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One N.A. Member FDIC. Hey

0:59

there, it's Stephen Dubner.

1:04

This week we wanted to share an

1:06

episode from our archives. That's one of

1:08

our most popular episodes. Perhaps because it

1:12

talks about a problem that many of us

1:14

have had. It's called, Why Are There So

1:16

Many Bad Bosses? I hope

1:18

you enjoy it. And if by chance you

1:20

have already listened to this episode, do stick

1:23

around to the end for a

1:25

career update from this person. My

1:28

name's Katie Johnson and I'm a

1:30

data scientist. Johnson is 33 years

1:33

old and lives in England. She grew

1:35

up near Bristol, went to university in

1:37

Birmingham and held a series of increasingly

1:39

impressive jobs at a series of companies.

1:42

These were all what are known as

1:44

IC jobs. IC standing for individual contributor,

1:47

which means what? It is

1:49

someone who makes as

1:52

opposed to managing people who make.

1:54

Johnson Loved being an IC.

1:56

She Loved analyzing data and.

2:00

The was really good at a job. But

2:02

after while she thought it might be

2:04

nice to become a boss. Yeah,

2:06

I wanted to manage more and more people.

2:08

And you wanted to manage more people? Because

2:11

why? You are just power hungry like the

2:13

rest of us? I think that's a couple

2:15

of reasons. Said a fast is that I

2:17

wanted to start getting more autonomy as a

2:19

while I was left on I would be

2:21

working on stuff in my eyes the bow

2:23

and think this isn't the most important thing

2:25

and I thought that is I became the

2:27

leader of the team than I. Would get

2:29

to pick why wet on. Okay,

2:32

that seems sensible. The other

2:34

reason was to have more impact at

2:36

the company's I was working at. Seated

2:38

described this as having a seat at

2:40

the table. Also. Sensible.

2:43

I guess the final reason is. That we all kind

2:45

is not everyone I guess. but I was

2:47

included in this sub at sunset that. Being.

2:50

Most successful mean being more senior

2:52

and so in order to not

2:54

necessarily so others the definitely myself,

2:56

the I had achieved and become

2:58

successful. I needed to keep moving

3:00

upwards within a company. Johnson's.

3:03

Father in his own career had seen

3:05

things differently. Save. My dad has

3:07

been a network engineer he recently retired that

3:09

he's done that for his whole career and

3:11

he had absolutely no aspiration to. Become the

3:13

manager and like why would I want to do that.

3:16

With. Katie Johnson did want to become

3:18

a manager and several firms were willing

3:20

to make her one. She.

3:22

Took the most appealing offer. Add.

3:25

A software from that helps companies

3:27

acquire new customers. And I

3:29

was sent on some management training and to

3:31

do what can only be described as a

3:34

very long personality tests and the idea was

3:36

to tell me what I was good at

3:38

being good at. And. What was

3:40

see particularly good at. Critical. Thinking

3:43

attention to detail, courage,

3:45

oldies entire know think

3:47

he had characteristics. You.

3:49

Can see why Tv Johnson would seem to

3:51

be a great boss. For. New

3:53

job title was Head of Data and

3:56

Analytics. She had roughly ten people reporting

3:58

to her. The. The Ocean

4:00

game with more money, more prestige,

4:02

more leverage to set the agenda.

4:05

Also. However, More. Meetings.

4:08

Oh so many meetings like compared to

4:10

being a data scientist I'd be the

4:12

hots up our meeting in the morning

4:14

and then I just be breaks to

4:17

do coding and thinking and making stuff

4:19

up a high was a meeting I

4:21

think she's days. I used to be

4:23

at me things like seven hours. No.

4:25

Offense, But did you not see that coming? Know.

4:29

I really didn't I thought it would just be

4:31

like my normal decide to stop with a few

4:33

one. To ones on the side that would

4:35

they take advantage of the you're talking about

4:37

the where he didn't get into planes with

4:39

my team is more like the meetings like

4:41

an hour's coffee with someone to try. And

4:44

south about a working relationship with that seems

4:46

times that by like five or ten of

4:48

a team. It's just draining. Keep.

4:53

In mind, this is happening during the

4:56

Pandemic shut down, so all these meetings

4:58

were virtual. And is drained as

5:00

Johnson was from all those meetings See

5:02

was getting good reviews as a manager.

5:04

yes people would tell me what a

5:06

great so by with Tanks I was

5:09

coming across. well. But see

5:11

bound that being a boss made

5:13

her. Miserable. Haven't

5:15

finished my day and my studied spoken to

5:17

the. Living rains blanket over my

5:19

head and cry because I was

5:22

in So much paid I bought,

5:24

I was. In

5:26

retrospect, Tv Johnson had playing

5:28

the aired in wanting to become a

5:30

boss. But it also felt

5:32

that management was the only sensible way

5:34

to advance her career. And if you

5:37

look at how most firms and institutions

5:39

around the world operate. You. Have to

5:41

agree with her. The question

5:43

is, does this standard operating

5:45

procedure produce good bosses? Were

5:47

bad boss or even horrible

5:49

Was. a horrible boss

5:51

is a familiar caricature we

5:54

all know the stereotypes screamer

5:56

see this the idea steeler

5:58

the passive aggressiveness These

6:01

are some of our most enduring characters

6:03

in film. You remember Blake

6:05

from Glengarry Glen Ross, played by Alec

6:07

Baldwin? Put that

6:09

coffee down. Coffee's

6:12

for clothes is on. Do

6:15

call yourself a salesman, you son of a ... Or

6:18

in the film Office Space, when Peter is

6:20

trying to escape the office on Friday

6:22

afternoon and he gets snagged by the boss.

6:25

Hello, Peter. What's

6:28

happening? Um, I'm

6:31

gonna need you to go ahead and

6:33

come in tomorrow. So

6:35

if you could be here

6:38

around 9, that would be

6:40

great. Mmkay? Oh,

6:42

oh, and I almost forgot. I'm

6:46

also gonna need you to go ahead and come in on Sunday,

6:49

too. Then

6:51

there's Miranda Priestley, played by Meryl

6:54

Streep, in The Devil Wears Prada. You

6:56

have no style or sense of fashion?

