The IQ Halo effect

On desirable correlates of intelligence
psychology, survey, IQ
2013-04-012017-02-02 notes certainty: highly likely importance: 9

In­creased IQ cor­re­lates with con­se­quen­tially morally de­sir­able ac­tions, at­ti­tudes, and be­liefs.

“All good things tend to go to­geth­er, as do all bad ones.” –Ed­ward Lee Thorndike

mil­i­tary: AFQT IQ test (sub­test of the ASVAB) ex­cel­lent pre­dic­tor of train­ing costs, suc­cess


the con­sen­sus re­ply to Bell Curve:

  1. IQ is strongly re­lat­ed, prob­a­bly more so than any other sin­gle mea­sur­able hu­man trait, to many im­por­tant ed­u­ca­tion­al, oc­cu­pa­tion­al, eco­nom­ic, and so­cial out­comes. Its re­la­tion to the wel­fare and per­for­mance of in­di­vid­u­als is very strong in some are­nas in life (e­d­u­ca­tion, mil­i­tary train­ing), mod­er­ate but ro­bust in oth­ers (so­cial com­pe­tence), and mod­est but con­sis­tent in oth­ers (law-abid­ing­ness). What­ever IQ tests mea­sure, it is of great prac­ti­cal and so­cial im­por­tance.

  2. A high IQ is an ad­van­tage in life be­cause vir­tu­ally all ac­tiv­i­ties re­quire some rea­son­ing and de­ci­sion-mak­ing. Con­verse­ly, a low IQ is often a dis­ad­van­tage, es­pe­cially in dis­or­ga­nized en­vi­ron­ments. Of course, a high IQ no more guar­an­tees suc­cess than a low IQ guar­an­tees fail­ure in life. There are many ex­cep­tions, but the odds for suc­cess in our so­ci­ety greatly fa­vor in­di­vid­u­als with higher IQs.

  3. That IQ may be highly her­i­ta­ble does not mean that it is not affected by the en­vi­ron­ment. In­di­vid­u­als are not born with fixed, un­change­able lev­els of in­tel­li­gence (no one claims they are). IQs do grad­u­ally sta­bi­lize dur­ing child­hood, how­ev­er, and gen­er­ally change lit­tle there­after.

  4. Al­though the en­vi­ron­ment is im­por­tant in cre­at­ing IQ differ­ences, we do not know yet how to ma­nip­u­late it to raise low IQs per­ma­nent­ly. Whether re­cent at­tempts show promise is still a mat­ter of con­sid­er­able sci­en­tific de­bate.

The con­tro­versy over The Bell Curve (Her­rn­stein & Mur­ray, 1994) was at its height in the fall of 1994. Many crit­ics at­tacked the book for sup­pos­edly re­ly­ing on out­dat­ed, pseu­do­sci­en­tific no­tions of in­tel­li­gence. In crit­i­ciz­ing the book, many crit­ics pro­moted false and highly mis­lead­ing views about the sci­en­tific study of in­tel­li­gence. Pub­lic mise­d­u­ca­tion on the topic is hardly new (S­ny­der­man & Roth­man, 1987, 1988), but never be­fore had it been so an­gry and ex­treme…It is ob­vi­ously not the case that there is no dis­agree­ment about these im­por­tant is­sues or that sci­en­tific truth is a mat­ter of ma­jor­ity rule. A sig­nifi­cant mi­nor­ity of the ex­perts who were con­tacted dis­agreed in part or in whole with the state­ment, and many of the sign­ers would have writ­ten the state­ment some­what differ­ent­ly. Rather, the les­son here is that what have often been car­i­ca­tured in the pub­lic press as dis­cred­it­ed, fringe ideas ac­tu­ally rep­re­sent the solid sci­en­tific cen­ter in the se­ri­ous study of in­tel­li­gence. As Sny­der­man and Roth­man’s (1988) sur­vey of IQ ex­perts and jour­nal­ists re­vealed, the me­dia, among oth­ers, have been turn­ing the truth on its head.

“Gen­eral Men­tal Abil­ity in the World of Work: Oc­cu­pa­tional At­tain­ment and Job Per­for­mance”, Schmidt & Hunter 2004 http://www.unc.e­du/~nielsen/­so­ci708/c­doc­s/Schmidt_Hunter_2004.pdf

The ac­cu­mu­lated ev­i­dence has be­come very strong that GMA is cor­re­lated with a wide va­ri­ety of life out­comes, rang­ing from risky health-re­lated be­hav­iors, to crim­i­nal offens­es, to the abil­ity to use a bus or sub­way sys­tem (Got­tfred­son, 1997; Lu­bin­ski & Humphreys, 1997). In ad­di­tion, the more highly a given GMA mea­sure loads on the gen­eral fac­tor in men­tal abil­ity (the g fac­tor), the larger are these cor­re­la­tions. The rel­a­tive stand­ing of in­di­vid­u­als on GMA has been found to be sta­ble over pe­ri­ods of more than 65 years (Deary, Whal­ley, Lem­mon, Craw­ford, & Starr, 2000). Find­ings in be­hav­ior ge­net­ics, in­clud­ing stud­ies of iden­ti­cal twins reared apart and to­gether (e.g., Bouchard, Lykken, McGue, Segal, & Tel­le­gen, 1990), have shown be­yond doubt that GMA has a strong ge­netic ba­sis (e.g., Bouchard, 1998; McGue & Bouchard, 1998).

  • Lu­bin­ski, D., & Humphreys, L. G. (1997). In­cor­po­rat­ing gen­eral in­tel­li­gence into epi­demi­ol­ogy and the so­cial sci­ences. In­tel­li­gence, 24, 159 -201
  • Deary, I. J., Whal­ley, L. J., Lem­mon, H., Craw­ford, J. R., & Starr, J. M. (2000). The sta­bil­ity of in­di­vid­ual differ­ences in men­tal abil­ity from child­hood to old age: Fol­low-up of the 1932 Scot­tish men­tal sur­vey. In­tel­li­gence, 28, 49 -55

Peo­ple’s rank­ings or rat­ings of the oc­cu­pa­tional level or pres­tige of differ­ent oc­cu­pa­tions are very re­li­able; cor­re­la­tions be­tween mean rat­ings across stud­ies are in the .95 to .98 range, re­gard­less of the so­cial class, oc­cu­pa­tion, age, or coun­try of the raters (Daw­is, 1994; Jensen, 1980, pp. 339 -347). These oc­cu­pa­tional level rat­ings cor­re­late be­tween .90 and .95 with av­er­age GMA scores of peo­ple in the oc­cu­pa­tions (Jensen, 1998, p. 293). In­di­vid­ual level cor­re­la­tions are of course not this large. In the U.S. Em­ploy­ment Ser­vice’s large data­base on the Gen­eral Ap­ti­tude Test Bat­tery (GATB; Hunter, 1980), the in­di­vid­ual level cor­re­la­tion be­tween the GMA mea­sure de­rived from that bat­tery and oc­cu­pa­tional level is .65 (.72 cor­rected for mea­sure­ment er­ror; Jensen, 1998). Much mil­i­tary data ex­ist from both world wars (when sam­ples of draftees were very rep­re­sen­ta­tive of the U.S. male pop­u­la­tion) show­ing an in­crease in mean GMA scores as oc­cu­pa­tional level (as de­ter­mined by rat­ings of the sort dis­cussed here) in­creases (Har­rell & Har­rell, 1945; Stew­art, 1947; Yerkes, 1921). Ta­ble 1, show­ing find­ings for 18,782 White en­listed men in the Army Air Force Com­mand (Har­rell & Har­rell, 1945), presents typ­i­cal find­ings. The GMA mea­sure used was the Army Gen­eral Clas­si­fi­ca­tion Test (Schmidt, Hunter, & Pearl­man, 1981). Mean GMA scores clearly in­crease with oc­cu­pa­tional lev­el.

Wilk, Des­marais, and Sack­ett (1995), us­ing the 3,887 young adults in the Na­tional Lon­gi­tu­di­nal Sur­vey-Y­outh Co­hort (NLSY; Cen­ter for Hu­man Re­source Re­search, 1989) for whom the re­quired data were avail­able, showed that over the 5-year pe­riod from 1982 to 1987, GMA mea­sured in 1980 pre­dicted move­ment in the job hi­er­ar­chy. Those with higher GMA scores in 1980 moved up the hi­er­ar­chy, whereas those with lower GMA scores moved down in the hi­er­ar­chy. In a larger fol­low-up study that was based on some­what differ­ent method­ol­o­gy, Wilk and Sack­ett (1996) ex­am­ined two large gov­ern­ment data­bas­es: the Na­tional Lon­gi­tu­di­nal Study of the Class of 1972 (NLS-72) and the Na­tional Lon­gi­tu­di­nal Sur­vey of La­bor Mar­ket Ex­pe­ri­ence-Y­outh Co­hort (NLSY). In both data­bas­es, Wilk and Sack­ett found that job mo­bil­ity was pre­dicted by the con­gru­ence be­tween in­di­vid­u­als’ GMA scores (mea­sured sev­eral years ear­lier) and the ob­jec­tively mea­sured com­plex­ity of their jobs. If their GMA ex­ceeded the com­plex­ity level of their job, they were likely to move into a higher com­plex­ity job. And if the com­plex­ity level of their job ex­ceeded their GMA lev­el, they were likely to move down into a less com­plex job.

In an­other study drawn from this same large data­base, Mur­ray (1998) found that GMA pre­dicted later in­come even with un­usu­ally thor­ough con­trol for so­cioe­co­nomic sta­tus (SES) and other back­ground vari­ables. This con­trol took ad­van­tage of the large vari­abil­ity of GMA within fam­i­lies and was achieved by use of a sam­ple of male full bi­o­log­i­cal sib­lings, hence con­trol­ling for home back­ground and many other vari­ables (e.g., schools, neigh­bor­hood­s). Mur­ray found that the sib­lings with higher GMA scores re­ceived more ed­u­ca­tion, en­tered more pres­ti­gious oc­cu­pa­tions, had higher in­come, and were em­ployed more reg­u­lar­ly. When the sib­lings were in their late 20s (in 1993), a per­son with av­er­age GMA was earn­ing on av­er­age al­most $18,000 less per year than his brighter sib­ling who had an IQ of 120 or higher and was earn­ing more than $9,000 more than his duller sib­ling who had an IQ of less than 80. This pat­tern of find­ings held up even in a sub­-sam­ple of per­sons who were all from “ad­van­taged” homes (his “utopian” sam­ple).

Judge, Hig­gins, Thore­sen, and Bar­rick (1999) re­lated GMA mea­sures taken at around 12 years of age to oc­cu­pa­tional out­comes in the age range of 41 to 50 years. They found that child­hood GMA scores pre­dicted adult oc­cu­pa­tional level with a cor­re­la­tion of .51 and pre­dicted adult in­come with a cor­re­la­tion of .53. Ball (1938) found that GMA mea­sured in child­hood cor­re­lated .47 with oc­cu­pa­tional level 14 years later and .71 with oc­cu­pa­tional level 19 years lat­er. Other such stud­ies in­clude Brown and Reynolds (1975), Dreher and Bretz (1991), Got­tfred­son and Crouse (1986), Howard and Bray (1990), Siegel and Ghis­elli (1971), and Thorndike and Ha­gen (1959).

Re­sults for GMA are typ­i­fied by the find­ings of the large study con­ducted by Hunter (1980; Hunter & Hunter, 1984) for the U.S. Em­ploy­ment Ser­vice us­ing the data­base on the Gen­eral Ap­ti­tude Test Bat­tery (GATB). On the ba­sis of 425 va­lid­ity stud­ies (N ϭ 32,124) con­ducted on civil­ian jobs span­ning the oc­cu­pa­tional spec­trum, Hunter and Hunter (1984) and Hunter (1980) re­ported the re­sults shown in Ta­ble 2. Hunter as­signed each job to one of five job fam­i­lies based on com­plex­ity (i.e., the in­for­ma­tion pro­cess­ing re­quire­ments of the job, mea­sured us­ing U.S. De­part­ment of La­bor job analy­sis data for each job). This is the largest data­base avail­able us­ing a mea­sure of per­for­mance on the job (mea­sured us­ing su­per­vi­sory rat­ings of job per­for­mance). As can be seen, va­lid­ity for pre­dict­ing per­for­mance on the job ranges from .58 for the high­est com­plex­ity jobs (pro­fes­sion­al, sci­en­tific, and up­per man­age­ment jobs) to .23 at the low­est com­plex­ity level (feed­ing/ off-bear­ing job­s). Job Fam­ily 2 (2.5% of all jobs in the econ­o­my) con­sists of com­plex tech­ni­cal jobs such as com­put­er-sys­tems trou­ble shoot­ing or com­plex man­u­fac­tur­ing set-up jobs. Job Fam­ily 3, with al­most 63% of all jobs in the econ­o­my, in­cludes skilled work­ers, tech­ni­cians, mid-level ad­min­is­tra­tors, para­pro­fes­sion­als, and sim­i­lar jobs. Job Fam­ily 4 is semi­skilled work. Clear­ly, GMA pre­dicts per­for­mance on higher level jobs bet­ter that it does for lower level jobs. How­ev­er, there is sub­stan­tial va­lid­ity for all job lev­els. In par­tic­u­lar, GMA pre­dicts per­for­mance even for the sim­plest 2.4% of jobs (Job Fam­ily 5). Other find­ings are re­ported in Ta­ble 3. On the ba­sis of 194 stud­ies (N ϭ 17,539) of per­for­mance in cler­i­cal jobs, Pearl­man, Schmidt, and Hunter (1980) re­ported a mean GMA va­lid­ity for job per­for­mance of .52. For law en­force­ment jobs, Hir­sh, Northrup, and Schmidt (1986) re­ported a mean va­lid­ity for job per­for­mance of .38. In a large scale, mul­ti­-year mil­i­tary study on en­listed Army per­son­nel (called “Project A”), McHen­ry, Hough, Toquam, Han­son, and Ash­worth (1990) re­ported that GMA pre­dicted “Core Tech­ni­cal Pro­fi­ciency” with a va­lid­ity of .63 and “Gen­eral Sol­dier­ing Per­for­mance” with a va­lid­ity of .65. Both job per­for­mance mea­sures were based on hand­s-on work-sam­ple mea­sures. (Va­lidi­ties were not as high for rat­ings of “Effort and Lead­er­ship” [.31], “Per­sonal Dis­ci­pline” [.16], and “Phys­i­cal Fit­ness and Mil­i­tary Bear­ing” [.20], which are sec­ondary cri­te­rion mea­sures with fewer cog­ni­tive de­mand­s.) Us­ing sim­i­lar job sam­ple mea­sures of job per­for­mance, Ree, Ear­les, and Tea­chout (1994) re­ported a mean value of .45 across seven Air Force jobs. Va­lidi­ties for the pre­dic­tion of per­for­mance in train­ing pro­grams are even larg­er. As can be seen in Ta­ble 2, in the GATB train­ing data­base (90 stud­ies; N ϭ 6,496) used by Hunter and Hunter (1984), GMA pre­dicted per­for­mance in job train­ing pro­grams for all job fam­i­lies for which data ex­isted with a cor­re­la­tion above .50. The data­base for train­ing per­for­mance is larger for mil­i­tary jobs. Hunter (1986) meta-an­a­lyzed mil­i­tary data­bases to­tal­ing over 82,000 trainees and re­ported an av­er­age va­lid­ity of .63 for GMA. On the ba­sis of 77,958 Air Force trainees, Ree and Ear­les (1991) re­ported a very sim­i­lar value of .60. On the ba­sis of 65 stud­ies with an N of 32,157, Pearl­man et al. (1980) re­ported a mean va­lid­ity of .71 for GMA in pre­dict­ing train­ing per­for­mance of cler­i­cal work­ers, whereas Hirsh et al. (1986) found a mean value of .76 for pre­dict­ing per­for­mance in po­lice and other train­ing acad­e­mies for law en­force­ment trainees. These find­ings and oth­ers are shown in Ta­ble 3. Ad­di­tional data of this sort are pre­sented in Schmidt (2002).

Differ­en­tial or spe­cific ap­ti­tude the­ory hy­poth­e­sizes that per­for­mance on differ­ent jobs re­quires differ­ent cog­ni­tive ap­ti­tudes and, there­fore, re­gres­sion equa­tions com­puted for each job and in­cor­po­rat­ing mea­sures of sev­eral spe­cific ap­ti­tudes will op­ti­mize the pre­dic­tion of per­for­mance on the job and in train­ing. In the last 10 years, re­search has strongly dis­con­firmed this the­o­ry. Differ­en­tially weight­ing spe­cific ap­ti­tude tests pro­duces lit­tle or no in­crease in va­lid­ity over the use of a mea­sure of GMA. An ex­pla­na­tion for this find­ing has been dis­cov­ered. It has been found that spe­cific ap­ti­tude tests mea­sure GMA; in ad­di­tion to GMA, each mea­sures some­thing spe­cific to that ap­ti­tude (e.g., specifi­cally nu­mer­i­cal ap­ti­tude, over and above GMA). The GMA com­po­nent ap­pears to be re­spon­si­ble for the pre­dic­tion of job and train­ing per­for­mance, whereas the fac­tors spe­cific to the ap­ti­tudes ap­pear to con­tribute lit­tle or noth­ing to pre­dic­tion. The re­search show­ing this is pre­sented and re­viewed in Hunter (1986); Jensen (1986); Thorndike (1986); Olea and Ree (1994); Ree and Ear­les (1992); Ree et al. (1994); Schmidt, Ones, and Hunter (1992); and Sack­ett and Wilk (1994), among other sources. A par­tic­u­larly dra­matic refu­ta­tion of spe­cific ap­ti­tude the­ory comes from the large sam­ple mil­i­tary re­search con­ducted by Hunter (1983b) for the De­part­ment of De­fense on the per­for­mance of mil­i­tary per­son­nel in job train­ing pro­grams. Four large sam­ples were an­a­lyzed sep­a­rate­ly: 21,032 Air Force per­son­nel, 20,256 Mari­nes, and two Army sam­ples of 16,618 and 79,926, re­spec­tive­ly. In all sam­ples, test data were ob­tained some months prior to mea­sure­ment of per­for­mance in job train­ing pro­grams. In all sam­ples, causal analy­sis mod­el­ing (with cor­rec­tions for mea­sure­ment er­ror and range re­stric­tion) was used to pit spe­cific ap­ti­tude the­ory against GMA in the pre­dic­tion of per­for­mance. In the case of all four sam­ples, mod­els with causal ar­rows from spe­cific ap­ti­tudes to train­ing per­for­mance failed to fit the da­ta. How­ev­er, in all the sam­ples, a hi­er­ar­chi­cal model show­ing a sin­gle causal path from GMA to per­for­mance - and no paths from spe­cific ap­ti­tudes to per­for­mance - fit the data quite well. …Train­ing per­for­mance is de­ter­mined only by GMA, with the stan­dard­ized path co­effi­cient from GMA to per­for­mance be­ing very large (.62). The find­ings for the other three sam­ples were es­sen­tially iden­ti­cal (Hunter, 1983b). It is well known that analy­sis of causal mod­els with cor­re­la­tional data can­not prove a the­o­ry. How­ev­er, such analy­ses - es­pe­cially when sam­ples are very large, as here - can dis­con­firm the­o­ries. The­o­ries that do not fit the data are dis­con­firmed. In these stud­ies, spe­cific ap­ti­tude the­ory is strongly dis­con­firmed.

Mc­Daniel (1985) an­a­lyzed United States Em­ploy­ment Ser­vices (USES) data for groups whose level of job ex­pe­ri­ence ex­tended be­yond 5 years. Con­trol­ling for differ­ences in vari­abil­ity of GMA across groups, Mc­Daniel cor­re­lated GMA with per­for­mance rat­ings for each level of ex­pe­ri­ence to 12 years and up. The re­sults are sum­ma­rized in Ta­ble 4. As the level of ex­pe­ri­ence in­creas­es, the pre­dic­tive va­lid­ity does not de­crease. Va­lid­ity goes from .36 for 0 - 6 years, up to .44 for 6 -12 years, up to .59 for more than 12 years (although the last value is based on a very small sam­ple). If any­thing, Mc­Daniel’s data sug­gest an in­crease in the va­lid­ity of GMA for pre­dict­ing per­for­mance rat­ings as level of worker ex­pe­ri­ence in­creas­es.

Many peo­ple may also be­lieve that per­son­al­ity is more im­por­tant than GMA in de­ter­min­ing ul­ti­mate oc­cu­pa­tional lev­el. How­ev­er, re­search sup­ports the con­clu­sion that per­son­al­ity is less im­por­tant than GMA in both ar­eas. In re­cent years, most per­son­al­ity re­search has been or­ga­nized around the Big Five model of per­son­al­ity (Gold­berg, 1990) …As in­di­cated ear­lier, Judge et al. (1999) found that three of the Big Five per­son­al­ity traits mea­sured in child­hood pre­dicted adult oc­cu­pa­tional level and in­come. For Con­sci­en­tious­ness, these lon­gi­tu­di­nal cor­re­la­tions were .49 and .41, re­spec­tive­ly; these val­ues are only slightly smaller than the cor­re­spond­ing cor­re­la­tions in this study for GMA (dis­cussed in the Lon­gi­tu­di­nal Stud­ies sec­tion, above) of .51 and .53, re­spec­tive­ly. For Open­ness to Ex­pe­ri­ence (which cor­re­lates pos­i­tively with GMA), the cor­re­la­tions were .32 and .26. Fi­nal­ly, Neu­roti­cism pro­duced lon­gi­tu­di­nal cor­re­la­tions of -.26 and -.34, for oc­cu­pa­tional level and in­come, re­spec­tive­ly. Be­cause of the unique na­ture of Judge et al.’s (1999) study, we con­ducted ad­di­tional analy­ses of the data from this study. Be­cause oc­cu­pa­tional level and in­come were highly cor­re­lated (r ϭ .83) and loaded on the same fac­tor, we com­bined them into one equally weighted mea­sure of ca­reer suc­cess. After cor­rect­ing for the bi­as­ing effects of mea­sure­ment er­ror, we found that the three Big Five per­son­al­ity traits pre­dicted this in­dex of ca­reer suc­cess with a (shrunk­en) mul­ti­ple cor­re­la­tion of .56. It is in­ter­est­ing to ex­am­ine the stan­dard­ized re­gres­sion weights (be­tas). For Neu­roti­cism, ␤ ϭ -.05 (SE ϭ .096); for Open­ness, ␤ ϭ .16 (SE ϭ .10); and for Con­sci­en­tious­ness, ␤ ϭ .44 (SE ϭ .123). Hence, in the re­gres­sion equa­tion, Con­sci­en­tious­ness is by far the most im­por­tant per­son­al­ity vari­able, and Neu­roti­cism ap­pears to have lit­tle im­pact after con­trol­ling for the other two per­son­al­ity traits. How­ev­er, it is also im­por­tant to con­trol for the effects of GMA. When GMA is added to the re­gres­sion equa­tion, the (shrunk­en) mul­ti­ple cor­re­la­tion rises to .63. Again, it is in­struc­tive to ex­am­ine the beta weights: Neu­roti­cism, ␤ ϭ -.05 (SE ϭ .096); Open­ness, ␤ ϭ -.03 (SE ϭ .113); Con­sci­en­tious­ness, ␤ ϭ .27 (SE ϭ .128); and GMA, ␤ ϭ .43 (SE ϭ .117). From these fig­ures, it ap­pears that the bur­den of pre­dic­tion is borne al­most en­tirely by GMA and Con­sci­en­tious­ness, with GMA be­ing 59% more im­por­tant than Con­sci­en­tious­ness (i.e., .43/.27 ϭ 1.59). In fact, when only GMA and Con­sci­en­tious­ness are in­cluded in the re­gres­sion equa­tion, the (shrunk­en) mul­ti­ple cor­re­la­tion re­mains the same, at .63. The stan­dard­ized re­gres­sion weights are then .29 for Con­sci­en­tious­ness (SE ϭ .102) and .41 for GMA (SE ϭ .096). These analy­ses sug­gest that Con­sci­en­tious­ness may be the only per­son­al­ity trait that con­tributes to ca­reer suc­cess. …The best meta-an­a­lytic es­ti­mate for the va­lid­ity of Con­sci­en­tious­ness, mea­sured with a re­li­able scale, for pre­dict­ing job per­for­mance is .31 (Mount & Bar­rick, 1995). Hence, the va­lid­ity of GMA is 60% to 80% larger (de­pend­ing on the GMA va­lid­ity es­ti­mate used) than that of Con­sci­en­tious­ness. How­ev­er, Con­sci­en­tious­ness mea­sures con­tribute to va­lid­ity over and above the va­lid­ity of GMA, be­cause the two are un­cor­re­lated (Schmidt & Hunter, 1998). As noted above, Hunter and Hunter (1984) es­ti­mated the va­lid­ity of GMA for medium com­plex­ity jobs (63% of all jobs) to be .51. The mul­ti­ple cor­re­la­tion pro­duced by use of mea­sures of both GMA and Con­sci­en­tious­ness in a re­gres­sion equa­tion for such jobs is .60, an 18% in­crease in va­lid­ity over that of GMA alone (Schmidt & Hunter, 1998). The best meta-an­a­lytic es­ti­mate of the va­lid­ity of Con­sci­en­tious­ness for pre­dict­ing per­for­mance in job train­ing is .30 (Mount & Bar­rick, 1995). The mul­ti­ple cor­re­la­tion pro­duced by si­mul­ta­ne­ous use of GMA and Con­sci­en­tious­ness mea­sures is .65 (vs. .56 for GMA alone; Schmidt & Hunter, 1998).

, Kell et al 2013

Youth iden­ti­fied be­fore age 13 (N = 320) as hav­ing pro­found math­e­mat­i­cal or ver­bal rea­son­ing abil­i­ties (top 1 in 10,000) were tracked for nearly three decades. Their awards and cre­ative ac­com­plish­ments by age 38, in com­bi­na­tion with spe­cific de­tails about their oc­cu­pa­tional re­spon­si­bil­i­ties, il­lu­mi­nate the mag­ni­tude of their con­tri­bu­tion and pro­fes­sional stature. Many have been en­trusted with oblig­a­tions and re­sources for mak­ing crit­i­cal de­ci­sions about in­di­vid­ual and or­ga­ni­za­tional well-be­ing. Their lead­er­ship po­si­tions in busi­ness, health care, law, the pro­fes­so­ri­ate, and STEM (science, tech­nol­o­gy, en­gi­neer­ing, and math­e­mat­ics) sug­gest that many are out­stand­ing cre­ators of mod­ern cul­ture, con­sti­tut­ing a pre­cious hu­man-cap­i­tal re­source. Iden­ti­fy­ing truly pro­found hu­man po­ten­tial, and fore­cast­ing differ­en­tial de­vel­op­ment within such pop­u­la­tions, re­quires as­sess­ing mul­ti­ple cog­ni­tive abil­i­ties and us­ing atyp­i­cal mea­sure­ment pro­ce­dures. This study il­lus­trates how ul­ti­mate cri­te­ria may be ag­gre­gated and lon­gi­tu­di­nally se­quenced to val­i­date such mea­sures.

