Steven Davis (Hoover Institution) on Job Flows, Policy Uncertainty and Work From Home

Jon Hartley and Steven Davis discuss Steven’s research career and seminal work on job flows, including the legacy of his classic book Job Creation and Destruction, co-authored with John Haltiwanger and Scott Schuh. They also discuss how we should think about full employment, how significant economic policy uncertainty is, and how important the shift to work from home has been and may continue to be in the future.

Recorded on August 27, 2024.

ABOUT THE SPEAKERS:

Steven Davis is the Thomas W. and Susan B. Ford Senior Fellow and Director of Research at the Hoover Institution, and Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR). He was on the faculty at the University of Chicago Booth School of Business for more than 35 years, including service as deputy dean of the faculty. 

He is also a research associate of the National Bureau of Economic Research, visiting scholar at the Federal Reserve Bank of Atlanta, senior adviser to the Brookings Papers on Economic Activity, advisor to the Monetary Authority of Singapore, elected fellow of the Society of Labor Economists,IZA Research Fellow, and senior academic fellow of the Asian Bureau of Finance and Economic Research. He hosts Economics, Applied – a video podcast series sponsored by the Hoover Institution.

Davis is a co-creator of the Economic Policy Uncertainty Indices, the Survey of Business Uncertainty, the U.S. Survey of Working Arrangements and Attitudes, the Global Survey of Working Arrangements, the Work-from-Home Map project, and the Stock Market Jumps project. He cofounded and co-organizes the Asian Monetary Policy Forum, held annually in Singapore.

Jon Hartley is a Research Associate at the Hoover Institution and an PhD candidate in economics at Stanford University, where he specializes in finance, labor economics, and macroeconomics. He is also currently a research fellow at the Foundation for Research on Equal Opportunity and a senior fellow at the Macdonald-Laurier Institute. Jon is also a member of the Canadian Group of Economists and serves as chair of the Economic Club of Miami.

Jon has previously worked at Goldman Sachs Asset Management as well as in various policy roles at the World Bank, the International Monetaty Fund, the Committee on Capital Markets Regulation, the US Congress Joint Economic Committee, the Federal Reserve Bank of New York, the Federal Reserve Bank of Chicago, and the Bank of Canada.

Jon has also been a regular economics contributor for National Review Online, Forbes, and the Huffington Post and has contributed to the
It's hard for me to think of anotherexample of such a big shift in
how we live and work that happened soabruptly outside of wartime.

(00:21):
And there have been bigger shifts.
The industrial revolutionwas a bigger deal, and
then the transition from factoriesto offices was a bigger deal.
But these things played outover centuries or decades.
And this big shift in how we live andwork with respect to remote work

(00:41):
played out initially in response tothe pandemic in a few weeks, and
then over the course of about took aboutthree years to settle down to where
we are now and have been for some time,which is roughly 28% to 30%.
As we measured in the survey of workingarrangements and attitudes, 28% to

(01:02):
30% of full paid days are worked fullyat home or some other remote location.
It could be a coffee shop, a library,even a friend's house or something, but
somewhere not at the employer's worksite and not at a client's work site.
[MUSIC]

>> Jon Hartley (01:27):
This is the Capitalism and Freedom in the 21st Century podcast,
an official podcast of the HooverInstitution Economic Policy Working Group,
where we talk about economics,markets, and public policy.
I'm Jon Hartley, your host.
Today, my guest is Steve Davis,who is the Thomas W and
Susan B Ford Senior Fellow and Directorof Research at the Hoover Institution and
senior fellow at the Stanford Institutefor Economic Policy Research, SIEPR.

(01:49):
He was previously on the faculty ofthe University of Chicago Booth School of
Business for more than 35 years, includingserving as deputy dean of the faculty.
Steve is also a research associate atthe National Bureau of Economic Research,
a visiting scholar atthe Federal Reserve bank of Atlanta,
a senior advisor to the Brookings Paperson economic activity,
advisor to the Monetary Authorityof Singapore.

(02:11):
He's been elected a fellow ofthe Society of Labor Economists.
He's an IZA research fellow and
a senior academic fellow of the AsianBureau of Finance and Economic Research.
He hosts Economics Applied,
a video podcast series sponsoredby the Hoover Institution.
Steve is also the co-creator ofthe Economic Policy Uncertainty Indices,

(02:34):
the Survey of Business Uncertainty,the US Survey of Working Arrangements and
Attitudes.
Attracts Work From Home,The Global Survey of Working Arrangements,
and the Work From Home Map project,as well as the Stock Market Jumps project.
He also co founded and co organizedthe Asian Monetary Policy Forum,

(02:54):
held annually in Singapore.
Thanks so much forjoining us today, Steve.

>> Steven J Davis (02:58):
Well, thanks for having me, Jon.
It's a pleasure to be here.

>> Jon Hartley (03:01):
I want to start really on your early life.
When you grew up, how did youfirst get interested in economics?
Were there any particularlyformative moments?

>> Steven J Davis (03:11):
Well, as I recall,
I took an economics courseas a senior in high school.
I don't remember anything about itexcept that I found it interesting.
So when they asked me to write downa major when I went to college,
I picked economics, started out there.
And I dabbled a bit in political science,sociology, law,

(03:33):
international relations, sometimes onmy own reading, sometimes in courses.
Somewhere along the line, I don't rememberexactly when the idea crystallized in my
head that I really wanted to understandhuman societies and the human condition.
And midway through my collegiate career,I think I came to

(03:54):
the conclusion that economicshad a better set of concepts and
tools fordoing that than other social sciences.
And so I decided I would go tograd school in economics so
I could learn how to thinkabout these matters.
And that was really the level atwhich I thought about it at the time.

