I consider this a typical case of statisticians massaging the data. In fact, I believe the Gini coefficient itself is flawed modeling. Even statisticians don't really use it much these days, right? The Gini index on Wikipedia actually reflects consumption inequality.
Also, high-fertility countries fundamentally have low contraception usage, low women's rights, and short periods of education. This is because labor is crucial in typical agrarian to light-industry economies.
Conversely, low-fertility countries are characterized by extended education and delayed job stability. The reason is simple: work that generates high added value requires a significant amount of time spent on education. Generally, the OECD doesn't view the decline in fertility rates in developed countries as being driven by just a single variable either.
Rather, there is a clear established relationship: higher housing costs correspond to a lower TFR (Total Fertility Rate) [1]. Classes exempt from these housing expenses—namely, individuals whose parents hold substantial real estate and assets—clearly have more children.
Actually, this kind of research has been conducted extensively in East Asian countries like Korea and Japan, yielding very similar conclusions.
> Rather, there is a clear established relationship: higher housing costs correspond to a lower TFR (Total Fertility Rate) [1]. Classes exempt from these housing expenses—namely, individuals whose parents hold substantial real estate and assets—clearly have more children.
This report is 122 pages. A cursory search did not turn up anything to support either of your claims. What page of the report are you drawing this conclusion from?
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Fertility
The total fertility rate (TFR) indicates the average number of children born per woman over a lifetime given current age‑specific fertility rates, assuming no female mortality during reproductive years. The population is replaced at a total fertility rate of about 2.1 children per woman.
Let's consider programming, which represents an entry into a high value-added industry.
To monetize programming, you fundamentally need to offer something more valuable than existing open-source software, or target a niche that open-source fails to address. However, this industry has now matured, and countless talented young individuals in the US have already contributed heavily to the open-source ecosystem.
As a result, it is exceedingly difficult for developers in non-English speaking countries to sell programs that are inferior to free open-source alternatives. This is exacerbated by the very nature of software, which can be instantly and perfectly replicated.
(Physical capital, on the other hand, is much easier for the establishment to control through protectionism. For instance, to foster the growth of Hyundai Motors, my country, South Korea, restricted American car imports, effectively forcing citizens to drive lower-quality domestic cars. That kind of national sacrifice was required.)
Consequently, citizens of non-English speaking countries face a double burden: the time required to master English, added to the time needed to learn programming and accumulate foundational experience. By the time they build this experience and reach a stable level of professional competency, they are typically in their early 30s. This inherently forces a delay in marriage and family formation.
If anything the trend is the other way; countries that are significantly more economically egalitarian tend to have lower fertility rates.
Here is a map of wealth inequality based on the Gini coefficient:
https://en.wikipedia.org/wiki/Gini_coefficient
Here is another showing total fertility rate:
https://en.wikipedia.org/wiki/List_of_countries_by_total_fer...