Reading up on the huge animal breeding literature gives a useful background to one’s thinking about what selection on humans will do in the future (embryo selection and direct editing á la CRISPR).

Embryo-selection

I made the above infograph some time ago, maybe 1-2 years. It is still pretty accurate. The newest data for genome sequencing does not look much different.

Steve Hsu has been following some of the animal breeding literature, e.g. Frontiers in cattle genomics.

I digged around a bit and found some reviews. They mentioned various interesting experiments. Of course, the most interesting experiment is still the Russian domesticated fox experiment (I want one of these!). Recently, there was an interesting one about breeding for brain size in guppies.

gr1

There is also the famous rat maze ability experiments. Solving mazes is g-loaded in humans (Jensen, 1980, book). A good review is Tolman and Tryon Early research on the inheritance of the ability to learn.

rat

The most new and interesting part in relationship to humans is using genomic predictors alone. There is a recent, easy to read review: Understanding genomic selection in
poultry breeding.

selection for eggs

Because the animal breeding field has been going for so long, one find 100s if not 1000s of these types of graphs, yet they are still exciting. One might wonder: is there nothing one cannot select for? It seems no matter the trait, evolution finds a way. Dawkins seems to agree:

Political opposition to eugenic breeding of humans sometimes spills over into the almost certainly false assertion that it is impossible. Not only is it immoral, you may hear it said, it wouldn’t work. Unfortunately, to say that something is morally wrong, or politically undesirable, is not to say that it wouldn’t work. I have no doubt that, if you set your mind to it and had enough time and enough political power, you could breed a race of superior body-builders, or high-jumpers, or shot-putters; pearl fishers, sumo wrestlers, or sprinters; or (I suspect, although now with less confidence because there are no animal precedents) superior musicians, poets, mathematicians or wine-tasters. The reason I am confident about selective breeding for athletic prowess is that the qualities needed are so similar to those that demonstrably work in the breeding of racehorses and carthorses, of greyhounds and sledge dogs. The reason I am still pretty confident about the practical feasibility (though not the moral or political desirability) of selective breeding for mental or otherwise uniquely human traits is that there are so few examples where an attempt at selective breeding in animals has ever failed, even for traits that might have been thought surprising. Who would have thought, for example, that dogs could be bred for sheep-herding skills, or ‘pointing’, or bull-baiting?

[from The Greatest Show on Earth]

Selection for High and Low Fatness in Swine

pig_backfat_1

Also interesting is that selective breeding makes it possible to estimate realized heritability, not just from family relationships.

pig_backfat_2pig_backfat_3

I think we will see some interesting humans in the future. The reason is this: embryo selection is very close and genetic engineering is fairly close. If some countries ban them, others will allow them. Or one can sail or fly to a seastead. Or use any number of black market solutions that will inevitably spring up. Probably, not all jurisdictions will ban it, so there will be reproductive havens+tourism just like there are tax havens and even suicide havens. I don’t think Western governments will dare to force abortions on pregnant returnees, so there is nothing much they can do at that point. There is also of course the near-impossibility of proving that a fetus is a result of embryo selection, not normal fertilization. After all, embryo selection is just choosing between actual possibilities (hopefully, philosophy readers will allow me the flagrant abuse of modal terminology). If everybody starts having healthier children by using this technology, there will be no way to prove that a particular couple ‘cheated’. It is only in the aggregate one can prove that something is going on. A particular couple may just have been lucky. As for direct editing, it may be possible to spot genetically, but I doubt this will happen.

In the EU, I suspect the legality of this practice will come down to legal interpretation. The EU has a CHARTER OF FUNDAMENTAL RIGHTS OF THE EUROPEAN UNION, in which one can read:

Article 3
Right to the integrity of the person
1.   Everyone has the right to respect for his or her physical and mental integrity.
2.   In the fields of medicine and biology, the following must be respected in particular:
(a) the free and informed consent of the person concerned, according to the procedures laid down by law;
(b) the prohibition of eugenic practices, in particular those aiming at the selection of persons;
(c) the prohibition on making the human body and its parts as such a source of financial gain;
(d) the prohibition of the reproductive cloning of human beings.

But given that selection of persons is widely done for e.g. Down’s syndrome, (b) is clearly ignored in practice. (c) is also ignored e.g. for sperm and egg selling, altho they call it donation (with a nice monetary benefit in return). So, the best hope is that embryo selection for medical reasons will sneak into practice and become so standard that it would seem outlandish to ban it. This is well underway. When the public comes to accept it, the judges will probably make up some legal reason to interpret (b) narrowly, e.g. as to refer to forced sterilization. One may be able to find support for this in the background work for this charter, altho I haven’t looked into it.

Given that the technology will likely come into wide-scale practice within the next couple of decades, what remains to be researched more — a lot more — is how people will actually make choices. When prospective parent(s) have to make decisions re. which embryos to implement, there will be a choice. With a limited choice of embryos, one cannot simultaneously maximize all desirable traits and minimize all undesirable traits. There will probably be clear trends in this: few will select against intelligence, few will select short boys, few will select nasty diseases, most will select for health and happiness. People like Helen Henderson are not common:

I can say, without hesitation, that my life has been richer because I have MS. How can anyone who has no experience with disabilities understand that?

[From Future Human Evolution.]

If they still try to get children with horrible genetic diseases, the government probably (should?) will step in and ban it.

Still, there will be lots of variation. This variation in selective pressure between people should — together with strong assortative mating — result in divergence of human lines. This is will somewhat akin to dog, cat and horse breeds. Assortative mating is apparently so strong that people even choose pets that are similar to themselves: Self seeks like: many humans choose their dog pets following rules used for assortative mating.

dog_pet

We truly live in interesting times. :)

If you want to read more like this, there was also recently the double paper: Eugenics, Ready or Not I, II. (I could not find a link to part 2.)

 

In trying to merge some data, I was confronted with a problem of matching up strings where the author had mutilated them. He had done so in two ways: cutting them off at the 8th character or using personal abbreviations. The first one is relatively easy to deal with. The second one is not.

So I looked around a bit and found that there are others who had similar problems:

The large table below shows the matching results. The leftmost column has the strings I need to match. The list to be matched against is the list of country and regional names and their ISO 3 abbreviations here: osf.io/59dr7/

It looks like for most cases the shortened version worked fine. 165 of 193 matches were found all of which were correct.

The agrep (with max distance = .1, the default), found a match in 175 cases, so only a little improvement there. But it gets worse, in many cases, it disagrees with the stricter matching method and gets it wrong. There is no case where it is correct over the simpler method. Strangely, in all 10 cases where the simple method failed, agrep got it right. But it got it wrong in the easier cases. In some cases, it is truly bizarre: given the string “United S”, it goes for “United Republic of Tanzania” instead of the much easier “United States”. Strangely, a common error is preferring a subset/longer version over an exact match. No human would make this error. E.g. given “Moldova”, it prefers “Moldova, Republic” of over just “Moldova”.

There are a number of different errors it makes. In the comments below I have noted the type of error (my judgment).

For the moment, I would caution the use of this algorithm.

