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With lockdowns easing and COVID-19 infections rebounding in many countries, policymakers and corporate leaders face a profoundly uncertain future for which historical data and precedents may not be reliable guides.

Against that backdrop, Randall S. Kroszner, deputy dean for Executive Programs and the Norman R. Bobins Professor of Economics, led a discussion with Mervyn King, former governor of the Bank of England and coauthor with John Kay of Radical Uncertainty: Decision-Making beyond the Numbers. This virtual event was hosted by Booth’s campus in the heart of the City of London and marked the first in Booth’s Road to Economic Recovery series on COVID-19.

Kroszner, a former governor of the Federal Reserve System, told the audience one of his takeaways from King’s book was that making rational choices under uncertainty was not a “simple card game where you know what the probabilities are.”

King agreed, saying it was time to move away from the classical economic idea that everyone is assumed to know the subjective probabilities of any given event. COVID-19 was a case in point.

For example, said King, he and his coauthor wrote their book in summer 2019, and in it they warned that people should expect an epidemic resulting from a virus that did not yet exist.

“But that didn’t mean we had the power of foresight and could make statements that the probability of a virus coming out of Wuhan in December 2019 was 17 percent or 32 percent or any other number,” King said. “There’s no basis for making that statement. The essence of our book is that we say, ‘Don’t pretend that we know more than we do.’”

Avoid the Data Trap

King said decision-makers should focus on collecting data that would help them understand key aspects of the problem. However, he cautioned against what he called “bogus quantification.”

He cited the 1980s HIV/AIDS crisis in southern Africa. The World Health Organization built a complex computer demographic model. Robert May, the late renowned scientist, pointed out that it used the average number of sexual contacts per person per year as a key parameter without stipulating whether that was 100 contacts with the same person, in which case the epidemic would die out, or 100 contacts with 100 different people, which would mean it would spread like wildfire. “Homing in on the key numbers that matter is of fundamental importance,” King said.

Focus on Resilience

Kroszner said CEOs facing uncertainty right now wanted to know what lessons they should learn. King said they should focus on resilience and robustness rather than just cost minimization or profit maximization. “It’s all very well to minimize your costs in normal times. But if a sudden event comes along and you fail to survive it as a business, then you’re gone,” he said.

“It makes sense to ask, ‘How would we cope with a pandemic?’ and forget the probability. Just as in the financial crisis, we had allowed the banking system to go into 2007 with little resilience. When there was a shock, it couldn’t withstand it.”

Have a Clear Narrative

Kroszner said one lesson he took from his time at the Fed was that Chairman Bernanke was open to criticisms that certain solutions were not addressing the problem. “He made sure people understood there’s a framework, but there’s also the flexibility to pivot.”

King said that there is now a common belief that central banks are “the only game in town” because of their ability to cut interest rates and inject stimuli. But he said when people are worried about contracting a virus, lower borrowing costs are unlikely to encourage consumers to visit shops and restaurants.

The focus should also be on governments’ ability to use taxpayer resources to replace businesses’ lost revenues, contingent on how effectively the country is coping.

“Governments need to have a clear narrative to navigate a path between two very unattractive rocks. One is the rock of a large number of infections and deaths, and the other is the rock of a very large cost in terms of lost economic output. We have to navigate it, discovering through trial and error what works and what doesn’t work.”

This virtual event was the first in Booth’s Road to Economic Recovery series on COVID-19. Watch the full presentation below, and visit the Road to Economic Recovery website to stay updated on future events in this series.

Road to Recovery-Video Thumbnail

- Good afternoon, everyone.

Welcome to the first virtual event

in the Booth Road to
Economic Recovery Series.

My name is Veronica Guerrieri
and I am the Ronald E. Tarrson

Professor of Economics and
Willard Graham Faculty Scholar

at the University of Chicago's
Booth School of Business.

I hope you're all doing
well in this difficult time.

And I'm very pleased to see
such an amazing turnout today.

Thank you to Lord King
for today's event.

Unfortunately the current pandemic has,

is an unprecedented event
that is having dramatic

consequences not only
in terms of human lives,

but also in terms of economic
impact all over the world.

So, policymakers, corporate
leaders and nonprofits

face uncertainty and daily challenges

in their decisions to
the road to recovery.

Today is the first event in Booth's

Road to Economic Recovery
Series on COVID-19.

All events will take place digitally

and will include discussions
with industry leaders,

policymakers, and other
experts from Booth and not only.

To open this series today we
have an excellent program.

I have the honor to introduce
our highly distinguished

speaker, Mervyn King, who
has served as a Governor

of the Bank of England
between 2003 and 2013.

He was previously deputy
governor from 1998 to 2003,

chief economist and
executive director from 1991

and nonexecutive director
of the bank from 1990 to 1991.

He was knighted in 2011,

made a life peer in 2013,

and appointed by the Queen to be a Knight

of the Garter in 2014.

Mervyn is the Allen Greenspan
Professor of Economics

and Professor of Law at
the New York University

and Emeritus Professor of Economics

at the London School of Economics.

He's also co-author jointly with

John Kay of the new book,
"Radical Uncertainty:

"Decision-Making Beyond the Numbers."

To moderate the conversation with Mervyn

we are thrilled to have Randy Kroszner

who is Deputy Dean for Executive Programs

and Norman Bobins Professor
of Economics at Chicago Booth.

Randy served as governor of
the Federal Reserve System

from 2006 until 2009.

And he chaired the
Committee on Supervision

and Regulation of Banking Institutions

and the Committee on Consumer
and Community Affairs.

In these capacities he took a leading role

in developing responses
to the financial crisis

and in undertaking new
initiatives to improve

consumer protection and disclosure,

including rules related to home
mortgages and credit cards.

So, let me leave
the floor now to Randy.

And enjoy the event.

- Great.

Thank you so much, Veronica.

I really appreciate that introduction.

And also I really appreciate
the incredible research

that you have been,

and with your co-authors have
been doing on the pandemic.

One of the things that's really
been amazing is how quickly

research has come out,
and I think particularly

from my colleagues here at Booth,

that are relevant for the current,

the current times and the current issues.

