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FRANK SCHILBACH: All right.

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Welcome, everyone, to
lecture 13 of 14.13.

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This is my last lecture
on social preferences.

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I hope all of you are
doing fine remotely.

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Hang in there.

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I know this is a
very difficult time.

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This lecture is a bit of
a more uplifting lecture

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than the previous ones.

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While the previous ones have
looked at social preferences,

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how we can measure
social preferences,

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and how can we perhaps
isolate pure altruism--

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do people really want others to
be better for their own sake,

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even if nobody knows about
it, either others or whether

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there's no feedback--

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we sort of found out that like
it seems like what looks a lot

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like altruism-- people
are nice to each other--

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is not pure altruism
in the sense

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that like they really
want others to do well.

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Instead, they want to either
look good in front of others,

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or they're worried about
reciprocity or sort

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of any other negative
feedback from others,

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or it might be they might want
to protect their self-image.

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They want to look good
in front of themselves

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and they try to figure out
ways to deceive themselves

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that they're nice.

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And given the opportunity
to not be nice,

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people actually
seem to be not as

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nice as one might think
from pure dictator games

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or ultimatum games
that we observe.

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This lecture is a bit more
uplifting in the sense

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that it talks about field
evidence on social preferences,

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and in particular you
can ask the question

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about can policies increase
pro-sociology in certain ways.

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We're going to talk about
three broad sets of studies.

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First, we're going to talk
about social preferences

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at the workplace.

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I'm going to talk about
the impact of relative pay

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on productivity.

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We talked about this
last time at the end

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of the lecture a
little bit, but I'm

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going to just restart from
scratch to sort of explain

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that a little bit better.

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Then we're going to
talk about the morale

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effects of pay inequality.

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What happens to
worker inequality

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when people are paid unequally,
and is that a good idea?

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There's also a very nice
paper in ethnic divisions

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and production in firms.

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We're going to talk about this
a little bit in recitation.

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Number two-- we're going to
talk about whether policies

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can induce pro-sociality.

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In particular we're
going to focus on mixing.

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This is sort of called
the contact hypothesis.

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When you mix people from
different backgrounds,

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do they become nicer to
each other in various ways?

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You'll discuss a very
nice paper by Gautam Rao

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when mixing rich and
poor children in school.

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And I talk very briefly about
mixing cricket players in India

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to reduce tensions across
castes or make people

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from different castes perhaps
more prosocial to each other.

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And then I'm going to
also briefly mention

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a study on mixing roommates
in college by Corno et al.

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Finally, we're
going to talk about

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whether people
perhaps underestimate

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the benefits of prosociality.

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In particular, I'm
going to show you

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a study on people
undervaluing gratitude.

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So when people write
letters of gratitude,

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do they understand how
others might react,

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and is there perhaps some
evidence that people are not

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prosocial enough because they
misunderstand the effects

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that that might have on others?

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OK, so let me start with social
preferences at the workplace.

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So Bandiera et al.
is a very nice study

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that looks at the impact of
relative pay on productivity.

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This is field evidence from
the fruit farm in the UK.

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It's literally field
evidence because it's

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from fruit-picking
farms from fruit fields.

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The study looks at two
types of payment schemes.

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These are piece rates--

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workers are paid
per unit of output.

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And the second scheme is
relative pay-- workers

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are paid relative to others.

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We might think that
relative pay is a good way

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to incentivize workers
because you want to do better

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than your neighbor, and there's
a computation of who does best.

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And in principle, it
could be that that,

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really, if you only care
about yourself, that really

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gets people to work very hard.

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Now, what Bandiera
et al. look at

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is, how does the
introduction of relative pay

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affect workers'
effort and output?

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Now, one thing to notice is that
while relative incentives might

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be good because you might induce
people to want to be better

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than their neighbors
or their co-workers,

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there's also a negative
externality of relative pay.

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That is the idea that
increasing your own pay

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comes at the costs
of others pay.

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That is to say, if
there's relative pay

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and I work really
hard, I'm paid how

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I do relative to my friends or
others that I'm working with.

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Well, if I'm working really hard
and do better than my friends,

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I'm going to be paid more.

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But at the same time, for
any effort that I put in,

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my friends are going
to be paid less.

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And so workers anticipating
these impacts on others

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might reduce their effort
if they care about others.

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If I'm really worried about
if there's relative pay

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and I work really hard, my
friends are going to look bad.

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They're going to be
relatively unproductive,

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and I worry that they
might not be paid enough.

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Then I might reduce my effort.

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And then everybody
might do that,

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and then the introduction
of relative pay

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or the relative pay
schemes might actually not

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be particularly effective or an
effective way of incentivizing

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and motivating workers.

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So there's sort of this tension
that relative pay in principle

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could work quite well.

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But if people really
care a lot about others,

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then it might not work at all.

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It might actually
backfire in some ways.

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Notice that it could be either
that people care about others

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for their own sake in the
sense that you might care

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about them because you want
them to have high earnings,

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or you might care
about others' earnings,

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not because you actually care
about the outcomes per se,

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but rather because
you're worried

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about negative repercussions.

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If these are your friends
and you work really hard,

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well, at nights, your friends
might really not be happy,

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and there might be retribution
against you socially

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or in some other ways if you
work too hard, if you caused

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them their earnings to go down.

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OK, so now, what
does this paper do?

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It looks at how does
switching to piece rates

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affect people's productivity?

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So the study has personnel data
from a food farm in the UK.

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They measure productivity as a
function of their compensation

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scheme.

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This is a quasi-field
experiment.

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The timeline is as follows.

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For the first eight weeks
of the 2002 picking season,

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fruit pickers were compensated
on a relative performance

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scheme.

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So the per fruit piece
rate is decreasing

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in average productivity.

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So if everybody
works really hard

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and I don't, if I'm relatively
bad compared to others,

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I get less than others.

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This is an incentive,
as I discussed,

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to keep the productivity low
if you care about others.

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And then in the
next eight weeks,

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compensation will switch to
a flat piece rate per fruit.

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So that's essentially
just you're

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paid by however how
much you produce,

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how many kilograms
of fruit you collect.

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And the payment is entirely
disjoint or independent of what

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other people do.

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That's a classic
piece rate payment.

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So the externalities
are entirely shut down.

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If I work really hard,
I'm going to be paid.

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There's no effect whatsoever
on my fellow workers.

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The switch was announced on
the day the change took place,

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so it came as a
surprise to workers.

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So we should not be
worried about sort

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of pre-trends or productivity
going up over time anyway.

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Now, what the paper finds
is a dramatic increase

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in productivity with the
introduction of piece rates.

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That is to say what you
see here in the figure

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is the average worker
productivity for two

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of the fields-- so
that's essentially

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like broad, big fields
where people work on,

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and I'm showing you
the data for two

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of those fields, for which,
essentially, on most days,

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there was actually production
workers were picking fruit.

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What you see on the left side
of the graph is when people were

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working under relative pay,
there you see, essentially,

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relatively flat productivity.

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People picked about
5 kilograms per hour.

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There doesn't seem to be
any trend upwards over time.

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So before the new
policy was introduced,

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essentially, workers
were producing or picking

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their fruit at a relatively
constant rate over time,

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about 5 kilograms per hour.

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There was no trends, as I said.

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Now, you see the vertical
line in the middle.

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This is when piece rates
were, in fact, introduced.

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So there was no
relative pay anymore,

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and now people were
paid by piece rate.

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And that increased
productivity by over 50%.

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So workers became a lot
more productive after that.

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That's true for both
of those fields.

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It is not the case that
the average payment

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per unit of output per kilogram
of fruit paid was actually

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was going up.

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So you might say, well, if
the piece rate was actually

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higher under the
piece rate payments

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on the right side of the
screen, of the graph,

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then maybe workers would
just be more productive.

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But in fact, if you
look at the piece rate

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over time, if anything,
the piece rates

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actually went down over time
what the company was paying.

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So that is to say,
the introduction

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of those piece rates,
the productivity effect

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is not explained
by workers being

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paid more per unit of output.

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Instead it seems
to be really coming

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from the different incentives
people workers have

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when it comes to how
their productivity affects

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their fellow workers
on the field.

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Now, you might have two
potential explanations

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here for this evidence.

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One is social preferences.

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So you might work
less to help others

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under relative incentives, even
less when your friends benefit.

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You have many friends
on a certain field

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that work with you.

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Well, then you might be
particularly inclined

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to not work very hard because
you care a lot more for them.

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And this is exactly what
they find in this paper.

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These effects are
stronger when there

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are more friends on the field
for a particular worker.

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Second, however, it's
also like a repeated game.

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That is to say, there's
a low effort equilibrium,

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where, essentially,
if there relative pay,

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we can just all agree
everybody on the field

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might agree that not
working particularly hard

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is a good idea because
we are essentially

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paid relatively anyway.

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So if everybody
doubles their effort,

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nothing is going to happen
to a worker's compensation.

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So you might as well just
decide everybody could just

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decide work half
as hard, and you're

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going to be paid the same.

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And as long as we can
sustain that equilibrium,

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that's a good idea.

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Of course, in any given
period, any given worker

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might have the incentive to
deviate from this equilibrium

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because on any given day,
if you work really hard,

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you're going to be paid
a lot more for that day.

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And so how might we be
able to disentangle that?

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Well, what Bandiera et al.

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have is they have also
some variation in the types

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of fruits that were collected.

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00:11:57,910 --> 00:11:59,320
What do I mean by that?

250
00:11:59,320 --> 00:12:01,140
Well, if you think
about what you

251
00:12:01,140 --> 00:12:04,770
need to be able to disentangle
these, what you might want

252
00:12:04,770 --> 00:12:09,470
to have is differences in the
observability of effort, right?

253
00:12:09,470 --> 00:12:11,980
So if I can see what
my co-worker does,

254
00:12:11,980 --> 00:12:13,950
I can punish them
very effectively.

255
00:12:13,950 --> 00:12:17,460
I can just know whenever
Frank is working really hard,

256
00:12:17,460 --> 00:12:20,717
my co-workers might
want to punish me.

257
00:12:20,717 --> 00:12:22,800
And then it's very hard
for me to work really hard

258
00:12:22,800 --> 00:12:25,110
because I'm getting
in trouble at nights

259
00:12:25,110 --> 00:12:27,810
after work with my co-workers.

260
00:12:27,810 --> 00:12:30,240
Or in the next day, everybody
might also work really hard,

261
00:12:30,240 --> 00:12:32,460
and I don't want that.

262
00:12:32,460 --> 00:12:35,040
On the other hand, if I can
hide how hard I'm working,

263
00:12:35,040 --> 00:12:38,370
well then, I might
secretly work really hard,

264
00:12:38,370 --> 00:12:40,980
and my co-workers might
not even find out.

265
00:12:40,980 --> 00:12:47,250
So then, if that's the case, in
the repeated game equilibrium

266
00:12:47,250 --> 00:12:49,787
might just not be enforceable,
as in I might just

267
00:12:49,787 --> 00:12:51,870
tell my friends, oh, I
wasn't really working hard.

268
00:12:51,870 --> 00:12:53,010
But in fact, I was.

269
00:12:53,010 --> 00:12:58,590
And they have no way of finding
out because I was unobservable.

270
00:12:58,590 --> 00:13:00,720
If, instead, I really
care about them,

271
00:13:00,720 --> 00:13:04,740
if I'm really interested
in their well-being

272
00:13:04,740 --> 00:13:06,600
overall and their
payment, it doesn't

273
00:13:06,600 --> 00:13:08,730
matter whether my
effort is observable.

274
00:13:08,730 --> 00:13:10,860
Regardless of how
observable my effort is,

275
00:13:10,860 --> 00:13:13,440
I will not work very
hard because not working

276
00:13:13,440 --> 00:13:15,430
hard makes them better off.

277
00:13:15,430 --> 00:13:18,420
So if you just had some
differences in observability

278
00:13:18,420 --> 00:13:21,165
in effort, how hard
people work, we

279
00:13:21,165 --> 00:13:25,260
could just disentangle
these two explanations.

280
00:13:25,260 --> 00:13:27,435
So Bandiera et al. have
this very nice variation.

281
00:13:27,435 --> 00:13:29,760
They have two fruits
there, fruit type

282
00:13:29,760 --> 00:13:31,770
1, which are
strawberries, and they

283
00:13:31,770 --> 00:13:34,620
have fruit of type 2,
which are raspberries.

284
00:13:34,620 --> 00:13:36,870
If you ever have
picked strawberries,

285
00:13:36,870 --> 00:13:39,270
you would know that
strawberries is very easy.

286
00:13:39,270 --> 00:13:43,800
These are essentially
very flat and low fruit

287
00:13:43,800 --> 00:13:45,810
that when somebody
picks strawberries,

288
00:13:45,810 --> 00:13:48,330
it's very easy to see
how fast somebody works,

289
00:13:48,330 --> 00:13:51,300
how much anybody picks and
so on because you can just

290
00:13:51,300 --> 00:13:52,650
see across the entire field.

291
00:13:52,650 --> 00:13:56,010
You can see essentially what
all of your coworkers are doing.

292
00:13:56,010 --> 00:14:00,930
In contrast, if you look at
raspberries, these are bushes.

293
00:14:00,930 --> 00:14:04,860
So you can essentially literally
hide behind the bushes and work

294
00:14:04,860 --> 00:14:08,250
really hard to pick
really hard and fast

295
00:14:08,250 --> 00:14:09,870
without others noticing.

296
00:14:09,870 --> 00:14:13,440
So essentially, productivity
is at least more unobserved

297
00:14:13,440 --> 00:14:15,780
than for strawberries.

298
00:14:15,780 --> 00:14:17,380
Now, what Bandiera
et al. then find,

299
00:14:17,380 --> 00:14:21,750
in fact, is no impact of the
piece rate on fruits of type 2,

300
00:14:21,750 --> 00:14:23,770
which is raspberries.

301
00:14:23,770 --> 00:14:27,960
So that suggests there is no
evidence of pure altruism,

302
00:14:27,960 --> 00:14:32,340
and the effects, perhaps,
could be driven by reciprocity.

303
00:14:32,340 --> 00:14:34,980
This is consistent with
what we had found before.

304
00:14:34,980 --> 00:14:37,260
When you remember the
dictator ultimatum games

305
00:14:37,260 --> 00:14:40,740
that we showed you, it looked a
lot like people are altruistic.

306
00:14:40,740 --> 00:14:42,060
They care about others.

307
00:14:42,060 --> 00:14:44,820
But when you look at
their actual motives,

308
00:14:44,820 --> 00:14:46,830
when you dig a little
deeper, we find out that,

309
00:14:46,830 --> 00:14:50,640
in fact, this is driven by
reciprocity, or by worries

310
00:14:50,640 --> 00:14:53,940
about repeated game effects,
or worries about essentially

311
00:14:53,940 --> 00:14:57,450
just retribution, either on the
field that other people will

312
00:14:57,450 --> 00:14:59,790
work also really hard if
I work really hard right

313
00:14:59,790 --> 00:15:03,360
now, or socially after
work, my coworkers

314
00:15:03,360 --> 00:15:04,635
might be really mean to me.

315
00:15:04,635 --> 00:15:06,510
They might even beat me
up, or they might not

316
00:15:06,510 --> 00:15:10,260
be my friends anymore.

317
00:15:10,260 --> 00:15:12,840
Here is regression tables
that show you this.

318
00:15:12,840 --> 00:15:16,350
The first column,
you see fruit type 2.

319
00:15:16,350 --> 00:15:17,820
Again, these are
the raspberries.

320
00:15:17,820 --> 00:15:20,700
This is when things
are unobserved.

321
00:15:20,700 --> 00:15:23,130
There's no effective
introducing the piece rate.

322
00:15:23,130 --> 00:15:27,120
In contrast, if you look at
column 2, this is fruit type 1.

323
00:15:27,120 --> 00:15:29,850
These are strawberries, and
there's large productivity

324
00:15:29,850 --> 00:15:33,600
effects of strawberries there.

325
00:15:33,600 --> 00:15:36,120
So these results, again,
highlight the importance

326
00:15:36,120 --> 00:15:39,060
of setting the
incentives carefully.

327
00:15:39,060 --> 00:15:40,800
Small details matter,
and you really

328
00:15:40,800 --> 00:15:42,660
want to be very
careful in how you

329
00:15:42,660 --> 00:15:45,720
said relative incentives
that in principle seemed

330
00:15:45,720 --> 00:15:47,520
like a good idea
because they provide

331
00:15:47,520 --> 00:15:51,900
steep incentives to workers, but
in practice might actually not

332
00:15:51,900 --> 00:15:54,300
work particularly well.

333
00:15:57,490 --> 00:15:59,530
OK, so the second paper
we're going to look at

334
00:15:59,530 --> 00:16:00,370
is Breza et al.

335
00:16:00,370 --> 00:16:03,190
These are the morale
effects of pay inequality.

336
00:16:03,190 --> 00:16:06,790
This is a very nice randomized
field experiment in rural India

337
00:16:06,790 --> 00:16:09,520
with low-skill
manufacturing workers.

338
00:16:09,520 --> 00:16:12,700
The question the authors
ask is do workers

339
00:16:12,700 --> 00:16:14,920
care about relative pay?

340
00:16:14,920 --> 00:16:19,720
And perhaps why do we see
so little variation in pay

341
00:16:19,720 --> 00:16:20,440
across settings?

342
00:16:20,440 --> 00:16:23,350
So what we often see in
many companies in India,

343
00:16:23,350 --> 00:16:27,940
but also in other places, also
in certain villages in India,

344
00:16:27,940 --> 00:16:31,070
what we see is wages tend
to be very compressed.

345
00:16:31,070 --> 00:16:34,480
So workers essentially
earn the exact same wage,

346
00:16:34,480 --> 00:16:38,650
regardless of their underlying
productivity, experience,

347
00:16:38,650 --> 00:16:39,520
and so on.

348
00:16:39,520 --> 00:16:43,000
Good and bad
workers tend to earn

349
00:16:43,000 --> 00:16:47,140
exactly the same amount
of money for per day

350
00:16:47,140 --> 00:16:49,270
or per hour and the like.

351
00:16:49,270 --> 00:16:50,890
Economists think
that's inefficient

352
00:16:50,890 --> 00:16:52,450
because if you want
to hire somebody

353
00:16:52,450 --> 00:16:53,950
who's really, really
productive, you

354
00:16:53,950 --> 00:16:55,930
might want to pay them more.

355
00:16:55,930 --> 00:16:58,330
If you want to hire somebody
who is not that productive,

356
00:16:58,330 --> 00:16:59,670
you might want to pay them less.

357
00:16:59,670 --> 00:17:02,170
You might actually want to pay
them less than the prevailing

358
00:17:02,170 --> 00:17:04,270
wage, but you might
not be able to do

359
00:17:04,270 --> 00:17:07,970
so because the norm is that you
have to pay them the prevailing

360
00:17:07,970 --> 00:17:08,470
wage.

361
00:17:08,470 --> 00:17:10,960
And then you might end up
not hiring that person,

362
00:17:10,960 --> 00:17:13,329
and that person might
end up being unemployed,

363
00:17:13,329 --> 00:17:16,599
even though you would be
very happy to pay them

364
00:17:16,599 --> 00:17:18,280
at a lower wage.

365
00:17:18,280 --> 00:17:20,780
This is a really
relevant question.

366
00:17:20,780 --> 00:17:22,569
Sorry, the second
question the authors ask

367
00:17:22,569 --> 00:17:26,260
is what is the notion of the
underlying notion of fairness?

368
00:17:26,260 --> 00:17:28,900
What is fair and
what's not fair?

369
00:17:28,900 --> 00:17:32,860
As in if there is wage
dispersion across workers

370
00:17:32,860 --> 00:17:37,060
within teams or within a certain
firm, under some circumstances

371
00:17:37,060 --> 00:17:38,385
under which that's OK to do?

372
00:17:38,385 --> 00:17:39,760
Are there some
good justification

373
00:17:39,760 --> 00:17:47,380
that workers are OK with, or
is it just always bad to do so?

374
00:17:47,380 --> 00:17:51,340
So can we find some situation
under which it's justified

375
00:17:51,340 --> 00:17:54,730
for workers or not, or is it
just like we cannot pay have

376
00:17:54,730 --> 00:17:57,760
pay inequality under any
circumstances in those kinds

377
00:17:57,760 --> 00:17:59,000
of firms?

378
00:17:59,000 --> 00:18:00,580
This is potentially
quite relevant

379
00:18:00,580 --> 00:18:03,170
for many features
of the labor market.

380
00:18:03,170 --> 00:18:05,170
It might explain
wage compression.

381
00:18:05,170 --> 00:18:06,670
That's what I'm
saying, like wages

382
00:18:06,670 --> 00:18:09,460
tend to be fairly compressed,
even if productivity is fairly

383
00:18:09,460 --> 00:18:11,500
dispersed across workers.

384
00:18:11,500 --> 00:18:13,450
There tends to be
lots of wage rigidity.

385
00:18:13,450 --> 00:18:17,170
Wages tend to not
move very much.

