Charles Wheelan:
Big data is the weapon, it's like
nuclear power as it becomes more powerful.
It also becomes more dangerous.
And it is incumbent upon people to appreciate
what could go right or wrong when they're
drawing conclusions based on
analysis of big data.
Harpreet Sahota:
What's up, everyone?
Welcome to another episode of
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Our guest today is a professor, journalist, speaker
and author, he holds a PHD in public
policy from the University Chicago, a
master's in public affairs from Princeton
University and a B.A.
from Dartmouth College. He's currently a senior
lecturer and policy fellow at the
Rockefeller Center at Dartmouth College, where he's
been a member of the faculty since
2012. Teaching courses on education policy, health
care tax policy, income inequality and
other related topics.
He's known for his engaging teaching style, which has
led to him being selected as one of
Dartmouth's 10 best professors by the
graduating classes 2015, 2016 and 2017.
In 2003, he published his first book,
Naked Economics, addressing the Dismal Science,
which is an accessible and entertaining introduction
to economics for the lay person
written in a clear, concise,
informative and witty style.
It's been selected as one of the 100 best
business books of all time and translated into
13 languages including Arabic and Hebrew.
His best selling book, Naked Money a revealing look
at what it is and why it matters.
Shows us how our banking and monetary
system should work in ideal situations.
He's also the author of books such as 10
and a Half Things No Commencement Speaker has
ever Said. The Centrist
Manifesto and the Rationing.
So please help me in
welcoming our guest today.
The New York Times best selling
author of Naked Statistics, Dr.
Charles J.
Wheelan. Dr. Wheelan, thank you so much for taking
time out of your schedule to be here
today. I really, really
appreciate having you here.
Charles Wheelan:
It's my pleasure. Thank you.
Harpreet Sahota:
Talk to us a bit about your story.
You've got quite an interesting background for
an author of a book on statistics.
So how did you become
so interested in statistics?
Charles Wheelan:
I think it's fair to say that none
of my books was particularly well planned.
They all arose out of a perceived
need where I was filling a vacuum.
You pointed out the naked
economics as my first book.
I did not set out to write that book.
I sat looking for someone else to write it.
I was teaching a class on economics to
journalists with a little statistics as well.
I assumed that somebody had
already written a book.
It wasn't a textbook, but still elucidated
why we should care about economics,
particularly journalists. I called my agent who was
trying to sell a book on the gambling
industry, which you will notice
I have never written.
She said that book doesn't exist.
You're gonna write it. We're going to call it
Economics for poets and I'm going to read
it. And that was the beginning of a series
of books where I thought somebody had already
done this. They hadn't.
In the case of statistics, I literally tore
a statistics textbook in half because I
thought that it was so opaque and so
bizarrely disconnected from all the real world
problems. Naked statistics like naked economics kind
of fill that void for people who
need to understand why this matters, how the
data can be abused, and not necessarily what
the difference between Alpha and Sigma is.
Harpreet Sahota:
So you mentioned that to
tear open a statistics book?
Charles Wheelan:
I tore into half, torn in half.It was
really expensive, so I didn't throw it away.
I had to tape it back up. No,
I ripped it in half out of frustration.
Harpreet Sahota:
So was there a lot of
self study involved in learning statistics?
And do you have any tips for our
listeners on how to learn something effectively?
Charles Wheelan:
This is a great question.
There was a lot of self study, but in the
course of myself study, I realized why some of
the things that have been covered
in my formal study were covered.
And I actually was somewhat frustrated that when
they were covered formally and I'd taken
a lot of statistics classes, why at the time
nobody connected the dots and said, OK, the
reason we want to know this is because
it illuminates this problem, where that phenomena
or here three examples of how this procedure
has been abused or used for good?
So I found myself through self study,
understanding more the formal study and
simultaneously becoming frustrated at the way some
of that formal study had been
presented.
Harpreet Sahota:
Talk about illuminating!
It was really such a well written book cause,
you know, me being a graduate student in
statistics myself, it's one thing to be exposed to
all of the formula and all of the kind
of structure and rigour behind it.
It was interesting with your book to see
it applied in different real world scenarios.
How is it that you're able to write a
book on statistics that is so much more
interesting, engaging and informative than anything I've
ever come across in grad school?
Charles Wheelan:
I think there are three reasons.
One is I'm a policy
person, not a statistics person.
In fact, when the Economist magazine reviewed
the book, the first line said something
like, Charles Wheelan is not a statistics expert,
which is why this book is so good.
The reason they said that is that people
who love statistics, love the methodology, they
love the numbers, and they kind of assume everybody
else is as adroit with the math as
they are. And they kind of gloss over things
that for the rest of us are not up.
The second reason is what I am
really good at is public policy.
I care about property.
I care about budget deficits.
I care about the process
for dealing with coronavirus.
Obviously, statistics and data are really, really
important to all of those things.
To every time I approach statistics, it's
within eye towards solving those problems.
So, , I start with why,
and then I back into how.
I think too often it starts and ends with
how, which is not an inherently interesting, at
least not to me. And then the last point
is, I'm just not very good at math.
I mean, I'm terrible. But as a
result, when someone does something mathematically or
tells you why statistics formula is what it is,
I have to kind of stop and translate that
out of math into something that's more intuitive,
at which point I can then write about
it in a way that other people
see it as intuitive as well.
Harpreet Sahota:
You've done an excellent job with it.
Really, really enjoyed your book.
I do want to take some time
now to get into the other book.
A lot of Data scientists, we spend a
lot of our time studying Data structures, algorithm,
coding, heavy quantitatively rigorous stuff.
Not unfortunately, not a lot of time
on economics and economics is pretty quantitatively
rigorousness in its own right.
But just for our listeners out there who have
their heads kind of stuck in the machine
learning world, Data science world, can you
give us your description of what economics
is? And you'll kind of make it
accessible for Data scientists out there.
Charles Wheelan:
If you were to do it in one sentence and
I think this is how Gary Becker at the University
of Chicago explained it, you'd
won the Nobel Prize.
It was my price theory instructor.
He said, Economics is how we allocate scarce
resources, We don't have enough of anything
that we need" And we're seeing that obviously
right now with personal protective gear and
ventilators and a vaccine and so on.
Some people are going to get
those things, some people are not.
That is of course, always true.
Some people have private airplanes.
Most people don't have private airplanes.
Some people have enough food to eat.
Some people don't have enough food.
So the question is, who gets what
we have in a centrally planned economy?
Most the people listening are too young to
remember the Soviet Union, but things were not
allocated by price.
