1 00:00:10,760 --> 00:00:15,120 Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast. 2 00:00:15,200 --> 00:00:19,640 Speaker 1: I'm Joe Wisenthal and I'm Tracy all Away. So, Tracy, 3 00:00:19,760 --> 00:00:23,479 Speaker 1: something I've noticed before is we talked about E s 4 00:00:23,520 --> 00:00:27,440 Speaker 1: G investing sometimes, and of course, as we've discussed in 5 00:00:27,480 --> 00:00:30,440 Speaker 1: the past, you know, it's become this huge industry. The 6 00:00:30,480 --> 00:00:32,280 Speaker 1: thing that I always think about in E s G 7 00:00:32,600 --> 00:00:35,480 Speaker 1: is the E the environment. There's a lot of talk 8 00:00:35,560 --> 00:00:38,360 Speaker 1: about climate and so forth, and it really is like 9 00:00:38,400 --> 00:00:40,240 Speaker 1: I don't really know anything about what the s of 10 00:00:40,320 --> 00:00:43,000 Speaker 1: the G are all about. Yeah, that would be the 11 00:00:43,159 --> 00:00:46,760 Speaker 1: social governance part of the equation. And it's absolutely true 12 00:00:46,760 --> 00:00:48,920 Speaker 1: when you think about the E s G space, most 13 00:00:48,960 --> 00:00:51,440 Speaker 1: of the action tends to be on doing things to 14 00:00:51,520 --> 00:00:56,320 Speaker 1: combat climate change. There's quite a bit um, but not 15 00:00:56,440 --> 00:01:01,480 Speaker 1: as much about things like gender equality. But it beyond that, 16 00:01:01,600 --> 00:01:05,200 Speaker 1: like this sort of social aspect you just don't hear 17 00:01:05,240 --> 00:01:08,360 Speaker 1: about that much. Yeah, exactly right. And this sort of 18 00:01:08,400 --> 00:01:13,280 Speaker 1: like the connection between these social aspects and returns is 19 00:01:13,319 --> 00:01:15,680 Speaker 1: always interesting and it's always sort of one of the 20 00:01:15,760 --> 00:01:17,640 Speaker 1: central questions of all the sort of E s G 21 00:01:17,959 --> 00:01:21,920 Speaker 1: is like how much is it about investing with one's values? 22 00:01:22,200 --> 00:01:25,360 Speaker 1: People want to invest in companies where they feel comfortable 23 00:01:25,360 --> 00:01:28,320 Speaker 1: with the values of the company versus searching out sort 24 00:01:28,319 --> 00:01:31,560 Speaker 1: of E. S G signals that can actually deliver alpha 25 00:01:31,760 --> 00:01:36,080 Speaker 1: and somehow generate higher returns. Yeah. I think that's a 26 00:01:36,080 --> 00:01:38,560 Speaker 1: really good way of framing it. And it's sort of 27 00:01:38,680 --> 00:01:41,679 Speaker 1: unclear at this moment of time whether or not you 28 00:01:41,680 --> 00:01:46,240 Speaker 1: would expect out performance of a good company because more 29 00:01:46,280 --> 00:01:50,840 Speaker 1: people hopefully are interested in, you know, participating in a 30 00:01:50,960 --> 00:01:54,280 Speaker 1: stock of a company that's doing good things, or if 31 00:01:54,320 --> 00:01:57,680 Speaker 1: the company itself is sort of outperforming because it's more 32 00:01:57,800 --> 00:02:02,480 Speaker 1: conscientious on things like the environment or gender or wealth 33 00:02:02,520 --> 00:02:05,800 Speaker 1: inequality and things like that. Exactly right, So let's just 34 00:02:05,840 --> 00:02:07,760 Speaker 1: get right into it. I'm really excited today we're gonna 35 00:02:07,760 --> 00:02:11,160 Speaker 1: be talking about the S in E S G. And 36 00:02:11,280 --> 00:02:15,000 Speaker 1: we have a world famous guest that I'm excited to 37 00:02:15,040 --> 00:02:17,880 Speaker 1: be speaking with. We're gonna be speaking with um Dan 38 00:02:18,000 --> 00:02:22,679 Speaker 1: ari Eli. He is a famous behavioral economist at Duke University, 39 00:02:22,720 --> 00:02:25,360 Speaker 1: and he is also the co founder of a firm 40 00:02:25,400 --> 00:02:29,600 Speaker 1: called Irrational Capital, who has formed five years ago, and 41 00:02:29,680 --> 00:02:33,760 Speaker 1: it pursues the idea of looking at a company's human 42 00:02:33,960 --> 00:02:37,480 Speaker 1: capital factor as a as something that could drive out 43 00:02:37,520 --> 00:02:40,359 Speaker 1: performance in an investment. So I don't even know what 44 00:02:40,440 --> 00:02:43,400 Speaker 1: the human capital factor is, but I'm excited to hear 45 00:02:43,960 --> 00:02:47,640 Speaker 1: Dan talk about it and what he's learned in five years. Dan, 46 00:02:47,639 --> 00:02:50,960 Speaker 1: thank you so much for coming on odd lot my pleasion. 47 00:02:51,040 --> 00:02:53,239 Speaker 1: Nice to be here. Well, first of all, I love 48 00:02:53,280 --> 00:02:56,880 Speaker 1: the name irrational I love the name irrational capital. It's perfect. 49 00:02:56,880 --> 00:02:59,800 Speaker 1: But I'm curious what is the founding story of this? 50 00:03:00,400 --> 00:03:02,840 Speaker 1: So the founding story is I'm a I'm a university professor. 51 00:03:02,880 --> 00:03:05,840 Speaker 1: I do research on a few things, but among the 52 00:03:05,960 --> 00:03:11,240 Speaker 1: human motivation. And in my academic career, I've from time 53 00:03:11,280 --> 00:03:13,840 Speaker 1: to time I go to a company and I change 54 00:03:13,880 --> 00:03:18,040 Speaker 1: things around. I change bonuses, I try to increase productivity, 55 00:03:18,160 --> 00:03:21,000 Speaker 1: try to get people to care more about work. And 56 00:03:21,040 --> 00:03:23,200 Speaker 1: my my experience has been that it's always been very 57 00:03:23,240 --> 00:03:26,480 Speaker 1: easy to come and improve what people do and how 58 00:03:26,520 --> 00:03:30,440 Speaker 1: to increase motivation because most companies just don't think about 59 00:03:30,480 --> 00:03:33,720 Speaker 1: it very carefully. You know, if you think about h R, 60 00:03:34,800 --> 00:03:37,920 Speaker 1: h R is usually a function that is about legal 61 00:03:38,000 --> 00:03:43,720 Speaker 1: issues and I don't know training modules, but but it's 62 00:03:43,720 --> 00:03:45,920 Speaker 1: not really a function that says, let's just get the 63 00:03:45,960 --> 00:03:48,480 Speaker 1: best out of people. Let's just think about how do 64 00:03:48,520 --> 00:03:50,480 Speaker 1: we motivate people, how do we get people to come 65 00:03:50,840 --> 00:03:53,240 Speaker 1: happy to work? So I've been doing this for a 66 00:03:53,240 --> 00:03:57,360 Speaker 1: long time and it's it's easy to do, and it's 67 00:03:57,440 --> 00:04:00,760 Speaker 1: it's it's helpful. But when I met Dave, my partner 68 00:04:01,680 --> 00:04:04,040 Speaker 1: can ask me whether I think that we could also 69 00:04:04,240 --> 00:04:06,920 Speaker 1: look at something broader than instead of one company at 70 00:04:06,920 --> 00:04:10,320 Speaker 1: a time, whether there's some way to look at companies, 71 00:04:10,880 --> 00:04:13,240 Speaker 1: see how they treat their employees, see how the employees 72 00:04:13,280 --> 00:04:17,440 Speaker 1: feel about the company, and whether this could predict stock 73 00:04:17,480 --> 00:04:20,479 Speaker 1: market return. And I said, I don't know, that's what 74 00:04:20,640 --> 00:04:23,719 Speaker 1: academics answer. I don't know that's the standard answer, but 75 00:04:23,839 --> 00:04:26,279 Speaker 1: we can try it out. So we went on the 76 00:04:26,360 --> 00:04:30,920 Speaker 1: hunt for data to see whether this hypothesis would ald 77 00:04:31,000 --> 00:04:33,000 Speaker 1: or not, and it turns out it holds very well. 78 00:04:33,960 --> 00:04:37,080 Speaker 1: So can I just press you on one point, which 79 00:04:37,120 --> 00:04:41,760 Speaker 1: is what exactly is the definition of human capital? What 80 00:04:41,800 --> 00:04:47,720 Speaker 1: are you looking at as you know undertake this exercise? Yea, 81 00:04:47,839 --> 00:04:49,840 Speaker 1: so in this, in this exercise, and there's lots of 82 00:04:49,839 --> 00:04:52,880 Speaker 1: ways to think about it, you're absolutely right. In this exercise, 83 00:04:52,920 --> 00:04:56,480 Speaker 1: we said, let's take everything we can measure, everything that 84 00:04:56,520 --> 00:04:59,960 Speaker 1: we can have access to, and then let's see which 85 00:05:00,120 --> 00:05:05,600 Speaker 1: one of those things correlate actually predict stock market return. 86 00:05:06,600 --> 00:05:09,159 Speaker 1: And and we we had some theories from the beginning 87 00:05:09,920 --> 00:05:12,880 Speaker 1: about what would matter and what would not, but we 88 00:05:12,960 --> 00:05:16,720 Speaker 1: also gave the data an opportunity to speak. And most 89 00:05:16,760 --> 00:05:19,200 Speaker 1: of the things we found are very, very consistent with 90 00:05:20,160 --> 00:05:22,839 Speaker 1: what we find in social science. So, for example, we 91 00:05:22,960 --> 00:05:27,200 Speaker 1: find that absolute salary levels of absolute salary don't matter 92 00:05:27,279 --> 00:05:30,880 Speaker 1: so much to our performance in the stock market, but 93 00:05:31,000 --> 00:05:34,760 Speaker 1: the perception of fairness of salary matters a lot. Right. 