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WHY DO MANAGEMENT PRACTICES DIFFER ACROSS FIRMS AND COUNTRIES?
Nick Bloom (Stanford University & SIEPR)
Blackrock, March 16th 2010
MOTIVATION
Large 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
2
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
3
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
4
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
5
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
6
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
7
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
8
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
9
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)
10
1. “Measuring” management practices
2. Evaluating the reliability of this measure
a) Internal/External validation
b) Measurement error/bias
3. Describing management across firms & countries
4. Accounting for management across firms & countries
5. Different sectors and evidence of causal impact
OUTLINE
11
12
34
5m
anag
eme
nt_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
12
EXTERNAL VALIDATION OF THE SCORING
Performance 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
13
Dependentvariable
Productivity(% increase)
Profits (ROCE)
5yr Salesgrowth
Share Price (Tobin Q)
Exit
Estimation OLS OLS OLS OLS Probit
Firm sample All All All Quoted All
Management 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
14
-10
01
02
03
04
05
06
0m
ean
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 r
etur
ns o
ver
2005
(%
)
Management score (to nearest 0.5) assessed in summer 2004
Significant at 1% level
4 29 57 58 49 37 19 # of firms
15
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)
16
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
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
18
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
De
nsi
ty
managementGraphs by country1
US SCORES HIGHLY BECAUSE OF FEW BAD FIRMS
Firm-Level Management Scores20
-.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)
21
1. “Measuring” management practices
2. Evaluating the reliability of this measure
3. Describing management across firms & countries
4. Accounting for management across firms & countries•Competition•Family firms•Multinationals•Labor market regulations•Education
5. Different sectors and evidence of causal impact
OUTLINE
22
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
23
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
25
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_
me
an
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,
firin
g, p
ay a
nd p
rom
otio
ns)
26
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-managers
Managers
27
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
28
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
29
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)
30
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)
31
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
32
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
35
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
36
05
01
00
15
0
-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
Qu
alit
y d
efec
ts in
dex
(h
igh
er s
core
=lo
wer
qu
alit
y)
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:
38
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
39
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
MY FAVOURITE QUOTES:
40
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
MY FAVOURITE QUOTES:
41
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”
MY FAVOURITE QUOTES:
42
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…]
MY FAVOURITE QUOTES:
43
BACK-UP
44
-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 (
$’00
0)
45
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
46
0.1
.2.3
.4D
en
sity
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”
47
-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 P
rodu
ctiv
ity
Self scored management* In comparison the management score has a 0.295 correlation with labor productivity
Correlation0.032*
48