it ain’t what you do it’s the way that you do i.t.: investigating the productivity miracle using...
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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
1. Stylized facts and motivation
2. Model outline and predictions
3. Testing this on UK establishment level data
OUTLINE
US productivity is accelerating away from the EU2
53
03
54
04
55
0L
ab
ou
r p
rod
uct
ivity
pe
r h
ou
r w
ork
ed,2
005
US
$
1980 1985 1990 1995 2000 2005year
EU 15 USA
Source: GGDC Dataset
Labor Productivity Levels
This is driven by the US “productivity miracle”.0
1.0
15
.02
.02
5.0
3G
row
th in
La
bo
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 “productivity miracle” appears linked to IT use
Source: O’Mahony and Van Ark (2003)
The US also started investing much more in IT….0
02
.00
4.0
06
.00
8.0
1.0
12
IT S
hare
in G
DP
, 2
00
0 p
rice
s, 5
ye
ar m
ovin
g a
vera
ge
1980 1985 1990 1995 2000 2005Year
USA EU
Sources: GGDC
Growth in IT Capital Stock Share in GDP
-.04
-.02
0.0
2.0
4.0
6N
on
IT
Sha
re in
GD
P, 20
00 p
rice
s, 5
ye
ar
mo
vin
g a
vera
ge
1980 1985 1990 1995 2000 2005year
USA EU 15
Source: GGDC Dataset
Change in Non IT Capital Stock Share in GDP
….but not much more in non-IT capital
All occurred as IT prices started to fall rapidly-.
3-.
25-.
2-.
15-.
1%
Fal
l in
Rea
l Co
mpu
ter
pric
es,
5 y
ear
mo
vin
g av
era
ge
1985 1990 1995 2000 2005Year
Source: Jorgenson (2001)
Fall in Real Computer Prices
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?
European Firms 4.13
4.93US Firms
Domestic Firms in Europe
4.87
3.67
4.11
Non-US MNEs in Europe
US MNEsin Europe
Organizational devolvement
European Firms
US Firms
3.74
3.12
3.11
Management practices
3.32
3.14
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.
Domestic Firms in Europe
Non-US MNEs in Europe
US MNEsin Europe
Organizational devolvement(firms located in Europe)
Management practices(firms located in Europe)
US and EU firms decentralization and managed
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
1. Stylized facts and motivation
2. Model outline and predictions
3. Testing this on UK establishment level data
OUTLINE
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
US decentralizing as IT prices fall rapidly
Initially centralized “Taylorism” best
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
Note: Assumed baseline TFP equal in US and EU, with no TFP growth
Higher IT inputs lead to higher productivity, particularly in more decentralized US
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
1 Need to assume some returns to IT accrue to firms – i.e. imperfect competition
2) If IT also complementary skilled labor, then rising skilled/unskilled wage differential, particularly in US
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
1. Stylized facts and motivation
2. Model outline and predictions
3. Testing this on UK establishment level data
OUTLINE
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 tominimize 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 generate establishment level estimates of IT stocks
• Assumptions– Initial Conditions
– Depreciation rates
– Deflators
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
Jjji
I
K
I
K
jt
jt
it
it
and
Methodological Choices
Data
Productivity Estimation
IT and Multinationals
Conclusions and next steps
Econometric Methodology
Estimate a standard Production Function (in logs):
Where
q = ln(Gross Output)
a = ln(TFP)
m = ln(Materials)
l = ln(Labour)
k = ln(Non-IT capital)
it = ln(IT capital)
z = Other controls (age, region, group)
ititCitit
Kitit
Litit
Mititit zitklmaq
Investigating the impact of foreign ownership
• TFP levels can depend on ownership status
• Factor coefficients can also depend on ownership status
In fact only IT coefficient varies significantly (table 2)
MNEit
MNEJh
USAit
USAJh
Jh
Jit DD ,,0,
MNEit
MNEh
USAit
USAhitit DDaa ~
US MNE Non-US MNE
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
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 -0.057 0.051 -0.167* 0.016
Non-US MNE 0.041*** 0.023 0.031 0.008 -0.009 0.045 Obs 22,736 22,736 7,905 14,831 7,905 14,831
Table 1: IT Coefficient by ownership status
Note: All regression include firm clustered SE
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
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 -0.010 0.010 0.048** -0.017 -0.009 Obs 2,365 2,365 3,353 3,353 3,353
Table 4: US Takeovers and IT Coefficients
Note: All include fixed effects, estimated on the IT using sectors, firm clustered SE
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*** 0.223
Ln(Labour) 1.110*** 1.011*** 1.010*** Obs 2,365 3,353 3,353
Table 5: US Takeovers and IT Investment
Note: All include fixed effects, estimated on the IT using sectors, firm clustered SE
US dummy significant higher than Non-US MNE dummy at 5% level
Summarizing last 2 slides, after US takeover establishments:• Become more productive due to higher IT productivity• Invest significantly more in IT
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 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 trade65 Financial intermediation66 Insurance and pension funding67 Activities related to financial intermediation71 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.75 Public Admin and welfare80 Education85 Health and Social Work90-93 Other community, social and personal services95 Private Household99 Extra-territorial organisations
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