94 00:05:34,880 --> 00:05:41,000 Speaker 1: We find that physical environment at work, things like tables, chairs, coffee, 95 00:05:41,680 --> 00:05:46,560 Speaker 1: don't matter so much. The sense of being appreciated matters 96 00:05:46,560 --> 00:05:49,680 Speaker 1: a lot. So so when we think about human capital, 97 00:05:49,760 --> 00:05:52,800 Speaker 1: we we start with the academic definition of let's take 98 00:05:52,839 --> 00:05:56,960 Speaker 1: everything we know about what creates motivation. And for example, 99 00:05:57,000 --> 00:06:00,599 Speaker 1: I did a study in which we showed that pizza 100 00:06:01,080 --> 00:06:03,440 Speaker 1: delivered to somebody at home it can be much more 101 00:06:03,480 --> 00:06:06,400 Speaker 1: motivating than a bonus if you think about the good 102 00:06:06,440 --> 00:06:09,720 Speaker 1: will that that results from that. Right, So we've did 103 00:06:09,920 --> 00:06:13,479 Speaker 1: lots of research on appreciation, so we said, hey, appreciation 104 00:06:13,560 --> 00:06:15,880 Speaker 1: is really important. Let's let's see if we can find 105 00:06:15,920 --> 00:06:18,839 Speaker 1: signals that companies that treat people in a way that 106 00:06:18,880 --> 00:06:21,599 Speaker 1: they feel appreciated actually do better in the stock market. 107 00:06:21,960 --> 00:06:24,640 Speaker 1: And and of course our exercise is to take the 108 00:06:24,760 --> 00:06:28,400 Speaker 1: human capital in company X in year one and predicts 109 00:06:28,440 --> 00:06:31,520 Speaker 1: the stock markets returned the following year. Right, we're not 110 00:06:31,560 --> 00:06:34,360 Speaker 1: trying to estimate at that moment. We're trying to say, 111 00:06:34,480 --> 00:06:38,200 Speaker 1: how is this human capital going to basically translate into 112 00:06:38,240 --> 00:06:42,240 Speaker 1: better procedures, better products, more more innovation and so on. 113 00:06:42,640 --> 00:06:44,880 Speaker 1: So so there's lots of things like that. And we 114 00:06:45,200 --> 00:06:47,400 Speaker 1: have data that goes back to two thousand and six 115 00:06:47,440 --> 00:06:51,560 Speaker 1: which we based a lot of our analysis on. But 116 00:06:51,640 --> 00:06:54,760 Speaker 1: we also went and and double down during COVID. We 117 00:06:54,760 --> 00:06:59,280 Speaker 1: studied fourteen hundred companies during COVID and and everything we 118 00:06:59,440 --> 00:07:03,680 Speaker 1: found that was important before COVID became even more important 119 00:07:04,640 --> 00:07:07,960 Speaker 1: during COVID and And the reason is think about kids, 120 00:07:08,680 --> 00:07:11,440 Speaker 1: and if a kid is in the classroom, the teacher 121 00:07:11,480 --> 00:07:14,520 Speaker 1: can control them to some degree. Right, it's straight, don't 122 00:07:14,520 --> 00:07:17,400 Speaker 1: look at your phone, focus on this. And the same 123 00:07:17,440 --> 00:07:19,520 Speaker 1: thing is true for us in the office. Right, nobody 124 00:07:19,560 --> 00:07:23,440 Speaker 1: says it's straight, but but we're being seen by other people. 125 00:07:23,560 --> 00:07:25,720 Speaker 1: We go to meetings, we have to focus. We can't 126 00:07:25,720 --> 00:07:29,480 Speaker 1: do completely different things all of a sudden when people 127 00:07:29,480 --> 00:07:33,720 Speaker 1: work from home. The role of intrinsic motivation is increasing dramatically, 128 00:07:34,960 --> 00:07:37,880 Speaker 1: and because the kind of things we test are basically 129 00:07:37,880 --> 00:07:40,320 Speaker 1: the things that lead to goodwill, the things that lead 130 00:07:40,360 --> 00:07:44,960 Speaker 1: to extra performance, extra motivation, the role of everything we 131 00:07:45,080 --> 00:07:50,480 Speaker 1: studied has increased quite dramatically during COVID. So this raises 132 00:07:50,480 --> 00:07:54,920 Speaker 1: the obvious question, which is with traditional factors. Of course, 133 00:07:54,960 --> 00:07:57,760 Speaker 1: you can look at the ten k or learning his 134 00:07:57,840 --> 00:08:02,760 Speaker 1: report with environment fair there's increasingly companies are putting out 135 00:08:02,840 --> 00:08:07,720 Speaker 1: some sort of separate perhaps an environmental scorecard that talks 136 00:08:07,720 --> 00:08:11,560 Speaker 1: about climate and what they're doing out sustainability. There, how 137 00:08:11,600 --> 00:08:14,880 Speaker 1: do you gather data in a systemic way on the 138 00:08:14,960 --> 00:08:17,840 Speaker 1: types of things you're talking about, such as fairness, perception, 139 00:08:18,040 --> 00:08:22,120 Speaker 1: and employee appreciation. Yeah, so there's lots of ways to 140 00:08:22,160 --> 00:08:25,120 Speaker 1: think about data. You can think about data from linked In, 141 00:08:25,240 --> 00:08:27,200 Speaker 1: and you can think about data from glass Door, and 142 00:08:27,240 --> 00:08:30,840 Speaker 1: you can think about those. There's also companies that collect 143 00:08:30,920 --> 00:08:34,240 Speaker 1: surveys about how employees feel about the company, and we 144 00:08:34,280 --> 00:08:37,760 Speaker 1: take all of them. And in the first few years 145 00:08:37,840 --> 00:08:42,360 Speaker 1: of our endeavor of irrational capital, we basically said, let's 146 00:08:42,360 --> 00:08:45,080 Speaker 1: just get as much data as possible and see what 147 00:08:45,320 --> 00:08:48,160 Speaker 1: matters and what doesn't matter. But once we found out 148 00:08:48,640 --> 00:08:51,760 Speaker 1: what matters, now we can focus. And I want to 149 00:08:51,800 --> 00:08:53,440 Speaker 1: kind of give you an example of why this is 150 00:08:53,440 --> 00:08:58,040 Speaker 1: so important. So think about gender equality. What an important topic? Right? 151 00:08:58,160 --> 00:09:01,800 Speaker 1: How important? It's incredibly important for investors to care about 152 00:09:02,600 --> 00:09:05,840 Speaker 1: gender equality. And and there are two reasons. One is, 153 00:09:06,400 --> 00:09:08,839 Speaker 1: you know, the moral issue, and the second one is performance. 154 00:09:09,000 --> 00:09:12,200 Speaker 1: If you discriminate half of your workforce, that can't possibly 155 00:09:12,240 --> 00:09:16,080 Speaker 1: be good for business. So there is something called the 156 00:09:16,120 --> 00:09:19,800 Speaker 1: She Index, and the She Index counts how many women 157 00:09:19,840 --> 00:09:22,679 Speaker 1: are in top positions and how many women are on 158 00:09:22,720 --> 00:09:26,440 Speaker 1: the board. And if you think about what that means 159 00:09:26,840 --> 00:09:29,880 Speaker 1: that the people who created this index had the thought 160 00:09:30,480 --> 00:09:32,679 Speaker 1: that this is a good proxy for how a company 161 00:09:32,720 --> 00:09:35,439 Speaker 1: is treating women. But if you look at the performance 162 00:09:35,440 --> 00:09:38,360 Speaker 1: of the SMP of the SMP five compared to the 163 00:09:38,400 --> 00:09:44,080 Speaker 1: SHE Index, the She Index is dramatically underperforming the dramatically 164 00:09:44,120 --> 00:09:49,280 Speaker 1: every year systematically. Now is this because it's not a 165 00:09:49,280 --> 00:09:52,800 Speaker 1: good idea to treat women equally? Of course not, but 166 00:09:52,800 --> 00:09:56,280 Speaker 1: but it's a proxy that doesn't really capture the essence 167 00:09:56,920 --> 00:10:00,240 Speaker 1: of what it means to be equal in country US. 168 00:10:00,400 --> 00:10:03,480 Speaker 1: We took our data and in our data, we looked 169 00:10:03,520 --> 00:10:06,160 Speaker 1: at how women feel compared to men on all kinds 170 00:10:06,200 --> 00:10:09,960 Speaker 1: of things, and it turns out that the delta between 171 00:10:10,000 --> 00:10:13,439 Speaker 1: women and men is incredibly important, you know it. It 172 00:10:13,760 --> 00:10:16,280 Speaker 1: doesn't matter so much if you're a company treats everybody 173 00:10:16,320 --> 00:10:19,960 Speaker 1: well or everybody badly, that doesn't matter so much. It's 174 00:10:20,000 --> 00:10:23,079 Speaker 1: the inequality that is very hard to get to. Because 175 00:10:23,480 --> 00:10:26,240 Speaker 1: if a company treats everybody by the way, it's good. 176 00:10:26,520 --> 00:10:29,319 Speaker 1: I'm not recommending that people start treating their employees badly, 177 00:10:29,360 --> 00:10:32,800 Speaker 1: but but in general, it matters less if you're equal 178 00:10:32,840 --> 00:10:35,959 Speaker 1: offender because people get used to it and that's the 179 00:10:36,360 --> 00:10:39,640 Speaker 1: level in which they evaluate themselves. But if you're a 180 00:10:39,679 --> 00:10:42,520 Speaker 1: woman and you feel that somebody next to in the 181 00:10:42,559 --> 00:10:45,520 Speaker 1: cubicle next to you is treated slightly better because they 182 00:10:45,520 --> 00:10:49,079 Speaker 1: have a different chromosome structure, that's just bothers you, no 183 00:10:49,280 --> 00:10:52,440 Speaker 1: end and it doesn't go away. So so as an example, 184 00:10:53,120 --> 00:10:55,720 Speaker 1: if we take our data set from two thousand and 185 00:10:55,760 --> 