Genes and Status Achievement. François Nielsen. Oxford Handbook of Evolution, Biology, and Society, edited by Rosemary L. Hopcroft. DOI: 10.1093/oxfordhb/9780190299323.013.22
Abstract: A number of human traits that are predictive of socioeconomic success (e.g., intelligence, certain personality traits, and educational attainment) or reflective of success (e.g., occupational prestige and earnings) have been found to be substantially affected by individual genetic endowments; some outcomes, such as educational attainment, are also affected by the family environment, although usually to a lesser extent. The associations among status-related traits are themselves largely due to genetic causes. By reshuffling the genes of parents at each generation, sexual reproduction produces a regression of status-relevant traits of offspring toward the population mean—downward for high-status parents, upward for low-status parents—generating social mobility in an achievement-oriented society. Incorporating the quantitative genetic decomposition of trait variance into genetic, shared environmental, and nonshared environmental sources into the classic sociological model of status achievement allows for a better understanding and measurement of central social stratification concepts, such as opportunity and ascription.
Keywords: social stratification, social mobility, inequality, genetic, socioeconomic success
Introduction: The Status Achievement Model
Since the 1960s, the dominant paradigm in studies of social stratification by sociologists and economists has been the status achievement model first proposed by Blau and Duncan (1967). The model represents various status-related outcomes—completion of formal education, first occupation, current occupation, and income—as arrayed in roughly successive stages along the life course (Blau and Duncan 1967). Later outcomes are represented as caused by earlier ones, with the most distal predictors of status consisting of socioeconomic characteristics of the family of origin, such as father’s education and occupation. The direct links among the various outcomes can be estimated from data as standardized regression coefficients. From the direct links, one can derive the indirect effect of a variable on a status outcome downstream in the causal flow. A typical status achievement model similar to that used by Blau and Duncan but estimated from more recent data is shown in Figure 15.1.
Genes and Status AchievementClick to view larger
Figure 15.1 A simple status achievement model. Numbers along straight arrows are standardized regression coefficients; numbers along curved arrows are correlations; and coefficients in parentheses are nonsignificant (p ≥ .05).
Source: Nielsen (2016).
Blau and Duncan (1967) drew three major substantive conclusions from their empirical analysis. First, direct effects of father’s occupation and father’s education on son’s occupation were small or nonsignificant, suggesting that there is little direct reproduction of social status. In Figure 15.1, for example, these effects are −.020 and .052, respectively, both nonsignificant. Second, there was a strong indirect effect of father’s occupation and father’s education on occupation through education, suggesting that education is a powerful mechanism of social reproduction. In Figure 15.1, the indirect effect of father’s education on respondent’s occupation was .452 × .520 = .235. Third, a large part of the total association between education and occupation was due to the indirect effect of residual factors of education, which are (by construction) independent (p. 306) of social origins, suggesting that achievement was influenced to a considerable extent by unmeasured personal motivations and abilities unrelated to parental socioeconomic status that could be conceptualized as merit. In Figure 15.1, the effect of residual factors on education is .881 (represented as the arrow pointing to respondent’s education and calculated as the square root of 1 − R2 for the regression of education on background measures) and the effect of education on occupation is .520, so the indirect effect of education residuals on occupation is .881 × .520 = .458, a large proportion of the bivariate correlation of .524 between education and occupation.
The third empirical pattern was particularly meaningful to Blau and Duncan (1967) because it appeared to reflect a major role of merit independent of social origins, as opposed to ascription at birth, in the determination of occupational status in modern industrial society. In a later publication, Duncan (1968) was able to estimate an expanded model including measures of mental ability (IQ), a putative component of the education residuals. He found strong effects of IQ on status achievement, which he took as confirming an important role of merit on mobility chances.
Later research found that other psychological traits affected achievement independently of the measured socioeconomic status of the family of origin. Noncognitive behavioral traits associated with achievement outcomes include perseverance, dependability, and consistency (Bowles and Gintis 1976); leadership, study habits, industriousness, and perseverance (Jencks 1979); the Big Five personality traits, especially conscientiousness and emotional stability (Barrick, Mount, and Judge 2001; Judge, Higgins, Thoresen, and Barrick 1999); and self-esteem and locus of control (Heckman and Rubinstein 2001; Heckman, Stixrud, and Urzua 2006).
There is relatively strong evidence that similar cognitive skills and noncognitive behavioral traits are key determinants of both educational and occupational outcomes (p. 307) (Farkas 2003). The considerable role of education residuals in occupational achievement had two implications that would slowly emerge in the literature. First, because unmeasured cognitive and noncognitive traits promoting educational attainment are also likely to enhance occupational success, residuals of these two outcomes are likely to be correlated. If this is the case, the estimated direct effect of education on occupation in the status achievement model is likely to be in part spurious (due to common causes) rather than causal. The causal language commonly used to interpret the associations among status-related outcomes as “effects” would then be misleading.
Second, the major role of individual psychological characteristics included in the education residuals, because they are unrelated by construction to measured family background, raises the question of the origin of these traits. They could be caused by other, unmeasured dimensions of the family environment. But they could also be a function of the individual’s genetic heritage. Duncan (1968) was well aware of the possibility that intelligence (measured by IQ) has a genetic origin. He viewed, in fact, possible influences of genes on ability as strengthening an interpretation of the role of education residuals as indicative of opportunity in an achievement-oriented society. The next section outlines the development of this idea.
Galton and His Legacy
Almost a century before Blau and Duncan (1967), polymath genius Francis Galton (1869) collected detailed genealogies of men who had attained “eminence” in a variety of fields. Galton’s data included judges, statesmen, military commanders, scientists, artists, and even oarsmen and wrestlers. In an insightful analysis well worth reading today, Galton found that among relatives of an eminent man, the numbers of men who are also eminent decline with the degree of relatedness: There were more eminent fathers, sons, and brothers than grandfathers, and the numbers among cousins and more distant relatives were smaller still. This pattern, found in all the fields of eminence, strongly suggested to him that eminence is hereditary. Galton also found a large number of remarkable women in the genealogies, which suggested to him that eminence is transmitted through mothers as well as fathers. He found evidence of assortative mating (homophily, or the tendency of like marrying like), which moved him to impugn the common stereotype of his days that “clever men marry silly women” (p. 324).
Galton (1869) found that in some fields, such as sciences and the arts, there are considerably more eminent men among sons than among brothers or fathers. He attributed this pattern to a stronger environmental effect of the family through example and encouragement—what we would call today “role modeling”—in some fields of achievement than in others.
Galton’s (1869) work predated current understanding of the laws of genetics, which emerged in the early 20th century from the combination of Gregor Mendel’s laws of particulate inheritance governing hereditary transmission of discrete traits (e.g., yellow (p. 308) or green color of peas) with Darwin’s theory of the evolution of continuous traits, that resulted in the modern evolutionary synthesis (Fisher 1918; Wright 1920).
The workhorse of the modern synthesis was the model proposed by Fisher (1918) for the determination of a continuous trait. The model assumes that the trait is polygenic—that is, determined by a large number of independent genetic and environmental causes, each of which has a small effect on the trait. The genetic causes of the trait sum up into an overall additive effect, commonly denoted as A.