7:00

Well, um, I think that

7:02

depends on what you're ... No,

7:04

no. That wasn't

7:06

a question. The Horrible Boss

7:09

motif is so attractive that the

7:11

director Seth Gordon made a film

7:13

called Horrible Bosses. Yeah, we gotta

7:15

trim some of the fat around here. Trim the ... What

7:17

do you mean by trim the fat? I want you to fire

7:19

the fat, people. Truly

7:23

horrible bosses do occasionally turn up

7:25

in real life, especially in Hollywood

7:28

itself. The producer Scott Rudin, for

7:30

instance, has been accused of years'

7:32

worth of alleged abuses, like

7:35

smashing an assistant's hand with a

7:37

computer model. But

7:39

even in Hollywood these days, and especially

7:41

in more normal industries, this

7:43

sort of grotesquely is harder to

7:45

get away with. Bosses

7:48

who are outright monsters are more

7:50

likely to lose their jobs. But

7:53

how much attention are we paying

7:55

to the more common type of

7:57

bad boss? Someone who's simply incompetent

7:59

or overstating? stretched, or even just

8:01

miserable being the boss, like Katie

8:03

Johnson was. Do we even know

8:06

how many bad bosses are out there? The

8:09

more you dig, the more you learn that

8:11

the science of boss behavior is not very

8:13

scientific. One Gallup poll shows

8:16

that roughly 50% of American employees have,

8:18

at some point in their career, left

8:20

a job because of a bad boss.

8:23

But an employee might have 10 or 20 bosses

8:26

over a career, so maybe that

8:28

number isn't so bad. A survey

8:31

of European employees found that only 13%

8:33

rated their current boss as bad. So

8:38

maybe the Hollywood caricature is way

8:40

off. Still, considering that

8:42

nearly all of us will at some point

8:44

in our lives have a boss or be

8:47

one, we thought there

8:49

might be some boss questions worth asking.

8:51

And so, today on Freakonomics Radio, when

8:54

a boss is a bad boss, have

8:56

you ever wondered why? There's

8:59

no reason to believe that a great salesperson

9:01

will be a great manager. And

9:03

yet this kind of promotion happens all the time.

9:06

Why is that? There are two ways to

9:08

motivate people. We can pay them a whole lot more, or

9:10

we can give them an opportunity for promotion. Today,

9:14

on the show, why good employees

9:16

become bad bosses and

9:18

whether that will ever change. Spoiler

9:21

alert, probably not. This

9:36

is Freakonomics Radio,

9:39

a podcast that explores the hidden

9:41

side of everything with

9:43

your host, Stephen Guevner. One

9:55

of the reasons I became a writer years ago

9:57

is because I Didn't particularly like.

10:00

Having a boss like eighty Johnson,

10:02

I prefer to set my own

10:04

agenda my own pace. I also

10:07

really like working alone. Also,

10:09

like Katie Johnson, I am not

10:11

particularly fond of meetings, so I

10:14

wouldn't be a very good boss

10:16

either. Fortunately, at Freakonomics Radio, there

10:18

are a couple other people who

10:20

do all the boss east, leaving

10:22

me pretty much free to do

10:25

this what we're doing right now,

10:27

asking questions. Trying to find

10:29

answers. So here's a question. I've

10:31

always been curious about how important

10:33

our bosses anyway, I

10:35

don't mean Ceos the ultimate Boss

10:38

if you're interested in that. We

10:40

once did a series called the

10:42

Secret Life of Ceos. Today we're

10:44

just talking about your standard issue

10:46

middle manager. Do they really matter?

10:51

Yes, broadly speaking, managers

10:53

matter, bosses, matter for

10:55

outcomes. That steve to

10:58

Dallas. He is an Economics professor

11:00

at U C Berkeley Class School

11:02

of Business training ground for future

11:04

bosses. Management is not

11:07

something that to delis himself

11:09

aspires to. Tell. Me: how close

11:11

you are two administrations I know how far

11:13

away to be from you. But. He

11:15

has spent time while on sabbatical working

11:17

as a boss at some well known

11:19

for hims. When. I was at

11:22

ebay and Amazon. I managed teams

11:24

and I enjoyed it very much.

11:26

How do you assess yourself as

11:28

a manager in that realm? I'm

11:30

blushing so. Because

11:33

you're the best ever! Provide

11:35

some pretty good so I'm feeling

11:38

a little uncomfortable. Your positive self

11:40

assessment is based on direct feedback

11:42

or just a general warm glow

11:45

feeling at ebay and Amazon. the

11:47

feedback was actually form of Truth

11:49

Surveys surveys that is. With questions

11:52

like. On. A scale of one to

11:54

five? How much do you agree with? the following

11:56

statement. My. boss generates a positive

11:58

attitude in the team Or,

12:00

my boss is someone I can trust.

12:03

Or, my boss provides continuous

12:05

coaching and guidance on how I

12:08

can improve my performance. These

12:10

surveys led Steve Tadellis to ask

12:12

his own bigger questions about bosses.

12:15

For instance, Does it really matter?

12:17

Do these measures of manager

12:20

skills or characteristics, do

12:23

they really have any value for the firm? Is

12:25

there some way in which managers

12:27

who score higher on these

12:29

surveys are actually contributing more? These

12:34

are eternal questions in the field

12:36

known as personnel economics. You

12:39

could ask the same questions about any manager.

12:41

The head coach of a football

12:44

team, the chairperson of your homeowners

12:46

association, the president of the

12:48

United States. But as

12:50

I mentioned earlier, the academic literature on

12:52

the impact of bosses is not particularly

12:55

advanced. You can see

12:57

why, if you think about it. There

12:59

are so many variables in the relationship between

13:01

a boss and their employees that it can

13:03

be hard to pinpoint the effects of the

13:05

boss. This is why

13:08

most research focuses on one

13:10

single quantifiable metric, productivity.

