AMERICANS DO I.T. BETTER: US Multinationals and the Productivity Miracle

1. 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

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. Interestingly, in the US the “miracle” appears linked in particular to the “IT using” sectors… Source: Oliner and Sichel (2000, 2005)[See also Jorgenson (2001, AER) and Stiroh (2002, AER)]

7. - Change in annual growth in output per hour from 1990 – 95 to 1995 – 2001 % Increase in annual growth rate – from 1.2% in 1990 – 95 to U.S. EU 4.7% from 1995 Static growth – at around 2% a year – during the early and ICT - using sectors late 1990s 3.5 -0.1 ICT - producing sectors 1.9 1.6 Non - ICT sectors -0.5 -1.1 3 … 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. 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): • 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.) • 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) Question

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 (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

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

12. 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 Why use UK micro data?

13. 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

14. Preliminary figures already show US multinationals are particularly different in terms of IT use % difference from 4 digit industry mean in 2001 Observations: 576 US; 2228 other MNE; 4770 Domestic UK

15. Econometric Methodology (1) 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)

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

17. Econometric Methodology (3): Other Issues • 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)

18. TABLE 3 – PRODUCTION FUNCTION

19. TABLE 3 – PRODUCTION FUNCTION, cont.

20. TABLE 4, SOME ROBUSTNESS TESTS (IT USING SECTORS)

21. 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)

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 3 (and interesting dynamics)

23. TABLE 5, PRODUCTIVITY BEFORE AND AFTER TAKEOVER

24. 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

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

26. 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) The US advantage is better organizational and managerial structures?

27. 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)) • 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) Effective IT use appears associated with these different organizational (and managerial) practices

28. Figure 3a: Organizational devolvement,firms by country of location European Firms US Firms Figure 3b: Organizational devolvement, firms by country of ownership Domestic Firms, in Europe Non-US Multinational subsidiaries, in EU US Multinational subsidiaries in EU

29. US multinationals also change their organizational structures more frequently Organizational change in the UK during 1981-1990 (WIRS data) Organizational change in the UK during 1998-2000 (CIS data) Domestic Firms Domestic Firms Non-US MNEs Non-US MNEs 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.

30. IT is complementary with newer organizational/managerial structures • IT prices are falling rapidly, especially since 1995, increasing IT inputs • US “re-organizes” more quickly because more flexible • Maybe because less labor market regulation and union restrictions One simple way to model the all this macro, micro and survey data is based on three simple elements

31. 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+σOKβ-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

32. Quadratic cost with ω EU > ωUS Fixed “Disruption” cost 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|

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, 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/

34. 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).

35. Figure 5: IT per unit of capital (C/K) in 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 1970-2025). Decentralization is the value of O (between 0 and 1).

36. Figure 6: Labor productivity (Q/L) in 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 1970-2025). Productivity is output per worker. Decentralization is the value of O.

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. Figure 7: Decentralization by firms taken over by US multinationals: model results US US takeover of European firm 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.

39. A rationale for differences in organizational structures between US and European firms • A simple way to interpret the macro stylized facts on productivity dynamics and IT investment in the US and Europe • A useful framework to link the micro findings on US multinationals active in the UK to the macro picture The model provides:

40. TABLE 6, IT AND LABOR MARKET REGULATIONS

41. Other extensions we consider to the model • 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) • Adjustment costs for IT capital • Qualitative findings the same • TFP also will appear to grow faster in the transition • Permanent differences in management quality • Possible alternative story: US firms able to transfer management practices across international boundaries Q = A OζCα+σOKβ - 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

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

43. 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? Conclusion

44. 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)

45. Back Up

46. TABLE 2: LABOR PRODUCTIVITY IN HIGH IT VS. LOW IT ESTABLISHMENTS

47. Stiroh/Van Ark “IT Intensive / Non-Intensive” and Services / Manufacturing split Industries (SIC-2) in blue are services and in black are manufacturing

48. TABLE A2 - DESCRIPTIVE STATISTICS

49. TABLE A3 – GMM AND OLLEY PAKES RESULTS

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