Oecd Compendium of Productivity Indicators 2005

OECD COMPENDIUM OF PRODUCTIVITY INDICATORS

2005

ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT

Pursuant to Article 1 of the Convention signed in Paris on 14th December 1960, and which came into force on 30th September 1961, the Organisation for Economic Co-operation and Development (OECD) shall promote policies designed:

• To achieve the highest sustainable economic growth and employment and a rising standard of living in member countries, while maintaining financial stability, and thus to contribute to the development of the world economy.

• To contribute to sound economic expansion in member as well as non-member countries in the process of economic development; and

• To contribute to the expansion of world trade on a multilateral, non-discriminatory basis in accordance with international obligations.

The original member countries of the OECD are Austria, Belgium, Canada, Denmark, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States. The following countries became members subsequently through accession at the dates indicated hereafter: Japan (28th April 1964), Finland (28th January 1969), Australia (7th June 1971), New Zealand (29th May 1973), Mexico (18th May 1994), the Czech Republic (21st December 1995), Hungary (7th May 1996), Poland (22nd November 1996), Korea (12th December 1996) and the Slovak Republic (14th December 2000). The Commission of the European Communities takes part in the work of the OECD (Article 13 of the OECD Convention).

Over the past few years, productivity and economic growth have been an important focus of OECD work. This work has included both efforts to improve the measurement of productivity growth, as shown in the development of the OECD Productivity Manual, published in 2001, as well as work to enhance the understanding of the drivers of productivity performance. In the course of this work, questions about data choices and the measurement of productivity were examined at several occasions. At the same time, OECD was confronted with a growing interest in internationally comparable statistics and indicators on productivity growth.

The continued interest of many OECD member countries in productivity led to a decision to develop an OECD Productivity Database, based on data that were considered to be as comparable and consistent across countries as possible. This database and related information on methods and sources was first made publicly available through the OECD Internet site on 15 March 2004, and is being updated and revised on a regular basis. The data is available (free of charge) at:

www.oecd.org/statistics/productivity

This compendium is the first time a large number of indicators on productivity are being combined in one document. The compendium draws heavily on the OECD Productivity Database, but also includes indicators drawn from other sources, such as the OECD STAN database, which enables productivity calculations for individual industries.

The compendium includes indicators as well as methodological notes and describes the measurement challenges and data choices that were made as well as the remaining measurement problems. Detail is available in a number of annexes.

The compendium was prepared for an OECD workshop on productivity measurement, held in Madrid from 17-19 October 2005. Agnes Cimper, Julien Dupont, Dirk Pilat, Paul Schreyer and Colin Webb prepared the text and tables in the compendium. Alessandra Colecchia, Chiara Criscuolo and Pascal Marianna contributed material. Beatrice Jeffries, Paula Venditti and Julie Branco-Marinho helped prepare the manuscript.

The data underlying this compendium is primarily based on the July 2005 update of the Productivity Database. Some minor revisions to these data were made in September, primarily affecting the estimates of productivity levels (see Annex 2) and the growth accounting results for a limited number of OECD countries. A full update of the OECD Productivity Database will be released on the OECD website by the end of 2005.

Compendium of Productivity Indicators

TABLE OF CONTENTS

Highlights 7 A) Economy-wide indicators of productivity growth 9 A.1. Growth in GDP per capita 10 A.2. Growth in GDP per hour worked 12 A.3. Alternative measures of labour productivity growth 14 A.4. Capital productivity 16 A.5. Growth accounts for OECD countries 18 A.6. The contribution of multi-factor productivity and ICT capital to GDP growth 20 A.7. Labour productivity growth in the business sector 22 B) Productivity levels 25 B.1. Income and productivity levels 26 B.2. Levels of GDP per capita and GDP per hour worked, 1950-2004 28 B.3. Alternative measures of output 30 B.4. Differences in labour productivity by economic activity 32 C) Productivity growth by industry 35 C.1. Contribution of key activities to aggregate productivity growth 36 C.2. Productivity growth in manufacturing 38 C.3. Productivity growth in services 40 C.4. Labour productivity of foreign affiliates 42 C.5. The contribution of multinationals to productivity growth 44

Annex 1 – OECD productivity database 46 Annex 2 – OECD estimates of labour productivity levels 54 Annex 3 – OECD databases relevant to productivity analysis 59 References 62

Compendium of Productivity Indicators

HIGHLIGHTS

This compendium presents productivity indicators in three broad areas. The first section presents economy-wide indicators of productivity growth. It shows that:

• Growth in GDP per hour worked over 1995-2004 was highest in Ireland, Korea and the Slovak Republic. In Greece, Iceland, Ireland and the United States, labour productivity grew much faster from 1995-2004 than from 1990-95. In many other OECD countries, labour productivity slowed down over this period. Alternative measures of labour productivity growth, based on net domestic product or gross domestic income, show a very similar picture, with some exceptions.

• Capital productivity declined in most OECD countries over the past decade. The decline has been most pronounced in Japan, but also in Canada, Denmark, Portugal and Spain. Finland and Ireland are exceptions; both countries have experienced a considerable increase in capital productivity.

• Stronger growth in some OECD countries, including Canada, France and the United States, over 1995-2003 was due to a combination of factors, including increased labour utilisation, strong capital deepening in information and communications technology (ICT) and more rapid multi-factor productivity (MFP) growth.

• Investment in ICT accounted for between 0.35 and 0.9 percentage points of growth in GDP over the period 1995-2003. Australia, Sweden and the United States received the largest boost from ICT capital. In Ireland, Finland and Greece, MFP was an important source of GDP growth.

The second section presents indicators of productivity levels. It shows that:

• Ireland, Luxembourg, Norway and the United States had the highest levels of per capita income in 2004, while Belgium, France, Ireland, Norway and the United States had the highest levels of GDP per hour worked. The different ranking of certain countries on these two measures is due to labour utilisation; many European countries have lower levels of labour utilisation than the United States. This implies that they have fewer people contributing to GDP; levels of GDP per capita are therefore lower than levels of GDP per hour worked.

• These rankings change little when alternative measures of output, such as net domestic product or gross national income, are used. Exceptions are Ireland and Luxembourg that have a lower ranking on gross national income per hour worked than on GDP per hour worked.

• Cross-country differences in income and productivity levels have eroded considerably since the 1950s. Japan and Korea have experienced the highest rates of catch-up since 1950. Many European countries experienced strong catch-up with the United States until 1980, but have fallen back since, Norway and Ireland being among the most notable exceptions.

• Australia, New Zealand, the United Kingdom and Canada already had relatively high income levels in 1950 and have done little catching up since. Central European countries, Mexico and Turkey started with low income levels in the 1950s and have only caught up a little.

• Industries predominantly involved in the extraction, processing and supply of fuel and energy goods produced the highest value added per labour unit. These industries were more than twice as productive as the average industry.

• Besides the energy-producing industries, those that yield the most value added per labour unit are industries considered more technology and/or knowledge intensive. In manufacturing, the chemical industry has the highest relative labour productivity level, while in services, finance, insurance and telecommunications lead the way.

The third section presents indicators of productivity growth by industry, and also includes indicators on the contribution of multinationals to productivity performance. It shows that:

• In many OECD countries, notably in Australia, Greece, Mexico, the United Kingdom and the United States, business sector services accounted for the bulk of labour productivity growth over 1995- 2003. However, the manufacturing sector remains important in the Czech Republic, Finland, Hungary, Poland, Korea, the Slovak Republic and Sweden.

• A large share of labour productivity growth in the non-agricultural business sector is attributable to knowledge-intensive activities. ICT manufacturing and services were particularly important in Finland and Sweden, whereas other high- and medium-high-technology industries were particularly important in Japan, Sweden and the United States. In Greece, Norway and the United States, wholesale and retail trade also contributed significantly to aggregate productivity growth.

• Within manufacturing, large differences in the rate of productivity growth can be observed. Electrical and optical equipment is often the industry with the highest rate of productivity growth, up to 20% annually in OECD countries such as Sweden, Hungary, Korea and Finland.

• The variation in productivity growth across services sectors is also considerable. Industries such as wholesale and retail trade, post and telecommunications and financial intermediation are typically the services sectors with the highest rate of productivity growth.

• Labour productivity of foreign affiliates in manufacturing in the United Kingdom is more than two times higher than the average labour productivity of domestic firms in the manufacturing sector.

• Foreign affiliates made an important contribution to labour productivity growth in the United States, accounting for almost a quarter of manufacturing productivity growth over 1995-2001. In the Czech Republic, France, Sweden and the United Kingdom, the bulk of productivity growth in manufacturing was due to foreign affiliates. In Japan, foreign affiliates made only a minor contribution to productivity growth.

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Compendium of Productivity Indicators

A. ECONOMY-WIDE INDICATORS OF PRODUCTIVITY GROWTH

GROWTH IN GDP PER CAPITA A.1. achieved by a greater use of capital or by dismissing • Growth in GDP per capita is one of the core (or not employing) low-productivity workers. indicators of economic performance. Estimates of the increase in GDP per capita for OECD countries • In the second half of the 1990s, many European for 1990-1995 and 1995-2004 show that rates of countries, improved their performance in terms of growth in GDP per capita were high for both periods labour utilisation, as unemployment fell and labour in Ireland, Korea, Luxembourg and the Slovak force participation increased. However, the growth in Republic. In most OECD countries, growth of GDP labour utilisation was accompanied by a sharp per capita was higher from 1995-2004 than from decline in labour productivity growth in many 1990-95. The Czech Republic, Finland, Hungary and European countries. In contrast, some other OECD Iceland were the countries with the largest countries, such as Canada and Ireland experienced improvement over the period. In a few countries, a pick-up in both labour utilisation and labour notably Korea and Norway, growth of GDP per productivity growth from 1990-95 to 1995-2004, capita was lower over 1995-2004 than from 1990-95. showing that there need not be a trade-off between The estimates shown here are not adjusted for labour productivity growth and increased labour use. differences in the business cycle; cyclically adjusted estimates might show a somewhat different pattern. Sources • OECD Productivity Database, July 2005: The growth in GDP per capita can be broken down www.oecd.org/statistics/productivity in a part which is due to labour productivity growth OECD, Annual National Accounts Database, July 2005. (see A.2) and a part that is due to increased labour utilisation, measured as hours worked per capita. For further reading Growing labour utilisation has had considerable OECD (2001), Measuring Productivity – OECD Manual, impacts on the growth of GDP per capita. In the first OECD, Paris. half of the 1990s, most OECD countries, in particular OECD (2004), Purchasing Power Parities and Real many European countries were characterised by a Expenditures, OECD, Paris. combination of high labour productivity growth and declining labour utilisation. The high productivity growth of these EU countries may thus have been

Growth in GDP per capita, 1990-951 and 1995-2004 Total economy, percentage change at annual rate

1990-1995 1995-2004 7

-1

e l ) s m g d d rk a ia m ia ce lic ry n u (2 en ea a land Italy gi land tate al ga r Japan rlands ustr el 14 ed Spain inlandKor olan rel enma Franceortug A - TurkeyMexicoea ingdoustr w IcelandGree F P I D RO zon P B U Norway Canada S Hun Germany U E Z A witze ethe E S N nited S Luxembour New U nited K lova k Repub Czech RepublicU S 1. Or earliest available year, i.e. 1991 for Hungary and 1992 for the Slovak Republic. 2. EU-14: Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and the United Kingdom.

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Compendium of Productivity Indicators A.1. GROWTH IN GDP PER CAPITA

Growth in GDP per capita – the contribution of productivity and labour utilisation Total economy, percentage change at annual rate

Average 1990-1995 (1) Average 1995-2004

Growth in labour utilisation Growth of GDP per capita Growth of GDP per hour = + (3) worked

Ireland

Luxembourg

Korea

Finland

Greece

Iceland

Spain

Sweden

Australia United Kingdom Canada United States Norway New Zealand EU-14 (2)

Belgium

Portugal

France

Netherlands

Denmark

Germany

Italy

Japan

Switzerland

-202468-202468 -202468 1. Or earliest available year, i.e. 1991 for Hungary and 1992 for the Slovak Republic. 2. EU-14: Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and the United Kingdom. 3. Labour utilisation is measured as hours worked per capita.

GROWTH IN GDP PER HOUR WORKED A.2.

• Productivity growth can be measured by relating Republic, Iceland and Spain being the main changes in output to changes in one or more inputs exceptions. to production. The most common productivity measure is labour productivity, which links changes • The estimates shown here are not adjusted for in output to changes in labour input. It is a key differences in the business cycle; cyclically adjusted economic indicator and is closely associated with estimates might show a somewhat different pattern. standards of living. Sources • Estimates of the increase in GDP per hour worked OECD Productivity Database: for OECD countries for 1990-1995 and 1995-2004 www.oecd.org/statistics/productivity show that rates of labour productivity growth were OECD, Annual National Accounts Database, July 2005. highest in Korea and Ireland. For 1995-2004, the For further reading Slovak Republic also had very high productivity growth. OECD (2001), Measuring Productivity – OECD Manual, OECD, Paris. • Labour productivity growth has varied considerably Ahmad, N., F. Lequiller, P. Marianna, D. Pilat, P. Schreyer and A. Wölfl (2003), “Comparing Labour over the past 15 years. In Greece, Iceland, Ireland Productivity Growth in the OECD Area: The Role of and the United States, it grew much faster from Measurement”, STI Working Papers 2003/14, OECD, 1995-2004 than from 1990-1995. In other OECD Paris. countries, notably Belgium, Germany, Italy, the Pilat, D. and P. Schreyer (2004), “The OECD Productivity Netherlands, Norway, Portugal and Spain, it slowed Database – An Overview”, International Productivity down over the period. Monitor, Number 8, Spring, pp. 59-65. OECD (2004), “Clocking In (and Out): Several Facets of • With the slowdown of the world economy since Working Time”, OECD Employment Outlook 2004, 2000, most OECD countries have experienced a Chapter 1, OECD, Paris. marked slowdown in labour productivity growth, some European countries, notably the Czech

OECD measures of labour productivity growth The OECD Productivity Manual. There are many different approaches to the measurement of productivity. The calculation and interpretation of the different measures are not straightforward, particularly for international comparisons. To give guidance to statisticians, researchers and analysts who work with productivity measures, the OECD released the OECD Productivity Manual in 2001. It is the first comprehensive guide to various productivity measures and focuses on the industry level. It presents the theoretical foundations of productivity measurement, discusses implementation and measurement issues and is accompanied by examples from OECD member countries to enhance its usefulness and readability. It also offers a brief discussion of the interpretation and use of indicators of productivity. See: www.oecd.org/sti/measuring-ind-performance OECD Productivity Database. Productivity measures rely heavily on the integration of measures of output and input. Some of the most important differences among studies of labour productivity growth are linked to choice of data, notably the combination of employment, hours worked and GDP. To address this problem, OECD has developed a reference database on productivity at the aggregate level, with a view to resolving the problem of data consistency. In deriving estimates of labour productivity growth for the economy as a whole, the database combines information on GDP, employment and hours worked. For employment and hours worked, a special effort is made to use the best available information for each country, based on a consistent matching of data on employment and annual hours worked per person employed (see Annex 1). The database adds to the already available OECD estimates of productivity growth. In particular, the OECD Economic Outlook currently includes estimates of labour productivity growth for the business sector in its Annex Tables (see A.7). These measures respond to different purposes and should thus be considered of equal value to those published in the Productivity Database. The following differences should be noted between the two series: 1) The measures for labour and multi-factor productivity in the OECD Productivity Database refer to the total economy. They are based on a detailed assessment of labour and capital input, which incorporates adjustments for average hours worked per person employed and for capital services. These economy-wide productivity measures provide a close link to changes in GDP per capita. 2) The measures of labour productivity in the OECD Economic Outlook cover the business sector only and do not adjust for average hours worked and for capital services. The main advantage of these measures is that it excludes a large part of the economy, i.e. the public sector, in which productivity is typically poorly measured. More sophisticated measures of productivity growth in the business sector are currently under development. More information is available on the special website for the database: www.oecd.org/statistics/productivity

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Compendium of Productivity Indicators A.2. GROWTH IN GDP PER HOUR WORKED

Growth in GDP per hour worked, 1990-1995 compared with 1995-2004 Total economy, percentage change at annual rate

% 1995-2004 1990-1995

-1

l y ic d s rg n m ia d l rea ce e nd r ny um xico tal b land o e ralia den la ada uga lan nds I elan tat e n do n rt lgi rland e a Ire K c S st i Japa France Aust M Spain rl epu Gre I Sw F mbou Norway ng Ca e R Republic Hungaryd Au Ki Po Germa Be Zea Denmarkitze te eth ch uxe d ew Sw L te N N ovak Uni ni Cze Sl U

Growth in GDP per hour worked, 1995-2000 compared with 2000-2004 Total economy, percentage change at annual rate

2000-2004 1995-2000 % 6

-1

l c d a d d e y s n ia y n d e k n ia y d a g d s co ly li lic n n c ar l m n c ai n d n i ta b b la e a o pa la n ar str a n a x I u la e tr d a n ra m u n our rla p o Kore e r s J i n Sp m a b rland Me P Ic IrelanG Norwang F F e A C ze Hung SwedeAu i Belgium it he Portuga Re K D Ger k d et a e ew Zeala LuxemSw N zech Repu United State N C nit Slov U

ALTERNATIVE MEASURES OF LABOUR PRODUCTIVITY GROWTH A.3.

