Growth and productivity accounts from EU KLEMS: an overview.

Timmer, Marcel P. ; O'Mahony, Mary ; van Ark, Bart 等

This paper gives an overview of the construction of and preliminary results from the EU KLEMS database which contains industry estimates of output, input and productivity growth for EU countries. The paper begins with a discussion of methodology and data sources covering output and intermediates, capital and labour services. The content and scope of the database is then briefly described. This is followed by a discussion of preliminary results focusing on comparisons between the EU and US. These confirm the relatively poor productivity performance of the EU relative to the US since the mid-1990s, mostly driven by low productivity growth in market services.

Keywords: Productivity; growth accounting; national accounts JEL Classifications: D24; E01; J24

1. Introduction (1)

The EU KLEMS Growth and Productivity Accounts described in this paper include measures of output growth, employment and skill creation, capital formation and multi-factor productivity (MFP) at the industry level for European Union member states from 1970 onwards. The input measures include various categories of capital (K), labour (L), energy (E), material (M) and service inputs (S). Thus the data are ideally suited to the study of the relationship between skill formation, investment, technological progress and innovation on the one hand, and productivity, on the other. A major advantage of growth accounts is that they are embedded in a clear analytical framework rooted in production functions and the theory of economic growth. They provide a conceptual framework within which the interaction between variables can be analysed, which is of fundamental importance for policy evaluation. The measures are developed for individual European Union member states, and are linked with 'sister'-KLEMS databases in the US and Japan.

The purpose of constructing the database is to support empirical and theoretical research in the area of economic growth and productivity and to inform policy which requires comprehensive measurement tools to monitor and evaluate progress. The construction of the database should also support the systematic production of high quality statistics on growth and productivity using the methodologies of national accounts and input-output analysis.

The layout of the paper is as follows. First we provide an overview of the growth accounting methodology underlying the analysis (section 2). This is followed by a discussion of the data sources and measurement methods (section 3). Section 4 presents an overview of the key characteristics of the database and the variables, country and industry coverage--essentially a user's guide. In section 5 we present an analysis of some of the major trends observed from the March 2007 release of the database. This brief overview paper will in due time be followed by more extensive reviews and research papers, which will also be available from the EU KLEMS website (http:// www.euklems.net). This site also contains more information on the methodology used in EU KLEMS--the document EU KLEMS Growth and Productivity Accounts, Version 1.0, PART I Methodology--with detailed source descriptions given in the document PART II Sources.

2. Growth accounting methodology

2.1 General framework

In this section we summarise the methodology used to develop our measures of industry-level total factor productivity growth. We begin with the industry-level production function and show how this allows us to quantify the sources of output growth. In general, we follow the growth accounting methodology as developed by Dale Jorgenson and associates as outlined in Jorgenson, Gollop and Fraumeni (1987) and more recently in Jorgenson, Ho and Stiroh (2005). We follow their notation as closely as possible. The method is based on production possibility frontiers where industry gross output is a function of capital, labour, intermediate inputs and technology, which is indexed by time, t. Each industry, indexed by j, can produce a set of products indexed by i indicated by the production possibility set g. Each industry has its own production function and purchases a number of distinct intermediate inputs indexed by i, capital service inputs indexed by k, and labour inputs indexed by l. The production functions are assumed to be separable in these inputs, so that:

[Y.sub.j] = [g.sub.j]([Y.sub.ij]) = [f.sub.j]([K.sub.j], [L.sub.j], [X.sub.j], T) (1)

where Y is output, K is an index of capital service flow, L is an index of labour service flows and X is an index of intermediate inputs. Under the assumptions of constant returns to scale and competitive markets, the value of output is equal to the value of all inputs:

[P.sup.Y.sub.j][Y.sub.j] = [P.sup.K.sub.j][K.sub.j] + [P.sup.L.sub.j][L.sub.j] + [P.sup.X.sub.j][X.sub.j] (2)

where [P.sup.Y.sub.j] denotes the price of output, [P.sup.X.sub.j] denotes the price of intermediate inputs, [P.sup.K.sub.j] denotes the price of capital services and [P.sup.L.sub.j] denotes the price of labour services. This expression is evaluated from the producer's point of view and thus excludes all taxes from the value of output, but includes producer subsidies. This is the basic price concept in the System of National Accounts 1993. The inputs are valued at purchasers' prices and reflect the marginal cost paid by the user. Therefore they should include taxes on commodities paid by the user (non-deductible VAT included) and exclude the subsidies on commodities. Margins on trade and transport should be included as well. The measurement of capital services and labour services is discussed in more detail below. It is important to note at this stage that the price of capital services is defined as a residual such that equation (2) holds.

Under the standard assumption of profit maximising behaviour and competitive markets, such that factors are paid their marginal product, and constant returns to scale, and assuming a Translog production function, we can define MFP growth ([DELTA]ln [A.sup.Y.sub.j]) using the Tornqvist index as follows:

[DELTA]ln[A.sup.Y.sub.j] = [DELTA]ln[Y.sub.jt] - [[bar.v].sup.X.sub.jt][DELTA]ln[X.sub.jt] - [[bar.v].sup.K.sub.jt][DELTA]ln[K.sub.jt] - [[bar.v].sup.L.sub.jt][DELTA]ln[L.sub.jt] (3)

Growth of MFP is derived as the real growth of output minus a weighted growth of inputs where [DELTA]x = [x.sub.t] - [x.sub.t-1] denotes the change between year t-1 and t, and [[bar.v].sub.jt] with a bar denotes period averages and [bar.v] is the two-period average share of the input in the nominal value of output. The value share of each input is defined as follows:

[v.sup.X.sub.jt] = [P.sup.X.sub.jt][X.sub.jt] / [P.sup.Y.sub.jt][Y.sub.jt]; [v.sup.L.sub.jt] = [P.sup.L.sub.jt][L.sub.jt] / [P.sup.Y.sub.jt][Y.sub.jt]; [v.sup.K.sub.jt] = [P.sup.K.sub.jt][K.sub.jt] / [P.sup.Y.sub.jt][Y.sub.jt] (4)

MFP indicates the efficiency with which inputs are being used in the production process and is an important indicator of technological change. (2) The assumption of constant returns to scale implies [v.sup.X.sub.jt] + [v.sup.L.sub.jt] + [v.sup.K.sub.jt] = 1 and allows the observed input shares to be used in the estimation of MFP growth in equation (3). This assumption is common in the growth accounting literature (see, for example, Schreyer, 2001). Alternatively, one can perform growth accounting without the imposition of constant returns to scale and use cost shares, rather than revenue shares, to weight input growth rates (Basu, Fernald, and Shapiro, 2001).

Rearranging (3) yields the standard growth accounting decomposition of output growth into the contribution of each input and MFP:

[DELTA]ln[Y.sub.jt] = [[bar.v].sup.X.sub.jt][DELTA]ln[X.sub.jt] + [[bar.v].sup.K.sub.jt][DELTA]ln[K.sub.jt] + [[bar.v].sup.L.sub.jt][DELTA]ln[L.sub.jt] + [DELTA]ln[A.sup.Y.sub.jt] (5)

where the contribution of each input is defined as the product of the input's growth rate and its two-period average revenue share. This decomposition is the basis of the sources of growth results in the EU KLEMS database.

