January 29, 2025 • 57 mins
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)
Social:
Instagram @evolvingleader
LinkedIn The Evolving Leader Podcast
Bluesky @evolvingleader.bsky.social
YouTube @evolvingleader
The Evolving Leader is researched, written and presented by Jean Gomes and Scott Allender with production by Phil Kerby. It is an Outside production.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jean Gomes (00:03):
We all love those aha Insights where we encounter
an idea or technique that worksbecause it overturns
conventional wisdom, but we onlylove it because it works up to
that point, the idea is oftenmet with eye rolling, cynicism,
resistance, dismissal or simplyinattention. In this show, we
get into a wonderfulconversation with the economics
(00:24):
researcher Anant misalon, whosepioneering studies into the
effectiveness of firms in thirdworld countries reveals a slew
of exciting, counter intuitiveinsights and evidence that have
the power to accelerate economicdevelopment in the world's most
impoverished countries. Tuneinto an important conversation
on The Evolving Leader.
Scott Allender (01:05):
Hi folks.
Welcome to The Evolving Leader,the show born from the belief
that we need deeper, moreaccountable and more human
leadership to confront theworld's biggest challenges. I'm
Scott Allender.
Jean Gomes (01:16):
and I'm Jean Gomes.
Scott Allender (01:17):
How are you doing today Mr. Gomes?
Jean Gomes (01:19):
I am feeling very good. I've had a amazing few
weeks of doing reallyinteresting research and
delivery and all sorts ofthings. I am feeling conflicted
about what's happening in theworld right now, as we ever
since we started a show that'sprobably like, there's ever been
(01:41):
a week where there isn't somemajor, major thing happening, it
probably it doesn't affect youmore than us, but you're, you're
at the epicenter of what'shappening in the US election. So
we won't talk about that, butjust suffice to say that we're
all feeling something. And yeah,and I'm really looking forward
to the show. How you feelingScott?
Scott Allender (02:02):
Yeah, we probably, probably should set up
a whole hour to talk to I'mfeeling, feeling a lot of things
today. My friend, yeah, feel it.
Feeling some I feel, I feel raw.
Maybe, is the is the best termfor it right now. We're only
sort of, I don't know when theshow will go up, but we're only
sort of a week on the otherside. And I certainly just, I'm
(02:22):
I'm in the middle of the fields,so I'm still processing, as I
know a lot of people are, andtry to make sense of things. So
but I am delighted to be backhere with you and having
conversations to influence theleadership discussion in terms
of what is leadership and whatdo we bring to it? How do we
make it more accountable? How dowe make it more human? And so
(02:42):
I'm delighted that we're joinedtoday by Anant Nyshadham. Anant
is an associate professor in thebusiness, economics and public
policy area of the Ross Schoolof Business at the University of
Michigan, a research associateof the National Bureau of
Economic Research, and he's anaffiliate of bread, which is a
non profit dedicated to researchand scholarship in economics,
(03:05):
and he is a research affiliateof the IGC, A J pal affiliated
professor and an affiliate ofthe Montreal partnership for
human resource management. Andif that's not impressive enough,
he is an accomplished guitarplayer, 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 alsoreceived his PhD in 2012 from
(03:27):
Yale University, and is the cofounder and chief strategy
officer of the good businesslab, a non profit seeking to
promote investment in workerwelfare as a business
imperative. His work focuses onenterprise, firm and worker
characteristics and decisionmaking, like labor contracting
and worker training andmanagerial quality and the
(03:48):
resulting performance dynamics,particularly in developing
countries. Anant Welcome to TheEvolving Leader.
Anant Nyshadham (03:57):
Thank you so much for having me, and thanks
for that great introduction. I'mglad you had to do it and not
and not me as a lot of as a lotof text, yeah.
Jean Gomes (04:07):
Anant, welcome to the show. Apart from just ogling
your Telecaster on the wallthere, how are you feeling
today?
Anant Nyshadham (04:15):
I am feeling, certainly, all the things that
people are feeling in the worldtoday, especially as an
American, lot of shock and a lotof stress about the way forward.
But also, you know, I thinksuccessfully looking inwards at
what I can do, trying to be abit more focused on what I can
(04:38):
control and the work that I'mdoing, which I'm very excited to
talk about today, and stayingkind of mentally fit to do that
work. So today I'm feelingequipped to do that. We'll see.
You know, it's a day to dayprocess,
Jean Gomes (04:52):
Excellent. Well, let's get to know you a little
bit. Imagine where a dinnerparty and you're surrounded by
people who are not in your worldand not in in the academic world
or in the nonprofit or any ofthose kind of things, and the
person turns next to you andsays, Hey, Anant. What do you
do? What would you say?
Anant Nyshadham (05:08):
I would say you're sure you want that. It
has to be that question.
Jean Gomes (05:13):
I'm really interested.
Anant Nyshadham (05:16):
So, yeah. So you know, broadly speaking, I
work on development issues. Soall the kinds of problems,
particularly in the developingworld, although I think these
problems by way of immigrationand by way of kind of persistent
class issues and so on, theyexist in the West, for sure,
also. So it's, it's kind of,it's about development both, you
(05:40):
know, in an economic or macroeconomic sense of countries,
whole economies, but also justin terms of individuals trying
to advance in their lives. AndI, you know, my path to this, I
think was a bit strange in thesense that it was late to get
here. I think I was late to takemyself very seriously. I grew up
(06:01):
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 longsince gone even before we got
there.
So I just grew up in a placethat had great sense of
community, but certainly wasrife with issues of racism and
class issues and extremelyimpoverished majority over the
(06:25):
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 alsospent recent trips, sorry,
frequent trips to India, to ourancestral land, which, sadly, we
don't have anymore, but to thevillage where I think I
(06:48):
experienced a lot of just acompletely different world. I
mean, I thought even what I wasseeing, you know, in the kind of
rural South, you know, lowerincome areas that we were living
in, you know, paled incomparison to what the
challenges that I saw in villageIndia, limited access to
resources and education in, youknow, pre internet, pre social
(07:12):
media, access to the to theexternal world. And so I think
this kind of stuck with me, andas I got into school and tried
to figure out what I was goingto do in the world. On the one
hand, I'd really just kind ofpursued the PhD in Economics in
a purely, like, romantic sense,like, I had some sense that I
wanted to be a thinker. And, youknow, I come from, actually, a
(07:34):
family of academics, and theidea, you know, my grandfather
was a physicist, and, you know,I had a sense that, like, Oh,
this is really nice, like, youget left alone and you can
think, but I didn't know what Iwas going to think about or, you
know, or how I was going toapply it to the world. And
honestly, it wasn't until I thesummer after I graduated from
the PhD, you know, I trained inmuch more techniques,
(07:57):
econometrics and so onmethodologies. But when I
graduated from the PhD thesummer after that, I had an
opportunity to go visit sometextile factories in India. So
it turns out, classmate of minefrom undergrad, his family was
a, you know, he was a secondgenerational industrialist. And,
you know, I didn't realize atthe time, but he was like, okay,
(08:17):
my family runs a few factoriesin India, and you should come
check it out. We're like, doingthese programs for female
garment workers. And then when Igot there, I realized they run
like all the factories, youknow. So there was a, it was a
really huge scope, and and Isaw, you know, something really
amazing there, in the sense thathere is a company that
(08:38):
represents and has kind of atouch point with over 100,000
people who need something, youknow, people who need help. They
have limited access toresources. They have limited
access to mobility. They'reconstrained. And so here's a
pretty powerful lever,potentially. But what we kind of
(09:02):
started talking about in thatfirst summer was this idea that,
like, well, maybe we can do someworker initiatives and, you
know, test them and so on. And Iwas explaining the idea of
running experiments to try toget, you know, kind of good
causal evidence of what mightwork and what might improve
things. And as we talked, Istarted to realize that, you
(09:23):
know, there was this internaldebate in that firm, and I've
now, since, over the last morethan decades, seen it in many,
many firms like it. Of you know,what are we doing for our
workers, and should we be doingit? Should we be doing more of
should we be doing less of it?
You know? And there's differentperspective. It's not that
anybody doesn't care. It's thatthat one manager thinks this
program is helpful and onemanager thinks that it's not
(09:46):
helpful and it's a distraction,and there's no evidence to make
these decisions, and so firmsare just kind of, you know, kind
of shooting in the dark a little
Scott Allender (09:56):
Can we talk about well being in the bit.
workplace? So I'm thinking aboutthe levels of stress and anxiety
and loneliness that areprevalent in workplaces all over
all over the place. Talk to us alittle bit about the work that
(10:18):
you've done at building wellbeing into core business models
at organizations where thetemptation and what gets
repeated a lot is that peopletry to kind of slap a little bit
of well being work on the sideof the of the agenda, but it's
not really ingrained into whateverybody's experiencing every
day, right? So the perspectiveof everyone and what they need
(10:39):
talk to us a little bit aboutthat.
Anant Nyshadham (10:41):
Yeah, um, I mean, I think that there's a lot
of different elements to that. Ithink, on the one hand, we've
been, you know, chipping away atat all the different elements of
well being, pointing out that insome sense, like, you know, well
being isn't just I'm fit, orI'm, you know, or I don't have
chronic health issues, it'smental health, it's loneliness,
(11:04):
it's being able to deal withstress, or maybe being insulated
to some degree from stress, youknow. And so when we I think we
want, as a scientist, the way wedo that is to then run a trial
on this, and run a trial onthat, and, you know, and try to
put them together and so on. Andwe have, I think we've got stuff
(11:25):
that, you know, documents thathow hot it is in the workplace,
whether the air is marginallydustier, can have tremendous
effects on productivity and andones that need to be monitored
by management and adjusted, andgood management can kind of
react to those things. You know,we've got studies on screening
(11:49):
for presbyopia and givingworkers glasses and nutrition
deficiencies and so all of thesetypes of maybe more traditional
things, but also, like buddysystems, you know, we, we work
in a lot of industries with alot of migrant labor. So these
are workers that have left theirnetwork, the only thing that,
the only reality they've everknown to come to a city, it's
(12:10):
totally new, with no one thatthey really know. And so can we
pair them with somebody who didthat two years ago, came, maybe
even from their area that speakstheir same language, you know,
likes the same types of foods,you know, and and connect,
create a connection there thatallows for some to break down
(12:30):
that isolation. Right? A lot oftimes when we're under stress,
it feels like we're the onlyones dealing with this sort of
thing, and that can be veryisolating. Just knowing that
it's not us alone. Can be, canbe really powerful, and so, you
know, so we've run trials onthat. So on the one hand, it's
kind of putting the evidencetogether. It's, it's why we've
(12:51):
kind of thought of this as alife's work, although every day
I wake up and feel like I wish Ihad done more up to now, or I
want to do more, you know,tomorrow, and do it faster to
get this evidence. But like wesaid, evidence isn't enough, and
so I think where it's beenimportant is, you know, at every
(13:11):
level, to change theconversation. And by that, it
means, like when I started doingthis, I would come in and I
would talk to CEOs, and theywould give me very confident
answers about what they thoughtwas going on on the ground and
what they thought their biggestproblems were, and so on. And
then I have yet to find a singlefirm or single setting in which
that notion wasn't ultimatelychallenged or contradicted when
(13:35):
I got enough layers down thatactually the way the managers
are dealing with this stuff atthe lowest possible level is
like this and not like that,and, you know, and then
actually, the biggest driver ofthat problem, you know, for
example, in a lot of thesegarment 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 overalmost our entire workforce
(13:56):
every year. So like, 100 plus1000 workers we're gonna hire
this year, and then we're gonnahire another 100 plus 1000 next
year. That's bananas. That'scrazy. So obviously, that's a
lot of money and a lot of effortin an entire, like, army of HR,
you know, just to do that. Butthen that's, you know, that's
what was said out of one side oftheir mouth, and the other side
(14:16):
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 getmarried, and then they drop out
of the workforce or whatever.
