AMERICANS DO I.T. BETTER: US Multinationals and the Productivity Miracle John Van Reenen, Department...
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Transcript of AMERICANS DO I.T. BETTER: US Multinationals and the Productivity Miracle John Van Reenen, Department...
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
Out
put p
er
ho
ur w
ork
ed, $
1000
's (
200
5 P
PP
)
1960 1970 1980 1990 2000 2010year
USA EU 15
Source: GGDC Dataset
Labor Productivity Levels
…but since 1995 US productivity accelerated away again from Europe.
25
30
35
40
45
50
Out
put p
er
ho
ur w
ork
ed, $
1000
's (
200
5 P
PP
)
1980 1985 1990 1995 2000 2005year
USA EU 15
Source: GGDC Dataset
Labor Productivity Levels
.01
.01
5.0
2.0
25
.03
Gro
wth
in L
abo
ur
pro
du
ctiv
ity p
er
ho
ur
wo
rked
, 5 y
ea
r m
ovi
ng a
vera
ge
1985 1990 1995 2000 2005year
EU 15 USA
Source: GGDC Dataset
Labor Productivity Growth
The US resurgence is known as the “productivity miracle”.
The “productivity miracle” started as quality adjusted computer price falls started to accelerate.
-.3
-.25
-.2
-.15
-.1
% F
all
in R
ea
l Co
mpu
ter
price
s, 5
yea
r m
ovi
ng a
vera
ge
1985 1990 1995 2000 2005Year
Source: Jorgenson (2001)
Fall in Real Computer Prices
Source: Oliner and Sichel (2000, 2005)[See also Jorgenson (2001, AER) and Stiroh (2002, AER)]
Interestingly, in the US the “miracle” appears linked in particular 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
Increase in annual growth rate – from 1.2% in 1990 –95 to
4.7% from 1995 Static growth – at around 2% a year –during the early and
late 1990s
… but no acceleration of productivity growth in Europe in the same IT using sectors.
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
02
46
8IT
Ca
pita
l S
tock p
er
Hou
rs W
ork
ed
, 20
00 E
uro
s
1980 1985 1990 1995 2000 2005year
USA EU 15
Source: GGDC
IT Capital Stock per Hours Worked
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
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
• 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
1,,, 1 tititi KIK
-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
Preliminary figures already show US multinationals are particularly different in terms of IT use
Observations: 576 US; 2228 other MNE; 4770 Domestic UK
% difference from 4 digit industry mean in 2001
Estimate a standard production function (in logs) for establishment i at time t:
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)
itCitit
Kitit
Litit
Mititit cklmaq
Econometric Methodology (1)
• 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 (table 2)
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)
• 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)
Econometric Methodology (3): Other Issues
Sectors (1) All (2) All (3) All (4) IT Using (5) Others
Fixed effects NO NO NO NO NO
USA*ln(C)- -
0.0086*(0.0048)
0.0196**(0.0078)
0.0033(0.0061)
MNE*ln(C)- -
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)
Obs 21,746 21,746 21,746 7,784 13,962
USA*ln(C)=MNE*ln(C), p-value 0.0944 0.0048 0.9614
USA=MNE 0.0206 0.0203 0.5198 0.0108 0.2296
TABLE 3 – PRODUCTION FUNCTION
Sectors (6) All Sectors (7) IT Using Intensive (8) Other Sectors
Fixed effects 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
TABLE 3 – PRODUCTION FUNCTION, cont.
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)
0.0114**(0.0047)
Ln(Wages) 0.2137***(0.0407)
Ln(Wages)*Ln(C)
0.0109*(0.0056)
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)
Before Takeover
Before takeover
AfterTakeover
After Takeover
After Takeover
USA*ln(C)-0.0322 (0.0277)
0.0224(0.0102)
MNE*ln(C) -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)
Ln(C), IT capital
0.0582***(0.0092)
0.0593***(0.0097)
0.0495***(0.0061)
0.0460***(0.0067)
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 1,422 3,466 3,466 3,466
USA*ln(C)=MNE*ln(C), p-value 0.5564 0.0880 0.0894
TABLE 5, PRODUCTIVITY BEFORE AND AFTER TAKEOVER
Sample All AllAll except domestic
All except domestic
Ln(C/L)t-1 -0.0029 -0.0003 -
ΔLn(C/L)t-1 - -0.0236 -0.0876
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
Obs 563 563 190 190
Tab A4: Probability of takeover by US multinational (compared to other forms of takeovers)
Note: LPM model, robust standard errors, controls include 2 digit industry dummies
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)
European Firms 4.13
4.93US Firms
Domestic Firms,
in Europe
4.87
3.67
4.11
Non-US Multinational subsidiaries, in EU
US Multinational subsidiaries in EU
Figure 3a: Organizational devolvement,firms by country of location
Figure 3b: Organizational devolvement, firms by country of ownership
0.40
0.42
0.52 0.75
0.65
0.42Domestic Firms
Non-US MNEs
US MNEs
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.
Domestic Firms
Non-US MNEs
US MNEs
Organizational change in the UK during 1981-1990 (WIRS data)
US multinationals also change their organizational structures more frequently
Organizational change in the UK during 1998-2000 (CIS data)
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 costwith
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).
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).
US
Europe
Figure 5: IT per unit of capital (C/K) in US and European firms, model results
Figure 6: Labor productivity (Q/L) in US and European firms, model results
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.
