Lies, damn lies, and statistics

Posted: 30 January 2017 in Uncategorized
Tags: , , , , ,

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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).

Comments
  1. Bruce says:

    I’ve made comments on GDP accounting here before, so I won’t go off on the same content again. But, when you write:

    “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”,

    I do agree (“how” commodities are “produced” — the conditions, the social relations, under which production occurs — matters, for anyone acquainted with the multiple strands of debate in the history of economics), but I don’t think that covers the full stakes of that debate. “Production” is not a self-evident category — not all performances of services are identical, not all social “hieroglyphs” that take the form of “commodities” are created equal, and the examples you (and most who deal with the topic) give above of problematic choices in terms of what is counted and how that counting is done don’t fully cover the issues. As I’m sure we both agree, one can’t begin to calculate S/V without asking and answering questions about the “productive” vs “unproductive” status of activities and their consequences.

    But I do appreciate the irony that Marxists of all sorts have something like a common cause with many of the mainstream statisticians in resisting the privatization of “public” data and the statistics derived from it.

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