• JobsTrabajosUnited StatesEE. UU.WorkersSector del trabajo
National Public Radio’s All Things Considered show had a piece on a bill that would make current national child labor standards apply to children working on farms. Currently, children as young as 12 can legally work in agriculture. The bill under consideration would impose the same rules for child labor on farms as apply to the rest of the economy.
At one point the piece gives a comment from Kristi Boswell, a Trump administration agriculture official, complaining that the bill would prevent people like her relatives from working on their family’s farm and learning the business. As the piece subsequently points out, this is not an issue since the bill has an exemption for family workers.
Since Ms. Boswell’s comment obviously misrepresented the issue, the question is why NPR felt the need to present it to listeners. The piece ran for just three minutes. In that context, there is no obvious reason why it should give opponents of the bill the time to make a blatantly false excuse for opposing the bill. If it could not find anyone to give an honest objection to the bill, it should have told its listeners of that fact and left it at that.
National Public Radio’s All Things Considered show had a piece on a bill that would make current national child labor standards apply to children working on farms. Currently, children as young as 12 can legally work in agriculture. The bill under consideration would impose the same rules for child labor on farms as apply to the rest of the economy.
At one point the piece gives a comment from Kristi Boswell, a Trump administration agriculture official, complaining that the bill would prevent people like her relatives from working on their family’s farm and learning the business. As the piece subsequently points out, this is not an issue since the bill has an exemption for family workers.
Since Ms. Boswell’s comment obviously misrepresented the issue, the question is why NPR felt the need to present it to listeners. The piece ran for just three minutes. In that context, there is no obvious reason why it should give opponents of the bill the time to make a blatantly false excuse for opposing the bill. If it could not find anyone to give an honest objection to the bill, it should have told its listeners of that fact and left it at that.
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• Food PricesInflationUnited StatesEE. UU.
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It is really painful to see the regular flow of pieces debating whether AI will lead to mass unemployment. Invariably, these pieces are written as though the author has taken an oath that they have no knowledge of economics whatsoever.
The NYT gave us the latest example on Sunday, in a piece debating how many jobs will be affected by AI. As the piece itself indicates, it is not clear what “affected by AI” even means.
What percent of jobs were affected by computers? The answer would probably be pretty close to 100 percent, if by “affected” we mean in some way changed. If by affected, we mean eliminated, then we clearly are talking about a much smaller number.
Thinking of AI like we did about computers is likely a good place to start. First of all, we should remember that there were predictions of massive layoffs and unemployment from computers and robots for decades. This did not happen.
In fact, we have a measure of the extent to which computers, robots, and other technology are displacing workers. It’s called “productivity growth,” and the Labor Department gives us data on it every quarter.
Productivity is the measure of the value of output that a worker can produce an hour. We expect this to increase through time as we get better equipment and software, we learn how to do things better, and workers get more educated.
For the last two centuries, productivity growth has been a normal feature of the U.S. economy, and in fact, most normally functioning economies around the world. This is the basis for rising living standards through time. It is the reason that we can feed our whole population, and still export food, even with just around 1.0 percent of the workforce in agriculture, as opposed to more than 50 percent in the 19th century.
The big question is the rate at which productivity grows. Productivity growth has actually been pretty slow in recent years. It averaged just 1.3 percent annually since 2006. By contrast, it averaged close to 3.0 percent in the quarter century from 1947 to 1973.
Rather than being a period of mass unemployment and declining living standards, the rapid productivity growth in that period was associated with widespread improvements in living standards. We went from depression era living standards in 1947 to a prosperous middle-class society by the end, as ordinary workers were able to afford to buy houses and cars, and send their kids to college.
We should think of the promise of AI in the same way. The first paragraph in the NYT piece warns/promises:
“In 2013, researchers at Oxford University published a startling number about the future of work: 47 percent of all United States jobs, they estimated, were ‘at risk’ of automation ‘over some unspecified number of years, perhaps a decade or two.’”
That warning is pretty vague but let’s say that we could use AI to eliminate 47 percent of current jobs over two decades. If we held GDP constant over this period, that would roughly correspond to the 3.0 percent annual productivity growth we saw during the post-World War II boom. And, just as we saw high levels of employment through the post-war boom (unemployment got down to 3.0 percent in 1969), we could maintain high employment if the economy had the same sort of rapid growth that we had in that quarter century. That will be a policy choice not an issue determined by technology.
Will Prosperity be Shared?
In the post-war boom the benefits from productivity growth were widely shared. To be clear, not everyone was doing great. Blacks were openly discriminated against, and virtually excluded from many better-paying jobs. The same was true of women, as the barriers were just beginning to come down. But the gains from productivity growth went well beyond just a small elite at the top.
