Latest / The Capitalism and Freedom in the Twenty-First Century Podcast / Steven Davis (Hoover Institution) on Job Flows, Policy Uncertainty and Work From Home

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Steven Davis (Hoover Institution) on Job Flows, Policy Uncertainty and Work From Home · The Capitalism and Freedom in the Twenty-First Century Podcast

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>> Steven J Davis: This is the most profound shift in how we live and work,
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certainly in such a compressed timeframe that's happened in decades.
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It's hard for me to think of another example of such a big shift in
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how we live and work that happened so abruptly outside of wartime.
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And there have been bigger shifts.
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The industrial revolution was a bigger deal, and
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then the transition from factories to offices was a bigger deal.
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But these things played out over centuries or decades.
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And this big shift in how we live and work with respect to remote work
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played out initially in response to the pandemic in a few weeks, and
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then over the course of about took about three years to settle down to where
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we are now and have been for some time, which is roughly 28% to 30%.
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As we measured in the survey of working arrangements and attitudes, 28% to
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30% of full paid days are worked fully at home or some other remote location.
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It could be a coffee shop, a library, even a friend's house or something, but
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somewhere not at the employer's work site and not at a client's work site.
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[MUSIC]
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>> Jon Hartley: This is the Capitalism and Freedom in the 21st Century podcast,
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an official podcast of the Hoover Institution Economic Policy Working Group,
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where we talk about economics, markets, and public policy.
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I'm Jon Hartley, your host.
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Today, my guest is Steve Davis, who is the Thomas W and
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Susan B Ford Senior Fellow and Director of Research at the Hoover Institution and
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senior fellow at the Stanford Institute for Economic Policy Research, SIEPR.
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He was previously on the faculty of the University of Chicago Booth School of
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Business for more than 35 years, including serving as deputy dean of the faculty.
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Steve is also a research associate at the National Bureau of Economic Research,
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a visiting scholar at the Federal Reserve bank of Atlanta,
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a senior advisor to the Brookings Papers on economic activity,
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advisor to the Monetary Authority of Singapore.
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He's been elected a fellow of the Society of Labor Economists.
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He's an IZA research fellow and
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a senior academic fellow of the Asian Bureau of Finance and Economic Research.
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He hosts Economics Applied,
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a video podcast series sponsored by the Hoover Institution.
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Steve is also the co-creator of the Economic Policy Uncertainty Indices,
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the Survey of Business Uncertainty, the US Survey of Working Arrangements and
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Attitudes.
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Attracts Work From Home, The Global Survey of Working Arrangements,
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and the Work From Home Map project, as well as the Stock Market Jumps project.
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He also co founded and co organized the Asian Monetary Policy Forum,
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held annually in Singapore.
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Thanks so much for joining us today, Steve.
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>> Steven J Davis: Well, thanks for having me, Jon.
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It's a pleasure to be here.
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>> Jon Hartley: I want to start really on your early life.
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When you grew up, how did you first get interested in economics?
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Were there any particularly formative moments?
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>> Steven J Davis: Well, as I recall,
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I took an economics course as a senior in high school.
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I don't remember anything about it except that I found it interesting.
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So when they asked me to write down a major when I went to college,
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I picked economics, started out there.
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And I dabbled a bit in political science, sociology, law,
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international relations, sometimes on my own reading, sometimes in courses.
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Somewhere along the line, I don't remember exactly when the idea crystallized in my
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head that I really wanted to understand human societies and the human condition.
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And midway through my collegiate career, I think I came to
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the conclusion that economics had a better set of concepts and
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tools for doing that than other social sciences.
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And so I decided I would go to grad school in economics so
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I could learn how to think about these matters.
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And that was really the level at which I thought about it at the time.
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>> Jon Hartley: Were there any particular recessions growing up or
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anything like that?
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>> Steven J Davis: Well, we were in the middle of the 70s inflation,
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the oil price shocks.
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So those were kind of formative external experiences,
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I would say, early in my development as an aware human being,
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say as a senior in high school and early years of college.
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So those were very much in the background.
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And so I had an early history in macroeconomics and unemployment,
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probably because I was living during times when inflation was high and
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unemployment was high in the mid 70s.
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>> Jon Hartley: I guess a very formal time for a lot of economists,
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certainly the rational expectations revolution coming out of that time.
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A lot of macroeconomic models coming out of that time.
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>> Steven J Davis: A lot of macro economists around my cohort as well
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because of the turbulent macroeconomic times in the US and elsewhere.
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>> Jon Hartley: Well, great.
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I mean, how did you settle on sort of macro labor
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unemployment dynamics as an area of interest?
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You graduated from Brown with your PhD and
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you spent 35 years on the faculty at University of Chicago.
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How did you stumble across labor, job flows and later,
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obviously, economic policy uncertainty and work from home?
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How did you stumble across labor jobs, firms?
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How did you get interested in that?
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>> Steven J Davis: I was interested in unemployment pretty early in my graduate
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school career, maybe even before I got there, I don't recall, but
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I definitely had an early interest in unemployment.
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My dissertation was inspired in part by David Lillian's work on sectoral shifts.
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So that was part of my thinking.
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I read pretty much on my own a lot of the early search theory work by Diamond and
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Mortensen and Pissarides and their various co authors.
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And I found it super interesting.
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It definitely influenced my thinking.
