Power and Progress (part 3)

August 28, 2025

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Acemoglu and Johnson argue that the link between technological progress and general prosperity is not automatic. It depends on other variables, especially how well new technologies sustain the demand for labor and how much workers share the benefits of rising productivity. Having supported their argument with historical examples, they now apply it to the more recent economy, especially the period since 1980.

The “graveyard of shared prosperity”

At the height of the postwar prosperity, believers in the “productivity bandwagon” expected further technological breakthroughs to raise productivity and wages, continuing and even surpassing the postwar prosperity. Digital technologies, like the mainframe computers already in use in the 1960s, looked very promising. As the digital revolution took off, the rate of innovation soared.

“Digital technologies, even more so than electricity…are general purpose, enabling a wide range of applications.” They have the potential not only to replace human labor with smart machines, but also to complement and enhance human labor. During my university career, I used computers to enhance my teaching, research and administrative work in numerous ways, but they never replaced me in any of those roles. Many manufacturing workers weren’t so lucky, as new technologies were used more to replace labor than to augment it. As the authors put it, “Digital technologies became the graveyard of shared prosperity.” I would emphasize the word “shared” in that claim, since no one disputes that digital technologies have created great riches for some and modest gains for others.

The authors attribute much of the decline of shared prosperity to a more conservative vision of progress that developed in the 1960s and 70s and became dominant after the “Reagan revolution” of 1980. In this vision, the path to prosperity started at the top, with wealthy investors, high-profit corporations, and well-rewarded shareholders. If left alone by government, they would create more wealth and income for all. But to maximize investment, the rich needed low taxes; and to maximize profits, corporations needed low taxes, minimal regulation, and low labor costs. “Many American managers came to see labor as a cost, not as a resource…This meant reducing the amount of labor used in production through automation.”

Americans may place most of the blame for lost manufacturing jobs on foreign competitors like China, but automation is responsible for more job losses and downward mobility. While foreign workers and immigrants did take many of the of the low-wage manufacturing jobs, automation destroyed more of the jobs that had been paying good wages.

The workers who remained in manufacturing were more productive, but the demand for additional workers fell. In addition, total factor productivity grew at a much slower rate after 1980 than in the previous four decades. Median wages grew even more slowly, less than 0.45% per year.

Inequality increased in a number of ways:

[T]he share of the richest 1 percent of US households in national income rose from around 10 percent in 1980 to 19 percent in 2019…Throughout most of the twentieth century, about 67-70 percent of national income went to workers, and the rest went to capital (in the form of payments for machinery and profits). From the 1980s onward, things started getting much better for capital and much worse for workers . By 2019, labor’s share of national income had dropped to under 60 percent…

What income did go to labor was divided more unevenly across educational levels, with college-educated workers gaining some ground, while less educated workers saw actual declines in real earnings. Rather than train less educated workers, employers more often replaced them with fewer but more educated workers. Along with the destruction of manufacturing jobs came the decline of unions and the reduced power of workers to fight for good wages and job training.

The value of the five biggest corporations—Google, Facebook, Apple, Amazon and Microsoft—grew to about 20 percent of GDP, twice as much as the value of the five biggest corporations at the height of the Gilded Age in 1900.

Artificial intelligence

Acemoglu and Johnson see artificial intelligence making matters worse, since so many employers are using it to replace human labor rather than augment it. Rather than ask how machines can be useful to workers, proponents of new technologies ask how machines can equal or surpass human workers. Taken to an extreme, the goal of AI enthusiasts is to achieve a general machine intelligence that can make any decision as well as a human. From a business standpoint, it is the ultimate way of cutting labor costs, by replacing educated as well as less-educated labor.

So far, the results have been a lot of what the authors call “so-so automation,” with only modest gains in productivity. The reason, they think: “Humans are good at most of what they do, and AI-based automation is not likely to have impressive results when it simply replaces humans in tasks for which we accumulated relevant skills over centuries.”

What makes us think that the way to prosperity is to devalue the human capacities of the workers who are trying to prosper? That may generate short-term profits for the owners of the machines, but not shared and sustained prosperity. The authors warn that “infatuation with machine intelligence encourages mass-scale data collection, the disempowerment of workers and citizens, and a scramble to automate work, even when this is no more than so-so automation—meaning that it has only small productivity benefits.”

