Beat the Press

Beat the press por Dean Baker

Beat the Press is Dean Baker's commentary on economic reporting. He is a Senior Economist at the Center for Economic and Policy Research (CEPR). To never miss a post, subscribe to a weekly email roundup of Beat the Press. Please also consider supporting the blog on Patreon.

People routinely tout Biden’s efforts to bring back manufacturing jobs as a way to rebuild the middle class and reduce inequality. Whatever the motives, there is not much reason to believe that it will have this effect.

When the United States opened up its market to freer trade in manufactured goods, through trade deals like NAFTA and admitting China to the WTO, manufacturing workers had a substantial pay premium over workers in the rest of the private sector. This was largely because manufacturing was much more highly unionized than other parts of the private sector.

However, this is no longer true. In 2022, 7.8 percent of manufacturing workers were unionized, compared to 6.0 percent for the private sector as a whole. As a result, the pay premium for workers in manufacturing has largely disappeared.

This means that there is little reason to believe that manufacturing jobs will be good-paying jobs, unless they are unionized. While the Biden administration has tried to push measures that increase the probability that the jobs created by his policies will be union jobs, it is not clear that they will be effective. In that case, the manufacturing jobs created producing semiconductors or clean energy may be little better than other jobs that workers might have taken.

The other side of this picture is that the owners of the companies getting the subsidies, in addition to well-placed high-end workers, are likely to put lots of money in their pocket as a result of Biden’s policies. Moderna provides an excellent example of what can happen. The government paid for the company to develop a Covid vaccine, and then allowed the company to maintain control of the vaccine. The result was that we created five Moderna billionaires by the summer of 2021.

Elon Musk is another great example. Tesla benefitted hugely in its early days from Obama administration loans at below market interest rates. It is also benefitting hugely from subsidies to electric cars. As a result, we have made Elon Musk the richest person the planet.

For whatever reason, maintaining control of the technology that the government funds is literally never mentioned in discussions of industrial policy and inequality. While it would be possible for the government to make the technology open-source (no government-granted patent monopolies or non-disclosure agreements for technology) as a condition of getting the money, this is not on anyone’s agenda in Washington.

Perhaps it is good that people are at least talking about inequality these days, but it doesn’t seem like there is much interest in doing anything about it.

People routinely tout Biden’s efforts to bring back manufacturing jobs as a way to rebuild the middle class and reduce inequality. Whatever the motives, there is not much reason to believe that it will have this effect.

When the United States opened up its market to freer trade in manufactured goods, through trade deals like NAFTA and admitting China to the WTO, manufacturing workers had a substantial pay premium over workers in the rest of the private sector. This was largely because manufacturing was much more highly unionized than other parts of the private sector.

However, this is no longer true. In 2022, 7.8 percent of manufacturing workers were unionized, compared to 6.0 percent for the private sector as a whole. As a result, the pay premium for workers in manufacturing has largely disappeared.

This means that there is little reason to believe that manufacturing jobs will be good-paying jobs, unless they are unionized. While the Biden administration has tried to push measures that increase the probability that the jobs created by his policies will be union jobs, it is not clear that they will be effective. In that case, the manufacturing jobs created producing semiconductors or clean energy may be little better than other jobs that workers might have taken.

The other side of this picture is that the owners of the companies getting the subsidies, in addition to well-placed high-end workers, are likely to put lots of money in their pocket as a result of Biden’s policies. Moderna provides an excellent example of what can happen. The government paid for the company to develop a Covid vaccine, and then allowed the company to maintain control of the vaccine. The result was that we created five Moderna billionaires by the summer of 2021.

Elon Musk is another great example. Tesla benefitted hugely in its early days from Obama administration loans at below market interest rates. It is also benefitting hugely from subsidies to electric cars. As a result, we have made Elon Musk the richest person the planet.