Country Genetic_distance_SA to_short_result agrep_result best_match agreement filled_in comments
Norway 1455.52 Norway Norway Norway TRUE Norway
Netherla 1453.28 Netherlands Netherlands Netherlands TRUE Netherlands
Ireland 1940.31 Ireland Iceland Ireland FALSE Ireland prefers substitution over exact
Liechste 1511.83 Liechtenstein Liechtenstein FALSE Liechtenstein correct
Germany 1484.92 Germany Germany Germany TRUE Germany
Sweeden 1453.79 Sweden Sweden FALSE Sweden correct
Switzerl 1557.96 Switzerland Switzerland Switzerland TRUE Switzerland
Iceland 1932.36 Iceland Iceland Iceland TRUE Iceland
Denmark 1472.52 Denmark Denmark Denmark TRUE Denmark
Belgium 1940.31 Belgium Belgium Belgium TRUE Belgium
Austria 1465.7 Austria Australia Austria FALSE Austria prefers part deleted
France 1896.22 France France France TRUE France
Slovenia 1292.32 Slovenia Slovenia Slovenia TRUE Slovenia
Finland 2420.3 Finland Finland Finland TRUE Finland
Spain 1929.44 Spain Saint Barth<U+FFFD>lemy Spain FALSE Spain no idea
Italy 1961.9 Italy Italy Italy TRUE Italy
Luxembur 1929.98 TRUE Luxembourg
Czech Re 1524.73 Czech Republic Czech Republic Czech Republic TRUE Czech Republic
U. K. 1916.91 TRUE UK
Greece 1283.05 Greece Greece Greece TRUE Greece
Cyprus 1288.53 Cyprus Cyprus Cyprus TRUE Cyprus
Estonia 2302.86 Estonia Estonia Estonia TRUE Estonia
Slovakia 1573.38 Slovakia Slovakia Slovakia TRUE Slovakia
Malta 1912.52 Malta Gibraltar Malta FALSE Malta prefers subset + substitution over exact
Poland 1905.67 Poland Poland Poland TRUE Poland
Lithuani 2389.28 Lithuania Lithuania Lithuania TRUE Lithuania
Portugal 1949.34 Portugal Portugal Portugal TRUE Portugal
Latvia 2256.69 Latvia Latvia Latvia TRUE Latvia
Croatia 1289.64 Croatia Croatia Croatia TRUE Croatia
Romania 1928.4 Romania Romania Romania TRUE Romania
Bulgaria 1399.01 Bulgaria Bulgaria Bulgaria TRUE Bulgaria
Serbia 1421.01 Serbia Serbia Serbia TRUE Serbia
Russia 1975.49 Russia Russia Russia TRUE Russia
Albania 1301.47 Albania Albania Albania TRUE Albania
Macedoni 1334.51 Macedonia Macedonia Macedonia TRUE Macedonia
Armenia 1558.32 Armenia Armenia Armenia TRUE Armenia
Moldova 1527.95 Moldova Moldova, Republic of Moldova FALSE Moldova prefers longer
Botswana 347.18 Botswana Botswana Botswana TRUE Botswana
South Af 0 South Africa South Africa South Africa TRUE South Africa
Ghana 395.9 Ghana Ghana Ghana TRUE Ghana
Eq Guine 373.2 TRUE Equatorial Guinea
Congo 452.9 Congo Congo Congo TRUE Congo
Kenya 366.78 Kenya Kenya Kenya TRUE Kenya
Cameroon 319.62 Cameroon Cameroon Cameroon TRUE Cameroon
Tanzania 352.54 Tanzania Tanzania Tanzania TRUE Tanzania
Nigeria 342.24 Nigeria Nigeria Nigeria TRUE Nigeria
Uganda 358.75 Uganda Uganda Uganda TRUE Uganda
Zambia 352.54 Zambia Gambia Zambia FALSE Zambia prefers substitution over exact
Sudan 316.95 Sudan Sudan Sudan TRUE Sudan
Zimbabwe 352.54 Zimbabwe Zimbabwe Zimbabwe TRUE Zimbabwe
Ethiopia 705.3 Ethiopia Ethiopia Ethiopia TRUE Ethiopia
Guinea 395.9 Guinea Guinea Guinea TRUE Guinea
CentAfrR 469.7 TRUE Central African Republic
SierraLe 395.9 TRUE Sierra Leone
Mozambiq 355.75 Mozambique Mozambique Mozambique TRUE Mozambique
CongoDR 410.17 Congo Republic of Congo Republic of FALSE Congo Republic of wrong but excuseable; Congo Democratic Republic
Andorra 1912.83 Andorra Andorra Andorra TRUE Andorra
Angola 353.49 Angola Angola Angola TRUE Angola
Belarus 1949.85 Belarus Belarus Belarus TRUE Belarus
Benin 394.54 Benin Benin Benin TRUE Benin
Bosnia 1337.37 Bosnia Bosnia and Herzegovina Bosnia FALSE Bosnia prefers longer
BurkinaF 378.36 Burkina Faso Burkina Faso FALSE Burkina Faso correct
Burundi 362.98 Burundi Burundi Burundi TRUE Burundi
Cape Ver 963.09 Cape Verde Cape Verde Cape Verde TRUE Cape Verde
Chad 537.81 Chad Chad Chad TRUE Chad
Comoros 352.54 Comoros Comoros Comoros TRUE Comoros
IvoryCoa 468.01 TRUE Ivory Coast
Djibouti 750.88 Djibouti Djibouti Djibouti TRUE Djibouti
Eritrea 665.96 Eritrea Eritrea Eritrea TRUE Eritrea
Gabon 360.07 Gabon Gabon Gabon TRUE Gabon
Gambia 395.9 Gambia Gambia Gambia TRUE Gambia
Georgia 1613.7 Georgia Georgia Georgia TRUE Georgia
Guinea-B 395.9 Guinea-Bissau Guinea-Bissau Guinea-Bissau TRUE Guinea-Bissau
Lesotho 352.54 Lesotho Lesotho Lesotho TRUE Lesotho
Liberia 395.9 Liberia Liberia Liberia TRUE Liberia
Malawi 352.54 Malawi Malawi Malawi TRUE Malawi
Mali 430.58 Mali Australia Mali FALSE Mali prefers subset + substitution over exact
Mauritan 681.23 Mauritania Mauritania Mauritania TRUE Mauritania
Namibia 419.32 Namibia Namibia Namibia TRUE Namibia
Niger 315.05 Niger Niger Niger TRUE Niger
Rwanda 364.26 Rwanda Rwanda Rwanda TRUE Rwanda
SaoTomeP 339.89 TRUE Sao Tome and Principe
Senegal 395.9 Senegal Senegal Senegal TRUE Senegal
Seychell 1709.06 Seychelles Seychelles Seychelles TRUE Seychelles
Somalia 500.74 Somalia Somalia Somalia TRUE Somalia
Swazilan 400.17 Swaziland Swaziland Swaziland TRUE Swaziland
Togo 395.9 Togo Togo Togo TRUE Togo
Ukraine 1947.94 Ukraine Ukraine Ukraine TRUE Ukraine
Australi 1971.39 Australia Australia Australia TRUE Australia
United S 1792.33 United States United Republic of Tanzania United States FALSE United States bizarre
New Zeal 2061.12 New Zealand New Zealand New Zealand TRUE New Zealand
Canada 1958.73 Canada Canada Canada TRUE Canada
Japan 2176.04 Japan Japan Japan TRUE Japan
Hong Kon 2674.63 Hong Kong Hong Kong Hong Kong TRUE Hong Kong
Korea 2399.11 Korea Korea Democratic People’s Republic of Korea FALSE Korea prefers subset + substitution over exact
Israel 1539.63 Israel Israel Israel TRUE Israel
Singapor 2459.04 Singapore Singapore Singapore TRUE Singapore
Qatar 1733.83 Qatar Qatar Qatar TRUE Qatar
Hungary 2432.96 Hungary Hungary Hungary TRUE Hungary
