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 EU

This is driven by the US “productivity miracle”

The “productivity miracle” appears linked to IT use Source: O’Mahony and Van Ark (2003)

The US also started investing much more in IT…

….but not much more in non-IT capital

All occurred as IT prices started to fall rapidly

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 US Firms Domestic Firms in Europe Non-US MNEs in Europe US MNEs in Europe Organizational devolvement European Firms US Firms Management practices 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 MNEs in 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 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 C α+λO X β-λO π = Y- p c C - p x X where: Y=output, A=TFP, C=IT, O=decentralization, X=other factors and π=profit, p c price of IT and p x 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) = ω i (O t -O t-1 ) 2 where ω EU > ω US

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 parameterisation 1 –Very much work in progress 1 Full Matlab code on

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 US 1 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 –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 (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

IssueChoiceNotes 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 NIESR  Robust to alternative methods Depreciation Rates How to choose δ ?Follow Oliner et al (2004) and set δ = 0.36 (obsolescence)  Basu and Oulton suggest 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 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)

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) US MNENon-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 Variableln(GO) SectorsAll IT UsingOthersIT UsingOthers Fixed effectsNo Yes Ln (IT) 0.043***0.041***0.036***0.044***0.021***0.027*** US MNE *ln(IT) 0.011**0.019** *0.001 Non- US MNE*ln(IT) * Ln(Materials) 0.539*** 0.614***0.501***0.560***0.412*** Ln(Non-IT K) 0.118*** 0.102***0.134***0.140***0.211*** Ln(Labour) 0.286*** 0.234***0.303***0.254***0.339*** US MNE 0.075*** *0.016 Non-US MNE 0.041*** Obs 22,736 7,90514,8317,90514,831 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) Timing versus TOBefore After US MNE *ln(IT), (all years) * US MNE *ln(IT), (1 year after TO) US MNE *ln(IT), (2+ years after TO) 0.037** Non-US MNE*ln(IT) Ln (IT) 0.056***0.044*** Ln(Materials) 0.510***0.497***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*** US MNE *** * Non-US MNE ** Obs 2,365 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 I IT /K IT Timing versus TOBeforeAfter US MNE, (all years) *** US MNE, (1 year after TO) 0.519*** US MNE, (2+ years after TO) 0.359** Non-US MNE ***0.223 Ln(Labour) 1.110***1.011***1.010*** Obs 2,3653,353 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 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 Railroad equipment and transport equipment 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 Professional business services

BREAKDOWN OF INDUSTRIES (2 of 3) Non- IT Intensive (Using Sectors) Non-IT intensive manufacturing 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 Other community, social and personal services 95 Private Household 99 Extra-territorial organisations Non-IT intensive other sectors 01 Agriculture 02 Forestry 05 Fishing Mining and quarrying 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