proposed one very
specific theory for that,

00:07:21.630 --> 00:07:25.060
which is, if you've never
experimented because you've

00:07:25.060 --> 00:07:28.670
always eaten same thing, then
you might not know what would

00:07:28.670 --> 00:07:31.050
happen outside of your
normal range.

00:07:31.050 --> 00:07:33.360
So that would be one reason
why you don't have the

00:07:33.360 --> 00:07:34.610
information.

00:07:36.545 --> 00:07:38.533
AUDIENCE: And then when
someone, or maybe the

00:07:38.533 --> 00:07:42.012
government, suggests a different
diet, or replacing

00:07:42.012 --> 00:07:46.485
what people are normally used to
eating, they're not really

00:07:46.485 --> 00:07:48.473
willing to take that advice.

00:07:48.473 --> 00:07:49.964
And so that can--

00:07:49.964 --> 00:07:53.443
they'll just continue
eating [INAUDIBLE]

00:07:53.443 --> 00:07:54.934
just normally eating.

00:07:54.934 --> 00:07:58.935
Their diet is based mainly
on grains and rice.

00:07:58.935 --> 00:08:01.531
And if someone in the government
says, well, there's

00:08:01.531 --> 00:08:02.340
a shortage of that.

00:08:02.340 --> 00:08:05.250
Maybe you should supplement
more vegetables for it.

00:08:05.250 --> 00:08:09.130
Then they aren't very
willing to switch.

00:08:11.910 --> 00:08:14.050
PROFESSOR: One reason why your
information might be limited

00:08:14.050 --> 00:08:16.350
is that even when you
get a source of

00:08:16.350 --> 00:08:17.590
information from outside--

00:08:17.590 --> 00:08:19.700
for example, the government--
tells you, you

00:08:19.700 --> 00:08:21.380
should eat your vegetable.

00:08:21.380 --> 00:08:26.450
You should eat these kinds of
cereals rather than those

00:08:26.450 --> 00:08:27.180
kinds of cereals.

00:08:27.180 --> 00:08:32.140
You should replace some rice
with pulses, or some rice with

00:08:32.140 --> 00:08:36.500
cereals, people are reluctant
to do it.

00:08:36.500 --> 00:08:42.460
And why would we think that
people are reluctant to follow

00:08:42.460 --> 00:08:45.518
this information from
the government?

00:08:45.518 --> 00:08:46.014
AUDIENCE: A bunch of reasons.

00:08:46.014 --> 00:08:49.982
One might be a variety of foods
might not be available

00:08:49.982 --> 00:08:50.974
in that region.

00:08:50.974 --> 00:08:53.950
And also, a lot of those things
are more expensive.

00:08:53.950 --> 00:08:56.926
So the cheaper things are very
carb-heavy things, which is

00:08:56.926 --> 00:08:58.414
quite filling.

00:08:58.414 --> 00:09:02.910
So you might not choose to go
with vegetables [INAUDIBLE].

00:09:02.910 --> 00:09:03.270
PROFESSOR: Right.

00:09:03.270 --> 00:09:04.530
So there are two reasons, two

00:09:04.530 --> 00:09:05.770
possibilities in what you said.

00:09:05.770 --> 00:09:07.890
The first one is a chicken
and egg problem.

00:09:07.890 --> 00:09:11.200
Because if no one eats
spinach in a place--

00:09:11.200 --> 00:09:14.280
and spinach is a great thing--

00:09:14.280 --> 00:09:16.410
but if no one eats it, it's
just not available, and

00:09:16.410 --> 00:09:18.250
therefore you cannot
try it out.

00:09:18.250 --> 00:09:19.690
What was the second
one, excuse me?

00:09:19.690 --> 00:09:22.045
AUDIENCE: The second one is
that the cheaper food is

00:09:22.045 --> 00:09:25.109
generally more carb-heavy, and
so it's quite filling, and you

00:09:25.109 --> 00:09:29.252
might choose to buy that over
vegetables which are much more

00:09:29.252 --> 00:09:30.750
expensive than the
rest of the food.

00:09:30.750 --> 00:09:31.175
PROFESSOR: Right.

00:09:31.175 --> 00:09:33.703
And the second one could
be a matter of costs.

00:09:33.703 --> 00:09:35.675
AUDIENCE: And I think that the
benefit of nutrient intakes

00:09:35.675 --> 00:09:37.154
are over the long-term.

00:09:37.154 --> 00:09:39.619
So if you're taking a small
iron tablet, the next day,

00:09:39.619 --> 00:09:42.084
miraculously you're not going
to perform better.

00:09:42.084 --> 00:09:45.042
But over the long-term,
you might see smaller

00:09:45.042 --> 00:09:46.030
improvements.

00:09:46.030 --> 00:09:46.410
PROFESSOR: Right.

00:09:46.410 --> 00:09:49.420
So it could be that if is
difficult to learn.

00:09:49.420 --> 00:09:52.880
So for example, one thing that
could happen is that someone

00:09:52.880 --> 00:09:56.050
from outside, very well-meaning,
say you should

00:09:56.050 --> 00:10:00.830
really eat iron-fortified
flour instead of

00:10:00.830 --> 00:10:02.710
your regular flour.

00:10:02.710 --> 00:10:04.020
And you try.

00:10:04.020 --> 00:10:07.090
And then, after one week, you
don't feel like Popeye.

00:10:07.090 --> 00:10:10.480
It's not that things have
dramatically changed.

00:10:10.480 --> 00:10:14.550
And in fact, if I put you on a
treadmill and I ask you to

00:10:14.550 --> 00:10:16.280
perform an exercise,
I will know

00:10:16.280 --> 00:10:19.130
that you are 10% stronger.

00:10:19.130 --> 00:10:20.820
But this is not something--

00:10:20.820 --> 00:10:28.040
if you are 10% stronger the next
month, are you going to

00:10:28.040 --> 00:10:30.770
be able to really see the
difference or not?

00:10:30.770 --> 00:10:34.400
So it might not be immediately
clear.

00:10:34.400 --> 00:10:37.320
And if that is the case, then
you might have a situation

00:10:37.320 --> 00:10:41.200
where people arrive from outside
and give you this

00:10:41.200 --> 00:10:44.340
message, and say, you should
really change your

00:10:44.340 --> 00:10:45.700
diet in this way.

00:10:45.700 --> 00:10:49.360
And you make, maybe, an effort
to follow them for some time.

00:10:49.360 --> 00:10:52.540
Spend a little bit more money,
or a little bit more effort

00:10:52.540 --> 00:10:55.740
into going into pasturizing
your food.

00:10:55.740 --> 00:10:58.310
And then it happens, and you're
not any stronger.

00:10:58.310 --> 00:11:00.990
And you're like, whatever
did they tell me?

00:11:00.990 --> 00:11:03.420
It's like, this is no better.

00:11:03.420 --> 00:11:08.240
Because your expectations were
set high enough to encourage

00:11:08.240 --> 00:11:09.760
you to do the switch.

00:11:09.760 --> 00:11:13.510
And the problem is there would
be a tendency to slightly

00:11:13.510 --> 00:11:17.230
oversell how much better
you're going to feel.

00:11:17.230 --> 00:11:19.490
Which then is going to
translate into a

00:11:19.490 --> 00:11:21.240
disappointment.

00:11:21.240 --> 00:11:26.230
So one example of that is
something that people have

00:11:26.230 --> 00:11:30.120
found in a deworming program in
Kenya, which we're going to

00:11:30.120 --> 00:11:32.450
discuss in a moment.

00:11:32.450 --> 00:11:34.950
So it's a charitable
deworming program.

00:11:34.950 --> 00:11:39.520
Deworming is, in some sense, a
nutrition program, because the

00:11:39.520 --> 00:11:43.600
worms are competing with
the kid for the food.

00:11:43.600 --> 00:11:48.500
So by removing the worms, you
are increasing the amount of

00:11:48.500 --> 00:11:50.260
food that stays with the kid.

00:11:50.260 --> 00:11:53.230
I'm sorry, this is not a great
conversation to have right

00:11:53.230 --> 00:11:53.880
after lunch.

00:11:53.880 --> 00:11:58.610
But that's kind of the biology
of it, in two words.

00:11:58.610 --> 00:12:00.640
So when you give deworming--

00:12:00.640 --> 00:12:02.330
and we are going to see that
in a minute-- that

00:12:02.330 --> 00:12:05.752
does make the kid--

00:12:05.752 --> 00:12:13.480
that reduces anemia,
that reduces the

00:12:13.480 --> 00:12:15.370
incidence of being sick.

00:12:15.370 --> 00:12:20.140
That reduces, therefore,
absence from school.

00:12:20.140 --> 00:12:27.050
So there was an NGO that was
trying to promote deworming in

00:12:27.050 --> 00:12:33.120
some randomly-selected schools
in the late '90s, early 2000s.

00:12:33.120 --> 00:12:35.650
And they went, and they
explained all of this with a

00:12:35.650 --> 00:12:38.970
lot of energy, and said, your
kid is going to feel much

00:12:38.970 --> 00:12:43.100
better, and is going to go to
school more, and all of that.

00:12:43.100 --> 00:12:48.350
And parents had to sign
a form to agree

00:12:48.350 --> 00:12:49.790
to get the kid dewormed.

00:12:49.790 --> 00:12:52.020
So it's not money.

00:12:52.020 --> 00:12:53.190
It's not a huge amount
of effort.

00:12:53.190 --> 00:12:55.390
But it's still a little
bit of effort.

00:12:55.390 --> 00:12:57.980
And also, you have to want it.

00:12:57.980 --> 00:12:59.950
And people were interested--

00:12:59.950 --> 00:13:03.740
the researches were interested
to know whether parents were

00:13:03.740 --> 00:13:07.760
more likely to sign the form
if they knew more people

00:13:07.760 --> 00:13:11.150
around them who got a chance
to get the deworming.

00:13:11.150 --> 00:13:15.775
So because it was a randomized
experiment, which was done at

00:13:15.775 --> 00:13:17.230
the level of the school--

00:13:17.230 --> 00:13:19.480
I'm going to show you
a map in a moment--

00:13:19.480 --> 00:13:23.130
some people got treated in some
schools, and some people

00:13:23.130 --> 00:13:25.380
didn't get treated
immediately.

00:13:25.380 --> 00:13:29.010
So people who are in a treated
schools may have had friends

00:13:29.010 --> 00:13:31.390
who were in neighboring schools,
which may have been

00:13:31.390 --> 00:13:33.280
treated a control.

00:13:33.280 --> 00:13:36.650
So what the researchers did is
to look at whether you were

00:13:36.650 --> 00:13:39.470
more likely to take up the
deworming once you got the

00:13:39.470 --> 00:13:42.560
option if you had more
friends who got the

00:13:42.560 --> 00:13:44.740
option the year before.

00:13:44.740 --> 00:13:48.360
And their prior going into
this, was that the more

00:13:48.360 --> 00:13:52.480
friends you have who got into
the deworming, the more you

00:13:52.480 --> 00:13:54.810
are likely to do it yourself,
because you

00:13:54.810 --> 00:13:56.960
will see the benefits.

00:13:56.960 --> 00:13:59.760
And what they found was
exactly the opposite.

00:13:59.760 --> 00:14:03.170
Which is the more friends you
had who had been a chance to

00:14:03.170 --> 00:14:06.360
get dewormed a year before,
the less you are likely to

00:14:06.360 --> 00:14:09.590
take up the deworming once
you got a chance.

00:14:09.590 --> 00:14:12.410
And what are the possible
interpretations for that

00:14:12.410 --> 00:14:13.540
somewhat weird results?

00:14:13.540 --> 00:14:15.530
Yeah, Zach?

00:14:15.530 --> 00:14:16.940
AUDIENCE: One possible
interpretation is that it

00:14:16.940 --> 00:14:19.105
depends on how you
get the worms.

00:14:19.105 --> 00:14:21.932
The fact that your friends are
being treated for you might be

00:14:21.932 --> 00:14:24.302
less likely to get it, like
in the case of malaria.

00:14:24.302 --> 00:14:26.198
If everybody in the community's
using the bednet,

00:14:26.198 --> 00:14:27.620
you probably don't have to.

00:14:27.620 --> 00:14:28.100
PROFESSOR: Exactly.

00:14:28.100 --> 00:14:30.900
So that's a first possible
interpretation, which is worms

00:14:30.900 --> 00:14:33.250
are, in fact, highly
contagious.

00:14:33.250 --> 00:14:37.330
So if most of your friends are
treated, then they probably

00:14:37.330 --> 00:14:38.390
don't have worms anymore.

00:14:38.390 --> 00:14:42.010
You might feel, well, I don't
need to go to the trouble of

00:14:42.010 --> 00:14:44.480
getting dewormed because they
did, therefore there are fewer

00:14:44.480 --> 00:14:45.290
worms around.

00:14:45.290 --> 00:14:46.550
And there is some side effect.

00:14:46.550 --> 00:14:48.150
Why would you take
the trouble?

00:14:48.150 --> 00:14:49.068
Yeah, Norm?

00:14:49.068 --> 00:14:52.510
AUDIENCE: Maybe people also,
since people get dewormed, and

00:14:52.510 --> 00:14:55.996
then their problems decrease,
people don't think it's as

00:14:55.996 --> 00:14:58.102
much of an issue, because
it's not as prominent.

00:14:58.102 --> 00:15:01.504
So it's the externality just
decreasing, they just don't

00:15:01.504 --> 00:15:03.450
realize that it's such
a big threat anymore.

00:15:03.450 --> 00:15:04.180
PROFESSOR: Exactly.

00:15:04.180 --> 00:15:05.750
So that could be
another thing.

00:15:05.750 --> 00:15:10.130
Which is, people learn that--

00:15:10.130 --> 00:15:14.810
So people say, oh, these other
kids got dewormed, but they

00:15:14.810 --> 00:15:17.912
are not much healthier
than me.

00:15:17.912 --> 00:15:20.440
And the fact is, you don't
realize that you are healthy

00:15:20.440 --> 00:15:23.060
because they are healthy, and
they made you healthier.

00:15:23.060 --> 00:15:27.870
So you are now comparing the
benefits of you as a control

00:15:27.870 --> 00:15:29.600
child-- you are not
yet treated--

00:15:29.600 --> 00:15:31.860
to the other kids
who got treated.

00:15:31.860 --> 00:15:33.870
And the difference is
not that large.

00:15:33.870 --> 00:15:37.000
It's not that large precisely
because of the contagion

00:15:37.000 --> 00:15:38.640
effect that Zack mentioned.

00:15:38.640 --> 00:15:40.845
But so you're trying to learn
the effect, and so it's not

00:15:40.845 --> 00:15:41.710
that large.

00:15:41.710 --> 00:15:43.910
And even if you don't understand
that it's due to

00:15:43.910 --> 00:15:46.320
the externality, so you don't
do this calculation, saying,

00:15:46.320 --> 00:15:47.470
it's not worthwhile.

00:15:47.470 --> 00:15:49.800
You just see it and think,
what did they sell me?

00:15:49.800 --> 00:15:51.970
This thing doesn't really
make any difference.

00:15:51.970 --> 00:15:53.360
And so you decide not to do it.

00:15:53.360 --> 00:15:53.854
Yeah.

00:15:53.854 --> 00:15:56.324
AUDIENCE: I was going to say
that, even if you don't really

00:15:56.324 --> 00:15:59.946
have any change in your health
status, maybe the change that

00:15:59.946 --> 00:16:02.086
the other people have is
not so great as to

00:16:02.086 --> 00:16:03.734
convince you to get it.

