How to Read Economics Research Papers: Randomized Controlled Trials (RCTs)

- [Narrator] On his quest
to master econometrics,

Grasshopper Kamal has
made great progress,

stretching his capabilities
and outsmarting his foes.

Alas, today he's despondent,

for one challenge remains unmet.

Kamal cannot yet decode
the scriptures of academic research,

journals like
"The American Economic Review"

and "Econometrica."

These seemed to him to be inscribed
in an obscure foreign tongue.

- [Kamal] Ugh, what the... ?

- These volumes are
opaque to the novice, Kamal,

but can be deciphered with study.

Let us learn to read them together.

Let's dive into the West Point study,

published in the "Economics
of Education Review."

This paper reports
on a randomized evaluation

of student electronics use
in Economics 101 classrooms.

First, a quick review
of the research design.

- Okay.

- [Josh] 'Metrics masters
teaching at West Point,

the military college that trains
American Army officers

designed a randomized trial
to answer this question.

These masters randomly assigned
West Point cadets

into Economics classes
operating under different rules.

Unlike most American colleges,

the West Point default
is no electronics.

For purposes of this experiment,
some students were left

in such traditional
technology-free classes,

no laptops, no tablets
and no phones!

[voice echoes]

This is the control group,
or baseline case.

Another group was allowed
to use electronics.

This is the treatment group,
subject to a changed environment.

The treatment in this case
is the unrestricted use

of laptops or tablets in class.

Every causal question
has a clear outcome,

the variables we hope to influence
defined in advance of the study.

The outcomes in the West Point
electronics study

are final exam scores.

The study seeks to answer
the following question,

what is the causal effect
of classroom electronics on learning

as measured by exam scores?

- Economics journal articles
usually begin with a table

of descriptive statistics,
giving key facts

about the study sample.

- Oh my gosh, I remember this table,
so confusing!

- [Narrator] Columns 1 to 3 report
mean, or average, characteristics.

These give a sense
of who we're studying.

Let's start with column 1
which describes covariates

in the control group.

Covariates are characteristics
of the control and treatment groups

measured before
the experiment begins.

For example, we see the control group
has an average age a bit over 20.

Many of these covariates
are dummy variables.

A dummy variable can only have
two values, a zero or a one.

For example, student gender
is captured by a dummy variable

that equals one for women
and zero for men.

The mean of this variable
is the proportion female.

We also see that the control group
is 13% Hispanic

and 19% had prior military service.

The table notes are key.

Refer to these
as you scan the table.

These notes explain what's shown
in each column and panel.

The notes tell us, for example,
that standard deviations

are reported in brackets.

Standard deviations tell us how
spread out the data are.

For example, a standard deviation
of 0.52 tells us that most

of the control group's GPAs
fall between 2.35,

which is 0.52 below
the mean GPA of 2.87,

and 3.39, which is 0.52 above 2.87.

A lower standard deviation
would mean the GPAs were

more tightly clustered
around the mean.

- [Kamal] Yeah, but they're missing
for most of the variables.

- [Narrator] That's right.

Masters usually omit
standard deviations for dummies

because the mean of this variable
determines its standard deviation.

This study compares two treatment
groups with the control group.

The first was allowed free use
of laptops and tablets.

The second treatment
was more restrictive,

allowing only tablets placed
flat on the desk.

The treatment groups
look much like the control group.

This takes us to the next feature
of this table, columns 4 through 6

use statistical tests to compare
the characteristics

of the treatment and control group
before the experiment.

In column 4, the two treatment
groups are combined.

You can see that the difference
in proportion female

between the treatment
and control group is only 0.03.

The difference is not
statistically significant.

It is the sort of difference
we can easily put down

to chance results
in our sample selection process.

- [Kamal] Hmm, how do we know that?

- [Narrator] Remember
the rule of thumb?

Statistical estimates
that exceed the standard error

by a multiple of 2
in absolute value

are usually said
to be statistically significant.

The standard error is 0.03,

same as the difference
in proportion female.

So the ratio of the latter
to the former is only 1,

which of course is less than 2.

- [Kamal] Uh huh. So none
of the treatment/control differences

in the table are more than twice
their standard errors.

- [Narrator] Correct.

The random division of students
appears to have succeeded

in creating groups
that are indeed comparable.

We can be confident therefore
that any later differences

in classroom achievement
are the result of the experimental

intervention rather
than a reflection

of preexisting differences.

Ceteris paribus achieved!

