Towards Productivity Comparisons using the KLEMS Approach: An Overview of Sources and Methods
Marcel Timmer Groningen Growth and Development Centre and The Conference Board
July 2000
Acknowledgements
This report is a feasibility study into an international productivity project using the KLEM growth accounting methodology. It could not have been written without the help of many people. Thanks are due to Bart van Ark, Robert McGuckin, Mary O'Mahony and Dirk Pilat who provided me with helpful comments on earlier drafts of this report. During my visit to the Kennedy School of Government, Harvard University, Mun Ho, Kevin Stiroh and Dale Jorgenson provided me with many insights into the theories and empirical applications of the KLEM-methodology. Discussions with Frank Lee, Wulong Gu and Jianmin Tang (Industry Canada), René Durand (Statistics Canada) and Svend E. Hougaard Jensen, Anders Sørensen, Mogens Fosgerau and Steffen Andersen of the Center of Economic and Business Research (CEBR) provided further insights and new ideas. At the meeting of the KLEM-research consortium on 9 and 10 December at the Centre of Economic and Business Research (CEBR), Copenhagen, consortium members provided me with relevant information, references on ongoing projects and helpful suggestions.Their help is gratefully acknowledged. Prasada Rao (University of New England) is thanked for his advice on the section on purchasing power parities. Finally, the Conference Board is acknowledged for financing the preparation of this report.
1 TABLE OF CONTENTS
EXECUTIVE SUMMARY 3 1. INTRODUCTION 5 2. EXISTING DATA SOURCES AND PREVIOUS RESEARCH 7 3. INPUT-OUTPUT TABLES 11 4. LABOUR INPUT 17 4.1 Hours worked 18 4.2 Labour costs 20 4.3 Proposed Cross-Classification for Labour Input 22 5. CAPITAL INPUT 23 5.1 Capital Stock 23 5.2 Rental prices 26 5.3 Proposed Classification for Capital Input 27 6. PURCHASING POWER PARITIES (PPPs) 39 7. SUMMARY AND CONCLUSIONS 32
APPENDIX I INDUSTRIAL CLASSIFICATIONS 33 APPENDIX II.1 OUTLINE OF JORGENSON APPROACH TO CAPITAL INPUT MEASUREMENT 36 APPENDIX II.2 INTERPOLATION TECHNIQUES 38 APPENDIX III COUNTRY EXPERIENCES WITH IMPLEMENTING KLEM 39 Appendix III.1 The Canadian Experience 39 Appendix III.2 The Danish Experience 41 Appendix III.3 Country Status Reports 44
REFERENCES 51
2 EXECUTIVE SUMMARY
Growth accounting provides an indispensable instrument to assess the importance of the changes in patterns of economic growth, productivity and competitiveness as shown, for example, by recent research into the effects of ICT on growth. To study patterns of input substitution, technical change and structural change, an international KLEM-type database is indispensable. A KLEM database provides detailed sectoral data on output, capital (K), labour (L), intermediate inputs (M) and energy (E). Recently, a proposal has been put forward by nine European research institutes and universities to construct a KLEM dataset for European countries. This report discusses the feasibility of this project and describes the requirements with respect to the level of detail of the dataset. Numerous data sources and studies exist from which data for this project can be drawn. These are discussed here, focussing on four classes of data: input-output tables, labour accounts, capital flow matrices and purchasing power parities. For the input-output tables, the OECD input-output table database provides the best starting point. For the labour accounts, a combination of Eurostat’s Labour Force Survey and Labour Cost Survey and the earnings surveys of the Luxembourg Income Studies Project is recommended. For capital input, use can be made of capital flow matrices database by the OECD. Annual output and input data from the national accounts can be exploited to develop the full dataset. For the purchasing power parities, a combination of data from the International Comparisons Project (ICP), Eurostat PRODCOM database and data from the International Comparisons of Output and Productivity Project (ICOP) can be made. To ensure international comparability, the report suggests a set of minimal requirements to which each national data set must adhere. The period of study starts in 1970. For input-output tables, a list of 31 industries based on the NACE rev 1 classification is proposed, and a conceptual framework similar to the one used by OECD (1995). For labour input the following cross-classification is suggested: by sex, by 4 types of education (university level and above, non-university tertiary education, upper secondary and below upper secondary education) and 4 age classes (below 25 years, 25-34, 35-54 and more than 54). Capital input should be subdivided in at least 4 types of assets: residential structures, non-residential structures, high-tech equipment (consisting of computers and peripheral equipment, communications equipment, instruments and photocopy and related instruments) and low-tech equipment (all other machinery and transport equipment). This is summarised in the table below.
3 Table 1 Proposed Minimal Requirements of European KLEM database Gross output Industries (according to NACE rev 1.) 31 industries Intermediate inputs Industries (according to NACE rev 1.) 31 industries Labour input (hours worked) 32 types - sex (male/female) 2 types - educational attainment (university level and above (> 16 years of schooling), 4 types non-university tertiary education (> 14 ), upper secondary (>12) and below upper secondary education (< 12)) - age classes (-24, 25-34, 35-54, 55+) 4 types Capital input (capital services) 4 types - residential building, non-residential building, high-tech equipment and low-tech equipment
4 1. INTRODUCTION
Aim of the KLEM data project for Europe In the past decades, important changes in the pattern of economic growth in OECD countries have taken place. Recent changes in growth, productivity and employment may be interpreted as a movement towards the so-called knowledge-based economy (OECD 1996, 1999b). Currently, output and employment are expanding fast in high-technology industries such as computers and electronics, as well as knowledge-based services such as financial and other business services. More and more resources are spent on the production and development of new technologies, in particular on information and communication technology. Computers and related equipment are now the fastest growing component of tangible investments. At the same time, a polarisation in European labour markets is taking place as skilled labour is increasing its demand, whereas demand for low-skilled workers is falling.
Growth accounting provides an indispensable instrument to assess the importance of these changes in economic growth, productivity and competitiveness. Most clearly this appears from recent research into the effects of ICT on growth (Jorgenson and Stiroh 1999, Schreyer 2000, van Ark 2000). Following the pioneering work of Solow, Kendrick, Denison and Jorgenson in developing growth accounting techniques, many growth accounting studies have been performed over the past decades (Maddison 1987, Hulten 2000). Apart from national studies, international comparisons have also been provided in particular by Denison, Jorgenson and Maddison. Most of these studies have been restricted to aggregate analyses of value added, labour input and capital input. However, a more detailed analysis is warranted when processes of structural change and input substitution have to be assessed. The use of value added measures and aggregate input measures is not satisfactorily because it not only ignores the substitution possibilities between the primary inputs capital (K) and labour (L), and energy (E) and intermediate materials (M), but also between various types of capital (e.g. ICT-capital and non-ICT-capital) and labour (e.g. skilled and unskilled). Jorgenson, Gollop and Fraumeni were the first scholars to outline and apply the basic KLEM-methodology for detailed industry-level analysis of productivity growth in the post-war US economy, which eventually evolved in their seminal 1987 publication.
Over the past years, KLEM studies have been carried out in various European countries, including Denmark, Germany, the Netherlands and the United Kingdom, and new work is embarked upon in these countries and elsewhere in Europe (see section 2 and Appendix III). However, so far little attention has been paid to the international comparability of the work undertaken in the various countries. The primary aim of the European KLEM project is to arrive at an internationally comparable data-set for a KLEM-type analysis of productivity growth for eight European countries: Denmark, Finland, France, Germany, Italy, Netherlands, Spain and
5 United Kingdom.1 At a later stage, this European dataset will be linked with a Canada-Japan- USA database to allow for international comparisons. The latter database is currently developed under the aegis of Jorgenson and associates, and sponsored by Industry Canada and MITI. Preliminary results for the Canada-US part are already available (see e.g. Lee and Tang, 1999).
The importance of the dataset is clear from the large range of possible applications. It will provide measures of output and productivity growth, structural change and input substitution which are internationally comparable across a wide range of countries. The data set can be used to analyse the issues of sectoral employment growth patterns, efficiency of factor allocation, processes of factor substitution, skill-biases in technological change, etc. In addition, the dataset, which includes the development of purchasing power parities, can be used for other purposes such as the analysis of international competitiveness and investment opportunities. Finally, it can serve as a base for further research into for example the impact of high-tech industries or human capital building on economic growth and productivity change. This project is not a one- time effort, but aims to build an infrastructure in which the data are regularly updated and available for public usage. The KLEM-project must be seen as an international platform in which national research and data collection efforts are being supported, and where necessary co-ordinated, with a clear emphasis on the need for international comparability (van Ark et al, 1999).
Aim of this report The feasibility of the European KLEM-project depends crucially on two questions: firstly, what data is needed for a KLEM data base, and secondly, what data is available both from national and international data sources? It is clear that the answer to the first question is closely related with the second one. Of course the KLEM data base must be as detailed as possible, but benefits must be weighted against costs. Whereas the data collection efforts will show rapidly diminishing returns, costs will rapidly increase. For example, comparability problems increase with a greater level of detail. It is between these counteracting forces that the European KLEM-platform plays a decisive role. This report reflects discussion among the consortium participants about the desirable format and level of detail of the KLEM-database. It also provides an overview of the international and national data sources which are available for the project, discusses the particular merits and demerits of each source, points out problematic areas and suggest possible solutions. The report is structured as follows. In the next section a short introduction is given of recent European KLEM-research and available international data sources for the proposed KLEM database. the data needed for the project consist of four classes: 1. input-output tables, 2. labour accounts, 3. capital flow matrices and 4. purchasing power parities. Input-output tables provide a consistent framework in which measures of output, energy and intermediate inputs are
1 Presently Austria is considered as a possible ninth candidate to be included in the KLEM project.
6 collected. The labour accounts specify in detail the labour inputs, as do the capital flow matrices for capital input. Purchasing power parities are needed to provide international level comparisons of output, input and productivity, in addition to growth rate analyses. In sections 3 to 6 I focus in turn on each of these four classes of data. For each class, the availability of international data sources is discussed first, and particular problems are pointed out. International databases can be used as a starting point, but given the problems encountered, more detailed national data will be needed in a later stage. The framework of construction used in compiling the international databases can provide a useful structure for this. Section 7 provides a summary of the main points of discussion.
2. EXISTING DATA SOURCES AND PREVIOUS RESEARCH
Basic growth accounting decomposes output growth into the contributions of growth in labour, capital, material and energy input and productivity change (total factor productivity or TFP). For a particular industry i, productivity growth is given by the following
& = & − α L & − α K & − α E & − α M & TFPt Yt t Lt t Kt t Et t M t
j 1 j j j with α = (v + v − ) and v t the share of input j in total output at time t, output (Y), labour input t 2 t t 1 (L), capital input (K), intermediate input (M) and energy input (E).2 The OECD Productivity Manual (OECD 1999a), provides a good overview of the art of productivity measurement and it will not discussed any further here. This report focuses on a discussion of the desirable level of detail of the various input and output measures, and provides an overview of the available data sources for international productivity comparisons.
As a starting point, I first discuss available international data sources for European countries. These data sources have been set up with the aim to ensure international comparability and hence they provide a good starting point for the KLEM-project. In section 2.2 some recent European KLEM-type growth accounting studies are discussed.