Ta­ble 1 re­veals the rich­ness and scope of par­tic­i­pants’ ac­tiv­i­ties. One in­di­ca­tion of the cal­iber of their con­tri­bu­tions is the pres­tige of the or­ga­ni­za­tions that have awarded them grants. The data on cre­ative ac­com­plish­ments speak for them­selves, but a few sum­mary re­marks are in or­der. In the arts and hu­man­i­ties, 24 in­di­vid­u­als had pro­duced 128 cre­ative writ­ten works (e.g., po­ems, nov­els, ref­er­eed pub­li­ca­tion­s), an av­er­age of 5.3 ac­com­plish­ments per in­di­vid­ual. In the same do­main, 52 peo­ple had pro­duced 1,069 achieve­ments in the fine arts (e.g., mu­sic, sculp­ture), an av­er­age of 20.6 ac­com­plish­ments per per­son. STEM achieve­ments are also note­wor­thy. Fifty-nine in­di­vid­u­als had pro­duced ref­er­eed STEM pub­li­ca­tions, in ar­eas rang­ing from bio­chem­istry to en­gi­neer­ing; the to­tal num­ber of STEM pub­li­ca­tions pro­duced was 392 (6.6 per per­son). In the case of soft­ware de­vel­op­ment and patents, 117 peo­ple had made 820 con­tri­bu­tions, an av­er­age of 7 per in­di­vid­ual. Thir­ty-one in­di­vid­u­als had re­ceived more than $25 mil­lion in grants, an av­er­age of $825,635 per per­son. The tally of awards and sig­nifi­cant ac­com­plish­ments for these 320 in­di­vid­u­als was 2,749, or an av­er­age of 8.6 per per­son.

…e­nough in­for­ma­tion is pro­vided to make clear that a num­ber of par­tic­i­pants are work­ing for world-class or­ga­ni­za­tions and hold im­por­tant po­si­tions of im­pact and re­spon­si­bil­ity in For­tune 500 com­pa­nies, tech­nol­o­gy, law, and med­i­cine. For the pro­fes­so­ri­ate in our sam­ple, Ta­ble 3 lists uni­ver­si­ties that ei­ther awarded them tenure or at­tracted them with tenure, plus some of their ref­er­eed pub­li­ca­tion out­lets. In to­tal, 11.3% of par­tic­i­pants had earned tenure at ac­cred­ited in­sti­tu­tions; 7.5% had tenure at re­search-in­ten­sive in­sti­tu­tions (Carnegie Foun­da­tion, 2010). This lat­ter per­cent­age is many, many times the base-rate ex­pec­ta­tion, given the 2% base rate for doc­tor­ates in the United States and the fact that only a tiny frac­tion of the in­di­vid­u­als with doc­tor­ates have tenure at re­search0in­ten­sive in­sti­tu­tions.

Al­though it would be diffi­cult to quan­tify par­tic­i­pants’ col­lec­tive ac­com­plish­ments in a sin­gle num­ber, by any stan­dard, it ap­pears that many in­di­vid­u­als iden­ti­fi­able by age 13 as hav­ing pro­found math­e­mat­i­cal and ver­bal rea­son­ing abil­ity de­velop into truly out­stand­ing con­trib­u­tors in their re­spec­tive fields. Not only did par­tic­i­pants choose pres­ti­gious oc­cu­pa­tions by age 38 (Fig. 2 and Ta­ble 2), but the or­ga­ni­za­tions em­ploy­ing them were im­pres­sive as well (Ta­bles 2 and 3). Al­though a num­ber of our data counts do not re­flect the qual­ity of par­tic­i­pants’ con­tri­bu­tions, the or­ga­ni­za­tions em­ploy­ing par­tic­i­pants (e.g., For­tune 500 com­pa­nies, ma­jor law firms, large med­ical fa­cil­i­ties, and re­search uni­ver­si­ties) and be­stow­ing awards on them (e.g., the U.S. De­part­ments of State and Jus­tice, the Na­tional Sci­ence Foun­da­tion, In­tel Cor­po­ra­tion, NASA, and The Wall Street Jour­nal) afford rea­son­able qual­ity ap­praisals of their cre­ative prod­ucts as well as the re­spon­si­bil­i­ties, re­sources, and trust that they have earned. More than 7% of par­tic­i­pants held tenure at re­search-in­ten­sive uni­ver­si­ties (in­clud­ing many con­sid­ered the best in the world) by the time they were age 38. The 14 at­tor­neys were pre­dom­i­nantly work­ing in po­si­tions of sig­nifi­cant re­spon­si­bil­ity for ma­jor firms or or­ga­ni­za­tions. The 19 physi­cians were also highly ac­com­plished: Seven were as­sis­tant pro­fes­sors, 2 were di­rec­tors of ma­jor pri­vate prac­tices, and 1 codi­rected a hos­pi­tal or­gan-trans­plant cen­ter serv­ing more than 3 mil­lion peo­ple. Rather than work­ing for es­tab­lished or­ga­ni­za­tions, 14 in­di­vid­u­als founded com­pa­nies of their own. Two in­di­vid­u­als were vice pres­i­dents at For­tune 500 com­pa­nies; 2 oth­ers were For­tune 500 se­nior hard­ware or soft­ware en­gi­neers. Sev­eral par­tic­i­pants were ac­tive in gov­ern­ment agen­cies at lo­cal and fed­eral lev­el­s-one ad­vised the pres­i­dent of the United States on na­tional pol­icy is­sues. Al­though par­tic­i­pants’ ac­com­plish­ments are im­pres­sive in va­ri­ety and scope, it is im­por­tant to note the mag­ni­tude of in­di­vid­ual differ­ences in out­put, even in this ex­cep­tion­ally tal­ented sam­ple. Within sev­eral ac­com­plish­ment group­ings, some in­di­vid­u­als far out­stripped their in­tel­lec­tual peers. For ex­am­ple, in the arts and hu­man­i­ties, one in­di­vid­ual pro­duced 500 mu­si­cal pro­duc­tions, ac­count­ing for more than 57% of the mu­si­cal pro­duc­tions re­ported here; three in­di­vid­u­als pro­duced 100 soft­ware con­tri­bu­tions each, or nearly 44% of the to­tal re­port­ed. Seven par­tic­i­pants re­ceived more than $1 mil­lion in grant fund­ing each; col­lec­tive­ly, their fund­ing amounted to nearly $20 mil­lion, more than 77% of the to­tal sam­ple’s grant fund­ing; one in­di­vid­ual alone re­ceived $9 mil­lion in grant fund­ing. Fi­nal­ly, one per­son founded three com­pa­nies, and an­other was re­spon­si­ble for rais­ing more than $65 mil­lion in pri­vate eq­uity in­vest­ment to fund his com­pa­ny. These find­ings mir­ror those in Gal­ton’s (1869/2006) in­ves­ti­ga­tion of the Cam­bridge Uni­ver­sity “wran­glers,” the 40 top-s­cor­ing stu­dents out of the ap­prox­i­mately 100 hon­ors math­e­mat­ics grad­u­ates each year (400-450 stu­dents grad­u­ated from Cam­bridge an­nu­al­ly). Wran­glers were rank-ordered ac­cord­ing to their scores on their fi­nal math­e­mat­ics exam (a 44-hr test spread over 8 days). Al­though be­ing even a low-ranked wran­gler was enough for a grad­u­ate to ob­tain a fel­low­ship at a small col­lege, Gal­ton found that the high­est-ranked wran­gler tended to do more than twice as well on the fi­nal exam as the sec­ond-ranked wran­gler and ap­prox­i­mately 4 times bet­ter than the low­est­-ranked wran­glers. Ex­am­in­ers em­pha­sized that the units of mea­sure­ment they em­ployed were de­signed to in­dex equal in­ter­vals, such that twice the score range trans­lated into ap­prox­i­mately twice the knowl­edge. Such out­ly­ing in­di­vid­ual differ­ences in ac­com­plish­ments, even among the most tal­ent­ed, are read­ily ob­served through­out his­tory (Mur­ray, 2003). This is one rea­son why O’Boyle and Agui­nis (2012) ar­gued that, given the out­put of truly out­stand­ing per­form­ers, per­for­mance in gen­eral is bet­ter mod­eled through Paret­ian (power law) dis­tri­b­u­tions as op­posed to Gauss­ian (nor­mal-curve) dis­tri­b­u­tions (Si­mon­ton, 1999a, 1999b).

Sipe and Curlette (1996) have found in their meta-syn­the­sis of ed­u­ca­tional re­search that on the in­di­vid­ual level the effect of in­tel­li­gence on ed­u­ca­tional at­tain­ment was .6 (r = .5). The effects of other vari­ables (mo­ti­va­tion, SES, teacher ed­u­ca­tion, etc.) were small­er.

The in­breed­ing de­pres­sion had been cal­cu­lated by Schull and Neel (1965) from 1854 cousin mar­riages in Japan on the WISC and showed an over­all 7.5 point decre­ment (0.50 SD) in the off­spring, with each sub­test show­ing a greater or lesser amount. There is no non-ge­netic ex­pla­na­tion for why Black­-White differ­ences in the US should be more pro­nounced on those sub­tests show­ing the most in­breed­ing de­pres­sion among the Japan­ese in Japan (Jensen also demon­strated in­breed­ing de­pres­sion effects on the Raven Ma­tri­ces in In­dia; Agrawal, Sin­ha, & Jensen, 1984).


Miller 2012, Sin­gu­lar­ity Ris­ing:

g - the let­ter used as short­hand for gen­eral men­tal abil­ity - is, in the words of Linda Got­tfred­son, a pro­lific scholar of hu­man in­tel­li­gence, “prob­a­bly the best mea­sured and most stud­ied hu­man trait in all of psy­chol­o­gy.” [Got­tfred­son, Linda S. 2002. “Where and Why g Mat­ters: Not a Mys­tery.” Hu­man Per­for­mance 15 (1/2): 25-46. http://www1.udel.e­du/e­duc/­got­tfred­son/reprints/2002no­tamys­tery.pdf ]

econ­o­mist Garett Jones notes, “Across thou­sands of stud­ies on the cor­re­la­tion across men­tal abil­i­ties across pop­u­la­tions, no one has yet found a re­li­able neg­a­tive cor­re­la­tion [be­tween per­for­mances on two differ­ent com­plex men­tal tasks]”; [117. Jones, Garett. 2011b. “Na­tional IQ and Na­tional Pro­duc­tiv­i­ty: The Hive Mind Across Asia.” Asian De­vel­op­ment Re­view 28 (1): 51-71 http://www.kvimis.­­/sites/de­fault­/­files/adr-vol28-1.pdf#­page=55 ]

Some might chal­lenge IQ’s im­por­tance by claim­ing that all chil­dren have about the same aca­d­e­mic po­ten­tial, but for so­cial rea­sons we feel the need to grade and rank chil­dren even though the rank­ings arise from differ­ences that are very small. But differ­ences in IQ cor­re­late with starkly dis­sim­i­lar lev­els of real aca­d­e­mic per­for­mance. (One strik­ing piece of ev­i­dence is that “the nineti­eth per­centile of nine-year-olds . . . per­forms in read­ing, math, and sci­ence at the level of the twen­ty-fifth per­centile of sev­en­teen-year-olds.”[120. Got­tfred­son, Linda S. 2005. “Sup­press­ing In­tel­li­gence Re­search: Hurt­ing Those We In­tend to Help,” In Rogers H. Wright and Nicholas A. Cum­mings (ed­s.), De­struc­tive Trends in Men­tal Health: The Well-In­ten­tioned Path to Harm. New York: Tay­lor and Fran­cis, 155-86. http://cite­­su.e­du/view­doc/­down­load­?­doi=­type­=pdf . Foot­note omit­ted.] Be­cause schools sort by age rather than abil­i­ty, we don’t find smart nine-year-olds in higher grades than not-so-s­mart sev­en­teen-year-olds, even when the for­mer are more ca­pa­ble than the lat­ter.)

IQ tests taken by chil­dren have been found to go a long way to­ward pre­dict­ing life span.[126. Got­tfred­son, Lin­da. S., and Ian J. Deary. 2004. “In­tel­li­gence Pre­dicts Health and Longevi­ty, But Why?” Cur­rent Di­rec­tions in Psy­cho­log­i­cal Sci­ence 13 (1): 1-4.­files/2006/02/22/20060131_­Got­tfred­son­In­tel­li­gence.pdf ] High­er-IQ in­di­vid­u­als also have bet­ter den­tal health, even when con­trol­ling for in­come and eth­nic­i­ty.[S­ab­bah, Wael, and Aubrey Shei­ham. 2010. “The Re­la­tion­ships Be­tween Cog­ni­tive Abil­ity and Den­tal Sta­tus in a Na­tional Sam­ple of USA Adults”, In­tel­li­gence 38 (6): 605-10] We don’t un­der­stand the causes of the health-IQ re­la­tion­ship, but plau­si­ble ex­pla­na­tions in­clude child­hood ill­nesses that may both re­duce a child’s IQ and shorten his life span and the pos­si­bil­ity that high­er-IQ in­di­vid­u­als make bet­ter health de­ci­sions, get into fewer ac­ci­dents, choose to live in a health­ier en­vi­ron­ment, fol­low doc­tors’ ad­vice bet­ter, and more often fol­low di­rec­tions when tak­ing med­ica­tion. Ge­net­ics would also ex­plain part of the cor­re­la­tion if the same genes that con­ferred high in­tel­li­gence also boosted longevi­ty.[128. Got­tfred­son and Deary (2004).] The pos­i­tive re­la­tion­ship be­tween a man’s se­men qual­ity and his IQ sup­ports the the­ory that genes play a role in the cor­re­la­tion.[Ar­den, Ros­alind, Linda S. Got­tfred­son, Ge­offrey Miller, and Arand Pierce. 2009. “In­tel­li­gence and Se­men Qual­ity are Pos­i­tively Cor­re­lat­ed.” In­tel­li­gence 37 (3): 277-82 http://j­­ing/in­tel­li­gence%20and%20sea­man%20qual­i­ty%20are%20­pos­i­tive­ly%20­cor­re­lat­ed.pdf] Com­pound­ing IQs im­pact on in­equal­i­ty, high­er-IQ peo­ple tend to be more phys­i­cally at­trac­tive.[Kanaza­wa, Satoshi 2011. “In­tel­li­gence and Phys­i­cal At­trac­tive­ness”. In­tel­li­gence 39 (1): 7-14 http://per­son­­wa/pdf­s/I2011.pdf ] Fur­ther­more, it’s pos­si­ble to make a de­cent guess at peo­ple’s in­tel­li­gence just by look­ing at them. From an ar­ti­cle in the on­line mag­a­zine Slate http://www.s­late.­com/ar­ti­cles/health_and_­science/­ex­plain­er/2012/01/are_s­mart_peo­ple_ug­ly_the_­ex­plain­er_s_2011_ques­tion_of_the_year_.s­in­gle.html : “In 1918, a re­searcher in Ohio showed a dozen pho­to­graphic por­traits of well-dressed chil­dren to a group of physi­cians and teach­ers, and asked the adults to rank the kids from smartest to dumb­est. A cou­ple of years lat­er, a Pitts­burgh psy­chol­o­gist ran a sim­i­lar ex­per­i­ment us­ing head­shots of 69 em­ploy­ees from a de­part­ment store. In both stud­ies, seem­ingly naive guesses were com­pared to ac­tual test scores and turned out to be ac­cu­rate more often than not.” Stare at a com­puter screen un­til a big green ball ap­pears, and then hit the space bar as quickly as you can. You have just taken a par­tially re­li­able IQ test, since a per­son’s IQ has a pos­i­tive cor­re­la­tion with re­ac­tion time.[132. Got­tfred­son (2002).]

…mag­netic res­o­nance imag­ing show­ing a strong pos­i­tive cor­re­la­tion be­tween brain size and IQ[136. Jones (2011b).]

A per­son’s IQ is large­ly, but not com­plete­ly, de­ter­mined by age eight.[Heck­man, James, Jora Stixrud, and Ser­gio Urzua. 2006. “The Effects of Cog­ni­tive and Noncog­ni­tive Abil­i­ties on La­bor Mar­ket Out­comes and So­cial Be­hav­ior”. Jour­nal of La­bor Eco­nom­ics 24 (3): 41 1-82 ] Tests given to in­fants mea­sur­ing how much at­ten­tion the in­fant pays to novel pic­tures have a pos­i­tive cor­re­la­tion with the IQ the in­fant will have at age twen­ty-one.[Hunt, Earl. 2011 . Hu­man In­tel­li­gence. Cam­bridge: Cam­bridge Uni­ver­sity Press http://www.a­ma­zon.­com/Hu­man-In­tel­li­gence-Ear­l-Hunt/d­p/0521707811/ ] The Scot­tish Men­tal Sur­vey of 1932 has helped show the re­mark­able sta­bil­ity of a per­son’s IQ across his adult life.[Deary, Ian, Martha C. White­man, John M. Starr, Lawrence J. Whal­ley, and He­len C. Fox 2004. “The Im­pact of Child­hood In­tel­li­gence on Later Life: Fol­low­ing Up the Scot­tish Men­tal Sur­veys of 1932 and 1947”. Jour­nal of Per­son­al­ity and So­cial Psy­chol­ogy 86 (1): 130-47 ] On June 1, 1932, al­most every child in Scot­land born in 1921 took the same men­tal test. Over sixty years lat­er, re­searchers tracked down some of the test tak­ers who lived in one par­tic­u­lar part of Scot­land and gave them the test they took in 1932. The re­searchers found a strong cor­re­la­tion be­tween most peo­ple’s 1932 and re­cent test re­sults.

IQ is the sin­gle best pre­dic­tor of job per­for­mance. [Got­tfred­son, Linda S. 1997. “Why g Mat­ters: The Com­plex­ity of Every­day Life.” In­tel­li­gence 24 (1): 79–132]

The effect of IQ on wages might be mit­i­gated by the pos­si­bil­ity that some high­-IQ peo­ple are drawn to rel­a­tively low-pay­ing pro­fes­sions. Con­sid­er, for ex­am­ple, Ter­ence Tao, a rea­son­able can­di­date for the smartest per­son alive to­day. Ter­ence works as a math pro­fes­sor, and ac­cord­ing to Wikipedia, his great­est ac­com­plish­ment to date is coau­thor­ing a the­o­rem on prime num­bers. Prime num­ber re­search does­n’t pay well, but you can’t do it, and would­n’t find it in­ter­est­ing, un­less you had a su­per-ge­nius level IQ. Sim­i­lar­ly, a poet with an ex­tremely high IQ might have be­come a lawyer had her IQ been a bit lower be­cause then she would­n’t have un­der­stood the sub­tleties of po­etry that drew her into a poorly re­mu­ner­ated pro­fes­sion. I sus­pect that many math pro­fes­sors and po­ets would have higher in­comes if some brain in­jury low­ered their IQs just enough to force them out of their pro­fes­sions. If you have an IQ of 135, then you’re al­ready smarter than 99% of hu­man­i­ty. [I’m as­sum­ing an IQ stan­dard de­vi­a­tion of 15 for this and all other IQ cal­cu­la­tions used in this book.] Would you do bet­ter in life if your IQ went well above 135? Re­search by Heck­man says yes. He found that among men with IQs in the top 1% of the pop­u­la­tion, hav­ing a higher IQ boosts wages through­out one’s en­tire work­ing life, and this effect ex­ists even after tak­ing into ac­count an in­di­vid­u­al’s level of ed­u­ca­tion.[­Gen­sowski, Miri­am, James J. Heck­man, and Pe­ter Save­lyev. 2011. “The Effects of Ed­u­ca­tion, Per­son­al­i­ty, and IQ on Earn­ings of High­-A­bil­ity Men” Work­ing pa­per http://www.roa.u­ni­maas.n­l/sem­i­nars/pdf2011/­gen­sows­ki.pdf [See also for ex­am­ple SMPY re­sults like Kell et al 2013 ]]

A re­searcher at the Lon­don School of Eco­nom­ics has even shown that one-fourth of the differ­ences in wealth be­tween differ­ent US states can be ex­plained by differ­ences in the av­er­age IQ of their pop­u­la­tion.[Kanaza­wa, Satoshi. 2006. “IQ and the Wealth of States.” In­tel­li­gence 34 (6): 593-600. http://per­son­­wa/pdf­s/I2006.pdf ]

a British study showed that IQ tests given to a group of ten and eleven-year-olds strongly cor­re­lated with the level of trust these sub­jects ex­pressed when they be­came adult­s.[S­tur­gis, Patrick, Sanna Read, and Nick Al­lum. 2010. “Does In­tel­li­gence Fos­ter Gen­er­al­ized Trust? An Em­pir­i­cal Test Us­ing the UK Birth Co­hort Stud­ies”. In­tel­li­gence 38 (1): 45-54 ]

Hav­ing a low IQ makes you, on av­er­age, more dis­posed to crime.[Beaver, Kevin M., and John Paul Wright. 2011. “The As­so­ci­a­tion be­tween Coun­ty-Level IQ and Coun­ty-Level Crime Rates”. In­tel­li­gence 39 (1): 22-26 ] Since crime re­duces pro­duc­tive eco­nomic ac­tiv­i­ty, this is an­other means by which high IQ con­tributes to eco­nomic growth. The higher a per­son’s IQ, the more likely he is to sup­port eco­nomic poli­cies that most econ­o­mists con­sider to be healthy for a na­tion’s econ­o­my.[­Ca­plan, Bryan, and Stephen C. Miller. 2010. “In­tel­li­gence Makes Peo­ple Think Like Econ­o­mists: Ev­i­dence from the Gen­eral So­cial Sur­vey”. In­tel­li­gence 38 (6): 636-47 http://e­con­fac­ul­ty.g­mu.e­du/b­ca­plan/pdf­s/in­tel­li­gence­thin­k­like.pdf ] Con­se­quent­ly, if you live in a democ­racy and trust econ­o­mists’ judg­ments, you should want your fel­low vot­ers to be smart. Hav­ing a high IQ makes you more long-term ori­ent­ed.[Warn­er, John T, and Saul Fleeter. 2001. ] Fu­ture-ori­ented peo­ple are more likely to make the kind of cal­cu­lated long-term in­vest­ments crit­i­cal to eco­nomic growth.

“In­tel­li­gence makes peo­ple think like econ­o­mists: Ev­i­dence from the Gen­eral So­cial Sur­vey”, Ca­plan & Miller 2010:

Us­ing data from the Gen­eral So­cial Sur­vey (GSS), we show that the es­ti­mated effect of ed­u­ca­tion sharply falls after con­trol­ling for in­tel­li­gence. In fact, ed­u­ca­tion is dri­ven down to sec­ond place, and in­tel­li­gence re­places it at the top of the list of vari­ables that make peo­ple “think like econ­o­mists.” Thus, to a fair de­gree ed­u­ca­tion is proxy for in­tel­li­gence, though there are some ar­eas-in­ter­na­tional eco­nom­ics in par­tic­u­lar-where ed­u­ca­tion still dom­i­nates. An im­por­tant im­pli­ca­tion is that the po­lit­i­cal ex­ter­nal­i­ties of ed­u­ca­tion may not be as large as they ini­tially ap­pear.

, Carl & Bil­lari 2014:

An ex­ten­sive em­pir­i­cal lit­er­a­ture has es­tab­lished that gen­er­al­ized trust is an im­por­tant as­pect of civic cul­ture. It has been linked to a va­ri­ety of pos­i­tive out­comes at the in­di­vid­ual lev­el, such as en­tre­pre­neur­ship, vol­un­teer­ing, self­-rated health, and hap­pi­ness. How­ev­er, two re­cent stud­ies have found that it is highly cor­re­lated with in­tel­li­gence, which raises the pos­si­bil­ity that the other re­la­tion­ships in which it has been im­pli­cated may be spu­ri­ous. Here we repli­cate the as­so­ci­a­tion be­tween in­tel­li­gence and gen­er­al­ized trust in a large, na­tion­ally rep­re­sen­ta­tive sam­ple of U.S. adults. We also show that, after ad­just­ing for in­tel­li­gence, gen­er­al­ized trust con­tin­ues to be strongly as­so­ci­ated with both self­-rated health and hap­pi­ness. In the con­text of sub­stan­tial vari­a­tion across coun­tries, these re­sults bol­ster the view that gen­er­al­ized trust is a valu­able so­cial re­source, not only for the in­di­vid­ual but for the wider so­ci­ety as well.

…Two re­cent stud­ies have doc­u­mented a strong cor­re­la­tion be­tween gen­er­al­ized trust and in­tel­li­gence [16], [17]. Stur­gis et al. [“Does in­tel­li­gence fos­ter gen­er­al­ized trust? An em­pir­i­cal test us­ing the UK birth co­hort stud­ies”] analyse data from the U.K., and show that in­tel­li­gence at age 10-11 pre­dicts gen­er­al­ized trust at age 34, even after con­di­tion­ing on a large num­ber of so­cio-e­co­nomic vari­ables, in­clud­ing self­-rated health and hap­pi­ness. Sim­i­lar­ly, Hooghe et al 2012 [“The cog­ni­tive ba­sis of trust. The re­la­tion be­tween ed­u­ca­tion, cog­ni­tive abil­i­ty, and gen­er­al­ized and po­lit­i­cal trust”] ex­am­ine Dutch data, and find that a large part of the as­so­ci­a­tion be­tween gen­er­al­ized trust and ed­u­ca­tion is ac­counted for by cog­ni­tive abil­i­ty.

Hooghe et al 2012:

Pre­vi­ous stud­ies - mainly based on UK data - in­deed show a pos­i­tive re­la­tion be­tween in­tel­li­gence and gen­er­al­ized and po­lit­i­cal trust (Ya­m­ag­ishi, 2001; Ya­m­ag­ishi, Kikuchi & Ko­sugi, 1999; Stur­gis, Read & Al­lum, 2010, p. 52; Schoon & Cheng, 2011; Schoon et al., 2010). In this line of rea­son­ing, Deary, Batty and Gale (2008, p. 1) stat­ed: “bright chil­dren be­come en­light­ened adults”.

  • Ya­m­ag­ishi, T. (2001). “Trust as a form of so­cial in­tel­li­gence”, pp. 121-147 in K. Cook (ed.), Trust in so­ci­ety. New York: Rus­sell Sage Foun­da­tion
  • Ya­m­ag­ishi, T., Kikuchi, M. & Ko­sugi, M. (1999). “Trust, gulli­bil­ity and so­cial in­tel­li­gence”. Asian Jour­nal of So­cial Psy­chol­ogy, 2(1), 145-161
  • Stur­gis, P., Read, S. & N. Al­lum (2010). “Does in­tel­li­gence fos­ter gen­er­al­ized trust? An em­pir­i­cal test us­ing the UK birth co­hort stud­ies”, In­tel­li­gence, 38(1), 45-54
  • Schoon, I. & H. Cheng (2011). “De­ter­mi­nants of po­lit­i­cal trust. A life-long learn­ing model”. De­vel­op­men­tal Psy­chol­ogy, 47(3), 619-631
  • Schoon, I., Cheng, H., Gale, C., Bat­ty, D. & Deary, I. (2010). “So­cial sta­tus, cog­ni­tive abil­i­ty, and ed­u­ca­tional at­tain­ment as pre­dic­tors of lib­eral so­cial at­ti­tudes and po­lit­i­cal trust”. In­tel­li­gence, 38(1), 144-150

“Sup­press­ing In­tel­li­gence Re­search: Hurt­ing Those We In­tend to Help”, Got­tfred­son 2005 http://cite­­su.e­du/view­doc/­down­load­?­doi=­type­=pdf

The re­sults of a 1984 sur­vey (S­ny­der­man & Roth­man, 1988) of ex­perts on in­tel­li­gence and men­tal test­ing there­fore sur­prised even Jensen. The ex­perts’ modal re­sponse on every ques­tion that in­volved the “hereti­cal” con­clu­sions from Jensen’s 1969 ar­ti­cle was the same as his (Jensen, 1998, p. 198). (The ex­perts’ mean re­sponse over­es­ti­mated test bi­as, how­ev­er, be­cause there is none against blacks or lower so­cial class in­di­vid­u­als; Jensen, 1980; Neisser et al., 1996; Sny­der­man & Roth­man, 1988, p. 134; Wig­dor & Gar­ner, 1982). Here in ab­bre­vi­ated form are the sur­vey’s ma­jor ques­tions and the 600 ex­perts’ re­spons­es.

