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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. Hello and welcome to

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Speaker 1: another episode of the Odd Lots podcast.

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Speaker 2: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 1: Tracy, remember that episode we did several weeks ago about

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Speaker 1: waste and fraud and abuse in medicare I do.

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Speaker 2: It was full of interesting facts and figures, such as

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Speaker 2: the US government spends one percent of the federal budget

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Speaker 2: on dialysis.

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Speaker 1: And the thing about that episode, so we were talking

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Speaker 1: to be you, Professor Jetson, Leader Luise, you know, the

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Speaker 1: thing about that episode that's striking is if you look

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Speaker 1: at government spending and probably a lot of all bureaucracies

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Speaker 1: even outside the government, but if you look at government

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Speaker 1: spending or government waste or government fraud, et cetera, there

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Speaker 1: are many things that everyone can point to and say

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Speaker 1: this is waste, this is bad, we should change this.

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Speaker 1: But like that's clearly just being able to like identify

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Speaker 1: some bad process or whether it's in how you you know,

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Speaker 1: how purchasing works or how hiring works or anything like,

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Speaker 1: clearly that's not enough to fix it. Like identification almost

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Speaker 1: seems like the easy part.

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Speaker 2: No, I think this is actually endemic in a lot

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Speaker 2: of large organizations, although I am certain there are specific

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Speaker 2: things about the government process that are unique to them,

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Speaker 2: But it feels like at large organizations everyone kind of

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Speaker 2: feels helpless, right, Like everyone can say we need to

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Speaker 2: do this better, or this is ridiculous, or we should

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Speaker 2: change this, but no one actually seems able to fix it.

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Speaker 1: Yeah, I anyway, Yes this is my this is my

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Speaker 1: perception as well. But we are in an era, you know,

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Speaker 1: the vibes have changed. We are in an era where,

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Speaker 1: once again and probably because inflation remains high, people see

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Speaker 1: the avious failures of what people call state capacity. You know,

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Speaker 1: there was a lot of stuff built under the Biden administration.

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Speaker 1: There was a lot of stuff allocated. People are frustrated

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Speaker 1: by the speed of things, whether you look at charging stations,

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Speaker 1: rural broadband, the speed with which any project gets implemented.

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Speaker 1: We're in a moment of frustration, and people want to

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Speaker 1: see two things. They want to see waste and spending

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Speaker 1: decline in many cases, and they also want to see

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Speaker 1: that the spending that we actually do allocate turns into action,

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Speaker 1: good results. Right, We're in a period where sitting aside,

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Speaker 1: what will happen, etc. Like there is this public impolicy.

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Speaker 1: We want to see results for things that we do.

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Speaker 2: Speaking of good results, can I just say the last

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Speaker 2: time we spoke to this guest who we are about

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Speaker 2: to speak to again, we were complaining about how clunky

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Speaker 2: the Treasury Direct website is and how you had to

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Speaker 2: click little buttons to get in instead of just typing

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Speaker 2: your password. And I think shortly after that someone sent

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Speaker 2: me a screenshot and said that they had fixed it. Amazing,

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Speaker 2: So you know this podcast can lead to real results.

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Speaker 1: Maybe that's the answer. We just need to do podcast

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Speaker 1: episodes on various things that aren't working and that someone

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Speaker 1: here is it and stuff like that. But yes, this

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Speaker 1: is a we are in an era for people demanding results. Anyway,

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Speaker 1: you mentioned that last episode we did with this guest,

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Speaker 1: she really is the perfect guest to talk about now

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Speaker 1: in this era of doge and the sort of impulse

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Speaker 1: to see government root out waste and fraud and abuse

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Speaker 1: and become more efficient. We're going to be speaking with

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Speaker 1: Jennifer Palka. She is the author of the book Recoding America,

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Speaker 1: currently serving as a Senior Fellow at the Nascannon Center.

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Speaker 1: She was the founder and former director of the Code

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Speaker 1: for America initiative. She's been on the Defense Innovation Board,

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Speaker 1: so has seen how government actually operates and spends and

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Speaker 1: works and builds technology at a very granular level. Perfect

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Speaker 1: guest to have back on. So, Jennifer, thank you so

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Speaker 1: much for coming back on out laws.

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Speaker 3: Yeah, no, thanks for having me.

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Speaker 1: You know, when it comes to government spending and waste,

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Speaker 1: you know, I think there's sort of two There are

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Speaker 1: sort of two things that people I think it's sort

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Speaker 1: of useful to divide the conversation in two things, because

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Speaker 1: there are major things that the government spends money on,

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Speaker 1: like various entitlements or having a large military, et cetera,

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Speaker 1: that some might consider wasteful per se, right, and then

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Speaker 1: there is the other aspect, which is just like within

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Speaker 1: the realm of things that many people agree we need,

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Speaker 1: is government getting the best bang for the book within

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Speaker 1: the sort of expectation that this is you know, legitimate spending.

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Speaker 1: And so some of these fights that in conversations that

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Speaker 1: are going to be held onto the new administration are

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Speaker 1: going to be in the first category, where it's like

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Speaker 1: big political questions about where we should be allocating our money.

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Speaker 1: But from your perspective, even setting that aside, when you

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Speaker 1: look at sort of the actual expenditure, like, do you

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Speaker 1: see big dials that can be turned in terms of

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Speaker 1: this can be done better.

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Speaker 3: Yeah, it's funny you say, like people get concerned about

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Speaker 3: the bang for the buck. I think people get really

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Speaker 3: concerned when they're like, any amount of books is not

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Speaker 3: getting a bang. Yeah, right, you know, we're it's it's less.

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Speaker 3: I understand the frustration and the switch to a dialogue

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Speaker 3: around government efficiency that Elon Musk is pushing, But I

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Speaker 3: think we've gotten to that place because people are saying,

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Speaker 3: you know, we're getting very little results. We would be

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Speaker 3: tolerant of, you know, reasonably high spending if something we're

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Speaker 3: coming out of this and you know, you talk about

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Speaker 3: the defense world for instance, I mean, our spending keeps

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Speaker 3: going up, and in theory, we're spending more so that

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Speaker 3: we can be safer and have greater deterrence. And in reality,

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Speaker 3: I think that that greater spending is actually keeping us

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Speaker 3: from making the changes that we need to make such

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Speaker 3: that the system is more effective in a fefficient you know,

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Speaker 3: people just still do these like big bang projects because

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Speaker 3: they have the money for them, they have all the

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Speaker 3: trappings around them that make them really slow all the procedure,

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Speaker 3: you know, instead of these you know fast you know procurements,

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Speaker 3: like we need a ton of drones, for instance, instead

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Speaker 3: of these you know, big heavy weapons platforms. But we're

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Speaker 3: still putting a lot of money into these things that

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Speaker 3: don't get us any results for twenty years. By the

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Speaker 3: time you know, we get the results, the thing isn't

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Speaker 3: actually needed anymore. What we need is drones instead of ships.

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Speaker 3: And you know, I really think that the dialogue is

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Speaker 3: changing for like, hey, where's the bang at all?

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Speaker 2: Right? Yeah, it feels like when organizations are sort of

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Speaker 2: swimming in money and have large budgets, they don't actually

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Speaker 2: have to think about how to best spend that money, right,

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Speaker 2: And so I well, I wanted to ask you more

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Speaker 2: your position on the Defense Innovation Board. One thing I've

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Speaker 2: been wondering is why does the Pentagon to this point

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Speaker 2: always seem to fail its audits.