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There was no labor economics course at Brown until my fifth year,
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when Robert Moffatt arrived.
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And by then I was seriously working on my thesis and getting ready for
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the job market.
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So I never took a labor economics course in grad school, oddly.
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But as I said, on the macro labor side, I had done a lot of reading on my own,
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and my dissertation was motivated by David Lillian's work.
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So that's how it started.
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And then sometime between I got my job at Chicago and
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I arrived at Chicago, it sort of dawned on me, partly based on.
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I'd read some work by Darby, Haltiwanger, and Plant, and
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I'd started working on my own with what we now call the CPS, gross flows data,
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gross flows across different states of the labor market.
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Unemployment, not in the labor force, in employment.
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And I said, wow, these flows are huge.
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This kind of net sectoral shifts, which was net movements of employment across
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industrial sectors that was the focus of David Lillian's work,
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is not really where it's at.
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Especially if you wanted to understand unemployment as a frictional
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phenomenon in the mold of the Mortensen,
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Pissarides type models that came to play such an important role in the profession.
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So it's the combination of the very early empirical work.
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And I was that time somewhat steeped in the.
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In the early theory of job search.
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And if you think back to these Mortensen and Pissarides style models,
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they typically feature some kind of matching function where the inputs
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are job seekers and open job positions in the form of vacancies.
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And at least in the early versions of those models, the job seekers were
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typically people who had lost their job via layoff or sometimes via quit.
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So you start to ask yourself, well, what are the central inputs to
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the matching function, sort of a production function for
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producing matches or new employment relationships?
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And it wasn't the intersectoral shifts that David Laidler was focused on.
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It was something closer to what was happening at the level of individual
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employers.
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So that led me down the path of thinking, and
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I soon started talking with John Halterwinger about this.
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Well, if we really wanted to be serious about this in terms of the empirical
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foundations or underpinnings of this frictional view of unemployment,
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we should go out and try to actually measure the loss of jobs and
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the flows of workers from other labor market states into the unemployment pool,
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into the pool of actively seeking work.
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And so that's kind of how we started early on and how I thought about it.
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>> Jon Hartley: That's fascinating.
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And I'm curious, did your time at Chicago shape your journey in any way?
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I feel like you're spending 35 years in such a storied department.
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It certainly, I'm sure, leaves an impression on anyone.
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And also you leave an impression on the department,
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of course, and the business school- >> Steven J Davis: At least the first
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one's definitely true.
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[LAUGH] I'll let others speak to the second one.
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Look, I came into the University of Chicago
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what I think of as one of its golden ages.
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So just first, it's interesting,
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the way that I learn changed when I went to Chicago.
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Brown was a small economics department and maybe two or
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three seminars a week and maybe one in macro.
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I did an awful lot of learning at Brown by just reading and thinking and
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arguing with my fellow grad students as well.
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But that was usually in the context of homework problems.
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But I really did spend a lot of time just reading on my own and
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going through articles, trying to understand them.
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And so when I got to Chicago, there's this,
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I don't know how many, 15, 20 seminars a week.
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And this tradition of very vigorous oral [LAUGH] discussion
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argumentation was still quite alive, at least some of the seminars in Chicago.
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And I went to a lot of workshops, and
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I often read the paper in advance and go to the workshop.
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I went to three or four a week.
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And I didn't just sit there like bump on the log.
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I was intensely engaged, at least in a listening capacity and
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sometimes in a speaking capacity.
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And so I shifted from learning about reading journal articles,
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which I continued to do, but all of a sudden, I'm learning tons orally
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by talking to people in seminars and out of seminars over coffee.
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So the way that I learn shifted, I remember that.
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And then the people who really had a profound impact on
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the intellectual environment for me in those days.
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So my favorite workshops were the applications workshop and the labor
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workshop, which was essentially the same cast of characters in both workshops.
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So we had Gary Becker, Sherwin Rosen, Eddie Lazear,
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Kevin Murphy, Robert Topel, and many others.
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I think Bob was away, maybe didn't come till my second year there.
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He was at UCLA.
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So if you just think about the people I listed, there were five or
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six others who were, broadly speaking, in the labor economic space.
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It was just a tremendous opportunity to build my human capital in labor
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economics as well as macro.
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I mean, I went to the macro money workshop as well.
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I just found them,
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they were a little less no holds barred in the style of discussion.
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It's a style of discussion which now is kinda gone out.
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It's no longer really accepted in the economics profession.
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It was very vigorous.
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People interrupted each other a lot.
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Especially in the applications and labor workshops,
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the speaker often spoke less than half the time.
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And that was not necessarily a bad thing, because Gary may have spoken
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more than anybody else, and that was probably the right allocation.
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[LAUGH] And so I learned a tremendous amount in these workshops.
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And I think back, those two workshops in particular, and the people
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I mentioned really made for profoundly rewarding intellectual experience.
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And so I kinda grew up in that culture and learned a lot about labor and
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about other things, but especially about labor.
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So my education the first five years in Chicago as a labor economist,
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that's where it happened, even though I never took a course in the subject.
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>> Jon Hartley: That's terrific.
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I know many people have gone through that labor group at
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Chicago under Gary Becker, either as faculty or students.
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I think of certainly folks like Glaeser, Steve Levitt,
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folks like that have gone through there.
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I'm curious, just getting back to firm dynamics and
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job vacancies, a lot of your work is hugely seminal in this space.