2 Gross output is the preferred measure for productivity analysis at lower levels of aggregation, but the problem of double accounting remains at higher levels. It seems that at the level of analysis proposed in this project, the benefits of gross-output measures are bigger than the potential disadvantages (see also OECD 1999a).
7 2.1 International data sources
The obvious starting point for a European KLEM-dataset is the Statistical Office of the European Union (Eurostat). Eurostat collects a wide range of data on all (potential) members of the European Union. Increasingly, it is harmonising statistical practices across the various national statistical institutes of the EU members and international comparability is constantly enhanced. Three important databases from a KLEM-perspective are the labour force survey, the input- output tables and the capital stock database. Eurostat also collects harmonised consumer price indices and purchasing power parities. Within each EU-country, the national statistical agencies carry out labour force surveys on a regular basis. Since 1983, these are held annually within a common framework, co-ordinated by Eurostat. Eurostat also centrally processes the data which ensures that the degree of comparability of the EU Labour Force Survey results is considerably higher than that of any other existing set of employment statistics. Another useful database related to labour input is the Labour Cost Survey which is four-annually establishment survey providing details on labour costs. In collaboration with Jörg Beutel, Eurostat has also contructed harmonised input-output tables and capital stock estimates for the EU members. The capital stock database covers 25 industries and three types of capital goods (buildings, machinery and transport equipment) for the period 1959-97. Harmonised input-output tables are available for five-year intervals starting in 1980. Because harmonisation efforts have only recently begun to pay off, a main problem of Eurostat data is that consistent series are available for short periods only. We will discuss the use of Eurostat sources extensively below.
The OECD has a long tradition in constructing international databases in which attempts are made to tackle both the problem of consistency in definitions and classifications over time and across countries. Important databases from a KLEM-perspective are the International Sectoral DataBase (ISDB), Labour Force Statistics and the Input-output database. The ISDB database provides data for value added, employment, hours worked (only for total economy), gross capital formation and capital stock. It covers the period 1960-1997 for 33 sectors and 14 countries (USA, Canada, Japan, Germany and Western Germany, France, Italy, United Kingdom, Australia, Netherlands, Belgium, Denmark, Norway, Sweden, Finland). For the manufacturing sector some more detailed databases are available such as the STAN database (covering 49 manufacturing industries). Currently, attempts are undertaken to merge the two databases and provide an update (see http://www.oecd.org/dsti/sti/stat-ana/index.htm for more information on OECD-statistics). The OECD Input-Output database provides internationally comparable input-output tables in both current and constant prices for several benchmark years between 1970 and 1990 for ten OECD countries. The unique features of this database are: international comparability based on
8 the use of a common industrial classification (ISIC Revision 2, 36 sectors), which includes distinction between high-technology, trade-oriented industries such as pharmaceuticals, computers, communication equipment, automobiles and aircraft; separation of transaction flows of goods and services by domestically produced and imported ones and inclusion of consistent capital investment flow matrices as supporting tables (OECD 1995). OECD announced a re- launch of this project for updating in the first half of 2000. The OECD skill database contains comparable data on employment by industry and occupation for 10 OECD countries. Four types of workers are distinguished (white-collar high- and low-skilled and blue-collar high- and low-skilled) at a detailed industry level including both manufacturing and service industries (OECD 1998a). OECD data can be used as important corner stones for the KLEM-database, but a number of weaknesses from a KLEM-perspective can be detected as discussed below.
The National Institute (NIESR) sectoral productivity database underlying the study by O'Mahony (1999) provides comparable measures of value added, number of employees, hours worked, capital services and labour skill levels for 34 sectors in five countries: France, Germany, Japan, UK and US for the period 1950-1996. Capital stock is subdivided into structures and equipment, and labour into three skill levels. The study contains an elaborate description of the various sources used and problems encountered in compiling the various national data sources.
Data for international productivity comparisons have also been constructed and used in a wide range of studies by the Groningen Growth and Development Centre (e.g., Maddison, 1987, 1995; Van Ark, 1996b). A particular strong point of the GGDC dataset is that it has moved beyond OECD countries, and covers about 10 Asian countries and various Latin American countries as well (Pilat, 1994; Hofman, 1998; Mulder, 1999; Timmer, 2000). In particular the GGDC Sectoral Database, which includes output and labour information for 10 sectors of the economy can be useful for growth accounting studies within and outside the OECD area (van Ark 1996b). However, presently the degree of disaggregation in capital and labour is still not up to the standards required for KLEM studies. GGDC also develops sector-specific purchasing power parities which can be used in KLEM studies (see Section 6). Recently the GGDC has begun to release data on a systematic basis on a website, which will be extended and updated on a regular basis in the future.3
2.2 Recent European KLEM research
An important attempt to develop growth accounts for European countries with explicit attention to the various types of labour and capital is provided by Dougerthy (1991), updated in Jorgenson
9 and Yip (1999). Dougerthy studied growth in business sector GDP in four European countries (France, Germany, Italy and UK) and Canada, Japan and the USA for the period 1960-89, using comparable concepts of value added, labour and capital input. He distinguished 21 categories of capital (9 asset types and 4 different ownership types) and 20 labour subcategories (by sex, educational attainment and employment status). The study contains separate chapters on each country, spelling out the various sources and studies which have been used. However, only total business sector GDP is studied and no industry detail is provided. Consequently, intermediate inputs have not been taken into account. More sectoral detail is provided by O'Mahony (1999) who presents a study on sectoral productivity growth in five countries: France, Germany, Japan, UK and US for the period 1950- 1996. In total 34 sectors are included, 16 non-manufacturing and 18 manufacturing. It does not only provide comparisons of productivity growth rates but also of productivity levels, using a combination of consumer and producer PPPs. Capital stock is subdivided into structures and equipment, and labour into three skill levels. The study contains an elaborate description of the various sources used and problems encountered in compiling the various national data sources. Eldon Ball, Bureau, Butault and Nehring (1999) provide a study of productivity growth in the agricultural sector in nine European countries (including Germany, France, Italy, The Netherlands, Belgium, UK, Ireland, Denmark and Greece) and the US for the period 1973-1993. They consider not only various types of land, capital and labour, but also take into account intermediate inputs.
In addition there are various national projects going on. These studies have a high level of sectoral detail. Oulton and O'Mahony (1994) replicated the Jorgenson et al. (1987) in a careful study for Britain and applied the KLEM-methodology to study productivity improvements for over 130 manufacturing industries over the period 1954-86. Workers were split into manual and non-manual workers, and by sex, three kinds of capital (plant & machinery, buildings and land, and vehicles) and two kinds of inventories (materials, stores and fuels, and work in progress and goods on hand for sale) are distinguished. Fosgerau and Sørensen (1999) present a KLEM study for growth in the Danish economy for the period 1966-98. Their main data-input consisted of annual input-output tables for deliveries between 19 industries and the use of 14 imported commodities in the 19 industries. Two types of capital input are distinguished, but labour has not been broken down by type yet. Results for Denmark are compared with those for the USA. For the Netherlands, van der Wiel (1999) provides a growth accounting study for 17 sectors for the period 1973-1995, using value added, labour cross-classified by age, gender and education, and 7 types of capital. For Germany, Falk and Koebel (1999) study determinants of factor demand in 27 manufacturing industries during the period 1978 to 1990, distinguishing
3 See http:/www.eco.rug.nl/ggdc/dseries/dataseries.htm.
10 capital, energy, three types of labour and intermediate materials. Other national databases are described in Appendix III.
3. INPUT-OUTPUT TABLES
To start with, we should use the recommendations of the Systems of National Accounts 1993 as our reference for a common language. The original production account framework used by Jorgenson and associates was explicitly based on the recommendations of the 1963 System of National Accounts (SNA). Aulin-Ahmavaara (2000) compares the Jorgenson production account with the new 1993 SNA and finds a high degree of consistency. This ensures that the use of the Jorgensonian framework will not conflict with data prepared according to the SNA standards.4
To get an index of the growth of material inputs used in industry i, Jorgenson, Gollop and Fraumeni (1987) (from here on JGF) assume that total intermediate input can be written as a translog function of the various individual inputs. Hence the corresponding index of intermediate T m input M is a translog quantity index of the individual intermediate inputs Mt (for convenience we consider material and energy inputs together) − = m m − m ln M t ln M t−1 ∑ vt [ln M t ln M t−1] m where weights are given by the average shares of each component in the total intermediate input m m m 1 m m m pt M t m value v = [v + v − ] , and v = with p the price of inputs from industry m. t 2 t t 1 t m m ∑ pt M t m The formula shows that to measure aggregate input in a particular industry, data on real growth of each input, and the current price share of each particular input m in the total value of intermediate inputs, is needed. This information can be derived from input-output tables. The input-output tables provide a consistent framework in which measures of output and intermediate inputs are collected. The backbone of the KLEM database should be a set of annual Input-Output tables in current and constant prices for each country, using a common industrial classification. The target-period is from 1970 to 1998 (including the latest I/O-benchmark in each country). The OECD I/O database is therefore a natural starting point.
4 However, she also points out some problems related to the classification of government activities and of valuation practices. These need to be studied more in-depth.
11 OECD Input-Output tables
The OECD I/O-database provides I/O-tables covering 10 OECD Member countries: Australia, Canada, Denmark, France, Germany, Italy, Japan, the Netherlands, the United Kingdom and the United States. In general, five-year benchmarks are presented, spanning the period 1970-1990. They are in both current and constant prices. Imported and domestic intermediate flows are given in separate matrices. The database uses a common industrial classification based on the ISIC Revision 2. 36 sectors are being distinguished (see Appendix Table I.1 for full listing). It is based on primary data supplied by statistical offices or experts in Member countries. Because large inconsistencies in definitions of concepts exist between national statistics and the international standards represented by the SNA, considerable efforts have been made to impose some uniformity on the data by sharing information about national practices with authorities in Member countries (OECD 1995).
From our European KLEM-perspective, there are a number of weaknesses in the OECD data base. These are discussed below.
Weaknesses in the OECD I/O-database:
Coverage of the dataset The coverage of the data set is limited in a number of respects:
- 1. The data set ends in 1990 and needs to be updated. Fortunately, the OECD will begin with updating in the near future.
- 2. With respect to the coverage of countries: - Finland and Spain are not included. - For Italy only one table is available (1985) due to a revision of their input-output exercises. - For Germany there is no pre-1973 table, nor one for the early 1980s.
- 3. The data set provides only tables on a 5-year interval. They need to be interpolated for the years in-between on basis of yearly national I/O-tables, or with some interpolation method such as RAS. This is further discussed in Appendix II.2. - 4. The dataset provides square industry-by-industry matrices, and not separate use matrices (which show purchases of commodities by industry) and make matrices (which show the principal and secondary production of commodities by industries). For most countries, the square industry-by-industry configuration was created by merging the use- and make-matrices. A few
12 countries, such as Japan compiled this matrix by simply converting commodity-based input- output tables using the commodity (activity) and industry correspondence (see OECD 1995, p.11). It would be desirable to have the two matrices separated in order to properly deflate the various cells and to facilitate the calculation of industry input and output PPPs and price indices.
