Transcript The Effects of ICT on Productivity

AMERICANS DO I.T. BETTER:
US Multinationals and the Productivity Miracle
John Van Reenen, Department of Economics, LSE; Director of the
Centre for Economic Performance, NBER & CEPR
Nick Bloom, Stanford, CEP & NBER
Raffaella Sadun, LSE & CEP
European productivity had been catching up with
the US for 50 years…
10
20
30
40
50
Labor Productivity Levels
1960
1970
1980
1990
2000
year
USA
Source: GGDC Dataset
EU 15
2010
…but since 1995 US productivity accelerated away
again from Europe.
25
30
35
40
45
50
Labor Productivity Levels
1980
1985
1990
1995
2000
year
USA
Source: GGDC Dataset
EU 15
2005
The US resurgence is known as the “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” started as quality
adjusted computer price falls started to accelerate.
-.3
-.25
-.2
-.15
-.1
Fall in Real Computer Prices
1985
1990
Source: Jorgenson (2001)
1995
Year
2000
2005
Interestingly, in the US the “miracle” appears linked
in particular to the “IT using” sectors…
Source: Oliner and Sichel (2000, 2005)
[See also Jorgenson (2001, AER) and Stiroh (2002, 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.
Increase in annual
growth rate – from
1.2% in 1990 –95 to
4.7% from 1995
ICT-using sectors
ICT-producing sectors
Non-ICT sectors -0.5
3.5
EU
-0.1
Static growth – at
around 2% a year –
during the early and
late 1990s
1.9
1.6
-1.1
Source: O’Mahony and Van Ark (2003, Gronnigen Data and European Commission)
And Europe also did not have the same IT
investment boom as the US
0
2
4
6
8
IT Capital Stock per Hours Worked
1980
1985
1990
1995
2000
year
USA
Source: GGDC
EU 15
2005
Question
Why did the US achieve a productivity miracle and not Europe?
[since ICT available in EU and US at similar price]
Two types of arguments proposed (not mutually exclusive):
1) Standard: US advantage lies in geographic/business
environment (e.g. less planning regulation, faster demand
growth, larger market size, better skills, younger labor force, etc.)
2) Alternative: US advantage lies in their firm
organization/management practices (e.g. Martin Bailey)
Paper will present micro evidence from UK data that supports (2)
-Key idea is to look within one country (holds environment
constant) but look across US multinationals vs. non-US MNEs
(including takeovers)
Summary of Results
• New micro data - unbalanced panel of c.11,000 establishments located
in UK 1995-2003
– US multinationals (MNE) more productive than non-US
multinationals
– US establishments have more IT capital, but higher US productivity
mainly due to higher (observed) impact of unit of IT on productivity
• Also true for US takeovers of UK establishments
• Result driven by same sectors responsible for US productivity
miracle (“IT using” sectors)
• Rationalize the results with a simple model
– Common production function (IT-org complementarity)
– But lower adjustment costs of changing organization in US relative
to Europe
Macro facts and motivation
New micro results
Our intuition and a possible model
Conclusion
Why use UK micro data?
