report 4th consortium meeting - eu klems · report 4th eu klems consortium meeting 16-17 march...

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Report 4th EU KLEMS Consortium Meeting 16-17 March 2007, Sheraton Hotel, Brussels Attendance: See appendix 1 Report: Gerard Ypma and Edwin Stuivenwold Friday 16 March Pierre Valette (DG Research) Socio-Economic Sciences and Humanities in FP7

Launching FP7 – Conference for Information Multipliers – Brussels, 7-8 February 2007 p 2

Socio-Economic Sciences and Humanities in FP 7

“Consolidation and new orientations”

• Addressing major challenges facing EU and the World :

growth, employment, competitiveness, knowledge society.

combining economic, social and environmental objectives: e.g. socio economic models, energy, agriculture, rural and urban issues.

major trends in Society: demography, quality of life, cultural interactions.

global interactions and interdependence; conflicts and peace.

participation, democracy, governance; European diversities and commonalities.



“Consolidation and new orientations”

• The research is problem oriented and policy relevant at the same time.

• A fair balance between economic, social, cultural and political issues and aspects.

• A major new focus on: the international /global dimension, humanities, methods of analysis and assessment.



“Practicalities”

Nature and scale of activities• Approach:

– Emphasis on knowledge generation (no NoEs / Smaller networking schemes in specific topics)

– ERA-NETs are included – All topics open to international cooperation

• Projects should:– Sufficiently broad European and comparative perspective – Cooperation between disciplines to the degree required by the

topics– Involve users and stakeholders as appropriate

• Indicative budgets: an average of between 3 and 4 M per topic




Funding schemes

• Collaborative projects:– Small / medium scale: 0.5 – 1.5 M – Large-scale: 1.5 – 4 M

• Research for the benefit of specific groups (CSOs) up to 1 M

• Coordination and Support Actions:

– CSA-coordinating– CSA-supporting

• Each topic uses specific scheme(s)!

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“Practicalities”Eligibility requirements

• Collaborative projects:– Min. 3 partners from 3 MS or associated countries– Financial thresholds

• Research for the benefit of specific groups (CSOs) :– Min. 3 CSOs (or 1 association of CSOs) + 2 R&D providers– Financial thresholds

• Coordination and Support Actions:– CSA-coordinating

• Min. 3 partners from 3 MS or associated countries– CSA-supporting

• Min. 1 organization• Each topic uses specified funding scheme(s)!




Call deadlines / proposal submission & evaluations

• Deadline 1: 10 May 2007– First contracts to be launched towards the end of 2007

• Deadline 2: 29 November 2007– First contracts to be launched in Autumn 2008

• One stage proposal submission only

• Remote evaluation will be used

Other points: -Projects with proposal submission deadline at 10 May will start at the end of this year. The second deadline (29 November) leads to a starting date in the middle of 2008. -EU KLEMS is linked to at least 2 or 3 of the FP7 projects. There is a lot of database work, but modelling is also very important. Modelling should be done in the first phase of the projects already. -Intangible investment and services are other important issues (2 small projects). -If you are interested in joining a new project, please contact Mary O’Mahony. Session I (WP7) Chair: Mary O’Mahony Mary O’Mahony, Fei Peng and Nikolai Zubanov (University of Birmingham) European productivity, lagged adjustments and industry competition

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

Summary – long run impact ICTMostly positive, some very large e.g. J &KBut warning – not well specified production

functions

AGRICULTURE, HUNTING, FORESTRY AND FISHING 0.040 MINING AND QUARRYING 0.124CONSUMER GOODS -0.006INTERMEDIATE GOODS 0.063INVESTMENT GOODS 0.082 ELECTRICITY, GAS AND WATER SUPPLY -0.013 CONSTRUCTION -0.014 WHOLESALE AND RETAIL TRADE 0.063 HOTELS AND RESTAURANTS 0.014 TRANSPORT AND STORAGE, POST AND TELECOM 0.057 FINANCIAL INTERMEDIATION 0.170RENTING OF MACHINERY&EQ, OTHR BUS. ACTIVITIES 0.092 OTHER COMMUNITY, SOCIAL AND PERSONAL SERVIC 0.017

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Summary – long run impact ICTDependent variable = MFP

Not highly correlated with previous estimatesSome wild swings in lag structure

AGRICULTURE, HUNTING, FORESTRY AND FISHING -0.015 MINING AND QUARRYING -0.684CONSUMER GOODS 0.113INTERMEDIATE GOODS 0.240INVESTMENT GOODS 0.010 ELECTRICITY, GAS AND WATER SUPPLY 0.066 CONSTRUCTION 0.038 WHOLESALE AND RETAIL TRADE 0.028 HOTELS AND RESTAURANTS -0.011 TRANSPORT AND STORAGE, POST AND TELECOM 0.080 FINANCIAL INTERMEDIATION 0.066RENTING OF MACHINERY&EQ, OTHR BUS. ACTIVITIES -0.061 OTHER COMMUNITY, SOCIAL AND PERSONAL SERVICES 0.000

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 1996-2005, 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

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Market structure – firm dataCompetitiveness and concentration:

AmadeusThree digit and two digit industry Herfindahl indices generated for 2004, calculated as:

The closer this is to 1, the more concentrated the industryAlso 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

∑= i iSH 2)(

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Market structure – firm dataCompetitiveness and concentration: Amadeus

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

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

Points of discussion -The Amadeus database contains information about origins, competitors, age of companies, but no data on commodities or the output side. It is not a very rich database, but the coverage of all countries is a major advantage. -Data will be available for interested consortium partners.

Formatted: English (U.S.)

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Robert Inklaar (GGDC, University of Groningen) ICT and Productivity in Service Sectors

GGDC

Main findings

Convergence has stopped in Europe and between EU and US in mid-1990sMarket services important sourceSkills, ICT and regulation are not the source of this divergenceTechnology transfer in services still not well understood

GGDC

Data on MFP growth and levels

Use MFP growth from EU KLEMSUse output PPPs and labour and capital data to come up with input PPPs (45xII, 2xL, 6xK)Multilateral MFP level comparison for 1997, extrapolated using relative growthLook at MFP level relative to the leading country

GGDC

More formal analysis

( )iFi MFPMFPMFP lnln β=∆

( ) ( )iFiFi MFPMFPZZMFPMFPMFP lnlnln ∗++=∆ δγβ

Positive: convergence

Innovation Imitation

Unconditional convergence:

Conditional convergence:

GGDC

Conclusions

End of convergence, but why?Technology transfer in manufacturing

R&D + skills (Griffith et al., 2004)Technology transfer in services

Organizational change, management practices?