- 5. Import matrices In the OECD data base domestic and imported intermediate matrices are given separately, although they are not independently derived. Every country in the database more or less made use of the import-proportionality assumption in the construction of the import matrices. This technique assumes that an industry uses an import of a particular product in proportion to its total use of that product (p.13). In first instance we might concentrate on total intermediate inputs. In a later stage the distinction between domestic and imports might be made again. This is especially important for smaller countries with high import shares (see e.g. Fosgerau and Sørenson 1999 for the case of Denmark).
Industry detail and comparability An important issue is the choice of the level of industrial detail and the industrial classification scheme in order to maintain international comparability.
- 6. The basic idea of the KLEM-project is to define a minimum level of industrial detail to which all national databases must conform. National databases could be at lower level of aggregation containing as much detail as desired but aggregation to this minimum set must be made available as well. In this way international comparability will be guaranteed. Actually this was the strategy chosen for the US-Canada comparison where the Canadian data-set was much more detailed than the actual number of industries for which comparisons were made (33 private business industries). For comparability reasons one has to chose for a particular industrial classification system, and a particular level of industrial detail. With respect to the industrial classification the following is of importance. Currently a number of industrial classification schemes are used. The United Nations adheres to the International Standard Industrial Classification, which is currently in its third revision since 1990 (ISIC rev 3). The general structure of rev 3 is the same as that of rev 2, but it provides a greater level of industrial detail (see UN 1990). In Europe, all countries are moving towards implementation of, or already have implemented, NACE (‘Nomenclature generale des activités economiques dans les Communautes europiennes') rev 1, developed by the Statistical Office of the European Communities (Eurostat) in 1990, as the succesor of NACE70. Member states were required to transmit data to Eurostat according to the NACE rev 1. It is currently used by Eurostat in the compilation of compatible 1995 I/O-tables (in co-operation with Beutel – see
13 below). NACE rev 1 is compatible with ISIC rev 3 up to the fourth digit, and only some minor coding differences exist.
The OECD data base presents data for 36 sectors using ISIC revision 2 as the industrial classification. As described above, ISIC rev 2 is not in use anymore by either European or North American statistical offices. Hence use of ISIC rev 2 will generate problems for updating and it will obstruct the attempt to institutionalise this work at statistical offices in the future. Therefore ISIC rev 3 or NACE rev 1 should be used as the reference system.5,6
As for the number of industries which could be distinguished, a first option is to adhere to 2-digit industries in NACE rev 1, as NACE rev 1 is used for all databases maintained by Eurostat (e.g. on labour input, discussed in the next section). A minimal set of 31 industries is given in Appendix Table 2.1, which is solely based on 2-digit NACE industries. This also ensures comparability of the European data-set with the US/Canada comparison after some aggregation at both sides.7 This proposal would imply that a number of industries distinguished in the OECD database have to be merged, notably drugs and medicines with chemicals, and shipbuilding and aircraft with other transport to keep the break with the OECD database will be minimal (these sectors are indicated by ± in the table).8
5 In Appendix Table I.1, I provide a correspondence table between ISIC rev 2 industries used by the OECD and the NACE 1970 and NACE rev 1 industries. More detailed tables can be found in Milana (1999) and Jon Haveman’s homepage (http://www.eiit.org/Trade.Resources/TradeConcordances.html). 6 A complication for the future will be that the industrial classification in the US and Canada has changed since 1997 with a switch towards the NAICS (North American Industrial Classification System). This system is a common system for US, Canada and Mexico. It is a ‘clean slate’ revision, that is, the changes made are fundamental. In addition to changes in the industrial classification, also some concept and definition changes take place including the treatment of auxiliaries (not anymore classified in the same industry as the parent company) and the classification of business based on the production processes it uses (previously it was based on a number of principles). Changes in the industrial classification include a number of important reallocations at the 2-digit level: publishing and logging industries are transferred from manufacturing to service industries, and eating and drinking places are moved from retail trade to a new industry called accommodation and food services. Otherwise, reconciliation with the SIC at a higher level of aggregation seems feasible. NAICS will be used for the new 1997 Benchmark I/O-table, scheduled in 2002. 7 The following industries should be merged with other industries, in the US/Canada data-set: mining industries, tobacco, textiles, lumber and paper industries; in the European data-set: office machinery and transport. 8 From the table, it appears that most OECD ISIC rev 2 industries correspond well with 1- or 2-digit NACE rev 1 industries. There are however a number of problems in the matching of the two classifications which require the use of more detailed data. As shown in the table, NACE 3-digit data is necessary to allocate some industries between wholesale and retail trade, and community, social and personal services (NACE industries 502, 504 and 527 which cover repair services), and between wood products and other manufacturing (361 furniture). Further, 3-digit data is needed to single out drugs and medicines, shipbuilding and aircraft industries. These industries were selected by the OECD because they are important technology-intensive and/or trade-intensive sectors. 4-digit data is needed to separate iron and steel from non-ferrous metals because both are included in one 2-digit sector in NACE rev 1. One can conclude that, at
14 However, there are good reasons to go beyond the NACE 2-digit level. First of all, as the so- called high-tech industries are an important dynamic part of current economic growth, it is highly desirable to single out some more detailed industries. The OECD determined on the basis of both direct and indirect R&D intensity the following high-tech manufacturing industries: aircraft (ISIC rev2 3845), office and computing machinery (3825), drugs and medicines (3522) and radio, TV and communication equipment (3832). In Nace rev 1, these industries are identified at a 2 or 3-digit level (see Milana 1999, Table 3c). From the investment side it is useful to separate communication equipment from radio and tv-production as the former is much more sophisticated than the latter. Similarly, it would be desirable to separate semi-conductors from other electrical apparatus nec (ISIC rev3, groups 321, 322 and 323). With respect to analysing the role of knowledge, it is desirable to subdivide the sector business services and real estate as the former sector is highly skill-based and shows dynamic growth in many countries (divisions 70-74). The production of software might also be considered as a separate industry as it is now in ISIC rev 3 (Division 72). Similarly, community, social and personal services are lumped together, but the health sector which is part of this, will be a large and growing sector by itself in the near future, especially as production in this sector in Europe will gradually move from public to private hands.9 As it might not be possible to distinguish these sectors for the whole period under consideration, attempts to do so for more recent periods are rewarding for future work. Provided that data becomes available at several aggregation levels, changes in the level of detail over time and across countries will not be a problem.
- 7. It is claimed that the OECD IO-tables data set was made comparable in terms of industrial classification and output concepts. However, there is some doubt about the consistency across the various countries, especially with regard to the service sector. One might need to go back to the original national sources for further investigation and reconciliation.
- 8. Imposing uniformity forced the OECD to formulate a number of conventions on their IO- tables, which are sometimes against the recommendations of the SNA, but were necessary to
least in terms of industrial classification, the NACE rev.1 at a 3-digit level can be easily matched with the OECD classification, except for the iron and steel industry. 9 It is well known that government services (which include in most countries government administration, education and health) are hard to measure. Output valuation in the latter sectors is partly estimated by employees’ income with some allowance for capital consumption. Hence it is recommended in the productivity analysis to look also at the market sector which excludes government services in the non- market sector (O’Mahony 1999, p.4).
15 ensure comparability.10 Because of this it is better to speak of tables in a common format rather than internationally comparable. Improvements might be made in this respect.
Hedonic prices - 9. In the OECD database deflators used for office and computing machinery in Japan and the US show particularly large declines relative to the other countries. This is due to the use of a quality adjusted deflator. It is clear that the use of hedonic price indices is to be preferred, especially for industries such as computers and telecommunication. However, until now they are not used in the national accounts for European countries, except in France and Denmark. The use of hedonics should preferably be harmonised across the different national databases. In a first stage one might opt to use the US hedonic deflators for the computer industry in all countries as suggested by Wyckoff (1995), not only for output but also for investment. Insofar developments in mark-ups and distribution margins do not differ too much in Europe from the US, this is a reasonable approximation for goods which are intensively internationally traded. A more sophisticated alternative is proposed by Schreyer (2000). He uses a harmonised price index based on the assumption that the difference between price changes for ICT-capital and non-ICT capital goods are the same across countries. At a second stage, one might decide to devote considerable attention to develop country- specific hedonics. Triplett (Brookings Institute) is embarking on a project to generate hedonics for various of the countries involved in the European KLEM project for computers, semi- conductors and telecommunication equipment. The basic idea of this project is to provide statistical agencies with a standard regression, assuming that hedonic functions are the same in all countries. These new deflators could be used for the KLEM-project in a later stage.
10 For comparability reasons and analytical purposes, national data has been adjusted in ways not recommended by the SNA. - This is the case with the treatment of government services and imputed interest of domestic banks and other financial institutions. Government services are included in final demand rather than in intermediate input. Imputed interest is distributed among sectors rather than treated as intermediate consumption of a fictive financial sector (OECD 1995, p.19/20). - The use of basic prices is recommended by the SNA. However, in all European countries except Denmark, use is made of producer prices rather than basic prices and hence net commodity taxes paid by the producer are included. This means that the I/O coefficients do not precisely describe technological relationships, but include distortions in the producer’s price system caused by these taxes. Value-added tax on the other hand is excluded, as it should be, in all countries but Denmark and Germany (ibid. p.10). - The difference between producer’s and purchasers’ prices- the trade and transport margins- have been allocated to the margin industries (retail and wholesale trade, transportation and ware housing, and insurance) (p.10) although it is unclear which proportions have been used. For Germany and France, all the margins associated with the intermediate flows have been included in the wholesale and retail sector (p. 13). - Specific rules were used for the so-called special industries (sectors 33-36), including a.o. the rule that government enterprises which sell products via market transactions should be assigned to the industry in which they compete (see p. 11).
16 Eurostat Input-Output tables Harmonised five-yearly input-output tables have been published for a number of European countries since the early sixties and are available in the input-output tables dataset of Eurostat. The national io-tables produced by the National Statistical Offices and sent to Eurostat, are expressed in national currencies. Based on them, Eurostat computes consolidated tables for the EC expressed in ECUs. The tables are in current prices and can be obtained from Eurostat. The data set is far from complete however and there is a large lag in delivery of the NSOs (Beutel 1999, Table 5). Consequently, the latest set of harmonised I-O tables is for 1985. Assigned by Eurostat, Beutel provides more recent updates of the tables based on national accounts data and a projection method called EURO, whose basic idea corresponds to the RAS method (see Beutel 2000). The results have been published in Beutel (1999). Other scholars have also worked at harmonising EU tables, including deriving a set of tables at constant prices (also up to 1985), see Hoen (1999). As yet, it is unclear what these databases offer in addition to the national publications. Interesting from the perspective of future development of the KLEM-project is the new input-output framework of the european system of accounts (ESA 95). Supply and use table are an integral part of the system. According to regulation, the member countries of the Union are now requested to submit annual supply and use tables and five-yearly symmetric input-output tables to Eurostat. The classifications are totally compatible with those used within the framework of the UN. Detailed (60 by 60) supply and use tables for 1995 to 1999 should be delivered in 2002 (Beutel 1999).
4. LABOUR INPUT
Labour input is often measured as the number of workers or total hours worked which does not take into account the quality of the labour force. Ideally, the labour input index would divide the employed labour force into groups according to every economically relevant characteristic such as age, sex, educational attainment, work experience, on the job training, and so on. These different labour groups are distinguished by differing levels of marginal productivity. The importance of a characteristic is determined by the extent to which it discriminates between classes with different productivity. For the aggregation of labour input it is generally assumed that the aggregate labour T T input (L ) is a translog function of the quantities of individual labour types (Ll ).