Whether that happens with AI and related technologies will depend on how we as a society choose to structure the rules around AI. One reason why Bill Gates and others in the tech industry became incredibly rich was that the government granted patent and copyright protection for computer software. That was a policy choice. If we did not have these government-granted monopolies, Bill Gates would probably still be working for a living. (Okay, maybe he would be collecting his Social Security by now.)
These monopolies serve a purpose, they provide an incentive to innovate, but it’s not clear they have to be as long and as strong as is currently the case. Also, there are other ways to provide incentives. For example, the government can pay for people to do the work, as it did when it paid Moderna roughly a $1 billion to develop and test its Covid vaccine. Of course, the government also gave Moderna control over the vaccine, allowing the company’s stock to generate five Moderna billionaires in a bit over a year.
It is not hard to envision routes through which AI can lead to widespread prosperity in a way comparable to what we saw in the post-war boom. Suppose that we don’t have government-granted monopolies restricted access to the technology, so that it can be freely used.
In that world, I could likely go to a medical technician (someone trained in performing clinical tests and entering data), who could plug various test results into an AI system, and it would tell me if I have heart problem, kidney problem, or anything else. Rather than seeing a highly paid physician, I could have most of my health care needs met with this technology and a reasonably compensated medical professional, who may get less than one-third of the pay of a doctor.
There would be a similar story with legal assistance. Certainly, for standard legal processes, like preparing a will or even arranging a divorce, AI would likely be up to the task. Even in more complicated cases, AI could likely prepare a brief, which a lawyer could evaluate and edit in a fraction of the time it would take them if they were working from scratch.
People have pointed out that AI makes mistakes. There have been many instances where we have heard of AI systems inventing facts that are not true or citing sources that don’t exist. This is a real problem, but presumably one that will be largely fixed in the not distant future. We shouldn’t imagine that AI systems will ever be perfect, but the number of errors they make will surely be reduced as the technology is developed further.
In addition, it is important to remember that humans also make errors. There are few of us that cannot recall a serious mistake that a doctor made in diagnosing or treating our own condition or a close family member. A world without mistakes does not exist and cannot be the basis of comparison. We need AI to be at least as good as the workers it is displacing, but that doesn’t mean perfect.
AI and the Distribution of Income
We structured our economy over the last four decades so that most of the gains from the productivity growth over this period went to those at the top. Contrary to what is often asserted, most of the gains actually did not go to corporate profits, they went to workers at the top of the pay ladder, like CEOs and other top management, Wall Street types, highly paid tech workers, and doctors and lawyers and other highly paid professionals. These workers used their political power to ensure that the rules of the economy were designed to benefit them.
Whether or not that continues in the era of AI will depend on the power of these groups relative to less highly paid workers. Just to take an obvious example, doctors may use their political power to have licensing restrictions that prevent less highly trained medical professionals from making diagnoses and recommending treatments based on AI.
If that seems far-fetched, we already have laws that make it very difficult for even very well-trained foreign doctors from practicing in the United States. While the cry of “free-trade” was used to expose manufacturing workers to international competition, and thereby depress their pay, it almost never came up with doctors and other highly paid professionals.
Anyhow, we may well see a similar story with AI, where highly paid professionals use their political power to limit the uses of AI and ensure that it doesn’t depress their incomes. This also is an issue with ownership of the technology itself. If we don’t allow for strong patent/copyright monopolies in AI, and make non-disclosure agreements difficult to enforce, we can ensure the technology is more widely spread and cheap. This would mean that the gains are widely shared and not going to a relatively small group of Bill Gates types.
It is also important to understand how high incomes for a small group depress incomes for everyone else. Most of us don’t directly pay for our own health care. We have insurance provided by an employer or the government. However, insurers are not charities. (You knew that.)
If insurers have to pay out lots of money to doctors, then it will mean that our employers pay higher premiums, which they will look to take out of our paychecks. Alternatively, if the government is picking up the tab, there will be less money to pay for child tax credits, day care, and other good things.
Also, when the lawyers, doctors, tech workers and other would be beneficiaries from AI get high incomes, they buy bigger and more houses. That raises the cost of housing for everyone else. We can and should build more housing, but when you have a small segment of the population that has far money than everyone else, it is difficult to keep housing affordable for ordinary workers.
Anyhow, the point here is straightforward. Keeping down the pay for those at the top is not an issue of jealously. The more money that goes to the top, the less there is for everyone else, as long as we have not structured the rules in a way that takes away the incentive to be innovative and productive.
Fear the Rich, Not AI
The moral of the story is that there is nothing about AI technology that should lead to mass unemployment and inequality. If those are outcomes, it will be the result of how we structured the rules, not the technology itself. We need to keep our eyes on the ball and remember that structuring the rules is a policy choice.
