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LABOR TOPICS Nick Bloom Learning. Technologies – like pineapples - are not used by everyone. Question is why?. Suri (2011, forthcoming Econometrica ). A few classic learning papers A learning related paper I know well….

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

Suri (2011, forthcoming Econometrica)

conley and udry 2008 is based around a learning story with some key points
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
slide6

The original classic – Griliches (1957) shows gradual learning about hybrid seed corn

  • 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
duflo kremer and robinson 2010 suggest other non learning stories
Duflo, Kremer and Robinson (2010) 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 at 50% subsidy
  • Interestingly, these are not persistent – it appears to be a commitment issue rather than a learning story
suri 2010 suggests a heterogeneity interpretation instead
Suri (2010) 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
slide10

Does management matter: evidence from IndiaNick Bloom (Stanford)Benn Eifert (Berkeley)Aprajit Mahajan (Stanford)David McKenzie (World Bank)John Roberts (Stanford)NBER WP16658

slide11

Management appears to be 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)Source: Bloom, Sadun and Van Reenen (2010, Annual Review)

11

slide12

Developing countries have more badly managed firms

US, manufacturing, mean=3.33 (N=695)

Density

India, manufacturing, mean=2.69 (N=620)

Density

Firm level management score, manufacturing firms 100 to 5000 employeesSource: Bloom and Van Reenen (2010, JEP)

12

but do we care does management matter
But do we care - does management matter?
  • Long debate between business practitioners versus academics
  • Evidence to date primarily case-studies and surveys. In fact Syverson’s(2010) productivity survey stated on management

“Perhaps no potential driver of productivity differences has seen a higher ratio of speculation to actual empirical study than management”

  • So in India we ran a management field experiment
investigate in large indian firms
Investigate in large Indian firms

Took large firms (≈ 300 employees) outside Mumbai making cotton fabric. Randomized treatment plants get 5 months management consulting, controls plants get 1 month consulting.

Collect weekly data on all plants from 2008 to 2010

1) Management ‘improves’

2) Productivity and profits up by about 10% to 20%

3) Decentralization of decision making within firms

4) Increased computerization

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

More photos and some basic video footage on http://worldmanagementsurvey.org/

slide19

Exhibit 3: Many parts of these Indian plants were dirty and unsafe

Garbage outside the plant

Garbage inside a plant

Flammable garbage in a plant

Chemicals without any covering

slide21

Exhibit 5: There was almost no routine maintenance – instead machines were only repaired when they broke down

slide22

Exhibit 6a: Inventory was not well controlled – firms had months of excess yarn, typically stored in an ad hoc way all over the factory

slide23

Exhibit 6b: Inventory was not well controlled – firms had months of excess yarn, typically stored in an ad hoc way all over the factory

slide24
Management practices before and after treatment

Performance of the plants before and after treatment

Why were these practices not introduced before?

Decentralization and IT

24

slide26

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

.6

.5

.4

.3

.2

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

Months after the diagnostic phase

Treated

Treatment plants

Control plants

Share of key textile management practices adopted

Control

Excluded plants(not treatment or control)

slide27

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

slide28

Poor quality meant 19% of manpower went on repairs

Large room full of repair workers (the day shift)

Workers spread cloth over lighted plates to spot defects

Defects are repaired by hand or cut out from cloth

Defects lead to about 5% of cloth being scrapped

previously mending was recorded only to cross check against customers complaints
Previously mending was recorded only to cross-check against customers’ complaints

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

now mending is recorded daily in a standard format for analysing by loom shift weaver
Now mending is recorded daily in a standard format, for analysing by loom, shift, & weaver

30

slide31

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

slide32

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

Start of Diagnostic

Start of Implementation

End of Implementation

97.5th percentile

Control plants

Average (♦ symbol)

Quality defects index (higher score=lower quality)

2.5th percentile

97.5th percentile

Average (+ symbol)

Treatment plants

2.5th percentile

Weeks after the start of the diagnostic

slide33

Differences are not driven by one firm

Control

Treatment

8

6

Density

4

2

0

-1

-.5

0

.5

1

-1

-.5

0

.5

1

Before/after difference in log(QDI)

QDI fell in every treatment firm by at least 10%.

can also run weekly performance regressions

Instrument “Management” with log(1+weeks of consulting)

Calculate standard errors using clustered bootstrap, and also using small-sample permutation and t-asymptotic tests

Can also run weekly performance regressions

34

quality a quality defects index
Quality (a Quality Defects Index)

Note: standard errors bootstrap clustered by firm. Instrument in second column in log(1+weeks treatment). ITT is intention to treat and regresses log(QDI) on a 0/1 indicator for treatment. IV instruments management with log (1+weeks of consulting)

slide36
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

36

slide38

Figure 4: Yarn inventory for the treatment and control plants

Start of Diagnostic

Start of Implementation

End of Implementation

97.5th percentile

Average (♦ symbol)

Control plants

97.5th percentile

Yarn inventory (normalized to 100 prior to diagnostic)

2.5th percentile

Average (+ symbol)

Treatment plants

2.5th percentile

Weeks after the start of the intervention

slide39

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.

slide40

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

Nuts & bolts sorted as per specifications

Parts like gears, bushes, sorted as per specifications

Tool

storage organized

slide41

Production data is now collected in a standardized format, for discussion in the daily meetings

After (standardized, so easy to enter daily into a computer)

Before(not standardized, on loose pieces of paper)

daily performance boards have also been put up with incentive pay for employees based on this
Daily performance boards have also been put up, with incentive pay for employees based on this
slide43

Figure 5: Output for the treatment and control plants

Start of Diagnostic

Start of Implementation

End of Implementation

97.5th percentile

Treatment plants

Average (+ symbol)

Output (normalized to 100 prior to diagnostic)

2.5th percentile

97.5th percentile

Average (♦ symbol)

Control plants

2.5th percentile

Weeks after the start of the intervention

slide44
Management practices before and after treatment

Performance of the plants before and after treatment

Decentralization and IT

Why were these practices not introduced before?

44

better management improved information flow enabling owners to trust managers more
Better management improved information flow enabling owners to trust managers more
  • The India firms hierarchical: owners take all major decisions
  • Reason is owners fear theft by managers:
  • punishment is limited (Indian courts are ineffective)
  • risk of getting caught is limited (little information to monitor)
  • Better management, increases information, so better monitoring
  • So owners delegate more: visit factories less, take less decisions
slide46

Better management led to decentralization in firms

Decentralization index is the principal component factor of 7 measures of decentralization around weaver hiring, manager hiring, spares purchases, maintenance planning, weaver bonuses, investment, and departmental co-ordination.

slide47

Better management also increased computerization

(pre-experiment mean=10)

Computerization index is the principal component factor of 10 measures around computerization, which are the use of an ERP system, the number of computers in the plant, the number of computers less than 2 years old, the number of employees using computers for at least 10 minutes per day, and the cumulative number of hours of computer use per week, an internet connection at the plant, if the plant-manager uses e-mail, if the directors use of e-mail, and the intensity of computerization in production.

slide48
Management practices before and after treatment

Performance of the plants before and after treatment

Decentralization and IT

Why were these practices not introduced before?

48

why does competition not fix bad management
Why does competition not fix bad management?

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

so why did these firms not improve themselves limited information learning
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

So why did these firms not improve themselves – limited information/learning

50

slide51

Adoption of these management practices was spread by firms to non-experimental plants: learning

Treatment plants (on-site)

Control plants (on-site)

Share of key textile management practices adopted

Excluded plants in treatment firms