The threat to democracy

The part of the book I found most disturbing was the chapter, “Democracy Breaks.” It describes what some have called a new “digital dictatorship,” most evident in China. With the help of some of the world’s largest AI companies, the Chinese government has turned the data-crunching capacities of new technologies into tools of mass surveillance and control. The aim is to monitor, rate, and sanction the behavior of any citizen. Forty years after Orwell’s imaginary 1984, Big Brother is watching more efficiently than ever. Other authoritarian governments—Russia, Iran, Saudi Arabia, Hungary, and even India—are developing similar capabilities.

In the United States, “The NSA cooperated with Google, Microsoft, Facebook, Yahoo!, various other internet service providers, and telephone companies such as AT & T and Verizon to scoop up huge amounts of data about American citizens’ internet searches, online communications and phone calls.”

Digital media have also played a role in polarizing Americans and debasing civil discourse. Media companies whose business model was based on selling ads, such as Facebook, wanted to keep their users as engaged as possible. “Any messages that garnered strong emotions, including of course hate speech and provocative misinformation, were favored by the platform’s algorithms because they triggered intense engagement from thousands, sometimes hundreds of thousands, of users.”

The hope that digital media would—like the printing press of an earlier era—empower citizens and strengthen democracy has not been fulfilled. The underlying problem, according to Acemoglu and Johnson, is that technology companies prefer a more “technocratic approach, which maintains that many important decisions are too complex for regular people.” In the economy, that encourages the devaluation and replacement of human  laborers and a flow of economic rewards to the rich. In government, it enables the surveillance and control of citizens and a flow of political power to authoritarian leaders.

Redirecting technology

In their final chapter, Acemoglu and Johnson describe a three-pronged formula for redirecting technology: “altering the narrative and changing norms…cultivating countervailing powers…[and] policy solutions.”

The new narrative would reject “trickle-down economics” and shift the emphasis back to shared prosperity. It would encourage decision-makers to address the wellbeing of ordinary people, instead of assuming that what’s good for corporate profits or large fortunes is good for everybody. Hopefully it would influence how business managers think and what they learn in business school.

Countervailing power against self-serving technocrats and corporations can come from many directions—government, civic organization and online communities. Now that blue-collar manufacturing workers are a smaller part of the labor force, organized labor should grow to embrace many occupations. Workers should organize on a broader level than the plant or the firm and play a major role in national politics.

Here are some of the policy changes they recommend:

  • Subsidize socially beneficial technologies, especially those that augment human labor rather than replace it
  • Support research on such technologies, especially in education and health care
  • Break up technology companies that have become too monopolistic
  • Reform tax policies that favor investments in equipment over hiring of workers
  • Increase tax incentives for worker training
  • Repeal the law that exempts internet platforms from any accountability for what they post
  • Tax platforms that rely on advertising in favor of those with alternative revenue streams, such as subscriptions or nonprofit contributions
  • Raise the minimum wage, but do not provide a Universal Basic Income

The authors regard a Universal Basic Income as “defeatist,” since it “fully buys into the vision of the business and tech elite that they are the enlightened, talented people who should generously finance the rest.” What they support instead is a new vision committed to seeing the value and productive potential in all of us and investing accordingly.

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Power and Progress

August 21, 2025

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Daron Acemoglu and Simon Johnson. Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity. New York: Hachette Book Group, 2024.

Daron Acemoglu and Simon Johnson won the 2024 Nobel Prize in Economics for their research on how political and economic institutions shape national prosperity. In this book, they tackle the relationship between technological innovation and prosperity.

No one doubts that new technologies have the potential to boost productivity and raise living standards. How and when they actually accomplish this is a more difficult question.

In the introduction and first three chapters, the authors lay out their general theory of technology and progress, considering the role of variations in labor demand, wages, and social power. The next four chapters discuss how these variations have played out in various historical situations, ranging from the failure of innovation to benefit farmworkers and early manufacturing workers before the late nineteenth century, to the more widespread prosperity of the mid-twentieth century. Armed with insights from economic theory and history, the authors then address the more recent revolution in digital technology. Readers who follow the argument all the way through should come away with a better understanding of our current technological age and its discontents. I know I did.

The productivity bandwagon

The conventional wisdom in economics, as well as a lot of public discussion, is that technological advances raise productivity, and higher productivity raises living standards. The authors cite Gregory Mankiw’s popular undergraduate textbook, which says that “almost all variation in living standards is attributable to differences in countries’ productivity.”