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Speaker 3: Hmm, okay, there's so many reasons for that. One of

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Speaker 3: them I think speaks to a cordys function that you

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Speaker 3: see across government, which is we do everything in this

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Speaker 3: very very bespoke way. So it used to be over

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Speaker 3: five thousand different back end systems, including accounting, systems just

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Speaker 3: within the DoD. I don't think that number has gone

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Speaker 3: down that much. How do you get so many different

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Speaker 3: systems in you know, it's obviously a huge institution, but

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Speaker 3: like you would think there would be a standard way

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Speaker 3: to start to do accounting. But what happens is that Congress,

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Speaker 3: you know, puts some new requirement for reporting on this

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Speaker 3: particular program or this particular department, and they go, okay, well,

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Speaker 3: we need really bespoke software to handle these, you know,

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Speaker 3: sort of ourcane and in the weeds kinds of requests

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Speaker 3: from Congress or someone else. Some of them are like real,

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Speaker 3: Congress isn't backing off on them, and some of them

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Speaker 3: are kind of imagined. But that's how we get this

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Speaker 3: very heavyweight requirements development process in government, where like the

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Speaker 3: job of a bunch of people for a long time

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Speaker 3: is to go around to everybody and say, what are

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Speaker 3: all the things that you're going to need for this

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Speaker 3: system to work. Well, if you do that, they come

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Speaker 3: up with a bunch of things. That means you can't

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Speaker 3: just buy an off the shelf commercial accounting software the

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Speaker 3: way that even a really big enterprise would, because you've

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Speaker 3: come up with all these like bespoke things that need

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Speaker 3: to happen. Some of them are rail and some of

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Speaker 3: them are invented. And one of the things we said

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Speaker 3: on the Defense Innovation Board was it's actually not about

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Speaker 3: like doing better software to accommodate all those requirements. It

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Speaker 3: is about getting rid of those requirements so that you

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Speaker 3: can just buy commodities where But that's just really hard

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Speaker 3: because it's sort of you know, flow that power is

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Speaker 3: flowing the wrong direction. You don't have people who do

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Speaker 3: software going telling Congress to remove this requirement. It's just like,

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Speaker 3: you know, the stream needs to be a reverse. So

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Speaker 3: one of the reasons is just that they literally have

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Speaker 3: all these different systems that are supposed to feed into

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Speaker 3: one big picture of what the Department of Defense is spending,

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Speaker 3: and you know what it's receiving, and it's just incredibly complicated.

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Speaker 3: Now there are other reasons as well, but I think

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Speaker 3: that's one of the core. It's functions that you see

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Speaker 3: at play.

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Speaker 1: Let's back up for a second. So you wrote your

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Speaker 1: book Recruiting America. You have a great substance right now

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Speaker 1: called eating policy, and I want to get into some

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Speaker 1: of we'll get into some of the posts you wrote

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Speaker 1: about fixing hiring practices, and so forth. But I'm curious, like,

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Speaker 1: did you have a moment in your life, like the

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Speaker 1: sort of like founding trauma or whatever, where you're like, oh, wow,

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Speaker 1: this really something that you know, something that scandalized you

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Speaker 1: such that you've now devoted your work to writing about

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Speaker 1: and elucidating the issues that come up with things like

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Speaker 1: government hiring and spending.

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Speaker 3: Yeah, that's a funny question. I think what happened was

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Speaker 3: I started looking at this whole issue of government technology

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Speaker 3: back right after Obama was elected. Was we were working

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Speaker 3: on this idea of web two dotto if anyone remembers that,

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Speaker 3: what would gov Dato look like? And some colleagues and

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Speaker 3: I were sort of going to DC and talking to

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Speaker 3: people about how software got done in government. And I

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Speaker 3: remember this story. This is not the traumatic part, but

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Speaker 3: I remember the story from this guy who I'm from

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Speaker 3: Silicon Valley and he was working with the Department of

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Speaker 3: Labor and he was like, let me explain to you

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Speaker 3: how we do stuff here. It's like you have to

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Speaker 3: get soldiers from New York to d C. And you

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Speaker 3: go out and like source the iron and like you know,

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Speaker 3: have engineers speck out the requirements for the engines, because

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Speaker 3: you're going to build an entire railway instead of just

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Speaker 3: buying the soldiers some train tickets, and that can't be right,

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Speaker 3: that's crazy. But as I got into it, actually that

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Speaker 3: really is how this works. Often there's just not this

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Speaker 3: sense of like just how to use what's out there

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Speaker 3: because of this obsession with you know, doing everything from

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Speaker 3: the ground up. But before I worked in tech, my

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Speaker 3: first job out of college, I worked in child welfare

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Speaker 3: agency and I saw how badly the operations were there,

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Speaker 3: and you really see up close what's at stake these

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Speaker 3: kids luck and I thought, oh my god, if we're

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Speaker 3: doing software for the Department of Labor, this bad. This

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Speaker 3: is also affecting things like child welfare. And we really

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Speaker 3: ought to be putting like the best our country has

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Speaker 3: to offer on the biggest problems. And it's kind of

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Speaker 3: the opposite. And that's really got what made me, you know,

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Speaker 3: want to start Code for America.

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Speaker 2: Say more about the process of building everything from the

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Speaker 2: ground up, because I remember the last time we spoke

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Speaker 2: to you, one of the crazy numbers in that conversation

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Speaker 2: was the amount of requirements for a specific software project.

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Speaker 2: What is the exact process like walk us through the

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Speaker 2: I guess the life cycle of putting out a government

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Speaker 2: You know, we can talk software in particular because you

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Speaker 2: have a lot of experience with that a government software project.

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Speaker 2: Where does it start, what are the various like hoops

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Speaker 2: that people have to jump through, and then where does

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Speaker 2: it end up?

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Speaker 3: Yeah, maybe I'll talk about the unemployment insurance system in California,

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Speaker 3: which it worked on very briefly as the coacher of

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Speaker 3: the strike team in that first year of the pandemic

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Speaker 3: when everyone was waiting for their checks, and it was

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Speaker 3: really a disaster when we came in because there was

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Speaker 3: this huge backlog of claims. In fact, just coincidentally, the

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Speaker 3: team at the Employment Development Department had just finally gotten

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Speaker 3: to the point where they were going to put a

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Speaker 3: bid for a sort of a modernization they would say,

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Speaker 3: an upgrade they call a business system modernization out to vendors.

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Speaker 3: And I don't remember the exact number of requirements in it,

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Speaker 3: but it was in the thousands and they had been

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Speaker 3: working on that for eleven years. So they started the

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Speaker 3: requirements gathering eleven years ago and they were just putting

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Speaker 3: it out to bid, and I think we became extremely

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Speaker 3: unpop popular by telling the governor's office you really should

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Speaker 3: not bid that out. It is even if it were

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Speaker 3: one year old, it's now totally outdated because now we

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Speaker 3: understand the problems that you need to be solved much

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Speaker 3: better than you did a year ago, but certainly better

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Speaker 3: than you did eleven years ago. And they did actually

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Speaker 3: stop that and sort of revise it. But like, why

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Speaker 3: are there those thousands of requirements that take I don't

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Speaker 3: think that I should be clear. I don't think the

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Speaker 3: requirements gathering was eleven years. It was a big chunk

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Speaker 3: of that eleven years. Then you have sort of putting

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Speaker 3: together the RFP and all getting it, you know, getting

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Speaker 3: it approved for the legislature, et cetera. So you know,

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Speaker 3: why does it get so big? Well, I think you I.

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Speaker 3: Unemployment insurance is a good example of this. So unemployment

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Speaker 3: insurance started in the nineteen thirty five Social Security Act.

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Speaker 3: So we are well now it's twenty twenty five. So

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Speaker 3: now we are ninety years of accumulation of policy and

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Speaker 3: process and regulatory cruft essentially, So think about like stuff,

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Speaker 3: you know, requirements and rules and guidance from both the executive,

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Speaker 3: legislative and judicial branches just sort of falling like garbage

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Speaker 3: onto something like unemployment insurance for ninety years. Like a

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Speaker 3: rule of thumb is we always add, we never subtract.

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Speaker 3: And this is a fault of our legislatures, primarily not

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Speaker 3: the people who run the unemployment insurance system. They kind

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Speaker 3: of feel like they can't do anything about this accumulation.

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Speaker 3: What they do then is try to accommodate all of

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Speaker 3: these changes over time while never actually fixing the underlying system.

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Speaker 3: So like one of the metaphors someone else used for

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Speaker 3: me is like layers of paint to just get put

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Speaker 3: on well, you know, especially if you've lived like an

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Speaker 3: apartment in New York that's been painted like ninety thousand times,

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Speaker 3: eventually it cracks. That's sort of what's happening. Or my

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Speaker 3: other metaphor, which is like archaeological layers, but they only

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Speaker 3: ever appropriate money to just like make the fixes, not

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Speaker 3: to go back and say, how would we actually design

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Speaker 3: this system if we were designing it today, not just

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Speaker 3: from a technological perspective, but how would we go back

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Speaker 3: and say, oh, that you know, requirement from the forties,

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Speaker 3: you know, that's been overwritten by these other things. Why

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Speaker 3: is there still code in the system that's you know,

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Speaker 3: that's dealing with that requirement from the forties and why

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Speaker 3: is there still you know, memos pointing to that, confusing

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Speaker 3: people about what the current state of the rules are.

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Speaker 3: But like, really, what we need to do is do

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Speaker 3: a full kind of regulatory simplification, policy and process simplification

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Speaker 3: alongside our modernizations, and we just don't do that.

