Educational Opportunity & Genetics

1. Educational Opportunity& Genetics François Nielsen Presentation at UNC-C 3 March 2006

2. Forthcoming in SF as:Achievement & Ascription inEducational Attainment Genetic & Environmental Influences on Adolescent Schooling

3. In memory of Bruce Eckland 1932—1999

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

5. Status Attainment Model • Classic substantive interpretations: • there is low ascription as FsOcc -> RsOcc is small (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 (independent of social origins)

6. Attainment Model Used: • In comparative social mobility research (e.g., Breen and Jonsson in 2005 ARS) • In policy-oriented debate on educational and occupational achievement (e.g., following The Bell Curve)

7. 3 Problems • Model parameters are ambiguous measures of opportunity for achievement versus ascription • Model is vulnerable to misspecification of family background • Parameter estimates confound environmental and genetic influences Each problem in more detail…

8. 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

9. Problem 1 (cont’d) • BUT these interpretations are questionable: • 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 (not native talent), so IQ effect measures ascription rather than achievement • Same ambiguity with effect of RsEd!

10. 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 (Fischer et al. 1996; Korenman & Winship 2000): • composite SES measure leaves out important aspects of background which: • inflates effect of IQ, thus evidence for opportunity • underestimates strength of social ascription

11. 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

12. Problem 3: Confounding 2 remarkable papers in ASR (!): • Eckland (1967): • Occupational mobility tables assume null model: sons from any origin equally likely to reach any destination • 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

13. 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

14. Problem 3 (cont’d) Conclusion : • Status attainment model confounds environmental & genetic influences on attainment • Effect of FsEd or FsOcc on RsEd or RsOcc may include genetic component, so effects are potentially biased measures of social inheritance or ascription

15. 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

16. Solution? (cont’d) BG model alleviates problems of status 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

17. Empirical Analysis • I illustrate these ideas by estimating a BG model of adolescent school achievement • 3 school outcomes (Verbal IQ, GPA & college plans) • Data on 6 types of sibling pairs from the AddHealth study (MZ twins, DZ twins, full sibs, half sibs, cousins, unrelated sibs)

19. 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)

20. 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

21. 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)

23. Model Comparisons Note: B = phenotypic paths; A = genetic paths; C = shared environment paths; E = specific environment paths; Ad, Cd, Ed: off diagonal elements of A, C, or E fixed (independent factors model); A1, C1: lower triangular matrix A, C reduced to single column vector (common factor model). a favored model

24. Estimated Model

27. Highlights • “phenotypic” path coefficients (i.e., VIQ -> GPA; VIQ -> CPL; GPA -> CPL) become n.s. when BG structure is controlled • heritability (= opportunity) high for all three outcomes (VIQ 54%, GPA 67%, CPL 60%) • environmentality (= ascription) only substantial for VIQ (14%); almost 0 for GPA or CPL • specificity (specific environment + measurement error) substantial for all three outcomes (33% to 37%)

28. Highlights (cont’d) • genetic influences cannot be reduced to a single latent academic ability factor; partially overlapping factors specific to each outcome • common environmental effects can be represented as a single (privilege?) environmental factor • specific environmental factors are largely independent across measures, behaving like measurement error

29. 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 potential basis of new approach to: • comparative social stratification research • normative discussions of social inequality

30. Discussion (cont’d) • Based on previous literature can construct “demo” of genetically-aware comparative mobility research