But the productivity gains from new technologies can only raise living standards if they improve real wages. What about labor-saving technologies that lower the demand for labor, causing unemployment and lower wages? Mankiw acknowledges the problem, but minimizes it by claiming that “most technological progress is instead labor-augmenting.” Most workers find some way to work with new technologies, and their increased productivity enables them to command a higher wage.

Acemoglu and Johnson call this optimistic view the “productivity bandwagon.” They argue to the contrary:

There is nothing in the past thousand years of history to suggest the presence of an automatic mechanism that ensures gains for ordinary working people when technology improves… New techniques can generate shared prosperity or relentless inequality, depending on how they are used and where new innovative effort is directed.

Rather than accept a broad generalization about technology and prosperity, the authors want to study historical variations and identify the key variables involved. The stories that people tell themselves about technology—including the ones economists tell—can both reflect and affect the historical variations. Writing in the Great Depression, John Maynard Keynes coined the term “technological unemployment.” He could imagine “the means of economising the use of labour outrunning the pace at which we can find new uses for labor.” More recently, robotics and artificial intelligence are raising that possibility again, but the productivity bandwagon remains a popular narrative.  Economic elites who profit from the application of new technologies are especially fond of it.

Variations in labor demand

Acemoglu and Johnson maintain that technological advances may or may not increase the demand for labor, depending on whether they are labor-augmenting or just labor-saving.

A classic example of technology that augmented labor, increased labor demand, and raised wages is the electrified assembly line introduced by Henry Ford. It not only raised the productivity of the existing autoworkers; it also enabled auto manufacturers to employ additional workers productively. (Economists call that variable the “marginal productivity of labor.”) By producing more cars at lower cost, car companies created a mass market for what had been a luxury item. In addition, they created additional jobs in related industries, such as auto repair, highway construction and tourism.

The effects of today’s robotics on automobile manufacturing may be very different. Carmakers can make just as many cars with less human labor, so labor productivity goes up. But demand for additional labor may go down, if factories are already turning out as many cars as their market can absorb. The marginal productivity of labor then falls, and the connection between technology and prosperity is weakened.

That is not to say that automation is always bad news for workers. That depends on the balance of labor-saving and labor-augmentation:

For most of the twentieth century, new technologies sometimes replaced people with machines in existing tasks but also boosted worker effectiveness in some other tasks while also creating many new tasks. This combination led to higher wages, increased employment, and shared prosperity.

The problem then is not just automation but excessive automation, especially if it is not really very productive in the fullest sense of the word. In economics “total factor productivity” refers to the relationship between economic output and all inputs, including capital as well as labor. Replacing workers with machines has costs as well as benefits, since machines cost money too, and displaced humans might have contributed something that machines cannot. The authors use the term “so-so automation” to refer to replacement of workers without much productivity gain. In that case, the classic gains of the earlier automobile boom—lower costs, expanded markets, rising labor demand, and widespread prosperity—do not occur.

Variations in wages

Even if new technologies are labor-enhancing, higher wages do not necessarily follow. They have not followed in societies where workers have been coerced to work without pay, or forbidden to leave their employer in search of better pay. The cotton gin enhanced the productivity of cotton workers in the Old South and expanded the areas where cotton could be profitably cultivated. But “the greater demand for labor, under conditions of coercion, translated not into higher wages but into harsher treatment, so that the last ounce of effort could be squeezed out of the slaves.”

In modern, free-market labor systems, wages are freer to rise along with labor demand. However, “wages are often negotiated rather than being simply determined by impersonal market forces.” A dominant employer may set wages for a multitude of workers, while the workers are too disorganized to bargain from strength. It was only in 1871 in Britain and 1935 in the United States that workers gained the legal right to organize and bargain collectively. Opponents of organized labor have continued to find ways of discouraging labor unions to this day. The share of national income going to labor rather than capital was highest when unions were strongest, in the 1950s.

Variations in power

Acemoglu and Johnson argue that the effects of technology depend on “economic, social, and political choices,” and that “choice in this context is fundamentally about power.”

What societies do with new technologies depends on whose vision of the future prevails. The most powerful segments of society have more say than others, although they can be contested by countervailing forces, especially in democratic societies where masses of workers vote. Although plenty of evidence points to the self-serving behavior of elites, they must at least appear to be promoting the common good for their views to be persuasive.

