The negative selection hypothesis may help explain why specific race/ethnic groups, especially subgroups of Black and Latino children, on average, perform below White and Asian children on measurements of cognitive achievement. If parents of immigrant children migrate because of high levels of inequality and lack of opportunity in the country of origin, the negative selection hypothesis suggests that immigrants are more likely to have lower levels of education and ability (Borjas, 1990). Hence, the parents in certain immigrant subgroups may not have the resources—in terms of economic or cultural capital—to help their children succeed academically once in the United States. Moreover, first generation Black and Latino children encounter many obstacles associated with minority status in the United States, including discrimination, racism, and spatial segregation, all of which are risk factors for educational achievement (Palacios et al., 2008).

Notice how “genetic capital” is omitted from discussion. Give non-neglible within population heritabilities, it’s inevitable the parents and their children will bring along these resources too. Regardless, the stumbling block for the “negative selection” hypothesis is that a large body of research, which is completely ignored by the authors, shows that in the case of Black immigrants the contrary is true (e.g., Easterly and Nyarko, 2005; Model, 2005; Feliciano, 2005; Bennet and Lutz, 2009).

The general findings are that differences start early (Glick and Hohmann-Marriott, 2007; Glick, Bates, and Yabiku, 2009.) — in early youth — persist with a bundle of controls — and are found on numerous measures of ability across numerous samples.

(Articles which I already touched upon are in parentheses.)

Kao and Tienda., 1995. Optimism and Achievement: The Educational Performance of Immigrant Youth.

Hao and Bonstead-Bruns, 1998. Parent-Child Differences in Educational Expectations and the Academic Achievement of Immigrant and Native Students

Glick and White, 2003. The Academic Trajectories of Immigrant Youths: Analysis Within and Across Cohorts

Kao, 2004. Parental Influences on the Educutional Outcomes of Immigrant Youth

Glick and Hohmann-Marriott, 2007. Academic performance of young children in immigrant families- The significance of race, ethnicity, and national origins

Pong and Hao, 2007. Neighborhood and School Factors in the School Performance of Immigrants’ Children

(Massey et al. 2007. Black Immigrants and Black Natives Attending Selective Colleges and Universities in the United States.)

Palacios et al., 2008. Early Reading Achievement of Children in Immigrant Families- Is There an Immigrant Paradox?

Glick, Bates, and Yabiku, 2009. Mother’s age at arrival in the United States and early cognitive development

De Feyter and Winsler, 2009. The early developmental competencies and school readiness of low-income, immigrant children: Influences of generation, race/ethnicity, and national origins

(Bennett and Lutz. 2009. How African American is the net black advantage? Differences in college attendance among immigrant blacks, native blacks, and whites.)

(Richwine, 2009. IQ and Immigration Policy)

Jaret and Reitzes, 2009. Currents in a Stream: College Student Identities and Ethnic Identities and Their Relationship with Self-Esteem, Efficacy, and Grade Point Average in an Urban University

Conger, 2010. Immigrant Peers in School and Human Capital Development

Stiefel et al., 2010. Age of entry and the high school performance of immigrant youth

Crosnoe and Turley, 2011. K–12 Educational Outcomes of Immigrant Youth

References

Feliciano, 2005. Educational Selectivity in U.S. Immigration: How Do Immigrants Compare to Those Left Behind?

Easterly and Nyarko, 2005. Is the brain drain good for Africa?

Model, 2008. The Secret of West Indian Success

Bennet and Lutz, 2009. Bennet and Lutz. How African American is the net black advantage? Differences in college attendance among immigrant blacks, native blacks, and whites

“The finding that [self-identifying B/W mixed race individuals who are school identified as Black] over-perform Blacks and that [self-identifying B/W mixed race individuals who are school identified as White] underperform Whites is somewhat informative. It could be that [self-identifying B/W mixed race individuals, in general] include a number of true Black and true White monoracials (i.e., monoracial Blacks and Whites who happened to check both “Black” and “White.”) The effect would be that the scores of [self-identifying B/W mixed race individuals, in general] fall in between those of “Whites” and “Blacks,” thus giving the illusion of intermediate mixed race performance. (Though, in reality, if this were the case, one would not expect the scores to fall in the middle, as they do, as monoracial Whites are 4 times as numerous as monoracial Blacks. As a result, in the above scenario, we would expect 4 times as many monoracial Whites to (mis)check both the “Black” and “White” box as monoracial Blacks and, so, expect the average scores to be skewed to the White end.) Such a “sampling” explanation, though, cannot explain the findings [above] since [self-identifying B/W mixed race individuals who are school identified as Black] presumably excludes monoracial Whites and [self-identifying B/W mixed race individuals who are school identified as White] presumably excludes monoracial Blacks (via school identification in both cases).”

