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While extensive literature is available on the impact of the early childhood nutritional food program, little is known about the nutritious food provision at a later stage. Narbadeshwar Mishra examines the causal effect of one such program.

Image courtesy of interviewee. December 20, 2023

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There is already a very big strand of literature,

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which says that if the child gets access

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to the early childhood nutrition,

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then the effects last long.

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And there is the spill of effect,

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whether it could be in terms of health

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or we talk about the education of the child,

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their cognitive level, their IQ level,

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their learning capability,

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or if we talk about the long-term impact,

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that is the labor market.

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So there are many studies which have already established,

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and it's kind of in economics, we call it

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as a stylized fact, that if the child is getting exposed

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to the nutritious food right from the beginning, then

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the overall development of the child would be very high.

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So what we do here is that

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what if the child has not received such development,

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and what happens if the child gets these nutritious food

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at a later stage of life?

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Does the child recovers at the same pace

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or there is some, some kind of lag,

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or the human capital formation is little bit low

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compared to the early stage.

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So that is where what we are seeing here,

01:08.835 --> 01:11.055
and our study is in the context of India.

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And the other thing, the major, the, the novelty

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of our paper is that the program which we are analyzing

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or evaluating is very unique.

01:22.435 --> 01:26.705
For example, the, the, the basic funder is that here is that

01:27.525 --> 01:30.745
you see many of the studies in the context of USA,

01:30.745 --> 01:32.785
where there is a school big first program,

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then there is a European big first program.

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Then in Sweden also there is the school feeding program.

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All these studies are establishing

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and where all the programs are

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or all the, the intervention is

01:46.765 --> 01:47.805
happening at the school level.

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That is, doesn't matter whether you are really in a need

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of food or whether you are coming from the backward society

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or the impoverished household, you'll get irrespective of

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what your background is or socioeconomic status is.

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Because once you're enrolled in school, you are entitled

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to get this food.

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But what we are seeing, so, so for example,

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let's say there are two boys

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or two children, one who is coming from the society.

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So he or she doesn't need

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to have this full school breakfast program.

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He'll come or she'll come in respect whether this program

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will be enrolled or not.

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But if there's other kid who are coming,

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who is coming from the backward

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or the very disadvantaged society, he

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or she's already making, struggling

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to make their ends meet

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and their family really need such kind

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of support from the government side.

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And that's where the school feeding program comes at a risk.

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Because the impact is,

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the impact which we are getting is not from those kids

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who actually need this program, who actually,

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but it is formed everyone.

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And that's where we take is a take home version.

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A take home version is something which is a targeted

02:58.105 --> 02:59.285
program program.

02:59.285 --> 03:00.885
It's really target those students

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who are really in need of that.

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And especially if you talk about the developing countries,

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for example, the subs, countries

03:07.785 --> 03:09.805
and countries like South Asian countries,

03:09.975 --> 03:11.245
India, Pakistan, China.

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There, the educational outcome is very low,

03:14.765 --> 03:18.245
especially when we go above the elementary education.

03:18.245 --> 03:20.965
There's a huge disparity, especially in terms of gender.

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So we also look at this study from the angle

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of the gender also.

03:26.345 --> 03:28.925
So what we do here is basically is that

03:29.645 --> 03:34.135
there is this program in India which was initiated

03:34.235 --> 03:37.175
or introduced in 2010 only

03:37.275 --> 03:39.375
to the girls age 11 to 80.

03:40.315 --> 03:44.695
So all the girls who are age 11 to 18 is entitled

03:46.025 --> 03:47.765
to receive the benefits under this program.

03:47.945 --> 03:49.885
And this program is called a sub law.

03:50.385 --> 03:52.805
So sub supply is a Hindi word, what's basically the meaning

03:52.805 --> 03:55.285
of sub supply, women empowerment, it's nothing

03:55.305 --> 03:57.765
but in simple templates called as a women empowerment.

03:58.745 --> 04:01.085
So there are multiple objective of this program.

04:01.225 --> 04:03.605
The first one to close the gender gap.

04:04.985 --> 04:08.285
So there, there is already a huge inequality based in terms

04:08.285 --> 04:09.405
of gender in India.