386
00:18:17,170 --> 00:18:20,500
Employers have trouble
adjusting wages,

387
00:18:20,500 --> 00:18:23,080
or they are reluctant to
adjust people's wages much,

388
00:18:23,080 --> 00:18:25,520
in particular when it comes
to like downward rigidity.

389
00:18:25,520 --> 00:18:26,895
We talked about
this a little bit

390
00:18:26,895 --> 00:18:31,120
already when we talked
about reference dependence.

391
00:18:31,120 --> 00:18:33,460
This can help us think about
like sorting of workers

392
00:18:33,460 --> 00:18:35,830
into firms and inequality.

393
00:18:35,830 --> 00:18:37,950
We can think about firm
boundaries in a sense

394
00:18:37,950 --> 00:18:41,680
and say should they
be really large firms

395
00:18:41,680 --> 00:18:43,420
or a number of small firms?

396
00:18:43,420 --> 00:18:48,070
If you think the relative
comparison is across workers

397
00:18:48,070 --> 00:18:50,380
within firms, that would
say, well, we should probably

398
00:18:50,380 --> 00:18:52,233
have a lot of small firms.

399
00:18:52,233 --> 00:18:53,650
And then each of
these small firms

400
00:18:53,650 --> 00:18:55,510
could have different
wages, as opposed

401
00:18:55,510 --> 00:18:58,360
to one large firm where
essentially everybody has to be

402
00:18:58,360 --> 00:19:00,377
paid more or less the same.

403
00:19:00,377 --> 00:19:02,710
And we might think that's
inefficient because as returns

404
00:19:02,710 --> 00:19:05,500
to scale of having a large firm.

405
00:19:05,500 --> 00:19:08,350
Think about it is also
relevant for some HR policies

406
00:19:08,350 --> 00:19:10,810
in terms of how do
you pay workers,

407
00:19:10,810 --> 00:19:13,130
how do you set wages, and so on.

408
00:19:13,130 --> 00:19:15,970
So as motivation in
the study, the authors

409
00:19:15,970 --> 00:19:19,460
ask people the
following questions.

410
00:19:19,460 --> 00:19:21,850
The questions are the following.

411
00:19:21,850 --> 00:19:29,080
This is rural India in Rissa, a
relatively poor area with lots

412
00:19:29,080 --> 00:19:31,600
of small-scale manufacturing.

413
00:19:31,600 --> 00:19:33,430
The question is three
people from a village

414
00:19:33,430 --> 00:19:36,760
get hired to work on a
construction site together.

415
00:19:36,760 --> 00:19:39,940
The prevailing
wage is 250 rupees.

416
00:19:39,940 --> 00:19:42,010
That's the standard
wage in the village.

417
00:19:42,010 --> 00:19:43,960
Each village tends to
have like a prevailing

418
00:19:43,960 --> 00:19:46,780
wage, which is how much
people are paid usually.

419
00:19:46,780 --> 00:19:51,460
The contractor pays
them 250 rupees per day.

420
00:19:51,460 --> 00:19:53,920
How well will they
work together?

421
00:19:53,920 --> 00:19:56,050
And what you see is what
respondents tend to say

422
00:19:56,050 --> 00:20:01,810
is 80% of respondents say people
worked very well together.

423
00:20:01,810 --> 00:20:06,650
20% say as well as usual, and
0% say there will be conflict.

424
00:20:06,650 --> 00:20:08,620
So that's seems like
paying everybody

425
00:20:08,620 --> 00:20:11,540
the same is the socially
acceptable thing to do.

426
00:20:11,540 --> 00:20:14,260
Everybody seems happy with that.

427
00:20:14,260 --> 00:20:16,430
In contrast, if you
ask the following,

428
00:20:16,430 --> 00:20:19,420
which is the contractor
pays them different wages.

429
00:20:19,420 --> 00:20:23,890
If the contractor pays
250 per day, 270 per day,

430
00:20:23,890 --> 00:20:28,790
and 290 per day, how well
will they work together?

431
00:20:28,790 --> 00:20:32,260
And so now, what
you see here is when

432
00:20:32,260 --> 00:20:37,180
there is differences
across pay, 94% of workers

433
00:20:37,180 --> 00:20:40,893
say there will be conflict.

434
00:20:40,893 --> 00:20:42,560
Listen, in this version
of the question,

435
00:20:42,560 --> 00:20:45,850
it doesn't specify whether
that's due with underlying

436
00:20:45,850 --> 00:20:47,000
productivity differences.

437
00:20:47,000 --> 00:20:50,060
So why is it that some workers
are paid different than others?

438
00:20:50,060 --> 00:20:52,690
However, it's very strong
suggestive evidence

439
00:20:52,690 --> 00:20:55,720
that workers might
be really unhappy,

440
00:20:55,720 --> 00:20:59,320
and there will be
conflict across workers

441
00:20:59,320 --> 00:21:03,340
if they're paid different
wages in the same kind of team,

442
00:21:03,340 --> 00:21:06,700
or at least on the
same construction site.

443
00:21:06,700 --> 00:21:08,840
And then, of course,
if you're an employer,

444
00:21:08,840 --> 00:21:13,240
you might be quite worried about
paying people different wages

445
00:21:13,240 --> 00:21:14,410
because of this conflict.

446
00:21:14,410 --> 00:21:17,770
Presumably, workers will not
work or collaborate as well

447
00:21:17,770 --> 00:21:20,500
as they could potentially.

448
00:21:20,500 --> 00:21:23,920
OK, so now the authors set up
a very nice field experiment

449
00:21:23,920 --> 00:21:25,070
that looks like this.

450
00:21:25,070 --> 00:21:30,850
So there's 10 production units
of 3 workers in each factory.

451
00:21:30,850 --> 00:21:35,470
So think of factories like work
sites, factories in quotation

452
00:21:35,470 --> 00:21:37,360
marks because it's
really not just a factory

453
00:21:37,360 --> 00:21:38,860
as you might imagine it.

454
00:21:38,860 --> 00:21:41,410
It's really like a small work
site, an office where people

455
00:21:41,410 --> 00:21:43,720
are working in different areas.

456
00:21:43,720 --> 00:21:45,670
There's 10 units
of 3 workers each,

457
00:21:45,670 --> 00:21:47,350
so it's not a huge office.

458
00:21:47,350 --> 00:21:51,920
Each unit of three workers
produces different products.

459
00:21:51,920 --> 00:21:54,700
So each unit has one product.

460
00:21:54,700 --> 00:21:56,650
For example, unit
1 makes brooms.

461
00:21:56,650 --> 00:21:59,140
Unit 2 makes incense sticks.

462
00:21:59,140 --> 00:22:03,000
Unit 3 makes leaf
plates, and so on.

463
00:22:03,000 --> 00:22:06,442
So each unit has one
item of production.

464
00:22:06,442 --> 00:22:07,900
Now, why are there
different units?

465
00:22:07,900 --> 00:22:09,430
It's essentially
to separate workers

466
00:22:09,430 --> 00:22:11,290
into different lines of work.

467
00:22:11,290 --> 00:22:13,390
So what we're going to
expect is that workers

468
00:22:13,390 --> 00:22:15,700
are going to compare
each other within unit,

469
00:22:15,700 --> 00:22:20,200
but not so much across units
because if I make brooms

470
00:22:20,200 --> 00:22:22,040
and you make incense
sticks, that's

471
00:22:22,040 --> 00:22:24,088
very different
work in some ways.

472
00:22:24,088 --> 00:22:25,630
But if you make the
exact same thing,

473
00:22:25,630 --> 00:22:27,910
the natural comparison
now for workers

474
00:22:27,910 --> 00:22:30,640
to compare yourself
with another worker.

475
00:22:30,640 --> 00:22:34,180
All unit members, so
everybody in a given unit,

476
00:22:34,180 --> 00:22:35,930
makes the exact same product.

477
00:22:35,930 --> 00:22:40,810
So everybody in unit 1, who is
three workers, makes brooms.

478
00:22:40,810 --> 00:22:44,390
Everybody in unit 2 makes
incense sticks, and so on.

479
00:22:44,390 --> 00:22:47,980
Now the key experimental
variation in the experiment

480
00:22:47,980 --> 00:22:52,900
is weight dispersion across
workers within teams.

481
00:22:52,900 --> 00:22:56,290
Finally, there's also-- and I'll
get back to that at the end--

482
00:22:56,290 --> 00:23:02,000
production tasks vary in their
observability of performance.

483
00:23:02,000 --> 00:23:04,300
So that is to say for
some tasks it just

484
00:23:04,300 --> 00:23:07,240
happens to be that
it's much easier

485
00:23:07,240 --> 00:23:10,150
to understand how productive
your co-worker is than

486
00:23:10,150 --> 00:23:12,430
in other tasks.

487
00:23:12,430 --> 00:23:17,050
OK, so now what is the
variation in relative pay.

488
00:23:17,050 --> 00:23:23,920
So here you see people are paid
depending on their worker rank.

489
00:23:23,920 --> 00:23:26,260
There's four different
regimes of pay

490
00:23:26,260 --> 00:23:28,990
in the different
columns, heterogeneous,

491
00:23:28,990 --> 00:23:32,950
compressed L, compressed M,
and compressed H. Workers

492
00:23:32,950 --> 00:23:33,753
are sorted.

493
00:23:33,753 --> 00:23:35,170
So there's a
baseline period where

494
00:23:35,170 --> 00:23:38,380
workers who work for a
few days, and the few days

495
00:23:38,380 --> 00:23:41,260
are used to assess their
baseline productivity.

496
00:23:41,260 --> 00:23:44,560
They're ranked into
three turnstyles,

497
00:23:44,560 --> 00:23:46,420
so there's like the
low productive workers,

498
00:23:46,420 --> 00:23:49,060
median productivity, and
high productivity workers.

499
00:23:49,060 --> 00:23:51,160
They're ranked essentially
based on how well they

500
00:23:51,160 --> 00:23:52,570
did at baseline.

501
00:23:52,570 --> 00:23:54,755
OK, so if you're like
really bad at the start,

502
00:23:54,755 --> 00:23:56,380
you would be a low
productivity worker.

503
00:23:56,380 --> 00:23:57,760
If you're like an
average worker,

504
00:23:57,760 --> 00:23:59,325
you would be median
productivity.

505
00:23:59,325 --> 00:24:01,450
And if you're really like
highly productive worker,

506
00:24:01,450 --> 00:24:06,280
you would be classified as
high productivity workers.

507
00:24:06,280 --> 00:24:08,780
Notice that's always done for
all of the different treatment

508
00:24:08,780 --> 00:24:09,530
groups.

509
00:24:09,530 --> 00:24:13,640
The difference now is across
these different regimes

510
00:24:13,640 --> 00:24:15,530
how workers are paid.

511
00:24:15,530 --> 00:24:19,280
In their heterogeneous
pay essentially, there's

512
00:24:19,280 --> 00:24:22,790
differences in cross
workers in the sense

513
00:24:22,790 --> 00:24:27,830
of like the low productivity
workers are paid low wage,

514
00:24:27,830 --> 00:24:29,570
the median productivity
paid workers

515
00:24:29,570 --> 00:24:32,990
are paid the median weight, and
the high productivity workers

516
00:24:32,990 --> 00:24:34,178
are paid the high wage.

517
00:24:34,178 --> 00:24:36,470
As you might expect, that's,
sort of, the natural thing

518
00:24:36,470 --> 00:24:39,590
to do in terms if wanted
more productivity, the most

519
00:24:39,590 --> 00:24:43,220
productive workers you're
going to pay the most.

520
00:24:43,220 --> 00:24:46,130
Notice that these wage
differences are modest.

521
00:24:46,130 --> 00:24:48,140
Even like the
difference between W

522
00:24:48,140 --> 00:24:54,472
high and W low are like
only about like up to 10%.

523
00:24:54,472 --> 00:24:56,180
So really these are
not huge differences.

524
00:24:56,180 --> 00:24:58,680
It's not that the other guy if
you are like low productivity

525
00:24:58,680 --> 00:25:01,870
worker if you get W low,
it's not the other guy

526
00:25:01,870 --> 00:25:03,120
gets like twice as much.

527
00:25:03,120 --> 00:25:06,350
It really is like modestly
more, but it is more money

528
00:25:06,350 --> 00:25:07,580
that they get.

529
00:25:07,580 --> 00:25:10,790
Now then if you look at the
other three regimes, compressed

530
00:25:10,790 --> 00:25:15,140
L, compressed M, and compressed
H, low productivity-- so

531
00:25:15,140 --> 00:25:18,980
in compressed L, everybody gets
to low wage, in compressed M,

532
00:25:18,980 --> 00:25:22,040
everybody gets the minimum
wage, and in compressed H,

533
00:25:22,040 --> 00:25:24,260
everybody gets the high wage.

534
00:25:24,260 --> 00:25:26,510
And now the study
now lets the authors

535
00:25:26,510 --> 00:25:31,100
compare the different
columns for holding exactly

536
00:25:31,100 --> 00:25:34,820
constant baseline
productivity and wage levels.

537
00:25:34,820 --> 00:25:39,800
That is to say, for instance, we
can compare the heterogeneous--

538
00:25:39,800 --> 00:25:41,810
the low productivity
workers that

539
00:25:41,810 --> 00:25:44,600
happen to be randomized
into like the heterogeneous

540
00:25:44,600 --> 00:25:49,130
treatment, they receive
W low or compare them

541
00:25:49,130 --> 00:25:51,530
with like low productivity
workers at baseline who

542
00:25:51,530 --> 00:25:54,530
happen to be randomized
into a compressed L.

543
00:25:54,530 --> 00:25:56,240
So I should have
said more clearly

544
00:25:56,240 --> 00:25:59,750
people are randomized into
any of these four groups,

545
00:25:59,750 --> 00:26:02,570
and then depending on what
your baseline productivity is

546
00:26:02,570 --> 00:26:05,970
you get the wages as I
show here in the table.

547
00:26:05,970 --> 00:26:08,780
So for example, if you are
like a low productivity worker,

548
00:26:08,780 --> 00:26:13,670
you might be randomized into the
heterogeneous treatment group.

549
00:26:13,670 --> 00:26:16,130
You might receive
W low, or you might

550
00:26:16,130 --> 00:26:18,260
be randomized into the low--

551
00:26:18,260 --> 00:26:23,150
compressed, low treatment
where you also receive W low.

552
00:26:23,150 --> 00:26:25,760
Notice that in both
cases, that worker

553
00:26:25,760 --> 00:26:28,100
is a low productivity worker.

554
00:26:28,100 --> 00:26:31,430
In both cases, the
worker receives W low.

555
00:26:31,430 --> 00:26:34,850
However, what's different here
now is that his co-worker--

556
00:26:34,850 --> 00:26:36,230
his or her--

557
00:26:36,230 --> 00:26:38,060
in this case,
these are all men--

558
00:26:38,060 --> 00:26:41,210
co-workers are receiving
either the same, which

559
00:26:41,210 --> 00:26:44,900
is in compressed L, or
they receive higher wages

560
00:26:44,900 --> 00:26:49,020
in the heterogeneous
weight treatment, right.

561
00:26:49,020 --> 00:26:50,720
So now we can look
at workers who

562
00:26:50,720 --> 00:26:53,000
have the exact same baseline
productivity on average

563
00:26:53,000 --> 00:26:56,330
at least and have the exact same
weight, but what's being varied

564
00:26:56,330 --> 00:26:58,700
is like how much other
workers are earning.

565
00:26:58,700 --> 00:27:01,698
We can do this for low
productivity workers.

566
00:27:01,698 --> 00:27:03,740
We can also do it for
median productivity workers

567
00:27:03,740 --> 00:27:05,667
or also for high
productivity workers.

568
00:27:05,667 --> 00:27:07,250
I skip the median
productivity worker,

569
00:27:07,250 --> 00:27:08,810
but that's exactly the same.

570
00:27:08,810 --> 00:27:10,220
For high productivity
workers, we

571
00:27:10,220 --> 00:27:15,800
can look at the workers
who had been randomized

572
00:27:15,800 --> 00:27:22,340
into the heterogeneous treatment
group or heterogeneous worker

573
00:27:22,340 --> 00:27:23,870
group--

574
00:27:23,870 --> 00:27:26,870
sorry, heterogeneous
wage group where

575
00:27:26,870 --> 00:27:29,150
the worker gets a
WH but everybody

576
00:27:29,150 --> 00:27:31,460
else gets like a
lower wage, or we

577
00:27:31,460 --> 00:27:35,120
can compare that to
compressed age where everybody

578
00:27:35,120 --> 00:27:37,340
gets WH in that group.

579
00:27:43,510 --> 00:27:45,820
OK, so now what do you
what do the authors find?

580
00:27:45,820 --> 00:27:48,820
Let's start with
the low productivity

581
00:27:48,820 --> 00:27:50,170
workers at baseline.

582
00:27:50,170 --> 00:27:52,120
Remember the collinear
comparison here

583
00:27:52,120 --> 00:27:56,200
is between pay disparity-- this
is a heterogeneous group-- pay

584
00:27:56,200 --> 00:27:58,330
disparity and
compressed L, which

585
00:27:58,330 --> 00:28:02,710
is like the group where
everybody is paid the same.

586
00:28:02,710 --> 00:28:05,500
Now what the authors find--
and you see this in the graph

587
00:28:05,500 --> 00:28:09,272
fairly nicely, which is
like the productivity--

588
00:28:09,272 --> 00:28:11,230
these are like the red
line and the blue line--

589
00:28:11,230 --> 00:28:12,910
the productivity
on the left side-

590
00:28:12,910 --> 00:28:17,440
this is before the treatment
starts for about over 10 days--

591
00:28:17,440 --> 00:28:20,180
the productivity looks
pretty much the same.

592
00:28:20,180 --> 00:28:22,390
But then when you go to the
right side of the graph,

593
00:28:22,390 --> 00:28:26,350
not immediately but after a
few days they seem to be a gap

594
00:28:26,350 --> 00:28:30,310
or a gap emerges between the red
line and the blue line, which

595
00:28:30,310 --> 00:28:36,160
is exactly the gap as you would
expect if the disparity makes

596
00:28:36,160 --> 00:28:40,120
workers less productive, which
is to say the workers who

597
00:28:40,120 --> 00:28:43,000
receive a low wage but
others in that group

598
00:28:43,000 --> 00:28:46,510
are receiving higher wages
are becoming less productive

599
00:28:46,510 --> 00:28:50,830
compared to workers that receive
a low wage where others have

600
00:28:50,830 --> 00:28:53,530
the exact same productivity.

601
00:28:53,530 --> 00:28:57,340
And you see this also in
the regression tables,

602
00:28:57,340 --> 00:29:01,690
you see about a 22%
reduction in mean output

603
00:29:01,690 --> 00:29:05,903
and a 9% reduction in earnings,
which are pretty large effects.

604
00:29:05,903 --> 00:29:07,570
So these are like
large effects compared

605
00:29:07,570 --> 00:29:10,810
to like other interventions
that people have tried.

606
00:29:10,810 --> 00:29:12,550
Interesting, you
see a little bit

607
00:29:12,550 --> 00:29:15,820
like while the treatment effect
when you look at the graph

608
00:29:15,820 --> 00:29:19,360
initially looks not particularly
large, if anything it might not

609
00:29:19,360 --> 00:29:22,660
even be there often like a few
days, that treatment effect

610
00:29:22,660 --> 00:29:23,890
increases over time.

611
00:29:23,890 --> 00:29:27,610
It becomes larger
so over time people

612
00:29:27,610 --> 00:29:33,550
become less and less productive
compared to the compressed wage

613
00:29:33,550 --> 00:29:34,690
treatment.

614
00:29:34,690 --> 00:29:36,940
Interestingly, we
find-- the authors

615
00:29:36,940 --> 00:29:40,720
find similar effects for high
ranked, high productivity

616
00:29:40,720 --> 00:29:41,740
workers.

617
00:29:41,740 --> 00:29:43,870
So notice that these
are not workers

618
00:29:43,870 --> 00:29:46,540
who either who are high
productivity workers

619
00:29:46,540 --> 00:29:49,600
to receive the same wage in
the pay disparity treatment

620
00:29:49,600 --> 00:29:53,200
and the compressed H treatment
that have the same wage.

621
00:29:53,200 --> 00:29:56,140
In one case, they are
paid exactly the same.

622
00:29:56,140 --> 00:29:58,630
In the other case,
they're paid more

623
00:29:58,630 --> 00:30:00,910
compared to their coworkers.

624
00:30:00,910 --> 00:30:03,190
And what happens what
seems to be the case

625
00:30:03,190 --> 00:30:08,410
is that the compressed high
paid workers are, in fact, more

626
00:30:08,410 --> 00:30:10,420
productive compared to others.

627
00:30:10,420 --> 00:30:13,498
You might have expected in some
ways like if one guy gets paid

628
00:30:13,498 --> 00:30:15,040
high payment but
then others get paid

629
00:30:15,040 --> 00:30:17,802
less, that makes the high
pay worker more productive,

630
00:30:17,802 --> 00:30:19,510
because maybe he feels
good about himself

631
00:30:19,510 --> 00:30:20,800
that he's a high paid workers.

