thecausaleﬀectsofeducationonadulthealth ...neil m. davies , matt dickson , george davey smith ,...

Mendelian Randomization vs. RoSLA

The Causal Effects of Education on Adult Health,Mortality and Income: Evidence from MendelianRandomization and the Raising of the School

Leaving Age

Neil M. Davies† Matt Dickson§ George Davey Smith†

Frank Windmeijer† Gerard van den Berg‡

†University of Bristol

‡University of Bristol, and IZA

§University of Bath, and IZA

March 2018

Dickson – NIESR seminar March 2018

Mendelian Randomization vs. RoSLA Introduction Literature Methods Data Results Discussion Robust

Motivation

On average those with more education are healthier, happier, moreintelligent, richer, and longer lived than those with less education.

Big question: WHY?

Education directly causes these outcomes?By increasing income (Devereux and Hart, 2010; Dickson, 2013;Buscha and Dickson, 2015; Delaney and Devereux, forthcoming) .By improving health knowledge and affecting behaviours: smoking,drinking, exercise etc ⇒ greater “health efficiency” (Grossman, 1972).By changing time-preferences and/or risk preferences (Becker andMulligan 1994, Perez-Arce 2011).

Or the education gradient in outcomes is due to other socioeconomic orgenomic differences: factors that determine educational attainment(discount rate, ‘ability’,. . . ) also impact health accumulation?

Or could be reverse causation: health → education?Dickson – NIESR seminar March 2018


Motivation

Need plausibly exogenous variation in education to identify causal impacton health (experiment or natural experiment).

Another BIG question is whether the impact of education on health isconstant across the education distribution – or does it depend on theeducation margin? i.e. LATE versus ATE.



Motivation

To address both of these questions, in this paper we look at two plausiblyexogenous sources of variation in educational attainment that work ondifferent education margins:

1 The good old ‘Raising of the School Leaving Age’ (RoSLA) in the UKin 1972 (note: whole UK 1972).

2 Genetic variants associated with education: Mendelian Randomization(MR).



Roadmap

Literature: (i) compulsory school changes exploited for causal effects ofeducation on health; (ii) use of MR to instrument education for healthoutcomes.

IV strategy – particularly the use of MR

Data – the UK BioBank

Results – balancing tests, first stage, second stage

Robustness

Conclusions



Literature – exploiting compulsory schooling changes toidentify causal effect of education on ‘health’

Lleras-Muney, ReStud (2005). US cross state variation/changes incompulsory schooling requirements. Finds: education reduces mortality.

Arendt, EconEdRev (2005). Denmark, schooling reforms (inc. compulsoryyears) over time. Finds: no signif. causal effect of education onself-reported health or BMI.

Albouy and Lequien, JHealthEcon (2009). France, changes in compulsoryschooling requirements over time. Finds: no signif. causal effect ofeducation on mortality.

Spasojevic, Contributions to Econ Analysis (2010). Sweden, changes incompulsory schooling requirements over time. Finds: education has apositive causal impact to reduce BMI and bad-health index.

Kemptner et al., JHealthEcon (2011). German cross statevariation/changes in compulosry schooling requirements. Finds: educationreduces incidence of long-term illness for men.



The 1972 Raising of the School Leaving Age in Englandand Wales (RoSLA)

A “natural experiment” in the UK. . .

The 1944 “Butler” Education Act: legislated for the increase in theminimum school leaving age in two-steps from 14 (the minimum in 1944)up to 16.

The first “step” took place in 1947 and affected cohorts born from April1933 onwards – from this point the minimum school leaving age was 15.

The second “step” did not take place until 1972 and affected cohorts bornfrom September 1957 onwards – from this point the minimum schoolleaving age was 16.

These two reforms have been exploited in a sub-literature within the healthreturns to education literature.



Literature – RoSLA specific

Oreopoulos, AER (2006), JPubEcon (2007), mimeo (2008). Self-reportedhealth/happiness. 1947 RoSLA. Finds: positive causal effects (2006, 2007),little or no effect (2008).

Silles, EconEdRev (2009). Self-reported health. 1947 and 1972 RoSLAs.Finds: signif. causal effect of education.

Clark and Royer, AER (2013). Mortality and self-reportedhealth/health-behaviours. Pools 1947 and 1972 reforms in many cases.Find: little or no effect on education on health.

Powdthavee, JHC (2010). Hypertension. Finds: a causal effect of the 1947RoSLA on hypertension (reducing the probability by 7 pp); 1972 RoSLA nosignif. impact.

Jürges et al., JPopEcon (2013). Blood fibrinogen and C-reactive proteinlevels – two biomarkers thought to be objective measures of health. Find:no causal effect of education via 1947 and 1972 RoSLAs.



