How to Stop Worrying and Love the Robot Apocalypse (Update) AI transcript and summary - episode of podcast Freakonomics Radio

Go to PodExtra AI's episode page (How to Stop Worrying and Love the Robot Apocalypse (Update)) to play and view complete AI-processed content: summary, mindmap, topics, takeaways, transcript, keywords and highlights.

Go to PodExtra AI's podcast page (Freakonomics Radio) to view the AI-processed content of all episodes of this podcast.

Freakonomics Radio episodes list: view full AI transcripts and summaries of this podcast on the blog

Episode: How to Stop Worrying and Love the Robot Apocalypse (Update)

Author: Freakonomics Radio + Stitcher
Duration: 00:48:36

Episode Shownotes

It’s true that robots (and other smart technologies) will kill many jobs. It may also be true that newer collaborative robots (“cobots”) will totally reinvigorate how work gets done. That, at least, is what the economists are telling us. Should we believe them? SOURCES:David Autor, professor of economics at the

Massachusetts Institute of Technology.James Rosenman, C.E.O. of Andrus on Hudson senior care community.Karen Eggleston, economist at Stanford University.Yong Suk Lee, professor of technology, economy, and global affairs at the University of Notre Dame. RESOURCES:"Robots and Labor in Nursing Homes," by Yong Suk Lee, Toshiaki Iizuka, and Karen Eggleston (NBER Working Paper, 2024)."Global Robotics Race: Korea, Singapore and Germany in the Lead," by International Federation of Robotics (2024)."Unmet Need for Equipment to Help With Bathing and Toileting Among Older US Adults," by Kenneth Lam, Ying Shi, John Boscardin, and Kenneth E. Covinsky (JAMA Internal Medicine, 2021)."Robots and Labor in the Service Sector: Evidence from Nursing Homes," by Karen Eggleston, Yong Suk Lee, and Toshiaki Iizuka (NBER Working Papers, 2021).The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, by David Autor, David Mindell, Elisabeth Reynolds, and the MIT Task Force on the Work of the Future (2020)."Robots and Jobs: Evidence from US Labor Markets," by Daron Acemoglu and Pascual Restrepo (University of Chicago Press, 2020)."The Slowdown in Productivity Growth and Policies That Can Restore It," by Emily Moss, Ryan Nunn, and Jay Shambaugh (The Hamilton Project, 2020)."The China Shock: Learning from Labor Market Adjustment to Large Changes in Trade," by David H. Autor, David Dorn, and Gordon H. Hanson (NBER Working Papers, 2016)."Deregulation at Heart of Japan's New Robotics Revolution," by Sophie Knight and Kaori Kaneko (Reuters, 2014). EXTRAS:"What Do People Do All Day?" by Freakonomics Radio (2024)."Did China Eat America’s Jobs?" by Freakonomics Radio (2017).

Summary

In this episode of Freakonomics Radio, the discussion revolves around the impact of robots and automation on the job market. David Autor highlights that while technology may eliminate some low-wage jobs, it simultaneously creates new opportunities for higher-skilled workers. The conversation emphasizes historical context, showing that fears of technology leading to job loss are often overstated. Insights from experts, including James Rosenman, illustrate that collaborative robots ('cobots') can enhance productivity in sectors like healthcare without reducing employment. Research indicates that automation in nursing homes can complement human labor and improve patient care, raising crucial discussions on the evolving dynamics between technology and the workforce.

Go to PodExtra AI's episode page (How to Stop Worrying and Love the Robot Apocalypse (Update)) to play and view complete AI-processed content: summary, mindmap, topics, takeaways, transcript, keywords and highlights.

Full Transcript

00:00:03 Speaker_06
Hey there, it's Stephen Dubner, and this is a bonus episode of Freakonomics Radio. In 2021, we put out an episode about the future of robots in the workforce.

00:00:13 Speaker_06
It featured a couple of economists who had been studying how robots, or cobots for collaborative robots, were being used in Japanese nursing homes.

00:00:23 Speaker_06
Those same economists recently put out a follow-up paper, so we thought we'd replay the original episode with updated facts and figures, and then hear about the new research findings. We've also got some robot news from an American nursing home.

00:00:38 Speaker_06
So here is the updated episode. It's called, How to Stop Worrying and Love the Robot Apocalypse.

00:00:52 Speaker_05
We might as well start with an economist. No, no, I'm not even a real economist. I just play one at MIT. That's David Autor.

00:01:01 Speaker_06
He is a real economist. He's been on the show a few times before. His path to economics professor was indirect.

00:01:10 Speaker_05
I started as an undergraduate at Columbia. I dropped out after three semesters. I worked, I rode a motorcycle. I went back and completed my undergraduate degree at Tufts a couple of years later. I studied psychology.

00:01:21 Speaker_05
with a concentration in computer science, and I really didn't know what to do with myself. So he did some temping.

00:01:27 Speaker_06
He did construction. He worked at McDonald's. Then he went back to school again and got a Ph.D. in public policy. So not the typical path for a labor economist at MIT. And that real-world experience is reflected in David Autor's work.

00:01:44 Speaker_05
My work is very concrete. I'm not a high theorist. I'm very much driven by practical problems. A lot of the questions I studied are related to things I worked on and saw firsthand.

00:01:54 Speaker_05
Working in poor communities, working in places undergoing political upheaval, watching the gulf of inequality expand in the information age.

00:02:04 Speaker_06
Watching the gulf of inequality expand in the information age. Yes, that does sound like a transformative idea, and it leads to a large question. Will new technologies make that inequality gulf bigger or smaller?

00:02:19 Speaker_06
You could see it going either way, right? On the one hand, technology democratizes. Many of us are now rich enough to afford what is essentially a butler.