632
00:30:20,800 --> 00:30:23,710
And maybe he feels he can
prove himself or the like,

633
00:30:23,710 --> 00:30:26,050
but instead what seems to
be the case the group that

634
00:30:26,050 --> 00:30:31,270
works in his team that does not
work well together or they just

635
00:30:31,270 --> 00:30:34,030
becomes uncomfortable to work
with somebody else who is like

636
00:30:34,030 --> 00:30:37,500
really mad at you for
earning more than they do.

637
00:30:37,500 --> 00:30:41,950
And so now the high pay workers
in the compressed treatment,

638
00:30:41,950 --> 00:30:44,140
in fact, earn more
or produce more

639
00:30:44,140 --> 00:30:49,160
than the high paid workers in
the pay disparity treatment.

640
00:30:49,160 --> 00:30:52,510
So that is to say
the pay disparity,

641
00:30:52,510 --> 00:30:55,630
pay inequality does
not only reduce

642
00:30:55,630 --> 00:30:58,360
worker's productivity
for the low pay workers,

643
00:30:58,360 --> 00:31:00,400
so not only the people--

644
00:31:00,400 --> 00:31:04,533
the workers who are earning less
compared to their co-workers,

645
00:31:04,533 --> 00:31:06,200
and that's in some
sense to be expected.

646
00:31:06,200 --> 00:31:08,492
You might just be mad at
everybody else or other people

647
00:31:08,492 --> 00:31:11,350
in your group are earning more.

648
00:31:11,350 --> 00:31:13,190
Then you might be unhappy
and mad about that

649
00:31:13,190 --> 00:31:14,950
and just then produce less.

650
00:31:14,950 --> 00:31:17,890
Instead it seems to be the
case even the high productivity

651
00:31:17,890 --> 00:31:21,130
workers, the workers who earn
more than others in their group

652
00:31:21,130 --> 00:31:25,330
are producing less compared
to the control group where

653
00:31:25,330 --> 00:31:27,890
everybody earns the same.

654
00:31:27,890 --> 00:31:29,320
So what did we learn from that?

655
00:31:29,320 --> 00:31:33,070
Well, pay disparity lowers
worker performance for all team

656
00:31:33,070 --> 00:31:36,790
members, and so the
interpretation of that

657
00:31:36,790 --> 00:31:42,070
is that pay disparity undermines
workers ability to cooperate

658
00:31:42,070 --> 00:31:46,000
in their own self-interest.

659
00:31:46,000 --> 00:31:47,740
The paper has some
additional evidence

660
00:31:47,740 --> 00:31:50,050
with some cooperative
tasks where

661
00:31:50,050 --> 00:31:53,060
essentially workers are worse
at cooperating with each other.

662
00:31:53,060 --> 00:31:55,480
It seems to really be
that workers are not

663
00:31:55,480 --> 00:31:57,760
happy to work in the
same place with somebody

664
00:31:57,760 --> 00:32:00,550
else who earns less.

665
00:32:00,550 --> 00:32:03,100
Importantly, the
perceived justification

666
00:32:03,100 --> 00:32:05,800
is essential in
mediating these effects.

667
00:32:05,800 --> 00:32:08,590
That is to say I told you
previously that for some task

668
00:32:08,590 --> 00:32:12,680
it was easier to see who was
more productive than others,

669
00:32:12,680 --> 00:32:15,400
especially at baseline
just because in the task

670
00:32:15,400 --> 00:32:19,570
the difference is across highly
and less highly productive.

671
00:32:19,570 --> 00:32:22,480
Or high and low productivity
workers was just wider,

672
00:32:22,480 --> 00:32:25,270
and it's just easy to see that
Frank is really bad at this

673
00:32:25,270 --> 00:32:26,800
and somebody else
is really good.

674
00:32:26,800 --> 00:32:29,320
And for some task, it's
really easy to see that,

675
00:32:29,320 --> 00:32:30,800
for others not.

676
00:32:30,800 --> 00:32:32,410
So if it's really
easy-- if there's

677
00:32:32,410 --> 00:32:34,397
a perceived justification
for workers,

678
00:32:34,397 --> 00:32:35,980
if workers, sort of,
thought, OK, it's

679
00:32:35,980 --> 00:32:39,550
really easy to see who is
more productive than others,

680
00:32:39,550 --> 00:32:42,250
the effects are much
weaker than when there

681
00:32:42,250 --> 00:32:44,440
is no receive justification.

682
00:32:44,440 --> 00:32:46,473
That is to say workers
themselves are saying,

683
00:32:46,473 --> 00:32:47,890
like, if I'm saying,
look, there's

684
00:32:47,890 --> 00:32:50,000
this other worker who
is really productive

685
00:32:50,000 --> 00:32:51,740
and if I'm sort of
saying, hey, look,

686
00:32:51,740 --> 00:32:53,198
this is really
obvious that they're

687
00:32:53,198 --> 00:32:54,910
more productive than
I am, it's only fair

688
00:32:54,910 --> 00:32:57,910
that they're earning
more, then the affects

689
00:32:57,910 --> 00:33:01,190
are much damage-- then there's
much less than an effect that's

690
00:33:01,190 --> 00:33:04,120
accepted, and there's much
less of a productivity

691
00:33:04,120 --> 00:33:08,048
effect, if any, if
there's pay inequality.

692
00:33:08,048 --> 00:33:10,090
However, if it seems to
be the case that they're,

693
00:33:10,090 --> 00:33:12,550
kind of, like producing
the same thing,

694
00:33:12,550 --> 00:33:16,150
if we like similarly
producing the same thing

695
00:33:16,150 --> 00:33:19,270
and we're equally good at it
or approximately equally good

696
00:33:19,270 --> 00:33:22,610
and one person is paid more
than I am for no good reason,

697
00:33:22,610 --> 00:33:26,200
so it appears that workers are
really not happy about that,

698
00:33:26,200 --> 00:33:33,160
then work performance, in
particular output falls.

699
00:33:33,160 --> 00:33:34,430
So what are the implications?

700
00:33:34,430 --> 00:33:39,370
Well, this evidence suggests
that weight compression

701
00:33:39,370 --> 00:33:42,490
may be more likely in some
settings than in others, right.

702
00:33:42,490 --> 00:33:45,220
That's to say like if
it's easy to justify,

703
00:33:45,220 --> 00:33:48,280
if the production function or
like the production process is

704
00:33:48,280 --> 00:33:52,030
such that all aspects of the
production and the output

705
00:33:52,030 --> 00:33:55,750
or the performance are easy,
measurable, and observable

706
00:33:55,750 --> 00:33:58,720
for workers, then
workers might say

707
00:33:58,720 --> 00:34:02,510
it's OK if some workers are
earning more than others.

708
00:34:02,510 --> 00:34:05,740
However, if that's not the case,
if there's a bunch of stuff

709
00:34:05,740 --> 00:34:08,020
in the work process that's
really not observed--

710
00:34:08,020 --> 00:34:11,170
maybe it's verbal or maybe just
really hard to justify or hard

711
00:34:11,170 --> 00:34:14,199
to sort of assess in
some tangible way--

712
00:34:14,199 --> 00:34:16,750
then workers might
be really unhappy

713
00:34:16,750 --> 00:34:19,060
and that might lead
to weight compression,

714
00:34:19,060 --> 00:34:23,110
because the employer
might anticipate that.

715
00:34:23,110 --> 00:34:28,239
So overall, this, sort of, says
that like relative comparisons

716
00:34:28,239 --> 00:34:29,500
matter quite a bit.

717
00:34:29,500 --> 00:34:32,860
Fairness matters quite
a bit, and what's

718
00:34:32,860 --> 00:34:34,909
really key for an
employer and anybody who

719
00:34:34,909 --> 00:34:39,130
sets incentives is to take
into account these fairness

720
00:34:39,130 --> 00:34:43,090
considerations, and one wants to
really understand what is fair

721
00:34:43,090 --> 00:34:44,300
and what is not.

722
00:34:44,300 --> 00:34:48,460
And upsetting workers by
violating these fairness

723
00:34:48,460 --> 00:34:52,030
considerations or norms might
be really, really costly

724
00:34:52,030 --> 00:34:55,060
for an employer.

725
00:34:55,060 --> 00:34:58,980
So the good news here is that if
one understands that well, one

726
00:34:58,980 --> 00:35:01,890
can really sort of produce
increased productivity quite

727
00:35:01,890 --> 00:35:06,660
a bit, and in some cases weight
inequalities actually fine.

728
00:35:06,660 --> 00:35:09,600
One just has to be careful in
figuring out when exactly is

729
00:35:09,600 --> 00:35:12,790
that the case, OK.

730
00:35:12,790 --> 00:35:14,290
As I said, there's
a very nice paper

731
00:35:14,290 --> 00:35:17,350
by Jonas Hjort on
ethnic divisions

732
00:35:17,350 --> 00:35:18,760
and production in firms.

733
00:35:18,760 --> 00:35:20,740
You're going to talk
about that in recitation.

734
00:35:23,960 --> 00:35:26,880
OK, the second part
of this lecture

735
00:35:26,880 --> 00:35:31,620
we'll talk about policies
to increase pro-sociality.

736
00:35:31,620 --> 00:35:34,290
And so the first
of these papers is

737
00:35:34,290 --> 00:35:37,320
very nice recent paper by Gautam
Rao that looks at the question

738
00:35:37,320 --> 00:35:39,660
are social
preferences malleable.

739
00:35:39,660 --> 00:35:41,920
Like, what are the origins
of social preferences?

740
00:35:41,920 --> 00:35:46,930
Why is it that some people
appear nicer than others?

741
00:35:46,930 --> 00:35:48,660
And then once we
understand that,

742
00:35:48,660 --> 00:35:51,960
perhaps, we can also understand
what policies, if anything,

743
00:35:51,960 --> 00:35:53,837
can affect social preferences.

744
00:35:56,460 --> 00:35:58,350
The main question
that this paper asks

745
00:35:58,350 --> 00:36:01,560
is how does being mixed
with poor students in school

746
00:36:01,560 --> 00:36:05,040
affect the social
preferences of rich students.

747
00:36:05,040 --> 00:36:08,790
Now in a lot of the,
kind of, policies

748
00:36:08,790 --> 00:36:11,430
where poor students are
mixed with rich students,

749
00:36:11,430 --> 00:36:13,320
you might ask the
question, how does

750
00:36:13,320 --> 00:36:14,500
it affect the poor student.

751
00:36:14,500 --> 00:36:16,410
If a poor student
for whatever reason

752
00:36:16,410 --> 00:36:20,130
would not be able to afford like
a rich school or rich student's

753
00:36:20,130 --> 00:36:22,320
school and you,
sort of, allow them

754
00:36:22,320 --> 00:36:26,610
by giving them scholarships
or other policies,

755
00:36:26,610 --> 00:36:28,110
often the question
that people ask

756
00:36:28,110 --> 00:36:30,450
is like what are the
benefits of doing that.

757
00:36:30,450 --> 00:36:34,035
Is the poor student
doing better in school,

758
00:36:34,035 --> 00:36:36,660
or do they have different types
of friends, different networks,

759
00:36:36,660 --> 00:36:39,520
and does it lead to better
jobs and so on and so forth?

760
00:36:39,520 --> 00:36:41,790
This question, this paper
ask a different question.

761
00:36:41,790 --> 00:36:45,270
It asks the question about what
is the effect of rich students

762
00:36:45,270 --> 00:36:47,100
if they randomly
or quasi randomly

763
00:36:47,100 --> 00:36:52,200
are exposed to being in class
with additional or some more

764
00:36:52,200 --> 00:36:54,150
poor students.

765
00:36:54,150 --> 00:36:56,490
The paper exploits
a policy change

766
00:36:56,490 --> 00:37:01,800
that introduced an admissions
quota of 20% for poor students

767
00:37:01,800 --> 00:37:04,000
in primary schools in Delhi.

768
00:37:04,000 --> 00:37:08,280
In Delhi, these are
rich primary schools.

769
00:37:08,280 --> 00:37:11,410
The paper looks at two
sources of variation.

770
00:37:11,410 --> 00:37:14,100
There's variation
across classrooms

771
00:37:14,100 --> 00:37:17,400
that allows the author to
look at the overall effects.

772
00:37:17,400 --> 00:37:21,370
So you can look at, like,
within schools there's

773
00:37:21,370 --> 00:37:26,650
going to be treated
and controlled cohorts,

774
00:37:26,650 --> 00:37:30,010
because the policy was
introduced at some point.

775
00:37:30,010 --> 00:37:33,280
So for some students,
they enjoyed

776
00:37:33,280 --> 00:37:36,760
the benefits of being in
class with poor students

777
00:37:36,760 --> 00:37:38,050
or the costs.

778
00:37:38,050 --> 00:37:39,550
We'll see about that.

779
00:37:39,550 --> 00:37:42,130
And for other schools, they're
already too far advanced.

780
00:37:42,130 --> 00:37:44,230
They were like
essentially before the--

781
00:37:44,230 --> 00:37:46,780
they went to school before
the policy was enacted,

782
00:37:46,780 --> 00:37:49,390
and therefore they
were not exposed

783
00:37:49,390 --> 00:37:52,270
to having poor
students in school.

784
00:37:52,270 --> 00:37:53,830
For these treated
schools there is

785
00:37:53,830 --> 00:37:57,340
essentially variation
within school across cohorts

786
00:37:57,340 --> 00:37:59,890
that we can look at.

787
00:37:59,890 --> 00:38:01,250
Second within cohorts.

788
00:38:01,250 --> 00:38:04,000
We can look at treated
and control schools.

789
00:38:04,000 --> 00:38:08,300
So some schools were getting
additional poor kids and others

790
00:38:08,300 --> 00:38:08,800
did not.

791
00:38:08,800 --> 00:38:11,180
I'll tell you about
this in a second.

792
00:38:11,180 --> 00:38:14,020
And then there's variation
within classrooms

793
00:38:14,020 --> 00:38:17,800
to allow us to look at the
role of personal interactions.

794
00:38:17,800 --> 00:38:19,780
There are, sort of,
idiosyncratic assignments

795
00:38:19,780 --> 00:38:22,210
to study groups,
so some students

796
00:38:22,210 --> 00:38:25,240
happen to be in study
groups with a poor kid,

797
00:38:25,240 --> 00:38:26,830
and others were not.

798
00:38:32,795 --> 00:38:34,170
Now what does this
study measure.

799
00:38:34,170 --> 00:38:36,750
The study measures three
broad set of outcomes.

800
00:38:36,750 --> 00:38:39,420
It measures pro-social
behavior and generosity.

801
00:38:39,420 --> 00:38:41,670
This is very much what we
have already looked at.

802
00:38:41,670 --> 00:38:45,810
In particular, it looks at
dictator games and volunteering

803
00:38:45,810 --> 00:38:47,670
for charities at school.

804
00:38:47,670 --> 00:38:51,318
The paper has a very nice
mix of laboratory outcomes,

805
00:38:51,318 --> 00:38:52,860
sort of, measured
in the field, which

806
00:38:52,860 --> 00:38:55,795
is, sort of, the dictator game
that you're all familiar with.

807
00:38:55,795 --> 00:38:57,420
And field outcomes,
were just, sort of,

808
00:38:57,420 --> 00:39:00,480
trying to collect
real world outcomes

809
00:39:00,480 --> 00:39:02,880
in the sense of like
things that are, perhaps,

810
00:39:02,880 --> 00:39:07,320
somewhat less contrived from the
perspective of kids in school.

811
00:39:07,320 --> 00:39:09,240
And what's very nice
about the study is

812
00:39:09,240 --> 00:39:12,780
that it seems to be that the
results are very much aligned

813
00:39:12,780 --> 00:39:15,540
between, sort of, these live
outcomes, the dictator game

814
00:39:15,540 --> 00:39:18,250
type outcomes, and
the field outcomes,

815
00:39:18,250 --> 00:39:21,870
which is volunteering for
charity at school in this case.

816
00:39:21,870 --> 00:39:26,010
Second, the author
looks at discrimination

817
00:39:26,010 --> 00:39:27,570
in social interactions.

818
00:39:27,570 --> 00:39:30,780
In particular, he does it
all sports contests and looks

819
00:39:30,780 --> 00:39:33,660
at teammates selection
among these students

820
00:39:33,660 --> 00:39:38,010
and then willingness to attend
play dates with poor students.

821
00:39:38,010 --> 00:39:41,370
Finally, the author also
looks at academic outcomes,

822
00:39:41,370 --> 00:39:44,970
in particular test scores
and disciplinary infractions.

823
00:39:44,970 --> 00:39:47,910
Now why might one want to
look at academic outcomes,

824
00:39:47,910 --> 00:39:51,960
and why we're interested
in social preferences,

825
00:39:51,960 --> 00:39:54,270
there's several
reasons for that.

826
00:39:54,270 --> 00:39:55,920
In particular, an
important reason

827
00:39:55,920 --> 00:39:59,830
here is that if you're
against this type of policy,

828
00:39:59,830 --> 00:40:04,560
I might, sort of, show you
that, oh, adding poor children

829
00:40:04,560 --> 00:40:07,140
might affect the social
preference of rich children

830
00:40:07,140 --> 00:40:08,400
and so on in various ways.

831
00:40:08,400 --> 00:40:10,830
Maybe discretization goes
down, but perhaps it's

832
00:40:10,830 --> 00:40:13,780
the case that all comes at the
cost of academic performance,

833
00:40:13,780 --> 00:40:14,280
right.

834
00:40:14,280 --> 00:40:16,405
If you're trying to, sort
of, implement this policy

835
00:40:16,405 --> 00:40:23,100
and persuade policymakers, maybe
teachers or parents of like,

836
00:40:23,100 --> 00:40:27,690
OK, let's have more poor
students in your school.

837
00:40:27,690 --> 00:40:29,640
Well, parents might,
sort of, be OK fine.

838
00:40:29,640 --> 00:40:32,310
There's going to be like some
change in social preferences,

839
00:40:32,310 --> 00:40:34,110
some change in
discrimination, but really

840
00:40:34,110 --> 00:40:39,390
what we care about is test
scores or discipline at school,

841
00:40:39,390 --> 00:40:42,180
and these poor
kids might not be--

842
00:40:42,180 --> 00:40:44,310
might be worse in
terms of test scores,

843
00:40:44,310 --> 00:40:46,560
and they might have
negative peer effects

844
00:40:46,560 --> 00:40:51,450
in terms of test scores
but also in terms

845
00:40:51,450 --> 00:40:53,400
of disciplinary infractions.

846
00:40:53,400 --> 00:40:55,300
And, sort of, as
a policy question,

847
00:40:55,300 --> 00:40:57,360
then it's really
important to understand

848
00:40:57,360 --> 00:40:59,010
if there are some
benefits in terms

849
00:40:59,010 --> 00:41:02,280
of poor sociology or
reduced discrimination,

850
00:41:02,280 --> 00:41:04,050
do these benefits
come at the cost

851
00:41:04,050 --> 00:41:07,005
of reduced academic performance?

852
00:41:12,093 --> 00:41:14,010
So now what is, in fact,
the policy innovation

853
00:41:14,010 --> 00:41:15,690
in Delhi in 2007.

854
00:41:15,690 --> 00:41:20,340
There was a 20% admissions quota
in private schools introduced

855
00:41:20,340 --> 00:41:22,980
for poor students in some
of these private schools.

856
00:41:22,980 --> 00:41:26,737
There's a household income
cutoff of $2,000 per year.

857
00:41:26,737 --> 00:41:28,320
So these are not,
sort of, the poorest

858
00:41:28,320 --> 00:41:32,820
of the pole of the
students who are qualified.

859
00:41:32,820 --> 00:41:35,760
Schools which received
subsidized land

860
00:41:35,760 --> 00:41:39,870
from the government
were essentially

861
00:41:39,870 --> 00:41:41,820
included in this policy change.

862
00:41:41,820 --> 00:41:44,693
That's over 90% of
elite private schools.

863
00:41:44,693 --> 00:41:46,360
So think of these
elite private schools.

864
00:41:46,360 --> 00:41:50,730
These are really, sort of,
like very rich kids or parents

865
00:41:50,730 --> 00:41:52,013
of these kids.

866
00:41:52,013 --> 00:41:53,430
There's no fees
for the poor kids,

867
00:41:53,430 --> 00:41:57,000
because they would not be able
to afford these fees anyway.

868
00:41:57,000 --> 00:41:58,980
There was importantly
also no tracking.

869
00:41:58,980 --> 00:42:03,870
So it wasn't that high ability
or high performance kids

870
00:42:03,870 --> 00:42:05,310
would do very well
where they like

871
00:42:05,310 --> 00:42:08,310
tracked into the high tracks
and the good tracks verses

872
00:42:08,310 --> 00:42:09,540
lower tracks.

873
00:42:09,540 --> 00:42:11,730
Instead everybody
was mixed together,

874
00:42:11,730 --> 00:42:15,600
poor and rich, high performing,
low performing students.

