1. It Ain’t What You Do It’s The Way That You Do I.T.: Investigating the US Productivity Miracle using Multinationals 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

2. European productivity had been catching up with the US for 50 years…

3. …but since 1995 US productivity accelerated away again from Europe.

4. The US resurgence is known as the “productivity miracle”.

5. The “productivity miracle” started as quality adjusted computer price falls started to accelerate.

6. 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…

7. - 3 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)

8. And Europe also did not have the same IT investment boom as the US

9. Question Why did the US achieve a productivity miracle and not Europe? 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

10. 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 returns to IT Also true for US takeovers of UK establishments Result driven by “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

11. Macro facts and motivation New micro results Our intuition and a possible model Conclusion

12. 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 data is excellent for this purpose –Data on IT and productivity for manufacturing and services (where much of the “US miracle” occurred) –Combined 4 unused surveys of IT expenditure with ABI (like US LRD) –About 23,000 observations from 1995 to 2003

13. Stiroh/Van Ark “IT Intensive / Non-Intensive” and Services / Manufacturing split IT Intensive# obsIT non-intensive# obs Wholesale trade2620Food, drink and tobacco1116 Retail trade1399Hotels & catering1012 Machinery and equipment 736Construction993 Printing and publishing 639Supporting transport services (travel agencies) 740 Professional business services 489Real estate700 Industries (SIC-2) in blue are services and in black are manufacturing

14. 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

15. 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) Econometric Methodology (1)

16. 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) US MNE Non-US MNE US MNE Non-US MNE Econometric Methodology (2)

17. Include full set of four-digit industry dummies interacted with year dummies to control for industry level shocks (e.g. output price differences) Main specifications also include establishment fixed effects Standard errors clustered by establishment Try to address endogeneity using GMM-SYS (Blundell and Bond, 1998, 2000) and Olley Pakes (1996) Also consider takeover sample (discuss below) Econometric Methodology (3): Other Issues

18. Dep Variableln(GO) SectorsAll IT UsingOthersIT UsingOthers Fixed effectsNo Yes Ln(C) 0.043***0.041***0.036***0.044***0.021***0.027*** US MNE *ln(C) -0.011**0.019**0.0070.029***0.000 Non- US MNE*ln(C) -0.004-0.0000.007*0.004-0.002 Ln(Materials) 0.538*** 0.614***0.501***0.559***0.411*** Ln(Non-IT K) 0.118*** 0.102***0.134***0.139***0.211*** Ln(Labour) 0.287***0.286***0.233***0.303***0.253***0.339*** US MNE 0.074***0.015-0.0550.050-0.166***0.014 Non-US MNE 0.041***0.0230.0310.008-0.0060.044* Obs 22,736 7,87614,8607,87614,860 Table 1: IT Coefficient (C) by ownership status Note: All regression SE are clustered by establishment

19. All inputs interacted Another IT measure 1 TranslogWages (Skills) Value added Fixed effectsYesNoYes Dependent :ln(GO) ln(VA) Ln(C)0.0184***0.0385***0.0181***-0.00280.0503*** USA*ln(C)0.0441***0.0311*0.0292***0.0163*0.0681*** MNE*ln(C)0.00590.00140.00020.0033-0.0104 Ln(Wages)* Ln(C) ---0.0048- Ln(Wages)---0.2455***- Obs7,8762,8597,8767,8727,876 Table 2: Robustness Checks (IT Intensive sectors) 1 log(No. of employees using a computer) from a matched computer use survey. Note: All columns estimated on IT intensive sample. All variables of Table 1 included (labour, non-IT, capital, materials,…). All regression SE clustered by establishment

20. Dep Variable ln(GO) Sectors All IT- intensive WholesaleRetail Rest of IT intensive Fixed effects Yes Ln(C)0.021***0.018***0.013***0.024*** US MNE *ln(C)0.029***0.0290.030**0.025* Non- US MNE*ln(C)0.004-0.001-0.0120.003 Ln(Materials)0.559***0.679***0.638***0.445*** Ln(Non-IT K)0.139***0.100***0.106***0.216*** Ln(Labour)0.253***0.177***0.219***0.311*** US MNE -0.166***-0.072-0.297***-0.163* Non-US MNE -0.0060.079*0.091-0.030 Obs 7,8762,6201,3993,857 IT Intensive industries in more detail Note: All regression SE are clustered by establishment

