Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of The Odd Laws podcast.
Speaker 2: I'm Joe Wisenthal and I'm Tracy Alloway. Tracy, what do
Speaker 2: you think about DOGE.
Speaker 1: The coin or the new Department of Government Efficiency the latter.
Speaker 1: Here's what I will say. Part of me hates that
Speaker 1: government efficiency is being politicized in this way, because if
Speaker 1: you think that government services are a desirable thing to have,
Speaker 1: then you should definitely be against waste and inefficiency and
Speaker 1: fraud in that market, because at a minimum, if you're
Speaker 1: wasting money, you could be using that money to do
Speaker 1: even more. And then, obviously, if you think that government
Speaker 1: is just bad in general, then I imagine that you
Speaker 1: also think that the government wasting money is also bad.
Speaker 2: There's no constituency there probably shouldn't be an ideological constituency
Speaker 2: or political constituency for waste, right.
Speaker 1: Yeah, exactly, Like I feel like we should all agree
Speaker 1: on this, But also I hate the way it's kind
Speaker 1: of unrolling. Let's put it that way.
Speaker 2: I would say I broadly agree waste is bad. Whether
Speaker 2: the existing makeup of this sort of quasi blue ribbon
Speaker 2: commission to get rid of waste whatever that means is
Speaker 2: actually going to do it. Look, I'll be open minded,
Speaker 2: but I like the premise of cracking down on waste.
Speaker 2: I will just say that, you know what the other
Speaker 2: thing is. So first of all, part of the issue
Speaker 2: here is that waste is probably difficult to define. Fraud
Speaker 2: in some cases is probably difficult to define. There will
Speaker 2: probably be political fights over certain types of spending that
Speaker 2: it's like, you call this waste, I call this a
Speaker 2: good allocation of whatever. I imagine many of the political fights
Speaker 2: around this will sort of revolve around some of these questions.
Speaker 2: There's a lot there. But I'm glad we generally agree
Speaker 2: that fraud is bad.
Speaker 1: Fraud is bad, waste is bad. But I think you're
Speaker 1: absolutely right that the definitions are going to be crucial, right,
Speaker 1: And this is where I worry about the politicization, because
Speaker 1: you can just come in and say like, oh, well,
Speaker 1: I don't like this, so I'm going to call this
Speaker 1: a waste and go on from there. But I think
Speaker 1: we should talk about it because you know, this has
Speaker 1: come up in a number of episodes, specifically on the
Speaker 1: PPP post pandemic, and so it's clearly something that is
Speaker 1: on people's minds totally.
Speaker 2: Right now, we are recording this Thursday, November fourteenth. We
Speaker 2: are in a moment where resources in the economy are constrained. Right,
Speaker 2: the unemployment rate is low, inflation continue used to be
Speaker 2: by some measures above the Fed's goal. It is not
Speaker 2: crazy from like a macro standpoint to think we need
Speaker 2: to make things more efficient and better to have a
Speaker 2: better use of our real resources right now.
Speaker 1: And one, this is your other middle aged man thing.
Speaker 1: You know, you've decided to become like a real resource
Speaker 1: constraint guy.
Speaker 2: Right yeah, I'm like, oh, we got we got to
Speaker 2: crack down. You have to make tough decisions, to make
Speaker 2: tough decisions. We're taking the candy away from the kids.
Speaker 2: I'm going to be one of those.
Speaker 1: Here's a tough decision. If you're going to talk to
Speaker 1: someone about government waste, would you talk to Elon or Vivek.
Speaker 1: There's two.
Speaker 2: Well, Elon would get more downloads if we had a podcast,
Speaker 2: so I would talk to Elon. But we actually have
Speaker 2: a better guest. I think we have the perfect guest
Speaker 2: to talk about government waste fraud where we might be
Speaker 2: able to move the dial in a substantive way on
Speaker 2: this kind of stuff. We're going to be speaking with
Speaker 2: Jetson Leader Luis. He is an assistant professor at the
Speaker 2: Questionroom School of Business at Boston University. He's also a
Speaker 2: faculty research fellow at the nb ARE and this is
Speaker 2: what he studies, particularly areas around fraud and how companies
Speaker 2: defraud the government wasting millions, probably billions of dollars. So, Jetson,
Speaker 2: thank you so much for coming.
Speaker 3: On odd Lots, Thanks so much for having me.
Speaker 2: Why do you describe your research? I kind of described it,
Speaker 2: but why did you describe what you do in your background?
Speaker 3: Yeah? That's great. So I'm an assistant professor. I study economics,
Speaker 3: and in particular I'm interested in questions about fraud in
Speaker 3: government spending. I receive my PhD from MIT Economics in
Speaker 3: twenty twenty, and I've written a number of papers trying
Speaker 3: to explore the mechanisms the government can and does use
Speaker 3: to cut out fraud in it in public expenditure.
Speaker 1: Was there a particular moment or reason that drew you
Speaker 1: to this particular field.
Speaker 3: I think fraud in government spending has historically been underanalyzed,
Speaker 3: and in particular in the healthcare system, where I've done
Speaker 3: a lot of work. There are really big and impactful
Speaker 3: policies that are being used to try to eliminate fraud,
Speaker 3: but historically we didn't really understand what worked and what
Speaker 3: didn't work and why it worked, and so I think,
Speaker 3: you know, there was a big opportunity in the research there.
Speaker 3: And I've always been interested in questions of bad behavior.
Speaker 3: I have a paper that I started early in my
Speaker 3: career on fraud in the World Bank that we just
Speaker 3: got accepted at the journal World Development. And so overall,
Speaker 3: I'm really interested in this question about, you know, where's
Speaker 3: the money going and how can we you know, fix
Speaker 3: that to make sure that the federal funds are being
Speaker 3: used for people who need them.
Speaker 2: I definitely want to talk a little bit about fixing
Speaker 2: the problem and identifying the problem and so forth, But
Speaker 2: actually I really want to start on how to defraud
Speaker 2: the government because no, for real, because I sometimes we'll
Speaker 2: see headlines it's like so and so arrested for insurance
Speaker 2: fraud of some sort, and you know that's bad, But
Speaker 2: I also like have there's like weird thing that happens
Speaker 2: in my head where I'm like, I wouldn't even know
Speaker 2: how to defraud.
Speaker 1: Tell us what's the most accessible way to defraud the government.
Speaker 2: So, like, give us an example, or when we talk about, okay,
Speaker 2: fraud in the medical system, what are people doing? What
Speaker 2: is a classical form of defrauding to government in the
Speaker 2: world of healthcare.
Speaker 3: So, in the world of healthcare, there are some really
Speaker 3: obvious frauds that have persisted for years and that I
Speaker 3: think we're finally maybe starting to wrap our hands around.