875
00:42:15,600 --> 00:42:18,720
And the poor kids were
selected using lotteries,

876
00:42:18,720 --> 00:42:21,600
which in principle also allows
the author or others to look

877
00:42:21,600 --> 00:42:24,330
at the effective on those
poor kids, the students who

878
00:42:24,330 --> 00:42:25,860
are selected versus not.

879
00:42:25,860 --> 00:42:29,040
To be clear this is not
the subject of this paper.

880
00:42:29,040 --> 00:42:33,900
Just to give you a sense of the
magnitude of like the mixing,

881
00:42:33,900 --> 00:42:38,400
you see is that the average
beneficiary was a, sort of,

882
00:42:38,400 --> 00:42:45,690
at the 25th percentile of the
income distribution in Delhi,

883
00:42:45,690 --> 00:42:51,130
and wealthy students are
very much on the right tail--

884
00:42:51,130 --> 00:42:54,540
sorry on the right tail of
the distribution, something

885
00:42:54,540 --> 00:42:58,170
like the 90th percentile,
95th, which is about--

886
00:42:58,170 --> 00:43:02,410
the US equivalent of that would
be about $200,000 per year.

887
00:43:02,410 --> 00:43:06,420
The average beneficiary
had like the US equivalent

888
00:43:06,420 --> 00:43:08,340
income of like $23,000.

889
00:43:08,340 --> 00:43:10,530
Again, these are not
the poorest of the pole

890
00:43:10,530 --> 00:43:14,460
but relatively poor as about
like an order of magnitude

891
00:43:14,460 --> 00:43:19,230
poorer than the average
person in the actual school.

892
00:43:19,230 --> 00:43:23,640
Now this policy then
induced large variation

893
00:43:23,640 --> 00:43:25,260
across classrooms.

894
00:43:25,260 --> 00:43:30,150
If you look at poor students
in the fraction of the numbers

895
00:43:30,150 --> 00:43:33,810
of poor students in
by grade in 2011,

896
00:43:33,810 --> 00:43:38,010
I told you the policy
innovation was in 2007.

897
00:43:38,010 --> 00:43:42,540
So if you look at 2011--
that's four years later--

898
00:43:42,540 --> 00:43:47,100
anybody who is in
grade four in 2011

899
00:43:47,100 --> 00:43:51,420
has essentially no poor
student in that class

900
00:43:51,420 --> 00:43:53,310
in those rich schools.

901
00:43:53,310 --> 00:43:57,870
So for students who were in 2011
in grade four, five, or six.

902
00:43:57,870 --> 00:43:59,610
That policy came too late.

903
00:43:59,610 --> 00:44:02,730
They did not have
any poor classmates.

904
00:44:02,730 --> 00:44:07,140
In contrast, if you look
at the lower grades,

905
00:44:07,140 --> 00:44:10,430
grades 3, 2, 1, and 0,
which essentially preschool

906
00:44:10,430 --> 00:44:13,500
or minus 1, which
is preschool grades,

907
00:44:13,500 --> 00:44:15,660
there's lots of
additional poor students

908
00:44:15,660 --> 00:44:19,750
now in those rich schools.

909
00:44:19,750 --> 00:44:22,560
Now again, there's
variation within schools

910
00:44:22,560 --> 00:44:23,490
and across schools.

911
00:44:23,490 --> 00:44:26,190
The variation within schools
is across classrooms.

912
00:44:26,190 --> 00:44:29,190
That's, kind of, what
I'm showing you here.

913
00:44:29,190 --> 00:44:31,883
But it's also a
variation across schools.

914
00:44:31,883 --> 00:44:33,300
So just to be
clear, the variation

915
00:44:33,300 --> 00:44:39,030
within schools across classrooms
is like in the treated schools

916
00:44:39,030 --> 00:44:41,850
there's some classes like
the fourth grade have

917
00:44:41,850 --> 00:44:45,090
like essentially zero poor
kids in their classes,

918
00:44:45,090 --> 00:44:48,210
and the third
grade, in contrast,

919
00:44:48,210 --> 00:44:52,390
have lots of poor
kids in the classroom.

920
00:44:52,390 --> 00:44:55,080
They can do that comparison
across classrooms

921
00:44:55,080 --> 00:44:56,340
within school.

922
00:44:56,340 --> 00:44:59,040
In addition, has also
variation across schools.

923
00:44:59,040 --> 00:45:01,920
There is, in particular,
there's treatment schools, which

924
00:45:01,920 --> 00:45:04,590
comply in 2007 as
they were supposed to,

925
00:45:04,590 --> 00:45:07,200
but then there's also
delayed treatment schools,

926
00:45:07,200 --> 00:45:08,940
which complied in 2008.

927
00:45:08,940 --> 00:45:11,100
So the jump shifted
by one cohort.

928
00:45:11,100 --> 00:45:13,350
So the cohort, the jump
that I showed you here,

929
00:45:13,350 --> 00:45:17,820
this is for the treatment
schools that complied

930
00:45:17,820 --> 00:45:20,580
or this for everybody
that complied in 2007.

931
00:45:20,580 --> 00:45:22,540
The delayed one
complied a year later,

932
00:45:22,540 --> 00:45:26,820
so you'll see them essentially,
somewhat later increasing--

933
00:45:26,820 --> 00:45:28,590
or one year later
exactly increasing

934
00:45:28,590 --> 00:45:31,020
the fraction of poor
kids in their classroom.

935
00:45:31,020 --> 00:45:32,970
And then there is
the control schools

936
00:45:32,970 --> 00:45:36,630
that were not subject to the
policy, at least until 2013.

937
00:45:36,630 --> 00:45:39,625
So they had received--

938
00:45:39,625 --> 00:45:42,000
they had received land from
federal government or private

939
00:45:42,000 --> 00:45:44,140
foundations.

940
00:45:44,140 --> 00:45:46,410
So essentially there
was no treatment.

941
00:45:46,410 --> 00:45:50,700
That is all students are rich
in these types of classes.

942
00:45:50,700 --> 00:45:53,630
So that is-- the land
receiving is essentially just--

943
00:45:53,630 --> 00:45:57,180
that was like a fairly
arbitrary rule in some ways.

944
00:45:57,180 --> 00:46:00,470
So that, sort of, leads to at
least quasi random variation.

945
00:46:00,470 --> 00:46:07,280
And some schools received the
treatment, and others did not.

946
00:46:07,280 --> 00:46:09,330
Now let me show
you some outcomes.

947
00:46:09,330 --> 00:46:11,960
So one outcome is our friend
the dictator game which you have

948
00:46:11,960 --> 00:46:15,350
seen quite a bit and played
already as well is students are

949
00:46:15,350 --> 00:46:18,860
allowed with 10 rupees,
which is not very much--

950
00:46:18,860 --> 00:46:21,380
that's about like $0.15--

951
00:46:21,380 --> 00:46:23,540
and they choose to share--

952
00:46:23,540 --> 00:46:25,760
notice that these are
pretty young kids,

953
00:46:25,760 --> 00:46:28,040
so 10 rupees is quite a
bit of money for them.

954
00:46:28,040 --> 00:46:33,170
They choose to share an amount
as before between 0 and 10.

955
00:46:33,170 --> 00:46:36,203
And Gautam sets this up
such that students could--

956
00:46:36,203 --> 00:46:38,120
it was not just about
the money, but they also

957
00:46:38,120 --> 00:46:41,850
can exchange the money for candy
later, add 1 rupee per piece.

958
00:46:41,850 --> 00:46:45,190
So that was a pretty good deal.

959
00:46:45,190 --> 00:46:48,490
Then the dictator games for
the order was randomized.

960
00:46:48,490 --> 00:46:50,350
The kids would play two games.

961
00:46:50,350 --> 00:46:53,800
Again, so all rich
kids would do that.

962
00:46:53,800 --> 00:46:57,340
They play first game one where
the recipient is a poor student

963
00:46:57,340 --> 00:46:59,170
in a school for poor children.

964
00:46:59,170 --> 00:47:01,990
And in game 2,
it's a rich student

965
00:47:01,990 --> 00:47:03,880
in a private control school.

966
00:47:03,880 --> 00:47:07,750
Notice it's a control
school to be clear,

967
00:47:07,750 --> 00:47:10,960
so it's not a kid
that they might know.

968
00:47:10,960 --> 00:47:15,430
So the kids are chosen
such that the kids were

969
00:47:15,430 --> 00:47:18,640
given the names and the
photographs of the school shown

970
00:47:18,640 --> 00:47:20,440
to the subject, to the kids.

971
00:47:20,440 --> 00:47:21,850
So you could sort of see--

972
00:47:21,850 --> 00:47:23,470
and this is verified
in the briefing

973
00:47:23,470 --> 00:47:26,710
by the author-- is that
the children understand

974
00:47:26,710 --> 00:47:30,370
very well who is a rich
kid and who's a poor kid,

975
00:47:30,370 --> 00:47:33,290
but these are not kids that
they would actually know.

976
00:47:33,290 --> 00:47:35,290
And something that's a
less interesting question

977
00:47:35,290 --> 00:47:38,530
if you have gone to school
with somebody for a long time,

978
00:47:38,530 --> 00:47:41,740
you might have seen them
around in your school yard,

979
00:47:41,740 --> 00:47:44,450
you might be nicer to
them or less nice to them.

980
00:47:44,450 --> 00:47:46,330
That's not what the
study is looking at.

981
00:47:46,330 --> 00:47:50,200
The study is looking at
other kids in other schools

982
00:47:50,200 --> 00:47:53,530
that look rich or look
poor both in terms

983
00:47:53,530 --> 00:47:56,930
of the way they're
dressed and so on,

984
00:47:56,930 --> 00:47:59,570
but in particular the way
their school looks like.

985
00:47:59,570 --> 00:48:01,085
So essentially these are--

986
00:48:01,085 --> 00:48:02,710
now we're looking at
social preferences

987
00:48:02,710 --> 00:48:07,310
towards other people, people
that these kids don't know.

988
00:48:07,310 --> 00:48:09,410
OK, so now what do we see.

989
00:48:09,410 --> 00:48:11,570
So this is a
generosity to the pole.

990
00:48:11,570 --> 00:48:14,690
So like this is essentially the
results from game number one.

991
00:48:14,690 --> 00:48:18,500
Again, remember these are all
rich kids who are giving now

992
00:48:18,500 --> 00:48:22,190
to poor kids, and the question
now is being exposed to

993
00:48:22,190 --> 00:48:27,860
or does being exposed to poor
children for several years,

994
00:48:27,860 --> 00:48:32,480
does that affect giving
in the form of dictator

995
00:48:32,480 --> 00:48:36,230
games towards poor
children to start with.

996
00:48:36,230 --> 00:48:39,270
And we're going to talk about
rich students after that.

997
00:48:39,270 --> 00:48:43,430
So here you see the generosity
to the poor in control schools.

998
00:48:43,430 --> 00:48:47,450
You see this is the percent
to the poor recipient.

999
00:48:47,450 --> 00:48:50,340
It's remarkably similar to
what we've seen previously,

1000
00:48:50,340 --> 00:48:52,120
which is about 20% to 30%.

1001
00:48:52,120 --> 00:48:56,570
About 25% is given to the
pole kid in the control

1002
00:48:56,570 --> 00:48:59,930
groups in the control schools.

1003
00:48:59,930 --> 00:49:04,190
This fraction seems to trend
up a little bit over time,

1004
00:49:04,190 --> 00:49:07,370
but it's also quite constant
over time across grades.

1005
00:49:07,370 --> 00:49:10,970
You see here by grades
two, three, four, and five

1006
00:49:10,970 --> 00:49:11,990
on the x-axis.

1007
00:49:11,990 --> 00:49:15,320
On the y-axis, it's the percent
given to the poor recipients.

1008
00:49:15,320 --> 00:49:17,240
Now adding the
treatment schools,

1009
00:49:17,240 --> 00:49:20,600
now we find that
for two grades--

1010
00:49:20,600 --> 00:49:23,030
for grade two and grade
three, remember the graph

1011
00:49:23,030 --> 00:49:24,940
that I showed you here.

1012
00:49:24,940 --> 00:49:27,610
You see that in grades
three, two, one,

1013
00:49:27,610 --> 00:49:29,200
and so on, these
are the kids who

1014
00:49:29,200 --> 00:49:33,130
are exposed to the poor
children in class in contrast

1015
00:49:33,130 --> 00:49:35,410
to grades four and five
that have not been exposed

1016
00:49:35,410 --> 00:49:37,540
to poor kids in their class.

1017
00:49:37,540 --> 00:49:41,620
So what this graph shows
is that exactly the grades

1018
00:49:41,620 --> 00:49:44,470
three and two that
have been exposed--

1019
00:49:44,470 --> 00:49:46,000
in the treatment
groups that have

1020
00:49:46,000 --> 00:49:48,280
been exposed to poor
classmates, these

1021
00:49:48,280 --> 00:49:55,030
are exactly the students, the
grades where giving is higher

1022
00:49:55,030 --> 00:49:58,690
towards the poor compared
to the control groups

1023
00:49:58,690 --> 00:50:02,710
but also compared to the
older cohorts four and five,

1024
00:50:02,710 --> 00:50:04,090
the grade four and five.

1025
00:50:04,090 --> 00:50:07,070
When you look at
grades four and five,

1026
00:50:07,070 --> 00:50:09,320
they see essentially no
difference between treatment

1027
00:50:09,320 --> 00:50:10,640
and control schools.

1028
00:50:10,640 --> 00:50:12,530
So they seem to be very similar.

1029
00:50:12,530 --> 00:50:15,170
In contrast, if you
look at grades three

1030
00:50:15,170 --> 00:50:18,770
and through where the
treatment kids are exposed

1031
00:50:18,770 --> 00:50:22,430
to the treatment by having the
poor kids in their classrooms,

1032
00:50:22,430 --> 00:50:27,020
these are exactly the years
where giving goes up and goes

1033
00:50:27,020 --> 00:50:27,890
up by quite a bit.

1034
00:50:27,890 --> 00:50:31,730
While the average was, as
I said before, about 25%,

1035
00:50:31,730 --> 00:50:34,010
that goes up to about 35%.

1036
00:50:34,010 --> 00:50:36,455
So that's a pretty
large relative increase.

1037
00:50:39,650 --> 00:50:42,530
Now in addition, as
I told you, there

1038
00:50:42,530 --> 00:50:45,800
are also delayed treatment
groups, treatment schools.

1039
00:50:45,800 --> 00:50:48,968
For those schools, if
you were in grade three,

1040
00:50:48,968 --> 00:50:51,260
it was also too late for you,
because the treatment was

1041
00:50:51,260 --> 00:50:53,490
introduced only a year later.

1042
00:50:53,490 --> 00:50:55,850
So if you were in
grade three, you

1043
00:50:55,850 --> 00:50:58,880
did not have any poor
kids in your school.

1044
00:50:58,880 --> 00:51:00,800
Now if you look at
the yellow line here,

1045
00:51:00,800 --> 00:51:03,620
the yellow line looks very
much like the green line.

1046
00:51:03,620 --> 00:51:06,200
That is for the delay
treatment schools,

1047
00:51:06,200 --> 00:51:08,180
there's no impact either.

1048
00:51:08,180 --> 00:51:11,360
They look very much like the
control group of schools,

1049
00:51:11,360 --> 00:51:13,710
as you would expect.

1050
00:51:13,710 --> 00:51:17,960
However, in the delayed
treatment schools

1051
00:51:17,960 --> 00:51:22,460
in grade number two,
that's the grade where

1052
00:51:22,460 --> 00:51:23,640
the students are exposed--

1053
00:51:23,640 --> 00:51:26,450
the rich students are exposed
to the poor kids using

1054
00:51:26,450 --> 00:51:31,340
the essentially similar effects
to the treatment schools,

1055
00:51:31,340 --> 00:51:32,520
as you expect.

1056
00:51:32,520 --> 00:51:34,070
So that's fairly
compelling evidence

1057
00:51:34,070 --> 00:51:39,230
that really the differences that
you see across these schools

1058
00:51:39,230 --> 00:51:43,790
are driven by the
treatment that was--

1059
00:51:43,790 --> 00:51:48,770
the timing of the treatment
as opposed to other potential

1060
00:51:48,770 --> 00:51:52,100
effects due to like selection
or maybe these schools

1061
00:51:52,100 --> 00:51:53,390
are different and so on.

1062
00:51:53,390 --> 00:51:55,070
We see essentially
no differences

1063
00:51:55,070 --> 00:51:58,650
in grades four and five for any
of the treatment and control

1064
00:51:58,650 --> 00:51:59,330
schools.

1065
00:51:59,330 --> 00:52:02,150
You also see no
difference in grades three

1066
00:52:02,150 --> 00:52:03,890
for the delayed
treatment schools

1067
00:52:03,890 --> 00:52:05,430
compared to the control group.

1068
00:52:05,430 --> 00:52:07,970
In contrast, we see
clear differences

1069
00:52:07,970 --> 00:52:11,180
in grades two for the delay
in the treatment school as

1070
00:52:11,180 --> 00:52:12,770
compared to the control schools.

1071
00:52:12,770 --> 00:52:16,040
We also see a clear difference
for the treatment schools

1072
00:52:16,040 --> 00:52:19,280
compared to both the control
schools and the delayed

1073
00:52:19,280 --> 00:52:22,650
treatment schools as well.

1074
00:52:22,650 --> 00:52:25,400
So that's fairly
compelling evidence

1075
00:52:25,400 --> 00:52:28,550
that game or play,
dictator games

1076
00:52:28,550 --> 00:52:32,690
changes when
students are exposed

1077
00:52:32,690 --> 00:52:37,750
for quite a long time to
poor kids in their classroom.

1078
00:52:37,750 --> 00:52:41,830
OK so now in
addition, we will have

1079
00:52:41,830 --> 00:52:44,270
variation within classroom.

1080
00:52:44,270 --> 00:52:46,690
So what I show you here,
this is all variation

1081
00:52:46,690 --> 00:52:48,400
across classrooms.

1082
00:52:48,400 --> 00:52:51,640
That is to say, if you have
a poor kid in your classroom,

1083
00:52:51,640 --> 00:52:55,760
you will behave differently, or
that will change your attitude

1084
00:52:55,760 --> 00:52:58,840
toward your giving
and dictator games

1085
00:52:58,840 --> 00:53:03,370
towards the poor or
other poor children.

1086
00:53:03,370 --> 00:53:06,490
Now, in addition,
the author also

1087
00:53:06,490 --> 00:53:10,750
has evidence of variation
within classroom.

1088
00:53:10,750 --> 00:53:12,500
Now, he lets you think
about for a second,

1089
00:53:12,500 --> 00:53:15,590
why is it important to also
have variation within classroom?

1090
00:53:15,590 --> 00:53:18,410
Isn't the evidence that we have
here already compelling enough?

1091
00:53:18,410 --> 00:53:20,643
Why do we need more?

1092
00:53:20,643 --> 00:53:22,310
And you can think
about it for a second.

1093
00:53:29,530 --> 00:53:33,250
The reason for that is that the
diff-in-diff, the difference

1094
00:53:33,250 --> 00:53:36,790
in differences comparing
essentially across classrooms,

1095
00:53:36,790 --> 00:53:39,190
identifies the overall
effective having

1096
00:53:39,190 --> 00:53:41,380
poor classmates in class.

1097
00:53:41,380 --> 00:53:44,920
That might be the result
of personal interactions.

1098
00:53:44,920 --> 00:53:48,160
But it might also be the
result of the teacher.

1099
00:53:48,160 --> 00:53:49,390
The curriculum might change.

1100
00:53:49,390 --> 00:53:52,010
Your parents might change in
some ways in just telling you,

1101
00:53:52,010 --> 00:53:52,510
look.

1102
00:53:52,510 --> 00:53:54,460
There's these poor
kids in your class.

1103
00:53:54,460 --> 00:53:56,350
The teachers might
tell everybody, oh, you

1104
00:53:56,350 --> 00:53:58,120
need to be nice to poor kids.

1105
00:53:58,120 --> 00:53:59,320
The curriculum might change.

1106
00:53:59,320 --> 00:54:00,820
It might sort of
include things that

1107
00:54:00,820 --> 00:54:02,770
tell you to be nice to
poor kids and so on.

1108
00:54:02,770 --> 00:54:04,937
You might be sort of exposed
to just different types

1109
00:54:04,937 --> 00:54:05,860
of material.

1110
00:54:05,860 --> 00:54:08,050
So really, given the
evidence that we have here,

1111
00:54:08,050 --> 00:54:10,480
when you just compare
across classrooms,

1112
00:54:10,480 --> 00:54:14,170
you cannot disentangle whether
this is driven from being

1113
00:54:14,170 --> 00:54:18,430
personally exposed in terms of
interacting with poor children

1114
00:54:18,430 --> 00:54:20,830
personally by just talking
to them in certain ways,

1115
00:54:20,830 --> 00:54:24,290
or being exposed to them, or
learning about them and so on.

