Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast.
Speaker 1: I'm Joe Wisenthal and I'm Tracy all Away. So, Tracy,
Speaker 1: something I've noticed before is we talked about E s
Speaker 1: G investing sometimes, and of course, as we've discussed in
Speaker 1: the past, you know, it's become this huge industry. The
Speaker 1: thing that I always think about in E s G
Speaker 1: is the E the environment. There's a lot of talk
Speaker 1: about climate and so forth, and it really is like
Speaker 1: I don't really know anything about what the s of
Speaker 1: the G are all about. Yeah, that would be the
Speaker 1: social governance part of the equation. And it's absolutely true
Speaker 1: when you think about the E s G space, most
Speaker 1: of the action tends to be on doing things to
Speaker 1: combat climate change. There's quite a bit um, but not
Speaker 1: as much about things like gender equality. But it beyond that,
Speaker 1: like this sort of social aspect you just don't hear
Speaker 1: about that much. Yeah, exactly right. And this sort of
Speaker 1: like the connection between these social aspects and returns is
Speaker 1: always interesting and it's always sort of one of the
Speaker 1: central questions of all the sort of E s G
Speaker 1: is like how much is it about investing with one's values?
Speaker 1: People want to invest in companies where they feel comfortable
Speaker 1: with the values of the company versus searching out sort
Speaker 1: of E. S G signals that can actually deliver alpha
Speaker 1: and somehow generate higher returns. Yeah. I think that's a
Speaker 1: really good way of framing it. And it's sort of
Speaker 1: unclear at this moment of time whether or not you
Speaker 1: would expect out performance of a good company because more
Speaker 1: people hopefully are interested in, you know, participating in a
Speaker 1: stock of a company that's doing good things, or if
Speaker 1: the company itself is sort of outperforming because it's more
Speaker 1: conscientious on things like the environment or gender or wealth
Speaker 1: inequality and things like that. Exactly right, So let's just
Speaker 1: get right into it. I'm really excited today we're gonna
Speaker 1: be talking about the S in E S G. And
Speaker 1: we have a world famous guest that I'm excited to
Speaker 1: be speaking with. We're gonna be speaking with um Dan
Speaker 1: ari Eli. He is a famous behavioral economist at Duke University,
Speaker 1: and he is also the co founder of a firm
Speaker 1: called Irrational Capital, who has formed five years ago, and
Speaker 1: it pursues the idea of looking at a company's human
Speaker 1: capital factor as a as something that could drive out
Speaker 1: performance in an investment. So I don't even know what
Speaker 1: the human capital factor is, but I'm excited to hear
Speaker 1: Dan talk about it and what he's learned in five years. Dan,
Speaker 1: thank you so much for coming on odd lot my pleasion.
Speaker 1: Nice to be here. Well, first of all, I love
Speaker 1: the name irrational I love the name irrational capital. It's perfect.
Speaker 1: But I'm curious what is the founding story of this?
Speaker 1: So the founding story is I'm a I'm a university professor.
Speaker 1: I do research on a few things, but among the
Speaker 1: human motivation. And in my academic career, I've from time
Speaker 1: to time I go to a company and I change
Speaker 1: things around. I change bonuses, I try to increase productivity,
Speaker 1: try to get people to care more about work. And
Speaker 1: my my experience has been that it's always been very
Speaker 1: easy to come and improve what people do and how
Speaker 1: to increase motivation because most companies just don't think about
Speaker 1: it very carefully. You know, if you think about h R,
Speaker 1: h R is usually a function that is about legal
Speaker 1: issues and I don't know training modules, but but it's
Speaker 1: not really a function that says, let's just get the
Speaker 1: best out of people. Let's just think about how do
Speaker 1: we motivate people, how do we get people to come
Speaker 1: happy to work? So I've been doing this for a
Speaker 1: long time and it's it's easy to do, and it's
Speaker 1: it's it's helpful. But when I met Dave, my partner
Speaker 1: can ask me whether I think that we could also
Speaker 1: look at something broader than instead of one company at
Speaker 1: a time, whether there's some way to look at companies,
Speaker 1: see how they treat their employees, see how the employees
Speaker 1: feel about the company, and whether this could predict stock
Speaker 1: market return. And I said, I don't know, that's what
Speaker 1: academics answer. I don't know that's the standard answer, but
Speaker 1: we can try it out. So we went on the
Speaker 1: hunt for data to see whether this hypothesis would ald
Speaker 1: or not, and it turns out it holds very well.
Speaker 1: So can I just press you on one point, which
Speaker 1: is what exactly is the definition of human capital? What
Speaker 1: are you looking at as you know undertake this exercise? Yea,
Speaker 1: so in this, in this exercise, and there's lots of
Speaker 1: ways to think about it, you're absolutely right. In this exercise,
Speaker 1: we said, let's take everything we can measure, everything that
Speaker 1: we can have access to, and then let's see which
Speaker 1: one of those things correlate actually predict stock market return.
Speaker 1: And and we we had some theories from the beginning
Speaker 1: about what would matter and what would not, but we
Speaker 1: also gave the data an opportunity to speak. And most
Speaker 1: of the things we found are very, very consistent with
Speaker 1: what we find in social science. So, for example, we
Speaker 1: find that absolute salary levels of absolute salary don't matter
Speaker 1: so much to our performance in the stock market, but
Speaker 1: the perception of fairness of salary matters a lot. Right.