I concluded that either a cultural explanation (being X, identifying as Y) or genetic explanation (being X and Y) could explain the results. I tested the latter and found that a cultural hypothesis was wanting. The results were odd, though, from the perspective of a genetic hypothesis. I noted:

“The most reliable method for identifying mixed race students is that which uses both student and school report. 4th grade mixed race students identified thusly do not behave in accordance with a hereditarian model. 8th grade students do. The results are odd, since we are dealing with overlapping populations. The 2003, 2005, and 2007 4th grade sample is supposedly a representative sample of the same population from which, respectively, the 2007, 2009, and 2011 8th grade sample came from. The mixed race 4th grade increase, relative to Whites, between 2005 and 2007 should have show up as an 8th grade increase, relative to Whites, between 2009 and 2011.”

Further investigation suggested a solution to the paradox found: the correspondence between self-identified and school-identified mixed race status is low. For example, only ½ of 4th grade students who took the NAEP reading test who were school identified as being mixed race, self identify the same way. This would explain why the 4th grade 2005 and 2007 results were at odds with the 8th grade 2009 and 2011 results, even though they were based on samples of mostly the same population. The lack of correspondence between self/school mixed race, of course, does not, in turn, necessarily support a cultural hypothesis, as it’s not clear if self-identified mixed race status is a poor predictor of “true” mixed race status. It could be that both self and school identified mixed race status are fair predictors of “true” status but not of each other.

Whatever the case, the results do call into question the significance of the findings here and elsewhere in context of reducing the hereditarian/environmental uncertainty. It may be that while self identifying “mixed race” individuals perform intermediate to the parental populations, school identified “mixed race” individuals don’t.

To determine if the latter is true, one would need to decompose school identified mixed race results by racial mix. To do this accurately one would need the school level data, which, unfortunately, is not available to us. Nonetheless, we can estimate the scores, if we are willing to make a simple assumption. To estimate the scores, we can cross tab school identified mixed race with self identified race. If we just make the assumption that school identified mixed race individuals who self identified as X are mixed race individual of X and some other parentage, we can estimate these scores based on racial intermarriage rates. (To clarify, we are assuming that a school classified mixed individual who self-classifies as “Black” is “really” Black + something — as opposed to, say, White + Asian.)

For example, in the 4th grade NAEP 2011 Math sample, there were (as estimated from s.e. and SD) about 402 school classified multiracial students who self-classified as Black, 217 who classified as White, 500 who classified as Mixed, and 249 who classified as Mixed Black/White.
And based on the 2000 census, 26% of interracial White marriages were to Blacks and 88% of Black interracial marriages were to Whites. Black/White interracial marriages comprised 25% of the total.

Based on our assumption, we can estimate the school mixed race scores as:

“Self classified B/W” score x sample size”
(Or we could use: Self classified Mixed, School classified Mixed” x sample size x % of Mixed interracial marriages that were B/W. But I am opting to use “Self classified B/W” because those results are less in line with a genetic hypothesis.)
+
“Self classified White, School classified Mixed” score x sample size x % of White interracial marriages to Blacks.”
+
“Self classified Black, School classified Mixed” score x sample size x % of Black interracial marriages to Whites.”

/ N(total)

We are just creating an N-weighted score based on the probability of admixture.

Doing this for 4th grade Math…Reading, 8th grade Math and Reading, we get:

(To be continued)

te Nijenhuis, 2012. The Flynn effect, group differences, and g loadings

Flynn effect gains are predominantly driven by environmental factors. Might these factors also be responsible for group differences in intelligence? Group differences in intelligence have been clearly shown to strongly correlate with g loadings. The empirical studies on whether the pattern of Flynn effect gains is the same as the pattern of group differences yield conflicting findings. We present new evidence on the topic using a number of datasets from the US and the Netherlands. Score gains and g loadings showed a small negative average correlation. The general picture is now that there is a small, negative correlation between g loadings and Flynn effect gains. It appears that the Flynn effect and group differences have different causes

[The continuing debate on the malleability of IQ.]

