The UK and Europe
Data, statistics and immigration
Jonathan Portes / Apr 2017
I take data and statistics seriously – it’s part of my job as an academic, researcher and public policy commentator with a focus on quantitative analysis. But, as well as writing research papers, I also use print, broadcast and social media to try to explain why they matter. And one of the occupational hazards of this is frequent accusations that my focus on hard numbers means that I have my head in the clouds, or buried in a computer, and am therefore somehow detached from “reality”. I’ve lost count of the number of times a Twitter link to a chart, research paper or OECD data, making a point about what the evidence shows, is greeted by some version of “why don’t you stop looking at spreadsheets and get out into the real world?”
This is particularly the case for immigration, in the UK and elsewhere in Europe. The response to the overwhelming evidence that, in the UK, there is no measurable impact of immigration on employment, and only a very modest impact on the wages of the low-paid, is often an anecdote, a reference to (perceived) personal experience, or just an injunction that I should spend more time in the pub. This was particularly true during the recent EU referendum campaign in the UK, when the attempts of economists and other experts to explain what the data and evidence actually showed about trade and immigration were frequently dismissed.
Here’s a recent typical example (amazingly and embarrassingly, from a lecturer at King’s College London, my own university, in a scientific discipline):
“Jonathan Portes gave an accomplished performance, asserting that immigration has boosted society, and that it hasn’t depressed wages. Such thinking may be lapped up by a forum of intelligentsia, but try telling that to my landscape gardener friend, who has been severely undercut by waves of east Europeans.”
But there is a fundamental problem with the argument by those like Dr McCrae, that “spreadsheets” or “databases” are somehow divorced from reality, while the experiences of (selected) individuals represents it. In fact, spreadsheets – or at least the ones used by labour market economists and, indeed, quantitative social scientists more broadly, are far more closely connected to the “real world” than any individuals’ experience can hope to be.
Consider the Labour Force Survey (LFS), the primary data source for economists analysing the UK labour market (similar surveys exist in almost all developed economies). Each quarter the LFS samples 40,000 households, covering 100,000 individuals, a representative sample of (broadly) the UK resident population; lengthy interviews are conducted in person (and subsequently by phone) and cover a wide range of topics in considerable detail, from education, earnings and employment to age, marital and family status, country of birth, and disability.
So when I say that the evidence is clear that immigration doesn’t impact on the employment of UK-born residents, this analysis is formulated in terms of numbers on a spreadsheet or data points in a regression. But behind those numbers are what tens of thousands of real people have told professional interviewers, and in a way which means that the results are in turn representative of lived experience of the UK population as a whole.
So the statement that, say, immigration hasn’t, on average, made it more likely that a young British person is unemployed is not (just) a statement about numbers. It is a statement about what has actually happened to millions of people. It is the spreadsheet, not what my nephew looking for a job, your cab driver, or Dr McCrae’s landscape gardener friend say that best reflect the real world. As the great Hans Rosling put it:
“Statistics tells us whether the things we think and believe in are actually true”
None of this means that talking to people – or, more relevantly for these topics, rigorous qualitative research, which is generally not what the critics mean – is not useful and sometimes necessary to get a full picture. Indeed, such research – which is the opposite of relying on anecdote or personal experience- is often essential to understand the causal mechanisms at work, and hence to properly understanding the data. But suggesting that data doesn’t represent reality as well as personal experience is simply the opposite of the truth. If you want to know what’s actually going on in the real world, look at the data.