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Speaker 2: Do government officials who are design these projects, how much

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Speaker 2: do they ever work in tandem with vendors? Like well,

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Speaker 2: they go out and say, hey, we're thinking about upgrading

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Speaker 2: the system. What do you think we should do? Or

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Speaker 2: does it always start with the requirement process and then

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Speaker 2: that just got sent out.

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Speaker 3: No, they do often work with vendors. The problem is

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Speaker 3: that the sort of way the vendor game is played,

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Speaker 3: and I'd say this is changing to some degree. It's

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Speaker 3: like the vendors benefit from all that complexity, So the

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Speaker 3: vendors never say, hey, why don't you go your legislature

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Speaker 3: and ask for like, you know, get can we you know,

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Speaker 3: collaborate in a project, sort of rationalize all of this

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Speaker 3: policy craft and then we can make you a system

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Speaker 3: that it's going to be you know, a lot more elegant,

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Speaker 3: a lot more stable, a lot more scalable. Right, scalability

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Speaker 3: is really really degraded by this complexity because like it's

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Speaker 3: they're huge projects. I mean, I think even after we

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Speaker 3: asked California to boot out the old business system monorization

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Speaker 3: and started again, they still went out with something that

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Speaker 3: I believe was almost two billion dollars. Might have been

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Speaker 3: one billion, So forgive me if I'm getting that wrong,

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Speaker 3: but that's still in the same range of like, that's

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Speaker 3: a lot of money to like, quote unquote like do

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Speaker 3: some upgrades on a system, because they really don't have

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Speaker 3: that relationship between the executive and legislative branch. And also

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Speaker 3: let's throw in the judicial to say, what this needs

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Speaker 3: is a real breeboot. I mean, unemployment insurance is not

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Speaker 3: that complicated in its basics, right, We're giving people a

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Speaker 3: certain amount of money for a certain amount of time

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Speaker 3: under certain conditions. But it's gotten wildly complicated, and that

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Speaker 3: like the vendors don't seem to most of the vendors

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Speaker 3: aren't really incentive to be part of that conversation about

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Speaker 3: the simplification because then the projects are going to be smaller.

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Speaker 1: Let's talk about government hiring and your experience, and you've

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Speaker 1: written some sort of wild stuff on your subject just

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Speaker 1: about how hard it is to get a talented person

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Speaker 1: in the door. And this is even sitting aside the

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Speaker 1: fact that government pay skills aren't the same as private

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Speaker 1: sector pay skills, et cetera. There's no like, you know,

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Speaker 1: equity upside for a talented engineer just working through the

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Speaker 1: government HR process.

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Speaker 3: What do you see the biggest problem with hiring to me?

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Speaker 3: And there's there are many isn't the pay though I

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Speaker 3: know that should be fixed and a lot of people

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Speaker 3: do talk about that. It's that we don't actually assess

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Speaker 3: candidates for their skills. So if you leave a part

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Speaker 3: the like political appointees and the people who get appointed

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Speaker 3: are things like JLA or schedule. See where you're using

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Speaker 3: an exception and just talk about like the regular uh,

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Speaker 3: open to the public competitive process. Ninety percent of those

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Speaker 3: use on self assessments of the person's own skill and

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Speaker 3: they tell you what level they are and a resume screen.

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Speaker 3: So how it goes is as a hiring manager, you're

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Speaker 3: like completely cut out of the process. The HR person

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Speaker 3: is in control, and you say, okay, I need this

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Speaker 3: job there's all this sort of back and forth about

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Speaker 3: what the job is. The job descriptions aren't often up

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Speaker 3: to date. Maybe there isn't a job description or a

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Speaker 3: classification for the kind of person you need, especially if

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Speaker 3: it's technical. Leaving all that aside, you finally post a

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Speaker 3: position and let's say five hundred resumes come in. Well,

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Speaker 3: your HR person has to give you a sert, a

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Speaker 3: list of people that they deem qualified. And the way

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Speaker 3: they get from five hundred to say ten on the

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Speaker 3: sert is first they look for everybody who has cut

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Speaker 3: and pasted from the job description into their resume and

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Speaker 3: cover letter. Oh, by the way, that person had to

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Speaker 3: have known to do a government resume which is usually

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Speaker 3: like seven pages long as opposed private sector resume, which

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Speaker 3: would be shorter. That's all another story. So anyone who

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Speaker 3: has exact matches between the language in the job description

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Speaker 3: and their resume can go through to the next step.

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Speaker 3: Of those the people who rate themselves as a master

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Speaker 3: on all levels then can go through to the next step,

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Speaker 3: and from there they apply veterans preference. Now I am

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Speaker 3: a fan of giving veterans preference, but when you have

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Speaker 3: down selected out all the people who didn't want to

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Speaker 3: cut in paste or didn't want to basically lie and

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Speaker 3: just say that they were a master on every competency.

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Speaker 3: The good veterans probably haven't ended up on that sert,

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Speaker 3: And so as a hiring manager, you're given a cert

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Speaker 3: of people who just knew how to play the game,

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Speaker 3: but don't necessarily have the skills, and half of the

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Speaker 3: time the hiring manager just rejects the sert. So, like

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Speaker 3: an example of how this works with that, you know,

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Speaker 3: looking for exact matches, was this guy that we found

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Speaker 3: when we were working on the Defense and Avation Board

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Speaker 3: who had literally won the hack the Pentagon contest, like

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Speaker 3: the top security researchers in the country came together and

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Speaker 3: this one guy he happened to be nineteen years old.

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Speaker 3: One that thing, he's like incredibly talented, and the people

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Speaker 3: there recognized this is the kind of guy that we

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Speaker 3: need in the Pentagon making our systems more secure. So

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Speaker 3: they try to hire him, but his resume like just details,

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Speaker 3: you know, all the programming languages and frameworks that he

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Speaker 3: knows how to use, because that's what you would, you know,

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Speaker 3: you would talk about your experience with specific frameworks and languages.

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Speaker 3: If you were applying for a private sector job, and

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Speaker 3: the you know, HR people look at that and they go,

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Speaker 3: we have no idea what that means. That doesn't match

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Speaker 3: at all our language. And he gets kicked out literally,

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Speaker 3: you know, in the first the first down select. But

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Speaker 3: it doesn't stop there, like the people at the Pentagon

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Speaker 3: continue to try to get this guy hired, and pretty

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Speaker 3: high level people kept in intervening as they tried again

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Speaker 3: and again to get Jack Cable hired, and it took

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Speaker 3: months for him to like finally get through the process.

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Speaker 3: In between, one of the HR people told him, why

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Speaker 3: don't you go work for Best Buy selling TVs for

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Speaker 3: a year because then you'll be qualified.

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Speaker 2: That is nuts. So just to be clear, because he

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Speaker 2: said on his CV specific programming languages like I can

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Speaker 2: use like babbel and I'm an expert in I don't know,

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Speaker 2: NPM or whatever. The HR manager didn't recognize it, and

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Speaker 2: they're like, this is not what we're looking for.

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Speaker 3: That's exactly right, and they explained to the Defense Innovation Board.

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Speaker 3: They pulled up the language in the job description, which

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Speaker 3: was like, you know, X years of experience doing a

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Speaker 3: very generic language about like the completion of it tech

404
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Speaker 3: you know, prods or something, which is like doesn't mean anything,

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Speaker 3: and they were like, we don't see this, and.

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Speaker 2: Just a question, this might be a cultural question, but

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Speaker 2: why does HR not listen to the expertise of these

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Speaker 2: specific hiring managers.

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Speaker 3: Okay, that is a fantastic question, and I've actually done

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Speaker 3: some research on like legally is that required? And I'll

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Speaker 3: give you the answer in a second. But I think

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Speaker 3: the real answer is that they're extremely risk averse and

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Speaker 3: one of the principles that they're supposed to hold is

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Speaker 3: uphold is a principle of fairness. And I like fairness.

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Speaker 3: I think that's great, But the way that it gets

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Speaker 3: applied is that essentially saying, we have all these very

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Speaker 3: very specific rules designed to keep bias out of this process,

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Speaker 3: and we are experts in those processes and those rules,

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Speaker 3: and so only we can ensure that this process occurs

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Speaker 3: without the introduction of bias and or you know, you know,

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Speaker 3: some perceived or real you know, deviation from the rules,

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Speaker 3: and so what they call them subject matter experts. Like

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Speaker 3: in other words, if you're hiring from a programmer, like

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Speaker 3: you would kind of want a programmer to evaluate that person.