So if there weren't enough pork chops, then the
people got the pork chops were the ones
who were lined up in front of a butcher.
First you queued up, which is
one way of allocating scarce resources.
We do it differently, which is to
say the butcher sets a price.
Those who are willing to pay
the price, get the pork chops.
If it turns out they sell out the next time
he or she decides to raise the price or maybe
raises the price. in the moment, things for which
there is a surplus we put on sale.
I mean, all these things are quite intuitive.
But at the end of the day,
it's about how we allocate resources.
And then in the process, you can add
layers of complexity, which is that process also
creates very powerful incentives.
If you or I create a vaccine for Covid-19
right now, we're gonna be rich super rich.
And you know what? We should be super rich
if we come up with a vaccine for Covid-19.
On the other hand, and I think there's
a really, really important point, markets reward
something that is scarce relative to demand.
That is an amoral judgment.
So, for example, we're seeing right now,
particularly the United States, that people who
are essential workers, we are saying literally
we cannot function without you are being
paid less than people who are
sitting at home doing relatively nothing.
Making a lot more money. So
markets are not a value judgment.
They don't tell us what's right.
They just tell us what's going to happen
when people act on their own preferences.
Harpreet Sahota:
How does the study or application of economics, how
is it going to be different or the
same, now, in the era of big data, maybe
more so than it was 50 years ago?
Charles Wheelan:
In some ways it won't be different.
The basic trade offs are always there.
I mean, you think about something
as fundamental and important as pollution.
Fifty years ago, the pollution
problem was water quality.
The reason we had a water quality problem is
that people just dumped things in our rivers
and our lake. You may or may not be
familiar with the impetus for the American Clean
Water Act, which there are people of good
mind advocating for years to clean up our
rivers and lakes but it didn't become
politically palatable until the Cuyahoga River in
Cleveland caught on fire.
The river caught on fire.
Now, that's the signal to the rest of ha!
I think we have a water quality
problem when our river catches on fire.
Now, what would economists say?
Well, the reason you a water quality problem
is that people don't fully internalize the
costs of their behavior.
If you dump paint into a river, it
poisons maybe hundreds of thousands of people.
You may be one of those persons, but you
only bear a tiny fraction of the cost.
So you're going to behave in a way that
is inimical to the interests of everybody else.
Environmental problems have always
been that an externality.
Now you fast forward to climate change.
It's a different pollutant, carbon,
but it's the same problem.
So in some ways, nothing yet which is why
I think economics is so important if you
understand the basics, I can
fast forward to 2080.
I don't know what the problems are going to be,
but I suspect that an Econ 1 textbook is
still going to be pretty damn important.
Now, what changes will big data?
And again, statistics is just pattern.
It's just pattern recognition.
And big data allows us to
see more patterns more cheaply.
So, again, the underlying statistics won't change,
but our purchase on the data will.
If you think about crime, for example.
We've always had crime data.
It's just that they were in the basement of
the police station in a filing cabinet so
nobody could take all those filing cabinets,
add them together and look for patterns.
Now we can. So, again, it's kind
of the old married to the new.
In a way, the. We use it right?
Can give us insight into things
that we care a lot about.
Harpreet Sahota:
Big Data, machine learning, and AI are becoming
more more you could as in nearly every
aspect of commerce.
What's the implication of this on,
you know, Adam Smith's invisible hand?
Charles Wheelan:
I don't think it changes Adam Smith's invisible hand
much at all in the sense that the
fundamental incentive hasn't changed at all you know,
he said it wasn't the butcher, the
baker out of their good
intentions that fed us.
That's not gonna change again. You know, whoever
is making the vaccine could be a very
generous person who cares a lot about humanity
or they could be the most avaricious,
selfish person you've ever met.
They're both going to work
really hard at a vaccine.
I don't think that changes much at all.
I think what changes with Big Data is
that, like all statistics, it's a powerful weapon.
And I use the word weapon quite deliberately.
It is like fire or a sharp
knife or dynamite, which is used properly.
It really can be put to great effect.
Use improperly. You can
do some enormous damage.
I think it's probably more important than ever
that people appreciate the use and misuse
of big data because anybody
can do the analysis.
You know, something that used to be the
province of somebody who had access to a
mainframe computer.
You can now probably run on your phone.
The question is, do you have the understanding
of what those patterns are and what the
erroneous conclusions might be?
And if not, they you come
up with some very dangerous conclusions.
Harpreet Sahota:
Tongue in cheek question here, based on the title
of one of your other books, Naked Money.
What is money and why does it matter?
Charles Wheelan:
Money is one of the strangest things
you are ever going to encounter.
If we were doing video rather than a podcast,
I would show you the hundred trillion dollar
bill that I have on my desk from Zimbabwe.
I bought on eBay for about 20 bucks.
Most of that, I think,
was shipping and novelty value.
Money is not the paper bill in your pocket.
Turns out that paper bill
can be used as money.
But money is really an agreement
between people that something has value.
If I show up and I rake your lawn,
don't the people rake lawns and candidates whom they
do not come out rake the leaves?
And I say, you know what?
It took me two hours.
You owe me two hours worth of labor.
And you say happily, of course, you
know, and you write something down.
You know, this is worth two hours of labor.
All right. Well, that could be money if I
then, you know, it snows heavily here in New
Hampshire, just like in Canada.
And I say, boy, I don't really want to
shovel my driveway, but I've got this certificate
where two hours of labor and I give it to a
neighbor, not to you, I give it to a neighbor
and I say, you know what, I rake the lawn.
And this is you know, if you take this back
to the guy whose lawn I raked, he'll do two
hours for you. We've created money right?
So it tends to
serve three different purposes.
It's a store of value.
So in this case, it's
that two hours of labor.
It's a medium of exchange.
For example, if I wanted to buy pork chops,
it gets a little more complicated by am I
going to say, hey, you
want your your yard rate?
Here's two hours worth of
breaking give me the porkchops.
Right. And so whether it's gold or diamonds
or currency or though or bitcoin, you can
shop and do things like that.
And then the last is it tends
to be a unit of account.
This is one where our two hours
of raking kind of falls down.
It tends to be the way
that people think about the world.
So if I say, boy, I'll give you one used
Camary, 11 hours of raking, and three bags of
dog food for someone, you're gonna be
like, well, how much is that worth?
You're going to do all these calculations.
Whereas if I say, it's going to be one
hundred and thirteen Canadian dollars, you have to
know exactly how much that is worth.