00:02:30 Speaker_06
Amazon.com, for instance, will bring you whatever you'd like quite quickly at the push of a button. On the other hand, much of the wealth produced by this kind of technology flows way up to the tippity top of the income ladder.

00:02:45 Speaker_06
So who are the winners and who are the losers when there is such a transformative shift in the global economy? Think about one of the last big shifts we lived through, the massive expansion of global trade, during which the U.S.

00:03:00 Speaker_06
intentionally sent millions of jobs to China. We actually had David Autor on the show a few years back to talk about that. Episode number 274, if you want to listen, it's called Did China Eat America's Jobs?

00:03:13 Speaker_05
So Autor has done a lot of thinking about these issues. No country has experienced the extremes of rising inequality that the United States has, and there's no evidence that the U.S. has gained much from it. We haven't grown faster than other countries.

00:03:27 Speaker_05
We don't have higher labor force participation rates. We don't have higher social mobility of people going from rags to riches.

00:03:33 Speaker_06
If you wanted a SparkNotes version of the U.S. economy over the past few decades, it would be this.

00:03:39 Speaker_06
Rising productivity, though not as fast a rise as the post-war era, and stagnant median wages, with the productivity gains largely benefiting the top of the income distribution.

00:03:51 Speaker_05
Yeah, it's just incredibly skewed. And so as far as we can measure it, the median is barely budging.

00:03:57 Speaker_06
And now, after all that, it's time to consider another very, very large disruption, because, you know, that robot future you've been hearing about?

00:04:07 Speaker_04
Open the pod bay doors, Hal. I'm sorry, Dave. I'm afraid I can't do that.

00:04:13 Speaker_06
Yeah, well, the future got here yesterday.

00:04:16 Speaker_01
Good to see you again. I like your shirt. Thank you. So tell me, how are you feeling today?

00:04:25 Speaker_07
I'm feeling pretty good.

00:04:27 Speaker_01
You're welcome.

00:04:38 Speaker_02
This is Freakonomics Radio, the podcast that explores the hidden side of everything, with your host, Stephen Dubner.

00:04:54 Speaker_06
In the spring of 2018, David Autor was asked to co-chair an MIT task force called The Work of the Future. It included researchers from a variety of disciplines, economics, engineering, political science, anthropology even.

00:05:09 Speaker_06
The mission was to explore how new technologies like robotics and automation will affect labor markets, especially whether certain groups of workers would be left behind.

00:05:20 Speaker_06
Keep in mind that this sort of prediction is really hard, as evidenced by the predictions that economists made about globalization. They predicted that when the U.S.

00:05:31 Speaker_06
offshored manufacturing jobs to China, that Americans who worked in manufacturing would be made better off, since they'd theoretically be reallocated into better jobs. But as David Autor told us in that earlier episode, this didn't happen.

00:05:46 Speaker_05
Some people are leaving the labor market. Some people are going into unemployment. Some people are going on to disability. And so the reallocation process seems to be slow, frictional, and scarring.

00:05:58 Speaker_05
The real differentiator is the skill level of the worker. So higher paid and more highly educated workers, they seem to reallocate successfully out of manufacturing into other jobs.

00:06:09 Speaker_06
So the HR person at a big textile firm gets an HR job elsewhere and the manufacturers on the line are probably not.

00:06:16 Speaker_05
And the line workers are much less likely to do so, exactly.

00:06:19 Speaker_06
So considering the difficulty of making predictions about the future of work,

00:06:25 Speaker_05
The MIT task force started with one thing they were pretty sure about. The one thing we were confident in was that the U.S. would keep generating lots of low wage jobs. Too many even, yes? Well, actually, too many is better than too few.

00:06:39 Speaker_05
When there's too many, at least they're competing hard for workers. When there are too few, workers are competing for them. And that means those jobs will get worse. And so the one positive thing you could say about the U.S.

00:06:49 Speaker_05
workforce, well, we had a lot of crappy jobs.

00:06:52 Speaker_06
When we spoke with Otter for this episode originally, the U.S. was still recovering from the covid-19 recession. And what kind of damage did that recession do?

00:07:01 Speaker_05
In the short run, it's just done enormous damage to most of the in-person service jobs, the ones that were absolutely necessary, like in grocery stores and health care, have persisted.

00:07:10 Speaker_05
But many of the jobs in retail, in restaurants and hospitality have not.

00:07:14 Speaker_06
A lot of those jobs paid only the minimum wage and they did come back after the recession. But other changes were more permanent.

00:07:22 Speaker_05
I actually think the biggest change is, you know, most obvious is telepresence, that we are just doing more things remotely.

00:07:29 Speaker_05
We've kind of broken the space time barrier in that we can't be in two places at once, but we can get to any two places instantly.

00:07:35 Speaker_06
But with lockdowns and covid precautions, some jobs simply couldn't be done remotely. During the pandemic, business travel dropped massively and that had all kinds of downstream labor effects.

00:07:49 Speaker_05
not just airplanes, right? It's Ubers and limos. It's expensive hotels that, you know, people pay full freight on weeknights and then go out to marquee restaurants and then go have their shoes shine and dry cleaners.

00:08:02 Speaker_05
And so I think that's the real challenge.

00:08:05 Speaker_06
The work of the Future Task Force took the pandemic into account as best as they could. They published their report in December 2021. It tried to answer three main questions. The first one,

00:08:17 Speaker_06
How are emerging technologies transforming the nature of human work and the set of skills that enable humans to thrive in the digital economy?

00:08:27 Speaker_05
You know, technology is always eliminating work and creating work simultaneously. We tend to focus on what is automated away, and that's completely reasonable.

00:08:36 Speaker_05
Simultaneously, new areas of expertise, new luxuries, new services, new demands are constantly being created. And that process, that kind of turnover is highly productive. Consider, for instance, how medicine is practiced these days.