In humans, who typically grow up in families, nongenetic influences on a trait such as height or IQ can be partitioned into environmental influences (called shared) that are common to all siblings, such as family socioeconomic status insofar as it affects nutrition levels and intellectual stimulation, and tend to make siblings similar on the trait, and environmental influences (called unshared) that are specific to an individual sibling, such as an accident or infectious disease that affects mental ability of one sibling and not others, and tend to make siblings different from each other. The combined effects of shared and unshared environmental causes are commonly denoted C and E, respectively. The unshared component E includes errors of measurement of the trait.
With this notation, the value y of a continuous trait can be written as y = aA + cC + eE, where the A, C, and E components are standardized (with mean 0 and standard deviation 1). When y is also standardized, and certain assumptions are satisfied, it can be shown that var(y) = a2 + c2 + e2 = 1, where a2, c2, and e2 denote the proportions of the variance in a trait due to additive effects of genes, the shared environment, and the unshared environment, respectively. (Purcell [2002] discusses the assumptions of the model and the consequences of violating them.)
The decomposition of y into the three components is called the ACE model. In humans, the variance components are estimated traditionally using so-called biometric methods based on designs involving relatives, most commonly pairs of identical and fraternal twins, or adopted children and their adoptive and biological parents. More recently developed molecular genetic methodologies provide alternative estimates of heritability using (sometimes very) large samples of unrelated individuals whose DNA has been sequenced (Chabris, Lee, Cesarini, Benjamin, and Laibson 2015). Heritability estimates are then derived from the associations between the presence of a genetic variant for a large number of genes and the measured value of the individual on the trait (Belsky and Israel 2014; Okbay et al. 2016). The classic twins design is based on a comparison of identical (monozygotic or MZ) and fraternal (dizygotic or DZ) twins raised together. MZ twins share all their genes, whereas DZ twins share only approximately half their genes (assuming random mating of parents). It can be shown that the correlations between twins on a trait are therefore rMZ = a2 + c2 for MZ twins and rDZ = ½a2 + c2 for DZ twins, so the variance components can be estimated with Falconer’s formulas as a2 = 2 × (rMZ − rDZ), c2 = 2 × rDZ − rMZ, and e2 = 1 − a2 − c2 (Falconer and Mackay 1996).
For example, in a sample of young adult twins in the United States, correlations for occupational education (average education of incumbents of an occupation, a measure of occupational status) are .547 for MZ twins and .258 for DZ twins (data discussed in Roos and Nielsen 2015). Applying Falconer’s formulas, a2 = 2 × (.547 − .258) = .578; (p. 309) c2 = 2 × .258 − .547 = −.031; and e2 = 1 − .578 − (−.031) = .453 (the small negative value for c2 is not statistically different from zero). Thus, sources of influences on occupational education of young adults appear largely genetic (58%) and nonshared environmental (45%), with little contribution of shared family influences.
Components of the variance in a trait have important substantive meanings in the context of social stratification and mobility. The additive genetic component a2 is called the heritability of the trait. It represents the proportion of the population variance in a trait that is contributed by all genetic sources of influence. As suggested by Duncan (1968), a larger role of genes in a trait measuring socioeconomic achievement indicates greater opportunity for individuals to achieve their native potential, high or low, and a lesser role of ascription based on status of the family of origin. Higher values of a2 thus indicate a more achievement-oriented society (Guo and Stearns 2002; Heath et al. 1985; Nielsen 2006).
The shared environment c2 represents the effects on a status-related outcome of all characteristics of the family or embedding social environment that tend to make siblings similar on the outcome; it includes effects of social class and of other factors such as ethnicity or the quality of local schools that tend to vary more between than within families. The shared environment component can be thought of as measuring the strength of ascription, or the degree to which status is socially reproduced (Conley 2008). From a policy-oriented viewpoint, c (the square root of c2) measures the potential effect on the trait (expressed in standard deviation units) of raising the quality of the family environment by one standard deviation. c2 thus reflects the extent of improvement on the trait achievable by an intervention modifying the environment within the existing range of environmental variation (Behrman and Taubman 1989; Rowe 1994; Taubman 1976). Highlighting in the previous sentence points to the fact that a low c2 should not be interpreted as a limit on environmental malleability of an outcome, because c2 does not include potential effects of environmental manipulations (e.g., effective but undiscovered or underutilized interventions) that are not currently contributing to population variation. In contrast with plant and animal breeding, where a2 is crucial in predicting success of artificial selection and the environment is largely under experimental control, social scientists also have a major interest in the size of c2, representing as it does a measure of social closure.
The unshared environment e2 represents the effects of all environmental influences that tend to make siblings different from each other, such as an accident, disease, parental preference, or an encounter with an inspiring role model that affects one sibling but not the other(s). e2 includes (but is not limited to) measurement error.
Genes and Environments in Status-Related Traits
I use the expression status-related trait or outcome to mean any measure, continuous or categorical, that either predicts or reflects status achievement of individuals. Over the (p. 310) life course, therefore, status-related outcomes range from measurement of IQ in childhood, through high school graduation and completion of formal education, to occupation, income, and subjective social status in adulthood.
Behavioral and social scientists have accumulated considerable research applying the ACE model to various human traits. Turkheimer (2000) summarized the results of that research with his “three laws of behavior genetics” (p. 160):
1. All human behavioral traits are heritable.
2. The effect of being raised in the same family is smaller than the effect of genes.
3. A substantial portion of the variation in complex human behavioral traits is not accounted for by the effects of genes or families.
In terms of the ACE decomposition, a2 ≠ 0, c2 < a2, and e2 ≫ 0 (see also Freese 2008). The three laws have been spectacularly confirmed in a massive meta-analysis of 2,748 twin studies of human traits published between 1958 and 2012 (Polderman et al. 2015).
Many status-related outcomes have been found to be affected by genes (Adkins and Guo 2008). Among predictors of educational and occupational achievements, intelligence (measured as IQ) has received the most attention. Abundant research based on twin or adoption studies has found substantial heritability of IQ. For example, the meta-analysis of a large number of studies by Devlin, Daniels, and Roeder (1997) found a2s in the 47–68% range (see also Plomin and Spinath 2004). However, studies summarized by these authors are dominated by studies of children because it is much easier to recruit and test samples of school-aged twins than samples of adults. More recent studies using molecular genetic technology have yielded similarly strong estimates of heritability and confirmed that intelligence is a highly polygenic trait (Davies et al. 2011; Martin et al. 2011).
Studies have documented a systematic pattern of change in the relative importance of genes and the shared environment for IQ during development, with heritability being contingent on age. A typical pattern is that exhibited by samples of Dutch twins varying in age from 5 to 50 years. At age 5 years, genes account for 20% and the shared environment for 55% of the variance in IQ. There is then a steady increase in the role of genes and decline in the role of the shared environment so that by age 12 genes account for more than 80% of the variance and the role of the shared environment dwindles to zero. From then on, heritability of IQ remains high (Bouchard and McGue 2003). The age dependence of the heritability of IQ implies that estimates from older studies of IQ largely based on children, such as the ones summarized by Devlin et al. (1997), were biased toward zero.
