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Measuring & explaining organizational practices

Nick Bloom (Stanford & NBER)

based on work with Raffaella Sadun (HBS) & John Van Reenen (LSE)

MIT/Harvard Org Econ Lecture 2 (February 2010)

http://www.youtube.com/watch?v=wBIov_CSqyI&feature=related






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Theory ahead of measurement in organizational economics

• Impressive theoretical literature on the organization of the firm

• Traditionally much less empirical work because of difficulties in defining “organization” and collecting data

• Today I will overview some current organizational data

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1. Measuring organization: spans and decentralization

2. Differences in organization across firms, countries & time

3. Factors driving the organization of firms

4. Organization and productivity

5. Conclusions

Lecture 2: Overview

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Manager

The simple model – organization within a 2-level firm

Workers

Span of control: the number of workers reporting to the

manager

Worker autonomy: low if managers take most

decisions; high if workers take most decisions

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CEO

Plant Manager 1

Workers

A more realistic multi-plant multi-level firm example

Plant Manager 2

Plant Manager 3

WorkersWorkers

CEO span: number of plants

Plant manager autonomy:low if CEO takes most non-

production decisions; high if plant manager takes most decisions

Plant manager span: people reporting to PM

Worker autonomy: low if plant managers take most production

decisions; high if workers take most

decisionsScranton Tokyo London

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1) Quantifying: measure decentralization of hiring, investment, sales and production decisions from CHQ to plant manager

•Also collect spans and worker decentralization (detailed in Bloom, Garicano, Sadun and Van Reenen, 2009)

2) Truth: use double blind as in Bloom and Van Reenen (2007)

3) Data: use various tricks as in Bloom and Van Reenen (2007)

•Introduced as “Lean-manufacturing” interview, no financials

•Official Endorsement: Bundesbank, Treasury, RBI , etc.

•Run by 45 MBA types (loud, assertive & business experience)

4) Sample: Manufacturing firms with 100 to 5000 employees

Bloom, Sadun and Van Reenen (2009) collect span and decentralization data on about 600 firms

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The decentralization survey page

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5. Conclusions

Lecture 2: Overview

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• Main measure averages the z-score (scores normalized to mean 0, standard-deviation 1) of each variable:

– Hiring senior employees (discrete, 1 to 5)

– Maximum Capital expenditure (continuous, in $)

– Introduction of new products (discrete, 1 to 5)

– Sales and marketing (discrete, 1 to 5)

The Bloom, Sadun and Van Reenen (2009) empirical decentralization measure

10-1 -.5 0 .5mean of zorg

Sweden

US

UK

Germany

Italy

Portugal

France

Poland

China

India

Japan

Greece

-1 -.5 0 .5mean of zorg

Sweden

US

UK

Germany

Italy

Portugal

France

Poland

China

India

Japan

Greece

Decentralization varies across countries

Most centralized

• Asia

• Southern Europe

Least centralized

• Scandinavian countries

• Anglo-Saxon countries

Decentralization measure

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Decentralization also varies across firms

Decentralization measure (higher number is more decentralized)

0.5

11

.50

.51

1.5

0.5

11

.5

-2 0 2 4 -2 0 2 4 -2 0 2 4 -2 0 2 4

China France Germany Greece

India Italy Japan Poland

Portugal Sweden UK US

De

nsity

zorgGraphs by Country where firm is located

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External validation – country level (1/2)

• Do these cross-country values look sensible?

• Only prior firm decentralization measure to cross-check against we are aware of is from Hofstede (1980)

– Surveyed c.100,000 IBM employees across 50 countries during the 1970s & early 1980s

– Questions on management style (autocractic/paternalistic or consultative) and preferences for delegation

– Combined into Power Distance index (1-100), low means limited (preference for) delegation

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China

France

Germany

Greece

India

Italy

Japan

Poland

Portugal

Sweden

UKUS

-1-.

50

.5D

ece

ntra

lizat

ion

20 30 40 50 60 70Power Distance Index (100=low power distance)

‘Power distance’ seems correlated with our firm decentralization

Dec

entr

aliz

atio

n

Power distance

Correlation= 0.80

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External validation – country level (2/2)

• There is also a cross-country index of Fiscal Decentralization from Arzaghi and Henderson (2005, JPubE)

• Index of Fiscal Decentralization based on 9 factors including:

• Government structure (e.g. unitary v federal)

• Local (regional/municipal) democratization & autonomy

• Local (regional/municipal) control over taxation and spending (education, police, transport etc.)

• Surveyed every country with >10 million people (in 1995)

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China

France

Germany

Greece

India

Italy

Japan

Poland

UK US

-1-.

