[pic] | COMMISSION OF THE EUROPEAN COMMUNITIES |

Brussels, 21.11.2007

SEC(2007) 1507

COMMISSION STAFF WORKING DOCUMENT Accompanying the COMMUNICATION FROM THE COMMISSION TO THE COUNCIL, THE EUROPEAN PARLIAMENT, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE, THE COMMITTEE OF THE REGIONS AND THE EUROPEAN CENTRAL BANK

The EU Economy: 2007 Review MOVING EUROPE'S PRODUCTIVITY FRONTIER {COM(2007) 721 final}

The “ EU economy: 2007 review – Moving Europe's productivity frontier ” was prepared under the responsibility of Klaus Regling, Director-General for Economic and Financial Affairs and Marco Buti, Deputy Director General for Economic and Financial Affairs and acting Director for Economic Studies and Research.

Executive responsibility was attached to Karl Pichelmann, Adviser in the Directorate for Economic Studies and Research. Karl Pichelmann, together with Paul van den Noord, also served as co-ordinating editor of the report.

Primary contributors to this report include Alfonso Arpaia, Daniel Grenouilleau, Sylvain Gouteron, Karel Havik, Jan in't Veld, Kieran Mc Morrow, Dario Paternoster, Werner Röger, Valerie Rouxel-Laxton, Michael Thiel, Alessandro Turrini, Janos Varga and Peter Weiss.

The report benefited from contributions and comments by staff of the Directorate General for Economic and Financial Affairs, including Sean Berrigan, Marco Buti, Giuseppe Carone, Declan Costello, Adriaan Dierx, Fabienne Ilzkovitz, Jarmo Kontulainen, Jürgen Kröger, Karolina Leib, Ulrike Mandl, Jan-Host Schmidt, Istvan Szekely and Max Watson.

Statistical assistance was provided by Karel Havik, Etienne Sail, Fabiana Pierini and Adam Kowalski, all of whom also helped to prepare tables and graphs. Kristine Vlagsma, George Alexakis, Adam Kowalski and Greta Haems were responsible for the lay-out of the report with the latter also providing excellent secretarial support, as did Honoree Rovers and Patricia Tuite.

Comments on the report would be gratefully received and should be sent, by mail or e-mail, to:

Mail:

European CommissionDirectorate-General for Economic and Financial AffairsDirectorate for Economic Studies and ResearchKarl PichelmannOffice BU-1 05-184B-1049 Brussels

E-mail : Karl.Pichelmann@ec.europa.eu

Communication from the Commission 1

Chapter 1: Productivity trends in Europe: finally turning the corner? 3

Summary 5

1. Introduction 6

2. Slowing productivity growth in the EU and the euro area 7

3. The recent pick-up in labour productivity growth 10

4. Cycle analysis 14

5. The trend component of labour productivity growth 17

6. Trend growth of total factor productivity 22

Concluding remarks 24

References 25

1.A.1. Cycle dating 26

1.A.2. Trend labour productivity growth 27

1.A.3. Specification of the Kalman-filter and trend labour productivity growth 29

1.A.4. Stability tests 30

1.A.5. Data definitions and sources 32

Chapter 2: Assessing productivity at the industry level 33

Summary 35

1. Introduction 36

2. EU productivity trends at the economy wide level: a comparison with the US 38

3. Productivity trends at the industry level: an analysis of the March 2007 EU KLEMS Datasets 40

3.1. Basic sectoral productivity trends 40

3.2. Detailed growth accounting analysis of the manufacturing and private services sectors 41

3.3. Industry level TFP trends 45

4. Understanding the determinants of TFP growth 47

4.1. Conceptual framework 47

4.2. Existing empirical work 48

4.3. Empirical strategy 49

4.4. Regression results 50

Concluding remarks 57

References 58

Chapter 3: Is there a trade-off between productivity and employment? 61

Summary 63

1. Introduction 64

2. The stylised facts 66

3. A framework for analysis 70

4. Gauging the trade-off between employment and productivity 72

4.1. Possible sources of employment-productivity trade-offs 72

4.2. Measuring the employment-productivity trade-off 76

4.3. The role of labour market reforms in boosting employment and productivity growth 84

4.4. The effect on employment and productivity of policies for marginally attached people 90

Concluding remarks 95

References 96

Chapter 4: Policies in the pursuit of higher productivity: another look 99

Summary 101

1. Introduction 102

2. Policies to foster investment in knowledge 103

2.1. Policy lines in the areas of research and education 103

2.2. Trends in inputs and outputs 105

2.3. Assessing the effectiveness and efficiency of public spending on the knowledge economy 106

2.4. The relationship between the knowledge economy and economic growth 108

2.5. The effect of higher R&D spending in the QUEST III model 110

3. Policies to enhance the impact of competition on innovation 114

3.1. Introduction 114

3.2. The link between competition and economic growth 114

3.3. Policies to foster competition and their effects 115

3.4. The effects of higher competition in the QUEST II model 120

3.5. Policy conclusions 121

4. Policies to foster reallocation 123

4.1. Introduction 123

4.2. How important is the reallocation effect? 123

4.3. Economic policy measures that impact on reallocation 127

4.4. The effect of lower administrative costs and entry in the QUEST model 131

Concluding remarks 134

References 135

Tables

1.3.1. Labour productivity, value added and employment, selected Member States 11

1.3.2. Labour productivity, value added and employment, selected Member States 12

1.A.1.1. Business cycle dating, private business sector 26

2.3.1. GDP, labour input in hours and labour productivity per hour (annual average volume growth rates in %), EU15ex5 + US 40

2.3.2. Results of growth accounting analysis – gross value added growth and contributions (annual average volume growth rates in %), EU15ex5 + US 44

2.4.1. Basic specification 51

2.4.2. The role of human capital and R&D 52

2.4.3. The role of regulations 53

2.4.4. Industry-specific models 55

3.2.1. Actual GDP, Population and GDP per capita 1996-2007 period average % changes for the US and EU aggregates 67

3.2.2. Key labour market indicators 68

3.3.1. Relationship between employment and productivity growth 70

3.4.1. Cross-country growth regression controlling for employment and labour force growth 79

3.4.2. Relationship between employment and productivity growth 84

3.4.3. Changes in labour market variables 87

3.4.4. Changes in the Fraser Institute Indicators of product market reform and estimated changes in mark-ups 89

3.4.5. Productivity and growth effect of wage moderation 89

3.4.6. Simulated long-run effects of changes in labour and product markets 1995-2003 on GDP and unemployment rate 90

3.4.7. Simulated long-run effects of changes in labour and product markets on GDP and the unemployment rate: 1995-2003 90

3.4.8. Effects of reforms for marginally attached people on employment rate: EU25 countries, 2001-2006 (difference in difference estimation) 92

3.4.9. Effects of reforms for marginally attached people on employment rate: EU15 countries, 2001-2006 (difference in difference estimation ) 93

3.4.10. Effects on productivity growth of reforms for marginally attached people 93

3.4.11. Effects on the growth rate of the capital-labour ratio of reforms for marginally attached people: EU15 countries 94

4.2.1. Efficiency gains of education spending using the same resources (in % of output) 108

4.2.2. Tax-cut financed from consumption tax single country scenario (percent deviations from baseline) 112

4.2.3. Tax-cut financed from consumption tax all member states scenario (percent deviations from baseline) 112

4.3.1. Griffith results (dependend variable: patents per 1000 population,and interaction with competition) 115

4.3.2. Forecasted changes in sectoral prices and productivity as a consequence of market opening 118

4.3.3. Change in TFP growth over 10 years from adopting regulatory best practices 120

4.3.4. The impact of changes in mark ups on labour productivity in the QUEST model 121

4.4.1. Economic performance in sectors with low productivity growth 1995-2004 126

4.4.2. Reducing administrative costs (% deviations from baseline) 132

Graphs

1.1.1. Labour market developments in the EU 6

1.1.2. GDP growth in the EU, the US and Japan 6

1.2.1. GDP per employee and GDP per head in the euro area and the US 7

1.4.1. Output and productivity growth in the euro area 14

1.4.2. Euro-area private business sector 15

1.4.3. Euro-area manufacturing 15

1.4.4. Euro-area construction 15

1.4.5. Euro-area trade services 15

1.4.6. Euro-area financial intermediation 15

1.4.7. Germany: private business sector 16

1.4.8. France: private business sector 16

1.4.9. Italy: private business sector 16

1.4.10. Spain: private business sector 16

1.5.1. Euro area private business sector 17

1.5.2. Euro area manufacturing 19

1.5.3. Euro area construction 19

1.5.4. Euro area trade services 19

1.5.5. Euro area financial services 19

1.5.6. Germany: private business sector 19

1.5.7. France: private business sector 19

1.5.8. Italy: private business sector 19

1.5.9. Spain: private business sector 19

1.6.1. Trend TFP growth-Euro area 22

1.6.2. Trend TFP growth-Germany 23

1.6.3. Trend TFP growth-France 23

1.6.4. Trend TFP growth-Italy 23

1.6.5. Trend TFP growth-Spain 23

1.A.2.1. Germany, manufacturing 27

1.A.2.2. Germany, construction 27

1.A.2.3. Germany, trade services 27

1.A.2.4. Germany, financial services 27

1.A.2.5. France, manufacturing 27

1.A.2.6. France, construction 27

1.A.2.7. France, trade services 27

1.A.2.8. France, financial services 27

1.A.2.9. Italy, manufacturing 28

1.A.2.10. Italy, construction 28

1.A.2.11. Italy, trade services 28

1.A.2.12. Italy, financial services 28

1.A.2.13. Spain, manufacturing 28

1.A.2.14. Spain, construction 28

1.A.2.15. Spain, trade services 28

1.A.2.16. Spain, financial services 28

1.A.4.1. Euro area, private business sector 31

1.A.4.2. Germany, private business sector 31

1.A.4.3. France, private business sector 31

1.A.4.4. Italy, private business sector 31

1.A.4.5. Spain, private business sector 31

1.A.4.6. Euro area, private business sector 31

1.A.4.7. Germany, private business sector 31

1.A.4.8. France, private business sector 31

1.A.4.9. Italy, private business sector 31

1.A.4.10. Spain, private business sector 31

2.2.1. Potential GDP per capita levels accounting 38

2.2.2. Labour productivity per hour levels accounting 38

2.2.3. Labour productivity per hour growth trends 38

2.2.4. Capital deepening 39

2.2.5. Total factor productivity 39

2.3.1. Total economy, annual % change 41

2.3.2. Total manufacturing, annual % change 41

2.3.3. Total private services, annual % change 41

2.3.4. Rest of economy, annual % change 41

2.3.5. Capital services 44

2.3.6. Total factor productivity 44

2.3.7. ICT capital services 44

2.3.8. Non-ICT capital services 44

2.3.9. Capital services 45

2.3.10. Total factor productivity 45

2.3.11. ICT capital services 45

2.3.12. Non-ICT capital services 45

2.3.13. Sectoral contributions from ICT capital services to the total economy change in value added growth US minus EU (1996-2004) 45

