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Measuring & explaining management practices Nick Bloom (Stanford & NBER) based on work with Raffaella Sadun (HBS) & John Van Reenen (LSE) MIT/Harvard Org Econ Lecture 1 (February 2010). Two part lecture course. Lecture 1: Measuring and explaining management practices

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slide1

Measuring & explaining management practices

Nick Bloom (Stanford & NBER)based on work with Raffaella Sadun (HBS) & John Van Reenen (LSE)

MIT/Harvard Org Econ Lecture 1 (February 2010)

slide2

Two part lecture course

  • Lecture 1: Measuring and explaining management practices
  • Overview what are management practices, how we measure them, why they vary and what effect they have on performance
  • Highlight how little is rigorously known – management is one of the major holes in social science, and a great research area
  • Lecture 2: Measuring and explaining organizational practices
  • As above overview what are organizational practices, how we measure them, why they vary and what effect this has
  • Again, large holes in rigorous large sample causal evidence
slide3

Other points

  • Meeting up: I am around until late Thursday so e-mail me if you would like to talk for individually about work or topics
  • Lectures: I will post all the lectures on my website (teaching page) on Thursday PM
  • http://www.stanford.edu/~nbloom/ (or Google Nick Bloom)
  • Breaks: I’ll take a 10 minute break at 10:30
  • Questions: Please feel free to ask any questions and/or make comments. I’ve not prepared material assuming this
slide4

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Management practices across firms and countries
  • Explaining why management practices vary
  • The effect of management practices on performance
slide5

Large GDP & TFP differences across countries

Average US worker makes more in 2 weeks than a Tanzanian in 1 year

Source: Jones and Romer (2009). US=1

slide6

These TFP & GDP differences are often persistent

Source: Maddison (2008) Data is smoothed by decade

productivity differences across firms are also large
Productivity differences across firms are also large

US plants at 90th percentile have 4x higher labor productivity than plant at the 10th percentile (Syverson, 2004 REStat)

Controlling for other inputs, TFP difference is about 2:1

In China and India this gap is about 5:1 (Hsieh and Klenow, 2009 QJE)

Not just mismeasured prices: in detailed industries with plant level prices like white pan bread, block ice, concrete productivity differences still 2:1 (Foster et al, 2008 AER)

slide8

TFP dispersion is particularly large in low competition industries

Low competition

High competition

Source: Syverson (2004, JPE)

slide9

Productivity difference between firms in one obvious motivation for looking at management

Persistent TFP differences a key part of many Macro, Trade, IO and Labor models – but these are typically silent on the casues

Could this be in part because of differences in management – even Adam Smith’s 1776 wealth of nations suggests this matters

Today will present a bunch of evidence showing that management differences do seem to be a major factor driving TFP differences

Of course, might also be interesting in management for a range of other reasons around growth, strategy and theory of the firm

slide10

Note, good productivity overview paper, Syversson (2010, NBER WP)

Since completing your lecture outline noticed a very nice overview of the productivity literature has been complied by Syversson

“What determines productivity”, NBER WP 15712 and forthcoming in the Journal of Economic Literature

http://home.uchicago.edu/~syverson/productivitysurvey.pdf

slide11

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Management practices across firms and countries
  • Explaining why management practices vary
  • The effect of management practices on performance
slide12

Before discussing “management practices”, want to point out that this is different from “managers”

There is also a large literature looking at CEOs (managers) – for example Jack Welch, Bill Walsh or Alex Ferguson

Best known paper is Bertrand and Schoar (2003, QJE)

They build a panel dataset tracking managers across US firms over time, and allow for firm and manager fixed effects

Focus on large US publicly traded firms – average of about 10,000 employees – so represents impact of strategy by the top manager

slide13

Summary of Bertrand and Schoar (2003)

  • Interesting results, and highly cited, finding:
  • Manager fixed effect exist, particularly for M&A, dividend policy, debt ratios and cost-cutting
  • Managers have styles - more/less aggressive and internal/external growth focus
  • Managers are also absolutely “better” or “worse” – performance fixed effects exist, and linked to compensation and governance
  • These styles and fixed effects also correlated with manager characteristics – particularly CEO age and having an MBA
slide14

Measuring management practices

  • Also a literature on management practices, which I will focus on in these lectures, as these are more about firms than individuals
  • Historically been strongly case study based – e.g. Ford, GM, Toyota, GE, Mayo Clinic, Citibank, Dabbawala etc.
  • Case-studies helpful for intuition and illustration, but potentially misleading because very selected sample – e.g. Enron
  • More recently work has focused on trying to systematically measure management practices in large samples of firms
    • First generation, single country studies & direct questions
    • Second generation, international studies & indirect questions
slide15