>> Jon Hartley (04:16):
Were there any particular recessions growing up or
anything like that?

>> Steven J Davis (04:19):
Well, we were in the middle of the 70s inflation,
the oil price shocks.
So those were kind of formativeexternal experiences,
I would say, early in my developmentas an aware human being,
say as a senior in high school andearly years of college.

(04:42):
So those were very much in the background.
And so I had an early history inmacroeconomics and unemployment,
probably because I was living duringtimes when inflation was high and
unemployment was high in the mid 70s.

>> Jon Hartley (04:54):
I guess a very formal time for a lot of economists,
certainly the rational expectationsrevolution coming out of that time.
A lot of macroeconomic modelscoming out of that time.

>> Steven J Davis (05:05):
A lot of macro economists around my cohort as well
because of the turbulent macroeconomictimes in the US and elsewhere.

>> Jon Hartley (05:13):
Well, great.
I mean,how did you settle on sort of macro labor
unemployment dynamicsas an area of interest?
You graduated from Brown with your PhD and
you spent 35 years on the facultyat University of Chicago.
How did you stumble across labor,job flows and later,

(05:36):
obviously, economic policy uncertainty andwork from home?
How did you stumble across labor jobs,firms?
How did you get interested in that?

>> Steven J Davis (05:46):
I was interested in unemployment pretty early in my graduate
school career, maybe even beforeI got there, I don't recall, but
I definitely had an earlyinterest in unemployment.
My dissertation was inspired in part byDavid Lillian's work on sectoral shifts.
So that was part of my thinking.

(06:08):
I read pretty much on my own a lot of theearly search theory work by Diamond and
Mortensen and Pissarides andtheir various co authors.
And I found it super interesting.
It definitely influenced my thinking.
There was no labor economics courseat Brown until my fifth year,

(06:29):
when Robert Moffatt arrived.
And by then I was seriously workingon my thesis and getting ready for
the job market.
So I never took a labor economicscourse in grad school, oddly.
But as I said, on the macro labor side,I had done a lot of reading on my own,

(06:50):
and my dissertation was motivatedby David Lillian's work.
So that's how it started.
And then sometime between Igot my job at Chicago and
I arrived at Chicago,it sort of dawned on me, partly based on.
I'd read some work by Darby,Haltiwanger, and Plant, and

(07:13):
I'd started working on my own with whatwe now call the CPS, gross flows data,
gross flows across differentstates of the labor market.
Unemployment, not in the labor force,in employment.
And I said, wow, these flows are huge.
This kind of net sectoral shifts, whichwas net movements of employment across
industrial sectors that wasthe focus of David Lillian's work,

(07:37):
is not really where it's at.
Especially if you wanted to understandunemployment as a frictional
phenomenon in the mold of the Mortensen,
Pissarides type models that came to playsuch an important role in the profession.
So it's the combination ofthe very early empirical work.
And I was that timesomewhat steeped in the.

(08:00):
In the early theory of job search.
And if you think back to theseMortensen and Pissarides style models,
they typically feature some kind ofmatching function where the inputs
are job seekers and open jobpositions in the form of vacancies.
And at least in the early versions ofthose models, the job seekers were

(08:24):
typically people who had lost theirjob via layoff or sometimes via quit.
So you start to ask yourself, well,what are the central inputs to
the matching function,sort of a production function for
producing matches ornew employment relationships?
And it wasn't the intersectoral shiftsthat David Laidler was focused on.

(08:45):
It was something closer to what washappening at the level of individual
employers.
So that led me down the path of thinking,and
I soon started talking withJohn Halterwinger about this.
Well, if we really wanted to be seriousabout this in terms of the empirical
foundations or underpinnings of thisfrictional view of unemployment,

(09:08):
we should go out and try to actuallymeasure the loss of jobs and
the flows of workers from other labormarket states into the unemployment pool,
into the pool of actively seeking work.
And so that's kind of how we startedearly on and how I thought about it.

>> Jon Hartley (09:24):
That's fascinating.
And I'm curious, did your time atChicago shape your journey in any way?
I feel like you're spending 35years in such a storied department.
It certainly, I'm sure,leaves an impression on anyone.
And also you leave an impressionon the department,

(09:45):
of course, and the business school->> Steven J Davis: At least the first
one's definitely true.
[LAUGH] I'll let othersspeak to the second one.
Look, I came intothe University of Chicago
what I think of as one of its golden ages.
So just first, it's interesting,

(10:06):
the way that I learn changedwhen I went to Chicago.
Brown was a small economics department andmaybe two or
three seminars a week andmaybe one in macro.
I did an awful lot of learning atBrown by just reading and thinking and
arguing with my fellowgrad students as well.

