How Much Does Good Management Matter? Evidence from India.

Does management matter:evidence from India
Nick Bloom (Stanford)Benn Eifert (Berkeley)Aprajit Mahajan (Stanford)David McKenzie (World Bank)John Roberts (Stanford GSB)
Barcelona Lecture
May 2010

Management appears worse in developing countries
# firms
695
336
270
122
344
312
188
762
382
92
231
102
140
559
620
524
171
Average country management score, manufacturing firms 100 to 5000 employees
(monitoring, targets and incentives management scored on a 1 to 5 scalemethodology: Bloom & Van Reenen (2007, QJE), data: Bloom, Sadun & Van Reenen (2010, AR))
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India’s low score is due to a tail of badly managed firms
US manufacturing, mean=3.33 (N=695)
Density
Indian manufacturing, mean=2.69 (N=620)
Density
Firm-Level Management Scores
Firm level histograms underlying the country averages

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This raises three obvious questions
Does “bad” management really exist?
Syversson (2010) notes: “no potential driving factor of productivity has seen a higher ratio of speculation to empirical study”.
Literature goes back to beginnings of social science, e.g. Francis Walker’s (1887) “On the sources of Business Profits”
2. Does “bad” management matter for development?
“Prominent development textbooks such as the four-volume Handbook of Development Economics do not contain a single chapter or substantial section on entrepreneurship” (Naude, 2009)
3. Why are so many Indian firms “badly” managed?

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Summary of the experiment
Experiment on 28 plants owned by 17 large firms (≈ 300 employees each) outside Mumbai making cotton fabric
Randomized treatment plants get heavy management consulting, controls plants get very light consulting (just enough to get data)
Collect weekly performance, management, organizational and IT data on all plants from 2008 to 2010

Summary of the results
Impact of better management:
Raised TFP by about 10% and profits by about 17%
Owners decentralized more decisions to plant managers
Computer use increased for data collection and processing
Why did firms not improve management earlier?
Information – firms not aware of existence or impact of many modern management practices (e.g. quality control)
Ability - family ownership limits pool of potential directors
Competition - high entry costs, and little reallocation (inability to decentralize limits ability of well managed firms to grow)
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Exhibit 1: Plants are large compounds, often containing several buildings.
Plant surrounded by grounds
Plant entrance with gates and a guard post
Front entrance to the main building
Plant buildings with gates and guard post

Exhibit 2: The plants operate 24 hours a day for 7 days a week producing fabric from yarn, with 4 main stages of production
(1) Winding the yarn thread onto the warp beam
(2) Drawing the warp beam ready for weaving
(3) Weaving the fabric on the weaving loom
(4) Quality checking and repair

Exhibit 3: Many parts of these Indian plants were dirty and unsafe
Garbage outside the plant
Garbage inside a plant
Chemicals without any covering
Flammable garbage in a plant

Exhibit 4: The plant floors were often disorganized and aisles blocked
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

Exhibit 5: The inventory rooms had months of excess yarn, often without any formal storage system or protection from damp or crushing
Yarn piled up so high and deep that access to back sacks is almost impossible
Yarn without labeling, order or damp protection
Different types and colors of yarn lying mixed
A crushed yarn cone, which is unusable as it leads to irregular yarn tension

Exhibit 6: The spare parts stores were also disorganized and dirty
No protection to prevent damage and rust
Spares without any labeling or order
Shelves overfilled and disorganized
Spares without any labeling or order

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These firms appear typical of large manufacturers in Brazil, China, India and Mexico
Experimental Firms, mean=2.60
Indian Textiles, mean=2.60
Indian Manufacturing, mean=2.69
Brazil and China Manufacturing, mean=2.67
Management scores (using Bloom and Van Reenen (2007) methodology)