Speaker 1: We find that physical environment at work, things like tables, chairs, coffee,
Speaker 1: don't matter so much. The sense of being appreciated matters
Speaker 1: a lot. So so when we think about human capital,
Speaker 1: we we start with the academic definition of let's take
Speaker 1: everything we know about what creates motivation. And for example,
Speaker 1: I did a study in which we showed that pizza
Speaker 1: delivered to somebody at home it can be much more
Speaker 1: motivating than a bonus if you think about the good
Speaker 1: will that that results from that. Right, So we've did
Speaker 1: lots of research on appreciation, so we said, hey, appreciation
Speaker 1: is really important. Let's let's see if we can find
Speaker 1: signals that companies that treat people in a way that
Speaker 1: they feel appreciated actually do better in the stock market.
Speaker 1: And and of course our exercise is to take the
Speaker 1: human capital in company X in year one and predicts
Speaker 1: the stock markets returned the following year. Right, we're not
Speaker 1: trying to estimate at that moment. We're trying to say,
Speaker 1: how is this human capital going to basically translate into
Speaker 1: better procedures, better products, more more innovation and so on.
Speaker 1: So so there's lots of things like that. And we
Speaker 1: have data that goes back to two thousand and six
Speaker 1: which we based a lot of our analysis on. But
Speaker 1: we also went and and double down during COVID. We
Speaker 1: studied fourteen hundred companies during COVID and and everything we
Speaker 1: found that was important before COVID became even more important
Speaker 1: during COVID and And the reason is think about kids,
Speaker 1: and if a kid is in the classroom, the teacher
Speaker 1: can control them to some degree. Right, it's straight, don't
Speaker 1: look at your phone, focus on this. And the same
Speaker 1: thing is true for us in the office. Right, nobody
Speaker 1: says it's straight, but but we're being seen by other people.
Speaker 1: We go to meetings, we have to focus. We can't
Speaker 1: do completely different things all of a sudden when people
Speaker 1: work from home. The role of intrinsic motivation is increasing dramatically,
Speaker 1: and because the kind of things we test are basically
Speaker 1: the things that lead to goodwill, the things that lead
Speaker 1: to extra performance, extra motivation, the role of everything we
Speaker 1: studied has increased quite dramatically during COVID. So this raises
Speaker 1: the obvious question, which is with traditional factors. Of course,
Speaker 1: you can look at the ten k or learning his
Speaker 1: report with environment fair there's increasingly companies are putting out
Speaker 1: some sort of separate perhaps an environmental scorecard that talks
Speaker 1: about climate and what they're doing out sustainability. There, how
Speaker 1: do you gather data in a systemic way on the
Speaker 1: types of things you're talking about, such as fairness, perception,
Speaker 1: and employee appreciation. Yeah, so there's lots of ways to
Speaker 1: think about data. You can think about data from linked In,
Speaker 1: and you can think about data from glass Door, and
Speaker 1: you can think about those. There's also companies that collect
Speaker 1: surveys about how employees feel about the company, and we
Speaker 1: take all of them. And in the first few years
Speaker 1: of our endeavor of irrational capital, we basically said, let's
Speaker 1: just get as much data as possible and see what
Speaker 1: matters and what doesn't matter. But once we found out
Speaker 1: what matters, now we can focus. And I want to
Speaker 1: kind of give you an example of why this is
Speaker 1: so important. So think about gender equality. What an important topic? Right?
Speaker 1: How important? It's incredibly important for investors to care about
Speaker 1: gender equality. And and there are two reasons. One is,
Speaker 1: you know, the moral issue, and the second one is performance.
Speaker 1: If you discriminate half of your workforce, that can't possibly
Speaker 1: be good for business. So there is something called the
Speaker 1: She Index, and the She Index counts how many women
Speaker 1: are in top positions and how many women are on
Speaker 1: the board. And if you think about what that means
Speaker 1: that the people who created this index had the thought
Speaker 1: that this is a good proxy for how a company
Speaker 1: is treating women. But if you look at the performance
Speaker 1: of the SMP of the SMP five compared to the
Speaker 1: SHE Index, the She Index is dramatically underperforming the dramatically
Speaker 1: every year systematically. Now is this because it's not a
Speaker 1: good idea to treat women equally? Of course not, but
Speaker 1: but it's a proxy that doesn't really capture the essence
Speaker 1: of what it means to be equal in country US.
Speaker 1: We took our data and in our data, we looked
Speaker 1: at how women feel compared to men on all kinds
Speaker 1: of things, and it turns out that the delta between
Speaker 1: women and men is incredibly important, you know it. It
Speaker 1: doesn't matter so much if you're a company treats everybody
Speaker 1: well or everybody badly, that doesn't matter so much. It's
Speaker 1: the inequality that is very hard to get to. Because
Speaker 1: if a company treats everybody by the way, it's good.
Speaker 1: I'm not recommending that people start treating their employees badly,
Speaker 1: but but in general, it matters less if you're equal
Speaker 1: offender because people get used to it and that's the
Speaker 1: level in which they evaluate themselves. But if you're a
Speaker 1: woman and you feel that somebody next to in the
Speaker 1: cubicle next to you is treated slightly better because they
Speaker 1: have a different chromosome structure, that's just bothers you, no
Speaker 1: end and it doesn't go away. So so as an example,
Speaker 1: if we take our data set from two thousand and
Speaker 1: six and every year we calculate which companies are the
Speaker 1: ones that are treating their women most equal to men,
Speaker 1: and we buy the top twenty percent companies every year
Speaker 1: who are doing that, and we construct a portfolio like this.
Speaker 1: That portfolio has a return of about five point four
Speaker 1: percent a year above the SNP just just by looking
Speaker 1: at equality nothing else. You're saying, let's just invest in
Speaker 1: the top twenty percent companies who are treating women the
Speaker 1: most equal, and you compare that to the she in
Speaker 1: the xtation the excess underperforming. So so it's incredibly important
Speaker 1: to actually do the research correctly and to start focusing
Speaker 1: on what's important to measure right. In the same way
Speaker 1: that I told you salary doesn't matter so much relative
Speaker 1: salary fairness in salary matters a lot. We need to
Speaker 1: figure out first what are the things that are actually
Speaker 1: good proxies of motivation, and what are the things that
Speaker 1: we think are predictive of motivation but really have thing
Speaker 1: to do with it. We found that sometimes when companies
Speaker 1: appoint women to high positions, it actually backfires, And and
Speaker 1: the reason it backfires is because they make it clear
Speaker 1: in some way to the women are working there that
Speaker 1: they don't care about women issues. They're only doing things
Speaker 1: for pr purposes. I think, think about the women engineer
Speaker 1: at company X, and one day the company is appointing
Speaker 1: a wonderful woman to the board. If if nothing follows
Speaker 1: in terms of her daily life. She is going to
Speaker 1: basically say, this company doesn't really care. They're doing things
Speaker 1: for pr they're checking the box. That's that's not so
Speaker 1: it actually creates sometimes what we call window dressing, that
Speaker 1: it's just for outside purposes, and then it backfires. So
Speaker 1: I love the nuance that you just described in measuring
Speaker 1: gender inequality, the difference between just counting up the number
Speaker 1: of women in leadership positions or who are on the board,
Speaker 1: versus the discrepancy in how women and men perceived that
Speaker 1: they are actually being treated. And I guess this is
Speaker 1: a topic sort of close to my heart, but I
Speaker 1: really think people should think more about it. But in
Speaker 1: thinking about this issue, there are clearly different ways, um
Speaker 1: to ask the question of how do you feel the
Speaker 1: company is doing on gender equality? Doesn't treat men and
Speaker 1: women the same, how do you feel the company treats you.
Speaker 1: I'm curious, just on the data collection side, how do
Speaker 1: you control for different surveys that are, you know, being
Speaker 1: done by different consultancies, for are different firms, Because that
Speaker 1: seems like a pretty big challenge. Yeah. So, so, first
Speaker 1: of all, we have we've worked with all kinds of
Speaker 1: data providers and of course it's easiest to work on
Speaker 1: with one right because then the consistency of the question
Speaker 1: is is equal, and then with the questions are not
Speaker 1: the same. It does provide a challenge, but because a
Speaker 1: lot of these data providers work with lots of companies,
Speaker 1: there are statistical ways to try and calibrate them to
Speaker 1: each other. But but we don't ask women how do
Speaker 1: you feel women are treated? It's not about asking them
Speaker 1: explicitly about this, but it's for example, you look at
Speaker 1: the difference between how women feel that promotions are fair
Speaker 1: versus how men feel that promotions are fair. By the way,
Speaker 1: the same thing that is true about men and women
Speaker 1: is also true about employees versus management. If you look
Speaker 1: at the company and you say, you know, feeling appreciated
Speaker 1: ends up being important. What happens to a company. Let's
Speaker 1: just take a simple case. Imagine it's a five point scare.
Speaker 1: All how appreciated do you feel? And let's say you
Speaker 1: have a company where the employees are at three and
Speaker 1: the management is at three and a half. All we
Speaker 1: have a company where the employees are at four, much
Speaker 1: higher and the management is at five. So the employees
Speaker 1: are better and the management is much better. The gap
Speaker 1: is different. Which of those companies is going to be
Speaker 1: more successful in the stock market. They're going to our results.
Speaker 1: It's the first one. Why because a lot of things
Speaker 1: are about relative happiness. Think about your own life, right,
Speaker 1: A lot of it is about where you feel compared
Speaker 1: to other people. And sources of injustice are usually asked
Speaker 1: compared to other people, men compared to women, women compared
Speaker 1: to men, management compared to the employees. So so having
Speaker 1: a consistent strategy is very important. And and one other
Speaker 1: thing that we found incredibly importan and for for motivation
Speaker 1: is so so I told you that feeling appreciated is
Speaker 1: one and of course during COVID it was harder to
Speaker 1: communicate to people that they matter, and the companies who
Speaker 1: did this did much better. But another important thing is
Speaker 1: feeling that people can make honest mistakes. And and this
Speaker 1: is why it's so important, you know, think about it.
Speaker 1: Every company wants people to innovate, but some companies want
Speaker 1: people to innovate, but they also get them to feel
Speaker 1: that if they will make a mistake, they will be punished, right,
Speaker 1: they might not get promoted, they might lose something. And
Speaker 1: every time you try something different, you take a risk.
Speaker 1: So on one hand, companies declare that they want people
Speaker 1: to take a risk. On the other hand, lots of
Speaker 1: companies behave as if if people take a risk and
Speaker 1: don't succeed, they will they will get penalized for it,
Speaker 1: and the company is that score high on that that
Speaker 1: people feel they will be penalized if they try something
Speaker 1: new and didn't succeed. Of course do much worse in
Speaker 1: the stock man. So you know, you you founded the
Speaker 1: firm five years ago with the intent to see if
Speaker 1: there was useful signal in searching out this data. And
Speaker 1: five years is not actually that long of a time
Speaker 1: really in terms of market history and what's a durable signal?
Speaker 1: And I know a lot of like approaches they talk about,
Speaker 1: like the idea of like out of sample data and
Speaker 1: so you're like, look at something, but you want to
Speaker 1: make sure you're not getting correlation and causation backwards or something.
Speaker 1: So you need time. How do you sort of as
Speaker 1: a as as an investor or someone selling investment products
Speaker 1: built on this, how do you establish that what you've
Speaker 1: identified these links between some of the survey data is uh,
Speaker 1: is this is real and not just you know, maybe
Speaker 1: a couple of years worth or something, and something that
Speaker 1: will actually stand the test of time. So so a
Speaker 1: couple of senses for this. The first one is, even
Speaker 1: though we started five years ago, we were lucky enough
Speaker 1: to get data that goes more backward in that so
Speaker 1: we have data going back to two thousand and six.
Speaker 1: So you know, it's slightly better than than five years,
Speaker 1: but you know, it doesn't solve the problem in general.
Speaker 1: The other thing is that you know, there's lots of
Speaker 1: modeling approaches they're out there that are kind of black
Speaker 1: box models. Our approach is not a black box model.
Speaker 1: Like I start with things that are already proven in
Speaker 1: the social science world. Right, so when I when I
Speaker 1: looked for relative salary matters, fairness matters a lot much
Speaker 1: more than absolute level. This is not hey, I just
Speaker 1: discovered the feature in the data. This is based on
Speaker 1: the hypothesis that we have from lots of other sources.
Speaker 1: So you know, it's a little bit like like you
Speaker 1: create a vaccine out there, you use a lot of
Speaker 1: data about biology, You're not still doing a random model
Speaker 1: and creating a vaccine. So so we have a better
Speaker 1: starting point because we start with what we know matters.
Speaker 1: We have a model of human behavior. But by the way,
Speaker 1: the reason that Fern you don't matter, and the social
Speaker 1: retirement benefits don't matter, and health benefits don't matter. These
Speaker 1: are things that we we predicted up front, and the
Speaker 1: reason they don't matter is they just fade to the background.
Speaker 1: You know, you might be excited when you get hired
Speaker 1: by all the benefits you have, but but how many
Speaker 1: days do you wake up or go to work and
Speaker 1: think about your retirement. But I love our ice coffee
Speaker 1: here at the Bloom office, and every day it's the
Speaker 1: first thing I do when I get it, as I
Speaker 1: pour myself a big cup of the cold brew that
Speaker 1: we have on tap here. So I just want to say,
Speaker 1: I just want to give a shout out to our
Speaker 1: cold break. I I hope they will not listen to
Speaker 1: our show and take and take you aware your eyes
Speaker 1: coffee to my intention. And the third thing, when you
Speaker 1: think about what's causal and correlational, you can actually look
Speaker 1: at the data. So we, as I told you, in
Speaker 1: our modeling approach, we take employees state of motivation in
Speaker 1: year one and we predict and let's in two thousands,
Speaker 1: says we predict the stock market in two thousand seven.
Speaker 1: We take two thousand nineteen, we predict E twenty. But
Speaker 1: also it's a question of what matters. You know, when
Speaker 1: the company does does well, there are some things they
Speaker 1: can do better easily. They can increase employees retirement benefit,
Speaker 1: they can buy better furniture and so on, and we
Speaker 1: see that happening, But it turns out that's not the
Speaker 1: important thing. If you if you look at what we found,
Speaker 1: it matters, for example, the sense of feeling appreciated. That's
Speaker 1: a deep cultural thing, not easy to measure, but it's
Speaker 1: a deep cultural thing. And when companies do better financially,
Speaker 1: it doesn't necessarily mean that all of a sudden we're
Speaker 1: better at appreciating our employees. So between all of those
Speaker 1: we do have more data than when we started. We're
Speaker 1: starting with a strong theory about what motivates you and being,
Speaker 1: and the data we explore it as much as possible
Speaker 1: to see what matters and not, and we feel quite
Speaker 1: confident that that it is a predictive model. So Joe
Speaker 1: and I started out this conversation by king about how
Speaker 1: the E and E s G tends to get a
Speaker 1: lot more attention. Why do you think that is? Is
Speaker 1: it a problem of data. This notion that maybe you
Speaker 1: can quantify more easily what a company is doing to
Speaker 1: reduce its carbon emissions, but quantifying what it's doing to
Speaker 1: make its employees feel a sense of well being and
Speaker 1: appreciated is much more difficult. I think, so you know,
Speaker 1: in the same way that I told you about the
Speaker 1: she in, because I think we often go to the
Speaker 1: things that are easy to measure rather the things that
Speaker 1: are important. Now, let's take let's take key and you
Speaker 1: mentioned it in the beginning, and you basically said there
Speaker 1: are two paths for it to be a good alpha strategy.
Speaker 1: One is more people would want that stock, so that
Speaker 1: stock will go up. That's one approach. The second approach
Speaker 1: is saying, somehow being environmental would create more motivation for employees,
Speaker 1: the employees will be more proud to work there. Well,
Speaker 1: that's actually true. We find that most of the effect
Speaker 1: of E comes from human motivation, right, And if you
Speaker 1: can measure human motivation directly, you do much better. And
Speaker 1: you could do You could be a an E company
Speaker 1: and the employees don't know about it, and therefore you're
Speaker 1: not increasing their motivation. And you can be an environmental
Speaker 1: company and the employees could be incredibly involved and connected
Speaker 1: and feel more proud and now you get also the
Speaker 1: benefit from that. So so I I look at companies
Speaker 1: as a mechanistic thing. I look at it's an engine.
Speaker 1: It's an engine of innovation and creativity and thought, and
Speaker 1: ask what what fuels that engine and what creates friction?
Speaker 1: And you know, dust and slows it down, and bureaucracy
Speaker 1: slows it down. And people feeling connected to the company,
Speaker 1: and feeling that their utility function is aligned with the company,
Speaker 1: and people wanting to be part of a successful company.
Speaker 1: All of those things are It's what fueled the machine.
Speaker 1: And from my perspective, the human capital party is to say,
Speaker 1: let's just quantify that, because you know, the reality is
Speaker 1: that lots of people are not happy enough it work,
Speaker 1: and it's kind of a waste. Partly, we're investing in
Speaker 1: this strategy because it has a good alpha, but partially
Speaker 1: I think it's just kind of the moral thing to do.
Speaker 1: You know, if people come to work unhappy, everybody loses.
Speaker 1: The people are miserable, management is miserable, shareholders are miserable.
Speaker 1: If people come to work happy, everybody benefits. Right. I
Speaker 1: I can't tell you what what it's like to to
Speaker 1: work in a place that you love. It's just a
Speaker 1: complete transformation. Why why don't we invest more in getting
Speaker 1: people to love the place that they work? Well, Actually,
Speaker 1: there is a question. You know you said at the
Speaker 1: very beginning that you could go into a company and
Speaker 1: without actually too much effort improve their ability to you know,
Speaker 1: employee motivation. Could you have a firm that took a
Speaker 1: stake in a company and then hired you as a consultant,
Speaker 1: improved employee motivation and then got better returned. So so
Speaker 1: absolutely yes. But I'll tell you even something else. I
Speaker 1: think when companies, when when investors do due diligence on companies,
Speaker 1: I think they have to understand the human capital in
Speaker 1: that company. If you if you look to get a
Speaker 1: big position in the company and hoping to to turn
Speaker 1: them around, and you're not getting a really good view
Speaker 1: of the human capital, you're missing something very big in
Speaker 1: your evaluation. And I know you know it's it doesn't
Speaker 1: feel as good to get numbers like four point five,
Speaker 1: and it feels much better to get a number with
Speaker 1: the dollar sign and two decimals. But but ignoring that
Speaker 1: information I think ignores way too much in the real
Speaker 1: value of the company. So I think I think it's
Speaker 1: very true for any evaluation of a company needs to
Speaker 1: take that into account. And of course, if you want
Speaker 1: to be activists, like think about Microsoft as an example,
Speaker 1: Microsoft change CEOs and had an amazing transformation. And if
Speaker 1: you look at transformation, it was largely cultural transformation, largely
Speaker 1: a transformation of how people looked at themselves, how management
Speaker 1: looked at at the employees. I mean that that was
Speaker 1: that was basically it, and you know, it's been very successful.
Speaker 1: Since you brought up Microsoft, I actually wanted to ask
Speaker 1: you about tech companies UM and maybe the difference with
Speaker 1: other firms. So one of the criticisms of the way
Speaker 1: E s G proponents go out and you know, typically
Speaker 1: E s G will say, well, we outperformed over the
Speaker 1: past year, especially so it proves that you should invest
Speaker 1: in E s G companies, UM, because we produced that alpha.
Speaker 1: But then a lot of people will point out in
Speaker 1: response that, well, the E s G statistics for tech
Speaker 1: look particularly good because they're not actually polluting that much.
Speaker 1: You know, most of what they do is intangibles. I
Speaker 1: imagine that could apply to the human capital argument as well.
Speaker 1: So tech companies would naturally be more aware and conscientious
Speaker 1: of their human capital than other companies. So I guess
Speaker 1: what I'm asking is is that true in your research?
Speaker 1: And then secondly, how does that fit into the alpha equation?
Speaker 1: Like are you outperforming simply because you're buying more tech
Speaker 1: companies with a strategy that's focused on human capital. So
Speaker 1: so my strategy is to look at all the companies
Speaker 1: and take the one that our top on human capital
Speaker 1: and and buy them. Now, I do have a bias
Speaker 1: in the portfolio towards tech, but it's not it's not
Speaker 1: fully tech, and it's not close to being half tech.
Speaker 1: It moves between depending on the ear between. And now,
Speaker 1: the interesting thing about our results that surprised me, I
Speaker 1: have to say, was that we didn't find the reason
Speaker 1: to create a different model in different sectors. So you
Speaker 1: would say something like in manufacturing, you could say, oh,
Speaker 1: in manufacturing, how important is it to be appreciated? It
Speaker 1: turns out to be just the same. And since since
Speaker 1: we got that results, I understand that phenomenal much better.
Speaker 1: But even think about something like Target in Walmart. There
Speaker 1: was recently a study that asked, like what happens when
Speaker 1: a customer shows up and there's something that is out
Speaker 1: of stock, and they asked one of the Cells people
Speaker 1: to to check if it's in the in the back
Speaker 1: room if they still have it. It turns out its Walmart.
Speaker 1: They never check, you know, they're just not that motivated,
Speaker 1: and in targets they go and check and they often
Speaker 1: find it. Now, now this is a it's not Google
Speaker 1: or Amazon, but but good will is incredibly important. So
Speaker 1: so we find that our model of saying, you know,
Speaker 1: what's important is to feel appreciated and you can make
Speaker 1: honest mistakes, and you feel connected to the company, and
Speaker 1: all the things that we find you find it promotion
Speaker 1: is is fair. We find that all of those things
Speaker 1: are equal across the different sectors, and we also find
Speaker 1: that they are important across the rank within the company.
Speaker 1: So to answer your question, I think, yes, we are
Speaker 1: slightly heavy on on tech compared to sector neutral strategy.
Speaker 1: Of course we can also have a sector neutral strategy.
Speaker 1: We don't think that's the right thing to do, but
Speaker 1: you can do it. But but it doesn't it doesn't
Speaker 1: only hold for tech company, it holds it holds for
Speaker 1: everybody equally. As far as we can tell, I just
Speaker 1: I mean, I guess we haven't talked what are the results? Like?
Speaker 1: What what have you seen? Like? What is the what
Speaker 1: is what do you anticipate getting? What have you gotten?
Speaker 1: What is the data show in terms of making money?
Speaker 1: So the return taste oracle return that we have is
Speaker 1: slightly more than seven percent just by looking at human capital. Right. So,
Speaker 1: by the way, this is a pure strategy because of
Speaker 1: course if you wanted to, you could combine it with
Speaker 1: other things. But it's important for us to prove like
Speaker 1: how is just this one signal? What is this one
Speaker 1: signal worth? And from two thousand and six it's it's
Speaker 1: about it's about seven pc over the the SNP. But
Speaker 1: but the things that are that are important underneath it
Speaker 1: is the thing that I told you about, right, It's
Speaker 1: a lot about fairness, it's a lot about being appreciated.
Speaker 1: There was another interesting fact that it became more important,
Speaker 1: which we call inclusive innovation. An inclusive innovation is how
Speaker 1: many voices around the table are being heard. And that
Speaker 1: was always important, but it became extra important during COVID.
Speaker 1: And the reason is that when you sit around the
Speaker 1: physical table, the people who don't like to talk that
Speaker 1: much still talk at some point and little bit. The
Speaker 1: social pressure is very high, but when people are on Zoom,
Speaker 1: lots of people are just silent. They just basically drop off.
Speaker 1: You lose some insights, you use some collaboration, you use
Speaker 1: some opinions, and the companies that they do better on
Speaker 1: that find things much more important. The other thing that
Speaker 1: increased over COVID, I told you was feeling appreciated companies.
Speaker 1: You know, it's it's hard. It's hard to say thank
Speaker 1: you over zoom compared to when you're in the room.
Speaker 1: In the room you can just tap on some of
Speaker 1: these shoulder you could wink at them. On zoom, it's
Speaker 1: a little extra tough. People need to work at it harder,
Speaker 1: and some people are better at it and and worse
Speaker 1: at it. And then feeling that people have a future
Speaker 1: with the company became more important. And I think it's
Speaker 1: because of this. There was so much uncertainty about what
Speaker 1: the the job market would hold, what would happen to
Speaker 1: the turmoils as so that also became much more important.
Speaker 1: So CEOs that's communicated and help people understand where the
Speaker 1: company is going did did much better. So since we're
Speaker 1: on the topic of what changed during COVID, I'm curious
Speaker 1: if you did any research on flexible work arrangements and
Speaker 1: the idea of companies, you know, allowing their employees to
Speaker 1: work from home as needed, because that's such a hot
Speaker 1: button issue right now. There's such a big debate over
Speaker 1: whether employees feel better about being able to do that,
Speaker 1: or whether or not they enjoy the camaraderie of an
Speaker 1: office environment. I'm just curious whether that came up at all. So, yes,
Speaker 1: we studied tens of thousands of people on this. On
Speaker 1: this question, I'll keep you kind of the highlights. The
Speaker 1: first highlight is that some flexibility is good, but people
Speaker 1: should go back to the office. It's also the fact
Speaker 1: that people are not good judges of whether they should
Speaker 1: go back to the office or not. You know, after
Speaker 1: a year or so being away, people forget what it
Speaker 1: is to work with people. You know, people are still
Speaker 1: a little bit afraid COVID distance. But but you know,
Speaker 1: on the things that happened in this interaction between people,
Speaker 1: you don't recognize it and appreciate it. It's it's hard
Speaker 1: to quantify. It's hard to quantify chit chat. It's the
Speaker 1: water cooler and over coffee. I think a work from
Speaker 1: home is important, and some of it should stay maybe
Speaker 1: a day a week. If it becomes just work the
Speaker 1: same work you do in the office, just do it
Speaker 1: from home, that's not as valuable. People should do different
Speaker 1: work at home, the kind of things that the home
Speaker 1: environment is more suitable for. Companies also need to collaborate
Speaker 1: on this. If you have a zoom meeting or whatever
Speaker 1: technology you use, where half the people are physical and
Speaker 1: half the people are distant, the people who are distant
Speaker 1: our second class citizens. And and because of that, unless
Speaker 1: companies coordinated and say Tuesday, the whole team can stay home.