T − T−1 = T − T−1 ln L ln L ∑ vl [ln L l ln L l ] l
17 where weights are given by the average shares of each labour type in the total value of labour T T 1 − p L compensation v = [v T + v T 1 ] and v T = l l with p the price of labour services (wages) l l l l T T l 2 ∑ p l L l l from labor type l.11 Ideally one would like to have annual series on hours worked, cross-classified by a number of important labour characteristics for each industry. Ho and Jorgenson (1999) distinguish 168 labour categories and show that for the growth of labour quality in the US during 1973-1995 changes in educational attainment were by far the most important determinant. The effects of trends in sex, age and class of employment were small and mutually offsetting, although for smaller subperiods this might not be the case. To apply the Divisia index for aggregation as shown above, quantities of each labour type need to be weighted by their marginal product. Hence data on labour costs per hour worked for each type of worker is needed, for each year and each of the 36 industries. Possible international datasources for hours worked and labour costs are discussed in turn below.
4.1 Hours worked
EUROSTAT Labour Force Survey Within each EU-country, the national statistical agencies carry out labour force surveys on a regular basis. Since 1983, these are held annually within a common framework, co-ordinated by Eurostat. Eurostat also centrally processes the data which ensures that the degree of comparability of the EU Labour Force Survey results is considerably higher than that of any other existing set of employment statistics. The characteristics of these Labour force surveys (LFS) are the following (Eurostat 1998a): - It is a household survey and covers both employees (full and part time), self-employed unpaid family workers and government workers. - The NACE industrial classification is used. - Employment and hours worked are available for the period 1983-1997 for different labour types by branch of activity. Eurostat has access to the individual household records for each country. This means that the possibilities for cross-classifications of labour-types are numerous. The records include details such as economic activity according to NACE 2-digit, sex, age and professional status of the labourer. Highest level of education or training completed is included in the survey from 1988 onwards.
11 The underlying assumption for this aggregation formula is that wages reflect marginal labour productivities. Due to labour market regulations, the validity of this assumption might differ across countries and over time. Econometric estimates might be used as an alternative.
18 - Data for the full period is available for Denmark, France, Germany, Italy, Netherlands, Spain and United Kingdom, but only for 1995-97 for Finland and Austria (Eurostat (1998c)).
To work with this data set a number of adjustment steps must be taken. 1. Hours worked per week which must be transformed in hours worked per year using assumptions about the number of weeks worked per year. 2. The data set uses NACE classification. This is not a problem if we chose to adopt NACE as well. 3. The LFS does not provide correct levels of employment (probably because it is based on a sample survey), and it is advised by Eurostat to use the Employment benchmark series for levels (Eurostat 1998a). These series are available from 1985 onwards and based on published national data sources. It is probably not cross-classified by labour types, but we could use proportions from the LFS for one year to get an accurate benchmark level comparison. Alternatively, Eurostat can deliver sample weights of the LFS which can be used to estimate population levels. As for the I-o-tables, control totals for total employment of the national accounts (when available) can be used in combination with proportions from the LFS. 4. The main problem with the EUROSTAT Labour Force Survey (LFS) is that the series start in 1983, and hence do not cover the period 1970-82.
The big advantage of using the LFS is that national series are put on a comparable basis and that it provides the opportunity to cross-classify labour input. The level of detail necessary for cross- classifications is difficult to assess. As shown above, educational attainment is by far the most important labour characteristic. At the moment, Eurostat distinguishes only 3 levels of educational attainment: workers with less than 5 years of secondary schooling, workers who finished secondary schooling and workers with more than secondary schooling. A more detailed classification should be possible, but there are serious problems in comparing educational systems, and hence levels of educational attainment, in the various European countries. Countries often report according to the levels distinguished in the ISCED (International Standard Classification of Education) which classifies educational attainment in seven categories, see http://unescostat.unesco.org/uisen/pub/pub0.htm). However, as schooling systems differ, international comparisons are better made using years of formal schooling. OECD Indicators (1998b, Annex I) provides an international comparison of ISCED levels and years of schooling. The number of years of schooling can be used to define particular types of labour by level of educational attainment. Unfortunately, this data is often not available at a sectoral level whereas data on occupation more often is. Occupation is reported according to the International Standard Classification of Occupation (ISCO) 1988. The ISCO classification relies on various criteria, including the complexity of the tasks performed. Occupational groups can be used a proxies for
19 levels of educational attainment, but only as a broad distinction between high and low skilled labourers as shown in OECD (1998a). The OECD Skill database gives employment by sector and occupation for a number of benchmark years between 1980-1991 (1970-90 for Finland) including all major European countries, but not Denmark and the Netherlands. As pointed out by Mason and O'Mahony (1999), even years of schooling is a deficient measure of human capital since they are based on a record of attendence rather than attainment. They do not allow for variation in the quality and content of schooling. Therefore, they rely on a proxy based on education and training output, such as certified qualifications. They show the difficulties involved in matching US data on educational attainment and attendance to data for European countries, especially with respect to vocational schooling. O’Mahony (1999) provides a subdivision by three skill levels for US, Germany and UK by sector of industry.
The cross-classification of labour quantity cannot be discussed adequately without considering the available data on labour costs. Data on wages need to be available at a corresponding level of detail as the quantity data. Hence we turn to a discussion of data sources on labour costs first.
4.2 Labour costs
The labour costs should be considered from the producer’s point of view, and hence not only include direct and indirect employee wages, but also other costs incurred by the employer, such as income taxes and social security contribution (insurance premiums, pensions etc.). The difference between the two (the tax wedge) differs considerably across countries ranging from 30 per cent in UK to over 50 per cent of total labour cost in Germany and Belgium (in 1998, source OECD). There are two international sources for data on labour costs which are useful: Eurostat’s Labour cost survey (LCS) and the surveys organised by the Luxembourg Income Studies (LIS). The last one is the most promising because it has earnings classified by type of worker. However, the LCS is probably still useful to tackle the biggest problem of the LIS which is that it collects earnings, rather than labour costs.
Luxembourg Income Studies for labour costs The Luxembourg Income Study LIS is a co-operative research project with a membership that includes 25 countries in Europe (including all our countries), America, Asia and Oceania. The LIS project began in 1983 under the joint sponsorship of the government of Luxembourg and the Centre for Population, Poverty and Policy Studies (CEPS) and is located at the CEPS/INSTEAD Institute in Differdange in Luxembourg. The project is mainly funded by the national science and social science research foundations of its member countries. It aims to unify and standardise
20 micro survey data on income and earnings on a household and personal level which have been collected by national statistical agencies. Present director is Timothy Smeeding.12
Characteristics of the Luxembourg Income Studies (LIS) are the following: - The surveys are held irregular, but mostly at an interval of about 5 years. The benchmark dates differ per country. Most series start in the 1980s, but earlier surveys are available for Germany and UK. - It collects earnings per hour worked for workers cross-classified by age, sex, education, occupation and industry. - In principal it only covers earnings, but for some countries it may also include (part of the) employers contributions. This can be checked for each individual country.
Problems with using the Luxembourg Income Studies - there are inconsistencies between benchmarks because the framework for conducting the surveys is sometimes altered. - the surveys are based on samples. Hence with a detailed cross-classification of the labour force, sample sizes for a particular group of workers can become too small. - there is no data for the 1970s for Denmark, France, Italy and the Netherlands. - In principal the survey covers only earnings. This problem is the most serious. As a solution we can use the Labour cost survey from Eurostat (Eurostat 1998b).
EUROSTAT Labour Cost Survey (LCS) Characteristics of the LCS are the following: - Establishment survey held in 1975, 78, 84, 88, 92, 96. - Covers industry and certain services sectors - Industrial classification NACE 70 up to 1988, NACE rev 1 from 1992. - Labour costs per employee and per hour worked. Labour costs are costs born by employers in order to employ workers (direct and indirect costs)
Problems with EUROSTAT Labour Cost Survey - No cross classification by labour type is available. - Only employees are covered. - years in-between need to be interpolated - not all economic sectors are covered - covers only firms with 10 + employees.
12 More info is available at http://lissy.ceps.lu/access.htm.
21 Because of these problems, the LCS is only useful as an addition to the Luxembourg Income Studies data to calculate an industry-specific ratio of employee earnings and total labour costs, which can subsequently be applied to the earnings data from the Luxembourg Income Studies.
( N.B. Eurostat has also a Structure of Earnings Statistics which is an establishment survey and gives cross classification by age, sex, occupation and level of education. However, it only covers employees, gives only employee earnings instead of costs and most importantly, is only available for 1995. Using 1995 ratios for the whole period 1970-1996 seems rather tricky.)
4.3 Proposed Cross-Classification for Labour Input
Given the discussion above, the following minimal set of characteristics for the cross- classification of labour-input is proposed: a cross classification by sex, educational attainment and age. These characteristics are considered to be the most important determinants of earnings (see also OECD 1998b, section F7). More specifically, educational attainment can be subdivided by 4 educational types: university level and above (> 16 years of schooling), non-university tertiary education (> 14 ), upper secondary (>12) and below upper secondary education (< 12). Similarly 4 age classes are distinguished: below 25 years, 25-34, 35-54 and more than 54. In effect, this means 32 labour types.13 A particular interesting expansion of the database would be to separate out R&D personnel often defined as professionals engaged in the conception and creation of new knowledge, products, processes, methods and systems and in the direct management of the projects concerned (see Mason and O'Mahony 1999).
For hours worked by labour type, the EUROSTAT Labour Force Survey can be used. For labour costs, use can be made primarily of the LIS dataset, using the Labour Cost Survey to convert employee earnings into total labour costs. There remain a number of points which require attention: - As the mentioned data sources mostly start in the beginning of the 1980s, members have to collect comparable data for the 1970s themselves using similar classifications. - Consortium members should check for benchmark consistency of the LIS data. - It should be kept in mind, that the more detailed the desired cross-classification of labour input, the less reliable the results will be due to the fact that the LFS is only a sample survey. Especially for countries with a small labour force, the number of observations for a particular labour-type can quickly become too low. There is a specific national trade-off between level of detail and
13 The Canada-US comparison uses 2 sex types (male/female), 7 age types (up to 18; 18-24; 25-34; 35-44; 45-54; 55-64; 65+ ), 2 class types (employees, and self-employed and unpaid workers) and 5 education types (up to elementary school; 1-3 years high school; 4 years high school; 1-3 years college; 4+ college). This leads to 2 x 7 x 2 x 5 = 140 types of workers.
22 accuracy of the estimate. Note also that as a first approximation we can use total economy estimates (or broad sectors), instead of detailed industry estimates. It is not clear whether education or age premiums differ across industries. If not, we can economise here on data collection efforts.
5. CAPITAL INPUT
Similar as for labour input, for capital input quantities of capital and the prices of capital services for the period 1970-1996 for each industry are needed. The Perpetual Inventory Method is commonly used to estimate capital stocks. This method requires series of real investment and efficiency patterns for each asset. To aggregate the different assets into one index of capital input, one needs rental prices of capital services (see appendix II for a summary of the JGF method for capital measurement). In this section international data sources on capital stocks are discussed first, followed by a discussion of rental prices.