In order to decompose growth at higher levels of aggregation, we also define a more restrictive industry value-added function, which gives the quantity of value-added as a function of only capital, labour and time as:

[V.sub.j] = [g.sub.j]([K.sub.j], [L.sub.j], T) (6)

where [V.sub.j] is the quantity of industry value-added. Value-added consists of capital and labour inputs, and the nominal value is:

[P.sup.V.sub.j][V.sub.j] = [P.sup.K.sub.j][K.sub.j] + [P.sup.L.sub.j][L.sub.j] (7)

where [P.sup.V] is the price of value-added. Under the same assumptions as above, industry value-added growth can be decomposed into the contribution of capital, labour and MFP ([A.sup.V]).

[DELTA]ln[V.sub.jt] = [[bar.w].sup.K.sub.jt][DELTA]ln[K.sub.jt] + [[bar.w].sup.L.sub.jt][DELTA]ln[L.sub.jt] + [DELTA]ln[A.sup.V.sub.jt] (8)

where [bar.w] is the two-period average share of the input in nominal value-added. The value share of each input is defined as follows:

[w.sup.L.sub.jt] = [([P.sup.V.sub.jt][V.sub.jt]).sup.-1][P.sup.L.sub.jt][L.sub.jt]; [w.sup.K.sub.jt] = [([P.sup.V.sub.jt][V.sub.jt]).sup.-1][P.sup.K.sub.jt][K.sub.jt] (9)

In order to define the quantity of value-added, we assume that the production function is separable in intermediate input and value-added. These value-added based measures are used in the summary of the EU KLEMS results presented in section 4 below.

Each element on the right-hand side of equation (5) indicates the proportion of output growth accounted for by growth in intermediate inputs, capital services, labour services and MFP, respectively. Accurate measures of labour and capital input are based on a breakdown of aggregate hours worked and aggregate capital stock into various components. Hours worked are cross-classified by various categories to account for differences in the productivity of various labour types, such as high-versus low-skilled labour. Similarly, capital stock measures are broken down into stocks of different asset types. Short-lived assets like computers have a much higher productivity than long-lived assets like buildings, and this should be reflected in the capital input measures. The contribution of intermediate inputs is broken down into the contribution of energy goods, intermediate materials and services.

2.2 Measurement of capital services

The availability of investment series by asset type and by industry is one of the unique characteristics of the EU KLEMS database. They are based on series obtained from national statistical institutes, allowing for a detailed industry-by-asset analysis. Importantly, we make a distinction between three ICT assets (office and computing equipment, communication equipment and software) and four non-ICT assets (transport equipment, other machinery and equipment, residential buildings and non-residential structures). ICT assets are deflated using a quality-adjusted investment deflator, except for those countries which have not yet implemented adequate quality adjustment, where we used the harmonisation procedure suggested by Schreyer (2002). The real investment series is used to derive capital stocks through the accumulation of investment into stock estimates using the Perpetual Inventory Method (PIM) and the application of geometric depreciation rates. Then capital service flows are derived by weighting the growth of stocks by the share of each asset's compensation in total capital compensation as follows:

[DELTA]ln[K.sub.t] = [summation over K][[bar.v].sub.k,t][DELTA]ln[S.sub.k,t] (10)

where [DELTA]ln[S.sub.k,t] indicates the growth of the stock of asset k and weights are given by the average shares of each asset in the value of total capital compensation. In this way, aggregation takes into account the widely different marginal products from the heterogeneous stock of assets. The weights are related to the user cost of each asset.

Rental prices, or user cost of capital, can be estimated using the standard approach grounded in the arbitrage equation derived from neo-classical theory of investment, introduced by Jorgenson (1963) and Jorgenson and Griliches (1967). In equilibrium, an investor is indifferent between two alternatives: buying a unit of capital at investment price [p.sup.I.sub.kt], collecting a rental fee and then selling the depreciated asset for (1 - [[delta].sub.k])[p.sup.I.sub.k,t+1] in the next period, or earning a nominal rate of return, [i.sub.t], on a different investment opportunity. In the absence of taxation the equilibrium condition can be rearranged, yielding the familiar cost-of-capital equation:

[p.sup.K.sub.k,t] = [p.sup.I.sub.k,t-1][i.sub.t] + [[delta].sub.k][p.sup.I.sub.k,t] - [[p.sup.I.sub.k,t] - [p.sup.I.sub.k,t-1] (11)

This formula shows that the rental fee is determined by the nominal rate of return, the rate of economic depreciation and the asset specific capital gains. The asset revaluation term can be derived from the investment price indices. The rate of depreciation is identical to the rate used in the construction of the capital stocks. The nominal rate of return can be estimated in two different ways. (3) The first is to use the opportunity, or ex ante, approach, which is based on some exogenous value for the rate of return, for example interest rates on government bonds. The second approach is the residual, or ex post approach, which estimates the internal rate of return as a residual given the value of capital compensation from the national accounts, depreciation and the capital gains. The attractive property of the latter approach is that it ensures complete consistency between income and production accounts. Hence an ex post approach is employed in this database.

The user cost approach is crucial for the analysis of the contribution of capital to output growth. This approach is based on the assumption that marginal costs reflect marginal productivity. For example, if the cost of leasing one euro of computer assets is higher than leasing one euro of buildings, computers have a higher marginal productivity, and this should taken into account. There are various reasons why the cost of computers is higher than for buildings. While computers may typically be scrapped after five or six years, buildings may provide services for several decades. Besides, prices of new computers are rapidly declining, whereas those of buildings are normally not. Hence the user cost of IT-machinery is typically 50 to 60 per cent of the investment price, while that of buildings is less than 10 per cent. Therefore one euro of computer capital stock should get a heavier weight in the growth of capital services than one euro of building stock. This is ensured by using the rental prices of capital services as weights.

2.3 Measurement of labour services

The productivity of various types of labour input, such as low- versus high-skilled workers, will also differ. Standard measures of labour input, such as numbers employed or hours worked, will not account for such differences. Hence one needs measures of labour input which take the heterogeneity of the labour force into account in analysing productivity and the contribution of labour to output growth. These measures are called labour services, as they allow for differences in the amount of services delivered per unit of labour in the growth accounting approach. It is assumed that the flow of labour services for each labour type is proportional to hours worked, and workers are paid their marginal products. Then the corresponding index of labour services input L is given by:

[DELTA]ln[L.sub.t] = [summation over l][[bar.v].sub.l,t][DELTA]ln[H.sub.l,t] (12)

where [DELTA]ln[H.sub.l,t] indicates the growth of hours worked by labour type l and weights are given by the average shares of each type in the value of labour compensation. In this way, aggregation takes into account the changing composition of the labour force. We cross-classify labour input by educational attainment, gender and age with the aim to proxy for differences in work experience, which provides eighteen labour categories (3x2x3 types). Typically, a shift in the share of hours worked by low-skilled workers to high-skilled workers will lead to a growth of labour services which is larger than the growth in total hours worked. We refer to this difference as the labour composition effect.

