High-quality studies on education, intelligence, and environmental effects

Heritability of intelligence

  • Vinkhuysen et al. (2012). “Psychometric IQ data were available for adult monozygotic and dizygotic twins, their siblings, the partners of the twins and siblings, and either the parents or the adult offspring of the twins and siblings (N = 1314). Two underlying processes of assortment were considered: phenotypic assortment and social homogamy. The phenotypic assortment model was slightly preferred over the social homogamy model, suggesting that assortment for intelligence is mostly due to a selection of mates on similarity in intelligence. Under the preferred phenotypic assortment model, the variance of intelligence in adulthood was not only due to non-shared environmental (18%) and additive genetic factors (44%) but also to non-additive genetic factors (27%) and phenotypic assortment (11%).” (Note: This adds up to 82% heritability.)
  • Schwabe, Janss, and van den Berg (2017). “Here we compare twin registry based estimates with a census-based heritability estimate, sampling from the same Dutch birth cohort population and using the same standardized measure for educational achievement. Including important covariates (i.e., sex, migration status, school denomination, SES, and group size), we analyzed 893,127 scores from primary school children from the years 2008–2014. For genetic inference, we used pedigree information to construct an additive genetic relationship matrix. Corrected for the covariates, this resulted in an estimate of 85%, which is even higher than based on twin studies using the same cohort and same measure.”

Effect of gene-environment interactions on intelligence

  • von Stumm, Kandaswamy, and Maxwell (2023). “We systematically tested GxE effects in the prediction of cognitive development from 2 to 4 years, using polygenic scores (PGS) for years spent in education and 39 measures of the home and neighborhood environment. Data came from up to 6973 unrelated individuals from the Twins Early Development Study (TEDS), a UK population-representative cohort. The environmental measures accounted together for 20.6% of the variance in cognitive development, while the PGS accounted for 0.5% (p < .001). We observed substantial gene-environment correlations but found no conclusive evidence for GxE effects. While associations between PGS and cognitive development were weak, genetic and environmental factors had direct and additive (i.e., main effects) rather than interactive influences on early life cognitive development.”

Effect of education on intelligence

  • Finn et al. (2013). “Random offers of enrollment to oversubscribed charter schools resulted in positive impacts of such school attendance on math achievement but had no impact on cognitive skills. These findings suggest that schools that improve standardized achievement-test scores do so primarily through channels other than improving cognitive skills.”
  • Ritchie et al. (2013). “Controlling for childhood IQ score, we found that education was positively associated with IQ at ages 79 (Sample 1) and 70 (Sample 2), and more strongly for participants with lower initial IQ scores. Education, however, showed no significant association with processing speed, measured at ages 83 and 70. Increased education may enhance important later life cognitive capacities, but does not appear to improve more fundamental aspects of cognitive processing.”
  • Carlsson et al. (2015). “To identify the causal effect of schooling on cognitive skills, we exploit conditionally random variation in the date Swedish males take a battery of cognitive tests in preparation for military service. We find an extra ten days of school instruction raises scores on crystallized intelligence tests (synonyms and technical comprehension tests) by approximately 1% of a standard deviation, whereas extra nonschool days have almost no effect. In contrast, test scores on fluid intelligence tests (spatial and logic tests) do not increase with additional days of schooling but do increase modestly with age.”
  • Ritchie, Bates, and Deary (2015). “We conducted structural equation modeling on data from a large (n=1,091), longitudinal sample, with a measure of intelligence at age 11 years and 10 tests covering a diverse range of cognitive abilities taken at age 70. Results indicated that the association of education with improved cognitive test scores is not mediated by g, but consists of direct effects on specific cognitive skills. These results suggest a decoupling of educational gains from increases in general intellectual capacity.”
  • Dahmann (2017). “First, I exploit the variation over time and across states to identify the effect of an increase in class hours on same-aged students’ intelligence scores, using data on seventeen year-olds from the German Socio-Economic Panel. Second, I investigate the influence of earlier instruction at younger ages, using data from the German National Educational Panel Study on high school graduates’ competences. The results show that, on average, neither instructional time nor age-distinct timing of instruction significantly improves students’ crystallized cognitive skills in adolescence.”
  • Karwowski and Milersky (2021). “Using data collected during the recent educational reform introduced in Poland, this study examined whether schooling’s intensity relates to changes in cognitive abilities. […] However, after separate analyses for verbal and nonverbal intelligence, together with additional robustness checks, we conclude that the effect of more intensive schooling on cognitive growth was not systematic and quite unstable. We discuss the consequences of these findings and future research directions.”
  • Lasker and Kirkegaard (2022). “We used the structural equation models from Ritchie, Bates & Deary (2015) on a longitudinal sample of over 4,000 American men who took an intelligence test near the end of high school and then took another around 37 years of age. Our results were consistent with theirs in that we found that the effect of education on intelligence test scores was not an improvement to intelligence itself, but instead was relegated to improvements to specific skills. Our results support the notion that education is not a source of enhanced intelligence, but it can help specific skills.”

