What If We Paid People Before the Problem Happened?

This is not a policy proposal. It is not ready for a city council presentation. It might not even work. But I keep thinking about it, and I figure if I am going to think about it anyway, I might as well write it down.

Here is the basic idea:

I currently operate an Airbnb property. Like most owners who are tech savvy, I use price labs as a dynamic pricing tool. Price labs looks at which weekends and days are most in demand and adjust the price accordingly so that supply equals demand. What if we applied that same logic to staffing?

Every fire department has mandatory overtime. You probably already know the cycle. A vacancy opens. Callbacks go out. Half the list is unavailable. Eventually someone gets held over. The shift gets covered, the money gets spent, and three days later it happens again.

The thing is, most of that is predictable. Not perfectly predictable. But close enough to matter.

Certain days just hit harder. Mother’s Day. Holiday weekends. School breaks. Certain months that historically drain leave like a leaky air tank.

So here is what I keep wondering.

What if instead of waiting for the staffing problem to show up and then throwing overtime money at it, we used some of that money earlier, on purpose, aimed at the days we already know are going to be a problem?

The Thought Experiment

Imagine you could build an overtime heat map.

You pull a few years of mandatory holdover data. You layer in leave usage, day of week, month, holiday proximity, shift patterns. You let it show you which days historically produce the most staffing pressure.

Green days are normal. Yellow days are elevated. Orange days are rough. Red days are the ones where someone is definitely getting held over.

Now imagine that before a red day arrives, the department attaches a small premium to that shift. Not overtime. Not a raise. Just a temporary bump for people already scheduled to work.

Three dollars an hour. Five dollars. Whatever number actually changes behavior.

The psychology is different than a standard callback. You are not reacting to a problem. You are telling people ahead of time: we know this day is going to be difficult, and we are willing to recognize that before it becomes a crisis.

Does the Math Actually Work?

This is the part I keep stress-testing, because a warm idea that loses money is just a warm idea.

Here is the rough sketch:

If you offer a three dollar per hour premium to 125 firefighters for a 24 hour shift, that costs about nine thousand dollars.

One mandatory holdover at time and a half, depending on the member’s rank for a 24 hour vacancy costs roughly a $1,200-$1,500.

So the question becomes: how many mandatory holdovers would that premium need to prevent to break even?

Something like 6-8.

If a targeted premium on a historically terrible staffing day prevents 6-8 mandatories, the department roughly breaks even. If it prevents more, the department may actually save money. And that is before you factor in fatigue, morale, the member who missed their kid’s birthday because of a holdover, or the firefighter who is getting quietly burned out.

Now, I am not saying the math always works. It probably does not work if you apply it too broadly. The model only makes sense on the days where staffing pressure is high enough to justify the investment.

That is the whole point. You are not paying a premium every day. You are being surgical about it.

Why This Feels Different

The current system rewards the response to the problem.

Someone gets held over. Someone collects overtime. The shift gets covered.

The system almost never rewards preventing the problem. This flips that, at least partially.

It creates an incentive for members who are already scheduled to work to stay available on the days where the department is most vulnerable. You are not manufacturing staffing. You are protecting the staffing you already have.

And honestly, the message is different too. Instead of “we may force someone to stay because too many people took leave,” the department is saying “we see this coming, and we are willing to compensate the people who hold the line.”

That is a better conversation to have.

The Part I Cannot Fully Answer Yet

There are real problems with this idea and I am not going to pretend otherwise.

A few extra dollars an hour probably does not stop someone who already booked a cruise. The premium has to be calibrated correctly or it just adds cost without changing behavior. People will figure out the system quickly and some will game it. Labor agreements and City policies make everything complicated. Fairness questions come up immediately. Why does this shift get premium pay and that one does not? The answers have to be grounded in data and new data needs to constantly update the premium pay logic.

These are not fatal flaws. They are design problems. But they are real.

The honest answer is that I do not know if this works until someone actually runs a pilot. And a pilot is the only responsible way to find out. Six months, ten to twenty historically difficult dates, a clear tracking system for leave usage, mandatory holdovers, callback volume, and actual overtime cost compared to premium pay cost.

At the end of it, you have real data instead of a theory.

The Bigger Picture

The reason I keep coming back to this idea is not just the firefighter staffing angle.

Any 24 hour operation with minimum staffing requirements deals with some version of this problem. EMS agencies. Police. Emergency communications. Hospitals. Corrections. Public utilities.

The pattern is universal. Predictable staffing pressure, reactive spending, repeated cycle.

If a data model could identify high risk windows accurately enough to justify targeted incentives, that is not just a department-level scheduling fix. That is eventually a workforce intelligence product.

A tool that learns from your historical data, surfaces upcoming risk, and helps leadership make proactive decisions before the callback list comes out.

I am not saying I have built that. I am saying the idea is worth following down the hallway a little further to see where it goes.

Final Thought

Mandatory overtime probably never fully goes away in public safety. But not every mandatory holdover has to be a surprise.

If the data already tells you which days are going to be hard, then responding to that data before the problem happens seems like a more intelligent use of the same dollars you are already spending.

The model is simple: look at the history, identify the pressure points, offer a targeted incentive, measure the result, adjust based on what you learn.

Whether it works or not, it is a better question than just accepting the same cycle every year. And if the math works, you improve morale, reduce forced overtime, and get smarter about your budget at the same time.

That seems worth at least running the experiment.