• Real depreciation has grown somewhat faster than real and/or where foreign trade accounts for a large share of GDP in the past decade in many OECD countries, GDP. reflecting investment in new technologies and a shift in the structure of investment and capital stocks towards Sources shorter-lived assets. As a consequence, NDP per hour OECD Productivity Database: worked has risen somewhat slower than GDP per hour www.oecd.org/statistics/productivity worked. The gap turned out to be relatively large in the OECD, Annual National Accounts Database, September United States, Denmark and Switzerland whereas in 2005. Finland, in the Slovak Republic and in the United For further reading Kingdom, NDP growth exceeded GDP growth. Commission of the European Communities, OECD, IMF, United Nations, World Bank (1993), System of National • Looking at trend growth of real GDP and GDI per hour Accounts 1993, Brussels/Luxembourg, New York, Paris, worked over the past ten years shows that differences Washington DC. between the two measures are relatively small, except Kohli, Ulrich (2004); “Real GDP, real domestic income and for a few countries (e.g. Finland, Sweden, Korea and terms of trade changes”; Journal of International Economics Switzerland). By definition, the difference between the 62. two measures is most important in those countries that experienced the largest shifts in their terms of trade

Alternative measures of output

GDP is a gross measure that does not account for capital used in production. The associated loss in value, depreciation, reduces the net value of production that is available as net income in any given year. The observation has often been made that a growing part of capital goods is short-lived (for example computers), and that this structural shift in the composition of assets brings with it a higher overall depreciation. A case could thus be made to measure productivity on the basis of net output as well as GDP. Countries with a structure of fixed assets that is biased towards short-lived assets would exhibit a relatively lower NDP per hour worked than GDP per hour worked, reflecting relatively higher depreciation.

However, net measures require reliable estimates of depreciation and the empirical basis for depreciation estimates is generally not well established. For similar types of assets, significant differences exist in the service lives and depreciation rates that are used by different countries. These rates are sometimes based on assumptions more than in-depth empirical studies or are based on evidence dating back a number of years. This reduces the quality of depreciation estimates as well as their international comparability. The international GDP-NDP comparisons should thus be interpreted with caution.

It is also useful to compare GDP per hour worked with Gross Domestic Income (GDI) per hour worked over time. Real gross domestic income (GDI) measures the purchasing power of the total incomes generated by domestic production (including the impact on those incomes of changes in the terms of trade); it is equal to gross domestic product at constant prices plus the trading gain (or less the trading loss) resulting from changes in the terms of trade. The terms-of-trade effect arises because real GDI is obtained by deflation with the price index for domestic final demand rather than the price index for GDP. It measures the rate at which exports can be traded against imports from the rest of the world. The difference between movements in GDP at constant prices and real GDI are not always small. If imports and exports are large relative to GDP, and if the commodity composition of the goods and services which make up imports and exports are very different, the scope for potential trading gains or losses may be large. If the prices of a country’s export rise faster (or fall more slowly) then the prices of its imports — that is if its terms of trade improve — less exports are needed to pay for a given volume of imports so that at a given level of domestic production of goods and services can be reallocated from exports to consumption or capital formation. Thus, an improvement in terms of trade makes it possible for an increased volume of goods and services to be purchased by residents out of the incomes generated by a given level of domestic production.

Despite its analytical usefulness, it should be borne in mind that GDI is a measure of income and not a measure of production. However, as has been pointed out by some authors (Kohli 2004), terms of trade effects resemble technical change. Changes in constant price GDP would only reflect technical change whereas GDI is a measure that reflects both technical change and terms of trade. This makes GDI a meaningful measure even in the context of production analysis.

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Compendium of Productivity Indicators A.3. ALTERNATIVE MEASURES OF LABOUR PRODUCTIVITY GROWTH

Growth in NDP per hour worked compared with growth in GDP per hour worked, 1995-2004 Total economy, average annual growth rate

NDP per hour w orked GDP per hour w orked 6

y ) y ) ic rk m m 3 l nds 03 u tria do b Ital g u rla elgi ra nce enma B Aus F Finland d States Iceland Greece the d (2003) D German e Sweden e n co (20 N la xi r nited Kin Unit Me Canada (2003) U Australia (200 Slovak Rep Switze

Growth in GDI per hour worked compared with growth in GDP per hour worked, 1995-2004 Total economy, percentage change at annual rate

GDI per hour w orked GDP per hour w orked 6

s rk m e m lic 3) u 3) o nd b Italy ates en lgi nla t d (2003) enma e rmany S Korea (2003) epu B Austria Franc Fi Iceland Greece d D co (200 Ge Swe n R and (200 alia (2003) a Netherland xi nited r ak Japan (2003)nited Kingd U Irel Me Canada (2003) lov Zeal U Aust S Switzerland ew N

CAPITAL PRODUCTIVITY A.4.

• While labour productivity is the most common partial • Although most countries experienced a decline in productivity measure, capital productivity provides capital productivity, the rates at which capital another, supplementary piece of information about productivity fell has varied significantly over time. In productivity growth. Capital productivity is measured Portugal, Denmark, the United States and New as the ratio between output and capital input. Zealand, the decline in capital productivity slowed markedly between the period 1990-95 and 1995- • Capital productivity has to be distinguished from the 2003. The most remarkable shift can be observed in rate of return to capital. The former is a physical, Finland where capital productivity declined in the first partial productivity measure; the latter is an income half of the 1990s and rose in the period afterwards. measure that relates capital income to the value of Like other productivity measures, capital productivity the capital stock. varies considerably with the business cycle; no adjustments have been made for variations in the rate of capacity utilization. • Capital productivity has declined in most OECD countries over the past decade. The fall in capital productivity since 1990 has been most pronounced Sources in Japan, but also occurred in Spain, Canada, OECD Productivity Database: Portugal and Denmark. Notable exceptions to the www.oecd.org/statistics/productivity secular decline in output per unit of capital input are For further reading Ireland and Finland where capital productivity grew OECD (2001), Measuring Productivity – OECD Manual, over most of the past decade. OECD, Paris. Schreyer P. (2003), Capital Stocks, Capital Services and • Two important drivers determine capital productivity: Multi-factor Productivity, OECD Economic Studies No 37. overall efficiency or multi-factor productivity growth Schreyer, P., P-E. Bignon and J. Dupont (2003), “OECD and the amount of labour input per unit of capital in Capital Services Estimates: Methodology and A First Set production. The fewer hours worked are available of Results”, OECD Statistics Working Papers 2003/6, per unit of capital, the lower capital productivity. OECD, Paris. Generally, the cost of using capital has declined relative to labour, so that the amount of labour input per capital input has declined as well, leading to the observed fall in capital productivity.

Measuring capital input From the viewpoint of the economic theory, the objective is to measure the flow of productive services that capital delivers in production. This measure relies on the assumption that capital services are a fixed proportion of the productive capital stock. With this assumption, the rate of change of capital services coincides with the rate of change of the capital stock. The latter is estimated by cumulating investment flows and correcting them for retirement, wear and tear and obsolescence. The aggregate flow of capital services is obtained by weighting the flow of capital services of each type of asset by its share in total capital income. More information on capital measurement can be found in the documentation for the OECD Productivity Database.

International comparability of price indices Price indices are key in measuring volume investment, capital services and user costs. Accurate price indices should be constant quality deflators that reflect price changes for a given performance of ICT investment goods. Thus, observed price changes of ‘computer boxes’ have to be quality-adjusted for comparison of different vintages. There are differences how countries deal with quality adjustment with possible consequences for the international comparability of price and volume measures of ICT investment. In particular, those countries that employ hedonic methods to construct ICT deflators tend to register a larger drop in ICT prices than countries that do not. The OECD uses a set of ‘harmonised’ deflators to control for some of the differences in methodology and assumes that the ratios between ICT and non-ICT asset prices evolve in a similar manner across countries, using the United States as the benchmark. Although no claim is made that the ‘harmonised’ deflator is necessarily the correct price index for a given country, the understanding is that the possible error due to using a harmonised price index is smaller than the bias arising from comparing capital services based on national deflators. However, from an accounting perspective, adjusting the price index for investment goods for any country implies an adjustment of the volume index of output. In most cases, such an adjustment would increase the measured rate of volume output change. At the same time, effects on the economy-wide rate of GDP growth appear to be contained (see Schreyer (2001) for a discussion). More information is available on the special website for the database: www.oecd.org/statistics/productivity

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Compendium of Productivity Indicators A.4. CAPITAL PRODUCTIVITY

Growth in capital productivity, 1990-1995 compared with 1995-2003 Total economy, average annual growth rate

% 1990-1995 1995-2003

3.0

2.0

1.0

0.0

-1.0

-2.0

-3.0

-4.0

al rk y m m a d a al en i ny ds 2) ug It 002) d a land 2 gdo ree ce an 00 States n (2002) ustr (2002) (2 Irelan in Port Canada we G Belgiu A F Denm S d Germ herl ia et l nce nited ted Ki lan N a Japan (2002) U Spain ( i a n ustra Fr U Ze A New

Growth in capital productivity, 1995-2000 compared with 2000-2003 Total economy, average annual growth rate

% 1995-2000 2000-2003

4.0

3.0

2.0

1.0

0.0

-1.0

-2.0

-3.0

-4.0 l a rk ly ) s a e m ) m d d a 2) e n It 02) la at nad giu 002 gdo e 200 rlands St a el n inlan ortug enma ( Austria Greec Ir F P D he C B Ki Sweden ted Germany pan pain (2002 ni alia (2 ed and (20 S Net r al Ja U France (2002) nit Aust U New Ze

GROWTH ACCOUNTS FOR OECD COUNTRIES A.5.

• Stronger growth in Canada, France, the United • In Ireland, Finland and Greece, MFP growth was Kingdom and the United States over the 1990s was also an important source of GDP growth. In due to several factors, including higher labour Denmark, Italy, the Netherlands and Spain, MFP utilisation, capital deepening, notably due to growth was very low or negative in the second half of investment in information and communications the 1990s. technology (ICT), and more rapid multi-factor productivity (MFP) growth. In France, Italy and the Source United Kingdom, the contribution of labour input to OECD Productivity Database, September 2005 growth was negative in the first half of the 1990s but www.oecd.org/statistics/productivity positive for 1995-2003. In Germany and Japan, For further reading labour utilisation continued to decline from 1995 OECD (2001), Measuring Productivity – OECD Manual, onwards. In several European countries, MFP OECD, Paris. growth fell from 1995 onwards, but it rose in Canada, France and the United States. Schreyer, P., P.E. Bignon and J. Dupont (2003), “OECD Capital Services Estimates: Methodology and a First Set of Results”, OECD Statistics Working Paper 2003/6, • Investment in ICT accounted for between 0.35 and OECD, Paris. 0.9 percentage points of growth in GDP over the Schreyer, P. (2004), “Capital Stocks, Capital Services period 1995-2003. Australia, Sweden and the and Multi-factor Productivity Measures”, OECD United States received the largest boost from ICT Economic Studies No. 37, 2003/2, OECD, Paris, pp. 163- capital; Japan and Canada a more modest one; and 184. Austria, France and Germany a much smaller one. Wölfl, A., and D. Hajkova (2005), “Measuring Multi-factor In several countries, ICT accounts for the bulk of Productivity Growth”, STI Working Papers, OECD, Paris, capital’s contribution to GDP growth. forthcoming.

• In Canada, Finland, Ireland, the Netherlands, New Zealand and Spain, increased labour utilisation made a large contribution to growth of GDP over 1995-2003.

Growth accounting Economic growth can be increased in several ways; by increasing the amount and types of labour and capital used in production, and by attaining greater overall efficiency in how these factors of production are used together, i.e. higher multi-factor productivity. Growth accounting involves breaking down growth of GDP into these contributions; i.e. labour input, capital input and MFP. The growth accounting model is based on the microeconomic theory of production and rests on a number of assumptions, among which the following are important: i) production technology can be represented by a production function relating total GDP to the primary inputs labour L and capital services K; ii) this production function exhibits constant returns to scale; and iii) product and factor markets are characterised by perfect competition. For any desired level of output, the firm minimises costs of inputs, subject to the production technology discussed above. Factor input markets are competitive, so that the firm takes factor prices as given and adjusts quantities of factor inputs to minimise costs. The rate of growth of output is a weighted average of the rates of growth of the various inputs and of the multi-factor productivity term. The weights attached to each input are the output elasticities for each factor of production. Output elasticities cannot be directly observed, however, and the factor shares of labour and capital are often used as weights.

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Compendium of Productivity Indicators A.5. GROWTH ACCOUNTS FOR OECD COUNTRIES

Contributions to growth of GDP, G7 countries, 1990-95 and 1995-20031 In percentage points Labour input ICT capital Non-ICT capital Multi-factor productivity % 5

4 1995-2003 3

2 1990-95

-1 Canada France Germany Italy Japan United United Kingdom States

Contributions to GDP growth, all OECD countries, 1995-20032 In percentage points

Labour input ICT capital Non-ICT capital Multi-factor productivity % 9

-1

y k l d n y l a m s e n s a d n e a d n r i a e m n i li a a r u d g c e d n a c n p a t a t i n d o a t a a I s u n d l a a la p e r l a m g a t a e t n e t m u l l r r g a n S r e J r n e r w e S a i s r A o F in I e e B e S Z C F G u h P d G D t K e A e d w it e e n N t i N U n U 1. 1991-1995 for Germany; 1995-2001 for Italy, 1995-2002 for France and Japan. 2. 1995-2001 for Italy; 1995-2002 for Australia, France, Japan, New Zealand and Spain.

THE CONTRIBUTION OF MULTI-FACTOR PRODUCTIVITY AND ICT A.6. CAPITAL TO GDP GROWTH

• Multi-factor productivity growth was one of the particularly large for Australia and Ireland, and factors that helped strengthen growth in Canada, smallest for Germany. Finland, France, Greece, Ireland, Portugal, Sweden and the United States over the 1990s. In other Source countries, including Germany, Italy, Japan, the OECD Productivity Database, September 2005 United Kingdom, Austria, Belgium, Denmark, the www.oecd.org/statistics/productivity Netherlands and Spain, MFP growth slowed down Groningen Growth and Development Centre, June 2005. over the 1990s. For further reading • Lequiller, F., N. Ahmad, S Varjonen, W Cave and Investment in ICT accounted for between 0.35 and K.H. Ahn (2003), “Report of the OECD Task Force on 0.9 percentage points of growth in GDP over the Software Measurement in the National Accounts”, OECD period 1995-2003 (see A.5). In all countries but Statistics Working Paper 2003/1, OECD, Paris. Finland, investment in ICT hardware accounted for Ahmad, N. (2003), “Measuring Investment in Software”, the bulk of the contribution of ICT capital. In several STI Working Paper, 2003/6, OECD, Paris. countries, notably Denmark, France, the Schreyer, P., P.E. Bignon and J. Dupont (2003), “OECD Netherlands, Sweden and the United States, Capital Services Estimates: Methodology and a First Set investment in software accounted for one-third of the of Results”, OECD Statistics Working Paper 2003/6, total contribution of ICT capital to GDP growth. In OECD, Paris. Finland, investment in communications equipment Wölfl, A., and D. Hajkova (2005), “Measuring Multi-factor was the most important component of ICT’s Productivity Growth”, STI Working Papers, OECD, Paris, contribution to GDP growth. Differences in the forthcoming. measurement of the components of ICT investment are likely to affect these comparisons.

