Management Survey Overview

WHY DO MANAGEMENT PRACTICES DIFFER ACROSS FIRMS AND COUNTRIES?

Nick Bloom (Stanford University & SIEPR)

Blackrock, March 16th 2010

MOTIVATIONLarge persistent productivity spread across firms and countries

• Britain less productive than the US since about 1900• Firms at 90th percentile of productivity distribution about

twice as productive at those as the 10th percentile

Could this be in part because of differences in management?

Summarize a ten-year LSE, Harvard, Stanford and McKinsey project to measure management across firms and countries

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1. “Measuring” management practices

2. Evaluating the reliability of this measure

3. Describing management across firms & countries

4. Accounting for management across firms & countries

5. Different sectors and evidence of causal impact

OUTLINE

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1) Developing management questions

•Scorecard for 18 monitoring, targets and incentives practices•≈45 minute phone interview of manufacturing plant managers

2) Obtaining unbiased comparable responses (“Double-blind”)•Interviewers do not know the company’s performance•Managers are not informed (in advance) they are scored

3) Getting firms to participate in the interview•Introduced as “Lean-manufacturing” interview, no financials•Official Endorsement: Bundesbank, PBC, CII & RBI, etc. •Run by 75 MBAs types (loud, assertive & business experience)

THE SURVEY METHODOLOGY

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Score (1): Measures tracked do not indicate directly if overall business objectives are being met. Tracking is an ad-hoc process (certain processes aren’t tracked at all)

(3): Most key performance indicators are tracked formally. Tracking is overseen by senior management.

(5): Performance is continuously tracked and communicated, both formally and informally, to all staff using a range of visual management tools.

(4) Performance tracking

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Score (1): Top management's main focus is on short term targets .

(3): There are short and long-term goals for all levels of the organization. As they are set independently, they are not necessarily linked to each other

(5): Long term goals are translated into specific short term targets so that short term targets become a "staircase" to reach long term goals

(10) Target time horizon

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Score (1): Goals are either too easy or impossible to achieve; managers provide low estimates to ensure easy goals

(3): In most areas, top management pushes for aggressive goals based on solid economic rationale. There are a few "sacred cows" that are not held to the same rigorous standard

(5): Goals are genuinely demanding for all divisions. They are grounded in solid, solid economic rationale

(11) Targets are stretching

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Score (1): Poor performers are rarely removed from their positions

(3): Suspected poor performers stay in a position for a few years before action is taken

(5): We move poor performers out of the company or to less critical roles as soon as a weakness is identified

(15) Removing poor performers

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Score (1): People are promoted primarily upon the basis of tenure

(3): People are promoted upon the basis of performance

(5): We actively identify, develop and promote our top performers

(16) Promoting high performers

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MANUFACTURING SURVEY SAMPLE

• Interviewed 7000 firms across Asia, Europe and the Americas• Obtained 45% coverage rate from sampling frame (with

response rates uncorrelated with performance measures)

Medium sized manufacturing firms:• Medium sized (100 - 5,000 employees, median ≈ 250)

because firm practices more homogeneous• Focus on manufacturing as easier to measure productivity

(but show results for Schools, Hospitals and Retail)

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2. Evaluating the reliability of this measurea) Internal/External validationb) Measurement error/bias




OUTLINE

11

12

34

5m

anag

emen

t_1

1 2 3 4 5management_2

INTERVAL VALIDATION: RE-SURVEY ANALYSIS

1st interview

2nd in

terv

iew

Re-interviewed 222 firms with different interviewers & managers

Firm average scores (over 18 question)

Firm-level correlation of 0.627

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EXTERNAL VALIDATION OF THE SCORINGPerformance measure

ci

ci

cim

cik

cil

ci

ci uxhklMNGy '

ln(capital)ln(materials)

management(average z-scores) ln(labor)

other controls

• Use most recent cross-section of data (typically 2006)

country c

• Note – not a causal estimation, only an association

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Dependentvariable

Productivity(% increase)

Profits (ROCE)

5yr Salesgrowth

Share Price (Tobin Q) Exit

Estimation OLS OLS OLS OLS ProbitFirm sample All All All Quoted AllManagement 28.7*** 2.018*** 0.047*** 0.250*** -0.262**Firms 3469 1994 1883 374 3161

EXTERNAL VALIDATION: BETTER PERFORMANCE IS CORRELATED WITH BETTER MANAGEMENT

Includes controls for country, with results robust to controls for industry, year, firm-size, firm-age, skills etc.

