EMPLOYMENT PAPER

2000/5

Productivity and unit labourcost comparisons:

a data base

Bart van ArkErik Monnikhof

Employment SectorInternational Labour Office Geneva

Copyright © International Labour Organization 2000 ISBN 92-2-112176-3

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Contents

Foreword..........................................................................................................................................1

1. Introduction.................................................................................................................................1

2. Measures of output, labour inputs and labour compensation.......................................................2

3. Currency conversion factors ........................................................................................................8

4. Groningen Growth and Development Centre DataBase ............................................................13

5. Some comparisons of productivity and unit labour cost............................................................14

6. Research agenda ........................................................................................................................21

Appendix I .....................................................................................................................................22

References......................................................................................................................................31

Foreword

In 1997, a collaborative effort involving the ILO, experts from the Organisation forEconomic Cooperation and Development (OECD) and several national statistical offices wasundertaken to complete the selection and refinement of indicators for a 1999 Key Indicators ofthe Labour Market (KILM) project. The KILM project was designed with two objectives: (1)to develop a set of labour market indicators; and (2) to widen the availability of the indicatorsto monitor new employment trends so as to arm policy-makers with the proper tools fordecision-making in labour market policies. The indicators were chosen based on three criteria:conceptual relevance, data availability and comparability across countries and regions. Theresulting set of 18 indicators was designed to satisfy the ever-increasing demands ofgovernments and the social partners for timely, accurate and accessible information on theworld’s labour markets.

One measure that generates continual interest world-wide is the measure of labourproductivity. Economic growth in a country or a sector can be linked either to increasedemployment or to more effective work of those who are employed, the latter of which can becaptured in the measurement of labour productivity. The understanding of what drives labourproductivity, be it the accumulation of machinery, improvements in physical and institutionalinfrastructures, streamlining of human capital, the generation of new technologies, etc., isimportant for formulating policies to support economic growth. The obvious first criterion ofpolicy formulation, however, is accurate and comparable data.

The KILM database includes data series for both labour productivity and unit labourcosts. The second measure, unit labour costs, represents a direct link between productivity andthe cost of labour used in generating output. This paper, prepared by researchers of theGroningen Growth and Development Centre at the University of Groningen in the Netherlands,presents the background information on data sources and methodologies used for the KILMmeasures. Some discussion focussed on the data estimates for the 31 countries included in thetotal economy series and the 23 countries included in the manufacturing series. Finally, thepaper sets out the research agenda for future work on KILM productivity measures.

Werner SengenbergerDirector

Employment Strategy Department

1. Introduction

This paper presents background information on data sources and methodologies onmeasures of labour productivity and unit labour cost, as presented in Chapter 6 of the KeyIndicators of the Labour Market (KILM) database of the International Labour Office as KILM17.1 The KILM database is meant to provide timely and accurate information on labourmarkets developments across the world. The first KILM publication covers labour marketindicators, including labour force participation rates, unemployment, working hours,educational attainment, wages and productivity for a wide range of countries since 1980. Aseparate CD-ROM was released with data for intermediate years and underlying basic data.The KILM database will be updated on a regular basis through the KILM website and futurepublications.2

The two major variables in KILM 17 are labour productivity and unit labour cost. Theestimates include 31 countries for the total economy (see Table 1) and 23 countries formanufacturing (see Table 2). The measures mostly cover the period 1980-1997, even though insome cases date only run to 1995 or 1996. Labour productivity is defined as the gross domesticproduct or value added per person employed or, when data on working hours were available,per hour worked. The measures are provided as indices (based on 1980=100) which onlymeasures changes from the reference year, but in most cases also in US dollars. The US dollar-based estimates of labour productivity are obtained on the basis of purchasing power paritiesfor the total economy and unit value ratios for manufacturing. Labour cost per unit of output(in short, unit labour cost) is defined as nominal labour compensation divided by real valueadded. Total labour compensation includes wage compensation and other labour costs such asemployers’ contributions to social security and pension schemes and labour cost of the self-employed. Unit labour costs are provided as indices (based on 1980=100) and in US dollars. Inthe latter case labour compensation is converted to US dollars on the basis of the officialmarket exchange rates. The actual construction of the output, labour input and compensationmeasures and the way these are combined into our key measures of labour productivity andunit labour cost is discussed in more detail in Section 2.

A unique feature of KILM 17 is that the indicators are provided not only in terms ofindices but also as levels expressed in US dollars. This adds substantially to the usefulness ofthese indicators for international comparisons. The labour productivity measure in US dollarsis corrected for differences in relative prices between countries using purchasing power parities(for the total economy) or unit value ratios (for manufacturing). Section 3 deals specificallywith the estimates of purchasing power parities and unit value ratios.

Section 4 of the paper gives a brief discussion of the underlying data sources for KILM17. Data are largely derived from the Groningen Growth and Development Centre (GGDC)Database, which contains internationally comparable information on growth and levels ofoutput, population and labour input.3 In addition the GGDC Database employs the purchasingpower parities and unit value ratios to convert output to a common currency. For KILM 17, weseparately developed measures of total labour compensation, mostly derived from national

1 ILO (1999), Key Indicators of the Labour Market 1999, Chapter 6: Labour productivity and unit labour costsindicator, Geneva.

2The KILM website is located at http://www.ilo.org/kilm.

3 In a limited number of cases the GGDC database also provides estimates of capital stock. The GGDC database isregularly updated and extended and can be consulted at: http://www.eco.rug.nl/ggdc/homeggdc.html

2

accounts, which are consistent with the output and labour input data from the GGDCDatabase.4 Sources are provided on a country-by-country basis in the Appendix of this paper.

Finally, in Section 5 we discuss some of the results on labour productivity and unit labourcost. In the concluding section we set the research agenda for future work on KILM 17.

2. Measures of output, labour inputs and labourcompensation

Chart 1 provides an overview of the major inputs to obtain internationally comparablemeasures of labour productivity and unit labour cost. The variables in the grey-shaded boxesare derived from the other variables that in turn are obtained from sources described below andin the appendix. The key variables that are briefly discussed here are output, labour inputs andlabour compensation.

Output is defined as “gross value added” (or, in national accounts terms, “gross domesticproduct”), which is the total domestic production value minus the value of purchasedintermediate inputs. These variables are primarily obtained from national accounts statisticswhich are calculated according to a common conceptual framework, the System of NationalAccounts. The extent to which this can compensate for differences in the nature and quality ofavailable statistics, in particular for developing countries, is unclear. In particular, internationalcomparisons of real GDP have become more troublesome during recent years. Firstly, as theshare of services in output has increased, distinguishing between price and quantitycomponents of the value of output has become increasingly difficult. This is partly because ofthe lack of primary statistics for services, such as censuses and price surveys. In addition, it isconceptually more difficult to define the quantity of a particular service delivered than thequantity of a tangible good (Griliches, 1992). This problem of increasing proportions of poorlymeasured services is common to all countries. It is of particular concern in internationalcomparisons because individual countries tend to follow their own procedures in estimatingservices output. Moreover, because the shares of services vary across national economies, theimpact of the problem is not uniform.

A second problem is that the weighting systems used to aggregate individual goods andservices into GDP measures at constant prices are not fully harmonized. Several advancedcountries - including, most recently, the United States - are now producing annually chainedseries for GDP. Because this procedure relies on very recent component weights, it is preferredfor statistical as well theoretical reasons. On the other hand, there are still a substantial numberof countries that use 5- or even 10-year base weights in developing their national accounts.These differences strongly affect the comparability of the time series of real GDP, as longerbase periods lead to an overstatement of growth rates when (as is usual) observations for thefirst year of the period are used as GDP component weights.

A third problem, one that becomes particularly important when low-income countries areincluded in the analysis, is undercoverage of output measures due to neglect of large parts ofthe informal economy. Even in advanced countries, adjustments for the underground economycan differ substantially, and the effect on GDP estimates can be as much as 15 to 20 per cent.(Maddison, 1996).

4 For an earlier study of unit labour cost comparisons using this type of data for the G-5 (France, Germany, Japan,the United Kingdom vis-à-vis the United States), see van Ark (1995, 1996).

3

Note: Unshaded boxes are basic variables; shaded boxes are derivatives.

Chart 1 - Stylized Structure of Data Base for Comparisons of Labour Productivity and Unit Labour Cost

Gross Value Added in National Currency

(constant prices)

Total Employee Compensation in National Currency

Employee Compensation per Unit

of Gross Value Added in National Currency

PurchasingPower Parity

Exchange Rates

Gross Value Added in US dollars

(constant prices)

Total Employee Compensation in

US Dollars

Employee Compensation per Unit of Gross Value Added in

US DollarsEmployees(wage and salary

earners)

All persons Employed

Gross ValueAdded perEmployee

Gross ValueAdded per

Person Employed

Annual Hoursper Employee

Annual Hoursper Person Employed

Gross ValueAdded per

Employee-Hour

Gross ValueAdded per PersonEmployed-Hour

Imputed Total Labour Compensation per Unit

of Gross Value Added in National Currency

Employee Compensation per Unit of Gross Value Added in

US Dollars

Imputed Total Labour Compensation per Unit of Gross Value Added in

US Dollars

4

The estimates in this paper are still based on guidelines laid down in the old UnitedNations System of National Accounts (SNA) of 1968. Since 1999 many countries have begunto apply guidelines from the new 1993 System of National Accounts. The new SNA impliessubstantial changes, including strong recommendations to switch to chain-linked volumeseries, the introduction of a broader concept on investment, including expenditure on software,changes in the treatment of taxes and subsidies, and increased coverage of the “grey” economy.However, countries are still at different stages in implementing the 1993 SNA. In some casesthe introduction of the 1993 SNA has caused very substantial adjustments to GDP levels inOECD countries, ranging from between 0.3 per cent for Belgium to 7.4 per cent for theRepublic of Korea (OECD, 1999 and 2000). The impact on growth rates is still unclear as theperiods over which the changes have been implemented differ across countries, with mostcountries, not going further back than the late 1980s. Many non-OECD countries have not yetimplemented the SNA 1993 guidelines at all.

Labour input in KILM 17 refers to the number of persons employed and where possiblealso to annual working hours. Estimates of employment are for the average number of personswith one or more paid jobs during the year. Unfortunately, even conceptually, labour accountsare not as well harmonized as are national accounts, although a small number of countriesprovide employment estimates within the national accounts framework. For most countriesemployment estimates are derived from labour force or population surveys. Using thesesources in combination with those from the national accounts assumes that the same populationof establishments is covered in both countries. With an adjustment for self-employed persons,which may introduce part of the “grey” economy into the labour measures, this assumptionmay not hold, in particular not for developing countries.

Comprehensive estimates of annual working hours are difficult to obtain and theirinternational comparability is limited. For example, hours often refer to paid hours rather thanhours actually worked. For a small number of countries in KILM 17, including Canada, theUnited Kingdom and the United States, estimates of hours actually worked (as opposed tohours paid) could be directly obtained on the basis of labour force survey information.However, the international comparability of these estimates is questionable. For most othercountries we therefore made use of some variant of the “component method” to arrive at hoursactually worked.5 This method begins with a measure of paid hours or “usual” hours, which issupplemented with estimates of “unusual” working hours (such as overtime) and various typesof hours not worked, including vacation and holidays, absences due to sickness and part-timework. Estimates for use with the component method generally are obtained by combininginformation on paid employee hours from establishment surveys and information on workinghours of self-employed and unpaid family workers from labour force surveys.

We followed Maddison (1980, 1991) by using as much as possible hours estimates basedon the component method. This has particularly large consequences for the estimates of annualworking hours in the United States, for which our estimate of 1,615 hours per person employedin 1996, which is updated from Maddison’s 1992-estimate of 1,589 hours (Maddison, 1995), isabout 340 hours below the US estimate reported by OECD (1998), and which is derived fromthe Current Population Survey in the United States (and subsequently reproduced in KILM 6.Hours of work). This large difference requires further investigation, but for this moment we usethe lower “component method” estimate because of its greater international comparability.6

5 See, for example, OECD (1998, p. 185) for OECD countries. See Maddison (1980) and van Ark (1993) for adetailed description of this method.