Bahrain 971.99 Bahrain Bahrain Bahrain TRUE Bahrain
Chile 2279.52 Chile Chile Chile TRUE Chile
Argentin 1994.59 Argentina Argentina Argentina TRUE Argentina
Barbados 468.02 Barbados Barbados Barbados TRUE Barbados
Uruguay 1918.61 Uruguay Uruguay Uruguay TRUE Uruguay
Cuba 1370.91 Cuba Aruba Cuba FALSE Cuba prefers subset + substitution over exact
Saudi Ar 1468.3 Saudi Arabia Saudi Arabia Saudi Arabia TRUE Saudi Arabia
Mexico 2024.64 Mexico Mexico Mexico TRUE Mexico
Malaysia 1922.77 Malaysia Malaysia Malaysia TRUE Malaysia
Trinidad 1024.1 Trinidad and Tobago Trinidad and Tobago Trinidad and Tobago TRUE Trinidad and Tobago
Kuwait 1081.15 Kuwait Kuwait Kuwait TRUE Kuwait
Lebanon 1543.46 Lebanon Lebanon Lebanon TRUE Lebanon
Venezuel 1280.81 Venezuela, Bolivarian Republic of Venezuela, Bolivarian Republic of Venezuela, Bolivarian Republic of TRUE Venezuela, Bolivarian Republic of
Mauritiu 1792.48 Mauritius Mauritius Mauritius TRUE Mauritius
Jamaica 595.5 Jamaica Jamaica Jamaica TRUE Jamaica
Peru 2096.08 Peru Hviderusland Peru FALSE Peru prefers subset + substitution over exact
Dominica 521.08 Dominica Dominica Dominica TRUE Dominica
SaintLuc 497.7 TRUE Saint Lucia
Ecuador 2228.58 Ecuador Ecuador Ecuador TRUE Ecuador
Brazil 1875.81 Brazil Brazil Brazil TRUE Brazil
SaintVin 395.9 TRUE Saint Vincent
Colombia 1973.6 Colombia Colombia Colombia TRUE Colombia
Iran 1945.07 Iran France Iran FALSE Iran prefers subset + substitution over exact
Tonga 2390.38 Tonga Tonga Tonga TRUE Tonga
Turkey 2167.95 Turkey Turkey Turkey TRUE Turkey
Belize 1481.26 Belize Belize Belize TRUE Belize
Tunisia 203.38 Tunisia Tunisia Tunisia TRUE Tunisia
Jordan 1539.63 Jordan Jordan Jordan TRUE Jordan
SriLanka 1783.84 TRUE Sri Lanka
DomRep 1206.72 TRUE Dominican Republic
W. Samoa 2388.58 W. Samoa W. Samoa W. Samoa TRUE W. Samoa
Fiji 2534.15 Fiji Fiji Fiji TRUE Fiji
China 2646.26 China China China TRUE China
Thailand 2068.81 Thailand Thailand Thailand TRUE Thailand
Surinam 1562.55 Suriname Suriname Suriname TRUE Suriname
Paraguay 2243.61 Paraguay Paraguay Paraguay TRUE Paraguay
Bolivia 2410.22 Bolivia Bolivia, Plurinational State of Bolivia FALSE Bolivia prefers longer
Philipin 2628.84 Philipines Philipines Philipines TRUE Philipines
Egypt 1401.52 Egypt Egypt Egypt TRUE Egypt
Syria 1590.05 Syria Syria Syria TRUE Syria
Honduras 1979.74 Honduras Honduras Honduras TRUE Honduras
Indonesi 2602.63 Indonesia Indonesia Indonesia TRUE Indonesia
VietNam 2264.3 Viet Nam Viet Nam FALSE Viet Nam correct, but odd, vs. Vietnam
Morocco 191.55 Morocco Morocco Morocco TRUE Morocco
Guatemal 2040.41 Guatemala Guatemala Guatemala TRUE Guatemala
Irak 1625.4 Irak Iran Irak FALSE Irak prefers substitution over exact
India 1888.5 India India India TRUE India
Laos 3012.42 Laos Lao People’s Democratic Republic Laos FALSE Laos prefers longer
Pakistan 1901.47 Pakistan Pakistan Pakistan TRUE Pakistan
Madagasc 1678.96 Madagascar Madagascar Madagascar TRUE Madagascar
Papua 3115.88 Papua New Guinea Papua New Guinea FALSE Papua New Guinea correct
Yemen 1190.13 Yemen Yemen Yemen TRUE Yemen
Nepal 2030.11 Nepal Nepal Nepal TRUE Nepal
CookIsla 2437.7 TRUE Cook Islands
Macau 2660.44 Macau Macao Macau FALSE Macau prefers substitution over exact
Marianas 2437.7 Mariana Isl. Mariana Isl. FALSE Mariana Isl. correct
Marshall 2437.7 Marshall Islands Marshall Islands Marshall Islands TRUE Marshall Islands
NCaledon 2437.7 TRUE New Caledonia
Taiwan 2673.71 Taiwan Taiwan, Province of China Taiwan FALSE Taiwan prefers longer
PuertoRi 1654.5 TRUE Puerto Rico
Afghanis 1962.74 Afghanistan Afghanistan Afghanistan TRUE Afghanistan
Algeria 185.65 Algeria Algeria Algeria TRUE Algeria
Antigua/ 491.84 Antigua and Barbuda Antigua and Barbuda FALSE Antigua and Barbuda correct
Azerbaij 2190.35 Azerbaijan Azerbaijan Azerbaijan TRUE Azerbaijan
Bahamas 594.17 Bahamas Bahamas Bahamas TRUE Bahamas
Banglade 1897.24 Bangladesh Bangladesh Bangladesh TRUE Bangladesh
Bhutan 2082.28 Bhutan Bhutan Bhutan TRUE Bhutan
Brunei 1904.48 Brunei Brunei Darussalam Brunei FALSE Brunei prefers longer
Burma 2138.54 Burma Burma Burma TRUE Burma
Cambodia 2254.37 Cambodia Cambodia Cambodia TRUE Cambodia
Costa Ri 1938.1 Costa Rica Costa Rica Costa Rica TRUE Costa Rica
El Salva 1016.14 El Salvador El Salvador El Salvador TRUE El Salvador
Grenada 537.25 Grenada Grenada Grenada TRUE Grenada
Guyana 1379.76 Guyana Guyana Guyana TRUE Guyana
Haiti 434.51 Haiti Haiti Haiti TRUE Haiti
Kazakhst 2122.18 Kazakhstan Kazakhstan Kazakhstan TRUE Kazakhstan
Kiribati 2281.44 Kiribati Kiribati Kiribati TRUE Kiribati
Korea (N 2399.11 Korea North Korea North FALSE Korea North correct
Kyrgysta 2143.13 TRUE Kyrgyzstan
Libya 185.65 Libya Libano Libya FALSE Libya prefers subset, deletion, insertion over exact
Maldives 1836.17 Maldives Maldives Maldives TRUE Maldives
Micrones 2437.7 Micronesia, Federated States of Micronesia, Federated States of Micronesia, Federated States of TRUE Micronesia, Federated States of
Mongolia 2542.15 Mongolia Mongolia Mongolia TRUE Mongolia
Nicaragu 1856.28 Nicaragua Nicaragua Nicaragua TRUE Nicaragua
Oman 1594.25 Oman Cayman Islands Oman FALSE Oman prefers subset + substitution over exact
Panama 1809.12 Panama Panama Panama TRUE Panama
SKittsNe 469.61 TRUE Saint Kitts and Nevis
Solomon 3050.76 Solomon Islands Solomon Islands FALSE Solomon Islands
Tajikist 2000.53 Tajikistan Tajikistan Tajikistan TRUE Tajikistan
TimorLes 2602.63 TRUE Timor–Leste
Turkmeni 2212.49 Turkmenistan Turkmenistan Turkmenistan TRUE Turkmenistan
UArabEm 1286.35 TRUE United Arab Emirates
Uzbekist 2193.47 Uzbekistan Uzbekistan Uzbekistan TRUE Uzbekistan
Vanuatu 2385.83 Vanuatu Vanuatu Vanuatu TRUE Vanuatu