Among noncognitive traits potentially related to socioeconomic achievement, personality traits conscientiousness and emotional stability (absence of neuroticism) have shown consistent associations with job performance (Barrick et al. 2001). Bouchard and McGue (2003, p. 21, Table 5) report the average heritability in four recent studies to be 49% for conscientiousness and 48% for neuroticism. The role of the shared environment for these traits is not significantly different from zero. Adkins and Guo (2008) cite (p. 311) literature suggesting that mental and physical health, both predictors of achievement, have a strong genetic substrate. For example, they report heritabilities on the order of 37% for depression, 81% for schizophrenia, and approximately 30% for overall longevity.
Among educational outcomes, educational attainment, measured as highest degree earned or years of completed schooling, has been studied most extensively due to ease of measurement and the view that it serves as a proxy for intelligence (although an imperfect one, as the education–IQ correlation of only .56 in Table 15.2 suggests). A meta-analysis of the international literature found that the mean contribution of genes to educational attainment in twin studies is 40% and that of the shared environment 36% (Branigan, McCallum, and Freese 2013), a pattern confirmed in the meta-analysis of all human traits by Polderman et al. (2015), who found similarly high effects of the shared environment, besides education, only for traits related to conduct disorders and religion and spirituality. The important role of the shared environment in educational attainment is therefore unusual and cannot be readily explained by the effect of cognitive and noncognitive abilities, which may be “ingredients” of educational attainment, because the latter traits themselves have near zero shared environmental variance in adults (Bouchard and McGue 2003). Nielsen and Roos (2015) find a similar pattern in a large US sample of variously related siblings, and they suggest that the large c2 may be the result of a persistent role of family financial resources in educational attainment. Freese and Jao (2017) discuss alternative explanations of the phenomenon.
For income, Björklund, Jäntti, and Solon (2005) find moderate heritabilities in the range of 20–30% depending on the model, with shared environmental component typically less than 10%. These results suggest heritability of adult financial success is non-zero but low relative to other status-related outcomes.
Roos and Nielsen (2015) estimate the ACE model for 15 status-related outcomes over the life course for the same large sample of adolescent siblings first interviewed during adolescence and followed up until young adulthood (average age ~28 years). Results for outcomes roughly arranged in order of occurrence in the life course are shown in Table 15.1 and Figure 15.2. Estimated heritabilities range from small (9%) for home ownership to a high of 60% for college graduation. The shared environment is substantial for some outcomes, such as educational attainment (29%), college plans (23%), verbal IQ (22%), and some college (21%), but zero for personal earnings. In agreement with Turkheimer’s (2000) second law, the effect of genes (a2) is invariably higher than the effect of the family (c2).
Some of the more salient findings pertain to status-related outcomes that are more rarely analyzed in a behavior genetic perspective. Heritability of personal earnings (34%) is at the high end for that outcome measured in a single year (compare with Björklund et al. 2005), and the absence of shared environment effect (c2 = 0) may be surprising. Two measures of occupational prestige (occupational education and occupational wages) that are of special interest to sociologists show moderately high heritabilities (33% and 25%, respectively) but low effects of the shared environment (13% and 3%, respectively), especially in comparison with educational outcomes. The moderate heritability (31%) but low impact of the shared environment (2%) for subjective social (p. 312) status may be surprising because one would expect any cultural mechanism of intergenerational transmission of status to affect this subjective dimension most strongly.
Table 15.1 Variance Components Decomposition of Status-Related Outcomes
Outcome
a2
c2
e2
lcpos
Verbal IQ
0.462
0.315
0.223
1
GPA
0.464
0.179
0.357
2
College plans
0.318
0.255
0.427
3
HS graduation
0.249
0.158
0.593
4
Some college
0.291
0.212
0.497
5
College graduation
0.611
0.138
0.251
6
Graduate school
0.368
0.097
0.535
7
Educational attainment
0.355
0.286
0.359
8
Occupational education
0.349
0.134
0.517
9
Occupational wages
0.281
0.030
0.689
10
Personal earnings
0.362
0
0.638
11
Household income
0.266
0.198
0.536
12
Household assets
0.160
0.127
0.713
13
Home ownership
0.071
0.175
0.754
14
Subjective social status
0.317
0.028
0.655
15
HS, high school; lcpos, approximate life course position.
Source: Data from Roos and Nielsen (2015).
Roos and Nielsen (2015) found systematic patterns in the genetic architecture of successive status-related outcomes over the life course that affected the overall impact of the family of origin on the outcome, from both genetic and environmental sources. First, both a2 and c2 tended to decrease, and e2 increase, with the position of an outcome later in the life course, resulting in a trend of declining family resemblance (which the authors measured as ½a2 + c2, the resemblance between ordinary siblings predicted by the ACE model, which they call familiality). The trend suggests that as individuals grow older, status-related outcomes become less affected by family influences (both genetic and shared environmental) and increasingly reflect idiosyncratic (nonshared) individual characteristics.
Genes and Status AchievementClick to view larger
Figure 15.2 Variance components decomposition of status-related outcomes.
Source: Data from Roos and Nielsen (2015).
Second, c2 represented a significantly greater proportion of family resemblance for outcomes related to the household and shared with a spouse or household partner (household income, household assets, and home ownership)—although family resemblance is typically low for these outcomes—than for individually defined outcomes such (p. 313) as personal earnings. This pattern may indicate a recrudescence of nongenetic shared influences of the family of origin on outcomes associated with (respondent’s own) family formation. See Roos and Nielsen (2015) for further discussion.
Gene–Environment Interactions
Scarr-Salapatek (1971) conjectured that the relative roles of genetic and shared environmental sources of intelligence and academic achievement would vary according to socioeconomic status of the family. In advantaged environments, genetic potential can be fully expressed so that heritability of mental ability will be high and the effect of the shared environment low. In deprived environments, expression of genes will be inhibited so that heritability will be lower and the impact of the shared environment greater. The conjecture has been called the Scarr–Rowe hypothesis of gene × socioeconomic status (SES) interaction (Tucker-Drobs and Bates 2016). In general interactions between genes and environment of this type are called G×E (Shanahan and Hofer 2005).
(p. 314) Significant moderation of genetic expression as a function of environment quality for intelligence has been documented in US studies, such as those by Rowe, Jacobsen, and Van den Oord (1999); Guo and Stearns (2002); and Turkheimer, Haley, Waldron, D’Onofrio, and Gottesman (2003). The latter study, for example, finds that for intelligence in young children in low SES environments, heritability is only 10%, with a strong impact of the shared environment (58%). In high SES environments, the pattern is reversed, with heritability at 72% and the shared environment at 15%.
