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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)




                                                        1
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

                                                         2
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



                                                          3
Lecture 1: Overview



1. Motivation: productivity across firms and countries


2. Measuring management practices


3. Management practices across firms and countries


4. Explaining why management practices vary


5. The effect of management practices on performance


                                                     4
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
                                                       5
These TFP & GDP differences are often persistent




Source: Maddison (2008) Data is smoothed by decade
                                                     6
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)




                                                           7
TFP dispersion is particularly large in low
competition industries




     Low competition                 High competition




            Source: Syverson (2004, JPE)
                                               8
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


                                                         9
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




                                                         10
Lecture 1: Overview



1. Motivation: productivity across firms and countries


2. Measuring management practices


3. Management practices across firms and countries


4. Explaining why management practices vary


5. The effect of management practices on performance


                                                         11
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



                                                         12
Summary of Bertrand and Schoar (2003)

Interesting results, and highly cited, finding:


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


                                                          13
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

                                                             14
Challenges to measuring management practices
Despite sounding easy, “measuring management” is fraud with
difficulties, which has held back research.


1) How to quantify (as in put numbers on) management practices


2) How to get data from firms – surveys are tough to do

3) How to get the truth – will badly managed firms ‘fess-up’


4) Building a representative population – e.g. not just targeting
   Compustat firms – especially important for cross-country work


                                                          15
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

1. Quantify: Asked a series of questions on employee recruitment,
   work organization, meetings and modern production practices
2. Get data: Administered by the US Census Bureau
3. Truth: Told respondents their answers were confidential
4. Population: stratified from the Census establishment database

Found large variations in management, and strong correlation of
management practices and performance

                                                       16
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

                                                         17
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

                                                           18
Monitoring - i.e. “How is performance tracked?”


Score (1): Measures       (3): Most key   (5): Performance is
      tracked do not      performance     continuously
      indicate directly   indicators      tracked and
      if overall          are tracked     communicated,
      business            formally.       both formally and
      objectives are      Tracking is     informally, to all
      being met.          overseen by     staff using a range
      Certain             senior          of visual
      processes aren’t    management      management tools
      tracked at all




                                                     19
Survey Video




               20
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




                                                        21
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)
                5
                4
2nd interview




                                                          Firm-level
                                                          correlation
                3




                                                          of 0.627
                2
                1




                    1       2           3         4   5
                                   management_2
                        1st interview                        22
External survey validation – another useful exercise
suggesting double-blind methodology may work
Performance country c
measure

  yi  MNGi  l li
    c         c       c
                              k ki
                                   c
                                        mhi
                                            c
                                                   ' xi
                                                        c
                                                              ui
                                                                c


   management                  ln(capital)         other controls
(average z-scores)     ln(labor)        ln(materials)

•   Use most recent cross-section of data (typically 2006)


•   Note – not a causal estimation, only an association


                                                            23
External validation: better performance is correlated
with better management
 Dependent Productivity Profits 5yr Sales Share Price
                                                                 Exit
 variable  (% increase) (ROCE) growth (Tobin Q)
Estimation         OLS         OLS       OLS         OLS        Probit
Firm sample         All         All       All       Quoted        All
Management        28.7***    2.018***   0.047***    0.250***   -0.262**
Firms              3469       1994       1883         374        3161

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



                                                                 24
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)




                                                       25
Consistent with Helpman, Melitz and Yeaple (2004,
AER) well managed firms also export more
                       50
                       40
Share of firms exporting
                       30
                       20
                       10
                           0




                               1       1.5   2    2.5    3    3.5   4     4.5

                                   Management score (rounded to nearest 0.5)    26
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)
                                                                      1 point higher
 Energy use, log( KWH/$ sales)




                                                                      management score
                                                                      associated with about
                                                                      20% less energy use




                                            Management
                                                                                                      27
Source: Bloom, Genakos, Martin and Sadun, (2010, EJ). Analysis uses Census of production data for UK firms
Lecture 1: Overview



1. Motivation: productivity across firms and countries


2. Measuring management practices


3. Management practices across firms and countries


4. Explaining why management practices vary


5. The effect of management practices on performance


                                                         28
US management practices score highest on
average, with developing countries lowest
                US
         Germany
          Sweden
             Japan
          Canada
            France
               Italy
     Great Britain
         Australia
  Northern Ireland
            Poland
Republic of Ireland
          Portugal
              Brazil                      Also have data from Chile,
              India                       Mexico and New Zealand, but
             China                        not yet publicly released
           Greece

