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Speaker 1: Where a change and I can Hello Stephanomics here the

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Speaker 1: podcast that brings you the global economy and a bit

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Speaker 1: of Jack Johnson. This week we're asking where did everybody go?

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Speaker 1: Why has the US workforce been declining for several years,

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Speaker 1: not just since the pandemic? And will America have enough

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Speaker 1: people left to wage that great battle for global economic

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Speaker 1: leadership that both President Biden and Chinese President shi Jing

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Speaker 1: Bing are now embarked upon. The Director of the Aspen

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Speaker 1: Economic Strategy Group, Melissa Khanni, has a lot to say

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Speaker 1: about that in a few minutes. She thinks we can

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Speaker 1: do plenty with smart machines, but the engine of ideas

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Speaker 1: and innovation is people. In recent years, neither the US,

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Speaker 1: nor China, nor anyone else has been able to persuade

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Speaker 1: women to give birth to more of them, quite the opposite.

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Speaker 1: That's a fascinating conversation. We also find out where the

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Speaker 1: Harvard Star economist Raj Chetti has the data to explain

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Speaker 1: how the US economy somehow lost two point six million

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Speaker 1: workers in the past three years. All to come. The

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Speaker 1: first Bloomberg Senior writer on economic Sean Donnan, has been

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Speaker 1: to Ohio. Okay, I'm standing by the side of the

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Speaker 1: road here in Licking County, Ohio, and I'm looking through

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Speaker 1: a chain link fence. There's a big no trespassing signs

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Speaker 1: keep out SIGNUS has no drones, snow flurries in the air.

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Speaker 1: And I'm looking through a chain link fence at some

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Speaker 1: big grassy burns. I can just see the start of

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Speaker 1: what is an enormous muddy expanse. And you wouldn't necessarily

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Speaker 1: note at first sight, but that big muddy former farm field,

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Speaker 1: it's quite literally the future of the American economy. That

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Speaker 1: at least is what Joe Biden is betting on. Thanks

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Speaker 1: to what's billed as the Chips Act that he signed

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Speaker 1: into law last year, the US is investing some fifty

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Speaker 1: billion dollars in new semiconductor plans. And that muddy field

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Speaker 1: in Licking County outside Columbus is the future home of

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Speaker 1: a twenty billion dollar Intel project that will take some

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Speaker 1: seven thousand people to build and house another three thousand

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Speaker 1: workers when it's done. The future of the chick industry

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Speaker 1: is going to be made in America, made in America.

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Speaker 1: But there's another big question hanging over that goal. Does

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Speaker 1: America have the people needed to meet it? Katherine hunt

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Speaker 1: Ryan is president of Manufacturing and Technology at contractor Becktel.

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Speaker 1: She's the person in charge of finding the people needed

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Speaker 1: to build that Intel plant, and she says finding seven

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Speaker 1: thousand skilled construction workers is going to be drawing people

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Speaker 1: from around the country to Central Ohio. That is significantly

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Speaker 1: outstripping the supply of labor in the local areas. It

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Speaker 1: will be pulling on people certainly in the region and

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Speaker 1: across the country in order to make that work, to

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Speaker 1: make sure it has the three thousand workers it needs

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Speaker 1: once the plant is built. Intel is investing a hundred

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Speaker 1: million dollars of its own money in universities and to

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Speaker 1: your technical colleges. It's also looking out of the world

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Speaker 1: in which demand for semiconductor plants and the workers in

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Speaker 1: them is booming. That has created a competition for talent.

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Speaker 1: It's unlike any scene in decades, says Gabby Cruz Thompson,

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Speaker 1: the Intel executive in charge of collaborating with universities to

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Speaker 1: build that workforce. I I think we haven't seen anything

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Speaker 1: like this, this volume of interest, at this level of interest. Ever,

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Speaker 1: like many industrialized economies, the US is in the midst

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Speaker 1: of a big demographic shift as baby Boomers, that generation

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Speaker 1: born in the wake of the Second World War leave

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Speaker 1: the workforce. That exit, which accelerated during the pandemic, is

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Speaker 1: colliding with a dip in a number of young people

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Speaker 1: entering the workforce. Then there's the effects of both the

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Speaker 1: more than a million people lost during the pandemic and

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Speaker 1: cuts an immigration in recent years. The US is working

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Speaker 1: age population has in recent years been growing at its

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Speaker 1: slowest rate since nineteen sixty, and in at least twenty

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Speaker 1: four states, including Ohio, the total population actually declined last year.

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Speaker 1: There were five hundred thousand fewer babies born in America

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Speaker 1: last year than there were in two thousand and five.

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Speaker 1: That's when most of this year's high school graduates were born,

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Speaker 1: which means that barring a major change in birth rates

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Speaker 1: or immigration policy, there will be far fewer high school

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Speaker 1: graduates available eighteen years from now. Gabby Cruz Thompson from

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Speaker 1: Intel argues that automation and other technologies will help fill

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Speaker 1: the gap in the long term. I think it gets challenging,

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Speaker 1: but I also we learn to leverage advances in technology.

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Speaker 1: Right the advent of artificial intelligence. The workforce that we

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Speaker 1: need to educate needs to be a workforce that learns

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Speaker 1: to forever learn. Fab has less people today than it

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Speaker 1: did twenty years ago. In the short term, though, Intel

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Speaker 1: is investing heavily in institutions like Central Ohio Technical College

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Speaker 1: in nearby Newark, Ohio. The goal is to build that

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Speaker 1: workforce there, and particularly the seventy of workers who won't

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Speaker 1: need a four year degree to do their job at Intel.