For whatever reason, maintaining control of the technology that the government funds is literally never mentioned in discussions of industrial policy and inequality. While it would be possible for the government to make the technology open-source (no government-granted patent monopolies or non-disclosure agreements for technology) as a condition of getting the money, this is not on anyone’s agenda in Washington.

Perhaps it is good that people are at least talking about inequality these days, but it doesn’t seem like there is much interest in doing anything about it.

For better or worse (worse in my view), President Biden has not done much to restrict drilling for new oil and gas. As a result, we are now producing more than when Donald Trump was in the White House. Nonetheless, there are still many people who want to blame Biden’s restrictions for the high price of oil.

Well, none of these claims make any sense. Biden has not done much to restrict the price of oil; we are producing more oil now than under Trump, and oil is not expensive. To see the last point, I adjusted the price of oil (West Texas Intermediate) for the inflation we have seen since 2000, using the GDP deflator.[1]

As can be seen, oil prices were somewhat lower at times in the last twenty-three years. They were lower at the start of the George W. Bush administration, but higher through most of his second term. They plunged in the Great Recession, but then were higher than the current level through the rest of President Obama’s first term.

Oil prices then fell sharply towards the end of the Obama administration, as a flood of fracked oil came on line. Oil prices then rise under Trump, passing the current level in 2018 and then falling again in 2019. Oil prices plunged with the pandemic shutdown, but then soared with the reopening and the Russian invasion of Ukraine.

They have now fallen back to a level that is below where they have been for most of the first two decades of this century. In spite of the widespread whining of Republican politicians about high oil prices, they are actually lower now than for roughly half the time that George W. Bush was in the White House.  

For better or worse (worse in my view), President Biden has not done much to restrict drilling for new oil and gas. As a result, we are now producing more than when Donald Trump was in the White House. Nonetheless, there are still many people who want to blame Biden’s restrictions for the high price of oil.

Well, none of these claims make any sense. Biden has not done much to restrict the price of oil; we are producing more oil now than under Trump, and oil is not expensive. To see the last point, I adjusted the price of oil (West Texas Intermediate) for the inflation we have seen since 2000, using the GDP deflator.[1]

As can be seen, oil prices were somewhat lower at times in the last twenty-three years. They were lower at the start of the George W. Bush administration, but higher through most of his second term. They plunged in the Great Recession, but then were higher than the current level through the rest of President Obama’s first term.

Oil prices then fell sharply towards the end of the Obama administration, as a flood of fracked oil came on line. Oil prices then rise under Trump, passing the current level in 2018 and then falling again in 2019. Oil prices plunged with the pandemic shutdown, but then soared with the reopening and the Russian invasion of Ukraine.

They have now fallen back to a level that is below where they have been for most of the first two decades of this century. In spite of the widespread whining of Republican politicians about high oil prices, they are actually lower now than for roughly half the time that George W. Bush was in the White House.  

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.

NYT readers should know that anyone who has seen the cost of their food basket rise by 66.7 percent is either very atypical or badly confused about how much they used to pay for food.
NYT readers should know that anyone who has seen the cost of their food basket rise by 66.7 percent is either very atypical or badly confused about how much they used to pay for food.

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.  

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.

It would be a huge step forward for both public health and U.S. foreign policy if we could begin down the road of freely sharing health care technology rather than trying to bottle it up so that a small number of people can get very rich.
It would be a huge step forward for both public health and U.S. foreign policy if we could begin down the road of freely sharing health care technology rather than trying to bottle it up so that a small number of people can get very rich.
If people feel they are doing poorly in today’s economy, we can’t tell them they are wrong to feel the way they do. We can say that, based on the consumption data, it doesn’t look like they are doing poorly.
If people feel they are doing poorly in today’s economy, we can’t tell them they are wrong to feel the way they do. We can say that, based on the consumption data, it doesn’t look like they are doing poorly.
Wage growth has slowed to a pace that is roughly consistent with the Fed’s inflation target.
Wage growth has slowed to a pace that is roughly consistent with the Fed’s inflation target.

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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