Results such as those of Turkheimer et al. (2003) have captured the imagination of social scientists because they appear to reaffirm a central role for the social environment as opposed to biological endowment (Nisbett 2009). In the case of intelligence, however, the systematic review by Tucker-Drob and Bates (2016) finds mixed support for the Scarr–Rowe hypothesis. The authors find that low SES was associated with attenuated genetic influences on intelligence in studies conducted in the United States, but that the interaction was not significantly different from zero in non-US studies (conducted in western Europe and Australia). Tucker-Drob and Bates believe that G×E may be higher in the United States because of a more inegalitarian access to economic resources in the society. Even in US studies, however, the size of the interaction is typically less than that found by Turkheimer et al.
The mixed support for G×E in the case of IQ should be related to the high heritability of IQ in adulthood, which ensures that there is little room for any environmental effect. It does not mean that the G×E mechanism is not at work for status-attainment outcomes other than IQ, such as educational attainment, that have a substantial shared environment component in adulthood. Further studies might illuminate this issue.
The Scarr–Rowe hypothesis assumes that heritability increases monotonically as a function of social status. The simplest model is that heritability increases linearly with SES. A variant model is that starting from the most deprived environment, heritability at first increases rapidly as conditions improve and then more slowly once environmental conditions are above a “humane threshold” bounding the normal species range (Scarr 1992). Sociological literature suggests alternative hypotheses to the Scarr–Rowe conjecture of monotonically increasing gene expression with environment quality. In his conception of social mobility and the circulation of elites, Pareto (1909/1971) conjectured that the effect of innate qualities on success traces an inverted-U shape with family SES: In the lowest stratum of SES, environmental conditions are so impoverished that individuals are held back irrespective of innate talent; in the highest SES stratum, resources are so readily available that even individuals with low levels of ability are protected from downward mobility. It is in the middle stratum of SES that the effect of innate qualities is strongest, as resources are sufficiently abundant to allow talented individuals to rise but not sufficient to prevent downward mobility of the less able. Pareto’s model thus predicts that heritability is highest in the middle stratum and lower in both bottom and top strata, a curvilinear relationship of a2 with environmental resources.
Still another hypothesis is implied by Saunders (2010), who found that in modern Great Britain the only deviation from perfect meritocratic mobility was a tendency for those born in the upper occupational classes to experience downward mobility at a lower rate than that expected in a fully meritocratic society. The rate of upward mobility (p. 315) from lower to upper classes, however, was consistent with the meritocratic hypothesis. Saunders’ interpretation of the finding is that being born a member of the upper stratum has a greater effect on achieved status (in preventing downward mobility) than has being born to the working class (in preventing upward mobility). In terms of the ACE, this would imply that the shared environment c2 increases monotonically with family SES and, conversely, that the effect (a2) of genes on status achievement declines apace. Nielsen (2016) suggests that the predicted negative relationship between heritability of individual socioeconomic success and family SES might be called the Saunders or reverse Scarr–Rowe hypothesis. Studies of G×E for educational attainment would seem a promising strategy for future research because this adult outcome is characterized by a substantial shared environment component.
The theoretical significance of G×E has been greatly expanded in discussions by Adkins and Guo (2008) and Adkins and Vaisey (2009). These authors contend that parameters of the ACE models are macro-sociological concepts characterizing the nature of the stratification system in a society, with average heritability in a society measuring opportunity to reach one’s potential and, conversely, the shared environment representing social ascription and social closure. They conjecture that more open and egalitarian societies will exhibit greater heritability and smaller effects of the shared environment.
Heath et al. (1985) illustrate the way the ACE decomposition of the variance in status provides macrosocial indicators of fluidity of the social structure of a society. The authors compared resemblances in educational attainment between DZ and MZ twins in a large Norwegian sample to estimate the components of the ACE model for different birth cohorts, separately by sex. Comparing older (born 1915–1939) and younger (born 1950–1960) cohorts, they found that, for males, the effect of genes on educational attainment increased (from 18% to 76%) and the role of the shared environment correspondingly decreased (from 68% to 9%). Heath et al. interpret these changes in the ACE parameters as resulting from liberal policy reforms in Norway that made access to education more open.
For females during the same period, the role of genes in schooling also increased (from 28% to 46%), and the role of the shared environment decreased (from 61% to 43%), but not as much as for males. Heath et al. (1985) conclude that equality of opportunity, measured as the relative size of the genetic component, did not improve to the same extent for females as for males so that the role of family privilege in educational attainment, measured as the shared environment component, remained correspondingly greater for females (see also Branigan et al. 2013). These interpretations are consistent with the theoretical discussions of Adkins and Guo (2008) and Adkins and Vaisey (2009).
Interrelationships of Status-Related Outcomes
It has been known since at least the beginning of status achievement research that status-related outcomes over the life course are correlated with each other and with (p. 316) characteristics of the family background. Average correlations from a meta-analysis of a large number of independent studies by Strenze (2007, p. 412, Table 1, column p) are shown in Table 15.2. Average correlations of the three principal status outcomes in adulthood (measured after age 29 years) with intelligence (measured before age 19 years) are .56 for education, .45 for occupation, and .23 for income, suggesting that intelligence predicts income less well than it does education or occupation. A similar pattern holds for academic performance. Adult outcomes are explained almost equally well by background characteristics, especially when combined into an SES index, which is correlated .55 with education, .38 with occupation, and .09 with income. A general pattern is that income is more weakly associated with predictors than other outcomes.
Table 15.2 Correlations Between Three Principal Measures of Status and Predictorsa
Measure of Status
Predictor
Education
Occupation
Income
Intelligence (all studies)
.56
.43
.20
Intelligence (best studies)
.56
.45
.23
Father’s education
.50
.31
.17
Mother’s education
.48
.27
.13
Father’s occupation
.42
.35
.19
Parental income
.39
.27
.20
SES index
.55
.38
.18
Academic performance
.53
.37
.09
(a) Correlations shown are sample size weighted averages corrected for unreliability and dichotomization.
Source: Data from Strenze (2007, p. 412, Table 1, column p).
The classic status achievement model interprets associations among status-related outcomes in a causal perspective. Rowe (1994) has noted that causal interpretations are likely to be spurious because successive outcomes (e.g., education and occupation) may be associated due to unmeasured common causes such as ability and ambition, including genetic ones, rather than causally (Eckland 1979). As an alternative account, Rowe (1994) proposed a multivariate extension of the ACE model in which associations among outcomes are due not to direct causal effects but, rather, to effects of latent (i.e., unmeasured) environmental and genetic sources that may themselves be correlated (see also Petrill and Wilkerson 2000). Structural equation modeling (SEM) methodology permits estimating the proportions of the zero-order association (correlation) between any two status-related outcomes that are due to genetic, shared-environmental and unshared-environmental sources, respectively.