• The contribution of ICT capital to GDP growth has increased in all OECD countries from 1990-95 to 1995-2003. The increase over this period was

The contribution of ICT capital to GDP growth

Correct measurement of investment in ICT in both nominal and volume terms is crucial for estimating its contribution to economic growth and performance. Data availability and measurement of investment in ICT based on national accounts (SNA93) vary considerably across OECD countries, especially as regards measurement of investment in software, deflators applied, breakdown by institutional sector and temporal coverage. In the national accounts, expenditure on ICT products is considered as investment only if the products can be physically isolated (i.e. ICT embodied in equipment is considered not as investment but as intermediate consumption). This means that investment in ICT may be underestimated and the order of magnitude of the underestimation may differ depending on how intermediate consumption and investment are treated in each country’s accounts.

In particular, expenditure on software has only very recently been treated as capital expenditure in the national accounts, and methodologies still vary considerably across countries. Difficulties for measuring software investment are also linked to the ways in which software can be acquired, e.g. via rental and licences or embedded in hardware. Moreover, software is often developed on own account. To tackle the specific problems relating to software in the context of the SNA93 revision of the national accounts, a joint OECD-EU Task Force on the Measurement of Software in the National Accounts has developed recommendations concerning the capitalisation of software (Lequiller, et al., 2003; Ahmad, 2003). These are now being implemented by OECD member countries.

20 © OECD 2005

Compendium of Productivity Indicators A.6. THE CONTRIBUTION OF MULTI-FACTOR PRODUCTIVITY AND ICT CAPITAL TO GDP GROWTH

Multi-factor productivity growth, 1990-95 and 1995-20031 In percentage points 1990-1995 1995-2003 5.0%

4.0%

3.0%

2.0%

1.0%

0.0%

-1.0%

ly k s n d y m e r i ia es lia nd Ita an ada ra a Spa gium ustr al rman gdo ranc st nland el Japan A n weden F Stat u Ir enma Ze e Can S Greece Fi D Bel G Portugal Ki ed A etherland N ew ted nit N ni U U

The contributions of ICT capital by component, 1995- The contributions of ICT capital, 1990-95 and 1995- 20032 20032 In percentage points In percentage points

Software Communication equipment IT equipment 1990-1995 1995-2003*

Australia Australia United States United States Sweden Sw eden Denmark Denmark United Kingdom United Kingdom Belgium Belgium Canada Canada Japan Japan New Zealand New Zealand Spain Spain Netherlands Netherlands Portugal Portugal

Finland Finland

Ireland Ireland

Greece Greece

Italy Italy

Germany Germany

France France

Austria Austria %

% 1.0 0.8 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0

1. 1991-1995 for Germany; 1995-2002 for Australia, France, Japan, New Zealand and Spain. 2. 1995-2002 for Australia, France, Japan, New Zealand and Spain.

LABOUR PRODUCTIVITY GROWTH IN THE BUSINESS SECTOR A.7.

• Productivity growth can be measured by relating down over the period. From 2000 onwards, business changes in output to changes in one or more inputs sector productivity slowed down in most OECD to production. The most common productivity countries, but picked up in a few countries. measure is labour productivity, which links output to labour input. The OECD publishes several labour Sources productivity measures: those presented in the OECD OECD (2005), OECD Economic Outlook, Vol. 2005/1, Productivity Database refer to the total economy and No. 77, June 2005. relate changes in output to changes in hours worked. For further reading In its Economic Outlook, the OECD publishes OECD (2001), Measuring Productivity – OECD Manual, another series of labour productivity, namely output OECD, Paris. per employed person in the business sector. Ahmad, N., F. Lequiller, P. Marianna, D. Pilat, P. Schreyer and A. Wölfl (2003), “Comparing Growth in • As with other productivity indicators, the growth in GDP and Labour Productivity: Measurement Issues”, business sector labour productivity is quite variable OECD Statistics Brief, No. 7 December, OECD, Paris. from year to year, because employment generally Ahmad, N., F. Lequiller, P. Marianna, D. Pilat, P. moves more slowly than value added. Businesses Schreyer and A. Wölfl (2003), “Comparing Labour do not immediately employ more staff when there is Productivity Growth in the OECD Area: The Role of an upturn and they do not immediately lay off staff Measurement”, STI Working Papers 2003/14, OECD, when there is a down turn. Paris. Pilat, D. and P. Schreyer (2004), “The OECD Productivity • Database – An Overview”, International Productivity Business sector labour productivity has grown in all Monitor, Number 8, Spring, pp. 59-65. countries although the charts show considerable differences in growth between countries. In Greece, Atkinson, Tony (2005) Atkinson Review: Final report; available on http://www.statistics.gov.uk. Iceland, Ireland and the United States, business sector labour productivity grew much faster from 1995-2004 than from 1990-1995. In other OECD countries, notably Belgium, Germany, Italy, the Netherlands, Norway, Portugal and Spain, it slowed

Measuring business sector productivity

Business sector output is defined as economy-wide GDP less the government wage bill, less net indirect taxes and government consumption of fixed capital. Business sector employment is defined as total economy employment less public sector employment. The business sector thus excludes outputs and inputs that relate to government. The main advantage of business sector measures is that they exclude a large part of the economy, i.e. the public sector, in which productivity is typically poorly measured. In the absence of market production and market prices (as is the case for example for general administration), government output is normally measured by the inputs that are used to produce this output, implying zero productivity growth.

Measures of business sector productivity are also important because this sector ultimately determines the development of an economy’s potential output and of the economy's tax base. At the same time, the precise definition of the business sector is not always straightforward. Also, it can be difficult to allocate certain enterprises to the market or to the non-market sector. In their national statistics, countries use different approaches towards measuring the output and inputs of the business sector. Differences arise in particular in the treatment of health or education services that may be considered part of non-market production in some countries and part of market production in others.

Over the past few years, several countries have started to develop output-based measures of production for non-market activities. For example, the United Kingdom has put in place measures of output of education and health services in their national accounts. A report to the U.K. Office of National Statistics (Atkinson 2005) provides a comprehensive discussion of the issues involved in measuring non- market output.

Despite their conceptual advantages, business sector measures of labour productivity are subject to greater constraints of data availability than labour productivity measures for the total economy. In particular, it is difficult to obtain reliable and internationally comparable data for the total number of hours worked in the business sector, making it necessary to revert to the total number of persons employed as a measure of labour input. As long as average hours worked per person do not change significantly over time, this makes little difference to the resulting productivity measures. However, when there are marked shifts in working hours, the two measures of labour input may give rise to quite different rates of labour productivity growth.

22 © OECD 2005

Compendium of Productivity Indicators A.7. LABOUR PRODUCTIVITY GROWTH IN THE BUSINESS SECTOR

Growth in GDP per employee, 1990-19951 compared with 1995-2004 Business sector, average annual growth rate

% 1990-1995 1995-2004 7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

-1.0

-2.0

-3.0

al m m rk y n e d co o ia a e c Italy pain al e key S land lands tug ed tates ur ra nce or elgiu Japan ustria inland Korea Irelan T Mexi F P ealandB Canada A enma F Norw w IcelandGre Germany Z Kingd D Austr S ed S ed Switzer Nether ew Luxembourg nit ch Republic N nit U e U Cz

1 Refers to 1991-1995 for Mexico and Germany.

Growth in GDP per employee, 1995-2000 compared with 2000-2004 Business sector, average annual growth rate

% 1995-2000 2000-2004 7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

-1.0

o g ly al a y in a e m n rk d d s d a e y d r d ds n ri d ia a om e lic xic Ita an a pa an lan way lan an e l lan giu ap n r reec ortug anad rm S ra nc J i Irelan ce Kore Turke Pol M P zer C e Aust F Bel enma ingd F No I G it her G Zeal Austral D K Sweden uxembou w et L S N ew nited Stat N nited U ze ch Repu b U C

Compendium of Productivity Indicators

B. PRODUCTIVITY LEVELS

INCOME AND PRODUCTIVITY LEVELS B.1.

• In 2004, GDP per capita in the OECD area ranged countries because of high unemployment and low from over USD 35 000 in Ireland, Luxembourg, participation of the working-age population in the Norway and the United States to less than labour force. In Iceland and Korea, however, labour USD 15 000 in Mexico, Poland, the Slovak Republic input per capita is considerably higher than in the and Turkey. For most OECD countries, income United States, owing to relatively long working hours levels are 70-85% of US income levels. and high rates of labour force participation. Labour input per capita is also relatively high in Australia, • Differences in income reflect a combination of labour Canada, the Czech Republic, Japan, New Zealand productivity, measured as GDP per hour worked, and Switzerland. and labour utilisation, measured as hours worked per capita. A country’s labour productivity level is Sources typically the most significant factor in determining OECD, Productivity Database, September 2005. income levels, particularly for countries with low Available at: www.oecd.org/statistics/productivity. levels of GDP per capita. OECD, Annual National Accounts Database, September 2005. • Most OECD countries have higher levels of GDP per For further reading hour worked compared with the United States than OECD (2001), Measuring Productivity – OECD Manual, GDP per capita. This is because they often have OECD, Paris. lower levels of labour utilisation than the United OECD (2004), “Clocking In (and Out): Several Facets of States. The difference between income and Working Time”, OECD Employment Outlook 2004, productivity levels is largest for European countries; Chapter 1, OECD, Paris. GDP per hour worked surpasses the US productivity OECD (2004), Purchasing Power Parities and Real level in Belgium, France, Ireland and Norway, Expenditures, OECD, Paris. whereas income levels in most of these countries are substantially below those for the United States.

• In many OECD countries, labour use, as measured by hours worked per capita, is substantially lower than in the United States. This is because of disparities in working hours but also in several

Comparisons of income and productivity levels Comparisons of income and productivity levels face several measurement problems (see Annex 2 for further detail). First, they require comparable data on output. In the 1993 System of National Accounts (SNA), the measurement and definition of GDP are treated systematically across countries. Most countries have implemented this system; in the OECD area, Turkey is the only exception, and its output is likely to be understated relative to other OECD countries. Other differences, such as the measurement of software investment, also affect the comparability of GDP across countries, although the differences are typically quite small. The second problem is the measurement of labour input. Some countries integrate the measurement of labour input in the national accounts; this may ensure that estimates of labour input are consistent with those of output. In most countries, however, employment data are derived from labour force surveys which are not entirely consistent with the national accounts. Labour input also requires measures of hours worked, which are typically derived either from labour force surveys or from business surveys. Several OECD countries estimate hours worked from a combination of these sources or integrate these sources in a system of labour accounts, which are comparable to the national accounts. The OECD Productivity Database includes estimates of total hours worked which aim at consistency between estimates of employment and hours worked. The cross-country comparability of hours worked remains somewhat limited, however, with a margin of uncertainty in estimates of productivity levels. Third, international comparisons require price ratios to convert output expressed in a national currency into a common unit. Exchange rates are of limited use for this purpose because they are volatile and reflect many influences, including capital movements and trade flows. The alternative is to use purchasing power parities (PPP), which measure the relative prices of the same basket of consumption goods in different countries. The estimates shown here are based on official OECD PPPs for 2004.

26 © OECD 2005

Compendium of Productivity Indicators B.1. INCOME AND PRODUCTIVITY LEVELS

Income and productivity levels, 2004 Percentage point differences with respect to the United States

Percentage gap in GDP per capita Effect of labour utilisation1 Gap in GDP per hour worked

Turkey (2)

Mexico

Poland

Slovak Republic

Hungary

Czech Republic

Portugal

Korea

Greece

New Zealand

Spain

EU-19 (3)

Italy

OECD

Euro-zone (4)

Germany

France (5)

Japan

Australia

Sweden

Finland

Belgium

Netherlands

Canada

United Kingdom

Denmark

Austria

Iceland

Switzerland

Ireland

Norway

-80 -60 -40 -20 0 -80 -60 -40 -20 0 -80 -60 -40 -20 0 1. Based on total hours worked per capita. 2. GDP for Turkey is based on the 1968 System of National Accounts. 3. EU member countries that are also member countries of the OECD. 4. Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain. 5. Includes overseas departments.

LEVELS OF GDP PER CAPITA AND GDP PER HOUR WORKED, 1950-2004 B.2.

• Cross-country differences in GDP per capita and • Changes in levels of GDP per hour worked show a labour productivity in the OECD area have eroded slightly different pattern. Out of 22 OECD countries considerably since the 1950s. Over the 1950s and for which data are available for a long period, only 1960s, income levels in OECD countries were Mexico, Canada, Australia and Switzerland have not catching up with those of the United States except in been catching up almost continuously with Australia, New Zealand and the United Kingdom. In US productivity levels over the post-war period. the 1970s, this phenomenon was less widespread Several European countries now stand even with the and the rate of catch-up fell except in Korea. In the United States in terms of average labour productivity 1980s, there was even less catch-up, as GDP per and some have surpassed US productivity levels capita grew more slowly than in the United States in (see B.1). 20 OECD countries. The same was true for 20 OECD countries from 1990-2004 with Ireland Sources and Korea being the most notable exceptions. OECD, Productivity database, July 2005. Available at: www.oecd.org/statistics/productivity. • Japan and Korea had the highest rates of catch-up OECD, Annual National Accounts Database, July 2005. over the period 1950-2004, with GDP per capita For further reading growing much more rapidly than in the United OECD (2001), Measuring Productivity – OECD Manual, States. Rates of catch-up were much lower, typically OECD, Paris. below 1% a year, in most of western Europe. Angus Maddison (2001), The World Economy: A Australia, New Zealand, the United Kingdom and Millennial Perspective, Development Centre Studies, Canada already had relatively high income levels in OECD, Paris 1950 and have done little catching up since. OECD (2004), Purchasing Power Parities and Real Switzerland has seen a marked decline in its relative Expenditures, OECD, Paris. income level. Central European countries, Mexico and Turkey started with low income levels in the 1950s and have only caught up a little.

Comparisons of income and productivity levels over time Comparisons of income and productivity levels for a particular year (see B.1) can be updated over time by using time series for GDP, population, employment and hours worked. Time series for GDP, population, employment and hours worked are all derived from the OECD Productivity Database. This OECD database only dates back to the early 1970s, however. For earlier years, estimates were derived by using data for GDP and population from Angus Maddison (2001), The World Economy: A Millennial Perspective, OECD Development Centre, OECD, Paris. GDP per hour worked in the OECD area, 1950, 1973 and 2004 United States = 100

1950 1973 2004

120

100

United States = 100 = States United 40

y al in y nd an en tes nd orea Ital pa d rway ala ap nada S e man ta Mexico K ortug e J a w nmark S Irela France Iceland C tzerland Finland ingdom e erlands No P Z Australia S D Ger h d Belgium w e Swi nite N ed K Net nit U U

28 © OECD 2005

Compendium of Productivity Indicators B.2. LEVELS OF GDP PER CAPITA AND GDP PER HOUR WORKED, 1950-2004

Catch-up and convergence in OECD income levels, 1950-2004, United States = 100

Medium rate of catch-up (<=1.1% annually) High-income, no catch-up (<0.1% annually)

140 140

Belgium Finland France 120 Germany Netherlands Norw ay 120 Italy

100 100

80 80

60 60

Australia Canada 40 40 New Zealand Sw eden Sw itzerland United Kingdom Denmark 20 20

0 0 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Rapid catch-up (>1.1% annually) Low starting point, low rates of catch-up (< 0.3% annually)

140 140

Czech Republic Hungary Greece Ireland Japan Korea 120 120 Mexico Poland Portugal Spain Austria Turkey Slovak Republic

100 100

80 80

60 60

40 40

20 20

0 0 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Source: 2004 income levels from B.1; previous years based on OECD productivity database and Angus Maddison (2001), The World Economy: A Millennial Perspective, Development Centre Studies, OECD, Paris.