  • Q: What are the im­por­tant el­e­ments of in­tel­li­gence?

  • A: “Near una­nim­ity” (96-99%) for ab­stract think­ing or rea­son­ing, prob­lem solv­ing abil­i­ty, and ca­pac­ity to ac­quire knowl­edge (p. 56).

  • Q: Is in­tel­li­gence best de­scribed as a sin­gle gen­eral fac­tor with sub­sidiaries or as sep­a­rate fac­ul­ties?

  • A: A gen­eral fac­tor (58%, or 67% of those re­spond­ing; p. 71).

  • Q: What her­i­tabil­ity would you es­ti­mate for IQ differ­ences within the white pop­u­la­tion?

  • A: Av­er­age es­ti­mate of 57% (p. 95).

  • Q: What her­i­tabil­ity would you es­ti­mate for IQ differ­ences within the black pop­u­la­tion?

  • A: Av­er­age es­ti­mate of 57% (p. 95).

  • Q: Are in­tel­li­gence tests bi­ased against blacks?

  • A: On a scale of 1 (not at all or in­signifi­cant­ly) to 4 (ex­treme­ly), mean re­sponse of 2 (some­what, p. 117).

  • Q: Are in­tel­li­gence tests bi­ased against lower so­cial class in­di­vid­u­als?

  • A: On a scale of 1 (not at all or in­signifi­cant­ly) to 4 (ex­treme­ly), mean re­sponse of 2 (some­what, p. 118).

  • Q: What is the source of av­er­age so­cial class differ­ences in IQ?

  • A: Both ge­netic and en­vi­ron­men­tal (55%, or 65% of those re­spond­ing; p. 126).

  • Q: What is the source of the av­er­age black­-white differ­ence in IQ?

  • A: Both ge­netic and en­vi­ron­men­tal (45%, or 52% of those re­spond­ing; p. 128).

The sup­pos­edly fringe sci­en­tist, Jensen, was ac­tu­ally in the main­stream be­cause the main­stream had silently come to him, where it re­mains to­day (Got­tfred­son, 1997a). Mean­while, pub­lic opin­ion was still be­ing pushed in the op­po­site di­rec­tion, cre­at­ing an ever greater gulf be­tween re­ceived opin­ion and sci­en­tifi­cally in­formed thought.

…And why keep silent when the me­dia pro­mul­gate clear false­hoods as sci­en­tific truth­s-e­spe­cially when, as Sny­der­man and Roth­man (1988) demon­strat­ed, the me­dia por­tray ex­pert opin­ion on in­tel­li­gence as the op­po­site of what it re­ally is?

Early in my ca­reer I re­ported that bright boys who had at­tended a school for dyslex­ics did not en­ter the usual high­-level jobs (med­i­cine, law, sci­ence, and col­lege teach­ing) but had nev­er­the­less suc­ceeded at a high level by en­ter­ing pres­ti­gious or re­mu­ner­a­tive oc­cu­pa­tions that re­quired above-av­er­age in­tel­li­gence but rel­a­tively lit­tle read­ing or writ­ing, specifi­cal­ly, top man­age­ment and sales po­si­tions. A col­league ac­cused me in that sem­i­nar of say­ing that “blacks can’t make it be­cause they are dumb.”

The best in­formed, who are often called upon for ex­pert com­ment, can­not en­dorse clear false­hoods with­out jeop­ar­diz­ing their own stand­ing within the dis­ci­pline, but they some­times dis­pute mi­nor is­sues in a man­ner that the un­in­formed mis­take for whole­sale re­pu­di­a­tion (Got­tfred­son, 1994a; Page, 1972).

The im­pli­ca­tion of ABC’s No­vem­ber 22, 1994, na­tional news­cast was surely not lost on view­ers when, while ex­pos­ing the sup­pos­edly un­sa­vory his­tory of in­tel­li­gence re­search be­hind The Bell Curve, news an­chor Pe­ter Jen­nings fol­lowed pho­tographs of Jensen and other sup­posed race sci­en­tists with footage of Nazi sol­diers and what ap­peared to be death camp doc­tors and pris­on­ers.

Crit­ics have as­so­ci­ated a be­lief in the hered­i­tary ba­sis of in­tel­li­gence with evil in­tent so fre­quently and for so long that merely men­tion­ing “IQ” is enough to trig­ger in many minds the words “pseu­do­science,” “racism,” and “geno­cide.” Even cur­rent APA pres­i­dent Robert Stern­berg keeps the ma­li­cious as­so­ci­a­tion alive by reg­u­larly ridi­cul­ing and be­lit­tling em­pir­i­cal­ly-minded in­tel­li­gence re­searchers (e.g., com­par­ing Jensen, in a book meant to honor him, to a child who would not grow up; Stern­berg, 2003), re­fer­ring to their work as “qua­si­-science” (“Sci­ence and pseu­do­science,” 1999, p. 27) that has “recre­ated a kind of night of the liv­ing dead” (Stern­berg, 1997, p. 55), and sprin­kling his de­scrip­tions of it with men­tions of racism, slav­ery, and even So­viet tyranny (e.g., Stern­berg, 2003; see also Stern­berg, 2000, Stern­berg & Wag­n­er, 1993). But why should we as­sume that a be­lief in the her­i­tabil­ity of many hu­man differ­ences is dan­ger­ous and a be­lief in man’s in­fi­nite mal­leabil­ity is not? Crit­ics have yet to ex­plain. Why is the for­mer be­lief al­ways yoked to Hitler, but the lat­ter never to Stal­in, who out­lawed both in­tel­li­gence tests and ge­netic think­ing? Stalin killed at least as many as did Hitler in his effort to re­shape the So­viet cit­i­zenry (Cour­tois, 1999).

Be­hav­ior ge­neti­cists dis­tin­guish be­tween two types of en­vi­ron­men­tal in­flu­ence: shared and non-shared (also called be­tween-fam­ily and with­in-fam­ily effect­s). Shared in­flu­ences are those that make sib­lings more alike. Pos­si­ble such in­flu­ences would in­clude parental in­come, ed­u­ca­tion, child-rear­ing style, and the like, be­cause they would im­pinge on all sib­lings in a house­hold. Non-shared in­flu­ences are those that affect in­di­vid­u­als one per­son at a time and there­fore make sib­lings less alike. Lit­tle is yet known about them, but they might in­clude ill­ness, ac­ci­dents, non-ge­netic in­flu­ences on fe­tal de­vel­op­ment, and the con­cate­na­tion of unique ex­pe­ri­ences. To the great sur­prise even of be­hav­ior ge­neti­cists, shared en­vi­ron­men­tal effects on in­tel­li­gence (within the broad range of typ­i­cal en­vi­ron­ments) wash away by late ado­les­cence. IQ differ­ences can be traced to both genes (40%) and shared en­vi­ron­ments (25%) in early child­hood, but ge­netic effects in­crease in im­por­tance with age (to 80% in adult­hood) while shared effects dis­si­pate (Plom­in, De­Fries, Mc­Clearn, & McGuffin, 2001). For ex­am­ple, adop­tive sib­lings end up no more alike in IQ or per­son­al­ity by ado­les­cence than are ran­dom strangers, and in­stead be­come sim­i­lar to the bi­o­log­i­cal rel­a­tives they have never met.

Cur­rently one of the biggest puz­zles for fam­ily effects the­ory is that aca­d­e­mic achieve­ment gaps do not nar­row even in set­tings where all the sup­pos­edly im­por­tant en­vi­ron­men­tal re­sources are present (Banchero & Lit­tle, 2002). For ex­am­ple, its ad­her­ents are now ar­gu­ing among them­selves (Lee, 2002) about the proper cul­tural ex­pla­na­tion for the large black­-white achieve­ment gaps that per­sist in the most so­cioe­co­nom­i­cally ad­van­taged, in­te­grat­ed, lib­er­al, sub­ur­ban school dis­tricts in the United States, such as Shaker Heights, Ohio (Og­bu, 2003) and Berke­ley, Cal­i­for­nia (Noguera, 2001). More­over, black­-white test score gaps (IQ, SAT, etc.) tend to be larger at higher so­cioe­co­nomic lev­els. This find­ing con­tra­dicts the pre­dic­tions of fam­ily effects the­o­ry. It is con­sis­tent with g-based the­o­ry, how­ev­er, be­cause the lat­ter pre­dicts that black and white chil­dren of high­-IQ par­ents will regress part way from their par­ents’ mean to­ward differ­ent pop­u­la­tion means, IQ 100 for whites and IQ 85 for blacks.

The fic­tions about in­tel­li­gence es­sen­tially deny that it ex­ists, which vir­tu­ally no one re­ally be­lieves. Many peo­ple just want a more “de­mo­c­ra­tic” view of it. Not sur­pris­ing­ly, psy­chol­o­gy’s sup­ply has risen to meet pub­lic de­mand, and the new egal­i­tar­ian per­spec­tives on hu­man in­tel­li­gence were in­stantly blessed by opin­ion mak­ers. Chief among them are the “mul­ti­ple in­tel­li­gence” the­o­ries by psy­chol­o­gists Howard Gard­ner (1983, 1998) and Robert Stern­berg (1997). The ea­ger ac­cep­tance of their the­o­ries by ed­u­ca­tors, psy­chol­o­gists, and oth­ers has oc­curred de­spite nei­ther of them pro­vid­ing cred­i­ble ev­i­dence that their pro­posed in­tel­li­gences ac­tu­ally ex­ist, that is, as in­de­pen­dent abil­i­ties of com­pa­ra­ble gen­er­al­ity and prac­ti­cal im­por­tance to g. Gard­ner has re­jected even mea­sur­ing his eight in­tel­li­gences, let alone demon­strat­ing that they pre­dict any­thing (Hunt, 2001; Lu­bin­ski & Ben­bow, 1995). Study-by-s­tudy dis­sec­tions of Stern­berg’s mul­ti­ple-in­tel­li­gence re­search pro­gram re­veal no such ev­i­dence (Brody, 2003a, b; Got­tfred­son, 2003a, c). If any­thing, they con­firm that all three of his pro­posed in­tel­li­gences are just differ­ent fla­vors of g it­self, as prob­a­bly are most of Gard­ner’s too (Car­roll, 1993, p. 641).

In 1991, the U.S. Con­gress voted over­whelm­ingly to out­law race-norm­ing in em­ploy­ment after it learned that the La­bor De­part­ment had al­ready been race-norm­ing its em­ploy­ment tests for a decade and that the U. S. Equal Em­ploy­ment Op­por­tu­nity Com­mis­sion (EEOC) had started threat­en­ing pri­vate em­ploy­ers if they did not adopt the “sci­en­tifi­cal­ly-jus­ti­fied” prac­tice. The racial pref­er­ences that race-norm­ing en­tails are hardly triv­ial. What the NRC re­port did not say was that blacks scor­ing at the 15th per­centile in skill level on DOL’s test would have been judged equal to whites and Asians scor­ing at the 50th per­centile, and blacks at the 50th per­centile would be rated com­pa­ra­bly skilled as whites and Asians at the 84th (Blits & Got­tfred­son, 1990a). Sel­dom be­ing ap­prised of such facts, most peo­ple greatly un­der­es­ti­mate how dis­crepant the pools of qual­i­fied ap­pli­cants are from which racial bal­ance is sup­posed to emerge. An­other il­lus­tra­tion, per­ti­nent to the next ex­am­ple, is that about 75% of whites vs. only 28% of blacks ex­ceed the min­i­mum IQ level (~IQ 91)-a ra­tio of 3 to 1-usu­ally re­quired for min­i­mally sat­is­fac­tory per­for­mance in the skilled trades, fire and po­lice work, and mid-level cler­i­cal jobs such as bank teller (Got­tfred­son, 1986, pp. 400-401). The po­ten­tial pools be­come in­creas­ingly racially lop­sided for more cog­ni­tively de­mand­ing jobs. Work­ers in pro­fes­sional jobs such as en­gi­neer, lawyer, and physi­cian typ­i­cally need an IQ of at least 114 to per­form sat­is­fac­to­ri­ly. About 23% of whites but only 1% of blacks ex­ceed this min­i­mum….De­vel­op­ing tests that mea­sure cog­ni­tive skills more effec­tively tends only to worsen the pro­scribed dis­parate im­pact. Adding rel­e­vant non-cog­ni­tive pre­dic­tors to the mix does lit­tle to re­duce the racial im­bal­ance (Schmitt, Rogers, Chan, Shep­pard, & Jen­nings, 1997).

The po­lice se­lec­tion test de­vel­oped in 1994 for Nas­sau Coun­ty, NY, rep­re­sents one such “tech­ni­cal ad­vance.” The 10 mem­bers of a joint Nas­sau Coun­ty-U.S. De­part­ment of Jus­tice (DOJ) team had set out to de­velop a po­lice se­lec­tion test with less dis­parate im­pact (more racially bal­anced re­sult­s). The county had not been able to sat­isfy the DOJ’s em­ploy­ment dis­crim­i­na­tion unit in sev­eral tries un­der its var­i­ous con­sent de­crees since 1977. (Re­call the 3 to 1 ra­tio given above for the pro­por­tion of whites vs. blacks ex­ceed­ing the abil­ity level be­low which per­for­mance in po­lice work tends to be un­sat­is­fac­to­ry.) Seven of the team’s eight psy­chol­o­gists con­sti­tuted a Who’s Who of APA’s large Di­vi­sion 14 (In­dus­trial and Or­ga­ni­za­tional Psy­chol­o­gy), four of them hav­ing pre­vi­ously served as its pres­i­dent. Sev­eral years and mil­lions of dol­lars lat­er, this high­-pow­ered team claimed to have suc­ceeded in de­vel­op­ing a test that vir­tu­ally elim­i­nated dis­parate im­pact while si­mul­ta­ne­ously im­prov­ing se­lec­tion va­lid­i­ty. Wa­ter could run up­-hill, after all. Once again, lead­ing psy­chol­o­gists found a seem­ingly sci­en­tific so­lu­tion to an in­tractable po­lit­i­cal-le­gal dilem­ma. DOJ im­me­di­ately be­gan press­ing other po­lice ju­ris­dic­tions na­tion­wide to re­place their more “dis­crim­i­na­tory” tests with the new se­lec­tion bat­tery. A close look at the sev­er­al-vol­ume tech­ni­cal re­port for the Nas­sau test bat­tery re­vealed that the team had suc­ceeded in re­duc­ing dis­parate im­pact by, in effect, ger­ry­man­der­ing the test to as­sess only traits on which the races differed lit­tle or not at all (Got­tfred­son, 1996a, b). The joint Nassau-DOJ team had ad­min­is­tered its nearly day-long, 25-part ex­per­i­men­tal bat­tery to all 25,000 ap­pli­cants, but set­tled on the bat­tery’s fi­nal com­po­si­tion only after ex­am­in­ing the scores it yielded for differ­ent races. The ex­per­i­men­tal bat­tery was then ap­par­ently stripped of vir­tu­ally all parts de­mand­ing cog­ni­tive abil­i­ty. The only parts ac­tu­ally used to rank ap­pli­cants were eight non-cog­ni­tive per­son­al­ity scales (all com­mer­cial prod­ucts owned by mem­bers of the team) and be­ing able to read above the 1st per­centile of cur­rently em­ployed po­lice offi­cers (n­ear il­lit­er­a­cy). Se­lec­tion for cog­ni­tive com­pe­tence had been re­duced to lit­tle more than the toss of a coin, de­spite the team’s own care­ful job analy­sis hav­ing shown that “rea­son­ing, judg­ment, and in­fer­en­tial think­ing” were the most crit­i­cal skills for good po­lice work. The new po­lice test was made to ap­pear more valid than the coun­ty’s pre­vi­ous ones by, among other things, omit­ting key re­sults re­quired by le­gal and pro­fes­sional guide­li­nes, trans­form­ing the data in ways that ar­ti­fi­cially re­duced the ap­par­ent va­lid­ity of the cog­ni­tive sub­tests rel­a­tive to the non-cog­ni­tive ones, and mak­ing a se­ries of sta­tis­ti­cal er­rors that more than dou­bled the fi­nal bat­tery’s ap­par­ent pre­dic­tive va­lid­ity (from .14 to .35). When ex­posed, the test cre­ated a scan­dal in Di­vi­sion 14 (“The Great De­bate of 1997” in Hakel, 1997, p. 116), partly be­cause other lead­ing se­lec­tion psy­chol­o­gists ex­pected its use would pro­duce less effec­tive polic­ing and de­grade pub­lic safety (Schmidt, 1996).

Even the most ob­jec­tive, most care­fully vet­ted pro­ce­dures for iden­ti­fy­ing tal­ent are in­stantly pro­nounced guilty of bias or “ex­clu­sion” when they yield dis­parate im­pact in hir­ing, col­lege ad­mis­sions, place­ment in gifted ed­u­ca­tion, and the like. In­deed, the very no­tions of ob­jec­tiv­ity and merit are now un­der at­tack by in­flu­en­tial in­tel­lec­tual elites (Far­ber & Sher­ry, 1997). When faith­ful and fair ap­pli­ca­tion of the law yields dis­parate im­pact in ar­rest or in­car­cer­a­tion rates, Amer­i­can ju­rispru­dence must be con­sid­ered in­her­ently racist (see ar­gu­ments in Cren­shaw, Gotan­da, Peller, & Thomas, 1995). When earnest, so­cially lib­eral teach­ers fail to nar­row the stub­born achieve­ment gaps be­tween races and class­es, they must be un­con­sciously dis­crim­i­na­tory and re­quire di­ver­sity train­ing. Be­cause Amer­i­can in­sti­tu­tions still rou­tinely and al­most every­where fail to yield the de­sired racial bal­ance, the Amer­i­cans who cre­ated and sup­pos­edly con­trol those in­sti­tu­tion­s-ma­jor­ity Amer­i­can­s-must be judged deeply, un­con­scious­ly, in­vet­er­ately racist and to have cre­ated a so­ci­ety where ap­pear­ances to the con­trary are just a smoke­screen to hide their built-in priv­i­leges. Un­der the equipo­ten­tial­ity fic­tion, there can be no other le­git­i­mate ex­pla­na­tion, and any at­tempt at one serves only to evade re­spon­si­bil­i­ty.

…Fewer but still many so­cial sci­en­tists hold to a fourth false cre­do-that in­tel­li­gence has lit­tle or no func­tional util­i­ty, at least out­side schools. More­over, they often add that the ad­van­tages and dis­ad­van­tages of high or low IQ are mostly “so­cially con­structed” to serve the in­ter­ests of the priv­i­leged. This view was ar­tic­u­lated in an in­flu­en­tial ar­ti­cle pub­lished soon after Jensen’s 1969 ar­ti­cle by econ­o­mists Samuel Bowles and Her­bert Gin­tis (1972/1973). They ar­gued that higher IQ does not have any func­tional util­i­ty, even within schools, and that IQ tests are sim­ply a tool cre­ated by the up­per classes to main­tain and jus­tify their priv­i­leges. They dis­missed talk of “ob­jec­tiv­ity” and “merit” as just smoke blown to ob­scure this fact. Psy­chol­o­gist Robert Stern­berg im­plies much the same when he sug­gests that the g fac­tor di­men­sion of in­tel­lec­tual differ­ences is an ar­ti­fact of West­ern school­ing (Stern­berg et al., 2000, p. 9) and that us­ing cog­ni­tive tests such as the SAT to sort peo­ple is akin to the way slav­ery and re­li­gious prej­u­dice were once used to keep dis­fa­vored groups down (Stern­berg, 2003).

How­ev­er, when crit­ics ar­gue that IQ differ­ences have lit­tle or no func­tional mean­ing be­yond that which cul­tures or their elites ar­bi­trar­ily at­tach to them for selfish pur­pos­es, they si­mul­ta­ne­ously turn at­ten­tion away from the very real prob­lems that lower in­tel­li­gence cre­ates for less able per­sons. As Her­rn­stein and Mur­ray (1994) note, the crit­ics gen­er­ally have lit­tle con­tact with the down­trod­den they would pro­tect. These bright opin­ion mak­ers may be liv­ing com­fort­ably with their fic­tions and benev­o­lent lies, but low­er-IQ in­di­vid­u­als must live daily with the con­se­quences of their weaker learn­ing and rea­son­ing skills. Their dis­tant pro­tec­tors would seem to be the lim­ou­sine lib­er­als of in­tel­li­gence.

I fo­cus be­low on every­day tasks that high­er-IQ in­di­vid­u­als con­sider so sim­ple that they do not re­al­ize how such tasks might cre­ate ob­sta­cles to the well-be­ing of oth­ers less cog­ni­tively blessed.

Func­tional lit­er­acy and daily self­-main­te­nance. Cit­i­zens of lit­er­ate so­ci­eties take for granted that they are rou­tinely called upon to read in­struc­tions, fill out forms, de­ter­mine best buys, de­ci­pher bus sched­ules, and oth­er­wise read and write to cope with the myr­iad de­tails of every­day life. But such tasks are diffi­cult for many peo­ple. The prob­lem is sel­dom that they can­not read or write the words, but usu­ally that they are un­able to carry out the men­tal op­er­a­tions the task calls for-to com­pare two items, grasp an ab­stract con­cept, pro­vide com­pre­hen­si­ble and ac­cu­rate in­for­ma­tion about them­selves, fol­low a set of in­struc­tions, and so on. This is what it means to have poor “func­tional lit­er­a­cy.” Func­tional lit­er­acy has been a ma­jor pub­lic pol­icy con­cern, as il­lus­trated by the U.S. De­part­ment of Ed­u­ca­tion’s var­i­ous efforts to gauge its level in differ­ent seg­ments of the Amer­i­can pop­u­la­tion. Tests of func­tional lit­er­acy es­sen­tially mimic in­di­vid­u­al­ly-ad­min­is­tered in­tel­li­gence tests, ex­cept that all their items come from every­day life, such as cal­cu­lat­ing a tip (see ex­tended dis­cus­sion in Got­tfred­son, 1997b). As on in­tel­li­gence tests, differ­ences in item diffi­culty rest on the items’ cog­ni­tive com­plex­ity (their ab­stract­ness, amount of dis­tract­ing ir­rel­e­vant in­for­ma­tion, and de­gree of in­fer­ence re­quired), not on their read­abil­ity per se or the level of ed­u­ca­tion test tak­ers have com­plet­ed. Lit­er­acy re­searchers have con­clud­ed, with some sur­prise, that func­tional lit­er­acy rep­re­sents a gen­eral ca­pac­ity to learn, rea­son, and solve prob­lem­s-a ver­i­ta­ble de­scrip­tion of g.

The Na­tional Adult Lit­er­acy Sur­vey (NALS; Kirsch, Junge­blut, Jenk­ins, & Kol­stad, 1993) groups lit­er­acy scores into five lev­els. In­di­vid­u­als scor­ing in Level 1 have an 80% chance of suc­cess­fully per­form­ing tasks sim­i­lar in diffi­culty to lo­cat­ing an ex­pi­ra­tion date on a dri­ver’s li­cense and to­tal­ing a bank de­posit slip. They are not rou­tinely able to per­form Level 2 tasks, such as de­ter­min­ing the price differ­ence be­tween two show tick­ets or fill­ing in back­ground in­for­ma­tion on an ap­pli­ca­tion for a so­cial se­cu­rity card. Level 3 diffi­culty in­cludes writ­ing a brief let­ter ex­plain­ing an er­ror in a credit card bill and us­ing a flight sched­ule to plan trav­el. Level 4 tasks in­clude re­stat­ing an ar­gu­ment made in a lengthy news ar­ti­cle and cal­cu­lat­ing the money needed to raise a child based on in­for­ma­tion in a news ar­ti­cle. Only at Level 5 are in­di­vid­u­als rou­tinely able to per­form men­tal tasks as com­plex as sum­ma­riz­ing two ways that lawyers chal­lenge prospec­tive ju­rors (based on a pas­sage dis­cussing such prac­tices) and, with a cal­cu­la­tor, de­ter­min­ing the to­tal cost of car­pet to cover a room.

Al­though these tasks might seem to rep­re­sent only the in­con­se­quen­tial minu­tiae of every­day life, they sam­ple the large uni­verse of mostly un­tu­tored tasks that mod­ern life de­mands of adults. Con­sis­tently fail­ing them is not just a daily in­con­ve­nience, but a com­pound­ing prob­lem. Liken­ing func­tional lit­er­acy to mon­ey-it al­ways helps to have more-, lit­er­acy re­searchers point out that rates of so­cioe­co­nomic dis­tress and pathol­ogy (unem­ploy­ment, adult pover­ty, etc.) rise steadily at suc­ces­sively lower lev­els of func­tional lit­er­acy (as is the pat­tern for IQ too; Got­tfred­son, 2002a)…­Such dis­ad­van­tage is com­mon, too, be­cause 40% of the adult white pop­u­la­tion and 80% of the adult black pop­u­la­tion can­not rou­tinely per­form above Level 2. Fully 14% and 40%, re­spec­tive­ly, can­not rou­tinely per­form even above Level 1 (Kirsch et al., 1993, pp. 119121). To claim that low­er-a­bil­ity cit­i­zens will only be vic­tim­ized by the pub­lic know­ing that differ­ences in in­tel­li­gence are re­al, stub­born, and im­por­tant is to ig­nore the prac­ti­cal hur­dles they face.

Health lit­er­a­cy, IQ, and health self­-care. The chal­lenges in self­-care for low­er-IQ in­di­vid­u­als are es­pe­cially strik­ing in health mat­ters, where the con­se­quences of poor per­for­mance are tal­lied in ex­cess mor­bid­ity and mor­tal­i­ty. Health psy­chol­o­gists have ig­nored the role of com­pe­tence in health be­hav­ior, fo­cus­ing in­stead on vo­li­tion. Pa­tient “non-com­pli­ance” is in­deed a huge prob­lem in med­i­cine, but health lit­er­acy re­searchers, un­like health psy­chol­o­gists, have con­cluded that it is more a mat­ter of pa­tients not un­der­stand­ing what is re­quired of them than be­ing un­will­ing to im­ple­ment it (re­views in Got­tfred­son, 2002a, in press).

…For ex­am­ple, 26% of out­pa­tients in sev­eral large ur­ban hos­pi­tals could not de­ter­mine from an ap­point­ment slip when the next visit was sched­uled and 42% could not un­der­stand in­struc­tions for tak­ing med­i­cine on an empty stom­ach. Among those with “in­ad­e­quate” lit­er­a­cy, the fail­ure rates on these two tasks were 40% and 65%, re­spec­tive­ly. Sub­stan­tial per­cent­ages of this low-lit­er­acy group were un­able to re­port, when given pre­scrip­tion la­bels con­tain­ing the nec­es­sary in­for­ma­tion, how to take the med­ica­tion four times a day (24%), how many times the pre­scrip­tion could be re­filled (42%), or how many pills of the pre­scrip­tion should be taken (70%). Tak­ing med­ica­tions im­prop­erly can be as harm­ful as not tak­ing them at all, and the phar­macy pro­fes­sion has es­ti­mated that about half of all pre­scrip­tions are taken in­cor­rect­ly. As in other per­for­mance do­mains, train­ing and mo­ti­va­tion do not erase the dis­ad­van­tages of lower com­pre­hen­sion abil­i­ties. For in­stance, many pa­tients who are un­der treat­ment for in­sulin-de­pen­dent di­a­betes do not un­der­stand the most el­e­men­tal facts for main­tain­ing daily con­trol of their dis­ease. In one study, about half of those with “in­ad­e­quate” lit­er­acy did not know the signs of very low or very high blood sug­ar, both of which re­quire ex­pe­di­tious cor­rec­tion, and 60% did not know the cor­rec­tive ac­tions to take. Like hy­per­ten­sion and many other chronic ill­ness­es, di­a­betes re­quires con­tin­ual self­-mon­i­tor­ing and fre­quent judg­ments by pa­tients to keep their phys­i­o­log­i­cal processes within safe lim­its dur­ing the day. Per­sis­tently high blood sugar lev­els can lead to blind­ness, heart dis­ease, limb am­pu­ta­tion, and much more. For per­sons in gen­er­al, low func­tional lit­er­acy has been linked to num­ber and sever­ity of ill­ness­es, worse self­-rated health, far higher med­ical costs, and (prospec­tive­ly) more fre­quent hos­pi­tal­iza­tion. These re­la­tions are not elim­i­nated by con­trol­ling for ed­u­ca­tion, so­cioe­co­nomic re­sources, ac­cess to health care, de­mo­graphic char­ac­ter­is­tics, and other such vari­ables.