00:08:51 Speaker_05
There's hundreds of medical specialties, way, way more than there used to be. And it's not because doctors have become narrower and narrower and they know less and less. It's that they know more and more in depth rather than breadth. Right.

00:09:02 Speaker_05
The extent of expertise required is just extraordinary. And humans have finite capacity. Where did all of that need for expertise come from? Well, it came from research and technology and so on.

00:09:13 Speaker_05
So often we're broadening expertise, but it's not just in the high-tech professions. You will find patents emerging for new ways of hardening nails. Fingernails, I mean, not the nails you pound into wood.

00:09:27 Speaker_05
Patents for solar voltaic electricians, people who install solar cells. You know, there's a lot of skilled work that's done hands on being an electrician, being a plumber, building a home or repairing an engine.

00:09:39 Speaker_05
And much of that work requires a combination of dexterity and flexibility and problem solving and also knowledge, knowledge on demand. A lot of people today consult YouTube when they want to learn how to sweat a pipe.

00:09:52 Speaker_05
We can augment people's capability to do that work by giving them VR tools, giving them information on demand. People could be much more effective in that work and more productive and therefore paid more if they were augmented in these ways.

00:10:07 Speaker_05
And so you can see in those examples how you could use the technology to not make people less necessary, but to make them more effective.

00:10:14 Speaker_06
That said, not every profession benefits from this kind of tech augmentation.

00:10:19 Speaker_05
If you're doing one of those things that all of a sudden a machine can do better than you, your opportunity set contracts.

00:10:24 Speaker_05
And usually the people who are on the one end of that seeing their work disappear are not the same people who are getting new opportunities.

00:10:31 Speaker_06
We saw this vividly when the U.S. offshored manufacturing jobs and we're seeing it now in other sectors.

00:10:38 Speaker_05
For the people who have been working in clerical jobs or many production jobs, what automation has done is made their work unnecessary.

00:10:47 Speaker_06
It's tempting to think that automation will replace only the simpler jobs that don't require heavy cognitive input. But that's not the case. Otter has seen this for himself at some of the firms he's visited.

00:10:59 Speaker_05
One of them was a big insurance company, and they do an enormous amount of claims adjudication, claims assessment. And they have these floors of, I guess you'd call them forensic accountants.

00:11:11 Speaker_05
And they go through a lot of material looking for anomalies, looking for fraud, looking for overpayment and so on.

00:11:17 Speaker_06
It is true that forensic accounting requires a high level of expertise, but combing through these files in search of anomalies is also a tedious task.

00:11:28 Speaker_05
And the automation has really accelerated that discovery work. Machines can actually do reasonably well at this. And simultaneously, they never run out of attention. They never run out of energy.

00:11:39 Speaker_06
Let's say machine learning and artificial intelligence can be used to find these anomalies. Does that mean that the people who used to find the anomalies are out of work or they have a different style of work, a different amount of work?

00:11:51 Speaker_05
So definitely the total headcount of people who need to do this work is shrinking. Now, they're mostly not firing people, but they slow down hiring. The work, I think, that remains is quite interesting. There's less tedium and more action.

00:12:04 Speaker_05
But it does ultimately mean, I think, reduction in the number of people doing that work.

00:12:09 Speaker_06
The automating of work is itself big business, and it's something we've heard a lot about since we first made this episode in 2021. To give you an example of how big,

00:12:20 Speaker_06
NVIDIA, the leading supplier of hardware and software for artificial intelligence, is now the most valuable publicly traded company in the world.

00:12:30 Speaker_06
If you are the kind of person who hears this and shudders at the thought that technology is destroying our way of life, well, there is a long history of such thought. Aristotle had the same concern.

00:12:46 Speaker_06
And in ancient Rome, some technologies were outlawed because of the expected job loss. In the most recent century, if you've ever watched a movie, you have likely come across at least one fever dream of technology run amok.

00:13:02 Speaker_01
It's alive! It's alive! It's alive! It's alive!

00:13:07 Speaker_06
And fears of a robot apocalypse.

00:13:11 Speaker_02
Skynet begins to learn at a geometric rate. It becomes self-aware at 2.14 a.m. Eastern Time, August 29th.

00:13:19 Speaker_01
We don't know who struck first, us or them.

00:13:22 Speaker_06
But if the overall goal is to make good policy and economic decisions about our collective future, we probably shouldn't base that policy on movie plots.

00:13:31 Speaker_06
As history has shown again and again and again, the fear of new technologies tends to be overstated, and the gains from technology make most people better off. But maybe, you're thinking, maybe this time is different.

00:13:47 Speaker_06
In the old days, when the automobile replaced the horse and carriage, if you lost your job as a carriage maker or a stable hand, you could probably find work in an auto plant. What about today?

00:14:00 Speaker_06
A 2020 paper by the economists Daron Acemoglu and Pascual Restrepo found that a single industrial robot will typically reduce employment by as many as six human workers. Here's David Autor again.

00:14:14 Speaker_05
And I can understand why companies would do that. It makes a lot of sense. Labor's a cost. No one hires workers for the fun of hiring workers. They hire workers because they need things done.

00:14:21 Speaker_05
If they could have machines that did it without complaining and cost less, that's what they would do. But we have a public interest in something more than that. We're going to have lots of people. The machines ultimately work for the people.

00:14:31 Speaker_05
We want to augment the people. And there are many highly valuable social problems that could use automation, could use investment, and we under-invest in. For example, healthcare.

00:14:46 Speaker_06
Consider this healthcare checkup.

00:14:48 Speaker_07
I have Dr. Samalusi. He's gonna be evaluating you today, okay? Okay.

00:14:55 Speaker_01
Hello, my dear, how are you?

00:14:56 Speaker_07
Hi, I'm okay.