• The UK has a lot of multinational activity
– In our sample, 40% plants are multinational (10% US, 30%
non-US)
– Frequent M&A generates lots of ownership change
• No productivity acceleration in UK
• UK census (ONS) data is excellent for this purpose
– Data on IT and productivity for manufacturing and services
(where much of the “US miracle” occurred)
– Combined unused surveys of IT expenditure with ABI (like
US LRD but includes most private services)
– About 23,000 observations from 1995 to 2003
IT Capital Stocks Estimates
• Methodology
– US assumptions over depreciation and hedonic prices for IT
– Construct IT capital using standard approaches (e.g. Jorgensen
(2001, AER and Stiroh, 2002, AER)
– Perpetual inventory method (PIM) to generate establishment level
estimates of IT stocks
Ki,t  Ii,t  1   Ki,t 1
• Robustness test assumptions on:
– Initial Conditions
– Depreciation and deflation rates
– Compare main results with a survey of IT use based on proportion
of workers using computers
Preliminary figures already show US multinationals
are particularly different in terms of IT use
% difference from 4 digit industry mean in 2001
60
50
40
US Multinationals
30
Non-US Multinationals
20
UK domestic
10
0
-10
-20
-30
Employment
Value added Non-IT Capital IT Capital per
per Employee per Employee Employee
Observations: 576 US; 2228 other MNE; 4770 Domestic UK
Econometric Methodology (1)
Estimate a standard production
establishment i at time t:
function
(in
logs)
for
qit  ait   mit   l   k   c
M
it
L
it it
K
it it
C
it it
Where
q = ln(Gross Output)
a = ln(TFP)
m = ln(Materials)
l = ln(Labor)
k = ln(Non-IT capital)
c = ln(IT capital)
Also include age, multi-plant dummy, region controls (z)
Econometric Methodology (2)
• TFP can depend on ownership (UK domestic is omitted base)
USA
USA
MNE
MNE
it
h
it
h
it
h
it
a 
D

D
~
 ' z
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 significantly (table 2)
Econometric Methodology (3): Other Issues
• Include full set of industry dummies interacted with year
dummies to control for industry level shocks (e.g. output
price differences)
• Main specifications also include establishment fixed effects
• Takeover sample: compare US takeovers of UK plants
compared to non-US multinational takeovers
• Standard errors clustered by establishment
• Robustness: address endogeneity using GMM-SYS
(Blundell and Bond, 1998, 2000) and Olley Pakes (1996)
TABLE 3 – PRODUCTION FUNCTION
(1) All
(2) All
(3) All
(4) IT Using
(5) Others
NO
NO
NO
NO
NO
-
0.0086*
(0.0048)
0.0196**
(0.0078)
0.0033
(0.0061)
-
-
0.0001
(0.0030)
-0.0030
(0.0041)
0.0037
(0.0042)
Ln(C), IT
capital
-
0.0457***
(0.0024)
0.0449***
(0.0026)
0.0399***
(0.0036)
0.0472***
(0.0035)
Ln(M),
materials
0.5575***
(0.0084)
0.5474***
(0.0083)
0.5475***
(0.0083)
0.6212***
(0.0142)
0.5065***
(0.0104)
Ln(K), non-IT
capital
0.1388***
(0.0071)
0.1268***
(0.0068)
0.1268***
(0.0068)
0.1108***
(0.0094)
0.1458***
(0.0092)
Ln(L), labor
0.2985***
(0.0062)
0.2690***
(0.0062)
0.2688***
(0.0062)
0.2179***
(0.0102)
0.2869***
(0.0076)
USA
0.0712***
(0.0140)
0.0642***
(0.0135)
0.0151
(0.0277)
-0.0824*
(0.0438)
0.0641*
(0.0354)
MNE
0.0392***
(0.0079)
0.0339***
(0.0078)
0.0338**
(0.0161)
0.0325
(0.0241)
0.0194
(0.0214)
21,746
21,746
21,746
7,784
13,962
0.0944
0.0048
0.9614
0.5198
0.0108
0.2296
Sectors
Fixed effects
USA*ln(C)
MNE*ln(C)
Obs
USA*ln(C)=MNE*ln(C), p-value
USA=MNE
0.0206
0.0203
TABLE 3 – PRODUCTION FUNCTION, cont.