5.1 Capital stock
For the measurement of productivity, the productive capital stock is the appropriate indicator for a quantity measure. The productive capital stock of a particular asset is the sum of all past investments with each age group adjusted for the loss in productive efficiency and for the retirements that have occurred since it was new (see discussion in OECD 1999a). Capital stock measurement varies greatly across statistical offices in Europe. Generally the PIM is used, but the assumptions for age-efficiency patterns differ considerably (OECD 1993). Insofar differences in assumptions are based on true national differences, this diverging practice can be justified. However, there is little empirical evidence that actual differences are indeed as big as suggested. Hence to ensure internationally comparability, the assumptions of common age-efficiency patterns seems to be fit in a first stage (as long as the basic investment series are available, various patterns can be tried).
Four basic ingredients are required to construct the productive capital stock: - time series of investment expenditure on each asset type. - purchaser price indices of investment goods to deflate investment expenditure - retirement patterns to account for discarded assets. - age-efficiency patterns which account for the loss of productive capacity of capital goods as they age.
Investment series at constant prices
23 The OECD provides detailed capital flow matrices for a number of benchmark years (OECD 1995), and aggregate annual investment series from the National Accounts (ISDB database).
OECD Capital flow matrices Together with the I/O-table data set described in section 3, the OECD compiled comparable reproducible capital flow matrices for the 10 countries using the same benchmarks years as for the I/O-tables. There are a number of comparability problems when using these tables (OECD 1995, pp.14 ff.)
Problems with using OECD capital flow matrices - 1. the sectoral detail of the capital flows is less than for the input-output tables. Especially in Denmark and France this is a big problem (ibid. Table 5) -2. as OECD series start only in 1960, they are not long enough to generate a full stock estimate for 1970. Longer investment series from Maddison might be used for this purpose, or national data sources. Otherwise a simple shortcut could be used, for example assuming that investments are a function of GDP and estimating the strength of the relationship. Or on the basis of historical time series estimates for longer periods (see OECD 1998c, p.45 vv). - 3. the tables are not on an annual basis but annual data is mandatory for using the perpetual inventory method to build up a stock estimate. To interpolate between the benchmarks, we can use annual series on aggregate investment from the national accounts, as published in the OECD, Annual National Accounts, Detailed Tables. This source gives annual series on total investment by industry (30 sectors). For the aggregate economy, the OECD National Accounts also provides a detailed breakdown of investment for the period 1960-1995 on residential building, non- residential building, other construction, land improvement, producer durable, subdivided by transport equipment and other equipment, breeding stock, and inventories. An industrial breakdown could be generated by a modified RAS procedure, taking the OECD benchmark capital flow matrices described above as an initial guess. National accounts data could be used as marginal control totals (see Appendix II.2 for interpolation techniques). - 4. conceptual problems such as ‘the identification and inclusion of government expenditures in capital, whether or not leased and rented structures and equipment are allocated to their users or to their owners, the inclusion or exclusion of residential expenditures on equipment and structures and the allocation of expenditures on repairs and maintenance on capital equipment and structures’ have not been solved.
Eurostat/Beutel dataset Another source is the project initiated by Eurostat and carried out by Jörg Beutel to derive comparable capital stocks for all EU members for the period 1960-1997. The database covers 25
24 industries and three types of capital goods (buildings, machinery and transport equipment) for the period 1959-97. This industry grouping covers 25 industries with less detail in manufacturing industries compared to the OECD grouping (see appendix table I.3) (Beutel 1997). Investment series were taken from Eurostat, including estimates backwards as far as to 1870. Aim of this project was to arrive at an estimate of the capital stock for the EU as a whole, including countries which NSOs had done little so far on collecting data on capital formation and stock. The capital stock estimates were based completely on data available within Eurostat. The data for France, Germany, Italy and UK was most reliable, for other countries various assumptions and proxies had to be used, which are documented within the worksheet files made by Beutel (Beutel, personal communication). The data set is currently not open for public use but can be consulted in part through Eurostat’s Cronos database. This capital stock data set might be used in a first stage, but needs to be cross-checked and supplemented with national sources, for example O’Mahony (1999) or Lutzel (1997).
Efficiency and retirement patterns
Different patterns of efficiency can be chosen such as one-hoss-shay, straight-line or geometric efficiency patterns. There are both theoretical and empirical reasons to opt for a geometric decline. A theoretical advantage is that a geometric pattern of efficiency decline generates a similar pattern of depreciation (i.e. with the same geometric decline). Hence the value of the stock is proportional to the services it delivers, that is, there is no difference between the wealth of the capital stock and the productive capital stock. Age-price profiles have the same shape as age-efficiency profiles which greatly enhances its analytical tractability and ensures consistency between efficiency used in estimate of the productive capital stock and the measure of depreciation used in the estimation of the user cost of capital. From an empirical point of view, several (US) studies have shown that the geometric decline pattern fits empirical data on depreciation rates. This pattern is extensively used by Jorgenson and associates and is now officially adopted by the BEA.14 With a geometric pattern, the rate of depreciation δ depends on the declining-balance rate R and the asset service lifetime T, which can be separately estimated (see Fraumeni 1997 for an overview and references). The seminal work of Wykoff and Hulten (1980) provides empirical estimates of geometric decline rates.
14 This method is still not undisputed in other countries which is clear from discussions within the so-called Canberra-group. This is a study group of national statistical agencies which looks into the question of standardization of capital stock estimation, stocktakes present methods and develops a manual on capital stock measurement.
25 ICT-capital and software
Capital assets are normally classified in three broad groups: buildings, machinery and transport equipment. Recently, attention has been focused on separating out ICT-capital. In principal the OECD capital flow matrices provide a breakdown of investments by origin. Non-electrical machinery, Office and computing machinery and Radio, TV and communication equipment are separately distinguished. This gives an idea about the proportion of ICT investment in total. Schreyer (2000) provides a first estimate of the contribution of ICT input to output growth in G7 countries, using an alternative source. He uses data from the International Data Corporation investments in IT hardware and telecommunications spending. Investment from this source is combined with national accounts data on total investment expenditure to ensure consistency. However, as the coverage and reliability of the IDC data is still unknown, additional sources are necessary, like census and automation surveys, to get a proper idea of investment in ICT. These are available for the UK (Lansbury, Soteri and Young 1997) and are now being produced for the Netherlands and France. Similarly, data on software investment is an important target for further research. The 1993 SNA recognises software as an investment good and some countries have started to implement this recommendation in their national accounts. It is uncertain whether historical series for software investment will be produced and international comparability of available data is still weak, for example with respect to the valuation and measurement of in-house produced software.
5.2 Prices of capital services
For the aggregation of capital services over the different asset types it is assumed that aggregate services are a translog function of the services of individual assets (see JGF 1987, Chapter 4). The corresponding index of capital input KT is a translog quantity index of individual capital T inputs K k.
T − T−1 = T − T−1 ln K ln K ∑ v k [ln K k ln K k ] k where weights are given by the average shares of each component in the value of property T T 1 − p K compensation v = [vT + vT 1 ] and v T = k k with p the rental price of capital services k k k k T T k 2 ∑ p k K k k from asset type k.
26 There are no international sources for capital costs at the detailed level required for this study. User cost of capital is defined in a standard way as the rate of return plus depreciation minus real capital gains. There are two principal ways to estimate user cost of capital, dependent on the approach towards measurement of the rate of return: the residual, or ex-post approach, and the opportunity, or ex-ante approach. The latter uses some exogenous value for the rate of return, for example interest rates on government bonds. The former approach in the spirit of Hall and Jorgenson (1967) estimates the internal rate of return with the help of an accounting identity. Given the value of capital compensation from the NA, depreciation and the capital gains, an internal rate of return can be estimates as a residual (see OECD 1999a for full discussion of these alternatives). As long as capital compensation data is available at the required level of detail, the latter approach is to be preferred and is frequently used by Jorgenson and associates because it ensures complete consistency between income and production accounts. Using this method, Dougherty (1991) provides a step-by-step procedure to arrive at rates of return. Normally, data on property compensation for an industry can be collected from the national accounts.15 Capital consumption follows from the efficiency patterns used in the estimation of the capital stock. Revaluation can be derived from asset price indices.
JGF (1987) and Jorgenson and Yun (1991) provide a more elaborated formula in which capital income taxes are included as well (see appendix II for outline). As a start one could apply the standard formula for user cost, ignoring the differences in tax treatment.16 The trade-off between benefits and costs of additional research seems most important here. For a full implementation, historical and current tax laws have to be monitored by type of asset and by industry. Additionally, one of the main findings of Jorgenson and Yun (1991) was that there were large differences in the tax treatment of capital in corporate and non-corporate enterprises in the US. Taking into account this difference would imply that capital stock estimates must be made separately for these sectors, but prospects are bleak (see Dougherty 1991, p.2.26). Taxes will also differ between asset types, especially between long- and short-lived assets.
5.3 Proposed Cross-Classification for Capital Input
The level of detail with regard to asset types depends both on the availability of quantity data and rental prices. Ho, Jorgenson and Stiroh (1999) provide a breakdown of the growth of fixed reproducible capital input in the US economy. Their table 3 shows that capital quality added 0.62% to the 3.28% annual growth in raw capital input for the period 1948-96. Of this 0.62%,
15 E.g. note that the value-added sector “Operating Surplus” in the OECD national accounts must be split up in compensation for self-employed and compensation for property. The last item must be added to capital compensation. (N.B. this is much easier when separate data on corporate and non-corporate capital stock is available see JGF, pp.124-128.)
27 0.42% was picked up by distinguishing 5 broad asset types: high-tech equipment, low-tech equipment, non-residential structures, residential structures and consumers' durable. The remainder of 0.2% was due to lower level asset type substitution. For the most recent period (1990-96), capital quality change was almost completely due to substitution between the 5 distinguished assets and in particular towards high-tech capital. Hence a classification of capital goods into 4 categories: residential structures, non- residential structures, high-tech equipment (consisting of computers and peripheral equipment, communications equipment, instruments and photocopy and related instruments) and low-tech equipment (all other machinery and transport equipment) is essential.
Possible extensions Of course, more detailed asset types could be considered, e.g. transport equipment could be separated out. It is also possible to include other asset classes: - Land Inclusion of agricultural land stock. For this we can use the results of work on international comparisons of agricultural productivity, which provides among others land estimates for US and nine European countries, including all but Finland and Spain, for the period 1973-93 (Eldon Ball, Bureau, Butault and Nehring 1999). The basic data was derived from Eurostat. They also give land values per hectare which were used in a hedonic regression technique to arrive at PPPs for quality adjusted land for 1990. Data on quantity and value of non-agricultural land is scarce. Proxies could be made using available data on the ratio of land value and the value of structures, and data on growth rates of urban land from the US and INSEE (Dougherty 1991, p.2.29) - Inclusion of consumer durable. This would require an extra sector (the private sector). Output of this sector should consist of consumer durable services and owner occupied residential housing. This would also require a distinction of residential housing into owner and tenant occupied. For consumer durable one can use OECD NA data on expenditure on durable to build a stock. - Inclusion of inventories. National data on inventory stock can be gathered from balance sheets. As countries have stock revaluation in their national accounts, the statistical agency must have industry detail of inventory. The OECD national accounts provide also changes in inventory.
