00:00:00
But then you also have, and you
use the term in your research,
00:00:04
the good bad job.
00:00:06
How does that come into play?
00:00:09
You know, I trust what my
00:00:10
workers tell me. You know, I
didn't love driving, but a lot
00:00:15
of my drivers did like driving.
And I think that was my first
00:00:18
"aha!" in the research. This is
some sort of tension I need to look
00:00:22
into. So yes, the driver is
telling me, "I'm earning more than
00:00:25
I was at Walmart, at the gas
station. I can take care of my
00:00:29
family. You know, I like driving
around town and showing people
00:00:32
the sights of my city."
00:00:33
Welcome to
<i>The Ripple Effect</i>, the
00:00:35
podcast that takes you on a
journey through the minds of
00:00:38
Wharton faculty. I'm your host,
Dan Loney, and in each episode,
00:00:42
we'll be diving deep into the
inspiration behind the
00:00:45
groundbreaking research that
Wharton professors have
00:00:47
conducted and exploring how
their findings resonate with the
00:00:51
world today.
00:00:53
Well, as more and more people add gig
work to their professional mix,
00:00:57
we also see an increase in
algorithmic management. This
00:01:00
means that workers are following
the instruction of so-called
00:01:03
managers, who are the process of
algorithms. Recent research
00:01:08
examines what that relationship
is like and how workers feel
00:01:12
about being managed by tech and
not by, directly, another human
00:01:16
being. Pleasure to be joined by
Lindsey Cameron, Assistant
00:01:19
Professor of Management here at
the Wharton School, who's led
00:01:22
this research. Lindsey, great to
talk to you. It's been a while.
00:01:25
Always great to touch base.
00:01:26
Yeah, definitely. Feel the same
over here.
00:01:29
I know how much you
have focused on a lot of these
00:01:32
elements in your research, but I
guess when you look at that
00:01:35
relationship, it becomes even
more interesting with how gig
00:01:39
work kind of factors in.
00:01:41
Exactly. You know, one thing I
00:01:43
love to say about the gig
economy is that it punches above
00:01:46
its weight class. It's only
about 1% of the US workforce,
00:01:50
but 97% of people have heard of
Uber and Lyft. You know, it's
00:01:54
about 60 to 70% of Americans
have gotten in one of those
00:01:57
cars. And you think about all
the sweeping legal regulations
00:02:01
that we've talked about before,
Dan, in California,
00:02:04
Massachusetts, Minnesota, all
about redefining gig work, which
00:02:07
has all these ripple effects for
other types of independent
00:02:10
contractors. So the gig economy
is really just a microcosm that
00:02:14
helps us understand broader
changes in our economy.
00:02:17
Right. And I guess, where this
research is concerned,
00:02:21
it's unique to
00:02:22
look at how these workers are
being managed, and what that
00:02:26
might mean for the concept of
management moving forward.
00:02:30
Exactly. We always think of
management as like, having a
00:02:33
boss. And like, how do I avoid
the bad boss and get the good
00:02:36
boss? But you know, we have
these longer conversations,
00:02:39
larger conversations, about AI
and control. And really what is
00:02:43
AI doing? It's trying to
algorithmically manage people.
00:02:46
Outsource part of the work, the
managerial function, to the
00:02:49
algorithms. And Uber is a really
great example, from end to end.
00:02:53
You know, I worked part time as
a driver for three years. Never
00:02:57
spoke to a single employee of
Uber. Hiring, firing,
00:03:01
evaluating, disciplining, all
done by an algorithmic
00:03:04
management system. It's really
gives us a precursor or a view
00:03:08
into the future.
00:03:09
What did that
experience really mean to you in
00:03:12
going through that? Because, I
mean— I mean, it's unique. As
00:03:16
you said, we're so used to
coming into the office and
00:03:20
meeting with a boss or talking
with other people. And it's—
00:03:23
it's— it's a totally different
experience, isn't it?
00:03:26
Yeah. It is— you know,
00:03:27
you do— you do have a lot
of schedule flexibility. You
00:03:30
can— you can work around your
schedule. But when things don't
00:03:33
go right, then you don't know
what to do. There's this whole
00:03:37
period I drove in the DC, you
know, Virginia area. And there
00:03:41
was, like three hours— I was
sitting near Dupont Circle. It
00:03:45
was raining. I was seeing all
these other cars go by and
00:03:48
people getting in the back seat.
I'm like, "That's an Uber. I
00:03:51
think it's an Uber. But why am I
not getting any rides? Why am I
00:03:55
here in the dark, not earning
income?" And I was texting, and
00:03:58
they were like,"No, we're— you're
logged on, you're eligible to
00:04:02
get rides." I had no real way to
have voice and resolve an issue.
