Posts Tagged ‘data’

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Special mention

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Special mention

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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).

Data and Taylorism

Posted: 19 August 2015 in Uncategorized
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Lots of folks are talking about the New York Times report on Amazon’s brutality toward its own white-collar workers.

What seems to be attracting attention is the fact that it’s about office workers—and not the blue-collar workers in the warehouses. Clearly, both groups are being subjected to a ruthless, data-driven effort to increase profits.

Benjamin Wallace-Wells [ht: sm] understands what these new data systems mean in the context of U.S. corporations:

throughout the Times’ account, another menace keeps creeping in — less vivid, but heavier with existential weight: data. In Amazon’s warehouses, we learn, workers “are monitored by sophisticated electronic systems to ensure that they are packing enough boxes.” In the white-collar jobs that are the story’s real subject, the company is exacting in similar ways. “The company is running a continual performance improvement algorithm on its staff,” a former marketer on the Kindle team explains. Before regular performance reviews, Amazon workers are given “printouts, sometimes up to 50 or 60 pages long,” that measure their performance on many different metrics. (It’s a little amazing that Amazon is still printing this stuff out on paper.) The totality of this measurement, the Times suggests, means not that Amazon is unique but merely that the company has been “quicker in responding to changes that the rest of the work world is now experiencing: data that allows individual performance to be measured continuously.”

Which is the line at which the average white-collar Times reader is meant to experience a sense of imminent collapse and dread. Obnoxious as most of the abuse is, as many lines as it crosses, a reader who works at another company can chalk it up to a particular sick corporate culture, located in Seattle and presided over by a megalomaniac. You can reassure yourself that you have a kinder boss and a more decent set of rules. But “continual improvement algorithms” are innovations, the Times explains, the kind that are now arriving in “the rest of the work world” and just happened to come to Amazon first. The real villain of the Times piece isn’t Bezos or his senior executives. Instead, it’s Taylorism for the professional class, in the guise of data.

Data-supported Taylorism is precisely what is breaking down the traditional distinction between the blue- and white-collar working-classes within the context of contemporary U.S. capitalism.

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The discussion these days seems to be all about foxes and hedgehogs.

Those are the terms Nate Silver borrows from a phrase originally attributed to the Greek poet Archilochus to define his new journalistic project—the fox who knows many things as against the the hedgehog who knows one big thing. (But see my critique here.)

The pair of animal also turns up in James Surowiecki’s review of Fortune Tellers: The Story of America’s First Economic Forecasters by Walter A. Friedman.

Philip Tetlock, a professor of psychology and management at Penn who conducted a 20-year study asking almost 300 experts to forecast political events, has shown that while experts in the political realm are not especially good at forecasting the future, those who did best were, in the terminology he borrowed from Isaiah Berlin, foxes as opposed to hedgehogs—that is, the best forecasters were those who knew lots of little things rather than one big thing. Yet forecasters are more likely to be hedgehogs, if only because it’s easier to get famous when you’re preaching a simple gospel. And hedgehogs are not good, in general, at adapting to changed conditions—think of those bearish commentators who correctly predicted the bursting of the housing bubble but then failed to see that the stock market was going to make a healthy recovery.

The fact is, the two periods that led to more sources of information for economic forecasting preceded the two greatest crises of capitalism we’ve witnessed during the past 100 years—after which new ideas and movements erupted that provided concrete alternatives to capitalism. It’s not that they had more information. They honestly used the data at hand about what was fundamentally wrong with existing economic arrangements and, instead of sticking with tired formulas and failed policies, dared to imagine a world beyond capitalism.

Someday, then, we too will be able to exclaim, “Well burrowed, old mole!”

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These charts illustrate both the good and the bad of Nate Silver’s new version of the FiveThirtyEight journalism project.

On one hand, they offer us important information about the growing portion of American workers who are attempting to support themselves on $10.10-an-hour jobs (more than half today compared to 39 percent in 1990) and the incomes of the families that have minimum-wage workers (a large majority of minimum-wage workers are in low- to moderate-income families, and a significant minority are just plain poor).

On the other hand, Ben Casselman’s analysis presumes that who is working at the current or higher proposed minimum wage is the only relevant information. In other words, he accepts the idea that the minimum wage is merely one among other anti-poverty tools (which include the oft-cited Earned Income Tax Credit) .

What Casselman seems not to consider or understand is that all workers benefit from a higher minimum wage, because it establishes a higher floor for all wages. That makes it more difficult for employers to follow a low-road strategy of extracting profits from poorly paid workers and of replacing—or threatening to replace—workers who currently earn a wage above the minimum wage with lower-wage workers.

That’s where theory and politics come in. You need a theory of the labor market and the role minimum wages play. And you need an analysis of the politics of the minimum-wage debate, including the attempt to reduce the debate to finding the best anti-poverty tool while making sure those at the bottom continue to be forced to have the freedom to sell their ability to work in order to eek out a living.

The Economic Policy Institute’s State of Working America, 2012 edition, is now available online. The entire report can also be downloaded.