00:14:57 Speaker_01
How are you? Good to see you again. I like your shirt. So tell me, how are you feeling today?

00:15:04 Speaker_07
Yeah, I'm feeling pretty good. No complaints today, really.

00:15:09 Speaker_06
Abiola Famalusi is a doctor who works with a nursing home in Westchester County, just outside of New York City. It is called Andrus on Hudson.

00:15:18 Speaker_01
Can you open your mouth for me? Say, ah. Ah. OK, good. Can you lift both hands up for me? Lift your arms up for me. Both of them. Excellent.

00:15:31 Speaker_06
But here's the thing, Dr. Famalusi isn't at the nursing home today. He is examining the patient remotely.

00:15:37 Speaker_03
Yeah, we are in our infancy of adopting certain robots. That is James Rosenman.

00:15:43 Speaker_06
He's the CEO of Andris on Hudson.

00:15:46 Speaker_03
We have two robots, one for the purposes of telemedicine so that physicians can go into patient rooms with the assistance of a nurse. when they can't be physically available on site.

00:16:02 Speaker_06
This telemedicine robot doesn't look like much, or at least not like what you might think a robot should look like.

00:16:08 Speaker_03
Yeah, like an iPad that is on a base that has wheels that can move to various areas. And we also have another robot that is a social robot to visit certain residents that may be less able to get up and walk around.

00:16:24 Speaker_06
I understand you had eight robotic dogs and 11 robotic cats. Did you have to pull them then because of COVID concerns?

00:16:30 Speaker_03
They've been put in the kennel for a little while. The problem with the robots in the environment we're in right now is that you can't have them just roaming about. So infection control has added this other layer of complexity to robotics.

00:16:44 Speaker_06
So the pandemic is the reason the robotic dogs and cats had to be sidelined, but the pandemic was also the reason that Andrus got the telemedicine robot.

00:16:55 Speaker_06
Nursing homes were a hot zone for COVID transmissions, so Rosenman wanted to minimize face-to-face contact. Are you pretty typical as far as a nursing facility with the amount of robots you have? Are you at the leading edge or are you lagging?

00:17:11 Speaker_03
It's hard to know where we stand in comparison to other providers because this isn't a topic that comes up very often. But we do know that a lot of the people that we talk to don't utilize those in their facilities.

00:17:25 Speaker_06
Also, James Rosenman is a self-proclaimed robot nerd.

00:17:30 Speaker_03
I think I watched Short Circuit when I was little, the movie.

00:17:35 Speaker_07
Oh, I get it.

00:17:39 Speaker_03
Johnny Five was a big inspiration for me.

00:17:42 Speaker_06
But there are other non-pandemic reasons that a nursing home or hospital might want to use robots.

00:17:48 Speaker_03
Yeah, we work very hard on staff retention and we do have a good retention rate, but we also have people, you know, they retire. We would love for them to work there forever and ever.

00:17:56 Speaker_03
And I'd love to clone people, but we can't, you know, maybe that's for another show, but we have a labor shortage in the market of nurses and of CNAs. A CNA is a certified nursing assistant.

00:18:10 Speaker_06
In the U.S. today, there are roughly 4 million RNs or registered nurses. A study in the American Journal of Medical Quality found that by 2030, there will be a shortage of half a million RNs.

00:18:23 Speaker_06
A subsequent study by the National Center for Health Workforce Analysis projects a smaller gap, but still a gap. This gap is driven by both demand. We have a large population of elderly and sick people and supply.

00:18:38 Speaker_06
There are more nurses aging out of the workforce than entering it.

00:18:43 Speaker_03
I've continued to see this labor shortage get worse and worse. How hard is it for you to hire already? It's incredibly difficult. It is a very difficult and demanding job. There is a critical shortage of those individuals.

00:18:58 Speaker_06
Andrus has about 190 residents and nearly 250 employees, not counting the robots. The typical resident is over 70 and has a variety of conditions.

00:19:10 Speaker_03
Respiratory conditions, COPD, general chronic respiratory failure. congestive heart failure, cancer.

00:19:18 Speaker_06
The nursing assistants manage a lot of the moment by moment care. Their wages start at twenty three dollars an hour. Registered nurses at Andrus earn around forty to fifty dollars an hour.

00:19:30 Speaker_06
Fifty dollars an hour works out to around one hundred thousand dollars a year.

00:19:34 Speaker_03
And what did these robots cost? It was $4,000 for one of the robots that we are using for socialization. And then for the medical robot, we lease that. We pay about $2,000 a month because it has all the equipment. Equipment meaning like EKG possibility.

00:19:52 Speaker_03
Exactly. With the telemedicine robot. One of the key components is not just that the clinician can look at the patient and assess them, but it has an array of tools connected with it. So you have what they call a smart stethoscope.

00:20:08 Speaker_03
So that directly feeds into what the physician can see on their end, you know, an EKG on site. And an ultrasound is something that we're looking on adding.

00:20:19 Speaker_01
Your pulse rate is very good.

00:20:22 Speaker_07
Oxygen saturation is 98%.

00:20:25 Speaker_06
James Rosenman says the robots have increased productivity at the nursing home. And better yet, they've helped improve patient outcomes.

00:20:34 Speaker_03
You know, one area that is always of concern, individuals who come to us for short term rehabilitation and then something happens medically with them. Then we have to send them back out to the hospital. It's called a readmission.

00:20:49 Speaker_03
And so we realized that by adding the robot and having faster access to clinicians to be able to view something in real time, assess it, we were able to fairly significantly reduce readmission rates to the hospital just through that alone.

00:21:06 Speaker_06
For David Autor, the MIT labor economist, these nursing home robots can help answer the second question that his work of the Future Task Force asked. How can we shape and catalyze technological innovation to complement and augment human potential?