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Speaker 3: But programmers are not allowed to valuate the person again

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Speaker 3: in ninety percent of these cases because they might introduce bias.

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Speaker 3: And it's a perfect example for me of like, if

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Speaker 3: you read the merit system principles that came out of

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Speaker 3: the Civil Service Reform Act of nineteen seventy eight, Thank you,

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Speaker 3: President Carter. That was, you know, an important thing that

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Speaker 3: he did. They sound really great and they talk about,

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Speaker 3: you know, hiring on the basis of merit, and fairness

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Speaker 3: is in there as well. And you read them, you

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Speaker 3: would say these are fantastic. Federal hiring is great, no problem.

435
00:25:58,440 --> 00:26:02,399
Speaker 3: But what happened is as they get operationalized, they go

436
00:26:02,440 --> 00:26:05,880
Speaker 3: through what I call the cascade of rigidity, where each

437
00:26:06,000 --> 00:26:09,240
Speaker 3: step down from sort of a law or policy into

438
00:26:09,400 --> 00:26:13,000
Speaker 3: like the way HR managers actually work on a daily basis,

439
00:26:13,320 --> 00:26:17,159
Speaker 3: just gets incredibly rigid. And the rigidity means that you

440
00:26:17,320 --> 00:26:21,520
Speaker 3: get kind of the opposite outcome of what the law intended.

441
00:26:21,560 --> 00:26:25,520
Speaker 3: The law intended merit, we're really not hiring on the

442
00:26:25,560 --> 00:26:28,240
Speaker 3: basis of merit because it's gotten so rigid as it's

443
00:26:28,280 --> 00:26:32,359
Speaker 3: been operationalized. And I asked one of our researchers to

444
00:26:32,400 --> 00:26:36,119
Speaker 3: go look into this, like is it in the law

445
00:26:36,240 --> 00:26:39,679
Speaker 3: is it in the regulations? Is it in the guidance?

446
00:26:39,840 --> 00:26:43,879
Speaker 3: Like who wrote in that HR managers control this process

447
00:26:44,080 --> 00:26:47,200
Speaker 3: and hiring managers don't have a say. And it turns

448
00:26:47,200 --> 00:26:49,320
Speaker 3: out it just sort of appears in this thing called

449
00:26:49,320 --> 00:26:54,760
Speaker 3: the Delegated Examiner's Operations Handbook, which means somebody wrote this

450
00:26:54,880 --> 00:26:59,320
Speaker 3: operational handbook and said, you know, this is HR manager's job,

451
00:26:59,520 --> 00:27:03,439
Speaker 3: like you know, and so that's what people do. But

452
00:27:03,520 --> 00:27:06,440
Speaker 3: like the law never said that in needed does the regulation?

453
00:27:07,000 --> 00:27:07,199
Speaker 2: You know?

454
00:27:07,280 --> 00:27:09,800
Speaker 1: I just you know, Okay, So five hundred people apply

455
00:27:09,880 --> 00:27:13,359
Speaker 1: for a job, you get ten. The hiring manager sees

456
00:27:13,760 --> 00:27:16,400
Speaker 1: ten names, they sort of might have a feeling that

457
00:27:16,520 --> 00:27:19,400
Speaker 1: these can't be the best of the five hundred. Can

458
00:27:19,440 --> 00:27:21,520
Speaker 1: you say, look, just give me the five hundred. Let

459
00:27:21,600 --> 00:27:23,840
Speaker 1: me scan through click on some of them, because a

460
00:27:23,920 --> 00:27:27,359
Speaker 1: good hiring manager probably has some intuitions about what a

461
00:27:27,359 --> 00:27:29,720
Speaker 1: good candidate might look like, like, will they let you

462
00:27:29,800 --> 00:27:31,480
Speaker 1: see that five hundred person list?

463
00:27:32,520 --> 00:27:35,639
Speaker 3: Well, I maintain that there is nothing legally that keeps

464
00:27:35,640 --> 00:27:38,840
Speaker 3: them from doing that, But on a practical basis, no,

465
00:27:39,040 --> 00:27:39,919
Speaker 3: they're not allowed to.

466
00:27:40,960 --> 00:27:43,840
Speaker 2: So you touched on this earlier. But part of the

467
00:27:43,880 --> 00:27:48,679
Speaker 2: reasons all these requirements have been codified is for reasons

468
00:27:48,680 --> 00:27:51,560
Speaker 2: of fairness, and the US presumably doesn't want to go

469
00:27:51,680 --> 00:27:56,560
Speaker 2: back to a system of patronage or nepotism or people

470
00:27:56,680 --> 00:28:00,199
Speaker 2: hiring their friends and stuff like that. What would be

471
00:28:00,480 --> 00:28:04,719
Speaker 2: I guess the suggested guard rails for stopping the government

472
00:28:04,720 --> 00:28:07,400
Speaker 2: from doing that or maybe balancing you know, the two

473
00:28:07,440 --> 00:28:10,760
Speaker 2: things fairness and hiring based on merit.

474
00:28:11,200 --> 00:28:15,360
Speaker 3: It's a complicated question because of what I described earlier,

475
00:28:15,440 --> 00:28:18,200
Speaker 3: which is, if you don't solve the problem of everything

476
00:28:18,240 --> 00:28:22,280
Speaker 3: getting more rigid as it gets operationalized, even you know,

477
00:28:22,359 --> 00:28:26,560
Speaker 3: acts of Congress to change you know, civil service rules

478
00:28:26,760 --> 00:28:29,879
Speaker 3: at the highest level kind of you know, don't have

479
00:28:29,920 --> 00:28:33,479
Speaker 3: the intended effect, but we are still always going to

480
00:28:33,480 --> 00:28:37,160
Speaker 3: be trying to balance those things. I maintain that essentially,

481
00:28:37,480 --> 00:28:40,600
Speaker 3: the merits and stant principles defined in that nineteen seventy

482
00:28:40,600 --> 00:28:44,760
Speaker 3: eight CSRA are great. What we just need to do

483
00:28:44,800 --> 00:28:47,640
Speaker 3: is sort of like pull the you know, pull the

484
00:28:47,840 --> 00:28:50,920
Speaker 3: edifice back to the studs, like it's a massive remodel,

485
00:28:51,400 --> 00:28:55,200
Speaker 3: but not like an upending of the foundation. The foundation

486
00:28:55,520 --> 00:28:58,840
Speaker 3: is solid, and so we need to go have a

487
00:28:58,880 --> 00:29:01,400
Speaker 3: full look up and down the stack. Where did we

488
00:29:01,440 --> 00:29:05,800
Speaker 3: go wrong in that cascade of rigidity. There's probably there

489
00:29:05,840 --> 00:29:08,040
Speaker 3: are tweaks that need to happen at the statute level,

490
00:29:08,040 --> 00:29:10,080
Speaker 3: So I think like OPM and Congress do need to

491
00:29:10,080 --> 00:29:11,840
Speaker 3: get involved. But a lot of it is just how

492
00:29:11,880 --> 00:29:15,880
Speaker 3: has this been operationalized? Now you probably know that there

493
00:29:16,040 --> 00:29:20,760
Speaker 3: is this sort of nuclear bomb that is being threatened.

494
00:29:20,800 --> 00:29:22,719
Speaker 2: Oh I was going to ask about that.

495
00:29:23,080 --> 00:29:30,800
Speaker 3: Yeah. Schedule F basically says if you can demonstrate that

496
00:29:30,840 --> 00:29:35,120
Speaker 3: this person in the who has civil service protections today

497
00:29:35,640 --> 00:29:40,720
Speaker 3: has sort of any adjacency to policy, you can reclassify

498
00:29:40,880 --> 00:29:42,920
Speaker 3: them as a political appointc and then they lose all

499
00:29:42,960 --> 00:29:47,000
Speaker 3: their civil service protections. And of course, uh Trump rolled

500
00:29:47,000 --> 00:29:50,280
Speaker 3: that up like in the last month or something of

501
00:29:50,360 --> 00:29:52,720
Speaker 3: his first term and has been very clear that he

502
00:29:52,720 --> 00:29:55,480
Speaker 3: wants to bring it back in the beginning of this

503
00:29:55,600 --> 00:29:59,760
Speaker 3: coming term. And you know, in my metaphor, that is

504
00:29:59,800 --> 00:30:03,200
Speaker 3: at pulling up the foundation, because that is saying we're

505
00:30:03,200 --> 00:30:07,680
Speaker 3: going to hire fire on the basis of loyalty essentially

506
00:30:08,120 --> 00:30:12,040
Speaker 3: to an administration, instead of on the basis of merit.

