Why People Aren't Robots

The $5 Bill That Nobody Paid:

Here's a story that every city finance director should know.

A city offered generous financial assistance and some customers paid just $5 per billing cycle for water service—less than what most people spend on coffee in a single morning. These were residents who qualified for assistance, received substantial discounts, and still had basic financial capacity.

Yet they didn't pay.

Not because they couldn't afford $5 every billing cycle. They didn't pay because the city had suspended its standard payment accountability measures and didn’t communicate to customers receiving assistance, what was expected of them.

Not paying a $5 water bill isn’t about affordability. This is a story about human behavior. And it reveals something fundamental that city leaders consistently get wrong.

People Are Not Robots

Every month, finance directors across America make decisions based on a simple, flawed assumption: that people respond predictably to price signals.

Raise the parking fine from $25 to $50? Surely that will deter violations.

Offer payment plans for water bills? Surely people will pay them off.

Increase assistance eligibility thresholds? Surely that will solve collection problems.

But here's what we've discovered: the same people who consistently paid their bills suddenly became consistently delinquent when policies changed. Same income. Same bills. Different behavior.

Why? This will change based on the context of each city but its because cities inadvertently send signals about payment expectations with their policies.

The Evidence Most City Leaders Don't Have

Here's the question that should haunt every policy decision: What evidence do you have that your intervention will work?

When a mayor decides to double parking fines, what data supports the assumption that higher penalties will increase compliance rather than simply creating more uncollectable debt?

When a finance director expands payment plan options, what evidence suggests these plans will reduce delinquency rather than just moving debt from one category to another?

In our work, we see this pattern repeatedly: well-intentioned policies that sound logical but lack any empirical foundation. Cities implement "solutions" without understanding why the problem exists in the first place.

The Real Challenge: Incentives and Accountability Work Together

Cities often approach revenue challenges with either compassion or enforcement, as if these are opposing forces. They offer discounts without accountability, or enforcement without understanding. Both approaches fail.

Our data reveals something counterintuitive: assistance programs require clear communication about payment expectations, not just reduced prices. Even with dramatic discounts, customers won't pay if there is no consistent enforcement.

But enforcement without understanding customer behavior is equally ineffective. Threatening consequences to someone who genuinely can't pay doesn't create compliance—it creates desperation.

The breakthrough comes when cities use data to understand the difference between "can't pay" and "won't pay," then design different pathways for each group.

The Stakes Are Rising

Local governments are about to face unprecedented pressure to fund their own projects. Federal and state support is becoming less reliable. Revenue optimization isn't just about balancing budgets anymore—it's about maintaining essential services.

Cities that continue making policy decisions based on assumptions rather than evidence will find themselves raising rates on residents while leaving millions in uncollected revenue on the table.

What You Should Be Asking

Before implementing your next policy change, ask yourself:

What data do I have about how my specific population responds to this type of intervention?

How will I measure whether this policy is working?

Have I tested this approach, or am I implementing city-wide based on assumptions?

If you can't answer these questions with concrete data, you're making decisions based on presumption, not evidence.

The Choice Every City Faces

Your residents are complex humans with varying circumstances, motivations, and behavioral patterns. They respond to incentives, social norms, communication style, and policy signals in ways that aren't always intuitive.

You can continue treating them like robots—assuming they'll respond predictably to price changes and policy adjustments—and wonder why your well-intentioned policies don't work.

Or you can start acknowledging that serving thousands or millions of people with different socioeconomic backgrounds requires a smarter approach.

These are people whose behavior can be understood, predicted, and influenced through thoughtful policy design based on real evidence rather than good intentions.

The cities that make this shift will build sustainable revenue while serving their residents well. The cities that don't will struggle with both.

The choice is yours. But the data shows which approach works.

At SERVUS, we help cities make evidence-based decisions about revenue policy. We don't just analyze—we implement alongside you, using rigorous testing to prove what works before you commit city-wide.