The technological choices a society makes can serve either to reinforce the power of elites or empower larger numbers of workers. This is especially true of general technologies with many applications. In the twentieth century, the benefits of electricity helped power a more egalitarian, broadly middle-class society. We cannot yet say the same about the digital technologies of the present century. The authors apply their theory, buttressed by historical evidence, to explain why.

Recall the subtitle of the book: “Our Thousand-Year Struggle Over Technology and Prosperity.” Making technology work for all of us has always been a struggle, and one that is related to the struggle for true democracy. Looking at it that way is more realistic and enlightening than seeing only a “productivity bandwagon” rolling smoothly toward mass prosperity.

Continued

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The Knowledge Economy (part 2)

March 24, 2021

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Roberto Unger’s book lays out his vision of the emerging knowledge economy, with rapidly innovative, knowledge-based processes in the vanguard of production. So far, however, these advanced practices of production remain highly restricted—employing few workers, controlled by technological elites, and mainly benefiting a small number of global corporations. Unger observes what he calls “pseudo-vanguardism,” in which a company uses the products of advanced production—such as sophisticated software—to run large, highly regimented operations, often in parts of the world far from corporate headquarters. The creative knowledge workers of Silicon Valley are supported by low-paid assembly workers in Asia. “Genuine vanguardism remains restricted to a small inner circle of entrepreneurs, managers, and technicians—an elite of capital and of knowledge—disengaged from the social entanglements of mass production.”

This has two unfortunate consequences: the domination of the global economy by large oligarchies and the weaker position of labor in relation to capital. Most workers don’t yet receive the potential benefits of the knowledge economy, but experience instead greater job insecurity and a declining share of income relative to the owners of capital. Innovative firms want flexible work forces, so they replace secure employment with subcontracts to low-wage firms or part-time and temporary hires. These trends contribute to the “hollowing out” of the middle of the wage distribution and the increase in overall inequality. The potential of the knowledge economy to unleash creative impulses, boost productivity and raise incomes across the board is yet to be realized.

These trends are especially noticeable in the United States, as evidence I have been citing from many sources supports. Frey reported that in the last 40 years, the share of US income going to labor rather than capital has declined from 64% to 58%. Among the 36 countries in the OECD, only three rank higher than the US on the Gini index of income inequality, based on after-tax income.

Unger’s explanation for these restrictions on the knowledge economy starts with the observation that the standardized, formulaic practices of industrial mass production are simply easier to spread from one place to another. Innovative, imaginative forms of work are harder to emulate because they “cannot, as mass production can, be reduced to a stock of readily transportable machines and procedures and easily acquired abilities.” The knowledge economy makes heavier demands on society to provide cultural and institutional support for economic growth and transformation.

What would it take to make the knowledge economy more inclusive? Unger identifies requirements of several kinds:

  • Cognitive-educational requirements include technical training that is not too job-specific, more emphasis on the imaginative side of the mind, in-depth study as opposed to “encyclopedic superficiality,” cooperative rather than authoritative learning, and discussion of contrasting points of view.
  • Social-moral requirements include more emphasis on the kinds of social interdependence that we more often associate with families, communities and churches, as opposed to the unbridled self-interest of traditional business. Social supports that could help compensate for greater flexibility of employment could include portable benefits that workers could take from job to job, or a “social inheritance” granted at birth, available to help finance human capital development and career transitions.
  • Legal-institutional requirements include new forms of coordination between governments and firms. The aim would be to help more firms acquire and use the new means of production, similar to how government helped small farmers in the nineteenth century with land grants, agricultural education and economic support. Analogous support today would include intellectual property reform to keep large corporations from monopolizing the ownership of online, user-generated data.

Unger advocates for a more vigorous democracy, since he sees today’s relatively weak democracies as too easily captured by powerful interests. He wants something in between the minimal government of laissez-faire capitalism and the more intrusive government of state socialism. A stronger democracy would respect and empower group differences, but also develop more rapid and effective means of resolving disputes among them. Otherwise, government may stand by helpless and gridlocked while the economy is generating undemocratic outcomes.

In the long run, Unger expects the emerging knowledge economy to support a more egalitarian society, recent trends in the opposite direction notwithstanding. He also expects it to ameliorate the chronic imbalances of economic supply and demand that create periods of recession and stagnation. He agrees with the Keynesian economists that supply does not create its own demand, and that there is no automatic connection between advances in productive capacity and the capacity to consume. That is the main reason for economic instability. A particularly innovative firm can expand a market by producing a product at lower cost, but that may not solve the problem of economy-wide aggregate demand.