2.3.14. Sectoral TFP contributions to total economy change in value added growth US minus EU (1996-2004) 46

2.3.15. 28 Industry breakdown of total TFP contribution to value added growth US minus EU (1996-2004) 46

2.4.1. EU+US – trend contributions from TFP in the Network Industries to the Value Added Growth Rate of the EU and US economies over the period 1981-2004 (annual % change) 54

3.2.1. Unemployment rate and the NAIRU in the EU15: 1981-2007 66

3.2.2. Contribution of labour productivity growth and employment to GDP growth in the EU and the US 67

3.2.3. Total factor productivity 69

3.4.1. Hourly productivity – comparisons of actual, trend, and balanced productivity growth – Germany 73

3.4.2. Hourly productivity – comparisons of actual, trend, and balanced productivity growth – Spain 73

3.4.3. Hourly productivity – comparisons of actual, trend, and balanced productivity growth – France 73

3.4.4. Hourly productivity – comparisons of actual, trend, and balanced productivity growth – Italy 73

3.4.5. Hourly productivity – comparisons of actual, trend, and balanced productivity growth – United Kingdom 73

3.4.6. Correlation between productivity growth and employment, labour force and unemployment rate 74

3.4.7. Hourly labour productivity and hours worked by low skilled: share in total hours 76

3.4.8. Speed of convergence controlling for employment growth (rolling regression over 10-years window) 77

3.4.9. Effect of 1% employment growth on yearly productivity (rolling regression over 10-years window) 77

3.4.10. Effect of 1% labour force growth on yearly productivity (rolling regression over 10-years window) 77

3.4.11. Speed of convergence controlling for labour force growth (rolling regression over 10-years window) 77

3.4.12. Cumulative effect of a 1% labour force growth on labour productivity growth 78

3.4.13. Cumulative effect of a 1% employment growth on labour productivity growth 78

3.4.14. Employment and productivity trade-off: an unstable relationship 79

3.4.15. Employment productivity trade-off: all sectors – change in HLP growth (avg 96-04 minus avg 85-95) 81

3.4.16. Employment productivity trade-off: all sectors – change in capital labour substitution growth (avg 96-04 minus avg 85-95) 81

3.4.17. Employment productivity trade-off: all sectors – change in TFP growth (avg 96-04 minus avg 85-95) 81

3.4.18. Employment productivity trade-off: manufacturing – change in HLP growth (avg 96-04 minus avg 85-95) 82

3.4.19. Employment productivity trade-off: manufacturing – change in capital labour substitution growth (avg 96-04 minus avg 85-95) 82

3.4.20. Employment productivity trade-off: manufacturing – change in TFP growth (avg 96-04 minus avg 85-95) 82

3.4.21. Employment productivity trade-off: private services – change in HLP growth (avg 96-04 minus avg 85-95) 83

3.4.22. Employment productivity trade-off: private services – change in capital labour substitution growth (avg 96-04 minus avg 85-95) 83

3.4.23. Employment productivity trade-off: private services – change in TFP growth (avg 96-04 minus avg 85-95) 83

4.1.1. Technology frontier and sources of growth 102

4.2.1. Public spending on education and R&D, EU-15 105

4.2.2. R&D expenditure and researchers in EU Member States 105

4.2.3. Public expenditure on research and education by Member States 106

4.2.4. Efficiency of public spending 107

4.3.1. Relationship between competition and innovation according to Aghion 115

4.3.2. OECD deregulation index for network industries, EU-15 aggregate 118

4.3.3. Simulation results: changes in mark-ups 121

4.4.1. Birth rate of new enterprises 2003-2004 124

4.4.2. Labour productivity by firm size 125

4.4.3. Contribution of negative productivity sectors to aggregate productivity growth, EU-15 126

4.4.4. Correlation between firm entry and exit 127

4.4.5. Employment protection legislation index 131

4.4.6. Fragmented industries 132

4.4.7. Segmented industries 132

Boxes

1.2.1. Different concepts of productivity growth 8

1.5.1. Disentangling trend and cycle 18

2.3.1. EU KLEMS Growth Accounting Methodology. 42

3.4.1. The effects of product and labour market reforms on the relationship between productivity and employment 85

3.4.2. QUEST II model 88

4.2.1. The impact social return of R&D on productivity growth: a brief review of the literature 104

4.2.2. A description of the QUEST III R&D-model 111

4.3.1. Recent evidence on the link between trade openness and productivity growth with firm data 117

The simultaneous occurrence of low output and productivity growth in the first half of this decade gave rise to concerns about the health of the EU and euro-area economies. Although the European economies' improved capacity to create new jobs was a welcome development, the downside was that it occurred against a background of falling productivity growth.

Along with the recovery in economic activity, productivity growth has seen a revival since mid-2005 in both the EU and the euro area. This has led commentators to conclude that the protracted period of slow productivity growth in the EU might have come to an end. However, since labour productivity growth is known to be subject to cyclical variations, the recent acceleration may reflect mainly cyclical factors rather than an improvement in the underlying trend.

The purpose of this chapter is to shed some light on this issue, exploiting available data on quarterly labour productivity for the larger euro-area Member States up to the first quarter of 2007. As the emphasis lies on data timeliness some compromise had to be accepted regarding the sector breakdown, which exists only at a highly aggregated level.

One of the main findings of the chapter is that trend productivity growth in the euro area saw a fairly sustained decline from the mid-1990s on. This is a common feature across sectors and Member States, although developments in manufacturing and trade services, from a sector perspective, and in Germany and Italy, from a country perspective, seem to have dominated the overall picture. There is moreover some evidence that sectoral composition effects played a role in the productivity slowdown, with lower-productivity sectors such as trade services having grown faster than higher-productivity sectors. Moreover, while there does not seem to exist a trade-off between employment growth and productivity growth in the longer term, there are indications that exceptionally buoyant employment growth maytemporarily lower productivity growth, as appears to have happened in Italy and Spain, particularly in the construction sector.

The trend decline in productivity growth seems to have come to a halt in the early years of this decade and this again is a development shared by most sectors and Member States. Actual data suggests a significant pick-up in labour productivity growth since mid-2005, which also took place against the background of fast and accelerating employment growth. While the quickening pace of productivity growth was fairly general across countries and sectors, a significant part of the acceleration at the euro-area level seems to be due to developments in the private business sector in Germany.

However, if the cyclical component is eliminated from from productivity growth, little of this acceleration remains. Thus it is clear that a large part of the apparent acceleration must be attributed to the cyclical upswing which the euro-area economy has enjoyed in this period. Neither estimates of trend productivity using filtering techniques nor statistical tests of structural shifts provide conclusive evidence of a strengthening of trend productivity growth in the past years. These results are corroborated by estimates of trend growth of total factor productivity which indicate that, to date, there is little evidence in favour of a pick-up in this component of labour productivity growth.

This might be simply due to the fact that the pick-up in productivity growth is too recent for statistical procedures to be able to detect underlying structural changes. As a matter of fact, evidence of the rise in productivity growth in the US from the mid-1990s on was available only several years after the turn-around.

In conclusion, while the slowdown of trend productivity growth from the mid-1990s on appears to have bottomed out, further data is needed to assess whether the recent pick-up in actual productivity growth is due also to changes in the underlying trend.

The economies of the EU and the euro area have experienced a robust economic upturn since mid-2005, with GDP in 2006 growing by around 3% over the preceding year. This was the best performance since the boom in 1999-2000, lifting the growth rate above those of both the US and Japan for the first time in a number of years (see Graph 1.1.2). Moreover, in the face of surging oil prices, consumer price inflation remained moderate.

Short-term indicators point to a deceleration in the pace of economic activity in the period ahead, but growth in 2007 should still be in a range of 2½ to 3% in both areas, which is clearly above potential. Further ahead, downside risks to the economic outlook have accumulated, however, not least due to the recent financial markets woes. In particular, on the back of a slowdown in the US a downshift in the global economy cannot be ruled out, despite continued brisk growth in many emerging market economies. This would inevitably have some spill-over effects on the EU and euro-area economies, their generally favourable fundamentals notwithstanding.

The economic revival in 2006-07 was greeted with significant relief. Indeed, a rather modest growth performance during the period 1996-2005 led to recurrent downward revisions of potential output growth, especially that of the euro-area ([1]), and became a source of growing concerns among economic analysts and policy makers. It is

noteworthy, however, that the labour market eschewed this general tendency of a weak performance. On the contrary, since the mid-1990s employment has expanded at a sustained pace and, except for a short interruption in 2002-2003, unemployment has fallen continuously to reach a rate not seen in more than two decades (see Graph 1.1.1).

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Since, by definition, output growth minus employment growth equals productivity growth, the slowdown in output growth coupled with faster employment creation implied a significant decline in labour productivity growth from the mid-1990s on. While the improved capacity of the EU and euro-area economies of creating jobs was a welcome development, the downside was that is occurred against a background of falling productivity growth. This contrasted with developments in the US, where productivity growth saw a sustained acceleration over the same period, leading to a widening productivity gap between Europe and the US.