1116
00:54:24,290 --> 00:54:27,820
So you cannot disentangle
personal interactions from

1117
00:54:27,820 --> 00:54:30,430
teacher or curriculum
changes that are essentially

1118
00:54:30,430 --> 00:54:32,960
at the classroom level.

1119
00:54:32,960 --> 00:54:35,320
So in addition,
then, Gautam also

1120
00:54:35,320 --> 00:54:40,360
has a within-classroom
strategy that

1121
00:54:40,360 --> 00:54:43,180
exploits the assignments
of study groups.

1122
00:54:43,180 --> 00:54:44,860
What is that doing
is essentially,

1123
00:54:44,860 --> 00:54:48,190
it isolates the role of
direct personal interactions

1124
00:54:48,190 --> 00:54:50,630
and is not subject
to sorting concerns.

1125
00:54:50,630 --> 00:54:53,260
Another concern might be
that you might sort of say,

1126
00:54:53,260 --> 00:54:57,820
oh, well, in this
school, they're

1127
00:54:57,820 --> 00:55:01,000
having poor kids
that are allowed

1128
00:55:01,000 --> 00:55:02,260
into this private school.

1129
00:55:02,260 --> 00:55:05,120
I'm going to send my
kids to somebody else.

1130
00:55:05,120 --> 00:55:10,150
So that's also an issue perhaps
potentially in the evidence

1131
00:55:10,150 --> 00:55:12,250
that I showed you before
across classrooms.

1132
00:55:12,250 --> 00:55:14,080
The within-classroom
strategy is now

1133
00:55:14,080 --> 00:55:17,170
looking at everybody
who is in that classroom

1134
00:55:17,170 --> 00:55:22,900
and looking at some kids
were just randomly assigned

1135
00:55:22,900 --> 00:55:25,810
to be in study groups with
poor kids, and others were not.

1136
00:55:25,810 --> 00:55:28,240
And that sort of allowed
us to disentangle

1137
00:55:28,240 --> 00:55:30,085
or allows us to
isolate the effect

1138
00:55:30,085 --> 00:55:32,870
of personal interactions.

1139
00:55:32,870 --> 00:55:36,670
So one hour a day, kids
were working in small groups

1140
00:55:36,670 --> 00:55:38,530
of 2 to 4 students.

1141
00:55:38,530 --> 00:55:41,200
Remember, this was for a
very long period of time.

1142
00:55:41,200 --> 00:55:43,450
So it's one hour per
day, but it was also

1143
00:55:43,450 --> 00:55:44,450
for quite a bit of time.

1144
00:55:44,450 --> 00:55:46,090
It's not just doing
that once or twice.

1145
00:55:46,090 --> 00:55:48,610
It's more an extended
period of time.

1146
00:55:48,610 --> 00:55:51,760
And crucially, some schools
use the alphabetic order

1147
00:55:51,760 --> 00:55:54,450
of first names to
assign study groups.

1148
00:55:54,450 --> 00:55:56,350
So that's essentially
exogenous variation

1149
00:55:56,350 --> 00:55:58,090
in personal interactions.

1150
00:55:58,090 --> 00:56:04,120
Other schools frequently
shuffled the groups and only--

1151
00:56:04,120 --> 00:56:08,020
which essentially sort of--

1152
00:56:08,020 --> 00:56:11,080
everybody was equally
exposed to those kinds of--

1153
00:56:11,080 --> 00:56:13,300
to different students.

1154
00:56:13,300 --> 00:56:16,270
So you are sometimes in study
groups with rich students

1155
00:56:16,270 --> 00:56:18,760
only and sometimes with
poor students only.

1156
00:56:18,760 --> 00:56:23,170
In the first group where the
alphabetic order was used,

1157
00:56:23,170 --> 00:56:25,600
you were either sort
of in the study group

1158
00:56:25,600 --> 00:56:32,170
where you happened to be
next to somebody who's

1159
00:56:32,170 --> 00:56:34,360
a poor kid because they
happened to be next to you

1160
00:56:34,360 --> 00:56:38,000
in the alphabet, or you're
not, and that stayed the same.

1161
00:56:38,000 --> 00:56:40,370
Now, what does this
let us look at?

1162
00:56:40,370 --> 00:56:44,190
Essentially, the alphabetical
order predicts study partners.

1163
00:56:44,190 --> 00:56:46,790
So now you might say people
with different names might also

1164
00:56:46,790 --> 00:56:47,520
be different.

1165
00:56:47,520 --> 00:56:49,400
So it could just be
that like my name--

1166
00:56:49,400 --> 00:56:51,350
because my name
is different, I'm

1167
00:56:51,350 --> 00:56:55,890
more likely to be next in the
alphabet to a poor student.

1168
00:56:55,890 --> 00:56:58,250
For example, if I'm
a rich kid and there

1169
00:56:58,250 --> 00:57:02,000
are some names that are more
similar to poor kids' names,

1170
00:57:02,000 --> 00:57:04,610
it could be that my parents
are particularly tolerant.

1171
00:57:04,610 --> 00:57:09,050
They also gave me a name that
sounds like a poor person.

1172
00:57:09,050 --> 00:57:11,510
And therefore, I'm more
likely to be in a study group

1173
00:57:11,510 --> 00:57:14,420
with the poor kids.

1174
00:57:14,420 --> 00:57:16,910
And that's not the impact
of being in the study group

1175
00:57:16,910 --> 00:57:18,170
or rather sort of selection.

1176
00:57:18,170 --> 00:57:20,075
It could be that
names are different

1177
00:57:20,075 --> 00:57:22,440
or people with different
names are different.

1178
00:57:22,440 --> 00:57:25,050
So that's why having this
control group is really nice.

1179
00:57:25,050 --> 00:57:26,900
So what we can look
at now is to say,

1180
00:57:26,900 --> 00:57:33,200
you can look at kids that have
names adjacent to rich students

1181
00:57:33,200 --> 00:57:36,810
and kids that have names
adjacent to poor students.

1182
00:57:36,810 --> 00:57:39,095
So if you look at
the left side, this

1183
00:57:39,095 --> 00:57:41,060
is essentially
looking at students

1184
00:57:41,060 --> 00:57:45,890
that have their names
adjacent to rich students only

1185
00:57:45,890 --> 00:57:48,350
or students that have
names adjacent to also

1186
00:57:48,350 --> 00:57:49,670
some poor students.

1187
00:57:49,670 --> 00:57:53,460
When you look at that, where the
alphabetic order was not used,

1188
00:57:53,460 --> 00:57:55,400
the outcome here is
what's your share

1189
00:57:55,400 --> 00:57:56,833
of having poor study partners?

1190
00:57:56,833 --> 00:57:58,250
And so there,
essentially, there's

1191
00:57:58,250 --> 00:58:01,490
no difference, as it should be,
because the alphabetic order

1192
00:58:01,490 --> 00:58:02,210
was not used.

1193
00:58:02,210 --> 00:58:04,040
There was a shuffling
around the kids

1194
00:58:04,040 --> 00:58:05,630
all the time, so
it didn't really

1195
00:58:05,630 --> 00:58:07,040
matter what your alphabet was.

1196
00:58:07,040 --> 00:58:08,040
It didn't really matter.

1197
00:58:08,040 --> 00:58:10,370
But crucially, we can
still look at the alphabet.

1198
00:58:10,370 --> 00:58:14,787
We can look at kids
that have names

1199
00:58:14,787 --> 00:58:16,370
where, Jason, in the
alphabet, there's

1200
00:58:16,370 --> 00:58:18,800
only rich kids, rich students.

1201
00:58:18,800 --> 00:58:22,160
Or they can look
at kids that have

1202
00:58:22,160 --> 00:58:24,380
names where, adjacent
to the alphabets,

1203
00:58:24,380 --> 00:58:25,650
there are poor students.

1204
00:58:25,650 --> 00:58:27,650
And, here you see on the
left side of the graph,

1205
00:58:27,650 --> 00:58:31,890
you see essentially no
difference across these groups.

1206
00:58:31,890 --> 00:58:35,270
So they were equally likely to
have a poor kid in their study

1207
00:58:35,270 --> 00:58:36,200
group.

1208
00:58:36,200 --> 00:58:38,690
In contrast, on the
right side of the graph,

1209
00:58:38,690 --> 00:58:45,150
you see the alphabetic order
was used to assign study groups.

1210
00:58:45,150 --> 00:58:50,450
So if you had a name adjacent to
a poor student in the alphabet,

1211
00:58:50,450 --> 00:58:54,260
you're very, very likely to
have a poor kid assigned to you

1212
00:58:54,260 --> 00:58:55,400
or be in your study group.

1213
00:58:55,400 --> 00:58:58,440
Not always because sometimes,
these are groups of three.

1214
00:58:58,440 --> 00:59:00,980
So sometimes, you
were lucky or unlucky

1215
00:59:00,980 --> 00:59:02,930
depending on how you view it.

1216
00:59:02,930 --> 00:59:06,350
The group was just above you
or below you in the alphabet.

1217
00:59:06,350 --> 00:59:07,880
But most of the
time, if you have

1218
00:59:07,880 --> 00:59:10,520
a name adjacent to a poor
student, that student

1219
00:59:10,520 --> 00:59:15,290
or any student would
be in your study group.

1220
00:59:15,290 --> 00:59:19,564
If you didn't have a
name adjacent to you or f

1221
00:59:19,564 --> 00:59:21,530
you only had rich
students adjacent to you,

1222
00:59:21,530 --> 00:59:23,030
there's still a
chance that you have

1223
00:59:23,030 --> 00:59:24,380
a poor student in
your study group

1224
00:59:24,380 --> 00:59:26,720
because it could be just like
not the person next to you

1225
00:59:26,720 --> 00:59:31,350
but another person further down
is, in fact, a poor student.

1226
00:59:31,350 --> 00:59:33,920
You end up in that
group with that student.

1227
00:59:33,920 --> 00:59:36,347
Remember, these are
groups of 2 to 4 students.

1228
00:59:36,347 --> 00:59:38,930
So it could just be that you're
in a group of 3 to 4 students,

1229
00:59:38,930 --> 00:59:42,230
and not the person adjacent to
you but the person after that

1230
00:59:42,230 --> 00:59:44,820
ends up in your study group.

1231
00:59:44,820 --> 00:59:47,570
So you're still likely to have--

1232
00:59:47,570 --> 00:59:50,570
you have still like about
like a 40% chance of having

1233
00:59:50,570 --> 00:59:54,500
a poor kid in your study group.

1234
00:59:54,500 --> 00:59:56,600
But there's a huge
difference between the two

1235
00:59:56,600 --> 00:59:58,190
types of groups.

1236
00:59:58,190 --> 01:00:00,710
The name adjacent
to the study group

1237
01:00:00,710 --> 01:00:05,330
has a huge fraction,
about 90%, versus 40%.

1238
01:00:05,330 --> 01:00:07,520
So crucially, now,
we can compare

1239
01:00:07,520 --> 01:00:10,040
for both types of
schools, for the schools

1240
01:00:10,040 --> 01:00:13,010
on the right-hand side where,
essentially, the names are very

1241
01:00:13,010 --> 01:00:18,200
predictive of having a poor
kid in your study group,

1242
01:00:18,200 --> 01:00:20,780
versus on the left-hand
side where the names are not

1243
01:00:20,780 --> 01:00:24,920
predicative at all whether you
have a poor kid in your study

1244
01:00:24,920 --> 01:00:25,680
group.

1245
01:00:25,680 --> 01:00:27,350
And so we can--

1246
01:00:27,350 --> 01:00:30,260
that way, we can keep selection
the same, the types of name

1247
01:00:30,260 --> 01:00:31,040
are the same.

1248
01:00:31,040 --> 01:00:33,080
We are isolating
the impact of having

1249
01:00:33,080 --> 01:00:35,540
a poor kid in your study group.

1250
01:00:35,540 --> 01:00:36,920
Now, what does the author find?

1251
01:00:36,920 --> 01:00:39,570
He finds that-- this is
what we showed you before,

1252
01:00:39,570 --> 01:00:43,430
which is in the control
group, we had about,

1253
01:00:43,430 --> 01:00:46,109
in the dictator game,
these kids gave about 27%.

1254
01:00:51,350 --> 01:00:53,060
Having a poor kid
in your classroom.

1255
01:00:53,060 --> 01:00:54,977
That's the evidence that
I already showed you.

1256
01:00:54,977 --> 01:00:58,980
The treatment effect is
about 12 percentage points.

1257
01:00:58,980 --> 01:00:59,940
That's fairly large.

1258
01:00:59,940 --> 01:01:02,900
That's almost like
a 50% increase.

1259
01:01:02,900 --> 01:01:04,290
A little bit less than that.

1260
01:01:04,290 --> 01:01:06,620
So that's a huge increase.

1261
01:01:06,620 --> 01:01:08,960
And then in addition,
when you look

1262
01:01:08,960 --> 01:01:14,330
at kids that have a
poor study partner,

1263
01:01:14,330 --> 01:01:16,580
notice that these are things
not necessarily additive,

1264
01:01:16,580 --> 01:01:19,220
but if you have a poor
study partner versus not,

1265
01:01:19,220 --> 01:01:23,340
there's an effective about
7.5 percentage points.

1266
01:01:23,340 --> 01:01:26,100
That's a pretty large effect.

1267
01:01:26,100 --> 01:01:29,690
So both of these
things seem to matter.

1268
01:01:29,690 --> 01:01:32,240
Personal interactions seem
to be quite important.

1269
01:01:32,240 --> 01:01:33,890
But in addition,
there seem to be also

1270
01:01:33,890 --> 01:01:35,600
some additional
effects perhaps coming

1271
01:01:35,600 --> 01:01:37,640
from the classroom level.

1272
01:01:37,640 --> 01:01:39,740
You might-- so there's
two types of effects

1273
01:01:39,740 --> 01:01:40,940
that are possible here.

1274
01:01:40,940 --> 01:01:44,030
Some types of effects could
be from the teacher, parents,

1275
01:01:44,030 --> 01:01:45,090
curriculum, et cetera.

1276
01:01:45,090 --> 01:01:46,190
Might be different.

1277
01:01:46,190 --> 01:01:47,720
Or other things
could be just like,

1278
01:01:47,720 --> 01:01:50,690
even if you don't have a
poor kid in your study group,

1279
01:01:50,690 --> 01:01:52,940
you might still play with
them or be friends with them

1280
01:01:52,940 --> 01:01:55,880
or just seeing them around in
the classroom might affect you.

1281
01:01:55,880 --> 01:01:59,690
But crucially, the
personal interactions

1282
01:01:59,690 --> 01:02:03,305
seem to be very
important as well.

1283
01:02:03,305 --> 01:02:05,430
Now, one question you might
ask is, well, so far, I

1284
01:02:05,430 --> 01:02:08,490
showed you generosity
towards poor children.

1285
01:02:08,490 --> 01:02:09,690
So that was game number 1.

1286
01:02:09,690 --> 01:02:14,190
We looked at what happens
in the dictator games

1287
01:02:14,190 --> 01:02:16,170
when a rich kid
plays with a poor kid

1288
01:02:16,170 --> 01:02:19,810
and the rich kids become
nicer towards the poor kids.

1289
01:02:19,810 --> 01:02:23,640
Now, how about generosity
towards other wealthy children?

1290
01:02:23,640 --> 01:02:27,000
Well, it turns out that
that increases as well.

1291
01:02:27,000 --> 01:02:29,850
So it's a smaller effect size,
but it's still substantial,

1292
01:02:29,850 --> 01:02:32,830
and, in fact,
statistically significant.

1293
01:02:32,830 --> 01:02:34,230
So why might that be?

1294
01:02:34,230 --> 01:02:37,030
You might sort of ask,
well, what's going on here?

1295
01:02:37,030 --> 01:02:38,790
So here's the evidence.

1296
01:02:38,790 --> 01:02:42,130
You sort of see the change in
giving to rich participants.

1297
01:02:42,130 --> 01:02:43,630
This is sort of
like a distribution.

1298
01:02:43,630 --> 01:02:47,550
You see this is the share
giving to the rich participant.

1299
01:02:47,550 --> 01:02:49,013
It seems to be what happens--

1300
01:02:49,013 --> 01:02:50,430
these are essentially
the fraction

1301
01:02:50,430 --> 01:02:53,190
of students in the different
treatment versus control

1302
01:02:53,190 --> 01:02:56,760
who give 0%, 10%,
20%, 30%, 40%, 50%.

1303
01:02:56,760 --> 01:02:59,850
And what you see-- there is
about a 10 percentage points

1304
01:02:59,850 --> 01:03:04,350
decrease in the fraction
who give 0% and about

1305
01:03:04,350 --> 01:03:07,110
an 8-something percentage
points increase

1306
01:03:07,110 --> 01:03:09,750
in the fraction who give 50%.

1307
01:03:09,750 --> 01:03:12,510
So it seems to be that there's
quite a few students who

1308
01:03:12,510 --> 01:03:14,310
move from 0%.

1309
01:03:14,310 --> 01:03:19,830
About 10% of them
move from 0% to 50%.

1310
01:03:19,830 --> 01:03:23,400
And 50-50 is kind of like
the equal allocation.

1311
01:03:23,400 --> 01:03:26,880
So rich kids also becoming
nicer towards rich kids

1312
01:03:26,880 --> 01:03:28,620
in these dictator games.

1313
01:03:28,620 --> 01:03:31,230
So what's going on here?

1314
01:03:31,230 --> 01:03:33,810
There's different
potential explanations.

1315
01:03:33,810 --> 01:03:37,590
Perhaps the most plausible
explanation-- the study

1316
01:03:37,590 --> 01:03:39,540
has some evidence on that.

1317
01:03:39,540 --> 01:03:42,060
At the end of the day, it's
hard to sort of entirely

1318
01:03:42,060 --> 01:03:47,230
nail this or rule out all
other potential explanation,

1319
01:03:47,230 --> 01:03:50,160
but it's quite plausible
that what's happening here

1320
01:03:50,160 --> 01:03:53,160
is that inequity,
students, when you're

1321
01:03:53,160 --> 01:03:58,530
exposed to poor children, you
are essentially a bit more

1322
01:03:58,530 --> 01:04:05,010
averse to inequality or inequity
across people, even in things,

1323
01:04:05,010 --> 01:04:08,610
in fairly trivial things
such as dictator games.

1324
01:04:08,610 --> 01:04:11,130
Now, I told you before,
that's a little bit funny

1325
01:04:11,130 --> 01:04:13,620
because dictator games
obviously are very narrowly

1326
01:04:13,620 --> 01:04:16,710
framing people and looking
at essentially very

1327
01:04:16,710 --> 01:04:18,900
narrow outcomes.

1328
01:04:18,900 --> 01:04:22,560
If we have, like, a 50/50
outcome in the dictator game,

1329
01:04:22,560 --> 01:04:25,410
that doesn't mean that
our life is the same.

1330
01:04:25,410 --> 01:04:27,660
You might be still
much richer than I am.

1331
01:04:27,660 --> 01:04:30,450
So having sort of equal
outcomes in the dictator,

1332
01:04:30,450 --> 01:04:32,910
the 50-50 allocation
in the dictator game,

1333
01:04:32,910 --> 01:04:35,910
might be narrowly framed, in
that particular game, fair.

1334
01:04:35,910 --> 01:04:38,730
But of course, it's not fair
in the grand scheme of things.

1335
01:04:38,730 --> 01:04:41,180
But what seems to be
the case is that--

1336
01:04:41,180 --> 01:04:43,230
and there's some other
evidence in the paper

1337
01:04:43,230 --> 01:04:45,420
that you can read
if you would like.

1338
01:04:45,420 --> 01:04:49,320
It seems to be the case
that rich children become

1339
01:04:49,320 --> 01:04:53,160
more averse to unequal outcomes
in the world in general

1340
01:04:53,160 --> 01:04:56,220
because they essentially
see these poor kids who

1341
01:04:56,220 --> 01:04:59,160
are very smart and
are disadvantaged

1342
01:04:59,160 --> 01:05:02,190
in terms of various
ways from having

1343
01:05:02,190 --> 01:05:03,780
lower wealth of their parents.

1344
01:05:03,780 --> 01:05:06,750
The rich kids become sort of
more adverse against that,

1345
01:05:06,750 --> 01:05:09,660
and that translates
even into dictator games

1346
01:05:09,660 --> 01:05:13,260
with other rich kids in a
very sort of minor thing

1347
01:05:13,260 --> 01:05:15,250
in the world and in
these dictator games,

1348
01:05:15,250 --> 01:05:17,250
again, even though these
dictator games actually

1349
01:05:17,250 --> 01:05:18,210
don't change very much.

1350
01:05:18,210 --> 01:05:21,550
But they really seem to
be averse to inequality

1351
01:05:21,550 --> 01:05:25,650
of these outcomes and move
them from the 10-0 allocation

1352
01:05:25,650 --> 01:05:31,910
to 50-50, even with
these rich kids.

1353
01:05:31,910 --> 01:05:32,660
OK.