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Speaker 1: John Barry, the college's president, says helping Intel fill the

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Speaker 1: vacancies it will have for those technicians on the factory

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Speaker 1: floor is not going to be easy. The college is

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Speaker 1: rapidly expanding its technology program, but that currently has just

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Speaker 1: a hundred and fifty or so students in it. Most

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Speaker 1: of our students are part time, so even if you're

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Speaker 1: going through a two year degree cycle, rarely does that

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Speaker 1: happen in two years. That reality has led Intel in

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Speaker 1: the college to work on a one year certificate that

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Speaker 1: will help some students with experience in other industries qualify quicker.

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Speaker 1: But the college also isn't just facing rising demand for

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Speaker 1: qualified chip technicians. The healthcare industry wants more nurses and radiologists,

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Speaker 1: municipalities need more police officers and firefighters, And then there's

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Speaker 1: the other manufacturers who want young workers able to operate robots.

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Speaker 1: All in a world in which the college and manufacturers

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Speaker 1: are competing with easy, low end service jobs that are

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Speaker 1: often paying equivalent wages. Today's world order is bizar. There

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Speaker 1: is just no other way to describe it. Because in

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Speaker 1: today's market, you think about this, I can go and

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Speaker 1: get a job at literally the drop of a hat,

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Speaker 1: and it's probably going to pay me eighteen to twenty

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Speaker 1: an hour to do things that used to pay a ten.

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Speaker 1: Catherine hunt Ryan, a contractor Becktel, says roughly thirty percent

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Speaker 1: of the seven thousand construction workers on the Intel site

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Speaker 1: will be apprentices. At least fort initially are likely to

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Speaker 1: be so called travelers from either elsewhere in Ohio or

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Speaker 1: out of state. Altogether, to lure those workers who are

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Speaker 1: also in demand and many other places where chip plants

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Speaker 1: and electric vehicle or battery factories are going up, Becktel

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Speaker 1: will be offering competitive wages. It also will be offering

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Speaker 1: benefits like on site or nearby medical and dental care,

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Speaker 1: and comfortable lounges for workers to take breaks. It's all

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Speaker 1: with an eye on the long term and a fight

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Speaker 1: for people that is only likely to get more intense

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Speaker 1: in the years to come. Forcing entertaining crop professionals is

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Speaker 1: the long pole and attention. It is a significant challenge

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Speaker 1: right now is not going to be solved through one program.

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Speaker 1: For Bloomberg News, I'm Sean Donnin. I'd like to pull

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Speaker 1: out some of the broader lessons of that piece now

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Speaker 1: with the director of the Aspen Economic Strategy Group, Melissa Khanni,

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Speaker 1: who has written about America's people problem in the group's

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Speaker 1: new publication, Economic Policy in a More Uncertain World. Now, Melissa,

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Speaker 1: thanks very much for joining us. We're not only focused

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Speaker 1: on demographics, and I know that that book on economic

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Speaker 1: policy is talking quite broadly about the US and its

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Speaker 1: economic challenges, but I think it would be good to

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Speaker 1: start there. You know, what is the demographic challenge that

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Speaker 1: the US is facing right now? The need for a

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Speaker 1: robust workforce comes down to not just needing a large

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Speaker 1: share of working age adults working, but it also comes

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Speaker 1: down to needing a large number of working age adults.

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Speaker 1: And the problem that the US is facing is that

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Speaker 1: birth rates have been declineing for a sustained amount of

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Speaker 1: time now in the US, well over a decade, such

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Speaker 1: that we now find ourselves well below replacement level fertility

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Speaker 1: for many decades. Women in the U s we're having

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Speaker 1: more than two children on average per woman, and now

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Speaker 1: that number is down to one point six six And

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Speaker 1: so barring a large reversal in the decline in US

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Speaker 1: birth rates or barring a large increase in the rate

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Speaker 1: of immigration, in not too many years, the US working

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Speaker 1: age population is going to start shrinking and whilst driving

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Speaker 1: that slowing of the birth rate. So this is the

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Speaker 1: big question, and it's actually harder to answer than we

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Speaker 1: might think. Looking at a lot of the sort of

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Speaker 1: explanations that are commonly thrown around. Childcare has become too expensive,

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Speaker 1: people have too much student debt, religiosity is declining. We

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Speaker 1: don't see any data support for the those sort of

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Speaker 1: common explanations, and rather what we see is just a

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Speaker 1: widespread decline across almost all groups of women across the country,

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Speaker 1: across education level of recent ethnic groups. And so it

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Speaker 1: just seems more like something universal such that young adults

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Speaker 1: today are choosing to have fewer children than young adults

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Speaker 1: in the recent past. And it could be some combination

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Speaker 1: of widespread perceptions about sort of how costly it is

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Speaker 1: to have kids in terms of how much other stuff

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Speaker 1: you have to give up, how much of your own

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Speaker 1: like freedom, you have to give up, how hard it

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Speaker 1: is to combine raising kids and having a career, something

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Speaker 1: that's sort of been a slow universal change across women

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Speaker 1: in the US in such a way that now women

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Speaker 1: in the US are finally reaching up to women in

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Speaker 1: other high income countries who stopped having you know, two

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Speaker 1: kids on average for person a couple of decades ago. Well,

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Speaker 1: it's interesting is that the red birth rate is also

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Speaker 1: coming with something that's been talked about a lot, especially

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Speaker 1: since the pandemic, which has reduced labor force participation. America

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Speaker 1: used to be much better than other countries on getting

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Speaker 1: women into the workforce, but we seem to be less

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Speaker 1: good at that now. So it's sort of odd in

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Speaker 1: a way that women are we were not. We haven't

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Speaker 1: seen continued growth and women in the workforce, but they're

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Speaker 1: also not having kids. So if this is really interesting,

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Speaker 1: So if you look across high income countries, sometime between

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Speaker 1: nineteen eighty and the relationship between fertility overall fertility and

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Speaker 1: female labor force participation rates flipped. So it used to

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Speaker 1: be in countries that had higher rates of fertility, they

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Speaker 1: had lower rates of female labor force participation in general.