How to find out?Measurement of intangibles

Points of discussion -The findings of Griffith et al. hold up with current EU KLEMS data. It should be possible to replicate their results. -The use of a leading country has not much impact on the results; it is not always the same leading country and especially the ranking matters. -The limited international trade in services could be a reason for the end of convergence, as trade is less exposed to international competition. The results suggest however that the impact of trade on convergence is not very high. -It can be interesting to look at the effect of the services directive in Europe. There are big differences across countries and industries but it is too early to say to which direction this would lead. -The role of regulation, rigidity of institutions and changed unemployment would be an interesting subject. There were significant changes in labour market and institutions. -Interaction terms are not always very satisfying; other methods like multi regime models can be useful in this respect. -Labour Force Surveys measure some organizational change and on the job training. This can be an interesting additional variable for the analysis.

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Hans Christian Kongsted (CEBR) ICT and Productivity Growth: the Timing of Structural Breaks in Productivity Growth and the Link to Information Technology

Points of discussion -Look at all-out analysis within industry and within countries. -Stiroh focuses on investment effects (capital deepening). It may be a good idea to focus on MFP growth here. -Is the subdivision in ICT using and ICT producing still useful now we have detailed data about IT intensity by sector? Main point is not look at the distinction, but at real data. -Playing around with different classifications can lead to interesting results. Retail is a pushing factor. -Why should you look for a specific breakpoint? Is it not more a gradual shift? Smooth transition models and groupings of countries may help in this respect.

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Session II (WP8) Chair: Kurt Kratena Kurt Kratena (WIFO) International Outsourcing and the Demand for Skills

Motivation

• Stylized facts: • Increase in the wage gap between skilled and unskilled

workers (U.S and UK) or employment shifts (increasing unemployment of unskilled) with stable wage structures (continental EU countries)

• Increased use of inputs, e.g.:• skilled labour• imported materials

• Decreased use of inputs:• Unskilled workers (in particular older workers)

Outsourcing & demand for skills

• Methodological questions (Feenstra, Hanson, 2001):

(i) single (relative) labour demand functions: Berman, Bound and Griliches, 1994; Feenstra, Hanson (1999), Amiti, Wei (2004).

(ii) system of labour demand equations derived from a flexible cost function:Morrison-Paul, Siegel, 2001; Eckholm, Hakkala 2006; Hijzen, Görg and Hine (2005)

advantage of (ii): theoretical consistency (elasticities), econometric results based on efficient estimation technique


• Methodological questions

(i) outsourcing as a “quasi fixed” factor like factor biased technical change: most studies(ii) outsourcing as a variable factor = imported intermediates: Falk, Koebel 2002; Tombazos, 1999. Measures of outsourcing:(iii) imports of intermediates from the same industry = narrow measure(iv) total imported intermediates (in EUKLEMS without energy!) = broad measureadvantage of (ii) over (i) : directly quantifying the role of prices for outsourcing (= substitution).


• Methodological approach

• Cost and factor demand functions with different skills of labour and outsourcing as imported intermediates (without energy): (i) own and cross price elasticities(ii) impact of outsourcing on costs (~ productivity)

• EU countries: Austria, Finland, Germany, Italy Sweden (geography criterion)

• Pooling across countries (not industries)

First empirical results (Austria)

• Unskilled labour and imported intermediates are substitutes in 7 out of 12 manufacturing industries (especially: transport equipment, machinery, textiles)

• Skilled labour and imported intermediates are substitutes in almost all manufacturing industries (especially transport equipment, wood, pulp and paper/printing)

• which skill aggregation is appropriate ? (aggegatingmedium skill category to high or low ?)

• Both categories of labour are complements in 5 out of 12 industries

• Domestic intermediates are rather a substitute for high skilled labour than for low skilled labour (domestic outsourcing)

Points of discussion -An increase in the division of three skill levels or the use of weighted graduate levels can lead to additional information. -Country of origin of intermediate goods can make some difference for the results. There is however no information on this in the input output tables (only EU versus non-EU). -The analysis will be extended to services.

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-Is there also an opposite trend like in-sourcing? -There is a high potential for substitution in international trade. Imports and exports are growing more rapidly than value added. Many commodities are traded in both directions. Robert Stehrer (WIIW) Outsourcing and Skills in CEEC Countries

Points of discussion -The product code does not tell in which industry the product has been produced. Furthermore the industry of use is not known. -The United Nations has constructed a concordance that assigns five digit product codes to industries. -Industry dummies lead to a loss of interesting detail on the industry dimensions. -No direct link is available between production data and industries. Try to move towards better measures of domestic and imported with import matrices. -There are hardly any import matrices available in Europe for longer time periods. It might be useful to team up with some people to come up with measures. Other points -The reason for the fact that there are no capital stocks in the EU KLEMS database is twofold. Firstly, we have always told the NSIs not to provide this data. We have to go back to the NSIs to ask for their permission. A second reason is that capital services are included. By taking the share of capital, one can calculate the stocks themselves. The most important point however is that stocks are available for consortium partners in the capital input files. -Drawback to the current capital services measure is that it is influenced by the assumptions we make. A user-oriented point is that capital stocks are easier to understand for the user.

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Matilde Mas and Mari Kangasniemi (IVIE and NIESR) Migration and Productivity

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Objectives

• To analyze the impact of migration on productivity growth,

• Comparing two complementary experiences:• Spain: with a very recent presence of migrants• UK: traditional recipient of migration flows

• From three perspectives:• Impact on GDP per capita. Alternative Scenarios• Growth Accounting• Econometric estimates

[ 10 ]

GDP PER CAPITA DECOMPOSITION

Equation (1)

Y = Real GDPN = Total PopulationWAP = Working Age PopulationAP = Active PopulationL = Employement

{ {{ { { employment productivityGDPpc age activity

demography

Y WAP AP L YN N WAP AP L

= ⋅ ⋅ ⋅

144424443

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GDP PER CAPITA DECOMPOSITION

• SPAIN, THREE SCENARIOS ACCORDING TO DEMOGRAPHY (INCLUDING LABOR MARKET) VARIABLES:

1. ACTUAL

2. VIRTUAL I: NO MIGRANTS (Substitute in (1) all the demography variables by the ones corresponding to Spanish nationals)

3. VIRTUAL II: UKneization (Substitute in (1) all the demography variables by the ones corresponding to UK aggregate (migrant+nationals) population)

[ 28 ]

Preliminary econometric analysis

• Standard Cobb-Douglas production fct• First difference and fixed effects estimations with year dummies (no

levels available for all variables)• Different specifications for migrant/native labour input: 1) number of

hours (share*total hours), 2) quality adjusted by the overall labour quality 3) quality adjusted by separate indices for migrants and non-migrants (for UK only) 4) migrant share as a separate regressor in standard prod fct

• Data on migrant shares by industry and characteristics available for UK 1993 onwards and Spain 2000 onwards

• Estimations for all observations and three industry groups (1) non-services, (2) retail, transport and communication and (3) other services

• Separately: regressions of tfp growth on changes in migrant share and migrant labour input

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Conclusions

• The characteristics of the migrant population, as well as its evolution over time and sectoral distribution are different between the two countries.