 

The R code:

gn = read.csv("genetic_distance.csv", encoding = "UTF-8", stringsAsFactors = F)
gn$abbrev = as_abbrev(gn$Country)

trans = read.csv("countrycodes.csv", sep=";", encoding = "UTF-8", stringsAsFactors = F)
trans$shorter = str_sub(trans$Names, 1, 8)

intersect(trans$shorter, gn$Country)

matches = data.frame(source_names = gn$Country,
                     to_short = pmatch (gn$Country, trans$shorter)
                     )

agrep(gn$Country[4], trans$shorter)

best_matches = matrix(nrow = nrow(gn))
for (idx in seq_along(gn$Country)) {
  match_idx = agrep(gn$Country[idx], trans$shorter, max.distance = .1, useBytes = T)
  #skip on no match
  if (length(match_idx) == 0) next
  #insert match
  best_matches[idx] = match_idx
}

matches$agrep = best_matches

matches$to_short_result = trans[matches$to_short, "Names"]
matches$agrep_result = trans[matches$agrep, "Names"]

for (idx in 1:nrow(matches)) {
  if (!is.na(matches[idx, "to_short_result"])) {
    matches[idx, "best_match"] = matches[idx, "to_short_result"]
  }
  if (is.na(matches[idx, "to_short_result"])) {
    matches[idx, "best_match"] = matches[idx, "agrep_result"]
  }
}

#output
write.table(matches, "clipboard", na = "", sep = "\t")

John Fuerst suggested that I write a meta-analysis, review and methodology paper on the S factor. That seems like a decent idea once I get some more studies done (data are known to exist on France (another level), Japan (analysis done, writing pending), Denmark, Sweden and Turkey (reanalysis of Lynn’s data done, but there is much more data).

However, before doing that it seems okay to post my check list here in case someone else is planning on doing a study.

A methodology paper is perhaps not too bad an idea. Here’s a quick check list of what I usually do:
  1. Find some country for which there exist administrative divisions that number preferably at least 10 and as many as possible.
  2. Find cognitive data for these divisions. Usually this is only available for fairly large divisions, like states but may sometimes be available for smaller divisions. One can sometimes find real IQ test data, but usually one will have to rely on scholastic ability tests such as PISA. Often one will have to use a regional or national variant of this.
  3. Find socioeconomic outcome data for these divisions. This can usually be found at some kind of official statistics bureau’s website. These websites often have English language editions for non-English speaker countries. Sometimes they don’t and one has to rely on clever use of guessing and Google Translate. If the country has a diverse ethnoracial demographic, obtain data for this as well. If possible, try to obtain data for multiple levels of administrative divisions and time periods so one can see changes over levels or time. Sometimes data will be available for a variety of years, so one can do a longitudinal study. Other times one will have to average all the years for each variable.
  4. If there are lots of variables to choose from, then choose a diverse mix of variables. Avoid variables that are overly dependent on local natural environment, such as the presence of a large body of water.
  5. Use the redundancy algorithm to remove the most redundant variables. I usually use a threshold of |.90|, such that if a pair of variables in the dataset correlate >= that level, then remove one of them. One can also average them if they are e.g. gendered versions, such as life expectancy or mean income by gender.
  6. Use the mixedness algorithms to detect if any cases are structural outliers, i.e. that they don’t fit the factor structure of the remaining cases. Create parallel datasets without the problematic cases.
  7. Factor analyze the dataset with outliers with ordinary factor analysis (FA), rank order and robust FA. Use ordinary FA on the dataset without the structural outliers. Plot all the FA loading sets using the loadings plotter function. Make note of variables that change their loadings between analyses, and variables that load in unexpected ways.
  8. Extract the S factors and examine their relationship to the ethnoracial variables and cognitive scores.
  9. If the country has seen substantial immigration over the recent decades, it may be a good idea to regress out the effect of this demographic and examine the loadings.
  10. Write up the results. Use lots of loading plots and scatter plots with names.
  11. After you have written a draft, contact natives to get their opinion. Maybe you missed something important about the country. People who speak the local language are also useful when gathering data, but generally, you will have to do things yourself.

 

If I missed something, let me know.

A recent paper informs us that we have now found a small number of SNPs that explain skin color in European samples.

In the International Visible Trait Genetics (VisiGen) Consortium, we investigated the genetics of human skin color by combining a series of genome-wide association studies (GWAS) in a total of 17,262 Europeans with functional follow-up of discovered loci. Our GWAS provide the first genome-wide significant evidence for chromosome 20q11.22 harboring the ASIP gene being explicitly associated with skin color in Europeans. In addition, genomic loci at 5p13.2 (SLC45A2), 6p25.3 (IRF4), 15q13.1 (HERC2/OCA2), and 16q24.3 (MC1R) were confirmed to be involved in skin coloration in Europeans. In follow-up gene expression and regulation studies of 22 genes in 20q11.22, we highlighted two novel genes EIF2S2 and GSS, serving as competing functional candidates in this region and providing future research lines. A genetically inferred skin color score obtained from the 9 top-associated SNPs from 9 genes in 940 worldwide samples (HGDP-CEPH) showed a clear gradual pattern in Western Eurasians similar to the distribution of physical skin color, suggesting the used 9 SNPs as suitable markers for DNA prediction of skin color in Europeans and neighboring populations, relevant in future forensic and anthropological investigations.

However:

All 9 SNPs listed in Table 1 were used to construct a genetically inferred skin color score in 940 samples from 54 worldwide populations (HGDP-CEPH samples), which showed a spatial distribution with a clear gradual increase in skin darkness from Northern Europe to Southern Europe to Northern Africa, the Middle East and Western Asia (Figure S2); in agreement with the known distribution of skin color across these geographic regions. Outside of these geographic regions, the inferred skin color score appeared rather similar (i.e., failing to discriminate), despite the known phenotypic skin color difference between generally lighter Asians/Native Americans and darker Africans. This demonstrates that although these 9 SNPs can explain skin color variation among Europeans, they cannot explain existing skin color differences between Asians/Native Americans and Africans. Therefore, these differences in skin color variation may partly be due to different DNA variants not identifiable by this European study with restricted genetic origin.

The same general problem may apply to the Piffer results. Perhaps the SNPs found only affect cognitive ability within European samples (or Euroasian, because there is one Chinese replication). This sounds like a case of epistasis, where the other necessary gene(s) for the identified SNPs to have an effect on cognitive ability have substantial frequencies in European populations, but don’t exist or are very rare in non-European populations.

As far as I know, this is a possible but unlikely scenario. It will perhaps serve as one of the remaining areas where non-hereditarians can point to and say that there is still reasonable doubt. The solution is to perform GWAS on African subjects. Luckily, a large number of such subjects live in or near (relatively) affluent countries in the Americas.

Many airports have free wifi services. The problem with these is that they are time-limited, usually to 1 to 3 hours. This can be very annoying if one is stuck in an airport for an extended period, as I am right now.

Non-technical solution

If you have spent your time on one device, you can simply switch to a new one. If you have brought a smartphone, tablet and a laptop, you can use the time on each of these.

This solution may be sufficient in some situations.

Technical solution

The wifi services rely on your computers MAC address to identity you. They keep track of these and so when you have used all the time on a given MAC, it will be temporarily blocked from using the internet again.

The solution is simple: we kill the batman we switch to a new MAC address every time one has expired. How do we do this? The built in network controller can change the MAC address, but this did not work for me. Instead I downloaded macchanger using:

sudo apt-get install macchanger

This is a small program that lets you easily change MAC addresses. I found a ton of guides, but they did not fully work.

Here’s my current routine.

  1. Disable the wifi using by clicking turn off in the dock-menu.
  2. Delete the previous connection to the network.
  3. Open a terminal as root.
  4. Type:
    macchanger -s wlan0

    to show the current MAC address.

  5. Type:
    macchanger -a wlan0

    to get a new similar MAC address.

