Does management matter:evidence from India

Nick Bloom (Stanford)Benn Eifert (Berkeley)

Aprajit Mahajan (Stanford)David McKenzie (World Bank)John Roberts (Stanford GSB)

LSE/UCL seminarMarch 1st 2010

We thank the Freeman Spogli Institute, the International Growth Centre,

the Kauffman Foundation, the Murty Family, and World Bank for funding

2.6 2.8 3 3.2 3.4mean of management

USGermanySweden

JapanCanadaFrance

ItalyGreat Britain

AustraliaNorthern Ireland

PolandRepublic of Ireland

PortugalBrazilIndia

ChinaGreece

2

Management appears worse in developing countries

Average country management score, manufacturing firms 100 to 5000 employees(monitoring, targets and incentives management scored on a 1 to 5 scaleusing the methodology developed in Bloom & Van Reenen (2007, QJE))

69533627012234431218876238292231102140

524171

620559

# firms

3Firm-Level Management Scores

0.2

.4

.6

.8

De

nsity

1 2 3 4 5management

0.2

.4

.6

.8

De

nsity

1 2 3 4 5management

US manufacturing, mean=3.33 (N=695)

Indian manufacturing, mean=2.69 (N=620)

India’s low score is due to a tail of badly managed firmsD

en

sity

De

nsi

ty

Firm level histograms underlying the country averages from the last figure

4

This raises two obvious questions

1. Does “bad” management really reduce productivity, or are Indian firms differently managed because, for example, wages are low?

2. If it does matter, why are so many Indian firms badly managed?

Also, links to a long literature in social science on the importance of management, from the earliest work on profit spreads (e.g. Walker, 1887) to recent work on productivity spreads (e.g. Syversson, 2010)

5

SummaryExperiment on plants in large (≈ 300 person) Indian textile firms

Randomized treatment plants get heavy management consulting, controls plants get very light consulting (just enough to get data)

Collect weekly performance data on all plants from 2008 to 2010

• Improving management practices led to large increases in productivity and profitability

• Reasons for bad management are informational (firms not aware of modern practices), and CEO capabilities & behavior

Before I show any data would like to show some photos of the plants to give context to the results

6

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.

Exhibit 1: Plants are large compounds, often containing several buildings.

Plant surrounded by grounds

Front entrance to the main building Plant buildings with gates and guard post

Plant entrance with gates and a 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 coveringFlammable garbage in a plant

Exhibit 4: The plant floors were disorganized

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

Yarn piled up so high and deep that access to back

sacks is almost impossible

Exhibit 5: 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

No protection to prevent damage and rustSpares without any labeling or order

Exhibit 6: The spare parts stores were also disorganized and dirty

Shelves overfilled and disorganizedSpares without any labeling or order

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

0.2

.4

.6

.8

De

nsity

1 2 3 4 5management

14Management scores (using Bloom and Van Reenen (2007) methodology)

Brazil and China Manufacturing,

mean=2.67

0.2

.4

.6

.8

De

nsity

1 2 3 4 5management

0.2

.4

.6

.8

1

De

nsity

1 2 3 4 5management

0.5

11

.5

De

nsity

1 3 5management

Indian Manufacturing,

mean=2.69

Indian Textiles, mean=2.60

Experimental Firms, mean=2.60

These firms appear typical of large manufacturers in India, China and Brazil

15

Management practices before and after treatment

Performance of the plants before and after treatment

• Quality

• Inventory

• Operational efficiency

Why were these practices not introduced before?

16

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: 34 willing to participate straight-away, so randomly picked 20 plants from these 17 firms

• A team of 6 consultants from Accenture, Mumbai was hired to help improve the practices in some of these firms• Control: 1 month of diagnostic• Treatment: 1 month diagnostic + 4 months implementation

• Collecting data from April 2008 to December 2010

17

Sample of firms we worked with

18

Our plants are large by Indian and US standardsSource: Hsieh and Klenow, 2009

Average size of our plants

Employment weighted size distributions, workers per plant

19

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

20

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

.2.3

.4.5

.6

2008.25 2008.5 2008.75 2009 2009.25 2009.5 2009.75ym

January 2009 April 2009 July 2009October 2008July 2008 October 2009April 2008 January 2010

21

Adoption of these 38 management practices did rise, and particularly in the treatment plants

Notes: Non-experiment plants are other plants in the wave 2 treatment firms that were not involved in the experiment. They improved practices because the firms internally copied these over. All initial differences not statistically significant (Table 2)

Wave 1 treatment plants: Diagnostic September 2008, implementation began October 2008

Control plants:Diagnostic July 2009

Wave 2 treatment plants: Diagnostic April 2009, implementation began May 2008

Non experiment plants (plants in wave 2 firms with no intervention)

Sha

re o

f the

38

man

agem

ent p

ract

ices

ado

pted

22

Management practices before and after treatment

Performance of the plants before and after treatment

• Quality

• Inventory

• Operational efficiency

Why were these practices not introduced before?