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The simultaneous occurrence of falling productivity growth in Europe and a sustained rise in productivity growth in the US from the mid-1990s on (see Graph 1.2.1) gave rise to increasing concerns about the health of the EU and euro-area economies. Even if in terms of GDP per capita the gap vis-à-vis the US was smaller, high productivity growth is considered to be closely associated with rising living standards, not least because it drives growth in real wages, while facilitating fiscal consolidation and assisting monetary policy in keeping inflation in check and interest rates low. Moreover, in view of the relationship between labour productivity and output growth (see Box 1.2.1), a longer-term decline in productivity growth is likely to have an adverse impact on the rate of potential output growth. This runs counter to the ambitions of the European Unions' Lisbon strategy that made raising potential growth one of the priorities of the policy agenda ([2]).

Since mid-2005, growth of labour productivity has seen a rebound in both the EU and the euro area. In the euro area, for instance, productivity growth accelerated from a year-on-year rate of 0.8% in the second quarter of 2005 to 1.8% in the first quarter of 2007. This has led commentators to conclude that the drawn-out period of slow productivity growth in the EU might have come to an end.

At the same time, the pick-up in productivity growth was not particularly pronounced, with the growth rate in 2006 still falling short of the average rate observed in the 1980s and 1990s. Moreover, labour productivity is known to be subject to cyclical variations. It could well be, therefore, that the recent productivity spurt reflects mainly cyclical factors rather than an improvement in the underlying trend.

The following sections aim to shed some light on this issue by assessing developments in labour productivity in the current cycle. For this purpose, quarterly national accounts data up to the first quarter of 2007 are used. With recent developments being the focus of the chapter, the emphasis lies on the timeliness of the data. This comes at the cost that a sector breakdown is only available for highly aggregated sectors.

Moreover, because of data limitations, when seeking to exploit the sectoral dimension of the data, country aggregates can only be constructed by using data from Member States for which such sectoral data is available (i.e.Germany, France, Italy, Spain, Belgium and Finland). In terms of coverage, these Member States represent about 61% of the total GDP of the EU and 83% of euro-area GDP. Since this is representative of the euro area but less so of the EU, the aggregate of the above-mentioned six Member States will be labelled "euro-area" when using sectoral data (Appendix 5 contains more details about data sources and data treatment).

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Finally, it is important to note that labour productivity is defined here as output (value added) per employee. While output per hour worked might be a more adequate measure of underlying labour productivity, data for hours worked are often not available at a quarterly frequency or, when available, of generally poor quality reflecting the still imperfect harmonisation of estimates for hours worked. In terms of the focus of this chapter, previous research has shown that, qualitatively, the results do not depend very much on whether output per worker or output per hour is used (e.g. Benati, 2007).

Section 3 of this chapter takes a closer look at the recent performance of productivity growth and

compares it with that in the past two decades. Since calendar periods may cover different phases of the business cycle, which may influence the results, section 4 takes averages over upswing phases of the business cycles in the past 25 years, comparing the recent recovery phase with the past recoveries. In order to shed some light on the question whether the productivity acceleration observed since mid-2005 constituted a cyclical or a structural change, section 5 uses various statistical techniques to disentangle the cyclical and trend component of productivity growth. Section 6 looks at total factor productivity in order to investigate the role this component has played in the recent evolution of labour productivity growth. Section 7 concludes.

After several years of sluggishness, recent developments in labour productivity in the euro area have surprised on the upside. Annual labour productivity growth measured as value-added per worker accelerated to 1.2% in 2006, compared with an average of 0.4% in the period from 2001 to 2005 (Table 1.3.1). Although this performance is not outstanding by historical standards, as the annual average growth rates of 1.7% in the 1980s and 1.4% in the 1990s demonstrate, it marks a clear change compared to the first half of this decade. In addition, in 2006 productivity accelerated despite a further strengthening of employment growth, as the dynamism of output more than offset the negative “contribution” from employment.

In the private business sector, developments in the 1980s and 1990s seemed to be similar to those in the whole economy. However, the pick-up in 2006 was somewhat more pronounced than for the whole economy, with both output and productivity growth being around 0.5 pp. higher. This conforms to the notion that, in the short term, productivity gains can be more easily achieved in the private business sector as compared to the public services sector ([3]), although, somewhat surprisingly, in the longer term there do not seem to be large differences in the developments of these two sectors.

Within the private business sector, the recent pick-up in labour productivity growth appears to be broadly shared between sub-sectors, albeit to differing degrees and with the exception of financial intermediation, where the acceleration occurred already in the first half of this decade ([4]). Specifically, annual productivity growth in manufacturing accelerated to 4.5% in 2006, up from an average of 1.8% in the previous five years. Productivity growth in the construction sector turned positive in 2006, after having been negative on average over the preceding 15 years, remaining nevertheless below the rates of the other sectors. As for the trade-related services, productivity gains quickened to a pace of 2% in 2006, up from 0.6% in the preceding half-decade and clearly improving on the performance of the previous two decades ([5]).

With productivity growth equalling output minus employment growth, it is interesting to see to what extent the former was determined by developments in the latter two variables. As shown in Table 1.3.1, the annual average rate of employment creation in the private business sector accelerated in 2001-2005 compared to the preceding decade, picking further up in 2006. This led to lower productivity growth in the first half of the decade as output growth weakened simultaneously, but in 2006 productivity growth re-accelerated owing to a strong rebound in output growth.

The pattern of development was fairly heterogeneous across sectors. In particular, in manufacturing the productivity slowdown in the first half of this decade was predominantly the mirror image of a lower rate of job shedding, whilst the marked acceleration in 2006 was chiefly a reflection of a sharp increase in output growth. In construction, productivity growth did not change significantly in 2001-2005 compared with the 1990s, owing to a higher rate of job creation being balanced by faster output growth, but rose in 2006 as a further acceleration in job creation was outpaced by even higher output growth. In the trade-related services, productivity weakened in 2001-2005, with slowing output growth being accompanied by faster employment growth, but re-accelerated in 2006 thanks to a significant pick-up in output growth combined with virtually unchanged employment growth. Finally, in financial intermediation the acceleration of productivity in 2001-2005 was due to faster output growth against stagnant employment, while in 2006 employment accelerated in step with output, leaving productivity growth unchanged.

Overall, for the euro area as a whole slowdown of productivity growth in 2001-2005 was largely concentrated in manufacturing and trade and dominated by employment-related changes in manufacturing (lower rate of job shedding) and output-related changes in trade (lower rate of output growth). The pick-up in productivity growth in 2006 was a reflection of faster output growth in manufacturing, construction and trade, which was only partly offset by employment growth, its acceleration vis-à-vis the period 2001-2005 notwithstanding.

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Looking at the euro area aggregate may conceal important differences between countries. This section therefore takes a closer look at the developments in the four largest euro-area countries, Germany, France, Italy and Spain.

As shown in Table 1.3.2, a comparison of productivity developments in 2006 with the longer-term averages across Member States indicates that productivity gains have been particularly large in Germany. Labour productivity in the German private business sector grew at an annual rate of 3.2% in 2006, placing Germany in the lead amongst the larger Member States. This is explained by rapid output growth, which outpaced employment growth by a significant margin. While the surge in the German manufacturing sector to an annual 6.7% stands out, it is remarkable that productivity growth was strong in all sectors of the German economy in 2006, including construction (+6.2%) and trade (+3.4%). Financial intermediation also saw dynamic productivity growth in 2006 (+4.7%), but this constituted a slowdown compared to the period 2001-05, where productivity was growing at an annual average of close to 6%.

France also experienced a revival of productivity growth in 2006, although to a much lesser extent than Germany. While labour productivity in the private business sector was up at 1.6% in 2006, from an average of 1.1% in the first half of the decade, this was still below the average growth rates experienced in the 1980s and 1990s. Similar to Germany, in France the acceleration of productivity growth in 2006 reflected almost fully faster output growth, with employment growth broadly unchanged. Differences across sectors were more pronounced than in Germany. Whereas productivity gains in the manufacturing sector accelerated to 4.1% in 2006, they were more muted in the trade sector and in financial intermediation. In the construction sector, labour productivity was already on a declining path during 2001-05 and deteriorated further to a rate of -1.3% in 2006.

In Italy, despite a fairly sharp swing from a strongly negative average growth rate in the period 2001-05, labour productivity growth in the private business sector remained at a subdued level in 2006. While output recovered from the very modest performance in 2001-05, its effects on productivity was largely offset by brisk employment growth. Differences across sectors were quite large. In particular, the economy-wide average was dragged down by the services sector, where growth was subdued in trade and even negative in financial intermediation.

Spain also performed poorly in terms of labour productivity growth, against a backdrop of rapid output and employment growth. After having been negative on average in the previous 15 years, labour productivity in the private business sector declined again in 2006. Job growth in Spain was very dynamic over this period. In the construction sector, for instance, employment expanded by a stunning 7.6% in 2006, following almost 6% average growth in the previous 5 years. While overall output growth remained comfortably above the euro-area average, it was insufficient to fully offset the drag on productivity from employment growth. Productivity growth was subdued in trade and was even strongly negative in the construction sector. Manufacturing was the only sector that gave a significant positive contribution to overall productivity growth in 2006, following a very moderate performance during 2001-2005.

Overall, while the acceleration of productivity growth in 2006 was fairly general across countries and sectors, a significant part of the pick-up at the euro-area level seems to be due to developments in the private business sector in Germany. Table 1.3.2 also shows that, in general, there is a close correspondence between changes in productivity growth and changes in output growth. This is likely to be linked to the pro-cyclicality of productivity growth. It illustrates, moreover, that generally there exists no longer-term trade-off between employment growth and productivity growth. Nevertheless, the table also provides some evidence that in specific instances particularly employment-rich growth may have acted as a damper to productivity growth. The construction sector in France, Italy and Spain during this decade seems to be a case in point. However, in the latter two countries recent productivity data may also have been downward biased by the regularisation of undeclared immigrant workers.