Obviously these are things
we've never seen before.

And so that's posing an enormous challenge

for doing research as
well as for doing policy.

And that's really the crux of the,

of the book that Mervyn and John Kay wrote

on radical uncertainty;
thinking about the challenges

of making decisions and

the whole decision-making
structure and process.

When it's not just a simple,

a simple set of,

it's not a simple card game where you know

what the probabilities are, you know what

everything is,

you don't know exactly the outcome,

you don't know which
card is gonna be drawn,

but you know how many
cards are in the deck.

Here we don't even know
what deck we're playing with

and that was I think
also true a decade ago

when we were at the,

when Mervyn and I were both
in our policy positions

at our respective central banks

dealing with the financial crisis.

So with that brief intro,
I now wanna turn to,

to Mervyn.

Again, thank you very much for being here

and being with us.

And one of the standards
that we typically teach

in economics is that we have

rational choice under uncertainty.

As I said, no one says that we know

what the outcomes are gonna be,

but we have a framework for
trying to make those decisions.

You just simply try to
gather data, quantify risks,

and then you make the trade-offs

to make the best decision.

Now, we call that decision,

rational choice under uncertainty.

When I read your and John's book,

I get the feeling that you think

that's irrational to do that.

So, maybe you could give us a little bit

of a framework for how you
think about decision-making

in these challenging times.

- Well, thank you, Randy.

And let me say first, it's
a pleasure to join you

and in particular to see you
in your splendid London campus

with St. Paul's behind you;
it looks really beautiful.

So, decision-making under uncertainty.

You're right that economists tend to say

that we know what is rational.

And that's because of the very
first line of the argument

they assume that we know
all possible future events.

We can write a long list of everything

that could conceivably happen,

and every one is assumed to have

some subjective probability
which we attach to those events.

And once you know that, then you can go on

to argue that the right thing to do

is to maximize your expected happiness,

utility, whatever you want to call it.

Rational decision-making becomes

a rather straightforward thing.

Although the application
to individual problems

can be complex and difficult.

And if you get it right, you
can get a Nobel Prize for it,

if the problems are difficult enough.

But no one goes back to
challenge that very first step,

which is that everyone is assumed to know

what the subjective probabilities
they attach to events are.

And that really goes back to the 1920s

and a young Cambridge
mathematician/philosopher/economist,

Frank Ramsey, who argued that

you can ask people what
odds they would bet

on various outcomes and events.

And provided their bets were bets

that didn't guarantee that
they would lose money,

that is they behaved in
that sense rationally,

you could infer from those bets

that they were subject to probabilities

that they attach to all events,

which obeyed the normal
laws of probability.

And the weakness in that
argument is a very simple one,

which is that most sensible
people don't bet on most things.

Partly because they don't believe
they know enough about it.

They believe that the
person offering them the bet

may well know more about
the outcomes than they do.

And so they're unwilling
to bet on those outcomes.

And there's just no basis for assuming

that people can attach
these probabilities.

The best illustration of
this is indeed COVID-19.

In the book John and I wrote

that, we wrote this last summer,

that we must expect an epidemic
of an infectious disease

resulting from a virus
that does not yet exist.

But that didn't mean that we
had the power of foresight

and could, say, make
statements of the kind,

the probability that there will be a virus

coming out of Wuhan in
China in December of 2019

is 17 percent or 32 percent,

or any other number; there's just no basis

for making that statement.

And what we have to do is to be content

with the statement: It's
likely that at some point

there will be an epidemic coming out

from a virus that doesn't yet exist.

And therefore we need to prepare for it.

Just as in the financial crisis,

we knew the banking crises existed

so that we knew something about them,

but that didn't enable us to say,

the probability that
there will be a collapse

of the US financial system
in September of 2008

is any particular number.

And so the essence of decision
making under uncertainty

is recognizing that most
of the big decisions

we have to take are forward
events which are one-offs.

They're not repetitions.

You know, your card game, tossing a dice.

They're not repetitions of a stationary

probability distribution.

The world is non-stationary.

That's what differentiates the behavior

of business, the economy, human behavior

from the behavior of the planets

or some scientific phenomenon.

It isn't just the playing out
of a stationary distribution

where we know the odds.

The laws, the situation is changing.

The model that's driving this
all the time is changing.

And so the essence of
our book is that we say

what we need to do is
in any given situation,

don't pretend that we
know more than we do.

We know something and we
must use that information.

We knew something about pandemics.

But don't pretend that
we can attach numbers

where we have to make them up

in order to fill in
cells in a spreadsheet.

Let's just ask ourselves the question:

Well, what is going on here?

And try to work out,
puzzle through the decision

we have to take; that is what
all good decision-takers,

whether they're in central
banks or businesses, actually do.

They don't sit down and maximize
some mathematical function.

- Yes, I certainly know that.

And we both know that from a decade ago.

You mentioned one very
important Frank, Frank Ramsey,

but there's another very important Frank,

a University of Chicago
economist, Frank Knight,

who in 1921 published a book,
"Risk, Uncertainty and Profit"

where he was coming at it
more from the economic side

rather than the statistics side,

but came to the same kind of conclusions

that there's a very important distinction

between things where we
know the probabilities,

we don't know the particular outcome.

Versus things we're just not sure about

what's going on here, getting
at exactly what you had said.

And he was a very important influence

at University of Chicago.

He actually trained Milton Friedman,

George Stigler, others who
went on to get Nobel Prizes.

Jim Buchanan for example.

And I think that's an important part

of the University of Chicago tradition

is drawing that important distinction.

Not that every University
of Chicago economist

adopted that, but I think
that is an important part

of the, important part of our tradition

and that's why I think it's great

to have you discussing these things.

One of the things that I
think is super important

for both policy makers and business people

is exactly what you were getting at.

What is the right question to be asking?

And this is something that comes up a lot

in my classes and also, and
now being an administrator

at the business school about well,

you know, what's going to happen next?

And what's gonna happen
next with the pandemic?

And it strikes me that
that's not the right question

to be asking, for exactly the reasons

that you outlined, Mervyn.