Keynesian demand-side stimulus by government can help, through easy credit or “redistributive social spending.” Government can tax or borrow under-invested savings from the wealthy in order to boost spending and consumption for all. But even that may not be enough, if the problems of stagnation and inequality are severe. This is where Unger sees an institutional solution in the transition to the knowledge economy:

[W]e eventually come to a class of solutions that do expand demand by the same means through which they increase supply: an institutionalized broadening of access to the resources, opportunities, and capabilities of production. At this point, and only at this point, that which increases demand also increases supply. What the prevalent way of thinking supposes to be the natural state of economic life—the reciprocal accommodation of supply and demand—is in fact a characteristic of exceptional varieties of economic organization: those that have the property of breaching the limits of both supply and demand by equipping more economic agents with the means and occasions for productive initiative.

To make this more concrete, consider service workers whose means of production consist mainly of personal computers, software and skills, both cognitive and social. They are both workers and owners of capital, and their capital is intellectual and social as well as material and financial. Assuming they are providing a desired social service, they simultaneously produce something of value and generate income for their own consumption. Anything that increases their access to capital—broadly defined—helps them do so. The sharp division of owners and workers so typical of industrial capitalism—and so central to its inequalities and instabilities—starts to break down.

Unger accuses mainstream economics of a “poverty of institutional imagination,” the kind of imagination he associates with Adam Smith and Karl Marx. The most “fundamentalist” of economists defend the institutional arrangements that developed in Europe and America as part of the fixed laws of capitalism. Others are “agnostic” about such arrangements, limiting the subject matter of economics to what they think would be true of any market economy. Unger doesn’t want to defend or ignore institutions like property law and labor law, but instead bring institutional change back to the center of economic analysis.

In his final chapter, Unger discusses the “higher purpose” of making the knowledge economy more inclusive. The present economy is not only vulnerable to stagnation and growing inequality, but it also wastes human potential.

By condemning the vast majority of the labor force in even the richest countries, with the most educated populations, to less productive jobs, it also belittles them. It forces them to live diminished lives, giving inadequate scope to the development of their powers and to the expression of their humanity. To overcome the evil of belittlement through the transformation of workday experience is the higher purpose of an inclusive knowledge economy.

Keynes looked forward to a time when industrial productivity would eliminate scarcity and free people from the demands of work. That made sense at a time when highly productive manufacturing workers were demanding both good wages and a shorter work week. While some material things have become more abundant, Unger doubts that we could ever have enough of every marketable commodity. He points instead to the human capacity to keep finding new things to desire, especially in an economy that can offer more customized goods and services. Even if we limit our consumption of material things—and I expect limitations on natural resources to make us do so—I agree with Unger that “there is no limit…to our desire for service and attention from one another.” Instead of “freedom from the economy,” he looks for more “freedom in the economy.” This is consistent with his willingness to let the robots do the formulaic work, while humans devote themselves to the more creative functions.

Here are Unger’s closing thoughts:

As it deepens and spreads, the knowledge economy makes the practice of production more closely resemble the workings of the imagination….

Imagination is freedom because it is transcendence in the working of the mind. A form of production giving more space to the imagination than any previous practice of production ever gave represents an advance in freedom. It justifies the hope that we might find freedom in the economy rather than only freedom from the economy.

A knowledge economy in which many can take part does more than increase productivity and diminish inequality. It has the potential to lift us up together, to offer us a shared bigness.

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The Technology Trap (part 2)

August 10, 2019

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Carl Benedikt Frey uses the distinction between labor-replacing and labor-enabling technologies to explain why industrialization can have quite different short-term effects on jobs, wages, and the demand for labor. The Second Industrial Revolution did more than the first to raise labor demand, create good jobs, and increase labor’s share of national income. Here I will take a closer look at that process for the United States in the twentieth century.

New technologies

Based on the research of Michelle Alexopoulos and Jon Cohen, Frey identifies electricity and the internal combustion engine as the most important general-purpose technologies of the Second Industrial Revolution. Both originated in the late nineteenth century but were widely applied in the twentieth. Both were essential to what became the country’s largest industry by 1940, automobile production.

