Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The causal effects of education on health outcomes in the UK Biobank


Educated people are generally healthier, have fewer comorbidities and live longer than people with less education1,2,3. Much of the evidence about the effects of education comes from observational studies, which can be affected by residual confounding. Natural experiments, such as laws that increase the minimum school leaving age, are a potentially more robust source of evidence about the causal effects of education. Previous studies have exploited this natural experiment using population-level administrative data to investigate mortality, and surveys to investigate the effect on morbidity1, 2,4. Here, we add to the evidence using data from a large sample from the UK Biobank5. We exploit the raising of the minimum school leaving age in the UK in September 1972 as a natural experiment6. We used a regression discontinuity design to investigate the causal effects of remaining in school. We found consistent evidence that remaining in school causally reduced the risk of diabetes and mortality in all specifications.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Years of full-time education by quarter of birth.
Fig. 2: The effect of the reform on each outcome estimated using the difference-in-difference approach accounting for age effects.
Fig. 3: The effect of the 1972 reform in Clark and Royer2 and UK Biobank.


  1. 1.

    Lager, A. C. J. & Torssander, J. Causal effect of education on mortality in a quasi-experiment on 1.2 million Swedes. Proc. Natl Acad. Sci. USA 109, 8461–8466 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Clark, D. & Royer, H. The effect of education on adult mortality and health: evidence from Britain. Am. Econ. Rev. 103, 2087–2120 (2013).

    Article  Google Scholar 

  3. 3.

    Cutler, D. & Lleras-Muney, A. Education and Health: Evaluating Theories and Evidence (National Bureau of Economic Research, 2006).

  4. 4.

    Dickson, M. The causal effect of education on wages revisited: the causal effect of education. Oxf. Bull. Econ. Stat. 75, 477–498 (2013).

    Article  Google Scholar 

  5. 5.

    Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).

    Article  PubMed  Google Scholar 

  6. 6.

    Harmon, C. & Walker, I. Estimates of the economic return to schooling for the United Kingdom. Am. Econ. Rev. 85, 1278–1286 (1995).

    Google Scholar 

  7. 7.

    Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Conti, G., Heckman, J. & Urzua, S. The education–health gradient. Am. Econ. Rev. 100, 234–238 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Naess, O., Hoff, D. A., Lawlor, D. & Mortensen, L. H. Education and adult cause-specific mortality—examining the impact of family factors shared by 871 367 Norwegian siblings. Int. J. Epidemiol. 41, 1683–1691 (2012).

    Article  PubMed  Google Scholar 

  10. 10.

    Meghir, C., Palme, M. & Simeonova, E. Education, Health and Mortality: Evidence from a Social Experiment (National Bureau of Economic Research, 2012).

  11. 11.

    Nordahl, H. et al. Education and cause-specific mortality: the mediating role of differential exposure and vulnerability to behavioral risk factors. Epidemiology 25, 389–396 (2014).

    Article  PubMed  Google Scholar 

  12. 12.

    Mackenbach, J. P. et al. Variations in the relation between education and cause-specific mortality in 19 European populations: a test of the ‘fundamental causes’ theory of social inequalities in health. Soc. Sci. Med. 127, 51–62 (2015).

    Article  PubMed  Google Scholar 

  13. 13.

    Strand, B. H. et al. Educational inequalities in mortality over four decades in Norway: prospective study of middle aged men and women followed for cause specific mortality, 1960–2000. BMJ 340, c654 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Baker, D. P., Leon, J., Smith Greenaway, E. G., Collins, J. & Movit, M. The education effect on population health: a reassessment. Popul. Dev. Rev. 37, 307–332 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Spearman, C. ‘General intelligence,’ objectively determined and measured. Am. J. Psychol. 15, 201–292 (1904).

    Article  Google Scholar 

  16. 16.

    Davey Smith, G. et al. Education and occupational social class: which is the more important indicator of mortality risk? J. Epidemiol. Community Health 52, 153–160 (1998).

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Schafer, M. H., Wilkinson, L. R. & Ferraro, K. F. Childhood (mis)fortune, educational attainment, and adult health: contingent benefits of a college degree? Soc. Forces 91, 1007–1034 (2013).

    Article  Google Scholar 

  18. 18.

    Clouston, S. A. et al. Benefits of educational attainment on adult fluid cognition: international evidence from three birth cohorts. Int. J. Epidemiol. 41, 1729–1736 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Richards, M. & Sacker, A. Is education causal? Yes. Int. J. Epidemiol. 40, 516–518 (2011).