13:13

For example, there is a

13:16

paper by the late, wonderful

13:18

economist Eddie Lazear, Catherine

13:20

Shaw and Chris Stanton, where

13:23

they show that there is

13:25

variation in output of

13:28

employees based on the managers that

13:30

are in charge of them. That

13:33

paper from 2015 analyzed data

13:35

from a single firm that the researchers

13:37

were not allowed to identify, but it

13:39

appears to be something like a call

13:41

center. The analysis looked

13:43

at what happened when a worker moved

13:45

from what the researchers identified as an

13:47

average boss to a high quality boss.

13:51

Such a move, they found, increased productivity

13:53

by as much as 50%. If

13:57

this were a call center and a given

13:59

worker handled 100 calls

14:01

per shift under an average boss,

14:03

an excellent boss could boost that

14:06

to 150 calls. So at

14:09

least in this type of setting, a

14:11

quote, good boss is doing something right,

14:13

but the data couldn't say what. Steve

14:16

Tadellis wanted to learn more. So

14:18

he teamed up with Mitchell Hoffman,

14:20

an economist at the University of

14:22

Toronto's Rotman School of Management, to

14:24

write a research paper. I

14:26

have access to interesting data and

14:29

people in this company that will

14:31

have to be unnamed, because

14:33

when it comes to personnel data,

14:35

companies are very hesitant.

14:38

Tadellis would only say that

14:41

this firm did high-tech, knowledge-based

14:43

work. Maybe, given his

14:45

history, you might picture a firm

14:47

like an eBay or an Amazon.

14:50

In any case, he is looking at

14:52

a very different type of work than

14:54

the earlier research with its narrow measure

14:56

of productivity. What we're doing

14:59

is opening the hood up a little bit. And

15:01

what sort of data did they have access to? We

15:04

have data that allows us to

15:06

measure the impact of a particular

15:08

manager skill that we're calling people

15:10

management skills, as opposed

15:12

to just do managers matter. People

15:15

management skills, meaning the sort of

15:17

things you find on those employee

15:19

feedback surveys, how well

15:22

the manager coaches and communicates, how

15:24

trustworthy they are. So

15:26

that's the boss data. On the

15:28

employee side, Tadellis and Hoffman had

15:30

a lot of concrete data, subjective

15:33

performance scores, as well as how often

15:35

a given employee was promoted or given

15:37

a raise, the number of patents

15:39

they filed, and whether they

15:41

stayed at the firm or left. In

15:44

these high-tech, knowledge-based companies,

15:47

retention is a very,

15:49

very important focus

15:51

because getting these

15:54

high-skilled workers is not easy, and

15:57

there's a lot of competition. And when you

15:59

lose a an employee, especially an

16:01

employee that's very valuable, then

16:04

it could take months

16:06

to replace them. So

16:08

Tadellis and Hoffman set about to sort

16:10

through all this data to look for

16:12

any causal relationships between the rating of

16:15

a given manager and the

16:17

various outcomes of the employees working under

16:19

them. What did they find? For

16:22

the most part, it was a big bag of

16:24

nothing. We didn't find

16:26

that the ratings of the

16:28

managers seemed to impact

16:31

the subjective performance of their employees,

16:33

their income, their promotions, or

16:35

patent applications in a meaningful way. That's

16:38

right. On all those employee

16:41

outcomes, performance, earnings,

16:43

patents, it just didn't

16:46

seem to matter whether the manager was

16:48

highly rated or poorly rated. But

16:51

there was one other outcome to look

16:53

at, employee retention.

16:56

Bingo. Tadellis and Hoffman looked

16:58

at employees at this one firm who moved

17:00

from a manager with a poor rating to

17:02

one with a high rating. That's

17:05

associated with an attrition drop of about 60%. That

17:08

is huge. And within that

17:11

huge effect was an important nuance.

17:13

What we see then is

17:16

that managers help retain

17:18

better employees more than worse employees,

17:20

which shows that the impact of

17:23

being a better manager is

17:25

strongest where it matters the most. So

17:29

a good boss seems capable of

17:31

keeping the best employees happy and

17:34

presumably productive. Conversely,

17:36

a bad boss might drive

17:38

away the best employees. The

17:41

Tadellis-Hoffmann paper was published in 2021

17:44

in the Journal of Political Economy,

17:46

one of the best econ journals.

17:49

So okay, the economics literature

17:51

on bosses and management just got a

17:53

little bit deeper. But

17:55

remember, employee retention was the only outcome

17:57

where it seemed to matter whether a

18:00

boss was good or bad. And

18:02

if you ask Steve Tadellis a

18:04

more fundamental question, like what does

18:07

a good boss actually do to

18:09

instill this loyalty? This is

18:11

where I have to take

18:13

a step back and say that there

18:15

are certain things that may be outside

18:17

the scope of what economists should be

18:20

dealing with. If you were to make

18:22

a list of things that you would like to measure were

18:24

it possible given the data, what would some of those things

18:26

be? That's a good question. Something

18:29

that's very hard to measure that

18:32

I believe is important

18:36

is compassion. I guess if

18:38

this is going to be on the radio I might lose

18:40

my economist card. Steve Tadellis is

18:42

not the only economist who's been frustrated

18:44

by the lack of evidence for what

18:46

makes a good boss good. Maybe

18:49

compassion is as important as

18:51

he suspects, but we just

18:53

don't have any large-scale empirical

18:56

evidence yet. The

18:58

Stanford economist Nicholas Bloom has

19:00

been studying leadership and management for

19:02

years. And yet, no

19:05

one could really give us a straight answer

19:07

on what to find a good or bad

19:09

leader. You look at the data and there's

19:11

ten different recipes for success. Maybe they each

19:13

work for a particular case study, but I've

19:15

still 20 years later struggled to find anything

19:18

that's the secret recipe beyond saying, sure, there are

19:20

some people the better than others, but it's damn

19:22

hard to tell what it is. This

19:25

has not stopped leadership gurus from

19:27

promoting their pet theories. As

19:30

Bloom puts it, there is a

19:33

ton of BS around this from

19:35

airport bookstore pulp fiction. And

19:38

here's another reason to question the

19:40

literature on management and bosses. As

19:42

we've been hearing, most of

19:45

the boss data comes from employee

19:47

surveys. Have you ever

19:49

taken a survey that rates your manager?