With respect to user-cost calculations. - Taxes could be added in the user-cost formula's at a later stage. Useful references for tax policies include Dougherty (1991), Jorgenson and Landau (1993), Chennells and Griffith (1997) which give the change in corporate tax rates from 1979-1994 for 10 countries (not including Netherlands and Denmark) and Ken Messere (ed 1998) (which studies all countries but
16 Unfortunately the discrepancy in results between the simple and more sophisticated method is unknown.
28 Denmark). The OECD also has a database on taxes which could be explored for its usefullness for the KLEM-database.
6. PURCHASING POWER PARITIES (PPPs)
International data sources for output PPPs Purchasing power parities are necessary to put the national growth experiences in an international level comparison. This requires a matrix of PPPs for final output and intermediate inputs. As the analysis is from the viewpoint of the producer, output PPPs should be at producer prices while the intermediate input PPPs should be at purchaser prices. Output PPPs are not available from international organisations and need to be constructed. The ICOP-project at the University of Groningen, NIESR (London) and CEPII (Paris) have constructed bilateral producer price parities for a large number of countries primarily with the USA as the benchmark country. Most countries cover manufacturing, and in some cases services are included. However, there are still major gaps in the data sets (van Ark 1996a). The use of data recently collected by Eurostat for the compilation of national producer price indices in manufacturing for a number of European countries (Prodcom database) will be investigated (see also Van Ark and Monnikhof 1996). There is a possibility to work in a cooperative setting with Eurostat to obtain underlying bilateral PPPs for all the countries involved. Use can also be made of expenditure ICP PPPs collected by OECD and EUROSTAT (since 1990 on an annual basis), which then have to be ‘peeled off’ for taxes and distribution margins, and corrected for imports and exports, to arrive at producer price parities using the method outlined by Hooper and Vrankovich (1995). Pilat (1996) discusses various problems of the peeling-off method. In principle peeling-off is useful when the expenditure PPP to start with are really based on a comparisons of expenditure prices, which is doubtful for a number of service sectors. Also the adjustment for export and import in the method is based on rather strong assumptions. A particular important problem is that ICP PPPs are based on final expenditure categories and hence are difficult to assign to a particular industry. Moreover, the output of intermediate products is not covered by the expenditure PPPs. The use of ICP proxies only, as for example in Jorgenson and Kuroda (1990) and Lee and Tang (1999) is not fully satisfactory. Using a combination of output PPPs from ICOP, which do cover intermediate inputs, and expenditure ICP PPPs seems to be the best way forward (van Ark 1996a).17
17 The Castles-report discusses some more fundamental weaknesses of the ICP PPP methodology and results in practice. The OECD PPP-web page provides a very useful source of further information on PPP- methodology and results, including the Castles-report (see http://www.oecd.org//std/ppp/pps.htm). One of
29 Method Using a translog aggregation scheme, the output PPP for an industry j can be defined as a weighted average of the output prices of all products i produced in industry j
j = ij ij ln PPPY ∑ v Y ln PPPY i j ij with PPPY the relative producer price of industry j (output indicated by Y), PPPY the relative 1 producer price of output i in industry j and v ij = [vij (Ger) + vij (USA)] the average of the Y 2 Y Y share of product i in output value of industry j in say Germany and the USA. An intermediate input PPP can be defined in a similar way.
Multilateralisation The method described above gives PPPs for bilateral comparisons. However, these comparisons are not transitive, i.e. PPPAB x PPPBC 333AC. This means that indirect comparisons via a link (base) country are different from the direct comparisons. Hence multilateral PPPs are preferable if all countries are directly compared with all other countries. Multilateralisation, when desired, can be attempted afterwards, using various methods (see Pilat and Rao 1996, Rao and Timmer 2000 for overviews).
International data sources for factor input PPPs Input PPPs for labour and capital services are not available from international sources and need to be developed from national data.
The relative price of labour is defined in a similar way as relative commodity prices using a translog index. It indicates the ratio of average labour compensation per hour worked in the two countries adjusted for differences in quality insofar different types of labour are distinguished.
W ij (Ger) ln PPP j = vij ln L ∑ L ij i W (USA)
j ij with PPPL the relative labour input price of industry j (labour input indicated by L), W the price of one hour of labour type i in industry j in Germany or the USA, and 1 v ij = [vij (Ger) + vij (USA)] the average of the share of labour type i in the total labour input L 2 L L
the issues concerns the intertemporal consistency of the results of the various ICP-rounds (see also http://www.oecd.org//std/nameet99/index.htm).
30 value in industry j in Germany and the USA. The price of labour should be at purchasers costs, and therefore should include all the costs which have to be incurred by the employer (including taxes, insurance and pension premiums etc). Data on wages has been discussed in Section 4 and especially data from the Luxembourg Income Project seems to be very useful.
Similarly as for labour, capital PPPs give the relative price of the use of capital in the two countries again from the purchasers standpoint. The calculation of the capital input PPP is less straightforward than for intermediate input PPPs. This is because PPPs for new investment goods are available from the ICP, but PPPs for capital input are not. Capital input depends on the investments made in the past and hence the aggregate capital input PPP will differ from the new investment good PPP if the rate of inflation is different between the countries, the rate of return is different, the rate of depreciation is different and/or the composition of the capital stock is different (see Jorgenson 1995b, p.191 and 340). First we need to derive an investment PPP for a particular industry which has to be transformed with so-called annualisation factors into PPPs for capital input. Using a translog aggregation scheme, the investment PPP for an industry j can be defined as a weighted average of the prices of all the investment flows into industry j
j = ij ij ln PPPI ∑ v I ln PPPI i j with PPPI the relative investment good price of industry j (investments goods indicated by I), ij PPPI the relative purchaser price of investment good i in industry j and 1 v ij = [vij (Ger) + vij (USA)] the average of the share in the value of deliveries of new I 2 I I investment goods i to industry j from other industries. These shares can be calculated from investment flow tables. Investment good PPPs can be transformed into an aggregate capital input PPPs using the following transformation j j = j C (Ger) PPPK PPPI C j (US) where C indicates the user cost of one currency unit’s worth of capital stock in industry j. User costs have been described in the previous section.
31 7. SUMMARY
This report has discussed the various international data sources which are available for setting up a KLEM-type international data base, focussing on four classes of data: input-output tables, labour accounts, capital accounts and PPPs. For the input-output tables, OECD (1995) provides the best starting point. For the labour accounts, a combination of the Eurostat Labour Force Survey, the Eurostat Labour Cost Survey and the LIS earnings surveys is recommended. For capital input, use can be made of OECD (1995) combined with national accounts data. The difficult task ahead is linking the different sources in a comprehensive framework, ensuring international comparability. This report proposes a set of minimal requirements to which each national data set must adhere, allowing for maximum freedom in all other respects. To recapitulate these minimal requirements: for the input-output tables a list of 31 industries based on NACE rev 1 (which is identical to ISIC rev 3), preferably to be extended by inclusion of a number of high-tech sectors and fast-growing service industries. Labour input should be cross classified by sex, by 4 educational types and 4 age classes, and capital input by at least 4 types: residential structures, non-residential structures, high-tech equipment and low-tech equipment. With respect to the PPPs, using a combination of output PPPs from ICOP, which cover intermediate inputs, and expenditure ICP PPPs seems to be the best way forward.
32 APPENDIX I INDUSTRIAL CLASSIFICATIONS Appendix Table I.1 Concordance Table ISIC Rev 2 (used in OECD I/O-Tables), 1970 NACE and NACE rev 1. Number ISIC Rev 2 codes Description OLD 1970 NACE NACE rev 1
1 1 Agriculture, forestry and fishery 0 0 2 2 Mining and quarrying 1+21+23 10+11+12+13+14 3 31 Food, beverages and tobacco 41+42 15+16 4 32 Textiles, apparel and leather 43+44+45 17+18+19 5 33 Wood products and furniture 46 20+361 6 34 Paper, paper products and printing 47 21+22 7 351+352-3522 Industrial chemicals 25-257+26-252 24-244 8 3522 Drugs and medicines 257 244 9 353+354 Petroleum and coal products 252 23 10 355+356 Rubber and plastic products 48 25 11 36 Non-metallic mineral products 24 26 12 371 Iron and steel 221+222+223+311+312 27-274-2753-2754 13 372 Non-ferrous metals 224 274+2753+2754 14 381 Metal products 31-311-312 28 15 382-3825 Non-electrical machinery 32 29 16 3825 Office and computing machinery 33 30 17 383-3832 Electric apparatus, nec 34-344-345 31 18 3832 Radio, TV and communication equipment 344+345 32 19 3841 Shipbuilding and repairing 361 351 20 3842+3844+3849 Other transport 36-361-364 35-351-353 21 3843 Motor vehicles 35 34 22 3845 Aircraft 364 353 23 385 Professional goods 37 33 24 39 Other manufacturing 49 36-361 25 4 Electricity, gas and water 40+41 26 5 Construction 50 45 27 61+62 Wholesale and retail trade 61+63+64+65 50+51+52-502-504-527 28 63 Restaurants and hotels 66 55 29 71 Transport and storage 7-79 60+61+62+63 30 72 Communication 79 64 31 81+82 Finance and insurance 81+82 65+66+67 32 83 Real estate and business services 83+84+85 70+71+72+73+74 33 9 Community, social and personal services 9+67 75+8+9+502+504+527 34 91-93?? Producers of government services 35 Other producers 62 37
33 Appendix Table I.2 Proposed minimal set of industries Number Description NACE rev 1 Original OECD industry number
1 Agriculture, forestry and fishery 0 1 2 Mining and quarrying 10+11+12+13+14 2 3 Food, beverages and tobacco 15+16 3 4 Textiles, apparel and leather 17+18+19 4 5 Wood products 20 ±5 6 Paper, paper products and printing 21+22 6 7 Chemicals 24 7+8 8 Petroleum and coal products 23 9 9 Rubber and plastic products 25 10 10 Non-metallic mineral products 26 11 11 Iron and steel and non-ferrous metals 27 12+13 12 Metal products 28 14 13 Non-electrical machinery 29 15 14 Office and computing machinery 30 16 15 Electric apparatus, nec 31 17 16 Radio, TV and communication equipment 32 18 17 Professional goods 33 23 18 Motor vehicles 34 21 19 Other transport 35 19+20+22 20 Other manufacturing 36 ±24 21 Electricity, gas and water 40+41 25 22 Construction 45 26 23 Wholesale and retail trade 50+51 ±27 24 Restaurants and hotels 55 28 25 Transport and storage 60+61+62+63 29 26 Communication 64 30 27 Finance and insurance 65+66+67 31 28 Real estate and business services 70+71+72+73+74 32 29 Community, social and personal services 75+8+9 ±33 30 Producers of government services 34 31 Other producers 37 35
34 Appendix Table I.3 EUROSTAT R25 Industrial classification used in Capital Stock estimates
1 Agriculture, forestry and fishery products 2 Fuel and power products 3 Ferrous and non-ferrous ores and metals 4 Non-metallic mineral products 5 Chemical products 6 Metal products except machinery 7 Agricultural and industrial machinery 8 Office and data processing machines 9 Electrical goods 10 Transport equipment 11 Food, beverages, tobacco 12 Textiles and clothing, leather and footwear 13 Paper and printing products 14 Rubber and plastic products 15 Other manufacturing products 16 Building and construction 17 Recovery, repair services, wholesale, retail 18 Lodging and catering services 19 Inland transport services 20 Maritime and air transport services 21 Auxiliary transport services 22 Communication services 23 Services of credit and insurance institutions 24 Other market services 25 Non-market services
35 APPENDIX II.1 OUTLINE OF JORGENSON APPROACH TO CAPITAL INPUT MEASUREMENT
For the measurement of capital services we need capital stock estimates for detailed assets and the shares of capital remuneration in total output value.