And don't get me wrong, that'snot untrue. There are lots of
women, not just in India, everycountry in the world, that work
before they've shared theirfamilies, and then when they
(14:37):
have families, will choose, ormaybe, unfortunately, have the
choice made for them to dropout. But when you're turning
over 100% of your workforce,there's not one answer, right?
There's like, who knows aninnumerable number of causes of
this. And so you've got toreally start by challenging
everything you think you knowabout all the. Problems, right?
(15:00):
You've got to start by actuallygoing down as far as you can,
right down to the ground, andasking people, and this person
might tell you one thing, andthat person might tell you
another, but starting to getthemes, you know, from the
actual people about what aretheir challenges, what makes
them potentially leave thesejobs or and so on. And then you
(15:23):
start to get a bunch ofdifferent stories. Yes, I, you
know, I'm going to get marriednext year, and I want to start a
family, but actually, I wasplanning to stay. It's just that
this is this. Work is reallyhard on my back, and I can't do
that. And also work, I mean, I'mjust, you know, that's one of
many stories we've heard. Sothen you start to understand
(15:43):
that I don't have to solve everyproblem or the whole problem.
There's value in solving a partof this problem and chipping
away at it, right? There's athere's a value in just saying,
Well, let me solve theergonomics. And some x percent
of people who were dropping outbefore that's the real thing,
they needed to stay you know?
Oh, I'll give you an examplethat I'm really, really excited
(16:05):
about. We recently, you know,it's true everywhere in the
world, including in the US. Thisnotion, in fact, the existence
of the payday loan industrybasically underscores the idea
that a lot of low incomehouseholds and families are
living paycheck to paycheck.
(16:26):
Shocks come as as economists, wewould call them shocks, but like
unexpected things come, you needto pay for them, and often they
might be, I don't know, 300bucks. 400 bucks, which
thankfully many of us think, isnot that big a deal, but in
every country in the world,there's quite a few people who
(16:46):
would never be able to come upwith that tomorrow. And that can
be the effects of that can justecho through their whole lives.
If I can't get it, I can't makemy 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 oftimes. I'm out on the curb, you
know. If I can't get it, I can'ttake my kid to the hospital
(17:09):
today, so I'm going to wait abit longer and hope that they
get better, and then there's achance they may never get
better, you know. And if you addup all of those things, you have
such a simple problem driving somuch persistent, you know,
adverse effects for thispopulation. So we did something
really simple, which was that,like, look, here's a firm.
(17:33):
They've got hundreds of 1000s ofworkers. They're not going to
just cut checks every day. Theyneed to be on a cycle like and
they need to manage theirliquidity and so on. So, but we
just created a tool where itsays, 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 endof the month or next month for
(17:54):
last month, right? So I'veearned this money. So we gave
these garment workers theability to draw up to 50% of
that of that wage that they hadearned up to date, and they
could do it throughout, wheneverthey needed it, throughout the
the month. And just that, noadditional money, nothing else,
(18:16):
just the ability to, like, go toa little tablet on the factory
floor. That's the I mean, we hadto, we had to know that we
couldn't make it an iPhone app,because then nobody would be
able to use it, right? So youhave to understand your
population. But here's a tableton the factory floor. I can go
there, I can draw my money. Andit dramatically reduced
(18:36):
attrition, wow. And evenincreased day to day
productivity. We're stillunpacking the why, but a lot of
it is that this is a thing thatthis job has that other jobs
don't have. I need to keep thisjob. This job allows me to pay
my bills. Another job is almostuseless. I can get paid twice as
(18:58):
much, but if I can't get it whenI need it, what good is it?
Jean Gomes (19:04):
That's fascinating.
I mean, what it brings out forme is like the disconnect
between the owners and managersof firms and the lives of the
people that you know make themsuccessful, and if you do not
understand the problems thatthey face. You can't possibly be
in a relationship with them thatactually is mutually beneficial
(19:26):
beyond time for money, becausethat's what you're basically
saying. You're creating a newvalue exchange between the
individual and the firm, whichis, goes beyond that, which is,
I understand you understand whatyou need. I'm going to help you
meet your needs in a creativeway, and actually from a many
ways, that doesn't cost the firmvery much at all. I mean,
(19:47):
there's a slight implication oncash flow, little bit of, you
know, of you know, financialmanagement that's required
there, but it's that seems verysimple,
Anant Nyshadham (19:57):
and to highlight what you're saying,
actually. We got tremendouspushback on this. I mean, we it
took us months to convince asingle factory to let us do
this. And the agreement was,you're going to do it and then
you're going to take it awayimmediately. So we're going to
do it for three months, and thenyou take it away, or six months,
I can't remember, and then takeit away. And the trial has just
(20:18):
now, it's just now wrapping up,and now they're begging us to
keep it and scale it up to therest of the factory, because now
they're seeing that, likeworkers 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, assoon as but the logic of that is
(20:39):
so strange, right? Like, if theonly reason why your workers are
here is because you're holdingtheir money, you've got a
problem, you know. And so Idon't think it didn't ring true
to me, and it didn't turn out tobe true. So thankfully, that's,
you know, that I was right thattime. That's
Jean Gomes (20:54):
interesting. So did was there? Was there any cases
of people, you know, takingthree months, or, you know,
sorry, three week salary andjust No, no.
Anant Nyshadham (21:02):
I mean, so I can't tell you every n, right,
every observation, but onaverage, it was tremendous. It
was like, very much theopposite, you know, very much
the opposite. And the average iswhat the firm should care about,
right? Even if there's a handfulof workers that are going to do
that, if the overwhelminglyworkers are going to see this as
(21:23):
a benefit that they want tostick around for and work harder
for, you know, that's amazing.
And the reality is, there's allthese nuances that we're still
unpacking. But you know, if Ican just we were finding it at
baseline before we intervened,that that many of these workers
were reporting running out ofmoney at the end of the month
and eating less forgoing medicalexpenses. These like real things
(21:45):
that very obviously will affectyour productivity or your
ability to show up to work, youknow. So the, you know, the
theory of change is like, prettyeasy 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, Ithink it's quite reasonable, or
(22:07):
maybe expected that, you know, asenior leader or somebody comes
from a different walk of life,who's running this company is
going to have troubleunderstanding the lives of their
workers. The idea is to not, isto is to just be aware of what
you don't know, to want to knowwhat you don't know you know, to
actually find out. And you don'thave to do it philanthropically.
(22:29):
That's been our whole kind ofmission 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 wejust wait around for the people
who want to think all day about,you know, other people and the
poor and so on. To makeprogress, let's just it should
be good for your business. Itshould be that I am spending a
lot of money on recruiting newworkers. Now I need to go
(22:51):
challenge everything I knowabout why workers are leaving,
and be a bit honest with myselfand 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 problemanymore.
Scott Allender (23:06):
Do you find that people are receptive to letting
the data do the talking and arepersuaded by that, or is there
still a kind of resistantresistance, because that legacy
mindset you talked about where,you know, we'll invest in
upgrading our machinery, butwe're not really going to do
that with our people. I'mactually thinking about, you
know, statistics aroundeffective leadership, you know,
(23:26):
70 to 75% at least, beingattributable to measures of
emotional intelligence. And yet,time and again, people are hired
into very important leadershiproles simply based on their sort
of hard skills or their sort ofprevious experience. And the
data that's there and undeniableis almost denied, and in spite
of it being undeniable, so I'mreally curious, like with the
(23:48):
data you're presenting with yoursort of own research, is it
persuasive, or what are youdoing or having to do to bust
through that sort of resistancethat you might experience?
Anant Nyshadham (23:58):
That's a great question. And maybe the crux of,
you know, the hardest elementsof my work, you know, for the
last decade, evidence isn'tenough. I mean, so we started
with the idea that the evidencewas bad. It either didn't exist
or it was bad. And while I stillthink that's true, and I still
think that, you know, pat myselfon the back, I think we made
better evidence. We ran reallytight, rigorous, gold standard
(24:22):
trials. And we, you know, we, wepresented it that was its own
challenge. Once you do that, youknow, five people are going to
read this paper, if I'm lucky.
So now, how do I translate thisinto an HBr piece or, you know,
or an NPR kind of podcast, or apodcast discussion, you know?
But even that just wasn'tenough. I think what we
(24:43):
realized, which is, I think, abroader, maybe epiphany, at
least on my part, is that, youknow, you have to meet there's a
cop, there's a there's a set ofdecision makers, or a sequence
of decision makers. You. Inorder to change this, to create
an organizational change,convincing the CEO does almost
(25:04):
nothing, right? I mean, it's anecessary but insufficient
condition, right? Like, if I goin there and I, you know,
convince the CEO, nobody else isnecessarily their day to day
lives are different than thatdecision, right? Yes, my boss
tells me, but my boss can't seewhether I'm really putting forth
effort into this. And day today, I'm gonna make a choice as
(25:26):
to what seems to benefit me, youknow. So we had to do these. And
then the opposite is also true.
I can go convince the frontlinemanager to adopt this thing, but
if the CEO doesn't understandwhy you're doing it, then
somewhere along the way, they'regonna say, what are you wasting
your time training? Theseworkers are doing this thing,
you know? So you need, in somesense, this, this coordinated
(25:48):
effort or big push, and in orderto do that, we really, I think
we've learned to take a humancentered approach to every
element of what we're doing. Andit started out where we had some
excellent people trained indesign who joined our team. I
heard all these buzz wordsaround this, who said to design
(26:09):
and all this stuff that as aneconomist, I didn't understand
what they were, but I really getit now, and it it really jives
with economics in a way that Idon't know that I realized
before, which is that this isabout understanding the
perspective of like everyonethat matters for this particular
decision. If I want to make onechange, I need to convince the
(26:33):
CEO that I've got good evidencethat it's going to drive
returns, and even if you'reskeptical, discount my returns,
but the returns are so big thateven if you're skeptical and you
think it might it may not be asgood 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 getdown all the way down to the
(26:55):
front line, manager or frontlineworker, and say this thing is
going to make your life better,because you don't. A lot of
those people don't, individuallyparticipate in the higher
productivity, or, you know,bigger margins, or whatever it
might be, or they or it's a verymuted participation. I get a
little bonus, but it's not, Idon't own this company, you
(27:18):
know, but I do make decisionsevery day about how to do my job
and what's a costly way to doit, what's a less costly way to
do it? Where should I put myeffort? And so then you have to
think about every change, Ithink, in that perspective, and
that means that you're kind ofmarketing to a very broad array
(27:39):
of people you know, so surethat's the second phase, or, I
don't know, the end phase, aftergetting the evidence. Yeah,
Jean Gomes (27:52):
we had a another guest on recently, Ellen Langer,
a psychologist who's done somepretty groundbreaking work in a
long career, and she was talkingabout the importance of
understanding that risk issubjective. It's not an
objective thing that youmeasure. 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 reallyinterested in, you know,
(28:14):
particularly when you're tryingto remove this kind of
paternalistic or judgmental typeof frame, particularly to first
world countries, looking atthird world countries. How do
you get rid of that? How do youkind of dial that out of your
analysis and the solutions youcome up with?