Gallowat and Brinch, 2012. Schooling in adolescence raises IQ scores

Although some scholars maintain that education has little effect on intelligence quotient (IQ) scores, others claim that IQ scores are indeed malleable, primarily through intervention in early child- hood. The causal effect of education on IQ at later ages is often difficult to uncover because analyses based on observational data are plagued by problems of reverse causation and self-selection into further education. We exploit a reform that increased com- pulsory schooling from 7 to 9 y in Norway in the 1960s to estimate the effect of education on IQ. We find that this schooling reform, which primarily affected education in the middle teenage years, had a substantial effect on IQ scores measured at the age of 19 y

[Comment: The continuing debate about why IQ is negatively correlated with various forms of conservatism. Curiously, no one has yet suggested stereotype threat.]

Leeson et al., 2012. Revisiting the link between low verbal intelligence and ideology

We address a series of criticisms, raised by Woodley (2011), of our paper “Cognitive ability, right-wing authoritarianism, and social dominance orientation: A five-year longitudinal study amongst adolescents” (Heaven, Ciarrochi, & Leeson, 2011). We argue that, while Wood- ley (2011) presents some interesting points, his criticisms do not alter our initial interpretation that verbal intelligence influences the individual’s ideological perspective. We also argue that the use of RWA and SDO in our paper is not problematic given that these variables are treated as ideological constructs and not measures of personality. We further challenge the assump- tion that our reported relationship between low IQ and conservative ideology reflects the greater flexibility of intelligent participants in endorsing liberal norms. Finally, as suggested by Woodley, we re-analysed our data using a General Factor of Personality (GFP). The results indicated that in predicting ideology, GFP did not uniquely account for variance above and be- yond that of intelligence, thus failing to support one of the central hypotheses of the cultural- mediation model.

Rindermann et al., 2011. Political orientations, intelligence and education

The social sciences have traditionally assumed that education is a major determinant of citizens’ po- litical orientations and behavior. Several studies have also shown that intelligence has an impact. According to a theory that conceptualizes intelligence as a burgher (middle-class, civil) phenome- non — intelligence should promote civil attitudes, habits and norms like diligence, order and liberty, which in turn nurture cognitive development — political orientations should be related to intelli- gence, with more intelligent individuals tending towards less extreme political orientations. In a Brazilian sample (N=586), individuals were given the Standard Progressive Matrices (SPM) and a questionnaire measuring age, gender, income, education and political orientations. Firstly, intelli- gence has a positive impact on having any political opinion. Among persons with opinions those with the highest IQ’s were found to be politically center-right and centrist respectively. The relation- ship held after correcting for gender, age, education and income. In a path-analysis, only intelligence had a positive impact on political centrality, whereas education promoted orientations that were farther from the center. These results are discussed in the context of results from other studies in different countries and in the context of different theoretical models on the relationship between political attitudes and IQ.

[Comment: More on Life History Strategy.]

McDonald et al., 2011. A life history approach to understanding the Dark Triad

Researchers adopting an evolutionary perspective have conceptualized the Dark Triad as an exploitative interpersonal style reflective of a fast life history strategy. However, not all research has supported this claim. We posit that different elements of the constructs associated with the Dark Triad may reflect different life history strategies. Our results indicate that the measures of the Dark Triad and other indicators of life history strategies form two distinct factors: (1) a fast life strategy factor that includes the impulsive antisociality facet of psychopathy, the entitlement/exploitativeness facet of narcissism, Machiavellianism, unrestricted sociosexuality, and aggression, and (2) a slow life strategy factor that includes the fearless dominance facet of psychopathy and both the leadership/authority and grandiose exhibitionism facets of narcissism. These factors differentially correlate with established measures of life history strategy. These findings add to the literature by clarifying how the Dark Triad fits into a life history framework.

[Comment: Age, heritability, stability.]

