Last week I attended an event in Geneva with an interesting premise — could its audience of international data experts prove that they were more familiar with trends in global statistics than a troop of chimpanzees? If you think it’s easy, try answering three simple multiple-choice questions.

This was the launch of Project Rosling, a Swiss Confederation initiative whose “beat the chimpanzees” metric has its origins in the work of the foundation’s family co-founders, Ola, Anna and the late Hans. According to the Roslings, whereas the chimps make completely random choices, there is a pattern to humanity’s collective ignorance — people routinely show a more pessimistic view of the world than the one described by our statistics.

Why is this a problem? Well, it’s difficult to produce evidence-based debates and decisions when even experts’ intuition leads us astray from the outset. Project Rosling proposes corrective treatment for our misperceptions, and two ideas in particular caught my eye.

The first — improving global data literacy — wants the public to know where and how to access data and statistics for making fact-based decisions. This is much easier said than done, partly because statistics agencies are among the lesser-known government bodies. According to a 2021 survey, 75 per cent of people in the UK had heard of the Office for National Statistics, which might sound impressive, but is well behind the 94 per cent public awareness of the Bank of England, for example.

And while nearly everyone — 96 per cent — agreed that it’s important that an independent body should speak out against the misuse of statistics by politicians and journalists, less than half can name the organisation responsible for doing so (the UK Statistics Authority, which commissioned the survey).

Greater visibility is important because it’s associated with trust — public awareness of the ONS has risen during the pandemic, as trust in its statistics edged upwards. An OECD review during the early stages of the pandemic reached a similar conclusion: statistics agencies need to be more visible and improve their communication (particularly on social media) to build trust.

The second idea — better funding for the production of statistics — highlights an acute problem facing poorer countries. As the Rosling-style bubble chart below reveals, there are stark differences in statistical capabilities around the world.

Weak statistical systems are problematic, not least because of the lost economic potential. According to the global Partnership for Sustainable Development Data, every $1 invested in data delivers an average of $32 in return.

In Geneva, Nicole Ruder, assistant director-general of the Swiss Agency for Development and Cooperation, highlighted how misperceptions affect areas such as foreign aid: donor countries tend to both overestimate the quality of data in recipient countries and underestimate the effort needed to make improvements.

Boosting statistical capability is a long-term effort, and the results are often not immediately tangible. Consequently, donors may invest in one-offs such as a census (which is “like trying to save democracy by funding one election”, according to Ruder) or create “data cemeteries” — silos full of data that’s collected, stored and never used again.

In fact, Ruder suggests, donor recipients often deprioritise data. “Investing in data doesn’t give you an immediate voter return on investment”, she says.

From deprioritisation, it’s only a small step to wilful self-harm — perhaps an appealing prospect for leaders who would rather avoid the scrutiny and accountability afforded by official data. This week I spoke with Pedro Silva, a prominent Brazilian statistician, and former president of the International Statistical Institute, about the difficulties the Brazilian statistical system faced under the Bolsonaro administration.

According to Silva, the visible legacies of Jair Bolsonaro’s government — which include a troubled and much-delayed census, and plunging child immunisation rates — are the result of a “systematic attack on the whole system of governance”, including IBGE, the national statistics agency.

Silva believes the damage would have been even more severe had Brazil’s democratic checks and balances, including a free press, failed to scrutinise the former president. For example, a consortium of media agencies sourced Covid-19 data directly from health boards when Bolsonaro’s government suppressed the publication of its own figures on the spread of the disease.

But those checks should not be taken for granted. Indeed, if we recolour our earlier bubble chart, this time shading each country according to their engagement with political rights and civil liberties, a disturbing pattern emerges. Many countries — such as China, India, Russia and Turkey — might not prove as resilient as Brazil was to a serious data deficit.

Bolsonaro may no longer be president, but the populist playbook is still in use elsewhere. So although the audience in Geneva narrowly managed to beat the chimps, far tougher challenges for the statistical community lie ahead.

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