And, one other point: those who want to structure the rules so that all the money goes to the top will want to say the problem is technology. It is much easier for them to tell the rest of us that they are rich and everyone else is not because of technology, rather than because they rigged the market. Keep that in mind, always.
It is really painful to see the regular flow of pieces debating whether AI will lead to mass unemployment. Invariably, these pieces are written as though the author has taken an oath that they have no knowledge of economics whatsoever.
The NYT gave us the latest example on Sunday, in a piece debating how many jobs will be affected by AI. As the piece itself indicates, it is not clear what “affected by AI” even means.
What percent of jobs were affected by computers? The answer would probably be pretty close to 100 percent, if by “affected” we mean in some way changed. If by affected, we mean eliminated, then we clearly are talking about a much smaller number.
Thinking of AI like we did about computers is likely a good place to start. First of all, we should remember that there were predictions of massive layoffs and unemployment from computers and robots for decades. This did not happen.
In fact, we have a measure of the extent to which computers, robots, and other technology are displacing workers. It’s called “productivity growth,” and the Labor Department gives us data on it every quarter.
Productivity is the measure of the value of output that a worker can produce an hour. We expect this to increase through time as we get better equipment and software, we learn how to do things better, and workers get more educated.
For the last two centuries, productivity growth has been a normal feature of the U.S. economy, and in fact, most normally functioning economies around the world. This is the basis for rising living standards through time. It is the reason that we can feed our whole population, and still export food, even with just around 1.0 percent of the workforce in agriculture, as opposed to more than 50 percent in the 19th century.
The big question is the rate at which productivity grows. Productivity growth has actually been pretty slow in recent years. It averaged just 1.3 percent annually since 2006. By contrast, it averaged close to 3.0 percent in the quarter century from 1947 to 1973.
Rather than being a period of mass unemployment and declining living standards, the rapid productivity growth in that period was associated with widespread improvements in living standards. We went from depression era living standards in 1947 to a prosperous middle-class society by the end, as ordinary workers were able to afford to buy houses and cars, and send their kids to college.
We should think of the promise of AI in the same way. The first paragraph in the NYT piece warns/promises:
“In 2013, researchers at Oxford University published a startling number about the future of work: 47 percent of all United States jobs, they estimated, were ‘at risk’ of automation ‘over some unspecified number of years, perhaps a decade or two.’”
That warning is pretty vague but let’s say that we could use AI to eliminate 47 percent of current jobs over two decades. If we held GDP constant over this period, that would roughly correspond to the 3.0 percent annual productivity growth we saw during the post-World War II boom. And, just as we saw high levels of employment through the post-war boom (unemployment got down to 3.0 percent in 1969), we could maintain high employment if the economy had the same sort of rapid growth that we had in that quarter century. That will be a policy choice not an issue determined by technology.
Will Prosperity be Shared?
In the post-war boom the benefits from productivity growth were widely shared. To be clear, not everyone was doing great. Blacks were openly discriminated against, and virtually excluded from many better-paying jobs. The same was true of women, as the barriers were just beginning to come down. But the gains from productivity growth went well beyond just a small elite at the top.
Whether that happens with AI and related technologies will depend on how we as a society choose to structure the rules around AI. One reason why Bill Gates and others in the tech industry became incredibly rich was that the government granted patent and copyright protection for computer software. That was a policy choice. If we did not have these government-granted monopolies, Bill Gates would probably still be working for a living. (Okay, maybe he would be collecting his Social Security by now.)
These monopolies serve a purpose, they provide an incentive to innovate, but it’s not clear they have to be as long and as strong as is currently the case. Also, there are other ways to provide incentives. For example, the government can pay for people to do the work, as it did when it paid Moderna roughly a $1 billion to develop and test its Covid vaccine. Of course, the government also gave Moderna control over the vaccine, allowing the company’s stock to generate five Moderna billionaires in a bit over a year.
It is not hard to envision routes through which AI can lead to widespread prosperity in a way comparable to what we saw in the post-war boom. Suppose that we don’t have government-granted monopolies restricted access to the technology, so that it can be freely used.
In that world, I could likely go to a medical technician (someone trained in performing clinical tests and entering data), who could plug various test results into an AI system, and it would tell me if I have heart problem, kidney problem, or anything else. Rather than seeing a highly paid physician, I could have most of my health care needs met with this technology and a reasonably compensated medical professional, who may get less than one-third of the pay of a doctor.
There would be a similar story with legal assistance. Certainly, for standard legal processes, like preparing a will or even arranging a divorce, AI would likely be up to the task. Even in more complicated cases, AI could likely prepare a brief, which a lawyer could evaluate and edit in a fraction of the time it would take them if they were working from scratch.
People have pointed out that AI makes mistakes. There have been many instances where we have heard of AI systems inventing facts that are not true or citing sources that don’t exist. This is a real problem, but presumably one that will be largely fixed in the not distant future. We shouldn’t imagine that AI systems will ever be perfect, but the number of errors they make will surely be reduced as the technology is developed further.