(10:28):
But that was usually inthe context of homework problems.
But I really did spend a lot oftime just reading on my own and
going through articles,trying to understand them.
And so when I got to Chicago,there's this,
I don't know how many,15, 20 seminars a week.
And this tradition of veryvigorous oral [LAUGH] discussion

(10:50):
argumentation was still quite alive,at least some of the seminars in Chicago.
And I went to a lot of workshops, and
I often read the paper in advance andgo to the workshop.
I went to three or four a week.
And I didn't just sit therelike bump on the log.
I was intensely engaged,at least in a listening capacity and

(11:13):
sometimes in a speaking capacity.
And so I shifted from learningabout reading journal articles,
which I continued to do, butall of a sudden, I'm learning tons orally
by talking to people in seminars andout of seminars over coffee.
So the way that I learn shifted,I remember that.
And then the people who reallyhad a profound impact on

(11:38):
the intellectual environment forme in those days.
So my favorite workshops werethe applications workshop and the labor
workshop, which was essentially the samecast of characters in both workshops.
So we had Gary Becker,Sherwin Rosen, Eddie Lazear,
Kevin Murphy, Robert Topel,and many others.

(12:01):
I think Bob was away, maybe didn'tcome till my second year there.
He was at UCLA.
So if you just think about the peopleI listed, there were five or
six others who were, broadly speaking,in the labor economic space.
It was just a tremendous opportunityto build my human capital in labor
economics as well as macro.
I mean, I went to the macromoney workshop as well.

(12:24):
I just found them,
they were a little less no holdsbarred in the style of discussion.
It's a style of discussionwhich now is kinda gone out.
It's no longer really acceptedin the economics profession.
It was very vigorous.
People interrupted each other a lot.

(12:44):
Especially in the applications andlabor workshops,
the speaker often spokeless than half the time.
And that was not necessarily a bad thing,because Gary may have spoken
more than anybody else, andthat was probably the right allocation.
[LAUGH] And so I learned a tremendousamount in these workshops.

(13:07):
And I think back, those two workshopsin particular, and the people
I mentioned really made for profoundlyrewarding intellectual experience.
And so I kinda grew up in that culture andlearned a lot about labor and
about other things, butespecially about labor.

(13:27):
So my education the first five yearsin Chicago as a labor economist,
that's where it happened, even thoughI never took a course in the subject.
That's terrific.
I know many people have gonethrough that labor group at
Chicago under Gary Becker,either as faculty or students.
I think of certainly folks like Glaeser,Steve Levitt,

(13:51):
folks like that have gone through there.
I'm curious,just getting back to firm dynamics and
job vacancies, a lot of your workis hugely seminal in this space.
I'm curious, what, in your mind,have we learned about job creation and
destruction in the recent decades as we'vegotten better data on labor and firms?

(14:18):
We often hear thingslike small businesses or
small firms that are responsible for themajority of job growth in the US economy.
Small businesses are good.
That's kind of an often repeated thing.
I mean,to what extent is this true in your mind?
And I know some of yourresearch says maybe otherwise,
maybe somewhat younger firms.

(14:39):
Do we see smaller firmsbeing the most innovative or
small businesses beingthe most innovative?
Is that necessarily true either?

>> Steven J Davis (14:47):
No, so let me say a few things first.
One of the big things we learned,which is now so widely accepted,
is kind of just everybody'sinternalized it.
But I think my early workwith John Halterwinger and
also with Scott Hsu was instrumental inestablishing this fact in an unambiguous,
pervasive way, is that every marketeconomy in all times and almost every

(15:10):
sector has lots of job creation anddestruction going on all the time.
Okay, and so that was, at least in earlyreceptions of our work in the late 80s and
early 90s,viewed as somewhat controversial.
And then when accepted for the US,okay, well, the US is different.
That's how the US operates.
Everybody knows it's kind ofa highly flexible labor market.

(15:32):
But the story we've learned since then is,as I said,
outside of centrally-planned economies,which really do in the state-owned sector,
often have very little grossjob creation and destruction.
The norm of a market economyis that every period,
there's jobs that disappear and thereare many other new jobs that open up.

(15:56):
That's job creation in our lexicon.
And so what you should think of the visionyou should have of the economy,
the labor market,at least a market economy is.
Yeah, there's a modest net change ofemployment from one period to the next.
So maybe in the United States wemight have employment growth by 1%,

(16:19):
so that might be 1.5,2 million new jobs in a year.
But underlying the extra 2 million jobsthat we gained on net, there might be 17
million newly created employmentpositions that weren't there say,
a year ago and 15 million of the positionsthat were there a year ago are now gone.
So that's a very basic fact,but it's important

(16:43):
to internalize in your thinking formany reasons.
First, back to the original motivationof why I went down this research path.
It helps you understand howfrictional unemployment can be
a significant phenomenon,
even in a well functioning market economybecause lots of jobs are disappearing and

(17:04):
those people either have to leavethe labor force or find a new job.
They often have an intervening spellof unemployment while they're seeking
a new job.
There's a lot of resource reallocationhappening in the economy all
the time through the labor market,directly from these statistics.
But often, capital andother intangible forms of factor

(17:24):
inputs are also beingreallocated at the same time.
And there's a strand of literaturewhich I played a modest role in,
bu John Haltiwanger andothers played a much bigger role.
That goes off and looks at the connectionbetween that resource reallocation
process, which is ongoing in the economyall the time, and productivity growth.
And I think the central messagefrom that literature as I take it,

(17:48):
there are many nuances.
But the central message is part of the waythat the economy revitalizes itself and
pushes productivity advances is bycontinuously reallocating labor and
other inputs from less productive,
less valued uses to more productive,more valued uses.
So that's really two, I kind ofgive you two big messages so far.

