1. WHY DO MANAGEMENT PRACTICES DIFFER ACROSS FIRMS AND COUNTRIES?
Nick Bloom (Stanford University & SIEPR)
Blackrock, March 16th 2010

2. 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

3. OUTLINE “Measuring” management practices
Evaluating the reliability of this measure
Describing management across firms & countries
Accounting for management across firms & countries
5. Different sectors and evidence of causal impact
OUTLINE 3

4. 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)

5. (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

6. (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

7. (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

8. (15) Removing poor performers
Regulation important but not only thing – MNE example, moving people out
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

9. (16) Promoting high performers
Open questions. Creates problems of misallocation of workers and exit of the more able workers.
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

10. 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 ( ,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)

11. OUTLINE “Measuring” management practices
Evaluating the reliability of this measure
Internal/External validation
Measurement error/bias
Describing management across firms & countries
Accounting for management across firms & countries
5. Different sectors and evidence of causal impact
OUTLINE

12. INTERVAL VALIDATION: RE-SURVEY ANALYSIS
Re-interviewed 222 firms with different interviewers & managers
Firm average scores (over 18 question)
Firm-level correlation of 0.627
2nd interview
1st interview

13. EXTERNAL VALIDATION OF THE SCORING
Performance measure
country c
management
(average z-scores)
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

14. Productivity (% increase)
EXTERNAL VALIDATION: BETTER PERFORMANCE IS CORRELATED WITH BETTER MANAGEMENT
Dependent variable
Productivity (% increase)
Profits (ROCE)
5yr Sales growth
Share Price (Tobin Q)
Exit
Estimation
OLS
Probit
Firm sample
All
Quoted
Management
28.7***
2.018***
0.047***
0.250***
-0.262**
Firms
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

15. EXTERNAL VALIDATION: FUTURE STOCK RETURNS
Most intriguingly, for an earlier (summer 2004) survey cohort of publicly quoted US firms we find correlated future (2005) stock holding returns 4 29 57 58 49 37 19
# of firms
Significant at 1% level
Stock holding returns over 2005 (%)
Management score (to nearest 0.5) assessed in summer 2004

16. 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)

17. EXTERNAL VALIDATION: WELL MANAGED FIRMS ALSO APPEAR TO BE MORE ENERGY EFFICIENT
1 point higher management score associated with about 20% less energy use
Energy use, log( KWH/$ sales)
Management
Source: Bloom, Genakos, martin and Sadun, NBER WP Analysis uses Census of production data for UK firms

18. OUTLINE “Measuring” management practices
Evaluating the reliability of this measure
Describing management across firms & countries
Accounting for management across firms & countries
5. Different sectors and evidence of causal impact
OUTLINE

19. US MANAGEMENT BEST ON AVERAGE WITH A TAIL OF DEVELOPING COUNTRIES
Average Country Management Score

20. US SCORES HIGHLY BECAUSE OF FEW BAD FIRMS
Firm-Level Management Scores

21. 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)

22. OUTLINE “Measuring” management practices
Evaluating the reliability of this measure
Describing management across firms & countries
Accounting for management across firms & countries
Competition
Family firms
Multinationals
Labor market regulations
Education
5. Different sectors and evidence of causal impact
OUTLINE

23. 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

24. Average Management Score
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
Average Management Score

25. MULTINATIONALS APPEAR ABLE TO TRANSPORT GOOD MANAGEMENT AROUND THE WORLD
Foreign multinationals
Domestic firms
Average Management Score

26. Average people management (hiring, firing, pay and promotions)
LIGHT LABOR REGULATION ALSO FACILITIATES GOOD MANAGEMENT (PITY THE FRENCH)
Average people management (hiring, firing, pay and promotions)
World Bank Employment Rigidity Index

27. EDUCATION IS ALSO STRONGLY LINKED WITH BETTER MANAGEMENT PRACTICES
Non-managers
Managers
Percent with a degree
Management score (rounded to nearest 0.5)

28. OUTLINE “Measuring” management practices
Evaluating the reliability of this measure
Describing management across firms & countries
Accounting for management across firms & countries
5. Different sectors and evidence of causal impact
OUTLINE

29. Overall management scores
ALSO RAN A SMALLER RETAIL MANAGEMENT SURVEY (USING AN ALMOST IDENTICAL GRID) WITH BROADLY SIMILAR RESULTS
Retail
United States
Found a strong correlation between management and profits and productivity in retail
Canada
United Kingdom
Overall management scores

30. RECENTLY ALSO BEEN RUNNING A HOSPITAL MANAGEMENT SURVEY
Hospitals
Again, found a strong correlation between management and performance (e.g. patient survival after heart-attacks)
Management practice scores

31. MAJOR REASON FOR HIGH US SCORES ARE PRIVATE HOSPITALS ARE MUCH BETTER RUN
Hospitals (US data)
Private
Public
Average management score

32. ALSO RUNNING A SCHOOLS MANAGEMENT SURVEY, IN WHICH US MANAGEMENT SCORES ARE POOR (THINK RUBBER ROOM & UNIONS)
Schools
Again, found a strong correlation between management and performance (e.g. pupil exam grades)
Average management score

33. 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

34. 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

35. The plant floors were also disorganized – the land that Lean forgot
Instrument not removed after use, blocking hallway.
Old warp beam, chairs and a desk obstructing the plant floor
Dirty and poorly maintained machines
Tools left on the floor after use

36. 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
Yarn piled up so high and deep that access to back sacks is almost impossible
Different types and colors of yarn lying mixed
A crushed yarn cone, which is unusable as it leads to irregular yarn tension

37. Not surprisingly, modern management practices led to large performance improvements – e.g. defects down by 50%
Start of Diagnostic
Start of Implementation
End of Implementation
97.5th percentile
Control plants
Average (♦ symbol)
2.5th percentile
Quality defects index (higher score=lower quality)
97.5th percentile
Treatment plants
Average (+ symbol)
2.5th percentile
Weeks after the start of the intervention
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

38. SUMMARY
Variations in management practices (for monitoring, targets and incentives) account for large differences in performance
Huge differences in these management practices across organizations in every sector and country we have looked at
Competition, ownership, regulations and education seem key factors in explaining these differences
Quotes:

39. BACK-UP

40. 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)
Log of Sales/employee ($’000)
Management score

41. 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

42. MANAGERS GENERALLY OVER-SCORED THEIR FIRM’S MANAGEMENT
“Worst Practice”
“Average”
“Best Practice”

43. SELF-SCORES ARE ALSO UNINFORMATIVE ABOUT FIRM PERFORMANCE
Correlation
0.032*
Labor Productivity
Self scored management
* In comparison the management score has a correlation with labor productivity