507
00:30:12,160 --> 00:30:15,840
Speaker 3: And we do really have problems with not being able

508
00:30:15,880 --> 00:30:19,719
Speaker 3: to fire underperformers. They're a very small percentage of people

509
00:30:19,880 --> 00:30:22,920
Speaker 3: who really should have been removed and it's super hard

510
00:30:22,960 --> 00:30:24,680
Speaker 3: to remove them. I can tell you a couple of

511
00:30:24,680 --> 00:30:27,479
Speaker 3: stories about that. But like, and we should fix that.

512
00:30:28,000 --> 00:30:32,440
Speaker 3: We should fix that, not take away the protections for

513
00:30:32,560 --> 00:30:34,800
Speaker 3: folks who stand up and say, hey, you need to

514
00:30:34,880 --> 00:30:37,160
Speaker 3: know this. Oh wait, I disagree with you, so suddenly

515
00:30:37,200 --> 00:30:42,120
Speaker 3: you're fired. Like, like, rebuild the thing from its strong foundation, don't.

516
00:30:42,360 --> 00:30:44,560
Speaker 3: Don't pull the foundation out of the soil.

517
00:31:00,080 --> 00:31:04,360
Speaker 1: The way I'm looking at the Delegated Examining Operations Handbook,

518
00:31:07,040 --> 00:31:11,800
Speaker 1: three hundred and eighteen pages, many flow charts, many sub sections.

519
00:31:11,840 --> 00:31:15,840
Speaker 1: It's many appendices. You know, I'm sure, look this serious stuff.

520
00:31:15,840 --> 00:31:17,960
Speaker 1: I'm sure that this needs to be a real document.

521
00:31:17,680 --> 00:31:18,160
Speaker 3: But it is.

522
00:31:18,680 --> 00:31:21,320
Speaker 1: There's a lot to it. This gets though to something

523
00:31:21,520 --> 00:31:25,320
Speaker 1: that I think is maybe part of the core question here,

524
00:31:25,480 --> 00:31:27,880
Speaker 1: and we sort of teased at it in the introduction.

525
00:31:28,360 --> 00:31:31,320
Speaker 1: It's easy enough to identify these issues and say, look,

526
00:31:31,360 --> 00:31:34,080
Speaker 1: this is absurd, and I imagine that if a lot

527
00:31:34,160 --> 00:31:37,640
Speaker 1: of people experiencing this would agree, and probably over the

528
00:31:37,720 --> 00:31:40,320
Speaker 1: years in government many people have come to the same

529
00:31:40,400 --> 00:31:44,280
Speaker 1: conclusion that you have about the absurdity of some of

530
00:31:44,320 --> 00:31:48,200
Speaker 1: these some of these workflows and so forth. In one

531
00:31:48,240 --> 00:31:51,720
Speaker 1: of your pieces recently, something that you implied is that

532
00:31:51,800 --> 00:31:56,640
Speaker 1: maybe the answer is not just more identification of the

533
00:31:56,680 --> 00:32:00,120
Speaker 1: problems or sort of good intention, but maybe someone who's

534
00:32:00,200 --> 00:32:03,840
Speaker 1: kind of a bully or someone who doesn't necessarily think

535
00:32:03,920 --> 00:32:08,600
Speaker 1: the rules apply, and perhaps someone like you know, Elon

536
00:32:08,720 --> 00:32:12,960
Speaker 1: Musk for example, someone with that someone with that character.

537
00:32:13,600 --> 00:32:16,680
Speaker 1: Gravitas might be a word that people use energy. I

538
00:32:16,720 --> 00:32:19,959
Speaker 1: don't know, talk to us more, because again, it doesn't

539
00:32:20,000 --> 00:32:22,800
Speaker 1: seem like just identifying these problems are enough.

540
00:32:24,680 --> 00:32:27,160
Speaker 3: Well, I think evidently hasn't been because we haven't done

541
00:32:27,240 --> 00:32:31,560
Speaker 3: that much. The chance to compete act is a you know,

542
00:32:31,600 --> 00:32:34,560
Speaker 3: a good move in the right direction. It's past both

543
00:32:34,760 --> 00:32:38,000
Speaker 3: houses and is I presume we'll be signed by President

544
00:32:38,000 --> 00:32:41,040
Speaker 3: Biden before he leaves office. And it does a lot

545
00:32:41,080 --> 00:32:44,640
Speaker 3: actually about the assessment problem that we just discussed, but

546
00:32:44,680 --> 00:32:47,640
Speaker 3: it doesn't it probably doesn't do enough in my view,

547
00:32:48,160 --> 00:32:50,760
Speaker 3: and again you'll have the problem of actually implementing it,

548
00:32:50,800 --> 00:32:53,000
Speaker 3: which the new head of OPM is going to find

549
00:32:53,040 --> 00:32:56,040
Speaker 3: out is pretty hard to implement when you have an

550
00:32:56,160 --> 00:32:59,520
Speaker 3: HR workforce that risk averse and that sort of process obsessed.

551
00:33:00,120 --> 00:33:04,400
Speaker 3: So yeah, I'm in that weird position as a Democrat

552
00:33:05,120 --> 00:33:07,440
Speaker 3: and someone who very much cares about the rule of

553
00:33:07,560 --> 00:33:10,880
Speaker 3: law and values the institution of government and wants to

554
00:33:10,880 --> 00:33:13,400
Speaker 3: see it succeed in part because we have all these

555
00:33:13,480 --> 00:33:16,760
Speaker 3: challenges that we government is the only institution that can

556
00:33:17,280 --> 00:33:19,239
Speaker 3: really meet them, in my view, at least meet them

557
00:33:19,280 --> 00:33:22,640
Speaker 3: in a way that is consistent with the democratic principles

558
00:33:23,160 --> 00:33:29,160
Speaker 3: of our society. And yet we have neglected that kind

559
00:33:29,200 --> 00:33:32,280
Speaker 3: of change, or we've just been not bold enough in

560
00:33:32,480 --> 00:33:36,280
Speaker 3: trying to change the system as it exists today. I

561
00:33:36,320 --> 00:33:40,080
Speaker 3: think it's part of culture of both the left and

562
00:33:40,120 --> 00:33:42,960
Speaker 3: the right to fight a lot about like i'll call

563
00:33:43,000 --> 00:33:46,480
Speaker 3: the what of government, Like what bills are we passing?

564
00:33:47,120 --> 00:33:49,640
Speaker 3: You know, the Chips and Science Act and the Inflational

565
00:33:49,680 --> 00:33:53,360
Speaker 3: Reduction Act and the by De Parson Instructure Law. Those

566
00:33:53,360 --> 00:33:56,200
Speaker 3: are all the what, and like they were really good,

567
00:33:56,320 --> 00:33:59,920
Speaker 3: I think in a lot of ways, but we neglect

568
00:34:00,120 --> 00:34:03,520
Speaker 3: did the how? And the how is all these things

569
00:34:03,600 --> 00:34:07,160
Speaker 3: like the functioning of government and hiring and again both

570
00:34:07,200 --> 00:34:11,200
Speaker 3: parties do that. And because we've neglected the how, it's

571
00:34:11,280 --> 00:34:16,640
Speaker 3: gotten really really sclerotic, I guess is a word, and

572
00:34:16,920 --> 00:34:19,520
Speaker 3: in effective, as you talked about at the start of this.