Anant Nyshadham (28:32):
Yeah, I mean, I think, Well, one way is to so
often if we don't trulyunderstand what drives
decisions. So the way that we dothis as a science is we might
write down some mathematicalequations that simplify the
trade offs that exist, but theykind of try to characterize and
the simplicity is not lost onus. We know that we're
(28:53):
simplifying, but we're trying tofocus on certain elements that
we think are important. Andthen, if you're me and you're an
applied economist, you try to goout into the world and see data
that you can fit to this andthen see, like when I analyze
the data, does it actuallyfollow what these mathematical
equations would say, or does itdeviate and why? But the kind of
(29:17):
most important thing foreconomists like myself that are
looking to, you know, testchanges that we might be able to
impose, and we might be able toinform a firm to do this and see
what happens, and so on. Youknow, we really don't want to
say that unless we know what'sthe causal effect of that,
right? So, like, can I say withsome amount of statistical
(29:38):
certainty, obviously never fullcertainty that if you do this,
this will happen, you know. Andin order to do that, you're
looking for either you know,perturbations in the world,
randomness in the world, thatyou can use to say, like, you
know, these two people areexactly the same. But this
purpose and happen to have the.
Ability to do this, and thisperson didn't. So now I can
(30:01):
compare and see like, well, whenyou had the ability to do X,
what happened? That's what wecall a natural experiment. And
there was a recent nobel prizegiven kind of for that study of
analysis. But the other thing wecan do is, just like any other
medical trial, we can imposeperturbations, right? We can
(30:21):
preemptively inject randomnessto help us understand so, like,
the way a medical trial works islike, you know, I can give the
drug to one set of people, and Ican perfectly exclude the other
set of people from having thedrug. And I can do that in a
random way where I make the twopopulations, you know, look as
similar as possible, and then I,you know, I flip a coin, or
(30:44):
whatever it might be. And sothen when I go forward, I have a
sense that whatever all theother stuff that's going on
doesn't matter, as far asfiguring out the effect of this
drug, because the only thing Iinjected that was different was
that I gave them the drug, orthat I gave them access to this
(31:04):
resource, or whatever it mightbe. And so now you need the weak
law of large numbers. You needyou can't do it for two people,
because then, you know, lots ofthings could be different
between those two people. But ifyou combine that with, you know,
enough observations, then youbasically get this kind of
repeatable sense that, likethis, you know, this is the a
reasonable idea of what thecause and effect is, and that
(31:27):
allows me to say, Okay, well, ifI do it over here, and I do it
over there, and I do it at abigger scale, I'll get the
effect. And so I think those arethe simplest ways to think
about, you know, these kinds ofconditions. And I think there's
another there was also a Nobelprize given recently for a
couple on bringing this kind ofexperiments to our field, to
economics, and in particular, indevelopment. And so I think
(31:51):
that's been a really powerfulthing, this idea that, you know,
so many billions, trillions ofdollars are being spent on
development efforts, byfoundations, by governments, you
know, but we have so little, atleast historically, idea of what
actually works, what drivesimpact, what's the you know, is
it worth spending the money onbed nets or Malaria pills, or
(32:14):
she'll be spending it on, youknow, building infrastructure or
whatever, and so over the lastcouple of decades, you know, our
field has been able to run thosetypes of experiments for all in
the public sector, almost everycountry in the world, and almost
every level of government.
(32:34):
Really exciting stuff where wenow, you know, both are able to
run those types of experimentsand get evidence. But we've also
generated an appetite for thatthat like policy makers are
saying, I'm not going to pullthe trigger on this big policy
until I know, until I've gotsome evidence that it works, you
know, and so I have a sense ofit.
Jean Gomes (32:54):
What examples in that wider research, not
necessarily yours, have beenmost exciting for you? You You
know, the way you can start tosee 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 youfound 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 ismostly in kind of public goods,
in like health, education andthese types, a little bit in
political representation and soon. And so there have been some
really exciting things there.
There's one classic result ondeworming that, you know,
(33:33):
demonstrated that you could, youknow, you didn't have to deworm
everybody. It's an infectiousparasite. You could get a
certain amount, and then therest would just, you know, die
out. And so it's kind of apublic health point, but in
economics, we had to thinkabout, well, how do you approach
those populations? How do youdrive the decision to actually
(33:53):
adopt it, you know, how do youdeal with the fact that some
people will be skeptical and notwant to do it, and so on, you
know, there have been similarresults on technologies of
teaching at the right level,which is really powerful thing
that we have technology now todo 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, isa school, I see that they really
(34:16):
try to vary the curriculum andmeet the students where they
are. But doing this in thedeveloping world, you know, in
India with, you know, it'shundreds of millions of
children, you know, in schoolsthat are super under resourced
and not well organized, that'sreally hard. And so that's where
technology we've seen canreally, like, you know, level
(34:37):
the playing field, we can createapps that adaptively teach at
the right level, and those canbe really impactful. So I think
all of that's been reallyexciting, but I think that the
main AHA that we had early onwas that despite economics
seeming like it's all about likebusiness and economics. Seem
(34:58):
like you're the same in somesense, there was very little
work being done where economistswere doing this type of stuff
with private firms. And thedeveloping world is filled with
these huge monoliths, these hugefirms that employ so many
workers, but there was nointeracting with them to kind of
help them make decisions. Imean, these are essential. These
(35:21):
are small governments. They'rethe huge representations of
people, and they're makingpolicies every day that affect
the lives of that person, butalso their families and their
children and their generationaleffects. But nobody's providing
them with evidence. And in fact,because of that, there's no
appetite for evidence. There'sjust a sense that we just do
(35:43):
what we think is best, and we gowith it and we move on. We
don't, you know, we don't havethe luxury of of, you know,
needing evidence. And so the bigaha that came about there was
when I was saying, you know, wewe had this first opportunity to
test a skilling intervention ingarment settings. So this is
like teaching, you know, there'sa broader program, but what we
(36:06):
kind of saw was that this wasessentially teaching what we
call soft skills, or noncognitive skills. These are all
terrible terms that we don'thave the right term for, but
they're like the non technicalaspects of any job, and usually
the transferable ones, like theones that communication, problem
solving, teamwork, and they wereteaching these skills to
frontline machine operators ingarment factories. And it was
(36:30):
kind of a really fascinatingnotion, because, I mean, these
are one person to machine, andyou know, you these are skills
are usually teach to, like whitecollar professionals and you
know, and consultants and youknow, not people you think, who
are sitting, you know, eight,nine hours a day in front of one
machine. But we found, we ran anexperiment to evaluate that, and
(36:52):
found tremendous effects onproductivity for even that
level, because everything iscoordination, everything is
teamwork. You know, there'salways an element of
relationship and trust andcoordination in every element of
productivity everywhere. And sothe big aha here was both
(37:12):
actually the power of that typeof relationship and that
investing in that relationship,wherever we find it can be
pretty powerful, but actuallymore broadly, the idea that look
at this like simple thing we didthat actually had a tremendous
effect, a flow benefit onproductivity. If the firm had
known that I can increaseproductivity by four percentage
(37:36):
points or whatever, you know,they would have done almost
anything to do that. It's reallystubborn. It's really hard to
move productivity, especially inthese kind of razor, thin
margin, kind of competitivefactory environments. But, but
there's such a blind spot withthe idea that you could put any
resources or money on thesefrontline workers and have it be
(37:56):
worthwhile, total blind spot. Sonow we've seen across
industries, electronicsmanufacturing, 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 lowincome workers or low skill
workers. Why should I invest alot in them if they're going to
(38:18):
leave? And so we've had to chipaway 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 worksa tremendous amount of the time.
I'm not going to say always, butit works a lot.
Scott Allender (38:34):
So is that, let me kind of zoom back out a bit,
because you're talking, there'sso many wonderful things you're
doing. So if you were to sortof, you know, define this
mission of good business lab,like, you know, how would you
sort of describe that? Whatproblems are you you're touching
on many of the problems. Butlike, how do you identify which
problems you want to solve? Howare you evangelizing the work
(38:58):
into these businesses that aremaybe resistant to educating
themselves in this space. Giveus a give us a better, or not a
better, but a deeper tour of thesort 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 ofbroad vision, or, I guess maybe
my broad vision, and now theorganizations, to some degree,
is to change the way thatemployers think about workers,
kind of every industry, everycorner of the world, from a cost
(39:33):
or a liability or kind of ausable input to a productive
asset, you know, and I don't,and I don't, I don't want to
dehumanize the workers Iactually, you know, I want, but
so much firms think aboutcoddling their machinery. They
invest in they take care of it.
They invest in maintenance.
(39:55):
They, you know, try to push offdepreciation or offset
depreciation. They upgrade it.
And. Make sure they're keepingup with the highest technology
of the times. But none of thatlogic really applies when we
think about these hugeworkforces that actually, you
know, make everything and doeverything like, you know, in
fact, even more so as technologyadvances and the role of the
(40:17):
human becomes very specific.
What's left is, in some sense,their emotions, their their soft
skills, their ability tocommunicate and problem solve.
You know, the machine doesn'tknow when it's wrong because it
always thinks it's right. Sothen a human has to figure this
(40:38):
out, and a few human has to beable to tell somebody, Hey, we
really need to, you know, have alook over here, or change
things. So that's, that's kindof like the overarching strategy
or idea we have, you know, Iguess that's the goal. And then
the strategy, in some sense, isto bring evidence to bear. You
(41:00):
know, we saw, we got the we hadthe fortunate kind of position
to see J pal, the Poverty ActionLab that was born out of MIT,
and founders and colleagues gotthe Nobel Prize recently for
bringing these experiments tothe public sector, so we could
see how transformative that was,to both introduce this notion
(41:21):
that you could do that type ofresearch, you could get that
type of evidence, but also tochange the way actual decisions
were being made in the world, arevolution towards evidence
based decision making, datadriven decision making. It seems
strange to think that you haveto do that for the private
sector, but you really, youreally do. I mean, very few
(41:43):
firms are Amazon running APexperiments, and even that
Amazon, historically, you know,is only doing it on the consumer
side, on the demand side.
They're not doing it internallywith their personnel policy.