Assortative mating on intelligence

  • Robinson et al. (2017). “We extend our analysis to the UK Biobank study (7,780 [spousal] pairs), finding evidence of a correlation at trait-associated loci for waist-to-hip ratio (0.101, 0.041 SE), systolic blood pressure (0.138, 0.064 SE) and educational attainment (0.654, 0.014 SE).”

Effect of famine on intelligence

  • Stein et al. (1972). “Prenatal exposure to the Dutch famine of 1944-1945 seems not related to mental performance at age 19.”
  • de Rooij et al. (2010). “At the end of World War II, a severe 5-mo famine struck the cities in the western part of The Netherlands. At its peak, the rations dropped to as low as 400 calories per day. In 1972, cognitive performance in 19-y-old male conscripts was reported not to have been affected by exposure to the famine before birth. In the present study, we show that cognitive function in later life does seem affected by prenatal undernutrition. We found that at age 56 to 59, men and women exposed to famine during the early stage of gestation performed worse on a selective attention task, a cognitive ability that usually declines with increasing age.” (Note: The other three cognitive tests assessed by the authors showed no difference, and the selective attention task scores only declined by a relatively small margin.)
  • Lumey et al. (2011).“We investigated the consequences of gestational exposure to the Dutch famine of 1944–45 for cognitive functioning at the age of 59 years. […] We found no overall association between maternal exposure to acute famine in pregnancy and cognitive performance of the offspring at the age of 59 years, but cannot rule out an association specific to early pregnancy exposure.”

Effect of deafness on intelligence

  • Review of Braden (1994) and related work. “Studies on deaf people provide the best natural experiment for these environmental-educational hypotheses. My earlier review of Braden’s book (1994) summarizes the important points. Deaf children experience a quite severe environmental deprivation, leading to social isolation which extends into adulthood. Parental practice is anything but cognitive stimulating. Mother-child interaction seems to be punitive, nonsupportive and oriented toward compliance rather than understanding. Ostracism against deaf children occurs in schools. Despite all this, deaf people perform almost equally well as normal-hearing people on performance IQ but 1 SD lower on verbal IQ. In a factor analysis, hearing loss, academic achievement and verbal IQ load on a common factor, but not nonverbal factor. In a correlated vector analysis, the environmental deprivation owing to deafness is inversely related with subtests’ g-loadings (Braden, 1989).”

Effect of parental death on life outcomes

  • Dribe, Debiasi, and Eriksson (2022). “We employ sibling fixed-effects models and the Spanish flu as an exogenous mortality shock to assess the importance of endogeneity bias in associations between parental loss and socioeconomic outcomes. Maternal death led to worse socioeconomic outcomes in adulthood in terms of occupational and class attainment, as well as for social mobility. The effects seem to be causal but the magnitudes were small. For paternal death, we find no consistent pattern, and in most models there was no effect on sons’ socioeconomic outcomes.”

Effect of education on life outcomes

  • Clark and Cummins (2020). “Here we measure the consequence of extending compulsory schooling in England to ages 14, 15 and 16 in the years 1919-22, 1947 and 1972. […] Compulsory schooling ages 14-16 had no effect, at the cohort level, on social outcomes in England.”

February 1st, 2023 | Posted in Biology

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