00:16:03.734 --> 00:16:07.200
You see that the medication
quote, unquote, doesn't work.

00:16:07.200 --> 00:16:08.040
PROFESSOR: Right.

00:16:08.040 --> 00:16:09.250
That also could be the case.

00:16:09.250 --> 00:16:11.540
Could be that, even without
this mechanism--

00:16:11.540 --> 00:16:14.610
which is a very nice one-- but
even without this mechanism,

00:16:14.610 --> 00:16:16.590
you could see the other children
and say, well, first

00:16:16.590 --> 00:16:19.620
thing, they got sick when they
ate the deworming pill.

00:16:19.620 --> 00:16:21.110
So the side effect
is immediate.

00:16:24.630 --> 00:16:25.820
It's getting worse and worse.

00:16:25.820 --> 00:16:31.010
But as the worm dies, this make
you pretty unwell for an

00:16:31.010 --> 00:16:34.770
hour or two, as your body
gets rid of them.

00:16:34.770 --> 00:16:36.310
And then you get better.

00:16:36.310 --> 00:16:38.950
But the side effect is salient
and immediate, and the

00:16:38.950 --> 00:16:43.010
benefits are a little
bit less apparent.

00:16:43.010 --> 00:16:46.530
And this, of course, is
reinforced by the

00:16:46.530 --> 00:16:47.790
point that Norm made.

00:16:47.790 --> 00:16:50.120
Which is that the externalities
make it

00:16:50.120 --> 00:16:53.030
difficult to compare treatment
and control.

00:16:53.030 --> 00:16:56.180
So for all of these reasons--

00:16:56.180 --> 00:17:01.390
so this is one example of why
it's very difficult for people

00:17:01.390 --> 00:17:08.099
to learn about relatively subtle
nutrition mechanisms.

00:17:08.099 --> 00:17:11.579
And so what is happening with
deworming, that's maybe made a

00:17:11.579 --> 00:17:16.319
little bit harder but the
externalities, which, A, gives

00:17:16.319 --> 00:17:16.849
[INAUDIBLE]

00:17:16.849 --> 00:17:19.869
like strategic reasons
not to do it.

00:17:19.869 --> 00:17:21.790
So worms give Norms' difficulty
of learning

00:17:21.790 --> 00:17:23.040
explanation.

00:17:25.650 --> 00:17:29.350
But that's could also be at
play with iron pill, or

00:17:29.350 --> 00:17:32.220
supplementing your
flour with iron.

00:17:32.220 --> 00:17:37.280
Where you're like, really not
that much is. happening.

00:17:37.280 --> 00:17:41.630
So these are possible reasons
why you wouldn't do what the

00:17:41.630 --> 00:17:45.360
good man, or well-meaning
NGO tells you to do.

00:17:45.360 --> 00:17:47.150
You don't have the
information.

00:17:47.150 --> 00:17:50.040
Learning is difficult, because
the effects are subtle.

00:17:50.040 --> 00:17:52.170
This implies spending
more money.

00:17:52.170 --> 00:17:55.450
Nd maybe those foods are not
even available for you in a

00:17:55.450 --> 00:17:56.780
convenient way.

00:17:56.780 --> 00:17:59.826
What else could be going
on, potentially?

00:17:59.826 --> 00:18:02.814
AUDIENCE: If the wages are set
wages, then even if you eat

00:18:02.814 --> 00:18:04.474
more stronger, you're still
going to get the

00:18:04.474 --> 00:18:05.304
same amount of money.

00:18:05.304 --> 00:18:08.160
So there's no point in being
more productive.

00:18:08.160 --> 00:18:08.590
PROFESSOR: Right.

00:18:08.590 --> 00:18:11.240
So another possible explanation
is you could

00:18:11.240 --> 00:18:13.500
realize that it's going
to make you a bit more

00:18:13.500 --> 00:18:17.400
productive, but you might
wonder, what's the use of me

00:18:17.400 --> 00:18:20.660
being more productive if, in
fact, the wages are not piece

00:18:20.660 --> 00:18:22.620
wage but day wage?

00:18:22.620 --> 00:18:24.420
And you are a little bit
more productive.

00:18:24.420 --> 00:18:27.810
But you need to go and convince
your employer that

00:18:27.810 --> 00:18:30.080
now I'm a little bit more
productive, so you need to pay

00:18:30.080 --> 00:18:32.080
me more on a daily basis.

00:18:32.080 --> 00:18:34.630
But your employer is not behind
your back, checking

00:18:34.630 --> 00:18:36.660
what it is you're eating
every day.

00:18:36.660 --> 00:18:39.960
And so your consumption is an
upsell from the point of view

00:18:39.960 --> 00:18:41.390
of the employer.

00:18:41.390 --> 00:18:43.310
But there is a more moral hazard
issue, where you could

00:18:43.310 --> 00:18:46.380
go and say, I'm telling you
I've eaten so much.

00:18:46.380 --> 00:18:47.380
I'm very strong.

00:18:47.380 --> 00:18:49.780
You can monitor, you can see.

00:18:49.780 --> 00:18:52.240
Unless your employer can really
be monitoring your

00:18:52.240 --> 00:18:56.640
output in a very close way,
which might not always be

00:18:56.640 --> 00:18:59.670
possible, then they might
say, whatever.

00:18:59.670 --> 00:19:03.510
I'm just assuming that you
are the average person.

00:19:03.510 --> 00:19:12.070
And there is one study that
shows that shows employers

00:19:12.070 --> 00:19:15.550
recognize that taller people
are more productive.

00:19:15.550 --> 00:19:18.680
Taller people usually have been
better fed, maybe, when

00:19:18.680 --> 00:19:19.930
they grew up.

00:19:19.930 --> 00:19:21.230
And they are stronger.

00:19:21.230 --> 00:19:23.380
Taller, maybe stronger,
more muscles.

00:19:23.380 --> 00:19:25.950
People are more productive,
they pay them more.

00:19:25.950 --> 00:19:29.800
But how much you've eaten and
how well you've eaten

00:19:29.800 --> 00:19:32.730
previously does not
affect wages.

00:19:32.730 --> 00:19:34.850
And that is because that isn't
observed from the point of

00:19:34.850 --> 00:19:35.760
view of the employer.

00:19:35.760 --> 00:19:41.320
And if they can't see the
output either, it's

00:19:41.320 --> 00:19:42.630
he said, she said.

00:19:42.630 --> 00:19:45.490
How do I know you're actually
more productive?

00:19:45.490 --> 00:19:47.870
So of course, the solution to
that would be for the employer

00:19:47.870 --> 00:19:50.490
to feed people iron supplement
on the job.

00:19:50.490 --> 00:19:53.490
And why they're not doing
that, I don't know.

00:19:53.490 --> 00:19:57.190
But that would be an interesting
thing to consider.

00:19:57.190 --> 00:19:58.250
Because then they could know.

00:19:58.250 --> 00:19:59.330
They could say, yeah.

00:19:59.330 --> 00:20:01.260
I can pay you a bit more, as
long as you are eating your

00:20:01.260 --> 00:20:02.510
iron supplement.

00:20:04.860 --> 00:20:09.930
So let's go to all of this in a
little more systematic way.

00:20:09.930 --> 00:20:13.910
So the first thing we need to
check is, all of this learning

00:20:13.910 --> 00:20:14.540
is going to be--

00:20:14.540 --> 00:20:19.310
I think people are very
naturally associating more

00:20:19.310 --> 00:20:21.750
calories with more strength.

00:20:21.750 --> 00:20:24.740
Even we have this in
mind-- to a point,

00:20:24.740 --> 00:20:27.730
until we eat too much.

00:20:27.730 --> 00:20:31.090
But this is probably harder to
learn about-- micronutrient

00:20:31.090 --> 00:20:32.620
deficiency.

00:20:32.620 --> 00:20:35.550
Because that's not something
that is as obvious, and you

00:20:35.550 --> 00:20:37.220
don't necessarily know
which foods have what

00:20:37.220 --> 00:20:38.880
nutrients, et cetera.

00:20:38.880 --> 00:20:41.620
And so the first thing we need
to establish is that

00:20:41.620 --> 00:20:44.520
micronutrient deficiency
actually matters.

00:20:44.520 --> 00:20:51.160
And in particular, that the poor
and even the not-so-poor

00:20:51.160 --> 00:20:59.680
could become more productive if
they got more micronutrient

00:20:59.680 --> 00:21:02.330
supplementation in their diet.

00:21:02.330 --> 00:21:08.600
For that, of course, we could
compound the wages of people

00:21:08.600 --> 00:21:11.820
who have more hemoglobin in
their blood and the wages of

00:21:11.820 --> 00:21:14.310
people who have less hemoglobin
in their blood.

00:21:14.310 --> 00:21:16.390
If we do that, what do you
think we will find?

00:21:20.950 --> 00:21:22.870
Most likely?

00:21:22.870 --> 00:21:25.870
We look at the data set, and we
look at the wages of anemic

00:21:25.870 --> 00:21:29.420
people versus the wages
of non-anemic people?

00:21:29.420 --> 00:21:30.626
Richard?

00:21:30.626 --> 00:21:32.944
AUDIENCE: Of course, the
non-anemic people have more

00:21:32.944 --> 00:21:35.484
strength to go to work, so their
wages are higher if they

00:21:35.484 --> 00:21:37.110
are paid by the [INAUDIBLE].

00:21:37.110 --> 00:21:38.940
PROFESSOR: So the non-anemic
people, when we do this

00:21:38.940 --> 00:21:40.960
comparison, will make
more money.

00:21:40.960 --> 00:21:43.540
That's sure poverty in
every data set, we're

00:21:43.540 --> 00:21:44.740
going to find that.

00:21:44.740 --> 00:21:48.300
But once we find that, can we
for sure say it's the effect

00:21:48.300 --> 00:21:49.550
of being anemic?

00:21:52.678 --> 00:21:53.666
AUDIENCE: Not necessarily.

00:21:53.666 --> 00:21:56.186
It could be environmental
factors.

00:21:56.186 --> 00:21:58.601
You could be anemic because
you don't make [INAUDIBLE]

00:21:58.601 --> 00:22:02.465
enough to have a proper diet,
or you could not have wages

00:22:02.465 --> 00:22:03.930
because you're anemic.

00:22:03.930 --> 00:22:04.320
PROFESSOR: Right.

00:22:04.320 --> 00:22:05.960
So there's two things.

00:22:05.960 --> 00:22:08.390
So first, they could be a
reverse causality at play.

00:22:08.390 --> 00:22:10.630
Which is, you could be anemic
because you don't own enough

00:22:10.630 --> 00:22:12.050
to buy spinach.

00:22:12.050 --> 00:22:12.810
That's one.

00:22:12.810 --> 00:22:14.570
And what else could
be at play?

00:22:17.720 --> 00:22:20.560
Even if we manage to shut down
this mechanism, or assume that

00:22:20.560 --> 00:22:22.320
specific mechanism
is not there?

00:22:27.130 --> 00:22:29.935
What could be other things
that would explain this

00:22:29.935 --> 00:22:31.700
correlation between anemia
and [INAUDIBLE]?

00:22:41.200 --> 00:22:42.450
AUDIENCE: It might be
some other third

00:22:42.450 --> 00:22:44.200
factor that causes both.

00:22:44.200 --> 00:22:46.950
For instance, your social
status, perhaps, means you can

00:22:46.950 --> 00:22:48.700
only get a certain
kind of job.

00:22:48.700 --> 00:22:53.450
And it also means that it's
harder for you to get good

00:22:53.450 --> 00:22:55.640
wages and then get
better diet.

00:22:55.640 --> 00:22:56.090
PROFESSOR: Right.

00:22:56.090 --> 00:22:59.460
There could be something
that explains both.

00:22:59.460 --> 00:23:03.610
For example, your social status,
or for example, how

00:23:03.610 --> 00:23:08.270
well-educated you are, or the
types of opportunities you

00:23:08.270 --> 00:23:09.080
have access to.

00:23:09.080 --> 00:23:12.140
Or anything like that would
both effect your

00:23:12.140 --> 00:23:13.560
anemia and your wage.

00:23:13.560 --> 00:23:15.750
So we don't know.

00:23:15.750 --> 00:23:18.880
So that's something which is
actually relatively easy to

00:23:18.880 --> 00:23:24.960
organize as a randomized
experiment, because you can

00:23:24.960 --> 00:23:28.240
pretty much cure anemia, at
least temporarily, by giving

00:23:28.240 --> 00:23:29.490
people iron supplements.

00:23:32.080 --> 00:23:35.290
So that's almost like a medical
study you can give

00:23:35.290 --> 00:23:35.720
some people.

00:23:35.720 --> 00:23:37.980
So this was done in Indonesia.

00:23:37.980 --> 00:23:43.720
The WISE stands for Work and
Iron Status Evaluation.

00:23:43.720 --> 00:23:43.830
They

00:23:43.830 --> 00:23:47.180
They worked with several
thousand households.

00:23:47.180 --> 00:23:51.090
And they provided them with
either an iron supplement or a

00:23:51.090 --> 00:23:54.790
placebo in a randomly-selected
way.

00:23:54.790 --> 00:23:56.660
So they randomized
the household.

00:23:56.660 --> 00:23:58.910
And once they pick a household's
treatment, they

00:23:58.910 --> 00:24:02.200
give everyone in the household
the iron supplement.

00:24:02.200 --> 00:24:06.260
It takes a few months for people
to absorb the iron and

00:24:06.260 --> 00:24:08.540
to become iron-replete.

00:24:08.540 --> 00:24:12.000
Once you're not anemic, you have
enough iron in your body,

00:24:12.000 --> 00:24:13.750
you get rid of the rest.

00:24:13.750 --> 00:24:16.430
So anemia is something which is,
either you are anemic or

00:24:16.430 --> 00:24:17.580
you're not.

00:24:17.580 --> 00:24:20.790
And once you're not-- that is,
once your hemoglobin is above

00:24:20.790 --> 00:24:25.940
13 for men, and for women
it's between 11 and 12.

00:24:25.940 --> 00:24:29.470
That's gram per deciliter.

00:24:29.470 --> 00:24:31.780
You just stop absorbing it.

00:24:31.780 --> 00:24:35.750
So what they found when they
gave this iron supplement is

00:24:35.750 --> 00:24:40.580
that there is no effect of
comparing the people who got

00:24:40.580 --> 00:24:43.910
the placebo and people who got
the pill if they were not

00:24:43.910 --> 00:24:44.680
anemic before.

00:24:44.680 --> 00:24:46.280
There is no impact on them.

00:24:46.280 --> 00:24:48.630
That's exactly what you would
expect, because once you have

00:24:48.630 --> 00:24:49.440
enough, you have enough.

00:24:49.440 --> 00:24:51.270
There is nothing more
we can tell you.

00:24:51.270 --> 00:24:54.440
On the other hand, the more
anemic you were before-- that

00:24:54.440 --> 00:24:57.180
is, the further you were from
12 grams per deciliter of

00:24:57.180 --> 00:25:00.140
hemoglobin in your blood--

00:25:00.140 --> 00:25:04.970
the larger the effect, in
terms of the increase in

00:25:04.970 --> 00:25:06.330
hemoglobin in your blood.

00:25:06.330 --> 00:25:08.710
That is, what they found is that
the people who got the

00:25:08.710 --> 00:25:13.110
supplement almost all got
to 12, or close to 12.

00:25:13.110 --> 00:25:15.700
So the further away
you were from 12,

00:25:15.700 --> 00:25:17.580
the bigger the effect.