Recently, they've, they've takensome things, they're doing some
things, but so you have firmsmaking these really impactful
decisions every day on the basisof nothing. And so the main I
(42:05):
feel like there's kind of twolayers of problems. There's the
there's the more the problem atthe ecosystem level, of saying,
Look, we need to make evidencebased decision making something
that seems valuable, andtherefore the evidence itself is
valuable. And and turn that evento things that are not like
(42:26):
immediate balance sheet numbers,right? So not just like input
costs, or, you know, auctionsfor inputs, or whatever it might
be, or, you know, scrap materialauctions, like all these, we do
so much around firms do so mucharound that stuff, and then they
just kind of shoot in the darkwhen it comes to personnel
policies or, you know, benefitsthat they're providing, or
(42:48):
training programs or whatever itis. And it's not because they
it's because there aren'tresources, there isn't evidence,
you know, for them to use for alot of those things. So that's,
I think there's that layer ofproblems, of just trying to
generate an appetite, and, like,want, create a desire, demand
for evidence to make decisionson. And then, of course, there's
(43:11):
the there's the frontline levelof problems that we actually are
trying to solve with thatevidence. And that is, you know,
we've seen a surprising amountof universality in some sense,
of these problems, so highabsenteeism, high worker
turnover, low output per workerand kind of some core reasons
(43:34):
for, you know, uncomfortableworking conditions, especially
if you broaden that to includephysical and psychological
conditions. I mean, these areubiquitous issues, and they're
persistent issues in so manyindustries, in so many
countries, in so many parts ofthe world. I mean, there's
(43:54):
progress to mediate almosteverywhere in that and so those
end up being the kind ofproblems we've we've tried to
take head on. So, you know,we've done this by trying to go
to the firms themselves, andstart at the CEOs, but go all
the way down to the ground andlet them tell us what their
problems are. Now, we're seeingpatterns. We're seeing trends,
(44:15):
certainly, you know, and it'snot always is high, worker
turnover or high workerabsenteeism, the issue, well,
the root cause might bedifferent in different places,
but the issue looks verysimilar, and the effects on the
firm's performance are verysimilar. So then, so then you
have to kind of really dive into understand, even if the
(44:37):
problem is the same, why thatproblem exists in each of these
settings.
Sara Deschamps (44:45):
If the conversations we've been having
on the evolving leader havehelped you in any way, please
share this episode with yournetwork, 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 thatwe're not talking about what
most of our guests areinterested in, which is the kind
of high end knowledge worker,the focus of the universities
and, you know, the kind ofmanagement literature is very
much focused on the that kind ofstrata of of worker. What you're
(45:20):
looking at is, you know, therest of the of humanity across
the world, who's not necessarilyin these these kind of
sophisticated jobs, but are justessential for for global
commerce. And really, whatyou're doing here in this
conversation is saying thatthere we're playing catch up in
that, moving the conversationfrom workers as a fungible
(45:43):
commodity that isn't reallyworth investing in. I'm really
interested in what your thoughtsare about how that might change
in the next decade. With AI, howdoes that large number of people
working in factories or workingin distribution centers or call
centers and so on. What do youthink is going to happen as that
(46:03):
starts to are they going to getpushed down? Are they? Is there
an opportunity for them? What'sgoing to happen?
Anant Nyshadham (46:09):
Oh, there's so much to unpack here. I think
that you know. So I'll caveatthis by saying that I'm a, I'm a
boots on the ground kind ofresearcher, you know? I'm a
applied, very applied and a verymicro economist. I'm going into
each firm and workplace andtrying to figure out what's
(46:31):
working and trying to makethings better today and for
tomorrow, but, but the long run,convergence, you know? You know,
I can concede that I may notknow what the long run effects
are, but the reality is, I'vebeen working in a lot of these
manufacturing settings, servicesettings, for over a decade, and
(46:55):
the boogeyman of technologyhasn't replaced all These jobs,
there are more or less as manyworkers or more, actually more,
in most of these firms. Youknow. 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 apocalypticchanges, you know, or you know,
(47:17):
but the nature of the work ischanging a lot, and that's where
skills matters a lot. That'swhere management matters a lot.
That's where organizationalpolicies matter a lot. Is that
the peoples are still requiredin the production function, but
what they need to do changes abit, step by step, and maybe
(47:37):
over the over a medium run, it'sgoing to change a lot. Like I
said, I've seen this notionthat, you know, the machine can
do the rote task better andbetter, and it can even now
generate its own, you know,understanding of things, and
make changes and so on. Butthere's always a limit where the
(47:57):
machine makes a mistake, and theonly the machine will never tell
itself that it made a mistake,right? A human being has to be
able to assess that and problemsolve, have a framework. Think
about, oh, well, how do thesystems put, you know, fit
together and so on. And we'veseen that. For example, we did
some work in the automotiveindustry in Latin America, and
(48:19):
we saw that this, you know, whatwas transformative there? As,
for example, new, moretechnically advanced models of
cars came in. And by the way,when you need to increase the
volume, was that problems gotmore complex. You needed to
bring managers closer to theproblems, and you needed to
(48:39):
train even lower level workersin these traditionally
managerial tasks of problemsolving, you know, tacit
knowledge and these types ofthings that we that we talk
about. And so then, if I zoomback out, this is not
disconnected from the macrotrends that models tend to
(49:01):
predict, which is that, overtime, you know what actually
happens as technology comes inis that it, on average, doesn't
replace labor labor. It augmentslabor in the sense that it makes
it more productive. Now it mightmean that you need less labor.
But the thing that and so I'mgetting very economics professor
(49:24):
about this, but the thing that Ithink people tend to
underestimate is that everythingthe potential to produce shifts
out. Things get cheaper. Peoplebuy more. You know, it's not,
there's still 2000 people thattouch almost every car that's
(49:44):
assembled. That's not 50,000people, but it's not 20, you
know, it's, it's, it's a quite alot of people involved, and
they're more skilled than theywere before. But what does that
mean? That means we have highertech. Cars than we have before,
and we have every household hastwo cars instead of one and a
(50:06):
half cars. Now, you know. Sowhat I mean to say is that
here's an example of even, forexample, fast food. So a study I
haven't yet completed yet, butwe're looking to kind of study
the introduction of these, justthe ordering kiosks, check out
kiosks in a fast food setting.
(50:29):
My hypothesis because I've seensimilar types of things in other
settings, and the reason why I'mexcited to do this analysis and
write this paper is that myhypothesis is that, yes, you
will need less cashiers.
Obviously, I've got a machine todo that now, to take orders, and
we actually saw in other paperswe've written in the space that
(50:50):
how fast the cashier takes thoseorders and how fast the orders
are filled is certainly abottleneck. But I foresee just
as a stylized story, that you'llneed less cashiers, but now that
the machine takes orders sofast, you're going to need to
evolve how you operate in thekitchen and how you deliver
(51:12):
orders. You're going to need,potentially, just every single
one of those cashiers might nowneed to be moved to a different
bottleneck that has been createdbecause the machine made one
part really efficient, right?
But the machine can't doeverything. We're always taking
(51:33):
one step. We're adding a machinehere, and it's making this part
more efficient, but that meansthat the other parts have to
catch up, and so we either haveto skill those people to catch
up, or we have to add people andskill them potentially to be
able to keep up. You know, Idon't know how that generalizes.
(51:53):
I'm not going to comment on thatmore broadly, but I have seen it
time and time again, and I thinkthat that's, that's where the
study of soft skills, that'swhere the study of even the
theories behind training. Wehave a series of papers now
thinking about whether, in whatcontext should you have
specialized workers? In whatcontext should you have
(52:15):
generalized workers, the classicnotion of, you know, comparative
advantage in economics withoutany other details. You know, is
very clear, like, you shouldspecialize, do one thing and do
it really well, and then Ishould have another worker that,
you know, specialize and doesthe other thing, does really
well. But that breaks down in aworld where there's chaos, you
(52:37):
know, where I don't know whichthing I'm going to need to do
more of, you know, then Iactually need to have people who
can generalize a bit. And amachine is the stylized
specialist, you know, I can't,it. Can't dynamically adapt to
the to the fact that I need tomake more fries, or whatever,
the fact that, you know, thesoccer games there, and so, you
(52:59):
know, the dinner rush is goingto come two hours earlier. You
know these types of things,that's a stylized example, but
my point is, human beings aremuch more adaptable, and so as
this kind of need for a morebroadly skilled worker comes
about, I think that's wherewe're going to see another
opportunity for workers to justbe more important as a counter
(53:24):
effect.
Scott Allender (53:26):
Well, Anant how can people get involved in the
work that you're doing, orconnect with you, to have you
come in and work with theirorganization?
Anant Nyshadham (53:35):
Yeah, thanks. I mean, I think there's quite a
bit of ways. So I think there'sat least, you know, early on, we
were generating all thisevidence only in some sense,
right? So, and that's still thecase. If you have, you know, if
you're running a large business,or a, you know, small
establishment in a largerbusiness, and you're interested
in solving a problem, and you'vegot a persistent problem, and
(53:58):
you think, you know, you want tosolve it, there's, you can
connect with us on our website.
There's, you know, communicatingwith us. You can actually Google
me and find my email, it seemsactually so, you know, we're
here. We're open to years. We'realways interested in new
problems or new settings forstubborn problems we know exist.
So that's always reallyexciting. And to be honest, it
(54:18):
takes progressive leaders. Ittakes courageous leaders who are
willing to say, look, I Theremust be a better way. Tell me
how. And I don't know what itis, let's go figure it out. You
know. So if you're one of thosepeople, please connect. But
we're also in a great positionnow to want to interact with
people who just want to prove orwant to implement what's already
(54:42):
been proven. Experimentation ishard, you know, like, it's
costly if you're a if you're nota large, very competitive firm,
if you're a small establishment,just trying to, like, you know,
make it through, eke out smallmargins. You don't want to come
and have us treat your firm as.
Playground. You know, we'refortunate to let have suburbs.
(55:02):
Let us do that. You just want toknow 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'reready for that. You know, we've
got tools on worker grievances,we've got trainings, we've got
lots of interventions that we'veyou know, now we're going to
look to scale up this payday,this on demand salary tool. So
(55:24):
if any of the problems I'vetalked about here, you know,
resonate with you, and even ifyou don't have the capacity or
interest to experiment, you justwant to see if we've got a
solution that might work foryou. You know, that's one way we
can certainly interact. And thelast I'll say is, you know, if I
go back to the beginning, youasked me, Scott, what my what
our mission is, and like I said,it's, I think, as grand as it
(55:47):
can be, which is to change theway that firms think about
workers as assets, and investingin those workers in every corner
of the world. And so if you justwant, if you just believe in
that, and think it makes sense,and you know, we could use all
the help we can get to spreadthat word. It's going to take,
you know, every leader of everyorganization at every level to
(56:11):
start to believe this, you know.
And so if that's something youthink you can do, just spread
the word. And that's that'stremendously helpful for us.
Scott Allender (56:20):
Excellent. Well, we'll put all that in the show
notes. And thank you for comingon and sharing your insights and
your and your wisdom and andthank you for the work you're
doing in the world. I love it,and I've benefited tremendously
from this conversation. So thankyou.
Anant Nyshadham (56:35):
Me as well.