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I'm curious, what, in your mind, have we learned about job creation and
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destruction in the recent decades as we've gotten better data on labor and firms?
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We often hear things like small businesses or
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small firms that are responsible for the majority of job growth in the US economy.
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Small businesses are good.
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That's kind of an often repeated thing.
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I mean, to what extent is this true in your mind?
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And I know some of your research says maybe otherwise,
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maybe somewhat younger firms.
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Do we see smaller firms being the most innovative or
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small businesses being the most innovative?
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Is that necessarily true either?
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>> Steven J Davis: No, so let me say a few things first.
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One of the big things we learned, which is now so widely accepted,
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is kind of just everybody's internalized it.
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But I think my early work with John Halterwinger and
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also with Scott Hsu was instrumental in establishing this fact in an unambiguous,
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pervasive way, is that every market economy in all times and almost every
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sector has lots of job creation and destruction going on all the time.
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Okay, and so that was, at least in early receptions of our work in the late 80s and
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early 90s, viewed as somewhat controversial.
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And then when accepted for the US, okay, well, the US is different.
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That's how the US operates.
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Everybody knows it's kind of a highly flexible labor market.
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But the story we've learned since then is, as I said,
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outside of centrally-planned economies, which really do in the state-owned sector,
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often have very little gross job creation and destruction.
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The norm of a market economy is that every period,
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there's jobs that disappear and there are many other new jobs that open up.
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That's job creation in our lexicon.
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And so what you should think of the vision you should have of the economy,
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the labor market, at least a market economy is.
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Yeah, there's a modest net change of employment from one period to the next.
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So maybe in the United States we might have employment growth by 1%,
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so that might be 1.5, 2 million new jobs in a year.
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But underlying the extra 2 million jobs that we gained on net, there might be 17
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million newly created employment positions that weren't there say,
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a year ago and 15 million of the positions that were there a year ago are now gone.
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So that's a very basic fact, but it's important
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to internalize in your thinking for many reasons.
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First, back to the original motivation of why I went down this research path.
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It helps you understand how frictional unemployment can be
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a significant phenomenon,
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even in a well functioning market economy because lots of jobs are disappearing and
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those people either have to leave the labor force or find a new job.
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They often have an intervening spell of unemployment while they're seeking
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a new job.
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There's a lot of resource reallocation happening in the economy all
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the time through the labor market, directly from these statistics.
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But often, capital and other intangible forms of factor
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inputs are also being reallocated at the same time.
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And there's a strand of literature which I played a modest role in,
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bu John Haltiwanger and others played a much bigger role.
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That goes off and looks at the connection between that resource reallocation
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process, which is ongoing in the economy all the time, and productivity growth.
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And I think the central message from that literature as I take it,
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there are many nuances.
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But the central message is part of the way that the economy revitalizes itself and
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pushes productivity advances is by continuously reallocating labor and
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other inputs from less productive,
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less valued uses to more productive, more valued uses.
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So that's really two, I kind of give you two big messages so far.
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Now, the small business piece that you asked me about.
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So let me back up.
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There's so many myths and half truths around the role of small
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businesses in the economy, it's kind of hard to cut through them all.
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And at some point in the mid-90s, I just got irked and
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sick of hearing all these ridiculous claims and
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misleading claims about the role of small business in the economy.
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So I thought I'd just write a paper, which seemed to me be stating a bunch of obvious
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points about the misleading nature of many of these empirical characterizations.
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Don't get me wrong, I don't dislike small businesses.
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I think they play a hugely valuable role in the economy in many respects,
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but there were so many outlandish claims about their role in the economy.
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Sometimes, often coming from the SBA, the small business advocacy or agency,
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whatever it's called which particularly irked me because they're a taxpayer funded
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organization, part of the government- >> Jon Hartley: Small business
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administration.
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>> Steven J Davis: Small business administration, right?
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So I decided, look, I'm gonna write a paper and just set the record straight.
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So I'm just tired of these things and then everybody will understand what's wrong
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with these characterizations that we can go on.
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And this paper which to me see, I really wrote it for an undergraduate.
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I was kind of hesitant about writing.
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It was still early in my career and I was a little concerned about,
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aren't my colleagues going to think that I'm just writing these papers that state
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the obvious?
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That paper, for whatever reason got an enormous amount of attention and
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I led to a picture on the front page of the New York Times
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business section with me in some factory, so on.
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I think it was a more glorious thing to be on the New York Times in those days than
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it is now, but let's go back.
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Here's the substance, there are many of these
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misleading characterizations made about small businesses.
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Let me take up a few of them.
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So one thing to understand, there's a statistical sleight of hand that
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comes up all the time, and not just with respect to small businesses.
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The scope for this mischaracterization arises particularly because the gross
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job flows that we talked about before, the newly created positions and the lost
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old positions are so much larger than the net changes from period to period.
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So net employment in the United States, if it changes by 2
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million in a given year, and you might then go look at, well,
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how much did the net position of small businesses change over that one year?
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And you can suppose it just happened to be, it was 2 million as well, and
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there was no net change in the number of jobs at larger businesses,
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however you wanted to find small and large,
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then there's a sense in which it's true to say, it's correct to say that
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all of the new jobs were created by small businesses over this period.
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Now there's a sense in which that's correct, but
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there's also a sense in which it's extremely misleading.
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And so why is that?