19:52

If so, were you told it

19:54

was anonymous? Did

19:56

you believe it was anonymous? The

20:00

answers Objective. Or.

20:03

Did you may be think? Well, my boss

20:05

thinks I'm good at my job, so I'm

20:07

going to say they're good at there's or

20:09

vice versa. I don't think my boss

20:12

likes me, so I'm sure I can give them a

20:14

good reading. As we

20:16

have said before on the show,

20:18

survey data can be the lowest

20:20

form of data Steer again is

20:22

Steve to Dallas. I'm. Sure,

20:24

you know that economists are very

20:27

weary about using surveys, and economists

20:29

believe in what we call a

20:31

revealed preference approach. Meaning how you

20:33

behave is telling me a lot

20:36

more about you than what you

20:38

say about yourself. Just how

20:40

big is the gap between what people

20:42

say. In how they behave. Over.

20:45

The years I've heard many economists

20:47

good many examples of this gap.

20:50

Steve. To Dallas. Example is my

20:52

all time favorite. There. Is

20:54

a lot of discussion about privacy and

20:56

privacy regulations these days and you hear

20:58

a lot of people saying how their

21:00

privacy is important to them and then

21:02

you turn of them and say here's

21:04

a Snickers Bar could I have your

21:07

mother's maiden name and they say yes,

21:09

Mister Fell It's a little bit confusing

21:11

when you tell me that you really

21:13

care about privacy and then you just

21:15

scroll down on every app you downloads

21:17

and click Yes, Yes, yes, that's doesn't

21:19

tell me that you really care about

21:21

privacy. So the same is true for.

21:23

Many other types of

21:25

behavior. So

21:28

let's keep in mind that much

21:30

of what we have been told

21:32

in the past about good bosses

21:34

and bad bosses is not exactly

21:36

evidence based. Researchers like to Delis

21:38

and Hoffman and Bloom have been

21:41

chipping away at the black box

21:43

of boss behavior, but we've got

21:45

a long way to go. This

21:47

means we need to keep looking

21:49

for good data and asking good

21:51

questions. So coming up after the

21:53

break, who becomes a boss and

21:55

why It's so many people think

21:57

the boss. selection process is so

21:59

stupid, why do firms keep

22:01

doing it? And whatever

22:04

happened to Katie Johnson? I

22:06

get to the end of the day and the last thing I want

22:08

to do is talk to someone else. I'm

22:10

Stephen Dubner. This is Freakonomics Radio.

22:13

And remember, you can get our

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24:01

What I mean is why a given

24:03

employee will rise from the ranks

24:05

to become a manager. Here's

24:08

someone who's been thinking about that a lot. My

24:11

name is Kelly Hsu. I'm a professor

24:13

of finance at the Yale School of

24:15

Management. Kelly Hsu, along

24:18

with Alan Benson and Danielle

24:20

Lee, published a paper in

24:22

the Quarterly Journal of Economics,

24:24

another top journal, called Promotions

24:26

and the Peter Principle. So

24:29

the Peter Principle is a very

24:31

funny and popular management book written

24:33

by Lawrence J. Peter, and his

24:35

book offers an explanation for why

24:38

we might see incompetent bosses everywhere.

24:41

Incompetent bosses everywhere? Okay,

24:44

I'm listening. What is this

24:46

explanation? Let's go back to

24:48

Lawrence J. Peter himself. This is from

24:51

a 1973 documentary. The

24:55

Peter Principle states very simply that

24:57

in any hierarchy, an employee tends

25:00

to rise to his level of incompetence.

25:04

I'm sorry. As many times as I've heard that

25:07

phrase, I still laugh at it just because it

25:10

sounds like it's going to be not

25:12

irreverent, and then it turns immediately irreverent,

25:14

which makes me chuckle. Oh, exactly. I

25:16

think it's a funny idea, but it

25:18

also rings true, and it's funny in

25:20

a kind of unpleasant way, because it

25:22

reminds people how much they dislike their

25:24

bosses. Peter was a

25:27

Canadian education scholar. He

25:29

used his daily observations to form

25:31

a theory about job promotions.

25:34

I saw that very often a competent

25:36

individual was promoted to something

25:39

he couldn't do. I

25:41

saw a competent mechanic where

25:44

I used to take my car. He

25:46

was terrific. He was very responsible,

25:49

very precise, knew exactly what he was

25:51

doing, so they made him form it.

25:53

Now, he's no longer fixing

25:55

cars, and he's trying to manage other mechanics,

25:58

and he's very incompetent. The more

26:01

Peter looked around, the more he

26:03

saw people who were good at

26:05

their jobs, routinely stumbling into bigger

26:07

jobs they weren't good at. In

26:10

any organization where

26:13

competence is essentially

26:15

eligibility for promotion and incompetence

26:17

is a bar to promotion,

26:20

people will rise to the level of incompetence

26:23

and tend to stay there. The

26:25

book he wrote with Raymond Hull

26:27

was called The Peter Principle, Why

26:30

Things Always Go Wrong. It

26:32

wound up selling millions of copies.

26:34

The book was meant to satirize

26:37

corporate strategy. Nevertheless, a

26:39

variety of big firms tried to

26:41

recruit Peter to become their management

26:43

guru. He declined saying

26:46

that he didn't wish to rise

26:48

to his own level of incompetence.

