Americans do I.T. Better:US Multinationals and the Productivity Miracle

Nick Bloom, Stanford & NBER

Raffaella Sadun, LSE

John Van Reenen, LSE, NBER & CEPR

March 2008

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Source: GGDC Dataset

Labor Productivity Levels

European productivity had been catching up with the US for 50 years…

…but since 1995 US productivity accelerated away again from Europe.

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USA EU 15

Source: GGDC Dataset

Labor Productivity Levels

The “productivity miracle” occurred as quality adjusted computer prices began to fall very rapidly

-.3

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Fall in Real Computer Prices

Sources: Stiroh (2002, AER)See also: Oliner and Sichel (2000 JEP, 2002 Fed) & Jorgenson (2001, AER),

In the US the “miracle” appears linked in to the “IT using” sectors…

-

Change in annual growth in output per hour from 1990 –95 to 1995 –2001%

3.5

1.9

-0.5

ICT-using sectors

ICT-producing sectors

Non-ICT sectors

U.S.

-0.1

1.6

-1.1

EU

… but no acceleration of productivity growth in Europe in the same “IT using” sectors.

Source: O’Mahony & Van Ark (2003, Gronnigen Data & European Commission)

So why did the US achieve a productivity miracle and not Europe?

Two types of arguments proposed (not mutually exclusive):

(1) Standard: US advantage lies in geographic, business or demographic environment (e.g. more space, younger workers)

(2) Alternative: US advantage lies in their firm organizational or management practices

Paper uses two micro data sets (one from the UK and one from Europe) that support (2)

-Idea is to look within UK and Europe (holds environment constant) and compare US and non-US multinationals

(1) Use new data on 11,000 UK establishments, 1995-03, find:• US multinationals use IT more effectively (and invest more in

IT) than non-US multinationals• This occurs in same sectors driving the macro story• Even true for takeovers (with a lag)

Summary of Results

One possible interpretation is

• US firms are managed in a way that make them more IT intensive, both in the US and as multinationals abroad

• When IT prices fell rapidly in mid-1990s onwards they benefited more than European firms

(2) Test with a second new dataset: on 720 firms, 1998-2005, which contains accounts, management and IT data, finding:

• US firms & multinationals are indeed differently managed

• This explains much of the higher US productivity of IT

Macro facts and motivation

Evidence from UK establishments

Evidence from an EU panel

Conclusion

Why use UK micro data?

• The UK has a lot of multinational activity– In our sample of 11,000 establishments 10% are US

multinational and 30% non-US multinational– Frequent M&A generates also lots of ownership change

• UK census data is well suited for this research– Data on IT and productivity for manufacturing and services

(where much of the “US miracle” occurred) – Data from 1995 to 2003, the productivity miracle period

(note: US Census has no annual service sector data)

-30

-20

-10

0

10

20

30

40

50

60

Employment Value addedper Employee

Non-IT Capitalper Employee

IT Capital perEmployee

US Multinationals

Non-US Multinationals

UK domestic

Descriptive statistics already show US multinationals are particularly different in IT use

Observations: 576 US; 2228 other MNE; 4770 Domestic UK

% difference from 4 digit industry mean in 2001

Conceptually want to see if there are differences between US and European production functions

Output (Q) function of TFP (A), Non-IT Capital (K), Labor (L), Materials (M) and IT-Capital (C)

Q = A KαLβMγCδ

Interested whether there is any difference between the US and Europe in the coefficients α, β, γ and δ

Empirically will show: δUS>δEU and βUS<βEU

Estimate a production function for establishment i at time t:

Allow TFP and factor coefficients to vary by ownership (US, non-US multinational and domestic firms)

Where

Q = Gross Output A = TFP

K = Non-IT capital L = Labor

M = Materials C = IT capital

itCMLK

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)/ln()/ln()/ln()ln()/ln(

)ln()ln()ln()ln()ln()ln( itC

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itit CMLKAQ

Econometric Methodology (1)

• Include full set of SIC-3 digit industry dummies interacted with year dummies to control for output price differences

