Covid19 – make or break

Some countries will wrack their economies and others won’t.

Some countries will stop the epidemic in its track and others will be overwhelmed.

It seems pretty unclear how these two bifurcations will correlate. It is entirely possible that a quick collapse with 100.000s of deaths during maybe two horrible months will leave the economy largely unscathed, while an extended lockdown might have much more severe economic consequences.

But of course quickly controlling the epidemic might lead to minor economic consequences while it is also possible to combine all the economic damage of an extended lockdown with not managing to keep the virus from spreading through the population.

There probably won’t be much middle ground in the epidemic bifurcation.

On a walk recently I was thinking about AGI and about what it would mean to slow the epidemic to such a degree that the healthcare system isn’t overwhelmed, without really stopping it. No, these two topics are not related.

In Germany there are 30.000 ICU units, I was calculating with 20 percent severe cases, of which 10 percent require a ICU unit. This is probably too high. Hey, I was taking a walk, had to work from memory here.

Anyway, with 10 percent requiring ICU to stay underwhelming the epidemic wouldn’t be allowed to hit more than 300.000 people at once. With a mean hospitalization duration of 3 weeks we end up with 80.000.000 / 300.000 * 3 weeks = 15 years. Which means that that scenario is never going to happen. Either the epidemic is stopped or it overwhelms the healthcare system.
In the same evening I came across an article making the same point. It used 2.5% ICU rate and was written by an AGI-researcher.

Of course my point still holds even with 2.5%, though that strikes me as too low, being even below some estimates for the completed mortality rate.

In the economic bifurcation things might be more gradual. If only because the plunging world economy will take everybody down a notch. Still some countries will enact a full scale lockdown, possibly over many weeks or even months. And others won’t.

The best case scenario seems to be that ramping up testing (PCR, X-ray, antigen + possibly test pooling), tracking and mandatory masks, gloves and disinfection will allow containment of the virus without extended lockdowns.

In any case, the next month is probably make or break for many countries. Unchecked doubling every three days will lead to 1000-fold increase, taking those countries who have a couple of hundred cases or thousand cases right now into disaster zone. Slowed doubling every 6 days would lead to a 32-fold increase, overloading the health system of most of those same countries.

Covid19 in Germany

There is a lot of data freely available, unfortunately it seems to reflect testing more than the actual spread of the virus and so far it is hard to know whether the Chinese death rates are representative of the overall lethality. But a comparison might be enlightening.

In the winter of 2017/18 there was a severe flu epidemic in Germany. Partly because the less expensive vaccine didn’t work against it and the more expensive one wasn’t paid by health care system. Within 3,5 months an estimated 10 million cases lead to 25.000 deaths. A lethality of 0.25%, somewhat high for the flu but not unusually so. 60.000 people were hospitalized.

The lethality of Covid19 is somewhere between 3-12 times higher than that flu. So a similar epidemic might lead to 75.000 to 300.000 deaths in Germany. This is quite possible, because Covid19 seems to be at least as contagious as the flu.

Additionally, the flu is generally somewhat similar to strains that have been common before, so part of the population is somewhat immune even without vaccination. Covid19 is completely new and therefore might achieve much higher penetration, with 50-60% of the population being infected at some point being a distinct possibility. In that case the worst case scenario is up to a million deaths.

In fact the toll might be even higher than that, because the German population is significantly older than the Chinese population. So the percentage of particularly susceptible people is quite a bit higher than in China.

Of course Covid19 is taken much more seriously than the flu and I assume that this worst case scenario would be avoided by draconian measures probably implemented several weeks or even months too late for optimal effect, that will completely crater the economy.

Standard deviations in population traits

Standard deviations are a somewhat neglected topic when it comes to the statistical analysis of group differences. And when it comes up, it usually only for the explanation of some tail effects: A larger standard deviation leads to more outliers and beats a higher mean if you go far out onto the tail of the distribution. Brilliant example: La Griyffe du Lion’s analysis of crime rates and serial killers [1].

But standard deviation are interesting beyond these tail effects. For example environmental hardship or strong environmental influences on a trait should generally increase the standard deviation. If half of all kids in a village get a disease that costs a few IQ points this will increase standard deviation in IQ, compared to a country where this disease has been eradicated. You can often see this effect in scholastic achievement studies where the standard deviation of second generation immigrants can be notably lower than that of first generation immigrants. In second generation immigrants language ability, health and malnutrition varies a lot less than among first generation immigrants and so does every trait downstream of these.