Deary et al., 2012. Genetic contributions to stability and change in intelligence from childhood to old age

Understanding the determinants of healthy mental ageing is a priority for society today1,2. So far, we know that intelligence differences show high stability from childhood to old age3,4 and there are estimates of the genetic contribution to intelligence at different ages5,6. However, attempts to discover whether genetic causes con- tribute to differences in cognitive ageing have been relatively uninformative7–10. Here we provide an estimate of the genetic and environmental contributions to stability and change in intelligence across most of the human lifetime. We used genome-wide single nucleotide polymorphism (SNP) data from 1,940 unrelated individuals whose intelligence was measured in childhood (age 11 years) and again in old age (age 65, 70 or 79 years)11,12. We use a statistical method that allows genetic (co)variance to be estimated from SNP data on unrelated individuals13–17. We estimate that causal genetic variants in linkage disequilibrium with common SNPs account for 0.24 of the variation in cognitive ability change from childhood to old age. Using bivariate analysis, we estimate a genetic correlation between intelligence at age 11 years and in old age of 0.62. These estimates, derived from rarely available data on lifetime cognitive measures, warrant the search for genetic causes of cognitive stability and change.

[Update: I repeated my previous analysis using Pisa 2006 Science data (age 15) and using International Explorer. In that data -- screen shot here -- there were 72 self identified mixed race students. The following averages were found: (non-hispanic) White 522, (non-hispanic) Mixed White-Black 470, (non-hispanic) Black 408.]

[Update: I repeated my previous analysis using Pisa 2006 Math data (age 15) and using International Explorer. In that data -- screen shot here -- there were 67 self identified mixed race students. The following averages were found: (non-hispanic) White 502, (non-hispanic) Mixed White-Black 464, (non-hispanic) Black 403.]

Since, I have found the self-report multiracial status does not correspond well with school reported status, at least at young ages (e.g., NAEP 4th grade). This calls into question the significance of the findings in context of reducing the hereditarian/environmental uncertainty. The findings are nonetheless interesting and important in their own right, since, as noted in discussion with JL, they can’t be explained purely in terms of sampling. Identification as mixed race is associated with intermediate scores. Further investigation could uncover strong support for a cultural (i.e., being-X and acting Y) or hereditarian (i.e., being 1/2 X and 1/2 Y) hypothesis.

Feeling down? You’ve been on a hot streak lately, so please don’t feel bad. We appreciate all the hard work you are doing.

I’m here for you Chuck. We’re all here for you.

Umm…no. You missed the point.

Bizarrely, I got the idea from the anti-White Mulatto blogger Abagond, who is fretting that the man will strip him of his right to post his racial grievances.

Umm…no.

But the man might block my site because I frequently link to HBD articles that I copy and download without permission, which is why I posted as I did.

But thanks for the concern anyways.

(1) When comparing mean differences, the accepted procedure is to pool standard deviations:

Gottfredson, L. S. (2005). Implications of cognitive differences for schooling within diverse societies.

Having compiled numbers that show the startling amount of money, time, and effort that the educators and political leaders in America have spent trying to close the racial gap in learning, this young member of Chinese intelligence – who studied at the University of California on a scholarship provided by American tax-payers – believes the US will bankrupt itself before it admits racial differences in intelligence, which could ultimately avert its decline and fall.

Addressing his audience of some of the most important people in China, he reads from a study:

For both blacks and whites, family income is one of the best predictors of a student’s SAT score. Students from families with high incomes tend to score higher. Students from low-income families on average have low SAT scores. Because the median black family income in the United States is about 60 percent of the median family income of whites, one would immediately seize upon this economic statistic to explain the average 200-point gap between blacks and whites on the standard SAT scoring curve.

But income differences explain only part of the racial gap in SAT scores. For black and white students from families with incomes of more than $200,000 in 2008, there still remains a huge 149-point gap in SAT scores. Even more startling is the fact that in 2008 black students from families with incomes of more than $200,000 scored lower on the SAT test than did students from white families with incomes between $20,000 and $40,000.

The room falls silent, until a small chuckle can be heard in the back. Someone clears their throat, but it’s too late; the entire room breaks out into laughter.