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Well, because within, among the set of small firms, there are lots of jobs being
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created, maybe 10 million, and lots of jobs being destroyed, maybe 8 million.
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And among the larger businesses, there's lots of jobs being created.
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And I guess if I wanna make my numbers add up right say there's a 7
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million new jobs created and 5 million that are disappearing, okay?
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So okay, it's true that in some accounting sense, you can say the number of
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net new jobs created by small business is equal to the number of net new jobs.
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But you could also go find a set of large businesses because I just told you there's
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7 million new job positions created, large businesses, you can go off and carve off
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a set of those and say, you know, those five industries, large businesses, and
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those five industries accounted for 100% of all the net new jobs.
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Or you could do something which governors who run for
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presidential in presidential elections are very fond of doing.
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If they come from a state that's had a lot of net job growth, you can go say,
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you know, from 19 x to 19 x, 85% of all the new jobs created in the United States
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were in Texas or Florida or New York or California, whatever it happened to be.
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The point I'm trying to make is all of these statements, in some sense,
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in an accounting sense, can be true.
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They're just very misleading in terms of sometimes the way they're said,
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but certainly is the way they're heard by their intended audience.
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So we wrote in this paper, we wrote in mid-90s.
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We call this netting out reality.
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So if you give me the micro data and
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especially if you're looking at a period where the net change is small,
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I can go find 100 different groups defined by size, by age,
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by geography, by industry where I could make this kind of statement
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that 100% of the jobs were created or accounted for by this group.
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Okay, so when you explain it that way, you see why this statement is.
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It can be both factually correct, but essentially nearly meaningless and
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quite misleading in the way it's often received by the audience.
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That's one of the problems.
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But as I tried to make with my geography examples, this statistical sleight
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of hand or mischaracterization Extends well beyond the small business issue.
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>> Jon Hartley: Its fascinating, I mean, a huge contribution.
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When we normally think about jobs,
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a lot of macro people tend to follow the BLS employment situation report.
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And you get the monthly non-farm payroll numbers,
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jobs numbers, and you hear 200,000 net new jobs.
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But people forget that that's just net, and
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there's plenty more gross job flows moving around.
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And that's, I think, a huge contribution of, I think,
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perhaps culminates in your 1996 book, Job Creation and
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Destruction with John Haltiwanger and Scott Schuh.
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And also I guess, it really speaks to, I guess this Schumpeterian point
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about creative destruction and provides a lot of empirical backing for,
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I guess, that sort of underlying theoretical kind of idea.
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I want to just shift to- >> Steven J Davis: On that point,
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I sometimes think of our work as Schumpeter with data.
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>> Jon Hartley: That's a great way to frame it.
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Yeah, I think you couldn't have put it any better.
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And it's amazing too.
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I mean, Schumpeterian creative destruction gets cited a lot,
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I feel, and it's amazing that you were really the first
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researchers to really emphasize that point in the data.
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I'm curious what you think about just to shift gears here a little bit,
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I'm curious what you think about the concept of full employment.
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It's this old keynesian labor market concept that suggests that
25:42
there's slack in the economy when the factories are empty and
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the workers are on the sidelines.
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But I feel like today in 2024, we don't really have a good way of say,
25:56
measuring it, particularly given how dominant the service economy is now.
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The CBO full employment measure, in my mind, is kind of a way of cheating or
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in part updates in a somewhat backward looking way.
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We discover that growth doesn't necessarily get back to
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its old trend like it didn't after the Great Recession.
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The sort of CBO full employment scores or GDP potential gets updated in this
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backward looking way to account for the fact that there actually was hysteresis.
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But some researchers say it's important to look at vacancies on top of unemployment.
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The beverage curve, I think is historically a bit more a pretty reliable,
26:50
stable relationship.
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I mean, Covid shifted things out quite a bit.
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But historically, I think the relationship between unemployment and
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vacancies has been a lot more stable than say,
27:00
the Phillips curve relationship between unemployment and [CROSSTALK].
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>> Steven J Davis: It's a low bar [LAUGH].
27:05
>> Jon Hartley: A low bar, but I'm curious, have we learned anything in your
27:09
mind about what full employment is in recent decades,
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or in your mind, is it kind of an outdated concept now?
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>> Steven J Davis: Here's how I think about it.
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Both full employment and in some senses mirror image slack,
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they are useful concepts.
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They're also difficult to measure.
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And I think it's a mistake to rely on any single indicator of either
27:30
full employment or slack.
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And because in almost any time period or episode you can tell me about,
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I'll tell you what was wrong with what indicator in that episode and
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why it gave you a misleading answer based on how it
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behaved then compared to the historical pattern.
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So I think we ought to look at a whole bunch of indicators, and
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this is typically what I do.
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We want to look at the employment to population ratio, probably adjusted for
28:02
the age mix of the workforce, the unemployment rate itself,
28:05
which comes in many varieties, as you know, it's not just a headline number.
28:10
Vacancies, yes, but also the vacancy to unemployment rate.
28:14
How long does it take to fill vacancies?
28:18
I also look at matching efficiency that pops out when you plug your favorite
28:23
measures of job seekers and open job positions into a matching function.
28:29
I tend to prefer here, I'm of the same view as Bob Hall that I find the matching
28:34
function to be actually a more useful construct than the beverage curve itself,
28:39
although I've used both.
28:42
And you could go on in this vein, I don't think there's a single
28:47
measure of full employment or slack that you want to hang your hat on.
