1 00:00:15,410 --> 00:00:15,890 Speaker 1: Pushkin. 2 00:00:18,650 --> 00:00:21,370 Speaker 2: Twenty years ago, I was working on the final draft 3 00:00:21,370 --> 00:00:25,250 Speaker 2: of my first book, The Undercover Economist, when my publisher 4 00:00:25,410 --> 00:00:28,690 Speaker 2: sent me a worried email. There's this new book about 5 00:00:28,730 --> 00:00:31,210 Speaker 2: to come out. He said, it's also about economics, and 6 00:00:31,250 --> 00:00:32,850 Speaker 2: it looks like it's going to scoop you, and that 7 00:00:32,930 --> 00:00:35,610 Speaker 2: might be a problem, But I wasn't worried. If the 8 00:00:35,610 --> 00:00:38,290 Speaker 2: book was a success, it would just make economics seem cool. 9 00:00:39,130 --> 00:00:43,090 Speaker 2: And anyway, how big a success could an economics book be. 10 00:00:43,890 --> 00:00:45,410 Speaker 2: At the time, I lived in the US, and I've 11 00:00:45,450 --> 00:00:48,050 Speaker 2: been doing some writing for the Financial Times, so I 12 00:00:48,130 --> 00:00:51,930 Speaker 2: pitched the idea of flying to Chicago to interview the 13 00:00:51,970 --> 00:00:54,970 Speaker 2: economist whose work was at the heart of this book. 14 00:00:55,330 --> 00:00:58,570 Speaker 2: It was my first ever interview, and little did either 15 00:00:58,610 --> 00:01:01,530 Speaker 2: of us know that his work was about to become 16 00:01:01,570 --> 00:01:04,610 Speaker 2: one of the biggest books of the decade. I walked 17 00:01:04,650 --> 00:01:07,850 Speaker 2: away from the interview with a copy to Tim the 18 00:01:07,930 --> 00:01:11,130 Speaker 2: first book I've ever signed. You can probably get at 19 00:01:11,210 --> 00:01:15,850 Speaker 2: least nine dollars fifty on eBay. Steve Levitt, the book, 20 00:01:16,010 --> 00:01:19,090 Speaker 2: co authored with Stephen J. Dubner, was called Free Economics 21 00:01:19,290 --> 00:01:22,010 Speaker 2: and a Combined Pop Culture with Economics. It was a 22 00:01:22,050 --> 00:01:26,010 Speaker 2: global smash hit. It spawned several sequels, a documentary film, 23 00:01:26,050 --> 00:01:29,370 Speaker 2: and a podcast, and twenty years after our first meeting, 24 00:01:29,530 --> 00:01:34,290 Speaker 2: I am reunited with Steve Levitt. Steve, welcome to Cautionary Tales. 25 00:01:35,010 --> 00:01:37,530 Speaker 1: A great to talk to you, Tim. We both had 26 00:01:37,730 --> 00:01:39,610 Speaker 1: a pretty good run for the last twenty years. 27 00:01:39,810 --> 00:01:42,130 Speaker 2: Well you've had an even better run than me, but yeah, 28 00:01:42,370 --> 00:01:45,370 Speaker 2: good enough. I can't believe it's been twenty years. It 29 00:01:45,410 --> 00:01:47,250 Speaker 2: was my first interview, so I was just terrified that 30 00:01:47,290 --> 00:01:49,890 Speaker 2: the digital recorder wouldn't work, and you kept picking it 31 00:01:50,010 --> 00:01:54,410 Speaker 2: up and going, sure, this is working. I was like, please, please, 32 00:01:54,410 --> 00:01:57,450 Speaker 2: don't mess with my tape recorder. And did you really 33 00:01:57,490 --> 00:01:59,850 Speaker 2: not know the book was going to be so big? 34 00:02:00,530 --> 00:02:02,890 Speaker 2: I mean, nobody thought the book would be successful. 35 00:02:03,050 --> 00:02:06,570 Speaker 1: After we signed the contract, I called my dad to celebrate, 36 00:02:07,170 --> 00:02:12,050 Speaker 1: and his only response was, this is completely immoral. I said, 37 00:02:12,090 --> 00:02:14,650 Speaker 1: what are you talking about? He said, it's a moral 38 00:02:14,730 --> 00:02:16,890 Speaker 1: to take the money from the publishers when you and 39 00:02:16,930 --> 00:02:19,010 Speaker 1: I both know no one wants to read the junk 40 00:02:19,050 --> 00:02:22,050 Speaker 1: that you write about. And I think that everyone kind 41 00:02:22,050 --> 00:02:24,410 Speaker 1: of had that view. I mean, Stephen Denner and I 42 00:02:24,490 --> 00:02:26,690 Speaker 1: we both agreed that we could use a little extra cash. 43 00:02:26,890 --> 00:02:29,290 Speaker 1: So when they were willing to overpay us for the book, 44 00:02:29,490 --> 00:02:33,010 Speaker 1: we agreed to write it, but honestly, it was just luck. Really. 45 00:02:33,050 --> 00:02:35,930 Speaker 1: The biggest thing that happened was they got me on 46 00:02:36,250 --> 00:02:40,290 Speaker 1: the Daily Show with John Stewart. And honestly, the way 47 00:02:40,330 --> 00:02:42,930 Speaker 1: they got me on, I know I'm not supped to 48 00:02:42,970 --> 00:02:46,570 Speaker 1: say this, but somebody traded a rent controled apartment to 49 00:02:46,850 --> 00:02:50,690 Speaker 1: someone who worked on Chance show in return for getting 50 00:02:50,690 --> 00:02:53,210 Speaker 1: me on to the show, and that changed everything. It 51 00:02:53,250 --> 00:02:56,010 Speaker 1: was pure graft, you know, that made this thing take off? 52 00:02:56,130 --> 00:02:59,010 Speaker 2: Well, I meant is cool these days? So you were 53 00:02:59,250 --> 00:03:01,810 Speaker 2: as so often, you were ahead of the curve. Although 54 00:03:01,810 --> 00:03:03,250 Speaker 2: I have to say I was on I was on 55 00:03:03,410 --> 00:03:07,090 Speaker 2: John Stewart's show, but I was on the Colbert Report. Yeah, 56 00:03:07,130 --> 00:03:10,410 Speaker 2: with my second book, and I can assure you myself, 57 00:03:10,450 --> 00:03:13,010 Speaker 2: and I don't think anybody trading in any rent control departments. 58 00:03:13,010 --> 00:03:15,690 Speaker 2: But I assure you my second book did not do 59 00:03:15,890 --> 00:03:18,770 Speaker 2: nearly as well as Freakonomics, so I think there was 60 00:03:18,850 --> 00:03:22,330 Speaker 2: more going on than just corruption. But first I need 61 00:03:22,370 --> 00:03:50,250 Speaker 2: to say, I'm Tim Harford and you're listening to cautionary tales, Steve. 62 00:03:50,290 --> 00:03:52,690 Speaker 2: If you can recall the elevator pitch from twenty years ago, 63 00:03:52,730 --> 00:03:54,010 Speaker 2: what was economics all about? 64 00:03:54,530 --> 00:03:59,770 Speaker 1: Yeah, so freakonomics was really my research. Up to that time. 65 00:03:59,810 --> 00:04:03,090 Speaker 1: I had written dozens and dozens of really odd ball 66 00:04:03,450 --> 00:04:07,450 Speaker 1: economics papers, so about how real estate agents rip you 67 00:04:07,490 --> 00:04:12,370 Speaker 1: off because they have bad incentives in the contract, how 68 00:04:12,970 --> 00:04:16,610 Speaker 1: getting the financial records of a crack selling gang and 69 00:04:16,650 --> 00:04:21,090 Speaker 1: looking at the economics of selling drugs and the names 70 00:04:21,090 --> 00:04:22,930 Speaker 1: you give to your kids doesn't expect the life. So 71 00:04:22,970 --> 00:04:24,930 Speaker 1: that's the kind of research I did. Now, of course 72 00:04:24,930 --> 00:04:28,330 Speaker 1: it was very dry and academic, but it was on 73 00:04:28,450 --> 00:04:31,690 Speaker 1: topics that lent themselves to storytelling, and so when I 74 00:04:31,690 --> 00:04:34,370 Speaker 1: got together with Stephen Tumner, we turned all of these 75 00:04:34,890 --> 00:04:38,970 Speaker 1: academic papers I had written into a series of interwoven 76 00:04:39,250 --> 00:04:43,570 Speaker 1: stories that were fun and exciting and really touching on 77 00:04:43,730 --> 00:04:47,610 Speaker 1: pop culture. So in many ways it was a rigorous 78 00:04:48,170 --> 00:04:50,930 Speaker 1: economics book because the ideas and the studies that we're 79 00:04:50,930 --> 00:04:54,730 Speaker 1: writing about were really serious and had the validation of 80 00:04:54,730 --> 00:04:58,090 Speaker 1: having been published in top economics jurnales, but done with 81 00:04:58,210 --> 00:05:03,130 Speaker 1: a tone that nobody had ever really done before. The 82 00:05:03,130 --> 00:05:05,370 Speaker 1: heart and soul of economics is how can you talk 83 00:05:05,410 --> 00:05:11,130 Speaker 1: about economics but as if you were kind of what 84 00:05:11,170 --> 00:05:13,370 Speaker 1: we call like a rogue sorts of rogue way of 85 00:05:13,970 --> 00:05:17,530 Speaker 1: having fun and making fun of a lot of things 86 00:05:17,530 --> 00:05:21,530 Speaker 1: and a lot of people while you're dabbling in big 87 00:05:21,610 --> 00:05:22,690 Speaker 1: economic ideas. 88 00:05:22,970 --> 00:05:26,330 Speaker 2: You may remember, you probably don't. I did actually auction 89 00:05:26,490 --> 00:05:29,570 Speaker 2: that book you signed for me on eBay. I got 90 00:05:29,610 --> 00:05:32,050 Speaker 2: quite a lot more than nine dollars fifty for it. 91 00:05:32,090 --> 00:05:32,650 Speaker 1: Do you remember that? 92 00:05:32,930 --> 00:05:33,010 Speaker 3: No? 93 00:05:33,130 --> 00:05:34,530 Speaker 1: I don't. I don't remember that at all. 94 00:05:34,770 --> 00:05:37,210 Speaker 2: I said, look, I'm going to auction this and give 95 00:05:37,210 --> 00:05:40,490 Speaker 2: them money to charity, and you very nobly said, well, 96 00:05:40,490 --> 00:05:42,930 Speaker 2: whatever they pay, I'll match it, so or'll double the money. 97 00:05:43,730 --> 00:05:46,050 Speaker 3: You know it's coming back to me. Yeah, yeah, it 98 00:05:46,090 --> 00:05:48,250 Speaker 3: wasn't it a few hundred dollars? It was a few hundred. 99 00:05:48,370 --> 00:05:50,490 Speaker 3: I think it was about six hundred dollars. I can't 100 00:05:50,490 --> 00:05:53,850 Speaker 3: remember exactly. Wow, So it was not nothing slightly self interestedly. 101 00:05:53,890 --> 00:05:55,850 Speaker 3: I sold it about the time my book was coming out, 102 00:05:55,850 --> 00:06:00,730 Speaker 3: which was about six months after Freakonomics, obviously, just you know, 103 00:06:00,770 --> 00:06:03,050 Speaker 3: anything to get a little bit of attention. 104 00:06:03,610 --> 00:06:05,610 Speaker 1: And I remember a six poker game too. 105 00:06:06,050 --> 00:06:09,210 Speaker 2: Ah yeah, you. So this is a separate interview the 106 00:06:09,610 --> 00:06:12,170 Speaker 2: Ft the Financial Times. Maybe me go and lose it 107 00:06:12,250 --> 00:06:15,370 Speaker 2: poker to you while trying to interview you. It was 108 00:06:15,810 --> 00:06:17,810 Speaker 2: really really difficult multitask. 109 00:06:17,930 --> 00:06:19,890 Speaker 1: I thought it was good though. I thought that turned 110 00:06:19,890 --> 00:06:20,490 Speaker 1: out really well. 111 00:06:20,530 --> 00:06:23,010 Speaker 2: It was a fun interview, but you took all my money, 112 00:06:23,170 --> 00:06:25,010 Speaker 2: and by the way, the expense claim on that was 113 00:06:25,090 --> 00:06:25,770 Speaker 2: quite difficult. 114 00:06:25,970 --> 00:06:29,050 Speaker 1: That's like when I invited a call girl, a prostitute, 115 00:06:29,050 --> 00:06:31,330 Speaker 1: to come in and guest lecture to my class at 116 00:06:31,330 --> 00:06:34,610 Speaker 1: the University of Chicago on the economics of crime, and 117 00:06:34,930 --> 00:06:36,410 Speaker 1: to get her to come, I had to agree to 118 00:06:36,450 --> 00:06:40,170 Speaker 1: pay her hourly wage. And I will say I did 119 00:06:40,210 --> 00:06:44,010 Speaker 1: not have success billing either the National Science Foundation or 120 00:06:44,050 --> 00:06:47,930 Speaker 1: the University of Chicago six hundred dollars to cover the 121 00:06:47,970 --> 00:06:50,050 Speaker 1: cost of hiring a prostitute to come to teach. 122 00:06:49,810 --> 00:06:52,170 Speaker 2: My class form because of the amount, or because of 123 00:06:52,290 --> 00:06:53,210 Speaker 2: who it was being paid to. 124 00:06:53,610 --> 00:06:56,890 Speaker 1: It was because I didn't even try. Could you imagine, 125 00:06:57,530 --> 00:07:01,490 Speaker 1: I can imagine. I can imagine the National Science Foundation 126 00:07:01,890 --> 00:07:06,530 Speaker 1: pays a freaking author six hundred dollars for prostitute. 127 00:07:06,850 --> 00:07:10,330 Speaker 2: With the benefit of twenty years of high any sense 128 00:07:10,370 --> 00:07:13,130 Speaker 2: of why the book was such a phenomenon. 129 00:07:14,490 --> 00:07:17,570 Speaker 1: It really was a case of being in the right 130 00:07:17,610 --> 00:07:20,930 Speaker 1: place at the right time. A combination of that and 131 00:07:21,370 --> 00:07:24,330 Speaker 1: the fact that Dubner and I didn't expect anyone to 132 00:07:24,370 --> 00:07:28,090 Speaker 1: ever read the book, so we wrote it in a 133 00:07:28,130 --> 00:07:32,810 Speaker 1: way that was very freeing because we started we started 134 00:07:32,810 --> 00:07:35,410 Speaker 1: out trying to write a regular book, and it was terrible, 135 00:07:35,450 --> 00:07:37,170 Speaker 1: and we knew it was terrible, and we were almost 136 00:07:37,210 --> 00:07:39,250 Speaker 1: going to write it anyway, because well, that's the way 137 00:07:39,290 --> 00:07:41,370 Speaker 1: you write books. And then we had the good sense 138 00:07:41,410 --> 00:07:43,250 Speaker 1: to sit back and look, since no one's going to 139 00:07:43,290 --> 00:07:45,090 Speaker 1: read it, why don't we try to write a book 140 00:07:45,090 --> 00:07:47,890 Speaker 1: that's fun for us. Dubnor is an amazing writer, and 141 00:07:47,930 --> 00:07:50,530 Speaker 1: I think he and I had the right tone. But 142 00:07:50,610 --> 00:07:52,490 Speaker 1: Dubnor had written this piece about me in the New 143 00:07:52,570 --> 00:07:57,250 Speaker 1: York Times, and he had cast me as this genius 144 00:07:57,290 --> 00:08:00,250 Speaker 1: srlock Homes of economics. You give him a pile of 145 00:08:00,330 --> 00:08:02,610 Speaker 1: data and he'll go bang away in his computer for 146 00:08:02,610 --> 00:08:04,610 Speaker 1: two hours and solve any problem. I mean, it was 147 00:08:04,690 --> 00:08:08,130 Speaker 1: completely and totally artificial, but people loved it. 148 00:08:08,130 --> 00:08:09,810 Speaker 2: It was a great piece. It was a read regret. 149 00:08:09,890 --> 00:08:11,050 Speaker 2: That was a great piece of writing. 150 00:08:11,410 --> 00:08:15,530 Speaker 1: So we just basically have been milking that completely fake 151 00:08:16,210 --> 00:08:18,450 Speaker 1: image he built about me for the last twenty years, 152 00:08:18,810 --> 00:08:20,850 Speaker 1: and it was a really easy voice for both of 153 00:08:20,930 --> 00:08:23,170 Speaker 1: us to fit into. And I think that's part of it. 154 00:08:23,250 --> 00:08:25,810 Speaker 1: I don't know why people were so eager to read it. Now. 155 00:08:25,970 --> 00:08:29,530 Speaker 1: What was really funny for me is we wrote that book, 156 00:08:29,650 --> 00:08:31,690 Speaker 1: and then, of course, if you have that kind of success, 157 00:08:31,730 --> 00:08:34,330 Speaker 1: you think, well, let's write a second book. And what 158 00:08:34,370 --> 00:08:36,850 Speaker 1: we thought is, oh, people loved our stories, and so 159 00:08:37,090 --> 00:08:38,370 Speaker 1: if we can come up with a whole bunch of 160 00:08:38,410 --> 00:08:41,330 Speaker 1: good stories, people will love the next book. But people 161 00:08:41,370 --> 00:08:43,650 Speaker 1: didn't really like it, and people didn't really buy it 162 00:08:43,730 --> 00:08:46,370 Speaker 1: nearly this much, and I came to realize it wasn't 163 00:08:46,410 --> 00:08:49,770 Speaker 1: actually the stories that people wanted. It was more the attitude, 164 00:08:50,210 --> 00:08:52,770 Speaker 1: and it was an attitude that people weren't used to, 165 00:08:52,810 --> 00:08:57,690 Speaker 1: a sassy kind of exploration of possibilities people hadn't thought about, 166 00:08:58,130 --> 00:09:01,290 Speaker 1: and getting that once was plenty, people didn't really need 167 00:09:01,330 --> 00:09:03,530 Speaker 1: any more of it, and so we had the good 168 00:09:03,570 --> 00:09:07,090 Speaker 1: sense eventually to stop writing books because I think of anything, 169 00:09:07,130 --> 00:09:12,850 Speaker 1: maybe reading Freakonomics people feel smart because it was really 170 00:09:12,850 --> 00:09:15,370 Speaker 1: easy to read what was written at the seventh grade level, 171 00:09:15,730 --> 00:09:19,410 Speaker 1: but it kind of invited you inside of academia in 172 00:09:19,450 --> 00:09:24,570 Speaker 1: a way that was more fun and unusual compared to 173 00:09:24,610 --> 00:09:27,050 Speaker 1: other books. Like nobody really knows why it worked. I'm sure, 174 00:09:27,050 --> 00:09:29,290 Speaker 1: if we did it again, it wouldn't work, but we 175 00:09:29,290 --> 00:09:30,170 Speaker 1: were super lucky. 176 00:09:30,410 --> 00:09:33,650 Speaker 2: You said it invited people into academia in a way 177 00:09:33,690 --> 00:09:35,370 Speaker 2: that made it seem really fun. 178 00:09:36,290 --> 00:09:37,410 Speaker 1: Is academia really fun? 179 00:09:37,450 --> 00:09:41,370 Speaker 2: I mean, you recently announced that you're leaving academia, and 180 00:09:41,770 --> 00:09:45,210 Speaker 2: listening to conversations you've had about that, not only are 181 00:09:45,210 --> 00:09:47,090 Speaker 2: you leaving academia, but you kind of wish you'd left 182 00:09:47,130 --> 00:09:50,370 Speaker 2: academia a long time ago. So what is it about 183 00:09:50,410 --> 00:09:53,010 Speaker 2: academia that, if anything that ever was fun? And why 184 00:09:53,010 --> 00:09:54,050 Speaker 2: did it stop being fun? 185 00:09:54,690 --> 00:09:58,730 Speaker 1: The fun part about academia for me is the freedom 186 00:09:59,290 --> 00:10:04,810 Speaker 1: to pursue ideas and the ability to tackle things you 187 00:10:04,810 --> 00:10:07,050 Speaker 1: don't know anything about, and then to take the time 188 00:10:07,090 --> 00:10:09,930 Speaker 1: to learn about them and to have the them to fail, 189 00:10:10,410 --> 00:10:13,570 Speaker 1: to not have a boss. Sometimes it's an incredible gift. 190 00:10:14,010 --> 00:10:17,970 Speaker 1: In another sense, it's complete travesty because it leads many 191 00:10:18,010 --> 00:10:20,490 Speaker 1: people to be very unproductive and get paid very well 192 00:10:20,530 --> 00:10:23,290 Speaker 1: for many, many years. That is all really fun. I 193 00:10:23,330 --> 00:10:27,570 Speaker 1: think what's not fun for me about academia is that 194 00:10:27,810 --> 00:10:31,770 Speaker 1: the standard of evidence is sky high, So when you 195 00:10:31,890 --> 00:10:36,530 Speaker 1: try to publish your work, you've got referees whose job 196 00:10:36,730 --> 00:10:40,690 Speaker 1: is to find every possible hole in the paper, and 197 00:10:41,250 --> 00:10:45,610 Speaker 1: there's an incredible bias there for in academics answering very 198 00:10:45,650 --> 00:10:51,090 Speaker 1: small questions very very convincingly, And ultimately I didn't find 199 00:10:51,170 --> 00:10:55,770 Speaker 1: that so exciting because anytime there was a little bit 200 00:10:55,850 --> 00:10:58,530 Speaker 1: of uncertain not that you thought it was wrong, you're 201 00:10:58,650 --> 00:11:02,250 Speaker 1: ninety seven percent sure that you're right. And even the 202 00:11:02,250 --> 00:11:05,210 Speaker 1: referees are ninety seven percent sure they're right. Their whole job, 203 00:11:05,290 --> 00:11:08,410 Speaker 1: all of their incentives, is to poke holes, and the 204 00:11:08,850 --> 00:11:12,050 Speaker 1: editor's job is not to get called out later for 205 00:11:12,090 --> 00:11:14,450 Speaker 1: having published a paper that's wrong. All of the work 206 00:11:14,610 --> 00:11:17,650 Speaker 1: goes into trying to polish off that last one or 207 00:11:17,690 --> 00:11:21,210 Speaker 1: two percent of certainty. It's really not fun and it 208 00:11:21,290 --> 00:11:23,850 Speaker 1: drives you away from tackling big problems. And so for me, 209 00:11:24,050 --> 00:11:26,970 Speaker 1: ultimately I found it really fun to be in the 210 00:11:27,050 --> 00:11:29,650 Speaker 1: real world after fre economics, so many doors opened up. 211 00:11:29,650 --> 00:11:31,650 Speaker 1: I got to talk to people like you and and 212 00:11:31,730 --> 00:11:34,650 Speaker 1: it just showed me how big the world was and 213 00:11:34,770 --> 00:11:39,490 Speaker 1: how many exciting things there were to do. And personally, 214 00:11:39,690 --> 00:11:44,210 Speaker 1: the part I like best about ideas is coming up 215 00:11:44,250 --> 00:11:47,970 Speaker 1: with them and testing them until I'm eighty five percent 216 00:11:47,970 --> 00:11:50,130 Speaker 1: sure they're right. That's good enough for me, and then 217 00:11:50,170 --> 00:11:52,050 Speaker 1: I like to move on to the next thing, and 218 00:11:52,410 --> 00:11:55,410 Speaker 1: so the real world is a much better lab for 219 00:11:55,450 --> 00:11:56,490 Speaker 1: doing that than academics. 220 00:11:57,010 --> 00:12:00,770 Speaker 2: You were talking about in another interview about your experiences 221 00:12:00,810 --> 00:12:03,730 Speaker 2: at the University of Chicago. The Economic Department of the 222 00:12:03,770 --> 00:12:06,890 Speaker 2: University of Chicago is very famous, lots of Nobel Prize winners. 223 00:12:07,210 --> 00:12:10,170 Speaker 2: But some of the stories you were telling about what 224 00:12:10,330 --> 00:12:12,730 Speaker 2: sounded like a really toxic culture, a lot of bullying. 225 00:12:13,490 --> 00:12:18,050 Speaker 1: It sounded really really bad. Actually, most of my colleagues 226 00:12:18,050 --> 00:12:20,410 Speaker 1: were wonderful. But the monster in my case is this guy, 227 00:12:20,490 --> 00:12:23,690 Speaker 1: Jim Heckman, who's a brilliant economist, a Nobel Prize winner, 228 00:12:23,770 --> 00:12:25,770 Speaker 1: but just to give you an example, at some point 229 00:12:25,810 --> 00:12:28,330 Speaker 1: he just started to hate me. But the most extreme 230 00:12:28,330 --> 00:12:31,410 Speaker 1: thing he ever did was to start a seminar series. 231 00:12:31,570 --> 00:12:36,450 Speaker 1: And typically the seminar series and academics are like macroeconomics, macroeconomics, 232 00:12:36,530 --> 00:12:39,650 Speaker 1: law and economics, trade things like that, and we only 233 00:12:39,690 --> 00:12:43,370 Speaker 1: invite academics and usually pretty esteemed academics to come and 234 00:12:43,490 --> 00:12:46,770 Speaker 1: visit and talk. The seminar that he put into place, 235 00:12:47,170 --> 00:12:49,930 Speaker 1: the only requirement to speak in that is that you 236 00:12:49,970 --> 00:12:52,850 Speaker 1: had to hate me and have written something critical about me. 237 00:12:53,410 --> 00:12:57,570 Speaker 1: And it was completely absurd, and eventually the powers that 238 00:12:57,690 --> 00:13:01,490 Speaker 1: be made him stop doing it. But there's a core 239 00:13:01,610 --> 00:13:04,570 Speaker 1: exam that every first year student has to pass in 240 00:13:04,690 --> 00:13:06,490 Speaker 1: order to stay into the program, and it would be 241 00:13:06,490 --> 00:13:09,610 Speaker 1: completely standard that the question he would put on would 242 00:13:09,650 --> 00:13:14,010 Speaker 1: be discuss what's wrong with Steve Levitt's work, which of 243 00:13:14,010 --> 00:13:17,370 Speaker 1: course puts the gradu students in a terrible position because 244 00:13:17,490 --> 00:13:21,130 Speaker 1: they have no choice except to say everything that is 245 00:13:21,170 --> 00:13:24,010 Speaker 1: supposedly wrong with my work when maybe they want to 246 00:13:24,010 --> 00:13:28,850 Speaker 1: actually be my student. But it was an extremely rambunctious 247 00:13:28,890 --> 00:13:32,970 Speaker 1: and difficult environment. The idea at some level was that 248 00:13:32,970 --> 00:13:37,610 Speaker 1: that kind of roughness would lead to big breakthroughs in scholarship. 249 00:13:38,010 --> 00:13:41,330 Speaker 1: Do you think it does? You know, I don't think so. 250 00:13:42,170 --> 00:13:45,730 Speaker 1: I think what is good for a scholarship is that 251 00:13:45,770 --> 00:13:47,610 Speaker 1: people will tell you the truth about what they think 252 00:13:47,610 --> 00:13:50,610 Speaker 1: about your work. But I think it also helps if 253 00:13:50,610 --> 00:13:53,330 Speaker 1: you do it nicely. And there are amazing scholars at 254 00:13:53,330 --> 00:13:56,570 Speaker 1: the UFC Economics Department. But when we were completely dysfunctional, 255 00:13:57,050 --> 00:13:58,490 Speaker 1: you know, a lot of people were willing to know 256 00:13:58,530 --> 00:14:02,010 Speaker 1: about prizes, and in part it was because Chicago was 257 00:14:02,050 --> 00:14:05,210 Speaker 1: willing to take folks who were thinking about things differently 258 00:14:05,770 --> 00:14:08,810 Speaker 1: and to invite them in and it didn't have to 259 00:14:08,850 --> 00:14:11,970 Speaker 1: come at such a high cost personality wise. 260 00:14:12,250 --> 00:14:14,530 Speaker 2: Obviously, Jim Hackwin's not on the podcast to give his 261 00:14:14,610 --> 00:14:17,290 Speaker 2: side of the story. No doubt he would describe everything 262 00:14:17,330 --> 00:14:21,330 Speaker 2: in his own way, but just to leave academia behind 263 00:14:21,530 --> 00:14:24,850 Speaker 2: on a high before we move on, what's the academic 264 00:14:24,850 --> 00:14:26,770 Speaker 2: paper you've written that you're most proud of. 265 00:14:27,130 --> 00:14:29,610 Speaker 1: So I wrote this paper on abortion and crime, which 266 00:14:29,650 --> 00:14:33,450 Speaker 1: got enormous amounts of attention. And the idea is that 267 00:14:33,490 --> 00:14:35,890 Speaker 1: I don't wanted children are at risk for crime, okay, 268 00:14:36,090 --> 00:14:40,730 Speaker 1: And that's I think very well documented from decades of 269 00:14:40,810 --> 00:14:44,930 Speaker 1: social science research that if your mother doesn't love you, 270 00:14:44,930 --> 00:14:47,330 Speaker 1: your life is going to be difficult on many dimensions. 271 00:14:47,330 --> 00:14:50,170 Speaker 1: And so we applied that very simple logic to the 272 00:14:50,330 --> 00:14:54,650 Speaker 1: changes in abortion laws in the US, the legalization abortion, 273 00:14:54,730 --> 00:14:59,570 Speaker 1: and we showed that pretty credibly a huge share of 274 00:14:59,610 --> 00:15:02,170 Speaker 1: the decline in crime that happened in the nineteen nineties 275 00:15:02,610 --> 00:15:04,850 Speaker 1: was due to the legalization of abortion. Okay. At the 276 00:15:05,010 --> 00:15:08,690 Speaker 1: end of that first paper, we in our conclusion said, well, 277 00:15:08,730 --> 00:15:13,410 Speaker 1: because use abortion only affects crime with a twenty year lag, 278 00:15:14,090 --> 00:15:16,730 Speaker 1: we can see as we write this paper the predictions 279 00:15:16,770 --> 00:15:18,810 Speaker 1: we can make about what will happen to crime over 280 00:15:18,810 --> 00:15:21,370 Speaker 1: the next twenty years, and we predict that crime will 281 00:15:21,410 --> 00:15:23,610 Speaker 1: continue to fall into US about one percent a year 282 00:15:23,650 --> 00:15:25,970 Speaker 1: for the next twenty years. So the paper I'm most 283 00:15:25,970 --> 00:15:28,970 Speaker 1: proud of it is that twenty years later, John Donnie, 284 00:15:28,970 --> 00:15:30,930 Speaker 1: who was my CA author, and I said, hey, we 285 00:15:31,010 --> 00:15:33,450 Speaker 1: made that prediction twenty years ago, why don't we go 286 00:15:33,530 --> 00:15:35,690 Speaker 1: test it. Let's see if it really worked. And it 287 00:15:35,770 --> 00:15:39,250 Speaker 1: was different from a typical academic paper because number one, 288 00:15:39,290 --> 00:15:41,970 Speaker 1: we had exactly the hypothesis we've made. We knew exactly 289 00:15:41,970 --> 00:15:44,530 Speaker 1: what we were testing. And number two, we had a 290 00:15:44,650 --> 00:15:47,850 Speaker 1: set of tables we had used in the initial paper 291 00:15:47,970 --> 00:15:50,610 Speaker 1: that we said, look, well, exactly replicate that methodology. So 292 00:15:50,650 --> 00:15:53,290 Speaker 1: we'll get twenty years of data that is completely out 293 00:15:53,290 --> 00:15:56,490 Speaker 1: of sample, and we'll just see what happens in the data. 294 00:15:56,570 --> 00:15:58,850 Speaker 1: And what was so interesting is that the data over 295 00:15:58,850 --> 00:16:02,330 Speaker 1: those next twenty years were completely consistent with our hypotsis, 296 00:16:02,370 --> 00:16:06,330 Speaker 1: I think, better even than we had expected. And I 297 00:16:06,370 --> 00:16:09,890 Speaker 1: do not know any economist who has made a prediction 298 00:16:09,930 --> 00:16:11,930 Speaker 1: and waited twenty years and then seen where that prediction 299 00:16:12,250 --> 00:16:15,370 Speaker 1: came true and done it with a real scientific rigor. 300 00:16:15,410 --> 00:16:18,650 Speaker 1: And it was so confirming. To me of what we 301 00:16:18,690 --> 00:16:23,290 Speaker 1: had done, and in some sense though, my deep pride 302 00:16:23,290 --> 00:16:27,930 Speaker 1: about that paper also led to my exit from the 303 00:16:28,010 --> 00:16:32,770 Speaker 1: profession because nobody cared. We couldn't get academics to care, 304 00:16:32,970 --> 00:16:36,410 Speaker 1: We couldn't get it published easily. I mean, I sent 305 00:16:36,490 --> 00:16:39,850 Speaker 1: it back to the original journal that our first paper 306 00:16:39,850 --> 00:16:42,330 Speaker 1: had been polished, and the responsors, well, we don't really 307 00:16:42,370 --> 00:16:46,210 Speaker 1: publish simple papers like that anymore. And so it really 308 00:16:46,250 --> 00:16:48,970 Speaker 1: pointed to me that what was important to me and 309 00:16:48,970 --> 00:16:51,930 Speaker 1: what I thought was exciting about academics, there was no 310 00:16:51,970 --> 00:16:54,370 Speaker 1: market for that anymore. And when there's no market for 311 00:16:54,450 --> 00:16:56,890 Speaker 1: what you're making, it's a really good idea to exit. 312 00:16:56,930 --> 00:16:57,690 Speaker 1: And that's what I did. 313 00:16:58,330 --> 00:17:01,170 Speaker 2: You're listening to Cautionary Tales with me, Tim Harford and 314 00:17:01,250 --> 00:17:05,730 Speaker 2: my special guest Steve Levitt. When we come back, will 315 00:17:05,730 --> 00:17:08,850 Speaker 2: be talking about how to teach maths in a way 316 00:17:08,930 --> 00:17:18,570 Speaker 2: that makes it exciting. We're back, and I'm here with 317 00:17:18,690 --> 00:17:23,130 Speaker 2: Steve Levitt. Steve, when you were at Chicago University, you 318 00:17:23,290 --> 00:17:27,730 Speaker 2: founded the Center for Radical Innovation for Social Change. It's 319 00:17:27,810 --> 00:17:29,330 Speaker 2: quite a grand sounding title. 320 00:17:29,410 --> 00:17:31,570 Speaker 1: We just got risk. I verrely remember what it says 321 00:17:31,610 --> 00:17:32,050 Speaker 1: for anymore. 322 00:17:32,090 --> 00:17:35,810 Speaker 2: Okay, Oh yeah, Radical Innovation for sarch changed risk. Yeah, 323 00:17:35,810 --> 00:17:37,970 Speaker 2: I got it. Okay, So you were just aiming for 324 00:17:38,010 --> 00:17:41,410 Speaker 2: the backronym. Now I understand what were you trying to 325 00:17:41,450 --> 00:17:42,130 Speaker 2: do with that center. 326 00:17:42,650 --> 00:17:45,370 Speaker 1: I had gotten tired, as we've talked about already about 327 00:17:45,450 --> 00:17:48,250 Speaker 1: what was going on in academics, but I wanted to 328 00:17:48,290 --> 00:17:53,450 Speaker 1: try to do something that put together the best of academics, 329 00:17:53,450 --> 00:17:56,850 Speaker 1: so real rigor based on innovative ideas and unpopular ideas. 330 00:17:56,890 --> 00:17:59,250 Speaker 1: I didn't want to shy away from things that were unpopular, 331 00:17:59,290 --> 00:18:02,210 Speaker 1: as academics don't have to shy away from unpopular things. 332 00:18:02,690 --> 00:18:05,770 Speaker 1: Mixed that with a kind of startup feel and with 333 00:18:06,450 --> 00:18:08,770 Speaker 1: a philanthropic feel, and to try to go out and 334 00:18:08,810 --> 00:18:14,170 Speaker 1: saw of really big, important problems, often problems that regular 335 00:18:14,650 --> 00:18:19,490 Speaker 1: nonprofits couldn't tackle because the ideas might be repugnant or unpopular. 336 00:18:19,890 --> 00:18:22,490 Speaker 1: I guess it's been six or seven years now we've 337 00:18:22,490 --> 00:18:24,570 Speaker 1: been at it, and like it's hard, it's hard to 338 00:18:24,570 --> 00:18:27,330 Speaker 1: make real world change. But I think we finally got 339 00:18:28,050 --> 00:18:30,010 Speaker 1: two or three successes on the book that we can 340 00:18:30,050 --> 00:18:33,130 Speaker 1: feel pretty good about. For example, for a long time 341 00:18:33,370 --> 00:18:37,810 Speaker 1: we've been working on the issue of kidney donations, and 342 00:18:37,850 --> 00:18:40,970 Speaker 1: so in the US in particular, there's this huge waiting 343 00:18:41,010 --> 00:18:45,530 Speaker 1: list of people who are on dialysis who need a kidney, 344 00:18:45,930 --> 00:18:49,370 Speaker 1: and there's a tremendous shortage of donors for kidneys. In 345 00:18:49,690 --> 00:18:52,810 Speaker 1: large part that is because it is against the law 346 00:18:53,050 --> 00:18:56,970 Speaker 1: to compensate financially people for giving their kidneys. It all 347 00:18:57,010 --> 00:19:01,490 Speaker 1: depends on altruism. So economists disagree with most people in 348 00:19:01,490 --> 00:19:03,570 Speaker 1: the sense that we think that does make sense, that 349 00:19:04,010 --> 00:19:08,250 Speaker 1: done well, we could give financial incentives for kidneys and 350 00:19:08,490 --> 00:19:10,610 Speaker 1: it would make the world a much better place. And 351 00:19:10,850 --> 00:19:13,890 Speaker 1: the value of a kidney to the recipient and to 352 00:19:13,930 --> 00:19:16,970 Speaker 1: the government who's paying for the dialysis is huge, hundreds 353 00:19:16,970 --> 00:19:20,010 Speaker 1: and hundreds of thousands of dollars. So we could offer 354 00:19:20,290 --> 00:19:23,010 Speaker 1: donors hundreds of thousands of dollars and still it would 355 00:19:23,010 --> 00:19:25,770 Speaker 1: be worth doing the transplant operation. And so the ethical 356 00:19:25,810 --> 00:19:28,530 Speaker 1: issues around kidney donation are such that people say, well, 357 00:19:28,530 --> 00:19:31,170 Speaker 1: we can't really have a market for kidneys because the 358 00:19:31,690 --> 00:19:34,570 Speaker 1: market price is ten thousand dollars, and that'll be exportation 359 00:19:34,690 --> 00:19:37,570 Speaker 1: of the poor. But it's not exploitation. If you actually 360 00:19:37,610 --> 00:19:40,090 Speaker 1: pay people two hundred thousand dollars for kidneys, then what 361 00:19:40,130 --> 00:19:42,810 Speaker 1: you have is you have a list of seven million 362 00:19:42,890 --> 00:19:45,610 Speaker 1: people all signed up begging to give their kidney. I 363 00:19:45,730 --> 00:19:47,530 Speaker 1: tried to do that for years and I failed. But 364 00:19:47,970 --> 00:19:51,450 Speaker 1: as part of Risk, as we were trying to push 365 00:19:51,450 --> 00:19:54,770 Speaker 1: that agenda, we were just learning a lot about kidneys. 366 00:19:54,810 --> 00:19:58,370 Speaker 1: And one of my young people, we really hire really twenty, 367 00:19:58,530 --> 00:20:00,570 Speaker 1: really talented twenty two year olds to come and work with. 368 00:20:01,290 --> 00:20:04,490 Speaker 1: He was talking with some doctors and actually maybe watching 369 00:20:04,530 --> 00:20:07,330 Speaker 1: an intake of a potential dollar. What he realized was 370 00:20:07,370 --> 00:20:10,930 Speaker 1: that it turns out that of every one hundred people 371 00:20:11,010 --> 00:20:13,690 Speaker 1: who show up and say I would like to donate 372 00:20:13,690 --> 00:20:16,930 Speaker 1: a kidney, in the end, only about three of them 373 00:20:16,970 --> 00:20:22,010 Speaker 1: make the donation, and fully forty percent of those are 374 00:20:22,170 --> 00:20:24,810 Speaker 1: kicked out of the process because they're either too heavy 375 00:20:25,050 --> 00:20:27,970 Speaker 1: or they smoke, and those are two things that will 376 00:20:28,130 --> 00:20:31,010 Speaker 1: make doctors unwilling to do with the transplant. And he 377 00:20:31,130 --> 00:20:34,530 Speaker 1: had this incredibly simple idea. He said, hey, why don't 378 00:20:34,530 --> 00:20:37,890 Speaker 1: we work with the transplant centers and when someone comes 379 00:20:37,890 --> 00:20:40,930 Speaker 1: in and the weight is too high, instead of saying hey, 380 00:20:40,970 --> 00:20:43,690 Speaker 1: you can't donate, we'll just have the transplant center say 381 00:20:43,970 --> 00:20:46,970 Speaker 1: hey you can't donate. Yeah, but if you lose weight 382 00:20:46,970 --> 00:20:49,530 Speaker 1: you could. And there's this group called Risk that will 383 00:20:49,570 --> 00:20:52,130 Speaker 1: for free help you lose weight. So all we did 384 00:20:52,250 --> 00:20:54,490 Speaker 1: was just work with transplant centers, give them flyers and 385 00:20:54,490 --> 00:20:57,010 Speaker 1: say send people our way, and all we would do 386 00:20:57,250 --> 00:21:01,010 Speaker 1: was just give them free access to weight watchers, and 387 00:21:01,170 --> 00:21:03,890 Speaker 1: we would have a young person who would text them 388 00:21:03,890 --> 00:21:05,490 Speaker 1: and try to give them moral support and be a 389 00:21:05,570 --> 00:21:09,010 Speaker 1: cheerleader to help them lose weight. And it seems really silly, 390 00:21:09,210 --> 00:21:11,970 Speaker 1: really simple, and so obvious that how could it make 391 00:21:11,970 --> 00:21:16,250 Speaker 1: any difference. But on a shoestring budget, our little group 392 00:21:16,370 --> 00:21:20,730 Speaker 1: has led to sixty extra kidney donations over the last 393 00:21:21,010 --> 00:21:25,450 Speaker 1: year and a half, and that's sixty lives changed or saved, 394 00:21:25,570 --> 00:21:30,210 Speaker 1: and the cost per life saved is something like five 395 00:21:30,250 --> 00:21:35,090 Speaker 1: thousand dollars nothing. It is about the best intervention that 396 00:21:35,410 --> 00:21:38,730 Speaker 1: anyone has ever seen from a simple cost benefit and 397 00:21:38,730 --> 00:21:40,970 Speaker 1: it's totally obvious. Why was no one else doing it? 398 00:21:41,410 --> 00:21:44,770 Speaker 1: We're not sure, but it's a really simple insight that 399 00:21:45,130 --> 00:21:47,690 Speaker 1: led to a big impact, and at scale we could 400 00:21:47,730 --> 00:21:50,690 Speaker 1: be doing thousands of these per year. We really did 401 00:21:50,730 --> 00:21:52,690 Speaker 1: it almost as a pilot, and now we're trying to 402 00:21:52,690 --> 00:21:56,090 Speaker 1: figure out how we can make it really transformational in 403 00:21:56,250 --> 00:21:58,850 Speaker 1: the kidney space. So that's one example of a success. 404 00:21:59,010 --> 00:22:01,450 Speaker 1: I'd say another one that is really interesting to a 405 00:22:01,530 --> 00:22:04,250 Speaker 1: sale con of the con Academy, and with Arizona State 406 00:22:04,330 --> 00:22:09,370 Speaker 1: University we helped start in online school. You know, we 407 00:22:09,410 --> 00:22:11,650 Speaker 1: had various ideas about what we thought was wrong with 408 00:22:11,690 --> 00:22:14,250 Speaker 1: the way current high schools are run in the US, 409 00:22:14,450 --> 00:22:17,490 Speaker 1: and it turned out that the online school was incredibly 410 00:22:17,530 --> 00:22:20,890 Speaker 1: successful in terms of test scores. And what we've learned 411 00:22:20,930 --> 00:22:23,970 Speaker 1: is that the idea that there'd be a teacher standing 412 00:22:24,010 --> 00:22:26,330 Speaker 1: in front of twenty five or thirty students lecturing them 413 00:22:26,330 --> 00:22:29,050 Speaker 1: about things, that turns out to be roughly the least 414 00:22:29,090 --> 00:22:31,450 Speaker 1: efficient way to learn that you can come up with. 415 00:22:31,530 --> 00:22:33,490 Speaker 1: And the only reason we do it is because it's 416 00:22:33,490 --> 00:22:35,450 Speaker 1: the technology. We had to do it in the eighteen 417 00:22:35,570 --> 00:22:39,210 Speaker 1: fifties when public education got going, And so now we're 418 00:22:39,290 --> 00:22:41,570 Speaker 1: launching an in person school at brick and Mortar School. 419 00:22:41,770 --> 00:22:43,530 Speaker 1: So I can't say it's going to be success that 420 00:22:43,570 --> 00:22:45,930 Speaker 1: because it has happened, but I have every reason to 421 00:22:45,970 --> 00:22:48,850 Speaker 1: believe that we are going to be able to deliver 422 00:22:49,010 --> 00:22:53,450 Speaker 1: a high school experience that is staggeringly better than what 423 00:22:53,610 --> 00:22:57,290 Speaker 1: is being done right now in terms of what kids learn, 424 00:22:58,130 --> 00:23:01,370 Speaker 1: how enjoyable it is to them. That we will build 425 00:23:01,410 --> 00:23:05,490 Speaker 1: in creativity rather than drive creativity out, and so for me, 426 00:23:05,610 --> 00:23:09,090 Speaker 1: that is the single most important and interesting and inspired 427 00:23:09,170 --> 00:23:12,250 Speaker 1: anything I'm working on right now is I just believe 428 00:23:12,370 --> 00:23:16,810 Speaker 1: that we are at the right moment post COVID to 429 00:23:16,930 --> 00:23:21,450 Speaker 1: create a completely different model for how we teach and 430 00:23:21,490 --> 00:23:24,490 Speaker 1: how we learn. And I hope that this is something 431 00:23:24,730 --> 00:23:26,930 Speaker 1: I'll do till the day I die, and that more 432 00:23:26,970 --> 00:23:28,890 Speaker 1: than freakonomics, that this will be my legacy. 433 00:23:29,450 --> 00:23:32,490 Speaker 2: You and I both have an interest in maths or 434 00:23:32,530 --> 00:23:34,730 Speaker 2: math as I guess you would say, what do you 435 00:23:34,810 --> 00:23:38,090 Speaker 2: think that we get wrong in the way the young 436 00:23:38,090 --> 00:23:40,570 Speaker 2: people are taught to engage with numbers. 437 00:23:40,810 --> 00:23:43,290 Speaker 1: So I think what we do up through about the 438 00:23:43,330 --> 00:23:45,530 Speaker 1: age of twelve or thirteen is not that bad. I 439 00:23:45,570 --> 00:23:49,050 Speaker 1: think knowing how to add and subtract and divide and 440 00:23:49,090 --> 00:23:51,490 Speaker 1: do fractions and things like that is the basic skill 441 00:23:51,530 --> 00:23:53,970 Speaker 1: in life that you need and everyone should have that, 442 00:23:54,090 --> 00:23:56,250 Speaker 1: and I don't think we do in the most efficient way, 443 00:23:56,290 --> 00:23:58,250 Speaker 1: but at least we have our eye and the prize 444 00:23:58,370 --> 00:24:01,570 Speaker 1: up through about the age of thirteen. High school math, 445 00:24:01,690 --> 00:24:03,770 Speaker 1: on the other hand, I think is a complete horror 446 00:24:03,810 --> 00:24:09,770 Speaker 1: show because we ask children to do a whole bunch 447 00:24:09,850 --> 00:24:14,690 Speaker 1: of math, which is complicated based on doing computations that 448 00:24:14,850 --> 00:24:17,330 Speaker 1: know what he does anymore. So one of the things 449 00:24:17,330 --> 00:24:19,410 Speaker 1: that I watch all of my high school kids do 450 00:24:19,730 --> 00:24:22,210 Speaker 1: is try to learn how to compute the minimums and 451 00:24:22,290 --> 00:24:25,890 Speaker 1: maximums or the zeros of polynomials. And that is something 452 00:24:25,930 --> 00:24:29,050 Speaker 1: that they did in Hidden Figures, that movie about getting 453 00:24:29,330 --> 00:24:31,810 Speaker 1: people to the Moon, because we didn't have good computers 454 00:24:31,810 --> 00:24:33,770 Speaker 1: and so people had to do that. But since computers 455 00:24:33,810 --> 00:24:36,490 Speaker 1: came in, there's not a person on the planet who's 456 00:24:36,530 --> 00:24:39,930 Speaker 1: had to do that manually other than a high school student. 457 00:24:40,530 --> 00:24:43,170 Speaker 1: And we try to teach kids how to do proofs. 458 00:24:43,250 --> 00:24:46,290 Speaker 1: Again great, I think knowing how to prove things is wonderful, 459 00:24:46,490 --> 00:24:48,530 Speaker 1: but we make them prove all sort of things about 460 00:24:48,570 --> 00:24:52,090 Speaker 1: triangles that who cares about. Back in ancient Greece, it 461 00:24:52,130 --> 00:24:55,450 Speaker 1: was important to know things about triangles. Now it really isn't. 462 00:24:55,690 --> 00:24:58,690 Speaker 1: What do people actually use in their daily life. They 463 00:24:58,770 --> 00:25:01,130 Speaker 1: add and subtract, They do simple arithmetic, and that's good 464 00:25:01,170 --> 00:25:04,730 Speaker 1: because they've usually learned that, and then they analyze data. 465 00:25:04,730 --> 00:25:08,130 Speaker 1: They use spreadsheets, they have to make sense of complicated relationships, 466 00:25:08,370 --> 00:25:10,650 Speaker 1: and we do all almost none of the teaching of 467 00:25:10,730 --> 00:25:14,290 Speaker 1: that in high school. And so data science might call 468 00:25:14,330 --> 00:25:17,450 Speaker 1: it data analytics, that those are the skills that people need, 469 00:25:17,570 --> 00:25:19,930 Speaker 1: and I believe we should teach people the things that 470 00:25:19,970 --> 00:25:23,010 Speaker 1: they will use. Many of the hardcore mathematicians get upset, 471 00:25:23,050 --> 00:25:25,770 Speaker 1: they say, oh, we're not teaching deep math. But I 472 00:25:25,850 --> 00:25:28,330 Speaker 1: will tell you there is very deep math in data 473 00:25:28,370 --> 00:25:32,530 Speaker 1: science and a lot of artistry and a lot of judgment. 474 00:25:32,730 --> 00:25:35,370 Speaker 1: And another thing that we've been doing at my Risk 475 00:25:35,450 --> 00:25:39,290 Speaker 1: Center is we ended up launching something called Data Science 476 00:25:39,290 --> 00:25:43,850 Speaker 1: for Everyone, which has become the leading think tank everything 477 00:25:43,930 --> 00:25:47,090 Speaker 1: group that's trying to change the way we teach math 478 00:25:47,250 --> 00:25:49,850 Speaker 1: to incorporate data science in the United States, and it's 479 00:25:49,890 --> 00:25:52,850 Speaker 1: been really, really quite successful. I'm sure you know. 480 00:25:53,610 --> 00:25:57,330 Speaker 2: Steve Strogatz, of course, has this idea that colleges should 481 00:25:57,370 --> 00:26:01,210 Speaker 2: teach math appreciation. We have art classes where you teach 482 00:26:01,250 --> 00:26:04,490 Speaker 2: people to draw. You have hours appreciation where you teach 483 00:26:04,490 --> 00:26:06,450 Speaker 2: people the history, and you know, you get people to 484 00:26:06,450 --> 00:26:09,290 Speaker 2: look at pretty pictures and discuss like why they're such 485 00:26:09,290 --> 00:26:13,890 Speaker 2: studding works of art. And he suggested for some students, Okay, fine, 486 00:26:13,890 --> 00:26:15,930 Speaker 2: they're not going to calculate a lot of stuff. They're 487 00:26:15,970 --> 00:26:18,930 Speaker 2: not going to be high powered mathematicians or statisticians, but 488 00:26:18,970 --> 00:26:22,050 Speaker 2: they could still have an appreciation of how math works, 489 00:26:22,050 --> 00:26:24,690 Speaker 2: how data works. I guess fre economics should be on 490 00:26:24,730 --> 00:26:28,330 Speaker 2: the course, right. It's an example of data appreciation. It 491 00:26:28,370 --> 00:26:31,050 Speaker 2: doesn't really tell you the details of how your detective 492 00:26:31,090 --> 00:26:32,650 Speaker 2: work was done, but it gives you a sense of 493 00:26:32,690 --> 00:26:34,450 Speaker 2: the kind of things you were doing and the kind 494 00:26:34,450 --> 00:26:36,930 Speaker 2: of things that you could do if you had control 495 00:26:36,930 --> 00:26:37,450 Speaker 2: of the data. 496 00:26:38,170 --> 00:26:40,610 Speaker 1: See, you've shutts and I have talked a lot about that. 497 00:26:40,730 --> 00:26:42,890 Speaker 1: Actually worked with the State of Utah in the United 498 00:26:42,930 --> 00:26:46,570 Speaker 1: States to try to implement some of those ideas. And 499 00:26:46,930 --> 00:26:49,130 Speaker 1: we're just getting gone and we'll see how that works. 500 00:26:49,890 --> 00:26:52,330 Speaker 1: The discoveries, the process of the history of math, I 501 00:26:52,370 --> 00:26:55,090 Speaker 1: think is actually fascinating once you get into it. Yeah, 502 00:26:55,250 --> 00:26:59,130 Speaker 1: But the real power of this idea of appreciation is 503 00:26:59,170 --> 00:27:02,530 Speaker 1: with young people, because so many twelve and thirteen year 504 00:27:02,570 --> 00:27:04,730 Speaker 1: olds are right at the point where they're deciding, oh, 505 00:27:05,810 --> 00:27:08,930 Speaker 1: I'm not a mass person. That's not for me, and 506 00:27:09,130 --> 00:27:12,650 Speaker 1: then they check out. And that's because they don't understand 507 00:27:12,970 --> 00:27:16,410 Speaker 1: why they're doing this complex math, and which makes sense 508 00:27:16,410 --> 00:27:18,970 Speaker 1: because I don't really understand why they're doing that complex math. 509 00:27:19,210 --> 00:27:22,690 Speaker 1: But on the other hand, if you instead focused with 510 00:27:22,770 --> 00:27:25,570 Speaker 1: young people and saying, hey, here's what's amazing about math. 511 00:27:25,890 --> 00:27:29,130 Speaker 1: Here's how math is useful, Here's how math is a 512 00:27:29,170 --> 00:27:32,450 Speaker 1: form of storytelling, or here's how math is the language 513 00:27:32,450 --> 00:27:34,930 Speaker 1: of the universe. I mean my own high school kids. 514 00:27:35,250 --> 00:27:37,330 Speaker 1: They just thought, well, the teacher told me I do this, 515 00:27:37,330 --> 00:27:39,490 Speaker 1: this and this could the answer, and so I do that. 516 00:27:39,530 --> 00:27:41,730 Speaker 1: But they didn't really understand it is true that if 517 00:27:41,730 --> 00:27:44,850 Speaker 1: you would do a different galaxy, it would also be true. 518 00:27:44,930 --> 00:27:47,410 Speaker 1: And so I think the idea, the most powerful part 519 00:27:47,450 --> 00:27:50,730 Speaker 1: of the appreciation idea is to take people who are 520 00:27:50,810 --> 00:27:52,810 Speaker 1: still trying to figure out who they are. They don't 521 00:27:52,810 --> 00:27:55,490 Speaker 1: know who they are, and they're making these choices, and 522 00:27:55,530 --> 00:27:58,690 Speaker 1: if they say, WHOA, maybe math is fun, then they 523 00:27:58,690 --> 00:28:03,330 Speaker 1: won't close off the path to eventually maybe saying that 524 00:28:03,530 --> 00:28:05,570 Speaker 1: math could be a part of your career from they 525 00:28:05,610 --> 00:28:07,930 Speaker 1: don't have to be afraid of science. And so that's 526 00:28:08,010 --> 00:28:09,650 Speaker 1: where I think the idea heads. 527 00:28:09,690 --> 00:28:13,010 Speaker 2: It's real legged you're listening to Cautionary Tales with me, 528 00:28:13,170 --> 00:28:16,890 Speaker 2: Tim Harford and my guest Steve Levitt. After the break, 529 00:28:16,930 --> 00:28:20,290 Speaker 2: we'll be talking about Steve's very own podcast, People I 530 00:28:20,410 --> 00:28:31,410 Speaker 2: mostly admire. I wanted to ask you about podcasting when 531 00:28:31,890 --> 00:28:36,970 Speaker 2: you and Dubna launched the Free Comics radio podcast. I 532 00:28:37,050 --> 00:28:40,690 Speaker 2: remember thinking, what are they doing. They've created this unbelievably 533 00:28:40,770 --> 00:28:43,890 Speaker 2: successful brand. People love the books, they got a New 534 00:28:43,970 --> 00:28:46,770 Speaker 2: York Times column, but podcasting, you're going to spend your 535 00:28:46,770 --> 00:28:50,530 Speaker 2: time podcasting. Obviously that worked out pretty well. So you 536 00:28:50,530 --> 00:28:52,170 Speaker 2: guys were really ahead of the curve there. 537 00:28:52,410 --> 00:28:54,650 Speaker 1: I was in the exact same boat as you were 538 00:28:54,690 --> 00:28:56,610 Speaker 1: when when Dumney said I'm going to start a podcast. 539 00:28:56,850 --> 00:28:59,250 Speaker 1: I think I said, what's a podcast? It spons what 540 00:28:59,410 --> 00:29:03,090 Speaker 1: fifteen years ago. He's one of the really early podcasters, 541 00:29:03,290 --> 00:29:06,810 Speaker 1: and I thought it was the silliest idea. You would say, hey, 542 00:29:06,850 --> 00:29:08,770 Speaker 1: can you come on my show? Could I interview you? 543 00:29:08,810 --> 00:29:10,930 Speaker 1: And I I'd always do it, and it's really out 544 00:29:10,930 --> 00:29:13,050 Speaker 1: of sympathy for him. And then I remember if we 545 00:29:13,050 --> 00:29:15,330 Speaker 1: were on a book tour, and I suppose it must 546 00:29:15,330 --> 00:29:17,490 Speaker 1: have been for our third book, Thinks like a Freak 547 00:29:17,930 --> 00:29:21,090 Speaker 1: And Dumna said to me, hey, how many downloads do 548 00:29:21,130 --> 00:29:24,090 Speaker 1: you think I get a month for Freakonomics? And I 549 00:29:24,130 --> 00:29:26,210 Speaker 1: didn't want to hurt his feelings, so I said, I 550 00:29:26,250 --> 00:29:30,050 Speaker 1: don't know, maybe ten thousand and fifteen thousand. I really 551 00:29:30,050 --> 00:29:31,890 Speaker 1: thought it was probably five hundred, but I didn't want 552 00:29:31,890 --> 00:29:33,290 Speaker 1: to say that, and make him think I had a 553 00:29:33,290 --> 00:29:36,370 Speaker 1: low opinion and said, oh no, it's actually over a million. 