• GDP per capita growth has been driven by productivity growth in UK while for Spain the engine of growth has been demography.

• In both countries total migrant contribution to GDP growth has been positive, while the effect on labor productivity has been negative.

• In both countries, migrants quantity effect has been positive on output growth and negative on labor productivity.

• In Spain, migrants quality effect has been negative, both on output growth and on labor productivity, while for the UK it has been positive.

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• Total migrants contribution to output growth is higher in Spain than in the UK with the exception of Transport & Storage & Communication and Finance & Insurance

• On the contrary, total migrant contribution to productivitygrowth is negative in almost all sectors and always higher in Spain than in the UK.

• Preliminary econometric results seem to support the suggestion that in the UK migrants are more productive than natives and in Spain vice versa. More work is needed to refine the econometric estimates.

Points of discussion -The exact definition of migrants is not clear. Are migrants defined by ethnic origin, passport or race? -Arrival date of migrants can make some differences. -The wage gap between migrants and nationals has been omitted in this study. -Results are not very plausible. It is hard to imagine that the effect on TFP of adding an additional variable is so large. -Reduction in labour productivity seems to be driven by the way of carrying out the analysis. Only the demographic effect has been measured.

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Session III (WP10) Chair: Eric Bartelsman Eric Bartelsman (AMBER, Free University) Summary of Work Linking Micro Data

WP10 Objectives

• Integrate (existing) micro measures of within industry firm-level distributions with the EU-KLEMS data.

• Conduct empirical work using the augmented EU KLEMS data to explore recent theoretical models linking growth to firm dynamics and heterogeneity.

• Facilitate the analysis of productivity in work package 9 on technology and innovation by providing firm-level data

• Investigate potential to use firm level data in future updates of EU KLEMS Productivity Database.

Overview• Healthy, market economies exhibit the following features at the

firm level:• Large dispersion of productivity across firms within narrowly

defined sectors.• Must be due to some form of “friction”

• Market power• Economies of scope/decreasing returns• Adjustment frictions and idiosyncratic shocks:

• Search and matching frictions• Labor and capital adjustment costs• Entry and exit costs

• Strong positive covariance between market share and productivity

• Static allocative efficiency• Market selection is productivity enhancing

• Market selection yields exit of less productive firms and establishments

• High pace of input (e.g. jobs) and output reallocation• Uncertainty, experimentation, learning and selection as well as time

varying idiosyncratic shocks

Distortions and Allocative Efficiency?• Working conjecture:

• Emerging and transition economies have market structure and institutions that distort allocative efficiency

• Distortions:• Imperfect competition• Subsidies (explicit) or quotas/rationing of allocation to

insiders/incumbents/favored businesses• Credit constraints to young and small businesses• Bribes and corruption (unevenly applied)• Doing Business inefficiencies:

• Costly to start up a business• Costly to adjust employment• Poor or inefficient infrastructure (telephones, roads,

electricity)• Key point: Distortions have idiosyncratic component:

cross-sectional allocation is distorted• Entry barriers act in this way naturally• Credit constraints for young and small businesses• Arbitrary and capricious application of regulations

Observations

• Distortions affect various margins• Entry: amount and characteristics• Allocation among survivors• OP cross term reflects allocation for given

survivors• Uncorrelated scale distortion

• Slight increase in mean TFP/LP• Lower survival

• Lower OP cross term for both TFP and LP• Affects entry and selection• Lower consumption (C) through lower

productivity and excess churn

Observations (2)

• Uncorrelated factor-mix distortion• Slight increase in Mean TFP and LP again

• Lower survival• Not much impact on OP cross term for either TFP and

LP• K/L too high• Lower C primarily through distorted high capital

investment• Correlated scale distortion

• Mean TFP/LP much lower• OP cross term very low• Adverse impact on survival• So: wrong firms survive, allocation poor amongst

survivors and too much churn (everything wrong!)• Much lower C from all of these factors

Open questions

• Can we account for differences across countries in productivity via the distribution of distortions?• If so, what are these distortions?

• Static vs. Dynamic distortions?• Is wedge between marginal product and factor prices because

of adjustment costs (dynamic) or a more permanent distortion (e.g., favored businesses)

• Can we quantify the reduction in distortions in transition economies that account for rising allocative efficiency?

• Implications for growth: transition dynamics vs. steady state growth?

• Missing pieces (many):• E.g., Differentiated products version of model• More frictions? (Experimentation/learning/adjustment costs)

Points of discussion -Allocative efficiency has not been taken into account as it does not really matter. Technical efficiency is part of the analysis.

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Martin Gornig (DIW) Product Diversification and Firm-Level Productivity of ICT Producers – A Micro-data Analysis for Germany

Brussels, March 2007EU 6. Framework Programme

WP 10: Product Diversification and Firm-level Productivity of ICT Producer

Research question:

Innovative diversification or concentration on core competencies -

What strategy is driving productivity of ICT producer?



Theoretical background:

Traditional view: Product specialization with concentration on core competencies reduces fixed cost and increases productivity and profits (Marshall 1920)

Industrial organisation theory: Product diversification and innovation is one of the most important strategies for firms to improve competitiveness and productivity (Chandler 1978)


Firms with increasing number of products show a low growth rate of productivity, but they are expanding their employment.


These results are in line with other empirical findings:• Diversification reduces the profit rates

(Montgomery 1985). • Diversification and firm growth are highly correlated

(Jovanovic/Gilbert 1993)

Conclusions:


Productivity growth rates of “dynamic” ICT producer, of both, specialising and diversifying firms, are lower than for Non-ICT producing firms.


We also have to look at the development of individual firms: Does the strong productivity growth of ICT producer with constant number of products result from the introduction of new products before 1995? - Methodology: Treatment analysis.