This makes it striking that the standard deviation of IQ in African Americans and of Africans generally, is usually lower than the SD of white Americans or European populations, despite the undoubtably worse environmental conditions. African American standard deviation in IQ for example varies between 11 and 14 points compared to a white SD of 15. Given that a worse environment should increase the SD, this lower SD most likely is due to genetic reasons. In this post I want to discuss possible influences on these differences in standard deviations.

Strikingly all tests in this study show lower SDs for the African students
In this study most subtests show lower SD for the South Africans with a four point differences in SD in the Performance and Full Scale IQ

One possible influence on the standard deviation would be admixture. If a population is a relatively recent mix of two populations with a different mean, the new population would have a higher SD. Basically the variation in admixture percentage would add to the trait SD. This can be observed in Hispanics, see my blogpost [2]. Of course African Americans are an admixed population with roughly 20% white admixture, while white Americans aren’t, so purely African African Americans should have an even lower standard dev than the current population.

In non-admixed populations the standard deviation of a trait ultimately directly depends on assortative mating for that trait. It is intuitive that random mating minimizes differences because people high on a trait and people low on a trait mix genes often. Strong assortative mating sees a widening of the bell curve up to a steady state influenced by the heritability of the trait.

So one interpretation of this observation would be that environments that select for a trait are environments in which this trait is valued, which means that assortative mating is strong. In that case we would expect to see populations with a high mean to also have a high standard dev and vice versa, which is kind of what we see in IQ. But as the blogpost linked above shows, the standard deviation of violent crime is higher in Whites although the mean is lower. This seems to constitute a counter example, until we realize that the trait under selection here might be peaceful behavior.

But there are also possible explanations that don’t invoke selection pressure. For example a population that has local mating, but a global cline in the trait in question, will have globally a higher standard deviation. Such a cline is often observed in IQ where the Northern parts of many countries are higher in IQ than the Southern parts, though occasionally it is the other way round. Nigeria is probably an extreme example for such an IQ cline, see [3]. So Nigerians as a whole population might have quite a high standard deviation. However, the resulting distribution in Nigeria would not be gaussian, but multimodal, because the different ethnic groups are very much endogamous. So Whites might have higher standard deviations simply because they have historically formed larger endogamous groups or rather endogamous groups that stretch over more terrain. This explanation would predict tails that are slimmer than expected, because the distribution is not fully gaussian. This scenario is somewhat comparable to the admixture case mentioned above.

A third and maybe most convincing scenario combines aspects of the other two ideas: Maybe standard deviations depend on the historical sophistication of societies. More advanced societies lead to stronger social stratification and this in turn leads to stronger assortative mating even without changing the preferences of the people involved. Assortative mating would partly be a byproduct of assortative socializing in socially stratified societies.

[1] Why most serial killers are white men.

[2] Hereditarianism III: Discussion

[3] An answer to Chanda Chisala

Demographic Change in France – Prenoms Rare

In my blogposts on demographic change in France I have discussed the growing percentage of kids with African origin in France. If estimated via the number of kids that are tested on sickle cell anemia, this percentage has surpassed 40% and has more than doubled since the year 2000. I counted the change in how many newborns are given typical Muslim names and could validate at least the growth rate of the sickle cell data, roughly a doubling between 2000 and 2015. It’s now 1.5 years since I analyzed the data and I decided to revisit the newest given name database, which is updated every year by the French bureau of statistics INSEE.

Re-checking the percentage of muslim names, I made the surprising discovery that since 2016 this percentage has stopped growing. For comparison, the number of names covered by my short list of muslim names is 13003, 26926, 25873 for the year 2000, 2015 and 2018. Given that the number of births has been dropping slightly, this translates into the percentage staying steady over 2015, 2016, 2017 and 2018.

If the muslim population from the Maghreb were the only sickle cell tested kids, this would translate to a steady 40% of kids with African origin. However, this is not the case. Subsaharan immigrants to France usually aren’t Muslims, so even if the percentage of Muslim kids has stopped growing, the original „sickle cell“ percentage might still be growing. (The sickle cell statistic has long been discontinued, for obvious reasons.)

