“People in this country have had enough of experts.” So said Michael Gove, then Justice Secretary, a month before the EU Referendum. The referendum was as much a referendum on experts as on the EU, dominated by two competing claims expressed as numbers: £4,300, which was produced by experts, and £350 million, which wasn’t. £350 million is a big number, much bigger than £4,300, especially when painted on the side of a bus.
The big number won, even though the big number was really the much smaller number, and according to almost all experts the wrong number. £4,300 was an estimate of the annual loss to the economy of leaving the EU, divided by the number of households. £350 million was claimed to be the amount ‘sent to’ the EU by the UK each week. With around 27 million households in the UK, £350 million equates to around £680 per household per year. Converted the other way, £4,300 per household per week equates to around £2,200 million per week, more than six times as much. And as a number of exasperated experts pointed out, the net contribution per week was nearer £160 million, implying around £315 per year per household, a more than ten-fold difference between the cost and benefit of leaving the EU. Delve into the 200 page technical report which gave rise to the £4,300 claim, and on page 158 we’re told that the ‘basic specification’ of the model used is:
ln(T_ijt ) = αij+ γt + α1 (Yit * Yjt) + α2 ln (POPit * POPjt) + α3 ln(DISTij) + α4 COMLANGij + α5 COLONYij + α6 BORDERij + ϵijt
Clear? Simple? Persuasive? The £350 million claim might have been false – clearly, demonstrably so – but at least it was comprehensible. By contrast, without expertise in econometrics and a willingness to spend a weekend digging through algebra and databases, for much of the general public the £4,300 claim had to be taken on trust: trust in economists, trust in politicians, trust in experts. The £4,300 claim fell, and the £350 million claim rose, no matter how often we experts tried to shoot it down. The academically trained economists at HM Treasury, and elsewhere, weren’t trusted as experts (perhaps justifiably given some of the modelling assumptions). If the EU referendum was about expertise, the experts lost.
To convince the general public of our expertise and relevance, we need to make suitable products. As well as spending years writing tightly referenced monographs and months writing academic articles for ourselves, we also need to be more willing, and more supported, in writing books, newspaper articles and blog posts for everyone else. We need to react to the news cycle rather than stand apart from it. This means learning to do many things quickly, not just some complicated things slowly. And we need to be able to say things clearly and simply. Quick and simple work may not win 4* REF assessments, but it can help win the public over. Ultimately, we are public servants, working for publicly funded institutions. Public engagement is not an optional extra, but a duty.
Here’s an example of doing and saying something simple: The figure below shows how GDP per person changed in the UK from 1950 to 2015. The line shows the trend from 1950 to 2008, and the points the actual values. Put simply – and we have to – something changed after 2008, we stopped getting richer as a nation as we used to, and if we were still getting richer the way we had been since the 1950s, there would be around £5,000 more in the UK per person. This wasn’t about migration, which rose after 2004, but something else.
The last sentence, with its simple statements, likely matters more than the figure. It’s the narrative, the claim I’m making, the way I want readers to think differently about the world. I would love to tell people that the R-squared on the regression model was over 0.98, the data sources I used, and my equivocation about whether to inflation adjust the GDP estimates and if so using which inflation index, but I won’t, as such details get in the way of the message.
To do simple things quickly, we need new skills in both the production and communication of information. For quantitative social scientists, this means learning how to fit the pipes connecting raw data to new insight together faster. Just as Keynes hoped that eventually economists would win a spot alongside dentists in public rankings of expertise, perhaps we should all aspire to be more like plumbers.
Submitted by Jonathan Minton, University of Glasgow on Monday, 28th November 2016