Introduction to Instrumental Variables (IV)

MIT's Josh Angrist introduces one of econometrics most powerful tools: instrumental variables.

Instrumental variables (IV, for those in the know), allow masters of econometrics to draw convincing causal conclusions when a treatment of interest is incompletely or imperfectly randomized.

For example, arguments over American school quality often run hot, boiling over with selection bias. See a school with strong graduation rates and enticing test scores? Is that a good school or just an ordinary school fortuitously located in a good neighborhood?

Lotteries that randomize offers of a school seat at in-demand schools should unravel the school quality conundrum. But lotteries only offer seats. Families are free to accept or go elsewhere and these choices are far from random.

IV provides a path to causal conclusions even in the face of this sort of incomplete randomization.

In this video, we cover the following:

- Incomplete random assignment

- IV terminology: first stage, second stage, instrument, reduced form

- Three key IV assumptions: substantial first stage, independence assumption, exclusion restriction

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- [Instructor] The path from cause
to effect is dark and dangerous.

But the weapons
of Econometrics are strong.

Attack with fierce
and flexible instrumental variables

when nature blesses you
with fortuitous random assignment.

[gong rings]

Randomized trials are the surest
path to ceteris parabus comparisons.

Alas, this powerful tool
is often unavailable.

But sometimes, randomization
happens by accident.

That's when we turn
to instrumental variables --

IV for short.

- [Voice whispers] Instrumental
variables.

- [Instructor] Today's lesson
is the first of two on IV.

Our first IV lesson begins
with a story of schools.

[school bell rings]

- [Josh] Charter schools
are public schools

freed from daily district oversight
and teacher union contracts.

The question of whether charters
boost achievement

is one of the most important

in the history
of American education reform.

- The most popular charter schools
have more applicants than seats

so the luck of a lottery draw
decides who's offered a seat.

A lot is at stake for the students
vying for their chance,

and waiting for the lottery results
brings up lots of emotions

as was captured
in the award-winning documentary

"Waiting For Superman."

- [Mother] Don't cry. You're gonna
make Mommy cry. Okay?

- Do charters really provide
a better education?

Critics most definitely say no,

arguing that charters enroll
better students to begin with,

smarter or more motivated,
so differences in later outcomes

reflects selection bias.

- [Kamal] Wait, this one seems easy.

In a lottery, winners
are chosen randomly,

so just compare winners and losers.
- [Student] Obviously.

- On the right track, Kamal,

but charter lotteries
don't force kids into

or out of a particular school.

They randomize offers
of a charter seat.

Some kids get lucky.

Some kids don't.

If we just wanted to know
the effect of charter school offers,

we could treat this
as a randomized trial.

But we we're interested
in the effects

of charter school attendance,
not offers.

And not everyone
who is offered, accepts.

IV turns the effect of being offered
a charter seat into the effect

of actually attending
a charter school.

- [Student] Cool.
- Oh nice.

- Let's look at an example,
a charter school from

the Knowledge Is Power
Program, or KIPP for short.

This KIPP school is in Lynn,

a faded industrial town
on the coast of Massachusetts.

The school has
more applicants than seats

and therefore picks its students
using a lottery.

From 2005 to 2008,
371 fourth and fifth graders

put their names
in the KIPP Lynn lottery,

253 students won a seat at KIPP,

118 students lost.

A year later, lottery winners had
much higher math scores

than lottery losers.

But remember,
we're not trying to figure out

whether winning a lottery
makes you better at math.

We want to know if attending KIPP
makes you better at math.

Of the 253 lottery winners,
only 199 actually went to KIPP.

The others chose
a traditional public school.

Similarly of the 118 lottery losers,
a few actually ended up at KIPP.

They got an offer later.

So what was the effect on test scores
of actually attending KIPP?

- [Kamal] Why can't we just
measure their math scores?

- [Instructor] Great question.

Who would you compare them to?

- [Kamal] Those who didn't attend.

- [Instructor] Is attendance random?

- [Camilla] No.

- Selection bias.

- [Instructor] Correct.
- [Otto] What?

- [Instructor] The KIPP offers
are random so we can be confident

of ceteris parabus,
but attendance is not random.

The choice to accept the offer
might be due to characteristics

that are related
to math performance --

say, for example,
that dedicated parents

are more likely
to accept the offer.

Their kids are also more likely
to do better in math,

regardless of school.

- [Student] Right.

- [Instructor] IV converts
the offer effect

into the effect of KIPP attendance,

adjusting for the fact
that some winners go elsewhere

and some losers manage
to attend KIPP anyway.

Essentially, IV takes
an incomplete randomization

and makes the appropriate
adjustments.

How? IV describes a chain reaction.

Why do offers affect achievement?

Probably because they affect
charter attendance

and charter attendance
improves math scores,

the first link in the chain
called the first stage

is the effect of the lottery
on charter attendance.

The second stage is the length
between attending a charter

and an outcome variable,

in this case, math scores.

The instrumental variable,
or instrument for short,

is the variable
that initiates the chain reaction.

The effect of the instrument
on the outcome is called

the reduced form.

This chain reaction can be
represented mathematically.