Challenges to measuring management practices

  • Despite sounding easy, “measuring management” is fraud with
  • difficulties, which has held back research.
  • How to quantify (as in put numbers on) management practices
  • How to get data from firms – surveys are tough to do
  • How to get the truth – will badly managed firms ‘fess-up’
  • Building a representative population – e.g. not just targeting Compustat firms – especially important for cross-country work
slide16

First generation surveys: single-country focus with direct survey techniques

  • Black and Lynch (2001, REStat) is a good example of a well
  • executed single country management survey
  • Surveyed about 3,000 establishments with the US Census bureau
  • Quantify: Asked a series of questions on employee recruitment, work organization, meetings and modern production practices
  • Get data: Administered by the US Census Bureau
  • Truth: Told respondents their answers were confidential
  • Population: stratified from the Census establishment database
  • Found large variations in management, and strong correlation of
  • management practices and performance
slide17

Second wave surveys: cross countries and tries to address response bias with indirect surveys

  • Cross country comparisons: identification of many factors driving management typically require cross-country data
  • Problems with direct surveys: unfortunately people typically do not tell the complete truth in open surveys:
    • Schwartz (1999, American Pschologist)
    • Opinion poll-evidence
    • Bertrand and Mullainathan (2001, AER P&P).
  • Bloom and Van Reenen (2007, QJE) is a good example of a second wave of management survey, which I’ll cover in detail
slide18

The Bloom and Van Reenen (2007) approach

  • 1) Quantifying: use scoring grid from a consulting firm
    • Scores 18 monitoring, targets and incentives practices
    • ≈45 minute phone interview of manufacturing plant managers
  • 2) Truth: use “Double-blind”
    • Interviewers do not know the company’s performance
    • Managers are not informed (in advance) they are scored
    • All interviews run from a single location with rotation by country
  • 3) Getting data: a variety of tricks
    • Introduced as “Lean-manufacturing” interview, no financials
    • Official Endorsement: Bundesbank, PBC, CII & RBI, etc.
    • Run by 75 MBAs types (loud, assertive & business experience)
  • 4) Population: sample randomly medium and large firms (100-5000 employees) from population databases across countries
slide21

Getting representative cross country samples

  • So far interviewed about 7,000 firms across about 20 countries
    • Obtained 45% coverage rate from sampling frame (with response rates uncorrelated with performance measures)
  • Currently being extended to Charities, Hospitals, Law Firms, Retail firms, PPPs, Schools and Tax Collection Agencies
    • So basic concept easily transported across industries
slide22

Internal survey validation – useful exercise suggesting double-blind methodology may work

Re-interviewed 222 firms with different interviewers & managers

Firm average scores (over 18 question)

Firm-level correlation of 0.627

2nd interview

1st interview

slide23

External survey validation – another useful exercise suggesting double-blind methodology may work

Performance measure

country c

management

(average z-scores)

ln(capital)

other controls

ln(labor)

ln(materials)

  • Use most recent cross-section of data (typically 2006)
  • Note – not a causal estimation, only an association
slide24

External validation: better performance is correlated with better management

Includes controls for country, with results robust to controls for industry, year, firm-size, firm-age, skills etc.

Significance levels: *** 1%, ** 5%, * 10%.

Sample of all firms where accounting data is available

slide25

External validation – robustness across countries (the “ooh la la” question)

  • Performance results robust in all main regions:
    • Anglo-Saxon (US, UK, Ireland and Canada)
    • Northern Europe (France, Germany, Sweden & Poland)
    • Southern Europe (Portugal, Greece and Italy)
    • East Asia (China and Japan)
    • South America (Brazil)
slide26

Consistent with Helpman, Melitz and Yeaple (2004, AER) well managed firms also export more

Share of firms exporting

Management score (rounded to nearest 0.5)

slide27

1 point higher management score associated with about 20% less energy use

Energy use, log( KWH/$ sales)

Management

Well managed firms also more energy efficient

  • Many US firms not operating using energy efficient technology (De Cannio, 1993 EP ), due in large part to a mix of management problems (Howarth, Haddad and Paton, 200 EP)
  • We find similar results in our management data (below)

Source: Bloom, Genakos, Martin and Sadun, (2010, EJ). Analysis uses Census of production data for UK firms

slide28

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Management practices across firms and countries
  • Explaining why management practices vary
  • The effect of management practices on performance
slide29

US management practices score highest on average, with developing countries lowest

Also have data from Chile, Mexico and New Zealand, but not yet publicly released

Average Country Management Score

slide31

Relative management practices also vary by country

Relatively better at ‘operations’ management (monitoring, continuous improvement, Lean etc)

Relatively better at ‘people’ management (hiring, firing, pay, promotions etc)

People management (hiring, firing, pay & promotions) – operations (monitoring, continuous improvement and Lean)

slide32

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Management practices across firms and countries
  • Explaining why management practices vary
  • The effect of management practices on performance
slide33

So why does management vary across countries and firms?

