Speaker 1: Hello, and welcome to another episode of the Odd Thoughts Podcast.
Speaker 1: I'm Tracy Alloway and I'm Joe. Joe. Do you remember
Speaker 1: when we had Gordon McGill on to talk about trucking. Yeah,
Speaker 1: that's what that's like an iconic. I mean, there's recent,
Speaker 1: but that's like a odd lots classic already and a
Speaker 1: bunch of people of reaching. That was a great episode.
Speaker 1: It was he said, I mean, he said a bunch
Speaker 1: of things that were very interesting, but he said something
Speaker 1: in particular where he was talking about an upcoming book
Speaker 1: by a Cornell University professor called Data Driven, and the
Speaker 1: idea was to explore the role of technology in the
Speaker 1: trucking industry. Yeah. Absolutely, And you know, I think this
Speaker 1: is a really important area to explore because, of course,
Speaker 1: as we've discussed with Gored, but also other episodes that
Speaker 1: we've done on trucking, truckers are monitored increasingly. They're these
Speaker 1: e l d s electronic logging devices, the track hours,
Speaker 1: things like that, and you know, obviously this affects truckers,
Speaker 1: but you know we may all be tracked by e
Speaker 1: l d s as workers were. Right, So there's two
Speaker 1: interesting things here. So one you would expect that monitor
Speaker 1: monitoring technology UM like the e l D s that
Speaker 1: they're using would be sort of like anatama to a
Speaker 1: lot of the truck driving industry, Like a lot of
Speaker 1: truckers go into it thinking they're going to be independent,
Speaker 1: they have the freedom of the road, and then it
Speaker 1: turns out that they're like eye movements and brain waves
Speaker 1: are going to be tracked and monitored. So that's interesting.
Speaker 1: And then secondly, given the shift towards work from home
Speaker 1: for a wide variety of workers, it does seem like
Speaker 1: surveillance technology in general could become more of a thing. Well,
Speaker 1: and the other really important thing that I think maybe
Speaker 1: I don't know something profound about how business works or
Speaker 1: capitalism works, etcetera, is like there is this core problem right,
Speaker 1: like why does e l D exist in the first place,
Speaker 1: attempt to solve problems truckers on the road for too
Speaker 1: long that creates fatigue, that creates accidents, like some sort
Speaker 1: of impulse behind these rules that curtail how long someone
Speaker 1: could be on the road, But rather than address why
Speaker 1: are they on the road to love, why is there
Speaker 1: truck or fatigue? Why has fatigue been part of trucking?
Speaker 1: And you know forever, rather than figuring out different ways
Speaker 1: to do that, let's just crow come up with a
Speaker 1: monitor system, rather than addressing the core issue that creates
Speaker 1: the need for these time constraints and monitoring systems in
Speaker 1: the first right. So the question is also whether or
Speaker 1: not technology is the right solution to the problem it's
Speaker 1: trying to solve. Okay, well, I'm very happy to say
Speaker 1: we have the perfect guest because we are going to
Speaker 1: be speaking with the author of the book that was
Speaker 1: referenced by Gord. We are going to be speaking with
Speaker 1: Karen Leavy. She is an associate professor at Cornell University
Speaker 1: and also the author of Data Driven Truckers Technology and
Speaker 1: the New Workplace Surveillance, which just just came out. So, Karen,
Speaker 1: thank you so much for coming on all thoughts, Thanks
Speaker 1: so much for having me. This is a really interesting
Speaker 1: topic and I'm glad we have the chance to dive
Speaker 1: into it further. Maybe just to begin with, you know,
Speaker 1: Joe kind of alluded to this in the intro, but
Speaker 1: what exactly is the problem that this workplace surveillance technology
Speaker 1: that's been mandatory in the trucking industry. I think since
Speaker 1: seen what exactly is it trying to solve Yeah, I
Speaker 1: mean the way you guys talked about it kind of
Speaker 1: in the lead up, I feel like perfectly encapsulates it.
Speaker 1: So the e l D, the electronic logging device, as
Speaker 1: you mentioned, is federally mandated in the trucking industry. All
Speaker 1: long haul truckers have to buy and install and use them.
Speaker 1: And ostensibly the goal is related to safety, right, as
Speaker 1: you alluded to, we know that truckers are really tired.
Speaker 1: We know they're incredibly overworked and underslept, and obviously that
Speaker 1: has you know, that's high stakes, right. There are thousands
Speaker 1: of truck related accidents a year and fatalities from those accidents.
Speaker 1: It's really cost billions of dollars every year. Um, So
Speaker 1: like nobody wants that, right, and including truckers, nobody wants
Speaker 1: the roads to be on safe. I don't want to
Speaker 1: be next to a tired trucker on the road, nobody, Like,
Speaker 1: we're all aligned on that. But what's interesting, as you
Speaker 1: point out, is not like is you know the way
Speaker 1: that we have sort of chosen to address that in
Speaker 1: this case is via technology. So the electronic logging device
Speaker 1: is sort of, um the centerpiece of this effort to
Speaker 1: make truckers comply with the timekeeping regulations that they're subject to.
Speaker 1: I think this is something Gordon talked about two in
Speaker 1: the episode he was on right, Like, truckers for since
Speaker 1: the thirties have been bound to, you know, work no
Speaker 1: more than a certain number of hours every day and
Speaker 1: every week, and that's for safety reasons. The problem, as
Speaker 1: I know, you know you'll have discussed, you know, on
Speaker 1: on Board show and on other shows, is that the
Speaker 1: way truckers are paid, like does not align with those incentives. Right.
Speaker 1: They're paid by the mile. Truckers have the saying if
Speaker 1: the wheel ain't turn in, you ain't earnin right, Like,
Speaker 1: they're only paid for the time that they're actually moving
Speaker 1: down the highway, not to like sit around resting, right,
Speaker 1: or not to do all the other things that are
Speaker 1: an important, essential part of being a trucker but aren't compensated.
