I’ve torn through the late Hans Rosling’s Factfulness, completed by his son and daughter-in-law, Ola Rosling and Anna Rosling Ronnlund. I wish everybody could read it. The message in a nutshell is: learn to reflect about the news you read/hear, and appreciate that there’s a difference between good and better.

The book is written around a multiple choice quiz Rosling administered online and to many audiences he spoke to, which revealed that large majorities of all kinds of audiences (including Nobel prize winners and the Davos elite) get certain basic facts very badly wrong. In a lot of ways, things are not at all great, around the world. But they are a lot better than they used to be, and a lot better than most people think they are. This applies to health and longevity, girls’ education, violent crime, and so on.

In terms of applying a bit of thought to the drama of the news, Rosling gives very practical advice: always think about the size of  number you hear – what is a good comparator, should it be a ratio; don’t always extrapolate in a straight line; don’t mistake the extremes for the typical experience; understand the power of exponential change. He means ‘factfulness’ as an analogue to ‘mindfulness’ (but much more worthwhile).

I like Rosling’s philsosophy: “The goal of higher income is not just bigger piles of money. The gola of longer lives is not just extra time. The ultimate goal is to have the freedom to do what we want.” I agree with his diagnosis that fear plays a big part in our responses to news: “Critical thinking is always difficult but it’s almost impossible when we’re scared.”

Although (to boast a bit) I got 12/13 on the quiz, I learned a lot from the book. For instance, I was struck by his argument that given we know 88% of the world’s children now get vaccinated, this tells us most countries can now sustain the logistics and infrastructure of the necessary cool chains; “This is exactly the same infrastructure needed to establish new factories,” Rosling writes. Yet 88% of the investors he quizzed thought only 20% of children get vaccinated so don’t know about that opportunity. He also argues that big pharma is missing a business opportunity in its focus on expensive new drugs for the richest markets, when they could offer older drugs at lower prices in the extensive middle income markets.

The book is also a delightful read, full of personal anecdote. One can hear his voice, which is a testament to the loving work his son and daughter-in-law put into preparing it for publication. Also worth a look is Anna Rosling Ronnund’s fascinating Dollar Street photography project.

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I’m thoroughly enjoying William Deringer’s Calculated Values: Finance, Politics and the Quantitative Age – almost finished. The book asks, why from the early 18th century did argument about numbers, statistics, come to have a special weight in political debate? Often, the growing role of statistics (as in numbers relevant to the state) is seen as part of the general Enlightenment spread of scientific discourse and rationality. Deringer argues that in fact the deployment of numerical calculation about the state of the nation was one of the weapons of choice in the bitter political division between Whigs and Tories. The sphere of public debate was, as he puts it, “uncivil, impolite, sometimes irrational”.

The book presents plentiful evidence of this use of numbers. For instance, pamphlets proliferated about hot issues such as the payment to be made to Scotland for its Union with England, or the balance of trade – and were packed with errors. Printers, and presumably readers, “did not seem to care about getting the numbers right.” They were only there to slug against somebody else’s numbers. Deringer writes (in the context of the fierce debate about the ‘Equivalent’ England should pay Scotland on their Union], “Critics of quantification in the modern era argue that efforts to address political questions through quantitative means often have the effect of foreclosing political controversy by translating substantive political questions into ‘technical’ ones. This was not the case in 1706; if anything, the opposite was true. The Equivalent calculation brought new (technical) questions into public view and revealed their political stakes.”

The book therefore argues that the emergence of partisan politics after the 1688 Revolution was a major reason for the emergence of the political life of numbers. Starting with a more or less blank slate, a good part of the contest was epistemological: what were the right categories and classifications to even begin to measure? What was the right methodology? For example, one innovation was the introduction of discounting future values, something many contemporaries found both hard to understand and unsettling: “It placed almotst no value on anything that happened beyond one human lifetime. This peculaira claim clashed violently with many Britons’ intuition about what the future was worth to them.” It probably still clashes with intuition, to the extent the typical Briton thinks about it.

Of course, one can’t read this history and not think about the notoriously misleading £350m claim on the Brexit bus, or the claim and counter-claim about the likely Brexit effect on UK trade. It isn’t that both sides are equally true or false – far from it – but the political clash is putting statistics centre stage. David Hume was sceptical about the usefulness of the quantitative debate for exactly this reason: “Every man who has ever reasoned on this subject, has always proved his theory, whatever it was, by facts and calculations.” I’m with Deringer in concluding that nevertheless the conversation about numbers is essential.

As Calculated Values concludes, “modern quantitative culture is fundamentally two-sided.” People think numerical evidence has a special, trustworthy status; and at the same time that it’s especially easy to lie with statistics. These are two sides of the same coin. The adversarial debate reveals how useful the numbers and calculations can be to interrogate and test the opposing claims.

Attitudes have shifted over time. In the 19th century, the partisan contest abated. Dickens for one saw numbers as a dry, soulless window on society when he portrayed Mr Gradgrind in Hard Times. The bureacratisation of statistics, and development of official agencies, made statistics seem just a tiny bit dull. Needless to say, they are centre stage again now for reasons of both political conflict and epistemological uncertainty. Once again, some politicians wield numbers without any great concern about their accuracy or meaningfulness; the victory in debate is all that matters. Once again, given the profound changes in the structure of the economy, we can’t be sure what categories and methods will give us the understanding we would like. This is a terrific book for reflecting on contested and uncertain statistical terrain.

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I thoroughly enjoyed reading Howard Wainer’s Truth or Truthiness: Distinguishing Fact From Fiction By Learning to Think Like a Data Scientist. I even laughed out loud occasionally, as there’s a lot of wit on display here, and one gets a strong sense of Wainer’s personality. This is not usual in a book about statistics (although having said that, Angrist and Pischke also do quite well on the clarity and fun front, especially for econometricians.)

Truth or Truthiness a collection of essays in effect, published as a response to this brave new world of truthiness (ie. lies that people believe because they want to) in politics and public debate. Wainer writes very clearly about statistics in general, and his main theme here, causal inference. This is of course dear to the heart of economists, and gratifyingly Wainer recognises that the profession is more scrupulous than most disciplines about causation. The book starts by underlining the importance of having a clear counterfactual in mind and thinking – thinking! – about how it might be possible to estimate the size of any causal effect. As Wainer puts it, “The real world is hopelessly multivariate,” so untangling the causality is never going to happen without careful thought.

I also discovered that one aspect of something that’s bugged me since my thesis days – when I started disaggregating macro data – namely the pitfalls of aggregation, has a name elsewhere in the scholarly forest: “The ecological fallacy, in which apparent structure exists in grouped (eg average) data that disappaears or even reverses on the individual level.” It seems it’s a commonplace in statistics – here’s one clear explanation I found. Actually, I think the aggregation issues are more extensive in economics; for example I once heard Dave Giles do a brilliant lecture on how time aggregation can lead to spurious autocorrelation results.

Having said how much I enjoyed reading Truth or Truthiness, I’m not sure who it’s aimed at who isn’t already really interested in statistics. For newcomers to Wainer, I’d recommend his wonderful earlier books, Picturing the Uncertain World, and Graphical Discovery. They’re up there with Edward Tufte’s books on intelligent visualisation (rather than the decorative visualisation that’s become unfortunately common).