26:51

Kelly Shue again. His idea

26:53

is that firms and organizations tend to

26:55

promote people based upon their performance so

26:58

far. What that means is a

27:00

worker who is good at her job will be

27:02

quickly promoted to a new job role, which might

27:04

require different set of skills. If

27:07

she's good at that new role, she's going

27:09

to be promoted again until she reaches a

27:11

position where she's actually a bad match for

27:13

that new job role and then she will

27:15

no longer be promoted. On the one

27:17

hand, it would seem to make perfect sense that you

27:19

promote someone who's good at their job. You're going to

27:22

promote the bad workers. On the

27:24

other hand, managing is not the same

27:26

as doing. There's no reason

27:28

to believe that a great salesperson who knows how

27:31

to negotiate deals will be a great manager. That

27:34

again is the Berkeley economist Steve

27:36

Tedellis. I look here in

27:38

my company, Berkeley, great researchers often make for

27:40

lousy department chairs. Great engineers

27:43

often make for lousy engineering managers. But

27:46

here's the thing about the Peter Principle. Even

27:49

though the theory had been around for half

27:51

a century, no one had

27:53

ever checked with real data from real

27:55

companies Whether Lawrence Peter

27:58

was right. A

28:00

few observations about car mechanic

28:02

or an academic research or

28:05

turned department chair. Those do

28:07

not constitute empirical proof. Especially.

28:10

In the realm of management and

28:12

all that airport bookstore pulp fiction,

28:14

this is where Kelly, Sue and

28:16

her coauthors come in. They. Wanted

28:19

to see if the Peter Principle

28:21

actually exists. In, it's so what

28:23

should be done about it. First.

28:25

Step. Get. Hold of some

28:28

data. We. Got our data from

28:30

a company that offers sale of

28:32

performance. Management Software and Services.

28:35

Shoe. Can't tell us the name of

28:38

the company but pictures something like

28:40

salesforce. A typical kind of

28:42

our data provider is a from that

28:44

employs. Business to business sales

28:46

workers. And that clients

28:48

from would implode the sales

28:50

numbers and the whole organizational

28:52

structure into. A. Software Program and

28:55

what we're doing is we're studying the

28:57

data that his client for hims uploaded

28:59

into the. Software. Program: How many firms

29:01

and how many workers. We. See

29:03

data for about forty thousand business

29:05

to business fails workers at over

29:08

one hundred and thirty different Us

29:10

based firms. And. How many

29:12

of those were in Managerial Rose? Five

29:14

thousand. Managers. And about

29:17

fifteen hundred promotion events.

29:19

So in terms of empirical studies in

29:21

your realm, this is considered a pretty

29:24

large and robust dataset. Or would you

29:26

like to to be even bigger than

29:28

that? I would. Always prefer a bitter

29:30

dataset. Vote for this type of question

29:32

of. Very large and comprehensive

29:35

dataset. So these are sales

29:37

workers and sales managers. What

29:40

makes sales a good business function

29:42

to study? One. Is important

29:44

a study sales. Workers. Has almost

29:46

ten percent of the Us labor force.

29:49

Or somehow involved in the sales

29:51

function. The other benefit is that

29:53

we have a very good measure.

29:55

Of their performance. So. He

29:57

can test or the stronger performers.

30:00

more likely to be promoted. So

30:02

that makes a lot of sense from

30:04

your perspective as the scholar. From my

30:06

perspective as someone who's not in sales,

30:09

I would think, well, your findings may

30:11

not translate very well that in a

30:13

field like journalism or in healthcare or

30:15

in many other fields, the

30:18

measurables aren't nearly as measurable as they

30:20

are in sales. So how generalizable do

30:22

you think your findings are? I

30:25

believe it's likely to apply to

30:27

other settings where the

30:29

skills required to succeed at one

30:31

level differ from skills required to succeed

30:33

in the next level. So

30:36

some examples are science,

30:38

manufacturing, academia,

30:41

entrepreneurship. Can

30:43

you think of industries or sectors

30:46

where this problem wouldn't apply? It's

30:49

actually hard for me to think of a setting in which

30:51

this problem wouldn't apply at all. I've

30:53

also seen it in the context of

30:55

government structures. A good

30:57

example is actually the ancient

31:00

Chinese imperial examination system. It's

31:03

famous for being a meritocracy even thousands

31:05

of years ago. So you

31:07

would take a test and the top

31:09

scores on the test would become administrators

31:11

within the government bureaucracy. But

31:14

their problem was they would make

31:16

the test based upon familiarity with

31:18

classical poetry and the people who

31:20

are best at that test would

31:22

then become tax collectors, which is a different

31:24

skill set. But ancient

31:27

Chinese poetry was an incredibly

31:29

rich and diverse body of literature, yes?

31:31

I could imagine how a mastery

31:34

or even a deep appreciation of that

31:36

could theoretically apply across

31:38

a number of skills. Theoretically,

31:41

yes. You sound

31:44

unconvinced. And to be fair, I

31:46

do not have the historical data to prove

31:48

that being the best

31:50

at classical poetry means

31:53

you're not the best at tax collection. Since

31:55

you don't have that data, let's look

31:57

at, say, modern US politics. would

32:00

you assess the relationship between

32:02

a person who's electable

32:04

and a person who will govern

32:06

well? Oh, that is

32:08

a very good point. So someone who

32:11

is electable might be very charismatic, very

32:13

good at public speaking, whereas

32:15

the actual function once someone

32:18

has been elected might involve

32:20

being good at dealmaking, back

32:23

office politics, or understanding the actual

32:25

details of the policies that they're

32:28

passing. Do you know anything about

32:30

that question empirically? I'm drawing a blank,

32:32

but you really did raise a very

32:34

good research idea. Maybe I will look into

32:36

this. We've been thinking about

32:38

settings where this type of problem might

32:41

apply for a long time, but somehow

32:43

I'd never thought about the government or

32:45

elected official example you just raised, but

32:47

it seems like spot on for having

32:49

potential as a problem. Okay,

32:52

before I hijacked this conversation with Kelly

32:54

Shue to talk about politics and ancient

32:56

Chinese poetry, we were talking

32:58

about her research paper that tried to

33:01

identify the Peter principle in the wild.

33:04

As Shue told us, she had

33:06

performance data on roughly 40,000 sales

33:09

workers and around 130 companies. The

33:12

next step was to confirm

33:14

that companies indeed use an

33:17

employee's job performance as a

33:19

trigger for promotion. The

33:21

answer? Yes. We

33:24

find that doubling in worker

33:26

sales corresponds to a 30%

33:29

increase in their probability of being

33:31

promoted. Another way to

33:34

look at it is if someone is

33:36

the top sales worker within their team

33:38

of five or six people, then

33:40

that top sales worker has about tripled

33:43

the probability of being promoted

33:46

relative to the average sales worker. Now

33:48

is that alone evidence of the

33:51

Peter principle? No,

33:53

just to promote based upon past

33:55

performance isn't necessarily a Peter principle

33:57

problem because it could be that

34:00

the best salespeople really are the best

34:02

managers of salespeople. In that case, you

34:04

want to promote the best salespeople. Okay.

