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

European productivity had been catching up withthe US for 50 years…

…but since 1995 US productivity accelerated awayagain from Europe.

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

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 “ITusing” sectors…

-

3

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 inEurope in the same “IT using” sectors.

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

So why did the US achieve a productivity miracleand not Europe?

Two types of arguments proposed (not mutually exclusive):

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

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

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

-Idea is to look within UK and Europe (holds environmentconstant) 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 added

per Employee

Non-IT Capital

per Employee

IT Capital per

Employee

US Multinationals

Non-US Multinationals

UK domestic

Descriptive statistics already show USmultinationals 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 differencesbetween 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 andEurope 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)

WhereQ = Gross Output A = TFPK = Non-IT capital L = LaborM = Materials C = IT capital

itCMLK

itC

itM

itK

itit

L

LCLMLKALQ

)ln()1(

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

!!!!

!!!

""""+

+++=

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

itL

itL

itK

itit CMLKAQ !!!! ++++=

Econometric Methodology (1)

• Include full set of SIC-3 digit industry dummies interactedwith 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

0.015 0.176 0.011 0.023 0.021USA=MNE 0.527 0.004 0.032USA×ln(C/L)=MNE×ln(C/L)13962 7784 21752174621746Obs 0.044*** 0.015 0.037*** 0.034*** 0.039***MNE 0.089*** 0.044** 0.073*** 0.064*** 0.071***USA-0.012***-0.009**-0.011***-0.011***-0.005*Ln(L) 0.146*** 0.111*** 0.127*** 0.127*** 0.139***Ln(K/L) 0.507*** 0.622*** 0.548*** 0.547*** 0.558***Ln(M/L) 0.046*** 0.037*** 0.043*** 0.046***Ln(C/L) 0.006-0.001 0.004MNE×ln(C/L) 0.012 0.038*** 0.020***USA×ln(C/L)

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

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’ andMNE=‘Non-US multinational’ (domestically owned is baseline).

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

700Real estate489Professional businessservices

740Supporting transportservices (travel agencies)

639Printing andpublishing

993Construction736Machinery andequipment

1012Hotels & catering1399Retail trade

1116Food, drink and tobacco2620Wholesale trade

# obsIT non-intensive# obsIT Intensive

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

0.815 0.430Test USA=MNE 0.521 0.009USA×ln(C/L)=MNE×ln(C/L) 13,962 7,784Observations-0.001 0.017MNE-0.007 0.045USA-0.247***-0.128***Ln(L) 0.067*** 0.106***Ln(K/L) 0.361*** 0.502***Ln(M/L) 0.016*** 0.012**Ln(C/L) 0.001-0.003MNE×ln(C/L)-0.006 0.037***USA×ln(C/L) YES YESFixed effects Others IT UsingSectors

Table 2, Production Functions with Fixed Effects

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

Quantification suggests UK micro data can accountfor 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 ITsectors 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-Pakesand find results robust (Table A4)

Table 3, Runs Some Robustness Tests

7,784 7,780 7,7842,1967,784Obs

0.046 0.058 0.0240.0120.022USA×ln(C)= MNE×ln(C)

-0.014Non-EU×ln(C/L) 0.002EU×ln(C/L)

0.012*Ln(Wage)×Ln(C/L) 0.280***Ln(Wage)

0.012**-0.025 0.0330.029***0.013**Ln(C/L)-0.005-0.0010.0030.000MNE×ln(C/L)

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

Splitout EU MNEs

Skills(wages)

Trans log

Another ITmeasure

All inputsinteract

Experiment

‘All inputs interacted’ allows labor, capital and materials to interact with ownership– these are individually and joint insignificant. ‘Another IT measure’ is “% ofemployees 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)

0.2510.0970.0530.2110.0760.031Test USA=MNE13,9627,78421,74613,9627,78421,746ObservationsYESYESYESNONONOExtra controls

0.123***0.194***0.151***0.133***0.212***0.163***MNE0.193***0.313***0.241***0.209***0.339***0.263***USA

OtherITUsing

AllOthersITUsing

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

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

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 istaken-over by US and non-US multinationals (domesticbaseline)

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

0.495USA×ln(C)=MNE*ln(C), 1 year after0.097USA×ln(C)=MNE*ln(C) 0.704

261 261

0.073USA×ln(C)=MNE*ln(C), 2+ years

1,0661,066Obs 0.012MNE×ln(C), 2+ years-0.009MNE×ln(C), 1 year after 0.066**USA×ln(C), 2+years 0.019USA×ln(C), 1 year after 0.029*** 0.029*** 0.094** 0.074***Ln(C)

0.021-0.001 0.032MNE 0.062-0.106-0.066USA 0.007-0.043MNE×ln(C) 0.054***-0.067USA×ln(C)

AfterAfterBeforeBeforeTakeover timing: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 anunobserved factor which is

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

Range of possible explanations – one we think may explainpart 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) andBresnahan et al. (2001)

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

Argument is IT changes informational flow, changing the optimalfirm 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 PracticesData 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).

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

People Management by Country of Origin

Aside: This is part of a set of results suggestingmultinationals take domestic organizational andmanagement 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 oforigin 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 ITsurvey 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 accountsand IT data, yields dataset on 719 firms with 2,555 obs

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

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

719 719719 7191633Firms 2555 25552555 25557420Observations YES NONO NONOFixed Effects

0.631 0.235 0.019(USA=MNE)×ln(C/L) 0.037**0.037** 0.043**Log(Degree) 0.162***0.160*** 0.160***0.193***MNE 0.084*0.111** 0.0780.270***USA

-0.049 0.146***0.143*** 0.126***Log(C/L) 0.235** 0.179***0.178*** 0.184***0.236***Log (K/L) 0.128* 0.140***0.145***Manag.×Log(C/L)

0.0190.019Management 0.022-0.024-0.026MNE×Log(C/L) 0.052 0.078 0.179**USA×Log(C/L)

TABLE 6: EU PANEL PRODUCTION FUNCTIONS

719 719719Firms 2555 25552555ObservationsNONO YESFixed Effects

0.0270.001 0.955(USA=MNE)×ln(C/L) 0.070Log(Degree)×Log(PC/L)

0.0370.049MNE0.215***0.260***USA

-0.228Log(PC/L) 0.232***Log (K/L) 0.099*Management×Log(PC/L)

0.088***People Management 0.023MNE×Log(PC/L) 0.019USA×Log(PC/L)

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

TABLE 6 CONTINUED: EU PANEL PRODUCTION FUNCTIONSAND IT INTENSITY

Macro facts and motivation

Evidence from UK establishments

Evidence from an EU panel

Conclusion

Currently looking at why US firms have betterpeople management

• Bloom and Van Reenen (2007) suggest two factors importantin 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 USBoth factors correlated with people management in our data

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

Labor market regulation and IT investment

Source: GGDC

Labor market regulation and productivity growth

Source: GGDC

40

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

IT Contribution to

output growth, 1990-93

France

German

y

USUK

Italy

0

0.2

0.4

0.6

0.8

1

10 20 30 40 50

Share with tertiary education

IT Contribution to

output growth, 1990-03

Italy

US

UK

German

yFrance

0

0.2

0.4

0.6

0.8

1

01234

Employment Protection Index

Sources: IT contribution to output growth (annual average, percentage points) and share withtertiary 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 otherfirms 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 (ITcoefficient in US higher than non-US MNEs)

MNE

it

MNEJ

h

USA

it

USAJ

h

J

h

J

itDD

,,0, !!!! ++=

ith

MNE

it

MNE

h

USA

it

USA

hitzDDa~'!"" ++=

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 products34 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