3. Data sources

This section considers the data sources and methods employed to go from the theoretical constructs in the previous section to practical implementation. It is a basic overview of the relevant issues; further details are contained in the two underlying documents on the website mentioned in the introduction.

3.1 Output and intermediate input accounts

A crucial element in KLEMS growth accounts is the consistency of inputs and outputs within and across industries. Therefore, the main building block of a KLEMS account is a series of input-output tables in which inter-industry flows are recorded in a consistent way. Until recently, the main bottleneck in productivity research for most countries was the lack of long-run series of these tables. But since the introduction and application of the European System of Accounts 1995 (ESA), this situation is changing rapidly for many European countries. Supply and use tables (SUTs) are a particular type of input-output table. They trace the supply and use of all commodities in the economy, as well as the payments for primary factors, labour and capital. The Supply table indicates for each industry the composition of output by product. This is used to derive industry gross output indices. The Use table indicates for each industry the product composition of its intermediate inputs and value-added components. This is used to derive the intermediate input and value-added series in the national accounts. Therefore, long series of SUTs seem to be a natural starting point for KLEMS accounts for European countries.

Since 1995, many countries have started to implement the SUT approach. However, the speed of implementation differs across countries and there is variation in the timeframe under which countries carry back the revisions. To solve this data availability, EU KLEMS takes a two-step procedure. First, we start from the most recent and revised series by industry on gross output (GO), total intermediate input (II) and value-added (VA) from the National Accounts. These series are extended back in time and, if needed, broken down into more industry detail using additional information on growth from business censuses and inquiries. (4) In a second step we use available Use tables to decompose total intermediate inputs into energy (IIE), materials (IIM) and services (IIS). In this way we retain a maximum of information as older vintage Use tables can also be used to provide a breakdown of intermediate inputs. Clearly, this procedure is a second-best solution and awaits further revisions of the SUTs by NSIs.

Even though we would prefer to obtain value-added series directly from the Supply and Use tables by separately deflating gross output and intermediate inputs, we report industry-level value-added volume indices based on the national accounts methodology of each individual country. In some cases the methodology to obtain industry-level value-added differs across countries, but differences are small. The motivation for this strategy is that value-added volume series for many countries are often longer and have more industry detail than the gross output and intermediate inputs series. Failure to use these would have resulted in an unacceptable loss of data. Especially in the past, value-added volumes in the national accounts were not always derived using the double deflation method. This is particularly true for several services industries and data derived from earlier vintages of the National Accounts before the ESA 1995 revisions.

To derive the factor input weights in the growth accounts the following nominal value-added components are needed: compensation of employees (COMP), gross operating surplus (GOS) and net taxes on production (TXSP). Energy, materials and services inputs are calculated by applying shares of E, M and S from the Use-tables to total intermediate inputs from the national account series. While for many countries (nominal) SUTs are available from 1995, few countries have long-term series extending back to 1980 or earlier. Therefore, sometimes use has been made of input-output tables, rather than SUTs, to derive measures of E, M and S. Energy inputs are defined as all energy mining products (NACE 10-12), oil refining products (NACE 23) and electricity and gas products (NACE 40). All services (products from industries NACE 50-99) are included in S. The remaining products are classified as materials. One has to keep in mind that the underlying Use tables are valued at purchasers' prices and hence all margins are included in the value of the products and have not been reallocated to the trade and transportation sectors (except for the US). This will only affect the relative contributions of E, M and S to gross output growth, but not the other growth accounting variables. Finally, for all aggregation (over products or industries) we use the T6rnqvist quantity index, which is a discrete time approximation to a Divisia index. This aggregation approach uses annual moving weights based on averages of adjacent points in time.

3.2 Capital accounts

In this section we discuss three major implementation issues in the measurement of capital service inputs: the asset types which are distinguished, the rate of depreciation used and investment deflators for computing equipment (IT). Ideally we would like to divide capital inputs into a large number of distinct asset types, as is available, for example, in the National Income and Product Accounts produced by the US Bureau of Economic Analysis (BEA). While some European countries have detailed capital formation matrices, most provide only a limited amount of asset detail. Therefore, a minimum level of asset type detail was defined to which all country databases more or less adhere. This minimum list includes nine asset types of which three assets are ICT assets: Computing equipment, Communications equipment and Software, and the remainder are residential and non-residential structures, transport equipment, other non-ICT equipment, other products and other intangibles. Where feasible, investment flows in current and constant prices are derived from National Accounts. As with output measures, additional sources are used to achieve finer industry splits.

In this database we use a harmonised approach to capital measurement and use one set of asset depreciation rates for all countries. These depreciation rates differ by asset type and industry, but not across countries and also not over time. They are based on the industry by asset type depreciation rates from the BEA as described in Fraumeni (1997). The advantage of using the BEA rates is that these are based on empirical research (albeit for many assets rather outdated), rather than ad hoc assumptions based on, for example, tax laws. (5) The BEA rates have much more asset detail than the investment series for most European countries. Therefore, we needed to aggregate the rates over BEA assets to arrive at a set of rates for the EU KLEMS assets. To achieve this we calculated an implicit aggregate geometric depreciation rate for each year based on capital stocks for each separate asset type available from the BEA data. The rates for other machinery, other tangibles, transport equipment and non-residential buildings differ by industry. The rates for the other asset types are the same for all industries. The rate for residential structures is set to 0.0114, the rate for 1-to-4-unit homes from the BEA. The three ICT assets, computers, software and communications equipment, were also assumed to have the same depreciation rate for all industries. These were set equal to the rates employed in Jorgenson, Ho and Stiroh (2005), i.e. 0.315 for computers and software and 0.115 for communications equipment.

A key assumption in the capital services approach outlined above is the measurement of investment in constant-quality efficiency units. Only under this assumption can different vintages of each asset be treated as perfect substitutes in production. This requires constant-quality price indices for each asset type, in particular those which are subject to rapid technological change and improvements in quality, such as IT assets. Generally there is support for the adoption of hedonic, or high-frequency matched model, deflators for ICT output and investment. However, there is still some discussion as to how these should be calculated (Triplett, 2004). The BEA was one of the first to adopt hedonics for computers, and recently more NSIs have adopted this approach. Others base their national deflators on the US hedonics, adjusting for international price or exchange rate movements. Only a few NSIs still use IT deflators which are clearly not adjusted for quality. We follow previous comparative studies such as Colecchia and Schreyer (2001), Timmer and van Ark (2005) and Inklaar, O'Mahony and Timmer (2005) and use the harmonisation procedure introduced by Schreyer (2002) for those countries for which IT deflators are clearly not adjusted for quality. (6)

3.3 Labour accounts

The aim of the labour accounts is to estimate total labour input so that it reflects the actual changes in the amount and quality of labour input over time. In this method the labour force is subdivided into types based on various characteristics, in this case age, gender and educational attainment. For all countries we have used National Accounts data as the starting point for constructing series for employment and hours. However, the national accounts themselves do not provide enough information to disaggregate the data into a large number of detailed industries and, in some cases, do not separate employees and self employed. Depending on the source, employment can be measured as persons or jobs, or some measure constructed from these concepts, like 'full-time equivalent'. A person can hold several jobs which are not necessarily in the same industry, so the two measures are not equal. National Accounts employment is often reported in persons. When employment is reported as persons, ideally hours worked in second jobs would be somehow allocated to the industries the jobs are actually in rather than, for example, to industries of the primary jobs of the jobholders. In the construction of National Accounts an attempt has often been made to reallocate hours worked in such a way.