A distinct “American system” of manufacturing was substantially boosting productivity by the 1920s. The model-T Ford was the first product to be assembled without any hand labor for fitting pieces together, since machine tools could now produce completely standardized and interchangeable parts. Another innovation was “unit drive”–machines with their own electric motors–which “allowed factory workflows to be reconfigured to accommodate assembly line techniques, as machinery could now be arranged according to the natural sequence of manufacturing operations.”

Electricity also enabled the production of new home appliances, “such as the iron (first introduced in the market in 1893), vacuum cleaner (1907), washing machine (1907), toaster (1909), refrigerator (1916), dishwasher (1929), and dryer (1938).” These time-savers made it easier for women to enter the labor force, earning money with which to buy more of the products being made.

The internal combustion engine revolutionized transportation, as the share of households with cars went from 2.3% in 1910 to 89.8% in 1930. The share of farms with tractors went from 3.6% in 1920 to 80% in 1960. The Federal Aid Highway Act of 1956 created better highways for cars and trucks to travel. Economists have attributed over a quarter of the increase in productivity between 1950 and 1970 to spending on highways.

Frey summarizes:

America’s great inventions of the period 1909–49 were predominantly of the enabling sort. Some jobs were clearly destroyed as new ones appeared, but overall, new technologies boosted job opportunities enormously. Indeed, gigantic new industries emerged, producing automobiles, aircraft, tractors, electrical machinery, telephones, household appliances, and so on, which created an abundance of new jobs. Vacancies rose and unemployment fell as the mysterious force of technology progressed.

Wages and working conditions

In general, wages rose along with productivity from 1870 to 1980. Since this hasn’t been true throughout history–and especially not lately–we have to say that rising productivity is helpful but not sufficient to produce wage increases. Frey suggests that concerns about worker turnover were one motive for employers to raise wages. “[T]he assembly line could be slowed if an experienced worker quit and was replaced by someone who could not initially keep pace.” Keeping labor peace in the face of worker organization and agitation was another motive.

A democratic society can also legislate on behalf of workers, especially if middle-class voters identify with their concerns. That was more the case during the Great Depression, when New Deal legislation supported worker interests. The National Labor Relations Act of 1935 guaranteed the right to organize and bargain with management, and the Fair Labor Standards Act of 1938 defined the standard work week as 40 hours and required employers to pay overtime for additional hours.

New technologies also created safer and less physically demanding workplaces. “Machines meant the end of the most hazardous, dirty, and backbreaking jobs,” and disabling injuries were cut in half. “Belts, gears, and shafts [of the pre-electric factory] were the main sources of factory accidents, posing a constant danger to workers’ fingers, arms, and lives.”

The “Great Leveling”

In retrospect, the twentieth century up until about 1980 is noted not only for its greater prosperity, but its reduction in economic inequality. Inequality had increased between the American Revolution and the Civil War, as artisan jobs had been lost to factories, large fortunes were being amassed, and large wage gaps had opened up between the most successful urban workers and the masses of poor people both on the farms and in the cities. The late nineteenth century is, of course, known as the “Gilded Age” for its conspicuous consumption by wealthy capitalists.

The twentieth century was different:

As Americans in the middle and at the lower end of the income distribution became the prime beneficiaries of progress, inequality went into reverse. Along with every other industrialized nation, America saw the share of income accruing to people at the top, fall.

Here, explanations differ. Thomas Piketty has argued that the general trend of capitalism is toward greater inequality, and it takes some unusual shock to the system to interrupt that process. As summarized by Frey:

In Piketty’s world, there are no forces within capitalism that serve to drive inequality down. From time to time, however, macroeconomic or political shocks may disrupt the normal equilibrium. Two world wars and the Great Depression served to destroy the riches of the wealthy.

Without denying that such shocks have played a role, Frey does see forces within capitalism to generate equality, the first of which is investment in labor-enabling technologies. That creates the potential to empower and enrich workers. A high rate of unionization is helpful for realizing that potential. Beyond that, workers must be able to keep up with the skill demands of new technologies.

“The leading explanation for the great leveling comes from pioneering work by Jan Tinbergen that conceptualized patterns of inequality as a race between technology and education….” The enabling technologies of the twentieth century favored more skilled workers. Jobs like mechanic or electrician paid well, but only for those who had the skills to do them. Semi-skilled assembly-line work could also pay pretty well, for workers with the discipline, stamina and dexterity to keep up. That could have created a wide gap between a skilled few and the unskilled many, except for the fact that so many workers were acquiring at least the basic skills they needed for an advanced industrial economy.