Does a different composition of the analysed sets of firms play a role? - Methodology: Decomposition by industries and firm size.

Questions:

Points of discussion -Decomposition by industries and firm size will be implemented. -Measuring growth of output will be very important in this analysis. The product mix should be measured by the average share in revenues, rather than just using the number of products as a variable. -The life cycle of products can clearly be distinguished in the results -The current analysis is especially focused on ICT. But DIW already carried out some additional research. Expanding firms usually start in a low profit position, but increase their productivity up to the average. Consolidating firms start with high profits and try to keep this level. -One should be aware of the fact that the Prodcom list is always outdated as the product list is mostly updated with a time lag of 10 years. Especially in ICT goods this is an important problem.

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Anita Wölfl (CEPII) Productivity Growth in Services and the Role of Measuring Service Price Indices

Why analysing the measurement of price indices in services?

• What we observe:– relatively weak productivity growth in services as compared to

manufacturing industries ...

– ... although some services show characteristics that would typically spur productivity growth.

• Maybe due to problems measuring the underlying price index ?

• Steps– Reasons and channels of measurement problems in services?

– How to analyse the extent of such measurement problems?

– Producer price changes and their variation - some results for two selected business services.

Some major problems measuring producer price indices and volumes series in services

Source: Eurostat Handbook on Price and Volume Measures in National Accounts, 2001

• Heterogeneity of services products– Specific services for individual customers (‘one-off ’),– Different types and levels of prices for different markets/consumers,– ‘Bundling’ of services products.

• Date of delivery different to date of payment

• Quality of service provided is important, but difficult to measure– Rapid change of technologies used,– Service that is provided ~ knowledge provided.

• Availability and comparability of data– Definition of service output,– Lack of necessary detail,– Lack of comparability across countries.

Channels of measurement problems II -An overview

The elementary price series

Following prices over time

Aggregation issues

• finding a « typical »services product

• defining the detailed product specification

• measuring the price for each specification

• estimating missing values

• adjusting for changes in product specifications

• adjusting for quality changes

• accounting for new products

• choice of the firm sample

• classification of services products

• weighting schemes

• choice of the index formula

• accounting for new firms

Conclusion

• Summarising:– Mean quarterly price change between 2 et 5% in absolute terms.

– Large heterogeneity across services activities, firms, product groups, and over time, both in terms of levels and timing of price changes.

– Time and firm-specific factors are most important determinants of price changes in services, both in terms of variation and timing, ...

– … but less so product groups.

• Does measurement matter ? – Conclusion on this basis would be premature:– Weak explanatory power of the estimated models -> better

specification, better measurement of variables necessary– Unexpectedly weak impact of product groups - careful interpretation– Impact of adjustments undertaken to construct the price index - linked

to other variable and their measures?

Channels of measurement problems I -

The steps in the construction of a PPI

Source: PPI Manual ILO, OECD, IMF, 2004

• Determining the objectives, scope, conceptual basis of the index,

• deciding on the index coverage and classifications structure,

• deriving the weighting pattern,

• designing the sample,

• collecting and editing the prices,

• adjusting for changes in quality,

• calculating the index,

• disseminating the indices,

• maintaining samples of businesses and product specifications,

• reviewing and reweighting the index.

Points of discussion - Statistical agencies have often split price and level departments, which may be another source of bias. -Information about the change of the product over time is hard to find as the survey samples change every two years. -It is very hard to find out if the prices reflect what you want to measure. Products from the service sector are more complicated than manufacturing products. It is therefore doubtful if practices in the manufacturing price measurement can be applied to services. -The distinction model pricing and actual pricing would be useful as a variable.

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Laurence Nayman and Laurent Wagner (CEPII) Vertical Production Networks: Evidence from France

What is it about?

• The rapid development of FDI causes concern over the decline of industrial activities in developed countries and their relocation in emerging economies.

• Horizontal FDI was viewed as the main channel through which this phenomenon occurred on account of higher trade and wage costs.

• Nonetheless, vertical FDI expands rapidly and trade in intermediate inputs amounts to 1/3 of world trade.

• Multinational firms (MNF) are the major actors of trade.

• A substantial part of exports of US parent companies to their manufacturing affiliates are inputs for further processing against about 40% for French ones.

The Empirical Model

• Intra-firm exports of int. inputs would increase with lower trade and wage costs and some policy characteristics of the host country, so argue Hanson, Mataloni & Slaughter (2005).

• There is then a continuum of production processes from vertical to horizontal FDI, depending on the elasticities of intra-firm exports to trade and wage costs.

• To test this, we will focus on three flows:

- the affiliate imports more or less intermediate inputs and services from the parent company,

- the affiliate re-exports the finished product to its parent company,

- the affiliate takes advantage of the market size of the host country to sell finished products at home or in the area.

The Empirical Model

• Objective: Tracing the determinants of intra-firm trade in intermediate inputs for French multinationals, using firm-level data.

The estimated equation is:

Sipcm = αip + β1 ln Wagec + β3 ln LPc + β3 ln Distancec

+ β3 ln (1-Corp Tax Ratec)+ β4 ln (1+Tariffscm)+ γXpc+ eaipcm

• The imported inputs in the affiliate total costs are measured by the share of imported inputs for further processing in the affiliate’s turnover, Sipcm, where istands for the sector, p the parent company, c the country, and m the imported input for further processing.

ResultsWages :

Wages per employee impact positively on the import to turnover ratio for the developed countries’sample, and negatively for the developing countries one. Cross-price elasticity of imported demand to wages=1.9 for the developed countries and -0.2 for the developing ones.

Wages per employee mirror the average skill intensity of jobs in a country: the higher wages are, the higher the average skill-content of jobs in the country.

The less-skilled labour intensive affiliates are located in countries where wage costs are relatively lower, and conversely for the developed countries.

Results

Labour productivity :

Labour productivity matters a lot in explaining patterns of intra-firm trade.

Real unit wages, the difference in the logarithms of wages per employee and labour productivity corroborate the dichotomy between developed and developing economies.

It results, for the developed countries, into a negative coefficient on unit wage costs. Conversely, in the developing countries, the coefficient is rather low but positive.

Results

Alternative Sample Splitting :

• 40% of export flows are matched by imports of parent companies from their affiliates. Hence, parent companies may export intermediate products and import assembled goods.

• The whole Sample is split twice according to whether products are reshipped to France or not and with respect to the capital to labour intensity of imported inputs. We used capital deepening from the EUKLEMS database for France.