00:25:17.580 --> 00:25:20.660
And so, once they do that, they
can separately at people

00:25:20.660 --> 00:25:23.350
who were anemic at baseline
and people who weren't.

00:25:23.350 --> 00:25:28.010
And they found that if you
focus on people who were

00:25:28.010 --> 00:25:35.120
anemic at baseline, and people
who were self-employed, those

00:25:35.120 --> 00:25:39.660
people made substantially more
money after they received the

00:25:39.660 --> 00:25:40.610
iron supplement.

00:25:40.610 --> 00:25:44.490
So they looked at the wages
eight months after the iron

00:25:44.490 --> 00:25:45.910
supplement starts.

00:25:45.910 --> 00:25:48.910
And then there is another end
line a few months later.

00:25:48.910 --> 00:25:51.320
And they find these people
to make more money.

00:25:51.320 --> 00:25:54.280
So about $40 more per year.

00:25:54.280 --> 00:25:58.140
Which is not nothing.

00:25:58.140 --> 00:25:59.750
This is not an enormous
amount.

00:25:59.750 --> 00:26:03.975
This is not a doubling of
the wage or anything.

00:26:03.975 --> 00:26:08.220
The yearly wages of these people
may have been around

00:26:08.220 --> 00:26:09.920
$500 or something like that.

00:26:09.920 --> 00:26:13.290
So it's maybe a little less
than 10% increase.

00:26:13.290 --> 00:26:16.470
But this is very cheap.

00:26:16.470 --> 00:26:18.470
Because if someone wanted to--

00:26:18.470 --> 00:26:21.400
well, actually the experiment
itself was very expensive.

00:26:21.400 --> 00:26:24.230
Because they had to go behind
people's backs and make sure

00:26:24.230 --> 00:26:26.110
that actually eat the pill.

00:26:26.110 --> 00:26:28.670
And they had so many nurses,
and they were really

00:26:28.670 --> 00:26:31.220
controlling that they were
following the protocol.

00:26:31.220 --> 00:26:33.870
So for the experiment itself,
costs much more

00:26:33.870 --> 00:26:36.120
than $40 per person.

00:26:36.120 --> 00:26:41.290
But what they argue in their
paper is that that's not

00:26:41.290 --> 00:26:44.660
really interesting, because if
someone wanted to do it, they

00:26:44.660 --> 00:26:46.690
could just buy 45 fish sauce.

00:26:46.690 --> 00:26:48.817
And that would cost
them only $6.

00:26:48.817 --> 00:26:49.314
Yep.

00:26:49.314 --> 00:26:52.129
AUDIENCE: In the experiment, do
they control for the fact

00:26:52.129 --> 00:26:55.775
that people usually earn higher
wages as time passes?

00:26:55.775 --> 00:26:58.757
So next year, my wage is
probably going to be higher

00:26:58.757 --> 00:27:00.248
than this year, because I
have more experience.

00:27:00.248 --> 00:27:02.236
It means I can get
a better wage.

00:27:02.236 --> 00:27:04.230
I'm better at catching fish.

00:27:04.230 --> 00:27:04.590
PROFESSOR: Right.

00:27:04.590 --> 00:27:05.190
That's an excellent point.

00:27:05.190 --> 00:27:10.250
You're saying you would want to
control for the fact that

00:27:10.250 --> 00:27:12.180
as time passes, you
earn more money.

00:27:12.180 --> 00:27:14.910
So how would they be able to do
that in the context of this

00:27:14.910 --> 00:27:15.640
experiment?

00:27:15.640 --> 00:27:20.122
AUDIENCE: Maybe there's a
historical [INAUDIBLE].

00:27:20.122 --> 00:27:22.861
I'd like to figure out how much
people would earn over

00:27:22.861 --> 00:27:25.102
their lifetime in that region,
and then control for that

00:27:25.102 --> 00:27:26.596
percentage.

00:27:26.596 --> 00:27:30.082
And they can account-- maybe in
the $40 increase, there is

00:27:30.082 --> 00:27:35.560
$22 into that that is perhaps
due to the [INAUDIBLE]

00:27:35.560 --> 00:27:37.580
average increase in wages.

00:27:37.580 --> 00:27:40.790
PROFESSOR: And so you're saying
what they could do to

00:27:40.790 --> 00:27:42.880
control for an historical trend
is to try to find out

00:27:42.880 --> 00:27:45.185
what the historical trend
would have been.

00:27:45.185 --> 00:27:49.490
And in particular, what is in
their data that tells them

00:27:49.490 --> 00:27:52.160
what the historical trend
would have been,

00:27:52.160 --> 00:27:53.500
directly free of charge.

00:27:53.500 --> 00:27:55.270
Not free of charge, because
that was in the design.

00:27:55.270 --> 00:27:56.520
But once you have
the experiment.

00:27:59.200 --> 00:27:59.410
Yeah.

00:27:59.410 --> 00:28:00.330
AUDIENCE: The control group.

00:28:00.330 --> 00:28:00.870
PROFESSOR: The control group.

00:28:00.870 --> 00:28:02.040
There is a placebo group.

00:28:02.040 --> 00:28:04.850
So half the sample
gets nothing.

00:28:04.850 --> 00:28:07.890
So what they actually do in the
experiment is they compare

00:28:07.890 --> 00:28:12.635
the wage growth of people who
got the program to people who

00:28:12.635 --> 00:28:13.700
got the placebo.

00:28:13.700 --> 00:28:15.830
In fact, here they compare
the wage growth of the

00:28:15.830 --> 00:28:20.320
self-employed people who were
anemic at baseline in the

00:28:20.320 --> 00:28:22.660
treatment group and in
the control group.

00:28:22.660 --> 00:28:26.160
And you are exactly right
that those wages

00:28:26.160 --> 00:28:28.610
increase in both cases.

00:28:28.610 --> 00:28:31.220
But they increased faster
in the treatment group.

00:28:31.220 --> 00:28:34.270
And the $40 is the difference
in the growth.

00:28:34.270 --> 00:28:35.770
So it's already accounting
for that.

00:28:38.650 --> 00:28:42.490
So what I say is that, well, if
someone wanted to do it on

00:28:42.490 --> 00:28:45.250
the own, that wouldn't cost
them so much money.

00:28:45.250 --> 00:28:52.420
That would just cost them $6
per year for a gain of $40.

00:28:52.420 --> 00:28:56.120
So this is a case where you
would think it's something

00:28:56.120 --> 00:28:58.630
that starts looking
like an S-shape.

00:28:58.630 --> 00:29:03.710
Which is, if you become rich
enough for spending an extra

00:29:03.710 --> 00:29:08.492
$6, you actually get a return
which is much higher than $6.

00:29:08.492 --> 00:29:13.030
So you may have this increasing
return that is

00:29:13.030 --> 00:29:16.465
necessary for the poverty trap
to emerge, where the slightly

00:29:16.465 --> 00:29:19.280
richer people get the fortified
fish sauce instead

00:29:19.280 --> 00:29:22.590
of the regular fish sauce
that costs them $6, and

00:29:22.590 --> 00:29:24.340
they make $40 extra.

00:29:27.100 --> 00:29:28.480
So you could say, well,
there is something.

00:29:28.480 --> 00:29:30.200
Except that, of course,
you have to ask.

00:29:30.200 --> 00:29:34.400
$6 is not all that much, so what
is preventing these poor

00:29:34.400 --> 00:29:38.470
people to pay $6?

00:29:38.470 --> 00:29:42.600
So that is the first place
where, if we compare this, 40

00:29:42.600 --> 00:29:45.555
to 6 is the first place where
we can see a poverty trap.

00:29:45.555 --> 00:29:48.140
Except we'll have to explain
why it's there.

00:29:48.140 --> 00:29:51.250
We'll have to explain why it
seems that the poor people are

00:29:51.250 --> 00:29:53.485
less likely to spend the
$6 on fortified fish

00:29:53.485 --> 00:29:54.735
sauce in their reach.

00:29:57.490 --> 00:29:58.850
That's for adults.

00:29:58.850 --> 00:30:01.800
So already, we saw that for
calories, we don't see such a

00:30:01.800 --> 00:30:03.810
big return to calorie
consumption.

00:30:03.810 --> 00:30:05.870
By for iron, we see it.

00:30:05.870 --> 00:30:09.480
Now, another place where we do
see, potentially, very large

00:30:09.480 --> 00:30:13.860
returns of investing into food
is when you're trying to

00:30:13.860 --> 00:30:17.800
invest in the nutrition
of your children.

00:30:17.800 --> 00:30:25.940
So why is it that, even
though if we're

00:30:25.940 --> 00:30:27.520
talking about calories--

00:30:27.520 --> 00:30:31.360
even more micronutrients,
but any kind of

00:30:31.360 --> 00:30:34.280
investment in your children--

00:30:34.280 --> 00:30:37.290
may have a larger impact
than the same

00:30:37.290 --> 00:30:40.610
investment for an adult?

00:30:40.610 --> 00:30:42.590
AUDIENCE: Because children are
still growing and developing.

00:30:42.590 --> 00:30:45.560
Their brains are still growing,
and their bones.

00:30:45.560 --> 00:30:49.355
Basically, the frame for who
their going to be is in

00:30:49.355 --> 00:30:51.005
development at this point
in their life.

00:30:51.005 --> 00:30:54.322
So it's important that they can
reach their potential by

00:30:54.322 --> 00:30:57.250
giving them the nutrients
that they need now.

00:30:57.250 --> 00:30:58.250
PROFESSOR: Right.

00:30:58.250 --> 00:31:00.180
AUDIENCE: [INAUDIBLE].

00:31:00.180 --> 00:31:00.610
PROFESSOR: Exactly.

00:31:00.610 --> 00:31:06.250
So the first reason, pure health
reason, is that when

00:31:06.250 --> 00:31:08.910
you're investing into a child's
nutrition, be it

00:31:08.910 --> 00:31:11.860
calorie or micronutrient, you
don't only make the child more

00:31:11.860 --> 00:31:15.340
productive tomorrow, you are
changing the adult that this

00:31:15.340 --> 00:31:16.780
child is going to be.

00:31:16.780 --> 00:31:19.740
You are making this person reach
their genetic potential

00:31:19.740 --> 00:31:22.270
in terms of height, for example,
that they might not

00:31:22.270 --> 00:31:23.950
otherwise be getting.

00:31:23.950 --> 00:31:28.210
You are helping this
person reach that

00:31:28.210 --> 00:31:29.890
potential in their brain.

00:31:29.890 --> 00:31:32.130
You're helping this person
develop the muscles that they

00:31:32.130 --> 00:31:33.270
would have gotten.

00:31:33.270 --> 00:31:37.320
And some of these, you might not
be able to recover later.

00:31:37.320 --> 00:31:41.850
In particular, some of the
nutritional deficiency that

00:31:41.850 --> 00:31:45.280
you get as very small children,
in between weaning

00:31:45.280 --> 00:31:51.660
at about six months and two
years, would be very easy to

00:31:51.660 --> 00:31:54.730
catch up once the child's
actually gone.

00:31:54.730 --> 00:31:57.224
Even once a child is
more than two.

00:31:57.224 --> 00:31:58.020
Yeah.

00:31:58.020 --> 00:31:59.958
AUDIENCE: There's no access
to things like

00:31:59.958 --> 00:32:01.446
education at this point.

00:32:01.446 --> 00:32:03.926
So if they're better nourished
now, then they

00:32:03.926 --> 00:32:04.918
can focus on that.

00:32:04.918 --> 00:32:07.563
Versus an adult probably
wouldn't be thinking about

00:32:07.563 --> 00:32:09.900
going back to school
at that point.

00:32:09.900 --> 00:32:10.155
PROFESSOR: Exactly.

00:32:10.155 --> 00:32:13.340
So the second reason is that,
even if we forget this

00:32:13.340 --> 00:32:17.760
biological phenomenon, the job
of a child is typically to be

00:32:17.760 --> 00:32:21.270
in school, or to learn
things around them.

00:32:21.270 --> 00:32:22.320
Not necessarily in school.

00:32:22.320 --> 00:32:25.835
Some can be outside of school.

00:32:25.835 --> 00:32:29.220
They are still getting all the
information in the world.

00:32:29.220 --> 00:32:31.040
That's what children do.

00:32:31.040 --> 00:32:36.060
And if you do this job better,
then you are building your

00:32:36.060 --> 00:32:37.560
human capitol.

00:32:37.560 --> 00:32:41.650
Really think of it as like, the
capitol of each of us is

00:32:41.650 --> 00:32:44.700
our health, which is affected
by how much we eat directly.

00:32:44.700 --> 00:32:47.540
But also what we know,
our experience, our

00:32:47.540 --> 00:32:49.340
education, et cetera.

00:32:49.340 --> 00:32:53.240
And if we do this job better
as children, we'll have a

00:32:53.240 --> 00:32:56.550
better stock of education for
the rest of our lives.

00:32:56.550 --> 00:32:58.760
Education and knowledge
generally.

00:32:58.760 --> 00:33:03.000
And we are be going to get the
return from that every year.

00:33:03.000 --> 00:33:07.035
So when we do our job better as
an adult, we earn a higher

00:33:07.035 --> 00:33:08.330
wage and that's it.

00:33:08.330 --> 00:33:12.900
When we do our job better at
your age, or even earlier,

00:33:12.900 --> 00:33:14.700
when you were a child, when you
were a small child trying

00:33:14.700 --> 00:33:18.000
to learn things, since your job
is to develop, that means

00:33:18.000 --> 00:33:19.524
you're better developed.

00:33:19.524 --> 00:33:21.612
AUDIENCE: Is it more important
to have good nutrition when

00:33:21.612 --> 00:33:25.340
the mom's pregnant, or after
the child's born?

00:33:25.340 --> 00:33:26.040
PROFESSOR: Both are important.

00:33:26.040 --> 00:33:29.920
We're going to get to
the mom in a minute.

00:33:29.920 --> 00:33:33.720
But both children are important,
and in utero is

00:33:33.720 --> 00:33:34.680
very important.

00:33:34.680 --> 00:33:36.570
Both of them are important.

00:33:36.570 --> 00:33:40.340
So for these reasons, if you
take a child and you say, I'm

00:33:40.340 --> 00:33:43.550
going to feed this child better,
if only between the

00:33:43.550 --> 00:33:45.930
time of six months
to two years--

00:33:45.930 --> 00:33:49.470
or let's say, even if you were
going from six months to ten

00:33:49.470 --> 00:33:53.000
years, when they are in full
development of their body and

00:33:53.000 --> 00:33:53.940
their mind.

00:33:53.940 --> 00:33:57.480
I'm going to then, on
return, potentially,

00:33:57.480 --> 00:33:58.820
for his entire life.

00:33:58.820 --> 00:34:02.390
That means the size in
difference in investment in

00:34:02.390 --> 00:34:05.130
how much you're going to get in
the future compared to the

00:34:05.130 --> 00:34:08.139
investment you are making
is much, much larger.

00:34:08.139 --> 00:34:11.489
And that can, again, give you
the potential for an S-shape.

00:34:11.489 --> 00:34:15.110
Where a poorer person is going
to invest a little less.

00:34:15.110 --> 00:34:17.799
And this difference at
this points can be--

00:34:17.799 --> 00:34:21.739
this difference in slightly
smaller investment at critical

00:34:21.739 --> 00:34:26.889
range could translate into much,
much smaller lifetime

00:34:26.889 --> 00:34:29.130
earning for a child.

00:34:29.130 --> 00:34:33.330
So let's see some examples
of that.