                   2.6          2.8           3          3.2            3.4
                                      mean of management
                       Average Country Management Score        29
Variation even greater across firms than across countries
               Australia                     Brazil                    Canada                   China
   1
  .5
   0




               France                       Germany                Great Britain                Greece
   1
  .5
   0




                 India                      Ireland                     Italy                   Japan
   1
  .5
   0




               Poland                       Portugal                   Sweden                    US
   1
  .5
   0




       1   2       3       4   5    1   2      3       4   5   1   2     3      4   5   1   2     3      4   5

                                            management
                                   Firm-Level Management Scores                                 30
Relative management practices also vary by country
          Sweden
            France
         Australia                                   Relatively better at
               Italy                                 ‘operations’
          Portugal
                                                     management
         Germany
             Japan
                                                     (monitoring, continuous
           Greece                                    improvement, Lean etc)
          Canada
     Great Britain
              Brazil
  Northern Ireland     Relatively better at
                 US    ‘people’
Republic of Ireland    management
             China     (hiring, firing, pay,
            Poland
                       promotions etc)
              India

                         -.4         -.2          0        .2        .4
     People management                        promotions) –
                                        mean of pay &
                                 (hiring, firing,peo_ops
     operations (monitoring, continuous improvement and31Lean)
Lecture 1: Overview

1. Motivation: productivity across firms and countries


2. Measuring management practices


3. Management practices across firms and countries


4. Explaining why management practices vary

5. The effect of management practices on performance




                                                         32
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




                                                          33
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
.4
.3




      “Worst                          “Average”                           “Best
     Practice”                                                           Practice”
.2
.1
 0




     0              2               4               6              8               10
         Their self-score: 1 (worst practice), 5 (average) to 10 (best practice)
The Brazilians and Greeks overscored the most, the
US and French the least
               Brazil
            Greece
               India
           Portugal
              China
 Republic of Ireland
   Northern Ireland
          Australia
           Canada
                Italy
      Great Britain
             Poland
          Germany
              Japan
           Sweden
             France
                 US

                        0    .5             1              1.5
                                    scale) –
   Self score (normalized to 1 to 5mean of gap Management score
   These self-scores also appear not only too high on
   average, but also uncorrelated with actual practices
                       Lowess smoother
                            2

                                                                                               Correlation
                                                                                                0.032*
       Labor Productivity
                            0
labp

                       -2
                       -4
                       -6




                                0               2               4               6              8               10
                                     Their self-score: 1 (worst practice), 5 (average) to 10 (best practice)
                                bandwidth = .8
                                                  Self scored management
       * In comparison the management score has a 0.295 correlation with labor productivity
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
Lecture 1: Overview
1. Motivation: productivity across firms and countries


2. Measuring management practices

3. Explaining why management practices vary
   • Competition
   • Family firms
   • Multinationals
   • Labor market regulations
   • Education

4. The effect of management practices on performance
Tough competition appears strongly linked to better
management practices
                                               Dependent variable:
Competition proxies
                                                  Management
Import penetration                       0.066**
(SIC-3 industry, 1995-99)                (0.033)
“1-Rents” measure1                                    1.964**
(SIC-3 except firm itself, 1995-99)                   (0.721)
# of competitors                                                   0.158***
(Firm level, 2004 and 2006)                                         (0.023)
Observations                               2499        2980          3589
Full controls2,3                           Yes          Yes          Yes

1 1-Rents  = 1- (operating profit – capital costs)/sales
2 Includes 108 SIC-3 industry, country, firm-size, public and interview noise

  (analyst, time, date, and manager characteristic) controls
3 S.E.s in ( ) below, robust to heteroskedasticity, clustered by country-industry

                                                                      40
We also have some management panel data, and
find similar results
                                        Dependent variable: Change in
Competition proxies
                                           Management 2006-2004
Change in Import penetration            0.013**
                                         (0.005)
Change in “1-Rents” measure1                         1.006**
                                                     (0.415)
                                                                  0.120**
Change in Number of rivals
                                                                  (0.052)
Observations                              421          404          432

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

                                                                     41
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




                                                       42
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.