(p. 317) Nielsen (2006, 2016) describes a multivariate ACE model of the interrelationships among verbal IQ (VIQ), high school grades (GPA), and college plans (CPL) estimated from a large sample of adolescent sibling pairs living in the same household and related to different degrees (MZ twins, DZ twins, full siblings, half siblings, cousins, and nonrelated siblings). SEM methodology was used to compare the fit of a variety of models and test hypotheses concerning the structure of latent factors responsible for the observed relationships. The analytical strategy consisted in initially fitting a maximal model with as many latent A, C, and E factors as there are outcomes. These latent factors were assumed uncorrelated among each other and linked to the observed outcomes in a triangular pattern called a Cholesky factorization. The original model was then simplified through successive steps testing structural hypotheses such as whether several outcomes can be explained by the same latent factor or whether factors corresponding to different outcomes are uncorrelated. The model was finally rotated so that each outcome is a function of only one A, C, and E factor, and factors for a given latent source may be correlated. Among the findings were the following:
1. Each of the outcomes was substantially affected by genes (a2 was 53% for VIQ, 67% for GPA, and 59% for CPL).
2. Only VIQ was substantially affected by shared-environment sources (c2 was 14% for VIQ, 0 for GPA, and 3% for CPL).
3. There were moderate correlations between genes affecting verbal IQ and GPA (.43) and GPA and college plans (.55); the correlation was lower for verbal IQ and college plans (.26), suggesting that genes affecting these two traits overlap only partially. The hypothesis that there is a single set of “ability genes” affecting all three outcomes was statistically rejected.
4. The hypothesis that there is a single “privilege” dimension of the shared environment affecting all three outcomes could not be rejected.
5. The associations among the three outcomes were largely the results of common genetic sources, as the proportion of the correlation due to genes was 70% for VIQ–CPL and 100% for both VIQ–GPA and GPA–CPL, with little contribution of either shared or nonshared environments.
Reports of large contributions of genetic sources to the association between status-related outcomes have become commonplace (Calvin et al. 2012; Marioni et al. 2014). Although much work remains to be done in that area, such findings contribute to the mounting evidence that the ostensibly “causal” links in the status achievement model are generated to a substantial extent by common genetic sources rather than environmental factors or direct causation. These findings suggest that much of career continuity (i.e., the tendency of most careers to trace a monotonic status trend, devoid of wild swings) has genetic origins.
One important limitation of the multivariate ACE is that it assumes associations among outcomes are entirely explained by latent factors. It is not possible to add direct causal links to the full ACE model (represented by its Cholesky factorization) because (p. 318) they would not be estimable. This is problematic in situations in which there are substantive reasons to expect a direct causal influence of one outcome on another. For example, in the model described previously, it would be reasonable to think that by providing the student with an external clue of his or her academic potential, GPA affects college plans directly and independently of abilities (which would perhaps disturbingly imply that deliberate manipulation of GPA by school officials might enhance or discourage college ambitions). As an important instance, much research in economics has focused on the effect of education on earnings because economists are interested in estimating the financial return to an additional year of education net of individual endowments (Ashenfelter and Krueger 1994; Behrman and Rosenzweig 1999; Miller, Mulvey, and Martin 2006).
Kohler, Behrman, and Schnittker (2011) have clarified the conditions under which causal effects of the type assumed in the classic status achievement model can be incorporated into the multivariate ACE. The authors show that a direct causal link can be estimated if sufficiently stringent assumptions are made. In the case of GPA → CPL, the most natural extra constraint would be that the unshared E components for GPA and CPL are uncorrelated—that is, idiosyncratic factors that affect the GPA of an individual sibling are uncorrelated with idiosyncratic factors affecting CPL for that sibling. If that assumption holds, the direct effect GPA → CPL can be estimated. Kohler et al. show convincing examples of application of the strategy, but the question whether it is plausible to assume uncorrelated unshared components for two status-related outcomes will likely arise in specific applications of the method.
Mobility Implications of the Role of Genes
The preceding discussion suggests that the traits that enhance socioeconomic success, such as intelligence and personality traits, are strongly influenced by genes. Thus, in a society with any degree of openness, individuals with favorable genes for these traits will tend to gravitate toward high-status positions so that social class of destination is determined in part by genes. This is the gist of Herrnstein’s (1973) syllogism, quoted by Rowe (1994, p. 141):
1. If differences in mental abilities are inherited, and
2. If success requires those abilities, and
3. If earnings and prestige depend on success,
4. Then social standing (which depends on earnings and prestige) will be based to some extent on inherited differences among people.
The syllogism can be extended in an obvious way from mental abilities to include the role of noncognitive traits that affect success.
(p. 319) The idea that social class might have a genetic basis generated strong negative reactions among the public and in academia. Herrnstein’s (1973) syllogism, and its later formulation in Herrnstein and Murray (1994), seemed to predict a dismal future in which society would be partitioned into residentially segregated and quasi-hereditary castes based on genes, a social arrangement all the more detestable because it could conceivably be morally justified on meritocratic grounds. However, Herrnstein’s scenario may have downplayed the potential role of a powerful mechanism of reproduction in preventing the hardening of social classes into ability-based hereditary castes: regression to the mean.
Human beings, as sexually reproducing organisms, do not produce clones of themselves. Instead, a couple produces offspring with whom parents each share a random 50% of their genes. For a continuous trait affected by many genes, each of which has a small effect (a “complex trait”), the parent–child correlation is rPC = ½a2 + c2—that is, half the heritability plus the shared environmental component—and the same correlation holds for other first-degree relatives such as ordinary (full) siblings. Thus, even for a perfectly heritable trait (with a2 = 1 and c2 = 0), the parent–child correlation due to genetic inheritance cannot be larger than .5.
The high heritabilities found for many traits may seem implausible or even scary because they seem to imply a strong, overwhelming genetic basis of “family resemblance.” Sociologists, like most people, base their intuition of family resemblance on the similarity of parents and offspring, and the similarity of ordinary siblings, with respect to visible outcomes such as college graduation. They know from their personal experience that children from a given family tend to be alike in obtaining college degrees. It seems natural to interpret the similarity in outcomes as resulting from similar expectations for siblings and their exposure to similar resources, role models, and encouragements. Invocation of a strong role of genes in college completion seems unnecessary and difficult to believe.
However, the shared environment plays a greater role in family resemblance than may at first appear. This is because for first-degree relatives such as ordinary siblings, who are genetically related at only 50%, the correlation on the trait involves the full contribution of the shared environment c2 but only half of the impact of genes denoted as a2. Therefore, the resemblance between ordinary siblings reflects the full contribution of the shared environment to trait variance but only half of the contribution of genes.
A concrete example is provided by the meta-analytic estimates of a2 = .40 and c2 = .36 for the variance components of educational attainment found by Branigan et al. (2013). In a population characterized by these parameters, the predicted correlation between ordinary siblings is rsibs = ½ × .40 + .36 = .56. Thus, the shared environment plays the major role in the resemblance of ordinary siblings (.36/.56 = 64%), and genetic factors play a relatively minor one (.20/.56 = 36%). An alternative interpretation is that shared environmental characteristics of the family induce a correlation of .36 between the educational attainments of siblings, while genetic factors induce an added .20. The role of genes, viewed in this way, might seem more plausible (and relatively nonthreatening), even for a social scientist convinced of the predominant importance of environmental (p. 320) factors. Genetic sources of variation in a trait are fully reflected only in the resemblance of identical twins, which, of course, can be astonishing. Most people, however, do not know more than a few pairs of identical twins—too few to ground their intuition about family resemblance.
