Robert Kaestner
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1) Adolescent cognitive ability and non-cognitive traits are associated with better adult physical and mental health and fewer depressive symptoms. 2) Adding family background factors attenuates but does not eliminate the associations, suggesting both shared family environments and individual attributes matter. 3) Further adding education and health behaviors further reduces the associations, indicating cognitive/non-cognitive traits may influence health through these mediating factors.
Robert Kaestner
- 1. Adolescent Cognitive and Non-cognitive Correlates of Adult Health Robert Kaestner Institute of Government and Public Affairs University of Illinois Department of Economics University of Illinois at Chicago Presentation XXX Jornadas de Economia de la Salud Asociacion de Economia de la Salud Valencia June 25, 2010
- 2. Why study adolescent correlates of adult health? Childhood (family) environments matter empirically. Mazumder (2008) and others reported that approximately 50% of the variation in wages, earnings and household income is due to differences between family (childhood) environments. Studies of health and longevity found significant sibling correlations in life expectancy, incidence of heart disease, and mental illness (Stunkard et al. 1986; Marenberg et al. 1994; Christensen and Vaupel 1996; Kiessepa et al. 2004; vB Hjelmborg et al. 2006; Petersen et al. 2008). Christensen and Vaupel (1996) reported that approximately 25% of variation in life expectancy is attributable to shared childhood environments.
- 3. Families Matter in My Sample Sibling Correlations for Socioeconomic Status and Health 754 Same-sex Sibling Pairs in NLSY79 Coefficient of Correlation Outcome Variation Coefficient P-value SF-12 Physical Score 0.17 0.07 0.05 SF-12 Mental Score 0.16 0.09 0.02 Self-reported Health 0.43 0.19 <0.01 Self-reported Good Health 0.13 <0.01 Self-reported Poor Health 0.15 <0.01 CESD Score 1.16 0.11 <0.01 Height (1985) 0.05 0.71 <0.01 Daily Smoker (1998) 0.27 <0.01 Binge Drinker Past Month 0.10 <0.01 AFQT Percentile Score 0.72 0.68 <0.01 Annual Earnings 0.98 0.33 <0.01
- 4. Why study adolescent correlates of adult health? Not yet known why childhood (family) matters? Families provide resources (e.g., medical care) and invest in cognitive and non-cognitive abilities of children that influence adult well being. It is not clear what is the causal mechanism that links family (childhood) to adult outcomes. It could be: Shared genetic factors Shared socioeconomic family environment (e.g., family income, parental education, family structure, number of siblings) Shared community environment (e.g., quality of child care, quality of elementary and secondary schooling. quality of public health infrastructure)
- 5. Why study adolescent correlates of adult health? Relatively little research focused on health. Most research in this area has focused on adult social and economic outcomes. Relatively little research on the influence of childhood environment on adult health Prior research examining adult health focused on the childhood health environment Barker hypothesis (in utero determinants); studies of famines and disease during prenatal period Studies of effects of low-birth weight Fogel’s work on nutrition, height and health Preston’s work on urban/rural environment
- 6. Why study adolescent correlates of adult health? The special importance of childhood. Different forms of human capital, or what Heckman refers to as capabilities, are complementary (Becker 2007; Cunha and Heckman 2007; Heckman 2007). Investments in one form of human capital, for example a non-cognitive factor such as rate of time preference, cause further investments in other forms of human capital (or capabilities) that also improve adult outcomes. Heckman and colleagues expand model of complementary investments in human capital by incorporating a developmental aspect that recognizes that the timing of investments is also important. Temporal investments in human capital are also complementary. Investments in cognitive ability during late adolescence (e.g., high school) are more productive (less costly) when earlier investments in cognitive ability have been made. In sum, childhood, and particularly early childhood, investments in human capital may be extremely important in determining adult outcomes.
- 7. Potentially High Rates of Return on Investments During Childhood Rate of Return to Investments Age Heckman (2008)
- 8. Purpose and Contributions of Current Research Add to the relatively small literature studying the early determinants of adult health by providing a descriptive analysis of the relationship between adult health and factors measured at end of childhood (ages 14 to 15) Include cognitive, non-cognitive (e.g., self esteem), and family background factors in the analysis—cognitive and non-cognitive factors have generally been ignored. Influence of non-cognitive factors on socioeconomic outcomes has become an important research area (Heckman et al. 2006) Differences in human capital accumulation and adult outcomes are too large to be explained by differences in monetary costs Non-cognitive abilities may significantly affect the non-monetary costs of investment and may therefore provide an explanation for the lack of investment
- 9. Summary of Previous Literature Auld and Sidhu (2005) Large effects of cognitive ability on health limitations Family background had little effect on whether a person had a health limitation. Elias (2005) Small effects of cognitive ability on self-rated health Family background had little effect on self-rated health Cutler and Lleras-Muney (2007) Found that cognitive ability and family background were significantly related to health behaviors such as smoking Influence of family background was larger than cognitive ability. Hartog and Osterbeek (1998) Reported that family background and cognitive ability are associated with adult health 50% of the effect of cognitive ability on health and nearly all of the effect of family background on health work through completed schooling.