US
Europe
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 well-known firms (P&G, Unilever, McKinsey, Starbucks etc..)
US
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.
Figure 7: Decentralization by firms taken over by US multinationals: model results
US takeover of European firm
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
(1) (2) (3) (4) (5)
Fixed Effects NO NO YES YES YES
Sample All MNE's All MNE's All MNE's All MNE's All MNE's
Dependent Variable ln(Q) ln(Q) ln(Q) ln(Q) ln(Q)
USA*ln(C) 0.0230*** - 0.0287* - 0.0161
USA ownership*IT capital (0.0081) (0.0161) (0.0154)
Ln(C) 0.0439*** 0.0134 0.0152** -0.0339 -0.0041
IT capital (0.0055) (0.0158) (0.0073) (0.0270) (0.0254)
Labor Regulation*ln( C ) - 0.0439** - 0.0702** 0.0295
World Bank Labour Regulation Index*IT capital (0.0193) (0.0358) (0.0332)
USA -0.1186*** - -0.1483 - -0.1600
(0.0453) (0.0988) (0.1058)
Labor Regulation - -0.1410 - -0.3651 -0.0666
World Bank Labour Regulation Flexibility Index
(0=inflexible, 1=most flexible) (0.0998) (0.2700) (0.2451)
Observations 3,144 3,144 3,144 3,144 3,144
TABLE 6, IT AND LABOR MARKET REGULATIONS
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
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 re-organization 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 decentralization (Acemoglu, Van Reenen et al, forthcoming QJE)
• More on IT endogeneity (e.g. regulatory decision on broadband roll-out)
• Structural estimation of the adjustment cost model (e.g. Simulated Method of Moments). See examples in Bloom, Bond and Van Reenen (ReStud, 2007)
DIFFERENCE IN DIFFERENCESValue Added per Employee
High IT establishments
Low IT establishment
Difference
US Multinationals 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)
Observations 4,014 2,827
Difference in Differences0.098**(0.048)
Notes: High IT are observations where the (de-meaned by 4 digit industry and year) ratio of IT capital to employment is greater than the median. 2787 Observations (only multinationals considered)
TABLE 2: LABOR PRODUCTIVITY IN HIGH IT VS.LOW IT ESTABLISHMENTS
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 services (travel agencies)
740
Professional business services
489 Real estate 700
Industries (SIC-2) in blue are services and in black are manufacturing
TABLE A2 - DESCRIPTIVE STATISTICS
Variable Frequency
Mean Median Standard Deviation
Employment 7,121 811.10 238.00 4,052.77
Gross Output 7,12187,966.3
820,916.4
8456,896.10
Value Added 7,12129,787.6
17,052.00 167,798.70
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
SampleAll
US OtherDomestic 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)
MNE*ln(C) 0.0092 - - -
Non-US multinational *IT capital
(0.0418)
Ln(C) 0.0793*** 0.0758** 0.0343** 0.0468***
IT capital (0.0382) (0.0383) (0.0171) (0.0116)
Ln(M) 0.4641*** 0.5874*** 0.6514*** 0.6293***
Materials (0.0560) (0.0312) (0.0187) (0.0267)
Ln(K) 0.2052*** 0.0713 0.1017*** 0.1110***
Non-IT Capital (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 - - -
Europe also did not have the same IT investment boom as the US
02
46
8IT
Ca
pita
l Sto
ck p
er
Hou
rs W
ork
ed, 2
00
0 E
uro
s
1980 1985 1990 1995 2000 2005year
USA EU 15
Source: GGDC
IT Capital Stock per Hours Worked
Non IT capital per hour worked
30
35
40
45
50
55
Non
IT S
tock
pe
r H
ours
Wo
rked
, 20
00 U
S$
1980 1985 1990 1995 2000 2005year
USA EU 15
Source: GGDC
Non IT Stock per Hours Worked
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
• Robustness test assumptions on:– Initial Conditions
– Depreciation and deflation rates
1,,, 1 tititi KIK
Issue Choice Notes
Initial Conditions
We do not observe all firms in their first year of activity.
How do we approximate the existing capital stock?
Use industry data (SIC2) and impute:
Similar to Martin (2002)Industry IT capital stocks from NIESRRobust to alternative methods
Depreciation Rates
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
Deflators
Need real investment to generate real capital
Use NIESR hedonic deflators (based on US estimates)
Re-evaluation effects included in deflators
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Methodological Choices
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*
IT capital (0.0022) (0.0030) (0.0057) (0.0082)
Observations 10,122 4,079 920 404
What do we expect in TFP regressions?
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CPS
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XOCOQA ln)1(ln)(lnln
cc SPQ
CPO
MTFP, measured TFP is
In our model “true” TFP is
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?
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Precise parameterization
variable Mnemonic Value Reference
C coefficient (IT capital)
α 0.025 Share of IT in value added
K coefficient β 0.3 Share of capital costs in value added
Complementarity σ 0.017 α (1-e-1)
Mark-up (p-mc)/mc 1/(e-1) 0.5 Hall (1988)
Relative quadratic adjustment cost of O
ωEU/ωUS 4 Nicoletti and Scarpetta (2003)
Disruption cost of O (as a % of sales)
η 0.2% Bloom (2006), Cooper & Haltiwanger (2003)
IT prices pc -15% p.a. until 1995 then -30%
BLS
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)
Non- IT Intensive (Using Sectors)
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
BREAKDOWN OF INDUSTRIES (3 of 3)
IT Producing 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