  • I will discuss five factors that seem important
  • Competition
  • Family firms
  • Multinationals
  • Labor market regulations
  • Education
  • But, before that, I that want to raise one informational constraint for why every firm does not adopt best practices
wanted to find out if firms were aware of their practices being good bad
Wanted to find out if firms were aware of their practices being good/bad?

We asked:

“Excluding yourself, how well managed would you say your firm is on a scale of 1 to 10, where 1 is worst practice, 5 is average and 10 is best practice”

We also asked them to give themselves scores on operations and people management separately

to the extent they are honest most managers seem to think they are well above average
To the extent they are honest, most managers seem to think they are well above average

“Worst Practice”

“Average”

“Best Practice”

the brazilians and greeks overscored the most the us and french the least
The Brazilians and Greeks overscored the most, the US and French the least

Self score (normalized to 1 to 5 scale) – Management score

slide37
These self-scores also appear not only too high on average, but also uncorrelated with actual practices

Correlation

0.032*

Labor Productivity

Self scored management

* In comparison the management score has a 0.295 correlation with labor productivity

slide38

So seems many firms are unaware of their poor management, consistent with a range of evidence that management practices are a type of technology

A number of studies, including several I will discuss later on this lecture, provide evidence that management is a technology

Innovations include the American System of Manufacturing, Scientific Management, Mass Production, M-form firm, Quality Movement and Lean

So one reason for bad management is like any other technology there is a diffusion curve, with many firms below the curve

Even so, many well informed firms are badly managed, hence why I will discuss a range of other factors

slide39

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Explaining why management practices vary
    • Competition
    • Family firms
    • Multinationals
    • Labor market regulations
    • Education
  • 4. The effect of management practices on performance
slide40

Tough competition appears strongly linked to better management practices

1 1-Rents = 1- (operating profit – capital costs)/sales2 Includes 108 SIC-3 industry, country, firm-size, public and interview noise (analyst, time, date, and manager characteristic) controls3 S.E.s in ( ) below, robust to heteroskedasticity, clustered by country-industry

slide41

We also have some management panel data, and find similar results

1 1-Rents = 1- (operating profit – capital costs)/sales S.E.s in ( ) below, robust to heteroskedasticity, clustered by country-industry

UK, US, France and Germany only

slide42

Competition also appears linked to selection

An additional point on the management score is associated with

an increase of employment

US 715 more workers

UK 546 more workers

India 263 more workers

Competitive forces of reallocation weak in India compared to US

slide43

So appears to be a mix of ways competition can improve management

“Incentives” (e.g. “Boot up the ass effect”) – competition forces badly managed firms to improve performance

“Selection” – competition selects out badly managed firms

“Learning” – competition provides more firms in and industry, increasing experimentation and learning.

slide44

Studies on TFP find similar results of competition on performance

Syversson (2004, JPE) looks at the concrete industry and finds that more competitive markets had higher average levels of TFP and less dispersion.

Pavcnik (2002, REStud) and Olley-Pakes (1996, Econometrica) also at changes in competition from trade-reforms and deregulations respectively, finding this weeds out low TFP firms

Schmitz (2005, JPE) shows great lakes iron-producers responded heavily to import competition

Nickell (1996, JPE) shows changes in competition lead to faster TFP growth within a panel of firms

slide45

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Explaining why management practices vary
    • Competition
    • Family firms
    • Multinationals
    • Labor market regulations
    • Education
  • 4. The effect of management practices on performance
slide46

Management practices vary strongly with ownership, even after controlling for industry, country, skills, size etc.

Distribution of firm management scores by ownership. Overlaid dashed line is approximate density for dispersed shareholders, the most common US ownership type

Average Management Score

slide47

share family CEO (2nd+ generation)

share founderCEO (1st generation)

share government owned

Ownership differences are another factor behind cross-country variations in management practices

share of ownership (for types associated with low management scores)

slide48

Results again consistent with other studies using other performance metrics

  • One nice study is by Perez-Gonzalez (2006, AER) looking at the impact of a family CEO
    • Finds stock prices fall the day a firm’s founder announces they are passing the CEO position down to one of their kids
    • Drops particularly for hand-downs to kids who went to non-selective schools
  • Another clever study by Bennedsen, Nielson, Perez-Gonalez, and Wolfenzon (2007, QJE) on family firms and performance
    • Use gender of the first born to instrument for family control
    • Find family CEOs reduce profitability and growth rates
slide49