04:09.665 --> 04:12.965
So the boys are performing better in terms of education.

04:13.075 --> 04:14.285
It's not because they are getting,

04:14.685 --> 04:18.085
because inherent bias in the Indian society

04:18.085 --> 04:19.365
towards the sun preference.

04:19.745 --> 04:22.165
And there is another whole lot of literature that they want.

04:22.165 --> 04:23.765
Some they consider girls as a border

04:24.075 --> 04:26.445
because they have to marry it off, marry her off.

04:27.105 --> 04:29.285
So they prefer girls and they have another

04:29.285 --> 04:30.325
intervention to stop that.

04:30.585 --> 04:34.405
So the government of India thought that, okay,

04:34.475 --> 04:35.485
this has to stop.

04:35.505 --> 04:38.045
And that's why they rolled out this program in 2010

04:38.805 --> 04:41.125
focusing only on the girls age, 11 to 18.

04:42.145 --> 04:45.525
And I'll come in a subsequent time that why 11 to 18?

04:45.525 --> 04:47.965
What was the intention behind this? But this is the program.

04:48.025 --> 04:50.685
And in this program started in the phase manner

04:51.465 --> 04:53.405
in 2010 in certain districts.

04:53.405 --> 04:56.085
So some counties will get this program

04:56.145 --> 04:57.485
and some counties will not.

04:57.585 --> 05:00.525
So they wanted to see how this program is playing out,

05:00.555 --> 05:02.085
whether if it is successful,

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and then we'll pan it out, we'll extend it,

05:05.265 --> 05:06.805
and if there is some shortcomings

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or demos, then we'll improve on it

05:08.205 --> 05:09.925
and then later on we'll extend it.

05:10.665 --> 05:14.605
So, so that was the context and we explored this

05:14.825 --> 05:17.445
and then we see that what exactly is the impact.

05:17.945 --> 05:21.045
The the second part is like I Afo mentioned,

05:21.175 --> 05:24.605
there have been studied by German, by alderman,

05:24.785 --> 05:28.765
by Denberg in different parts of the countries, Western,

05:28.765 --> 05:30.285
especially in the western countries.

05:30.285 --> 05:31.285
For example, USA,

05:31.625 --> 05:33.965
the school big first program in Philippines.

05:33.965 --> 05:37.165
Also, some authors have studied lundberg in recent 2022,

05:37.715 --> 05:42.085
they see whether the school feeding program has any spill

05:42.145 --> 05:43.245
effect in the long term.

05:43.425 --> 05:46.815
And what they found is that those students

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who have received this program,

05:49.325 --> 05:52.095
they are earning 3% more than their counterpart.

05:53.145 --> 05:55.125
So that is a long-term impact of this program.

05:56.305 --> 05:58.315
But the, the, the way we place our,

05:58.315 --> 06:01.875
there have been very limited literature where we talk about,

06:02.415 --> 06:04.795
or where the studies talk about, okay, what if

06:05.545 --> 06:08.925
the intervention or the nutrition has been

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provided not from right, from the childhood

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or right from the beginning, but at a later state,

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do we get the similar impact

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or do the child like fails to come back to the school?

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So that is where we place our paper.

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So that is the dear of the ation.

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I, we, to the best of our knowledge,

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we do not see any credible study

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or any vigorous study that has been,

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implemented such kind of setting.

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Unfortunately in India we have found such setting.

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So we exported this.

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So coming to what exactly is the setting?

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Now, I'll, I'll briefly talk about the,

06:44.665 --> 06:46.045
the, the intervention.

06:46.265 --> 06:48.565
So this, this program bla program,

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which means the women empowerment, this came in 2010,

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late 2010 to be specific in October, 2010.

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And there the government,

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the federal government announced that, okay,

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in few districts we'll roll out this program

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where the girls age 11 to 18,

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which they call in their official document as,

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adolescent girls, because this is the adolescent age,

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they are entitled to get some benefits.

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And what are those benefits?

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Is that 600 calories worth of food they will get

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and 18 to 20 gram of protein also,

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they will get apart from that.

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Additionally, they will also get micronutrients 300

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grams per year.

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And like the standard program,

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they will also get the hundred days

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or the hundred tablets of iron and folic acid.

