Toby Young is in the news again. The Tories’ favourite born again educationalist has been given a seat on the board of Jo Johnson’s new universities “regulator”, the Office for Students. This gives me an excuse to publish something I failed to put out last time he was in the news, for claiming that schools can’t do much to reduce inequalities in attainment.
Toby Young has been at the centre of some controversy about the ability of schools to help disadvantaged children. He wrote an article for Teach First arguing that schools can’t really achieve much, which the charity subsequently took down because they disagreed with it. The article is now published on Toby’s blog, and he has been anointed by some as a free speech martyr (although he very modestly says that “martyr is putting it a bit strongly”).
But what about that article? Is it right?
There are a few different threads to it, including Toby’s usual futurology about IQ-enhancing drugs, but the central claim about the efficacy of schools is based on research that attributes variation in GCSE results to different causes. According to Toby, this research finds that IQ accounts for 60-70% of the observed variation in results, differences between schools (such as funding, class size and quality of teachers) account for 10% and the other 20-30% is accounted for by other environmental factors.
I don’t know this research so I’ll leave it for others to debate whether it’s any good and whether Toby is describing it correctly. The results are presumably from a multiple regression of observational data, so the usual caveats about causation versus correlation and unobserved variables will apply. But let’s set that to one side and take the results at face value: what do they mean for schools policy?
The conclusion Toby draws is a tempting one: that schools can’t do much to ameliorate the effects of inequalities. I think that’s the wrong conclusion to draw from these numbers for three reasons, which I’ll address in order of increasing complexity.
The first is trivial: reducing inequalities in attainment by 10% sounds like a major achievement to me. We should do this! (In fact it may be slightly unfair to suggest Toby is arguing otherwise.)
The second is more subtle and requires us to think about what those numbers actually mean. 10% of the observed variation in GCSE results is accounted for by the observed variation in school characteristics. So if we were to equalise all schools on these characteristics (things like funding, class size and quality of teachers) then variation in results would reduce by 10%. If the only intervention we could possibly make in schooling was to equalise these things across schools, then we could only eliminate 10% of current variation in attainment. But this isn’t the only thing we can do. What if we made schools in deprived areas better than those in more affluent areas? What if we gave additional help to the children who face the greatest disadvantages at home? The 10% figure tells us nothing about the efficacy of these things.
The third also relates to the way that 10% figure is constructed. It’s the variation attributed to differences between schools divided by total observed variation, so it’s a function of three things: how big the differences are between schools, how strong an effect school differences have on attainment, and how much variation there is from other sources (like home environment and IQ). So we can’t just look at the 10% figure and say that’s a small number so schools can’t have a strong effect on children’s attainment. If our schools were much more unequal in funding and class size then this number would go up, while if they were identical on these measures it would go down to zero – but these changes would tell us nothing about the power of education to drive attainment. If we were able to reduce variation due to other environmental factors (say, by reducing income inequality between the families of schoolchildren) then the 10% schools figure would increase. This would not mean that schools had become more effective at driving attainment.
So taking all of this into account, what can these figures tell us about schools policy? What would we do differently if this figure were 50% instead of 10%? It seems to me that the answer to both these questions is “very little”. Either way we should make sure that already-disadvantaged children don’t end up in schools that have fewer resources, larger class sizes and worse teaching, since these factors do compound their disadvantage. Either way we should consider helping disadvantaged children with targeted policies, about which these numbers tell us nothing. Where interventions cost money, we will want to know if the effect size is large enough to justify that expenditure, but these numbers tell us nothing about the absolute effect size of school interventions.
In fact, it’s hard to conclude that these numbers are much use at all for policy. The nature versus nurture debate has become an ideological battleground, but its relevance to education policy seems very limited. We can’t control nature, but we can decide how to nurture our children. We can do that by designing, testing and implementing good education policies. Whether in the end these policies explain more or less of the population-level variation in attainment than genetics is rather beside the point, if they are effective and cost-effective at improving attainment and reducing inequalities. It’s easy to see this if you consider an example from another policy area: if you have a demonstrably cost-effective behavioural intervention that reduces the chances of high-risk people developing cancer, but I tell you that only 10% of the observed variation in cancer risk is related to behaviours, does that have any bearing on whether you implement your policy?