1
00:00:00,040 --> 00:00:02,460
The following content is
provided under a Creative

2
00:00:02,460 --> 00:00:03,970
Commons license.

3
00:00:03,970 --> 00:00:06,910
Your support will help MIT
OpenCourseWare continue to

4
00:00:06,910 --> 00:00:10,660
offer high-quality educational
resources for free.

5
00:00:10,660 --> 00:00:13,460
To make a donation or view
additional materials from

6
00:00:13,460 --> 00:00:17,390
hundreds of MIT courses, visit
MIT OpenCourseWare at

7
00:00:17,390 --> 00:00:18,640
ocw.mit.edu.

8
00:00:28,190 --> 00:00:35,200
PROFESSOR: Where we left it at
the end of last time was the

9
00:00:35,200 --> 00:00:38,080
mechanism for poverty trap
that [INAUDIBLE]

10
00:00:38,080 --> 00:00:42,530
explained, and that was kind of
a workhorse of development

11
00:00:42,530 --> 00:00:45,540
economics for many years,
since the 1950s, might

12
00:00:45,540 --> 00:00:48,650
actually, surprisingly,
not be at play.

13
00:00:48,650 --> 00:00:54,510
In that, number one, the
effect of your calorie

14
00:00:54,510 --> 00:00:59,320
consumption on your productivity
in the immediate

15
00:00:59,320 --> 00:01:02,650
next few days is probably
not large enough.

16
00:01:02,650 --> 00:01:06,490
And, perhaps as a consequence,
or perhaps just because they

17
00:01:06,490 --> 00:01:09,140
have other things to do with
their money, we don't see the

18
00:01:09,140 --> 00:01:14,160
poor also consuming as
much as they can.

19
00:01:14,160 --> 00:01:18,790
And therefore, we don't see a
very high elasticity of food

20
00:01:18,790 --> 00:01:21,790
consumption with respect
to wages.

21
00:01:21,790 --> 00:01:25,140
So if we don't have a very high
elasticity of wages with

22
00:01:25,140 --> 00:01:28,560
respect to consumption, and
we don't have a very high

23
00:01:28,560 --> 00:01:32,020
elasticity of consumption with
respect to wages, then we are

24
00:01:32,020 --> 00:01:35,360
not going to get a very highest
elasticity of wages

25
00:01:35,360 --> 00:01:37,780
tomorrow with respect
to wages yesterday.

26
00:01:37,780 --> 00:01:40,990
And therefore, the whole thing
of I am poor because I am

27
00:01:40,990 --> 00:01:45,900
poor, based on how much
food I can consume

28
00:01:45,900 --> 00:01:47,730
is not really there.

29
00:01:47,730 --> 00:01:51,060
So on the one hand, you could
say fine, that great.

30
00:01:51,060 --> 00:01:53,470
It means we can start focusing
on other programs, and

31
00:01:53,470 --> 00:01:55,890
nutrition is not really
an issue.

32
00:01:55,890 --> 00:01:58,320
And for some people, that
has been the conclusion.

33
00:01:58,320 --> 00:02:04,360
For example, on Tuesday, we
showed some graphs coming from

34
00:02:04,360 --> 00:02:09,270
the paper by Angus Deaton and
Jean Dreze about the fact that

35
00:02:09,270 --> 00:02:13,130
people are consuming less and
less calories in India.

36
00:02:13,130 --> 00:02:16,060
So they are becoming richer, so
they are moving along the

37
00:02:16,060 --> 00:02:17,760
angle curve.

38
00:02:17,760 --> 00:02:20,680
And that would make
them consume more,

39
00:02:20,680 --> 00:02:22,450
everything else equal.

40
00:02:22,450 --> 00:02:24,920
But the thing is, not everything
else is equal.

41
00:02:24,920 --> 00:02:27,590
And at the same time, we
have the angle of curve

42
00:02:27,590 --> 00:02:29,370
shifting to the right.

43
00:02:29,370 --> 00:02:31,840
So that it's a swimming upstream
movement, where

44
00:02:31,840 --> 00:02:34,590
you're trying to go up the angle
curve, but the angle

45
00:02:34,590 --> 00:02:37,050
curve is shifting right, so you
end up actually consuming

46
00:02:37,050 --> 00:02:43,050
less, fewer calories than you
would otherwise consume,

47
00:02:43,050 --> 00:02:47,090
So for some people in India,
this is a sign that there's

48
00:02:47,090 --> 00:02:50,790
much more poverty than
the official

49
00:02:50,790 --> 00:02:52,610
statistics are saying.

50
00:02:52,610 --> 00:02:55,890
Because if we define poverty as
not having enough to eat,

51
00:02:55,890 --> 00:02:58,400
then we have more and more
people who in fact don't have

52
00:02:58,400 --> 00:03:00,380
enough to eat.

53
00:03:00,380 --> 00:03:03,030
But what is strange is that if
we look at the other things

54
00:03:03,030 --> 00:03:06,070
that people consume, and we
measure poverty in this way,

55
00:03:06,070 --> 00:03:09,360
which is, if you look at the
entire budget, are you below--

56
00:03:09,360 --> 00:03:11,830
are you someone who consumes
less than a dollar a day per

57
00:03:11,830 --> 00:03:15,180
capita, of 16 rupees a day,
because it's India?

58
00:03:15,180 --> 00:03:16,210
And you don't find that.

59
00:03:16,210 --> 00:03:21,400
You find that actually there are
fewer and fewer people who

60
00:03:21,400 --> 00:03:22,830
are below a dollar a day.

61
00:03:22,830 --> 00:03:23,940
There is still a number.

62
00:03:23,940 --> 00:03:25,190
It's about 13%.

63
00:03:25,190 --> 00:03:27,330
but it's certainly going down.

64
00:03:27,330 --> 00:03:30,340
So it has to be that people
exercise a choice

65
00:03:30,340 --> 00:03:32,810
not to eat as much.

66
00:03:32,810 --> 00:03:38,856
So Deaton and Dreze who wrote
this paper and documented the

67
00:03:38,856 --> 00:03:42,680
decline in calorie consumption
in India, have one

68
00:03:42,680 --> 00:03:43,850
explanation.

69
00:03:43,850 --> 00:03:47,540
And their explanation is that
people's need for calories has

70
00:03:47,540 --> 00:03:51,950
gone down because they are
less ill, they have fewer

71
00:03:51,950 --> 00:03:54,865
children, they are doing less
intense physical work.

72
00:03:54,865 --> 00:03:57,560
A lot of people have moved
to the urban areas.

73
00:03:57,560 --> 00:03:59,900
So it's just they eat
less because they

74
00:03:59,900 --> 00:04:01,780
need less of the strength.

75
00:04:01,780 --> 00:04:05,370
And therefore we have nothing
to worry about, in a sense.

76
00:04:05,370 --> 00:04:08,380
The fact that people are eating
less is, in a sense, a

77
00:04:08,380 --> 00:04:15,840
sign of success of India's
economic growth.

78
00:04:15,840 --> 00:04:19,380
But if it were the case, then
we should find that the

79
00:04:19,380 --> 00:04:23,080
nutritional status of people
would be adequate.

80
00:04:23,080 --> 00:04:24,920
Defined in more objective
terms.

81
00:04:24,920 --> 00:04:28,190
Not the calories you're
consuming, but what is your

82
00:04:28,190 --> 00:04:30,660
weight, what is your height.

83
00:04:30,660 --> 00:04:32,580
Whether you're anemic or not.

84
00:04:32,580 --> 00:04:35,400
We should find an improvement
in that.

85
00:04:35,400 --> 00:04:38,100
Because, to the extent that
people are getting richer,

86
00:04:38,100 --> 00:04:40,240
they should want a little bit
of improvement in their

87
00:04:40,240 --> 00:04:41,790
nutritional status.

88
00:04:41,790 --> 00:04:44,700
And what is striking and
surprising, which is why they

89
00:04:44,700 --> 00:04:50,240
might be hidden traps is that
by all accounts, in India in

90
00:04:50,240 --> 00:04:53,710
particular but in other places
as well, people are still not

91
00:04:53,710 --> 00:04:55,440
very well-nourished.

92
00:04:55,440 --> 00:04:58,880
And it is more a matter
of there is some

93
00:04:58,880 --> 00:05:02,380
undernourishment, which is
people are not eating that

94
00:05:02,380 --> 00:05:03,790
many calories.

95
00:05:03,790 --> 00:05:08,470
And also, maybe something that
people referred to as hidden

96
00:05:08,470 --> 00:05:12,110
hunger, and you can think
about as malnutrition.

97
00:05:12,110 --> 00:05:15,270
Which is even the condition of
having enough calories if

98
00:05:15,270 --> 00:05:19,540
people are not getting enough
of the other micronutrients

99
00:05:19,540 --> 00:05:22,770
that they need-- for
example, anemia.

100
00:05:22,770 --> 00:05:26,980
So here is a number for India.

101
00:05:26,980 --> 00:05:35,440
33% of men and 36% of women
have a BMI below 18.5.

102
00:05:35,440 --> 00:05:40,750
And meanwhile, iron deficiency
anemia affects maybe something

103
00:05:40,750 --> 00:05:44,230
like a billion people
worldwide.

104
00:05:44,230 --> 00:05:48,590
And iron deficiency anemia means
that people are in fact

105
00:05:48,590 --> 00:05:52,840
less strong, because the ability
of their body or their

106
00:05:52,840 --> 00:05:56,940
blood to process the
oxygen is limited.

107
00:05:56,940 --> 00:06:00,700
Because we process the oxygen
with other blood cells in our

108
00:06:00,700 --> 00:06:02,710
body, the hemoglobin
in our blood.

109
00:06:02,710 --> 00:06:04,620
And if we don't have enough of
that, we're not very good at

110
00:06:04,620 --> 00:06:06,440
processing the oxygen.

111
00:06:06,440 --> 00:06:08,760
So you put people on the
treadmill that are anemic, and

112
00:06:08,760 --> 00:06:11,680
they are not able to
make it go as far.

113
00:06:14,740 --> 00:06:18,370
So we have here a puzzle that,
on the one hand, we don't see

114
00:06:18,370 --> 00:06:21,160
people appearing to be
hungry for calories.

115
00:06:21,160 --> 00:06:29,750
In fact, in China we see this
Jensen, Miller evidence, which

116
00:06:29,750 --> 00:06:30,760
goes the opposite way.

117
00:06:30,760 --> 00:06:33,030
Which is, you make the cheaper
source of calories cheaper,

118
00:06:33,030 --> 00:06:36,670
and people eat fewer calories,
at least in one region.

119
00:06:36,670 --> 00:06:41,620
And yet, they seem to be not
very well-nourished.

120
00:06:41,620 --> 00:06:44,410
so what could be going on?

121
00:06:47,620 --> 00:06:49,620
Let me start with you,
your reason.

122
00:06:49,620 --> 00:06:50,610
And I will present it.

123
00:06:50,610 --> 00:06:53,340
AUDIENCE: Their diets are
very narrow and the

124
00:06:53,340 --> 00:06:54,280
same all the time.

125
00:06:54,280 --> 00:06:58,192
So they don't really correlate
what they eat to--

126
00:06:58,192 --> 00:07:00,148
It's just kind of an
informational thing.

127
00:07:00,148 --> 00:07:02,430
They don't realize what kinds
of nutrients they actually

128
00:07:02,430 --> 00:07:04,304
need, so they just have what
tastes good or what they're

129
00:07:04,304 --> 00:07:05,530
used to eating.

130
00:07:05,530 --> 00:07:07,150
PROFESSOR: So it could
be, for example--

131
00:07:07,150 --> 00:07:09,380
I'm just rephrasing for
everyone, because you speak in

132
00:07:09,380 --> 00:07:11,980
this very nice and soft tone--

133
00:07:11,980 --> 00:07:16,210
It could be that they don't
have the information that

134
00:07:16,210 --> 00:07:18,560
nutrition affects
your strength.

135
00:07:18,560 --> 00:07:21,630
In fact, you proposed one very
specific theory for that,

136
00:07:21,630 --> 00:07:25,060
which is, if you've never
experimented because you've

137
00:07:25,060 --> 00:07:28,670
always eaten same thing, then
you might not know what would

138
00:07:28,670 --> 00:07:31,050
happen outside of your
normal range.

139
00:07:31,050 --> 00:07:33,360
So that would be one reason
why you don't have the

140
00:07:33,360 --> 00:07:34,610
information.

141
00:07:36,545 --> 00:07:38,533
AUDIENCE: And then when
someone, or maybe the

142
00:07:38,533 --> 00:07:42,012
government, suggests a different
diet, or replacing

143
00:07:42,012 --> 00:07:46,485
what people are normally used to
eating, they're not really

144
00:07:46,485 --> 00:07:48,473
willing to take that advice.

145
00:07:48,473 --> 00:07:49,964
And so that can--

146
00:07:49,964 --> 00:07:53,443
they'll just continue
eating [INAUDIBLE]

147
00:07:53,443 --> 00:07:54,934
just normally eating.

148
00:07:54,934 --> 00:07:58,935
Their diet is based mainly
on grains and rice.

149
00:07:58,935 --> 00:08:01,531
And if someone in the government
says, well, there's

150
00:08:01,531 --> 00:08:02,340
a shortage of that.

151
00:08:02,340 --> 00:08:05,250
Maybe you should supplement
more vegetables for it.

152
00:08:05,250 --> 00:08:09,130
Then they aren't very
willing to switch.

153
00:08:11,910 --> 00:08:14,050
PROFESSOR: One reason why your
information might be limited

154
00:08:14,050 --> 00:08:16,350
is that even when you
get a source of

155
00:08:16,350 --> 00:08:17,590
information from outside--

156
00:08:17,590 --> 00:08:19,700
for example, the government--
tells you, you

157
00:08:19,700 --> 00:08:21,380
should eat your vegetable.

158
00:08:21,380 --> 00:08:26,450
You should eat these kinds of
cereals rather than those

159
00:08:26,450 --> 00:08:27,180
kinds of cereals.

160
00:08:27,180 --> 00:08:32,140
You should replace some rice
with pulses, or some rice with

161
00:08:32,140 --> 00:08:36,500
cereals, people are reluctant
to do it.

162
00:08:36,500 --> 00:08:42,460
And why would we think that
people are reluctant to follow

163
00:08:42,460 --> 00:08:45,518
this information from
the government?

164
00:08:45,518 --> 00:08:46,014
AUDIENCE: A bunch of reasons.

165
00:08:46,014 --> 00:08:49,982
One might be a variety of foods
might not be available

166
00:08:49,982 --> 00:08:50,974
in that region.

167
00:08:50,974 --> 00:08:53,950
And also, a lot of those things
are more expensive.

168
00:08:53,950 --> 00:08:56,926
So the cheaper things are very
carb-heavy things, which is

169
00:08:56,926 --> 00:08:58,414
quite filling.

170
00:08:58,414 --> 00:09:02,910
So you might not choose to go
with vegetables [INAUDIBLE].

171
00:09:02,910 --> 00:09:03,270
PROFESSOR: Right.

172
00:09:03,270 --> 00:09:04,530
So there are two reasons, two

173
00:09:04,530 --> 00:09:05,770
possibilities in what you said.

174
00:09:05,770 --> 00:09:07,890
The first one is a chicken
and egg problem.

175
00:09:07,890 --> 00:09:11,200
Because if no one eats
spinach in a place--

176
00:09:11,200 --> 00:09:14,280
and spinach is a great thing--

177
00:09:14,280 --> 00:09:16,410
but if no one eats it, it's
just not available, and

178
00:09:16,410 --> 00:09:18,250
therefore you cannot
try it out.

179
00:09:18,250 --> 00:09:19,690
What was the second
one, excuse me?

180
00:09:19,690 --> 00:09:22,045
AUDIENCE: The second one is
that the cheaper food is

181
00:09:22,045 --> 00:09:25,109
generally more carb-heavy, and
so it's quite filling, and you

182
00:09:25,109 --> 00:09:29,252
might choose to buy that over
vegetables which are much more

183
00:09:29,252 --> 00:09:30,750
expensive than the
rest of the food.

184
00:09:30,750 --> 00:09:31,175
PROFESSOR: Right.

185
00:09:31,175 --> 00:09:33,703
And the second one could
be a matter of costs.

186
00:09:33,703 --> 00:09:35,675
AUDIENCE: And I think that the
benefit of nutrient intakes

187
00:09:35,675 --> 00:09:37,154
are over the long-term.

188
00:09:37,154 --> 00:09:39,619
So if you're taking a small
iron tablet, the next day,

189
00:09:39,619 --> 00:09:42,084
miraculously you're not going
to perform better.

190
00:09:42,084 --> 00:09:45,042
But over the long-term,
you might see smaller

191
00:09:45,042 --> 00:09:46,030
improvements.

192
00:09:46,030 --> 00:09:46,410
PROFESSOR: Right.

193
00:09:46,410 --> 00:09:49,420
So it could be that if is
difficult to learn.

194
00:09:49,420 --> 00:09:52,880
So for example, one thing that
could happen is that someone

195
00:09:52,880 --> 00:09:56,050
from outside, very well-meaning,
say you should

196
00:09:56,050 --> 00:10:00,830
really eat iron-fortified
flour instead of

197
00:10:00,830 --> 00:10:02,710
your regular flour.

198
00:10:02,710 --> 00:10:04,020
And you try.

199
00:10:04,020 --> 00:10:07,090
And then, after one week, you
don't feel like Popeye.

200
00:10:07,090 --> 00:10:10,480
It's not that things have
dramatically changed.

201
00:10:10,480 --> 00:10:14,550
And in fact, if I put you on a
treadmill and I ask you to

202
00:10:14,550 --> 00:10:16,280
perform an exercise,
I will know

203
00:10:16,280 --> 00:10:19,130
that you are 10% stronger.

204
00:10:19,130 --> 00:10:20,820
But this is not something--

205
00:10:20,820 --> 00:10:28,040
if you are 10% stronger the next
month, are you going to

206
00:10:28,040 --> 00:10:30,770
be able to really see the
difference or not?

207
00:10:30,770 --> 00:10:34,400
So it might not be immediately
clear.