What basis can we draw from

making any statements about that?

What we can do is perhaps
ask other questions

that are more specific
to some of the risks

that may be posed by
that, to try to get at it.

So I wanna try to move from,

'cause I think the point that you make

is extraordinarily important
in that we shouldn't have

an excess of confidence in our ability to,

to put down numbers that have
very tight standard errors

on them and know what's happening.

But then, how do we take
that next step to say,

okay, well how do we make decisions?

'Cause obviously we're gonna
make decisions in this world.

So how do you ask the right questions?

And then how do you make those decisions?

- So I think the key
is to ask the question

that I mentioned before:
What is going on here?

To try to tease out the things
that are really important.

To focus attempts to collect data

on those aspects of the decision.

So, John and I are not against
models or quantification.

But we are definitely
against bogus quantification

of which there is a very great deal.

One of my favorite examples is

that when AIDS started to spread,

particularly in Southern Africa,

the World Health Organization
started to put together

a very large, very complex computer model

of the demographics of the
whole of Southern Africa,

linking together demographic
models of different countries.

And they wanted to predict
how quickly AIDS would spread.

The one good decision they
made was to invite Bob May,

Australian, went to Princeton,
then Oxford in England.

Became chief government
scientist in the UK.

A mathematician, biologist,
he and Roy Anderson

at the Imperial College
literally wrote the textbook

on the epidemiological
models people are now using.

And he went out and he
looked at their work.

And he said, look, there's
a key parameter here

in your model,

and it's the average
number of sexual contacts

per person, per year in each country.

So this is a key parameter,
but don't you realize

that if, let's suppose the
answer that they collected

from surveys with say, 100
contacts per person, per year.

Don't you realize that
if it's 100 contacts

with the same person that
it's utterly different

from 100 contacts with
100 different people?

The former means the
epidemic will die out.

The latter means it will
spread like wildfire.

So what you need to do is forget
the complexity of a model,

because that's irrelevant.

What you need to do is
to go out and discover

more about the sexual mores of the people

who may be spreading AIDS
through Southern Africa.

And that's what they
did and they discovered

that it was being spread
very often by lorry drivers

moving through South Africa.

Homing in on the key numbers that matter

is of fundamental importance.

And yet that is violated
in almost all attempts

to apply cost/benefit analysis

to a well-defined problem
in, say, public policy.

Where you're given a
spreadsheet and you have

to fill in every cell in the spreadsheet.

And you end up making up numbers

and you don't realize which ones

are the really important ones

that are driving the result.

And so, what quantitative
work should be doing

is try to tease out from the problem

which are the numbers that
will really matter here

for this decision?

And get the orders of magnitude right.

It doesn't matter if it's
to the third decimal place.

What does matter is that
you've got the right numbers

that matter that will
determine the outcome.

And you have some feel for
what the order of magnitude

of those numbers are.

And that's not something that
fits naturally into a belief.

But what we're observing
is the playing out

of a stationary distribution
playing out day after day,

day after day, and all we need to do

is just to feed in the
numbers into the spreadsheet

and out will come a number that tells us

whether the decision is a good one or not.

And it's very rarely of that kind.

Most of these decisions are one-offs.

Then understanding the nature
of it is far more important

than pretending there is a

spreadsheet where we
just fill in the numbers

into each cell, press a button
and out comes the answer.

- I think that's incredibly important

because it's about, as you said,

asking what's going on here.

What is important about this issue?

And what data might we be able to get

to actually help us to deal with it

rather than rely on the sophistication

of a model or something like that.

I recently had a conversation with,

with one of our Nobel Prize
winners Gene Fama, and you know,

he's one of the founders of and really

developers of using data in finance

to try to think about
risk and risk trade offs.

You know, he had this
incredibly sophisticated

econometric link, but I
asked him about his approach

to the data and he said,
you never wanna use

those complicated things.

You really wanna, he
basically said the same thing

that you had said: You
really wanna understand

what's going on here.

Try to understand the
basics, the fundamentals.

Sure, you can be fancy and
put all these other things

to dot the Is and cross the
Ts, but you really wanna get

the big picture and the understanding.

And I think that's
something that is often lost

both in policymaking and in business

especially when something
like this comes up

that's so sort of, scary.

People wanna sort of,
fall back on quote "science"

or something that seems
very sophisticated.

But what you really want is something

that's very straightforward,
asking what's the relevant

issue, what's the relevant question?

And so I wanna ask, and I wanna move

from this sort of, bigger picture to some

of the challenges now, what
would be the key questions

that you think other business
people should be asking

in thinking about what
they should be doing next?

Or that the policymakers should be asking

in what they do next for policy?

What are the best questions?

And where should we be
focusing our

data-gathering efforts?

- Well, I'll give you one
example from COVID-19.

The epidemiological models that are used

are helpful in understanding
the nature of an epidemic.

That is, it tends to start slowly;

you don't really notice
or have enough information

about it until it suddenly accelerates,

reaches a peak, and comes down again.

And early on, various modelers,

particularly at Imperial
College, were asked

to make predictions about what the cause

of the epidemic would be.

And they turn out to be
not very good predictions

which is hardly surprising
because in these highly

non-linear phenomena,
the results are sensitive

to the values of the
parameters that you feed in.

And there were many key parameter values

that we just didn't know.

It was a new virus. What was
the nature of this virus?

We didn't know the
fatality rate, for example.

We didn't know enough about
the nature of the virus.

We didn't know how people would respond

to various lockdown measures,
and we certainly don't know

how they're responding as we try to ease

the restrictions that have
been imposed on people.

But one of the messages from that was

well, we need to know far
more about the fatality rate

of this virus, but to do
that what we need to do

is to do a large random
sample test of the population

in order to find out how many
people have got the virus,

and then to infer what
the mortality rate is.

Because you may feel you
can measure the number

of deaths reasonably accurately,

or that even that is
mired in controversy now,

at least in the United
Kingdom where many people

have been described as
dying from COVID-19.

When in fact they were
tested months before

and died from something else.

The realization that we needed

to know more about this parameter,

and therefore the way to do it

was to carry out a
large random-sample test

was not followed for a very long time.

And the reason, I think,
was that understandably,

all the people in the medical profession

are basically focused
on treating patients.

They're brought up to help a patient.

And what you need to do in
a public health emergency

like this can be very different.

So, despite the fact that
both the United States

and the United Kingdom
were described by various

international bodies as
the best-prepared countries

for a pandemic, it turned out we weren't.

And we weren't in a number
of different dimensions,

one of which critically was
that we didn't have in place

a series of procedures
to learn about the nature

of the pandemic; we assumed,
we'll know about the pandemic.

How do we respond to it?

But we didn't know the
nature of this pandemic

and we therefore ought to
have put in place measures

to find out more about it.

And I think this is one of the key lessons

that you know, you mustn't sort of,

pretend through bogus quantification

that we know enough to make predictions.

And I was very struck in Britain

that early on the chief medical officer

was asked by a journalist in
their daily press conferences,

"So, how many people are gonna die?"

And he said, "I don't know."

And that was the best
and most honest answer

that anyone gave in this whole episode.

As the episode continued,

what you could see was that
many of the medical experts

were sucked into giving
very positive advice

about exactly what you should do.

And the trouble was that as we
learned more about the virus,

that advice changed.

And you lose credibility if you say,

I'm certain today that you should

follow this course of action.

Then next week you say,
but I'm equally certain

that today you should
do something different.

You have to tell a narrative
in which you explain

to people, we don't know
very much about this virus.

This is the sort of advice
we think makes sense today.

But we're gonna try and learn more about it

and modify the advice we
give you as time goes by.

That way you build up credibility.

Because people think you're
being honest with the public.

But if you pretend to the
public that you know more

than in fact you do,
they will come to realize

that over time and then
you lose credibility.

And you can have some
very adverse consequences

as a result of that, as I think we've seen

in other medical areas such as
the anti-vaccination campaign

which has been very costly to society

in terms of deaths
particularly of children.

So I think credibility
comes from being honest.

And being prepared to say in response

to a number of questions, "I don't know."

But that answer is one
that very few politicians

feel able to give.

- I agree, I think that there's
so much pressure to say,

we know what's going on and
we're gonna do this to solve it.

But I think you're exactly right that,

what, I think it's difficult
to just say, I don't know.

You have to have sort of, the next step.

And the next step was obvious;

to do randomized, controlled trials.

I mean, the most recent
Nobel Prize winners

in economics received the Nobel
Prize for doing exactly this

in emerging markets.

People weren't sure what
was effective for education.

They weren't sure what was effective

and actually did a lot of
work on infectious diseases.

And one of the challenges is

that people were estimating the frequency

of a particular disease by
looking at the data they had.

It gets back to exactly
what you're saying, Mervyn.

What they were doing
is collecting the data

from the local health clinic.

Well of course a very high
fraction of the people

who go to the health clinic
have a health challenge.

And if you try to estimate
the frequency of the disease

from that data, you're gonna get a number

that has nothing to do with reality.

You have to go out and do
randomized controlled testing.

And you can do that in health,
you can do that in so many

other areas and they've
actually done a lot in this.

So there's a well-worked-out
field in this,

just got a Nobel Prize.

But what's amazing is that we
haven't seen this anywhere.

People should've said I
think, exactly what you said.

We're not sure but we're gonna find out.

And this is how we're going to find out.

Instead, as you said, they didn't do that.

I recently gave a talk on a,

a joint seminar that
we did with the Centre

for Economic Policy
and Research that Booth

did a joint workshop with.

And I titled my talk, "The Data Deficit."

A lot of people have been
talking about the fiscal deficit

but not about the data deficit.

If you look at the fiscal

expenditures from the $3 trillion,

so 15 percent of US GDP that
has been put forward,

at most generous, less
than 8 percent could be said

to go for gathering data,
for directly addressing

the disease issues and
finding out more about it.

It's just not sexy to do that.

And it's also suggesting
that you don't really know

what's going on, but it's
super, super important.

Is there a way to get over this
in the public policy realm?

And also, is there a
way for CEOs to do this?

'Cause obviously they're facing something

very similar within their companies,

either talking to their employees

or talking to their investors.

- Well you're right, it does
apply to businesses, too.

I hope that one thing that
will come out of this episode

is a greater focus on the importance

of resilience and robustness,

rather than just cost minimization
or profit maximization.

It's all very well to try
and minimize your costs

in normal times, but if a
sudden event comes along

like this episode and you fail
to survive it as a business,

then you're gone.

And for most good companies, that's not

what they want to see happen.

So it makes sense to ask the question:

How would we cope with a pandemic?

Forget the probability of it.

We know enough to say that it is likely

that there will be another pandemic

at some point in the future.

We can't be more precise than that.

But that's enough, to know that we

need to think about how
we would cope with it.

And I think there will be an adjustment

of business practices to focus more

on resilience and robustness,

just as in the financial
crisis it became very clear

that when we asked a question in 2008,

early on in 2008, what is going on here?

The answer was not, a
shortage of liquidity,

but deep concerns about the underlying

solvency position of
financial institutions.

Which then led to the symptom

that people weren't
willing to lend to banks

because they didn't know
whether they were short

or long in many of these
complex financial instruments.

And we had allowed the banking
system going into 2007/08

to go into it with very little resilience.

The equity buffers were too small.

There were no holdings of liquid assets.

And the result was that
when there was a shock,

then the banking system
couldn't withstand it.

And afterwards we then put
in place regulatory measures

to improve the resilience and robustness

of the banking system.

What we didn't do was to ask the question,

are there other parts of the economy

that we also need to think about

the resilience and robustness of?

And there are many
aspects of our economies,

ranging from potential
weaknesses in the cyber area.

Are our IT systems resilient?

Are we absolutely confident
that we have an electricity

supply system that is resilient and robust

through a number of different outcomes?

The risks of terrorists
with nuclear weapons

in a suitcase in a major city.

These are things which
threaten our survival

and they're worth spending
time and effort thinking about.

How we can improve our
resilience of our businesses

and our economic and society systems

to prevent the downside risks
from these things occurring.

And I think to go back to something

that you mentioned earlier,
one of the important

conceptual distinctions
we need to re-introduce

into economic debate is the difference

between risk and uncertainty.

That was Frank Knight's
great contribution.

It's rather unfortunate that the person

who argued against it was
another Chicago economist,

Milton Friedman--
- Yeah.

And Frank's student actually.

- Sorry?
- And he was a student

of Frank Knight.
- He was, he was.

- So it's interesting, yes.

Yeah, so--

- And he argued against it
and I think he was wrong.

And I think there is a real distinction

and it matters because risk
is something we want to avoid.

A risk is a bad outcome

relative to what John Kay and
I call a reference narrative.

That is, a story of how
we think our business

or our lives will evolve.

And a risk is a downside
outcome relative to that.

And we need to take steps
to insure against that

or to ameliorate the
event if it were to occur.

Uncertainty, on the other
hand, is something we

in many ways we should welcome.

Because as Frank Knight pointed out,

uncertainty, that is
where you can't quantify

the uncertainty is the essence

of what entrepreneurs are all about.

And entrepreneurship is something

that's essentially been missing

from a large part of
conventional economic analysis.

A lot of work is done on technology

and patents, et cetera, et cetera.

But the process by which
people come up with new ideas

which no one has had before,

work with other people to
put them into practice,

is the essence of entrepreneurship

and it's part of uncertainty.

And uncertainty is
indeed the spice of life.

You know, if

we could tell all our students
on the day they graduate

that these are the three or
four careers they could follow

and these are the probabilities attached

to each of these career paths,

here's a list of the seven people

who could be their life partners,

with probabilities
attached to each of them,

they would go home deeply depressed.

So when the students say to me, you know,

I'm very uncertain, there's
a lot of uncertainty

about my future, I say,

"That's the most
wonderful thing about it."

When you get to the end of your life

and there isn't a lot of
uncertainty left about your life,

that's when you start to worry.

So, uncertainty is the spice
of life but risk isn't.

Risk is something we should guard against

and take actions now
to minimize the chances

or ameliorate the
consequences of those risks.

And that's why it is
important to distinguish

between risk on the one hand
and uncertainty on the other.

- I very much agree.

I try to describe
things in a similar way,

and some of my inspiration
comes from a great

entrepreneur in the US, Peter Thiel,

who wrote a book called, "Zero to One."

And he said, it's the creation
of something out of nothing

that people hadn't seen before,

that's what true entrepreneurship is.

It's sort of, easy if
someone has already done this

for you to just maybe do
it a little bit better.

But to see something that
other people hadn't seen.

To create something.

And in some sense it gets
back to these broader

discussions of, you
know, deeper discussions

of sort of, free will and such.

Is everything determined or not?

The kind of, probabilistic world is,

well, everything is kind of determined

and you just have to figure
out the probabilities.

In this, what I would consider,

and I would agree with you,
sort of optimistic world,

people can create new things.

You can create the future,
you're not just given it.

And you figure out what
the probabilities are

and you're just kind of stuck.

You go, you create that new future.

You create the possibility
of finding new partners,

the possibility of new things

that people might be interested in

that they've never thought about.

And I think that's a positive
message in all of this.

Obviously we're facing the challenges now.

But that uncertainty is not something

that is bad, but is actually something

that can be inspiring.

But I wanted to turn to
some of the questions.

We're getting questions in now,

and so please feel free
to send more questions in.

And it gets at exactly this.

One of the questions was,

in both, within the firm context

as well as in the policy context.

You certainly wanna be honest
that we're not quite sure

what's going on here, but
how do you still inspire

hope and confidence
rather than people saying,

oh my goodness, he doesn't
know what he's doing

or she doesn't know what he's doing

so I'm either gonna sell the shares

or I'm gonna move out of the UK.

How do you get that balance right?

- I think by telling a narrative

which is as compelling as can be

given our lack of knowledge.

So I'll just give one example.

I would sometimes go to
the parliamentary committee

in London and be asked a question:

So where will interest
rates be in a year's time?

And I'd say, "I don't know."

What do you mean, you don't
know? It's your job to know.

And I'd say, "No it's not.

"I don't know where interest
rates will be a year from now

"because I don't know how
the economy will evolve.

"If you tell me how the
economy will evolve,

"then I can tell you how
we'll respond to it."

So we have a stable response
function or reaction function

in the jargon of economists,

but what will actually
happen to interest rates

depends on what will happen in the world.

I don't know what will happen.

We can tell a story about
what we think is going on

and we'll do that and
we'll revise our narrative

every three months and
tell you what we think

we've learnt over the
previous three months

and whether our narrative has changed.

And you should trust us because if over

the previous 10 years you have seen

that at least we can give
an intelligent description

of what's going on
without pretending to have

a sort of crystal ball that gives us

a magic view of the future.

We don't have that and
it doesn't make sense

to pretend that we do.

And I think one of the
mistakes that central banks

have made in recent
years is to be sucked in

to this position of
saying, well, we've done

what we can in terms of
cutting interest rates

and printing money and let's
think of something new to do.

So, why don't we tell
people where interest rates

are gonna be in the
next two or three years.

But they can't possibly know that

because they don't know
what the economy is.

And what you saw therefore,
was that both at the Fed

and at the Bank of England,
the statements they made

under the heading of forward guidance

basically had to be retracted
because the world changed

and therefore they didn't
want to raise interest rates

in the way that they
previously said they would.

And they changed tack.

It was sensible to change tack.

It wasn't sensible to try and predict

what you would yourself
in the next 12, 24 months.

And so I think that it is a question

of trying to explain to people

why we don't know certain things.

Because people are perfectly
capable of understanding that.

I think, I've been astonished
in the last 10 years

in politics in the UK, at how politicians

talk down to the population and assume

that they really are dumb.

They're not; they may
not be technical experts

but they can certainly tell when someone

is spinning them a lie.

And we saw that in several cases.