One of the main problems with estimating hours worked is that definition of hours varies across data sources. The most reliable data concern contractual hours or paid hours as these data tend to come from employer payroll records or other such sources. The hours measure of interest for productivity measurement, however, is for hours actually worked. This also includes unpaid hours but excludes hours that are paid but not worked. National accounts often provide actual hours worked and this is the concept of hours also used in EU KLEMS. When national accounts did not provide measures of hours worked, these have been estimated from other sources--this includes the hours of self-employed persons.

Series on hours worked by labour type are not part of the standard statistics reported by NSIs, not even at the aggregate economy level. Also, there is no single international database on skills which can be used for this purpose. For each country, a choice has been made to use survey data which provide the best sources for consistent wage and employment data at the industry level. In most cases this was a labour force survey (LFS), in some cases together with an earnings survey when wages were not included in the LFS. In other cases, the establishment survey or a social-security database, or a mix of sources has been used. Care has been taken to arrive at series which are consistent over time, which was important as most employment surveys are not designed to track developments over time, and breaks in methodology or coverage frequently occur.

4. The EU KLEMS database

The methodology used to derive multi-factor productivity (MFP) growth rates as outlined above has rarely been applied comprehensively in practice, particularly in Europe. (7) This section outlines the coverage of data in terms of variables, industries, countries and time periods in the EU KLEMS database.

The first public release of the EU KLEMS database covers 25 EU countries (EU-25), (8) as well as Japan and the United States. In general, data for 1970-2004 are available for the 'old' EU-15 countries (9) and for the US. Series from 1995 onwards are available for the new EU member states which joined the EU on 1 May 2004 (EU-10). Due to data limitations, the coverage differs across countries, industries and variables. Appendix table 1 provides an overview of all the series included in the EU KLEMS database. The variables covered can be split into three main groups: (1) basic variables; (2) growth accounting variables and (3) additional variables. The basic series contain all the data needed to construct single productivity measures, such as labour productivity (output per hour worked). These series include nominal, volume and price series of output and intermediate inputs, and volumes and prices of employment. Most series are part of the present European System of National Accounts (ESA, 1995) and are reported in the National Accounts of all individual countries, at least for the most recent period. The main adjustments to these series were related to filling gaps in industry detail (using industry statistics) and to link series over time. The variables in the growth accounting series are of an analytical nature and cannot be derived directly from published National Accounts data without additional assumptions. These include series of capital services, of labour services, and of multi-factor productivity which are at the heart of the EU KLEMS Growth and Productivity Accounts. Finally, additional series are given which have been used in generating the growth accounts and are informative by themselves. These include, for example, various measures of the relative importance of ICT-capital and non-ICT capital, and of the various labour types within EU KLEMS.

At the lowest level of aggregation, data were collected for 71 industries. The industries are classified according to the European NACE revision 1 classification. But the level of detail varies across countries, industries and variables due to data limitations. In order to ensure a minimal level of industry detail for which comparisons can be made across all countries, so-called 'minimum lists' of industries have been used. All national datasets have been constructed in such a way that these minimum lists are met. The minimum lists are different for particular groups of variables and time-periods. Two groups of variables can be distinguished: variables needed for the computation of labour productivity growth and unit labour cost analysis and a set of additional variables required to execute growth accounting (including gross output, intermediate inputs, labour composition and capital). The number of industries covered include 62 (for labour productivity post-1995), 48 (for labour productivity pre-1995) and 31 industries (for growth accounts). The industry detail for each country conforms at least to the minimum list of industries, but often more detail is available. Appendix table 2 provides a listing of industries for which growth accounting variables are available. This list also includes higher level aggregates provided in the EU KLEMS database. Finally, data are provided for four institutional country groupings: EU-25, 'old' EU-15, 'new' EU-10 and Eurozone. To aggregate across countries use is made of industry-specific Purchasing Power Parities (PPPs) for gross output.

5. Some descriptive results from the EU KLEMS Growth and Productivity Accounts (10)

The EU KLEMS database confirms earlier observations, that the growth performance of the European Union has undergone a marked change during the second half of the 1990s (O'Mahony and van Ark, 2003). Even though average GDP growth of the EU-15 remained constant at 2.2 per cent, labour productivity growth slowed dramatically from 2.4 per cent from 1970-1995 to 1.4 per cent from 1995-2004 (see table 1). Even after including the significantly better productivity growth performance of the new member states of the Union (see table 2), given their relatively small GDP, the labour productivity growth of the aggregate EU-25 was only slightly higher at 1.7 per cent from 1995-2004 (see table 3). This structural slowdown in productivity for the European Union as a whole is striking in the light of a comparison with the United States, where productivity growth accelerated significantly from 1.3 per cent, averaged over 1970-1995, to 2.4 per cent from 1995-2004 (see van Ark, O'Mahony and Ypma, 2007). Even in Japan, which showed an even bigger slowdown in productivity growth than Europe, productivity growth during 1995-2004 was still higher than in the EU at 1.8 per cent. When looking at the market economy alone, the forging ahead of the US becomes even more pronounced (see below). (11)

However, the EU KLEMS database documents a wide variation in productivity growth rates across EU member states. Among the 'old' member states, the fastest productivity growth rates were recorded in Finland and Sweden. (12) Among the large countries in the 'old' EU-15, the UK has shown the fastest productivity growth since 1995, ahead of France and Germany. At the lower end of the productivity growth ranks are the two large countries in the southern part of the EU, i.e. Italy and Spain. The dismal productivity performance of the latter two countries impacts significantly on the average growth rate in the Union. However, whereas slow productivity growth in Spain was related to rapid improvement in labour input growth, the Italian economy experienced no compensating effect from an acceleration in employment growth. In general, the productivity growth rates from 1995-2005 were by far the highest for the new member states, reflecting the restructuring of the economies in Central and Eastern Europe. However, labour input growth in the new member states has generally been negative, in particular in manufacturing (see table 2).

The underlying analysis of the industry contributions to labour productivity since 1995 shows that the manufacturing sector continues to contribute significantly to European growth, partly through high labour productivity growth in the electrical machinery sector (which includes, for example, all the ICT production industries), and partly from the rest of the manufacturing sector (0.5 percentage points) (see table 3). Also, growth in distribution services and in other goods producing industries each contributed 0.4 percentage points to post-1995 growth in the EU-25. Nevertheless, compared to the United States, the striking differences in labour productivity growth originate from the much smaller contribution of market services, notably the distribution sector as well as finance and business services, which contributed 1.3 percentage points in the US (see Van Ark, O'Mahony and Ypma, 2007). The EU aggregates hide considerable country variation in terms of industries driving growth. For example, some European countries (Finland, Sweden and Ireland as well as Estonia, Hungary and Latvia) showed a major contribution from ICT production. Some other countries show larger contributions from other manufacturing industries, such as some fast-growing new EU-countries, Austria, Ireland and Sweden, while in other countries again 'other goods' production (which includes agriculture, mining, utilities and construction) is an important source of growth. Also differences in the productivity contribution of market services appear to be a major driver of divergence within Europe.