208
00:10:34,400 --> 00:10:37,320
And if that is the case, then
you might have a situation

209
00:10:37,320 --> 00:10:41,200
where people arrive from outside
and give you this

210
00:10:41,200 --> 00:10:44,340
message, and say, you should
really change your

211
00:10:44,340 --> 00:10:45,700
diet in this way.

212
00:10:45,700 --> 00:10:49,360
And you make, maybe, an effort
to follow them for some time.

213
00:10:49,360 --> 00:10:52,540
Spend a little bit more money,
or a little bit more effort

214
00:10:52,540 --> 00:10:55,740
into going into pasturizing
your food.

215
00:10:55,740 --> 00:10:58,310
And then it happens, and you're
not any stronger.

216
00:10:58,310 --> 00:11:00,990
And you're like, whatever
did they tell me?

217
00:11:00,990 --> 00:11:03,420
It's like, this is no better.

218
00:11:03,420 --> 00:11:08,240
Because your expectations were
set high enough to encourage

219
00:11:08,240 --> 00:11:09,760
you to do the switch.

220
00:11:09,760 --> 00:11:13,510
And the problem is there would
be a tendency to slightly

221
00:11:13,510 --> 00:11:17,230
oversell how much better
you're going to feel.

222
00:11:17,230 --> 00:11:19,490
Which then is going to
translate into a

223
00:11:19,490 --> 00:11:21,240
disappointment.

224
00:11:21,240 --> 00:11:26,230
So one example of that is
something that people have

225
00:11:26,230 --> 00:11:30,120
found in a deworming program in
Kenya, which we're going to

226
00:11:30,120 --> 00:11:32,450
discuss in a moment.

227
00:11:32,450 --> 00:11:34,950
So it's a charitable
deworming program.

228
00:11:34,950 --> 00:11:39,520
Deworming is, in some sense, a
nutrition program, because the

229
00:11:39,520 --> 00:11:43,600
worms are competing with
the kid for the food.

230
00:11:43,600 --> 00:11:48,500
So by removing the worms, you
are increasing the amount of

231
00:11:48,500 --> 00:11:50,260
food that stays with the kid.

232
00:11:50,260 --> 00:11:53,230
I'm sorry, this is not a great
conversation to have right

233
00:11:53,230 --> 00:11:53,880
after lunch.

234
00:11:53,880 --> 00:11:58,610
But that's kind of the biology
of it, in two words.

235
00:11:58,610 --> 00:12:00,640
So when you give deworming--

236
00:12:00,640 --> 00:12:02,330
and we are going to see that
in a minute-- that

237
00:12:02,330 --> 00:12:05,752
does make the kid--

238
00:12:05,752 --> 00:12:13,480
that reduces anemia,
that reduces the

239
00:12:13,480 --> 00:12:15,370
incidence of being sick.

240
00:12:15,370 --> 00:12:20,140
That reduces, therefore,
absence from school.

241
00:12:20,140 --> 00:12:27,050
So there was an NGO that was
trying to promote deworming in

242
00:12:27,050 --> 00:12:33,120
some randomly-selected schools
in the late '90s, early 2000s.

243
00:12:33,120 --> 00:12:35,650
And they went, and they
explained all of this with a

244
00:12:35,650 --> 00:12:38,970
lot of energy, and said, your
kid is going to feel much

245
00:12:38,970 --> 00:12:43,100
better, and is going to go to
school more, and all of that.

246
00:12:43,100 --> 00:12:48,350
And parents had to sign
a form to agree

247
00:12:48,350 --> 00:12:49,790
to get the kid dewormed.

248
00:12:49,790 --> 00:12:52,020
So it's not money.

249
00:12:52,020 --> 00:12:53,190
It's not a huge amount
of effort.

250
00:12:53,190 --> 00:12:55,390
But it's still a little
bit of effort.

251
00:12:55,390 --> 00:12:57,980
And also, you have to want it.

252
00:12:57,980 --> 00:12:59,950
And people were interested--

253
00:12:59,950 --> 00:13:03,740
the researches were interested
to know whether parents were

254
00:13:03,740 --> 00:13:07,760
more likely to sign the form
if they knew more people

255
00:13:07,760 --> 00:13:11,150
around them who got a chance
to get the deworming.

256
00:13:11,150 --> 00:13:15,775
So because it was a randomized
experiment, which was done at

257
00:13:15,775 --> 00:13:17,230
the level of the school--

258
00:13:17,230 --> 00:13:19,480
I'm going to show you
a map in a moment--

259
00:13:19,480 --> 00:13:23,130
some people got treated in some
schools, and some people

260
00:13:23,130 --> 00:13:25,380
didn't get treated
immediately.

261
00:13:25,380 --> 00:13:29,010
So people who are in a treated
schools may have had friends

262
00:13:29,010 --> 00:13:31,390
who were in neighboring schools,
which may have been

263
00:13:31,390 --> 00:13:33,280
treated a control.

264
00:13:33,280 --> 00:13:36,650
So what the researchers did is
to look at whether you were

265
00:13:36,650 --> 00:13:39,470
more likely to take up the
deworming once you got the

266
00:13:39,470 --> 00:13:42,560
option if you had more
friends who got the

267
00:13:42,560 --> 00:13:44,740
option the year before.

268
00:13:44,740 --> 00:13:48,360
And their prior going into
this, was that the more

269
00:13:48,360 --> 00:13:52,480
friends you have who got into
the deworming, the more you

270
00:13:52,480 --> 00:13:54,810
are likely to do it yourself,
because you

271
00:13:54,810 --> 00:13:56,960
will see the benefits.

272
00:13:56,960 --> 00:13:59,760
And what they found was
exactly the opposite.

273
00:13:59,760 --> 00:14:03,170
Which is the more friends you
had who had been a chance to

274
00:14:03,170 --> 00:14:06,360
get dewormed a year before,
the less you are likely to

275
00:14:06,360 --> 00:14:09,590
take up the deworming once
you got a chance.

276
00:14:09,590 --> 00:14:12,410
And what are the possible
interpretations for that

277
00:14:12,410 --> 00:14:13,540
somewhat weird results?

278
00:14:13,540 --> 00:14:15,530
Yeah, Zach?

279
00:14:15,530 --> 00:14:16,940
AUDIENCE: One possible
interpretation is that it

280
00:14:16,940 --> 00:14:19,105
depends on how you
get the worms.

281
00:14:19,105 --> 00:14:21,932
The fact that your friends are
being treated for you might be

282
00:14:21,932 --> 00:14:24,302
less likely to get it, like
in the case of malaria.

283
00:14:24,302 --> 00:14:26,198
If everybody in the community's
using the bednet,

284
00:14:26,198 --> 00:14:27,620
you probably don't have to.

285
00:14:27,620 --> 00:14:28,100
PROFESSOR: Exactly.

286
00:14:28,100 --> 00:14:30,900
So that's a first possible
interpretation, which is worms

287
00:14:30,900 --> 00:14:33,250
are, in fact, highly
contagious.

288
00:14:33,250 --> 00:14:37,330
So if most of your friends are
treated, then they probably

289
00:14:37,330 --> 00:14:38,390
don't have worms anymore.

290
00:14:38,390 --> 00:14:42,010
You might feel, well, I don't
need to go to the trouble of

291
00:14:42,010 --> 00:14:44,480
getting dewormed because they
did, therefore there are fewer

292
00:14:44,480 --> 00:14:45,290
worms around.

293
00:14:45,290 --> 00:14:46,550
And there is some side effect.

294
00:14:46,550 --> 00:14:48,150
Why would you take
the trouble?

295
00:14:48,150 --> 00:14:49,068
Yeah, Norm?

296
00:14:49,068 --> 00:14:52,510
AUDIENCE: Maybe people also,
since people get dewormed, and

297
00:14:52,510 --> 00:14:55,996
then their problems decrease,
people don't think it's as

298
00:14:55,996 --> 00:14:58,102
much of an issue, because
it's not as prominent.

299
00:14:58,102 --> 00:15:01,504
So it's the externality just
decreasing, they just don't

300
00:15:01,504 --> 00:15:03,450
realize that it's such
a big threat anymore.

301
00:15:03,450 --> 00:15:04,180
PROFESSOR: Exactly.

302
00:15:04,180 --> 00:15:05,750
So that could be
another thing.

303
00:15:05,750 --> 00:15:10,130
Which is, people learn that--

304
00:15:10,130 --> 00:15:14,810
So people say, oh, these other
kids got dewormed, but they

305
00:15:14,810 --> 00:15:17,912
are not much healthier
than me.

306
00:15:17,912 --> 00:15:20,440
And the fact is, you don't
realize that you are healthy

307
00:15:20,440 --> 00:15:23,060
because they are healthy, and
they made you healthier.

308
00:15:23,060 --> 00:15:27,870
So you are now comparing the
benefits of you as a control

309
00:15:27,870 --> 00:15:29,600
child-- you are not
yet treated--

310
00:15:29,600 --> 00:15:31,860
to the other kids
who got treated.

311
00:15:31,860 --> 00:15:33,870
And the difference is
not that large.

312
00:15:33,870 --> 00:15:37,000
It's not that large precisely
because of the contagion

313
00:15:37,000 --> 00:15:38,640
effect that Zack mentioned.

314
00:15:38,640 --> 00:15:40,845
But so you're trying to learn
the effect, and so it's not

315
00:15:40,845 --> 00:15:41,710
that large.

316
00:15:41,710 --> 00:15:43,910
And even if you don't understand
that it's due to

317
00:15:43,910 --> 00:15:46,320
the externality, so you don't
do this calculation, saying,

318
00:15:46,320 --> 00:15:47,470
it's not worthwhile.

319
00:15:47,470 --> 00:15:49,800
You just see it and think,
what did they sell me?

320
00:15:49,800 --> 00:15:51,970
This thing doesn't really
make any difference.

321
00:15:51,970 --> 00:15:53,360
And so you decide not to do it.

322
00:15:53,360 --> 00:15:53,854
Yeah.

323
00:15:53,854 --> 00:15:56,324
AUDIENCE: I was going to say
that, even if you don't really

324
00:15:56,324 --> 00:15:59,946
have any change in your health
status, maybe the change that

325
00:15:59,946 --> 00:16:02,086
the other people have is
not so great as to

326
00:16:02,086 --> 00:16:03,734
convince you to get it.

327
00:16:03,734 --> 00:16:07,200
You see that the medication
quote, unquote, doesn't work.

328
00:16:07,200 --> 00:16:08,040
PROFESSOR: Right.

329
00:16:08,040 --> 00:16:09,250
That also could be the case.

330
00:16:09,250 --> 00:16:11,540
Could be that, even without
this mechanism--

331
00:16:11,540 --> 00:16:14,610
which is a very nice one-- but
even without this mechanism,

332
00:16:14,610 --> 00:16:16,590
you could see the other children
and say, well, first

333
00:16:16,590 --> 00:16:19,620
thing, they got sick when they
ate the deworming pill.

334
00:16:19,620 --> 00:16:21,110
So the side effect
is immediate.

335
00:16:24,630 --> 00:16:25,820
It's getting worse and worse.

336
00:16:25,820 --> 00:16:31,010
But as the worm dies, this make
you pretty unwell for an

337
00:16:31,010 --> 00:16:34,770
hour or two, as your body
gets rid of them.

338
00:16:34,770 --> 00:16:36,310
And then you get better.

339
00:16:36,310 --> 00:16:38,950
But the side effect is salient
and immediate, and the

340
00:16:38,950 --> 00:16:43,010
benefits are a little
bit less apparent.

341
00:16:43,010 --> 00:16:46,530
And this, of course, is
reinforced by the

342
00:16:46,530 --> 00:16:47,790
point that Norm made.

343
00:16:47,790 --> 00:16:50,120
Which is that the externalities
make it

344
00:16:50,120 --> 00:16:53,030
difficult to compare treatment
and control.

345
00:16:53,030 --> 00:16:56,180
So for all of these reasons--

346
00:16:56,180 --> 00:17:01,390
so this is one example of why
it's very difficult for people

347
00:17:01,390 --> 00:17:08,099
to learn about relatively subtle
nutrition mechanisms.

348
00:17:08,099 --> 00:17:11,579
And so what is happening with
deworming, that's maybe made a

349
00:17:11,579 --> 00:17:16,319
little bit harder but the
externalities, which, A, gives

350
00:17:16,319 --> 00:17:16,849
[INAUDIBLE]

351
00:17:16,849 --> 00:17:19,869
like strategic reasons
not to do it.

352
00:17:19,869 --> 00:17:21,790
So worms give Norms' difficulty
of learning

353
00:17:21,790 --> 00:17:23,040
explanation.

354
00:17:25,650 --> 00:17:29,350
But that's could also be at
play with iron pill, or

355
00:17:29,350 --> 00:17:32,220
supplementing your
flour with iron.

356
00:17:32,220 --> 00:17:37,280
Where you're like, really not
that much is. happening.

357
00:17:37,280 --> 00:17:41,630
So these are possible reasons
why you wouldn't do what the

358
00:17:41,630 --> 00:17:45,360
good man, or well-meaning
NGO tells you to do.

359
00:17:45,360 --> 00:17:47,150
You don't have the
information.

360
00:17:47,150 --> 00:17:50,040
Learning is difficult, because
the effects are subtle.

361
00:17:50,040 --> 00:17:52,170
This implies spending
more money.

362
00:17:52,170 --> 00:17:55,450
Nd maybe those foods are not
even available for you in a

363
00:17:55,450 --> 00:17:56,780
convenient way.

364
00:17:56,780 --> 00:17:59,826
What else could be going
on, potentially?

365
00:17:59,826 --> 00:18:02,814
AUDIENCE: If the wages are set
wages, then even if you eat

366
00:18:02,814 --> 00:18:04,474
more stronger, you're still
going to get the

367
00:18:04,474 --> 00:18:05,304
same amount of money.

368
00:18:05,304 --> 00:18:08,160
So there's no point in being
more productive.

369
00:18:08,160 --> 00:18:08,590
PROFESSOR: Right.

370
00:18:08,590 --> 00:18:11,240
So another possible explanation
is you could

371
00:18:11,240 --> 00:18:13,500
realize that it's going
to make you a bit more

372
00:18:13,500 --> 00:18:17,400
productive, but you might
wonder, what's the use of me

373
00:18:17,400 --> 00:18:20,660
being more productive if, in
fact, the wages are not piece

374
00:18:20,660 --> 00:18:22,620
wage but day wage?

375
00:18:22,620 --> 00:18:24,420
And you are a little bit
more productive.

376
00:18:24,420 --> 00:18:27,810
But you need to go and convince
your employer that

377
00:18:27,810 --> 00:18:30,080
now I'm a little bit more
productive, so you need to pay

378
00:18:30,080 --> 00:18:32,080
me more on a daily basis.

379
00:18:32,080 --> 00:18:34,630
But your employer is not behind
your back, checking

380
00:18:34,630 --> 00:18:36,660
what it is you're eating
every day.

381
00:18:36,660 --> 00:18:39,960
And so your consumption is an
upsell from the point of view

382
00:18:39,960 --> 00:18:41,390
of the employer.

383
00:18:41,390 --> 00:18:43,310
But there is a more moral hazard
issue, where you could

384
00:18:43,310 --> 00:18:46,380
go and say, I'm telling you
I've eaten so much.

385
00:18:46,380 --> 00:18:47,380
I'm very strong.

386
00:18:47,380 --> 00:18:49,780
You can monitor, you can see.

387
00:18:49,780 --> 00:18:52,240
Unless your employer can really
be monitoring your

388
00:18:52,240 --> 00:18:56,640
output in a very close way,
which might not always be

389
00:18:56,640 --> 00:18:59,670
possible, then they might
say, whatever.

390
00:18:59,670 --> 00:19:03,510
I'm just assuming that you
are the average person.

391
00:19:03,510 --> 00:19:12,070
And there is one study that
shows that shows employers

392
00:19:12,070 --> 00:19:15,550
recognize that taller people
are more productive.

393
00:19:15,550 --> 00:19:18,680
Taller people usually have been
better fed, maybe, when

394
00:19:18,680 --> 00:19:19,930
they grew up.

395
00:19:19,930 --> 00:19:21,230
And they are stronger.

396
00:19:21,230 --> 00:19:23,380
Taller, maybe stronger,
more muscles.

397
00:19:23,380 --> 00:19:25,950
People are more productive,
they pay them more.

398
00:19:25,950 --> 00:19:29,800
But how much you've eaten and
how well you've eaten

399
00:19:29,800 --> 00:19:32,730
previously does not
affect wages.

400
00:19:32,730 --> 00:19:34,850
And that is because that isn't
observed from the point of

401
00:19:34,850 --> 00:19:35,760
view of the employer.

402
00:19:35,760 --> 00:19:41,320
And if they can't see the
output either, it's

403
00:19:41,320 --> 00:19:42,630
he said, she said.

404
00:19:42,630 --> 00:19:45,490
How do I know you're actually
more productive?

405
00:19:45,490 --> 00:19:47,870
So of course, the solution to
that would be for the employer

406
00:19:47,870 --> 00:19:50,490
to feed people iron supplement
on the job.

407
00:19:50,490 --> 00:19:53,490
And why they're not doing
that, I don't know.

408
00:19:53,490 --> 00:19:57,190
But that would be an interesting
thing to consider.

409
00:19:57,190 --> 00:19:58,250
Because then they could know.

410
00:19:58,250 --> 00:19:59,330
They could say, yeah.

411
00:19:59,330 --> 00:20:01,260
I can pay you a bit more, as
long as you are eating your

412
00:20:01,260 --> 00:20:02,510
iron supplement.

413
00:20:04,860 --> 00:20:09,930
So let's go to all of this in a
little more systematic way.

414
00:20:09,930 --> 00:20:13,910
So the first thing we need to
check is, all of this learning

415
00:20:13,910 --> 00:20:14,540
is going to be--

416
00:20:14,540 --> 00:20:19,310
I think people are very
naturally associating more

417
00:20:19,310 --> 00:20:21,750
calories with more strength.

418
00:20:21,750 --> 00:20:24,740
Even we have this in
mind-- to a point,

419
00:20:24,740 --> 00:20:27,730
until we eat too much.