Be­cause health lit­er­acy is a rough sur­ro­gate for g, it pro­duces re­sults con­sis­tent with re­search on IQ and health. To take sev­eral ex­am­ples, in­tel­li­gence at time of di­ag­no­sis cor­re­lates .36 with di­a­betes knowl­edge mea­sured one year later (Tay­lor, Frier, Gold, & Deary, in press). IQ mea­sured at age 11 pre­dicts longevi­ty, in­ci­dence of can­cer, and func­tional in­de­pen­dence in old age, and these re­la­tions re­main ro­bust after con­trol­ling for de­prived liv­ing con­di­tions (Deary, White­man, Starr, & Whal­ley, in press). An­other prospec­tive epi­demi­o­log­i­cal study found that the mo­tor ve­hi­cle death rate for men of IQ 80-85 was triple and for men of IQ 85-100 it was dou­ble the rate for men of IQ 100-115 (O’­Toole, 1990). Youth­ful IQ was the best pre­dic­tor of al­l-cause mor­tal­ity by age 40 in this large na­tional sam­ple of Aus­tralian Army vet­er­ans, and IQ’s pre­dic­tive value re­mained sig­nifi­cant after con­trol­ling for all 56 de­mo­graph­ic, health, and other at­trib­utes mea­sured (O’­Toole & Stankov, 1992). As in ed­u­ca­tion, equal re­sources do not pro­duce equal out­comes in health. Like ed­u­ca­tional in­equal­i­ties, health in­equal­i­ties in­crease when health re­sources be­come equally avail­able to all, such as hap­pened to the British gov­ern­men­t’s dis­may after it in­sti­tuted free na­tional health care. Health im­proves over­all, but least for less ed­u­cated and lower in­come per­sons. They seek more but not nec­es­sar­ily ap­pro­pri­ate care when cost is no bar­ri­er; ad­here less often to treat­ment reg­i­mens; learn and un­der­stand less about how to pro­tect their health; seek less pre­ven­tive care, even when free; and less often prac­tice the healthy be­hav­iors so im­por­tant for pre­vent­ing or slow­ing the pro­gres­sion of chronic dis­eases, the ma­jor killers and dis­ablers in de­vel­oped na­tions.

…In­fus­ing more knowl­edge into the pub­lic sphere about health risks (smok­ing) and new di­ag­nos­tic op­tions (Pap smears) re­sults in al­ready-in­formed per­sons learn­ing the most and more often act­ing on the new in­for­ma­tion. This may ex­plain why an SES-mortality gra­di­ent fa­vor­ing ed­u­cated women de­vel­oped for cer­vi­cal can­cer after Pap smears be­came avail­able.

…After it be­came clear that health in­equal­i­ties could not be ex­plained by in­equal­i­ties in ma­te­r­ial re­sources and ac­cess to health care, it be­came fash­ion­able in health epi­demi­ol­ogy to blame class and race differ­ences in health on the psy­chic dam­age done by so­cial in­equal­i­ty. We are now to be­lieve that so­cial in­equal­ity per se is lit­er­ally a killer (Wilkin­son, 1996). Physi­cians, like teach­ers, are in­creas­ingly be­ing ac­cused of racism and given sen­si­tiv­ity train­ing when they fail to pro­duce racial par­ity in out­comes (Satel, 2000). Mind­ful of ide­o­log­i­cally cor­rect thought, health lit­er­acy re­searchers who men­tion in­tel­li­gence do so only to re­ject out of hand the no­tion that lit­er­acy might re­flect in­tel­li­gence, be­cause any such no­tion would be racist and de­mean­ing.

In the mean­time, in­ad­e­quate learn­ing and rea­son­ing abil­i­ties put many peo­ple at risk of tak­ing med­ica­tions in health-dam­ag­ing ways, not grasp­ing the mer­its of pre­ven­tive pre­cau­tions against chronic dis­ease and ac­ci­dents, and fail­ing to prop­erly im­ple­ment po­ten­tially more effec­tive but com­plex new treat­ment reg­i­mens for heart dis­ease, hy­per­ten­sion, and other killers.

…To in­ten­tion­ally ig­nore differ­ences in men­tal com­pe­tence is un­con­scionable. It is so­cial sci­ence mal­prac­tice against the very peo­ple whom the “un­truth” is sup­pos­edly meant to pro­tect.

Got­tfred­son, Linda S. 2002. “Where and Why g Mat­ters: Not a Mys­tery.” Hu­man Per­for­mance 15 (1/2): 25-46. http://www1.udel.e­du/e­duc/­got­tfred­son/reprints/2002no­tamys­tery.pdf

g is a highly gen­eral ca­pa­bil­ity for pro­cess­ing com­plex in­for­ma­tion of any type. This ex­plains its great value in pre­dict­ing job per­for­mance. Com­plex­ity is the ma­jor dis­tinc­tion among jobs, which ex­plains why g is more im­por­tant fur­ther up the oc­cu­pa­tional hi­er­ar­chy. The pre­dic­tive va­lidi­ties of g are mod­er­ated by the cri­te­ria and other pre­dic­tors con­sid­ered in se­lec­tion re­search, but the re­sult­ing gra­di­ents of g’s effects are sys­tem­at­ic. The pat­tern pro­vides per­son­nel psy­chol­o­gists a road map for how to de­sign bet­ter se­lec­tion bat­ter­ies.

…One of the sim­plest facts about men­tal abil­i­ties pro­vides one of the most im­por­tant clues to the na­ture of g. Peo­ple who do well on one kind of men­tal test tend to do well on all oth­ers. When the scores on a large, di­verse bat­tery of men­tal abil­ity tests are fac­tor an­a­lyzed, they yield a large com­mon fac­tor, la­beled g. Pick any test of men­tal ap­ti­tude or achieve­men­t-say, ver­bal ap­ti­tude, spa­tial vi­su­al­iza­tion, the SAT, a stan­dard­ized test of aca­d­e­mic achieve­ment in 8th grade, or the Block De- sign or Mem­ory for Sen­tences sub­tests of the Stan­ford-Bi­net in­tel­li­gence test- and you will find that it mea­sures mostly g. All efforts to build mean­ing­ful men­tal tests that do not mea­sure g have failed…In con­trast, no gen­eral fac­tor emerges from per­son­al­ity in­ven­to­ries, which shows that gen­eral fac­tors are not a nec­es­sary out­come of fac­tor analy­sis. (See Jensen, 1998, and Got­tfred­son, 1997, 2000a, 2002, for fuller dis­cus­sion and doc­u­men­ta­tion of these and fol­low­ing points on g.)

The im­por­tant point is that the pre­dic­tive va­lidi­ties of g be­have law­ful­ly. They vary, but they vary sys­tem­at­i­cally and for rea­sons that are be­gin­ning to be well un­der­stood. Over 2 decades of meta-analy­ses have shown that they are not sen­si­tive to small vari­a­tions in job du­ties and cir­cum­stance, after con­trol­ling for sam­pling er­ror and other sta­tis­ti­cal ar­ti­facts. Com­plex jobs will al­ways put a pre­mium on higher g. Their per­for­mance will al­ways be no­tably en­hanced by higher g, all else equal. Higher g will also en­hance per­for­mance in sim­ple jobs, but to a much smaller de­gree.


Gen­eral in­tel­li­gence is an im­por­tant hu­man quan­ti­ta­tive trait that ac­counts for much of the vari­a­tion in di­verse cog­ni­tive abil­i­ties. In­di­vid­ual differ­ences in in­tel­li­gence are strongly as­so­ci­ated with many im­por­tant life out­comes, in­clud­ing ed­u­ca­tional and oc­cu­pa­tional at­tain­ments, in­come, health and lifes­pan1,2. Data from twin and fam­ily stud­ies are con­sis­tent with a high her­i­tabil­ity of in­tel­li­gence3, but this in­fer­ence has been con­tro­ver­sial. We con­ducted a genome-wide analy­sis of 3511 un­re­lated adults with data on 549 692 SNPs and de­tailed phe­no­types on cog­ni­tive traits. We es­ti­mate that 40% of the vari­a­tion in crys­tal­lized-type in­tel­li­gence and 51% of the vari­a­tion in flu­id-type in­tel­li­gence be­tween in­di­vid­u­als is ac­counted for by link­age dis­e­qui­lib­rium be­tween geno­typed com­mon SNP mark­ers and un­known causal vari­ants. These es­ti­mates pro­vide lower bounds for the nar­row-sense her­i­tabil­ity of the traits. We par­ti­tioned ge­netic vari­a­tion on in­di­vid­ual chro­mo­somes and found that, on av­er­age, longer chro­mo­somes ex­plain more vari­a­tion. Fi­nal­ly, us­ing just SNP data we pre­dicted ap­prox­i­mately 1% of the vari­ance of crys­tal­lized and fluid cog­ni­tive phe­no­types in an in­de­pen­dent sam­ple (P = 0.009 and 0.028, re­spec­tive­ly). Our re­sults un­equiv­o­cally con­firm that a sub­stan­tial pro­por­tion of in­di­vid­ual differ­ences in hu­man in­tel­li­gence is due to ge­netic vari­a­tion, and are con­sis­tent with many genes of small effects un­der­ly­ing the ad­di­tive ge­netic in­flu­ences on in­tel­li­gence.

“Com­mon DNA Mark­ers Can Ac­count for More Than Half of the Ge­netic In­flu­ence on Cog­ni­tive Abil­i­ties”:

For nearly a cen­tu­ry, twin and adop­tion stud­ies have yielded sub­stan­tial es­ti­mates of her­i­tabil­ity for cog­ni­tive abil­i­ties, al­though it has proved diffi­cult for genome-wide-as­so­ci­a­tion stud­ies to iden­tify the ge­netic vari­ants that ac­count for this her­i­tabil­ity (i.e., the miss­ing-her­i­tabil­ity prob­lem). How­ev­er, a new ap­proach, genome-wide com­plex-trait analy­sis (GCTA), for­goes the iden­ti­fi­ca­tion of in­di­vid­ual vari­ants to es­ti­mate the to­tal her­i­tabil­ity cap­tured by com­mon DNA mark­ers on geno­typ­ing ar­rays. In the same sam­ple of 3,154 pairs of 12-year-old twins, we di­rectly com­pared twin-s­tudy her­i­tabil­ity es­ti­mates for cog­ni­tive abil­i­ties (lan­guage, ver­bal, non­ver­bal, and gen­er­al) with GCTA es­ti­mates cap­tured by 1.7 mil­lion DNA mark­ers. We found that DNA mark­ers tagged by the ar­ray ac­counted for .66 of the es­ti­mated her­i­tabil­i­ty, reaffirm­ing that cog­ni­tive abil­i­ties are her­i­ta­ble. Larger sam­ple sizes alone will be suffi­cient to iden­tify many of the ge­netic vari­ants that in­flu­ence cog­ni­tive abil­i­ties.

…Cog­ni­tive abil­i­ties pre­dict ed­u­ca­tional at­tain­ment, in­come, health, and longevi­ty, and thus con­tribute im­por­tantly to the in­tel­lec­tual cap­i­tal of knowl­edge-based so­ci­eties (Deary, 2012). Since the 1920s, twin and adop­tion stud­ies have in­ves­ti­gated the ge­netic and en­vi­ron­men­tal ori­gins of in­di­vid­ual differ­ences in cog­ni­tive abil­i­ties; scores of such stud­ies have con­sis­tently yielded es­ti­mates of sub­stan­tial her­i­tabil­ity (i.e., the ex­tent to which ge­netic vari­ance can ac­count for ob­served, or phe­no­typ­ic, vari­ance; Deary, John­son, & Houli­han, 2009). Meta-analy­ses of these stud­ies have yielded her­i­tabil­ity es­ti­mates of about .50 for gen­eral cog­ni­tive abil­i­ty, the most well-s­tud­ied cog­ni­tive trait (Plom­in, De­Fries, Knopik, & Neu­houser, 2013).

…One of the most far-reach­ing re­sults of GWA stud­ies is to show that there are no genes of large effect size in the pop­u­la­tion, which means that the her­i­tabil­ity of com­plex traits is prob­a­bly due to many genes of small effect size, and this means that as­so­ci­a­tions will be diffi­cult to de­tect and repli­cate (Plom­in, 2012). For ex­am­ple, the first GWA stud­ies of gen­eral cog­ni­tive abil­ity (Davies et al., 2011; Davis et al., 2010) were pow­ered to de­tect as­so­ci­a­tions that ac­count for as lit­tle as .01 of the vari­ance, but they came up emp­ty-handed be­cause the as­so­ci­a­tions with the largest effect ac­counted for less than .005 of the vari­ance. One of many pos­si­ble rea­sons for the miss­ing-her­i­tabil­ity prob­lem is that the com­mon SNPs (i.e., SNPs for which the fre­quency of the less fre­quent al­lele is greater than .01) in­cor­po­rated in com­mer­cially avail­able DNA ar­rays miss the con­tri­bu­tion of rare DNA vari­ants (Cir­ulli & Gold­stein, 2010). An­other pos­si­bil­ity is that her­i­tabil­ity has been over­es­ti­mated by twin and adop­tion stud­ies.

…The study re­ported here ad­dressed both of these pos­si­bil­i­ties by com­par­ing twin-based es­ti­mates of her­i­tabil­ity for cog­ni­tive abil­i­ties with es­ti­mates from a new method that is pop­u­la­tion based rather than fam­ily based. The method, called genome-wide com­plex-trait analy­sis (GCTA), can be used to es­ti­mate ge­netic vari­ance ac­counted for by all the SNPs that have been geno­typed in any sam­ple, not just sam­ples con­sist­ing of spe­cial fam­ily mem­bers such as twins or adoptees (Lee, Wray, God­dard, & Viss­cher, 2011; Yang, Lee, God­dard, & Viss­cher, 2011; Yang, Mano­lio, et al., 2011)…GCTA does not iden­tify spe­cific genes as­so­ci­ated with traits. In­stead, it uses chance sim­i­lar­ity across hun­dreds of thou­sands of SNPs to pre­dict phe­no­typic sim­i­lar­ity pair by pair in a large sam­ple of un­re­lated in­di­vid­u­als. The essence of GCTA is to es­ti­mate ge­netic in­flu­ence on a trait by pre­dict­ing phe­no­typic sim­i­lar­ity for each pair of in­di­vid­u­als in the sam­ple from their to­tal SNP sim­i­lar­i­ty. In con­trast to the twin method, which es­ti­mates her­i­tabil­ity by com­par­ing phe­no­typic sim­i­lar­ity of iden­ti­cal and fra­ter­nal twin pairs, whose ge­netic sim­i­lar­ity is roughly 1.00 and .50, re­spec­tive­ly, GCTA re­lies on com­par­isons of pairs of in­di­vid­u­als whose ge­netic sim­i­lar­ity varies from .00 to .02. GCTA ex­tracts this tiny ge­netic sig­nal from the noise of hun­dreds of thou­sands of SNPs us­ing the mas­sive in­for­ma­tion avail­able from a ma­trix of thou­sands of in­di­vid­u­als, each com­pared pair by pair with every other in­di­vid­ual in the sam­ple; for ex­am­ple, the 3,000-plus in­di­vid­u­als in the present sam­ple pro­vided nearly 5 mil­lion pair­wise com­par­isons

GCTA has been used to es­ti­mate her­i­tabil­ity as cap­tured by geno­typ­ing ar­rays for height (Yang et al., 2010), weight (Yang, Mano­lio, et al., 2011), psy­chi­atric and other med­ical dis­or­ders (Lee et al., 2012; Lee et al., 2011; Lubke et al., 2012), and per­son­al­ity (Vinkhuyzen, Ped­er­sen, et al., 2012). GCTA was first ap­plied to cog­ni­tive abil­ity in a study of 3,500 un­re­lated adults, which yielded her­i­tabil­ity es­ti­mates of .40 and .51 for crys­tal­lized and fluid in­tel­li­gence, re­spec­tively (Davies et al., 2011). The GCTA es­ti­mate for gen­eral cog­ni­tive abil­ity was .47 in a meta-analy­sis across three stud­ies in­volv­ing nearly 10,000 adults (Chabris et al., 2012) and .48 in a study of nearly 2 thou­sand 11-year-old chil­dren (Deary et al., 2012)…GCTA has been used to es­ti­mate her­i­tabil­ity as cap­tured by geno­typ­ing ar­rays for height (Yang et al., 2010), weight (Yang, Mano­lio, et al., 2011), psy­chi­atric and other med­ical dis­or­ders (Lee et al., 2012; Lee et al., 2011; Lubke et al., 2012), and per­son­al­ity (Vinkhuyzen, Ped­er­sen, et al., 2012). GCTA was first ap­plied to cog­ni­tive abil­ity in a study of 3,500 un­re­lated adults, which yielded her­i­tabil­ity es­ti­mates of .40 and .51 for crys­tal­lized and fluid in­tel­li­gence, re­spec­tively (Davies et al., 2011). The GCTA es­ti­mate for gen­eral cog­ni­tive abil­ity was .47 in a meta-analy­sis across three stud­ies in­volv­ing nearly 10,000 adults (Chabris et al., 2012) and .48 in a study of nearly 2 thou­sand 11-year-old chil­dren (Deary et al., 2012).

GCTA has been used to es­ti­mate her­i­tabil­ity as cap­tured by geno­typ­ing ar­rays for height (Yang et al., 2010), weight (Yang, Mano­lio, et al., 2011), psy­chi­atric and other med­ical dis­or­ders (Lee et al., 2012; Lee et al., 2011; Lubke et al., 2012), and per­son­al­ity (Vinkhuyzen, Ped­er­sen, et al., 2012). GCTA was first ap­plied to cog­ni­tive abil­ity in a study of 3,500 un­re­lated adults, which yielded her­i­tabil­ity es­ti­mates of .40 and .51 for crys­tal­lized and fluid in­tel­li­gence, re­spec­tively (Davies et al., 2011). The GCTA es­ti­mate for gen­eral cog­ni­tive abil­ity was .47 in a meta-analy­sis across three stud­ies in­volv­ing nearly 10,000 adults (Chabris et al., 2012) and .48 in a study of nearly 2 thou­sand 11-year-old chil­dren (Deary et al., 2012)…This is the first study in which GCTA es­ti­mates of her­i­tabil­ity for di­verse cog­ni­tive abil­i­ties were com­pared di­rectly with twin-based es­ti­mates us­ing the same mea­sures at the same age in the same sam­ple. The Affymetrix 6.0 DNA ar­ray yielded GCTA es­ti­mates that ac­counted on av­er­age for .66 of the twin her­i­tabil­ity es­ti­mates for lan­guage, ver­bal, non­ver­bal, and gen­eral cog­ni­tive abil­i­ties. Note that the GCTA es­ti­mates ac­counted for a greater pro­por­tion of the twin her­i­tabil­ity es­ti­mates in the case of cog­ni­tive abil­i­ties than in the case of height (.44) and weight (.50).

…Why might these com­mon SNPs tag gen­eral cog­ni­tive abil­ity more than height and weight? Com­mon SNPs are likely to be com­mon be­cause they are old, hav­ing spread through the pop­u­la­tion over many gen­er­a­tions, but there seems no ob­vi­ous rea­son why the evo­lu­tion­ary ar­chi­tec­ture for gen­eral cog­ni­tive abil­ity should differ from height in this way. How­ev­er, there is one ma­jor ge­netic differ­ence be­tween cog­ni­tive and phys­i­cal traits: As­sor­ta­tive mat­ing (non­ran­dom mat­ing) is at least twice as great for gen­eral cog­ni­tive abil­ity (cor­re­la­tion be­tween spous­es: ~.45) as for height and weight (~.20; Plomin et al., 2013). The effect of as­sor­ta­tive mat­ing is to in­crease ad­di­tive ge­netic vari­ance be­cause chil­dren re­ceive cor­re­lated ge­netic in­flu­ences from their par­ents, which spreads out the dis­tri­b­u­tion; more­over, the effects of as­sor­ta­tive mat­ing ac­cu­mu­late gen­er­a­tion after gen­er­a­tion. If as­sor­ta­tive mat­ing is re­spon­si­ble for the fact that com­mon SNPs tag gen­eral cog­ni­tive abil­ity more than height and weight, then ver­bal abil­i­ties should show greater GCTA/twin her­i­tabil­ity ra­tios than non­ver­bal abil­i­ties do be­cause ver­bal abil­i­ties show more as­sor­ta­tive mat­ing than non­ver­bal abil­i­ties (cor­re­la­tion be­tween spous­es: ~.50 vs. .30). The re­sults in Ta­ble 1 are con­sis­tent with this hy­poth­e­sis: The GCTA/twin her­i­tabil­ity ra­tio is .65 for ver­bal abil­ity and .48 for non­ver­bal abil­i­ty.

Above a cer­tain lev­el, in­tel­li­gence does­n’t mat­ter. There was no sig­nifi­cant differ­ence in max­i­mum in­come earned by men with IQs in the 110-115 range and men with IQs higher than 150. http://www.busi­nessin­sid­er.­com/­grant-s­tudy-re­veal­s-what-makes-us-hap­py-2013-4

TODO: what’s go­ing on there? weasel word­ing on ‘max­i­mum’? not a big enough sam­ple size to reach sta­tis­ti­cal-sig­nifi­cance

, Ham­brick et al 2014:

…Global mea­sures of in­tel­li­gence (IQ) have also been found to cor­re­late with per­for­mance in chess and mu­sic, con­sis­tent with the pos­si­bil­ity that a rel­a­tively high level of in­tel­li­gence is nec­es­sary for suc­cess in these do­mains. Fry­d­man and Lynn (1992) found that young chess play­ers had an av­er­age per­for­mance IQ of 129, com­pared to a pop­u­la­tion av­er­age of 100, and that the av­er­age was higher for the best play­ers (top-third avg. = 131) in the sam­ple than the weak­est play­ers (bot­tom-third avg. = 124). Fur­ther­more, Grab­n­er, Neubauer, and Stern (2006) found that, even in highly rated play­ers, IQ pos­i­tively pre­dicted per­for­mance on rep­re­sen­ta­tive chess tasks (e.g., next best move). Bi­lalić et al. (2007) found that IQ was not a sig­nifi­cant pre­dic­tor of chess rat­ing in the sam­ple of elite young chess play­ers listed in Ta­ble 1 after sta­tis­ti­cally con­trol­ling for prac­tice. How­ev­er, the sam­ple size for the elite group was only 23, and mean IQ was sig­nifi­cantly higher for the elite group (M = 133) than for the rest of the sam­ple (M = 114).

  • Fry­d­man, M., & Lynn, R. (1992). The gen­eral in­tel­li­gence and spa­tial abil­i­ties of gifted young Bel­gian chess play­ers. British Jour­nal of Psy­chol­o­gy, 83, 233-235. http://dx.­­b02437.x.
  • Grab­n­er, R. H., Neubauer, A. C., & Stern, E. (2006). Su­pe­rior per­for­mance and neural effi­cien­cy: The im­pact of in­tel­li­gence and ex­per­tise. Brain Re­search Bul­let­in, 69, 422-439. http://dx.­ j.brain­res­bul­l.2006.02.009.
  • Bi­lal­ić, M., McLeod, P., & Go­b­et, F. (2007). Does chess need in­tel­li­gence? A study with young chess play­ers. In­tel­li­gence, 35, 457-470. http://dx.­ 10.1016/­tel­l.2006.09.005.

IQ cor­re­lates pos­i­tively with mu­sic per­for­mance, as well. Luce (1965) found a cor­re­la­tion of .53 (p b .01) be­tween IQ and sight-read­ing per­for­mance in high school band mem­bers, and Salis (1977) re­ported a cor­re­la­tion of .58 be­tween these vari­ables in a uni­ver­sity sam­ple. Gromko (2004) found pos­i­tive cor­re­la­tions be­tween both ver­bal abil­ity and spa­tial abil­ity (rs = .35-.49) and sight-read­ing per­for­mance in high school wind play­ers, and Hay­ward and Gromko (2009) found a sig­nifi­cant pos­i­tive cor­re­la­tion (r = .24) be­tween a mea­sure of spa­tial abil­ity based on three ETS tests and sight-read­ing per­for­mance in uni­ver­sity wind play­ers. Ruth­satz et al. (2008) found that Raven’s scores cor­re­lated pos­i­tively and sig­nifi­cantly with mu­si­cal achieve­ment in high school band mem­bers (r = .25). This cor­re­la­tion was not sta­tis­ti­cally sig­nifi­cant in a sam­ple of more highly ac­com­plished con­ser­va­tory stu­dents and mu­sic ma­jors, but this could have been due to a ceil­ing effect on Raven’s, as these par­tic­i­pants had been heav­ily se­lected for cog­ni­tive abil­i­ty.

  • Luce, J. R. (1965). Sight-read­ing and ear-play­ing abil­i­ties as re­lated to in­stru­men­tal mu­sic stu­dents. Jour­nal of Re­search in Mu­sic Ed­u­ca­tion, 13, 101-109. http://dx.­
  • Sal­is, D. L. (1977). The iden­ti­fi­ca­tion and as­sess­ment of cog­ni­tive vari­ables as­so­ci­ated with read­ing of ad­vanced mu­sic at the pi­ano (un­pub­lished doc­toral dis­ser­ta­tion). Pitts­burgh, PA: Uni­ver­sity of Pitts­burgh.
  • Gromko, J. E. (2004). Pre­dic­tors of mu­sic sight-read­ing abil­ity in high school wind play­ers. Jour­nal of Re­search in Mu­sic Ed­u­ca­tion, 52, 6-15. http://dx.­
  • Hay­ward, C. M., & Gromko, J. E. (2009). Re­la­tion­ships among mu­sic sight-read­ing and tech­ni­cal pro­fi­cien­cy, spa­tial vi­su­al­iza­tion, and au­ral dis­crim­i­na­tion. Jour­nal of Re­search in Mu­sic Ed­u­ca­tion, 57, 29-36. http://dx.­
  • Ruth­satz, J., Det­ter­man, D. K., Griscom, W. S., & Cir­ul­lo, B. A. (2008). Be­com­ing an ex­pert in the mu­si­cal do­main: It takes more than just prac­tice. In­tel­li­gence, 36, 330-338. http://dx.­­tel­l.2007.08.003.

…Ruth­satz and Ur­bach (2012) ad­min­is­tered a stan­dard­ized IQ test (the Stan­ford-Bi­net) to eight child prodigies, six of whom were mu­si­cal prodi­gies. De­spite ful­l-s­cale IQs that ranged from 108 to 147-just above av­er­age to above the con­ven­tional cut­off for “ge­nius”-all of the prodi­gies were at or above the 99th per­centile for work­ing mem­ory (in­deed, six scored at the 99.9th per­centile).