Management practices before and after treatment
Performance of the plants before and after treatment
Decentralization and IT
Why were these practices not introduced before?
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The experiment used consulting to randomly change management practices
Obtained details of the population of 529 woven cotton fabric firms (SIC 2211) near Mumbai with 100 to 5000 employees.
Selected 66 firms in the largest cluster (Tarapur)
Contacted every firm: 17 participated containing 28 plants
A team of 6 consultants from Accenture, Mumbai was hired to help improve the practices in some of these firms
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Treatment and control, on-site and off-site splits
Intervention and performance data collection is time consuming, so focused on 20 plants – the “on-site” plants:
Treatment: 1 month diagnostic + 4 months implementation
Control: 1 month diagnostic
Collecting management, organization and IT data is easier as slow moving (bi-monthly) so gathered for all 28 plants
Hence, performance regressions on 20 plants only, all other regressions on all 28 plants.
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Intervention aimed to improve 38 core textile management practices in 6 areas
Targeted practices in 6 areas: operations, quality, inventory, loom planning, HR and sales & orders

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Intervention aimed to improve 38 core textile management practices in 6 areas
Targeted practices in 6 areas: operations, quality, inventory, loom planning, HR and sales & orders

Adoption of these 38 management practices did rise, and particularly in the treatment plants
Treatment plants (on-site)
Control plants (on-site)
Share of key textile management practices adopted
Off-site plants (treatment and control)
Notes: Average adoption rates of the 38 key textile manufacturing management practices listed in Table 2. Shown separately for the 14 on-site treatment plants (round symbol), 6 Control plants (diamond symbol) and the 8 off-site plants (+ symbol). Scores range from 0 (if none of the group of plants have adopted any of the 38 management practices) to 1 (if all of the group of plants have adopted all of the 38 management practices). Initial differences across all the groups are not statistically significant (e.g. the initial difference between treatment and control has a p-value of 0.248).

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Quality
Inventory
Output

Poor quality meant 19% of manpower went on repairs
Workers spread cloth over lighted plates to spot defects
Large room full of repair workers (the day shift)
Defects lead to about 5% of cloth being scrapped
Defects are repaired by hand or cut out from cloth

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Previously mending was recorded only to cross-check against customers’ claims for rebates
Defects log with defects not recorded in an standardized format. These defects were recorded solely as a record in case of customer complaints. The data was not aggregated or analyzed

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Now mending is recorded daily in a standard format, so it can analyzed by loom, shift, design & weaver
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The quality data is now collated and analyzed as part of the new daily production meetings
Plant managers now meet regularly with heads of quality, inventory, weaving, maintenance, warping etc. to analyze data

Figure 3: Quality defects index for the treatment and control plants
Start of Diagnostic
Start of Implementation
End of Implementation
97.5th percentile
Control plants
Average (♦ symbol)
Quality defects index (higher score=lower quality)
2.5th percentile
97.5th percentile
Average (+ symbol)
Treatment plants
2.5th percentile
Weeks after the start of the diagnostic
Notes: Displays the average weekly quality defects index, which is a weighted index of quality defects, so a higher score means lower quality. This is plotted for the 14 on-site treatment plants (+symbols) and the 6 on-site control plants (♦symbols). Values normalized so both series have an average of 100 prior to the start of the intervention. To obtain confidence intervals we bootstrapped the firms with replacement 250 times.

Estimating management effect in regressions
(A) OLS: plant FEs and weekly time dummies
Outcomei,t=αi+ λt + βmanagementi,t + vi,t
(B) IV: 2nd stage as above, 1st stage instruments management
Managementi,t=αi+ λt + β1log(1+intervention weeks)i,t + ei,t
(C) ITT: regress on outcome on intervention
Outcomei,t=αi+ λt + βinterventioni,t + vi,t
All standard errors bootstrapped clustered at firm level

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Impact of management on quality
Data is weekly at the plant level. Standard errors are boostrap clustered at the firm level.

Quality
Inventory
Output
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Organizing and racking inventory enables firms to substantially reduce capital stock
Stock is organized, labeled, and entered into an Electronic Resource Planning (ERP) system which has details of the type, age and location.
Bagging and racking yarn reduces waste from rotting (keeps the yarn dry) and crushing
Computerized inventory systems help to reduce stock levels.