00:21:23 Speaker_05
You could introduce so much technology into healthcare without reducing employment and yet expanding the quality of care and the quantity of care. And of course, you'll need tons and tons of people to actually do the hands-on care work.

00:21:37 Speaker_06
But is that reading of the situation too optimistic? Coming up after the break, a fascinating new study about Japanese nursing homes.

00:21:46 Speaker_00
What we're really worried about are the lower skilled workers that might be completely replaced.

00:21:54 Speaker_06
And why is the Andrus nursing home an outlier? Why is the U.S. a laggard when it comes to health care robots? Sort of hard to understand.

00:22:04 Speaker_06
And if you like what you're hearing on Freakonomics Radio today, why don't you give us a rating or write a review on your podcast app? We'll be right back with robots and cobots. Welcome back.

00:22:26 Speaker_06
Today we are playing an update of an episode we originally recorded in 2021. The MIT labor economist David Autor was co-chair of a task force on the future of work, specifically how the U.S. workforce is integrating and adapting to new technologies.

00:22:45 Speaker_06
The task force found that the U.S. is not nearly as adept as one might hope in this regard. Here's what they wrote in their final report. So David, of all the rich countries in the world, how would you rank the U.S.

00:23:08 Speaker_06
in terms of successfully adapting to the future of work? And assuming that we are not in, let's say, the 90th percentile or above, why are we trailing?

00:23:19 Speaker_05
I would put the U.S. maybe at the bottom of the top dozen. On the plus side, let's give the U.S. a little bit of credit. It's incredibly creative and entrepreneurial. A lot of the technologies originate here. Right.

00:23:30 Speaker_05
But in terms of dealing with the consequences as opposed to the opportunities, that's where we have been extremely poor. Low wage workers in Canada make 25 percent more per hour than low wage workers in the United States.

00:23:42 Speaker_05
It's hard to believe that Canadian workers are actually 25 percent more productive per hour at McDonald's than U.S. workers. That seems very unlikely. How are those wages so much higher in Canada? There are minimum wages.

00:23:52 Speaker_05
And then there are just norms about what is acceptable. And the U.S. has kind of thrown away those norms to a substantial extent. We've convinced ourselves that those norms are the problem, not the solution.

00:24:06 Speaker_06
Aside from those norms, there's also the fear that new technologies will destroy more good jobs than they create, or at least that the productivity trade-off won't be worth it. But not all countries feel that way, especially when it comes to robots.

00:24:21 Speaker_00
I think a lot of people just weren't aware that Japan's been subsidizing robot adoption since 2015.

00:24:28 Speaker_06
Karen Eggleston is an economist at Stanford. It's beautiful. You can hear the birds chirping. A lot of Eggleston's research looks at health care and technology in Asia. Why that focus?

00:24:40 Speaker_00
Well, Asia is a very important part of the world and a part of the global economy. I also have family connections to Asia.

00:24:47 Speaker_06
When you look at the countries with the highest per capita share of robots in the workforce, Asia is well represented. Number one, by a long shot, is South Korea. Singapore is number two, and Japan is number four. Germany is third.

00:25:03 Speaker_06
Most of these are industrial robots used in the production of electronics and automobiles. The countries with a lot of robots tend to be high wage countries, which makes sense since higher wages create more incentives to replace human workers.

00:25:18 Speaker_06
The exception is China, which is now at number five, even though labor there is relatively cheap, at least for now. When it comes to Japan, Karen Eggleston says that robots have been embraced for several reasons.

00:25:32 Speaker_00
First of all, we know Japan is a very developed economy and invests a lot in many kinds of new technologies from so-so technologies to brilliant technologies. So investing in robots was natural in that context.

00:25:46 Speaker_06
A so-so technology is economist speak for something that just doesn't perform very well, especially when it's new. Think of automated phone services and self-checkouts in grocery stores.

00:25:58 Speaker_00
Second, and more related to what I usually study, is that the population age structure in Japan is such that it's leading the world in the demographic transition.

00:26:08 Speaker_00
and so therefore has an overall declining population and a declining working age population.

00:26:16 Speaker_06
Japan, in fact, has the oldest population in the world.

00:26:20 Speaker_00
So you have an increasing demand for long-term care and a declining supply of workers to staff that long-term care.

00:26:30 Speaker_06
This is the same dynamic that James Rosenman of the Andrus Nursing Home told us about, but it's even more pronounced in Japan. A lot of countries ease the burden of an aging population by importing labor.

00:26:43 Speaker_00
But as many people know, Japan is less welcoming of immigrant labor than many other countries in the world and has actually had a longstanding acceptance of robots.

00:26:56 Speaker_06
I feel like I read that a few years ago, Japan had finally started to loosen up some of the immigration. Is that right?

00:27:03 Speaker_00
Japan does continue to loosen immigration. So it's certainly not a black or white thing, but it's just relative to many other countries where the labor market conditions might be different.

00:27:14 Speaker_06
In other words, Japan might have opted for more immigrant labor to help care for its aging population, but instead it invested heavily in robots.

00:27:23 Speaker_00
So they don't all look like R2D2 or C3PO, but they have functionality that enables them to take actions based on what they're monitoring. And a cobot is a term that's developed for robots that work alongside humans.

00:27:38 Speaker_06
Cobot as in a collaborative robot. It is a very different machine than the kind of robots used in something like auto manufacturing.

00:27:46 Speaker_00
Correct. Yeah. Those robots can kind of swing their arms without worrying that they're going to knock over a human and damage them.

00:27:54 Speaker_06
And then a cobot is defined as necessarily working alongside humans, is that right?

00:28:00 Speaker_00
That's the idea, is that they can work alongside. They're not only aware physically of the human's presence, but they can productively interact with the human.