Thank you so much for theopportunity really fun
conversation. And I love whatyou what you both are doing with
this, so I'm glad I couldcontribute a little bit.
Scott Allender (56:44):
Thank you. Thank you and folks, until next time,
remember the world is evolving.
Are you?
We all love those aha Insights where we encounter
an idea or technique that worksbecause it overturns
conventional wisdom, but we onlylove it because it works up to
that point, the idea is oftenmet with eye rolling, cynicism,
resistance, dismissal or simplyinattention. In this show, we
get into a wonderfulconversation with the economics
(00:24):
researcher Anant misalon, whosepioneering studies into the
effectiveness of firms in thirdworld countries reveals a slew
of exciting, counter intuitiveinsights and evidence that have
the power to accelerate economicdevelopment in the world's most
impoverished countries. Tuneinto an important conversation
on The Evolving Leader.
Scott Allender (01:05):
Hi folks.
Welcome to The Evolving Leader,the show born from the belief
that we need deeper, moreaccountable and more human
leadership to confront theworld's biggest challenges. I'm
Scott Allender.
Jean Gomes (01:16):
and I'm Jean Gomes.
Scott Allender (01:17):
How are you doing today Mr. Gomes?
Jean Gomes (01:19):
I am feeling very good. I've had a amazing few
weeks of doing reallyinteresting research and
delivery and all sorts ofthings. I am feeling conflicted
about what's happening in theworld right now, as we ever
since we started a show that'sprobably like, there's ever been
(01:41):
a week where there isn't somemajor, major thing happening, it
probably it doesn't affect youmore than us, but you're, you're
at the epicenter of what'shappening in the US election. So
we won't talk about that, butjust suffice to say that we're
all feeling something. And yeah,and I'm really looking forward
to the show. How you feelingScott?
Scott Allender (02:02):
Yeah, we probably, probably should set up
a whole hour to talk to I'mfeeling, feeling a lot of things
today. My friend, yeah, feel it.
Feeling some I feel, I feel raw.
Maybe, is the is the best termfor it right now. We're only
sort of, I don't know when theshow will go up, but we're only
sort of a week on the otherside. And I certainly just, I'm
(02:22):
I'm in the middle of the fields,so I'm still processing, as I
know a lot of people are, andtry to make sense of things. So
but I am delighted to be backhere with you and having
conversations to influence theleadership discussion in terms
of what is leadership and whatdo we bring to it? How do we
make it more accountable? How dowe make it more human? And so
(02:42):
I'm delighted that we're joinedtoday by Anant Nyshadham. Anant
is an associate professor in thebusiness, economics and public
policy area of the Ross Schoolof Business at the University of
Michigan, a research associateof the National Bureau of
Economic Research, and he's anaffiliate of bread, which is a
non profit dedicated to researchand scholarship in economics,
(03:05):
and he is a research affiliateof the IGC, A J pal affiliated
professor and an affiliate ofthe Montreal partnership for
human resource management. Andif that's not impressive enough,
he is an accomplished guitarplayer, 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 alsoreceived his PhD in 2012 from
(03:27):
Yale University, and is the cofounder and chief strategy
officer of the good businesslab, a non profit seeking to
promote investment in workerwelfare as a business
imperative. His work focuses onenterprise, firm and worker
characteristics and decisionmaking, like labor contracting
and worker training andmanagerial quality and the
(03:48):
resulting performance dynamics,particularly in developing
countries. Anant Welcome to TheEvolving Leader.
Anant Nyshadham (03:57):
Thank you so much for having me, and thanks
for that great introduction. I'mglad you had to do it and not
and not me as a lot of as a lotof text, yeah.
Jean Gomes (04:07):
Anant, welcome to the show. Apart from just ogling
your Telecaster on the wallthere, how are you feeling
today?
Anant Nyshadham (04:15):
I am feeling, certainly, all the things that
people are feeling in the worldtoday, especially as an
American, lot of shock and a lotof stress about the way forward.
But also, you know, I thinksuccessfully looking inwards at
what I can do, trying to be abit more focused on what I can
(04:38):
control and the work that I'mdoing, which I'm very excited to
talk about today, and stayingkind of mentally fit to do that
work. So today I'm feelingequipped to do that. We'll see.
You know, it's a day to dayprocess,
Jean Gomes (04:52):
Excellent. Well, let's get to know you a little
bit. Imagine where a dinnerparty and you're surrounded by
people who are not in your worldand not in in the academic world
or in the nonprofit or any ofthose kind of things, and the
person turns next to you andsays, Hey, Anant. What do you
do? What would you say?
Anant Nyshadham (05:08):
I would say you're sure you want that. It
has to be that question.
Jean Gomes (05:13):
I'm really interested.
Anant Nyshadham (05:16):
So, yeah. So you know, broadly speaking, I
work on development issues. Soall the kinds of problems,
particularly in the developingworld, although I think these
problems by way of immigrationand by way of kind of persistent
class issues and so on, theyexist in the West, for sure,
also. So it's, it's kind of,it's about development both, you
(05:40):
know, in an economic or macroeconomic sense of countries,
whole economies, but also justin terms of individuals trying
to advance in their lives. AndI, you know, my path to this, I
think was a bit strange in thesense that it was late to get
here. I think I was late to takemyself very seriously. I grew up
(06:01):
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 longsince gone even before we got
there.
So I just grew up in a placethat had great sense of
community, but certainly wasrife with issues of racism and
class issues and extremelyimpoverished majority over the
(06:25):
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 alsospent recent trips, sorry,
frequent trips to India, to ourancestral land, which, sadly, we
don't have anymore, but to thevillage where I think I
(06:48):
experienced a lot of just acompletely different world. I
mean, I thought even what I wasseeing, you know, in the kind of
rural South, you know, lowerincome areas that we were living
in, you know, paled incomparison to what the
challenges that I saw in villageIndia, limited access to
resources and education in, youknow, pre internet, pre social
(07:12):
media, access to the to theexternal world. And so I think
this kind of stuck with me, andas I got into school and tried
to figure out what I was goingto do in the world. On the one
hand, I'd really just kind ofpursued the PhD in Economics in
a purely, like, romantic sense,like, I had some sense that I
wanted to be a thinker. And, youknow, I come from, actually, a
(07:34):
family of academics, and theidea, you know, my grandfather
was a physicist, and, you know,I had a sense that, like, Oh,
this is really nice, like, youget left alone and you can
think, but I didn't know what Iwas going to think about or, you
know, or how I was going toapply it to the world. And
honestly, it wasn't until I thesummer after I graduated from
the PhD, you know, I trained inmuch more techniques,
(07:57):
econometrics and so onmethodologies. But when I
graduated from the PhD thesummer after that, I had an
opportunity to go visit sometextile factories in India. So
it turns out, classmate of minefrom undergrad, his family was
a, you know, he was a secondgenerational industrialist. And,
you know, I didn't realize atthe time, but he was like, okay,
(08:17):
my family runs a few factoriesin India, and you should come
check it out. We're like, doingthese programs for female
garment workers. And then when Igot there, I realized they run
like all the factories, youknow. So there was a, it was a
really huge scope, and and Isaw, you know, something really
amazing there, in the sense thathere is a company that
(08:38):
represents and has kind of atouch point with over 100,000
people who need something, youknow, people who need help. They
have limited access toresources. They have limited
access to mobility. They'reconstrained. And so here's a
pretty powerful lever,potentially. But what we kind of
(09:02):
started talking about in thatfirst summer was this idea that,
like, well, maybe we can do someworker initiatives and, you
know, test them and so on. And Iwas explaining the idea of
running experiments to try toget, you know, kind of good
causal evidence of what mightwork and what might improve
things. And as we talked, Istarted to realize that, you
(09:23):
know, there was this internaldebate in that firm, and I've
now, since, over the last morethan decades, seen it in many,
many firms like it. Of you know,what are we doing for our
workers, and should we be doingit? Should we be doing more of
should we be doing less of it?
You know? And there's differentperspective. It's not that
anybody doesn't care. It's thatthat one manager thinks this
program is helpful and onemanager thinks that it's not
(09:46):
helpful and it's a distraction,and there's no evidence to make
these decisions, and so firmsare just kind of, you know, kind
of shooting in the dark a little
Scott Allender (09:56):
Can we talk about well being in the bit.
workplace? So I'm thinking aboutthe levels of stress and anxiety
and loneliness that areprevalent in workplaces all over
all over the place. Talk to us alittle bit about the work that
(10:18):
you've done at building wellbeing into core business models
at organizations where thetemptation and what gets
repeated a lot is that peopletry to kind of slap a little bit
of well being work on the sideof the of the agenda, but it's
not really ingrained into whateverybody's experiencing every
day, right? So the perspectiveof everyone and what they need
(10:39):
talk to us a little bit aboutthat.
Anant Nyshadham (10:41):
Yeah, um, I mean, I think that there's a lot
of different elements to that. Ithink, on the one hand, we've
been, you know, chipping away atat all the different elements of
well being, pointing out that insome sense, like, you know, well
being isn't just I'm fit, orI'm, you know, or I don't have
chronic health issues, it'smental health, it's loneliness,
(11:04):
it's being able to deal withstress, or maybe being insulated
to some degree from stress, youknow. And so when we I think we
want, as a scientist, the way wedo that is to then run a trial
on this, and run a trial onthat, and, you know, and try to
put them together and so on. Andwe have, I think we've got stuff
(11:25):
that, you know, documents thathow hot it is in the workplace,
whether the air is marginallydustier, can have tremendous
effects on productivity and andones that need to be monitored
by management and adjusted, andgood management can kind of
react to those things. You know,we've got studies on screening
(11:49):
for presbyopia and givingworkers glasses and nutrition
deficiencies and so all of thesetypes of maybe more traditional
things, but also, like buddysystems, you know, we, we work
in a lot of industries with alot of migrant labor. So these
are workers that have left theirnetwork, the only thing that,
the only reality they've everknown to come to a city, it's
(12:10):
totally new, with no one thatthey really know. And so can we
pair them with somebody who didthat two years ago, came, maybe
even from their area that speakstheir same language, you know,
likes the same types of foods,you know, and and connect,
create a connection there thatallows for some to break down
(12:30):
that isolation. Right? A lot oftimes when we're under stress,
it feels like we're the onlyones dealing with this sort of
thing, and that can be veryisolating. Just knowing that
it's not us alone. Can be, canbe really powerful, and so, you
know, so we've run trials onthat. So on the one hand, it's
kind of putting the evidencetogether. It's, it's why we've
(12:51):
kind of thought of this as alife's work, although every day
I wake up and feel like I wish Ihad done more up to now, or I
want to do more, you know,tomorrow, and do it faster to
get this evidence. But like wesaid, evidence isn't enough, and
so I think where it's beenimportant is, you know, at every
(13:11):
level, to change theconversation. And by that, it
means, like when I started doingthis, I would come in and I
would talk to CEOs, and theywould give me very confident
answers about what they thoughtwas going on on the ground and
what they thought their biggestproblems were, and so on. And
then I have yet to find a singlefirm or single setting in which
that notion wasn't ultimatelychallenged or contradicted when
(13:35):
I got enough layers down thatactually the way the managers
are dealing with this stuff atthe lowest possible level is
like this and not like that,and, you know, and then
actually, the biggest driver ofthat problem, you know, for
example, in a lot of thesegarment 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 overalmost our entire workforce
(13:56):
every year. So like, 100 plus1000 workers we're gonna hire
this year, and then we're gonnahire another 100 plus 1000 next
year. That's bananas. That'scrazy. So obviously, that's a
lot of money and a lot of effortin an entire, like, army of HR,
you know, just to do that. Butthen that's, you know, that's
what was said out of one side oftheir mouth, and the other side
(14:16):
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 getmarried, and then they drop out
of the workforce or whatever.
