americans do i.t. better: us multinationals and the productivity miracle
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Nick Bloom, Stanford & NBER Raffaella Sadun, LSE John Van Reenen, LSE, NBER & CEPR March 2008. Americans do I.T. Better: US Multinationals and the Productivity Miracle. European productivity had been catching up with the US for 50 years…. - PowerPoint PPT PresentationTRANSCRIPT
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
10
20
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1960 1970 1980 1990 2000 2010year
USA EU 15
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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20
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1960 1970 1980 1990 2000 2010year
USA EU 15
Source: GGDC Dataset
Labor Productivity Levels
The “productivity miracle” occurred as quality adjusted computer prices began to fall very rapidly
-.3
-.25
-.2
-.15
-.1
% F
all
in R
ea
l Co
mpu
ter
price
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yea
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1985 1990 1995 2000 2005Year
Source: Jorgenson (2001)
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
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 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
US50
010
0015
0020
0025
0030
00IT
Exp
endi
ture
s pe
r em
ploy
ee (
2000
Eur
os)
, 199
6-20
04
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
Greece
Italy
Netherlands
Portugal
Spain
Sweden
United Kingdom
US
0.0
05.0
1.0
15.0
2.0
25La
bor
pro
duct
ivity
gro
wth
, 19
96-2
004
40 60 80 100World Bank Employment Rigidity Index, 100=most flexible, 0=most rigid
Source: GGDC
40
Flexible labor markets are correlated with IT use and productivity growth —but so is higher education
IT Contribution to output growth, 1990-93
FranceGerman
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
USUK
GermanyFrance
0
0.2
0.4
0.6
0.8
1
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