34:07

So the next step, I guess, is

34:09

seeing whether the best salespeople indeed do

34:11

become the best managers. How do you

34:14

do that? So first, we're

34:16

going to measure the quality of each

34:18

manager. Managers in

34:20

our data are no longer directly

34:22

involved in sales. Their job as

34:24

a manager is to coordinate and

34:27

facilitate the sales of their subordinates.

34:29

And presumably, those subordinates are people they worked

34:31

with side by side and maybe competed against

34:33

just the week before they were promoted. Is

34:36

that the case often? We

34:38

actually see for the most part,

34:40

people when they're promoted, they're rotated

34:42

to a different team, possibly because

34:45

the firm overall is exactly afraid

34:47

of those internal team dynamics that

34:49

you've just described. So

34:52

we don't want to call someone a good manager

34:54

just because her team sells a lot, because

34:56

we're worried that maybe she was lucky and she

34:58

was assigned to great sales workers.

35:01

And those sales workers could have been

35:03

great regardless of her managerial input. To

35:06

get around that problem, we're going to

35:08

measure manager quality as the

35:10

manager's value added to her

35:12

subordinate sales. If my

35:14

subordinates sell more when they work under

35:16

me than when they worked under other

35:19

managers, then I would

35:21

be considered a high quality manager. So

35:27

here's the key question Kelly Shue was asking.

35:30

Does being a good salesperson make you

35:33

a good manager of other salespeople? Here's

35:36

what she found. The manager with

35:38

double, the pre-promotion sales

35:41

as another manager leads

35:43

to about a 6% decline

35:45

in subordinate sales. Oh my

35:47

goodness. Yes, what we find

35:49

is that among promoted managers, those

35:52

with low sales prior

35:54

to their promotion, they are actually

35:56

better at managing their subordinates.

36:00

Let me say that again. Oh my

36:03

goodness. When these firms

36:05

select people to be managers based

36:07

on their current job performance, they

36:09

are actively making themselves worse off.

36:12

In other words, the Peter principle is

36:14

as real as Lawrence Peter said it was.

36:18

And, I'm editorializing here, it

36:20

would also seem to be

36:22

incredibly stupid. If

36:25

the firm's only goal were to

36:27

have the best possible managers, then

36:30

the firm could, by putting more

36:32

weight on collaboration experience and less

36:35

weight on sales numbers, the

36:37

firm could promote better managers and

36:39

raise overall firm sales numbers by

36:41

about 30%. That's

36:44

assuming that collaboration experience is in

36:46

fact more important for a manager

36:48

than just high sales numbers. Still,

36:51

a 30% increase in revenue

36:54

simply by killing off the Peter

36:56

principle? That would seem to be

36:58

a no-brainer. So, does

37:00

this mean that modern firms simply aren't

37:02

aware of the age-old Peter

37:04

principle? Most firms

37:06

are aware of the Peter principle

37:08

problem, and it's a problem

37:10

that they purposely choose to live with.

37:14

Some evidence we have indicating that

37:17

in situations when the

37:19

firm is trying to select a new manager who is going

37:21

to be in charge of a very large team.

37:24

So, that's a situation in which manager quality

37:26

matters a lot. In

37:28

those situations, firms put less weight

37:31

on a worker's sales numbers, probably

37:33

because they know they're going to end up with a

37:36

bad manager. So, Shue is

37:38

arguing that firms know they will get

37:40

worse managers by simply promoting people who've

37:42

been good at their previous jobs rather

37:44

than people who might actually be good

37:46

managers. And yet, for

37:49

the most part, they continue to do

37:51

it, even though it hurts their profits. Why

37:54

would they do that? Economists

37:56

are always telling us

37:58

that companies are, by

38:01

definition, profit-maximizing machines. Knowingly

38:04

promoting a bad manager does

38:06

not sound very profit-maximizing. So

38:08

are companies just making a mistake? A

38:11

firm having a Peter Principle problem

38:13

doesn't necessarily mean that the firm

38:15

doesn't understand what it's doing or

38:17

is making a mistake. So

38:22

what is going on? After the

38:24

break, Kelly Shue gives us some answers. You don't

38:26

want to brag about your pay on your resume.

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SI Pc. For

42:00

the break, the Yale Finance professor Kelly

42:02

Sue was telling us about a study

42:05

she and her colleagues published about the

42:07

Peter Principle that the idea that a

42:09

good employee will be promoted to bigger

42:11

and bigger jobs until they get to

42:14

a job they're not been and then

42:16

they tend to stay there for for

42:18

years. The Peter principle was just a

42:21

theory Kelly. She wanted to see if

42:23

it's real using data on thousands of

42:25

promotions. She did find that when top

42:28

performing sales people were promoted into. Management:

42:30

The sales performance on the teams

42:32

to be managed declined. In other

42:34

words, just because somebody is good

42:36

at their job doesn't mean they'll

42:38

be good at managing people doing

42:40

that same job. Shoe also found

42:43

evidence that firms know that the

42:45

best sales people make bad managers

42:47

and they choose to promote them

42:49

anyway. So what is happening? What

42:52

we believe it's happening is the From

42:54

is doing. It's best to motivate workers

42:56

and they face a trade off. Up.

42:59

This is where it gets really interesting.

43:01

Promoting. Based upon past

43:03

performance is very motivating.