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Sales are also informed about excess yarn stock so they can incorporate this in new designs.
Shade cards now produced for all surplus yarn. These are sent to the design team in Mumbai to use in future products

Figure 4: Yarn inventory for the treatment and control plants
Start of Diagnostic
Start of Implementation
End of Implementation
97.5th percentile
Average (♦ symbol)
Control plants
2.5th percentile
Yarn inventory (normalized to 100 prior to diagnostic)
97.5th percentile
Average (+ symbol)
Treatment plants
2.5th percentile
Weeks after the start of the intervention
Notes: Displays the weekly average yarn inventory plotted for 12 on-site treatment plants (+symbols) and the 6 on-site control plants (♦symbols). Values normalized so both series have an average of 100 prior to the start of the intervention. To obtain confidence intervals we bootstrapped the firms with replacement 250 times. Slow moving fluctuations due to seasonality. 2 treatment plants maintain no on-site yarn inventory so are not included in the figures.

Impact of management on inventory
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Quality
Inventory
Output
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Many treated firms have also introduced basic initiatives (called “5S”) to organize the plant floor
Worker involved in 5S initiative on the shop floor, marking out the area around the model machine
Snag tagging to identify the abnormalities on & around the machines, such as redundant materials, broken equipment, or accident areas. The operator and the maintenance team is responsible for removing these abnormalities.

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Spare parts were also organized, reducing downtime (parts can be found quickly) and waste
Nuts & bolts sorted as per specifications
Parts like gears, bushes, sorted as per specifications
Tool
storage organized

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Production data is now collected in a standardized format, for discussion in the daily meetings
Before(not standardized, on loose pieces of paper)
After (standardized, so easy to enter daily into a computer)

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Daily performance boards have also been put up, with incentive pay for employees based on this

Impact of management on output
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Impact on productivity and profitability looks large
Estimate increased profit by about $322,000 per firm (≈16.8%)
Productivity increased by about 10.5%
Long-run impacts potentially much larger as flexibility on changing inputs & product choice, but also chance of slippage

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Improved management also led to more decentralization in decision making
Firms in developing countries are typically very centralized (Bloom, Sadun and Van Reenen, 2009)
Owners take decisions to avoid expropriation by their middle managers, which they risk because:
Rule of law is weak, so punishing theft is hard
Management is poor, so detecting theft is hard
When management improves the ability to detect theft increases, so we should see more decentralization
This matters for growth since the inability to decentralize limits the growth of productive firms, impeding reallocation

Figure 6: Decentralization and changes in management practices
1=treatment plant, 0=control plant
correlation 0.594(p-value 0.001)
Change in the decentralization index
Change in management practices
Note: Decentralization index is the principal component factor of 8 measures of decentralization around weaver hiring, manager hiring, spares purchases, maintenance planning, weaver bonuses, new products, investment, and departmental co-ordination. Changes defined over the period from pre-treatment to March 2010.

Decentralization regression results
Data is pre-intervention and March 2010. Standard errors are boostrap clustered at the firm level.

Improved management also led to greater use of computers
Most modern management practices involve extensive data collection, analysis and presentation
Suggests that computer use (hours and number of users) should rise with the adoption of better management
Better management appears to be one type of skill-biased technical change

Figure 7: Computerization and changes in management practices
1=treatment plant, 0=control plant
correlation 0.778(p-value 0.001)
Change in the computerization index
Change in management practices
Note:Computerization index is the principal component factor of 10 measures, which are the use of an ERP system, the number of computers, the number of computers less than 2 years old, the number of employees using computers for at least 10 minutes per day, and the cumulative number of hours of computer use per week, an internet connection at the plant, the firm has a web-site, if the plant-manager uses e-mail, if the directors use of e-mail, and the intensity of computerization in production.

Computerization regression results
Data is pre-intervention and March 2010. Standard errors are boostrap clustered at the firm level.

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So why did these firms have bad management?
Asked the consultants to investigate the non-adoption of each of the 38 practices, in each plant, every other month
They did this by discussion with the owners, managers and workers, observation of the factory, and from their experiences of trying to change management practices.
We also spent many months visiting the firms, and spending time with the managers and owners
The next slide shows this data over time
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Reason for the non-adoption of the practices in the treatment plants (as a % of all 38 practices)
Notes: covers 532 practices (38 practices in 14 plants) in the treatment plants. Table 9 (in the paper) also has values for control and non-experimental plants.
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Transition matrix for the reasons for non-adoption
2 months ahead (t+1)
Current (t)
Note: All blank cells are zero. Shows transition of reasons for non adoption to other reasons or implementation (“doing”) over each two month period. Averaged over all treatment firms for months 1 to 11.
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Why does competition not fix badly managed firms?
Bankruptcy is not (currently) a threat: at weaver wage rates of $5 a day these firms are profitable
Reallocation appears limited: Owners take all decisions as they worry about managers stealing. But owners time is constrained – they already work 72.4 hours average a week – limiting growth. One explanation for Hsieh and Klenow (2009) results.
As an illustration firm size is more linked to number of male family members (corr=0.689)- who are trusted to be given managerial positions -than management scores (corr=0.223)
Entry appears limited: Capital intensive ($13m assets average per firm), and no guarantee new entrants are any better

