Educated people are generally healthier, have fewer comorbidities and live longer than people with less education.1–3 Much of the evidence about the effects of education comes from observational studies, which can be affected by residual confounding. Natural experiments, such as increases to minimum school leaving age laws, 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 morbidity.1,2,4 Here, we add to the evidence using data from a large sample from the UK Biobank.5 We exploit the raising of the minimum school leaving age in the UK in September 1972 as a natural experiment.6 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 risk of diabetes and mortality in all specifications. We do not know if the differences in outcomes across education groups is because education directly causes these outcomes, by affecting behaviors, such as smoking, or if these differences are due to other factors, such as socioeconomic or genomic differences. Whether education causes differences in outcomes later in life has been the subject of considerable debate by epidemiologists, economists and other social scientists.1–3,7–17 Economists have argued that in addition to its effects on income, a substantial portion of the benefits of education accrue via its potential effects on mortality and morbidity.3 Epidemiologists have found that people who attended university have higher fluid intelligence in adulthood.18 These associations are robust to adjustment for parental social class and adolescent cognition, which has been taken by some as proof that education causes later outcomes.19 Despite this, many epidemiologists and economists are acutely aware that correlations and multivariable adjusted regressions can be unreliable evidence of causation.20–22 The ideal experiment to test this hypothesis, randomizing the age at which children leave school, is unlikely to be ethical, cost-effective, or timely. A more feasible, and potentially robust, research design is to exploit natural experiments that affected when people left school but are not related to confounding factors.23,24 One widely used natural experiment are changes to the legal minimum school leaving age. These changes forced some people to stay in school for longer than they would have otherwise chosen. In September 1972, the school leaving age increased from age 15 to 16 for children in England. Before the reform, the vast majority of those who left school at age 15 went into the labor force and found employment. The 1971 census indicated that in April 1971 32% of 15-year olds were non-students, of whom 87% were in the labor force. At this time, the unemployment rates in this group were 21.7% and 14.9% for males and females respectively.25 Government discussions at the time of the reform raised concerns at the impact of the immediate withdrawal of 400,000 15-year olds from the labor force as a result of the reform. School leavers at this time were strongly attached to the labor market.26 Researchers have previously used this policy change to investigate the effects of forcing students to stay in school longer using administrative data and longitudinal cohort studies.2,4,27,28 However, the cohort studies had relatively small samples and, as a result, produced relatively imprecise estimates of the effects of education. Previous results from administrative data lacked detailed information needed to identify people born in England affected by the reform, or on many outcomes of interest such as cognition or clinical measures of aging such as grip strength. In the current study, we used the raising of the school leaving age in 1972 as a natural experiment to estimate the causal effects of schooling. We used a regression discontinuity design and data from the UK Biobank.29,30 We add to the literature in two ways. First, this is the largest sample with detailed individual-level information from the school years immediately before and after the reform. Second, we used genome-wide data to demonstrate that the observational associations of education and other outcomes are likely to suffer from genomic confounding. Of the 502,644 participants in the UK Biobank, who were all aged between 37 and 74 at recruitment in 2008, 390,412 were born in England, (see Supplementary Figure 1 for a flow diagram of inclusion and exclusion of participants in this study, and Supplementary Table 1 for a description of their characteristics). The youngest participants, those born between 1960 and 1971, obtained more education than those born earlier in the twentieth century (Figure 1). This is consistent with the well-documented secular increase in the length of education over the period.2 UK Biobank includes 11,240 and 10,898 participants who turned 15 years old in the last year before and the first year after the school leaving age increased. Before the reform, 85% of participants remained in school after the age of 15, whereas after the reform almost 100% of participants remained in school after the age of 15. The proportions of men and women who remained in school after age 15 increased over time (Supplementary Figure 2). Participants born in July and August could still technically leave school before their 16th birthday, this is why participants born in the summer term were more likely to report leaving school before the age of 16. People who remained in school after age 15 had higher birth weights, their mothers were less likely to smoke during pregnancy, were more likely to have been breastfed, were more likely to have parents who were alive, and had fewer siblings (Supplementary Table 2). In addition they had more genetic variants (single nucleotide polymorphism (SNPs)) known to associate with higher educational attainment31 (Supplementary Table 2). This suggests that the association of educational attainment and later outcomes will suffer from residual genomic confounding. In comparison, there were few detectable pre-existing differences between people affected and unaffected by the reform. The only detectable difference was that the parents of participants in the first year affected by the reform were more likely to be alive when they attended the assessment center in 2008-2010 (4.3 95% confidence intervals (95%CI): 2.5 to 6.1) and 3.7 (95%CI: 2.6 to 4.8) percentage points for father and mother respectively). These associations could be due to age effects, because on average the parents of those in the first year affected by the reform will be a year younger than parents’ of those in the previous school year. Alternatively, having more educated, and potentially richer offspring may increase parents’ longevity, perhaps via improved care.32 There was some evidence that fewer participants in the younger cohort were breastfed. On average, participants in the cohorts before and after the reform had similar numbers of education associated genetic variants. This suggests that associations of the reform and later outcomes are unlikely to suffer from residual genomic confounding. The