• Main specifications also include establishment fixed effects

• Standard errors clustered by establishment

Econometric Methodology (2): Other Issues

Depend Var Ln(Q/L) Ln(Q/L) Ln(Q/L) Ln(Q/L) Ln(Q/L)

Sectors All All All IT Using Others

USA×ln(C/L) 0.020*** 0.038*** 0.012

MNE×ln(C/L) 0.004 -0.001 0.006

Ln(C/L) 0.046*** 0.043*** 0.037*** 0.046***

Ln(M/L) 0.558*** 0.547*** 0.548*** 0.622*** 0.507***

Ln(K/L) 0.139*** 0.127*** 0.127*** 0.111*** 0.146***

Ln(L) -0.005* -0.011*** -0.011*** -0.009** -0.012***

USA 0.071*** 0.064*** 0.073*** 0.044** 0.089***

MNE 0.039*** 0.034*** 0.037*** 0.015 0.044***

Obs 21746 21746 2175 7784 13962

USA×ln(C/L)=MNE×ln(C/L) 0.032 0.004 0.527

USA=MNE 0.021 0.023 0.011 0.176 0.015

TABLE 2: PRODUCTION FUNCTIONS

Notes: Log (output/employees) is the dependent variable. C=‘IT Capital’, M=‘Materials’, K=‘Non-IT Capital’, L=‘Employees’, USA=‘USA Multinational’ and MNE=‘Non-US multinational’ (domestically owned is baseline).

Stiroh (2002) “IT Intensive / Non-Intensive” and Services / Manufacturing split

IT Intensive # obs IT non-intensive # obs

Wholesale trade 2620 Food, drink and tobacco 1116

Retail trade 1399 Hotels & catering 1012

Machinery and equipment

736 Construction 993

Printing and publishing

639 Supporting transport services (travel agencies)

740

Professional business services

489 Real estate 700

Industries (SIC-2) in blue are services and in black are manufacturing

Sectors IT Using Others

Fixed effects YES YES

USA×ln(C/L) 0.037*** -0.006

MNE×ln(C/L) -0.003 0.001

Ln(C/L) 0.012** 0.016***

Ln(M/L) 0.502*** 0.361***

Ln(K/L) 0.106*** 0.067***

Ln(L) -0.128*** -0.247***

USA 0.045 -0.007

MNE 0.017 -0.001

Observations 7,784 13,962

USA×ln(C/L)=MNE×ln(C/L) 0.009 0.521

Test USA=MNE 0.430 0.815

Table 2, Production Functions with Fixed Effects

Note: C=‘IT Capital’, M=‘Materials’, K=‘Non-IT Capital’, L=‘Employees’, USA=‘USA Multinational’, MNE=‘Non-US multinational’ (domestic owned the baseline)

Quantification suggests UK micro data can account for about half of US macro productivity surge

• US firms have a 0.037 larger coefficient on IT (in IT sectors)• IT grew at around 22% per year 1995-2005 in (US and EU)• This implies a faster Q/L growth rate of 0.81% in the US

(calculated as: 0.81%=0.037×22%)• IT sectors about ½ of all employment – so if applied to US

economy would imply faster Q/L growth in US of about 0.4%

• Since US productivity growth about 0.8% faster over 1995-2005 this suggests UK results can account for half of the gap

• Even this probably an underestimate as IT grew faster in IT sectors than non-IT sectors

Robustness Tests (1/2) - Endogeneity

• Results due to reverse causation – e.g.– IT in US firms correlated with productivity shocks, but

• Only in IT intensive industries (IT/non-IT > median, including retail, wholesale & high-tech manufacturing)

• Only for US firms (not other multinationals)• Only for IT in US firms (not labor, capital or materials)

• Unfortunately no clean natural experiment

• As a partial check use Blundell-Bond GMM and Olley-Pakes and find results robust (Table A4)

Table 3, Runs Some Robustness Tests

Experiment All inputs interact

Another IT measure

Trans log

Skills (wages)

