Sachs – who made history by becoming a Harvard Professor at age 28 – is a heavyweight in the field of economic development, so it’s worth listening when he writes “technological progress can be immiserating.”
The paper acknowledges that such predictions have been made before, and proved wrong. There were some details in the history of Ludditism that I didn’t know, particularly the role of the state in defending novel production methods.
“Concern about the downside to new technology dates at least to Ned Ludd’s destruction of two stocking frames in 1779 near Leichester, England. Ludd, a weaver, was whipped for indolence before taking revenge on the machines. Popular myth has Ludd escaping to Sherwood Forest to organize secret raids on industrial machinery, albeit with no Maid Marian. More than three decades later – in 1812, 150 armed workers – self-named Luddites – marched on a textile mill in Huddersfield, England to smash equipment. The British army promptly killed or executed 19 of their number. Later that year the British Parliament passed The Destruction of Stocking Frames, etc. Act, authorizing death for vandalizing machines. Nonetheless, Luddite rioting continued for several years, eventuating in 70 hangings.”
The model constructed by Sachs and his co-authors has no role for hangings. It simplifies the economy into a technology sector producing “goods” and a residual sector staffed by humans, producing “services.”
The model tries to answer the question:
“Will the reduction in the cost of goods produced by more advanced robots compensate workers for the lower wages?”
The team runs the models several times and gets a range of different answers depending on assumptions. But the news is certainly not all good.
“A second prediction of our model is a decline, over time, in labor’s share of national income.”
The model has ‘retention of code’ as a central feature. They argue that over time, useful code builds up so that new code is less and less necessary, leaving less and less work for people engaged in its production.
Code is defined as “not just software but, more generally, rules and instructions for generating output from capital.”
It assumes over time code becomes more durable, driving unwanted “high tech workers” to go and work in the services space, where they drive down wages.
“The price of services peaks and then declines thanks to the return of high-tech workers to the sector. This puts downward pressure on low-tech workers’ wages and, depending on the complementarity of the two inputs in producing services, low-tech workers may also see their wages fall”
The ‘retention of code’ is a key feature of the model. When the researchers ramp up the coefficient on that, the model has gloomier and gloomier predictions.
The mechanism by which this works is because each more poorly compensated generation can add less and less to the economy’s capital stock:
“The long run in such cases is no techno-utopia. Yes, code is abundant. But capital is dear. And yes, everyone is fully employed. But no one is earning very much. Consequently, there is too little capacity to buy one of the two things, in addition to current consumption, that today’s smart machines (our model’s non-human dependent good production process) produce, namely next period’s capital stock. In short, when smart machines replace people, they eventually bite the hands of those that finance them.”
But is code different to any stock of knowledge? Humans have invented designs for thousands of perfectly functional cars, yet there’s work being done on inventing new and better ones at a fantastic rate. Computer code may accumulate, but “rules and instructions for generating output from capital” sounds like management. I don’t see managers being replaced by computers soon.
The model also has no room in it for the rapid expansion of the service sector. I’ve written about this before, and I think it is a central to an economy operated by the fanciful and idiosyncratic species we call humans. If our needs are met cheaply, we will invent new things to want.
Nevertheless, the paper adds to the rich debate over what might happen in an economy where humans are not directly engaged in the tasks most important for their survival.
I’ll leave you with the working paper’s dystopian predictions:
“Will smart machines, which are rapidly replacing workers in a wide range of jobs, produce economic misery or prosperity? Our two-period, OLG model admits both outcomes. But it does firmly predict three things – a long-run decline in labor share of income (which appears underway in OECD members), techbooms followed by tech-busts, and a growing dependency of current output on past software investment.”
“Our simple model illustrates the range of things that smart machines can do for us and to us. Its central message is disturbing. Absent appropriate fiscal policy that redistributes from winners to losers, smart machines can mean long-term misery for all.”