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So all these were included under this program.

07:41.695 --> 07:45.235
And the major intention of this program was one twofold.

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One to improve the health status of these adults and girls.

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And second, to bring these girls back to either the formal

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or education, informal education system.

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So this program was exclusively targeting these adults

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and girls, one, the gender lens

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and second to those girls who have out of school,

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who have left school because of many reasons, maybe

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because their family needs their support

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or their family thinks that okay,

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they have done their elementary education, now they need

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to take care of the siblings, they need

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to do household chores and tasks.

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So there are multiple reasons

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or in India, in some of the states

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where the thinking is still orthodox or superstition.

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What they think is that we need to marry her off just

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by completing that elementary education.

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So government of India thought that no, let's,

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let's aware them these households

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and this is how we'll aware.

08:40.395 --> 08:43.005
Another thing which is very,

08:43.665 --> 08:45.365
is very interesting about this study is

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that these households,

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especially if you think about the India's demographic, 65

08:51.165 --> 08:56.085
to 70% and only 30

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to 35% lives in the urban area.

08:59.565 --> 09:01.105
So these rural population,

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these rural households really struggle

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because to meet their daily needs,

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because they mainly depend on the agriculture.

09:08.045 --> 09:10.225
And you see how the weather conditions are

09:10.225 --> 09:11.625
happening in recent years.

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It is not, it is not like very certain everything

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is becoming uncertain.

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And that's where there comes the financial

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constraints for these households.

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And it becomes very difficult for them to send their kids

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to the school because they don't see

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anything extra output out of that.

09:30.735 --> 09:33.145
They think that it's better for them to help my children,

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help my kid and help me in the field

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instead of going to the school.

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So what government does is that to stop this

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or to to to inculcate this habit

09:44.565 --> 09:47.305
of parent towards the human capital formation is they say

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that, okay, let's do one thing will provide the food free

09:51.605 --> 09:53.985
of course to you or to your kids.

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And in that sense, at least if what parents does in response

09:59.245 --> 10:02.185
to this program, they say that, okay, I do not have

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to worry about the food of my child, especially the girls.

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So at least she could go to school, she could get the food.

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And in that way they start sending the food,

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they start sending their girls to the schools.

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And in in that, in, in that way,

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this program was rolled out.

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So they get take home Russian where these,

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these particular things were checked out

10:24.305 --> 10:27.105
and these girls were getting these food, okay?

10:27.765 --> 10:31.025
And sometimes the food was given as take home version

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that okay, you will get this amount of food worth

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of these quantities.

10:35.165 --> 10:36.905
And sometimes the food was also cooked

10:36.905 --> 10:39.265
and given to them via the centers.

10:40.095 --> 10:42.505
Okay? Now the other thing is

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that this financial constraint thing is lifted off from

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their households and slowly

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and slowly the girls start getting interest in the school

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and in some sense they will start coming back to the school.

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This was the notion.

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So either bring them back to the formal education system

10:58.845 --> 11:00.425
or the informal education system.

11:01.215 --> 11:05.185
This was the setting. Now the other part which I mentioned

11:05.185 --> 11:06.745
is that why 11 to 80?

11:07.405 --> 11:09.345
Why not like starting from the childhood,

11:09.685 --> 11:10.865
why exactly 11 to 80?

11:11.245 --> 11:15.985
So there is already a program in India which started in 1995

11:16.165 --> 11:18.465
and it became Pan India in 2005.

11:18.465 --> 11:20.385
That is called as a midday meal program.

11:21.775 --> 11:24.345
What exactly this program is, this program is similar

11:24.345 --> 11:28.185
to S'S school big first program where every child

11:28.285 --> 11:31.185
who is going to school or attending school will get a lunch

11:31.565 --> 11:32.585
in the school itself.

11:33.005 --> 11:34.785
It they, the lunch will not be given

11:34.785 --> 11:35.825
to them outta the school,

11:35.845 --> 11:39.265
but in the school to, so to be the beneficiary

11:39.265 --> 11:42.025
of this midday meal program, you have to be in school.