Speaker 1: So the result is that truck ers just want to
Speaker 1: stay on the road as much as they can because
Speaker 1: that's how they make a living. They're in pretty dire
Speaker 1: economic straits to begin with, Like, it's not a mystery
Speaker 1: why they why they end up doing these things. And
Speaker 1: so the E L D is sort of scene is
Speaker 1: like an answer to this, right, a way to sort
Speaker 1: of police this behavior by making it ostensibly harder, although
Speaker 1: not impossible, for truckers to tamper with the log books
Speaker 1: that they used to keep. So they used to do
Speaker 1: this using paper and pencil um and that is like
Speaker 1: a really easy system to kind of falsify or make
Speaker 1: it look like you're running legal when you're not. And
Speaker 1: so the e L the sort of scene as an
Speaker 1: answer to that. You know, I I always was a
Speaker 1: big fan of the song Convoy and there's a line
Speaker 1: of tearing up your swindle sheets, which I didn't really
Speaker 1: you realize the origin of that term until I read
Speaker 1: your book, and the swindle sheets was a nickname for
Speaker 1: the old physical pieces of paper that the truckers used
Speaker 1: to log their hours talk about like, you know, they
Speaker 1: had all different ways I guess of sort of swindle
Speaker 1: lying on these cards. How long was that push to
Speaker 1: How long had that effort been in place to get
Speaker 1: truckers to give up the physical pieces of paper in
Speaker 1: favor of these e L D s. So ELD is
Speaker 1: like first start popping up in you know, discussion around
Speaker 1: regulation in the nineteen eighties, and there's a long history
Speaker 1: of kind of how they eventually become mandatory. At first,
Speaker 1: the government starts to do things like, well, you know,
Speaker 1: maybe we'll make these mandatory for people who have like
Speaker 1: really bad safety records, right for some subset of the
Speaker 1: industry that we kind of think of as habitual VIL violators.
Speaker 1: Then there's a proposed mandate in the that goes nowhere
Speaker 1: right there, legal challenges. It's kind of like a long
Speaker 1: and rocky road to get to the e L demandate
Speaker 1: that we eventually end up with in SEV. But we
Speaker 1: do eventually get there, right And you know, a lot
Speaker 1: of the kind of the research that's in for my
Speaker 1: book is trying to understand that transition looking at the
Speaker 1: arguments that get made on either side of the e
Speaker 1: L DE mandate. You know, from the nineteen eighties and
Speaker 1: even before right, there's some predecessor technologies to that that
Speaker 1: you know, I think set the stage for this up
Speaker 1: to you know, post mandate, looking at what the effects
Speaker 1: have been on the industry. So we definitely want to
Speaker 1: dive into those various arguments. But before we do, can
Speaker 1: you talk little bit about the technology that goes into
Speaker 1: e L d s Because my understanding is. They sort
Speaker 1: of range from pretty basic models to more sophisticated things
Speaker 1: that actually, you know, can measure your brain waves to
Speaker 1: see how awake you are or how much attention you're
Speaker 1: paying to the road. So talk to us about the
Speaker 1: variety of e l d s that we have right now. Yeah,
Speaker 1: this is really crucial. So the e l D itself
Speaker 1: captures some important data, but like, as you say, sometimes
Speaker 1: maybe not that much, like a fairly minimal amount. Basically
Speaker 1: where the truck is and how long it's been being driven, right, Like,
Speaker 1: those are basically the core requirements in the regulations. However,
Speaker 1: it's almost as though the government told everybody, like you
Speaker 1: have to buy a phone that makes phone calls or something, right,
Speaker 1: like you can. It's very hard to find a phone
Speaker 1: that only makes phone calls. Right. If you're going to
Speaker 1: buy a phone that makes phone calls, you're also going
Speaker 1: to buy a phone that takes photos and connects to
Speaker 1: the internet and all this other stuff. Right, Because the
Speaker 1: tech tends to like bundle these things together, and the
Speaker 1: same thing happens in e l d s. So e
Speaker 1: l d s are actually more commonly like a module
Speaker 1: in what's sometimes called the fleet management system or FMS.
Speaker 1: People sometimes call them like a qual calm because qual
Speaker 1: Calm used to be like have a big market share
Speaker 1: in this area. They're all kinds of names for him.
Speaker 1: But now what people think of is the E L D. Actually,
Speaker 1: it's kind of like, you know, it's almost like naming
Speaker 1: the part for the whole, Like it sort of stands
Speaker 1: in for this much broader range of data collection technologies.
Speaker 1: And as you point out, Tracy, those can include things.
Speaker 1: You know, it commonly includes stuff like two way messaging
Speaker 1: or you know, alerting a back office about your fuel
Speaker 1: use or how hard you're breaking or how fast you're going,
Speaker 1: or you know, whether you're changing lanes without signaling, like
Speaker 1: all kinds of like pretty fine grain driving behavior data,
Speaker 1: which is just like very different from what trucking work
Speaker 1: has look like for a long time. Right, Like, if
Speaker 1: you ask folks why they get into trucking, many of
Speaker 1: them say explicitly because they don't want that, right, they
Speaker 1: don't want someone looking over their shoulder kind of measuring
Speaker 1: how they do their work. And you know, some of
Speaker 1: the stuff that's coming up on the horizon more recently
Speaker 1: includes the types of stuff you allude to, right, things
Speaker 1: like driver facing cameras have become much more common. Sometimes
Speaker 1: those are augmented by artificial intelligence that will do things
Speaker 1: like try to assess how fatigued a driver is, and
Speaker 1: they might do that by doing things like measuring, um,
Speaker 1: whether the dry driver's eyelids are fluttering, you know, as
Speaker 1: you know, like when you start to fall asleep, you're
Speaker 1: lets start to flutter your head nods. They can measure
Speaker 1: those kinds of things, and like you know, infer that
Speaker 1: the driver is tired. UM. Some of them, you know,
Speaker 1: they are wearable devices that sometimes get sort of integrated
Speaker 1: into these systems or use kind of um in a
Speaker 1: complimentary way to these systems that do things like you
Speaker 1: said that measure a driver's brain waves, measure a driver's
Speaker 1: heart rate, you know, collecting these other kind of biometric
Speaker 1: signals to alert the back office or to alert you know,
Speaker 1: the driver's safety manager whoever kind of what state they're in.