1354
01:05:32,660 --> 01:05:35,240
So that was evidence
on social preferences

1355
01:05:35,240 --> 01:05:37,070
as measured by dictator games.

1356
01:05:37,070 --> 01:05:41,120
But people also had some
evidence on discrimination.

1357
01:05:41,120 --> 01:05:45,210
In particular, a small field
experiment on team selection.

1358
01:05:45,210 --> 01:05:47,390
So what does this study do?

1359
01:05:47,390 --> 01:05:49,790
So its subjects
are, again, students

1360
01:05:49,790 --> 01:05:51,660
from two elite private schools.

1361
01:05:51,660 --> 01:05:55,970
So now, it's like two of these
selected schools are selected.

1362
01:05:55,970 --> 01:05:57,650
One is a treatment school.

1363
01:05:57,650 --> 01:05:59,060
One is a control group.

1364
01:05:59,060 --> 01:06:03,830
And in addition, Gautam
invited athletic poor students

1365
01:06:03,830 --> 01:06:04,790
from a public school.

1366
01:06:04,790 --> 01:06:07,280
Importantly, he said,
athletic students-- these

1367
01:06:07,280 --> 01:06:10,240
are students who are better
at sports than the rich kids.

1368
01:06:10,240 --> 01:06:11,990
You might think the
poorer kids are better

1369
01:06:11,990 --> 01:06:14,940
at sports than the rich kids
anyway, which is probably true.

1370
01:06:14,940 --> 01:06:18,380
But now, these are particularly
athletic students on purpose

1371
01:06:18,380 --> 01:06:22,940
who are invited to attend
a sports event as well.

1372
01:06:22,940 --> 01:06:25,920
Now, students in this
experiment, in this game,

1373
01:06:25,920 --> 01:06:29,570
must choose teammates
to run a relay race.

1374
01:06:29,570 --> 01:06:35,480
Now, when you're a rich kid
who is thinking about who

1375
01:06:35,480 --> 01:06:37,310
should be in my
team, you can either

1376
01:06:37,310 --> 01:06:41,780
choose a rich kid who is
kind of like similar to you

1377
01:06:41,780 --> 01:06:46,730
in social ways, or you
can choose a poor kid

1378
01:06:46,730 --> 01:06:48,110
who you don't know very much.

1379
01:06:48,110 --> 01:06:50,660
You might actually not
like the poor kids.

1380
01:06:50,660 --> 01:06:54,840
But the poor kid is a lot
better in the running,

1381
01:06:54,840 --> 01:06:56,990
so it might be much
better for you.

1382
01:06:56,990 --> 01:06:59,150
You might be much more
likely to win in the game

1383
01:06:59,150 --> 01:07:02,190
because now your partner in
the relay race is much faster.

1384
01:07:02,190 --> 01:07:04,520
So it's a very nice
trade-off between ability--

1385
01:07:04,520 --> 01:07:08,960
choosing the fast runner
versus social similarity.

1386
01:07:08,960 --> 01:07:12,440
And now, what
Gautam then is doing

1387
01:07:12,440 --> 01:07:16,340
is like, if you sort of
choose the rich kid, that's

1388
01:07:16,340 --> 01:07:19,430
then a measure of discrimination
because essentially, you're

1389
01:07:19,430 --> 01:07:23,870
choosing a worse
runner in favor of

1390
01:07:23,870 --> 01:07:26,660
or because you want
more social similarity.

1391
01:07:26,660 --> 01:07:28,820
You don't want to hang
out with a poor kid.

1392
01:07:28,820 --> 01:07:30,680
Instead, you choose
the rich kid,

1393
01:07:30,680 --> 01:07:32,990
which reduces your
chances in the race

1394
01:07:32,990 --> 01:07:36,350
but increases your time
spent with the rich kids

1395
01:07:36,350 --> 01:07:38,360
compared to poor kids.

1396
01:07:38,360 --> 01:07:41,900
Let me tell you a little bit
more detail of the experiment.

1397
01:07:41,900 --> 01:07:43,220
Stage 1 is randomization.

1398
01:07:43,220 --> 01:07:47,810
So the people were randomized
to sessions with varying stakes.

1399
01:07:47,810 --> 01:07:53,630
There's 50 rupees, 20 rupees,
500 rupees per student

1400
01:07:53,630 --> 01:07:55,340
for the winning team.

1401
01:07:55,340 --> 01:07:58,300
This is a lot of money compared
to students usual pocket money,

1402
01:07:58,300 --> 01:07:58,800
right?

1403
01:07:58,800 --> 01:08:02,450
So they would get something
like $10 or 500 rupees

1404
01:08:02,450 --> 01:08:04,690
is like one month's
of pocket money.

1405
01:08:04,690 --> 01:08:07,370
So that's really high
stakes for these kids.

1406
01:08:07,370 --> 01:08:09,140
The price is
varied, essentially,

1407
01:08:09,140 --> 01:08:10,760
because it lets us price out.

1408
01:08:10,760 --> 01:08:12,770
It gives us a price
of discrimination.

1409
01:08:12,770 --> 01:08:15,620
Lets us understand how much
are students willing to give up

1410
01:08:15,620 --> 01:08:19,500
in order to not have to--

1411
01:08:19,500 --> 01:08:23,450
or in order to be able to
socialize with the rich kids

1412
01:08:23,450 --> 01:08:24,890
compared to the poor kid.

1413
01:08:24,890 --> 01:08:28,250
There was a brief mixing
to start with to judge

1414
01:08:28,250 --> 01:08:30,170
socioeconomic status.

1415
01:08:30,170 --> 01:08:32,810
So the kids were allowed
to mingle a little bit.

1416
01:08:32,810 --> 01:08:36,050
That would allow them to
fairly easily understand

1417
01:08:36,050 --> 01:08:39,109
who was a poor kid
and who's a rich kid.

1418
01:08:39,109 --> 01:08:40,069
OK.

1419
01:08:40,069 --> 01:08:43,670
Stage number 2 was ability
revelation and team selection.

1420
01:08:43,670 --> 01:08:47,029
So you could essentially
observe a two-person race.

1421
01:08:47,029 --> 01:08:49,850
Usually, it's one poor
and one rich students.

1422
01:08:49,850 --> 01:08:51,283
Neither is from the old school.

1423
01:08:51,283 --> 01:08:52,700
So these are not
students that you

1424
01:08:52,700 --> 01:08:56,540
would know from school anyway.

1425
01:08:56,540 --> 01:08:59,149
But the uniforms make
the school identifiable.

1426
01:08:59,149 --> 01:09:01,819
You kind of know who is the
rich kid and who's the poor kid.

1427
01:09:01,819 --> 01:09:05,029
Now, then you can pick
which of these two runners

1428
01:09:05,029 --> 01:09:07,140
you want to have
as your partners.

1429
01:09:07,140 --> 01:09:10,939
So that is to say you
see them run one by one.

1430
01:09:10,939 --> 01:09:13,609
It's very easy to see
who's faster and who's not.

1431
01:09:13,609 --> 01:09:15,290
And so again,
discrimination here

1432
01:09:15,290 --> 01:09:18,600
is interpreted as
picking the slow runner.

1433
01:09:18,600 --> 01:09:22,189
So if you pick
the slower runner,

1434
01:09:22,189 --> 01:09:25,370
then it must be because
you like something

1435
01:09:25,370 --> 01:09:28,250
some other characteristics
about that person more.

1436
01:09:28,250 --> 01:09:29,840
The obvious
characteristic here is

1437
01:09:29,840 --> 01:09:34,380
that it's most likely going
to be that kid is rich.

1438
01:09:34,380 --> 01:09:35,250
OK.

1439
01:09:35,250 --> 01:09:41,189
Then stages 3 and 4 are
the choice implementation

1440
01:09:41,189 --> 01:09:42,270
relay race.

1441
01:09:42,270 --> 01:09:43,896
So students are
randomly picked to have

1442
01:09:43,896 --> 01:09:44,979
their choices implemented.

1443
01:09:44,979 --> 01:09:46,260
So some of those choices--

1444
01:09:46,260 --> 01:09:48,029
this is, again, the
strategy method.

1445
01:09:48,029 --> 01:09:50,790
Some of these choices were
actually randomly implemented.

1446
01:09:50,790 --> 01:09:54,660
So there's plausible
deniability for the students

1447
01:09:54,660 --> 01:10:01,440
in the sense of you could
just happen to be randomized

1448
01:10:01,440 --> 01:10:05,520
or you happen to pick some
students versus another.

1449
01:10:05,520 --> 01:10:07,080
It could just be
by chance that you

1450
01:10:07,080 --> 01:10:09,058
are with one student
versus another.

1451
01:10:09,058 --> 01:10:11,100
So you could-- your freedom
to choose essentially

1452
01:10:11,100 --> 01:10:12,030
provide you cover.

1453
01:10:12,030 --> 01:10:14,070
Sometimes, as we
discussed before,

1454
01:10:14,070 --> 01:10:16,380
the computer is choosing, so
you always have an excuse.

1455
01:10:16,380 --> 01:10:20,280
So it's intended to reveal
student's true preferences as

1456
01:10:20,280 --> 01:10:22,140
opposed to perhaps
what they think

1457
01:10:22,140 --> 01:10:26,190
that their friends want them
to choose or the other runners.

1458
01:10:26,190 --> 01:10:28,020
Then the relay races
were actually held

1459
01:10:28,020 --> 01:10:30,630
and prizes were
distributed as promised.

1460
01:10:30,630 --> 01:10:34,840
Number 4, crucially, there
was a social interaction.

1461
01:10:34,840 --> 01:10:37,770
So if you picked your
teammate, you actually

1462
01:10:37,770 --> 01:10:40,980
had to spend two hours of
playing with a teammate.

1463
01:10:40,980 --> 01:10:43,930
Board games, sports,
playgrounds, and so on.

1464
01:10:43,930 --> 01:10:45,690
Importantly, this
was preannounce.

1465
01:10:45,690 --> 01:10:49,680
So now, again, as I said before,
when picking your partner,

1466
01:10:49,680 --> 01:10:52,350
you have the choice
between either picking

1467
01:10:52,350 --> 01:10:55,230
the fast, poor kid,
which will really

1468
01:10:55,230 --> 01:10:57,960
increase your
probability of winning,

1469
01:10:57,960 --> 01:10:59,820
or the rich kid,
who is kind of slow

1470
01:10:59,820 --> 01:11:03,240
and will reduce your
probability of winning.

1471
01:11:03,240 --> 01:11:05,670
But if you pick that
poor kid or the rich kid,

1472
01:11:05,670 --> 01:11:08,850
you have to actually spend
two hours with that teammate

1473
01:11:08,850 --> 01:11:11,370
playing board games, sports,
playground, and so on,

1474
01:11:11,370 --> 01:11:16,540
and you might not want to
do that with a poor kid.

1475
01:11:16,540 --> 01:11:17,380
OK.

1476
01:11:17,380 --> 01:11:21,310
So now, first, what is the
demand for discrimination?

1477
01:11:21,310 --> 01:11:22,660
You can look at this graph.

1478
01:11:22,660 --> 01:11:25,330
It shows very nicely for
the different prices.

1479
01:11:25,330 --> 01:11:28,840
As I said, 500 rupees for
winning the race, 200 rupees,

1480
01:11:28,840 --> 01:11:30,340
or 500 rupees.

1481
01:11:30,340 --> 01:11:32,440
If you look at 500
rupees, this is again

1482
01:11:32,440 --> 01:11:34,120
one months of pocket money.

1483
01:11:34,120 --> 01:11:36,430
There's no difference
between treated and untreated

1484
01:11:36,430 --> 01:11:37,190
classrooms.

1485
01:11:37,190 --> 01:11:38,800
That is to say--

1486
01:11:38,800 --> 01:11:41,560
so there's 10% of
people are, as you

1487
01:11:41,560 --> 01:11:42,820
want, discriminating the poor.

1488
01:11:42,820 --> 01:11:48,820
So this is less than 10%
is about 7%, 8% of students

1489
01:11:48,820 --> 01:11:55,120
pick the rich kids, even in
the really, really high stakes

1490
01:11:55,120 --> 01:11:55,870
race.

1491
01:11:55,870 --> 01:11:58,360
That is to say, when the
stakes are 500 rupees,

1492
01:11:58,360 --> 01:12:00,970
there are, like,
about 6%, 7%, 8%

1493
01:12:00,970 --> 01:12:04,210
of students who still
pick the rich kids,

1494
01:12:04,210 --> 01:12:06,130
and they sort of
take into account

1495
01:12:06,130 --> 01:12:11,800
the chance that that
might lose them the race.

1496
01:12:11,800 --> 01:12:16,450
But they don't have to spend
two hours with a poor kid then

1497
01:12:16,450 --> 01:12:17,710
socializing.

1498
01:12:17,710 --> 01:12:20,560
Now, when you look at the
lower prices, the fraction,

1499
01:12:20,560 --> 01:12:23,020
as you expect-- this
is the red line.

1500
01:12:23,020 --> 01:12:25,960
Sorry, this is the green
line, the upper line.

1501
01:12:25,960 --> 01:12:28,270
The fraction who
choose the rich kids,

1502
01:12:28,270 --> 01:12:29,770
the fraction who
were discriminating

1503
01:12:29,770 --> 01:12:33,360
against the poor,
increases as you expect.

1504
01:12:33,360 --> 01:12:35,200
So now, it becomes cheaper.

1505
01:12:35,200 --> 01:12:37,990
The race is only 200
rupees or 50 rupees.

1506
01:12:37,990 --> 01:12:41,110
You might be more inclined to
pick the rich kid because you

1507
01:12:41,110 --> 01:12:43,510
know the value of
socializing stays the same,

1508
01:12:43,510 --> 01:12:45,520
but the costs of
picking the rich kid,

1509
01:12:45,520 --> 01:12:47,680
the cost of losing
the race, potentially

1510
01:12:47,680 --> 01:12:51,760
at least is reduced.

1511
01:12:51,760 --> 01:12:53,620
If you look at 50
rupees, the price

1512
01:12:53,620 --> 01:12:59,890
of 50 rupees for the game,
for the relay race, that's

1513
01:12:59,890 --> 01:13:03,460
about almost 40% of students
now pick the rich kid,

1514
01:13:03,460 --> 01:13:05,650
even though that might
lose them the race.

1515
01:13:05,650 --> 01:13:09,400
And now, crucially, in
red, the dashed line

1516
01:13:09,400 --> 01:13:11,920
below, you can see the
treated classrooms.

1517
01:13:11,920 --> 01:13:14,860
And what he finds is
that for 500 rupees, when

1518
01:13:14,860 --> 01:13:17,518
the stakes are really, really
high, there's no effect.

1519
01:13:17,518 --> 01:13:19,060
Essentially, it
doesn't really matter

1520
01:13:19,060 --> 01:13:21,393
whether you are in a treated
or a not treated classroom,

1521
01:13:21,393 --> 01:13:25,060
in part perhaps because there's
not much room for going lower

1522
01:13:25,060 --> 01:13:25,940
than that.

1523
01:13:25,940 --> 01:13:29,382
So essentially, there's
no effect on there

1524
01:13:29,382 --> 01:13:31,840
because you know the stakes
are really, really high anyway.

1525
01:13:31,840 --> 01:13:33,460
There's not much discrimination.

1526
01:13:33,460 --> 01:13:35,555
And having had a treated--

1527
01:13:35,555 --> 01:13:37,180
having had a poor
kid in your classroom

1528
01:13:37,180 --> 01:13:39,220
doesn't really change that.

1529
01:13:39,220 --> 01:13:42,820
But then, very clearly, for 250
rupees and 50 rupees, the lower

1530
01:13:42,820 --> 01:13:45,670
stakes, there's a
clear difference

1531
01:13:45,670 --> 01:13:48,050
between the green
and the red lines.

1532
01:13:48,050 --> 01:13:51,400
That is to say, there's
a lot less discrimination

1533
01:13:51,400 --> 01:13:52,780
towards the poor kids.

1534
01:13:52,780 --> 01:13:54,970
Poor kids are a lot
more likely to be

1535
01:13:54,970 --> 01:14:00,430
chosen when a student
has-- in treated classrooms

1536
01:14:00,430 --> 01:14:03,070
when somebody had a poor
kid, another poor kid

1537
01:14:03,070 --> 01:14:05,480
in that classroom
for several years.

1538
01:14:05,480 --> 01:14:07,840
So that's to say there's--

1539
01:14:07,840 --> 01:14:09,880
being exposed to
these poor children

1540
01:14:09,880 --> 01:14:14,740
reduces discrimination among
the poor among the rich students

1541
01:14:14,740 --> 01:14:15,520
subsequently.

1542
01:14:18,260 --> 01:14:20,723
This is sort of the same graph
that I showed you before.

1543
01:14:20,723 --> 01:14:23,140
Again, that's sort of consistent
with what we have before.

1544
01:14:23,140 --> 01:14:26,170
It gets a little messier
than we had seen previously.

1545
01:14:26,170 --> 01:14:29,440
But similarly, even
within classrooms,

1546
01:14:29,440 --> 01:14:33,520
if you look at grades
2 and 3 versus 4 and 5,

1547
01:14:33,520 --> 01:14:36,250
the effects seem to
be concentrated more

1548
01:14:36,250 --> 01:14:40,680
pronounced in grades 2 and 3.

1549
01:14:40,680 --> 01:14:46,290
Now, finally, as I said, we look
at test scores and discipline.

1550
01:14:46,290 --> 01:14:48,390
So arguably, there are
some positive effects

1551
01:14:48,390 --> 01:14:49,590
on social preferences.

1552
01:14:49,590 --> 01:14:53,040
And as I said before, the
policy question now is,

1553
01:14:53,040 --> 01:14:55,950
does that come at the cost
of academic achievement

1554
01:14:55,950 --> 01:14:57,070
in some ways?

1555
01:14:57,070 --> 01:14:59,520
So is it that the rich
kid now, by being exposed

1556
01:14:59,520 --> 01:15:02,010
to the poor kids, may be
somewhat nicer and more

1557
01:15:02,010 --> 01:15:05,460
friendly and less
discriminating against the poor?

1558
01:15:05,460 --> 01:15:06,330
That's all and good.

1559
01:15:06,330 --> 01:15:09,960
But is it the case now
that test scores go down?

1560
01:15:09,960 --> 01:15:14,250
So Gautam finds no effect on
aggregate test score index

1561
01:15:14,250 --> 01:15:16,440
or zero effect in
Hindi and math.

1562
01:15:16,440 --> 01:15:22,230
There's a little bit of a
reduction in English scores

1563
01:15:22,230 --> 01:15:25,320
of 0.9 standard deviations.

1564
01:15:25,320 --> 01:15:27,480
That's marginally significant.

1565
01:15:27,480 --> 01:15:34,810
That's suggestive but not
perhaps particularly large.

1566
01:15:34,810 --> 01:15:36,807
So these effects are
not particularly large.

1567
01:15:36,807 --> 01:15:39,390
And in particular, in aggregate,
so if you agree to everything

1568
01:15:39,390 --> 01:15:43,350
together, there don't seem to
be any significant effects.

1569
01:15:43,350 --> 01:15:47,550
So perhaps the English scores
are suggestive but sort of not

1570
01:15:47,550 --> 01:15:48,870
particularly large.

1571
01:15:48,870 --> 01:15:51,000
There's some mild
effect on discipline.

1572
01:15:51,000 --> 01:15:52,980
Interestingly, there's
an increase in swearing.

1573
01:15:52,980 --> 01:15:54,188
You think that's good or bad.

1574
01:15:54,188 --> 01:15:55,260
You can think about that.

1575
01:15:55,260 --> 01:15:58,710
But there seems to be a little
bit of an effect in terms

1576
01:15:58,710 --> 01:16:01,620
of language uses.

1577
01:16:01,620 --> 01:16:04,020
There's no effect on violent
and disruptive behavior,

1578
01:16:04,020 --> 01:16:11,200
which you might think is a lot
more damaging, potentially.

1579
01:16:11,200 --> 01:16:11,860
OK.

1580
01:16:11,860 --> 01:16:14,230
So just summarizing,
what does the paper find?

1581
01:16:14,230 --> 01:16:17,260
Well, having poor classmates
makes wealthy students

1582
01:16:17,260 --> 01:16:19,180
more prosocial and generous.

1583
01:16:19,180 --> 01:16:22,385
They're more likely to
volunteer for charities.

1584
01:16:22,385 --> 01:16:24,760
I didn't show you that evidence,
but that's another piece

1585
01:16:24,760 --> 01:16:26,110
of evidence that he finds.

1586
01:16:26,110 --> 01:16:29,500
They're more likely to give
in money in dictator games

1587
01:16:29,500 --> 01:16:32,500
to give them more higher
fractions of their shares

1588
01:16:32,500 --> 01:16:33,820
in dictator games.

1589
01:16:33,820 --> 01:16:37,940
They also choose more
equitable outcomes

1590
01:16:37,940 --> 01:16:41,890
in sort of disinterested
third party games

1591
01:16:41,890 --> 01:16:45,730
where essentially, you choose
between two other students

1592
01:16:45,730 --> 01:16:47,073
and their allocation.