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Speaker 1: Like those two things were sort of substituting things that

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Speaker 1: women could do. But if you work, working, you were

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Speaker 1: having babies. Yeah, right right, And now what we see

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Speaker 1: again across high income countries is basically like in all countries,

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Speaker 1: about of women work, but the fertility rate ranges wide,

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Speaker 1: like ranges a lot, and so it's you know, let's

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Speaker 1: take Scandinavia. Scandinavia was always a place where it was

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Speaker 1: perceived as being easier for women to combine work and

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Speaker 1: having kids. There was more egalitarian sharing of household responsibilities.

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Speaker 1: There's obviously a really um comprehensive welfare stage, there's generous

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Speaker 1: leave policies, and women there were having, you know, at

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Speaker 1: high rates of work participation and also high rates of fertility.

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Speaker 1: Even in those countries. Now, uh, fertility is way down.

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Speaker 1: So so fertility in Finland and Norway is far below

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Speaker 1: what it is in the US. UH. And the thinking

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Speaker 1: that we had to make it easier for women to

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Speaker 1: combine work and having a family and that would keep

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Speaker 1: fertility rates um elevated, that's all seemed to have come unhinged.

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Speaker 1: Are there any proven ways in the rest of the

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Speaker 1: world for for increasing birth rates. No, and so we

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Speaker 1: can learn a lot from other countries, and that because

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Speaker 1: in other high income countries their fertility rate really fell

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Speaker 1: below replacement level in the eighties and nineties, a lot

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Speaker 1: of those countries have been experimenting with two types of

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Speaker 1: what's called them pro natalist policies. So one is like

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Speaker 1: baby bonuses and tax credits, explicit government checks being sent

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Speaker 1: to people when they had babies, and and often in

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Speaker 1: explicitly uh, you know, ways to incentivize people having births.

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Speaker 1: The evidence on those kinds of policies is that maybe

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Speaker 1: some of them lead to an increase in births, small

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Speaker 1: to modest in the short run, but none of them

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Speaker 1: seem to have had a sustained effect increasing birth rates.

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Speaker 1: The other types of policies are things like expanding parent

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Speaker 1: to leave, having more childcare, etcetera. There the evidence seems

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Speaker 1: to suggest that there's not much of a link between um,

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Speaker 1: longer paid leaves, and fertility in any persistent way, a

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Speaker 1: more of a timing effect, if anything, nothing that would

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Speaker 1: be of the order the size that would need to

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Speaker 1: bring us back to replacement level. Japan has basically quadrupled

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Speaker 1: the amount of their GDP that they spend on a

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Speaker 1: family friendly policies, childcare, early childhood education. Their fertility rate

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Speaker 1: is still one point five, so far below replacement level,

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Speaker 1: but it's sort of stalled out. My read of the

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Speaker 1: evidence makes me very skeptical that policymakers can do anything

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Speaker 1: it's going to turn things around. Um And so so

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Speaker 1: in my mind, that sort of leads us to think

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Speaker 1: about what are other ways we can replenish our working

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Speaker 1: age population or keep the level of skilled workers at

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Speaker 1: a high level. And I guess the sort of leading

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Speaker 1: way you could do it is just by taking other

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Speaker 1: countries people through immigration. Yeah, at least that buys you sometime.

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Speaker 1: Right at some point, like a lot of these host countries,

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Speaker 1: their fertility rates are falling as well. Um. Again certainly

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Speaker 1: in high income countries. Um. But yes, so immigration is

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Speaker 1: the obvious way to bring in working age people right away. Um,

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Speaker 1: and you know, even targeted, so you're bringing in skilled

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Speaker 1: working age people right away. But we know how complicated

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Speaker 1: the issue of immigration is in the US, and so

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Speaker 1: even while there might be obvious ways we could reform

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Speaker 1: our immigration system so that we let in more people

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Speaker 1: and we let in more skilled workers. That seems to

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Speaker 1: be a very politically difficult thing to do in the US.

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Speaker 1: But just to give us a sense of the numbers,

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Speaker 1: and you may not have these on the top of

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Speaker 1: your head, but I mean, there was a there's been

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Speaker 1: a long period where a good chunk of US labor

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Speaker 1: force growth was coming from immigration, um and certainly quite

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Speaker 1: a lot of the birth rate was coming from sort

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Speaker 1: of relatively recent immigrants. How is that shifted? How is

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Speaker 1: the immigration flows doing right now for the US or

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Speaker 1: the last few years compared to that previous period. So

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Speaker 1: the annual that flows of immigrants has really fallen since

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Speaker 1: right and some of that was a shift in our administration.