50

.5D

ece

ntra

lizat

ion

0 1 2 3 4Fiscal Decentralization Index

‘Fiscal decentralization’ is also correlated with firm decentralization

Firm

Dec

entr

aliz

atio

n

Fiscal Decentralization

Correlation= 0.83

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-10

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Org

- 2

nd S

core

-1 -.5 0 .5 1Org - 1st Score

Double Interviews - Deviations2Correlation between 1st and 2nd interviews (72 firms)

Internal validation – the re-rater survey

correlation 0.51(p-value <0.001)

Decentralization – 1st interview

Dec

entr

aliz

atio

n –

2n

d in

terv

iew

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We also collected data on spans & worker autonomy

• Turn out spans of control is only weakly correlated with decentralization (about 0.10)

• I think reason is formal authority not the same as real authorityhttp://www.youtube.com/watch?v=5FRVvjGL2C0 but measurement error with span undoubtedly also a problem

• Worker decentralization is quite strongly correlated with plant manager span (about 0.20)

• Suggests factors that drive decentralization in upper and lower parts of the firms are probably correlated

http://www.youtube.com/watch?v=5FRVvjGL2C0

Nick Bloom and John Van Reenen, Management Practices, 2010 1818

Business Week The 21st Century Corporation, cover story August 21-28, 2000.

"Globalization and the arrival of the information economy have rapidly demolished all the old precepts. The management of global companies, which must innovate simultaneously and speed information through horizontal globe-spanning networks, has become a daunting challenge. Old, rigid hierarchies are out ...."

What about changes in decentralization over time?


Increasing Delayering – positions reporting to CEO

Source: Rajan and Wulf, 2006, 300+ large US corporations

SP

AN

year1985 1990 1995 2000

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5

6

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5. Conclusions

Lecture 2: Overview

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General modeling framework

• Principal-agent

– Principal is the Corporate Head Quarters (CHQ)

– Agent is the plant manager

• Optimal decentralization depends on trade-off between:

– Managers typically have better local information than CHQ

– Manager’s incentives diverge from firm’s (agency problem)

• This can of course be extended in many ways – for example:

• Need for coordination (Alonso et al. 2008)

• Incentives to communicate

• Multi-level agency problems with CEO and owners

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Trust and decentralization

• Trust may affect optimal decentralization

– Facilitate cooperative solutions in repeated game settings: e.g. Baker, Gibbons and Murphy (1999)

– Proxy the congruence of incentives: e.g. Aghion and Tirole (1997)

– More broadly, reliability of manager and/or information: Rajan & Zingales (2002), Hart & Moore (2005)

• We find evidence of robust positive relationship between trust in region where plant is located and decentralization

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• Measure trust using the World Value Survey, from the question:

“Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?”

Trust by region of the country defined as % of people answering “yes” to first part of the trust question

• Experimental studies show this question linked with trust/trusting behavior (Glaeser et al, 2000, Sapienza et al, 2007)

• Used in prior social capital literature: e.g. Knack & Keefer (1997); Guiso, Sapienza, Zingales (2004);

Measuring trust

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Trust (region) 1.196*** 0.825*** 0.732**

(0.429) (0.290) (0.298)

Rule of law (country) 0.473***

(0.102)

No. Competitors

Plant Skills 0.094***

(0.016)

Firm Size 0.044*

(0.021)

Plant Size 0.091***

(0.029)

Observations 3549 3549 3549

Country dummies no no yes

Other controls no no yes

Trust and decentralization

Notes: Other controls are SIC3 dummies, noise Controls (interviewer dummies, Interviewee tenure and seniority, etc.), public Listing, CEO onsite, plant size, regional GDP/head, Regional population, domestic multinational. Weighted by % of WVS respondents in region in country.SE clustered by 112 regions.

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Use multinationals as a second test for trust

• Could worry about bias due to trust proxying for other country/regional variables

• So look at affiliates of foreign multinationals and investigate whether trust in their home country also matters

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Central HQ(New York Site)

Example A: Domestic Firm2 Sites, Single Plant

Plant(Phoenix Site)

D, Decentralization

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Plant 1(Lund Site)

Global HQ(Tokyo Site)

French CHQ(Paris Site)

Example DJapanese MNE

Sweden CHQ(Stockholm Site)

Plant 2 (Lyon Site)

Do not observe D

Observe D Observe D

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Sample: Multinational Firms

Trust (region of location) 0.609 0.563 0.446

(0.592) (0.843) (1.908)

Trust (country of origin) 0.749*** 0.698*** 0.152

(0.301) (0.331) (0.152)