- [Kamal] Cool. Wait,
what about the bottom,

the numbers with the stars?

Those differences are a lot more
than double the standard error.

- [Narrator] Good eye, Kamal!

The table has many numbers.

Those in Panel B are important too.

This panel measures the extent
to which students in treatment

and control groups actually use
computers in class.

The treatment here was
to allow computer use.

The researchers must show
that students allowed

to use computers took advantage
of the opportunity to do so.

If they didn't, then there's
really no treatment.

Luckily, 81% of those
in the first treatment group

used computers compared
with none in the control group.

And many in the second
tablet treatment group

used computers as well.

These differences
in computer use are large

and statistically significant.

We also get to see
the sample size in each group.

- [Kamal] The stars
are just like decoration?

- [Narrator] Some academic papers
use stars to indicate differences

that are statistically significant.

This makes them jump out at you.

Here three stars indicate that
the result is statistically different

from zero with a p value
less than 1%.

In other words, there's less
than a 1 in 100 chance

this result is purely
a chance finding.

[applause]

Two stars indicate a 1 in 20
or 5% chance of a chance finding.

And one star denotes results
we might see as often as 10%

of the time merely due to chance.

Today, stars are seen
as a little old fashioned.

Some journals omit them.

- [Kamal] What about
those last two columns?

- [Narrator] Unlike column 4,
which combines

both treatment groups into one,
these last two columns

look separately
at treatment/control differences

for each treatment group.

This provides a more detailed
analysis of balance.

Also, for now,
you can ignore this row

which provides
another test of significance.

Now we get to the article's
punchline, table 4.

This table reports
regression estimates

of the effects of electronics use
on measures of student learning.

- [Kamal Why does the study
report regression estimates?

See, that's why I was getting lost.

I thought one reason
why we liked randomized trials

is that we use them
to obtain causal effects

simply by comparing
treatment and control groups.

Since these groups are balanced,
no need to use regression.

- [Narrator] Well said, Kamal.

In practice, it's customary
to report regression estimates

for two reasons.

First, evidence of balance
not withstanding, an abundance

of caution might lead the analyst
to allow for chance differences.

Second, regression estimates
are likely to be more precise.

That is, they have lower
standard errors than

the simple treatment
control comparisons.

The dependent variable
in this study

is the outcome of interest.

Since the question at hand
is how classroom electronics

affect learning, a good outcome
is the economics final exam score.

Each column reports results
from a different regression model.

Models are distinguished
by the control variables

or covariates they include
besides treatment status.

Estimates with no covariates
are simple comparisons

of treatment and control groups.

- [Kamal] I thought
they just forgot to fill it out.

- [Narrator] Column 1 suggests
electronics use reduced

final exam scores
by 0.28 standard deviations.

In our last lesson, Master Joshway
explained, we use standard deviation

units because these units
are easily compared across studies.

Column 2 reports results
from a model

that adds demographic controls.

Here we're comparing test scores
but holding constant factors

such as age and sex.

Column 3 reports results
from a model that adds GPA

to the list of covariates.

Column 4 adds ACT scores.

Analysts often report
results this way,

starting with models that include
few or no covariates

and then reporting estimates
from models that add more

and more covariates
as we move across columns.

Looking across columns,
what do you notice?

- [Kamal] Well, the coefficient
on using a computer is always

a pretty big negative number.

- [Narrator] That's right!

We can also see that
the standard errors are small enough

to make these negative results
statistically significant.

In other words, the primary
takeaway from this experiment

is that electronics in the classroom
reduce student learning.

- [Kama] GPA and ACT scores
are also significant.

Why is that?

- [Narrator] Good observation!

That's not surprising.

We expect these variables
to predict college performance.

- [Kamal] Oh right, of course.

Kids who got better grades before
are more likely to get

a better grade in this course.

- [Narrator] You'll also notice a lot
of other information on this table.

Remaining panels in the table
report effects of electronics use

on components of the final exam,

such as the multiple
choice questions.

These results are mostly consistent
with computer use effects

on overall scores.

- [Kamal] What about the rows
not in English?

- [Narrator] These rows give
additional statistical information.

R-squared is a measure
of goodness of fit.

This isn't too important, though
some readers may want to know it.

Other rows report on alternative
tests of statistical significance

that you can ignore for now.

- [Kamal] Oh my gosh,
these tables aren't that hard.

Thank you so much.

Next up is regression.

See you then!

♪ [music] ♪

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