Davies et al. 2018

Davies et al., Nature Human Behaviour (2018). Finds: a causal effect ofthe 1972 RoSLA on grip strength (0.26kg), and risk of diabetes (0.81ppreduction), stroke (0.28pp reduction), mortality (0.37pp reduction). Showthat cross-sectional association of education and health outcomes suffersresidual genomic confounding.

First paper to use genome-wide allele scores to investigate theeducation-health gradient and potential genetic confounding in thecross-sectional relationship

Clinical health measures – height, weight, BMI, blood pressure(systolic, diastolic), hand-grip strength, arterial stiffness plus diagnosisinformation/history for cancer, diabetes, stroke, heart attack.

Very large sample: 503,325 participants in the UK biobank, allowstight focus on 22,138 individuals born in the period comprising lastyear before the reform and first year of the reform.

Use diff-in-diff to deal with impact of age difference in key cohorts.Dickson – NIESR seminar March 2018


Literature – exploiting MR to identify causal effect ofeducation on ‘health’

von Hinke et al. (2016). Effect of fat mass on educational outcomes andblood pressure. Find causal effect of fat mass on blood pressure but noevidence of causal effect on education. Establish conditions for use of MRas IV.

Nguyen et al. (2016). Effect of education on cognitive impairment(dementia risk) – find causal effect.

Hagenaars et al (2017). Effect of education on cognition and other traitslater in life. No causal effects found (but data/measures not great, IVsweak).

Tillmann, T. et al. (2017) Education and coronary heart disease. Findcausal effect to reduced heart disease.



Method – instrument #1

Exploit the 1972 RoSLA to test whether remaining in school post-15 affectslater health outcomes. Relevance and validity criteria for RoSLA as an IV:

1 Relevance: those attending school post-reform must be more likely toremain in school beyond 15 (testable via first stage regression).

2 No pre-existing differences between those affected by the reform andthose not i.e. no common cause affecting instrument and outcome(falsifiable to an extent via balancing tests, see below).

3 Validity: no impact of the reform on health outcomes other thanthrough impact on education i.e. exclusion restriction (not formallytestable but literature supports validity).

(Economists usually combine the latter two into a single validityassumption: IV has no correlation with the error term in the structuralequation and can itself be excluded from that equation.)



RoSLA – first stage




Exploit the natural experiment that happens at conception to test whethereducation affects later health outcomes.

Okbay et al. (2016) identified 74 single nucleotide polymorphism (SNPs)associated with years of education at genome-wide significance levels in thediscovery sample of the educational attainment genome wide associationstudy (GWAS). genetics

Each child inherits 1/2 of each parent’s genomes: at each locus 50%chance of getting each parent’s allele.

Segregation of alleles means education variants are independent of otheralleles inherited ⇒ genetic factors for one trait do not associate with othertraits.

Genes set at conception ⇒ no reverse causality of education → genes orother outcomes/choices → genes. Note: re epigenetics.




Constructed weighted allele scores for educational attainment in the UKBio Bank data.

The allele scores are the weighted sum of the number of educationincreasing alleles for each participant. The contribution of each SNP to thescore was weighted by the size of the coefficient reported by the GWAS.

design

Allele score mean 0.325, s.d. 0.101.

Checked consistency between the effect allele frequency between the GWASand UK Biobank data. The allele frequencies were correlated 0.9913.




Relevance and validity criteria for MR as an IV:

1 Relevance: those with higher allele scores must be more likely toattain more education (testable via first stage regression).

2 No pre-existing differences between those with the higher allele scoresand those not i.e. no common cause affecting instrument and outcome(falsifiable to an extent via balancing tests, see below).

3 Validity: no impact of the education alleles on health outcomes otherthan through impact on education (aka no horizontal pleiotropy).(Can test for impact on cognition.)




Potential sources of bias in MR:

Weak instrument

Pleiotropy - horizontal would be the issue (aka endogeneity): IV impactsoutcome through a channel other than education ⇒ invalid IV. Can testrobustness to controls.

Residual population stratification – can control for.

Dynastic effects

Assortative mating

Caveat: MR more plausible strategy for biologically proximal phenotypes(i.e. variation in ALDH2 genes for alcohol) than for education where themediating pathways are less well understood.



Method – instruments

RoSLA well understood to estimate a Local Average Treatment Effect i.e.the estimated ‘return’ to education is the impact for compliers: thosewhose behaviour was affected by the instrument.

Given nature of the IV, the complier group is by definition at the lower endof the education distribution.

The MR instrument however works across the whole distribution ofeducation leaving ages – implies may be able to recover something closer toan ATE rather than a LATE (see next slides. . . ). The estimates areintention to treat (ITT) as not everyone will attain increased educationgiven these genes.