6 For further discussion, see van Ark and McGuckin (1999)

5

As Chart 1 shows the measures of output and labour input in principle provide us withfour possible measures of labour productivity, namely gross value added per person employed(in 1990 US $), per hour worked (in 1990 US $), per person employed (1980 = 100), and perhour worked (1980 = 100). In practice we were mostly able to obtain measures of value addedper hour worked, although in some cases no measure of working hours could be obtained formanufacturing. In other cases, in particular for non-OECD countries, the measure of workinghours mostly only covers employees. In those cases we often assumed working hours peremployee were representative for those of a self-employed person, so that we could stillprovide a measure of gross value added per person-employed hour.

Total labour compensation is also derived from national accounts statistics. Theadvantage of using the national accounts is that the definition of compensation is the mostcomprehensive. It is meant to include not only gross wages and salaries of employees, but alsolabour costs paid by employers. However, national accounts measures of labour costs refer toemployee compensation only, and therefore do not include the compensation of self-employedpersons which is by definition part of other income, including income on capital, etc. Hence, asChart 1 shows the unit labour cost measure is in first instance expressed as employeecompensation per unit of output. To obtain a measure of total labour compensation per unit ofoutput, we needed to impute the labour income for self-employed persons assuming the samelabour compensation per employee and per self-employed person. The latter estimate could beobtained when measures of employees as well as total persons employed were available.

In contrast to the productivity measures, the labour compensation measures wereexpressed in current prices and not adjusted for relative price differences between country. Asa result the unit labour cost measure is constructed from a numerator (labour compensation) innominal terms and a denominator (output) in real terms. This apparent contrast can beunderstood when interpreting the unit labour cost measure as an indicator of costcompetitiveness. It then adequately represents the current cost of labour per “quantity unit” ofoutput produced. Similarly for comparisons across countries, labour compensation wasconverted to US dollars on the basis of exchange rates. For this purpose the unit labour costequation can be written as the ratio of nominal labour compensation per hour worked and reallabour productivity per hour worked:

where ERXU is the exchange rate between country X and the United States, PPPXU is thepurchasing power parity (or unit value ratio) between country X and the United States, LCHX(X)

are the labour cost per hour in country X in prices of X and OHX(X) is output (value added) perhour in country X in prices of country X. Equation (1) can be rewritten to decompose thedifference in unit labour cost between country X and country U into three components, i.e., thedifference in nominal labour cost per person, the difference in nominal labour productivity (thatis unadjusted for differences in price levels) and the differences in relative price levels:

)PPP - ER( - )OH - ER

OH(- )LCH - ER

LCH( = )ULC - ULC( XUXUUXU

XU

XU

XUX(U) loglogloglog (2)

All these three measures contribute in their own way to differences in costcompetitiveness between the two countries. The measure of PPPs and unit value ratios arediscussed in more detail in the next section.

Tables 1 and 2 show the availability of the underlying data on output in US dollars,labour input (persons employed or working hours) and labour compensation for the countries

PPP / OH

ER / LCH = ULC XUX(X)

XUX(X)UX )( (1)

6

included in KILM 17. In most cases the key variables were available to calculate output perhour worked in US$ and total labour compensation per unit of output in US$ were available.However, in a limited number of cases we had to rely on alternative measures. For example,for some countries (Philippines, Brazil, Chile, Colombia and Venezuela) only labourcompensation for employees could be obtained and no adjustment to total labour compensationwas possible. Secondly, in some cases (Austria, Denmark, Greece, Portugal, Philippines,Thailand and Mexico) manufacturing value added could not be converted to US dollars due toa lack of a currency conversion factor, so that we can only provide estimates on productivityand unit labour cost on the basis of national currency estimates. Finally, for the manufacturingestimates of Korea, Taiwan-China, Philippines and Mexico and for the total economy-estimateof Hong Kong-China, a wage index instead of full compensation was used. This latter type ofmeasure is less useful for the purpose of constructing unit labour cost estimates than the figuresbased on compensation obtained from national accounts for several reasons. Firstly, wage orearnings indices do not cover all labour cost, as these exclude employers’ contributions.Secondly, these indices often refer to particular types of workers or particular economicactivities. In particular, the agricultural sector is usually not covered by wage statistics. For thisreason, this proxy measure is usually not applied beyond the manufacturing sector.7

7As an exception, a proxy wage measure was also used for the total economy in the case of Hong Kong, China,given the negligible size of its agricultural sector.

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Table 1: Availability of underlying data for KILM 17 (1999), total economy

Country GDP in Persons Hours per Total labour Employee Wage1990 US$ employed person compensation compensation index

employed in US$ in US$Major EuropeAustria X X X XBelgium X X X XDenmark X X X XFinland X X X XFrance X X X XGermany (West) X X X XGreece X X X XIreland X X X XItaly X X X XNetherlands X X X XPortugal X X X XSpain X X X XSweden X X X XUnited Kingdom X X X XMajor non-EuropeAustralia X X X XCanada X X X XJapan X X X XUnited States X X X XAsia and the PacificEastern AsiaHong Kong,China

X X X X

Korea, Republicof

X X X X

Taiwan, China X X XSouth-central AsiaIndia X XSri Lanka X XSouth-easternAsiaIndonesia X XPhilippines X X XThailand X XLatin America and The CaribbeanBrazil X X X XChile X X X XColombia X X X XMexico X X X XVenezuela X X X XSource: See ILO ( 1999), table 17a)

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Table 2 – Availability of underlying data for KILM 17 (1999), manufacturingCountry GDP in Persons Hours per Total labour GDP in Wage

1990 US$ employed person compensation own currency indexemployed in US$ (constant prices)

Major EuropeAustria X XBelgium X X X XDenmark X X XFinland X X X XFrance X X X XGermany (West) X X X XGreece X XNetherlands X X X XPortugal X XSpain X XSweden X X X XUnited Kingdom X X X XMajor non-EuropeAustralia X X X XCanada X X X XJapan X X X XUnited States X X X XAsia and the PacificEastern AsiaKorea, Republic of X X X X XTaiwan, China X X X XSouth-central AsiaIndia X XSouth-eastern AsiaIndonesia X XPhilippines X X XThailand X XLatin America and the CaribbeanMexico X X XSee ILO (1999), table 17b

3. Currency conversion factors

Much of the work on international comparisons is based on comparisons of growthrates across countries. This reduces measurement errors from methodological differencesbetween countries because many of these errors remain relatively constant over time andtherefore drop out when the growth rate is calculated.8 An important element of the work forKILM 17 is the use of currency conversion factors to express all indicators in US dollars. Inthis way comparisons can be made not only over time but also across space. For outputcomparisons, use of market exchange rates does not take account of differences in relativeprice levels across countries. Hence the use of purchasing power-adjusted exchange rates is afundamental component of the level comparisons pursued for this project. Purchasing powerparities (PPPs) are the amount of a country’s currency that is required to purchase a standardset of goods and services worth one unit of the currency of another (base) country. Whenconverting output measured in one currency into the currency of another country, PPPs takeaccount of the price differences between the countries.

8 Maddison (1996) suggests that over the past 40 to 50 years differences between growth measures of OECDcountries are due to non-comparability of the measure and “…less than 0.2% a year should probably not beregarded as significant.” p. 30.

9

For comparisons of total economy output, purchasing power parities for GDP can beused. The use of purchasing power parities has a long history going back to work done at theOECD during the 1950s (Gilbert and Kravis, 1954; Gilbert and Associates, 1958). Since 1975,the construction of PPPs has been a regular aspect of the statistical programs of internationalagencies such as Eurostat, OECD, the United Nations and the World Bank.9 PPPs are estimatedfor 251 expenditure categories and aggregated to total GDP. An important feature of the PPPestimates since the 1970s is that the aggregation procedures are such that multilateralcomparisons between groups of countries can be made. Hence the results are transitive, so that,for example, a comparison of the US/Germany PPP with the Germany/France PPP gives thesame result as the U.S/France PPP.

For European countries, purchasing power parities are now estimated on an annualbasis, and for the non-European OECD countries estimates are provided on a three-year basis(1990, 1993, 1996, etc.).10 For non-OECD countries, benchmark estimates are provided on amore irregular basis. For most Asian countries, PPP estimates can be obtained for 1975 (fromKravis, Heston and Summer, 1982), 1980 (from United Nations, 1986) and 1985 (from UnitedNations, 1994), whereas for Latin American countries the 1975-round was the latest fromwhich PPP estimates can be obtained. Maddison implicitly obtained 1990 PPPs by updating theGDP in US$ for the benchmark year to 1990 using the real GDP trend in the given country andthe GDP deflator for the United States:

GDPX(U)90 = GDPX(U)

t * REALGDPX90/t * DEFLU

90/t (3)

where GDPX(U)t represents GDP of country X in US dollars in year t, REALGDPX

90/t representsthe real GDP index of country X between year t and 1990, and DEFLU

90/t the GDP deflator ofthe United States between year t and 1990. By dividing 1990 GDP in US dollars through the1990 GDP in national currencies of country, the implicit 1990 PPP is obtained. For KILM 17we applied Maddison’s 1990 PPPs for total GDP that made it possible to cover the widestpossible sample of countries in a consistent way for a most recent year.11

The total economy PPPs are all aggregated using the Geary-Khamis method. Thisimplies that purchasing power parities are simultaneously developed with so-calledinternational prices. The Geary-Khamis PPPs give weights to countries according to their sizemeasured in terms of gross national product (GNP). For example, in application of this method,the GDP of the United States counts for approximately 5 times as much in the determination ofinternational prices as that of India and about 7.5 times as much as that of Brazil.12 (See table3)

9 See, for example, Kravis, Heston and Summers (1982), Eurostat (1983, 1988), OECD (1996, 1999), UnitedNations (1986, 1994).

10 The latest PPP estimates for OECD countries are now benchmarked on 1996 (OECD, 1999). See the GGDCwebsite for regular updates of GDP estimates on the basis of the latest PPP estimates(http://www.eco.rug.nl/ggdcc/homeggdc.html). van Ark and McGuckin (1999) provide comparisons of PPP-adjusted estimates of per capita income and labour productivity for OECD and non-OECD countries.

11 See Heston and Summers (1991) for a review of the PPP-adjusted estimates of the Penn World Tables, whichare converted at 1985 PPPs.

12 An alternative method that is nowadays mostly used in the OECD countries aggregates PPPs on the basis of theEKS (Eltetö, Köves and Sculz) method. EKS PPPs are basically geometric averages of binary PPPs (which are notindividually transitive). This means that they represent averages that do not account for differences in countrysize. For a review of PPP methods, see Hill (1982). For updates, recent development and references to

10

PPP estimates have been criticized for many reasons, as set out in recent reports byCastles (1997) and Ryten (1999). Indeed the precise nature of the price surveys can differacross countries. The pricing procedures have been criticized for lack of comparability andreflection of the specified items between countries. Furthermore, the multilateral character ofthe estimates is affected by the fact that the PPPs are, in fact, estimated for six differentregions, which are “globalized” with particular interregional (binary) links. Finally, within eachof the regions the aggregation procedures of the PPPs differ. For example, within the EuropeanUnion, country PPPs are unweighted for size of GDP, whereas the PPPs for non-EuropeanOECD countries are combined with those for the European Union by weighting for size ofGDP.

For manufacturing, appropriate currency conversion factors are more difficult to obtainthan for the total economy, as there are no specific international surveys of producer prices. Fora limited number of countries in Europe, North America and Asia, measures of manufacturingunit value ratios are obtained on the basis of industry of origin method by the ICOP(International Comparisons of Output and Productivity) research group at the GroningenGrowth and Development Centre (GGDC) at the University of Groningen.13 With this method,industry-specific purchasing power parities are obtained from ratios of unit values. The unitvalues represent sales values divided by quantities for similar products or product groups,derived from national production censuses or industry surveys, which are matched betweencountries. The unit value ratios are weighted at product and industry output values and thenused to convert manufacturing output to US dollars. In contrast to the total economy PPPs theunit value ratios for manufacturing are of a bilateral nature. The ICOP project now coversabout 30 countries in Asia, East and West Europe, and North and South America. Comparisonsof manufacturing output and productivity are disaggregated into 16 manufacturing branchesand are available for almost all of these countries.14

The manufacturing unit value ratios (UVRs) used for KILM 17 were obtained for 1987and updated to 1990 on the basis of the ratio of the 1990/1987 deflators of each countryrelative to the United States (see table 3). With the exception of Spain and the UnitedKingdom, the differences between the total economy PPPs and the manufacturing UVRs arerather small for the OECD economies. However, for the non-OECD countries, manufacturingUVRs are substantially higher than those for the total economy, confirming the stylized factthat relative prices of non-manufacturing goods are much lower than those of manufacturedgoods in low-income countries.

methodological studies, see the OECD website on purchasing power parities(http://www.oecd.org/std/ppp/pps.htm).