The growth accounting analysis from the EU KLEMS Growth and Productivity Accounts is the most innovative and hitherto unavailable component of the database. It concentrates on a sub-sample of ten 'old' EU countries and four new member states for which full labour and capital accounts could be constructed over a sufficient length of time and with the same variables. (13) In table 4, a decomposition of value-added growth in the market economy of the old EU countries is given. GDP growth accelerated from 1.9 to 2.2 per cent after 1995, completely due to a strong improvement in the contribution of labour input, increasing from a zero contribution to a 0.7 percentage point contribution. About two thirds of this came from faster growth in total hours worked and one third from improved labour composition, as the overall skill level of the workforce has continued to increase significantly (see table 4).

The contribution of capital input to value-added growth has not changed much at the aggregate level, but the distribution has shifted somewhat from non-ICT capital to ICT capital. However, compared to the United States, the shift towards intensive use of ICT capital has generally not been as pronounced. Notably, when comparing the ratio of capital to labour contributions to growth in the EU, there are signs of a declining capital intensity in the EU. This development is in sharp contrast to the US trend in capital intensity since 1995 (see table 4).

The factor contributing most to the diverging trends in Europe and the US is the trend in multi-factor productivity growth. While contributing 0.7 per cent to market economy GDP during 1980-1995 in both regions, the trend accelerated to 1.6 per cent in the US, but declined to 0.3 percent in the ten old EU after 1995 (see figure 1). This slowdown in MFP growth is recorded almost everywhere across the Union, with the exception of Finland and the Netherlands, where it has improved since 1995. In France, MFP growth in the market economy has remained stable at 0.7 per cent, but it slowed sharply in Germany and in the United Kingdom. In Italy and Spain, MFP growth was even negative, reflecting the lack of technology and innovation spillovers and market rigidities, in particular in services industries (see figure 2).

[FIGURES 1-2 OMITTED]

When decomposing growth to both industry and the sources of growth, it appears that market services have accounted for a major part of the divergent performance of European economies since 1995, both among themselves and relative to the United States. Table 4 shows causes of the slowing or stagnation of output growth in market services. While the contribution of factor inputs to growth has generally stayed up, multi-factor productivity growth in the market services stagnated or even turned negative in many European countries. The reasons for the slowdown in multi-factor productivity growth in market services are an important topic for further research.

6. Concluding remarks: a glimmer of hope?

The EU KLEMS Growth and Productivity Accounts provide a new set of data that provide researchers, policymakers, media and others with a rich source of information on the sources of growth by industry and country in the European Union. Using national accounts and supplementary official statistics in combination with state-of-the-art growth accounting techniques, this database allows one to detect the key areas of growth and slowdown for individual countries, as well as convergence and divergence across economies. More precise measurement of the sources of growth at industry level is important for the analysis of the causes of the growth slowdown. In particular the breakdown of capital and labour inputs into asset types and labour categories (skill, gender and age) is an important step towards a more adequate assessment of the growth sources and less biased measures of multi-factor productivity growth.

The first release of the EU KLEMS database confirmed the view that European countries showed a significant slowdown in productivity growth on average from 1995 to 2004, which is shown to be widespread across countries and industries but with notable differences. For example, productivity growth rates in Spain and Italy seriously declined, while they slowed moderately in France and Germany. The productivity slowdown in the United Kingdom has been more limited, and in some smaller economies (Greece, Ireland, and the Netherlands) productivity growth even accelerated, at least in the market sector of those economies. Productivity growth in most new member states of the European Union has been much faster as these countries have been catching up on the productivity levels of the 'old' EU-15, but this has often gone together with a sharp contraction in employment.

Recent productivity trends suggest some signs of a recovery in EU labour productivity growth, in particular for the EU15 since about 2004 (see table 5). At the same time US productivity appears to be slowing. This may offer a glimmer of hope that the EU has turned the corner and will experience a productivity spurt similar to that achieved in the US. However such a conclusion must be treated with some caution, since it involves extrapolating from one or two years of relatively favourable performance. Some estimates suggest much of this may be due to cyclical movements, with little underlying difference in trend growth in each region. Thus trend productivity growth in the US has not decelerated significantly whereas EU-15 trend productivity growth still follows a downward path (van Ark and Fosler, 2007). It is just too early yet to say whether we have entered a new era of renewed productivity growth in the EU. What is beyond dispute is that identifying the industry and country location of any growth acceleration, and explaining this in terms of investment in inputs or MFP growth, requires the type of detailed analysis outlined in this paper. The EU KLEMS project has established the foundations for such an analysis and updates and further research on the database will allow a speedier resolution of debates on international productivity performance.

REFERENCES

Basu, S., Fernald, J.G. and Shapiro, M.D. (2001), 'Productivity growth in the 1990s: technology, utilization, or adjustment?' Carnegie-Rochester Conference Series on Public Policy, 55, pp. 117-66.

Colecchia, A. and Schreyer, P. (2001), ICT Investment and Economic Growth in the 1990s: Is the United States a Unique Case?, Paris, OECD.

EU KLEMS Database, March 2007, http://www.euklems.net.

Fraumeni, B. (1997), 'The measurement of depreciation in the US National Income and Product Accounts', Survey of Current Business, July.

Inklaar, R., O'Mahony, M. and Timmer, M.P. (2005), 'ICT and Europe's productivity performance: industry-level growth account comparisons with the United States', Review of Income and Wealth, 51(4), pp. 505-36.

Jorgenson, D.W. and Griliches, Z. (1967), 'The explanation of productivity change', Review of Economic Studies, 34(3), pp. 249-83.

Jorgenson, D.W., Gollop, F.M. and Fraumeni, B.M. (1987), Productivity and U.S. Economic Growth, Cambridge, MA, Harvard Economic Studies.

Jorgenson, D.W., Ho, M. and Stiroh, K.J. (2005), Information Technology and the American Growth Resurgence, Cambridge, MIT.

O'Mahony, M. and van Ark, B. (eds.) (2003), EU Productivity and Competitiveness: An Industry Perspective. Can Europe Resume the Catching-up Process? Luxembourg, Office for Official Publications of the European Communities.

Schreyer, P. (2002), 'Computer price indices and international growth and productivity comparisons', Review of Income and Wealth, 48(1), pp. 15-31.

Statistical Commission and Economic Commission for Europe (2004), Survey of National Practices in Estimating Service Lives of Capital Assets, CES/AC.68/2004/18, Paper presented on the joint meeting of national accounts in Geneva, 28-30 April.