                                                          43
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
                                                        44
Lecture 1: Overview
1. Motivation: productivity across firms and countries


2. Measuring management practices

3. Explaining why management practices vary
   • Competition
   • Family firms
   • Multinationals
   • Labor market regulations
   • Education

4. The effect of management practices on performance


                                                         45
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

           Dispersed Shareholders             Family, external CEO           Family, family CEO
   1
  .5
   0




                   Founder                        Government                       Managers
   1
  .5
   0




                    Other                        Private Equity              Private Individuals
   1
  .5
   0




       1       2      3      4      5     1      2     3     4       5   1     2      3       4        5
                                        Average Management Score                                  46
Ownership differences are another factor behind
cross-country variations in management practices
          Sweden
                                              share family CEO (2nd+ generation)
             Japan
                  US                          share founder CEO (1st generation)
            France                            share government owned
            Poland
          Canada
         Australia
         Germany
              China
     Great Britain
Republic of Ireland
  Northern Ireland
                Italy
              Brazil
          Portugal
           Greece
               India

                        0       .2            .4             .6              .8
         share of ownership (for types associated with low management scores)
                             mean of family                       47
                                                             mean of founder
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



                                                           48
Lecture 1: Overview
1. Motivation: productivity across firms and countries


2. Measuring management practices

3. Explaining why management practices vary
   • Competition
   • Family firms
   • Multinationals
   • Labor market regulations
   • Education

4. The effect of management practices on performance


                                                         49
Multinationals appear to always be well managed,
consistent with selection and most trade models
                                                          Foreign multinationals
                                                          Domestic firms
                US
             Japan
          Sweden
         Germany
          Canada
         Australia
               Italy
     Great Britain
            France
            Poland
  Northern Ireland
Republic of Ireland
              India
             China
          Portugal
              Brazil
           Greece

                   2.4   2.6       2.8      3      3.2         3.4
                               Average Management Score                   50
Multinational presence also linked to cross-country
differences in average management practices
               India                            Foreign multinationals
               Brazil                           Domestic multinationals
              China
            Greece
              Japan
             Poland
                Italy
   Northern Ireland
 Republic of Ireland
           Portugal
           Canada
                 US
      Great Britain
          Australia
          Germany
             France
           Sweden

                        0   .2       .4          .6           .8
                                 share of multinationals
                                                                   51
Lecture 1: Overview
1. Motivation: productivity across firms and countries


2. Measuring management practices

3. Explaining why management practices vary
   • Competition
   • Family firms
   • Multinationals
   • Labor market regulations
   • Education

4. The effect of management practices on performance


                                                         52
            Labor market regulations appear linked to worse
            management, particularly incentives management
                                                   3.4
            (hiring, firing, pay and promotions)
             Average incentives management


                                                                 US
                                                   3.2




                                                                       Canada
peop_mean




                                                         3




                                                                                                                           Germany
                                                                                      Japan
                                                                                  Great Britain
                                                                                  Northern Ireland                Poland   Sweden
                                                   2.8




                                                                      Australia       Republic of Ireland
                                                                                                                                              France
                                                                                                                   Italy
                                                                                                          India
                                                                                                      China
                                                   2.6




                                                                                                                                Portugal
                                                                                                                             Brazil

                                                                                                                                     Greece
                                                   2.4




                                                             0                          20                  40                                   60
                                                                                         WB_RigidityEmployment
                                                                  World Bank Employment Rigidity Index                               53
Lecture 1: Overview
1. Motivation: productivity across firms and countries


2. Measuring management practices

3. Explaining why management practices vary
   • Competition
   • Family firms
   • Multinationals
   • Labor market regulations
   • Education

4. The effect of management practices on performance


                                                         54
Finally, education is highly correlated with better
management, but no idea on causation
                        80


                                  Non-managers
                                  Managers
                        60
Percent with a degree
                        40
                        20
                         0




                             1    1.5    2       2.5   3   3.5    4     4.5
                             Managementofscore (rounded to nearest 0.5)
                                   mean degree_nm        mean of degree_m   55
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….”
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”
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…”
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…]
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
Lecture 1: Overview