Speaker 1: So technically the law just require something the bare minimum
Speaker 1: of like okay, something track the truck, the hours on
Speaker 1: the road, etcetera. That needed an electronic version of the
Speaker 1: log book. But it's like the trojan Trojan horse atone,
Speaker 1: like once you get that electronic device in the truck itself,
Speaker 1: then why not add all these other things. Where is
Speaker 1: the impulse coming from to add these other features onto
Speaker 1: the field? Is it from the sort of like yeah,
Speaker 1: who who's benefiting from this and who's making the decision? Yeah,
Speaker 1: we want to stick a camera in the face of
Speaker 1: the trucker and see their face at all time and
Speaker 1: start counting, uh, you know, eyelids opening and closing. Yeah,
Speaker 1: I think that's exactly right. It's like a scaffold, or
Speaker 1: it's like a you know, a Christmas tree, and once
Speaker 1: you have the Christmas tree, you can just pay more
Speaker 1: ornaments on the Christmas tree because you got the Christmas tree. Now.
Speaker 1: So what has happened is that, yes, the government has
Speaker 1: you know, started to say like, we need to collect
Speaker 1: a certain sort of information digitally, and then a lot
Speaker 1: of the impetus for more data collection comes not necessarily
Speaker 1: from the government per se, but from trekking companies. Right
Speaker 1: because those workplace analytics, as you alluded to right in
Speaker 1: the setup to the conversation, workplaces like across all kinds
Speaker 1: of industries and all kinds of professions are increasingly interested
Speaker 1: in using software, sensors or other technologies that are cheaper
Speaker 1: and easy to come by to measure stuff about what
Speaker 1: their workers are doing. And so in some sense, truck
Speaker 1: like trucking companies are doing what all kinds of employers
Speaker 1: are doing, which is measuring more information about what their
Speaker 1: workers are doing on It Actually doesn't end there though,
Speaker 1: right Like on top of that, there are third parties
Speaker 1: that become very interested in this data too, right, So
Speaker 1: insurance companies become interested in it. Um companies that sell
Speaker 1: like parking space reservations to truckers other stuff like that, like,
Speaker 1: they become interested in it. So the data becomes very
Speaker 1: valuable to many different parties. It's just not valuable to
Speaker 1: the trucker, right The trucker is kind of isolated against
Speaker 1: all these parties that are interested in gathering the stata
Speaker 1: about what he's doing. Can you talk a little bit
Speaker 1: more about the insurance angle, because this is sort of
Speaker 1: a a recent theme for us, this idea of insurers
Speaker 1: sort of incentivizing different types of behavior collecting more granular
Speaker 1: data in order to do that. Does the installation of
Speaker 1: y l d s does that actually you know, reduce
Speaker 1: insurance rates for instance, for truck drivers or the truck
Speaker 1: driving companies yeah. So in what I have seen, um,
Speaker 1: it does or it can, and that some insurers insure,
Speaker 1: as I think at this point, are mostly just interested
Speaker 1: in getting the data rather than like really doing much
Speaker 1: with it beyond just like getting the data and integrating
Speaker 1: it into their modeling. So what I've seen is is
Speaker 1: insurance companies offering what they sometimes call like a plug
Speaker 1: in discount. This is not so different from like what
Speaker 1: Progressive does with commercial or with passenger vehicles, right where
Speaker 1: you get some discount on your premia for just installing
Speaker 1: the thing or like just giving data access. I haven't
Speaker 1: yet seen that it's actually had a measurable impact on
Speaker 1: like actual rates within the industry, but you can imagine
Speaker 1: right like this is this is the goal, right is
Speaker 1: to do something like that. So, I mean, we have
Speaker 1: had this mandate in effect for a few years now.
Speaker 1: Is there evident stead is making the road safer? No,
Speaker 1: there's not. There's not evidence that there's making the read safe.
Speaker 1: Thank you for that set up. Um No. So what
Speaker 1: we have seen is that in the few years after
Speaker 1: the e l D mandate took effect, traffic like or
Speaker 1: truck crash fatalities went up. So they hit a thirty
Speaker 1: year high the year after the mandate. UM crash rates
Speaker 1: haven't gone down in some some segments of the industry,
Speaker 1: they've gone up. This is not based on my analysis.
Speaker 1: This is based on like quantitative analysis by some business
Speaker 1: school professors that I talked about in the book. UM.
Speaker 1: But yeah, like if the goal is safety, right, like,
Speaker 1: there is no demonstration that even on its own terms,
Speaker 1: the e l D is succeeding in making the road safer.
Speaker 1: And if you think about why that is, there are
Speaker 1: a couple of reasons that maybe happening. So one of
Speaker 1: them that's like really intuitive is if you tell people, like, hey,
Speaker 1: you know, you need to get from point A to
Speaker 1: point B in eleven hours, like about roughly, Like let
Speaker 1: me try to get home for Christmas in eleven hours,
Speaker 1: Like I will do that. But if it's eleven hours
Speaker 1: and five minutes or if it's twelve hours, like, it's
Speaker 1: not the end of the world. And if I need
Speaker 1: to stop and get a cup of coffee, or if
Speaker 1: I need to like pull over and see if my
Speaker 1: tire looks weird or something like, I can do those things, right,
Speaker 1: I'm not going to like drive like about at a
Speaker 1: hell to do it because I know that that flexibility
Speaker 1: is sort of built into the system. If I tell
Speaker 1: somebody like you have exactly eleven hours and you have
Speaker 1: no more because I'm like I'm tracking this, like I'm
Speaker 1: monitoring you, people will drive very differently, right, And that's
Speaker 1: very intuitive. So what we have seen is that like
Speaker 1: rates of speeding and reckless driving have gone up because
Speaker 1: people feel this extra rigidity in the rules and so,
Speaker 1: like not shockingly, they compensate for this lost productivity by
Speaker 1: trying to make up for that in the way that
Speaker 1: they drive. So that's like one clear mechanism. Another thing
Speaker 1: that like we don't have as clear up data on this,
Speaker 1: but clearly seems to be happening based on my conversations
Speaker 1: with drivers. You know, trucking is kind of an aging
Speaker 1: workforce anyway, like the median trucker ages I think in
Speaker 1: the late forties, and a lot of folks if you
Speaker 1: talk to them, you know, like they are not interested
Speaker 1: in this, right, Veteran drivers who have been driving professionally
Speaker 1: for me be decades, millions of miles without accidents. You
Speaker 1: tell them like, guess what, guess what we're putting in
Speaker 1: the truck now, Like they're not going to stick around
Speaker 1: for that. Trucking has this very high turnover rate anyway,
Speaker 1: people turn in and out of the industry all the time,
Speaker 1: And like, those are the drivers that are the safest.