Step 1: construction of capital stock estimates for all asset types. The common approach in capital stock measurement is the use of the Perpetual Inventory Method (PIM). In the PIM, capital stock is defined as a weighted sum of past investments with weights given by the relative efficiencies of capital goods at different ages according to (see JGF 1987, pp.40-49 for discussion).
∞ = ∂ A T ∑ t IT−t t=0
with AT the capital stock (for a particular asset type) at time T, ∂t the efficiency of a capital good of age t relative to the efficiency of a new capital good and IT-t the investments in period T-t. (An important implicit assumption made here is that the services by assets of different vintages are perfect substitutes for each other). The formula shows that for each asset type the following data should be available:
1. investment series at constant prices Ideally one needs investment series going back in time infinitely. In practice one can truncate after a certain period because of declining efficiency older vintages will not add to the capital stock anymore. 2. for each asset type a particular pattern of efficiency. The geometric pattern is used, hence with ∂ ∂ ∂ = − ∂ t−1 a given constant rate of depreciation different for each asset type, t is given by t (1 ) and it follows
∞ = − ∂ t = − ∂ + A T ∑ (1 ) IT−t A T (1 ) I T t=0
Step2 Aggregation over different capital types For the aggregation of capital services over the different asset types it is assumed that aggregate services are a translog function of the services of individual assets (see JGF 1987, Chapter 4). The corresponding index of capital input KT is a translog quantity index of individual capital T inputs K k.
36 T − T−1 = T − T−1 ln K ln K ∑ v k [ln K k ln K k ] k where weights are given by the average shares of each component in the value of property T T 1 − p K compensation v = [vT + vT 1 ] and v T = k k with p the rental price of capital services k k k k T T k 2 ∑ p k K k k from asset type k.
As long as we assume that the flow of capital services for each asset type is proportional to its stock (independent of time) we can rewrite aggregate capital input in terms of capital stock A
T − T−1 = T − T−1 ln K ln K ∑ v k [ln A k ln A k ] k
(N.B. in JGF 1987 it is assumed that capital services in period T are delivered by the stock at time T-1, rather than T. In that case the above formula should be changed accordingly.)
For the weighting of the different asset type inputs one needs the rental prices of capital services. These are difficult to observe in practice. In JGF an elegant method is proposed to solve for this problem ensuring consistency in the estimates of prices of capital services with the corresponding estimates of capital stock outlined above.
In the absence of taxation, the rental price of capital services at time T is given by T = T−1 T + ∂ T−1 − T − T−1 p k q k r q k [q k q k ]
with r the rate of return and qk the acquisition price of investment good k. This can be rewritten as pT k = (r T − πT ) + (1 + πT )∂ T−1 k k k q k q T − q T−1 with πT = k k , the rate of inflation in the price of investment goods (see Jorgenson and k T−1 q k Yun (1991) for a derivation). This formula shows that the cost of capital (rental price divided by the acquisition price) is determined by the rate of return, net of inflation, and the rate of depreciation, corrected for inflation. The cost of capital is an annualization factor that transforms the acquisition price of investment goods into the price of capital input.
37 The inflation rate can be derived from the investment price indices and the rate of depreciation from the rate used in the construction of the capital stock estimates. The problem is how to estimate the rate of return. To this end, the following assumptions are made: 1. there is perfect competition and no profits are made 2. the nominal rate of return (after tax) is the same for all assets in an industry 3. the sum of rental payments for all assets is equal to total property compensation Using these assumptions and data on property compensation for each industry, the rate of return in each industry can be determined.18 Finally, the determined rate of return in each industry can be used to calculate the rental prices and value shares (v) needed in the aggregation formula. Ideally taxes should be included to account for differences in tax treatment of the different asset types and different legal forms (household, corporate and non-corporate). The formulas above should then be adjusted to take into account these tax rates (see Jorgenson and Yun 1991).
APPENDIX II.2 Interpolation techniques
In general, use is made of a (modified) RAS procedure for interpolation between benchmark I-o tables. in which annual industrial output values are used to control for columns and row sums. This procedure is applied to I/O shares, not to absolute levels. However, it has been shown that RAS introduces significant errors (OECD 1995, p.9). Polenske (1997) provides an overview of the weaknesses of the RAS-method, stressing that overall mean errors from RAS are still very high. This was confirmed by our US partners. The intra-sector transactions estimates are particularly troublesome, since the diagonal, as it turns out, is particularly to any RAS procedure.19
Alternative measures have been developed, among others by Prof. Kuroda in which the resulting interpolated table is much closer to the original transactions table than the table generated by RAS (see Wilcoxen 1989). However, problems with negative numbers still remain if the row and column targets differ substantially from the corresponding totals of the starting point. Beutel (2000) also discusses the merits of the RAS method and variants. Further development of these methods is warranted. Also, for most countries there are more national I/O-tables available than given in the OECD data set. One might use this additional information as well in the interpolation exercise.
18 In practice, the estimated rates of return can lead to negative capital service prices. The basic procedure followed by the US counterparts was to never change the basic data from the national accounts, but instead change the rate of return by interpolation etc. 19 Personal communication with Bill Gullickson from BLS, 18 April 2000.
38 APPENDIX III COUNTRY EXPERIENCES WITH IMPLEMENTING KLEM
Appendix III.1 The Canadian Experience
[This text is based on meetings with Frank Lee, Wulong Gu and Jianmin Tang (Industry Canada) and René Durand (Statistics Canada) in March 1999]
Since January 1998, a KLEM-project for a US-Canada comparison is being implemented, similar to the proposed US-Europe project which is discussed in this report. To get an insight in the problems which are encountered when trying to implement the Jorgenson-methodology, a brief description of the Canadian experience follows. In general, it is clear that the whole exercise is highly labor-intensive and requires much time, even though parts of the data were already available from Statistics Canada beforehand. Also, the exercise can only be performed satisfactorily when the national statistical agency is closely involved in the project. For the Canada-US comparison using the Jorgenson-methodology, Industry Canada (Frank Lee) and Statistics Canada (René Durand) were involved for the Canadian part of the data, including the development of the PPPs. The period under consideration is 1961-1996 and 33 industries are covered (roughly 2-digit). The government sector was excluded beforehand, only private business was included.
Below we discuss particular problems with each of the four data-areas: Input-output tables, labor input, capital input and PPPs. Preliminary results are currently available, see e.g. Lee and Tang (1999).
I/O-tables Statistics Canada had already implemented a KLEM-database, which means that annual I/O- tables were already available. The harmonization of industrial classifications with the US however, gave severe problems, especially with respect to mining, forestry and some services. This took much time, and not all problems could be solved. Also the handling of government enterprises was different in the countries and required adaptations. Canadian gross output figures included intra-industry sales between establishments (also referred to as gross-gross output) whereas US figures excluded these sales. This depends on the basis on which output data is gathered (industry vs. establishment level).
Labor input Statistics Canada also worked on labor quality indices. Initially labor input was cross-classified by 2 sexes, 8 age groups, 5 educational groups and 2 employment status (employed and self- employed). Although some differences in the use of basic datasources (household vs. enterprise survey) and methodology in estimating self-employed earnings, harmonization on these issues has not been tried yet and are probably not important.
39 Capital input The biggest problem in the comparison involved the capital stock estimates. The estimates available for the US were more detailed than the data available for Canada. As for now (17 March) capital stock estimates were available for only three asset types for each industry (machinery and equipment, buildings and other structures). Estimates for land, inventories and consumer durable have not been attempted yet. Also the cross-classification by legal form of organization (important for the calculation of rental prices) has not been tried due to data limitations. This is less problematic for the manufacturing sector where most enterprises are corporate business, but might be a bigger problem for agriculture and certain services. From the methodological point of view, Statistics Canada prefers to use its own depreciation patterns in the calculation of capital stocks, as they are based on recent surveys within Canada. These are significantly different from the depreciation rates used in the US-data. For the calculation of the rental prices, a wealth of data on capital cost allowance, tax parameters and financing ratios was used. One needed to assume that cost of capital for corporate business was the same for non-corporate due to the unavailability of data to distinguish the two.
PPPs Bilateral PPPs for the US-Canada level comparisons were developed for the year 1993, see Lee and Tang (1999).
Output PPPs Use was made of the ICP expenditure PPPs for 201 basic headings. To arrive at industry level PPPs for output at producer prices, these were peeled off for taxed and distribution margins and international trade following Hooper and Vrankovich (1995). Two main problems were encountered. First, the expenditure PPPs do not cover intermediate goods. For this the exchange rate was used instead. The use of PPPs at producer prices for these goods would be an important improvement but has not been attempted. Secondly, PPPs refer to goods, while the aim is to arrive at PPPs for industries. This requires aggregation and hence the development of a common (goods x industries) table for the two countries using input-output tables to construct weights for the aggregation. A 35 industries x 300 goods table was created for this purpose.
Intermediate input PPPs: These are PPPs at purchaser’s prices, hence no peeling off is required, only aggregation using the common goods x industry table.
Capital input PPPs: This required PPPs for investment goods and weights to aggregate, together with user cost estimates for capital in each industry for both countries. Two capital input types were used due to data limitations described above.
Labor input PPPs: The distinction between employed and self-employed was dropped in this calculation.
40 Appendix III.2 The Danish Experience
[This text is based on meeting with Svend E. Hougaard Jensen, Anders Sørensen, Mogens Fosgerau and Steffen Andersen of the Center of Economic and Business Research (CEBR) within the Danish Ministry of Trade and Industry, held in Copenhagen on September 3, 1999.]
General The Centre had developed fruitful links with Statistics Denmark (SD) for this project and the collection of data does not appear to be a problem. Moreover, SD has expressed great interest in the project and is willing to co-operate. In general the quality and availability of data in Denmark is very good. There are no major problems with the I/O-tables and the capital input data. For labor input there might be some problems with collecting wage data. Industry PPPs need still to be developed. The Centre is determined to go ahead with the project and will start on a preliminary aggregate analysis using a data base for 19 industries (excluding quality of labor issues for the moment). A first study was presented at the KLEM-meeting in Copenhagen Dec. 1999 by Fosgerau and Sørensen (Fosgerau and Sørensen (1999)).
Input-output table - At the moment, there are annual series of I/O-Tables from Statistics Denmark from 1966-1992 (117 x 117 industries) in constant and current prices. Next year new series will become available on the basis of NACE rev. 1 spanning the period 1966-96 (130 x 130 industries). This level of detail is sufficient to distinguish the 36 industries as proposed in the project so far. - Comparison of the original I/O- tables and the tables in the OECD data set reveal that modifications made by the OECD were only minor and can easily be replicated using the original data. - Tables are at basic prices but can be converted to producer prices if necessary. - No hedonic prices are used at the moment. No plans for the near future. - Marketed government services are placed in the industries which produce these kinds of services rather than reported separately.