And don't get me wrong, that'snot untrue. There are lots of
women, not just in India, everycountry in the world, that work
before they've shared theirfamilies, and then when they
(14:37):
have families, will choose, ormaybe, unfortunately, have the
choice made for them to dropout. But when you're turning
over 100% of your workforce,there's not one answer, right?
There's like, who knows aninnumerable number of causes of
this. And so you've got toreally start by challenging
everything you think you knowabout all the. Problems, right?
(15:00):
You've got to start by actuallygoing down as far as you can,
right down to the ground, andasking people, and this person
might tell you one thing, andthat person might tell you
another, but starting to getthemes, you know, from the
actual people about what aretheir challenges, what makes
them potentially leave thesejobs or and so on. And then you
(15:23):
start to get a bunch ofdifferent stories. Yes, I, you
know, I'm going to get marriednext year, and I want to start a
family, but actually, I wasplanning to stay. It's just that
this is this. Work is reallyhard on my back, and I can't do
that. And also work, I mean, I'mjust, you know, that's one of
many stories we've heard. Sothen you start to understand
(15:43):
that I don't have to solve everyproblem or the whole problem.
There's value in solving a partof this problem and chipping
away at it, right? There's athere's a value in just saying,
Well, let me solve theergonomics. And some x percent
of people who were dropping outbefore that's the real thing,
they needed to stay you know?
Oh, I'll give you an examplethat I'm really, really excited
(16:05):
about. We recently, you know,it's true everywhere in the
world, including in the US. Thisnotion, in fact, the existence
of the payday loan industrybasically underscores the idea
that a lot of low incomehouseholds and families are
living paycheck to paycheck.
(16:26):
Shocks come as as economists, wewould call them shocks, but like
unexpected things come, you needto pay for them, and often they
might be, I don't know, 300bucks. 400 bucks, which
thankfully many of us think, isnot that big a deal, but in
every country in the world,there's quite a few people who
(16:46):
would never be able to come upwith that tomorrow. And that can
be the effects of that can justecho through their whole lives.
If I can't get it, I can't makemy 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 oftimes. I'm out on the curb, you
know. If I can't get it, I can'ttake my kid to the hospital
(17:09):
today, so I'm going to wait abit longer and hope that they
get better, and then there's achance they may never get
better, you know. And if you addup all of those things, you have
such a simple problem driving somuch persistent, you know,
adverse effects for thispopulation. So we did something
really simple, which was that,like, look, here's a firm.
(17:33):
They've got hundreds of 1000s ofworkers. They're not going to
just cut checks every day. Theyneed to be on a cycle like and
they need to manage theirliquidity and so on. So, but we
just created a tool where itsays, 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 endof the month or next month for
(17:54):
last month, right? So I'veearned this money. So we gave
these garment workers theability to draw up to 50% of
that of that wage that they hadearned up to date, and they
could do it throughout, wheneverthey needed it, throughout the
the month. And just that, noadditional money, nothing else,
(18:16):
just the ability to, like, go toa little tablet on the factory
floor. That's the I mean, we hadto, we had to know that we
couldn't make it an iPhone app,because then nobody would be
able to use it, right? So youhave to understand your
population. But here's a tableton the factory floor. I can go
there, I can draw my money. Andit dramatically reduced
(18:36):
attrition, wow. And evenincreased day to day
productivity. We're stillunpacking the why, but a lot of
it is that this is a thing thatthis job has that other jobs
don't have. I need to keep thisjob. This job allows me to pay
my bills. Another job is almostuseless. I can get paid twice as
(18:58):
much, but if I can't get it whenI need it, what good is it?
Jean Gomes (19:04):
That's fascinating.
I mean, what it brings out forme is like the disconnect
between the owners and managersof firms and the lives of the
people that you know make themsuccessful, and if you do not
understand the problems thatthey face. You can't possibly be
in a relationship with them thatactually is mutually beneficial
(19:26):
beyond time for money, becausethat's what you're basically
saying. You're creating a newvalue exchange between the
individual and the firm, whichis, goes beyond that, which is,
I understand you understand whatyou need. I'm going to help you
meet your needs in a creativeway, and actually from a many
ways, that doesn't cost the firmvery much at all. I mean,
(19:47):
there's a slight implication oncash flow, little bit of, you
know, of you know, financialmanagement that's required
there, but it's that seems verysimple,
Anant Nyshadham (19:57):
and to highlight what you're saying,
actually. We got tremendouspushback on this. I mean, we it
took us months to convince asingle factory to let us do
this. And the agreement was,you're going to do it and then
you're going to take it awayimmediately. So we're going to
do it for three months, and thenyou take it away, or six months,
I can't remember, and then takeit away. And the trial has just
(20:18):
now, it's just now wrapping up,and now they're begging us to
keep it and scale it up to therest of the factory, because now
they're seeing that, likeworkers 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, assoon as but the logic of that is
(20:39):
so strange, right? Like, if theonly reason why your workers are
here is because you're holdingtheir money, you've got a
problem, you know. And so Idon't think it didn't ring true
to me, and it didn't turn out tobe true. So thankfully, that's,
you know, that I was right thattime. That's
Jean Gomes (20:54):
interesting. So did was there? Was there any cases
of people, you know, takingthree months, or, you know,
sorry, three week salary andjust No, no.
Anant Nyshadham (21:02):
I mean, so I can't tell you every n, right,
every observation, but onaverage, it was tremendous. It
was like, very much theopposite, you know, very much
the opposite. And the average iswhat the firm should care about,
right? Even if there's a handfulof workers that are going to do
that, if the overwhelminglyworkers are going to see this as
(21:23):
a benefit that they want tostick around for and work harder
for, you know, that's amazing.
And the reality is, there's allthese nuances that we're still
unpacking. But you know, if Ican just we were finding it at
baseline before we intervened,that that many of these workers
were reporting running out ofmoney at the end of the month
and eating less forgoing medicalexpenses. These like real things
(21:45):
that very obviously will affectyour productivity or your
ability to show up to work, youknow. So the, you know, the
theory of change is like, prettyeasy 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, Ithink it's quite reasonable, or
(22:07):
maybe expected that, you know, asenior leader or somebody comes
from a different walk of life,who's running this company is
going to have troubleunderstanding the lives of their
workers. The idea is to not, isto is to just be aware of what
you don't know, to want to knowwhat you don't know you know, to
actually find out. And you don'thave to do it philanthropically.
(22:29):
That's been our whole kind ofmission 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 wejust wait around for the people
who want to think all day about,you know, other people and the
poor and so on. To makeprogress, let's just it should
be good for your business. Itshould be that I am spending a
lot of money on recruiting newworkers. Now I need to go
(22:51):
challenge everything I knowabout why workers are leaving,
and be a bit honest with myselfand 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 problemanymore.
Scott Allender (23:06):
Do you find that people are receptive to letting
the data do the talking and arepersuaded by that, or is there
still a kind of resistantresistance, because that legacy
mindset you talked about where,you know, we'll invest in
upgrading our machinery, butwe're not really going to do
that with our people. I'mactually thinking about, you
know, statistics aroundeffective leadership, you know,
(23:26):
70 to 75% at least, beingattributable to measures of
emotional intelligence. And yet,time and again, people are hired
into very important leadershiproles simply based on their sort
of hard skills or their sort ofprevious experience. And the
data that's there and undeniableis almost denied, and in spite
of it being undeniable, so I'mreally curious, like with the
(23:48):
data you're presenting with yoursort of own research, is it
persuasive, or what are youdoing or having to do to bust
through that sort of resistancethat you might experience?
Anant Nyshadham (23:58):
That's a great question. And maybe the crux of,
you know, the hardest elementsof my work, you know, for the
last decade, evidence isn'tenough. I mean, so we started
with the idea that the evidencewas bad. It either didn't exist
or it was bad. And while I stillthink that's true, and I still
think that, you know, pat myselfon the back, I think we made
better evidence. We ran reallytight, rigorous, gold standard
(24:22):
trials. And we, you know, we, wepresented it that was its own
challenge. Once you do that, youknow, five people are going to
read this paper, if I'm lucky.
So now, how do I translate thisinto an HBr piece or, you know,
or an NPR kind of podcast, or apodcast discussion, you know?
But even that just wasn'tenough. I think what we
(24:43):
realized, which is, I think, abroader, maybe epiphany, at
least on my part, is that, youknow, you have to meet there's a
cop, there's a there's a set ofdecision makers, or a sequence
of decision makers. You. Inorder to change this, to create
an organizational change,convincing the CEO does almost
(25:04):
nothing, right? I mean, it's anecessary but insufficient
condition, right? Like, if I goin there and I, you know,
convince the CEO, nobody else isnecessarily their day to day
lives are different than thatdecision, right? Yes, my boss
tells me, but my boss can't seewhether I'm really putting forth
effort into this. And day today, I'm gonna make a choice as
(25:26):
to what seems to benefit me, youknow. So we had to do these. And
then the opposite is also true.
I can go convince the frontlinemanager to adopt this thing, but
if the CEO doesn't understandwhy you're doing it, then
somewhere along the way, they'regonna say, what are you wasting
your time training? Theseworkers are doing this thing,
you know? So you need, in somesense, this, this coordinated
(25:48):
effort or big push, and in orderto do that, we really, I think
we've learned to take a humancentered approach to every
element of what we're doing. Andit started out where we had some
excellent people trained indesign who joined our team. I
heard all these buzz wordsaround this, who said to design
(26:09):
and all this stuff that as aneconomist, I didn't understand
what they were, but I really getit now, and it it really jives
with economics in a way that Idon't know that I realized
before, which is that this isabout understanding the
perspective of like everyonethat matters for this particular
decision. If I want to make onechange, I need to convince the
(26:33):
CEO that I've got good evidencethat it's going to drive
returns, and even if you'reskeptical, discount my returns,
but the returns are so big thateven if you're skeptical and you
think it might it may not be asgood 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 getdown all the way down to the
(26:55):
front line, manager or frontlineworker, and say this thing is
going to make your life better,because you don't. A lot of
those people don't, individuallyparticipate in the higher
productivity, or, you know,bigger margins, or whatever it
might be, or they or it's a verymuted participation. I get a
little bonus, but it's not, Idon't own this company, you
(27:18):
know, but I do make decisionsevery day about how to do my job
and what's a costly way to doit, what's a less costly way to
do it? Where should I put myeffort? And so then you have to
think about every change, Ithink, in that perspective, and
that means that you're kind ofmarketing to a very broad array
(27:39):
of people you know, so surethat's the second phase, or, I
don't know, the end phase, aftergetting the evidence. Yeah,
Jean Gomes (27:52):
we had a another guest on recently, Ellen Langer,
a psychologist who's done somepretty groundbreaking work in a
long career, and she was talkingabout the importance of
understanding that risk issubjective. It's not an
objective thing that youmeasure. 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 reallyinterested in, you know,
(28:14):
particularly when you're tryingto remove this kind of
paternalistic or judgmental typeof frame, particularly to first
world countries, looking atthird world countries. How do
you get rid of that? How do youkind of dial that out of your
analysis and the solutions youcome up with?