13 An earlier pioneering industry-of-origin study of comparative output and productivity is Paige and Bombach(1959).

14 In addition, ICOP estimates are available for agriculture and (for a limited number of countries) for particularservice sectors. See the ICOP website at http://www.eco.rug.nl/ggdc/icop.html, which also includes a list of ICOPand ICOP-related publications and reports since 1983.

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Table 3. Purchasing power parities for total economy unit value ratios for manufacturing, national currency to US dollar, 1990.

Purchasing Power Unit Value Ratio Exchange RateParity (1990) (1990) (1990)

Major EuropeAustria 13.90 11.37Belgium 38.36 42.05 33.42Denmark 8.70 6.19Finland 6.22 5.67 3.82France 6.45 6.99 5.45Germany (West) 2.05 2.10 1.62Greece 129.55 158.51Ireland 0.688 0.605Italy 1384 1198Netherlands 2.08 2.21 1.82Portugal 91.7 142.6Spain 105.7 139.0 101.9Sweden 8.98 8.39 5.92United Kingdom 0.587 0.720 0.563Major non-EuropeAustralia 1.35 1.51 1.28Canada 1.27 1.29 1.17Japan 185.3 152.8 144.8United States 1.00 1.00 1.00Asia and the Pacific Eastern AsiaHong Kong, China 5.84 7.79Korea, Republic of 481.1 631.15 707.8Taiwan, China 21.5 28.2 26.9South-central AsiaIndia 4.88 11.66 17.50Sri Lanka 7.55 40.06South-eastern AsiaIndonesia 468 1599 1843Philippines 7.53 24.31Thailand 8.54 25.59Latin America and the CaribbeanBrazil 40.3 68.3Chile 110.0 305.1Colombia 127.2 502.3Mexico 1.43 2.80Venezuela 14.2 46.9Source: Total economy PPPs for OECD countries are obtained from Maddison (1995), Table C-6. For non-OECD countries PPPs are implicitly obtained from updated GDP in US$ for earlier benchmark (see text andMaddison, 1995) and are kindly provided by Maddison. Manufacturing unit value ratios are from various ICOPstudies: Belgium from Soete (1984); Finland and Sweden from Maliranta (1994); France from van Ark andKouwenhoven (1994); Germany from van Ark and Pilat (1993); Netherlands from Kouwenhoven (1993); Spain/UK(1984) from van Ark (1995a), linked to UK/USA (1987) from van Ark (1992); Australia from Pilat, Rao andShepherd (1993); Canada from de Jong (1996); Korea and Japan from Pilat (1994); India, Indonesia and Taiwanfrom Timmer (1999). In most cases the PPPs are updated to 1990 using manufacturing price deflators from nationalaccounts.

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The UVR-method for manufacturing faces three major problems that affect thecomparability of the estimates across countries:15

1) Unit value ratios are based on a limited sample of items, and rather far-reachingassumptions are employed concerning their representativity for non-measured pricerelatives. For example, in manufacturing where the average percentage of outputcovered by unit value ratios is between 15 and 45 per cent, it is usually assumed thatUVRs for matched items within a manufacturing branch are representative for non-matched items.

2) Comparisons of unit values are affected by differences in product mix, becauseproduction censuses often include output values for product groups rather than forspecified products. In international comparisons the problem aggravates because ofthe lack of a harmonized product coding system, so that items need to be furtheraggregated in order to obtain a correct match between countries.

3) UVRs need to be adjusted for differences in product quality across countries. Thisproblem can be particularly serious for international comparisons, as the frequency ofso-called "unique products" (which are products which are only observed in one case)is higher than for comparisons over time.

It has been suggested that pricing of narrowly specified items, which is, for example,applied in the GDP PPP approach, is superior to the use of unit values and that expenditure PPPsshould therefore also be used for industry of origin comparisons. In some cases, GDP PPPs havebeen applied to individual sectors or industries which assumes that differences in price levels areequal across industries.16 A more sophisticated method is to select expenditure PPPs forrepresentative items that are allocated to specific industries. One problem with this so-called"proxy PPP method" is that it is based on market prices for final products. In a comparisonbetween Japan and the United States, Jorgenson and Kuroda (1990) applied PPPs that were"peeled off" for indirect taxes and transport and trade margins. Hooper (1996) went a step furtherand adjusted the expenditure PPPs for import and export prices: "output PPPs" should of courseinclude prices of exported goods but exclude those of imported goods. As Hooper acknowledges,the latter step is difficult and, in particular in open economies, might introduce a lot of noise inthe estimates. The most important problem of the “proxy PPP” approach for industry of origincomparisons is that it does not cover intermediate products, which in manufacturing account forat least one third of output.

Whichever concept of PPP or UVR is chosen, the main problem in industry of origincomparisons is that ideally one requires a currency conversion factor not only for output but alsofor inputs. Preferably industry productivity should be measured as gross output per unit of input.In contrast ICOP comparisons apply output-weighted unit value ratios to value added. This maybe referred to as the “single deflation” method, which implicitly uses one and the same UVR foroutput and for intermediate inputs. The reason why this relatively simple method still has usefulapplication in international comparisons is due to measurement problems related to the pricesof intermediate inputs. Earlier attempts to change ICOP studies from “single deflation” to“double deflation” (i.e., deducting UVR-deflated intermediate inputs from UVR-deflated grossoutput) led to volatile results because the estimates were sensitive to the weights used in theindex. Moreover, adequate measurement of the value and quantities of intermediate inputs

15 For more detail on how these problems are dealt with see van Ark (1993, 1996a). van Ark (1993) can also bedownloaded in full from http://www.eco.rug.nl/ggdc/icop.html.

16 See, for example, Dollar and Wolff (1993).

13

requires larger coverage percentages for inputs than for output, as in one and the same industrythe output is more homogeneous than inputs. In particular, when intermediate inputs make up alarge part of gross output, small measurement errors show up strongly in the end results of valueadded (in the ICOP case) or in the contribution of intermediate inputs to gross output in casedouble deflation is applied. Hence the deflation problem is not any less serious in using the proxyPPP approach for productivity measures than in a "double deflation" procedure. In practice,therefore, the single deflation method still provides more robust results for internationalcomparisons than the double deflation method when applied to either value added or to theseparate estimation of intermediate input PPPs or UVRs.

4. Groningen Growth and Development CentreDataBase

The data for KILM 17 are largely derived from the Groningen Growth and DevelopmentCentre (GGDC) Database at the University of Groningen (the Netherlands). The GGDC haslong-standing expertise in development and analysis of data on productivity performance, inparticular on comparisons of levels of productivity by sector and industry. The GGDCDatabase consists of three subsets of data, namely the GGDC Total Economy Database, theGGDC Sectoral Database and the ICOP Industry Database. All three databases have been usedfor KILM 17 with KILM 17 consisting of an additional measure of labour compensation toobtain unit labour cost.17

Real GDP and labour input data for the total economy are derived from the GGDC TotalEconomy Database. The GGDC Total Economy Database is strongly rooted in the work ofAngus Maddison. For most countries movements in GDP and population before 1990, as wellbenchmark year estimates on employment and hours, are derived from Maddison (1995) aswell as some of Maddison’s other publications. Maddison’s estimates of Geary-Khamispurchasing power parities for 1990 were used to convert output to US dollars.18 Maddison’sown series often go back well into the 19th century, so that one can link these historical seriesto the series presented in the GGDC Total Economy Database.

Manufacturing output and labour input is taken from the GGDC Sectoral Database, whichconsists of series on real GDP in national currencies, employment and, in some cases, annualworking hours for ten sectors of the economy and 20 countries in Asia and the OECD areafrom 1950 onwards.19 The unit value ratios are taken from the ICOP database which isdescribed in more detail in the previous section.

The basic sources used are best described by making a distinction between countries thatare members of the OECD and those that are not.20 For OECD countries, indicated in KILM 17

17 Underlying data on KILM 17 from 1980 onwards can be derived from the KILM CD-ROM. Data furtherbackwards to 1950 are available on the GGDC website: http://www.eco.rug.nl/ggdc/Dseries/dataseries.html. Bothdatasets are updated on a regular basis.

18 See the GGDC website (http:/www.eco.rug.nl/ggdc/Dseries/dataseries.html) for GDP estimates of OECDcountries using more recent purchasing power parities, based on the benchmark year, 1993.

19 The ten sectors of the economy are: agriculture, mining, manufacturing, construction, public utilities, retail andwholesale trade, transport and communication, finance and business services, other market services andgovernment services. See van Ark (1996b) for a description and discussion of OECD data.

20 A full overview of data sources is provided in appendix A.

14

in the “major Europe” and "major non-Europe" categories, value added and labourcompensation is mostly obtained from the OECD, National Accounts, Volumes I and II (annualissues). Employment is mostly taken from OECD, Labour Force Statistics (annual issues).These data, originally obtained from national statistical offices and, where possible,harmonized for differences in concepts and industry classification, have been supplemented,where necessary, with national accounts and labour force statistics obtained directly from theindividual countries. For some countries, the database of the US Bureau of Labour Statistics(BLS) was used, in particular on employment and on data for manufacturing.21 The estimatesof working hours were obtained from various sources. Maddison (1995) provides benchmarkestimates of annual working hours for most OECD countries and a significant number of non-OECD countries. These were complemented with movements on hours derived from theOECD Employment Outlook (annual issues) and the BLS database.22

For the non-OECD countries, the national accounts and labour statistics publications ofindividual countries were often taken as the point of departure. The statistics from thesesources were supplemented with statistics from international organizations such as the WorldBank, the Asian Development Bank, the United Nations Statistical Office and the InternationalLabour Organization.23 Furthermore, estimates on GDP and employment from Maddison(1995) were used to obtain consistent benchmark estimates in 1990 US dollars.

Where possible, labour compensation is obtained from the national accounts so that valueadded (GDP) and labour cost are compatible. However, the national accounts of manydeveloping countries do not provide estimates of labour compensation. In a limited number ofcases, a separate measure of unit labour costs is provided which is based on wage or earningsindices derived from the ILO Yearbook of Labour Statistics.

5. Some comparisons of productivity and unitlabour cost24

The results on productivity and unit labour cost are presented in the 1999 Key Indicatorsfo the Labour Market for 1980 and 1990 to 1996 or 1997. The KILM CD-ROM provides dataon an annual basis since 1980 as well as underlying data on output, labour input and labourcompensation. It is also possible to link these data with those from the GGDC Database goingback to 1960 for most non-OECD countries and to 1950 for many OECD countries.

Table 4 provides the growth rates of labour productivity for the total economy andmanufacturing back to 1960 divided into three sub-periods. The growth estimates show,amongst other things, the relatively rapid growth of labour productivity during the period 1960

21 BLS databases on Foreign labor statistics and Manufacturing unit labor cost can be accessed throughhttp://stats.bls.gov/datahome.htm.

22 While it is not possible to fully assess which working hours estimates are “best,” the data produced byMaddison are largely based on the component method, as described in Section 2, and have been adjusted wherepossible to improve international comparability. However, a final judgment cannot be made before more detailedwork in this field is carried out. For a more detailed assessment of the impact of working hours estimates oninternational comparisons of output and productivity, see van Ark and McGuckin (1999).

23World Bank: World Development Indicators (various issues); Asian Development Bank: Key Indicators ofDeveloping Asian and Pacific Countries (annual issues); United Nations: National Accounts Statistics (annualissues); and ILO: Yearbook of Labour Statistics (annual issues).

24 See also ILO (1999), Chapter 6.

15

to 1973, and the even more rapid productivity growth in manufacturing. This period isnowadays characterised as the “Golden Era” of the twentieth century. During these days manycountries in the OECD league (in particular those in Europe and Japan) were realizing much ofthe “catch up” potential which arose following the two World Wars and were benefiting fromrapid technological diffusion from the United States (Maddison, 1991; Crafts and Toniolo,1996). The Japanese economy, which started from a relatively low level of productivity (seetable 5) showed the fastest productivity growth, running into 2-digit annual growth rates for theperiod 1960 to 1973. The growth performance during this period was slower for most countriesin Asia and Latin America. Most of these countries remained either relatively closed tointernational trade or faced unfavourable terms of trade, particularly when exports weredominated by primary products. In the present country sample only the Republic of Korea andTaiwan-China entered a phase of industrialization which was accompanied by productivitygrowth. Industrialization in these countries was characterized by a mix of export-orientationtowards labour intensive goods accompanied by import substitution of more capital-intensivecommodities.