(18:14):
Now, the small business piecethat you asked me about.
So let me back up.
There's so many myths andhalf truths around the role of small
businesses in the economy,it's kind of hard to cut through them all.
And at some point in the mid-90s,I just got irked and

(18:34):
sick of hearing all theseridiculous claims and
misleading claims about the roleof small business in the economy.
So I thought I'd just write a paper, whichseemed to me be stating a bunch of obvious
points about the misleading nature of manyof these empirical characterizations.
Don't get me wrong,I don't dislike small businesses.

(18:58):
I think they play a hugely valuablerole in the economy in many respects,
but there were so many outlandishclaims about their role in the economy.
Sometimes, often coming from the SBA,the small business advocacy or agency,
whatever it's called which particularlyirked me because they're a taxpayer funded

(19:20):
organization, part of the government->> Jon Hartley: Small business
administration.
Small business administration, right?
So I decided, look, I'm gonna writea paper and just set the record straight.
So I'm just tired of these things and theneverybody will understand what's wrong
with these characterizationsthat we can go on.
And this paper which to me see,I really wrote it for an undergraduate.

(19:43):
I was kind of hesitant about writing.
It was still early in my career andI was a little concerned about,
aren't my colleagues going to think thatI'm just writing these papers that state
the obvious?
That paper, for whatever reason gotan enormous amount of attention and
I led to a picture on the frontpage of the New York Times

(20:07):
business section with me in some factory,so on.
I think it was a more glorious thing to beon the New York Times in those days than
it is now, but let's go back.
Here's the substance,there are many of these
misleading characterizationsmade about small businesses.
Let me take up a few of them.

(20:29):
So one thing to understand,there's a statistical sleight of hand that
comes up all the time, andnot just with respect to small businesses.
The scope for this mischaracterizationarises particularly because the gross
job flows that we talked about before,the newly created positions and the lost

(20:51):
old positions are so much larger thanthe net changes from period to period.
So net employment in the United States,if it changes by 2
million in a given year, andyou might then go look at, well,
how much did the net position of smallbusinesses change over that one year?

(21:13):
And you can suppose it just happenedto be, it was 2 million as well, and
there was no net change in the numberof jobs at larger businesses,
however you wanted to find small andlarge,
then there's a sense in which it'strue to say, it's correct to say that
all of the new jobs were created bysmall businesses over this period.

(21:35):
Now there's a sense inwhich that's correct, but
there's also a sense in whichit's extremely misleading.
And so why is that?
Well, because within, among the set ofsmall firms, there are lots of jobs being
created, maybe 10 million, and lots ofjobs being destroyed, maybe 8 million.
And among the larger businesses,there's lots of jobs being created.

(21:56):
And I guess if I wanna make mynumbers add up right say there's a 7
million new jobs created and5 million that are disappearing, okay?
So okay, it's true that in some accountingsense, you can say the number of
net new jobs created by small businessis equal to the number of net new jobs.
But you could also go find a set of largebusinesses because I just told you there's

(22:19):
7 million new job positions created, largebusinesses, you can go off and carve off
a set of those and say, you know, thosefive industries, large businesses, and
those five industries accounted for100% of all the net new jobs.
Or you could do somethingwhich governors who run for
presidential in presidentialelections are very fond of doing.

(22:40):
If they come from a state that's hada lot of net job growth, you can go say,
you know, from 19 x to 19 x, 85% of allthe new jobs created in the United States
were in Texas or Florida or New York orCalifornia, whatever it happened to be.
The point I'm trying to make is allof these statements, in some sense,
in an accounting sense, can be true.

(23:00):
They're just very misleading in termsof sometimes the way they're said,
but certainly is the way they'reheard by their intended audience.
So we wrote in this paper,we wrote in mid-90s.
We call this netting out reality.
So if you give me the micro data and
especially if you're looking ata period where the net change is small,

(23:22):
I can go find 100 differentgroups defined by size, by age,
by geography, by industry where Icould make this kind of statement
that 100% of the jobs were created oraccounted for by this group.
Okay, so when you explain it that way,you see why this statement is.
It can be both factually correct, butessentially nearly meaningless and

(23:46):
quite misleading in the way it'soften received by the audience.
That's one of the problems.
But as I tried to make with my geographyexamples, this statistical sleight
of hand or mischaracterization Extendswell beyond the small business issue.

>> Jon Hartley (24:02):
Its fascinating, I mean, a huge contribution.
When we normally think about jobs,
a lot of macro people tend to followthe BLS employment situation report.
And you get the monthlynon-farm payroll numbers,
jobs numbers, andyou hear 200,000 net new jobs.

(24:23):
But people forget that that's just net,and
there's plenty more grossjob flows moving around.
And that's, I think,a huge contribution of, I think,
perhaps culminates in your 1996 book,Job Creation and
Destruction with John Haltiwanger andScott Schuh.

(24:46):
And also I guess, it really speaks to,I guess this Schumpeterian point
about creative destruction andprovides a lot of empirical backing for,
I guess, that sort of underlyingtheoretical kind of idea.
I want to just shift to->> Steven J Davis: On that point,
I sometimes think of our workas Schumpeter with data.