• Results point to a mix of vertical and horizontal trade.

Points of discussion -Horizontal FDI means that firms perform a set of activities that are similar to the activities of the mother company. -The subject can be put in a wider picture of internationalization of production. Networks can have large influence, but should be picked up by the variable special partnership.

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Marcel Timmer and Bart Los (GGDC, University of Groningen) InnovaTion, InvesTment and ImiTation: How information and communication technology affected European Productivity Performance

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ICT as a GPT

• ICT as GPT (see David, AER 1990; Brynjolfsson & Hitt, JEPersp 2000; Hall & Trajtenberg 2004 NBER)

• Europe has exhausted imitation in old technologies, and is lagging in application of new ICT-based innovations (Aghion and Howitt 2006, JEEA).

• Divergence is possible. Degree to which imitation can lead to productivity gains depends on technology operated (“appropriate technology”, Basu & Weil, QJE, 1998)

• Traditional growth accounting findings: Total Factor Productivity growth in market services in US, but not in Europe (see Jorgenson, Ho and Stiroh 2005; Triplett and Bosworth 2004; Inklaar, O’Mahony and Timmer, RIW, 2003; Timmer & Van Ark, OxEP 2005.)

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This paper

• TFP should be divided into pure technological change (“innovation”) and efficiency changes (“imitation”) through estimation of global production frontier. (see Los & Timmer, JDevE 2005, Timmer and Los, JPA 2005)

• ICT capital is a critical input

Questions• How many years are European countries lagging behind in ICT?• How efficient are European countries in using “old-vintage” ICT? • Does this differ for various sectors?• How much of European growth is due to innovation and how much

due to imitation?

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Frontier Estimation

• Data Envelopment Analysis on 1 output (GDP) and 3 inputs (labour, IT and non-IT), assuming CRS (Färe et all, 1994, AER)

• Non-parametric approach with very few restrictions on production technology

• Advantage is flexible functional form, which allows for localized technological change

• DEA with intertemporal dataset, to avoid technological regress.– Frontier for year y based on all observations from 1980 up to y – First frontier for 1990.

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Main findings and road ahead• Global production frontier is driven by investment in ICT

capital goods

• European countries lag US in application of ICT technology (4 to 16 years)

• Some countries are succesful imitators (FR, UK, DK), but others face divergence (IT, PT, ES)

• Different pattern at industry level: innovation in manufacturing in some countries

24

Main findings and road ahead• Industry-level analysis, in particular services: EUKLEMS

data project

• Explanation of divergence in terms of “Imitation”(inefficiency model) including regulation and skilled labour supply as determinants

Points of discussion -There are three developments possible compared to the frontier country: falling behind, catching up and leapfrogging. -Localized phenomena can only be explored with non-parametric method, which is the reason for not using a stochastic frontier. -Global production is pushed out more in more ICT intensive regions. -The interpretation of inefficiency is not directly linked to the TFP levels in one year. -GDP is a composite figure and because shares in private consumption is different between (European) countries. The use of the aggregated EU15 can be helpful in this respect.

14

Business Meeting and Discussions on Volume II Bart van Ark (GGDC, University of Groningen)

3

• Remaining work on analytical module of the database• Update to 2005• Extension (e.g. for non-core countries, experimental variables)• Revision of some series, repairs and fill remaining gaps• Make plan on a bilateral basis between Groningen/London and

consortium • How to do updates?

• Work on updates and extension on rolling basis?• Release on rolling basis or only December 2007?

• Preparation of statistical module of database• Eurostat will publish statistical modules (variables as in analytical

database, industry detail might differ)• Guidelines from NSI’s are not always precise and need to be worked out

in direct contact with NSI’s• Propose that consortium partners will enter discussion with NSI’s on

precise implementation• Technical support from Groningen

Database and related actions until end of 2007

4

• Who will do the work on analytical module?• Match incentives and interest for party to “run” the database• Keep EU KLEMS consortium alive to back up with data provision? Opt

out? New partners? For example, do more with interested NSI’s?• Simplify structure from consortium to subcontracting system?

• Who will pay?• 7th framework?• EU institutions• National institutions on country specific parts?• …

• EU KLEMS is and will remain an open network database• Add-ons to database are automatically part of public domain• Intellectual property is linked to papers, publications, etc.

Post-2007 steps on EU KLEMS

5

Publication Plan: Three Categories

I. Documentation of EU KLEMS database• User Manual (methods and sources)• Vol. I: Productivity in Europe: Measurement, Trends and Explanations

(authored by Timmer, O’Mahony and van Ark)• Special issue of Economic Systems Research (edited by Timmer and

Aulin)• Special Issue of National Institute Economic Review (Spring 2007)

(edited by O’Mahony and Robinson)

II. Joint publications of consortium• EU KLEMS Productivity Report: second issue?• Vol. II: Trends and Comparisons of Productivity in Europe (edited by van

Ark and Stehrer) • “Country narratives” using EU KLEMS database• First presentation on 12 January in Amsterdam• Outline• Time schedule

6

Volume II outline

• Introduction (Bart van Ark and Robert Stehrer)• Europe’s Productivity Performance (Bart van Ark, Mary O’Mahony and

Marcel Timmer)• Trends in economic growth in OECD (Jorgenson and Timmer)• Germany, France and UK (Bernd Görzig, Laurence Nayman and Mary

O’Mahony)• Denmark, Sweden and Finland (Martin Junge, Hans Olof Hagén and

Matti Pohjola)• Italy and Spain (Matilde Mas & Carlo Milana)• Austria, Belgium and Netherlands (Michael Peneder, Chantal Kegels

and Henry van der Wiel)• Central and Eastern Europe (Robert Stehrer)• Japan and Korea (Kyoji Fukao and Hak Pyo)

7

Volume II time schedule

• First half April 07: outline paper Volume II• Second half April 07: full proposal to publisher• June 07: contract with publisher• July 07: working papers on Volume II• September 07: workshop ??• November 07: first draft Volume II• January 08: final manuscript Volume II• Autumn 08: publication Volume II

8

Publication Plan: Three Categories• Vol. III: Explaining Productivity in Europe: The Role of Labour, Technology

and Firms • Selection of research papers from analytical research WPs (7-10)• Edited by O’Mahony (+ Falk, Los and Bartelsman?)• Give priority to joint papers• Plans can be drafted on basis of discussions of joint work in Brussels and workshops• Deadline for final drafts: Autumn 2007 (to present at final conference)

Free space• Based on data research or analytical work in analytical work• Also bring out as EU KLEMS working papers• Make reference to:

““This paper is based on the EU KLEMS database which has been funded by the European Commission, Research Directorate General as part of the 6th Framework Programme, Priority 8, "Policy Support and Anticipating Scientific and Technological Needs (project 502049)"

• Also after release of public version, please, check use of underlying data with local consortium partner and EU KLEMS consortium

15

9

Events

• Analytical workshops for WP8-10 (Spring and Autumn 2007)• Policy & adacemic related events in individual countries

• Separate workshop (lunch, seminar) with policy makes• Tie session into current conference or meeting schedule in country• Co-organized like ministry or NSI• Please, let us know upcoming events

• Final conference in 12-14 December 2007• 2 or 2,5 days• Internal and external participation on open invitation & paper proposals

(+/- 200 people)• Location: Netherlands (Amsterdam, Groningen, The Hague) ?