554 00:29:37,290 --> 00:29:40,770 Speaker 1: I said over a million a month, and he said yeah, 555 00:29:40,890 --> 00:29:43,290 Speaker 1: and I couldn't believe it. And that was one of 556 00:29:43,330 --> 00:29:45,970 Speaker 1: those epiphanies, those moments of truth where you say, wait 557 00:29:45,970 --> 00:29:50,010 Speaker 1: a second. So in a year you have twice as 558 00:29:50,050 --> 00:29:53,370 Speaker 1: many downloads of the podcast we've ever sold the books, 559 00:29:53,410 --> 00:29:55,490 Speaker 1: even though we had a really excise books, And it 560 00:29:55,610 --> 00:29:58,890 Speaker 1: really opened my eyes that as a vehicle for talking 561 00:29:58,930 --> 00:30:03,370 Speaker 1: about ideas, it is amazing. And so I never could 562 00:30:03,370 --> 00:30:07,170 Speaker 1: have imagined that I would end up doing my own podcast. 563 00:30:07,290 --> 00:30:11,170 Speaker 1: But when I decided that I wanted to leave academia, 564 00:30:11,530 --> 00:30:15,410 Speaker 1: I thought, well, you know, for twenty five for thirty years, 565 00:30:16,010 --> 00:30:20,210 Speaker 1: I've been so helpent on being a producer of ideas. 566 00:30:20,330 --> 00:30:23,210 Speaker 1: I literally spent all my time trying to come up 567 00:30:23,210 --> 00:30:26,690 Speaker 1: with Grady cannowming ideas that I had closed myself off 568 00:30:26,810 --> 00:30:31,330 Speaker 1: completely to learning or knowing about anything else. And I said, 569 00:30:31,370 --> 00:30:33,530 Speaker 1: I just want to relax for a while and be 570 00:30:33,610 --> 00:30:37,290 Speaker 1: a consumer. And a podcast was a way that I 571 00:30:37,330 --> 00:30:40,010 Speaker 1: could convince all sorts of amazing people to come and 572 00:30:40,050 --> 00:30:44,330 Speaker 1: talk to me. Nobel Prize winning chemists and things that 573 00:30:44,450 --> 00:30:47,770 Speaker 1: I could just have the joy of having really amazing 574 00:30:47,770 --> 00:30:51,570 Speaker 1: conversations with incredible people and completely switch my own role 575 00:30:51,730 --> 00:30:55,250 Speaker 1: from being the person who's supposed to be the genius 576 00:30:55,530 --> 00:30:57,610 Speaker 1: to being the person who doesn't know anything, and this 577 00:30:57,730 --> 00:31:00,610 Speaker 1: is asking the questions and got I've I found him 578 00:31:00,770 --> 00:31:03,890 Speaker 1: very comfortable with that second role. I love being the 579 00:31:03,890 --> 00:31:07,490 Speaker 1: person who doesn't know anything, and then he's trying to 580 00:31:08,490 --> 00:31:11,970 Speaker 1: extract information from experts. And one of the things I've 581 00:31:12,010 --> 00:31:16,770 Speaker 1: learned is that many experts are incredibly bad at explaining 582 00:31:16,770 --> 00:31:20,730 Speaker 1: what they do and making it approachable. And I think 583 00:31:20,810 --> 00:31:24,090 Speaker 1: one of my real talents is that I'm pretty good 584 00:31:24,570 --> 00:31:28,370 Speaker 1: at hearing something complex, grasping on to the end of 585 00:31:28,410 --> 00:31:32,690 Speaker 1: a string, and pulling on it until I can get 586 00:31:32,690 --> 00:31:35,930 Speaker 1: people to explain things in ways that I and other 587 00:31:36,010 --> 00:31:37,330 Speaker 1: regular people can understand. 588 00:31:37,450 --> 00:31:40,650 Speaker 2: The podcast is called People I mostly Admire, and it's 589 00:31:40,690 --> 00:31:43,370 Speaker 2: a really wonderful podcast. I love listening to it. I'm 590 00:31:43,410 --> 00:31:45,610 Speaker 2: sure you'll take this as a compliment. You are quite 591 00:31:45,690 --> 00:31:48,810 Speaker 2: a weird interviewer in a way that it's hard to 592 00:31:48,810 --> 00:31:50,370 Speaker 2: put my finger on exactly what it is. But I 593 00:31:50,370 --> 00:31:55,490 Speaker 2: think it is partly this. You don't mind appearing like 594 00:31:55,530 --> 00:31:57,130 Speaker 2: you don't know what you're talking about. You don't mind 595 00:31:57,170 --> 00:32:00,490 Speaker 2: seeming a little bit stupid, and I think it's because 596 00:32:00,530 --> 00:32:03,650 Speaker 2: actually everybody knows you're not stupid. You're a professor at Chicago. 597 00:32:04,130 --> 00:32:06,410 Speaker 2: You want all these prizes, so you don't mind asking 598 00:32:06,450 --> 00:32:09,210 Speaker 2: the dumb questions. There's an artlessness about it, which. 599 00:32:09,530 --> 00:32:13,810 Speaker 1: I really like. I'm very antisocial in general, and I 600 00:32:13,850 --> 00:32:18,570 Speaker 1: have very little human interaction. But for these podcasts, I 601 00:32:18,730 --> 00:32:23,730 Speaker 1: come prepared and I think I asked them different questions 602 00:32:23,770 --> 00:32:27,810 Speaker 1: and they used to being asked sometimes and sometimes because 603 00:32:27,810 --> 00:32:30,370 Speaker 1: of that, it gets very personal and we end up 604 00:32:30,410 --> 00:32:34,490 Speaker 1: talking about things that might be seen as out of bounds, 605 00:32:34,530 --> 00:32:38,090 Speaker 1: but build this strange bond. And for me, that's what 606 00:32:38,250 --> 00:32:42,490 Speaker 1: is completely unexpected. But there's something about the artificiality of 607 00:32:42,890 --> 00:32:47,970 Speaker 1: a podcast interview that allows this emotional connection. You wouldn't 608 00:32:47,970 --> 00:32:50,370 Speaker 1: expect it, but that's how I judge whether a podcast 609 00:32:50,370 --> 00:32:55,130 Speaker 1: episode is successful. And it happens often in the most unexpected, 610 00:32:56,010 --> 00:32:59,050 Speaker 1: strange ways, and that's but so fun about it for me. 611 00:32:59,210 --> 00:33:02,010 Speaker 2: Yeah, there's definitely some podcasts you listen to that you're 612 00:33:02,050 --> 00:33:06,170 Speaker 2: clearly just like having the best time. I mean, there's 613 00:33:06,170 --> 00:33:10,450 Speaker 2: an incredible podcast where you interviewed, you will daughters, which 614 00:33:10,490 --> 00:33:14,090 Speaker 2: is not a normal podcast behavior for a podcast, like people, 615 00:33:14,090 --> 00:33:16,810 Speaker 2: I mostly in my interviewing all these academics, and there's 616 00:33:16,810 --> 00:33:21,650 Speaker 2: one point where you're in tears, She's in tears. I'm listening. 617 00:33:21,730 --> 00:33:25,970 Speaker 2: I'm in tears. I mean, you know, it's it's heavy stuff. 618 00:33:25,970 --> 00:33:27,770 Speaker 2: It was an incredible interview. 619 00:33:28,050 --> 00:33:30,610 Speaker 1: So yeah, I did interview my two oldest daughters, and 620 00:33:30,650 --> 00:33:34,850 Speaker 1: one of my daughters was anorexic, seriously inerrexit in hospitalized, 621 00:33:35,050 --> 00:33:39,530 Speaker 1: in your death. And we never really talk about the 622 00:33:39,690 --> 00:33:43,570 Speaker 1: deepest issues in regular life because there's always some excuse 623 00:33:43,690 --> 00:33:45,970 Speaker 1: not to. But the idea that we would sit down 624 00:33:46,010 --> 00:33:50,650 Speaker 1: in a recording studio and for you know, an hour, 625 00:33:50,930 --> 00:33:56,410 Speaker 1: she and I would talk and it was a conversation 626 00:33:56,610 --> 00:33:59,970 Speaker 1: that for me is so special and honestly, we hadn't 627 00:34:00,490 --> 00:34:03,090 Speaker 1: had a conversation ever before that, and we really haven't 628 00:34:03,130 --> 00:34:07,330 Speaker 1: had a conversation ever that special since. Again, it was 629 00:34:07,370 --> 00:34:12,330 Speaker 1: this weird thing about it being an interview being a 630 00:34:12,370 --> 00:34:18,410 Speaker 1: podcast that somehow got us to open up. And I 631 00:34:18,450 --> 00:34:20,450 Speaker 1: have to say, I'm so glad that I did that. 632 00:34:21,050 --> 00:34:23,570 Speaker 1: Everyone else thought it was a terrible idea. My producer 633 00:34:23,610 --> 00:34:26,290 Speaker 1: thought it was a terrible idea. I wasn't sure if 634 00:34:26,330 --> 00:34:29,010 Speaker 1: it work or not, but it was really magical and 635 00:34:29,050 --> 00:34:34,730 Speaker 1: I think it's pulled my daughter, Lily and I closer 636 00:34:34,810 --> 00:34:38,010 Speaker 1: together than we ever would have been otherwise. But again, 637 00:34:38,050 --> 00:34:40,890 Speaker 1: it's almost like that initial Freakonomics somehow captured light thing 638 00:34:40,890 --> 00:34:42,570 Speaker 1: in a bottle. It's the right thing at the right time, 639 00:34:42,890 --> 00:34:45,570 Speaker 1: and that conversation with Lily was just a I'm so 640 00:34:45,770 --> 00:34:48,370 Speaker 1: glad that I had that, and I think I don't 641 00:34:48,370 --> 00:34:49,970 Speaker 1: really have it in me to have those kind of 642 00:34:50,250 --> 00:34:52,690 Speaker 1: conversations with her outside of that setting. It was the 643 00:34:52,770 --> 00:34:56,530 Speaker 1: artificiality that somehow allowed both of us to open up 644 00:34:56,730 --> 00:34:58,050 Speaker 1: in a way we don't Usually. 645 00:34:58,490 --> 00:35:01,290 Speaker 2: You're kind of trapped. You might make an excuse and 646 00:35:01,330 --> 00:35:05,050 Speaker 2: walk away from the conversation. But yeah, I wanted to 647 00:35:05,050 --> 00:35:11,370 Speaker 2: ask you, Steve, about your perspective on parenting. And when 648 00:35:11,410 --> 00:35:13,890 Speaker 2: I first met you, I was about to become a 649 00:35:14,010 --> 00:35:17,890 Speaker 2: dad for the first time. That my oldest daughter is 650 00:35:17,890 --> 00:35:22,690 Speaker 2: is slightly younger than pre Economics. You have been through 651 00:35:22,770 --> 00:35:26,970 Speaker 2: some very difficult experiences as a parent. You lost your 652 00:35:27,010 --> 00:35:29,690 Speaker 2: youngest son when he was very young. You had this 653 00:35:29,770 --> 00:35:32,970 Speaker 2: experience with your daughter, Lily, who was so ill. But 654 00:35:33,010 --> 00:35:35,410 Speaker 2: you know, you're still going you've got a young family, 655 00:35:35,450 --> 00:35:39,050 Speaker 2: you've got more children under the age of five, You've 656 00:35:39,050 --> 00:35:40,770 Speaker 2: got a chance to do it all again. And I 657 00:35:40,850 --> 00:35:43,930 Speaker 2: just think people who have had to deal with these 658 00:35:44,010 --> 00:35:48,850 Speaker 2: really difficult challenges learn something that those of us who 659 00:35:49,010 --> 00:35:51,490 Speaker 2: get a bit more lucky and don't have to deal 660 00:35:51,530 --> 00:35:54,530 Speaker 2: with so much, maybe we don't learn. So I just 661 00:35:54,570 --> 00:35:58,130 Speaker 2: wanted to get your thoughts on what you've learned from everything. 662 00:35:59,250 --> 00:36:03,090 Speaker 1: Well, I really thought after I went through my older 663 00:36:03,170 --> 00:36:05,730 Speaker 1: children and then I did it, she said, I started 664 00:36:05,730 --> 00:36:08,210 Speaker 1: a new family. Oh my god, all the lessons I learned, 665 00:36:08,330 --> 00:36:10,410 Speaker 1: I'm going to be such a better parent. The second 666 00:36:10,490 --> 00:36:14,250 Speaker 1: num around, and two things happen. Number One, I realized 667 00:36:14,330 --> 00:36:16,970 Speaker 1: I was so tired of those first kids I can 668 00:36:17,010 --> 00:36:21,050 Speaker 1: barely remember what I did. And number two, in the 669 00:36:21,130 --> 00:36:24,930 Speaker 1: actual practice of it, I literally made all the same mistakes, 670 00:36:25,050 --> 00:36:27,650 Speaker 1: and it was really disappointing to me. I have two 671 00:36:27,690 --> 00:36:30,690 Speaker 1: simple pieces of advice about parenting, one of which I 672 00:36:30,850 --> 00:36:33,330 Speaker 1: take and the other which I don't take, even though 673 00:36:33,330 --> 00:36:35,650 Speaker 1: it's my own advice. The first simple one is that 674 00:36:36,130 --> 00:36:39,690 Speaker 1: I believe that if your kids feel loved, most other 675 00:36:39,770 --> 00:36:43,410 Speaker 1: things will take care of themselves. I don't do a 676 00:36:43,410 --> 00:36:46,770 Speaker 1: lot of helicoptering of my kids. I don't care whether 677 00:36:46,810 --> 00:36:49,890 Speaker 1: they are good at school, and I don't push them 678 00:36:49,970 --> 00:36:52,530 Speaker 1: or pressure them anyway. I think that's so much less 679 00:36:52,530 --> 00:36:56,450 Speaker 1: important than just having kids who can see and know 680 00:36:56,570 --> 00:36:58,850 Speaker 1: that you love them deeply and that no matter what happens, 681 00:36:58,890 --> 00:37:01,570 Speaker 1: you're going to be behind them. So that's the piece 682 00:37:01,610 --> 00:37:04,090 Speaker 1: of advice that I actually do a good job of parenting. 683 00:37:05,610 --> 00:37:07,850 Speaker 1: The thing I don't do wish I wish I would 684 00:37:07,850 --> 00:37:10,730 Speaker 1: do is not trying to do other things while you parent. 685 00:37:11,410 --> 00:37:14,010 Speaker 1: What I do all day long is I have take 686 00:37:14,050 --> 00:37:16,730 Speaker 1: care of my kids and I have work on my 687 00:37:16,850 --> 00:37:20,290 Speaker 1: podcast or my new school, and it's frustrating to everyone 688 00:37:20,690 --> 00:37:24,210 Speaker 1: and it's no fun at all. And whenever I have 689 00:37:24,290 --> 00:37:26,730 Speaker 1: the discipline, which isn't very often, to say, hey, for 690 00:37:26,810 --> 00:37:29,810 Speaker 1: the next four hours, I'm gonna sit with my kids 691 00:37:29,930 --> 00:37:32,090 Speaker 1: and I'm not even gonna think about anything else. I'm 692 00:37:32,090 --> 00:37:35,010 Speaker 1: just gonna be with them and let them dictate what happens. 693 00:37:35,250 --> 00:37:39,050 Speaker 1: I'm always happier, there's always more rewarding. The kids love it. 694 00:37:39,050 --> 00:37:41,170 Speaker 1: It's good for me. I have such a lack of 695 00:37:41,170 --> 00:37:45,610 Speaker 1: discipline that I cannot do that on a regular basis. 696 00:37:45,690 --> 00:37:49,210 Speaker 1: There's just this outside polls, I me are so strong. 697 00:37:49,490 --> 00:37:51,410 Speaker 1: But if I were to give advice to others, if 698 00:37:51,410 --> 00:37:53,490 Speaker 1: you're gonna work work, if you're gonna be with the kids, 699 00:37:53,530 --> 00:37:55,050 Speaker 1: to be with the kids, try not to do both 700 00:37:55,090 --> 00:37:55,410 Speaker 1: at once. 701 00:37:56,410 --> 00:37:58,850 Speaker 2: Steve, thank you so much for joining us on Coation Retales. 702 00:37:59,330 --> 00:38:01,130 Speaker 1: Oh, thank you, Jim. It's all so good. We got 703 00:38:01,130 --> 00:38:03,370 Speaker 1: to do this more often. Yeah, I'll take you up 704 00:38:03,370 --> 00:38:03,610 Speaker 1: on that. 705 00:38:05,050 --> 00:38:08,330 Speaker 2: I'm sure this conversation would have whetted your appetites to 706 00:38:08,370 --> 00:38:12,050 Speaker 2: hear Steve. We've interview a huge variety of guests on 707 00:38:12,090 --> 00:38:15,250 Speaker 2: his podcast, people I mostly admire. You can of course 708 00:38:15,330 --> 00:38:20,210 Speaker 2: find that anywhere you get your podcasts. As for Cautionary Tales, 709 00:38:20,330 --> 00:38:23,410 Speaker 2: we will be back very soon in your feed with 710 00:38:23,530 --> 00:38:30,370 Speaker 2: another of our episodes. For a full list of our sources, 711 00:38:30,690 --> 00:38:39,450 Speaker 2: see the show notes at Timharford dot com. Cautionary Tales 712 00:38:39,450 --> 00:38:42,970 Speaker 2: as written by me Tim Harford with Andrew Wright, Alice Fines, 713 00:38:43,210 --> 00:38:47,250 Speaker 2: and Ryan Dilly. It's produced by Georgia Mills and Marilyn Rust. 714 00:38:47,850 --> 00:38:50,570 Speaker 2: The sound design and original music are the work of 715 00:38:50,690 --> 00:38:54,770 Speaker 2: Pascal Wise. Additional sound design is by Carlos San Juan 716 00:38:55,010 --> 00:38:59,970 Speaker 2: at Brain Audio Bend and Dapfhaffrey edited the scripts. The 717 00:39:00,010 --> 00:39:03,810 Speaker 2: show features the voice talents of Melanie Guttridge, Stella Harford, 718 00:39:04,010 --> 00:39:09,010 Speaker 2: Oliver Hembrough, Sarah Jopp, Messaam Monroe, Jamal Westman and rufus Right. 719 00:39:09,810 --> 00:39:12,650 Speaker 2: The show also wouldn't have been possible without the work 720 00:39:12,690 --> 00:39:17,570 Speaker 2: of Jacob Weisberg, Greta Cohene, Sarah Nix, Eric Sandler, Carrie Brody, 721 00:39:17,810 --> 00:39:23,170 Speaker 2: Christina Sullivan, Kira Posey and Owen Miller. Cautionary Tales is 722 00:39:23,170 --> 00:39:27,450 Speaker 2: a production of Pushkin Industries. It's recorded at Wardour Studios 723 00:39:27,570 --> 00:39:31,050 Speaker 2: in London by Tom Berry. If you like the show, 724 00:39:31,330 --> 00:39:34,450 Speaker 2: please remember to share, rate and review. It really makes 725 00:39:34,490 --> 00:39:36,050 Speaker 2: a difference to us and if you want to hear 726 00:39:36,090 --> 00:39:39,410 Speaker 2: the show, add free sign up to Pushkin Plus on 727 00:39:39,530 --> 00:39:43,290 Speaker 2: the show page, on Apple Podcasts or at pushkin dot fm, 728 00:39:43,410 --> 00:39:44,450 Speaker 2: slash plus