ALTERNATIVE MEASURES OF OUTPUT B.3.

• When data in the national accounts are taken at face 2004 (or latest year available), but the largest value, a comparison between GDP and NDP per increase is registered for Ireland, which is also an hour worked hardly changes the ranking of countries example for a country where GDP was significantly with respect to relative levels of labour productivity. higher than GNI, whereas Switzerland is a case On average, NDP accounts for about 85% of GDP, where the opposite holds. although there is some variation across countries. Nonetheless, in Japan, the Czech Republic and the Sources Slovak Republic, depreciation is large in relation to OECD Productivity Database: GDP. By contrast, Greece, Mexico, or the United www.oecd.org/statistics/productivity States are countries where NDP is relatively high OECD, Annual National Accounts Database, September compared to GDP. Similar to the level comparisons 2005. of NDP and GDP per hour worked, the growth rates For further reading of NDP and GDP per hour worked can be compared Commission of the European Communities, OECD, IMF, (see A.3). United Nations, World Bank (1993), System of National Accounts 1993, Brussels/Luxembourg, New York, Paris, • In most OECD countries, the difference between Washington DC. GDP, NDP and GNI per hour worked is small since OECD (2001), Measuring Productivity – OECD Manual, gross income inflows from abroad tends to be offset OECD, Paris. by gross outflows. This indicates that GDP per hour Ahmad, N., F. Lequiller, P. Marianna, D. Pilat, P. worked is a useful proxy for other measures of Schreyer and A. Wölfl (2003), “Comparing Labour output in productivity calculations, especially when Productivity Growth in the OECD Area: The Role of data availability restricts international comparability. Measurement”, STI Working Papers 2003/14, OECD, However, the difference is significant for a few Paris. countries, notably Ireland. GDP per hour worked, OECD (2004), Purchasing Power Parities and Real GNI per hour worked and GDI per hour worked Expenditures, OECD, Paris. increased in all OECD countries between 1995 and

Alternative measures of output – level comparisons

GDP is a gross measure that does not account for capital used in production. The associated loss in value, depreciation, reduces the net value of production that is available as net income in any given year. The observation has often been made that a growing part of capital goods is short-lived (for example computers), and that this structural shift in the composition of assets leads to higher overall depreciation. A case could thus be made to measure productivity on the basis of net domestic product as well as GDP. Countries with a structure of fixed assets that is biased towards short-lived assets would exhibit a relatively lower NDP per hour worked than GDP per hour worked, reflecting relatively higher depreciation.

It is also useful to compare GDP with gross national income (GNI). This comparison permits to take into account the payments received from or sent to the rest of world. GNI is equal to GDP less net taxes on production and imports, less compensation of employees and property income payable to the rest of the world plus the corresponding items receiving from the rest of the world. For example, when company profits are transferred abroad, this leads to GNI being lower than GDP. Conversely, when foreign affiliates or domestic firms or residents abroad transfer payment to the domestic economy, this will raise GNI relative to GDP.

At the same time it should be noted that income-based measures, while useful from a welfare perspective, are not necessarily the preferred indicator when it comes to measuring productivity. The purpose of productivity measures is to capture inputs and outputs in a production process and GNI may not be the best measure of output for this purpose.

30 © OECD 2005

Compendium of Productivity Indicators B.3. ALTERNATIVE MEASURES OF OUTPUT

GDP and NDP per hour worked, 2004 Total economy, USD, current prices, current PPPs

NDP per hour w orked GDP per hour w orked 60

a al e d a ) in d m rk ly y s d m ) 3) 3) lic 3) 3) 3) 3 n 3) u 3 ay b ug 0 lan pa an Ita an t land el gi w (200 Kore or 200 20 ce 200 200 S ngdo rma Ir or epu P ( Greec ( I ( Canad ( Finl wedenAustria enma e (200 FranceBel N R a Ki S D G s her and apan land ted Net Mexico (200 J ni epublic ustrali zer U R Slovak Zeal A t ed State h w t e Swi ni Luxembourg (200 ze c N U C

GDP and GNI per hour worked, 2004 Total economy, USD, current prices, current PPPs

GNI per hour w orked GDP per hour w orked 60

0 l a ) e d a n d rk ly y s d 3) 3) e lic a 3 3) 3) d i m ia 3) e m 3) y b en a Ita nd u ug eec a anc gi Kor 200 r (200 celan anad Spa 200 Irelan r epu Port G I C Finlan ingdo Austr ( F Bel Norwa K Swed Denm Germans nd ( witzerlan e etherl a S at N Mexico (200 Japan St Slova k R Zeal Australia (200 United h Republic (200 ted c Luxembourg (200 ze New Uni C

DIFFERENCES IN LABOUR PRODUCTIVITY BY ECONOMIC ACTIVITY B.4.

• The ratio of value added to employment provides an • The relatively low ratios of value added to indication of which industries yield relatively high employment for business services are partly due to value added per unit of labour input. Although total the fact that this category covers a diverse range of employment is not the best measure of labour input activities from renting of machinery and equipment, for this purpose (see box), a reasonably clear pattern computer services and R&D to legal, accounting, emerges. architectural and engineering services. Use of a more detailed activity breakdown would allow more • In 2002, industries predominantly involved in the high value added activities to be revealed. extraction, processing and supply of fuel and energy goods continued to produce the highest value added Data Resources: per labour unit. These industries were more than OECD, STAN Indicators Database, August 2005. twice as productive as the average industry. They OECD, STAN Database, August 2005. account for about 4% of total OECD value added For further reading and are typically highly capital-intensive. Ahmad, Nadim, Francois Lequiller, Pascal Marianna, Dirk Pilat, Paul Schreyer and Anita Wölfl (2003), “Comparing • Besides the energy-producing industries, those that Labour Productivity Growth in the OECD Area: The Role yield the most value added per labour unit are those of Measurement”, STI Working Paper 2003/14, OECD, considered more technology and/or knowledge Paris. intensive (see box). In manufacturing, the chemical OECD (2001), Measuring Productivity – OECD Manual, industry has the highest relative labour productivity OECD, Paris. level, while in services, finance, insurance and OECD (2001), OECD Science, Technology and Industry telecommunications lead the way. Scoreboard, D.4. OECD, Paris. OECD (2003), OECD Science, Technology and Industry • Construction, wholesale and retail trade, hotels and Scoreboard section D and Annex 1. OECD, Paris restaurants and textiles show relatively low levels of labour productivity in all three major OECD regions. These industries are typically highly labour-intensive, have a high proportion of low-skilled jobs and are not considered high-technology sectors.

Measuring labour productivity by industry

The indicators shown here are determined by data availability and simply measure value added per person employed. Further adjustments to labour input, including adjustment for part-time work and hours worked per worker, can be made for certain OECD countries but international comparisons by industry are not yet feasible. A few other notes apply to the indicators:

• For the labour productivity levels, 2002 value added at current prices was used. For the European Union, member countries' value added data were aggregated after applying 2002 US dollar GDP purchasing power parities (PPPs). Industry-specific PPPs are preferable, but are not available for all sectors and countries.

• The labour productivity levels by industry are relative to the total non-agriculture business sector. This consists of all industries except i) Agriculture, hunting, forestry and fishing (ISIC 01-05); ii) Real estate activities (ISIC 70 - for value added this includes imputed rent of owner occupied dwellings); and iii) Community, social and personal services (ISIC 75-99 - this includes mainly non-market activities such as public administration, education and health for which, in some countries, there are still some measurement problems). This activity-based definition is not to be confused with an institutional definition of the business sector (see A.7).

• Sectors that are considered technology- and/or knowledge-intensive are highlighted in the graphs. For manufacturing, R&D intensity is the main determinant broadly identifying Chemicals (ISIC 24), Machinery and equipment (ISIC 29-33) and Transport equipment (ISIC 34-45) as high or medium-high technology sectors (see Annex 1, of STI Scoreboard 2003 for more details). For services, preliminary analyses of embodied technology (based on input-output tables) and composition of workforce skills have complemented limited R&D intensity information to describe the following broad service sectors as knowledge intensive: Post and telecommunications (ISIC 64); Finance and Insurance (ISIC 65-67) and Business services (ISIC 71-74).

• The European Union in this section covers the 15 member countries up to 2004 plus two new members, the Czech Republic and Hungary.

• Data on value added for the EU countries is expressed in basic prices; for Japan and the United States, it is at factor costs.

32 © OECD 2005

Compendium of Productivity Indicators B.4. DIFFERENCES IN LABOUR PRODUCTIVITY BY ECONOMIC ACTIVITY Labour productivity levels relative to the total non-agriculture business sector, 2002 United States

Electricity, gas and water supply Petroleum refining Mining and quarrying Chem icals Finance and Insurance Post and telecom m unications Transport equipment Paper, printing, publishing Machinery and equipment Food, drink, tobacco Non-metallic m inerals Rubber and plastics Basic m etals and products Business services Transport and storage W ood and m anufacturing nec Construction Textiles, clothing Trade, hotels and restaurants

012345

Japan

P e tro leu m re finin g

E le ctricity, gas and w ater sup ply

Chemicals

Finance and Insurance

Post and telecom m unications

Transport equipment

Business services

Paper, printing, publishing

Mining and quarrying

M achinery and equipm ent

Non-metallic m inerals

Basic m etals and products

Rubber and plastics

Transport and storage

Food, drink, tobacco

Trade, hotels and restaurants

Construction

W ood and m anufacturing nec

Textiles, clothing 012345 European Union

Petroleum refining

Mining and quarrying

Electricity, gas and water supply

Chemicals

Post and telecommunications

Finance and Insurance

Transport equipment

Paper, printing, publishing

Transport and storage

Non-metallic minerals

Business services

Machinery and equipment

Food, drink, tobacco

Rubber and plastics

Basic metals and products

C onstruction

W ood and manufacturing nec

Trade, hotels and restaurants

Textiles, clothing 012345

Compendium of Productivity Indicators

C. PRODUCTIVITY GROWTH BY INDUSTRY

CONTRIBUTION OF KEY ACTIVITIES TO AGGREGATE PRODUCTIVITY C.1. GROWTH

• A breakdown of productivity growth by activity can technology and medium-high-technology show which industries are particularly important for manufacturing. ICT manufacturing and services were overall productivity performance. In many OECD particularly important in Finland and Sweden, countries, notably in Australia, Greece, Mexico, the whereas other high- and medium-high-technology United Kingdom and the United States, business industries were particularly important in Japan, sector services have accounted for the bulk of labour Sweden and the United States. In Greece, Norway productivity growth over 1995-2003. However, the and the United States, wholesale and retail trade manufacturing sector remains important in the Czech also contributed significantly to aggregate Republic, Finland, Hungary, Poland, Korea, the productivity growth. Slovak Republic and Sweden. Data Resources: • For several countries, the contribution of business OECD, STAN Indicators Database, August 2005. services has increased from 1990-95. This is linked OECD, STAN Database, August 2005. to their growing share in total value added. However, For further reading it also reflects stronger labour productivity growth in OECD (2001), Measuring Productivity – OECD Manual, some OECD countries, such as Canada, Sweden OECD, Paris. and the United States. In France, Germany, the Netherlands and Spain, on the other hand, labour OECD (2005), Science, Technology and Industry Scoreboard 2005, OECD, Paris. productivity growth in business services slowed over the past decade, reflecting a slowdown that can also Wölfl, A. (2003), “Productivity Growth in Service Industries: An Assessment of Recent Patterns and the be observed at the aggregate level (see A.2). Role of Measurement”, STI Working Paper 2003/7, OECD, Paris. • A large share of labour productivity growth in the Wölfl, A. (2005), “The Service Economy in OECD non-agricultural business sector is attributable to Countries”, STI Working Paper 2005/3, OECD, Paris. knowledge-intensive activities, notably ICT

manufacturing and services and other high-

Measuring labour productivity growth by industry

Labour productivity growth can be calculated as the difference between the rate of growth of output or value added and the rate of growth of labour input. Calculating a sector’s contribution to aggregate productivity growth requires a number of simple steps, as explained in the OECD Productivity Manual. First, the aggregate rate of change in value added is a share-weighted average of the industry-specific rate of change in value added, with weights reflecting the current price share of each industry in value added. On the input side, aggregation of industry-level labour input is achieved by weighting the growth rates in hours worked by industry with each industry’s share in total labour compensation. Aggregate labour productivity growth can then be calculated as the difference between aggregate growth in value added and aggregate growth in labour input. An industry’s contribution to aggregate labour productivity growth is therefore the difference between its contribution to total value added and total labour input. If value added and labour shares are the same, total labour productivity growth is a simple weighted average of industry-specific labour productivity growth. Similar approaches can be followed when production, instead of value added, is used as the output measure. However, OECD work on the basis of the STAN database has typically focused on value added, since constant price series of value added are more widely available across OECD countries than constant price series of production. Difficulties in measuring output and productivity in services sectors should also be taken into consideration when interpreting the results (see Wölfl, 2003).

36 © OECD 2005

Compendium of Productivity Indicators C.1. CONTRIBUTION OF KEY ACTIVITIES TO AGGREGATE PRODUCTIVITY GROWTH

Contribution to growth of value added per person employed Annual average growth rates and contributions in percentage points

1990-1995

Business sector services Other services Manufacturing Other industries 5.0 3.0 4.8 4.6 2.7 total labour productivity grow th 4.0 3.0 3.2 3.7 2.1 3.0 2.0 2.0 2.3 1.8 1.8 1.8 1.8 1.5 1.4 2.0 0.9 1.2 1.1 1.3 0.7 1.0 0.6

0.0

-1.0 l n m a rk s rg a li nd da ny a and do a rea a nd ico nce ain ary l tra l na Italy ug m a x p g Jap s a Ko rma rt ra S mbou weden Fin ing Po Austria e o en erl Me F e S NorwayZealand K C Belgium P D h Hun d Au G t w e e Lux e N N nit United States U 1995-20031

Business sector services Other services Manuf ac tur ing Other industries 5.0 total labour productivity grow th 4.2 4.0 3.3 2.7 2.9 3.6 2.1 3.0 1.7 1.8 1.9 1.7 1.9 2.4 1.5 1.5 1.5 1.6 1.4 2.0 1.4 1.3 0.8 0.9 0.9 0.6 0.9 1.0 0.5 0.3 0.0

-1.0

s a m d a d al rk g lic y lic te li n ay e n g a ny r ds nd n l a a la r a u pan n a ary ium ain a t rw l m a la l g de lg p It ub S str Ko rma J r a e e S p Greeceu Mexico Po o Fin en e e Austria France d N Portu CanadaD h Hun Sw B te A d Kingdo G xembo t i e u w Ze n L Ne e U nit N lovak Repub zech Re U S C

Contributions to average annual growth of value added per person employed non-agricultural business sector, 1990-20031

ICT manufactures and services Financial intermediation % Wholesale and retail trade; hotels and restaurants Other high and medium-high tech. industries 4.0 3.7 Labour productivity growth of total non-agricultural 3.4 3.5 business sector 2.9 2.9 3.0 2.5 2.3 2.5 2.0 2.1 2.1 2.0 1.6 1.5 1.1 1.1 0.9 1.0 -0.1 0.7 0.5 0.0 -0.5

y s n a a s d y e in e ly d k ia i e n an ta a r e pa I ap na tat eec S ranc J a mar ust S inlan orwa r erm F A ustral F wed G C en A d N S G etherland D N nite U

1. Or latest year available.

PRODUCTIVITY GROWTH IN MANUFACTURING C.2.