(6) All Sectors
(7) IT Using Intensive
(8) Other Sectors
YES
YES
YES
USA*ln(C)
0.0049
(0.0064)
0.0278***
(0.0105)
-0.0085
(0.0071)
MNE*ln(C)
0.0042
(0.0034)
0.0055
(0.0052)
0.0034
(0.0044)
Ln(C)
0.0146***
(0.0028)
0.0114**
(0.0047)
0.0150***
(0.0034)
Ln(M)
0.4032***
(0.0178)
0.5020***
(0.0280)
0.3605***
(0.0209)
Ln(K)
0.0902***
(0.0159)
0.1064***
(0.0229)
0.0664***
(0.0209)
Ln(L)
0.2917***
(0.0173)
0.2475***
(0.0326)
0.3108***
(0.0195)
USA
-0.0110
(0.0424)
-0.1355*
(0.0768)
0.0472
(0.0405)
MNE
-0.0162
(0.0198)
-0.0160
(0.0327)
-0.0204
(0.0254)
Observations
21,746
7,784
13,962
USA*ln(C)=MNE*ln(C)
0.9208
0.0403
0.1340
Test USA=MNE
0.9072
0.1227
0.9665
Sectors
Fixed effects
TABLE 4, SOME ROBUSTNESS TESTS (IT USING SECTORS)
Experiment
All Inputs
interacted
Alternative IT
measure
Translog
Skills (wages)
Split out EU
MNEs
USA*ln(C)
0.0328**
(0.0141)
0.0711**
(0.0294)
0.0268**
(0.0102)
0.0208**
(0.0096)
0.0283**
(0.0105)
MNE*ln(C)
0.0002
(0.0065)
0.0056
(0.0131)
0.0028
(0.0050)
0.0021
(0.0047)
Ln(C), IT
capital
0.0126**
(0.0050)
0.0285***
(0.0083)
0.0327
(0.0463)
-0.0227*
(0.0163)
Ln(Wages)
0.2137***
(0.0407)
Ln(Wages)*
Ln(C)
0.0109*
(0.0056)
0.0114**
(0.0047)
EU*ln(C)
0.0065
(0.0051)
Non-EU*
*ln(C)
-0.0079
(0.0158)
USA*ln(C)=
MNE*ln(C)
0.0224
0.0122
0.0244
0.0575
0.0457
Obs
7,784
7,784
7,784
7,784
7,784
Other Issues
•
Transfer pricing (must be changing over time and effect IT)?
– Higher US coefficient not observed for any other factor inputs (e.g.
intermediates)
– Observed in retail and wholesale (final services)
– Dynamic changes (see takeover table 5)
•
US firms select into high IT sectors? Use % of US establishments in 4 digit
industry (col 6 table 4)
•
Unobserved US HQ inputs (e.g. software)?
– But why larger than non-US MNE inputs (US firms similar median size to
non US MNEs)
– No significant interaction of IT with global firm size and US*IT result
unaffected
– Software results
•
Revenue productivity? But in standard Klette-Griliches this implies different
coefficients on all factor inputs if US mark-ups different (col 3 of table 4)
Worried about unobserved heterogeneity?
• Maybe US firms “cherry pick” plants with high IT productivity?
• Or maybe some kind of other unobserved difference
• So test by looking at production functions before and after
establishment is take-over by US firms (compared to other
takeovers)
• No difference before takeover. After takeover results look
very similar to table 3 (and interesting dynamics)
TABLE 5, PRODUCTIVITY BEFORE AND AFTER TAKEOVER
Before
Takeover
Before
takeover
After
Takeover
-0.0322
(0.0277)
USA*ln(C)
MNE*ln(C)
After
Takeover
0.0224
(0.0102)
-0.0159
(0.0118)
0.0031
(0.0079)
USA
-0.0031
(0.0335)
0.1634
(0.1357)
0.0827***
(0.0227)
-0.0345
(0.0550)
MNE
-0.0221
(0.0226)
0.0572
(0.0598)
0.0539***
(0.0188)
0.0412
(0.0380)
0.0582***
(0.0092)
0.0593***
(0.0097)
0.0495***
(0.0061)
0.0460***
(0.0067)
Ln(C), IT
capital
After
Takeover
0.0459***
(0.0067)
USA*ln(C)
1 year after
0.0095
(0.0149)
USA*ln(C)
2+years
0.0274**
(0.0115)
MNE*ln(C)
1 year after
0.0003
(0.0109)
MNE*ln(C)
2+ years after
0.0041
(0.0085)
Obs
1,422
USA*ln(C)=MNE*ln(C), p-value
1,422
0.5564
3,466
3,466
3,466
0.0880
0.0894
Tab A4: Probability of takeover by US multinational
(compared to other forms of takeovers)
Sample
Ln(C/L)t-1
ΔLn(C/L)t-1
All
All
-0.0029
All except
domestic
All except
domestic
-0.0003
-
-
-0.0236
Ln(L)t-1
0.0140
0.0108
-0.0183
-0.0222
Ln(K/L)t-1
0.0108
0.0109
-0.0174
-0.0346
Ln(Y/L)t-1
0.0236
0.0270
0.0333
0.0580
Aget-1
-0.0014
0.0017
-0.0003
-0.0014
563
563
190
190
Obs
-0.0876
Note: LPM model, robust standard errors, controls include 2 digit industry dummies
Macro facts and motivation
New micro results
Our intuition and a possible model
Conclusion
The US advantage is better organizational and
managerial structures?