11:43.045 --> 11:45.705
So in that sense, this BLA program is different

11:46.385 --> 11:49.165
bla program targeted only girls so

11:49.165 --> 11:50.405
that there is a gender lens

11:50.785 --> 11:53.285
and exclusively to girls who are not coming

11:53.305 --> 11:54.605
to school are outta school.

11:55.465 --> 11:59.645
Second is that BLA program was targeted to 11

11:59.705 --> 12:04.645
to 18 years, but mid is school work program was from

12:04.645 --> 12:06.085
only six to 10 years.

12:06.085 --> 12:07.565
That is only till grade five.

12:07.945 --> 12:10.605
So there was a continuation for girls so

12:10.605 --> 12:12.325
that the girls can come back

12:12.465 --> 12:14.325
and they can revive their education

12:14.325 --> 12:15.485
and resume their education.

12:15.865 --> 12:18.405
And the, the gender gap in terms

12:18.405 --> 12:21.125
of education can be narrowed down if cannot be

12:21.125 --> 12:22.205
eliminated completely.

12:22.705 --> 12:23.885
So this was the notion

12:24.775 --> 12:28.775
and what we did here is that we exploit this variation

12:28.775 --> 12:31.215
because some of the counties got this program

12:31.355 --> 12:33.135
and some counties did not get this program.

12:33.675 --> 12:37.255
So we explored this variation and the gender variation

12:37.355 --> 12:39.455
and we use a SSI experimental design,

12:39.625 --> 12:41.535
which is a very standard approach

12:41.765 --> 12:43.535
because this is not an experiment.

12:43.635 --> 12:45.015
The program is already rolled out.

12:45.875 --> 12:50.135
And so we ex we did the Causa experimental design call

12:50.155 --> 12:51.375
as a defense in defense.

12:52.355 --> 12:56.175
And so what we see is that we were restricted,

12:56.175 --> 12:59.215
we restricted outcomes to the educational outcome

12:59.235 --> 13:00.295
and the learning outcomes.

13:01.355 --> 13:04.095
So by educational outcome, I meant the enrollment,

13:04.825 --> 13:08.295
which is the first order, which is the direct,

13:08.345 --> 13:10.735
motivation or the objective of the program.

13:10.765 --> 13:13.375
That is whether actually the program is working

13:13.395 --> 13:15.175
or not, whether the girl is actually coming

13:15.175 --> 13:16.375
back to the school or not.

13:16.715 --> 13:19.695
So if the girls have started coming back to school,

13:19.905 --> 13:22.255
we'll see there is a slowly increase in

13:22.255 --> 13:23.295
the enrollment of girls.

13:24.195 --> 13:27.415
And so we took that as a first order of outcome.

13:27.635 --> 13:29.495
The second was the learning outcome.

13:29.495 --> 13:31.615
That is a test score, the cognitive ability.

13:32.075 --> 13:34.015
So once they start coming to the school,

13:34.015 --> 13:36.735
they'll start enrolling, they'll start attending the school

13:37.355 --> 13:39.855
and slowly, slowly their cognitive ability will start

13:39.855 --> 13:41.095
improving and they'll start

13:41.345 --> 13:42.975
performing better in their school.

13:43.875 --> 13:45.575
So second was a learning outcome.

13:46.755 --> 13:50.205
Now coming to the other thing is that

13:50.235 --> 13:52.045
what is the mechanism behind this?

13:52.435 --> 13:54.125
What is driving? So the main results,

13:54.185 --> 13:57.405
and I'll discuss later on, so what, who which is connecting.

13:57.425 --> 13:59.965
So this nutritious food is being provided to this girl

14:00.385 --> 14:02.445
and now we are seeing the what is the impact

14:02.465 --> 14:05.485
of this nutritious food to the educational outcome.

14:05.985 --> 14:08.445
Now we want to see how these two are connected.

14:09.195 --> 14:11.005
Like what is the theory of change?

14:11.825 --> 14:15.325
So based on the literature, the the things

14:15.325 --> 14:17.605
that since the major part of this program

14:18.305 --> 14:21.165
was the nutrition food, the major chunk

14:21.165 --> 14:22.685
of this program was a nutrition.

14:23.145 --> 14:26.725
So what we thought that if actually this program is working,

14:27.075 --> 14:29.485
then we should definitely see a change in the

14:29.485 --> 14:30.605
anthropo outcome.

