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Gross Value Added

Gross Value added (GVA) is the typical measure of goods and services produced when analysing productivity. GVA is the difference between total output and intermediate consumption in the economy. In other words, it is the difference between the value of goods and services produced and the cost of raw materials and other inputs that are used up in the production process.

GVA reported in current prices is the value for that particular year, while GVA at constant prices presents the data for each year in the value of a particular base year. GVA at constant prices are used since current prices are influenced by inflation. GVA is sourced from the National Income and Expenditure dataset, which is published annually by the CSO.

Relationship between GVA and GDP and GNI

GVA is Gross Domestic Product (GDP) excluding taxes and subsidies on products. Gross National Income is equal to GDP at market prices plus net factor income from the rest of the world plus EU subsidies less EU taxes.

Foreign and Domestic and other sectors of the economy

This publication separates the economy into sectors that are Foreign dominated and Domestic and Other. Foreign-owned Multinational Enterprise (MNE) dominated NACE A64 sectors occur where MNE turnover on average exceeds 85% of the sector total. These sectors are Chemicals and chemical products (NACE 20), Software and Communications sectors (NACE 58-63) and Reproduction of recorded media, Basic pharmaceutical products and Pharmaceutical preparations, Computer, electronic and optical products, Electrical equipment, Medical and dental instruments and supplies (NACE 18.2, 21, 26, 27 and 32.5). Redomiciled PLCs (also known as corporate inversions) are foreign-owned MNEs in this analysis. All other sectors are categorised as domestic and other sectors.

Current and Constant data

This publication uses two methods for converting data from current to constant prices. One is the previous year’s prices method (PYP). This is used in calculating capital services where data aggregation is required for weighting. Chain linked GVA is used in the rest of the publication. 

Labour Input

Labour input is the change in hours worked multiplied by the two-period average of the labour share of GVA. Hours worked is usually considered to be a more precise measure of labour than employment as it takes account of differences in hours worked in different jobs due to factors such as leave, part time working arrangements and time unemployed during the year. The measurement of hours worked in this publication includes both employees and self-employed people. Hours worked for employees and self-employed were sourced from the Quarterly National Household Survey (QNHS) up until 2011. From 2011 onwards, hours of the self-employed continued to be sourced from the QNHS, while hours worked by employees is now sourced from the Earnings, Hours and Employment Cost Survey (EHECS), except for the hours for those in Agriculture, which continues to be sourced from the QNHS. The number of people in employment includes both employees and self-employed. Employees, except for Agriculture are sourced from the Earnings, except for Agriculture are sourced from the Earnings, Hours and Employment cost survey. The self-employed are sourced from the Quarterly National Household Survey.

The QNHS is a large-scale, nationwide survey of households in Ireland. It is designed to produce quarterly labour force estimates that include the official measure of employment and unemployment in the state (ILO basis). The survey size is 26000 households each quarter. EHECS is a quarterly survey designed to produce indices for monitoring change in labour costs in Ireland and across the European Union. The survey size is 7500 enterprises each quarter. It includes all enterprises in the NACE sectors B-S with 50 or more employees and a sample of those with 3 to 49 employees are surveyed each quarter.

Illustration of difference between labour productivity using hours worked and employment

X-axis labelGVA per EmployeeGVA per Hour
20003.519144351837014.00970337360381
20010.6550261461081881.13587809878957
20024.450736868430285.50678529593646
2003-0.5685483976719910.353444437513784
20041.702875520321822.32791917539636
20051.056449932010080.591859298243131
2006-0.01477252915953330.205584598606149
20073.337466007471794.10641528362753
2008-1.78636393568279-0.657963827089513
20091.456952611505663.22384371574961
20106.1048686026235711.7861976851709
20112.955707809276092.03331506333873
2012-1.28412948489417-1.35970742245691
2013-1.35667080628148-1.67407111760887
20144.521865704989663.71553688757876
201521.465556665054720.5357588158118
20160.9194905057047841.35804404129742
20173.823689783999323.13159829557982
20184.623578574830314.24505259569338

The above chart compares growth of GVA per Hour and GVA per Employee. GVA per Hour and GVA per Employee are calculated as GVA divided by the total number of hours and the total employment of both the self-employed and the employees. GVA per hour is usually considered to be a more precise measure of labour productivity as it takes account of differences in hours worked in different jobs due to factors such as leave, part time working arrangements and time unemployed during the year. Both measures mostly follow a very similar trend over the period. However, there over a five-percentage point difference in labour productivity increase measured by GVA per hour rather than GVA per employee in 2011 and a one percentage point difference 2009.