The imperfect correlation between parent and child in a trait produces the phenomenon of regression to the mean, another discovery of Francis Galton (1886). Taking height (a highly heritable trait) as an example, regression to the mean implies that the height of offspring of tall parents will exhibit considerable scatter, with a mean in between the average height of the parents and the average height of the population. Conversely, the offspring of short parents will be taller on average than the parents but shorter than the population average.
The extent to which the average trait in the parents regresses to the mean in the offspring is a function of the heritability of the trait. Specifically, the offspring average on the trait is equal to the population mean plus the deviation of the parental average (called the midparent) from the population mean times the heritability (Falconer and Mckay 1996).1 Taking intelligence as an example, suppose an exceptionally intelligent couple has an average IQ of 130. Because IQ is distributed with mean 100 and standard deviation of 15, the midparent is 2 standard deviations above the mean, corresponding to the 98th percentile of the IQ scale. Assuming heritability is .6, the expected IQ of the offspring of the couple is 100 + .6 × 30 = 118, which corresponds to the 88th percentile of IQ, well above average but less exceptional than that of the parents. Actual IQ scores for the offspring will vary considerably around this expected value. Although highly successful couples with a disappointing child may discover regression to the mean with consternation, it is important to keep in mind that the phenomenon is symmetrical so that a below average couple will produce offspring with IQ higher than theirs on average.
If people chose their mate randomly, irrespective of the status-related traits of spouses, the average midparent over all couples would be equal to the population mean, and the average trait in the offspring would regress to the population mean in each generation. However, this does not happen due to assortative mating, or homophily, the tendency of spouses to choose partners similar to themselves. Homophily, measured as the correlation of the trait between spouses, tends to be high for status-related traits, such as intelligence (~.4) and education (~.6), in comparison with physical traits such as height and weight (~.2) (Plomin and Deary 2015). Assortative mating of parents on a trait results in an increase in the additive genetic variance of the trait in the offspring, relative to what it would be under random mating. The increase in variance occurs each generation until an asymptote is reached. For example, Jensen (1972, pp. 106–108), assuming heritability of .8 and spousal correlation of .6, calculates that without assortative mating, the standard deviation of IQ would decline from 15 to 12.9.
A crucial implication of the regression to the mean of a trait that promotes socioeconomic success is that biological inheritance by itself will produce considerable intergenerational mobility in the trait. Sexual reproduction ensures that there is a natural reshuffling of genes in each generation so that many individuals in the higher classes are born with abilities lower than those of their parents, and many in the lower classes are (p. 321) born with abilities higher than those of their parents. To the extent that socioeconomic achievement in a society is based on merit, biological inheritance will thus naturally induce a certain amount of social mobility, both downward and upward. Rowe (1994, p. 142) estimates that in industrialized societies, in each generation 30% of individuals move up from the class of their parents, 30% move down, and the rest remain in the class of origin. This conception corresponds to (with respect to political power) the “circulation of elites” of Pareto (1909/1971, 1917–1919/1965) and (with respect to intelligence and social status) approaches such as hinted at by Duncan (1968) and explicitly proposed by Burt (1961), Herrnstein (1973), Marks (2014), and Saunders (2010).
Ascriptive mechanisms can slow down the natural rate of intergenerational mobility caused by genetic regression to the mean. Although there is probably some degree of mobility in all societies, ascriptive mechanisms by which offspring of higher strata are protected from downward mobility while upward mobility of offspring of the lower strata is stunted can reduce overall mobility to near zero, as in extreme cases such as endogamous castes of traditional India or hereditary aristocracies of medieval Europe (Scarr-Salapatek 1971). The relative strength of ascriptive mechanisms related to unequal quality of rearing environments or deliberate social closure can be estimated at the societal level as the size of the shared environment component c2 (Conley 2008; Nielsen 2006).
The observed association between parent and child on a status-related outcome such as occupation or income has traditionally been used in social mobility research as a measure of social closure, or inverse measure of social fluidity, with a strong association implying a closed social structure in which ascription plays a paramount role and a weak association implying an open structure with more opportunities for achievement (Ganzeboom, Luijkx, and Treiman 1989; Solon 2008).
The quantitative genetic decomposition of the parent–offspring correlation together with the substantial role of genes documented for many status-related outcomes imply that intergenerational associations of status-related traits are ambiguous because a strong association may be due to high heritability of the trait—in which case it actually reflects high opportunities for achievement in a more open society—or to a high value of the shared family environment—in which case it does indeed reflect greater social closure (Eckland 1967). This implies that variance components a2 and c2 from the ACE decomposition of the trait variance, considered separately, are better measures of the degrees of opportunity and ascription, respectively, than the overall intergenerational association (Heath et al. 1985; Jencks and Tach 2006; Nielsen 2006).
Conclusion
Genetic inheritance play a central role in all aspects of social stratification and mobility.
Most status-related outcomes—individual characteristics predicting or reflecting socioeconomic success—are substantially affected by genes. Some outcomes, notably (p. 322) educational attainment, are also appreciably affected by circumstances of the family of origin (shared environment). There is evidence that the extent to which genes affect an outcome depends on the social context, particularly the availability of resources (gene–environment interaction).
Genetic inheritance plays an important role in career continuity, the serial correlation of status outcomes at successive stages of the life course, reflected in findings that associations among outcomes are often predominantly or entirely explained by effects of common genes on the outcomes but only in smaller measure by shared environmental effects.
Because parents individually transmit only one-half of their genes to the offspring, the mechanism of genetic inheritance ensures that levels of status-relevant traits of parents and offspring are imperfectly correlated. There is a regression to the mean in which the expected offspring level of a trait is in between the average levels of parents and the population mean.
In a society in which status achievement is related to any appreciable extent to individual qualities (“merit”), genetic inheritance will thus produce a degree of intergenerational mobility as individuals with low qualities born to higher strata will tend to move downward in status, whereas individuals with high qualities born to lower strata will tend to move upward. These movements tend to generate (and restore) an association between status-related qualities and social strata.
Components of the variance in a status-related outcome representing the relative effects of genes (heritability) versus the family environment of origin (shared environment) are direct measures of, respectively, the degree of opportunity versus the weight of social ascription facing individuals in a society. These measures are to be preferred to measures of overall intergenerational association that are ambiguous because they confound the two components.
Research is ongoing on all these issues, and much remains to be learned.