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Explaining why management practices vary
    • Competition
    • Family firms
    • Multinationals
    • Labor market regulations
    • Education
  • 4. The effect of management practices on performance
slide50

Multinationals appear to always be well managed, consistent with selection and most trade models

Foreign multinationals

Domestic firms

Average Management Score

slide51

Multinational presence also linked to cross-country differences in average management practices

Foreign multinationals

Domestic multinationals

share of multinationals

slide52

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Explaining why management practices vary
    • Competition
    • Family firms
    • Multinationals
    • Labor market regulations
    • Education
  • 4. The effect of management practices on performance
slide53

Labor market regulations appear linked to worse management, particularly incentives management

Average incentives management(hiring, firing, pay and promotions)

World Bank Employment Rigidity Index

slide54

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Explaining why management practices vary
    • Competition
    • Family firms
    • Multinationals
    • Labor market regulations
    • Education
  • 4. The effect of management practices on performance
slide55

Finally, education is highly correlated with better management, but no idea on causation

Non-managers

Managers

Percent with a degree

Management score (rounded to nearest 0.5)

slide56

MY FAVOURITE QUOTES:

The traditional British Chat-Up

[Male manager speaking to an Australian female interviewer]

Production Manager: “Your accent is really cute and I love the way you talk. Do you fancy meeting up near the factory?”

Interviewer “Sorry, but I’m washing my hair every night for the next month….”

slide57

MY FAVOURITE QUOTES:

The traditional Indian Chat-Up

Production Manager: “Are you a Brahmin?’

Interviewer “Yes, why do you ask?”

Production manager “And are you married?”

Interviewer “No?”

Production manager “Excellent, excellent, my son is looking for a bride and I think you could be perfect. I must contact your parents to discuss this”

slide58

MY FAVOURITE QUOTES:

The difficulties of defining ownership in Europe

Production Manager: “We’re owned by the Mafia”

Interviewer: “I think that’s the “Other” category……..although I guess I could put you down as an “Italian multinational” ?”

Americans on geography

Interviewer: “How many production sites do you have abroad?

Manager in Indiana, US: “Well…we have one in Texas…”

slide59

MY FAVOURITE QUOTES:

The bizarre

Interviewer: “[long silence]……hello, hello….are you still there….hello”

Production Manager: “…….I’m sorry, I just got distracted by a submarine surfacing in front of my window”

The unbelievable

[Male manager speaking to a female interviewer]

Production Manager: “I would like you to call me “Daddy” when we talk”

[End of interview…]

slide60

References for the management data

  • The management data I showed you comes from two sources:
  • Bloom, Nicholas, and John Van Reenen (2010) “Why do management practices differ across firms and countries”, Journal of Economic Perspectives, 24(1).

http://www.stanford.edu/~nbloom/JEP.pdf

  • Bloom, Nicholas, Sadun, Raffaella and John Van Reenen (2010), “Recent advances in the empirics of organizational economics”, forthcoming Annual Review of Economics
  • http://www.stanford.edu/~nbloom/AR.pdf
slide61

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Explaining why management practices vary
  • The effect of management practices on performance
slide62

Estimating effect of management on performance

  • Is there really “bad” management, or are management variations just response to different environments?
    • For example, in Brazil does corruption make is better not to monitor performance?
  • Management discipline is big on “contingent” management (Woodward, 1958), while the Chicago school would claim bad managed firms would be driven out of the market.
  • Three types of approaches to investigating this:
    • Repeated arms-length surveys
    • Longitudinal ground-based studies
    • Experiments on management
slide63

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Explaining why management practices vary
  • The effect of management practices on performance
    • Repeated arms-length surveys
    • Longitudinal ground-based studies
    • Experiments on management
slide64

Black and Lynch (2004) and Cappelli and Neumark (2001) as good examples of panel survey-data

  • The survey data in Black and Lynch (2001, REStat) was actually collected in two waves (1994 and 1997)
  • This was matched into performance panel data to create a management and performance panel
  • Black & Lynch (2004, EJ) and Cappelli & Neumark (2001, ILRR) run panel regressions of performance on management finding:
    • Significant effects in the cross-sectional regressions
    • Nothing when fixed effects are included
  • Appears to be because the Census management data is noisy and management changes slowly (e.g. too little signal:noise)
slide65

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Explaining why management practices vary
  • The effect of management practices on performance
    • Repeated arms-length surveys
    • Longitudinal ground-based studies
    • Experiments on management
slide66

Longitudinal ground based surveys

Classic paper is Ichniowski, Shaw and Prennushi (1997, AER) (and more recently Bartel, Ichniowski and Shaw (2007, QJE))

Ichniowski et al. (1997) collected detailed monthly performance and management data on 36 steel lines owned by 17 firms.