28:53
Now why are these useful?
28:56
Broadly speaking, we want to get some handle on how close we think
29:01
the economy is currently operating at close to, I left off, by the way,
29:05
capacity utilization measures.
29:08
We can also measure the utilization rate of the physical capital.
29:11
So there are others as well I should mention, that want to get some sense of
29:17
how close is the economy to operating at its full capacity in some sustainable way.
29:23
That's interesting all by itself, because if we, in the extreme cases,
29:28
when there's millions of workers, or tens of millions of workers who
29:33
are out of work, who don't seem to have any near term options to find work.
29:38
So we're not talking about a situation of just frictional unemployment,
29:43
then something is amiss in the economy.
29:45
So we want to know whether the economy is operating close to its capacity.
29:52
And then in many, many theories, Keynesian theories in particular,
29:57
and I put some weight on this idea, although not too much.
30:01
But some weight on the idea that if labor markets are extremely tight
30:06
in that there's not much slack, or that we're at full employment,
30:11
or even above various measures of full employment,
30:14
we might expect wage pressures to be pretty strong and
30:18
wage inflation, if not already, then in the near future to tick up.
30:23
So that's why I think these measures are useful, but they all have
30:28
their weaknesses, and hence it's most useful to look at several of them.
30:34
In recent years, in particular during COVID, but
30:38
in the wake of COVID and partly related to the shift to work from home and
30:44
what that did to the workforce, the usual relationships in the beverage curve and
30:51
vacancy durations, I think these measures differed quite a bit
30:56
in terms of what they were telling you about the tightness or
31:00
slack in the labor market at that time relative to what you would
31:06
have inferred from the same statistics before the pandemic.
31:11
>> Jon Hartley: That's passing, it's interesting to think, too,
31:15
concepts like Nairu was very much Milton Friedman concept,
31:20
but is Keynesian in many ways.
31:23
But it's interesting just to think over the past
31:28
few years how unemployment's fallen to such
31:33
a low level that essentially it's forced people
31:38
to revise their estimates of what Nauru is.
31:44
And so people thought it was 5%, then unemployment falls below 5%,
31:49
pretty meaningfully, closer to 4 or 3%.
31:52
And yet inflation didn't really start rising at that point.
32:00
At least in the pre sort of pandemic period.
32:04
I feel like the Fed has really moved away from thinking
32:09
about concepts like, say, U star or R star, and
32:14
are kind of more just looking at the actual prints themselves and
32:20
just trying to shift in the direction against the winds of inflation.
32:27
Or I guess conversely, from unemployment's skyrocketing to levels that
32:32
are just well above, really just the historical means.
32:35
Forget about what some sort of idea of U star or R star are.
32:41
In my mind, they've just sort of assumed now that U star is just
32:46
the long run historical mean.
32:49
>> Steven J Davis: Yeah, that's not a very satisfactory approach, in my view.
32:53
And even when moving beyond that, much of the literature about the evolutions of,
32:58
say, the natural rate of unemployment are really focused on just
33:02
the demographic mix of the work of the working age population.
33:06
I don't think that's very satisfactory.
33:08
And I can explain briefly a couple of reasons why that's so,
33:14
and this predates the pandemic.
33:17
One has to do with both back to our job, our earlier discussion of job creation and
33:21
destruction flows.
33:23
Those flows have trended downward.
33:24
The rate of gross job creation and destruction and the rate at which workers
33:30
lose jobs to vacancies has been trending downward for a long time.
33:34
And part of that has to do with the aging of the workforce.
33:38
Part of it has to do with, even conditional on the age of the workforce
33:43
and the age of existing employers,
33:45
there's been a reduction in the rate at which people get laid off.
33:50
Okay, so other things equal,
33:52
you would expect that to reduce the extent of frictional unemployment.
33:56
I've written a few papers in this space with others.
34:00
I think there's a pretty compelling case that there's been a long-term
34:05
downward drift in the natural rate of unemployment, partly for that reason,
34:11
even over and above any contribution you would get from demographic shifts alone.
34:16
So I think part of the low unemployment rates that we've had for
34:21
quite some time now, apart from the pandemic episode, but
34:25
in the years since the global financial crisis fully played out,
34:30
we've had pretty low unemployment rates.
34:33
And many people have often, I think, misinterpreted that as an indication
34:38
that labor markets were really tight and that inflationary pressures would be high.
34:44
One more point on this part of what's happened, and not the whole story at all,
34:52
is the way in which matches formed is changed over time.
34:57
There's less ex post experimentation.
35:00
That is, we're gonna hire some person and see how they work out.
35:03
And if they don't, work out, we're gonna fire them.
35:05
So there's less ex post experimentation, there's more rigorous ex ante selection.
35:12
I think that has happened both for regulatory reasons,
35:15
that the costs of getting rid of somebody who didn't work out have grown higher for
35:20
regulatory and legal liability reasons.
35:23
But also the technology for
35:24
evaluating people before you actually hire them has probably also increased.
35:30
We live in a much more data rich world.
35:33
You can acquire more information,
35:35
you can rely on algorithms to help you evaluate people if you'd like.
35:39
That has its own drawbacks.
35:41
But there's a whole set of reasons, and
35:42
I don't think I confidently can parse it out.