X-axis labelTotal GVA per Hour GrowthTotal GVA per Employee GrowthTotal Labour Hours GrowthTotal Employment GrowthTotal Real GVA Growth
20004.009703373603813.519144351837013.911987940883934.384751068258378.15918486297108
20011.135878098789570.6550261461081882.588071192383513.064656462267553.78751170151616
20025.506785295936464.450736868430280.5544979173473961.560470289761726.09344322296574
20030.353444437513784-0.5685483976719910.9166051434373641.839597220301211.27751785323902
20042.327919175396361.702875520321822.723685891240873.336383216016385.15331303451974
20050.5918592982431311.056449932010085.217278366025794.756484563647135.97933439458294
20060.205584598606149-0.01477252915953334.285591269956824.505738452994974.59333538098741
20074.106415283627533.337466007471793.536780841622634.2781406669617.85431791149604
2008-0.657963827089513-1.78636393568279-1.76594448856839-0.623570425285758-2.39688961667911
20093.223843715749611.45695261150566-9.92108649051276-8.19455911053017-6.52546883666764
201011.78619768517096.10486860262357-9.29542474075786-4.079408120940811.86351716685403
20112.033315063338732.95570780927609-1.28035818267357-2.180307807614090.735250833564657
2012-1.35970742245691-1.28412948489417-0.471671705469869-0.548262109410304-1.82387024984045
2013-1.67407111760887-1.356670806281483.228171510620082.90588712052931.55184745660333
20143.715536887578764.521865704989663.438941117842352.664504981581017.34429087132519
201520.535758815811821.46555666505474.322469699289653.554042120544425.8601235265878
20161.358044041297420.9194905057047843.215790581098893.6494069229064.67048154858917
20173.131598295579823.823689783999323.63789533830982.969060949110416.95249666292785
20184.245052595693384.623578574830313.480133288327123.117679250646447.93678539278478

The differences in measured labour productivity growth in 2011 and 2009 are due to larger falls in labour hours than employment. These instances are a form of labour hoarding where employers reduce the hours of employees rather than making them redundant.

Calculating Labour Productivity

Labour productivity measures output in the economy relative to labour input. It is calculated as GVA at constant prices divided by labour hours in the economy.

Labour Productivity = GVA
Total Hours of the Employed and Self-Employed

 

 

Contributions to Labour Productivity Growth

In order to look at labour productivity in more detail, it is possible to break labour productivity growth into the contribution of capital deepening and MFP.

The contribution to labour productivity growth is calculated as follows:

Labour Productivity Growth = ln ( Labour Productivityt ) = ln ( GVAt ) - ln ( Hours Workedt )
 Labour Productivityt-1  GVAt-1  Hours Workedt-1

Capital deepening, otherwise known as the growth in capital services per hour worked, is calculated as follows:

Capital Deepening = ln ( Capital Servicest ) - ln ( Hours Workedt )
 Capital Servicest-1  Hours Workedt-1

The contribution of capital deepening to labour productivity growth is calculated below:

Capital Share two-period average ( ln ( Capital Servicest ) - ln ( Hours Worked t ))
 Capital Servicest-1  Hours Worked t-1

Further information can be found here: see OECD

GVA Labour Share

The labour share is defined as the proportion of GVA growth attributed to labour with the remainder being attributed to capital. The labour share reflects the proportion of national income received by workers in the form of wages and salaries. A falling labour share often reflects an increase in the return to capital. The labour share was calculated following OECD methodology:

Labour Share = COE + labour share of GMI + labour share of taxes
 COE + GMI + taxes + GOS

 

The labour share of GMI=     ( COE ) x Self employed

 
 Employees

 

The capital share is 1- labour share.

*COE=Compensation of Employees

*GMI=Gross Mixed Income

*GOS=Gross Operating Surplus

Nominal Unit Labour Costs

Nominal unit labour cost (ULC) measures employee compensation relative to real labour productivity. Growth in an economy’s unit labour cost suggests that the cost of labour in the economy is rising relative to labour productivity, decreasing competitiveness. On the other hand, a decline in unit labour cost suggests that the cost of labour is declining relative to labour productivity, increasing competitiveness.