Split out EU MNEs

USA×ln(C/L) 0.033** 0.065** 0.033** 0.028** 0.038**

MNE×ln(C/L) 0.000 0.003 -0.001 -0.005

Ln(C/L) 0.013** 0.029*** 0.033 -0.025 0.012**

Ln(Wage) 0.280***

Ln(Wage)×Ln(C/L) 0.012*

EU×ln(C/L) 0.002

Non-EU×ln(C/L) -0.014

USA×ln(C)= MNE×ln(C)

0.022 0.012 0.024 0.058 0.046

Obs 7,784 2,196 7,784 7,780 7,784

‘All inputs interacted’ allows labor, capital and materials to interact with ownership – these are individually and joint insignificant. ‘Another IT measure’ is “% of employees using a computer”

Robustness Tests (2/2)

• Could this all be due to transfer pricing?– Higher US coefficient not observed for any other factor

inputs (e.g. materials)– Takes time to arise (see takeover table 5)

• Software – US multinationals have more/better software?– US multinationals global size the same as non-US

multinationals (i.e. not a simple HQ fixed cost story)– Within US multinationals global size plays no role (the

interaction global size with IT negative & insignificant)

(1) (2) (3) (4) (5) (6)

Dependent var: ln(C/L) ln(C/L) ln(C/L) ln(C/L) ln(C/L) ln(C/L)

Sectors All IT Using

Others All IT Using

Other

USA 0.263*** 0.339*** 0.209*** 0.241*** 0.313*** 0.193***

MNE 0.163*** 0.212*** 0.133*** 0.151*** 0.194*** 0.123***

Extra controls NO NO NO YES YES YES

Observations 21,746 7,784 13,962 21,746 7,784 13,962

Test USA=MNE 0.031 0.076 0.211 0.053 0.097 0.251

TABLE 4, IT INTENSITY EQUATION

Notes: All columns include SIC3 * time dummies & ln(Q).Additional controls = age, region & multi-plant. SE clustered by establishment.

What About Unobserved Heterogeneity?

• Maybe US firms “cherry pick” plants with high IT productivity?

• Look at production functions before & after establishment is taken-over by US and non-US multinationals (domestic baseline)

• No difference before takeover. After takeover results look very similar to table 3 (and interesting dynamics)

Takeover timing: Before Before After After

USA×ln(C) -0.067 0.054***

MNE×ln(C) -0.043 0.007

USA -0.066 -0.106 0.062

MNE 0.032 -0.001 0.021

Ln(C) 0.074*** 0.094** 0.029*** 0.029***

USA×ln(C), 1 year after 0.019

USA×ln(C), 2+years 0.066**

MNE×ln(C), 1 year after -0.009

MNE×ln(C), 2+ years 0.012

Obs 261 261 1,066 1,066

USA×ln(C)=MNE*ln(C) 0.704 0.097

USA×ln(C)=MNE*ln(C), 1 year after 0.495

USA×ln(C)=MNE*ln(C), 2+ years 0.073

Table 5, Before and After Takeovers

Macro facts and motivation

Evidence from UK establishments

Evidence from an EU panel

Conclusion

Why Do US firms have Higher IT productivity?

Macro and micro estimates consistent with the idea of an unobserved factor which is

• Complementary with IT• Abundant in US firms relative to others

Range of possible explanations – one we think may explain part of this is the different management practices of US firms

• Briefly sketch out the idea (model in the paper)• Provide a test using a new cross-country firm-level

management, IT and performance dataset

The Management Story Based on Prior Literature

Literature suggests tough “people” management (hiring, firing,

promotions & rewards) associated with higher IT productivity:

• Econometric evidence in Caroli and Van Reenen (2001) and

Bresnahan et al. (2001)

• Case study evidence surveyed in Blanchard et al. (2004)

Argument is IT changes informational flow, changing the optimal

firm structure (Arrow, 1974). Good “people” management enables:• reorganization more quickly to exploit this• decentralization more effectively to allow experimentation

Developed questions on managerial & organizational practices

• ~45 minute phone interview of manufacturing plant managers

• Randomized from medium sized firms (100 to 5000 employees)