14:30.635 --> 14:34.125
That is the BMI weight four height, height, four age,

14:34.585 --> 14:37.245
and all other such outcome variables.

14:38.105 --> 14:40.885
The other thing also, we first look at their health status

14:41.105 --> 14:42.685
as a potential mechanism channel,

14:42.695 --> 14:44.005
which is driving these factors.

14:45.785 --> 14:48.565
The other thing is that the response to the household,

14:49.325 --> 14:50.745
so like I mentioned since

14:50.805 --> 14:54.005
The government is now exogenously providing this food

14:54.665 --> 14:56.285
to these potential households

14:56.285 --> 14:59.845
or potential beneficiaries, what the households are going

14:59.845 --> 15:01.845
to do now, they do not have

15:01.845 --> 15:04.845
to worry about the the health expenditure of their girls

15:04.845 --> 15:06.085
because already they are getting

15:06.085 --> 15:07.365
these food from the government.

15:08.065 --> 15:09.245
So some amount

15:09.245 --> 15:12.405
of money which they could have spent if the government would

15:12.405 --> 15:14.045
have not been sponsoring such food.

15:14.425 --> 15:16.605
Now they, they this amount is getting saved

15:17.875 --> 15:21.495
and how they are going to spend in this amount is, is

15:22.255 --> 15:23.515
in any way they can spend.

15:23.655 --> 15:25.675
Now what we want to see whether

15:26.565 --> 15:30.635
households are in any way spending this amount towards the

15:30.635 --> 15:31.995
education of the child

15:32.385 --> 15:35.075
because now they think that yes, I do not have

15:35.075 --> 15:37.115
to worry more about their health and education.

15:37.145 --> 15:39.395
Also health is already taken care about,

15:39.405 --> 15:40.835
taken care of by the government.

15:40.935 --> 15:44.955
Now I can spend or I can send these girls, my child,

15:45.095 --> 15:46.395
my daughters to the schools

15:46.495 --> 15:48.155
and I can take care of their education.

15:49.275 --> 15:52.335
And so this is the another channel what we see

15:52.435 --> 15:53.455
or what we exploit.

15:53.955 --> 15:55.135
We see that yes,

15:55.305 --> 15:58.695
there has been a significant improvement in the enrollment

15:59.205 --> 16:02.455
that the girls are literally coming back to school

16:02.515 --> 16:07.135
and they enrolling and the the impact is not very strong.

16:07.315 --> 16:10.135
It is like modestly five to 10% in the range

16:10.195 --> 16:12.055
of somewhere around 10%.

16:12.355 --> 16:15.295
So there has been improvement in education enrollment

16:15.915 --> 16:17.895
and the test is scores.

16:18.115 --> 16:20.335
How we measure, what is the instrument that we take

16:20.335 --> 16:21.855
for measuring the learning outcome is

16:21.855 --> 16:22.935
that the test is score.

16:23.875 --> 16:27.495
So we use test score as a proxy for the learning outcome

16:27.715 --> 16:30.375
and we see that yes, girls are able

16:30.395 --> 16:32.855
to perform better in terms of their learning outcome.

16:33.365 --> 16:37.535
They are able to read the basic English, they're like able

16:37.535 --> 16:40.775
to identify the letters, they're able to identify words,

16:41.235 --> 16:44.175
but not the higher level of English that is,

16:44.175 --> 16:46.895
they cannot read the full paragraph without any difficulty

16:46.895 --> 16:48.255
or without any single mistake.

16:49.075 --> 16:51.095
And same goes for the mathematics also

16:51.095 --> 16:53.295
that is the mathmatic skills, the basic maths.

16:53.995 --> 16:57.495
So what we see here is that they can identify the words,

16:58.125 --> 16:59.295
they can do subtraction,

16:59.395 --> 17:02.335
but not the division, not the higher level of maths.

17:03.265 --> 17:05.245
The other thing, what we see here is

17:05.245 --> 17:08.385
that the prob conjecture we had is

17:08.385 --> 17:11.505
that since this intervention is not right from the

17:11.505 --> 17:14.545
childhood, but it is coming in the beginning to some

17:14.545 --> 17:16.385
of the girls who have dropped off

17:16.385 --> 17:17.505
the school, there is a lag.