Between 1973 and 1987 growth in OECD countries slowed down substantially. In mostcases the growth rates were half or even less that of the “Golden Era”. The breakdown of theinternational monetary arrangements of Bretton Woods, which had provided much stability tointernational macroeconomic relations during the 1950s and 1960s, and the oil crises of the1970s, were responsible for part of the growth slowdown. However, the continued slowdownduring the much of the 1980s must be attributed to underperformance of OECD economiesbecause of orthodox economic policies characterized by tight monetary policies and largegovernment budget cuts and a painful industrial restructuring with a large rise inunemployment. The period 1973 to 1987 is characterized by much diversity across thecountries in Asia and Latin America. The Republic of Korea and Taiwan-China continued toshow rapid productivity growth, and some Southeast Asian countries followed the sameindustrialization path. In contrast, Latin America was hit by one of the worst debt crises in theirhistory, causing zero or negative growth in productivity for most of the 1980s.

Since the mid 1980s growth performance of the OECD countries has been characterizedby more diversity than before. Indeed the coefficient of variation (i.e., the standard deviation ofthe growth rates divided by the mean) of the labour productivity growth rates for the OECDcountries increased from 0.34 for 1973 to 1987 to 0.41 for 1987 to 1997. Some countries suchas Denmark, France, Ireland and Portugal have shown an accelerated productivity growth since1987, but other countries such as Austria, France, the Netherlands, Spain, Sweden and theUnited Kingdom have experienced a substantive slowdown. There are indications of differentsuccess rates in structural reform policies that may account for varying success rates of thesecountries. Manufacturing productivity growth has mostly been faster during this period than forthe total economy. One reason for this is probably the greater effect of new technologies onmanufacturing productivity growth. The other reason is that the increasing share of servicesector in the economies of OECD countries accounts for much of the slower growth at the totaleconomy level. Both reasons also play a role in accounting for the relatively large growthdifferential between manufacturing and the total economy for the United States compared toOECD economies.

Among the lower income countries, productivity growth rates remained high among EastAsian countries and accelerated substantially in other Asian countries including India,Indonesia and Thailand. The performance among Latin American countries was much moremixed, partly depending on the success of structural market reforms since the 1980s and thedegree of isolation from financial crises.

Table 5 shows the relative levels of labour productivity and unit labour cost in 1980,1987 and 1997 for the total economy. Productivity gaps narrowed among OECD countries, asthe coefficient of variation declined from 0.225 in 1980 to 0.182 in 1997. Indeed most

16

countries improved labour productivity relative to the United States, in particular Finland,Ireland and the East Asian countries. In 1987, the United States was still the productivityleader, but by 1997 two countries in the present sample, France and the Netherlands, showedhigher levels of output per hour than the United States in 1997. Some countries in LatinAmerica (Mexico and Venezuela) experienced a widening of the productivity gap with theUnited States.

In manufacturing, less productivity convergence among OECD countries took place (table6).25 Some countries (Belgium and Finland) overtook the US productivity level inmanufacturing by several percentage points, but others even showed some divergence (forexample, Australia and Canada). East Asian industrializing countries, like Korea and Taiwan,showed considerable catching up to the US level of manufacturing productivity but still were atnot more than one third of the US productivity level by 199726.

The unit labour cost data, which are presented in the last columns of tables 5 and 6show substantially more variation that the productivity estimates. This is due to the fact that theunit labour cost indicators are influenced by diverse factors like hourly compensation,productivity and the US dollar exchange rate. Except for the poorest OECD economies (forexample, Greece and Portugal) and for Japan, unit labour costs levels were higher for mostOECD countries relative to the United States. However, the unit labour cost gap relative to theUnited States was smallest in 1987, when the dollar-exchange rate was relatively high andtherefore labour in other countries relative to US was cheap. For manufacturing, one of themost striking observations is the rapid rise in unit labour cost in Japan, from 70 per cent of theUS level to over 120 per cent in 1996, which was also higher than in most European countriesexcept Germany (West), Sweden and the United Kingdom. The high Japanese level of unitlabour costs is related to the comparatively low productivity level for the total economy and therapid appreciation of the yen since 1990.

25 This table substitutes for figures published in KILM 17b (ILO, 1999). For that publication manufacturing UVRswere accidentally updated to 1990 on the basis of the current value index of each country relative to the UnitedStates. In this table we correctly carried out the updating of the UVRs on the basis of the manufacturing deflatorrelative to the United States. In most cases this implied that the correct UVRs came out lower and therefore therelative productivity levels are higher than those published in ILO (1999). The corrected Table 17b is also shownin the appendix to this paper.

26 It should be emphasized that these estimates of relative productivity levels differ, in some cases, substantiallyfrom those directly be obtained for the ICOP Industry Database. This is due to the fact that the ICOP comparisonsare originally based on output and employment information from production censuses or industrial surveys. Thesesources often provide more disaggregated information than the national accounts and ensure that output and inputinformation are derived from the same primary source. However, production censuses and industrial surveys oftenprovide no full coverage of all manufacturing activities, including those of the smallest firms (see, e.g., van Ark,1993; Timmer, 1999). Another difference between the estimates of manufacturing productivity presented in KILM17b and those in the ICOP Industry Database is that we updated the UVRs for KILM 17b to 1990, whereas theywere left at the benchmark year in ICOP.

17

Table 4. Annual growth rates of labour productivity, total economy and manufacturing, 1960-1997

Total economy ManufacturingGDP per personemployed

GDP per personhour worked

Value added perperson employed

Value added perperson hour worked

Country 1960-1973

1973-1987

1987-1997

1960-1973

1973-1987

1987-1997

1960-1973

1973-1987

1987-1997

1960-1973

1973-1987

1987-1997

Major EuropeAustria 5.2 1.8 1.4 5.9 2.6 1.3 3.7 (a)Belgium 4.3 2.0 1.9 5.5 3.0 1.8 4.3 2.3 2.9 6.9 5.4 2.8Denmark 3.4 1.3 1.9 5.0 1.6 2.1 2.1 (b)Finland 4.7 2.0 2.7 6.1 2.2 2.8 5.5 4.9 5.8 (a) 5.3 4.3France 4.3 2.1 1.4 (a) 5.0 3.1 1.6 6.3 2.0 3.2 (a) 6.2 3.5 3.2 (a)Germany (West) 4.0 1.8 2.0 (a) 5.2 2.6 2.7 5.1 2.2 2.2 (a) 5.8 2.6 3.2 (a)Greece 6.6 1.6 1.2 7.1 2.4 1.6 15.5 (b)Ireland 5.0 3.0 4.0 5.5 3.9 4.5Italy 5.8 2.2 2.2 7.8 2.6 2.3Netherlands 3.2 1.2 1.1 (a) 4.5 2.9 1.7 6.5 1.8 2.1 (a) 7.8 3.6 2.6 (a)Portugal 6.5 0.9 1.8 7.2 1.5 2.1 7.7 2.0 1.4 (b)Spain 8.0 3.0 1.5 8.1 3.7 1.8 9.7 4.0 0.9 (a) 8.4 7.8Sweden 3.5 1.1 2.2 4.7 1.6 1.4 6.4 4.5 4.9 (a) 7.1 2.7 4.1 (a)United Kingdom 2.8 1.7 1.3 (a) 3.8 2.3 1.7 1.9 0.1 3.1 (a) 4.7 3.2 3.6 (a)Major non-EuropeAustralia 2.3 1.4 1.7 2.5 1.7 1.7 2.5 0.9 1.8 (a) 3.2 2.8 1.4 (a)Canada 2.5 0.9 0.8 2.9 1.4 0.8 3.5 2.5 1.9 4.8 1.9 1.8Japan 8.1 2.7 1.9 (a) 9.1 2.9 2.5 12.8 7.1 2.8 (a) 11.3 4.5 4.1 (a)United States 2.3 0.7 1.1 2.7 1.2 1.0 2.0 4.0 2.9 (a) 3.6 2.4 2.7 (a)Asia and the PacificEastern AsiaHong Kong,China

1.5 4.7 3.8 (a) 4.1 (a)

Korea, Republicof

4.8 5.3 5.1 (a) 5.6 (a) 12.3 9.8 7.8 (a) 6.3 8.6 (a)

Taiwan, China 6.6 5.2 5.0 (a) 6.1 (b) 5.8 (a) 6.3 (b)South-central AsiaIndia 2.0 1.6 4.0 (b) 2.8 -1.9 4.6 (b) 2.4 3.8Sri Lanka 0.3 3.6 3.8 (b)South-eastern Asia

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Indonesia 2.2 0.6 5.3 (b) 2.7 3.9 (b) 3.1 1.5Philippines 2.3 0.2 0.6 (b) 0.9 (b)Thailand 4.8 3.1 7.9 (a) 4.3 (a)Latin America and the CaribbeanBrazil 4.0 1.1 0.5 (a) 0.7 (a) 5.2 1.9 4.2 -0.3Chile 1.7 0.1 3.5 (a) 3.3 (a)Colombia 2.5 1.3 1.8 (a) 1.8 (a)Mexico 3.7 1.1 -3.1 -2.7 (a) 6.0 4.4 3.2 (a) 3.0 -0.1Venezuela 0.5 -1.8 -1.9 (a) -1.8 (a)(a) 1987-1996; (b) 1987-1995Source: GGDC Total Economy Database, Sectoral Database and ICOP Industry Database. See Appendix for 1980-1997. Pre-1980 see source descriptions onhttp://www.eco.rug.nl/ggdc/Dseries/dataseries.html.

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Table 5. Labour productivity and unit labour cost, total economy, 1960-1977, 1990, US$GDP per person employed GDP per person hour worked Unit labour cost per unit of

outputCountry 1980 1987 1997 1980 1987 1997 1980 1987 1996Major EuropeAustria 32,154 34,866 40,020 23.00 25.70 28.90 0.478 0.644 0.938Belgium 36,849 40,737 49,187 22.70 25.00 29.90 0.622 0.644 0.954Denmark 30,009 33,183 40,214 17.50 19.90 24.60 0.596 0.733 1.031Finland 25,719 30,314 39,722 14.50 18.20 24.00 0.573 0.784 0.919France 36,850 42,194 47,958 22.20 27.40 32.20 0.570 0.627 0.858Germany(West)

35,073 38,458 46,100 20.30 23.20 30.30 0.567 0.674 0.938

Greece 26,257 26,477 29,868 13.90 14.80 17.40 0.347 0.384 0.683Ireland 24,290 29,808 44,253 12.90 16.80 26.00 0.550 0.660 0.730Italy 30,200 33,038 41,010 19.00 21.60 27.10 0.419 0.591 0.736Netherlands 35,276 37,725 41,453 22.30 27.20 32.20 0.586 0.609 0.805Portugal 20,123 21,852 26,203 11.00 12.50 15.40 0.266 0.297 0.490Spain 29,711 35,439 41,138 14.40 18.00 21.60 0.458 0.476 0.708Sweden 29,331 32,910 40,741 20.20 22.40 25.70 0.717 0.747 1.058UnitedKingdom

29,166 34,747 38,890 18.30 22.30 26.40 0.478 0.499 0.712

Major non-EuropeAustralia 32,283 35,582 42,263 19.90 21.80 25.70 0.498 0.487 0.689Canada 35,361 39,142 42,384 20.50 23.40 25.30 0.415 0.509 0.623Japan 27,666 33,256 39,434 13.80 16.50 21.20 0.523 0.882 1.225United States 41,034 44,610 49,905 25.50 27.70 30.70 0.453 0.601 0.776Asia and the PacificEastern AsiaHong Kong,China

23,220 31,800 44,412 9.80 13.70 19.60

Korea,Republic of

11,430 18,066 28,166 4.50 6.90 11.30 0.336 0.331 0.632

Taiwan,China

15,176 21,499 33,438 5.90 8.50 13.70

South-central AsiaIndia 2,638 3,156 4,325Sri Lanka 7,223 8,441 11,384South-eastern AsiaIndonesia 5,350 5,266 7,961Philippines 7,364 5,910 6,192Thailand 4,943 6,028 9,103Latin America and the CaribbeanBrazil 14,427 13,765 14,366 7.27 7.20 7.72 (a) (a) (a)Chile 19,184 17,681 24,143 9.90 9.00 12.06 (a) (a) (a)Colombia 13,476 14,711 17,232 6.50 7.40 8.73 (a) (a) (a)Mexico 17,099 18,096 13,169 8.34 8.80 6.88 0.500 0.186 0.350Venezuela 32,368 29,406 24,795 16.21 15.30 12.98 (a) (a) (a)(a) Unit labour cost levels for these countries are only provided in terms of employee cost per unit of

output and therefore are not comparable to the other figures, which represent total labourcompensation per unit of output.