Poor quality meant 19% of manpower went on repairs

Workers spread cloth over lighted plates to spot defectsLarge room full of repair workers (the day shift)

Defects lead to about 5% of cloth being scrappedDefects are repaired by hand or cut out from cloth

24

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

2525

Now mending is recorded daily in a standard format, so it can analyzed by loom, shift, design & weaver

26

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

05

01

00

15

0

-20 -10 0 10 20 30 40timing

Figure 3: Quality defects index for the treatment and control plants

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.

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 + β1(Intervention weeks)i,t + β1(Intervention weeks)2i,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

29Data is weekly at the plant level. Standard errors are boostrap clustered at the firm level.

30

Management practices before and after treatment

Performance of the firms before and after treatment

• Quality

• Inventory

• Operational efficiency

Why were these practices not introduced before?

31

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.

Organizing and racking inventory enables firms to substantially reduce capital stock

32

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

33Data is weekly at the plant level. Standard errors are boostrap clustered at the firm level.

34

Management practices before and after treatment

Performance of the firms before and after treatment

• Quality

• Inventory

• Operational efficiency

Why were these practices not introduced before?

35

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.

36

Spare parts were also organized, reducing downtime (parts can be found quickly) and waste

Nuts & bolts sorted as per specifications

Tool

storage organized

Parts like gears,

bushes, sorted as per specifications

37

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)

38

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

39Data is weekly at the plant level. Standard errors are boostrap clustered at the firm level.

40

Impact on productivity and profitability looks large

Estimate increased profit by about $475,000 per firm (≈ 24%)

Productivity increased by about 15%

Long-run impacts potentially much larger as more flexibility on changing inputs and product choice

41

Management practices before and after treatment

Performance of the firms before and after treatment

• Quality

• Inventory

• Operational efficiency

Why were these practices not introduced before?

42

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.

The next slide shows this data over time

43

1 month before

1 month after

3 months

after

5 months

after

7 months

after

9 months

after

Lack of information(not aware of the practice)

38.6 12.8 2.2 0.5 0.4 0.3

Incorrect information(wrong cost-benefit analysis)

29.3 33.3 31.9 29.2 28.5 27.5

Owner ability, time and/or procrastination

1.3 9.1 7.2 7.5 7 6.7

Manager incentives and/or authority

0 2.1 2.4 3.0 3 3.2

Not profitable(non-adoption is correct)

0 0.2 0.4 0.5 0.5 0.5

Other(variety of other reasons)

0 0.2 0.4 0.2 0.5 0.5

Total(% practices not adopted)

73 57.7 44.3 40.9 39.8 38.6

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.

44

Lack of information

Incorrect infor-

mation

Owner ability pro-

crastination

Plant manager

incentives or authority

Other Doing

Lack of information

33 22.3 13.5 1.8 0.9 28.4

Incorrect information

92 0.4 7.6

Owner ability, time or procrastination 81.8 18.2

Manager incentive and/or authority 100

Not profitable 100

Other100

Transition matrix for the reasons for non-adoption2 months ahead (t+1)

Cu

rren

t (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.

45

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

46

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)

47

SummaryFirms 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

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

Warp beam

Krill

The warping looms at Lowell Mills in 1854, Massachusetts

52

“Non adoption flow chart” used to collect data

Was the firm previously awarethat the practice existed? Lack of information

Can the firm adopt the practice with existing staff & equipment?

Did the owner believe introducing the practice would be profitable?

Low ability of the owner and/or procrastination

Does the firm have enough internal financing or access to credit?

Do you think the CEO was correct about the cost-benefit tradeoff?

Could the firm hire new employees or consultants

to adopt the practice?

Credit constraints

External factors (legal, climate etc)Is the reason for the non adoption of the practice internal to the firm?

Could the CEO get his employees to introduce the practice?

Did the firm realize this would be

profitable?

Would this adoption be profitable Not profit maximizing

Incorrect information

Lack of local skills

Other reasons

Limited incentives and/or authority for employees

Yes

No

Legend

Conclusion

Hypothesis

No

Yes

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