10

• Possible areas:• More intensive use of supply-use tables (Bart Los, Joerg Beutel)• Trade and FDI data (…)• Intangible capital (…)• General equilibrium modelling (…)• Labour market and demography (…)• …

• How to move ahead?• Assign coordinating person(s)• Form smaller and extended teams from EU KLEMS• Seek appropriate parts of FP7 programmes (keep track of deadlines for

application) and other funding schemes• Use EU KLEMS network as way of planning• NOT EU KLEMS 2 – but use it as support activity

Next steps on “EU KLEMS related” research

11

Administrative Issues

• Record activities (workshops, visits to NSI’s, etc.) it all counts for the EU KLEMS output and for your auditor (we don’t get everything !)

• Financial accounts 2005/2006 approved early: keep it up !!• Send financial statements in advance to [email protected] to

check for EU conditions; only after that get approval from auditor

• Also for final year: audit-approved reports:• We will check with Commission on reporting data but count on

September• Also count on surprise audits in final round

Points of discussion Database related -For the update of the database the European Commission is very interested in MFP levels, a split in domestic versus imported and capital stock level data. -The development of the statistical database is in the hands of the consortium partners. They can take the analytical output file as a starting point and remove the figures NSIs do not want in. Some NSIs have mentioned that non-official data should be flagged. Making estimated data grey is the perhaps the most consistent way of doing this. -We do indeed end up with two databases, as has been mentioned from the start of the project. -In the longer run it means that any update of the analytical database should be followed by an update of the statistical database. -A coordinating group is needed to keep the database running. It depends on the consortium and the funding who is going to do this work. Publications -Mary O’Mahony is preparing a special issue for the National Institute Economic Review. This will include short papers, the productivity report, methods and sources. It is not restricted to EU KLEMS data and issues only. -There are plans for writing another productivity report in December. -To get an idea of how the Volume II is going to look like, Bart van Ark refers to the Crafts and Toniolo book, Economic Growth in Europe Since 1945, CUP, 1996.. -It is suggested that it would be a good idea to use both internal and external referees for Vol. II. -The first part of each chapter will have a similar order with harmonized tables and figures. Second part will be free. We will start basically from the productivity report.

16

-The length of each chapter should be about 30 printed papers. Saturday 17 March Session IV (WP9) Chair: Bart Los Bart Los (GGDC, University of Groningen) International Technological Specialization in Important Innovations: Some Industry-Level Explorations

3

Measures of innovation output:patent indicators

• Body of literature on patents as output indicator (Schmookler, Scherer, Griliches, etc). Conclusion: patents useful but noisy indicator of innovation• Patents very heterogeneous in importance (Hall, Pakes,

Schankerman, Harhoff, etc.)• In some industries, patenting is not seen as the most

appropriate method to protect intellectual property (Cohen, Walsh, Nelson)

• Patent offices are not always functioning as they should, with imperfect examination procedures of ‘prior art’ (Jaffe, Lerner)

• Citation counts can help in identifying important indicators (Trajtenberg, Jaffe, Hall)

5

Problems to cope with…

• Point of departure: patents that receive more citations in subsequent patents are more important• Problem 1: Patenting behavior varies across industries• Problem 2: Citation behavior varies over time• Problem 3: Citations are not received immediately

• Important innovations determined by constructing citation-based rankings by industry and year of grant for all patents issued;

• Distinction between important innovations and other innovations based on stylized fact concerning frequency distributions.

7

Data Sources

• NBER Patent-Citations Datafile• Numbers of citations (1975-1999) to all utility patents granted

by USPTO in 1963-1999• Our subset: 1970-1999 (>2.4M patents, of which 1.0M to non-US

inventors)• Country of first inventor

• USPTO’s PATSIC-CONAME Database• Industry of manufacture (OTAF: 42 industries)• “Fractional counting” in case of multiple OTAF codes • Matching to 20 EUKLEMS industries• 26 countries

15

Further research

• Use of OECD PatStat database on international patent citations instead of NBER database

• More systematic analysis of distribution of cut-off point estimator

• Industry-of-use instead of industry-of-manufacture (Johnson’s concordance), to link innovation indicator to EUKLEMS productivity indicators

• Study of relationship between important innovations and industry profitability using core EUKLEMS data

• Investigations to see whether techniques can be found to reduce time lag in identification process

Points of discussion -The Community Innovation Survey could be used to check if same industries and effects show up. -At the moment an ‘Industry-of-manufacture’ approach is used (so ‘manufacture of new seeds’ ends up in chemicals, while it is an agricultural innovation). In the future an ‘industry-of-use’ approach will be used as well. This can be done with the new OECD database on innovation. -With the NBER innovation database a distinction could be made between product and production process innovations, but for this project that will not be looked into.

17

Carolina Castaldi (GGDC and University of Utrecht) Technological Regimes and Structural Change: Evidence from Productivity Growth in Europe

2

Motivation• Differences in industrial structures have been used to explain the productivity gap between EU and the US.

• Sectors contribute to aggregate productivity growth in heterogenous ways.

• What is the the role of knowledge and innovation in explaining these differences?