Speaker 3: And one that comes to mind immediately is the ambulance market.
Speaker 2: Okay, say more so.
Speaker 3: This was actually described to me when I first heard
Speaker 3: it by a colleague friend who works for the federal
Speaker 3: government as the perfect healthcare fraud. And so ambulance services
Speaker 3: are paid for by Medicare. Medicare is the old age
Speaker 3: health insurance program for Americans. We spend more than eight
Speaker 3: hundred billion dollars a year on this program. We spend
Speaker 3: another seven hundred billion dollars a year on Medicaid. So
Speaker 3: we're talking about one point five trillion dollars of outlays
Speaker 3: to these programs. It's very hard for the government to
Speaker 3: ensure that every dollar that's going out is legitimate, right,
Speaker 3: It's a volume problem. Ambulance services are highly reimbursed and
Speaker 3: low overhead. If you want to start an ambulance company,
Speaker 3: you need to buy an ambulance. That's like thirty grand
Speaker 3: used online. You can actually go and google it yourself.
Speaker 3: You can go buy an ambulance, and then you need
Speaker 3: a couple of employees. It's actually a super low overhead business,
Speaker 3: which means it's easy for people to start starting around.
Speaker 3: We think two thousand and three, the market for ambulance services,
Speaker 3: and in particular repetitive non emergency ambulance services, started getting
Speaker 3: saturated by intentional fraudulent actors.
Speaker 1: Wait, what's a non emergency ambulance?
Speaker 3: Non emergency ambulance is a patient who needs to go
Speaker 3: to a service because they are sick enough that they
Speaker 3: can't ride in a taxi or take the train, and
Speaker 3: they the only safe way for them to get to
Speaker 3: a service is in an ambulance. And in particular, this
Speaker 3: really blew up in the dialysis industry. Dialysis patients, there
Speaker 3: are about half a million of them. We actually spend
Speaker 3: I think you know this, one percent of the federal
Speaker 3: budget on the dialysis program.
Speaker 2: Incredible, set not.
Speaker 3: One percent of medicare. One percent of the federal budget
Speaker 3: is the dialysis program. We do not in general pay
Speaker 3: for ambulance rides or taxi rides for these people to
Speaker 3: go to and from the visits. They are responsible for
Speaker 3: getting themselves to the clinic every day, three times a week,
Speaker 3: generally for a few hours, and that's in perpetuity. It's
Speaker 3: very challenging to get a kidney and therefore to get
Speaker 3: off of diale. So we had this system, and this
Speaker 3: is sort of the canonical Medicare fraud. We build in
Speaker 3: a little thing for the few people who need it,
Speaker 3: and that turns into a loophole through which bad actors
Speaker 3: drive a truck. So we built in this provision, which
Speaker 3: is if the only safe way that you can get
Speaker 3: to the dialysis clinic is in an ambulance, Medicare will
Speaker 3: pay for an ambulance, and they pay for it at
Speaker 3: a competitive rate for the ambulance companies, at say two
Speaker 3: hundred and fifty dollars for a one way ride. Now
Speaker 3: that's not that much money for a real ambulance, but
Speaker 3: it's a heck of a lot of money for a taxi.
Speaker 3: And what happened is thousands of firms around the country
Speaker 3: opened with the express intention not of giving people serious
Speaker 3: medical care, but of becoming an expensive ambulance taxi and
Speaker 3: build the government. We have one hundred percent data from
Speaker 3: the dialysis system. We can see all of these payments
Speaker 3: more than seven billion dollars for non emergency ambulance transportation
Speaker 3: over the following ten years, seven point seven billion dollars,
Speaker 3: and a lot of it was fraud, and the government
Speaker 3: cracked down. The government really tried to crack down, and
Speaker 3: in particular, the used a number of tools. The first
Speaker 3: one is they started throwing people in prison. This is
Speaker 3: what you say, you see in the headlines. Yeah, but
Speaker 3: it's so easy to start an ambulance company that we'd
Speaker 3: see these stories where, you know, someone gets busted and
Speaker 3: their family member goes and opens a company next door
Speaker 3: the next day. And this persisted for years, with thousands
Speaker 3: of companies and billions of dollars of spending just down
Speaker 3: the drain.
Speaker 1: I imagine some of the difficulty is also deciding who genuinely
Speaker 1: needs an ambulance ride and who doesn't. Right, So this
Speaker 1: is something that I never quite understand about US healthcare
Speaker 1: in general. The first ten years of my adulthood were
Speaker 1: in the UK, and there's a national health service there,
Speaker 1: and if the doctor told you you needed something. You know,
Speaker 1: you got that something. Maybe it would take a while,
Speaker 1: but eventually you would get it, whereas in the US
Speaker 1: you seem to have all these decision makers in the process,
Speaker 1: and yet we're talking about medical care, which you would
Speaker 1: think would need to be dictated by like highly trained
Speaker 1: medical doctors.
Speaker 3: This is super interesting that that's your intuition, because that's
Speaker 3: exactly how they fixed it. So the way that Medicare
Speaker 3: closed this loophole was by requiring what's called prior authorization.
Speaker 3: Now if instead of going to an ambulance company and saying,
Speaker 3: you know, please give me this ride, or the even worse,
Speaker 3: the ambulance company coming to the patient and saying do
Speaker 3: you want a taxi ride, which is actually what was happening,
Speaker 3: instead they required that a physicians signed off and say, look,
Speaker 3: this patient is actually sick, they're bedridden. There's no other
Speaker 3: way that they can get to dialysis this week. And
Speaker 3: if you didn't have the doctor's note, Medicare didn't pay.
Speaker 3: And we estimate that around the timing of them implementing
Speaker 3: this prior authorization requirement, which they rolled out in different
Speaker 3: states at different times. This is like what economists love
Speaker 3: in our research and actual test a difference, and difference
Speaker 3: exactly when they rolled this out in different places, in
Speaker 3: different times. We see a sixty seven percent drop in
Speaker 3: spending the next month, persistent, and not only that, we
Speaker 3: can then trace the patients and say were these patients harmed?
Speaker 3: Did they actually miss their dialysis visits and end up
Speaker 3: in the hospital, And we find no evidence at all
Speaker 3: of negative patient health effects, And so we actually saved
Speaker 3: billions of dollars by putting in something that was so basic,
Speaker 3: which is this the doctor sign?
Speaker 1: Why didn't they do it before?
Speaker 3: The structure of Medicare is largely disaggregated, where individuals are
Speaker 3: able to go to different services as long as they
Speaker 3: qualify for them, and some of those are doctor's visits, hospitals,
Speaker 3: medical equipment, pharmaceuticals. We pay for a lot of things,
Speaker 3: and there is a real worry that requiring too much
Speaker 3: paperwork can burden the system, and we don't want to
Speaker 3: turn the Medicare system into an even more heavily administrative
Speaker 3: burden system. So there are different qualification rules, but largely
Speaker 3: what happens is the qualification rules are not always enforced upfront.