00:34:33.330 --> 00:34:36.489
So the first one is the
deworming example that I was

00:34:36.489 --> 00:34:38.110
talking about.

00:34:38.110 --> 00:34:41.590
And this was done, also, in
a randomized experiment.

00:34:41.590 --> 00:34:43.340
That's the one I was talking
to you about, where they

00:34:43.340 --> 00:34:51.600
realized that the more people
you knew who took the

00:34:51.600 --> 00:34:54.100
deworming, the less likely
you were to take it.

00:34:54.100 --> 00:34:55.940
Well, it turned out that
was actually a mistake.

00:34:55.940 --> 00:35:00.570
Because being dewormed
is extremely helpful.

00:35:00.570 --> 00:35:05.220
So what I did is, this is the
region where they worked,

00:35:05.220 --> 00:35:08.600
where you had a bunch
of schools.

00:35:08.600 --> 00:35:10.350
This is a map.

00:35:10.350 --> 00:35:16.500
You can see that the region
is close to Lake Victoria.

00:35:16.500 --> 00:35:21.410
Worms, particularly
schistosomasis, is something

00:35:21.410 --> 00:35:25.080
that you're much more likely to
get if you are walking in

00:35:25.080 --> 00:35:27.710
the fresh water.

00:35:27.710 --> 00:35:29.170
Particularly when it's
not that clean, but

00:35:29.170 --> 00:35:30.660
when it's not salty.

00:35:30.660 --> 00:35:35.140
So ones basically climb from the
sole of your feet inside.

00:35:35.140 --> 00:35:38.560
So when these kids go fishing
in the lake, or just go hang

00:35:38.560 --> 00:35:41.640
out in the lake, much more
likely to get worms.

00:35:41.640 --> 00:35:44.350
So this region is infected
by worms.

00:35:44.350 --> 00:35:47.810
About a quarter of the worm
children suffer from worms.

00:35:47.810 --> 00:35:50.950
One thing with worms is that
they've never killed anybody.

00:35:50.950 --> 00:35:52.720
At least, not these worms.

00:35:52.720 --> 00:35:55.480
There are some worms
that gives you very

00:35:55.480 --> 00:35:58.250
spectacular, big legs.

00:35:58.250 --> 00:36:00.410
And those worms are a little
bit more fashionable.

00:36:00.410 --> 00:36:03.990
But these little hookworm,
schistosomasis,

00:36:03.990 --> 00:36:06.550
doesn't kill people.

00:36:06.550 --> 00:36:08.910
You can't really see that
someone has them.

00:36:08.910 --> 00:36:10.990
So as a reason, it's not a
disease that anybody's

00:36:10.990 --> 00:36:13.960
particularly excited about.

00:36:13.960 --> 00:36:17.536
I want to make you excited about
worms for about, like,

00:36:17.536 --> 00:36:19.555
at least 15 minutes.

00:36:19.555 --> 00:36:22.140
You can come back and say, well,
these worms, there is

00:36:22.140 --> 00:36:23.710
something with them.

00:36:23.710 --> 00:36:30.120
So the researcher went to this
area, and they separated to

00:36:30.120 --> 00:36:32.840
schools into three
groups randomly.

00:36:32.840 --> 00:36:36.400
Why did they pick the school?

00:36:36.400 --> 00:36:38.810
Why did they decide to randomize
at the school level

00:36:38.810 --> 00:36:41.280
instead of doing it
within school?

00:36:41.280 --> 00:36:43.960
For example, if you remember
the bednet experiment, the

00:36:43.960 --> 00:36:46.540
bednet experiment was done
at the individual level.

00:36:46.540 --> 00:36:49.790
Here, they treated all the
children in the school.

00:36:49.790 --> 00:36:52.260
All the children was
left as control.

00:36:52.260 --> 00:36:56.190
Why did they decide to do
it at the school level?

00:36:56.190 --> 00:36:57.020
Yup.

00:36:57.020 --> 00:36:59.440
AUDIENCE: So kids may
affect one another.

00:36:59.440 --> 00:37:03.150
So if one child in a classroom
is dewormed and the other is

00:37:03.150 --> 00:37:04.764
not, they may be learning
better.

00:37:04.764 --> 00:37:06.538
And because they're learning
better, the other child may

00:37:06.538 --> 00:37:07.668
also be increasing their
understanding.

00:37:07.668 --> 00:37:10.572
If you do it at a school level,
they can cancel out

00:37:10.572 --> 00:37:11.056
that effect.

00:37:11.056 --> 00:37:13.330
So they can compare schools
where all children are and

00:37:13.330 --> 00:37:14.580
schools where all
children aren't.

00:37:14.580 --> 00:37:14.950
PROFESSOR: Right.

00:37:14.950 --> 00:37:17.010
So kids' education could
affect one another.

00:37:17.010 --> 00:37:17.610
What else?

00:37:17.610 --> 00:37:20.613
In what way could they also
affect one another?

00:37:20.613 --> 00:37:23.103
AUDIENCE: Isn't there the
externality, because they're

00:37:23.103 --> 00:37:24.850
very contagious, you said?

00:37:24.850 --> 00:37:25.150
PROFESSOR: Right.

00:37:25.150 --> 00:37:29.930
There is the direct deworming
externality that Zach and Noah

00:37:29.930 --> 00:37:30.870
mentioned earlier.

00:37:30.870 --> 00:37:34.260
Which is actually, worms
are hyper-contagious.

00:37:34.260 --> 00:37:37.990
So if you compare, when they
have done randomized

00:37:37.990 --> 00:37:40.450
experiments before within
schools, they were very

00:37:40.450 --> 00:37:42.420
surprised, because they're
saying, we are deworming these

00:37:42.420 --> 00:37:45.500
children, and we see no
effect on anything.

00:37:45.500 --> 00:37:49.650
And the thing is, the control
kids were re-infecting the

00:37:49.650 --> 00:37:51.900
treated kids, and the treated
kids were also making the

00:37:51.900 --> 00:37:53.640
control kids less sick.

00:37:53.640 --> 00:37:56.140
So the effect was zero.

00:37:56.140 --> 00:37:58.640
So here, they decided, let's
go and randomize the at the

00:37:58.640 --> 00:37:59.980
school level.

00:37:59.980 --> 00:38:04.890
And the first thing they
did is that they

00:38:04.890 --> 00:38:08.370
went into the schools.

00:38:08.370 --> 00:38:10.480
So they did the school
in three groups.

00:38:10.480 --> 00:38:14.680
They dewormed Group 1 in
'98-2003, and then dewormed

00:38:14.680 --> 00:38:18.220
the Group 2 in 1999-2003,
and dewormed the

00:38:18.220 --> 00:38:21.560
group three in 2001-2003.

00:38:21.560 --> 00:38:32.880
So the Group 3 three children
got, on average, less two

00:38:32.880 --> 00:38:37.080
fewer years of deworming
compared to the Group 1 and 2.

00:38:37.080 --> 00:38:40.190
There was a first study they
did, which was they collected

00:38:40.190 --> 00:38:42.790
data in 2000.

00:38:42.790 --> 00:38:45.590
And in 2000, they compared
children in Group 1 and 2 to

00:38:45.590 --> 00:38:46.950
children in Group 3.

00:38:46.950 --> 00:38:49.630
So children in Group 1 and 2 had
been treated either one or

00:38:49.630 --> 00:38:51.930
two years, and children
in Group 3 had not

00:38:51.930 --> 00:38:53.390
being treated yet.

00:38:53.390 --> 00:38:56.060
And what they found at this time
was children, of course,

00:38:56.060 --> 00:38:58.390
were less likely to have worms
if they had been dewormed.

00:38:58.390 --> 00:39:01.670
Otherwise, it's not much
study to talk about.

00:39:01.670 --> 00:39:05.225
Number one is children who had
been dewormed [INAUDIBLE],

00:39:05.225 --> 00:39:07.500
they are less likely
to be anemic.

00:39:07.500 --> 00:39:10.630
And importantly, they are less
likely to miss school.

00:39:10.630 --> 00:39:15.220
So they find that there was
an increase of about 15%.

00:39:15.220 --> 00:39:20.790
So 1/6 of a year in
participation in school.

00:39:20.790 --> 00:39:24.900
So what this study that we are
doing now does is that it's

00:39:24.900 --> 00:39:28.280
tracking the children who were
in primary school at this time

00:39:28.280 --> 00:39:30.400
later when they go up.

00:39:30.400 --> 00:39:35.490
So the date we're going to
look at is in 2007-2009.

00:39:35.490 --> 00:39:43.940
So a kid who was 10 in 1998 is
now 20, and is therefore

00:39:43.940 --> 00:39:46.350
usually doing something,
working.

00:39:46.350 --> 00:39:48.890
And therefore, they can start
looking at whether these

00:39:48.890 --> 00:39:50.420
people are now earning
more money.

00:39:54.940 --> 00:39:58.650
So it's a big project, because
these children have gone all

00:39:58.650 --> 00:40:00.270
over the place.

00:40:00.270 --> 00:40:04.570
So they have had some difficulty
finding them.

00:40:04.570 --> 00:40:08.210
One of them was in London, and
they went and interviewed a

00:40:08.210 --> 00:40:11.600
person in London.

00:40:11.600 --> 00:40:13.620
Many of them had moved to
Nairobi or had moved to

00:40:13.620 --> 00:40:15.940
Mombasa or had moved
to Uganda.

00:40:15.940 --> 00:40:18.510
So what they did is they did a
first wave of it where they

00:40:18.510 --> 00:40:20.430
tried to track everyone.

00:40:20.430 --> 00:40:22.850
And they found about
60% of the people.

00:40:22.850 --> 00:40:27.740
And that's not enough, because
the 40% you don't find might

00:40:27.740 --> 00:40:29.960
be the ones that have
the bigger effect.

00:40:29.960 --> 00:40:32.320
They might be the one that have
moved to London, because

00:40:32.320 --> 00:40:34.300
of the extra education
they are getting.

00:40:34.300 --> 00:40:37.700
So then they decided, let's take
a smaller number of kids,

00:40:37.700 --> 00:40:40.060
and track them wherever
they are.

00:40:40.060 --> 00:40:41.780
Really find them.

00:40:41.780 --> 00:40:43.750
And when you do that,
they found a quite a

00:40:43.750 --> 00:40:44.760
large number of them.

00:40:44.760 --> 00:40:49.060
So that altogether in the
sample, they have about 85% of

00:40:49.060 --> 00:40:52.680
tracking rate, in
treatment and in

00:40:52.680 --> 00:40:55.220
controls very similarly.

00:40:55.220 --> 00:40:58.350
So therefore, we can now look
at what happened to wages.

00:40:58.350 --> 00:41:04.650
So this is the empirical
distribution of log wages.

00:41:04.650 --> 00:41:20.530
So what this tells you
is, roughly, if you

00:41:20.530 --> 00:41:22.090
take any line here--

00:41:22.090 --> 00:41:25.020
for example, it says
log earning of 7.

00:41:25.020 --> 00:41:28.300
So wages tend to
be log numbers.

00:41:28.300 --> 00:41:31.020
So we like to show logs.

00:41:31.020 --> 00:41:36.190
So in the treatment group,
about 10% of

00:41:36.190 --> 00:41:39.310
people, a log of 7.

00:41:39.310 --> 00:41:46.720
And in the control group,
that's about 21%, 25%,

00:41:46.720 --> 00:41:48.260
something like that.

00:41:48.260 --> 00:41:49.770
So what does this mean?

00:41:49.770 --> 00:41:55.780
This What happened to the
distribution of wage between

00:41:55.780 --> 00:41:59.240
treatment and control, and
what does this mean?

00:41:59.240 --> 00:42:00.490
How do we read this?

00:42:04.090 --> 00:42:07.140
You can do it.

00:42:07.140 --> 00:42:09.880
You've seen a distribution,
any distribution before.

00:42:09.880 --> 00:42:11.130
I know that.

00:42:13.100 --> 00:42:16.720
Just describe what happens
to these two curves.

00:42:19.940 --> 00:42:20.780
You, you, you, you.

00:42:20.780 --> 00:42:21.900
I was talking to you.

00:42:21.900 --> 00:42:23.911
Just describe what happens
to these two curves.

00:42:27.140 --> 00:42:33.462
Just tell me, physically, what
happens to these two curves.

00:42:33.462 --> 00:42:34.863
AUDIENCE: [INAUDIBLE].

00:42:34.863 --> 00:42:36.370
PROFESSOR: It moved right.

00:42:36.370 --> 00:42:37.346
Right?

00:42:37.346 --> 00:42:38.322
AUDIENCE: Yeah.

00:42:38.322 --> 00:42:39.220
PROFESSOR: Right?

00:42:39.220 --> 00:42:40.540
That was hard.

00:42:40.540 --> 00:42:42.240
They moved right.

00:42:42.240 --> 00:42:45.150
Now, what is hard is saying,
well, now that they move

00:42:45.150 --> 00:42:47.100
right, what does this mean?

00:42:47.100 --> 00:42:47.677
Noah.

00:42:47.677 --> 00:42:50.062
AUDIENCE: Well, I think
two things.

00:42:50.062 --> 00:42:53.876
Well, first of all, the on
average peaks higher, which

00:42:53.876 --> 00:42:58.836
means that the distribution in
any case, on average, people

00:42:58.836 --> 00:42:59.828
[INAUDIBLE].

00:42:59.828 --> 00:43:04.292
And also, it looks like it's
narrower, which means that

00:43:04.292 --> 00:43:07.268
more people are also earning
more, as opposed to just the

00:43:07.268 --> 00:43:08.770
average also earning more.

00:43:08.770 --> 00:43:09.630
PROFESSOR: Right.

00:43:09.630 --> 00:43:12.070
So those two things
are exactly true.

00:43:12.070 --> 00:43:14.290
So what we see is, number
one, here is the peak.

00:43:14.290 --> 00:43:19.200
So this is where, in the control
group, we get 45% of

00:43:19.200 --> 00:43:24.440
people earning about
a wage of 8.

00:43:24.440 --> 00:43:26.550
That's the mode of
the distribution.

00:43:26.550 --> 00:43:29.240
Then, the nice thing with wages
is they're going to be

00:43:29.240 --> 00:43:33.460
log normal, which means that the
mode is about the medium.

00:43:33.460 --> 00:43:37.910
It also means that 50% of the
people in the control group

00:43:37.910 --> 00:43:40.750
earn less than 8.

00:43:40.750 --> 00:43:44.360
Whereas here, we find that, if
we want to find 50% of the

00:43:44.360 --> 00:43:49.480
people earning less than
something, it's closer.

00:43:49.480 --> 00:43:51.720
So for the control group, it's
like 7 and 1/2, and the

00:43:51.720 --> 00:43:53.930
treatment group is 8.

00:43:53.930 --> 00:43:57.520
So in the control group, 50% of
people earn less than 7 and

00:43:57.520 --> 00:44:00.060
1/2, and in the treatment
group, 50% of people

00:44:00.060 --> 00:44:02.070
earn less than 8.

00:44:02.070 --> 00:44:05.840
And in fact, we could transform
this graph into a

00:44:05.840 --> 00:44:08.910
cumulative distribution function
instead of density.

00:44:08.910 --> 00:44:11.310
And we would find that, given
this graph, given that it's

00:44:11.310 --> 00:44:14.680
nicely shifted to the right
and it's also a little bit

00:44:14.680 --> 00:44:18.500
less valuable, as Noah pointed
out, we would find that at

00:44:18.500 --> 00:44:22.250
every percentage, we have more
people in the control group

00:44:22.250 --> 00:44:25.460
who make less than that
at every level.