    Article  PubMed  Google Scholar 

  20. 20.

    Davey Smith, G. & Ebrahim, S. Epidemiology—is it time to call it a day? Int. J. Epidemiol. 30, 1–11 (2001).

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Leamer, E. Let’s take the con out of econometrics. Am. Econ. Rev. 73, 31–43 (1983).

    Google Scholar 

  22. 22.

    Deary, I. J. & Johnson, W. Intelligence and education: causal perceptions drive analytic processes and therefore conclusions. Int. J. Epidemiol. 39, 1362–1369 (2010).

    Article  PubMed  Google Scholar 

  23. 23.

    Angrist, J. D. & Krueger, A. B. Instrumental variables and the search for identification: from supply and demand to natural experiments. J. Econ. Perspect. 15, 69–85 (2001).

    Article  Google Scholar 

  24. 24.

    Nguyen, T. T. et al. Instrumental variable approaches to identifying the causal effect of educational attainment on dementia risk. Ann. Epidemiol. 26, 71–76.e3 (2016).

    Article  PubMed  Google Scholar 

  25. 25.

    Layard, R. in The Youth Labor Market Problem: Its Nature, Causes, and Consequences (eds Freeman, R. B. & Wise, D. A.) 499–542 (Univ. Chicago Press, Chicago, IL, 1982).

  26. 26.

    McCulloch, G., Cowan, S. & Woodin, T. The British Conservative Government and the raising of the school leaving age, 1959–1964. J. Educ. Policy 27, 509–527 (2012).

    Article  Google Scholar 

  27. 27.

    Powdthavee, N. Does education reduce the risk of hypertension? Estimating the biomarker effect of compulsory schooling in England. J. Hum. Cap. 4, 173–202 (2010).

    Article  Google Scholar 

  28. 28.

    Jürges, H., Kruk, E. & Reinhold, S. The effect of compulsory schooling on health—evidence from biomarkers. J. Popul. Econ. 26, 645–672 (2013).

    Article  Google Scholar 

  29. 29.

    Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012).

    Article  PubMed  Google Scholar 

  30. 30.

    O’Keeffe, A. G. et al. Regression discontinuity designs: an approach to the evaluation of treatment efficacy in primary care using observational data. BMJ 349, g5293 (2014).

    Article  PubMed  Google Scholar 

  31. 31.

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Torssander, J. From child to parent? The significance of children’s education for their parents’ longevity. Demography 50, 637–659 (2013).

    Article  PubMed  Google Scholar 

  33. 33.

    McCrary, J. Manipulation of the running variable in the regression discontinuity design: a density test. J. Econom. 142, 698–714 (2008).

    Article  Google Scholar 

  34. 34.

    Calonico, S., Cattaneo, M. D. & Titiunik, R. Robust nonparametric confidence intervals for regression-discontinuity designs: robust nonparametric confidence intervals. Econometrica 82, 2295–2326 (2014).

    Article  Google Scholar 

  35. 35.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).

    Google Scholar 

  36. 36.

    National Life Tables, UK Statistical Bulletins (Office for National Statistics, accessed 21 February 2017);

  37. 37.

    Richards, M. & Sacker, A. Lifetime antecedents of cognitive reserve. J. Clin. Exp. Neuropsychol. 25, 614–624 (2003).

    Article  PubMed  Google Scholar 

  38. 38.

    Gilman, S. E. et al. Educational attainment and cigarette smoking: a causal association? Int. J. Epidemiol. 37, 615–624 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Turley, P. Heterogeneous Impacts of Education on Health. PhD dissertation, Harvard Univ. (2016).

  40. 40.

    Pei, Z., Pischke, J.-S. & Schwandt, H. Poorly Measured Confounders are More Useful on the Left Than on the Right NBER Working Paper No. 23232 (National Bureau of Economic Research, 2017);

  41. 41.

    Rothman, K. J., Gallacher, J. E. & Hatch, E. E. Why representativeness should be avoided. Int. J. Epidemiol. 42, 1012–1014 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Cole, S. R. et al. Illustrating bias due to conditioning on a collider. Int. J. Epidemiol. 39, 417–420 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Imbens, G. W. & Angrist, J. D. Identification and estimation of local average treatment effects. Econometrica 62, 467–475 (1994).

    Article  Google Scholar 

  44. 44.

    Clarke, P. S. & Windmeijer, F. Instrumental variable estimators for binary outcomes. J. Am. Stat. Assoc. 107, 1638–1652 (2012).

    CAS  Article  Google Scholar 

  45. 45.