And so you use a lots of
different things to serve those three purposes.
But anything that doesn't do those three things
fairly well, will probably lapse as a
source of money.
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Somebody I'm a big fan of Naval Ravikant
I'm not sure if you're familiar with him.
Pretty much all he says is
that money is essentially an IOU.
OK, thank you. We owe you something in the future
for the work that you did in the past,
here's a little IOU. Let's
let's call that money.
Charles Wheelan:
It's exactly what it is.
And you can see why it
collapses if people lose faith.
Right. You move out of a neighborhood.
Nobody really wants something saying that you're going
to rake my lawn for two hours or
if I moved into a different town, this guy Canada
come rake your - you're like who is he?
So a lot of money
does come down to credibility.
We can talk about Bitcoin and those kinds
of things, but most current modern currencies,
though historically, they derived their value from
the fact that they could be redeemed
for something that didn't have intrinsic
worth that could show up.
People give you gold.
And therefore, that was why I didn't have to
trust [inaudible] I thought I could get my
gold. That turns out if the bank is
fraudulent or isn't keeping its accounts on the
level, then you might not be
able to get your your gold.
So even under that
standard, trust was involved.
Nowadays, you take your dollars to the Bank
of Canada or to the Federal Reserve, you'll
get a whole lot of nothing.
You can bang on the door.
I'm not even sure they're gonna let you
in and they're not redeemable for anything.
But why do they have value?
Well, because they're years and years in which
those central banks and others around the
world have worked very, very hard to maintain
the purchasing power of those pieces of
paper. That is not
a negligible accomplishment.
Harpreet Sahota:
Due to our current global situation every place
I go to now really, only the grocery
store, no longer accepts
paper physical currency.
They're instead shifting to like
contactless forms of payment.
What, if any, implication does this
have for the future of money?
Charles Wheelan:
Relatively little, if you think about it.
In some ways, it's not any different than
switching from dollars and cents to writing
cheques. Writing cheques is just a paper
version of the digital transfer that you're
doing now. They both say, hey, transfer
money from my account to his account.
Unit of account hasn't changed at all.
Central banks are still defining the dollar,
whether it's going through the ether or
whether it's coming out of my pocket,
whether I'm writing one cheques, they're still
maintaining the unit of account.
Always changed is kind of the means by
which we keep track of those accounts.
We can either do it physically by
stacks of dollars in my basement.
We can do it on paper with cheques where the
bank just kind of takes out a ledger and
moves money from my out to your account when
they get the check Or we can do it
digitally, which is just really a
fancy version of the cheque.
That doesn't change at all And we should
point out that those kinds of digital
transactions are very different than digital
currencies like Bitcoin, because when you
take your debit card to the grocery store and
they swipe it, it's still in dollars and
the federal government is still overseeing that
currency and those transactions and the
security and everything else. Bitcoin is an
entirely different unit of account, which
bounces around a value all the time by the
way, it makes it relatively poor for doing
commerce. That is an entirely different currency,
not just a different way of transacting
with the currency that we've already had.
Harpreet Sahota:
How do you think the future will be
impacted by like these digital currencies like you're
mentioning? What implications will
this have for society?
Charles Wheelan:
I'm told by people who know more than I
do about this that the underlying technology, the
blockchain technology is very valuable because it
allows for the sharing of information
without a central mediator.
So you can bypass, for example, the bank or
the entity if you're trying to transfer money
to your family in El Salvador or you used
to have to go to somebody would take two
percent, then they would wire something to the
bank in El Salvador or they might take
another commission, their credit card, it's just a
different form of a short term loan.
You go pay by credit card, do the
store then reach out to Visa or MasterCard.
They mediate the transaction.
My understanding is BlockChain gets rid of
that mediator, which has all kinds of
potential implications, not least that you don't have
to pay a fee to somebody to deal
with it. It can be distributed in ways that
are lower cost, higher trust and so on.
In terms of the future of
currencies based on block chain.
I've yet to see a profound need for it
other than in places like Venezuela, where your
currency may be collapsing and you
have no faith in the government.
There are extreme cases where just like transferring
your assets into gold, it might make
sense to transfer them into
some electronic digital currency.
But for the rest of us, what Bitcoin and
others offer is the ability to make large
transfers undetected with no
record across international boundaries.
Well, who likes to do that?
Mostly drug kingpins and arms merchants and terrorists
and kidnappers and for the rest of
us. I don't really care
if I buy something abroad.
If there's a record, I'd like to pay a
lower fee but other than that, I think digital
currencies, other than for some nefarious characters,
are a solution in search of a
problem.
Harpreet Sahota:
Quite interesting thing this money is, I
think, hands down probably the greatest
technology invented.
Yuval Noah Harrari, I think, in his
book Sapience mentioned something like inter
subjective reality or something like that.
We all agree that this thing is a
valuable but entirely between our imaginations, right?
Yeah, it's interesting.
Charles Wheelan:
If you think about why it's valuable, it's
not because the money itself has great value
and for the most part, you can't eat it.
You can't keep you warm - if you're
burning it that's a really bad sign.
What's valuable is human commerce, the
ability to specialize in something.
We can become really good at it and
then trade with other people who've become really
good at something else.
And you're now much better off because I
have more meat, you have more econ textbooks,
and so on. Money is
what facilitates that specialization.
And then trust in money is what
allows specialization amongst strangers, which is perhaps
the greatest innovation of all that tens of
thousands of people who don't know each other
can somehow engage in something as complicated
and, frankly, amazing as a modern economy.
Harpreet Sahota:
So I want to jump into some topics
that you covered in your book, Naked Statistics.
Start off with a question about how we
can use statistics to make business work better,
make our lives more comfortable, more productive
and improve what the government does and
doesn't do, especially now
in this post-COVID world?
Charles Wheelan:
What did you just think about some of
the most important questions that we're asking right
now? So what is the fatality rate from COVID?
This is a fundamental question.
If it's a benign disease,
then maybe we're overreacting.
If it's as fatal as the
Spanish flu, we're not reacting enough.
We can't know how fatal it is until we
can figure out how many people have gotten it
without knowing it and have recovered because
you can't know the fatality rate without,
my colleagues we say, what's the denominator?
What's the denominator?
So if we're looking at how many people
die of the disease, who presented hospital?
Well, you're totally missing it.
Those are the people who are the sickest.
If we say, we taking a bath is really
dangerous because everybody who showed up in the
emergency room and taken a bath had a broken
lip like, well, OK but remember that a lot
of people took baths who didn't show up here.