00:44:25.460 --> 00:44:27.850
We have a more people in the
control group earning less

00:44:27.850 --> 00:44:29.940
than that than in the
treatment group.

00:44:29.940 --> 00:44:31.170
Which means that--

00:44:31.170 --> 00:44:32.740
Well, it has to mean that
the people in the

00:44:32.740 --> 00:44:34.710
treatment group earn more.

00:44:34.710 --> 00:44:37.800
And not only that, but--

00:44:37.800 --> 00:44:42.120
not every single person,
but statistically--

00:44:42.120 --> 00:44:44.500
everybody in the treatment
does somewhat better.

00:44:44.500 --> 00:44:46.530
So we are saying the
distribution in the treatment

00:44:46.530 --> 00:44:49.180
group statistically dominates
the distribution in the

00:44:49.180 --> 00:44:50.650
control group.

00:44:50.650 --> 00:44:54.720
If you had to choose which
society to live in, without

00:44:54.720 --> 00:44:58.540
knowing, you would pick
the treatment group.

00:44:58.540 --> 00:45:02.005
Because the chance that you are
earning more is better in

00:45:02.005 --> 00:45:03.450
one place than the other.

00:45:03.450 --> 00:45:06.490
So that's what happens with
this distribution.

00:45:06.490 --> 00:45:09.070
We can just look at them and
say, yeah, we have more people

00:45:09.070 --> 00:45:12.205
earning less and we have here
more people earning more.

00:45:15.320 --> 00:45:19.840
So now we can say, well,
how does it look like?

00:45:19.840 --> 00:45:23.550
This could just all be nice
in graph, but there is no

00:45:23.550 --> 00:45:24.490
standard error here.

00:45:24.490 --> 00:45:25.730
There is no confidence
interval.

00:45:25.730 --> 00:45:29.750
Maybe this is not really
very solid.

00:45:29.750 --> 00:45:32.420
So we can look at that
in a regression.

00:45:32.420 --> 00:45:35.810
So this is a simple regression,
which gives us

00:45:35.810 --> 00:45:37.770
directly the difference--

00:45:37.770 --> 00:45:41.760
what you can read here is the
difference between the log

00:45:41.760 --> 00:45:46.800
earning of the treatment group
and the log earning of the

00:45:46.800 --> 00:45:48.270
control group.

00:45:48.270 --> 00:45:52.250
That means I could have plotted
bar charts like we had

00:45:52.250 --> 00:45:53.230
with the bednet.

00:45:53.230 --> 00:45:57.910
It's saying, this is
the mean here.

00:45:57.910 --> 00:46:01.550
The mean wage in the control
group is 7.8, which

00:46:01.550 --> 00:46:04.230
corresponds to above the median
and above the mode of

00:46:04.230 --> 00:46:06.750
the distribution, 7.8.

00:46:06.750 --> 00:46:15.650
And the mean wage for the
treatment group is log.18.

00:46:15.650 --> 00:46:18.050
So that means about 18--

00:46:18.050 --> 00:46:18.980
19, sorry.

00:46:18.980 --> 00:46:21.910
19 percentage points higher
than the mean in

00:46:21.910 --> 00:46:22.840
the treatment group.

00:46:22.840 --> 00:46:26.970
So when we run regression in
logs, the advantage is we can

00:46:26.970 --> 00:46:28.870
read the coefficient
directly as the

00:46:28.870 --> 00:46:30.660
percentage point increases.

00:46:30.660 --> 00:46:34.010
So if we wanted to know, what's
the mean log wages in

00:46:34.010 --> 00:46:34.730
the treatment group?

00:46:34.730 --> 00:46:37.460
What do we need to do
from this graph?

00:46:37.460 --> 00:46:39.030
So make sure that you
have it well.

00:46:41.890 --> 00:46:44.778
Yeah.

00:46:44.778 --> 00:46:49.210
AUDIENCE: Take the median
and multiply it by 1.19.

00:46:49.210 --> 00:46:49.390
PROFESSOR: No.

00:46:49.390 --> 00:46:50.640
So what you would do--

00:46:52.960 --> 00:46:55.080
This is the mean of the log.

00:46:55.080 --> 00:46:58.430
And this is the log point
that they get.

00:46:58.430 --> 00:47:02.950
So if we wanted to know the log
wages for the treatment

00:47:02.950 --> 00:47:09.350
group, all we would need to
do is to add 0.19 to 7.8.

00:47:09.350 --> 00:47:11.180
So that would be about 8.

00:47:11.180 --> 00:47:14.580
And then if we wanted to know
the level then we would take

00:47:14.580 --> 00:47:16.690
the exponential of 8.

00:47:16.690 --> 00:47:17.290
Right?

00:47:17.290 --> 00:47:22.005
So when you have experiments,
you can just take the mean,

00:47:22.005 --> 00:47:24.650
and you can calculate the mean
in the treatment group or the

00:47:24.650 --> 00:47:26.390
mean the control group.

00:47:26.390 --> 00:47:29.600
But in the papers in studies,
what you generally see is

00:47:29.600 --> 00:47:33.500
people running a very simple,
ordinary [INAUDIBLE] square

00:47:33.500 --> 00:47:40.390
regression on wages of whether
you are a treatment person.

00:47:40.390 --> 00:47:43.070
And the way we'll read this is
just saying, this is the

00:47:43.070 --> 00:47:45.110
difference between treatment
and control.

00:47:45.110 --> 00:47:47.520
And this is the mean
for control.

00:47:47.520 --> 00:47:49.870
And then, once we've done that,
we can add other things

00:47:49.870 --> 00:47:52.920
that absolves the noise,
and we'll get

00:47:52.920 --> 00:47:54.040
slightly different results.

00:47:54.040 --> 00:47:55.740
But not very different,
because everything is

00:47:55.740 --> 00:47:57.300
randomized.

00:47:57.300 --> 00:47:59.214
What is this one?

00:47:59.214 --> 00:48:00.590
Over here?

00:48:04.794 --> 00:48:06.690
What is this little
[INAUDIBLE]

00:48:06.690 --> 00:48:07.440
in [INAUDIBLE]?

00:48:07.440 --> 00:48:09.200
Sorry?

00:48:09.200 --> 00:48:09.990
AUDIENCE: Errors?

00:48:09.990 --> 00:48:11.260
PROFESSOR: The standard error.

00:48:11.260 --> 00:48:12.210
Exactly.

00:48:12.210 --> 00:48:14.180
This guy is the standard
error.

00:48:14.180 --> 00:48:17.490
So this is saying there is some
noise around these wages.

00:48:17.490 --> 00:48:20.460
So the difference, the mean,
because we have the

00:48:20.460 --> 00:48:21.350
distribution of wages.

00:48:21.350 --> 00:48:27.760
So there is some variation
around the estimate.

00:48:27.760 --> 00:48:31.840
And therefore, there is some
noise around our estimate of

00:48:31.840 --> 00:48:34.660
the difference between treatment
and control wages.

00:48:34.660 --> 00:48:35.910
And that tells us the
standard error.

00:48:39.170 --> 00:48:42.630
So now we need to know, well,
how do I know whether this

00:48:42.630 --> 00:48:47.300
effect is just due to chance,
or if it's a real effect.

00:48:47.300 --> 00:48:49.100
Once I give you the
coefficient, and

00:48:49.100 --> 00:48:50.960
the standard error.

00:48:50.960 --> 00:48:51.440
Yeah.

00:48:51.440 --> 00:48:53.380
AUDIENCE: If it's more than two
standard errors, isn't it

00:48:53.380 --> 00:48:54.850
significant?

00:48:54.850 --> 00:48:55.770
PROFESSOR: Right.

00:48:55.770 --> 00:48:59.160
So if you divide the coefficient
by the standard

00:48:59.160 --> 00:49:02.760
error, it gives you something
we call the t-statistic.

00:49:02.760 --> 00:49:06.470
For the hypothesis that
the effect is 0.

00:49:06.470 --> 00:49:08.530
So when we divide the
coefficient by the standard

00:49:08.530 --> 00:49:14.130
error, we get the t-statistic,
and the t-statistic is for the

00:49:14.130 --> 00:49:17.170
test that the coefficient
is not 0.

00:49:17.170 --> 00:49:20.190
So the hypothesis is, is
this coefficient 0?

00:49:20.190 --> 00:49:27.470
So each test goes with a level
of confidence, which is the

00:49:27.470 --> 00:49:30.100
probability of a
type one error.

00:49:30.100 --> 00:49:32.900
That is, the probability that
you are saying there is an

00:49:32.900 --> 00:49:35.182
effect when in fact,
there is not.

00:49:35.182 --> 00:49:36.490
Generally in economics--

00:49:36.490 --> 00:49:38.660
I don't know in other fields,
but in economics-- generally

00:49:38.660 --> 00:49:41.580
we go with sizes of 5%.

00:49:41.580 --> 00:49:45.880
So we accept to say that
something has an effect when

00:49:45.880 --> 00:49:49.530
in fact it doesn't with
a probability of 5%.

00:49:49.530 --> 00:49:55.420
And 5% corresponds to a
t-statistic of 1.96.

00:49:55.420 --> 00:49:59.270
So when you see regression table
like this, it's very

00:49:59.270 --> 00:50:01.890
simple if things
are randomized.

00:50:01.890 --> 00:50:04.330
When you see a regression,

00:50:04.330 --> 00:50:06.630
looking at these effects,
gives you the difference

00:50:06.630 --> 00:50:08.440
between treatment and control.

00:50:08.440 --> 00:50:09.940
Divided by it's standard
error.

00:50:09.940 --> 00:50:12.690
And if it's above 1.96, it tells
you that the effect is

00:50:12.690 --> 00:50:14.820
significantly different
from 0.

00:50:14.820 --> 00:50:16.310
That is, there is
a real effect.

00:50:16.310 --> 00:50:18.290
Not an effect due to chance.

00:50:18.290 --> 00:50:21.100
So here of course, it's
much above 2.

00:50:21.100 --> 00:50:23.290
And it's about 19%.

00:50:23.290 --> 00:50:27.110
So it tells you that the wage
of the treated guys is 19%

00:50:27.110 --> 00:50:29.600
higher than the wage of
the control guys.

00:50:29.600 --> 00:50:30.850
Which is a fair amount.

00:50:44.890 --> 00:50:48.280
So why do I say that
19% wage is high?

00:50:55.120 --> 00:50:57.970
What was the economic growth
in Kenya over this period,

00:50:57.970 --> 00:50:59.190
give or take?

00:50:59.190 --> 00:51:00.440
An order of magnitude?

00:51:02.830 --> 00:51:04.320
AUDIENCE: 10%?

00:51:04.320 --> 00:51:05.740
PROFESSOR: 10% would be nice.

00:51:05.740 --> 00:51:07.470
[LAUGHTER]

00:51:07.470 --> 00:51:10.000
PROFESSOR: I don't know if they
had any single year where

00:51:10.000 --> 00:51:11.250
they had 10% growth.

00:51:14.214 --> 00:51:15.100
AUDIENCE: Like 4?

00:51:15.100 --> 00:51:16.280
PROFESSOR: Yeah, 3 4.

00:51:16.280 --> 00:51:16.650
3, 4.

00:51:16.650 --> 00:51:17.908
AUDIENCE: Do you know
what inflation is?

00:51:17.908 --> 00:51:20.790
PROFESSOR: So that would
be in real time.

00:51:20.790 --> 00:51:21.110
AUDIENCE: Adjusted.

00:51:21.110 --> 00:51:22.280
All right.

00:51:22.280 --> 00:51:24.810
PROFESSOR: But this is,
remember, we are comparing

00:51:24.810 --> 00:51:26.280
treatment to control.

00:51:26.280 --> 00:51:28.460
So there is no inflation here,
because our treatment people

00:51:28.460 --> 00:51:31.670
were measured at
the same time.

00:51:31.670 --> 00:51:32.890
Take real growth.

00:51:32.890 --> 00:51:37.470
If we are saying 3% to 4% a
year, we are being generous to

00:51:37.470 --> 00:51:39.790
Kenya for the average.

00:51:39.790 --> 00:51:43.570
So that means that these guys
got the equivalent of several

00:51:43.570 --> 00:51:48.130
years of good economic growth,
except there has not been many

00:51:48.130 --> 00:51:51.610
years in Kenya where there has
been several years of good

00:51:51.610 --> 00:51:53.790
economic growth.

00:51:53.790 --> 00:51:56.330
So that's why I wanted to get
you excited about worms for

00:51:56.330 --> 00:51:58.090
five minutes.

00:51:58.090 --> 00:52:03.000
So this thing corresponds to
giving the kids a pill which

00:52:03.000 --> 00:52:06.720
costs about, including the
delivery cost and all of that,

00:52:06.720 --> 00:52:11.640
about $0.60 of delivering
the pill.

00:52:11.640 --> 00:52:14.090
You need to do that
twice a year.

00:52:14.090 --> 00:52:16.590
And this is a difference between
doing it for three

00:52:16.590 --> 00:52:18.290
years versus one.

00:52:18.290 --> 00:52:20.780
So this is your investment,
it's probably a good

00:52:20.780 --> 00:52:24.590
investment, that was delivered
by society here in the form of

00:52:24.590 --> 00:52:28.290
this NGO, was a [INAUDIBLE].

00:52:28.290 --> 00:52:30.290
And that's 19% per year.

00:52:30.290 --> 00:52:32.910
That's a lot.

00:52:32.910 --> 00:52:35.280
Even people, if they are to do
it themselves, maybe they have

00:52:35.280 --> 00:52:38.640
to do to the shop so they
don't get it for $0.60.

00:52:38.640 --> 00:52:39.890
They have to pay $1.

00:52:42.030 --> 00:52:45.410
Then they get several years of
good growth for the entire

00:52:45.410 --> 00:52:47.140
lifetime of the child.

00:52:47.140 --> 00:52:49.690
So we are talking about, for
a lifetime [INAUDIBLE]

00:52:49.690 --> 00:52:53.310
of several thousand dollars
of extra wages.

00:52:53.310 --> 00:52:56.280
And we can see it here.

00:52:56.280 --> 00:53:00.770
So what this is is these are
the benefits that you're

00:53:00.770 --> 00:53:04.540
getting from this 19% increase
in earnings.

00:53:04.540 --> 00:53:08.260
So imagine that you get 19%
increase in earnings.

00:53:08.260 --> 00:53:13.130
Take the GDP of Kenya, or the
average wage level of Kenya.

00:53:13.130 --> 00:53:14.540
Multiplied by 19%.

00:53:14.540 --> 00:53:16.720
That's how much you're
getting every year.

00:53:16.720 --> 00:53:19.900
Then you have to compute
the net present value.

00:53:19.900 --> 00:53:24.150
Because the benefit that you're
getting if you have to

00:53:24.150 --> 00:53:27.180
pay the investment today, but
you're starting to get the

00:53:27.180 --> 00:53:29.660
return when you're 20 and then
over your lifetime, it's not

00:53:29.660 --> 00:53:31.330
as valuable.

00:53:31.330 --> 00:53:34.690
So we are using some
[INAUDIBLE], let's say 5%.

00:53:34.690 --> 00:53:36.910
And we are computing the net
present value of those

00:53:36.910 --> 00:53:40.990
earnings, like we would for
the investment in a stock.

00:53:40.990 --> 00:53:46.760
So when you do that, you get
over $1,000 increasing in your

00:53:46.760 --> 00:53:47.630
lifetime earnings.

00:53:47.630 --> 00:53:48.482
So this is that.

