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.

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Demographics, Economic Theory and Acadmicsclaus vistesendemographics, birth rates, fertility, tempo effects, birth postponement, Malthus, Neo-Malthusianism, fertility trap, low fertility, total fertility rate, cohort fertility
Cruising for a Bruising

Financial market pundits are a bit like dogs chasing cars; they wouldn’t know what to do if they caught one. And so it is that after trying to figure out whether the economy and markets would achieve a soft landing in the wake of the post-Covid tightening cycle, no one quite knows what to think now that the soft landing appears to have arrived.

Let’s list the key requirements for a soft landing.

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Global Economy, Markets and Trading, Monetary Policy, US Economy, UK, General info, Eurozone watchclaus visteseninflation, interest rates, growth, global economy, David Zervos, Dario Perkins, larry summers, Philips Curve, prices, CPI, markets, equities, bonds, leading indicators
The BIS gets it wrong on AI/LLM and feminism & reproduction

The BIS has a Bulletin out on the usefulness of AI and large language models. They’re not terribly impressed.

When posed with a logical puzzle that demands reasoning about the knowledge of others and about counterfactuals, large language models (LLMs) display a distinctive and revealing pattern of failure. 

The LLM performs flawlessly when presented with the original wording of the puzzle available on the internet but performs poorly when incidental details are changed, suggestive of a lack of true understanding of the underlying logic. 

Our findings do not detract from the considerable progress in central bank applications of machine learning to data management, macro analysis and regulation/supervision. They do, however, suggest that caution should be exercised in deploying LLMs in contexts that demand rigorous reasoning in economic analysis.

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Demographics, Global Economy, Politics and societyclaus vistesenfeminism, AI, BIS, ChatGPT, OpenAI, the Nation, Moira Donegan
Stock market signals with Chat GPT 4

This is my third use case for how to do quantitative analysis with Chat GPT 4. The two others, on Eurozone inflation and times series regression with macro data, can be found here and here. I started in the industry as Head of Research for Variant Perception, a research shop that specialises, among other things, in quantitative trading models, asset allocation tools, and trade signalling analysis. One tool that came up again and again in my analyses was binary signals to identify turning points in asset classes, stocks or economic data series. The idea is simple. First, you create a binary indicator which takes the value of 1, if a certain threshold in the data is breached to the upside or downside, and zero otherwise. Secondly, you investigate what happens after such a signal has gone off, either in the original data set or mapped to a separate data set. You can combine signals across datasets to get a rolling series of signals, which can be compared to asset prices or economic data. You can see an example of such an analysis with the Nasdaq here.

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Markets and Tradingclaus vistesenChatGPT, Microsoft, stock market, stock price signals, AI