Speaker 3: We do a lot of requiring people to follow the rules,
Speaker 3: maybe we do some audits, maybe we chase after them
Speaker 3: with criminal lawsuits afterwards. But largely there are circumstances where
Speaker 3: nefarious actors, it's a relatively high trusystem. Nefarious actors will
Speaker 3: find these loopholes and drive a truck through them. And
Speaker 3: I can talk about a million examples of this.
Speaker 2: Well, let's talk about so Okay, it sounds like the
Speaker 2: ambulance from taken care of more or less.
Speaker 3: So the ambulance fraud is less than it used to be.
Speaker 3: And that's the dialysis ambulance fraud. Oh yeah, this paper
Speaker 3: was just accepted at the General Political Economy, So we're
Speaker 3: super happy. Shout out to my.
Speaker 2: Congratulations who did a great job there.
Speaker 3: The ambulance market itself has other frauds, right, we're talking
Speaker 3: about one type, which is this repetitive dialysis fraud. There
Speaker 3: are still lots of unnecessary ambulance rides, ghost ambulance rides
Speaker 3: where patients don't even get in the ambulance and bills
Speaker 3: are sent. One question is what can the government do?
Speaker 3: And part of it is I think that there needs
Speaker 3: to be a better focus on using data to detect
Speaker 3: and stop the fraud.
Speaker 1: Yeah, talk to us about the data because I imagine,
Speaker 1: like you're talking about government spending and programs, plus in
Speaker 1: some instances the medical industry, there must be interesting data
Speaker 1: available to you.
Speaker 3: So I have fantastic access to data. I use one
Speaker 3: hundred percent sample Medicare claims data from nineteen ninety nine
Speaker 3: through twenty nineteen for all inpatient and outpatient services, durbal
Speaker 3: medical equipments. I can see twenty percent of physician office
Speaker 3: visits and party pharmaceuticals. So it's an ridiculous volume. And
Speaker 3: you know, I'm very equipped with data. I teach data
Speaker 3: analysis and I have you know, PhD students and other
Speaker 3: professors who work with me. And even for us, it's
Speaker 3: a big problem. How do we actually wrap our hands
Speaker 3: around this? Yeah, the government has not historically invested very
Speaker 3: well in its data analysis for anti fraud. Part of
Speaker 3: the reason is that the organizations that are responsible for this,
Speaker 3: which are the Department of Justice and the Office of
Speaker 3: the Inspector General. Those career lawyers are fantastic. I cannot
Speaker 3: say enough positive things about my colleagues at the Department
Speaker 3: of Justice and the Office of the Inspector General, but
Speaker 3: there are too few of them. We do not pay
Speaker 3: them very well, and they are not data analysts. They
Speaker 3: are lawyers.
Speaker 2: Okay, so you mentioned there's still some ambulance the dialysis specifically,
Speaker 2: sounds like that was mostly taken care of. There's other
Speaker 2: ambulance fraud out there. You mentioned that some people that
Speaker 2: there's billing ghost ambulances or people never even ride the ambulance.
Speaker 2: What's hot right now, what's the new ambulance fraud?
Speaker 3: So what's amazing here is that it seems like it's
Speaker 3: a constantly evolving marketplace. Right We have to think fraud
Speaker 3: is a technology where people figure out a loophole and
Speaker 3: then they tell their friends and these things spread and
Speaker 3: eventually the government catches up, and so we're playing cat
Speaker 3: and mouse every year. Right now. I think it's wound care.
Speaker 2: Okay, say more about wound care.
Speaker 3: There's been a rise in these expensive treatments for patients
Speaker 3: with non healing wounds. So, if you're diabetic, you're likely
Speaker 3: to have neuropathy, and one of the things that comes
Speaker 3: with diabetic neuropathy is that you often have wounds, particularly
Speaker 3: on their feet, where the patient doesn't heal. There are
Speaker 3: some modern technology skin substitute things you can graft onto
Speaker 3: these wounds that seem like they pay pretty well and
Speaker 3: potentially even work, and then a few doctors have just
Speaker 3: started spending millions of dollars on that. But that's a
Speaker 3: flash in the pan. Historically, we see fraud really rife
Speaker 3: in the durable medical equipment industry. There's been fraud in
Speaker 3: basically everything that healthcare touches. Another thing right now that's
Speaker 3: really popular. We're seeing a lot of fraud in vascular
Speaker 3: care that is helping patients who have collapsed vain be
Speaker 3: able to receive intravenous treatments. But again, it's just like
Speaker 3: just to.
Speaker 2: Drill into specifics, let's just durable medical equipment fraud. Yes,
Speaker 2: I want to get into it. What am I doing so?
Speaker 3: Or you want to get into durable medical equipment fraud,
Speaker 3: Well you're in luck because it's you know, durable medical
Speaker 3: equipment has been the wild West of the healthcare system
Speaker 3: for twenty years with billions of dollars of fraud. I
Speaker 3: have to admit I'm writing a paper on it right now.
Speaker 3: That's I'm excited. I know a lot about it. So
Speaker 3: you remember the scooter store they used to like advertise
Speaker 3: on late night TV. Are you an old person?
Speaker 1: Number.
Speaker 3: Would you like a free wheelchair? Yeah? So there's been
Speaker 3: some excellent kind of investigative journalism on this so durable
Speaker 3: medical equipment. I want people to think walkers, wheelchairs, oxygen pumps,
Speaker 3: hospital beds in their home. Things that people need that
Speaker 3: are supposed to be permanent. You know, seatpat machines, yep.
Speaker 3: And these are largely given by suppliers that are sometimes
Speaker 3: big national firms and sometimes small mom and pop shops.
Speaker 3: I want you to think about Flora. This is sort
Speaker 3: of a Florida story. If you're interested in selling someone
Speaker 3: a you know, a fraudulent walker or pomp or something
Speaker 3: like that, it's actually.
Speaker 2: Wait, what does it mean? A fraudulent walker one that
Speaker 2: doesn't work well.
Speaker 1: That you don't need.
Speaker 3: I guess this is super interesting, right, So what do
Speaker 3: we mean when we say fraud? Healthcare fraud has different types, right,
Speaker 3: and I can break it. There are three types of
Speaker 3: health care fraud. There's upcoding. That's where I sell you
Speaker 3: a little push wheelchair, but I go build a government
Speaker 3: for a super lux automatic wheelchair. We call that upcoding.
