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<\/a><\/p>\nIt regularly goes viral on X<\/a>\u00a0with the usual abortion debate to follow. The source is this site<\/a>, where we can also learn that this plot is 15 years old. Here’s the newest one they have from 2022:<\/p>\n The newer map shows Portugal now has a higher rate, and Poland is still the big outlier together with Austria (in their data). I think these rates are based on abortions in that country, so does not include medical tourism, which is now very easy to do. Poland’s effective rate is thus much higher than the <2% seen here. Naturally, abortions lower the fertility rate, so you would expect that if we plot these together, we get a decent negative correlation:<\/p>\n But that’s not what we see, instead it is positive for European countries, and worldwide it is:<\/p>\n In neither case is the relationship stronger than chance level, despite we knowing that it has a causal effect. It tells us that abortion rates per pregnancy is not the cause of national differences taken as a snapshot (cross-sectional). Clearly, what matters must be the other part of the equation, the pregnancy rate:<\/p>\n Countries above the regression line are those with few failed pregnancies, whether from abortion or miscarriage. If you look closely you can see that Poland is a bit above the line. And in general, Poland does not have higher TFR than its neighbors:<\/p>\n The issue with the focus on abortion is that it is not the only way women can control fertility. If you ban abortion, they will just take more care to not get pregnant in the first place and not rely on abortion as a backup solution This is what Polish women do:<\/p>\n Only Ukraine has a lower pregnancy rate.<\/p>\n We have time series data, so we can also try the country fixed effect model, which controls for anything that doesn’t change for a given country (geography, neighbors). Using this, can we show that changing the abortion rate per pregnancy boosts fertility? No, within country variation in TFR is not much linked to %aborted either, 1.4% variance explained, and with the wrong sign. Fitting the more correct model — TFR = pregnancy rate * (1 – abortion_rate_pp) — explains 90% of the variance and change is almost entire due to changes in the pregnancy rate, not the aborted%. Abortions are a distraction, communist Romania aside<\/a>. The model doesn’t fit perfectly because the pregnancy rate is given in per 1000 women, and the TFR is a metric that attempts to predict the lifetime fertility of a woman using the current age specific birth rates. This forecasting model implicitly assumes that these rates do not change, which of course we know they do<\/a>. There is also a minor effect of the age and sex composition of countries, but these are minor compared to the main cause, the pregnancy rate. To boost fertility rates, countries must focus on this variable, not having religious or quasi-religious arguments about abortion. You must make women want to have children. Make Motherhood Great Again.<\/p>\n Some more maps for those interested. Aborted% by country:<\/p>\n Pregnancy rate map:<\/p>\n TFR map:<\/p>\n Cross-sectional fit of TFR ~ pregnancy_rate * (1-aborted%):<\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n