  • Ruth­satz, J., & Ur­bach, J. B. (2012). Child prodi­gy: A novel cog­ni­tive pro­file places el­e­vated gen­eral in­tel­li­gence, ex­cep­tional work­ing mem­ory and at­ten­tion to de­tail at the root of prodi­gious­ness. In­tel­li­gence, 40, 419-426. http://dx.­­tel­l.2012.06.002.

…Gen­eral in­tel­li­gence does not al­ways pre­dict per­for­mance. In a study of foot­ball play­ers, Lyons, Hoff­man, and Michel (2009) found that scores on the Won­der­lic Per­son­nel Test, a widely ad­min­is­tered group in­tel­li­gence test, cor­re­lated es­sen­tially zero with suc­cess in the Na­tional Foot­ball League, even in the quar­ter­back po­si­tion, which is be­lieved to place the high­est de­mand on in­for­ma­tion pro­cess­ing. Fur­ther­more, Ham­brick et al. (2012) found that spa­tial abil­ity pos­i­tively pre­dicted suc­cess in a com­plex ge­o­log­i­cal prob­lem solv­ing task in novice ge­ol­o­gists, but not in ex­perts.

  • Lyons, B., Hoff­man, B., & Michel, J. (2009). Not much more than g? An ex­am­i­na­tion of the im­pact of in­tel­li­gence on NFL per­for­mance. Hu­man Per­for­mance, 22, 225-245. http://dx.­
  • Ham­brick, D. Z., & Meinz, E. J. (2012). Work­ing mem­ory ca­pac­ity and mu­si­cal skill. In T. P. Al­loway, & R. G. Al­loway (Ed­s.), Work­ing mem­o­ry: The con­nected in­tel­li­gence (pp. 137-155). New York: Psy­chol­ogy Press.

“In­tel­li­gence and se­men qual­ity are pos­i­tively cor­re­lated”, Ar­den et al 2009

If the cor­re­la­tions among cog­ni­tive abil­i­ties are part of a larger ma­trix of pos­i­tive as­so­ci­a­tions among fit­ness-re­lated traits, then in­tel­li­gence ought to cor­re­late with seem­ingly un­re­lated traits that affect fit­ness-such as se­men qual­i­ty. We found sig­nifi­cant pos­i­tive cor­re­la­tions be­tween in­tel­li­gence and 3 key in­dices of se­men qual­i­ty: log sperm con­cen­tra­tion (r = .15, p = .002), log sperm count (r = .19, p b .001), and sperm motil­ity (r = .14, p = .002) in a large sam­ple of US Army Vet­er­ans. None was me­di­ated by age, body mass in­dex, days of sex­ual ab­sti­nence, ser­vice in Viet­nam, or use of al­co­hol, to­bac­co, mar­i­jua­na, or hard drugs.

…in­tel­li­gence cor­re­lates with many im­por­tant health out­comes, even longevity (Bat­ty, Deary, & Got­tfred­son, 2007).

  • Bat­ty, G. D., Deary, I. J., & Got­tfred­son, L. S. (2007). Pre­mor­bid (early life) IQ and later mor­tal­ity risk: Sys­tem­atic re­view. An­nals of Epi­demi­ol­o­gy, 17 (4), 278−288.

…the effect size is con­gru­ent with phe­no­typic cor­re­la­tions ob­served for other bod­ily cor­re­lates of in­tel­li­gence such as height (r = .14, r = .15) (Sil­ven­toinen, Posthu­ma, van Bei­jster­veldt, Bar­tels, & Booms­ma, 2006; Sun­det, Tambs, Har­ris, Mag­nus, & Tor­jussen, 2005).

  • Sil­ven­toinen, K., Posthu­ma, D., van Bei­jster­veldt, T., Bar­tels, M., & Booms­ma, D. I. (2006). Ge­netic con­tri­bu­tions to the as­so­ci­a­tion be­tween height and in­tel­li­gence: Ev­i­dence from Dutch twin data from child­hood to mid­dle age. Genes, Brain and Be­hav­ior, 5(8), 585−595.
  • Sun­det, J. M., Tambs, K., Har­ris, J. R., Mag­nus, P., & Tor­jussen, T. M. (2005). Re­solv­ing the ge­netic and en­vi­ron­men­tal sources of the cor­re­la­tion be­tween height and in­tel­li­gence: A study of nearly 2600 Nor­we­gian male twin pairs. Twin Re­search and Hu­man Ge­net­ics, 8(4), 307−311.


  1. Deary IJ, Strand S, Smith P, Fer­nan­des C (2007) In­tel­li­gence and ed­u­ca­tional achieve­ment. In­tel­li­gence 35: 13-21 doi:10.1016/­tel­l.2006.02.001. . doi: 10.1016/­tel­l.2006.02.001. IQ scores are often used as an in­dex of gen­eral cog­ni­tive abil­i­ties. Such IQ mea­sures ex­hibit sub­stan­tial cor­re­la­tions from late child­hood through adult­hood (e.g., IQ scores were es­ti­mated to cor­re­late 0.73 from ages 11 through 77 in a lon­gi­tu­di­nal study [2]).
  2. Deary IJ, Whal­ley LJ, Lem­mon H, Craw­ford J, Starr JM (2000) The Sta­bil­ity of In­di­vid­ual Differ­ences in Men­tal Abil­ity from Child­hood to Old Age: Fol­low-up of the 1932 Scot­tish Men­tal Sur­vey. In­tel­li­gence 28: 49-55 doi:10.1016/S0160-2896(99)00031-8. . doi: 10.1016/S0160-2896(99)00031-8.

“Child­hood cog­ni­tive abil­ity ac­counts for as­so­ci­a­tions be­tween cog­ni­tive abil­ity and brain cor­ti­cal thick­ness in old age”, Karama et al 2013

We an­a­lyzed data on 588 sub­jects from the Loth­ian Birth Co­hort 1936 who had in­tel­li­gence quo­tient (IQ) scores from the same cog­ni­tive test avail­able at both 11 and 70 years of age as well as high­-res­o­lu­tion brain mag­netic res­o­nance imag­ing data ob­tained at ap­prox­i­mately 73 years of age. Cor­ti­cal thick­ness was es­ti­mated at 81,924 sam­pling points across the cor­tex for each sub­ject us­ing an au­to­mated pipeline. Mul­ti­ple re­gres­sion was used to as­sess as­so­ci­a­tions be­tween cor­ti­cal thick­ness and the IQ mea­sures at 11 and 70 years. Child­hood IQ ac­counted for more than two-third of the as­so­ci­a­tion be­tween IQ at 70 years and cor­ti­cal thick­ness mea­sured at age 73 years. This warns against as­crib­ing a causal in­ter­pre­ta­tion to the as­so­ci­a­tion be­tween cog­ni­tive abil­ity and cor­ti­cal tis­sue in old age based on as­sump­tions about, and ex­clu­sive ref­er­ence to, the ag­ing process and any as­so­ci­ated dis­ease.

, Chi­ang et al 2011

We de­vel­oped an analy­sis pipeline en­abling pop­u­la­tion stud­ies of HARDI data, and ap­plied it to map ge­netic in­flu­ences on fiber ar­chi­tec­ture in 90 twin sub­jects. We ap­plied ten­sor-driven 3D fluid reg­is­tra­tion to HARDI, re-sam­pling the spher­i­cal fiber ori­en­ta­tion dis­tri­b­u­tion func­tions (ODFs) in ap­pro­pri­ate Rie­mann­ian man­i­folds, after ODF reg­u­lar­iza­tion and sharp­en­ing. Fit­ting struc­tural equa­tion mod­els (SEM) from quan­ti­ta­tive ge­net­ics, we eval­u­ated ge­netic in­flu­ences on the Jensen-Shan­non di­ver­gence (JSD), a novel mea­sure of fiber spa­tial co­her­ence, and on the gen­er­al­ized fiber anisotropy (GFA) a mea­sure of fiber in­tegri­ty. With ran­dom-effects re­gres­sion, we mapped re­gions where diffu­sion pro­files were highly cor­re­lated with sub­jects’ in­tel­li­gence quo­tient (IQ). Fiber com­plex­ity was pre­dom­i­nantly un­der ge­netic con­trol, and higher in more highly anisotropic re­gions; the pro­por­tion of ge­netic ver­sus en­vi­ron­men­tal con­trol var­ied spa­tial­ly. Our meth­ods show promise for dis­cov­er­ing genes affect­ing fiber con­nec­tiv­ity in the brain.

“In­ves­ti­gat­ing Amer­i­ca’s elite: Cog­ni­tive abil­i­ty, ed­u­ca­tion, and sex differ­ences”, Wai 2013

Are the Amer­i­can elite drawn from the cog­ni­tive elite?…How­ev­er, whether the elite are pri­mar­ily com­posed of in­di­vid­u­als in the top per­centiles of the abil­ity dis­tri­b­u­tion who have at­tended the most pres­ti­gious col­leges and uni­ver­si­ties has not yet been em­pir­i­cally ex­am­ined…To ad­dress this, five groups of Amer­i­ca’s elite (to­tal N = 2254) were ex­am­ined: For­tune 500 CEOs, fed­eral judges, bil­lion­aires, Sen­a­tors, and mem­bers of the House of Rep­re­sen­ta­tives. Within each of these groups, nearly all had at­tended col­lege with the ma­jor­ity hav­ing at­tended ei­ther a highly se­lec­tive un­der­grad­u­ate in­sti­tu­tion or grad­u­ate school of some kind. High av­er­age test scores re­quired for ad­mis­sion to these in­sti­tu­tions in­di­cated those who rise to or are se­lected for these po­si­tions are highly fil­tered for abil­i­ty…Fe­males were un­der­rep­re­sented among all groups, but to a lesser de­gree among fed­eral judges and De­moc­rats and to a larger de­gree among Re­pub­li­cans and CEOs. Amer­i­ca’s elite are largely drawn from the in­tel­lec­tu­ally gift­ed, with many in the top 1% of abil­i­ty…­Mur­ray (2008) was cor­rect that a large por­tion of Amer­i­ca’s elite are drawn from the in­tel­lec­tu­ally gift­ed. This held for every group ex­cept the House of Rep­re­sen­ta­tives, which had a lower per­cent­age hav­ing at­tended an Elite School. If the de­fi­n­i­tion of elite is broad­ened to in­clude ei­ther at­ten­dance at an Elite School or Grad­u­ate School then the ma­jor­ity met these cri­te­ria and are likely in the top per­centiles of abil­i­ty. This would in­clude 56.6% of the bil­lion­aires, 67.0% of the CEOs, 68.1% of the House, 83.0% of the Sen­ate, and all of the judges. All the fed­eral judges and Sen­a­tors and nearly all the other groups at­tended col­lege…This study used av­er­age SAT or ACT scores of a col­lege or uni­ver­sity (Amer­i­ca’s Best Col­leges, 2013) as an ap­prox­i­ma­tion for abil­ity level (Frey & Det­ter­man, 2004; Koenig et al., 2008), which may not hold for each in­di­vid­ual case. It would have been op­ti­mal to have ac­cess to in­di­vid­ual test scores, but un­for­tu­nately this data was not pub­licly avail­able. How­ev­er, us­ing av­er­age SAT and ACT scores as an ap­prox­i­ma­tion for abil­ity level may give an un­der­es­ti­mate be­cause ex­tremely smart peo­ple may not have cho­sen to at­tend a top school for mul­ti­ple rea­sons (e.g., fi­nan­cial, schol­ar­ship, stay­ing close to home). Al­ter­na­tive­ly, us­ing this method may also give an over­es­ti­mate be­cause there are many lega­cies and ath­letic ad­mits to elite in­sti­tu­tions who do not usu­ally meet the typ­i­cal test score cri­te­ria (E­spen­shade & Rad­ford, 2009). …This study demon­strates that in Amer­i­ca, De­moc­rats were more likely than Re­pub­li­cans to have a higher per­cent­age of Sen­ate and House mem­bers who at­tended an Elite School which places these in­di­vid­u­als in the top 1% in abil­ity (see Fig. 1 panel B and Ap­pen­dix A). There­fore, among the elected elite, De­moc­rats had a higher abil­ity and ed­u­ca­tion lev­el, on av­er­age, than Re­pub­li­can­s…This also shows that Bill Gates and Mark Zucker­berg (in­cluded in the Tech­nol­ogy sec­tor), who are often used as promi­nent ex­am­ples in the me­dia as to why go­ing to col­lege is not nec­es­sary for suc­cess (e.g., Lin, 2010: “Top 10 col­lege dropouts”; Williams, 2012: “Say­ing no to col­lege”), are ac­tu­ally ex­cep­tions to the rule. Within the bil­lion­aire sam­ple, 37 (8.7%) were clearly marked as a col­lege drop out by the Forbes staff who com­piled the da­ta. The ma­jor­ity of the bil­lion­aires (88%) went to col­lege and grad­u­at­ed. …Even within a group in the top 0.0000001% of wealth and a group of CEOs who were com­pen­sated quite highly (well within the top 1% of wealth), there were differ­ences in the ed­u­ca­tion and abil­ity level be­tween those who earned more money com­pared to those who earned less. The analy­ses in Ta­ble 3a and b demon­strate that even within bil­lion­aires and CEOs, higher ed­u­ca­tion and abil­ity level is re­lated to higher net worth and com­pen­sa­tion. Prior re­search demon­strated that even within a group in the top 1% in abil­i­ty, higher abil­ity is as­so­ci­ated with higher in­come (Wai et al., 2005). The analy­ses in Ta­ble 3c demon­strated that even within the top 1% of abil­i­ty, higher abil­ity is as­so­ci­ated with higher net worth and com­pen­sa­tion. There­fore, this study adds to, ex­pands, and strength­ens the lit­er­a­ture link­ing ed­u­ca­tion, abil­i­ty, and wealth (Mur­ray, 1998; Ny­borg & Jensen, 2001; Zax & Rees, 2002), and pro­vides fur­ther ev­i­dence that does not sup­port an abil­ity thresh­old hy­poth­e­sis (Kun­cel & He­zlett, 2010; Park et al., 2007; Wai et al., 2005) - or the idea that more abil­ity does not mat­ter be­yond a cer­tain point in pre­dict­ing real world out­comes.

  • Kuncel, N. R., & He­zlett, S. A. (2010). “Fact and fic­tion in cog­ni­tive abil­ity test­ing for ad­mis­sions and hir­ing de­ci­sions”. Cur­rent Di­rec­tions in Psy­cho­log­i­cal Sci­ence, 19, 339-345
  • Mur­ray, C. (1998). In­come in­equal­ity and IQ. Wash­ing­ton, D.C.: AEI Press.
  • Ny­borg, H., & Jensen, A. R. (2001). “Oc­cu­pa­tion and in­come re­lated to psy­cho­me­t­ric g”. In­tel­li­gence, 29, 45-55
  • Park, G., Lu­bin­ski, D., & Ben­bow, C. P. (2007). “Con­trast­ing in­tel­lec­tual pat­terns pre­dict cre­ativ­ity in the arts and sci­ences”. Psy­cho­log­i­cal Sci­ence, 18, 948-952
  • Wai, J., Lu­bin­ski, D., & Ben­bow, C. P. (2005). “Cre­ativ­ity and oc­cu­pa­tional ac­com­plish­ments among in­tel­lec­tu­ally pre­co­cious youths: An age 13 to age 33 lon­gi­tu­di­nal study”. Jour­nal of Ed­u­ca­tional Psy­chol­o­gy, 97, 484-492
  • Zax, J. S., & Rees, D. L. (2002). “IQ, aca­d­e­mic per­for­mance, en­vi­ron­ment, and earn­ings”. The Re­view of Eco­nom­ics and Sta­tis­tics, 84, 600-614.

This might seem ob­vi­ous (“elite schools pro­duce the elites”) but is worth ver­i­fy­ing. It’s also in­ter­est­ing that be­ing elected does­n’t sub­stan­tially affect the ed­u­ca­tional cre­den­tials & in­ferred IQ (even Rep­re­sen­ta­tives are still 20x more likely to be from an elite school), since given their be­hav­ior/poli­cies/s­tate­ments one might as­sume elected politi­cians are medi­oc­ri­ties or oth­er­wise not es­pe­cially in­tel­li­gent. I can give a per­sonal ex­am­ple: I grew up in New York, where one of the Sen­a­tors has been for as long as I can re­mem­ber, , who never struck me as very sub­tle or in­tel­li­gent, an im­pres­sion fur­thered when he made his il­l-fated pub­lic call years ago for the - still op­er­at­ing - Silk Road to be shut down; so I was some­what shocked to learn via Steve Sailer that he claimed a per­fect 1600 on the pre-cen­ter­ing SAT (and then went to Har­vard where he was Phi Beta Kap­pa) which im­plies that he was more in­tel­li­gent than my­self or most of Less­Wrong, and com­bined with his flaw­less elec­tion record, fur­ther sug­gests that I have badly mis­un­der­stood him and he is ac­tu­ally a bril­liant po­lit­i­cal mas­ter­mind.

, McIn­tosh et al 2013:

Back­ground: Genome-wide as­so­ci­a­tion stud­ies (GWAS) have shown a poly­genic com­po­nent to the risk of schiz­o­phre­nia. The dis­or­der is as­so­ci­ated with im­pair­ments in gen­eral cog­ni­tive abil­ity that also have a sub­stan­tial ge­netic con­tri­bu­tion. No study has de­ter­mined whether cog­ni­tive im­pair­ments can be at­trib­uted to schiz­o­phre­ni­a’s poly­genic ar­chi­tec­ture us­ing data from GWAS.

Meth­ods: Mem­bers of the Loth­ian Birth Co­hort 1936 (LBC1936, n = 14,937) were as­sessed us­ing the Moray House Test at age 11 and with the Moray House Test and a fur­ther cog­ni­tive bat­tery at age 70. To cre­ate poly­genic risk scores for schiz­o­phre­nia, we ob­tained data from the lat­est GWAS of the Psy­chi­atric GWAS Con­sor­tium on Schiz­o­phre­nia. Schiz­o­phre­nia poly­genic risk pro­file scores were cal­cu­lated us­ing in­for­ma­tion from the Psy­chi­atric GWAS Con­sor­tium on Schiz­o­phre­nia GWAS.

Re­sults: In LBC1936, poly­genic risk for schiz­o­phre­nia was neg­a­tively as­so­ci­ated with IQ at age 70 but not at age 11. Greater poly­genic risk for schiz­o­phre­nia was as­so­ci­ated with more rel­a­tive de­cline in IQ be­tween these ages. These find­ings were main­tained when the re­sults of LBC1936 were com­bined with that of the in­de­pen­dent Loth­ian Birth Co­hort 1921 (n 14,517) in a meta-analy­sis.

Con­clu­sions: In­creased poly­genic risk of schiz­o­phre­nia is as­so­ci­ated with lower cog­ni­tive abil­ity at age 70 and greater rel­a­tive de­cline in gen­eral cog­ni­tive abil­ity be­tween the ages of 11 and 70. Com­mon ge­netic vari­ants may un­der­lie both cog­ni­tive ag­ing and risk of schiz­o­phre­nia.

fat/obe­si­ty; “Child­hood In­tel­li­gence and Adult Obe­sity”, Kanazawa 2013

De­sign and Meth­ods: A pop­u­la­tion (n=17,419) of British ba­bies has been fol­lowed since birth in 1958 in a prospec­tively lon­gi­tu­di­nal study. Child­hood gen­eral in­tel­li­gence is mea­sured at 7, 11, and 16, and adult BMI and obe­sity are mea­sured at 51. Re­sults: Child­hood gen­eral in­tel­li­gence has a di­rect effect on adult BMI, obe­si­ty, and weight gain, net of ed­u­ca­tion, earn­ings, moth­er’s BMI, fa­ther’s BMI, child­hood so­cial class, and sex. More in­tel­li­gent chil­dren grow up to eat more healthy foods and ex­er­cise more fre­quently as adults. Con­clu­sion: Child­hood in­tel­li­gence has a di­rect effect on adult obe­sity un­medi­ated by ed­u­ca­tion or earn­ings. Gen­eral in­tel­li­gence de­creases BMI only in adult­hood when in­di­vid­u­als have com­plete con­trol over what they eat.

…Obe­sity is just one of a large num­ber of health prob­lems that afflict less in­tel­li­gent in­di­vid­u­als, in­crease their mor­tal­i­ty, and de­crease their life ex­pectancy (7-9)…Thus, rel­a­tive to their less in­tel­li­gent coun­ter­parts, more in­tel­li­gent chil­dren are more likely to grow up to es­pouse the evo­lu­tion­ar­ily novel val­ues of left­-wing lib­er­al­ism or athe­ism (13); to be noc­tur­nal (15); to con­sume the evo­lu­tion­ar­ily novel sub­stances of al­co­hol, to­bac­co, and psy­choac­tive drugs (16); to pre­fer evo­lu­tion­ar­ily novel in­stru­men­tal mu­sic such as clas­si­cal and light mu­sic (17); and re­gard­less of their ge­netic and hor­monal pre­dis­po­si­tion, to en­gage in evo­lu­tion­ar­ily novel ho­mo­sex­ual be­hav­ior (18)…The avail­able ev­i­dence sug­gests that more in­tel­li­gent in­di­vid­u­als are more likely to ex­er­cise more fre­quently than less in­tel­li­gent in­di­vid­u­als (21,22)…­Con­sis­tent with the pre­dic­tion of the Hy­poth­e­sis, Teas­dale et al. (23) re­port that, among a sam­ple of 26,274 young Dan­ish men, in­tel­li­gence and body-mass in­dex (BMI) are sig­nifi­cantly neg­a­tively cor­re­lated even net of ed­u­ca­tion.

  • Batty GD, Deary IJ, Got­tfred­son LS. Pre­mor­bid (early life) IQ and later mor­tal­ity risk: sys­tem­atic re­view. Ann Epi­demiol 2007;17:278-288.
  • Got­tfred­son LS, Deary IJ. In­tel­li­gence pre­dicts health and longevi­ty, but why? Curr Di­rect Psy­chol Sci 2004;13:1-4
  • Kanazawa S. Mind the gap…in in­tel­li­gence: re­ex­am­in­ing the re­la­tion­ship be­tween in­equal­ity and health. Br J Health Psy­chol 2006;11:623-642
  • Kanazawa S. Why lib­er­als and athe­ists are more in­tel­li­gent. Soc Psy­chol Q 2010; 73:33-57
  • Kanazawa S, Pe­rina K. Why night owls are more in­tel­li­gent. Pers In­di­vid Differ­ences 2009;47:685-690
  • Kanazawa S, Hell­berg JEEU. In­tel­li­gence and sub­stance use. Rev Gen Psy­chol 2010;14:382-396
  • Kanazawa S, Pe­rina K. Why more in­tel­li­gent in­di­vid­u­als like clas­si­cal mu­sic. J Be­hav­ior De­cis Mak­ing 2012;25:264-275.
  • Kanazawa S. In­tel­li­gence and ho­mo­sex­u­al­i­ty. J Biosoc Sci 2012;44:595-623.
  • Hall PA, Elias LJ, Fong GT, Har­ri­son AH, Borowsky R, Sarty GE. A so­cial neu­ro-
  • sci­ence per­spec­tive on phys­i­cal ac­tiv­i­ty. J Sport Ex­er­cise Psy­chol 2008;30:432-449.
  • Kingma EM, Tak LM, Huis­man M, Ros­malen JGM. In­tel­li­gence is neg­a­tively as­so­ci­ated with the num­ber of func­tional so­matic symp­toms. J Epi­demiol Com­mun Health 2009;63:900-906.
  • Teas­dale TW, Sørensen TIA, Stunkard AJ. In­tel­li­gence and ed­u­ca­tional level in re­la­tion to body mass in­dex of adult males. Hum Biol 1992;64:99-106.

, Yeo et al 2011

Phe­no­typic vari­a­tion in hu­man in­tel­lec­tual func­tion­ing shows sub­stan­tial her­i­tabil­i­ty, as demon­strated by a long his­tory of be­hav­ior ge­netic stud­ies. Many re­cent mol­e­c­u­lar ge­netic stud­ies have at­tempted to un­cover spe­cific ge­netic vari­a­tions re­spon­si­ble for this her­i­tabil­i­ty, but iden­ti­fied effects cap­ture lit­tle vari­ance and have proven diffi­cult to repli­cate. The present study, mo­ti­vated an in­ter­est in “mu­ta­tion load” emerg­ing from evo­lu­tion­ary per­spec­tives, ex­am­ined the im­por­tance of the num­ber of rare (or in­fre­quent) copy num­ber vari­a­tions (CNVs), and the to­tal num­ber of base pairs in­cluded in such dele­tions, for psy­cho­me­t­ric in­tel­li­gence. Ge­netic data was col­lected us­ing the Il­lu­mina 1M­DuoBead­Chip Ar­ray from a sam­ple of 202 adult in­di­vid­u­als with al­co­hol de­pen­dence, and a sub­set of these (N = 77) had been ad­min­is­tered the Wech­sler Ab­bre­vi­ated Scale of In­tel­li­gence (WASI). After re­mov­ing CNV out­liers, the im­pact of rare ge­netic dele­tions on psy­cho­me­t­ric in­tel­li­gence was in­ves­ti­gated in 74 in­di­vid­u­als. The to­tal length of the rare dele­tions sig­nifi­cantly and neg­a­tively pre­dicted in­tel­li­gence (r = −.30, p = .01). As prior stud­ies have in­di­cated greater her­i­tabil­ity in in­di­vid­u­als with rel­a­tively higher parental so­cioe­co­nomic sta­tus (SES), we also ex­am­ined the im­pact of eth­nic­ity (An­glo/White vs. Oth­er), as a proxy mea­sure of SES; these groups did not differ on any ge­netic vari­able. This cat­e­gor­i­cal vari­able sig­nifi­cantly mod­er­ated the effect of length of dele­tions on in­tel­li­gence, with larger effects be­ing noted in the An­glo/White group. Over­all, these re­sults sug­gest that rare dele­tions (be­tween 5% and 1% pop­u­la­tion fre­quency or less) ad­versely affect in­tel­lec­tual func­tion­ing, and that pleiotropic effects might partly ac­count for the as­so­ci­a­tion of in­tel­li­gence with health and men­tal health sta­tus. Sig­nifi­cant lim­i­ta­tions of this re­search, in­clud­ing is­sues of gen­er­al­iz­abil­ity and CNV mea­sure­ment, are dis­cussed.

“Effi­ciency of func­tional brain net­works and in­tel­lec­tual per­for­mance”, van den Heuvel 2009

Our brain is a com­plex net­work in which in­for­ma­tion is con­tin­u­ously processed and trans­ported be­tween spa­tially dis­trib­uted but func­tion­ally linked re­gions. Re­cent stud­ies have shown that the func­tional con­nec­tions of the brain net­work are or­ga­nized in a highly effi­cient smal­l­-world man­ner, in­di­cat­ing a high level of lo­cal neigh­bor­hood clus­ter­ing, to­gether with the ex­is­tence of more long-dis­tance con­nec­tions that en­sure a high level of global com­mu­ni­ca­tion effi­ciency within the over­all net­work. Such an effi­cient net­work ar­chi­tec­ture of our func­tional brain raises the ques­tion of a pos­si­ble as­so­ci­a­tion be­tween how effi­ciently the re­gions of our brain are func­tion­ally con­nected and our level of in­tel­li­gence. Ex­am­in­ing the over­all or­ga­ni­za­tion of the brain net­work us­ing graph analy­sis, we show a strong neg­a­tive as­so­ci­a­tion be­tween the nor­mal­ized char­ac­ter­is­tic path length lambda of the rest­ing-s­tate brain net­work and in­tel­li­gence quo­tient (IQ). This sug­gests that hu­man in­tel­lec­tual per­for­mance is likely to be re­lated to how effi­ciently our brain in­te­grates in­for­ma­tion be­tween mul­ti­ple brain re­gions. Most pro­nounced effects be­tween nor­mal­ized path length and IQ were found in frontal and pari­etal re­gions. Our find­ings in­di­cate a strong pos­i­tive as­so­ci­a­tion be­tween the global effi­ciency of func­tional brain net­works and in­tel­lec­tual per­for­mance.