Speaker 3: There's medical necessity fraud. That's where we say that a
Speaker 3: patient needs something and they don't. And then there's substandard care.
Speaker 3: That's where we have a patient who actually does need
Speaker 3: something and we give them junk. And all of them
Speaker 3: happen in all forms of medicine, but in particular and
Speaker 3: durable medical equipment. I think it's a lot of medical
Speaker 3: necessity fraud. I think we have patients who are getting
Speaker 3: a knock on the door, high do you want this
Speaker 3: fancy new device free to you? Now, what's really interesting
Speaker 3: is we're actually supposed to collect a twenty percent copay
Speaker 3: for the durable medical equipment products. But the fraud and
Speaker 3: that's designed by medicare to make sure that patients aren't
Speaker 3: getting stuff that they don't need, so they.
Speaker 2: Have to have some skin in the game.
Speaker 3: They're supposed to do. But if you're a fraudulent firm,
Speaker 3: you just don't collect it. You're very happy to have
Speaker 3: the government's eighty percent, and the patient wouldn't take it
Speaker 3: if they had to pay.
Speaker 1: Yeah, what's been the I'm trying to think how to
Speaker 1: frame this, but I guess what's been the cultural or
Speaker 1: incentive approach in government to stamping out fraud. If I
Speaker 1: am a government official and I design a poor social
Speaker 1: service program of some sort that has a bunch of
Speaker 1: loopholes that ends up costing lots of money. Do I
Speaker 1: get in trouble or do I get rewarded if I
Speaker 1: managed to tweak the program so that it doesn't have
Speaker 1: a lot of fraud in it.
Speaker 3: The government generally underinvests and miss prices. It's anti fraud investments,
Speaker 3: by which I mean when we consider how we are
Speaker 3: measuring what the government is doing to stop fraud. You're
Speaker 3: talking about these career civil servants and how we reward them. Historically,
Speaker 3: the focus has been on how much are you getting back?
Speaker 3: And I've made this point in a bunch of research,
Speaker 3: and I recently released a white paper through the Center
Speaker 3: for Medicare and Medicare Medicaid Services saying how much money
Speaker 3: you get back is irrelevant. That is the wrong number.
Speaker 3: That is really the number that everyone in government is
Speaker 3: focused on. When you say anti fraud recovery. We caught
Speaker 3: this many people, we put this many people in jail,
Speaker 3: and we got a billion dollars back this year. And
Speaker 3: the point that I've made is the money you're getting
Speaker 3: back is just a small share of the effect of
Speaker 3: your anti fraud efforts. What you should really care about
Speaker 3: is your deterrence effect. And I've shown in research deterrence
Speaker 3: effects are in many cases like ten times larger than
Speaker 3: these recovery dollars. So you go to the Partner of
Speaker 3: Justice and you talk to their healthcare fraud people. They
Speaker 3: write a report to Congress every year. It's called the
Speaker 3: Healthcare Fraud and Abuse Report, and they put a number
Speaker 3: there for return on investment, and Congress asks them, tell
Speaker 3: us how much money did you spend and how what
Speaker 3: was your return on investment? And they say the number
Speaker 3: is four, And okay, first of all, four ex return
Speaker 3: on investment already very good. That immediately means that we
Speaker 3: should spend more resources there. But I think that's actually
Speaker 3: the wrong number. I think the number is forty because
Speaker 3: the four is only counting money that they're getting written
Speaker 3: to them in terms of checks back. But if you
Speaker 3: count to terrence, and you have to count to terrence,
Speaker 3: the value of these anti fraud efforts is huge. So
Speaker 3: do we reward people in terms of the value. They
Speaker 3: bring this to Tracy's question in some sense, yes, right, Look,
Speaker 3: there's a press release they trot out the attorney general,
Speaker 3: maybe the civil servant gets exact and.
Speaker 1: A minimum I could go to my boss and be like,
Speaker 1: I saved US twenty million dollars.
Speaker 3: But largely no, we don't pay these people. Well, we
Speaker 3: don't retain them. Well, if you look at your average
Speaker 3: assistant US attorney, they go to the government for a
Speaker 3: few years, do it fantastic work, and then realize that
Speaker 3: private industry pays three times as much.
Speaker 2: In the league, when we were talking about the original
Speaker 2: dialysis ambulance fraud, it sounded like there was a very
Speaker 2: elegant solution, just get a doctor sign off. When you
Speaker 2: look at some of these other emerging frauds, and you
Speaker 2: mentioned wound care for example, or you mentioned someone gets
Speaker 2: a really nice scooter that they didn't really need, or
Speaker 2: something like that, because and you mentioned that the requirement
Speaker 2: of putting up twenty percent copay is not that effective
Speaker 2: because the seller is just like, you know what, We'll
Speaker 2: take the hit and only take the eighty percent. Is
Speaker 2: no big deal. How much of the challenge is on
Speaker 2: the data and identification side, which we should talk a
Speaker 2: little bit more, versus the versus the mechanism which you
Speaker 2: use to crack down on it.
Speaker 3: So I'm actually really in favor of this program. We
Speaker 3: use the big whistleblower program called the False Claims Act. Okay,
Speaker 3: and I've written extensively. Do you guys know how this works?
Speaker 2: No?
Speaker 3: No, it's the weirdest, coolest thing. I mean for an economist. Okay,
Speaker 3: So there is a private market for anti fraud in
Speaker 3: the US. If you know about a company or person
Speaker 3: that is defrauding any public program, not specific to healthcare,
Speaker 3: you can hire your own attorney and there are firms
Speaker 3: that specialize in this. You can sue that person in
Speaker 3: federal civil court, and the whistleblower gets a share of
Speaker 3: the money they bring back to the government. And this
Speaker 3: is a super effective program because it means that every nurse,
Speaker 3: every billing agent, every doctor in a hospital, if that organization.
Speaker 2: This is what we're going to get into. Tracy, I'm
Speaker 2: not gonna do fraud because even though I want to
Speaker 2: know how it works, but I do want to make that.
Speaker 1: I'm going to become a fraud bounty hunter.
Speaker 3: It's bounty hunting. But bounty hunting is great because of
Speaker 3: two reasons. The first is there's this private information component.
Speaker 3: Rather than waiting for some analyst in Washington to figure
Speaker 3: out today's fraud, just make a huge reward available for
Speaker 3: the individuals who know about it, because there's lots of information.
Speaker 3: Right from an economist perspective, the problem here is the
Speaker 3: problem we have in a lot of healthcare. It's information asymmetry.
Speaker 3: The doctors and the hospitals and the nursing homes know
Speaker 3: so much more about the patient than the insurance company.