17:18.165 --> 17:20.745
So the human capital formation or the IQ level

17:21.165 --> 17:22.465
or the brain development,

17:23.135 --> 17:25.505
that would've been already happened in the early stage.

17:26.165 --> 17:29.145
And the same thing, we will not expect if the same

17:29.145 --> 17:31.025
intervention is given at the later stage.

17:31.935 --> 17:34.515
So that is why the improvement or the impact

17:34.855 --> 17:37.995
or the results which we are seeing is not very strong,

17:38.025 --> 17:40.115
very high like 50% or 60%,

17:40.135 --> 17:42.915
but somewhere close in the range of 10 to 15%,

17:42.915 --> 17:45.715
which is again good and very similar to some

17:45.715 --> 17:48.235
of the recent studies that have been done in other context

17:48.295 --> 17:50.325
of the country, especially in the developing countries.

17:51.945 --> 17:55.445
Now one may say the way we have our identification

17:55.445 --> 17:58.445
strategies, there are other factors also the confounding

17:58.445 --> 18:00.045
factor that the impact

18:00.065 --> 18:03.295
or the results which we are seeing is something else.

18:03.295 --> 18:06.215
Because at the same time, if you talk about the,

18:06.215 --> 18:10.895
if you see the Indian political economy during 2010,

18:11.845 --> 18:13.455
many intervention had happened.

18:13.515 --> 18:16.335
One of the intervention was right to education act where

18:16.865 --> 18:19.695
every child age six to 14 has

18:19.695 --> 18:21.615
to be given compulsory and free education.

18:22.555 --> 18:26.695
So that we control for, we did a battery of robustness test

18:26.795 --> 18:28.215
and that has been taken care of.

18:28.215 --> 18:31.575
So our results are net of these intervention.

18:32.635 --> 18:35.135
The second major confounding factor is that,

18:36.155 --> 18:38.895
and it happens in India p predominantly,

18:38.895 --> 18:40.495
and this is very common practice, is that

18:41.005 --> 18:44.175
because of the sun preference nomenclature,

18:44.175 --> 18:45.695
what payments might do is that,

18:46.075 --> 18:47.615
and this is a take conversion program.

18:47.615 --> 18:48.975
This is not a school feeding program

18:48.985 --> 18:50.935
where the girls will go and they'll get the food.

18:52.275 --> 18:53.565
What happens in this program is

18:53.565 --> 18:56.005
that girls will get the food outta the school.

18:56.945 --> 18:59.165
So if the food is given to the girls,

18:59.395 --> 19:01.405
what payments could do basically is that

19:01.955 --> 19:03.885
they could take the food from the girl

19:03.945 --> 19:07.005
and give it to their maybe younger son or younger siblings

19:07.185 --> 19:09.925
or the elder dot eldest elder brother

19:10.665 --> 19:14.085
or the other thing they could do if the girl is eating the

19:14.085 --> 19:16.805
food outside, they could reduce their food intake

19:16.805 --> 19:18.645
inside the home thinking that,

19:18.705 --> 19:21.245
my daughter has already taking the food inside, outside

19:21.625 --> 19:22.805
of because of this program.

19:23.305 --> 19:27.805
Now let's reduce her intake of nutrients and give it to

19:27.805 --> 19:30.005
or push it towards my son who is going

19:30.005 --> 19:31.805
to help me in my latest stage of life.

19:32.505 --> 19:34.165
So this is called as the intra

19:34.165 --> 19:35.445
households resource allocation.

19:35.445 --> 19:37.325
This is the big problem in our identification.

19:37.945 --> 19:42.565
So to welcome or to to smm this issue,

19:42.565 --> 19:44.765
what we did, we restrict our study

19:44.945 --> 19:46.965
or sample to only those households

19:47.495 --> 19:51.205
where only girls are there, only the daughters,

19:51.865 --> 19:53.245
and we get similar impact.

19:54.385 --> 19:56.045
The results are qualitatively similar,

19:56.185 --> 19:57.445
the impact is little low,

19:58.025 --> 20:00.205
but the results are qualitatively similar

20:00.205 --> 20:01.405
because of the sample size.