Method – instruments



Data: UK Biobank

9.2 million invitations sent, final recruited sample: 503,325 individualsrecruited between 2006 and 2010 and were aged between 40 and 69 whenrecruited; 315,436 usable observations.

Non-random sample but health measures representative compared toHealth Survey of England map .

Clinical measures: height and weight, two measures of diastolic and systolicblood pressure (recorded via an electronic blood pressure monitor, with themeasurements taken two minutes apart), atrial stiffness (measured using anelectronic measure device), grip strength (measured in kilos using ahydraulic hand dynamometer)

Fluid intelligence measured via 13 logic puzzles

Link to national cancer and mortality registries ⇒ long-term follow-up

Self-reported measures: asked if ever been diagnosed by a doctor with thefollowing health conditions: high blood pressure, stroke, diabetes, or heartattack; asked re depression



Data: UK Biobank

Health behaviour self-reported measures:how frequently they consumed alcoholcurrent, ex or never smokerhow often they vigorously and moderately exercised in a typical week

Also asked if their pre-tax income was below £18,000; between £18,000and £30,999; between £31,000 and £50,999; between £52,000 and£100,000; or above £100,000



Data: UK Biobank

Genetic measures – key to the MR instrument:Participants provided a blood sample and this was used to extractDNA and genotype.

Extracted genetic information allowed the genotyping of around800,000 single nucleotide polymorphism (SNPs) for each participant.

As noted above, we created a genome-wide allele score by calculatingthe weighted sum of the number of genetic variants they had thatwere associated with higher educational attainment (using the weightsderived from the GWAS).

Allele score mean 0.325, s.d. 0.101. Normalized to have mean zeroand standard deviation one. The allele score represents the effects ofthe known genetic variants on educational attainment.



Method – measuring education

Participants were asked re a college/university degree and if they did nothave a degree, were asked what age they left full-time education. (Degreeholders left after age 15 assumed. . . )

UK literature suggests the impact of the 1972 RoSLA was compelindividuals to stay just one more year, little or no ripple effect furtherup the distribution.

Participants also asked re qualifications; we map these to InternationalStandard Classification for Education (ISCED) levels and impute years ofschooling

Left at age 15Left at age 16 (O-levels)Left at age 18 (A-levels)Left at 20 (post secondary vocational training)Left at 21 (college degree)

ISCED levels used to classify education level into years to match theprevious epidemiology literature – use this measure for cross-sectional andMR estimates.



Method – 25 outcome variables

Group the outcome variables into those related to Morbidity, Mortality, HealthBehaviours, Income, Ageing, Blood Pressure, and Neuro-cognitive:

MorbiditySelf reported hypertension, diabetes, stroke, depressive episodesRegistry linked cancer diagnosis

MortalityNHS linked mortality records (7.75 years of follow-up)

Health behaviorsSmoking (current and ever)Alcohol consumptionExercise (weekly vigorous or moderate)Hours watching TV per day



Method – 25 outcome variables

IncomeHousehold, 4 item scale

Indicators of agingGrip strengthArterial stiffness

Blood PressureSystolic and diastolic

NeurocognitiveIntelligenceHappiness



Results – baseline characteristics



Method – statistical analyses

Estimate effect of education2SLS for continuous outcomesAdditive structural mean models for binary outcomes Clarke andWindmeijer (2012)Standard errors corrected for two-stage process.

Baseline results include gender, month of birth, and the 10 principalcomponents of population (genetic) stratification as covariates

Control for the most common sets of variation in genetic make-upwhich reflect regional differences and differences in ancestry.

(Results robust to inclusion of additional controls: breastfed, mothersmoked during pregnancy, birth weight, birth location and deprivation.Important re pleiotropy, especially birth-weight.)

Standard errors clustered by month of birth.



Method – statistical analyses

UK BioBank under-samples lower educatedUK BioBank: 41.0% degree, 64.0% any post-16 education, 82.1% anyqualificationsUK population: 27.9% degree, 61.8% any post-16 education, 76.5%any qualifications

In raw data only approx. 20% suggest leaving school at 15 compared toapprox. 25-30% in LFS. Use weights to adjust to population proportions.



Results – first stage

Participants affected by the RoSLA were 23.0% more likely to remain inschool (95% confidence interval: 21.7 to 24.4).

F-stats range from 788–2286 depending on the specification/outcome

Each unit increase in the raw Okbay (allele) score was associated with 1.36additional years of education (95% confidence interval: 1.29 to 1.44)

F-stats range from 288–1118 depending on the specification/outcome

In no case issue of weak instruments.