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Speaker 1: Some of that than was COVID policy, And so we

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Speaker 1: had over a million immigrants coming in the official numbers

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Speaker 1: now or it's down to two hundred and fifty thousand,

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Speaker 1: So there's a real deficit of immigrants too. So it's

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Speaker 1: it's really this combination of a reduction in immigration, a

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Speaker 1: reduction in birth rates and arising death rate because the

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Speaker 1: population is aging, has led to US population growth being

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Speaker 1: at its lowest level in recent history. I guess we

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Speaker 1: should remember because as we are partly thinking about sort

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Speaker 1: of competition between countries. The US is still relatively well

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Speaker 1: off compared to China, which we saw just in the

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Speaker 1: last week has its overall population shrinking, which is is

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Speaker 1: different is in contrast to many other countries, they've had

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Speaker 1: a really extreme a graphic shift. And it's sort of

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Speaker 1: fascinating when you think about how for how long China

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Speaker 1: had a one child policy, and then you know explicit

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Speaker 1: policy goals of keeping fertility rates low, family size small,

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Speaker 1: and now they're trying to implement all sorts of policies

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Speaker 1: and subsidies to increase the rate um and and it

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Speaker 1: turns out it's not so easy to turn back on

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Speaker 1: right now. That yeah, and I guess your your point

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Speaker 1: from before about we've learned from other countries trying and failing.

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Speaker 1: You know, if this famously authoritarian command control country has

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Speaker 1: still not been able to this is one thing that

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Speaker 1: they can't force people to do, even with quite significant measures.

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Speaker 1: That is, that is not a bit discouraging for those

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Speaker 1: who are trying to devise these policies. So I just

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Speaker 1: want to shift a little bit because I know also

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Speaker 1: that the book that the Strategy Group produced was thinking

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Speaker 1: about not just this challenge that the US economy faces,

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Speaker 1: but the challenge of sort of maintaining innovation um and

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Speaker 1: also helping people through the sort of technological transition as

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Speaker 1: we have AI and all these things. So I want

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Speaker 1: to get onto a little bit of that. I mean,

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Speaker 1: because it's not just how many people you have. It

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Speaker 1: is also about what they're doing and the efficiency they

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Speaker 1: have and how they do it. How should we think

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Speaker 1: about productivity and innovation in this context? If we've got

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Speaker 1: few fewer people, we surely should be focused on getting

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Speaker 1: more out of them. I should say, I generally like

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Speaker 1: to be a half glassful and an optimistic person. But

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Speaker 1: I'm going to bile on here. So we've got this

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Speaker 1: demographic headwins um, and on top of that, we've had,

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Speaker 1: now for many decades, a decline in you know, prime

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Speaker 1: age mail employment. And then on top of that, as

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Speaker 1: you're alluding to, we've had a decline in productivity, growth

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Speaker 1: and business dynamism in the US. So what do I

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Speaker 1: mean by that? We have fewer firm entries and exits,

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Speaker 1: we have fewer young firms as a proportion economic activity.

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Speaker 1: We have a greater dispersion in productivity, you know, across firms.

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Speaker 1: So it's sort of like some market sector leading firms

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Speaker 1: are are bigger, and there's just fewer, smaller dynamic firms

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Speaker 1: that we usually think of as growth engines. Patents and

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Speaker 1: investors are inclasingly clustered in large firms. So all of

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Speaker 1: this is also it's also worrying and suggests that, you know,

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Speaker 1: setting aside the issue of workers, we need policies and

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Speaker 1: conditions and regulatory reform that really spurs innovation among firms.

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Speaker 1: So there's both the workers side and the firm side

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Speaker 1: where received struggling. Do you think the administration is moving

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Speaker 1: in the direction on that? I mean, there was this

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Speaker 1: sort of much touted Chips Act last year, very focused

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Speaker 1: on education and training for science and sort of industrial

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Speaker 1: policy approach. We also had the ridiculously named Inflation Reduction Act,

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Speaker 1: which is also going to try and encourage lots of

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Speaker 1: investment in clean energies. I mean, do you think that

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Speaker 1: the administration is kind of pushing in the right direction

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Speaker 1: on some of these policies. So yeah, So I think

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Speaker 1: the sort of policy agenda from the past few years

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Speaker 1: has been very encouraging in the sense that we also

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Speaker 1: have the Infrastructure Bill, so investments in physical infrastructure sort

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Speaker 1: of an obvious way for the US to try and

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Speaker 1: increase our productivity UM and now, you know, industrial policy

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Speaker 1: is famously hard to get right, and innovation policy is

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Speaker 1: very challenging, so that it's often that the devils and

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Speaker 1: the details and those kinds of things. But an emphasis

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Speaker 1: and a recognition that we need to be putting money

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Speaker 1: towards building talent, investing in you know, training stem workers,

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Speaker 1: all of that I think is very encouraging. This also,

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Speaker 1: I should say, before are all that UM as part

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Speaker 1: of the Build Back Better proposal that you know didn't

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Speaker 1: get enacted, there was a lot of investment in UM

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Speaker 1: in youth and families and the kinds of stuff we

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Speaker 1: were talking about earlier, like early childcare, early childhood education,

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Speaker 1: and I have to say, UM, even though I don't

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Speaker 1: think those things will meaningfully turn around fertility rates, investments

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Speaker 1: in early childhood education are a clear way for us

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Speaker 1: to build up our talent pool. We just have mounds

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Speaker 1: of evidence that you know that has long term effects

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Speaker 1: UM and so so that was very disappointing that we