00:04:06
And the next day, they
apologized. They said something
00:04:08
was down to the system. I was
logged online, but not getting
00:04:11
rides. So there's this joy in
schedule flexibility, and then
00:04:15
there's this issue of when
things don't go right, whether
00:04:18
it's pay, something with a
customer, not getting rides— then
00:04:22
you're really— you're talking to
a robo bot.
00:04:25
And it's hard to get
resolution.
00:04:27
What does it mean,
then, for the types of tasks that
00:04:30
may be brought that person's way
in terms of dealing with them
00:04:34
and handling them, depending on
who or what is kind of managing
00:04:39
the process?
00:04:41
So the types of
tasks that you're going to see
00:04:43
algorithms sort of step in and
be the boss are going to be
00:04:46
sliced down to the smallest unit
possible. We call that de-
00:04:52
skilling. But it's at a much
deeper way than, like, factory
00:04:56
work, when you're at a assembly
line. Then it's a bit of de-skilling.
00:05:00
But this is like, "Can a task be
completed in two seconds?" You
00:05:03
know, a micro task. So when you
think about, say, ride hailing,
00:05:07
for example, it's lots of little
micro tasks. Will I accept this
00:05:10
ride or not? Am I going to
follow the GPS out, Ubers or
00:05:15
Waze, to go where I'm going? Am
I going to talk to the customer?
00:05:18
Which way will I drive? And then
will I rate the— do I rate them
00:05:21
or not? They're all these
very, very small components, but
00:05:25
because they're so small, they
can be algorithmically managed,
00:05:29
and at the same time, the
workers feel like they have
00:05:31
choice, because they're all
these little individual
00:05:34
elements. I have a very small
but very real amount of choice.
00:05:39
And I think that's one of the
reasons why people like this
00:05:41
work so much, is this feeling of
choice, Small but real.
00:05:45
But then you also have—
and you use the term
00:05:48
in your research, the good
00:05:50
bad job. How does that come into
play?
00:05:54
You know, I trust what my
00:05:56
workers tell me. You know, I
didn't love driving, but a lot
00:06:00
of my drivers did like driving,
and I think that was my first
00:06:04
"aha!" in the research. This is
some sort of tension I need to
00:06:07
look into. So yes, the driver is
telling me, "I'm earning more than
00:06:11
I was at Walmart, at the gas
station. I can take care of my
00:06:14
family. You know, I like driving
around town and showing people
00:06:17
the sights of my city." But then
if you zoom out of the workers,
00:06:21
you gotta look at what's the
larger legal, social,
00:06:25
environmental influx. These
people don't have insurance
00:06:29
coverage if they get into an
accident. You know. This— you
00:06:31
might get in coverage when the
passenger's in the car, but what
00:06:35
happens when you're driving? You
know, waiting to get that ride.
00:06:38
You're not covered in the same
amount. You saw during COVID how
00:06:41
they— there were questions about
whether or not they would be
00:06:43
covered to get the same sort of
employment protections. There's
00:06:46
issues about not being able to
earn the minimum wage. So
00:06:49
there's a broader context that
you've got to understand the
00:06:52
work in, even if workers like
it. And that's why I call it the
00:06:56
good bad job. And the thing is,
it's here to stay, and it's not
00:06:59
just the gig economy. Most of
our work is becoming both good
00:07:04
and bad, because these tensions
are generative.
00:07:07
So then, when
you're talking about working on
00:07:09
these types of platforms, I
00:07:10
guess you can consider it a bad
job for a variety of different
00:07:14
reasons, right?
00:07:15
Yeah, there are
reasons you can consider it a
00:07:17
bad job. And I think just
leaving it like, "It's a bad job,
00:07:20
workers are exploited. They
don't have insurance, they don't
00:07:23
have minimum wage." It's true,
but it's only part of the story,
00:07:27
because people are also enjoying
this work. And for those that
00:07:31
have been shut out of the labor
market, particularly if you're a
00:07:33
first generation immigrant, many
of the people doing this work,
00:07:36
you know, they come over to the
States, and their credentials
00:07:39
are not transferring, even
though they have the skills. So
00:07:41
they go into this type of work.
Or there's someone that's been
00:07:44
out of the labor market. Maybe
they were incarcerated, or they
00:07:46
were at home taking care of
kids, or they were sick. It does
00:07:49
provide an income-earning
opportunity. So the fact they
00:07:53
enjoy the work is real. In the
same time, it exists in these
00:07:56
larger conditions. And I think
we're finding more and more
00:07:59
jobs, particularly as benefits
for workers continue to be
00:08:03
eroding, holding this tension.
00:08:06
So when you look at
where we are
00:08:08
right now, in many cases, gig
work is preferred to people over
00:08:14
a traditional office.
00:08:17
Ah, so that's a tricky one.