Significance levels: *** 1%, ** 5%, * 10%.

Sample of all firms where accounting data is available

Standard errors clustered by firm

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-1

00

1020

3040

5060

mea

n of

retu

rn

1.5 2 2.5 3 3.5 4 4.5

EXTERNAL VALIDATION: FUTURE STOCK RETURNSMost intriguingly, for an earlier (summer 2004) survey cohort of publicly quoted US firms we find correlated future (2005) stock holding returns

Sto

ck h

oldi

ng re

turn

s ov

er 2

005

(%)

Management score (to nearest 0.5) assessed in summer 2004

Significant at 1% level

4 29 57 58 49 37 19 # of firms

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EXTERNAL VALIDATION – ROBUSTNESS

Performance results robust in all main regions:

• Anglo-Saxon (US, UK, Ireland and Canada)

• Northern Europe (France, Germany, Sweden & Poland)

• Southern Europe (Portugal, Greece and Italy)

• East Asia (China and Japan)

• South America (Brazil)

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EXTERNAL VALIDATION: WELL MANAGED FIRMS ALSO APPEAR TO BE MORE ENERGY EFFICIENT

Ene

rgy

use,

log(

KW

H/$

sal

es)

Management

1 point higher management score associated with about 20% less energy use

17Source: Bloom, Genakos, martin and Sadun, NBER WP14394. Analysis uses Census of production data for UK firms






OUTLINE

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2.6 2.8 3 3.2 3.4mean of management

USGermany

SwedenJapan

CanadaFrance

ItalyGreat Britain

AustraliaNorthern Ireland

PolandRepublic of Ireland

PortugalBrazilIndia

ChinaGreece

US MANAGEMENT BEST ON AVERAGE WITH A TAIL OF DEVELOPING COUNTRIES

Average Country Management Score 19

0.5

10

.51

0.5

10

.51

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Australia Brazil Canada China

France Germany Great Britain Greece

India Ireland Italy Japan

Poland Portugal Sweden US

Den

sity

managementGraphs by country1

US SCORES HIGHLY BECAUSE OF FEW BAD FIRMS

Firm-Level Management Scores 20

-.4 -.2 0 .2 .4mean of peo_ops

IndiaPolandChina

Republic of IrelandUS

Northern IrelandBrazil

Great BritainCanadaGreeceJapan

GermanyPortugal

ItalyAustralia

FranceSweden

COUNTRY LEVEL RELATIVE MANAGEMENT

Relatively better at ‘operations’ management (monitoring, continuous improvement, Lean etc)

Relatively better at ‘people’ management (hiring, firing, pay, promotions etc)

People management (hiring, firing, pay & promotions) – operations (monitoring, continuous improvement and Lean)

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4. Accounting for management across firms & countries•Competition•Family firms•Multinationals•Labor market regulations•Education


OUTLINE

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TOUGH COMPETITION LINKED TO MUCH BETTER MANAGEMENT PRACTICES

Various ways to measure competitive intensity (long-run market profits, trade-openess, market concentration, surveys etc.)

In every case more competition leads to better management

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0.5

10

.51

0.5

1

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Dispersed Shareholders Family, external CEO Family, family CEO

Founder Government Managers

Other Private Equity Private Individuals

Density PEy

Graphs by Who owns the firm?