00:53:48.482 --> 00:53:50.680
And this is how much it cost.

00:53:50.680 --> 00:53:55.980
So you need to deliver the
pills, $0.65 per year, and

00:53:55.980 --> 00:53:57.880
then they wanted to--

00:53:57.880 --> 00:54:03.130
so that would be a huge benefit
of, like, $1,500 or

00:54:03.130 --> 00:54:06.310
$1,100 divided by $0.65.

00:54:06.310 --> 00:54:08.770
That would be pretty gigantic.

00:54:08.770 --> 00:54:11.450
That's why worms are exciting.

00:54:11.450 --> 00:54:13.580
Well, they don't want to make
it too exciting, so they are

00:54:13.580 --> 00:54:17.300
saying, well, let's see what all
the costs we need to add.

00:54:17.300 --> 00:54:19.720
Well, these kids have gone to
school a little longer.

00:54:19.720 --> 00:54:21.920
They've gone to school
more every year.

00:54:21.920 --> 00:54:25.440
So while in school, they are not
playing, or they are not

00:54:25.440 --> 00:54:27.430
earning some wage.

00:54:27.430 --> 00:54:29.510
So they are making some
assumption of what is this

00:54:29.510 --> 00:54:31.390
opportunity cost.

00:54:31.390 --> 00:54:33.900
Other wage, unskilled wage.

00:54:33.900 --> 00:54:35.510
All of the day they spend
in school, they

00:54:35.510 --> 00:54:38.206
assign them the wage.

00:54:38.206 --> 00:54:40.680
That's an over-estimate, because
usually the kids are

00:54:40.680 --> 00:54:42.840
just doing nothing, because
they are sick.

00:54:42.840 --> 00:54:44.990
So this is being very
generous for the

00:54:44.990 --> 00:54:47.330
cost of being in school.

00:54:47.330 --> 00:54:49.535
And then, they also add the
fact that if you have more

00:54:49.535 --> 00:54:52.693
kids in school, you need to
have, maybe, a little bit more

00:54:52.693 --> 00:54:53.770
teachers and all that.

00:54:53.770 --> 00:54:56.800
So they also can create
how much that can be.

00:54:56.800 --> 00:55:00.340
So these things, you might want
to put them, or you might

00:55:00.340 --> 00:55:01.980
not want to put them.

00:55:01.980 --> 00:55:04.820
But the bottom line is that when
you do that, this bar is

00:55:04.820 --> 00:55:07.460
pretty huge, and this bar
is pretty minimal.

00:55:07.460 --> 00:55:08.408
Yeah.

00:55:08.408 --> 00:55:11.252
AUDIENCE: If they're so clear,
why doesn't Kenya's government

00:55:11.252 --> 00:55:12.680
support it?

00:55:12.680 --> 00:55:15.020
PROFESSOR: Well, the
answer is they do.

00:55:15.020 --> 00:55:18.810
Because until this study, it
wasn't so obvious that the

00:55:18.810 --> 00:55:20.000
benefits are so large.

00:55:20.000 --> 00:55:22.775
Because how would you know?

00:55:22.775 --> 00:55:26.330
You only had those experiments
where you were comparing

00:55:26.330 --> 00:55:27.920
people within the same school.

00:55:27.920 --> 00:55:30.490
And you found no effect
of deworming.

00:55:30.490 --> 00:55:32.280
So this study came.

00:55:32.280 --> 00:55:34.660
That's an interesting political
economic story.

00:55:34.660 --> 00:55:37.520
This study came-- the first one,
not even the second one.

00:55:37.520 --> 00:55:39.850
And showed that it basically
costs nothing

00:55:39.850 --> 00:55:41.625
to put kids in school.

00:55:41.625 --> 00:55:44.390
The cheapest way to
get kids to attend

00:55:44.390 --> 00:55:46.270
to school more regularly.

00:55:46.270 --> 00:55:51.470
So the researchers and us here
at Poverty Action have started

00:55:51.470 --> 00:55:54.820
to advertise this as, you might
not have thought it that

00:55:54.820 --> 00:55:56.530
way, but deworming is
the cheapest way

00:55:56.530 --> 00:55:59.450
to get kids in school.

00:55:59.450 --> 00:56:00.860
We went to Davos.

00:56:00.860 --> 00:56:04.820
Davos is this world congress
of rich people.

00:56:04.820 --> 00:56:08.525
And we presented this kind of
data, and showed to them, you

00:56:08.525 --> 00:56:11.240
know what, you might not think
deworming is so exciting, but

00:56:11.240 --> 00:56:11.920
in fact it is.

00:56:11.920 --> 00:56:13.780
Because it's a great
investment.

00:56:13.780 --> 00:56:16.510
So they kind of liked
the idea.

00:56:16.510 --> 00:56:17.920
Well, we started
an organization

00:56:17.920 --> 00:56:19.330
called deworm the world.

00:56:19.330 --> 00:56:22.140
And started just diffusing
these kind of results.

00:56:22.140 --> 00:56:24.440
We didn't even have the
wage results yet.

00:56:24.440 --> 00:56:27.080
It was just education results,
saying, deworming is a

00:56:27.080 --> 00:56:28.650
sensible education policy.

00:56:28.650 --> 00:56:31.090
It's a very cheap way to
get kids in school.

00:56:31.090 --> 00:56:33.920
And started working with the
government to get this

00:56:33.920 --> 00:56:35.890
information out.

00:56:35.890 --> 00:56:38.710
One complicated thing with
deworming from a political

00:56:38.710 --> 00:56:42.380
economic point of view is that
it's a health program that you

00:56:42.380 --> 00:56:44.720
want to do in school.

00:56:44.720 --> 00:56:46.430
The reason why you want to do
it in school is you have all

00:56:46.430 --> 00:56:46.770
the kids there.

00:56:46.770 --> 00:56:48.430
That's why it's cheap.

00:56:48.430 --> 00:56:51.430
But when you want to do a health
program in school, you

00:56:51.430 --> 00:56:54.630
need the Health Ministry and
the Education Ministry to

00:56:54.630 --> 00:56:57.120
collaborate, or you need the
Finance Ministry to tell

00:56:57.120 --> 00:56:58.370
them, you do it.

00:56:58.370 --> 00:57:01.760
So that takes some effort,
but that effort got done.

00:57:01.760 --> 00:57:05.300
And in fact, in Kenya they are
now deworming everywhere.

00:57:05.300 --> 00:57:08.130
So that's millions, millions
of children.

00:57:08.130 --> 00:57:10.120
And then this is also
moving up and down.

00:57:10.120 --> 00:57:12.550
They're going to start doing it
in Bihar, which is a state

00:57:12.550 --> 00:57:14.930
in India where they also
have a lot of worms.

00:57:14.930 --> 00:57:16.910
They have started doing it in
Andhra Pradesh, where there is

00:57:16.910 --> 00:57:19.570
not that many worms, but they
have subregions in Andhra

00:57:19.570 --> 00:57:20.900
Pradesh with a lot of worms.

00:57:20.900 --> 00:57:23.710
And in this way, the information
gets out, and

00:57:23.710 --> 00:57:25.275
progressively it's taken up.

00:57:25.275 --> 00:57:27.887
AUDIENCE: In Kenya, did the
government sponsor the

00:57:27.887 --> 00:57:30.510
deworming program, or was
it outside donors?

00:57:30.510 --> 00:57:36.750
PROFESSOR: In Kenya, the
answer is yes and no.

00:57:36.750 --> 00:57:42.090
The direct answer is yes, but
it is subsidized in part by

00:57:42.090 --> 00:57:51.720
the Fast Track Initiative, which
is international money

00:57:51.720 --> 00:57:55.790
that government can access to do
things that help education.

00:57:55.790 --> 00:58:00.110
So Kenya can elect to use Fast
Track Initiative money to do

00:58:00.110 --> 00:58:03.890
textbooks, or to do computers
in school, or to do

00:58:03.890 --> 00:58:06.160
blackboards, or to pay
teachers more.

00:58:06.160 --> 00:58:07.570
And what they did is
they took some of

00:58:07.570 --> 00:58:09.960
that money to do deworming.

00:58:09.960 --> 00:58:13.670
The thing is, deworming is cheap
enough that once you

00:58:13.670 --> 00:58:16.940
realize that it is a good thing
to do, money is less the

00:58:16.940 --> 00:58:20.200
issue than getting everybody
on board and organized.

00:58:20.200 --> 00:58:20.450
Yeah.

00:58:20.450 --> 00:58:22.109
AUDIENCE: And so I'm thinking
there's probably other

00:58:22.109 --> 00:58:25.190
developing countries that have
significantly worm issues.

00:58:25.190 --> 00:58:27.086
And then why aren't those
countries doing it?

00:58:27.086 --> 00:58:29.470
You mentioned India, but I'd
imagine there's a lot more.

00:58:29.470 --> 00:58:29.960
PROFESSOR: Yes.

00:58:29.960 --> 00:58:35.010
So the answer is slowly, slowly
they are getting into

00:58:35.010 --> 00:58:36.070
the bandwagon.

00:58:36.070 --> 00:58:38.440
But that's a very good question,
which is, number one

00:58:38.440 --> 00:58:40.470
you need to have the
evidence out.

00:58:40.470 --> 00:58:42.720
And until fairly recently,
in particular until this

00:58:42.720 --> 00:58:45.150
experiment, the evidence
wasn't out.

00:58:45.150 --> 00:58:47.400
And this is not something
that people could just

00:58:47.400 --> 00:58:48.720
make up on their own.

00:58:48.720 --> 00:58:51.910
I think in particular, the
effect on education, I don't

00:58:51.910 --> 00:58:54.335
think the first thing that
comes to an education

00:58:54.335 --> 00:58:57.130
minister, or the first thing
that would come to you, if I'd

00:58:57.130 --> 00:58:59.800
asked you in principle, how
would you increase education?

00:58:59.800 --> 00:59:01.300
What's the cheapest
way to do it?

00:59:01.300 --> 00:59:03.380
I don't think deworming would
have been very high on your

00:59:03.380 --> 00:59:04.250
radar screen.

00:59:04.250 --> 00:59:06.600
It's not very high on anybody's
radar screen,

00:59:06.600 --> 00:59:08.130
precisely because worms
don't kill.

00:59:08.130 --> 00:59:11.030
So people think of HIV as being
important, which it is.

00:59:11.030 --> 00:59:12.230
But people don't
think of worms.

00:59:12.230 --> 00:59:13.410
So that's the first reason.

00:59:13.410 --> 00:59:17.200
Once the information is out,
then it needs to be

00:59:17.200 --> 00:59:17.750
percolated.

00:59:17.750 --> 00:59:20.500
People need to absorb It.

00:59:20.500 --> 00:59:22.500
And I think this is happening,
actually.

00:59:22.500 --> 00:59:26.230
This is one of the pretty
hopeful stories, in terms of

00:59:26.230 --> 00:59:27.690
that the evidence can
make a difference.

00:59:30.915 --> 00:59:33.390
AUDIENCE: I can understand
where you argue with

00:59:33.390 --> 00:59:37.350
government about education
effects,

00:59:37.350 --> 00:59:38.340
especially in children.

00:59:38.340 --> 00:59:41.310
But when you get something
as long as wage

00:59:41.310 --> 00:59:44.280
effects, pretty long time.

00:59:44.280 --> 00:59:49.725
Are you assuming that no other
health hazards would offset

00:59:49.725 --> 00:59:55.210
the gains which can be obtained
from deworming.

00:59:55.210 --> 00:59:55.460
PROFESSOR: Right.

00:59:55.460 --> 00:59:58.040
So the question is whether I'm
assuming that there are no

00:59:58.040 --> 00:59:59.730
other things that will happen.

00:59:59.730 --> 01:00:02.165
And the beauty of this is I'm
not assuming anything.

01:00:05.790 --> 01:00:07.010
In fact, I didn't.

01:00:07.010 --> 01:00:11.610
But Ted Miguel and Michael
Kremer dewormed

01:00:11.610 --> 01:00:14.400
the children in 1999.

01:00:14.400 --> 01:00:16.610
And then they had the foresight
of deciding, we need

01:00:16.610 --> 01:00:19.570
to continue to track them to
find out whether or not there

01:00:19.570 --> 01:00:21.500
is a wage effect.

01:00:21.500 --> 01:00:24.120
If you want to know my prior
when they started this

01:00:24.120 --> 01:00:27.690
exercise, very honestly, is that
you're wasting your time.

01:00:27.690 --> 01:00:29.600
All of these other things
will be happening.

01:00:29.600 --> 01:00:31.570
You're never going to
find an effect.

01:00:31.570 --> 01:00:35.100
And so when this came up, I
was very surprised in a

01:00:35.100 --> 01:00:37.900
positive way.

01:00:37.900 --> 01:00:40.450
But these results were not
even used to sell the

01:00:40.450 --> 01:00:42.730
deworming to the government,
because we didn't have them

01:00:42.730 --> 01:00:45.210
till very recently.

01:00:45.210 --> 01:00:46.710
Only the education results
were used,

01:00:46.710 --> 01:00:48.400
which are very immediate.

01:00:48.400 --> 01:00:50.410
But the point here, you see,
you don't assume anything.

01:00:50.410 --> 01:00:53.490
Whatever things would have
happened, happened.

01:00:53.490 --> 01:00:55.560
And surprisingly,
didn't offset.

01:00:55.560 --> 01:00:57.210
That's what the standard
error tells you.

01:01:03.800 --> 01:01:06.510
So deworming is an interesting
policy, because it's a good

01:01:06.510 --> 01:01:08.610
policy that's not
obviously good.

01:01:08.610 --> 01:01:11.480
So it is nobody's
first choice.

01:01:11.480 --> 01:01:13.390
So you have to make it people's
first choice.

01:01:13.390 --> 01:01:16.904
The evidence plays a role,
and then some convincing.

01:01:16.904 --> 01:01:22.540
And what is interesting is that
the parents themselves,

01:01:22.540 --> 01:01:24.520
they could do with
them as well.

01:01:24.520 --> 01:01:26.780
And so the second question we
want to ask, which is the

01:01:26.780 --> 01:01:29.530
individual version of
the same, why don't

01:01:29.530 --> 01:01:30.120
government do it?

01:01:30.120 --> 01:01:32.210
Is why don't parents do it?

01:01:32.210 --> 01:01:34.440
Which is the same question
as, why don't people

01:01:34.440 --> 01:01:36.290
buy the fish sauce.

01:01:36.290 --> 01:01:38.630
We'll get to it in a moment,
we'll collect the thing.

01:01:38.630 --> 01:01:39.640
Unless you want to have a--

01:01:39.640 --> 01:01:42.617
AUDIENCE: For deworming, could
you just treat the water that

01:01:42.617 --> 01:01:46.030
the children walk in, so that
the worms don't go in the

01:01:46.030 --> 01:01:48.560
water, so the kids
won't get worms.

01:01:48.560 --> 01:01:50.170
PROFESSOR: So the question is,
could you treat the water

01:01:50.170 --> 01:01:51.370
instead of treating the kids?

01:01:51.370 --> 01:01:53.370
I think that's an excellent
idea, because you could do it.

01:01:53.370 --> 01:01:56.303
Except that Lake Victoria
is really big.

01:01:56.303 --> 01:02:00.440
So I think for Lake Victoria it
would be a bit difficult.

01:02:00.440 --> 01:02:01.920
It's really, really big.

01:02:01.920 --> 01:02:04.895
It's almost like a freshwater
sea in the middle.