(25:10):
That's a great way to frame it.
Yeah, I think you couldn'thave put it any better.
And it's amazing too.
I mean, Schumpeterian creativedestruction gets cited a lot,
I feel, and it's amazing thatyou were really the first
researchers to really emphasizethat point in the data.
I'm curious what you think about justto shift gears here a little bit,

(25:33):
I'm curious what you think aboutthe concept of full employment.
It's this old keynesian labormarket concept that suggests that
there's slack in the economywhen the factories are empty and
the workers are on the sidelines.
But I feel like today in 2024,we don't really have a good way of say,

(25:56):
measuring it, particularly given howdominant the service economy is now.
The CBO full employment measure, in mymind, is kind of a way of cheating or
in part updates in a somewhatbackward looking way.
We discover that growth doesn'tnecessarily get back to

(26:20):
its old trend like it didn'tafter the Great Recession.
The sort of CBO full employment scores orGDP potential gets updated in this
backward looking way to account for thefact that there actually was hysteresis.
But some researchers say it's important tolook at vacancies on top of unemployment.

(26:45):
The beverage curve, I think ishistorically a bit more a pretty reliable,
stable relationship.
I mean,Covid shifted things out quite a bit.
But historically, I thinkthe relationship between unemployment and
vacancies has been a lotmore stable than say,
the Phillips curve relationshipbetween unemployment and [CROSSTALK].

>> Steven J Davis (27:03):
It's a low bar [LAUGH].

>> Jon Hartley (27:05):
A low bar, but I'm curious, have we learned anything in your
mind about what full employmentis in recent decades,
or in your mind,is it kind of an outdated concept now?

>> Steven J Davis (27:16):
Here's how I think about it.
Both full employment andin some senses mirror image slack,
they are useful concepts.
They're also difficult to measure.
And I think it's a mistake to relyon any single indicator of either
full employment or slack.
And because in almost any time period orepisode you can tell me about,

(27:38):
I'll tell you what was wrong withwhat indicator in that episode and
why it gave you a misleadinganswer based on how it
behaved then compared tothe historical pattern.
So I think we ought to look ata whole bunch of indicators, and
this is typically what I do.
We want to look at the employment topopulation ratio, probably adjusted for

(28:02):
the age mix of the workforce,the unemployment rate itself,
which comes in many varieties, as youknow, it's not just a headline number.
Vacancies, yes, butalso the vacancy to unemployment rate.
How long does it take to fill vacancies?
I also look at matching efficiency thatpops out when you plug your favorite

(28:23):
measures of job seekers and open jobpositions into a matching function.
I tend to prefer here, I'm of the sameview as Bob Hall that I find the matching
function to be actually a more usefulconstruct than the beverage curve itself,
although I've used both.
And you could go on in this vein,I don't think there's a single

(28:47):
measure of full employment orslack that you want to hang your hat on.
Now why are these useful?
Broadly speaking, we want to getsome handle on how close we think
the economy is currently operatingat close to, I left off, by the way,
capacity utilization measures.

(29:08):
We can also measure the utilizationrate of the physical capital.
So there are others as well I shouldmention, that want to get some sense of
how close is the economy to operating atits full capacity in some sustainable way.
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
are out of work, who don't seem to haveany near term options to find work.
So we're not talking about a situationof just frictional unemployment,
then something is amiss in the economy.
So we want to know whether the economyis operating close to its capacity.

(29:52):
And then in many, many theories,Keynesian theories in particular,
and I put some weight on this idea,although not too much.
But some weight on the idea that iflabor markets are extremely tight
in that there's not much slack,or that we're at full employment,
or even above variousmeasures of full employment,

(30:14):
we might expect wage pressuresto be pretty strong and
wage inflation, if not already,then in the near future to tick up.
So that's why I think these measuresare useful, but they all have
their weaknesses, and hence it's mostuseful to look at several of them.
In recent years,in particular during COVID, but

(30:38):
in the wake of COVID and partly relatedto the shift to work from home and
what that did to the workforce, the usualrelationships in the beverage curve and
vacancy durations, I think thesemeasures differed quite a bit
in terms of what they were tellingyou about the tightness or

(31:00):
slack in the labor market at thattime relative to what you would
have inferred from the samestatistics before the pandemic.

>> Jon Hartley (31:11):
That's passing, it's interesting to think, too,
concepts like Nairu was verymuch Milton Friedman concept,
but is Keynesian in many ways.
But it's interesting justto think over the past
few years how unemployment'sfallen to such

(31:33):
a low level that essentiallyit's forced people
to revise their estimatesof what Nauru is.
And so people thought it was 5%,then unemployment falls below 5%,
pretty meaningfully, closer to 4 or 3%.
And yet inflation didn't reallystart rising at that point.

(32:00):
At least in the pre sortof pandemic period.
I feel like the Fed has reallymoved away from thinking
about concepts like, say,U star or R star, and
are kind of more just looking atthe actual prints themselves and

(32:20):
just trying to shift in the directionagainst the winds of inflation.
Or I guess conversely, fromunemployment's skyrocketing to levels that
are just well above,really just the historical means.
Forget about what some sort ofidea of U star or R star are.

(32:41):
In my mind, they've just sort ofassumed now that U star is just
the long run historical mean.

>> Steven J Davis (32:49):
Yeah, that's not a very satisfactory approach, in my view.
And even when moving beyond that, much ofthe literature about the evolutions of,
say, the natural rate of unemploymentare really focused on just
the demographic mix of the workof the working age population.
I don't think that's very satisfactory.
And I can explain briefly a coupleof reasons why that's so,

(33:14):
and this predates the pandemic.
One has to do with both back to our job,our earlier discussion of job creation and
destruction flows.
Those flows have trended downward.
The rate of gross job creation anddestruction and the rate at which workers
lose jobs to vacancies has beentrending downward for a long time.