AimAssess the contribution of both manufacturing and service industries to labor productivity growth using an innovation-based sectoral taxonomy

3

Theoretical background• Structural change necessary for growth • Schumpeterian processes of creative destruction are

at the heart of sectoral productivity differences (supply side)

• Structural bonus when labor shifts to higher productivity sectors

• Shift to services as a structural burden?• National specialization patterns and growth

differentials

4

Empirical literature on structural change and growth

• Shift-share exercises looking for evidence of shift effects (Fagerberg 2000, Timmer and Szirmai 2000, Peneder 2003)

• Regression analysis including structural variables: higher shares of high-tech industries has positive impact on growth

• Growth accounting (Jorgenson and Stiroh 2003, van Ark et al 2003)

5

Sectoral differences in innovation

• Technological regimes

• Pavitt (1984) taxonomy based on several dimensions of innovation

• Recent efforts at new innovation-based classification in services

• Miozzo and Soete (2001) extends the Pavitt taxonomy to services

6

Innovation-based taxonomies1. Pavitt taxonomy (manufacturing)

• Supplier dominated

• Scale intensive

• Specialized suppliers

• Science based

2. Miozzo and Soete (services)

Focus on the linkages between manufacturing and service and on the increased importance of knowledge and information technologies

• Supplier dominated services

• Physical networks

• Information networks

• Science-based/specialized services (also KIBS) 13

Findings• Major weight of within growth contribution, but shift effects are significant and they indicate a shift from manufacturing to services

• Productivity growth higher in manufacturing than in services

• Some services escape the Baumol disease and show already high productivity growth (PN and IN services)

• Large cross-country variation in group contributions

18

14

Discussion of the findings• Prominent role of networks services in the innovation-based taxonomy

• Key knowledge/technology generating role of SB (ICT production) and SI sectors

• Marginal role of SD and SDS

• Disappointing contribution of KIBS can be explained with:

• measurement errors (Wőlfl, 2003)

• reliance on outsourcing as a constraint to productivity gains (Fixler and Siegel, 1999)

15

Conclusions• Use of innovation taxonomies as lenses to interpret structural change and growth

• Modified shift-share decomposition to account for direction and quality of shift effects

•More on the cross-country variation in the contribution of different groups of sectors

• Further step: Relate to links between the properties of national systems of innovation and the differences in sectoral performance

Points of discussion -KIBS (Knowledge Intense Business Services) are defined by the following industries: Computer Services (NACE 72), R&D (NACE 73) and Other Business Services (NACE 74.5-74.8), as is defined in Miozzo and Soete (2001). -The modified Shift-Share analysis as proposed by Van Ark and Timmer (2003) enables us to analyse shifts towards high labour productivity growth sectors, it does not enable us to distinguish the movement of skilled people between growing sectors. -A distinction between Consumer services and Business services is very important as Baumol’s disease concerns Consumer services. -Using more detailed data (for this analysis the minimum of 48 industries was used, as this was readily available for most countries) could improve the results. Nick Oulton (LSE and NIESR) Stress Testing the EU KLEMS Database: Alternative Measures of Capital Input

The spirit of the new SNA

• Capital stocks, capital services and capital consumption should all be estimated in an intellectually consistent way. All underlying data should be consistent (see OECD manuals).

• Gross operating surplus is the return to capital = the sum of the returns to each asset

User cost of asset Capital stock of asset j

j j= ×∑

Stress testing

• The user cost has three elements:• Depreciation• Capital gain/loss• Rate of return

• The average rate of return is implicit in the other estimates

• Is the implied rate of return plausible?

19

Reasons why the estimates may be wrong

1. Asset stocks: allocation of investment across industries may be wrong

2. Tax factors are needed for calculating depreciation and capital gains, but are not (yet) available in EU KLEMS

3. Some assets that generate profits may be missing, eg

inventories, land, R&D, intangibles

1

-1

( )

Value of capital stocks

mt jt jtj

tt

GOS D CGr =

− −=

∑

Is there a pattern? (1)

Dummy variable model

Dependent variable: real rate of returnIndependent variables: dummies for countries, sectors, and

years

Drop extreme observations (r < -50 or r > 50): N was 7859, now 5744 (2115 obs. dropped)

Conclusions

• The differences between countries and across sectors in estimated real rates of return are larger than can be explained by differences in economic fundamentals

• Tentative explanations: 1. The allocation of investment across

industries may be at fault2. The initial capital stocks may be too

high

Further work…

• Extend analysis to rest of EU-25 countries• Second part of stress-testing: calculate

sensitivity of capital input estimates to method employed – Using estimates of rates of return, calculate

contribution of capital to growth by ex-post, ex-ante and hybrid methods

Points of discussion -Capital services (CAP variable from the EU KLEMS database) is used as measure for GOS, as CAP has been adjusted for the income for self-employed. -Inventory rates could be part of the explanation, for the UK this data is available. It is not clear if this is also the case for other countries. -Intangibles are another possible explanation; Bart van Ark and Jukka Jalava are looking into this for some countries. -Inflation rates are used to convert the nominal rates of return into real rates of return. This makes rates of return comparable over countries. However as inflation rates were very high in the beginning of the database period (the seventies), a nominal rate of return could also be considered. -Starting capital stocks might explain the negative returns in early years -it is important to keep in mind the difference between rates of return and negative user costs. The latter can be negative for a number of years

20

Carlo Milana (ISAE) Alternative Measures of Productivity in a Changing Structure

22

OverviewOverview

II. Measurement problems with non-invariant index numbers

III. Empirical evidence in Italy

IV. Upper and lower bounds in the case of only two observations

V. Afriat’s tight bounds in a multilateral context

I. Defining the problem: Building blocks of economic theory of index numbers

VI. Conclusion1212

GeneralGeneral nonnon--homothetichomothetic casecase

• In the non-homothetic case economic index numbers are non-invariant

(this is because it is not possible to disentangle univocally the mutual effects of variables)

• If we deflate a nominal value by means of a non-invariant price index numberthe resulting implicit quantity index is not in general homogeneous of degree 1(if, for example, the elementary quantities double, in general the quantity index does not double).

• This undesirable behaviour is related to an anomalous position of the “true”index number with respect to the Laspeyres and Paasche index numbers.

1414

SinceSince a a geometricgeometric averageaverage of of twotwo nonnon--invariantinvariant economiceconomic indexindex numbersnumbers isisgenerallygenerally nonnon--invariantinvariant withwith respectrespect toto referencereference variablesvariables, , the the ““superlativesuperlative”” indexindex numbersnumbers are are alsoalso nonnon--invariantinvariant in the in the nonnon--homothetichomotheticcase.case.

WhileWhile the price the price economiceconomic indexindex numbernumber isis linearlylinearly homogeneoushomogeneous bybyconstructionconstruction, in , in generalgeneral the the correspondingcorresponding quantityquantity indexindex numbernumber failsfails totosatisfysatisfy the the linearlinear homogeneityhomogeneity requirementsrequirements in the in the nonnon--homothetichomothetic case.case.((seesee, , forfor exampleexample, , SamuelsonSamuelson and and SwamySwamy, 1974, , 1974, DiewertDiewert, 1983, p. 179, 1983, p. 179).).