Source: GGDC Total Economy Database. See Appendix.

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Table 6. Labour productivity and unit labour cost, manufacturing,1960-1996, 1990 US$

GDP per person employed GDP per person hour worked Unit labour cost per unit ofoutput

Country 1980 1987 1996 1980 1987 1996 1980 1987 1996Major EuropeBelgium 30,581 41,657 55,600 20.1 27.7 36.5 0.83 0.71 0.84Finland 24,353 34,491 57,430 14.1 20.4 35.2 0.60 0.70 0.70France 33,373 38,407 50,926 19.5 23.8 31.5 0.64 0.71 0.85Germany(West)

33,680 36,577 44,471 19.6 22.3 29.4 0.62 0.78 1.14

Netherlands 27,678 34,428 41,569 17.7 23.6 29.6 0.68 0.70 0.82Spain 19,916 28,367 30,703Sweden 25,283 32,054 49,398 17.6 21.5 30.9 0.82 0.79 0.93UnitedKingdom

17,793 25,718 33,753 10.3 14.6 20.1 0.84 0.81 0.96

Major non-EuropeAustralia 26,109 30,842 36,070 13.6 15.8 18.0 0.63 0.57 0.85Canada 30,469 37,524 45,428 16.0 19.4 23.1 0.51 0.59 0.67Japan 32,613 43,668 56,127 15.0 19.9 28.5 0.40 0.62 0.84United States 37,714 50,844 65,966 20.3 26.6 33.7 0.57 0.62 0.69Asia and the PacificEastern AsiaKorea,Republic of

8,994 14,436 28,437 3.3 5.2 10.9 0.32 0.33 0.45

Taiwan, China 11,644 16,764 27,850 4.4 6.7 10.9South-central AsiaIndia 1,675 2,333 3,342South-eastern AsiaIndonesia 2,233 3,358 4,547Note: This table substitutes for figures published in KILM 17b (ILO, 1999). For that publication manufacturingUVRs were accidentally updated to 1990 on the basis of the current value index of each country relative to theUnited States. In this table we correctly carried out the updating of the UVRs on the basis of the manufacturingdeflator relative to the United States. In most cases this implied that the correct UVRs came out lower andtherefore the relative productivity levels are higher than those published in 1999 KILM. The corrected table 17bis also shown in the appendix to this paper.Source: GGDC Sectoral Database and ICOP Industry Database. See Appendix

Similar data on productivity and unit labour cost were recently made available by otherscholars including the World Bank (1999) and Golub (1999). The World DevelopmentIndicators 1999 include wages and productivity measures for manufacturing for a wide rangeof countries. However, the measures are all converted to US dollars on the basis of the averageexchange rate for each year (World Bank, 1999, Table 2.6).

Golub (1999) provides productivity and unit labour cost data for a smaller set of 14countries, including the G-5 (France, Germany, Japan, United Kingdom, United States), 7major Asian countries (India, Indonesia, Korea, Malaysia, Philippines, Singapore andThailand) and 2 Latin American countries (Chile and Mexico), most of which are also in KILM17. Golub’s results largely confirm those of KILM 17. In particular, he emphasizes that relativelevels of unit labour cost are much closer between countries than those of labour productivityand compensation separately, as differences in the relative levels of both indicators more orless offset each other. Despite similar outcomes, however, Golub’s dataset differs in manyrespects from KILM. Firstly, Golub’s estimates are only for manufacturing. Secondly, he doesnot provide estimates of output per hour worked but only output per person employed. Thirdly,

21

in converting manufacturing output, Golub makes use of purchasing power parity for producerdurables obtained from the Penn World Tables (see Summers and Heston, 1991). Fourthly,Golub makes use of “real product wages” which are earnings deflated by the value-addeddeflator for manufacturing and converted to US dollar at the market exchange rate in the baseyear. This is, therefore, not exactly a nominal index as used in KILM. Finally, labourcompensation, obtained from UNIDO, does not include employer contributions to socialinsurance.

6. Research agenda

For future versions of the productivity and unit labour cost database for KILM, prioritywill be given to expanding the estimates to more countries than the current sample of 66countries in the Groningen Growth and Development Database, of which 31 countries areincluded in KILM 17 so far. Estimates of total labour compensation and manufacturing unitvalue ratios are the current bottlenecks in extending the database.

Secondly, presently research activity in this field focuses on extending estimates toservice sectors of the economy. ICOP-type studies for services have been carried out by Pilat(1994) for Japan and Korea and by Mulder (1999) for Brazil and Mexico. Productivity levelestimates for transport and communication and for wholesale and retail trade are compiled byvan Ark, Monnikhof and Mulder (1999) for Canada, France, Germany, the Netherlands and theUnited States and are currently extended to another ten to 15 countries.

Finally, estimation of total factor productivity growth and levels is also needed. Amongother things this requires internationally consistent series of capital stock and capital flows andof human capital. Lack of reliable data in combination with the sensitivity of the proceduresseriously limits the number of countries for which one can derive reliable estimates of thecapital stock (Nehru and Dhareshwar, 1993). Many studies, in particular those that made use ofcross-country regressions, have used investment-output ratios as a proxy for the change in thecapital stock.27 Scholars that constructed capital stock estimates either reverted to wealthsurveys that value the capital stock in place at user value or construct estimates based on theperpetual inventory method (PIM) which are obtained by cumulating investment data usingassumptions concerning the life time of assets and the depreciation pattern. Such series are stillavailable for a limited number of countries and need to be further extended.28 Moreover, forsectoral estimation of total factor productivity levels a detailed input-output framework isrequired to measure substitution effects between inputs and flows between sectors,29 as well asthe adoption of a growth accounting framework.

27 This procedure assumes that marginal and average capital-output ratios are the same, which is a particularlyunrealistic assumption for rapidly industrializing economies which are often characterised by relatively lowcapital-output ratios in combination with high rates of capital accumulation (Fukuda and Toya, 1999).

28 The PIM approach has been applied in two international datasets that aimed to include as many countries aspossible, namely the World Bank dataset on physical capital (Nehru and Dhareshwar, 1993) and the Penn WorldTables (Summers and Heston, 1991). The series from both datasets involve very substantial measurementproblems, as the estimates are either based on indirect procedures, such as using investment/GDP ratios (PennWorld Tables) or rough methods to derive a reliable benchmark estimates for the stock (World Bank dataset).Maddison (1995a) provided PIM estimates for six advanced countries, which is extended by O’Mahony (1996,1999) for sectoral estimates. Timmer and van Ark (2000) provide comparable PIM estimates of the capital stockfor Taiwan, China and the Republic of Korea.

29 See, for example, Jorgenson (1995).

22

Appendix I

Detailed source descriptions

The full reference is given only the first time it is mentioned.

Total Economy Data (KILM 17a)

Austria: GDP in 1990 US$: 1980-1990 from A. Maddison (1995), Monitoring the World Economy,1980-1992 (OECD Development Centre); 1991-1996 extrapolated from 1990 with GDP trend fromOECD, National Accounts Vol. I; 1997 from OECD Online website. Persons employed: 1984-1994from OECD, Labour Force Statistics; 1995-1997 extrapolated from 1990 with employment trend fromOECD, Economic Outlook; 1980-1983 linearly interpolated between 1979 (from A. Maddison (1982),Phases of Capitalist Development, Oxford University Press) and 1984 figure. Hours worked: 1979 fromMaddison (1982); 1987 from A. Maddison (1991), Dynamic Forces in Capitalist Development (OxfordUniversity Press); 1992 from Maddison (1995); years in between are linearly interpolated; 1992-1997kept constant at 1992 level. Labour compensation: 1980-1996 from OECD, National Accounts, Vol. I.

Belgium: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990with GDP trend from OECD, National Accounts Vol. I; 1997 from OECD Online website). Personsemployed: 1980-1993 from OECD, Labour Force Statistics; 1994-1997 extrapolated from 1993 withemployment trend from OECD, Economic Outlook. Hours worked: 1979 from Maddison (1982); 1987from Maddison (1991); 1990 from A. Maddison (1996), "Macroeconomic Accounts for EuropeanCountries", in B. van Ark and N.F.R. Crafts, eds., Quantitative Aspects of Postwar European EconomicGrowth (Cambridge University Press); 1992 from Maddison (1995); years in between are linearlyinterpolated; 1992-1997 kept constant at 1992 level. Labour compensation: 1980-1996 from OECD,National Accounts, Vol. I.

Denmark: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990with GDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1996 from OECD, Labour Force Statistics; 1997 extrapolated from 1996 withemployment trend from OECD, Economic Outlook. Hours worked: 1979 from Maddison (1982); 1987from Maddison (1991); 1990 from A. Maddison (1996); 1992 from Maddison (1995); years in betweenare linearly interpolated; 1992-1997 kept constant at 1992 level. Labour compensation: 1980-1996 fromOECD, National Accounts, Vol. I.

Finland: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990 withGDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1996 from OECD, Labour Force Statistics; 1997 extrapolated from 1996 withemployment trend from OECD, Economic Outlook. Hours worked: 1979 from Maddison (1982); 1987from Maddison (1991); 1992 from Maddison (1995); years in between are linearly interpolated; 1992-1997 based on trend in OECD, Employment Outlook. Labour compensation: 1980-1996 from OECD,National Accounts, Vol. I.

France: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990 withGDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1996 from OECD, Labour Force Statistics; 1997 extrapolated from 1995 withemployment trend from OECD, Economic Outlook. Hours worked: 1980-1996 from INSEE, Comptes etIndicateurs Économiques. Rapport sur les comptes de la Nation; 1997 based on trend in OECD,Employment Outlook. Labour compensation: 1980-1992 from INSEE, Comptes et IndicateursÉconomiques; 1992-1996 from OECD, Economic Outlook.

23

Germany, Federal Republic of (Western): GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1997 extrapolated from 1990 with GDP trend from Statistisches Bundesamt, VolkswirtschaftlichesGesamtrechnungen. Persons employed: Statistisches Bundesamt, VolkswirtschaftlichesGesamtrechnungen. Hours worked: Institut fuer Arbeidsmarkt und Berufsforschung, Arbeitszeit undArbeitsvolumen in Deutschland. Labour compensation: Statistisches Bundesamt, VolkswirtschaftlichesGesamtrechnungen.

Greece: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990 withGDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1995 from OECD, Labour Force Statistics; 1996-1997 extrapolated from 1996 withemployment trend from OECD, Economic Outlook. Hours worked: 1980-1992 based on linearinterpolation between 1973 and 1992 from Maddison (1995); 1992-1997 kept constant at 1992 level.Labour compensation: 1980-1996 from OECD, National Accounts, Vol. I.

Ireland: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990 withGDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1996 from OECD, Labour Force Statistics; 1997 extrapolated from 1996 withemployment trend from OECD, Economic Outlook. Hours worked: 1980-1992 based on linearinterpolation between 1973 and 1992 from Maddison (1995); 1992-1997 kept constant at 1992 level.Labour compensation: 1980-1996 from OECD, National Accounts, Vol. I.

Italy: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990 withGDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1987-1996 from OECD, Labour Force Statistics, but increased by 17.6% to match theupward adjustment in the Italian national accounts in 1982 to allow for “underground employment” (seeMaddison, 1996); 1980-1986 backwardly extrapolated from 1987 on the basis of civilian employment(US concept) from Bureau of Labor Statistics website; 1997 extrapolated from 1996 with employmenttrend from OECD, Economic Outlook. Hours worked: 1979 from Maddison (1982); 1987 fromMaddison (1991); 1990 from Maddison (1996); 1992 from Maddison (1995); years in between arelinearly interpolated; 1992-1994 based upon trend in OECD, Employment Outlook; 1994-1997 keptconstant at 1994 level. Labour compensation: 1980-1996 from OECD, National Accounts, Vol. I.