Used “Double-blind” interviews to try to reduce survey bias

• Interviewers do not know the company performance in advance

• Managers are not informed (in advance) they are scored

Getting firms to participate in the interview

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

• Official Endorsements (e.g. Bundesbank, PBC, RBI)

• Run by 51 MBA types (loud, persistent & business experience)

Test Using New Firm-Level Management Practices Data Across Countries

Example Management Question on Promotions

See Appendix and Bloom and Van Reenen (2007) for details

People Management by Country of Location

Note: Uses 4,003 firms. Z-score of 4 people management questions (hiring, firing, promotion and rewards).

-.4 -.2 0 .2 .4 .6mean of peeps

US

Germany

Japan

Poland

UK

France

Sweden

China

Italy

Portugal

India

Greece

-.2 0 .2 .4 .6mean of peeps

US

Germany

France

Switzerland

UK

Denmark

Sweden

Holland

Finland

Japan

Note: Uses 631 multinational subsidiaries in Europe. Z-score of 4 people management questions (hiring, firing, promotion and rewards)

People Management by Country of Origin

Aside: This is part of a set of results suggesting multinationals take domestic organizational and management practices abroad

• Growing literature on multinationals often assumes they take firm-level ‘attributes’ across countries• Productivity – Helpman, Melitz and Yeapple (2004)• Communication/organization – Antras, Garicano & Rossi-

Hansberg (2008)• Management - Burstein and Monge (2008)

• These results, and those in Bloom, Sadun and Van Reenen (2008) are completely consistent with this• Multinationals appear to have management and

organizational characteristics partly based on their country of origin and partly based on their country of location

• Obtained accounts for all European firms (public and private)

• Purchased firm-level IT panel data from Harte-Hanks (an IT survey firm) for the European firms

• HH runs annual surveys on all firms with 100+ employees

• HH achieves about a 50% coverage ratio of this group

• High quality data as sold for marketing purposes

• Join cross-sectional management data with panel accounts and IT data, yields dataset on 719 firms with 2,555 obs

We Matched the Firm-Level Management Data to Panel Company Accounts and IT Data

Dependent Var: Ln(Q/L) Ln(Q/L) Ln(Q/L) Ln(Q/L) Ln(Q/L)

USA×Log(C/L) 0.179** 0.078 0.052

MNE×Log(C/L) -0.026 -0.024 0.022

Management 0.019 0.019

Manag.×Log(C/L) 0.145*** 0.140*** 0.128*

Log (K/L) 0.236*** 0.184*** 0.178*** 0.179*** 0.235**

Log(C/L) 0.126*** 0.143*** 0.146*** -0.049

USA 0.270*** 0.078 0.111** 0.084*

MNE 0.193*** 0.160*** 0.160*** 0.162***

Log(Degree) 0.043** 0.037** 0.037**

(USA=MNE)×ln(C/L)  0.019   0.235 0.631

Firms 1633 719 719 719 719

Observations 7420 2555 2555 2555 2555

Fixed Effects NO NO NO NO YES

TABLE 6: EU PANEL PRODUCTION FUNCTIONS

Dependent Variable Ln(Q/L) Ln(PC/L) Ln(PC/L)

USA×Log(PC/L) 0.019

MNE×Log(PC/L) 0.023

People Management 0.088***

Management×Log(PC/L) 0.099*

Log (K/L) 0.232***

Log(PC/L) -0.228

USA 0.260*** 0.215***

MNE 0.049 0.037

Log(Degree)×Log(PC/L) 0.070

(USA=MNE)×ln(C/L) 0.955 0.001 0.027

Firms 719 719 719

Observations 2555 2555 2555

Fixed Effects YES NO NO

TABLE 6 CONTINUED: EU PANEL PRODUCTION FUNCTIONS AND IT INTENSITY

Macro facts and motivation

Evidence from UK establishments

Evidence from an EU panel

Conclusion

Currently looking at why US firms have better people management

• Bloom and Van Reenen (2007) suggest two factors important in improving overall US management practices– Greater product market competition– Fewer primo geniture family firms