[E]ven if technological progress favors skilled workers, growing wage inequality does not have to be the result. The return to human capital depends on demand as well as supply. As long as the supply of skilled workers keeps pace with the demand for them, the wage gap between skilled and unskilled workers will not widen. While a number of short-run events and government interventions contributed to the great leveling, the most pervasive force—and certainly the best documented one—behind its long-run egalitarian impact was the upskilling of the American workforce, which depressed the skill premium.

The percentage of young people who completed a high-school education went from 9% to 40% just between 1910 and 1935, and proceeded upward from there.

The combination of enabling technology and a more skilled population created the largest middle class the country had ever seen. But that made the shrinking of the middle class that occurred after 1980 all the more surprising and alarming. Frey calls this the “Great Reversal,” and that is the topic of the next post.

Continued

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Automation and New Tasks

February 22, 2019

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Daron Acemoglu and Pascual Restrepo, “Automation and New Tasks: The Implications of the Task Content of Production for Labor Demand.” Prepared for Journal of Economic Perspectives, November 6, 2018.

This paper really helped me think more systematically about the impact of automation on the demand for labor, something I’ve been thinking a lot about lately. My last post on the subject, which also featured a paper by Acemoglu and Restrepo, is here.

Technology and labor demand

One of the key issues is how to reconcile the recent concern that robotics will destroy too many good jobs with the traditional optimism of economists about the effects of technology. Throughout most of U.S. history, new technologies like farm machinery or industrial assembly lines have not stood in the way of economy-wide gains in labor demand, as reflected in real wages per capita. And yet, too many workers today are experiencing unemployment or wage loss because of automation. How do we sort this out in a way that enables us to anticipate where we may be headed and formulate sensible policy responses?

Acemoglu and Restrepo conceptualize the labor demand of an industry as a product of “value added” and “labor share”:

Labor demand = Value added x Labor share

Value added is an industry’s addition to economic output and income. Labor share is the portion of that added value that is received by labor. Labor demand is the resulting wages per capita.

The authors say that economists have primarily conceptualized technological progress as “factor-augmenting.” That is, new technologies increase the productivity of labor or machinery or both. More productive factors of production mean more value added. Assuming that labor’s share doesn’t decline, labor demand as reflected in real wages ought to increase. “Factor augmenting technologies affect labor demand mostly via the productivity effect and have a small impact on the labor share of an industry.” That was the experience, for example, of manufacturing industries in the mid-20th century.

However, an analysis that focuses on value added while holding labor share constant is not complete. Technological change can also effect how much labor is needed in a particular process of production, and how much of the income from that process goes to workers.

The task content of production

The authors’ key concept is the task content of production, which involves the allocation of tasks between labor and capital (the latter including machines and software). Focusing on the potential of new technologies to add value “often misses the major implications of technological changes that directly alter the allocation of tasks to factors.”

What makes the implications of automation hard to assess is that it has contradictory effects on labor demand–a productivity effect and a displacement effect. By raising productivity and increasing value added, it increases the potential income to be distributed to the human workers who remain. But by replacing labor with machinery, it reduces labor’s share of income. Whether wages actually rise or fall depends on the relative strength of the two effects.

To complicate things further, displaced labor does not necessarily go missing from an industry, let alone the economy as a whole. The introduction of new tasks for humans to do has a reinstatement effect that raises the labor share of production and therefore labor demand. (Personally, I’m not too fond of the term “reinstatement”, since it sounds as if workers are getting their old jobs back, which is not at all what is intended. I would prefer the term “redeployment”.) But whatever term is used, the point is important. As machines take over familiar tasks, humans can move into new economic activities where they have some advantage over machines, especially because of their general intelligence, flexibility and creativity. This has been true in the past, and how much it remains true in the robotic age is an important question for the future of work.

Labor demand in a multi-sector economy

When the authors move from discussing individual industries to discussing the entire economy, they introduce an additional effect on labor demand, the composition effect. This occurs when labor moves from one economic sector to another, and the sectors differ in labor demand. If workers move out of a sector where wages and labor’s share of added value are falling, and into a sector where they are higher, that contributes positively to labor demand.