Samuelson and Samuelson and SwamySwamy (1974, p. 576)(1974, p. 576) observed that, in the general nonobserved that, in the general non--homothetic case, the corresponding quantity index obtained implihomothetic case, the corresponding quantity index obtained implicitly by citly by deflating the nominal cost by means of the economic price index deflating the nominal cost by means of the economic price index fails to be fails to be linearly homogeneous. linearly homogeneous.

Samuelson and Samuelson and SwamySwamy (1974, p. 570) noted: (1974, p. 570) noted: ““[[t]het]he invariance of the price invariance of the price index is seen to imply and to be implied by the invariance of thindex is seen to imply and to be implied by the invariance of the quantity e quantity index from its reference price baseindex from its reference price base””..

GeneralGeneral nonnon--homothetichomothetic casecase

2323

ConclusionConclusionMain steps towards the construction of Afriat’s tight bounds of the unknown “true” index:

Step 2: Test consistency of the data with a well behaved utility or technology function (if the test is passed, then go to step 3; if not, then go to step 1).

Step 3: Construct the matrix of Laspeyres and Paasche indexes.

Step 4: Devise an efficient algorithm to derive the matrix of minimum (maximum) path of chained Laspeyres (Paasche) indexes.

Step 1: Construct/revise an appropriate economic-theoretic model of demand (supply) behaviour

Step 5: Derive Afriat’s tight bounds from the results of Step 4.

Points of discussion -The model cost function used in the empirical part of this paper was non-homothetic, therefore the results were not between the Paasche and Laspeyres index numbers. The model will be adjusted, which should result in better outcomes. -Redoing the work using a true econometric approach could be problematic as the time span is too short.

21

Planning Volume III Mary O’Mahony and Bart Los (NIESR, University of Birmingham and GGDC, University of Groningen)

1

Note on Vol. III of EUKLEMS CUP series • Objective: to make results of state-of-the-art research into

European productivity issues accessible to a broad, mainly academic, audience

• (Very) provisional title: “European Productivity Growth Performance: The Roles of Technology and Skills in an International Comparative Perspective”

• Editorship by WP8-leader(?), Bart Los and Mary O’Mahony• Contents: 10-12 chapters on effects of technology, skills,

outsourcing, innovation, and intangibles. The focus will be on studies with an international comparative perspective

• Selection will be based on one-page abstracts, which should clearly describe 1) the research objective, 2) the data, and 3) the methodology

• Abstracts should be submitted before April 30, to [email protected] and ([email protected]). Authors will be notified before June 15 about inclusion or exclusion of their proposal in the plans we submit to CUP

• Full chapters should be ready by October 30.

Points of discussion -Time schedule for Volume III is not necessarily the same as for Volume I and II. It is useful however to put the deadlines for Volume III at April 30th and October 30th as well. The first deadline is important because some general information is needed on what Volume III will contain for the negotiations with Cambridge University Press. The second deadline cannot be postponed, as people will start working on other projects after the December conference and work for Volume III will not fit in their schedules anymore. The work handed in for the October deadline does not have to be completely finished chapters, but they should be reasonably complete so the editors can judge if they fit in the volume. Later a number of outside referees (as also proposed for Volume I and II) will review the chapters. -A thematic Volume could be very useful to policy makers and have more ‘staying power’ then Volume II, as data tends to become outdated. -In principle consortium members would get priority to publish in Volume III. If the number of abstracts (or chapters in the later stage) of sufficient quality handed in is too low, collaborations between various people within the consortium could be suggested by the editors of the Volume. Finally, we can consider to include joint work with outside people as well.

22

Appendix 1 List of participants Fourth EU KLEMS Meeting Brussels, 16-17 March 1 Ana Rincon NIESR 2 Andreas Metz The Conference Board Brussels 3 Anita Wölfl CEPII 4 Bart Hertveldt FPB 5 Bart Los GGDC, University of Groningen 6 Bart van Ark GGDC, University of Groningen 7 Bart Van den Cruyce FPB 8 Battista Severgnini CEBR 9 Bernadette Biatour FPB 10 Bernd Görzig DIW 11 Bernhard Michel FPB 12 Carlo Milana ISAE 13 Carolina Castaldi GGDC, University of Groningen 14 Chantal Kegels FPB 15 Charles-Henri Dimaria STATEC 16 Dale Jorgenson Harvard University 17 Edwin Stuivenwold GGDC, University of Groningen 18 Eric Bartelsman AMBER 19 Francesco Zollino Bank of Italy 20 Gerard Ypma GGDC, University of Groningen 21 Hans Christian Kongsted CEBR and University of Kopenhagen 22 Hans-Olof Hagén Statistics Sweden 23 Henri Bogaert FPB 24 Henry van der Wiel CPB 25 Hyeog Ug Kwon Nihon University 26 Ian Perry DG Research 27 Jari Hyvärinen Tekesä Finnish Funding Agency for Technology and Innovation 28 Jeroen Fiers FPB 29 Joerg Beutel Konstanz University of Applied Sciences 30 Joost Verlinden FPB 31 Jukka Jalava PTT 32 Keun Hee Rhee Korea Productivity Center 33 Kieran McMorrow DG ECFIN 34 Kurt Kratena WIFO 35 Kyoji Fukao Hitotsubashi University 36 Laurence Nayman CEPII 37 Laurent Wagner CEPII 38 Lourens Broersma GGDC, University of Groningen 39 Marcel Timmer GGDC, University of Groningen 40 Mari Kangasniemi NIESR 41 Martin Gornig DIW 42 Martin Junge CEBR 43 Mary O'Mahony NIESR and Birmingham University 44 Matilde Mas IVIE 45 Matti Pohjola Helsinki School of Economics 46 Michel Fouquin CEPII 47 Mickey Petersen CEBR 48 Monika Schwarzhappel Wiiw 49 Mun Ho Harvard University

23

50 Nick Oulton LSE 51 Peter Havlik Wiiw 52 Pierre Valette DG Research 53 Robert Inklaar GGDC, University of Groningen 54 Robert Stehrer Wiiw 55 Tomas Skyttesvall Statistics Sweden 56 Tomohiko Inui Nihon University 57 Ton van Moergastel GGDC, University of Groningen 58 Tsutomu Miyagawa Gakushuin University 59 Vincent Geortay FPB 60 Vlad Manole The Conference Board

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