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Speaker 1: weren't able to make progress on spending more money on

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Speaker 1: kids because they also you know, are crucial built to

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Speaker 1: our talent pool going forward. Now, okay, so I have

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Speaker 1: a question for you. As an economist, I mean the

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Speaker 1: devil's world that kind of make formed last week, there

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Speaker 1: was quite a loss of You can imagine these people

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Speaker 1: who have cited about the US talking about industrial policy

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Speaker 1: and investing in key industries and and and also thinking

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Speaker 1: about talent in a kind of long term way. But

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Speaker 1: the big kind of negative that goes along with that

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Speaker 1: is this much more nationalistic approach to policy and what

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Speaker 1: you might call a zero sum approach to sort of

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Speaker 1: international competition that it's you know, my gain is another

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Speaker 1: country's loss, and vice versa. So how do you think

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Speaker 1: about that? Because I see in your report there's a

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Speaker 1: lot of the traditional economic view that openness and innovation

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Speaker 1: and trade is all. I mean, openness is good for

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Speaker 1: being more productive um and those investment in the strategic industries.

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Speaker 1: But it seems like we're going to invest in strategic

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Speaker 1: industries but become less open. So how is that going

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Speaker 1: to net out? Do you think? Yeah? So, you know,

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Speaker 1: I struggle a lot with this As an economist, I

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Speaker 1: truly understand the need to do more to protect US

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Speaker 1: workers and and help the regions that have been hard

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Speaker 1: hit by previous transitions. So, for example, when we you know,

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Speaker 1: in the US dramatically increased imports from China, you know,

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Speaker 1: that's great for most people, Prices go way down. This

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Speaker 1: is a good thing, UM, But there were certain workers

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Speaker 1: and certain regions that were hard hit, and our policies

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Speaker 1: didn't adequately compensate those sort of you know, I hate

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Speaker 1: to use the terms winners and losers, but you know

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Speaker 1: what I mean by that, UM, And so I understand

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Speaker 1: where the impetus for becoming more of a closed economy

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Speaker 1: comes from. UM, but that's sort of a situation where

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Speaker 1: everybody will be worse off. And so instead, you know,

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Speaker 1: in my in my view, which would be sort of

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Speaker 1: a standard economist view, I think is we need to

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Speaker 1: remain open to trade, we need to remain open to

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Speaker 1: global talent, and yet we need to do more to

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Speaker 1: make sure that the sort of disparate you know, impacts

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Speaker 1: of those of those activities, that we take care of

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Speaker 1: the people who are left behind or who need to

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Speaker 1: transition to different industries, who need to you know, redevelop skills,

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Speaker 1: or just have their wages supplemented, or have some income

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Speaker 1: support and so you know, I don't. I certainly don't

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Speaker 1: think we should be doing things that lowers global innovation,

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Speaker 1: raises US prices, and sort of makes us a less

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Speaker 1: dynamic economy. Um, but I'm not, you know, just blase

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Speaker 1: about saying no trade is good for everybody, and importing

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Speaker 1: immigrants is good for everybody, and we just have to

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Speaker 1: be honest about the trade offs, and our policies need

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Speaker 1: to reflect them. I guess the final question, it seems

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Speaker 1: an implication of of of your work and the work

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Speaker 1: of your colleagues that we should expect as we get

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Speaker 1: older and as populations eventually shrink, that we will we

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Speaker 1: might become poorer as well, because the productivity will be

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Speaker 1: less from being a smaller economy. Is that really the case?

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Speaker 1: I mean, I sort of like to think if we

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Speaker 1: have all this golden age of robots, we might all

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Speaker 1: have superpowers and become very productive. Yeah, it's it's certainly

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Speaker 1: not Um, it's not destiny, but it's uh, it's a

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Speaker 1: possibility that we should be aware of and frankly a

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Speaker 1: bit worried about. And so this, you know, this idea

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Speaker 1: comes from um, the economic insight that people are actually

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Speaker 1: the engines of idea generation and technological and medical advances,

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Speaker 1: and so there's just this idea that it's not just

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Speaker 1: you know, one person creates one more thing, and so

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Speaker 1: if you have one fewer person, well you have one

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Speaker 1: fewer things. But it's okay because income per person's days concept.

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Speaker 1: The issue is if you have fewer workers overall, there's

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Speaker 1: less specialization happening, there's less idea generation, and so actually

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Speaker 1: what happens is income per person shrinks and living standards shrink.

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Speaker 1: Now again it doesn't have to be that way, um,

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Speaker 1: but that's a possibility. And so you know, if you know,

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Speaker 1: you bring up, well, maybe robots can replace people and

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Speaker 1: they can make these innovations. Maybe right, Um, so we

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Speaker 1: might get richer, but the robots might be running the show.

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Speaker 1: That would be a bit unfortunate. Still being image there,

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Speaker 1: all right, well that's a great image to leave our

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Speaker 1: audience with. Melissa Carnie, Thank you very much, Thank you

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Speaker 1: so much. Now, Sean talked about the long term forces

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Speaker 1: which have helped produce these real time labor shortages in

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Speaker 1: places like Licking County, and we're going to have more

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Speaker 1: on that in a minute. Also some discussion of whether

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Speaker 1: the Biden administration is doing the right things to help

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Speaker 1: us work has become more productive. But first I wanted

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Speaker 1: to quit update on a shorter term mystery that has

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Speaker 1: made the US labor shortage a lot worse since the pandemic,

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Speaker 1: and that's the mystery of the seeing two point six million,

435
00:27:02,560 --> 00:27:05,520
Speaker 1: that's roughly how many more Americans will be working or

436
00:27:05,520 --> 00:27:08,600
Speaker 1: looking for jobs right now if the economy's labor force

437
00:27:08,680 --> 00:27:12,640
Speaker 1: participation rate was the same as it was before COVID hit.

