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
OUTLINE
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
3
THE SURVEY METHODOLOGY
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)
4
(4) Performance tracking
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.
5
(10) Target time horizon
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
6
(11) Targets are stretching
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
7
(15) Removing poor performers
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
8
(16) Promoting high performers
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
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
OUTLINE
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
11
INTERVAL VALIDATION: RE-SURVEY ANALYSIS
Re-interviewed 222 firms with different interviewers & managers
2
3
Firm-level
correlation
of 0.627
1
2nd interview
4
5
Firm average scores (over 18 question)
1
2
3
st interview
1management_2
4
5
12
EXTERNAL VALIDATION OF THE SCORING
Performance country c
measure
yic  MNGic  l lic
management
(average z-scores)
 k kic  mhic   ' xic  uic
ln(capital)
other controls
ln(labor)
ln(materials)
•
Use most recent cross-section of data (typically 2006)
•
Note – not a causal estimation, only an association
13
EXTERNAL VALIDATION: BETTER PERFORMANCE
IS CORRELATED WITH BETTER MANAGEMENT
Dependent Productivity Profits 5yr Sales Share Price
variable
(% increase) (ROCE) growth
(Tobin Q)
Estimation
Firm sample
Management
Firms
Exit
OLS
OLS
OLS
OLS
Probit
All
All
All
Quoted
All
28.7***
2.018***
0.047***
0.250***
-0.262**
3469
1994
1883
374
3161
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
EXTERNAL VALIDATION: FUTURE STOCK RETURNS
60
29
57
58
49
37
19
# of firms
Significant
at 1% level
0
10
20
30
40
50
4
-10
Stock holding returns over 2005 (%)
Most intriguingly, for an earlier (summer 2004) survey cohort of
publicly quoted US firms we find correlated future (2005) stock
holding returns
1.5
2
2.5
3
3.5
4
4.5
Management score (to nearest 0.5) assessed in summer 2004
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
Energy use, log( KWH/$ sales)
1 point higher
management score
associated with about
20% less energy use
Management
Source: Bloom, Genakos, martin and Sadun, NBER WP14394. Analysis uses Census of production data for UK firms
17
OUTLINE
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
18
US MANAGEMENT BEST ON AVERAGE WITH A TAIL
OF DEVELOPING COUNTRIES
US
Germany
Sweden
Japan
Canada
France
Italy
Great Britain
Australia
Northern Ireland
Poland
Republic of Ireland
Portugal
Brazil
India
China
Greece
2.6
2.8
3
3.2
of management
Averagemean
Country
Management Score
3.4
19
US SCORES HIGHLY BECAUSE OF FEW BAD FIRMS
Brazil
Canada
China
France
Germany
Great Britain
Greece
India
Ireland
Italy
Japan
Poland
Portugal
Sweden
US
0
.5
1
0
.5
1
0
.5
1
0
.5
1
Australia
1
2
3
4
5
1
2
3
4
5
1
2
3
4
management
Firm-Level
Management Scores
5
1
2
3
4
5
20
COUNTRY LEVEL RELATIVE MANAGEMENT
Sweden
France
Australia
Italy
Portugal
Germany
Japan
Greece
Canada
Great Britain
Brazil
Northern Ireland
US
Republic of Ireland
China
Poland
India
Relatively better at
‘operations’
management
(monitoring, continuous
improvement, Lean etc)
Relatively better at
‘people’
management
(hiring, firing, pay,
promotions etc)
-.4
-.2
0
.2
.4
mean
of peo_ops – operations (monitoring,
People management (hiring, firing, pay
& promotions)
continuous improvement and Lean)
21
OUTLINE
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
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
OWNERSHIP MATTERS – FIRMS WITH
PROFESSIONAL CEOS ARE WAY BETTER RUN
THAN FAMILY, FOUNDER OR GOVERNMENT FIRMS
Distribution of firm management scores by ownership. Overlaid dashed line is approximate density
for dispersed shareholders, the most common US and Canadian ownership type
Family, external CEO
Family, family CEO
Founder
Government
Managers
Other
Private Equity
Private Individuals
0
.5
1
0
.5
1
0
.5
1
Dispersed Shareholders
1
2
3
4
5
1
2
3
4
5
Average Management Score
1
2
3
4
5
24
MULTINATIONALS APPEAR ABLE TO TRANSPORT
GOOD MANAGEMENT AROUND THE WORLD
Foreign multinationals
Domestic firms
US
Japan
Sweden
Germany
Canada
Australia
Italy
Great Britain
France
Poland
Northern Ireland
Republic of Ireland
India
China
Portugal
Brazil
Greece
2.4
2.6
2.8
3
3.2
Average Management Score
3.4
25
3.4
3.2
US
3
Canada
Germany
2.8
GreatJapan
Britain
Northern Ireland
Australia
Poland
Sweden
Republic of Ireland
France
Italy
2.6
India
China
Portugal
Brazil
Greece
2.4
Average people management
(hiring, firing, pay and promotions)
peop_mean
LIGHT LABOR REGULATION ALSO FACILITIATES
GOOD MANAGEMENT (PITY THE FRENCH)
0
20
40
WB_RigidityEmployment
World Bank
Employment Rigidity Index
60
26
20
40
60
Non-managers
Managers
0
Percent with a degree
80
EDUCATION IS ALSO STRONGLY LINKED WITH
BETTER MANAGEMENT PRACTICES
1
1.5
2
2.5
3
3.5
4
Management score (rounded to nearest 0.5)
4.5
27
OUTLINE
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
28
ALSO RAN A SMALLER RETAIL MANAGEMENT
SURVEY (USING AN ALMOST IDENTICAL GRID)
WITH BROADLY SIMILAR RESULTS
Retail
3.32
United States
Manufacturing
Canada
3.15
United Kingdom
2.99
3.14
Retail
3.07
2.83
1
2
3
Found a strong
correlation
between
management and
profits and
productivity in
retail
4
Overall management scores
29
RECENTLY ALSO BEEN RUNNING A HOSPITAL
MANAGEMENT SURVEY
Hospitals
3.00
US
2.82
UK
Germany
2.65
2.57
Sweden
2.52
Canada
2.48
Italy
France
2.39
0
1
2
3
Again, found a
strong correlation
between
management and
performance (e.g.