• The manufacturing sector has traditionally been the and Finland. Due to rapid technological progress, main driver of aggregate productivity growth in measuring productivity growth in this industry is OECD countries. While its contribution to aggregate particularly difficult. Some countries have introduced productivity growth has become less important in so-called hedonic deflators to address the rapid recent years (see C.1), notably in some OECD changes in quality and characteristics that countries, it still shows strong performance in many characterise this industry. Other countries have not, industries. which implies that the international comparability of productivity growth rates in this industry is likely to • For most OECD countries, with the exceptions of be somewhat limited. Iceland and Japan, manufacturing productivity growth over 1995-2003 was slower than over 1990- Data Resources: 95. The difference is particularly marked in Finland, OECD, STAN Indicators Database, August 2005. Hungary and Poland, where the early 1990s marked OECD, STAN Database, August 2005. strong structural changes in the manufacturing For further reading: sector. OECD (2001), Measuring Productivity – OECD Manual, OECD, Paris. • Within manufacturing, large differences in the rate of Pilat, D., A. Cimper, K. Olsen and C. Webb (2005), “The productivity growth can be observed. High- and Changing Nature of Manufacturing in OECD Economies”, medium-high technology industries, such as STI Working Paper, OECD, Paris, forthcoming. machinery, electrical and optical equipment, as well Triplett, Jack (2004), “Handbook on Hedonic Indexes as chemicals, have typically experienced relatively and Quality Adjustment in Price Indexes: Special high rates of productivity growth. Low-technology Application to Information Technology Products”, STI manufacturing industries, such as food products and Working Papers 2004/9, OECD, Paris. textiles, have often observed slightly lower rates of Wölfl, A. (2003), “Productivity Growth in Service productivity growth. Industries: An Assessment of Recent Patterns and the Role of Measurement”, STI Working Paper 2003/7, OECD, Paris, www.oecd.org/sti/working-papers • Developments in specific industries are also noteworthy. Electrical and optical equipment is among the manufacturing industries with the highest rate of productivity growth, up to 20% annually in OECD countries such as Sweden, Hungary, Korea

Hedonic methods

Examining the role of ICT-producing sectors in economic growth is heavily influenced by measurement problems, both regarding outputs and inputs. The key measurement problem for the manufacturing of ICT goods on both the output and input side concerns prices, in particular how to statistically capture significant quality improvements associated with technological advances in goods such as computers and semi-conductors. The use of hedonic deflators is generally considered as the best way to address these problems. Several countries currently use hedonic methods to deflate output in the computer industry (e.g. Canada, Denmark, France, Sweden and the United States). However, these countries do not use exactly the same method. Some countries, such as the United States , apply their own hedonic deflator, others apply the US hedonic deflator adjusted for exchange rates, and yet other OECD countries apply conventional deflators.

Adjusting for these methodological differences in computer deflators for the purpose of a cross-country comparison is difficult, since there are considerable cross-country differences in industrial specialisation. Only few OECD countries produce computers, where price falls have been very rapid; many only produce peripheral equipment, such as computer terminals. Similar differences in industry composition exist in Radio, Television and Communication Equipment (ISIC 32), which includes the semi-conductor industry. The differences in the composition of output are typically larger than in computer investment, where standardised approaches have been applied (e.g. Schreyer, et al. 2003).

38 © OECD 2005

Compendium of Productivity Indicators C.2. PRODUCTIVITY GROWTH IN MANUFACTURING

Annual average growth of value added per person employed 1990-1995 and 1995-20031 Total manufacturing % 16 14 12 10 8 6 4 2 0 -2

a y d n e k a d y d s y e r en ic nd a m r lia d al m ly r a pa a ri ece a ico g an nd wa ain a o d a st ma na tu do a r p It olandK ng e J inl u e ranc a r g rl rl o S P u w epublIcelan F A Gr F ustr Mexo ermanze e N H S R Belgiu C P Zealan t h DenA G wi t ew S e Luxembourg N N lovak United States CzechS Republic United Kin % High- and medium-high technology manufactures % Low technology manufactures 16 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 -2 -2 l ic d s y n e ia m o a y a a d e k e g y n l y n ea n e ga r o d ds ain ic m d n lic r c r a a a m i ry r den t pa u a n p Italy a a ico w g taly o e a a t st rlandla m x ap I do pa K J r u an e r S Kore publ lgiu e or J g rman S Finla FrancGreece o A MexicC e Finla Greec M Fran N ortu in Sw Hungar BelgiumP Denmark German Be Can en P Hunga R D xembou Ge Switz u nited St Nethe Luxembourg ak Repub L U v United States Slovak Republ United Kingd Czech Slo United K 2 2 % Machinery % Electrical & Optical equipment 16 16 20.4 14 14 19.7 12 12 10 10 8 8 6 6 4 4 2 2 0 0 -2 -2 l y g a e n y n a y ic n d y y d n s e m ia e y ic rg in a s ly ry ea r ri c co i l n ar te c om u r c ga a a r t i ium de a om b tal lan pa blic n d t u an nd Ita tates ou s ee x p d land rla I ng n ta u ra us m Sp a Ko u r lg S pu Japae u Korea Ja F ng elgi ort r epubl erland A G Me e Francewe No rwa Canad e Fin Sweden Fi S B A Gree e Mexico CanadNorwatz erl Hungd S Portugal B S R itz H Rep Ki Denmark P G R i h GermanDenmark w ted h uxemb S ak Luxembou Sw Net nite ech RepublicL Netherlands ited U z Uni n C UnitedSlovak King Czec U Slov Chemicals 3 % % Motor vehicles 10 18.1 16.3 10 10.4 8 8 6 6 4 4 2 2 0 0 -2 -2 -4.2 -4.3 -6.3 -4 -6 -4 -11.8-12.2 a n y s a y y c n e d a y y ce k m m ia s in d n li a c ia d m ic r en u tr d ece p n r n tes a tal a ugal ore n d pa do rwa nd Italy an a a st la a I g t ra mar g la us na Spa rl mark re J Spainin rlands n K F we n Ja elgi o a State n Fr Au e u Greece S e Mexico FinlandB N A C tze e ermanG Sweden F Can Mexico Belgium H Por D Kin ther Germa D G Repub d St e h eth d Kingdo N Swi c N ited United ze Unite lovak Republ n C Unite S U Textiles % Food products % 10 10 8 8 6 6 4 4 2 2 0 0 -2 - -2 -5.9 -4 -4

l y d y y y a rk d a e o a d m s a n ly m n e ic rg lic r s a ia rg e m s s e rk y d m n ry al a in n ia d o re a n c ic d n g ain a b a te r lic nd c lic d n n a a d a o rali e la nd Ita p nc ou u a st b iu n te ec la rw de g a pa p ral Italy re ustria r lgiu a rtu S ap ra b ng lg ub a ta re ma do S a t K ana J F p Kore pu Frane p rl nmar in o g we ortugan J Finla G Mex A C Be erl Norwa Sweden Au B S G e e F N S C us Mexic Denm Aust th Po German Hu d St Re Re d D G HunP A e k Re h ethe e Switze ew Zeala uxema ite uxembouew Zeala c it d Kin Switzerlan N N ited Kingdo L L vak N e N n ite n lov Un lo z U n U Czech RepublS S C U 1. Or latest year available. 2. Data for the United States refer to ISIC sector 29-33 “machinery and equipment”. 3. Data for Belgium, Japan, Korea, Netherlands, Portugal, Spain and United Kingdom refer to ISIC sector 34-35 “transport equipment”.

PRODUCTIVITY GROWTH IN SERVICES C.3.

• In several OECD countries, productivity growth in Data Resources: services accelerated from 1990-95 to 1995-2003. OECD, STAN Indicators Database, August 2005. This was notably the case in Australia, Greece, OECD, STAN Database, August 2005. Mexico, the Slovak Republic and the United States. For further reading In most other countries, including most large European countries as well as Japan, productivity Ahmad, Nadim, Francois Lequiller, Pascal Marianna, Dirk Pilat, Paul Schreyer and Anita Wölfl (2003), “Comparing growth in services was less rapid over 1995-2003 Labour Productivity Growth in the OECD Area: The Role than over 1990-95. of Measurement”, STI Working Paper 2003/14, OECD, Paris. • The variation across services sectors and across OECD (2001), Measuring Productivity – OECD Manual, countries is considerable. Industries such as OECD, Paris. wholesale and retail trade, post and OECD (2005), Enhancing Services Sector Performance, telecommunications and financial intermediation are OECD, Paris. typically the services sectors with the highest rate of Wölfl, A. (2003), “Productivity Growth in Service productivity growth. Productivity growth in hotels and Industries: An Assessment of Recent Patterns and the restaurants, transport and storage as well as Role of Measurement”, STI Working Paper 2003/7, business services is typically lower, although these OECD, Paris. industries have been relatively important in some Wölfl, A. (2005), “The Service Economy in OECD OECD countries. Productivity growth rate in social Countries”, STI Working Paper 2005/3, OECD, Paris. services are not shown in the charts, as many OECD countries still measure the output of these services through their inputs.

Measuring productivity in services The service sector now accounts for about 70 to 80% of aggregate production and employment of OECD economies and continues to grow. But measuring output and productivity growth in many services is not straightforward. What exactly does a lawyer or economist produce? How can the rapidly-changing pricing schemes of telecommunications providers be compared over time? And how should one measure the ‘quantity’ of health services provided by public hospitals? These and similar questions arise when statisticians attempt to measure the production of services and the output of service industries and the difficulty of this task is hard to overstate. Generally, measuring volumes in the national accounts requires that current-price values of flows of goods and services can be divided into volume and price components. Typically, this is more difficult for services than for goods. Characteristics of goods can normally be identified and changes in quantities and qualities are measurable in principle, whereas for services even quantitative changes are often hard to measure, let alone quality change. Omitting qualitative changes does not necessarily mean that volume of services will be underestimated. For example, technical progress may improve the quality of medical services but may also lead to a decline in quality when, for example, self-service in post offices, longer queuing times or increased distances to the nearest post office all of which reflect an increased burden on customers and a lower quality of services. At the level of individual industries, two types of measurement issues arise: comparability of current-price measures, and comparability of the price indices used to deflate the latter or, in some cases, the comparability of volume indices. When it comes to the impact of service industry measures on the comparability of aggregate GDP series, two other factors come into play. The first is the extent to which a particular service industry’s output is bought as intermediate input by other domestic producers as opposed to the share of output that is delivered to final demand (private consumption, investment, government and export). The larger the share of the former, the smaller the impact of a measurement error at the industry level on measures of aggregate GDP growth, and vice versa: if a service industry delivers exclusively for final demand purposes, any possible measurement will carry over to measures of total GDP. The second factor is the size of the industry in question, or more precisely, its share in economy-wide value-added. The larger the share, the bigger the potential impact of an incorrect measure of industry-level output.

40 © OECD 2005

Compendium of Productivity Indicators C.3. PRODUCTIVITY GROWTH IN SERVICES

Annual average growth of value added per person employed 1990-1995 and 1995-20031 % Total services 5 4 3 2 1 0 -1

d s y o d a d s in ly lic n ce lia a ry da mre rk n en al ny nd an d ce m a ia rga nd b la te a w a a o o a la n n u It u e a or exic n d K m n Jap la ra Sp Ic Polanung a n i r F Austr ep Gree N M H C e F SwedPortug e Belgiu R Austr D Germa th k ited St e n N LuxemboSwitzerla U New Zeala Slova United King 7.9 Wholesale & Retail Trade % 5.4 % Hotels & Restaurants 5 5.1 8

4 6 4 3 2 2 0 1 -2 -4 0 -6 -1 -8 c s y d g a y s d y n e i n r en a r d d m c lic y d l e ico tri ark n an aly ai n d d e n ia a e d a m rg d s s ry in ly a rk lic y ic rwa la ou x d s lan l gium It p n n c e l ria n u e a publ tat o o elandKorea m rla gdol S ra a d a t a o an an It bl e S P anadIc Au unga e n F epub el tr nad b land at lgiumnga rtug manu N Greece C MeSwe H Fin Be Portugal ree s Kore erl St Spa R Australia Den th Ki German R Ic PolaG AusNorwaMexicoFrancFinlCa tz Zeal er h ited e SweAu ther Be Hu PoDenma G uxemb N ew SwitzerlandZeaed xem ted w Rep ec n L N t Swi i h U lovak Lu Ne Ne Cz Uni S Un United Kingdo Slovak RepubCzec

% Transport & Storage % Post & Telecommunications 8 18 6 16 4 14 12 2 10 0 8 -2 6 -4 4 -6 2 -8 0

e rk ia a y y d n in e s s ly n d n y d y d ly s in e e k ia n a s a a a n m e ia e a a a m c r l e ri ral an la o d pa nc nd It an an an wa an It nd n a t ad t nad n d S ra la r exico la Spa ra ed us ate nm a F Jap erl Japa o an Greece us C Norw Fi ing MexicoAustr er tz N M Finl er F Greec A St C D A Germ K Swe d Statth i Germ th DenmaAustr Sw e w witzerl ted e S S e i ited N N n n Unit U U United Kingdo % Financial Intermediation % Busine ss se rvice s 12 5 10 4 8 6 3 4 2 2 0 1 -2 0 -4 -6 -1

l l a o d rk a ry d n in ly m ia d s a o d rk d a ry n m ia g in ly g ic lic n nd es a an ce e aya ny m lia nd e da ds lic ce rg n nd e lia ay e ce ic n m da an n ny e ds nd lic r r ce nd la e g lan o a d a Ita n b iu str ra or e la o la ug ga iu la b n Ita a at m e n d Spa n u lg at e ex o d na ma rt n lan a u oura Spa in n Japr Kor Po g str cela rla Fran orwK P Japn b F Mex F St G Norw u I Swe Ca e Be Au zerla Icela St N Gr M Ca Fin Swed Belg Aust Portu d De HuGerma A Aust De PoGermaHu ther Rep h Repub Kin wit k ite Neth S ited Ne Switzerl ec New Zeala Luxembou n ited King New Ze Luxem z Un nited lovak Rep U C U S Un Slova 1. Or latest year available.

LABOUR PRODUCTIVITY OF FOREIGN AFFILIATES C.4.

• Foreign affiliates are often considered to make an productivity, while in Spain and Portugal the growth important contribution to productivity performance. of labour productivity of these affiliates was negative. OECD data on foreign affiliates in manufacturing and services help point to their role in economic • During the same period, the growth of labour performance. productivity of foreign affiliates in the service sector was high in Japan, but negative in the Netherlands, • In 2002, labour productivity of manufacturing foreign Portugal, Finland, the Czech Republic and France. affiliates in the United Kingdom was more than two times higher than the average labour productivity of Sources all manufacturing domestic firms. OECD, Activity of Foreign Affiliates (AFA) database, June 2005. • In the United States, Finland and France, labour OECD, Foreign Affiliates’ Trade in Services (FATS) productivity of manufacturing foreign affiliates was database, June 2005. almost at the same level as the national average. For further reading OECD (2005), Measuring Globalisation – OECD • With respect to the service sector, foreign affiliates in Handbook on Economic Globalisation Indicators, OECD, Portugal were two times more productive than the Paris. Available at: www.oecd.org/sti/measuring- national average, while in Finland and the globalisation United States, labour productivity of foreign affiliates OECD (2005), Measuring Globalisation – Economic was lower than the national average. Globalisation Indicators, OECD, Paris, forthcoming. C. Criscuolo (2005), “The Contribution of Foreign • Between 1995 and 2001, in the manufacturing Affiliates to Productivity Growth: Evidence from OECD sector, foreign affiliates in Sweden, Finland and the Countries”, STI working papers 2005-9, OECD, Paris. Available at: www.oecd.org/sti/working-papers United States recorded the highest growth of labour

Measuring labour productivity of foreign affiliates in host countries

In the calculations shown here, the choice of measurement of labour productivity was largely determined by the availability of data. Even at the level of total manufacturing, it was not possible to calculate total factor productivity, since no data on capital stock were available for foreign affiliates. Labour productivity is measured on the basis of gross value added divided by the number of employees.