This raises the more general question of the factors behind the widely different experiences of sectors and countries in terms of labour productivity growth during this decade. In addition to the fundamental drivers of productivity growth, i.e. capital deepening and technical and organisational progress, which will be analysed more in-depth in later chapters, “statistical” factors seem to partially account for this. In particular, due to measurement errors and differences in national accounting practices the comparison of labour productivity across countries is subject to considerable uncertainty ([6]).

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More importantly, composition effects, which are due to differences in sectoral specialisation and sectoral growth between countries, may play a significant role in shaping productivity developments. While the boom in construction activity in Spain over past years is the obvious example, this effect may have been more generally relevant, especially in the services sector ([7]). Finally, the degree countries and sectors resort to “outsourcing” may be an important source of divergence. Outsourcing may affect measured productivity in two ways. First, it may lead to a better division of labour and therefore improve overall efficiency. But it may also have purely “statistical” effects in that certain activities move from one sector or country to another. If the productivity of these activities differs from the average productivity of the sector or country of origin, measured productivity levels will change across sectors and countries.

Labour productivity growth varies with the cycle, with productivity growth usually increasing above trend in a cyclical upswing and falling below trend during a downswing. A plausible explanation for this is that in the early phases of an economic upswing additional demand addressed to the enterprise sector is met by making incumbent employees work more efficiently (and longer hours). As the upswing matures and to the extent that the recovery is perceived as lasting, companies increase hiring. During this phase, while GDP may start to slow, employment growth accelerates, leading to slower growth of output per person employed ([8]).

The cyclicality of productivity growth is illustrated by Graph 1.4.1 which depicts the developments in output and productivity growth in the euro area over the period 1980-2007. As can be seen, the two variables move very closely together, with a contemporaneous correlation of 0.76. While it is often claimed that productivity is a variable that leads the cycle ([9]), this is not borne out by the data here. The maximum cross-correlation is reached at a lead/lag of zero, with cross-correlations of increasing leads and lags decaying quickly. Examining the turning points, the productivity variable displays a small lead at some peaks, but on average the lead is less than one quarter.

The cyclicality of productivity growth implies that the average growth rates as shown in Tables 1.3.1 and 1.3.2 may be distorted by the fact that they reflect different phases of the business cycle. One way of eliminating this distortion would be to take averages over full business cycles ([10]). This would net out the effects of the cycle and the result could therefore be interpreted as the "structural" growth rate of labour productivity over a business cycle. However, this would not allow the comparison of the current cycle with those in the past, as knowledge on the actual state of the present cycle is highly uncertain. The approach adopted in this section is, therefore, to compare cyclical upswing phases rather than full cycles, i.e. to average only over the expansion phase of current and past cycles. This approach is used to calculate average growth rates of labour productivity, value added and employment and compare the current cycle with the average of previous cycles. Appendix 1 provides more detailed information on the cycle dating procedure.

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Graph 1.4.2 shows the average growth rates of value added, employment and labour productivity in the euro area in the current upswing compared with that of previous ones. It can be seen that output growth in the current upswing lags behind that of previous cycles, while the opposite holds for employment growth. As a consequence, output per worker grew at a significantly lower rate during the latest upturn than in past upswings.

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Labour productivity in the euro-area private business sector was dragged down in the current recovery by weakness in trade and financial intermediation (Graphs 1.4.3-1.4.6). Specifically, labour productivity in trade increased at an average pace of 1.6% in the current upswing, 0.8 pp. less than in previous expansions. The drop is even larger for financial intermediation, with the average growth rate having slowed down to just above 3% in the current recovery from an average of almost 5% in past recoveries. By contrast, in manufacturing and in construction the performance of labour productivity in the current upswing was more favourable than previously, with growth in the former sector averaging 4.1% in this upswing compared to 3.8% before, while in the latter sector growth accelerated noticeably to a rate of 1.9%, from 0.6% in previous cycles.

The productivity slowdown in this cyclical recovery is thus mainly attributable to developments in the services sector. In addition, composition effects seem to have played a role. In particular, the construction sector, which in terms of productivity growth was a “low-performer” in the past, expanded by much above the economy-wide growth rate during this upswing. And in financial intermediation, where despite their recent moderation productivity gains have been comparatively high, output growth was lagging behind in this upturn compared to previous upturns.

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Comparing these results with those of the previous section, two main differences can be noted. Controlling for the effect of the cycle shows that the construction sector has seen a strong, output-driven acceleration of productivity growth in recent years. This did not become visible in the result of the previous section which averaged over calendar-specific sub-periods. The opposite holds for financial intermediation, where the results of this section indicate that a slowdown in productivity growth took hold during the latest cycle compared to past cycles while the previous section showed an acceleration in this decade compared with the previous two decades.

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The slowdown of productivity growth during the current recovery can be observed across all large euro-area Member States, although to varying degrees (Graphs 1.4.7-1.4.10). Italy and Spain stand out as experiencing considerably lower productivity growth during this recovery than in previous upswing episodes. In Italy, productivity grew by 0.9% in the current expansion, against 2.8% average growth in the past. In Spain, productivity remained flat during this cyclical upswing, compared to 1.2% average growth in the past. The gap is significantly smaller in the case of Germany and France. In the former country, output per worker in the current recovery broadly matched the pace of past recoveries and in the latter the gap amounted to 0.4 pp.

The developments in Italy and Spain in the current expansion appear to explain most of the underperformance of labour productivity growth at the euro-area level. This seems to be due above all to a pick-up in employment growth, which in Italy has taken place against a background of a decline in output growth, while in Spain output growth accelerated but was significantly outpaced by employment growth.

The previous sections indicated that the euro area was stalled in a state of historically low productivity growth for most of the present cyclical expansion. A question that remains unanswered is whether the rebound in productivity growth since mid-2005 represents a cyclical or a structural change in productivity growth. As shown by Graph 1.4.1, the recent acceleration of labour productivity in the euro area is closely related to the strengthening of economic activity, suggesting that the up-tick in productivity growth is mainly of a cyclical nature. To shed more light on this issue this section will use statistical techniques with a view to disentangling the trend and the cyclical components of productivity growth.

A commonly-used method of separating out the trend component from an economic time series is the Hodrick-Prescott (HP) filter. The HP-filter was used, for example, in the calculation of trend labour productivity growth for the euro area in European Commission (2006). An alternative to the HP-filter is the Kalman-filter, which estimates the trend from a time-varying parameter time-series model (see e.g. Gordon (2003) or Fritsche and Erber (2005)). Finally, statistical test procedures can be used to determine whether productivity growth is subject to structural shifts (see e.g., Jimeno et al. (2006) or Benati (2007)). Box 1.5.1 presents a more detailed discussion of these methods.

Using filters to extract trend productivity

Graph 1.5.1 shows the development of trend productivity growth in the euro-area private business sector since 1980, where the trend has been estimated with the two techniques mentioned above (HP-filter and Kalman-filter). As can be seen, the two methods yield fairly similar results except that the HP-filtered trend is somewhat smoother than the Kalman-filtered trend ([11]).

In accordance with these estimates, trend labour productivity in the euro area expanded at a year-on-year growth rate of around 2% during most years between the first half of the 1980s and the early 1990s. Thereafter, trend labour productivity growth declined rapidly, reaching a minimum at the beginning of this decade. The declining trend came to a halt between 2000 and 2002, depending on which trend curve is used. The Kalman-filter indicates a halt in 2000, with a slight pick-up during 2000-2002 and a stabilisation thereafter. The HP-filter suggests a somewhat later turning point at around 2002, with a gradual but more persistent recovery thereafter.

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The development of trend productivity in the euro-area private business sector as depicted in Graph 1.5.1 allows a slightly differentiated interpretation of the results obtained previously and presented in Table 1.3.1. In the 1980s, the average growth rate of actual labour productivity of 1.7% matches a more or less stable trend growth rate of around the same size. In the 1990s, the average growth rate of actual productivity falls to 1.5% as a consequence of a decline in the underlying trend growth, which starts towards the end of the first half of that decade. In the first half of the current decade, the actual productivity growth was dragged down (to an average of 0.5%) by a combination of a low underlying trend and unfavourable cyclical conditions. In 2006, actual productivity growth picked up to 1.7% on the back of a strong cyclical upswing, while trend productivity growth remained at around 0.6-0.8%.

These results differ from previous research (e.g. Gomez-Salvador et al. (2006), Dew-Becker and Gordon (2006)) as the latter points to the slowdown in the 1990s but fails to detect the stabilisation that occurred in the early years of this decade ([12]). This is partly related to the fact that most of this research is based on data which does not include most recent developments, partly to methodological differences.

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Looking into the sectoral dimension, Graphs 1.5.2-1.5.5 show that trend productivity has been very heterogeneous across sectors, confirming the results of previous sections. There is some evidence that trend productitivy growth started to increase gently in manufacturing and construction, in the latter sector somewhat earlier than in the former. Such a reversal in the trend decline cannot be observed in the trade sector, even if the negative trend seems to have bottomed out. Finally, for financial intermediation strong fluctuations make the picture somewhat blurred, but the underlying tendency points to an upward trend.

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Turning to cross-country differences, the trend decline in the 1990s and the subsequent stabilisation is a common feature for all four Member States (Graphs 1.5.6-1.5.9). However, the decline in the 1990s was less pronounced for Germany and France than for Italy and Spain. In Germany, trend productivity growth, after peaking at some 2.3% in 1992, declined to about 1.5% at the end of the 1990s, before picking up slightly. In France, the decline in trend productivity growth started earlier and followed a gentle downward path until the beginning of this decade when it started to bottom out. In Italy, trend productivity growth oscillated sideways during the 1980s before dropping in the 1990s, getting negative in 2000 and tailing off thereafter.