Speaker 1: You want those drivers who are the most resistant in
Speaker 1: many ways to this kind of oversight. That's the guy
Speaker 1: you want to be next to on the road. You
Speaker 1: don't want to be next to the eighteen year old
Speaker 1: who just got their CDL who like maybe it doesn't
Speaker 1: know any different, right, like, never grew up trucking any
Speaker 1: other way, It's hard to find a blue collar job
Speaker 1: that doesn't involve a lot of oversight and managerial surveillance.
Speaker 1: So those are the folks that will stomach this kind
Speaker 1: of thing, but they're not the safest drivers. Um. Actually,
Speaker 1: this reminds me. I want to ask you. You know
Speaker 1: you studied this issue. I think when we spoke to
Speaker 1: Gored he mentioned a tenure study or something like that.
Speaker 1: I'm not sure if that time frame is entirely accurate,
Speaker 1: but talk to us about how you actually went about
Speaker 1: gathering data and anecdotes for your work. Yeah, so it
Speaker 1: has been cord is right. It has taken me a
Speaker 1: very long time to process the study but that's been good.
Speaker 1: Like I've spent like a quarter of my life thinking
Speaker 1: about trucking at this point, which you know, life comes
Speaker 1: at a best. But um, I started the study inn
Speaker 1: I was a grad student. I was studying sociology um
Speaker 1: and I was a lawyer before that, so I was
Speaker 1: really interested in kind of thinking about law and how
Speaker 1: does it how does it function on the ground, and
Speaker 1: how do people respond to it? And especially I was
Speaker 1: really interested in kind of wine technology and what happens
Speaker 1: when we like surveil people, what happens when we collect
Speaker 1: a bunch of information about what people are doing to
Speaker 1: kind of assess whether they're breaking the rules. And I
Speaker 1: was like sort of looking around for like where's a
Speaker 1: place where I can see this transition in action, Like
Speaker 1: I can really see that happening on the ground, so
Speaker 1: I can get a good sense for like how that
Speaker 1: unfolds or how it changes the way people relate to
Speaker 1: one another. And just by chance, it was like completely
Speaker 1: a fluke. I heard a story on the radio about
Speaker 1: trucking and how like truckers were upset because they were
Speaker 1: dealing with this. But this was again twenty eleven, so
Speaker 1: the e l Demandate was being discussed, although it wasn't
Speaker 1: effective for several years after that, But about this mandate,
Speaker 1: and I thought like, well, that's maybe it. And I
Speaker 1: didn't know any truckers, like I have no truck truckers
Speaker 1: in my family, but I went that day to a
Speaker 1: truck stop just to see, like, what is it like
Speaker 1: to talk to a truck driver? Like is it easy?
Speaker 1: Will they talk to me? And I was like instantaneously hooked.
Speaker 1: I found that truckers were like, they had such interesting stories,
Speaker 1: they were so forthcoming, they were incredibly generous, you know,
Speaker 1: in talking about the stuff that I admit I had
Speaker 1: never thought about. Right, it sounds like the origins of
Speaker 1: of odd lots interest in trucking as well. Yeah, so
Speaker 1: we keep doing trucking episodes. Obviously it's a fascinating, fascinating,
Speaker 1: fascinating angle. You know what you mentioned, you know, the
Speaker 1: legal background, and this brings up something a point that
Speaker 1: I thought was really interesting in your book, which is
Speaker 1: that like society kind of depends on people being able
Speaker 1: to break the law a little bit, that you can't
Speaker 1: actually run a society on everywhere everyone always strictly hewing
Speaker 1: to the law and being punished if they don't you
Speaker 1: explain that a little bit more, because I think that's
Speaker 1: key when you talk about you know what the E
Speaker 1: L D. You can't be ten minutes later, you can't
Speaker 1: drive an extra ten minutes. But what is this concept
Speaker 1: of like we need a little bit of loose areas? Yeah, no,
Speaker 1: I think this is exactly right. Right. It's very easy
Speaker 1: and intuitive to say, like, well, if we have a law,
Speaker 1: people should follow the law, and like therefore, you know,
Speaker 1: if we assume that the law is legitimate or has
Speaker 1: been reached in like a democratic way or whatever, like
Speaker 1: people should just follow it. But like that doesn't really
Speaker 1: hold up too much scrutiny, right, And there's tons of
Speaker 1: places where we see that actually, like social life would
Speaker 1: kind of fall apart if people actually followed laws to
Speaker 1: the letter. Um, you know, like one of the examples
Speaker 1: days in the book is just like speeding, right, Like
Speaker 1: I think if you were told that you were if
Speaker 1: you got a ticket for driving sixty six and a
Speaker 1: sixty five, not as a trucker but just as like
Speaker 1: a regular car driver, people like we would be like, what,
Speaker 1: that's not the law, and like it is technically, but
Speaker 1: like nobody really expects that that's the law. Another thing
Speaker 1: I talked about in the book that kind of hits
Speaker 1: this home is um in in like labor actions and
Speaker 1: you know collective action, like a pretty common worker strategy
Speaker 1: is the work to rule labor action. I don't know
Speaker 1: if this is something it's familiar to odd lets listeners
Speaker 1: that we're in work to rule. What you do, it's
Speaker 1: it's like a work slowdown, and you achieve it by
Speaker 1: like suddenly you like really pay attention to all the
Speaker 1: rules that are in like the cool books. You start
Speaker 1: track like spells out that you work this amount of
Speaker 1: hours every week, and you work exactly that amount of
Speaker 1: hours and you just do it right. And like the
Speaker 1: whole point is that by following the rules like that
Speaker 1: is not actually what we necessarily wanted people to do,
Speaker 1: because if you do it like, that's the slowdown. Right.
