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. Another method is simply to use the midpoints. So for using midpoints, all we're doing is we're taking the jobs that we can benchmark, we're fitting lines between them, and then we're filling in the gaps from there. So let's give a rehash. So the first step is going to be to pick the jobs that we're going to benchmark. Typically these are going to the jobs for which we can easily go off and we can find good survey data to find out what the medium pay is for those jobs. They're typically going to be the largest grades or the most standard grades within our organizational hierarchy. The next step we're going to do is we're going to benchmark pay at our control rates. That is, we're going to figure out what the medians are given those surveys. The third type is going to be we're going to fit our pay policy line through regression or some other method. And then fourth, we're going to set pay for our non-benchmarked jobs using that pay policy line. And then lastly, we're going to determine our ranges based on whatever our firm policies are. And so all together, these five steps are how you'd create a structure from those jobs that you are able to benchmark. So let's look at the big picture. So in our previous lesson we also talked about how pay was determined by labor markets. And so if the pay is set by labor markets, well, how can it be set by a structure? But really these two are very complementary. So for example, let's say that we looked at a labor market, and we graphed both the pay for senior engineers and also the pay for junior engineers. And remember, we said that pay is set at the intersection of labor supply and labor demand. And then we said, when we're doing a survey, all we're really doing is kind of taking a little sample from the intersection of labor supply and labor demand to determine what people are usually paid in the labor market. And that's essentially what we're doing here. So we're saying that for our junior engineers, we're saying that our survey data, if we're just taking a sample from the labor supply and labor demand of junior engineers, this can map onto our structure. And then likewise, when we do the same thing for our senior engineers, then we're also just taking a sample of labor supply and labor demand from that senior engineering labor market and then putting it onto our structure as well. What does that mean? Well, that means that a lot of the same rules that apply to labor supply and labor demand also apply when we're talking about our structures. So for example, let's ask this question, why is it that senior engineers have higher pay? Well, of course they're higher up on the job hierarchy. But another way to look at it are from the labor supply perspective and the labor demand perspective. From the perspective of labor supply, it's expensive for engineers to acquire greater skills and experience. Those skills that might make a programmer or an engineer, or what have you, more productive are relatively scarce, and they're expensive to attain. On the demand side, employers are also willing to pay more for those skills. Just because a skill is expensive to obtain or it's rare doesn't per se make it more highly paid. But rather it's that they're both scarce, and also that employers are willing to pay a lot to get it, that makes those jobs pay more. In the next step, we're going to be discussing how to establish criteria for raises and promotions from our salary structures. All right, see you soon.