(33:34):
And part of that has to do withthe aging of the workforce.
Part of it has to do with, evenconditional on the age of the workforce
and the age of existing employers,
there's been a reduction in the rateat which people get laid off.
Okay, so other things equal,
you would expect that to reducethe extent of frictional unemployment.

(33:56):
I've written a few papersin this space with others.
I think there's a pretty compellingcase that there's been a long-term
downward drift in the natural rate ofunemployment, partly for that reason,
even over and above any contribution youwould get from demographic shifts alone.

(34:16):
So I think part of the lowunemployment rates that we've had for
quite some time now,apart from the pandemic episode, but
in the years since the globalfinancial crisis fully played out,
we've had pretty low unemployment rates.
And many people have often, I think,misinterpreted that as an indication

(34:38):
that labor markets were really tight andthat inflationary pressures would be high.
One more point on this part of what'shappened, and not the whole story at all,
is the way in which matchesformed is changed over time.
There's less ex post experimentation.

(35:00):
That is, we're gonna hire some person andsee how they work out.
And if they don't, work out,we're gonna fire them.
So there's less ex post experimentation,there's more rigorous ex ante selection.
I think that has happened both forregulatory reasons,
that the costs of getting rid of somebodywho didn't work out have grown higher for

(35:20):
regulatory and legal liability reasons.
But also the technology for
evaluating people before you actuallyhire them has probably also increased.
We live in a much more data rich world.
You can acquire more information,
you can rely on algorithms to helpyou evaluate people if you'd like.
That has its own drawbacks.

(35:41):
But there's a whole set of reasons, and
I don't think I confidentlycan parse it out.
But there's a whole set of reasonswhy we've shifted from ex post
experimentation, which tends to generatea lot of frictional unemployment,
to more ex ante selection andlonger term employment relationships.
That also feeds into some extent tothe reduction in the extent of frictional

(36:03):
unemployment.
So these are just some of the reasonswhy a simple statistical approach to
characterizing the evolution ofU star just as a function of,
of demographics,would lead you astray, in my view.

>> Jon Hartley (36:17):
Absolutely, it's a fascinating concept and
fascinating debate.
I wanna shift towardeconomic policy uncertainty.
This is an area which you've beenvery active in the recent decade.
You, along with your co authorsNick Bloom and Scott Baker,
developed economic policyuncertainty indices based on various

(36:41):
uncertainty keywords in the news andthe frequency at which they appear.
These measures have been hugelyinfluential in both academia and
the private sector as well.
How much, in your mind, does economicpolicy uncertainty matter for
firms and growth?

>> Steven J Davis (36:59):
So I'll give you my overall assessment is that in most times
and places, economic uncertainty andeconomic policy uncertainty
are modest factors in explainingfluctuations in firm level outcomes.
And certainly I should explainthe aggregate economic fluctuations.
But these fluctuations in economicuncertainty are also highly skewed.

(37:21):
And there are episodes, and I think ofthe period during the global financial
crisis and in the years afterthe financial crisis as one example.
The COVID period is another example,
where there were extraordinarilyhigh rates of both uncertainty and
policy related uncertainty that didsignificantly restrain investment

(37:45):
in hiring decisions andmattered quite a bit at the macro level.
So I wouldn't put them in the samecategory as many of the first moment
shocks that are often drivingeven the everyday fluctuations,
the more quarter to quarterfluctuations in income activity.
But I do think that there are,are episodes in which they really matter

(38:08):
a lot, andoften episodes in which times are bad or
you're being hit by bad firstmoment shock, so to speak.
So these things aren't uncorrelated withother things happening in the economy.
So the extra uncertaintyis often being layered onto
other stresses andstrains that the economy is facing and

(38:29):
in effect,amplifying those negative effects.

>> Jon Hartley (38:34):
And it's interesting, and
I wonder too if there's maybe an optimalamount of policy uncertainty.
Like obviously there'sgonna be more policy
uncertainty in democraticregimes compared to, say,
autocratic regimes where thingsare probably pretty low uncertainty.

(38:57):
But I remember when talking about some ofyour research, which kind of came out of,
I think, heavily sought after,particularly during a lot of this
sort of deadlock congressionalperiods in the 2010s.
I remember working Goldman Sachs and
a colleague from China who saysthe problem with the US, they can never

(39:20):
get anything done amidst all this policyuncertainty and things like that.
I also think, well, having completepolicy certainty might not be so
great if the policies aren't sofriendly toward markets in general.
But I'm curious if youhave any thoughts on that.

>> Steven J Davis (39:41):
Yeah, a few thoughts.
First, there's always going to be somelevel of economic uncertainty and
even policy related uncertainty with us,so let's just put that on the table.
But much of the motivation, much ofthe reason, Nick and I, well, speak for
myself, much of my motivation to getinto the business of trying Trying to

(40:01):
measure policy related uncertainty earlyon arose because there was a lot of,
in the United States,at least politically, manufactured
policy uncertainty that didn't seemto serve any productive purpose.
So if you remember, we had a lotof fiscal cliff-type episodes,
and tax code expiration episodes,and debt ceiling fiascos in

(40:23):
which these are all things wherethe policy related uncertainty
wasn't built into the system the way,say, an election cycle is.
It was a consequence of the waythat policies were designed and
the negotiations betweenthe different competing political

(40:44):
factions in the Congress.
So perhaps the clearest example of thiswere debt ceiling crises, where Democrats
and Republicans were essentially playingchicken cuz they were trying to get
what they wanted with respect to otheraspects of the fiscal legislation, and
they weren't gonna come to an agreementuntil the other side blinked.