In addition, it is important to remember that humans also make errors. There are few of us that cannot recall a serious mistake that a doctor made in diagnosing or treating our own condition or a close family member. A world without mistakes does not exist and cannot be the basis of comparison. We need AI to be at least as good as the workers it is displacing, but that doesn’t mean perfect.
AI and the Distribution of Income
We structured our economy over the last four decades so that most of the gains from the productivity growth over this period went to those at the top. Contrary to what is often asserted, most of the gains actually did not go to corporate profits, they went to workers at the top of the pay ladder, like CEOs and other top management, Wall Street types, highly paid tech workers, and doctors and lawyers and other highly paid professionals. These workers used their political power to ensure that the rules of the economy were designed to benefit them.
Whether or not that continues in the era of AI will depend on the power of these groups relative to less highly paid workers. Just to take an obvious example, doctors may use their political power to have licensing restrictions that prevent less highly trained medical professionals from making diagnoses and recommending treatments based on AI.
If that seems far-fetched, we already have laws that make it very difficult for even very well-trained foreign doctors from practicing in the United States. While the cry of “free-trade” was used to expose manufacturing workers to international competition, and thereby depress their pay, it almost never came up with doctors and other highly paid professionals.
Anyhow, we may well see a similar story with AI, where highly paid professionals use their political power to limit the uses of AI and ensure that it doesn’t depress their incomes. This also is an issue with ownership of the technology itself. If we don’t allow for strong patent/copyright monopolies in AI, and make non-disclosure agreements difficult to enforce, we can ensure the technology is more widely spread and cheap. This would mean that the gains are widely shared and not going to a relatively small group of Bill Gates types.
It is also important to understand how high incomes for a small group depress incomes for everyone else. Most of us don’t directly pay for our own health care. We have insurance provided by an employer or the government. However, insurers are not charities. (You knew that.)
If insurers have to pay out lots of money to doctors, then it will mean that our employers pay higher premiums, which they will look to take out of our paychecks. Alternatively, if the government is picking up the tab, there will be less money to pay for child tax credits, day care, and other good things.
Also, when the lawyers, doctors, tech workers and other would be beneficiaries from AI get high incomes, they buy bigger and more houses. That raises the cost of housing for everyone else. We can and should build more housing, but when you have a small segment of the population that has far money than everyone else, it is difficult to keep housing affordable for ordinary workers.
Anyhow, the point here is straightforward. Keeping down the pay for those at the top is not an issue of jealously. The more money that goes to the top, the less there is for everyone else, as long as we have not structured the rules in a way that takes away the incentive to be innovative and productive.
Fear the Rich, Not AI
The moral of the story is that there is nothing about AI technology that should lead to mass unemployment and inequality. If those are outcomes, it will be the result of how we structured the rules, not the technology itself. We need to keep our eyes on the ball and remember that structuring the rules is a policy choice.
And, one other point: those who want to structure the rules so that all the money goes to the top will want to say the problem is technology. It is much easier for them to tell the rest of us that they are rich and everyone else is not because of technology, rather than because they rigged the market. Keep that in mind, always.
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Seems the NYT still can’t get access to the government’s data on consumption. It ran an article telling readers that demand for wedding rings has fallen. This is very plausibly explained by fewer people having met up during the pandemic, and therefore fewer people getting married. However, the subhead adds “Inflation and anxiety among shoppers haven’t helped.”
In spite of this astute diagnosis of the country’s economic ills by the NYT, the Commerce Department’s data tell a very different story. These data show that real (inflation-adjusted) sales of jewelry were 26.4 percent higher in the first quarter of 2023 than in the fourth quarter of 2019 (Line 62).
This seems like yet another case where the country’s media are determined to tell a horrible economy story, bravely refusing to let the data get in the way.
Seems the NYT still can’t get access to the government’s data on consumption. It ran an article telling readers that demand for wedding rings has fallen. This is very plausibly explained by fewer people having met up during the pandemic, and therefore fewer people getting married. However, the subhead adds “Inflation and anxiety among shoppers haven’t helped.”
In spite of this astute diagnosis of the country’s economic ills by the NYT, the Commerce Department’s data tell a very different story. These data show that real (inflation-adjusted) sales of jewelry were 26.4 percent higher in the first quarter of 2023 than in the fourth quarter of 2019 (Line 62).
This seems like yet another case where the country’s media are determined to tell a horrible economy story, bravely refusing to let the data get in the way.
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• COVID-19CoronavirusHealthcarePost-COVIDUnited StatesEE. UU.
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• Consumer Price IndexEconomic Crisis and RecoveryCrisis económica y recuperaciónEconomic GrowthEl DesarolloUnited StatesEE. UU.