  6. Re-do step (4) to see that it worked.
  7. Turn on wifi.
  8. Connect to the network.
  9. Enjoy internet for as long as it lasts, start over from step (1).

I’m not sure if everything here is strictly necessary, but this works for me.

Due to lengthy discussion over at Unz concerning the good performance of some African groups in the UK, it seems worth it to review the Danish and Norwegian results. Basically, some African groups perform better on some measures than native British. The author is basically arguing that this disproves global hereditarianism. I think not.

The over-performance relative to home country IQ of some African countries is not restricted to the UK. In my studies of immigrants in Denmark and Norway, I found the same thing. It is very clear that there are strong selection effects for some countries, but not others, and that this is a large part of the reason why the home country IQ x performance in host country are not higher. If the selection effect was constant across countries, it would not affect the correlations. But because it differs between countries, it essentially creates noise in the correlations.

Two plots:

NO_S_IQ DK_S_IQ

The codes are ISO-3 codes. SO e.g. NGA is Nigeria, GHA is Ghana, KEN = Kenya and so on. They perform fairly well compared to their home country IQ, both in Norway and Denmark. But Somalia does not and the performance of several MENAP immigrants is abysmal.

The scores on the Y axis are S factor scores for their performance in these countries. They are general factors extracted from measures of income, educational attainment, use of social benefits, crime and the like. The S scores correlate .77 between the countries. For details, see the papers concerning the data:

I did not use the scores from the papers, I redid the analysis. The code is posted below for those curious. The kirkegaard package is my personal package. It is on github. The megadataset file is on OSF.


 

library(pacman)
p_load(kirkegaard, ggplot2)

M = read_mega("Megadataset_v2.0e.csv")

DK = M[111:135] #fetch danish data
DK = DK[miss_case(DK) <= 4, ] #keep cases with 4 or fewer missing
DK = irmi(DK, noise = F) #impute the missing
DK.S = fa(DK) #factor analyze
DK_S_scores = data.frame(DK.S = as.vector(DK.S$scores) * -1) #save scores, reversed
rownames(DK_S_scores) = rownames(DK) #add rownames

M = merge_datasets(M, DK_S_scores, 1) #merge to mega

#plot
ggplot(M, aes(LV2012estimatedIQ, DK.S)) + 
  geom_point() +
  geom_text(aes(label = rownames(M)), vjust = 1, alpha = .7) +
  geom_smooth(method = "lm", se = F)
ggsave("DK_S_IQ.png")


# Norway ------------------------------------------------------------------

NO_work = cbind(M["Norway.OutOfWork.2010Q2.men"], #for work data
                M["Norway.OutOfWork.2011Q2.men"],
                M["Norway.OutOfWork.2012Q2.men"],
                M["Norway.OutOfWork.2013Q2.men"],
                M["Norway.OutOfWork.2014Q2.men"],
                M["Norway.OutOfWork.2010Q2.women"],
                M["Norway.OutOfWork.2011Q2.women"],
                M["Norway.OutOfWork.2012Q2.women"],
                M["Norway.OutOfWork.2013Q2.women"],
                M["Norway.OutOfWork.2014Q2.women"])

NO_income = cbind(M["Norway.Income.index.2009"], #for income data
                  M["Norway.Income.index.2010"],
                  M["Norway.Income.index.2011"],
                  M["Norway.Income.index.2012"])

#make DF
NO = cbind(M["NorwayViolentCrimeAdjustedOddsRatioSkardhamar2014"],
           M["NorwayLarcenyAdjustedOddsRatioSkardhamar2014"],
           M["Norway.tertiary.edu.att.bigsamples.2013"])


#get 5 year means
NO["OutOfWork.2010to2014.men"] = apply(NO_work[1:5],1,mean,na.rm=T) #get means, ignore missing
NO["OutOfWork.2010to2014.women"] = apply(NO_work[6:10],1,mean,na.rm=T) #get means, ignore missing

#get means for income and add to DF
NO["Income.index.2009to2012"] = apply(NO_income,1,mean,na.rm=T) #get means, ignore missing

plot_miss(NO) #view is data missing?

NO = NO[miss_case(NO) <= 3, ] #keep those with 3 datapoints or fewer missing
NO = irmi(NO, noise = F) #impute the missing

NO_S = fa(NO) #factor analyze
NO_S_scores = data.frame(NO_S = as.vector(NO_S$scores) * -1) #save scores, reverse
rownames(NO_S_scores) = rownames(NO) #add rownames

M = merge_datasets(M, NO_S_scores, 1) #merge with mega

#plot
ggplot(M, aes(LV2012estimatedIQ, NO_S)) +
  geom_point() +
  geom_text(aes(label = rownames(M)), vjust = 1, alpha = .7) +
  geom_smooth(method = "lm", se = F)
ggsave("NO_S_IQ.png")

sum(!is.na(M$NO_S))
sum(!is.na(M$DK.S))

cor(M$NO_S, M$DK.S, use = "pair")

 

Abstract

A reanalysis of (Carl, 2015) revealed that the inclusion of London had a strong effect on the S loading of crime and poverty variables. S factor scores from a dataset without London and redundant variables was strongly related to IQ scores, r = .87. The Jensen coefficient for this relationship was .86.

 

Introduction

Carl (2015) analyzed socioeconomic inequality across 12 regions of the UK. In my reading of his paper, I thought of several analyses that Carl had not done. I therefore asked him for the data and he shared it with me. For a fuller description of the data sources, refer back to his article.

Redundant variables and London

Including (nearly) perfectly correlated variables can skew an extracted factor. For this reason, I created an alternative dataset where variables that correlated above |.90| were removed. The following pairs of strongly correlated variables were found:

  1. median.weekly.earnings and log.weekly.earnings r=0.999
  2. GVA.per.capita and log.GVA.per.capita r=0.997
  3. R.D.workers.per.capita and log.weekly.earnings r=0.955
  4. log.GVA.per.capita and log.weekly.earnings r=0.925
  5. economic.inactivity and children.workless.households r=0.914

In each case, the first of the pair was removed from the dataset. However, this resulted in a dataset with 11 cases and 11 variables, which is impossible to factor analyze. For this reason, I left in the last pair.

Furthermore, because capitals are known to sometimes strongly affect results (Kirkegaard, 2015a, 2015b, 2015d), I also created two further datasets without London: one with the redundant variables, one without. Thus, there were 4 datasets:

  1. A dataset with London and redundant variables.
  2. A dataset with redundant variables but without London.
  3. A dataset with London but without redundant variables.
  4. A dataset without London and redundant variables.

Factor analysis

Each of the four datasets was factor analyzed. Figure 1 shows the loadings.

loadings

Figure 1: S factor loadings in four analyses.

Removing London strongly affected the loading of the crime variable, which changed from moderately positive to moderately negative. The poverty variable also saw a large change, from slightly negative to strongly negative. Both changes are in the direction towards a purer S factor (desirable outcomes with positive loadings, undesirable outcomes with negative loadings). Removing the redundant variables did not have much effect.

As a check, I investigated whether these results were stable across 30 different factor analytic methods.1 They were, all loadings and scores correlated near 1.00. For my analysis, I used those extracted with the combination of minimum residuals and regression.

Mixedness

Due to London’s strong effect on the loadings, one should check that the two methods developed for finding such cases can identify it (Kirkegaard, 2015c). Figure 2 shows the results from these two methods (mean absolute residual and change in factor size):

mixedness
Figure 2: Mixedness metrics for the complete dataset.

As can be seen, London was identified as a far outlier using both methods.

S scores and IQ

Carl’s dataset also contains IQ scores for the regions. These correlate .87 with the S factor scores from the dataset without London and redundant variables. Figure 3 shows the scatter plot.

IQ_S
Figure 3: Scatter plot of S and IQ scores for regions of the UK.