Netherlands: GDP in 1990 US$: 1990 from Maddison (1995); 1980-1996 extrapolated from 1990 withGDP trend from Centraal Bureau voor de Statistiek, Nationale Rekeningen. Persons employed: CentraalBureau voor de Statistiek, Arbeidsrekeningen. Hours worked: 1980-1987 on the basis of linearinterpolation between 1979 and 1987; 1979 obtained from 1973-1979 according to Maddison (1982)linked to 1973 from Maddison (1995); 1987-1997 from Centraal Bureau voor de Statistiek,Arbeidsrekeningen. Labour compensation: Centraal Bureau voor de Statistiek, Nationale Rekeningen.Portugal: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990with GDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1996 from OECD, Labour Force Statistics; 1997 extrapolated from 1996 withemployment trend from OECD, Economic Outlook. Hours worked: 1980-1992 based on linearinterpolation between 1973 and 1992 from Maddison (1995); 1992-1994 based upon trend in OECD,Employment Outlook; 1994-1997 kept constant at 1994 level. Labour compensation: 1980-1996 fromOECD, National Accounts, Vol. I.

Spain: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990 withGDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1996 from OECD, Labour Force Statistics; 1997 extrapolated from 1995 withemployment trend from OECD, Economic Outlook. Hours worked: 1979 from Maddison (1982); 1987from Maddison (1991); 1990 from Maddison (1996); 1992 from Maddison (1995); years in between arelinearly interpolated; 1992-1997 based upon trend in OECD, Employment Outlook. Labourcompensation: 1980-1996 from OECD, National Accounts, Vol. I.

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Sweden: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990 withGDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1996 from Bureau of Labor Statistics website; 1997 extrapolated from 1996 withemployment trend from OECD, Economic Outlook. Hours worked: 1979 from Maddison (1982); 1987from Maddison (1991); 1990 from Maddison (1996); 1992 from Maddison (1995); years in between arelinearly interpolated; 1992-1997 based upon trend in OECD, Employment Outlook. Labourcompensation: 1980-1996 from OECD, National Accounts, Vol. I.

United Kingdom: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from1990 with GDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website.Persons employed: 1980-1996 from Bureau of Labor Statistics website; 1997 extrapolated from 1995with employment trend from OECD, Economic Outlook. Hours worked: 1980-1987 on the basis oflinear interpolation between 1979 and 1987; 1979 obtained from 1973-1979 according to Maddison(1982) linked to 1973 from Maddison (1995); 1987 from Maddison (1991); 1990 from Maddison(1996); 1992 from Maddison (1995); years in between are linearly interpolated; 1992-1997 based upontrend in OECD, Employment Outlook. Labour compensation: 1980-1996 from Government StatisticalService, National Accounts, 1997.

Australia: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990with GDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1996 from Bureau of Labor Statistics website; 1997 extrapolated from 1995 withemployment trend from OECD, Economic Outlook. Hours worked: 1979 from Maddison (1982); 1987from Maddison (1991); 1990 from Maddison (1996); 1992 from Maddison (1995); years in between areinterpolated, from 1983-1987 using trend in man hours from OECD, National Accounts, Vol. II; 1992-1997 based upon trend in OECD, Employment Outlook. Labour compensation: 1980-1996 from OECD,National Accounts, Vol. I.

Canada: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990 withGDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1996 from Bureau of Labor Statistics website; 1997 extrapolated from 1995 withemployment trend from OECD, Economic Outlook. Hours worked: 1979 from Maddison (1982); 1987from Maddison (1991); 1990 from Maddison (1996); 1992 from Maddison (1995); years in between arelinearly interpolated; 1981-1996 based upon trend in OECD, Employment Outlook; 1997 kept constantat 1996 level. Labour compensation: 1980-1996 from OECD, National Accounts, Vol. I.

Japan: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990 withGDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website. Personsemployed: 1980-1996 from OECD, Labour Force Statistics; 1997 extrapolated from 1996 withemployment trend from OECD, Economic Outlook. Hours worked: 1980-1990 from D. Pilat, (1994),The Economics of Rapid Growth: the Experience of Japan and Korea, Edward Elgar; 1990-1996 fromMinistry of Labour, Monthly Report on the Labour Force Survey. 1997 kept constant at 1996 level.Labour compensation: 1980-1995 from OECD, National Accounts, Vol. I; 1996 from OECD, EconomicOutlook.

United States: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from1990 with GDP trend from OECD, National Accounts, Vol. I; 1997 from OECD Online website.Persons employed: 1980-1996 from Bureau of Labor Statistics website; 1997 extrapolated from 1995with employment trend from OECD, Economic Outlook; Hours worked: 1980-1987 on the basis oflinear interpolation between 1979 and 1987; 1979 obtained from 1973-1979 according to Maddison(1982) linked to 1973 from Maddison (1995); 1987 from Maddison (1991); 1990 from Maddison(1996); 1992 from Maddison (1995); years in between are linearly interpolated; 1992-1997 based upontrend in OECD, Employment Outlook. Labour compensation: 1980-1996 from US Department ofCommerce, National Income and Product Accounts.

25

Hong Kong, China: GDP in 1990 US$: 1990 from Maddison (1995); 1980-1996 extrapolated from1990 with GDP trend from Asian Development Bank, Key Indicators of Developing Asian and PacificCountries. Persons employed: 1980-1996 from Asian Development Bank, Key Indicators of developingAsian and Pacific Countries. Hours worked: 1973 and 1992 from N. Crafts (1997), "Economic Growthin East Asia and Western Europe since 1950: Implications for Living Standards", National InstituteEconomic Review, No. 4; years in between are linearly interpolated; 1992-1997 based upon the yearlytrend in the earlier series. Wage index: daily wage rate for wage earners in non-agricultural activitiesfrom ILO, Yearbook of Labour Statistics.

Korea, Republic of: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolatedfrom 1990 with GDP trend from Asian Development Bank, Key Indicators of Developing Asian andPacific Countries. Persons employed: 1980-1996 from OECD, Labour Force Statistics. Hours worked:1980-1990 from Pilat (1994); 1991-1996 trend linked to 1990 from Ministry of Labour, Yearbook ofLabour Statistics. Labour compensation: 1980-1982 from Bank of Korea, National Accounts, 1990;1983-1996 from OECD, National Accounts, Vol. I.

Taiwan, China: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from1990 with GDP trend from Asian Development Bank, Key Indicators of Developing Asian and PacificCountries. Persons employed: 1980-1996 from Asian Development Bank, Key Indicators of DevelopingAsian and Pacific Countries. Hours worked: 1980-1995 from DOBAS, Monthly Bulletin of Earningsand Productivity Statistics; 1996 kept constant at 1995 level.

India: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990 withGDP trend from Asian Development Bank, Key Indicators of Developing Asian and Pacific Countries.Persons employed: 1980 and 1990 on the basis of labour force figures from the population census.Interpolated and extrapolated from 1990 with population growth rates.

Sri Lanka: GDP in 1990 US$: 1990 from Maddison (1995); 1980-1996 extrapolated from 1990 withGDP trend from Asian Development Bank, Key Indicators of Developing Asian and Pacific Countries.Persons employed: 1980-1996 from Asian Development Bank, Key Indicators of Developing Asian andPacific Countries.

Indonesia: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990with GDP trend from Asian Development Bank, Key Indicators of Developing Asian and PacificCountries. Persons employed: 1980-1996 from Asian Development Bank, Key Indicators of DevelopingAsian and Pacific Countries.

Philippines: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1996 extrapolated from 1990with GDP trend from Asian Development Bank, Key Indicators of Developing Asian and PacificCountries. Persons employed: 1980-1989 from National Statistical Coordination Board, PhilippineStatistical Yearbook; 1990-1995 from Asian Development Bank, Key Indicators of Developing Asianand Pacific Countries. Labour compensation: 1980-1989 from National Statistical Coordination Board,Philippine Statistical Yearbook; 1990-1995 extrapolated from 1990 with United Nations, NationalAccount Statistics.

Thailand: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1995 extrapolated from 1990with GDP trend from Asian Development Bank, Key Indicators of Developing Asian and PacificCountries. Persons employed: 1980-1995 from Asian Development Bank, Key Indicators of DevelopingAsian and Pacific Countries.

Brazil: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1994 extrapolated from 1990 withGDP trend from A. Hofman (1998), Latin American Economic Development: A Casual Analysis inHistorical Perspective, Groningen Growth and Development Centre, monograph series no. 3.; 1994-1996 extrapolated from IBGE, Annuario Estadistico do Brasil. Persons employed: 1980, 1989, 1990

26

and 1994 from Hofman (1998); years in between are interpolated and 1995-1996 is extrapolated from1994 using trend from N. Mulder, The Economic Performance of Services in Brazil, Mexico and theUSA. in Comparative Perspective, Groningen Growth and Development Centre, MonographSeries, No. 4. Hours worked: 1980, 1989, 1990 and 1994 from Hofman (1998); years in between arelinearly interpolated; 1994-1996 kept constant at 1994 level. Labour compensation: 1980-1996 fromUnited Nations ECLAC database.

Chile: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1994 extrapolated from 1990 withGDP trend from Hofman (1998); 1994-1996 from United Nations, Statistical Yearbook of LatinAmerica. Persons employed: 1980, 1989, 1990 and 1994 from Hofman (1998); years in between areinterpolated and 1995-1996 is extrapolated from 1994 using trend from ILO, Yearbook of LabourStatistics; 1996 estimated using the average growth rate over the period 1990-1995. Hours worked:1980, 1989, 1990 and 1994 from Hofman (1998); years in between linearly interpolated; 1994-1996kept constant at 1994 level. Labour compensation: 1980-1996 from United Nations ECLAC database.

Colombia: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1994 extrapolated from 1990with GDP trend from Hofman (1998); 1994-1996 from United Nations, Statistical Yearbook of LatinAmerica. Persons employed: 1980, 1989, 1990 and 1994 from Hofman (1998); years in between areinterpolated and 1995-1996 is extrapolated from 1994 using trend from ILO, Yearbook of LabourStatistics. Hours worked: 1980, 1989, 1990 and 1994 from Hofman (1998); years in between linearlyinterpolated; 1994-1996 kept constant at 1994 level. Labour compensation: 1980-1996 from UnitedNations, Statistical Yearbook of Latin America.

Mexico: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1994 extrapolated from 1990 withGDP trend from Hofman, (1998); 1995-1996 extrapolated from 1994 from OECD, National Accounts,Vol. I; 1997 from OECD Online website. Persons employed: 1980 and 1990-1996 from OECD, LabourForce Statistics; 1981-1989 interpolated using trend for 7 main cities from INEGI, Annuaire Estadisticode los Estados Unidos Mexicanos, 1994; 1997 extrapolated from 1996 from OECD, Economic Outlook.Hours worked: 1980, 1989, 1990 and 1994 from Hofman (1998); years in between linearly interpolated;1994-1996 kept constant at 1994 level. Labour compensation: 1980-1996 from OECD, NationalAccounts, Vol. I.

Venezuela: GDP in 1990 US$: 1980-1990 from Maddison (1995); 1991-1994 extrapolated from 1990with GDP trend from Hofman (1998); 1995-1996 extrapolated from 1994 from United Nations,Statistical Yearbook of Latin America. Persons employed: 1980, 1989, 1990 and 1994 from Hofman(1998); years in between are interpolated and 1995 is extrapolated from 1994 using trend from ILO,Yearbook of Labour Statistics; 1996 kept constant at 1995 level. Hours worked: 1980, 1989, 1990 and1994 from Hofman (1998); years in between linearly interpolated; 1994-1996 kept constant at 1994level. Labour compensation: 1980-1996 from United Nations ECLAC database.

Manufacturing data (KILM 17b)

Austria: Manufacturing GDP in constant national currency: 1980-1996 from OECD, National Accounts,Vol. II. Persons employed: 1980-1996 from OECD, National Accounts, Vol. II.