Macro and micro estimates consistent with the idea of an
unobserved factor which is:
• Complementary with IT
• Abundant in US firms relative to others
We think the unobserved factor is the different organizational
and managerial structure of US firms (see next slide)
Effective IT use appears associated with these
different organizational (and managerial) practices
1. Econometric firm level evidence, i.e.
• Complementarity of IT and organizational practices in
production functions (Bresnahan, Brynjolfsson & Hitt
(QJE, 2002), Caroli and Van Reenen (QJE, 2002))
2. Case study evidence, i.e.
• Introduction of ATMs & PCs in banking (Hunter, 2002)
– Teller positions reduced due to ATM’s
– “Personal banker” role expanded using CRM software
and customer databases to cross-sell
– Remaining staff have more responsibility, skills and
decision making
– Not all banks did this smoothly or successfully (e.g.
much slower in EU)
Figure 3a: Organizational devolvement,
firms by country of location
European Firms
4.13
US Firms
4.93
Figure 3b: Organizational devolvement,
firms by country of ownership
Domestic Firms,
in Europe
Non-US Multinational
subsidiaries, in EU
US Multinational
subsidiaries in EU
4.11
3.67
4.87
US multinationals also change their organizational
structures more frequently
Organizational change in the UK
during 1981-1990 (WIRS data)
Organizational change in the UK
during 1998-2000 (CIS data)
Domestic Firms
0.40
Domestic Firms
Non-US MNEs
0.42
Non-US MNEs
US MNEs
0.52 US MNEs
0.42
0.65
0.75
Source: WIRS data (1984 and 1990) plots the proportion of establishments experiencing organizational
change in previous 3 years (all establishments in the UK). US MNEs (N=190), Non-US MNEs (N=147),
Domestic (N=2848). Senior manager is asked “whether there has been any change in work organization
not involving new plant/equipment in the past three years” CIS data: we plot the proportion of
establishments experiencing organizational or managerial change in previous 3 years. The firm is
asked “Did your enterprise make major changes in the following areas of business structure and
practices during the three year period 1998-2001?” with answers to either “Advanced Management
techniques” or “Major changes in organizational structure” recorded as an organizational change.
One simple way to model the all this macro, micro
and survey data is based on three simple elements
1. IT is complementary with newer organizational/managerial
structures
2. IT prices are falling rapidly, especially since 1995, increasing
IT inputs
3. US “re-organizes” more quickly because more flexible
• Maybe because less labor market regulation and union
restrictions
Organizational structure (O) as an optimal choice
(1) Firms optimally choose their organization
– Example: Old-style centralized “Fordism” complementary
with physical capital, but new style organizational structures
complementary with
IT (“decentralized”)
Q = A Cα+σO Kβ-σO L1-α- β
π = PQ- G(ΔO)- ρCC – ρKK – WL
Where:
Q = Output, A=TFP, π=profits
C = IT capital, K = non-IT capita, L=Labor
O = organizational structure (between 0 and 1)
σ = Indexes complementarity between IT and organizational structure
G(ΔO)= Organizational adjustment costs
IT price and organizational adjustment
(2) IT prices fall fast so firms want to re-organize quickly
(3) But rapid re-organization is costly, with adjustment costs
higher in EU than US,
G(ΔO) = ωm(Ot-Ot-1)2 + ηPQ| ΔO≠0|
Quadratic
cost
with
ω EU > ωUS
Fixed
“Disruption”
cost
Other details
The model is:
– “De-trended” so no baseline TFP growth
– Deterministic so IT price path known
– Allows for imperfect (monopolistic) competition
– EU and US identical except organization adjustment costs
In the long run US and EU the same, but transition dynamics different
Solving the model
– Almost everywhere unique continuous solution and policy
correspondences: O*(O-1, ρC),K*(O-1, ρC),C*(O-1, ρC), L*(O-1, ρC)
– But need numerical methods for precise parameterization1
1 Full
Matlab code on http://cep.lse.ac.uk/matlabcode/
Figure 4: Decentralization by US and European
firms, model results
US
Europe
Notes: Results from the numerical simulation of the theoretical model 1980-2015 (the full simulation was run 1970-2035). See text for details.