So if we're going to try and figure out how
to take it, how dangerous take a bath is, we
need to know the denominator.
So, I mean, that's a simple but
powerful, powerful example of how data collection.
that's not a very complicated thing by the
way, how many people or probably have it?
But it is a crucial piece of
data to understanding what's going on.
And then, of course, you can just go
deeper in terms of rates of transmission and
breaking out the rates of transmission by age
and geography and indoors and outdoors so I
would say that statistics are at the
core of understanding everything related to COVID.
There's the science of the disease which
is important, but then epidemiology, which is
bringing statistics to disease, is
every bit as important.
Harpreet Sahota:
So you talk about you look to the different
types of biases that we can commit when we're
performing any type of statistical analysis.
In the scenario you just described, which form of
bias do you think would be most, the
one that we should be most wary of?
Charles Wheelan:
I think we should always be wary of selection
bias, which is when the sample that you're
seeing is not representative
of the true population.
In this case, the extreme example is
the people show up at hospitals.
They are the sickest.
We may, for example, find that the disease.
I think we are finding that it is more
dangerous for men than for women but Maybe there
is an underlying but biological difference there, or
it could be that men are just more
stubborn about seeking medical help and the
virus actually is invariant between the two.
In that case, we might tease out
the difference in how they respond.
I would say, you know, I headed about
Canada, but here in the United States, we've
obviously got a robust
political debate going on.
Got a presidential election
coming up in November.
I think one of things that really skews
our politics is because we have sorted ourselves
as Americans around other people who
tend to share political views.
We are all living selection bias.
We've kind of selected news
that reinforces our views.
Neighbors who tell us what we want to think.
Social media is going to repeat
our brilliant thoughts into selection bias.
And politics is quite dangerous because we are
really unaware of how the whole country is
interpreting different events.
Harpreet Sahota:
Curious as to your thoughts as to the
election and how social distancing is going to
impact our elections?
Are going to go digital at all in the future.
We're in this covered world
or does that play out?
Charles Wheelan:
Yeah. I do have a professional interest in this
in that I read the Centerist Manifesto but
what grew out of that is this larger
effort to re-empower, the American political middle,
which is most observers know is kind of being
beaten down by the extremes as part of
that. I was the founder of a group called
Unite America, and we are proponents of a
number of process reforms to
help the system work better.
So, for example, he may or may not be
familiar with the term gerrymandering, which is the
mechanism by which state legislatures
draw the congressional boundaries.
And of course, they can draw them in
ways that are electorally favorable to whichever
party controls power.
So you get these crazy looking
congressional boundaries in United States.
So we're working on processes to put
an end to that independent redistricting commissions
and so on. Well, one of our reforms that
we were proposing before, COVID-19 is vote from
home. It doesn't have to be vote by mail.
You could probably also come up with a secure
electronic way but the key insight is that
even before this came along, it is really important
to make it easier for people to vote.
Colorado has already done that again long
before COVID-19, they established a system
whereby people were automatically registered when they
got a driver's license unless they
didn't want to be, and
then they could opt out.
They automatically were sent a ballot for every
election, which they could return by mail
if they wanted, or they could bring that ballot
to a polling station where they agree to
any other polling station.
That turns out that only
it favors Republicans or Democrats.
Then, of course, it has been has been the
debate here but it does favor young people
because they're more likely to vote.
It favors independents and
those kinds of things.
You know, my view is that broadening
the electorate is a good thing.
And that vote by mail, which is proven
secure, is an effective way of doing that.
Harpreet Sahota:
Few more questions based on naked statistics.
I wantto start with, why are humans
so bad at appreciating conceptualizing probabilities?
Charles Wheelan:
Humans are hard wired to live in
caves and run from snakes [inaudible] fire.
In other words, you know, our brains have
very successfully evolved, the reason you and I
are alive is because our relatives know
when it snakes through the grass.
Didn't say, hey, let's lean over and take
this home and see whether it's poisonous or
not. People who did that are with us anymore.
Their genes were eliminated.
You know, hey, this is fire.
What happens if I had. No, don't do that.
Those are immediate, palpable threats.
As a result, we're less good
at more abstract long term threats.
Climate change, for example, is
the opposite of fire.
Your garage is on fire.
You're going to run immediately.
Climate change like it doesn't seem that much
different than yesterday and only as a
resource. So the brain has developed
weaknesses as well as strengths.
It turns out that abstract concepts like risk
are also prone to those kinds of mistakes
for assorted reasons.
I think behavioral economics is a major step
word and people are interested can read more
deeply about why we make these mistakes but
for example, many people are afraid to fly.
Almost nobody is afraid to drive.
The United States only driving something like, depending
on how you measure it, a thousand
times more dangerous than flying.
My understanding is there a couple reasons
if people are afraid to fly.
One is they read in
the newspaper about plane crashes.
They almost never read about car crashes, even
though in the United States, 40 or 50
thousand people a year are dying in cars
and probably since some years, no plane crashes.
In some years, several hundred people die
but those are what you read about.
So they get tankered. The other is
I think there's some sense of control.
We have a sense that when we're driving,
we're in control, even though from a statistical
standpoint, you really have the pilot flying a
plane that's regulated heavily by lots of
governments. But that combination of control, what
we're exposed to and other things
explains we're just not really
good at appreciating risks.
And then there's some things
that combine two of them.
So, for example, things like smoking and
sun exposure, which are quite devastatingly
dangerous, are long term harms that
you don't read about very often.
And therefore, you're more likely to be worried
about Harp being abducted by ISIS or
something like that, where there's almost no
chance that that's actually going to happen.
So there's a lot of reason for it but the
short one is that our brains just aren't as
good as they should be at dealing with modern
risks and the tools by which we use to
evaluate.
Harpreet Sahota:
So for Data scientists who are working with
vast quantities of data, why is it important
that we cultivate an intuition
for what probabilities represent?
Charles Wheelan:
I think it's essential to making better
decision makers out of our electorate.
So just today, for example, I read about
somebody was trying to quantify the harm being
done COVID-19 in terms of plane crashes.
I can't remember the exact
number, but it was.
Nine or twelve or thirteen
plane crashes a day.
And again, if you're in a place that's
not directly affected or you don't know somebody
who's died, when someone says one hundred thousand
people died, I'd say that is just so
abstract that it doesn't directly affect you.
So by putting it in terms of people do
understand the tragedy of a plane crash, maybe you
can motivate action.
The same is true with something like malaria,
which kills hundreds of thousands of people
every year around the globe but it's
not really a first world problem.