- 10. Specific Contributions of this Study I focus on attributes at age 14 and 15 (end of childhood) and relate these to adult health 25 years later at age 41 I focus on the direct effects of cognitive and non-cognitive ability—not how the effect of education is mediated by addition of cognitive ability. I consider an extensive set of factors. I assess how much of the effect of cognitive ability and non-cognitive ability can be explained by family background—the family environment is the precursor to cognitive and non-cognitive ability. I assess whether the associations between adolescent factors and adult health are mediated by completed schooling and health behaviors—two factors known to affect health
- 11. Illustrative Empirical Model Cunha and Heckman (2007) and Heckman (2007) Hit = g (α0 H ,α0C ,α0 NC , I H1,..., I H (t −1), IC1,..., IC (t −1), I NC1,..., I NC(t −1) , f H , fC , f NC ) i = 1,...,N (persons) t = 0,...,t (age) Model assumes three distinct forms of human capital (H, C, and NC) Human capital accumulation at age t depends on initial endowments of human capital Human capital accumulation at age t depends on history of investments in different forms of human capital Production technology should incorporate complementarities between investments at different ages and between forms of human capital at same age
- 12. My Ad-hoc Approach Intended as a Descriptive Analysis H i = β 0 + ∑ δ k DEMOGik + ∑ λk COG _ 14ik + ∑ γ k NONCOG _ 14ik k k k + ∑ δ k FAMILYik + ei k A comparison of this approach with correct approach reveals that this approach omits important determinants of adult health and fails to incorporate any of the complementarities described by Becker (2007) and Heckman (2007). Omissions are likely to result in estimates of the associations between cognitive and non-cognitive attributes at age 14, and adult health that are too large (from a structural point of view). The likely upward bias (in terms of their interpretation as structural estimates) of the estimated associations is important information for determinants that do not have statistically significant associations because failing to reject the null hypothesis in this case is relatively strong evidence that these factors and earlier investments in these factors, are not likely to be important determinants of adult health
- 13. Causal Mechanisms Linking Non-cognitive Attributes and Health Time preference—here measured by use of tobacco, alcohol and drugs by age 14 Ability to appreciate future increases likelihood of investment in health Locus of control Those who have an internal locus of control may be more likely to seek and appreciate health information Self Esteem Self esteem may affect health by allowing a person to communicate better with his or her doctor about symptoms, diagnoses and treatment regimes. In sum, factors that affect a person’s appreciation of future benefits, ability to communicate (e.g., agreeableness) and motivation to take action and follow through (conscientiousness) may all affect health. These factors are distinct from cognitive factors
- 14. Data NLSY79 Health measured at around age 41 Johnson and Schoeni (2003) reported sibling correlations (PSID) in health (0.6—very large compared to anything reported elsewhere) that remain constant from age 25 to 55 Other estimates of sibling correlations for health at older ages such as life expectancy are around 0.25 Correlations reported here are somewhat lower—0.1 to 0.2 Using health at age 41 may be informative for older ages when health begins to deteriorate Adolescent characteristics measured at ages 14 to 15
