The National Institute Sectoral Productivity Database:

Sources and Methods.

May 1999

1

1. General Remarks: sector and data coverage.

These data series are constructed to allow a comprehensive study of trends in

productivity by sector for five major industrial economies, the United States, the

United Kingdom, France, Germany and Japan. The plan was to provide annual data

for the post-war period from 1950 to 1995/96 for as many sectors as the data allow.

The variables included in the data set are real output, persons engaged, annual

average hours worked, labour productivity, capital input, labour force skills and

labour’s share of value added. These series are generally annual observations from

1950 to 1996, (1995 for capital and labour’s share). Relative levels of labour

productivity and capital intensity are given for a single year, 1993. Thus sufficient

information exists in the dataset to allow a researcher to calculate joint factor

productivity, growth rates or relative levels, and relative unit labour costs, using the

formulae given in parts I and II.

For each of the five countries series were constructed for ten broad sectors

comprising 1. agriculture, forestry & fishing, 2. Mining, extraction and oil refining,

3.utilities, 4.manufacturing, 5.construction, 6. transport & communications, 7.

distributive trades, 8. financial & and business services, 9. other personal services

and 10. non-market services1. Within the broad sectors, where feasible the dataset

includes the following breakdown: 2.mining and oil refining: (2.1.oil & gas

extraction, 2.2.other mining, 2.3.mineral oil refining); 3.utilities: (3.1.electricity,

3.2.gas, 3.3.water supply); 6.transport & communications: (6.1.rail transport,

6.2.water transport, 6.3.air transport, 6.4.other transport & transport services,

6.5.communications); 7.distributive trades: ( 7.1.wholesale trade, 7.2.retail trade,

7.3.hotels & catering); and 8.financial & business services: (8.1.banking and

finance, 8.2.insurance, 8.3.business services).

In manufacturing the data were split into six main branches, and further divided into

individual industries as follows. 5.1.Chemicals & allied trades (5.11.chemicals, 5.12.

rubber & plastics); 5.2. Metals (5.21.basic metals, 5.22.fabricated metal

products),5.3. engineering and transport equipment (5.31. mechanical engineering,

1 Comprising health, education and government.

2

5.32. office machinery and computers, 5.33.electrical engineering, 5.34. motor

vehicles, 5.35.other transport equipment, 5.36. instrument engineering); 5.4.textiles,

clothing and leather (5.41. textiles, 5.42.clothing & leather); 5.5.food,drink and

tobacco and 5.6. Other manufacturing (5.61.non-metallic mineral products,

5.62.timber, wood products and furniture, 5.63.paper, paper products, printing &

publishing, 5.64. miscellaneous manufacturing2).

It was possible to construct data series for all sub-sectors for the US for employment

and capital and all bar water for output. Slightly less sectoral detail is available for

hours worked and labour’s share. In all series oil and gas extraction is not given

separately for Germany but, to the extent that this industry exists in that country, it is

included with other mining. Also for that country air transport is included with other

transport. Otherwise the sectoral breakdown is virtually complete for Germany.

There were a number of subsectors where it was not possible to calculate time series

for one or more variables for the UK. The most notable case is financial and business

services where it was not possible to separate output or capital into its components,

but a division of this sector is included for persons engaged. Output indices were

only constructed for two of the transport sectors, i.e. rail and air and output series

were not available for water and wholesale trade. Output indices for retail trade and

hotels & catering were only constructed from the mid-1950s. Again less sectoral

detail is available for hours worked and labour’s share. In general it was not possible

to include any division of either the utilities or transport for France and data were not

available for mineral oil refining which is included with oil & gas extraction. Finally

data for Japan are only available for broad sectors. Workforce skills are given only

for the US, the UK and Germany for selected years between 1979 and 1993. In

manufacturing, the data series show both mechanical engineering and office

machinery separately but also includes these industries combined since data are not

always available for the two components. In France office machinery is included

with electrical. It was not possible to construct a breakdown of capital within

manufacturing for Japan.

2 Comprising toys, sports and recreational goods and jewellery.

3

This paper is divided into three sections. The first outlines the methodology

employed to estimate relative labour productivity levels and to calculate capital

stocks. The second section details the data sources and outlines adjustments to render

the data internationally consistent. This considers in turn the measurement of output,

persons engaged, annual average hours worked, investment data, labour force skills

and labour’s share of value added. Finally the third section considers industry

classification.

2. Methodology.

2.1. The estimation of relative productivity levels.

Studies of productivity convergence require estimates of levels of output per hour for

a benchmark year. In this book the benchmark year was generally chosen to be 1993

but there are some exceptions which are discussed below. The time series were then

used to derive productivity levels for all years from 1950 to 1996. In constructing

productivity levels the most difficult problem is how to measure output in a manner

which is comparable across countries. There are two methods to achieve this, either

deflate bilateral ratios of nominal output in a sector by the ratios of the country’s

output prices or base output on some quantity indicator. Productivity ratios at the

broad sector level were constructed using the former approach. Quantity indicators

such as Kilowatt hours of electricity produced, number of telephone calls or

passenger km were employed, where necessary, in more detailed sectors.

The price ratios used to deflate nominal value added can be based on price

quotations for specific products, unit value ratios (the ratios of values of sales

divided by quantities produced) or purchasing power parities (the ratios of final

expenditure prices estimated periodically by international bodies such as Eurostat or

OECD). The relevant price ratio depends on the sector under consideration. In

production industries the deflator should be the ratio of producer prices whereas final

goods prices are appropriate for service sectors. A discussion of the relative merits of

different deflators can be found in O’Mahony (1996).

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Producer price quotations are rarely available in practice and here form the basis for

only one sector, agriculture. forestry and fishing. In this sector output per person

engaged in 1990 is measured for a large number of countries in Prasada Rao (1993).

Producer prices were derived in this study from surveys of farm-gate prices

undertaken by the Food and Agricultural Organisation (FAO). These 1990 price

ratios were updated to 1993 using agricultural price indices and then applied to value

added from the national accounts.

Purchasing power parities for 1993 were used to convert nominal outputs to £

sterling in sectors other than manufacturing, mining and electricity. Price ratios for

each sector were calculated using unpublished data, provided by Eurostat, giving

PPPs and expenditures for about 150 individual categories. The sector PPPs were

constructed by choosing the items which corresponded most closely to the outputs of

the sector The price ratios for retail distribution were based on PPPs for consumer

goods whereas the price ratios for wholesale trade also included PPPs for machinery.

The price ratios for construction were based on PPPs for buildings, for utilities on

PPPs for gas and water, for transport on PPPs for transport services, for

communications on PPPs for telecommunications and postal charges, for hotels &

catering on PPPs for restauraunts, drinking places and short stay accomodation and

for miscellanous personal services on PPPs for consumer services (excluding health

and education). The published PPP for financial services was considered unreliable

and PPPs for business services, where they existed, were included with other

personal services. Hence the price ratio for this sector was based on the PPPs for all

items of consumer expenditure, again excluding health & education, and were

adjusted to exclude value added tax, (see the discussion in O’Mahony, Oulton and

Vass (1998)).

In constructing aggregate sector price ratios individual PPPs were weighted by

expenditure shares using the EKS multilateral weighting system for the five

countries. The latter ensures that pairwise comparisons between countries are

transitive. The EKS system generates transitive international comparisons that

minimise deviations from direct binary comparisons. It begins by calculating binary

price ratios. For each sector the aggregate PPPs were calculated for each pair of

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countries by weighting the PPPs for i individual items by expenditures as follows.

Letting p denote retail price and e denote expenditures, then for each sector i,

comparing country J with country K, two PPPs were calculated as:

(1) PPP

p

p

e

ee J i

J

iK

i

iJ

J( )

(2) PPP

p

p

e

ee K i

K

iJ

i

iK

K( ) / 1

where eJ = Si eiJ . The sector price ratio was then derived as the geometric mean of

(1) and (2). The resulting price ratio, denoted by P(JK) has the property that it

satisfies the country reversal test, i.e. P(JK) . P(KJ) equals one. Suppose there are M

countries for which price comparisons are required. The EKS procedure incorporates

the principle that a multilateral price ratio P(JK)M should deviate the least from the

binary price ratio P(JK). Thus it minimises the distance:

(3) SJ (ln(P(JK)M ) - log (P(JK))2

The first order conditions for minimisation of (II.3) yields the following multilateral

price ratio:

(4) P JK P JL P LKM

J

M M

( ) [ ( ) * ( )]/

1

1

Thus the EKS procedure takes geometric means of indirect price comparisons

between J, K and other countries included in M. Formula (4) was used to derive

price ratios for all sectors which use Eurostat PPPs. In practice this yielded results

which were not very different from the bilateral PPPs with the UK as base country

and probably reflects common consumer expenditure patterns in these advanced

countries.3

3 This discussion of the EKS procedure was taken from Prasada Rao (1993). A number of other multi-lateral formulas exist which satisfy various properties, and as in common in index number theory, none satisfy all desirable properties, see e.g. the discussion in Diewert (1990). However since the EKS multilateral index is found to be very close to the bilateral index it is doubtful if the use of any other formula would result in significant differences.

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Unit value ratios have been used extensively by researchers at both the University of

Groningen, the Netherlands and the National Institute of Economic and Social

Research, London to estimate productivity levels in manufacturing. The productivity

ratios for manufacturing employ the Anglo-German results in M. O'Mahony (1992),

the results comparing the UK with both the US and France were taken from B. van

Ark (1993), and the ratio for Japan was derived indirectly using the estimate for

Japan compared to the US in Pilat (1994) with that comparing the US with the UK in

van Ark (1993).4 The benchmark year in these studies varies but all refer to some

year in the 1980s. The productivity ratios were updated from the benchmark year to

1993 using labour productivity time series with data sources as detailed below. These

manufacturing studies were based on data from each country’s census of production

rather than on data from the national accounts. The use of the census ensures that

output and employment referred to the same firms which was considered more

reliable than the national accounts where output and employment are often derived

from different sources. Also manufacturing output in the censuses is measured at

factor cost, consistent with the unit value ratios, whereas national accounts output is

measured at market prices. In addition to manufacturing unit value ratios were also

employed in mining and electricity.

GDP purchasing power parities, published in OECD 1995, were used as conversion

factors for the aggregate economy. These PPPs also use an EKS multilateral

weighting system but involving a larger number of countries. Results on aggregate

productivity reported by other authors tend to use these PPPs and so they were

included here for purposes of comparability.

The price ratio for the total market economy was calculated using formula (II.4)

above but using all individual PPPs except the services of government, health,

education and residential buildings.5 It is useful to know if the total market economy

4It was not possible to construct multilateral indices for manufacturing the studies have been undertaken by researchers at different institutions. Unfortunately transitivity is a significant problem in manufacturing. Thus the implied gaps between Germany and France, on the one hand, and the US on the other are considerably greater here than those reported in Van Ark (1996). But since this book is primarily concerned with British performance it seems appropriate, where feasible, to use the direct binary comparisons with the UK.

5 In measuring output it was also to ensure all countries measured output at market prices (since the PPPs are final demand prices) which involved adding VAT to the total over the nine market sectors

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productivity ratio is in fact consistent with results for the aggregate market economy,

i.e. do the sectoral results sum to a plausible result. To do so the market sector price

ratios were compared with implicit ratios derived by converting nominal output to £

sterling in each sector, summing over sectors and then taking the ratio of this to

nominal output in domestic currencies. A comparison of the two market sector price

ratios are shown in Table 1. The two measures of relative prices for the US and

Germany are virtually the same but there is a small discrepancy in the case of France

and Japan. Nevertheless even these price ratios are close enough to conclude that

there is no great discrepancy between relative price ratios for total market sectors

versus those at the individual sector level.

Table 1. 1993 PPP’s, domestic currencies per £ sterling.US France Germany Japan

Market sector PPP 1.34 10.38 3.06 272Implied PPP 1.35 9.99 3.08 286

ratio market to implied 0.99 1.04 0.99 1.05

2.2. The estimation of capital input.

The standard approach used by national statistical offices to measure capital stocks is

the perpetual inventory method (PIM) which is based on cumulating investments

over periods of time and allowing for retirement or replacement of assets. If we let K

denote capital stocks and I denote investment, then a general form of the PIM

calculation is given by:

(5) K d It t

s

0

where s is the maximum service life of the asset and, following Jorgenson (1989), d

is the relative efficiency weight attached to each period’s investment. Both the form

and interpretation of d, however, has been the subject of considerable controversy.

in France and Germany.

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It has become traditional in the national accounts to distinguish between gross and

net capital stocks where the latter, but not the former, allows for ‘depreciation’. In

fact this is somewhat misleading as both are special cases of the specification in

equation (5). Within the general framework of equation (5) the measurement of

gross capital stocks assumes that each year’s investment up to period s is equally

productive so that d equals zero. The assumption that assets retire entirely after s

periods is known in the literature as ‘one-hoss shay’ pattern of decline in relative

efficiency. In practice gross capital stocks are estimated assuming assets retire in a

range of years around s so that (5) becomes:

(6) K I d It t t

s m

s ms m

0

1

The second term in equation (6) is generally included to take account of the

possibility that assets may be retired over a range of years rather than at one specific

date and in turn is due to the fact that investment in equipment or structures typically

incorporate a wide range of asset types.

Net capital stocks allow d to take a positive value for each periods’ investment.

There are a number of alternative forms for the function d which have been

employed in calculating net capital stocks. These include straight line depreciation,

double declining balance and geometric decay. Both Jorgenson (1989) and Hulten

and Wykoff (1981) present empirical evidence in favour of geometrically declining

efficiency functions. Geometric decay also has the advantage that it is easy to

implement empirically. Thus the relative efficiency function is given by:

(7) d = (1-d)

In this case the summation in equation (5) goes from zero to infinity, rather than

ending at some finite level s, since the contribution of each period’s investment

approaches but does not reach zero. Substituting (7) into (5) and rearranging gives

period t capital stock as:

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(8) Kt = It + (1-d) Kt-1

Should gross or net capital stocks be used as measures of the productivity capacity of

the capital input. If the productive capacity stays constant throughout its life then

gross capital stocks are the appropriate measure in an analysis of productivity. On

the other hand if productivity capacity declines with age then net capital stocks

should be used. Which assumption is closer to the true picture is an empirical issue

that is difficult to resolve. It is easy to think of assets, such as computers, where

each new vintage is obviously less productive than the previous one but one can also

be sceptical in employing geometric decay for say buildings where their productive

capacity does not obviously vary from year to year. It is important to note that

‘depreciation’ in this context should only include decreases in relative efficiency and

not ‘financial depreciation’ which is the decline in the value of assets as they near

the end of their service lives.6 The empirical evidence mentioned above does lend

support to the notion that the productivity capacity of assets decline with time. Hence

net capital stocks were employed in this dataset, calculated using equation (8)

separately for equipment and structures.

The next issue to be resolved is what assumptions should be employed on the

average service lives of assets and the amount of depreciation. Much of the research

in O’Mahony (1997) was devoted to gauging the sensitivity of capital stock

estimates to assumptions on the length of asset lives, in particular, to the differences

in assumptions used by national statistical offices. The conclusion reached in that

paper was that the scarce information on actual service lives makes it difficult to

justify anything other than common lives. While recognising that this is a

controversial position in this paper we only present estimates on this basis.

The depreciation rates used were those estimated by Hulten and Wykoff (1981),

reproduced in and Fraumeni (1997). These rates were available by asset type and so

needed to be converted to sector specific depreciation rates for equipment and

structures. This was achieved by using detailed investment data by sector and asset

for the US. Both equipment and structures consist of a number of asset types. The

6 Hulten and Wykoff (1981) make this distinction in their measures of efficiency functions based on the prices of second-hand assets.

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number of types of machinery ranged from five in some sectors (largely comprising

furniture & fixtures, office machinery, communication equipment and photocopying

equipment to well over ten types in other sectors including vehicles generally

consisting of trucks and autos but also including ships and aircraft in some sectors,

mainly transport. Structures generally comprise buildings (industrial, office

commercial, etc.) but also include railway tracks and mine shafts. Suppose sector j

uses i types of equipment and let di equal the depreciation rate for asset i. Then the

depreciation rate for equipment in sector j is given by:

(9) dej = i sijdij

where sij = Iij / i Iij is the share of asset i in total real equipment investment. The

investment shares in equation (9) were based on average investment over the period

1970-1994. A similar calculation was carried out for structures but based on

investment shares over the period 1950 to 1994.7 The depreciation rates by sector

are shown in Table 2 and for manufacturing in Table 3.

7 Use of investment shares averaged over longer periods did not alter the results appreciably.

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Table 2 Depreciation Rates by sector.Equipment Structures

Agriculture, forestry & fishing 0.139 0.0238Mining & Oil refining 0.149 0.0507 Oil & gas extraction 0.152 0.0539 Metal, coal & mineral mining 0.153 0.0371 Mineral oil refining 0.138 0.0310Electricity, Gas & Water 0.120 0.0218 Electricity 0.103 0.0211 Gas 0.160 0.0237 Water 0.206 0.0229Manufacturing 0.136 0.0308Construction 0.161 0.0286Transport & Communications 0.128 0.0252 Transport 0.134 0.0232 Railroads 0.084 0.0228 Other inland transport 0.144 0.0242 Water transportation 0.078 0.0248 Air transportation 0.135 0.0234 Other transport 0.158 0.0237 Communications 0.122 0.0261Distributive Trades 0.177 0.0256 Wholesale Trade 0.186 0.0239 Retail Trade 0.170 0.0256 Hotels 0.162 0.0281Finance, insurance, & business services 0.206 0.0230 Financial Services 0.249 0.0251 Insurance 0.260 0.0247 Real Estate& Business Services 0.174 0.0225Miscellaneous personal services 0.157 0.0263 Non-Market Services 0.177 0.0192 Health 0.167 0.0214 Education 0.197 0.0231Government 0.178 0.0190

Total Economy 0.160 0.0272Total Market Sectors 0.159 0.0284

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Table 3 Depreciation rates in manufacturing.

Equipment StructuresChemicals 0.119 0.0308Rubber & Plastics 0.129 0.0308Basic Metals 0.115 0.0308Metal Products 0.137 0.0308Mechanical Engineering 0.176 0.0308Office Machinery 0.133 0.0308Electrical Engineering 0.133 0.0308Motor Vehicles 0.132 0.0308Other transport Equipment 0.141 0.0308Instrument Engineering 0.161 0.0308Textiles 0.119 0.0308Clothing & Leather 0.151 0.0308Food, Drink & Tobacco 0.138 0.0308Mineral products 0.171 0.0308Wood products 0.143 0.0308Paper, printing & publishing 0.129 0.0308Miscellaneous manufacturing 0.144 0.0308

In calculating total capital stocks it is necessary to aggregate structures and

equipment. National statistical offices typically simply sum these two types of assets.

An alternative is to calculate capital services from both types of assets by weighting

each by asset specific user costs of capital; this is the methodology employed by

Jorgenson et al. (1987) and Oulton and O’Mahony (1994). Thus the growth in

capital input for each country was constructed as:

(10) dlnK = 0.5.(vet + ve

t-1) dlnKe + 0.5.(vst + vs

t-1) dlnKs

where e and s denote equipment and structures, respectively, and v denotes shares in

the value of capital. The latter is in turn derived by multiplying the net stocks of

each asset type by its user cost of capital, given by the standard formula of the real

interest rate plus depreciation minus real capital gains. The real rate of interest was

assumed equal to 5% in all countries, an estimate chosen to ensure a low incidence

of negative user costs. Real capital gains were calculated as the rate of price increase

for asset i minus the price increase for all assets. When the calculation yielded

negative user costs the price changes were smoothed over a number of years.

This methodology is a compromise between using the ideal user cost formula as

outlined in Jorgenson et al. (1987), employing the nominal internal rate of return

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plus nominal capital gains adjusting for differences across countries in the tax

treatment of assets, and simply summing net stocks. Experiments suggested that the

latter gave unsatisfactory results, largely due to the fact that the share of structures

differs across countries in a way that suggests that the definition of this asset also

varies across countries. Thus in France, and probably also in Germany, it is likely

that more short-lived assets are included with structures. The value share weighted

formula captures the fact that the services of relatively long lived assets are

proportionally less than the services of short lived assets.

In calculating relative levels of capital input, purchasing power parities for 1993

were used to convert investment in domestic currencies to US$. The equipment PPPs

were sector specific estimates, derived as weighted averages of PPPs for three asset

types, machinery, vehicles and computers, with weights equal to each sector’s share

of US investment in these assets. The structures PPP was chosen in each sector to

correspond with the buildings used in that sector, e.g. farm buildings for agriculture,

industrial buildings for manufacturing, commercial buildings for distribution etc.

Leased assets were allocated where feasible to the sector of use rather than sector of

ownership. Finally investment in computers in the US was deflated by an index

which incorporated quality change comparable to that in use in the European

countries rather than using the hedonic index currently employed by the US

statistical offices. This was justified on the grounds that the quality adjusted price of

computers has been falling by similar amounts in all countries but the US hedonic

index implies a reductions in the hedonic price by about a factor of twelve in the past

two decades whereas other countries assume only a halfing of the quality adjusted

price. The theoretically more attractive option of employing a hedonic index for

other countries was not feasible since only the US statistical offices separately

identify investment in computers.

3. Data Sources.

3.1. Output.

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These data are presented in index form with 1993 set equal to 100. For the broad

sectors, and where possible for the sub sectors, data were taken from the national

accounts and generally are equal to gross value added at constant prices. In the US

and Germany value added is double deflated, i.e. it is calculated as gross output

deflated by producer prices minus intermediate input deflated by intermediate input

prices. The broad sectoral estimates for the UK are based on indexes of real output at

constant factor cost as given in that country’s national accounts. These are

frequently based on measures of deflated sales, with value added weights but they

also make use of quantity indicators in some cases. Output in some subsectors such

as electricity, gas, and transport sectors in the UK make use of quantity indicators

such as kilowatt hours of electricity produced, passenger km carried etc.

A. United Kingdom.

The series 'gross domestic product at constant factor cost by industry of output',

published in various issues of the United Kingdom National Accounts, Office for

National Statistics (ONS) (formerly the Central Statistical Office, (CSO)) were used

to construct real output at the broad sector level. The Annual Censuses of

Production, ONS (formerly Business Statistics Office) were used to provide detailed

estimates within manufacturing - series on nominal value added from this source

were deflated by producer price indexes taken from the Annual Abstract of Statistics,

ONS.

Output in the distributive trade sectors were derived as deflated gross margin using

information in the British Business Monitor series SDA 25: Retailing, SDA 26:

Motor Trades and SDA 28: Catering and Allied Trades. Additional information on

distribution was obtained from the Annual Abstract of Statistics and the periodic

Censuses of Distribution, CSO. Retail prices were used to deflate output in this

sector, these were taken from the Monthly Digest of Statistics, ONS and CSO,

various issues.

Quantity indicators were employed in the utilities, transport and communications

sectors. These comprised the physical production of electricity, gas and water for

15

the utilities, passenger km or freight tonne km for transport (using revenue weighted

averages in sectors which carry both passengers and freight), number of pieces of

mail handled in the postal service and a revenue weighted average of telephone calls

and access lines in telecommunications. The source for the above quantity indicators

was the Annual Abstract of Statistics ONS, (CSO).

B. United States.

1977-1996: From Survey of Current Business, US Department of Commerce, Bureau

of Economic Analysis (BEA), various issues. From 1987 to 1995 this is the recently

constructed chain linked index, recently available from the BEA website but from

1977 to 1987 the series use constant 1985 weights. From 1950 to 1977 the series are

taken from the National Income and Product Accounts of the US, 1929-82, BEA,

September 1986, Washington, DC. - the pre 1977 series use fixed 1982 weights.

These sources only produce a total for the utilities. For the individual sectors we use

quantity indicators, i.e. billion Kwh of electricity produced, trillion BTU of gas

produced - it was not possible to calculate output separately for water. Eating and

drinking places were taken out of retailing and added to hotels using proportions of

sales. The output of the postal service was calculated as number of pieces of mail

handled. Total output of the communications sector was calculated by combining the

indexes for telecommunications and postal services using revenue weights of the

telecommunications companies and the US Postal services. The sources for the

above quantity indicators and adjustments were various issues of the Statistical

Abstract of the United States, US Department of Commerce and from Historical

Statistics of the United States: colonial times to 1970, US Department of Commerce,

Bureau of the Census, September 1975, Washington, DC.

C. France

16

The basic source for output from 1970 to 1995 is Comptes et Indicateurs

Economiques: Rapport sur les comptes de la Nation, Institut National de la

Statistique et des Etudes Economiques (INSEE), and from 1950 to 1970 in Le

Mouvement Economique en France, 1949-1979: series longues macroeconomiques,

INSEE, Paris, May 1981. The series pre 1970 are not as detailed by sector as those

from 1970, in particular one large category ‘services’ includes both personal and

non-market services. Some additional detail by sector was taken from Carre Dubois

and Malinvaud (1976) for the period 1950 to 1966. Additional data for

manufacturing, employed to breakdown aggregates to constituent parts, were taken

from The OECD STAN Database for Industrial Analysis, OECD Paris.

D. Germany

The basic source for real output were series from the national accounts published in

Volkswirtschaftliche Gesamtrechnungen, Fachserie 18, Reihe 1.3, Konten und

Standardtabellen, Statistisches Bundesamt, Wiesbaden, 1998 and

Volkswirtschaftliche Gesamtrechnungen Reihe S.15 Revidierte Ergebnisse, 1950 bis

1990, Statistisches Bundesamt, Wiesbaden, 1991. Real output was only available at

the broad sector level between 1950 and 1960. Additional detail by sector was taken

from Hoffmann (1965).

E. Japan

Data from 1953 to 1990 primarily from Pilat (1994) supplemented by data from

OECD National Accounts, OECD, Paris and The OECD STAN Database for

Industrial Analysis, OECD Paris.

.

3.2. Persons engaged

17

Persons engaged includes all full-time and part-time employees and self-employed

persons. In general standard sources include the self-employed but for the UK

published sources by sector generally only include employees in employment.

Therefore some additional estimation was required for the UK - details are given

below.

A. United Kingdom.

The basic data source was the Censuses of Employment carried out between 1971

and 1995 - these referred to employees only. These data were available in various

issues of The Department of Employment Gazette or more recently Labour Market

Trends from 1979 to 1994 on a Standard Industrial Classification (SIC) 1980 basis.

From 1971 to 1978 unpublished data classified according to SIC 1980 were made

available from The Department of Employment. 1995 and 1996 data were only

available on a SIC 1992 basis so that growth rates between 1994 and 1995 and 1996

in this new classification were applied to the original series. From 1950 to 1971 data

on employees in employment classified according to SIC 1968 were available in

British Labour Statistics: Historical Abstract, 1886-1968, Department of

Employment and Productivity, HMSO, London, 1971 and British Labour Statistics,

Year Books, Department of Employment, HMSO, London. These series were

reclassified to SIC 1980 using 1971 employment available on both bases.

Official time series do not exist for the self-employed by sector in the UK. It was

therefore necessary to resort to the population censuses for 1951, 1961, 1966, 1971,

1981 and 1991 to estimate the proportion of self-employed in total persons engaged

which was then applied to the employees in employment series. Linear interpolation

was used to estimate these proportions in inter-censal years. Adjustments to include

self-employed had large impacts in agriculture, construction, distributive trades,

business services and personal services. The general pattern was one of decreases in

the proportions of self-employed through the 1950s and 1960s, followed by a period

of stability in the 1970s and considerable increases in the 1980s. In these sectors

trends in employees in employment are not good indicators of trends in persons

engaged. The census publications, for Great Britain only, were: Census 1951,

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England and Wales: Industry Tables, General Register Office, HMSO, 1957; Census

1951, Scotland: Occupations and Industries, General Registry Office, Edinburgh,

HMSO, 1956; Census 1961, England and Wales: Industry Tables, General Register

Office, HMSO, 1966; Census 1961, Scotland: Occupations and Industries, General

Registry Office, Edinburgh, HMSO, 1966; Sample Census 1966, Great Britain,

Economic Activity Tables, Part I, General Register Office, London, General Register

Office, Edinburgh, HMSO,1968; Census 1971, Great Britain: Economic Activity,

Part III, Office of Population Censuses and Surveys, London, General Register

Office, Edinburgh, HMSO, 1975; Census 1981, Great Britain: Economic Activity

Part I, Office of Population Censuses and Surveys, London, 1984; Census 1991,

Great Britain: Economic Activity Part I, Office of Population Censuses and Surveys,

London, 1995.

B. United States.

The basic source for data on persons engaged are the ‘National Income and Product

Accounts of the US’, (NIPA), which comprise annual series from 1948 to 1991 on

diskette, produced by the US, Bureau of Economic Analysis. These were

supplemented from 1992 from series made available on the BEA website. Note the

main NIPA series are based on three Tables, Table 6.4C ‘Full-time and part time

employees’, Table 6.5C ‘Full-time equivalent employees, and Table 6.8C, ‘Persons

engaged’ where the latter is derived by adding to Table 6.5C estimates of the number

of self-employed persons. A headcount of persons engaged is therefore derived as

Table 6.4C + (Table 6.8C - Table 6.5C).

Catering was taken out of retail trade and added to hotels using information for

selected years on the proportions of catering in total retail in the Censuses of

Distribution, U.S. Department of Commerce, Bureau of the Census, reproduced in

various issues of The Statistical Abstract of the United States. Interpolation was

employed to fill in the missing years. Postal employees were taken from The

Statistical Abstract of the United States; these were added to communications and

subtracted from Government employees. In the utilities, annual series exist in The

Statistical Abstract of the United States from 1960 to 1995 (and for 1950) for four

19

categories, electricity, gas, combination industries (firms who produce both

electricity and gas) and water. Employment in the combination industries was broken

down into electricity and gas according to these industries relative importance but

constrained to equal the electricity and gas totals for 1992 derived from the Census

of Transportation, Communications and Utilities, 1992, U.S. Department of

Commerce, Bureau of the Census, which allows a breakdown of the combination

utilities.

C. France

Data Sources were as for Output.

D. Germany

Data Sources were as for Output.

E. Japan.

Data Sources were as for Output supplemented by information in Labour Force

Statistics, OECD, Paris.

3.3. Annual average hours worked.

Series showing annual average hours worked per employee were constructed for

each of the five countries from a variety of sources. These series are less reliable and

involve more international non-comparabilities than numbers of persons engaged and

are often available only at broad sector levels. Each country employs a different

methodology in constructing hours worked and in the case of the UK, official series

have only recently been published (since 1992).

Average annual hours worked is the product of two components, average weekly

hours and average weeks worked per year. The former should allow for differences

in hours worked by full-time and part-time employees, manual and non-manual

workers and males and females and include overtime hours. The latter needs to take

20

account of annual paid holidays, public holidays and days lost due to sickness,

maternity leave and strikes. The extent to which all these elements are taken into

account varies across country.

Traditionally weekly hours worked series tended to be based on surveys of firms

which often only included manual workers. If an individual was absent for the entire

week of the survey then they were not included, hence the need for a separate

estimate of weeks worked per year. Series on weeks worked per year also were

based on single series for the entire economy. In recent years both the US and the

UK have constructed series based on sampling individuals rather than firms so that

individuals on paid hoilday or sick leave were included. In the US this information

was gathered in the Current Population Survey and in the UK in the Labour Force

Survey. In principle these series are more accurate than the firm based series since

they take account of workers of all types and time not working. However the new

series generally imply longer annual hours than the firm based series which are

difficult to reconcile and may be due to different perceptions by firms and

individuals of how many hours are actually worked in a given week. For reasons of

international comparability hours worked in this paper are primarily based on firm

level data on weekly hours combined with weeks worked per year. The new UK

series are employed, however, to estimate trends from 1992.

A. United Kingdom

From 1984 to 1992 weekly hours are taken from labour force survey data, by broad

sector, published in Labour Market Trends. Before 1984 weekly hours are based on

hours of manual and non-manual workers from the New Earnings Survey, and

proportions of males and females from the Censuses of Employment. Female

employment is divided into full-time and part-time and a part-time worker is

assumed to work half the hours of a full-time worker. Data for the 1950s and 1960s

are based on manual workers only, based on series available in British Labour

Statistics: Historical Abstract, 1886-1968, Department of Employment and

Productivity, HMSO, London, 1971 and British Labour Statistics, Year Books,

Department of Employment, HMSO, London. Weeks worked per year is a single

21

series which takes account of paid holidays, public holidays and time lost due to

sickness, maternity and strikes. This series is an update to 1992 of that discussed in

Oulton and O’Mahony (1994); data sources are given in that publication. The annual

average hours worked series were updated from 1992 to 1995 using LFS weekly

hours, averaged over four quarters, multiplied by fifty-two.

B. United States

The primary source is a series constructed by the US Bureau of Economic Analysis

on total hours worked for employees by broad sector. Annual average hours worked

are derived by dividing total hours by the number of full-time and part-time workers.

These series are available in the US NIPA with sources are as for numbers of

persons engaged.

C. France

From 1976 the basic source is the series ‘ Duree annuelle effective du travail par

branche’ published in Comptes et Indicateurs Economiques: Rapport sur les comptes

de la Nation, INSEE, Paris. 1966 to 1976 trends are based on weekly hours from

Annuaire Statistique de la France, INSEE, various issues and 1950 to 1966 from

Carre, Dubois and Malinvaud (1976).

D. Germany

From 1970 to 1994 the basic series were available in various issues of Arbeitszeit

und Arbeitsvolumen in der Bundesrepublik Deutschland, Institut fuer Arbeitsmarkt

und Berufsforschung, Nurnberg. These were updated to 1995 using trends in series

developed by the Deutches Institute fuer Wirtschaftforschung, Produktionsvolumen

und –potential, produktionsfaktoren des Bergbaus und des Verarbeitenden

Gewerbes, DIW, Berlin, various issues, and using trends in manual hours from the

Statistisches Jahrbuch, 1997, Statistisches Bundesamt. Hours series for five yearly

intervals between 1950 and 1970 were unpublished series made available by the

DIW, Berlin. The series from 1950 to 1970 were completed by linear interpolation.

22

E. Japan

Data from 1953 to 1990 were taken from Pilat (1994). These series were updated to

1990 to 1995 using monthly hours worked reported in various issues of Japan

Statistical Yearbook, ,Statistics Bureau, Management and Coordination Agency,

Tokyo.

3.4. Investment.

A. United Kingdom.

The primary source for investment in both current and constant prices is an

unpublished series from the CSO which is used in their construction of post-war

capital stocks. The CSO series distinguish about 40 sectors and three asset types,

structures, machinery, and vehicles. These series were generally easy to match with

the US sectors. However no breakdown was possible in the broad group financial,

insurance and business services. Also retail trade included repairing. An estimate of

the repairing investment proportion was derived from the UK annual inquiries on the

distributive trades (Business Monitors SDA25, Retailing and SDA27 Motor Trades)

and their predecessors, The Censuses of Distribution. War damage was assumed to

equal 3% of the capital stock between 1939 and 1945, an estimate taken from

Maddison (1991).

B. United States.

The US investment data come directly from the BEA, U.S. Department of

Commerce. Detailed data are available for 61 sectors (21 in manufacturing)

and fifty asset types both at current and constant prices. Investment in the postal

services were contained within federal government investment and it was not

possible to separate this sector using the BEA source. Series for investment in the

postal services were obtained by setting the 1993 value equal to that reported in

‘Generations: the 1994 Annual Report of the Postmaster General, United States

23

Postal Service. Time series were derived using data from The Budget of the US

Government. These series were then subtracted from the total federal investments

and added to communications. The proportion of catering in the total retail trade

investment series was derived from various years’ Censuses of the Distributive

Trades, US Bureau of the Census. These were available only at five year intervals

so the proportions in intervening years were interpolated.

C. France

Unpublished data on investment by industry and asset type, from 1970 to 1997, at

both current and constant prices, were obtained from INSEE. These cover 36

sectors of which 19 are in manufacturing and distinguish 10 asset types. From 1950

to 1970 real investment by broad sector, with some breakdown in manufacturing

were also available from INSEE. But this source does not give a breakdown by asset

type. This was achieved by using the proportions in an investment series kindly

made available by Pierre Villa, at CEPII, Paris. Villa’s constant price investment

series were used for years prior to 1950. War damage was assumed to be 8% of the

pre-1919 capital stock and a further 8% of the pre-1945 capital stock - these

estimates come from Maddison (1991).

D. Germany

Investment data for West Germany come from two sources. From 1960 to 1995 data

come from Volkswirtschaftliche Gesamtrechnungen, Statistisches Bundesamt. Data

for the period 1850-1960 are available in Kirner (1968). These distinguish

equipment and structures for 20 sectors and adjust for changes in the geographic

boundaries in Germany over this period. The data are presented in constant prices

but Kirner also presents the price deflators used. Kirner adjusted his investment

data for war damage. Data for the period 1960-95 are contained in the German

national accounts, i.e. Volkswirtschaftliche Gesamtrechnungen, Statistisches

Bundesamt, various years. These data distinguish structures and equipment and are

available for over 50 sectors at current and constant prices. Separate data on

investment in vehicles are not recorded. The transport sector was broken down into

24

its constituent sector using data obtained from the Deutsches Institute fuer

Wirtschaftforschung, Berlin. In common with other variables fifty per cent of total

investment in ‘other services’ was allocated to business services, but on the basis of

information for the US and France, this was divided into 40% of structures and 60%

of equipment.

E. Japan

In terms of coverage, the Japanese data is by far the poorest of the five countries

considered here. From 1965 to 1995 data are available for private enterprises from

the Economic Planning Agency (EPA) at constant prices for ten broad industrial

sectors. Business services were separated from other services assuming this sector

represented 40% of private services, which is approximately the average proportion

in the US and France. In addition information was available for some individual

sectors such as mineral oil refining, electricity, and wholesale trade and for fourteen

sub-sectors in manufacturing. These data are limited in that they do not distinguish

investment by asset type or include estimates for the public sector. Also a single

deflator is used for all forms of investment.

Data on private enterprise investment are available, at current prices, from the

OECD national accounts for the years 1952 to 1970, for six industrial sectors.

These were deflated by the investment deflator for those years given in Ohkawa

and Shinohara (1979). The average proportions for the first five years of the

seventies were used to breakdown these six sectors into the ten available in the

EPA data. From 1905 to 1940 Ohkawa and Rosovsky (1973) give investment at

constant prices for five sectors, agriculture, mining & manufacturing,

facilitating industries (transport and communication and public utilities),

construction and services. There are no data by industry from 1941 to 1951 but

Ohkawa and Rosovsky present estimates of non-residential investment, in

constant prices, in the private sector. The average proportions for the early

fifties were used to breakdown investment data into sectors pre 1952.

25

For the public sector, investment data were taken from the OECD national

accounts, for the years 1970 to 1989, excluding investment in residential buildings

by public enterprises. From 1940 to 1970 data were taken from Ohkawa and

Rosovsky (1973) and for the pre-1940 period from Ohkawa and Shinohara

(1979). All series were available at constant prices. Following Maddison (1991)

war damage was assumed to equal 27.5% of the 1939-1945 capital stock.

The Economic Planning Agency has not published investment data by asset type

since the second world war. Nevertheless, the OECD national accounts do include

such a breakdown, for aggregate investment, from 1970. Ohkawa and Shinohara

(1979) include a breakdown into structures and plant & machinery for aggregate

investment for the period before 1940. The proportions for the aggregate

economy in the intervening years were estimated by simple linear interpolation

from 1946 to 1970 where the proportions in 1946 were set equal to their 1935

values. The share of structures declined dramatically after 1935 with the pre-war

build-up. This emphasis on plant & machinery probably continued throughout the

war so the 1940 structures share was used for the years 1941-1945.

Examination of the data by sector for other countries showed that the importance of

structures in total investment varied considerably across sectors so that use of the

single aggregate proportions for all sectors would not be appropriate. The data

were separated into structures and machinery by first taking the average proportions

of the other four countries for each sector for the period 1970-95 and using trends in

the aggregate proportions of structures and equipment applied to each sector. The

investment series for the two asset types were then summed and constrained, by a

process of iteration, to equal the aggregate proportions.

26

3.5. Workforce skills.

Data limitations constrained the measurement of this variable to only three countries,

the US, the UK and Germany. With respect to inter-country differences in human

capital endowments, the most commonly used proxy measures are those based on

education inputs (e.g. years of schooling or enrolment) But these are deficient as

measures of human capital since they are a record of attendance rather than

attainment and do not allow for variations in the quality and content of schooling.

Therefore the estimates here rely in the main on proxy measures of skills based on

education and training outputs, for example, the proportions of the workforce with

different levels of certified qualifications. However, even these measures are

deficient in that many skills may be acquired by informal on-the-job training and

remain uncertified. Furthermore, although data on vocational qualifications are

readily obtainable for Britain and Germany, the same is not true for the US where we

have been constrained to use a mix of educational attainment and attendance data.

Workforce skills are divided into three categories, those with higher level

qualifications (degree or above), intermediate qualifications (vocational

qualifications above high school but below degree) and those with low or no skills.

A. United Kingdom.

The basic source was the Labour Force Survey, ONS, London. The skill groups were

divided as follows: higher level: degree and above, membership of professional

institutions; intermediate level: BTEC HNC/HND, teaching and nursing, BTEC

ONC/OND, City & Guilds, apprenticeships; low or no skills: all with high school or

no qualifications. After 1993 the data are classified according to SIC 1992, which is

difficult to reclassify to SIC 1980 without an overlapping year, and so were not

included in the dataset. Note that the 1979 data were classified according to SIC

1968. An attempt was made to reclassify to SIC 1980 but the lack of an overlapping

year means that the adjustments are crude and so the data for 1979 are not strictly

comparable with other years.

27

B. United States.

The basic source for US data is the Current Population Survey (CPS), US Bureau of

the Census. For 1992 and 1993 data on educational attainment/attendance from the

are divided into the following categories: school grades up to high school graduation,

some college but no degree, associate degrees, and bachelor and advanced degrees.

The bachelor and advanced degrees may be regarded as broadly equivalent to British

and German higher level qualifications. Comparisons based on mechanical

engineering exam papers suggest that, in this subject at least, American associate

degrees are comparable to the British Higher National Diploma, an upper

intermediate-level qualification, see Mason and Finegold (1995).

The category ‘some college but no degree’ presents problems since it includes both

people who left college with no qualifications and those who gained vocational

certificates/diplomas of various kinds. In the light of more disaggregated US

qualifications information gathered in a national Survey of Income and Program

Participation (SIPP) in 1984 (US Department of Commerce, 1987), it was decided to

classify half of the ‘some college but no degree’ category as holding intermediate

vocational qualifications and half as low- or unskilled. 8 In addition, all those whose

highest educational attainment is high school graduation are classified as low- or

unskilled. It is recognised that these are very crude assumptions. ‘Some college but

no degree’ includes both general studies and vocational courses and the latter

sometimes fall well short of the craft standards achieved by British and German

apprentice-trained workers. On the other hand, some US high school graduates will

have taken vocational courses.9

8 The Survey of Income and Program Participation (SIPP) results for 1984, based on the total US noninstitutional resident population aged 18 years and over, show only 23% of those in the ‘one to three years college’ category as holding either associates degrees or vocational certificates/diplomas (US Department of Commerce, 1987, Table 1). (Up to 1993 the CPS category ‘one to three years college’ included both associate degrees and ‘some college but no degree’). Hence, even allowing for growth in the numbers holding vocational qualifications since 1984, our assumption that half of those in the ‘some college but no degree’ category hold qualifications equivalent to European intermediate vocational awards is unlikely to be an under-estimate. 9 The SIPP results for 1984 suggest that approximately 18% of persons who had attended at least 12 years of high school had followed predominantly vocational or business courses (US Department of Commerce, 1987, Table 6). Some of these people will have undoubtedly gone on to further studies and acquired intermediate or even higher-level vocational qualifications.

28

Up to 1991 the CPS data was available on an attendance basis only - i.e. showing

number of years spent in schools or colleges. Estimates for 1990 were employed on

both bases to convert the attendence data to the attainment categories included in

more recent years - these were based on information in R. Kominski and P.M.

Siegel, 'Measuring education in the Current Population Survey', Monthly Labour

Review, US Bureau of Labour Statistics. It is assumed that those attending college

for four years or more are equivalent to BA and above. The proportions of one, two

and three years of college which were deemed to have intermediate skills were 0.52,

0.68 and 0.62, respectively.

C. Germany

Data were taken from the 'Microzensus', Statistisches Bundesamt, Wiesbaden. The

skill groups were divided as follows: higher level: Hochschulabschluss and

Fachhochschulabschluss; intermediate level: Meister/Techniker gleichwertig

Fachschulabschluss, Lehr-/Anlehrausbildung gleichwertig Berufsfachschulabschluss,

berufliches Praktikum; low or no skills: all with high school or no qualifications. As

in Britain the 1995 'Microzensus' is not comparable with earlier years.

Relative Wages

The sources for relative wages of workers with various skill types were : UK:

estimates from the occupational earnings data from The New Earnings Survey; US:

Eck (1993) based on information from the Current Population Survey; Germany:

Abraham and Houseman (1993) based on information from the German

Socioeconomic Panel. These sources cover all full-time adult workers and imply

wage weights for workers with higher level and intermediate level skills,

respectively, relative to those with low skills equal to 1.75 and 1.29 for the US, 1.97

and 1.19 for the UK and 2.0 and 1.25 for Germany.

3.6. Labour Compensation and Labour's share of Value Added.

29

III.3.6.1. Labour compensation.

This includes wages and salaries for employees and non-wage labour costs such as

employer's contributions to national insurance and pensions. The basic sources for

labour compensation were taken from the following sources: US: same sources as for

persons engaged; UK: at the broad sector level from 'gross domestic product at

current factor cost: by industry and type of income, and for manufacturing from the

series income from employment in the production industries, both from the ‘United

Kingdom National Accounts, ONS, additional information was taken from the

Annual Censuses of Manufactures,ONS.; France: 'Remuneration des salaries par

branche', Comptes et Indicateurs Economiques: Rapport sur les comptes de la

Nation, INSEE; Germany: 'Einkommen aus unseldstaendiger arbeit', from

Volkswirtschaftliche Gesamtrechnungen, Statistisches Bundesamt; Japan: from

OECD National Accounts. The German data begin only in 1970. From 1950 to 1970

trends in labour compensation were estimated using trends in wages and salaries

multiplied by number of employees. An adjustment was made to allow for the

growth in non-wage labour costs using average growth rates in the early 1970s. Data

are unavailable for France pre 1977 and Japan pre 1971.

3.6.2. Labour's share of value added.

This is calculated as labour compensation plus the imputed labour income of the

self-employed divided by nominal gross domestic product. The sources for the latter

are as for labour compensation. In general the average wage of employees in any

sector is taken to be the labour compensation of the self-employed as this is a

measure of the opportunity cost of their labour. Unfortunately in Germany and Japan

in the agricultural sector this yields a labour share greater than one (significantly so

in some years). Therefore in this case we included an (arbitrary) adjustment of using

one half the wages of employees.

Also there is a problem estimating labour's share in non-market services. In the UK

before 1972 outputs of government services, public health and public education

were estimated using labour compensation so labour's share was identically equal to

30

one. At that time the small private health and education sectors were included with

personal services in the national accounts. Since 1972 private operators were

included with public providers and an allowance was made for capital consumption

in the government sector so labour's share fell below one. For the non-market service

sector for the UK we assume labour's share was constant pre 1972. Note, however,

that this problem of estimating output by labour input also applies to parts of this

sector in other countries so estimates of labour's share should not be seen as reliable

in non-market services.

Problem arose in estimating labour's share in sectors which were either heavily

subsidised, such as rail transport or have incurred heavy losses such as mining. Some

smoothing over a number of years was carried out when initial calculations led to

labour’s share in these sector being greater than one.

Finally the historical series for France (pre 1977) and Japan (pre 1971) uses trends in

labour compensation for the total economy. For France this is derived from data on

total labour compensation from the OECD National Accounts combined with data on

employees and self-employed from the sources listed above under persons engaged.

For Japan we take estimates in Pilat (1994) (including income from land) from 1953

spliced to our estimate in 1971.

III.4. Industry Classifications.

This paper attempted as far as feasible to match the industry classifications in the

five countries. The starting point was generally taken to be the UK Standard

Industrial Classification, 1980 version (SIC80) and other countries classification

systems were adjusted to render them as close as possible to the British system.

Details of the adjustments and the data sources are outlined in general terms above.

In addition, it was decided to exclude agricultural services, forestry and fishing in the

US from Agriculture and include this with non-market services. Unfortunately a

31

large proportion of this is veterinary services which are classified with health in

other countries. This sector’s employment trends behave more like those in service

sectors than agriculture and their inclusion in the latter led to labour productivity

trends which were deemed to be outside the bounds of plausibility. The required data

did not exist to separate forestry and fishing from other agricultural services.

It was not possible to do the same detailed industrial classification matching for

Japan as for other countries but the broad sectors corresponded fairly closely to the

British system. The exceptions were that distribution includes catering but not hotels.