00:28:10 Speaker_06
In Japanese nursing homes, there are a variety of cobots designed to accomplish a variety of tasks. One type, for instance, is designed to monitor patients.

00:28:20 Speaker_00
So these can help both the caregivers and the people themselves to avoid falls, particularly if they roll out of bed at night or they get up and then trip on something.

00:28:33 Speaker_06
There are also cobots to help the nursing home staff move their patients.

00:28:38 Speaker_00
They have these big robots with big arms that help to pick people up. Others that actually are worn by the caregiver really need to strap onto the body when they're trying to move someone from the bed to a chair or back again.

00:28:54 Speaker_00
so they're not shaped like a human but to fit onto a human body. And these robots are trying to address the issue of back pain that caregivers often experience and leads to turnover and therefore poor outcomes for long-term care.

00:29:11 Speaker_00
Other robots help with other activities of the individual, such as being able to move directly themselves and to function independently, to help with taking a bath or walking around.

00:29:23 Speaker_06
So unlike the typical robot, a cobot is designed to complement human labor rather than replace it. That, at least, is the theory. Karen Eggleston, being an economist, wanted to test this theory.

00:29:36 Speaker_06
She and two colleagues, Yang Li and Toshiaki Iizuka, set out to gather and analyze data from 860 nursing homes in Japan.

00:29:47 Speaker_00
We focused on nursing homes partly because that's where this population aging question is really most manifest, and also because the huge debate about technologies is, yes, we know that surgeons' jobs will be affected by technology, but what we're really worried about are the lower skilled workers that might be completely replaced

00:30:09 Speaker_00
A lot of the research in manufacturing has shown that to be certainly a worry that has foundation.

00:30:17 Speaker_06
Eggleston and her co-authors were able to collect a variety of data for this study. First, wage and employment data from these nursing homes.

00:30:27 Speaker_06
This included whether a given employee was a so-called regular worker, which was usually a full-time position and paid fairly well, or a lower paid non-regular, meaning a part-time or flex worker.

00:30:40 Speaker_06
The researchers also measured the degree of cobot adoption in a given nursing home, but they needed to introduce a random variable to prove causality between the adoption of robots and the effects on staffing.

00:30:54 Speaker_06
Luckily for them, different prefectures across Japan subsidized cobots at different rates, some as high as 50%. This variation in subsidies gave the researchers a nice natural experiment.

00:31:07 Speaker_00
And we use the variation in those subsidies to help figure out which way the causality arrow goes.

00:31:15 Speaker_06
Eggleston and her colleagues have written a working paper called Robots and Labor in the Service Sector, Evidence from Nursing Homes. Would they find?

00:31:24 Speaker_00
What we find is that robot adoption is strongly correlated with having a much larger nursing home. And it appears to be a causal impact that adopting robots is associated with more care workers rather than fewer.

00:31:43 Speaker_00
But these additional care workers are the non-regular type on more flexible contracts.

00:31:49 Speaker_06
So that sounds as if it could mean that robots are bad for the upper end of that employment spectrum, considering that this is relatively low paid work anyway. It sounds like it would promote more human workers, but at a lower wage.

00:32:08 Speaker_06
Is that about right?

00:32:09 Speaker_00
Well, yes, it is possible, although we also know that the most commonly adopted robot is the monitoring robots we were talking about, and they are helping to reduce the long night shifts that nurses and care workers have to do.

00:32:27 Speaker_00
So we think that part of the effect is that the workers have a reduced burden of care.

00:32:33 Speaker_00
And yes, we do find a lower wage of a modest amount for the regular nurses, but if the case is that they have shorter work days, then it's not clear that that's actually a welfare loss.

00:32:47 Speaker_06
When I first read your paper, the sort of sunny headline that I wrote in my head was, we thought robots were the enemy of workers, and now it looks like they are best friends. That's a little bit too sunny, isn't it?

00:33:03 Speaker_00
Yeah, I think it is a little sunny, although it is a little bit surprising. And depending on how they're adapted, this automation, yes, it will replace some of the tasks that care workers do.

00:33:15 Speaker_00
But the ones that do end up staying in this profession, maybe they will have more support, less back pain, have the education to work alongside robots, and may find that a more enjoyable experience as well as better for the people they serve.

00:33:32 Speaker_00
A lot of the workforce feels burned out, not necessarily because they don't like doing what they do, but they don't like doing all that paperwork and all that other stuff. And they want to interact one-on-one with the people they care for.

00:33:47 Speaker_00
And co-bots, if they work properly, will enable that. Humans have these qualities of being very dexterous and being able to care directly to the patient and communicate and have compassion with them.

00:34:00 Speaker_06
And what's next for our relationship with the robots? That's coming up. I'm Stephen Dubner. You are listening to Freakonomics Radio. We'll be right back. You could argue that healthcare is the ideal scenario for the blending of human and robot labor.

00:34:32 Speaker_06
There are countless tasks and procedures where technology can plainly be helpful. But the human appetite for compassion also seems boundless. And for now, at least, humans are better at compassion.

00:34:46 Speaker_06
You could see cobots helping mightily, not just in hospitals and nursing homes, but in at-home care as well. A recent study from the Journal of the American Medical Association found that some 5 million older adults in the U.S.

00:34:59 Speaker_06
need help with bathing or using the bathroom. In Japan and elsewhere in Asia, and also in Europe, it is increasingly possible for a robot to assist with such tasks. That's not just because robots have been subsidized, they've also been deregulated.

00:35:18 Speaker_03
In the United States, we don't have access to a lot of these types of robots.

00:35:25 Speaker_06
That, again, is James Rosenman, CEO of the Andresson-Hudson Nursing Home. And why don't we have more access to these types of robots?