- 15. Data Health Short Form-12 (SF12) mental and physical health Center for Epidemiological Studies Depression Scale (CES-D). Self-rated general health: good health defined as self-rated health that is excellent or good, and poor health defined as self-rated health that is poor or fair. Cognitive ability at age 14 or 15 Armed Forces Qualification Test (AFQT) percentile taken at age 14 or 15 adjusted for differences in age at time of test. used the sample distribution of adjusted percentile scores to classify people into quartiles of cognitive ability
- 16. Data Non-cognitive traits at age 14 or 15 Rosenberg self-esteem scale (measured in 1980 at ages 15 and 16); divided into low-, moderate- or high-self esteem Rotter locus of control scale; divided into low- moderate or –high- external locus of control church attendance (never/rarely, sometimes, often) history of stealing (never, sometimes, often) use of tobacco, alcohol and marijuana by age 14
- 17. Data Family Background mother’s education (<9, 9-11 years, 12 years, 13 to 15 years, 16 or more years, missing) number of siblings (none, one, two to three, four or more) family structure (two biological parents, two parents, mother only, other) 1978 family income (0-4,999, 5-9,9999, 10-19,999, 20-29,999, 30,000 or more, missing) whether childhood household had library card, magazines or newspapers whether influential person would approve of not going to college
- 18. Data Years of Completed Education (<12 inc GED, 12, 13-15, 16+) Health Behaviors daily smoker in 1998 (last year available) binged drank in past month in 2002 (last year available) obese (self-reported BMI>30) engaged in any vigorous physical activity recently Initial Health age 14 or 15 health limitation height and height squared father deceased by age 40 Demographics age (measured in six-month intervals) race/ethnicity (non-Hispanic Black, non-Hispanic While, Hispanic, other) respondent and mother’s natality (foreign-born) whether foreign-language was spoken in the home
- 19. Sample Means of Outcomes Females Males Mean Std.Dev. Mean Std.Dev. SF-12 Physical 51.2 8.8 52.8 6.7 SF-12 Mental 52.1 8.5 54.3 7.0 CESD Score 3.7 4.3 2.7 3.6 Good Health 0.56 0.61 Poor Health 0.15 0.12
- 20. Sample Means Selected Variables Females Males Mean Dev. Mean Dev. Locus of Control Score (1979) 9.4 2.0 9.3 2.1 Mother-Father Most Influential 0.69 0.71 Approve Not Going College 0.22 0.26 Likely go on Food Stamps 0.47 0.42 Attend Church Sometime 0.21 0.24 Attend Church Often 0.49 0.40 Stole Sometimes 0.20 0.23 Stole Often 0.11 0.23
- 21. Sample Means Selected Variables Females Males Mean Dev. Mean Dev. Two Biological Parents 0.62 0.62 Two Parents 0.08 0.08 Mother Only 0.27 0.23 Number of Sibling 2.2 1.6 2.4 1.8 Mother’s Education 9-11 0.27 0.22 Mother’s Education 12 0.35 0.39 Mother’s Education 13-15 0.08 0.11 Mother’s Education 16+ 0.08 0.08 Family Income in 1978 15958 12164 15497 11990 Daily Smoker 0.27 0.29 Binge Drinker 0.11 0.26 Obese 0.30 0.29 Engaged Vigorous Activity 0.68 0.81