(41:06):
And what was at stake then?
What was at stake then was the capacityof the federal government to
make timely payments on its variousobligations, including even possibly,
interest payments on Treasury securities.
And so there was a non-trivialprobability of some type of
default event in US Treasury securities.

(41:29):
And given the central roles they playin the monetary and financial system in
the United States and globally, that's notsomething you want to mess around with.
I could go on and give you manyother examples like this, but
in this period in particular, inthe wake of the global financial crisis,
there were many such episodesinvolving fiscal policy.

(41:53):
The debt ceiling crisis.
We talked about health care policy.
Remember, we had at least twoSupreme Court decisions where the.
The Affordable Care Act,Obamacare, hung in the balance.
That was partly as a consequence ofthe way the legislation had been designed.
So this was not inevitable formsof economic policies uncertainty.

(42:15):
This was built into the system consciouslyby policy design decisions and
political decisions.
So that's the second thingI want to get on the table.
Third thing, I'm gonna push back a littlebit on your claim that at least in
democratic regimes, yes, there's policyrelated uncertainty in democratic regimes.

(42:36):
We've got a presidentialelection coming up.
It looks like a lot rides onthat a lot road on other recent
presidential elections.
Certainly, there was a bigfallout in financial markets in
the cross-section in Trump's surprisingvictory over Hillary Clinton.

(42:57):
So in much of my work inthe policy related vein kind of
draws out the role ofelection related uncertainty
as one source of policyrelated uncertainty.
But you don't wanna giveautocratic regimes a pass.
I mean, just think whatRussia's been doing in Ukraine.

(43:17):
You want to talk about a generator ofpolicy related economic uncertainty.
That's an enormous one, and
that may be the biggest one inmuch of Europe in recent years.
So autocratic regimes generateuncertainty politically.
Policy related uncertainty as well.
It's just not soclosely tied to the election cycle.

>> Jon Hartley (43:38):
That's a great point, yeah, it's interesting.
Even dictators can be very volatile andvery unpredictable, and
that's an excellent point.
So I want to shift to yourwork on work from home.
You have a famous paperwith Jose Barrero and

(43:58):
Nick Bloom titled Why WorkingFrom Home Will Stick in the US.
It does seem,according to the data that you track,
the percentage of full-time dayswork from home is stabilized around,
say 30% or so over the past,say year or two.
Now, that number,
which if I understand correctly,only applies to service sector jobs.

(44:23):
So we're talking about 30% ofservice sector jobs, roughly.

>> Steven J Davis (44:29):
This number applies across the board for employees.
Now, actually,
I'm trying to remember whether we havethe self employed group in that or not.
But most of the US economy isin the service sector now.
So it's also true thatmost goods producing jobs
don't lend themselves to remote work.

>> Jon Hartley (44:52):
Right, exactly, so it stabilized, this is number workdays,
work from home, stabilized around 30%.
It's still up from, say around 6% orso where it was before
the pandemic, butit's also down considerably from where
it was at the height of the pandemic,understandably so.

(45:15):
When many people who are able to workfrom home are working from home cuz they
couldn't go to work.
I'm curious, what in your mind have beensome of the largest impacts of work from
home, whether it's real estate orin the labor market?
And how do you see the futureof work from home?
Do you see remote workbecoming more common?

(45:38):
I know your co-author, Nick Bloom,
often talks about maybe like a j curvewhere there being sort of a rebound
in the share of work from home jobs orthe number of days work from home.
Curious where you stand on that.

>> Steven J Davis (45:50):
So I think this is the most profound shift in how we live and
work.
Certainly in such a compressedtimeframe that's happened in decades.
It's hard for me to think of anotherexample, such a big shift in
how we live and work that happened soabruptly outside of wartime.

(46:12):
And there have been bigger shifts.
The Industrial Revolutionwas a bigger deal than this.
The transition from factories tooffices was a bigger deal, but
these things played out over centuries ordecades.
And this big shift in how we live andwork with respect to remote work

(46:32):
played out initially in response tothe pandemic in a few weeks, and
then over the course of about tookabout three years to settle down to
where we are now and have been forsome time, which is roughly 28% to 30%.
As we measured in the survey of workingarrangements and attitudes, 28%

(46:53):
to 30% of full pay days are worked fullyat home or some other remote location.
It could be a coffee shop, a library,even a friend's house or something, but
somewhere not at the employer's worksite and not at a client's work site.
So that's just an enormous shift.
There are many aspects of it, somepositive, many positive, some negative,

(47:16):
some of the positive ones.
The most obvious one, perhaps,is the time people save by not commuting.
The time andthe money they save by not commuting.
That's a really big deal.
It's kind of a mechanical effect.
But it's not to be taken lightlybecause the average American
on the margin between working from home orworking on site,

(47:38):
spends about 65 75 minutesin extra time on commuting.
Plus a little extra time on groomingis a little extra time grooming.
When you go into the work site,maybe you put on better clothes,
make sure you shave, that kind of thing.
So that's a lot of time.
It means if you work fromhome three days a week,

(47:59):
you're saving more than3 hours out of your day.
So that's a huge benefit.
There's also just the flexibilityin time use over the day,
which appears to be especially valuableto people who have young kids.
So maybe you take 15 or
20 minutes out of your workday whenyour kids come home from school, or
if you need to take your kid to the doctoror the dentist one morning, you do that.