Decentralization is the value of O (between 0 and 1).
Figure 5: IT per unit of capital (C/K) in US and
European firms, model results
US
Europe
Notes: Results from the numerical simulation of the theoretical model 1980-2015 (the full
simulation was run 1970-2025). Decentralization is the value of O (between 0 and 1).
Figure 6: Labor productivity (Q/L) in US and
European firms, model results
US
Europe
Notes: Results from the numerical simulation of the theoretical model 1980-2015 (the full simulation was
run 1970-2025). Productivity is output per worker. Decentralization is the value of O.
Extension: Multinationals
What happens when a firm expands abroad?
Assumption:
Costly for multinationals to have different management and
organizational structures (easier to integrate managers, HR,
training, software etc. if org is similar across borders)
Implication:
Then US multinationals and EU multinationals abroad will
adjust to their parent’s organizational structure
Consistent with range of case-study evidence (e.g. Bartlett &
Ghoshal, 1999, Muller-Camen et al. 2004) and true for wellknown firms (P&G, Unilever, McKinsey, Starbucks etc..)
Figure 7: Decentralization by firms taken over by
US multinationals: model results
US
US takeover of
European firm
Europe
Notes: Results from the numerical simulation of the theoretical model 1980-2015 (the full simulation was run 1965-2025). See text for details.
Productivity is output per worker. Decentralization is the value of O.
The model provides:
1. A rationale for differences in organizational structures
between US and European firms
1. A simple way to interpret the macro stylized facts on
productivity dynamics and IT investment in the US and
Europe
1. A useful framework to link the micro findings on US
multinationals active in the UK to the macro picture
TABLE 6, IT AND LABOR MARKET REGULATIONS
Fixed Effects
Sample
Dependent Variable
USA*ln(C)
USA ownership*IT capital
Ln(C)
IT capital
Labor Regulation*ln( C )
(1)
(2)
(3)
(4)
(5)
NO
NO
YES
YES
YES
All MNE's
ln(Q)
ln(Q)
ln(Q)
ln(Q)
ln(Q)
0.0230***
-
0.0287*
-
0.0161
(0.0081)
(0.0161)
0.0134
0.0152**
-0.0339
-0.0041
(0.0055)
(0.0158)
(0.0073)
(0.0270)
(0.0254)
-
0.0439**
-
0.0702**
0.0295
(0.0358)
(0.0332)
-
-0.1600
(0.0193)
-0.1186***
-
(0.0453)
Labor Regulation
(0.0154)
0.0439***
World Bank Labour Regulation Index*IT capital
USA
All MNE's All MNE's All MNE's All MNE's
-
World Bank Labour Regulation Flexibility Index
-0.1483
(0.0988)
-0.1410
-
(0.0998)
(0.1058)
-0.3651
-0.0666
(0.2700)
(0.2451)
3,144
3,144
(0=inflexible, 1=most flexible)
Observations
3,144
3,144
3,144
Other extensions we consider to the model
1. Industry heterogeneity
– If the degree of complementarity is higher in some sectors (e.g. “IT
intensive using” industries) and zero in others, then these patterns will be
sector specific
– EU does just as well as US when no complementarity (σ = 0)
2. Adjustment costs for IT capital
– Qualitative findings the same
– TFP also will appear to grow faster in the transition
3. Permanent differences in management quality
– Possible alternative story: US firms able to transfer management practices
across international boundaries
Q = A OζCα+σO Kβ - σO L1-α- β- ζ
-
But implies a permanently higher US labor productivity even after
controlling for IT level and higher coefficient (we don’t find this)
-
Can test using new management data we are collecting
Macro facts and motivation
New micro results
A possible model
Conclusion
Conclusion
New micro evidence (cross section, panel and takeovers)
– US establishments have higher TFP than non-US
multinationals
– This is almost all due to higher coefficient on IT (“the way
that you do I.T.”)