participants affected by the reform are, by definition, an average of one year younger than those who were not affected. The raw differences above do not account for this age difference. There was little evidence of manipulation around the discontinuity (McCrary robust bias-corrected regression discontinuity manipulation test p=0.21).33 In this section we report two comparisons: first, the differences between participants who chose to stay in school after the age of 15 and those who left, and second, the regression discontinuity results. The regression discontinuity results are the difference between participants not affected by the reform (those born before September 1957) and those affected by it (those born in or after September 1957). On average, participants who chose to stay in school after age 15 had better outcomes later in life. They were less likely: to be diagnosed with hypertension, diabetes, a stroke or a heart attack, to die, smoke or have ever smoked, and were more likely to be diagnosed with depression (left columns in Table 1). Rates of cancer diagnoses were similar across education levels. Participants who remained in school had stronger grips, lower arterial stiffness, and lower systolic and diastolic blood pressure. They also reported higher incomes, were taller, thinner, achieved higher scores on the intelligence test, drank more, watched less television, and exercised less. There was little difference in happiness. Turning to the regression discontinuity results, there was little evidence that the reform affected rates of depression, diastolic blood pressure, and rates of moderate and vigorous exercise (right columns in Table 1). For the other outcomes, the effect of the reform was consistent in direction with the association of choosing to remain in school and the outcomes. We found some evidence that the reform may have had a larger effect on male’s likelihood of earning more than £31,000 (p-value for interaction=0.008), but little evidence of interactions by gender with any other outcomes (Supplementary Tables 3 and 4). There was some evidence that the reform had larger effects on participants predicted to leave before the age of 16: specifically increasing the likelihood of earning over £18,000 or £31,000, increasing grip strength and happiness, and alcohol consumption (Supplementary Table 5). As a sensitivity analysis we repeated the analyses reported in Table 1 using Calonico, Cattaneo, and Titiunik (2014) optimal bandwidths (reported in Supplementary Tables 6, sex stratified in 7 and 8). These bandwidths are calculated using each outcome and the running variable (the difference between the participant’s date of birth and 1st of September 1957 in months). They minimize the mean squared error of the estimates. The bandwidths ranged from 24 to 65.4 months, greater than the 12 months used for the results above. These analyses allow for differential linear time trends either side of the reform. This substantially increased the sample size and statistical power (standard errors fell by a factor of between 1.25 and 4). The results were consistent in direction with the main results reported in Table 1, except for cancer, income over £100,000 and happiness. However, these differences are consistent with sampling error. Supplementary Tables 9, 10 and 11 provide the results for the regression discontinuity results using a one year bandwidth without using inverse probability weights (see methods below). The associations reported in Table 1 are valid tests of the null hypotheses that education does not affect the outcomes. However, these associations are not informative about the size of the effect of remaining school. We estimated the effect of remaining in school using instrumental variable analysis. Participants affected by the reform were 23.0 (95%CI: 21.7, 24.4) percentage points more likely to remain in school past age 15 than those who were unaffected. This suggests that these analyses are unlikely to suffer from weak instrument bias (min partial F-statistic=811). In Supplementary Table 12 we report instrumental variable estimates of the effect of remaining in school past the age of 15. The instrumental variable estimates are consistent in direction with the effect of the reform described above. There was evidence that the linear regression overestimated the effect of remaining in school on rates of ever or current smoking, income, intelligence, sedentary behavior, and exercise (all Hausman test for difference p 0]. This requires that there were no participants who were “defiers”, who would have remained in school if they were not affected by the reform, but would have left school if they were affected by the reform. Under monotonicity, the instrumental variable estimators estimate a local average treatment effect. This is the effects of treatment in the sub-group of participants whose decisions were affected by the reform.48 That is the people in the year after the reform who would have chosen to leave school at 15 had the reform not been introduced. Finally, we could assume that the effects of education are not affected by the reform (no effect modification). This would identify the effects of education on participants who remained in school. We report the partial F-statistic of the association of remained in school Eict and the reform Dic . We also report the test for endogeneity (using a C-statistic, which is a heteroskedasticity robust Hausman test 59,60, that E[Eict wict ] = 0). This implicitly tests for differences between the linear regression and instrumental variable estimates.60 All estimates allow for clustered standard errors by year and month of birth and include controls for sex and month of birth. C Difference-in-difference We were concerned that differences between the two school years may occur because of the participants affected by the reform were a year younger on average than participants unaffected by the reform. To investigate this, we estimated the year-on-year differences in each outcome for the five non-overlapping two-year cohorts in the 10 years before and after the reform. Otherwise, we used an identical specification to the regression discontinuity analysis above. There are no changes to the school leaving ages between each of these years. Therefore any year-on-year differences observed in these “negative control cohorts” must be due other factors, such as age effects, and cannot be an effect of raising the school leaving age in 1972. We compared these estimates using forest plots, which are reported in the supplementary materials. We pooled the year-on-year differences from the 5 negative control samples from before and after the reform using the Stata command metan. We calculated the difference between this pooled estimate and difference between the years before and after the reform. We estimated the difference and the standard error of this difference using Bland-Altman tests.61 Supplementary Material Reporting summary Supplementary materials