Nominal ULC (ULC) is calculated as:

Compensation of employees in current prices/Total employment, not including self-employed
Chain-linked GDP at market prices/Total employment, including self-employed

 

 

The sectoral breakdowns in unit labour cost between the Domestic & Other and Foreign sectors in this publication are calculated using GVA rather than GDP since taxes and subsidies, which are included in GDP, cannot be disaggregated by sector.

Capital Input

Capital input is the flow of capital services multiplied by the two-period average of the capital share of GVA. This publication terms capital input as capital services in charts for clarity. Capital services rather than capital stocks are used to measure capital deepening, capital input and calculate multi-factor productivity. The main difference between the volume index of capital services and the stock measure of capital is the way in which different types and ages of assets are aggregated together. In the volume index of capital services, each capital asset class is weighted by its user cost. The user cost is the estimated price that the user would have to pay to hire the asset for a period. In contrast, capital stock values are calculated using asset price weights for each asset type and period.

Calculating Capital Services

Capital services are the services derived from the net capital stock of produced fixed assets. Data on produced fixed assets are available in the CSO’s Estimates of the Capital Stock of Fixed Assets release.

The aggregate capital services index is obtained using a chained superlative Törnqvist index aggregation of the capital stocks of the six asset categories using estimated user costs (also known as rental prices) for each asset type. Each user cost reflects the nominal rate of return to all assets within the industry and rates of economic depreciation and revaluation for the specific asset. The steps in calculating capital services as follows:

1. The nominal rate of return is calculated for all assets. The numerator consists of capital compensation plus the value change in the deflator for constant productive stocks minus the product of the asset price deflator, depreciation and constant price net capital stocks. The denominator consists of the asset price deflator multiplied by productive stocks, summed for all asset types. Depreciation rates are obtained for each asset category by dividing consumption of fixed capital by constant price net capital stocks (also known as productive stocks). Capital compensation is calculated as gross value added minus labour compensation. Labour compensation is calculated by adding employee and self-employed compensation.

Rate of Return = Capital compensation + numerator term 2 + numerator term 3
 Denominator

Term 2 of Numerator =

Σ
Asset Types

(Asset Price Deflatort - Asset Price Deflatort-1) x Constant Productive Stocks

Term 3 of Numerator =

Σ
Asset Types

Asset Price Deflator x Depreciation x Productive Stock

Denominator =

Σ
Asset Types

Asset Price Deflator x Productive Stock
X-axis labelNominal Rate of Return
200030.4208043283036
200126.7897880769222
200223.5948147963535
200322.3145531081396
200420.5369803077896
200514.6804176963957
200615.9745791835574
20077.01880398627724
2008-3.04601435077645
2009-2.15243161918096
201011.5438078368048
201118.1597943411076
201221.2345831541831
201320.403243531115
201422.0230397772018
201519.3762442375373
201618.4098150193812
201719.1506592534536
201819.7630912947033

2. The rate of return is then revaluated according to the depreciation rate and deflation rate for the specific asset to form user costs.

User Cost = (Overall Rate of Returnt x Asset Price Deflatort-1) + (Depreciation Rate x Asset Price Deflator) - (Asset Price Deflatort - Asset Price Deflatort-1)
Other Building and StructuresTransport and EquipmentOther Machinery and EquipmentCultivated AssetsIntangiblesDwellings
200019.304789389353434.63565819583836.884019922361628.508824985576825.47159354018117.1372578139641
200119.852940201145631.694001249933837.39829636794518.938459306516325.827088962490116.3656319265213
200223.307972432789328.56542383057338.745504495068416.960527274652828.192741215604715.4854100545686
200323.850574453563928.074579479395441.992113174834813.589104018640724.751651022943313.1014878357672
200419.37197527782126.299916378687732.02261833942069.2874899009085123.165324988557314.7544425735059
200514.410544020126519.795600089573223.05357497435279.8948429229357819.180725471204115.6570014130452
200616.539057349651521.465063129268219.52376183514577.5631191392612322.569776768665414.2641247075956
20079.702913596011213.445886818026716.75644007525836.5983372588342712.055745020551817.8274734517598
20088.870390763258523.032240048739283.9535624499697302.3762778631252318.9328175638882
200912.43732472214763.695280407663282.119764445883447.7392476391159.2596116895348317.7617729720509
201017.413122003659319.099377052972316.53808812162627.5172760616021722.904813723245814.4699397671026
201115.827022833986324.270626637543624.29492327756921.2445049720310630.867656474364715.1739015490814
201216.405134870756624.29049599218226.246683099673913.035391724981124.766470386993616.502753619503
201316.205040702203223.772973492960526.206140982007533.730438123135529.943228817135715.9450952548312
201416.912987041046226.052756270401724.023756492487420.295448345190533.362677752984616.147843610856
201514.859871432743922.663415532378823.80484864924316.413458809764727.642712321708315.9079827041507
201614.093232181315222.594423121417129.93716620524533.035428654017425.250650114410115.1530657233588
201715.777254879209723.42270079219824.389839500595816.95139772614727.113050335459615.9052389268768
201816.721385585901624.738423102716925.866044176721123.110993597706428.782390204814519.7606702291187