Speaker 1: The reason it's effective is because that's clearly not the
Speaker 1: expectation most of the time. And there's this happens just
Speaker 1: all over the place. UM there was like a few
Speaker 1: years ago in New York City and in many cities
Speaker 1: right there are these big moves like um these vision
Speaker 1: zero kind of projects where they're like we should have
Speaker 1: no jaywalking because jaywalking leads to traffic accidents. And then
Speaker 1: like when police start really cracking down on jaywalking, there's
Speaker 1: like some economics analysis where people say, like, you know,
Speaker 1: actually jaywalking is really efficient, and if we like we
Speaker 1: would lose like a good deal of economic efficiency if
Speaker 1: people actually follow these rules, because like we actually want
Speaker 1: people to bend the rules a lot of the time, right,
Speaker 1: We've built a society in which, like, following a bunch
Speaker 1: of rules to the letter can be really inefficient or
Speaker 1: ineffective or can cause other harms. And that's definitely the
Speaker 1: case in trucking, right, Like, clearly a lot of problems
Speaker 1: with the status quo and trucking. Like I'm not suggesting
Speaker 1: that everything is great and trucking and the labor conditions
Speaker 1: are wonderful and if we got rid of the E
Speaker 1: L D s everything would be fine, Like, I don't
Speaker 1: view it that way, but it is definitely the case
Speaker 1: that we have all sort of come to rely on
Speaker 1: the rules being more like guidelines in this context, right,
Speaker 1: and that people budge them in order to move stuff
Speaker 1: at the pace at which we kind of all demand it. Um,
Speaker 1: So when we crack down on a rule without kind
Speaker 1: of considering that broader context, you end up with this
Speaker 1: weird situation where you know you haven't addressed the underlying
Speaker 1: reasons people are breaking the rule. You're just kind of
Speaker 1: policing them harder for it. You know. Joe mentioned the
Speaker 1: swindle books earlier, like the the analog log books that
Speaker 1: truck drivers used to keep. And I remember I used to, Um,
Speaker 1: I used to fly planes when I was like nine
Speaker 1: years old. I don't know that you flew because my
Speaker 1: dad's out. Yeah, but I didn't know you were a pilot.
Speaker 1: Well not, but I had a pilot's log book and
Speaker 1: I was supposed to put in like my hours in
Speaker 1: my tiny little cessna, but like I was nine years old,
Speaker 1: and so I just used to pretend to fill it
Speaker 1: out all the time. I didn't know. So my question is, like,
Speaker 1: moving from the analog log books to the e l
Speaker 1: D S how infallible actually is that system? Can that
Speaker 1: information be tampered with or falsified? Yeah, it is a
Speaker 1: great question, and I love that you yourself have falsified
Speaker 1: a log book. So yeah, right, So the whole idea writer,
Speaker 1: like the rhetoric around the e l D is like, oh,
Speaker 1: this is tamper This is a tamper proof version of
Speaker 1: the thing that people have been doing for a long time,
Speaker 1: and certainly it is more difficult writer. It presents different
Speaker 1: kinds of challenges to falsify data and an e l
Speaker 1: D than it would be on a you know, using
Speaker 1: pencil and paper, where like anybody could do it with
Speaker 1: five minutes of thought. Um, but it's not impossible, right,
Speaker 1: as you point out, um, in one of the chapters
Speaker 1: of the book, I go through, like all of these
Speaker 1: different strategies that truckers and other folks used to kind
Speaker 1: of still bend the rules sometimes, right, because like as
Speaker 1: I mentioned, right, you still sometimes need that, or like
Speaker 1: the economy depends on that, or they're expected to do
Speaker 1: it by their managers or whatever it is. Um, Like
Speaker 1: one of the things people will do is what and
Speaker 1: they did this with paper lugs too, is what sometimes
Speaker 1: called a ghost log. So like you sign in as Karen, right,
Speaker 1: if you're Karen, and you do your eleven hours, and
Speaker 1: then you sign in as Joe or as Tracy right
Speaker 1: using some other right, which is like that's not and
Speaker 1: then you have eleven more hours, right, And sometimes like
Speaker 1: some companies they often get like a demo account like
Speaker 1: a John Do kind of account when they sign up
Speaker 1: with the E l D vendor, and so they like
Speaker 1: have these kind of like slush you know, drivers that
Speaker 1: they can use in some cases. I don't think like
Speaker 1: not everybody is doing this, but the strategy that was
Speaker 1: described to me, um other things folks will do. I
Speaker 1: once I was watching I was sitting with a safety
Speaker 1: or with a dispatcher at a trucking company and she
Speaker 1: was talking to this driver and the driver was like, well,
Speaker 1: I'm like four miles from where I need to be,
Speaker 1: but I'm out of hours. What do I do? And
Speaker 1: she was like, well, pull over and then roll to
Speaker 1: the place you have to get to roll to the
Speaker 1: drop off point at less than fifteen miles an hour.
Speaker 1: And the reason was because the e l D s
Speaker 1: all have like a threshold which is usually set by
Speaker 1: the company, and that threshold is like, under fifteen miles
Speaker 1: an hour, it won't register is driving, right, So she
Speaker 1: knew that like, it wouldn't register is driving. What's interesting
Speaker 1: and like both of these examples actually is that it's
Speaker 1: not necessarily the trucker that's like pushing this, right, it's
Speaker 1: oftentimes the company that's telling the driver like here's what
Speaker 1: you need to do. Right, Like it's kind of like
Speaker 1: a winky strategy because they kind of wanted both ways
Speaker 1: in some sense, right, Like they want the control that E.
Speaker 1: L D S afford them, but they also like just
Speaker 1: want the stuff to move. So sometimes they kind of
Speaker 1: coerce or compel drivers to do these things. So, you know,
Speaker 1: one of the things, and I think this is a
Speaker 1: thing that's gonna matter for all workers who are especially
Speaker 1: you know, work from home. That's gonna we know that
Speaker 1: that's going to increase the amount of surveillance on workers
Speaker 1: like are you actually at your desk responding to customers, etcetera?
Speaker 1: Whatever the job. But I think like one thing that
Speaker 1: all this data collection is going to affect everyone is
Speaker 1: like who who knows about how we're done? How who? Who?