Capital input - Investment flow matrices are available from 1966-1992 in constant and current prices. Next year, the series will probably be extended to 1996. - The number of industries distinguished is less than in the I/O-table (43 industries now, 56 later). This will create problems for some detailed industries such as drugs and medicines and computer machinery. A breakdown has to be attempted or results for these industries have to be aggregated into higher level industry figures. - Data on hardware and software investments will become available in the near future. - For buildings there are series available going back to 1947. This will remove most of the problems of not having a benchmark capital stock.
41 - There are series for land and inventory investment. - Distinguishing between legal forms can only be done on an aggregate basis and applying some scaling method. - Consumer durable can be included as investment data for this is available. - Rental prices will be more of a problem. Some studies are available but especially the tax part might be a problem. Suggestions would be welcomed.
Labour input - Sectoral labor input is available for 1982-1996, subdivided by age, sex, education and occupation. Both in number of employees and hours worked. - Before 1982, there is nothing on educational levels of the workforce, but by using surveys and population census it should be possible to come up with some approximations. - The biggest problem is wage compensation. Statistics Denmark does not collect this data as it is collected by the Employers Association. However, they cover only 35% of the business sector. Further investigation in this matter is required. As an alternative one can turn to the Luxembourg Income Studies project to collect additional data.
PPPs Denmark is included in ICP-rounds but there are no industry-of-origin PPPs available at the moment. From a policy perspective, interest in industry PPPs is high and the Centre plans to start on a binary comparison of the Danish manufacturing sector with the USA using the industry-of- origin approach. The Groningen Growth and Development Centre is glad to co-operate on this topic.
Computer programme for KLEM-productivity estimation
For the Danish KLEM-study (Fosgerau and Sørenson 1999), a special computer programme has been developed to aid the set up of a KLEM-database and which can be of use for other consortium members. The programme is described below.
Data management Data manipulations are organised in two databases. The first database extracts data from ADAM and sets up tables that are used to perform calculations for the growth account in the second database.
First database
Manipulations of ADAM data are programmed in a database allowing easy updates when the calculations are changed or when new data become available. In the program three tables with identical layout are produced. The following table shows the fields in the tables:
42 Year Output sector Category Input type or Measure output Table 1 19XX j F F(1) W
K Kk(2)
L Ll(1)
E Ei(6)
M Mi(11)
I Ii(11)
S Si(5) Table 2 -do- -do- -do- -do- X Table 3 -do- -do- -do- -do- P=W/X
The category field is used for grouping of inputs (or output). In addition to the output, F, six input categories are employed in the analysis. These are KLEMIS for capital, labour, energy, domestically produced materials, import, and services. In total, the data on input are divided into 36 different types of input. The number of different types of input belonging to each single category is shown in parentheses. The only entry that differs between tables is the measure field. In Table 1 of the database, values of sectoral output and compensation of different input types, W, enter the measure field; in Table 2 the quantity of sectoral output and quantities of different input types, $X$, enter the measure field; and in Table 3, prices of sectoral output and inputs, P, enter the measure field. The prices are constructed by dividing values, W, by quantities, X, from Table 1 and 2.
Second database The calculations for the growth account take place in a second database linked into the first. The program in the second database automatically scales calculations, such that the program is independent of industry and commodity nomenclature and of the period covered in the analysis. Consequently, the second database is programmed such that it can be re-used without changes when we shift from the ADAM database to another data source. Changes can be made in a small auxiliary table relating the type of input to KLEMIS\ categories if a different grouping of data is chosen. The database will adapt accordingly when the programs are run.
Manipulations for presentation of the data take place within a single spreadsheet linked to the second database.
43 Appendix III. 3 Country Databases and Status Reports
In this appendix existing databases of the consortium members are described, including a description of current plans. For France and Italy, existing databases do not extend beyond what is given in the OECD ISDB and I/O tables database. Finland and Spain are not included in these databases. CEPPI will start to collect French data this year, as will Statistics Finland for the Finnish data. For the other countries the consortium members have already started to build up own databases.
Denmark
Current state of data base time-span: 1966-1992 number of industries: 19 output measure: gross output and value added. Annual constant and current price I/O tables for the period 1966-1992. labor: types: by age, sex, education and occupation for 1982-1996. quantity: number of employees and hours worked price: - capital: types: at least three quantity: investment flows available from 1966 onwards. For buildings back to 1947. price: -
Availability of additional data Capital: IT capital can be distinguished in later stage. Rental prices can be constructed. Labor: For period before 1982 approximations on basis of population census etc. are possible.
Particular problems Wage data are difficult to get. Are not collected by Statistics Denmark. Alternatives are being investigated.
Current plans See report of meeting in Appendix III.2.
44 Finland, Statistics Finland
KLEM-project
The short term aim of the project is to show the benefits of the KLEM approach in productivity measurement. For this we are using data to which we have easy access and classifications as well as time series that are readily available. The first calculations concern labour productivity with classification of labour input by educational attainment for the entire economy as well as well by industry, with a rather rough classification by industry. The time series for these calculations cover only the 90’s.
The work with the estimates of capital input by asset class and industry will be started during the summer with the help of our capital stock model. These time series will start from 1970 and with a very detailed classification by industry. We have also started the work to get the existing input- output tables from 1970 onwards available in a form that would enable the modifications and reclassifications that will be needed. We will probably first make calculations only for a few years, again in order to demonstrate the benefits of the approach.
A national seminar on the work with productivity issues within Statistics Finland is planned to be held during the autumn.
France, CEPII
Since the past few years, CEPII has undertaken international productivity comparisons in both manufacturing and services using the ICOP (International Comparison of Output and Productivity) method set out by the University of Groningen. With regard to the manufacturing sector, CEPII is currently undertaking two research projects. The first project has a trans-Atlantic dimension, given that France and Germany are compared with the United States (the France-USA comparison is being carried out jointly with the University of Groningen). This picks up from previous research conducted by the Centre, which used 1987 as a base year for productivity calculations. The new study will be able to include the significant economic developments of the 1990s, such as German reunification and large productivity gains in the United States. The second project concerns Euro-Mediterranean integration, with a comparison being made between France, Turkey, Egypt, Morocco, Portugal and Spain. The aim here is to assess the possible consequences of association agreements on the industrial development of the Mediterranean countries and on their trade flows. In services, CEPII has carried out, also in collaboration with the University of Groningen, international comparisons of prices and productivity in transport, communications and wholesale and retail trade. The transport comparisons covered several European countries,
45 the USA, Canada and Japan, while the study on distribution included only five countries. Both used 1992 as the benchmark year. Finally, recently detailed estimates of capital stocks and services have been compiled by sub-sector of transport in France. For this study, various sources, such as the national accounts, census and survey data, have been exploited which will also be use for the construction of capital accounts of other sectors of the French economy. The capital services series have been used, in combination with output and labour input data, to analyse the French productivity performance in international perspective.
France (National Institute sectoral productivity data set) Current state of data base time-span: 1950-1996 number of industries: 34 output measure: value added labor: types: by 3 class of education (1979-93) quantity: number of employees and hours worked price: wages from earnings survey capital: types: 2 quantity: PIM-estimate using geometric depreciation price: defined as real interest ate plus depreciation minus real capital gains
46 Germany (ZEW dataset) Current state of data base time-span: 1975-1995 for West Germany, 1991-95 for East Germany number of industries: 31 manufacturing and 27 non-manufacturing industries output measure: value added labor: types: by educational qualifications, age and sex. quantity: number of employees and hours worked for homogeneous labor (58 industries) from Institute of Employment Research. price: wages are drawn from an one percent random sample of the social security statistics (IABS) capital: types: structures and equipment quantity: PIM-estimated price: user cost is computed using investment prices, nominal interest rate and depreciation rates. intermediate inputs: aggregate.
Availability of additional data I/O-tables: Tables for 58 industries are available for the years 78/80/82/84- 88/90,891,93,95 Labor: Wage data drawn from the GSOEP will be used for the period between 1995 and 1998. In addition frequent Labor Force Surveys (individual data) up from 1970 will be used for additional detailed information.
Particular problems - One major data-related problem is the construction of separate input-output tables for East and West German industries. For the years, 1991, 1993 and 1995 separate input-output tables for East and West Germany are not available. The 1990 input-output table refers to West Germany. - In general no IT data available. - German I/O-tables are based on the product concept rather than the establishment concept.
Current plans - Work on update the database for West German industries, 1975-1995 - I/O tables are used to split intermediate inputs into three components: energy, non-energy imported materials, non-energy domestic materials. - Currently working on the distinction between East and West Germany using additional sources (intra German trade data, DIW input-output table for 11 East German industries)
47 Germany (National Institute sectoral productivity data set)
Current state of data base time-span: 1950-1996 number of industries: 34 output measure: value added labor: types: by 3 class of education (1979-93) quantity: number of employees and hours worked price: wages from earnings survey capital: types: 2 quantity: PIM-estimate using geometric depreciation price: defined as real interest ate plus depreciation minus real capital gains
Netherlands
Current state of data base time-span: 1974-1995 number of industries: 17 output measure: output and value added labor: types: by age, gender and education. quantity: hours worked price: wages estimated by standard semi-log wage equation. capital: types: by 7 assets (building, machinery and equipment, cars and road transport, rail, vessels, aircraft and civil engineering). quantity: stocks are PIM-based with beta-decay function. price: service flows calculated using long-term interest rates, depreciation, asset price and fiscal investment facilities. intermediate inputs: aggregate only.
Availability of additional data I/O-tables: Annual I/O tables at constant prices from 1981 onwards are available for in-between 60 to 100 industries.
Particular problems - There are no I/O-tables before 1980.
48 - I/O tables are not consistent over time. - Investments series available from 1948 onwards. Statistics Netherlands will carry out project in near future to construct new investment time-series for the past. - No good wage data for different types of labor. - In general no IT data available.
Current plans - Further disaggregation is not planned as it will generate little additional insight.
Spain
Current state of data base time-span: 1970-1995 number of industries: 17 output measure: value added labor: types: 1 quantity: number of employees price: - capital: types: 1 quantity: stock with PIM, comparable to OECD ISDB method price: - intermediate inputs: aggregate.
Availability of additional data I/O-tables: under investigation Capital: under investigation Labor: under investigation
Particular problems - National data is on NACE-CLIO base which has now been converted to ISIC rev 2.
Future plans - Include data for Spain in the OECD ISDB data base.
49 United Kingdom
Current state of data base time-span: 1950-1996 number of industries: 34 output measure: value added labor: types: by 3 class of education (1979-93) quantity: number of employees and hours worked price: wages from earnings survey capital: types: 2 quantity: PIM-estimate using geometric depreciation price: defined as real interest rate plus depreciation minus real capital gains
Availability of additional data I/O-tables: for the years 1968/74/79/84/89 and annually from 1991, but only in current prices. Capital: 40 sectors, 3 asset types, IT can probably be separated Labor: Division by age and sex possible is possible from the Labor Force Survey, and another source will give information on wages for the various categories.
Particular problems - I/O tables are only shown at current prices - Serious break in the series with the change from SIC80 to SIC92.
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