European productivity, lagged adjustments and industry competition Mary OMahony, Fei Peng and Nikolai Zubanov University of Birmingham

Presentation Overview Context First Regression Results Econometric Issues Industry competition measures – company accounts Herfindahl indexes, France and UK Industry dynamics Linking the macro and micro

Context Consider impact of technology shock on output growth –specific lagged impact of ICT What factors can explain differences in speeds of adjustment and long run impacts Preliminary Regressions –production function ln(Q) on labour, non-ICT capital and ICT capital, lagged up to five years – useful for stress testing –Dependent variable, MFP and ICT sole explanatory variable – difficult to justify Panel regressions, fixed effects and time dummies included Panel is by country not conventional industry panel But include some groups of like industries

Regression results (1) AtB

Regression results (2) C

Regression results (3) D1 Consumer goods

Regression results (4) D2 Intermediate goods

Regression results (5) D3 Investment goods

Regression results (6) E

Regression results (7) F

Regression results (8) G

Regression results (9) H

Regression results (9) I

Regression results (10) J

Regression results (11) K

Regression results (12) O

Summary – long run impact ICT Mostly positive, some very large e.g. J &K But warning – not well specified production functions

Summary – long run impact ICT Dependent variable = MFP Not highly correlated with previous estimates Some wild swings in lag structure

Econometric estimation issues The impact of ICT capital on output is distributed across many years We need to allow for dynamic adjustment of output to inputs Estimation options available: Short-run vs. long-run coefficient estimation (like Engle- Granger two-stage procedure) One-stage autoregressive distributed lag estimation estimators available: OLS, fixed-effects, pooled or weighted mean group other options: instrumental variables, GMM or system estimators

Industry competition and dynamics need to integrate country and industry context into the model, in particular, market concentration and firm dynamics Using AMADEUS database, 250,000 large to medium size companies – very small unincorporated companies not available Data from , all EU countries included estimate concentration rates by country and industry Cannot use for entry/exit but know when company was incorporated So can derive variables such as average age of companies (sales weighted) Shares of new companies in turnover First results 133 three digit industries, all manufacturing, 50-52,55,63, 71-74

2.4.2 Market structure – firm Competitiveness and concentration: Amadeus UK

2.4.2 Market structure – firm data Competitiveness and concentration: Amadeus FRANCE

Market structure – firm data Competitiveness and concentration: Amadeus Three digit and two digit industry Herfindahl indices generated for 2004, calculated as: The closer this is to 1, the more concentrated the industry Also can calculate a normalised Herfindahl – useful if comparing companies across countries where there is a possibly of different reporting rates H* = (H-1/N) / (1- 1/N) Shown in charts for UK and France

Market structure – firm data Competitiveness and concentration: Amadeus Normalised Herfindahl Index, Company accounts, 2004

Market structure – firm data Competitiveness and concentration: Amadeus Shows wide variance in index in both countries but again suggests UK more concentrated. Correlation H (UK, France) = 0.31 Examples - UK highest 283 (manufacture of boilers), only ranked 44 in France France highest 323 (TV and radio transmitters) – ranked 45 in UK Both countries show greater concentration in manufacturing

Market structure – firm data Competitiveness and concentration: Amadeus

Market structure – firm data Competitiveness and concentration: Amadeus

Market structure – firm data Competitiveness and concentration: Amadeus

Market structure – firm data Competitiveness and concentration: Amadeus

Industry competition and dynamics Reporting rates may vary across country – can cross check with industry data, e.g. UK Amadeus covers over 90% employment Econometric difficulty is that market concentration data are available for a short period of time can regress the industry fixed effects on market concentration or/and introduce cross-products of production factors and market concentration