Looking through the data I came across the prenoms rare, the rare first names. Under this moniker the kids are counted that have been given a relatively rare name. Contrary to the Muslim names, the percentage of prenoms rare has been steadily rising even in the last few years. I looked back a bit and made the discovery that this percentage has been doubling every twenty years or so since world war 2.

Given that the Muslim names had stopped increasing recently and given that this exponential growth seems to have started very early, surely before mass immigration, I was ready to interpret it as a trend of the French society towards greater and greater individualism or something. Then I decided to check whether the percentage of Muslim names in the different departments correlates with the percentage of prenoms rare:

It turns out that there is a significant correlation starting in the year 1949, that steadily increases as more and more departments have a count of Muslim names above zero until it reaches almost 0.80. In the early 2000s it suddenly starts dropping and has vanished by 2014. So what’s going on?

One thing that might contribute to killing off the correlation is that names of a 20% minority just aren’t as rare as names of a <10% minority. So Muslims might have slowly outgrown the prenoms rare marker. But the prenoms rare keep rising exponentially, so if Muslim names are getting less rare there has to be another driver of that growth. If we look at the intercept of the linear fit between the Muslim names and the prenoms rare percentage, that is the extrapolated prenoms rare for a department with zero Muslims, we get the following pattern over time:

There is something like 1% of rare names in the original French population and this probably doesn’t change too much. But starting in the 1970s there is an increase in the rare names independent of Muslims names that shows roughly a doubling every ten years or so. It seems probable that this is driven by non-Muslim immigration. However, when I look for the names that correlate strongest with prenoms rare over the different departments I get anything but rare names:

(0.47688124948411037, 1.8063429505759013e-76, ‘GABRIEL’),
(0.470505323840092, 3.2246147618498544e-74, ‘LIAM’),
(0.4416925115802408, 1.2859076223422156e-64, ‘MAËL’),
(0.4326625169376073, 8.57024632603287e-62, ‘MIA’),
(0.4206020094129121, 3.743600297108489e-58, ‘TIMÉO’),
(0.4190831094973139, 1.0503598293466153e-57, ‘ADAM’),
(0.4149723540036518, 1.6689711293740196e-56, ‘NOÉ’),
(0.4079050152026234, 1.7728242577990175e-54, ‘TIAGO’),
(0.4039208782138888, 2.342116862082597e-53, ‘KAÏS’),
(0.3975213210364356, 1.3754944258800354e-51, ‘EDEN’),
(0.39080880395597334, 8.963524732093063e-50, ‘ISSA’),
(0.39039340121367977, 1.1570834678410338e-49, ‘MILA’),
(0.3895565952224957, 1.9331033907637143e-49, ‘ISMAËL’) …

This seems to mostly pick up on urbanisation, which is in line with prenoms rare being driven by immigration. However, the hope to find actual names that represent the prenoms rare population comes to nothing. So again we are left without a sensible measure of the non-Muslim African population growth in France. But with the massive increase in prenoms rare it seems unlikely the entire „sickle cell“ percentage has petered off like the Muslim names.

Why I do not believe in a big dysgenic effect in the West

There are now several convincing papers that show a dysgenic effect in Western countries, but when I say big dysgenic effect what I mean are the estimates given by Woodley of Menie. He claims that in great Britain over the last hundred years average IQ has dropped by more than 10 points, that is 1 point per decade.

There are several reasons why I find these estimates unrealistic. One reason is that for such a big dysgenic effect presumably limited to western countries, the IQ gaps we see today are remarkably similar to Galton’s estimates 160 years ago.

Another reason to be skeptical is that we actually live in the golden age of mathematics. It seems unrealistic that after a drop of more than 10 points we would still have the geniuses to solve century old problems like the Poincare conjecture or Fermat’s last theorem.

I would also assume that some of the normal IQ tests used in the Wechsler test, would show a negative Flynn effect over the last decades. In actual fact the Flynn effect of the different subtests ranges from 0.07 to 1.59 standard deviations for the second five decades of the last century in the US. If the effect of environmental improvements can range from 0 to „a lot“, it seems a priori unlikely that the subtests with the weakest Flynn effect more or less exactly cancels a large dysgenic effect.