And again others have tried to emphasize in
ways that are understandable to our reptilian
brain. Why do we care about this?
If we don't do it, then we can't expect
the electorate to support things such as efforts
to ameliorate climate change if they don't appreciate
in the present that long term risk.
Harpreet Sahota:
Why shouldn't we buy the extended
warranty, the ninety nine dollar printer?
Charles Wheelan:
So the way extended warranty is
just the kind of insurance.
It's no different than life insurance or
car insurance, you're just insuring a different
product over some term.
You should always buy insurance if the
outcome you're insuring against is something that
you cannot tolerate either because of your willingness
to take risk or because it's going
to destroy your life. So why
do I insure my home?
Because if the house burns down, that's a
matter of money that I don't have sitting
around and that would change
the life of my family.
Do I think my house is going to burn down?
No, I do not. I had some concerns last
night because my daughter had a bonfire in the
backyard. But that's the exception.
For the most part, why would you buy life
insurance if you think your family won't be
able to send the kids to college
or we'll be able survive without you?
You know, I've cancel my life insurance, why?
Because my kids are now
almost out of college.
My wife has a good job.
You know, if I go, I don't plan on it.
But they don't need it.
So it's not worth the premium.
It was really important when I was 30.
All right. So back
to the extended warranties.
If the toaster oven breaks, you're a nine
dollar toaster oven, is this going to change
your life? And I hope the answer is no.
You don't need to insure against it.
And then we take the second piece of it.
Remember, insurance companies, including whoever's offering
you these awardee, are for
profit companies. They'd run the numbers.
So they think wherever they're gonna have to pay
out if things break is less than what
they're going to take in. So we know that all
insurance tends to be a bad bet, but some
insurance is worth taking a bad benefit
protects against something you can't tolerate and
of course, insuring the toaster oven is a bad
bet and it's not one that protects against
something that you can't tolerate.
Harpreet Sahota:
Once upon a time, I used to be an actuary
and I did warranty pricing that's coming up with
premiums warranties, yeah, don't buy them.
Charles Wheelan:
And they hawk them so aggressively at the
counter because they're doing a great job for
you.
Harpreet Sahota:
Shifting gears here, wanna get
your perspective on this.
A lot of up and coming Data scientists and
just data scientists in general, they tend to
focus primarily on on hard technical skills
and rightfully so it's exceedingly important
for the work that we do. You know they
think that is what's going to separate them from
the rest of the world,
the rest the competition.
So what are some soft skills Data scientists
are missing that you think are really going
to help separate them from the
other data scientists out there?
Charles Wheelan:
I am a huge proponent of the Liberal Arts, which
is to say that even if you are very
technically oriented, you have got to have
an awareness of sociology, psychology, great
literature and the like.
And the reason is that the hard questions,
things like income inequality and why people
are poor and why humans do what they do
are wrapped around everything that on its face
might seem like just a Data issue.
And if you don't have a greater appreciation
for those big questions, you're going to
miss some things. So, for example, I heard
a commentator say something that was probably
a little snarky about Facebook and in particular
about its founder and she who dropped
out of college to found Facebook in her
point was, well, maybe he should've stayed in
college and Harvard because then he would
have had a greater appreciation for Russian
motivation and how fake news confect society.
And there are all these other big
issues that Facebook has stepped into.
Now, I think the comments unfair because I not
sure another year at Harvard is going to
solve these hard problems but I think the
point is nonetheless valid, which is, wow, if
you're gonna be as powerful and prevalent as
Facebook, you better be thinking long and
hard about freedom of speech and hate speech
and why hate speech exists and international
relations and what the Russians want because if
you don't, then you can be the greatest
coder in the world and
you are completely blind.
You're the guy carrying the dynamite
around saying, hey, isn't this great?
Well, what happens when you
light the fuse, right?
You you have to read the safety label.
I'll give you a perfect example of one of
a policy problem where I think Data can be
really helpful or make things worse.
And that bail reform.
So I don't know in Canada if you commit
a crime, I don't know if you have bail.
In the United States, if it's a really serious
crime, they just send you to jail until
your trial comes up. But if it's a more
minor offense and they don't think you're going to
flee before trial, then you post some money
and then you come back to your trial.
Well, what's important here is figuring out who's
going to flee and who might commit
another serious crime while they're on bail.
And we have Data on that.
Right. We've got decades
and decades of records.
And the question is, can we use those
records to create a more humane bail system?
Because you really don't want to hold someone in
jail, doesn't have to be held in jail
because they haven't found guilty yet.
It's inhumane.
It's harsh, they have to quit their job
and of course, they might be found innocent.
So let's look at the data and
see who should get bail or not.
And maybe, by the way, that liberates us
from some of the racial stereotypes that have
been perpetrated. So if we find, for example, that
people of color are less likely to get
bail for similar crimes, then we can
the Data will set us free.
All right. Well, that's good.
But let's suppose that while people are
on bail, there's certain groups who law
enforcement are likely to more
likely to prey on.
So we said, well, this group, they seem to
commit more crimes while they're on bail than
somebody else. Well, if the way we're policing
isn't fair, then our Data may actually
just be reflecting a problem.
Not illuminating it.
And so we may say, well, don't give those
folks bail when in fact they are equally
deserving. So if you're Data reflects some
underlying problem, then any model you build
from those Data will just embed
it more firmly in cement.
So, you know, on the other hand, maybe that's not
the case and we can't come up with a
more humane bail system.
You got to be really careful about the
system that generated those Data before you make
conclusions using your statistical methods
by the way, maybe terrific.
But this isn't about statistics.
This is about law enforcement and
the behaviors that generate the data.
Harpreet Sahota:
That's why I think it's super important to have
an awareness of all the different types of
biases that you can commit?
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I think you may have a crystal ball somewhere
because back in 2019 you go to fiction
book, the rationing and going to Twitter, May 2018
you tweeted that for the first time in
my writing and political life I
get to just make stuff up
Charles Wheelan:
Right, and it turns out I
wasn't really making stuff up.
Harpreet Sahota:
Yeah. Fast forward March 2020 you tweeted, I
thought I was writing a dystopian fiction
apparently not. So talk to us about your book,
The Rationing, can you give our listeners a
synopsis of the book and maybe draw parallels
between the fiction you wrote and the
reality that we're experiencing today?
Charles Wheelan:
Yes, well, I'll describe the book.
The comparison to reality will become quite
obvious as I give us a synopsis.
This is a book novel, my first fiction
that takes place in the near future.