However, it is possible that IQ is not really related to the latent S factor, just the other variance of the extracted S scores. For this reason I used Jensen’s method (method of correlated vectors) (Jensen, 1998). Figure 4 shows the results.

Jensen_method
Figure 4: Jensen’s method for the S factor’s relationship to IQ scores.

Jensen’s method thus supported the claim that IQ scores and the latent S factor are related.

Discussion and conclusion

My reanalysis revealed some interesting results regarding the effect of London on the loadings. This was made possible by data sharing demonstrating the importance of this practice (Wicherts & Bakker, 2012).

Supplementary material

R source code and datasets are available at the OSF.

References

Carl, N. (2015). IQ and socioeconomic development across Regions of the UK. Journal of Biosocial Science, 1–12. doi.org/10.1017/S002193201500019X

Jensen, A. R. (1998). The g factor: the science of mental ability. Westport, Conn.: Praeger.

Kirkegaard, E. O. W. (2015a). Examining the S factor in Mexican states. The Winnower. Retrieved from thewinnower.com/papers/examining-the-s-factor-in-mexican-states

Kirkegaard, E. O. W. (2015b). Examining the S factor in US states. The Winnower. Retrieved from thewinnower.com/papers/examining-the-s-factor-in-us-states

Kirkegaard, E. O. W. (2015c). Finding mixed cases in exploratory factor analysis. The Winnower. Retrieved from thewinnower.com/papers/finding-mixed-cases-in-exploratory-factor-analysis

Kirkegaard, E. O. W. (2015d). The S factor in Brazilian states. The Winnower. Retrieved from thewinnower.com/papers/the-s-factor-in-brazilian-states

Revelle, W. (2015). psych: Procedures for Psychological, Psychometric, and Personality Research (Version 1.5.4). Retrieved from cran.r-project.org/web/packages/psych/index.html

Wicherts, J. M., & Bakker, M. (2012). Publish (your data) or (let the data) perish! Why not publish your data too? Intelligence, 40(2), 73–76. doi.org/10.1016/j.intell.2012.01.004

1There are 6 different extraction and 5 scoring methods supported by the fa() function from the psych package (Revelle, 2015). Thus, there are 6*5 combinations.

Abstract

A dataset of 127 variables concerning socioeconomic outcomes for US states was analyzed. Of these, 81 were used in a factor analysis. The analysis revealed a general socioeconomic factor. This factor correlated .961 with one from a previous analysis of socioeconomic data for US states.

 

Introduction

It has repeatedly been found that desirable outcomes tend to be associated with other desirable outcomes and likewise for undesirable outcomes. When this is the case, one can extract a general factor — the general socioeconomic factor (S factor) — such that the desirable outcomes load positively and the undesirable outcomes negatively. This pattern has been found at the country level (1), within country divisions of many countries (2–10), at the city district level (11), at the level of first names (12) and at the level of country of origin groups in two countries (13,14).

A previous study have found that the pattern holds for US states too (7). However, a new and larger dataset has been found, so it is worth examining whether the pattern holds in it, and if so, how strongly correlated the extracted factor scores are between the datasets. This would function as a kind of test-retest reliability.

Data sources

The previous study (7) of the S factor among US states used a dataset of 25 variables compiled from various official statistics found at The 2012 Statistical Abstract website. The current study relies upon a dataset compiled by Measure of America, a website that visualizes social inequality. It is possible to download the datasets their maps rely upon here.

As done with earlier studies, I excluded the capital district. I also excluded the data for US as a whole since it was not a state like the other cases.

The dataset contains a total of 127 variables. However, not all of these are useful for examining the S factor:

  • 4 variables are the composite indexes calculated by Measure of America. These are fairly similar to the Human Development Index scores, except that they are scaled differently.
  • 6 variables concern the population sizes in percent of 6 sociological race categories: Non-Hispanic White, Latino, African American, Asian, Amerindian (Native American) and other.
  • 1 variable contains the total population size for each state.
  • A number of variables were not given in a form adjusted for population size e.g. per capita, percent or rate per 100k persons. These variables were excluded: Rape (total number), Homeless Population (total number), Medicare Recipients (thousands), Medicaid Recipients (thousands), Army Recruits (total), Total Military Casualties in Operations Enduring Freedom and Iraqi Freedom to April 2010, Prisoners State or Federal Jurisdiction (total number), Women in Congressional Delegation (total), Men in Congressional Delegation (total), Carcinogen Releases (pounds), Lead Releases (pounds), Dioxin Releases (grams), Superfund Sites (total), Protected Forest (acres), and Protected Farm and Ranch Land (acres).
  • 1 variable was excluded due to being heavily reliant on local natural environment (presence of water and forests): Farming fishing and forestry occupations (%).
  • 1 variable was excluded because most of its data was missing: State Earned Income Tax Credit (% of federal Earned Income Tax Credit).

The variables that were not given in per population format almost always had a sibling variable that was given in a suitable format and which was included in the analysis. After these exclusions, 101 variables remained for analysis.

Missing data

An analysis of missing data showed that some variables still had missing data. Because the dataset had more variables than cases, it was not possible to impute the missing data using multiple regression as commonly done in these analyses. For this reason, these variables were excluded. After this, 93 variables remained for analysis.

Duplicated, reverse-coded and highly redundant variables

An analysis of correlations among variables showed that 2 of them had duplicates (r = 1): Diabetes (% age 18 and older) and Low-Birth-Weight Infants (% of all infants). I’m not sure why this is the case.

Furthermore, 4 variables had a reverse-coded sibling (r = -1):

  1. Less Than High School (%) + At Least High School Diploma (%)
  2. 4th Graders Reading Below Proficiency (%) + 4th Grade National Assessment of Educational Progress in Reading (% at or above proficient)
  3. Urban Population (%) + Rural Population (%)
  4. Public High School Graduation Rate (%) + High School Freshmen Not Graduating After 4 Years (%).

Finally, some variables were so strongly related to other variables that keeping both would perhaps result in factor analytic errors or headily influence the resulting factor. I decided to use a threshold of |.9| as the limit. If any pair of variables correlated at this level or above, one of them was excluded. There were 6 pairs of variables like this and the first of the pair was excluded:

  1. Poverty Rate (% below federal poverty threshold) + Child Poverty (% living in families below the poverty line), r = .985.
  2. Poverty Rate (% below federal poverty threshold) + Children Under 6 Living in Poverty (%), r = .968.
  3. Management professional and related occupations (%) + At Least Bachelor’s Degree (%), r = .925.
  4. Preschool Enrollment (% enrolled ages 3 and 4) + 3- and 4-year-olds Not Enrolled in Preschool (%), r = -.925.
  5. Army Recruits (per 1000 youth) + Army Recruits (per 1000 youth), r = .914.
  6. Graduate Degree (%) + At Least Bachelor’s Degree (%), r= .910.

The army recruit variable seems to be a duplicate, but the numbers are not identical for all cases. The two preschool enrollment variables seem to be meant to be a reverse-coding of each other, but they don’t correlate perfectly negatively.

After exclusion of these variables, there were 81 remaining.

Factor analysis

Next I extracted a general factor from the data. Since one previous study had found instability across extraction methods when extracting factors from datasets with more variables than cases (2), I examined the stability across all possible extraction and scoring methods, 30 in total (6 extraction methods, 5 scoring methods). 11 of these 30 methods did not result in an error tho they gave warnings. There was no loading instability or scoring instability across methods: all correlations >.996.1 I saved the results from the minres+regression combination.

Inspection of the loadings revealed no important variables with the ‘wrong loading’ i.e., either a desirable outcome but with a negative loading or an undesirable outcome with a positive loading. Some variables are debatable. E.g. binge drinking in adults has a loading of .566, but this could be seen as a good thing (sufficient free time and money to spend it drinking large quantities of alcohol), or a bad thing (binge drinking is bad for one’s health). Figure 1 shows the loadings plot.