In the Brexit referendum,
both sides of the argument

in the public debate
engaged in what we call

bogus quantification,
and they misused numbers

or they made up numbers to
bolster their arguments.

So that the Remain side said,

every family in the UK
will be 4,300 pounds

worse-off a year if
we were to leave the EU.

I met many people who said, look,

I have no idea whether this
number is right or not,

but I'm sure that they
can't possibly know that

with that degree of precision.

They can't know that; there's
too much uncertainty about it.

And so they just infer that politicians

were making up the numbers,
which they large part were.

And the economists advising them.

Bogus assumptions, false comparisons

between episodes in the past and today.

And it, you do not gain
credibility by doing that.

So I think that regular,
frequent communication

with your staff, your team
around you, the wider public,

is essential to build up a reputation

that you're open and
honest and don't pretend

to know things that you
can't possibly know.

- Right, I very much agree.

And I think it's important to
do that not just in a crisis,

but before the crisis comes.

So as you did in your testimony before

the parliamentary committees,
it wasn't just in a crisis

saying well, I don't know where
interest rates are gonna be.

You gave a foundation,
you gave a structure

for people being able to understand that,

so that when the crisis then comes

you can say, well remember
I told you that before.

That's how I think about these problems,

and then this is how I'm gonna go about

trying to answer the problems.

That's actually another one of
the questions that's come up.

Are there ways in which
we could accelerate

gathering data, particularly
in a challenging situation

like this, sort of, getting to one,

asking the right questions about the data

and two, accelerating getting that data,

both for CEOs in a business context

as well as policymakers?

- I think that it depends very much

on the situation and the question.

I mean here, with COVID-19
it would've made sense

to have launched, this could've
been done relatively quickly

I think, a program to do mass testing.

You know, several hundred
thousand people being tested

to find out whether or
not they had the disease.

But there are other situations

where you have to make
decisions much more quickly.

You know, if you're managing
an emergency response

to a situation, be it a fire or a riot.

Or take the Cuban Missile Crisis,

where I think what President Kennedy did,

having learnt from the
Bay of Pigs episode earlier

in which he began to
realize that the advice

he was getting from the military then

or the intelligence
services was misleading.

What he decided in the
Cuban Missile Crisis

was to create a small group of people,

you know, less than 20, and
divided them into two groups

when it became clear that there were

two major courses of action.

An air strike on Cuba or a naval blockade.

And to get them to come
up with the strongest

possible argument for their
particular preference.

Then to get them to
critique the other side.

So that he built into this process,

without much delay because
this was taking place

over days, not weeks.

The process in which the narrative

of each alternative course of
action was being challenged.

And this is vitally important

because you need a narrative.

But you need to challenge
the narrative all the time.

And there are different ways of doing it.

At the Fed and the Bank of England

we did it with committees
making our decisions

on interest rates where the
debates among the committee

were a way of challenging the narratives

that different people held.

And in the White House in that
Cuban Missile Crisis episode,

it was a very effective way of ensuring

that people were
challenging the narrative.

And interestingly, Kennedy

deliberately didn't go to
many of those discussions.

Because he didn't want to be in the room

where people were thinking,

what can I say that the
president would approve of?

That way you don't get the
right discussion and debate.

So you have to think about
the mechanisms for doing it.

And then get, you want alternative views.

You make your decision.

You don't sort of, criticize or dismiss

the group whose advice you didn't take.

You thank them for it.

And you create an atmosphere
in which people feel

that their job is to
come up with arguments

and counter-arguments, and then you all

stick with the decision that's been made.

- I think that's very important

and that's very similar
to the kind of approach

that Ben Bernanke took at the Fed.

He was always open for people
to raise issues and questions.

And he would give responses to that.

Some people in crisis situations

manage by raising their voice

and shouting people down.

He didn't do that, but he would also say,

well we do need to move to a
decision relatively quickly

on things, and understanding
that we didn't always know

that this was gonna be the
solution to the problem.

And being open to revisiting something.

So, many of the programs that
we stood up during that time,

particularly things related
to the money markets,

we had to do multiple programs

to try to address the underlying issue.

And we were ready to do that.

We were willing to be
open to hear criticisms

and concerns, both from
market participants

as well as from others
in other central banks

about well, that's not really working

and that's not really
addressing the problem.

I think having that,

of making sure that people understand

that there's a framework, but
also having the flexibility

to pivot when you're
getting more information

and to show that you are learning.

And that that is part of the
process of what you're doing

I think is very important in
maintaining that credibility.

And we've got a number of
questions about policy.

And so, I'm gonna kind of

put a few of them together.

And say, okay, so now you've
been talking about this,

you know, given the data that we have,

if you were back in a policy position,

not necessarily at the central bank

but let's say in a
broader policy position,

what would you think would
be the most important thing

to either do in the US or in
the UK or in continental Europe

right now to try to deal with
what's going on here today?

- Well, I'm not in a position

where I have access to the information

which governments have.

Let me start with central banks.

I think there has been a
very unfortunate tendency

to describe banks as the only game in town

and to think that if economic
growth falters for any reason,

the answer must be the central banks

to regenerate growth,
ease monetary policies.

There is no basis for that
in theory, whatsoever.

There are many reasons why
economic growth could falter.

Only some of those are amenable
to central bank action.

Monetary policy is essentially

a short-term, counter-cyclical instrument.

And after 10 years of slow growth

we ought to have come to the realization

that further cuts in interest rates

and yet more money printing
isn't really the answer.

There was of course,
when the pandemic started

in earnest in the West (sneezes)

excuse me, a concern about
financial instability

in mid-March and then central
bank–created liquidity

to prevent disturbances
in financial markets.

But this is not a moment to
argue for monetary stimulus.

The idea that we are either,

first of all, when the lockdown came in

and it's still being implemented
in many parts of the world,

if the government is trying to shut down

the economy with one hand,

it makes no sense to try and
stimulate it with the other.

If you're told you can't go to work,

you must stay indoors, you
can't go to restaurants

or bars, no point giving
people money to spend.