    Deaton, A. Instruments, randomization, and learning about development. J. Econ. Lit. 48, 424–455 (2010).

    Article  Google Scholar 

  46. 46.

    Imbens, G. W. Better late than nothing: some comments on Deaton (2009) and Heckman and Urzua (2009). J. Econ. Lit. 48, 399–423 (2010).

    Article  Google Scholar 

  47. 47.

    Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).

  48. 48.

    Hagenaars, S. P. et al. Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia. Mol. Psychiatry 21, 1624–1632 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Angrist, J. D., Imbens, G. W. & Rubin, D. B. Identification of causal effects using instrumental variables. J. Am. Stat. Assoc. 91, 444–455 (1996).

    Article  Google Scholar 

  50. 50.

    Wooldridge, J. M. Asymptotic properties of weighted M-estimators for variable probability samples. Econometrica 67, 1385–1406 (1999).

    Article  Google Scholar 

  51. 51.

    Solon, G., Haider, S. J. & Wooldridge, J. M. What are we weighting for? J. Hum. Resour. 50, 301–316 (2015).

    Article  Google Scholar 

  52. 52.

    Canan, C., Lesko, C. & Lau, B. Instrumental variable analyses and selection bias. Epidemiology 28, 396–398 (2017).

    Article  PubMed  Google Scholar 

  53. 53.

    Buscha, F. & Dickson, M. The Wage Returns to Education over the Life-Cycle: Heterogeneity and the Role of Experience (Institute for the Study of Labor (IZA), 2015).

  54. 54.

    Denman, J. & McDonald, P. Unemployment statistics from 1881 to the present day. Labour Market Trends 104, 5–18 (1996).

    Google Scholar 

  55. 55.

    Cattaneo, M. D., Jansson, M. & Ma, X. rddensity: manipulation testing based on density discontinuity. Stata J. (in the press);

  56. 56.

    Keller, M. C. Gene × environment interaction studies have not properly controlled for potential confounders: the problem and the (simple) solution. Biol. Psychiatry 75, 18–24 (2014).

    Article  PubMed  Google Scholar 

  57. 57.

    Lee, D. S. & Lemieux, T. Regression discontinuity designs in economics. J. Econ. Lit. 48, 281–355 (2010).

    Article  Google Scholar 

  58. 58.

    Hernán, M. A. & Robins, J. Instruments for causal inference: an epidemiologist’s dream? Epidemiology 17, 360–372 (2006).

    Article  PubMed  Google Scholar 

  59. 59.

    Hayashi, F. Econometrics (Princeton Univ. Press, Princeton, NJ, 2000).

  60. 60.

    Hausman, J. A. Specification tests in econometrics. Econometrica 46, 1251–1271 (1978).

    Article  Google Scholar 

  61. 61.

    Altman, D. G. & Bland, J. M. Interaction revisited: the difference between two estimates. BMJ 326, 219 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Stata Statistical Software: Release 14 (StataCorp, 2015).

Download references


We thank the Social Science Genetic Association Consortium for providing the coefficients from the educational attainment genome-wide association study, and G. Hemani, L. Paternoster, D. Carslake, J. Bowden, L. Zuccolo, E. Stergiakouli and E. Sanderson for helpful comments on an earlier draft. All mistakes remain our own. The Medical Research Council (MRC) and the University of Bristol fund the MRC Integrative Epidemiology Unit (MC_UU_12013/1 and MC_UU_12013/9). N.M.D. is supported by the Economics and Social Research Council (ESRC) via a Future Research Leaders grant (ES/N000757/1). The research described in this paper was specifically funded by a grant from the ESRC for Transformative Social Science. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This work was carried out using the computational facilities of the Advanced Computing Research Centre ( and the Research Data Storage Facility of the University of Bristol ( This research was conducted using the UK Biobank Resource.

Author information




N.M.D. obtained funding for this study, analysed and cleaned the data, interpreted results, and wrote and revised the manuscript. M.D. interpreted the results and wrote and revised the manuscript. G.J.v.d.B. interpreted the results and wrote and revised the manuscript. G.D.S. obtained funding for this study, interpreted results and wrote and revised the manuscript. F.W. obtained funding for this study, interpreted results and wrote and revised the manuscript.

Corresponding author

Correspondence to Neil M. Davies.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Tables 1–14, Supplementary Figures 1–32, Supplementary References, Supplementary Methods.

Life Sciences Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Davies, N.M., Dickson, M., Davey Smith, G. et al. The causal effects of education on health outcomes in the UK Biobank. Nat Hum Behav 2, 117–125 (2018).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing