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Speaker 1: Hi. This is Laura Vanderkam. I'm a mother of four,

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Speaker 1: an author, journalist, and speaker. And this is Sarah Hart Hunger.

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Speaker 1: I'm a mother of three, practicing physician and blogger. On

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Speaker 1: the side, we are two working parents who love our

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Speaker 1: careers and our families. Welcome to best of both worlds.

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Speaker 1: Here we talk about how real women manage work, family,

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Speaker 1: and time for fun, from figuring out childcare to mapping

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Speaker 1: out long term career goals. We want you to get

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Speaker 1: the most out of life. Welcome to best of both worlds.

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Speaker 1: This is Laura. This is episode ninety seven. We're excited

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Speaker 1: to be interviewing Emily Oster today. She is a professor

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Speaker 1: at Brown, an economist, and the author of the new

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Speaker 1: book Cribsheet, which is a data driven guide to better,

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Speaker 1: more relaxed parenting from birth to preschool. So lots of

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Speaker 1: great things we're going to be talking about with her

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Speaker 1: later in this episode. The reason we reached out to

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Speaker 1: Emily is because a lot of people mentioned on my

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Speaker 1: blog that she would make a great guest, and then

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Speaker 1: Sarah'll so recently heard her on Freakonomics, right, that's yeah,

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Speaker 1: it aired just recently. The episode aired in early May

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Speaker 1: if you want to go back and look back, because

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Speaker 1: it's a fantastic episode if you enjoy hearing her on

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Speaker 1: this podcast, and specifically she delved into some topics that

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Speaker 1: I felt were right up our Best of Both Worlds

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Speaker 1: Ali and thought she'd make a fantastic guest for us.

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Speaker 1: So we were thrilled when you know, she was able

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Speaker 1: to make room in her guest speaking calendar right after

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Speaker 1: Freakonomics to hang out with us Freakonomics and Best of

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Speaker 1: Both Worlds, which you listened to both of those, Sarah,

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Speaker 1: what well else are you listening to today? Yeah? I was.

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Speaker 1: I was wondering if you had any new podcasts you liked.

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Speaker 1: I you know, I haven't had that many new ones.

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Speaker 1: I actually there's a finance one that I've been enjoying

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Speaker 1: called how to Money. Although it's very male perspective, I'll

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Speaker 1: have to look to see if they're any you know

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Speaker 1: more more, although they have a very nice family perspective.

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Speaker 1: They both have large families and they feel like spending

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Speaker 1: out on the families is worth it. So I really

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Speaker 1: like that. Maybe they just need to have their partners speak.

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Speaker 1: I am actually a guest on this show with How

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Speaker 1: To Money at some point. I'm not sure if it

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Speaker 1: will have aired by the time this is I didn't

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Speaker 1: not air yet because it heard it between when we're

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Speaker 1: recording this and the But yeah, no, they're Matt and

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Speaker 1: Joel are very fun and yeah, they're both big expanding families,

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Speaker 1: so it was good good to talk with them from

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Speaker 1: that perspective as well. Well. I'm also super excited to

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Speaker 1: say that Note to Self is returning. Manush has had

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Speaker 1: another podcast in the meantime, but I am just in

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Speaker 1: love with Note to selfs topics and I'm excited that

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Speaker 1: that's coming back. And I still listen to a lot

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Speaker 1: of a lot of different things. I do like in

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Speaker 1: the if you're looking for something sort of similar to

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Speaker 1: our kind of content, maybe less work related. I still

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Speaker 1: like the mom our a lot. Yeah, what about you?

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Speaker 1: I listened to Happier. I listen to what should I

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Speaker 1: read next? That's That's been great. I still love that

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Speaker 1: one as well. You know, I'm not in the car

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Speaker 1: all that much in terms of longer chunks of time,

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Speaker 1: and so I find that there's less space for listening

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Speaker 1: to a lot of podcasts. But you know, I certainly

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Speaker 1: like those and I've enjoyed listening to ones like how

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Speaker 1: to Money, you know, trying trying that out before I

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Speaker 1: was a guest on that. I definitely, I mean I

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Speaker 1: listened to everything that I'm on a guest that I'm

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Speaker 1: a guest on, like before I'm on the show. So

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Speaker 1: I feel like there's a wide variety of podcasts that

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Speaker 1: I've tried out here and there. There's also another I've

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Speaker 1: been listening to, not Safe for Work or no, maybe

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Speaker 1: it's called Safe for Work. Yeah, Safe for Work, Safe

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Speaker 1: for Work. It's a good it's like a career podcast.

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Speaker 1: It's like it's similar to us without the family side.

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Speaker 1: Got so those of you who enjoy us might want

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Speaker 1: to check that out as well. That's cool. Yeah, and uh,

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Speaker 1: we we continue to record this. We've been batching a

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Speaker 1: lot to get us ahead of the summer because it's

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Speaker 1: going to be a lot of back and forth of

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Speaker 1: both of us being off at different points in the summer.

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Speaker 1: So yeah, that's uh some of these the timing maybe

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Speaker 1: a little bit odd, but hopefully our listeners will forgive us.

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Speaker 1: As we're talking about August and it's like June, so

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Speaker 1: we thought ahead about that. So pretty much we try

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Speaker 1: not to do that. Yeah, yeah, all right, Well, here

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Speaker 1: is Emily. I'm so excited to speak with her. So,

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Speaker 1: Sarah and I are thrilled to welcome Emily Ostar to

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Speaker 1: the podcast today. As we said, she's the author of

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Speaker 1: the new book The Crib Sheet, which is a data

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Speaker 1: driven guide to better, more relaxed parenting from birth to preschool. So, Emily,

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Speaker 1: could you introduce yourself to our listeners. Yes, Hi, I'm Emily.

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Speaker 1: I am a professor of economics at Brown University in

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Speaker 1: my day job, and I am the author of this book, Cribsheet,

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Speaker 1: which is about using data to parent. And I also

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Speaker 1: have an earlier book called Expecting Better, which is about

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Speaker 1: using data to be a better pregnant person. And I'm

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Speaker 1: really excited to be here with you guys. Yeah, and

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Speaker 1: you have two children who have been the subject of

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Speaker 1: much of this research as well. Right, yes, sorry, I

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Speaker 1: should have mentioned the most important thing. I have a

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Speaker 1: four year old son and an eight year old daughter

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Speaker 1: at home. Excellent. Well, one of the things that intrigued

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Speaker 1: us and intrigues me in general, is that economists are

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Speaker 1: kind of special. They have a different way of thinking

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Speaker 1: about the world. I know, I enjoy listening to Freakonomics

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Speaker 1: is one example of a podcast that explores that kind

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Speaker 1: of thinking. Can you tell our listeners like, what does

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Speaker 1: it mean to be an economists? How do you see

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Speaker 1: things a little bit differently? Yeah, So I think there's

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Speaker 1: many kinds of economists, and we run the range from

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Speaker 1: what I think a lot of people would think about,

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Speaker 1: which are like people who study the Federal Reserve and

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Speaker 1: think about interest rates and think about stocks, which not

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Speaker 1: really the kind of economics I do. But I think

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Speaker 1: that there's a large pool of economists now who are

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Speaker 1: kind of really into analyzing data and trying to get

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Speaker 1: cause or relationships out of data, in many cases around

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Speaker 1: issues that are maybe less traditionally associated with economics, like parenting,

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Speaker 1: but more like health or behavior, behavior, behavior, and so

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Speaker 1: you know, for me, I think of economics as kind

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Speaker 1: of being partly about about data and evidence and trying

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Speaker 1: to get causality out of out of data, and partly

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Speaker 1: about decision making and thinking about how people should make

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Speaker 1: decisions and what is the right way to make choices.

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Speaker 1: And so I think that that for me, those two

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Speaker 1: things kind of come together and you can apply them

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Speaker 1: to in a lot of different areas, some of which

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Speaker 1: are more traditional than others for economists. Yeah, and so

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Speaker 1: your book takes this approach of looking at data and

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Speaker 1: looking at how people should make decisions and turns it

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Speaker 1: on this topic of you know, first pregnancy for the

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Speaker 1: first book, and now parenting, especially during the early years.

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Speaker 1: And you know, one of the reasons we're very excited

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Speaker 1: to have you on. It's one of my personal biggest

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Speaker 1: pet peeves that suddenly somebody will get very authoritative and say, well,

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Speaker 1: all the research says kids turn out best if you

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Speaker 1: do X. Now what maybe you could explain to people

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Speaker 1: what are the inherent problems of a statement like that.

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Speaker 1: So there is almost nothing about which all the research

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Speaker 1: says the same thing. And I think this is one

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Speaker 1: of the things that frustrates me also so much, is

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Speaker 1: that this sort of phrase US studies show like studies show,

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Speaker 1: studies show everything. Okay, there's always the studies show everything,

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Speaker 1: And the challenge is to say, you know, what is it?

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Speaker 1: What are all the studies show all the studies show

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Speaker 1: When you look at them all together and you try

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Speaker 1: to understand which are the best studies and which are

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Speaker 1: the are the least good studies, and you know which

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Speaker 1: ones should we should we rely on. And I think

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Speaker 1: in some sense, saying all the research shows just betrays

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Speaker 1: like a lack of understanding in some ways of how

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Speaker 1: this works. Because even for something where it's very clear

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Speaker 1: that it's it's sort of the data is in one

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Speaker 1: way or the other, you will still often find a

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Speaker 1: study that shows the opposite, because you know, studies are

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Speaker 1: not perfect, and sometimes there are some good studies and

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Speaker 1: less good studies, and some studies are wrong. And so

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Speaker 1: I think we need to take a more holistic view,

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Speaker 1: but it is very hard to do that in the

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Speaker 1: face of so many pieces of information that come at you. Yeah. Well,

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Speaker 1: also the question of how kids turn out. I mean

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Speaker 1: this is also a problem because any study needs a

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Speaker 1: measurable endpoint, and I'm just not sure how turning out

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Speaker 1: translates into a measurable endpoint. Yeah, no, it's it's right.

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Speaker 1: And I think even you know, even in the book,

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Speaker 1: there's this sort of there's all the limitations I talk about,

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Speaker 1: and then there's the on top of that, the limitation

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Speaker 1: that like, in our best case scenario, the outcomes we're

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Speaker 1: looking at are like test scores, right, or you know, behavior,

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Speaker 1: like some measure of behavior, right, And those are the

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Speaker 1: things we can measure. But of course, as a parent, like, yeah,

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Speaker 1: maybe those are things that you care about, but they're

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Speaker 1: certainly not the limit of the things you care about.

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Speaker 1: You know, I want my kids to be like happy

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Speaker 1: and nice and and there's no nice, like forget about

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Speaker 1: like a nice. They will measure nice. Nobody's like, hey,

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Speaker 1: is your kid like kind to other children? That's not

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Speaker 1: the thing. You know, there's externalizing behavior, there's like pieces

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Speaker 1: of that, but there isn't there. We're sort of missing

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Speaker 1: some of these more holistic things because they're hard to measure.

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Speaker 1: And I think that you know, that's an inherent limitation,

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Speaker 1: but it does mean that that these these conversations end

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Speaker 1: up sounding sort of like, oh, my goal is to

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Speaker 1: like optimize my kid on IQ and behavior outcomes, And

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Speaker 1: I don't think there's anybody's actual goal. And the funny thing,

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Speaker 1: like you talk about, everything tends to have two sides.

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Speaker 1: So even if you did manage to use data to

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Speaker 1: create the smartest kid, that could backfire anyway, because you

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Speaker 1: may have sacrificed something else equally valuable, like being nice

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Speaker 1: or creative or not having a psychiatric disease or right

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Speaker 1: or whatever. It is just like, for example, all bad

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Speaker 1: things you know, yes, yes, exactly. So you know you're

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Speaker 1: building your career as an economist, and then you know,

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Speaker 1: you and your husband start a family, and you know,

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Speaker 1: you decide to take this approach to pregnancy and childcare.

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Speaker 1: What were some of your biggest surprises as you looked

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Speaker 1: into this. Was there anything that you were surprised to

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Speaker 1: find or like, WHOA, I didn't think that would be

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Speaker 1: there in the data, So I think there was more

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Speaker 1: of that in the case of pregnancy. And so when

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Speaker 1: I sort of think about the two books, you know,

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Speaker 1: there's a lot of things in the in the first

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Speaker 1: book about pregnancy that are kind of like, you know,

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Speaker 1: people tell you you can't have any sushi, but actually

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Speaker 1: like it's fine to have sushi. And so there were

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Speaker 1: sort of many many more things like that or like

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Speaker 1: the one there that I was super surprised by that

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Speaker 1: remain very surprised by is about bed rest. So a

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Speaker 1: lot of people are told to go on bed rest,

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Speaker 1: and it turns out like there is like almost there's

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Speaker 1: basically nothing for which bed rest is a good idea,

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Speaker 1: and there are a lot of reasons it's not a

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Speaker 1: good idea. And yet it is still prescribed all the time.

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Speaker 1: So there were there were many things like that in pregnancy,

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Speaker 1: I think in the parenting stuff, you know, there's there's

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Speaker 1: a few things that kind of I think people find

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Speaker 1: somewhat surprising, but a lot of I guess a lot

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Speaker 1: of what's surprising about this about the book is that

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Speaker 1: there are many good choices, and so we are sort

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Speaker 1: of coming in always with like the idea that like

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Speaker 1: there's there's like a right choice, like there's like a

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Speaker 1: right way to sleep with your kind of a right

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Speaker 1: way to feed them, and and you know, in most

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Speaker 1: of these or many of the decisions there, there isn't

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Speaker 1: really a right way. And so that means that there

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Speaker 1: isn't a fact that is surprising. There is an approach

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Speaker 1: that is surprising. But I will I will highlight one

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Speaker 1: thing that I think that we that I didn't didn't

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Speaker 1: realize until I did the book, which is about the

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Speaker 1: way that you introduce food. So there when you I

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Speaker 1: don't know if you guys have you muys have little

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Speaker 1: kids or big kids. And I'm actually a pediatric ercnologist,

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Speaker 1: so I am like gonna listen with ears open. But actually,

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Speaker 1: so when I was thinking about feeding our dog, like

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Speaker 1: introducing solid food. You got this like very specific list

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Speaker 1: of like first you give them ry cereal and then

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Speaker 1: you know, every three days you give them a new vegetable,

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Speaker 1: and first it's vegetables and then it's fruit, and like

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Speaker 1: there's there's a more of like very very very specific instructions.

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Speaker 1: And I was like, Okay, there must be a reason

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Speaker 1: that it is exactly like this, Like there must be

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Speaker 1: a reason that you have to start with this and

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Speaker 1: then you have to do this, and then you have

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Speaker 1: to do this, because why would it be so specific

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Speaker 1: if there wasn't a reason. And then the answer was

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Speaker 1: like this is a fine way. There's really wrong with

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Speaker 1: this way I'm doing. It's like perfectly fine, but there

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Speaker 1: isn't anything wrong with like giving them oatmeal first, or

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Speaker 1: you know, like giving them some fruit before that. I mean,

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Speaker 1: there's like it was sort of like there's a logic,

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Speaker 1: but there didn't. It didn't feel like there was a

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Speaker 1: deep underlying evidence base for like why you had to

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Speaker 1: start and do it exactly this this way. And I

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Speaker 1: was surprised. I think that's so funny. And my gut

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Speaker 1: feeling is that people made a list because it was

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Speaker 1: like an easier answer, Like parents wanted a prescription and

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Speaker 1: they're let someone wrote it down and they're like, oh,

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Speaker 1: that looks good, and then that's the new handout and

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Speaker 1: you can sound all good saying it. And it wasn't

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Speaker 1: hurting anyone. It isn't like unlike something's like bed rest.

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Speaker 1: This isn't like a problem, Like this is totally fine

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Speaker 1: way to do it. It's just it's like if you

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Speaker 1: didn't want to, you know, I remember my son didn't

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Speaker 1: like rice cereal. He just like hated it. And he

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Speaker 1: was and my husband's like, why, like but what, we're family,

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Speaker 1: why does he like this? And it was like and

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Speaker 1: then he was like, maybe he'll like, you know, like kanji,

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Speaker 1: and then he did. And then it was like, I

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Speaker 1: guess we're gonna give him. This's this kid. Number two, Yeah,

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Speaker 1: number two. But also the risk that people are like,

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Speaker 1: oh I did something terribly wrong. If they introduced yellow

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Speaker 1: vegetables before red vegetables or whatever the order was right,

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Speaker 1: because yeah, I remember it's very yellow and then the

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Speaker 1: orange is you know, it turns out it doesn't matter.

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Speaker 1: It's okay. All the vegetables are going yeah. Well, and

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Speaker 1: the funny chicks out. You know, people make life altering

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Speaker 1: decisions based on things. I mean probably you know, what

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Speaker 1: order you feed your kid is not terribly life altering,

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Speaker 1: but things like parental employment are a lot more life altering.

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Speaker 1: And you know, you looked into the data on this,

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Speaker 1: you know, and how how people wind up being very

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Speaker 1: judgmental in both directions without particularly great evidence. So what

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Speaker 1: did you find in terms of the data on parental

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Speaker 1: implement And I'm not saying here whether mom stays home

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Speaker 1: or not, because I love the way you put it,

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Speaker 1: What is the optimal configuration of adult work hours for

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Speaker 1: your house? Yes, yes, it's very catchy. Yeah you won't

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Speaker 1: see that in the headline anytime, but I'm not going

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Speaker 1: to tell you don't think so it's too many words.

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Speaker 1: So you know, first of all, I think this is

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Speaker 1: this is a place where it's very it's this is

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Speaker 1: a place where the data is somewhat limited, or you know,

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Speaker 1: the approaches are somewhat limited because this choice is really

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Speaker 1: wrapped up in other things about the family, and so

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Speaker 1: it's just it's a hard question to answer. So the

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Speaker 1: best we can do is we can say, Okay, what

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Speaker 1: is the data to say of the data that we have.

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Speaker 1: What does it say? And so these studies compare the

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Speaker 1: kids of moms who stay home to the kids of

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Speaker 1: moms who who work, and or parents where both parents

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Speaker 1: work versus one parent staying home. It's usually the mom,

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Speaker 1: although not not always of course. And you know, what

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Speaker 1: they show is that the outcomes are very similar. That

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Speaker 1: there isn't any particular reason to think that. And again

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Speaker 1: here we're in outcomes like IQ or behavior, things you

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Speaker 1: can measure in test scores and in schools, and the

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Speaker 1: kids look look the same. There. You know, maybe is

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Speaker 1: a little bit of a hint that kids where one

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Speaker 1: parent works part time and one parent works full time

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Speaker 1: maybe do a little bit better. But it seems like

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Speaker 1: that is probably because that's like a very unusual family configuration,

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Speaker 1: which only happens in particular you're kind of heavily selected,

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Speaker 1: uh selected families. So I think that that what this means,

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Speaker 1: and you know what I'm what I took from this

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Speaker 1: is that the choice of you know, how to organize

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Speaker 1: your adult work hours should be mostly about what is

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Speaker 1: going to work for the family overall. And I think

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Speaker 1: we have this sense that we like start this conversation

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Speaker 1: with what is the best for the baby, Like, let

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Speaker 1: me do the thing that's the best for the kid,

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Speaker 1: the best for the kid. And they said, well, there's

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Speaker 1: just like there's no best for the kid. They're like this,

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Speaker 1: anything is fine. And so you need to figure out

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Speaker 1: what's best for your for your family, and one part

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Speaker 1: of that is budgeting. But one part of that is

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Speaker 1: what you want to do. So when I talk about this,

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Speaker 1: I say, you should think each person the family should

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Speaker 1: think about what do you want to do? Do you

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Speaker 1: want to work? Do not? Do not want to work?

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Speaker 1: And as I've talked about this book with women, there

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Speaker 1: are a number of women who have told me it

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Speaker 1: never even occurred to me to ask that question. Like

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Speaker 1: when I was making a decision about whether to work

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Speaker 1: after I had a kid, it never occurred to me

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Speaker 1: to think about whether I wanted to. But why is

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Speaker 1: parental preference view it as not relevant variable in so

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Speaker 1: much of this? I mean, I think it it's because

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Speaker 1: this has gotten wrapped up in this general you know,

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Speaker 1: modern approach to parenting. Their culture. Yeah, I trying to

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Speaker 1: like optimize, try our kids enjoy our lives. That's not

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Speaker 1: what we're here for right now, right, I mean here

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Speaker 1: for a podcast that serves to hopefully go against that.

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Speaker 1: And I love that you're actually bringing numbers into that.

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Speaker 1: That's fascinating. Yeah. Yeah, And I personally enjoyed that you

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Speaker 1: looked at the marginal value of each extra hour with

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Speaker 1: your kids versus the marginal utility of like the sixth

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Speaker 1: hour with your kids is probably lower than the no.

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Speaker 1: But I mean, I think there is this this resistance

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Speaker 1: in their culture to to saying that, as particularly as

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Speaker 1: a mom, but as a parent in general. Like people

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Speaker 1: don't somehow saying like, yeah, I have a job because

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Speaker 1: I like to have my job. I like it. I

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Speaker 1: like to go to work. It's fun, I enjoy it. It

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Speaker 1: It gives me fulfillment that is different from the fulfillment

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Speaker 1: I get from my kids. And I don't like my kids.

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Speaker 1: I like my kids, they're great, they're the best. But

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Speaker 1: I also like my job. And I think we are

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Speaker 1: we have become kind of resistant to saying that, and

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Speaker 1: we're kind of resistant to saying in the other direction too.

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Speaker 1: I think people who stay at home there's also a

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Speaker 1: resistance to saying, you know, I'm staying at home because

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Speaker 1: I like that that's the thing that I enjoy doing, right,

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Speaker 1: now because there's so much resistance to saying that, it

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Speaker 1: ends up being much more fraud than it would be

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Speaker 1: in much more conflict driven than if everyone could just

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Speaker 1: be like, Yeah, I'm doing the thing that I want

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Speaker 1: that works for me. So of course maybe it isn't

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Speaker 1: the thing that works for you because we're not the

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Speaker 1: same person, and that's fine. We could still be friends

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Speaker 1: and not in the two tribes people, yeah, or the

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Speaker 1: tribes split their the tribe tribes. So if you you know,

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Speaker 1: obviously if both parents are employed outside the home, then

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Speaker 1: childcare comes into this. There's probably going to need to

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Speaker 1: be some sort of paid childcare. And you looked at

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Speaker 1: this too, so you know both the question of like

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Speaker 1: nanny and daycare and what makes good care for that?

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Speaker 1: Is there any negative outcomes for having done this? Are

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Speaker 1: there positive outcomes for you know, putting your child in daycare?

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Speaker 1: So I wonder if you could talk a little bit

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Speaker 1: about that. Yeah, So I think again, like I'll say,

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Speaker 1: this is a hard thing to study because the kinds

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Speaker 1: of choices that people make about childcare are wrapped up

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Speaker 1: in other things that they that they do. But to

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Speaker 1: the extent we have data, you know, I think what

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Speaker 1: it suggests is that there are maybe you know, daycare

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Speaker 1: very early on maybe has some small impacts on behavior,

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Speaker 1: and daycare later on has some those are small negative impacts,

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Speaker 1: and daycare later on has some small positive impacts on

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Speaker 1: test scores. But all of these effects are small. When

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Speaker 1: you say early on, like in the first two it's

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Speaker 1: sort of in the first like eighteen months versus like

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Speaker 1: eighteen months to four and a half, okay, so so

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Speaker 1: sort of like yeah, the first year, year and a

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Speaker 1: half versus the like slightly later period, but all of

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Speaker 1: those effects are really little. So like relative to other

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Speaker 1: kinds of variation across kids, they're they're just not that

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Speaker 1: important in terms of size, and they're totally swamped by

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Speaker 1: home environment. So things like you know, how what is

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Speaker 1: happening in your house is way, way, way, way more

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Speaker 1: important than the differences in childcare that you are having.

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Speaker 1: And so in some sense, you know that's again like boy,

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Speaker 1: it's really important to be a good parent. So maybe

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Speaker 1: that's more stressful, but there is a sort of less

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Speaker 1: stressful aspect of it that says that you're making these

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Speaker 1: choices is not this is not the choice that is

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Speaker 1: going to make your kid smart. Or not smart or

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Speaker 1: badly behaved or better behaved than So a lot of

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Speaker 1: it should be about again sort of what is going

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Speaker 1: to work for your for your family. And then I

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Speaker 1: took a little bit about, you know, particularly in the

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Speaker 1: case of daycare, like how do you choose a good

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Speaker 1: like a good daycare? Like what is what does it

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Speaker 1: mean for a daycare to be to be good? Because

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Speaker 1: we do know that daycare quality or nanny quality as

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Speaker 1: measured and these studies, is associated with better outcomes. And

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Speaker 1: I try to be clear that quality is not like

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Speaker 1: organic snacks. And I think that's sometimes it's easy to

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Speaker 1: slip into the idea that like I should go to

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Speaker 1: the daycare with the fanciest stuff and the like. That's

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Speaker 1: probably correlated. But then ultimately, really what matters is like

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Speaker 1: an engaged caregiver who's paying attention to their kids and

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Speaker 1: not you know, whether the I don't know whether there's

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Speaker 1: like a particular kind of blocks or something like that. Yeah,

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Speaker 1: any kind of blocks or any kind of blocks are fine, Yeah, exactly.

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Speaker 1: And so you mentioned these tiny effects, and that's looking

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Speaker 1: at daycare versus home with parent. What about home with nanny? No,

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Speaker 1: So that's looking at daycare versus home. So these these

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Speaker 1: studies are are kind of limited, but that that statement

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Speaker 1: is looking at daycare versus home with caregiver, not the mom. Oh,

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Speaker 1: so that's daycare. So I try to sort of pull

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Speaker 1: out this study that's looks at like so that could

430
00:21:01,320 --> 00:21:03,119
Speaker 1: be a family member. So it's like a little tricky,

431
00:21:03,160 --> 00:21:05,720
Speaker 1: so it's not nanny's, but it could be a grandparent.

432
00:21:05,760 --> 00:21:08,280
Speaker 1: But I think it's it's distinctly not stay at home parent.

433
00:21:08,800 --> 00:21:12,280
Speaker 1: Got it interesting? Well, Sarah is particularly interested in your

434
00:21:12,440 --> 00:21:16,560
Speaker 1: research on screen time. This This is something she brings

435
00:21:16,640 --> 00:21:19,159
Speaker 1: up a lot here. And you know your husband is

436
00:21:19,160 --> 00:21:20,920
Speaker 1: also an economist, and I know one of the studies

437
00:21:21,000 --> 00:21:25,080
Speaker 1: you looked at in your book is one that he did. Correct. Yes,

438
00:21:25,280 --> 00:21:27,440
Speaker 1: it is a study. It is a study a television

439
00:21:27,480 --> 00:21:31,200
Speaker 1: exposure and kids, and so in that study, you know,

440
00:21:31,280 --> 00:21:33,640
Speaker 1: I think that the challenge in looking at things about

441
00:21:33,680 --> 00:21:35,080
Speaker 1: screen time. We can talk about how I think the

442
00:21:35,160 --> 00:21:37,199
Speaker 1: data in general is very poor, but you know, the

443
00:21:37,320 --> 00:21:40,399
Speaker 1: challenge in looking even at something like TV exposure and

444
00:21:40,760 --> 00:21:44,440
Speaker 1: kids outcomes is that the kinds of kids who watch

445
00:21:44,440 --> 00:21:46,600
Speaker 1: more TV are from families that are different from the

446
00:21:46,640 --> 00:21:48,600
Speaker 1: kinds of kids who watch less TV. And so it's

447
00:21:48,600 --> 00:21:52,000
Speaker 1: hard to know if it's the TV. So Jesse's paper

448
00:21:52,520 --> 00:21:54,399
Speaker 1: so if you want to study this and get some

449
00:21:54,480 --> 00:21:58,480
Speaker 1: causal impact, you need to actually find some sort of

450
00:21:58,560 --> 00:22:01,240
Speaker 1: random or plausibly random variation and the amount of TV

451
00:22:01,280 --> 00:22:03,120
Speaker 1: the kids watch. So what Jesse does is they look

452
00:22:03,320 --> 00:22:05,760
Speaker 1: at he and his co author look at when TV

453
00:22:05,920 --> 00:22:11,200
Speaker 1: was introduced in the nineteen fifties, and some places got

454
00:22:11,200 --> 00:22:13,360
Speaker 1: TV earlier than others. So there were some kids who

455
00:22:13,359 --> 00:22:16,280
Speaker 1: were exposed to TV for more of their like young childhood,

456
00:22:16,280 --> 00:22:18,240
Speaker 1: and some kids who are exposed for less. And when

457
00:22:18,280 --> 00:22:21,199
Speaker 1: they compare those those groups, it doesn't look like the

458
00:22:21,240 --> 00:22:25,439
Speaker 1: TV is having negative impacts. Now, I think that's like

459
00:22:25,440 --> 00:22:28,119
Speaker 1: a very compelling argument. Their paper is very compelling on

460
00:22:28,160 --> 00:22:30,919
Speaker 1: the topic of TV in the nineteen fifties, which is

461
00:22:30,960 --> 00:22:35,400
Speaker 1: maybe some has some relevance. Actually people watch more TV

462
00:22:35,440 --> 00:22:37,760
Speaker 1: than you might expect given that there were like three channels,

463
00:22:38,680 --> 00:22:41,960
Speaker 1: but it is it is probably not the answer to

464
00:22:42,080 --> 00:22:45,240
Speaker 1: like can my kids play iPad apps? Which I think

465
00:22:45,240 --> 00:22:47,840
Speaker 1: we don't have an answer too. That's fascinating. Yeah, it's

466
00:22:47,880 --> 00:22:49,960
Speaker 1: I mean even in the short time. So yeah, you

467
00:22:50,000 --> 00:22:51,880
Speaker 1: asked of Lauren, I had kids, We've got kids, ranging

468
00:22:51,960 --> 00:22:55,000
Speaker 1: from how many kids we have between us seven, ranging

469
00:22:55,080 --> 00:22:58,560
Speaker 1: between eighteen months and twelve and even in the time

470
00:22:58,680 --> 00:23:01,600
Speaker 1: since my oldest who's now seven, was a baby, and

471
00:23:01,640 --> 00:23:04,240
Speaker 1: now I feel like the quote unquote rules have changed,

472
00:23:04,280 --> 00:23:05,880
Speaker 1: and then kind of knowing that there's not a lot

473
00:23:05,920 --> 00:23:08,920
Speaker 1: of substance behind those rules in the first place, Like again,

474
00:23:09,000 --> 00:23:12,040
Speaker 1: being a pediatrician, sometimes I turn to AAP, but you know,

475
00:23:12,400 --> 00:23:14,640
Speaker 1: you have to also look at what's behind that, right,

476
00:23:14,640 --> 00:23:17,800
Speaker 1: because that's still just somebody's opinion summing up the quote

477
00:23:17,840 --> 00:23:21,399
Speaker 1: unquote data. Yea, and that's being looked at properly is

478
00:23:21,640 --> 00:23:23,840
Speaker 1: sometimes in question, Yeah, And I think in this case

479
00:23:23,880 --> 00:23:27,800
Speaker 1: it's not. It goes beyond like maybe their understanding of

480
00:23:28,160 --> 00:23:30,119
Speaker 1: the data isn't isn't that good, but just that there

481
00:23:30,200 --> 00:23:32,280
Speaker 1: isn't a lot of data. So if you want to know,

482
00:23:32,320 --> 00:23:35,560
Speaker 1: like what's the impact of these kind of frequent screen phones,

483
00:23:35,640 --> 00:23:38,959
Speaker 1: iPads whatever on like high school graduation, well, there's like

484
00:23:39,280 --> 00:23:41,120
Speaker 1: no way we could ever know that because the kids

485
00:23:41,119 --> 00:23:43,760
Speaker 1: who are graduating from high school now are are We're

486
00:23:43,800 --> 00:23:46,640
Speaker 1: not exposed to screens as little kids, and so we're

487
00:23:46,680 --> 00:23:52,480
Speaker 1: sort of relying on an expert opinion about these topics,

488
00:23:52,520 --> 00:23:56,199
Speaker 1: which has some value, and I think that is it

489
00:23:56,320 --> 00:23:58,719
Speaker 1: sort of has to be the case that having your

490
00:23:58,760 --> 00:24:02,400
Speaker 1: kid watch nine hours TV a day is bad because

491
00:24:02,560 --> 00:24:05,320
Speaker 1: there wouldn't be any other time to do stuff. And

492
00:24:05,480 --> 00:24:07,760
Speaker 1: you know, similarly, having your three year old stay up

493
00:24:07,800 --> 00:24:10,080
Speaker 1: late at night playing Xbox games instead of sleeping, we

494
00:24:10,119 --> 00:24:14,000
Speaker 1: can that's also not good. On the other hand, I

495
00:24:14,000 --> 00:24:16,399
Speaker 1: think the guidelines make it seem like if you have

496
00:24:16,520 --> 00:24:18,600
Speaker 1: your kid, your two year old watch a half an

497
00:24:18,640 --> 00:24:21,680
Speaker 1: hour of Kayu while you take a shower and make dinner,

498
00:24:21,760 --> 00:24:25,600
Speaker 1: that that's like, oh so terrible, And it seems much

499
00:24:25,720 --> 00:24:29,439
Speaker 1: less likely that the data supports that conclusion. Yeah. I

500
00:24:29,520 --> 00:24:32,480
Speaker 1: actually the moment that I finally was like I'm no

501
00:24:32,520 --> 00:24:34,760
Speaker 1: longer listening to this was when Well suggested that parents

502
00:24:34,800 --> 00:24:37,679
Speaker 1: need to watch along with theirs. I'm not watching is

503
00:24:37,880 --> 00:24:40,119
Speaker 1: one time you get a break, like I'm not gonna

504
00:24:40,160 --> 00:24:42,600
Speaker 1: sit there and watch Bears Seem Bears or Kayu or

505
00:24:42,640 --> 00:24:45,600
Speaker 1: whatever like that is no. No, I will say. My

506
00:24:45,720 --> 00:24:48,639
Speaker 1: husband is actually this is like his dream because there

507
00:24:48,680 --> 00:24:50,760
Speaker 1: are so many My kids are now old enough that

508
00:24:50,800 --> 00:24:55,760
Speaker 1: they both like to watch like animated shows like made

509
00:24:55,840 --> 00:24:58,440
Speaker 1: for you know, seven year olds. But that's like that's

510
00:24:58,480 --> 00:25:02,080
Speaker 1: his thing. So they've been watching like Shira. There's like

511
00:25:02,119 --> 00:25:05,359
Speaker 1: a Shia reboot on Netflix, and so he just like

512
00:25:05,400 --> 00:25:07,480
Speaker 1: sits like for TV time, Like I'm like, I'm gonna

513
00:25:07,480 --> 00:25:09,400
Speaker 1: go upstairs the hand glass of wine. I'm gonna make dinner,

514
00:25:09,440 --> 00:25:10,720
Speaker 1: like it's gonna be read. But you like, he's like,

515
00:25:11,000 --> 00:25:12,600
Speaker 1: he's like, this is so creticve to like stay with

516
00:25:12,600 --> 00:25:14,480
Speaker 1: the kids on the couch and watch her. So it's great,

517
00:25:14,720 --> 00:25:16,359
Speaker 1: but it's not because he if you told them to it,

518
00:25:17,160 --> 00:25:19,199
Speaker 1: because he loves Shira. And then they ran out of

519
00:25:19,200 --> 00:25:21,200
Speaker 1: Shara and the kids started watching a show called p

520
00:25:21,400 --> 00:25:24,000
Speaker 1: J Mask. Oh my kids love that. Yeah, and Jesse

521
00:25:24,200 --> 00:25:26,639
Speaker 1: was like, no, I'm not going to work. What was

522
00:25:26,680 --> 00:25:29,080
Speaker 1: the station? Check out power? Rightow? I bet they'd like

523
00:25:29,119 --> 00:25:35,000
Speaker 1: it's like the Netron. We're onto Vultron. Also, yeah, I know.

524
00:25:35,520 --> 00:25:39,440
Speaker 1: So what happens when you have two parents who are economists?

525
00:25:39,920 --> 00:25:42,240
Speaker 1: I think I was. I laughed so hard when I

526
00:25:42,280 --> 00:25:46,000
Speaker 1: saw that you too communicate via task management software of

527
00:25:46,080 --> 00:25:51,199
Speaker 1: the asana whatever it is. So, how did we actually

528
00:25:51,359 --> 00:25:53,280
Speaker 1: tell us how it came to be and also actually

529
00:25:53,359 --> 00:25:56,520
Speaker 1: give our listeners a flavor of those exact logistics because

530
00:25:56,760 --> 00:26:00,080
Speaker 1: people always tell us they want those details right. So

531
00:26:00,080 --> 00:26:03,000
Speaker 1: so a sauna is a task management tool that I

532
00:26:03,119 --> 00:26:05,400
Speaker 1: use at work. Also I think it's sort of commonly

533
00:26:05,680 --> 00:26:07,960
Speaker 1: used it at work. And the idea is that, you know,

534
00:26:08,000 --> 00:26:12,320
Speaker 1: if there's a task so like buy new summer clothes

535
00:26:12,720 --> 00:26:15,320
Speaker 1: for the children, one way you could approach that is,

536
00:26:15,359 --> 00:26:17,800
Speaker 1: you know, you could just say, like send an email like, hey,

537
00:26:17,840 --> 00:26:19,560
Speaker 1: you have to buy new summer clothes. Somebody has to

538
00:26:19,560 --> 00:26:21,800
Speaker 1: buy new summer clothes for the kids. And in a

539
00:26:21,880 --> 00:26:23,760
Speaker 1: task like that, it might be okay to send an email,

540
00:26:23,840 --> 00:26:25,680
Speaker 1: but you know what if there's a lot of discussion

541
00:26:26,160 --> 00:26:28,640
Speaker 1: like well, which you know, what do they need and

542
00:26:28,680 --> 00:26:30,440
Speaker 1: like did you talk to them about it? And did

543
00:26:30,440 --> 00:26:32,359
Speaker 1: you do the review of the existing clothes and do

544
00:26:32,400 --> 00:26:34,400
Speaker 1: we have any leftover clothes or in Penelope they still

545
00:26:34,440 --> 00:26:37,639
Speaker 1: fit thin. You know, like there's some topics and it

546
00:26:38,080 --> 00:26:41,080
Speaker 1: fills up your inbox and you also then maybe you

547
00:26:41,119 --> 00:26:43,080
Speaker 1: can't find it. What if the person starts a new

548
00:26:43,160 --> 00:26:45,680
Speaker 1: thread with like links to the things that they bought,

549
00:26:45,760 --> 00:26:47,720
Speaker 1: that's not very helpful. Then you're like trying to get

550
00:26:47,720 --> 00:26:50,000
Speaker 1: your emails altogether. This is the reason people use task

551
00:26:50,040 --> 00:26:53,960
Speaker 1: management products at work, and so we we use them

552
00:26:53,960 --> 00:26:55,680
Speaker 1: at our house. So we have so we like log

553
00:26:55,720 --> 00:26:57,440
Speaker 1: onto a Sana and you open a task it says

554
00:26:57,480 --> 00:27:00,760
Speaker 1: like review, you know, review summer clothing, and then people

555
00:27:00,840 --> 00:27:02,800
Speaker 1: can comment like I looked it through the clothes and

556
00:27:02,840 --> 00:27:04,720
Speaker 1: she only has two pairs of shorts and you know,

557
00:27:04,800 --> 00:27:07,760
Speaker 1: I'm going to order these shorts and I'm ordering these things.

558
00:27:07,800 --> 00:27:09,760
Speaker 1: And then the other person say, well, what about like

559
00:27:09,880 --> 00:27:11,720
Speaker 1: what about the shorts that I left in the closet

560
00:27:11,760 --> 00:27:14,400
Speaker 1: from last year? And you know, you can like you've

561
00:27:14,440 --> 00:27:16,400
Speaker 1: thread and then when you're done, you can click complete

562
00:27:16,440 --> 00:27:18,359
Speaker 1: and then you feel like you've gotten something done. I

563
00:27:18,400 --> 00:27:21,879
Speaker 1: think I'd really like the complete button. That's a complete comment.

564
00:27:21,960 --> 00:27:23,719
Speaker 1: But I mean, I think this is this is like

565
00:27:23,800 --> 00:27:26,199
Speaker 1: part of the general thing where I think that we

566
00:27:26,240 --> 00:27:29,359
Speaker 1: don't always take the lessons from our work into our house,

567
00:27:29,480 --> 00:27:31,879
Speaker 1: even though sometimes we even though there is like an

568
00:27:31,920 --> 00:27:35,199
Speaker 1: element of management in parenting that is not dissimilar from

569
00:27:35,240 --> 00:27:37,080
Speaker 1: the element of management that you have at work. And

570
00:27:37,119 --> 00:27:40,320
Speaker 1: you guys are actual colleagues though, right, I mean you're yeah,

571
00:27:41,640 --> 00:27:45,080
Speaker 1: we are actual colleagues in the same department. Well, many

572
00:27:45,160 --> 00:27:47,959
Speaker 1: questions about that. I mean, one sort of we do

573
00:27:48,040 --> 00:27:51,040
Speaker 1: have definitely academic listeners and the question of going on

574
00:27:51,080 --> 00:27:54,280
Speaker 1: the job market to gather what that was like, because

575
00:27:54,280 --> 00:27:56,439
Speaker 1: I'm sure that the search was with then you know,

576
00:27:56,600 --> 00:27:58,960
Speaker 1: very sort of different than if it's you know, one

577
00:27:59,000 --> 00:28:02,040
Speaker 1: party doing that. But also then what that leads to

578
00:28:02,040 --> 00:28:04,240
Speaker 1: in terms of how you guys split things in terms

579
00:28:04,280 --> 00:28:06,320
Speaker 1: of parenting. I mean, given that you have again the

580
00:28:06,400 --> 00:28:10,719
Speaker 1: exact same job, that probably changes the discussion versus in

581
00:28:10,760 --> 00:28:14,439
Speaker 1: many couples. Yeah, so I think, you know, in the

582
00:28:14,560 --> 00:28:16,600
Speaker 1: job market case, I think we're sort of lucky to

583
00:28:16,600 --> 00:28:18,719
Speaker 1: be in a field where it is not there are

584
00:28:18,760 --> 00:28:21,240
Speaker 1: a lot of jobs, so I think if you you know,

585
00:28:21,320 --> 00:28:25,840
Speaker 1: and is not uncommon for universities or departments to be

586
00:28:25,920 --> 00:28:29,199
Speaker 1: hiring more than one person in the same hiring cycle.

587
00:28:29,640 --> 00:28:32,119
Speaker 1: And so we did go on the job market together twice,

588
00:28:32,160 --> 00:28:35,240
Speaker 1: once for junior jobs and once for senior jobs. And

589
00:28:35,640 --> 00:28:38,080
Speaker 1: you know it like getting a job is stressful, getting

590
00:28:38,080 --> 00:28:40,320
Speaker 1: two jobs is more stressful. And I think there are

591
00:28:40,360 --> 00:28:43,239
Speaker 1: a lot of particularly in the senior job search, there

592
00:28:43,240 --> 00:28:46,720
Speaker 1: were some you know, trade offs between what was best

593
00:28:46,760 --> 00:28:48,600
Speaker 1: for each person, but I think in the end they

594
00:28:48,640 --> 00:28:51,000
Speaker 1: worked out it worked at good and we're lucky to

595
00:28:51,000 --> 00:28:52,120
Speaker 1: be in the same place and in a place that

596
00:28:52,160 --> 00:28:54,600
Speaker 1: we really that we really like. It's been super supportive.

597
00:28:55,120 --> 00:28:58,680
Speaker 1: I think splitting stuff in your house is is tricky.

598
00:28:59,320 --> 00:29:03,000
Speaker 1: I do more more parenting than my husband does, and

599
00:29:03,400 --> 00:29:07,240
Speaker 1: I think that is mostly taste. Actually, so I think

600
00:29:07,280 --> 00:29:09,920
Speaker 1: that that is mostly that I like it, not that

601
00:29:10,000 --> 00:29:11,560
Speaker 1: I like it more that it's not the way I

602
00:29:11,600 --> 00:29:13,440
Speaker 1: would put it, but that I have a higher taste

603
00:29:13,480 --> 00:29:16,520
Speaker 1: for time with the kids, like more hours of the day.

604
00:29:17,080 --> 00:29:19,440
Speaker 1: And then you know, so, but I think that we

605
00:29:19,440 --> 00:29:24,360
Speaker 1: we try to split things, like you know, who's responsible

606
00:29:24,440 --> 00:29:27,200
Speaker 1: if somebody is if the childcare is sick. We try

607
00:29:27,240 --> 00:29:29,880
Speaker 1: to we try to balance that a little bit. And

608
00:29:30,040 --> 00:29:31,920
Speaker 1: you know, the other thing that happens is that like

609
00:29:31,960 --> 00:29:35,200
Speaker 1: sometimes one person is really is more busy than the other.

610
00:29:35,440 --> 00:29:37,600
Speaker 1: So the last several weeks, as I have been promoting

611
00:29:37,600 --> 00:29:39,720
Speaker 1: this book, I have been super busy and I have

612
00:29:39,800 --> 00:29:41,440
Speaker 1: been away a lot, and he has been doing a

613
00:29:41,440 --> 00:29:44,320
Speaker 1: lot of additional additional parenting. And he learned to make

614
00:29:44,360 --> 00:29:49,320
Speaker 1: the lunches, which is not been that successful, but everyone

615
00:29:49,360 --> 00:29:55,640
Speaker 1: has been eating, so it's good. I want to ask

616
00:29:55,640 --> 00:29:58,320
Speaker 1: for details, but I'm going to restrain exactly what I

617
00:29:58,600 --> 00:30:02,240
Speaker 1: want to It's not that complicated to make the lunches.

618
00:30:02,280 --> 00:30:05,200
Speaker 1: But my kids I have my kids are unfortunately very

619
00:30:05,320 --> 00:30:08,520
Speaker 1: very specific about what they want. And so the first

620
00:30:08,520 --> 00:30:10,160
Speaker 1: time I would just tell you, like the first time

621
00:30:10,160 --> 00:30:11,720
Speaker 1: I did it, he spent forty five minutes in the

622
00:30:11,760 --> 00:30:13,760
Speaker 1: morning making the lunches. Okay, I just it's not like

623
00:30:13,880 --> 00:30:16,440
Speaker 1: a displayed Vento box. Okay, it's a small amount of

624
00:30:16,480 --> 00:30:20,040
Speaker 1: like frozen vegetables and a bagel. And and my daughter

625
00:30:20,120 --> 00:30:23,000
Speaker 1: came home and she was like, Mom, he put way

626
00:30:23,040 --> 00:30:25,000
Speaker 1: too much jam. Didn't you tell him not to put

627
00:30:25,040 --> 00:30:26,280
Speaker 1: a lot of jam on the bil? I was like, yeah,

628
00:30:26,280 --> 00:30:27,840
Speaker 1: I actually did. I put in the notes don't put

629
00:30:27,880 --> 00:30:29,880
Speaker 1: a lot of jam on the bagel. And then she's like,

630
00:30:30,200 --> 00:30:31,400
Speaker 1: you have to look at this. You have to look

631
00:30:31,440 --> 00:30:32,800
Speaker 1: at this. And she like takes out her thing and

632
00:30:32,840 --> 00:30:36,000
Speaker 1: she's like wiped off the jam with her with her napkin.

633
00:30:36,040 --> 00:30:38,120
Speaker 1: And then she's like saved the nag. But she can

634
00:30:38,280 --> 00:30:42,800
Speaker 1: tell me it was like too much jam, too much jam,

635
00:30:42,880 --> 00:30:45,960
Speaker 1: you know, too much jam. He didn't read the notes.

636
00:30:47,320 --> 00:30:50,960
Speaker 1: He did read? Was that an enough? No? No, just

637
00:30:51,000 --> 00:30:53,160
Speaker 1: that I just that was just and and I had

638
00:30:53,160 --> 00:30:56,880
Speaker 1: written I emailed him that clearly in the task management software.

639
00:30:56,960 --> 00:30:58,320
Speaker 1: Yeah it needed to be No, he needed to be

640
00:30:58,320 --> 00:31:02,280
Speaker 1: able to provide feedback. It's totally right, exactly well and

641
00:31:02,920 --> 00:31:04,959
Speaker 1: just to I mean, one of the things that I

642
00:31:05,000 --> 00:31:06,680
Speaker 1: loved it the last chapter you talk about some of

643
00:31:06,680 --> 00:31:09,320
Speaker 1: the best parenting advice you ever got, which was from

644
00:31:09,800 --> 00:31:13,240
Speaker 1: your pediatrician when you guys were in Chicago. Maybe you

645
00:31:13,240 --> 00:31:15,560
Speaker 1: could tell listeners that story. You were you were very

646
00:31:15,560 --> 00:31:17,680
Speaker 1: worked up about, you know, a trip to France you

647
00:31:17,680 --> 00:31:20,719
Speaker 1: were taking with with your daughter. Yeah, so we uh

648
00:31:21,040 --> 00:31:24,200
Speaker 1: so we were we were going to France and my

649
00:31:24,280 --> 00:31:26,440
Speaker 1: daughter was almost two and we had come. We used

650
00:31:26,440 --> 00:31:28,760
Speaker 1: to come to our pediatrician visits with these like long

651
00:31:28,800 --> 00:31:30,719
Speaker 1: lists of questions that we would have. I don't think

652
00:31:30,760 --> 00:31:32,479
Speaker 1: with my second kid, I never have any questions. Can

653
00:31:32,680 --> 00:31:35,200
Speaker 1: be all these questions, And in this one it was like, Okay,

654
00:31:35,440 --> 00:31:37,760
Speaker 1: we're going to France and this place that we're going

655
00:31:37,800 --> 00:31:40,400
Speaker 1: and has a lot of bees, and I'm she's never

656
00:31:40,440 --> 00:31:42,440
Speaker 1: been sung, and I'm afraid that she's gonna get stung

657
00:31:42,760 --> 00:31:44,640
Speaker 1: and if she gets stung, we're kind of far. It's

658
00:31:44,680 --> 00:31:46,960
Speaker 1: really isolated, and like what are we going to do?

659
00:31:47,120 --> 00:31:49,120
Speaker 1: And like what you know, like should I take an EpiPen?

660
00:31:49,200 --> 00:31:51,160
Speaker 1: Should we test her in advance? I had this whole

661
00:31:51,160 --> 00:31:53,400
Speaker 1: like series of different plans that we like, different levels

662
00:31:53,400 --> 00:31:55,680
Speaker 1: of planning that we could engage in to deal with

663
00:31:55,720 --> 00:31:58,640
Speaker 1: this major problem. And I sort of finished my like

664
00:31:58,720 --> 00:32:02,040
Speaker 1: hyped up story and doctor Lee is like a very

665
00:32:02,040 --> 00:32:05,200
Speaker 1: calm person. She just looks at me and she sort

666
00:32:05,200 --> 00:32:08,280
Speaker 1: of nods her head and she's like, yeah, I would

667
00:32:08,320 --> 00:32:11,080
Speaker 1: just try not to think about that, And it was

668
00:32:11,160 --> 00:32:13,840
Speaker 1: like it was it's sort of that moment where you realize,

669
00:32:13,880 --> 00:32:17,400
Speaker 1: like I am being insane, like this is you know,

670
00:32:17,440 --> 00:32:19,800
Speaker 1: and she's edited in like a super nice way. She

671
00:32:19,840 --> 00:32:21,640
Speaker 1: doesn't like, get out of my office. You're weird, though,

672
00:32:21,920 --> 00:32:24,320
Speaker 1: But I think it did highlight and I think about

673
00:32:24,360 --> 00:32:27,800
Speaker 1: that all the time in these points of parenting where

674
00:32:28,080 --> 00:32:31,840
Speaker 1: you're just you get really worked off about some little

675
00:32:32,160 --> 00:32:36,240
Speaker 1: tiny thing and it's not important and there's there's nothing

676
00:32:36,280 --> 00:32:38,680
Speaker 1: you can do about it, Like you can't you can't

677
00:32:38,720 --> 00:32:41,680
Speaker 1: control that. There are so many things in your parenting

678
00:32:41,720 --> 00:32:43,840
Speaker 1: that you wish you could control, but you can't, and

679
00:32:43,880 --> 00:32:45,239
Speaker 1: you have to just let it go a little bit.

680
00:32:45,240 --> 00:32:47,560
Speaker 1: And I think that that's I try to think about

681
00:32:47,720 --> 00:32:49,600
Speaker 1: just you know, okay, just try not to think about that,

682
00:32:49,640 --> 00:32:51,240
Speaker 1: and it's probably going to be fine. And if there's

683
00:32:51,280 --> 00:32:53,080
Speaker 1: something bad happens, we'll you know, we'll work on it

684
00:32:53,320 --> 00:32:56,520
Speaker 1: when it happens. That's great advice. Well, I think this

685
00:32:56,520 --> 00:32:59,000
Speaker 1: brings us to our Love of the Week segment where

686
00:32:59,200 --> 00:33:01,680
Speaker 1: we talk about something that's really working out well for

687
00:33:01,800 --> 00:33:04,040
Speaker 1: us this week or that we're enjoying. So Sarah and

688
00:33:04,120 --> 00:33:05,800
Speaker 1: I can go first to Emily so to give you

689
00:33:05,840 --> 00:33:08,360
Speaker 1: time to think about it. So, Sarah, what do you

690
00:33:08,440 --> 00:33:11,440
Speaker 1: have for us this week? Mind sort of a strategy

691
00:33:11,520 --> 00:33:13,400
Speaker 1: which is that if you have an item that you

692
00:33:13,480 --> 00:33:16,400
Speaker 1: need you can, and it's not a terribly expensive item,

693
00:33:16,440 --> 00:33:18,880
Speaker 1: but you need it in different places, should just buy

694
00:33:19,080 --> 00:33:21,840
Speaker 1: multiple of that item. Because I bought my headphones the

695
00:33:21,840 --> 00:33:24,280
Speaker 1: ones i'm wearing now. They're like fifteen bucks, and I

696
00:33:24,320 --> 00:33:26,040
Speaker 1: would always scramble for them when we had to record

697
00:33:26,080 --> 00:33:27,760
Speaker 1: our podcast, and I was like, why don't I just

698
00:33:27,840 --> 00:33:31,040
Speaker 1: buy one and keep it with my microphone? So yes,

699
00:33:31,120 --> 00:33:36,400
Speaker 1: I'm in love with the convenient headphones. Mine is a

700
00:33:36,440 --> 00:33:39,320
Speaker 1: statistical one, which was I was reminded of with reading

701
00:33:39,400 --> 00:33:41,760
Speaker 1: Emily's book when you talk about so you know, we

702
00:33:41,760 --> 00:33:43,960
Speaker 1: always get wrapped up in these studies where like you know,

703
00:33:44,000 --> 00:33:46,640
Speaker 1: it doubles your risk of whatever to do this and

704
00:33:46,680 --> 00:33:49,280
Speaker 1: you're looking at it, it's often extremely small numbers, like

705
00:33:49,320 --> 00:33:52,400
Speaker 1: it goes from you know, point oh one in one

706
00:33:52,480 --> 00:33:55,320
Speaker 1: thousand to point oh two in one thousand. So yes,

707
00:33:55,360 --> 00:34:00,000
Speaker 1: it did double, but you know we're still talking relatively small. Absolutely,

708
00:34:00,440 --> 00:34:02,640
Speaker 1: So there was this fascinating number I saw in terms

709
00:34:02,640 --> 00:34:07,880
Speaker 1: of health economics, the number needed to treat I love that, yeah,

710
00:34:08,040 --> 00:34:11,800
Speaker 1: because how many people would have to do this intervention

711
00:34:11,880 --> 00:34:15,400
Speaker 1: in order to prevent one adverse event? And so you

712
00:34:15,440 --> 00:34:17,040
Speaker 1: looked at it in terms of, like, you know, how

713
00:34:17,080 --> 00:34:19,279
Speaker 1: many families would have to co sleep for there to

714
00:34:19,320 --> 00:34:23,160
Speaker 1: be one additional adverse event. It was something like ten thousand.

715
00:34:23,360 --> 00:34:26,360
Speaker 1: I mean it's not a small number, and you know,

716
00:34:26,440 --> 00:34:29,560
Speaker 1: same as like various drug interventions, like you know, okay,

717
00:34:29,920 --> 00:34:32,960
Speaker 1: fifty thousand people have to take this drug over and

718
00:34:33,000 --> 00:34:36,520
Speaker 1: over again and we will prevent one incidence of this.

719
00:34:36,640 --> 00:34:40,879
Speaker 1: And it's just a totally different statistic and makes it

720
00:34:41,080 --> 00:34:43,880
Speaker 1: so much less likely that you will go with whatever

721
00:34:43,960 --> 00:34:46,640
Speaker 1: that is versus saying, oh, it doubles your risk. So

722
00:34:47,160 --> 00:34:50,440
Speaker 1: I enjoy looking at it from that side as well.

723
00:34:50,680 --> 00:34:53,799
Speaker 1: So I will tell you the sort of fun thing

724
00:34:53,840 --> 00:34:56,720
Speaker 1: that is happening, a fun, nice thing that made me happy,

725
00:34:56,760 --> 00:34:59,400
Speaker 1: which is that my daughter has been asking for a dog,

726
00:35:00,200 --> 00:35:04,160
Speaker 1: which I don't want right now because there's a lot

727
00:35:04,160 --> 00:35:05,799
Speaker 1: of other things, and she asks for it every day

728
00:35:05,840 --> 00:35:09,480
Speaker 1: and I find it, you know, annoying, and so I've

729
00:35:09,480 --> 00:35:11,520
Speaker 1: told her to ask my husband, and so she started

730
00:35:11,520 --> 00:35:15,879
Speaker 1: asking him r every day, and then yesterday he told

731
00:35:15,920 --> 00:35:18,960
Speaker 1: the kids that instead we could get venus fly traps,

732
00:35:19,520 --> 00:35:23,480
Speaker 1: and they were incredibly excited. And it's like taken on,

733
00:35:23,640 --> 00:35:26,080
Speaker 1: I would say, it's it's about as exciting as a

734
00:35:26,120 --> 00:35:28,840
Speaker 1: dog would be. And they're making plans for their venus

735
00:35:28,840 --> 00:35:30,760
Speaker 1: fly traps, and a venus fly traps just a plant.

736
00:35:30,880 --> 00:35:33,120
Speaker 1: It's just a plant, and so you know, it's gonna

737
00:35:33,160 --> 00:35:35,160
Speaker 1: we're just gonna have it. And it's not a dog,

738
00:35:35,239 --> 00:35:36,640
Speaker 1: and I'm not gonna have to do anything to it.

739
00:35:37,040 --> 00:35:38,839
Speaker 1: And I'm just really pleased that my kids are still

740
00:35:38,880 --> 00:35:42,040
Speaker 1: young enough that they can be dissuaded from their desire

741
00:35:42,120 --> 00:35:45,480
Speaker 1: for a dog by a plant. That's awesome. And yes,

742
00:35:45,560 --> 00:35:47,160
Speaker 1: that to me, I would definitely go for the venus

743
00:35:47,160 --> 00:35:50,160
Speaker 1: fly trap over the dog. We've had one before. They

744
00:35:50,200 --> 00:35:52,200
Speaker 1: actually they keep, you know, they catch. That's good and

745
00:35:52,200 --> 00:35:54,520
Speaker 1: that's a good thing to help. Yeah, amazing, Well, Emily,

746
00:35:54,560 --> 00:35:56,319
Speaker 1: thanks so much for coming on the program. We were

747
00:35:56,320 --> 00:35:59,120
Speaker 1: so glad you could make the time to do this. Yeah,

748
00:35:59,160 --> 00:36:01,480
Speaker 1: thank you guys so much for there and so for

749
00:36:01,560 --> 00:36:04,920
Speaker 1: our listeners again. Her book is Cribsheet, a data driven

750
00:36:04,960 --> 00:36:08,400
Speaker 1: guide to better more relaxed parenting from Birth to Preschool,

751
00:36:08,400 --> 00:36:13,440
Speaker 1: So please check that out well. Today's question comes from

752
00:36:13,680 --> 00:36:17,080
Speaker 1: somebody who says she worked as a research scientist for

753
00:36:17,120 --> 00:36:19,360
Speaker 1: a decade and then moved into the tech field for

754
00:36:19,440 --> 00:36:22,799
Speaker 1: data analytics about two years ago, and she's asking about

755
00:36:22,800 --> 00:36:26,319
Speaker 1: the transition between academia and industry and how this has

756
00:36:26,360 --> 00:36:28,359
Speaker 1: played out in how she uses her time. She says,

757
00:36:28,400 --> 00:36:31,919
Speaker 1: in academia, years are spent gathering information, then a few

758
00:36:31,960 --> 00:36:35,040
Speaker 1: months summarizing culminating in a paper or grant. As I

759
00:36:35,080 --> 00:36:37,600
Speaker 1: switched into working at tech companies, most of the deliverables

760
00:36:37,640 --> 00:36:39,840
Speaker 1: are based on a four to six week work cycle,

761
00:36:40,200 --> 00:36:43,680
Speaker 1: and she's found that she over researches and over prepares,

762
00:36:44,000 --> 00:36:46,480
Speaker 1: so once, for instance, she put together a technical report,

763
00:36:46,520 --> 00:36:49,320
Speaker 1: then spent days making an hour long presentation to present

764
00:36:49,360 --> 00:36:51,480
Speaker 1: to the team, only to find out I'd only have

765
00:36:51,560 --> 00:36:53,640
Speaker 1: the floor for the first fifteen minutes of a meeting.

766
00:36:54,160 --> 00:36:56,719
Speaker 1: Nobody speaks for an hour straight. Ever, I was used

767
00:36:56,760 --> 00:36:58,919
Speaker 1: to filling an hour usually by preparing for one hundred

768
00:36:58,960 --> 00:37:01,040
Speaker 1: and twenty five percent of that seminar then cutting down

769
00:37:01,080 --> 00:37:04,360
Speaker 1: to size. She says, aside from asking questions about the

770
00:37:04,400 --> 00:37:07,160
Speaker 1: size of deliberals, which she figured out on her own,

771
00:37:07,320 --> 00:37:09,319
Speaker 1: as would be a good thing to ask, how do

772
00:37:09,360 --> 00:37:12,839
Speaker 1: I avoid exhaustively exploring many branches, even if I can

773
00:37:12,880 --> 00:37:17,040
Speaker 1: sometimes make an exciting paradigm changing finding. She mentioned that

774
00:37:17,160 --> 00:37:19,480
Speaker 1: something I had written in the past. She mentioned that

775
00:37:19,480 --> 00:37:21,440
Speaker 1: as you progressed as a writer, you learn to scale

776
00:37:21,480 --> 00:37:23,720
Speaker 1: your research and work to the size of the article.

777
00:37:23,760 --> 00:37:26,160
Speaker 1: So if somebody assigns me a four thousand word article,

778
00:37:26,480 --> 00:37:28,520
Speaker 1: I do a different amount of research than if they

779
00:37:28,560 --> 00:37:30,799
Speaker 1: assign me an eight hundred word article, and that you

780
00:37:30,840 --> 00:37:33,520
Speaker 1: sort of learn how much fits this space. She says, well,

781
00:37:33,560 --> 00:37:39,080
Speaker 1: what helped you learn that skill? So time, trial and error.

782
00:37:39,120 --> 00:37:42,680
Speaker 1: I don't know that there's any better answer to that.

783
00:37:43,120 --> 00:37:45,840
Speaker 1: Time estimation is hard and general. I mean, this is

784
00:37:45,880 --> 00:37:48,160
Speaker 1: not just a challenge for people who switch careers, though

785
00:37:48,200 --> 00:37:50,759
Speaker 1: it's definitely a challenge for people who switch careers. But

786
00:37:50,800 --> 00:37:53,759
Speaker 1: there are people who drive to work every single day,

787
00:37:53,840 --> 00:37:55,680
Speaker 1: and in their mind it takes thirty minutes, and it

788
00:37:55,719 --> 00:37:58,520
Speaker 1: doesn't it takes forty five, because in their mind it's like, well,

789
00:37:58,600 --> 00:38:01,200
Speaker 1: once it took thirty, Like I guess when you drove

790
00:38:01,239 --> 00:38:03,680
Speaker 1: to work at five am or something, But every other

791
00:38:03,760 --> 00:38:06,000
Speaker 1: time you're driving at eight when there's more people on

792
00:38:06,040 --> 00:38:08,200
Speaker 1: the road, and it actually takes forty five minutes, and

793
00:38:08,480 --> 00:38:11,680
Speaker 1: you'd be amazed how many people haven't quite figured this out. So,

794
00:38:11,880 --> 00:38:13,480
Speaker 1: I mean, one of the reasons I do tell people

795
00:38:13,560 --> 00:38:16,200
Speaker 1: to track time is that it gives you a better

796
00:38:16,239 --> 00:38:18,759
Speaker 1: sense of how long things actually take. Like it's very

797
00:38:18,800 --> 00:38:21,360
Speaker 1: difficult to look at a spreadsheet where every day it

798
00:38:21,360 --> 00:38:22,960
Speaker 1: takes you forty five minutes, see it's to work, and

799
00:38:23,000 --> 00:38:25,200
Speaker 1: continue to tell yourself that it takes thirty. I mean,

800
00:38:25,200 --> 00:38:27,239
Speaker 1: you could try, but it's a lot more challenging to

801
00:38:27,280 --> 00:38:30,360
Speaker 1: have that cognitive dissonance there. So first, just you know,

802
00:38:30,400 --> 00:38:33,040
Speaker 1: paying attention to all these things that you'll learn, like

803
00:38:33,080 --> 00:38:35,040
Speaker 1: how much time do things take? Like how much time

804
00:38:35,040 --> 00:38:37,120
Speaker 1: does it take me to do a fifteen minute presentation

805
00:38:37,280 --> 00:38:40,920
Speaker 1: versus an hour presentation? How much time does it take

806
00:38:41,000 --> 00:38:45,200
Speaker 1: me to get you know, the amount of research that

807
00:38:45,239 --> 00:38:48,279
Speaker 1: would fit in this amount of space. But there's also

808
00:38:48,360 --> 00:38:50,840
Speaker 1: just a difference between being well prepared and over prepared.

809
00:38:50,840 --> 00:38:53,200
Speaker 1: And well prepared is great, like everybody loves people who

810
00:38:53,239 --> 00:38:55,799
Speaker 1: are well prepared. But since she's a scientist, I think

811
00:38:55,840 --> 00:38:57,960
Speaker 1: it might help to think in terms of probability. So

812
00:38:58,040 --> 00:39:01,000
Speaker 1: well prepared is probably thinking about in the ten to

813
00:39:01,120 --> 00:39:04,680
Speaker 1: ninetieth percentile of what might come up, right, So think

814
00:39:04,719 --> 00:39:08,520
Speaker 1: about you know, you know, maybe probably the guy will

815
00:39:08,560 --> 00:39:10,920
Speaker 1: ask me this. He probably won't, but there's a reasonable

816
00:39:10,960 --> 00:39:13,279
Speaker 1: likelihood he will ask me this. Let me come up

817
00:39:13,320 --> 00:39:15,319
Speaker 1: with that. But then you don't need to go out

818
00:39:15,360 --> 00:39:18,799
Speaker 1: to the point one percent probability of like, you know, well,

819
00:39:18,800 --> 00:39:21,960
Speaker 1: what if we, you know, launch a mission to Mars,

820
00:39:22,040 --> 00:39:24,520
Speaker 1: like how will our thing look? Then you know, that's

821
00:39:24,520 --> 00:39:26,799
Speaker 1: that's just not something you really need to concern yourself with.

822
00:39:27,400 --> 00:39:29,520
Speaker 1: So it's just part of part of the learning curve.

823
00:39:29,560 --> 00:39:32,719
Speaker 1: And then recognizing that you don't have to do everything.

824
00:39:33,000 --> 00:39:34,880
Speaker 1: Think about you know, ten to ninety percent. What do

825
00:39:34,880 --> 00:39:38,320
Speaker 1: you think, Sarah? Yeah, you know, I know this person

826
00:39:38,360 --> 00:39:40,760
Speaker 1: who wrote in kind of attributed to a job change,

827
00:39:40,840 --> 00:39:42,719
Speaker 1: and you gave her that grace. But I have a

828
00:39:42,960 --> 00:39:45,600
Speaker 1: feeling just from the way she writes this, then it

829
00:39:45,640 --> 00:39:49,120
Speaker 1: may be just her nature to do a little more

830
00:39:49,480 --> 00:39:52,920
Speaker 1: than she needs to be doing, and we actually this

831
00:39:53,080 --> 00:39:54,480
Speaker 1: is giving around in time a bit. But we did

832
00:39:54,520 --> 00:39:56,719
Speaker 1: have a recent guest talk about how you know, over

833
00:39:56,760 --> 00:40:00,520
Speaker 1: conscientiousness can be a problem with women and as opposed

834
00:40:00,520 --> 00:40:03,120
Speaker 1: to men, Like there are actually studies that show that, like,

835
00:40:03,600 --> 00:40:07,200
Speaker 1: particularly in school, like, there's a tendency towards putting a

836
00:40:07,239 --> 00:40:11,080
Speaker 1: lot more in for incremental gains. You have more girls

837
00:40:11,120 --> 00:40:13,719
Speaker 1: trying to get like the ninety nine percent, where as

838
00:40:13,719 --> 00:40:15,439
Speaker 1: a boy might get a ninety five. I'd be like, great,

839
00:40:15,480 --> 00:40:18,960
Speaker 1: onto the next thing. So I guess I would caution

840
00:40:19,120 --> 00:40:22,480
Speaker 1: this person to try to challenge herself if she really

841
00:40:22,600 --> 00:40:26,120
Speaker 1: tends to fit in that overdoing it place, which again

842
00:40:26,160 --> 00:40:27,799
Speaker 1: I don't know for sure, but I sort of get

843
00:40:27,840 --> 00:40:30,440
Speaker 1: that sense. Even in the way she writes this email,

844
00:40:30,440 --> 00:40:33,000
Speaker 1: like there are so many specifics in it, it was

845
00:40:33,080 --> 00:40:36,799
Speaker 1: like like like I probably would have not spend as

846
00:40:36,880 --> 00:40:40,000
Speaker 1: much time writing the email as she did. Then figure

847
00:40:40,000 --> 00:40:42,680
Speaker 1: out how good the job needs to be, and maybe

848
00:40:42,680 --> 00:40:44,920
Speaker 1: go a little beyond that if that feels helps her

849
00:40:44,920 --> 00:40:46,799
Speaker 1: feel like she meets her standard. But try to make

850
00:40:46,840 --> 00:40:49,400
Speaker 1: sure that she's not going far far beyond that, because

851
00:40:49,400 --> 00:40:52,040
Speaker 1: that could be crippling and that could be inefficient and

852
00:40:52,120 --> 00:40:55,359
Speaker 1: nobody really gains anything. So I guess I think as

853
00:40:55,400 --> 00:40:57,560
Speaker 1: women and as people in general, if that's your tendency,

854
00:40:57,600 --> 00:41:00,160
Speaker 1: you probably need to be aware of it. Yeah, you

855
00:41:00,239 --> 00:41:02,640
Speaker 1: might look at what your colleagues are doing too to

856
00:41:02,719 --> 00:41:04,680
Speaker 1: pay attention to that, and you can decide to be

857
00:41:04,800 --> 00:41:06,960
Speaker 1: a little better than that in terms of preparation, but

858
00:41:07,000 --> 00:41:09,799
Speaker 1: not you know, in an entirely different realm, because then

859
00:41:09,840 --> 00:41:12,360
Speaker 1: it's just you know, wasting your time. And you know,

860
00:41:12,400 --> 00:41:14,239
Speaker 1: when you spend too much time on one thing, there

861
00:41:14,239 --> 00:41:17,080
Speaker 1: won't be time available to do something else because again,

862
00:41:17,120 --> 00:41:19,120
Speaker 1: there are only twenty four hours in a day. So

863
00:41:19,280 --> 00:41:22,440
Speaker 1: when you've massively over prepared for the hour long presentation

864
00:41:22,520 --> 00:41:25,440
Speaker 1: that's only fifteen minutes anyway, well, that means you weren't

865
00:41:25,480 --> 00:41:28,200
Speaker 1: doing something else. No, maybe it was sleeping, maybe it

866
00:41:28,239 --> 00:41:29,920
Speaker 1: was hanging out with your family and relaxing, but it's

867
00:41:29,920 --> 00:41:33,000
Speaker 1: also probably something work related you weren't getting to as well.

868
00:41:33,480 --> 00:41:35,080
Speaker 1: So you kind of got to keep your eyes on

869
00:41:35,120 --> 00:41:39,040
Speaker 1: the big prize and recognize that everything has a limit.

870
00:41:39,560 --> 00:41:42,320
Speaker 1: In general, as we say, done is better than perfect,

871
00:41:42,400 --> 00:41:45,640
Speaker 1: because there is no perfect without being done, and better

872
00:41:45,680 --> 00:41:48,759
Speaker 1: is the enemy of good. Better, Yes, exactly all right, Well,

873
00:41:48,800 --> 00:41:50,720
Speaker 1: this has been best of both worlds. We've been talking

874
00:41:50,719 --> 00:41:55,440
Speaker 1: with Emily Auster about data driven approaches to raising children.

875
00:41:55,719 --> 00:41:57,680
Speaker 1: We'll be back next week with more on making work

876
00:41:57,680 --> 00:42:03,520
Speaker 1: in life fit together. Thanks for listening. You can find

877
00:42:03,560 --> 00:42:06,960
Speaker 1: me Sarah at the shoebox dot com or at the

878
00:42:07,200 --> 00:42:11,200
Speaker 1: Underscore Shoebox on Instagram, and you can find me Laura

879
00:42:11,600 --> 00:42:14,920
Speaker 1: at Laura vandercam dot com. This has been the best

880
00:42:14,960 --> 00:42:18,480
Speaker 1: of both worlds podcasts. Please join us next time for

881
00:42:18,600 --> 00:42:20,960
Speaker 1: more on making work and life work together.