Also notable that the raw Okbay (allele) score has a sizeable impact on thetreatment variable (education) ⇒ increases chance that impacts can beidentified (see von Hinke et al, 2016).



Results – balancing tests for potential IV confounding

Bias component plots: plot the Wald estimate of associations betweeninstrument, education and outcomes: genotypic and phenotypic.Standardized scale for outcomes – Wald makes it impact of additional yearof schooling.

GenotypicConstructed weighted allele scores 45 traits (p<5e-5) – liberalthresholdExcluded variants within 500kb of the 74 Okbay SNPs

Phenotypic (background characteristics)Little information about pre-conception or family backgroundPlace of birth, breast-fed, mother smoked in pregancyBirthweight – most single measure of in-utero conditions, pre-birthcircumstancesAlso have comparative height and comparative size at age 10




No evidence of association between education and genotypic measures.RoSLA estimates less precise but cover zero.

Very similar for phenotypic results for RoSLA, imprecise estimates coverzero (apart from mother/father alive). Differences can be controlled inrobustness specifications.

Phenotypic characteristics associated with genetic variation small effectsand controlled in robustness specifications.

Comparative height at age 10 suggests dynastic/assortative mating effect,though comparative body size at age 10 does not correlate – pleiotropyrobust estimation shows reduced height effect.



Results – causal effect of education on health



Results – discussion

Causal impacts of education onHypertension, diabetes, stroke, heart attack, mortalitySmoking behaviourIncome: over £18k, over £31k for RoSLA; over £52k and £100k forgenetic variationGrip strength, BMIMR suggests lower blood pressureSmall impacts on other behaviours (alcohol, TV)



Results – discussion

Striking how similar the RoSLA and MR results are in many caseshypertension, diabetes, stroke, heart attack, ever/currently smoke,BMI, intelligence, TV

Results for the RoSLA IV would be biased by age effects (younger moreeducated and also less likely to have conditions) but diff-in-diff specificationremoves the year-to-year age effect.

MR results unlikely to be affected by this as IV not tied to age variation.



Results – robustness

With or without weighting for under-sampling of less educated robustness 1

Exclude controls for sex, month and year of birth, principal components ofgenetic stratification robustness 2

Include controls for breastfed, mother smoked during pregnancy,birthweight, birth location and deprivation robustness 3

Pleiotriopy robustness regression techniques: Inverse variance weighting,MR-Egger, Weighted median, Weighted mode robustness 4




Concern that impact of education allele score works through impact onintelligence (i.e. horizontal pleiotropy/endogeneity)




Instrument intelligence/cognition in addition to education.

Use a weighted genetic allele score for intelligence summing variants andusing coefficients from another GWAS as weights (as per main educationallele score).

Smaller sample size ⇒ less precise. F-statistics ranged from 16.1 to 20.9for both education and cognition, exceeded thresholds for multipleendogenous regressors. robustness 5

No evidence that education effect mediated through intelligence (i.e.education genes affect intelligence directly). It may be that child cognition→ child education → health outcomes but this is fine.



Discussion continued

Dynastic effects/assortative mating will impact results to the extent thatintergenerational transmission of education works through behaviours ofhigh educated parents.

Can get so far with pleiotropy robust methods but need genetic level datain two generations of same family to investigate further (possible, work inprogress. . . )

Pleiotropy robust methods find robust results for: mortality, heart attack,diabetes, smoking, income, grip strength, BMI, blood pressure, intelligenceand alcohol.

Limitation for both IV approaches is the non-random sample not ideal butcan correct.



Conclusions

Work in progress!

Strength and balance of confounders similar for MR as ROSLA.

Education variation due to MR and RoSLA similar size treatment andsimilar results.

MR affects education across the distribution, closer to ATE whereas RoSLAidentifies a LATE.

Similarity in estimates requires rethink of RoSLA LATE applicability andconsistency of results from different IVs supports causal effect of educationon health.



Measuring genetics. . .



Measuring genetics. . .

back



Data: UK Biobank sample

back



Robustness 1: With (darker)/Without (lighter) weighting



Robustness 1: With (darker)/Without (lighter) weighting

back



Robustness 2: With (darker)/Without (lighter) basecontrols



Robustness 2: With (darker)/Without (lighter) basecontrols

back



Robustness 3: With (darker)/Without (lighter) Phenotypiccontrols



Robustness 3: With (darker)/Without (lighter) Phenotypiccontrols

back



Robustness 4: Pleiotropy Robustness



Robustness 4: Pleiotropy Robustness

back



Robustness 5: Bivariate MR



Robustness 5: Bivariate MR

back


thecausaleﬀectsofeducationonadulthealth ...neil m. davies , matt dickson , george davey smith ,...

Documents