35:45
But there's a whole set of reasons why we've shifted from ex post
35:49
experimentation, which tends to generate a lot of frictional unemployment,
35:54
to more ex ante selection and longer term employment relationships.
35:58
That also feeds into some extent to the reduction in the extent of frictional
36:03
unemployment.
36:04
So these are just some of the reasons why a simple statistical approach to
36:09
characterizing the evolution of U star just as a function of,
36:13
of demographics, would lead you astray, in my view.
36:17
>> Jon Hartley: Absolutely, it's a fascinating concept and
36:21
fascinating debate.
36:23
I wanna shift toward economic policy uncertainty.
36:26
This is an area which you've been very active in the recent decade.
36:32
You, along with your co authors Nick Bloom and Scott Baker,
36:36
developed economic policy uncertainty indices based on various
36:41
uncertainty keywords in the news and the frequency at which they appear.
36:46
These measures have been hugely influential in both academia and
36:51
the private sector as well.
36:53
How much, in your mind, does economic policy uncertainty matter for
36:57
firms and growth?
36:59
>> Steven J Davis: So I'll give you my overall assessment is that in most times
37:02
and places, economic uncertainty and economic policy uncertainty
37:07
are modest factors in explaining fluctuations in firm level outcomes.
37:12
And certainly I should explain the aggregate economic fluctuations.
37:18
But these fluctuations in economic uncertainty are also highly skewed.
37:21
And there are episodes, and I think of the period during the global financial
37:27
crisis and in the years after the financial crisis as one example.
37:32
The COVID period is another example,
37:34
where there were extraordinarily high rates of both uncertainty and
37:39
policy related uncertainty that did significantly restrain investment
37:45
in hiring decisions and mattered quite a bit at the macro level.
37:49
So I wouldn't put them in the same category as many of the first moment
37:54
shocks that are often driving even the everyday fluctuations,
37:59
the more quarter to quarter fluctuations in income activity.
38:03
But I do think that there are, are episodes in which they really matter
38:08
a lot, and often episodes in which times are bad or
38:12
you're being hit by bad first moment shock, so to speak.
38:16
So these things aren't uncorrelated with other things happening in the economy.
38:20
So the extra uncertainty is often being layered onto
38:24
other stresses and strains that the economy is facing and
38:29
in effect, amplifying those negative effects.
38:34
>> Jon Hartley: And it's interesting, and
38:36
I wonder too if there's maybe an optimal amount of policy uncertainty.
38:40
Like obviously there's gonna be more policy
38:44
uncertainty in democratic regimes compared to, say,
38:50
autocratic regimes where things are probably pretty low uncertainty.
38:57
But I remember when talking about some of your research, which kind of came out of,
39:03
I think, heavily sought after, particularly during a lot of this
39:08
sort of deadlock congressional periods in the 2010s.
39:12
I remember working Goldman Sachs and
39:15
a colleague from China who says the problem with the US, they can never
39:20
get anything done amidst all this policy uncertainty and things like that.
39:26
I also think, well, having complete policy certainty might not be so
39:32
great if the policies aren't so friendly toward markets in general.
39:38
But I'm curious if you have any thoughts on that.
39:41
>> Steven J Davis: Yeah, a few thoughts.
39:42
First, there's always going to be some level of economic uncertainty and
39:46
even policy related uncertainty with us, so let's just put that on the table.
39:51
But much of the motivation, much of the reason, Nick and I, well, speak for
39:56
myself, much of my motivation to get into the business of trying Trying to
40:01
measure policy related uncertainty early on arose because there was a lot of,
40:05
in the United States, at least politically, manufactured
40:09
policy uncertainty that didn't seem to serve any productive purpose.
40:13
So if you remember, we had a lot of fiscal cliff-type episodes,
40:18
and tax code expiration episodes, and debt ceiling fiascos in
40:23
which these are all things where the policy related uncertainty
40:29
wasn't built into the system the way, say, an election cycle is.
40:34
It was a consequence of the way that policies were designed and
40:39
the negotiations between the different competing political
40:44
factions in the Congress.
40:46
So perhaps the clearest example of this were debt ceiling crises, where Democrats
40:52
and Republicans were essentially playing chicken cuz they were trying to get
40:57
what they wanted with respect to other aspects of the fiscal legislation, and
41:02
they weren't gonna come to an agreement until the other side blinked.
41:06
And what was at stake then?
41:09
What was at stake then was the capacity of the federal government to
41:13
make timely payments on its various obligations, including even possibly,
41:18
interest payments on Treasury securities.
41:21
And so there was a non-trivial probability of some type of
41:25
default event in US Treasury securities.
41:29
And given the central roles they play in the monetary and financial system in
41:33
the United States and globally, that's not something you want to mess around with.
41:38
I could go on and give you many other examples like this, but
41:42
in this period in particular, in the wake of the global financial crisis,
41:47
there were many such episodes involving fiscal policy.
41:53
The debt ceiling crisis.
41:54
We talked about health care policy.
41:57
Remember, we had at least two Supreme Court decisions where the.
42:02
The Affordable Care Act, Obamacare, hung in the balance.
42:05
That was partly as a consequence of the way the legislation had been designed.
42:10
So this was not inevitable forms of economic policies uncertainty.
42:15
This was built into the system consciously by policy design decisions and
42:21
political decisions.
42:23
So that's the second thing I want to get on the table.