00:35:33 Speaker_03
It's a good question. When I look at a lot of these things or I'll find something, my eyes will get huge. You know, I'll do some research and not late at night.

00:35:40 Speaker_03
And then I find out, you know, it's only available in, you know, Japan or in the EU, actually in many, many markets. And the glimmer goes out of my eye because I know that we can't legally import that to the United States.

00:35:54 Speaker_03
The other day, I was just looking at, for example, to reduce the incidence of individuals developing pressure ulcers for people who are more bed bound.

00:36:03 Speaker_03
The current thinking is that you rotate people so that you can increase blood flow and reduce pressure on one given part of the body.

00:36:10 Speaker_03
So the idea that I was thinking about was maybe there are beds beyond just the mattresses that are pressure relieving, a robotic bed that literally move people. Right now, that's being done by humans. It's not available in the United States.

00:36:23 Speaker_06
So is it regulation that's preventing this right now? And if so, what kind of regulation is it? Technical regulation? Is it medical regulation, et cetera?

00:36:33 Speaker_03
Sort of hard to understand. I think that some of it is like a pie chart, if you will, of different reasons.

00:36:39 Speaker_03
I don't think there's one sort of smoking gun or people in the back room that are saying, all right, let's not get these things rolled out because it goes against our interests. It's just very fragmented.

00:36:51 Speaker_03
And so you have these different regulatory authorities, you have who's going to pay for it, how's it going to be used? You know, you can have it approved, but then you have how is it used in practical terms on site?

00:37:02 Speaker_03
I think that first and foremost, there need to be more pilots, you know, studies, models. There are pilots going on every day. Medicare funds those or they're funded by other agencies of the federal government.

00:37:16 Speaker_03
But there haven't been a lot of pilots that include robotics in our settings.

00:37:25 Speaker_06
So if you're thinking big picture about the future of work, one of the most compelling questions is the degree to which robotics will complement human labor versus replace it.

00:37:35 Speaker_04
One example that I've encountered is in a construction company.

00:37:41 Speaker_06
That is Yong Lee, one of Karen Eggleston's co-authors on the Japanese nursing home paper. He is an economist at Notre Dame.

00:37:49 Speaker_04
They initially created robots so they could replace workers, for instance, digging out certain parts of the land to lay the foundation. But they needed people who had years of experience, more than 10 or 20 years of experience.

00:38:04 Speaker_04
And it was just difficult to find that labor anymore.

00:38:07 Speaker_04
So what they decided to do is to create a robot where an individual with maybe only one year of experience could operate a machine that could perform the tasks that a skilled laborer with 20 years of experience could perform.

00:38:23 Speaker_04
So in this sense, they were designing a robot not to replace the skilled individual, but actually to augment an individual with less skill.

00:38:32 Speaker_06
In another study, Li looked at robots in the manufacturing sector, a study that covered 11 years. There, too, he found that robots at first were replacing workers. But later, as the technology matured, the robots became more collaborative.

00:38:48 Speaker_04
Robots 10 years ago that did welding and robots 10 years later will likely be different.

00:38:54 Speaker_06
So how do economists see this relationship unfolding between human workers and smart machines? How can that relationship be optimized? Karen Eggleston again.

00:39:06 Speaker_00
It won't surprise you to know as an educator and a researcher that I believe that investment in human capital is really, really important.

00:39:14 Speaker_00
And we need to be investing in young people and everyone else to enable them to be lifelong learners and to be adaptable.

00:39:21 Speaker_00
If we give support to people to be adaptable to changes in the labor markets, there really is a possibility that it will work on behalf of a very broad spectrum of society.

00:39:35 Speaker_06
In other words, every piece of technology in a way could become a cobot if we humans are skilled enough to collaborate with them.

00:39:44 Speaker_00
Yes. Yes. I think there really is a potential for technology to make our lives better. But I'm not of that opinion that it's going to automatically happen.

00:39:56 Speaker_00
I think it comes down to the choices that we make, particularly in policy on behalf of the most vulnerable in our society.

00:40:07 Speaker_05
we have time to adapt our institutions, our educational systems and the way we work.

00:40:12 Speaker_06
And that, again, is the MIT economist David Autor. The third and final question from his task force on the future of work was this.

00:40:21 Speaker_06
How can our civic institutions ensure that the gains from these emerging innovations contribute to equality of opportunity, social inclusion and shared prosperity?

00:40:36 Speaker_06
The problem strikes me as a layperson is maybe a gigantic coordination problem because we look to our governments to coordinate the way jobs and the economy will flow and take care of everybody.

00:40:48 Speaker_06
But in fact, governments aren't really very equipped to do that, whereas firms have a different set of incentives. So can you just describe how that will unfold in a way that leaves people not either out of work or grotesquely underpaid or

00:41:03 Speaker_06
Working in an economy where the gap between the high and low just gets bigger and bigger.

00:41:08 Speaker_05
So first, I want to argue that the government actually can do a lot and that we in America tend to deride our government and assume it can't be effective. But in many ways, history demonstrates just the opposite.

00:41:20 Speaker_05
And you don't have to look very far back in history.

00:41:21 Speaker_05
Just look back when the government passed the CARES Act and overnight essentially took 10 percent of GDP and said, hey, we're going to send this to households, to businesses and to the unemployed to keep this pandemic from turning into an economic catastrophe.

00:41:36 Speaker_05
And it was highly effective. And the government similarly has been effective in shaping technology over many generations. Right. The U.S. had a leading patent system. It's in our constitution. But the U.S.

00:41:48 Speaker_05
has also invested in R&D through our universities in health development and so on. So it actually plays a big role in even setting the rules of the road.

00:41:56 Speaker_06
To that end, the MIT Work of the Future Task Force had some concrete recommendations.