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In Spain, the deterioration started already in the mid-1980s and bottomed out in the mid-1990s at a negative rate of around 0.5%, without however seeing a distinct reversal since then. It is possible that these different trends are explained by the sectoral composition of each economy or by differences in sectoral growth.

Trend labour productivity in the manufacturing sector differed considerably across Member States (see graphs in Appendix 2). Notably, in the 1990s a declining trend cannot be observed in the German or French manufacturing sector. Trend productivity growth in the German manufacturing sector increased steadily, starting from around 1.5% in the 1980s to above 3.5% at around 2000. In France, however, it seems that the upward trend has come to a halt, stabilising at slightly above 3% towards the end of the 1990s. In contrast, Spain and Italy experienced an unabated downward trend in labour productivity growth in manufacturing activities from the 1980s on. However, the decline in the trend seems to have come to a halt and even reversed in Spain over the most recent period. The pick-up in the manufacturing sector in Spain is noteworthy, but some stabilisation seems to have occurred also in the trade services. In the cases of both Spain and Italy, the construction sector appears to have dragged down trend growth.

The financial services sector is a very special case. The fairly positive picture at the aggregated euro-area level is predominantly explained by the strong pick-up in Germany. Developments in financial services in Spain and Italy since the end of the 1990s clearly point in the opposite direction and in France a deceleration is also visible, although to a lesser extent.

Testing for structural breaks

In order to assess the significance of the recent acceleration in labour productivity, the existence of shifts in the trend rate of labour productivity growth has been tested by way of two different types of statistical tests. The first test (Nyblom-Hansen) checks for overall stability of productivity growth. In case of a rejection, the second test (Bai-Perron) examines how many times a shift ("break point") occurred using the "Bayesian Information Criterion" to determine the "optimal" number of shifts. The tests were carried out on the series of quarter-on-quarter productivity growth in the private business sector for the euro area and the four largest euro-area Member States. The conclusions that can be drawn from these tests are the following (for further details see Appendix 4):

- Trend productivity growth over the period 1981-2006 was unstable for the euro area and the four largest Member States, with France being a borderline case.

- Based on the a priori assumption that three shifts in the trend of productivity growth occurred during the period 1981-2006 in the euro area, shifts are identified in the first half of the 1980s (upward shift), in the second half of the 1990s (downward shift) and at the beginning of this decade (upward shift). However, the productivity shift in the early years of this decade is statistically not robust since the assumption of a single shift (the downshift in the second half of the 1990s) yields a superior model in terms of the applied criterion for model selection.

- Regarding the results for the four largest Member States, for Germany the test procedure identifies a single shift as describing the data "optimally", with the shift occuring in the second half of the 1980s (upward shift). In other words, the statistical evidence in favour of a downward shift of productivity growth in the 1990s and a subsequent upward shift during this decade is weak. For France, there is no conclusive evidence in favour of any number of shifts (which is in line with the inconclusiveness of the stabiliy test). For Italy, two downward shifts (in the first half of the 1980s and in the second half of the 1990s) appear to best describe the data. Finally, for Spain three downward shifts (one in the mid-1980s and two in the first half of the 1990s) are chosen by the selection criterion as being "optimal".

These results suggest that, while the bottoming out of the decline in trend productivity growth at the start of this decade is broadly confirmed, evidence of a turning point beyond that date indicating a recent pick-up in trend productivity growth is weak. A caveat as to the implications of these results is, however, in order. Due to the high volatility of the underlying series (see the graphs in Appendix 4), the applied test procedures may not be able to discriminate very precisely between alternative assumptions. This is reflected by the values of the test criterion which are not very far from each other under different assumption. Moroever, as noted also by Benati (2006), the power of these tests may be very low when the underlying data generation process is characterised by gradual change, as seems to be the case for trend productivity growth, rather than sudden shifts. For this reason, a very cautious interpretation of the results seems approproate in the sense that, while there is no strong evidence of a recent pick-up in trend productivity growth, there is also no direct evidence against it, at least for the euro area as whole.

Summing up, the fall in trend productivity growth in the euro-area is a phenomenon that is largely concentrated in (the second half of) the 1990s. The decline bottomed out at the beginning of this decade, with the Kalman-filter suggesting a slightly earlier date than the HP-filter. While there are also indications that for some sectors and countries a turn-around occurred during this decade, the strong pick-up in productivity growth since mid-2005 seems to be mainly due to favourable cyclical conditions.

Labour productivity defined as output per employee is one among several concepts of productivity. As discussed in Box 1.2.1, labour productivity growth equals TFP growth plus capital deepening. In the long run, under certain assumptions labour productivity growth converges to TFP growth up to a constant factor. TFP growth is therefore of some interest in itself as it shows how labour productivity contributes to overall growth if capital and labour were to grow at the same rate.

TFP growth cannot be directly measured. It is usually calculated as a residual after accounting for the contribution of capital and labour input to output growth ([13]). As can be seen from Box 1.2.1, TFP growth can be calculated as the difference between output growth and capital deepening. Since both output growth and capital deepening are subject to cyclical variations, TFP growth will also vary with the cycle.

For this reason, to obtain information on the longer-term trend of TFP growth similar procedures as in the case of labour productivity can be used to extract the trend component of the series. Since the cyclicality of measured TFP growth reflects to a large extent the degree of factor utilisation that varies over the cycle (see Gomez-Salvador et al. (2006)), capacity utilisation may be a valuable piece of information in extracting trend TFP growth. As it turns out, the rate of capacity utilisation is useful in identifying the trend component of TFP growth (for further details see Annex 4). Results from such an exercise are presented below.

Graph 1.6.1 shows that trend TFP growth in the euro area drifted downwards from the second half of the 1980s up to the early years of this decade. A stabilisation at a low level is suggested thereafter, particularly when looking at the results from the HP filter, which show a marginal improvement in recent years, while the results derived from the Kalman filter are less clear in this respect. Taken together, the results may be interpreted as indicating that, similarly to the developments of labour productivity growth, the long-term decline in TFP growth has come to a halt, but evidence for a trend reversal is not robust.

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Looking at the country-specific estimates (Graphs 1.6.2-1.6.5), the only country for which some trend increase in recent years seems indicated is Germany. However, it should be noted that Germany has the most volatile TFP trend among the countries considered and the recent increase should rather be seen as a return to the normal trend growth level following several years of steeply declining growth rates after 2000. Moreover, based on the results of the Kalman filter, only the last year of the sample exhibits a rise in trend TFP growth. But inference on the last data point is statistically fragile, since the estimate is potentially subject to revisions when additional observations become available.

In the case of France, trend TFP growth has been heading downwards for most of the past 20 years. Although the relatively volatile Kalman-filter estimates suggest intermittent recoveries, these were more than offset by subsequent declines. While the estimates obtained with the HP filter could be interpreted as evidence for a stabilisation in TFP growth in recent years, the comparison with the results derived from the Kalman filter suggests that some caution is warranted before jumping to conclusions. In particular, given its problems at end-points, the HP filter may not have fully eliminated the cyclical component of TFP growth for the most recent data points.

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For Italy, the development of trend TFP growth bears resemblance to that of the euro area as a whole, albeit at a lower trajectory. According to the Kalman-filter estimates, trend TFP growth was falling for most of the past 20 years, with a stabilisation at a negative rate of growth in the last three years. The results obtained with the HP filter indicate that the stabilisation could have occurred somewhat earlier, in which case trend TFP growth would have remained in positive territory. But this could again be a reflection of the HP filter's potential failure to properly disentangle the cyclical component from the trend component at end-points.

A similar conclusion holds for Spain, where the apparent recovery in trend TFP based on the HP-filter estimates could well be a statistical artefact, given that the estimates obtained with Kalman filter show almost no growth over the last years.

A common element of the above estimates seems to be that the results derived from the HP filter show a slightly more positive picture than those obtained with the Kalman filter. The most likely reason for this is that, due to the end-point problem, the HP-filter does not separate out the cyclical component from TFP growth for the most recent period, leading to an overestimation of TFP growth in the recent cyclical upswing. Taking this into account, the conclusion that can be drawn from these estimates is that there is no robust evidence of a trend reversal of TFP growth in the euro area. Among the larger euro-area Member States, the only country for which a trend increase appears possible is Germany, but according to the Kalman filter estimates the turn-around occurred only in 2006, which seems to be too short a period for drawing firm conclusions.

Summing up, the analysis of labour productivity growth using quarterly data shows that, for the euro area as a whole, trend labour productivity growth declined relatively sharply from the mid-1990s until the early years of the current decade. This applies across sectors and Member States, though the decline at euro-area level seems to have been dominated by developments in manufacturing and trade services, on the one hand, and in Germany and Italy, on the other. While the decline in trend labour productivity growth seems to have bottomed out, there is little evidence of a marked pick-up so far in the current decade.

A sectoral analysis of the data shows that the German and French manufacturing sector are exceptions to the overall pattern of a declining trend in the 1990s.

While there does not seem to exist a trade-off between employment growth and productivity growth in the longer term, there are some indications that exceptionally buoyant employment growth may have temporarily lowered productivity growth in Italy and Spain

Actual data suggests a strong pick-up in labour productivity growth since mid-2005. However, a large part of this apparent acceleration must be attributed to the cyclical upswing which the EU economy has enjoyed in this period. These results are corroborated by estimates of trend growth of total factor productivity which do not so far show much sign of a pick-up in this component of labour productivity growth.

Overall, while productivity growth appears to have stopped slowing down, it has not yet turned the corner.

Bai, J. and P. Perron, 'Computation and Analysis of Multiple Structural Change Models', Journal of Applied Econometrics , Vol. 18, 2003, pp. 1-22.

Benati, L., 'Drift and breaks in labor productivity', Journal of Economic Dynamics and Control , Vol. 31, 2007, pp. 2847-2877.

Denis, C., D. Grenouilleau, K. Mc Morrow and W. Roeger, 'Calculating potential growth rates and output gaps - a revised production function approach', European Economy. Economic Papers No 247, Directorate General for Economic and Financial Affairs, European Commission, 2006.