21. Other Issues US firms have to “cross the Ocean” so have to be more efficient? Divide into EU and non-EU MNEs – no different US firms select into high IT sectors – use % of US establishments in 4 digit industry (col 7 table 2) Revenue productivity? But in standard Klette-Griliches this implies different coefficients on all factor inputs if US mark-ups different (col 2 of table 2) Unobserved US HQ inputs (e.g. software)? –But why larger than non-US MNE inputs –Software results –No significant interaction of IT with global firm size in UK sample –US firms global size same at median compared to non-US MNE global size Endogeneity of IT: GMM-SYS and Olley-Pakes

22. 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 1 (and interesting dynamics)

23. Table 3: US Takeovers and IT Coefficients Note: All variables of Table 1 included, SE clustered by establishment Sample Before takeover Before takeover After takeover After takeover After takeover US MNE, all years0.0470.1700.087***-0.035 NON- US MNE, all years-0.0120.0010.048**-0.017 US MNE*ln(C), all years-0.0220.023* NON-US MNE*ln(C), all yrs-0.0020.013 US*ln(C), 1 year after TO -0.005 US*ln(C), 2+ yrs after TO 0.038** NON-US*ln(C),1 yr after TO 0.009 NON-US*ln(C), 2+ yrs after 0.014 US MNE, 1 year after TO 0.107 US MNE, 2+ yrs after TO -0.113 NON-US, 1 year after TO -0.044 NON-US, 2+ yrs after TO 0.004 Obs2,365 3,353

24. Dep. Variable Ln(I IT ) Timing versus TOBeforeAfter US MNE, (all years) 0.0400.424*** US MNE, (1 year after TO) 0.519*** US MNE, (2+ years after TO) 0.359** Non-US MNE 0.0660.222***0.223 Ln(Labour) 1.110***1.011***1.010*** Obs 2,3653,353 Table 4: US Takeovers and IT Investment US dummy significant higher than Non-US MNE dummy at 5% level Note: All variables of Table 1 included, firm clustered SE

25. Macro facts and motivation New micro results Our intuition and a possible model Conclusion

26. 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)

27. 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 (2006) 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 firms are organized and managed differently

28. 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)

29. Domestic 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 enterprize 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)

30. 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

31. organizational structure (O) as an optimal choice (1) Firms optimally choose their organization between: –Old-style “Fordism”, complementary with physical capital –New style organizational structures complementary with IT (“decentralized”) Q = A C α+σO K β-σO L 1-α- β π = PQ- G(ΔO)- p c I C – p K I K – p L L Where: Q=Output, A=TFP, π=profits C = IT capital (I C = investment in IT), K = non-IT capital (I K =investment), L=Labor O=organizational structure (between 0 and 1) σ=Complementarity between IT and organizational structure G(ΔO)= Organizational adjustment costs

32. 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) = ω k (O t -O t-1 ) 2 + ηPQ| ΔO≠0| Quadratic cost with ω EU > ω US Fixed “Disruption” cost

33. 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,P c ),K*(O -1,P c ),C*(O -1,P c ), L*(O -1,P c ) –But need numerical methods for precise parameterisation 1 1 Full Matlab code on http://cep.lse.ac.uk/matlabcode/

34. US re-organizes first due to lower adjustment costs US re-organizes, particularly as IT prices start falling rapidly Initially “centralized” best EU re-organizes later and more slowly

35. IT intensity (C/K) rises everywhere, but faster in US US decentralization increases optimal IT investment US EU

36. Decentralized US obtains higher labor productivity Note: Assumed baseline TFP equal in US and EU, with no TFP growth Higher IT inputs lead to higher productivity (Q/L), particularly in more decentralized US US EU

37. 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..)

38. Plants rapidly reorganize after a US takeover US firm EU firmUS firm takes- over an EU firm Note: Assumes cost of non-alignment = sales x (O PARENT - O SUBSIDIARY ) 2

39. 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

40. 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 O ζ C α+σO K β - σO L 1-α- β- ζ -But implies a permanently higher US labor productivity even after controlling for IT level and higher coefficient -Can test using new management data we are collecting

41. Macro facts and motivation New micro results A possible model Conclusion

42. 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?

