THE INVERTED WORLD REPORT – Dragon Theory

Edition #2, September 28th 2025

In my first report, I described the birthrate collapse as the crisis to end all crises — a global, synchronized decline unlike anything societies have faced before. But before we can answer why it began, or how it spread so quickly, we need to fix the way we measure it. Because if your tools are blunt or broken, you may never see what’s hiding in plain sight. This is where demography has misled itself for decades — and why my story of dragons and fairy-tale figures matters.

Imagine little dragons suddenly appearing in Japan and parts of Europe — overnight, and at the same time. Now imagine those dragons multiplying, then nation by nation spreading in lockstep across the developed world. You wouldn’t shrug it off as mere coincidence, with dragons independently appearing due to local factors. Yet “independent dragon theory” is precisely how we’ve responded to the low birthrate crisis: a phenomenon that began in Japan and Europe in the early 1970s and has since widened and spread relentlessly. Today, three-quarters of the world’s population lives in countries below the tipping point demographers call the “Replacement Level” — what I prefer to call the “Stability Level” – approximately two surviving children per woman.

Independent Dragon Theorists

Ask almost anyone why birthrates are falling, and you’ll hear a list of reasons, usually delivered with great confidence. Housing costs. Salaries. Childcare. Work–life balance. Gender roles. The list goes on. But for each reason, there are obvious counter-examples. Education is expensive in Japan but free in much of Europe. Housing is affordable in Japan but not in parts of the UK or US. Scandinavia tops the world in gender equality and work–life balance, yet its birthrates are as low as those in countries without those advantages.

When pressed, demographic experts often fall back on a shrug: “no one factor,” they insist, pointing to the complexities of modern life. In contrast, few have dared to argue that there might be a single “silver bullet” — one powerful force that explains this collapse across such diverse cultures and economies. Indeed, if such an explanation exists, surely it would have been uncovered by now. Demography, after all, is not short of data. Yet coming from a world of Data Science, it didn’t take long to understand why researchers might have missed something: much of demography was built on measuring an idea as fanciful as a fire-breathing dragon — the so-called “average woman.”

When I entered the field in 2016, I was struck by how little effort was being made to search for a unifying cause. Instead, the field was crowded by a plethora of “independent dragon theories” — based on the assumption that each region faced its own unique monster. This wasn’t just opinion; it shaped the way data itself was analyzed. Policymakers, too, fought their own local dragons. Sweden pioneered long parental leaves and heavily subsidized childcare. Japan expanded daycare provision and introduced paid paternity leave. Singapore rolled out “baby bonuses” worth thousands of dollars per child. Yet across all these cases, the effect on birthrates was marginal at best. Perhaps unsurprisingly, almost none of it moved the needle.

Slaying the Myth of the Average Woman

As I set out on what often felt at times like a lonely nine-year research project, I avoided averages wherever possible in the national statistical databases. Just as importantly, as I traveled across five continents speaking with people young and old, my focus was on real stories, not abstract summaries. My aim was to confirm what soon became clear: the “average woman” is neither alive, nor well – anywhere!

As an aside, if I had my way, solitary averages would be banned outright. There’s a reason they’re called the mean, after all! A single average, stripped of its shape, can be as fanciful as any fairytale. What averages hide is often more important than what they reveal. To understand birthrates — or anything human, really — you need the full shape of the data: the spread, the peaks, the skew, the clusters, the outliers. I once tried to make this point to my film crew while shooting Birthgap in Switzerland: “What is the average height of a mountain in the Alps?” A meaningless question produces only meaningless answers — or, in this case, no answer at all.

I’m not alone in this fight. I’ve just added Todd Rose’s *The End of Average* (2016) to my reading list — an acclaimed book that argues how averages erase individuality and lead to one-size-fits-none policies. Demography, sadly, is a prime example. Its central myth — the so-called “average woman,” measured by Total Fertility Rate (TFR) — has misled us for decades, and obscured the reality of what is happening in our societies that triggered low birth rates.

