Welcome to the site! First off, I will start by saying that you might be better off asking this in a statistics SE because this isn't really data science. But I will still try to help you.
I will assume that age and salary are the only items you have and that salary is the only thing you are trying to predict. If those assumptions are correct then there really is no algorithm, you can just do some distribution "tricks" to get you there.
You're on the right path of thinking about things on a proportional basis and I would proceed accordingly. However, I wouldn't think of it in whole numbers (like your 20k example), I would think about using some scaling. So, take the max value of all your salaries and create scales with something like
salary / max salary. From there, you should should know how many people will have X salary (in a scaled, decimal format).
Now, you need to decide how much salary inflation (or deflation) has happened in the 3 years since your data was available. Take that percentage and assign it back to your scaled values and now you can have a pretty good idea of how much each person makes in today's dollars (otherwise, you'd end up with salaries in 2016 dollars).