I think it's in the late twenty twenties.
A pandemic has broken out, there's a virus that's
not fully understood as a result of the
pandemic, there's a panic.
The political powers, United States focused
on what becomes global very quickly.
The president and his immediate staff are trying
to deal with this first in secret, later
becomes public and then, of course,
it becomes a political battle.
The president's opponents seek to seize on this
as a way to defeat him politically.
And then it becomes
an international challenges.
China and the United States go
head to head over the pandemic.
So that's all the fiction.
Ostensibly, it just happened to be largely what
happened a year after it was ours.
And we can talk about kind
of how I stumbled upon that.
But, yes, that is the short answer is really
that the world racing to deal with a virus
before the virus dispatches
with the population.
Harpreet Sahota:
Yeah, definitely. Let's talk about how you stumbled
upon this and how you came up with
this concept of this book.
Charles Wheelan:
It's interesting. The title was The Rationing, because
what I wanted to elucidate is some
of the trade offs that
are inherent to economics.
What we talked about at the beginning of
this conversation that you're never going to
have enough of everything.
And I thought, all right, well, how well,
what kind of situation what kind of fictional
situation would throw that into stark
contrast and make it really clear?
I figured, oh, all right. Well, if you had a
pandemic and you only had a finite amount of
something, you need it now because this
book takes place in the near future.
We do have kind of
a miracle antibiotic called, dormagen.
Unfortunately, we don't have that right now.
That's the only thing that
I wish were not fiction.
But there's not enough of it in the book
for reasons that have to do with the plot.
So the explicit question is,
who gets the dormagen?
And there's a chapter in the book that really is
the kernel of it, which is OK, do we
give it to all people?
Do we give it to prisoners?
Do we give it to people who've been in
prison because somebody is going to die and, oh,
we're going to have to
explicitly decide who that is.
So I just wanted people to understand
that life involves those kinds of tradeoffs.
The pandemic, I think, was just kind of dumb
luck, although I will say that I've studied
enough public policy to know the people been
warning about something like this since I
was with The Economist, which is the late 90s,
many people said, and this thread is not
off the table, that it
might be antibiotic resistant tuberculosis.
It was the kind of antibiotic resistance piece
that many experts thought would be more
likely to generate
something really dangerous.
But in any event, this is one of those
termites in the basement kind of problems like
climate change that we're just not willing to act
on until it shows up as a garage fire.
So I figured that would be I like viruses.
I think they're just fascinating.
That was really a setup for a larger
discussion of human nature, of rationing, of our
political system.
Partisanship is of several fake news strands that play
out in the novel, kind of all the
things that I thought would happen if there were
a pandemic and sadly, most of them have
happen.
Harpreet Sahota:
Which aspect of human nature do you think from
your fiction has shown itself to become a
reality with our current situation?
Charles Wheelan:
I think it's the political tribalism.
And this also is where my work with
Unite America and the Centrist Manifesto intersects
with what we're watching in terms of
political response and the book, the rationing.
And this also has an evolutionary biology
piece to it, which is OK.
How do you survive?
We survive by running from snakes, by
not touching fire and by distrusting strangers.
Unfortunately, I mean, the reality is, if you're living
in a band of 40 people, you trust
each other and somebody shows
up whom you've never met.
You can ask a lot of
questions or you can react violently.
So there is kind of this inner distrust.
Now, I think many historians and economists
say one of society's greatest achievements is
the ability to trust a wider
and wider circle of people.
For reasons we've already talked about,
then you enjoy all the benefits.
First, are not a country at war, but also
you get to enjoy the products of their
productivity and so on.
But deep in the reptilian brain is some
distrust of people who don't look like us.
Right, for whatever reason.
And so I think in this country, I
can't speak for Canada, that tribalism, it's less,
although sadly it hasn't disappeared
about race and ethnicity.
That's still there. But a lot of
it has evolved to political identification.
So I think what you're seeing now
is people retreating to their political tribes,
regardless of what happens.
But it has certainly happened
with our response to COVID-19.
And that has been exacerbated by some underlying
realities of the virus, which is it has
hit blue states worse than red states.
So it's not just that Republicans and Democrats
are looking at this through their own
lens, which they are, or that they're tuning
on to separate news sources, which they are.
It's also that what they're seeing outside their
doors is different in response in terms
of the virus. So all of those things
lead to the hyperpartisan reaction that we're seeing
which obviously doesn't serve us when we're dealing
with a public health problem, not the
one that's narrowly political.
Harpreet Sahota:
Thank you so much.
That's very, very insightful.
So we've got the last question here
before I jump into the lightning round.
What's the one thing you want
people to learn from this story?
Charles Wheelan:
[inaduble] I'm gonna say two things. One is,
I think they're always going to be tradeoffs.
I actually walked in and some of my fellow
economists as I was sitting down to do this
podcast and he said, you know,
there are no solutions, only tradeoffs.
And I think if you think about hard problems
as tradeoffs, then you're going to get more
traction issues like dealing
with hard diseases.
No doctor, no Oncologists says, yeah, here's the
miracle cure for this really bad cancer.
He or she says, look, it's a bad disease.
Here's a pill or medicine.
Here are the 17 side effects but we think
that still it's better off that's how we deal
with all of our private problems.
So understand public tradeoffs.
Think about policy challenges
in terms of tradeoffs.
Appreciate why different people might with
different weight on those different tradeoffs.
And the second is
something we've talked about.
But I'll just make it more explicit is
that the big data is a weapon.
It's like nuclear power.
And that as it becomes more
powerful, it also becomes more dangerous.
And it is incumbent upon people to appreciate
what could go right or wrong when they're
drawing conclusions based on
analysis of big data.
Harpreet Sahota:
What's a academic topic or research area
a data scientist should spend some time
researching up on?
Charles Wheelan:
I think every academic worth his or her
salt, or data scientist, or should read fiction.
What fiction does is illuminate the great questions
and distill human nature in ways that
we cannot even approximate with non-fiction, what we
can do in such narrow ways if you
read great novels.
What characterizes them as great novels is that
they've just gotten to some eternal truth
through fiction. I read a short story that
was written, I think, before 1920 called the
Garden Party, and it was just the best
distillation of income inequality that I've read
in a long time is about a little girl who
goes to visit the home of one of the servants
who works in their giant home on the
day that they're planning this elaborate garden
party. And it just nails the
fundamental unfairness of a market economy.
And again, that's a hundred years old.
And so I would say read fate wouldn't
be more complete person and a more effective
researcher or academic or
business person read fiction.