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That’s what the Census Bureau data for the first quarter of 2023 showed, in a report completely ignored by the media. While NPR was telling us that the homeownership rate reported in the 2020 Census hit its lowest level in half a century (this was the top of the hour news summary, no link), the data the Census Bureau puts out quarterly tell the opposite story.
Homeownership rates have been rising throughout the pandemic and the recovery, hitting levels not seen since the collapse of the housing bubble. The homeownership rate for households with less than the median income hit 53.4 percent in the first quarter of 2023. That’s up from 51.4 percent in the fourth quarter of 2019, and the highest at any point since 1994 when the series begins. For some reason, it doesn’t seem the media think a record high homeownership rate for moderate-income people is newsworthy.
There is a comparable story for homeownership rates for Black people, which rose to 45.8 percent in the first quarter, compared to 44.0 percent before the pandemic. For Hispanics, the homeownership rate in the first quarter stood at 49.7 percent, up from 48.1 percent in the fourth quarter of 2019. The homeownership rate for people under age 35 was 39.3 percent in the first quarter, a 1.7 percentage point increase from 37.6 percent pre-pandemic rate.
The quarterly data are erratic, and this picture could change with high current mortgage rates, which could go higher with more rate hikes from the Fed. But the point here is that there is a really good story on homeownership that the media is not only ignoring it, but telling people the opposite. I would be tempted to say “FAKE NEWS!,” but I can’t afford the royalty payments to Donald Trump.
That’s what the Census Bureau data for the first quarter of 2023 showed, in a report completely ignored by the media. While NPR was telling us that the homeownership rate reported in the 2020 Census hit its lowest level in half a century (this was the top of the hour news summary, no link), the data the Census Bureau puts out quarterly tell the opposite story.
Homeownership rates have been rising throughout the pandemic and the recovery, hitting levels not seen since the collapse of the housing bubble. The homeownership rate for households with less than the median income hit 53.4 percent in the first quarter of 2023. That’s up from 51.4 percent in the fourth quarter of 2019, and the highest at any point since 1994 when the series begins. For some reason, it doesn’t seem the media think a record high homeownership rate for moderate-income people is newsworthy.
There is a comparable story for homeownership rates for Black people, which rose to 45.8 percent in the first quarter, compared to 44.0 percent before the pandemic. For Hispanics, the homeownership rate in the first quarter stood at 49.7 percent, up from 48.1 percent in the fourth quarter of 2019. The homeownership rate for people under age 35 was 39.3 percent in the first quarter, a 1.7 percentage point increase from 37.6 percent pre-pandemic rate.
The quarterly data are erratic, and this picture could change with high current mortgage rates, which could go higher with more rate hikes from the Fed. But the point here is that there is a really good story on homeownership that the media is not only ignoring it, but telling people the opposite. I would be tempted to say “FAKE NEWS!,” but I can’t afford the royalty payments to Donald Trump.
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When I saw that two of the country’s most prominent economists wrote a book on “our 1000-year struggle over technology and prosperity,” I expected a lot. I was disappointed. To be clear, there is much here to like and I’m sure that most readers will get much from it, as I did. But, the book fails to follow through adequately on the key point in its analysis, which is that the gains from technology are a matter of struggle, not an outcome given by the technology itself.
I’ll start with the positives. The book gives a cursory, but useful, account of the major developments in technology going back more than a thousand years. Some of their discussion deals with the origins of agriculture, an innovation that goes back many thousands of years. However, most of the book does describe events in the promised thousand-year horizon.
It points out that there were important technological innovations in the Middle Ages, which did allow for very modest gains in productivity and living standards across much of Europe until the 13th or 14th century. At that point, the rate of increase accelerated, although it was still much slower than in later centuries.
One of the points it makes, which was underappreciated (at least by me), was the extent to which the gains in this period were siphoned off by the church. The huge cathedrals and monasteries constructed in this period absorbed a huge chunk of the surplus produced in agriculture. This both depressed living standards and prevented resources from going to investments that would increase productivity.
As technology continued to advance in the subsequent centuries, there were modest gains in living standards for large sections of the population as the demands of the church were reined in. However, the book makes clear that there was no automatic transmission from improvements in technology to gains in living standards for the bulk of the population.
In particular, the book points out that the early years of the industrial revolution in 19th century England were associated with a deterioration in living standards for large segments of the working class. Factory workers, and especially children, were forced to work longer hours under worse conditions than ever would have been the case in agriculture. In addition, the living conditions in cities were far more unhealthy than what they faced in the countryside.
When conditions for the English working class did subsequently improve in the second half of the 19th century, it was due to the growing political power of the working class, as well as the growing importance of labor unions.
