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Speaker 1: Pushkin. Welcome back to Risky Business, a show about making

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Speaker 1: better decisions. I'm Maria Kannakova.

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Speaker 2: And I'm Nate Silker.

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Speaker 1: So today on the show, we've got a fun one

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Speaker 1: for you. We'll be playing a game that we've played

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Speaker 1: on the show before, Good Call, Bad Call, and we're

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Speaker 1: going to be starting up by talking about wealthiest man

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Speaker 1: in the world, Elon Musk, who's about to become even

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Speaker 1: wealthier because Tesla has given him quite the compensation package.

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Speaker 1: So we'll discuss whether that's a good call or a

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Speaker 1: bad call.

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Speaker 2: And we'll be talking about the purported crisis of masculinity.

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Speaker 2: I am trying not to add skeptical inflection to my voice.

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Speaker 2: In fact, as we'll hear, you know, I don't think

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Speaker 2: there's any particularly good reason to be skeptical, but maybe

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Speaker 2: maybe Aria disagree. We'll have a fun segment, I predict.

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Speaker 1: All right, Nate, le let's get into it. But before

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Speaker 1: we do, I want to welcome you back to the

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Speaker 1: United States.

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Speaker 2: Thank you. No, I mean I if I had learned

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Speaker 2: a word of Norwegian or Finnish, I would greet you

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Speaker 2: in that language, but they're very fluent in English, and

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Speaker 2: so it was not necessary to be honest.

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Speaker 1: You and I were both abroad, but I came back

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Speaker 1: a week before you from Barcelona, and you were in

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Speaker 1: northern Europe. Was summer over in northern Europe.

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Speaker 2: It was quite pleasantly temperate. I was in Norway. Saw

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Speaker 2: some fjords, pretty good fucking fjords.

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Speaker 1: Right.

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Speaker 2: The first day we kind of drove around. We're like, oh,

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Speaker 2: this is nice. Looks like Maine or something, right, And

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Speaker 2: then the next day we took a fucking cruise on

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Speaker 2: the real fjords. One of the most spectacular natural things

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Speaker 2: I've ever seen. Hawaii is up there, but you know,

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Speaker 2: top top three in terms of things that will hopefully

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Speaker 2: outlast the human race. That sounds pessimistic, but you know

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Speaker 2: what I mean, right, Eternal, I do know what.

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Speaker 1: You mean, and I do I hope they outlast as well,

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Speaker 1: and I hope that they last long enough at least

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Speaker 1: for me to see them, because those are one that's

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Speaker 1: one of the things that I've never seen that I

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Speaker 1: would love to see one of these days.

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Speaker 2: Then, since sent some time in Helsinki, Finland, you can

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Speaker 2: tell I have some pronouncing it Helsinki, not Helsinki, which

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Speaker 2: is how uneducated Americans say, very nice town, great food,

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Speaker 2: which I wasn't expecting. And then Amsterdam.

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Speaker 1: Briefly, where there's also delicious food. Hmm, you didn't agree.

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Speaker 2: No, I think I like Amsterdam and Robbie and I

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Speaker 2: have some friends there. I have visited a number of times,

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Speaker 2: had a great visit. I think Amsterdam is pound for

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Speaker 2: one of the worst food cities.

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Speaker 1: Huh.

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Speaker 2: And it's good like Nipalese and stuff, there's good. It's

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Speaker 2: mixed at Nikolie right uh. And so like you know,

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Speaker 2: the international food's good, but the Dutch food, the continental food,

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Speaker 2: I know.

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Speaker 1: So. So I think this is where you and I

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Speaker 1: kind of differ because someone as someone who grew up

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Speaker 1: eating hariring, I absolutely love it. And you can get

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Speaker 1: really incredible hairing.

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Speaker 2: And you get hair. We took the boat, the ferry

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Speaker 2: from Helsinki to Tallinn, Estonia for a day too. Pretty

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Speaker 2: good fucking haring on that boat.

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Speaker 1: Oh absolutely, I'm sure. I'm sure it was delicious. Well,

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Speaker 1: welcome back to the United States. You were missed. I

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Speaker 1: think New York missed both of us. But but now

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Speaker 1: we're now we're all back.

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Speaker 2: How did how did Barcelona but the Barcelona? How did

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Speaker 2: Barcelona go how the epint go.

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Speaker 1: It was fine. You know, it's very funny because I actually,

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Speaker 1: you know, I'm normally pretty good at not cashing and

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Speaker 1: then like having one big score when it comes to these.

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Speaker 1: I actually cashed in I think half of my event

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Speaker 1: and had no big scores, so ended down for the trip.

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Speaker 1: The deepest I went was well, actually I got fourth

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Speaker 1: in one event, but I missed out Nate on a

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Speaker 1: thirty seven thousand euro page jump and it was really

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Speaker 1: it was really heartbreaking. Had I just made it and

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Speaker 1: had I lasted, and had I been in third, I

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Speaker 1: would have been way up for the trip. So we

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Speaker 1: were very close. And you know, we've talked on the

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Speaker 1: show before about butterfly effects and how you know one

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Speaker 1: thing affects another. There was a player who was the

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Speaker 1: short stack, so people who don't play poker. He had

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Speaker 1: the fewest chips, and it seemed pretty likely that I

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Speaker 1: would end up outlasting him, and he ended up getting

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Speaker 1: it all in with ACE five off suit and getting

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Speaker 1: called by Ace King or actually Ace three, not ACE five,

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Speaker 1: Ace three getting called by Ace King, and he hit

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Speaker 1: his three and ended up doubling, and then he knocked

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Speaker 1: me up out a little while later, so you know

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Speaker 1: butterfly effects.

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Speaker 2: It was it was a good player or bad player.

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Speaker 1: Incredibly wealthy player who doesn't really care. He is capable

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Speaker 1: of playing well, but at that point he was not

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Speaker 1: playing well because he was kind of in and I

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Speaker 1: don't give a fuck, but you guys all care about

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Speaker 1: the money type of mood. He is a businessman, he's

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Speaker 1: not a professional poker player.

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Speaker 2: You know, sometimes people with a fucket attitude toward ICM. ICM.

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Speaker 2: If you don't play poker, is the strategy the changes

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Speaker 2: you make when you're trying to cash a tournament I've had,

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Speaker 2: By the way, this is probably cashed a lot and

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Speaker 2: nothing too exciting beyond that, right, But like, if you're

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Speaker 2: really rich, though, and you just care about winning, then

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Speaker 2: you don't have to play with ICM. And it's like

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Speaker 2: annoying to all the other knits at the table who are.

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Speaker 1: Yeah, there's a t shirt that some poker players like

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Speaker 1: to wear that says ICM is for poor people. That

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Speaker 1: means that people who care about the independent hip model.

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Speaker 1: You know, the value of your of your chips actually

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Speaker 1: changes as these huge payge ump sloom. That means you're poor.

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Speaker 1: And if you can say fuck it. Sometimes that's that's

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Speaker 1: the way to do it, and you know, there is

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Speaker 1: It's a it's an interesting analogy kind of to to

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Speaker 1: life because there are definitely moments in life where you

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Speaker 1: know it pays to hang around a little bit, but

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Speaker 1: sometimes you're like, ah, fuck it, I want to play

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Speaker 1: to win, right like I want to I want to

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Speaker 1: get I want to take a big swing. I really

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Speaker 1: want to go for it. And sometimes that feels really good,

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Speaker 1: even if it's not necessarily correct, and you have to

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Speaker 1: just try to think, you know what, what risks? Am

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Speaker 1: I willing to take? What's the reward here? Is it

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Speaker 1: worth it? And am I I think this is actually

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Speaker 1: really important? Am I willing to walk away? Right? Like?

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Speaker 1: It's kind of like the fuck you money type of

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Speaker 1: type of concept, where will I walk away? Like if

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Speaker 1: this doesn't go right? And ICM as for poor people,

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Speaker 1: that person says, I am willing to walk away, right,

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Speaker 1: I am willing to forego you know, forty thousand dollars,

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Speaker 1: four hundred thousand dollars. It doesn't matter because I'm not

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Speaker 1: poor and I don't care, and you do and.

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Speaker 2: Your time is valuable too, you know what I mean?

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Speaker 2: I mean obviously, sometimes you're just giving up thousands of

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Speaker 2: dollars by not adapting your raine is right, but like, yeah,

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Speaker 2: I think it's anything wrong with we're saying opportunity cost

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Speaker 2: is high. I mean, it's a very very very very

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Speaker 2: very slippery slope, but.

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Speaker 1: Yeah, it certainly is. And on that note, Nate, maybe

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Speaker 1: we should talk about someone who has the most fuck

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Speaker 1: you money in the entire world, Elon Musk. So we

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Speaker 1: play a game good call, bad call? You know, is

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Speaker 1: this a good call? Is this a bad call? And

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Speaker 1: right now we're going to be putting that lens not

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Speaker 1: on Musk, but on Tesla's board because earlier this week,

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Speaker 1: so we are recording on Monday, September eighth, So I

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Speaker 1: guess it was last week that Tesla offered it's CEO,

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Speaker 1: Elon Musk, a new compensation package that's going to be

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Speaker 1: worth one trillion dollars over the next ten years and

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Speaker 1: by the end of it if he actually earns out

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Speaker 1: this compensation package, which is an if, right, there are

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Speaker 1: a lot of things that have to go right, but

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Speaker 1: he would own almost twenty nine twenty eight point eight

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Speaker 1: percent of Tesla's stock because that's how his compensation would

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Speaker 1: be paid out, which is huge, right, and that's a

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Speaker 1: lot more than most CEOs ever get to earn of

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Speaker 1: their of their company's stock, which would put him in

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Speaker 1: a really interesting position. So basically, you know, the board

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Speaker 1: has thrown money at him to try to get him

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Speaker 1: to stick around. Nate, do you want to talk about

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Speaker 1: some of the things that he has to do or

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Speaker 1: should I just go through that quickly?

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Speaker 2: Why don't you go You know, I'm still on finish

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Speaker 2: time here, so why don't you go through it? Maria?

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Speaker 1: All right? So, basically he needs to meet some milestones,

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Speaker 1: which include everything from profits that have to increase over

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Speaker 1: twenty four times to one million autonomous taxis that are

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Speaker 1: going to be deployed, one million humanoid robots, and car

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Speaker 1: sales obviously, so there are a lot of things that

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Speaker 1: have to happen. So Tesla has to reach four hundred

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Speaker 1: billion in adjusted earnings before interest, taxes, depreciation and amortization annually,

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Speaker 1: and right now it is earning less than seventeen billion

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Speaker 1: dollars a year, so that's quite a high number. He

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Speaker 1: needs to have an eight point five trillion dollar market cap.

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Speaker 1: Current market cap is about one trillion. The robotaxis is

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Speaker 1: a huge ask, right because right now they're way behind

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Speaker 1: schedule on that. So they have to deliver twenty million teslas,

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Speaker 1: which is actually probably the most attainable, but I don't

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Speaker 1: know we can talk about that.

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Speaker 2: I could deliver fourteen million teslas, and.

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Speaker 1: And they're humanoid robots. Optimus is another one where they've

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Speaker 1: had a lot of issues, including in their demos. The

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Speaker 1: Optimus robots have not hit their benchmarks over time. I

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Speaker 1: think Elon has been over optimistic with all of these,

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Speaker 1: including the robots. But these are the things that have

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Speaker 1: to happen for him to get his compensation. Except and

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Speaker 1: this is one big except. So I was reading all

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Speaker 1: of this and I was like, huh, interesting thing that

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Speaker 1: they put in there. If there are factors quote outside

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Speaker 1: Tesla's control end quote that hurts Tesla's ability to reach

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Speaker 1: these milestones and reach these delivery targets, then Musk will

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Speaker 1: still get compensated. And it seems to me that that's

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Speaker 1: a pretty big out right that must can say, oh, well,

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Speaker 1: you know this was up might.

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Speaker 2: Have the best lawyer billing cycles in history. If there's

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Speaker 2: a dispute over this, I would think.

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Speaker 1: Yeah, I think so. So that's kind of the that's

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Speaker 1: the one caveat This will come to a vote on

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Speaker 1: November six. Listeners might remember as well that Musk already

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Speaker 1: has a prior compensation package that is in courts right

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Speaker 1: now because it was challenged. So yeah, so that's where

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Speaker 1: we are. And the question is right, like good call

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Speaker 1: or bad call on the part of the board of Tesla.

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Speaker 1: Everything is going down. Profits are going down, Tesla's market

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Speaker 1: share is going down. Is it a good call to

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Speaker 1: just throw money at Elon and try to kind of

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Speaker 1: get him to hit all of these targets to get

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Speaker 1: his attention back? Do we think this is a good

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Speaker 1: use of capital? Do we think Elon will be motivated

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Speaker 1: by it? He's already the world's richest man. Do we

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Speaker 1: think he's going to perform? Like? What do we think

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Speaker 1: about all of this? I think it's actually a really

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Speaker 1: kind of interesting psychological question about someone who's already insanely wealthy.

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Speaker 1: You know, how do we motivate someone like that? And

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Speaker 1: is this, you know, is this the way to do it?

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Speaker 2: Yeah? I think it's a smart package. Actually, so you

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Speaker 2: can burn me at the stake for being such a capitalist.

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Speaker 2: But no, the reason why it's smart is because it

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Speaker 2: would seemed to me like these targets are a not

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Speaker 2: terribly easy to hit, notwithstanding the ambiguity of some of

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Speaker 2: these you know, acts of God clauses, right and b.

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Speaker 2: If the company is doing that well, then there's a

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Speaker 2: lot of wealth to go around, right. We're talking about

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Speaker 2: much higher than market growth. The fact that Tesla you know,

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Speaker 2: is facing more competition from Chinese automakers. It's products you're

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Speaker 2: not seen as innovative. They are all these things they

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Speaker 2: are trying to do, robo taxis and stuff like that,

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Speaker 2: but they're kind of like not as market leading right now.

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Speaker 2: But like, but if he turns it around, then I

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Speaker 2: think it strikes me as like fairly rational if it's

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Speaker 2: unlikely to be hit these benchmarks. Although Elon has bet

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Speaker 2: on himself before and been right, it's part of why

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Speaker 2: he is already the world's richest man, I'm all for it.

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Speaker 1: Well, so theoretically, I don't mind, you know, great compensation

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Speaker 1: packages if there's a good end result. I'm wondering, though,

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Speaker 1: if you want to get you know, the world's richest

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Speaker 1: man right to focus on Tesla, even though his attention

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Speaker 1: has been elsewhere, his ambitions have been elsewhere. You know,

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Speaker 1: he's interested in X and SpaceX and all these other things.

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Speaker 1: Is just like throwing dollar signs at it. Is that

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Speaker 1: really going to be the best motivator? And the reason

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Speaker 1: I asked that, and maybe the answer is yes, but

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Speaker 1: the answer could also be no, because there is a

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Speaker 1: lot of really great psychological research about intrinsic versus extrinsic motivation,

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Speaker 1: and a lot of times the reason that games players

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Speaker 1: want to play, you know, are four flow states and

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Speaker 1: kind of things that are intrinsic to the game, right,

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Speaker 1: Like they want to do better, they want to level up,

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Speaker 1: they want to do this, they want to get that,

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Speaker 1: like things that make them a good player within the game, right,

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Speaker 1: that convey some sort of prestige on them.

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Speaker 2: And it fits.

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Speaker 1: And I'm not talking about people for whom like this

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Speaker 1: is their profession and like they just need to get paid.

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Speaker 1: But when you start replacing intrinsic motivation with extrinsic motivation,

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Speaker 1: right when you start putting dollar values on everything, sometimes

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Speaker 1: motivation actually lacks. So there can be this you know,

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Speaker 1: ironic effect where where when you actually get money, like

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Speaker 1: all of a sudden, it's not fun anymore. And that's

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Speaker 1: why a lot of people. You know, you sometimes hear

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Speaker 1: the advice like keep your hobbies, your hobbies right, don't

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Speaker 1: try to make everything into a side hustle, because you

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Speaker 1: might find that you no longer enjoy the types of

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Speaker 1: things that you used to really enjoy when you feel.

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Speaker 2: Like you have to experience. Yeah, I'm curious, Nate.

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Speaker 1: Why don't you actually say a little bit more about that,

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Speaker 1: because I think that that is part of the question

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Speaker 1: here right about whether this is the best motivator.

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Speaker 2: Well, look, I'll talk about the two big things that

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Speaker 2: I find very motivating, and maybe they're not so different

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Speaker 2: than the way Elon thinks about the world.

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Speaker 1: Right. You know.

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Speaker 2: One is just to build a product that you think

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Speaker 2: is really great. Right, to do something that nobody else

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Speaker 2: has done, to differentiate, to add intellectual value, artistic value,

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Speaker 2: aesthetic value, whatever it might be. Right, But it's like, Okay,

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Speaker 2: here's the world. Nobody is building a good election model

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Speaker 2: or writing a book like the book I wrote, or

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Speaker 2: whatever else, right, and so I am going to do

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Speaker 2: that really well. Right, And I think Elon is or

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Speaker 2: maybe was motivated by that type of thing, right, he

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Speaker 2: definitely has vision. I think you have to be pretty biased,

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Speaker 2: not to give him credit for that, right, And the

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Speaker 2: other big motivator is like the desire to prove people

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Speaker 2: wrong and the fact that you'll have people like us say, oh,

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Speaker 2: these targets seem very hard to achieve. They're kind of crazy.

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Speaker 2: It can't be done. How can he do all these

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Speaker 2: things where they're not even like leading the robotaxi market

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Speaker 2: for example, Like, like that could be very motivating too,

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Speaker 2: I think I think more so than the money, really, right,

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Speaker 2: It's like they're not like that. You have very many

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Speaker 2: anyone in Silicon Valley, be the founders or vcs or

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Speaker 2: whatever else. They don't like just go and say, Okay,

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Speaker 2: well I made a lot of money. I'm going to

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Speaker 2: go right in the sunset and hang out at my

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Speaker 2: private villa in Tuscany and show up and go to

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Speaker 2: really nice three star Michelin restaurants. Right, They usually are

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Speaker 2: still motivated to prove their doubters wrong, which makes them

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Speaker 2: assholes sometimes. And I don't think anybody would would shy

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Speaker 2: away from accusing Elon of that either, right, But yeah.

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Speaker 1: Yeah, no, I think that that is a very powerful

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Speaker 1: motivator for people like Elon, Right, like, prove the hater's wrong.

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Speaker 1: Prove that I can do it, prove that I'm better

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Speaker 1: than they think I am because I know better now.

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Speaker 1: He has shown a pension for overconfidence. Right. He has

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Speaker 1: failed to hit very ambitious targets in the past, but

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Speaker 1: they're actually you know, you know, I mean this is

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Speaker 1: and listeners of the show no, no fan of Elon,

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Speaker 1: but this is something that is actually pretty not smart,

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Speaker 1: but like it is something that can work when you

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Speaker 1: do set these ambitious targets, right, even if you don't

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Speaker 1: actually hit them, you might hit a much higher target

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Speaker 1: than you otherwise would have done in trying to hit

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Speaker 1: this crazy target. Right. So it's kind of the opposite

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Speaker 1: of customer service business or consulting when you say under promise,

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Speaker 1: over deliver, right, Like, that's how you get people to

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Speaker 1: be really happy. Like you tell them a little bit

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Speaker 1: less than you think you're capable of doing, and then

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Speaker 1: once you hit what you're actually capable of doing, they'll

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Speaker 1: be like, holy shit, this is even better than I thought.

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Speaker 1: And he is kind of doing the opposite, right. Historically,

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Speaker 1: what he's done is over promise and underdeliver. That's not

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Speaker 1: necessarily great for Tesla's stock in the short term, but

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Speaker 1: it might not be necessarily a horrible strategy for motivating

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Speaker 1: people within the company to actually strive to hit kind

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Speaker 1: of these other milestones. And one of the reasons why

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Speaker 1: I think that probably the board wants him there and

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Speaker 1: is willing to kind of do any throw anything at

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Speaker 1: the problem to get him there is that anecdotally, Musk

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Speaker 1: does have a very strong motivating influence on workers when

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Speaker 1: he's present, right, and when he's not, motivation tends to sag. Like.

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Speaker 1: It is a very kind of cultive personality type of

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Speaker 1: business from what we've heard and from what we've seen,

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Speaker 1: and so maybe they're just trying to do everything they

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Speaker 1: can to try to get him physically there and really

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Speaker 1: trying to motivate people directly. I'm torn, right, Like, I

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Speaker 1: don't know. Obviously, I don't think they should be throwing

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Speaker 1: this amount of money at anyone, but like, let's just

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Speaker 1: put that aside and just think about.

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Speaker 2: Type of socialists are you running your rand moom Donnie's campaign?

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Speaker 1: I know, I know, right, but so let's let's just

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Speaker 1: put those considerations aside.

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Speaker 2: And you believe billionaires shouldn't exist? Are billionaires Okay.

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Speaker 1: I'm fine with billionaires existing, but but yeah, so so

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Speaker 1: the I think the question here is is an interesting

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Speaker 1: one in terms of how do we, you know, how

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Speaker 1: do we get people like that to perform at peak? They,

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Speaker 1: I think, are trying to throw everything into the equation,

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Speaker 1: you know, all these targets, money, all all of these

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Speaker 1: different things. The one thing that does worry me is

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Speaker 1: that out clause that I read to you, right, because like,

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Speaker 1: if he can just get the money without doing anything,

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Speaker 1: I think that there's a good chance that he is

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Speaker 1: going to potentially go there. So I might be worried

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Speaker 1: about language, but that's my biggest that's one of my

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Speaker 1: biggest Oh.

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Speaker 2: Look, I assume that, I mean, Tesla's stock was up

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Speaker 2: on this, right. I assume that, like the lawyers have

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Speaker 2: thought about how that clause might be interpreted in their

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Speaker 2: outclauses and in clauses. And I'm making that term up right.

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Speaker 2: I assume that, like it's not just you know, writing

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Speaker 2: things up on a white board, that they thought this

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Speaker 2: through a little bit. But yeah, look, to me, the

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Speaker 2: question is also, you know, does elon add that much

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Speaker 2: marginal value over another CEO? That kind of thing is

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Speaker 2: very hard to test, right if you go and I

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Speaker 2: asked my handy assistant Claude the AI. But I asked Claude,

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Speaker 2: what's the empirical literature say on whether CEO pay packages

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Speaker 2: are worth it?

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Speaker 1: Right?

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Speaker 2: And Claude wasn't sure. There does not seem to be

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Speaker 2: a clear consensus on this question in general and specifically.

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Speaker 2: I mean, you know, Elon got very distracted by politics

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Speaker 2: for some period of time. Well, that's stracts the right word.

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Speaker 2: I mean, I think most people would say that XAI

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Speaker 2: is doing interesting things behind kind of the big three

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Speaker 2: AI labs, but is a serious player, right, there's SpaceX

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Speaker 2: There's a lot of things, so yeah, you know. Meanwhile,

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Speaker 2: Elon has also been accused of recreational drug use in

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Speaker 2: well sourced stores in the Wall Street Journal. He is

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Speaker 2: a polarizing figure. I don't believe that his approval writing.

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Speaker 2: We've been tracking favorability of branks for Elon. They haven't

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Speaker 2: really improved very much. And see let the White House.

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Speaker 2: In fact, if anything, Republicans are more mad at him

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Speaker 2: now than they once were. Democrats haven't really you know,

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Speaker 2: returned to admiration for him yet. So there are a

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Speaker 2: lot of disadvantages too. But you know, I mean, look,

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Speaker 2: one of the other options he owns somewhere in the

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Speaker 2: range of fifteen percent of Tesla stock, right, so who's

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Speaker 2: the largest shareholder. If you wanted to oust him, you

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Speaker 2: would have to have the majority of other shareholders wanting

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Speaker 2: him out, which is very difficult for lots and lots

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Speaker 2: of reasons. So, you know, maybe it's not a choice

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Speaker 2: between Elon and generic CEO so much as motivated Elon

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Speaker 2: versus unmotivated Elon.

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Speaker 1: Yeah, so at the end of the day, night, good

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Speaker 1: call or bad call. I think you know where you

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Speaker 1: come down, all right, and I am it's hard going.

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Speaker 1: I'm gonna waffle on this one. I'm going to say,

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Speaker 1: I really I'm not sure I can see it going

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Speaker 1: either way. And this is a very waffly answer, But

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Speaker 1: that's at the end of the day where I'm coming

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Speaker 1: out and yeah, I'm hedging, Nate, I'm hedging. So yeah,

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Speaker 1: so that's where we are. Let's take a quick break now,

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Speaker 1: and then when we come back, we're going to talk

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Speaker 1: about the Battle of the sexes or you know, is

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Speaker 1: there actually a crisis of masculinity. We'll find out after

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Speaker 1: the break. Nate. Normally, when I think of the Battle

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Speaker 1: of the Sexes, I'm thinking game theory. Right, there's actually

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Speaker 1: a game that's called Battle of the Sexes. But in

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Speaker 1: today's segment, that is not what we're talking about, well

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Speaker 1: sort of, but not really. We're talking about kind of

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Speaker 1: the headlines that have been in the news over the

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Speaker 1: last few years about kind of a crisis of modern

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Speaker 1: masculinity that men are not doing as well as they

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Speaker 1: once were. Basically, you know, suicide as up, education levels

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Speaker 1: are down, loneliness is up. You know, we're seeing a

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Speaker 1: loneliness epidemic. And that's actually true of both sexes, but

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Speaker 1: more for men than for women's suicide actually is also

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Speaker 1: true that it's going up for both men and women,

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Speaker 1: but more for men, and on and on. So the

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Speaker 1: question here is is this actually a crisis of masculinity

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Speaker 1: or are these overblown headlines that basically are just like

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Speaker 1: little data blifts that really do not tell the whole

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Speaker 1: story where there's actually, you know, just as much male

427
00:23:52,044 --> 00:23:55,004
Speaker 1: dominance as there ever was, and maybe on the margins

428
00:23:55,124 --> 00:23:59,084
Speaker 1: certain things are changing a little bit, but it's still

429
00:23:59,844 --> 00:24:03,884
Speaker 1: basically same old story. So that is that is the

430
00:24:03,924 --> 00:24:08,044
Speaker 1: topic for this Battle of the Sexes. And obviously I'm female,

431
00:24:08,244 --> 00:24:10,764
Speaker 1: you're male, but I'm gonna let you kick this one

432
00:24:10,844 --> 00:24:14,524
Speaker 1: OFFN eight. So what is your kind of initial reaction

433
00:24:14,644 --> 00:24:16,244
Speaker 1: and then we can get into some data.

434
00:24:16,804 --> 00:24:21,524
Speaker 2: Yeah, look, maybe I'm playing into the stereotype a little

435
00:24:21,564 --> 00:24:23,764
Speaker 2: bit here. I think there's something real. I mean, one

436
00:24:23,804 --> 00:24:25,364
Speaker 2: reason why I say that is because you can see

437
00:24:25,404 --> 00:24:28,804
Speaker 2: it reflected in voting patterns too, which is something that

438
00:24:28,844 --> 00:24:32,684
Speaker 2: I study quite a bit, where among young voters in

439
00:24:32,724 --> 00:24:36,764
Speaker 2: the US and elsewhere, there are bigger gender splits than

440
00:24:36,764 --> 00:24:41,364
Speaker 2: there ever have been before. Historically, gender is only mildly

441
00:24:41,364 --> 00:24:44,404
Speaker 2: predictive of voting share. Usually women are a bit more

442
00:24:44,684 --> 00:24:47,724
Speaker 2: liberal than men, but that's been more profound, for example,

443
00:24:47,804 --> 00:24:50,364
Speaker 2: for Kamala Harris, for Donald Trump. And I think it

444
00:24:50,404 --> 00:24:55,564
Speaker 2: reflects the fact that, like some men feel a little

445
00:24:55,604 --> 00:24:59,324
Speaker 2: bit left out of the direction that society is headed, right,

446
00:24:59,324 --> 00:25:03,044
Speaker 2: They feel society as too risk averse, They feel emasculated,

447
00:25:03,164 --> 00:25:07,444
Speaker 2: They feel that, you know, institutions may be led. Well,

448
00:25:07,484 --> 00:25:11,564
Speaker 2: I don't know certainly what I call the village in

449
00:25:11,604 --> 00:25:14,244
Speaker 2: my book, right, the village is kind of academia and

450
00:25:14,284 --> 00:25:17,044
Speaker 2: media and so forth. Certainly in that field you see

451
00:25:17,044 --> 00:25:21,484
Speaker 2: more and more women leaders. Right in like democratic politics,

452
00:25:21,484 --> 00:25:23,004
Speaker 2: a lot of people who work on campaigns you know,

453
00:25:23,044 --> 00:25:25,044
Speaker 2: the women who ran Joe Biden's campaign was a woman.

454
00:25:26,324 --> 00:25:29,004
Speaker 2: So yeah, I think there's I think there's something there. Maria.

455
00:25:29,164 --> 00:25:32,604
Speaker 1: Yeah, well, having there be something there and having it

456
00:25:32,644 --> 00:25:36,404
Speaker 1: be like a full blown crisis are two different things.

457
00:25:36,644 --> 00:25:40,444
Speaker 1: So I do think that there's definitely I agree that

458
00:25:40,524 --> 00:25:42,844
Speaker 1: there is dissatisfaction. I mean, I think a lot of

459
00:25:42,844 --> 00:25:45,764
Speaker 1: what you're talking about is probably what got us you know,

460
00:25:45,844 --> 00:25:48,604
Speaker 1: Trump two point zero, right, what got us to that,

461
00:25:49,284 --> 00:25:51,844
Speaker 1: to that moment in time where a lot of people

462
00:25:51,884 --> 00:25:54,724
Speaker 1: felt really disaffected. And by the way, you're seeing this

463
00:25:54,844 --> 00:25:58,284
Speaker 1: in women too, where it's kind of a dovetailing that

464
00:25:58,324 --> 00:26:02,404
Speaker 1: where you're seeing the tradwife movement right where women are saying, yeah,

465
00:26:02,484 --> 00:26:06,684
Speaker 1: you know, we want to be at home and be

466
00:26:06,844 --> 00:26:10,884
Speaker 1: wives and full time homemakers and cook elaborate meals and

467
00:26:10,924 --> 00:26:13,084
Speaker 1: take care of our children and have lots of children.

468
00:26:13,124 --> 00:26:14,764
Speaker 1: We've talked on the show a little bit about this

469
00:26:14,844 --> 00:26:16,844
Speaker 1: movement in the past, but I think that these two

470
00:26:16,924 --> 00:26:19,324
Speaker 1: things are kind of going hand in hand, right. So

471
00:26:19,364 --> 00:26:22,484
Speaker 1: it's not just men who feel disaffected. There is a

472
00:26:22,524 --> 00:26:25,364
Speaker 1: subset of women who think, oh, you know, it's good

473
00:26:25,404 --> 00:26:27,884
Speaker 1: that women are staying at home. They shouldn't be in

474
00:26:27,924 --> 00:26:30,644
Speaker 1: the workforce in the same numbers as men. Men should

475
00:26:30,644 --> 00:26:33,884
Speaker 1: be the breadwinners. You know, we like traditional gender roles,

476
00:26:34,124 --> 00:26:38,884
Speaker 1: So you definitely have this current, this subcurrent in society.

477
00:26:39,484 --> 00:26:42,364
Speaker 1: That said, I think that some of the data that

478
00:26:42,564 --> 00:26:47,364
Speaker 1: has been cited that shows that basically men are not

479
00:26:47,484 --> 00:26:50,804
Speaker 1: doing as well as women. I think that a lot

480
00:26:50,844 --> 00:26:54,044
Speaker 1: of those data points, if you dig a little bit deeper,

481
00:26:54,804 --> 00:26:59,084
Speaker 1: don't hold a lot of water. So, for instance, when

482
00:26:59,084 --> 00:27:02,164
Speaker 1: it comes to the workforce, we still do have quite

483
00:27:02,204 --> 00:27:07,004
Speaker 1: a significant gender pay gap. So women on average make

484
00:27:07,084 --> 00:27:10,604
Speaker 1: eighty three cents to eighty five on the dollar.

485
00:27:11,084 --> 00:27:12,724
Speaker 2: Of what I'm going to I'm going to note for

486
00:27:12,764 --> 00:27:15,484
Speaker 2: the record that I think people would be the.

487
00:27:15,324 --> 00:27:19,724
Speaker 1: Causes of that, the causes of that. Yeah, that's that's

488
00:27:19,764 --> 00:27:21,924
Speaker 1: totally fine. I'm just saying that this that this still

489
00:27:21,964 --> 00:27:25,044
Speaker 1: exists right when, because if we're posing the question of

490
00:27:25,084 --> 00:27:27,244
Speaker 1: men are not doing as well as women, I'm just

491
00:27:27,404 --> 00:27:29,324
Speaker 1: kind of saying that there is a still a gender

492
00:27:29,324 --> 00:27:33,564
Speaker 1: pay gap that is in favor of men, and it

493
00:27:33,724 --> 00:27:39,404
Speaker 1: holds true over all education levels. So basically, men who

494
00:27:39,684 --> 00:27:41,844
Speaker 1: have no education are able to get better jobs and

495
00:27:41,884 --> 00:27:44,964
Speaker 1: women who have no education. Women with PhDs are paid

496
00:27:45,004 --> 00:27:48,884
Speaker 1: more than women with PhDs, and we still don't see

497
00:27:48,924 --> 00:27:53,444
Speaker 1: women progressing into positions of power at nearly the same

498
00:27:53,524 --> 00:27:56,124
Speaker 1: rates as we see men. So we do. We do

499
00:27:56,244 --> 00:27:59,444
Speaker 1: have that sort of thing and the fact that, oh

500
00:27:59,524 --> 00:28:02,924
Speaker 1: that there's a crisis in higher education, or that women

501
00:28:03,924 --> 00:28:07,044
Speaker 1: graduate from college more often than men. This has been

502
00:28:07,084 --> 00:28:10,604
Speaker 1: the case for kind of a long time, Like women

503
00:28:10,684 --> 00:28:14,084
Speaker 1: tend to do better academically and have for a very

504
00:28:14,124 --> 00:28:17,044
Speaker 1: long time. So this is not something new. It's not

505
00:28:17,124 --> 00:28:19,164
Speaker 1: like all of a sudden we have different education.

506
00:28:19,284 --> 00:28:21,284
Speaker 2: I've got to push back. I mean, it has gotten

507
00:28:21,284 --> 00:28:23,644
Speaker 2: I believe significantly big. I mean in terms of like

508
00:28:23,684 --> 00:28:26,204
Speaker 2: college graduation rates is going to like three to two

509
00:28:26,284 --> 00:28:29,404
Speaker 2: women or might go to two to one, and eventually,

510
00:28:29,484 --> 00:28:31,284
Speaker 2: I mean this kind of leads to interesting things like

511
00:28:31,324 --> 00:28:35,124
Speaker 2: should there be affirmative action for men on campus? It

512
00:28:35,124 --> 00:28:38,284
Speaker 2: also affects things like the campus dating scene potentially.

513
00:28:38,444 --> 00:28:42,164
Speaker 1: That's actually so nate, that's actually the kind of a

514
00:28:42,444 --> 00:28:45,804
Speaker 1: data point in the opposite direction. If this is If

515
00:28:45,804 --> 00:28:48,804
Speaker 1: it's true that it has gotten worse and there is

516
00:28:48,884 --> 00:28:51,044
Speaker 1: still a huge gender pay gap and that men are

517
00:28:51,124 --> 00:28:53,764
Speaker 1: still getting paid more than women, even though women are

518
00:28:53,764 --> 00:28:56,564
Speaker 1: pushing to get educated and are more educated than men.

519
00:28:56,644 --> 00:28:58,844
Speaker 1: That that actually shows that men are getting even more

520
00:28:58,844 --> 00:29:01,044
Speaker 1: privileged when it comes to how much they're being paid

521
00:29:01,044 --> 00:29:03,524
Speaker 1: than women. Right, Because if.

522
00:29:03,324 --> 00:29:07,124
Speaker 2: You're no, I don't think necessary, I mean, are there

523
00:29:07,124 --> 00:29:08,084
Speaker 2: are a few things here right.

524
00:29:09,484 --> 00:29:11,084
Speaker 1: This is, by the way, By the way, the reason

525
00:29:11,124 --> 00:29:13,444
Speaker 1: we're going back and forth on this is even economists

526
00:29:13,524 --> 00:29:15,364
Speaker 1: go back and forth on this, like the data is,

527
00:29:15,804 --> 00:29:18,684
Speaker 1: the data is really complicated, and this is a very

528
00:29:18,764 --> 00:29:22,604
Speaker 1: muddy subject with tons of confounds, and it's really really

529
00:29:22,644 --> 00:29:25,884
Speaker 1: hard to control for all of the variables. So I'm

530
00:29:26,124 --> 00:29:28,604
Speaker 1: I'm just doing a little bit of a caveat on that.

531
00:29:29,844 --> 00:29:31,884
Speaker 2: Let me go through a couple of arguments that I

532
00:29:31,924 --> 00:29:35,004
Speaker 2: think the kind of economists on the other side of

533
00:29:35,004 --> 00:29:38,204
Speaker 2: this issue we'd go through, and some of them are

534
00:29:38,484 --> 00:29:41,444
Speaker 2: are explanations liberals would like more, some conservatives would like more. Right,

535
00:29:42,404 --> 00:29:48,564
Speaker 2: you know, one belief is that women often take more

536
00:29:48,604 --> 00:29:53,164
Speaker 2: time out of their careers to care for children, to

537
00:29:53,324 --> 00:29:56,164
Speaker 2: care sometimes for other like ailing family members, for things

538
00:29:56,204 --> 00:29:59,044
Speaker 2: like that, and that you know, if you count for that,

539
00:29:59,124 --> 00:30:02,004
Speaker 2: then there's questions about how much of the gender wage

540
00:30:02,124 --> 00:30:05,684
Speaker 2: gap remains. You know. The more plocularly incorrect thing too,

541
00:30:05,724 --> 00:30:07,724
Speaker 2: This is kind of what got Larry Summer's in a

542
00:30:07,764 --> 00:30:11,524
Speaker 2: lot of trouble at Harvard University. Right, you know, there

543
00:30:11,524 --> 00:30:15,524
Speaker 2: are some evidence that men have higher variance, that they

544
00:30:16,564 --> 00:30:19,964
Speaker 2: end up in really bad circumstances, commit substantially more violence

545
00:30:20,324 --> 00:30:24,404
Speaker 2: and more crime. Right, can crash out in different ways.

546
00:30:25,084 --> 00:30:29,404
Speaker 2: But if there's more genetic variation in a population, then

547
00:30:29,444 --> 00:30:31,764
Speaker 2: maybe the very top earners Elon Musk of the world

548
00:30:31,804 --> 00:30:34,604
Speaker 2: to bring him up again might tend to be men more, right,

549
00:30:34,644 --> 00:30:37,364
Speaker 2: I mean, those are those are theories that compete with this,

550
00:30:37,644 --> 00:30:42,524
Speaker 2: I guess, right, you know, I think one could ask

551
00:30:43,164 --> 00:30:49,484
Speaker 2: how predictive is college completion of success in career anyway?

552
00:30:49,724 --> 00:30:54,284
Speaker 1: Right, yep, that's a very valid question, especially now when

553
00:30:54,324 --> 00:30:56,124
Speaker 1: you have you know, also the Pewter teals of the

554
00:30:56,124 --> 00:30:58,804
Speaker 1: world paying people not to go to college. You know. There,

555
00:30:58,844 --> 00:31:02,244
Speaker 1: I think we are seeing a very very different society

556
00:31:02,284 --> 00:31:04,284
Speaker 1: than even ten years ago, and I think that that

557
00:31:04,364 --> 00:31:07,444
Speaker 1: shift is going to all the all the evidence shows

558
00:31:07,444 --> 00:31:11,404
Speaker 1: that that shift is not a prairiy blip that people

559
00:31:11,524 --> 00:31:15,364
Speaker 1: are putting less value on. Okay, where did you go

560
00:31:15,404 --> 00:31:16,764
Speaker 1: to school? Et cetera, et cetera.

561
00:31:17,324 --> 00:31:19,764
Speaker 2: Yeah, and tech, as you said, is like the field

562
00:31:19,764 --> 00:31:23,644
Speaker 2: where this is most pronounced. Right, there's now a you know,

563
00:31:23,764 --> 00:31:29,004
Speaker 2: anti higher education quadrant in tech I would see, right,

564
00:31:29,044 --> 00:31:31,684
Speaker 2: But it hasn't spread to like finances much, which is

565
00:31:31,724 --> 00:31:34,204
Speaker 2: still a bit more hierarchical. But like all this is

566
00:31:34,244 --> 00:31:41,164
Speaker 2: tied together, right, the backlash against elite higher education and

567
00:31:41,564 --> 00:31:44,804
Speaker 2: the backlash against women's roles in society. These things are

568
00:31:45,004 --> 00:31:49,004
Speaker 2: tied together, I think to like a greater and greater extent, right,

569
00:31:49,724 --> 00:31:51,884
Speaker 2: you know, and again you know, Look, my premise is

570
00:31:51,884 --> 00:31:55,044
Speaker 2: always at like, it doesn't do anybody any good to

571
00:31:55,044 --> 00:31:59,284
Speaker 2: pretend that, on average, that gender differences don't exist.

572
00:31:59,444 --> 00:31:59,644
Speaker 1: Right.

573
00:32:00,044 --> 00:32:03,644
Speaker 2: If you look at measures of self reported well being,

574
00:32:03,724 --> 00:32:07,724
Speaker 2: if you ask people are you happy or not? Basically? Right,

575
00:32:08,404 --> 00:32:12,484
Speaker 2: First of all, there are enormous generational divides where a

576
00:32:12,484 --> 00:32:16,004
Speaker 2: lot of gen Z reports very poor mental health, But

577
00:32:16,084 --> 00:32:20,084
Speaker 2: there is a gender gap too. Men tend to report

578
00:32:21,004 --> 00:32:26,124
Speaker 2: higher happiness, lower levels of anxiety and depression. If you

579
00:32:26,164 --> 00:32:30,324
Speaker 2: look at like what circumstances, what life circumstances affect people's

580
00:32:30,364 --> 00:32:33,164
Speaker 2: sense of well being? Right? For some types of things,

581
00:32:33,244 --> 00:32:37,444
Speaker 2: men are more resilient, but not for work related stuff.

582
00:32:37,484 --> 00:32:37,564
Speaker 1: Right.

583
00:32:37,604 --> 00:32:39,524
Speaker 2: If you ask kind of questions about like what would

584
00:32:39,564 --> 00:32:41,964
Speaker 2: happen if you got demoted at work or you weren't

585
00:32:41,964 --> 00:32:46,844
Speaker 2: performing expectations that you know, men have a bigger psychological

586
00:32:46,844 --> 00:32:49,284
Speaker 2: effect from that than women do on average, all these

587
00:32:49,524 --> 00:32:50,164
Speaker 2: our averages.

588
00:32:50,484 --> 00:32:54,884
Speaker 1: Yeah, absolutely, I will just add to that to kind

589
00:32:54,884 --> 00:32:59,924
Speaker 1: of give a broader picture that usually the gender differences

590
00:33:00,444 --> 00:33:04,244
Speaker 1: within genders are much much larger than the gender differences

591
00:33:04,284 --> 00:33:07,764
Speaker 1: between genders on almost any data point you can find. Right.

592
00:33:07,764 --> 00:33:10,004
Speaker 1: That means men differ more from men, and women differ

593
00:33:10,044 --> 00:33:12,764
Speaker 1: more from women than men on average, and women on

594
00:33:12,804 --> 00:33:15,884
Speaker 1: average differ from each other. That is something that is

595
00:33:15,964 --> 00:33:18,924
Speaker 1: just basically true of almost anything you can find in

596
00:33:18,964 --> 00:33:21,964
Speaker 1: the psychology literature. So I just want people to keep

597
00:33:21,964 --> 00:33:23,284
Speaker 1: that in the back of their minds.

598
00:33:23,724 --> 00:33:26,204
Speaker 2: Yeah. Look, you know, I just came from the self

599
00:33:27,044 --> 00:33:30,324
Speaker 2: professed happiest country in the world, Finland. I was trying

600
00:33:30,364 --> 00:33:31,204
Speaker 2: to do it. Oh my god.

601
00:33:31,244 --> 00:33:33,684
Speaker 1: That that always just I get such a kick out

602
00:33:33,684 --> 00:33:34,084
Speaker 1: of that.

603
00:33:35,284 --> 00:33:38,404
Speaker 2: They seem pretty happy. They drink a lot, you get offered,

604
00:33:38,724 --> 00:33:40,204
Speaker 2: Oh that's probably why.

605
00:33:40,324 --> 00:33:45,404
Speaker 1: Yeah, they haven't bought into the alcohol is bad for

606
00:33:45,444 --> 00:33:48,604
Speaker 1: you yet, so yes, their life satisfaction is probably higher.

607
00:33:48,684 --> 00:33:51,084
Speaker 2: This could be, this could be a whole different episode.

608
00:33:51,084 --> 00:33:52,684
Speaker 2: Not that we could do the boost thing, right, but

609
00:33:52,764 --> 00:33:55,404
Speaker 2: like national happiness rankings, what do they really tell you, right,

610
00:33:56,604 --> 00:34:03,004
Speaker 2: I think they're probably somewhat real. One thing you actually notice,

611
00:34:03,044 --> 00:34:05,164
Speaker 2: I haven't published this work. Maybe I'll get around to

612
00:34:05,164 --> 00:34:09,004
Speaker 2: it eventually. If you look at fertility rates by country,

613
00:34:09,844 --> 00:34:14,644
Speaker 2: then happiness is somewhat predictive of that when you control

614
00:34:14,764 --> 00:34:17,124
Speaker 2: for GDP. I mean, basically, you know, the big thing

615
00:34:17,124 --> 00:34:18,484
Speaker 2: that no one wants to talk about is that, like

616
00:34:19,204 --> 00:34:22,764
Speaker 2: both men and women have seen to discover around the

617
00:34:22,764 --> 00:34:25,764
Speaker 2: world that like, when you get wealthier than maybe there

618
00:34:25,764 --> 00:34:27,924
Speaker 2: are better things to do with your life and to

619
00:34:27,964 --> 00:34:31,524
Speaker 2: have children, right, and this is causing potential decline in

620
00:34:31,564 --> 00:34:33,964
Speaker 2: the global population. Right. That's a big difference between not

621
00:34:33,964 --> 00:34:36,844
Speaker 2: really between men and women, but it's also a generational difference.

622
00:34:36,924 --> 00:34:37,124
Speaker 1: Right.

623
00:34:37,924 --> 00:34:43,084
Speaker 2: But people actually have, controlling for other factors, more kids

624
00:34:43,124 --> 00:34:45,404
Speaker 2: in happier countries, which suggests to me that there is

625
00:34:45,484 --> 00:34:49,684
Speaker 2: like some tangible difference here potentially. I mean, you could

626
00:34:49,724 --> 00:34:52,964
Speaker 2: look at things like suicide rates. I haven't looked at that. Actually,

627
00:34:53,604 --> 00:34:56,164
Speaker 2: you know, men commit suicide much more offer than women,

628
00:34:56,244 --> 00:35:01,804
Speaker 2: even though even though they report higher self reported mental health,

629
00:35:01,804 --> 00:35:03,884
Speaker 2: which is interesting. Maybe it's another case of like having

630
00:35:03,964 --> 00:35:07,844
Speaker 2: more variation potentially, but I think these things are at

631
00:35:07,924 --> 00:35:10,644
Speaker 2: least partly real, and some of us that it's more

632
00:35:10,684 --> 00:35:14,324
Speaker 2: you know, the notion about like men not feeling like

633
00:35:14,364 --> 00:35:16,644
Speaker 2: they have a lot of close friends, right.

634
00:35:16,884 --> 00:35:19,444
Speaker 1: That's actually a huge thing. I think that there's a

635
00:35:19,484 --> 00:35:22,844
Speaker 1: lot to that, and I think that there you know,

636
00:35:22,924 --> 00:35:30,084
Speaker 1: women historically socially have had to kind of be more socialized, right.

637
00:35:30,124 --> 00:35:32,644
Speaker 1: It's it's one of those things where women, I think,

638
00:35:33,644 --> 00:35:37,444
Speaker 1: just in general in society have kind of had to

639
00:35:37,644 --> 00:35:40,244
Speaker 1: get support groups, right, and be nice and be the

640
00:35:40,284 --> 00:35:43,404
Speaker 1: people in the family who take care of calendars and

641
00:35:43,444 --> 00:35:47,044
Speaker 1: appointments and keep up with other parents and like and

642
00:35:47,084 --> 00:35:50,004
Speaker 1: do all of the social stuff. And for men, that

643
00:35:50,564 --> 00:35:54,724
Speaker 1: once again historically hasn't always been their dominant social role.

644
00:35:54,804 --> 00:35:57,124
Speaker 1: So we actually do see that that men tend to

645
00:35:57,124 --> 00:36:00,724
Speaker 1: have smaller circles, and that those circles often shrink with

646
00:36:00,884 --> 00:36:05,164
Speaker 1: age unless they do something specifically to counteract it. Something

647
00:36:05,164 --> 00:36:07,964
Speaker 1: else that we mentioned briefly but have not gone into

648
00:36:08,004 --> 00:36:10,804
Speaker 1: in detail, But I think it's important here, including in

649
00:36:10,884 --> 00:36:14,524
Speaker 1: social circles who you interact with, and I think that

650
00:36:14,644 --> 00:36:19,484
Speaker 1: this is affecting both genders, is that you know, teens

651
00:36:19,644 --> 00:36:24,244
Speaker 1: right now. Tweens are coming up in an era where

652
00:36:24,604 --> 00:36:28,084
Speaker 1: so much activity is online, right, and there's this atomization

653
00:36:28,244 --> 00:36:30,724
Speaker 1: of society that we didn't experience you and I and

654
00:36:30,724 --> 00:36:33,164
Speaker 1: A when we were when we were growing up. And

655
00:36:33,204 --> 00:36:36,684
Speaker 1: I think that there are generational differences that we have

656
00:36:36,764 --> 00:36:38,964
Speaker 1: no idea yet how that's going to play out, but

657
00:36:39,444 --> 00:36:41,444
Speaker 1: as of now, I think that those do not have

658
00:36:41,484 --> 00:36:44,484
Speaker 1: a great effect on mental health. Right if you socialize

659
00:36:44,484 --> 00:36:47,164
Speaker 1: online versus in person, there is a difference. We do

660
00:36:47,244 --> 00:36:49,404
Speaker 1: have data on that, and so I do think that

661
00:36:49,444 --> 00:36:52,724
Speaker 1: we're going to be seeing kind of some spillover effects

662
00:36:52,764 --> 00:36:55,564
Speaker 1: from that in the future. And it's too early to

663
00:36:55,604 --> 00:36:58,244
Speaker 1: say whether you know what the gender differences will be,

664
00:36:59,044 --> 00:37:01,644
Speaker 1: what kind of what the fall from that will be,

665
00:37:01,724 --> 00:37:04,604
Speaker 1: But there is an important kind of generational shift going

666
00:37:04,644 --> 00:37:07,004
Speaker 1: on right now that we should be paying attention to.

667
00:37:10,684 --> 00:37:12,564
Speaker 2: And we'll be right back after this break.

668
00:37:25,964 --> 00:37:26,164
Speaker 1: Yeah.

669
00:37:26,324 --> 00:37:28,924
Speaker 2: Look, I think some stereotypes are true on an average,

670
00:37:28,964 --> 00:37:32,484
Speaker 2: right If you let's say I get dinner with a

671
00:37:32,604 --> 00:37:36,084
Speaker 2: male friend playtonic right the next day, do I get

672
00:37:36,124 --> 00:37:39,884
Speaker 2: like a follow up thank you We'll stening out again

673
00:37:39,924 --> 00:37:43,684
Speaker 2: soon text? Probably not? Right with a female friend over

674
00:37:43,724 --> 00:37:46,244
Speaker 2: fifty to fifty I would say, right, just an observation

675
00:37:46,324 --> 00:37:47,684
Speaker 2: I think will hold true for a lot of people.

676
00:37:48,604 --> 00:37:52,644
Speaker 2: You know, men stereotypically like to hang up by doing

677
00:37:52,684 --> 00:37:55,644
Speaker 2: things together. Right, you're watching a football game, you're watching

678
00:37:55,684 --> 00:37:59,324
Speaker 2: a movie, you're playing a video game. Right, maybe playing poker,

679
00:37:59,804 --> 00:38:03,444
Speaker 2: women tend to you know, involve more talking on average,

680
00:38:03,604 --> 00:38:07,124
Speaker 2: don't shoot the messenger, right, There is data on this, right,

681
00:38:07,164 --> 00:38:09,244
Speaker 2: and like and that might be more threatened by a

682
00:38:09,524 --> 00:38:12,204
Speaker 2: like a model where now you're hanging out in person

683
00:38:12,444 --> 00:38:14,884
Speaker 2: or you're or you're not hanging out in person as much. Right,

684
00:38:14,924 --> 00:38:17,484
Speaker 2: you know, I think poker is interesting and there is

685
00:38:17,564 --> 00:38:22,124
Speaker 2: certainly outright sexism in poker, for sure, and yes, but

686
00:38:22,764 --> 00:38:25,564
Speaker 2: you know, I also think it's a male bonding experience

687
00:38:25,804 --> 00:38:28,644
Speaker 2: and an outlet for some men who feel like they

688
00:38:28,644 --> 00:38:32,164
Speaker 2: don't have another way to like to make friends. Right,

689
00:38:32,324 --> 00:38:34,644
Speaker 2: you go to the poker room, and believe me, there

690
00:38:34,644 --> 00:38:36,844
Speaker 2: are some poker players who are very sociable and eloquent

691
00:38:36,884 --> 00:38:39,684
Speaker 2: and everything else, but like you want, Yeah, some guys

692
00:38:39,764 --> 00:38:42,724
Speaker 2: just want to play poker and talk about crypto and

693
00:38:42,804 --> 00:38:44,644
Speaker 2: sports and women.

694
00:38:45,004 --> 00:38:45,204
Speaker 1: Right.

695
00:38:45,924 --> 00:38:48,804
Speaker 2: You know, that's one of the reasons why poker is

696
00:38:48,804 --> 00:38:51,844
Speaker 2: a popular hobby with with with men right, and might

697
00:38:51,924 --> 00:38:54,324
Speaker 2: encourage men who are like, Okay, this is my this

698
00:38:54,404 --> 00:38:56,524
Speaker 2: is my safe space a little bit when you do

699
00:38:56,524 --> 00:38:59,444
Speaker 2: see some of the more sexist behaviors, Oh for sure.

700
00:38:59,484 --> 00:39:03,004
Speaker 1: I mean I've said that often that you know, especially

701
00:39:03,044 --> 00:39:05,204
Speaker 1: at lower stakes, when you go to your local casino,

702
00:39:05,244 --> 00:39:08,804
Speaker 1: that's where I've experienced some of the greatest misogyny because

703
00:39:09,124 --> 00:39:11,484
Speaker 1: I think that people there are playing for different reasons

704
00:39:11,484 --> 00:39:13,164
Speaker 1: than when you get to like high stakes pros for

705
00:39:13,204 --> 00:39:15,084
Speaker 1: whom like this is a career, they're taking it like

706
00:39:15,124 --> 00:39:18,124
Speaker 1: it's their profession. And at those lower stakes you often

707
00:39:18,164 --> 00:39:21,044
Speaker 1: get people for whom like this is their socializing, right,

708
00:39:21,124 --> 00:39:23,364
Speaker 1: like this is there, like I just want to be

709
00:39:23,444 --> 00:39:25,284
Speaker 1: with the guys and like I don't want to watch

710
00:39:25,284 --> 00:39:27,724
Speaker 1: my fucking mouth. Like you're the one who chose to

711
00:39:27,764 --> 00:39:30,244
Speaker 1: sit at this table. If I want to, you know,

712
00:39:30,484 --> 00:39:34,044
Speaker 1: hit on the dealer and like talk about her boobs

713
00:39:34,084 --> 00:39:36,444
Speaker 1: and like mention your ass, that's fine, And if you're

714
00:39:36,444 --> 00:39:38,764
Speaker 1: not comfortable, you leave, right, I'm not going to change

715
00:39:38,764 --> 00:39:41,124
Speaker 1: my behavior. I'm here with the boys. I want to drink,

716
00:39:41,164 --> 00:39:43,524
Speaker 1: I want to have fun. So I think that there's

717
00:39:43,564 --> 00:39:47,844
Speaker 1: definitely something to that. And yeah, so I do think

718
00:39:47,844 --> 00:39:51,204
Speaker 1: that all of these factors matter when we're thinking about, like,

719
00:39:51,724 --> 00:39:54,484
Speaker 1: is there actually a crisis here? Right, So at the

720
00:39:54,564 --> 00:39:56,604
Speaker 1: end of the day and night, how would you weigh

721
00:39:56,724 --> 00:39:59,364
Speaker 1: all of this to say, you know, is there a

722
00:39:59,444 --> 00:40:02,204
Speaker 1: crisis or are these just kind of some issues where

723
00:40:02,204 --> 00:40:05,084
Speaker 1: men are winning some where they're losing. And it's true

724
00:40:05,084 --> 00:40:07,884
Speaker 1: of women as well, and we're just in a moment

725
00:40:07,884 --> 00:40:11,124
Speaker 1: of social change that's not necess necessarily gender related, which

726
00:40:11,164 --> 00:40:14,044
Speaker 1: is how I would kind of think about it, as

727
00:40:14,084 --> 00:40:16,724
Speaker 1: opposed to saying, yes, it's a crisis of masculinity, that

728
00:40:16,724 --> 00:40:18,404
Speaker 1: would be my answer. How would you answer it?

729
00:40:19,884 --> 00:40:22,804
Speaker 2: Well, look, I think most people are will tend to

730
00:40:22,804 --> 00:40:25,004
Speaker 2: treat everything as a crisis, whether or not it's actually

731
00:40:25,484 --> 00:40:30,924
Speaker 2: incret nor. The mental health numbers for young people are

732
00:40:31,044 --> 00:40:34,164
Speaker 2: very bad, and I think it has to constitute a crisis. Now,

733
00:40:34,164 --> 00:40:39,524
Speaker 2: they're worse for women than men. But look, okay, let

734
00:40:39,564 --> 00:40:41,204
Speaker 2: me be a little spicier here, right, I think there

735
00:40:41,284 --> 00:40:48,204
Speaker 2: is often implicit bias against men in how the village, right,

736
00:40:48,404 --> 00:40:50,564
Speaker 2: the kind of progressive communities talk about some of these

737
00:40:51,124 --> 00:40:53,844
Speaker 2: things potentially, Right, if you kind of get at the

738
00:40:53,924 --> 00:40:58,604
Speaker 2: question of, like to go back to self diagnosed mental health, right,

739
00:40:58,724 --> 00:41:02,044
Speaker 2: some people will say, Okay, women are higher and liberals

740
00:41:02,084 --> 00:41:03,884
Speaker 2: are higher. This isn't even bigger different talking way, Liberals

741
00:41:03,884 --> 00:41:06,484
Speaker 2: are higher than conservatives by a wide margin.

742
00:41:06,764 --> 00:41:06,924
Speaker 1: Right.

743
00:41:08,324 --> 00:41:11,604
Speaker 2: You know, they will say, well, that's because there is

744
00:41:11,644 --> 00:41:17,964
Speaker 2: a stigma against declaring mental health conditions, and that women

745
00:41:18,684 --> 00:41:21,724
Speaker 2: and concern or women and liberals don't suffer from the stigma,

746
00:41:22,084 --> 00:41:29,484
Speaker 2: whereas conservatives and men do, right, to which I would say, okay,

747
00:41:30,284 --> 00:41:36,084
Speaker 2: maybe there is something to stoicism basically, right, and you

748
00:41:36,084 --> 00:41:39,324
Speaker 2: know they're popular still assists books and podcasts and websites

749
00:41:39,324 --> 00:41:41,564
Speaker 2: and things like that where it's like, okay, you know,

750
00:41:41,644 --> 00:41:44,924
Speaker 2: maybe there's something to be said for like toughing it

751
00:41:44,964 --> 00:41:50,044
Speaker 2: out under pressure. Maybe this is actually an evolutionarily smart strategy, right,

752
00:41:50,084 --> 00:41:54,084
Speaker 2: Maybe it's okay to like compartmentalize things a little bit, right,

753
00:41:54,164 --> 00:41:55,564
Speaker 2: or you know, to kind of the example of what

754
00:41:55,644 --> 00:41:57,684
Speaker 2: was kind of half joking about before. We're like men

755
00:41:57,804 --> 00:41:59,764
Speaker 2: like to do stuff, women like to talk with one another,

756
00:42:00,124 --> 00:42:03,564
Speaker 2: you know. Now and then I'll see like some op

757
00:42:03,724 --> 00:42:06,324
Speaker 2: ed in the Atlantic or whatever. Right you Usually when

758
00:42:06,364 --> 00:42:08,444
Speaker 2: by women they're like, well, men don't know how to

759
00:42:09,484 --> 00:42:11,164
Speaker 2: good friendships. It's like fuck you if I want to

760
00:42:11,204 --> 00:42:14,244
Speaker 2: go out and watch a baseball game with my friend, Like,

761
00:42:14,284 --> 00:42:16,284
Speaker 2: don't tell me it's like any less valid than like

762
00:42:16,644 --> 00:42:18,644
Speaker 2: chatting chatting chatting pressure. Yeah.

763
00:42:18,724 --> 00:42:21,564
Speaker 1: Yeah, no. And by the way, I actually think there's

764
00:42:21,564 --> 00:42:24,044
Speaker 1: good data for both men and women that you bond

765
00:42:24,044 --> 00:42:26,684
Speaker 1: more through experiences that like that kind of stuff and

766
00:42:26,724 --> 00:42:28,844
Speaker 1: doing things together is just good for friendship period.

767
00:42:29,244 --> 00:42:31,844
Speaker 2: Yeah, people talk so much, you know, what's what's they

768
00:42:31,924 --> 00:42:34,124
Speaker 2: to talk about? Go do something?

769
00:42:35,084 --> 00:42:38,044
Speaker 1: But yeah, I mean, but it sounds like it sounds

770
00:42:38,084 --> 00:42:40,924
Speaker 1: like you're you're kind of on the same page as

771
00:42:40,964 --> 00:42:44,084
Speaker 1: I am when it comes to like, yeah, there are

772
00:42:44,084 --> 00:42:48,244
Speaker 1: obviously some issues happening, but it's less clear how much

773
00:42:48,284 --> 00:42:51,684
Speaker 1: of it is. Gender is kind of a crisis of

774
00:42:51,804 --> 00:42:55,564
Speaker 1: masculinity as such, right, because the mental health stuff that's

775
00:42:55,764 --> 00:42:59,204
Speaker 1: that's a very different crisis like that is not The data,

776
00:42:59,284 --> 00:43:02,404
Speaker 1: as you said, are not great for women either. So

777
00:43:02,444 --> 00:43:04,604
Speaker 1: I think there are lots of things going on. I

778
00:43:04,684 --> 00:43:06,444
Speaker 1: do think we need to there are lots of trends

779
00:43:06,484 --> 00:43:08,524
Speaker 1: that we should be paying attention to. But I think

780
00:43:08,564 --> 00:43:11,244
Speaker 1: the data inconclusive. When it comes to, oh, you know,

781
00:43:11,364 --> 00:43:14,404
Speaker 1: is there is there a crisis for one gender or another?

782
00:43:14,444 --> 00:43:17,324
Speaker 1: I think the answer there is no, there's not enough

783
00:43:17,684 --> 00:43:21,004
Speaker 1: evidence for that, but there are some undercurrents that we

784
00:43:21,004 --> 00:43:24,964
Speaker 1: should be paying attention to and monitoring, especially with the

785
00:43:25,004 --> 00:43:26,324
Speaker 1: next generation coming up.

786
00:43:27,364 --> 00:43:28,924
Speaker 2: Yeah, look, I mean I don't know. If you look

787
00:43:28,964 --> 00:43:33,964
Speaker 2: at like the male versus female employment population ratio, right,

788
00:43:34,004 --> 00:43:38,124
Speaker 2: it's dropped actually for everybody, in part because you have

789
00:43:38,204 --> 00:43:40,964
Speaker 2: like the population aging outside of prime working years, but

790
00:43:41,604 --> 00:43:44,444
Speaker 2: it's dropped quite a bit more for men. Right in

791
00:43:44,524 --> 00:43:47,524
Speaker 2: two thousand and five according to the Bureau of Labor

792
00:43:47,564 --> 00:43:52,524
Speaker 2: Cystix before before the site gets compromised by Trump. So

793
00:43:52,804 --> 00:43:57,524
Speaker 2: in two thousand and five, seventy three percent of men

794
00:43:57,604 --> 00:44:02,564
Speaker 2: aged twenty and older were employed. Now it's sixty seven percent. Right,

795
00:44:02,604 --> 00:44:04,764
Speaker 2: there's a drop off for women, but it's been less

796
00:44:04,804 --> 00:44:08,124
Speaker 2: from fifty eight to fifty six. It looks like here, right, So,

797
00:44:08,204 --> 00:44:11,644
Speaker 2: like I think, I think there's plenty of evidence for this, right,

798
00:44:12,004 --> 00:44:14,844
Speaker 2: quantitative and qualitative and everything and everything else.

799
00:44:15,524 --> 00:44:18,084
Speaker 1: But there's also evidence against it that we've talked about.

800
00:44:18,124 --> 00:44:20,204
Speaker 1: I don't think we need to rehash it once again.

801
00:44:20,204 --> 00:44:22,124
Speaker 1: But it sounds like you're coming out more on the

802
00:44:22,204 --> 00:44:24,684
Speaker 1: side of crisis. I'm coming out less on the side

803
00:44:24,684 --> 00:44:27,764
Speaker 1: of crisis. And I don't just don't like the crisis language.

804
00:44:27,804 --> 00:44:30,964
Speaker 1: I think that is I think that's way too alarmist,

805
00:44:31,164 --> 00:44:34,004
Speaker 1: and that is not how we should be approaching this.

806
00:44:34,164 --> 00:44:37,164
Speaker 1: I'm much more in the camp of stoicism, you know,

807
00:44:37,204 --> 00:44:41,244
Speaker 1: that's one of the underlying philosophies of my writing. So

808
00:44:41,804 --> 00:44:44,244
Speaker 1: I'm all for that. This has been fun, though, and

809
00:44:44,284 --> 00:44:47,124
Speaker 1: I think that it is interesting to look through these data.

810
00:44:47,404 --> 00:44:50,484
Speaker 1: It's a really tough topic one that I'm sure some

811
00:44:50,564 --> 00:44:52,884
Speaker 1: of these things will will be revisiting. But it also

812
00:44:53,004 --> 00:44:55,404
Speaker 1: it goes to show that, you know, assessing these sources

813
00:44:55,404 --> 00:44:59,884
Speaker 1: of risks, it's tough, right, and it's a really difficult

814
00:44:59,964 --> 00:45:03,444
Speaker 1: job for economists try to parse the numbers, and there's

815
00:45:03,444 --> 00:45:06,284
Speaker 1: so much going on that it's just not going to

816
00:45:06,324 --> 00:45:08,604
Speaker 1: be a clear story ever, which is why people can

817
00:45:09,484 --> 00:45:12,764
Speaker 1: right authoritative op eds on opposite sides of this insight

818
00:45:12,884 --> 00:45:17,084
Speaker 1: data that seems to support their point of view. So yeah,

819
00:45:17,084 --> 00:45:20,004
Speaker 1: this has been a fun show, Nate. I you know,

820
00:45:20,244 --> 00:45:22,964
Speaker 1: one of the other things about northern Europe where you

821
00:45:23,044 --> 00:45:25,724
Speaker 1: came from, is that there also is a lot more

822
00:45:25,724 --> 00:45:28,684
Speaker 1: gender equality there than there is in other parts of

823
00:45:28,724 --> 00:45:31,364
Speaker 1: the world. So curious if that has something to do

824
00:45:31,404 --> 00:45:33,444
Speaker 1: with the happiness levels as well. But maybe we can.

825
00:45:34,084 --> 00:45:36,284
Speaker 1: Maybe we can leave that for another show.

826
00:45:37,204 --> 00:45:38,724
Speaker 2: Look, I think if you look at the happiest countries

827
00:45:38,724 --> 00:45:41,164
Speaker 2: in the world, they're also smaller countries, right, I think

828
00:45:41,204 --> 00:45:42,004
Speaker 2: that absolutely.

829
00:45:42,044 --> 00:45:45,364
Speaker 1: Yeah, so many confounding factors. Yeah, lots and lots of

830
00:45:45,404 --> 00:45:48,924
Speaker 1: confounding factors there. And on that note, Nate, I hope

831
00:45:48,924 --> 00:45:51,444
Speaker 1: you have a lovely week, and let me invite you

832
00:45:52,124 --> 00:45:55,444
Speaker 1: to dinner so that we can talk, because I'm female

833
00:45:55,564 --> 00:45:57,364
Speaker 1: and you know I like to I like to bond

834
00:45:57,404 --> 00:46:00,204
Speaker 1: over talking and good food. So let's get a dinner

835
00:46:00,204 --> 00:46:01,084
Speaker 1: from the calendar.

836
00:46:01,284 --> 00:46:03,404
Speaker 2: You know, maybe go to a Yankees game, Maria, you

837
00:46:03,404 --> 00:46:04,204
Speaker 2: can get dinner there.

838
00:46:04,324 --> 00:46:07,204
Speaker 1: Okay, all right, I will. I will go to a

839
00:46:07,284 --> 00:46:16,924
Speaker 1: Yankees game, Nate, because I am a good friend. Let

840
00:46:17,044 --> 00:46:18,964
Speaker 1: us know what you think of the show. Reach out

841
00:46:19,004 --> 00:46:23,124
Speaker 1: to us at Risky Business at pushkin dot fm. And

842
00:46:23,244 --> 00:46:25,364
Speaker 1: by the way, if you're a Pushkin Plus subscriber, we

843
00:46:25,404 --> 00:46:28,124
Speaker 1: have some bonus content for you that's coming up right

844
00:46:28,164 --> 00:46:29,124
Speaker 1: after the credits.

845
00:46:29,324 --> 00:46:32,564
Speaker 2: And if you're not subscribing yet, consider signing up for

846
00:46:32,764 --> 00:46:34,924
Speaker 2: just six ninety nine a month. What a nice price.

847
00:46:35,284 --> 00:46:37,924
Speaker 2: You get access to all that premium content and ad

848
00:46:38,084 --> 00:46:40,884
Speaker 2: for listening across Pushkin's entire network of shows.

849
00:46:41,564 --> 00:46:44,644
Speaker 1: Risky Business is hosted by me Maria Kanikova.

850
00:46:44,324 --> 00:46:46,684
Speaker 2: And by me Nate Silver. The show is a co

851
00:46:46,764 --> 00:46:50,644
Speaker 2: production of Pushkin Industries and iHeartMedia. This episode was produced

852
00:46:50,644 --> 00:46:54,404
Speaker 2: by Isabelle Carter. Our associate producer is Sonia Gerwit. Sally

853
00:46:54,444 --> 00:46:57,484
Speaker 2: Helm is our editor, and our executive producer is Jacob Goldstein.

854
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Speaker 2: Mixing by Sarah Bruger.

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Speaker 1: Thanks so much for tuning in.