While the sample size is small, by looking at one very detailed industry – steel finishing – can control for host of other factors

Also collected a many other control variables from frequent plant visits - so it’s a mix of case-study and econometrics approaches

slide67

Summary Ichniowski, Shaw and Prennushi (1997), slide (1/2)

  • Key findings:
  • Strong cross-sectional and panel correlation between adoption of modern management practices and productivity
      • Appears robust to controls for other factors like rotation of individual managers to external threats
  • 2) Clustering of management practices in firms, and empirical results strongest for clusters rather than individual practices
      • Both consistent with complementarity across practices
slide68

Summary Ichniowski, Shaw and Prennushi (1997), slide (2/2)

  • Main issues:
  • Performance and management relationship not fully identified – practices and performance could change in response to an external shock. No good instrument, although plausible story
  • 2) Clustering of management practices, and empirical results being strongest for clusters, does not prove complementarity
    • Clustering could equally reflect differences across firms
    • Question level measurement error gives the results that clusters are more significant than individual practices
  • Despite this hugely cited and shows that impact that a good empirical management paper can have
slide69

Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Explaining why management practices vary
  • The effect of management practices on performance
    • Repeated arms-length surveys
    • Longitudinal ground-based studies
    • Experiments on management
slide70

Very recently, academics have started running experiments on changing management practices

  • Running management experiments is expensive, so this is to date this has been limited to:
  • Developing countries, typically on micro-enterprises (i.e. 5 to 10 person firms), or
  • Single firms (i.e. fruit-picking firms) in developed countries
slide71

Evidence from micro-enterprises in developed countries (1/2)

A few projects are in progress (nothing published) - Karlan and Valdivia (2010, R&R REStat) in Peru; Bruhn, Karlan and Schoar in Mexico; Karlan and Udry in Ghana; McKenzie and Woodruff in Sri Lanka

These provide a limited amount (≈50 hours) of basic trainings to small firms – e.g. accounting, marketing, pricing, strategy etc.

This training is provided randomly and performance measured before and after the intervention

slide72

Evidence from micro-enterprises in developed countries (2/2)

Data so far extremely preliminary – my evidence on them is mainly from discussing this directly with the authors.

Some studies find evidence of impact of management training on performance, others do not (so far)

Maybe management does not matter? But I’ll present in detail another study I’ve been involved in showing large effects

Or maybe these firms are very small, so management matters less in small firms?

Or maybe to have an impact need extensive intervention – a few hours of training not sufficient to have much effect?

slide73

Evidence from the single firms in developing countries (1/2)

The team of Bandiera, Barankay and Rasul have produced an impressive set of papers (2005, QJE; 2007, QJE; 2009, Econometrica; and 2010, REStud)

Run experiments on incentives for workers and managers, team selection and task division on a fruit picking farm

Typically introduce managerial changes part-way through season to look at change in performance, plus use last seasons output as seasonality controls

slide74

Evidence from the single firms in developing countries (2/2)

  • Bandiera, Barakalay and Rasul find large effects of varying management practices on fruit-picking performance:
    • Worker incentive pay increases their performance, especially absolute (rather than relative) incentives
    • Peer monitoring effects are strong between workers when absolute group incentives exist
    • Manager incentive pay improves team selection (less favoritism) and the effort they put into monitoring workers
slide75

Finally, a management experiment on large firms

The only experiment I know on panels of large firms is Bloom, Eifert, Mahajan, McKenzie and Roberts (2010).

Randomize management practices delivered by Accenture to 20 plants in large (300 person) textile firms in Mumbai, India

Control firms get one month of diagnostic (≈ 200 hours of help) and then monthly monitoring (≈ 20 hours a month).

Treatment firms get one month of diagnostic, four months of intervention (≈ 800 hours of help) and then monthly monitoring.

Collect weekly data for all plants from 2008 to 2010

Before discussing the details show some pictures for context

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

Plant entrance with gates and a guard post

Plant surrounded by grounds

Front entrance to the main building

Plant buildings with gates and guard post

slide77
Exhibit 2: The plants operate 24 hours a day for 7 days a week producing fabric from yarn, with 4 main stages of production

(1) Winding the yarn thread onto the warp beam

(2) Drawing the warp beam ready for weaving

(3) Weaving the fabric on the weaving loom

(4) Quality checking and repair

the production technology has not changed much over time
The production technology has not changed much over time

Krill

Warp beam

The warping looms at Lowell Mills in 1854, Massachusetts

slide79

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

slide80

Exhibit 4: The plant floors were disorganized

Instrument not removed after use, blocking hallway.