01:02:04.895 --> 01:02:06.200
AUDIENCE: It's not just
a lake, right?

01:02:06.200 --> 01:02:08.802
It's also puddles and
things like that.

01:02:08.802 --> 01:02:11.628
People walking through
there with no shoes.

01:02:11.628 --> 01:02:12.100
PROFESSOR: Yeah.

01:02:12.100 --> 01:02:18.100
It's any body of fresh water
that creates the problem.

01:02:18.100 --> 01:02:21.130
So that's general nutrition.

01:02:21.130 --> 01:02:22.970
There are other examples of
effective [INAUDIBLE]

01:02:22.970 --> 01:02:23.700
of nutrition.

01:02:23.700 --> 01:02:26.750
But now let's skip to the
third one, which is the

01:02:26.750 --> 01:02:29.260
nutrition in the womb, which
is what you were asking.

01:02:29.260 --> 01:02:31.610
Whether it's not even more
important to feed

01:02:31.610 --> 01:02:32.930
the pregnant woman.

01:02:32.930 --> 01:02:35.180
And the answer is that it is.

01:02:35.180 --> 01:02:40.250
So there is a doctor in the UK
called Dr. Barker who this

01:02:40.250 --> 01:02:41.765
hypothesis has his name.

01:02:41.765 --> 01:02:43.730
It's called the Barker
Hypothesis.

01:02:43.730 --> 01:02:47.030
What he found is that basically,
he found that the

01:02:47.030 --> 01:02:51.120
region which had the highest
child mortality, infant

01:02:51.120 --> 01:02:53.660
mortality, neo-natal mortality,
were also the

01:02:53.660 --> 01:02:57.110
places where people, once they
were born, had the lowest life

01:02:57.110 --> 01:02:58.080
expectancy.

01:02:58.080 --> 01:03:02.080
And he concluded that this was
a sign that your condition of

01:03:02.080 --> 01:03:04.180
life in utero were
really important.

01:03:04.180 --> 01:03:06.090
Of course, that was not
convincing at all, because the

01:03:06.090 --> 01:03:09.920
regions that have the highest
infant mortality also are

01:03:09.920 --> 01:03:12.220
pretty bad in many
other respects.

01:03:12.220 --> 01:03:15.110
And you will expect that these
people live less long.

01:03:15.110 --> 01:03:18.620
But still, he's the first one
who formulated the hypothesis.

01:03:18.620 --> 01:03:21.530
And despite the fact that his
evidence was weak for it, the

01:03:21.530 --> 01:03:25.150
hypothesis was right, as we
subsequently discovered.

01:03:25.150 --> 01:03:28.450
I'm going to give you a few
examples where it was seen

01:03:28.450 --> 01:03:29.620
very clearly.

01:03:29.620 --> 01:03:32.700
One of the big names in this
is an economist at Colombia

01:03:32.700 --> 01:03:34.740
named Doug Almond.

01:03:34.740 --> 01:03:38.470
And the first thing that Doug
Almond found is that he looked

01:03:38.470 --> 01:03:47.390
at people who were born just
after 1918, which is the

01:03:47.390 --> 01:03:51.510
period where there was a big,
big flew epidemic in the US.

01:03:51.510 --> 01:03:54.250
So many people died
of the flu.

01:03:54.250 --> 01:03:55.710
Adults died of the flu.

01:03:55.710 --> 01:03:58.450
But many people didn't,
and still had it.

01:03:58.450 --> 01:04:01.370
And in particular, a lot of kids
were born from moms who

01:04:01.370 --> 01:04:03.260
had had the flu.

01:04:03.260 --> 01:04:10.480
And the paper here was very
simple, which was to compare

01:04:10.480 --> 01:04:15.090
the life outcomes of people who
were in utero during the

01:04:15.090 --> 01:04:16.540
period of the flu.

01:04:16.540 --> 01:04:19.430
He doesn't even know whether
their mother had the flu.

01:04:19.430 --> 01:04:23.340
It just makes it quite likely
that their mother had the flu

01:04:23.340 --> 01:04:26.570
if they were born during
that period.

01:04:26.570 --> 01:04:29.360
And they found that children who
were in this period during

01:04:29.360 --> 01:04:33.000
the big flu pandemics were
sicker as adults.

01:04:33.000 --> 01:04:35.260
They were more likely to have
all sorts of diseases.

01:04:35.260 --> 01:04:36.700
Name a disease, they have it.

01:04:36.700 --> 01:04:38.450
Or they are more likely
to have it.

01:04:38.450 --> 01:04:39.680
They were earning less money.

01:04:39.680 --> 01:04:42.840
They were less likely to
have gone to college.

01:04:42.840 --> 01:04:45.630
And they died earlier,
they died younger.

01:04:45.630 --> 01:04:48.330
So that was one of
the first people.

01:04:48.330 --> 01:04:52.290
So particularly if your mom had
the flu when you were in

01:04:52.290 --> 01:04:53.420
utero, that's not good.

01:04:53.420 --> 01:04:55.450
That's not nutrition.

01:04:55.450 --> 01:04:56.640
Other effects--

01:04:56.640 --> 01:04:58.620
still a paper by Doug Almond--

01:04:58.620 --> 01:05:03.420
is that people who are born
during or just after the

01:05:03.420 --> 01:05:04.410
Chinese famine--

01:05:04.410 --> 01:05:08.780
or even just after is
a better number.

01:05:08.780 --> 01:05:11.280
Children who are born just after
the Chinese famine, so

01:05:11.280 --> 01:05:14.200
who were in utero during
the famine, they of

01:05:14.200 --> 01:05:16.380
course live less long.

01:05:16.380 --> 01:05:17.300
They are shorter.

01:05:17.300 --> 01:05:19.290
They have lower wages.

01:05:19.290 --> 01:05:23.710
And even the children of the
children of these people are

01:05:23.710 --> 01:05:26.650
shorter and doing less
well in life.

01:05:26.650 --> 01:05:29.810
So there is even a second
generation that's let's

01:05:29.810 --> 01:05:32.300
productive, fertile,
et cetera if you

01:05:32.300 --> 01:05:35.610
were born in the famine.

01:05:35.610 --> 01:05:38.430
There is, of course, a bias in
this, when we look at the

01:05:38.430 --> 01:05:40.860
children who were born just
after the famine.

01:05:40.860 --> 01:05:42.110
Which comes from what?

01:05:46.137 --> 01:05:48.265
AUDIENCE: They probably also
experienced ramifications of

01:05:48.265 --> 01:05:50.400
the famine afterwards.

01:05:50.400 --> 01:05:50.580
PROFESSOR: Right.

01:05:50.580 --> 01:05:52.690
So it was afterwards.

01:05:52.690 --> 01:05:55.280
The famine was very brutal,
and ended and

01:05:55.280 --> 01:05:56.840
started very brutally.

01:05:56.840 --> 01:05:59.560
So we might expect that there
is not so much effect after.

01:05:59.560 --> 01:06:03.030
That but on the other hand,
what do you expect happens

01:06:03.030 --> 01:06:04.280
during the famine?

01:06:07.874 --> 01:06:09.216
AUDIENCE: Probably disease.

01:06:09.216 --> 01:06:10.950
PROFESSOR: A lot of diseases
in particular.

01:06:10.950 --> 01:06:12.810
A lot of adults died.

01:06:12.810 --> 01:06:15.790
We are talking about 59
million adults dying.

01:06:15.790 --> 01:06:19.450
And a lot of people probably
were never born.

01:06:19.450 --> 01:06:23.360
And in particular, there were
stillborns or miscarriages.

01:06:23.360 --> 01:06:26.570
So the people who made it
despite the fact that they

01:06:26.570 --> 01:06:29.430
were in utero doing this period,
the babies who managed

01:06:29.430 --> 01:06:32.210
to get born are probably pretty
good genetic potential

01:06:32.210 --> 01:06:33.990
to start with.

01:06:33.990 --> 01:06:37.500
And despite that, they are doing
much less well in life.

01:06:37.500 --> 01:06:40.420
So there is a bias, but it goes
in the direction of not

01:06:40.420 --> 01:06:42.150
finding an effect
of the famine.

01:06:42.150 --> 01:06:44.630
Because surviving during the
famine already indicates that

01:06:44.630 --> 01:06:48.067
you're a pretty feisty child.

01:06:48.067 --> 01:06:50.490
So that's quite extreme.

01:06:50.490 --> 01:06:53.460
You would say, yes, of course
being in utero during a famine

01:06:53.460 --> 01:06:54.410
is a bad idea.

01:06:54.410 --> 01:06:56.780
You should avoid it at
all costs if you can.

01:06:56.780 --> 01:07:00.170
But maybe it's not particularly
relevant.

01:07:00.170 --> 01:07:02.580
Because after all, we are not
talking about famine for most

01:07:02.580 --> 01:07:03.140
poor people.

01:07:03.140 --> 01:07:06.490
We are talking about
malnutrition and

01:07:06.490 --> 01:07:08.020
ill-nutrition.

01:07:08.020 --> 01:07:11.220
So here is one example
of that.

01:07:11.220 --> 01:07:18.270
Is that children who were in
utero during Ramadan--

01:07:18.270 --> 01:07:22.610
and Ramadan shifts, so it's
not a particular season.

01:07:22.610 --> 01:07:25.490
So we can look at kids who were
in utero doing Ramadan

01:07:25.490 --> 01:07:27.780
who were born in September, who
were born in October, who

01:07:27.780 --> 01:07:28.890
were born in December.

01:07:28.890 --> 01:07:30.935
All over the year.

01:07:30.935 --> 01:07:33.550
This is a paper that
looks at Uganda.

01:07:33.550 --> 01:07:40.190
Children born of Muslim mothers
and who were in utero

01:07:40.190 --> 01:07:42.510
during Ramadan, in particular
in the first trimester of

01:07:42.510 --> 01:07:46.780
pregnancy during the Ramadan,
are less educated.

01:07:49.460 --> 01:07:54.220
It's many less educated and
earn less as adults.

01:07:54.220 --> 01:08:00.620
And with Ramadan, it's not even
that you are not eating.

01:08:00.620 --> 01:08:02.280
You're not eating
during the day.

01:08:02.280 --> 01:08:03.670
But people eat during
the night.

01:08:03.670 --> 01:08:06.990
But these long periods of
fasting are no good.

01:08:06.990 --> 01:08:11.830
That's interesting, because
you don't have to observe

01:08:11.830 --> 01:08:14.760
Ramadan when you're pregnant.

01:08:14.760 --> 01:08:16.380
You could not do it.

01:08:16.380 --> 01:08:18.439
And if you're really observant,
in fact, you have

01:08:18.439 --> 01:08:22.229
the option of not doing
it and doing it later.

01:08:22.229 --> 01:08:28.300
But pregnant women tend to do
Ramadan anyway because other

01:08:28.300 --> 01:08:29.795
people around them do it.

01:08:29.795 --> 01:08:32.569
And what is interesting here
is that, in terms of policy

01:08:32.569 --> 01:08:37.569
implication, it could be
encouraged to say, you can not

01:08:37.569 --> 01:08:38.680
observe the Ramadan.

01:08:38.680 --> 01:08:42.790
Not everybody does it because
it's acceptable not to observe

01:08:42.790 --> 01:08:44.340
it, potentially.

01:08:44.340 --> 01:08:45.910
But most women do.

01:08:45.910 --> 01:08:51.939
And this is not good
for their children.

01:08:51.939 --> 01:08:55.490
And even though it's not
something massive, it's this

01:08:55.490 --> 01:08:56.519
shift in the consumption.

01:08:56.519 --> 01:09:00.390
The calories probably stay
relatively constant.

01:09:00.390 --> 01:09:03.439
Another example-- which, again,
is nothing extreme--

01:09:03.439 --> 01:09:08.149
the paper by Erica Field and
Maximo Torero, which looks at

01:09:08.149 --> 01:09:11.720
one particular micronutrient,
which is iodine.

01:09:11.720 --> 01:09:20.029
So iodine deficiency in
adulthood create this thyroid

01:09:20.029 --> 01:09:23.170
insuffiency, so it makes
you a bit slow.

01:09:23.170 --> 01:09:26.569
So in French, the expression
"cretin" comes from that.

01:09:26.569 --> 01:09:31.170
In French, we say "cretin of the
Alps," because people from

01:09:31.170 --> 01:09:33.130
the Alps were very
far from the sea.

01:09:33.130 --> 01:09:35.529
So their salt came from the
mountain, not from the sea.

01:09:35.529 --> 01:09:36.790
So it wasn't iodized.

01:09:36.790 --> 01:09:41.180
So you had more thyroid
problems due to iodine

01:09:41.180 --> 01:09:44.240
deficiency in the Alps
and elsewhere.

01:09:44.240 --> 01:09:51.010
So now, iodized salt is
available on a large scale.

01:09:51.010 --> 01:09:53.630
But before that, when it was
not available on a large

01:09:53.630 --> 01:09:56.820
scale, at some point governments
realized this

01:09:56.820 --> 01:09:59.620
problem and tried to
have programs of

01:09:59.620 --> 01:10:01.310
distribution of iodine.

01:10:01.310 --> 01:10:03.690
And what these people look at is
they look at the program in

01:10:03.690 --> 01:10:08.150
Tanzania, which attempted to
reach every pregnant woman,

01:10:08.150 --> 01:10:09.680
but failed.

01:10:09.680 --> 01:10:12.650
So some kids, normally
you would have five

01:10:12.650 --> 01:10:15.310
waves of the program.

01:10:15.310 --> 01:10:19.770
A pill is sufficient
for several months.

01:10:19.770 --> 01:10:23.220
So they were attempting to reach
people frequently enough

01:10:23.220 --> 01:10:27.100
that all the pregnant woman
would have a pill covering

01:10:27.100 --> 01:10:29.090
them for the duration
of the pregnancy.

01:10:29.090 --> 01:10:31.590
But they failed to do that
because they were not

01:10:31.590 --> 01:10:32.810
particularly organized.

01:10:32.810 --> 01:10:35.410
So in some districts they went
in sometimes, and some

01:10:35.410 --> 01:10:37.460
district they went in
some other times.

01:10:37.460 --> 01:10:41.850
So what you can look is kids who
were lucky enough to be in

01:10:41.850 --> 01:10:45.030
utero when their mother
was covered.

01:10:45.030 --> 01:10:48.345
Compared to kids who were not
lucky, and who where in utero,

01:10:48.345 --> 01:10:50.410
in particular first
trimester, when

01:10:50.410 --> 01:10:52.430
their mom was not covered.

01:10:52.430 --> 01:10:56.270
And what they look at is
education down the line.

01:10:56.270 --> 01:10:59.590
And they found that the covered
kids have about a

01:10:59.590 --> 01:11:04.280
third of a year more education
than the uncovered kids, for

01:11:04.280 --> 01:11:07.710
receiving this iodine
supplementation.

01:11:07.710 --> 01:11:11.380
So again, a pretty small
intervention makes a big

01:11:11.380 --> 01:11:14.300
effect down in life.

01:11:14.300 --> 01:11:18.010
So all of these create potential
for poverty traps,

01:11:18.010 --> 01:11:19.300
because if the poor--

01:11:19.300 --> 01:11:21.570
these are investments that
are not costly and

01:11:21.570 --> 01:11:24.270
that have high return.

01:11:24.270 --> 01:11:27.940
Even micronutrients for adults,
childhood pregnancy,

01:11:27.940 --> 01:11:28.590
in this order.

01:11:28.590 --> 01:11:32.090
You are asking, pregnancy is a
very short period of time.

