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... ?

- [Narrator] These volumes are
opaque to 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 lap tops, 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!

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 a 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 woman
and zero for man.

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
from most of the variables.

- [Narrator] That's right.

Masters usually omit
standard deviations for dummies

because the mean of this variable
determines its standard deviaton.

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.

Hmmm...

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

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.