Does management matter? Evidence from India Nick Bloom (Stanford) Benn Eifert (Berkeley) Aprajit Mahajan (Stanford) David McKenzie (World Bank) John Roberts

Does management matter?Evidence from India

Nick Bloom (Stanford)Benn Eifert (Berkeley)

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

Labor Studies, July 25th 2011

Management scoreRandom sample of manufacturing population firms 100 to 5000 employees.Source: Bloom and Van Reenen (2007, QJE) and Bloom and Van Reenen (2010, JEP)

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

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

And firm management spreads look like TFP spreads

But does management cause any of these TFP 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 practices on TFP

• 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.

Plants are large compounds, with several buildings

Plants operate day and night making cotton fabric

Garbage outside the plant Garbage inside a plant

Chemicals without any coveringFlammable garbage in a plant

They are typically dirty and disorganized

They have extensive quality repair halls

They also have large scattered inventories of yarn

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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?

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 we have weekly data for

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)

Estimate Intention to Treat (ITT) and also regressions:

Run in OLS and also instrument management with treatment.

15

OUTCOMEi,t = αi + βt + θMANAGEMENTi,t+νi,t

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

17

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

18

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.

0

20

40

60

80

100

120

140

-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

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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

6

08

01

001

20

-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


23

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

24

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

Nuts & bolts

Tools

Spare parts

25

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)

26

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

8

01

001

201

40

-15 -10 -5 0 5 10 15 20 25 30 35 40 45

TFP rose in treatment plants vs controls

Control plants

Treatment plants


Tota

l fa

cto

r p

rod

uct

ivit

y


Intention to Treat estimations

Standard errors bootstrap clustered by firm.Intervention dummy zero before the intervention and 1 afterwards for the treatment plants. Dropped the 6+ months of data spanning the intervention itself

Dep. Var. QualityDefectsi,t

Inventoryi,t Outputi,t TFPi,t

Interventioni,t -0.565*** -0.273** 0.098*** 0.169**

(0.231) (0.116) (0.036) (0.067)

Small sample robustness Ibragimov-Mueller (95% Conf. Intervals)

[-0.782,-0.441]

[-0.219,0.001]

[0.218,0.470]

[0.183,0.511]

Permutation Test (p-values) 0.04 0.13 0.04 0.05

Time FEs 125 122 125 122Observations 1396 1627 1966 1447

OLS and IV estimations

Standard errors bootstrap clustered by firm. The IV for management is cumulative weeks of treatment.

Dep. Var. QualityDefects

QualityDefects

Invent. Invent. Output Output TFP TFP

Specification OLS IV OLS IV OLS IV OLS IV

Managementi,t -0.561 -1.675** -0.639*** -0.921*** 0.127 0.320** 0.160 0.488**

(0.440) (0.763) (0.242) (0.290) (0.099) (0.118) (0.179) (0.227)

1st stage Fstat 67.51 63.76 91.20 74.68Time FEs 113 113 113 113 114 114 113 113Plant FEs 20 20 18 18 20 20 20 20Observations 1732 1732 1977 1977 2312 2312 1779 1779

OUTCOMEi,t = αi + βt + θMANAGEMENTi,t+νi,t

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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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BASIC (>50% initial adoption, 9 practices)

1 month before

1 month after

3 months after

5 months after

9 months after

No information 3.3 3.2 0.5 0 0

Wrong information 30 22.4 15.4 15.2 14.4

Owner ability/time 1.1 0.8 0.5 0.8 0.8

Other 0 0 0 0 0

Total non-adoption 34.6 26.4 16.3 16.0 15.2

Basic practices were most constrained by wrong info (bad priors), advanced practices by lack of info

ADVANCED (<5% initial adoption, 10 practices)

1 month before

1 month after

3 months after

5 months after

9 months after

No information 64.0 19.1 2.9 1.5 0

Wrong information 30.9 50.7 50.7 49.3 47.1

Owner ability/time 3.7 13.2 13.2 13.2 14.0

Other 2.1 1.5 1.5 2.2 2.2

Total non-adoption 98.5 84.6 78.2 66.2 63.2

Note: 14 treatment plants. Basic mainly quality and downtime recording, & worker bonuses. Advanced mainly review meetings, standard procedures & managers bonuses.

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Summary

Management matters in Indian firms – large impacts on productivity and profitability from more modern practices

A primary reason for bad management appears to be lack of information, 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

Back Up

35

Can we learn from this small sample? (1/3)

Small sample because this is expensive! (~75K per treated plant), why also no prior large-firm management experiments

1) Is this sample large enough to get significant results? Yes:

- Homogeneous production, location, and technology, so most external shocks controlled for with time dummies.

- Large plants with 80 looms and 130 employees so individual machine and employee shocks average out

- Data from machines & logs so little measurement error- High frequency data: 114 weeks of data (large T)


2) Need to use appropriate statistical inference:– Use bootstrap firm-clustered standard-errors as baseline– Also use permutation tests (12,376 possible ways of

choosing 11 treated from 17 firms) to get test statistics which don’t rely on asymptotics.

– Use large T-asymptotics from Ibramigov-Mueller (2009)• Remove time effects• Estimate parameter of interest separately for each treatment firm, then

treat resultant 11 estimates as a draw from a t distribution with 10 d.f.• This provides robustness to heterogeneity across firms also.

All three methods give similar results


3) External validity: are these firms relatively representative of large firms in developing countries?

• While we focus on one region and one industry in one country, it is India’s largest industry in its commercial hub.

• Our firms seem at least broadly representative of firms in developing countries in terms of basic management practices (see next slide).

Table 1

All Treatmnt Contrl DiffMean Median Min Max Mean Mean p-value

Sample sizes:Number of plants 20 n/a n/a n/a 14 6 n/aNumber of firms 17 n/a n/a n/a 11 6 n/aPlants per firm 1.65 2 1 4 1.73 1.5 0.393Firm/plant sizes:Employees per firm 273 250 70 500 291 236 0.454Employees per plant 134 132 60 250 144 114 0.161Hierarchical levels 4.4 4 3 7 4.4 4.4 0.935Annual sales $m per firm 7.45 6 1.4 15.6 7.06 8.37 0.598Current assets $m per firm 12.8 7.9 2.85 44.2 13.3 12.0 0.837Management and plant ages:BVR Management score 2.60 2.61 1.89 3.28 2.50 2.75 0.203Management adoption rates 0.262 0.257 0.08 0.553 0.255 0.288 0.575Age, experimental plant (years) 19.4 16.5 2 46 20.5 16.8 0.662

Note - the production technology has not changed much over time

Warp beam

Krill

The warping looms at Lowell Mills in 1854, Massachusetts

Documents

Does management matter? Evidence from India Nick Bloom (Stanford) Benn Eifert (Berkeley) Aprajit Mahajan (Stanford) David McKenzie (World Bank) John Roberts