(48:23):
And that's pretty, that's much easierto do if you can go from home rather
than have to connect it to your commute,that's 30, 45 minutes away.
So people value this flexibility andtime use over the day.
Many of them also value justthe personal autonomy that comes.
I've before you, before I joinedthis podcast, I had a little soft

(48:46):
jazz going on in the backgroundwhile I was trying to write a memo.
I don't usually have music onwhen I'm writing my papers, but
if I'm writing some administrative memo, Iwanna hear something soothing and calming.
I'm defining my background,I'm doing that here in my office, but
for people who work in a cubicle,that's not so easy.

(49:07):
Those are some of the benefits.
We can get into the productivityside if you want,
but that's a complex thicketof the productivity effects.
But it does have a whole rangeof effects on productivity.
It affects how companies organizetheir productive activity and
how managers operate.
So managers who have hybrid or

(49:28):
fully remote workers have had to learnnew management skills and styles.
Organizations have to adapt theirHR practices and compensation and
evaluation policies if they're goingto have some workers who are remote.
So big changes in organizationalpractices management, it's had an effect,
in my view, on the structure of wages.

(49:50):
So wages for people in oftenhighly compensated professionals,
but say in the finance sector,the business services sector,
they've seen rather slowcompensation growth since 2021.
Even though these are the industries andoccupations that tended to see rather

(50:10):
rapid compensation growth relative to mostother sectors in the previous decades.
I think, and we have plentyof evidence to back this up,
some of that slow compensation growthis because people in these sectors have
decided to take some of their compensationin the form of the benefits that
come along with working remotely.

(50:33):
So could go on in this vein,we can drill in there more if you'd like.
But you also asked me,where do I think it's going?
So I think the main changes,the main rapid changes are behind us.
I base that partly because not reallymuch has changed in the last 18 months.
Even though you hear all these newsaccounts about calling workers back

(50:55):
to the office.
And there continue to be,in some organizations, struggles between
management and staff as to exactly howmuch remote work there will be and
what the parameters are around that.
And there may be changes.
In some organizations,the overall data don't show much change,

(51:16):
both our data and other data sources.
In the past twelve to 18 months, we have,
through the survey of businessuncertainty at the Atlanta Fed,
asked business executivesforward looking questions.
And what we do is we ask senior businessexecutives about the outlook at their own

(51:37):
firm, and we've asked themto project five years ahead.
So we say first we ask them, well,
what fraction of your workforcehours are currently remote?
And so on.
But then we ask them,well, projecting forward,
what do you think is gonna happento the extent of your hybrid work,
your workforce, andyour fully remote workforce?

(51:59):
And we break down those two.
And if you average across all ofthe employer executives that we surveyed,
and you weight them bythe size of the employer, and
here it doesn't really matterwhether you weight them or not.
What you find out is executives,when you ask them about their own firm,
they foresee over the next fiveyears modest further increases in

(52:22):
the extent of work from home, both onthe hybrid side and the fully remote side.
Now, I'm a little less confidentin that projection for
the fully remote workers because Ithink the workers who are fully remote
are also the ones who are potentiallyvulnerable to domestic offshoring.

(52:45):
And it may be harder forbusiness executives to factor into their
own thinking the changes thatmight happen in that regard.
But I take away from all this evidence and
other evidence that we could talkabout is kind of in a new normal.
Best guess is probably overthe next five to ten years,

(53:09):
modest further increases from where weare now in the extent of remote work.
And by that I don'tmean fully remote work.
I mean, the fraction of workdaysthat are performed remotely.

>> Jon Hartley (53:23):
That's fascinating.
I, too wonder to what degree, say,
remote jobs might be more susceptible to,say, generative AI.
There's speaking of offshore jobs or
remote jobs being morelikely to be put offshore.
My understanding of generative AI jobs andtasks that are being somewhat more quickly

(53:48):
automated away, at least in terms of whatsort of data and evidence we have so
far know jobs like those that say callcenters that are obviously largely
offshore are more susceptible todisplacement from generative AI.
I wonder if there's maybe somesort of intersection there.

>> Steven J Davis (54:07):
I think so.
And call centers would be the firstone that comes to my mind too.
There's already been a lot ofoffshoring of call centers,
as you know, especially to placeslike India and the Philippines,
where there's a large domesticpopulation that speaks English and
is reasonably well-educated andis willing to work for

(54:31):
wages that are below American wages.
And so they're often handlingmaybe the more routine type
customer service queries orcustomer relationship type queries.
Those do seem amenable to AI,generative AI, more so
than most jobs,at least in the next few years.

(54:55):
And so I do think there is that theintersection exactly that you described,
those jobs that are currentlyfully remote in the United States
are relatively susceptible todisplacement through generative AI,
but they're also closer tothe margin of being offshored.

>> Jon Hartley (55:16):
Well, it's fascinating and really a fascinating discussion and
real honor to have you on, Steven,here about your amazing career and ideas.
Really want to thank you somuch for joining us today.

>> Steven J Davis (55:26):
Thanks for having me, John.
And good luck as youcontinue your podcast.

>> Jon Hartley (55:30):
This is the Capitalism and Freedom, the 21st Century Podcast,
an official podcast ofthe Hoover Economic Policy Working Group,
where we talk about economics,markets and public policy.
I'm Jon Hartley, your host.
Thanks so much for joining us.
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