Flynn effect in the US between 1947 and 2001, don’t ask me why vocabulary is marked.

Woodley’s shtick is to find different traits that correlate with IQ and show that some sample many decades ago scored better than the average person does today. Unfortunately, this amounts to cherry picking and the long time between the studies makes sampling problems impossible to rule out.

Myopia for example correlates with IQ and has become much more prevalent in the last century. The correlation is even due to an overlap of genetic factors. Does the increasing prevalence of myopia prove a eugenic effect? Hardly. The true dysgenic effect is probably 2 to 5 times weaker as estimated via polygenic scores for the population of Iceland.

Chess psychometrics – Ashkenazi grit

In earlier posts we have shown that Ashkenazi Jews have higher average Elo than other chess players in the US. This corresponds to the well-established IQ advantage of roughly 10 points enjoyed by the Ashkenazim.

However, in many areas Ashkenazi Jews are even more over represented than can be explained by IQ alone. This is not surprising, if the reason for the IQ advantage is evolutionary selection for success in white collar jobs. Evolution doesn’t act on IQ-tests, so the higher proficiency of Ashkenazim in cognitive occupations should only partly be explained by IQ.

According to Richard Lynn, a big part of the remaining gap between Ashkenazim and Gentiles is explained by „will to succeed“ or „grit“. In chess, grit should be measurable by examining the length of games. The stronger your will to succeed, the longer you will try to win, or try to avoid defeat.

In this post we do a little foray into Ashkenazi grit in chess. Instead of trying to connect the identities of US players that we peg as Ashkenazim with the names in our games database, we just do a quick and dirty look for games of players with very typical Ashkenazi names: Names that start with „Fine“, „Gold“, „Rubin“ or the name „Cohen“.

While this should guarantee a very Ashkenazi set of players, we still come up empty this time around. While the average game in our database has a length of 38.89 moves, the Golds, Cohens, Fines and Rubins come in at 39.23, 38.26, 38.00, 40.94 calculated from 3210, 379, 1621 and 2071 games.

Along the same vein, the number of draws should be lower among people more driven to succeed. After all, if you are very peacefully inclined you cannot be too driven. Also here we see no significant deviation in our small sample with the average percentage of draws being 31.4 and the Ashkenazi values varying with 29.9, 16.6, 31.8, 33.4 with the 16.6 value being of the very small sample of Cohen games.

This is somewhat surprising, but could be due to the small sample size. After all these few thousand games only correspond to a couple of dozen players. So it seems like we will have to bring the entire power of my chess database trickery to bear on this question in some post in the future.

First year review

I started this blog exactly one year ago and I tried to publish a post a week for at least a year. The year has now concluded and I almost matched my goal. In the final months I couldn’t quite keep up the frequency so we are still a few posts short to match the number of weeks in a year.

The motivation for the blog was to publish investigations and thoughts into HBD. Human Bio-Diversity is an important topic that explains big parts of the geopolitical picture of economic and scientific progress as well social dynamics, crime and education within countries. It is, however, so villified that I couldn’t easily trust existing sources. My own independent investigations and ideas seemed worth publishing.

Given that I analysed a lot of data before I actually started the blog and then also proceeded to put out the most interesting ideas as quickly as possible, it is maybe inevitable that I couldn’t keep up the quality of earlier posts. Analysing original data takes a lot of time, with no interesting result guaranteed. Instead, when time was short I made do with „Random thoughts“.

I would have preferred to stick with data and scientific and mathematical ideas. „Random thoughts“ veered too easily into the political realm and if not seen in the context of pushback against increasingly woke discourse also might give a distorted impression of my persuasions.

Readership has been low, with little effort of mine to drive it up. Feedback has been very complimentary.

I am unsure about the future of the blog. On the one hand I hate quitting. On the other hand I should probably put this effort into one of my many other fields of interest, where I can actually reap some personal and professional benefit. A wise course of action would probably be to relax my publishing schedule. Given that I haven’t inspired much of a dedicated following, this also doesn’t disappoint anybody. However, it is unclear to me at the moment whether publishing once a month is actually easier than once per week. Maybe I’ll just post more ad libitum whenever I have something interesting to discuss.

We’ll see.