438
00:27:13,160 --> 00:27:15,639
Speaker 1: You know, it's a hugely important question for anyone wanting

439
00:27:15,680 --> 00:27:18,680
Speaker 1: to know what's going to happen to unemployment or inflation

440
00:27:18,720 --> 00:27:21,480
Speaker 1: in America in the next year. And it's also pretty

441
00:27:21,480 --> 00:27:25,800
Speaker 1: crucial for any business owner who is short on workers now.

442
00:27:25,880 --> 00:27:29,480
Speaker 1: One of the most celebrated economists of his generation, Raj Chesti,

443
00:27:29,680 --> 00:27:33,200
Speaker 1: has weighed in with a pretty interesting answer, and Bloomberg

444
00:27:33,240 --> 00:27:36,800
Speaker 1: Wealth reporter Ben Stephnman had the story in Business Week

445
00:27:36,960 --> 00:27:40,800
Speaker 1: about it. Ben, thanks for doing this late notice. I

446
00:27:40,840 --> 00:27:43,600
Speaker 1: guess you should tell us quickly. First you know who

447
00:27:43,720 --> 00:27:48,240
Speaker 1: rushed Chetti is and why the data he's used might

448
00:27:48,359 --> 00:27:53,600
Speaker 1: be interesting in this context. Rush Jetty is a economist

449
00:27:53,760 --> 00:27:58,080
Speaker 1: at Harvard. He has a research lab. They're called Opportunity

450
00:27:58,119 --> 00:28:01,400
Speaker 1: Insights that he's founded with a couple of other academics,

451
00:28:01,440 --> 00:28:03,920
Speaker 1: and he has really a team of people that pour

452
00:28:04,160 --> 00:28:10,160
Speaker 1: through giant data sets two come to conclusions about what's

453
00:28:10,200 --> 00:28:12,920
Speaker 1: happening in the economy. So he's done some great work

454
00:28:13,000 --> 00:28:17,560
Speaker 1: on inequality over the years that has really got some

455
00:28:17,640 --> 00:28:21,800
Speaker 1: big headlines. But when UM, when the pandemic hit, he

456
00:28:22,840 --> 00:28:25,080
Speaker 1: and the rest of his team, which was dozens of people,

457
00:28:25,400 --> 00:28:29,399
Speaker 1: mobilized UM and they built what they called an Economic Tracker,

458
00:28:29,520 --> 00:28:35,800
Speaker 1: which used private data UM basically transaction data from private

459
00:28:35,840 --> 00:28:40,240
Speaker 1: companies UM that we're willing in that moment to offer

460
00:28:40,280 --> 00:28:43,719
Speaker 1: them some data feeds, and they used that to build

461
00:28:43,920 --> 00:28:47,480
Speaker 1: basically it was a online tool to track what was

462
00:28:47,520 --> 00:28:50,480
Speaker 1: happening in the economy down to the zip code level.

463
00:28:51,440 --> 00:28:54,200
Speaker 1: And UM, this was really important in twenty if there

464
00:28:54,240 --> 00:28:57,720
Speaker 1: was pandemonium in the economy, but we really didn't understand

465
00:28:57,760 --> 00:29:00,600
Speaker 1: what was actually happening at the time. What we're talking

466
00:29:00,600 --> 00:29:05,000
Speaker 1: about now is some new research using the same data

467
00:29:05,040 --> 00:29:09,160
Speaker 1: set UM that really looks at where these missing workers

468
00:29:09,200 --> 00:29:13,760
Speaker 1: are and and tries to use that private data in

469
00:29:13,760 --> 00:29:17,360
Speaker 1: in another innovative way. And so where is that hole

470
00:29:17,440 --> 00:29:21,440
Speaker 1: I mean I mentioned the two point six million are

471
00:29:21,480 --> 00:29:24,360
Speaker 1: they what do we know about where they are in

472
00:29:24,400 --> 00:29:26,960
Speaker 1: the workforce? I mean, I guess geographically, but also the

473
00:29:27,040 --> 00:29:29,560
Speaker 1: kind of worker they are. When they look at their

474
00:29:29,640 --> 00:29:35,160
Speaker 1: employment data, they're finding a big shortage of low income workers. UM,

475
00:29:35,160 --> 00:29:39,240
Speaker 1: basically a drop in the pre pandemical low income workforce.