OWNERSHIP MATTERS – FIRMS WITH PROFESSIONAL CEOS ARE WAY BETTER RUN THAN FAMILY, FOUNDER OR GOVERNMENT FIRMSDistribution of firm management scores by ownership. Overlaid dashed line is approximate density for dispersed shareholders, the most common US and Canadian ownership type

Average Management Score 24

2.4 2.6 2.8 3 3.2 3.4

USJapan

SwedenGermany

CanadaAustralia

ItalyGreat Britain

FrancePoland

Northern IrelandRepublic of Ireland

IndiaChina

PortugalBrazil

Greece

MULTINATIONALS APPEAR ABLE TO TRANSPORT GOOD MANAGEMENT AROUND THE WORLD

Average Management Score

Foreign multinationalsDomestic firms

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Australia

Brazil

Canada

China

France

Great Britain

Germany

Greece

India

Republic of Ireland

Italy

JapanNorthern Ireland Poland

Portugal

Sweden

US

2.4

2.6

2.8

33.

23.

4pe

op_m

ean

0 20 40 60WB_RigidityEmployment

LIGHT LABOR REGULATION ALSO FACILITIATES GOOD MANAGEMENT (PITY THE FRENCH)

World Bank Employment Rigidity Index

Ave

rage

peo

ple

man

agem

ent

(hiri

ng, f

iring

, pay

and

pro

mot

ions

)

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0

2040

6080

1 1.5 2 2.5 3 3.5 4 4.5

mean of degree_nm mean of degree_m

EDUCATION IS ALSO STRONGLY LINKED WITH BETTER MANAGEMENT PRACTICES

Management score (rounded to nearest 0.5)

Per

cent

with

a d

egre

e

Non-managersManagers

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OUTLINE

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ALSO RAN A SMALLER RETAIL MANAGEMENT SURVEY (USING AN ALMOST IDENTICAL GRID) WITH BROADLY SIMILAR RESULTS

2.83

2.99

3.07

3.15

3.14

3.32

1 2 3 4

Retail

Manufacturing

United States

Canada

United Kingdom

Overall management scores

Retail

Found a strong correlation between management and profits and productivity in retail

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3.00

2.82

2.65

2.57

2.52

2.48

2.39

0 1 2 3 4

US

UK

Germany

Sweden

Canada

Italy

France

RECENTLY ALSO BEEN RUNNING A HOSPITAL MANAGEMENT SURVEY

Management practice scores

Hospitals

Again, found a strong correlation between management and performance (e.g. patient survival after heart-attacks)

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MAJOR REASON FOR HIGH US SCORES ARE PRIVATE HOSPITALS ARE MUCH BETTER RUN

2.59

2.97

2.5 2.6 2.7 2.8 2.9 3.0 3.1

Public

Private

Average management score

Hospitals (US data)

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2.95

2.80

2.70

2.70

2.54

2.30 2.40 2.50 2.60 2.70 2.80 2.90 3.00

UK

Sweden

US

Canada

Germany

Schools

ALSO RUNNING A SCHOOLS MANAGEMENT SURVEY, IN WHICH US MANAGEMENT SCORES ARE POOR (THINK RUBBER ROOM & UNIONS)

Again, found a strong correlation between management and performance (e.g. pupil exam grades)

Average management score

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FINALLY, IN SEARCH OF CAUSATION WE ARE RUNNING MANAGEMENT EXPERIMENTS IN INDIA

To investigate the causal impact of management I am working with the World Bank to run experiments in large Indian firms

Find large performance impact from improving basic management for operations, quality, inventory and HR

Outside a typical Indian factory in our experiments Inside a typical Indian factory in our experiments33

Many parts of these Indian plants – as in most developing countries - were dirty and unsafe

Garbage outside the plant Garbage inside a plant

Chemicals without any coveringFlammable garbage in a plant 34

The plant floors were also disorganized – the land that Lean forgot

Instrument not

removed after use, blocking hallway.

