It Ain’t What You Do It’s The Way That You Do I.T.:
Investigating the Productivity Miracle
using Multinationals*
Bank of England, February 2006
Nick Bloom, Stanford & Centre for Economic Performance
Raffaella Sadun, LSE & Centre for Economic Performance
John Van Reenen, LSE & Centre for Economic Performance
* The paper formerly known as: “Nobody does I.T. better”
Overview (1)
Recent US “productivity miracle” not occurred in Europe
– Evidence is this is being driven by IT intensive sectors
– But why only in US as IT globally available?
Three types of arguments proposed:
1) US geographic advantage (skills, land, planning, clean air…)
2) US good luck (first mover advantage)
3) US better management/organisation
We present a model and range of evidence supporting the third
Overview (2)
Model has three elements
– IT prices falling rapidly
– IT complementary with newer organisation/management
– US “decentralized” first because lower labor regulations
Empirical evidence supporting this from three blocks
– Macro evidence: fits the well-known macro data
– Survey evidence: fits new organisational/management data
– Micro evidence: fits new micro data
• US MNEs more productive than non-US MNEs in UK
• Higher US productivity due to higher returns to IT
– Particularly in IT intensive sectors
– Very robust and also true for US takeovers
OUTLINE
1. Stylized facts and motivation
2. Model outline and predictions
3. Testing this on UK establishment level data
US productivity is accelerating away from the EU
25
30
35
40
45
50
Labor Productivity Levels
1980
1985
1990
1995
2000
year
EU 15
Source: GGDC Dataset
USA
2005
This is driven by the US “productivity miracle”
.01
.015
.02
.025
.03
Labor Productivity Growth
1985
1990
1995
year
EU 15
Source: GGDC Dataset
2000
USA
2005
The “productivity miracle” appears linked to IT use
100304LN(M)ZWG126RSAL-P1
Change in annual growth in output per hour from 1990–95 to 1995–2001
%
U.S.
Increase in annual
growth rate – from
1.2% in 1990–95 to
4.7% from 1995
ICT-using sectors
3.5
ICT-producing sectors
Non-ICT sectors
-0.5
Source: O’Mahony and Van Ark (2003)
Source: O’Mahoney and Van Ark, 2003
EU
-0.1
1.9
Static growth – at
around 2% a year –
during the early and
late 1990s
1.6
-1.1
3
The US also started investing much more in IT…
.002
.004
.006
.008
.01
.012
Growth in IT Capital Stock Share in GDP
1980
1985
1990
1995
2000
Year
USA
Sources: GGDC
EU
2005
….but not much more in non-IT capital
-.04
-.02
0
.02
.04
.06
Change in Non IT Capital Stock Share in GDP
1980
1985
1990
1995
2000
year
USA
Source: GGDC Dataset
EU 15
2005
All occurred as IT prices started to fall rapidly
-.3
-.25
-.2
-.15
-.1
Fall in Real Computer Prices
1985
1990
Source: Jorgenson (2001)
1995
Year
2000
2005
So what is behind the US “productivity miracle”?
• Superior US geographic factors:
–Greater supply of skilled/younger workers
–Higher competition
–Lower planning regulation
but link to IT in mid 1990s and US MNEs in UK?
• US good luck:
–US firms invested in IT first
but why don’t Europeans copy this
• US firms better organised and managed:
–Organisation/management important for the productivity of
IT (Brynjolfsson, Bresnahan & Hitt, 2002)
but are US firms better organised & managed?
US and EU firms decentralization and managed
Organizational devolvement
Management practices
European Firms
European Firms
4.13
US Firms
4.93
3.14
US Firms
3.32
Organizational devolvement
(firms located in Europe)
Management practices
(firms located in Europe)
Domestic Firms
in Europe
Domestic Firms
in Europe
3.11
Non-US MNEs
in Europe
3.12
Non-US MNEs
in Europe
US MNEs
in Europe
4.11
3.67
4.87
US MNEs
in Europe
3.74
Source: Bloom and Van Reenen (2005) survey of 732 firms in the US, UK, France and
Germany. Differences between “US-multinational” and “Domestic” firms significant at 1%
level in all panels except bottom left which is significant at the 10% level.
