[MUSIC] Okay, now that we've measured our variables, our outcome variable, and our policy
variable, we move to the first step of data analysis: describing the data.
So for outcome variable, for example GDP, we want to know things like what's the average
GDP across all countries?
What's the largest?
And what's the smallest?
And we also want to know these for the policy variable.
How much property rights there are?
So what's the average amount of property rights across countries?
What's the largest?
And what's the smallest?
That's always the first step in a data analysis.
Now, we ultimately want to learn about causality, and remember causality refers to how an intervention
to change the policy variable affects the outcome variable.
So we must observe variation in the policy variable in the data if we want to learn about
causality, because causality is about what happens when we change something, when we
change the policy variable.
And by variation I mean that the value of the policy variable is not the same for all
units.
So suppose that all countries in our data had the same level of property rights.
Well, that would mean that there's zero variation in property rights in the data, everyone has
the same thing, and that would make it difficult to learn about causality because there's not
any change in the policy variable.
If instead we had some countries that have high property rights and some countries that
have low property rights, then there would be variation in the data, and we'd have some
hope about learning about causality.
Now statistically, variation is measured by using two numbers, the variance and the standard
deviation.
And these numbers are usually always reported in published papers and elsewhere where you
see these kinds of data analysis.
And the way to interpret them is that larger numbers mean there is more variation, so a
larger standard deviation means that there's more variation in the data in that variable.
Now why exactly do we need variation specifically?
Well, learning about causality is going to ultimately boil down to comparing units with
different values of the policy variable.
So comparing countries that have strong property rights with countries that have weak property
rights.
So we can't make this comparison if there's no variation in the policy variable.
Now let's look at an example.
So, in 2012, an Australian nurse, Bronnie Ware, wrote a book called The Top Five Regrets
of the Dying from her experience as a nurse, and here they are: Number 1: I wish I had
the courage to live a life true to myself, not the life others expected of me.
Number 2: I wish I hadn't worked so hard.
Number 3: I wish I’d had the courage to express my feelings.
Number 4: I wish I had stayed in touch with my friends.
And finally number 5: I wish that I'd let myself be happier.
So what lessons can we take from this list about how we should live life if we want to
avoid death bed regrets?
So for example, based on number two, should we work less?
Spend more time at the beach, for example?
Well, from her data it's actually not clear what we should do, because we need to make
comparisons.
What we need to do is compare the regrets of people who worked a lot with the regrets
of people who didn't work a lot, and see if there's a big difference.
But as we'll see in later modules, even that comparison is not good enough for learning
about causality, but it's a good start.
If all we observed was the first group - the regrets of people who had worked a lot - then
there wouldn't be any variation in the policy variable, and we couldn’t made these comparisons.
So to learn about causality, we need variation in the data, and variation in the treatment
and the policy variable.
[MUSIC].