Trust (bilateral from origin cty to location cty)

1.809*** 2.101***

(0.768) (1.035)

Full set of controls Yes Yes Yes Yes Yes

Regional dummies No No Yes No Yes

Country origin dummies No No No No Yes

Clustering Region Region Origin country

Origin country

Origin country

Observations 867 867 867 280 280

Decentralization and trust: multinationals

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Competition and decentralization – basic theory• Prior work had found a strong positive link – Guadalupe and

Wulf (2008) look at Canadian free trade experiment

• But theory is actually ambiguous

• Competition may affect information:

– Improves the value of timely responses to local conditions (e.g. Aghion & Tirole, 1997)

– But, reduces value of local information as more firms for the principal to learn from (e.g. Acemoglu et al. 2007)

• Competition may also affect incentives:

– Lower risk of manager abusing autonomy as incentives more aligned with firm (e.g. Schmidt 1997, Vives 2005)

– Less incentive to co-ordinate prices (Alonso et al. 2008)

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Import Penetration

0.131*** 0.184***

(0.050) (0.073)

1 – Lerner Index

6.537*** 2.265***

(1.176) (1.081)

Number of competitors

0.134*** 0.094**

(0.036) (0.034)

Plant Skills 0.081*** 0.090*** 0.090***

(0.018) (0.016) (0.016)

Ln(Firm Size) 0.076** 0.068*** 0.066***

(0.026) (0.017) (0.018)

Ln(Plant size) 0.119** 0.091** 0.090**

(0.024) (0.022) (0.022)

Observations 2,497 2,497 3,587 3,587 3,587 3,587

Country & Ind. dummies

no yes no yes no yes

Clustering Cty *Sic2 Cty *Sic2 Cty *Sic3 Cty *Sic3 Firm Firm

Decentralization higher with more competition

Notes: Other controls are SIC3 dummies, 12 country dummies, noise controls (interviewer dummiesInterviewee tenure and seniority, etc.), public listing, CEO onsite, plant size, Number of competitors (0=none, 1=between 1 and 4, 2=5 or more (as reported by plant manager).

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Industry volatility and age• Acemoglu, Aghion, Le Large Van Reenen and Zillabotti (2007,

QJE) develop a learning model

• Firm adopts/develops a new technology

• agent (manager) is informed about useful of technology

• principal (CEO) is correctly aligned with owners incentives

• Predictions are that in more volatile and younger industries technology more uncertainty – so more decentralization

• AALVZ indeed find strong evidence that industry volatility (SD of levels and changes of TFP) and firm age ↑ decentralization

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Other factors• Collection of other factors which have robust signs in AALZV

(2007) and BSV (2009)

• Size: larger firms and larger plants robustly more decentralization

• Skills: firms with more education employees and more education managers more decentralized

• Multinationals: controlling for size and industry, still more decentralized

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5. Conclusions

Lecture 2: Overview

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Two channels for the impact of decentralization (1/2)

Firm size:Early work on the structure of firms argued that decentralization was critical for large firms, Penrose (1959) & Chandler (1962)

Indeed, see that larger firms are more decentralized

Essential for productivity growth as reallocation - which accounts for ≈¾ of US TFP growth - needs productive firms to grow

Also important in development as low productivity due to lack of reallocation as too few large firms: e.g. Banerjee & Duflo, 2004; Hsieh & Klenow (2008) Pawasutipaisit & Townsend (2008)

Hence, factors driving decentralization – trust, rule of law, competition – also drive growth via facilitating decentraliztion

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Two channels for the impact of decentralization (2/2)

Firm level productivity:Typically think of decentralization as a control variable – firms choose the right level – so not “right” or “wrong” level

But this level may be hard to change, so can be “right” or “wrong” level in the short-run if situation changes

Good example is IT – changed information flow around the firm, so changed optimal decentralization, so not all firms optimal

Bresnahan, Brynjolfsson and Hitt (2002, QJE); Bloom, Sadun and Van Reenen (2009, NBER) find strong complementarity between IT and decentralization

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Summary

• Decentralization probably key organizational trait of firms

• Varies by country – Northern Europe and North America decentralized, Southern Europe and Asian centralized

• Varies by firm – high trust, strong rule-of-law, tough competition, rapid industry change and education all linked to decentralization

• Essential for macro growth: firms need to decentralize to grow, and firm growth required for productivity enhancing reallocation

• Important for firm level productivity of certain factors – e.g. IT

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My five outstanding research questions

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

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

3. How quickly can firms change real organizational structures?

4. Is the optimal organizational structure changing over time, and if so why?

5. Why are academics so disorganized….