Harpreet Sahota:
So do you have any
book recommendations for our audience?
Charles Wheelan:
Well, other than The Rationing, I
think they want to start there.
I'm a big Somerset Maugham fan is somebody who
writes a lot about human nature if you
read his short stories, they're set in
the South Pacific, a lot of them.
They deal with colonialism and clashes that
are rooted in racism and other things.
And they really pushed to the surface often
a surprising or any entertaining way, some
human truths. In his novels, I
think get to the same.
Harpreet Sahota:
It's just storytelling in general is an important
skill, I think, for anybody working in a
Data related profession that's something
that they should possess strength.
So you kind of pick that
up through reading fiction as well.
That's awesome. Thank you so much.
So what are the 11th and 12th things
that no commencement speaker has ever said?
Charles Wheelan:
I'm going to paraphrase, I think this is
Richard Branson's line, anybody who thinks that
money will make you happy
has never been rich.
There are other strands in the book that make
the point that you've got to find a journey
and self-fulfilment away from money.
But I would probably put a sharper point on
it that the dirt, and particularly for the
audience that speech was addressed to, which is
an Ivy League audience who are relatively
privileged and are going to have a lot of
career choices that you have the luxury for the
most part of not having to
work to feed your family.
You're going to have some choice about your
avocation and that the nature of the working
with you enjoy it or not is going to
determine your sense of self worth more than the
balance sheet.
And then I guess I don't
why I'm so obsessed with money.
Maybe because I looked out as we did in the
last one, which I tell myself all the time,
because he wants to be an entrepreneur.
If you want to get
rich, stop worrying about money.
Steve Jobs was not like he didn't say,
boy, I really want a fancy car.
What can I do to get a fancy car?
If that's kind of 11.1 or 12.1 would
be stop thinking about making technology better and
start thinking about
solving people's problems.
Right. I mean, one of my pet peeves is
what every day some software that I generally like
gets reengineered in a way that
the people around somewhere love it.
But I'm the guy using it.
I really care about that.
I'm the writer. This is
a word processing software.
So stop making it elegant for yourself.
And again, any great entrepreneur is somebody who
just found it if you think about naked
economics, we don't think
of authors as entrepreneurs.
That was entrepreneurial.
That was. Look, there's a need.
Write the book for people who don't currently
have access that books stop thinking about
what you're doing and look around
the world and see what's missing.
And that will both lead you to be
a more effective entrepreneur and probably make you
richer too.
Harpreet Sahota:
Dig into that a little bit more.
First of all, if you can give us a synopsis
of what Ten and a Half Things No Commencement
Speakers Has Ever Said, the kind of moral of
the story from that, if you share that with
us,
Charles Wheelan:
It was based on a speech that I gave
at Dartmouth that was originally called Five Things n
o commencement speaker has ever said.
It was quite deliberate. I went to Dartmouth, so
here I was 20 some years later, giving a
speech that I have heard.
Then considering the speech was, OK, if somebody
while I was sitting on the grass that
day listening to the speech, what do I wish
20 some years later that somebody had told
me? So now, like when I sat down to
write and in particular I wanted to enumerate things
that commencement speakers
don't typically say.
So what unexpected would I
have wanted to hear?
From there, it just came out with some
things, many which are supported by Data.
Number one, I think, was your time in
fraternity basements was not wasted, which I can
assure you most commencement speakers are not,
say, and the university the college
president was sitting behind me and you could
see him shift uncomfortably in his seat
because there's a whole lot of bad
things that go on in the fraternity.
But the larger point was, look, happiness.
We know this based on research.
Happiness depends on your social network,
on having meaningful friends, connections with
other people. And I was just using
fraternities as a metaphor for that.
It could be the chess club.
It could be hiking outdoors, could be anything
that connects you with people with whom
you have something in common.
Do not undervalue that.
And that is an important part of college.
So that was and we do it, but
we have the happiness research that shows that.
That's was number one, I think the other
ones married somebody who's smarter than you are.
I married someone who was a classmate and I
said, look, it's never going to be easier
because at commencement, the people were Phi Beta
Kappa, the top 10 percent, they have
red ribbons on their gowns.
He will never be easier to
find that people are really smart.
Then tomorrow and my wife was Phi Beta Kappa.
I was not. I look at work for me.
So that was no way.
I think and again, you know, having a
meaningful partner is really important in terms of
happiness, a Data quite clear on that in terms
of the way my wife and I have worked
career wise, we each supported the
other person at times of risk.
She was able to quit a software job
and start her own software services company.
I was able to get my PHD,
which is a long slog without income.
Later on, she was able to go
back and get her teaching license.
So it just feels like we've been
able to do Ying and Yang.
So that was another one.
They were kind of in that spirit things
that was mildly surprising, but usually supported
by either life experience Data or both.
Harpreet Sahota:
Follow up question to that, based on
what you were saying earlier about entrepreneurship
and how if you're going to become an
entrepreneur think more about solving a problem
instead of getting rich.
What are some qualities that an entrepreneur are
cultivating or a would be entrepreneur in
concert cultivating within themselves
to make that happen?
Charles Wheelan:
One is intellectual curiosity.
Just understanding how the world works,
why people do what they do.
The second is clearly passion, if you look
at the early tech entrepreneurs, it's because
they care deeply about something.
The personal computer, in the case of Bill
Gates computing, married to design and a
consumer friendly facing product in the case
of Steve Jobs, these weren't somebody
sitting around saying, how can I get rich?
These were deep seated passions, married to
lots of personality traits that are
associated with success.
So then I think also some
competence in an area of study.
So I couldn't have written an economics book.
I didn't have a PHD in public policy
and hadn't spent a lot of time writing.
He also got to be pretty good at something
but I think that that marriage of capacity
with intellectual interests, with passion for some
subject is usually at the heart of
what leads some entrepreneurial breakthrough.
Harpreet Sahota:
So if we can get a magic telephone that
allowed us to contact 20 year old Charlie, what
would you tell him. First, tell us what 20 year
old Charlie was up to and what would what
advice would you give him to to get him
through whatever he was going through at that
moment?
Charles Wheelan:
I think first thing I would say
is, yes, you will become a writer.
Just stick with it. My mother, who is a
wonderful person but not always the most willing
to challenge the status quo, used to tell
me, like, stop writing in your journal and
focus on your schoolwork, because that was
clearly a passion that I had developed.
By the way, I've kept a journal ever since I
was 19, 18, 17 to this day, volumes and
volumes. So really, that was my passion and
she just couldn't understand I was going to
turn that into a professional career.