Relative labour productivity of foreign affiliates is the ratio of labour productivity of foreign affiliates compared to the national total for labour productivity in the manufacturing and services sectors.

To measure labour productivity in terms of growth for total manufacturing and services, value added data for foreign affiliates have been deflated using sectoral deflators of the national industry and re-weighing them according to the sectoral structure of foreign affiliates. These calculations have been made where possible. For some sectors, this approach was not possible due to missing data. In these cases, deflators at a higher level of aggregation were used.

42 © OECD 2005

Compendium of Productivity Indicators C.4. LABOUR PRODUCTIVITY OF FOREIGN AFFILIATES

Relative labour productivity of foreign affiliates, 2002

Manufacturing sector1 Service sector2

2.2 2.3

2.1 2

1.9 1.8

1.7 1.6 1.5 1.4 1.3 1.2 1.1 1 0.9 in s n al c y y n s d e n n e s d s m a li r e al ry a lic p b S rwa nga ed ga and ate gdo Japa o u w State inlan Jap ranc inlan n epub N S F Franc un wede F F Ki therland Portug R H Portug H S erl d St d e h ited N n h Repu eth U ec N Czec z Unite Unite C Average annual labour productivity growth, 1995-2001 Percentage points

Manufacturing sector1 Service sector2

Foreign affiliates All firms Foreign affiliates All firms 12% 10%

8% 10% 6% 8% 4% 6% 2%

4% 0%

2% -2% -4% 0% -6% -2% -8%

-4% -10%

c in d e y n s al n n li a c m r d ay e es c nd ce a s en p n w ug d n ary d gal tes ub S ra apa r rt tat bli ap a inlan F unga J rlan o o we u inla ra J lan wed ep F ngdo e N S S ep F F ung ortu St Ki H th P d H P S R e ite ther ted ch d n e i e ite N N n z n U zech R U C U C 1. Or nearest available year: Czech Republic 1997-2002; United Kingdom 1995-1999; Finland 1995-2002; Hungary 1996-2002; Spain 1999-2001 and Portugal 1996-2002. 2. Or nearest available year: Czech Republic 1995-2002; Sweden 1997-2000; Hungary 1998-2002; Netherlands 1997-2001; Japan 1997-2000 and Portugal 1996-2002. Sources: OECD, AFA, FATS and STAN databases, June 2005.

THE CONTRIBUTION OF MULTINATIONALS TO PRODUCTIVITY C.5. GROWTH

• Multinationals often also make an important contribution of foreign affiliates to productivity contribution to productivity growth. In the growth. Hungary is an exception. manufacturing sector, foreign affiliates contributed from 6.7% in the Czech Republic to -0.4% in • In both the manufacturing and services sector, the Portugal to annual productivity growth. contribution of foreign affiliates is largest in the Czech Republic and Sweden and smallest in Japan • In the Czech Republic, the United Kingdom and and Portugal. Norway, the contribution of foreign affiliates is greater than that of the total manufacturing sector. • In France and the United States, the contribution of This is due to sharp growth in foreign affiliates’ share foreign affiliates to labour productivity growth is of employment in the Czech Republic and Norway much smaller in the services sector than in the and to negative productivity growth in UK domestic manufacturing sector. firms. Sources • The contribution of foreign affiliates most often OECD, Structural Analysis database, May 2005. See comes from the “between” effect, i.e. a rise in foreign also www.oecd.org/sti/stan affiliates’ share of employment. OECD, Activity of Foreign Affiliates (AFA) database, March 2005. • The contribution of foreign affiliates in the services OECD, Foreign Affiliates’ Trade in Services (FATS) sector ranges from 1.2% in the Czech Republic to database, March 2005. -0.2% in Portugal and is much smaller than in the For further reading manufacturing sector. C. Criscuolo (2005), “The Contribution of Foreign Affiliates to Productivity Growth: Evidence from OECD • As in the manufacturing sector, the between effect in Countries”, STI working papers 2005-9, OECD, Paris. the services sector accounts for most of the Available at: www.oecd.org/sti/working-papers

Calculating foreign affiliates’ contribution to productivity growth To measure the contribution of foreign affiliates to productivity growth, the OECD has put together a database with information from the AFA, FATS and STAN databases. The database contains information on the growth of labour productivity, measured as deflated value added over employment of affiliates and non-affiliates for the manufacturing sector of 12 OECD countries and for the services sector of 9 OECD countries. Total annualised labour productivity growth is defined as the weighted sum of domestic firms’ productivity growth and foreign affiliates’ productivity growth, where the weights used are the shares of domestic and foreign affiliates in total employment, as shown in the formula below: EMP EMP it − it−k LPi t LPi t−k 1 ∆LP EMP EMP− 1 ∗ t = ∑ t t k * k LPt−k i=DOM ,FOR LPt−k k

Where LP is labour productivity calculated as the ratio of real value added to labour input (EMP), ∆ indicates change; k indicates the number of years between observations, so that the left-hand side is aggregate annualised labour productivity growth. For each sector therefore the contribution to labour productivity growth of foreign affiliates can be calculated as: ∆  EMP EMP −   1 LP 1 LP 1 k ∗  FOR,t * LP − FOR,t k * LP  LP  = * FOR,t * w + ∆w * * FOR  FOR,t FOR,t −k  t −k  FOR FOR,t  EMPt EMPt −k  k LPt −k k LPt −k   14244 4344 14244 4344 within between Foreign affiliates’ contribution to productivity growth derives from switches in labour resources between domestic and more productive foreign affiliates, the “between effect”, and from labour productivity growth within the group of foreign affiliates, the “within effect”. The first term of the right-hand side is the “within” effect and the second is the “between” effect. Thus, foreign affiliates’ contribution to labour productivity growth might increase if there is an increase in its rate of productivity growth or if their average employment share is higher (from the first term); and if their employment share increases or their labour productivity level is higher relative to the domestic average (from the second term).

44 © OECD 2005

Compendium of Productivity Indicators

C.5. THE CONTRIBUTION OF MULTINATIONALS TO PRODUCTIVITY GROWTH

Labour productivity growth and average contribution of foreign affiliates, 1995-2001 Percentage points Manufacturing sector1 Services sector2

Labour productivity growth Contribution of foreign affiliates Labour productivity growth Contribution of foreign affiliates

Czech Republic Czech Republic

Sweden Sweden United Kingdom

France Hungary

Norway Finland Finland Netherlands Hungary

United States United States

Netherlands France Japan Japan Spain

% Portugal Portugal % 7 6 5 4 3 2 1 0 - 1 - 1 0 1 2 3

Breakdown of foreign affiliates’ contribution, 1995-2001 Percentage points Manufacturing sector1 Services sector2

Within effect Between effect Within effect Between effect

Czech Republic Czech Republic Sweden Sweden United Kingdom Hungary France Norway Finland Finland Netherlands Hungary United States United States Netherlands France Japan Japan Spain Portugal Portugal % % 7 6 5 4 3 2 1 0 -1 -1 0 1 1 2

Note: Labour productivity is measured as value added in constant prices over employment. 1. Or nearest available year: Czech Republic 1997-2002; United Kingdom 1995-1999; Finland 1995-2002; Hungary 1996- 2002; Spain 1999-2001 and Portugal 1996-2002. 2. Or nearest available year: Czech Republic 1995-2002; Sweden 1997-2000; Hungary 1998-2002; Netherlands 1997-2001; Japan 1997-2000 and Portugal 1996-2002.

ANNEX 1: OECD PRODUCTIVITY DATABASE

Over several years, the analysis of productivity and economic growth has been an important focus of OECD work. To carry out this work, OECD needs comparable data on productivity growth and its underlying drivers. There has also been significant interest in productivity data by researchers and analysts outside the OECD and this led to the development of the OECD Productivity Database, designed to be as comparable and consistent across countries as is currently possible. The database and related information on methods and sources were made publicly available through the OECD Internet site on 15 March 2004.

Coverage

The OECD Productivity Database currently provides annual estimates of

• Labour productivity growth, measured as GDP per hour worked, for 29 OECD countries and the period 1970-2004;

• Capital services and multi-factor productivity for 19 OECD countries for the period 1985-2003;

• Labour productivity levels for 2004.

All of the above data relate to the economy as a whole. In addition, the Productivity Database features industry-level measures of labour productivity growth, drawn from the OECD STAN Database, see www.oecd.org/sti/stan

The OECD Productivity Database is updated on a regular basis as new data become available. It is accessible through the OECD Internet site, at: www.oecd.org/statistics/productivity

Sources of data underlying the OECD Productivity Database

The OECD productivity database combines – to the extent possible – a consistent set of data on GDP, labour input measured as total hours worked, and capital services. The following sources are used in the productivity database.

Gross Domestic Product

Estimates for GDP are derived from OECD's Annual National Accounts (ANA). The data from ANA are based on OECD’s annual national accounts questionnaire to OECD member countries. The data resulting from this questionnaire differ somewhat from national sources and are more comparable across countries than those derived from OECD’s quarterly national accounts, thanks to several small methodological adjustments that are made. However, the differences with other OECD sources, such as the Quarterly National Accounts and the Economic Outlook database, are minor for most countries.

Employment

Equally important for international comparisons of productivity growth are comparable measures of labour input. In most comparisons of labour productivity growth, labour input is measured along only

46 © OECD 2005

Compendium of Productivity Indicators

two dimensions: the number of persons employed and the total number of hours worked of all persons employed.1

Basic data for employment can be derived from several sources, including administrative records, labour force surveys and establishment or enterprise-based surveys. Labour force surveys are typically conducted to provide reliable information about personal characteristics of the labour force, such as educational attainment, age, or the occurrence of multiple job holding, as well as information about the jobs (e.g. hours at work, industry, occupation and type of contract). Compared with most other statistical sources on employment, labour force surveys are quite well standardised across OECD countries as most countries collect their numbers on the basis of agreed guidelines, and therefore they pose few problems for international comparisons. In addition, labour force surveys have fairly comprehensive coverage of the economy. However, they are based on a national concept, which implies that they exclude non-resident workers (commuters) that are quite important for some OECD countries. Moreover, they may have lower and upper age thresholds and may exclude institutional households. Despite these shortcomings, labour force surveys are often an important source of information for comparisons of productivity growth for the aggregate economy.

The main difficulty with employment estimates from labour force surveys is that the data are not necessarily consistent in coverage with other data needed, notably GDP and hours worked. Labour force surveys are mostly defined within geographic boundaries, whereas, for example, national accounts are defined within economic boundaries. This implies, for example, that a country’s military bases and diplomatic premises on foreign soil are part of its economic territory, and that the residence of an enterprise is determined according to its “centre of economic interest”.

A second major source of employment data is therefore the national accounts. Following the introduction of SNA 1993/ESA 1995 many countries now provide data on employment in the framework of the national accounts. In principle, national accounts information on employment is preferable over labour force surveys, due to the conceptual issues discussed above and since the national accounts are likely to integrate a wider range of basic source data on employment.

In practice, the concepts and actual compilation of the national accounts estimates of employment are not yet as well standardised as labour force surveys. One issue is conceptual; most European countries provide data on persons employed in their national accounts, as is also the case with the labour force surveys. In several other OECD countries, including Austria, Canada, Greece, Japan, the United Kingdom and the United States, the national accounts data on employment refers to the number of jobs, which is closer to the concept used in establishment or enterprise statistics. This conceptual difference can be important for the resulting estimate of employment, notably in countries with a high rate of multiple job-holding, such as the United States.

Previous OECD estimates of productivity growth have sometimes used labour force surveys as a source of employment data for productivity estimates at the aggregate level. Recently, and in close co- ordination with Eurostat, OECD decided to move to the national accounts as the preferred source of employment data. This methodological change was driven by three motivations:

1. Consistency between GDP and employment. Employment estimates from the national accounts are likely to be more consistent with GDP estimates than employment estimates from labour force surveys, since the employment estimates may incorporate information from other sources of employment. For example, in several countries, e.g. Italy, GDP includes an adjustment for the informal economy. This adjustment is also reflected in the employment estimate in the national accounts. The employment number in the labour force survey is substantially lower; using this number for comparisons of productivity growth might lead to a bias in Italian estimates of labour productivity growth.

1 . A possible third dimension concerns labour composition. This dimension is currently being investigated (see Colecchia, 2005).

2. Conceptual. The national accounts are more appropriate from a conceptual point of view, since they use economic boundaries instead of national boundaries.

Despite this choice for the source of employment data for OECD estimates of productivity growth, it is important to be cognisant of the statistical problems that are still associated with national accounts information on employment. The first important limitation is that only 19 OECD countries currently include data on total hours worked in the framework of national accounts. These are: Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Italy, Korea, Netherlands, Norway, the Slovak Republic, Spain, Sweden, Switzerland and the United States. For most of these countries, the OECD has now moved to estimates of total hours worked from the national accounts. For the United States, the OECD does not use the BEA estimates of total hours worked, but those of the BLS, as these are the official US estimates of total labour input. For the remaining countries, the OECD continues to use data on hours worked that are collected for the OECD Employment Outlook from a variety of sources, including labour force surveys, and combines these with employment figures from national accounts to derive an estimate of total hours worked (see also Annex 2).

An additional problem is that little is known about how countries currently integrate different sources of employment information in the national accounts. It is not clear whether this is done in a similar way across countries. Moreover, some countries supply data on persons employed, others on jobs or full-time equivalents. Work is currently underway in several organisations, including the OECD and Eurostat, to examine and improve the measures of employment (and hours worked) that are included in the national accounts. This work will hopefully reduce some of the uncertainties that are associated with employment estimates in the national accounts, and the adjustments that countries make in integrating different sources of employment information.

Hours worked

Estimates of levels of GDP per hour worked require estimates of total hours worked that are consistent across countries and consistent with GDP. For most of the 19 countries for which total hours worked are available in the national accounts (with the exception of the United States), these data are used in the productivity calculations. For the other countries, consistency is currently achieved by matching the hours worked per person that are collected by the OECD for its annual OECD Employment Outlook with the national accounts measure of employment for each individual country. Estimates of average hours actually worked per year per person in employment are currently available on an annual basis for 27 OECD countries from the OECD Employment Outlook, whereas estimates of average hours per person in dependent employment are available for two additional countries (see OECD (2004), Statistical Annex Table F). These estimates are available from National Statistical Offices for 20 countries. Employment Outlook estimates for several countries (including Australia, Canada, France, Norway, Sweden and Switzerland) are fully consistent with National Accounts concepts and coverage and are thus typically the same as those derived from the OECD Annual National Accounts Database.

To develop their estimates of average hours worked, countries use the best available data sources for different categories of workers, industries and components of variation from usual or normal working time (e.g. public holidays, annual leave, overtime, absences from work due to illness and to maternity, etc.). For example, in 2 countries (Japan and United States) actual hours are derived from establishment surveys for regular or production/non-supervisory workers in employee jobs and from labour force surveys (LFS) for non-regular or managers/non-supervisory employees, self- employed, farm workers and employees in the public sector. In 3 other countries (France, Germany and Switzerland), the measurement of annual working time relies on a component method based on standard working hours minus hours not worked due to absences plus hours worked overtime. Standard working hours are derived from an establishment survey (hours offered), an administrative source (contractual hours) and the labour force survey (normal hours), respectively. The coverage of workers is extended using standard hours reported in labour force surveys or other sources as hours

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worked overtime. Vacation time is either derived from establishment-survey data on paid leave or the number of days of statutory leave entitlements. Hours lost due to sickness are estimated from the number of days not worked from social security registers and/or health surveys.

