does management matter: evidence from india
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Does management matter: evidence from India. Nick Bloom (Stanford) Benn Eifert (Berkeley) Aprajit Mahajan (Stanford) David McKenzie (World Bank) John Roberts (Stanford) July 22 nd 2010. Management appears better in rich countries. - PowerPoint PPT PresentationTRANSCRIPT
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Does management matter:evidence from India
Nick Bloom (Stanford)Benn Eifert (Berkeley)
Aprajit Mahajan (Stanford)David McKenzie (World Bank)
John Roberts (Stanford)
July 22nd 2010
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2.6 2.8 3 3.2 3.4mean of management
USJapan
GermanySwedenCanada
ItalyFrance
Great BritainAustralia
New ZealandPolandIreland
PortugalChile
MexicoGreece
BrazilChina
ArgentinaIndia
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Management appears better in rich countries
Average country management score, manufacturing firms 100 to 5000 employees(monitoring, targets and incentives management scored on a 1 to 5 scale, see Bloom and Van
Reenen (2007, QJE) and Bloom, Sadun and Van Reenen (2010, JEP))
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3
0.2
.4.6
.8D
ensi
ty
1 2 3 4 5management
0.2
.4.6
.8D
ensi
ty
1 2 3 4 5management
US, manufacturing, mean=3.33 (N=695)
India, manufacturing, mean=2.69 (N=620)
Den
sity
Den
sity
With a huge spread within almost all countries
Firm level management score, manufacturing firms 100 to 5000 employees
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But do we care - does management matter?• Long debate between business practitioners versus economists
• Evidence to date primarily case-studies and surveys
• So in India we ran a management field experiment
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We investigate these questions in large Indian firms
Took large firms (≈ 300 employees) outside Mumbai making cotton fabric. Randomized treatment plants get heavy management consulting, controls plants get very light consulting.
Collect weekly data on all plants from 2008 to 2010
1) Profits and productivity up by about 20%2) Decentralization of power within firms3) Increased computerization
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Exhibit 1: Plants are large compounds, often containing several buildings.
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Exhibit 2a: Plants operate continuously making cotton fabric from yarn
Fabric warping
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Fabric weaving
Exhibit 2b: Plants operate continuously making cotton fabric from yarn
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Quality checking
Exhibit 2c: Plants operate continuously making cotton fabric from yarn
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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
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Exhibit 4: The plant floors were often disorganized and aisles blocked
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Exhibit 5: There was almost no routine maintenance – instead machines were only repaired when they broke down
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Exhibit 6a: Inventory was not well controlled – firms had months of excess yarn, typically stored in an ad hoc way all over the factory
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Exhibit 6b: Inventory was not well controlled – firms had months of excess yarn, typically stored in an ad hoc way all over the factory
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Management practices before and after treatment
Performance of the plants before and after treatment
Why were these practices not introduced before?
Decentralization and IT
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Intervention aimed to improve 38 core textile management practices in 6 areas
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Intervention aimed to improve 38 core textile management practices in 6 areas
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.2.3
.4.5
.6
-10 -8 -6 -4 -2 0 2 4 6 8 10 12Months after the diagnostic phase
Treatment plants (on-site) Control plants
(on-site)
Sha
re o
f key
text
ile m
anag
emen
t pra
ctic
es a
dopt
ed
Excluded plants(not treatment or control)
Adoption of these 38 management practices did rise, and particularly in the treatment plants
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Management practices before and after treatment
Performance of the plants before and after treatment• Quality• Inventory• Output
Why were these practices not introduced before?
Decentralization and IT
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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
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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
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020
4060
8010
012
014
0
-20 -10 0 10 20 30 40weeks since diagnostic phase
2.5th percentile
Figure 3: Quality defects index for the treatment and control plants
Control plants
Treatment plants
Weeks after the start of the diagnostic
Qua
lity
defe
cts
inde
x (h
ighe
r sco
re=l
ower
qua
lity)
Start of Diagnostic
Start of Implementation
Average (+ symbol)
97.5th percentile
Average (♦ symbol)
97.5th percentile
End of Implementation
2.5th percentile
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Quality results in regression format
Note: standard errors boostrap clustered by firm
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Management practices before and after treatment
Performance of the plants before and after treatment• Quality• Inventory• Output
Why were these practices not introduced before?
Decentralization and IT
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Organizing and racking inventory enables firms to slowly reduce their capital stock
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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
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Inventory results in regression format
Note: standard errors boostrap clustered by firm
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Management practices before and after treatment
Performance of the plants before and after treatment• Quality• Inventory• Output
Why were these practices not introduced before?
Decentralization and IT
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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
Toolstorage organized
Parts like gears,
bushes, sorted as per specifications
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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
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Output results in regression format
Note: standard errors boostrap clustered by firm
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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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00
111
1
1
1 00
1
0
1
1
1
0
1
1
01 1
1 001
11
1
0.2
.4.6
.8
0 .2 .4 .6Change in management practices
Cha
nge
in th
e de
cent
raliz
atio
n in
dex
Change in management practices
correlation 0.594(p-value 0.001)
1=treatment plant, 0=control plant
Figure 6: Changes in decentralization and management practices
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0
0
1
1
00
1
0
11
0
1
0
1
1
0
0
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0.5
11.
52
0 .2 .4 .6Change in management practices
Cha
nge
in th
e co
mpu
teriz
atio
n in
dex
Change in management practices
correlation 0.778(p-value 0.001)
1=treatment plant, 0=control plant
Figure 7: Changes in computerization and management practices
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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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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.
Entry is limited: Capital intensive ($13m assets average per firm), and no guarantee new entrants are any better
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So why did these firms not improve themselves?
Collected panel data on reasons for non implementation, and main (initial) reason was a lack of information• Firms either never heard of these practices (no information)• Or, did not believe they were relevant (wrong information)
Later constraints after informational barriers overcome primarily around limited CEO time and CEO ability
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SummaryImproving management practices improves productivity, leads to more decentralized decision making and greater use of IT
A primary reason for bad management appears to be lack of information, which limited competition allows to persist
Policy implicationsA) 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