• Currently investigating two other factors that may play a role:– Lower labor market regulation in US– Higher skill levels in the US

Both factors correlated with people management in our data

• These two factors are also correlated with cross-country IT investment and productivity experience

Labor market regulation and IT investment

Belgium

Denmark

Finland

France

Germany

Greece

Ireland

Italy

Netherlands

Portugal

Spain

Sweden

UK

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40 60 80 100World Bank Employment Rigidity Index, 100=most flexible, 0=most rigid

Source: GGDC

Labor market regulation and productivity growth

Belgium

Denmark

Finland

France

Germany

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Flexible labor markets are correlated with IT use and productivity growth —but so is higher education

IT Contribution to output growth, 1990-93

FranceGerman

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USUK

Italy

0

0.2

0.4

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10 20 30 40 50

Share with tertiary education

IT Contribution to output growth, 1990-03

Italy

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GermanyFrance

0

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0.4

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01234Employment Protection Index

Sources: IT contribution to output growth (annual average, percentage points) and share with tertiary education from OECD. Employment Protection Index from Nicoletti et al (2000).

(Increasing flexibility →)

Source: John Fernald,EF&G discussion Fall 2007

Conclusions

1) New UK census micro data:– US MNEs higher intensity of IT than non-US MNEs– Driven by sectors responsible for US “productivity miracle”– Magnitudes can account for ≈ ½ US productivity miracle

2) New international firm IT and management data:– Suggests US firms differently managed at home & abroad– This can explain much of the higher US intensity of IT use

Currently working on trying to understand why US and other

firms are differently managed and organized across countries

Back Up

• TFP can depend on ownership (UK domestic is omitted base)

• Coefficient on factor J depends on ownership (and sector, h)

Empirically, only IT coefficient varies significantly (IT coefficient in US higher than non-US MNEs)

MNEit

MNEJh

USAit

USAJh

Jh

Jit DD ,,0,

ithMNEit

MNEh

USAit

USAhit zDDa ~'

US MNE Non-US MNE

US MNE Non-US MNE

Econometric Methodology (2)

Table A1 BREAKDOWN OF INDUSTRIES (1 of 3)

IT Intensive (Using Sectors)

IT-using manufacturing18 Wearing apparel, dressing and dying of fur22 Printing and publishing29 Machinery and equipment31, excl. 313 Electrical machinery and apparatus, excluding insulated wire33, excl. 331 Precision and optical instruments, excluding IT instruments351 Building and repairing of ships and boats353 Aircraft and spacecraft352+359 Railroad equipment and transport equipment36-37 miscellaneous manufacturing and recycling

IT-using services51 Wholesale trades52 Retail trade71 Renting of machinery and equipment73 Research and development741-743 Professional business services

BREAKDOWN OF INDUSTRIES (2 of 3)

IT Producing Sectors (Other Sectors)

IT Producing manufacturing30 Office Machinery313 Insulated wire321 Electronic valves and tubes322 Telecom equipment323 radio and TV receivers331 scientific instruments

IT producing services64 Communications72 Computer services and related activity

BREAKDOWN OF INDUSTRIES (3 of 3)

Non- IT Intensive (Other sectors – cont.)

Non-IT intensive manufacturing15-16 Food drink and tobacco17 Textiles19 Leather and footwear20 wood21pulp and paper23 mineral oil refining, coke and nuclear24 chemicals25 rubber and plastics26 non-metallic mineral products27 basic metals28 fabricated metal products 34 motor vehicles

Non-IT Services50 sale, maintenance and repair of motor vehicles55 hotels and catering60 Inland transport61 Water transport62 Air transport

63 Supporting transport services, and travel agencies70 Real estate749 Other business activities n.e.c.90-93 Other community, social and personal services95 Private Household99 Extra-territorial organizations

Non-IT intensive other sectors01 Agriculture02 Forestry05 Fishing10-14 Mining and quarrying50-41 Utilities45 Construction