3. The user costs are then weighted by industry productive stocks.

User Cost Weight = (

Σ
Asset Types

User Cost x Productive Stock)-1 User cost for all assets x Productive stock for all assets
Cultivated AssetsOther Machinery and EquipmentTransport EquipmentIntangiblesOther Building and StructuresDwellingsConfidential
20001.42823023246518.65566841309299.337204756399253.1624023867601926.215256937059141.20123727422350
20010.74720068261607518.27914394332028.678435185369863.9410253148794427.936550573096940.41764430071750
20020.54990551439925316.75269015181868.738629327195224.8391351478539431.750043295667237.36959656306580
20030.4117765224942317.68506283871069.248933727467444.6267760729143433.063433285870734.96401755254260
20040.27549379291007812.16694665484549.825594501887994.9330135787656627.20319901039445.59575246119690
20050.2508374779602118.3668401477605811.24175721847254.7062805376642921.021527545767554.4127570723750
20060.1820944236518746.6750149922831812.16850199204965.9561609333783324.438585691053650.57964196758340
20070.158588455796845.903187758100587.729044658463383.2839711309415415.04101200303867.88419599365960
200801.538463827113471.905106604266030.79220333387404516.549263328858279.21496290588830
20090.2497156413457880.869244714928232.736746438272643.8484915491675223.874599710142468.42120194614340
20100.1950739635363676.2365036132032614.3211889060919.327428592862127.405902683243742.51390224106360
20110.03147090595969368.8103963264747617.404572805099712.03866780083621.873426111630339.84146604999960
20120.3902747012864168.9209850333062817.67768514755799.7567516593801122.000508440824541.25379501764480
20131.04411160736198.9580720941033317.452653532622812.350154967860921.721437844640338.47356995341070
20140.4830896789828767.7564758543631221.979083432528213.283089750225321.20880115121535.28946013268550
20150.09838387120466965.225749083252910012.279138702437722.286405584288360.1103362167178
20160.5795677425820196.798988508605120012.406578466211721.432573837396258.7822882029413
20170.2385214227636434.816917747721170013.980767590062621.366249391508559.5975441391375
20180.2936963580684624.819497066161110014.693653126245925.124770215443255.0683855628471

4. The change in capital stocks is then weighted by the two-period average of the user cost and multiplied together to form a Törnqvist index of capital services. The log of these values can be taken to show the contributions to capital services by asset.