01:11:32.090 --> 01:11:34.730
Then it will affect the child
for their entire life.

01:11:34.730 --> 01:11:38.000
So if the poor are less likely
to undertake the investment,

01:11:38.000 --> 01:11:40.370
then there is a potential
for a poverty trap here.

01:11:44.510 --> 01:11:46.855
So is it the case that the poor
are likely to undertake

01:11:46.855 --> 01:11:48.430
this investment?

01:11:48.430 --> 01:11:50.710
And the answer is yes.

01:11:50.710 --> 01:11:52.930
Most of the poor still
consume a diet that's

01:11:52.930 --> 01:11:55.300
very poor in Iran.

01:11:55.300 --> 01:11:57.670
The vast majority of the
quarter of the world's

01:11:57.670 --> 01:12:02.370
children who should get worms
are still not dewormed.

01:12:02.370 --> 01:12:05.820
The WHO estimates that
40% of pregnant women

01:12:05.820 --> 01:12:08.170
worldwide are anemic.

01:12:08.170 --> 01:12:09.960
Not all of that is due continue
to iron deficiency

01:12:09.960 --> 01:12:12.660
anemia, but probably
at least a half.

01:12:12.660 --> 01:12:18.040
So these are three examples of
saying, these investments are

01:12:18.040 --> 01:12:21.720
not undertaken, even though they
are potentially highly

01:12:21.720 --> 01:12:22.970
productive.

01:12:24.610 --> 01:12:26.730
And so you are saying, well,
maybe it's not undertaken, but

01:12:26.730 --> 01:12:28.440
it's not because of poverty.

01:12:28.440 --> 01:12:30.800
So is money an issue?

01:12:30.800 --> 01:12:34.470
And it does seem to be that a
very small cost, even a very

01:12:34.470 --> 01:12:36.570
small cost, seems to
discourage people.

01:12:39.320 --> 01:12:41.260
Asking the question that
you were asking before.

01:12:41.260 --> 01:12:42.800
At the level of government.

01:12:42.800 --> 01:12:47.350
If 45 fish sauce costs only $6,
it seems the investment is

01:12:47.350 --> 01:12:52.441
worthwhile, and yet no
poor family does it.

01:12:52.441 --> 01:12:56.200
In Kenya, in the deworming
program, in the first group of

01:12:56.200 --> 01:12:58.760
schools, at some point
the NGO wanted to do

01:12:58.760 --> 01:13:00.750
the sustainable thing.

01:13:00.750 --> 01:13:05.420
And the sustainable thing was
to ask people to cost-share.

01:13:05.420 --> 01:13:09.250
So they had to pay a little
fee for their children.

01:13:09.250 --> 01:13:11.630
Small fee for the
entire family.

01:13:11.630 --> 01:13:14.530
And this is believed to
be help maintaining

01:13:14.530 --> 01:13:17.270
the program, et cetera.

01:13:17.270 --> 01:13:20.240
The moment where they introduced
the cost sharing,

01:13:20.240 --> 01:13:22.720
the take up of the program
went to zero.

01:13:22.720 --> 01:13:24.130
Nobody took it up.

01:13:24.130 --> 01:13:25.495
So that goes back to this.

01:13:25.495 --> 01:13:27.550
They didn't know the
effect, maybe.

01:13:27.550 --> 01:13:30.290
Interestingly, it means asking
people to pay is not

01:13:30.290 --> 01:13:30.880
sustainable.

01:13:30.880 --> 01:13:33.940
Because it's the costlier thing
about the deworming

01:13:33.940 --> 01:13:37.650
program is to drive your
car to the place.

01:13:37.650 --> 01:13:39.020
So once you're there,
you want to do all

01:13:39.020 --> 01:13:40.300
many people as possible.

01:13:40.300 --> 01:13:43.910
So if the take up falls down to
zero, you've really lost a

01:13:43.910 --> 01:13:46.940
lot of chances.

01:13:46.940 --> 01:13:48.110
Another example.

01:13:48.110 --> 01:13:49.280
It's not only money.

01:13:49.280 --> 01:13:50.320
The thing is that it's not only

01:13:50.320 --> 01:13:51.630
money that is the problem.

01:13:51.630 --> 01:13:56.280
So it's not only poverty,
as in lack of income.

01:13:56.280 --> 01:14:01.410
Because in India, we tried
something so to fight anemia.

01:14:01.410 --> 01:14:02.400
We said, OK, fine.

01:14:02.400 --> 01:14:06.100
People are not going
to buy iron pills.

01:14:06.100 --> 01:14:11.410
But let's introduce a program
where the local miller who

01:14:11.410 --> 01:14:15.580
mills the grain of everyone,
will add the iron.

01:14:15.580 --> 01:14:19.410
But we only had money to install
the machine and pay

01:14:19.410 --> 01:14:24.150
the miller to do it for
one minute a village.

01:14:24.150 --> 01:14:26.580
And what we saw is that--

01:14:26.580 --> 01:14:29.620
so people who were already
walking with that miller

01:14:29.620 --> 01:14:31.644
continue to do so.

01:14:31.644 --> 01:14:33.350
But the other people
didn't switch.

01:14:36.750 --> 01:14:39.260
So the people who happened
to be close by

01:14:39.260 --> 01:14:41.120
benefited from the program.

01:14:41.120 --> 01:14:44.810
But no one was willing to work
the extra five minutes to

01:14:44.810 --> 01:14:46.540
benefit from the program.

01:14:46.540 --> 01:14:49.905
And moreover, the miller thought
it was a lot of effort

01:14:49.905 --> 01:14:52.030
to add the iron.

01:14:52.030 --> 01:14:54.935
So even though the rules were
you're supposed to do it

01:14:54.935 --> 01:14:58.130
unless the family asks, they
switched to do the opposite,

01:14:58.130 --> 01:14:59.390
which is you're supposed--

01:14:59.390 --> 01:15:02.380
they wouldn't not do it if
the family didn't ask.

01:15:02.380 --> 01:15:05.435
And the family didn't
really ask.

01:15:05.435 --> 01:15:08.630
They didn't say no, but
they didn't say yes.

01:15:08.630 --> 01:15:10.950
To the [INAUDIBLE], which was
very high at the beginning

01:15:10.950 --> 01:15:14.170
when the miller did it by
default, it progressively went

01:15:14.170 --> 01:15:18.300
to a very low number, and
the program collapsed.

01:15:18.300 --> 01:15:20.200
Which suggests that it's
not only money,

01:15:20.200 --> 01:15:23.270
it's any form of costs.

01:15:23.270 --> 01:15:26.150
Which brings to these
other issues.

01:15:26.150 --> 01:15:29.730
One is what Steve said earlier,
which is are the

01:15:29.730 --> 01:15:33.270
workers going to reap the
benefit, or is the employer

01:15:33.270 --> 01:15:34.950
going to reap the benefit?

01:15:34.950 --> 01:15:38.195
And one sign that it might be
the employer rather than the

01:15:38.195 --> 01:15:41.530
worker is that in Indonesia,
it's only the wages of the

01:15:41.530 --> 01:15:43.090
self-employed that increased.

01:15:43.090 --> 01:15:44.390
Not the wages, the
earnings of the

01:15:44.390 --> 01:15:45.890
self-employed that increased.

01:15:45.890 --> 01:15:50.360
The wages of people working
for a wage didn't go up.

01:15:50.360 --> 01:15:51.590
In Kenya, it was different.

01:15:51.590 --> 01:15:53.940
But in Kenya there is all
this education effect.

01:15:53.940 --> 01:15:55.830
And one thing that we have
in Kenya is that

01:15:55.830 --> 01:15:57.120
people switch sectors.

01:15:57.120 --> 01:16:00.400
The young kids just started
workings in different sectors

01:16:00.400 --> 01:16:01.680
altogether.

01:16:01.680 --> 01:16:03.350
But for adults, it's
too late for them.

01:16:03.350 --> 01:16:05.790
They're just going to do the
same thing a little better.

01:16:05.790 --> 01:16:09.080
And they are not really
rewarded for that.

01:16:09.080 --> 01:16:11.140
The other thing are the
information things we

01:16:11.140 --> 01:16:13.170
discussed earlier.

01:16:13.170 --> 01:16:15.780
It's very difficult to find out
on your own what makes a

01:16:15.780 --> 01:16:18.240
difference and what doesn't.

01:16:18.240 --> 01:16:20.340
Is it iron?

01:16:20.340 --> 01:16:21.870
How do you know that
iron matters?

01:16:21.870 --> 01:16:24.580
Until recently, scientists
didn't know.

01:16:24.580 --> 01:16:27.010
In the '70s and '80s, scientists
were still

01:16:27.010 --> 01:16:29.650
convinced that the big
problem was proteins.

01:16:29.650 --> 01:16:31.270
And protein is a problem.

01:16:31.270 --> 01:16:35.400
But they didn't think of
micronutrients as an issue.

01:16:35.400 --> 01:16:37.620
So number one, the information
is very difficult to acquire.

01:16:37.620 --> 01:16:39.750
So you need to trust outsider.

01:16:39.750 --> 01:16:42.950
It's not very clear you
would trust them.

01:16:42.950 --> 01:16:43.490
Finally--

01:16:43.490 --> 01:16:45.260
and I will finish on that--

01:16:45.260 --> 01:16:48.330
is that consumption
is a decision.

01:16:48.330 --> 01:16:50.500
And people are not machines.

01:16:50.500 --> 01:16:53.140
So they are not maximizing their
productivity, they are

01:16:53.140 --> 01:16:55.340
maximizing their utility.

01:16:55.340 --> 01:16:58.650
And their utility is made
of other things than our

01:16:58.650 --> 01:17:00.970
productivity can be.

01:17:00.970 --> 01:17:03.910
There is the food that you
have to eat every day.

01:17:03.910 --> 01:17:09.630
And if you don't like it,
then this is horrible.

01:17:09.630 --> 01:17:13.520
Because eating is the only thing
that we are doing day

01:17:13.520 --> 01:17:14.100
in, day out.

01:17:14.100 --> 01:17:17.330
So if we don't like to eat,
then it's kind of awful.

01:17:17.330 --> 01:17:21.950
And in particular, this may
be one reason why it's

01:17:21.950 --> 01:17:26.760
particularly difficult to have
people switch their diet.

01:17:26.760 --> 01:17:28.790
This is something
where this is so

01:17:28.790 --> 01:17:30.720
ingrained into our habits.

01:17:30.720 --> 01:17:33.400
And if we are used do eating
in a particular way, we may

01:17:33.400 --> 01:17:35.870
know that the best way is to eat
something else, but we may

01:17:35.870 --> 01:17:37.900
be very reluctant to switch.

01:17:37.900 --> 01:17:40.250
Now, this is a pattern that we
are seeing of course in this

01:17:40.250 --> 01:17:43.430
country, as well as
anywhere else.

01:17:43.430 --> 01:17:46.520
The second thing is you may care
about your social status.

01:17:46.520 --> 01:17:51.500
Which might be related to how
big a party you throw for your

01:17:51.500 --> 01:17:54.850
son's birthday or for your
daughter's wedding, or even

01:17:54.850 --> 01:17:57.110
for your dad's funeral.

01:17:57.110 --> 01:18:01.780
Which may be related to some
goods you may want to have,

01:18:01.780 --> 01:18:03.360
like a TV or things like that.

01:18:03.360 --> 01:18:06.370
People can care deeply about
these things, which may mean

01:18:06.370 --> 01:18:09.450
that they decide to forego
nourishing their [INAUDIBLE]

01:18:09.450 --> 01:18:12.620
to make sure that they can
actually do the things.

01:18:12.620 --> 01:18:13.820
It's not like--

01:18:13.820 --> 01:18:16.240
and finally, to diversity
of goods you have.

01:18:16.240 --> 01:18:19.490
Like cell phones,
TVs, et cetera.

01:18:19.490 --> 01:18:22.200
And all of that means that it
has very important policy

01:18:22.200 --> 01:18:23.880
implications, of course.

01:18:23.880 --> 01:18:26.210
Because it means that it's
not going to be trivial.

01:18:26.210 --> 01:18:29.720
It's going to eventually be
quite difficult to get people

01:18:29.720 --> 01:18:32.400
to convince to switch the
type of their diet.

01:18:32.400 --> 01:18:35.470
And also, because of what we saw
last time, it's not such a

01:18:35.470 --> 01:18:38.700
great idea to try and
subsidized grains

01:18:38.700 --> 01:18:39.500
or things like that.

01:18:39.500 --> 01:18:42.480
Because it's not going to
lead to an improvement.

01:18:42.480 --> 01:18:45.240
It's not so much of the quantity
of food, because it's

01:18:45.240 --> 01:18:47.050
not that useful to it more.

01:18:47.050 --> 01:18:49.360
Nor in the quality of food,
because it's not the fasting

01:18:49.360 --> 01:18:52.140
people will want to do with
the extra income.

01:18:52.140 --> 01:18:56.870
That means that you would want
to do things that have a

01:18:56.870 --> 01:18:59.230
chance directly to affect
the quality of the

01:18:59.230 --> 01:19:00.190
food people are eating.

01:19:00.190 --> 01:19:03.570
And in particular, children and
pregnant woman are eating.

01:19:03.570 --> 01:19:06.680
So one is making it as
easy as possible to

01:19:06.680 --> 01:19:08.430
do the right thing.

01:19:08.430 --> 01:19:12.140
So invent foods that people
want to eat, but the

01:19:12.140 --> 01:19:14.210
micronutrients is in them.

01:19:14.210 --> 01:19:17.660
So there is something called
golden rice, which is rice

01:19:17.660 --> 01:19:19.190
which is already fortified
in iron.

01:19:19.190 --> 01:19:22.500
But that's GMOs, we might
like that or not.

01:19:22.500 --> 01:19:25.620
But it's also like hybrids
foods, like yams, which are

01:19:25.620 --> 01:19:28.620
very rich in vitamin A that
can grow in Africa.

01:19:28.620 --> 01:19:31.140
So there are organizations
that work on this.

01:19:31.140 --> 01:19:34.310
So the organizations that work
on this bioengineering--

01:19:34.310 --> 01:19:36.980
like HarvestPlus, these types
of organizations--

01:19:36.980 --> 01:19:39.410
historically have been focused
on making the food more

01:19:39.410 --> 01:19:41.060
productive.

01:19:41.060 --> 01:19:44.410
And what is needed is a shift
to making the food

01:19:44.410 --> 01:19:47.150
higher-quality from the point
of view of nutrition.

01:19:47.150 --> 01:19:50.660
And this is happening,
but slowly, slowly.

01:19:50.660 --> 01:19:55.710
Other thing is when you have the
kids, you should invest in

01:19:55.710 --> 01:19:57.420
the quality of their food.

01:19:57.420 --> 01:19:59.640
Because the parents might not
know or might not do it.

01:19:59.640 --> 01:20:01.720
But you have the kids right
in front of you.

01:20:01.720 --> 01:20:02.530
So it's easy to do.

01:20:02.530 --> 01:20:03.870
So deworming.

01:20:03.870 --> 01:20:05.660
Make the school meal nutritious,
for example, by

01:20:05.660 --> 01:20:07.863
sprinkling micronutrient
on them.

01:20:07.863 --> 01:20:10.350
And the parents are not going
to compensate for that by

01:20:10.350 --> 01:20:12.050
giving them less, because
they have no idea what

01:20:12.050 --> 01:20:14.470
you're doing anyway.

01:20:14.470 --> 01:20:17.920
And then you can think
of other things.

01:20:17.920 --> 01:20:19.170
I'm going to let you move now.