- 22. Regression Sequence First estimate a model including only cognitive and non-cognitive factors. Add family background Include what I refer to as initial health or health determined by family under assumption that cognitive and non-cognitive factors do not influence health at this age Family background is pre-cursor to (origin of) cognitive and non-cognitive factors—evidence to assess whether it is all family or whether there is scope for intervention (raise cognitive ability) Add completed years of schooling and health behaviors Evidence that effects of cognitive and non-cognitive factors are working through education and health behaviors
- 23. SF-Physical SF-Mental CESD (1) (2) (3) (1) (2) (3) (1) (2) (3) 1.37* 1.41* 1.51* 1.01 1.04 0.76 -0.96** -0.94* -0.85* (0.68) (0.70) (0.72) (0.74) (0.76) (0.78) (0.37) (0.38) (0.39) 2.48** 2.15** 2.28** 1.58* 1.56 1.03 -1.51** -1.45** -1.27** (0.72) (0.76) (0.80) (0.78) (0.83) (0.88) (0.38) (0.41) (0.43) 3.33** 2.86** 2.58** 1.75* 1.82 1.12 -2.09** -2.13** -1.77** (0.79) (0.86) (0.95) (0.85) (0.94) (1.04) (0.42) (0.47) (0.51) 6.52** 3.95** 3.14** 1.73 1.49 0.54 8.79** 7.26** 4.22** 872 866 854 872 866 854 858 852 841 SF-Physical SF-Mental CESD (1) (2) (3) (1) (2) (3) (1) (2) (3) 1.93* 1.46 0.92 1.29 1.27 0.29 -0.71 -0.38 -0.02 (0.96) (1.01) (1.02) (0.91) (0.95) (0.98) (0.45) (0.47) (0.48) 2.37* 1.90 0.80 0.37 0.55 -0.74 -0.84 -0.54 -0.04 (1.02) (1.09) (1.13) (0.97) (1.03) (1.08) (0.48) (0.51) (0.53) 2.97** 1.90 0.02 0.20 0.37 -1.50 -1.05* -0.68 0.17 (1.14) (1.27) (1.35) (1.09) (1.20) (1.29) (0.53) (0.59) (0.63) 2.62* 1.16 0.51 0.82 0.68 0.93 1.52 0.51 0.06 Observations 798 792 783 798 792 783 780 775
- 24. SF-Physical SF-Mental CESD (1) (2) (3) (1) (2) (3) (1) (2) (3) Self Esteem-Middle 0.86 0.73 0.99 0.35 0.37 0.36 -0.95** -0.96** -0.98** (0.68) (0.69) (0.69) (0.74) (0.75) (0.76) (0.36) (0.37) (0.37) Self Esteem-Top 1.03 0.85 1.06 1.61* 1.64* 1.66* -1.23** -1.24** -1.25** (0.63) (0.64) (0.64) (0.68) (0.69) (0.70) (0.33) (0.34) (0.34) Internal Locus-Middle -0.04 -0.02 -0.10 0.48 0.54 0.70 -0.70* -0.66* -0.68* (0.59) (0.60) (0.60) (0.64) (0.65) (0.66) (0.31) (0.32) (0.32) Internal Locus-Top -0.71 -0.89 -0.92 0.84 0.90 0.98 -0.55 -0.46 -0.45 (0.60) (0.61) (0.61) (0.65) (0.66) (0.67) (0.32) (0.32) (0.33) Partial F 1.10 1.17 1.41 2.32* 2.33* 2.42* 4.74** 4.47** 4.42** Religious Att.-Sometimes 0.63 0.16 0.21 0.93 0.68 0.64 -0.56 -0.52 -0.53 (0.62) (0.64) (0.64) (0.67) (0.69) (0.70) (0.33) (0.34) (0.34) Religious Att.- Frequent 0.98 0.71 0.63 0.25 -0.00 -0.26 -0.31 -0.30 -0.18 (0.54) (0.56) (0.57) (0.58) (0.61) (0.62) (0.29) (0.30) (0.30) Stole Sometimes -0.01 0.00 -0.11 1.30 1.34 1.10 -0.52 -0.62 -0.51 (0.70) (0.71) (0.72) (0.75) (0.77) (0.78) (0.37) (0.38) (0.38) Stole Never -0.39 -0.41 -0.46 1.05 0.99 0.85 -0.18 -0.24 -0.17 (0.62) (0.64) (0.64) (0.68) (0.70) (0.70) (0.33) (0.34) (0.35) Partial F 0.98 0.59 0.45 1.31 1.06 0.92 1.27 1.31 1.04 No Cigarette Use 1.78** 1.78** 1.55** 0.34 0.08 -0.19 -0.39 -0.28 -0.15 (0.51) (0.52) (0.52) (0.55) (0.57) (0.57) (0.27) (0.28) (0.28) No Alcohol Use -0.23 -0.13 0.04 0.60 0.68 0.76 -0.10 -0.06 -0.12 (0.69) (0.70) (0.71) (0.75) (0.77) (0.77) (0.37) (0.37) (0.38) No Marijuana Use -1.09 -1.17 -0.86 -0.08 -0.23 0.02 0.14 0.11 0.04 (0.67) (0.68) (0.68) (0.72) (0.74) (0.74) (0.35) (0.36) (0.37) Partial F 3.77** 3.97** 3.21** 0.97 0.80 0.62 0.69 0.49 0.19 Observations 872 866 854 872 866 854 858 852 841
