LABOR TOPICS Nick Bloom Learning

LABOR TOPICSNick BloomLearning

Technologies – like pineapples - are not used by everyone. Question is why? Suri (2011, forthcoming Econometrica)

A few classic learning papersA 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

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 • 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 • 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

A few classic learning papersA learning related paper I know well…

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

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

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

Exhibit 2a: Plants operate continuously making cotton fabric from yarn Fabric warping

Exhibit 2b: Plants operate continuously making cotton fabric from yarn Fabric weaving

Exhibit 2c: Plants operate continuously making cotton fabric from yarn Quality checking

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

Exhibit 4: The plant floors were often disorganized and aisles blocked

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

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

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

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

Intervention aimed to improve core textile management practices in 6 areas – e.g.

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)

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

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 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 30

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

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

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%.

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) 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)

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

Organizing and racking inventory enables firms to slowly reduce their capital stock

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

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.

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

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

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

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 • 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

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.

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.

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? 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

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

LABOR TOPICS Nick Bloom Learning