They also point out that the direction of technology itself is very much determined by power relations. In particular, they note that the enclosure movement in England was not, in fact, about allowing for the transition to more modern crop rotations. There were many places where improved crop rotation systems were adopted in open fields. Rather, enclosure was about giving landlords more control over land and displacing the peasantry.
None of this would be new to people familiar with the history of this period, but it is still refreshing to see prominent economists make these points. The distribution of the benefits of technology is far from being determined by strictly economic factors. It depends very much on the institutional structure and power relations in society.
It is Not Just the Masses Who Rely on Political Power to Secure the Gains from Technology
However, having said this, my major criticism of Acemoglu and Johnson is that they fail to carry this critique far enough. While they discuss extensively the factors that have allowed for gains in the past for ordinary workers, and which could go further in the future, they don’t consider the ways in which the winners have structured the economy to ensure that they get the bulk of the benefits of growth. In effect, the book treats the upward redistribution as a natural outcome of the development of the market and technology, whereas it requires interventions to ensure that gains are widely shared.
Just to take the most obvious case (and my favorite target), the money that goes to the top end of the income distribution due to patent and copyright monopolies is entirely due to the winners’ ability to write the laws on intellectual products in ways that benefit them. If the government didn’t threaten to imprison people who copied Microsoft software without his permission, Bill Gates would still be working for a living. It wasn’t technology that created five Moderna billionaires, it was the fact that the government paid the company to develop a Covid vaccine, and then allowed Moderna to have monopoly control over its distribution.
If it is difficult to understand the idea that government policy on technology, rather than the technology itself, created a huge amount on inequality, imagine a world where we had no patent and copyright monopolies and non-disclosure agreements were unenforceable. Arguably, we would have seen less technological progress, depending on what, if any, alternative incentive mechanism we used; but surely we would see nothing like the inequality that has resulted from preserving and expanding these monopolies over the last four decades.
Similarly, it was not just technology that allowed us to subject manufacturing workers to competition from their much lower paid counterparts, but protected highly paid professionals. There is no technical reason that we can’t all have Zoom consultations with doctors in India who get paid one-tenth as much as doctors in the United States. Globalization has not had as much impact on the pay of highly paid professions because of their power, not because of technology.
And of course, the big bucks made by the top people in finance is not due to the wonders of technology, but the fact that a politically powerful industry was able to rig the rules. The too-big-to-fail insurance on vivid display in the Great Financial Crisis, and again following the collapse of the Silicon Valley Bank, is only part of the story.
We have the technology to make the financial system far more efficient, starting with Federal Reserve banking accounts and running into just about every area of finance, but it doesn’t happen because the big players in the financial industry don’t want to see a hit to their income. No one knows this better than Simon Johnson, who has written extensively on the abuses and corruption in the financial industry, but for some reason this is mentioned only in passing in Power and Progress.[1]
These points are not nit-picking. It is not just the masses who need to change the rules to get their fair share of the gains from productivity growth, it is also the rich who change the rules to their benefit. This point is essential both as a matter of logic and politics.
It is also important to recognize that we improve the living standards of ordinary workers not only by getting them more money, but by giving the rich less. If the rich have less money to buy multiple houses, then housing is cheaper for everyone else. And, if they have fewer personal servants fulfilling their whims, there are more workers available to meet the needs of the rest of the population.
There are no preset given rules of a market economy. We make them up as we go along. If we want to see more equality and a system where the vast majority, and not just a lucky few, share in the benefits of progress, we can go two routes. One is the route Acemoglu and Johnson advocate throughout the book: strengthen workers’ bargaining power and expand the welfare state. But, we can also work the other side; weaken the institutional structures the rich have been putting in place to make themselves richer. We should, of course, be opportunistic and make advances on both sides wherever we can, but it makes no sense to ignore the other side.
Will AI and Related Technologies Change the World?
I have one other small bone to pick with Acemoglu and Johnson. They look at the developments in AI and are largely dismissive of the idea that it will lead to another productivity boom. As they note, we have been hearing the promises of a computer-driven productivity boom for decades and have yet to see it.
I share their skepticism, having listened to the same promises. However, I will cautiously suggest that this time might be different. According to the analyses of Nick Bloom and his colleagues, between 25-30 percent of work days are now remote. This is up from around 5 percent before the pandemic.
This is a huge change in the lives of tens of millions of people. They are saving hundreds of hours a year in commuting time and thousands of dollars in commuting related expenses. These benefits are not picked up in our productivity or GDP data. Commuting time is not counted as work hours, even though most commuters would probably rather be spending that time at work than commuting.
Also, expenses associated with commuting, like gas, train, and bus fares, as well as dry-cleaning services and the costs of business clothes, are just treated like any other consumption. Most immediately, less spending on these items would actually be treated as a drop in GDP.