Erber, G. and U. Fritsche, 'Estimating and forecasting aggregate productivity growth trends in the US and Germany', DIW Discussion Papers , No 471, 2005.

Quarterly Report on the Euro Area , Vol. 5, No 4, Directorate General for Economic and Financial Affairs, European Commission, 2006.

Gomez-Salvador, G., A. Musso, M. Stocker and J. Turunen, 'Labour productivity developments in the euro area', European Central Bank Occasional Papers Series , No 53, 2006.

Gordon, R.J., 'Exploding productivity growth: context, causes and implications', Brookings Papers on Economic activity , Vol. 34, No 2, 2003, pp. 1-73.

Gordon, R. J. and I. D. Becker, 'Why Did Europe’s Productivity Catch-up Sputter Out? A Tale of Tigers and Tortoises', Paper Presented at the Federal Reserve Bank/CSIP Conference , San Francisco, November 18, 2005.

Hansen, B.E., 'Testing for Parameters Instability in the Linear Models', Journal of Policy Modelling , Vol. 14, No 4, 1992, pp. 517-533.

Harvey, A. C., Forecasting, Structural Time Series Models and the Kalman Filter , Cambridge University Press, Cambridge, 1989.

Jimeno, J.F., E. Moral and L. Saiz, 'Structural breaks in labour productivity growth: the United States vs. the European Union', Banco de España Working Papers , No 625, 2006.

Nyblom, J., 'Testing for constancy of parameters over time', Journal of the American Statistical Association , Vol. 48, 1989, pp. 223-230.

Okun, A.M., The Political Economy of Prosperity , Washington, D.C.: The Brookings Institution, 1970.

OECD, Compendium of productivity indicators , Organisation of Economic Development and Co-operation: Paris, 2006.

Planas, C. and A. Rossi, 'The GAP program', mimeo , Joint Research Centre of the European Commission: Ispra, 2004.

The quarterly series of value-added at constant prices in a specific sector is taken as the reference series to track and date the business cycle in that sector, against which the cyclical evolution of value-added, employment and labour productivity are assessed. The (asymmetric) Christiano-Fitzgerald filter, applied on the (log)level of the reference series, was used to extract the cyclical component for each reference series and to identify peaks and troughs.

The euro-area private business sector has experienced a total of five recoveries and recessions since 1980 (Table 1.A.1.1). The third quarter of 2003 marks the latest trough and the turning point in economic activity, which means that the euro area had been in recovery for fourteen quarters since. The four previous recoveries began in 1982Q4, 1987Q3, 1993Q2 and 1999Q1, respectively.

The business cycles and turning points for the four largest euro-area Member States and for the four sub-sectors of the private business sector are similar to those of the euro-area private business sector as a whole. However, the degree of synchronisation varies across time and his is especially true for the acceleration of economic activity in the first half of the 1980s and the latest upturn.

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For each expansion phase from trough to peak, the growth rate of value added, employment and labour productivity were then computed on the original level of the series. As these growth rates control for the length of each specific cycle, averaging over business cycles of different lengths one obtains an average growth rate of the variable in question. The performance of value-added, employment and labour productivity during the current expansion phases is then compared with the average performance of these variables in previous upswings.

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(a) Labour productivity growth

The Kalman filter for the extraction of trend labour productivity growth was specified in the following way:

Measurement equation:

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[pic]denotes actual labour productivity, [pic]denotes the unobserved trend and c ycle it are additional (observed) variables measuring the state of the cycle (typically the output gap).

State equation:

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According to the state equation,[pic] follows a random walk ε t and ν t are independent random variables.

In the empirical analysis of this chapter the variable cycle it is chosen to be the output gap, more precisely the gap between actual value added and its trend extracted by a Christiano-Fitzgerald filter. Since productivity is often considered to be a leading variable, cycle it is entering the equation with a number of leads. The number of leads is chosen according to their statistical significance which, as a matter of fact, proves to be zero in almost all cases except for France (n=1) and Belgium (n=3). In the other cases, only the contemporaneous output gap remains at the right-hand side of the equation.

In the measurement equation, actual productivity growth is regressed on the trend component of productivity growth. The change in output gap is added to eliminate the influence of business cycle fluctuations from the trend component. As a result, [pic]represents the trend of the signal. The variance ratio of the disturbance in the measurement and state equation (the so-called "signal-to-noise ratio") was constrained in way so as to produce a series which does not deviate too much from the HP-filtered series.

(b) Total factor productivity growth

The Kalman filter for the extraction of trend TFP growth was specified in a slightly different way (all variables in log-levels):

Measurement equation:

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TFP growth, tfp t , is decomposed into a trend component, [pic] and a cyclical component, [pic]. These two components are specified in the following way:

Trend specification:

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Cycle specification:

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The trend is specified as a random walk process. The cyclical component is specified as a second-order auto-regressive process. cuk t is the capacity utilisation rate which is used to identify the cyclical component of trend productivity growth ( cuk t fulfils a similar role as the output gap variable in the estimation of trend labour productivity growth).

The model can be cast into state-space form and estimated with the Kalman filter. Estimates were carried out in GAP, a programme developed by Planas and Rossi (2004).

The main principle behind the tests implemented in this section is the following:

If [pic]denotes quarter-on-quarter labour productivity growth, cycle it the cyclical component of value added extracted by a Christiano-Fitzgerald filter, c a constant and ut and εt error terms, the model is specified as:

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As in the case of the Kalman-filter estimates, cycle it is chosen to be the contemporaneous output gap and leads thereof with the number of leads chosen according to their statistical significance.

Results of the Nyblom-Hansen stability test

The stability test of Nyblom-Hansen (see Nyblom, 1989; Hansen, 1992) is applied to the second equation: [pic] . The test checks the stability of c and the error variance of εt, separately and jointly.

The parameters are assumed to be stable under the null hypothesis, under the alternative hypothesis there is at least one-time shift in the parameters. The results are presented in the table below. With the exception of France, which is a borderline case, the hypothesis of parameter stability is rejected in all cases.

EA | DE | FR | ES | IT |

C | 0.43* | 0.05 | 0.32* | 1.73§ | 0.53§ |

Var ε | 0.76§ | 0.83§ | 0.11 | 0.20 | 0.74§ |

Joint test | 0.98+ | 0.88§ | 0.43 | 2.28§ | 1.63+ |

§,+,* …… H0 rejected at 1%, 5%, 10% |

Results of Bai-Perron test of multiple structural breaks

The test of Bai-Perron (2003) is also carried out on the second equation: [pic].

The procedure of Bai-Perron consists in investigating all possible models under the assumption of a given number of breakpoints and a given minimum distance between the break points. The optimal model is chosen according to the Bayesian Information Criterion (BIC).

The results of the Bai-Perron tests, assuming 1, 2 and 3 breakpoints and a minimum distance between two breakpoints of three years, are presented hereafter.

Number of breaks: 1

EA | DE | FR | ES | IT |

Break date | 1998:1 | 1987:1 | 1998:2 | 1985:1 | 1997:2 |

BIC | -10.66 | -9.32 | -10.91 | -10.05 | -9.57 |

Number of breaks: 2

EA | DE | FR | ES | IT |

Break dates | 1983:3 1998:1 | 1987:1 1996:4 | 1998:2 2001:4 | 1985:1 1994:4 | 1983:2 1997:2 |

BIC | -10.65 | -9.30 | -10.91 | -10.06 | -9.63 |

Number of breaks: 3

EA | DE | FR | ES | IT |

Break dates | 1983:3 1998:1 2001:4 | 1987:1 1997:4 2000:4 | 1983:4 1998:2 2001:4 | 1985:1 1991:1 1994:4 | 1983:2 1988:1 1997:2 |

BIC | -10.62 | -9.27 | -10.91 | -10.07 | -9.61 |

Using again the BIC to discriminate between the three assumptions on the number of breakpoints, the optimal number of breakpoints is 1 for the euro area and Germany, 2 for Italy and 3 for Spain. For France, the BIC is identical across assumptions, in line with the Nyblom-Hansen test which gives only weak evidence of a structural break.

The graphs below show on the left-hand side actual productivity growth and on the right-hand side trend productivity growth, superimposing on both sides the results of the Bai-Perron test under the assumption of 1 and 3 breakpoints. As can be seen from the graphs on the left-hand side, the quarter-on-quarter growth rate of productivity is a very volatile variable. The power of the tests to discriminate between alternative hypotheses is therefore fairly low.

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- Quarterly national accounts: gross value added (in real terms, constant prices, in 1995 prices and exchange rates) and employment (number of persons employed).

- By sector, using the classification by NACE-6 branches, except for the sector 'financial intermediation', where the aggregate NACE category labelled 'J to K' was used in order to obtain the 'total' and 'private business sector'; however, for the purpose of the detailed sectoral analysis, a more restrictive definition (category J only) applied in order to better reflect the dynamics of the financial sector, thus leaving out category K that contains a variety of sub-activities, which are not necessarily financial services.

- Starting from 1981Q1 (starting date common to all 6 countries).

- Output gap data: a Christiano-Fitzgerald filter was applied to all the series using assumptions of a random walk with drift, and a window of 6 to 32 quarters.

Coverage :

- Germany: data from 1991Q1 are readily available in Newcronos (Eurostat), and series were backcast up to 1981Q1 using growth rates for West Germany.

- France: data from 1980Q1 are available, except for employment in services, where data from 1991Q1 are in Newcronos (Eurostat), and before that, annual data were used and interpolated using the share of each sub-sector in the economy.

- Italy: data from 1981Q1 are readily available in Newcronos (Eurostat).

- Spain, Finland and Belgium: data from 1980Q1 are readily available in Newcronos (Eurostat).

Methodology :

- Aggregation: data on value-added and employment have been added in levels, in order to obtain a "euro-area" aggregate (EA = sum(DE, FR, ES, IT, BE, FI)), bearing in mind that these countries represent 85% of the euro area in terms of real GDP.

- The time series for labour productivity in levels were obtained by dividing the fictitious "euro-area" value-added by the "euro-area" total employment.