1. Motivation: productivity across firms and countries


2. Measuring management practices


3. Explaining why management practices vary


4. The effect of management practices on performance




                                                         61
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

                                                          62
Lecture 1: Overview
1. Motivation: productivity across firms and countries


2. Measuring management practices


3. Explaining why management practices vary


4. The effect of management practices on performance
    • Repeated arms-length surveys
    • Longitudinal ground-based studies
    • Experiments on management




                                                         63
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)

                                                      64
Lecture 1: Overview
1. Motivation: productivity across firms and countries


2. Measuring management practices


3. Explaining why management practices vary


4. The effect of management practices on performance
    • Repeated arms-length surveys
    • Longitudinal ground-based studies
    • Experiments on management




                                                         65
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




                                                            66
Summary Ichniowski, Shaw and Prennushi (1997),
slide (1/2)
Key findings:

1) 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




                                                          67
Summary Ichniowski, Shaw and Prennushi (1997),
slide (2/2)
Main issues:

1) 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
                                                        68
Lecture 1: Overview
1. Motivation: productivity across firms and countries


2. Measuring management practices


3. Explaining why management practices vary


4. The effect of management practices on performance
    • Repeated arms-length surveys
    • Longitudinal ground-based studies
    • Experiments on management




                                                         69
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




                                                            70
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




                                                        71
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?
                                                       72
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




                                                       73
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




                                                             74
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
                                                        75
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
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




Krill




Warp
beam




The warping looms at Lowell Mills in 1854, Massachusetts
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 disorganized

Instrument
     not                                              Old warp
 removed                                            beam, chairs
 after use,                                          and a desk
  blocking                                         obstructing the
  hallway.                                           plant floor




 Dirty and
  poorly                           Tools left on
maintained                          the floor
machines                            after use
Exhibit 5: The inventory rooms had months of excess yarn, often without
any formal storage system or protection from damp or crushing




        Yarn without                                  Yarn piled up so high and
     labeling, order or                               deep that access to back
      damp protection                                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
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
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.
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
1.5


                                                               Experimental Firms, mean=2.60
      1
.5
      01




           1                                  3                                            5
                                          management


                                                                     Indian Textiles, mean=2.60
.8
.6
.4
 .8 .2
0




           1                2                3                   4                     5
                                         management


                                                              Indian Manufacturing, mean=2.69
.6
.4
.2
      0




               1                2              3                     4                     5
.8




                                          management


                                                    Brazil and China Manufacturing, mean=2.67
.6
.4
.2




                                                                                 85
      0




           1                 2               3                    4                        5

                   Management scores (using Bloom and Van Reenen (2007) methodology)
                                         management
Sample of firms we worked with




                                 86
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)


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




                                            88
Adoption of these 38 management practices did
rise, and particularly in the treatment plants
                                               .6
  Share of the 38 management practices adopted




                                                                 Wave 1 treatment plants:       Wave 2 treatment plants:
                                   .5




                                                                 Diagnostic September           Diagnostic April 2009,
                                                                 2008, implementation           implementation began
                                                                 began October 2008             May 2008
                       .4




                                                                                                                                       Control plants:
                                                                                                                                       Diagnostic July 2009
             .3




                                                                                                                                        Non-experiment plants:
                                                                                                                                        No intervention
.2




                                                    2008.25
                                                    April 2008       2008.5
                                                                   July 2008      2008.75
                                                                               October 2008      2009
                                                                                              January 2009   2009.25
                                                                                                             April 2009     2009.5
                                                                                                                           July 2009         2009.75
                                                                                                                                            October 2009
                                                                                                  ym
                                                                                                                         89
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)
Adoption of these 38 management practices led to
clear improvements in 3 areas of these India firms




  • Quality

  • Inventory

  • Operational efficiency




                                            90
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




                                                                                            91
Defects are repaired by hand or cut out from cloth   Non-fixable defects lead to discounts of up to 75%
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




                                           92
Now mending is recorded daily in a standard format,
so it can analyzed by loom, shift, design & weaver




                                            93
                                                  93
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
                                            94
Figure 3: Quality defects index for the treatment and control plants
                                                     Start of Diagnostic      Start of Implementation
Quality defects index (higher score=lower quality)

                                                                                                                                    Spline, 95% upper CI
                                           140



                                                                                                 Control plants
                                                                                                                                    Data (+ symbol)
                                                                                                                                    Cubic Spline
                                     120