Speaker 3: And here the insurance company is the government. And so
Speaker 3: rather than trying to make just top down solutions, and
Speaker 3: there are some top down solutions, don't get me wrong,
Speaker 3: it's really important that we also allow the individuals who
Speaker 3: have the information, the valuable information, to be rewarded for that.
Speaker 3: So the whistleblower program private information. There's also the private
Speaker 3: cause of action, the idea that every person can become
Speaker 3: Literally the legal term sometimes use is private attorney general. Right,
Speaker 3: you can go and hire a lawyer and sue, and
Speaker 3: that lawsuit is on behalf of the United States of America.
Speaker 3: And this has been an extremely effective program historically.
Speaker 2: Say more about that.
Speaker 3: Yeah, So I ran a Freedom of Information Act request
Speaker 3: on the Department of Justice for data on every whistleblower
Speaker 3: lawsuit from nineteen eighty seven through about twenty seventeen. When
Speaker 3: I first filed this. There are thousands of these cases.
Speaker 3: They've brought in billions of dollars for the government. Fifty
Speaker 3: five percent of them are in healthcare, but they're also
Speaker 3: used all over the government. We see cases related to
Speaker 3: the Transportation Department, the Department of Education, the Department of Defense,
Speaker 3: and the ideas the same. Instead of trying to have
Speaker 3: the government figure out how to run anti fraud, anti
Speaker 3: fraud can pay for itself. There are lots of people
Speaker 3: who would love to earn a million dollars being a whistleblower,
Speaker 3: and these whistleblowers get paid pretty well.
Speaker 1: Since you mentioned transportation and education, just then, can you
Speaker 1: talk about some examples of fraud outside of the medical sector.
Speaker 3: Absolutely. So. I have a recent paper about the unemployment
Speaker 3: insurance market during COVID, so I'd be happy to talk
Speaker 3: about that.
Speaker 1: Oh, that's great.
Speaker 3: So during COVID we had the biggest expansion of unemployment
Speaker 3: insurance in history. I think you guys know about this.
Speaker 3: You've talked a little bit about PPP, and there's some
Speaker 3: great research there on fraud. PPP was for companies. Unemployment
Speaker 3: insurance was for individuals who lost their job, and we
Speaker 3: expanded it to also include gig economy workers through a
Speaker 3: program called PUA. You guys probably know about this.
Speaker 2: So this is starting to feel like history imade so
Speaker 2: crazy because this is like yesterday, but also it's it
Speaker 2: feels so long. Yeah, the time distortion is real.
Speaker 3: So unemployment insurance rose heavily during the pandemic, and with
Speaker 3: it came a lot of fraud. Now why is there
Speaker 3: fraud in the unemployment insurance sector. Well, the government's cutting checks,
Speaker 3: and the government's cutting checks really fast. So you might
Speaker 3: remember at the beginning of the pandemic, there was this
Speaker 3: immediate recession, and there was fear of the big macroeconomic
Speaker 3: consequences of everybody being out of a job on many people,
Speaker 3: I should say being out of a job, and so
Speaker 3: the government really loosened and expanded its use of unemployment insurance.
Speaker 3: But whenever the government says, hey, we're going to write
Speaker 3: eight hundred billion dollars in checks, people get creative about
Speaker 3: ways to builk the government. And in particular, this type
Speaker 3: of fraud was really identity theft. It is super easy,
Speaker 3: super easy to go on the dark web and buy
Speaker 3: a social Security number, and so that's what a lot
Speaker 3: of criminals and organized criminal groups did during the pandemic,
Speaker 3: there was a widespread fraud where individuals would go purchase identities,
Speaker 3: apply en mass to state unemployment insurance programs, collect them,
Speaker 3: take it out of the system, and then we think
Speaker 3: in many cases it was either offshore or rerouted to
Speaker 3: criminal organizations. So this was so incredibly wides But actually
Speaker 3: my wife got a prepaid debit card in the mail
Speaker 3: from the unemployment agency and she didn't lose her job.
Speaker 3: I've talked to just dozens of people across the country
Speaker 3: and in all sorts of sectors, who said, yeah, this
Speaker 3: actually happened to me.
Speaker 1: Wait, this leads to something that I want to ask
Speaker 1: as well, which is how is fraud propagated? Because this
Speaker 1: kind of gets to Joe's question earlier, how do people
Speaker 1: actually do this? How do I learn to do fraud?
Speaker 1: Is it like I just look it up on the internet,
Speaker 1: or is it like someone I'm associated with tells me.
Speaker 3: So different frauds have different mechanisms by which people learn them,
Speaker 3: but in general, there is a social learning component to this. Absolutely.
Speaker 3: So with some of the institutional frauds that we've been
Speaker 3: talking about, a hospital that decides that they're going to
Speaker 3: suddenly charge a lot of money through some loophole. Often
Speaker 3: there are business decisions being made by executives. Sometimes there
Speaker 3: are consultants involved, and big national chains often are the
Speaker 3: ones that's read these because they have kind of centralized management.
Speaker 3: The hospital administrators from different hospitals look at this. I'm thinking,
Speaker 3: for example, of the tenant hospitals which paid nine hundred
Speaker 3: million dollars back to the government for a small little
Speaker 3: loophole that was supposed to be for an outlier payments program,
Speaker 3: and they just drove a truck through that loophole they
Speaker 3: ended up stealing. So if you go in patient in
Speaker 3: a hospital, the hospital gets paid fixed themount under Medicare
Speaker 3: through what's called a prospective payment system. They don't pay
Speaker 3: per cost. They just say, you know you have a pneumonia,
Speaker 3: we're going to pay this much. The government was worried
Speaker 3: when they set this program up that some very expensive
Speaker 3: patients wouldn't get treatment because if the hospital knows that
Speaker 3: and they know that, they're going to lose money on you,
Speaker 3: so they made this asterisk. A lot of these things
Speaker 3: are asterisks to have an outlier payment system where if
Speaker 3: a patient is super expensive, then they pay extra and
Speaker 3: the tenant hospitals figured out how to make every patient
Speaker 3: look super expensive by manipulating some of their balance sheets,
Speaker 3: and they ended up spending nine hundred million dollars to
Speaker 3: excuse me, they end up to settle claims, so that
Speaker 3: Tenant never admitted fault, I should say, but the government
Speaker 3: received nine hundred million dollars back from Tenant because you know,
Speaker 3: these allegations were I think true that that Tenant had
Speaker 3: done this, and I estimate actually that the government lost
Speaker 3: billions of dollars to that. Okay, So but I want
Speaker 3: to go back to this question, So how did that
Speaker 3: one happen? Well, there was a consultancy in New Jersey
Speaker 3: that was going around telling people, hey, do you know
Speaker 3: about this outlier payment system? And that's how we think
Speaker 3: that fraud spread, and it's spread all over the country.