Speaker 1: Where is the knowledge stored about how to do a
Speaker 1: good job? And you know, so in the case of trucking,
Speaker 1: like at one point the idea is like, Okay, the
Speaker 1: truck drivers they really like know their area, they know
Speaker 1: the road, they know the warehouses that they go to,
Speaker 1: and there's some sort of like knowledge base that exists
Speaker 1: primarily in the head of the truck driver, that's difficult
Speaker 1: to replicate. And then I imagine, you know, okay, now
Speaker 1: with e l D s, maybe a lot of that
Speaker 1: knowledge isn't at the level of the driver, but maybe
Speaker 1: at uh, you know, whatever, the fleet owner or some
Speaker 1: other entity. But I also have to imagine that even
Speaker 1: for the owner of the trucking fleet, they may be
Speaker 1: losing to some third party company that's building a black
Speaker 1: box AI model that even the company can't access. And
Speaker 1: so like the locusts of like all this knowledge and
Speaker 1: you know, the power that one has with that knowledge
Speaker 1: gets sort of distributed. Can you talk a little bit about,
Speaker 1: like I guess I would say the shifting distribution of
Speaker 1: power and knowledge within the industry as these manufacturers E
Speaker 1: l D manufacturers, third party data providers, software companies that
Speaker 1: provide uh you know, become like, oh, we have we
Speaker 1: have the database, we have the information. Yeah, no, I
Speaker 1: think that's exactly right, right, Like, so the information moves
Speaker 1: right in trucking like people used to talk about it,
Speaker 1: like you're the captain of your ship, right, you get
Speaker 1: to make all the calls because like you're there, nobody
Speaker 1: else is there, and you know what the conditions are,
Speaker 1: like and nobody else does because nobody else is there.
Speaker 1: But as you point out, right, like now more of
Speaker 1: that data is distributed to the firm or to a
Speaker 1: third party. And what's important to recognize about that, I
Speaker 1: think is that like, not only does that change the
Speaker 1: game because somebody else has the data, but the data
Speaker 1: they have is like not quite the same as the
Speaker 1: data you have, right, Like they have some kind of
Speaker 1: abstracted guests about what things are like, Like they know
Speaker 1: what you're how many hours register on your eel D
Speaker 1: and they're going to use that to kind of assess
Speaker 1: whether you have the right to be tired, right, or
Speaker 1: like whether you have the right to stop. Or they
Speaker 1: have some sense of what the weather is like because
Speaker 1: they can look at the weather channel. But that's not
Speaker 1: the same as being in the place and knowing what
Speaker 1: it's like, right. So as things get abstracted, a lot
Speaker 1: of times those you know, kind of more abstract data
Speaker 1: sources get used to challenge the accounts of workers on
Speaker 1: the ground because they're measurable, right, They're the kinds of
Speaker 1: things we can measure. And this happens again not just
Speaker 1: in trucking but all over the place. So, like I
Speaker 1: have a study from a different contacts where I like
Speaker 1: looked at retail workers than kind of how technology is
Speaker 1: affecting them, and they're too, right, Like tail workers and
Speaker 1: some set in some settings do this practice that's called
Speaker 1: client telling, where you like keep a book of business
Speaker 1: and you kind of like know the sizes of your
Speaker 1: favorite customers and stuff, and you know when their birthdays
Speaker 1: are and so like that really is empowering exactly right,
Speaker 1: Like sex stores, those workers like have at the work,
Speaker 1: they work on commission and they have like a really
Speaker 1: good relationship to certain customers. Increasingly, like now they're not
Speaker 1: allowed to keep their own books in business. They have
Speaker 1: to like input that data into some centralized system. And
Speaker 1: what that means is that when they it comes time
Speaker 1: for them to argue for a raise or something like,
Speaker 1: they don't have those bargaining chips because, as you said,
Speaker 1: the data is not in their head. It's now owned
Speaker 1: by the company or it's owned by the software vendor
Speaker 1: or whatever, and so that reduces like they're more substitute
Speaker 1: herbal right, it's it reduces their bargaining power of easy
Speaker 1: the company. And I think you see this kind of
Speaker 1: thing happening just across the board really and a lot
Speaker 1: of different labor contexts. So just on this topic, and
Speaker 1: this is kind of a Devil's Advocate question, but you know,
Speaker 1: our last trucking episode, we were talking a lot about
Speaker 1: how truck drivers end working more hours than they're actually
Speaker 1: paid for because they end up going to warehouses or
Speaker 1: depots or whatever and they have to wait hours and
Speaker 1: hours and hours to pick up or drop off a load.
Speaker 1: If you have this kind of surveillance technology, could it
Speaker 1: not be used to measure, for instance, how much time
Speaker 1: you're idling and then maybe like used to avoid inefficient
Speaker 1: warehouses or make some of those problems, you know, try
Speaker 1: to ameliorate some of those problems. Yeah, definitely, I definitely
Speaker 1: think that can be. I don't think it like redeems
Speaker 1: the whole thing, but it is a silver lining, right that.
Speaker 1: And and exactly those kinds of tools have been built
Speaker 1: using e l D data that kind of give you
Speaker 1: It's almost like the thing where Google will tell you like,
Speaker 1: is there a long wait at this restaurant? Right like
Speaker 1: it will do the same thing for truckers, being like,
Speaker 1: you know, is there a long detention period on average
Speaker 1: at a particular shipper and that potentially can be useful
Speaker 1: to you, right, like to the extent you get to
Speaker 1: control whether you know which which loads are going to
Speaker 1: take to help you kind of control your business. So
Speaker 1: I agree that that kind of transparency can be useful,
Speaker 1: right And it's one example of where you know, it's
Speaker 1: kind of like a more labor friendly way of using
Speaker 1: this data to actually help truckers out. I don't think
Speaker 1: it like redeems the whole because it's like a nice
Speaker 1: thing to kind of put on top of all of
Speaker 1: this additional control. There would be like other ways we
Speaker 1: could ensure that truckers don't lose a bunch of time
Speaker 1: to detention that would be much more direct. But I'm
Speaker 1: in agreement with you that that is like kind of
Speaker 1: a bright spot. Well, although it also seems like, look,
Speaker 1: we just had these two years of like terrible supply
Speaker 1: chain disruptions, Like we're why didn't all this data help us? Then?