AHDI_S
Figure 1: Loadings on the S factor. Some variable names were too long and were cut at the 40th character. Consult the main data file to see the full name.

Factor scores

The extracted factor scores were compared with previously obtained similar measures:

  • HDI2010 scores calculated from HDI2002 scores found in (16).
  • Measure of America’s own American Human Development Index found in the dataset.
  • The S factor scores from the previous study of US states (7).

The correlation matrix is shown in Table 1.

 

HDI2010

S_previous

S_current

AHDI

HDI2010

0.868

0.843

0.750

S_previous

0.852

0.961

0.922

S_current

0.826

0.961

0.941

AHDI

0.724

0.913

0.945

 

Table 1: Correlation matrix of S and HDI scores. Weighted correlations below the diagonal (sqrt of population).

The correlation between the previously obtained S factor and the new one was very strong at .961. The two different HDI measures had the lowest correlation. This is the expected result if they are the worst approximations of the S factor. Note however that the HDI2010 is rescaled from 2002 data, whereas the AHDI and current S factor are based on 2010 data. The previous S factor is based on data from approximately the last 10 years that were averaged.

Mixedness

Finally, factorial mixedness was examined using two methods detailed in a previous paper (17). In short, mixedness is when cases are incongruent with the overall factor structure found for the data. The methods showed convergent results (r = .65). Figure 2 shows the results.

mixedness
Figure 2: Factorial mixedness in cases.

If one was doing a more detailed study, one could examine the residuals at the case level and see if one can find the reasons for why an outlier state is an outlier. In the case of Alaska, the residuals for each variable are shown in Table 2.

 

Variable

Residual

Population.over.65….

-3.34

Renters.with.Severe.Housing.Cost.Burden..gross.rent…50..of.household.income.

-2.64

School.Enrollment….

-2.44

High.School.Graduates.Enrolling.in.College….

-2.08

Infant.Mortality.Rate..per.1000.live.births.

-2.07

Low.Birth.Weight.Infants….of.all.infants.

-1.99

Change.in.Turnout.from.2004

-1.89

Adults.65.and.Older.Living.in.Poverty….

-1.83

Gini.Coefficient

-1.74

Bankruptcies..filings.per.1000.

-1.67

Less.Than.High.School….

-1.34

Children.Under.6.Living.in.Poverty….

-1.32

Diabetes….age.18.and.older.

-1.29

Individuals.with.Home.Internet.Access….ages.3.and.older.

-1.14

Child.Immunization.Rate….

-1.12

Economically.Disadvantaged.Students….public.K.12.

-1.01

Renters.Spending.30..or.More.on.Housing….

-0.85

Housing.Units.Occupied.by.Owner….

-0.80

Production.transportation.and.material.moving.occupations….

-0.80

Children.on.Medicaid….age.0..18.

-0.75

Commute.60.Minutes.or.More….of.workers.16.and.over.

-0.73

Prisoners.State.or.Federal.Jurisdiction..total.number.

-0.68

Change.in.Army.Recruits.from.2007.to.2008….

-0.53

Food.Stamps.Use….

-0.52

Service.occupations….

-0.50

Foreclosures..per.10000.homes.

-0.47

Urban.Population….

-0.44

Owners.Spending.30..or.More.on.Housing….

-0.39

Annual.Costs.of.Public.4.Year.College..average…

-0.38

Property.Crime..per.100000.

-0.28

Unemployment.Rate….ages.16.and.over.

-0.28

Obesity….age.20.and.older.

-0.25

Homicide..per.100000.

-0.23

Practicing.Physicians..per.10000.population.

-0.22

Water.Consumption..gallons.per.day.per.capita.

-0.22

Seats.in.State.Legislatures.Held.by.Women….

-0.21

Sales.and.office.occupations….

-0.06

Turnout….of.eligible.voters.who.voted.

-0.04

Life.Expectancy.at.Birth..years.

-0.02

State.Spending.on.Academic.Research.and.Development…..per.capita.

0.03

Farming.fishing.and.forestry.occupations….

0.17

Mercury.Releases..pounds.per.1000.population.

0.18

Births.to.Teenage.Girls..per.1000.age.15.19.

0.20

Medicaid.Eligibility.Cutoff..income.as…of.poverty.line.

0.21

8th.Grade.National.Assessment.of.Educational.Progress.in.Math….at.or.above.proficient.

0.25

At.Least.Bachelor.s.Degree….

0.30

Smoking….age.18.and.older.

0.31

High.School.Freshmen.Not.Graduating.After.4.Years….

0.33

Child.Mortality..age.1.4.per.100000.population.

0.46

Food.Insecure.Households….

0.47

Uninsured….of.individuals.lacking.coverage.

0.66

Labor.Force.Participation.Rate….ages.16.to.64.

0.74

Army.Recruits..per.1000.youth.

0.87

Binge.Drinking….adults.in.past.30.days.

0.96

Annual.Costs.of.Public.2.Year.College..average…

0.97

Homeless….of.population.

0.98

State.Expenditure.on.Corrections….per.prisoner.

1.02

4th.Graders.Reading.Below.Proficiency….

1.05

Marginally.Attached.Workers..per.10000.working.age.Adults.

1.16

Median.Earnings..2010.dollars.

1.25

Population.under.18….

1.25

Teenagers.Not.in.School.and.Not.Working….ages.16.19.

1.36

Trauma.Related.Death.Rate..per.100000.population.

1.45

3..and.4.year.olds.Not.Enrolled.in.Preschool….

1.50

Ineligible.to.Vote.Due.to.Felony.Convictions..per.100000.voting.age.population.

1.53

Construction.extraction.maintenance.and.repair.occupations….

1.57

State.Medicaid.Spending..per.recipient.

1.70

State.Spending.on.Higher.Education….per.capita.

1.77

Violent.Crime…per.100000.

1.82

Union.Membership….

2.02

Commute.by.Carpool….of.workers.

2.02

Per.Pupil.Spending.Public.K.12….

2.06

Housing.Units.with.1.01.or.More.Occupants.per.Room….

2.10

Carbon.Dioxide.Emissions..metric.tons.per.capita.

2.16

Federal.Revenue.to.Each.State….per.capita.

2.22

Suicide..per.100000.age.adjusted.

2.35

State.Police.Expenditure….per.resident.

2.39

State.Per.Capita.GDP….

2.67

Rape..per.100000.

4.06

Energy.Consumption..BTUs.per.capita.

4.41

State.Expenditure.on.Transportation….per.person.

6.41

 

Table 2: Residuals per variable for Alaska.

The meaning of the numbers is this: It is the number of standard deviations that Alaska is above or below on each variable given its score on the S factor (-.24); How much it deviates from the expected level. We see that the Alaskan state spends a much more on transportation per person than expected (more than 6 standard deviations). This is presumably due to it being located very far north compared to the other states and has the lowest population density. It also spends more energy per citizen, again presumably related to the climate. I’m not sure why rape is so common, however.

One could examine the other outlier states in a similar fashion, but this is left as an exercise to the reader.

Discussion and conclusion

The present analysis used a much larger dataset of 81 very diverse variables than the previous study of the S factor in US states which used 25, yet the findings were almost identical (r = .961). This should probably be interpreted as being because the S factor can be very reliably measured when an appropriate number of and diversity of socioeconomic variables are used. It should be noted however that many of the variables between the datasets overlapped in content, e.g. expected life span at birth.

Supplementary material

Data files and source code is available on OSF.

