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01 / 05
The Long Thread of Lessening Labor

Blog Post | Workforce Hours

The Long Thread of Lessening Labor

We have more time to do as we wish and fewer needs that force us to do as we must.

Summary: This article challenges the common perception that human progress has made us work harder and deprived us of leisure. It shows how both market and domestic labor have declined over time, thanks to technological innovations and economic changes. It traces the history of labor alleviation from the Viking era to the present day, and celebrates the benefits of having more time and freedom to pursue our interests.


People often complain that we are all working too hard and that human progress is pointless if we have to labor and strain to achieve our current lifestyles. They say we would be better off curtailing our desires and returning to some Edenic life with more time for ourselves. The problem is that Edenic life never existed. In fact, over the past millennium, humanity has been working less and less. And a thousand years is probably long enough to make the claim that working less is a trend, not a blip.

To understand working hours, it is important to recognize two points. First, households (some societies define households as containing only parents and children, while others extend the definition to cousins and nephews and so on) are the central economic units that should be discussed. Second, labor comes in two flavors: domestic work and market work. Domestic work includes food preparation, childcare, cleaning, or any other labor within the household. Market work generates money or goods to trade for any goods and services that the household does not produce. The “labor burden” on the household is the combined number of hours spent doing those two types of work.

Market and domestic labor can substitute one another. Children can go to kindergarten, and food can be bought as take-out. Likewise, clothes can be purchased from a factory, or they can be stitched at home. People tend to use the option that gets them the desired good or service with the least amount of work. As I will show below, the net effect of changes in those two forms of labor determines the total household labor supply. Or, to put it less formally, it determines how long people have to work to gain what they want. Once those two kinds of labor are added up, a declining trend in the total number of hours worked becomes apparent. Consider this report from the Federal Reserve Bank of Boston:

Specifically, we document that leisure for men increased by 6-8 hours per week (driven by a decline in market work hours) and for women by 4-8 hours per week (driven by a decline in home production work hours). 

And that was just for the period between 1965 and 2003. A closer look at the 20th century suggests that market working hours fell for men and rose for women. The rise in female market working hours was precipitated by technological innovation and a concomitant decline in the number of domestic working hours. Domestic working hours fell greatly for women and, less dramatically, for men. The net effect of the four processes was that leisure hours rose, and total working hours fell, for both men and women.

In his 1930 essay Economic Possibilities for our Grandchildren, the British economist John Maynard Keynes predicted that the next century would usher in an age of prosperity. He also forecasted that people would work less and spend more time at leisure. Keynes turned out to be right. But many modern readers, who come across Keynes’ prediction of a 15-hour workweek, wonder why they are still putting in 40 at the office. The answer is that the work we killed was the domestic labor done largely by women. 

One author estimates that it took 60 hours a week of physical labor to keep a 1930 household working. Today, it takes perhaps 15 hours. Those numbers are not exact, but when you consider the washing machine, the gas oven, the vacuum cleaner, prepared food, and steam irons, the amount of household work eliminated is immense. One of Hans Rosling’s TED talks recounts how the washing machine brought him books. According to the Swedish physician, once the washing machine liberated his mother from laundry, she had more time to read to him. The South Korean economist Ha Joon Chang claims that the washing machine – by which he really means all domestic labor-saving technologies – changed the world more than the internet.

But labor alleviation did not begin in the 20th century. In her new book (The Fabric Of Civilisation came out in November 2020), the American writer Virginia Postrel estimates that it took 365 full days of work to spin enough thread to make a Viking sail. Days and days of work to create enough thread to weave a bandana and weeks to make a pair of jeans. The Vikings used the drop spindle to make thread – a basic technology that humans used for millennia. The spinning wheel, which partly mechanized the process, arrived in Europe sometime in the 11th or the 12th century A.D. 

Then came the Industrial Revolution, first with the Spinning Jenny, which was followed by Crompton’s Mule and endless other derivatives. These machines progressively automated what was a horrendously time-consuming and nearly exclusively female domestic task for centuries. The economic historian Brad Delong has remarked that when women of any class are depicted in older literature, there is always reference to their spinning. By the time of Jane Austen’s novels in the late 18th and early 19th century A.D., spinning is never mentioned – it was all done in the factories by then.

We all have more leisure now than our forebears did. We have more time to do as we wish and fewer needs that force us to do as we must. But this wonderful outcome of human progress is obscured by the fact that, in large part, it is the household labor that has been automated away. Sure, the Roomba might not be a great leap forward, but it is just the latest iteration of a process that began a thousand years ago. And there is no sign of it ending.

BBC | Labor Productivity

How Robots Are Taking over Warehouse Work

“In its warehouses, Asda uses a system from Swiss automation firm Swisslog and Norway’s AutoStore. In the US, Walmart has been automating parts of its supply chain using robotics from an American company called Symbotic.

Back in Luton, Ocado has taken its automation process to a higher level.

The robots which zoom around the grid, now bring items to robotic arms, which reach out and grab what they need for the customer’s shop.

Bags of rice, boxes of tea, packets of crumpets are all grabbed by the arms using a suction cup on the end.”

From BBC.

Associated Press | Labor Productivity

Productivity Surge Helps Explain US Economy’s Resilience

“Chronic worker shortages have led many companies to invest in machines to do some of the work they can’t find people to do. They’ve also been training the workers they do have to use advanced technology so they can produce more with less.

The result has been an unexpected productivity boom.”