While the option to work at home is disproportionately available to more educated workers, it clearly goes beyond a small elite. In this respect, it is worth noting that after the surge in remote work and an unprecedented rate of job switching, a Conference Board survey shows workers report their job satisfaction is the highest in the four decades the survey has been taken.
To my view, we have not yet reaped the full benefits of this revolution in the opportunity to work remotely. The option to do things remotely is likely to be extended in many different directions, most obviously with telemedicine.
Again, many of these benefits will not be picked up in GDP and productivity data, but they will make a real difference in people’s lives. The fact that a person in bad health doesn’t have to travel across town or across country to consult with a medical professional makes a huge difference to that person and their family, but does not show up anywhere in the GDP accounts. So, while I respect Acemoglu and Johnson’s skepticism on this topic, I think there is a real basis for guarded optimism.
In short, Acemoglu and Johnson have given us much to think about in this book. It didn’t go everywhere I would have liked to see it go, but that’s what happens when you talk about a thousand years.
[1] I was very happy to see Johnson and Simon take up one of my recurring themes in this area, Section 230 protection for Internet platforms. This both allows for platforms to have inordinate power in shaping our politics and allows for their shareholders and top execs to get rich. They go in a somewhat different direction for a remedy. I would take away the protection for sites that relied on advertising or selling personal information, requiring platforms to respond to takedown notices, as is currently the case with allegations of copyright infringement. They would take it away for promoting incendiary material, but the key point is that we need a serious discussion on the matter, which has not taken place to date.
When I saw that two of the country’s most prominent economists wrote a book on “our 1000-year struggle over technology and prosperity,” I expected a lot. I was disappointed. To be clear, there is much here to like and I’m sure that most readers will get much from it, as I did. But, the book fails to follow through adequately on the key point in its analysis, which is that the gains from technology are a matter of struggle, not an outcome given by the technology itself.
I’ll start with the positives. The book gives a cursory, but useful, account of the major developments in technology going back more than a thousand years. Some of their discussion deals with the origins of agriculture, an innovation that goes back many thousands of years. However, most of the book does describe events in the promised thousand-year horizon.
It points out that there were important technological innovations in the Middle Ages, which did allow for very modest gains in productivity and living standards across much of Europe until the 13th or 14th century. At that point, the rate of increase accelerated, although it was still much slower than in later centuries.
One of the points it makes, which was underappreciated (at least by me), was the extent to which the gains in this period were siphoned off by the church. The huge cathedrals and monasteries constructed in this period absorbed a huge chunk of the surplus produced in agriculture. This both depressed living standards and prevented resources from going to investments that would increase productivity.
As technology continued to advance in the subsequent centuries, there were modest gains in living standards for large sections of the population as the demands of the church were reined in. However, the book makes clear that there was no automatic transmission from improvements in technology to gains in living standards for the bulk of the population.
In particular, the book points out that the early years of the industrial revolution in 19th century England were associated with a deterioration in living standards for large segments of the working class. Factory workers, and especially children, were forced to work longer hours under worse conditions than ever would have been the case in agriculture. In addition, the living conditions in cities were far more unhealthy than what they faced in the countryside.
When conditions for the English working class did subsequently improve in the second half of the 19th century, it was due to the growing political power of the working class, as well as the growing importance of labor unions.
They also point out that the direction of technology itself is very much determined by power relations. In particular, they note that the enclosure movement in England was not, in fact, about allowing for the transition to more modern crop rotations. There were many places where improved crop rotation systems were adopted in open fields. Rather, enclosure was about giving landlords more control over land and displacing the peasantry.
None of this would be new to people familiar with the history of this period, but it is still refreshing to see prominent economists make these points. The distribution of the benefits of technology is far from being determined by strictly economic factors. It depends very much on the institutional structure and power relations in society.
It is Not Just the Masses Who Rely on Political Power to Secure the Gains from Technology
However, having said this, my major criticism of Acemoglu and Johnson is that they fail to carry this critique far enough. While they discuss extensively the factors that have allowed for gains in the past for ordinary workers, and which could go further in the future, they don’t consider the ways in which the winners have structured the economy to ensure that they get the bulk of the benefits of growth. In effect, the book treats the upward redistribution as a natural outcome of the development of the market and technology, whereas it requires interventions to ensure that gains are widely shared.
Just to take the most obvious case (and my favorite target), the money that goes to the top end of the income distribution due to patent and copyright monopolies is entirely due to the winners’ ability to write the laws on intellectual products in ways that benefit them. If the government didn’t threaten to imprison people who copied Microsoft software without his permission, Bill Gates would still be working for a living. It wasn’t technology that created five Moderna billionaires, it was the fact that the government paid the company to develop a Covid vaccine, and then allowed Moderna to have monopoly control over its distribution.