Speaker 3: So there's some of this corporate learning from other companies,
Speaker 3: and there's also a lot of social learning. So in
Speaker 3: the case of unemployment insurance fraud or PPP fraud, you
Speaker 3: can go and you can find Telegram groups and Facebook
Speaker 3: groups of people that are like, here's how you apply
Speaker 3: for a PPP loan. There's a great new paper by
Speaker 3: John Griffin at the University of Texas on these Facebook
Speaker 3: groups and like they're called like fraud kings, like they
Speaker 3: know what there were maybe PPP loan kings. There's something
Speaker 3: that it's like really obvious what they're doing and everyone knows.
Speaker 3: And so in the case of health care fraud, in
Speaker 3: the case of non healthcare fraud, often it's you're surrounded
Speaker 3: by people who know how to do this, or you
Speaker 3: meet them through digital platforms, and then people learn and
Speaker 3: so a lot of the fraud we see propagates through communities.
Speaker 3: In the case of the ambulance market, we saw that
Speaker 3: there were certain Eastern European groups that were responsible for
Speaker 3: this in different parts of the country and often from
Speaker 3: the same from original areas, and so you know, generally
Speaker 3: the understanding, at least from the government is that there
Speaker 3: must have been some social learning going out there.
Speaker 2: It's very hard to prove, of course, right, someone figures
Speaker 2: it out and then tell family that's right, that makes sense.
Speaker 2: Let's talk about detection via data and more, and you
Speaker 2: talked about how you have access to all of this
Speaker 2: data and so forth. Obviously you can describe fraud qualitatively
Speaker 2: by saying this is how an ambulance company cheats the government,
Speaker 2: et cetera. What do the fingerprints of fraud look like
Speaker 2: when you look at it on the macro scale, What
Speaker 2: pops up in the data that would at least be
Speaker 2: a yellow flag and say this is something we need
Speaker 2: to look at more.
Speaker 3: So there are huge run ups in spending in every
Speaker 3: type of fraud I've ever seen, and every type of
Speaker 3: I mean, the whole point is if you're not making money,
Speaker 3: it's not a good fraud, right, And so if you
Speaker 3: just make a plot of bending against you know, time,
Speaker 3: you can often just see these really big exponential growth. Now,
Speaker 3: some of those are legitimate, because if there's a new
Speaker 3: great medical technology and people start using it, that also
Speaker 3: looks like a technological adoption curve, right of a kind
Speaker 3: of big upper rank. But when it's fraud, it you know,
Speaker 3: first of all, it looks like massive year over year increases.
Speaker 3: The second is that it'll often be way too much,
Speaker 3: to the point where it's obvious that nobody's getting this.
Speaker 3: For example, sometimes you'll see a doctor who's just billing
Speaker 3: from too many home care visits, but like the homecare
Speaker 3: visits are sixty minutes, and the doctor's billing for five
Speaker 3: thousand of them a year. It's like, well, that's five
Speaker 3: thousand hours. There aren't five thousand work hours in the year.
Speaker 3: The government should just be able to detect that. You
Speaker 3: should just not pay those So where the failure is
Speaker 3: is not in how hard it is to detect in
Speaker 3: data such and not that hard to detect in data.
Speaker 3: It's on the incentives for the enforcers to look at
Speaker 3: that data and use it appropriately. And that's where we
Speaker 3: get back to this limited enforcement capacity.
Speaker 1: How do you measure benefits on the other side, because
Speaker 1: that's it seems again like a potential avenue where there
Speaker 1: could be some disagreement.
Speaker 3: So I think it's super important that we preserve access
Speaker 3: to the public programs. And my research is not focused
Speaker 3: at all on, you know, how do we take things
Speaker 3: away from people? And in particular, I always try to
Speaker 3: measure very hard whether there are health effects associated with this.
Speaker 3: So in the case of the ambulances, we're able to
Speaker 3: show pretty definitively that there are no negative health effets
Speaker 3: associated with cutting this, and this is something I do
Speaker 3: in all my papers. You really have to ask the question,
Speaker 3: were people losing care that they needed? And so super
Speaker 3: great question in the context of some of these very
Speaker 3: obvious frauds. Sometimes people aren't even getting the service. So
Speaker 3: if the government's paying for something and nobody ever got it,
Speaker 3: taking it away is costless. So that's the best efficient
Speaker 3: thing that we could do, is just stop paying for
Speaker 3: things that aren't even happening. Right, Let's not even talk
Speaker 3: about waste. Let's just talk about these outright frauds. And
Speaker 3: so how do you measure it? I mean, if you
Speaker 3: look at very large scale claims data, as I do,
Speaker 3: and the government can, you can see, Okay, we cut
Speaker 3: out this provider, let's look at their patients. Did they
Speaker 3: go to the hospital more? That's an empirical question, right,
Speaker 3: And so there's no reason that it just has to
Speaker 3: be a gas, right. This is something that we should
Speaker 3: be measuring as part of our data analysis associated with
Speaker 3: handy front.
Speaker 2: So it's very obvious why, whether we're talking about the
Speaker 2: government or private insurance, that it's just rules on top
Speaker 2: of rules on top of rules and asterisks and so forth,
Speaker 2: because this is very complicated stuff. Rules also create problems
Speaker 2: and compliance by the rules. Can you know strict adherence
Speaker 2: to the rules can also have negative effects. I have
Speaker 2: to imagine, for example, that there are say, many people
Speaker 2: in the US currently on some sort of GLP one drug,
Speaker 2: but maybe technically they don't have the thing, but you
Speaker 2: know a lot of it. There seem to be a
Speaker 2: lot of benefits from weight loss and so forth. Have
Speaker 2: you found examples in your research in which there is
Speaker 2: some sort of positive externality from deviation from the rules?
Speaker 3: Absolutely? Absolutely, Thanks for cueuing this up. I have a
Speaker 3: great story. So let's talk about the hospice industry. Hospice
Speaker 3: care is an end of life benefit for patients who
Speaker 3: have a prognosis of six months or less. So if
Speaker 3: you're dying and a physician certifies that you're dying within
Speaker 3: six months, you qualify for hospice. What is hospice? You
Speaker 3: give up the curative care, and you stop taking all
Speaker 3: of these meds with the horrible side effects, and you
Speaker 3: stop going to the hospital as often, and you can
Speaker 3: die peacefully at home with pain medication. And I think
Speaker 3: that this is a great program. I think it's really important.