Anant Nyshadham (28:32):
Yeah, I mean, I think, Well, one way is to so
often if we don't trulyunderstand what drives
decisions. So the way that we dothis as a science is we might
write down some mathematicalequations that simplify the
trade offs that exist, but theykind of try to characterize and
the simplicity is not lost onus. We know that we're
(28:53):
simplifying, but we're trying tofocus on certain elements that
we think are important. Andthen, if you're me and you're an
applied economist, you try to goout into the world and see data
that you can fit to this andthen see, like when I analyze
the data, does it actuallyfollow what these mathematical
equations would say, or does itdeviate and why? But the kind of
(29:17):
most important thing foreconomists like myself that are
looking to, you know, testchanges that we might be able to
impose, and we might be able toinform a firm to do this and see
what happens, and so on. Youknow, we really don't want to
say that unless we know what'sthe causal effect of that,
right? So, like, can I say withsome amount of statistical
(29:38):
certainty, obviously never fullcertainty that if you do this,
this will happen, you know. Andin order to do that, you're
looking for either you know,perturbations in the world,
randomness in the world, thatyou can use to say, like, you
know, these two people areexactly the same. But this
purpose and happen to have the.
Ability to do this, and thisperson didn't. So now I can
(30:01):
compare and see like, well, whenyou had the ability to do X,
what happened? That's what wecall a natural experiment. And
there was a recent nobel prizegiven kind of for that study of
analysis. But the other thing wecan do is, just like any other
medical trial, we can imposeperturbations, right? We can
(30:21):
preemptively inject randomnessto help us understand so, like,
the way a medical trial works islike, you know, I can give the
drug to one set of people, and Ican perfectly exclude the other
set of people from having thedrug. And I can do that in a
random way where I make the twopopulations, you know, look as
similar as possible, and then I,you know, I flip a coin, or
(30:44):
whatever it might be. And sothen when I go forward, I have a
sense that whatever all theother stuff that's going on
doesn't matter, as far asfiguring out the effect of this
drug, because the only thing Iinjected that was different was
that I gave them the drug, orthat I gave them access to this
(31:04):
resource, or whatever it mightbe. And so now you need the weak
law of large numbers. You needyou can't do it for two people,
because then, you know, lots ofthings could be different
between those two people. But ifyou combine that with, you know,
enough observations, then youbasically get this kind of
repeatable sense that, likethis, you know, this is the a
reasonable idea of what thecause and effect is, and that
(31:27):
allows me to say, Okay, well, ifI do it over here, and I do it
over there, and I do it at abigger scale, I'll get the
effect. And so I think those arethe simplest ways to think
about, you know, these kinds ofconditions. And I think there's
another there was also a Nobelprize given recently for a
couple on bringing this kind ofexperiments to our field, to
economics, and in particular, indevelopment. And so I think
(31:51):
that's been a really powerfulthing, this idea that, you know,
so many billions, trillions ofdollars are being spent on
development efforts, byfoundations, by governments, you
know, but we have so little, atleast historically, idea of what
actually works, what drivesimpact, what's the you know, is
it worth spending the money onbed nets or Malaria pills, or
(32:14):
she'll be spending it on, youknow, building infrastructure or
whatever, and so over the lastcouple of decades, you know, our
field has been able to run thosetypes of experiments for all in
the public sector, almost everycountry in the world, and almost
every level of government.
(32:34):
Really exciting stuff where wenow, you know, both are able to
run those types of experimentsand get evidence. But we've also
generated an appetite for thatthat like policy makers are
saying, I'm not going to pullthe trigger on this big policy
until I know, until I've gotsome evidence that it works, you
know, and so I have a sense ofit.
Jean Gomes (32:54):
What examples in that wider research, not
necessarily yours, have beenmost exciting for you? You You
know, the way you can start tosee 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 youfound 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 ismostly in kind of public goods,
in like health, education andthese types, a little bit in
political representation and soon. And so there have been some
really exciting things there.
There's one classic result ondeworming that, you know,
(33:33):
demonstrated that you could, youknow, you didn't have to deworm
everybody. It's an infectiousparasite. You could get a
certain amount, and then therest would just, you know, die
out. And so it's kind of apublic health point, but in
economics, we had to thinkabout, well, how do you approach
those populations? How do youdrive the decision to actually
(33:53):
adopt it, you know, how do youdeal with the fact that some
people will be skeptical and notwant to do it, and so on, you
know, there have been similarresults on technologies of
teaching at the right level,which is really powerful thing
that we have technology now todo 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, isa school, I see that they really
(34:16):
try to vary the curriculum andmeet the students where they
are. But doing this in thedeveloping world, you know, in
India with, you know, it'shundreds of millions of
children, you know, in schoolsthat are super under resourced
and not well organized, that'sreally hard. And so that's where
technology we've seen canreally, like, you know, level
(34:37):
the playing field, we can createapps that adaptively teach at
the right level, and those canbe really impactful. So I think
all of that's been reallyexciting, but I think that the
main AHA that we had early onwas that despite economics
seeming like it's all about likebusiness and economics. Seem
(34:58):
like you're the same in somesense, there was very little
work being done where economistswere doing this type of stuff
with private firms. And thedeveloping world is filled with
these huge monoliths, these hugefirms that employ so many
workers, but there was nointeracting with them to kind of
help them make decisions. Imean, these are essential. These
(35:21):
are small governments. They'rethe huge representations of
people, and they're makingpolicies every day that affect
the lives of that person, butalso their families and their
children and their generationaleffects. But nobody's providing
them with evidence. And in fact,because of that, there's no
appetite for evidence. There'sjust a sense that we just do
(35:43):
what we think is best, and we gowith it and we move on. We
don't, you know, we don't havethe luxury of of, you know,
needing evidence. And so the bigaha that came about there was
when I was saying, you know, wewe had this first opportunity to
test a skilling intervention ingarment settings. So this is
like teaching, you know, there'sa broader program, but what we
(36:06):
kind of saw was that this wasessentially teaching what we
call soft skills, or noncognitive skills. These are all
terrible terms that we don'thave the right term for, but
they're like the non technicalaspects of any job, and usually
the transferable ones, like theones that communication, problem
solving, teamwork, and they wereteaching these skills to
frontline machine operators ingarment factories. And it was
(36:30):
kind of a really fascinatingnotion, because, I mean, these
are one person to machine, andyou know, you these are skills
are usually teach to, like whitecollar professionals and you
know, and consultants and youknow, not people you think, who
are sitting, you know, eight,nine hours a day in front of one
machine. But we found, we ran anexperiment to evaluate that, and
(36:52):
found tremendous effects onproductivity for even that
level, because everything iscoordination, everything is
teamwork. You know, there'salways an element of
relationship and trust andcoordination in every element of
productivity everywhere. And sothe big aha here was both
(37:12):
actually the power of that typeof relationship and that
investing in that relationship,wherever we find it can be
pretty powerful, but actuallymore broadly, the idea that look
at this like simple thing we didthat actually had a tremendous
effect, a flow benefit onproductivity. If the firm had
known that I can increaseproductivity by four percentage
(37:36):
points or whatever, you know,they would have done almost
anything to do that. It's reallystubborn. It's really hard to
move productivity, especially inthese kind of razor, thin
margin, kind of competitivefactory environments. But, but
there's such a blind spot withthe idea that you could put any
resources or money on thesefrontline workers and have it be
(37:56):
worthwhile, total blind spot. Sonow we've seen across
industries, electronicsmanufacturing, 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 lowincome workers or low skill
workers. Why should I invest alot in them if they're going to
(38:18):
leave? And so we've had to chipaway 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 worksa tremendous amount of the time.
I'm not going to say always, butit works a lot.
Scott Allender (38:34):
So is that, let me kind of zoom back out a bit,
because you're talking, there'sso many wonderful things you're
doing. So if you were to sortof, you know, define this
mission of good business lab,like, you know, how would you
sort of describe that? Whatproblems are you you're touching
on many of the problems. Butlike, how do you identify which
problems you want to solve? Howare you evangelizing the work
(38:58):
into these businesses that aremaybe resistant to educating
themselves in this space. Giveus a give us a better, or not a
better, but a deeper tour of thesort 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 ofbroad vision, or, I guess maybe
my broad vision, and now theorganizations, to some degree,
is to change the way thatemployers think about workers,
kind of every industry, everycorner of the world, from a cost
(39:33):
or a liability or kind of ausable input to a productive
asset, you know, and I don't,and I don't, I don't want to
dehumanize the workers Iactually, you know, I want, but
so much firms think aboutcoddling their machinery. They
invest in they take care of it.
They invest in maintenance.
(39:55):
They, you know, try to push offdepreciation or offset
depreciation. They upgrade it.
And. Make sure they're keepingup with the highest technology
of the times. But none of thatlogic really applies when we
think about these hugeworkforces that actually, you
know, make everything and doeverything like, you know, in
fact, even more so as technologyadvances and the role of the
(40:17):
human becomes very specific.
What's left is, in some sense,their emotions, their their soft
skills, their ability tocommunicate and problem solve.
You know, the machine doesn'tknow when it's wrong because it
always thinks it's right. Sothen a human has to figure this
(40:38):
out, and a few human has to beable to tell somebody, Hey, we
really need to, you know, have alook over here, or change
things. So that's, that's kindof like the overarching strategy
or idea we have, you know, Iguess that's the goal. And then
the strategy, in some sense, isto bring evidence to bear. You
(41:00):
know, we saw, we got the we hadthe fortunate kind of position
to see J pal, the Poverty ActionLab that was born out of MIT,
and founders and colleagues gotthe Nobel Prize recently for
bringing these experiments tothe public sector, so we could
see how transformative that was,to both introduce this notion
(41:21):
that you could do that type ofresearch, you could get that
type of evidence, but also tochange the way actual decisions
were being made in the world, arevolution towards evidence
based decision making, datadriven decision making. It seems
strange to think that you haveto do that for the private
sector, but you really, youreally do. I mean, very few
(41:43):
firms are Amazon running APexperiments, and even that
Amazon, historically, you know,is only doing it on the consumer
side, on the demand side.
They're not doing it internallywith their personnel policy.