ln (1+Capital Services)=

Σ
Asset Type

Two-period average of user cost weight x Δ ln (productive stock)
Other Building and StructuresTransport and EquipmentOther Machinery and EquipmentCultivated AssetsIntangiblesDwellingsConfidentialTotal
20001.596356790260542.059905797102881.707504047487820.02223390281341150.2758479262983862.1526695435106407.81451800747368
20011.575537901076491.206310881303670.80987972125592-0.1156164054944170.9101066514399522.2636781012027106.64989685078433
20021.705340620213242.34370562875760.51637390570269-0.01507545333968071.038184431478282.1004141061555607.68894323896769
20031.813361930378660.9779731168623590.924086842339125-0.009376663124830330.6960444410915312.1196368856477106.52172655319455
20041.518442908968251.512621823952340.6648454082699620.006210734776250550.6613023344938772.4992579804177806.86268119087847
20051.330720445476844.934507094361930.617998274561965-0.02720042690169520.724533870864963.46161728168705011.042176540051
20061.239657786516610.880061063114080.3923918402030020.006695861261276020.6017463346506163.4511322666587606.57168515240435
20071.126072220739520.6265490926837820.3890392215949270.009433349044258850.3298105608820673.3177571040649105.79866154900947
20081.07493451416578-0.09400237515088170.132128800713394-0.001045512993221640.07523552936287993.1950265246379904.38227748073594
20091.366454888323070.169415361526327-0.02225457431018460.01521156203831470.1771313027214111.5026854013845803.20864394168352
20100.9941253723747250.698709482585519-0.0914414497263687-0.03104537654649920.4103664129743240.35522379415171502.33593823581342
20110.3653111034996080.5801025542553890.09883381288791650.003886051711376020.332826318412299-0.011883900614179401.36907594015241
20120.2973968963140791.41216772850515-0.06932851226398490.04592951288244271.17673397965275-0.19051396152841902.67238564356202
20130.4337606074142660.8955997181533580.506515319457740.06379104752834520.394911601259342-0.16345767906477602.13112061474828
20140.5739682878567814.957117574022790.610063515443745-0.1053228497738161.0740480545458-0.098167954025002207.01170662807031
20150.49611877973828700.4156923199668570.01651370373121070-0.048980511115120561.616246906876462.4955911991976
20160.40850623057489800.1911078951790140.054540456980977400.0339006542005184.124223673562474.81227891049788
20170.49016409776698300.0656821939626812-0.050240072923872600.1314285963626211.538444144992132.17547896016055
20180.61514040102519300.2077659805612350.003228082095904800.272160378189647-2.60971071946175-1.51141587758977

Further information on calculating capital services can be found in the following publications:

Aggregate and Industry-level Productivity Growth: OECD Manual. Organisation for Economic Co-operation and Development (2001).

Biatour, Bernadette, Geert Bryon, and Chantal Kegels. "Capital services and total factor productivity measurements: impact of various methodologies for Belgium." Federal Planning Bureau, Working Paper (2007): 3-07. 

Capital Stocks

This publication uses net capital stocks rather than gross capital stocks because, unlike the latter, they incorporate depreciation. Produced fixed assets are assets which result from human effort.  They exclude financial assets and natural assets such as land, mineral deposits etc. Produced fixed assets comprise Dwellings and other buildings and structures (excluding the land on which they are built), Machinery and equipment (including transport equipment), Cultivated assets (e.g. Livestock for breeding such as dairy cattle) and Intangible fixed assets (Research and development, Computer software, Original works of art including musical and literary works, Mineral exploration).

Capital Intensity and Capital Deepening

Capital intensity is the ratio of capital services to hours worked in the economy (i.e. capital services per hour). The higher the capital to hours ratio, the more capital intensive the production process becomes. Capital deepening is the growth in capital services per hour worked. It is also possible to show the contribution of capital deepening to labour productivity growth by weighting capital deepening by the two period average capital share of GVA, as shown in the subsection contributions to labour productivity.

Capital intensity is calculated as follows:

Capital Intensity =   Capital Services
 Hours Worked

 Multi-factor Productivity

Multi-factor productivity (MFP) measures improvements in the efficiency in the utilisation of labour and capital. It is the residual output growth of an industry after calculating the contribution from capital and labour. Positive MFP results from factors such as technological change, efficiency improvements, returns to scale and reallocation of resources. Negative MFP indicates lower output from current capital and labour input relative to the output from current capital and labour input in the previous period.

Calculating Multi-Factor Productivity

The following methodology shows the log approach for calculating multi-factor productivity. The first step is to create a quantity index of combined inputs:

Quantity Index of Combined Inputs = (  Labour Inputt )  2 year average of the labour share of GVA  x (Capital Services) 2 year average of the Capital Share of GVA
 Labour Inputt-1

Then one creates an index of GVA divided by the previous period:

GVA Index = GVA Constant Basic Pricest
GVA Constant Basic Pricest-1

Then one divides the GVA index by the quantity of combined index. Subtract one from this to calculate multi-factor productivity – the residual of GVA growth that is not explained by capital or labour inputs.