So I think I'd tell the 20
Charlie, like, you know, you're right here.
Stick with it. Don't let them
beat it out of you.
And then I think the other thing I would say
is continue to buck the trend when you think
it makes sense. Most people, I hope my
classmates mostly went on to do consulting and
finance. There was nobody in my immediate
peer circle who got a PhD.
My parents dont anybody with a PhD.
They thought it was somewhere between a
curiosity and an insane thing to do.
In fact, it was exactly what was important to set
me on a career path where I could be a
writer and have deep,
substantive knowledge of something.
So I think at age 20, I had this sense
that people were running in a direction that I
didn't want to run. And there was a herd mentality
and a lot of people telling me I'd go
with the herd. And it does take some courage, I
think, just to step aside and say, no, I
want to go over that direction.
So I think I would encourage that 20
year old just to stick by my guns.
Harpreet Sahota:
It's really good advice even today.
I mean, like me for example. I'm an
Indian male, grew up in an immigrant family.
There's only three career choices.
Doctor, lawyer, engineer.
And now it's it's like the Internet has opened
up the space of possible career choices in
such a way that my parents
can even fathom it, right?
I remember I lived in Chicago for a
while, I went to grad school there.
And my dad can visit me and he's just
baffled by all these buildings and all these condos.
He's like, what do all these people do?
He's like they can't all
be lawyers, doctors, engineers.
Charles Wheelan:
I had a student story in that regard.
Yeah, yeah. Students coming
to ask for advice.
Right. So I had a young Indian male.
So first generation parents, an immigrant and
they know their career choices were about
what you elucidated, right?
You know, they're trying to decide whether
I should be a lawyer, engineer, doctor.
And I said, all right, well, like,
what do you want to do?
And he said, I really
want to be a playwright.
I thought, oh, dear God, I can only imagine.
How well you should try to be a playwright
but I imagine him going back and telling his
parents, oh, Professor Wheelan said I should try
to be a playwriting and imagine them
storming my office.
That's you know, you've got to you
got to do what you want.
And I think the other thing is if at
some point lots of evidence suggests you're not very
good at playwrighting, then you got to pivot.
I mean, there is know that's
what the standard commensal speak.
If you stick, follow your
heart, you'll be successful.
I'm never going to be
an NBA basketball player.
That's just not in the car.
So there is a certain amount
of kind of being self aware.
But if you've got the passion and the
rudiments of the skill, then you certainly won't
become a playwright. If you go to law
school or if you become a lawyer.
Harpreet Sahota:
What motivates you?
Charles Wheelan:
I really like what I do at present.
I'm writing a lot of fiction.
There've been so many points in my life where I've
said to myself, is this crazy or am I
wasting my time at this?
And began, for example even during my
PhD like this is so long.
All my friends are business
school is two years.
And you know exactly when you're done.
And I'm four years are
five years and it's indeterminate.
You're not done until your thesis Advisers say
you're done, which is a strange kind of
uncertainty. And my parents are like,
why are you getting abused again?
Later on, when I was trying to get a job
with the economists, I spent a year reaching out
to them, sending them articles and
faxes and calling periodically anything.
You know, you might not hear that.
Of course, things fall in my direction.
Fast forward a long way to the novel.
I wrote the novel before I
had any hope of publishing it.
I remember sitting here in this desk, copy
editing it, thinking like, is this crazy?
Like, Wow, why am I spending
so much time on fiction?
So I think what motivates me is because I like
doing it and I think it's good and it's
consistent with my long term interests.
So I think there's some kind of inner compass
that tells me what I should spend my time
on right now, writing short stories which
are even less commercial than novels.
But I feel like I've got things to say.
So I would describe myself as
a very intrinsically, almost exclusively, intrinsically
motivated person.
I left one job and went to another job and
the first job like, well, you know, can we pay
you more? And I thought, am I like, no, actually
the place I'm going is going to pay me
less. How do you respond to that?
Oh, we'll pay you even less than their pay.
Like I said, it just didn't.
I probably should have asked for more, but
it just wasn't just spelling a better fit.
Harpreet Sahota:
I mean, what you just said
resonated with me so much.
I was having this exact
conversation with my wife yesterday.
It's like what the hell am
I doing with this podcast?
Am I crazy spending every waking hour outside of
my full-time job, like, I'm up at 4:00
a.m. editing the podcast, transcribing it, mixing it,
putting each - like what am I
doing? Like, literally exactly
what you're saying.
Am I crazy? What am I doing.
Charles Wheelan:
You know, often when things don't work, there's
some residual value there that turns out
to be a game changer as well.
So when I was in grad school, I got in
my mind that I really wanted to write screenplays.
I probably wrote like nine or 10 screenplays.
I didn't actually sell one.
It was never made into a movie but so
far it say I'm not a screenwriter right now.
So that was a lot of time and effort.
But, you know, when I sat down to write
the novel, I was really good at dialogue.
And in fact, I just sold a short story and
part of the appeal was like, wow, this is
really snappy dialogue.
In fact the reviews, the novel said, you know,
part of what makes the book work is you
said, well, a screenplay
is all dialogue, right?
That's pretty much all
the screenwriter puts in.
The rest of is gonna be
picked up by the camera.
So what are you like twenty five years later, this
skill that I refine that seem to be a
dead end at the time turns out to
not have been a dead end at all.
Harpreet Sahota:
It's very, very inspiring.
So what song do you have on repeat right now?
Charles Wheelan:
I've got Gloria Naylor, I will survive.
I've been listening to that.
I do a lot of bike
riding and I've got this playlist.
And that's the one that
I've added most recently to.
I think it's kind of a catchy tune.
It's nostalgic from when I grew up but as
we're all doing quarantine and the like, you
know, I will survive has even more
resonance than it did in the past.
Harpreet Sahota:
Dr. Wheelan, how can people connect with you?
Where can they find you?
Charles Wheelan:
Yes. So my Web site is Nakadeconomics.com that's kind
of a landing page for all the other
things going on in my life.
I think therationing.com also well
before I did there.
You can find that book anywhere but I tend to
pop up, as you know, in lots of different
domains.
Harpreet Sahota:
Thank you so, so much for
popping off in my domain.
I really appreciate you taking time out
your schedule to be here today.
Thank you. Thank you so much.
Charles Wheelan:
Thank you. Good luck with the podcast,
with parenting, which is, I will say
parenthetically a humbling process.
Good luck. It was fun.