- This chapter exploits the detailed sectoral and industry-level information about productivity developments contained in the recently released EU KLEMS databank. An analysis of this data not only confirms the broad macro trends which have been known for some time but also adds interesting details regarding the contributions from particular sectors and industries. It is found that whilst the deterioration in the EU's productivity performance relative to the US was concentrated in the high technology part of the manufacturing sector, it was particularly entrenched in the EU's private services sector. In addition, a small group of industries are highlighted as being responsible for the post-1995 trends, namely electrical and optical equipment; wholesale and retail trade; financial services; and real estate and other business services. With regard to the nature of the reversal in productivity trends, the analysis shows that most of the EU-US differences are not to be found in investment patterns but are mainly driven by developments in TFP, the structural component of productivity.

In terms of understanding the underlying driving factors behind the TFP divergences which emerged, the analysis suggests that TFP growth is increasingly associated, especially over the post-1995 period, with innovation and technological spillovers from countries positioned "at the frontier". These results suggest that there is a growing need for a more intensive use of R&D and highly skilled human capital in frontier economies. With regard to regulatory issues, it is not possible to conclude that they are playing a significant role in explaining international TFP differentials. In particular one must be careful in drawing sweeping, macro-level, conclusions from the available evidence. The analysis suggests that industry-specific regulatory indicators are needed to better understand the effects of a more competition-friendly regulatory environment on TFP trends. In particular, research is required to assess the effects of labour, financial and product market regulations both on the innovation capacity of different industries and on their ability to introduce more advanced technologies and business practices. Regarding those specific industries where EU-US TFP differences are most concentrated, the analysis shows that a relatively wide spectrum of factors are implicated, so that there is a need to adopt a more targeted, industry-level, approach to structural reform efforts in the EU.

Finally, the analysis supports the growing view in the literature that when a country reaches, or comes close to, the technology frontier, it must re-focus its policies and institutions towards a more innovation-based economic model, with less emphasis on the imitation of available leading-edge technologies and practices. The hallmarks of an open, innovation-driven developmental model are world-class educational establishments; higher levels of excellence-driven and better targeted R&D; more market-based financing systems; and more flexible regulatory and institutional frameworks delivering a dynamic and competitive business environment. Whilst many aspects of this approach have been introduced in recent years in individual EU countries, the "mindset" shift needed to make an overall success of the process has unfortunately not yet occurred on a sufficiently large scale at the European level, despite the fact that "Lisbon" provides an effective vehicle for managing this essential transition process.

Despite the recent upturn in growth across the EU, Europe’s overall growth performance since the mid-1990s has been relatively disappointing. While many EU countries managed to improve their labour market positions, this unfortunately was accompanied by a slowdown on the productivity side in a significant number of Member States. This experience was in sharp contrast to many other developed economies around the world, in particular the US. For the US, the secular downward movement in productivity growth rates experienced since the 1970s was spectacularly reversed around the mid-1990s, aided by a strong performance in both the production and diffusion of information and communications technologies (ICT).

These growing divergences in the productivity performance of many developed world economies, and especially the size of the divergences presently being experienced between some of the EU's Member States, has provoked an ongoing debate in the EU regarding the implications of recent trends for future economic prospects:

- The “pessimistic view”, largely supported by the Sapir report ([14])/van Ark analyses ([15]), suggests that the EU might be unable to achieve a shift in its resources to sectors with high productivity growth prospects and will continue with production in areas where it has traditionally held a global advantage, namely medium-technology manufacturing industries. This overall strategy appears increasingly threatened with the emergence of a number of strong competitors around the world in these more traditional industries, most notably China and India.

- The “more optimistic view”, as enunciated by Blanchard amongst others ([16]), is that part of the explanation for Europe’s poor productivity performance could be measurement problems/adjustment lags, with perhaps the basis for a future pick-up already firmly established due to the labour, capital and product market reforms which have been progressively introduced since the early 1990s. Under this view the EU may now simply be in a transition phase whereby some of the negative effects of those reforms (e.g. a temporary decrease in productivity due to labour market changes) are visible, whilst the gains to be reaped in the future are not.

Most observers, "optimists" and "pessimists" alike, would agree that restrictions concerning labour and product markets, lack of openness to trade and/or foreign direct investment, as well as barriers in terms of access to/generation of new technologies and the diffusion of existing innovations are the key determinants of EU productivity growth. International comparisons reveal sizeable disparities in investments with regard to physical capital (especially in terms of ICT capital spending), human capital and in R&D spending. The present study will examine those countries and industries where the differences are most acute and assess the extent to which these differences can be linked with overall growth divergences.

Whilst such an analysis at the macro level has been possible for some time, a detailed cross-country examination at the industry level has been more problematic due to the fact that long runs of official industry level data were only available for a relatively small number of countries, industries and variables. This situation has significantly improved with the March 2007 release of the EU KLEMS datasets. The provision in EU KLEMS of detailed industry level datasets on economic growth, productivity, employment creation, capital formation and technological change for a large range of manufacturing and service industries is particularly noteworthy. A degree of caution is warranted however since the overall quality of the datasets has yet to be thoroughly evaluated by the national statistical institutes and Eurostat. In addition, according to EU KLEMS, the EU-US productivity differences are heavily concentrated in the market services sector where the conceptual and empirical problems in accurately measuring output and price developments have been well documented.

Sections 2 and 3 present the broad stylised facts concerning growth and productivity trends at the economy-wide and industry levels for the US and the EU. Both analyses conclude that cross-country differences in labour productivity growth rates predominantly reflect differences in TFP performances, although ICT investment patterns also played a role in a number of specific industries, especially over the second half of the 1990s. Since TFP is normally regarded as constituting the structural component of labour productivity, Section 4 of the paper goes on to examine in more detail the possible sources of the industry level divergences in TFP performance. Using EU KLEMS and a wide range of pertinent datasets for the explanatory variables, panel regressions are exploited to assess the degree of statistical support which exists for the major hypotheses explaining TFP divergences over time ([17]) i.e. the role played by the regulatory environment (product, labour and financial markets) ([18]); by the degree of openness of economies ([19]); by demographics ([20]); and finally by the efficiency of knowledge production (R&D and education) ([21]). The summary and concluding remarks section provides an overview of the main points from the paper and draws some tentative conclusions on the implications of the analysis for the direction of economic policy in the EU over the coming years.

At the moment, EU living standards (as measured by potential GDP per capita ([22])) are at roughly 77% of US levels, with about 1/3 of the gap due to labour productivity differences and with the remaining 2/3 due to differences in the utilisation of labour (i.e. differences in hours worked per worker and the employment rate). In terms of the key drivers of per capita income developments, the EU has also experienced some important changes over the course of the 1990s with, on the positive side, the previously downward movement in the EU’s labour input relative to the US coming to an end and, on the negative side, the post World War II convergence to US productivity levels going into reverse (graphs 2.2.1-2.2.2). In fact, after having peaked in the mid-1990s at around 98% of US levels, EU labour productivity per hour is estimated to have backtracked to around 93% in 2006, which is close to its relative level in the late 1980s. As shown in graph 2.2.2, this deterioration is more strongly related to developments in terms of TFP rather than to changes in capital intensity.

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The post 1995 deterioration in relative EU productivity levels reflects a sharp decline in EU productivity growth rates compared to those of the US over the period in question. This is shown clearly in Graph 2.2.3 which examines labour productivity per hour growth rates since the mid-1960s. Over most of that time, and indeed for most of the post World War II period, the EU has enjoyed productivity growth rates well in excess of those prevailing in the US. Given relatively low employment rates, the EU was able to use its superior productivity performance to broadly maintain its relative living standards.

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Graphs 2.2.4-2.2.5 show that the relative deterioration in the EU's labour productivity per hour growth performance since the first half of the 1990s is structural in nature, with 2/3 of the change emanating from deterioration in total factor productivity i.e. a relative decline in the overall efficiency of the EU's production processes.

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Are the productivity differences between the US and Europe signalled earlier confined to the manufacturing, private services or rest of the economy sectors or linked to particularly dynamic industries within these broad sectors in the US? Due to the lack of capital stock data for some of the EU15 countries analysed in section 2, a detailed growth accounting analysis at the industry level is only possible for 10 of the "old" EU15 countries, with these ten countries grouped together to form an EU15ex5 aggregate ([23]). Fortunately, the EU15ex5 (henceforth EU) grouping includes all of the larger EU15 Member States, hence the period average growth rates for the basic industry series are almost identical for both EU aggregates.

3.1. Basic sectoral productivity trends

Before presenting the detailed growth accounting results, a few comments on the broad industry level patterns is warranted. Given the amount of industry level detail which is available in EU KLEMS, for the purposes of the present exercise, we aggregated the 60-70 industries into the three broad sectors of manufacturing, private services and rest of the economy (i.e. primary industries plus public services). Table 2.3.1 shows that the period average trends for the EU's "total industries" aggregate mirrors the downward movement in EU labour productivity which was shown for the total economy in section 2. According to EU KLEMS, EU labour productivity per hour growth rates fell from over 2% over the 1981-95 period to 1.5% (1996-2000) and then to 1% (2001-04). These trends are in marked contrast to those experienced in the US which witnessed a sharp acceleration in its productivity performance over the same time periods, with annual average growth rates doubling from 1.3% over the period 1981-95 to 2.6% for 2001-04. The apparent outperformance of the US relative to the EU has been widespread at the industry level, with both the manufacturing and private services sectors as a whole both highlighting the contrasting fortunes of both areas ([24]).

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In order to bring out the essential longer term patterns more clearly with regard to labour productivity per hour developments, graphs 2.3.1-2.3.4 look at trends for this specific variable for the EU and the US for the total period 1981-2004. The graphs show the contributions to the total economy change in labour productivity (i.e. the combined effect of labour productivity growth and the output shares of the respective sectors) from manufacturing, private services and "other" industries. The trends have been calculated using a Hodrick-Prescott (HP) filter which is effectively equivalent to applying a centred moving average ([25]). The set of graphs have all the same scale and are additive (i.e. manufacturing + services + rest of economy = total economy). The graphs show clearly that there is a trend EU-US productivity gap of the order of 0.8 percentage points for the most recent years, with roughly 25% of the gap emanating from manufacturing and 75% from private services, with the detailed growth accounting exercise described in 3.2 concentrating on these two broad sectors.

