- [Host] Welcome everyone.
Thank you for joining
us for today's webinar.
Economic Impact of an Outbreak.
Throughout this session,
please type your question
into the Q and A box
located in the top right
or bottom section of your WebX screen.
We will attempt to answer all questions
in the time allowed.
Now, I would like to hand it over
to BC's M.S. in Applied
Economics program director
and the moderator for tonight's panel.
Dr. Aleksandar Tomic Sasha.
- [Dr.Sasha] To say that
these are unprecedented times
would be an understatement, well,
of at least a decade, if not the century.
And we are dealing with
a multidimensional issue.
So, to get to the economic impact,
we will look first at what,
how do we model this infection data
because that seems to be driving
the decisions about the lock downs.
What are the impacts on
the healthcare system,
because again, that's what
is also driving decisions,
because you don't want
the healthcare systems
to be overwhelmed.
We looked at the economic impact
and then the broader impact.
So, in other words,
we want to pull back the curtain
on all things COVID.
To do that, I'm joined
by four of my colleagues.
Faculty in the Applied Economics program
and they are presented here in the order
in which I do ask the
initials questions of them.
First is Larry Fulton.
He's the Associate Professor
and Undergraduate Programs Director
in the Health Sciences of the University,
at the Texas State University.
In our program, he teaches Data Analysis
and Predictive Analytics and Forecasting
and from time to time a big data course.
Next will be Diana Bowser.
She's the Associate Professor
of Brandeis University,
Heller School for Social Policy
and Management and she's
Head of Executive Programs
of Harvard T.H. Chan
School of Public Health.
Diana's currently teaching
Empirical Health Economics
for us and she's a health economist.
After Diana we'll talk
to Ataman Ozyildirim.
Ataman is a Senior Director
in Economics and Global Research Chair
of the Conference Board.
Basically the person behind
the Leading Economic Indicator
and the Consumer Confidence Indexes,
which you'll hear all about.
In our program he teaches
Measuring Business Cycles,
Trends and Growth Cycles.
And finally we have Professor Can Erbil
who's the Professor of the Practice
of Boston College with
tremendous experience consulting
around the world and a long, long history
of interaction with students.
In our program he teaches
Applied Macroeconomic Theory
and he will talk to us
about any outstanding issues
at the very end.
So, with that, I know everybody's eager
to hear from our experts.
Let's start with Larry.
Larry, in order to
(audio distorts)
around a number of COVID
(audio distorts)
This is what seems to be driving
all the decisions that then
have impact down the line
on the healthcare system and the economy
and even beyond.
Talk to us a little bit
about all of these numbers.
I mean, what methods are we using
to come up with these projections?
Where is the curve headed?
It seems like every day we hear
of a new model and another
model gets discredited.
Just walk us briefly through all of this.
When are we shaded in the
informs of the number of cases?
- [ Larry] Well terms of
cases in case the tallies,
I can give you right up front,
that they're gonna be somewhere
between zero and 328 billion
in the United States.
So haven't forecast
we're good to go, right?
(laughs).
And that's what it seems like,
if you look at the forecasting
models or sometimes,
but actually the forecasting models
were based upon sound science.
By way of background,
I just want to say,
is I do teach predictive
analytics and machine learning.
And I used to be the chief
of operations research
for the army medical department.
But that said, I am not
a public health expert,
although I did stay at a
holiday Inn express last night.
It opened it.
I decided to get out of
the house for a minute.
At anyway, what I wanna cover today
is four classes of models.
And those four classes of models,
include kind of a basic 1927
era differential equation set.
More advanced statistical
stochastic processes,
MAKOV models,
models that have
incorporated machine learning
in some of the ensemble
models employed by the CDC.
And I think the CDC has done a good job.
If you take a look at what they're doing,
of taking models from various
respected institutions and their own
and combining them.
And that's the ensemble
models I'll discuss.
But before we get started
with those models,
I think we should ask ourselves,
truly what are the challenges
in the forecasting models.
We've seen some that have projected that
the entire world would die
and some that have projected
that nobody would die.
All right.
So the hard part is that this
is a very, very new virus.
And the data we have is suspect.
So unlimited, Dr. Rob Hyndman,
who is a forecasting guru,
from the New Zealand,
Australia region of the world,
puts it this way.
We have limited and misleading data.
And when you have that becomes a problem.
We are just getting into the part,
where we have more and more data.
But even so with that
data that we do have,
we cannot properly estimate
exactly what's going on.
For example, John Hopkins reports that,
the case fatality rate,
those people who have COVID-19,
SAR-COVID-2 divided by
those who die from it,
divided by those who have it,
but about 6%. Okay?
That's certainly an upper bound,
but the problem is,
that many may have had mild cases,
never received testing
and have no idea,
that they had SARS-COVID-2 have any form.
Some of you may fall into that,
group yourself.
I know that my wife and I
went on a cruise in January
and we came back and the whole ship,
had an upper respiratory infection,
never tested, not going
to be in all likelihood,
I'll get tested for the
antigen, but that's about it at,
another problem is the
global reporting system.
We get data from other countries.
Now we can report truthfully by the state,
but when we get data from other countries,
that data may be poorly reported,
or incomplete or just inaccurate.
So trying to use that,
for our own models is difficult at best.
So that becomes another
issue with forecasting.
And because COVID-19,
so now we have very
limited experience with it.
What percent of the population
acquire immunity after exposure?
We don't know,
any of the models that basic models,
which I'm just about to discuss,
what suggests that almost everybody,
if not everybody acquire immunity.
So there is a transition state,
that's called recovered
and once you recovered you
never become susceptible again.
Well that doesn't hold
at least in some cases,
with COVID-19.
But anyway we do have tools,
but I really wish we could
go back to something simple
like the 1850s cholera epidemic in London,
where John Snow mapped out all the cases,
found the well and pulled
the well handle off,
which we could do that.
But unfortunately we're gonna be stuck,
trying to figure out how this is gonna go.
So some of the basic
epidemiological models,
that start our forecasting,
come from 1927 Carmack McKendrick theory.
And really these models
were real basic differential equations.
There were three States
proposed by this theory.
You have a susceptible population,
you have a population that gets infected,
and population that gets recovered
and the susceptible population,
some transitions to be infected,
some transitions to recovered or died.
But at the end of the day,
when you add all those together,
the N or the population
amount stays constant.
And that's a good basic
way for looking at,
a pandemic model. It set the stage.
The problem is there's some
assumptions in those models,
that are really difficult to understand.
How strong is the contagion?
Okay, what is the probability
that you come in contact
with an individual who has
it and you are infected?
That type of information is
really needed for this model
and it's hard to get.
You have to back into it with data
and if the data are not there or inferior,
then you're backing into
it with incomplete set.
So really it's all about the data,
the way to get around that somewhat.
It's sensitivity analysis,
but truthfully it becomes a problem.
Fortunately, we're gathering enough data
in the United States that,
our models will be become better
and better as we go along.
Okay. So the model was
called a ensemble model,
susceptible, infected and recovered model.
And from a susceptible,
you had it at time T,
any amount becoming infected
based upon transmission rates
and how strong or
contagious that virus is.
And then you had at any time T,
infections moving to the
recovered or dead pile,
there's links of time between there,
and what probability people
move out of that group.
It has to be established.
So again, we have to get distributions.
That's the basic epidemiological model.
We moved along quite a bit
from that over the years,
since 1927 one might hope,
and we have a lot of decent
stochastic process models,
that add complexity into the backs.
One of them is from Dr.
Alison Hall from Harvard,
and he is posted in our shiny application,
which allows you to kind
of play with some of the
components of the model
and see what different
rates of infection, different
susceptibility rates,
et cetera would do to
the curve, if you will.
Alright.
Some of the more powerful models include
immigrants and emmigrants,
and those immigrants
and emmigrants can come
into change your susceptible population.
So when President Trump caught off travel
from trying to China,
what he was doing was he's
making the modeling easier.
When he cut it off from Europe,
he made it even easier,
so that we're not having
the same immigrant problem
that goes into the pro,
to the susceptible population,
or to what's called the exposed.
And the previous model,
we are only had susceptible,
infected, and recovered.
Now we have susceptible and exposed.
So if those individuals
coming from overseas
had been exposed and they
were in an incubation period
and they don't yet show symptoms,
that could still bring the
disease into the population.
So it becomes a more complex model
with immigrants and emmigrants.
And then from the exposed,
you can go into the infectious stage
just like in the previous model.
But here's where it becomes
important to understand
why flattening the curve
and why modeling this is
a little more complex.
All levels infection are not different.
Dr. Hall's model suggests mild, moderate,
and severe classifications.
I would add a different one.
No symptoms whatsoever that
you're just the carrier.
Okay, so you can have three or four state,
or more if you'd like,
of categorizations,
of those with the infection.
The none mild or moderate
probably are not the problem.
It's going to be the severe,
when you get those severe cases.
The point is, you can outstrip
the ability of facilities
like the New York hospitals
to provide the treatment to the patients.
Patients need ventilators when
they're struggling to breathe
and coughing up pink stuff. Okay.
You got to put them on ventilators.
You have a finite set of ventilator.
Do you have a finite for
the nurses and doctors,
so that is the group that
really gets the focus.
If we can slow the contagion down,
through social distancing
or other measures,
if we can just slow it down.
That allows us to get
the resources into play,
so that there is not a
mass casualty situation,
which a mass casual situation is anytime,
that at a hospital is overwhelmed
in terms of requirements.
Also there's some level where,
you might actually consider
from a policy piece,
how you almost nationalized or federalized
or whatever you wanna call it,
some of the medical assets
when certain regions have problems.
But that's for policy
discussion not for me.
Alright(mumbles) go ahead.
- [Dr.Sasha] It's all these models.
Well do you think we (mumbles)
on the court of meeting
the pick that (mumble)
we need to successfully.
[ Larry] Well, I actually
like the CDC forecast,
and it looks to me like it
is flattening pretty well.
So their latest forecast,
looks to me like it's
going to flatten sometime
in just after the beginning of June.
Totally be right near the top
and then it'll start coming down.
That's what the CDCs and Sambal
forecast could looks like.
And I kinda buy into that.
I think they're doing a pretty good job
pulling the best brains into the business
who are building those models
and then putting together
as a linear combination.
And I'll talk about that just two seconds.
I'm almost done.
I don't wanna access with (mumble).
I've already taken five minutes of the 10.
So after you got the infection piece,
you've got the dead piece.
But the part that we don't know is
what if you had the infection,
what proportion of those
transitioned from having infection
back to the susceptible?
Because often the models just ignore that,
because once you've had the flu,
you're not likely to
have it again in season.
Flu dies out, blah, blah, blah.
But if you can return to susceptible,
and you don't really have immunity,
then this is the concern obviously
of you now have another
group of susceptible people
and your immunity of the herd drops,
as people lose any sort of
immunity from the disease itself
or if they never required
it to begin with.
All right.
And lastly, a lot of the models consider
what social distance you can contributes
at some level. Some do not.
They assume no intervention,
some assume social distancing,
and some have kind of a moderate,
effectiveness, social distancing model.
And all those models we'll talk about
this are used by the CDC.
So CDC, I mean, you look at their stuff,
they're not trying to be biased at all.
They're just trying to make
forecasts on the best brains
with data that are unfortunately flawed.
But it is what we have,
class two of the models is,
one of the ones I'm most interested
in is some of the machine learning models.
These models above,
have been used coupled
with neural networks.
So a neural network is kind of
an automatic learning approach
and we don't have time to discuss it now.
But bottom line up front,
it uses layers and activation functions,
and it tries to adjust pathways
along a network to improve
performance on what we know
so that we can get generate
better forecasting models.
I'll give you an example.
Babba car, GBaya, and DIA.
I hope I didn't butcher the name at all.
I have worked on one that
literally couples of sir model
with neural networks,
but unfortunately even the best tune,
machine learning algorithm,
can't work well if the data
are not representative of the population.
And that's our biggest issue is,
why testing so important.
But testing is important.
Even if you aren't sick,
we need to find out who had the antigen
and who is sick truthfully
to get the exact,
well you'll never get the exact,
but to get a good estimate or
a better estimate at least.
The final category of
models are the sambal models
and CDC uses about 12 of them.
He uses about 12 models
from different places.
For example, they use some from UCLA,
one from UCLA, a couple for
them from the Imperial college
with different assumptions
and a couple from here,
a couple from there.
And what they do is they
take a linear combination
or all those models to
produce a single forecast
with air bands,
and in bling,
those models produces a
better forecast overall.
The last thing I'm gonna talk about,
because I'm really just about out of time,
is things for you to think about.
Forecasting models suffer
from denominator problems
and numerator problems.
So we have lack of certainty.
That's number one.
Number two,
many forecasting models
are likely to underestimate
the true susceptible due
to those who have recovered
some asymptomatically.
Number three,
forecasting models are being
applied to the entirety
of the population.
In fact, the case fatality rate,
which is overestimated due
to lack of information,
is probably much different than that,
that's being reported in New York city.
You can take a look
but only a fraction of their death.
Small fraction of their deaths,
were associated with
individuals who did not
have known underlying conditions.
So there are different risk factors
for different population groups.
So think about that.
Other than that, just
recall that I did not
get a master's or
doctorate in public health
and I do not have any vested
interest in any model.
But I think that the CDC
is doing a reasonable job
at trying to get the forecast right
and given the forecast,
I'm looking at their curves,
and it looks like by the middle of June,
we'll definitely be at the flat spot.
So a month from now we'll
definitely be at the flat spot.
If I were to make a prediction, of course,
all predictions are wrong.
So that's all I have, Sir.
- [Dr.Sasha] Thank you Larry.
I will do a shameless spot for the program
that some of these models
that you do cover in that
(mumbles) for us.
So Diana, we heard uncertainty number one,
we've heard the issue with,
what is the true rate
of illness and death.
But one thing that's constant,
if you will is the biggest problem
is when the healthcare
systems get overwhelmed.
So all of these cases have had profound
effect on healthcare
systems around the world.
We see different types of
responses different levels,
of being overwhelmed and such.
So what are some of the
examples of these effects?
And then what do you think
will be an effect of,
COVID on the healthcare system in U.S.?
Not just during the
epidemic, but also beyond.
And then are there any features,
that you see that are maybe
working better than others
when you look at the different
systems around the world?
So with that item throw it to you.
- [Diana] Great.
thanks so much.
And Larry that was a great overview
of all the different models.
I'm not quite as optimistic
as you are with June,
but I guess we can debate that offline.
This is a really great question, Sasha.
And thank you so much.
I'm delighted to be here on the panel,
and I do have to say that,
"As a health economist, this
is what I do for a living."
Not study COVID but I
essentially look at different,
health policy changes that are happening
in countries around the world.
And I use that natural
variation in these policies
to understand the impact on health system
and then eventually to
measure how that impacts
mortality and other health
outcomes in specific populations.
So the Larry and I,
this usually takes me years and years
to measure the impact.
So the last three months
has just been fascinating for me
because what we've been seeing
really is natural variation
in all countries around the world
with regards to how
they are dealing with the COVID outbreak,
not only in terms of the
public health policies,
that they're putting in place
and the social distancing policies,
but again, how their health
systems are handling,
this new outbreak,
this new disease that we really,
no we're learning about
what as of January,
we didn't really know that much about.
So for me it's just a
fascinating question,
and I think that Larry is correct
that the data is really important.
But I think one of the problems
with the models is that,
it's changing continuously
and some of the key inputs,
that regulate the models have to do with
how we're containing the spread of COVID,
which really has to do with policies
and has to do with how individuals are
following those policies.
So behavior change, which is really hard.
So looking at countries around the world,
like I said, all
countries are dealing with
this COVID outbreak differently.
And so what I want to do is
just take a handful of countries
and what you could do really is
modeling is hard 'cause you
have to pick it into the future.
But looking at policies retrospectively
and seeing which ones
had the biggest impact
on containing the spread
is actually a lot easier and
probably a little more fun.
So what you could do really is
sort of look at the policies
that countries have put in place
with regard to public
health and social distancing
and see which ones are having
some of the largest impacts.
And I think already we can make
some of those associations,
some countries that really came out early
and put in strong public health
and social distancing
measures seems to be the ones
that are containing the spread
and then have less impact
on their health systems,
which we would measure in
terms of the number of deaths
per population size.
So for example,
Germany is one place
that's been in the news
on Angela Merkel as a scientist.
She took this pandemic very
seriously from the start.
She not only did lots of testing,
but she put in contact tracing very early,
in terms of containing the spread
and lots of rules and regulations,
which have been followed
quite closely in Germany.
And the mortality rate there
is quite low nine per 100,000
about throughout the
course of the pandemic.
New Zealand is another place,
that also the prime minister
there took this very seriously,
not only did testing quite early,
but they also have put in public policies
that are easy to understand.
Models are hard to understand,
but they put in a policy,
it's a color coded policy that,
at the local level in New Zealand,
you know where you live,
you're either red or blue
or green or whatever colors.
And based on those colors,
you know, if your location
is doing a good job.
containing the spread
and whether it's safe
to move around or not.
So those policies are easy to understand.
The Czech Republic also
took this very seriously
and they took a different route.
They very early onset,
everyone has to wear masks.
March 21st masks were mandated
in the Czech Republic.
And if you went outside your home,
you had to wear them with
serious repercussions.
Masks were not put in place here,
in Massachusetts until April six.
So we're behind in terms
of these policies that,
have been happening.
I don't want to get into
politics in the United States,
but we know that there's lots
of policies and different,
ideas around public health
and social distance.
But every state in the U.S. has done it,
quite differently,
which makes modelers crazy.
And Larry will attest to that.
You have to keep going back
and tweaking your model
every time a different
state decides to open up
or close or wear a mask
or don't wear a mask.
So it's a really interesting question
and I think that you can look across
these countries retrospectively,
and say, "Well, which
of these policies work?"
It's really a cost benefit analysis
that you can look back retrospectively
and look and just have the
countries that I've mentioned,
Germany nine per 100,000
in terms of mortality.
New Zealand is only had 20 deaths,
throughout the entire time.
Czech Republic has a low mortality rate.
The U.S. mortality rate,
I think is upwards of 26 or
2024 right now per 100,000.
And the UK, I didn't mention that country,
but now they have some policies in place.
But early on they were
toying with the idea,
of just letting the virus run its course,
and trying to see if they
could reach herd immunity,
which then of course would
put a lot of pressure on that,
healthcare system in a
very short period of time
and would have been catastrophic.
Now they have a high death rate.
They're catching up now,
but their death rate is higher than safe.
So you can see,
globally that there's
been a lot of variation
and all of these public health policies
and social distancing policies
have a direct impact on systems.
So I'll just take the last
few minutes to mention,
the United States.
And I don't think I need to
go into a lot of detail on,
what's happening with health
systems here in the U.S.
'cause it's on the news, you see it,
these system impacts in the U.S.
that we see in our
predicted in the models,
I call them short term impacts,
because they're taxing
our health system today.
That's the nurses and the doctors,
that we need on the front lines,
the ICU that we need in the models
count how many we need some ventilators,
the drugs, the tests.
Those are things that we need now,
and our systems are doing well,
Massachusetts and New York I would say,
or some of the places
that the biggest outbreaks
and I know in Massachusetts
hospitals there,
there's room that we have more
ICU beds that are available.
If it escalates further,
I think what I'd like to do though,
is end on the longterm impacts
looking into the future
for the healthcare system.
And I'd like to end on a positive note,
because I do feel like,
there may be some good things
that come out of this epidemic.
I've worked with the
U.S. healthcare system
for a number of years,
and it's a very complicated system,
and we all know that,
there's a lot of criticisms
of our healthcare system,
and we've been trying to make changes
in how care is delivered
in our system for years.
Trying to create ways
to create efficiencies,
and improve access to care.
One of the ways that
we've tried to do this,
for a number of years is
to increase telemedicine,
and virtual visits.
People working on this for years,
it's very hard to get
physicians to get providers
and patients to change behaviors,
and embrace virtual visits.
So with like a light switch with COVID,
hospitals are closed,
facilities are closed,
and overnight we had to
move to virtual visits,
and part of it then economic reason,
revenues were down,
and providers needed to get those patients
into their facilities.
And the other reason is that
people needed to be seen.
So over night we had this switch
and virtual visits are now being used.
They're being paid for.
And anecdotally I've talked to providers
and patients that say that
they like the virtual visits.
I'm sure many of you on this call,
have needed to see your provider
in the last three months,
and maybe you had a virtual visit.
So there's probably a
segment of the population
where virtual visits could continue,
could be paid for.
They're more efficient,
and you can,
access services easier for many
people with virtual visits.
Now this isn't for everyone,
so I'm not saying,
this should continue for everyone,
but there are some positive things
that are happening in our
U.S. health versus him.
I think we need to look
really closely at those,
and see what are,
some of these efficiencies,
that have been gained,
through this horrible pandemic,
that we can continue on with,
that may provide a better
healthcare system in the end.
So I'll stop there.
I'll take,
questions,
if you'd like,
there's other things,
that I can talk about with
the U.S. healthcare system,
because I think there's other changes,
that have happened,
that are really important.
But I'll stop there,
and I'm delighted to take
any further questions.
- [Dr.Sasha] Thank you Diana.
There are actually questions coming in
specifically for you,
but we will do that at
the end if that is okay.
So Optima known to you,
conference board has been
publishing a lot of research
on the economic impacts of this song.
So we are now looking at hygiene.
V-shaped recovery is if
the U-shaped recovery,
varies the bottom is it all maybe,
I saved or L saved New York
times published a front page
in order to illustrate the drive losses.
They actually had the graph across the top
and then it went all the way
to the bottom of the page,
because we are moving scales
to accommodate this economic data.
So lead us through these economic impacts.
What do we expect in a
short term, medium term?
Do you see any silver
lining like Diana did?
Talk to us about that a little bit.
Both in U.S. and globally.
- [Ataman] Yeah, sure.
Well first of all Sasha,
thank you for inviting
me to be on this panel.
It's an honor to be here with the palace.
And it was great to hear
from Diana and Larry,
because first and foremost,
the issue that we're faced
with is a public health crisis.
It's a pandemic it's a global pandemic.
Larry was talking about
the lack of information
and the data flaws for estimating
the models about the epidemic,
latter on estimating the
impact of the economic impact.
So we are faced with
really unprecedented times.
There is nothing,
in our memory or even in
history to compare it to,
and that makes understanding
what we're going through
in terms of the economic
impacts. Very, very difficult.
And the uncertainties
don't come from the usual
economic uncertainties
we're used to.
They're really coming from this whole big,
public health issue.
Will the public health
sector be able to handle
the pandemic, the cases,
how will those curves evolve over time?
When is the peak going to be reached?
So those are the biggest uncertainties,
in the environment,
and that really makes it
very difficult to understand.
And as a result we end up
with this alphabet soup
of different types of recessions,
different types of recoveries.
It's going to be V-shaped,
which we already know that
there's a very sharp contraction
that's one side of the V,
and then how is it going to
recover on the other side.
The reason that we see
this big contraction
is because of the rational
response to a pandemic
is to essentially keep people
apart to stop transmission.
And that's a major, major supply shock,
to productive economy in the world.
You have to tell factories to shut down.
You don't have to tell
workers to stay home.
Now that is followed immediately
by a major demand shock
because the demand side is basically,
people having to stay home,
and curtailing their spending.
So it's followed by a big
drop in consumer spending.
And this is really what
we saw happening in China
and Taiwan.
And all the other
countries around the world
are essentially showing the same playbook.
But then depending when the peak hits
and how long the stringent
containment measures
are kept in place are
the major uncertainties
about how the recovery is going to unfold.
So that V-shaped connection
that we're talking about
could turn into U-shaped recovery,
if the containment measures,
are kept in place for a long time.
That spreads out the economic pain.
But it also allows a
health system to be able,
to manage the case load
so that the hospitals
and the doctors, nurses
are not so overwhelmed,
so that we can treat
everybody and save more lives.
In terms of the epidemiologic models,
we're also seeing that there
could be the secondary waves,
or secondary peaks.
That's what happened with the Spanish flu.
And if a second peak occurs,
later in the fall
and as it might be likely to
because of the nature of this virus,
it's similar to a flu virus
which comes back in the fall.
Then we might end up with,
another downturn because those
stringent containment measures
that were being lifted on
suddenly brought back on.
But hopefully we've learned from
our initial experience early in the year,
so maybe they won't be as drastic,
or as long lasting,
because people have
changed their behaviors.
So that might lead us to a
double U-type of a scenario.
So if the alphabet soup keeps growing,
because of the uncertainty
around all of these things.
And I think the ultimately,
the uncertainties that determine
which scenario plays out is
really dependent on those.
The peaks is it May, is it June,
and how long that containment measure,
is going to be kept in place.
I think that the
complicating factor is that,
it is a global epidemic.
So containing it in one country,
or one region is not really enough
to address the whole issue,
because you could end up here in New York,
we're finding out that,
the curve is kind of coming back down
and we're thinking about re-opening,
but then are we going
to end up re-importing,
the virus back from some other region
that didn't quite go
through the same experience
and still has an outbreak.
So there are lots of complicating issues.
That makes it forecasting
even more difficult
than what Larry was talking about.
In that environment in order to be,
I think more resilient to
the facts as they come in.
And to be more agile in decision making,
it's better to look at all
those different scenarios,
separately to kind of plot the course.
It helps to keep it, keep it
more open minded about it.
And I really liked that.
Larry mentioned the ensemble models,
because that type of
looking at many models,
and looking at whether
you're having consensus,
or whether you can average them together
is going to be really more reliable
than relying on a single model
regardless of how good it is.
I think especially in this environment,
you don't want to put all
your eggs in one basket
and to really keep an open mind about it,
but regardless of whichever
model is you look at it,
and we've crunched through
a lot of different,
models that the conference board,
I think the end of 2020,
ends up being lower than
the beginning of 2020.
So we're going to end up,
finishing the year with a smaller economy,
and it's going to take much longer,
to dig out of the hall.
That we're finding ourselves in,
whether it's V-shaped or W-shaped.
So maybe I want to end on a positive note.
We're seeing that many
businesses around the world,
have responded by taking sort
of very drastic measures.
the crisis management mode was in early,
April, March,
depending on what region you're in,
what sector you're in.
But people have started
to think about the future.
And part of that thinking involves,
well, how is consumer
psychology going to change?
How are the,
what are those behavioral
changes going to be?
What will the future look like?
And I think part of that
is the realization that,
there's going to be a lot
more social distancing.
Restaurants will have to
rearrange their dining rooms,
even their business models,
let people in smaller groups at a time,
more spacing and so on.
But we can use some of
the digital technologies,
that are available to us,
whether it's mobile apps, scheduling apps,
things like that will give us,
a new way to kind of re-imagine,
what business life is going to be
with commercial license going to be.
And I think out of that,
we're going to end up
seeing much more innovation.
So in the longer run,
with all all these positive impacts,
we might end up with a
more positive trajectory
for economies around the world.
So let me stop there.
I'm sure there'll be more questions
that we can get to.
Especially with the economic
impact and the global impact.
- [Dr.Sasha] So thank you Ataman.
And just to put things in a context,
we saw 4.8% drop in the
first quarter GDP dropped
and then what's lost in that number,
I think is the fact that we were open
for most of the first quarter.
They basically, it was
roughly about two weeks,
of the first quarter that
we went into this walk down,
so to speak.
So as we all eagerly the
second quarter numbers,
I'm not going to put you
on the spot quite yet,
to give me the number,
but let me, that moved to Can.
Can, with everything that
everybody has said so far,
we are basically looking at,
what are some longer term impacts?
What are some changes in habits,
are we returning to normal
or will there be some new
normal that will be on,
like anything else we know,
what are some policies,
some attitudes which are
changing for better or for worse.
The floor is yours.
- [Can] Thank you Dr.Sasha.
I also want to thank you for including me,
in this panel and I wanna make sure that,
I emphasize that I have
probably the most fun part,
in this panel in terms of,
talking about something that
is going to be in the future
that we don't have any data about yet.
But let me take you to some
of the notes that I took
and a couple of different things.
One, Larry mentioned that it's difficult
to do the podcasting
because of data issues.
I'm gonna throw another ranch
and make it even more difficult.
It is even more difficult
because the economic agents
are changing behavior
as we speak.
So some of the audience,
includes my current students,
actually my no longer
students as of today,
I gave that grade
and I taught a principles
course this semester.
A big one.
So I wanna take us to the fundamentals,
economic fundamentals to start with.
And if you look atthe economic agents,
and if you look at the demand side
and if you go to the very basics,
what is behind the demand,
it is the willingness enabled you to pay.
And both of them are taking a hit.
Let's look at the ability to pay.
This is the income, this
is the unemployment.
This is what was mentioned,
by the speaker before me.
Someone just talked about how
big they call me or a hit.
That means people have less
paychecks that are less income,
hence the ability to pay gets a hit.
But something that people mentioned less
is also the willingness to pay.
So the agents are changing
behavior in terms of also,
psychology.
So even when we have a vaccine
and even when there are some,
medicines that are effective,
that will be what we
call some residual fear.
And some of the economic
agents do have resistance to,
to go and consume.
For instance,
I don't think we are gonna go to
a movie theater in the
future even if they're open.
But there is also Scan document.
So if you think about,
some people who are really ready to go out
spend and go back to the normal,
you are going to see two opposing forces.
But what you're gonna to
see is definitely a shift,
a change in the behavior
of the communications.
This is the demand side.
But if you look at the supply side,
it is even more interesting
in the supply side.
There are many things separating
some of the notes that I have.
First, the supply chains are
restructuring as we talk now.
There is localization of
production that's going on.
We had a lot of buzzwords.
We had the buzzword of
flattening the curb,
social distancing, guess what
the next buzzwords will be.
Ataman Just mentioned that a little bit
with the restaurants.
The next buzzword is D densifying.
So we are gonna have to figure out
how to D densify many
different units of production.
People are talking about factories,
operating in shifts at night
when they are usually closed,
upgrading in shifts work,
the workload is going
to be divided in three,
the day shift,
night shift and the shift from home.
We also see that,
there is increased automation.
So three days ago
an interesting article came out from MIT
from Garena Shimolo talking about AI
and the increase of automation.
I think the health epidemic now
has accelerated investment
into automation.
Here's number will tell us that,
on the average one robot
replaces about 2.3 workers,
and in some industries
the number goes up to 6.6.
That is a complete change
in the supply behavior
that is going to be contributing
to many different shifts in the economy.
Their own makes a point that
this will make the income
inequality even deeper,
in many countries.
The last one was the work from home
or someone also mentioned that,
you probably heard that,
Twitter just announced that
all the workers now you can
stay at home indefinitely
so you can work from home from now on.
And that is not necessarily a good thing.
If you look at another recent article,
you find that on the average
people who work from home,
are working two days at
three hours longer per day.
If you have been working from home,
you probably experienced it too.
This has been one of my busiest periods
since because teaching
200 student course online,
an introduction course online,
for me much more time
consuming than what I do
in the class.
So if you put all of these together,
I agree that Ataman yes,
there are some innovations
and there will be some positive change,
but there are also many, many drawbacks
that are happening that are
affecting the economy now.
So couple of other points.
So we have been locked in our houses
for more than two months.
And guess how long it
takes to build a habit.
It takes two months to be the habit.
So what's happening now is the consumers
are also building new habits.
So they had the time to reflect.
They had the time to think.
I am hearing from my colleagues,
from my friends that they
are being now ashamed
that they have so many
clothes that they had.
They had so many things that
they think they are starting to question,
do we really need this?
Is this really necessary?
So these are going to really,
change the behavior of
the economic agents.
Another big question that we have is
do we really need to travel that much?
If you look at the data
in the U.S. right now,
the airline passenger traffic is at 5%
of what it was during the normal times.
Couple of other things,
in my notes, these are a
lot of them are questions.
I will not provide too many answers,
but I think these are
interesting questions.
Another question that
people have been asking is,
is this a crisis of
the capitalistic system
based on perpetual growth and consumption?
So do we have to reconsider
how the divide the pie?
This is important.
This brings us to the
income inequality part.
So people started to ask,
is there room for industrial policy?
Is there room for a government policy,
that will pick into some of the vendors?
In this crazy times?
Are the limitations of the markets?
People have been writing
about how the stock market,
the markets have been
divorced of the reality,
of the economy that
Ataman was talking about.
The U.S. consumers have
never been this bullish.
So it is a really interesting divide,
that we should take note of,
a couple of another important thing is
what's going to happen
to the globalization
and how integrated you are.
And again, last week the
neurologic from Harvard,
had a piece on that and he said,
"Well, the retreat from what
he calls hyperglobalization
can lead to two different things."
If he says maybe to
escalate the trade Wars
and it will cause a
rising Aetna a nationalism
or he says, maybe we will
end up with a more sensible,
more inclusive model of
economic globalization.
This is really an interesting time.
It also showed us the vulnerabilities
in the economic and financial
system that we have.
And maybe at this juncture,
we need to make a decision,
and the decision that
the policy makers make,
will probably shape how the
new economy is going to look.
I'm gonna maybe re-put up with,
also talking just a little bit about,
developing countries,
not only the U.S.
the outside of the United States.
What's happening.
I've been here in Boston for 27 years,
but I'm still keeping my accent.
I'm originally from Turkey,
and what's happening in Turkey,
it's been different,
completely different than here.
My parents who are above 65 years old
have been basically in house arrest
in Turkey more than two months.
If you were also younger than 20 years,
you were also not allowed
to leave your home.
You would be facing a really high fight.
And the logic behind this was that
we need the people who are
in the workforce to go work
the students, the schools
are closed anyway.
we don't want the students running around
and spreading the virus so
much like them in the house
and the vulnerable people over 65,
they should also not leave the house.
And that had couple of consequences.
One of the consequences is that
the toll of the epidemic was much higher,
for the lower income working class.
So actually, they were
continuing to go to work
and they were hit hard.
The second thing is,
an interesting juxtaposition
of that to the U.S. economy.
If you think about the consumption,
and one of the agents,
who operate in the economy,
the demand side in the United States,
more than 40% of the spending is done
by people 55 years or older.
So if you keep them in the house,
you may think that you are
keeping the supply side going.
But the demand side still
gets a big, big hit.
I will stop here.
I have more notes.
I have more interesting questions,
rather than answers.
But I will stop here and
leave some time for questions.
Thank you.
- [Dr.Sasha] Thank you Can
and thanks everybody also,
we do share quite a few questions
so I will try to lump
some of them together,
because they ask about some common themes.
And the first one, and Diana,
it's actually directed to you,
is we considering the
African-American and Latinos
are more impacted in United
States than other groups,
do we see this around the
world and are drawing to that.
Another question that also talks about
the impact on the blue collar workers,
versus white collar workers.
Is this something that's U.S. specific
or is this something that
you're seeing around the world?
- [Diana] Yeah, thanks so much.
I think that's a good question.
And yes, in the United
States we're seeing,
differential effects for
different parts of the
United States and also
different populations
in the United States.
And I think that we're seeing
that around the globe as well.
And it goes back to
what I said in my first,
part of the talk,
is that the social distancing measures,
and some of these public health policies
are hard to implement in places.
Where there's individuals that
are living in crowded spaces,
lots of people living in the same home.
It gets in the employment a little bit,
because those are people
that have to go out and work.
They can't afford to stay home.
They don't have jobs like us,
where they can video online into work.
We're very fortunate
to be able to do that.
So in those situations
you're gonna have more vulnerable groups.
I have some funding from
the world bank actually
where we're looking at some
of these vulnerable groups
in migrant populations in countries,
to see the impact of COVID,
on these vulnerable
populations in comparison to
some other populations.
So I think it's a valid point.
And it needs to be looked at.
Again, it's data,
it's hard to get data
at the country level.
It's even harder to get data
within specific populations
in within a country.
So in the studies that we're doing,
we're actually trying to
collect data on our own
to get this information.
But yeah, it's hard to get that data.
- [Dr.Sasha] Thank you.
And Dr. Man, white collar
versus blue collar workers,
is it a skill story or
is it industry story?
'Cause it's just seems,
there is a lot of demand
for certain jobs in some industries.
And what would I be looking at is this,
what is the study of the
impact on the workers?
- [Ataman] Well, clearly,
there is a divergence,
in terms of the type that's,
the type of work that's being done.
And in some cases it is possible,
especially in the white collar professions
to be able to switch to working from home.
And that was a very big,
switch for a lot of companies,
a large portion of the workforce.
Unfortunately it's not
always available to,
what we're calling essential workers now
or blue collar workers.
And it's possible in some situations,
to design workforce policies,
which would allow that
type of work to go on,
to be able to...
a very simple example might be
if you're gonna get an
appliance repair person
in your apartment,
Is there a way to kind
of do some pre-screening
or arrange for different times,
I don't know, I'm kind
of speculating here,
but we can find creative imaginative ways
to bring some of those,
some of the blue collar work,
back into the workplace as well.
But others might be more difficult
and requires a very, very different
type of work environment.
- [Dr.Sasha] Thank you.
So another question and I will couch it in
a little bit to my own
predictions from 2008, 2009.
I thought we will certainly see inflation,
given how much money was pumped
into the economy back then.
Well, what we are seeing
now looks like the stimulus
on both monetary and fiscal side,
they would watch what
was done in 2008, 2009,
and it will be a worldwide.
So we are seeing a decrease in prices
or have been some goods.
I mean oil was famously
below zero for the while,
we are seeing spikes in
other goods with glossaries
obviously are getting much more expensive,
do at this point due to
the demand and supply.
She was more so than the
monetary and fiscal policy.
A big question.
Ataman, Can and Diana,
I'm late if you want to try me.
How are you going to be able
to avoid inflation this time?
- [Ataman] Well, that's a great question
and I can try to take a stab at it.
Just to get us started.
It's very hard to predict.
It's just a combination of
the supply and demand shock.
But I think what we will see,
is a lot more volatility in prices.
As you mentioned, in some cases,
even though there's no
demand where we're seeing,
price spikes,
so there a lot more
variability across sectors
and industries.
Of course, you know, what's
happening in oil prices,
what's probably a combination of things,
with the jail politics going on
between Russia and Saudi Arabia.
Compounded by the fact that
there's this pullback in demand.
But so that doesn't
necessarily lead us immediately
to an inflationary environment.
I think in the immediate short term,
we're looking at much more
disinflation or deflation,
because of what's
happening to the economy.
Inflation if it does show up
is going to be a much more
longer term prospect, I think.
- [Dr.Sasha] Can and your opinions.
- [Can] I completely agree with Ataman.
I actually hope that we
will see some inflation
because I think at that time,
we will be dealing with something
that we know much better.
At this point, I am not
worried about inflation.
We have much more immediate,
many more immediate problems
that we have to solve.
I also think that,
it's the money that's
pumped into the economy,
transforms into tangible
goods and services,
that are then absorbed
by the economic agents.
It will not necessarily create
a high level of inflation.
So it also depends on
how well the economy will be functioning
once we gradually return
to the new normal.
The key words here are gradual.
And then another keyword
is another question,
is once we find the new normal,
or what the economy is
called the steady state,
what is the damage going to be?
The recent article from
last week in the Economist
was talking about the 90% economy,
that it was predicting that a 10%,
of the output will be unreliable.
And they'll end up with
the 90%, the economy.
And it sounds good that
90% is a big number,
but the article was
I think laying out many
problems that it can bring
along with it.
So again inflation is for me,
not one of the top priorities
when it comes to the problems
that we need to solve now.
- [Dr.Sasha] Okay. Thank you Can.
So next question,
and I will again start
with the three phase.
So we do not shy away from hard questions,
at Boston college, no, in this program.
And I don't think I will
let you slide on it either.
But the question is basically
how do we balance the economic
damage with saving lives?
In other words, there will
be, that's from COVID,
that will be, that's from unemployment
and mental issues and such.
That's what I really like.
So that's the basic question,
but let me thank the person
who rephrased it in a much nicer way,
which is how can we
process doing the same time
to save lives and improve
the economic situation?
So I'm gonna call on your Can Erbil.
I see you in the screan,
and then say what you have to say. So Can.
- [Can] thank you.
So I did the tricks that
everybody is trying to do
is not to have the trade off
or to minimize the trade off.
So that's why we have
been staying at home,
for a long time.
That's why everybody
has been talking about
flattening the curve.
So there'll be some trade off. Yes.
And if you twist my arm
and if you say, okay,
you have to tell me,
who's gonna be sacrificed, how much?
I will tell you that this
will probably differ from,
local governance to local governance.
And it will differ from the precautions
that we are going to take.
It will also differ from
the level of economic,
comfort or economy buffer
that people have at different locations.
There is different
levels of economic buffer
that they can still stay
at home, stay viable.
But to give you the
importance of the urgency,
I can tell you two things.
Even in the United States.
Now the conversation started to shift
from a liquidity problem
into solvency problem,
which is much more difficult to handle.
And in developing countries
it became actually survival.
People staying at home,
are actually not bringing
any food into the family.
So here in the United States
we are relatively much luckier.
My answer to that, it will
depend on the location.
It will depend on also local governance
and what they are imposing.
- [Dr.Sasha] Thank you, Can.
Ataman?
- [ Ataman] while I tend to agree that,
there is no one size fits all,
solution to something like this.
It's a very complicated
public policy issue.
You probably need to approach it,
on a more sort of local or regional level,
but with a lot of coordination,
and you need the coordination because
it's a public good that
we're dealing with.
Public health and it has
a lot of externality,
and it's really encouraging
to see people coming together
and understanding that
there is a short term cost
and that helps us to kind
of get through the crisis
and managed through it.
But I think in order to make
the right public policy choices,
we need a lot of good
information, good data,
we need to keep working on those models
and we need also a
transparency and sort of good
governance infrastructure.
- [Dr.Sasha] Thank you Ataman. Diana.
- [Diana] So I would say that,
there are trade offs,
but I think if it's done right,
the trade off is minimized because,
if you put the public
policies in place or really,
and you have strict rules
and you can come back sooner
and we're seeing some of
these countries that did that.
They took it very seriously,
maybe even Turkey.
So they're gonna to be able to open up
and start some pieces of the economy.
And I think that's another
thing that you need the data.
We need to know which industries
are the safest to open first.
So there need to be strict
policies in place with regard
to where should we be opening regionally,
locally versus nationally,
and then which industries
are the safe because yes.
Saving lives is the most important for me.
And then we have to balance that
with making sure that the
economy comes back as well.
- [Dr.Sasha] Yeah, Larry.
- [Larry] Yeah, I agree that
saving lives is important,
but unemployment kills people.
So the suicide rate is gonna go up.
Everything else is gonna go up.
So we have to take,
let me give you an example from,
I live in San Antonio Texas,
San Antonio, Texas we've had 55 deaths,
from COVD-19 since its inception 55.
We get more car accident
deaths in this region
and a couple months than that.
So we've shut down an entire economy.
I get it because we're trying to contain,
but there are logical and reasonable steps
to put those at risk in better position,
while still letting the
economy burn a little bit
without shutting everything down.
And I think my biggest
thing is being from Texas,
I'm pretty independent
and if you're going to do stuff
to the entire economy
that's gonna have effects.
It's gonna cost lives on one end
and on the other end it's going to cost
with judge Nepal Pro and some others
would call civil liberties.
So at what point are we actually
infringing on the rights of others
to carry out their lives?
We got people here,
our food lines are
wrapped around the block
for people trying to get
food because a lot of people
have paycheck to paycheck.
Some families, the food
banks are having problems
keeping up.
The farmers have started delivering food,
'cause they can't sell it.
They just started delivering for free.
So stop riding in their fields.
What we've done has maybe gone too far
when we know that this is a
select group in the population
that are higher at risk of the elderly,
those with underlying conditions.
And now there are some uses, very few,
but some use that you
have to be cautious with,
particularly after they've had COVID-19 ,
they can that inflammatory response.
But providing common sense measures.
I mean Massachusetts, I didn't know that
you guys didn't want covering
your mouth until recently.
I mean, we've been doing that for awhile,
so that's kind of weird.
For a person at Texas,
we've been covering
mouths for a long time,
so maybe that's the benefit of that.
But common sense controls
without destroying the
economy that we're opening up
partially again,
and we're not having any major spikes.
You got to watch it. Right.
I understand that.
But yeah,
I have been in healthcare my entire life
and I love people
and I don't, I hate to see people die.
I've seen enough of that.
But you kill it people's as well,
when you shut down and an entire economy.
And I just gotta leave it at that.
So you got, you hit my passionate,
but on that one side.
- [Can] Okay.(mumbles)--
- [Dr.Sasha] We also demonstrated that,
there are differences obviously
not just internationally
but regionally.
Can, I do see which you raised a hand,
but I think those who will
say that there are differences
obviously like I say,
regionally and nationally,
in the approach and the sentiment towards.
So Can, you were going to say something.
- [Can] I just want to add to Larry.
I think he makes a great point.
But I mean what he said,
the food lines,
I think the epidemic
has also showed us that,
there are really
significant vulnerabilities
in the economic system that we have dealt,
and maybe it constitutes
an opportunity for us.
We are talking about going back to normal.
The question is,
do we really want that
normal to be exactly the same
as it was before the epidemic?
Or can we improve on that?
Can we make sure that we
don't have that many people
who live paycheck to paycheck.
I think we may actually achieve both,
if we think a little bit more creative
and if we are not just forcing
to just go back to the
normal as we used it,
as we are used to and
then have another shock,
in some time and discount this
can come from environment,
from other resources
and find ourselves in
the exact same situation.
So this may constitute
an opportunity for us.
That's just what I wanted to do.
- [Dr.Sasha] I have used the
horse prerogative to upline
on anything I feel like combining on.
So when I think of great depression,
obviously it was a huge economic event.
But what I really think
are all of the people
that will share certain kinds of behavior,
which was eight,
whenever the food is available,
save, do not go into debt.
And people around them will say
he lived through the depression,
if left, It was a
behavioral correcting event
for the whole generation of people.
And it is quite possible.
I know you are talking about
the safety net most likely,
but living paycheck to paycheck
can also be an attitude
that I had, which we saw in the twenties
was very much present seize the day.
And so then great depression
came and everything changed
and we are seeing that now as well.
I mean credit card debt has
plummeted something like 20%
or since the lockdown savings
rate is now approaching,
the levels of 1980s.
So I do also believe that there will be
some behavior corrections
that it will be interesting to watch.
So I will close with one question
that we will again ask everybody
but just in the reverse order,
what are we need to do? Larry.
- [Larry] (laughs).
That's a good one.
Well first of all,
if we can start at the macro level,
'cause I like what Diana is saying,
about reviewing from policies down.
We've got to take a look
at the large scale problem we've had with
inconsistent incoherent
policies across the nation.
I mean, he illustrated it,
we illustrate it when
we're talking about the...
- [Dr.Sasha] Larry you
were muted all of a sudden.
- [Larry] Okay, let's try that again.
Can you hear me now?
- [Dr.Sasha] Okay.We can.
- [Larry] Okay. So I don't
know what I left off.
Yeah.
- [Dr.Sasha] This isn't
inconsistent policies.
- [Larry] Inconsistent
incoherent policies.
And I think Diana brought it up
really well when she was talking about,
how it difference from state
to state, region to region.
And the fact that Massachusetts
wasn't required to wear,
you know, in that area,
no face coverings until recently.
It's kinda kind of blows
my mind being from Texas.
So the policy piece at the
macro level has to be addressed
also at the micro level.
I just love what the behavior correcting
stuff you guys talked about.
And it's particularly
what Can talked about,
was opportunity
for us to change the way we
do business to be smarter
in such that it's not business as usual.
It's improved business
and improve savings and
improved functionality.
I love your analogy, Sasha,
about the depression era, World War II.
I thought that was good.
And then I'm not smart
enough to talk about
the mid level Ray,
so I'm gonna let you guys talk about that.
But in terms of individuals,
I can say this,
that we have to take a look and scan
and kind of do some reflection
on what we are doing right
and wrong and this piece,
like it's up to every individual
to look at their behaviors,
their actions and their essentially
what they're doing
in their lives to make
adjustments that make sense.
I'm not gonna go visit my grandma,
(laughs)
but after I'm just not gonna go visit her,
without this thing being gone.
I mean, we resumed, believe it or not,
(laughs)
my old grandmother (mumble). Okay.
So there's some behavior changes
that are responsible for everybody.
Health is everybody's responsibility.
It's not just a single person.
Public health requires that.
So that's enough for me.
- [Dr.Sasha] Okay. Diana.
- [Diana] So yeah, this
is another great question.
I think that what I'd like
to see us focus on next,
and it hasn't come up much on this call,
which is interesting,
is testing how can we use that
behavior to change is one way to do it.
And I think that's important,
but it's hard to get people to change.
What about testing?
And especially at the educational level,
we need to get these kids back to school.
I'm at home with kids
here, a working parent,
and you can't open up the economy
until you send those kids back.
So let's see how we can get
some industry going and
educational systems as well.
And if it means testing at the university,
or at educational level,
then let's do it.
We can do some analysis to sync your,
figure out how much it's gonna cost,
get the private sector involved.
You know this is the United States.
Some say we're the one of
the most innovative places
in the world and we can't figure this out.
I think we can,
and I think that there's, in a way to ways
that we can get back to work,
making sure that it's safe
and using all the resources
that we have to do that.
- [Dr.Sasha] And I will second that,
heavy also been formed with two kids.
it will be difficult to
get the economy going
until they're in school.
Ataman.
- [Ataman] Yeah.
So just building on
everything that's been said,
I think the important
things are to save lives,
but also find ways to keep the economy
from sliding further down.
And finally I think we
should do the things that
will set the stage to help unleash,
more innovation and more sort of (mumble).
We have lots of great digital technologies
that we don't use especially,
effectively or efficiently,
think of what different,
new innovations might come out of that
and we should just set the
stage to unleash all of that.
- [Dr.Sasha] Thank you Ataman,
Can?
- [Can] Thank you.
I think we have a couple of
things that we need to do.
First I agree with you that,
there is behavior change
but there is also behavior
change in the opposite direction.
So according to one study in London,
the renters were paying
their rent to the landlords.
90% of them are paying regularly.
It's down to 40%.
And one of the reasons of that
is what also was mentioned before,
because they cannot pay.
So I think at this point
we have to make the right choice.
We have to make two choices.
One, I think we have to figure out,
if we are going to continue
with the economic models that
we have been using so far
or if we are going to
focus on more inclusive prosperity,
the inclusive prosperity doesn't mean
that we have to fall for growth markets
and the business as usual.
It is actually more clever
way to do the business.
This is what Larry was saying.
And the second one is also
what kind of government
are we going to choose?
This is important,
because two separate kind of approach is,
emerging in this times.
One of them is more totalitarian,
the other one is more egalitarian
and this is a decision
that we will have to make.
We have to reward some
leadership and vice versa.
So those are the two really
big things that we need to do.
And I also agree with Larry that,
everybody has to do whatever they can
in their individual capacity.
To give you an example,
I'm trying to do my part to,
raise awareness and education
on the income inequality problem.
That's I think in the source
of many of the vulnerabilities
of the economy here
starting in the principles course.
So all of my students at,
have been exposed to big data
and they have been
exposed to zip code level
income inequality and
opportunity inequality
in executives were able to have a living.
I think this is where we
can start the conversation
and hope that it will lead to a change
that will make our economy
our lives less vulnerable.
- [Dr.Sasha] That's all.
Thank you very much Can
and thank you all for tonight,
both to the panelists
and to about 120 people
who have been with us,
for the last hour and 17 minutes.
There was a lot of good information,
I will just close with,
quoting the government
knew some of California.
"This is not the permanent state.
This is a moment in time
a crisis come and go."
For many of you on the call,
this is probably the first one.
For some of the others,
it's not, especially with people
who have lived in other countries,
these things come and go.
They always seem like they will never end,
but they fade into the memory.
I think Spanish food was
mentioned only once tonight
and it was much more dramatic.
And nobody remembers is still.
So if there was one thing that I would say
is basically figured out
what to prepare you better to prevail
the next time the crisis hits,
not just this time.
And then there's an individual level.
And we also have some ways
we can help in the higher
education institutions,
that make sure to foster
those relationships
that you have to bring up the skills
that you have an understand
this too shall pass.
And as we have seen tonight
and I made all of the negative impacts,
there are some silver linings,
that I'm sure we will take advantage of.
So with that,
I thank you all and wish
you all a good night.
We will make the recording available
and we will send you the link
to those of you who attended.
Thank you.
Good night.