Tools left on the floor after use

Dirty and poorly

maintained machines

Old warp beam, chairs and a desk

obstructing the plant floor

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Yarn piled up so high and deep that access to back

sacks is almost impossible

The inventory rooms had months of excess yarn, often without any formal storage system or protection from damp or crushing

Different types and colors of

yarn lying mixed

Yarn without labeling, order or damp protection

A crushed yarn cone, which is unusable as it leads to

irregular yarn tension

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050

100

150

-20 -10 0 10 20 30 40timing

Not surprisingly, modern management practices led to large performance improvements – e.g. defects down by 50%

2.5th percentile

Control plants

Treatment plants

Weeks after the start of the intervention

Qua

lity

defe

cts

inde

x (h

ighe

r sco

re=l

ower

qua

lity)

Start of Diagnostic

Start of Implementation

Average (+ symbol)

97.5th percentile

Average (♦ symbol)

2.5th percentile

97.5th percentile

End of Implementation

Notes: Average quality defects index, which is a weighted index of quality defects, so a higher score means lower quality. Plotted for the 14 treatment plants (+ symbols) and the 6 control plants (♦ symbols). Values normalized so both series have an average of 100 prior to the start of the intervention. Confidence intervals from plant block bootstrapped. 37

SUMMARY

1. Variations in management practices (for monitoring, targets and incentives) account for large differences in performance

2. Huge differences in these management practices across organizations in every sector and country we have looked at

3. Competition, ownership, regulations and education seem key factors in explaining these differences

Quotes:

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MY FAVOURITE QUOTES:

[Male manager speaking to an Australian female interviewer]

Production Manager: “Your accent is really cute and I love the way you talk. Do you fancy meeting up near the factory?”

Interviewer “Sorry, but I’m washing my hair every night for the next month….”

The traditional British Chat-Up

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Production Manager: “Are you a Brahmin?’

Interviewer “Yes, why do you ask?”

Production manager “And are you married?”

Interviewer “No?”

Production manager “Excellent, excellent, my son is looking for a bride and I think you could be perfect. I must contact your parents to discuss this”

The traditional Indian Chat-Up


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Interviewer: “How many production sites do you have abroad?Manager in Indiana, US: “Well…we have one in Texas…”

Americans on geography

Production Manager: “We’re owned by the Mafia”Interviewer: “I think that’s the “Other” category……..although I guess I could put you down as an “Italian multinational” ?”

The difficulties of defining ownership in Europe


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Don’t get sick in Britain

Interviewer : “Do staff sometimes end up doing the wrong sort of work for their skills?

NHS Manager: “You mean like doctors doing nurses jobs, and nurses doing porter jobs? Yeah, all the time. Last week, we had to get the healthier patients to push around the beds for the sicker patients”


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The bizarre

Interviewer: “[long silence]……hello, hello….are you still there….hello”

Production Manager: “…….I’m sorry, I just got distracted by a submarine surfacing in front of my window”

The unbelievable

[Male manager speaking to a female interviewer]Production Manager: “I would like you to call me “Daddy” when we talk”[End of interview…]


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BACK-UP

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-6-4

-20

24

labp

1 2 3 4 5management

WE USE LARGE SAMPLES BECAUSE THE WIDE VARIATION IN MANAGEMENT MEANS SMALL SAMPLES CAN BE POTENTIALLY MISLEADING

Case studies provide rich firm-level details, but the variation in management practices means these can easily be misleading(e.g. Enron, was a case-study favorite with many HBS Enron cases)

Management score

Log

of S

ales

/em

ploy

ee ($

’000

)

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WE ALSO GOT MANAGERS TO SELFSCORE THEMSELVES AT THE END OF THE INTERVIEW

We asked:

“Excluding yourself, how well managed would you say your firm is on a scale of 1 to 10, where 1 is worst practice, 5 is average and 10 is best practice”

We also asked them to give themselves scores on operations and people management separately

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0.1

.2.3

.4D

ensi

ty

0 2 4 6 8 10Their self-score: 1 (worst practice), 5 (average) to 10 (best practice)

MANAGERS GENERALLY OVER-SCORED THEIR FIRM’S MANAGEMENT

“Average”“Worst Practice”

“Best Practice”

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-6-4

-20

2la

bp

0 2 4 6 8 10Their self-score: 1 (worst practice), 5 (average) to 10 (best practice)

bandwidth = .8

Lowess smoother

SELF-SCORES ARE ALSO UNINFORMATIVE ABOUT FIRM PERFORMANCE

Labo

r Pro

duct

ivity

Self scored management* In comparison the management score has a 0.295 correlation with labor productivity

Correlation0.032*

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Management Survey Overview

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