EU + US – Trend Contributions to the Total Change in Labour Productivity per Hour – Breakdown into Manufacturing, Private Services and Rest Economy.

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3.2. Detailed growth accounting analysis of the manufacturing and private services sectors

Decomposing real GDP growth into its main determinants can be done using a wide variety of methods, one variant of which is applied by the EU KLEMS research consortium (see Box 2.3.1 for details). This variant essentially uses a production function which includes productive capital (a volume index of capital services); human capital (a skills based indicator of the average qualifications of the labour force); employment levels adjusted for hours worked; and a residual term which, amongst other things, includes an estimate of the level of efficiency associated with the use of the various factors of production.

Table 2.3.2 gives the results for the EU and the US using the EU KLEMS growth accounting approach, with value added being decomposed into labour services, capital services and TFP. The table shows that the big labour productivity gap between the EU and the US over the period since 1995 has been mainly driven by TFP developments although differences in the value added contribution of ICT capital services was a significant additional explanatory factor over the period 1996-2000. Over the most recent period, 2001-2004, it is clearly TFP which is driving the EU-US productivity differences. At the level of total industries, the TFP growth rate differential since 2000 is an alarming 1.7 percentage points, compared with a TFP gap of only 0.5 a percentage point over the earlier period 1996-2000. This gap in TFP growth rates is widespread at the sectoral level, with very large EU-US TFP growth rate differentials for both the manufacturing and private services sectors.

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Graphs 2.3.5-2.3.12 examine in more detail the capital services and TFP trends over time in the manufacturing and private services sectors. This graph is constructed on the same basis as graphs 2.3.1-2.3.4 and show the HP-filtered contributions of capital services and TFP to the total change in "total industries" GDP from these two sectors (i.e. the combined effect of the growth rate of each of these different components of value added growth and their respective output shares). The graphs for the manufacturing sector (graphs 2.3.5-2.3.8) confirm that TFP trends are driving the EU-US productivity gap in this sector and that this EU-US TFP gap is rising over time. Graphs 2.3.5-2.3.8 also show that capital services trends in the manufacturing sector in both areas appear to be broadly converging over time, with this pattern a feature of both ICT and non-ICT capital deepening.

With regard to the private services sector (graphs 2.3.9-2.3.12), again the EU-US productivity gap is being driven by TFP trends rather than by capital services although it is interesting to note for this sector that there is a clear compositional shift in the US towards greater levels of ICT capital deepening. As graphs 2.3.9-2.3.12 indicate, while there is virtually no gap between both areas with regard to total capital services, there is evidence that the US's capital spending is increasingly being focussed on ICT rather than on the more traditional forms of capital expenditure. Furthermore, one should note, as shown in graph 2.3.13, that the bulk of the overall economy wide differences in ICT capital spending between the EU and the US since the mid-1990s is due to higher ICT investment spending in the private services sector. This is a feature which will be looked at in more detail in the regression analysis in section 4.

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EU + US – Growth Accounting Analysis – Graphs of Trend Contribution to the Total Change in GDP from Capital Services (ICT/Non-ICT) and TFP:

Manufacturing sector (annual % change)

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Private services sector (annual % change)

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3.3. Industry level TFP trends

Due to the availability of capital stock data for a large number of individual industries in EU KLEMS, it is possible to do a more detailed productivity breakdown of the manufacturing, private services and "rest of economy" sectors (described in 3.2) by examining those industries in the databank (28 in total – 14 manufacturing, 7 private services and 7 "other industries") where capital stock data exists and which are therefore amenable to growth accounting analysis. Graphs 2.3.14-2.3.15 focus on showing the differences in contributions to "total industries" TFP growth for these 28 industries in order to isolate the small number of industries which are the key drivers of the overall EU-US TFP growth gap. The relative performance of both areas is established by taking the US TFP contributions and subtracting the equivalent figures for the EU for the various industries and sectors. The graphs show the sectoral (i.e manufacturing, private services and "rest of economy") TFP differences as well as those for the 28 individual industries, with each of the latter in turn attributed to their respective sectors (i.e. M = manufacturing; P.S. = private services and O = "other industries").

With regard to overall EU-US differences in TFP growth rates, graph 2.3.14 confirms the points made earlier that the large "total industries" EU-US TFP gap over the period 1996-2004 reflects similar gaps in favour of the US in both the manufacturing and private services sectors. Graph 2.3.15 then goes on to decompose the "total industries" TFP gap over this period into the respective contributions from the 28 industries. The graph shows the highly industry specific nature of the TFP differences, with only a small handful of industries explaining the diverging EU-US trends, namely wholesale and retail trade; real estate and other business services; electrical and optical equipment (which includes semiconductors, the main ICT-producing industry); and to a lesser extent financial services. On a more positive note, graph 2.3.15 also shows some industries where the EU has done better, with a number of the so-called "network" industries doing particularly well.

Given the highly industry specific pattern of EU and US developments, it is evident that the regression analysis in the next section will need to look at this small group of industries in more detail in order to try to decipher the set of factors which have contributed to the superior performance of the US and the EU in such a diverse grouping. While the TFP patterns in "electrical and optical equipment" (the only one of the industries located in the manufacturing sector) are relatively easy to explain given that this industry has benefited enormously from the TFP enhancing effect of "Moore's Law" ([26]), the TFP determinants of the remaining industries would appear to be a more difficult challenge for the regression analysis to explain. One particularly interesting hypothesis to be tested is the extent to which ICT capital deepening trends in some of the US market services industries played a role in the divergences which have emerged. More specifically, is there any evidence to support the view that TFP spillover effects may be starting to manifest themselves due to the growing pervasiveness of, general purpose, ICT technologies?

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The previous sections have focused on a description of macro trends and sectoral/industry level patterns in productivity growth. The analysis contained in this section aims at isolating those factors which are critical in explaining differences in the evolution of total factor productivity (TFP), which, as shown earlier, accounts for the most important share of the gap in productivity growth between the EU and the US over the last decade. In addition, as we have seen in section 3, EU-US TFP growth differences over this period can be traced to a small number of industries in the manufacturing and private services sectors. As a result of this highly concentrated TFP pattern, there will be a consistent effort in the present section to focus the analysis on deriving the specific determinants for this small group of industries. This approach should help in identifying those policies which could potentially have the strongest impact in those areas of the economy where the TFP gap is largest.

4.1. Conceptual framework

A better understanding of the key determinants of TFP growth has been high on the research agenda of international organisations and the academic community over the past decade ([27]). For a long period of time growth theory was not endowed with an appropriate paradigm to explain the determinants of TFP growth. For example, in the neoclassical growth framework, TFP corresponds to a residual (the "Solow residual") and consequently these models are largely silent on the role which could be played by specific policies in affecting the growth rate. In addition, the early wave of endogenous growth models (the so-called "AK models") put the emphasis on capital accumulation as being the key driver of growth, with the implication being that growth-friendly policies should be focussed on promoting savings and investment. The limitations of such models have become increasingly evident, with their predictions failing to provide an adequate interpretation of the developments in TFP growth which have taken place over recent decades, especially in the post-1995 period. For example, investment rates, as shown in levels of capital intensity, were higher in Europe over this latter period and yet the US managed to substantially leapfrog the EU in terms of TFP growth rates.

Due to the limitations of existing paradigms, there is a growing consensus in the literature that recent growth theories, based on "Schumpeterian" creative destruction mechanisms, seem better equipped to interpret recent developments (see, for example, Aghion and Howitt (2005)) ([28]). This theory focuses on innovation as the key driver of growth in economies at, or close to, the "technology frontier" ([29]). Innovators, by introducing superior product varieties and technologies, have the effect of both displacing existing firms and of inducing the adoption of new products and techniques at the wider industry level. At the aggregate level, the innovation rate depends on the resources devoted to the innovation effort (i.e. R&D and human capital) and on the stock of existing knowledge (knowledge spillovers). The growth rate of the economy will depend not only on the rate of innovation but also on the rate at which "state-of-the-art" technologies are adopted/diffused throughout the wider economy. Countries that are close to the technology frontier will mainly grow thanks to the introduction of new technologies, whilst the "follower" grouping of countries will derive the largest share of their TFP growth from the adoption of better, but already existing, technologies which are available "at the frontier".

In this "Schumpeterian" world, institutions and policies play a key role in determining the coherence of the relative position of countries in the global innovation race. These framework conditions directly impact on the relative ability of countries to innovate at the frontier or to adopt existing, leading-edge, technologies. While follower countries would gain from institutions and policies favouring the cost efficient adoption of existing technologies, countries operating at the frontier would, on the other hand, profit from policies that promote excellence in higher education and R&D; financial markets that reward risky projects; and regulations that do not put an excessively heavy burden on either incumbent firms nor on potential entrants.

4.2. Existing empirical work

A number of papers in the literature have already analysed the determinants of TFP in a Schumpeterian framework. Most of the existing analyses use panel data information, pooling together data on TFP levels and growth rates over several years and countries. Some papers also use information at the sectoral/industry levels, with the datasets usually obtained from the OECD's STAN database ([30]). The available empirical specifications normally reflect a reduced form of the basic innovation-imitation model, with most of them regressing TFP growth on two essential variables:

- firstly, a measure of the technology gap (i.e., the distance between the TFP of the country analysed and that of the country with the highest level of efficiency);

- secondly, an estimate of the growth rate of TFP at the frontier (i.e. the TFP growth rate of the most efficient country).

The first variable captures the extent to which TFP growth in a specific country can be explained by the adoption of more efficient existing technologies. The assumption here is simply that the larger the technology gap, the higher the potential gains from ad