Timmer, M.P. and van Ark, B. (2005), 'IT in the European Union: a driver of productivity divergence?', Oxford Economic Papers, 57(4), pp. 693-716.

Triplett, J.E. (2004), Handbook on Hedonic Indexes and Quality Adjustments in Price Indexes: Special Application to Information Technology Products, Brookings Institution, July.

van Ark, B. and Fosler, G. (2007), 'Is ICT's contribution to productivity growth peaking', The Conference Board, Executive Action No. 224, January.

van Ark, B., O'Mahony, M. and Ypma, G. (eds) (2007), 'The EU KLEMS Productivity Report', Issue I, University of Groningen & University of Birmingham, March.

NOTES

(1) This project has been the joint research effort of fifteen research institutes across Europe, including Rijksuniversiteit Groningen (NL), National Institute of Economic and Social Research (UK), Centre d'Etudes prospectives et d'informations internationales (F), Centre for Economic and Business Research (DK), CPB Netherlands Bureau for Economic Policy Analysis (NL), Deutsches Institut fur Wirtschaftsforschung e.V. Berlin (DE), Federaal Planbureau (BE), Istituto di Studi e Analisi Economica (I) Instituto Valenciano De Investigaciones Economicas, S.A. (ES), Helsingin kauppakorkeakoulu (Helsinki School of Economics) (FI), Austrian Institute of Economic Research (AT), The Vienna Institute for International Economic Studies (AT), Amsterdam Institute for Business and Economic Research, Free University (NL) and The Conference Board Europe, Economics Department (BE). In addition, various national statistical offices within Europe have offered their help in collecting and interpreting the data. The project could not have succeeded without the dedicated effort of many people within each institute.

(2) Under strict neo-classical assumptions, MFP growth measures disembodied technological change. In practice, MFP is derived as a residual and includes a host of effects such as improvements in allocative and technical efficiency, changes in returns to scale and mark-ups and technological change proper. All these effects can be broadly summarised as 'improvements in efficiency', as they improve the productivity with which inputs are being used in the production process. In addition, being a residual measure, MFP growth also includes measurement errors and the effects from unmeasured output and inputs.

(3) See Schreyer (2001) for a discussion of these alternatives.

(4) In some cases additional information is not available in particular for earlier periods. In those cases missing data were filled by applying higher-level growth rates to more detailed levels.

(5) See Statistical Commission and Economic Commission for Europe (2004).

(6) NB. We only harmonise the investment series for IT, but not the IT output series as the composition of IT output varies highly across countries, and this cannot be taken into account.

(7) The OECD and the Groningen Growth and Development Centre maintain MFP series for aggregate OECD economies, but not at the industry level with the exception of a single study by Inklaar et al. (2005) including four European countries (France, Germany, the Netherlands and the United Kingdom). The main bottleneck has been the lack of available statistics on the composition of labour and capital at the industry level for a sufficient number of European countries. As a result, many studies resorted to cruder measures of output, inputs and MFP, mostly based on the OECD Structural Analysis database, STAN and its predecessor the International Sectoral Database--ISDB.

(8) All member states of the EU as of 1 May 2004 but excluding Bulgaria and Romania which only joined in 2007.

(9) All member states of the EU on 1 January 1995.

(10) For a country-specific analysis of results from the first release in March 2007, see van Ark, O'Mahony and Ypma (2007).

(11) Market economy excludes health (ISIC industry N), education (ISIC M) and government sectors (ISIC L). We also exclude real estate (ISIC 70), because output in this industry mostly reflects imputed housing rents rather than sales of firms.

(12) Greece and Ireland also showed rapid productivity growth but, just as in the new member states, this largely reflects 'catching up' growth.

(13) The ten 'old' EU countries in the growth accounts analysis refer to Austria, Belgium, Denmark, Finland, France, Germany, Italy, the Netherlands, Spain, and the United Kingdom. The four new member states refer to Czech Republic, Hungary, Poland and Slovenia.

Marcel P. Timmer *, Mary O'Mahony ** and Bart van Ark *

* Groningen Growth and Development Centre, University of Groningen, ** University of Birmingham and National Institute of Economic and Social Research, *** National Institute of Economic and Social Research. Contact author Mary O'Mahony, e-mail: m.omahony@bham.ac.uk.

Appendix table 1. Variables in EU KLEMS database

Basic variables

Values
GO Gross output at current basic prices (in millions of local
 currency)
II Intermediate inputs at current purchasers' prices (in
 millions of local currency)
IIE Intermediate energy inputs at current purchasers' prices
 (in millions of local currency)
IIM Intermediate material inputs at current purchasers' prices
 (in millions of local currency)
IIS Intermediate service inputs at current purchasers' prices
 (in millions of local currency)
VA Gross value-added at current basic prices (in millions of
 local currency)
COMP Compensation of employees (in millions of local currency)
GOS Gross operating surplus (in millions of local currency)
TXSP Taxes minus subsidies on production (in millions of local
 currency)
EMP Number of persons engaged (thousands)
EMPE Number of employees (thousands)
H_EMP Total hours worked by persons engaged (millions)
H_EMPE Total hours worked by employees (millions)

Prices
GO_P Gross output, price indices, 1995 = 100
II_P Intermediate inputs, price indices, 1995 = 100
VA_P Gross value-added, price indices, 1995 = 100

Volumes
GO_QI Gross output, volume indices, 1995 = 100
II_QI Intermediate inputs, volume indices, 1995 = 100
IIE_QI Intermediate energy inputs, volume indices, 1995 = 100
IIM_QI Intermediate material inputs, volume indices, 1995 = 100
IIS_QI Intermediate service inputs, volume indices, 1995 = 100
VA_QI Gross value-added, volume indices, 1995 = 100
LP_I Gross value-added per hour worked, volume indices,
 1995=100

Growth accounting variables
LAB Labour compensation (in millions of local currency)
CAP Capital compensation (in millions of local currency)
LAB_QI Labour services, volume indices, 1995 = 100
CAP_QI Capital services, volume indices, 1995 = 100
VA_Q Growth rate of value-added volume (% per year)
VAConL Contribution of labour services to value-added growth
 (percentage points)
VAConH Contribution of hours worked to value-added growth
 (percentage points)
VAConLC Contribution of labour composition change to value-added
 growth (percentage points)
VAConKIT Contribution of ICT capital services to output growth
 (percentage points)
VAConKNIT Contribution of non-ICT capital services to output growth
 (percentage points)
VAConTFP Contribution of TFP to value-added growth (percentage
 points)
TFPva_I TFP (value-added based) growth, 1995=100
GO_Q Growth rate of gross output volume (% per year)
GOConII Contribution of intermediate inputs to output growth
 (percentage points)
GOConIIM Contribution of intermediate energy inputs to output
 growth (percentage points)
GOConIIE Contribution of intermediate material inputs to output
 growth (percentage points)
GOConIIS Contribution of intermediate services inputs to output
 growth (percentage points)
GOConL Contribution of labour services to output growth
 (percentage points)
GOConK Contribution of capital services to output growth
 (percentage points)
GOConTFP Contribution of TFP to output growth (percentage points)
TFPgo_I TFP (gross output based) growth, 1995=100

