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Nick Bloom, Labor Topics 247, 2012
LABOR TOPICS
Nick Bloom
Learning
Nick Bloom, Labor Topics 247, 2012
Technologies – like pineapples - are not used by everyone. Question is why?
Suri (2011, Econometrica)
Nick Bloom, Labor Topics 247, 2012
A few classic learning papers
A learning related paper I know well…
Nick Bloom, Labor Topics 247, 2012
Conley and Udry (2008) is based around a learning story, with some key points
• Learning appears to happen slowly over time – pineapple does not immediately spread to every farmer in every village
• Information spreads best through friends and close contacts, suggesting people do not trust all information equally
• Spread also depends on success of trusted contacts, suggesting process of discovery – not everything known at t=0
Nick Bloom, Labor Topics 247, 2012
The original classic – Griliches (1957) – also focused on learning and discovery
Nick Bloom, Labor Topics 247, 2012
• Hybrid seen corn is a way of developing appropriate corn for different growing conditions – breeding is done for each area
• A single impactful technology that spread slowly across the US
• So Griliches splits adoption delays into– The “acceptance” problem (the lag in uptake by farmers) which is learning within markets
– The “availability” problem (breeding appropriateseed corn by market) which is discovery acrossmarkets, driven by profits
The original classic – Griliches (1957) shows gradual learning about hybrid seed corn
Nick Bloom, Labor Topics 247, 2012
Duflo, Kremer and Robinson (2011, AER) suggest other non-learning stories• Experiment on fertilizer use in Kenya where returns to fertilizer is about 50% to 100% per year – so a highly profitable investment
• Despite this farmers do not take up fertilizer, and this is despite being a well known effective technology (i.e. not learning issues)
• They has a model around hyperbolic discounting, and show in experiments with pre-commitment get large (profitable) uptake
– Discount at harvest (rather than planting) time increases adoption by 17%, equivalent to a 50% subsidy
• Interestingly, these are not persistent – it appears to be a commitment issue rather than a learning story
Nick Bloom, Labor Topics 247, 2012
Suri (2011) suggests a heterogeneity interpretation instead• Looks at hybrid maize adoption in Kenya over 1996-2004
• Stable rates of adoption and 30% of households switch (upside of using panel data, which Besley and Case 1993 also push)
• Find heterogeneity in costs and returns explains apparent adoption paradox, in particular three groups of households:
– Small group very high returns, but blocked by distance to seed/fertilizer distributors– Larger group of adopters with high returns– Larger group of switchers that have about zero returns
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A few classic learning papers
A learning related paper I know well…
Nick Bloom, Labor Topics 247, 2012
Does management matter?Evidence from India
Nick Bloom (Stanford)Benn Eifert (Berkeley)
Aprajit Mahajan (Stanford)David McKenzie (World Bank)John Roberts (Stanford GSB)
(NBER WP 2012, R&R QJE)
Nick Bloom, Labor Topics 247, 2012
Management scoreRandom sample of manufacturing population firms 100 to 5000 employees.
Source: Bloom & Van Reenen (2007, QJE); Bloom, Genakos, Sadun & Van Reenen (2011, AMP)
2.6 2.8 3 3.2 3.4
USJapan
GermanySwedenCanada
AustraliaUK
ItalyFrance
New ZealandMexicoPoland
Republic of IrelandPortugal
ChileArgentina
GreeceBrazilChina
India
One motivation for looking at management is that country management scores are correlated with GDP
Nick Bloom, Labor Topics 247, 2012 Management score
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 (N=695 firms)
India (N=620 firms)
De
nsi
tyD
en
sity
Firm management spreads like productivity spreads
Nick Bloom, Labor Topics 247, 2012
But does management cause any of these productivity differences between firms and countries?
Massive literature of case-studies and surveys but no consensus
Syverson (2011, JEL) “no potential driving factor of productivity has seen a higher ratio of speculation to empirical study”.
Nick Bloom, Labor Topics 247, 2012
So we run an experiment on large firms to evaluate the impact of modern management on productivity
• Experiment on 20 plants in large multi-plant firms (average 300 employees and $7m sales) near Mumbai making cotton fabric
• Randomized treatment plants get 5 months of management consulting intervention, controls get 1 month
• Consulting is on 38 specific practices tied to factory operations, quality and inventory control
• Collect weekly data on all plants from 2008 to 2010.
Nick Bloom, Labor Topics 247, 2012
Exhibit 1: Plants are large compounds, often containing several buildings.
Nick Bloom, Labor Topics 247, 2012
Exhibit 2a: Plants operate continuously making cotton fabric from yarn
Fabric warping
Nick Bloom, Labor Topics 247, 2012 Fabric weaving
Exhibit 2b: Plants operate continuously making cotton fabric from yarn
Nick Bloom, Labor Topics 247, 2012Quality checking
Exhibit 2c: Plants operate continuously making cotton fabric from yarn
Nick Bloom, Labor Topics 247, 2012
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
Nick Bloom, Labor Topics 247, 2012
Exhibit 4: The plant floors were often disorganized and aisles blocked
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
Nick Bloom, Labor Topics 247, 2012
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
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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?
