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

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

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) 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

(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

(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

(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

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)

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

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

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

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

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

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)

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 WP14394. Analysis uses Census of production data for UK firms

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

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

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

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)

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

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

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

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

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

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

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

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

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

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

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

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. Old warp beam, chairs and a desk obstructing the plant floor Dirty and poorly maintained machines Tools left on the floor after use

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

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

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:

BACK-UP

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

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

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

SELF-SCORES ARE ALSO UNINFORMATIVE ABOUT FIRM PERFORMANCE Correlation 0.032* Labor Productivity Self scored management * In comparison the management score has a 0.295 correlation with labor productivity