1593
01:16:47,073 --> 01:16:48,490
So it's not just
that they're more

1594
01:16:48,490 --> 01:16:51,310
likely to be willing to give
up money that others get,

1595
01:16:51,310 --> 01:16:55,390
but also, they're more likely
to choose equal allocations

1596
01:16:55,390 --> 01:16:57,845
in third party games
and disinterested games.

1597
01:16:57,845 --> 01:16:59,470
Again, I didn't show
you that evidence.

1598
01:16:59,470 --> 01:17:01,870
But it seems to be that
what's increasing here

1599
01:17:01,870 --> 01:17:04,780
is sort of like
inequality aversion

1600
01:17:04,780 --> 01:17:11,290
in these sort of disinterested
dictator or the types of games,

1601
01:17:11,290 --> 01:17:14,080
where essentially, these
students, by being exposed

1602
01:17:14,080 --> 01:17:19,420
to poor kids, are now more
averse against unequal outcomes

1603
01:17:19,420 --> 01:17:21,130
in these types of games.

1604
01:17:21,130 --> 01:17:23,710
Second, there is
less discrimination

1605
01:17:23,710 --> 01:17:27,640
and more higher willingness
to socialize with the poor.

1606
01:17:27,640 --> 01:17:31,300
They're more likely to
choose poor teammates

1607
01:17:31,300 --> 01:17:32,830
in sports contests.

1608
01:17:32,830 --> 01:17:34,810
They're also more willing
to attend playdates

1609
01:17:34,810 --> 01:17:35,650
with poor children.

1610
01:17:35,650 --> 01:17:38,200
Again, I didn't show
you that evidence.

1611
01:17:38,200 --> 01:17:40,600
And then there's some
small, negative effect

1612
01:17:40,600 --> 01:17:46,960
on academic outcomes that I
think are mostly negligible.

1613
01:17:46,960 --> 01:17:50,230
Now, there's other work
on the contact hypothesis

1614
01:17:50,230 --> 01:17:50,980
that I showed you.

1615
01:17:50,980 --> 01:17:53,470
So the contact-- what is
the contact hypothesis?

1616
01:17:53,470 --> 01:17:57,190
The contact hypothesis goes
back to at least Allport

1617
01:17:57,190 --> 01:18:01,600
in 1954, which is the idea
that interpersonal contacts

1618
01:18:01,600 --> 01:18:05,020
reduces prejudice until
certain conditions.

1619
01:18:05,020 --> 01:18:08,290
And not just prejudice,
but also changes

1620
01:18:08,290 --> 01:18:11,440
attitudes and social
preferences potentially.

1621
01:18:11,440 --> 01:18:14,290
So Matt Lowe, who was a
PhD student here at MIT,

1622
01:18:14,290 --> 01:18:17,680
has a very nice paper that
looks at cricket tournaments

1623
01:18:17,680 --> 01:18:20,950
and asks the question whether
cricket leagues in India

1624
01:18:20,950 --> 01:18:25,900
can increase cross-class
interaction in pro-sociology.

1625
01:18:25,900 --> 01:18:28,810
So what he does is he
randomizes, essentially,

1626
01:18:28,810 --> 01:18:31,300
cricket leagues and
teams in cricket leagues

1627
01:18:31,300 --> 01:18:33,370
where people across
different castes

1628
01:18:33,370 --> 01:18:36,710
are now more or less likely
to play with each other,

1629
01:18:36,710 --> 01:18:39,790
both in terms of-- he
varies or there's variation

1630
01:18:39,790 --> 01:18:42,010
in the study within teams.

1631
01:18:42,010 --> 01:18:44,080
So are you more or
less likely to have

1632
01:18:44,080 --> 01:18:46,780
a-- or some people are
more or less likely to have

1633
01:18:46,780 --> 01:18:48,640
a lower or higher caste.

1634
01:18:48,640 --> 01:18:51,550
So a person from a different
cast in their team.

1635
01:18:51,550 --> 01:18:55,870
And he has variation in what
he calls adversarial contact,

1636
01:18:55,870 --> 01:19:00,850
which is they're more or less
likely to be exposed to players

1637
01:19:00,850 --> 01:19:04,450
from other teams in
higher or different castes

1638
01:19:04,450 --> 01:19:05,840
from themselves.

1639
01:19:05,840 --> 01:19:08,770
So if you're on the
team and have a person

1640
01:19:08,770 --> 01:19:11,500
from a different caste on your
team, you're on the same team.

1641
01:19:11,500 --> 01:19:15,170
You share the same objective,
and you want to win together.

1642
01:19:15,170 --> 01:19:17,410
So now, having somebody
from a different caste

1643
01:19:17,410 --> 01:19:20,230
or just somebody who's different
in various ways in your team

1644
01:19:20,230 --> 01:19:23,620
might make you like them better.

1645
01:19:23,620 --> 01:19:26,500
You might be sort of
more positive about them.

1646
01:19:26,500 --> 01:19:28,900
You might be sort of more
likely to talk to them.

1647
01:19:28,900 --> 01:19:30,233
You might learn about them.

1648
01:19:30,233 --> 01:19:31,900
You might sort of see
some sides in them

1649
01:19:31,900 --> 01:19:33,245
that you hadn't seen before.

1650
01:19:33,245 --> 01:19:35,620
So you might be more like they
sort of empathize and look

1651
01:19:35,620 --> 01:19:37,300
at sort of nice
characteristic of them

1652
01:19:37,300 --> 01:19:39,790
and sort of update them
positively about people

1653
01:19:39,790 --> 01:19:42,910
from other tasks, and that
changes your attitudes

1654
01:19:42,910 --> 01:19:45,340
towards them in general.

1655
01:19:45,340 --> 01:19:48,737
If, however, you play against
somebody from different castes,

1656
01:19:48,737 --> 01:19:51,070
you might, actually-- that
might, actually, if anything,

1657
01:19:51,070 --> 01:19:54,790
backfire, because you really
don't like your opponents.

1658
01:19:54,790 --> 01:19:56,260
You might see them
very negatively.

1659
01:19:56,260 --> 01:19:57,802
You might be aggressive
towards them.

1660
01:19:57,802 --> 01:19:59,620
You might be unfriendly
towards them.

1661
01:19:59,620 --> 01:20:01,660
You might sort of
not like that they

1662
01:20:01,660 --> 01:20:03,320
win against you or the like.

1663
01:20:03,320 --> 01:20:06,190
So I'll just add, these
adversarial interactions

1664
01:20:06,190 --> 01:20:07,990
might actually
backfire in the sense

1665
01:20:07,990 --> 01:20:09,760
that they might not
foster integration

1666
01:20:09,760 --> 01:20:14,900
but actually sort of
make things worse.

1667
01:20:14,900 --> 01:20:21,490
So Matt runs this experiment
and finds evidence of increased

1668
01:20:21,490 --> 01:20:22,820
cross-caste interactions.

1669
01:20:22,820 --> 01:20:25,810
So people are more likely
to be friends, more likely

1670
01:20:25,810 --> 01:20:27,280
to hang out with others.

1671
01:20:27,280 --> 01:20:30,850
They also are more generous
in dictator and other types

1672
01:20:30,850 --> 01:20:31,870
of games.

1673
01:20:31,870 --> 01:20:35,980
They also are more
likely to engage in trade

1674
01:20:35,980 --> 01:20:37,630
or in economic exchange.

1675
01:20:37,630 --> 01:20:40,390
So what Matt does is he
sort of randomizes gloves.

1676
01:20:40,390 --> 01:20:42,490
It's like left gloves
and right gloves.

1677
01:20:42,490 --> 01:20:43,990
And he does the
same for flip-flops.

1678
01:20:43,990 --> 01:20:46,360
Left flip-flops and
right flip-flops.

1679
01:20:46,360 --> 01:20:49,120
And people are more likely
to trade with somebody

1680
01:20:49,120 --> 01:20:53,300
from another caste if they
ever been on the same team

1681
01:20:53,300 --> 01:20:54,550
with people from other castes.

1682
01:20:54,550 --> 01:20:56,217
If they have a higher
fraction of people

1683
01:20:56,217 --> 01:21:00,610
on their team of people
from other castes,

1684
01:21:00,610 --> 01:21:03,400
they're more likely to engage
in all these behaviors.

1685
01:21:03,400 --> 01:21:05,710
They're more likely to have
cross-class interaction.

1686
01:21:05,710 --> 01:21:07,690
More likely to be prosocial.

1687
01:21:07,690 --> 01:21:10,060
More likely to engage
in economic exchange

1688
01:21:10,060 --> 01:21:13,110
with people from other castes.

1689
01:21:13,110 --> 01:21:17,270
So that's all true for
collaborative contact.

1690
01:21:17,270 --> 01:21:21,470
That is to say, that's contact
with people on the same team.

1691
01:21:21,470 --> 01:21:24,470
In contrast, for adversarial
interactions, when people

1692
01:21:24,470 --> 01:21:28,460
are on the opposite team, having
more people from other castes

1693
01:21:28,460 --> 01:21:31,730
or being exposed to more
people from other castes

1694
01:21:31,730 --> 01:21:33,860
does not have these
positive effects

1695
01:21:33,860 --> 01:21:36,630
and, for some of these outcomes,
have even negative effects.

1696
01:21:36,630 --> 01:21:38,660
So if anything, that
sort of backfires.

1697
01:21:38,660 --> 01:21:42,050
It doesn't-- just-- a
mere exposure to others,

1698
01:21:42,050 --> 01:21:45,320
if you are sort of in an
adversarial contact situation,

1699
01:21:45,320 --> 01:21:49,580
does not really foster
prosociology or any of these

1700
01:21:49,580 --> 01:21:50,930
types of integration.

1701
01:21:50,930 --> 01:21:52,970
If anything, it backfires.

1702
01:21:52,970 --> 01:21:54,188
Now, why is that important?

1703
01:21:54,188 --> 01:21:55,730
If you think about
like, for example,

1704
01:21:55,730 --> 01:21:58,280
attitudes towards
immigrants, it really

1705
01:21:58,280 --> 01:22:01,670
matters hugely what types of
contacts people are exposed to.

1706
01:22:01,670 --> 01:22:04,880
If people have worked together
in the same team, if they have,

1707
01:22:04,880 --> 01:22:09,440
perhaps, team pay, if they were
to work towards the same goal,

1708
01:22:09,440 --> 01:22:11,990
really, it seems this
evidence suggests

1709
01:22:11,990 --> 01:22:15,260
can foster prosociology
and so on and so forth

1710
01:22:15,260 --> 01:22:18,590
that leads to integration,
reduce discrimination,

1711
01:22:18,590 --> 01:22:19,980
and so on and so forth.

1712
01:22:19,980 --> 01:22:22,010
So it's sort of the
incentives are aligned

1713
01:22:22,010 --> 01:22:25,010
or if you could sort of set up
incentives that are aligned,

1714
01:22:25,010 --> 01:22:26,990
people might become
nicer to each other

1715
01:22:26,990 --> 01:22:31,040
and interactions
might be fostered.

1716
01:22:31,040 --> 01:22:34,248
In contrast, if
contact is adversarial,

1717
01:22:34,248 --> 01:22:36,290
if you're worried that
immigrants are taking away

1718
01:22:36,290 --> 01:22:39,410
your jobs, being
exposed to immigrants

1719
01:22:39,410 --> 01:22:41,240
might just do the opposite.

1720
01:22:41,240 --> 01:22:43,010
So you might see a
lot of immigrants.

1721
01:22:43,010 --> 01:22:44,840
But in a way, if
you feel like you're

1722
01:22:44,840 --> 01:22:46,910
in computation with them,
if they're adversarial,

1723
01:22:46,910 --> 01:22:49,010
if they're sort of your
enemies in some ways

1724
01:22:49,010 --> 01:22:54,440
or your opponents in some
computation for a job,

1725
01:22:54,440 --> 01:22:59,300
being exposed to them might
things, in fact, if anything,

1726
01:22:59,300 --> 01:23:01,650
worse.

1727
01:23:01,650 --> 01:23:04,790
Finally, there's another
piece of very nice evidence

1728
01:23:04,790 --> 01:23:06,470
by Corno et al.

1729
01:23:06,470 --> 01:23:09,710
This paper is
considering the impact

1730
01:23:09,710 --> 01:23:11,750
of random interracial
interactions

1731
01:23:11,750 --> 01:23:16,850
among college roommates in
South Africa on stereotypes,

1732
01:23:16,850 --> 01:23:19,470
attitudes, and performance.

1733
01:23:19,470 --> 01:23:21,230
So what they look
at, essentially,

1734
01:23:21,230 --> 01:23:26,820
is roommates of
different race reduces--

1735
01:23:26,820 --> 01:23:28,460
so these are black
and white students--

1736
01:23:28,460 --> 01:23:31,880
reduces white students
stereotypes towards blacks

1737
01:23:31,880 --> 01:23:35,360
and increases
interracial friendships.

1738
01:23:35,360 --> 01:23:39,410
It also improves grades and
lowers dropouts among blacks.

1739
01:23:39,410 --> 01:23:42,740
So there's sort of a
number of positive effects

1740
01:23:42,740 --> 01:23:46,670
of a very simple
policy of increasing

1741
01:23:46,670 --> 01:23:48,770
contacts among roommates.

1742
01:23:48,770 --> 01:23:50,783
And again, if you think
about this evidence,

1743
01:23:50,783 --> 01:23:53,450
it seems to suggest that perhaps
that's kind of like if you have

1744
01:23:53,450 --> 01:23:56,090
a roommate that you're
going to live with for quite

1745
01:23:56,090 --> 01:23:58,978
a while, that feels a lot
like collaborative contact

1746
01:23:58,978 --> 01:24:00,770
in the sense of, like,
you're sort of stuck

1747
01:24:00,770 --> 01:24:02,312
with that roommate
for quite a while,

1748
01:24:02,312 --> 01:24:05,030
so you might as well sort
make the best out of it,

1749
01:24:05,030 --> 01:24:07,103
even if you initially
don't like that person

1750
01:24:07,103 --> 01:24:09,770
and you have the same objectives
of being happy together, living

1751
01:24:09,770 --> 01:24:10,460
together.

1752
01:24:10,460 --> 01:24:12,530
And that really seems
to foster and have

1753
01:24:12,530 --> 01:24:14,570
some positive benefits.

1754
01:24:17,680 --> 01:24:20,760
So taking together,
that sort of says, so A,

1755
01:24:20,760 --> 01:24:24,270
the contact hypothesis
seems to be broadly right.

1756
01:24:24,270 --> 01:24:27,480
Contact to people who
are different from you

1757
01:24:27,480 --> 01:24:31,195
might make you more tolerant
or more prosocial towards them.

1758
01:24:31,195 --> 01:24:32,820
There might be more
sort of integration

1759
01:24:32,820 --> 01:24:34,180
of different groups.

1760
01:24:34,180 --> 01:24:36,180
But what really seems
to matter quite a bit

1761
01:24:36,180 --> 01:24:40,890
is the type of contact
that people are exposed to.

1762
01:24:40,890 --> 01:24:43,170
So finally and
relatively quickly,

1763
01:24:43,170 --> 01:24:45,840
I'll tell you a little bit about
whether people underestimate

1764
01:24:45,840 --> 01:24:47,850
the benefits of prosociology.

1765
01:24:47,850 --> 01:24:50,910
I should mention
that problem set

1766
01:24:50,910 --> 01:24:54,960
3 question 2 is, in fact, trying
to ask you to do something

1767
01:24:54,960 --> 01:24:56,220
related to that.

1768
01:24:56,220 --> 01:24:58,470
So you might want to actually
do the problems at first

1769
01:24:58,470 --> 01:25:00,280
before you finish this lecture.

1770
01:25:00,280 --> 01:25:02,280
At least sort of-- sorry,
not the entire problem

1771
01:25:02,280 --> 01:25:05,670
set, but question 2
of problems set 3.

1772
01:25:05,670 --> 01:25:09,810
That will not take you
very long to do so,

1773
01:25:09,810 --> 01:25:11,310
and it's more like
a fun exercise

1774
01:25:11,310 --> 01:25:15,460
that I thought would be
nice for you to engage in.

1775
01:25:15,460 --> 01:25:20,670
But anyway, this is a very
nice paper by Kumar and Epley.

1776
01:25:20,670 --> 01:25:22,200
That's a typo here.

1777
01:25:22,200 --> 01:25:25,230
It should say Kumar and Epley.

1778
01:25:25,230 --> 01:25:27,660
And the officer is asked
the question about,

1779
01:25:27,660 --> 01:25:32,110
do we have correct beliefs
about the impacts of generosity?

1780
01:25:32,110 --> 01:25:34,260
And so what's the
underlying reason here is,

1781
01:25:34,260 --> 01:25:37,710
well, many prosocial acts
require estimating the impacts

1782
01:25:37,710 --> 01:25:39,150
on the recipient.

1783
01:25:39,150 --> 01:25:41,400
If you give money to
somebody in Africa,

1784
01:25:41,400 --> 01:25:46,800
if you help anybody and people
in need, if you donate money

1785
01:25:46,800 --> 01:25:50,860
in general, if you write
letters of gratitude

1786
01:25:50,860 --> 01:25:53,100
or if you do random
acts of kindness,

1787
01:25:53,100 --> 01:25:55,970
it requires some sort
of estimation of,

1788
01:25:55,970 --> 01:26:01,140
how is the other person going
to feel if they receive--

1789
01:26:01,140 --> 01:26:04,380
if they are on the receiving
end of this prosocial act?

1790
01:26:04,380 --> 01:26:06,750
Now, what Epley
and Kumar argue is

1791
01:26:06,750 --> 01:26:10,710
people are subject to
egocentric bias, which

1792
01:26:10,710 --> 01:26:13,290
may lead them to
systematically underestimate

1793
01:26:13,290 --> 01:26:15,390
the positive impact
of prosociology.

1794
01:26:15,390 --> 01:26:18,960
In this case, gratitude letters.

1795
01:26:18,960 --> 01:26:20,040
And so why is that?

1796
01:26:20,040 --> 01:26:23,130
Well, it's because predicting
others mental states is

1797
01:26:23,130 --> 01:26:27,090
difficult. It's
really hard for people

1798
01:26:27,090 --> 01:26:29,640
to understand how another
person might feel.

1799
01:26:29,640 --> 01:26:31,710
Usually, people would sort
think about themselves

1800
01:26:31,710 --> 01:26:35,590
and sort think about how would I
feel, and what would things be,

1801
01:26:35,590 --> 01:26:36,690
and how are things like.

1802
01:26:36,690 --> 01:26:42,100
And it's very hard to understand
how others might react to you.

1803
01:26:42,100 --> 01:26:43,890
It sort of requires
perspective taking,

1804
01:26:43,890 --> 01:26:48,300
and that's sometimes
tricky for people to do.

1805
01:26:48,300 --> 01:26:50,790
So this is again a
very nice experiment

1806
01:26:50,790 --> 01:26:54,390
by Kumar and Nicholas Epley.

1807
01:26:54,390 --> 01:26:57,300
Not Nicholas last name,
but Nicholas Epley.

1808
01:26:57,300 --> 01:27:00,630
And they test whether people
misunderstand the consequences

1809
01:27:00,630 --> 01:27:02,530
of showing appreciation.

1810
01:27:02,530 --> 01:27:04,410
And so what are
these experiments?

1811
01:27:04,410 --> 01:27:06,510
There's a series of
experiments that look at this.

1812
01:27:06,510 --> 01:27:07,830
What do they look like?

1813
01:27:07,830 --> 01:27:10,920
But what they do
is they ask people,

1814
01:27:10,920 --> 01:27:14,280
MBA students or
subjects in experiments,

1815
01:27:14,280 --> 01:27:15,900
to pick a prosocial act.

1816
01:27:15,900 --> 01:27:18,990
That's writing a gratitude
letter, for example.

1817
01:27:18,990 --> 01:27:22,603
And then ask how the giver and
the recipient will be affected.

1818
01:27:22,603 --> 01:27:25,020
In some cases, it's only the
recipient, and in some cases,

1819
01:27:25,020 --> 01:27:27,300
also the giver.

1820
01:27:27,300 --> 01:27:30,580
Then they perform the
prosocial act by, for example,

1821
01:27:30,580 --> 01:27:32,350
writing a letter of gratitude.

1822
01:27:32,350 --> 01:27:34,290
And then assess how the
giver under recipients

1823
01:27:34,290 --> 01:27:36,850
were actually affected
by doing that.

1824
01:27:36,850 --> 01:27:38,970
And then you can compare
that to item number 2.

1825
01:27:38,970 --> 01:27:42,750
To the estimation x ante.

1826
01:27:42,750 --> 01:27:44,130
Now, what do they find?

1827
01:27:44,130 --> 01:27:47,548
They find clear evidence
that when you look at,

1828
01:27:47,548 --> 01:27:49,590
like, in particular, when
you look at the giver--

1829
01:27:49,590 --> 01:27:53,670
so these graphs all show kind
of the predicted ratings.

