[MUSIC] So that's what I talked about before.
Now let’s keep reading this table to see what all these numbers is going on about.
So first, let's jump down to the footnotes.
There's a lot of footnotes here.
So we see notes: “standard error shown in parenthesis.”
So that is good; that let's us know this is a standard error, we can see whether our causal
difference-in-difference estimate is statistically significant or not.
And that's usually the case: parentheses usually mean standard errors.
Here they just double reinforce that that's the case.
Next, they say “The sample consists of all stores with available data on employment.”
So in all of these numbers, they've got data on different fast food stores, and if they
had some data from that store, they included it in their sample.
“FTE means full-time equivalent employment:” so now we know what FTE means, full-time equivalent.
And they're defining here what that means, it “counts each part-time worker as half
a full-time worker.”
So what they are saying is, well, we know that not everyone who works at fast food stores
works full time, some people work part time, so how do we count that?
Well, just half, so 0.5 of a worker is somebody who works part-time.
So that's why we have 0.33, 0.17, that let's us interpret what it means to have fractional
workers.
Anyway let's keep going.
“Employment at six closed stores is set to zero,” so some stores were closed in
some time periods, they’re just going to set the employment to zero.
“Employment at four temporarily closed stores is treated as missing,” which means we don't
even include them in our averages.
Data is not perfect, so they're going to have to deal with that somehow, they're just explaining
what they're doing when they don't see some data for some stores.
They're going to treat it as missing for these four temporarily closed stores, and when six
stores were closed, they set it equal to zero.
So that's just this first block that we've talked about before.
Well, let's keep reading down this way.
Let's look at row four, “Change in mean FTE, full-time equivalent employment balanced
sample stores.”
What does that mean?
Oh!
There's a footnote, see footnote C: “Subset of stores with available employment data in
wave one and wave two.”
So that means that they are now only going to include stores where they saw data for
that store both before and after the policy change, whereas earlier we were including
stores in Pennsylvania before the policy change in this average, even if we didn't have data
on that particular store after the policy change.
Okay, so now we’re going to use a little bit of the subset of our data where we only
observe for those stores where we see the data for both time periods, and if we just
use that subset of data, we again find basically the same causal effect.
So this is a robustness check, it's seeing whether this number is robust in some sets.
We change exactly which stores we keep in the dataset, or we change our definitions
a little bit, then does the number hold up?
So this robustness is: yes, it does.
Now, let's go to the next line five: “Change in main FTE appointment, setting FTE at temporary
closed stores to zero.”
Okay, so here in the earlier footnote it said, “employment at four temporarily closed stores
is treated as missing.”
That means we're not even including the stores in our average.
So now we're going to do something a little bit different to treat this data.
We're not going to treat it as missing, we're going to include it in our average and set
employment to zero.
So if we do that, then we find our difference-in-difference estimate changes a little bit, down to 2.51,
but it's pretty similar to the previous two numbers.
So our qualitative conclusion doesn't change.
So this another robustness check that shows that our main estimate holds up.
So that's what all the second two lines are.
Now what's going on over here?
Well, let's start reading.
“Stores in New Jersey” and it's a little “a” here.
Okay, well, what's going on?
Let's read what they say: “Stores in New Jersey were classified by what their starting
wage is in wave one,” okay, wave one is before that the policy was changed, which
they don't explain, but if you go to the body text you could figure it out, or you could
just think, you know, typically wave one is the beginning of any survey or any panel data,
wave one then wave two, wave three, etc.
Okay, so “starting wage in wave one equals $4.25 an hour,” that's 101 stores, “or
it's between $4.26 and $4.99 per hour,” that's 140 stores, “or whether it's $5 an
hour or higher,” that's 73 stores.
So, they take all the fast food stores they see in the data in New Jersey, look at what
they're paying their employees before the minimum wage was increased.
Okay, we split them into two groups: low wage stores, this is where most of the employees
were getting paid the minimum wage, mid-range stores where their employees were getting,
they're getting more than minimum wage but not much more.
And then high-wage stores, stores where they were getting paid $5 or more on average.
So these are stores where even though minimum wage is $4.25 before the policy change, these
stores are paying their workers $5 or more.
Okay, so that's what is going out here, we’ve categorized the stores in New Jersey.
And then for just the stores paying the minimum wage before, we can look at their full-time
employment before, it’s about 19.5 people and then it increased to about 21 people after
the policy changed.
Same thing, we can do the same analysis for the mid-range stores and the high-wage stores.
So, what this will lets us do is we can think about the effect of the minimum wage on just
the low-wage stores right here.
So we can compare the difference in employment for the low wage stores to what happened in
the common trend in Pennsylvania, and we see that even in low wage stores, employment went
up a little bit.
It went up by 1.32.
So again our same difference-in-difference analysis suggests that the minimum wage is
not lowering employment.
So, and that's the same conclusion here for the mid-range stores: mid-range stores’
employment went up just a little bit.
And for the high-wage stores, employment actually went down from before to after so by about
2 people.
But the overall trend was also downwards by about two people.
So the difference-in-difference estimate for the high-wage stores is zero.
Minus two, minus two, that's about zero.
So this shows that over here, we were grouping all the stores together, just getting one
difference-in-difference estimate for all the stores.
It's showing that where that estimate is coming from is mainly effects of the increase in
employment at the low-wage stores and the mid-range stores, and the high-wage stores,
they pretty much didn't change their wage at all.
And then you can do the same robustness checks for these, that’s in some sense a robustness
check for your robustness check, by changing exactly which stores in your sample you got,
or by treating missing data slightly differently, and you get sort of similar-ish conclusions
for that.
If you compare this difference to what was happening in Pennsylvania, or this to was
happening in Pennsylvania.
[MUSIC].