“Ge­net­ics of Brain Fiber Ar­chi­tec­ture and In­tel­lec­tual Per­for­mance”, Chi­ang et al 2009

The study is the first to an­a­lyze ge­netic and en­vi­ron­men­tal fac­tors that affect brain fiber ar­chi­tec­ture and its ge­netic link­age with cog­ni­tive func­tion. We as­sessed white mat­ter in­tegrity vox­el-wise us­ing diffu­sion ten­sor imag­ing at high mag­netic field (4 Tes­la), in 92 iden­ti­cal and fra­ter­nal twins. White mat­ter in­tegri­ty, quan­ti­fied us­ing frac­tional anisotropy (FA), was used to fit struc­tural equa­tion mod­els (SEM) at each point in the brain, gen­er­at­ing three­-di­men­sional maps of her­i­tabil­i­ty. We vi­su­al­ized the anatom­i­cal pro­file of cor­re­la­tions be­tween white mat­ter in­tegrity and ful­l-s­cale, ver­bal, and per­for­mance in­tel­li­gence quo­tients (FIQ, VIQ, and PIQ). White mat­ter in­tegrity (FA) was un­der strong ge­netic con­trol and was highly her­i­ta­ble in bi­lat­eral frontal (a2 = 0.55, p = 0.04, left; a2 = 0.74, p = 0.006, right), bi­lat­eral pari­etal (a2 = 0.85, p < 0.001, left; a2 = 0.84, p < 0.001, right), and left oc­cip­i­tal (a2 = 0.76, p = 0.003) lobes, and was cor­re­lated with FIQ and PIQ in the cin­gu­lum, op­tic ra­di­a­tions, su­pe­rior fron­to-oc­cip­i­tal fas­ci­cu­lus, in­ter­nal cap­sule, cal­losal isth­mus, and the corona ra­di­ata (p = 0.04 for FIQ and p = 0.01 for PIQ, cor­rected for mul­ti­ple com­par­ison­s). In a cross-trait map­ping ap­proach, com­mon ge­netic fac­tors me­di­ated the cor­re­la­tion be­tween IQ and white mat­ter in­tegri­ty, sug­gest­ing a com­mon phys­i­o­log­i­cal mech­a­nism for both, and com­mon ge­netic de­ter­mi­na­tion. These ge­netic brain maps re­veal her­i­ta­ble as­pects of white mat­ter in­tegrity and should ex­pe­dite the dis­cov­ery of sin­gle-nu­cleotide poly­mor­phisms affect­ing fiber con­nec­tiv­ity and cog­ni­tion.

“Smarter peo­ple are (a bit) more sym­met­ri­cal: A meta-analy­sis of the re­la­tion­ship be­tween in­tel­li­gence and fluc­tu­at­ing asym­me­try”, Banks et al 2010

In­di­vid­ual differ­ences in gen­eral men­tal abil­ity (g) have im­por­tant im­pli­ca­tions across mul­ti­ple dis­ci­plines. Re­search sug­gests that the vari­ance in g may be due to a gen­eral fit­ness fac­tor. If this is the case, a re­la­tion­ship should ex­ist be­tween g and other re­li­able in­di­ca­tors of fit­ness. Some em­pir­i­cal re­sults in­di­cate a re­la­tion­ship be­tween g and fluc­tu­at­ing asym­me­try. How­ev­er, there have been in­con­sis­ten­cies in the re­sults, some of which may be due to ran­dom sam­pling er­ror, and some of which may be due to mod­er­at­ing vari­ables, pub­li­ca­tion bi­as, and method­olog­i­cal is­sues. To help clar­ify the lit­er­a­ture, a meta-analy­sis was con­ducted on the re­la­tion­ship be­tween g and fluc­tu­at­ing asym­me­try. Based on 14 sam­ples across 1871 peo­ple, es­ti­mates of the pop­u­la­tion cor­re­la­tion ranged from −.12 to −.20. There was a differ­ence in the mag­ni­tude of the cor­re­la­tion be­tween pub­lished stud­ies and un­pub­lished stud­ies with pub­lished stud­ies show­ing larger mag­ni­tude neg­a­tive cor­re­la­tions and un­pub­lished stud­ies yield­ing re­sults closer to ze­ro. The im­pli­ca­tions for our un­der­stand­ing of g and its re­la­tion­ship with fluc­tu­at­ing asym­me­try are dis­cussed.

“The In­her­i­tance of In­equal­ity”, Bowles & Gin­tis 2002

Cor­re­la­tions of IQ be­tween par­ents and off­spring range from 0.42 to 0.72, where the higher fig­ure refers to mea­sures of av­er­age parental and av­er­age off­spring IQ (Bouchard and McGue, 1981; Plomin et al., 2000).

We have lo­cated 65 es­ti­mates of the nor­mal­ized re­gres­sion co­effi­cient of a test score in an earn­ings equa­tion in 24 differ­ent stud­ies of U.S. data over a pe­riod of three decades. Our meta-analy­sis of these stud­ies is pre­sented in Bowles, Gin­tis and Os­borne (2002). The mean of these es­ti­mates is 0.15, in­di­cat­ing that a stan­dard de­vi­a­tion change in the cog­ni­tive score, hold­ing con­stant the re­main­ing vari­ables (in­clud­ing school­ing), changes the nat­ural log­a­rithm of earn­ings by about one-sev­enth of a stan­dard de­vi­a­tion. By con­trast, the mean value of the nor­mal­ized re­gres­sion co­effi­cient of years of school­ing in the same equa­tion pre­dict­ing the nat­ural log of earn­ings in these stud­ies is 0.22, sug­gest­ing a some­what larger in­de­pen­dent effect of school­ing. We checked to see if these re­sults were de­pen­dent on the weight of over­rep­re­sented au­thors, the type of cog­ni­tive test used, at what age the test was taken and other differ­ences among the stud­ies and found no sig­nifi­cant effects. An es­ti­mate of the causal im­pact of child­hood IQ on years of school­ing (also nor­mal­ized) is 0.53 (Win­ship and Ko­ren­man, 1999). A rough es­ti­mate of the di­rect and in­di­rect effect of IQ on earn­ings, call it b, is then b ϭ 0.15 ϩ (0.53)(0.22) ϭ 0.266.

  • Bowles, Gin­tis and Os­borne (2002) “The De­ter­mi­nants of In­di­vid­ual Earn­ings: Skills, Pref­er­ences, and School­ing.” Jour­nal of Eco­nomic Lit­er­a­ture. De­cem­ber, 39:4, pp. 1137-176
  • Win­ship and Ko­ren­man, 1999 “Eco­nomic Suc­cess and the Evo­lu­tion of School­ing with Men­tal Abil­ity”, in Earn­ing and Learn­ing: How Schools Mat­ter. Su­san Mayer and Paul Pe­ter­son, eds. Wash­ing­ton, D.C.: Brook­ings In­sti­tu­tion, pp. 49-78

A meta-analy­sis of 63 stud­ies showed a sig­nifi­cant neg­a­tive as­so­ci­a­tion be­tween in­tel­li­gence and re­li­gios­i­ty. The as­so­ci­a­tion was stronger for col­lege stu­dents and the gen­eral pop­u­la­tion than for par­tic­i­pants younger than col­lege age; it was also stronger for re­li­gious be­liefs than re­li­gious be­hav­ior. For col­lege stu­dents and the gen­eral pop­u­la­tion, means of weighted and un­weighted cor­re­la­tions be­tween in­tel­li­gence and the strength of re­li­gious be­liefs ranged from −.20 to −.25 (mean r = −.24). Three pos­si­ble in­ter­pre­ta­tions were dis­cussed. First, in­tel­li­gent peo­ple are less likely to con­form and, thus, are more likely to re­sist re­li­gious dog­ma. Sec­ond, in­tel­li­gent peo­ple tend to adopt an an­a­lytic (as op­posed to in­tu­itive) think­ing style, which has been shown to un­der­mine re­li­gious be­liefs. Third, sev­eral func­tions of re­li­gios­i­ty, in­clud­ing com­pen­satory con­trol, self­-reg­u­la­tion, self­-en­hance­ment, and se­cure at­tach­ment, are also con­ferred by in­tel­li­gence. In­tel­li­gent peo­ple may there­fore have less need for re­li­gious be­liefs and prac­tices.

“The Re­la­tion Be­tween In­tel­li­gence and Re­li­gios­i­ty: A Meta-Analy­sis and Some Pro­posed Ex­pla­na­tions” Zuck­er­man et al 2013:

A meta-analy­sis of 63 stud­ies showed a sig­nifi­cant neg­a­tive as­so­ci­a­tion be­tween in­tel­li­gence and re­li­gios­i­ty. The as­so­ci­a­tion was stronger for col­lege stu­dents and the gen­eral pop­u­la­tion than for par­tic­i­pants younger than col­lege age; it was also stronger for re­li­gious be­liefs than re­li­gious be­hav­ior. For col­lege stu­dents and the gen­eral pop­u­la­tion, means of weighted and un­weighted cor­re­la­tions be­tween in­tel­li­gence and the strength of re­li­gious be­liefs ranged from −.20 to −.25 (mean r = −.24). Three pos­si­ble in­ter­pre­ta­tions were dis­cussed. First, in­tel­li­gent peo­ple are less likely to con­form and, thus, are more likely to re­sist re­li­gious dog­ma. Sec­ond, in­tel­li­gent peo­ple tend to adopt an an­a­lytic (as op­posed to in­tu­itive) think­ing style, which has been shown to un­der­mine re­li­gious be­liefs. Third, sev­eral func­tions of re­li­gios­i­ty, in­clud­ing com­pen­satory con­trol, self­-reg­u­la­tion, self­-en­hance­ment, and se­cure at­tach­ment, are also con­ferred by in­tel­li­gence. In­tel­li­gent peo­ple may there­fore have less need for re­li­gious be­liefs and prac­tices.

…To our knowl­edge, the first stud­ies on in­tel­li­gence and re­li­gios­ity ap­peared in 1928, in the Uni­ver­sity of Iowa Stud­ies se­ries, Stud­ies in Char­ac­ter (How­ells, 1928; Sin­clair, 1928). These stud­ies ex­am­ined sen­so­ry, mo­tor, and cog­ni­tive cor­re­lates of re­li­gios­i­ty. In­tel­li­gence tests were in­cluded in the bat­tery of ad­min­is­tered tasks. Both How­ells (1928) and Sin­clair (1928) found that higher lev­els of in­tel­li­gence were re­lated to lower lev­els of re­li­gios­i­ty. Ac­cu­mu­la­tion of ad­di­tional re­search dur­ing the sub­se­quent three decades prompted Ar­gyle (1958) to re­view the avail­able ev­i­dence. He con­cluded that “in­tel­li­gent stu­dents are much less likely to ac­cept or­tho­dox be­liefs, and rather less likely to have pro-re­li­gious at­ti­tudes” (p. 96). Ar­gyle also noted that, as of 1958, all avail­able ev­i­dence was based on chil­dren or col­lege stu­dent sam­ples. He spec­u­lat­ed, how­ev­er, that the same re­sults might be ob­served for adults of post-col­lege age. In the sub­se­quent decade, the pen­du­lum swung in the op­po­site di­rec­tion. Kosa and Schom­mer (1961) and Hoge (1969) drew con­clu­sions from their data that were in­con­sis­tent with those of Ar­gyle (1958). Ac­cord­ing to Kosa and Schom­mer, “so­cial en­vi­ron­ment reg­u­lates the re­la­tion­ship of men­tal abil­i­ties and re­li­gious at­ti­tudes by chan­nel­ing the in­tel­li­gence into cer­tain ap­proved di­rec­tions: a sec­u­lar-ori­ented en­vi­ron­ment may di­rect it to­ward skep­ti­cism, a church-ori­ented en­vi­ron­ment may di­rect it to­ward in­creased re­li­gious in­ter­est” (p. 90). They found that in a Catholic col­lege, more in­tel­li­gent stu­dents knew more about re­li­gious doc­trine and par­tic­i­pated more in strictly re­li­gious or­ga­ni­za­tions.

…Hoge (1969, 1974) tracked changes in re­li­gious at­ti­tudes on 13 Amer­i­can cam­pus­es. He com­pared sur­vey data, most of which were col­lected be­tween 1930 and 1948, with data that he col­lected him­self in 1967 and 1968. On four cam­pus­es, Hoge also ex­am­ined the re­la­tion be­tween SAT scores and re­li­gious at­ti­tudes. Cor­re­la­tions were small and mostly neg­a­tive. Hoge (1969) con­cluded that “no or­ganic or psy­chic re­la­tion­ship ex­ists be­tween in­tel­li­gence and re­li­gious at­ti­tudes and . . . the re­la­tion­ships found by re­searchers are ei­ther due to ed­u­ca­tional in­flu­ences or bi­ases in the in­tel­li­gence tests” (p. 215). Hoge ac­knowl­edged that range re­stric­tions of col­lege stu­dents’ in­tel­li­gence scores may de­crease cor­re­la­tions be­tween in­tel­li­gence and other vari­ables. Nev­er­the­less, he con­cluded that the low neg­a­tive-in­tel­li­gence-re­li­gios­ity cor­re­la­tions im­plied that there is no re­la­tion be­tween in­tel­li­gence and re­li­gios­i­ty.

…As if in re­sponse to Beit-Hal­lahmi and Ar­gyle’s (1997) call, the last decade has seen a num­ber of large-s­cale stud­ies that ex­am­ined the re­la­tion be­tween in­tel­li­gence and re­li­gios­ity (Kanaza­wa, 2010a; Lewis, Ritchie, & Bates, 2011; Ny­borg, 2009; Sherkat, 2010). Kanazawa (2010a), Sherkat (2010), and Lewis et al. (2011) all found neg­a­tive re­la­tions be­tween in­tel­li­gence and re­li­gios­ity in post-col­lege adults. Ny­borg (2009) found that young athe­ists (age 12 to 17) scored sig­nifi­cantly higher on an in­tel­li­gence test than re­li­gious youth. The last decade also saw stud­ies on the re­la­tion be­tween in­tel­li­gence and re­li­gios­ity at the group lev­el. Us­ing data from 137 na­tions, Lynn, Har­vey, and Ny­borg (2009) found a neg­a­tive re­la­tion be­tween mean in­tel­li­gence scores (com­puted for each na­tion) and mean re­li­gios­ity scores. How­ev­er, IQ scores from un­de­vel­oped and/or non-West­ern­ized coun­tries might have lim­ited va­lid­ity be­cause most tests were de­vel­oped for West­ern cul­tures. Low lev­els of lit­er­acy and prob­lems in ob­tain­ing rep­re­sen­ta­tive sam­ples in some coun­tries may also un­der­mine the va­lid­ity of these find­ings (Hunt, 2011; Richards, 2002; Volken, 2003). In re­sponse to these cri­tiques, Reeve (2009) re­peated the analy­sis but set all na­tional IQ scores lower than 90 to 90. The re­sult­ing IQ-re­li­gios­ity cor­re­la­tion was not lower than what had been re­ported in prior stud­ies (see Reeve, 2009, for a dis­cus­sion of his trun­cat­ing pro­ce­dure). In the same vein, Pes­ta, Mc­Daniel, and Bertsch (2010) found a neg­a­tive re­la­tion be­tween in­tel­li­gence and re­li­gios­ity scores that were com­puted for all 50 states in the United States. These re­sults are less sus­cep­ti­ble to the prob­lems (e.g., cul­tural differ­ences) that plagued stud­ies at the coun­try lev­el.

…S­tud­ies in this area have found that, rel­a­tive to the gen­eral pub­lic, sci­en­tists are less likely to be­lieve in God. For ex­am­ple, Leuba (1916) re­ported that 58% of ran­domly se­lected sci­en­tists in the United States ex­pressed dis­be­lief in, or doubt re­gard­ing the ex­is­tence of God; this pro­por­tion rose to nearly 70% for the most em­i­nent sci­en­tists. Lar­son and Witham (1998) re­ported sim­i­lar re­sults, as ev­i­denced by the ti­tle of their ar­ti­cle-“Lead­ing sci­en­tists still re­ject God.” Of course, higher in­tel­li­gence is only one of a num­ber of fac­tors that can ac­count for these re­sults.

…Out­side of aca­d­e­mic jour­nals, how­ev­er, there have been at least two re­views (Beck­with, 1986; Bell, 2002). Beck­with (1986) con­cluded that 39 of the 43 stud­ies that he sum­ma­rized sup­ported a neg­a­tive re­la­tion be­tween in­tel­li­gence and re­li­gios­i­ty, and Bell (2002) sim­ply re­peated this tal­ly. How­ev­er, some of the stud­ies re­viewed by Beck­with were only in­di­rectly rel­e­vant (e.g., com­par­isons be­tween more and less pres­ti­gious uni­ver­si­ties), and some rel­e­vant stud­ies were ex­clud­ed.

…The first row of Ta­ble 2 presents ba­sic sta­tis­tics de­scrib­ing the re­la­tion be­tween in­tel­li­gence and re­li­gios­ity for all 63 stud­ies. Re­sults are pre­sented for ran­dom-effects analy­ses (un­weighted mean cor­re­la­tions) and fixed-effects analy­ses (weighted mean cor­re­la­tion­s). Fifty-three stud­ies showed neg­a­tive cor­re­la­tions while 10 stud­ies showed pos­i­tive cor­re­la­tions. Thir­ty-seven stud­ies showed sig­nifi­cant cor­re­la­tions; of the­se, 35 were neg­a­tive and 2 were pos­i­tive. The un­weighted mean cor­re­la­tion (r) be­tween in­tel­li­gence and re­li­gios­ity was −.16, the me­dian r was −.14, and the weighted mean r was −.13. The sim­i­lar­ity of these three in­di­ca­tors of cen­tral ten­dency in­di­cates that the dis­tri­b­u­tion was ap­prox­i­mately sym­met­ri­cal and was not skewed by sev­eral very large stud­ies that were in the data­base. Ran­dom- and fixed-effects mod­els yielded sig­nifi­cant ev­i­dence that the higher a per­son’s in­tel­li­gence, the lower the per­son scored on the re­li­gios­ity mea­sures.

…When GPA[grade-point-average]-religiosity cor­re­la­tions from the five stud­ies us­ing only GPA are com­bined with GPA-religiosity cor­re­la­tions from the four stud­ies us­ing GPA as well as other in­tel­li­gence mea­sures, the mean GPA-religiosity cor­re­la­tion was not sig­nifi­cantly differ­ent from ze­ro, MGPA = −.027, p = .33. It was con­cluded that GPA had no mean­ing­ful re­la­tion to re­li­gios­ity and, ac­cord­ing­ly, all sub­se­quent analy­ses omit­ted the five stud­ies that used only GPA.

…As ex­pect­ed, the ex­treme groups effect size (M = −.43) that was sig­nifi­cantly more neg­a­tive than that of the un­bi­ased stud­ies (p < .001 by post hoc least sig­nifi­cant differ­ence [LSD] test).

…The fixed-effects trim and fill method for de­tect­ing pos­si­ble pub­li­ca­tion bias yielded neg­li­gi­ble im­pact for the pre-col­lege and non-col­lege groups. For the col­lege group, how­ev­er, there was ev­i­dence of pub­li­ca­tion bi­as, such that nine neg­a­tive effect sizes would need to be added to yield a sym­met­ri­cal dis­tri­b­u­tion. The im­pu­ta­tion of these effects re­sulted in an ad­justed mean effect of −.21, no­tice­ably quite differ­ent from the ob­served weighted mean effect of −.15. Be­cause the ad­justed effect size is hy­po­thet­i­cal, it will not be in­cor­po­rated into sub­se­quent analy­ses. How­ev­er, this re­sult and the range re­stric­tion in in­tel­li­gence scores in this group sug­gest that the true in­tel­li­gence-re­li­gios­ity re­la­tion in the col­lege pop­u­la­tion may be more neg­a­tive than the lit­er­a­ture in­di­cates.

…As an ex­ploratory analy­sis, we ex­am­ined the re­la­tion be­tween per­cent­age of males in each study and effect size of the in­tel­li­gence-re­li­gios­ity re­la­tion. In the 34 stud­ies in which it could be de­ter­mined, per­cent­age of males was pos­i­tively cor­re­lated with un­weighted effect sizes, r(32) = .50, p < .01. This cor­re­la­tion in­di­cates that the neg­a­tive in­tel­li­gence-re­li­gios­ity re­la­tion was less neg­a­tive in stud­ies with more males. This re­la­tion held in terms of mag­ni­tude for the pre-col­lege and col­lege groups, r(6) = .48, ns, and r(12) = .51, p = .06, but was weaker at the non-col­lege lev­el, r(10) = .19, ns. When an­a­lyzed as a fixed-effects re­gres­sion, the re­la­tion be­tween per­cent­age of males and effect size was also markedly pos­i­tive, p < .001. A more di­rect test of the pos­si­bil­ity that the in­tel­li­gence-re­li­gios­ity re­la­tion is less neg­a­tive for males is a with­in-s­tudy com­par­i­son be­tween males and fe­males. Kanaza­wa1 con­ducted this test for two stud­ies (Kanaza­wa, 2010a; com­bined N = 21,437). If any­thing, the re­sults pointed in the op­po­site di­rec­tion. The in­tel­li­gence-re­li­gios­ity cor­re­la­tions for fe­males and males, re­spec­tive­ly, were −.11 and −.12 in Study 1, and −.14 and −.16 in Study 2. Al­though the differ­ence be­tween fe­males and males was not sig­nifi­cant, even when com­bined meta-an­a­lyt­i­cally across stud­ies (Z = 1.39, p = .16), the di­rec­tion of this differ­ence is in­con­sis­tent with the be­tween-s­tud­ies find­ing of the meta-analy­sis.

…As pre­vi­ously not­ed, some in­ves­ti­ga­tors sug­gested that ed­u­ca­tion me­di­ates the re­la­tion be­tween in­tel­li­gence and re­li­gios­ity (Hoge, 1974; Reeve & Basa­lik, 2011). In­ter­est­ing­ly, Kanazawa (S. Kanaza­wa, per­sonal com­mu­ni­ca­tion, Jan­u­ary 2012) es­pouses an op­pos­ing view, namely that in­tel­li­gence ac­counts for any neg­a­tive re­la­tion be­tween ed­u­ca­tion and re­li­gios­i­ty. Ta­ble 8 presents re­sults that ad­dress the two com­pet­ing hy­pothe­ses. The analy­ses are based on seven stud­ies from three sources. Re­sults from the stu­dent sam­ple stud­ied by Blan­chard-Fields, Hert­zog, Stein, and Pak (2001; first row in Ta­ble 8) can be ex­cluded be­cause of range re­stric­tion for in­tel­li­gence and ed­u­ca­tion (in­deed, all cor­re­la­tions for that study were weak). The re­sults of the re­main­ing six stud­ies in­di­cate that ed­u­ca­tion does not me­di­ate the in­tel­li­gence-re­li­gios­ity re­la­tion. To be­gin with, in­tel­li­gence was more neg­a­tively re­lated to re­li­gios­ity than was ed­u­ca­tion (un­weighted mean cor­re­la­tions were −.18 and −.06, re­spec­tive­ly). We tested the sig­nifi­cance of this differ­ence sep­a­rately for each study, us­ing a pro­ce­dure for com­par­ing non­in­de­pen­dent cor­re­la­tions (Meng, Rosen­thal, & Ru­bin, 1992); the com­bined differ­ence across the six stud­ies was highly sig­nifi­cant, Z = 9.32, p < .001. Fur­ther­more, con­trol­ling for ed­u­ca­tion did not have much of an effect on the in­tel­li­gence-re­li­gios­ity re­la­tion-un­weighted means of the six ze­ro-order and par­tial cor­re­la­tions were −.18 and −.17, re­spec­tive­ly. In con­trast, con­trol­ling for in­tel­li­gence led to a some­what greater change in the ed­u­ca­tion-re­li­gios­ity re­la­tion; the un­weighted means for the six ze­ro-order and par­tial cor­re­la­tions were −.06 and .00, re­spec­tive­ly. This find­ing is con­sis­tent with S. Kanaza­wa’s (per­sonal com­mu­ni­ca­tion, Jan­u­ary 2010) view that in­tel­li­gence ac­counts for the ed­u­ca­tion-re­li­gios­ity re­la­tion. How­ev­er, given that the analy­sis is based on only six stud­ies, our con­clu­sions are ten­ta­tive.

…Table 9 presents the find­ings. In all four com­par­isons, Ter­man’s sam­ple scored sig­nifi­cantly lower on re­li­gios­ity than the gen­eral pub­lic (the av­er­age of these effects was used in the meta-analy­sis as one of the ex­treme groups’ stud­ies). Ad­mit­ted­ly, the years of data col­lec­tion and ages of the two groups do not match per­fect­ly. How­ev­er, the re­sults are so strong that it is diffi­cult to imag­ine that more ex­act match­ing would make a differ­ence. These re­sults are even more strik­ing if the Ter­mites’ re­li­gious up­bring­ing is con­sid­ered. Ter­man and Oden (1959) re­ported that close to 60% of Ter­mites re­ported that they re­ceived “very strict” or “con­sid­er­able” re­li­gious train­ing; ap­prox­i­mately 33% re­ported re­ceiv­ing lit­tle train­ing, and about 6% re­ported no re­li­gious train­ing. This sug­gests that the Ter­mites un­der­went changes in their re­li­gios­ity after their child­hood. …In Ter­man’s sam­ple (N = 410), 1.2% checked the re­li­gious op­tion, com­pared with .4% in the Hunter group (N = 139), Z < 1. These re­sults sug­gest that on an ab­solute lev­el, re­li­gion was rel­a­tively unim­por­tant to mid­dle-aged adults who were iden­ti­fied as gifted in child­hood in both sam­ples. In ad­di­tion, we spec­u­late that if the Hunter sam­ple is sim­i­lar to the Ter­man sam­ple with re­spect to re­li­gios­i­ty, it too may be less re­li­gious than the gen­eral pop­u­la­tion. In the Ter­man and the Hunter sam­ples, a high in­tel­li­gence level at an early age pre­ceded lower re­li­gios­ity many years lat­er. How­ev­er, our analy­ses of these re­sults nei­ther con­trolled for pos­si­ble rel­e­vant fac­tors at an early age (e.g., so­cioe­co­nomic sta­tus) nor ex­am­ined pos­si­ble me­di­a­tors (e.g., oc­cu­pa­tion) of this re­la­tion.

…In­tel­li­gence can be re­li­ably mea­sured at a very early age while re­li­gios­ity can­not (e.g., Jensen, 1998; Larsen, Hart­mann, & Ny­borg, 2008). In their clas­sic study, for ex­am­ple, H. E. Jones and Bay­ley (1941) showed that the mean of in­tel­li­gence scores as­sessed at ages 17 and 18 (a) cor­re­lated .86 with the mean scores as­sessed at ages 5, 6, and 7; and (b) cor­re­lated .96 with the mean of in­tel­li­gence scores as­sessed at ages 11, 12, and 13. Be­cause in­tel­li­gence can be mea­sured at an early age, it can be used to pre­dict out­comes ob­served years lat­er. For ex­am­ple, Deary, Strand, Smith, and Fer­nan­des (2007) re­ported a .69 cor­re­la­tion be­tween in­tel­li­gence mea­sured at age 11 and ed­u­ca­tional achieve­ment at age 16. Un­like in­tel­li­gence, re­li­gios­ity as­sessed at an early age is a weak pre­dic­tor of re­li­gios­ity as­sessed years lat­er. For ex­am­ple, Willits and Crider (1989) found only small to mod­er­ate cor­re­la­tions be­tween re­li­gios­ity at age 16 and that at 27 (.28 for church at­ten­dance and .36 for be­lief­s). O’­Con­nor, Hoge, and Alexan­der (2002) found no re­la­tion­ship be­tween mea­sures of church in­volve­ment at ages 16 and 38.

