Marie Antoinette and Piketty’s French revolution
The advent of “big data” is a well-documented aspect of our digital age. But the abuse of statistics is also a well-known phenomenon; telling lies with numbers predates the dawn of the Internet. All sorts of data traps are set for the unwary that lead to muddled thinking and poor policymaking.
One of the most common fundamental errors of analysis is where we mix up correlation with causation. Simply because two events happen at the same time does not mean one causes the other. Even if there is a causal link, we often can’t figure out which event causes which.
Economists have struggled with this for as long as there has been data and have even given the problem a Latin name: Post Hoc Ergo Propter Hoc, (after this, because of this). Take for example Vincent Browne’s recent statistical analysis, in this newspaper, of Premier League soccer success. Vincent argues that the size of a club’s wage bill “determines” or “explains” how many trophies a team will win; the manager is largely irrelevant.
In which case I suppose the best thing that David Moyes’s successor should do would be to double the wages of each member of the existing squad and wait for Champions League glory to inevitably follow. QPR fans would in all likelihood demur, pointing out that their club tried this with not too successful results. High wages, of course, are highly correlated with success. But the causal picture is much more complicated than this correlation might suggest. The best players might always attract the highest wages but choose to only work for the best managers.
The latest data set to attract remarks about both correlation and causation – and a related storm of debate – is contained in the work of Thomas Piketty, summarised in his best-selling work, just published in English, Capital in the 21st Century. It is a monumental study, a feat of scholarship not seen for a long time. And, for a modern work of macroeconomics, mercifully free of stochastic calculus. This week alone, Piketty has been feted at Ivy League universities in the US and interviewed in Britain by Jeremy Paxman.
Piketty has put much flesh upon what many of us have been writing about for some time: inequality in many economies has been on the increase; sometimes back to levels not seen for nearly a century. His policy prescriptions have attracted the most publicity: higher income taxes and a global wealth tax.
One of the most depressing things about the response to Piketty is that it has fallen along predictable lines: we could have forecast what Paul Krugman would have made of Piketty’s conclusions (he liked them). Equally, it would have been straightforward to predict that commentators associated with Fox news in the US would neither read nor admire the French economist’s work. More generally, it is somewhat sobering to reflect on just how many preconceived notions have, historically, been affected by new data. Just how many people change their minds when confronted with evidence that offends their ideologies? I can’t claim to have read every review or of Piketty, but of the dozens I have ploughed through, not one has said “I used to believe x but Piketty has shown I was wrong”. In fact, I cannot think of many empirical studies in finance or economics where this sort of change of mind has occurred.
Piketty’s work deserves serious thought. It is a simple fact that inequality is on the rise. We might ask, as Paxman did of Piketty, “why should I care about this”? The answer will determine what should be done about it. Whether inequality is a cause or consequence of capitalism is another, very important, correlation/causation problem. The level of inequality that will generate social unrest is a very pragmatic question, in part answerable by data (just ask Marie Antoinette).
We might ask why some of the most unequal societies on earth, the US and UK, have their domestic political debates utterly poisoned by the immigration debate: why, if things are so awful, so unequal, do so many people want to live there? The relationships between inequality, growth, political freedom and economic efficiency are tricky in the extreme.
Whatever the past causes of inequality, the “rise of the robots” – technology that is destroying traditional jobs – is only going to make things a whole lot worse.