Data and Governments

By definition, any statistical analysis is based on data. Setting aside genuine errors (wrong sample size, unrepresentative sample set, wrong questions asked), Tim Harford talks at length about the problem of data used by governments, in his book, How to Make the World Add Up.

 

As we know all too well, data can be misleading, even without malice. With governments though, the danger of such wrong data is enormous:

“It’s one thing to be wrong… (but) because the state is powerful, its misperceptions of the world often take physical form, producing well-meaning but clumsy and oppressive schemes.”

 

But of course, with governments, the problem is worse. They often pressurize agencies to come up with data that suits them. I’ll avoid such examples from India since that just becomes a political discussion in no time.

 

Independent agencies are not a magic bullet that solves the problem:

“(Independent) official agencies often get them wrong… (just that) they don’t make politically expedient errors, systematically warping their forecasts to fit a political agenda.”

Even when that’s true, we have cases like this. Trump poured scorn on unemployment figures when he was running for President (the first time), calling it ‘phony’ and ‘total fiction’. Once he was President, he liked the data! He was quoted as saying:

“They may have been phony in the past, but it’s very real now.”

This level of shamelessness isn’t just amusing (to outsiders), but worse:

“Trump’s opponents will start to distrust official statistics just as much as his supporters do.”

After all, as Harford says:

“Trump is a man who polarises opinion: you suspect that if he said ice cream was a pleasant treat on a sunny day, it would lead to some Americans refusing to eat anything but ice cream while others protested loudly outside ice cream parlours.”

(Harford is British, in case you wondered…).

 

Then there’s an example from Greek data. Being part of the EU, they had to keep their budget deficit at a certain level (3%). A number that made sense for the richer EU countries, but not poorer Greece. Hence:

“That target was onerous, so why not tweak the figures until all seemed well?”

And then the 2008 financial crisis broke out, and suddenly Greek data was as trustworthy as their “giant wooden horses”. The EU demanded to know the true situation, and a new man, Andreas Georgiou, was appointed. He raised the number from 3% to 13.6%! Still a lie, screamed the EU. Ok, it was 15%, said Georgiou. Suddenly, Georgiou was a traitor in Greek eyes: he’d made Greece look bad. He was the man whose numbers were now leading to severe EU imposed austerity measures. Bogus charges were filed, he was found guilty, then acquitted. And the cycle was repeated. 6 different times.

 

In many Western countries, government officials are told of the data to be announced before it is made public. This allows them time to prep up responses/explanations/even raise doubts about the data, thereby muddying the waters wrt any bad news. How convenient.

 

But not collecting any data is not an option for governments or countries. No matter what the problems are, the alternative (not collecting data) is even worse.