…First, al­though the preva­lence of re­li­gios­ity varies widely among coun­tries and cul­tures, more than 50% of the world pop­u­la­tion con­sider them­selves re­li­gious. Us­ing sur­vey data col­lected by P. Zuck­er­man (2007) from 137 coun­tries, Lynn et al. (2009) and Reeve (2009) ob­served a preva­lence of 89.9% be­liev­ers in the world and 89.5% be­liev­ers in the United States. How­ev­er, a re­cent Win-Gallup In­ter­na­tional (2012) poll of 59,927 per­sons in 57 coun­tries found that only 59% of the re­spon­dents (60% in the United States) con­sider them­selves re­li­gious, a de­cline of 9% (13% in the United States) from a sim­i­lar 2005 poll. Athe­ism might be con­sid­ered a case of non­con­for­mity in so­ci­eties where the ma­jor­ity is re­li­gious. This is not so, how­ev­er, if one grows up in largely athe­ist so­ci­eties, such as those that ex­ist in Scan­di­navia (P. Zuck­er­man, 2008).

…There is also em­pir­i­cal ev­i­dence sug­gest­ing that re­li­gios­ity may be an in­-group phe­nom­e­non, re­in­forc­ing proso­cial ten­den­cies within the group (see a re­view by Noren­za­yan & Ger­vais, 2012), but also pre­dis­pos­ing be­liev­ers to re­ject out­-groups mem­bers (see meta-analy­sis by D. L. Hall, Matz, & Wood, 2010). To be­come an athe­ist, there­fore, it may be nec­es­sary to re­sist the in­-group dogma of re­li­gious be­liefs. Not sur­pris­ing­ly, there is ev­i­dence of an­ti-athe­ist dis­trust and prej­u­dice (Ger­vais, Shar­iff, & Noren­za­yan, 2011; Ger­vais & Noren­za­yan, 2012b; for a re­view, see Noren­za­yan & Ger­vais, 2012).

…In­tel­li­gence also con­fers a sense of per­sonal con­trol. We iden­ti­fied eight stud­ies that re­ported cor­re­la­tions be­tween in­tel­li­gence and be­lief in per­sonal con­trol (Grover & Hert­zog, 1991; Lach­man, 1983; Lach­man, Bal­tes, Nes­sel­roade, & Willis, 1982; Martel, McK­elvie, & Stand­ing, 1987; Miller & Lach­man, 2000; Prenda & Lach­man, 2001; Tolor & Reznikoff, 1967; P. Wood & En­glert, 2009). All eight cor­re­la­tions were pos­i­tive, with a mean cor­re­la­tion (weighted by df of each study) of .29. In ad­di­tion, higher in­tel­li­gence is as­so­ci­ated with greater self­-effi­ca­cy-the be­lief in one’s own abil­ity to achieve val­ued goals (Ban­dura, 1997). This con­struct is sim­i­lar to per­sonal con­trol be­liefs but has been ex­am­ined sep­a­rately in the lit­er­a­ture. In a meta-analy­sis of 26 stud­ies, the mean cor­re­la­tion be­tween in­tel­li­gence and self­-effi­cacy was .20 (Judge, Jack­son, Shaw, Scott, & Rich, 2007).

…Choos­ing the large de­layed re­ward serves as an in­di­ca­tor of self­-con­trol. Shamosh and Gray (2008) meta-an­a­lyzed the re­la­tion be­tween in­tel­li­gence and de­lay dis­count­ing (the lat­ter con­struct is iden­ti­cal to de­lay of grat­i­fi­ca­tion ex­cept that high de­lay dis­count­ing in­di­cates poor self­-con­trol). Their analy­sis, based on 26 stud­ies, yielded a mean r of −.23. This sug­gests that in­tel­li­gent peo­ple are more likely to de­lay grat­i­fi­ca­tion (i.e., less likely to en­gage in de­lay dis­count­ing).

…On the other hand and in line with Kanaza­wa’s (2010a) mod­el, ge­netic in­flu­ences have been im­pli­cated not only in in­tel­li­gence (cf., Nis­bett et al., 2012b), but also in re­li­gios­ity (D’Onofrio, Eaves, Mur­relle, Maes, & Spilka, 1999; Koenig, McGue, & Ia­cono, 2008). Fur­ther­more, the model was used to pre­dict other cor­re­lates of in­tel­li­gence (e.g., po­lit­i­cal lib­er­al­ism and, for men, monogamy), and those pre­dic­tions re­ceived em­pir­i­cal sup­port. In con­clu­sion, Kanaza­wa’s (2010a) in­ter­pre­ta­tion re­mains an in­trigu­ing pos­si­bil­i­ty.

…This func­tion was not in­cluded in our dis­cus­sion of func­tional equiv­a­lence be­cause, to the best of our knowl­edge, there is no ev­i­dence per­tain­ing to the re­la­tion be­tween in­tel­li­gence and death anx­i­ety. Al­though this logic sug­gests that the neg­a­tive re­la­tion be­tween in­tel­li­gence and re­li­gios­ity might de­cline at the end of life, the rel­e­vant ev­i­dence we have in­di­cates oth­er­wise. The highly in­tel­li­gent mem­bers of Ter­man’s sam­ple re­tained lower re­li­gios­ity scores (rel­a­tive to the gen­eral pop­u­la­tion) even at 75 to 91 years of age (Table 9). Ad­di­tional re­search is needed to re­solve this is­sue.

“In­tel­li­gence (IQ) as a Pre­dic­tor of Life Suc­cess”, Firkowska-Mankiewicz 2002

“Lead­ing sci­en­tists still re­ject God”, Lar­son & Witham 1998

Re­search on this topic be­gan with the em­i­nent US psy­chol­o­gist James H. Leuba and his land­mark sur­vey of 1914. He found that 58% of 1,000 ran­domly se­lected US sci­en­tists ex­pressed dis­be­lief or doubt in the ex­is­tence of God, and that this fig­ure rose to near 70% among the 400 “greater” sci­en­tists within his sam­ple^1. Leuba re­peated his sur­vey in some­what differ­ent form 20 years lat­er, and found that these per­cent­ages had in­creased to 67 and 85, re­spec­tive­ly^2.

In 1996, we re­peated Leuba’s 1914 sur­vey and re­ported our re­sults in Na­ture^3. We found lit­tle change from 1914 for Amer­i­can sci­en­tists gen­er­al­ly, with 60.7% ex­press­ing dis­be­lief or doubt. This year, we closely im­i­tated the sec­ond phase of Leuba’s 1914 sur­vey to gauge be­lief among “greater” sci­en­tists, and find the rate of be­lief lower than ever - a mere 7% of re­spon­dents.

…Our cho­sen group of “greater” sci­en­tists were mem­bers of the Na­tional Acad­emy of Sci­ences (NAS). Our sur­vey found near uni­ver­sal re­jec­tion of the tran­scen­dent by NAS nat­ural sci­en­tists. Dis­be­lief in God and im­mor­tal­ity among NAS bi­o­log­i­cal sci­en­tists was 65.2% and 69.0%, re­spec­tive­ly, and among NAS phys­i­cal sci­en­tists it was 79.0% and 76.3%. Most of the rest were ag­nos­tics on both is­sues, with few be­liev­ers. We found the high­est per­cent­age of be­lief among NAS math­e­mati­cians (14.3% in God, 15.0% in im­mor­tal­i­ty). Bi­o­log­i­cal sci­en­tists had the low­est rate of be­lief (5.5% in God, 7.1% in im­mor­tal­i­ty), with physi­cists and as­tronomers slightly higher (7.5% in God, 7.5% in im­mor­tal­i­ty). Over­all com­par­i­son fig­ures for the 1914, 1933 and 1998 sur­veys ap­pear in Ta­ble 1.


The sig­nifi­cance of vari­a­tions in in­tel­li­gence has also been ex­am­ined among in­di­vid­u­als with al­co­hol de­pen­dence, as lower in­tel­li­gence as as­sessed in child­hood or in early adult­hood pre­dicts greater co­mor­bid­ity [14], a greater propen­sity for hang­overs [15], greater mor­tal­ity from al­co­hol-re­lated health prob­lems [16], and poor treat­ment out­comes [17].

“Ear­ly-Life In­tel­li­gence Pre­dicts Midlife Bi­o­log­i­cal Age”, Schae­fer et al 2015:

Ob­jec­tives. Ear­ly-life in­tel­li­gence has been shown to pre­dict mul­ti­ple causes of death in pop­u­la­tions around the world. This find­ing sug­gests that in­tel­li­gence might in­flu­ence mor­tal­ity through its effects on a gen­eral process of phys­i­o­log­i­cal de­te­ri­o­ra­tion (i.e., in­di­vid­ual vari­a­tion in “bi­o­log­i­cal age”). We ex­am­ined whether in­tel­li­gence could pre­dict mea­sures of ag­ing at midlife be­fore the on­set of most age-re­lated dis­ease.

Meth­ods. We tested whether in­tel­li­gence as­sessed in early child­hood, mid­dle child­hood, and midlife pre­dicted midlife bi­o­log­i­cal age in mem­bers of the Dunedin Study, a pop­u­la­tion- rep­re­sen­ta­tive birth co­hort.

Re­sults. Lower in­tel­li­gence pre­dicted more ad­vanced bi­o­log­i­cal age at midlife as cap­tured by per­ceived fa­cial age, a 10-bio­marker al­go­rithm based on data from the Na­tional Health and Nu­tri­tion Ex­am­i­na­tion Sur­vey (NHANES), and Fram­ing­ham heart age (r = 0.1-0.2). Cor­re­la­tions be­tween in­tel­li­gence and telom­ere length were less con­sis­tent. The as­so­ci­a­tions be­tween in­tel­li­gence and bi­o­log­i­cal age were not ex­plained by differ­ences in child­hood health or parental so­cioe­co­nomic sta­tus, and in­tel­li­gence re­mained a sig­nifi­cant pre­dic­tor of bi­o­log­i­cal age even when in­tel­li­gence was as­sessed be­fore Study mem­bers be­gan their for­mal school­ing.

Dis­cus­sion. These re­sults sug­gest that ac­cel­er­ated ag­ing may serve as one of the fac­tors link­ing low ear­ly-life in­tel­li­gence to in­creased rates of mor­bid­ity and mor­tal­i­ty.

“In­tel­li­gence Pre­dicts Health and Longevi­ty, but Why?”, Got­tfred­son & Deary 2004:

Large epi­demi­o­log­i­cal stud­ies of al­most an en­tire pop­u­la­tion in Scot­land have found that in­tel­li­gence (as mea­sured by an IQ-type test) in child­hood pre­dicts sub­stan­tial differ­ences in adult mor­bid­ity and mor­tal­i­ty, in­clud­ing deaths from can­cers and car­dio­vas­cu­lar dis­eases. These re­la­tions re­main sig­nifi­cant after con­trol­ling for so­cioe­co­nomic vari­ables. One pos­si­ble, par­tial ex­pla­na­tion of these re­sults is that in­tel­li­gence en­hances in­di­vid­u­als’ care of their own health be­cause it rep­re­sents learn­ing, rea­son­ing, and prob­lem-solv­ing skills use­ful in pre­vent­ing chronic dis­ease and ac­ci­den­tal in­jury and in ad­her­ing to com­plex treat­ment reg­i­mens.

…O’­Toole and Stankov (1992) used IQ at in­duc­tion into the mil­i­tary, along with 56 other psy­cho­log­i­cal, be­hav­ioral, health, and de­mo­graphic vari­ables, to pre­dict non­com­bat deaths by age 40 among 2,309 Aus­tralian vet­er­ans. When all other vari­ables were sta­tis­ti­cally con­trolled, each ad­di­tional IQ point pre­dicted a 1% de­crease in risk of death. Al­so, IQ was the best pre­dic­tor of the ma­jor cause of death, mo­tor ve­hi­cle ac­ci­dents. Ve­hic­u­lar death rates dou­bled and then tripled at suc­ces­sively lower IQ ranges (100-115, 85-100, 80-85; O’­Toole, 1990).

…To date, Scot­land is the only coun­try to have con­ducted IQ test­ing on al­most a whole year-of-birth co­hort. This took place in the re­mark­able Scot­tish Men­tal Sur­vey of 1932 (SMS1932). Us­ing these pro­ce­dures, the re­searchers traced 2,230 (79.9%) of those chil­dren who took the MHT in Ab­erdeen: 1,084 were dead, 1,101 were alive, and 45 had moved away from Scot­land. In ad­di­tion, 562 were un­traced… IQ at age 11 had a sig­nifi­cant as­so­ci­a­tion with sur­vival to about age 76. On av­er­age, in­di­vid­u­als who were at a 1s­tan­dard­-de­vi­a­tion (15-point) dis­ad­van­tage in IQ rel­a­tive to other par­tic­i­pants were only 79% as likely to live to age 76. The effect of IQ was stronger for women (71%) than for men (83%), partly be­cause men who died in ac­tive ser­vice dur­ing World War II had rel­a­tively high mean IQ scores. Fur­ther analy­ses of the Ab­erdeen sub­jects found that a drop of 1 stan­dard de­vi­a­tion in IQ was as­so­ci­ated with a 27% in­crease in can­cer deaths among men and a 40% in­crease in can­cer deaths among women (Deary, Whal­ley, & Starr, 2003). The effect was es­pe­cially pro­nounced for stom­ach and lung can­cers, which are specifi­cally as­so­ci­ated with low so­cioe­co­nomic sta­tus (SES) in child­hood.

…Higher in­tel­li­gence might lower mor­tal­ity from all causes and from spe­cific causes partly by affect­ing known risk fac­tors for dis­ease, such as smok­ing. In the com­bined SMS1932-Midspan data­base, there was no sig­nifi­cant child­hood IQ differ­ence be­tween par­tic­i­pants who had ever smoked and those who had never smoked (Tay­lor, Hart, et al., 2003). How­ev­er, at the time of the Midspan stud­ies, par­tic­i­pants who were cur­rent smok­ers had sig­nifi­cantly lower child­hood IQs than ex-smok­ers. For each stan­dard de­vi­a­tion in­crease in IQ, there was a 33% in­creased rate of quit­ting smok­ing. Ad­just­ing for so­cial class re­duced this rate only mild­ly, to 25%. Thus, child­hood IQ was not as­so­ci­ated with start­ing smok­ing (mostly in the 1930s, when the pub­lic were not aware of health risks), but was as­so­ci­ated with giv­ing up smok­ing as health risks be­came ev­i­dent.

…How­ev­er, health in­equal­i­ties tend to in­crease when health re­sources be­come more avail­able to every­one (Got­tfred­son, in press). That is, in­creased avail­abil­ity of health re­sources im­proves health over­all, but the im­prove­ments are smaller for peo­ple who are poorly ed­u­cated and have low in­comes than for peo­ple with more ed­u­ca­tion and bet­ter in­comes. Com­pared with peo­ple in high-SES groups, peo­ple with low SES seek more but not nec­es­sar­ily ap­pro­pri­ate care when cost is no bar­ri­er; ad­here less often to treat­ment reg­i­mens; learn and un­der­stand less about how to pro­tect their health; seek less pre­ven­tive care, even when it is free; and less often prac­tice the healthy be­hav­iors so im­por­tant for pre­vent­ing or slow­ing the pro­gres­sion of chronic dis­eases, the ma­jor killers and dis­ablers in de­vel­oped na­tions to­day.

Yet so­cial class cor­re­lates with vir­tu­ally every in­di­ca­tor of health, health be­hav­ior, and health knowl­edge. The link be­tween SES and health tran­scends the par­tic­u­lars of ma­te­r­ial ad­van­tage, decade, na­tion, health sys­tem, so­cial change, or dis­ease, re­gard­less of its treata­bil­i­ty. Health sci­en­tists view the per­va­sive­ness and finely graded na­ture of this re­la­tion­ship be­tween SES and health as a para­dox, lead­ing them to spec­u­late that SES cre­ates health in­equal­ity via some yet-to-be-i­den­ti­fied, highly gen­er­al­iz­able “fun­da­men­tal cause” (Got­tfred­son, in press). The so­cioe­co­nomic mea­sures that best pre­dict health in­equal­ity also cor­re­late most with in­tel­li­gence (e­d­u­ca­tion best, then oc­cu­pa­tion, then in­come). This means that in­stead of IQ be­ing a proxy for SES in health mat­ters, SES mea­sures might be op­er­at­ing pri­mar­ily as rough prox­ies for so­cial-class differ­ences in men­tal rather than ma­te­r­ial re­sources.

…Health work­ers can di­ag­nose and treat in­cu­bat­ing prob­lems, such as high blood pres­sure or di­a­betes, but only when peo­ple seek pre­ven­tive screen­ing and fol­low treat­ment reg­i­mens. Many do not. In fact, per­haps a third of all pre­scrip­tion med­ica­tions are taken in a man­ner that jeop­ar­dizes the pa­tien­t’s health. Non-ad­her­ence to pre­scribed treat­ment reg­i­mens dou­bles the risk of death among heart pa­tients (Gal­lagher, Vis­coli, & Hor­witz, 1993). For bet­ter or worse, peo­ple are sub­stan­tially their own pri­mary health care providers.

For in­stance, one study (Williams et al., 1995) found that, over­all, 26% of the out­pa­tients at two ur­ban hos­pi­tals were un­able to de­ter­mine from an ap­point­ment slip when their next ap­point­ment was sched­uled, and 42% did not un­der­stand di­rec­tions for tak­ing med­i­cine on an empty stom­ach. The per­cent­ages specifi­cally among out­pa­tients with “in­ad­e­quate” lit­er­acy were worse: 40% and 65%, re­spec­tive­ly. In com­par­ison, the per­cent­ages were 5% and 24% among out­pa­tients with “ad­e­quate” lit­er­a­cy. In an­other study (Williams, Bak­er, Park­er, & Nurss, 1998), many in­sulin-de­pen­dent di­a­bet­ics did not un­der­stand fun­da­men­tal facts for main­tain­ing daily con­trol of their dis­ease: Among those clas­si­fied as hav­ing in­ad­e­quate lit­er­a­cy, about half did not know the signs of very low or very high blood sug­ar, and 60% did not know the cor­rec­tive ac­tions they needed to take if their blood sugar was too low or too high. Among di­a­bet­ics, in­tel­li­gence at time of di­ag­no­sis cor­re­lates sig­nifi­cantly (.36) with di­a­betes knowl­edge mea­sured 1 year later (Tay­lor, Frier, et al., 2003).

Duar­te, Craw­ford, Stern, Haidt, Jus­sim, and Tet­lock:

[T]he ob­served re­la­tion­ship be­tween in­tel­li­gence and con­ser­vatism largely de­pends on how con­ser­vatism is op­er­a­tional­ized. So­cial con­ser­vatism cor­re­lates with lower cog­ni­tive abil­ity test scores, but eco­nomic con­ser­vatism cor­re­lates with higher scores (Iy­er, Kol­e­va, Gra­ham, Dit­to, & Haidt, 2012; Kem­melmeier 2008). Sim­i­lar­ly, Feld­man and John­ston (2014) find in mul­ti­ple na­tion­ally rep­re­sen­ta­tive sam­ples that so­cial con­ser­vatism neg­a­tively pre­dicted ed­u­ca­tional at­tain­ment, whereas eco­nomic con­ser­vatism pos­i­tively pre­dicted ed­u­ca­tional at­tain­ment. To­geth­er, these re­sults likely ex­plain why both Heaven et al. (2011) and Hod­son and Busseri (2012) found a neg­a­tive cor­re­la­tion be­tween IQ and con­ser­vatism–be­cause “con­ser­vatism” was op­er­a­tional­ized as Right-Wing Au­thor­i­tar­i­an­ism, which is more strongly re­lated to so­cial than eco­nomic con­ser­vatism (van Hiel et al., 2004). In fact, Carl (2014) found that Re­pub­li­cans have higher mean ver­bal in­tel­li­gence (up to 5.48 IQ points equiv­a­lent, when co­vari­ates are ex­clud­ed), and this effect is dri­ven by eco­nomic con­ser­vatism (which, as a Eu­ro­pean, he called eco­nomic lib­er­al­ism, be­cause of its em­pha­sis on free mar­ket­s). Carl sug­gests that lib­er­tar­ian Re­pub­li­cans over­power the neg­a­tive cor­re­la­tion be­tween so­cial con­ser­vatism and ver­bal in­tel­li­gence, to yield the ag­gre­gate mean ad­van­tage for Re­pub­li­cans. More­over, the largest po­lit­i­cal effect in Kem­melmeier’s (2008) study was the pos­i­tive cor­re­la­tion be­tween an­ti-reg­u­la­tion views and SAT-V scores, where β = .117, p < .001 (by com­par­ison, the re­gres­sion co­effi­cient for con­ser­vatism was β = −.088, p < .01, and for be­ing African Amer­i­can, β = −.169, p < .001).


Many key de­ter­mi­nants of well-be­ing cor­re­late highly with the re­sults of IQ tests, and other mea­sures of in­tel­li­gence. Many spe­cific life out­comes have been shown to cor­re­late highly with in­tel­li­gence (Her­rn­stein and Mur­ray 1994; Kirsch et al. 1993). While no causal link has been demon­strated be­tween higher lev­els of cog­ni­tion and hap­pi­ness (Gow et al. 2005), nu­mer­ous stud­ies have high­lighted that in­creased cog­ni­tion im­proves the like­li­hood of spe­cific mark­ers of well­be­ing, while lower lev­els of gen­eral in­tel­li­gence pre­dis­poses an in­di­vid­ual to var­i­ous forms of so­cial dis­ad­van­tage (Her­rn­stein and Mur­ray 1994; Kirsch et al. 1993).

Sev­eral promi­nent in­tel­li­gence stud­ies have demon­strated that higher gen­eral in­tel­li­gence cor­re­lates with such life out­comes as in­creased in­come (Rowe et al. 1998; Zagorsky 2007; Got­tfred­son 2003), im­proved qual­ity of health and re­duced mor­tal­ity (Batty et al. 2007; Whal­ley and Deary 2001; Got­tfred­son and Deary 2004) and over­all in­creased life chances (Mur­ray 2002; Her­rn­stein and Mur­ray 1994; Kirsch et al. 1993; Got­tfred­son 2011). In­tel­li­gence ap­pears to have a promi­nent effect over a broad range of so­cial and eco­nomic life out­comes. Life is diffi­cult for in­di­vid­u­als with bor­der­line in­tel­lec­tual dis­abil­i­ty; an IQ be­low 75. This group is at a high risk of fail­ing el­e­men­tary ed­u­ca­tion, be­ing un­able to mas­ter sim­ple daily tasks, be­ing clas­si­fied as un­em­ploy­able, and are at an in­creased risk of be­ing so­cially iso­lated (Edger­ton 1993; Koegel and Edger­ton 1984; Her­rn­stein and Mur­ray 1994; Kirsch et al. 1993). In­di­vid­u­als with bor­der­line in­tel­lec­tual dis­abil­ity are at a great risk of liv­ing in poverty (30 %), hav­ing il­le­git­i­mate chil­dren (32 %), be­ing a chronic wel­fare de­pen­dent (31 %) and have very poor em­ploy­ment op­por­tu­ni­ties (Got­tfred­son 1997; Her­rn­stein and Mur­ray 1994; Kirsch et al. 1993). While in­di­vid­u­als within this group are ca­pa­ble of lead­ing sat­is­fy­ing lives, they will most likely re­quire sig­nifi­cant so­cial sup­port in or­der to do so. Those in this group who do live in­de­pen­dently have a ten­dency to live volatile and un­pre­dictable lives due to the lack of sta­bil­is­ing re­sources that come with in­creased in­tel­lec­tual com­pe­tence (Edger­ton 1993; Got­tfred­son 1997).

…Over half of these in­di­vid­u­als fail to reach the min­i­mum re­cruit­ment stan­dards for the US mil­i­tary (Hunter and Schmidt 1996). In­di­vid­u­als with mi­nor to mod­er­ate low­er, nor­mal in­tel­li­gence are still at sig­nifi­cant risk of liv­ing in poverty (16 %), be­ing a chronic wel­fare de­pen­dent (17 %) and are much more likely to drop out of school (35 %) com­pared to in­di­vid­u­als with av­er­age in­tel­li­gence (Her­rn­stein and Mur­ray 1994; Kirsch et al. 1993; Got­tfred­son 2011). The odds of in­car­cer­a­tion re­main steady for all low­er, nor­mal in­tel­li­gence groups (7 %) but re­duce by more than half for av­er­age in­tel­li­gence lev­els (3 %), in­di­cat­ing a par­tic­u­lar sus­cep­ti­bil­ity to in­car­cer­a­tion at lower in­tel­li­gence lev­els (Got­tfred­son 1997; Her­rn­stein and Mur­ray 1994).

  • Bat­ty, G.D., I.J. Deary, and L.S. Got­tfred­son. 2007. Pre-mor­bid (early life) IQ and later mor­tal­ity risk: Sys­tem­atic re­view. An­nals of Epi­demi­ol­ogy 17(4): 278-288.
  • Edger­ton, R.B. 1993. The cloak of com­pe­tence, re­vised and up­dated edi­tion. Berke­ley: Uni­ver­sity of Cal­i­for­nia Press
  • Got­tfred­son, L.S. 1997. Why g mat­ters: The com­plex­ity of every­day life. In­tel­li­gence 24: 79-132.
  • Got­tfred­son, L.S. 2003. g, jobs, and life. In The sci­en­tific study of gen­eral in­tel­li­gence: Trib­ute to Arthur R. Jensen, ed. H. Ny­borg. New York: Perg­a­mon.
  • Got­tfred­son, L.S. 2011. In­tel­li­gence and so­cial in­equal­i­ty: Why the bi­o­log­i­cal link? In Hand­book of in­di­vid­ual differ­ences, ed. T. Chamor­ro-Pre­muz­ic, A. Furhnam, and S. von strumm. New York: Wi­ley.
  • Got­tfred­son, L.S., and I.J. Deary. 2004. In­tel­li­gence pre­dicts health and longevi­ty, but why? Cur­rent Di­rec­tions in Psy­cho­log­i­cal Sci­ence 13: 1-4.
  • Gow, A.J., M.C. White­man, A. Pat­tie, L. Whal­ley, J. Starr, and I.J. Deary. 2005. Life­time in­tel­lec­tual func­tion and sat­is­fac­tion with life in old age: Lon­gi­tu­di­nal co­hort study. BMJ 331: 141-142.
  • Her­rn­stein, R.J., and C.A. Mur­ray. 1994. The bell curve: In­tel­li­gence and class struc­ture in Amer­i­can life. Salt Lake: Free Press.
  • Hunter, J.E., and F.L. Schmidt. 1996. In­tel­li­gence and job per­for­mance: Eco­nomic and so­cial im­pli­ca­tions. Psy­chol­o­gy, Pub­lic Pol­i­cy, and Law 2: 447-472.
  • Kirsch, I.S., A. Junge­blut, L. Jenk­ins, and A. Kol­stad. 1993. Adult lit­er­acy in Amer­i­ca: A first look at the re­sults of the na­tional adult lit­er­acy sur­vey. Prince­ton: Ed­u­ca­tional Test­ing Ser­vice.
  • Koegel, P., and R.B. Edger­ton. 1984. “Black ‘six-hour re­tarded chil­dren’ as young adults”. Mono­graphs of the Amer­i­can As­so­ci­a­tion on Men­tal De­fi­ciency 6: 145-171.
  • Mur­ray, C. 2002. IQ and in­come in­equal­ity in a sam­ple of sib­ling pairs from ad­van­taged fam­ily back­grounds. The Amer­i­can Eco­nomic Re­view 92: 339-343.
  • Rowe, D.C., W.J. Ves­terdal, and J.L. Rodgers. 1998. Her­rn­stein’s syl­lo­gism: ge­netic and shared en­vi­ron­men­tal in­flu­ences on IQ, ed­u­ca­tion, and in­come. In­tel­li­gence 26: 405-423
  • Whal­ley, L.J., and I.J. Deary. 2001. Lon­gi­tu­di­nal co­hort study of child­hood IQ and sur­vival up to age 76. BMJ 322: 819.
  • Zagorsky, J.L. 2007. Do you have to be smart to be rich? The im­pact of IQ on wealth, in­come and fi­nan­cial dis­tress. In­tel­li­gence 35: 489-501.

