David Graeber's "Bullshit Jobs": why does the economy sustain jobs that no one values?
David Graeber defined a "bullshit job" in his viral 2013 essay as jobs that no one -- not even the people doing them -- valued, and he clearly struck a chord: in the years since, Graeber, an anthropologist, has collected stories from people whose bullshit jobs inspired them to get in touch with him, and now he has synthesized all that data into a beautifully written, outrageous and thought-provoking book called, simply, Bullshit Jobs.
Graeber's research into the phenomenon of bullshit jobs has helped him refine the theory and make some moves towards explaining the phenomenon. He's created a typology of five kinds of bullshit jobs ("duct taper," "goon," "box-ticker," "flunkies," and "taskmasters") and quantified the premium paid for bullshit work, and the discount applied to the wages of people who do non-bullshit work (especially caring work).
Graeber has heard a lot from market-orthodox libertarians who dispute that there can be such a thing as a bullshit job: the market would discipline any firm that wasted money on this sort of thing and it would sink, so by definition, any job that is persistent over time can't be bullshit. It must just be that low-level functionaries can't see the big picture and thus labor in ignorance of the difference their work makes.
But Graeber is an anthropologist, so he goes looking in our cultural stories for reasons that bullshit jobs might persist in firms, even ones in competitive markets. His investigation turns up many potential culprits, like the elevation of work itself (even meaningless work) to a virtue, an idea with its roots in the industrial revolution.
Another major contributor has to be the need for the top power brokers in large firms to surround themselves with corporate retainers and aristocrats, who help shore up the top peoples' power and thus their wages -- maybe the market force at work is big salaries for the C-suite, the VPs, the top deans and other administrators whose retinues sprawl beyond all measure.
I like this theory best, because it explains why firms are so quick to cut real jobs (teachers, clerks, waiters, ticket-takers, and other people who interact with the public and do the business of the business) but so reluctant to trim the thick bureaucratic layer surmounting all. From the perspective of a VP hoping to get a raise, the company's profitability (driven by a motivated, valued and robust workforce of non-bullshit workers) is secondary; the primary factor is beating all the other VPs. When asked to make cuts, any VP who erodes his retinue suffers hits to his bonus, while making cuts to useful workers may erode the firm's profitability and cause it eventually crash and burn, but that will be someone else's problem.
Graeber's big point is that a titanic amount of our economy is presently bullshit, and we're working crazy hours to ensure that all the bullshit gets done. If the anthropological pressure to work can be abated and the individual greed of a class of super-rich can be dampened or neutralized, maybe we could realize that 15-hour week Keynes predicted in the 1930s.
Because, as Graeber points out, bullshit jobs -- even ones that leave you free to dick around on the internet or write your novel all day -- make the people who do them miserable.
Bullshit Jobs: A Theory [David Graeber/Simon & Schuster]
In 1975, Noam Chomsky and Jean Paiget held a historic debate about the nature of human cognition; Chomsky held that babies are born with a bunch of in-built rules and instincts that help them build up the knowledge that they need to navigate the world; Piaget argued that babies are effectively blank slates that acquire […]
Big Tech got big because we stopped enforcing antitrust law (not because tech is intrinsically monopolistic)
Tim Wu (previously) is a legal scholar best known for coining the term "Net Neutrality" -- his next book, The Curse of Bigness: Antitrust in the New Gilded Age (previously) challenges the accepted wisdom about today's digital monopolists, which is that they grew so big because of some underlying truth about online business ("first-mover advantage," […]
When you train a machine learning system, you give it a bunch of data -- a simulation, a dataset, etc -- and it uses statistical methods to find a way to solve some task: land a virtual airplane, recognize a face, match a block of text with a known author, etc.
In case you hadn’t noticed from the sleigh bell-heavy music and the hues on your Starbucks cup, the holiday season hasn’t shown any more patience this year. But that doesn’t need to be a bad thing, especially if you’re hoping to get a jump on your shopping. Retailers aren’t waiting til Black Friday to dish […]
What do you get for the techie who has everything? How about giving them a Raspberry Pi and letting them make pretty much anything. Or better yet, do it for yourself with the Ultimate Raspberry Pi eBook Bundle. This trove of ideas and education unlocks the unlimited potential of this mini-computer, whose affordability and versatility […]
Note-taking just caught up to the digital age. For most of us, writing freehand is quicker and more convenient than pecking away on a tablet, but what to do when you need those scribbles on file? Grab a Rocketbook Everlast Reusable Notebook, which seamlessly fuses analog and digital notes. Just jot down your thoughts, journals […]