– Driven by same sectors responsible for US “productivity
miracle”
Micro, macro and survey findings consistent with a simple reorganization model
– IT changes the 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)
– When will the EU resume the catching up process?
Next Steps
• Bringing management and organizational data together with firm IT,
organization and productivity data. New survey data following up
Bloom and Van Reenen, 2006, forthcoming QJE. 12 countries
(including US, UK, France, Germany, India, Japan, Poland), 3500+
firms
• Understanding determination of organizational
(Acemoglu, Van Reenen et al, forthcoming QJE)
decentralization
• More on IT endogeneity (e.g. regulatory decision on broadband rollout)
• Structural estimation of the adjustment cost model (e.g. Simulated
Method of Moments). See examples in Bloom, Bond and Van Reenen
(ReStud, 2007)
Back Up
TABLE 2: LABOR PRODUCTIVITY IN HIGH IT VS.
LOW IT ESTABLISHMENTS
DIFFERENCE IN DIFFERENCES
Value Added per Employee
High IT
establishments
Low IT
establishment
Difference
3.893
3.557
0.336***
(0.742)
(0.698)
(0.043)
Observations
1,076
729
Other Multinationals
3.771
3.473
0.238***
(0.756)
(0.664)
(0.022)
4,014
2,827
US Multinationals
Observations
Difference in Differences
0.098**
(0.048)
Stiroh/Van Ark “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 A2 - DESCRIPTIVE STATISTICS
Variable
Frequenc
y
Mean
Median
Standard
Deviation
Employment
7,121
811.10
238.00
4,052.77
Gross Output
7,121
87,966.3
8
20,916.4
8
456,896.10
Value Added
7,121
29,787.6
1
7,052.00
167,798.70
IT Capital
7,121
1,030.60
77.44
10,820.69
ln(IT Capital)
7,121
4.46
4.35
2.03
Value Added per worker
7,121
40.43
29.53
55.19
Gross Output per worker
7,121
124.74
86.03
136.55
Materials per worker
7,121
82.38
47.23
103.52
Non-IT Capital per worker
7,121
85.28
48.56
112.54
IT Capital per worker
7,121
0.96
0.34
2.08
IT expenditure per worker
7,121
0.41
0.14
0.89
Material costs as a share of revenues
7,121
0.57
0.60
0.23
Employment costs as a share of revenues
7,121
0.83
0.64
0.86
Non-IT Capital as a share of revenues
7,121
0.30
0.26
0.20
IT Capital as a share of revenues
7,121
0.010
0.004
0.018
Age
7,121
8.38
5.00
6.74
Multigroup dummy (i.e. is establishment part of larger
group?)