420
00:20:27,730 --> 00:20:31,090
But this is probably harder to
learn about-- micronutrient

421
00:20:31,090 --> 00:20:32,620
deficiency.

422
00:20:32,620 --> 00:20:35,550
Because that's not something
that is as obvious, and you

423
00:20:35,550 --> 00:20:37,220
don't necessarily know
which foods have what

424
00:20:37,220 --> 00:20:38,880
nutrients, et cetera.

425
00:20:38,880 --> 00:20:41,620
And so the first thing we need
to establish is that

426
00:20:41,620 --> 00:20:44,520
micronutrient deficiency
actually matters.

427
00:20:44,520 --> 00:20:51,160
And in particular, that the poor
and even the not-so-poor

428
00:20:51,160 --> 00:20:59,680
could become more productive if
they got more micronutrient

429
00:20:59,680 --> 00:21:02,330
supplementation in their diet.

430
00:21:02,330 --> 00:21:08,600
For that, of course, we could
compound the wages of people

431
00:21:08,600 --> 00:21:11,820
who have more hemoglobin in
their blood and the wages of

432
00:21:11,820 --> 00:21:14,310
people who have less hemoglobin
in their blood.

433
00:21:14,310 --> 00:21:16,390
If we do that, what do you
think we will find?

434
00:21:20,950 --> 00:21:22,870
Most likely?

435
00:21:22,870 --> 00:21:25,870
We look at the data set, and we
look at the wages of anemic

436
00:21:25,870 --> 00:21:29,420
people versus the wages
of non-anemic people?

437
00:21:29,420 --> 00:21:30,626
Richard?

438
00:21:30,626 --> 00:21:32,944
AUDIENCE: Of course, the
non-anemic people have more

439
00:21:32,944 --> 00:21:35,484
strength to go to work, so their
wages are higher if they

440
00:21:35,484 --> 00:21:37,110
are paid by the [INAUDIBLE].

441
00:21:37,110 --> 00:21:38,940
PROFESSOR: So the non-anemic
people, when we do this

442
00:21:38,940 --> 00:21:40,960
comparison, will make
more money.

443
00:21:40,960 --> 00:21:43,540
That's sure poverty in
every data set, we're

444
00:21:43,540 --> 00:21:44,740
going to find that.

445
00:21:44,740 --> 00:21:48,300
But once we find that, can we
for sure say it's the effect

446
00:21:48,300 --> 00:21:49,550
of being anemic?

447
00:21:52,678 --> 00:21:53,666
AUDIENCE: Not necessarily.

448
00:21:53,666 --> 00:21:56,186
It could be environmental
factors.

449
00:21:56,186 --> 00:21:58,601
You could be anemic because
you don't make [INAUDIBLE]

450
00:21:58,601 --> 00:22:02,465
enough to have a proper diet,
or you could not have wages

451
00:22:02,465 --> 00:22:03,930
because you're anemic.

452
00:22:03,930 --> 00:22:04,320
PROFESSOR: Right.

453
00:22:04,320 --> 00:22:05,960
So there's two things.

454
00:22:05,960 --> 00:22:08,390
So first, they could be a
reverse causality at play.

455
00:22:08,390 --> 00:22:10,630
Which is, you could be anemic
because you don't own enough

456
00:22:10,630 --> 00:22:12,050
to buy spinach.

457
00:22:12,050 --> 00:22:12,810
That's one.

458
00:22:12,810 --> 00:22:14,570
And what else could
be at play?

459
00:22:17,720 --> 00:22:20,560
Even if we manage to shut down
this mechanism, or assume that

460
00:22:20,560 --> 00:22:22,320
specific mechanism
is not there?

461
00:22:27,130 --> 00:22:29,935
What could be other things
that would explain this

462
00:22:29,935 --> 00:22:31,700
correlation between anemia
and [INAUDIBLE]?

463
00:22:41,200 --> 00:22:42,450
AUDIENCE: It might be
some other third

464
00:22:42,450 --> 00:22:44,200
factor that causes both.

465
00:22:44,200 --> 00:22:46,950
For instance, your social
status, perhaps, means you can

466
00:22:46,950 --> 00:22:48,700
only get a certain
kind of job.

467
00:22:48,700 --> 00:22:53,450
And it also means that it's
harder for you to get good

468
00:22:53,450 --> 00:22:55,640
wages and then get
better diet.

469
00:22:55,640 --> 00:22:56,090
PROFESSOR: Right.

470
00:22:56,090 --> 00:22:59,460
There could be something
that explains both.

471
00:22:59,460 --> 00:23:03,610
For example, your social status,
or for example, how

472
00:23:03,610 --> 00:23:08,270
well-educated you are, or the
types of opportunities you

473
00:23:08,270 --> 00:23:09,080
have access to.

474
00:23:09,080 --> 00:23:12,140
Or anything like that would
both effect your

475
00:23:12,140 --> 00:23:13,560
anemia and your wage.

476
00:23:13,560 --> 00:23:15,750
So we don't know.

477
00:23:15,750 --> 00:23:18,880
So that's something which is
actually relatively easy to

478
00:23:18,880 --> 00:23:24,960
organize as a randomized
experiment, because you can

479
00:23:24,960 --> 00:23:28,240
pretty much cure anemia, at
least temporarily, by giving

480
00:23:28,240 --> 00:23:29,490
people iron supplements.

481
00:23:32,080 --> 00:23:35,290
So that's almost like a medical
study you can give

482
00:23:35,290 --> 00:23:35,720
some people.

483
00:23:35,720 --> 00:23:37,980
So this was done in Indonesia.

484
00:23:37,980 --> 00:23:43,720
The WISE stands for Work and
Iron Status Evaluation.

485
00:23:43,720 --> 00:23:43,830
They

486
00:23:43,830 --> 00:23:47,180
They worked with several
thousand households.

487
00:23:47,180 --> 00:23:51,090
And they provided them with
either an iron supplement or a

488
00:23:51,090 --> 00:23:54,790
placebo in a randomly-selected
way.

489
00:23:54,790 --> 00:23:56,660
So they randomized
the household.

490
00:23:56,660 --> 00:23:58,910
And once they pick a household's
treatment, they

491
00:23:58,910 --> 00:24:02,200
give everyone in the household
the iron supplement.

492
00:24:02,200 --> 00:24:06,260
It takes a few months for people
to absorb the iron and

493
00:24:06,260 --> 00:24:08,540
to become iron-replete.

494
00:24:08,540 --> 00:24:12,000
Once you're not anemic, you have
enough iron in your body,

495
00:24:12,000 --> 00:24:13,750
you get rid of the rest.

496
00:24:13,750 --> 00:24:16,430
So anemia is something which is,
either you are anemic or

497
00:24:16,430 --> 00:24:17,580
you're not.

498
00:24:17,580 --> 00:24:20,790
And once you're not-- that is,
once your hemoglobin is above

499
00:24:20,790 --> 00:24:25,940
13 for men, and for women
it's between 11 and 12.

500
00:24:25,940 --> 00:24:29,470
That's gram per deciliter.

501
00:24:29,470 --> 00:24:31,780
You just stop absorbing it.

502
00:24:31,780 --> 00:24:35,750
So what they found when they
gave this iron supplement is

503
00:24:35,750 --> 00:24:40,580
that there is no effect of
comparing the people who got

504
00:24:40,580 --> 00:24:43,910
the placebo and people who got
the pill if they were not

505
00:24:43,910 --> 00:24:44,680
anemic before.

506
00:24:44,680 --> 00:24:46,280
There is no impact on them.

507
00:24:46,280 --> 00:24:48,630
That's exactly what you would
expect, because once you have

508
00:24:48,630 --> 00:24:49,440
enough, you have enough.

509
00:24:49,440 --> 00:24:51,270
There is nothing more
we can tell you.

510
00:24:51,270 --> 00:24:54,440
On the other hand, the more
anemic you were before-- that

511
00:24:54,440 --> 00:24:57,180
is, the further you were from
12 grams per deciliter of

512
00:24:57,180 --> 00:25:00,140
hemoglobin in your blood--

513
00:25:00,140 --> 00:25:04,970
the larger the effect, in
terms of the increase in

514
00:25:04,970 --> 00:25:06,330
hemoglobin in your blood.

515
00:25:06,330 --> 00:25:08,710
That is, what they found is that
the people who got the

516
00:25:08,710 --> 00:25:13,110
supplement almost all got
to 12, or close to 12.

517
00:25:13,110 --> 00:25:15,700
So the further away
you were from 12,

518
00:25:15,700 --> 00:25:17,580
the bigger the effect.

519
00:25:17,580 --> 00:25:20,660
And so, once they do that, they
can separately at people

520
00:25:20,660 --> 00:25:23,350
who were anemic at baseline
and people who weren't.

521
00:25:23,350 --> 00:25:28,010
And they found that if you
focus on people who were

522
00:25:28,010 --> 00:25:35,120
anemic at baseline, and people
who were self-employed, those

523
00:25:35,120 --> 00:25:39,660
people made substantially more
money after they received the

524
00:25:39,660 --> 00:25:40,610
iron supplement.

525
00:25:40,610 --> 00:25:44,490
So they looked at the wages
eight months after the iron

526
00:25:44,490 --> 00:25:45,910
supplement starts.

527
00:25:45,910 --> 00:25:48,910
And then there is another end
line a few months later.

528
00:25:48,910 --> 00:25:51,320
And they find these people
to make more money.

529
00:25:51,320 --> 00:25:54,280
So about $40 more per year.

530
00:25:54,280 --> 00:25:58,140
Which is not nothing.

531
00:25:58,140 --> 00:25:59,750
This is not an enormous
amount.

532
00:25:59,750 --> 00:26:03,975
This is not a doubling of
the wage or anything.

533
00:26:03,975 --> 00:26:08,220
The yearly wages of these people
may have been around

534
00:26:08,220 --> 00:26:09,920
$500 or something like that.

535
00:26:09,920 --> 00:26:13,290
So it's maybe a little less
than 10% increase.

536
00:26:13,290 --> 00:26:16,470
But this is very cheap.

537
00:26:16,470 --> 00:26:18,470
Because if someone wanted to--

538
00:26:18,470 --> 00:26:21,400
well, actually the experiment
itself was very expensive.

539
00:26:21,400 --> 00:26:24,230
Because they had to go behind
people's backs and make sure

540
00:26:24,230 --> 00:26:26,110
that actually eat the pill.

541
00:26:26,110 --> 00:26:28,670
And they had so many nurses,
and they were really

542
00:26:28,670 --> 00:26:31,220
controlling that they were
following the protocol.

543
00:26:31,220 --> 00:26:33,870
So for the experiment itself,
costs much more

544
00:26:33,870 --> 00:26:36,120
than $40 per person.

545
00:26:36,120 --> 00:26:41,290
But what they argue in their
paper is that that's not

546
00:26:41,290 --> 00:26:44,660
really interesting, because if
someone wanted to do it, they

547
00:26:44,660 --> 00:26:46,690
could just buy 45 fish sauce.

548
00:26:46,690 --> 00:26:48,817
And that would cost
them only $6.

549
00:26:48,817 --> 00:26:49,314
Yep.

550
00:26:49,314 --> 00:26:52,129
AUDIENCE: In the experiment, do
they control for the fact

551
00:26:52,129 --> 00:26:55,775
that people usually earn higher
wages as time passes?

552
00:26:55,775 --> 00:26:58,757
So next year, my wage is
probably going to be higher

553
00:26:58,757 --> 00:27:00,248
than this year, because I
have more experience.

554
00:27:00,248 --> 00:27:02,236
It means I can get
a better wage.

555
00:27:02,236 --> 00:27:04,230
I'm better at catching fish.

556
00:27:04,230 --> 00:27:04,590
PROFESSOR: Right.

557
00:27:04,590 --> 00:27:05,190
That's an excellent point.

558
00:27:05,190 --> 00:27:10,250
You're saying you would want to
control for the fact that

559
00:27:10,250 --> 00:27:12,180
as time passes, you
earn more money.

560
00:27:12,180 --> 00:27:14,910
So how would they be able to do
that in the context of this

561
00:27:14,910 --> 00:27:15,640
experiment?

562
00:27:15,640 --> 00:27:20,122
AUDIENCE: Maybe there's a
historical [INAUDIBLE].

563
00:27:20,122 --> 00:27:22,861
I'd like to figure out how much
people would earn over

564
00:27:22,861 --> 00:27:25,102
their lifetime in that region,
and then control for that

565
00:27:25,102 --> 00:27:26,596
percentage.

566
00:27:26,596 --> 00:27:30,082
And they can account-- maybe in
the $40 increase, there is

567
00:27:30,082 --> 00:27:35,560
$22 into that that is perhaps
due to the [INAUDIBLE]

568
00:27:35,560 --> 00:27:37,580
average increase in wages.

569
00:27:37,580 --> 00:27:40,790
PROFESSOR: And so you're saying
what they could do to

570
00:27:40,790 --> 00:27:42,880
control for an historical trend
is to try to find out

571
00:27:42,880 --> 00:27:45,185
what the historical trend
would have been.

572
00:27:45,185 --> 00:27:49,490
And in particular, what is in
their data that tells them

573
00:27:49,490 --> 00:27:52,160
what the historical trend
would have been,

574
00:27:52,160 --> 00:27:53,500
directly free of charge.

575
00:27:53,500 --> 00:27:55,270
Not free of charge, because
that was in the design.

576
00:27:55,270 --> 00:27:56,520
But once you have
the experiment.

577
00:27:59,200 --> 00:27:59,410
Yeah.

578
00:27:59,410 --> 00:28:00,330
AUDIENCE: The control group.

579
00:28:00,330 --> 00:28:00,870
PROFESSOR: The control group.

580
00:28:00,870 --> 00:28:02,040
There is a placebo group.

581
00:28:02,040 --> 00:28:04,850
So half the sample
gets nothing.

582
00:28:04,850 --> 00:28:07,890
So what they actually do in the
experiment is they compare

583
00:28:07,890 --> 00:28:12,635
the wage growth of people who
got the program to people who

584
00:28:12,635 --> 00:28:13,700
got the placebo.

585
00:28:13,700 --> 00:28:15,830
In fact, here they compare
the wage growth of the

586
00:28:15,830 --> 00:28:20,320
self-employed people who were
anemic at baseline in the

587
00:28:20,320 --> 00:28:22,660
treatment group and in
the control group.

588
00:28:22,660 --> 00:28:26,160
And you are exactly right
that those wages

589
00:28:26,160 --> 00:28:28,610
increase in both cases.

590
00:28:28,610 --> 00:28:31,220
But they increased faster
in the treatment group.

591
00:28:31,220 --> 00:28:34,270
And the $40 is the difference
in the growth.

592
00:28:34,270 --> 00:28:35,770
So it's already accounting
for that.

593
00:28:38,650 --> 00:28:42,490
So what I say is that, well, if
someone wanted to do it on

594
00:28:42,490 --> 00:28:45,250
the own, that wouldn't cost
them so much money.

595
00:28:45,250 --> 00:28:52,420
That would just cost them $6
per year for a gain of $40.

596
00:28:52,420 --> 00:28:56,120
So this is a case where you
would think it's something

597
00:28:56,120 --> 00:28:58,630
that starts looking
like an S-shape.

598
00:28:58,630 --> 00:29:03,710
Which is, if you become rich
enough for spending an extra

599
00:29:03,710 --> 00:29:08,492
$6, you actually get a return
which is much higher than $6.

600
00:29:08,492 --> 00:29:13,030
So you may have this increasing
return that is

601
00:29:13,030 --> 00:29:16,465
necessary for the poverty trap
to emerge, where the slightly

602
00:29:16,465 --> 00:29:19,280
richer people get the fortified
fish sauce instead

603
00:29:19,280 --> 00:29:22,590
of the regular fish sauce
that costs them $6, and

604
00:29:22,590 --> 00:29:24,340
they make $40 extra.

605
00:29:27,100 --> 00:29:28,480
So you could say, well,
there is something.

606
00:29:28,480 --> 00:29:30,200
Except that, of course,
you have to ask.

607
00:29:30,200 --> 00:29:34,400
$6 is not all that much, so what
is preventing these poor

608
00:29:34,400 --> 00:29:38,470
people to pay $6?

609
00:29:38,470 --> 00:29:42,600
So that is the first place
where, if we compare this, 40

610
00:29:42,600 --> 00:29:45,555
to 6 is the first place where
we can see a poverty trap.

611
00:29:45,555 --> 00:29:48,140
Except we'll have to explain
why it's there.

612
00:29:48,140 --> 00:29:51,250
We'll have to explain why it
seems that the poor people are

613
00:29:51,250 --> 00:29:53,485
less likely to spend the
$6 on fortified fish

614
00:29:53,485 --> 00:29:54,735
sauce in their reach.

615
00:29:57,490 --> 00:29:58,850
That's for adults.

616
00:29:58,850 --> 00:30:01,800
So already, we saw that for
calories, we don't see such a

617
00:30:01,800 --> 00:30:03,810
big return to calorie
consumption.

618
00:30:03,810 --> 00:30:05,870
By for iron, we see it.

619
00:30:05,870 --> 00:30:09,480
Now, another place where we do
see, potentially, very large

620
00:30:09,480 --> 00:30:13,860
returns of investing into food
is when you're trying to

621
00:30:13,860 --> 00:30:17,800
invest in the nutrition
of your children.

622
00:30:17,800 --> 00:30:25,940
So why is it that, even
though if we're

623
00:30:25,940 --> 00:30:27,520
talking about calories--

624
00:30:27,520 --> 00:30:31,360
even more micronutrients,
but any kind of

625
00:30:31,360 --> 00:30:34,280
investment in your children--

626
00:30:34,280 --> 00:30:37,290
may have a larger impact
than the same

627
00:30:37,290 --> 00:30:40,610
investment for an adult?

628
00:30:40,610 --> 00:30:42,590
AUDIENCE: Because children are
still growing and developing.

629
00:30:42,590 --> 00:30:45,560
Their brains are still growing,
and their bones.

