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