Old warp beam, chairs and a desk obstructing the plant floor

Dirty and poorly maintained machines

Tools left on the floor after use

slide81

Exhibit 5: The inventory rooms had months of excess yarn, often without any formal storage system or protection from damp or crushing

Yarn without labeling, order or damp protection

Yarn piled up so high and deep that access to back sacks is almost impossible

Different types and colors of yarn lying mixed

A crushed yarn cone, which is unusable as it leads to irregular yarn tension

slide82

Exhibit 6: The spare parts stores were also disorganized and dirty

Spares without any labeling or order

No protection to prevent damage and rust

Spares without any labeling or order

Shelves overfilled and disorganized

slide83

Exhibit 7: The path for materials flow was often obstructed

Unfinished rough path along which several 0.6 ton warp beams were taken on wheeled trolleys every day to the elevator, which led down to the looms.This steep slope, rough surface and sharp angle meant workers often lost control of the trolleys. They crashed into the iron beam or wall, breaking the trolleys. So now each beam is carried by 6 men.

A broken trolley (the wheel snapped off)

At another plant both warp beam elevators had broken down due to poor maintenance. As a result teams of 7 men carried several warps beams down the stairs every day. At 0.6 tons each this was slow and dangerous - two serious accidents occurred in our time at the plant.

slide84

Exhibit 8: Routine maintenance was usually not carried out, with repairs only undertaken when breakdowns arose, leading to frequent stoppages.

Broken machine parts being repaired

Parts being cleaned and replaced on jammed loom

Workers investigating a broken loom

Loom parts being disassembled for diagnosis

these firms appear typical of large manufacturers in india china and brazil
These firms appear typical of large manufacturers in India, China and Brazil

Experimental Firms, mean=2.60

Indian Textiles, mean=2.60

Indian Manufacturing, mean=2.69

Brazil and China Manufacturing, mean=2.67

85

Management scores (using Bloom and Van Reenen (2007) methodology)

intervention aimed to improve 38 core textile management practices in 6 areas 1 2
Intervention aimed to improve 38 core textile management practices in 6 areas (1/2)

Targeted practices in 6 areas: operations, quality, inventory, loom planning, HR and sales & orders

87

intervention aimed to improve 38 core textile management practices in 6 areas 2 2
Intervention aimed to improve 38 core textile management practices in 6 areas (2/2)

Targeted practices in 6 areas: operations, quality, inventory, loom planning, HR and sales & orders

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

Wave 1 treatment plants: Diagnostic September 2008, implementation began October 2008

Wave 2 treatment plants: Diagnostic April 2009, implementation began May 2008

Share of the 38 management practices adopted

Control plants:

Diagnostic July 2009

Non-experiment plants:

No intervention

April 2008

July 2008

October 2008

January 2009

April 2009

July 2009

October 2009

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Notes: Non-experiment plants are other plants in the treatment firms not involved in the experiment. They improved practices over this period because the firm internally copied these over themselves. All initial differences not statistically significant (Table 2)

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Quality

Inventory

Operational efficiency

Adoption of these 38 management practices led to clear improvements in 3 areas of these India firms

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Exhibit 10: Quality was so poor that 19% of manpower was spent on repairing defects at the end of the production process

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

Non-fixable defects lead to discounts of up to 75%

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

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

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

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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

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

Start of Diagnostic

Start of Implementation

Spline, 95% upper CI

Control plants

Data (+ symbol)

Cubic Spline

Spline, 95% lower CI

Quality defects index (higher score=lower quality)

Treatment plants

Spline, 95% upper CI

Cubic Spline

Data (♦ symbol)

Spline, 95% lower CI

Weeks after the start of the intervention

Notes: Displays the average quality defects index, which is a weighted index of quality defects, so a higher score means lower quality. This is plotted for the 14 treatment plants (♦ symbols) and the 6 control plants (+ symbols). Values normalized so both series have an average of 100 prior to the start of the intervention. “Data” is plotted using a 5 week moving average. To obtain series (rather than point-wise) confidence intervals we used a cubic-spline with one knot at the start of the implementation period. The spline estimate is labeled (“Cubic Spine”), the 95% confidence upper and lower intervals labeled (“Spline, 95% upper CI”) and (“Spline, 95% lower CI”) from plant-wise block boostrap. Timing based on weeks after the intervention (positive values) or before the intervention (negative values). For wave 1 treatment plants this is relative to September 1st 2008, for Wave 2 treatment and control firms April 7th 2009. The control group’s rise in weeks 10+ are due to the pre Diwali and Ede production increase, which usually leads to a deterioration in quality due to increased speeds of production.

