Other parts of this series:
- Avoid the storm: what Europe needs to know to enact a new public services model
- What public leaders need to know to make a decision for change
- Turning ambition into action: How to launch the future of public service
- Tax compliance in the age of Ultron
- Preparing for the possible: the public service transition to a post-digital era
- Why has the civil servant been forgotten?
- Is singularity the end of the world as we know it?
- Is ZMOC a dirty word? Not for a data scientist
We’ve all been there. You glance at an item on Amazon or a movie title on Netflix. You weren’t planning on buying, but it looks pretty cool. Your finger hovers over “Add to Cart” or “Play”. Do you click?
This moment is what data scientists deem the Zero Moment of Truth, or ZMOT. Companies have used this science for years to study why people buy and what it will take to influence them to buy. Companies like Amazon and Netflix have mastered this concept, offering recommendations based on your previous purchases or views.
But there are numerous factors that come into play to get to that moment where a person says, “I’m buying it.” It could be after a thorough weighing of your options or various specifications, or perhaps it comes highly recommended by a friend. It’s not the same for everyone. That’s why companies spend significant time and effort analysing the full experience to know when ZMOT happens to close the deal.
What then, is ZMOC?
Working in tax compliance is as much an art as it is a science. And the Zero Moment of Compliance, or ZMOC, is a clever cousin to ZMOT. It is the point when someone looks at their income and circumstances and decides whether to declare it all – or not.
When it comes to tax evasion, many economists put it in the context of a deterrence model. This theory says that people are amoral and seek to maximise expected utility, regardless of legality. Businesses, likewise, look to maximise expected after-tax profits. Deciding whether to evade – or how much to evade – is a decision with a great deal of uncertainty, much like a portfolio choice or investment risk.
To evade or not to evade – that is the question
Imagine you are a 24-year-old with your first job. You’re just out of college and you’ve started a small online business selling decorated sneakers. Occasionally, you rent out your spare room. Now it’s time to declare your income. You ask yourself, am I running the risk of detection for doubling my income? Is the tax rate too high for my small income? What’s the risk of punishment if the tax authorities find out?
How much evasion depends on a number of factors:
- The probability of detection and punishment
- The punishment
- The individual taxpayer’s risk aversion
- The tax rate
Based on these criteria, how do you model ZMOC for the various segments of taxpayers? As I’ve described above, a person’s tendency is to avoid paying as much taxes as he or she can. Finding loopholes is a source of pride for many and the job of many others. The ZMOC is high and almost decided from the outset. But there are ways to influence the moment.
Much like the sales process, the goal is to understand taxpayers’ attitudes toward tax and use that information to interact and target their behavior.
Implementing a ZMOC model can improve compliance and give tax agencies additional benefits and leverage. But it requires the right knowledge and balance of regulation and a full grip on the data and science behind ZMOC.
 Slemrod, Joel. “Cheating Ourselves: The Economics of Tax Evasion,” Journal of Economic Perspectives, Winter 2007, 21(1), pp. 25-48.