Appendix table 2. Industry lists for growth accounting variables

Description Code

TOTAL INDUSTRIES TOT
MARKET ECONOMY MARKT
ELECTRICAL MACHINERY, POST AND
 COMMUNICATION SERVICES ELECOM
 Electrical and optical equipment 30t33
 Post and telecommunications 64
GOODS PRODUCING, EXCLUDING ELECTRICAL
 MACHINERY GOODS
TOTAL MANUFACTURING, EXCLUDING
 ELECTRICAL MexElec
 Consumer manufacturing Mcons
 Food products, beverages and tobacco 15t16
 Textiles, textile products, leather and footwear 17t19
 Manufacturing nec; recycling 36t37
 Intermediate manufacturing Minter
 Wood and products of wood and cork 20
 Pulp, paper, paper products, printing and publishing 21t22
 Coke, refined petroleum products and nuclear fuel 23
 Chemicals and chemical products 24
 Rubber and plastics products 25
 Other non-metallic mineral products 26
 Basic metals and fabricated metal products 27t28
 Investment goods, excluding hightech Minves
 Machinery, nec 29
 Transport equipment 34t35
OTHER PRODUCTION OtherG
 Mining and quarrying C
 Electricity, gas and water supply E
 Construction F
 Agriculture, hunting, forestry and fishing AtB
MARKET SERVICES, EXCLUDING POST AND
 TELECOMMUNICATIONS MSERV
DISTRIBUTION DISTR
 Trade 50t52
 Sale, maintenance and repair of motor vehicles
 and motorcycles; retail sale of fuel 50
 Wholesale trade and commission trade, except
 of motor vehicles and motorcycles 51
 Retail trade, except of motor vehicles and
 motorcycles; repair of household goods 52
 Transport and storage 60t63
FINANCE AND BUSINESS, EXCEPT REAL ESTATE FINBU
 Financial intermediation J
 Renting of m&eq and other business activities 71t74
PERSONAL SERVICES PERS
 Hotels and restaurants H
 Other community, social and personal services O
 Private households with employed persons P
NON-MARKET SERVICES NONMAR
 Public admin, education and health LtN
 Public admin and defence; compulsory social security L
 Education M
 Health and social work N
 Real estate activities 70

Source: EU KLEMS Database, March 2007, http://www.euklems.net

Table 1. Gross value-added, labour input and labour productivity,
1970-1995 and 1995-2004, European Union-15 (old EU-15)

 Gross Total Total GVA
 value- persons hours per hour
 added engaged worked worked

 (annual average volume
 growth rates, in %)

1970-1995
TOTAL INDUSTRIES 2.2 0.4 -0.2 2.4
Electrical machinery, post and
 communication 4.2 -0.4 -0.8 5.0
Manufacturing, excluding
 electrical 1.8 -1.2 -1.6 3.4
Other goods producing industries -0.2 -2.0 -2.4 2.1
Distribution services 2.7 0.8 0.3 2.4
Finance and business services 3.9 3.4 2.9 1.0
Personal and social services 2.1 2.0 1.6 0.5
Non-market services 2.8 2.1 1.6 1.3
Reallocation of labour effect

1995-2004
TOTAL INDUSTRIES 2.2 1.2 0.8 1.4
Electrical machinery, post and
 communication 6.3 -0.5 -0.9 7.2
Manufacturing, excluding
 electrical 1.2 -0.7 -0.9 2.1
Other goods producing industries 1.4 -0.2 -0.5 1.9
Distribution services 2.5 1.2 0.8 1.7
Finance and business services 3.6 3.6 3.3 0.3
Personal and social services 1.8 2.6 2.0 -0.2
Non-market services 1.6 1.4 1.0 0.6
Reallocation of labour effect

 Average Contribution
 share in to LP growth
 total hours in total
 worked (%) industries

1970-1995
TOTAL INDUSTRIES 100.0 2.4
Electrical machinery, post and
 communication 4.1 0.2
Manufacturing, excluding 21.6 0.9
 electrical
Other goods producing industries 20.7 0.6
Distribution services 19.5 0.4
Finance and business services 8.1 0.1
Personal and social services 8.1 0.0
Non-market services 17.8 0.2
Reallocation of labour effect 0.0

1995-2004
TOTAL INDUSTRIES 100.0 1.4
Electrical machinery, post and
 communication 3.4 0.3
Manufacturing, excluding 16.4 0.4
 electrical
Other goods producing industries 14.5 0.3
Distribution services 20.3 0.3
Finance and business services 13.5 0.0
Personal and social services 10.8 0.0
Non-market services 21.0 0.1
Reallocation of labour effect 0.0

Source: EU KLEMS Database, March 2007, http://www.euklems.net.

Table 2. Gross value-added, labour input and labour productivity,
1995-2004, European Union-10 (EU-10, new member states)

 Gross Total Total GVA
 value- persons hours per hour
 added engaged worked worked

 (annual average volume growth
 rates, in % p.a.)

1995-2004
TOTAL INDUSTRIES 2.3 1.0 0.6 1.7
Electrical machinery, post and
 communication 6.6 -0.4 -0.7 7.2
Manufacturing, excluding
 electrical 1.4 -0.9 -1.1 2.5
Other goods producing industries 1.3 -0.5 -0.7 2.0
Distribution services 2.6 1.1 0.6 2.0
Finance and business services 3.8 3.7 3.4 0.4
Personal and social services 1.8 2.5 1.9 -0.1
Non-market services 1.7 1.2 0.9 0.8
Reallocation of labour effect

 Average Contribution
 share in to LP growth
 total hours in total
 worked (%) industries

1995-2004
TOTAL INDUSTRIES 100.0 1.7
Electrical machinery, post and
 communication 3.4 0.3
Manufacturing, excluding
 electrical 16.8 0.5
Other goods producing industries 17.2 0.4
Distribution services 20.2 0.4
Finance and business services 12.3 0.0
Personal and social services 9.9 0.0
Non-market services 20.3 0.2
Reallocation of labour effect 0.0

Source: EU KLEMS Database, March 2007, http://www.euklems.net.

Table 3. Gross valued-added, labour input and labour productivity,
1995-2004, European Union-25

 Gross Total Total GVA
 value- persons hours per hour
 added engaged worked worked

 (annual average volume growth
 rates, in % p.a.)

1995-2004
TOTAL INDUSTRIES 3.1 -0.2 -0.4 3.5
Electrical machinery, post and
 communication 11.5 0.6 0.4 11.2
Manufacturing, excluding
 electrical 4.7 -1.7 -1.8 6.5
Other goods producing industries 0.9 -1.2 -1.3 2.2
Distribution services 4.0 0.4 0.0 4.0
Finance and business services 6.2 4.0 3.7 2.5
Personal and social services 1.5 1.3 0.8 0.7
Non-market services 2.1 0.2 0.1 1.9
Reallocation of labour effect

 Average Contribution
 share in to LP growth
 total hours in total
 worked (%) industries

1995-2004
TOTAL INDUSTRIES 100.0 3.5
Electrical machinery, post and
 communication 3.4 0.4
Manufacturing, excluding
 electrical 18.9 1.3
Other goods producing industries 29.6 0.7
Distribution services 19.3 0.8
Finance and business services 6.4 0.1
Personal and social services 5.5 0.0
Non-market services 16.9 0.3
Reallocation of labour effect 0.0

Source: EU KLEMS Database, March 2007, http://www.euklems.net.