Papers claims organisation/management the story
Build simple model explaining the macro data
• Centralized “Taylorism” complementary with traditional
capital, decentralization complementary with IT
• IT prices fall fast prompting firms to decentralize
• US more flexibility in hiring/firing so decentralize first
Test on panel of 7,500 UK establishment from 1995-2003
• US MNEs more productive than non-US MNEs
• From higher productivity of IT in US MNEs v non-US MNEs
– Particularly IT intensive sectors as in “Productivity Miracle”
• US firms also more IT intensive
• Robust to range of different measures and take-overs
OUTLINE
1. Stylized facts and motivation
2. Model outline and predictions
3. Testing this on UK establishment level data
Model is very simple – has three ingredients
(1) Old-style “Taylorism” complementary with traditional capital,
new-style “decentralization” complementary with IT
Y = A Cα+λO Xβ-λO
π = Y- pcC - pxX
where: Y=output, A=TFP, C=IT, O=decentralization, X=other
factors and π=profit, pc price of IT and px price of other factors.
(2) IT prices fall fast so firms want to decentralize quickly
(3) Rapid decentralisation costly. Costs higher in EU than US
Cost(ΔO) = ωi(Ot-Ot-1)2
where ωEU > ωUS
Model – results
Other simplifying assumptions:
– Firms always optimising (no European “stupidity”)
– Model “detrended”:
• No baseline TFP growth
– Deterministic
• No other stochastics and IT price path known
So fall in IT prices driving everything
Solving the model
– Unique continuous solution and policy correspondences
– But need numerical methods for precise parameterisation1
– Very much work in progress
1 Full
Matlab code on http://cep.lse.ac.uk/matlabcode/
Prices assumed falling 15% until 1995, 30% after
US decentralizes first due to lower adjustment costs
Initially centralized US decentralizing as
“Taylorism” best
IT prices fall rapidly
EU decentralizes
later as more costly
IT factor shares rise as US and EU decentralize
US decentralizes so IT
productivity rises
EU decentralizes later so
IT productivity rises later
Note: IT input quantity always rising as IT price always falling
Decentralized US obtains higher productivity
Higher IT inputs lead to higher productivity,
particularly in more decentralized US
Note: Assumed baseline TFP equal in US and EU, with no TFP growth
US also obtains higher productivity growth
Growth from
accumulation of IT
and decentralisation
US growth slows
as decentralisation
complete
Model also makes other interesting predictions
1) Rising stock
market values,
particularly in US1
2) If IT also complementary skilled labor, then rising
skilled/unskilled wage differential, particularly in US
1 Need
to assume some returns to IT accrue to firms – i.e. imperfect competition
Model – taking this to UK establishment data
Need one additional assumption:
– Multinationals like globally similar management and
organisational structures
• Easy to integrate managers, HR, software etc..
• Seems reasonable and is true for well-known firms
(P&G, McKinsey, MacDonalds, Starbucks etc..)
– Then US MNEs and EU MNEs in the UK adopt their
parents organisational structure
• Pay the adjustment cost for this for integration benefit
OUTLINE
1. Stylized facts and motivation
2. Model outline and predictions
3. Testing this on UK establishment level data
Why UK micro data is a good way to test
explanations of the US “productivity miracle”
With just Macro data other possible explanations possible, i.e.
– Weaker US retail planning laws and IT important for retail
Need to controlling for other factors, so look in 1 country. UK ideal:
– 50% establishments foreign owned (10% US, 40% non-US)
– Census data on IT in 7,500 establishment 1995-2003
– Covers manufacturing and services
Looking at this data find strong support for the better US
management/organisation story
Data
Productivity Estimation
IT and Multinationals
Conclusions and next steps
Characteristics of IT Data
Four ONS surveys (FAR, ABI, BSCI, QICE) combined to
minimize missing observations (similar to LRD data):
– Data on IT expenditures,
– Combine with ABI data on output, materials,
capital, employment, etc.