It’s actually worse than that. The gap doesn’t merely persist with SES, but — as with the heritability of cognitive ability, incidentally — it generally increases. The SAT SES data Kersey cited was somewhat of an anomaly. This phenomenon of increasing magnitude with SES puts the lie to the frequent claim that controlling for SES drastically reduces the gap. I’m sure you’ve heard it. The claim is accurate, but deceptive. Because Blacks have a disproportionately lower SES than Whites and as the gap itself is generally positively correlated with SES (See: Jensen 1973, 1998; and the graphs below), when scores are regressed on SES, one gets a larger reduction in the gap’s magnitude than one might otherwise anticipate. Raising the SES of Blacks relative to Whites will reduce the gap overall, of course, but it will also increase the gap relative to Whites of the same status.

Anyways, I created some new RACE by SES graphs.

I uncovered some more data on 2nd gen US immigrant IQ/cognitive ability by race using the International Explorer. We’re interested in these scores for reasons discussed before. I’m interested specifically because 2nd gen performance was used against me in a Bell Curve argument. And since Jason Malloy and Steve Sailer were the only ones in the HBD-sphere who had blogged about this — in context to actual data points — I had little to cite in defense of my position.)

TIMSS (2007) had some information. (I included s.e. and SD so you could calculate the sample size.) Math (grade 4) and Science (grade 4):

PIRLS (2006) also had some information. Reading (Grade 4):

I’m not seeing the supposed stellar performance of 2nd Gen Blacks. I have now identified eight independent sources of data which collectively show that 2nd Gen Blacks significantly underperform 2nd and 3rd Gen Whites.

[PISA (2006, 2009) Math (Age 15) had some information on Hispanic and Asian performance.

[Immigration status [Native: self or at least one parent born in the country of assessment; Second generation: self born in country but both parents born in another; First generation: self and both parents born in another country]

]

(There will be no part II if I am unable to come up with a coherent hereditarian explanation for the results)

(This is dissertation material people. I just want to dirty it up with my smelly racist paws before some SWPL gets to it.)

I again looked at NAEP math and reading

Method

For grades 4 and 8, I Identified mixed race students 4 ways. which after some consideration, I decided were the most reliable:

1) Student-identified as “Black,” “White,” and “non-Hispanic”
(In NAEP Explorer, cross tab BB21201 “Black”, BA21201 “White”, and DRACE “Two or more races”)

Pros: High face validity, relatively large Ns
Cons: Questionable reliability (the population identified could be an aggregate of monoracial students who cross identify)

2) Student-identified as “Black,” “White,” and school identified as “(non-Hispanic) Mixed”
(In NAEP Explorer, cross tab BB21201 “Black,” BA21201 “White”, and SDRACE “Two or more races”)

Pros: High face validity, Higher reliability (eliminates both monoracial White and Black students as a confounder)
Cons: Small Ns

3) Student-identified as “Black” and “White” and school identified as “(non-Hispanic) Black”
(In NAEP explorer, cross tab BB21201 “Black,” BA21201 “White,” and SDRACE “(non-Hispanic) Black”

Pros: High face validity, intermediate reliability (eliminates monoracial White students as a confounder)
Cons: small Ns

4) Student-identified as “Black” and “White” and school identified as “(non-Hispanic White”
(In NAEP explorer, cross tab BB21201 “Black,” BA21201 “White,” and SDRACE “(non-Hispanic) White”

Pros: High face validity, intermediate reliability (eliminates monoracial Black students as a confounder)
Cons: small Ns

I did this for all years for which there was relevant information. Since small sample sizes precluded, I was unable to do this for grades 12.

Results

Self-identified mixed race individuals (1) fell intermediate to the parental races in 4th and 8th grade for all years. However, only for grade 8, did self-identified mixed race individuals who were also school identified as being mixed (2) consistently significantly fall intermediate to the parental races. (See the two graphs on the left.)

In all cases self-identified mix race individuals who were school identified as being monoracial Black (3) or monoracial White (4) fell in between the parental races. In all cases, those who were school identified as Black (3) performed worse than those who were school identified as White (4). (See the two graphs on the right.)

