Home > Uncategorized > Lies, damn lies, and statistics

Lies, damn lies, and statistics

from David Ruccio

Regular readers know I take statistics quite seriously. So, as it turns out, did Stephen Jay Gould who, in the most poignant story about statistics of which I am aware, explained how important it is to go beyond the abstractions of central tendencies and understand the distribution of variation within the numbers.

And right now, when the numbers are under attack—when, for example, the new Trump administration is threatening to purge the inconvenient numbers about climate change—it is even more important to understand the role statistics play in economic and social life.*

William Davies [ht: ja] offers one story about statistics, starting with the recent populist attacks on public statistics and the questioning of the experts that produce and interpret them. His view is that, for all their faults, the numbers collected and disseminated by technical experts within national statistical offices need to be defended—as the representation of “common ideas of society and collective progress”—against the rise of private “data.”

A post-statistical society is a potentially frightening proposition, not because it would lack any forms of truth or expertise altogether, but because it would drastically privatise them. Statistics are one of many pillars of liberalism, indeed of Enlightenment. The experts who produce and use them have become painted as arrogant and oblivious to the emotional and local dimensions of politics. No doubt there are ways in which data collection could be adapted to reflect lived experiences better. But the battle that will need to be waged in the long term is not between an elite-led politics of facts versus a populist politics of feeling. It is between those still committed to public knowledge and public argument and those who profit from the ongoing disintegration of those things.

I understand the threat posed by big, private data—all those numbers that are collected “behind our backs and beyond our knowledge” when we travel, make purchases, and participate in social media, and in turn are utilized to sell us even more commodities (including, of course, political candidates).

But I also think Davies, in his rush to condemn private control over big data, presents too uncritical of a defense of “the kinds of unambiguous, objective, potentially consensus-forming claims about society that statisticians and economists are paid for.”

Consider, for example, one of the “unambiguous, objective, potentially consensus-forming claims about society” Davies himself cites: GDP. Just last Friday, the headlines reported that the U.S. economy grew “only” 1.6 percent during the last quarter of 2016, “the lowest level in five years.”

The presumption was that the decline in the number (with respect to both previous quarters and economists’ forecasts) represented a fundamental problem. But why should it—why should a decline in the growth rate of GDP be taken as a sign of something that needs to be fixed?

Davies does mention that GDP “only captures the value of paid work, thereby excluding the work traditionally done by women in the domestic sphere, has made it a target of feminist critique since the 1960s.” But the controversies surrounding that particular statistic are much more widespread than Davies would have us believe. As a number of recent books (including Ehsan Masood’s The Great Invention: The Story of GDP and the Making and Unmaking of the Modern World) have clearly explained, the initial formulation of that particular measure of national income as well as subsequent revisions have involved theoretical and political choices about what should and should not be included—government expenditures but not labor within households, the production of fossil fuels but not the destruction of the natural environment, sales of private security but not the growing inequality it is designed to protect against.**

Even more fundamentally, GDP is a measure of market transactions, of goods and services produced—and thus the contemporary counting of the elements celebrated by Adam Smith’s notion of the “wealth of nations.” But what it doesn’t measure are the conditions under which those commodities are produced.

Me, I’d be much more willing to join forces with Davies and defend the claims about society that statisticians and economists are paid for if they were also paid to calculate and publicly report one other number, S/V, the rate of exploitation.


**We should remember that perhaps the real hero of volume 1 of Capital was Leonard Horner, who as a factory inspector “carried on a life-long contest, not only with the embittered manufacturers, but also with the Cabinet, to whom the number of votes given by the masters in the Lower House, was a matter of far greater importance than the number of hours worked by the ‘hands’ in the mills.”

**Other useful books on GDP include the following: Philipp Lepenies’s The Power of a Single Number: A Political History of GDP (Columbia University Press, 2016), Lorenzo Fioramonti’s Gross Domestic Problem: The Politics Behind the World’s Most Powerful Number (Zed Books, 2013), and Thomas A. Stapleford’s The Cost of Living in America: A Political History of Economic Statistics, 1880-2000 (Cambridge University Press, 2009).


  1. February 4, 2017 at 5:45 am

    The person who developed GDP in 1934 (Kuznets) warned at that time of using it as a measure of social welfare, and also pointed out that at the time available data were limited and unreliable, and statistical tools for analyses were equally limited. It was really just an advanced back of the envelope calculation or hand waving argument. Even stats on population, income, formal employment, etc were un reliable. It was like biology—noone had any estimate of what, and how many animals lived on earth but they started makign estimates.

    There were whole social indicators movements in the 60’s to try to redefine GDP –many were cut off from academic funding due to politics. In 80’s H Daly came up with Index of sustainable social welfare and other ones derived from that (genuine profgress indicator—done by a sort of conservative pro-growth group). France, Bhutan, etc have the ‘Happiness Index’ (done in part by Sen and Stiglitz.) All of these just capture part of the story (just as any set of ecological indicators describes health of an ecosystem).

    There are quite a few op-eds in major uS newspapers on this topic—mostly ignored in economics discussions.

    I favor numbers like this (there is a special name for them in physics—but those in many ways are simpler than a social system and allow more precise measurement) but there is no consensus on how to calculate them. I was part of a group that tried to devlop for my local city but our group was diverse–some economists, some lay people—and they couldnt decide what to calculate—some wanted to include things like how many times a day they petted their puppy or miles they had to drive to get veggie produce.

  2. February 6, 2017 at 2:40 am

    In Dante’s “The Inferno,” Hell is depicted as nine concentric circles of suffering located within the Earth; it is the realm of those who have rejected spiritual values by yielding to bestial appetites or violence, or by perverting their human intellect to fraud or malice against their fellow humans. I can never decide whether economists belong in the eighth circle (fraud) or ninth circle (treachery) of hell. Like you and Gould I too am serious about statistics. In my view, it is the way economists use statistics and numbers generally that condemns them to Hell. Gould began with the statistic but immediately began to search for the story behind it. The actual events and actions, the feelings and concerns the statistic summarizes. Economists tend to go the other direction. They search for ever shorter, less detailed, and more detached ways to summarize events, concerns, and feelings with as few numbers as possible. GDP is an example of this tendency. But only one. I’ve concluded that economists are uncomfortable with actual events and actual actors. They find remoteness and disinterestedness more appealing. Covering it up with the explanation of “being objective.” In their view statistics is objective. Listening to and observing actual events and people is not. But that’s the lesson Gould teaches us so well. Statistics is not an end of the search. And certainly not the source of understanding and explanation So, which level of Hell fits them best?

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