In fairness, perhaps 50 years ago you could squint and imagine an average woman in developed countries — a time when most married in their early twenties and started families soon after. But today? That abstraction is pure fiction. Family formation now spans a huge age range — from teen mothers to women in their 40s — yet demographers still chase Ms. Average like she’s real in contemporary society.

Even worse, TFR continues to be used not just descriptively, but as a key explanatory factor as if it were a tangible number. Yes, a fabricated average of a figment of a demographer’s mind is being deployed to explain a real-world birthrate collapse. Better luck chasing dragons.

The (Potential) Rise of Microdemography

My view is blunt: it’s time to retire TFR - and quickly, while there’s still time to act on what it has been hiding. That won’t be easy for a discipline so wedded to TFR as its cornerstone, with the media faithfully marching to the same discordant tune. But there is a middle ground during a transition: for a time, report TFR alongside more meaningful measures, as I argue in my recent Scientific Reports paper (Nature Portfolio, August 21, 2025).

By meaningful, I mean avoiding falling into the “averagist” trap by breaking TFR into its natural parts:

• the rate at which women become mothers at all - what I call the Total Maternal Rate (TMR), and

• the average number of children among those who do - what I term Children per Mother (CPM)

For those who like a formula, it’s simple: TFR = TMR x CPM. This isn’t just about tidying up the math to sidestep “Ms. Average” - it massively increases explanatory power, boosting the Adjusted R² by +0.385 (for those not steeped in statistics, that’s a big deal).

Yet remarkably, this way of framing things has rarely been used in demography, even though it directly exposes the common factor driving ultra-low birthrates worldwide, beginning in Japan and Europe in the 1970s. I believe this approach is valuable enough to deserve its own umbrella: Microdemography. Just as Microeconomics emerged - with some resistance - during the Great Depression to unify more granular approaches in economics, I believe this is the moment for its cousin in demography to be formalized, bringing less mainstream methods to the forefront.

But wait — I can already hear the protests! Surely TFR is still useful as a macro descriptor of how a nation is faring? After all, governments and headlines alike point to numbers like South Korea’s horrifying “0.75 children per woman.” But pause for a moment: what do those numbers really mean? A TFR of 1.4 translates to birthrates one-third below the Stability Level. A TFR of 0.7 is two-thirds below. Framed this way, the picture becomes far more intuitive — “two-thirds below stability” is instantly clearer than simply saying “TFR = 0.7” to any lay person - or I would argue, even to most experts.

That’s why my paper calls for a new measure: the RLI, or Replacement Level Index. But I now prefer a naming refinement: the Stability Level Index (SLI), which would show South Korea at roughly Negative 65%.

So what’s left for TFR, except to quickly skip over it and focus on the more intuitive and incisive SLI, TMR and CPM? I confess that my hope is to see a world better informed — and no longer in need of, or misled by, TFR — within my lifetime.

Naming the Dragon: Unplanned Childlessness

Microdemography, and the measures I formalized in my paper as the Microdemographic Framework (MDF), offer no less than a clear explanation for ultra-low birthrates. Once you clear away the statistical fog that TFR represents, the story is unmistakable: the true driver isn’t a patchwork of local quirks but a single unifying force – a single ever-spreading breed of dragon: Unplanned Childlessness.

In my next article, I’ll turn to the deeper question — why did Unplanned Childlessness appear in the first place, why has it spread so fast, and why has it proved so stubbornly resilient. And why is it governed by an apparent law of nature?

Ultimately, this is much more than an academic question - unless we confront Unplanned Childlessness, nations will continue to wither, and wither. That is the stark reality before us — but at least we now have an explanation, and with it, the possibility of a cure.

As I’ve come to accept, arithmetically, mass Unplanned Childlessness is how civilizations end. My task these days is to persuade nations that they must reinvent themselves or fade relentlessly and without hope into the sunset. At least my crusade has one dragon to face — not a blaze of independent ones.