00:11:01,400 Speaker 1: six and every year we calculate which companies are the 186 00:11:01,440 --> 00:11:04,600 Speaker 1: ones that are treating their women most equal to men, 187 00:11:05,480 --> 00:11:08,520 Speaker 1: and we buy the top twenty percent companies every year 188 00:11:08,679 --> 00:11:11,560 Speaker 1: who are doing that, and we construct a portfolio like this. 189 00:11:12,600 --> 00:11:16,240 Speaker 1: That portfolio has a return of about five point four 190 00:11:16,280 --> 00:11:19,920 Speaker 1: percent a year above the SNP just just by looking 191 00:11:19,960 --> 00:11:23,240 Speaker 1: at equality nothing else. You're saying, let's just invest in 192 00:11:23,280 --> 00:11:27,720 Speaker 1: the top twenty percent companies who are treating women the 193 00:11:27,800 --> 00:11:30,360 Speaker 1: most equal, and you compare that to the she in 194 00:11:30,360 --> 00:11:35,119 Speaker 1: the xtation the excess underperforming. So so it's incredibly important 195 00:11:35,440 --> 00:11:40,199 Speaker 1: to actually do the research correctly and to start focusing 196 00:11:40,240 --> 00:11:43,320 Speaker 1: on what's important to measure right. In the same way 197 00:11:43,320 --> 00:11:46,680 Speaker 1: that I told you salary doesn't matter so much relative 198 00:11:46,720 --> 00:11:50,640 Speaker 1: salary fairness in salary matters a lot. We need to 199 00:11:50,679 --> 00:11:52,800 Speaker 1: figure out first what are the things that are actually 200 00:11:52,840 --> 00:11:56,120 Speaker 1: good proxies of motivation, and what are the things that 201 00:11:56,160 --> 00:12:00,240 Speaker 1: we think are predictive of motivation but really have thing 202 00:12:00,240 --> 00:12:04,480 Speaker 1: to do with it. We found that sometimes when companies 203 00:12:04,559 --> 00:12:09,400 Speaker 1: appoint women to high positions, it actually backfires, And and 204 00:12:09,440 --> 00:12:13,080 Speaker 1: the reason it backfires is because they make it clear 205 00:12:13,160 --> 00:12:17,480 Speaker 1: in some way to the women are working there that 206 00:12:17,559 --> 00:12:20,560 Speaker 1: they don't care about women issues. They're only doing things 207 00:12:20,600 --> 00:12:25,560 Speaker 1: for pr purposes. I think, think about the women engineer 208 00:12:25,600 --> 00:12:29,280 Speaker 1: at company X, and one day the company is appointing 209 00:12:29,600 --> 00:12:34,640 Speaker 1: a wonderful woman to the board. If if nothing follows 210 00:12:34,960 --> 00:12:37,840 Speaker 1: in terms of her daily life. She is going to 211 00:12:37,880 --> 00:12:42,520 Speaker 1: basically say, this company doesn't really care. They're doing things 212 00:12:42,600 --> 00:12:46,400 Speaker 1: for pr they're checking the box. That's that's not so 213 00:12:46,480 --> 00:12:49,840 Speaker 1: it actually creates sometimes what we call window dressing, that 214 00:12:49,880 --> 00:13:08,760 Speaker 1: it's just for outside purposes, and then it backfires. So 215 00:13:09,040 --> 00:13:13,040 Speaker 1: I love the nuance that you just described in measuring 216 00:13:13,200 --> 00:13:16,800 Speaker 1: gender inequality, the difference between just counting up the number 217 00:13:17,080 --> 00:13:21,080 Speaker 1: of women in leadership positions or who are on the board, 218 00:13:21,280 --> 00:13:26,000 Speaker 1: versus the discrepancy in how women and men perceived that 219 00:13:26,040 --> 00:13:29,720 Speaker 1: they are actually being treated. And I guess this is 220 00:13:29,720 --> 00:13:31,360 Speaker 1: a topic sort of close to my heart, but I 221 00:13:31,640 --> 00:13:35,040 Speaker 1: really think people should think more about it. But in 222 00:13:35,080 --> 00:13:38,840 Speaker 1: thinking about this issue, there are clearly different ways, um 223 00:13:38,880 --> 00:13:42,720 Speaker 1: to ask the question of how do you feel the 224 00:13:42,760 --> 00:13:46,440 Speaker 1: company is doing on gender equality? Doesn't treat men and 225 00:13:46,440 --> 00:13:49,679 Speaker 1: women the same, how do you feel the company treats you. 226 00:13:50,320 --> 00:13:52,680 Speaker 1: I'm curious, just on the data collection side, how do 227 00:13:52,720 --> 00:13:57,280 Speaker 1: you control for different surveys that are, you know, being 228 00:13:57,320 --> 00:14:01,880 Speaker 1: done by different consultancies, for are different firms, Because that 229 00:14:01,880 --> 00:14:05,440 Speaker 1: seems like a pretty big challenge. Yeah. So, so, first 230 00:14:05,440 --> 00:14:07,640 Speaker 1: of all, we have we've worked with all kinds of 231 00:14:07,720 --> 00:14:10,160 Speaker 1: data providers and of course it's easiest to work on 232 00:14:10,400 --> 00:14:13,440 Speaker 1: with one right because then the consistency of the question 233 00:14:13,559 --> 00:14:16,200 Speaker 1: is is equal, and then with the questions are not 234 00:14:16,559 --> 00:14:20,400 Speaker 1: the same. It does provide a challenge, but because a 235 00:14:20,440 --> 00:14:22,800 Speaker 1: lot of these data providers work with lots of companies, 236 00:14:22,800 --> 00:14:27,200 Speaker 1: there are statistical ways to try and calibrate them to 237 00:14:27,280 --> 00:14:31,400 Speaker 1: each other. But but we don't ask women how do 238 00:14:31,440 --> 00:14:33,800 Speaker 1: you feel women are treated? It's not about asking them 239 00:14:33,840 --> 00:14:37,720 Speaker 1: explicitly about this, but it's for example, you look at 240 00:14:37,720 --> 00:14:41,560 Speaker 1: the difference between how women feel that promotions are fair 241 00:14:41,680 --> 00:14:45,880 Speaker 1: versus how men feel that promotions are fair. By the way, 242 00:14:45,920 --> 00:14:47,960 Speaker 1: the same thing that is true about men and women 243 00:14:48,040 --> 00:14:51,640 Speaker 1: is also true about employees versus management. If you look 244 00:14:51,680 --> 00:14:54,400 Speaker 1: at the company and you say, you know, feeling appreciated 245 00:14:54,560 --> 00:14:57,720 Speaker 1: ends up being important. What happens to a company. Let's 246 00:14:57,720 --> 00:14:59,960 Speaker 1: just take a simple case. Imagine it's a five point scare. 247 00:15:00,000 --> 00:15:03,760 Speaker 1: All how appreciated do you feel? And let's say you 248 00:15:03,840 --> 00:15:07,840 Speaker 1: have a company where the employees are at three and 249 00:15:07,880 --> 00:15:10,960 Speaker 1: the management is at three and a half. All we 250 00:15:11,040 --> 00:15:15,560 Speaker 1: have a company where the employees are at four, much 251 00:15:15,600 --> 00:15:19,880 Speaker 1: higher and the management is at five. So the employees 252 00:15:19,880 --> 00:15:22,440 Speaker 1: are better and the management is much better. The gap 253 00:15:22,560 --> 00:15:26,200 Speaker 1: is different. Which of those companies is going to be 254 00:15:26,400 --> 00:15:29,880 Speaker 1: more successful in the stock market. They're going to our results. 255 00:15:30,720 --> 00:15:34,320 Speaker 1: It's the first one. Why because a lot of things 256 00:15:34,360 --> 00:15:39,120 Speaker 1: are about relative happiness. Think about your own life, right, 257 00:15:39,160 --> 00:15:41,240 Speaker 1: A lot of it is about where you feel compared 258 00:15:41,280 --> 00:15:45,320 Speaker 1: to other people. And sources of injustice are usually asked 259 00:15:45,360 --> 00:15:49,040 Speaker 1: compared to other people, men compared to women, women compared 260 00:15:49,080 --> 00:15:52,920 Speaker 1: to men, management compared to the employees. So so having 261 00:15:53,640 --> 00:15:57,880 Speaker 1: a consistent strategy is very important. And and one other 262 00:15:57,920 --> 00:16:01,960 Speaker 1: thing that we found incredibly importan and for for motivation 263 00:16:02,840 --> 00:16:05,680 Speaker 1: is so so I told you that feeling appreciated is 264 00:16:05,720 --> 00:16:08,640 Speaker 1: one and of course during COVID it was harder to 265 00:16:08,680 --> 00:16:11,760 Speaker 1: communicate to people that they matter, and the companies who 266 00:16:11,760 --> 00:16:15,080 Speaker 1: did this did much better. But another important thing is 267 00:16:15,400 --> 00:16:19,680 Speaker 1: feeling that people can make honest mistakes. And and this 268 00:16:19,720 --> 00:16:21,480 Speaker 1: is why it's so important, you know, think about it. 269 00:16:21,560 --> 00:16:25,600 Speaker 1: Every company wants people to innovate, but some companies want 270 00:16:25,600 --> 00:16:28,040 Speaker 1: people to innovate, but they also get them to feel 271 00:16:28,040 --> 00:16:32,440 Speaker 1: that if they will make a mistake, they will be punished, right, 272 00:16:32,480 --> 00:16:36,960 Speaker 1: they might not get promoted, they might lose something. And 273 00:16:37,000 --> 00:16:39,280 Speaker 1: every time you try something different, you take a risk. 274 00:16:40,240 --> 00:16:43,200 Speaker 1: So on one hand, companies declare that they want people 275 00:16:43,240 --> 00:16:44,920 Speaker 1: to take a risk. On the other hand, lots of 276 00:16:44,960 --> 00:16:48,320 Speaker 1: companies behave as if if people take a risk and 277 00:16:48,400 --> 00:16:51,400 Speaker 1: don't succeed, they will they will get penalized for it, 278 00:16:52,200 --> 00:16:54,920 Speaker 1: and the company is that score high on that that 279 00:16:55,040 --> 00:16:57,280 Speaker 1: people feel they will be penalized if they try something 280 00:16:57,320 --> 00:17:00,600 Speaker 1: new and didn't succeed. Of course do much worse in 281 00:17:00,640 --> 00:17:04,879 Speaker 1: the stock man. So you know, you you founded the 282 00:17:04,920 --> 00:17:08,199 Speaker 1: firm five years ago with the intent to see if 283 00:17:08,240 --> 00:17:11,440 Speaker 1: there was useful signal in searching out this data. And 284 00:17:11,880 --> 00:17:15,320 Speaker 1: five years is not actually that long of a time 285 00:17:15,400 --> 00:17:19,920 Speaker 1: really in terms of market history and what's a durable signal? 286 00:17:19,960 --> 00:17:22,200 Speaker 1: And I know a lot of like approaches they talk about, 287 00:17:22,240 --> 00:17:24,879 Speaker 1: like the idea of like out of sample data and 288 00:17:24,920 --> 00:17:26,560 Speaker 1: so you're like, look at something, but you want to 289 00:17:26,600 --> 00:17:29,920 Speaker 1: make sure you're not getting correlation and causation backwards or something. 290 00:17:29,960 --> 00:17:33,640 Speaker 1: So you need time. How do you sort of as 291 00:17:33,680 --> 00:17:38,000 Speaker 1: a as as an investor or someone selling investment products 292 00:17:38,000 --> 00:17:40,560 Speaker 1: built on this, how do you establish that what you've 293 00:17:40,600 --> 00:17:44,600 Speaker 1: identified these links between some of the survey data is uh, 294 00:17:44,720 --> 00:17:47,040 Speaker 1: is this is real and not just you know, maybe 295 00:17:47,040 --> 00:17:48,960 Speaker 1: a couple of years worth or something, and something that 296 00:17:49,000 --> 00:17:52,560 Speaker 1: will actually stand the test of time. So so a 297 00:17:52,560 --> 00:17:54,960 Speaker 1: couple of senses for this. The first one is, even 298 00:17:54,960 --> 00:17:57,520 Speaker 1: though we started five years ago, we were lucky enough 299 00:17:57,560 --> 00:18:00,399 Speaker 1: to get data that goes more backward in that so 300 00:18:00,440 --> 00:18:02,440 Speaker 1: we have data going back to two thousand and six. 301 00:18:03,280 --> 00:18:05,840 Speaker 1: So you know, it's slightly better than than five years, 302 00:18:05,880 --> 00:18:08,200 Speaker 1: but you know, it doesn't solve the problem in general. 303 00:18:09,040 --> 00:18:10,879 Speaker 1: The other thing is that you know, there's lots of 304 00:18:11,280 --> 00:18:14,040 Speaker 1: modeling approaches they're out there that are kind of black 305 00:18:14,080 --> 00:18:17,840 Speaker 1: box models. Our approach is not a black box model. 306 00:18:18,359 --> 00:18:21,280 Speaker 1: Like I start with things that are already proven in 307 00:18:21,320 --> 00:18:24,760 Speaker 1: the social science world. Right, so when I when I 308 00:18:24,800 --> 00:18:29,800 Speaker 1: looked for relative salary matters, fairness matters a lot much 309 00:18:29,840 --> 00:18:33,040 Speaker 1: more than absolute level. This is not hey, I just 310 00:18:33,080 --> 00:18:35,439 Speaker 1: discovered the feature in the data. This is based on 311 00:18:35,480 --> 00:18:38,680 Speaker 1: the hypothesis that we have from lots of other sources. 312 00:18:39,800 --> 00:18:41,800 Speaker 1: So you know, it's a little bit like like you 313 00:18:41,920 --> 00:18:44,920 Speaker 1: create a vaccine out there, you use a lot of 314 00:18:45,040 --> 00:18:49,159 Speaker 1: data about biology, You're not still doing a random model 315 00:18:49,200 --> 00:18:51,600 Speaker 1: and creating a vaccine. So so we have a better 316 00:18:51,640 --> 00:18:55,600 Speaker 1: starting point because we start with what we know matters. 317 00:18:55,600 --> 00:18:58,080 Speaker 1: We have a model of human behavior. But by the way, 318 00:18:58,400 --> 00:19:02,440 Speaker 1: the reason that Fern you don't matter, and the social 319 00:19:02,960 --> 00:19:06,520 Speaker 1: retirement benefits don't matter, and health benefits don't matter. These 320 00:19:06,520 --> 00:19:09,639 Speaker 1: are things that we we predicted up front, and the 321 00:19:09,680 --> 00:19:12,040 Speaker 1: reason they don't matter is they just fade to the background. 322 00:19:12,960 --> 00:19:15,159 Speaker 1: You know, you might be excited when you get hired 323 00:19:15,200 --> 00:19:17,400 Speaker 1: by all the benefits you have, but but how many 324 00:19:17,480 --> 00:19:19,159 Speaker 1: days do you wake up or go to work and 325 00:19:19,200 --> 00:19:21,880 Speaker 1: think about your retirement. But I love our ice coffee 326 00:19:22,080 --> 00:19:24,439 Speaker 1: here at the Bloom office, and every day it's the 327 00:19:24,480 --> 00:19:26,080 Speaker 1: first thing I do when I get it, as I 328 00:19:26,119 --> 00:19:29,359 Speaker 1: pour myself a big cup of the cold brew that 329 00:19:29,400 --> 00:19:31,000 Speaker 1: we have on tap here. So I just want to say, 330 00:19:31,040 --> 00:19:32,480 Speaker 1: I just want to give a shout out to our 331 00:19:32,480 --> 00:19:35,040 Speaker 1: cold break. I I hope they will not listen to 332 00:19:35,040 --> 00:19:37,320 Speaker 1: our show and take and take you aware your eyes 333 00:19:37,400 --> 00:19:41,240 Speaker 1: coffee to my intention. And the third thing, when you 334 00:19:41,320 --> 00:19:44,800 Speaker 1: think about what's causal and correlational, you can actually look 335 00:19:44,840 --> 00:19:47,800 Speaker 1: at the data. So we, as I told you, in 336 00:19:47,840 --> 00:19:52,399 Speaker 1: our modeling approach, we take employees state of motivation in 337 00:19:52,560 --> 00:19:54,679 Speaker 1: year one and we predict and let's in two thousands, 338 00:19:54,680 --> 00:19:57,000 Speaker 1: says we predict the stock market in two thousand seven. 339 00:19:57,000 --> 00:20:01,240 Speaker 1: We take two thousand nineteen, we predict E twenty. But 340 00:20:01,320 --> 00:20:03,840 Speaker 1: also it's a question of what matters. You know, when 341 00:20:03,840 --> 00:20:07,080 Speaker 1: the company does does well, there are some things they 342 00:20:07,080 --> 00:20:11,640 Speaker 1: can do better easily. They can increase employees retirement benefit, 343 00:20:11,720 --> 00:20:14,280 Speaker 1: they can buy better furniture and so on, and we 344 00:20:14,359 --> 00:20:16,520 Speaker 1: see that happening, But it turns out that's not the 345 00:20:16,520 --> 00:20:19,560 Speaker 1: important thing. If you if you look at what we found, 346 00:20:19,640 --> 00:20:23,080 Speaker 1: it matters, for example, the sense of feeling appreciated. That's 347 00:20:23,119 --> 00:20:25,679 Speaker 1: a deep cultural thing, not easy to measure, but it's 348 00:20:25,720 --> 00:20:29,840 Speaker 1: a deep cultural thing. And when companies do better financially, 349 00:20:30,080 --> 00:20:32,480 Speaker 1: it doesn't necessarily mean that all of a sudden we're 350 00:20:32,520 --> 00:20:36,399 Speaker 1: better at appreciating our employees. So between all of those 351 00:20:36,440 --> 00:20:39,720 Speaker 1: we do have more data than when we started. We're 352 00:20:39,720 --> 00:20:43,000 Speaker 1: starting with a strong theory about what motivates you and being, 353 00:20:43,000 --> 00:20:46,720 Speaker 1: and the data we explore it as much as possible 354 00:20:47,040 --> 00:20:50,440 Speaker 1: to see what matters and not, and we feel quite 355 00:20:50,480 --> 00:20:57,080 Speaker 1: confident that that it is a predictive model. So Joe 356 00:20:57,119 --> 00:21:00,640 Speaker 1: and I started out this conversation by king about how 357 00:21:00,640 --> 00:21:02,720 Speaker 1: the E and E s G tends to get a 358 00:21:02,800 --> 00:21:05,679 Speaker 1: lot more attention. Why do you think that is? Is 359 00:21:05,720 --> 00:21:08,280 Speaker 1: it a problem of data. This notion that maybe you 360 00:21:08,320 --> 00:21:11,840 Speaker 1: can quantify more easily what a company is doing to 361 00:21:11,920 --> 00:21:15,879 Speaker 1: reduce its carbon emissions, but quantifying what it's doing to 362 00:21:16,040 --> 00:21:19,560 Speaker 1: make its employees feel a sense of well being and 363 00:21:19,680 --> 00:21:24,919 Speaker 1: appreciated is much more difficult. I think, so you know, 364 00:21:25,200 --> 00:21:26,679 Speaker 1: in the same way that I told you about the 365 00:21:26,720 --> 00:21:28,760 Speaker 1: she in, because I think we often go to the 366 00:21:28,800 --> 00:21:31,520 Speaker 1: things that are easy to measure rather the things that 367 00:21:31,560 --> 00:21:35,800 Speaker 1: are important. Now, let's take let's take key and you 368 00:21:35,840 --> 00:21:38,080 Speaker 1: mentioned it in the beginning, and you basically said there 369 00:21:38,080 --> 00:21:42,920 Speaker 1: are two paths for it to be a good alpha strategy. 370 00:21:43,280 --> 00:21:45,520 Speaker 1: One is more people would want that stock, so that 371 00:21:45,600 --> 00:21:49,000 Speaker 1: stock will go up. That's one approach. The second approach 372 00:21:49,119 --> 00:21:55,119 Speaker 1: is saying, somehow being environmental would create more motivation for employees, 373 00:21:55,720 --> 00:21:58,800 Speaker 1: the employees will be more proud to work there. Well, 374 00:21:58,840 --> 00:22:02,199 Speaker 1: that's actually true. We find that most of the effect 375 00:22:02,280 --> 00:22:07,040 Speaker 1: of E comes from human motivation, right, And if you 376 00:22:07,080 --> 00:22:09,480 Speaker 1: can measure human motivation directly, you do much better. And 377 00:22:09,520 --> 00:22:13,200 Speaker 1: you could do You could be a an E company 378 00:22:13,280 --> 00:22:15,760 Speaker 1: and the employees don't know about it, and therefore you're 379 00:22:15,760 --> 00:22:19,040 Speaker 1: not increasing their motivation. And you can be an environmental 380 00:22:19,040 --> 00:22:21,760 Speaker 1: company and the employees could be incredibly involved and connected 381 00:22:22,160 --> 00:22:24,800 Speaker 1: and feel more proud and now you get also the 382 00:22:24,840 --> 00:22:28,360 Speaker 1: benefit from that. So so I I look at companies 383 00:22:28,400 --> 00:22:30,760 Speaker 1: as a mechanistic thing. I look at it's an engine. 384 00:22:31,480 --> 00:22:34,440 Speaker 1: It's an engine of innovation and creativity and thought, and 385 00:22:34,760 --> 00:22:39,240 Speaker 1: ask what what fuels that engine and what creates friction? 386 00:22:39,320 --> 00:22:43,240 Speaker 1: And you know, dust and slows it down, and bureaucracy 387 00:22:43,280 --> 00:22:47,240 Speaker 1: slows it down. And people feeling connected to the company, 388 00:22:47,280 --> 00:22:50,080 Speaker 1: and feeling that their utility function is aligned with the company, 389 00:22:50,080 --> 00:22:55,000 Speaker 1: and people wanting to be part of a successful company. 390 00:22:55,040 --> 00:22:57,360 Speaker 1: All of those things are It's what fueled the machine. 391 00:22:58,040 --> 00:23:01,240 Speaker 1: And from my perspective, the human capital party is to say, 392 00:23:01,320 --> 00:23:04,840 Speaker 1: let's just quantify that, because you know, the reality is 393 00:23:04,880 --> 00:23:08,040 Speaker 1: that lots of people are not happy enough it work, 394 00:23:08,800 --> 00:23:12,479 Speaker 1: and it's kind of a waste. Partly, we're investing in 395 00:23:12,480 --> 00:23:15,040 Speaker 1: this strategy because it has a good alpha, but partially 396 00:23:15,520 --> 00:23:17,480 Speaker 1: I think it's just kind of the moral thing to do. 397 00:23:17,840 --> 00:23:20,880 Speaker 1: You know, if people come to work unhappy, everybody loses. 398 00:23:21,119 --> 00:23:24,359 Speaker 1: The people are miserable, management is miserable, shareholders are miserable. 399 00:23:24,400 --> 00:23:29,159 Speaker 1: If people come to work happy, everybody benefits. Right. I 400 00:23:29,280 --> 00:23:31,199 Speaker 1: I can't tell you what what it's like to to 401 00:23:31,280 --> 00:23:34,000 Speaker 1: work in a place that you love. It's just a 402 00:23:34,040 --> 00:23:37,000 Speaker 1: complete transformation. Why why don't we invest more in getting 403 00:23:37,000 --> 00:23:39,480 Speaker 1: people to love the place that they work? Well, Actually, 404 00:23:39,480 --> 00:23:41,480 Speaker 1: there is a question. You know you said at the 405 00:23:41,560 --> 00:23:43,679 Speaker 1: very beginning that you could go into a company and 406 00:23:43,720 --> 00:23:48,840 Speaker 1: without actually too much effort improve their ability to you know, 407 00:23:48,920 --> 00:23:52,439 Speaker 1: employee motivation. Could you have a firm that took a 408 00:23:52,480 --> 00:23:55,360 Speaker 1: stake in a company and then hired you as a consultant, 409 00:23:55,800 --> 00:24:00,199 Speaker 1: improved employee motivation and then got better returned. So so 410 00:24:00,280 --> 00:24:02,720 Speaker 1: absolutely yes. But I'll tell you even something else. I 411 00:24:02,760 --> 00:24:07,560 Speaker 1: think when companies, when when investors do due diligence on companies, 412 00:24:08,440 --> 00:24:11,040 Speaker 1: I think they have to understand the human capital in 413 00:24:11,040 --> 00:24:13,919 Speaker 1: that company. If you if you look to get a 414 00:24:13,920 --> 00:24:17,120 Speaker 1: big position in the company and hoping to to turn 415 00:24:17,160 --> 00:24:19,160 Speaker 1: them around, and you're not getting a really good view 416 00:24:19,200 --> 00:24:21,639 Speaker 1: of the human capital, you're missing something very big in 417 00:24:21,640 --> 00:24:25,119 Speaker 1: your evaluation. And I know you know it's it doesn't 418 00:24:25,200 --> 00:24:29,240 Speaker 1: feel as good to get numbers like four point five, 419 00:24:29,359 --> 00:24:32,239 Speaker 1: and it feels much better to get a number with 420 00:24:32,440 --> 00:24:36,639 Speaker 1: the dollar sign and two decimals. But but ignoring that 421 00:24:36,760 --> 00:24:41,160 Speaker 1: information I think ignores way too much in the real 422 00:24:41,240 --> 00:24:42,960 Speaker 1: value of the company. So I think I think it's 423 00:24:43,040 --> 00:24:46,560 Speaker 1: very true for any evaluation of a company needs to 424 00:24:46,600 --> 00:24:48,600 Speaker 1: take that into account. And of course, if you want 425 00:24:48,640 --> 00:24:52,280 Speaker 1: to be activists, like think about Microsoft as an example, 426 00:24:53,520 --> 00:24:59,560 Speaker 1: Microsoft change CEOs and had an amazing transformation. And if 427 00:24:59,560 --> 00:25:04,240 Speaker 1: you look at transformation, it was largely cultural transformation, largely 428 00:25:04,280 --> 00:25:07,680 Speaker 1: a transformation of how people looked at themselves, how management 429 00:25:07,680 --> 00:25:10,159 Speaker 1: looked at at the employees. I mean that that was 430 00:25:10,320 --> 00:25:13,879 Speaker 1: that was basically it, and you know, it's been very successful. 431 00:25:30,080 --> 00:25:33,160 Speaker 1: Since you brought up Microsoft, I actually wanted to ask 432 00:25:33,200 --> 00:25:37,199 Speaker 1: you about tech companies UM and maybe the difference with 433 00:25:37,240 --> 00:25:41,720 Speaker 1: other firms. So one of the criticisms of the way 434 00:25:41,920 --> 00:25:45,160 Speaker 1: E s G proponents go out and you know, typically 435 00:25:45,280 --> 00:25:48,240 Speaker 1: E s G will say, well, we outperformed over the 436 00:25:48,320 --> 00:25:51,879 Speaker 1: past year, especially so it proves that you should invest 437 00:25:52,080 --> 00:25:55,400 Speaker 1: in E s G companies, UM, because we produced that alpha. 438 00:25:55,880 --> 00:25:58,000 Speaker 1: But then a lot of people will point out in 439 00:25:58,160 --> 00:26:01,879 Speaker 1: response that, well, the E s G statistics for tech 440 00:26:02,080 --> 00:26:07,119 Speaker 1: look particularly good because they're not actually polluting that much. 441 00:26:07,560 --> 00:26:10,560 Speaker 1: You know, most of what they do is intangibles. I 442 00:26:10,560 --> 00:26:14,600 Speaker 1: imagine that could apply to the human capital argument as well. 443 00:26:14,720 --> 00:26:19,760 Speaker 1: So tech companies would naturally be more aware and conscientious 444 00:26:19,840 --> 00:26:23,480 Speaker 1: of their human capital than other companies. So I guess 445 00:26:23,480 --> 00:26:26,560 Speaker 1: what I'm asking is is that true in your research? 446 00:26:26,640 --> 00:26:31,440 Speaker 1: And then secondly, how does that fit into the alpha equation? 447 00:26:31,520 --> 00:26:36,000 Speaker 1: Like are you outperforming simply because you're buying more tech 448 00:26:36,040 --> 00:26:40,800 Speaker 1: companies with a strategy that's focused on human capital. So 449 00:26:40,800 --> 00:26:43,719 Speaker 1: so my strategy is to look at all the companies 450 00:26:43,960 --> 00:26:47,440 Speaker 1: and take the one that our top on human capital 451 00:26:47,600 --> 00:26:51,640 Speaker 1: and and buy them. Now, I do have a bias 452 00:26:51,680 --> 00:26:55,600 Speaker 1: in the portfolio towards tech, but it's not it's not 453 00:26:55,720 --> 00:26:58,000 Speaker 1: fully tech, and it's not close to being half tech. 454 00:26:58,080 --> 00:27:02,840 Speaker 1: It moves between depending on the ear between. And now, 455 00:27:02,960 --> 00:27:05,720 Speaker 1: the interesting thing about our results that surprised me, I 456 00:27:05,760 --> 00:27:09,520 Speaker 1: have to say, was that we didn't find the reason 457 00:27:10,440 --> 00:27:14,119 Speaker 1: to create a different model in different sectors. So you 458 00:27:14,119 --> 00:27:17,680 Speaker 1: would say something like in manufacturing, you could say, oh, 459 00:27:17,720 --> 00:27:22,480 Speaker 1: in manufacturing, how important is it to be appreciated? It 460 00:27:22,560 --> 00:27:25,840 Speaker 1: turns out to be just the same. And since since 461 00:27:25,880 --> 00:27:28,520 Speaker 1: we got that results, I understand that phenomenal much better. 462 00:27:28,560 --> 00:27:32,800 Speaker 1: But even think about something like Target in Walmart. There 463 00:27:32,880 --> 00:27:35,400 Speaker 1: was recently a study that asked, like what happens when 464 00:27:35,400 --> 00:27:38,920 Speaker 1: a customer shows up and there's something that is out 465 00:27:38,920 --> 00:27:41,720 Speaker 1: of stock, and they asked one of the Cells people 466 00:27:41,760 --> 00:27:43,840 Speaker 1: to to check if it's in the in the back 467 00:27:43,920 --> 00:27:47,120 Speaker 1: room if they still have it. It turns out its Walmart. 468 00:27:47,240 --> 00:27:50,680 Speaker 1: They never check, you know, they're just not that motivated, 469 00:27:51,720 --> 00:27:54,320 Speaker 1: and in targets they go and check and they often 470 00:27:54,359 --> 00:27:57,280 Speaker 1: find it. Now, now this is a it's not Google 471 00:27:57,480 --> 00:28:02,640 Speaker 1: or Amazon, but but good will is incredibly important. So 472 00:28:02,640 --> 00:28:05,119 Speaker 1: so we find that our model of saying, you know, 473 00:28:05,160 --> 00:28:07,680 Speaker 1: what's important is to feel appreciated and you can make 474 00:28:08,080 --> 00:28:10,560 Speaker 1: honest mistakes, and you feel connected to the company, and 475 00:28:10,560 --> 00:28:13,320 Speaker 1: all the things that we find you find it promotion 476 00:28:14,680 --> 00:28:18,840 Speaker 1: is is fair. We find that all of those things 477 00:28:18,880 --> 00:28:24,400 Speaker 1: are equal across the different sectors, and we also find 478 00:28:24,440 --> 00:28:28,760 Speaker 1: that they are important across the rank within the company. 479 00:28:28,880 --> 00:28:30,880 Speaker 1: So to answer your question, I think, yes, we are 480 00:28:30,960 --> 00:28:36,080 Speaker 1: slightly heavy on on tech compared to sector neutral strategy. 481 00:28:36,119 --> 00:28:38,080 Speaker 1: Of course we can also have a sector neutral strategy. 482 00:28:38,120 --> 00:28:39,480 Speaker 1: We don't think that's the right thing to do, but 483 00:28:39,600 --> 00:28:42,520 Speaker 1: you can do it. But but it doesn't it doesn't 484 00:28:42,600 --> 00:28:45,560 Speaker 1: only hold for tech company, it holds it holds for 485 00:28:45,640 --> 00:28:48,600 Speaker 1: everybody equally. As far as we can tell, I just 486 00:28:48,760 --> 00:28:50,680 Speaker 1: I mean, I guess we haven't talked what are the results? Like? 487 00:28:50,720 --> 00:28:53,160 Speaker 1: What what have you seen? Like? What is the what 488 00:28:53,360 --> 00:28:55,640 Speaker 1: is what do you anticipate getting? What have you gotten? 489 00:28:55,680 --> 00:28:58,040 Speaker 1: What is the data show in terms of making money? 490 00:28:58,400 --> 00:29:01,680 Speaker 1: So the return taste oracle return that we have is 491 00:29:01,800 --> 00:29:06,560 Speaker 1: slightly more than seven percent just by looking at human capital. Right. So, 492 00:29:06,600 --> 00:29:08,960 Speaker 1: by the way, this is a pure strategy because of 493 00:29:09,000 --> 00:29:10,880 Speaker 1: course if you wanted to, you could combine it with 494 00:29:10,920 --> 00:29:13,800 Speaker 1: other things. But it's important for us to prove like 495 00:29:13,960 --> 00:29:16,240 Speaker 1: how is just this one signal? What is this one 496 00:29:16,360 --> 00:29:20,440 Speaker 1: signal worth? And from two thousand and six it's it's 497 00:29:20,440 --> 00:29:26,040 Speaker 1: about it's about seven pc over the the SNP. But 498 00:29:26,040 --> 00:29:28,680 Speaker 1: but the things that are that are important underneath it 499 00:29:28,720 --> 00:29:30,959 Speaker 1: is the thing that I told you about, right, It's 500 00:29:31,000 --> 00:29:34,040 Speaker 1: a lot about fairness, it's a lot about being appreciated. 501 00:29:34,840 --> 00:29:37,600 Speaker 1: There was another interesting fact that it became more important, 502 00:29:37,640 --> 00:29:42,360 Speaker 1: which we call inclusive innovation. An inclusive innovation is how 503 00:29:42,360 --> 00:29:46,600 Speaker 1: many voices around the table are being heard. And that 504 00:29:46,720 --> 00:29:50,600 Speaker 1: was always important, but it became extra important during COVID. 505 00:29:51,640 --> 00:29:53,520 Speaker 1: And the reason is that when you sit around the 506 00:29:53,560 --> 00:29:56,560 Speaker 1: physical table, the people who don't like to talk that 507 00:29:56,640 --> 00:30:00,400 Speaker 1: much still talk at some point and little bit. The 508 00:30:00,800 --> 00:30:04,800 Speaker 1: social pressure is very high, but when people are on Zoom, 509 00:30:05,000 --> 00:30:08,720 Speaker 1: lots of people are just silent. They just basically drop off. 510 00:30:09,200 --> 00:30:12,360 Speaker 1: You lose some insights, you use some collaboration, you use 511 00:30:12,440 --> 00:30:16,480 Speaker 1: some opinions, and the companies that they do better on 512 00:30:16,560 --> 00:30:20,479 Speaker 1: that find things much more important. The other thing that 513 00:30:20,600 --> 00:30:24,880 Speaker 1: increased over COVID, I told you was feeling appreciated companies. 514 00:30:24,920 --> 00:30:26,880 Speaker 1: You know, it's it's hard. It's hard to say thank 515 00:30:26,920 --> 00:30:32,000 Speaker 1: you over zoom compared to when you're in the room. 516 00:30:32,000 --> 00:30:33,600 Speaker 1: In the room you can just tap on some of 517 00:30:33,640 --> 00:30:36,360 Speaker 1: these shoulder you could wink at them. On zoom, it's 518 00:30:36,400 --> 00:30:38,440 Speaker 1: a little extra tough. People need to work at it harder, 519 00:30:38,480 --> 00:30:41,000 Speaker 1: and some people are better at it and and worse 520 00:30:41,080 --> 00:30:45,840 Speaker 1: at it. And then feeling that people have a future 521 00:30:45,920 --> 00:30:50,240 Speaker 1: with the company became more important. And I think it's 522 00:30:50,280 --> 00:30:53,640 Speaker 1: because of this. There was so much uncertainty about what 523 00:30:53,680 --> 00:30:57,680 Speaker 1: the the job market would hold, what would happen to 524 00:30:57,760 --> 00:31:02,200 Speaker 1: the turmoils as so that also became much more important. 525 00:31:02,200 --> 00:31:05,560 Speaker 1: So CEOs that's communicated and help people understand where the 526 00:31:05,560 --> 00:31:09,640 Speaker 1: company is going did did much better. So since we're 527 00:31:09,640 --> 00:31:13,680 Speaker 1: on the topic of what changed during COVID, I'm curious 528 00:31:13,720 --> 00:31:18,360 Speaker 1: if you did any research on flexible work arrangements and 529 00:31:18,720 --> 00:31:22,000 Speaker 1: the idea of companies, you know, allowing their employees to 530 00:31:22,400 --> 00:31:25,120 Speaker 1: work from home as needed, because that's such a hot 531 00:31:25,120 --> 00:31:27,720 Speaker 1: button issue right now. There's such a big debate over 532 00:31:28,000 --> 00:31:30,840 Speaker 1: whether employees feel better about being able to do that, 533 00:31:31,320 --> 00:31:34,320 Speaker 1: or whether or not they enjoy the camaraderie of an 534 00:31:34,360 --> 00:31:39,320 Speaker 1: office environment. I'm just curious whether that came up at all. So, yes, 535 00:31:39,440 --> 00:31:42,840 Speaker 1: we studied tens of thousands of people on this. On 536 00:31:42,920 --> 00:31:46,600 Speaker 1: this question, I'll keep you kind of the highlights. The 537 00:31:46,880 --> 00:31:51,080 Speaker 1: first highlight is that some flexibility is good, but people 538 00:31:51,080 --> 00:31:54,320 Speaker 1: should go back to the office. It's also the fact 539 00:31:54,320 --> 00:31:56,600 Speaker 1: that people are not good judges of whether they should 540 00:31:56,600 --> 00:31:59,080 Speaker 1: go back to the office or not. You know, after 541 00:31:59,120 --> 00:32:03,040 Speaker 1: a year or so being away, people forget what it 542 00:32:03,080 --> 00:32:05,600 Speaker 1: is to work with people. You know, people are still 543 00:32:05,600 --> 00:32:09,600 Speaker 1: a little bit afraid COVID distance. But but you know, 544 00:32:09,640 --> 00:32:12,440 Speaker 1: on the things that happened in this interaction between people, 545 00:32:13,120 --> 00:32:15,600 Speaker 1: you don't recognize it and appreciate it. It's it's hard 546 00:32:15,640 --> 00:32:18,760 Speaker 1: to quantify. It's hard to quantify chit chat. It's the 547 00:32:18,840 --> 00:32:23,080 Speaker 1: water cooler and over coffee. I think a work from 548 00:32:23,120 --> 00:32:26,240 Speaker 1: home is important, and some of it should stay maybe 549 00:32:26,280 --> 00:32:29,920 Speaker 1: a day a week. If it becomes just work the 550 00:32:29,960 --> 00:32:31,680 Speaker 1: same work you do in the office, just do it 551 00:32:31,760 --> 00:32:35,360 Speaker 1: from home, that's not as valuable. People should do different 552 00:32:35,440 --> 00:32:37,479 Speaker 1: work at home, the kind of things