573
00:34:19,719 --> 00:34:27,040
Speaker 3: And so that democratic approach generally of let's be very thoughtful,

574
00:34:27,360 --> 00:34:31,719
Speaker 3: and let's listen to all the voices and make sure

575
00:34:31,719 --> 00:34:35,480
Speaker 3: we're making very measured decisions here, which is like the

576
00:34:35,520 --> 00:34:37,879
Speaker 3: world I'd like to live in. That sounds great, and

577
00:34:38,440 --> 00:34:40,400
Speaker 3: I like to think of myself as a thoughtful and

578
00:34:40,440 --> 00:34:45,480
Speaker 3: measured person. But that kind of approach tends to like

579
00:34:45,640 --> 00:34:49,680
Speaker 3: tends basically to add more process and more procedure when

580
00:34:49,719 --> 00:34:51,960
Speaker 3: we already have a lot, and try to make sure

581
00:34:51,960 --> 00:34:55,200
Speaker 3: that everybody is happy, and the result of that has

582
00:34:55,320 --> 00:34:58,960
Speaker 3: been that we're not achieving our policy goals. And so

583
00:34:59,360 --> 00:35:06,000
Speaker 3: I'm in that comfortable space of saying, I don't really

584
00:35:06,080 --> 00:35:09,080
Speaker 3: agree with a lot of the policy goals of the

585
00:35:09,080 --> 00:35:14,439
Speaker 3: incoming administration, but I have to acknowledge that a sort

586
00:35:14,520 --> 00:35:20,560
Speaker 3: of extremely disruptive approach might be needed for us to

587
00:35:21,280 --> 00:35:23,160
Speaker 3: just get out of the rut that we're in of

588
00:35:23,200 --> 00:35:27,360
Speaker 3: always you know, always adding and never subtracting, and hope

589
00:35:27,480 --> 00:35:31,759
Speaker 3: that the cycles of you know, change in power mean that,

590
00:35:32,400 --> 00:35:35,120
Speaker 3: you know, if the party that I prefer ever gets

591
00:35:35,200 --> 00:35:39,200
Speaker 3: back into power, some cleanup would have happened, and then

592
00:35:39,239 --> 00:35:43,400
Speaker 3: they can sort of rebuild in that thoughtful, considered way

593
00:35:43,719 --> 00:35:47,040
Speaker 3: that Democrats tend to do, you know, rebuild something that

594
00:35:47,040 --> 00:35:52,000
Speaker 3: that's quite healthy and not you know, you know, out

595
00:35:52,080 --> 00:35:55,680
Speaker 3: of sort of this cleanup, but recognizing that this period

596
00:35:55,760 --> 00:36:01,360
Speaker 3: of disruption could be quite bad in many ways and

597
00:36:01,680 --> 00:36:02,440
Speaker 3: yet maybe needed.

598
00:36:02,960 --> 00:36:07,880
Speaker 2: I mean, there have been some virtually process free suggestions

599
00:36:07,920 --> 00:36:12,480
Speaker 2: for firing workers made by Musk and Favec Ramaswani. I

600
00:36:12,480 --> 00:36:16,040
Speaker 2: think Ramaswani wanted to didn't you want to like fire

601
00:36:16,719 --> 00:36:21,040
Speaker 2: all employees with like odd numbered Social Security numbers or something?

602
00:36:22,520 --> 00:36:26,239
Speaker 3: He did, And I will say that, a, that's really

603
00:36:26,400 --> 00:36:30,600
Speaker 3: dumb idea, because the people in government who are the

604
00:36:30,600 --> 00:36:33,800
Speaker 3: biggest advocate for change, who could be the vek in

605
00:36:33,880 --> 00:36:37,759
Speaker 3: Elon's you know, greatest allies in making the kind of

606
00:36:37,840 --> 00:36:41,160
Speaker 3: changes that they or at least reasonable changes, would probably

607
00:36:41,440 --> 00:36:44,920
Speaker 3: get fired, leaving you with the ones that should have

608
00:36:44,960 --> 00:36:48,000
Speaker 3: gotten fired. But I will also say that he said

609
00:36:48,040 --> 00:36:51,440
Speaker 3: that on the campaign trail, and he's since given interviews

610
00:36:51,680 --> 00:36:53,560
Speaker 3: that and I'm not trying to defend him, but he's

611
00:36:53,600 --> 00:36:57,960
Speaker 3: since given interviews that demonstrate quite a bit more understanding

612
00:36:58,600 --> 00:37:04,160
Speaker 3: of the right ways to do this. He's acknowledged, for instance,

613
00:37:04,239 --> 00:37:09,080
Speaker 3: that you know, it's Congress who's in charge of laws

614
00:37:09,080 --> 00:37:12,680
Speaker 3: and regulations, and they can't just ride rushshot over that

615
00:37:12,680 --> 00:37:15,520
Speaker 3: that there's probably better ways to deal with the workforce.

616
00:37:15,920 --> 00:37:19,920
Speaker 3: But he's certainly coming from this place of just completely

617
00:37:19,960 --> 00:37:23,360
Speaker 3: crazy ideas that at least maybe move the Overton window,

618
00:37:23,440 --> 00:37:25,560
Speaker 3: but should not actually be operationalized.

619
00:37:26,040 --> 00:37:29,000
Speaker 2: Can you give an example of I understand we haven't

620
00:37:29,120 --> 00:37:32,400
Speaker 2: done any big reforms to the hiring process, but I imagine

621
00:37:32,280 --> 00:37:36,200
Speaker 2: there have been some attempts previously to improve it at

622
00:37:36,200 --> 00:37:40,640
Speaker 2: the margins. Are there any successful demonstrations of changes here?

623
00:37:42,239 --> 00:37:44,719
Speaker 3: Under the first Trump administration, a team at the United

624
00:37:44,719 --> 00:37:48,120
Speaker 3: States Digital Service started something with the absolutely awful name

625
00:37:48,160 --> 00:37:55,719
Speaker 3: of SMEKUA Expertise subject Matter Expert qualifying Assessments. I know,

626
00:37:55,760 --> 00:37:56,919
Speaker 3: but it just doesn't sound okay.

627
00:37:56,920 --> 00:37:59,120
Speaker 1: At least it's not dog right like. At least it

628
00:37:59,239 --> 00:38:02,520
Speaker 1: sounds serious speaks, Yeah, keep going.

629
00:38:02,640 --> 00:38:04,439
Speaker 3: Well, the funny thing is it sounds.

630
00:38:04,160 --> 00:38:06,880
Speaker 1: Very governmentment, which I'm okay with.

631
00:38:08,040 --> 00:38:12,640
Speaker 3: But essentially, this team looked at that problem of ninety

632
00:38:12,719 --> 00:38:17,200
Speaker 3: percent of hires happening without a reasonable assessment of the

633
00:38:17,239 --> 00:38:20,319
Speaker 3: candidate's skills and said, you know, we don't need to

634
00:38:20,360 --> 00:38:23,200
Speaker 3: do this. The law and policy absolutely allow for real

635
00:38:23,239 --> 00:38:27,000
Speaker 3: assessments and put together a process that hiring managers and

636
00:38:27,440 --> 00:38:32,360
Speaker 3: HR their HR partners could use to for instance, assess

637
00:38:32,520 --> 00:38:35,759
Speaker 3: you know, programmers by actual other programmers, you know. But

638
00:38:35,800 --> 00:38:39,880
Speaker 3: it's because it's working within existing not only law in policy,

639
00:38:39,920 --> 00:38:43,360
Speaker 3: but sort of the culture of the Office of Personnel Management.

640
00:38:43,360 --> 00:38:47,239
Speaker 3: It's still a pretty heavyweight process. But the people I

641
00:38:47,280 --> 00:38:50,759
Speaker 3: know who used it to hire people love it. They

642
00:38:51,320 --> 00:38:55,440
Speaker 3: get great candidates through it, and it just it hasn't scaled,

643
00:38:56,000 --> 00:38:58,520
Speaker 3: in part because I don't think it was a huge

644
00:38:58,520 --> 00:39:01,480
Speaker 3: priority of the Biden industry, and in part because it's

645
00:39:01,480 --> 00:39:05,000
Speaker 3: still just a lot of work to get all the

646
00:39:05,280 --> 00:39:09,080
Speaker 3: people who can review these programmers resumes, for instance, to

647
00:39:09,080 --> 00:39:12,120
Speaker 3: take time out of their jobs to do this in

648
00:39:12,120 --> 00:39:17,440
Speaker 3: a very prescribed kind of controlled way, Like you can't

649
00:39:17,440 --> 00:39:19,200
Speaker 3: just take someone's time and say, hey, look at this

650
00:39:19,200 --> 00:39:20,480
Speaker 3: guy's resume, do you think he's good.

651
00:39:21,880 --> 00:39:24,240
Speaker 1: What happens when you try to fire someone in the government.

652
00:39:24,520 --> 00:39:28,040
Speaker 1: You know, we did that episode several weeks ago about

653
00:39:28,920 --> 00:39:32,160
Speaker 1: medicure fraud and the importance of having good data scientists

654
00:39:32,160 --> 00:39:34,360
Speaker 1: to look at. And let's say there's a data scientist

655
00:39:34,360 --> 00:39:36,759
Speaker 1: who isn't good or isn't finding any whatever, it is

656
00:39:36,840 --> 00:39:38,520
Speaker 1: what happens when you try to fire them.