patient survival
after heart-attacks)
4
Management practice scores
30
MAJOR REASON FOR HIGH US SCORES ARE
PRIVATE HOSPITALS ARE MUCH BETTER RUN
Hospitals
(US data)
Private
2.97
2.59
Public
2.5
2.6
2.7
2.8
2.9
3.0
3.1
Average management score
31
ALSO RUNNING A SCHOOLS MANAGEMENT
SURVEY, IN WHICH US MANAGEMENT SCORES
ARE POOR (THINK RUBBER ROOM & UNIONS)
Schools
2.95
UK
Sweden
2.80
US
2.70
Canada
2.70
Germany
2.30
Again, found a
strong correlation
between
management and
performance (e.g.
pupil exam grades)
2.54
2.40
2.50
2.60
2.70
2.80
2.90
3.00
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 experiments
33
Many parts of these Indian plants – as in most developing countries were dirty and unsafe
Garbage outside the plant
Garbage inside a plant
Flammable garbage in a plant
Chemicals without any covering
34
The plant floors were also disorganized – the land that Lean forgot
Instrument
not
removed
after use,
blocking
hallway.
Dirty and
poorly
maintained
machines
Old warp
beam, chairs
and a desk
obstructing the
plant floor
Tools left on
the floor
after use
35
The inventory rooms had months of excess yarn, often without any formal
storage system or protection from damp or crushing
Yarn without
labeling, order or
damp protection
Different types
and colors of
yarn lying mixed
Yarn piled up so high and
deep that access to back
sacks is almost impossible
A crushed yarn cone, which
is unusable as it leads to
irregular yarn tension
36
Not surprisingly, modern management practices led to large
performance improvements – e.g. defects down by 50%
100
150
quality)
Quality defects index (higher score=lower
50
0
Start of
Diagnostic
Start of
Implementation
End of
Implementation
97.5th percentile
Control plants
Average (♦ symbol)
2.5th percentile
97.5th percentile
Average (+ symbol)
Treatment plants
2.5th percentile
-20
-10
0
10
20
Weeks after the start
of the intervention
timing
30
40
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:
The traditional British Chat-Up
[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….”
39
MY FAVOURITE QUOTES:
The traditional Indian Chat-Up
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”
40
MY FAVOURITE QUOTES:
The difficulties of defining ownership in Europe
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” ?”
Americans on geography
Interviewer: “How many production sites do you have abroad?
Manager in Indiana, US: “Well…we have one in Texas…”
41
MY FAVOURITE QUOTES:
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”
42
MY FAVOURITE QUOTES:
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…]
43
BACK-UP
44
2
0
-4
-2
Log of Sales/employee ($’000)
Case studies
provide rich firmlevel 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)
-6
labp
4
WE USE LARGE SAMPLES BECAUSE THE WIDE
VARIATION IN MANAGEMENT MEANS SMALL
SAMPLES CAN BE POTENTIALLY MISLEADING
1
2
3
managementscore
Management
4
5
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
.3
.4
MANAGERS GENERALLY OVER-SCORED THEIR
FIRM’S MANAGEMENT
“Average”
“Best
Practice”
0
.1
.2
“Worst
Practice”
0
2
4
6
8
Their self-score: 1 (worst practice), 5 (average) to 10 (best practice)
10
47
2
SELF-SCORES ARE ALSO UNINFORMATIVE ABOUT
FIRM PERFORMANCE
Lowess smoother
-6
labp
Labor Productivity
-4
-2
0
Correlation
0.032*
0
2
4
6
8
10
Their self-score: 1 (worst
practice),
5 (average) to 10 (best practice)
Self scored
management
bandwidth = .8
* In comparison
the management score has a 0.295 correlation with labor productivity
48