When you come out of the restrictions,

if people are nervous about
going to restaurants and bars,

and certainly in the upper age groups

I think people will be nervous about that

until there's a vaccine,

then the idea that a
slightly lower interest rate

is gonna make them willing to save,

you know, save a bit
less, borrow a bit more

in order to go to their
favorite restaurant

is not very plausible.

You need to worry about their concerns

about the health situation.

So the focus should not
be on central banks,

it should be on governments.

Governments, I think,
have a responsibility

to support business through a period

in which business is being
told, you can't open.

This isn't a recession in
the normal sense of the word,

and I don't think it's helpful

to use words like recession,

because then people
think of the normal tools

to deal with a recession;
we don't want to do that.

This is a situation in which

the government is basically
saying to business,

we're suspending a market
economy for the time being.

Therefore I think government
has the responsibility

to replace the lost revenues of business

in one way or another, with transfers

from taxpayers in
general until such point

as we can get the economy back

to somewhere close to where it was

before the restrictions were imposed.

And we don't know when that will be.

So I think having arbitrary time limits

to some of these schemes
isn't very sensible.

They need to be conditional
on how effectively,

effectively we're coping with
the spread of the disease

and how far we've brought it down.

So I think the focus
should be on governments,

not on central banks.

And on governments, they obviously need

to have a clear narrative.

I think you don't want
a situation in which

the health experts say,

these are the health measures
that have to be implemented

because we don't know enough to give

a precise interpretation of that.

And the cost of the
economy of the lost incomes,

output, jobs, and GDP is massive.

Indeed, if you were to
calculate how much money

you would be willing to spend when using

the normal measurement of
a value of a life saved,

we've lost in GDP far more than that

relative to the number of deaths.

So I think that the,

the measures that have to be taken

are to navigate a path between two very,

very unattractive sets
of rocks on either side.

One of which is the rocks of large numbers

of infections and deaths, and the other is

the rocks of a very large cost

in terms of lost economic output.

Both are very unattractive.

There is no simple way to
say, "This is the right path."

We have to navigate it,
discovering as we go

through trial and error what
works and what doesn't work.

And therefore I think absolutism

in terms of a policy response

is not very sensible at this stage.

I think it is important to try

to find ways to reopen the economy.

But with testing and
tracing so that if you see

outbreaks of infections
in certain areas pick up,

then you have to reverse course

and institute a localized shutdown.

And there's no doubt
that as we are learning

more and more about this virus,

we are probably seeing
that the fatality rate

from now on is a bit lower
than we thought it was

at the beginning, which is encouraging.

But we're also seeing how
infectious this disease is.

I mean, it's extraordinarily infectious.

Look what's happened in Australia.

Australia had very good marks

for the way it handled this episode.

Much smaller numbers of
infections and deaths

than the US or the UK.

Really very small numbers,
and everyone thought

they'd done it; slightly reopened
the economy in Australia.

What happens? Melbourne is now shut down.

Five million people in
Melbourne told to stay at home.

This is a very, very difficult situation.

We shouldn't pretend there's
any easy way through it.

It is trial and error.

But finding ways of
getting more information,

the thing that we ought
to be willing to do

is to spend very large amounts of money,

not only on treatment, because that,

if you can minimize the fatality rate

through better treatment,
that's a big plus.

But obviously most important
of all is a vaccine.

That won't kill the,

the disease, I mean, we will,

we'll still be living with this disease

for many, many years, I suspect.

But it will be, with a vaccine,

if people take their
vaccination seriously,

it won't be as big a threat,

well not a big enough threat

to justify shutting
down the economy again.

- Right.
- So, spending more money

on finding a vaccine, I think we are.

I mean, I don't criticize
what governments are doing.

I think they are putting
a lot of money into it.

And there are many
attempts around the world.

Maybe this is one of
the rare opportunities

in which international cooperation

will naturally come forward, in which,

you know, we'll all support
teams around the world.

And then when the first
two or three come up

with a successful vaccine,

we'll all share it around the world.

- And speaking of
international cooperation,

I mean, the European Union
has just done something

unprecedented; they have gotten together

and agreed on a three-quarters
of a trillion Euro package

to provide support, budgetary support

as well as undertake other
actions, European wide.

This hadn't been, hadn't been done before.

So I wanted to get your thought on

what do you think of that
approach that they are taking?

And then, what metrics would you use

to judge whether they're using
that three-quarters of a trillion Euros

wisely 'cause this is
really unprecedented.

This is a very big move forward

in cooperation within the EU.

- Well, up to a point.

It is at one level.

But it certainly is not
a ship to a fiscal union.

- For sure.
- It took them five days

to agree on this, half
the money approximately

is in grants and half in loans
that will have to be repaid.

So that's much less of a burden sharing.

Clearly the scenes of
distress in Italy and Spain,

you know, were very important

in influencing the attitude
of countries in the north

to be willing to make special payments.

But this, you know, the future occasion

on which there could be
challenges to the monetary union

are unlikely to be ones
in which people can see

that the cause of the problem
was some external event

such as either a pandemic or some,

an earthquake or whatever
where people are willing

to make transfers.

It's more likely to be in the context

of a sovereign debt crisis

where countries in the north will not want

to take responsibility
for the consequences

of the profligacy of
countries in the south.

So I don't think they've resolved
their underlying problem.

And it's obviously all being exaggerated,

the benefits of this.

People are talking up the whole
scheme as a great new event

in Europe; I think that's an exaggeration.

After all, some of the
countries in Europe put up,

frontier ban, the whole spirit

of free movement of people just went

out of the window when
the pandemic came along.

So I think they're a long, long way

from moving to a
political or fiscal union.

So I wouldn't want to exaggerate that.

You know, the scale of it
looks very large on paper,

but actually the amount of
money which the UK Treasury

has been handing out to the
private sector in the UK

in terms of either direct
payments or lost tax revenue

is much greater relative to GDP

than this particular package.

I don't want to underestimate it.

It's obviously taken them a
long time to agree on this

and it's something which is better

that they agree than if
they fail to agree.

But equally I think
one shouldn't just say,

oh this is a big new
chapter in European history.

I think that will be an exaggeration.