References
Adkins, Daniel E. and Guang Guo. 2008. “Societal Development and the Shifting Influence of the Genome on Status Attainment.” Research in Social Stratification and Mobility 26: 3 (September): 235–255.Find this resource:
Adkins, Daniel E. and Stephen Vaisey. 2009. “Toward a Unified Stratification Theory: Structure, Genome, and Status Across Human Societies.” Sociological Theory 27: 2 (June): 99–121.Find this resource:
(p. 323) Ashenfelter, Orley and Alan Krueger. 1994. “Estimates of the Economic Return to Schooling from a New Sample of Twins.” American Economic Review 84: 5: 1157–1173.Find this resource:
Barrick, Murray R., Michael K. Mount, and Timothy A. Judge. 2001. “Personality and Performance at the Beginning of the New Millennium: What Do We Know and Where Do We Go Next?” Personality and Performance 9: 1/2 (March–June): 9–30.Find this resource:
Behrman, Jere R. and M. R. Rosenzweig. 1999. “‘Ability’ Biases in Schooling Returns and Twins: A Test and New Estimates.” Economics of Education Review 18: 2 (April): 159–167.Find this resource:
Behrman, Jere R. and Paul Taubman. 1989. “Is Schooling Mostly in the Genes? Nature–Nurture Decomposition Using Data on Relatives.” Journal of Political Economy 97: 6: 1425–1446.Find this resource:
Belsky, Daniel W. and Salomon Israel. 2014. “Integrating Genetics and Social Science: Genetic Risk Scores.” Biodemography and Social Biology 60: 2: 137–155.Find this resource:
Björklund, Anders, Markus Jäntti, and Gary Solon. 2005. “Influences of Nature and Nurture on Earnings Variation: Preliminary Results from a Study of Various Sibling Types in Sweden.” Pp. 145–164 in Unequal Chances: Family Background and Economic Success, edited by Samuel Bowles, Herbert Gintis, and Melissa Osborne Groves. New York, NY: Russell Sage.Find this resource:
Blau, Peter M. and Otis Dudley Duncan. 1967. The American Occupational Structure. New York, NY: Wiley.Find this resource:
Bouchard, Thomas J., Jr. and Matt McGue. 2003. “Genetic and Environmental Influences on Human Psychological Differences.” Journal of Neurobiology 54: 1 (January): 4–45.Find this resource:
Bowles, Samuel and Herbert Gintis. 1976. Schooling in Capitalist America. New York, NY: Basic Books.Find this resource:
Branigan, Amelia R., Kenneth J. McCallum, and Jeremy Freese. 2013. “Variation in the Heritability of Educational Attainment: An International Meta-Analysis.” Social Forces 92: 1 (September): 109–140.Find this resource:
Burt, Cyril. 1961. “Intelligence and Social Mobility.” British Journal of Statistical Psychology 14 (May): 3–24.Find this resource:
Calvin, Catherine M., Ian J. Deary, Dinand Webbink, Pauline Smith, Cres Fernandes, Sang Hong Lee, et al. 2012. “Multivariate Genetic Analyses of Cognition and Academic Achievement from Two Population Samples of 174,000 and 166,000 School Children.” Behavior Genetics 42: 5 (September): 699–710.Find this resource:
Chabris, Christopher F., James J. Lee, David Cesarini, Daniel J. Benjamin, and David I. Laibson. 2015. “The Fourth Law of Behavior genetics.” Current Directions in Psychological Science 44: 4 (August): 304–312.Find this resource:
Conley, Dalton. 2008. “What Do Low (or High) Sibling Correlations Tell Us About Social Ascription?” Pp. 596–609 in Social Stratification (3rd ed.), edited by David B. Grusky. Boulder, CO: Westview.Find this resource:
Davies, G., A. Tenesa, A. Payton, J. Yang, S. E. Harris, D. Liewald, et al. 2011. “Genome-Wide Association Studies Establish That Human Intelligence Is Highly Heritable and Polygenic.” Molecular Psychiatry 16: 996–1005. doi:10.1038/mp.2011.85; published online 9 August 2011.Find this resource:
Devlin, B., Daniels, M., and Roeder, K. 1997. “The Heritability of IQ.” Nature 388: 468–471.Find this resource:
Duncan, Otis Dudley. 1968. “Ability and Achievement.” Eugenics Quarterly 15: 1: 1–11.Find this resource:
Eckland, Bruce K. 1967. “Genetics and Sociology: A Reconsideration.” American Sociological Review 32: 2 (April): 173–194.Find this resource:
Eckland, Bruce K. 1979. “Genetic Variance in the SES-IQ Correlation.” Sociology of Education 52: 3: 191–196.Find this resource:
Falconer, D. S. and Trudy F. C. Mackay. 1996. Introduction to Quantitative Genetics (4th ed.). Harlow, UK: Pearson.Find this resource:
(p. 324) Farkas, George. 2003. “Cognitive Skills and Noncognitive Traits and Behaviors in Stratification Processes.” Annual Review of Sociology 29: 541–562.Find this resource:
Fisher, Ronald A. 1918. “The Correlation Between Relatives on the Supposition of Mendelian Inheritance.” Transactions of the Royal Society of Edinburgh 52, Part 2: 399–433.Find this resource:
Freese, Jeremy. 2008. “Genetics and the Social Science Explanation of Individual Outcomes.” American Journal of Sociology 114 Supplement: S1–S35.Find this resource:
Freese, Jeremy and Yu-Han Jao. 2017. “Shared Environment Estimates for Educational Attainment: A Puzzle and Possible Solutions.” Journal of Personality 85: 1 (February): 79–89.Find this resource:
Galton, Francis. 1869. Hereditary Genius. London, UK: Macmillan.Find this resource:
Galton, Francis. 1886. “Regression Towards Mediocrity in Hereditary Stature.” Journal of the Anthropological Institute of Great Britain and Ireland 15: 246–263.Find this resource:
Ganzeboom, H. B. G., Luijkx, R., and Treiman, D. J. 1989. “Intergenerational Class Mobility in Comparative Perspective.” Research in Social Stratification and Mobility 8: 3–84.Find this resource:
Guo, Guang, and Elizabeth Stearns. 2002. “The Social Influences on the Realization of Genetic Potential for Intellectual Development.” Social Forces 80: 881–910.Find this resource:
Heath, A. C., K. Berg, L. J. Eaves, M. H. Solaas, L. A. Corey, J. Sundet, et al. 1985. “Education Policy and the Heritability of Educational Attainment.” Nature 314 (6013): 734–736.Find this resource:
Heckman, James J. and Yona Rubinstein. 2001. “The Importance of Noncognitive Skills: Lessons from the GED Testing Program.” American Economic Review 91: 2 (May): 145–149.Find this resource:
Heckman, James J., 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 (July): 411–482.Find this resource:
Herrnstein, Richard J. 1973. I.Q. in the Meritocracy. Boston, MA: Little, Brown.Find this resource:
Herrnstein, Richard J. and Charles Murray. 1994. The Bell Curve: Intelligence and Class Structure in American Life. New York, NY: Free Press.Find this resource:
Jencks, Christopher. 1979. Who Gets Ahead? The Determinants of Economic Success in America. New York, NY: Basic Books.Find this resource:
Jencks, Christopher and Laura Tach. 2006. “Would Equal Opportunity Mean More Mobility?” Pp. 23–58 in Mobility and Inequality: Frontiers of Research from Sociology and Economics, edited by Stephen Morgan, David Grusky, and Gary Fields. Stanford, CA: Stanford University Press.Find this resource:
Jensen, Arthur R. 1972. Genetics and Education. New York, NY: Harper & Row.Find this resource:
Judge, Timothy A., Chad A. Higgins, Carl J. Thoresen, and Murray R. Barrick. 1999. “The Big Five Personality Traits, General Mental Ability, and Career Success Across the Life Span.” Personnel Psychology 52: 3 (September): 621–652.Find this resource:
Kohler, Hans-Peter, Jere R. Behrman, and Jason Schnittker. 2011. “Social Science Methods for Twins Data: Integrating Causality, Endowments, and Heritability.” Biodemography and Social Biology 57: 1: 88–141.Find this resource:
Marioni, Riccardo E., Gail Davies, Caroline Hayward, Dave Liewald, Shona M. Kerr, Archie Campbell, et al. 2014. “Molecular Genetic Contributions to Socioeconomic Status and Intelligence.” Intelligence 44: May–June: 26–32.Find this resource:
Marks, Gary N. 2014. Education, Social Background and Cognitive Ability: The Decline of the Social. New York, NY: Routledge.Find this resource:
Martin, Nicolas W., Sarah E. Medland, Karin J. H. Verweij, S. Hong Lee, Dale R. Nyholt, Pamela A. Madden, et al. 2011. “Educational Attainment: A Genome Wide Association Study in 9538 Australians.” PLoS One 6: 6 (June): e20128.Find this resource:
Miller, Paul, Charles Mulvey, and Nick Martin. 2006. “The Return to Schooling: Estimates from a Sample of Young Australian Twins.” Labour Economics 13: 5 (October): 571–587.Find this resource:
(p. 325) Nielsen, François. 2006. “Achievement and Ascription in Educational Attainment: Genetic and Environmental Influences on Adolescent Schooling.” Social Forces 85: 1 (September): 193–216.Find this resource:
Nielsen, François. 2016. “The Status-Achievement Process: Insights from Genetics.” Frontiers in Sociology 1: Article 9. doi:10.3389/fsoc.2016.00009Find this resource:
Nielsen, François and J. Micah Roos. 2015. “Genetics of Educational Attainment and the Persistence of Privilege at the Turn of the 21st Century.” Social Forces 94: 2 (December): 535–561.Find this resource:
Nisbett, Richard E. 2009. Intelligence and How to Get It: Why Schools and Cultures Count. New York, NY: Norton.Find this resource:
Okbay, Aysu, Jonathan P. Beauchamp, Mark Alan Fontana, James J. Lee, Tune H. Pers, et al. 2016. “Genome-Wide Association Study Identifies 74 Loci Associated with Educational Attainment.” Nature 533 (7604): 539–542.Find this resource:
Pareto, Vilfredo. [1909] 1971. Manual of Political Economy. Translated by A. S. Schwier, edited by A. S. Schwier and A. N. Page. New York, NY: A. M. Kelley.Find this resource:
Pareto, Vilfredo. [1917–1919, 1935] 1965. Mind and Society: A Treatise on General Sociology. Translated by A. Bongiorno and A. Livingston, edited by A. Livingston. New York, NY: Dover.Find this resource:
Petrill, Stephen A. and Bessie Wilkerson. 2000. “Intelligence and Achievement: A Behavioral Genetic Perspective.” Educational Psychology Review 12: 2 (June): 185–199.Find this resource:
Plomin, R. and I. J. Deary. 2015. “Genetics and Intelligence Differences: Five Special Findings.” Molecular Psychiatry 20: 98–108. doi:10.1038/mp.2014.105Find this resource:
Plomin, R. and F. M. Spinath. 2004. “Intelligence: Genetics, Genes, and Genomics.” Journal of Personality and Social Psychology 86: 1 (January): 112–129.Find this resource:
Polderman, Tinca J. C., Beben Benyamin, Christiaan A de Leeuw, Patrick F. Sullivan, Arjen van Bochoven, Peter M. Visscher, and Danielle Posthuma. 2015. “Meta-Analysis of the Heritability of Human Traits Based on Fifty Years of Twin Studies.” Nature Genetics 47: 7 (July): 702–709.Find this resource:
Purcell, Shaun. 2002. “Variance Components Models for Gene–Environment Interaction in Twin Analysis.” Twin Research 5: 6: 554–571.Find this resource:
Roos, J. Micah and François Nielsen. 2015. “Genetic and Environmental Influences on Status-Related Outcomes in the Early Lifecourse.” American Sociological Association annual meeting, Chicago, IL, August.Find this resource:
Rowe, David C. 1994. The Limits of Family Influence: Genes, Experience, and Behavior. New York, NY: Guilford.Find this resource:
Rowe, David C., Kristen C. Jacobson, and Edwin J. C. G. Van den Oord. 1999. “Genetic and Environmental Influences on Vocabulary IQ: Parental Education Level as Moderator.” Child Development 70: 1151–1162.Find this resource:
Saunders, Peter. 2010. Social Mobility Myths. London, UK: Civitas.Find this resource:
Scarr, Sandra. 1992. “Developmental Theories for the 1990s: Development and Individual Differences.” Child Development 63: 1 (February): 1–19.Find this resource:
Scarr-Salapatek, Sandra. 1971. “Unknowns in the IQ Equation.” Science 174: 4015 (December 17): 1223–1228.Find this resource:
Shanahan, Michael J. and Scott H. Hofer. 2005. “Social Context in Gene–Environment Interactions: Retrospect and Prospect.” Journal of Gerontology Series B 60B: Special Issue 1: 65–76.Find this resource:
Solon, Gary. 2008. “Intergenerational Income Mobility.” Pp. 481–485 in Social Stratification: Class, Race, and Gender in Sociological Perspective, edited by David B. Grusky. Boulder, CO: Westview.Find this resource:
(p. 326) Strenze, Tarmo. 2007. “Intelligence and Socioeconomic Success: A Meta-Analytic Review of Longitudinal Research.” Intelligence 35: 5 (September–October): 401–426.Find this resource:
Taubman, Paul. 1976. “The Determinants of Earnings: Genetics, Family and Other Environments; A Study of White Male Twins.” American Economic Review 66: 5 (December): 858–870.Find this resource:
Tucker-Drob, Elliot and Timothy C. Bates. 2016. “Large Cross-National Differences in Gene × Socioeconomic Status Interaction on Intelligence.” Psychological Science 27: 2 (February): 138–149.Find this resource:
Turkheimer, Eric. 2000. “Three Laws of Behavior Genetics and What They Mean.” Current Directions in Psychological Science 9: 5 (October): 160–164.Find this resource:
Turkheimer, Eric, Andreana Haley, Mary Waldron, Brian D’Onofrio, and Irving I. Gottesman. 2003. “Socioeconomic Status Modifies Heritability of IQ in Young Children.” Psychological Science 14: 623–628.Find this resource:
Wright, Sewall. 1920. “The Relative Importance of Heredity and Environment in Determining the Piebald Pattern of Guinea-Pigs.” Proceedings of the National Academy of Science of the USA 6: 6 (June 1): 320–332.Find this resource:
Notes:
(1.) Technically, parent–offspring resemblance is a function of narrow-sense heritability, which is the proportion of trait variance due to the additive effects of genes, excluding variance due to interaction of genes due to mechanisms of dominance or epistasis (Falconer and MacKay 1996). It appears that resemblance between relatives for a majority of continuous human traits is due to additive effects exclusively (Polderman et al. 2015).
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