                                                                                                                                    Spline, 95% lower CI
                              100
                 60    80




                                                                                                      Treatment plants
                                                                                                                                    Spline, 95% upper CI
                                                                                                                                    Cubic Spline
                                                                                                                                    Data (♦ symbol)
         40




                                                                                                                                    Spline, 95% lower CI
                                                     -10      -5       0        5       10       15        20
                                                                                 timing
                                                                   Weeks after the start of the intervention


     Note s: Di splays the average quality defects index, which is a weighted index of quality defects, so a higher score means lower quality. This i s
     plotted for the 14 treatment plants (♦ symbol s) and the 6 control plants (+ symbol s). 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 thi s i s relative to
                                                                                                                                       95
     September 1st 2008, for Wave 2 treatment and control firms April 7th 2009. The control group’ s ri se 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.
Management impact on quality, regressions




                                        96
Adoption of these 38 management practices led to
clear improvements in 3 areas of these India firms




  • Quality

  • Inventory

  • Operational efficiency




                                            97
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.




                                                 98
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




                                              99
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




                                         100
Management impact on inventory, regressions




                                       101
Adoption of these 38 management practices led to
clear improvements in 3 areas of these India firms




  • Quality

  • Inventory

  • Operational efficiency




                                            102
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
                                                                       103
Spare parts were also organized, reducing downtime
(parts can be found quickly), capital stock and waste




 Nuts & bolts                                Parts like
sorted as per                                 gears,
specifications                               bushes,
                                           sorted as per
                                           specifications
 Tool
 storage
 organized
                                            104
Production data is now collected in a standardized
format, for discussion in the daily meetings




                Before                            After
     (not standardized, on loose   (standardized, so easy to enter
           pieces of paper)                                105
                                        daily into a computer)
Daily performance boards have also been put up,
with incentive pay for employees based on this




                                          106
Management impact on efficiency, regressions




                                         107
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)


                                                      108
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




                                                       109
“Non adoption flow chart” used to collect data
         Legend                                        Is the reason for the non adoption        No
                                                      of the practice internal to the firm?                         External factors (legal, climate etc)
             Hypothesis
                                                                         Yes
             Conclusion
                                                         Was the firm previously aware
                                                                                                                            Lack of information
                                                           that the practice existed?
                  No

                                                                                     Could the firm hire new
                            Can the firm adopt the practice with                    employees or consultants                 Lack of local skills
                                existing staff & equipment?                           to adopt the practice?




Limited incentives and/or   Did the CEO believe introducing the                      Would this adoption be
                                                                                           profitable                      Not profit maximizing
 authority for employees       practice would be profitable?




                            Could the CEO get his employees to                   Do you think the CEO was correct
                                  introduce the practice?                        about the cost-benefit tradeoff?




                                                                                              Did the firm
 Low ability of the owner   Does the firm have enough internal                                 realize this
                                                                                                would be                   Incorrect information
  and/or procrastination      financing or access to credit?
                                                                                              profitable?




                                      Other reasons                                      Credit constraints

                                                                                                                                  110
Non adoption data in treatment firms




1) Lack of, or incorrect, information was the major reason. Firms
   not aware of, or incorrectly evaluated, modern practices.

2) Took time to change incorrect information, as initial trust in
   consultants was limited, but grew as their advice worked.

3) Blockages later arose with the owners ability or procrastination

4) Ongoing issues with managerial incentives – currently trying to
   introduce management incentive systems to address this
                                                             111
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.


                                                        112
Lecture 1: Overview



1. Motivation: productivity across firms and countries


2. Measuring management practices


3. Explaining why management practices vary


4. The effect of management practices on performance


5. Conclusions


                                                         113
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
                                                       114
My five outstanding research questions
The key questions which I think remain substantially unanswered

1. What fraction of the differences in TFP across firms and
   countries can management causally explain?

2. What are the key factors causing difference in management?

3. Why do management practices take so long to change?

4. Are different management practices complementary, or are
   their impacts more or less additive?

5. What broad types of management practices are universally
   good and what types of contingent on firm’s environment

                                                       115
Backup slides




                116
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




                                                                                                          117
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.




                                                                                                         118
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




                                                         119

				
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