So I would
00:08:20
say for my gig workers that I
focused on— so I've studied all
00:08:24
the big platforms. Instacart,
DoorDash, Amazon Flex, ride
00:08:29
hailing, they typically have a
lot of bad options. So it's
00:08:33
warehouse, manufacturing, gas
station, cater waiter. And so
00:08:38
they see gig work as being the
best option out of a set of
00:08:42
good options. But these were
never people that had
00:08:45
traditional office jobs. And so
when you're talking about those
00:08:49
people who are preferring gig
work over traditional, you know,
00:08:52
office jobs, there, you're
talking about more high skilled
00:08:55
employees, or higher— you know,
folks who are, like, doing Upwork,
00:08:59
or doing coding or copy editing.
And that's a different
00:09:03
conversation, because you're
talking about a different type
00:09:05
of labor or skill.
00:09:07
You talk in
the paper about the element of
00:09:09
consent, and the fact that that
can be very important in terms
00:09:13
of the perception of how good or
bad a job may be. And I guess
00:09:18
you have to look at it from
those two perspectives, one
00:09:21
being the human relationship,
but two being the algorithmic
00:09:25
relationship as well.
00:09:26
That's it.
You have to have conversations
00:09:29
with both. And particularly in
this context, it's conversations
00:09:32
with the technology. But the
classic question that paper
00:09:35
looks at— and this is— you
know, this question has been
00:09:38
looked at over 100 years by
different scholars— is, why do
00:09:41
people keep on doing jobs—
apologize for my French— that are
00:09:45
kind of shitty? That are kind of
bad? Why do people enjoy work
00:09:49
that qualitatively looks like
it's in bad conditions? And
00:09:53
that's the issue. That's the
tension of the good bad job
00:09:56
that I'm hoping to unpack. You
know, and deeper in the paper
00:10:00
I talk about, people have small
amounts of agency in their work.
00:10:04
So they might decline a whole
bunch of rides in a row because
00:10:07
they don't want a shared ride.
Because it's very— you know, you
00:10:10
can get fights with passengers.
You have three or four people,
00:10:12
you know, that are in the car.
Or they might use geo-spoofing
00:10:16
apps to try to get higher paid
work. Or they can try to inflate
00:10:20
surges. This is one of my
funnest stories. It was the
00:10:24
Thurs— no, the Wednesday
before Thanksgiving. You know,
00:10:26
everybody's heading out to the
airport. And I interviewed
00:10:29
somebody who primarily drove
in a college city. And he's
00:10:32
like, "They weren't paying me
enough to go to the airport, so
00:10:35
I just stayed in front of the
dorms and I click, decline,
00:10:37
decline, decline. And then I got
a ride for $160
00:10:41
to go to the airport, and it's
usually 40." So there are ways— he
00:10:45
quadrupled his wage. So there are
ways they're able to have small
00:10:48
amounts of agency within this
larger system of work that's
00:10:52
really not set up to,
00:10:54
you know, give them any
protection or benefits.
00:10:56
How do you think,
then, that that technology like
00:11:00
we see used in these types of
jobs potentially impacts our
00:11:04
larger workforce as we move into
the future?
00:11:10
You know, the— it's a great
question. And my first answer, I—
00:11:13
it's— I think it's honestly
scary. You know, you think about
00:11:16
those means of recourse. You
know, I didn't get paid for
00:11:18
three hours. There are people
that I interviewed who got
00:11:21
kicked off the app for three
days, couldn't pay a utility
00:11:25
bill, and supposedly they said
they were kicked out of the app
00:11:28
because they had said something
00:11:31
they shouldn't have said to a
woman. You know, there is
00:11:33
something that crossed gender
norms, and then he got an
00:11:37
apology message three days
later, saying, "Oh, it wasn't you.
00:11:40
The algorithmic management
system made the wrong reference.
00:11:43
It was actually a different
driver. You could come back on
00:11:46
the app." But for those three
days, he had no income and he
00:11:49
had no way to navigate the
system to get back on the app.
00:11:52
So there is a concern that as
you keep on de-skilling and de-
00:11:56
splicing the work, one, it
drives down wages because people
00:12:00
can't really build skill. But
two, there's no one to— like,
00:12:03
algorithms make mistakes, and
without a human in the loop, the
00:12:07
human is lost.
00:12:08
Is the expectation
that we probably
00:12:12
still will have some of those
conditions where algorithms do
00:12:16
make errors from time to time
for a longer time? Because I
00:12:19
think it's— I mean, look. Humans
are putting a lot of the effort
00:12:22
in behind the algorithm, and we
certainly know that humans are
00:12:25
not perfect.
- Yes.
00:12:26
So, I mean,
how can we expect the algorithms
00:12:28
to be perfect if the humans
are not?
00:12:30
It is—
00:12:32
The fact that people hold these
algorithms to god-like status.