Interpretation

Mixed race (1) and (2)

The most reliable method for identifying mixed race students is that which uses both student and school report. (2) above. 4th grade mixed race students identified thusly do not behave in accordance with a hereditarian model. 8th grade students do. The results are odd, since we are dealing with overlapping populations. The 2003, 2005, and 2007 4th grade sample is supposedly a representative sample of the same population from which, respectively, the 2007, 2009, and 2011 8th grade sample came from. The mixed race 4th grade increase, relative to Whites, between 2005 and 2007 should have show up as an 8th grade increase, relative to Whites, between 2009 and 2011.

Problem 1. It’s not clear how to best interpret these results. One could interpret the lack of difference between 4th grade mixed race students ( method 2) as indicating the absence of genetic difference between Whites and Blacks and then attribute the 8th grade results to the effects of culture (e.g., the Black-cultural undertow). Alternatively, one could interpret the increase in difference between mixed race students and whites as an effect of gene expression.

Mixed race (3) and (4)

Presumably, self-identified mixed race students who were school identified as being White (4) outperform self-identified mixed race students who were school identified as being Black (3) because these groups are confounded, respectively, with White and Black monoracials.

Problem 2. It’s not clear, though, why (3) outperform Self and School identified Blacks or why (4 ) underperform Self and School identified Whites. Mixed race self-identity has a clear effect on tests scores.

A cultural theory could possibly explain the above. It could be argued that identification with White and Black culture leads respectively to (3) outperforming Blacks and (4) underperforming Whites. Let’s call this the “Black cultural undertow hypothesis.” Accordingly, Black identity/culture confers a disadvantage to Whites and White identity/culture confers an advantage to Blacks.

It’s not clear how such a hypothesis can be reconciled with a cultural explanation for the gap between Whites and Mixed race 8th graders (by 2) and the lack of a gap between Whites and Mixed race (by 2) 4th graders (above). As noted, such a hypothesis contends that the presence of a gap between Whites and mixed race (by 2) students in eight grade is due to cultural effects. A corollary to this is that the absence of a gap between Whites and mixed race (by 2) students in the fourth grade is due to the absence of cultural effects. If there are no race cultural effects in grades 4, then a cultural hypothesis can not explain the grade 4 results for (3) and (4).

To investigate further, I compared (3) and (4) to two other classifications for 4th and 8th graders:

5) Student-identified as “Black” (monoracial) and school identified as “White” (monoracial)
(In NAEP explorer, cross tab DRACE and SDRACE)

6) Student-identified as “White” (monoracial) and school identified as “Black” (monoracial)
(In NAEP explorer, cross tab DRACE and SDRACE)

The following hierarchy was found for 8th graders:

1) White
2) W/B-Self, White-School
3) Black-Self, White-School
W/B self, Black-School
5) White-Self, Black School
6) Black

For 4th graders

1) White
2) W/B-Self, White-School
3) W/B-Self, Black-School
4) Black-Self, White-School
5) Black
White-Self, Black-School

Grade 8: School classified Blacks who self classified as White perform significantly better than School classified Blacks who self classified as Black. They tend to perform slightly worse than their school classified Black peers who self-classified as Mixed.

This is not fully consistent with the above cultural explanation. Self-identification as White alone does not provide an advantage relative to self-identification as Mixed.

School classified Whites who self classified as Black perform significantly worse than School classified Whites who classified as White. They tend to perform slightly worse than their school classified White peers who self-classify as Mixed.

This is consistent with the above cultural explanation.

Grade 4: School classified Blacks who self classified as White perform the same as School classified Blacks who self classified as Black. They tend to perform significantly worse than their school classified Black peers who self-classified as Mixed.

This is inconsistent with the above cultural explanation. Self-identification as White provides not advantage relative to self-identification as Mixed or Black.

School classified Whites who self classified as Black perform significantly worse than School classified Whites who classified as White. They tend to perform slightly worse than their school classified White peers who self-classified as Mixed.

This is consistent with the above cultural explanation.

Overall, while “Black cultural undertow hypothesis” can explain the pattern of performance for school identified Whites (via a wigger effect) it can not alone explain the pattern for school identified Blacks. Moreover, as discussed above, “Black cultural undertow hypothesis” is unable to account for the either the grade 4 results for (3) and (4) or the grade 4 results for (1) and (2). See above.)

How then do we account for the results?