630
00:30:45,560 --> 00:30:49,355
Basically, the frame for who
their going to be is in

631
00:30:49,355 --> 00:30:51,005
development at this point
in their life.

632
00:30:51,005 --> 00:30:54,322
So it's important that they can
reach their potential by

633
00:30:54,322 --> 00:30:57,250
giving them the nutrients
that they need now.

634
00:30:57,250 --> 00:30:58,250
PROFESSOR: Right.

635
00:30:58,250 --> 00:31:00,180
AUDIENCE: [INAUDIBLE].

636
00:31:00,180 --> 00:31:00,610
PROFESSOR: Exactly.

637
00:31:00,610 --> 00:31:06,250
So the first reason, pure health
reason, is that when

638
00:31:06,250 --> 00:31:08,910
you're investing into a child's
nutrition, be it

639
00:31:08,910 --> 00:31:11,860
calorie or micronutrient, you
don't only make the child more

640
00:31:11,860 --> 00:31:15,340
productive tomorrow, you are
changing the adult that this

641
00:31:15,340 --> 00:31:16,780
child is going to be.

642
00:31:16,780 --> 00:31:19,740
You are making this person reach
their genetic potential

643
00:31:19,740 --> 00:31:22,270
in terms of height, for example,
that they might not

644
00:31:22,270 --> 00:31:23,950
otherwise be getting.

645
00:31:23,950 --> 00:31:28,210
You are helping this
person reach that

646
00:31:28,210 --> 00:31:29,890
potential in their brain.

647
00:31:29,890 --> 00:31:32,130
You're helping this person
develop the muscles that they

648
00:31:32,130 --> 00:31:33,270
would have gotten.

649
00:31:33,270 --> 00:31:37,320
And some of these, you might not
be able to recover later.

650
00:31:37,320 --> 00:31:41,850
In particular, some of the
nutritional deficiency that

651
00:31:41,850 --> 00:31:45,280
you get as very small children,
in between weaning

652
00:31:45,280 --> 00:31:51,660
at about six months and two
years, would be very easy to

653
00:31:51,660 --> 00:31:54,730
catch up once the child's
actually gone.

654
00:31:54,730 --> 00:31:57,224
Even once a child is
more than two.

655
00:31:57,224 --> 00:31:58,020
Yeah.

656
00:31:58,020 --> 00:31:59,958
AUDIENCE: There's no access
to things like

657
00:31:59,958 --> 00:32:01,446
education at this point.

658
00:32:01,446 --> 00:32:03,926
So if they're better nourished
now, then they

659
00:32:03,926 --> 00:32:04,918
can focus on that.

660
00:32:04,918 --> 00:32:07,563
Versus an adult probably
wouldn't be thinking about

661
00:32:07,563 --> 00:32:09,900
going back to school
at that point.

662
00:32:09,900 --> 00:32:10,155
PROFESSOR: Exactly.

663
00:32:10,155 --> 00:32:13,340
So the second reason is that,
even if we forget this

664
00:32:13,340 --> 00:32:17,760
biological phenomenon, the job
of a child is typically to be

665
00:32:17,760 --> 00:32:21,270
in school, or to learn
things around them.

666
00:32:21,270 --> 00:32:22,320
Not necessarily in school.

667
00:32:22,320 --> 00:32:25,835
Some can be outside of school.

668
00:32:25,835 --> 00:32:29,220
They are still getting all the
information in the world.

669
00:32:29,220 --> 00:32:31,040
That's what children do.

670
00:32:31,040 --> 00:32:36,060
And if you do this job better,
then you are building your

671
00:32:36,060 --> 00:32:37,560
human capitol.

672
00:32:37,560 --> 00:32:41,650
Really think of it as like, the
capitol of each of us is

673
00:32:41,650 --> 00:32:44,700
our health, which is affected
by how much we eat directly.

674
00:32:44,700 --> 00:32:47,540
But also what we know,
our experience, our

675
00:32:47,540 --> 00:32:49,340
education, et cetera.

676
00:32:49,340 --> 00:32:53,240
And if we do this job better
as children, we'll have a

677
00:32:53,240 --> 00:32:56,550
better stock of education for
the rest of our lives.

678
00:32:56,550 --> 00:32:58,760
Education and knowledge
generally.

679
00:32:58,760 --> 00:33:03,000
And we are be going to get the
return from that every year.

680
00:33:03,000 --> 00:33:07,035
So when we do our job better as
an adult, we earn a higher

681
00:33:07,035 --> 00:33:08,330
wage and that's it.

682
00:33:08,330 --> 00:33:12,900
When we do our job better at
your age, or even earlier,

683
00:33:12,900 --> 00:33:14,700
when you were a child, when you
were a small child trying

684
00:33:14,700 --> 00:33:18,000
to learn things, since your job
is to develop, that means

685
00:33:18,000 --> 00:33:19,524
you're better developed.

686
00:33:19,524 --> 00:33:21,612
AUDIENCE: Is it more important
to have good nutrition when

687
00:33:21,612 --> 00:33:25,340
the mom's pregnant, or after
the child's born?

688
00:33:25,340 --> 00:33:26,040
PROFESSOR: Both are important.

689
00:33:26,040 --> 00:33:29,920
We're going to get to
the mom in a minute.

690
00:33:29,920 --> 00:33:33,720
But both children are important,
and in utero is

691
00:33:33,720 --> 00:33:34,680
very important.

692
00:33:34,680 --> 00:33:36,570
Both of them are important.

693
00:33:36,570 --> 00:33:40,340
So for these reasons, if you
take a child and you say, I'm

694
00:33:40,340 --> 00:33:43,550
going to feed this child better,
if only between the

695
00:33:43,550 --> 00:33:45,930
time of six months
to two years--

696
00:33:45,930 --> 00:33:49,470
or let's say, even if you were
going from six months to ten

697
00:33:49,470 --> 00:33:53,000
years, when they are in full
development of their body and

698
00:33:53,000 --> 00:33:53,940
their mind.

699
00:33:53,940 --> 00:33:57,480
I'm going to then, on
return, potentially,

700
00:33:57,480 --> 00:33:58,820
for his entire life.

701
00:33:58,820 --> 00:34:02,390
That means the size in
difference in investment in

702
00:34:02,390 --> 00:34:05,130
how much you're going to get in
the future compared to the

703
00:34:05,130 --> 00:34:08,139
investment you are making
is much, much larger.

704
00:34:08,139 --> 00:34:11,489
And that can, again, give you
the potential for an S-shape.

705
00:34:11,489 --> 00:34:15,110
Where a poorer person is going
to invest a little less.

706
00:34:15,110 --> 00:34:17,799
And this difference at
this points can be--

707
00:34:17,799 --> 00:34:21,739
this difference in slightly
smaller investment at critical

708
00:34:21,739 --> 00:34:26,889
range could translate into much,
much smaller lifetime

709
00:34:26,889 --> 00:34:29,130
earning for a child.

710
00:34:29,130 --> 00:34:33,330
So let's see some examples
of that.

711
00:34:33,330 --> 00:34:36,489
So the first one is the
deworming example that I was

712
00:34:36,489 --> 00:34:38,110
talking about.

713
00:34:38,110 --> 00:34:41,590
And this was done, also, in
a randomized experiment.

714
00:34:41,590 --> 00:34:43,340
That's the one I was talking
to you about, where they

715
00:34:43,340 --> 00:34:51,600
realized that the more people
you knew who took the

716
00:34:51,600 --> 00:34:54,100
deworming, the less likely
you were to take it.

717
00:34:54,100 --> 00:34:55,940
Well, it turned out that
was actually a mistake.

718
00:34:55,940 --> 00:35:00,570
Because being dewormed
is extremely helpful.

719
00:35:00,570 --> 00:35:05,220
So what I did is, this is the
region where they worked,

720
00:35:05,220 --> 00:35:08,600
where you had a bunch
of schools.

721
00:35:08,600 --> 00:35:10,350
This is a map.

722
00:35:10,350 --> 00:35:16,500
You can see that the region
is close to Lake Victoria.

723
00:35:16,500 --> 00:35:21,410
Worms, particularly
schistosomasis, is something

724
00:35:21,410 --> 00:35:25,080
that you're much more likely to
get if you are walking in

725
00:35:25,080 --> 00:35:27,710
the fresh water.

726
00:35:27,710 --> 00:35:29,170
Particularly when it's
not that clean, but

727
00:35:29,170 --> 00:35:30,660
when it's not salty.

728
00:35:30,660 --> 00:35:35,140
So ones basically climb from the
sole of your feet inside.

729
00:35:35,140 --> 00:35:38,560
So when these kids go fishing
in the lake, or just go hang

730
00:35:38,560 --> 00:35:41,640
out in the lake, much more
likely to get worms.

731
00:35:41,640 --> 00:35:44,350
So this region is infected
by worms.

732
00:35:44,350 --> 00:35:47,810
About a quarter of the worm
children suffer from worms.

733
00:35:47,810 --> 00:35:50,950
One thing with worms is that
they've never killed anybody.

734
00:35:50,950 --> 00:35:52,720
At least, not these worms.

735
00:35:52,720 --> 00:35:55,480
There are some worms
that gives you very

736
00:35:55,480 --> 00:35:58,250
spectacular, big legs.

737
00:35:58,250 --> 00:36:00,410
And those worms are a little
bit more fashionable.

738
00:36:00,410 --> 00:36:03,990
But these little hookworm,
schistosomasis,

739
00:36:03,990 --> 00:36:06,550
doesn't kill people.

740
00:36:06,550 --> 00:36:08,910
You can't really see that
someone has them.

741
00:36:08,910 --> 00:36:10,990
So as a reason, it's not a
disease that anybody's

742
00:36:10,990 --> 00:36:13,960
particularly excited about.

743
00:36:13,960 --> 00:36:17,536
I want to make you excited about
worms for about, like,

744
00:36:17,536 --> 00:36:19,555
at least 15 minutes.

745
00:36:19,555 --> 00:36:22,140
You can come back and say, well,
these worms, there is

746
00:36:22,140 --> 00:36:23,710
something with them.

747
00:36:23,710 --> 00:36:30,120
So the researcher went to this
area, and they separated to

748
00:36:30,120 --> 00:36:32,840
schools into three
groups randomly.

749
00:36:32,840 --> 00:36:36,400
Why did they pick the school?

750
00:36:36,400 --> 00:36:38,810
Why did they decide to randomize
at the school level

751
00:36:38,810 --> 00:36:41,280
instead of doing it
within school?

752
00:36:41,280 --> 00:36:43,960
For example, if you remember
the bednet experiment, the

753
00:36:43,960 --> 00:36:46,540
bednet experiment was done
at the individual level.

754
00:36:46,540 --> 00:36:49,790
Here, they treated all the
children in the school.

755
00:36:49,790 --> 00:36:52,260
All the children was
left as control.

756
00:36:52,260 --> 00:36:56,190
Why did they decide to do
it at the school level?

757
00:36:56,190 --> 00:36:57,020
Yup.

758
00:36:57,020 --> 00:36:59,440
AUDIENCE: So kids may
affect one another.

759
00:36:59,440 --> 00:37:03,150
So if one child in a classroom
is dewormed and the other is

760
00:37:03,150 --> 00:37:04,764
not, they may be learning
better.

761
00:37:04,764 --> 00:37:06,538
And because they're learning
better, the other child may

762
00:37:06,538 --> 00:37:07,668
also be increasing their
understanding.

763
00:37:07,668 --> 00:37:10,572
If you do it at a school level,
they can cancel out

764
00:37:10,572 --> 00:37:11,056
that effect.

765
00:37:11,056 --> 00:37:13,330
So they can compare schools
where all children are and

766
00:37:13,330 --> 00:37:14,580
schools where all
children aren't.

767
00:37:14,580 --> 00:37:14,950
PROFESSOR: Right.

768
00:37:14,950 --> 00:37:17,010
So kids' education could
affect one another.

769
00:37:17,010 --> 00:37:17,610
What else?

770
00:37:17,610 --> 00:37:20,613
In what way could they also
affect one another?

771
00:37:20,613 --> 00:37:23,103
AUDIENCE: Isn't there the
externality, because they're

772
00:37:23,103 --> 00:37:24,850
very contagious, you said?

773
00:37:24,850 --> 00:37:25,150
PROFESSOR: Right.

774
00:37:25,150 --> 00:37:29,930
There is the direct deworming
externality that Zach and Noah

775
00:37:29,930 --> 00:37:30,870
mentioned earlier.

776
00:37:30,870 --> 00:37:34,260
Which is actually, worms
are hyper-contagious.

777
00:37:34,260 --> 00:37:37,990
So if you compare, when they
have done randomized

778
00:37:37,990 --> 00:37:40,450
experiments before within
schools, they were very

779
00:37:40,450 --> 00:37:42,420
surprised, because they're
saying, we are deworming these

780
00:37:42,420 --> 00:37:45,500
children, and we see no
effect on anything.

781
00:37:45,500 --> 00:37:49,650
And the thing is, the control
kids were re-infecting the

782
00:37:49,650 --> 00:37:51,900
treated kids, and the treated
kids were also making the

783
00:37:51,900 --> 00:37:53,640
control kids less sick.

784
00:37:53,640 --> 00:37:56,140
So the effect was zero.

785
00:37:56,140 --> 00:37:58,640
So here, they decided, let's
go and randomize the at the

786
00:37:58,640 --> 00:37:59,980
school level.

787
00:37:59,980 --> 00:38:04,890
And the first thing they
did is that they

788
00:38:04,890 --> 00:38:08,370
went into the schools.

789
00:38:08,370 --> 00:38:10,480
So they did the school
in three groups.

790
00:38:10,480 --> 00:38:14,680
They dewormed Group 1 in
'98-2003, and then dewormed

791
00:38:14,680 --> 00:38:18,220
the Group 2 in 1999-2003,
and dewormed the

792
00:38:18,220 --> 00:38:21,560
group three in 2001-2003.

793
00:38:21,560 --> 00:38:32,880
So the Group 3 three children
got, on average, less two

794
00:38:32,880 --> 00:38:37,080
fewer years of deworming
compared to the Group 1 and 2.

795
00:38:37,080 --> 00:38:40,190
There was a first study they
did, which was they collected

796
00:38:40,190 --> 00:38:42,790
data in 2000.

797
00:38:42,790 --> 00:38:45,590
And in 2000, they compared
children in Group 1 and 2 to

798
00:38:45,590 --> 00:38:46,950
children in Group 3.

799
00:38:46,950 --> 00:38:49,630
So children in Group 1 and 2 had
been treated either one or

800
00:38:49,630 --> 00:38:51,930
two years, and children
in Group 3 had not

801
00:38:51,930 --> 00:38:53,390
being treated yet.

802
00:38:53,390 --> 00:38:56,060
And what they found at this time
was children, of course,

803
00:38:56,060 --> 00:38:58,390
were less likely to have worms
if they had been dewormed.

804
00:38:58,390 --> 00:39:01,670
Otherwise, it's not much
study to talk about.

805
00:39:01,670 --> 00:39:05,225
Number one is children who had
been dewormed [INAUDIBLE],

806
00:39:05,225 --> 00:39:07,500
they are less likely
to be anemic.

807
00:39:07,500 --> 00:39:10,630
And importantly, they are less
likely to miss school.

808
00:39:10,630 --> 00:39:15,220
So they find that there was
an increase of about 15%.

809
00:39:15,220 --> 00:39:20,790
So 1/6 of a year in
participation in school.

810
00:39:20,790 --> 00:39:24,900
So what this study that we are
doing now does is that it's

811
00:39:24,900 --> 00:39:28,280
tracking the children who were
in primary school at this time

812
00:39:28,280 --> 00:39:30,400
later when they go up.

813
00:39:30,400 --> 00:39:35,490
So the date we're going to
look at is in 2007-2009.

814
00:39:35,490 --> 00:39:43,940
So a kid who was 10 in 1998 is
now 20, and is therefore

815
00:39:43,940 --> 00:39:46,350
usually doing something,
working.

816
00:39:46,350 --> 00:39:48,890
And therefore, they can start
looking at whether these

817
00:39:48,890 --> 00:39:50,420
people are now earning
more money.

818
00:39:54,940 --> 00:39:58,650
So it's a big project, because
these children have gone all

819
00:39:58,650 --> 00:40:00,270
over the place.

820
00:40:00,270 --> 00:40:04,570
So they have had some difficulty
finding them.

821
00:40:04,570 --> 00:40:08,210
One of them was in London, and
they went and interviewed a

822
00:40:08,210 --> 00:40:11,600
person in London.

823
00:40:11,600 --> 00:40:13,620
Many of them had moved to
Nairobi or had moved to

824
00:40:13,620 --> 00:40:15,940
Mombasa or had moved
to Uganda.

825
00:40:15,940 --> 00:40:18,510
So what they did is they did a
first wave of it where they

826
00:40:18,510 --> 00:40:20,430
tried to track everyone.

827
00:40:20,430 --> 00:40:22,850
And they found about
60% of the people.

828
00:40:22,850 --> 00:40:27,740
And that's not enough, because
the 40% you don't find might

829
00:40:27,740 --> 00:40:29,960
be the ones that have
the bigger effect.

830
00:40:29,960 --> 00:40:32,320
They might be the one that have
moved to London, because

831
00:40:32,320 --> 00:40:34,300
of the extra education
they are getting.

832
00:40:34,300 --> 00:40:37,700
So then they decided, let's take
a smaller number of kids,

833
00:40:37,700 --> 00:40:40,060
and track them wherever
they are.

834
00:40:40,060 --> 00:40:41,780
Really find them.

835
00:40:41,780 --> 00:40:43,750
And when you do that,
they found a quite a

836
00:40:43,750 --> 00:40:44,760
large number of them.

837
00:40:44,760 --> 00:40:49,060
So that altogether in the
sample, they have about 85% of

838
00:40:49,060 --> 00:40:52,680
tracking rate, in
treatment and in

839
00:40:52,680 --> 00:40:55,220
controls very similarly.

840
00:40:55,220 --> 00:40:58,350
So therefore, we can now look
at what happened to wages.

841
00:40:58,350 --> 00:41:04,650
So this is the empirical
distribution of log wages.