MFP =

 GVA Index - 1
Quantity Index of Combined Inputs

Since MFP, capital and labour are multiplicatively linked, we add one to MFP, take the natural log of it and add it to similarly calculated capital and labour input growth rates to show the additive composition of GVA growth by these three factors.

ln (  GVA Constant Basic Pricest ) = ln ( Labour Inputt ) 2 year average of the labour share of GVA
 GVA Constant Basic Pricest-1 Labour Inputt-1
+ln (Capital Services)2 year average of the labour share of GVA + ln (1 + MFP Growth Rate)

This can be more simply expressed as:

ln (GVA index) = ln (Labour input index)ln (Capital input index) + ln (1 + MFP Growth Rate)

Calculating QALI (Quality Adjusted Labour Input)

These first set of comprehensive QALI estimates produced by the CSO and are of an experimental nature, therefore a note of caution is advised.

The Quality Adjusted Labour Input (QALI) is an input into measuring productivity that measures the growth in hours worked accounting for the composition of the workforce. As a result, QALI provides a more complete picture of the labour input, as it no longer assumes that each hour worked is of the same quality e.g. it does not assume that an hour worked by a highly experienced surgeon and an hour worked by a newly hired teenager at a fast food restaurant are the same. A key assumption underlying QALI is that higher wages reflect higher productivity. To perform the quality adjustment, hours worked are differentiated into n types of workers determined by their characteristics: age, education, industry and gender.

The gender categories used are Male and Female, while age is broken out into 3 categories, 16-29, 30-49 and 50-65. Education is classified into 4 categories: Low-Below Level 5 (education up to Junior Cert level), Medium to Low -Level 5 (education up to Leaving Cert), Medium to High-Levels 6 and 7 (Higher Cert, Advanced cert, Ordinary bachelor’s degree) and High-Levels 8,9 and 10 (Honours bachelor’s degree, Masters, PhD). The industry classification that was used was A64.

To perform the analysis for the period 2000-2018, income was taken from a variety of sources. For the years 2011 to 2013, income, hours worked, and education levels were taken from combined social insurance and revenue files linked to the Labour Force Survey (LFS). The variables for the latter years of 2013 to 2018 were sourced from the “Earnings Analysis Using Administrative Sources” publication. Data for the remaining years were sourced from the National Employment survey (NES). These data sets were then merged and linked together to arrive at a complete data set. Hours worked were scaled up to align with National Accounts employee hours. As a result, the Agricultural sector is not included in the analysis due to the large proportion of self-employed working in the sector.

To calculate the Quality Adjusted Labour Input (QALI), the approach used by OECD will be used.  A Toernqvist Index (Qtt-1) is usually the preferred method and CSO follow this approach in the analysis.

     Δ L(t)     =Σi (  wi(t)+wi(t-1) )    Δ hi(t)  
L(t)   2 hi(t)


Where:

Weightit=  Earningsit
 Σ Earningsit

 The hours worked can be broken out into n groups, in this case, it is high, medium to high, medium to low and low skilled labour, age groups of 16-29, 30-49 and 50-65 and gender of male or female. The growth in hours worked over the period is represented with the Toernqvist index, which is typically defined as the weighted geometric average of growth rates of hours worked. The weights used are the income shares across the different groups, which are calculated by expressing earnings of each group over the total earnings for all the groupings. As a result, the income shares will sum to one.

The labour composition can be found by calculating the difference between the QALI Adjusted Labour Input measure and aggregate hours.

Further information can be found at: OECD and Eurostat

KLEMS

It is important to point out, that this is the second time the CSO has released KLEMS results for Ireland. As a result, these are still considered experimental statistics in the publication.

As discussed in the glossary, KLEMS (stands for Capital, Labour, Energy, Materials and Services) provides a more detailed statistical decomposition on the inputs contributing to output growth and production efficiency. This helps policy makers and economists to identify factors associated with economic growth and allows for a more disaggregated analysis of aggregate and industry productivity growth, such as changes in the relative importance of input components over time periods. Under the KLEMS framework, gross output can be broken down into the contributions from Labour, Capital and Multi-factor productivity, as well as contributions from intermediate inputs. The intermediate inputs can be broken down into contributions from Energy, Materials and Services.