– YEARS: From 1995 to 2003, but most of
observations regard 2000-2003 (QICE)
– SECTORS: Manufacturing and Services (Services
data usually not available)
22,736 observations
IT Capital Stocks Estimates
• Methodology
Perpetual inventory method (PIM) to
establishment level estimates of IT stocks
K i ,t  I i ,t  1   K i ,t 1
• Assumptions
– Initial Conditions
– Depreciation rates
– Deflators
generate
Methodological Choices
Issue
Initial
Conditions
Notes
We do not observe all Use industry data
firms in their first
(SIC2) and impute:
year of activity.
K jt
K it

How do we
I it
I jt
approximate the
 i  j and j  J
existing capital
stock?
Similar to
Martin (2002)
Industry IT
capital stocks
from NIESR
Robust to
alternative
methods
How to choose δ ?
Follow Oliner et al
(2004) and set δ =
0.36 (obsolescence)
 Basu and
Oulton suggest
0.31. Results not
affected by
alternative δ
Need real investment
to generate real
capital
Use NIESR hedonic
deflators (based on
US estimates)
 Re-evaluation
effects included
in deflators
Depreciation
Rates
Deflators
Choice
Data
Productivity Estimation
IT and Multinationals
Conclusions and next steps
Econometric Methodology
Estimate a standard Production Function (in logs):
qit  ait   mit   l   kit   it it  zit
M
it
Where
q
a
m
l
k
it
z
=
=
=
=
=
=
=
L
it it
K
it
ln(Gross Output)
ln(TFP)
ln(Materials)
ln(Labour)
ln(Non-IT capital)
ln(IT capital)
Other controls (age, region, group)
C
it
Investigating the impact of foreign ownership
• TFP levels can depend on ownership status
USA
USA
MNE
MNE
~
ait  ait   h Dit   h Dit
US MNE
Non-US MNE
• Factor coefficients can also depend on ownership status
 
J
it
J ,0
h

J ,USA
h
USA
it
D

J , MNE
h
MNE
it
D
In fact only IT coefficient varies significantly (table 2)
Other Econometric Issues
• Unobserved “industry effects”, so all variables transformed
in deviations from 4 digit industry mean (Klette, 1999)
• Some specifications also include establishment fixed effects
• All standard errors clustered for arbitrary serial correlation
• Try to address endogeneity use GMM and Olley Pakes
Data
Productivity Estimation
IT and Multinationals
Conclusions and next steps
Table 1: IT Coefficient by ownership status
Dep Variable
ln(GO)
ln(GO)
ln(GO)
ln(GO)
ln(GO)
ln(GO)
Sectors
All
All
IT Using
Others
IT Using
Others
Fixed effects
No
No
No
No
Yes
Yes
Ln (IT)
0.043*** 0.041*** 0.036***
0.044*** 0.021***
0.027***
US MNE
*ln(IT)
0.011**
0.019**
0.007
0.030*
0.001
Non- US
MNE*ln(IT)
0.004
-0.000
0.007*
0.005
-0.002
Ln(Materials) 0.539*** 0.539*** 0.614***
0.501*** 0.560***
0.412***
Ln(Non-IT K)
0.118*** 0.118*** 0.102***
0.134*** 0.140***
0.211***
Ln(Labour)
0.286*** 0.286*** 0.234***
0.303*** 0.254***
0.339***
US MNE
0.075*** 0.016
0.051
0.016
Non-US MNE 0.041*** 0.023
Obs
22,736 22,736
-0.057
0.031
7,905
Note: All regression include firm clustered SE
0.008
14,831
-0.167*
-0.009
7,905
0.045
14,831
Some Robustness Checks (Table 2)
• Try factors all varying by ownership – only IT different
• Try alternative IT measure – US*IT interaction significant
• Try translog functional form – US*IT interaction significant
• Try IT share (IT cap /All cap) – US*IT interaction significant
• Try using VA (not output) – US*IT interaction significant
• Try US industry FDI control – US*IT interaction significant
• Try skills controls – US*IT interaction significant
Worried about unobserved heterogeneity?