00:42:02 Speaker_06
They include heavy investment in education and job training, both in schools and through private firms, improving the quality of existing jobs via policies like a higher minimum wage and labor organizing protections, and reforming the tax incentives that privilege capital investments over labor.

00:42:22 Speaker_06
If you think all that sounds a lot like the recommendations we've been hearing about for a few decades now, I agree. So you might be forgiven for thinking these adjustments won't happen, at least not in time to deal with the robotic revolution.

00:42:37 Speaker_06
But David Autor isn't panicking. The revolution may be inevitable, but it's not instantaneous.

00:42:44 Speaker_05
The technology is spectacular and it's going to have momentous impacts, but they're unfolding gradually. They often take years to decades.

00:42:52 Speaker_05
You know, think about the gap between the hype about driverless cars and the number that you don't yet see on the roads. And many of the things are still a ways off. I mean, these things will happen, but they take time.

00:43:03 Speaker_06
Let me ask you to cast your mind forward, let's say, between 10 and 20 years. It's pretty easy to foresee that a lot of low-skill jobs will be replaced or very much amended. But let's say even a lot of medium and high-skill ones.

00:43:17 Speaker_06
Let's say economists and writers and podcasters and forensic insurance agents. Let's say that Many, many, many of those jobs get essentially wiped out by some combination of robots and cobots and artificial intelligence and machine learning.

00:43:33 Speaker_05
Wouldn't that mostly be a wonderful thing? So it's wonderful in one sense. It means we are now much richer. We can do everything we were doing and yet not use any labor to do it. So we have incredible leisure opportunities.

00:43:46 Speaker_05
Therefore, we have incredible productivity, incredible wealth. The problem that creates is twofold. One is a huge distributional challenge.

00:43:54 Speaker_05
Our main method of income distribution in this country and in most industrialized economies is ownership of labor, right?

00:44:00 Speaker_05
You have some labor, you invest in your skills, and then you sell those skills and labor to the market for 30, 35 years, you save up some money, you retire. If labor is no longer scarce, what claim do you have on the assets of that society?

00:44:12 Speaker_05
So I worry about that problem, the problem of abundance, actually, the problem of lack of labor scarcity. The other is, I do think work, you know, one can oversell it, but work should be venerated to some degree.

00:44:26 Speaker_05
It gives people identity, it gives them structure, it gives them purpose. I mean, this is what the Calvinists have always told us, but how do we know this is true? Well, we know when people lose work, they are miserable.

00:44:37 Speaker_05
So if we're going to have less work, I'd like to see everybody have a little bit less rather than many people not working at all.

00:44:48 Speaker_06
David Autor is a lot smarter than me, so I am inclined to believe him when he says that people are miserable when they lose work.

00:44:56 Speaker_06
On the other hand, could it be that people who've lost work in the past have been miserable because our civilization is built around work as the primary means to satisfy your basic needs?

00:45:11 Speaker_06
If the assets of society, as Otter puts it, are so bountiful at some point in the future, shouldn't there be a way to share in those assets while our robot and cobot friends do most of the work? Some people are lucky enough to love their work.

00:45:28 Speaker_06
I'll be honest, that describes me most days at least, and I'm guessing it describes David Otter too. But many, many, many people have jobs they do not love and which keep them from what they do love.

00:45:41 Speaker_06
Economists are pretty good at measuring utility, but they're not very good yet at measuring things like love. Maybe if the robots and cobots are really smart, they can teach the economists how to do that.

00:45:59 Speaker_06
Since we originally published this episode, Yang Li and Karen Eggleston have come out with a new paper about robots and labor in nursing homes in Japan.

00:46:08 Speaker_06
They found that introducing cobots did not reduce the number of human workers, but it did reduce employee turnover, which is a good thing. And it also improved patient outcomes. Also a good thing. Here's what Eggleston told us by email.

00:46:25 Speaker_06
These patterns suggest that robots have the potential to enhance quality of care while augmenting care workers so they can focus more on human touch care and less on the back pain inducing physical tasks that contribute to making care work such a high turnover job.

00:46:44 Speaker_06
We also reached out to James Rosenman of the Andrus-on-Hudson Nursing Home to ask how his cobots are doing.

00:46:50 Speaker_06
He told us that since 2021, the facility has expanded its telehealth robot program and added some new devices, including a semi-robotic system that helps nursing assistants rotate bed-bound patients and a robotic exoskeleton that can help stroke patients stand up and walk.

00:47:10 Speaker_06
I hope you enjoyed this bonus episode. We will be back very soon with a brand new episode of Freakonomics Radio. Until then, take care of yourself. And if you can, someone else too. Although maybe a co-bot is taking care of them.

00:47:25 Speaker_06
Freakonomics Radio is produced by Stitcher and Renbud Radio. You can find our entire archive on any podcast app. Also at Freakonomics.com, where we publish transcripts and show notes.

00:47:36 Speaker_06
This episode was produced by Zach Lipinski and updated by Augusta Chapman.

00:47:41 Speaker_06
Our staff also includes Alina Kullman, Dalvin Abouagy, Eleanor Osborne, Ellen Frankman, Elsa Hernandez, Gabriel Roth, Greg Rippin, Jasmine Klinger, Jason Gambrel, Jeremy Johnston, John Schnarrs, Lyric Bowditch, Morgan Levy, Neil Carruth, Rebecca Lee Douglas, Sarah Lilly, and Tao Jacobs.

00:47:58 Speaker_06
Our theme song is Mr. Fortune by The Hitchhikers. Our composer is Luis Guerra. As always, thank you for listening.

00:48:10 Speaker_03
What do you call these two robots? Do they have names? Right now, the Stephen, you know, Dubnit robot. And that's what we're going to after this.

00:48:23 Speaker_02
The Freakonomics Radio Network. The hidden side of everything. Stitcher.