Within intermediate inputs, the classification into energy (E), materials (M) and services (S) is beneficial in that they have distinctively different roles in the production process. This helps in evaluating trends in the way industries interact.

Supply and Use tables are the building blocks behind the KLEMS framework. They trace the supply and use of all commodities in the economy, as well as the payments for primary factors labour and capital. The supply table indicates for each industry the composition of output by product. This is used to derive industry gross output indices. The Use table indicates for each industry the product composition of its intermediate inputs and value-added components. This is used to derive the intermediate input and value-added series in the national accounts (EUKLEMS Methodology Part 1). Due to the change in ESA standards in 2008 from NACE Rev 1 to NACE Rev 2, a bridging table was used to map sectors from NACE Rev 1 to NACE Rev 2. Because of this change, the Manufacturing sector excludes NACE Code (18) and includes NACE Code (95), while the Information and Communications sector includes NACE Code (18). The sector Other Service Activities excludes NACE Code 95.

The methodology followed by the CSO closely follows that as presented in the paper by the Australian Bureau of Statistics (2015). The production function is defined as, where Y= Index of gross output, A is growth in gross output MFP, K is an index of capital input and L is an index of labour input, while IL is an index of intermediate inputs. To estimate the growth in combined inputs, a toernqvist index with nominal shares as weights is used. KLEMS MFP is the same as GO based MFP except that in the growth accounts, the energy, materials and services input indexes are separately derived, which aggregate to the intermediate index. Therefore,

( Intermediate Inputt ) ( Energyt ) 2 period average Energy ( Materialst ) 2 period average Materials  
    
 Intermediate Inputt-1  Energyt-1  Materialst-1

 

(  Servicest ) 2 period average Sevices
 Servicest-1  

 These 2 period averages are the shares of the intermediate input costs.  In full,

Δ ln Gross Output = (2 period average Income Share of Capital x Δ ln Capital) + (2 period average Income Share of Labour x Δ ln Labour) + (2 period average Income Share of Energy x Δ ln Energy) + (2 period average Income Share of Materials x Δ ln Materials) + (2 period average Income Share of Services x Δ ln Services) + Δ ln MFP

 

To calculate the shares used in calculation of the contributions to gross output, total income will be equal to nominal industry gross output in year t.

Total Income = Gross Operating Surplus (GOS) + Capital Share of Gross Mixed Income + Labour Share of Gross Mixed Income + Compensation of Employees (COE) + Taxes and Subsidies attributed to Capital + Taxes and Subsidies attributed to labour + Nominal Intermediate Inputs

 

The income shares of labour, capital and intermediate inputs are calculated as follows:

Income Share of Capital =  GOS + Capital Share of Gross Mixed Income + Taxes and Subsidies attributed to capital
 Total Income

 

 

Income Share of Labour =  COE + Labour Share of Gross Mixed Income + Taxes and Subsidies attributed to labour
 Total Income

 

Income Share of Intermediate Inputs=  Nominal Intermediate Inputs
 Total Income

 

The 2-period average can be calculated as follows:

 

2 period average of capital = (Income Share of Capitalt + Income Share of Capitalt-1)/2

2 period average of labour = (Income Share of Labourt + Income Share of Labourt-1)/2
2 period average of Intermediate Inputs = (Income Share of Intermediate Inputst + Income Share of Intermediate Inputst-1)/2

 

MFP is calculated as a residual from the equation detailing Δ ln Gross Output.

APPENDIX/ DOMAR AGGREGATION RELATIONSHIP

For the aggregate economy, Domar aggregation implies that the relationship between the Value-added (VA) and Gross Output (GO) MFP is the following:

 

TNVAi

(  PNgoGON )  TNGO  
  pNvaVAN

 

The industry-level Domar Aggregation Relationship will then state that its respective VA MFP will equal the GO MFP weighted by the ratio of its Gross Output to its Value-added:

TiVA =

(  PigoGOi )  TiGO  
  pivaVAi

 In the KLEMS analysis, GO MFP was then re-estimated by imputing the original VA MFP estimates from our GVA estimates into the Domar relationship to give the following:

TiGO =

(  PivaVAi )  TiVA  
  pigoGOi

 

Further information on the KLEMS framework can be found below. The bridging table used for the supply use tables is available below:

Show Table: Table 2.1 NACE Converter - Bridging Table


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