Nick Bloom, Labor Topics 247, 2012 LABOR TOPICS Nick Bloom Learning.

Nick Bloom, Labor Topics 247, 2012

LABOR TOPICS

Nick Bloom

Learning


Technologies – like pineapples - are not used by everyone. Question is why?

Suri (2011, Econometrica)


A few classic learning papers

A learning related paper I know well…


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


The original classic – Griliches (1957) – also focused on learning and discovery


• 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


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


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

Nick Bloom, Labor Topics 247, 2012 9

A few classic learning papers

A learning related paper I know well…


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)


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


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”.


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.


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


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


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 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


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


Management practices before and after treatment

Performance of the plants before and after treatment

Why were these practices not introduced before?


Intervention aimed to improve 38 core textile management practices in 5 areas

Targeted

practices in 5

areas:

operations,

quality,

inventory, HR

and sales &

orders


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


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


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


Previously mending was recorded only to cross-check against customers’ claims for rebates


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


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.


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


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%.


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


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


Ya

rn in

ven

tory



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


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

Nuts & bolts

Tools

Spare parts


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)


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


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


Tota

l fa

cto

r p

rod

uct

ivit

y



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


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)


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

Nick Bloom, Labor Topics 247, 2012 LABOR TOPICS Nick Bloom Learning.

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