“Clever Enough to Tell the Truth”, Ruffle & To­bol 2017:

We con­duct a field ex­per­i­ment on 427 Is­raeli sol­diers who each rolled a six-sided die in pri­vate and re­ported the out­come. For every point re­port­ed, the sol­dier re­ceived an ad­di­tional half-hour early re­lease from the army base on Thurs­day after­noon. We find that the higher a sol­dier’s mil­i­tary en­trance score, the more hon­est he is on av­er­age. We repli­cate this find­ing on a sam­ple of 156 civil­ians paid in cash for their die re­ports. Fur­ther­more, the civil­ian ex­per­i­ments re­veal that two mea­sures of cog­ni­tive abil­ity pre­dict hon­esty, whereas self­-re­port hon­esty ques­tions and a con­sis­tency check among them are of no val­ue. We pro­vide a ra­tio­nale for the re­la­tion­ship be­tween cog­ni­tive abil­ity and hon­esty and dis­cuss the gen­er­al­iz­abil­ity of this re­sult.

“In­tel­li­gence and so­cioe­co­nomic suc­cess: A meta-an­a­lytic re­view of lon­gi­tu­di­nal re­search”, Strenze 2007

The re­la­tion­ship be­tween in­tel­li­gence and so­cioe­co­nomic suc­cess has been the source of nu­mer­ous con­tro­ver­sies. The present pa­per con­ducted a meta-analy­sis of the lon­gi­tu­di­nal stud­ies that have in­ves­ti­gated in­tel­li­gence as a pre­dic­tor of suc­cess (as mea­sured by ed­u­ca­tion, oc­cu­pa­tion, and in­come). In or­der to bet­ter eval­u­ate the pre­dic­tive power of in­tel­li­gence, the pa­per also in­cludes meta­analy­ses of parental so­cioe­co­nomic sta­tus (SES) and aca­d­e­mic per­for­mance (school grades) as pre­dic­tors of suc­cess. The re­sults demon­strate that in­tel­li­gence is a pow­er­ful pre­dic­tor of suc­cess but, on the whole, not an over­whelm­ingly bet­ter pre­dic­tor than parental SES or grades. Mod­er­a­tor analy­ses showed that the re­la­tion­ship be­tween in­tel­li­gence and suc­cess is de­pen­dent on the age of the sam­ple but there is lit­tle ev­i­dence of any his­tor­i­cal trend in the re­la­tion­ship.

“Does in­tel­li­gence fos­ter gen­er­al­ized trust? An em­pir­i­cal test us­ing the UK birth co­hort stud­ies”, Stur­gis et al 2010

So­cial, or ‘gen­er­al­ized’ trust is often char­ac­terised as the ‘at­ti­tu­di­nal di­men­sion’ of so­cial cap­i­tal. It has been posited as key to a host of nor­ma­tively de­sir­able out­comes at the so­ci­etal and in­di­vid­ual lev­els. Yet the ori­gins of in­di­vid­ual vari­a­tion in trust re­main some­thing of a mys­tery and con­tinue to be a source of dis­sensus amongst re­searchers across and within aca­d­e­mic dis­ci­plines. In this pa­per we use data from two British birth co­hort stud­ies to test the hy­poth­e­sis that a propen­sity to ex­press gen­er­al­ized trust varies sys­tem­at­i­cally as a func­tion of in­di­vid­ual in­tel­li­gence. In­tel­li­gence, we ar­gue, fos­ters greater trust in one’s fel­low cit­i­zens be­cause more in­tel­li­gent in­di­vid­u­als are more ac­cu­rate in their as­sess­ments of the trust­wor­thi­ness of oth­ers. This means that, over the life-course, their trust is less often be­trayed and they are able to ac­crue the ben­e­fits of norms of rec­i­proc­i­ty. Our re­sults show that stan­dard mea­sures of in­tel­li­gence ad­min­is­tered when co­hort mem­bers were aged 10 and 11 can ex­plain vari­abil­ity in ex­pressed trust in early mid­dle age, net of a broad range of the­o­ret­i­cally re­lated co­vari­ates.

“As­so­ci­a­tions be­tween IQ and cig­a­rette smok­ing among Swedish male twins”, Wen­ner­stad et al 2010

It has been sug­gested that cer­tain health be­hav­iours, such as smok­ing, may op­er­ate as me­di­a­tors of the well-estab­lished in­verse as­so­ci­a­tion be­tween IQ and mor­tal­ity risk. Pre­vi­ous re­search may be afflicted by un­ad­justed con­found­ing by so­cioe­co­nomic or psy­choso­cial fac­tors. Twin de­signs offer a unique pos­si­bil­ity to take ge­netic and shared en­vi­ron­men­tal fac­tors into ac­count. The aim of the present na­tional twin study was to de­ter­mine the in­ter­re­la­tions be­tween IQ at age 18, child­hood and at­tained so­cial fac­tors and smok­ing sta­tus in young adult­hood and mid-life. We stud­ied the as­so­ci­a­tion be­tween IQ at age 18 and smok­ing in later life in a pop­u­la­tion of 11 589 male Swedish twins. IQ was mea­sured at mil­i­tary con­scrip­tion, and data on smok­ing and zy­gos­ity was ob­tained from the Swedish Twin Reg­is­ter. In­for­ma­tion on so­cial fac­tors was ex­tracted from cen­sus­es. Data on smok­ing was self­-re­ported by the twins at the age of 22-47 years. Lo­gis­tic re­gres­sion mod­els es­ti­mated with gen­er­alised es­ti­mat­ing equa­tions were used to ex­plore pos­si­ble as­so­ci­a­tions be­tween IQ and smok­ing among the twins as in­di­vid­u­als as well as be­tween-and within twin-pairs.

A strong in­verse as­so­ci­a­tion be­tween IQ and smok­ing sta­tus emerged in un­matched analy­ses over the en­tire range of IQ dis­tri­b­u­tion. In with­in-pair and be­tween-pair analy­ses it tran­spired that shared en­vi­ron­men­tal fac­tors ex­plained most of the in­verse IQ-smok­ing re­la­tion­ship. In ad­di­tion, these analy­ses in­di­cated that non-shared and ge­netic fac­tors con­tributed only slightly (and non-sig­nifi­cant­ly) to the IQ-smok­ing as­so­ci­a­tion. Analy­sis of twin pairs dis­cor­dant for IQ and smok­ing sta­tus dis­played no ev­i­dence that non-shared fac­tors con­tribute sub­stan­tially to the as­so­ci­a­tion. The ques­tion of which shared en­vi­ron­men­tal fac­tors might ex­plain the IQ-smok­ing as­so­ci­a­tion is an in­trigu­ing one for fu­ture re­search.

“The as­so­ci­a­tion be­tween coun­ty-level IQ and coun­ty-level crime rates”, Beaver & Wright 2011:

An im­pres­sive body of re­search has re­vealed that in­di­vid­u­al-level IQ scores are neg­a­tively as­so­ci­ated with crim­i­nal and delin­quent in­volve­ment. Re­cent­ly, this line of re­search has been ex­tended to show that state-level IQ scores are as­so­ci­ated with state-level crime rates. The cur­rent study uses this lit­er­a­ture as a spring­board to ex­am­ine the po­ten­tial as­so­ci­a­tion be­tween coun­ty-level IQ and coun­ty-level crime rates. Analy­sis of data drawn from the Na­tional Lon­gi­tu­di­nal Study of Ado­les­cent Health re­vealed sta­tis­ti­cally sig­nifi­cant and neg­a­tive as­so­ci­a­tions be­tween coun­ty-level IQ and the prop­erty crime rate, the bur­glary rate, the lar­ceny rate, the mo­tor ve­hi­cle theft rate, the vi­o­lent crime rate, the rob­bery rate, and the ag­gra­vated as­sault rate. Ad­di­tional analy­ses re­vealed that these as­so­ci­a­tions were not con­founded by a mea­sure of con­cen­trated dis­ad­van­tage that cap­tures the effects of race, pover­ty, and other so­cial dis­ad­van­tages of the coun­ty. We dis­cuss the im­pli­ca­tions of the re­sults and note the lim­i­ta­tions of the study.

tat­toos and pierc­ings: http://emilkirkegaard.d­k/en/?p=5616

pg406, Stren­ze, “In­tel­li­gence and Suc­cess”, ch25 of Hand­book of In­tel­li­gence Evo­lu­tion­ary The­o­ry, His­tor­i­cal Per­spec­tive, and Cur­rent Con­cepts, ed Gold­stein et al 2015:

Ta­ble 25.1 Re­la­tion­ship be­tween in­tel­li­gence and mea­sures of suc­cess (Re­sults from meta-analy­ses)
Mea­sure of suc­cess r k N Source
Aca­d­e­mic per­for­mance in pri­mary ed­u­ca­tion 0.58 4 1791 Poropat (2009)
Ed­u­ca­tional at­tain­ment 0.56 59 84828 Strenze (2007)
Job per­for­mance (su­per­vi­sory rat­ing) 0.53 425 32124
Oc­cu­pa­tional at­tain­ment 0.43 45 72290 Strenze (2007)
Job per­for­mance (work sam­ple) 0.38 36 16480 Roth et al. (2005)
Skill ac­qui­si­tion in work train­ing 0.38 17 6713
De­gree at­tain­ment speed in grad­u­ate school 0.35 5 1700 Kun­cel et al. (2004)
Group lead­er­ship suc­cess (group pro­duc­tiv­i­ty) 0.33 14 Judge et al. (2004)
Pro­mo­tions at work 0.28 9 21290 Schmitt et al. (1984)
In­ter­view suc­cess (in­ter­viewer rat­ing of ap­pli­cant) 0.27 40 11317
Read­ing per­for­mance among prob­lem chil­dren 0.26 8 944 Nel­son et al. (2003)
Be­com­ing a leader in group 0.25 65 Judge et al. (2004)
Aca­d­e­mic per­for­mance in sec­ondary ed­u­ca­tion 0.24 17 12606 Poropat (2009)
Aca­d­e­mic per­for­mance in ter­tiary ed­u­ca­tion 0.23 26 17588 Poropat (2009)
In­come 0.20 31 58758 Strenze (2007)
Hav­ing anorexia ner­vosa 0.20 16 484 Lopez et al. (2010)
Re­search pro­duc­tiv­ity in grad­u­ate school 0.19 4 314 Kun­cel et al. (2004)
Par­tic­i­pa­tion in group ac­tiv­i­ties 0.18 36 Mann (1959)
Group lead­er­ship suc­cess (group mem­ber rat­ing) 0.17 64 Judge et al. (2004)
Cre­ativ­ity 0.17 447 Kim (2005)
Pop­u­lar­ity among group mem­bers 0.10 38 Mann (1959)
Hap­pi­ness 0.05 19 2546 DeN­eve & Cooper (1998)
Pro­cras­ti­na­tion (need­less de­lay of ac­tion) 0.03 14 2151 Steel (2007)
Chang­ing jobs 0.01 7 6062 Griffeth et al. (2000)
Phys­i­cal at­trac­tive­ness -0.04 31 3497 Fein­gold (1992)
Re­cidi­vism (re­peated crim­i­nal be­hav­ior) -0.07 32 21369 Gen­dreau et al. (1996)
Num­ber of chil­dren -0.11 3 Lynn (1996)
Traffic ac­ci­dent in­volve­ment -0.12 10 1020 Arthur et al. (1991)
Con­for­mity to per­sua­sion -0.12 7 Rhodes and Wood (1992)
Com­mu­ni­ca­tion anx­i­ety -0.13 8 2548 Bourhis and Allen (1992)
Hav­ing schiz­o­phre­nia -0.26 18

r cor­re­la­tion be­tween in­tel­li­gence and the mea­sure of suc­cess, k num­ber of stud­ies in­cluded in the meta-analy­sis, N num­ber of in­di­vid­u­als in­cluded in the meta-analy­sis

“In­tel­li­gence in young adult­hood and cause-spe­cific mor­tal­ity in the Dan­ish Con­scrip­tion Data­base – A co­hort study of 728,160 men”, Chris­tensen et al 2016:

An in­verse as­so­ci­a­tion has been re­ported be­tween early life in­tel­li­gence and al­l-cause mor­tal­i­ty. The aim of this study was to in­ves­ti­gate whether this well-estab­lished as­so­ci­a­tion differed ac­cord­ing to the un­der­ly­ing cause of death and across differ­ent birth co­horts. The as­so­ci­a­tions be­tween young adult in­tel­li­gence and mor­tal­ity from nat­ural and ex­ter­nal causes were in­ves­ti­gated in the Dan­ish Con­scrip­tion Data­base (DCD), which is a co­hort of more than 700,000 men born 1939–1959 and fol­lowed in Dan­ish reg­is­ters from young adult­hood un­til late mid-life. Young adult in­tel­li­gence was in­versely re­lated to al­l-cause mor­tal­ity with a 28% higher risk of dy­ing dur­ing the study pe­riod per 1 stan­dard de­vi­a­tion (SD) de­crease in in­tel­li­gence test score (HR = 1.28 95% CI = 1.27–1.29). The strength of the ob­served in­verse as­so­ci­a­tions did not vary much across main groups of nat­ural and ex­ter­nal causes with the ex­cep­tion of the as­so­ci­a­tions for mor­tal­ity from res­pi­ra­tory dis­eases (HR = 1.61 95% CI = 1.55–1.67) and homi­cide (HR = 1.65 95% CI = 1.46–1.87) which were more pro­nounced com­pared to the rest. More­over, for skin can­cer mor­tal­i­ty, each SD in­crease in in­tel­li­gence test score was as­so­ci­ated with a small in­crease in mor­tal­ity risk (HR = 1.03 95% CI = 1.01–1.15). Fur­ther­more, the as­so­ci­a­tion be­tween in­tel­li­gence and mor­tal­ity was stronger for those born 1950–1959 com­pared to those born 1939–1949 for al­most all nat­ural and ex­ter­nal causes of death.

“As­so­ci­a­tion of Fluid In­tel­li­gence and Psy­chi­atric Dis­or­ders in a Pop­u­la­tion-Rep­re­sen­ta­tive Sam­ple of US Ado­les­cents”, Keyes et al 2017:

Ob­jec­tive: To in­ves­ti­gate the as­so­ci­a­tion of fluid in­tel­li­gence with past-year and life­time psy­chi­atric dis­or­ders, dis­or­der age at on­set, and dis­or­der sever­ity in a na­tion­ally rep­re­sen­ta­tive sam­ple of US ado­les­cents.

De­sign, Set­ting, and Par­tic­i­pants: Na­tional sam­ple of ado­les­cents as­cer­tained from schools and house­holds from the Na­tional Co­mor­bid­ity Sur­vey Repli­ca­tion-Ado­les­cent Sup­ple­ment, col­lected 2001 through 2004. Face-to-face house­hold in­ter­views with ado­les­cents and ques­tion­naires from par­ents were ob­tained. The data were an­a­lyzed from Feb­ru­ary to De­cem­ber 2016. DSM-IV men­tal dis­or­ders were as­sessed with the World Health Or­ga­ni­za­tion Com­pos­ite In­ter­na­tional Di­ag­nos­tic In­ter­view, and in­cluded a broad range of fear, dis­tress, be­hav­ior, sub­stance use, and other dis­or­ders. Dis­or­der sever­ity was mea­sured with the Shee­han Dis­abil­ity Scale.

Main Out­comes and Mea­sures: Fluid IQ mea­sured with the Kauf­man Brief In­tel­li­gence Test, normed within the sam­ple by 6-month age groups.

Re­sults: The sam­ple in­cluded 10 073 ado­les­cents (mean [SD] age, 15.2 [1.50] years; 49.0% fe­male) with valid data on fluid in­tel­li­gence. Lower mean (SE) IQ was ob­served among ado­les­cents with past-year bipo­lar dis­or­der (94.2 [1.69]; P = .004), at­ten­tion-d­eficit/hy­per­ac­tiv­ity dis­or­der (96.3 [0.91]; P = .002), op­po­si­tional de­fi­ant dis­or­der (97.3 [0.66]; P = .007), con­duct dis­or­der (97.1 [0.82]; P = .02), sub­stance use dis­or­ders (al­co­hol abuse, 96.5 [0.67]; P < .001; drug abuse, 97.6 [0.64]; P = .02), and spe­cific pho­bia (97.1 [0.39]; P = .001) after ad­just­ment for a wide range of po­ten­tial con­founders. In­tel­li­gence was not as­so­ci­ated with post-trau­matic stress dis­or­der, eat­ing dis­or­ders, and anx­i­ety dis­or­ders other than spe­cific pho­bia, and was pos­i­tively as­so­ci­ated with past-year ma­jor de­pres­sion (mean [SE], 100 [0.5]; P = .01). As­so­ci­a­tions of fluid in­tel­li­gence with life­time dis­or­ders that had re­mit­ted were at­ten­u­ated com­pared with past-year dis­or­ders, with the ex­cep­tion of sep­a­ra­tion anx­i­ety dis­or­der. Mul­ti­ple past-year dis­or­ders had a larger pro­por­tion of ado­les­cents less than 1 SD be­low the mean IQ range than those with­out a dis­or­der. Across dis­or­ders, higher dis­or­der sever­ity was as­so­ci­ated with lower fluid in­tel­li­gence. For ex­am­ple, among ado­les­cents with spe­cific pho­bia, those with se­vere dis­or­der had a mean (SE) of 4.4 (0.72) points lower IQ than those with­out se­vere dis­or­der (P < .001), and those with al­co­hol abuse had a mean (SE) of 5.6 (1.2) points lower IQ than those with­out se­vere dis­or­der (P < .001).

Con­clu­sions and Rel­e­vance: Nu­mer­ous psy­chi­atric dis­or­ders were as­so­ci­ated with re­duc­tions in fluid in­tel­li­gence; as­so­ci­a­tions were gen­er­ally small in mag­ni­tude. Stronger as­so­ci­a­tions of cur­rent than past dis­or­ders with in­tel­li­gence sug­gest that ac­tive symp­toms of psy­chi­atric dis­or­ders in­ter­fere with cog­ni­tive func­tion­ing. Early iden­ti­fi­ca­tion and treat­ment of chil­dren with men­tal dis­or­ders in school set­tings is crit­i­cal to pro­mote aca­d­e­mic achieve­ment and long-term suc­cess.


  • http://­less­wrong.­com/l­w/7e1/ra­tional­i­ty_quotes_sep­tem­ber_2011/4r01
  • http://www1.udel.e­du/e­duc/­got­tfred­son/reprints/1997whyg­mat­ter­s.pdf
  • http://blogs.dis­cov­er­magazine.­com/gnx­p/2012/01/­so­cial-con­ser­v­a­tives-have-a-low­er-i-q-prob­a­bly/
  • http://­science­blogs.­com/gnx­p/2010/03/04/in­tel­li­gence-pol­i­tic­s-re­li­gion/
  • http://blogs.dis­cov­er­magazine.­com/gnx­p/2012/09/in­tel­li­gence-chal­lenged-peo­ple-and-free-speech/
  • Lead: http://www.­moth­er­jones.­com/en­vi­ron­men­t/2013/01/lead­-crime-link-ga­so­line http­s://en.wikipedi­­i/Lead­_poi­son­ing#N­er­vous_sys­tem
  • http://­less­wrong.­com/l­w/1e/rais­ing_the_san­i­ty_wa­ter­line/

http://hu­man­va­ri­­cho­me­t­ric-g-a-myth/ :

These ex­am­ples show that, con­trary to Shal­iz­i’s claims, all cog­ni­tive abil­i­ties are in­ter-cor­re­lat­ed. We can be con­fi­dent about this be­cause the best ev­i­dence for it comes not from the pro­po­nents of g but from nu­mer­ous com­pe­tent re­searchers who were hel­l-bent on dis­prov­ing the gen­er­al­ity of the pos­i­tive man­i­fold, only to be re­futed by their own work .­tel­li­gence-mat­ter­s-more-than-y­ou-think-for-ca­reer-suc­cess

De­pend­ing type of job and how per­for­mance is mea­sured GMA ex­plains be­tween 30% and 70% of the vari­a­tion in peo­ple’s work per­for­mance (i.e. cor­re­la­tions of be­tween .56 and .84), which is larger than any other known pre­dic­tor.4 [“When per­for­mance is mea­sured ob­jec­tively us­ing care­fully con­structed work sam­ple tests (sam­ples of ac­tual job tasks), the cor­re­la­tion (va­lid­i­ty) with in­tel­li­gence mea­sures is about .84—84% as large as the max­i­mum pos­si­ble value of 1.00, which rep­re­sents per­fect pre­dic­tion. When per­for­mance is mea­sured us­ing rat­ings of job per­for­mance by su­per­vi­sors, the cor­re­la­tion with in­tel­li­gence mea­sures is .66 for medium com­plex­ity jobs (over 60% of all job­s). For more com­plex jobs, this value is larger (e.g. .74 for pro­fes­sional and man­age­r­ial job­s), and for sim­pler jobs this value is not as high (e.g. .56 for semi­-skilled job­s). An­other per­for­mance mea­sure that is im­por­tant is the amount learned in job train­ing pro­grams (Hunter et al., 2006). Re­gard­less of job lev­el, in­tel­li­gence mea­sures pre­dict amount learned in train­ing with va­lid­ity of about .74 (Schmidt, Shaffer, and Oh, 2008).” From: Schmidt, Frank L, and John E Hunter. “Se­lect on in­tel­li­gence.” Hand­book of prin­ci­ples of or­ga­ni­za­tional be­hav­ior(2000): 3-14.]

Ev­i­dence from sev­eral meta-s­tud­ies shows that when per­for­mance is mea­sured us­ing work-sam­ple tests, the cor­re­la­tion be­tween GMA and per­for­mance is 0.84. When su­per­vi­sor rat­ings are used, the cor­re­la­tion is low­er, at 0.74 for high­-com­plex­ity jobs.5 [Schmidt, Frank L, and John E Hunter. “Se­lect on in­tel­li­gence.” Hand­book of prin­ci­ples of or­ga­ni­za­tional be­hav­ior(2000): 3-14.]

GMA also pre­dicts how high up you get in the job hi­er­ar­chy - i.e. your oc­cu­pa­tional lev­el.6 US Em­ploy­ment Ser­vice data shows a strong cor­re­la­tion (0.72) be­tween GMA and oc­cu­pa­tional level and US mil­i­tary data shows that mean GMA scores are higher at higher oc­cu­pa­tional lev­els. Al­so, there is a wider va­ri­ety of GMA scores at lower oc­cu­pa­tional lev­els than at higher ones. It seems that there are high­-s­cor­ing peo­ple in low-level oc­cu­pa­tions, but low-s­cor­ing peo­ple are un­likely to get pro­moted to higher lev­el­s.7[Schmidt, Frank L, and John Hunter. “Gen­eral men­tal abil­ity in the world of work: oc­cu­pa­tional at­tain­ment and job per­for­mance.” Jour­nal of per­son­al­ity and so­cial psy­chol­ogy 86.1 (2004): 162.]

But to fully show the link we need to track peo­ple with known GMA over time to see if high GMA in­di­vid­u­als end up be­ing more suc­cess­ful. This has been done.8 [Schmidt, Frank L, and John Hunter. “Gen­eral men­tal abil­ity in the world of work: oc­cu­pa­tional at­tain­ment and job per­for­mance.” Jour­nal of per­son­al­ity and so­cial psy­chol­ogy 86.1 (2004): 162.] In a lon­gi­tu­di­nal study of 3,887 young adults, GMA pre­dicted move­ment in the job hi­er­ar­chy 5 years lat­er. An­other study found that if peo­ple were in a job that was less com­plex than their GMA would pre­dict, they moved up to a more com­plex job and vice ver­sa. The pre­dic­tiv­ity of GMA even holds when con­trol­ling for so­cioe­co­nomic sta­tus by com­par­ing bi­o­log­i­cal sib­lings. “When the sib­lings were in their late 20s (in 1993), a per­son with av­er­age GMA was earn­ing on av­er­age al­most $18,000 less per year than his brighter sib­ling who had an IQ of 120 or higher and was earn­ing more than $9,000 more than his duller sib­ling who had an IQ of less than 80.”9 [Schmidt, Frank L, and John Hunter. “Gen­eral men­tal abil­ity in the world of work: oc­cu­pa­tional at­tain­ment and job per­for­mance.” Jour­nal of per­son­al­ity and so­cial psy­chol­ogy 86.1 (2004): 162.]

The link has also been con­firmed by two nat­ural ex­per­i­ments. [Schmidt, Frank L, and John E Hunter. “Se­lect on in­tel­li­gence.” Hand­book of prin­ci­ples of or­ga­ni­za­tional be­hav­ior(2000): 3-14.]

With high GMA, peo­ple are more able to go be­yond ex­ist­ing job knowl­edge and make judge­ments in un­fa­mil­iar sit­u­a­tions.12 [Schmidt, Frank L, and John E Hunter. “Se­lect on in­tel­li­gence.” Hand­book of prin­ci­ples of or­ga­ni­za­tional be­hav­ior(2000): 3-14.]

Al­though GMA pre­dicts per­for­mance in all jobs the more com­plex the job is13, the stronger the re­la­tion­ship be­tween GMA and per­for­mance.14[Hunter, John E. “Cog­ni­tive abil­i­ty, cog­ni­tive ap­ti­tudes, job knowl­edge, and job per­for­mance.” Jour­nal of vo­ca­tional be­hav­ior 29.3 (1986): 340-362.] And the more com­plex the job, the more vari­a­tion there is be­tween top per­form­ers and bot­tom per­form­ers.15 [Hunter, John E, Frank L Schmidt, and Michael K Jud­i­esch. “In­di­vid­ual differ­ences in out­put vari­abil­ity as a func­tion of job com­plex­i­ty.” Jour­nal of Ap­plied Psy­chol­ogy 75.1 (1990): 28.] So if you have one of the high­est lev­els of GMA in a highly com­plex job, you’ll have a high out­put com­pared to the av­er­age per­former.

“Child­hood in­tel­li­gence in re­la­tion to ma­jor causes of death in 68 year fol­low-up: prospec­tive pop­u­la­tion study”, Deary et al 2017: http://www.b­mj.­com/­con­tent/357/b­mj.j2708

“In­tel­li­gence and per­sist­ing with med­ica­tion for two years: Analy­sis in a ran­domised con­trolled trial”, Deary et al 2009: http://www.­sci­encedi­rec­t.­com/­science/ar­ti­cle/pi­i/S016028960900004X