that the home 553 00:32:37,600 --> 00:32:42,080 Speaker 1: environment is more suitable for. Companies also need to collaborate 554 00:32:42,160 --> 00:32:44,280 Speaker 1: on this. If you have a zoom meeting or whatever 555 00:32:44,360 --> 00:32:47,520 Speaker 1: technology you use, where half the people are physical and 556 00:32:47,520 --> 00:32:50,360 Speaker 1: half the people are distant, the people who are distant 557 00:32:50,360 --> 00:32:54,240 Speaker 1: our second class citizens. And and because of that, unless 558 00:32:54,240 --> 00:32:57,880 Speaker 1: companies coordinated and say Tuesday, the whole team can stay home. 559 00:32:57,960 --> 00:33:01,280 Speaker 1: Unless unless it's coordinated, you know, people would say, oh, 560 00:33:01,320 --> 00:33:03,200 Speaker 1: I have I was going to stay at home, but 561 00:33:03,240 --> 00:33:05,280 Speaker 1: I have this one important meeting. I'm not going to 562 00:33:05,320 --> 00:33:07,200 Speaker 1: be the only one on zoom because I have something 563 00:33:07,240 --> 00:33:09,440 Speaker 1: to say. I want them to pay attention. And if 564 00:33:09,440 --> 00:33:12,120 Speaker 1: I'm going, I might as well go early. I think 565 00:33:12,200 --> 00:33:16,000 Speaker 1: unless companies kind of coordinate on that, it would very 566 00:33:16,040 --> 00:33:19,040 Speaker 1: quickly go back to people being in the office the 567 00:33:19,040 --> 00:33:20,920 Speaker 1: whole time. So I think it's important to keep a 568 00:33:20,920 --> 00:33:23,440 Speaker 1: little bit of it. It needs to be a different 569 00:33:23,520 --> 00:33:27,720 Speaker 1: kind of work rather than work remotely, but but do 570 00:33:27,800 --> 00:33:30,240 Speaker 1: the same things. And I think companies need to put 571 00:33:30,280 --> 00:33:33,360 Speaker 1: some effort into it so that the coordination allows that 572 00:33:33,520 --> 00:33:36,920 Speaker 1: to to stay. And I'll say one last thing. I 573 00:33:36,960 --> 00:33:39,200 Speaker 1: think that people are going to be concerned when they 574 00:33:39,280 --> 00:33:42,680 Speaker 1: first come back. So I have a research lab here 575 00:33:42,720 --> 00:33:45,960 Speaker 1: with Duke. We're about fifty people. I'm waiting for Duke 576 00:33:46,000 --> 00:33:47,880 Speaker 1: to allow us in. I'm I'm in the office right now, 577 00:33:47,960 --> 00:33:50,400 Speaker 1: but I'm the only one in the office. I'm waiting 578 00:33:50,400 --> 00:33:54,120 Speaker 1: for people to come to come back. And some people 579 00:33:54,160 --> 00:33:57,360 Speaker 1: are going to be concerned. Right, It's been It's been 580 00:33:57,400 --> 00:34:01,760 Speaker 1: a long time people were anxious COVID related kind of things. 581 00:34:01,960 --> 00:34:05,040 Speaker 1: What can we do to get people to to drop 582 00:34:05,120 --> 00:34:07,800 Speaker 1: their their concern And what I'm going to do is 583 00:34:07,800 --> 00:34:10,719 Speaker 1: I'm going to try and get people to come. I'm 584 00:34:10,760 --> 00:34:13,600 Speaker 1: going to ask people to come every day, but tell 585 00:34:13,640 --> 00:34:15,319 Speaker 1: them they can come for a short time if they 586 00:34:15,360 --> 00:34:18,520 Speaker 1: want to. And and the reason I want is is 587 00:34:18,560 --> 00:34:22,560 Speaker 1: that we learn about risk from experience and not from 588 00:34:22,560 --> 00:34:26,080 Speaker 1: statistics that we read. So I want for people to 589 00:34:26,120 --> 00:34:28,040 Speaker 1: have the experience of coming to the office and nothing 590 00:34:28,080 --> 00:34:30,400 Speaker 1: bad happened coming to the office. That's going to be 591 00:34:30,520 --> 00:34:34,800 Speaker 1: the best medicine against fear. Right, just practice it a 592 00:34:34,840 --> 00:34:37,040 Speaker 1: few times and see that nothing that happens, and then 593 00:34:37,080 --> 00:34:39,680 Speaker 1: in a few weeks people would be relaxed. But if 594 00:34:39,719 --> 00:34:41,480 Speaker 1: we tell people, oh, you know, if you don't want 595 00:34:41,520 --> 00:34:43,160 Speaker 1: to come to the office, don't come to the office, 596 00:34:43,600 --> 00:34:46,959 Speaker 1: we're not going to help them subside that that fear 597 00:34:47,000 --> 00:34:51,720 Speaker 1: as much. So I think we need the correcting experience 598 00:34:52,360 --> 00:34:55,320 Speaker 1: of coming to the office, meeting people, seeing that nothing, 599 00:34:55,640 --> 00:34:58,799 Speaker 1: nothing bad happened, and experiencing that for a while. So 600 00:34:58,920 --> 00:35:01,400 Speaker 1: I just have one more fushan and we've we've you 601 00:35:01,520 --> 00:35:03,440 Speaker 1: kind of hinted at this when you talked about your 602 00:35:03,480 --> 00:35:05,920 Speaker 1: studies of Target versus Walmart, But we've been telling you 603 00:35:05,960 --> 00:35:08,600 Speaker 1: a lot about the zoom class and the people who 604 00:35:08,680 --> 00:35:10,719 Speaker 1: are sort of lucky enough to have been able to 605 00:35:10,760 --> 00:35:13,759 Speaker 1: do their jobs over the last year over zoom, or 606 00:35:13,800 --> 00:35:17,160 Speaker 1: the people who are lucky enough to have coffee, free 607 00:35:17,200 --> 00:35:19,560 Speaker 1: coffee and water coolers at work when they come back, 608 00:35:19,600 --> 00:35:22,520 Speaker 1: and things like that. But obviously that is not the 609 00:35:22,680 --> 00:35:25,400 Speaker 1: entirety of the workforce by any stretch. And we've seen 610 00:35:25,640 --> 00:35:28,680 Speaker 1: a lot of stories in the last several months about 611 00:35:28,880 --> 00:35:34,120 Speaker 1: lower paid workers companies having trouble hiring them for various reasons, 612 00:35:34,160 --> 00:35:36,920 Speaker 1: which you know, economists are trying to debate whether it's 613 00:35:37,000 --> 00:35:39,720 Speaker 1: you know, all kinds of things, lots of frustration among 614 00:35:40,120 --> 00:35:43,160 Speaker 1: fast food companies and retailers and restaurants and other sort 615 00:35:43,200 --> 00:35:46,160 Speaker 1: of service labor that they can't hire, and maybe wages 616 00:35:46,200 --> 00:35:49,000 Speaker 1: are part of the answer. What is your sense, like, 617 00:35:49,120 --> 00:35:51,400 Speaker 1: you know, going at that level. I'm curious if you 618 00:35:51,400 --> 00:35:53,480 Speaker 1: could talk a little bit more about how strong your 619 00:35:53,520 --> 00:35:58,000 Speaker 1: signals are and what your research says about hiring and 620 00:35:58,080 --> 00:36:02,000 Speaker 1: keeping those workers happy and motivated, you know, in what 621 00:36:02,120 --> 00:36:07,200 Speaker 1: it appears to be a very competitive job market right now. Yeah, so, 622 00:36:07,200 --> 00:36:10,160 Speaker 1: so I think I think it boils down to giving 623 00:36:10,239 --> 00:36:14,280 Speaker 1: people the sense that there are substitute herbal very easily, 624 00:36:16,080 --> 00:36:19,400 Speaker 1: and that just makes people behave this way. Right. So, 625 00:36:19,400 --> 00:36:21,280 Speaker 1: so if you think about a lot of those places, 626 00:36:21,320 --> 00:36:25,680 Speaker 1: think about restaurants, a lot of them make people feel 627 00:36:25,760 --> 00:36:28,719 Speaker 1: that we don't need you. We need some a body here, 628 00:36:29,920 --> 00:36:32,120 Speaker 1: but we don't need you. And then one day somebody 629 00:36:32,160 --> 00:36:36,120 Speaker 1: wakes up and the bus is late, or they don't 630 00:36:36,160 --> 00:36:38,080 Speaker 1: feel as good as they have something else, and they 631 00:36:38,120 --> 00:36:40,560 Speaker 1: don't feel any obligation to show up, and then they 632 00:36:40,560 --> 00:36:42,120 Speaker 1: don't show up, and then they feel bad about not 633 00:36:42,120 --> 00:36:45,160 Speaker 1: showing up, so they don't show up. Again, you know 634 00:36:45,200 --> 00:36:48,640 Speaker 1: a lot of those high frequency jobs, people don't resign, 635 00:36:48,920 --> 00:36:51,560 Speaker 1: They just stopped showing up. And and it's a part 636 00:36:51,560 --> 00:36:54,120 Speaker 1: of no commitment. They have no commitment to their team, 637 00:36:55,200 --> 00:36:57,440 Speaker 1: they have no commitment to the to the place, and 638 00:36:57,440 --> 00:36:59,920 Speaker 1: and you you want to try and create that commitment. 639 00:37:00,560 --> 00:37:03,920 Speaker 1: That was true even with with with jails. If you 640 00:37:03,920 --> 00:37:06,360 Speaker 1: think about a place like Saint Quentin and you know 641 00:37:06,520 --> 00:37:10,440 Speaker 1: some some some tough, tough jails, the moment you have 642 00:37:11,560 --> 00:37:15,560 Speaker 1: team cohesiveness, people feel like they owe something to each other. 643 00:37:17,320 --> 00:37:18,880 Speaker 1: By the way, that's true also for high tech it 644 00:37:18,960 --> 00:37:21,000 Speaker 1: if you know, one of our studies, we found that 645 00:37:21,080 --> 00:37:24,120 Speaker 1: people are more willing to stay late at night to 646 00:37:24,239 --> 00:37:27,319 Speaker 1: help a friend then to finish their own project. Right 647 00:37:27,360 --> 00:37:29,440 Speaker 1: because if I need to stay until two am for 648 00:37:29,560 --> 00:37:33,040 Speaker 1: my own project, you know, maybe I'll finish another day. 649 00:37:33,040 --> 00:37:34,920 Speaker 1: But if a friend is asking to it now, now, 650 00:37:34,920 --> 00:37:37,120 Speaker 1: the social pressure is higher and people are more likely 651 00:37:37,160 --> 00:37:40,040 Speaker 1: to do it. The way in which our commitment to 652 00:37:40,120 --> 00:37:42,440 Speaker 1: the work shows up, it's it's to the mission of 653 00:37:42,480 --> 00:37:47,000 Speaker 1: the company, it's to the CEO, it's for the direct management, 654 00:37:47,080 --> 00:37:50,799 Speaker 1: but also to our fellow fellow employees. And we need 655 00:37:50,880 --> 00:37:53,560 Speaker 1: to create those conditions. You can't expect it to just 656 00:37:53,640 --> 00:37:56,680 Speaker 1: to emerge by itself. And and what so many companies 657 00:37:56,680 --> 00:38:02,040 Speaker 1: are doing under the idea of kind of mass production 658 00:38:02,239 --> 00:38:06,720 Speaker 1: is treating employees is mass production? Come in, here's the training, 659 00:38:06,760 --> 00:38:11,799 Speaker 1: here's your badge, here's the thing. Start working. You're you're basically, UM, 660 00:38:12,520 --> 00:38:16,440 Speaker 1: a part of an efficient process that doesn't create loyalty, 661 00:38:17,000 --> 00:38:21,160 Speaker 1: right man. I could talk about this literally, UM for 662 00:38:21,200 --> 00:38:25,239 Speaker 1: another you know, five hours at least, and do really 663 00:38:25,280 --> 00:38:29,560 Speaker 1: really deep dives into workplace policies UM that work the 664 00:38:29,600 --> 00:38:32,759 Speaker 1: best in terms of employee satisfaction. I would love to 665 00:38:32,800 --> 00:38:35,920 Speaker 1: do that, But Dan, I'm very aware that you have 666 00:38:35,960 --> 00:38:38,480 Speaker 1: an appointment and so you have to go. But thank you. 667 00:38:38,600 --> 00:38:42,799 Speaker 1: This was so fascinating. Yeah, this is great, Dan, really 668 00:38:42,920 --> 00:38:46,520 Speaker 1: really appreciate you taking your time and joining us. My 669 00:38:46,520 --> 00:38:49,640 Speaker 1: my pleasure, you know it is it is the question 670 00:38:49,640 --> 00:38:55,320 Speaker 1: of how you improve the work conditions for people is central, 671 00:38:55,400 --> 00:38:57,319 Speaker 1: and I think it's one of the places where the 672 00:38:57,440 --> 00:39:00,560 Speaker 1: financial market can play a big role. Right if we 673 00:39:00,600 --> 00:39:03,640 Speaker 1: only invest more in companies that treat employees better, if 674 00:39:03,680 --> 00:39:06,240 Speaker 1: we make it clear what it means to treat people better, 675 00:39:06,360 --> 00:39:09,200 Speaker 1: if we've got people to think about it more, I 676 00:39:09,200 --> 00:39:12,720 Speaker 1: think I think everybody could be better of great stuff. 677 00:39:12,719 --> 00:39:16,000 Speaker 1: I will continue to uh, continue to watch your work, Dana, really, 678 00:39:16,040 --> 00:39:32,520 Speaker 1: thank you so much, my pleasure. Take care, Tracy. I 679 00:39:32,560 --> 00:39:35,080 Speaker 1: didn't know this was such a huge topic for it. 680 00:39:35,920 --> 00:39:41,840 Speaker 1: I feel very passionately about several of these themes I'm trying. 681 00:39:42,080 --> 00:39:43,719 Speaker 1: I had to hold back quite a lot because I 682 00:39:44,200 --> 00:39:46,480 Speaker 1: don't want to make the podcast about you know how 683 00:39:46,560 --> 00:39:50,120 Speaker 1: Tracy feels about flexible work conditions. But man, as someone 684 00:39:50,120 --> 00:39:52,520 Speaker 1: who works across time zones, you know we're recording this 685 00:39:52,680 --> 00:39:58,680 Speaker 1: at nine fifty pm, I have strong opinions about flexible work. 686 00:39:59,360 --> 00:40:01,600 Speaker 1: Come on, come back, just come back to New York, Tracy, 687 00:40:01,600 --> 00:40:03,319 Speaker 1: come up. Then we don't have to worry about that. 688 00:40:03,719 --> 00:40:06,600 Speaker 1: It would make my life easier. But I gotta say, like, 689 00:40:06,800 --> 00:40:11,040 Speaker 1: one thing I really loved about that conversation was Dan's 690 00:40:11,080 --> 00:40:16,560 Speaker 1: emphasis on relative versus absolute gains because I think so 691 00:40:16,640 --> 00:40:20,240 Speaker 1: much of finance and economics, and we've spoken about this before, 692 00:40:20,360 --> 00:40:23,360 Speaker 1: but so much of it is based around the idea 693 00:40:23,400 --> 00:40:28,280 Speaker 1: of people acting rationally and kind of working together and saying, well, 694 00:40:29,200 --> 00:40:31,719 Speaker 1: this person might you know, if we do something, this 695 00:40:31,760 --> 00:40:34,160 Speaker 1: person might get more out of it than I will, 696 00:40:34,440 --> 00:40:38,200 Speaker 1: but we both benefit in one way or another. But 697 00:40:39,080 --> 00:40:42,040 Speaker 1: as Dan was saying, in the real world, people often 698 00:40:42,120 --> 00:40:46,279 Speaker 1: aren't that rational. In the workplace, especially, there's much more 699 00:40:46,360 --> 00:40:49,200 Speaker 1: of a tendency to look at the colleagues around you 700 00:40:49,239 --> 00:40:51,799 Speaker 1: and say, hey, they're getting a better deal than me, 701 00:40:52,320 --> 00:40:54,960 Speaker 1: and much more of a tendency to focus on sort 702 00:40:55,000 --> 00:41:00,840 Speaker 1: of relative advantages versus what's going on absolutely at the firm. Yeah, 703 00:41:01,000 --> 00:41:02,520 Speaker 1: I mean that makes a lot of sense, and it's 704 00:41:02,560 --> 00:41:05,839 Speaker 1: definitely highly intuitive. I just want to say, like, and 705 00:41:06,080 --> 00:41:09,400 Speaker 1: I totally believe Dan and the data, but like, I 706 00:41:09,440 --> 00:41:11,600 Speaker 1: still want to like check back like in twenty years, 707 00:41:12,640 --> 00:41:15,160 Speaker 1: because you know, it still seems like obviously this is 708 00:41:15,160 --> 00:41:18,080 Speaker 1: like the collective of this type of data is new, 709 00:41:18,600 --> 00:41:22,759 Speaker 1: the data, the period is not that long, even going 710 00:41:22,760 --> 00:41:25,160 Speaker 1: back to say two thousand six is not that long. 711 00:41:25,239 --> 00:41:28,160 Speaker 1: And market time, as you pointed out, a lot of 712 00:41:28,200 --> 00:41:30,440 Speaker 1: this may you know, they've been overweight tech for some 713 00:41:30,560 --> 00:41:33,880 Speaker 1: of these, although not radically. So I do want to 714 00:41:33,920 --> 00:41:35,960 Speaker 1: have Dan back on in twenty years when we get 715 00:41:36,000 --> 00:41:38,880 Speaker 1: like a really long stretch of out of sample data 716 00:41:38,920 --> 00:41:40,880 Speaker 1: to see how well it holds up. But it is, uh, 717 00:41:40,960 --> 00:41:43,320 Speaker 1: it is fascinating and if you can sort of capture 718 00:41:43,320 --> 00:41:46,120 Speaker 1: it with the numbers. It's also it's intuitive. Well that's 719 00:41:46,480 --> 00:41:48,600 Speaker 1: I mean, that's the other big takeaway from this, right, 720 00:41:48,680 --> 00:41:51,640 Speaker 1: is how much of it depends on how you're measuring it, 721 00:41:51,920 --> 00:41:54,280 Speaker 1: the strength of the data, whether or not it actually 722 00:41:54,360 --> 00:41:58,080 Speaker 1: bears out over a longer time period. And that's basically 723 00:41:58,080 --> 00:42:01,040 Speaker 1: the reason that so far there's been this emphasis on 724 00:42:01,239 --> 00:42:03,359 Speaker 1: the E in E. S G and not so much 725 00:42:03,400 --> 00:42:07,320 Speaker 1: on the social governance side. So maybe that's starting to change, 726 00:42:07,360 --> 00:42:10,560 Speaker 1: and maybe we're going to see more rigorous collection of 727 00:42:11,000 --> 00:42:14,560 Speaker 1: social and human capital related data. But we are in 728 00:42:14,600 --> 00:42:18,520 Speaker 1: the early days. That's very true, exactly right. Shall we 729 00:42:18,640 --> 00:42:22,160 Speaker 1: leave it there? Sure, let's leave it there. All right. 730 00:42:22,680 --> 00:42:25,800 Speaker 1: This has been another episode of the All Thoughts podcast. 731 00:42:25,880 --> 00:42:28,640 Speaker 1: I'm Tracy Alloway. You can follow me on Twitter at 732 00:42:28,680 --> 00:42:32,120 Speaker 1: Tracy Alloway. And I'm Joe Wisenthal. You can follow me 733 00:42:32,200 --> 00:42:35,400 Speaker 1: on Twitter at The Stalwart. Follow our guest on Twitter, 734 00:42:35,520 --> 00:42:40,160 Speaker 1: Dan Arielli, He's at Dan Arielli. Follow our producer Laura Carlson, 735 00:42:40,360 --> 00:42:43,680 Speaker 1: She's at Laura M. Carlson. Followed the Bloomberg head of 736 00:42:43,680 --> 00:42:48,200 Speaker 1: podcast Francesco Levi at Francesca Today. And check out all 737 00:42:48,200 --> 00:42:51,799 Speaker 1: of our podcasts at Bloomberg under the handle at Podcasts. 738 00:42:52,120 --> 00:43:10,480 Speaker 1: Thanks for listening to