We multiply the first stage,
the effect of winning

on attendance, by the second stage,

the effect of attendance on scores.

And we get the reduced form,

the effect of winning
the lottery on scores.

The reduced form and first stage
are observable and easy to compute.

However, the effect of attendance
on achievement

is not directly observed.

This is the causal effect
we're trying to determine.

Given some important assumptions
we'll discuss shortly,

we can find the effect
of KIPP attendance

by dividing the reduced form
by the first stage.

This will become more clear
as we work through an example.

- [Student] Let's do this.

- A quick note on measurement.

We measure achievement
using standard deviations,

often denoted
by the Greek letter sigma (σ).

One σ is a huge move
from around the bottom 15%

to the middle of most
achievement distributions.

Even a ¼ or ½ σ difference is big.

- [Instructor] Now we're ready
to plug some numbers

into the equation
we introduced earlier.

First up, what's the effect

of winning the lottery
on math scores?

KIPP applicants' math scores
are a third of a standard deviation

below the state average in
the year before they apply to KIPP.

But a year later, lottery winners
score right at the state average

while the lottery losers
are still well behind

with an average score
around - 0.36 σ.

The effect of winning the lottery
on scores is the difference

between the winners' scores
and the losers' scores.

Take the winners'
average math scores,

subtract the losers'
average math scores,

and you will have 0.36 σ .

Next up: what's the effect
of winning the lottery on attendance?

In other words,
if you win the lottery,

how much more likely are you
to attend KIPP than if you lose?

First, what percentage
of lottery winners attend KIPP?

Divide the number of winners
who attended KIPP

by the total number
of lottery winners -- that's 78%.

To find the percentage
of lottery losers who attended KIPP,

we divide the number of losers
who attended KIPP

by the total number
of lottery losers -- that's 4%.

Subtract 4 from 78, and we find
that winning the lottery

makes you 74%
more likely to attend KIPP.

Now we can find
what we're really after,

the effect of attendance on scores,
by dividing 0.36 by 0.74.

Attending KIPP raises math scores

by 0.48 standard deviations
on average.

That's an awesome achievement gain,

equal to moving
from about the bottom third

to the middle
of the achievement distribution.

- [Student] Whoa, half a sig.

- [Instructor] These estimates
are for kids opting in

to the KIPP lottery,
whose enrollment status

is changed by winning.

That's not necessarily
a random sample

of all children in Lynn.

So we can't assume
we'd see the same effect

for other types of students.
- [Student] Huh.

- But this effect
on keen for KIPP kids

is likely to be a good indicator
of the consequences

of adding additional charter seats.

- [Student] Cool.
- [Student] Got it.

- IV eliminates selection bias,
but like all of our tools,

the solution builds on a set
of assumptions

not to be taken for granted.

First, there must be
a substantial first stage --

that is the instrumental variable,
winning or losing the lottery,

must really change the variable
whose effect we're interested in --

here, KIPP attendance.

In this case, the first stage
is not really in doubt.

Winning the lottery makes
KIPP attendance much more likely.

Not all IV stories are like that.

Second, the instrument
must be as good

as randomly assigned,
meaning lottery winners and losers

have similar characteristics.

This is the independence assumption.

Of course, KIPP lottery wins
really are randomly assigned.

Still, we should check for balance
and confirm that winners and losers

have similar family backgrounds,

similar aptitudes and so on.

In essence, we're checking
to ensure KIPP lotteries are fair

with no group of applicants
suspiciously likely to win.

Finally, we require
the instrument change outcomes

solely through
the variable of interest,

in this case, attending KIPP.

This assumption is called
the exclusion restriction.

- IV only works if you can satisfy
these three assumptions.

- I don't understand
the exclusion restriction.

How could winning the lottery
affect math scores

other than by attending KIPP?

- [Student] Yeah.
- [Instructor] Great question.

Suppose lottery winners
are just thrilled to win,

and this happiness motivates them
to study more and learn more math,

regardless of where
they go to school.

This would violate
the exclusion restriction

because the motivational effect
of winning is a second channel

whereby lotteries
might affect test scores.

While it's hard
to rule this out entirely,

there's no evidence
of any alternative channels

in the KIPP study.

- IV solves the problem
of selection bias

in scenarios like the KIPP lottery
where treatment offers are random

but some of those offered opt out.

This sort of intentional
yet incomplete random assignment

is surprisingly common.

Even randomized clinical trials
have this feature.

IV solves the problem
of non-random take up

in lotteries or clinical research.

But lotteries are not the only
source of compelling instruments.

Many causal questions can be
addressed by naturally occurring

as good as randomly
assigned variation.

Here's a causal question for you --

do women who have children early
in their careers suffer

a substantial earnings penalty
as a result?

After all, women earn less than men.

We could, of course, simply
compare the earnings of women

with more and fewer children.

But such comparisons are fraught
with selection bias.

If only we could randomly assign
babies to different households.

Yeah, right,
sounds pretty fanciful.

Our next IV story -- fantastic
and not fanciful --

illustrates an amazing,
naturally-occurring instrument

for family size.

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click for the next video.

You can also check out
MRU's website for more courses,

teacher resources, and more.

♪ [music] ♪