Business services were part of other services, but a similar adjustment to that

employed for Germany was used, i.e. business services were assumed to consist of

40% of the ‘personal services’ group for all variables. Finally any health or

education services provided privately are included in personal services.

Note finally that in all countries employment and related variables (hours, labour

compensation and skills) in the real estate industry is included in business services

but output and investment for this sector are not. The former includes imputed rent

on owner occupied housing and the latter is residential buildings.

The following is a list of industry codes and descriptions of the sector for four

countries, the UK, the US, Germany and France. It begins with the UK groups

according to their SIC80 codes, listed in Standard Industrial Classification: Revised

1980, CSO, HMSO, London, 1979. We then detail which groups they correspond to

in remaining countries. The US codes are those for SIC 1987 taken from Standard

Industrial Classification Manual 1987 (SIC87), (Executive Office of the President

Office of Management and Budget. The French codes are those used in the 1993

French national accounts (Comptes et Indicateurs Economiques: Rappport sur les

comptes de la Nation 1993 (INSEE 1994)). The German classification codes are

Systematik der Wirtschaftszweige, which we have abbreviated to SW.

32

A. Agriculture, Forestry and Fishing.UK SIC80, 001 (Agriculture and Horticulture), 002 (Forestry) and 003 (Fishing).

= US (SIC87 01 , 02,)= Germany (WZ 01, 03, 07)= France ( T01)

B. Mining and Oil Refining

Oil and Gas Extraction: UK SIC80 1300 (Extraction of Mineral Oil & Natural Gas). = US (SIC87 13)= Germany part of (WZ 11)= France ( T05)

Other Mining: UK SIC80 111 (Coal Extraction and Manufacture of Solid Fuels), 1200 (Coke Ovens).= US (SIC87 10, 12, 14)= Germany part of (WZ 11)= France ( T04)

Mineral Oil Refining: UK SIC80 140 (Mineral Oil Processing) = US (SIC87 29)= Germany (WZ 205)= France part of ( T05)

C. Gas, Electricity and Water.

Electricity: UK SIC80 1610 (Production and Distribution of Electricity), 1630 (Production and Distribution of Other Forms of Energy).= US (SIC87 491,493)= Germany (WZ 101, 105)= France part of ( T06)

Gas: UK SIC80 1620 (Public Gas Supply).= US (SIC87 492)= Germany (WZ 103)= France part of ( T06)

Water: UK SIC80 1700 (Water Supply Industry).= US (SIC87 494-497)= Germany (WZ 107)= France part of ( T06)

D. Manufacturing

33

(UK SIC80 Divisions 2-4 (Manufacturing).= US (SIC87 20-28, 30-39)= Germany (WZ 200,201,21-29)= France ( T02, T03,T07-T22)

E. Construction

UK SIC80 Division 5 (Construction).= US (SIC87 15-17)= Germany (WZ 30,31)= France ( T24)

F. Transport

Railways: UK SIC80 (Railways). = US (SIC87 40)= Germany (WZ 511)= France part of ( T31)

Water transport: UK SIC80 7400 (Sea Transport).= US (SIC87 44)= Germany (WZ 513, 514)= France part of ( T31)

Air Transport: UK SIC80 7500 (Air Transport)= US (SIC87 45)= Germany part of 'other transport' (WZ 512, 515, 516, 55)= France part of ( T31)

Other Inland Transport & Transport Services: (UK SIC80 72 (Other Inland Transport), 76 (Supporting Services to Transport) and 77 (Miscellaneous Transport Services and Storage).= US (SIC87 41, 42, 46, 47)= Germany part of 'other transport' (WZ 512, 515, 516, 55)= France part of ( T31)

34

G. Communications UK SIC80 7900 (Postal Services and Telecommunications).= US (SIC87 43, 481, 482)= Germany (WZ 517)= France ( T32)

H. Distributive trades.Wholesale trade: UK SIC80 61 (Wholesale Distribution), 62 (Dealing in Scrap and Waste Materials), 63 (Commission Agents).= US (SIC87 50, 51)= Germany (WZ 40, 41, 42)= France part of ( T25-8, T29)

Retail Trade: UK SIC80 64/65 (Retail Distribution).= US (SIC87 52-57, 59)= Germany (WZ 43)= France part of ( T25-8, T29)

Hotels & catering: UK SIC80 66 (Hotels & Catering)= US (SIC87 58, 70)= Germany (WZ 71, 72)= France ( T30)

I. Financial & business services

Finance: UK SIC80 81 (Banking and Finance).= US (SIC87 60, 61, 62, 64, 67)= Germany (WZ 60)= France ( T37)

35

Insurance: UK SIC80 82 (Insurance except Compulsory Social Insurance).= US (SIC87 63)= Germany (WZ 61)= France ( T36)

Business Services: UK SIC80 83 (Business Services), 84 (Renting of Movables), 85 ( Owning and Dealing in Real Estate).= US (SIC87 65, 73, 81, 87)= Germany 50% of 'uebrige dienstleistungen' , (WZ 73, 74, 78, 79) Note: The 50% figure was based on the 1987 employment census and was applied to all series for all years. A search of data sources failed to yield any method of splitting business services from other services.= France ( T33)

J. Miscellaneous Personal Services. UK SIC80 67 (Repairing), 92 (Sanitary Services), 94 (Research & Development), 96 (Other Services Provided to the General Public) 97 (Recreational and Cultural Services), 98 (Personal Services), 99 (Domestic Services). = US SIC87 07, 08, 09, 483-489, 72, 75, 76, 78, 79, 83, 84, 86, 88, 89. = Germany 50% of 'uebrige dienstleistungen' , (WZ 73, 74, 76, 78, 79) + (WZ 81, 83, 85)= France part of T34 (Services Marchands aux Particuliers minus Health and Education)

K. Non-Market Services.

Health: UK SIC80 95 (Medical, Other Health and Veterinary Services). = US SIC87 80.= Germany WZ 77.= France part of T34 and part of T38 (Services Non-Marchands).

Education: UK SIC80 93 (Education). = US SIC87 82.= Germany WZ 75.= France part of T34 and part of T38.

Government (UK SIC80 91 (Public Administration). = US SIC87 91-97.= Germany WZ 9.= France part of T38.

36

Manufacturing Industry Classifications.

The starting point was the UK 1980 SIC. Industries in other countries were chosen as those with similar overall coverage as that in the UK. No attempt was made to reclassify subsectors within the industry groups. Hence the matching is not exact but the division of manufacturing is at a sufficiently broad level that incompatabilities due to classification should be minimal. The UK SIC codes were as follows:

Chemicals: SIC80 25,26Rubber & plastics: SIC80 48Basic metals: SIC80 22Fabricated metal products: SIC80 31Mechanical engineering: SIC80 32Office machinery: SIC80 33Electrical engineering: SIC80 34Motor vehicles: SIC80 35Other transport equipment: SIC80 36Instrument engineering: SIC80 37Food, drink & tobacco: SIC80 41, 42Textiles: SIC80 43Clothing & leather: SIC80 44, 45Mineral products: SIC80 24Wood and furniture: SIC80 46 Paper, printing and publishing: SIC80 47Miscellaneous manufacturing: SIC80 49

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