842
00:41:04,650 --> 00:41:20,530
So what this tells you
is, roughly, if you

843
00:41:20,530 --> 00:41:22,090
take any line here--

844
00:41:22,090 --> 00:41:25,020
for example, it says
log earning of 7.

845
00:41:25,020 --> 00:41:28,300
So wages tend to
be log numbers.

846
00:41:28,300 --> 00:41:31,020
So we like to show logs.

847
00:41:31,020 --> 00:41:36,190
So in the treatment group,
about 10% of

848
00:41:36,190 --> 00:41:39,310
people, a log of 7.

849
00:41:39,310 --> 00:41:46,720
And in the control group,
that's about 21%, 25%,

850
00:41:46,720 --> 00:41:48,260
something like that.

851
00:41:48,260 --> 00:41:49,770
So what does this mean?

852
00:41:49,770 --> 00:41:55,780
This What happened to the
distribution of wage between

853
00:41:55,780 --> 00:41:59,240
treatment and control, and
what does this mean?

854
00:41:59,240 --> 00:42:00,490
How do we read this?

855
00:42:04,090 --> 00:42:07,140
You can do it.

856
00:42:07,140 --> 00:42:09,880
You've seen a distribution,
any distribution before.

857
00:42:09,880 --> 00:42:11,130
I know that.

858
00:42:13,100 --> 00:42:16,720
Just describe what happens
to these two curves.

859
00:42:19,940 --> 00:42:20,780
You, you, you, you.

860
00:42:20,780 --> 00:42:21,900
I was talking to you.

861
00:42:21,900 --> 00:42:23,911
Just describe what happens
to these two curves.

862
00:42:27,140 --> 00:42:33,462
Just tell me, physically, what
happens to these two curves.

863
00:42:33,462 --> 00:42:34,863
AUDIENCE: [INAUDIBLE].

864
00:42:34,863 --> 00:42:36,370
PROFESSOR: It moved right.

865
00:42:36,370 --> 00:42:37,346
Right?

866
00:42:37,346 --> 00:42:38,322
AUDIENCE: Yeah.

867
00:42:38,322 --> 00:42:39,220
PROFESSOR: Right?

868
00:42:39,220 --> 00:42:40,540
That was hard.

869
00:42:40,540 --> 00:42:42,240
They moved right.

870
00:42:42,240 --> 00:42:45,150
Now, what is hard is saying,
well, now that they move

871
00:42:45,150 --> 00:42:47,100
right, what does this mean?

872
00:42:47,100 --> 00:42:47,677
Noah.

873
00:42:47,677 --> 00:42:50,062
AUDIENCE: Well, I think
two things.

874
00:42:50,062 --> 00:42:53,876
Well, first of all, the on
average peaks higher, which

875
00:42:53,876 --> 00:42:58,836
means that the distribution in
any case, on average, people

876
00:42:58,836 --> 00:42:59,828
[INAUDIBLE].

877
00:42:59,828 --> 00:43:04,292
And also, it looks like it's
narrower, which means that

878
00:43:04,292 --> 00:43:07,268
more people are also earning
more, as opposed to just the

879
00:43:07,268 --> 00:43:08,770
average also earning more.

880
00:43:08,770 --> 00:43:09,630
PROFESSOR: Right.

881
00:43:09,630 --> 00:43:12,070
So those two things
are exactly true.

882
00:43:12,070 --> 00:43:14,290
So what we see is, number
one, here is the peak.

883
00:43:14,290 --> 00:43:19,200
So this is where, in the control
group, we get 45% of

884
00:43:19,200 --> 00:43:24,440
people earning about
a wage of 8.

885
00:43:24,440 --> 00:43:26,550
That's the mode of
the distribution.

886
00:43:26,550 --> 00:43:29,240
Then, the nice thing with wages
is they're going to be

887
00:43:29,240 --> 00:43:33,460
log normal, which means that the
mode is about the medium.

888
00:43:33,460 --> 00:43:37,910
It also means that 50% of the
people in the control group

889
00:43:37,910 --> 00:43:40,750
earn less than 8.

890
00:43:40,750 --> 00:43:44,360
Whereas here, we find that, if
we want to find 50% of the

891
00:43:44,360 --> 00:43:49,480
people earning less than
something, it's closer.

892
00:43:49,480 --> 00:43:51,720
So for the control group, it's
like 7 and 1/2, and the

893
00:43:51,720 --> 00:43:53,930
treatment group is 8.

894
00:43:53,930 --> 00:43:57,520
So in the control group, 50% of
people earn less than 7 and

895
00:43:57,520 --> 00:44:00,060
1/2, and in the treatment
group, 50% of people

896
00:44:00,060 --> 00:44:02,070
earn less than 8.

897
00:44:02,070 --> 00:44:05,840
And in fact, we could transform
this graph into a

898
00:44:05,840 --> 00:44:08,910
cumulative distribution function
instead of density.

899
00:44:08,910 --> 00:44:11,310
And we would find that, given
this graph, given that it's

900
00:44:11,310 --> 00:44:14,680
nicely shifted to the right
and it's also a little bit

901
00:44:14,680 --> 00:44:18,500
less valuable, as Noah pointed
out, we would find that at

902
00:44:18,500 --> 00:44:22,250
every percentage, we have more
people in the control group

903
00:44:22,250 --> 00:44:25,460
who make less than that
at every level.

904
00:44:25,460 --> 00:44:27,850
We have a more people in the
control group earning less

905
00:44:27,850 --> 00:44:29,940
than that than in the
treatment group.

906
00:44:29,940 --> 00:44:31,170
Which means that--

907
00:44:31,170 --> 00:44:32,740
Well, it has to mean that
the people in the

908
00:44:32,740 --> 00:44:34,710
treatment group earn more.

909
00:44:34,710 --> 00:44:37,800
And not only that, but--

910
00:44:37,800 --> 00:44:42,120
not every single person,
but statistically--

911
00:44:42,120 --> 00:44:44,500
everybody in the treatment
does somewhat better.

912
00:44:44,500 --> 00:44:46,530
So we are saying the
distribution in the treatment

913
00:44:46,530 --> 00:44:49,180
group statistically dominates
the distribution in the

914
00:44:49,180 --> 00:44:50,650
control group.

915
00:44:50,650 --> 00:44:54,720
If you had to choose which
society to live in, without

916
00:44:54,720 --> 00:44:58,540
knowing, you would pick
the treatment group.

917
00:44:58,540 --> 00:45:02,005
Because the chance that you are
earning more is better in

918
00:45:02,005 --> 00:45:03,450
one place than the other.

919
00:45:03,450 --> 00:45:06,490
So that's what happens with
this distribution.

920
00:45:06,490 --> 00:45:09,070
We can just look at them and
say, yeah, we have more people

921
00:45:09,070 --> 00:45:12,205
earning less and we have here
more people earning more.

922
00:45:15,320 --> 00:45:19,840
So now we can say, well,
how does it look like?

923
00:45:19,840 --> 00:45:23,550
This could just all be nice
in graph, but there is no

924
00:45:23,550 --> 00:45:24,490
standard error here.

925
00:45:24,490 --> 00:45:25,730
There is no confidence
interval.

926
00:45:25,730 --> 00:45:29,750
Maybe this is not really
very solid.

927
00:45:29,750 --> 00:45:32,420
So we can look at that
in a regression.

928
00:45:32,420 --> 00:45:35,810
So this is a simple regression,
which gives us

929
00:45:35,810 --> 00:45:37,770
directly the difference--

930
00:45:37,770 --> 00:45:41,760
what you can read here is the
difference between the log

931
00:45:41,760 --> 00:45:46,800
earning of the treatment group
and the log earning of the

932
00:45:46,800 --> 00:45:48,270
control group.

933
00:45:48,270 --> 00:45:52,250
That means I could have plotted
bar charts like we had

934
00:45:52,250 --> 00:45:53,230
with the bednet.

935
00:45:53,230 --> 00:45:57,910
It's saying, this is
the mean here.

936
00:45:57,910 --> 00:46:01,550
The mean wage in the control
group is 7.8, which

937
00:46:01,550 --> 00:46:04,230
corresponds to above the median
and above the mode of

938
00:46:04,230 --> 00:46:06,750
the distribution, 7.8.

939
00:46:06,750 --> 00:46:15,650
And the mean wage for the
treatment group is log.18.

940
00:46:15,650 --> 00:46:18,050
So that means about 18--

941
00:46:18,050 --> 00:46:18,980
19, sorry.

942
00:46:18,980 --> 00:46:21,910
19 percentage points higher
than the mean in

943
00:46:21,910 --> 00:46:22,840
the treatment group.

944
00:46:22,840 --> 00:46:26,970
So when we run regression in
logs, the advantage is we can

945
00:46:26,970 --> 00:46:28,870
read the coefficient
directly as the

946
00:46:28,870 --> 00:46:30,660
percentage point increases.

947
00:46:30,660 --> 00:46:34,010
So if we wanted to know, what's
the mean log wages in

948
00:46:34,010 --> 00:46:34,730
the treatment group?

949
00:46:34,730 --> 00:46:37,460
What do we need to do
from this graph?

950
00:46:37,460 --> 00:46:39,030
So make sure that you
have it well.

951
00:46:41,890 --> 00:46:44,778
Yeah.

952
00:46:44,778 --> 00:46:49,210
AUDIENCE: Take the median
and multiply it by 1.19.

953
00:46:49,210 --> 00:46:49,390
PROFESSOR: No.

954
00:46:49,390 --> 00:46:50,640
So what you would do--

955
00:46:52,960 --> 00:46:55,080
This is the mean of the log.

956
00:46:55,080 --> 00:46:58,430
And this is the log point
that they get.

957
00:46:58,430 --> 00:47:02,950
So if we wanted to know the log
wages for the treatment

958
00:47:02,950 --> 00:47:09,350
group, all we would need to
do is to add 0.19 to 7.8.

959
00:47:09,350 --> 00:47:11,180
So that would be about 8.

960
00:47:11,180 --> 00:47:14,580
And then if we wanted to know
the level then we would take

961
00:47:14,580 --> 00:47:16,690
the exponential of 8.

962
00:47:16,690 --> 00:47:17,290
Right?

963
00:47:17,290 --> 00:47:22,005
So when you have experiments,
you can just take the mean,

964
00:47:22,005 --> 00:47:24,650
and you can calculate the mean
in the treatment group or the

965
00:47:24,650 --> 00:47:26,390
mean the control group.

966
00:47:26,390 --> 00:47:29,600
But in the papers in studies,
what you generally see is

967
00:47:29,600 --> 00:47:33,500
people running a very simple,
ordinary [INAUDIBLE] square

968
00:47:33,500 --> 00:47:40,390
regression on wages of whether
you are a treatment person.

969
00:47:40,390 --> 00:47:43,070
And the way we'll read this is
just saying, this is the

970
00:47:43,070 --> 00:47:45,110
difference between treatment
and control.

971
00:47:45,110 --> 00:47:47,520
And this is the mean
for control.

972
00:47:47,520 --> 00:47:49,870
And then, once we've done that,
we can add other things

973
00:47:49,870 --> 00:47:52,920
that absolves the noise,
and we'll get

974
00:47:52,920 --> 00:47:54,040
slightly different results.

975
00:47:54,040 --> 00:47:55,740
But not very different,
because everything is

976
00:47:55,740 --> 00:47:57,300
randomized.

977
00:47:57,300 --> 00:47:59,214
What is this one?

978
00:47:59,214 --> 00:48:00,590
Over here?

979
00:48:04,794 --> 00:48:06,690
What is this little
[INAUDIBLE]

980
00:48:06,690 --> 00:48:07,440
in [INAUDIBLE]?

981
00:48:07,440 --> 00:48:09,200
Sorry?

982
00:48:09,200 --> 00:48:09,990
AUDIENCE: Errors?

983
00:48:09,990 --> 00:48:11,260
PROFESSOR: The standard error.

984
00:48:11,260 --> 00:48:12,210
Exactly.

985
00:48:12,210 --> 00:48:14,180
This guy is the standard
error.

986
00:48:14,180 --> 00:48:17,490
So this is saying there is some
noise around these wages.

987
00:48:17,490 --> 00:48:20,460
So the difference, the mean,
because we have the

988
00:48:20,460 --> 00:48:21,350
distribution of wages.

989
00:48:21,350 --> 00:48:27,760
So there is some variation
around the estimate.

990
00:48:27,760 --> 00:48:31,840
And therefore, there is some
noise around our estimate of

991
00:48:31,840 --> 00:48:34,660
the difference between treatment
and control wages.

992
00:48:34,660 --> 00:48:35,910
And that tells us the
standard error.

993
00:48:39,170 --> 00:48:42,630
So now we need to know, well,
how do I know whether this

994
00:48:42,630 --> 00:48:47,300
effect is just due to chance,
or if it's a real effect.

995
00:48:47,300 --> 00:48:49,100
Once I give you the
coefficient, and

996
00:48:49,100 --> 00:48:50,960
the standard error.

997
00:48:50,960 --> 00:48:51,440
Yeah.

998
00:48:51,440 --> 00:48:53,380
AUDIENCE: If it's more than two
standard errors, isn't it

999
00:48:53,380 --> 00:48:54,850
significant?

1000
00:48:54,850 --> 00:48:55,770
PROFESSOR: Right.

1001
00:48:55,770 --> 00:48:59,160
So if you divide the coefficient
by the standard

1002
00:48:59,160 --> 00:49:02,760
error, it gives you something
we call the t-statistic.

1003
00:49:02,760 --> 00:49:06,470
For the hypothesis that
the effect is 0.

1004
00:49:06,470 --> 00:49:08,530
So when we divide the
coefficient by the standard

1005
00:49:08,530 --> 00:49:14,130
error, we get the t-statistic,
and the t-statistic is for the

1006
00:49:14,130 --> 00:49:17,170
test that the coefficient
is not 0.

1007
00:49:17,170 --> 00:49:20,190
So the hypothesis is, is
this coefficient 0?

1008
00:49:20,190 --> 00:49:27,470
So each test goes with a level
of confidence, which is the

1009
00:49:27,470 --> 00:49:30,100
probability of a
type one error.

1010
00:49:30,100 --> 00:49:32,900
That is, the probability that
you are saying there is an

1011
00:49:32,900 --> 00:49:35,182
effect when in fact,
there is not.

1012
00:49:35,182 --> 00:49:36,490
Generally in economics--

1013
00:49:36,490 --> 00:49:38,660
I don't know in other fields,
but in economics-- generally

1014
00:49:38,660 --> 00:49:41,580
we go with sizes of 5%.

1015
00:49:41,580 --> 00:49:45,880
So we accept to say that
something has an effect when

1016
00:49:45,880 --> 00:49:49,530
in fact it doesn't with
a probability of 5%.

1017
00:49:49,530 --> 00:49:55,420
And 5% corresponds to a
t-statistic of 1.96.

1018
00:49:55,420 --> 00:49:59,270
So when you see regression table
like this, it's very

1019
00:49:59,270 --> 00:50:01,890
simple if things
are randomized.

1020
00:50:01,890 --> 00:50:04,330
When you see a regression,

1021
00:50:04,330 --> 00:50:06,630
looking at these effects,
gives you the difference

1022
00:50:06,630 --> 00:50:08,440
between treatment and control.

1023
00:50:08,440 --> 00:50:09,940
Divided by it's standard
error.

1024
00:50:09,940 --> 00:50:12,690
And if it's above 1.96, it tells
you that the effect is

1025
00:50:12,690 --> 00:50:14,820
significantly different
from 0.

1026
00:50:14,820 --> 00:50:16,310
That is, there is
a real effect.

1027
00:50:16,310 --> 00:50:18,290
Not an effect due to chance.

1028
00:50:18,290 --> 00:50:21,100
So here of course, it's
much above 2.

1029
00:50:21,100 --> 00:50:23,290
And it's about 19%.

1030
00:50:23,290 --> 00:50:27,110
So it tells you that the wage
of the treated guys is 19%

1031
00:50:27,110 --> 00:50:29,600
higher than the wage of
the control guys.

1032
00:50:29,600 --> 00:50:30,850
Which is a fair amount.

1033
00:50:44,890 --> 00:50:48,280
So why do I say that
19% wage is high?

1034
00:50:55,120 --> 00:50:57,970
What was the economic growth
in Kenya over this period,

1035
00:50:57,970 --> 00:50:59,190
give or take?

1036
00:50:59,190 --> 00:51:00,440
An order of magnitude?

1037
00:51:02,830 --> 00:51:04,320
AUDIENCE: 10%?

1038
00:51:04,320 --> 00:51:05,740
PROFESSOR: 10% would be nice.

1039
00:51:05,740 --> 00:51:07,470
[LAUGHTER]

1040
00:51:07,470 --> 00:51:10,000
PROFESSOR: I don't know if they
had any single year where

1041
00:51:10,000 --> 00:51:11,250
they had 10% growth.

1042
00:51:14,214 --> 00:51:15,100
AUDIENCE: Like 4?

1043
00:51:15,100 --> 00:51:16,280
PROFESSOR: Yeah, 3 4.

1044
00:51:16,280 --> 00:51:16,650
3, 4.

1045
00:51:16,650 --> 00:51:17,908
AUDIENCE: Do you know
what inflation is?

1046
00:51:17,908 --> 00:51:20,790
PROFESSOR: So that would
be in real time.

1047
00:51:20,790 --> 00:51:21,110
AUDIENCE: Adjusted.

1048
00:51:21,110 --> 00:51:22,280
All right.

1049
00:51:22,280 --> 00:51:24,810
PROFESSOR: But this is,
remember, we are comparing

1050
00:51:24,810 --> 00:51:26,280
treatment to control.

1051
00:51:26,280 --> 00:51:28,460
So there is no inflation here,
because our treatment people

1052
00:51:28,460 --> 00:51:31,670
were measured at
the same time.

1053
00:51:31,670 --> 00:51:32,890
Take real growth.

1054
00:51:32,890 --> 00:51:37,470
If we are saying 3% to 4% a
year, we are being generous to

1055
00:51:37,470 --> 00:51:39,790
Kenya for the average.