- 25. SF-Physical SF-Mental CESD (1) (2) (3) (1) (2) (3) (1) (2) (3) Self Esteem-Middle 1.28 1.52 1.13 1.63 1.87* 1.51 -1.43** -1.52** -1.30** (0.97) (1.00) (1.00) (0.93) (0.95) (0.95) (0.46) (0.47) (0.47) Self Esteem-Top 1.73* 1.80* 1.42 1.82* 1.78* 1.43 -1.20** -1.12** -0.95* (0.83) (0.85) (0.86) (0.79) (0.81) (0.82) (0.39) (0.40) (0.41) Internal Locus-Middle 0.62 0.61 0.87 -0.61 -0.71 -0.55 -0.13 -0.10 -0.22 (0.82) (0.84) (0.84) (0.78) (0.80) (0.80) (0.38) (0.39) (0.39) Internal Locus-Top -0.68 -0.94 -0.81 -0.22 -0.37 -0.40 -0.17 -0.06 -0.16 (0.86) (0.89) (0.88) (0.82) (0.84) (0.84) (0.40) (0.41) (0.41) Partial F 1.60 1.87 1.64 1.66 1.67 1.03 3.16* 3.02* 2.17 Religious Att.-Sometimes -0.78 -1.09 -0.54 0.66 0.49 0.74 -0.07 -0.01 -0.11 (0.94) (0.98) (0.97) (0.89) (0.92) (0.93) (0.44) (0.45) (0.46) Religious Att.- Frequent -0.75 -1.00 -1.07 2.48** 2.50** 2.58** -0.41 -0.41 -0.36 (0.76) (0.80) (0.79) (0.73) (0.76) (0.76) (0.36) (0.37) (0.37) Stole Sometimes 0.40 0.18 0.27 0.39 0.37 0.27 0.04 -0.07 0.05 (1.28) (1.32) (1.31) (1.22) (1.25) (1.25) (0.60) (0.61) (0.61) Stole Never 0.10 -0.10 -0.17 0.35 0.23 -0.29 0.32 0.27 0.50 (1.14) (1.18) (1.18) (1.08) (1.12) (1.13) (0.53) (0.55) (0.55) Partial F 0.33 0.51 0.54 3.25* 3.17* 3.23* 0.54 0.58 0.65 No Cigarette Use -0.28 -0.36 -0.29 -0.14 -0.22 -0.22 -0.04 0.09 0.13 (0.73) (0.74) (0.74) (0.69) (0.71) (0.70) (0.34) (0.34) (0.35) No Alcohol Use 1.35 1.16 1.23 2.38* 2.46* 2.03 -0.78 -0.72 -0.55 (1.12) (1.14) (1.13) (1.06) (1.08) (1.08) (0.52) (0.52) (0.53) No Marijuana Use -0.33 -0.58 -0.89 -0.18 -0.71 -0.84 -0.76 -0.64 -0.62 (1.02) (1.05) (1.04) (0.97) (1.00) (1.00) (0.48) (0.49) (0.49) Partial F 0.58 0.55 0.54 1.39 1.73 1.74 1.50 1.04 0.93 Observations 798 792 783 798 792 783 780 775 767
- 26. Males Females SF-PhysicalSF-MentalCESDSF-PhysicalSF-MentalCESD HS 1.21 2.31* -0.84 2.34 2.38 -0.29 (0.88) (0.96) (0.48) (1.28) (1.23) (0.61) Some College 0.71 2.84* -0.86 4.20** 4.49** -1.19 (1.04) (1.14) (0.56) (1.43) (1.37) (0.68) BA or more 2.08 2.84* -1.38* 5.53** 4.80** -1.60* (1.17) (1.28) (0.63) (1.62) (1.55) (0.77) Partial F 1.49 2.29 1.64 4.73** 4.57** 2.86* Daily Smoker -0.04 -0.56 0.31 -1.27 -1.42 0.91* (0.56) (0.61) (0.30) (0.81) (0.78) (0.38) Binge Drinker 0.44 -0.46 -0.03 3.19** 1.00 -0.06 (0.56) (0.61) (0.30) (1.12) (1.07) (0.53) Vigorous Activity 0.00 0.01* -0.00 0.00 -0.00 -0.00 (0.00) (0.00) (0.00) (0.01) (0.01) (0.00) Obese -2.27** -1.52** 0.58* -2.51** 0.15 0.12 (0.53) (0.58) (0.29) (0.75) (0.72) (0.35) Partial F 4.58** 3.26** 1.31 4.85** 1.04 1.29 Observations 872 858 872 783 783 767
- 27. Conclusions Cognitive ability and one non-cognitive trait—self esteem— have significant, direct associations with adult health. For males, direct (net of other factors) associations are of same relative magnitude for cognitive ability and self esteem. For females, direct associations are larger (relatively) for self esteem than cognitive ability, and cognitive ability has small direct associations with health.
- 28. Conclusions Completed education continues to be significantly associated with adult health net of adolescent and family background characteristics. Among males, there was more evidence that obtaining an educational threshold was associated with adult health than there was evidence of an education gradient in health. For females, there was consistent evidence of an education gradient for health and associations are of the same order of magnitude as the (direct) associations of cognitive ability and self esteem.
- 29. Conclusions Finally, we assessed whether adolescent cognitive and non- cognitive factors are potential explanations of gender and racial disparities in health. Overall, we found little evidence that these factors can explain much of the differences in health we observe between men and women, and black and white persons.