Nick Bloom, Labor Topics 247, 2012
Intervention aimed to improve 38 core textile management practices in 5 areas
Targeted
practices in 5
areas:
operations,
quality,
inventory, HR
and sales &
orders
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Intervention aimed to improve 38 core textile management practices in 5 areas
Targeted
practices in 5
areas:
operations,
quality,
inventory, HR
and sales &
orders
Nick Bloom, Labor Topics 247, 2012
Months after the diagnostic phase
.2.3
.4.5
.6
-10 -8 -6 -4 -2 0 2 4 6 8 10 12
Adoption of the 38 management practices over time
Treatment plants
Control plants
Sh
are
of 3
8 p
ract
ice
s a
dop
ted
Non-experimental plants in treatment firms
Months after the start of the diagnostic phase
Nick Bloom, Labor Topics 247, 2012
Management practices before and after treatment
Performance of the plants before and after treatment
Why were these practices not introduced before?
Nick Bloom, Labor Topics 247, 2012
Look at four outcomes with weekly data
Quality: Measured by Quality Defects Index (QDI) – a weighted average of quality defects (higher=worse quality)
Inventory: Measured in log tons
Output: Production picks (one pick=one run of the shuttle)
Productivity: Log(VA) – 0.42*log(K) – 0.58*log(L)
27
Nick Bloom, Labor Topics 247, 2012
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
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Now mending is recorded daily in a standard format, so it can analyzed by loom, shift, design & weaver
Nick Bloom, Labor Topics 247, 2012
The quality data is now collated and analyzed as part of the new daily production meetings
Plant managers meet with
heads of departments for
quality, inventory, weaving,
maintenance, warping etc.
Nick Bloom, Labor Topics 247, 2012
02
04
06
08
01
001
201
40
-15 -10 -5 0 5 10 15 20 25 30 35 40 45
Quality improved significantly in treatment plants
Control plants
Treatment plants
Weeks after the start of the experiment
Qu
alit
y d
efe
cts
ind
ex (
hig
he
r sc
ore
=lo
we
r q
ual
ity)
Note: solid lines are point estimates, dashed lines are 95% confidence intervals
Nick Bloom, Labor Topics 247, 2012
Differences are not driven by one firm
02
46
8
-1 -.5 0 .5 1 -1 -.5 0 .5 1
0 1
Den
sity
Before/after difference in log(qdi)Graphs by Treatment group
QDI fell in every treatment firm by at least 10%.
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Stock is organized, labeled, and
entered into the computer with
details of the type, age and location.
Organizing and racking inventory enables firms to substantially reduce capital stock
Nick Bloom, Labor Topics 247, 2012
60
80
100
120
-15 -10 -5 0 5 10 15 20 25 30 35 40 45
Inventory fell in treatment plants
Control plants
Treatment plants
Weeks after the start of the experiment
Ya
rn in
ven
tory
Note: solid lines are point estimates, dashed lines are 95% confidence intervals
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Many treated firms have also introduced basic initiatives (called “5S”) to organize the plant floor
Marking out the area around the model machine
Snag tagging to identify the abnormalities
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Spare parts were also organized, reducing downtime (parts can be found quickly)
Nuts & bolts
Tools
Spare parts
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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
Nick Bloom, Labor Topics 247, 2012
80
100
120
140
-15 -10 -5 0 5 10 15 20 25 30 35 40 45
Productivity rose in treatment plants vs controls
Control plants
Treatment plants
Weeks after the start of the experiment
Tota
l fa
cto
r p
rod
uct
ivit
y
Note: solid lines are point estimates, dashed lines are 95% confidence intervals
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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?
Nick Bloom, Labor Topics 247, 2012
Why doesn’t competition fix badly managed firms?
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. As a result firm size is more linked to number of male family members (corr=0.689) than management scores (corr=0.223)
Entry appears limited: capital intensive due to minimum scale (for a warping loom and 30 weaving looms at least $1m)
Trade is restricted: 50% tariff on fabric imports from China
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Why don’t these firms improve themselves (even worthwhile reducing costs for a monopolist…)?
Asked the consultants to investigate the non-adoption of each of the 38 practices, in each plant, every other month
Did this by discussion with the owners, managers, observation of the factory, and from trying to change management practices.
Find this is primarily an information problem - Wrong information (do not believe worth doing) - No information (never heard of the practices)
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SummaryManagement matters in Indian firms – large impacts on productivity and profitability from more modern practices
Primary reason for bad management appears to be lack of information and slow learning, which limited competition allows to persist
Potential policy implications
A) Competition and FDI: free product markets and encourage foreign multinationals to accelerate spread of best practices
B) Training: improved basic training around management skills
C) Rule of law: improve rule of law to encourage reallocation and ownership and control separation