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 is a 5.7% point difference in labour productivity growth measured by GVA per hour rather than GVA per employee in 2011 and a 1.8% difference for 2009.

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.

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:

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

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

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:

The labour share of GMI is set to the total GMI figure if the equation above implies a labour share of GMI greater than total GMI. In this case, the capital share of GMI is set to zero.

The capital share is 1- labour share.

*COE=Compensation of Employees

*GMI=Gross Mixed Income

*GOS=Gross Operating Surplus

KLEMS Gross Output Shares

In calculating a Gross Output-based MFP measure, the contributions of the primary inputs (Labour and Capital) and the intermediate inputs (Energy, Materials and Services) to Gross Output growth are weighted using a two-period average of their cost shares. The cost shares of Labour and Capital are equivalent to their GVA shares multiplied by the ratio of Gross Output to GVA, while the cost shares for the intermediate inputs are calculated as the ratio of their total cost to Gross Output.

Nominal Unit Labour Costs

Nominal Unit Labour Cost (ULC) measures hourly 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:

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.

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

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

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.

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:

 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:

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

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.

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.

This can be more simply expressed as:

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 2000-2019, the primary data source was the CSO Survey of Income and Living Conditions which was used in order to obtain a consistent series that provides Earnings, Education Gender and Economic Sector for the sample population covered by the survey.  The survey is relatively small compared to the LFS but has the advantage of reporting the necessary variables for the QALI. Some interpollations were necessary to arrive at estimates for the earlier years. 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 (Q^t_t-1) is usually the preferred method and CSO follow this approach in the analysis.

Where:

 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 third 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,

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

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.

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

The 2-period average can be calculated as follows:

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

DOMAR AGGREGATION RELATIONSHIP

Domar Aggregation or Domar Weights are used to combine our A21 industry-level, gross output-based MFP to higher-level aggregates. Domar aggregation imposes a relationship between the value-added MFP and the gross output MFP. It states that an industry’s value-added MFP is equal to its respective gross output MFP multiplied by its Domar weighting. This weighting is the ratio of its gross output to aggregated value added in the A21 sector Irish economy. The advantage of the Domar methodology of estimating MFP is that it imposes a direct relationship between the value-added MFP estimate and the gross output MFP estimate, resulting in consistency between the GVA and GO MFP estimates and near perfect positive correlations. The sum of the Domar weights are greater than 1 as the sum of each industry’s output will be greater than the sum of value-added in each industry. The economic rationale for this is that industries interact by using each other’s output as intermediate consumption, thus contributing to productivity and gross output in the aggregate economy. The Domar methodology was used to validate the KLEMS results in the publication.

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

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:

 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:

Further information on the KLEMS framework is available at the EU KLEMS website. The bridging table used for the Supply and Use tables is available below: