Rethinking Driving Productivity in Emerging Markets with Anant Nyshadham

During this episode of The Evolving Leader podcast, co-hosts Jean Gomes and Scott Allender are in conversation with Anant Nyshadham who’s work includes studying the effectiveness of firms in developing countries with the intention of accelerating economic development. Anant is an associate Professor in the Business Economics and Public Policy area of the Ross School of Business at the University of Michigan and a research associate of the National Bureau of Economic Research. He is also an affiliate of BREAD, which is a nonprofit dedicated to research and scholarship in development economics. And he is a research affiliate of the IGC, a J-PAL affiliated professor and an affiliate of the Montreal Partnership for Human Resource Management. 

Anant is also co-founder and chief strategy officer of the Good Business Lab, a nonprofit seeking to promote investment in worker welfare as a business imperative. His work focuses on enterprise, firm and worker characteristics and decision-making like labour contracting and worker training and managerial quality and the resulting performance dynamics, particularly in developing countries.
 
 Other reading from Jean Gomes and Scott Allender:
 Leading In A Non-Linear World (J Gomes, 2023)
The Enneagram of Emotional Intelligence (S Allender, 2023) 

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The Evolving Leader is researched, written and presented by Jean Gomes and Scott Allender with production by Phil Kerby. It is an Outside production.

Chapters

• 0:00 Introduction
• 4:07 Check-in
• 4:53 What’s your story?
• 10:00 Wellbeing in the workplace.
• 19:03 Creating a new value exchange.
• 23:05 Are people persuaded by the statistics?
• 27:52 Accepting that risk is subjective, how do you dial that out of your analysis and the solutions that you come up with?
• 32:54 What has been most exciting in both your and wider research?
• 38:34 How would you define the mission of Good Business Lab?
• 44:57 How will AI impact those not in sophisticated jobs?
• 53:26 How can people connect with you?

Transcript

Jean Gomes: 0:03

We all love those aha Insights where we encounter an idea or technique that works because it overturns conventional wisdom, but we only love it because it works up to that point, the idea is often met with eye rolling, cynicism, resistance, dismissal or simply inattention. In this show, we get into a wonderful conversation with the economics researcher Anant misalon, whose pioneering studies into the effectiveness of firms in third world countries reveals a slew of exciting, counter intuitive insights and evidence that have the power to accelerate economic development in the world's most impoverished countries. Tune into an important conversation on The Evolving Leader.

Scott Allender: 1:05

Hi folks. Welcome to The Evolving Leader, the show born from the belief that we need deeper, more accountable and more human leadership to confront the world's biggest challenges. I'm Scott Allender.

Jean Gomes: 1:16

and I'm Jean Gomes.

Scott Allender: 1:17

How are you doing today Mr. Gomes?

Jean Gomes: 1:19

I am feeling very good. I've had a amazing few weeks of doing really interesting research and delivery and all sorts of things. I am feeling conflicted about what's happening in the world right now, as we ever since we started a show that's probably like, there's ever been a week where there isn't some major, major thing happening, it probably it doesn't affect you more than us, but you're, you're at the epicenter of what's happening in the US election. So we won't talk about that, but just suffice to say that we're all feeling something. And yeah, and I'm really looking forward to the show. How you feeling Scott?

Scott Allender: 2:02

Yeah, we probably, probably should set up a whole hour to talk to I'm feeling, feeling a lot of things today. My friend, yeah, feel it. Feeling some I feel, I feel raw. Maybe, is the is the best term for it right now. We're only sort of, I don't know when the show will go up, but we're only sort of a week on the other side. And I certainly just, I'm I'm in the middle of the fields, so I'm still processing, as I know a lot of people are, and try to make sense of things. So but I am delighted to be back here with you and having conversations to influence the leadership discussion in terms of what is leadership and what do we bring to it? How do we make it more accountable? How do we make it more human? And so I'm delighted that we're joined today by Anant Nyshadham. Anant is an associate professor in the business, economics and public policy area of the Ross School of Business at the University of Michigan, a research associate of the National Bureau of Economic Research, and he's an affiliate of bread, which is a non profit dedicated to research and scholarship in economics, and he is a research affiliate of the IGC, A J pal affiliated professor and an affiliate of the Montreal partnership for human resource management. And if that's not impressive enough, he is an accomplished guitar player, and I'm staring at his guitars behind him right now, and I'm envious of them, and want to talk all things music. But in addition to that, he also received his PhD in 2012 from Yale University, and is the co founder and chief strategy officer of the good business lab, a non profit seeking to promote investment in worker welfare as a business imperative. His work focuses on enterprise, firm and worker characteristics and decision making, like labor contracting and worker training and managerial quality and the resulting performance dynamics, particularly in developing countries. Anant Welcome to The Evolving Leader.

Anant Nyshadham: 3:57

Thank you so much for having me, and thanks for that great introduction. I'm glad you had to do it and not and not me as a lot of as a lot of text, yeah.

Jean Gomes: 4:07

Anant, welcome to the show. Apart from just ogling your Telecaster on the wall there, how are you feeling today?

Anant Nyshadham: 4:15

I am feeling, certainly, all the things that people are feeling in the world today, especially as an American, lot of shock and a lot of stress about the way forward. But also, you know, I think successfully looking inwards at what I can do, trying to be a bit more focused on what I can control and the work that I'm doing, which I'm very excited to talk about today, and staying kind of mentally fit to do that work. So today I'm feeling equipped to do that. We'll see. You know, it's a day to day process,

Jean Gomes: 4:52

Excellent. Well, let's get to know you a little bit. Imagine where a dinner party and you're surrounded by people who are not in your world and not in in the academic world or in the nonprofit or any of those kind of things, and the person turns next to you and says, Hey, Anant. What do you do? What would you say?

Anant Nyshadham: 5:08

I would say you're sure you want that. It has to be that question.

Jean Gomes: 5:13

I'm really interested.

Anant Nyshadham: 5:16

So, yeah. So you know, broadly speaking, I work on development issues. So all the kinds of problems, particularly in the developing world, although I think these problems by way of immigration and by way of kind of persistent class issues and so on, they exist in the West, for sure, also. So it's, it's kind of, it's about development both, you know, in an economic or macro economic sense of countries, whole economies, but also just in terms of individuals trying to advance in their lives. And I, you know, my path to this, I think was a bit strange in the sense that it was late to get here. I think I was late to take myself very seriously. I grew up in a very small town in Georgia, very impoverished. It was an old kind of factory mill town, textile town, but the textile jobs, factory jobs, had long since gone even before we got there. So I just grew up in a place that had great sense of community, but certainly was rife with issues of racism and class issues and extremely impoverished majority over the poverty line and and, you know, majority, majority minorities in the country. And then, kind of, in some sense, as I'm as I was going through that, we also spent recent trips, sorry, frequent trips to India, to our ancestral land, which, sadly, we don't have anymore, but to the village where I think I experienced a lot of just a completely different world. I mean, I thought even what I was seeing, you know, in the kind of rural South, you know, lower income areas that we were living in, you know, paled in comparison to what the challenges that I saw in village India, limited access to resources and education in, you know, pre internet, pre social media, access to the to the external world. And so I think this kind of stuck with me, and as I got into school and tried to figure out what I was going to do in the world. On the one hand, I'd really just kind of pursued the PhD in Economics in a purely, like, romantic sense, like, I had some sense that I wanted to be a thinker. And, you know, I come from, actually, a family of academics, and the idea, you know, my grandfather was a physicist, and, you know, I had a sense that, like, Oh, this is really nice, like, you get left alone and you can think, but I didn't know what I was going to think about or, you know, or how I was going to apply it to the world. And honestly, it wasn't until I the summer after I graduated from the PhD, you know, I trained in much more techniques, econometrics and so on methodologies. But when I graduated from the PhD the summer after that, I had an opportunity to go visit some textile factories in India. So it turns out, classmate of mine from undergrad, his family was a, you know, he was a second generational industrialist. And, you know, I didn't realize at the time, but he was like, okay, my family runs a few factories in India, and you should come check it out. We're like, doing these programs for female garment workers. And then when I got there, I realized they run like all the factories, you know. So there was a, it was a really huge scope, and and I saw, you know, something really amazing there, in the sense that here is a company that represents and has kind of a touch point with over 100,000 people who need something, you know, people who need help. They have limited access to resources. They have limited access to mobility. They're constrained. And so here's a pretty powerful lever, potentially. But what we kind of started talking about in that first summer was this idea that, like, well, maybe we can do some worker initiatives and, you know, test them and so on. And I was explaining the idea of running experiments to try to get, you know, kind of good causal evidence of what might work and what might improve things. And as we talked, I started to realize that, you know, there was this internal debate in that firm, and I've now, since, over the last more than decades, seen it in many, many firms like it. Of you know, what are we doing for our workers, and should we be doing it? Should we be doing more of should we be doing less of it? You know? And there's different perspective. It's not that anybody doesn't care. It's that that one manager thinks this program is helpful and one manager thinks that it's not helpful and it's a distraction, and there's no evidence to make these decisions, and so firms are just kind of, you know, kind of shooting in the dark a little

Scott Allender: 9:56

Can we talk about well being in the bit. workplace? So I'm thinking about the levels of stress and anxiety and loneliness that are prevalent in workplaces all over all over the place. Talk to us a little bit about the work that you've done at building well being into core business models at organizations where the temptation and what gets repeated a lot is that people try to kind of slap a little bit of well being work on the side of the of the agenda, but it's not really ingrained into what everybody's experiencing every day, right? So the perspective of everyone and what they need talk to us a little bit about that.

Anant Nyshadham: 10:41

Yeah, um, I mean, I think that there's a lot of different elements to that. I think, on the one hand, we've been, you know, chipping away at at all the different elements of well being, pointing out that in some sense, like, you know, well being isn't just I'm fit, or I'm, you know, or I don't have chronic health issues, it's mental health, it's loneliness, it's being able to deal with stress, or maybe being insulated to some degree from stress, you know. And so when we I think we want, as a scientist, the way we do that is to then run a trial on this, and run a trial on that, and, you know, and try to put them together and so on. And we have, I think we've got stuff that, you know, documents that how hot it is in the workplace, whether the air is marginally dustier, can have tremendous effects on productivity and and ones that need to be monitored by management and adjusted, and good management can kind of react to those things. You know, we've got studies on screening for presbyopia and giving workers glasses and nutrition deficiencies and so all of these types of maybe more traditional things, but also, like buddy systems, you know, we, we work in a lot of industries with a lot of migrant labor. So these are workers that have left their network, the only thing that, the only reality they've ever known to come to a city, it's totally new, with no one that they really know. And so can we pair them with somebody who did that two years ago, came, maybe even from their area that speaks their same language, you know, likes the same types of foods, you know, and and connect, create a connection there that allows for some to break down that isolation. Right? A lot of times when we're under stress, it feels like we're the only ones dealing with this sort of thing, and that can be very isolating. Just knowing that it's not us alone. Can be, can be really powerful, and so, you know, so we've run trials on that. So on the one hand, it's kind of putting the evidence together. It's, it's why we've kind of thought of this as a life's work, although every day I wake up and feel like I wish I had done more up to now, or I want to do more, you know, tomorrow, and do it faster to get this evidence. But like we said, evidence isn't enough, and so I think where it's been important is, you know, at every level, to change the conversation. And by that, it means, like when I started doing this, I would come in and I would talk to CEOs, and they would give me very confident answers about what they thought was going on on the ground and what they thought their biggest problems were, and so on. And then I have yet to find a single firm or single setting in which that notion wasn't ultimately challenged or contradicted when I got enough layers down that actually the way the managers are dealing with this stuff at the lowest possible level is like this and not like that, and, you know, and then actually, the biggest driver of that problem, you know, for example, in a lot of these garment settings, you know, there's a super high turnover. And when I started, they said, Look, you know, work turnover is crazy high. We're turning over almost our entire workforce every year. So like, 100 plus 1000 workers we're gonna hire this year, and then we're gonna hire another 100 plus 1000 next year. That's bananas. That's crazy. So obviously, that's a lot of money and a lot of effort in an entire, like, army of HR, you know, just to do that. But then that's, you know, that's what was said out of one side of their mouth, and the other side of the mouth they're saying, Yeah, but those are, like, inevitabilities, right? Like, oh, in India, women come when they're young, and then they get married, and then they drop out of the workforce or whatever. And don't get me wrong, that's not untrue. There are lots of women, not just in India, every country in the world, that work before they've shared their families, and then when they have families, will choose, or maybe, unfortunately, have the choice made for them to drop out. But when you're turning over 100% of your workforce, there's not one answer, right? There's like, who knows an innumerable number of causes of this. And so you've got to really start by challenging everything you think you know about all the. Problems, right? You've got to start by actually going down as far as you can, right down to the ground, and asking people, and this person might tell you one thing, and that person might tell you another, but starting to get themes, you know, from the actual people about what are their challenges, what makes them potentially leave these jobs or and so on. And then you start to get a bunch of different stories. Yes, I, you know, I'm going to get married next year, and I want to start a family, but actually, I was planning to stay. It's just that this is this. Work is really hard on my back, and I can't do that. And also work, I mean, I'm just, you know, that's one of many stories we've heard. So then you start to understand that I don't have to solve every problem or the whole problem. There's value in solving a part of this problem and chipping away at it, right? There's a there's a value in just saying, Well, let me solve the ergonomics. And some x percent of people who were dropping out before that's the real thing, they needed to stay you know? Oh, I'll give you an example that I'm really, really excited about. We recently, you know, it's true everywhere in the world, including in the US. This notion, in fact, the existence of the payday loan industry basically underscores the idea that a lot of low income households and families are living paycheck to paycheck. Shocks come as as economists, we would call them shocks, but like unexpected things come, you need to pay for them, and often they might be, I don't know, 300 bucks. 400 bucks, which thankfully many of us think, is not that big a deal, but in every country in the world, there's quite a few people who would never be able to come up with that tomorrow. And that can be the effects of that can just echo through their whole lives. If I can't get it, I can't make my car payment. Now my car is gone and I can't get to work. Now I'm done. If I can't get it, I can't make my rent. I've already missed rent a couple of times. I'm out on the curb, you know. If I can't get it, I can't take my kid to the hospital today, so I'm going to wait a bit longer and hope that they get better, and then there's a chance they may never get better, you know. And if you add up all of those things, you have such a simple problem driving so much persistent, you know, adverse effects for this population. So we did something really simple, which was that, like, look, here's a firm. They've got hundreds of 1000s of workers. They're not going to just cut checks every day. They need to be on a cycle like and they need to manage their liquidity and so on. So, but we just created a tool where it says, Look, you can whatever wages you've earned up till now. This is not a loan, right? Everybody gets paid in arrears, mostly, right? Like, I work this month and I get paid at the end of the month or next month for last month, right? So I've earned this money. So we gave these garment workers the ability to draw up to 50% of that of that wage that they had earned up to date, and they could do it throughout, whenever they needed it, throughout the the month. And just that, no additional money, nothing else, just the ability to, like, go to a little tablet on the factory floor. That's the I mean, we had to, we had to know that we couldn't make it an iPhone app, because then nobody would be able to use it, right? So you have to understand your population. But here's a tablet on the factory floor. I can go there, I can draw my money. And it dramatically reduced attrition, wow. And even increased day to day productivity. We're still unpacking the why, but a lot of it is that this is a thing that this job has that other jobs don't have. I need to keep this job. This job allows me to pay my bills. Another job is almost useless. I can get paid twice as much, but if I can't get it when I need it, what good is it?

Jean Gomes: 19:04

That's fascinating. I mean, what it brings out for me is like the disconnect between the owners and managers of firms and the lives of the people that you know make them successful, and if you do not understand the problems that they face. You can't possibly be in a relationship with them that actually is mutually beneficial beyond time for money, because that's what you're basically saying. You're creating a new value exchange between the individual and the firm, which is, goes beyond that, which is, I understand you understand what you need. I'm going to help you meet your needs in a creative way, and actually from a many ways, that doesn't cost the firm very much at all. I mean, there's a slight implication on cash flow, little bit of, you know, of you know, financial management that's required there, but it's that seems very simple,

Anant Nyshadham: 19:57

and to highlight what you're saying, actually. We got tremendous pushback on this. I mean, we it took us months to convince a single factory to let us do this. And the agreement was, you're going to do it and then you're going to take it away immediately. So we're going to do it for three months, and then you take it away, or six months, I can't remember, and then take it away. And the trial has just now, it's just now wrapping up, and now they're begging us to keep it and scale it up to the rest of the factory, because now they're seeing that, like workers are happier. They're here, they're working harder, you know. So the notion, powerful notion, was that, well, they're going to take their money and run like, you know, as soon as but the logic of that is so strange, right? Like, if the only reason why your workers are here is because you're holding their money, you've got a problem, you know. And so I don't think it didn't ring true to me, and it didn't turn out to be true. So thankfully, that's, you know, that I was right that time. That's

Jean Gomes: 20:54

interesting. So did was there? Was there any cases of people, you know, taking three months, or, you know, sorry, three week salary and just No, no.

Anant Nyshadham: 21:02

I mean, so I can't tell you every n, right, every observation, but on average, it was tremendous. It was like, very much the opposite, you know, very much the opposite. And the average is what the firm should care about, right? Even if there's a handful of workers that are going to do that, if the overwhelmingly workers are going to see this as a benefit that they want to stick around for and work harder for, you know, that's amazing. And the reality is, there's all these nuances that we're still unpacking. But you know, if I can just we were finding it at baseline before we intervened, that that many of these workers were reporting running out of money at the end of the month and eating less forgoing medical expenses. These like real things that very obviously will affect your productivity or your ability to show up to work, you know. So the, you know, the theory of change is like, pretty easy to draw, you know, just to sketch out. But I think that, like you're saying, you know, I don't think it's, I mean, I think it's quite reasonable, or maybe expected that, you know, a senior leader or somebody comes from a different walk of life, who's running this company is going to have trouble understanding the lives of their workers. The idea is to not, is to is to just be aware of what you don't know, to want to know what you don't know you know, to actually find out. And you don't have to do it philanthropically. That's been our whole kind of mission or agenda. You know, I would love for that to be true, but that's silly. We're not going to make any progress if we just wait around for the people who want to think all day about, you know, other people and the poor and so on. To make progress, let's just it should be good for your business. It should be that I am spending a lot of money on recruiting new workers. Now I need to go challenge everything I know about why workers are leaving, and be a bit honest with myself and figure out how I can solve it. For me. Yeah, they benefit, but for me, right? So I don't have to deal with this problem anymore.

Scott Allender: 23:06

Do you find that people are receptive to letting the data do the talking and are persuaded by that, or is there still a kind of resistant resistance, because that legacy mindset you talked about where, you know, we'll invest in upgrading our machinery, but we're not really going to do that with our people. I'm actually thinking about, you know, statistics around effective leadership, you know, 70 to 75% at least, being attributable to measures of emotional intelligence. And yet, time and again, people are hired into very important leadership roles simply based on their sort of hard skills or their sort of previous experience. And the data that's there and undeniable is almost denied, and in spite of it being undeniable, so I'm really curious, like with the data you're presenting with your sort of own research, is it persuasive, or what are you doing or having to do to bust through that sort of resistance that you might experience?

Anant Nyshadham: 23:58

That's a great question. And maybe the crux of, you know, the hardest elements of my work, you know, for the last decade, evidence isn't enough. I mean, so we started with the idea that the evidence was bad. It either didn't exist or it was bad. And while I still think that's true, and I still think that, you know, pat myself on the back, I think we made better evidence. We ran really tight, rigorous, gold standard trials. And we, you know, we, we presented it that was its own challenge. Once you do that, you know, five people are going to read this paper, if I'm lucky. So now, how do I translate this into an HBr piece or, you know, or an NPR kind of podcast, or a podcast discussion, you know? But even that just wasn't enough. I think what we realized, which is, I think, a broader, maybe epiphany, at least on my part, is that, you know, you have to meet there's a cop, there's a there's a set of decision makers, or a sequence of decision makers. You. In order to change this, to create an organizational change, convincing the CEO does almost nothing, right? I mean, it's a necessary but insufficient condition, right? Like, if I go in there and I, you know, convince the CEO, nobody else is necessarily their day to day lives are different than that decision, right? Yes, my boss tells me, but my boss can't see whether I'm really putting forth effort into this. And day to day, I'm gonna make a choice as to what seems to benefit me, you know. So we had to do these. And then the opposite is also true. I can go convince the frontline manager to adopt this thing, but if the CEO doesn't understand why you're doing it, then somewhere along the way, they're gonna say, what are you wasting your time training? These workers are doing this thing, you know? So you need, in some sense, this, this coordinated effort or big push, and in order to do that, we really, I think we've learned to take a human centered approach to every element of what we're doing. And it started out where we had some excellent people trained in design who joined our team. I heard all these buzz words around this, who said to design and all this stuff that as an economist, I didn't understand what they were, but I really get it now, and it it really jives with economics in a way that I don't know that I realized before, which is that this is about understanding the perspective of like everyone that matters for this particular decision. If I want to make one change, I need to convince the CEO that I've got good evidence that it's going to drive returns, and even if you're skeptical, discount my returns, but the returns are so big that even if you're skeptical and you think it might it may not be as good as I'm saying it is, it's worth a try. The costs are low, the potential returns are high, okay, but then I've got to get down all the way down to the front line, manager or frontline worker, and say this thing is going to make your life better, because you don't. A lot of those people don't, individually participate in the higher productivity, or, you know, bigger margins, or whatever it might be, or they or it's a very muted participation. I get a little bonus, but it's not, I don't own this company, you know, but I do make decisions every day about how to do my job and what's a costly way to do it, what's a less costly way to do it? Where should I put my effort? And so then you have to think about every change, I think, in that perspective, and that means that you're kind of marketing to a very broad array of people you know, so sure that's the second phase, or, I don't know, the end phase, after getting the evidence. Yeah,

Jean Gomes: 27:52

we had a another guest on recently, Ellen Langer, a psychologist who's done some pretty groundbreaking work in a long career, and she was talking about the importance of understanding that risk is subjective. It's not an objective thing that you measure. We all look at risk. What what looks like risk to me, might look like a terrible uncertainty to you and so on. And so I'm just really interested in, you know, particularly when you're trying to remove this kind of paternalistic or judgmental type of frame, particularly to first world countries, looking at third world countries. How do you get rid of that? How do you kind of dial that out of your analysis and the solutions you come up with?

Anant Nyshadham: 28:32

Yeah, I mean, I think, Well, one way is to so often if we don't truly understand what drives decisions. So the way that we do this as a science is we might write down some mathematical equations that simplify the trade offs that exist, but they kind of try to characterize and the simplicity is not lost on us. We know that we're simplifying, but we're trying to focus on certain elements that we think are important. And then, if you're me and you're an applied economist, you try to go out into the world and see data that you can fit to this and then see, like when I analyze the data, does it actually follow what these mathematical equations would say, or does it deviate and why? But the kind of most important thing for economists like myself that are looking to, you know, test changes that we might be able to impose, and we might be able to inform a firm to do this and see what happens, and so on. You know, we really don't want to say that unless we know what's the causal effect of that, right? So, like, can I say with some amount of statistical certainty, obviously never full certainty that if you do this, this will happen, you know. And in order to do that, you're looking for either you know, perturbations in the world, randomness in the world, that you can use to say, like, you know, these two people are exactly the same. But this purpose and happen to have the. Ability to do this, and this person didn't. So now I can compare and see like, well, when you had the ability to do X, what happened? That's what we call a natural experiment. And there was a recent nobel prize given kind of for that study of analysis. But the other thing we can do is, just like any other medical trial, we can impose perturbations, right? We can preemptively inject randomness to help us understand so, like, the way a medical trial works is like, you know, I can give the drug to one set of people, and I can perfectly exclude the other set of people from having the drug. And I can do that in a random way where I make the two populations, you know, look as similar as possible, and then I, you know, I flip a coin, or whatever it might be. And so then when I go forward, I have a sense that whatever all the other stuff that's going on doesn't matter, as far as figuring out the effect of this drug, because the only thing I injected that was different was that I gave them the drug, or that I gave them access to this resource, or whatever it might be. And so now you need the weak law of large numbers. You need you can't do it for two people, because then, you know, lots of things could be different between those two people. But if you combine that with, you know, enough observations, then you basically get this kind of repeatable sense that, like this, you know, this is the a reasonable idea of what the cause and effect is, and that allows me to say, Okay, well, if I do it over here, and I do it over there, and I do it at a bigger scale, I'll get the effect. And so I think those are the simplest ways to think about, you know, these kinds of conditions. And I think there's another there was also a Nobel prize given recently for a couple on bringing this kind of experiments to our field, to economics, and in particular, in development. And so I think that's been a really powerful thing, this idea that, you know, so many billions, trillions of dollars are being spent on development efforts, by foundations, by governments, you know, but we have so little, at least historically, idea of what actually works, what drives impact, what's the you know, is it worth spending the money on bed nets or Malaria pills, or she'll be spending it on, you know, building infrastructure or whatever, and so over the last couple of decades, you know, our field has been able to run those types of experiments for all in the public sector, almost every country in the world, and almost every level of government. Really exciting stuff where we now, you know, both are able to run those types of experiments and get evidence. But we've also generated an appetite for that that like policy makers are saying, I'm not going to pull the trigger on this big policy until I know, until I've got some evidence that it works, you know, and so I have a sense of it.

Jean Gomes: 32:54

What examples in that wider research, not necessarily yours, have been most exciting for you? You You know, the way you can start to see that. And also, if you could just give us maybe a sense of, you know, any aha moments you've had in your research where you found something that you're again excited about.

Anant Nyshadham: 33:11

Yeah, both great questions. I mean, I think where the field has, you know, applied this technique and had this understanding more is mostly in kind of public goods, in like health, education and these types, a little bit in political representation and so on. And so there have been some really exciting things there. There's one classic result on deworming that, you know, demonstrated that you could, you know, you didn't have to deworm everybody. It's an infectious parasite. You could get a certain amount, and then the rest would just, you know, die out. And so it's kind of a public health point, but in economics, we had to think about, well, how do you approach those populations? How do you drive the decision to actually adopt it, you know, how do you deal with the fact that some people will be skeptical and not want to do it, and so on, you know, there have been similar results on technologies of teaching at the right level, which is really powerful thing that we have technology now to do this, you know, we've seen it a bit in the US, for sure, already that like, you know, even in my sis, my daughter, is a school, I see that they really try to vary the curriculum and meet the students where they are. But doing this in the developing world, you know, in India with, you know, it's hundreds of millions of children, you know, in schools that are super under resourced and not well organized, that's really hard. And so that's where technology we've seen can really, like, you know, level the playing field, we can create apps that adaptively teach at the right level, and those can be really impactful. So I think all of that's been really exciting, but I think that the main AHA that we had early on was that despite economics seeming like it's all about like business and economics. Seem like you're the same in some sense, there was very little work being done where economists were doing this type of stuff with private firms. And the developing world is filled with these huge monoliths, these huge firms that employ so many workers, but there was no interacting with them to kind of help them make decisions. I mean, these are essential. These are small governments. They're the huge representations of people, and they're making policies every day that affect the lives of that person, but also their families and their children and their generational effects. But nobody's providing them with evidence. And in fact, because of that, there's no appetite for evidence. There's just a sense that we just do what we think is best, and we go with it and we move on. We don't, you know, we don't have the luxury of of, you know, needing evidence. And so the big aha that came about there was when I was saying, you know, we we had this first opportunity to test a skilling intervention in garment settings. So this is like teaching, you know, there's a broader program, but what we kind of saw was that this was essentially teaching what we call soft skills, or non cognitive skills. These are all terrible terms that we don't have the right term for, but they're like the non technical aspects of any job, and usually the transferable ones, like the ones that communication, problem solving, teamwork, and they were teaching these skills to frontline machine operators in garment factories. And it was kind of a really fascinating notion, because, I mean, these are one person to machine, and you know, you these are skills are usually teach to, like white collar professionals and you know, and consultants and you know, not people you think, who are sitting, you know, eight, nine hours a day in front of one machine. But we found, we ran an experiment to evaluate that, and found tremendous effects on productivity for even that level, because everything is coordination, everything is teamwork. You know, there's always an element of relationship and trust and coordination in every element of productivity everywhere. And so the big aha here was both actually the power of that type of relationship and that investing in that relationship, wherever we find it can be pretty powerful, but actually more broadly, the idea that look at this like simple thing we did that actually had a tremendous effect, a flow benefit on productivity. If the firm had known that I can increase productivity by four percentage points or whatever, you know, they would have done almost anything to do that. It's really stubborn. It's really hard to move productivity, especially in these kind of razor, thin margin, kind of competitive factory environments. But, but there's such a blind spot with the idea that you could put any resources or money on these frontline workers and have it be worthwhile, total blind spot. So now we've seen across industries, electronics manufacturing, auto manufacturing, retail services, fast food. You know that there's this general sense that, well, workers come and go. These are not forever jobs. They're low income workers or low skill workers. Why should I invest a lot in them if they're going to leave? And so we've had to chip away at this notion that, like, it's the ROI isn't there, because it turns out it really is. It's it, you know, it works a tremendous amount of the time. I'm not going to say always, but it works a lot.

Scott Allender: 38:34

So is that, let me kind of zoom back out a bit, because you're talking, there's so many wonderful things you're doing. So if you were to sort of, you know, define this mission of good business lab, like, you know, how would you sort of describe that? What problems are you you're touching on many of the problems. But like, how do you identify which problems you want to solve? How are you evangelizing the work into these businesses that are maybe resistant to educating themselves in this space. Give us a give us a better, or not a better, but a deeper tour of the sort of model and mission of your organization, if you could. Yeah,

Anant Nyshadham: 39:13

absolutely. I think, you know, if we start at the top, I think GBL is kind of broad vision, or, I guess maybe my broad vision, and now the organizations, to some degree, is to change the way that employers think about workers, kind of every industry, every corner of the world, from a cost or a liability or kind of a usable input to a productive asset, you know, and I don't, and I don't, I don't want to dehumanize the workers I actually, you know, I want, but so much firms think about coddling their machinery. They invest in they take care of it. They invest in maintenance. They, you know, try to push off depreciation or offset depreciation. They upgrade it. And. Make sure they're keeping up with the highest technology of the times. But none of that logic really applies when we think about these huge workforces that actually, you know, make everything and do everything like, you know, in fact, even more so as technology advances and the role of the human becomes very specific. What's left is, in some sense, their emotions, their their soft skills, their ability to communicate and problem solve. You know, the machine doesn't know when it's wrong because it always thinks it's right. So then a human has to figure this out, and a few human has to be able to tell somebody, Hey, we really need to, you know, have a look over here, or change things. So that's, that's kind of like the overarching strategy or idea we have, you know, I guess that's the goal. And then the strategy, in some sense, is to bring evidence to bear. You know, we saw, we got the we had the fortunate kind of position to see J pal, the Poverty Action Lab that was born out of MIT, and founders and colleagues got the Nobel Prize recently for bringing these experiments to the public sector, so we could see how transformative that was, to both introduce this notion that you could do that type of research, you could get that type of evidence, but also to change the way actual decisions were being made in the world, a revolution towards evidence based decision making, data driven decision making. It seems strange to think that you have to do that for the private sector, but you really, you really do. I mean, very few firms are Amazon running AP experiments, and even that Amazon, historically, you know, is only doing it on the consumer side, on the demand side. They're not doing it internally with their personnel policy. Recently, they've, they've taken some things, they're doing some things, but so you have firms making these really impactful decisions every day on the basis of nothing. And so the main I feel like there's kind of two layers of problems. There's the there's the more the problem at the ecosystem level, of saying, Look, we need to make evidence based decision making something that seems valuable, and therefore the evidence itself is valuable. And and turn that even to things that are not like immediate balance sheet numbers, right? So not just like input costs, or, you know, auctions for inputs, or whatever it might be, or, you know, scrap material auctions, like all these, we do so much around firms do so much around that stuff, and then they just kind of shoot in the dark when it comes to personnel policies or, you know, benefits that they're providing, or training programs or whatever it is. And it's not because they it's because there aren't resources, there isn't evidence, you know, for them to use for a lot of those things. So that's, I think there's that layer of problems, of just trying to generate an appetite, and, like, want, create a desire, demand for evidence to make decisions on. And then, of course, there's the there's the frontline level of problems that we actually are trying to solve with that evidence. And that is, you know, we've seen a surprising amount of universality in some sense, of these problems, so high absenteeism, high worker turnover, low output per worker and kind of some core reasons for, you know, uncomfortable working conditions, especially if you broaden that to include physical and psychological conditions. I mean, these are ubiquitous issues, and they're persistent issues in so many industries, in so many countries, in so many parts of the world. I mean, there's progress to mediate almost everywhere in that and so those end up being the kind of problems we've we've tried to take head on. So, you know, we've done this by trying to go to the firms themselves, and start at the CEOs, but go all the way down to the ground and let them tell us what their problems are. Now, we're seeing patterns. We're seeing trends, certainly, you know, and it's not always is high, worker turnover or high worker absenteeism, the issue, well, the root cause might be different in different places, but the issue looks very similar, and the effects on the firm's performance are very similar. So then, so then you have to kind of really dive in to understand, even if the problem is the same, why that problem exists in each of these settings.

Sara Deschamps: 44:45

If the conversations we've been having on the evolving leader have helped you in any way, please share this episode with your network, friends and family. Thank you for listening. Now, let's get back to the conversation.

Jean Gomes: 44:57

Just thinking about, you know, this conversation, I. What I love about it is that we're not talking about what most of our guests are interested in, which is the kind of high end knowledge worker, the focus of the universities and, you know, the kind of management literature is very much focused on the that kind of strata of of worker. What you're looking at is, you know, the rest of the of humanity across the world, who's not necessarily in these these kind of sophisticated jobs, but are just essential for for global commerce. And really, what you're doing here in this conversation is saying that there we're playing catch up in that, moving the conversation from workers as a fungible commodity that isn't really worth investing in. I'm really interested in what your thoughts are about how that might change in the next decade. With AI, how does that large number of people working in factories or working in distribution centers or call centers and so on. What do you think is going to happen as that starts to are they going to get pushed down? Are they? Is there an opportunity for them? What's going to happen?

Anant Nyshadham: 46:09

Oh, there's so much to unpack here. I think that you know. So I'll caveat this by saying that I'm a, I'm a boots on the ground kind of researcher, you know? I'm a applied, very applied and a very micro economist. I'm going into each firm and workplace and trying to figure out what's working and trying to make things better today and for tomorrow, but, but the long run, convergence, you know? You know, I can concede that I may not know what the long run effects are, but the reality is, I've been working in a lot of these manufacturing settings, service settings, for over a decade, and the boogeyman of technology hasn't replaced all These jobs, there are more or less as many workers or more, actually more, in most of these firms. You know. Now I'm not, I don't know about the industry as a whole. I'm not saying that that's true, but broadly speaking, they're not. They're not apocalyptic changes, you know, or you know, but the nature of the work is changing a lot, and that's where skills matters a lot. That's where management matters a lot. That's where organizational policies matter a lot. Is that the peoples are still required in the production function, but what they need to do changes a bit, step by step, and maybe over the over a medium run, it's going to change a lot. Like I said, I've seen this notion that, you know, the machine can do the rote task better and better, and it can even now generate its own, you know, understanding of things, and make changes and so on. But there's always a limit where the machine makes a mistake, and the only the machine will never tell itself that it made a mistake, right? A human being has to be able to assess that and problem solve, have a framework. Think about, oh, well, how do the systems put, you know, fit together and so on. And we've seen that. For example, we did some work in the automotive industry in Latin America, and we saw that this, you know, what was transformative there? As, for example, new, more technically advanced models of cars came in. And by the way, when you need to increase the volume, was that problems got more complex. You needed to bring managers closer to the problems, and you needed to train even lower level workers in these traditionally managerial tasks of problem solving, you know, tacit knowledge and these types of things that we that we talk about. And so then, if I zoom back out, this is not disconnected from the macro trends that models tend to predict, which is that, over time, you know what actually happens as technology comes in is that it, on average, doesn't replace labor labor. It augments labor in the sense that it makes it more productive. Now it might mean that you need less labor. But the thing that and so I'm getting very economics professor about this, but the thing that I think people tend to underestimate is that everything the potential to produce shifts out. Things get cheaper. People buy more. You know, it's not, there's still 2000 people that touch almost every car that's assembled. That's not 50,000 people, but it's not 20, you know, it's, it's, it's a quite a lot of people involved, and they're more skilled than they were before. But what does that mean? That means we have higher tech. Cars than we have before, and we have every household has two cars instead of one and a half cars. Now, you know. So what I mean to say is that here's an example of even, for example, fast food. So a study I haven't yet completed yet, but we're looking to kind of study the introduction of these, just the ordering kiosks, check out kiosks in a fast food setting. My hypothesis because I've seen similar types of things in other settings, and the reason why I'm excited to do this analysis and write this paper is that my hypothesis is that, yes, you will need less cashiers. Obviously, I've got a machine to do that now, to take orders, and we actually saw in other papers we've written in the space that how fast the cashier takes those orders and how fast the orders are filled is certainly a bottleneck. But I foresee just as a stylized story, that you'll need less cashiers, but now that the machine takes orders so fast, you're going to need to evolve how you operate in the kitchen and how you deliver orders. You're going to need, potentially, just every single one of those cashiers might now need to be moved to a different bottleneck that has been created because the machine made one part really efficient, right? But the machine can't do everything. We're always taking one step. We're adding a machine here, and it's making this part more efficient, but that means that the other parts have to catch up, and so we either have to skill those people to catch up, or we have to add people and skill them potentially to be able to keep up. You know, I don't know how that generalizes. I'm not going to comment on that more broadly, but I have seen it time and time again, and I think that that's, that's where the study of soft skills, that's where the study of even the theories behind training. We have a series of papers now thinking about whether, in what context should you have specialized workers? In what context should you have generalized workers, the classic notion of, you know, comparative advantage in economics without any other details. You know, is very clear, like, you should specialize, do one thing and do it really well, and then I should have another worker that, you know, specialize and does the other thing, does really well. But that breaks down in a world where there's chaos, you know, where I don't know which thing I'm going to need to do more of, you know, then I actually need to have people who can generalize a bit. And a machine is the stylized specialist, you know, I can't, it. Can't dynamically adapt to the to the fact that I need to make more fries, or whatever, the fact that, you know, the soccer games there, and so, you know, the dinner rush is going to come two hours earlier. You know these types of things, that's a stylized example, but my point is, human beings are much more adaptable, and so as this kind of need for a more broadly skilled worker comes about, I think that's where we're going to see another opportunity for workers to just be more important as a counter effect.

Scott Allender: 53:26

Well, Anant how can people get involved in the work that you're doing, or connect with you, to have you come in and work with their organization?

Anant Nyshadham: 53:35

Yeah, thanks. I mean, I think there's quite a bit of ways. So I think there's at least, you know, early on, we were generating all this evidence only in some sense, right? So, and that's still the case. If you have, you know, if you're running a large business, or a, you know, small establishment in a larger business, and you're interested in solving a problem, and you've got a persistent problem, and you think, you know, you want to solve it, there's, you can connect with us on our website. There's, you know, communicating with us. You can actually Google me and find my email, it seems actually so, you know, we're here. We're open to years. We're always interested in new problems or new settings for stubborn problems we know exist. So that's always really exciting. And to be honest, it takes progressive leaders. It takes courageous leaders who are willing to say, look, I There must be a better way. Tell me how. And I don't know what it is, let's go figure it out. You know. So if you're one of those people, please connect. But we're also in a great position now to want to interact with people who just want to prove or want to implement what's already been proven. Experimentation is hard, you know, like, it's costly if you're a if you're not a large, very competitive firm, if you're a small establishment, just trying to, like, you know, make it through, eke out small margins. You don't want to come and have us treat your firm as. Playground. You know, we're fortunate to let have suburbs. Let us do that. You just want to know what's worked. And if you've got that problem, like, let me see if we can solve it with the same tool. And so we're ready for that. You know, we've got tools on worker grievances, we've got trainings, we've got lots of interventions that we've you know, now we're going to look to scale up this payday, this on demand salary tool. So if any of the problems I've talked about here, you know, resonate with you, and even if you don't have the capacity or interest to experiment, you just want to see if we've got a solution that might work for you. You know, that's one way we can certainly interact. And the last I'll say is, you know, if I go back to the beginning, you asked me, Scott, what my what our mission is, and like I said, it's, I think, as grand as it can be, which is to change the way that firms think about workers as assets, and investing in those workers in every corner of the world. And so if you just want, if you just believe in that, and think it makes sense, and you know, we could use all the help we can get to spread that word. It's going to take, you know, every leader of every organization at every level to start to believe this, you know. And so if that's something you think you can do, just spread the word. And that's that's tremendously helpful for us.

Scott Allender: 56:20

Excellent. Well, we'll put all that in the show notes. And thank you for coming on and sharing your insights and your and your wisdom and and thank you for the work you're doing in the world. I love it, and I've benefited tremendously from this conversation. So thank you.

Anant Nyshadham: 56:35

Me as well. Thank you so much for the opportunity really fun conversation. And I love what you what you both are doing with this, so I'm glad I could contribute a little bit.

Scott Allender: 56:44

Thank you. Thank you and folks, until next time, remember the world is evolving. Are you?