From Associated Press.

Blog Post | Science & Technology

AI, Tractors, and the Slow Diffusion of Labor-Saving Devices

Productivity and economic growth are a tide that lifts all boats, whether we're talking about agricultural machinery or ChatGPT.

Summary: The proliferation of large language models, such as ChatGPT, raises concerns about job displacement, particularly in professions like writing and law. However, historical transitions, like the shift from horses to tractors, suggest that the adoption of new technologies is gradual, with human labor and machines coexisting and evolving together. While AI may revolutionize certain sectors, its widespread impact on employment is likely to be evolutionary rather than revolutionary.


“You can see the computer age everywhere but in the productivity statistics,” wrote economist and Nobel laureate Robert Solow in a 1987 book review. While computing was making massive strides in the 1980s and ’90s—cumulating in the internet mania and dot-com bubble and its burst—it took a long time before we saw large, economy-wide impacts of this revolutionary general-purpose technology. Computers, internet, and (smart) screens have made the modern world unrecognizable from what it was just a few decades ago, but the process took a lot longer than most techno-optimists suggested then—and their followers are suggesting now.

Last year had a strong 1990s feel to it: 2023 became the year of AI worries, with ChatGPT and other large language models making a bid for disrupting all manner of industries. Writers, musicians, lawyers, editors, physicians, graphic designers, and more were some of the professions seemingly at risk of losing their livelihood to machines that quickly replicate their work at a fraction of the cost.

We should draw two conclusions from these large language models and past technological transitions: First, some of their features are truly remarkable and can very well compete with (some) lawyers, writers, musicians, or graphic designers over the next few years. Second, radical, labor-saving technologies are slow to diffuse across the economy, so these mainly middle-class professions are probably safe from mechanization and automation a while longer.

Take the iconic horses-to-tractor transition in agriculture over the 20th century. From invention and commercial availability, tractors took decades to overtake horses. The same was true for steam engines, that iconic energy invention powering the Industrial Revolution—which itself was both one of most radical and life-changing events in all of human history and pretty dull on account of its slow changes.

“Over the sweep of history the tractor has indeed had an immense impact on people’s lives,” The Economist said in December 2023, “but it conquered the world with a whimper, not a bang.” In all ages, tech innovations have defused across societies slowly: the old technology remains relevant for decades. Even in the 1930s, two to three decades after the first tractors were built, there was more equine than mechanical horsepower on American farms. Scholars usually put the cutoff point for when tractors overtook horses on farms after World War II, a generation or more after they were first employed.

The story in The Economist also highlights the connection to real wages and overall economy. Productivity and economic growth are a tide that lifts all boats. A rule of thumb is that labor-saving technology gets adopted only once it makes sense to replace labor with machines. That happens when the machines get good enough, the cost of running them falls sufficiently, or the alternative uses for labor get high enough.

For the first few decades of the tractor’s existence, they weren’t obviously better than the source of literal horsepower that preceded them. They were expensive to run, were not that powerful, and couldn’t do many things that a horse could. For one, they compelled farmers to purchase gasoline—a resource they didn’t have—rather than putting horses out to grazing on land or having children (or cheap labor) tend to the animals—resources that they did have. And during the Great Depression in America, cheap labor wasn’t that hard to come by.

In time, those trends reversed; real incomes and better-paid manufacturing jobs bid labor away from the farms, and the demographic transition to smaller families meant that children couldn’t be routinely relied upon to, for example, care for the animals. In economic speech, the optimal size of farms grew, and on these larger farms the now much better tractors came into their own right.

In contrast, the labor that large language models like ChatGPT purport to displace is often highly remunerated, while the costs of running the operations are pretty low, which suggest that artificial intelligence (AI) might displace some such workers.

Yann LeCun, chief AI scientist at Meta/Facebook, is less convinced. In an interview with Wired magazine’s Steven Levy, LeCun says that “chatbots can produce very fluent text with very good style. But they’re boring, and what they come up with can be completely false.” For Reason magazine last year, I reflected on the promise of ChatGPT along similar lines: “Bad writers, cheating students, lazy professors, and journalists echoing press releases have clearly met their match. But the more human, creative, authentic storytelling that is the center of all meaningful writing has not.”

The tractor attempted to compete directly with a farming industry routinely employing over a third of the labor force in the 1800s and early 1900s in the United States, constituting some 15 percent of gross domestic product. What is the total addressable market for large language models and by extension general AI? Statista estimates that the market size of AI in the United States, a $27 trillion economy, is some $100 billion—a fraction of a percent. Even if that grows seven-fold this decade in line with projections (nothing to scoff at!), that’s also not as revolutionary as most pundits have claimed in this the first year of their widespread adoption.

Solow, who died late in 2023, might have repeated his statement about this new form of the computer age. Like the computer in the 1990s, the onset of AI and large language models make more media puff and noise than they have real-world, real-economy impact.

There is some suggestion that adoption of new technology and consumer items happen somewhat faster today than, say, in the 1940s or 1980s, but not by a lot. Still, what is more likely to happen than mass white-collar unemployment is that we’re going to have a symbiosis for a while. The old human-labor technology and the new machines will coexist, and human workers will gradually employ the machines in different ways. An evolution, not a revolution.

At the end of the day, writes Mike Munger for the American Institute for Economic Research, ChatGPT is just a calculator. And like calculators did, it will obsolete some human labor. And that will be fine.