Welcome back.

Today, we're talking about salary structures and internal benchmarks.

And so in our previous video, we looked at this world in which you can actually

observe all of the different benchmarks for

all of the different grades that you have, but that's kind of an ideal circumstance.

Often times,

we won't be able to observe the benchmarks for all of our different grades.

So for example, we might be able to adequately benchmark an engineer and

a senior engineer, but we won't be able to adequately benchmark also the lead

engineer and the principle engineer and so on.

And so what are you supposed to do?

Well, so let's take an example where we are able to benchmark two of these

jobs in our hierarchy, but we're not able to benchmark those middle engineers.

And we're going to see what we're supposed to do.

So the first thing that we're going to do is we're going to establish control rates.

That is, for those jobs that we actually are able to benchmark,

we're going to essentially calibrate our whole organizational hierarchy,

in our pay hierarchy.

And from these control rates, we're going to fit a pay policy line.

That is, we're going to take the data that we have available, and we're going to

fit a line, and then we're going to fill in the gaps using that pay policy line.

And that pay policy line is essentially a method for

mapping our pay grades and our job grades to median pays.

So let's take an example from a slightly bigger pay structure.

So suppose we have seven different levels.

And here we're just going to put our midpoints for our salary structure.

So suppose that we're able to find good benchmarks for levels 1, 2, 4, and 7.

But we don't have very good benchmarks for 3, 5, and 6.

So again, our first step is to establish our control rates,

which are going to be the rates for those jobs that we can benchmark.

And then we're going to fit our pay policy line.

And so one method for doing this is called regression.

Regression is just simply a method for

fitting a best fit line through our available data.

So we're going to use regression to fit a line through those

jobs that we can actually benchmark.

And then we're going to fill in the midpoints from that regression line.

And this is an example of what it might look like.

So those predicted values from that regression would

establish the midpoints for the structure for

the specific grades, and then we can add and subtract maybe ten or

15% from those different grades to establish our full structure.