20:01.405 --> 20:03.805
Because sample size has greatly reduced signif

20:03.805 --> 20:05.245
substantially it has been reduced.

20:05.905 --> 20:09.845
And so we get qualitatively similar, which says that yes,

20:10.265 --> 20:13.125
our results are good and holding in all the conditions.

20:14.115 --> 20:17.125
Overall, if I need to summarize my paper, like

20:17.355 --> 20:19.405
what exactly is the paper all about?

20:19.625 --> 20:23.795
So one that yes, there is already a big chunk

20:23.815 --> 20:27.795
of literature which talks about if you give the food right

20:27.795 --> 20:30.595
from the childhood, the impact will last long

20:30.855 --> 20:33.395
and the impact will last long in several parameters.

20:33.395 --> 20:35.875
Whether it's health, whether it's education,

20:36.095 --> 20:39.115
or whether it's the labor market, the labor productivity,

20:39.165 --> 20:40.315
everything will improve

20:40.315 --> 20:42.475
because the brain doubles right from the early stage.

20:43.895 --> 20:46.715
But what if some children have not

20:46.715 --> 20:48.605
Been exposed to these intervention,

20:48.915 --> 20:52.245
have not received such food in the early childhood,

20:52.395 --> 20:57.325
what about them thus providing them these nutritious food at

20:57.325 --> 21:00.405
a later stage of life give a similar result or,

21:01.425 --> 21:03.645
or have they lost such stage

21:03.745 --> 21:06.405
or have they cell paths a stage that they cannot come back?

21:07.265 --> 21:09.565
So that is where, we are placing a paper

21:10.025 --> 21:13.085
and we conduct a study in the context of India,

21:13.255 --> 21:15.165
which is a great example

21:15.315 --> 21:18.245
because it is the world's most populous country

21:18.905 --> 21:21.725
and there's a huge gender disparity in terms of education

21:21.725 --> 21:24.885
where the child sex ratio is, is very high

21:25.755 --> 21:30.275
and the sun preference scenario is prevailing

21:30.275 --> 21:33.075
and is very ramped and many, many intervention

21:33.175 --> 21:35.595
and many such programs have been introduced by government

21:36.055 --> 21:37.195
to curb this practice.

21:37.335 --> 21:40.545
But still they, they, this this is happening in,

21:40.545 --> 21:42.185
especially in the rural regions.

21:43.085 --> 21:44.465
So this is one program

21:45.195 --> 21:48.105
which again makes the people aware

21:48.765 --> 21:52.425
and test whether this program is helping girls to evolve

21:52.485 --> 21:55.665
or to become, like self-dependent or not.

21:56.645 --> 21:59.865
So what we see here is that yes, if

22:00.565 --> 22:02.625
the food is nutritious, food is being

22:02.865 --> 22:05.585
provided to girls, they are, they,

22:05.585 --> 22:08.345
there is an improvement in the health educational outcome

22:08.555 --> 22:12.605
first that their VMI increases their rate

22:12.625 --> 22:13.685
for height increases.

22:14.225 --> 22:16.245
So there have been implement in their health status.

22:17.185 --> 22:20.925
Second, the payments has also been, have also been nudged

22:20.925 --> 22:23.045
because of the government they have started

22:23.645 --> 22:26.245
spending towards the education of their children

22:26.905 --> 22:28.765
and how we measure the health,

22:28.765 --> 22:33.015
the education expenditure, the the books, the book fees,

22:33.515 --> 22:36.375
the uniform fees, the transport fees,

22:36.915 --> 22:41.375
and the private tuition fees, all such fees of the children.

22:41.715 --> 22:44.695
We add them up and then we keep

22:44.835 --> 22:47.615
and call it as a child specific education expenditure.

22:47.995 --> 22:50.855
So we get the data for every child,

22:50.955 --> 22:53.415
how much the parents are spending towards the

22:53.415 --> 22:54.575
education of every child.

22:55.275 --> 22:57.775
So we see that this educational expenditure,

22:57.955 --> 22:59.935
the payments have started increasing this

22:59.935 --> 23:01.335
expenditure for the culture.