7,121
0.53
1.00
0.50
TABLE A3 – GMM AND OLLEY PAKES RESULTS
All
Sample
US
Other
Domestic UK
establishments
Estimation Method
GMM
Olley Pakes
Olley Pakes
Olley Pakes
Dependent Variable
Ln(Q)
ln(Q)
ln(Q)
ln(Q)
USA*ln(C)
0.1176*
-
-
-
USA ownership*IT capital
(0.0642)
-
-
-
0.0793***
0.0758**
0.0343**
0.0468***
(0.0382)
(0.0383)
(0.0171)
(0.0116)
0.4641***
0.5874***
0.6514***
0.6293***
(0.0560)
(0.0312)
(0.0187)
(0.0267)
0.2052***
0.0713
0.1017***
0.1110***
(0.0532)
(0.0674)
(0.0285)
(0.0270)
Ln(L)
0.2264***
0.1843***
0.2046***
0.2145***
Labor
(0.0728)
(0.0337)
(0.0139)
(0.0173)
Observations
1,074
615
2,022
3,692
First order correlation, p value
0.0100
-
-
-
Second order correlation, p value
0.3480
Sargan-Hansen, p-value
0.4570
-
-
-
MNE*ln(C)
Non-US multinational *IT
capital
Ln(C)
IT capital
Ln(M)
Materials
Ln(K)
Non-IT Capital
0.0092
(0.0418)
Europe also did not have the same IT investment
boom as the US
0
2
4
6
8
IT Capital Stock per Hours Worked
1980
1985
1990
1995
2000
year
USA
Source: GGDC
EU 15
2005
Non IT capital per hour worked
30
35
40
45
50
55
Non IT Stock per Hours Worked
1980
1985
1990
1995
2000
year
USA
Source: GGDC
EU 15
2005
organization matters for the productivity of IT
Source: Bresnahan, Brynjolfsson & Hitt (2002) “Information
Technology, Workplace Organization and the Demand for skilled labor”
Quarterly Journal of Economics
IT Capital Stocks Estimates
• Methodology
Perpetual inventory method (PIM) to generate
establishment level estimates of IT stocks
Ki,t  Ii,t  1   Ki,t 1
• Robustness test assumptions on:
– Initial Conditions
– Depreciation and deflation rates
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
Other Notes on Results
• Higher coefficient on IT than expected from share in gross output, but
not as large as Brynjolfsson and Hitt (2003) on US firm-level data
(example of TFP specification over)
• Methodological and data differences from BH (e.g. firms vs.
establishments; BH pre 1995 we are post 1995; we use standard
investment method BH use stock survey; we have more observations)
• But may be because we are looking at different countries
TFP BASED SPECIFICATIONS
(1)
(2)
(3)
(4)
Dependent
variable
Δln(TFP)
Δln(TFP)
Δln(TFP)
Δln(TFP)
Length of
differencing
first
second
third
fourth
(e.g. first differencing vs. longer differencing)
Sectors
All
All
All
All
ΔLn(C)
0.0137***
0.0150***
0.0154***
0.0155*
(0.0022)
(0.0030)
(0.0057)
(0.0082)
10,122
4,079
920
404
IT capital
Observations
What do we expect in TFP regressions?
MTFP, measured TFP is
ln Q  S c ln C  S x ln X ;
PcC
Px X
Sc 
; Sx 
PQ
PQ
In our model “true” TFP is
ln A  ln Q  (  O) ln C  (1    O) ln X
PcC
  O 
 Sc
PQ
So we measure TFP correctly even in presence of O
Using micro data
• In the data the higher O firms will have higher C so on average
coefficient on C is positive in TFP regressions unless we use exact
factor share of C by firm.
• On average, US firms will have no higher coefficient on C in TFP
equation if we use the US revenue share
• Extensions
– Allow adjustment costs on C. Implies that IT share “too low” when
calculating TFP, so measured TFP higher for high O firms
What do we expect in TFP regressions?
 d TFPit  bh0  d c~it  bhUSA  d ( DitUSA c~it )  bhMNE  d ( DitMNE c~it ) 
 USA  DUSA   MNE  D MNE   0  D 0   '  ~z   u
h
d
it
h
d
it
h
d
it
h
d it i
d
h ,it
Precise parameterization
variable
C coefficient
capital)
Mnemonic
(IT α
Value
Reference
0.025
Share of IT
value added
in
K coefficient
β
0.3
Share
costs
added
Complementarity
σ
0.017
α (1-e-1)
Mark-up (p-mc)/mc 1/(e-1)
0.5
Hall (1988)
Relative quadratic ωEU/ωUS
adjustment cost of
O
4
Nicoletti
and
Scarpetta (2003)
Disruption cost of η
O (as a % of sales)
0.2%
Bloom
(2006),
Cooper
&
Haltiwanger (2003)
IT prices
pc
-15% p.a. until BLS
1995 then -30%
of
in
capital
value
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)
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.
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
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