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Quality

Inventory

Operational efficiency

Adoption of these 38 management practices led to clear improvements in 3 areas of these India firms

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Organizing and racking inventory enables firms to reduce capital stock and reduces waste

Stock is organized, labeled, and entered into an Electronic Resource Planning (ERP) system which has details of the type, age and location.

Bagging and racking yarn reduces waste from rotting (keeps the yarn dry) and crushing

Computerized inventory systems help to reduce stock levels.

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sales are also informed about excess yarn stock so they can incorporate this in new designs
Sales are also informed about excess yarn stock so they can incorporate this in new designs.

Shade cards now produced for all surplus yarn. These are sent to the design team to use in future designs

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and yarn for products ranges no longer made by the firm e g suiting fabric was sold
And yarn for products ranges no longer made by the firm (e.g. suiting fabric) was sold

This firms used to make suiting and shirting yarn, but stopped making suiting yarn 2 years ago

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Quality

Inventory

Operational efficiency

Adoption of these 38 management practices led to clear improvements in 3 areas of these India firms

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The treated firms have also started to introduce basic initiatives (called “5S”) to organize the plant

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.

This is all part of the routine maintenance

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

Nuts & bolts sorted as per specifications

Parts like gears, bushes, sorted as per specifications

Tool

storage organized

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

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estimated impacts on productivity and profitability are large and rising
Estimated impacts on productivity and profitability are large and rising

Estimate the intervention has increase profits by about $250,00 per firm and productivity by 9% so far from:

reduced repair manpower costs

reduced wasted materials (from less defects)

lower inventory

higher efficiency levels

Full impacts of better management should be much larger:

short-run impacts only

narrow set of management practices (almost no HR)

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so why did these firms have bad management
So why did these firms have bad management?

Asked the consultants to investigate the non-adoption of each practice in each firm every other month

They did this by discussion with the owners, observation of factory, and from their experiences of changing practices

To collect this information systematically we developed a “management non-adoption” flow-chart

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non adoption flow chart used to collect data
“Non adoption flow chart” used to collect data

Legend

Is the reason for the non adoption of the practice internal to the firm?

External factors (legal, climate etc)

No

Hypothesis

Yes

Conclusion

Was the firm previously awarethat the practice existed?

Lack of information

No

Could the firm hire new employees or consultants to adopt the practice?

Can the firm adopt the practice with existing staff & equipment?

Lack of local skills

Limited incentives and/or authority for employees

Did the CEO believe introducing the practice would be profitable?

Not profit maximizing

Would this adoption be profitable

Do you think the CEO was correct about the cost-benefit tradeoff?

Could the CEO get his employees to introduce the practice?

Did the firm realize this would be profitable?

Low ability of the owner and/or procrastination

Incorrect information

Does the firm have enough internal financing or access to credit?

Other reasons

Credit constraints

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non adoption data in treatment firms
Non adoption data in treatment firms
  • Lack of, or incorrect, information was the major reason. Firms not aware of, or incorrectly evaluated, modern practices.
  • Took time to change incorrect information, as initial trust in consultants was limited, but grew as their advice worked.
  • Blockages later arose with the owners ability or procrastination
  • Ongoing issues with managerial incentives – currently trying to introduce management incentive systems to address this

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why does competition not fix badly managed firms
Why does competition not fix badly managed firms?

Bankruptcy is not (currently) a threat: a weaver wage rates of $5 a day, so these firms can be profitable with bad management.

Reallocation appears limited: Owners take all decisions as they worry about managers stealing. But owners time is constrained – they already work 66.2 hours average a week – limiting growth.

As an illustration firm size is more linked to number of male family members (corr=0.689)- who are trusted to be given managerial positions -than management scores (corr=0.223)

Entry appears limited: Capital intensive ($13m assets average per firm), and no guarantee new entrants are any better.

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Lecture 1: Overview

  • Motivation: productivity across firms and countries
  • Measuring management practices
  • Explaining why management practices vary
  • The effect of management practices on performance
  • Conclusions
summary
Summary
  • Management practices do vary widely across firms and countries, much like productivity
  • Factors associated with good management are competition, professional ownership (not families or Government), international exposure, light regulations & education
  • There is “good” and “bad” management, in that monitoring, targets and incentives certain practices appear to causally improve performance
  • Lastly, change appears slow to tough with many badly run firms. Suggests good management is a type of technology, with informational, incentive and behavorial barriers to adoption

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my five outstanding research questions
My five outstanding research questions
  • The key questions which I think remain substantially unanswered
  • What fraction of the differences in TFP across firms and countries can management causally explain?
  • What are the key factors causing difference in management?
  • Why do management practices take so long to change?
  • Are different management practices complementary, or are their impacts more or less additive?
  • What broad types of management practices are universally good and what types of contingent on firm’s environment