1830
01:27:53,670 --> 01:27:59,700
The ratings is between 0 and
10 about what's the experience?

1831
01:27:59,700 --> 01:28:02,070
How happy is the other person?

1832
01:28:02,070 --> 01:28:05,820
What is the surprise about
receiving this letter

1833
01:28:05,820 --> 01:28:07,630
of gratitude in this example?

1834
01:28:07,630 --> 01:28:10,200
So people are more
surprised than predicted.

1835
01:28:10,200 --> 01:28:12,300
They are also more
surprised about the content

1836
01:28:12,300 --> 01:28:15,180
than predicted.

1837
01:28:15,180 --> 01:28:17,550
They're also-- the
recipients mood

1838
01:28:17,550 --> 01:28:19,830
is actually better
than predicted.

1839
01:28:19,830 --> 01:28:23,670
And the awkwardness is
also better than predicted.

1840
01:28:23,670 --> 01:28:26,760
So when you ask people to
write letters of gratitude,

1841
01:28:26,760 --> 01:28:29,640
people tend to
say, oh, you know,

1842
01:28:29,640 --> 01:28:33,810
it's going to be tedious
to do, and the other person

1843
01:28:33,810 --> 01:28:35,550
might feel it's awkward.

1844
01:28:35,550 --> 01:28:37,210
And what am I going to say?

1845
01:28:37,210 --> 01:28:39,330
And is it going to be weird
when I'm going to say?

1846
01:28:39,330 --> 01:28:41,850
Is the person-- don't
they know already

1847
01:28:41,850 --> 01:28:45,360
anyway that I'm really
appreciate of that person?

1848
01:28:45,360 --> 01:28:46,330
And so on.

1849
01:28:46,330 --> 01:28:49,830
So people come up with all sorts
of reasons why they might not

1850
01:28:49,830 --> 01:28:50,910
want to do that.

1851
01:28:50,910 --> 01:28:53,580
If you sort of introspect and
ask yourself how many letters

1852
01:28:53,580 --> 01:28:57,930
of gratitude have you written
in the last year, that doesn't--

1853
01:28:57,930 --> 01:29:00,510
few people actually
do that in practice.

1854
01:29:00,510 --> 01:29:03,390
And perhaps some of these
reasons like the perceived

1855
01:29:03,390 --> 01:29:05,910
awkwardness or perhaps
sort of the underestimation

1856
01:29:05,910 --> 01:29:09,973
of the recipient's mood
might contribute to that.

1857
01:29:09,973 --> 01:29:11,640
Now, if you also think
for a little bit,

1858
01:29:11,640 --> 01:29:15,210
like, how would one actually
feel to receive these letters,

1859
01:29:15,210 --> 01:29:17,005
it's pretty obvious
once you think about it

1860
01:29:17,005 --> 01:29:19,380
that most people are actually
quite happy about receiving

1861
01:29:19,380 --> 01:29:19,980
such letters.

1862
01:29:19,980 --> 01:29:21,930
It's kind of nice if
somebody tells you, look.

1863
01:29:21,930 --> 01:29:25,200
You did something really
nice for me some time

1864
01:29:25,200 --> 01:29:28,560
a long time ago, and this
helped me a lot in my career

1865
01:29:28,560 --> 01:29:32,700
in whatever way, or getting
into school, into college

1866
01:29:32,700 --> 01:29:33,900
or whatever it might be.

1867
01:29:33,900 --> 01:29:35,880
It's really nice to sort
of hear from somebody

1868
01:29:35,880 --> 01:29:38,190
that you did something
nice in their life

1869
01:29:38,190 --> 01:29:42,270
that people are
quite happy about.

1870
01:29:42,270 --> 01:29:46,830
Now, what's sort of some
evidence, some summary of that?

1871
01:29:46,830 --> 01:29:49,230
Well, so we tend to
systematically underestimate

1872
01:29:49,230 --> 01:29:53,010
other's appreciated and
expressions of gratitude.

1873
01:29:53,010 --> 01:29:56,220
That's also true for some
other types of effect.

1874
01:29:56,220 --> 01:29:59,640
That tends to be also true
for random acts of kindness.

1875
01:29:59,640 --> 01:30:04,050
People tend to be surprisingly
more happy about those

1876
01:30:04,050 --> 01:30:05,520
compared to predicted.

1877
01:30:05,520 --> 01:30:07,770
It's also true-- so
Epley and Schroeder

1878
01:30:07,770 --> 01:30:10,360
argue for social connections.

1879
01:30:10,360 --> 01:30:13,200
So people also
underestimate on average

1880
01:30:13,200 --> 01:30:14,880
how they themselves
and others feel

1881
01:30:14,880 --> 01:30:16,047
when starting conversations.

1882
01:30:16,047 --> 01:30:18,570
So what this experiment does
is this gets people to-- it

1883
01:30:18,570 --> 01:30:22,620
randomized people to start
conversations during commuting

1884
01:30:22,620 --> 01:30:24,190
on buses or trains.

1885
01:30:24,190 --> 01:30:26,430
And once people start
doing that-- again,

1886
01:30:26,430 --> 01:30:29,640
before, when you ask them, like,
how is the other person going

1887
01:30:29,640 --> 01:30:31,140
to feel, how are
you going to feel,

1888
01:30:31,140 --> 01:30:34,920
and so on, people will say,
well, it's going to be awkward.

1889
01:30:34,920 --> 01:30:36,540
And what are we
going to talk about?

1890
01:30:36,540 --> 01:30:38,207
And is the other
person even interested?

1891
01:30:38,207 --> 01:30:39,348
And so on.

1892
01:30:39,348 --> 01:30:40,890
But when you actually
do that, people

1893
01:30:40,890 --> 01:30:43,260
seem to be quite happy to
have started conversations

1894
01:30:43,260 --> 01:30:45,600
and making human connection.

1895
01:30:45,600 --> 01:30:47,195
To be clear, not
everybody is happy.

1896
01:30:47,195 --> 01:30:48,570
Some people might
be also grumpy.

1897
01:30:48,570 --> 01:30:51,360
But the vast majority, at
least in these types of study,

1898
01:30:51,360 --> 01:30:54,480
seem to be quite
happy about initiating

1899
01:30:54,480 --> 01:30:57,180
social contact,
about expressions,

1900
01:30:57,180 --> 01:31:00,750
letters of gratitude, or things
like random acts of kindness.

1901
01:31:00,750 --> 01:31:02,250
It's really nice
if somebody happens

1902
01:31:02,250 --> 01:31:04,830
to be something to do
something nice towards you.

1903
01:31:04,830 --> 01:31:07,980
Could be like a random person
who you've never seen before

1904
01:31:07,980 --> 01:31:09,030
and you never see again.

1905
01:31:09,030 --> 01:31:11,530
Just some random person on the
street is really nice to you.

1906
01:31:11,530 --> 01:31:12,970
That might make your day.

1907
01:31:12,970 --> 01:31:15,690
It could be also
somebody who quite well,

1908
01:31:15,690 --> 01:31:18,990
and he's a good
friend, and who just

1909
01:31:18,990 --> 01:31:20,910
wants to do something
nice for you

1910
01:31:20,910 --> 01:31:23,080
for no good, apparent reasons.

1911
01:31:23,080 --> 01:31:25,230
So one caveat to these
kinds of experiments

1912
01:31:25,230 --> 01:31:28,265
is these are very much
like one-short, short

1913
01:31:28,265 --> 01:31:28,890
interreactions.

1914
01:31:28,890 --> 01:31:31,200
These are one-time interactions,
and then the effects

1915
01:31:31,200 --> 01:31:32,080
are measured.

1916
01:31:32,080 --> 01:31:33,780
So there are some
questions about,

1917
01:31:33,780 --> 01:31:36,430
do these effects persist
for repeat interactions?

1918
01:31:36,430 --> 01:31:40,190
That is to say, like, maybe
if you do that once, people

1919
01:31:40,190 --> 01:31:41,340
are quite happy.

1920
01:31:41,340 --> 01:31:44,320
But when you try it more often,
these effects tend to go away.

1921
01:31:44,320 --> 01:31:46,820
So there's a bit of a question
kind of like, how persistent,

1922
01:31:46,820 --> 01:31:48,450
how important are
these in practice

1923
01:31:48,450 --> 01:31:52,160
if you do that more often in
particular in the long run?

1924
01:31:52,160 --> 01:31:55,010
And then another
important question

1925
01:31:55,010 --> 01:31:57,380
here is then the
question about always

1926
01:31:57,380 --> 01:32:00,960
under-investing in prosociology
in terms of our behavior.

1927
01:32:00,960 --> 01:32:04,760
So lots of people who
tend to be quite selfish

1928
01:32:04,760 --> 01:32:07,220
and do good things for
themselves and not so much

1929
01:32:07,220 --> 01:32:08,288
others--

1930
01:32:08,288 --> 01:32:09,830
presumably, they do
that because they

1931
01:32:09,830 --> 01:32:11,930
want to make themselves happy.

1932
01:32:11,930 --> 01:32:14,600
Presumably, people who do
nice things towards others

1933
01:32:14,600 --> 01:32:17,120
to some degree do
that because they want

1934
01:32:17,120 --> 01:32:18,830
to make the other person happy.

1935
01:32:18,830 --> 01:32:23,960
In part perhaps
because of social image

1936
01:32:23,960 --> 01:32:26,130
and self-image concerns.

1937
01:32:26,130 --> 01:32:30,170
But one important hypothesis
that Epley and others raises

1938
01:32:30,170 --> 01:32:34,640
the question of, like, well, are
we underestimating how good it

1939
01:32:34,640 --> 01:32:37,070
might not only make
others feel but also

1940
01:32:37,070 --> 01:32:39,350
ourselves feel from being nice?

1941
01:32:39,350 --> 01:32:43,190
That is to say, perhaps, one
easy way of making ourselves

1942
01:32:43,190 --> 01:32:47,840
happy is not just by being
selfish and maximizing whatever

1943
01:32:47,840 --> 01:32:51,980
outcomes but really, being
nice towards others, in part

1944
01:32:51,980 --> 01:32:54,170
perhaps because it just
makes us happy to see

1945
01:32:54,170 --> 01:32:55,400
when others are happy.

1946
01:32:55,400 --> 01:32:57,770
Perhaps it makes us
happy because others

1947
01:32:57,770 --> 01:32:59,600
will be then nicer to us.

1948
01:32:59,600 --> 01:33:01,100
And there's a
question about, can we

1949
01:33:01,100 --> 01:33:04,070
make others and
ourselves happier

1950
01:33:04,070 --> 01:33:08,030
by being more prosocial,
perhaps because we

1951
01:33:08,030 --> 01:33:13,113
underestimate to start with
what these effects might be?

1952
01:33:13,113 --> 01:33:14,780
I don't think there's
that much evidence

1953
01:33:14,780 --> 01:33:17,430
on this specific question,
but I'd love to learn more.

1954
01:33:17,430 --> 01:33:19,170
And when you think
about your own life,

1955
01:33:19,170 --> 01:33:21,860
you might want to
experiment for a while

1956
01:33:21,860 --> 01:33:24,800
and seeing trying
to be nice or trying

1957
01:33:24,800 --> 01:33:28,910
to engage in random acts of
kindness, letters of gratitude

1958
01:33:28,910 --> 01:33:31,760
and so on, that might be
a nice habit to acquire.

1959
01:33:31,760 --> 01:33:35,990
And you might see it may or
might not make you happier.

1960
01:33:35,990 --> 01:33:39,230
Surely, it will make the other
person on the receiving end

1961
01:33:39,230 --> 01:33:40,710
happier.

1962
01:33:40,710 --> 01:33:41,210
OK.

1963
01:33:41,210 --> 01:33:44,090
So let me sort of
summarize what we learned

1964
01:33:44,090 --> 01:33:46,220
or what we studied on
social preferences.

1965
01:33:46,220 --> 01:33:49,940
So first, others' outcomes and
utility matter for people's

1966
01:33:49,940 --> 01:33:51,960
choices quite a bit.

1967
01:33:51,960 --> 01:33:54,380
So in various situations,
essentially, people

1968
01:33:54,380 --> 01:33:56,600
are willing to give to
others, and they're influenced

1969
01:33:56,600 --> 01:33:58,760
by others in their choices.

1970
01:33:58,760 --> 01:34:01,010
Now, upon closer
look, there's not

1971
01:34:01,010 --> 01:34:02,900
much evidence of pure altruism.

1972
01:34:02,900 --> 01:34:05,810
Rarely does it seem that
people just do stuff for others

1973
01:34:05,810 --> 01:34:08,520
just for the sake of
others doing well.

1974
01:34:08,520 --> 01:34:11,990
That is to say, if people
get the chance of hiding

1975
01:34:11,990 --> 01:34:14,630
they're not so nice
actions, they will often

1976
01:34:14,630 --> 01:34:16,770
take advantage of that.

1977
01:34:16,770 --> 01:34:19,190
The motivation there
often is instead

1978
01:34:19,190 --> 01:34:21,650
motivation to give to
others or be nice to others

1979
01:34:21,650 --> 01:34:25,470
to be prosocial is often saving
face in front of others--

1980
01:34:25,470 --> 01:34:26,690
this is social image--

1981
01:34:26,690 --> 01:34:30,330
or themselves,
which is self-image.

1982
01:34:30,330 --> 01:34:34,310
So here-- and because of that,
situational circumstances

1983
01:34:34,310 --> 01:34:35,090
matter greatly.

1984
01:34:35,090 --> 01:34:38,160
Societal norms are really
important to consider.

1985
01:34:38,160 --> 01:34:39,830
So when you think
about incentives

1986
01:34:39,830 --> 01:34:42,750
or any sort of types of
structures and organizations,

1987
01:34:42,750 --> 01:34:44,030
how might you--

1988
01:34:44,030 --> 01:34:48,500
incentives, or how might be sort
of a structure certain groups

1989
01:34:48,500 --> 01:34:51,830
of people working together,
understanding these norms

1990
01:34:51,830 --> 01:34:55,170
and circumstances is key
for fostering prosociology.

1991
01:34:55,170 --> 01:34:57,090
If you want people to
be nice to each other,

1992
01:34:57,090 --> 01:35:00,710
you have to set it up in a
way that it's maybe observable

1993
01:35:00,710 --> 01:35:02,280
what people do.

1994
01:35:02,280 --> 01:35:03,110
It's encouraged.

1995
01:35:03,110 --> 01:35:06,660
There's opportunities for
reciprocity, and so on.

1996
01:35:06,660 --> 01:35:08,720
So a lot of the
design of society,

1997
01:35:08,720 --> 01:35:10,880
of a firm, of a group
that you work in

1998
01:35:10,880 --> 01:35:14,180
or a team that you work
in really seems to matter.

1999
01:35:14,180 --> 01:35:16,050
So on the one hand,
as I said before,

2000
01:35:16,050 --> 01:35:18,050
it's a little bit
disappointing that there's not

2001
01:35:18,050 --> 01:35:19,280
much pure altruism.

2002
01:35:19,280 --> 01:35:21,890
But on the other hand,
if you sort of understand

2003
01:35:21,890 --> 01:35:24,950
the motivations for
people being to engage

2004
01:35:24,950 --> 01:35:28,640
in acts that are
good for others,

2005
01:35:28,640 --> 01:35:31,430
you can sort of design
incentives and structures

2006
01:35:31,430 --> 01:35:36,470
that people work in accordingly,
which will then create people

2007
01:35:36,470 --> 01:35:40,110
being friendly and
cooperative to each other.

2008
01:35:40,110 --> 01:35:42,020
Second, I showed you
that social preferences

2009
01:35:42,020 --> 01:35:44,990
matter at workplaces.

2010
01:35:44,990 --> 01:35:47,720
Relative pay can depress
incentives to work.

2011
01:35:47,720 --> 01:35:49,100
This is evidenced by Bandiera.

2012
01:35:49,100 --> 01:35:50,930
The fruit pickers
that I showed you.

2013
01:35:50,930 --> 01:35:53,990
Pay inequality can lower
performance via reduced morale.

2014
01:35:53,990 --> 01:35:56,030
So in particular,
if pay inequality

2015
01:35:56,030 --> 01:36:01,250
is seemingly unjustified, that's
really bad for worker morale

2016
01:36:01,250 --> 01:36:05,180
and might lower our
worker outputs and sort

2017
01:36:05,180 --> 01:36:08,460
of attendance and so on.

2018
01:36:08,460 --> 01:36:12,200
Third, social preferences appear
to be malleable and shaped

2019
01:36:12,200 --> 01:36:14,810
by external factors.

2020
01:36:14,810 --> 01:36:17,720
In particular, there's
evidence in favor

2021
01:36:17,720 --> 01:36:20,030
of the contact hypothesis.

2022
01:36:20,030 --> 01:36:23,600
Being exposed to others from
different backgrounds really

2023
01:36:23,600 --> 01:36:26,930
seems to make us more
tolerant and more

2024
01:36:26,930 --> 01:36:30,420
understanding, more prosocial
towards these other groups.

2025
01:36:30,420 --> 01:36:33,410
But also more prosocial
perhaps in general, which

2026
01:36:33,410 --> 01:36:35,630
is the evidence by Gautam Rao.

2027
01:36:35,630 --> 01:36:38,910
And then finally,
there's some evidence

2028
01:36:38,910 --> 01:36:43,090
that biased beliefs may lower
prosociology in the sense

2029
01:36:43,090 --> 01:36:47,370
that people might under-invest
potentially in how nice

2030
01:36:47,370 --> 01:36:50,700
they are to others perhaps
because they misunderstand

2031
01:36:50,700 --> 01:36:53,970
what the effective of
engaging on such a prosocial

2032
01:36:53,970 --> 01:36:56,080
act might have on others.

2033
01:36:56,080 --> 01:36:59,550
And so potentially correcting
these beliefs or experimenting

2034
01:36:59,550 --> 01:37:02,220
might sort of increase
prosociology, at least

2035
01:37:02,220 --> 01:37:04,830
in some settings.

2036
01:37:04,830 --> 01:37:06,285
Now, what's coming next?

2037
01:37:06,285 --> 01:37:07,410
What are the next lectures?

2038
01:37:07,410 --> 01:37:10,180
Lecture number 14 is
about limited attention.

2039
01:37:10,180 --> 01:37:13,240
15 is about projection
and attribution bias.

2040
01:37:13,240 --> 01:37:15,420
This is the idea
that people have

2041
01:37:15,420 --> 01:37:18,000
trouble projecting
how they might

2042
01:37:18,000 --> 01:37:19,810
feel on different
states of the world.

2043
01:37:19,810 --> 01:37:21,900
That is the idea if
you're really hungry,

2044
01:37:21,900 --> 01:37:24,630
it's really hard to project how
you might feel when you're not

2045
01:37:24,630 --> 01:37:26,220
hungry and vise versa.

2046
01:37:26,220 --> 01:37:27,870
We're going to look at that.

2047
01:37:27,870 --> 01:37:30,960
In lecture 16 and 17, we'll
look more specifically

2048
01:37:30,960 --> 01:37:32,460
about beliefs and learning.

2049
01:37:32,460 --> 01:37:34,020
We talked a little
bit about beliefs

2050
01:37:34,020 --> 01:37:35,520
already in various ways.

2051
01:37:35,520 --> 01:37:37,320
But now, we're going
to talk specifically

2052
01:37:37,320 --> 01:37:40,140
about biases in
beliefs and learning,

2053
01:37:40,140 --> 01:37:44,640
as in people sub-optimally
learning perhaps because

2054
01:37:44,640 --> 01:37:46,860
of computational issues
in the sense that it's

2055
01:37:46,860 --> 01:37:48,990
really hard to learn
in some settings,

2056
01:37:48,990 --> 01:37:51,390
in part because of
motivated beliefs.

2057
01:37:51,390 --> 01:37:56,250
People might drive utility
from their beliefs.

2058
01:37:56,250 --> 01:37:58,080
As in, for example,
I might want to think

2059
01:37:58,080 --> 01:38:03,220
that I'm a good-looking,
smart, and a good teacher.

2060
01:38:03,220 --> 01:38:06,030
And if I get feedback
on one way or the other,

2061
01:38:06,030 --> 01:38:08,820
I might react to positive
feedback a lot more.

2062
01:38:08,820 --> 01:38:11,145
I might sort of update
positively if somebody says,

2063
01:38:11,145 --> 01:38:12,270
Frank, you're really smart.

2064
01:38:12,270 --> 01:38:14,070
I might sort of
update positively

2065
01:38:14,070 --> 01:38:15,960
because I feel
really good about it.

2066
01:38:15,960 --> 01:38:18,180
If somebody says instead,
Frank, you're not so smart,

2067
01:38:18,180 --> 01:38:20,100
I might mostly
ignore that feedback

2068
01:38:20,100 --> 01:38:24,290
because it might make me
feel bad about myself.

2069
01:38:24,290 --> 01:38:26,080
So that's all for now.

2070
01:38:26,080 --> 01:38:27,950
Thank you so much.