References

1. Kirkegaard EOW. The international general socioeconomic factor: Factor analyzing international rankings. Open Differ Psychol [Internet]. 2014 Sep 8 [cited 2014 Oct 13]; Available from: openpsych.net/ODP/2014/09/the-international-general-socioeconomic-factor-factor-analyzing-international-rankings/

2. Kirkegaard EOW. Examining the S factor in Mexican states. The Winnower [Internet]. 2015 Apr 19 [cited 2015 Apr 23]; Available from: thewinnower.com/papers/examining-the-s-factor-in-mexican-states

3. Kirkegaard EOW. S and G in Italian regions: Re-analysis of Lynn’s data and new data. The Winnower [Internet]. 2015 Apr 23 [cited 2015 Apr 23]; Available from: thewinnower.com/papers/s-and-g-in-italian-regions-re-analysis-of-lynn-s-data-and-new-data

4. Kirkegaard EOW. The S factor in the British Isles: A reanalysis of Lynn (1979). The Winnower [Internet]. 2015 Mar 28 [cited 2015 Apr 23]; Available from: thewinnower.com/papers/the-s-factor-in-the-british-isles-a-reanalysis-of-lynn-1979

5. Kirkegaard EOW. Indian states: G and S factors. The Winnower [Internet]. 2015 Apr 23 [cited 2015 Apr 23]; Available from: thewinnower.com/papers/indian-states-g-and-s-factors

6. Kirkegaard EOW. The S factor in China. The Winnower [Internet]. 2015 Apr 23 [cited 2015 Apr 23]; Available from: thewinnower.com/papers/the-s-factor-in-china

7. Kirkegaard EOW. Examining the S factor in US states. The Winnower [Internet]. 2015 Apr 23 [cited 2015 Apr 23]; Available from: thewinnower.com/papers/examining-the-s-factor-in-us-states

8. Kirkegaard EOW. The S factor in Brazilian states. The Winnower [Internet]. 2015 Apr 30 [cited 2015 May 1]; Available from: thewinnower.com/papers/the-s-factor-in-brazilian-states

9. Kirkegaard EOW. The general socioeconomic factor among Colombian departments. The Winnower [Internet]. 2015 Jun 16 [cited 2015 Jun 16]; Available from: thewinnower.com/papers/1390-the-general-socioeconomic-factor-among-colombian-departments

10. Carl N. IQ AND SOCIOECONOMIC DEVELOPMENT ACROSS REGIONS OF THE UK. J Biosoc Sci. 2015 Jun;FirstView:1–12.

11. Kirkegaard EOW. An S factor among census tracts of Boston. The Winnower [Internet]. 2015 Jun 2 [cited 2015 Jun 2]; Available from: thewinnower.com/papers/an-s-factor-among-census-tracts-of-boston

12. Kirkegaard EOW, Tranberg B. What is a good name? The S factor in Denmark at the name-level. The Winnower [Internet]. 2015 Jun 4 [cited 2015 Jun 6]; Available from: thewinnower.com/papers/what-is-a-good-name-the-s-factor-in-denmark-at-the-name-level

13. Kirkegaard EOW. Crime, income, educational attainment and employment among immigrant groups in Norway and Finland. Open Differ Psychol [Internet]. 2014 Oct 9 [cited 2014 Oct 13]; Available from: openpsych.net/ODP/2014/10/crime-income-educational-attainment-and-employment-among-immigrant-groups-in-norway-and-finland/

14. Kirkegaard EOW, Fuerst J. Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark. Open Differ Psychol [Internet]. 2014 May 12 [cited 2014 Oct 13]; Available from: openpsych.net/ODP/2014/05/educational-attainment-income-use-of-social-benefits-crime-rate-and-the-general-socioeconomic-factor-among-71-immmigrant-groups-in-denmark/

15. Revelle W. psych: Procedures for Psychological, Psychometric, and Personality Research [Internet]. 2015 [cited 2015 Apr 29]. Available from: cran.r-project.org/web/packages/psych/index.html

16. Stanton EA. Inequality and the Human Development Index [Internet]. ProQuest; 2007 [cited 2015 Jun 25]. Available from: www.google.com/books?hl=en&lr=&id=87oZlFPLCykC&oi=fnd&pg=PR5&dq=INEQUALITY+AND+THE+HUMAN+DEVELOPMENT+INDEX+&ots=l1FCqCH_fZ&sig=7WbAexkbLK8zwxudyDqF72SfDTw

17. Kirkegaard EOW. Finding mixed cases in exploratory factor analysis. The Winnower [Internet]. 2015 Apr 28 [cited 2015 May 1]; Available from: thewinnower.com/papers/finding-mixed-cases-in-exploratory-factor-analysis

Footnotes

1 The factor analysis was done with the fa() function from the psych package (15). The cross-method check was done with a home-made function, see the supplementary material.

Some time ago, I stumbled upon this paper:
Searls, D. T., Mead, N. A., & Ward, B. (1985). The relationship of students’ reading skills to TV watching, leisure time reading, and homework. Journal of Reading, 158-162.

Sample is very large:

To enlarge on such information, the National Assessment of Educational Progress (NAEP) gathered data on the TV viewing habits of 9, 13, and 17 year olds across the U.S. during its 1979-80 assessment of reading skills. In this survey, 21,208 9 year olds, 30,488 13 year olds, and 25,551 17 year olds responded to questions about their back- grounds and to a wide range of items probing their reading comprehension skills. These data provide information on the amount of TV watched by different groups of students and allow comparisons of reading skills and TV watching.

The relationship turns out to be interestingly nonlinear:

TV reading compre age

For understanding, it is better to visualize the data anew:

tv_age_reading_comprehension

I will just pretend that reading comprehension is cognitive ability, usually a fair approximation.

So, if we follow the smarties: At 9 they watch a fairly amount of TV (3-4 hours per day), then at 13, they watch about half of that (1-2), and then at age 17, they barely watch it (<1).

Developmental hypothesis: TV is interesting but only to persons at a certain cognitive ability level. Young smart children fit in the target group, but as they age and become smarter, they grow out of the target group and stop watching.

Alternatives hypotheses?

R code

The code for the plot above.

d = data.frame(c(1.5, 2.2, 2.3),
               c(3, 3, 1.3),
               c(5.2, .2, -2.2),
               c(-1.7, -6.9, -8.1))
d

colnames(d) = c("<1 hour", "1-2 hours", "3-4 hours", ">4 hours")
d$age = factor(c("9", "13", "17"), levels = c("9", "13", "17"))

d = melt(d, id.vars = "age")

d

ggplot(d, aes(age, value)) +
  geom_point(aes(color = variable)) +
  ylab("Relative reading comprehension score") +
  scale_color_discrete(name = "TV watching per day") +
  scale_shape_discrete(guide = F)

Since James Thompson is posting statistics, here are some for comparison.

Note that the statistics for this covers all sites hosted by this server, so that includes: both Danish and English blogs, lyddansk.dk, legaliser.nu, Understanding Statistics, as well as a host of other subsites that can be found via the old front page. Note that the large traffic is due to the PDFs hosted on the site. Lots of visitors never really visit the site, just download the PDFs — fine by me — but it inflates the statistics.

Click the image, then click download. The images are huge, not small, and cannot be shown on one screen.

2015, so far

Statistics_for_emilkirkegaard.dk_(2015-06)_-_main_-_2015-06-20_11.21.00

2014

Statistics_for_emilkirkegaard.dk_(2014)_-_main_-_2015-06-20_11.32.26

2013

Statistics_for_emilkirkegaard.dk_(2013)_-_main_-_2015-06-20_11.32.44

2012

Statistics_for_emilkirkegaard.dk_(2012)_-_main_-_2015-06-20_11.32.59

2011

Statistics_for_emilkirkegaard.dk_(2011)_-_main_-_2015-06-20_11.33.22

2010

Statistics_for_emilkirkegaard.dk_(2010)_-_main_-_2015-06-20_11.33.33