The authors of this book belong to the elite of British economic policymaking. John Kay has held some of the UK’s leading academic, think-tank and advisory positions. Mervyn King completed a long career at the Bank of England by serving as its governor for 10 years, spanning the global financial crisis.
Forty years after they first wrote a book together, they have joined forces again to produce a rant. An eloquent, highbrow, entertaining and enlightening rant, but a rant all the same. The authors’ bugbear is the standard approach to uncertainty in economics and related disciplines, which requires a comprehensive list of possible outcomes with well-defined numerical probabilities attached.
This is an impoverished and, at times even fraudulent, approach to decision-making, they argue. Apart from stable and repeated situations, they explain, probabilities do not exist; or they and their possible outcomes are unknowable; or all the above at once. All that probabilistic analysis does in other cases — usually where good decision-making matters most — is, at best, give a false sense of precision. By an arrogation of impossible scientific respectability, probability analysis can turn evidence-based policy into policy-based evidence.
This is a necessary critique and they make it with verve, knowledge and a wealth of stories, ranging from former US president Barack Obama’s decision whether to launch the attack to kill Osama bin Laden, who may or may not have been in the compound in Abbottabad; to whether to build the UK’s HS2 rail project; all via the global financial crisis and a Goldman Sachs executive who claimed that such market drops were “25-sigma” (inordinately improbable) events.
In that wealth of anecdote, however, the exact object of their criticism gets a little blurred. Are they aiming at the use of incorrect probabilities, such as when investors valued mortgage-backed securities on the basis that US house prices could not fall nationwide as never before? Or at thinking that a complex economy can be adequately captured by stylised mathematical models? Or at any attempt to put numbers on an uncertain future?
These are all different claims. Yet one can think of delineated problems where each of these approaches has its legitimate application — indeed the book includes many of them.
It laments, for example, that, as big data becomes ubiquitous, so much can be known about any individual that conventional insurance becomes impossible — there will not be enough uncertainty to pool risk. But that is only so if the probabilistic methods that are used in our still-insurable world are indeed useful enough that we should regret their passing.
They also criticise the rules for corporate contributions to employer pension schemes. They are right that it is silly to think we can imagine all possible futures decades down the line, let alone put probabilities on them. But what should pension providers do? They suggest one may not want to have a system of employer-based pension at all: “Mutualisation [of pensions] will in future be based on other criteria, whether through the state or other collective entities”. But even such arrangements cannot responsibly decline attempts to quantify future outcomes in some way or another.
Or the burning issue du jour: how should we make policy in the face of coronavirus? Kay and King would rightly dismiss the notion of an objective, knowable probability distribution of epidemiological and economic outcomes. But surely any responsible approach includes tentatively putting numbers on these and the likelihood that they will ensue under different policies.
Their alternative to probability models seems to be, roughly, experienced judgment informed by credible and consistent “narratives” in a collaborative process. They say little about how those exercising such judgment would be held to account. The argument would be more convincing if it also explained, say, how this approach enabled the Bank of England to leave the UK well-prepared for the financial crisis, or why it wasn’t employed.
If, however, it just comes down to not giving formal models the final word, one cannot disagree with that.
Martin Sandbu is the FT’s European economics editor
Radical Uncertainty: Decision-Making Beyond the Numbers, by John Kay and Mervyn King, Norton, $30, 384 pp
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