43:06

Co. Workers so it's a very strong

43:08

incentive system we can also work

43:10

out is in some ways cheaper

43:12

than offering. Really strong pay for

43:14

performance. For. Their to way

43:16

to motivate people, read and pay them a

43:19

whole lot more. or we can give them

43:21

an opportunity for promotion which they might value

43:23

a whole lot because that's something that they

43:25

can put on their resume and it increases

43:28

their status in society. You. Want

43:30

to brag about your pay on your resume? I

43:32

mean the minute you say that it makes

43:34

me think we maybe we should make it

43:36

more acceptable for people to brag about their

43:39

pay because when that be more efficient in

43:41

the end and encourage less promotion of people

43:43

who are going to be bad managers. That's.

43:46

A fantastic idea. I don't know

43:48

of research testing that directly, but

43:51

I do know in other cultures

43:53

there's differences in it being more

43:55

socially acceptable to talk about your

43:57

compensation. Hundred. Hundred. Half

44:00

joking, but. He. Would be interesting

44:02

if there was some metric are bad

44:04

you get saying. I'm. Really

44:06

good at what I do, and I'm

44:09

so good that I've been rewarded a

44:11

lot of raises and plainly I'm very

44:13

valuable to the from and I could

44:15

be a manager if I wanted, but

44:18

I'm better than that. Battered.

44:20

Incredibly ham handed naive way of

44:23

putting it. But is there any

44:25

mechanism in managerial science for that

44:27

kind of. Delineation between

44:30

success on a financial level,

44:32

of success on a managerial

44:34

lemme. There has been some

44:36

interesting attempts in that direction so

44:38

I've heard of many technology focused

44:40

firms especially those in Silicon Valley.

44:42

They see as as problem that

44:44

they have a pool a very

44:47

talented and skilled engineers and those

44:49

engineers may not be the best

44:51

managers of engineers. Many of those

44:53

firms offer something called a dual

44:55

career track where someone ten rise

44:58

in the ranks of being an

45:00

engineer basically having a higher and

45:02

higher title to to start as

45:04

engineer. Than distinguished engineers

45:06

and lifetime distinguish engineer And

45:09

that's away for. The.

45:11

From to recognize someone's contributions in

45:13

a public way without moving them

45:16

over to management. The.

45:20

Berkeley economist Steve to Dallas has

45:23

also noticed this movement. And

45:25

companies like Bb, Google, Amazon,

45:27

Facebooks, there's the of icy

45:29

and were independent contributor and

45:31

you will have people who

45:33

are at the level of

45:35

V P not managing a

45:37

single person because they are

45:39

just gods in their realm

45:41

of engineering, war, coding or

45:43

architecture and and so on

45:45

by distinguishing between I Seized

45:48

and the so called management

45:50

talents The Sun The look

45:52

We are going to promote

45:54

people. In ways that reward

45:56

them for what they're gray at. You're

45:58

not a great manager. Your. I'm going

46:00

to get incentivized by becoming a

46:02

manager. Has that model.

46:05

Trickled out at all of that hi

46:07

tech realm. One area

46:09

where I have seen it

46:11

is in consulting companies were

46:13

you have the kind of

46:16

deep technical talent think of

46:18

phds, etc that will remain

46:20

and be very heavily rewarded

46:22

for the work they do

46:24

and they will not manage

46:26

people. The.

46:30

Fact that Kelly Sue Ann Steve

46:32

to Dallas can identify handful of

46:34

cases were career success is not

46:36

tied to a promotion into management.

46:39

Well. Those are exceptions that prove

46:41

the rule. As. Shoe found

46:44

in her research, the Peter Principle

46:46

is alive and well. As absurd

46:48

as that may seem, it is

46:50

yet another confirmation that management science

46:53

as lovely of phrases that may

46:55

seem, is not yet very scientific.

46:58

Most. From stick with what they've

47:00

always done. When and employees

47:02

good at what they do. You.

47:04

Turn them into a manager to oversee

47:06

other people who do what they used

47:08

to do, even if they are not

47:10

cut out to be a manager. Lights.

47:13

Our Friends Tv Johnson. The.

47:16

English data scientists we met earlier. I.

47:18

Didn't see that there was another past swear

47:20

by I could be director levels but not

47:22

have died a couple. it's I just didn't

47:25

As a see that. Looking back,

47:27

there were some clues that

47:29

Johnson wasn't quite manager material.

47:32

You. Member: During management training, she

47:34

took that personality test. And.

47:37

She told us the areas where sky high

47:39

scores. Critical. Thinking attention

47:41

to detail parades only

47:43

ten entire know. Pinkie

47:45

Pie Characteristics: Well. Those

47:47

were not be only results of this

47:50

test. Things. That I can

47:52

do by struggle with was compassion

47:54

and save the laces. Sit thirty.

47:56

I saw this eighth and I

47:59

was like. Why didn't anyone see

48:01

this to me before I got this

48:03

job? Because this just screams great data

48:06

scientist, not so great manager. But.

48:08

It was too late. She'd. Already been

48:10

made a manager and as you'll recall

48:12

it was not going well. I

48:14

haven't finished my day and my study Spoken to

48:16

the. Living. Room star blank over my

48:19

head and price. Soon.

48:23

That say, we're tapping the scale

48:25

of zero to ten. Where would

48:27

you put your median the satisfaction

48:29

when you were in I see

48:32

it or a maker. When I

48:34

was amazed that I that myself as

48:36

an eighth and a half by actually

48:38

loved why. Did I absolutely love that?

48:40

The only reason I even to ducks one

48:42

point five points is because that was some

48:44

frustration as I mentioned about not being heard

48:47

and not being autonomous. And then where would

48:49

you put it zero to ten when you'd

48:51

become a full on manager? I would

48:53

say I'd put myself more like a four

48:56

or five or six would be a great

48:58

day. Okay, that's your

49:00

personal satisfaction. I do see

49:03

however on linked in a

49:05

review from your manager. He

49:08

writes: Td is a rounded and

49:10

passionate data leader with all the

49:13

qualities required to inspire, manage, and

49:15

be the team. Plus she's got

49:17

brilliant I see skills to boot

49:20

and he notes that you are

49:22

a real unicorn in the data

49:24

analytics feel, so that sounds like

49:27

you were the best manager ever.

49:29

Yes, It's very nice

49:31

of them. It. Is really nice. Did

49:34

he write that before ft decided to quit?

49:36

He wrote the off death. You.