476
00:29:39,440 --> 00:29:41,960
Speaker 1: So one in five, one in five low income workers

477
00:29:42,040 --> 00:29:45,600
Speaker 1: that we have in effective before COVID has just disappeared. Yeah,

478
00:29:45,720 --> 00:29:48,120
Speaker 1: and it's that is that quite that bad? Because some

479
00:29:48,240 --> 00:29:52,160
Speaker 1: of those people have moved up are now making more

480
00:29:52,240 --> 00:29:55,880
Speaker 1: money and are further up the income scale. But only

481
00:29:55,880 --> 00:29:57,800
Speaker 1: about a third of the missing workers have been able

482
00:29:57,840 --> 00:29:59,800
Speaker 1: to move up, and about two thirds are still the

483
00:30:00,040 --> 00:30:04,560
Speaker 1: It just basically vanished. And um. They looked through a

484
00:30:04,600 --> 00:30:07,960
Speaker 1: bunch of different scenarios and reasons why that might have happened,

485
00:30:07,960 --> 00:30:11,000
Speaker 1: and they test various correlations. But the thing that was

486
00:30:11,080 --> 00:30:15,920
Speaker 1: the strongest predictor of a missing a place that had

487
00:30:15,960 --> 00:30:19,240
Speaker 1: a missing worker was a place that had the strongest

488
00:30:19,560 --> 00:30:24,720
Speaker 1: shock in And so this goes back to Chetty's research

489
00:30:24,760 --> 00:30:27,400
Speaker 1: in twenty What he found was that the low income

490
00:30:27,440 --> 00:30:31,520
Speaker 1: workers that were hit the hardest by the pandemic were

491
00:30:31,560 --> 00:30:36,600
Speaker 1: the ones who worked in the highest rent areas. Basically, professionals,

492
00:30:36,680 --> 00:30:43,400
Speaker 1: wealthy people, affluent people change their spending patterns very rapidly.

493
00:30:43,440 --> 00:30:48,480
Speaker 1: In March stopped going out and that caused this cascade

494
00:30:48,520 --> 00:30:54,320
Speaker 1: of job losses and um business closures that impacted workers

495
00:30:54,320 --> 00:30:57,880
Speaker 1: who worked in those neighborhoods particularly. Of course, everybody was

496
00:30:57,920 --> 00:31:01,200
Speaker 1: affected across the entire economy, but if you compare the Bronx,

497
00:31:01,240 --> 00:31:03,280
Speaker 1: which is a low income part of New York City,

498
00:31:03,280 --> 00:31:08,920
Speaker 1: to um wealthier parts of Manhattan, Um, the difference was

499
00:31:09,000 --> 00:31:13,240
Speaker 1: really stark there um in terms of the workers up

500
00:31:13,240 --> 00:31:16,080
Speaker 1: in up in the Bronx were still doing okay, I

501
00:31:16,080 --> 00:31:19,280
Speaker 1: mean they were, they were affected, but in wealthier neighborhoods

502
00:31:19,280 --> 00:31:23,280
Speaker 1: all across the country there was this devastation among low

503
00:31:23,360 --> 00:31:26,200
Speaker 1: income workers. And so basically to fast forward to or

504
00:31:26,240 --> 00:31:29,200
Speaker 1: three years, what they found in this new research is

505
00:31:29,280 --> 00:31:33,040
Speaker 1: that those are the places where the workers are most missing.

506
00:31:33,080 --> 00:31:36,200
Speaker 1: And that suggests that something about actually losing your job in,

507
00:31:37,440 --> 00:31:41,200
Speaker 1: and especially losing a low income job in, really scrambled

508
00:31:41,200 --> 00:31:44,400
Speaker 1: people's lives and through them out of the workforce. Is

509
00:31:44,440 --> 00:31:46,280
Speaker 1: there any sense that these people will have gone into

510
00:31:46,360 --> 00:31:48,640
Speaker 1: the informal economy that they're now in the sort of

511
00:31:48,680 --> 00:31:51,400
Speaker 1: being paid in cash or just do we have any

512
00:31:51,480 --> 00:31:54,160
Speaker 1: sense of that where they might be. Well that these

513
00:31:54,160 --> 00:31:57,160
Speaker 1: are probably the hardest people to track in the in

514
00:31:57,240 --> 00:32:00,720
Speaker 1: the United States. These are people obviously at the margins

515
00:32:00,720 --> 00:32:04,320
Speaker 1: of the workforce. Um. And so when I went out

516
00:32:04,360 --> 00:32:06,680
Speaker 1: and did some reporting and tried to talk to people,

517
00:32:06,760 --> 00:32:11,640
Speaker 1: I mean, there there were some interesting ideas, um that

518
00:32:11,720 --> 00:32:14,400
Speaker 1: came back to me from business owners and from workers themselves.

519
00:32:14,400 --> 00:32:18,840
Speaker 1: I Mean, one thing is that we're all three years older, um,

520
00:32:18,920 --> 00:32:22,640
Speaker 1: since the pandemic happened. And you know, some of these

521
00:32:22,640 --> 00:32:24,800
Speaker 1: low income jobs are really hard. For example, I talked

522
00:32:24,840 --> 00:32:27,640
Speaker 1: to one person who worked in a kitchen. He he

523
00:32:27,640 --> 00:32:30,600
Speaker 1: he's now fifty seven years old. He's like, he liked

524
00:32:30,600 --> 00:32:33,040
Speaker 1: that restaurant job that where he was working before, but

525
00:32:33,080 --> 00:32:36,480
Speaker 1: the restaurants now closed, and he doesn't trust another restaurant

526
00:32:36,560 --> 00:32:39,760
Speaker 1: job to be as good and easy, especially now that

527
00:32:39,800 --> 00:32:44,160
Speaker 1: he's fifty seven. That's hard work. Um. So and then

528
00:32:44,240 --> 00:32:46,480
Speaker 1: the other thing that seems to have happened maybe is

529
00:32:46,520 --> 00:32:51,040
Speaker 1: that in these expensive areas people have moved so um.