657
00:39:39,840 --> 00:39:42,160
Speaker 3: So most of the time on a practical basis. What

658
00:39:42,280 --> 00:39:46,160
Speaker 3: happens is they say that HR will tell you it's

659
00:39:46,160 --> 00:39:48,879
Speaker 3: going to be impossible to fire this person. Don't give

660
00:39:48,920 --> 00:39:51,040
Speaker 3: them a bad rating, because if you give them a

661
00:39:51,040 --> 00:39:53,960
Speaker 3: bad rating, we won't be able to transfer them. So

662
00:39:54,239 --> 00:39:56,520
Speaker 3: let's work on finding a transfer so they can be

663
00:39:56,600 --> 00:40:01,640
Speaker 3: someone else's problem. Not great. I heard from a friend

664
00:40:01,719 --> 00:40:07,200
Speaker 3: recently at an agency who got a complaint one of

665
00:40:07,239 --> 00:40:09,280
Speaker 3: his One of the employees on his team, complained about

666
00:40:09,280 --> 00:40:12,040
Speaker 3: the team leader who reports to him. She felt it

667
00:40:12,120 --> 00:40:18,560
Speaker 3: a formal complaint because that manager hadn't given her an

668
00:40:18,600 --> 00:40:22,600
Speaker 3: outstanding or exceptional rating of this person had been absent

669
00:40:22,680 --> 00:40:25,200
Speaker 3: a ton of the year, her skills were way outdated,

670
00:40:25,640 --> 00:40:29,520
Speaker 3: and the HR team said, well, you know, the best

671
00:40:29,520 --> 00:40:32,799
Speaker 3: thing to do is to, you know, assign her some

672
00:40:33,320 --> 00:40:36,880
Speaker 3: work that she can do, or very specific work that

673
00:40:36,920 --> 00:40:39,799
Speaker 3: you don't think she'll be able to achieve. But you know,

674
00:40:39,880 --> 00:40:43,319
Speaker 3: you can't push back on this. We would lose, and

675
00:40:43,440 --> 00:40:45,760
Speaker 3: you know, so we're just going to sort of manage

676
00:40:45,800 --> 00:40:48,520
Speaker 3: the situation. And oh, by the way, your manager is

677
00:40:48,920 --> 00:40:51,719
Speaker 3: potentially in trouble here because of this, this threat of

678
00:40:51,719 --> 00:40:55,359
Speaker 3: a lawsuit, so it's like completely upside down, and what

679
00:40:55,440 --> 00:40:57,440
Speaker 3: they will do is try to get that person to

680
00:40:57,480 --> 00:41:01,439
Speaker 3: be moved on to be someone else's problem. It's really

681
00:41:01,440 --> 00:41:05,040
Speaker 3: important to note that there is nothing that says you

682
00:41:05,120 --> 00:41:09,440
Speaker 3: can't fire people in government, and there is some number

683
00:41:09,560 --> 00:41:12,920
Speaker 3: and forgetting it that do get fired every year, so

684
00:41:13,160 --> 00:41:17,480
Speaker 3: it's not impossible. The law doesn't prevent it by any means.

685
00:41:17,840 --> 00:41:24,200
Speaker 3: It's more that there are many many pathways for employees

686
00:41:24,440 --> 00:41:27,799
Speaker 3: to protest, you know, not just being fired, but like

687
00:41:27,960 --> 00:41:31,160
Speaker 3: being given anything other than the top rating. And because

688
00:41:31,160 --> 00:41:37,319
Speaker 3: they have all those pathways to protest, they can sort

689
00:41:37,360 --> 00:41:40,360
Speaker 3: of run the clock out on all of those, sucking

690
00:41:40,440 --> 00:41:43,360
Speaker 3: up all of the time of the managers and the

691
00:41:43,520 --> 00:41:46,400
Speaker 3: HR people and the lawyers in a way that just nobody,

692
00:41:46,600 --> 00:41:48,400
Speaker 3: you know, everyone's like it's not worth it, Like we

693
00:41:48,400 --> 00:41:51,120
Speaker 3: can't get our work done if we're going to also

694
00:41:51,160 --> 00:41:53,359
Speaker 3: try to manage this person out, and so they give up.

695
00:41:54,120 --> 00:41:57,160
Speaker 2: It feels like there's also an incentive problem where if

696
00:41:57,160 --> 00:42:01,560
Speaker 2: you're a manager dealing with a bad employemployee, you know,

697
00:42:01,640 --> 00:42:04,720
Speaker 2: even if you manage to give them to another team,

698
00:42:05,239 --> 00:42:07,560
Speaker 2: there's no guarantee that you're going to get the head

699
00:42:07,560 --> 00:42:10,560
Speaker 2: count to replace them. And of course you have to

700
00:42:10,560 --> 00:42:13,040
Speaker 2: start the hiring process all over again, and you don't

701
00:42:13,080 --> 00:42:15,400
Speaker 2: know who you're going to get. And I feel like

702
00:42:15,560 --> 00:42:19,239
Speaker 2: in politics, especially or government bureaucracy, where there's a lot

703
00:42:19,320 --> 00:42:24,640
Speaker 2: of presumably internal infighting and you know, office politics, that

704
00:42:24,800 --> 00:42:27,520
Speaker 2: there's a lot of empire building as well, right Like

705
00:42:27,600 --> 00:42:30,440
Speaker 2: people do not want to see their teams reduce and

706
00:42:30,480 --> 00:42:31,480
Speaker 2: their budgets reduced.

707
00:42:31,600 --> 00:42:34,120
Speaker 3: That is undred present true, and I think it's an

708
00:42:34,200 --> 00:42:38,080
Speaker 3: interesting dynamic right now with this sort of threat of

709
00:42:38,320 --> 00:42:42,719
Speaker 3: you know, mass firings going on, that that instinct of

710
00:42:43,880 --> 00:42:46,640
Speaker 3: I am more important the more people report into me

711
00:42:47,000 --> 00:42:49,359
Speaker 3: is is sort of rearing its head in a way

712
00:42:49,440 --> 00:42:52,720
Speaker 3: that I wish it weren't, because I think public sector

713
00:42:52,719 --> 00:42:55,520
Speaker 3: employees have a lot to be proud of in terms

714
00:42:55,600 --> 00:42:58,640
Speaker 3: of the results they can produce for the American people,

715
00:42:59,040 --> 00:43:01,960
Speaker 3: and that ought to be the metric of of pride

716
00:43:02,440 --> 00:43:06,040
Speaker 3: and status, not how many people report to them. And

717
00:43:06,239 --> 00:43:09,680
Speaker 3: again I'm not a big fan of this, like let's,

718
00:43:09,719 --> 00:43:13,840
Speaker 3: you know, make every civil servant afraid to come in

719
00:43:13,880 --> 00:43:16,440
Speaker 3: every day. That kind of rhetoric is very sad to me,

720
00:43:17,200 --> 00:43:19,719
Speaker 3: but I think it's it's also true that that the

721
00:43:19,760 --> 00:43:23,319
Speaker 3: civil service, should you know, measure its value a little

722
00:43:23,320 --> 00:43:25,560
Speaker 3: differently than it often does today. And I'm not saying

723
00:43:25,600 --> 00:43:29,239
Speaker 3: it's everyone, but but that instinctive of empire building isn't

724
00:43:29,280 --> 00:43:30,920
Speaker 3: as healthy as as we would like.

725
00:43:31,760 --> 00:43:34,960
Speaker 1: Jennifer Pelka, thank you so much for coming back on

726
00:43:35,000 --> 00:43:37,640
Speaker 1: odd lots. Maybe we'll u we should have you back,

727
00:43:37,680 --> 00:43:39,040
Speaker 1: like in a year. We could do it. You can

728
00:43:39,120 --> 00:43:42,919
Speaker 1: sort of give your assessment on what you've seen so far.

729
00:43:42,960 --> 00:43:46,960
Speaker 1: We could sort of do regular uh regulator. Yeah, regular

730
00:43:47,040 --> 00:43:49,120
Speaker 1: doge report cards and updates.

731
00:43:49,560 --> 00:43:52,040
Speaker 3: I love that. That's great. We'll see if dog is

732
00:43:52,040 --> 00:43:52,560
Speaker 3: even around you.

733
00:43:52,680 --> 00:43:54,520
Speaker 1: Yeah, right, we'll see if it even exist. Certainly.

734
00:43:54,520 --> 00:43:55,080
Speaker 2: All good for it.

735
00:43:55,200 --> 00:44:11,600
Speaker 1: All right, thank you so much. That was fantastic. That

736
00:44:11,719 --> 00:44:14,600
Speaker 1: was really good. Tracy just recketing and hearing some of

737
00:44:14,600 --> 00:44:15,840
Speaker 1: those stories.