Table 4. Gross value-added growth and contribution, 1980-1995 and
1995-2004 (annual average volume growth rates, in % p.a.)

 VA L H
 (1)=(2)+(5) (2)=(3) (3)
 +(8) +(4)

A. European Union-15 (excluding Greece, Ireland, Luxembourg, Portugal
and Sweden)
1980-1995
MARKET ECONOMY 1.9 0.0 -0.3
Electrical machinery, post and
 communication 3.9 -0.7 -0.8
Manufacturing, excluding electrical 1.2 -1.3 -1.5
Other goods producing industries -0.2 -1.2 -1.4
Distribution services 2.6 0.4 0.0
Finance and business services 3.6 2.2 1.9
Personal and social services 1.8 1.8 1.5

1995-2004
MARKET ECONOMY 2.2 0.7 0.4
Electrical machinery, post and
 communication 6.0 -0.4 -0.6
Manufacturing, excluding electrical 1.0 -0.3 -0.6
Other goods producing industries 1.2 0.0 -0.2
Distribution services 2.3 0.7 0.6
Finance and business services 3.5 2.1 1.9
Personal and social services 1.7 1.5 1.4

B. United States
1980-1995
MARKET ECONOMY 3.0 1.2 1.0
Electrical machinery, post and
 communication 6.6 0.1 -0.3
Manufacturing, excluding electrical 1.7 0.1 -0.2
Other goods producing industries 0.7 0.7 0.4
Distribution services 3.9 1.3 1.2
Finance and business services 4.4 2.9 2.7
Personal and social services 2.8 2.5 2.5

1995-2004
MARKET ECONOMY 3.7 0.7 0.3
Electrical machinery, post and
 communication 8.9 -0.3 -0.9
Manufacturing, excluding electrical 0.7 -1.1 -1.5
Other goods producing industries 1.6 1.0 0.9
Distribution services 4.7 0.5 0.2
Finance and business services 4.9 2.0 1.6
Personal and social services 2.6 1.7 1.4

 LC K KIT KNIT
 (4) (5)=(6) (6) (7)
 +(7)

A. European Union-15 (excluding Greece, Ireland, Luxembourg, Portugal
and Sweden)
1980-1995
MARKET ECONOMY 0.3 1.1 0.4 0.7
Electrical machinery, post and
 communication 0.2 1.6 0.9 0.8
Manufacturing, excluding electrical 0.3 0.8 0.2 0.6
Other goods producing industries 0.2 0.9 0.2 0.7
Distribution services 0.3 0.8 0.3 0.5
Finance and business services 0.3 1.9 0.8 1.0
Personal and social services 0.3 1.0 0.3 0.7

1995-2004
MARKET ECONOMY 0.2 1.2 0.6 0.6
Electrical machinery, post and
 communication 0.2 1.7 1.2 0.5
Manufacturing, excluding electrical 0.3 0.7 0.3 0.4
Other goods producing industries 0.2 0.7 0.1 0.6
Distribution services 0.1 1.2 0.5 0.7
Finance and business services 0.3 2.3 1.3 1.0
Personal and social services 0.1 0.9 0.3 0.7

B. United States
1980-1995
MARKET ECONOMY 0.2 1.1 0.5 0.6
Electrical machinery, post and
 communication 0.4 1.9 1.0 0.9
Manufacturing, excluding electrical 0.3 0.6 0.3 0.3
Other goods producing industries 0.3 0.7 0.2 0.5
Distribution services 0.2 1.2 0.6 0.6
Finance and business services 0.2 1.8 1.0 0.9
Personal and social services 0.1 0.5 0.2 0.3

1995-2004
MARKET ECONOMY 0.3 1.4 0.8 0.6
Electrical machinery, post and
 communication 0.6 2.5 1.5 0.9
Manufacturing, excluding electrical 0.3 0.7 0.4 0.3
Other goods producing industries 0.1 0.9 0.2 0.6
Distribution services 0.3 1.4 1.0 0.4
Finance and business services 0.4 2.0 1.2 0.7
Personal and social services 0.2 1.0 0.4 0.6

 MFP
 (8)

A. European Union-15 (excluding Greece, Ireland, Luxembourg, Portugal
and Sweden)
1980-1995
MARKET ECONOMY 0.7
Electrical machinery, post and
 communication 2.9
Manufacturing, excluding electrical 1.7
Other goods producing industries 0.2
Distribution services 1.4
Finance and business services -0.7
Personal and social services -1.1

1995-2004
MARKET ECONOMY 0.3
Electrical machinery, post and
 communication 4.7
Manufacturing, excluding electrical 0.6
Other goods producing industries 0.5
Distribution services 0.4
Finance and business services -1.3
Personal and social services -0.9

B. United States
1980-1995
MARKET ECONOMY 0.7
Electrical machinery, post and
 communication 4.6
Manufacturing, excluding electrical 0.9
Other goods producing industries -0.7
Distribution services 1.3
Finance and business services -0.3
Personal and social services -0.2

1995-2004
MARKET ECONOMY 1.6
Electrical machinery, post and
 communication 6.8
Manufacturing, excluding electrical 1.1
Other goods producing industries -0.3
Distribution services 2.8
Finance and business services 0.9
Personal and social services 0.0

Source: EU KLEMS Database, March 2007, http://www.euklems.net

Notes: VA= Gross value-added growth; L= Contribution of labour input
growth; H= Contribution of total hours worked; LC= Contribution of
labour composition; K= Contribution of capital input growth;
KIT= Contribution of ICT capital; KNIT= Contribution of non-ICT
capital; MFP= Contribution of multi-factor productivity growth.

Table 5. GDP, hours and labour productivity growth in the US and EU,
2000-2006 (% p.a.)

 US EU-15 EU-27 US EU-15
 Real GDP Total hours

2000-006 2.5 1.8 2.0 0.2 0.6
2004 3.9 2.2 2.6 1.4 0.8
2005 3.2 1.5 1.8 1.4 0.7
2006 * 3.2 2.7 3.0 1.8 1.4
Acceleration 2006
Over 2005 0.0 1.1 1.2 0.4 0.7

 EU-27 US EU-15 EU-27
 Labour productivity

2000-006 0.9 2.3 1.1 1.5
2004 0.8 2.5 1.5 1.8
2005 1.0 1.8 0.9 0.9
2006 * 1.5 1.4 1.3 1.5
Acceleration 2006
Over 2005 0.5 -0.4 0.4 0.7

Source: University of Groningen and The Conference Board. 2007
(http://www.ggdc.net/dseries/totecon.shtml); 2006 is forecast based on
OECD, Economic Outlook, 2006.

Notes: * 2006 is preliminary estimate. For 2006 assume unchanged hours
from 2005.