On the other hand, the national estimates for 12 more countries (i.e. Australia, Canada, Czech Republic, Finland, Iceland, Mexico, New Zealand, Poland, Slovak Republic, Spain, Sweden and United Kingdom) rely mainly on labour force survey results. Annual working hours are derived using a direct method annualising actual weekly hours worked, which cover all weeks of the year in the case of continuous surveys. But, for labour force surveys with fixed monthly reference weeks, this method results in averaging hours worked during 12 weeks in the year and, therefore, necessitates adjustments for special events, such as public holidays falling outside the reference week (i.e. Canada and Finland). Finally, estimates of annual working time for 7 other EU member states (Austria, Belgium, Greece, Ireland, Italy, the Netherlands and Portugal) are derived by the OECD Secretariat for the OECD Employment Outlook by applying a variant of the component method to the results of the Spring European Labour Force Survey (ELFS).

Two other considerations should be kept in mind. First, annual working-time measures are reported either on a job or on a worker basis. To harmonise the presentation, annual hours worked measures can be converted between the two measurement units by using the share of multiple job holders in total employment, which is available in labour force surveys, albeit no further distinction is possible between second and more jobs.2 This difference is particularly important in matching annual working time estimates to employment estimates. Some countries provide employment estimates in the national accounts on the basis of jobs; for these countries, it is important to ensure that the measure of annual working-time per person that is used reflects jobs instead of workers. In Canada and Japan, the estimates of average hours worked already refer to hours per job. For the United Kingdom, the national accounts estimates of employment also refer to jobs, but preference has been given to employment estimates from the ONS labour force survey, since these are consistent with official UK productivity estimates.

Second, given the variety of data sources, of hours worked concepts retained in data sources, and of measurement methodologies (direct measures or component methods3) to produce estimates of annual working time, the quality and comparability of annual hours worked estimates are constantly questioned, and are subject to at least two probing issues:

• Labour force survey-based estimates are suspected of over-reporting hours worked compared to work hours reported in time-use surveys, in particular for those working long hours, like managers and professionals.

• Employer survey-based estimates do not account for unpaid overtime hours and are sometimes suspected of under-reporting hours worked, with consequences on productivity levels and growth.

The comparability of measures of hours worked across OECD countries thus remains an issue, and work is currently underway, notably through the Paris Group, the UN city group on Labour and Compensation, to further improve the available measures of hours worked. Data on hours worked are best suited for comparisons of trends over time, although comparisons of the level of average annual hours of work for a given year can be informative if the cross-country differences are sufficiently large.

2. For example, the BLS-Office of Productivity and Technology (OPT) estimates of annual hours of work for the United States are reported on a (per) job basis and are later converted by the OECD Secretariat to a per worker basis by multiplying the job-based annual hours of work by (1 + CPS based share of multiple jobholders in total employment). 3 . However, both methods can be summarised by the following identity: Annual hours per worker = Standard weekly hours worked x Number of weeks actually worked over the year = Weekly hours actually worked x 52 weeks, considering weekly reference period for reporting hours worked.

A summary of the various measures used in the July 2005 update of the OECD Productivity Database is shown in Annex Table 1 below.

Annex Table 1: Measures of annual working time and employment included in the OECD Productivity Database as of July 20051 Source for annual actual working hours Source for employment data Australia OECD Employment Outlook Annual National Accounts Austria OECD Employment Outlook Annual National Accounts Belgium OECD Employment Outlook Annual National Accounts Canada Annual National Accounts Annual National Accounts Czech Republic OECD Employment Outlook Annual National Accounts Denmark Annual National Accounts Annual National Accounts Finland Annual National Accounts Annual National Accounts France INSEE Annual National Accounts Germany Annual National Accounts Annual National Accounts Greece OECD Employment Outlook Annual National Accounts Hungary Annual National Accounts Annual National Accounts Iceland OECD Employment Outlook Labour Force Statistics Ireland OECD Employment Outlook Annual National Accounts Italy OECD Employment Outlook Annual National Accounts Japan OECD Employment Outlook Annual National Accounts Korea Annual National Accounts Annual National Accounts Luxembourg OECD Employment Outlook Annual National Accounts Mexico OECD Employment Outlook Annual National Accounts Netherlands OECD Employment Outlook Annual National Accounts New Zealand OECD Employment Outlook Labour Force Statistics Norway Annual National Accounts Annual National Accounts Poland OECD Employment Outlook Annual National Accounts Portugal OECD Employment Outlook Annual National Accounts Slovak Republic Annual National Accounts Annual National Accounts Spain Annual National Accounts Annual National Accounts Sweden Annual National Accounts Annual National Accounts Switzerland Annual National Accounts Annual National Accounts Turkey No data Labour Force Statistics United Kingdom OECD Employment Outlook UK Office of National Statistics United States US Bureau of Labor Statistics US Bureau of Labor Statistics 1. Over the recent period, several countries, including Austria, Belgium, the Czech Republic, Greece, Italy and the Netherlands have started to include measures of total hours worked in their national accounts. These measures are not yet fully incorporated in the OECD Productivity Database, but will be included in the next revision, due for the end of 2005. These data are incorporated, to the extent available, in the OECD estimates of productivity levels for 2004 presented in this compendium. Source: OECD (2005).

Capital input

The appropriate measure for capital input within the growth accounting framework is the flow of productive services that can be drawn from the cumulative stock of past investments in capital assets (see OECD, 2001a). These services are approximated by the rate of change of the ‘productive capital stock’ – a measure that takes account of wear and tear, retirements and other sources of reduction of the productive capacity of fixed assets. Flows of productive services of an office building, for instance, are the protection against rain or the comfort and storage services that the building provides to personnel during a given period (Schreyer et al., 2003). The price of capital services per asset is measured as their rental price. If there are markets for capital services, as is the case for office buildings, for instance, rental prices could be directly observed. For most assets, however, rental prices have to be imputed. The implicit rent that capital good owners ‘pay’ themselves gives rise to the terminology ‘user costs of capital’.

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Capital input (S) is measured as the volume of capital services, assumed to be in a fixed proportion to the productive capital stock (see Schreyer, et al., 2003 for a more extensive explanation and for details of the computation of capital services). The productivity database publishes capital services data with calculations based on the perpetual inventory method (PIM) The PIM calculations are carried out by OECD, using service lives for different assets that are common across countries and correcting for differences in deflators for information and communication technology assets. Sources for the investment series by type of asset underlying the capital services series are national statistical offices4 and the Groningen Growth and Development Centre Total Economy Growth Accounting Database5 (http://www.ggdc.net).

Measures of multi-factor productivity in the OECD Productivity Database

The following methodology has been applied for the computation of multi-factor productivity (MFP) measures:

Rates of change of output

Output (Q) is measured as GDP at constant prices for the entire economy. Year-to-year changes Q are computed as logarithmic differences: ln( t ) Q t−1

Rates of change of labour input

Labour input (L) is measured as total hours actually worked in the entire economy. Data on total hours has been specifically developed for the present purpose, see above. Year-to-year changes are L computed as logarithmic differences: ln( t ) . Lt−1

Rates of change of capital input

i = Capital services are computed for six different types of assets (St i 1,2,...7 ) and aggregated to an overall rate of change of capital services by means of a Törnqvist index:

i i i  St  7 1 i i  St  i u tSt ln  = ()v + v − ln  with v ≡   ∑i=1 2 t t 1  i  t 7 i i St−1 St−1 u S     ∑i=1 t t

7 where vi is the share of each asset in the total value of capital services uiSi . In this t ∑i=1 t t i i i expression, the value of capital services for each asset is measured by u tSt where u t is the user i cost price per unit of capital services and St is the quantity of capital services in year t.

Cost shares of inputs

The total cost of inputs is the sum of the remuneration for labour input and the remuneration for capital services. Remuneration for labour input has been computed as the average remuneration per employee multiplied by the total number of persons employed. This adjustment was necessary to

3. For Australia, Canada, France, Japan, Italy, Germany, United States. 4. For Austria, Belgium, Denmark, Finland, Greece, Ireland, Netherlands, Portugal, Spain, Sweden, United Kingdom.

correct for self-employed persons whose income is not part of the compensation of employees as registered in the national accounts. The source for data on compensation of employees and for the number of employees as well as the number of self employed is the OECD Annual National Accounts.

 COMP  =  t  w t L t  E t  EE t 

where

w t Lt : remuneration for labour input in period t

COMPt : compensation of employees in period t

EE t : number of employees in period t

E t : total number employed (employees plus self-employed) in period t.

Total cost of inputs is then given by:

7 C = w L + uiSi and the corresponding cost shares are t t t ∑i=1 t t

L ≡ w t Lt st for labour input and Ct

6 uiSi S ≡ ∑i=1 t t st for capital input. Ct

Total inputs

The rate of change of total inputs is a weighted average of the rate of change of labour and capital input with the respective cost shares as weights. Aggregation is by way of a Törnqvist index number formula:

 X   L   S  ln t  = 1 ()s L + s L ln t  + 1 ()sS + sS ln t    2 t t−1   2 t t−1    X t−1   L t−1   St−1 

Multi-factor productivity

Multi-factor productivity is measured as the difference between output and input change, or as ‘apparent multi-factor productivity’:

 MFP   Q   X  t = t − t ln  ln  ln  .  MFPt−1   Q t−1   X t−1 

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Why does the OECD publish different productivity measures?

The release of the OECD Productivity Database adds several new measures to the already available OECD estimates of productivity growth. In particular, the OECD Economic Outlook currently includes estimates of labour productivity growth for the business sector in its Annex Tables (see A.7). These measures respond to different purposes and should thus be considered of equal value to those published in the Productivity Database. The following differences should be noted between the two series:

1. The measures for labour and multi-factor productivity in the OECD Productivity Database refer to the total economy. They are based on a detailed assessment of labour and capital input, which incorporates adjustments for average hours worked per person employed and for capital services. These economy-wide productivity measures provide a close link to changes in GDP per capita.

2. The measures of labour productivity in the OECD Economic Outlook cover the business sector only and do not adjust for average hours worked and for capital services. The main advantage of these measures is that it excludes a large part of the economy, i.e. the public sector, in which productivity is typically poorly measured. Measures of business sector productivity are also important because this sector ultimately determines the development of potential output and the economy’s tax base.

In the medium term, OECD intends to develop more sophisticated measures of productivity for the business sector, incorporating adjustments for hours worked and capital services. Data constraints currently do not permit this for most OECD countries, primarily due to lack of data on capital services for the business sector.

ANNEX 2: OECD ESTIMATES OF LABOUR PRODUCTIVITY LEVELS

Introduction

International comparisons of productivity growth can give useful insights in the growth process, but should ideally be complemented with international comparisons of income and productivity levels. An examination of income and productivity levels may give insights into the possible scope for further gains, and also places a country’s growth experience in the perspective of its current level of income and productivity. OECD has published estimates of labour productivity levels in various studies (e.g. Scarpetta, et al., 2000; OECD, 2003), and has recently released estimates of productivity on the OECD Internet site, at: www.oecd.org/statistics/productivity

Since the release of OECD estimates of productivity growth in the OECD Productivity Database in March 2004 (see Annex 1), more attention has turned to the measurement of productivity levels, since these serve as a yardstick of economic performance in many OECD countries. Several statistical agencies and international organisations, including Eurostat, the UK Office of National Statistics, the US Bureau of Labor Statistics, and the International Labour Organisation, now release estimates of labour productivity levels, as do some academic institutions, such as the Groningen Growth and Development Centre, and some private institutions, such as the Conference Board. In several instances, notably in the case of Eurostat and the ONS, estimates of labour productivity levels serve as official yardsticks of economic performance and are used to measure progress with regards to explicit policy targets.

Given the importance attached to labour productivity levels, it is unfortunate that there is still considerable variation in the currently available estimates. Primarily, this seems due to differences in the choice of basic data. Indeed, much of the differences can be brought back to how different organisations select and combine information on the three components of labour productivity levels at the economy-wide level. These components are gross domestic product, labour input and a conversion factor for total GDP (typically a purchasing power parity or PPP) that is needed to translate output in national currency units to a common currency.

This annex briefly discusses some of the main measurement issues for these components, as well as the different data choices that can be made. It focuses on the current OECD approach to measuring labour productivity levels, but also refers to other possible approaches, where appropriate. The discussion focuses on comparisons of labour productivity at the economy-wide level; the estimation of productivity levels for individual industries raises additional measurement issues that go beyond the scope of this annex.6

Output: comparability and data choices

Most comparisons of labour productivity levels focus on GDP as the measure of output. Other measures of aggregate output, such as GNP or national income, have also been used in a few studies, but are not considered here. The measurement and definition of economic output is treated systematically across countries in the 1993 System of National Accounts (SNA 93). Most countries in the OECD area have now implemented the 1993 SNA, Turkey being the only exception, which implies that its level of GDP is likely to be somewhat understated relative to other OECD countries. Despite the harmonisation of GDP estimates through the 1993 SNA, there are some differences in estimation methods across countries, however (Ahmad, et al., 2003). These typically have only a small effect on growth rates, but may be substantially more important for comparisons of output and productivity

6 . A forthcoming OECD reader discusses these issues in greater detail (OECD, forthcoming).

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levels. Some of the main differences that are known to affect GDP levels are the following (Ahmad, et al., 2003):

• Expenditure on military equipment. The coverage of government investment in the US National Income and Product Accounts (NIPA) is more extensive than that recommended by the SNA, since it includes expenditures on military equipment (aircraft, ships, missiles) that are not considered assets by the SNA. The national accounts in most other OECD countries strictly follow the SNA in this matter. As the amount of public investment affects GDP, this results in a statistical difference in the measurement of GDP. Convergence on this issue is expected in the next edition of the SNA, in 2008. In the meantime, the OECD publishes data in its Annual National Accounts Database for the United States which adjust for this difference.

• Financial Intermediation Services. Most banking services are not explicitly charged. Thus, in the SNA, the implicit production of banks is estimated using the difference between interests received and paid. All OECD member countries have estimated this part of bank production, known as “Financial Intermediation Service Indirectly Measured” or “FISIM”. While it is relatively straightforward to recognise and estimate FISIM, the key problem is breaking it down between final consumers (households) and intermediate consumers (business and government). Only the first part has an overall impact on GDP. In the United States, Canada and Australia, such a breakdown has been estimated in the national accounts for some time, in accordance with the SNA. In Europe and Japan, the implementation of a breakdown between final and intermediate consumers has been delayed. The recent comprehensive revision of the US accounts has significantly reduced the difference in GDP levels linked to this factor to just over 1% of GDP, roughly halving the impact on growth. The EU member states and Japan are currently in the process of implementing the allocation of FISIM in their accounts. This methodological difference should be mostly eliminated in 2005, but will still affect comparisons of GDP and productivity levels as long as countries have not all implemented the new method.

• Software investment. Another significant issue in the comparability of GDP concerns the measurement of software. The 1993 SNA recommended that software expenditures be treated as investment as long as the acquisition satisfied conventional asset requirements. This change added nearly 2% to GDP for the United States, around 0.7% for Italy and France, and about 0.5% for the United Kingdom. Doubts on the comparability of these data were raised when comparing “investment ratios”, which are defined as the share of software expenditures that are recorded as investment to total expenditures in software. These ratios range from under 4% in the United Kingdom to over 70% in Spain (Lequiller, et al., 2003; Ahmad, 2003). A priori, one would expect that these are roughly the same across OECD countries. An OECD-Eurostat Task Force confirmed that differences in estimation procedures contributed significantly to the differences in software capitalisation rates, and a set of recommendations describing a harmonised method for estimating software were formulated (Lequiller, et al, 2003; Ahmad, 2003). Most of these recommendations will be implemented by countries, but this will only happen gradually. Differences in software measurement will therefore continue to have an impact on the comparability of GDP levels for some time to come.

• The informal economy. Another factor that may influence the comparability of GDP across countries is size of the non-observed economy. In principle, GDP estimates in the national accounts take account of this part of the economy. In practice, questions can be raised about the extent to which official estimates have full coverage of economic activities that are included in GDP according to the SNA, or to which extent there some under-reporting is involved. Large differences in coverage could substantially affect comparisons of productivity levels.

It is not clear, a priori, how large the impact of these, and possible other, differences is on GDP levels. What is clear, however, is that there is a margin of uncertainty associated with the comparability of levels of GDP across countries. Consequently, there is also a range of uncertainty associated with estimates of productivity levels; small differences between countries (of a few percentage points) will obviously fall within this range of uncertainty. This is important in interpreting estimates of productivity levels; countries within a small range of income and productivity levels may not have income and productivity differences that are statistically or economically significant (Schreyer and Koechlin, 2002).