Speaker 3: We spend twenty billion dollars a year on the federal
Speaker 3: hospice program, and more than fifty percent of Medicare patients
Speaker 3: who die every year will have had a hospice claim, Okay,
Speaker 3: this is important. This is the way that we're treating
Speaker 3: people at the end of life. It's really hard to
Speaker 3: know who's dying within six months. That is not a
Speaker 3: trivial estimate, and historically there was subjectivity in this. So
Speaker 3: over the twenty year period nineteen ninety nine through twenty nineteen,
Speaker 3: there was a quadrupling of for profit hospices, and many
Speaker 3: of them increasingly took Alzheimer's and dementia patients. Why because
Speaker 3: they stay for a long time on these hospice programs,
Speaker 3: and the hospice programs are paid about two hundred dollars
Speaker 3: a day. And the federal government cried fraud, and we
Speaker 3: saw one hundred and sixty three federal whistleblower lawsuits against
Speaker 3: for profit hospice companies more than three hundred million dollars
Speaker 3: in settlements, saying this is fraud. You shouldn't have taken
Speaker 3: the patients. You should have known that they weren't dying
Speaker 3: fast enough that they did not qualify for hospice. So
Speaker 3: I wrote a paper with John Gruber at MIT, as
Speaker 3: well as one of our PhD students and David Howard
Speaker 3: at Emery, some really top notch economists and we looked
Speaker 3: at this program. We said, Okay, this is something people
Speaker 3: are really concerned about. And what we found really knocked
Speaker 3: our socks off. The fraud actually was not as bad
Speaker 3: as people said, in particular because did some patients go
Speaker 3: to hospice who may otherwise not have because they were
Speaker 3: not dying fast enough. Sure, but it's still a heck
Speaker 3: of a lot cheaper to go to hospice for six
Speaker 3: months than it is to go to the hospital and
Speaker 3: the nursing home and the homecare agency and the Durbal
Speaker 3: medical equipment and the pharmaceuticals. And we estimate that these
Speaker 3: patients saved tens of thousands of dollars. And the patients
Speaker 3: liked it. They and their families are picking hospice. This
Speaker 3: is something that they want. They don't want the hospital,
Speaker 3: so they're getting a service they want and the government
Speaker 3: is saving money. And yet we've decided that this is
Speaker 3: a fraud because there's this rule. Now, when you think
Speaker 3: about it from that way, six months is totally arbitrary.
Speaker 3: Why is it six months and not eight months? And
Speaker 3: so this paper, it's called Dying or Lying, It just
Speaker 3: was accepted last week at the AER. It shows pretty
Speaker 3: conclusively that the policies aimed at limiting fraud threw the
Speaker 3: baby out with the bathloar.
Speaker 1: You know, we started this conversation mentioning DOJE the new
Speaker 1: Department of Government Efficiency. I guess one obvious question to
Speaker 1: ask you would be, you know, if you were in
Speaker 1: Elon or Vivek's shoes, what would be your you know,
Speaker 1: the first thing you would start with when it comes
Speaker 1: to rooting out fraud or I don't know if you
Speaker 1: want to get into wider types of inefficiencies, but what's
Speaker 1: the first thing you would do?
Speaker 2: Yeah, they make you the real point person on this.
Speaker 2: How do you start?
Speaker 3: So first, I'm optimistic about the Department of Government Efficiency
Speaker 3: because I think that there are some clear evidence based,
Speaker 3: obvious wins that we have left on the table in
Speaker 3: terms of saving government money. The first is, we know
Speaker 3: that anti fraud efforts, particularly in the Medicare and Medicaid programs,
Speaker 3: have huge return on investments, and we underfund them. So
Speaker 3: we could staff up those offices at the Department of Justice,
Speaker 3: at the Department of Justice Districts, at the Office of
Speaker 3: the Inspector General and literally just chase down the leads
Speaker 3: that we already know about we don't even have to
Speaker 3: go after other leads. If you look at your average
Speaker 3: civil assistant US attorney, they've got thirty good cases on
Speaker 3: their desk and they get to pick two of them sore.
Speaker 3: And many of these cases allege millions of dollars of
Speaker 3: fraud and they say, I'm sorry, we have a number.
Speaker 3: We can't go under anything right now less than ten million,
Speaker 3: and they drop the case. So even if we just
Speaker 3: doubled the number of people or at the Department of
Speaker 3: Justice who focus on fraud against the government, that would
Speaker 3: pay for itself many times over. Okay, So that's an
Speaker 3: obvious one. The second is to start getting serious about data.
Speaker 3: If we do not hire and recruit excellent analysts to
Speaker 3: look at government data from the government, we are going
Speaker 3: to miss obvious frauds. Everything I've talked about so far
Speaker 3: is not rocket science. We're talking about huge amounts of
Speaker 3: billing for patients who obviously don't need it in some
Speaker 3: of these cases, and the government pays it and they
Speaker 3: miss it. They have access to that data in real time,
Speaker 3: they're writ in the checks. Why are we not screening that?
Speaker 3: And the answer is not a lot of data analysts
Speaker 3: work at the Department of Justice, and there are some.
Speaker 3: There are some that work at the Office and the
Speaker 3: Inspector General of Health and Human Services, but not that many.
Speaker 3: Why because they pay like seventy thousand dollars a year.
Speaker 3: And if you're a good data analyst, you don't go
Speaker 3: to work for the government for seventy thousand dollars a year.
Speaker 3: If the government wants top talent, it's got to be
Speaker 3: willing to recruit, and those are competitive positions. And so
Speaker 3: if we get serious about data, we get serious about
Speaker 3: machine learning, we get serious about investing in the attorneys
Speaker 3: who are doing the good work, and just the programs
Speaker 3: work really well. The second is rigorous evaluation, right, we
Speaker 3: need to know when we try policies, do they work
Speaker 3: or do they not work. When we put in a
Speaker 3: prior authorization program, when we put in a new screening
Speaker 3: for people to take a selfie on their phone before
Speaker 3: we pay their unemployment insurance claim, that's actually how we
Speaker 3: fix the unemployment insurance problem. It was an identity theft problem.
Speaker 3: You just got to take a selfie on your phone.
Speaker 3: These ideas work, but we need to evaluate them as
Speaker 3: serious policy.
Speaker 2: As I just have one more question, and I don't
Speaker 2: even know whether this is capable of being ascertained in
Speaker 2: a substantive way. But when you think about the stuff
Speaker 2: that you research and we're talking about, like, you know,
Speaker 2: big question is can we meaningfully move the dial on
Speaker 2: fraudulent spending or maybe wasteful spending? And do we have
Speaker 2: a number it exists? Do we know how much could
Speaker 2: potentially be saved? So?