42:26
Third thing, I'm gonna push back a little bit on your claim that at least in
42:31
democratic regimes, yes, there's policy related uncertainty in democratic regimes.
42:36
We've got a presidential election coming up.
42:40
It looks like a lot rides on that a lot road on other recent
42:44
presidential elections.
42:47
Certainly, there was a big fallout in financial markets in
42:51
the cross-section in Trump's surprising victory over Hillary Clinton.
42:57
So in much of my work in the policy related vein kind of
43:01
draws out the role of election related uncertainty
43:06
as one source of policy related uncertainty.
43:11
But you don't wanna give autocratic regimes a pass.
43:14
I mean, just think what Russia's been doing in Ukraine.
43:17
You want to talk about a generator of policy related economic uncertainty.
43:22
That's an enormous one, and
43:25
that may be the biggest one in much of Europe in recent years.
43:29
So autocratic regimes generate uncertainty politically.
43:32
Policy related uncertainty as well.
43:35
It's just not so closely tied to the election cycle.
43:38
>> Jon Hartley: That's a great point, yeah, it's interesting.
43:43
Even dictators can be very volatile and very unpredictable, and
43:47
that's an excellent point.
43:50
So I want to shift to your work on work from home.
43:55
You have a famous paper with Jose Barrero and
43:58
Nick Bloom titled Why Working From Home Will Stick in the US.
44:04
It does seem, according to the data that you track,
44:08
the percentage of full-time days work from home is stabilized around,
44:13
say 30% or so over the past, say year or two.
44:16
Now, that number,
44:18
which if I understand correctly, only applies to service sector jobs.
44:23
So we're talking about 30% of service sector jobs, roughly.
44:29
>> Steven J Davis: This number applies across the board for employees.
44:36
Now, actually,
44:37
I'm trying to remember whether we have the self employed group in that or not.
44:40
But most of the US economy is in the service sector now.
44:44
So it's also true that most goods producing jobs
44:48
don't lend themselves to remote work.
44:52
>> Jon Hartley: Right, exactly, so it stabilized, this is number workdays,
44:57
work from home, stabilized around 30%.
45:00
It's still up from, say around 6% or so where it was before
45:05
the pandemic, but it's also down considerably from where
45:10
it was at the height of the pandemic, understandably so.
45:15
When many people who are able to work from home are working from home cuz they
45:19
couldn't go to work.
45:21
I'm curious, what in your mind have been some of the largest impacts of work from
45:25
home, whether it's real estate or in the labor market?
45:29
And how do you see the future of work from home?
45:34
Do you see remote work becoming more common?
45:38
I know your co-author, Nick Bloom,
45:40
often talks about maybe like a j curve where there being sort of a rebound
45:44
in the share of work from home jobs or the number of days work from home.
45:49
Curious where you stand on that.
45:50
>> Steven J Davis: So I think this is the most profound shift in how we live and
45:55
work.
45:56
Certainly in such a compressed timeframe that's happened in decades.
46:01
It's hard for me to think of another example, such a big shift in
46:06
how we live and work that happened so abruptly outside of wartime.
46:12
And there have been bigger shifts.
46:13
The Industrial Revolution was a bigger deal than this.
46:18
The transition from factories to offices was a bigger deal, but
46:23
these things played out over centuries or decades.
46:27
And this big shift in how we live and work with respect to remote work
46:32
played out initially in response to the pandemic in a few weeks, and
46:37
then over the course of about took about three years to settle down to
46:42
where we are now and have been for some time, which is roughly 28% to 30%.
46:48
As we measured in the survey of working arrangements and attitudes, 28%
46:53
to 30% of full pay days are worked fully at home or some other remote location.
46:59
It could be a coffee shop, a library, even a friend's house or something, but
47:04
somewhere not at the employer's work site and not at a client's work site.
47:09
So that's just an enormous shift.
47:12
There are many aspects of it, some positive, many positive, some negative,
47:16
some of the positive ones.
47:18
The most obvious one, perhaps, is the time people save by not commuting.
47:22
The time and the money they save by not commuting.
47:25
That's a really big deal.
47:26
It's kind of a mechanical effect.
47:27
But it's not to be taken lightly because the average American
47:33
on the margin between working from home or working on site,
47:38
spends about 65 75 minutes in extra time on commuting.
47:44
Plus a little extra time on grooming is a little extra time grooming.
47:47
When you go into the work site, maybe you put on better clothes,
47:52
make sure you shave, that kind of thing.
47:54
So that's a lot of time.
47:57
It means if you work from home three days a week,
47:59
you're saving more than 3 hours out of your day.
48:02
So that's a huge benefit.
48:04
There's also just the flexibility in time use over the day,
48:08
which appears to be especially valuable to people who have young kids.
48:12
So maybe you take 15 or
48:14
20 minutes out of your workday when your kids come home from school, or
48:18
if you need to take your kid to the doctor or the dentist one morning, you do that.
48:23
And that's pretty, that's much easier to do if you can go from home rather
48:28
than have to connect it to your commute, that's 30, 45 minutes away.
48:34
So people value this flexibility and time use over the day.
48:38
Many of them also value just the personal autonomy that comes.
48:41
I've before you, before I joined this podcast, I had a little soft
48:46
jazz going on in the background while I was trying to write a memo.
48:50
I don't usually have music on when I'm writing my papers, but
48:54
if I'm writing some administrative memo, I wanna hear something soothing and calming.