1056
00:51:39,790 --> 00:51:43,570
So that means that these guys
got the equivalent of several

1057
00:51:43,570 --> 00:51:48,130
years of good economic growth,
except there has not been many

1058
00:51:48,130 --> 00:51:51,610
years in Kenya where there has
been several years of good

1059
00:51:51,610 --> 00:51:53,790
economic growth.

1060
00:51:53,790 --> 00:51:56,330
So that's why I wanted to get
you excited about worms for

1061
00:51:56,330 --> 00:51:58,090
five minutes.

1062
00:51:58,090 --> 00:52:03,000
So this thing corresponds to
giving the kids a pill which

1063
00:52:03,000 --> 00:52:06,720
costs about, including the
delivery cost and all of that,

1064
00:52:06,720 --> 00:52:11,640
about $0.60 of delivering
the pill.

1065
00:52:11,640 --> 00:52:14,090
You need to do that
twice a year.

1066
00:52:14,090 --> 00:52:16,590
And this is a difference between
doing it for three

1067
00:52:16,590 --> 00:52:18,290
years versus one.

1068
00:52:18,290 --> 00:52:20,780
So this is your investment,
it's probably a good

1069
00:52:20,780 --> 00:52:24,590
investment, that was delivered
by society here in the form of

1070
00:52:24,590 --> 00:52:28,290
this NGO, was a [INAUDIBLE].

1071
00:52:28,290 --> 00:52:30,290
And that's 19% per year.

1072
00:52:30,290 --> 00:52:32,910
That's a lot.

1073
00:52:32,910 --> 00:52:35,280
Even people, if they are to do
it themselves, maybe they have

1074
00:52:35,280 --> 00:52:38,640
to do to the shop so they
don't get it for $0.60.

1075
00:52:38,640 --> 00:52:39,890
They have to pay $1.

1076
00:52:42,030 --> 00:52:45,410
Then they get several years of
good growth for the entire

1077
00:52:45,410 --> 00:52:47,140
lifetime of the child.

1078
00:52:47,140 --> 00:52:49,690
So we are talking about, for
a lifetime [INAUDIBLE]

1079
00:52:49,690 --> 00:52:53,310
of several thousand dollars
of extra wages.

1080
00:52:53,310 --> 00:52:56,280
And we can see it here.

1081
00:52:56,280 --> 00:53:00,770
So what this is is these are
the benefits that you're

1082
00:53:00,770 --> 00:53:04,540
getting from this 19% increase
in earnings.

1083
00:53:04,540 --> 00:53:08,260
So imagine that you get 19%
increase in earnings.

1084
00:53:08,260 --> 00:53:13,130
Take the GDP of Kenya, or the
average wage level of Kenya.

1085
00:53:13,130 --> 00:53:14,540
Multiplied by 19%.

1086
00:53:14,540 --> 00:53:16,720
That's how much you're
getting every year.

1087
00:53:16,720 --> 00:53:19,900
Then you have to compute
the net present value.

1088
00:53:19,900 --> 00:53:24,150
Because the benefit that you're
getting if you have to

1089
00:53:24,150 --> 00:53:27,180
pay the investment today, but
you're starting to get the

1090
00:53:27,180 --> 00:53:29,660
return when you're 20 and then
over your lifetime, it's not

1091
00:53:29,660 --> 00:53:31,330
as valuable.

1092
00:53:31,330 --> 00:53:34,690
So we are using some
[INAUDIBLE], let's say 5%.

1093
00:53:34,690 --> 00:53:36,910
And we are computing the net
present value of those

1094
00:53:36,910 --> 00:53:40,990
earnings, like we would for
the investment in a stock.

1095
00:53:40,990 --> 00:53:46,760
So when you do that, you get
over $1,000 increasing in your

1096
00:53:46,760 --> 00:53:47,630
lifetime earnings.

1097
00:53:47,630 --> 00:53:48,482
So this is that.

1098
00:53:48,482 --> 00:53:50,680
And this is how much it cost.

1099
00:53:50,680 --> 00:53:55,980
So you need to deliver the
pills, $0.65 per year, and

1100
00:53:55,980 --> 00:53:57,880
then they wanted to--

1101
00:53:57,880 --> 00:54:03,130
so that would be a huge benefit
of, like, $1,500 or

1102
00:54:03,130 --> 00:54:06,310
$1,100 divided by $0.65.

1103
00:54:06,310 --> 00:54:08,770
That would be pretty gigantic.

1104
00:54:08,770 --> 00:54:11,450
That's why worms are exciting.

1105
00:54:11,450 --> 00:54:13,580
Well, they don't want to make
it too exciting, so they are

1106
00:54:13,580 --> 00:54:17,300
saying, well, let's see what all
the costs we need to add.

1107
00:54:17,300 --> 00:54:19,720
Well, these kids have gone to
school a little longer.

1108
00:54:19,720 --> 00:54:21,920
They've gone to school
more every year.

1109
00:54:21,920 --> 00:54:25,440
So while in school, they are not
playing, or they are not

1110
00:54:25,440 --> 00:54:27,430
earning some wage.

1111
00:54:27,430 --> 00:54:29,510
So they are making some
assumption of what is this

1112
00:54:29,510 --> 00:54:31,390
opportunity cost.

1113
00:54:31,390 --> 00:54:33,900
Other wage, unskilled wage.

1114
00:54:33,900 --> 00:54:35,510
All of the day they spend
in school, they

1115
00:54:35,510 --> 00:54:38,206
assign them the wage.

1116
00:54:38,206 --> 00:54:40,680
That's an over-estimate, because
usually the kids are

1117
00:54:40,680 --> 00:54:42,840
just doing nothing, because
they are sick.

1118
00:54:42,840 --> 00:54:44,990
So this is being very
generous for the

1119
00:54:44,990 --> 00:54:47,330
cost of being in school.

1120
00:54:47,330 --> 00:54:49,535
And then, they also add the
fact that if you have more

1121
00:54:49,535 --> 00:54:52,693
kids in school, you need to
have, maybe, a little bit more

1122
00:54:52,693 --> 00:54:53,770
teachers and all that.

1123
00:54:53,770 --> 00:54:56,800
So they also can create
how much that can be.

1124
00:54:56,800 --> 00:55:00,340
So these things, you might want
to put them, or you might

1125
00:55:00,340 --> 00:55:01,980
not want to put them.

1126
00:55:01,980 --> 00:55:04,820
But the bottom line is that when
you do that, this bar is

1127
00:55:04,820 --> 00:55:07,460
pretty huge, and this bar
is pretty minimal.

1128
00:55:07,460 --> 00:55:08,408
Yeah.

1129
00:55:08,408 --> 00:55:11,252
AUDIENCE: If they're so clear,
why doesn't Kenya's government

1130
00:55:11,252 --> 00:55:12,680
support it?

1131
00:55:12,680 --> 00:55:15,020
PROFESSOR: Well, the
answer is they do.

1132
00:55:15,020 --> 00:55:18,810
Because until this study, it
wasn't so obvious that the

1133
00:55:18,810 --> 00:55:20,000
benefits are so large.

1134
00:55:20,000 --> 00:55:22,775
Because how would you know?

1135
00:55:22,775 --> 00:55:26,330
You only had those experiments
where you were comparing

1136
00:55:26,330 --> 00:55:27,920
people within the same school.

1137
00:55:27,920 --> 00:55:30,490
And you found no effect
of deworming.

1138
00:55:30,490 --> 00:55:32,280
So this study came.

1139
00:55:32,280 --> 00:55:34,660
That's an interesting political
economic story.

1140
00:55:34,660 --> 00:55:37,520
This study came-- the first one,
not even the second one.

1141
00:55:37,520 --> 00:55:39,850
And showed that it basically
costs nothing

1142
00:55:39,850 --> 00:55:41,625
to put kids in school.

1143
00:55:41,625 --> 00:55:44,390
The cheapest way to
get kids to attend

1144
00:55:44,390 --> 00:55:46,270
to school more regularly.

1145
00:55:46,270 --> 00:55:51,470
So the researchers and us here
at Poverty Action have started

1146
00:55:51,470 --> 00:55:54,820
to advertise this as, you might
not have thought it that

1147
00:55:54,820 --> 00:55:56,530
way, but deworming is
the cheapest way

1148
00:55:56,530 --> 00:55:59,450
to get kids in school.

1149
00:55:59,450 --> 00:56:00,860
We went to Davos.

1150
00:56:00,860 --> 00:56:04,820
Davos is this world congress
of rich people.

1151
00:56:04,820 --> 00:56:08,525
And we presented this kind of
data, and showed to them, you

1152
00:56:08,525 --> 00:56:11,240
know what, you might not think
deworming is so exciting, but

1153
00:56:11,240 --> 00:56:11,920
in fact it is.

1154
00:56:11,920 --> 00:56:13,780
Because it's a great
investment.

1155
00:56:13,780 --> 00:56:16,510
So they kind of liked
the idea.

1156
00:56:16,510 --> 00:56:17,920
Well, we started
an organization

1157
00:56:17,920 --> 00:56:19,330
called deworm the world.

1158
00:56:19,330 --> 00:56:22,140
And started just diffusing
these kind of results.

1159
00:56:22,140 --> 00:56:24,440
We didn't even have the
wage results yet.

1160
00:56:24,440 --> 00:56:27,080
It was just education results,
saying, deworming is a

1161
00:56:27,080 --> 00:56:28,650
sensible education policy.

1162
00:56:28,650 --> 00:56:31,090
It's a very cheap way to
get kids in school.

1163
00:56:31,090 --> 00:56:33,920
And started working with the
government to get this

1164
00:56:33,920 --> 00:56:35,890
information out.

1165
00:56:35,890 --> 00:56:38,710
One complicated thing with
deworming from a political

1166
00:56:38,710 --> 00:56:42,380
economic point of view is that
it's a health program that you

1167
00:56:42,380 --> 00:56:44,720
want to do in school.

1168
00:56:44,720 --> 00:56:46,430
The reason why you want to do
it in school is you have all

1169
00:56:46,430 --> 00:56:46,770
the kids there.

1170
00:56:46,770 --> 00:56:48,430
That's why it's cheap.

1171
00:56:48,430 --> 00:56:51,430
But when you want to do a health
program in school, you

1172
00:56:51,430 --> 00:56:54,630
need the Health Ministry and
the Education Ministry to

1173
00:56:54,630 --> 00:56:57,120
collaborate, or you need the
Finance Ministry to tell

1174
00:56:57,120 --> 00:56:58,370
them, you do it.

1175
00:56:58,370 --> 00:57:01,760
So that takes some effort,
but that effort got done.

1176
00:57:01,760 --> 00:57:05,300
And in fact, in Kenya they are
now deworming everywhere.

1177
00:57:05,300 --> 00:57:08,130
So that's millions, millions
of children.

1178
00:57:08,130 --> 00:57:10,120
And then this is also
moving up and down.

1179
00:57:10,120 --> 00:57:12,550
They're going to start doing it
in Bihar, which is a state

1180
00:57:12,550 --> 00:57:14,930
in India where they also
have a lot of worms.

1181
00:57:14,930 --> 00:57:16,910
They have started doing it in
Andhra Pradesh, where there is

1182
00:57:16,910 --> 00:57:19,570
not that many worms, but they
have subregions in Andhra

1183
00:57:19,570 --> 00:57:20,900
Pradesh with a lot of worms.

1184
00:57:20,900 --> 00:57:23,710
And in this way, the information
gets out, and

1185
00:57:23,710 --> 00:57:25,275
progressively it's taken up.

1186
00:57:25,275 --> 00:57:27,887
AUDIENCE: In Kenya, did the
government sponsor the

1187
00:57:27,887 --> 00:57:30,510
deworming program, or was
it outside donors?

1188
00:57:30,510 --> 00:57:36,750
PROFESSOR: In Kenya, the
answer is yes and no.

1189
00:57:36,750 --> 00:57:42,090
The direct answer is yes, but
it is subsidized in part by

1190
00:57:42,090 --> 00:57:51,720
the Fast Track Initiative, which
is international money

1191
00:57:51,720 --> 00:57:55,790
that government can access to do
things that help education.

1192
00:57:55,790 --> 00:58:00,110
So Kenya can elect to use Fast
Track Initiative money to do

1193
00:58:00,110 --> 00:58:03,890
textbooks, or to do computers
in school, or to do

1194
00:58:03,890 --> 00:58:06,160
blackboards, or to pay
teachers more.

1195
00:58:06,160 --> 00:58:07,570
And what they did is
they took some of

1196
00:58:07,570 --> 00:58:09,960
that money to do deworming.

1197
00:58:09,960 --> 00:58:13,670
The thing is, deworming is cheap
enough that once you

1198
00:58:13,670 --> 00:58:16,940
realize that it is a good thing
to do, money is less the

1199
00:58:16,940 --> 00:58:20,200
issue than getting everybody
on board and organized.

1200
00:58:20,200 --> 00:58:20,450
Yeah.

1201
00:58:20,450 --> 00:58:22,109
AUDIENCE: And so I'm thinking
there's probably other

1202
00:58:22,109 --> 00:58:25,190
developing countries that have
significantly worm issues.

1203
00:58:25,190 --> 00:58:27,086
And then why aren't those
countries doing it?

1204
00:58:27,086 --> 00:58:29,470
You mentioned India, but I'd
imagine there's a lot more.

1205
00:58:29,470 --> 00:58:29,960
PROFESSOR: Yes.

1206
00:58:29,960 --> 00:58:35,010
So the answer is slowly, slowly
they are getting into

1207
00:58:35,010 --> 00:58:36,070
the bandwagon.

1208
00:58:36,070 --> 00:58:38,440
But that's a very good question,
which is, number one

1209
00:58:38,440 --> 00:58:40,470
you need to have the
evidence out.

1210
00:58:40,470 --> 00:58:42,720
And until fairly recently,
in particular until this

1211
00:58:42,720 --> 00:58:45,150
experiment, the evidence
wasn't out.

1212
00:58:45,150 --> 00:58:47,400
And this is not something
that people could just

1213
00:58:47,400 --> 00:58:48,720
make up on their own.

1214
00:58:48,720 --> 00:58:51,910
I think in particular, the
effect on education, I don't

1215
00:58:51,910 --> 00:58:54,335
think the first thing that
comes to an education

1216
00:58:54,335 --> 00:58:57,130
minister, or the first thing
that would come to you, if I'd

1217
00:58:57,130 --> 00:58:59,800
asked you in principle, how
would you increase education?

1218
00:58:59,800 --> 00:59:01,300
What's the cheapest
way to do it?

1219
00:59:01,300 --> 00:59:03,380
I don't think deworming would
have been very high on your

1220
00:59:03,380 --> 00:59:04,250
radar screen.

1221
00:59:04,250 --> 00:59:06,600
It's not very high on anybody's
radar screen,

1222
00:59:06,600 --> 00:59:08,130
precisely because worms
don't kill.

1223
00:59:08,130 --> 00:59:11,030
So people think of HIV as being
important, which it is.

1224
00:59:11,030 --> 00:59:12,230
But people don't
think of worms.

1225
00:59:12,230 --> 00:59:13,410
So that's the first reason.

1226
00:59:13,410 --> 00:59:17,200
Once the information is out,
then it needs to be

1227
00:59:17,200 --> 00:59:17,750
percolated.

1228
00:59:17,750 --> 00:59:20,500
People need to absorb It.

1229
00:59:20,500 --> 00:59:22,500
And I think this is happening,
actually.

1230
00:59:22,500 --> 00:59:26,230
This is one of the pretty
hopeful stories, in terms of

1231
00:59:26,230 --> 00:59:27,690
that the evidence can
make a difference.

1232
00:59:30,915 --> 00:59:33,390
AUDIENCE: I can understand
where you argue with

1233
00:59:33,390 --> 00:59:37,350
government about education
effects,

1234
00:59:37,350 --> 00:59:38,340
especially in children.

1235
00:59:38,340 --> 00:59:41,310
But when you get something
as long as wage

1236
00:59:41,310 --> 00:59:44,280
effects, pretty long time.

1237
00:59:44,280 --> 00:59:49,725
Are you assuming that no other
health hazards would offset

1238
00:59:49,725 --> 00:59:55,210
the gains which can be obtained
from deworming.

1239
00:59:55,210 --> 00:59:55,460
PROFESSOR: Right.

1240
00:59:55,460 --> 00:59:58,040
So the question is whether I'm
assuming that there are no

1241
00:59:58,040 --> 00:59:59,730
other things that will happen.

1242
00:59:59,730 --> 01:00:02,165
And the beauty of this is I'm
not assuming anything.

1243
01:00:05,790 --> 01:00:07,010
In fact, I didn't.

1244
01:00:07,010 --> 01:00:11,610
But Ted Miguel and Michael
Kremer dewormed

1245
01:00:11,610 --> 01:00:14,400
the children in 1999.

1246
01:00:14,400 --> 01:00:16,610
And then they had the foresight
of deciding, we need

1247
01:00:16,610 --> 01:00:19,570
to continue to track them to
find out whether or not there

1248
01:00:19,570 --> 01:00:21,500
is a wage effect.

1249
01:00:21,500 --> 01:00:24,120
If you want to know my prior
when they started this

1250
01:00:24,120 --> 01:00:27,690
exercise, very honestly, is that
you're wasting your time.

1251
01:00:27,690 --> 01:00:29,600
All of these other things
will be happening.

1252
01:00:29,600 --> 01:00:31,570
You're never going to
find an effect.

1253
01:00:31,570 --> 01:00:35,100
And so when this came up, I
was very surprised in a

1254
01:00:35,100 --> 01:00:37,900
positive way.

1255
01:00:37,900 --> 01:00:40,450
But these results were not
even used to sell the

1256
01:00:40,450 --> 01:00:42,730
deworming to the government,
because we didn't have them

1257
01:00:42,730 --> 01:00:45,210
till very recently.

1258
01:00:45,210 --> 01:00:46,710
Only the education results
were used,

1259
01:00:46,710 --> 01:00:48,400
which are very immediate.

1260
01:00:48,400 --> 01:00:50,410
But the point here, you see,
you don't assume anything.

1261
01:00:50,410 --> 01:00:53,490
Whatever things would have
happened, happened.