• Maybe US firms only buy plants with higher IT productivity?
• Or maybe US firms only is certain sectors?
– We control for 4-digit SIC industry
– But could argue should divide further (5 or 6 digit)?
• Or maybe some kind of other unobserved difference
– Local skill supplies, type of product etc…
• So test by looking at establishment take-overs by US firms
Table 4: US Takeovers and IT Coefficients
Dep. Variable
ln(GO)
ln(GO)
ln(GO)
ln(GO)
ln(GO)
Timing versus TO
Before
Before
After
After
After
US MNE *ln(IT),
(all years)
-0.022
0.023*
US MNE *ln(IT),
(1 year after TO)
-0.005
US MNE *ln(IT),
(2+ years after TO)
0.037**
Non-US MNE*ln(IT)
-0.025
0.013
0.014
Ln (IT)
0.056***
0.044*** 0.044***
Ln(Materials)
0.510*** 0.497*** 0.538***
0.538*** 0.536***
Ln(Non-IT K)
0.162*** 0.146*** 0.110***
0.117*** 0.113***
Ln(Labour)
0.314*** 0.280*** 0.287***
0.285*** 0.285***
US MNE
0.044
0.170
0.087***
-0.035
-0.167*
Non-US MNE
Obs
-0.010
2,365
0.010
2,365
0.048**
3,353
-0.017
3,353
-0.009
3,353
Note: All include fixed effects, estimated on the IT using sectors, firm clustered SE
Table 5: US Takeovers and IT Investment
Dep. Variable
IIT/KIT
IIT/KIT
IIT/KIT
Timing versus TO
Before
After
After
US MNE,
(all years)
0.040
0.424***
US MNE,
(1 year after TO)
0.519***
US MNE,
(2+ years after TO)
0.359**
Non-US MNE
0.066
0.222***
Ln(Labour)
Obs
1.110*** 1.011***
2,365
3,353
0.223
US
dummy
significant
higher
than
Non-US MNE
dummy at 5%
level
1.010***
3,353
Summarizing last 2 slides, after US takeover establishments:
• Become more productive due to higher IT productivity
• Invest significantly more in IT
Note: All include fixed effects, estimated on the IT using sectors, firm clustered SE
Conclusions
US “productivity miracle” matches a simple decentralisation model
– IT changes optimal structure of the firm
– So as IT prices fall firms want to restructure
– Occurred in the US but much less in the EU (regulations)
Consistent with the macro, survey and micro evidence
Three predictions for US-EU growth gap going forwards
• EU Optimist (EC) – EU firms will decentralize and catch-up
• Moderate – ongoing technical change so permanent gap
• EU Pessimist (me) – technical change accelerating so EU falling
further and further behind US
Back Up
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
65 Financial intermediation
66 Insurance and pension funding
67 Activities related to financial intermediation
71 Renting of machinery and equipment
73 Research and development
741-743 Professional business services
BREAKDOWN OF INDUSTRIES (2 of 3)
Non- IT Intensive (Using Sectors)
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.
75 Public Admin and welfare
80 Education
85 Health and Social Work
90-93 Other community, social
and personal services
95 Private Household
99 Extra-territorial organisations
Non-IT intensive other sectors
01 Agriculture
02 Forestry
05 Fishing
10-14 Mining and quarrying
50-41 Utilities
45 Construction
BREAKDOWN OF INDUSTRIES (3 of 3)
IT Producing 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