Speaker 1: Unless unless it's coordinated, you know, people would say, oh,
Speaker 1: I have I was going to stay at home, but
Speaker 1: I have this one important meeting. I'm not going to
Speaker 1: be the only one on zoom because I have something
Speaker 1: to say. I want them to pay attention. And if
Speaker 1: I'm going, I might as well go early. I think
Speaker 1: unless companies kind of coordinate on that, it would very
Speaker 1: quickly go back to people being in the office the
Speaker 1: whole time. So I think it's important to keep a
Speaker 1: little bit of it. It needs to be a different
Speaker 1: kind of work rather than work remotely, but but do
Speaker 1: the same things. And I think companies need to put
Speaker 1: some effort into it so that the coordination allows that
Speaker 1: to to stay. And I'll say one last thing. I
Speaker 1: think that people are going to be concerned when they
Speaker 1: first come back. So I have a research lab here
Speaker 1: with Duke. We're about fifty people. I'm waiting for Duke
Speaker 1: to allow us in. I'm I'm in the office right now,
Speaker 1: but I'm the only one in the office. I'm waiting
Speaker 1: for people to come to come back. And some people
Speaker 1: are going to be concerned. Right, It's been It's been
Speaker 1: a long time people were anxious COVID related kind of things.
Speaker 1: What can we do to get people to to drop
Speaker 1: their their concern And what I'm going to do is
Speaker 1: I'm going to try and get people to come. I'm
Speaker 1: going to ask people to come every day, but tell
Speaker 1: them they can come for a short time if they
Speaker 1: want to. And and the reason I want is is
Speaker 1: that we learn about risk from experience and not from
Speaker 1: statistics that we read. So I want for people to
Speaker 1: have the experience of coming to the office and nothing
Speaker 1: bad happened coming to the office. That's going to be
Speaker 1: the best medicine against fear. Right, just practice it a
Speaker 1: few times and see that nothing that happens, and then
Speaker 1: in a few weeks people would be relaxed. But if
Speaker 1: we tell people, oh, you know, if you don't want
Speaker 1: to come to the office, don't come to the office,
Speaker 1: we're not going to help them subside that that fear
Speaker 1: as much. So I think we need the correcting experience
Speaker 1: of coming to the office, meeting people, seeing that nothing,
Speaker 1: nothing bad happened, and experiencing that for a while. So
Speaker 1: I just have one more fushan and we've we've you
Speaker 1: kind of hinted at this when you talked about your
Speaker 1: studies of Target versus Walmart, But we've been telling you
Speaker 1: a lot about the zoom class and the people who
Speaker 1: are sort of lucky enough to have been able to
Speaker 1: do their jobs over the last year over zoom, or
Speaker 1: the people who are lucky enough to have coffee, free
Speaker 1: coffee and water coolers at work when they come back,
Speaker 1: and things like that. But obviously that is not the
Speaker 1: entirety of the workforce by any stretch. And we've seen
Speaker 1: a lot of stories in the last several months about
Speaker 1: lower paid workers companies having trouble hiring them for various reasons,
Speaker 1: which you know, economists are trying to debate whether it's
Speaker 1: you know, all kinds of things, lots of frustration among
Speaker 1: fast food companies and retailers and restaurants and other sort
Speaker 1: of service labor that they can't hire, and maybe wages
Speaker 1: are part of the answer. What is your sense, like,
Speaker 1: you know, going at that level. I'm curious if you
Speaker 1: could talk a little bit more about how strong your
Speaker 1: signals are and what your research says about hiring and
Speaker 1: keeping those workers happy and motivated, you know, in what
Speaker 1: it appears to be a very competitive job market right now. Yeah, so,
Speaker 1: so I think I think it boils down to giving
Speaker 1: people the sense that there are substitute herbal very easily,
Speaker 1: and that just makes people behave this way. Right. So,
Speaker 1: so if you think about a lot of those places,
Speaker 1: think about restaurants, a lot of them make people feel
Speaker 1: that we don't need you. We need some a body here,
Speaker 1: but we don't need you. And then one day somebody
Speaker 1: wakes up and the bus is late, or they don't
Speaker 1: feel as good as they have something else, and they
Speaker 1: don't feel any obligation to show up, and then they
Speaker 1: don't show up, and then they feel bad about not
Speaker 1: showing up, so they don't show up. Again, you know
Speaker 1: a lot of those high frequency jobs, people don't resign,
Speaker 1: They just stopped showing up. And and it's a part
Speaker 1: of no commitment. They have no commitment to their team,
Speaker 1: they have no commitment to the to the place, and
Speaker 1: and you you want to try and create that commitment.
Speaker 1: That was true even with with with jails. If you
Speaker 1: think about a place like Saint Quentin and you know
Speaker 1: some some some tough, tough jails, the moment you have
Speaker 1: team cohesiveness, people feel like they owe something to each other.
Speaker 1: By the way, that's true also for high tech it
Speaker 1: if you know, one of our studies, we found that
Speaker 1: people are more willing to stay late at night to
Speaker 1: help a friend then to finish their own project. Right
Speaker 1: because if I need to stay until two am for
Speaker 1: my own project, you know, maybe I'll finish another day.
Speaker 1: But if a friend is asking to it now, now,
Speaker 1: the social pressure is higher and people are more likely
Speaker 1: to do it. The way in which our commitment to
Speaker 1: the work shows up, it's it's to the mission of
Speaker 1: the company, it's to the CEO, it's for the direct management,
Speaker 1: but also to our fellow fellow employees. And we need
Speaker 1: to create those conditions. You can't expect it to just
Speaker 1: to emerge by itself. And and what so many companies
Speaker 1: are doing under the idea of kind of mass production
Speaker 1: is treating employees is mass production? Come in, here's the training,
Speaker 1: here's your badge, here's the thing. Start working. You're you're basically, UM,
Speaker 1: a part of an efficient process that doesn't create loyalty,
Speaker 1: right man. I could talk about this literally, UM for
Speaker 1: another you know, five hours at least, and do really
Speaker 1: really deep dives into workplace policies UM that work the
Speaker 1: best in terms of employee satisfaction. I would love to
Speaker 1: do that, But Dan, I'm very aware that you have
Speaker 1: an appointment and so you have to go. But thank you.
Speaker 1: This was so fascinating. Yeah, this is great, Dan, really
Speaker 1: really appreciate you taking your time and joining us. My
Speaker 1: my pleasure, you know it is it is the question
Speaker 1: of how you improve the work conditions for people is central,
Speaker 1: and I think it's one of the places where the
Speaker 1: financial market can play a big role. Right if we
Speaker 1: only invest more in companies that treat employees better, if
Speaker 1: we make it clear what it means to treat people better,
Speaker 1: if we've got people to think about it more, I
Speaker 1: think I think everybody could be better of great stuff.
Speaker 1: I will continue to uh, continue to watch your work, Dana, really,
Speaker 1: thank you so much, my pleasure. Take care, Tracy. I
Speaker 1: didn't know this was such a huge topic for it.
Speaker 1: I feel very passionately about several of these themes I'm trying.
Speaker 1: I had to hold back quite a lot because I
Speaker 1: don't want to make the podcast about you know how
Speaker 1: Tracy feels about flexible work conditions. But man, as someone
Speaker 1: who works across time zones, you know we're recording this
Speaker 1: at nine fifty pm, I have strong opinions about flexible work.
Speaker 1: Come on, come back, just come back to New York, Tracy,
Speaker 1: come up. Then we don't have to worry about that.
Speaker 1: It would make my life easier. But I gotta say, like,
Speaker 1: one thing I really loved about that conversation was Dan's
Speaker 1: emphasis on relative versus absolute gains because I think so
Speaker 1: much of finance and economics, and we've spoken about this before,
Speaker 1: but so much of it is based around the idea
Speaker 1: of people acting rationally and kind of working together and saying, well,
Speaker 1: this person might you know, if we do something, this
Speaker 1: person might get more out of it than I will,
Speaker 1: but we both benefit in one way or another. But
Speaker 1: as Dan was saying, in the real world, people often
Speaker 1: aren't that rational. In the workplace, especially, there's much more
Speaker 1: of a tendency to look at the colleagues around you
Speaker 1: and say, hey, they're getting a better deal than me,
Speaker 1: and much more of a tendency to focus on sort
Speaker 1: of relative advantages versus what's going on absolutely at the firm. Yeah,
Speaker 1: I mean that makes a lot of sense, and it's
Speaker 1: definitely highly intuitive. I just want to say, like, and
Speaker 1: I totally believe Dan and the data, but like, I
Speaker 1: still want to like check back like in twenty years,
Speaker 1: because you know, it still seems like obviously this is
Speaker 1: like the collective of this type of data is new,
Speaker 1: the data, the period is not that long, even going
Speaker 1: back to say two thousand six is not that long.
Speaker 1: And market time, as you pointed out, a lot of
Speaker 1: this may you know, they've been overweight tech for some
Speaker 1: of these, although not radically. So I do want to
Speaker 1: have Dan back on in twenty years when we get
Speaker 1: like a really long stretch of out of sample data
Speaker 1: to see how well it holds up. But it is, uh,
Speaker 1: it is fascinating and if you can sort of capture
Speaker 1: it with the numbers. It's also it's intuitive. Well that's
Speaker 1: I mean, that's the other big takeaway from this, right,
Speaker 1: is how much of it depends on how you're measuring it,
Speaker 1: the strength of the data, whether or not it actually
Speaker 1: bears out over a longer time period. And that's basically
Speaker 1: the reason that so far there's been this emphasis on
Speaker 1: the E in E. S G and not so much
Speaker 1: on the social governance side. So maybe that's starting to change,
Speaker 1: and maybe we're going to see more rigorous collection of
Speaker 1: social and human capital related data. But we are in
Speaker 1: the early days. That's very true, exactly right. Shall we
Speaker 1: leave it there? Sure, let's leave it there. All right.
Speaker 1: This has been another episode of the All Thoughts podcast.
Speaker 1: I'm Tracy Alloway. You can follow me on Twitter at
Speaker 1: Tracy Alloway. And I'm Joe Wisenthal. You can follow me
Speaker 1: on Twitter at The Stalwart. Follow our guest on Twitter,
Speaker 1: Dan Arielli, He's at Dan Arielli. Follow our producer Laura Carlson,
Speaker 1: She's at Laura M. Carlson. Followed the Bloomberg head of
Speaker 1: podcast Francesco Levi at Francesca Today. And check out all
Speaker 1: of our podcasts at Bloomberg under the handle at Podcasts.
Speaker 1: Thanks for listening to