Summary
Firms in developing countries often have poor management practices, which lowers their productivity
Reasons include lack of information about modern management practices, and limited CEO ability and procrastination
Policy implications
A) Competition and FDI: free product markets and encourage foreign multinationals
B) Rule of law: improve rule of law to encourage reallocation and ownership and control separation
C) Training: improved basic training around management skills
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Finally, not to pick on the Indians, one country even exports TV shows about bad managers.....
Michael Scott(USA)
David Brent (Britain)
Basil Fawlty (Britain)

The production technology has not changed much over time
Krill
Warp beam
The warping looms at Lowell Mills in 1854, Massachusetts

“Non adoption flow chart” used to collect data
Legend
External factors (legal, climate etc)
Is the reason for the non adoption of the practice internal to the firm?
No
Hypothesis
Yes
Conclusion
Was the firm previously awarethat the practice existed?
Lack of information
Yes
No
Could the firm hire new employees or consultants to adopt the practice?
Can the firm adopt the practice with existing staff & equipment?
Lack of local skills
Limited incentives and/or authority for employees
Did the owner believe introducing the practice would be profitable?
Not profit maximizing
Would this adoption be profitable
Do you think the CEO was correct about the cost-benefit tradeoff?
Could the CEO get his employees to introduce the practice?
Did the firm realize this would be profitable?
Low ability of the owner and/or procrastination
Incorrect information
Does the firm have enough internal financing or access to credit?
Credit constraints
Other reasons
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Are these Hawthorne effects (temporary increases in performance due to monitoring?
Treatment and control plants both had initial 1 month of diagnostics and extended follow-up
Improvements take time to arise, and in areas (quality, inventory and efficiency) where practices are changing
Improvements persisted for several months after the intervention phase (although still collecting data)
Firms themselves also believe the improvements work and have rolled these out to other plants

But before the photos, I want to note that this is not a cost-benefit evaluation of management consulting
We hire consultants as a practical mechanism to achieve an improvement in management practices.
Our findings suggest a large impact of this in the treatment plants, but much less impact in the control plants.
Assessing the cost-benefit for both groups depends on a number of assumptions around long-run output and cost impacts, open market consulting costs, discount rates, and rival firm copying.
We have not done this, and it is not the focus of the paper.
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Exhibit 7: The path for materials flow was often heavily obstructed
Unfinished rough path along which several 0.6 ton warp beams were taken on wheeled trolleys every day to the elevator, which led down to the looms.This steep slope, rough surface and sharp angle meant workers often lost control of the trolleys. They crashed into the iron beam or wall, breaking the trolleys. So now each beam is carried by 6 men.
A broken trolley (the wheel snapped off)
At another plant both warp beam elevators had broken down due to poor maintenance. As a result teams of 7 men carried several warps beams down the stairs every day. At 0.6 tons each this was slow and dangerous

Our plants are large by Indian and US standards
Source: Hsieh and Klenow, 2009
Average size of our plants
Employment weighted size distributions, workers per plant
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Sample of firms and plants we worked with
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Why doesn’t the consulting market fix this?
90% of the reason for non-adoption is informational, so firms not aware they are badly managed
But, surely consultants could contact firms telling them about their services?
In India there is an active telesales market selling variety of cost reduction services, so not easy
But, why don’t consultants advertise free consulting and get paid through profit sharing?
But, firms not reporting honest profits to the tax authorities so unlikely to do so to consulting firms
And, firms are breaking tax, labor and safety laws so are also nervous about outsiders (we had WB and Stanford endorsement)
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How Much Does Good Management Matter? Evidence from India.

by Barcelona Graduate School of Economics (GSE)

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How Much Does Good Management Matter? Evidence from India. Presentation Transcript