If it is difficult to understand the idea that government policy on technology, rather than the technology itself, created a huge amount on inequality, imagine a world where we had no patent and copyright monopolies and non-disclosure agreements were unenforceable. Arguably, we would have seen less technological progress, depending on what, if any, alternative incentive mechanism we used; but surely we would see nothing like the inequality that has resulted from preserving and expanding these monopolies over the last four decades.
Similarly, it was not just technology that allowed us to subject manufacturing workers to competition from their much lower paid counterparts, but protected highly paid professionals. There is no technical reason that we can’t all have Zoom consultations with doctors in India who get paid one-tenth as much as doctors in the United States. Globalization has not had as much impact on the pay of highly paid professions because of their power, not because of technology.
And of course, the big bucks made by the top people in finance is not due to the wonders of technology, but the fact that a politically powerful industry was able to rig the rules. The too-big-to-fail insurance on vivid display in the Great Financial Crisis, and again following the collapse of the Silicon Valley Bank, is only part of the story.
We have the technology to make the financial system far more efficient, starting with Federal Reserve banking accounts and running into just about every area of finance, but it doesn’t happen because the big players in the financial industry don’t want to see a hit to their income. No one knows this better than Simon Johnson, who has written extensively on the abuses and corruption in the financial industry, but for some reason this is mentioned only in passing in Power and Progress.[1]
These points are not nit-picking. It is not just the masses who need to change the rules to get their fair share of the gains from productivity growth, it is also the rich who change the rules to their benefit. This point is essential both as a matter of logic and politics.
It is also important to recognize that we improve the living standards of ordinary workers not only by getting them more money, but by giving the rich less. If the rich have less money to buy multiple houses, then housing is cheaper for everyone else. And, if they have fewer personal servants fulfilling their whims, there are more workers available to meet the needs of the rest of the population.
There are no preset given rules of a market economy. We make them up as we go along. If we want to see more equality and a system where the vast majority, and not just a lucky few, share in the benefits of progress, we can go two routes. One is the route Acemoglu and Johnson advocate throughout the book: strengthen workers’ bargaining power and expand the welfare state. But, we can also work the other side; weaken the institutional structures the rich have been putting in place to make themselves richer. We should, of course, be opportunistic and make advances on both sides wherever we can, but it makes no sense to ignore the other side.
Will AI and Related Technologies Change the World?
I have one other small bone to pick with Acemoglu and Johnson. They look at the developments in AI and are largely dismissive of the idea that it will lead to another productivity boom. As they note, we have been hearing the promises of a computer-driven productivity boom for decades and have yet to see it.
I share their skepticism, having listened to the same promises. However, I will cautiously suggest that this time might be different. According to the analyses of Nick Bloom and his colleagues, between 25-30 percent of work days are now remote. This is up from around 5 percent before the pandemic.
This is a huge change in the lives of tens of millions of people. They are saving hundreds of hours a year in commuting time and thousands of dollars in commuting related expenses. These benefits are not picked up in our productivity or GDP data. Commuting time is not counted as work hours, even though most commuters would probably rather be spending that time at work than commuting.
Also, expenses associated with commuting, like gas, train, and bus fares, as well as dry-cleaning services and the costs of business clothes, are just treated like any other consumption. Most immediately, less spending on these items would actually be treated as a drop in GDP.
While the option to work at home is disproportionately available to more educated workers, it clearly goes beyond a small elite. In this respect, it is worth noting that after the surge in remote work and an unprecedented rate of job switching, a Conference Board survey shows workers report their job satisfaction is the highest in the four decades the survey has been taken.
To my view, we have not yet reaped the full benefits of this revolution in the opportunity to work remotely. The option to do things remotely is likely to be extended in many different directions, most obviously with telemedicine.
Again, many of these benefits will not be picked up in GDP and productivity data, but they will make a real difference in people’s lives. The fact that a person in bad health doesn’t have to travel across town or across country to consult with a medical professional makes a huge difference to that person and their family, but does not show up anywhere in the GDP accounts. So, while I respect Acemoglu and Johnson’s skepticism on this topic, I think there is a real basis for guarded optimism.
In short, Acemoglu and Johnson have given us much to think about in this book. It didn’t go everywhere I would have liked to see it go, but that’s what happens when you talk about a thousand years.
[1] I was very happy to see Johnson and Simon take up one of my recurring themes in this area, Section 230 protection for Internet platforms. This both allows for platforms to have inordinate power in shaping our politics and allows for their shareholders and top execs to get rich. They go in a somewhat different direction for a remedy. I would take away the protection for sites that relied on advertising or selling personal information, requiring platforms to respond to takedown notices, as is currently the case with allegations of copyright infringement. They would take it away for promoting incendiary material, but the key point is that we need a serious discussion on the matter, which has not taken place to date.
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