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Summary reading list, plus papers cited in slides

  • 9:00-12:00 Tuesday February 9th:
  • Readings by category: (* are core readings)
  • Bertrand, Marianne, and Antoinette Schoar. 2003. “Managing with Style: The Effect of Managers on Firm Policies.” Quarterly Journal of Economics, 118 (4): 1169-1208.
  • * Ichniowski, Casey, Kathryn Shaw and Giovanna Prenushi. (1997), “The Effects of Human Resource Management: A Study of Steel Finishing Lines”, American Economic Review, LXXXVII (3), 291-313.
  • Black, Sandra, and Lisa Lynch. 2001. “How to Compete: The Impact of Workplace Practices and Information Technology on Productivity.” Review of Economics and Statistics, 88(3): 434-45.
  • * Bloom, Nicholas, and John Van Reenen (2007) “Measuring and Explaining Management Practices across Firms and Countries”, Quarterly Journal of Economics, 122(4), 1341-1408.
  • Burstein, Ariel, and Alexander Monge-Naranjo. 2009. “Foreign Know-how, Firm Control, and the Income of Developing Countries.” Quarterly Journal of Economics, 124(1): 149-195.
  • Bloom, Nicholas, and John Van Reenen (2010) “Why do management practices differ across firms and countries”, Journal of Economic Perspectives, 24(1).
  • Syverson Chad. 2004b. “Market Structure and productivity: A Concrete Example.” Journal of Political Economy, 112(6): 1181-1222.
  • * (read the empirical part only) Foster Lucia, John Haltiwanger, and Chad Syverson. 2009. “Reallocation, Firm Turnover and Efficiency: Selection on Productivity or Profitability.” American Economic Review, 98(1): 394-425.
  • Hall, Robert, and Charles Jones. 1999. “Why Do Some Countries Produce So Much More Output Per Worker Than Others?” Quarterly Journal of Economics, 114(1): 83-116.
  • Hsieh, Chiang-Tai, and Pete Klenow. 2009. “Misallocation and Manufacturing TFP in China and India.” Quarterly Journal of Economics,
  • * Bloom, Nicholas, Eifert, Benn, Mahajan, Aprajit, McKenzie, David and John Robert. (2010), “Management matters: evidence from Indian firms”, Stanford mimeo
  • Bloom, Nicholas, Mahajan, Aprajit, McKenzie, David and John Robert. (2010), “Why do firms in developing countries have low productivity”, forthcoming American Economic Review papers and proceedings
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Summary reading list, plus papers cited in slides

  • 9:00-12:00 Thursday February 11th: (* are core readings)
  • * Rajan, Raghuram, and Julie Wulf (2006) “The Flattening Firm: Evidence from Panel Data on the Changing Nature of Corporate Hierarchies”, Review of Economics and Statistics, 88(4), 759-773.
  • Acemoglu Daron, Philippe Aghion, Claire Lelarge, John Van Reenen, and Fabrizio Zilibotti (2007) “Technology, Information and the Decentralization of the Firm”, Quarterly Journal of Economics, 122(4), 1759–1799.
  • * Bloom, Nicholas, Sadun, Raffaella and John Van Reenen. (2009). “The organization of firms across countries”, 2009, NBER WP15129.
  • Bloom, Nicholas, Sadun, Raffaella and John Van Reenen. (2010). “Does product market competition lead firms to decentralize?”, forthcoming American Economic Review papers and proceedings
  • Organizational structures, IT and performance:
  • * Bresnahan, Brynjolfsson and Hitt (2002, QJE) Bresnahan, Timothy, Erik Brynjolfsson, and Lorin Hitt (2002) “Information Technology, Workplace Organization and the Demand for Skilled Labor: Firm-level Evidence”, Quarterly Journal of Economics, 117(1), 339-376.
  • Baker, George, and Thomas Hubbard (2003) “Make Versus Buy in Trucking: Asset Ownership, Job Design and Information”, American Economic Review, 93(3), 551-572.
  • Bloom, Nicholas, Raffaella Sadun, and John Van Reenen (2007) “Americans do IT Better: American Multinationals and the Productivity Miracle”, NBER WP 13085, and revise and resubmit for the American Economic Review.
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Lazear (2000, AER) study on Safelite glass

  • Another classic, which studies the introduction of one type of management practice – piece-rate pay – on performance
  • The setting is Safelite Glass, who replace car windscreens, who rolled out a switch from flat to piece-rate across regions.
  • Examines performance-data for 19 months before and after the switch from hourly rates to piece-rate and finds:
      • Increase in productivity of 44%
      • Combination of selection and effort effects