43. 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 China, Japan), 3,000+ firms Understanding determination of organizational decentralization (Acemoglu and Van Reenen et al, 2006) Structural estimation of the adjustment cost model (e.g. Simulated Method of Moments). See examples in Bloom, Bond and Van Reenen (forthcoming ReStud) More on IT endogeneity (e.g. broadband natural experiment)

44. Back Up

45. European Firms US Firms “Operations” management“Monitoring” management Source: Bloom and Van Reenen (2006) survey of 732 firms in the US, UK, France and Germany. “Targets” and “incentives” management differences significant at the 1% level. US firm also have different management “styles” “Targets” management“Incentives” management European Firms US Firms European Firms US Firms European Firms US Firms

46. Europe also did not have the same IT investment boom as the US

47. Non IT capital per hour worked

48. 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

49. 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

50. 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 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 Methodological Choices

51. Basic Production functions (Table A4) (at least 3 continuous time series observations)

52. Dep Variableln(GO) SectorsAll IT UsingOthersIT UsingOthers Fixed effectsNo Yes Ln (C) 0.043***0.041***0.036***0.044***0.021***0.027*** US MNE *ln(C) -0.011**0.019**0.0070.029***0.000 Non- US MNE*ln(C) -0.004-0.0000.007*0.004-0.002 Ln(Materials) 0.547***0.538*** 0.614***0.501***0.559***0.411*** Ln(Non-IT K) 0.130***0.118*** 0.102***0.134***0.139***0.211*** Ln(Labour) 0.315***0.287***0.286***0.233***0.303***0.253***0.339*** US MNE 0.085***0.074***0.015-0.0550.050-0.166***0.014 Non-US MNE 0.048***0.041***0.0230.0310.008-0.0060.044* Obs 22,736 7,87614,8607,87614,860 Table 1: IT Coefficient by ownership status Note: All regression SE are clustered by establishment

53. BASIC PRODUCTION FUNCTION ESTIMATES, CONT. (TABLE A4) (1)(2)(3)(4)(5)(6)(7) Estimation MethodOLS, No FE OLS, FE OLS, FE GMM, Static GMM, Dynamic (Unrestricted) GMM COMFAC (Restricted) Olley-Pakes Dependent variable: ln(GO) = ln(Gross Output) Ln(C t ) 0.0440***0.0299***0.0265***0.0391***0.0656*0.0430**0.0204*** IT capital (0.0023)(0.0040)(0.0063)(0.0171)(0.0373)(0.0211)(0.0030) Ln(C t-1 ) ---- -0.0343- - IT capital, lagged (0.0242) Ln(M t ) 0.5384***0.4665***0.4702***0.3998***0.3293***0.3595***0.5562*** Materials (0.0080)(0.0193)(0.0283)(0.0402)(0.0750)(0.0494)(0.0102) Ln(M t-1 ) -----0.0715-- Materials, lagged (0.0534) Ln(K t ) 0.1193***0.1650***0.1953***0.1584***0.3618***0.2937***0.1511*** Non-IT Capital (0.0063)(0.0153)(0.0234)(0.0410)(0.0869)(0.0526)(0.0115) Ln(K t-1 ) ----0.1815***- Non-IT Capital, lagged (0.0592) Ln(L t ) 0.2868***0.3177***0.2979***0.4158***0.2981***0.3524***0.2611*** Labour (0.0062)(0.0198)(0.0209)(0.0479)(0.0829)(0.0560) (0.0080) Ln(L t-1 ) --- 0.0091- Labour, lagged (0.0624) Ln(Y t-1 ) ----0.2330***-- Gross Output, lagged (0.0581)

54. 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

55. TFP BASED SPECIFICATIONS (1)(2)(3)(4) Dependent variableΔln(TFP) Length of differencingfirstsecondthirdfourth (e.g. first differencing vs. longer differencing) SectorsAll ΔLn(C)0.0137***0.0150***0.0154***0.0155* IT capital (0.0022)(0.0030)(0.0057)(0.0082) Observations10,1224,079920404

56. What do we expect in TFP regressions? MTFP, measured TFP is In our model “true” TFP is So we measure TFP correctly even in presence of O

57. 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

58. What do we expect in TFP regressions?

59. Precise parameterization variableMnemonicValueReference C coefficient (IT capital) α0.025Share of IT in value added K coefficientβ0.3Share of capital costs in value added Complementarityσ0.017α (1-e -1 ) Mark-up (p-mc)/mc1/(e-1)0.5Hall (1988) Relative quadratic adjustment cost of O ω EU /ω US 4Nicoletti and Scarpetta (2003) Disruption cost of O (as a % of sales) η0.2%Bloom (2006), Cooper & Haltiwanger (2003) IT pricespcpc -15% p.a. until 1995 then -30% BLS

60. 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

61. 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

62. 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