Speaker 1: I mean maybe because this is like a once in
Speaker 1: a hundred year crisis we just experienced. But you figured, like, well,
Speaker 1: if all this data was so great at improving supply chains,
Speaker 1: you know, it didn't exactly step up to the plate.
Speaker 1: There's so many things I fascinating angles of this something
Speaker 1: I I wanna again something else in your book that
Speaker 1: really struck me, the social that you mentioned, Like, Okay,
Speaker 1: in many of the cases, the truckers you really want
Speaker 1: on the road are the ones who might be most
Speaker 1: repelled by having this electronic monitor device staring them in
Speaker 1: the face all day. Can you talk a little bit
Speaker 1: about these efforts that UM, the E l D makers
Speaker 1: and companies have put forth to sort of like get
Speaker 1: people comfortable at them. Yeah. So one thing that companies
Speaker 1: sometimes will do, and I've talked to E l D
Speaker 1: vendors that have talked about these strategies and to trucking
Speaker 1: companies themselves that will use them, is that, like in
Speaker 1: rolling out an E l D system or a fleet
Speaker 1: management system, you know, a lot does depend on kind
Speaker 1: of the messaging around that or like what kind of
Speaker 1: culture the company builds around that. UM, And there's a
Speaker 1: bunch of obviously, like the tools can be used in
Speaker 1: all kinds of different ways, and there's a bunch of
Speaker 1: variation and how companies roll them out. One kind of
Speaker 1: interesting thing I saw happening is that, you know, because
Speaker 1: there's now all this data on like like their scorecards,
Speaker 1: that can be generated for each driver based on fuel
Speaker 1: efficiency and how many safety incidents they have, and that
Speaker 1: kind of thing like that becomes really easy. And then
Speaker 1: that also facilitates like not only measuring the driver, but
Speaker 1: comparing the driver against other workers, right, or incentivizing the
Speaker 1: driver kind of like making a like gamifying it, right,
Speaker 1: making it a game about like, well, who can have
Speaker 1: the lowest fuel efficiency. That's a big one, right, because
Speaker 1: that's a big cost driver for trucking firms. And so
Speaker 1: companies will do all kinds of stuff to sort of
Speaker 1: incentivize drivers to sort of comply. Um, you know, like
Speaker 1: a really basic thing is just putting up a list
Speaker 1: of who's the most fuel efficient driver, which they of
Speaker 1: course now have that data, like putting that up you
Speaker 1: know in the central office or something. I've there's some
Speaker 1: pictures in the book of um you know some of
Speaker 1: these what these charts look like they might attach like
Speaker 1: a small financial incentive to it. So, like I talked
Speaker 1: to a company that it was like they drove for
Speaker 1: restaurants and they're like, oh, well, we'll give you a
Speaker 1: gittu divigate to the restaurant that you drive to, which
Speaker 1: doesn't cost them very much, but I guess it's nice
Speaker 1: for the driver. My favorite one is, um where a
Speaker 1: company they were interested in kind of um the drivers
Speaker 1: driving efficiently, and so what they decided to do was
Speaker 1: issue these like small bonuses two drivers wives in the
Speaker 1: wives names when their husbands like met whatever the fuel
Speaker 1: efficiency goal was. And the thinking I asked like, well,
Speaker 1: why the wives, and like, well, you know, once you
Speaker 1: get the wife fund board. Funny, I'm so proud of
Speaker 1: your efficient driving. I'm so proud of your e l
Speaker 1: D compliance fuel efficient driver. Thrilled what people want to
Speaker 1: here when they get home. I know that's what I
Speaker 1: say to my husband every night. No, I mean like
Speaker 1: you have to sort of admire the antinuity. But it
Speaker 1: is an interesting strategy, right to like yeah, exactly like
Speaker 1: you said, to sort of bring the family dynamic into
Speaker 1: this kind of datification of the work. Incredible. Um, well, Karen,
Speaker 1: one of the reasons we wanted to have you on
Speaker 1: is I mean, a, we're fascinated by the trucking industry
Speaker 1: in general. But be we mentioned this in the intro.
Speaker 1: As the world sort of shifts to work from home,
Speaker 1: or at least as there's a little bit more work
Speaker 1: from home than there was before the pandemic. It does
Speaker 1: feel like there is an opportunity for more of this
Speaker 1: kind of workplace surveillance technology to be deployed to a
Speaker 1: broader um workforce. Can you talk to us a little
Speaker 1: bit about what your research tells us about the application
Speaker 1: of this tech to the rest of the labor market. Yeah,
Speaker 1: I definitely agree that workplace surveillance has a very long history, right,
Speaker 1: and the motivations behind it are like not new. So
Speaker 1: companies have always wanted more productivity, more efficiency, and watching
Speaker 1: your workers to kind of get at that is something
Speaker 1: that like you know, get dates back to at least
Speaker 1: the Industrial Revolution, probably before that, right, Like, this is
Speaker 1: not a new strategy in some ways, but it is
Speaker 1: changing in some important respects, right, And one of those
Speaker 1: that I talked about in the book is like, well,
Speaker 1: first just that you can integrate these things into new workplaces.
Speaker 1: So truck cabs, for a long time, we're pretty immune
Speaker 1: to the kinds of workplace oversight that you saw like
Speaker 1: in warehouses or in factories or even in offices, right,
Speaker 1: just because of the nature of the work and like,
Speaker 1: in some ways they're like a lagging indicator, right, Like
Speaker 1: they're they're catching up with some other types of blue
Speaker 1: collar work. Um, but there are other things I think
Speaker 1: about kind of what the trucking case tells us. And
Speaker 1: as you mentioned right, like post pandemic, there's a lot
Speaker 1: more concern from managers about workers kind of not given
Speaker 1: it they're roll or shirking or not paying attention or
Speaker 1: doing those kinds of things. And so these types of
Speaker 1: data like data tracking, whether that's like um, you know,
Speaker 1: location tracking where your workers are, or detecting what they're
Speaker 1: looking at on a screen right when they're supposed to
Speaker 1: be working, or what windows they have open. Um. You
Speaker 1: see the stuff in school sittings to like the same
Speaker 1: kind of like procting software that you sometimes see for
Speaker 1: taking remote exams. Functionally is not so different from some
Speaker 1: of this workplace technology tracking like productivity monitoring. More and
Speaker 1: more kind of back end workplace systems like Microsoft three
Speaker 1: sixty five include these capabilities that will like report back
Speaker 1: to a manager about whether the like how many you know,
Speaker 1: minutes you spend in power Point or whatever stuff like that.