Belgium: Manufacturing GDP in constant national currency: 1980-1997 from Bureau of LaborStatistics, Foreign Labor Statistics (underlying data); conversion to US$ on the basis of ICOP UnitValue Ratio for 1987, provided by A. Soete, and converted to 1990 level with manufacturing GDPdeflator derived from OECD, National Accounts, Vol. II. Persons employed: 1980-1997 from Bureau ofLabor Statistics, Foreign Labor Statistics (underlying data). Hours worked: 1987 figure provided by A.Soete; trend in hours from 1980-1997 from Bureau of Labor Statistics, Foreign Labor Statistics(website). Labour compensation: 1980-1997 from Bureau of Labor Statistics, Foreign Labor Statistics(underlying data).

27

Denmark: Manufacturing GDP in constant national currency: 1980-1995 from OECD, NationalAccounts, Vol. II. Persons employed: 1980-1995 from OECD, National Accounts, Vol. II. Labourcompensation: 1980-1995 from OECD, National Accounts, Vol. II.

Finland: Manufacturing GDP in constant national currency: 1980-1996 from OECD, NationalAccounts, Vol. II. Persons employed: 1980-1996 from OECD, National Accounts, Vol. II. Hoursworked: 1987 level estimate from M. Maliranta (1994), Comparative Levels of Labour Productivity inSwedish, Finnish and American Manufacturing, Helsinki School of Economics, mimeographed andconverted to 1990 level with manufacturing GDP deflator derived from OECD, National Accounts, Vol.II. Persons employed: 1980-1996 trend from OECD, National Accounts, Vol. II. Labour compensation:1980-1996 from OECD, National Accounts, Vol. II.

France: Manufacturing GDP in constant national currency: 1980-1996 from OECD, National Accounts,Vol. II; conversion to US$ on the basis of ICOP Unit Value Ratio for 1987 from B. van Ark and R.Kouwenhoven (1994), "La productivité du secteur manufacturier français en comparaisoninternationale", Économie Internationale, no. 60, CEPII, Paris, and converted to 1990 level withmanufacturing GDP deflator derived from INSEE, Comptes et Indicateurs Economiques, 1998. Personsemployed: 1980-1995 from OECD, National Accounts, Vol. II; 1996 from INSEE, Comptes etIndicateurs Economiques, 1998. Hours worked: 1980-1996 from INSEE, Comptes et IndicateursEconomiques, 1998. Labour compensation: 1980-1996 from Bureau of Labor Statistics, Foreign LaborStatistics (underlying data).

Germany, Federal Republic of (Western): Manufacturing GDP in constant national currency: 1980-1996 from Statistisches Bundesamt, Volkswirtschaftliches Gesamtrechnungen; conversion to US$ onthe basis of ICOP Unit Value Ratio for 1987 from B. van Ark and D. Pilat (1993), Cross CountryProductivity Levels: Differences and Causes, Brookings Papers on Economic Activity(Microeconomics 2), 1993, pp. 1-69, and converted to 1990 level with manufacturing GDP deflatorderived from Statistisches Bundesamt, Volkswirtschaftliches Gesamtrechnungen. Persons employed:1980-1996 from Statistisches Bundesamt, Volkswirtschaftliches Gesamtrechnungen. Hours worked:1980-1996 from Institut fuer Arbeidsmarkt und Berufsforschung, Arbeitszeit und Arbeitsvolumen inDeutschland. Labour compensation: 1980-1996 from Bureau of Labor Statistics, Foreign LaborStatistics (underlying data).

Greece: Manufacturing GDP in constant national currency: 1980-1995 from OECD, National Accounts,Vol. II. Persons employed: 1980-1995 from OECD, National Accounts, Vol. II.

Netherlands: Manufacturing GDP in constant national currency: 1980-1996 from Centraal Bureau voorde Statistiek, Nationale Rekeningen; conversion to US$ on the basis of ICOP Unit Value Ratio for 1987from R. Kouwenhoven, Analysing Dutch Manufacturing Productivity, 1993, and converted to 1990level with manufacturing GDP deflator derived from Centraal Bureau voor de Statistiek, NationaleRekeningen. Persons employed: 1980-1987 trend from adjusted employment series provided byCentraal Bureau voor de Statistiek, which were linked to estimates for 1987-1994 from Centraal Bureauvoor de Statistiek, Arbeidsrekeningen; 1995-1996 trend linked to 1994 on the basis of Centraal Bureauvoor de Statistiek, Enquete Beroepsbevolking. Hours worked: 1987 benchmark based on CentraalBureau voor de Statistiek, Sociaal-Economische Maandstatistiek; 1980-1994 trend on the basis ofcontractual hours from Centraal Bureau voor de Statistiek, Arbeidsrekeningen; 1995-1996 linked to1994 on the basis of series provided by Centraal Plan Bureau. Labour compensation: 1980-1996 fromCentraal Bureau voor de Statistiek, Nationale Rekeningen.

Portugal: Manufacturing GDP in constant national currency: 1980-1995 from OECD, NationalAccounts, Vol. II. Persons employed: 1980-1995 from OECD, National Accounts, Vol. II.

Spain: Manufacturing GDP in constant national currency: 1980-1996 from OECD, National Accounts,

28

Vol. II; conversion to US$ on the basis of ICOP Unit Value Ratio for 1992 from ICOP/LCRA project(University of Groningen), and converted to 1990 level with manufacturing GDP deflator derived fromOECD, National Accounts, Vol. II. Persons employed: 1980-1996 from OECD, National Accounts, Vol.II.

Sweden: Manufacturing GDP in constant national currency: 1980-1996 from OECD, NationalAccounts, Vol. II; conversion to US$ on the basis of ICOP Unit Value Ratio for 1987 from M. Maliranta(1994), converted to 1990 level with manufacturing GDP deflator derived from OECD, NationalAccounts, Vol. II. Persons employed: 1980-1994 from OECD, National Accounts, Vol. II; 1995-1996trend linked to 1994 on the basis of trend from Bureau of Labor Statistics, Foreign Labor Statistics(underlying data). Hours worked: 1987 benchmark from Maliranta (1994); 1980-1996 trend fromBureau of Labor Statistics, Foreign Labor Statistics (website). Labour compensation: 1980-1996 fromBureau of Labor Statistics, Foreign Labor Statistics (underlying data).

United Kingdom: Manufacturing GDP in constant national currency: 1980-1992 from OECD, NationalAccounts, Vol. II; 1993-1996 trend linked to 1992 from Government Statistical Office, NationalAccounts, 1997; conversion to US$ on the basis of ICOP Unit Value Ratio for 1987 from B. van Ark(1992). "Comparative Productivity in British and American Manufacturing", National Institute EconomicReview, November, and converted to 1990 level with manufacturing GDP deflator derived from OECD,National Accounts, Vol. II. Persons employed: 1980-1992 from OECD, National Accounts, Vol. II;1993-1996 from Government Statistical Office, National Accounts, 1997. Hours worked: 1987benchmark from van Ark (1992); 1980-1996 trend from Bureau of Labor Statistics, Foreign LaborStatistics (website). Labour compensation: 1980-1996 from Bureau of Labor Statistics, Foreign LaborStatistics (underlying data).

Australia: Manufacturing GDP in constant national currency: 1980-1996 from OECD, NationalAccounts, Vol. II; conversion to US$ on the basis of ICOP Unit Value Ratio for 1987 from D. Pilat,D.S. Prasada Rao and W. Shepherd (1993), Comparison of Real Output, Productivity Levels andPurchasing Power in Australia/US Manufacturing 1970-1989, COPPAA Research Paper, No. 1, Centrefor the Study of Australia-Asia Relations, Griffith University, Brisbane, and converted to 1990 levelwith manufacturing GDP deflator derived from OECD, National Accounts, Vol. II. Persons employed:1980-1996 from OECD, National Accounts, Vol. II. Hours worked: 1980-1983 from Pilat, Rao andShepherd (1993); 1984-1996 linked to 1983 based upon trend from OECD, National Accounts, Vol. II.Labour compensation: 1980-1996 from OECD, National Accounts, Vol. II.

Canada: Manufacturing GDP in constant national currency: 1980-1996 from OECD, National Accounts,Vol. II; conversion to US$ on the basis of ICOP Unit Value Ratio for 1987 from G. de Jong (1997),Canada’s Post-war Manufacturing Performance: a Comparison with the United States, GroningenGrowth and Development Centre Research Memorandum, GD-32, and converted to 1990 level withmanufacturing GDP deflator derived from OECD, National Accounts, Vol. II. Persons employed: 1980-1997 from Bureau of Labor Statistics, Foreign Labor Statistics (underlying data).

Hours worked: 1987 benchmark from De Jong (1996); 1980-1997 trend from Bureau of LaborStatistics, Foreign Labor Statistics (website). Labour compensation: 1980-1997 from Bureau of LaborStatistics, Foreign Labor Statistics (underlying data).

Japan: Manufacturing GDP in constant national currency: 1980-1996 from EPA, Annual Report on theNational Accounts; conversion to US$ on the basis of ICOP Unit Value Ratio for 1987 from D. Pilat(1994), The Economics of Rapid Growth: The Experience of Japan and Korea, Edward Elgar Ltd., andconverted to 1990 level with manufacturing GDP deflator derived from EPA, Annual Report on theNational Accounts. Persons employed: 1980-1996 from EPA, Annual Report on the National Accounts.Hours worked: 1980-1996 from Ministry of Labour, Monthly Report on the Labour Force Survey.Labour compensation: 1980-1996 from Bureau of Labor Statistics, Foreign Labor Statistics (underlyingdata).

29

United States: Manufacturing GDP in constant national currency: 1980-1996 from US Department ofCommerce, National Income and Product Accounts, with 1980-1987 at fixed 1982 weights, and 1987-1996 at 1992 chained weights. Persons employed: 1980-1996 from US Department of Commerce,National Income and Product Accounts. Hours worked: 1987 benchmark from B. van Ark (1993),International Comparisons of Output and Productivity. Manufacturing Productivity Performance of TenCountries from 1950 to 1990, Monograph Series No. 1, Groningen Growth and Development Centre.1980-1996 trend from US Department of Commerce, National Income and Product Accounts. Labourcompensation: 1980-1997 from Bureau of Labor Statistics, Foreign Labor Statistics (underlying data).

Korea, Republic of: Manufacturing GDP in constant national currency: 1980-1996 from Bank of Korea,National Accounts; conversion to US$ on the basis of ICOP Unit Value Ratio for 1987 from D. Pilat(1994), The Economics of Rapid Growth: The Experience of Japan and Korea, Edward Elgar Ltd., andconverted to 1990 level with manufacturing GDP deflator derived from Bank of Korea, NationalAccounts. Persons employed: 1980-1990 from EPB, Annual Report on the Economically ActivePopulation; 1991-1996 trend linked to 1990 from OECD, Labour Force Statistics. Hours worked:1980-1996 from Ministry of Labor, Report on the Monthly Labor Survey. Labour compensation: 1980-1982 from Bank of Korea, National Accounts, 1990; 1983-1995 from OECD, National Accounts, Vol.I1; 1996 linked to 1995 from Bureau of Labor Statistics, Foreign Labor Statistics (underlying data).Wage trend: 1980-1996 trend in monthly earnings of employees ILO, Yearbook of Labour Statistics.

Taiwan, China: Manufacturing GDP in constant national currency: 1980-1996 from DOBAS, NationalIncome in Taiwan; conversion to US$ on the basis of ICOP Unit Value Ratio for 1987 from M.Timmer, (1998), Catch-up Patterns in Newly Industrializing Countries: an International Comparison ofManufacturing Productivity in Taiwan, 1961-1993, Groningen Growth and Development CentreResearch Memorandum, GD-40, and converted to 1990 level with manufacturing GDP deflator derivedfrom DOBAS, National Income in Taiwan. Persons employed: 1980-1996 from DOBAS, MonthlyBulletin of Labour Statistics. Hours worked: 1987 benchmark from Timmer (1998); 1980-1996 trendfrom DOBAS, Monthly Bulletin of Earnings and Productivity Statistics. Wage trend: 1980-1996 trendin monthly earnings from DOBAS, Monthly Bulletin of Earnings and Productivity Statistics.

India: Manufacturing GDP in constant national currency: 1980-1995 from Central Statistical Office,National Accounts; conversion to US$ on the basis of ICOP Unit Value Ratio for 1983-84 from A.Szirmai and M. Timmer (1997), Growth and Divergence in Manufacturing Performance in South andEast Asia, Groningen Growth and Development Centre Research Memorandum, GD-37, and convertedto 1990 level with manufacturing GDP deflator derived from Central Statistical Office, NationalAccounts. Persons employed: 1980 and 1990 on the basis of labour force in the population census.Interpolated and extrapolated from 1990 with population growth rates.