The data choices for GDP are fairly uniform across different sources. In the OECD estimates of productivity levels, data on GDP are derived from OECD's Annual National Accounts (ANA). The data from ANA are based on OECD’s annual national accounts questionnaire to OECD member countries. The data resulting from this questionnaire may differ somewhat from national sources and are more comparable across countries than those derived from OECD’s quarterly national accounts (or the OECD Economic Outlook database), thanks to some small methodological adjustments that are made. For example, the US GDP estimates are adjusted for expenditure on military equipment, as discussed above. However, the differences with other OECD sources, such as the Quarterly National Accounts and the Economic Outlook database, are minor for most countries.

For two countries, Australia and New Zealand, the OECD’s Annual National Accounts provides GDP estimates for fiscal years. This creates an inconsistency with other countries, since comparisons of productivity levels ideally should correspond to the same (calendar) year. However, this problem is considered relatively limited and no adjustment is made.

Labour input

Of great importance for international comparisons of productivity levels are comparable measures of labour input. The OECD estimates of labour productivity levels are typically based on the same data choices as the OECD estimates of labour productivity growth.7 Annex 1 therefore provides a more detailed discussion of the measures of labour input used in the calculations of productivity levels.

Purchasing power parities for international comparisons

The comparison of income and productivity across countries also requires purchasing power parity (PPP) data for GDP. Exchange rates are not suitable for the conversion of GDP to a common currency, since they do not reflect international price differences, and since they are heavily influenced by short-term fluctuations. The estimates used by the OECD are derived from its joint programme with Eurostat and refer to current-price PPPs (Schreyer and Koechlin, 2002). For the current set of comparisons, the most recent PPP benchmark comparison (for 2002) is used as the basis for the estimates.

The OECD does not recommend the use of PPP-adjusted estimates of GDP in time series, because of the difficulty to obtain PPPs that are consistent over time. This is why only one year of productivity level comparisons is included in the OECD Productivity Database. Users interested in adding a time dimension to this one year level comparison should use the corresponding database on productivity growth, which gives appropriate indices of productivity growth for individual OECD countries over a long time period.

7. Some OECD countries have only very recently included estimates of hours worked in the national accounts. To the extent possible, these estimates have been incorporated in the OECD estimates of productivity levels, as presented in Annex Table 2. They have not yet been fully incorporated in the OECD estimates of productivity growth and in the data choices for those calculations, as shown in Annex Table 1. This difference will be addressed in the next update of the OECD Productivity Database, which is planned for end-2005.

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OECD estimates of labour productivity levels for 2004, September 2005

Clearly, data for international comparisons of income and productivity are not perfect and some choices between different sources have to be made. In the OECD approach, GDP is derived from the OECD ANA database, which incorporates the latest comparative information on GDP from OECD member countries. Data on employment for most countries are also from the OECD national accounts as these should have a better correspondence to the estimates of GDP. For a limited number of countries, no appropriate employment estimates are currently available from the national accounts, in which case employment is derived from the OECD Labour Force Statistics. Estimates of hours worked are either from the national accounts, or from the OECD Employment Outlook, as discussed in Annex 1. To convert GDP to a common currency, the OECD uses current PPPs, which are developed in the OECD-Eurostat PPP programme.

Annex Table 2 presents the data choices and the resulting productivity level estimates for 2004. These estimates still require further work in the following ways:

1. The estimates of annual hours worked per person for several OECD countries are not yet consistent with the national accounts. Data on hours worked currently reach the OECD through two data collections. First, 29 OECD countries currently provide data on hours worked to the annual data collection for the OECD Employment Outlook; 7 of these countries provide the OECD with estimates of annual hours worked that are consistent with national accounts concepts and coverage. Secondly, 19 countries currently provide estimates of total hours worked in the framework of the national accounts for inclusion in OECD’s Annual National Accounts. Further investigation of these estimates of hours worked is needed.

2. The employment estimates that are currently incorporated in the national accounts are not necessarily consistent across countries or with the corresponding estimate of GDP. Addressing this problem will require further statistical work.

For analytical purposes, it is important that estimates of GDP per hour worked are combined with estimates of GDP per capita and estimates of GDP per person in the labour force and GDP per person of working age. The national accounts currently often do not include the necessary information on working-age population and labour force, and such data have commonly been derived from labour force statistics. The OECD’s change in method towards the national accounts as the main source of employment information requires that the link between labour force statistics (i.e. national concepts) and national accounts estimates of productivity (i.e. domestic concepts) is addressed.

Annex Table 2: OECD estimates of labour productivity for 2004, September 2005

Annual average GDP, million GDP per GDP per PPP for GDP, million Employment OECD source hours worked, OECD source Total hours national currency hour hour total GDP, USD (Col. 1 / (1000 for corresponding to for average worked (million units, based on worked, worked, 2004 Col. 2) persons)2 employment1 employment hours worked1 hours) ANA USD USA=100 estimate3

(1) (2) (3) (4) (5) (6) (7) (8)

4 Australia 837,104 1.37 611,025 9,703 ANA 1816 Empl. Outl. 17,617 34.7 75 Austria 237,039 0.91 260,482 4,145 ANA 1636 ANA 6,781 38.4 83 Belgium 283,752 0.88 322,445 4,167 ANA 1522 Empl. Outl. 6,342 50.8 110 Canada 1,270,760 1.27 1,000,598 16,232 ANA 1751 ANA 28,425 35.2 76 Czech Republic 2,750,256 14.58 188,632 4,704 ANA 1938 ANA 9,115 20.7 45 Denmark 1,446,471 8.46 170,978 2,758 ANA 1517 ANA 4,183 40.9 88 Finland 149,725 0.94 159,282 2,367 ANA 1719 ANA 4,068 39.2 85 France 1,648,369 0.90 1,831,521 24,873 ANA 1543 ANA 38,369 47.7 103 Germany 2,215,650 0.94 2,357,074 38,868 ANA 1440 ANA 55,962 42.1 91 Greece 167,169 0.70 238,813 4,018 ANA 2075 ANA 8,337 28.6 62 Hungary 20,338,182 126.19 161,171 3,879 ANA 1933 ANA 7,498 21.5 46 Iceland 858,921 90.08 9,535 156 LFS 1810 Empl. Outl. 283 33.7 73 Ireland 146,279 1.01 144,831 1,873 ANA 1642 Empl. Outl. 3,076 47.1 102 Italy 1,351,328 0.84 1,608,724 24,496 ANA 1810 ANA 44,343 36.3 78 Japan 505,160 133.28 3,790,215 65,224 ANA 1789 Empl. Outl. 116,697 32.5 70 Korea 778,444,600 774.37 1,005,262 22,533 ANA 2394 ANA 53,947 18.6 40 Luxembourg 25,664 0.98 26,188 301 ANA 1556 Empl. Outl. 468 55.9 121 Mexico 7,634,926 7.29 1,047,315 42,059 ANA 1848 Empl. Outl. 77,734 13.5 29 Netherlands 466,310 0.92 506,859 8,233 ANA 1394 ANA 11,476 44.2 95 New Zealand4 145,752 1.49 97,820 2,026 LFS 1826 Empl. Outl. 3,700 26.4 57 Norway 1,685,552 9.48 177,801 2,303 ANA 1364 ANA 3,140 56.6 122 Poland 883,656 1.83 482,872 13,795 ANA 1983 Empl. Outl. 27,355 17.7 38 Portugal 135,079 0.66 204,665 5,057 ANA 1694 Empl. Outl. 8,567 23.9 52 Slovak Republic 1,325,486 17.21 77,018 2,056 ANA 1735 ANA 3,567 21.6 47 Spain 837,557 0.77 1,087,736 18,232 ANA 1633 ANA 29,779 36.5 79 Sweden 2,545,750 9.32 273,149 4,322 ANA 1585 ANA 6,849 39.9 86 Switzerland 445,931 1.77 251,938 4,178 ANA 1641 ANA 6,856 36.7 79 Turkey 430,511,477 0.78 551,938 22,291 LFS 1943 GGDC 43,311 12.7 28 United Kingdom 1,164,439 0.62 1,878,127 28,463 ONS 1668 Empl. Outl. 47,464 39.6 86 United States 11,679,200 1.00 11,679,200 138,320 BLS 1825 BLS 252,380 46.3 100

OECD 32,203,216 521,631 1778 927,689 34.7 75 G7 24,145,460 336,475 1735 583,640 41.4 89 North America 13,727,113 196,611 1824 358,539 38.3 83 5 OECD-Europe 12,419,843 203,243 1643 333,877 37.2 80 EU-196 11,980,569 196,606 1646 323,599 37.0 80 7 Euro-zone 8,748,621 136,630 1592 217,567 40.2 87

Notes: (1) ANA = Annual National Accounts, LFS = OECD Labour Force Statistics, Empl. Outl. = OECD Employment Outlook; ONS = UK Office of National Statistics; GGDC = Groningen Growth and Development Centre; BLS = US Bureau of Labor Statistics. (2) The employment estimates for Canada and Japan refer to jobs. (3) The estimates of annual hours worked for Canada and Japan refer to hours worked per job. ANA data for the Czech Republic, France, Greece, Hungary, Spain and Switzerland are estimates. (4) GDP estimates for Australia and New Zealand refer to fiscal years. (5) Excluding Turkey. (6) All EU members that are also OECD member countries. (7) Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain. Source: OECD estimates, September 2005.

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ANNEX 3: OECD DATABASES RELEVANT TO PRODUCTIVITY ANALYSIS

Annual National Accounts (ANA): The OECD’s Annual National Accounts presents a consistent set of data mainly compiled on the basis of the 1993 System of National Accounts (SNA). It contains data from 1970 whenever possible for OECD member countries. The database contains a wide selection of accounts: main aggregates (GDP by expenditure, GDP by kind of activity, GDP by income and disposable income, saving and net lending), detailed breakdown by kind of activity for gross value added (at current and constant prices), components of value added, gross fixed capital formation, employment and hours worked. It also includes final consumption expenditure of households by purpose and simplified accounts for general government. Detailed accounts by institutional sector are also available. The database also provides comparative tables based on exchange rates and purchasing power parities.

Publications: OECD, National Accounts of OECD Countries, Volume I, 1992-2003, Main Aggregates, 2005 Edition; OECD, National Accounts of OECD Countries: Detailed Tables 1992/2003, Volume II, 2005 Edition. Also available annually on line on SourceOECD (www.sourceoecd.org).

Labour Force Statistics (LFS): The OECD’s Labour Force Statistics provides detailed statistics on population, labour force, employment and unemployment, broken down by gender, as well as unemployment duration, employment status, employment by sector of activity and part-time employment. It also contains participation and unemployment rates by gender and detailed age groups, as well as comparative tables for the main components of the labour force. Data are available for each OECD member country and for OECD-Total, Euro zone and EU15. The time series cover 20 years for most countries. It also provides information on the sources and definitions used by Member countries in the compilation of statistics.

Publication: OECD, Labour Force Statistics: 1984-2004, 2005 Edition. Also available annually on line on SourceOECD (www.sourceoecd.org).

Economic Outlook (EO): The OECD Economic Outlook Database is a comprehensive and consistent macroeconomic database of the OECD economies, covering expenditures, foreign trade, output, employment and unemployment, interest rates and exchange rates, the balance of payments, outlays and revenues of government and of households, and government debt. For the non-OECD regions, foreign trade and current account series are available. The database contains yearly and quarterly information, both for the historical period and for the projection period. The historical data is based on data from national statistical agencies and OECD sources such as the Quarterly National Accounts, the Annual National Accounts, the Quarterly Labour Force Statistics, the Annual Labour Force Statistics and the Main Economic Indicators.

Publication: OECD, Economic Outlook, twice yearly. The database is available on CD-ROM: http://www.oecd.org/dataoecd/47/40/32108765.pdf A statistical annex to the OECD Economic Outlook is available at: http://www.oecd.org/document/61/0,2340,en_2649_34109_2483901_1_1_1_1,00.html

Productivity Database: The OECD Productivity Database is a database on productivity at the aggregate level that is designed to be as comparable and consistent across countries as is currently possible (see Annex 1). The database and related information on methods and sources were made publicly available through the OECD Internet site on 15 March 2004. The OECD Productivity Database currently provides annual estimates of:

• Labour productivity growth, measured as GDP per hour worked, for 29 OECD countries and the period 1970-2004 (PROD);

• Capital services and multi-factor productivity for 19 OECD countries for the period 1985-2003 (CAP);

• Labour productivity levels for 2004 (see Annex 2; PRODL).

The OECD Productivity Database is updated on a regular basis as new data become available. It is accessible through the OECD Internet site, at: www.oecd.org/statistics/productivity

STAN: The STAN database for Industrial Analysis includes annual measures of output, labour input, investment and international trade by economic activity which allow users to construct a wide range of indicators focused on areas such as productivity growth, competitiveness and general structural change. The industry list based on the International Standard Industrial Classification (ISIC) Rev. 3, provides sufficient details to enable users to highlight high-technology sectors and is compatible with those lists used in related OECD databases in the ‘STAN’ family (see below). STAN- Industry is primarily based on member countries’ annual National Accounts by activity tables and uses data from other sources, such as national industrial surveys/censuses, to estimate any missing detail. Since many of the data points in STAN are estimated, they do not represent the official member country submissions. See: www.oecd.org/sti/stan

Publication: STAN-industry is available on line via SourceOECD ( www.sourceoecd.org) where it is regularly updated (new tables are posted as soon as they are ready). A “snapshot” of STAN- industry is also available on CDROM together with the latest versions of STAN – R&D (ANBERD), STAN – Bilateral Trade and a set of derived STAN Indicators. See www.oecd.org/sti/stan/indicators.

AFA: The Activities of Foreign Affiliates database presents detailed data on the performance of foreign affiliates in the manufacturing industry of OECD countries (inward and outward investment). The data indicate the increasing importance of foreign affiliates in the economies of host countries, particularly in production, employment, value added, research and development, exports, wages and salaries. AFA contains 18 variables broken down by country of origin and by industrial sector (based on ISIC Rev. 3) for 23 OECD countries.

Publication: OECD, Measuring Globalisation: Economic Globalisation Indicators, 2005 Edition. Also available annually on line on SourceOECD (www.sourceoecd.org).

FATS: This database gives detailed data on the activities of foreign affiliates in the services sector of OECD countries (inward and outward investment). The data indicate the increasing importance of foreign affiliates in the economies of host countries and of affiliates of national firms implanted abroad. FATS contains five variables (production, employment, value added, imports and exports) broken down by country of origin (inward investments) or implantation (outward investments) and by industrial sector (based on ISIC Rev. 3) for 21 OECD countries.

Publication: OECD, Measuring Globalisation: Economic Globalisation Indicators, 2005 Edition. Also available annually on line on SourceOECD (www.sourceoecd.org).

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Current country coverage of main databases used in this publication General databases Productivity Database Industry-level ANA LFS EO PROD CAP PRODL STAN AFA FATS Australia X X X X X X X X Austria X X X X X X X X Belgium X X X X X X X X Canada X X X X X X X X X Czech Republic X X X X X X X X Denmark X X X X X X X Finland X X X X X X X X X France X X X X X X X X X Germany X X X X X X X X X Greece X X X X X X X X X Hungary X X X X X X X X Iceland X X X X X X Ireland X X X X X X X X X Italy X X X X X X X X X Japan X X X X X X X X X Korea X X X X X X Luxembourg X X X X X X X X Mexico X X X X X X X Netherlands X X X X X X X X X New Zealand X X X X X X X Norway X X X X X X X X Poland X X X X X X X X Portugal X X X X X X X X X Slovak Republic X X X X X X X Spain X X X X X X X X X Sweden X X X X X X X X X Switzerland X X X X X X Turkey X X X X X X United Kingdom X X X X X X X X X United States X X X X X X X X X

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