530
00:32:51,120 --> 00:32:54,080
Speaker 1: You know, in a place like New York City, you

531
00:32:54,200 --> 00:32:56,280
Speaker 1: might end up living an hour and a half by

532
00:32:56,320 --> 00:32:59,240
Speaker 1: training your bus outside the city to get to your

533
00:32:59,320 --> 00:33:03,000
Speaker 1: job in a place like the Upper West Side. Well,

534
00:33:03,440 --> 00:33:05,560
Speaker 1: people just said, I'm not going to do that anymore.

535
00:33:05,960 --> 00:33:08,280
Speaker 1: I'm going to move to North Carolina which is cheaper,

536
00:33:08,400 --> 00:33:11,200
Speaker 1: or or or some other part of the country that's

537
00:33:11,280 --> 00:33:13,800
Speaker 1: that's the less expensive. It's interesting that you mentioned the

538
00:33:13,800 --> 00:33:16,360
Speaker 1: age element, and actually, in a bit in in a minute,

539
00:33:16,360 --> 00:33:17,719
Speaker 1: we're going to get into a bit more of than

540
00:33:17,720 --> 00:33:20,720
Speaker 1: the implications of an aging population before I let you go, though,

541
00:33:20,720 --> 00:33:22,480
Speaker 1: I'm interested. I mean that you've come back to it

542
00:33:22,640 --> 00:33:27,720
Speaker 1: various times, this this puzzle, Um, and I wondered when

543
00:33:27,720 --> 00:33:30,040
Speaker 1: you came away from this research and talking to the

544
00:33:30,040 --> 00:33:32,160
Speaker 1: people that you talked to on the ground, what do

545
00:33:32,200 --> 00:33:34,080
Speaker 1: you think the lessons might be for the future. I mean,

546
00:33:34,120 --> 00:33:36,160
Speaker 1: you know, obviously the FED is sitting there wondering whether

547
00:33:36,160 --> 00:33:38,000
Speaker 1: any of these people are going to come back, and

548
00:33:38,760 --> 00:33:41,880
Speaker 1: whether as a result, we might have a bit more room,

549
00:33:42,200 --> 00:33:44,320
Speaker 1: um in the economy, in the labor force than it

550
00:33:44,320 --> 00:33:46,560
Speaker 1: currently looks like. When you just look at that very

551
00:33:46,600 --> 00:33:50,160
Speaker 1: low three point six percent unemployment rate, do you do

552
00:33:50,240 --> 00:33:51,800
Speaker 1: you feel these people are going to come back or

553
00:33:51,800 --> 00:33:53,360
Speaker 1: they're going to pop up in some other part of

554
00:33:53,360 --> 00:33:56,360
Speaker 1: the economy. You know, I think part of the problem

555
00:33:56,400 --> 00:33:59,000
Speaker 1: here is that this is a long term problem of

556
00:33:59,080 --> 00:34:04,280
Speaker 1: labor force participates in the United States. UM, it's especially

557
00:34:04,360 --> 00:34:10,120
Speaker 1: for low income workers. UM, childcare, affordable housing has just

558
00:34:10,200 --> 00:34:13,200
Speaker 1: gotten really difficult. The most economically dynamic parts of the

559
00:34:13,200 --> 00:34:17,440
Speaker 1: country are the places that where workers are needed. So, um,

560
00:34:17,680 --> 00:34:20,800
Speaker 1: it's hard for people to afford to go to a job.

561
00:34:21,040 --> 00:34:23,600
Speaker 1: And the reasons for that are long term. You know,

562
00:34:23,760 --> 00:34:27,560
Speaker 1: there's not enough housing supply, there's not enough affordable childcare.

563
00:34:28,080 --> 00:34:30,720
Speaker 1: And so this is this problem decades in the making,

564
00:34:30,760 --> 00:34:34,400
Speaker 1: and it's probably going to require decades of policies that

565
00:34:34,400 --> 00:34:37,360
Speaker 1: could could address that. And we basically need policies to

566
00:34:37,440 --> 00:34:41,600
Speaker 1: create a labor supply increase in this country and get

567
00:34:41,880 --> 00:34:44,960
Speaker 1: more for example, parents or sick people or older people

568
00:34:45,280 --> 00:34:48,200
Speaker 1: comfortable getting to get getting in the workforce. And that

569
00:34:48,320 --> 00:34:50,799
Speaker 1: is a battle that many countries are raging right now.

570
00:34:50,880 --> 00:35:01,040
Speaker 1: Ben Stephen, and thank you very much. You're welcome. That's

571
00:35:01,080 --> 00:35:04,720
Speaker 1: it for this season of Stephanomics. We're officially back in April,

572
00:35:05,239 --> 00:35:07,200
Speaker 1: but I have a sneaking feeling I might have some

573
00:35:07,400 --> 00:35:10,120
Speaker 1: bonus interviews for you before then, so keep an eye

574
00:35:10,120 --> 00:35:12,480
Speaker 1: on this feed. And of course you can keep getting

575
00:35:12,480 --> 00:35:16,200
Speaker 1: informed on economic news and views, and much else on

576
00:35:16,280 --> 00:35:20,840
Speaker 1: the Bloomberg Terminal website or app. This entire series was

577
00:35:20,880 --> 00:35:24,600
Speaker 1: produced by Yang Yang, Summer Sadi and Mangus Henrickson, with

578
00:35:24,680 --> 00:35:28,759
Speaker 1: special thanks to Sean Donald, Ben Stevenman, Melissa Carney, and

579
00:35:28,840 --> 00:35:33,279
Speaker 1: Kelly Friendly. Mike Sasso is the executive producer of Stephanomics