- And so if we wanted to think then,

because there's another question about

what metrics we'd use to
evaluate the performance

and management of this,

of this tragedy, both on the economic side

and the health side, what
particular metrics would you

look at if you wanted to

assess the relative performance of

US, UK, countries in Asia, et cetera?

- So I think it's important not to,

to do an exercise with the intent

of apportionng blame.

- For sure.
- Nor is it a good idea

to do it with the benefit of hindsight.

So I think we have to know at each stage,

what did people know at the time?

What efforts did they make
to try to discover more?

How do they try to get more information?

Metrics in terms of

ability to acquire and deliver

both protective personal equipment

for people working in the
health services and care homes.

I think certainly in Europe

there's gonna be a

postmortem into the

what happened in care
homes to the elderly.

Where in countries with
very different approaches

to this episode, we have seen

seriously large numbers
of deaths in care homes,

often linked, I think, to the fact that

the attempt to sort of, quarantine them

overlooked the fact that the
people working in the care home

A, often came from abroad.

And B, worked in more than one care home.

So they were taking the virus themselves

from one care home to another.

Understanding that, I think,
is gonna be very important.

Trying to get a proper

estimate of the, what you
might think of as the true

mortality rate, and I think
this is very difficult.

Because clearly the
consequence of COVID-19

is that what leads to death is often

a result of co-morbidity.

That seems very obvious in terms

of the nature of the virus.

We still don't really
understand why the virus

has apparently a differential effect,

not only by age but by ethnic background.

Often geographical area.

I think it, a postmortem needs to be

not just a question of saying well,

this country gets eight out of 10

and another country four out of 10.

I think it's hard to do that.

There's no doubt that in the UK,

and I suspect the US as well,

we didn't score very highly
in terms of logistics.

So that the ability to
acquire and distribute

PPE equipment quickly,

the ability to implement
a testing strategy

and to ramp it up very quickly,

that varied a lot between countries.

Why did it vary between countries?

What was it used for?

Were decisions about the
population who would be tested,

the sample population,

were they good or bad decisions

based on what people knew at the time?

I think these are the things to look at.

But I don't think it
makes sense at this stage

just to say, oh the mortality rate

per 100,000 of the population
was X1 in one country

and a smaller X2 in another country.

Therefore the second country did better.

I think that's too simple.

I mean, just obvious points.

There seems to be a
correlation between mortality

with COVID-19 and obesity.

And countries vary in
the degree of obesity.

That was, you know, when we were thinking

about strategies and
policies about obesity

in years before, no one ever said,

oh, when that COVID-19
comes along you know,

we'll realize the cost of obesity.

It never occurred to anyone.

So I do think that we,

we have to do the postmortem

without using the benefit of hindsight.

- Sure.

Sure, which is always a challenge.

And certainly in the
press, as we well know

from being policy makers,
the press never follows that.

And with 20/20 hindsight they always know

that you had done something wrong

or why didn't you do this at that time.

I think it's very important, as you said,

to think about what data are available.

One of the criteria that I would use

is very much consistent
with what you're saying,

is what were they doing at the time

to try to get data, to try to
understand what was going on?

Were they just saying,
well, we'll do some testing.

We'll do this or that.

Or were they really trying
to be systematic about it?

And saying, well, let's try

to set up randomized controlled trials.

And actually, Germany I think has done

reasonably well on this.

They've, in Munich a few months ago,

or just a month ago they started doing

some randomized trials;
they're doing this in the UK,

they're starting to do some of that.

That's something that if we could somehow

get people to realize that
that's super important.

That the learning part,
it's not the fatality rate

per million people, that
eventually will be affected by that

but in the short run there
are a lot of other factors

that are coming into
that and also we can see

that as these things evolve,

somewhere that had a very
low death rate earlier

may have a high death
rate now and visa versa.

So, really understanding that,

as you said, the co-morbidities,

but also I think the
process that they undertake

of taking seriously that we're not so sure

and we wanna find out what's going on here

is super, super important, which I think

is the fundamental of your book.

- And I think--
- We just passed our time,

so just very quickly, Mervyn.

- There's a very important point

which is the countries that were thought

to be well-prepared for pandemics

had prepared for pandemics
on the assumption

that it would be very similar
to ordinary influenza.

And if you get such detailed
preparations in place,

because you believe you know
what the disease will be,

you can go badly wrong.

So the first thing you need
to do when there's a pandemic

is to say, well what is this virus?

What is going on here?
- Exactly.

So understanding that you maybe,

you can try to draw on some history,

but being fully straightforward

we need to have the data to understand

whether it is like the past or not.

And I think that's a
crucial, crucial point.

Well Mervyn, thank you so much

for participating in our kickoff.

This was a great way to kick this off.

And globally, I know we've
got people from the US,

from all throughout Europe,
Middle East, Africa,

and some people from Asia in.

We will be taking a break
from this in August.

But starting again in September.

One thing that I wanted to highlight

is we will be doing a
virtual global conference

on September 10th, that
is gonna be related

to corporate social responsibility.

And we call it Corporate Social
Responsibility Revisited.

We've talked about Milton
Friedman a number of times here.

September is the 50th
anniversary of Milton Friedman's

famous essay on corporate
social responsibility

which has been interpreted as him saying,

it's all about profits,
not about anything else.

I think a more careful
reading of the paper

doesn't say exactly that,

but that's exactly what
we're gonna be debating

and thinking about how things have evolved

in the last 50 years in that conference.

We'll be starting in Asia then coming

to Europe, Middle East, Africa,

then finishing up in the US.

Then the Stigler Center will have
a more academic conference

that will flesh that out.

And I think many of the things
that we're discussing today

really get at these issues of
what are the responsibilities

of businesses for keeping people safe?

For keeping their employees safe?

What are their responsibilities?

What data should they be gathering?

How they should be acting?

And those will be some of the questions

that we'll be looking at then.

So until September, I look forward to,

well I look forward to
seeing you again in September

as the road to recovery continues.

And for the,

the Corporate Social
Responsibility Revisited Conference

on September 10th.

Thank you so much, bye bye.

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