00:12:36
Like, can't make a mistake. I mean,
that's such a blind, naive view
00:12:41
of technology. There's always,
always going to be a gap between
00:12:46
the how the technology is
designed and how it's used and
00:12:49
implemented by the workers. And
in that space, you see agency,
00:12:54
like what I talked about in the
paper, but you also see mistakes
00:12:57
the technical system is making.
And I think the more we start—
00:13:01
get blinded by techno Utopia-ism,
that increases the risk. And the
00:13:06
risk, particularly, for the most
marginalized parts of our
00:13:09
society. Because these
algorithms, they're refined and
00:13:13
they're tested on the most
vulnerable parts of the
00:13:16
population. Like, you think about
predictive policing, you think
00:13:19
about whether or not I'm going
to give somebody that has a low
00:13:22
credit score a loan, or whether
or not I'm going to let them go
00:13:25
out for a parole. These were
tested on disenfranchised parts
00:13:29
of the population, and once they
were fine, bam, they come out
00:13:32
for the masses. And so I think
that's a way to think about the
00:13:35
gig economy, and why it punches
above its weight class, and why
00:13:38
it's having such a ripple
effect. It's— from— it started off
00:13:41
with a group of more
marginalized people who are shut
00:13:44
out on the edges of the labor
market. They're refining these
00:13:47
tools, and now they're going
after the middle paying, the
00:13:50
higher paying, skilled jobs. And
it's just changing— changing the
00:13:53
landscape of work.
00:13:55
But does the—
kind of the concept of
00:13:57
innovation really have the
opportunity to grow and develop,
00:14:02
because we're adding in this
component of technology? I mean,
00:14:05
before, a lot of it was just
innovation based on humans. Now
00:14:09
we're bringing in some other
components to it.
00:14:12
Maybe, if you
00:14:13
want to be optimistic. I mean,
you know, I think it's the
00:14:17
question like, has Uber done
more good for the world or more
00:14:20
harm for the world? And it's
hard, you know, as me, as an
00:14:22
academic, to see so— so black and
white. I do think there are
00:14:26
innovative things that they've
done. You know, in many ways,
00:14:29
they've dismantled a taxi
medallion system that wasn't
00:14:32
working for a lot of people. And
at the same time, I think the—
00:14:35
the feeling of that, what they
accomplished or— and I just want
00:14:39
to— I don't want to pick on a
ride hailing company. But
00:14:41
like, when you have those easy
wins, it can sort of blind you
00:14:44
about the future, and then think
that everything you're doing is
00:14:47
right. Like you have a mandate
to go forward. And that's where
00:14:50
I get worried about the boundary
of innovation. And what does it
00:14:53
mean for people and human
capital?
00:14:55
Where do you think you
00:14:56
would like to take this path of
research?
00:14:58
And what's that next
step for you, do you think?
00:15:01
I'm doing a lot of
research in the Global South
00:15:03
right now. Spent the summers in
Brazil, in Nigeria, in Ghana,
00:15:09
and I think— you know, back to
that earlier point where I
00:15:11
talked about how technology and
these algorithmic skills are
00:15:14
sort of honed on the most
disenfranchised groups. I think
00:15:18
the Global South is the future.
And that—and I see it in many
00:15:21
ways in thinking about how the
gig economy and algorithmic
00:15:24
management is evolving. It's— I'm
seeing the big changes happen in
00:15:27
the Global South, and I see it,
like, refined, when I'm looking in
00:15:30
the US. And it's easy to miss
it, because it looks like noise
00:15:33
within the data. And so, you
know, I'm looking at my research
00:15:36
in a more global scale.
00:15:39
I'm also thinking about, what are the
boundaries of liability? Like, I
00:15:42
do not want to point my fingers
at these companies and be like,
00:15:45
"You're wrong, big tech." But I
think there are questions about,
00:15:49
what are the boundaries of the
firm, and what responsibilities
00:15:52
do you have if you're trying to
build a marketplace and if you
00:15:55
have independent contractors? We
need to think about them in a
00:16:00
more thoughtful way. Because
this is not Walmart, but nor is
00:16:04
it really like a bunch of, like,
free floating, you know,
00:16:07
consultants, you know, that are
like, meeting through job— like a
00:16:10
job board, the correct place.
It's a new organizational form,
00:16:14
and it does have some—
00:16:15
some responsibilities and
liabilities in play.
00:16:18
Accountability.
- Yeah.
00:16:20
Lindsey, always great to
talk with you.
00:16:22
Thanks very much. All the best.
00:16:23
Thank you. You too.
- You got it.
00:16:25
Lindsey Cameron, Assistant
Professor of Management here at
00:16:28
the Wharton School.
00:16:29
Thank you for listening
to <i>The Ripple Effect</i>.
00:16:31
We hope you found this
episode informative and
00:16:34
engaging. Don't forget to
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00:16:37
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00:16:40
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