1 00:00:00,200 --> 00:00:02,160 Speaker 1: I was just saying to Joe, I'm annoyed that we 2 00:00:02,200 --> 00:00:04,760 Speaker 1: didn't go to the Freight Rate Waves conference this year 3 00:00:04,840 --> 00:00:08,760 Speaker 1: because they had a puppy playpen just like a bunch 4 00:00:08,760 --> 00:00:12,719 Speaker 1: of puppies at this conference. And we went last year 5 00:00:12,760 --> 00:00:14,040 Speaker 1: and they did not have puppies. 6 00:00:14,480 --> 00:00:18,320 Speaker 2: Yeah, we had some like work thing, the one there's 7 00:00:18,360 --> 00:00:21,200 Speaker 2: a board build or board group that's over an international square, 8 00:00:21,239 --> 00:00:23,840 Speaker 2: which is not like in the FED complex. And they did. 9 00:00:23,880 --> 00:00:25,840 Speaker 2: They had a day where they had all this stuff 10 00:00:25,840 --> 00:00:27,440 Speaker 2: for the people in the building, and when was a 11 00:00:27,480 --> 00:00:28,200 Speaker 2: puppy play pen. 12 00:00:28,480 --> 00:00:31,120 Speaker 3: So this is a new thing adult to need, like 13 00:00:31,200 --> 00:00:32,879 Speaker 3: puppy play pens at events. 14 00:00:32,880 --> 00:00:36,680 Speaker 1: To be sorry, why not wait the food trucks and 15 00:00:36,800 --> 00:00:39,960 Speaker 1: puppies preferably ones that are up for adoption, you know, 16 00:00:40,000 --> 00:00:44,040 Speaker 1: from animal shelters, and you have the complete the ideal conference, 17 00:00:44,200 --> 00:00:45,279 Speaker 1: in my opinion. 18 00:00:47,680 --> 00:00:54,760 Speaker 3: I did a deadlift one, two, three, Jimmy Okay, up barges. 19 00:00:54,880 --> 00:00:57,000 Speaker 3: This isn't after school special except. 20 00:00:56,640 --> 00:00:59,200 Speaker 1: I've decided I'm going to base my entire personality going 21 00:00:59,200 --> 00:01:02,440 Speaker 1: forward on painting for a strategic pork reserve in the US. 22 00:01:02,560 --> 00:01:04,280 Speaker 3: Where's the best with imposta? 23 00:01:04,440 --> 00:01:05,880 Speaker 1: These are the important question. 24 00:01:06,000 --> 00:01:07,400 Speaker 2: Is it robots taking over the world. 25 00:01:07,520 --> 00:01:10,360 Speaker 3: No, I think that like in a couple of years, 26 00:01:10,520 --> 00:01:12,800 Speaker 3: the AI will do a really good job of making 27 00:01:12,800 --> 00:01:13,920 Speaker 3: the out launch podcast. 28 00:01:14,600 --> 00:01:16,800 Speaker 2: And people say, I don't really need to listen to 29 00:01:16,920 --> 00:01:21,440 Speaker 2: Joe and Tracy anymore. We do have the perfect. 30 00:01:22,800 --> 00:01:25,400 Speaker 1: Well in the meantime, this is lots more. 31 00:01:25,480 --> 00:01:27,920 Speaker 3: A weekly chat about whatever is on our minds. 32 00:01:31,640 --> 00:01:35,600 Speaker 1: We are speaking with Claudia Palm, the former Fed economist 33 00:01:35,640 --> 00:01:39,920 Speaker 1: who now has her own consultancy, Psalm Consulting. Claudia, it's 34 00:01:39,920 --> 00:01:41,400 Speaker 1: good to talk again. It's been a while. 35 00:01:42,280 --> 00:01:44,520 Speaker 2: Yeah, no, it's great to be on. I really appreciate it. 36 00:01:45,000 --> 00:01:47,680 Speaker 1: I have a really important question, which is do you 37 00:01:47,720 --> 00:01:50,240 Speaker 1: get tired of having to convince people that you do, 38 00:01:50,360 --> 00:01:53,800 Speaker 1: in fact know how the Psalm rule actually works, the 39 00:01:53,880 --> 00:01:56,600 Speaker 1: rule that's named after you. 40 00:01:56,720 --> 00:02:01,760 Speaker 2: Yeah. No, men are fascinating. I agree. It's it's interesting. 41 00:02:01,800 --> 00:02:03,960 Speaker 2: I have people that ask me like they can't quite 42 00:02:04,080 --> 00:02:06,600 Speaker 2: you know, match it, so they email me very politely, 43 00:02:06,760 --> 00:02:09,520 Speaker 2: we you know, because it like details matter, right, exactly 44 00:02:09,560 --> 00:02:12,320 Speaker 2: what average you're taking and stuff. But the people who 45 00:02:12,800 --> 00:02:15,760 Speaker 2: try to explain to me that I've calculated it wrong, 46 00:02:16,040 --> 00:02:19,040 Speaker 2: it's like, and then get really not happy with me 47 00:02:19,080 --> 00:02:22,280 Speaker 2: when I don't agree with them and just anyways, So 48 00:02:22,639 --> 00:02:24,639 Speaker 2: that's I guess just part of I mean, I'm glad 49 00:02:24,639 --> 00:02:27,360 Speaker 2: they're touching data. They clearly don't know what to do 50 00:02:27,440 --> 00:02:28,400 Speaker 2: with it, but it's good. 51 00:02:28,760 --> 00:02:31,799 Speaker 3: Can we run a Bloomberg headline some call and men 52 00:02:31,840 --> 00:02:32,680 Speaker 3: are fascinating? 53 00:02:32,840 --> 00:02:34,520 Speaker 1: I think that that could be one. 54 00:02:34,400 --> 00:02:36,760 Speaker 3: Of those Bloomberg red headlines. I think that goes across 55 00:02:36,840 --> 00:02:38,000 Speaker 3: the wire when this comes out. 56 00:02:38,160 --> 00:02:40,720 Speaker 2: Yeah, you should ask my editor Bob Burgess a Bloomberg 57 00:02:40,800 --> 00:02:43,119 Speaker 2: opinion if I can write that piece. I'm not sure 58 00:02:43,120 --> 00:02:44,239 Speaker 2: how green light that one. 59 00:02:44,840 --> 00:02:46,200 Speaker 1: I thought we could get that commission. 60 00:02:46,880 --> 00:02:50,040 Speaker 3: Maybe you know, a lot of our listeners started listening 61 00:02:50,080 --> 00:02:52,280 Speaker 3: to us in sort of like twenty twenty, the pandemic era. 62 00:02:52,320 --> 00:02:54,120 Speaker 3: But we've we had called it you on I think 63 00:02:54,160 --> 00:02:59,160 Speaker 3: in January or February twenty twenty before COVID hit, just 64 00:02:59,240 --> 00:03:02,280 Speaker 3: sort of talking theoretically about this idea of okay, what 65 00:03:02,320 --> 00:03:06,480 Speaker 3: are these indicators of recession, et cetera. And then it 66 00:03:06,680 --> 00:03:10,120 Speaker 3: was literally six weeks later and we're like, oh, well, 67 00:03:10,120 --> 00:03:12,760 Speaker 3: we were talking about theoretically six weeks ago, and now 68 00:03:12,800 --> 00:03:15,520 Speaker 3: we have to talk about real So here we are. 69 00:03:15,520 --> 00:03:17,560 Speaker 3: I don't think we've had you I don't think we've 70 00:03:18,040 --> 00:03:19,320 Speaker 3: chatted with you since then, though. 71 00:03:19,880 --> 00:03:23,079 Speaker 2: Yeah, no, I show up when the world is creening 72 00:03:23,120 --> 00:03:25,720 Speaker 2: towards the cliff, so I you know, I'm happy to 73 00:03:25,760 --> 00:03:27,880 Speaker 2: be helpful, and you know we can do good policy, 74 00:03:27,919 --> 00:03:30,080 Speaker 2: we can do it better. And yet it is not 75 00:03:30,760 --> 00:03:34,960 Speaker 2: cheerful when I start getting press calls, lots of press calls. 76 00:03:35,080 --> 00:03:38,960 Speaker 1: I remember, though, that was actually an incredibly prescient conversation 77 00:03:39,280 --> 00:03:42,440 Speaker 1: in many ways because we did talk about the Psalm rule, 78 00:03:42,520 --> 00:03:45,720 Speaker 1: and we should maybe mention on this podcast what exactly 79 00:03:45,720 --> 00:03:48,040 Speaker 1: it is. But the whole idea of the Psalm rule 80 00:03:48,200 --> 00:03:51,760 Speaker 1: was that it would be an early indicator of recession, 81 00:03:52,120 --> 00:03:56,600 Speaker 1: so that policy makers would be able to actually start 82 00:03:56,760 --> 00:04:01,480 Speaker 1: doing something about, you know, impending a contraction. And I 83 00:04:01,520 --> 00:04:04,240 Speaker 1: think people kind of forget about that now. 84 00:04:04,320 --> 00:04:07,760 Speaker 2: Right, Like you said, this is a recession indicator, so 85 00:04:07,800 --> 00:04:10,560 Speaker 2: it doesn't forecast a recession, but very early in a recession, 86 00:04:10,800 --> 00:04:14,680 Speaker 2: way before like the NBER would call the recession, way 87 00:04:14,680 --> 00:04:17,320 Speaker 2: before we'd even have a lot of information on GDP. 88 00:04:18,040 --> 00:04:20,359 Speaker 2: This unemployment rate should tell us we're in a recession. 89 00:04:21,000 --> 00:04:23,200 Speaker 2: Let's get going, right, And there are certain things that 90 00:04:23,240 --> 00:04:26,000 Speaker 2: Congress does in almost every recession, though, I'm not holding 91 00:04:26,000 --> 00:04:29,560 Speaker 2: my breath if one comes soon with sending out stimulus checks, 92 00:04:29,640 --> 00:04:34,200 Speaker 2: enhancing unemployment benefits, maybe getting money to communities. So just 93 00:04:34,520 --> 00:04:37,520 Speaker 2: tie it to an economic indicator. I think the other 94 00:04:37,560 --> 00:04:40,080 Speaker 2: thing that's important with the stimulus checks, which should be 95 00:04:40,080 --> 00:04:43,679 Speaker 2: a lesson from this last cycle, is you can ahead 96 00:04:43,720 --> 00:04:46,719 Speaker 2: of time determine how big should they be, who should 97 00:04:46,760 --> 00:04:50,760 Speaker 2: they go to, and if we should repeat them, right, 98 00:04:50,800 --> 00:04:52,640 Speaker 2: and how big they should be if you repeated them, 99 00:04:52,640 --> 00:04:55,520 Speaker 2: because you can absolutely see the politics that got wrapped 100 00:04:55,560 --> 00:04:59,320 Speaker 2: into the stimulus checks and not a lot of guidance 101 00:04:59,839 --> 00:05:01,919 Speaker 2: right in terms of exactly when should we do it, 102 00:05:01,960 --> 00:05:05,120 Speaker 2: how much should we do it after the initial response. 103 00:05:05,360 --> 00:05:07,560 Speaker 2: So yes, I think taking the politics out of the 104 00:05:07,600 --> 00:05:10,080 Speaker 2: things we are always do and then let Congress focus 105 00:05:10,120 --> 00:05:12,920 Speaker 2: on what's new in that recession. 106 00:05:13,400 --> 00:05:16,440 Speaker 1: Yeah, so speaking of what's new and kind of going 107 00:05:16,480 --> 00:05:20,520 Speaker 1: back to the men are fascinating topic. But I think 108 00:05:20,600 --> 00:05:24,280 Speaker 1: one of the controversies with the discussion around the SAM 109 00:05:24,400 --> 00:05:27,680 Speaker 1: rule right now is you've said that you know, in 110 00:05:27,720 --> 00:05:31,760 Speaker 1: some respects this is kind of a backward looking empirical measure. 111 00:05:31,880 --> 00:05:35,479 Speaker 1: So the Sam rule has happened for previous recessions, but 112 00:05:35,720 --> 00:05:38,680 Speaker 1: that doesn't necessarily mean that it's going to happen this 113 00:05:38,800 --> 00:05:43,039 Speaker 1: time around. And I saw one guy on Twitter slash 114 00:05:43,360 --> 00:05:46,080 Speaker 1: x who just was furious that you were sort of 115 00:05:46,120 --> 00:05:49,000 Speaker 1: like doubting your own rule. And it was just hilarious 116 00:05:49,000 --> 00:05:51,960 Speaker 1: to me because you know, who knows better about the 117 00:05:52,040 --> 00:05:55,800 Speaker 1: limitations or the possibilities of this particular theory than its 118 00:05:55,839 --> 00:05:58,440 Speaker 1: actual creator. And yet this guy was like, I don't 119 00:05:58,520 --> 00:06:01,680 Speaker 1: understand why you're not into your own rule? Why would 120 00:06:01,760 --> 00:06:02,360 Speaker 1: you doubt it? 121 00:06:03,000 --> 00:06:04,760 Speaker 2: Yeah? No, I had someone last night told me I 122 00:06:04,839 --> 00:06:09,440 Speaker 2: was having an identity crisis and someone else, I mean, 123 00:06:09,480 --> 00:06:11,919 Speaker 2: And I've gotten emails, I think well intentioned ones of 124 00:06:12,040 --> 00:06:14,919 Speaker 2: like you're being too self deprecating and you're you know, 125 00:06:14,960 --> 00:06:16,520 Speaker 2: and it's like, no, no, no, see, I'm just I'm 126 00:06:16,520 --> 00:06:20,440 Speaker 2: being honest here, right. This is an empirical regularity. It's 127 00:06:20,480 --> 00:06:24,120 Speaker 2: a pattern that's true of every single model, every single 128 00:06:24,480 --> 00:06:28,680 Speaker 2: indicator forecast that we have in macroeconomics. They're all trained 129 00:06:28,720 --> 00:06:32,359 Speaker 2: on the past, and this present has been really unlike 130 00:06:32,400 --> 00:06:32,799 Speaker 2: the past. 131 00:06:33,279 --> 00:06:34,920 Speaker 3: Oh yeah, it's a I mean that makes a ton 132 00:06:34,960 --> 00:06:37,919 Speaker 3: of sense, right, All these different models have broken apart 133 00:06:38,560 --> 00:06:41,360 Speaker 3: this current cycle almost like nothing else that we've seen. 134 00:06:41,520 --> 00:06:44,239 Speaker 3: So it's good to have some humility about whether things 135 00:06:44,240 --> 00:06:46,640 Speaker 3: that empirically seem to be true in the past. Well, 136 00:06:46,720 --> 00:06:49,360 Speaker 3: what do you since you said, you know, when your 137 00:06:49,400 --> 00:06:51,719 Speaker 3: phone is ringing off the hook, that's usually a sign 138 00:06:51,920 --> 00:06:54,919 Speaker 3: of maybe things are going wrong. And it does happen 139 00:06:54,960 --> 00:06:57,560 Speaker 3: to be true that the unemployment rate has ticked up 140 00:06:57,600 --> 00:07:00,480 Speaker 3: two three point nine percent. Earlier in the year it 141 00:07:00,600 --> 00:07:03,760 Speaker 3: was three point four percent. Three point nine percent happens 142 00:07:03,800 --> 00:07:06,320 Speaker 3: to be the highest since January twenty twenty two. So 143 00:07:06,800 --> 00:07:10,480 Speaker 3: it's obviously called people's attention. It's not necessarily recession indicate 144 00:07:10,680 --> 00:07:13,120 Speaker 3: recession sign yet, but it's real. What don't you, just 145 00:07:13,360 --> 00:07:17,320 Speaker 3: for the sake of listeners, et cetera, situate the current 146 00:07:17,560 --> 00:07:21,480 Speaker 3: unemployment trajectory within the context of your rule. 147 00:07:22,280 --> 00:07:25,080 Speaker 2: Okay, so I guess, just so we don't dance around, 148 00:07:25,120 --> 00:07:26,240 Speaker 2: what is this rule? Yeah? 149 00:07:26,280 --> 00:07:28,480 Speaker 3: Yeah, what's the oil and where are we right now? 150 00:07:28,920 --> 00:07:32,120 Speaker 2: Yeah? So we take the monthly unemployment rate. We take 151 00:07:32,160 --> 00:07:34,520 Speaker 2: a three month moving average. I mean, if you look 152 00:07:34,520 --> 00:07:37,320 Speaker 2: at the data, things bounce around. Even the unemployment rate, 153 00:07:37,360 --> 00:07:39,440 Speaker 2: it's pretty well measured, it bounces around. So you take 154 00:07:39,480 --> 00:07:42,880 Speaker 2: three month average, you compare in that series, you compare 155 00:07:43,040 --> 00:07:47,840 Speaker 2: the current data point with the lowest over the prior 156 00:07:47,920 --> 00:07:50,760 Speaker 2: twelve months. If you see an increase of a half 157 00:07:50,800 --> 00:07:54,400 Speaker 2: a percentage point or more, we are in the early 158 00:07:54,480 --> 00:07:58,480 Speaker 2: months of a recession. That indicator, the Samb rule, has 159 00:07:59,040 --> 00:08:03,640 Speaker 2: been highly accurate since the nineteen seventies. It's triggered in 160 00:08:03,720 --> 00:08:06,800 Speaker 2: every recession. It is not triggered outside of any recession. 161 00:08:07,400 --> 00:08:10,320 Speaker 2: You go back to World War Two. It's really pretty good. 162 00:08:10,360 --> 00:08:12,680 Speaker 2: There are some places where it does trigger outside of 163 00:08:12,720 --> 00:08:16,360 Speaker 2: a recession. And as you said, the unemployment rate has 164 00:08:16,400 --> 00:08:18,320 Speaker 2: been rising. If you look, you do not need my 165 00:08:18,400 --> 00:08:20,400 Speaker 2: rule to tell you it has been rising. You know, 166 00:08:20,520 --> 00:08:23,280 Speaker 2: since the middle of the year. The Psalm rule is 167 00:08:23,400 --> 00:08:26,080 Speaker 2: currently at three tenths, so it is short of its trigger. 168 00:08:26,880 --> 00:08:31,000 Speaker 2: That three tenths is not It is not a good sign, 169 00:08:31,320 --> 00:08:33,000 Speaker 2: right like, there are more people out of jobs that 170 00:08:33,320 --> 00:08:38,320 Speaker 2: are looking for jobs, and it's often it does go 171 00:08:38,400 --> 00:08:41,640 Speaker 2: into a recession once the unemployment but not always, right like, 172 00:08:41,679 --> 00:08:45,280 Speaker 2: this is not we're not in the we're over the 173 00:08:45,320 --> 00:08:47,600 Speaker 2: cliff stage of this. But if you look just at 174 00:08:47,600 --> 00:08:51,080 Speaker 2: the monthly unemployment rates, they're up a half a percentage point. 175 00:08:51,240 --> 00:08:53,680 Speaker 2: So I do a lot of education about the Psalm rule, 176 00:08:54,280 --> 00:08:56,720 Speaker 2: right like, people get the half a percentage point trigger 177 00:08:56,760 --> 00:09:00,559 Speaker 2: in their head, but like that's not for this rule, right, 178 00:09:00,640 --> 00:09:02,000 Speaker 2: Like and it is, like I said, this is not 179 00:09:02,080 --> 00:09:05,920 Speaker 2: a good sign. One of the things that has frustrated 180 00:09:05,960 --> 00:09:09,120 Speaker 2: me both with the inflation debate and now what I'm 181 00:09:09,160 --> 00:09:11,720 Speaker 2: seeing with unemployment is we got to look under the 182 00:09:11,720 --> 00:09:16,360 Speaker 2: hood here, like why are things moving around the way 183 00:09:16,440 --> 00:09:21,240 Speaker 2: they are? And it's the case that we finally in 184 00:09:21,280 --> 00:09:25,079 Speaker 2: the labor market, have really seen the supply of workers, 185 00:09:25,480 --> 00:09:28,400 Speaker 2: you know, the people that want jobs coming back. And 186 00:09:28,679 --> 00:09:31,080 Speaker 2: at the same time, we've seen the pace of hiring 187 00:09:31,640 --> 00:09:34,280 Speaker 2: slow down. The job games we're seeing every month slow down. 188 00:09:34,320 --> 00:09:37,640 Speaker 2: They're still good, but we're in this space of now 189 00:09:37,679 --> 00:09:41,120 Speaker 2: we've got more workers and the jobs have to catch 190 00:09:41,200 --> 00:09:44,240 Speaker 2: up with them, Whereas when we were in labor shortage world, 191 00:09:44,760 --> 00:09:47,000 Speaker 2: it was turned around. We had all these jobs and 192 00:09:47,000 --> 00:09:50,080 Speaker 2: the workers had to catch up. So to me, it's 193 00:09:50,120 --> 00:09:53,600 Speaker 2: like that's the story because otherwise, like if this were 194 00:09:53,679 --> 00:09:58,280 Speaker 2: a demand driven recession, a demand or a demand driven recovery, 195 00:09:58,320 --> 00:10:02,680 Speaker 2: a demand driven inflation, then the logic of once the 196 00:10:02,720 --> 00:10:06,120 Speaker 2: unemployment rate gets going, it keeps going, right, You've got 197 00:10:06,160 --> 00:10:08,360 Speaker 2: to have you got to have a story here, and 198 00:10:08,400 --> 00:10:11,640 Speaker 2: one that is grounded in reality. Those are the best stories, 199 00:10:12,240 --> 00:10:16,000 Speaker 2: at least if you do macro. 200 00:10:24,559 --> 00:10:27,200 Speaker 3: Speaking of macro, Tracy, you and I we went to 201 00:10:27,280 --> 00:10:30,119 Speaker 3: separate parties last night. We went to separate events. 202 00:10:30,440 --> 00:10:33,720 Speaker 1: Yes, you abandoned me again the second you're supposed to 203 00:10:33,720 --> 00:10:35,480 Speaker 1: come with me, and then you're like, oh oops, I 204 00:10:35,600 --> 00:10:36,800 Speaker 1: r s VP for something else. 205 00:10:36,840 --> 00:10:37,640 Speaker 3: I double book. 206 00:10:38,000 --> 00:10:40,960 Speaker 1: I'm sensing a pattern here of ditching Tracy. 207 00:10:41,160 --> 00:10:42,880 Speaker 3: No, but this came up, and so I was at 208 00:10:42,880 --> 00:10:46,640 Speaker 3: this sort of cocktail thing that Rick Plosius and previous 209 00:10:46,720 --> 00:10:49,280 Speaker 3: lots more guests, Neil Dunna read, and some of this 210 00:10:49,440 --> 00:10:52,360 Speaker 3: question came up about Okay, the rise in the unemployment rate, 211 00:10:52,400 --> 00:10:55,160 Speaker 3: how much how much is this due to weakening demand 212 00:10:55,240 --> 00:10:57,439 Speaker 3: for labor versus how much is this due to increase 213 00:10:57,440 --> 00:11:00,640 Speaker 3: supply of labor people coming off the sideline. The one 214 00:11:00,679 --> 00:11:02,680 Speaker 3: thing that seems to be true, as he pointed out, 215 00:11:03,160 --> 00:11:04,760 Speaker 3: and I thought this was sort of a good thing 216 00:11:04,800 --> 00:11:07,320 Speaker 3: to think about, is that either one of the stories, 217 00:11:07,320 --> 00:11:10,600 Speaker 3: whether it's about slowing demand for labor increased supply, probably 218 00:11:10,720 --> 00:11:14,040 Speaker 3: going to depress the state of wage growth, which is, 219 00:11:14,080 --> 00:11:16,840 Speaker 3: if you're the fed, probably something you want to see. 220 00:11:16,880 --> 00:11:21,360 Speaker 3: Regardless in part of feeling confident that the inflation problem 221 00:11:21,400 --> 00:11:22,680 Speaker 3: is getting close to being solved. 222 00:11:22,960 --> 00:11:25,240 Speaker 1: Wait, just going back to reality. I actually have a 223 00:11:25,240 --> 00:11:29,480 Speaker 1: personal anecdote on this, which is my mother retired during 224 00:11:29,559 --> 00:11:32,640 Speaker 1: COVID and she is now unretired really and she's back 225 00:11:32,640 --> 00:11:35,199 Speaker 1: in the workforce as of about a month ago. 226 00:11:35,360 --> 00:11:38,560 Speaker 3: When is she coming out that lot? Why did she? Yeah, 227 00:11:38,600 --> 00:11:40,120 Speaker 3: we gotta get this story then. 228 00:11:40,240 --> 00:11:43,040 Speaker 1: Wait, Claudia, can I ask did you see this morning? 229 00:11:43,040 --> 00:11:46,880 Speaker 1: So Brent Donnelly, another All Thoughts guest, he published a 230 00:11:46,920 --> 00:11:50,280 Speaker 1: sort of modified PSALM rule that was based on initial 231 00:11:50,360 --> 00:11:53,880 Speaker 1: claims instead of the unemployment rate, and it looked kind 232 00:11:53,880 --> 00:11:55,440 Speaker 1: of interesting. I don't know if you've had a chance 233 00:11:55,480 --> 00:11:56,200 Speaker 1: to look at it yet. 234 00:11:56,440 --> 00:11:59,760 Speaker 2: Yeah. Branda actually sent me his piece ahead of time 235 00:11:59,800 --> 00:12:02,280 Speaker 2: to make sure he was characterizing this SAM rule correctly. 236 00:12:02,760 --> 00:12:05,439 Speaker 2: Oh night, So some men are smart about how they 237 00:12:05,480 --> 00:12:07,760 Speaker 2: do this. So yeah, so I got a preview of 238 00:12:07,800 --> 00:12:12,440 Speaker 2: the piece. It's really interesting. This the context of the 239 00:12:12,480 --> 00:12:14,440 Speaker 2: same rules. It was designed as we were talking about 240 00:12:14,480 --> 00:12:17,720 Speaker 2: for the automatic stablers and the fiscal policy. It needed 241 00:12:17,720 --> 00:12:20,080 Speaker 2: to be simple like this. It's supposed to be like 242 00:12:20,120 --> 00:12:24,840 Speaker 2: in legislation. It's something everyone follows, and it's a well 243 00:12:25,000 --> 00:12:30,120 Speaker 2: measured kind of stable creature. Right, So I never thought 244 00:12:30,120 --> 00:12:31,680 Speaker 2: I was gonna get in the business of are we 245 00:12:31,720 --> 00:12:34,280 Speaker 2: in a recession? I was supposed to be about now 246 00:12:34,280 --> 00:12:37,360 Speaker 2: we help people, right, So in any case, I'm happy 247 00:12:37,400 --> 00:12:40,520 Speaker 2: to be useful as I can. So I've always said 248 00:12:40,720 --> 00:12:43,760 Speaker 2: it's it's entirely possible that you end up with there 249 00:12:43,800 --> 00:12:48,160 Speaker 2: are better indicators of recessions, and better in the sense 250 00:12:48,240 --> 00:12:50,240 Speaker 2: that I mean, mine is highly accurate, but it better 251 00:12:50,280 --> 00:12:55,360 Speaker 2: would be like signaling, and even sooner. The SAM usually triggers, 252 00:12:55,520 --> 00:12:57,640 Speaker 2: you know, two to three months inside of a recession. 253 00:12:58,120 --> 00:13:01,600 Speaker 2: You could nail it right on day one. Be super claims. 254 00:13:01,600 --> 00:13:05,720 Speaker 2: We get claims weekly. It's faster moving. I never looked 255 00:13:05,720 --> 00:13:07,760 Speaker 2: at claims I have, I mean, having been at the 256 00:13:07,800 --> 00:13:11,280 Speaker 2: FED and following data all these kind of data before 257 00:13:11,320 --> 00:13:15,800 Speaker 2: I left claims. I just don't feel comfortable using a 258 00:13:15,880 --> 00:13:21,640 Speaker 2: data series where actually deer season can cause strange aberrations 259 00:13:21,679 --> 00:13:23,439 Speaker 2: and the data to the point Department of Labor has 260 00:13:23,440 --> 00:13:25,440 Speaker 2: to publish that like in the little header. 261 00:13:25,760 --> 00:13:29,080 Speaker 3: Oh my god, so this is people what taking off 262 00:13:29,120 --> 00:13:30,480 Speaker 3: time from I did not. 263 00:13:30,600 --> 00:13:35,160 Speaker 2: Yeah, you're more often you'll see like plant closures like 264 00:13:35,240 --> 00:13:37,840 Speaker 2: regular like in the auto industry. There's regular closures at 265 00:13:37,880 --> 00:13:40,760 Speaker 2: certain times the year to do maintenance. So that's a 266 00:13:40,800 --> 00:13:43,840 Speaker 2: more typical one to show in the headline. But years 267 00:13:43,840 --> 00:13:45,640 Speaker 2: ago when I was at the FED, there was one 268 00:13:45,679 --> 00:13:48,120 Speaker 2: on deer season in Michigan because it you know, these 269 00:13:48,160 --> 00:13:52,840 Speaker 2: are monthly things, but it's extremely timely. It's something absolutely 270 00:13:52,920 --> 00:13:54,600 Speaker 2: to Faull like, I keep an eye on. I mean, 271 00:13:54,679 --> 00:13:56,800 Speaker 2: came out today, right, you know, and it does tell 272 00:13:56,880 --> 00:13:59,400 Speaker 2: us something, so I wasn't. And I've had other friends 273 00:13:59,400 --> 00:14:02,240 Speaker 2: who have, you know, push claims as a way to 274 00:14:02,280 --> 00:14:04,319 Speaker 2: do this. I think that's fine. It makes a lot 275 00:14:04,320 --> 00:14:08,200 Speaker 2: of sense to me, you know, in the forecasting. I mean, heck, 276 00:14:08,240 --> 00:14:10,680 Speaker 2: if we still use the yield curve to say anything 277 00:14:10,679 --> 00:14:14,080 Speaker 2: about a recession coming, I would feel much more comfortable 278 00:14:14,120 --> 00:14:16,400 Speaker 2: at having something claims in, you know, in the labor 279 00:14:16,440 --> 00:14:19,960 Speaker 2: market space, just because you know, I said, like, the 280 00:14:20,040 --> 00:14:24,400 Speaker 2: labor market is so central to this recovery, any recovery. 281 00:14:24,760 --> 00:14:28,560 Speaker 2: It's been really strong. Most Americans spend their paychecks as 282 00:14:28,560 --> 00:14:31,040 Speaker 2: long as they still have jobs, they're still out there spending. 283 00:14:31,200 --> 00:14:35,000 Speaker 2: If they're unable to spend, like we're done, right, there's 284 00:14:35,040 --> 00:14:38,200 Speaker 2: seventy percent of the economy, so you have this change. 285 00:14:38,200 --> 00:14:40,680 Speaker 2: So to me, thinking about anything tied to the labor 286 00:14:40,720 --> 00:14:43,320 Speaker 2: market as an early indicator, it makes so much sense. 287 00:14:43,640 --> 00:14:46,440 Speaker 3: The other thing that I find to be very powerful 288 00:14:46,560 --> 00:14:51,800 Speaker 3: about this idea and the importance of trying to capture 289 00:14:51,800 --> 00:14:54,760 Speaker 3: something that happens early in the recession is that one 290 00:14:54,760 --> 00:14:58,120 Speaker 3: thing that you just see and Alex Williams over at 291 00:14:58,160 --> 00:15:01,680 Speaker 3: Employee America has talked about the as well, and obviously 292 00:15:01,760 --> 00:15:04,840 Speaker 3: it informs some of the core logic of what we're 293 00:15:04,840 --> 00:15:07,240 Speaker 3: working on. Is once it gets going, the rise and 294 00:15:07,280 --> 00:15:10,760 Speaker 3: the unemployment rate, it really gets moving. And so maybe 295 00:15:10,800 --> 00:15:14,240 Speaker 3: three in the three point three, three point nine percent, 296 00:15:14,760 --> 00:15:16,480 Speaker 3: it's a you know, it's still a blow four percent 297 00:15:16,520 --> 00:15:19,400 Speaker 3: that's really good, but historically, like once it gets going, 298 00:15:19,880 --> 00:15:22,480 Speaker 3: you know, you'd expect it to seriously, if the recession 299 00:15:23,160 --> 00:15:25,880 Speaker 3: starts to snowball, it could go to six or seven 300 00:15:25,920 --> 00:15:29,400 Speaker 3: percent very fast or in a fairly short period of time. 301 00:15:30,240 --> 00:15:33,560 Speaker 3: And so the idea, I guess, you know, in the 302 00:15:33,640 --> 00:15:38,360 Speaker 3: dream world of rules based fiscal automatic stabilizers, that seems 303 00:15:38,400 --> 00:15:40,520 Speaker 3: like the process that you really want a short circuit. 304 00:15:40,920 --> 00:15:43,280 Speaker 2: Yeah no, and I mean over in the monetary policy 305 00:15:43,360 --> 00:15:46,080 Speaker 2: side too, yeah right, and they I mean they you know, 306 00:15:46,120 --> 00:15:49,280 Speaker 2: at the FED, getting ahead of it would be more 307 00:15:49,320 --> 00:15:51,520 Speaker 2: would be ideal, not waiting until we're in it. And 308 00:15:51,600 --> 00:15:53,880 Speaker 2: to your point, the you know, looking at the kind 309 00:15:53,920 --> 00:15:57,840 Speaker 2: of recent set, like since the seventies of recessions, the 310 00:15:57,880 --> 00:16:00,200 Speaker 2: mildest one for two thousand and one, you saw two 311 00:16:00,240 --> 00:16:04,080 Speaker 2: percentage point increase in the unemployment or two percentage point 312 00:16:04,120 --> 00:16:06,480 Speaker 2: increase the unmployment rates. That would put us well above 313 00:16:06,560 --> 00:16:10,760 Speaker 2: five and the typical increase is more like four percentage 314 00:16:10,760 --> 00:16:14,480 Speaker 2: points or close to four. So yeah, I mean, if 315 00:16:14,680 --> 00:16:17,040 Speaker 2: anything you can do to show well, if you can 316 00:16:17,120 --> 00:16:21,920 Speaker 2: short circuit the cycle, that's amazing. If you can tamp 317 00:16:22,000 --> 00:16:25,440 Speaker 2: it down right, and the sooner you get out relief, 318 00:16:25,480 --> 00:16:28,520 Speaker 2: whether it's from the FED or from Congress, the better 319 00:16:28,640 --> 00:16:33,880 Speaker 2: chance you have at softening the blow, decreasing the hardship, 320 00:16:34,080 --> 00:16:37,480 Speaker 2: and like making this not one of the bad ones. 321 00:16:51,880 --> 00:16:53,920 Speaker 1: Can I get your view on something as an economist 322 00:16:53,920 --> 00:16:56,120 Speaker 1: and someone who is sort of dealing with data on 323 00:16:56,160 --> 00:16:58,760 Speaker 1: a day to data basis, I have this pet theory 324 00:16:58,960 --> 00:17:02,640 Speaker 1: and I'm kind of in early stages of actually seeing 325 00:17:02,800 --> 00:17:06,359 Speaker 1: if it's true or not. This is the way journalism works, 326 00:17:06,400 --> 00:17:09,679 Speaker 1: but it's intuitively attractive to me, but the idea is 327 00:17:09,720 --> 00:17:13,480 Speaker 1: that a lot of the economic data that we have now, 328 00:17:13,560 --> 00:17:18,200 Speaker 1: which is you know, de facto aggregate, isn't as informative 329 00:17:18,280 --> 00:17:22,280 Speaker 1: because the sort of distribution within those indices is a 330 00:17:22,280 --> 00:17:25,840 Speaker 1: lot more extreme, maybe than it used to be. So 331 00:17:26,800 --> 00:17:29,359 Speaker 1: a classic example would be if you look at the 332 00:17:29,400 --> 00:17:33,359 Speaker 1: Michigan Survey of Consumer sentiment, you look at the aggregate number, 333 00:17:33,359 --> 00:17:36,320 Speaker 1: but of course if you break it down by Republicans 334 00:17:36,400 --> 00:17:41,560 Speaker 1: or Democrats, they're going in almost completely opposite directions. And 335 00:17:41,600 --> 00:17:45,359 Speaker 1: I have a feeling that that might be the case 336 00:17:45,480 --> 00:17:47,879 Speaker 1: for a lot of different things right now, but I 337 00:17:47,960 --> 00:17:51,600 Speaker 1: haven't actually started breaking down the data to see if 338 00:17:51,640 --> 00:17:53,840 Speaker 1: it's true or not. So I guess my question is, like, 339 00:17:54,280 --> 00:17:58,000 Speaker 1: how reliable are a lot of these aggregate figures at 340 00:17:58,000 --> 00:18:00,760 Speaker 1: the moment or is there really a diff difference in 341 00:18:00,840 --> 00:18:03,840 Speaker 1: the way we're measuring the economy or the suitability of 342 00:18:03,920 --> 00:18:07,399 Speaker 1: economic data for the post COVID economy versus how we 343 00:18:07,400 --> 00:18:08,439 Speaker 1: were doing things before. 344 00:18:09,280 --> 00:18:12,399 Speaker 2: Right You're you're absolutely onto something, and it goes in 345 00:18:12,760 --> 00:18:16,760 Speaker 2: the category of know thy data, like who is in 346 00:18:16,800 --> 00:18:18,960 Speaker 2: this and how are we measuring? So something like the 347 00:18:19,000 --> 00:18:22,199 Speaker 2: unemployment rate or the Michigan Survey Sentiment which I'm a 348 00:18:22,280 --> 00:18:26,560 Speaker 2: huge fan of too. Every single person counts the same, right, 349 00:18:26,640 --> 00:18:29,440 Speaker 2: Like these are take all of the people that are surveyed, 350 00:18:29,560 --> 00:18:32,640 Speaker 2: take an average, and you get more. You can get 351 00:18:32,680 --> 00:18:35,360 Speaker 2: more detail, whether it's in the household survey that goes 352 00:18:35,400 --> 00:18:37,800 Speaker 2: into the unemployment rate or the Michigan survey that's got 353 00:18:37,800 --> 00:18:41,119 Speaker 2: individual respondents, right, so you can do a lot and 354 00:18:41,160 --> 00:18:44,679 Speaker 2: they're everybody, everybody is equal. If you go into something 355 00:18:44,720 --> 00:18:51,760 Speaker 2: like GDP inflation consumer spending, there, we don't all count 356 00:18:51,760 --> 00:18:55,080 Speaker 2: the same, right, Like even with inflation, which I think 357 00:18:55,080 --> 00:18:57,359 Speaker 2: that's one where people kind of miss this one more. 358 00:18:58,040 --> 00:19:00,439 Speaker 2: Inflation is like what is the price of the shopping 359 00:19:00,520 --> 00:19:04,280 Speaker 2: cart this month versus last month? Some people put a 360 00:19:04,320 --> 00:19:08,000 Speaker 2: lot more into the US shopping cart than others. Right. 361 00:19:08,080 --> 00:19:12,840 Speaker 2: So they're represented now, thankfully. And this has been because 362 00:19:12,880 --> 00:19:16,480 Speaker 2: the concern that you raised is one that's out there, right, 363 00:19:16,520 --> 00:19:18,560 Speaker 2: Like for years when I was at the FED, because 364 00:19:18,560 --> 00:19:20,520 Speaker 2: I worked on consumer spending, it's like, okay, let's think 365 00:19:20,520 --> 00:19:23,280 Speaker 2: about the distributions. And there were times where some patterns 366 00:19:23,280 --> 00:19:28,159 Speaker 2: were not holding up, and inequality on what looked like 367 00:19:28,280 --> 00:19:30,840 Speaker 2: it could have been doing something like the wealth effect 368 00:19:30,840 --> 00:19:34,120 Speaker 2: has really moved around in strange ways, not strange necessarily, 369 00:19:34,200 --> 00:19:37,440 Speaker 2: but so there's an awareness of this, and I give 370 00:19:38,080 --> 00:19:43,199 Speaker 2: huge kudos to some of the official statistical agencies and 371 00:19:43,240 --> 00:19:46,320 Speaker 2: that we have so much more data on the distribution. 372 00:19:47,880 --> 00:19:53,000 Speaker 2: All of my academic style research is using household micro data, 373 00:19:53,400 --> 00:19:56,439 Speaker 2: but I'm always using it to answer macro questions. So 374 00:19:56,480 --> 00:19:58,679 Speaker 2: I'm a big fan of taking the micro up to 375 00:19:58,680 --> 00:20:01,480 Speaker 2: the macro and the macro down to the micro like 376 00:20:01,520 --> 00:20:04,639 Speaker 2: in terms of conversations, and there's a lot more data 377 00:20:04,720 --> 00:20:06,960 Speaker 2: like that. I'll give you a shout out to the 378 00:20:06,960 --> 00:20:09,199 Speaker 2: FED because it's one of my favorite data says to 379 00:20:09,200 --> 00:20:11,760 Speaker 2: go look at. For a long time, they've published the 380 00:20:11,760 --> 00:20:14,240 Speaker 2: financial Accounts or what was called the Flow of funds 381 00:20:14,680 --> 00:20:16,800 Speaker 2: before that had all these different pieces of wealth in 382 00:20:16,840 --> 00:20:21,560 Speaker 2: the economy quarterly, and now they also published the distributional 383 00:20:21,600 --> 00:20:25,280 Speaker 2: financial accounts. They use the Survey of Consumer Finances, which 384 00:20:25,280 --> 00:20:29,119 Speaker 2: is the household survey to split apart, quarter by quarter 385 00:20:30,119 --> 00:20:33,480 Speaker 2: on the household side, who's got this wealth, And it's 386 00:20:33,560 --> 00:20:36,399 Speaker 2: absolutely fascinating and it's one where you can see these 387 00:20:36,440 --> 00:20:40,480 Speaker 2: distributions and how much they've expanded. I mean, the reality 388 00:20:40,560 --> 00:20:42,760 Speaker 2: is the United States has been a very unequal country 389 00:20:42,800 --> 00:20:46,120 Speaker 2: for quite some time, but you do have to think 390 00:20:46,119 --> 00:20:49,359 Speaker 2: about when you're doing the macro, how things could be 391 00:20:49,400 --> 00:20:52,760 Speaker 2: spreading out and leading you astray. And yet you can 392 00:20:52,840 --> 00:20:54,639 Speaker 2: look out at the recovery. Now I've had a lot 393 00:20:54,640 --> 00:20:55,960 Speaker 2: of people will be like, oh, this is just the 394 00:20:56,040 --> 00:20:57,880 Speaker 2: rich and the poor. It's like, no, no, no, See 395 00:20:57,920 --> 00:21:02,280 Speaker 2: this has been good, not for every single person, but 396 00:21:02,560 --> 00:21:05,200 Speaker 2: up and down the distributed, like this is not normal 397 00:21:05,720 --> 00:21:09,560 Speaker 2: for a recovery in terms of how much people you know, 398 00:21:09,760 --> 00:21:12,159 Speaker 2: bottom half of the income distribution, like they're in a 399 00:21:12,160 --> 00:21:14,600 Speaker 2: better place than they were going into COVID. Yeah. 400 00:21:14,640 --> 00:21:17,200 Speaker 1: Actually, just on that note, and this kind of goes 401 00:21:17,240 --> 00:21:20,720 Speaker 1: against my very half formed thesis at the moment, but 402 00:21:20,760 --> 00:21:22,800 Speaker 1: I think there was a paper from the Boston Fed 403 00:21:23,000 --> 00:21:26,920 Speaker 1: that looked at excess savings or just personal savings post 404 00:21:27,000 --> 00:21:30,880 Speaker 1: COVID to see whether or not a, you know, savings 405 00:21:30,920 --> 00:21:34,240 Speaker 1: were coming down, but b whether or not lower income 406 00:21:34,320 --> 00:21:38,320 Speaker 1: households were burning through their savings faster than wealthier households. 407 00:21:38,600 --> 00:21:41,200 Speaker 1: And they found that I think it was pretty much 408 00:21:41,240 --> 00:21:44,320 Speaker 1: even keel like everyone was kind of reducing their savings 409 00:21:44,320 --> 00:21:47,600 Speaker 1: at the same rate, which was somewhat surprising to me 410 00:21:47,680 --> 00:21:51,000 Speaker 1: but kind of speaks to your point about how kind 411 00:21:51,080 --> 00:21:54,760 Speaker 1: of unusual this recovery has been in that it has 412 00:21:54,920 --> 00:21:58,240 Speaker 1: benefited lower income households. I don't want to say as 413 00:21:58,320 --> 00:22:01,080 Speaker 1: much as wealthier households, but like you have seen that. 414 00:22:01,040 --> 00:22:06,040 Speaker 2: Effect absolutely, I mean in an unprecedented way, and it's 415 00:22:06,160 --> 00:22:10,520 Speaker 2: very heartening right to see this now. I okay, so 416 00:22:10,560 --> 00:22:14,080 Speaker 2: I understand the exercise. I have found excess savings to 417 00:22:14,119 --> 00:22:18,480 Speaker 2: be somewhat defensive. But it's bothered me because, first of all, 418 00:22:18,680 --> 00:22:20,760 Speaker 2: just the I'm very big on labels, which is not 419 00:22:21,200 --> 00:22:23,600 Speaker 2: normal for a macro economist, but it's like excess savings, 420 00:22:23,720 --> 00:22:25,439 Speaker 2: especially when talk about bottom It's like, you know what, 421 00:22:25,520 --> 00:22:27,800 Speaker 2: if I had to pick who's got the excess, it 422 00:22:27,840 --> 00:22:30,200 Speaker 2: would not be at the bottom, right, Like, what is 423 00:22:30,280 --> 00:22:31,800 Speaker 2: the definition of excess savings? 424 00:22:31,840 --> 00:22:34,480 Speaker 1: I've never actually thought about it, but like what determines 425 00:22:34,560 --> 00:22:35,160 Speaker 1: the excess? 426 00:22:35,400 --> 00:22:38,720 Speaker 3: I'm glad you say this, Claudia, I've I've never understood this, 427 00:22:38,760 --> 00:22:40,520 Speaker 3: so I'm curious you're answer to this question. 428 00:22:40,720 --> 00:22:45,439 Speaker 2: Yeah, So how people define the reference point? Very some 429 00:22:45,600 --> 00:22:48,240 Speaker 2: across the research paper. It's usually something in the space 430 00:22:48,320 --> 00:22:52,880 Speaker 2: of what was the wealth before the savings before COVID 431 00:22:52,960 --> 00:22:55,719 Speaker 2: and here's savings is usually like what's in your checking account? 432 00:22:55,760 --> 00:22:58,720 Speaker 2: What's a mutual fight? Like things you could get too quickly. Okay, 433 00:22:58,760 --> 00:23:02,160 Speaker 2: So some people it's like more of a just before COVID. 434 00:23:02,240 --> 00:23:05,280 Speaker 2: Most of the time it's like some trend, right, like 435 00:23:05,280 --> 00:23:07,960 Speaker 2: how savings was growing, you know, because it was with 436 00:23:08,040 --> 00:23:10,600 Speaker 2: income and whatever. Anyway, so they've got this kind of 437 00:23:10,640 --> 00:23:14,400 Speaker 2: trend line and it's simply comparing, Okay, how much savings 438 00:23:14,440 --> 00:23:18,639 Speaker 2: do these groups have relative to where we would have 439 00:23:18,720 --> 00:23:22,760 Speaker 2: thought if COVID had never happened, right, And so what's 440 00:23:22,760 --> 00:23:26,520 Speaker 2: been fascinating this Later everybody's got an estimate this thing 441 00:23:27,480 --> 00:23:30,239 Speaker 2: that you know, oh there's more, there's more. And I 442 00:23:30,240 --> 00:23:35,960 Speaker 2: can remember having a conversation this was twenty one or 443 00:23:36,000 --> 00:23:40,040 Speaker 2: maybe early twenty two, where everybody, even the administration, was 444 00:23:40,040 --> 00:23:42,119 Speaker 2: talking about, well, when the excess savings is gone, the 445 00:23:42,160 --> 00:23:44,480 Speaker 2: inflation will come down. And I'm just like, this is 446 00:23:44,480 --> 00:23:47,280 Speaker 2: not something to wish for here in terms of the savings. 447 00:23:47,359 --> 00:23:50,040 Speaker 2: You want people to have a buffer. And I was 448 00:23:50,080 --> 00:23:52,760 Speaker 2: talking with one macroeconomist and this just shows some of 449 00:23:52,800 --> 00:23:56,439 Speaker 2: the biases of my tribe. Was just shocked that the 450 00:23:56,480 --> 00:23:59,440 Speaker 2: savings was still there, because in most of our models, 451 00:23:59,480 --> 00:24:02,120 Speaker 2: people who don't have wealth. The way we model that 452 00:24:02,400 --> 00:24:06,560 Speaker 2: is they have no impulse control. They're not patient. And 453 00:24:06,600 --> 00:24:08,359 Speaker 2: I was telling this person to say, you know, maybe 454 00:24:08,400 --> 00:24:12,720 Speaker 2: they just don't have income to save like most people 455 00:24:12,800 --> 00:24:14,200 Speaker 2: want to. It's hard to. 456 00:24:14,080 --> 00:24:16,200 Speaker 1: Save if you don't actually have any money. 457 00:24:16,720 --> 00:24:19,639 Speaker 2: Yeah, and so that's what because I think what comes 458 00:24:19,800 --> 00:24:22,040 Speaker 2: what's missing a lot of these excess savings. It's not 459 00:24:22,080 --> 00:24:25,159 Speaker 2: just those stimulus checks we got years ago. Isn't like 460 00:24:25,200 --> 00:24:26,400 Speaker 2: the paychecks are bigger. 461 00:24:26,680 --> 00:24:31,240 Speaker 3: Yeah. It's so interesting because I guess people moralize savings 462 00:24:31,280 --> 00:24:33,760 Speaker 3: so much, right, this is what this idea of, Oh, 463 00:24:33,800 --> 00:24:36,240 Speaker 3: it's lack of impulse control. If we're just better people, 464 00:24:36,960 --> 00:24:38,960 Speaker 3: then we would have more savings. Of course, it makes 465 00:24:38,960 --> 00:24:41,960 Speaker 3: no sense because if we saved more than that's less 466 00:24:41,960 --> 00:24:45,720 Speaker 3: income from for someone else, theoretically crimps their ability to save. 467 00:24:45,760 --> 00:24:49,560 Speaker 3: But it the moralization comes so clear when people start 468 00:24:49,600 --> 00:25:07,399 Speaker 3: talking about saving two quick things. I really like your 469 00:25:07,440 --> 00:25:10,960 Speaker 3: point about data. I guess because I'm getting old. Occasionally, 470 00:25:11,280 --> 00:25:13,640 Speaker 3: people now more and more in my life reach out 471 00:25:13,640 --> 00:25:17,320 Speaker 3: to me for career advice and financial journalism, which is 472 00:25:17,320 --> 00:25:18,760 Speaker 3: something I've noticed in the last couple of years. I 473 00:25:18,760 --> 00:25:21,119 Speaker 3: guess it's a sign that, like I'm a gray beard 474 00:25:21,119 --> 00:25:24,200 Speaker 3: in this space. But I always say that just get 475 00:25:24,240 --> 00:25:26,840 Speaker 3: to really know a data point. All the smartest people. 476 00:25:26,960 --> 00:25:29,119 Speaker 3: I feel that Tracy and I talked to you. Actually, 477 00:25:29,119 --> 00:25:31,640 Speaker 3: I would say there's two categories of people that I'm 478 00:25:31,640 --> 00:25:35,280 Speaker 3: always impressed by. People who really understand how banks work 479 00:25:36,160 --> 00:25:39,359 Speaker 3: seriously are consistently a cut above. And people who have 480 00:25:39,480 --> 00:25:42,760 Speaker 3: really spent time understanding what a data point is actually 481 00:25:42,840 --> 00:25:45,760 Speaker 3: saying and how it's collected and what's underneath the guts 482 00:25:46,160 --> 00:25:48,040 Speaker 3: is opposed to just sort of shooting from the hip 483 00:25:48,080 --> 00:25:52,160 Speaker 3: from what the headline says. So I strongly agree that. 484 00:25:52,200 --> 00:25:53,879 Speaker 3: One other thing, I just want to say, you know, 485 00:25:54,080 --> 00:25:57,160 Speaker 3: we have this discord where we chat and I went 486 00:25:57,200 --> 00:26:00,600 Speaker 3: in I said, does anyone have any questions for Claudia. 487 00:26:01,600 --> 00:26:04,960 Speaker 3: We're having around lots more and there actually haven't been 488 00:26:05,040 --> 00:26:07,600 Speaker 3: a lot of questions, but you are getting a lot 489 00:26:07,640 --> 00:26:10,560 Speaker 3: of praise in there. Someone says Claudia is great. If 490 00:26:10,600 --> 00:26:12,920 Speaker 3: you ever want to stare into the mouth of madness, 491 00:26:13,280 --> 00:26:17,440 Speaker 3: check her replies on Twitter. Yes. Another person says you'll 492 00:26:17,440 --> 00:26:20,639 Speaker 3: see enough man explaining to drive a person to drink. 493 00:26:21,640 --> 00:26:24,560 Speaker 3: Another person says are over says you're in the arena 494 00:26:24,640 --> 00:26:29,119 Speaker 3: trying stuff successfully. Another person says nothing but respect for 495 00:26:29,359 --> 00:26:32,879 Speaker 3: real heroes, the people who EJ. M R hate, And 496 00:26:32,920 --> 00:26:37,760 Speaker 3: I know that's an entire separate world. Of My understanding 497 00:26:37,840 --> 00:26:43,520 Speaker 3: is it's sort of basically four Chan for economic students. Yeah, anyway, 498 00:26:43,720 --> 00:26:47,800 Speaker 3: many big fans of yours in the discord and particularly 499 00:26:48,400 --> 00:26:50,480 Speaker 3: the way you deal with people on Twitter. 500 00:26:51,480 --> 00:26:55,960 Speaker 2: So I do that's good. And I've tried to pace 501 00:26:56,119 --> 00:26:59,080 Speaker 2: myself some like you can't fight every battle, so I 502 00:26:59,080 --> 00:26:59,880 Speaker 2: think I've been doing better. 503 00:27:00,680 --> 00:27:04,679 Speaker 1: It takes so much patience and so much emotional energy. 504 00:27:04,760 --> 00:27:07,080 Speaker 1: And I really don't think, Sorry, this is gonna be 505 00:27:07,160 --> 00:27:09,920 Speaker 1: me ranting for a second. I don't think guys get it. 506 00:27:10,119 --> 00:27:13,280 Speaker 1: Really And speaking of data, Joe, this is actually interesting. 507 00:27:13,280 --> 00:27:17,440 Speaker 1: I once did a spreadsheet on Twitter replies to very 508 00:27:17,480 --> 00:27:19,760 Speaker 1: similar tweets that we both put out. It was about 509 00:27:19,880 --> 00:27:23,359 Speaker 1: bitcoin being an inflation hedge, so you can imagine what 510 00:27:23,400 --> 00:27:25,400 Speaker 1: it was. And you said something that was like very 511 00:27:25,400 --> 00:27:28,880 Speaker 1: similar to what I said, and I thought, because these 512 00:27:28,880 --> 00:27:32,399 Speaker 1: tweets are quite similar, it's a really good test case 513 00:27:32,520 --> 00:27:35,800 Speaker 1: to see and gauge the amount of abuse that each 514 00:27:35,840 --> 00:27:38,840 Speaker 1: one gets from crypto bros on Twitter. And I can 515 00:27:38,880 --> 00:27:42,760 Speaker 1: tell you, like sheer volume, I got multiples of what 516 00:27:42,800 --> 00:27:46,720 Speaker 1: you did. But the other interesting thing was the insults 517 00:27:46,760 --> 00:27:51,400 Speaker 1: themselves varied, So most of yours were calling you stupid, 518 00:27:52,000 --> 00:27:55,560 Speaker 1: and most of mine were just ad hominem attacks either 519 00:27:55,760 --> 00:27:59,119 Speaker 1: you know, like attacking my what I look like or 520 00:27:59,240 --> 00:28:03,080 Speaker 1: just calling me names, which I also thought was interested anyway, 521 00:28:03,640 --> 00:28:08,480 Speaker 1: So you know, data analysis, data analysis, yeah. 522 00:28:08,240 --> 00:28:10,000 Speaker 2: No, and I will say too, I mean one of 523 00:28:10,000 --> 00:28:12,760 Speaker 2: the things that I do a lot of macro and policy. 524 00:28:12,840 --> 00:28:17,719 Speaker 2: I am very passionate about economics becoming more diverse, and 525 00:28:17,840 --> 00:28:20,280 Speaker 2: especially in the policy world. That's my space, right is 526 00:28:20,320 --> 00:28:22,080 Speaker 2: at the FED and White House and all that, and 527 00:28:22,320 --> 00:28:24,480 Speaker 2: there's an aspect of me trying to be out there 528 00:28:24,560 --> 00:28:28,120 Speaker 2: just as an example. Right, It's really hard to get 529 00:28:28,119 --> 00:28:30,440 Speaker 2: that kind of abuse, and it can get in your 530 00:28:30,480 --> 00:28:33,840 Speaker 2: head at least it gets in mind sometimes. But I 531 00:28:33,880 --> 00:28:36,720 Speaker 2: do want people to see and not just other women, 532 00:28:36,840 --> 00:28:39,680 Speaker 2: like you don't have to do this like the rest 533 00:28:39,720 --> 00:28:42,840 Speaker 2: of the macro bros, right, Like we each have our style, 534 00:28:42,880 --> 00:28:44,760 Speaker 2: but I try it, and I've had students come up 535 00:28:44,760 --> 00:28:47,560 Speaker 2: to me like it's very encouraging, and you know, so 536 00:28:47,640 --> 00:28:50,200 Speaker 2: hopefully those that come after us won't have to deal 537 00:28:50,240 --> 00:28:52,080 Speaker 2: with that kind of abuse or at least less and 538 00:28:52,200 --> 00:28:54,840 Speaker 2: less of it, but yeah go team here. 539 00:28:59,440 --> 00:29:02,560 Speaker 3: Lots More is produced by Carmen Rodriguez and dash Ol Bennett, 540 00:29:02,640 --> 00:29:03,840 Speaker 3: with help from Moses Anda. 541 00:29:04,240 --> 00:29:06,080 Speaker 1: Our sound engineer is Blake Maple. 542 00:29:06,240 --> 00:29:08,240 Speaker 3: Sage Bauman is our head of Podcasts. 543 00:29:08,320 --> 00:29:10,280 Speaker 1: We'll catch you next time for lots more. 544 00:29:10,480 --> 00:29:11,240 Speaker 3: Thanks for listening. 545 00:29:27,320 --> 00:29:28,760 Speaker 2: That's it cool. 546 00:29:29,200 --> 00:29:31,800 Speaker 1: Half an hour until the next one. Half an hour 547 00:29:31,920 --> 00:29:32,520 Speaker 1: free time. 548 00:29:33,160 --> 00:29:33,600 Speaker 2: Enjoy it.