Slides - HBS People Space

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 with
the US for 50 years…
…but since 1995 US productivity accelerated away
again from Europe.
The “productivity miracle” occurred as quality
adjusted computer prices began to fall very rapidly
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),
… 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
ICT-producing sectors
Non-ICT sectors -0.5
3.5
1.9
-0.1
1.6
-1.1
Source: O’Mahony & Van Ark (2003, Gronnigen Data & European Commission)
3
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
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
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)
Descriptive statistics already show US
multinationals are particularly different in IT use
% difference from 4 digit industry mean in 2001
60
50
40
US Multinationals
Non-US Multinationals
UK domestic
30
20
10
0
-10
-20
-30
Employment
Value added
per Employee
Non-IT Capital
per Employee
IT Capital per
Employee
Observations: 576 US; 2228 other MNE; 4770 Domestic UK
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
Econometric Methodology (1)
Estimate a production function for establishment i at time t:
ln(Qit ) = ln( Ait ) + ! K ln( K it ) + ! L ln( Lit ) + ! L ln( M it ) + ! C ln(Cit )
K
M
C
ln(Q / L)it = ln( Ait ) + ! ln( K / L)it + ! ln( M / L)it + ! ln(C / L)it
+ (1 " ! K " ! L " ! M " ! C ) ln( L)it
Allow TFP and factor coefficients to vary by ownership (US,
non-US multinational and domestic firms)
Where
Q = Gross Output
K = Non-IT capital
M = Materials
A = TFP
L = Labor
C = IT capital
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
TABLE 2: PRODUCTION FUNCTIONS
Depend Var
Sectors
Ln(Q/L)
Ln(Q/L)
Ln(Q/L)
Ln(Q/L)
Ln(Q/L)
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
0.046***
0.043***
0.037***
0.046***
Ln(C/L)
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
0.011
0.176
0.015
0.021
0.023
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
740
services (travel agencies)
Professional business 489
services
Real estate
Industries (SIC-2) in blue are services and in black are manufacturing
700
Table 2, Production Functions with Fixed Effects
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
0.815
0.430
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 19952005 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
USA×ln(C/L)
0.033**
0.065**
0.033**
MNE×ln(C/L)
0.000
0.003
-0.001
-0.005
Ln(C/L)
0.013**
0.029***
0.033
-0.025
Skills
(wages)
0.028**
Ln(Wage)
0.280***
Ln(Wage)×Ln(C/L)
0.012*
Split
out EU
MNEs
0.038**
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)
TABLE 4, IT INTENSITY EQUATION
Dependent var:
Sectors
(1)
(2)
(3)
(4)
(5)
(6)
ln(C/L)
ln(C/L)
ln(C/L)
ln(C/L)
ln(C/L)
ln(C/L)
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
0.076
0.211
0.053
0.097
0.251
Test USA=MNE 0.031
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)
Table 5, Before and After Takeovers
Takeover timing:
Before
Before
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***
After
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
USA×ln(C)=MNE*ln(C)
261
1,066
0.704
0.097
1,066
USA×ln(C)=MNE*ln(C), 1 year after
0.495
USA×ln(C)=MNE*ln(C), 2+ years
0.073
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
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)
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).
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)
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 & RossiHansberg (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
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
TABLE 6: EU PANEL PRODUCTION FUNCTIONS
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*
0.184***
0.178***
0.179***
0.235**
0.126***
0.143***
0.146***
-0.049
Log (K/L)
0.236***
Log(C/L)
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
Firms
1633
719
Observations
7420
Fixed Effects
NO
0.235
0.631
719
719
719
2555
2555
2555
2555
NO
NO
NO
YES
TABLE 6 CONTINUED: EU PANEL PRODUCTION FUNCTIONS
AND IT INTENSITY
Dependent Variable
Ln(Q/L)
USA×Log(PC/L)
0.019
MNE×Log(PC/L)
0.023
Ln(PC/L)
People Management
Ln(PC/L)
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
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
Source: GGDC
Labor market regulation and productivity growth
Source: GGDC
Source: John Fernald,
EF&G discussion Fall 2007
Flexible labor markets are correlated with IT use and
productivity growth —but so is higher education
IT Contribution to
output growth, 1990-03
IT Contribution to
output growth, 1990-93
1
1
0.8
0.8
US
UK
0.6
Italy
German
Francey
0.4
0.2
0.6
UK
German
y
France
0.4
0.2
US
Italy
0
0
4
3
2
1
Employment Protection Index
(Increasing flexibility →)
0
10
20
30
40
50
Share with tertiary education
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). 40
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
Econometric Methodology (2)
• TFP can depend on ownership (UK domestic is omitted base)
ait = "
USA
h
USA
it
D
+"
MNE
h
MNE
it
D
~
+ ! h ' zit
Non-US MNE
US MNE
• Coefficient on factor J depends on ownership (and sector, h)
J
it
! =!
J ,0
h
+!
J ,USA
h
USA
it
D
US MNE
+!
J , MNE
h
MNE
it
D
Non-US MNE
Empirically, only IT coefficient varies
coefficient in US higher than non-US MNEs)
significantly (IT
Table A1 BREAKDOWN OF INDUSTRIES (1 of 3)
IT Intensive (Using Sectors)
IT-using manufacturing
18 Wearing apparel, dressing and dying of fur
22 Printing and publishing
29 Machinery and equipment
31, excl. 313 Electrical machinery and apparatus, excluding insulated wire
33, excl. 331 Precision and optical instruments, excluding IT instruments
351 Building and repairing of ships and boats
353 Aircraft and spacecraft
352+359 Railroad equipment and transport equipment
36-37 miscellaneous manufacturing and recycling
IT-using services
51 Wholesale trades
52 Retail trade
71 Renting of machinery and equipment
73 Research and development
741-743 Professional business services
BREAKDOWN OF INDUSTRIES (2 of 3)
IT Producing Sectors (Other Sectors)
IT Producing manufacturing
30 Office Machinery
313 Insulated wire
321 Electronic valves and tubes
322 Telecom equipment
323 radio and TV receivers
331 scientific instruments
IT producing services
64 Communications
72 Computer services and related activity
BREAKDOWN OF INDUSTRIES (3 of 3)
Non- IT Intensive (Other sectors – cont.)
Non-IT intensive manufacturing
15-16 Food drink and tobacco
17 Textiles
19 Leather and footwear
20 wood
21pulp and paper
23 mineral oil refining, coke and nuclear
24 chemicals
25 rubber and plastics
26 non-metallic mineral products
27 basic metals
28 fabricated metal products
34 motor vehicles
Non-IT Services
50 sale, maintenance and
repair of motor vehicles
55 hotels and catering
60 Inland transport
61 Water transport
62 Air transport
63 Supporting transport services, and
travel agencies
70 Real estate
749 Other business activities n.e.c.
90-93 Other community, social
and personal services
95 Private Household
99 Extra-territorial organizations
Non-IT intensive other sectors
01 Agriculture
02 Forestry
05 Fishing
10-14 Mining and quarrying
50-41 Utilities
45 Construction