Indonesia: Manufacturing GDP in constant national currency: 1980-1995 based on Asian DevelopmentBank, Key Indicators of Developing Asian and Pacific Countries; conversion to US$ on the basis ofICOP Unit Value Ratio for 1987 from Szirmai and Timmer (1997), and converted to 1990 level withmanufacturing GDP deflator derived from Asian Development Bank, Key Indicators of DevelopingAsian and Pacific Countries. Persons employed: 1980-1995 from Asian Development Bank, KeyIndicators of Developing Asian and Pacific Countries.

Philippines: GDP in constant national currency: 1980-1989 from National Statistical CoordinationBoard, Philippine Statistical Yearbook; 1990-1995 from Asian Development Bank, Key Indicators ofDeveloping Asian and Pacific Countries. Persons employed: 1980-1989 from National StatisticalCoordination Board, Philippine Statistical Yearbook; 1990-1995 from Asian Development Bank, KeyIndicators of Developing Asian and Pacific Countries. Wage trend: 1980-1993 trend in wage rates perday from ILO, Yearbook of Labour Statistics.

30

Thailand: GDP in constant national currency: 1980-1996 from Asian Development Bank, KeyIndicators of Developing Asian and Pacific Countries. Persons employed: 1980-1985 from N.Vanderveen (1987), Postwar Economic Growth and Structural Change in Thailand, University ofGroningen (mimeographed); 1986-1996 trend from United Nations, Statistical Yearbook.

Mexico: GDP in constant national currency: 1980-1996 from INEGI, Cuentas Nacionales de Mexico.Persons employed: 1980-1996 from N. Mulder, The Economic Performance of Services in Brazil,Mexico and the USA. in Comparative Perspective, Groningen Growth and Development Centre,Monograph Series, No. 4. Wage trend: 1980-1996 trend in earnings per month from ILO, Yearbook ofLabour Statistics.

31

References

Ark, B. van (1992), "Comparative Productivity in British and American Manufacturing", National InstituteEconomic Review, November.

Ark, B. van (1993), International Comparisons of Output and Productivity, Monograph Series No. 1, GroningenGrowth and Development Centre (downloadable from http://www.eco.rug.nl/ggdc/icop.html).

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Table 17b. Labour productivity and unit labour costs, manufacturing (revised)(figures in italics indicate changes compared to publication in KILM 17b)

Year Labour productivity Unit labour costs Wages or NoteValue added Value added Value added Value added Labour Labour Labour earnings No.

per person per person per hour per hour compensation compensation compensation per unit ofemployed employed worked worked per unit of per unit of per unit of output on US

(1990 US $) (1980 = 100) (1990 US $) (1980 = 100) output on US output on US output onnational

dollar basis

dollar basis dollar basis currency basis (1980 = 100)(1990 US $) (1980 = 100) (1980 = 100)

Developed (industrialized) countriesMajor EuropeAustria 1980 100 1

1990 141 11991 146 11992 149 11993 152 11994 162 11995 165 11996 172 1

Belgium 1980 30581 100 20.13 100.0 0.83 100.0 100.0 21990 46583 152 30.24 150.2 0.83 99.8 114.1 21991 46812 153 30.94 153.7 0.84 101.4 118.4 21992 47248 155 31.23 155.1 0.93 111.9 123.0 21993 48425 158 32.49 161.4 0.87 105.1 124.4 21994 52575 172 34.34 170.6 0.89 107.0 122.4 21995 51736 169 34.16 169.7 1.04 125.6 126.6 21996 52543 172 34.55 171.6 1.00 120.8 127.9 21997 55600 182 36.49 181.3 0.84 101.1 123.7 2

Denmark 1980 100 100.0 100.0 31990 105 155.7 171.0 31991 107 153.4 174.1 31992 110 165.4 177.2 31993 116 147.9 170.2 31994 118 153.6 173.4 3

1995 118 181.9 180.8 3Finland 1980 24353 100 14.06 100.0 0.60 100.0 100.0 3

1990 39113 161 23.85 169.6 0.94 155.5 159.4 31991 37627 155 23.86 169.6 0.94 155.4 168.5 31992 42413 174 27.04 192.3 0.78 129.2 155.2 31993 47836 196 30.21 214.8 0.57 94.6 144.9 31994 53525 220 33.05 235.0 0.59 98.3 137.7 31995 56113 230 34.37 244.4 0.74 121.7 142.4 31996 57430 236 35.16 250.0 0.70 115.4 142.1 3

France 1980 33373 100 19.45 100.0 0.64 100.0 100.0 41990 43747 131 27.15 139.6 0.79 123.5 159.2 41991 43635 131 27.04 139.0 0.80 125.5 167.5 41992 44247 133 27.30 140.3 0.87 136.7 171.2 41993 44992 135 27.99 143.9 0.83 129.1 173.0 41994 48399 145 30.16 155.0 0.80 125.3 164.6 51995 49946 150 30.97 159.2 0.88 137.1 161.9 41996 50926 153 31.55 162.2 0.85 133.6 161.7 6

Germany, Federal 1980 33680 100 19.62 100.0 0.62 100.0 100.0 7Republic of (Western) 1990 39565 118 24.92 127.0 0.91 146.9 130.6 7

1991 40462 120 25.66 130.8 0.92 147.8 134.8 71992 40048 119 25.20 128.4 1.04 168.3 144.6 71993 38979 116 25.30 128.9 1.04 168.0 152.8 71994 41945 125 27.22 138.7 1.03 167.6 149.6 71995 43202 128 28.19 143.6 1.19 192.3 151.6 71996 44471 132 29.44 150.0 1.14 184.3 152.6 7

Greece 1980 100 11990 336 11991 398 11992 445 11993 613 11994 665 11995 688 1

Netherlands 1980 27678 100 17.71 100.0 0.68 100.0 100.0 91990 36367 131 25.46 143.7 0.75 110.3 101.0 91991 36404 132 25.58 144.4 0.76 111.2 104.6 91992 36676 133 25.69 145.1 0.85 124.0 109.7 9

1993 36830 133 25.97 146.6 0.82 119.8 112.0 91994 40463 146 28.59 161.4 0.78 114.2 104.5 91995 41004 148 29.03 163.9 0.86 126.5 102.1 101996 41569 150 29.65 167.4 0.82 120.7 102.3 10

Portugal 1980 100 11990 110 11991 110 11992 118 11993 117 11994 117 11995 126 1

Spain 1980 19916 100 111990 28877 145 111991 29609 149 111992 30639 154 111993 31337 157 111994 33394 168 111995 31125 156 111996 30703 154 11

Sweden 1980 25283 100 17.64 100.0 0.82 100.0 100.0 121990 34291 136 22.81 129.3 1.05 127.3 178.1 121991 34534 137 23.00 130.4 1.11 134.9 192.8 121992 36661 145 24.22 137.3 1.15 139.5 192.0 121993 40110 159 25.83 146.4 0.80 97.6 179.5 121994 44778 177 27.99 158.7 0.78 94.3 172.0 121995 48022 190 29.73 168.6 0.84 101.7 171.5 131996 49398 195 30.87 175.0 0.93 112.7 178.7 13

United Kingdom 1980 17793 100 10.30 100.0 0.84 100.0 100.0 141990 28187 158 16.71 162.3 1.00 119.2 156.0 141991 28623 161 17.26 167.6 1.07 126.8 167.2 141992 29659 167 17.87 173.5 1.08 128.1 169.8 141993 31162 175 18.87 183.2 0.93 110.0 170.4 151994 32604 183 19.60 190.3 0.94 111.1 168.8 151995 32878 185 19.61 190.4 0.98 116.0 171.0 151996 33753 190 20.05 194.7 0.96 114.4 170.5 15

Major non-EuropeAustralia 1980 26109 100 13.59 100.0 0.63 100.0 100.0 16

1990 32282 124 16.39 120.6 0.75 119.2 174.0 171991 33110 127 16.91 124.4 0.76 120.8 176.7 171992 33625 129 17.08 125.7 0.73 116.4 180.6 171993 35153 135 17.52 129.0 0.67 106.7 178.8 171994 35735 137 17.66 130.0 0.73 116.2 181.1 171995 36282 139 18.04 132.8 0.77 123.5 189.7 171996 36070 138 17.96 132.1 0.85 135.5 197.3 17

Canada 1980 30469 100 16.04 100.0 0.51 100.0 100.0 181990 39220 129 20.22 126.1 0.75 147.2 147.0 181991 38862 128 20.15 125.6 0.81 159.3 156.1 181992 40886 134 21.16 131.9 0.78 151.7 156.9 181993 43087 141 21.99 137.1 0.70 136.3 150.5 181994 43926 144 22.29 139.0 0.65 128.0 149.5 181995 44985 148 22.93 143.0 0.65 127.9 150.1 181996 44188 145 22.48 140.2 0.69 134.2 156.5 181997 45428 149 23.10 144.0 0.67 131.9 156.3 19

Japan 1980 32613 100 15.00 100.0 0.40 100.0 100.0 201990 52438 161 24.66 164.4 0.59 148.3 94.7 201991 53177 163 25.54 170.2 0.65 162.8 96.6 201992 51578 158 25.54 170.2 0.71 180.2 100.7 201993 50099 154 25.44 169.6 0.85 215.5 105.7 201994 50658 155 25.73 171.5 0.94 236.7 106.7 201995 54706 168 27.66 184.4 0.98 246.4 102.2 201996 56127 172 28.51 190.1 0.84 210.8 101.2 20

United States 1980 37714 100 20.30 100.0 0.57 100.0 100.0 211990 52614 140 27.40 134.8 0.68 119.5 119.5 221991 52514 139 27.50 135.6 0.71 125.3 125.3 221992 54249 144 28.20 138.6 0.73 128.5 128.5 221993 56111 149 28.80 141.5 0.74 129.4 129.4 221994 59952 159 30.30 149.2 0.72 125.6 125.6 221995 63481 168 32.40 159.5 0.69 121.3 121.3 221996 65966 175 33.70 165.7 0.69 120.1 120.1 22

Asia and the PacificEastern Asia

Korea, Republic of 1980 8994 100 3.30 100.0 0.32 100.0 100.0 100.0 231990 17113 190 6.48 196.3 0.48 152.1 177.2 211.7 241991 18358 204 7.02 212.8 0.49 153.8 185.6 230.6 251992 19951 222 7.72 233.9 0.46 146.0 187.5 245.4 251993 21745 242 8.39 254.2 0.44 138.9 183.5 249.7 251994 23798 265 9.20 278.8 0.43 137.2 181.7 263.4 251995 25940 288 9.94 301.1 0.45 143.4 182.1 265.7 251996 28437 316 10.90 330.1 0.45 142.1 188.1 271.9 26

Taiwan, China 1980 11644 100 4.40 100.0 100.0 271990 19187 165 7.92 180.0 222.8 271991 20908 180 8.64 196.6 227.5 271992 21694 186 8.96 203.9 257.6 271993 23143 199 9.54 217.1 246.1 271994 24460 210 10.09 229.4 247.6 271995 26306 226 10.88 247.4 242.8 271996 27850 239 230.2 27

South-central AsiaIndia 1980 1675 100 28

1990 2669 159 281991 2504 149 291992 2567 153 291993 2745 164 291994 2984 178 291995 3342 199 29

South-eastern AsiaIndonesia 1980 2233 100 30

1990 3543 159 301991 3793 170 301992 4022 180 301993 4210 189 301994 3833 172 301995 4547 204 30

Philippines 1980 100 100.0 311990 99 502.2 321991 90 624.6 321992 83 754.7 32

1993 87 748.7 321994 87 321995 94 32

Thailand 1980 100 331990 125 341991 126 341992 135 341993 137 341994 154 341995 156 341996 148 34

Latin America and the CaribbeanLatin AmericaMexico 1980 100 100.0 35

1990 101 8769.9 351991 104 10780.3 351992 108 12678.5 351993 113 13535.3 351994 120 12863.6 351995 121 14533.3 351996 125 17188.4 35

This Table is a revised version of KILM 17b. All level estimates have been changed because of a correction in the updating of Unit Value Rations to 1990 (see Section 3 ofthis paper).