[MUSIC] Obesity is a big problem in the US, and policy makers have come up with many different
ways to combat it.
Anything from taxing sugary soft drinks, to banning them altogether, or to removing them
from the elementary, middle, and high schools.
Well, another thing they've tried in New York City in April 2008 was mandating that restaurants
post their calories counts on their menus, so that people can see exactly how many calories
are in each food and beverage item that they are sold.
So what was the effect of that policy on people's consumption?
Did people start consuming less food and beverages because they now know how many calories are
actually in what they are buying?
Well, that's the question that these researchers here set out to answer.
They gathered data from every Starbucks in New York City from January 2008 up to February
2009, and remember, the law was passed in April 2008 so their data includes times before
the law was passed and after the law was passed.
They also got data from Boston and Philadelphia Starbucks from the same time period, so they're
going to use Boston and Philadelphia as their control group.
Both of those cities did not have a law that mandated restaurants post their calories.
So we're in the classic difference-in-difference setup, and we can apply all of the ideas we've
talked about to learn about the causal effect of the policy mandated calorie count posting
on outcomes: how many calories people are actually consuming.
Now, because they have very rich transaction store level data, they're going to do a little
bit of fancier statistical methods to compute their causal effects, they're not going to
do this simple difference-in-differences comparison as we've done in the previous examples.
But the way they get identification, the way they can actually compute these effects in
the first place, is exactly as before.
They make the common trends assumption, which says that if the law was not passed in New
York City, then calorie consumptions would have continued along the same trend as what
happened in Boston and Philadelphia.
So we use Boston and Philadelphia to construct the counterfactual of what would have happened
in New York City if the law wasn't actually passed.
So once they actually do all of their statistics, they're going to compute a causal effect,
which is what they've shown in this table here.
Now there are six different causal effects shown for two different data sets, and three
different outcome variables.
Now the important thing I want to talk about this table is that usually this is the kind
of thing you'll see when you're looking at papers about panel data and causality.
The difference-in-differences kind of calculations are a little more special.
This is the kind of thing you'll see most of the cases.
You'll go straight to this table where they're showing you the causal effects.
But remember the main idea is the same as in difference-in-differences.
So let's just look at their first dataset they call the transaction data.
And their first outcome variable, log Beverage Calories.
So they're looking at the logarithm of how many calories are consumed through beverages,
and their estimate of the causal effect is -.003.
Now because the outcome variable is a log, this can be interpreted as a percentage, so
it's actually -.3 percentage points decrease in beverage calories consumed because of the
law.
That's our causal effect on beverage calorie consumption.
So that seems pretty small.
Let's look at food.
Over for food, it's a -14.7 percentage points.
So actually it seems like there's a pretty big effect on the law on food consumption.
Food consumption went down.
If you add food and beverage together then we are -6% points effect on total food and
beverage consumption.
So that's their main finding in the paper: that the mandated calorie count posting law
seemed to cause food and beverage consumption to go down by 6 percentage points, and most
of that is coming through a decease in food, not beverage consumption.
[MUSIC].