The classic example of this process is the mechanization of agriculture, “which started in the first half of the 19th century with the cotton gin and continued with horse-powered reapers, harvesters and plows later in the century and with tractors and combine harvesters in the 20th century.” That process displaced massive amounts of farm labor and reduced labor’s share of agricultural activity and income. Much of that displaced labor went into manufacturing. Manufacturing was mechanizing too, but it maintained labor demand by increasing output and creating new manufacturing tasks. “The composition and reinstatement effects explain why, despite the mechanization of a sector making up a third of the economy [agriculture in 1850], labor demand increased and the share of labor in national income remained stable during this period.”

Labor demand 1947-1987

For two recent periods, Acemoglu and Restrepo analyze changes in labor demand, measuring the relative contributions of the effects they have discussed. They chart developments in six industries: agriculture, mining, manufacturing, construction, transportation and services.

The postwar era of 1947-1987 was a period of strong labor demand and rising real wages, which grew at an average rate of 2.4% per year.

Nothing comparable to the displacement of labor by the mechanization of agriculture occurred during this time. The only industries to suffer a loss of labor share were the relatively small industries of mining and transportation.

There was some displacement of labor due to automation in the large manufacturing industry, but it was offset by the creation of new manufacturing jobs, such as managerial and clerical jobs in corporate bureaucracies, as well as jobs in the expanding service industries. “[T]here was plenty of automation, especially in manufacturing, but this was accompanied with the introduction of new tasks (or other changes increasing the task content of production in favor of labor) in both manufacturing and the rest of the economy that offset the adverse labor demand consequences of automation.”

With displacements due to automation balanced by reinstatements due to the creation of new tasks, labor’s share of output and income remained steady. The increase in actual real wages that occurred is accounted for almost entirely by the other effect that supports labor demand, higher productivity. New technologies added value, and labor got its share of that added value in the form of higher real wages.

If technological change always worked that way, we wouldn’t be so worried about the future. But there was trouble ahead. In their data for those years, we can already see labor’s share within manufacturing peaking around 1980 and starting downward.

Labor demand 1987-2017

In this more recent period, wage growth was slower, averaging only 1.3% per year.

Labor demand suffered in both ways suggested by the same formula:

Labor demand = Value added x Labor share

Value added increased more slowly because of slower growth in productivity. In addition, labor’s share of value added declined in manufacturing, construction and mining. That was because labor displacement due to automation accelerated, while labor reinstatement due to new task creation slowed down.

Deeper explanations for these trends are harder to agree on. One puzzle is why the accelerating automation hasn’t done more to raise productivity. The authors point out that productivity gains from automation depend on the actual superiority of machines over humans for a given task. A rush to automate because a company gets caught up in a wave of technological enthusiasm or receives a tax break on new equipment may not be that helpful.

This analysis clarifies that automation will reduce labor demand when the productivity effect is not very large. Contrary to a common presumption in popular debates, it is not the “brilliant” automation technologies but those that are “so-so” and generate only small productivity improvements that will reduce labor demand. This is because the positive productivity effect of so-so technologies is not sufficient to offset the decline in labor demand due to displacement.

Favoring machines over human workers may also result in a lack of investment in education and training:

…[T]here may be a mismatch between the available skills of the workforce and the needs for new technologies, which could further reduce productivity gains from automation and hamper the introduction of new tasks—because the lack of requisite skills reduces the efficiency with which new technologies can be deployed.

The future of work

By considering a variety of things affecting labor demand, Acemoglu and Restrepo avoid a simple preoccupation with a positive factor like the productivity effect or a negative factor like the displacement effect. “Our evidence and conceptual approach support neither the claims that the end of human work is imminent nor the presumption that technological change will always and everywhere be favorable to labor.” It will be the balance of the various effects that will matter.

The authors don’t make specific policy recommendations, but some very general prescriptions follow from their analysis. We shouldn’t be afraid to automate tasks where machines have a clear advantage, since the gains in value added are too good to pass up. But we should also enhance the value of human labor, both by giving the workers who remain in automating industries their share of the gains, and also by investing in the education and training workers need to perform new tasks that humans can do better than machines. Where private employers don’t find it profitable to nurture and reward human labor, government must play a strong role for the general good.

As it stands now, too much displaced labor is crowding into low-skill, low-tech, low-wage work. That is a waste of both our human and technological resources. A jobless future where robots work, their owners get rich, and most people live off public assistance is also a dismal prospect. If we are to have a hi-tech economy, let it be one where the average worker can reap the benefits in creative work, good pay, and rewarding leisure.

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Posted by Ed Steffes

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