1262
01:00:53,490 --> 01:00:55,560
And surprisingly,
didn't offset.

1263
01:00:55,560 --> 01:00:57,210
That's what the standard
error tells you.

1264
01:01:03,800 --> 01:01:06,510
So deworming is an interesting
policy, because it's a good

1265
01:01:06,510 --> 01:01:08,610
policy that's not
obviously good.

1266
01:01:08,610 --> 01:01:11,480
So it is nobody's
first choice.

1267
01:01:11,480 --> 01:01:13,390
So you have to make it people's
first choice.

1268
01:01:13,390 --> 01:01:16,904
The evidence plays a role,
and then some convincing.

1269
01:01:16,904 --> 01:01:22,540
And what is interesting is that
the parents themselves,

1270
01:01:22,540 --> 01:01:24,520
they could do with
them as well.

1271
01:01:24,520 --> 01:01:26,780
And so the second question we
want to ask, which is the

1272
01:01:26,780 --> 01:01:29,530
individual version of
the same, why don't

1273
01:01:29,530 --> 01:01:30,120
government do it?

1274
01:01:30,120 --> 01:01:32,210
Is why don't parents do it?

1275
01:01:32,210 --> 01:01:34,440
Which is the same question
as, why don't people

1276
01:01:34,440 --> 01:01:36,290
buy the fish sauce.

1277
01:01:36,290 --> 01:01:38,630
We'll get to it in a moment,
we'll collect the thing.

1278
01:01:38,630 --> 01:01:39,640
Unless you want to have a--

1279
01:01:39,640 --> 01:01:42,617
AUDIENCE: For deworming, could
you just treat the water that

1280
01:01:42,617 --> 01:01:46,030
the children walk in, so that
the worms don't go in the

1281
01:01:46,030 --> 01:01:48,560
water, so the kids
won't get worms.

1282
01:01:48,560 --> 01:01:50,170
PROFESSOR: So the question is,
could you treat the water

1283
01:01:50,170 --> 01:01:51,370
instead of treating the kids?

1284
01:01:51,370 --> 01:01:53,370
I think that's an excellent
idea, because you could do it.

1285
01:01:53,370 --> 01:01:56,303
Except that Lake Victoria
is really big.

1286
01:01:56,303 --> 01:02:00,440
So I think for Lake Victoria it
would be a bit difficult.

1287
01:02:00,440 --> 01:02:01,920
It's really, really big.

1288
01:02:01,920 --> 01:02:04,895
It's almost like a freshwater
sea in the middle.

1289
01:02:04,895 --> 01:02:06,200
AUDIENCE: It's not just
a lake, right?

1290
01:02:06,200 --> 01:02:08,802
It's also puddles and
things like that.

1291
01:02:08,802 --> 01:02:11,628
People walking through
there with no shoes.

1292
01:02:11,628 --> 01:02:12,100
PROFESSOR: Yeah.

1293
01:02:12,100 --> 01:02:18,100
It's any body of fresh water
that creates the problem.

1294
01:02:18,100 --> 01:02:21,130
So that's general nutrition.

1295
01:02:21,130 --> 01:02:22,970
There are other examples of
effective [INAUDIBLE]

1296
01:02:22,970 --> 01:02:23,700
of nutrition.

1297
01:02:23,700 --> 01:02:26,750
But now let's skip to the
third one, which is the

1298
01:02:26,750 --> 01:02:29,260
nutrition in the womb, which
is what you were asking.

1299
01:02:29,260 --> 01:02:31,610
Whether it's not even more
important to feed

1300
01:02:31,610 --> 01:02:32,930
the pregnant woman.

1301
01:02:32,930 --> 01:02:35,180
And the answer is that it is.

1302
01:02:35,180 --> 01:02:40,250
So there is a doctor in the UK
called Dr. Barker who this

1303
01:02:40,250 --> 01:02:41,765
hypothesis has his name.

1304
01:02:41,765 --> 01:02:43,730
It's called the Barker
Hypothesis.

1305
01:02:43,730 --> 01:02:47,030
What he found is that basically,
he found that the

1306
01:02:47,030 --> 01:02:51,120
region which had the highest
child mortality, infant

1307
01:02:51,120 --> 01:02:53,660
mortality, neo-natal mortality,
were also the

1308
01:02:53,660 --> 01:02:57,110
places where people, once they
were born, had the lowest life

1309
01:02:57,110 --> 01:02:58,080
expectancy.

1310
01:02:58,080 --> 01:03:02,080
And he concluded that this was
a sign that your condition of

1311
01:03:02,080 --> 01:03:04,180
life in utero were
really important.

1312
01:03:04,180 --> 01:03:06,090
Of course, that was not
convincing at all, because the

1313
01:03:06,090 --> 01:03:09,920
regions that have the highest
infant mortality also are

1314
01:03:09,920 --> 01:03:12,220
pretty bad in many
other respects.

1315
01:03:12,220 --> 01:03:15,110
And you will expect that these
people live less long.

1316
01:03:15,110 --> 01:03:18,620
But still, he's the first one
who formulated the hypothesis.

1317
01:03:18,620 --> 01:03:21,530
And despite the fact that his
evidence was weak for it, the

1318
01:03:21,530 --> 01:03:25,150
hypothesis was right, as we
subsequently discovered.

1319
01:03:25,150 --> 01:03:28,450
I'm going to give you a few
examples where it was seen

1320
01:03:28,450 --> 01:03:29,620
very clearly.

1321
01:03:29,620 --> 01:03:32,700
One of the big names in this
is an economist at Colombia

1322
01:03:32,700 --> 01:03:34,740
named Doug Almond.

1323
01:03:34,740 --> 01:03:38,470
And the first thing that Doug
Almond found is that he looked

1324
01:03:38,470 --> 01:03:47,390
at people who were born just
after 1918, which is the

1325
01:03:47,390 --> 01:03:51,510
period where there was a big,
big flew epidemic in the US.

1326
01:03:51,510 --> 01:03:54,250
So many people died
of the flu.

1327
01:03:54,250 --> 01:03:55,710
Adults died of the flu.

1328
01:03:55,710 --> 01:03:58,450
But many people didn't,
and still had it.

1329
01:03:58,450 --> 01:04:01,370
And in particular, a lot of kids
were born from moms who

1330
01:04:01,370 --> 01:04:03,260
had had the flu.

1331
01:04:03,260 --> 01:04:10,480
And the paper here was very
simple, which was to compare

1332
01:04:10,480 --> 01:04:15,090
the life outcomes of people who
were in utero during the

1333
01:04:15,090 --> 01:04:16,540
period of the flu.

1334
01:04:16,540 --> 01:04:19,430
He doesn't even know whether
their mother had the flu.

1335
01:04:19,430 --> 01:04:23,340
It just makes it quite likely
that their mother had the flu

1336
01:04:23,340 --> 01:04:26,570
if they were born during
that period.

1337
01:04:26,570 --> 01:04:29,360
And they found that children who
were in this period during

1338
01:04:29,360 --> 01:04:33,000
the big flu pandemics were
sicker as adults.

1339
01:04:33,000 --> 01:04:35,260
They were more likely to have
all sorts of diseases.

1340
01:04:35,260 --> 01:04:36,700
Name a disease, they have it.

1341
01:04:36,700 --> 01:04:38,450
Or they are more likely
to have it.

1342
01:04:38,450 --> 01:04:39,680
They were earning less money.

1343
01:04:39,680 --> 01:04:42,840
They were less likely to
have gone to college.

1344
01:04:42,840 --> 01:04:45,630
And they died earlier,
they died younger.

1345
01:04:45,630 --> 01:04:48,330
So that was one of
the first people.

1346
01:04:48,330 --> 01:04:52,290
So particularly if your mom had
the flu when you were in

1347
01:04:52,290 --> 01:04:53,420
utero, that's not good.

1348
01:04:53,420 --> 01:04:55,450
That's not nutrition.

1349
01:04:55,450 --> 01:04:56,640
Other effects--

1350
01:04:56,640 --> 01:04:58,620
still a paper by Doug Almond--

1351
01:04:58,620 --> 01:05:03,420
is that people who are born
during or just after the

1352
01:05:03,420 --> 01:05:04,410
Chinese famine--

1353
01:05:04,410 --> 01:05:08,780
or even just after is
a better number.

1354
01:05:08,780 --> 01:05:11,280
Children who are born just after
the Chinese famine, so

1355
01:05:11,280 --> 01:05:14,200
who were in utero during
the famine, they of

1356
01:05:14,200 --> 01:05:16,380
course live less long.

1357
01:05:16,380 --> 01:05:17,300
They are shorter.

1358
01:05:17,300 --> 01:05:19,290
They have lower wages.

1359
01:05:19,290 --> 01:05:23,710
And even the children of the
children of these people are

1360
01:05:23,710 --> 01:05:26,650
shorter and doing less
well in life.

1361
01:05:26,650 --> 01:05:29,810
So there is even a second
generation that's let's

1362
01:05:29,810 --> 01:05:32,300
productive, fertile,
et cetera if you

1363
01:05:32,300 --> 01:05:35,610
were born in the famine.

1364
01:05:35,610 --> 01:05:38,430
There is, of course, a bias in
this, when we look at the

1365
01:05:38,430 --> 01:05:40,860
children who were born just
after the famine.

1366
01:05:40,860 --> 01:05:42,110
Which comes from what?

1367
01:05:46,137 --> 01:05:48,265
AUDIENCE: They probably also
experienced ramifications of

1368
01:05:48,265 --> 01:05:50,400
the famine afterwards.

1369
01:05:50,400 --> 01:05:50,580
PROFESSOR: Right.

1370
01:05:50,580 --> 01:05:52,690
So it was afterwards.

1371
01:05:52,690 --> 01:05:55,280
The famine was very brutal,
and ended and

1372
01:05:55,280 --> 01:05:56,840
started very brutally.

1373
01:05:56,840 --> 01:05:59,560
So we might expect that there
is not so much effect after.

1374
01:05:59,560 --> 01:06:03,030
That but on the other hand,
what do you expect happens

1375
01:06:03,030 --> 01:06:04,280
during the famine?

1376
01:06:07,874 --> 01:06:09,216
AUDIENCE: Probably disease.

1377
01:06:09,216 --> 01:06:10,950
PROFESSOR: A lot of diseases
in particular.

1378
01:06:10,950 --> 01:06:12,810
A lot of adults died.

1379
01:06:12,810 --> 01:06:15,790
We are talking about 59
million adults dying.

1380
01:06:15,790 --> 01:06:19,450
And a lot of people probably
were never born.

1381
01:06:19,450 --> 01:06:23,360
And in particular, there were
stillborns or miscarriages.

1382
01:06:23,360 --> 01:06:26,570
So the people who made it
despite the fact that they

1383
01:06:26,570 --> 01:06:29,430
were in utero doing this period,
the babies who managed

1384
01:06:29,430 --> 01:06:32,210
to get born are probably pretty
good genetic potential

1385
01:06:32,210 --> 01:06:33,990
to start with.

1386
01:06:33,990 --> 01:06:37,500
And despite that, they are doing
much less well in life.

1387
01:06:37,500 --> 01:06:40,420
So there is a bias, but it goes
in the direction of not

1388
01:06:40,420 --> 01:06:42,150
finding an effect
of the famine.

1389
01:06:42,150 --> 01:06:44,630
Because surviving during the
famine already indicates that

1390
01:06:44,630 --> 01:06:48,067
you're a pretty feisty child.

1391
01:06:48,067 --> 01:06:50,490
So that's quite extreme.

1392
01:06:50,490 --> 01:06:53,460
You would say, yes, of course
being in utero during a famine

1393
01:06:53,460 --> 01:06:54,410
is a bad idea.

1394
01:06:54,410 --> 01:06:56,780
You should avoid it at
all costs if you can.

1395
01:06:56,780 --> 01:07:00,170
But maybe it's not particularly
relevant.

1396
01:07:00,170 --> 01:07:02,580
Because after all, we are not
talking about famine for most

1397
01:07:02,580 --> 01:07:03,140
poor people.

1398
01:07:03,140 --> 01:07:06,490
We are talking about
malnutrition and

1399
01:07:06,490 --> 01:07:08,020
ill-nutrition.

1400
01:07:08,020 --> 01:07:11,220
So here is one example
of that.

1401
01:07:11,220 --> 01:07:18,270
Is that children who were in
utero during Ramadan--

1402
01:07:18,270 --> 01:07:22,610
and Ramadan shifts, so it's
not a particular season.

1403
01:07:22,610 --> 01:07:25,490
So we can look at kids who were
in utero doing Ramadan

1404
01:07:25,490 --> 01:07:27,780
who were born in September, who
were born in October, who

1405
01:07:27,780 --> 01:07:28,890
were born in December.

1406
01:07:28,890 --> 01:07:30,935
All over the year.

1407
01:07:30,935 --> 01:07:33,550
This is a paper that
looks at Uganda.

1408
01:07:33,550 --> 01:07:40,190
Children born of Muslim mothers
and who were in utero

1409
01:07:40,190 --> 01:07:42,510
during Ramadan, in particular
in the first trimester of

1410
01:07:42,510 --> 01:07:46,780
pregnancy during the Ramadan,
are less educated.

1411
01:07:49,460 --> 01:07:54,220
It's many less educated and
earn less as adults.

1412
01:07:54,220 --> 01:08:00,620
And with Ramadan, it's not even
that you are not eating.

1413
01:08:00,620 --> 01:08:02,280
You're not eating
during the day.

1414
01:08:02,280 --> 01:08:03,670
But people eat during
the night.

1415
01:08:03,670 --> 01:08:06,990
But these long periods of
fasting are no good.

1416
01:08:06,990 --> 01:08:11,830
That's interesting, because
you don't have to observe

1417
01:08:11,830 --> 01:08:14,760
Ramadan when you're pregnant.

1418
01:08:14,760 --> 01:08:16,380
You could not do it.

1419
01:08:16,380 --> 01:08:18,439
And if you're really observant,
in fact, you have

1420
01:08:18,439 --> 01:08:22,229
the option of not doing
it and doing it later.

1421
01:08:22,229 --> 01:08:28,300
But pregnant women tend to do
Ramadan anyway because other

1422
01:08:28,300 --> 01:08:29,795
people around them do it.

1423
01:08:29,795 --> 01:08:32,569
And what is interesting here
is that, in terms of policy

1424
01:08:32,569 --> 01:08:37,569
implication, it could be
encouraged to say, you can not

1425
01:08:37,569 --> 01:08:38,680
observe the Ramadan.

1426
01:08:38,680 --> 01:08:42,790
Not everybody does it because
it's acceptable not to observe

1427
01:08:42,790 --> 01:08:44,340
it, potentially.

1428
01:08:44,340 --> 01:08:45,910
But most women do.

1429
01:08:45,910 --> 01:08:51,939
And this is not good
for their children.

1430
01:08:51,939 --> 01:08:55,490
And even though it's not
something massive, it's this

1431
01:08:55,490 --> 01:08:56,519
shift in the consumption.

1432
01:08:56,519 --> 01:09:00,390
The calories probably stay
relatively constant.

1433
01:09:00,390 --> 01:09:03,439
Another example-- which, again,
is nothing extreme--

1434
01:09:03,439 --> 01:09:08,149
the paper by Erica Field and
Maximo Torero, which looks at

1435
01:09:08,149 --> 01:09:11,720
one particular micronutrient,
which is iodine.

1436
01:09:11,720 --> 01:09:20,029
So iodine deficiency in
adulthood create this thyroid

1437
01:09:20,029 --> 01:09:23,170
insuffiency, so it makes
you a bit slow.

1438
01:09:23,170 --> 01:09:26,569
So in French, the expression
"cretin" comes from that.

1439
01:09:26,569 --> 01:09:31,170
In French, we say "cretin of the
Alps," because people from

1440
01:09:31,170 --> 01:09:33,130
the Alps were very
far from the sea.

1441
01:09:33,130 --> 01:09:35,529
So their salt came from the
mountain, not from the sea.

1442
01:09:35,529 --> 01:09:36,790
So it wasn't iodized.

1443
01:09:36,790 --> 01:09:41,180
So you had more thyroid
problems due to iodine

1444
01:09:41,180 --> 01:09:44,240
deficiency in the Alps
and elsewhere.

1445
01:09:44,240 --> 01:09:51,010
So now, iodized salt is
available on a large scale.

1446
01:09:51,010 --> 01:09:53,630
But before that, when it was
not available on a large

1447
01:09:53,630 --> 01:09:56,820
scale, at some point governments
realized this

1448
01:09:56,820 --> 01:09:59,620
problem and tried to
have programs of

1449
01:09:59,620 --> 01:10:01,310
distribution of iodine.

1450
01:10:01,310 --> 01:10:03,690
And what these people look at is
they look at the program in

1451
01:10:03,690 --> 01:10:08,150
Tanzania, which attempted to
reach every pregnant woman,

1452
01:10:08,150 --> 01:10:09,680
but failed.

1453
01:10:09,680 --> 01:10:12,650
So some kids, normally
you would have five

1454
01:10:12,650 --> 01:10:15,310
waves of the program.

1455
01:10:15,310 --> 01:10:19,770
A pill is sufficient
for several months.

1456
01:10:19,770 --> 01:10:23,220
So they were attempting to reach
people frequently enough

1457
01:10:23,220 --> 01:10:27,100
that all the pregnant woman
would have a pill covering

1458
01:10:27,100 --> 01:10:29,090
them for the duration
of the pregnancy.

1459
01:10:29,090 --> 01:10:31,590
But they failed to do that
because they were not

1460
01:10:31,590 --> 01:10:32,810
particularly organized.

1461
01:10:32,810 --> 01:10:35,410
So in some districts they went
in sometimes, and some

1462
01:10:35,410 --> 01:10:37,460
district they went in
some other times.

1463
01:10:37,460 --> 01:10:41,850
So what you can look is kids who
were lucky enough to be in

1464
01:10:41,850 --> 01:10:45,030
utero when their mother
was covered.

1465
01:10:45,030 --> 01:10:48,345
Compared to kids who were not
lucky, and who where in utero,

1466
01:10:48,345 --> 01:10:50,410
in particular first
trimester, when

1467
01:10:50,410 --> 01:10:52,430
their mom was not covered.