23:02.235 --> 23:06.375
So the government in some sense has been successful in

23:06.495 --> 23:07.535
nudging the payments also

23:07.535 --> 23:11.265
because this financial constraint has been taken care of,

23:11.445 --> 23:12.505
at least to some extent.

23:12.935 --> 23:16.545
Okay? So now they're not worrying much and

23:16.665 --> 23:18.345
because of these, their health is better

23:19.045 --> 23:22.985
and their financial situation has become better.

23:23.485 --> 23:25.225
Now the girls have started going to school,

23:25.375 --> 23:26.545
they've started studying

23:26.925 --> 23:29.785
and their cognitive ability has improved.

23:30.725 --> 23:32.585
And slowly, slowly we are going to also see,

23:32.725 --> 23:35.105
extend the paper, seeing the labor market outcomes.

23:35.125 --> 23:36.465
So what if now, now

23:36.465 --> 23:39.945
because they are more educated relatively, so

23:39.945 --> 23:41.665
what if they're performing better?

23:42.165 --> 23:46.755
Are they earning better? So that is, that is the whole,

23:46.755 --> 23:47.955
this paper is all about.

23:47.975 --> 23:49.555
And then we did, many checks

23:49.555 --> 23:51.675
because there are many confounding factors also.

23:51.695 --> 23:52.915
And then we take care of this,

23:52.935 --> 23:55.075
but our results hold in all the situation

23:55.775 --> 23:59.645
and we use a host a whole lot of data sets.

23:59.905 --> 24:02.125
The one is the National Sample Survey Organization,

24:02.865 --> 24:05.605
the other is the NFHS,

24:05.605 --> 24:07.245
that is the National Family health survey

24:07.505 --> 24:08.845
for measuring the health outcome.

24:09.435 --> 24:12.325
Then we use AER annual status education report

24:12.425 --> 24:14.685
for measuring the learning outcome, the test scores.

24:15.115 --> 24:17.565
Then we did the analysis at the district level also

24:17.905 --> 24:19.525
to see the district level.

24:19.825 --> 24:21.965
So the results are watertight.

24:22.545 --> 24:24.805
So we are getting the consistent results.

24:25.425 --> 24:29.195
So, and fortunately what we see here,

24:29.295 --> 24:32.555
the last bit of this paper is that what we see here is

24:32.555 --> 24:33.755
that the impact

24:33.855 --> 24:37.915
or the effect of this program is very directly proportional

24:37.915 --> 24:39.155
to the time of access.

24:39.655 --> 24:41.835
So what do I mean by that?

24:41.855 --> 24:45.635
Is this the girl child who has received this program

24:46.375 --> 24:48.395
as early, the impact is higher.

24:49.565 --> 24:52.465
So for example, if the girls, the program came in 2010

24:52.765 --> 24:54.785
and the girl child received the program

24:55.005 --> 24:59.185
or became eligible for the program in 2010 itself, there

25:00.005 --> 25:04.185
the impact is higher if the girl child is 17 years

25:04.205 --> 25:05.265
old in 2010.

25:05.365 --> 25:07.265
So she's eligible for only one year more

25:07.265 --> 25:09.145
because the program is 11 to 80.

25:09.805 --> 25:12.065
So there the, the impact is not that much.

25:12.975 --> 25:16.235
So that means the more nutritious food she get,

25:16.775 --> 25:18.355
the better she performs.

25:18.525 --> 25:22.035
There is a high probability of she and enrolling into school

25:22.415 --> 25:25.515
and performing better in school than the girl

25:25.815 --> 25:28.075
who has exposed, who has been exposed

25:28.075 --> 25:29.355
to the program relatively less.

25:29.935 --> 25:32.355
So again, this comes back to the stylist factor

25:32.355 --> 25:35.475
that we should give such intervention right from the

25:35.475 --> 25:38.235
beginning because the time matters.

25:38.775 --> 25:39.795
The number of times

25:40.055 --> 25:42.155
or the number of years the girl is exposed to the program.

25:42.535 --> 25:45.595
So this comes back to the stylist fact that such kind

25:45.595 --> 25:50.035
of intervention helps a lot if these intervention given

25:50.725 --> 25:51.955
right from the early childhood

25:52.615 --> 25:54.945
because of the world development of the children.

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