Speaker 3: I think healthcare fraud alone in the United States is
Speaker 3: something like one hundred billion dollars a year. Now that's
Speaker 3: across private and public. But we spend one point five
Speaker 3: trillion dollars on Medicare and Medicaid. So the idea that
Speaker 3: it's a big chunks, now, it's not that big of
Speaker 3: a chunk. We spent you know, two point three trillion
Speaker 3: dollars overall, and so to say fifty to one hundred
Speaker 3: billion dollars a year of fraud, yeah, I think that
Speaker 3: number is reasonable. Yeah, So can we move the needle
Speaker 3: on it? Absolutely? There are things that we know work
Speaker 3: really well, huge treatment effects at very low cost. Now,
Speaker 3: government spending overall, we spend what six seven trillion dollars
Speaker 3: a year in the government. When we look at these
Speaker 3: other public programs there are some that have obvious frauds
Speaker 3: going on, Like I mentioned the unemployment insurance system that
Speaker 3: had tens or possibly one hundred billion dollars of fraud
Speaker 3: there as well the PPP program. We spent one hundred
Speaker 3: billion dollars or at least on fraud in that program.
Speaker 3: And so not every program is rife with fraud. There
Speaker 3: are programs that are very hard to defraud. It's hard
Speaker 3: to defraud social Security why because they have your full
Speaker 3: earnings record and they pick the number and they send
Speaker 3: you a check. Very limited fraud in the social security system.
Speaker 3: I think that there's probably a lot of fraud in
Speaker 3: some of the infrastructure and defense, and so our framework
Speaker 3: has to be like where does the government know the least,
Speaker 3: the government doesn't really know what's going into every element
Speaker 3: of a defense spend or every element of you know,
Speaker 3: how is the road being built or what is the
Speaker 3: hospital doing? And when you have those big information, these symmetries,
Speaker 3: that's where the big frauds. So if I think that
Speaker 3: there's one hundred billion dollars just for Medicare and medicaid spending,
Speaker 3: or maybe you know, throw in the advantage spending and
Speaker 3: the you know, other the federal employees health benefits and
Speaker 3: the VA. To get to the hundred billion number of
Speaker 3: that healthcare fraud, I think that it very easily that
Speaker 3: hundred billion dollars also occurs. Yet again in some of
Speaker 3: these other like defense, I'm sure.
Speaker 2: That there's plan Jensen leader luis amazing. Thank you so
Speaker 2: much for coming on them.
Speaker 3: Park, Thanks for having me.
Speaker 2: Tracy. I am pro getting rid of fraud. No, I've
Speaker 2: for real, I don't take a lot of it.
Speaker 1: It seems like a low bar. Although you did ask
Speaker 1: repeatedly how you could commit it, but I know that
Speaker 1: was for informational purposes only.
Speaker 2: No, I'm going to actually I'm not going to get
Speaker 2: into the business of medical fraud. I'm going to get
Speaker 2: into the business of being one of those independent whistleblowers
Speaker 2: to bounty hunters. Yeah, that sounds great, but I feel
Speaker 2: like I need to know how it works so I
Speaker 2: can identify it. But I really do think, you know,
Speaker 2: setting aside, setting aside everything fraud is bad.
Speaker 1: I do think, and I kind of said this in
Speaker 1: the beginning, that we can all agree that fraud and
Speaker 1: waste is bad, like no matter where you fall on
Speaker 1: the political spectrum, because if you free up money that's
Speaker 1: not doing anything, then you could, in theory, put it
Speaker 1: to a different use and get more bang for your bucks,
Speaker 1: so to speak. I think obviously, and we touched on this,
Speaker 1: like a lot of these decisions over what's fraudulent or
Speaker 1: especially what's wasteful maybe not necessarily fraud come down to
Speaker 1: specific judgments and they can be subjective, and I think
Speaker 1: that's where a lot of the disagreement is going to
Speaker 1: be going forward. But I do think the point about
Speaker 1: using the data better. The government must have some amazing data.
Speaker 1: We kind of talked about it, especially in these specific
Speaker 1: sectors like healthcare.
Speaker 2: Two things on the data that were really interesting. So
Speaker 2: one is just this idea that if you're a talented
Speaker 2: data scientist.
Speaker 1: Go to the doge.
Speaker 2: And this has come up in some of our past
Speaker 2: episodes that we've done with a few other guests. There
Speaker 2: does seem to be this structural issue, right of how
Speaker 2: government pays and whether the how compelling a job in
Speaker 2: government is, et cetera. And so there does seem to
Speaker 2: be an issue with how do you staff up a
Speaker 2: big team of data scientists that are incentive and have
Speaker 2: the agency and capacity to use that data. Do something
Speaker 2: when they discover it. And then you know, very interesting
Speaker 2: comment that last one about it's very hard to defraud
Speaker 2: social security, and so this idea of like where is
Speaker 2: the fraud most likely to exist? Areas in which there
Speaker 2: is some limited asymmetrical as economists like to use information,
Speaker 2: which is, you know, the government doesn't know what happens
Speaker 2: when you are not you and I go into a doctor, right,
Speaker 2: there's some level of information asymmetry there. It doesn't really
Speaker 2: know what kind of walker you or I are going
Speaker 2: to need in thirty or forty years to get around,
Speaker 2: et cetera. And then furthermore, we didn't touch on it,
Speaker 2: but I do think or absolutely as a podcast, are
Speaker 2: going to need to do more, should do way more
Speaker 2: on defense spending. There's a million angles that we have
Speaker 2: to do on that. But you could see like we
Speaker 2: don't really know what went into the assembly of this
Speaker 2: and the cost of this program and is this part
Speaker 2: really worth this much money or maybe as a competition thing.
Speaker 2: There's a lot of fruit there for future episodes.
Speaker 1: But I do think one of the big tensions here
Speaker 1: is the sort of government generalists versus like the experts
Speaker 1: that they're listening to what I mean is, for instance,
Speaker 1: if you're a defense contractor and you're building I don't know,
Speaker 1: like a submarine launch pad or whatever, like the government
Speaker 1: official isn't necessarily going to know all the nuts and
Speaker 1: bolts that need to go into that. And so yeah,
Speaker 1: I always wonder how you sort of overcome that informational gap.
Speaker 2: There's a lot there. Let's do more on this topic.
Speaker 1: All right, shall we leave it there for now?
Speaker 2: Let's leave it there.
Speaker 1: This has been another episode of the All Thoughts podcast.
Speaker 1: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.
Speaker 2: Follow our guest Jetson leader Luis. He's at jetson Econ. Also,
Speaker 2: he has a number of papers on his website that
Speaker 2: you can just click on and go read. They're all
Speaker 2: really fascinating. Follow our producers Carmen Rodriguez at Carman Armann
Speaker 2: dash Ol Bennett at Dashbot and kill Brooks at Kilbrooks.
Speaker 2: Thank you to our producer Moses Onam. For more Odd
Speaker 2: Laws content, go to Bloomberg dot com slash oddlog. We
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