1468
01:10:52,430 --> 01:10:56,270
And what they look at is
education down the line.

1469
01:10:56,270 --> 01:10:59,590
And they found that the covered
kids have about a

1470
01:10:59,590 --> 01:11:04,280
third of a year more education
than the uncovered kids, for

1471
01:11:04,280 --> 01:11:07,710
receiving this iodine
supplementation.

1472
01:11:07,710 --> 01:11:11,380
So again, a pretty small
intervention makes a big

1473
01:11:11,380 --> 01:11:14,300
effect down in life.

1474
01:11:14,300 --> 01:11:18,010
So all of these create potential
for poverty traps,

1475
01:11:18,010 --> 01:11:19,300
because if the poor--

1476
01:11:19,300 --> 01:11:21,570
these are investments that
are not costly and

1477
01:11:21,570 --> 01:11:24,270
that have high return.

1478
01:11:24,270 --> 01:11:27,940
Even micronutrients for adults,
childhood pregnancy,

1479
01:11:27,940 --> 01:11:28,590
in this order.

1480
01:11:28,590 --> 01:11:32,090
You are asking, pregnancy is a
very short period of time.

1481
01:11:32,090 --> 01:11:34,730
Then it will affect the child
for their entire life.

1482
01:11:34,730 --> 01:11:38,000
So if the poor are less likely
to undertake the investment,

1483
01:11:38,000 --> 01:11:40,370
then there is a potential
for a poverty trap here.

1484
01:11:44,510 --> 01:11:46,855
So is it the case that the poor
are likely to undertake

1485
01:11:46,855 --> 01:11:48,430
this investment?

1486
01:11:48,430 --> 01:11:50,710
And the answer is yes.

1487
01:11:50,710 --> 01:11:52,930
Most of the poor still
consume a diet that's

1488
01:11:52,930 --> 01:11:55,300
very poor in Iran.

1489
01:11:55,300 --> 01:11:57,670
The vast majority of the
quarter of the world's

1490
01:11:57,670 --> 01:12:02,370
children who should get worms
are still not dewormed.

1491
01:12:02,370 --> 01:12:05,820
The WHO estimates that
40% of pregnant women

1492
01:12:05,820 --> 01:12:08,170
worldwide are anemic.

1493
01:12:08,170 --> 01:12:09,960
Not all of that is due continue
to iron deficiency

1494
01:12:09,960 --> 01:12:12,660
anemia, but probably
at least a half.

1495
01:12:12,660 --> 01:12:18,040
So these are three examples of
saying, these investments are

1496
01:12:18,040 --> 01:12:21,720
not undertaken, even though they
are potentially highly

1497
01:12:21,720 --> 01:12:22,970
productive.

1498
01:12:24,610 --> 01:12:26,730
And so you are saying, well,
maybe it's not undertaken, but

1499
01:12:26,730 --> 01:12:28,440
it's not because of poverty.

1500
01:12:28,440 --> 01:12:30,800
So is money an issue?

1501
01:12:30,800 --> 01:12:34,470
And it does seem to be that a
very small cost, even a very

1502
01:12:34,470 --> 01:12:36,570
small cost, seems to
discourage people.

1503
01:12:39,320 --> 01:12:41,260
Asking the question that
you were asking before.

1504
01:12:41,260 --> 01:12:42,800
At the level of government.

1505
01:12:42,800 --> 01:12:47,350
If 45 fish sauce costs only $6,
it seems the investment is

1506
01:12:47,350 --> 01:12:52,441
worthwhile, and yet no
poor family does it.

1507
01:12:52,441 --> 01:12:56,200
In Kenya, in the deworming
program, in the first group of

1508
01:12:56,200 --> 01:12:58,760
schools, at some point
the NGO wanted to do

1509
01:12:58,760 --> 01:13:00,750
the sustainable thing.

1510
01:13:00,750 --> 01:13:05,420
And the sustainable thing was
to ask people to cost-share.

1511
01:13:05,420 --> 01:13:09,250
So they had to pay a little
fee for their children.

1512
01:13:09,250 --> 01:13:11,630
Small fee for the
entire family.

1513
01:13:11,630 --> 01:13:14,530
And this is believed to
be help maintaining

1514
01:13:14,530 --> 01:13:17,270
the program, et cetera.

1515
01:13:17,270 --> 01:13:20,240
The moment where they introduced
the cost sharing,

1516
01:13:20,240 --> 01:13:22,720
the take up of the program
went to zero.

1517
01:13:22,720 --> 01:13:24,130
Nobody took it up.

1518
01:13:24,130 --> 01:13:25,495
So that goes back to this.

1519
01:13:25,495 --> 01:13:27,550
They didn't know the
effect, maybe.

1520
01:13:27,550 --> 01:13:30,290
Interestingly, it means asking
people to pay is not

1521
01:13:30,290 --> 01:13:30,880
sustainable.

1522
01:13:30,880 --> 01:13:33,940
Because it's the costlier thing
about the deworming

1523
01:13:33,940 --> 01:13:37,650
program is to drive your
car to the place.

1524
01:13:37,650 --> 01:13:39,020
So once you're there,
you want to do all

1525
01:13:39,020 --> 01:13:40,300
many people as possible.

1526
01:13:40,300 --> 01:13:43,910
So if the take up falls down to
zero, you've really lost a

1527
01:13:43,910 --> 01:13:46,940
lot of chances.

1528
01:13:46,940 --> 01:13:48,110
Another example.

1529
01:13:48,110 --> 01:13:49,280
It's not only money.

1530
01:13:49,280 --> 01:13:50,320
The thing is that it's not only

1531
01:13:50,320 --> 01:13:51,630
money that is the problem.

1532
01:13:51,630 --> 01:13:56,280
So it's not only poverty,
as in lack of income.

1533
01:13:56,280 --> 01:14:01,410
Because in India, we tried
something so to fight anemia.

1534
01:14:01,410 --> 01:14:02,400
We said, OK, fine.

1535
01:14:02,400 --> 01:14:06,100
People are not going
to buy iron pills.

1536
01:14:06,100 --> 01:14:11,410
But let's introduce a program
where the local miller who

1537
01:14:11,410 --> 01:14:15,580
mills the grain of everyone,
will add the iron.

1538
01:14:15,580 --> 01:14:19,410
But we only had money to install
the machine and pay

1539
01:14:19,410 --> 01:14:24,150
the miller to do it for
one minute a village.

1540
01:14:24,150 --> 01:14:26,580
And what we saw is that--

1541
01:14:26,580 --> 01:14:29,620
so people who were already
walking with that miller

1542
01:14:29,620 --> 01:14:31,644
continue to do so.

1543
01:14:31,644 --> 01:14:33,350
But the other people
didn't switch.

1544
01:14:36,750 --> 01:14:39,260
So the people who happened
to be close by

1545
01:14:39,260 --> 01:14:41,120
benefited from the program.

1546
01:14:41,120 --> 01:14:44,810
But no one was willing to work
the extra five minutes to

1547
01:14:44,810 --> 01:14:46,540
benefit from the program.

1548
01:14:46,540 --> 01:14:49,905
And moreover, the miller thought
it was a lot of effort

1549
01:14:49,905 --> 01:14:52,030
to add the iron.

1550
01:14:52,030 --> 01:14:54,935
So even though the rules were
you're supposed to do it

1551
01:14:54,935 --> 01:14:58,130
unless the family asks, they
switched to do the opposite,

1552
01:14:58,130 --> 01:14:59,390
which is you're supposed--

1553
01:14:59,390 --> 01:15:02,380
they wouldn't not do it if
the family didn't ask.

1554
01:15:02,380 --> 01:15:05,435
And the family didn't
really ask.

1555
01:15:05,435 --> 01:15:08,630
They didn't say no, but
they didn't say yes.

1556
01:15:08,630 --> 01:15:10,950
To the [INAUDIBLE], which was
very high at the beginning

1557
01:15:10,950 --> 01:15:14,170
when the miller did it by
default, it progressively went

1558
01:15:14,170 --> 01:15:18,300
to a very low number, and
the program collapsed.

1559
01:15:18,300 --> 01:15:20,200
Which suggests that it's
not only money,

1560
01:15:20,200 --> 01:15:23,270
it's any form of costs.

1561
01:15:23,270 --> 01:15:26,150
Which brings to these
other issues.

1562
01:15:26,150 --> 01:15:29,730
One is what Steve said earlier,
which is are the

1563
01:15:29,730 --> 01:15:33,270
workers going to reap the
benefit, or is the employer

1564
01:15:33,270 --> 01:15:34,950
going to reap the benefit?

1565
01:15:34,950 --> 01:15:38,195
And one sign that it might be
the employer rather than the

1566
01:15:38,195 --> 01:15:41,530
worker is that in Indonesia,
it's only the wages of the

1567
01:15:41,530 --> 01:15:43,090
self-employed that increased.

1568
01:15:43,090 --> 01:15:44,390
Not the wages, the
earnings of the

1569
01:15:44,390 --> 01:15:45,890
self-employed that increased.

1570
01:15:45,890 --> 01:15:50,360
The wages of people working
for a wage didn't go up.

1571
01:15:50,360 --> 01:15:51,590
In Kenya, it was different.

1572
01:15:51,590 --> 01:15:53,940
But in Kenya there is all
this education effect.

1573
01:15:53,940 --> 01:15:55,830
And one thing that we have
in Kenya is that

1574
01:15:55,830 --> 01:15:57,120
people switch sectors.

1575
01:15:57,120 --> 01:16:00,400
The young kids just started
workings in different sectors

1576
01:16:00,400 --> 01:16:01,680
altogether.

1577
01:16:01,680 --> 01:16:03,350
But for adults, it's
too late for them.

1578
01:16:03,350 --> 01:16:05,790
They're just going to do the
same thing a little better.

1579
01:16:05,790 --> 01:16:09,080
And they are not really
rewarded for that.

1580
01:16:09,080 --> 01:16:11,140
The other thing are the
information things we

1581
01:16:11,140 --> 01:16:13,170
discussed earlier.

1582
01:16:13,170 --> 01:16:15,780
It's very difficult to find out
on your own what makes a

1583
01:16:15,780 --> 01:16:18,240
difference and what doesn't.

1584
01:16:18,240 --> 01:16:20,340
Is it iron?

1585
01:16:20,340 --> 01:16:21,870
How do you know that
iron matters?

1586
01:16:21,870 --> 01:16:24,580
Until recently, scientists
didn't know.

1587
01:16:24,580 --> 01:16:27,010
In the '70s and '80s, scientists
were still

1588
01:16:27,010 --> 01:16:29,650
convinced that the big
problem was proteins.

1589
01:16:29,650 --> 01:16:31,270
And protein is a problem.

1590
01:16:31,270 --> 01:16:35,400
But they didn't think of
micronutrients as an issue.

1591
01:16:35,400 --> 01:16:37,620
So number one, the information
is very difficult to acquire.

1592
01:16:37,620 --> 01:16:39,750
So you need to trust outsider.

1593
01:16:39,750 --> 01:16:42,950
It's not very clear you
would trust them.

1594
01:16:42,950 --> 01:16:43,490
Finally--

1595
01:16:43,490 --> 01:16:45,260
and I will finish on that--

1596
01:16:45,260 --> 01:16:48,330
is that consumption
is a decision.

1597
01:16:48,330 --> 01:16:50,500
And people are not machines.

1598
01:16:50,500 --> 01:16:53,140
So they are not maximizing their
productivity, they are

1599
01:16:53,140 --> 01:16:55,340
maximizing their utility.

1600
01:16:55,340 --> 01:16:58,650
And their utility is made
of other things than our

1601
01:16:58,650 --> 01:17:00,970
productivity can be.

1602
01:17:00,970 --> 01:17:03,910
There is the food that you
have to eat every day.

1603
01:17:03,910 --> 01:17:09,630
And if you don't like it,
then this is horrible.

1604
01:17:09,630 --> 01:17:13,520
Because eating is the only thing
that we are doing day

1605
01:17:13,520 --> 01:17:14,100
in, day out.

1606
01:17:14,100 --> 01:17:17,330
So if we don't like to eat,
then it's kind of awful.

1607
01:17:17,330 --> 01:17:21,950
And in particular, this may
be one reason why it's

1608
01:17:21,950 --> 01:17:26,760
particularly difficult to have
people switch their diet.

1609
01:17:26,760 --> 01:17:28,790
This is something
where this is so

1610
01:17:28,790 --> 01:17:30,720
ingrained into our habits.

1611
01:17:30,720 --> 01:17:33,400
And if we are used do eating
in a particular way, we may

1612
01:17:33,400 --> 01:17:35,870
know that the best way is to eat
something else, but we may

1613
01:17:35,870 --> 01:17:37,900
be very reluctant to switch.

1614
01:17:37,900 --> 01:17:40,250
Now, this is a pattern that we
are seeing of course in this

1615
01:17:40,250 --> 01:17:43,430
country, as well as
anywhere else.

1616
01:17:43,430 --> 01:17:46,520
The second thing is you may care
about your social status.

1617
01:17:46,520 --> 01:17:51,500
Which might be related to how
big a party you throw for your

1618
01:17:51,500 --> 01:17:54,850
son's birthday or for your
daughter's wedding, or even

1619
01:17:54,850 --> 01:17:57,110
for your dad's funeral.

1620
01:17:57,110 --> 01:18:01,780
Which may be related to some
goods you may want to have,

1621
01:18:01,780 --> 01:18:03,360
like a TV or things like that.

1622
01:18:03,360 --> 01:18:06,370
People can care deeply about
these things, which may mean

1623
01:18:06,370 --> 01:18:09,450
that they decide to forego
nourishing their [INAUDIBLE]

1624
01:18:09,450 --> 01:18:12,620
to make sure that they can
actually do the things.

1625
01:18:12,620 --> 01:18:13,820
It's not like--

1626
01:18:13,820 --> 01:18:16,240
and finally, to diversity
of goods you have.

1627
01:18:16,240 --> 01:18:19,490
Like cell phones,
TVs, et cetera.

1628
01:18:19,490 --> 01:18:22,200
And all of that means that it
has very important policy

1629
01:18:22,200 --> 01:18:23,880
implications, of course.

1630
01:18:23,880 --> 01:18:26,210
Because it means that it's
not going to be trivial.

1631
01:18:26,210 --> 01:18:29,720
It's going to eventually be
quite difficult to get people

1632
01:18:29,720 --> 01:18:32,400
to convince to switch the
type of their diet.

1633
01:18:32,400 --> 01:18:35,470
And also, because of what we saw
last time, it's not such a

1634
01:18:35,470 --> 01:18:38,700
great idea to try and
subsidized grains

1635
01:18:38,700 --> 01:18:39,500
or things like that.

1636
01:18:39,500 --> 01:18:42,480
Because it's not going to
lead to an improvement.

1637
01:18:42,480 --> 01:18:45,240
It's not so much of the quantity
of food, because it's

1638
01:18:45,240 --> 01:18:47,050
not that useful to it more.

1639
01:18:47,050 --> 01:18:49,360
Nor in the quality of food,
because it's not the fasting

1640
01:18:49,360 --> 01:18:52,140
people will want to do with
the extra income.

1641
01:18:52,140 --> 01:18:56,870
That means that you would want
to do things that have a

1642
01:18:56,870 --> 01:18:59,230
chance directly to affect
the quality of the

1643
01:18:59,230 --> 01:19:00,190
food people are eating.

1644
01:19:00,190 --> 01:19:03,570
And in particular, children and
pregnant woman are eating.

1645
01:19:03,570 --> 01:19:06,680
So one is making it as
easy as possible to

1646
01:19:06,680 --> 01:19:08,430
do the right thing.

1647
01:19:08,430 --> 01:19:12,140
So invent foods that people
want to eat, but the

1648
01:19:12,140 --> 01:19:14,210
micronutrients is in them.

1649
01:19:14,210 --> 01:19:17,660
So there is something called
golden rice, which is rice

1650
01:19:17,660 --> 01:19:19,190
which is already fortified
in iron.

1651
01:19:19,190 --> 01:19:22,500
But that's GMOs, we might
like that or not.

1652
01:19:22,500 --> 01:19:25,620
But it's also like hybrids
foods, like yams, which are

1653
01:19:25,620 --> 01:19:28,620
very rich in vitamin A that
can grow in Africa.

1654
01:19:28,620 --> 01:19:31,140
So there are organizations
that work on this.

1655
01:19:31,140 --> 01:19:34,310
So the organizations that work
on this bioengineering--

1656
01:19:34,310 --> 01:19:36,980
like HarvestPlus, these types
of organizations--

1657
01:19:36,980 --> 01:19:39,410
historically have been focused
on making the food more

1658
01:19:39,410 --> 01:19:41,060
productive.

1659
01:19:41,060 --> 01:19:44,410
And what is needed is a shift
to making the food

1660
01:19:44,410 --> 01:19:47,150
higher-quality from the point
of view of nutrition.

1661
01:19:47,150 --> 01:19:50,660
And this is happening,
but slowly, slowly.

1662
01:19:50,660 --> 01:19:55,710
Other thing is when you have the
kids, you should invest in

1663
01:19:55,710 --> 01:19:57,420
the quality of their food.

1664
01:19:57,420 --> 01:19:59,640
Because the parents might not
know or might not do it.

1665
01:19:59,640 --> 01:20:01,720
But you have the kids right
in front of you.

1666
01:20:01,720 --> 01:20:02,530
So it's easy to do.

1667
01:20:02,530 --> 01:20:03,870
So deworming.

1668
01:20:03,870 --> 01:20:05,660
Make the school meal nutritious,
for example, by

1669
01:20:05,660 --> 01:20:07,863
sprinkling micronutrient
on them.

1670
01:20:07,863 --> 01:20:10,350
And the parents are not going
to compensate for that by

1671
01:20:10,350 --> 01:20:12,050
giving them less, because
they have no idea what

1672
01:20:12,050 --> 01:20:14,470
you're doing anyway.

1673
01:20:14,470 --> 01:20:17,920
And then you can think
of other things.

1674
01:20:17,920 --> 01:20:19,170
I'm going to let you move now.