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

1
Americans do I.T. BetterUS Multinationals and
the Productivity Miracle
Nick Bloom, Stanford NBER Raffaella Sadun,
LSE John Van Reenen, LSE, NBER CEPR March
2008
2
European productivity had been catching up with
the US for 50 years
3
but since 1995 US productivity accelerated away
again from Europe.
4
The productivity miracle occurred as quality
adjusted computer prices began to fall very
rapidly
5
In the US the miracle appears linked in to the
IT using sectors
Sources Stiroh (2002, AER) See also Oliner and
Sichel (2000 JEP, 2002 Fed) Jorgenson (2001,
AER),
6
but no acceleration of productivity growth in
Europe in the same IT using sectors.
-
Change in annual growth in output per hour from
1990

95 to 1995

2001

U.S.
EU
-
ICT
using sectors
3.5
-0.1
ICT
-
producing sectors
1.9
1.6
Non
-
ICT sectors
-1.1
Source OMahony Van Ark (2003, Gronnigen Data
European Commission)
3
7
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

8
Summary of Results

• (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)

• 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

9

• Macro facts and motivation
• Evidence from UK establishments
• Evidence from an EU panel
• Conclusion

10
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 MA 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)

11
Descriptive statistics already show US
multinationals are particularly different in IT
use
difference from 4 digit industry mean in 2001
Observations 576 US 2228 other MNE 4770
Domestic UK
12
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 KaLßM?Cd
• Interested whether there is any difference
  between the US and Europe in the coefficients a,
  ß, ? and d
• Empirically will show dUSgtdEU and ßUSltßEU

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Econometric Methodology (1)

• 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

14
Econometric Methodology (2) Other Issues

• 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

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TABLE 2 PRODUCTION FUNCTIONS
Notes Log (output/employees) is the dependent
variable. CIT Capital, MMaterials,
KNon-IT Capital, LEmployees, USAUSA
Multinational and MNENon-US multinational
(domestically owned is baseline).
16
Stiroh (2002) IT Intensive / Non-Intensive and
Services / Manufacturing split
Industries (SIC-2) in blue are services and in
black are manufacturing
17
Table 2, Production Functions with Fixed Effects
Note CIT Capital, MMaterials, KNon-IT
Capital, LEmployees, USAUSA Multinational,
MNENon-US multinational (domestic owned the
baseline)
18
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 0.810.03722)
• 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

19
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 gt
  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)

20
Table 3, Runs Some Robustness Tests
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
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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)

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TABLE 4, IT INTENSITY EQUATION
Notes All columns include SIC3 time dummies
ln(Q).Additional controls age, region
multi-plant. SE clustered by establishment.
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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)

24
Table 5, Before and After Takeovers
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• Macro facts and motivation
• Evidence from UK establishments
• Evidence from an EU panel
• Conclusion

26
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

27
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

28
Test Using New Firm-Level Management Practices
Data Across Countries

• 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)

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Example Management Question on Promotions

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

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People Management by Country of Location
Note Uses 4,003 firms. Z-score of 4 people
management questions (hiring, firing, promotion
and rewards).
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People Management by Country of Origin
Note Uses 631 multinational subsidiaries in
Europe. Z-score of 4 people management questions
(hiring, firing, promotion and rewards)
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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

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

• 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

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TABLE 6 EU PANEL PRODUCTION FUNCTIONS
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TABLE 6 CONTINUED EU PANEL PRODUCTION FUNCTIONS
AND IT INTENSITY
36

• Macro facts and motivation
• Evidence from UK establishments
• Evidence from an EU panel
• Conclusion

37
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

38
Labor market regulation and IT investment
Source GGDC
39
Labor market regulation and productivity growth
Source GGDC
40
Flexible labor markets are correlated with IT use
and productivity growth but so is higher
education
Source John Fernald, EFG discussion Fall 2007
(Increasing flexibility ?)
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).
41
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

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Back Up
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Econometric Methodology (2)

• 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)

Non-US MNE
US MNE
Non-US MNE
US MNE
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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 spacecraft352359 Railroad
equipment and transport equipment36-37
miscellaneous manufacturing and
recyclingIT-using services51 Wholesale
trades52 Retail trade71 Renting of machinery
and equipment73 Research and development741-743
Professional business services
45
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 instrumentsIT producing services64
Communications72 Computer services and related
activity
46
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 vehiclesNon-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 organizationsNon-
IT intensive other sectors01 Agriculture02
Forestry05 Fishing10-14 Mining and
quarrying50-41 Utilities45 Construction