Speaker 1: So this kind of like measuring work, this is just
Speaker 1: happening all over the place, and I really don't see
Speaker 1: that going away anytime soon, you know. One of the
Speaker 1: tricks here, one of the problems is that when you
Speaker 1: measure stuff, you like inherently lose a lot of contexts,
Speaker 1: and oftentimes the things you can measure, like how many
Speaker 1: emails somebody said or whatever, is like not an amazing
Speaker 1: indicator of whether they're doing like meaningful work. And so
Speaker 1: workers a lot of times, you know, still have to
Speaker 1: do the meaningful work, but they also are sort of
Speaker 1: burdened with making themselves legible to these systems that are
Speaker 1: like sort of incentivizing or gamifying these things that may
Speaker 1: or may not be all that meaningful. So I do
Speaker 1: think that those those dynamics are just popping up, like
Speaker 1: across a lot of blue collar work, but also like
Speaker 1: what we think of as kind of more white color
Speaker 1: professionalized industries like medicine and law and other contexts. Um.
Speaker 1: There was this really great article in The New York
Speaker 1: Times a few months ago that maybe folks saw um
Speaker 1: by Jodie Cantor and Aria Sundaram, where they talk about
Speaker 1: this kind of productivity monitoring that's happening, and like one
Speaker 1: of the examples that they use is of a hospice
Speaker 1: chaplain like it gets some certain number of points from
Speaker 1: her employer based on how many visits she makes to
Speaker 1: like dying people. I mean, it's like just super super dystopiated.
Speaker 1: But it's like they talk about a range of different
Speaker 1: industries and it's clear that this is just sort of
Speaker 1: coming for everybody in its own Alright, Well, I look
Speaker 1: forward to our dystopian future where Joe and I are
Speaker 1: just sending each other to word emails, constantly productive game
Speaker 1: the algorithm you're prepping for a podcast. Yeah, all right, Karen,
Speaker 1: we're going to have to leave it there. Thank you
Speaker 1: so much for coming on all thoughts and talking about
Speaker 1: your work. Absolutely fascinating. Oh it's my pleasure. Thanks for
Speaker 1: having me, Joe. That was interesting. That was so interesting.
Speaker 1: There's so many I feel like we've gould have talked
Speaker 1: to Karen for a long time, so many different threads
Speaker 1: about how like business worked, where power is the appoint
Speaker 1: the purpose of like you know, letter of the law,
Speaker 1: a letter of the rules, so many interesting things. Yeah,
Speaker 1: I do think that point about how we have rules
Speaker 1: in place, but actually flexibility to work outside of the
Speaker 1: rules is really important, like both because it kind of
Speaker 1: lubricates just what we're doing, like living in New York
Speaker 1: would be really really difficult if we actually never j walked,
Speaker 1: for instance. Um, but also there's there's an element of
Speaker 1: power to it. And you mentioned this, but if you
Speaker 1: have a manager in a workplace and they say like, oh,
Speaker 1: you know, don't worry about this deadline, or you're supposed
Speaker 1: to work from nine to five, but actually I don't
Speaker 1: mind if you come in slightly late. That's also a
Speaker 1: bargaining chip for workplace relationships. Absolutely, And then you think like, Okay,
Speaker 1: all these businesses like, oh yeah, we love the idea
Speaker 1: of having this software that allows us to track our
Speaker 1: workers and track our worker productivity. But then the software
Speaker 1: maker then itself becomes a source of power because then
Speaker 1: they see the equivalent data for fifty other companies that
Speaker 1: they service, and then they can aggregate that data, and
Speaker 1: then they know things about how your employees benchmark against
Speaker 1: employees from other companies that you, as a business owner
Speaker 1: don't have, and then they sell you things on top
Speaker 1: of that. So I think that like, as more jobs
Speaker 1: are sort of under this surveillance, like again, it seems
Speaker 1: like it's gonna be this sort of like fascinating power dynamic.
Speaker 1: Who is the information about how stuff really works. Yeah,
Speaker 1: and also the thorny issue of measuring productivity in quality right,
Speaker 1: and this is something not to get really naval gay
Speaker 1: zy here, but in the media industry, this is something
Speaker 1: that's been going on for years where you know, we
Speaker 1: can look at pure traffic conumbers and say, like, this
Speaker 1: article generated a lot of traffic, but you can compare
Speaker 1: it with something that maybe didn't get as much traffic
Speaker 1: and say, well, this is objectively a better article like
Speaker 1: this one has more nuance and more detail, but maybe
Speaker 1: it wasn't as popular. That's always been a sort of
Speaker 1: issue in journalists. But Tracy, you and I we've always
Speaker 1: been good at traffic on two both, like I always
Speaker 1: liked those rules because I usually like to pretty good
Speaker 1: at that. We do quality and quantity here. But the
Speaker 1: gamification of a place absolutely is an interesting one. Tons
Speaker 1: of stuff to talk about that was fasting. Shall we
Speaker 1: leave it there, Let's leave it there, all right. This
Speaker 1: has been another episode of the All Thoughts podcast. I'm
Speaker 1: Tracy Alloway. You can follow me on Twitter at Tracy
Speaker 1: Alloway and I'm Joe wi Isn't Though. You can follow
Speaker 1: me on Twitter at the Stalwart Follow our guest Karen Leavy.
Speaker 1: She's at Karen Underscore, ec Underscore Levy. Follow our producer
Speaker 1: Carmen Rodriguez at Carmen armand and check out all of
Speaker 1: our podcasts Bloomberg under the handle at podcasts, and for
Speaker 1: more Odd Lots content, go to Bloomberg dot com slash
Speaker 1: odd Lots, where we push transcripts. Tracy and I blog,
Speaker 1: and we have a weekly newsletter that you can go
Speaker 1: to and subscribe to. Thanks for listening to