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The fertility wars

The political flurry in the US over the virtues of parenthood and a high birth rate is part of a much larger cultural moment in which the debate on the significance of falling global fertility is pitting two increasingly militant and unyielding sides against each other. We have trade wars, culture wars, even actual wars; we can now add fertility wars to the list. When Elon Musk, a US entrepreneur and businessman, calls Ms. Harris an “extinctionist”, because she has linked the reluctance of young people to have children to “climate anxiety”, he means it, just as he means it when he concludes that “the natural extension of her philosophy would be a de facto holocaust for all of humanity!”

How to get handle on this? With difficulty, but in the end, hopefully with precision and clarity. First, I will briefly show that the fertility wars have been fought for a long time. I will then draw the contours of three separate positions in the fertility wars today—on the Conservative right, on the left, and a feminist perspective—before offering a suggestion on where this discourse goes next, and where it ultimately ends up, if we are sufficiently unlucky or un-attentive.

Portfolio optimization with US large cap equity sectors

I am still in a quant-mood at the moment, so today I will go through some work I’ve done on portfolio optimization with US large cap equity sectors. I am doing this to augment my current MinVar framwork, which I use for my own investments. A quick re-cap on the basics of portfolio optimization, with advance apologies to PMs reading this and lamenting that I’ve missed something. Finance has two workhorse models; the tangent portfolio, which places the investor on the efficient frontier, where risk-adjusted return—or the Sharpe ratio—is maximised. Or the minimum variance portfolio, which offers exposure to the combination of assets with the lowest variance, or standard deviation, regardless of return. These portfolios often are estimated given a set of constraints, as I explain below. Assuming most portfolio allocation decisions start with one of these ideal models in mind—you either want to achieve the best risk adjusted return or the lowest volatility—the difference between the textbook models and real-time allocations is governed by the following layers of complexity.

Delayed Gratification - Why are global birth rates falling, and does it matter?

This is the final chapter in the first part of my long-running demographics project. In the previous chapter I described the quantum effect of fertility, which hypothesises a negative relationship between fertility and rising incomes as parents substitute quantity for quality in their reproductive decisions and child-rearing. But can the quantum effect explain why birth rates in one country after the other appears to be stuck below the replacement level, and why global fertility will soon drop below that same level? The answer is no.

To understand current and more recent post WWII global fertility trends—broadly since the 1970s—we need to introduce tempo effects to the analysis. Tempo effects describe the tendency of women to postpone the timing of their first child. By mathematical logic, prolonged tempo effects can drive significant population ageing, but a more fundamental question is whether birth postponement also has a lagged effect on quantum, or more precisely, cohort fertility. This chapter discusses these question in the context of the hypothesis of a Second Demographic Transition, SDT, and presents a number of case studies to explore the specifics of recent fertility trends in key countries and regions. The chapter finishes by discussing the idea of a fertility trap, and whether the increasingly accelerating decline in global birth rates are a problem, drawing on the recent polarisation in the debate on this issue.

Time Series Regression Analysis with Chat GPT4

The following chart is one of hundreds that I use in my day-job as Chief Eurozone Economist at Pantheon Macroeconomics. It plots a normalised Z-score index of surveyed new manufacturing orders in Germany alongside year-over-year growth in factory orders, ex-major orders. It’s worthwhile spelling out the meaning of this chart in the world of economic research and forecasting. The factory orders numbers are so-called hard data, which in this case means that they’re official numbers of real activity reported by the statistical office. The PM new orders index, by contrast, is my home-cooked index of so-called soft data. Specifically, these are survey data, compiled by the likes of the EU Commission, IFO, S&P, and national statistical offices. We’re only interested in these numbers to the extent that they tell us something about the official/hard new orders data, which in turn could help us pin down trends in industrial production, exports, GDP growth, employment and so on. From simply eye-balling the chart, the two series look coincident, but note that the surveys are released ahead of the official data, so that we always have survey numbers that are one-to-two months ahead of the official data. In other words, when it comes time to forecast new orders for the month of December, we will already have survey data for that month. This should, in theory, help us to better forecast the official real new orders data.

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