738
00:44:15,440 --> 00:44:18,360
Speaker 2: Like about hiring, the stories are amazing. You kind of

739
00:44:18,400 --> 00:44:20,960
Speaker 2: just want to your head against them going well.

740
00:44:21,000 --> 00:44:23,400
Speaker 1: Like Also, it makes me wonder if it would be

741
00:44:23,840 --> 00:44:26,120
Speaker 1: I don't know anything about applying for a government job,

742
00:44:26,120 --> 00:44:28,399
Speaker 1: but if you just sort of know the hacks, right,

743
00:44:28,520 --> 00:44:30,960
Speaker 1: if you just sort of know what a government resume

744
00:44:31,080 --> 00:44:33,400
Speaker 1: is supposed to look like, and the importance of copying

745
00:44:33,440 --> 00:44:37,279
Speaker 1: and pasting keywords and getting past that initial round, that

746
00:44:37,360 --> 00:44:39,799
Speaker 1: seems like that's a good alpha for listeners if you're

747
00:44:39,800 --> 00:44:42,960
Speaker 1: ever applying for a government job. Some good advice there

748
00:44:42,960 --> 00:44:43,560
Speaker 1: from Jennifer.

749
00:44:43,719 --> 00:44:43,959
Speaker 3: Yeah.

750
00:44:44,000 --> 00:44:47,000
Speaker 2: The best government officials, the most effective government officials, are

751
00:44:47,000 --> 00:44:48,640
Speaker 2: the ones who know how to copy and paste.

752
00:44:49,040 --> 00:44:50,399
Speaker 1: Suppressing it is.

753
00:44:50,520 --> 00:44:52,680
Speaker 2: Oh gosh, there was one thing I actually forgot to

754
00:44:52,719 --> 00:44:54,799
Speaker 2: ask jen But you know, you brought up that like

755
00:44:54,880 --> 00:44:59,000
Speaker 2: three hundred page document, and it struck me that people,

756
00:44:59,440 --> 00:45:02,319
Speaker 2: I doubt anyone is reading that from cover to cover, right,

757
00:45:02,640 --> 00:45:06,240
Speaker 2: probably not. And so if you were looking to streamline

758
00:45:06,440 --> 00:45:09,719
Speaker 2: some of those processes again, I know, identifying the problem

759
00:45:09,920 --> 00:45:12,640
Speaker 2: isn't the biggest issue here. It's actually trying to fix

760
00:45:12,680 --> 00:45:15,719
Speaker 2: it and act on it. But it seems like you

761
00:45:15,760 --> 00:45:17,240
Speaker 2: could use AI to go through.

762
00:45:17,080 --> 00:45:19,640
Speaker 1: A lot of those rules, right yeah, yeah.

763
00:45:19,320 --> 00:45:21,919
Speaker 2: And say, can you make this flow chart that goes

764
00:45:21,960 --> 00:45:25,279
Speaker 2: on for five pages reduce it down to I don't know,

765
00:45:25,400 --> 00:45:26,280
Speaker 2: half a page.

766
00:45:26,440 --> 00:45:28,279
Speaker 1: The other thing I would just say is that a

767
00:45:28,320 --> 00:45:31,319
Speaker 1: lot of the sort of I would say pathologies of

768
00:45:31,360 --> 00:45:36,719
Speaker 1: the government are in my experienced pathologies of large organizations period, right,

769
00:45:36,719 --> 00:45:39,440
Speaker 1: And I think you find people, you know, there are

770
00:45:39,440 --> 00:45:42,440
Speaker 1: frustrations you have as a customer to large companies, There

771
00:45:42,440 --> 00:45:45,480
Speaker 1: are frustrations you have as an employee of large companies.

772
00:45:45,960 --> 00:45:49,319
Speaker 1: These things exist the way sort of certain institutions within

773
00:45:49,360 --> 00:45:52,960
Speaker 1: an organization perpetuate themselves by holding onto power by sort

774
00:45:53,000 --> 00:45:56,439
Speaker 1: of a strict adherence a handful of people that really

775
00:45:56,480 --> 00:45:57,200
Speaker 1: know the rules.

776
00:45:57,120 --> 00:45:59,719
Speaker 2: People that know the system, people that build the systems

777
00:45:59,719 --> 00:46:00,520
Speaker 2: that no one else can.

778
00:46:00,480 --> 00:46:01,439
Speaker 3: I They're just.

779
00:46:01,360 --> 00:46:04,840
Speaker 1: Really hard to root out, and so they're hard in

780
00:46:04,920 --> 00:46:10,000
Speaker 1: any organization. And obviously, you know, private sector entities sometimes

781
00:46:10,120 --> 00:46:12,879
Speaker 1: benefit because someone says, look, we really need to cut

782
00:46:12,920 --> 00:46:15,440
Speaker 1: this area or whatever in a way that often hasn't

783
00:46:15,480 --> 00:46:19,520
Speaker 1: happened in the government. But these are really tough problems

784
00:46:19,600 --> 00:46:22,480
Speaker 1: in organizations. And I think anyone who probablys in business

785
00:46:22,480 --> 00:46:25,879
Speaker 1: school and goes into any Fortune five hundred company would

786
00:46:25,960 --> 00:46:30,000
Speaker 1: probably be faced with sort of similar challenges in reforming

787
00:46:30,000 --> 00:46:30,840
Speaker 1: any organization.

788
00:46:31,080 --> 00:46:33,880
Speaker 2: Absolutely, and I will say this is obviously a highly

789
00:46:33,960 --> 00:46:38,319
Speaker 2: politically charged issue, but you know, I think everyone on

790
00:46:38,360 --> 00:46:40,680
Speaker 2: both sides of the aisle can agree that they want

791
00:46:41,160 --> 00:46:44,920
Speaker 2: an effective government, right. You want a government that is

792
00:46:45,040 --> 00:46:49,440
Speaker 2: doing good and productive things and not wasting people's money,

793
00:46:49,440 --> 00:46:53,759
Speaker 2: because at a minimum, that feeds into a perception problem.

794
00:46:54,520 --> 00:46:57,239
Speaker 2: And so this is a really interesting topic. I think

795
00:46:57,280 --> 00:46:59,719
Speaker 2: we'll do more on it absolutely, Okay, shall we leave

796
00:46:59,719 --> 00:47:00,000
Speaker 2: it there?

797
00:47:00,040 --> 00:47:00,719
Speaker 1: Let's leave it there.

798
00:47:00,920 --> 00:47:03,879
Speaker 2: This has been another episode of the Authots podcast. I'm

799
00:47:03,880 --> 00:47:06,560
Speaker 2: Tracy Alloway. You can follow me at Tracy Alloway.

800
00:47:06,800 --> 00:47:09,800
Speaker 1: And I'm Jill Wisenthal. You can follow me at the Stalwart.

801
00:47:10,080 --> 00:47:13,400
Speaker 1: Follow our guest Jennifer Pelka, She's at Pelka dot and

802
00:47:13,520 --> 00:47:16,720
Speaker 1: definitely go check out her substack and her book. Follow

803
00:47:16,760 --> 00:47:20,200
Speaker 1: our producers Carmen Rodriguez at carman Armann dash O Bennett

804
00:47:20,239 --> 00:47:23,840
Speaker 1: at Dashbot and Kilbrooks at Kilbrooks. From our Oddlogs content,

805
00:47:23,880 --> 00:47:26,120
Speaker 1: go to Bloomberg dot com slash odd Lots, where you

806
00:47:26,120 --> 00:47:28,680
Speaker 1: have transcripts, a blog, and a newsletter and you can

807
00:47:28,760 --> 00:47:30,840
Speaker 1: chat about all of these topics twenty four to seven

808
00:47:30,920 --> 00:47:34,480
Speaker 1: in our discord Discord dot gg slash od lots.

809
00:47:34,680 --> 00:47:37,120
Speaker 2: And if you enjoy odd Lots, if you like it

810
00:47:37,200 --> 00:47:40,879
Speaker 2: when we dive deep into various bureaucracies, then please leave

811
00:47:40,960 --> 00:47:44,960
Speaker 2: us a positive review on your favorite podcast platform. And remember,

812
00:47:45,000 --> 00:47:47,560
Speaker 2: if you are a Bloomberg subscriber, you can listen to

813
00:47:47,680 --> 00:47:50,640
Speaker 2: all of our episodes absolutely add free. All you need

814
00:47:50,719 --> 00:47:53,480
Speaker 2: to do is find the Bloomberg channel on Apple Podcasts

815
00:47:53,480 --> 00:48:20,480
Speaker 2: and follow the instructions there. Thanks for listening in in