49:40

Heard that right? Tt. Johnson

49:43

quit. That. Management Job. See.

49:46

Quit being a boss entirely.

49:48

She. Went back to working as a

49:50

data scientist at a different from. I

49:53

don't know if you can ever be

49:55

successful at something. You'd and like I

49:57

want to see something the I Love. I'm

50:00

really passionate by because that be only bates Many other

50:02

people are different the I have to love it have

50:04

to be like on a Sunday nights. I can't wait

50:06

to thought my what's mind get that's why with drinks

50:08

and I with. Nasa, Esa as the Us

50:11

gonna. Have thought my management job so

50:13

before you ever became a manager as

50:15

a maker you said your. Average

50:17

satisfaction. Their happiness as around eight and

50:19

a half. When. You became a

50:21

manager. Dropped her with Call It Five. Six.

50:24

And a great day. What Is

50:26

it now? I said the nine and

50:28

a half nine. I'm super happy. Are you

50:31

getting paid less now as the data centers

50:33

Then you are? As a manager, I'm getting

50:35

paid more. How did that happen? I think.

50:37

That are more individual country to both.

50:40

neither paid good money. I think that

50:42

Discs technical specialists route is becoming more

50:44

prominent and more who boarded and people

50:46

do realize that thou are going to

50:48

be a lot people. Who don't want

50:50

to become the manager and have you

50:53

met as a then I believe you

50:55

looked at the Peter Principle paper. Is

50:57

that right? Yeah, did the way the

50:59

Peter Principle is usually described his to

51:01

me almost comical that people rise to

51:03

the level of their incompetence, which I

51:05

find is a bit cruel sounding because

51:07

one could also say that people rise

51:09

to their ceiling of competence right? And

51:11

then maybe they're not as good as

51:13

out of not like they suddenly turn

51:15

into idiots. but I am curious to

51:17

see your thoughts on the notion of

51:19

promotion. Into Management as a reward for

51:21

being good at what you been doing

51:23

all along To. Me, This is where

51:26

the idea of split in a

51:28

daze. Levels of seniority, sales, Maybe

51:30

you don't become the manager that you can become

51:32

a technical expert and you are. Paid and

51:34

rewarded for that's is something that helps

51:36

the incentives. Why would say on that

51:38

Though it's often we have to steal

51:40

career track of of faith in the

51:43

a manager or you can be authentic

51:45

the specialists that even though you might

51:47

get a quote and quote from Mason

51:49

and be paid more, the technical specialists

51:51

still might be excluded from high level

51:53

conversation. Say it's been a manager just

51:55

had this connotation of seniority stuff a

51:57

typical specialist doesn't necessarily. and you still

51:59

might. Beeping

52:04

he hit. The police in the evening

52:06

of regular people would tunnel and that having

52:08

of the avocado people calling what you folks

52:10

have to come with that. I.

52:15

Would think that many people who are

52:17

promoted from some sort of maker to

52:19

some sort of manager that it would

52:21

be hard. To. Step back if for

52:23

no other reason than seems like losses

52:25

status. Yes, it definitely felt like a

52:28

lot of states. As I guess the

52:30

Me: I'm lucky that I don't care

52:32

what people say as much as other

52:34

people are. so. I'm sure that

52:36

was identified him Personality tests as

52:38

well. Yeah, Complete Faith Seven Power.

52:40

Of think people judge he was as

52:42

as interesting because I didn't know anyone

52:45

who likes the job as much as

52:47

I do says the people to look

52:49

upon me and feel sorry for me

52:51

in that sense the I have chosen

52:53

to go backwards and sense of korea

52:55

hierarchy of a pen palling and sense.

52:58

Of what we value as a career and

53:00

you can tell them that if you hadn't

53:02

done this you and be and signified yeah

53:04

well exactly how I got what I wanted

53:06

a with the. Plan all along the as

53:09

good as a long day. Thanks

53:15

to Tv Johnson for sharing her

53:17

boss and back story. Recently she

53:19

became her own boss. Working as

53:21

a freelance data coach. She. Wrote

53:23

to us. I am optimistic that my

53:25

new venture will allow me to grow

53:27

as a data professional, focusing on the

53:30

elements of leadership I value while minimizing

53:32

my exposure to the parts of leadership

53:34

that I did not enjoy. Thanks

53:37

also to Kelly Sue, Steve to

53:39

Dallas, Nick Bloom and all their

53:41

collaborators for trying to make this

53:43

thing We call management science a

53:45

bit more scientific coming up. Next

53:47

time on the So it's time

53:49

to take a look at one

53:51

of our favorite law's the Law

53:54

of Unintended Consequences. In this case

53:56

applied to the modern workplace. we

53:58

will hear how law those and

54:00

rules that are meant to protect

54:02

certain workers. Next

54:04

time on the show. Until then, take care

54:07

of yourself and if you can someone else

54:09

to. Freakonomics reduce

54:11

produced. I stutter and read but

54:13

radio. You can find our entire

54:15

archive on any podcast app off

54:17

with freakonomics.com where we publish transcripts

54:19

and show notes. This episode was

54:22

produced by Ryan Kelly. We had

54:24

help from Jared Hope. Our staff

54:26

also includes Alina Coleman, Augusta Chapman,

54:28

Eleanor Osborne's Elsa Hernandez Gabriel. Roth.

54:30

Greg Ribbons, Jasmine Klinger, Jeremy Johnson, Juli

54:32

Canter A Weird Bout It's Morgan Levy,

54:34

Neil Caruso, Rebecca We Douglas, Sarah, Lily

54:37

and Zapped with Pinsky Or theme song

54:39

is Mister Fortune by the Hitchhikers. The

54:41

rest of our music is composed by

54:43

Do is Get as always. thank you

54:45

for listening. Do.

54:49

You ever. Want. To

54:51

tell your dean. Thanks so much

54:54

for giving me all this managerial responsibility

54:56

but I don't wanna. I don't want

54:58

my reward for being a good scholar

55:00

to be that I have to do

55:02

a bunch of management. I

55:05

haven't thought about that. The. Radio

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