Achievement & Ascription in Educational Attainment

1. Achievement & Ascription inEducational Attainment Genetic & Environmental Influences on Adolescent Schooling François Nielsen

2. In memory of Bruce Eckland 1932—1999

3. Blau & Duncan’s (1967) model of attainment

4. Classic Attainment Model • Classic substantive interpretations: • there is low ascription as direct occupational inheritance FsOcc -> RsOcc is only .115 • education serves to reproduce inequality as most of r(FsOcc, RsOcc) = .405 is indirect, thru RsEd • there is much opportunity as the major part (.859 x .394) of r(RsEd, RsOcc) = .596 is driven by RsEd residual, thus independent of social origins

5. 3 Problems • Model parameters are ambiguous measures of ascription versus opportunity for achievement in a system of stratification • Model is vulnerable to specification bias with respect to family background • Estimates of associations between explanatory variables and outcomes confound environmental and genetic influences Each problem in more detail…

6. Problem 1: Interpretation • Traditionally: • effects of background variables (e.g., FsOcc, FsEd) associated with ascription or social reproduction • effects of intermediate variables (e.g., RsIQ, RsEd) associated with opportunity for achievement

7. Problem 1 (cont’d) • BUT classic interpretations are ambiguous: • Herrnstein & Murray (1994): • strong effect of IQ on educational & occupational outcomes indicates high opportunity for achievement • Fischer et al. (1996) counter: • IQ effect is not that strong • IQ score measures exposure to curricula & social inheritance rather than native talent, so IQ effect measures ascription rather than achievement • Same ambiguity with effect of RsEd!

8. Problem 2: Specification • If family background is not completely specified: • opportunity for achievement overestimated • strength of ascription underestimated • Herrnstein & Murray (1994): • use composite SES measure based on parental education & income • Critics (Korenman & Winship 2000; Fischer et al. 1996): • composite SES measure leaves out important aspects of background causing specification bias which: • inflates effect of IQ, thus evidence for achievement opportunity • underestimates strength of social ascription

9. Problem 2 (cont’d) • So Fischer et al. (1996): • re-estimate H&M’s (1994) model of being in poverty, including IQ plus 28 control variables • find that the effect of IQ is reduced by half, but still significant • In general: • no way to guarantee that all relevant aspects of family background have been explicitly measured and included in the model • thus that (ascription / opportunity) has not been (under / over) -estimated

10. Problem 3: Confounding 2 remarkable papers in ASR: • Eckland (1967): • Occupational mobility tables assume null model in which sons from any origin category are equally likely to reach any destination category • If ability to reach certain destinations is in part genetically determined and unequally distributed among sons from different origins, so that sons from certain origins are more likely to reach certain destinations, resulting asymmetry falsely attributed to a lack of perfect mobility • Thus to estimate social mobility one must control for origin / destination association due to genetic inheritance of abilities

11. Problem 3 (cont’d) 2. Scarr & Weinberg (1978), study of adopted children: • Correlation of adoptive parents IQ with adopted children IQ is low • Correlation of parents IQ with biological children IQ is high • Correlation of adopted child IQ with education of biological mother (proxy for cognitive ability) is high • Conclude: association between “family background” and child achievement in biological families largely reflects genetic inheritance of abilities that enhance achievement, rather than environmental / social influences

12. Problem 3 (cont’d) Conclusion : • The classic attainment model confounds environmental & genetic influences on attainment • Effect of FsEd or FsOcc on RsEd or RsOcc may include a genetic component, thus is potentially biased measure of social inheritance or ascription

13. A Solution? • Using data on siblings with different degrees of biological relatedness (MZ twins, DZ twins, full sibs, half sibs, cousins, unrelated sibs) • Estimate behavior genetic (BG) model that partitions variance in attainment into components due to • genes • common (shared) environment of siblings • specific (unshared) environment of siblings

14. Solution? (cont’d) BG model alleviates problems of classical attainment model: • BG model explicitly separates genetic and environmental influences • environmentality (= proportion of attainment variance due to common environment of sibs) measures social ascription / inheritance • heritability (= proportion of attainment variance due to genes) measures opportunity for achievement • Specification problem eliminated as BG model estimates family environmental effects in “black box” fashion

15. Empirical Analysis • I illustrate these ideas by estimating a BG model of adolescent school achievement (Verbal IQ, GPA & college plans) • Using data on 6 types of sibling pairs from the AddHealth study (MZ twins, DZ twins, full sibs, half sibs, cousins, unrelated sibs)

17. Model Variables • Measured variables: • VIQ = verbal IQ • GPA = grade point average • CPL = college plans • Latent variables (Cholesky factorizations): • A1, A2, A3: genetic factors • C1, C2, C3: common environment • E1, E2, E3: specific environment (includes measurement error)

18. Model Assumptions • Genetic factors Aj (j=1…3) correlated across siblings by a quantity k: • k represents degree of relatedness of siblings • assuming (for the moment) no assortative mating • MZ: k=1; DZ, FS: k=.5; HS: k=.25; CO: k=.125; NR: k=0

19. Model Assumptions (cont’d) • Each common environmental factor Cj (j=1…3) assumed perfectly correlated (r=1) across siblings • Variances of all latent variables are set to 1.0 • Estimate by ML with Mx program (Mike Neale)

24. Highlights • “phenotypic” path coefficients (i.e., VIQ -> GPA; VIQ -> CPL; GPA -> CPL) become n.s. when BG structure of achievement process is controlled • heritability (= a measure of opportunity) high for all three achievement measures (VIQ 54%, GPA 67%, CPL 60%), even though measurement error not corrected • environmentality (= a measure of ascription) only substantial for VIQ (14%); it is hardly significant for GPA or CPL • specificity (effect of specific environment of sibling; includes measurement error) substantial for all three outcomes (33% to 37%)

25. Highlights (cont’d) • genetic influences cannot be reduced to a single latent factor representing academic ability; they are better represented as relatively independent factors specific to each educational outcome • by contrast, common environmental effects can be represented as a single environmental factor • specific environmental factors are largely independent across outcomes, suggesting they largely consist of measurement error

26. Discussion BG parameters as macro-social variables? • heritability, environmentality, and specificity characterize a population, not a trait • parameter values characterize stratification system with respect to ascription versus opportunity for achievement: • high heritability = high opportunity, low ascription • high environmentality = high ascription, low opportunity • thus, BG model parameters potential basis of new approach to: • comparative social stratification research • normative discussions of social inequality