Recently, they've, they've takensome things, they're doing some
things, but so you have firmsmaking these really impactful
decisions every day on the basisof nothing. And so the main I
(42:05):
feel like there's kind of twolayers of problems. There's the
there's the more the problem atthe ecosystem level, of saying,
Look, we need to make evidencebased decision making something
that seems valuable, andtherefore the evidence itself is
valuable. And and turn that evento things that are not like
(42:26):
immediate balance sheet numbers,right? So not just like input
costs, or, you know, auctionsfor inputs, or whatever it might
be, or, you know, scrap materialauctions, like all these, we do
so much around firms do so mucharound that stuff, and then they
just kind of shoot in the darkwhen it comes to personnel
policies or, you know, benefitsthat they're providing, or
(42:48):
training programs or whatever itis. And it's not because they
it's because there aren'tresources, there isn't evidence,
you know, for them to use for alot of those things. So that's,
I think there's that layer ofproblems, of just trying to
generate an appetite, and, like,want, create a desire, demand
for evidence to make decisionson. And then, of course, there's
(43:11):
the there's the frontline levelof problems that we actually are
trying to solve with thatevidence. And that is, you know,
we've seen a surprising amountof universality in some sense,
of these problems, so highabsenteeism, high worker
turnover, low output per workerand kind of some core reasons
(43:34):
for, you know, uncomfortableworking conditions, especially
if you broaden that to includephysical and psychological
conditions. I mean, these areubiquitous issues, and they're
persistent issues in so manyindustries, in so many
countries, in so many parts ofthe world. I mean, there's
(43:54):
progress to mediate almosteverywhere in that and so those
end up being the kind ofproblems we've we've tried to
take head on. So, you know,we've done this by trying to go
to the firms themselves, andstart at the CEOs, but go all
the way down to the ground andlet them tell us what their
problems are. Now, we're seeingpatterns. We're seeing trends,
(44:15):
certainly, you know, and it'snot always is high, worker
turnover or high workerabsenteeism, the issue, well,
the root cause might bedifferent in different places,
but the issue looks verysimilar, and the effects on the
firm's performance are verysimilar. So then, so then you
have to kind of really dive into understand, even if the
(44:37):
problem is the same, why thatproblem exists in each of these
settings.
Sara Deschamps (44:45):
If the conversations we've been having
on the evolving leader havehelped you in any way, please
share this episode with yournetwork, 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 thatwe're not talking about what
most of our guests areinterested in, which is the kind
of high end knowledge worker,the focus of the universities
and, you know, the kind ofmanagement literature is very
much focused on the that kind ofstrata of of worker. What you're
(45:20):
looking at is, you know, therest of the of humanity across
the world, who's not necessarilyin these these kind of
sophisticated jobs, but are justessential for for global
commerce. And really, whatyou're doing here in this
conversation is saying thatthere we're playing catch up in
that, moving the conversationfrom workers as a fungible
(45:43):
commodity that isn't reallyworth investing in. I'm really
interested in what your thoughtsare about how that might change
in the next decade. With AI, howdoes that large number of people
working in factories or workingin distribution centers or call
centers and so on. What do youthink is going to happen as that
(46:03):
starts to are they going to getpushed down? Are they? Is there
an opportunity for them? What'sgoing to happen?
Anant Nyshadham (46:09):
Oh, there's so much to unpack here. I think
that you know. So I'll caveatthis by saying that I'm a, I'm a
boots on the ground kind ofresearcher, you know? I'm a
applied, very applied and a verymicro economist. I'm going into
each firm and workplace andtrying to figure out what's
(46:31):
working and trying to makethings better today and for
tomorrow, but, but the long run,convergence, you know? You know,
I can concede that I may notknow what the long run effects
are, but the reality is, I'vebeen working in a lot of these
manufacturing settings, servicesettings, for over a decade, and
(46:55):
the boogeyman of technologyhasn't replaced all These jobs,
there are more or less as manyworkers or more, actually more,
in most of these firms. Youknow. 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 apocalypticchanges, you know, or you know,
(47:17):
but the nature of the work ischanging a lot, and that's where
skills matters a lot. That'swhere management matters a lot.
That's where organizationalpolicies matter a lot. Is that
the peoples are still requiredin the production function, but
what they need to do changes abit, step by step, and maybe
(47:37):
over the over a medium run, it'sgoing to change a lot. Like I
said, I've seen this notionthat, you know, the machine can
do the rote task better andbetter, and it can even now
generate its own, you know,understanding of things, and
make changes and so on. Butthere's always a limit where the
(47:57):
machine makes a mistake, and theonly the machine will never tell
itself that it made a mistake,right? A human being has to be
able to assess that and problemsolve, have a framework. Think
about, oh, well, how do thesystems put, you know, fit
together and so on. And we'veseen that. For example, we did
some work in the automotiveindustry in Latin America, and
(48:19):
we saw that this, you know, whatwas transformative there? As,
for example, new, moretechnically advanced models of
cars came in. And by the way,when you need to increase the
volume, was that problems gotmore complex. You needed to
bring managers closer to theproblems, and you needed to
(48:39):
train even lower level workersin these traditionally
managerial tasks of problemsolving, you know, tacit
knowledge and these types ofthings that we that we talk
about. And so then, if I zoomback out, this is not
disconnected from the macrotrends that models tend to
(49:01):
predict, which is that, overtime, you know what actually
happens as technology comes inis that it, on average, doesn't
replace labor labor. It augmentslabor in the sense that it makes
it more productive. Now it mightmean that you need less labor.
But the thing that and so I'mgetting very economics professor
(49:24):
about this, but the thing that Ithink people tend to
underestimate is that everythingthe potential to produce shifts
out. Things get cheaper. Peoplebuy more. You know, it's not,
there's still 2000 people thattouch almost every car that's
(49:44):
assembled. That's not 50,000people, but it's not 20, you
know, it's, it's, it's a quite alot of people involved, and
they're more skilled than theywere before. But what does that
mean? That means we have highertech. Cars than we have before,
and we have every household hastwo cars instead of one and a
(50:06):
half cars. Now, you know. Sowhat I mean to say is that
here's an example of even, forexample, fast food. So a study I
haven't yet completed yet, butwe're looking to kind of study
the introduction of these, justthe ordering kiosks, check out
kiosks in a fast food setting.
(50:29):
My hypothesis because I've seensimilar types of things in other
settings, and the reason why I'mexcited to do this analysis and
write this paper is that myhypothesis is that, yes, you
will need less cashiers.
Obviously, I've got a machine todo that now, to take orders, and
we actually saw in other paperswe've written in the space that
(50:50):
how fast the cashier takes thoseorders and how fast the orders
are filled is certainly abottleneck. But I foresee just
as a stylized story, that you'llneed less cashiers, but now that
the machine takes orders sofast, you're going to need to
evolve how you operate in thekitchen and how you deliver
(51:12):
orders. You're going to need,potentially, just every single
one of those cashiers might nowneed to be moved to a different
bottleneck that has been createdbecause the machine made one
part really efficient, right?
But the machine can't doeverything. We're always taking
(51:33):
one step. We're adding a machinehere, and it's making this part
more efficient, but that meansthat the other parts have to
catch up, and so we either haveto skill those people to catch
up, or we have to add people andskill them potentially to be
able to keep up. You know, Idon't know how that generalizes.
(51:53):
I'm not going to comment on thatmore broadly, but I have seen it
time and time again, and I thinkthat that's, that's where the
study of soft skills, that'swhere the study of even the
theories behind training. Wehave a series of papers now
thinking about whether, in whatcontext should you have
specialized workers? In whatcontext should you have
(52:15):
generalized workers, the classicnotion of, you know, comparative
advantage in economics withoutany other details. You know, is
very clear, like, you shouldspecialize, do one thing and do
it really well, and then Ishould have another worker that,
you know, specialize and doesthe other thing, does really
well. But that breaks down in aworld where there's chaos, you
(52:37):
know, where I don't know whichthing I'm going to need to do
more of, you know, then Iactually need to have people who
can generalize a bit. And amachine is the stylized
specialist, you know, I can't,it. Can't dynamically adapt to
the to the fact that I need tomake more fries, or whatever,
the fact that, you know, thesoccer games there, and so, you
(52:59):
know, the dinner rush is goingto come two hours earlier. You
know these types of things,that's a stylized example, but
my point is, human beings aremuch more adaptable, and so as
this kind of need for a morebroadly skilled worker comes
about, I think that's wherewe're going to see another
opportunity for workers to justbe more important as a counter
(53:24):
effect.
Scott Allender (53:26):
Well, Anant how can people get involved in the
work that you're doing, orconnect with you, to have you
come in and work with theirorganization?
Anant Nyshadham (53:35):
Yeah, thanks. I mean, I think there's quite a
bit of ways. So I think there'sat least, you know, early on, we
were generating all thisevidence only in some sense,
right? So, and that's still thecase. If you have, you know, if
you're running a large business,or a, you know, small
establishment in a largerbusiness, and you're interested
in solving a problem, and you'vegot a persistent problem, and
(53:58):
you think, you know, you want tosolve it, there's, you can
connect with us on our website.
There's, you know, communicatingwith us. You can actually Google
me and find my email, it seemsactually so, you know, we're
here. We're open to years. We'realways interested in new
problems or new settings forstubborn problems we know exist.
So that's always reallyexciting. And to be honest, it
(54:18):
takes progressive leaders. Ittakes courageous leaders who are
willing to say, look, I Theremust be a better way. Tell me
how. And I don't know what itis, let's go figure it out. You
know. So if you're one of thosepeople, please connect. But
we're also in a great positionnow to want to interact with
people who just want to prove orwant to implement what's already
(54:42):
been proven. Experimentation ishard, you know, like, it's
costly if you're a if you're nota large, very competitive firm,
if you're a small establishment,just trying to, like, you know,
make it through, eke out smallmargins. You don't want to come
and have us treat your firm as.
Playground. You know, we'refortunate to let have suburbs.
(55:02):
Let us do that. You just want toknow 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'reready for that. You know, we've
got tools on worker grievances,we've got trainings, we've got
lots of interventions that we'veyou know, now we're going to
look to scale up this payday,this on demand salary tool. So
(55:24):
if any of the problems I'vetalked about here, you know,
resonate with you, and even ifyou don't have the capacity or
interest to experiment, you justwant to see if we've got a
solution that might work foryou. You know, that's one way we
can certainly interact. And thelast I'll say is, you know, if I
go back to the beginning, youasked me, Scott, what my what
our mission is, and like I said,it's, I think, as grand as it
(55:47):
can be, which is to change theway that firms think about
workers as assets, and investingin those workers in every corner
of the world. And so if you justwant, if you just believe in
that, and think it makes sense,and you know, we could use all
the help we can get to spreadthat word. It's going to take,
you know, every leader of everyorganization at every level to
(56:11):
start to believe this, you know.
And so if that's something youthink you can do, just spread
the word. And that's that'stremendously helpful for us.
Scott Allender (56:20):
Excellent. Well, we'll put all that in the show
notes. And thank you for comingon and sharing your insights and
your and your wisdom and andthank you for the work you're
doing in the world. I love it,and I've benefited tremendously
from this conversation. So thankyou.
Anant Nyshadham (56:35):
Me as well.
Thank you so much for theopportunity really fun
conversation. And I love whatyou what you both are doing with
this, so I'm glad I couldcontribute a little bit.
Scott Allender (56:44):
Thank you. Thank you and folks, until next time,
remember the world is evolving.
Are you?
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