48:59
I'm defining my background, I'm doing that here in my office, but
49:02
for people who work in a cubicle, that's not so easy.
49:07
Those are some of the benefits.
49:09
We can get into the productivity side if you want,
49:12
but that's a complex thicket of the productivity effects.
49:16
But it does have a whole range of effects on productivity.
49:19
It affects how companies organize their productive activity and
49:24
how managers operate.
49:26
So managers who have hybrid or
49:28
fully remote workers have had to learn new management skills and styles.
49:33
Organizations have to adapt their HR practices and compensation and
49:37
evaluation policies if they're going to have some workers who are remote.
49:43
So big changes in organizational practices management, it's had an effect,
49:48
in my view, on the structure of wages.
49:50
So wages for people in often highly compensated professionals,
49:55
but say in the finance sector, the business services sector,
50:00
they've seen rather slow compensation growth since 2021.
50:05
Even though these are the industries and occupations that tended to see rather
50:10
rapid compensation growth relative to most other sectors in the previous decades.
50:16
I think, and we have plenty of evidence to back this up,
50:20
some of that slow compensation growth is because people in these sectors have
50:24
decided to take some of their compensation in the form of the benefits that
50:29
come along with working remotely.
50:33
So could go on in this vein, we can drill in there more if you'd like.
50:39
But you also asked me, where do I think it's going?
50:41
So I think the main changes, the main rapid changes are behind us.
50:47
I base that partly because not really much has changed in the last 18 months.
50:52
Even though you hear all these news accounts about calling workers back
50:55
to the office.
50:57
And there continue to be, in some organizations, struggles between
51:02
management and staff as to exactly how much remote work there will be and
51:07
what the parameters are around that.
51:11
And there may be changes.
51:12
In some organizations, the overall data don't show much change,
51:16
both our data and other data sources.
51:19
In the past twelve to 18 months, we have,
51:22
through the survey of business uncertainty at the Atlanta Fed,
51:27
asked business executives forward looking questions.
51:32
And what we do is we ask senior business executives about the outlook at their own
51:37
firm, and we've asked them to project five years ahead.
51:41
So we say first we ask them, well,
51:44
what fraction of your workforce hours are currently remote?
51:49
And so on.
51:50
But then we ask them, well, projecting forward,
51:52
what do you think is gonna happen to the extent of your hybrid work,
51:56
your workforce, and your fully remote workforce?
51:59
And we break down those two.
52:01
And if you average across all of the employer executives that we surveyed,
52:05
and you weight them by the size of the employer, and
52:08
here it doesn't really matter whether you weight them or not.
52:11
What you find out is executives, when you ask them about their own firm,
52:17
they foresee over the next five years modest further increases in
52:22
the extent of work from home, both on the hybrid side and the fully remote side.
52:28
Now, I'm a little less confident in that projection for
52:32
the fully remote workers because I think the workers who are fully remote
52:38
are also the ones who are potentially vulnerable to domestic offshoring.
52:45
And it may be harder for business executives to factor into their
52:49
own thinking the changes that might happen in that regard.
52:54
But I take away from all this evidence and
52:58
other evidence that we could talk about is kind of in a new normal.
53:05
Best guess is probably over the next five to ten years,
53:09
modest further increases from where we are now in the extent of remote work.
53:16
And by that I don't mean fully remote work.
53:18
I mean, the fraction of workdays that are performed remotely.
53:23
>> Jon Hartley: That's fascinating.
53:24
I, too wonder to what degree, say,
53:27
remote jobs might be more susceptible to, say, generative AI.
53:33
There's speaking of offshore jobs or
53:37
remote jobs being more likely to be put offshore.
53:43
My understanding of generative AI jobs and tasks that are being somewhat more quickly
53:48
automated away, at least in terms of what sort of data and evidence we have so
53:54
far know jobs like those that say call centers that are obviously largely
53:59
offshore are more susceptible to displacement from generative AI.
54:04
I wonder if there's maybe some sort of intersection there.
54:07
>> Steven J Davis: I think so.
54:08
And call centers would be the first one that comes to my mind too.
54:12
There's already been a lot of offshoring of call centers,
54:17
as you know, especially to places like India and the Philippines,
54:22
where there's a large domestic population that speaks English and
54:27
is reasonably well-educated and is willing to work for
54:31
wages that are below American wages.
54:34
And so they're often handling maybe the more routine type
54:39
customer service queries or customer relationship type queries.
54:45
Those do seem amenable to AI, generative AI, more so
54:50
than most jobs, at least in the next few years.
54:55
And so I do think there is that the intersection exactly that you described,
55:01
those jobs that are currently fully remote in the United States
55:06
are relatively susceptible to displacement through generative AI,
55:11
but they're also closer to the margin of being offshored.
55:16
>> Jon Hartley: Well, it's fascinating and really a fascinating discussion and
55:19
real honor to have you on, Steven, here about your amazing career and ideas.
55:23
Really want to thank you so much for joining us today.
55:26
>> Steven J Davis: Thanks for having me, John.
55:27
And good luck as you continue your podcast.
55:30
>> Jon Hartley: This is the Capitalism and Freedom, the 21st Century Podcast,
55:33
an official podcast of the Hoover Economic Policy Working Group,
55:36
where we talk about economics, markets and public policy.
55:39
I'm Jon Hartley, your host.
55:40
Thanks so much for joining us.
55:43
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