G6 INTRO KIT (GEN

Note 1 EU-15 ex represents the 10 out of the 15 EU member states as of 1 January 1995 for which total factor productivity measures are available including Austria, Belgium, Denmark, Finland, France, Germany, Italy, Netherlands, Spain, and the United Kingdom. The 5 excluded are Greece, Ireland, Luxembourg, Portugal and Sweden.

Note 2 EU 15 represents the EU member states as of 1 January 1995. It includes the EU-15 ex states, plus Greece, Ireland, Luxembourg, Portugal and Sweden.

Note 3 EU 10 represents the 10 EU members states joined on 1 May 2004, comprising Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovak Republic and Slovenia.

Note 4 EU 25 represents the 25 states in EU15 and EU10.

Note 5 EurozoneEx represents the 8 countries in the Eurozone for which total factor productivity measure are available, comprising Austria, Belgium, Finland, France, Germany, Italy, Netherlands, and Spain.

Table 6.1: Labour Productivity and Multi-Factor Productivity Growth Rates Source: EU KLEMS ISIC Rev. 3 updated March 2011and EU KLEMS ISIC Rev. 4

Making use of the EU KLEMS database, the average labour productivity growth and multi-factor productivity growth of manufacturing sectors, construction sectors and the whole economy are calculated for the EU-15 ex and a number of other advanced economies including the US and Japan in table 6.1. Labour productivity growth is measured by the growth of gross value added output volume per hour worked and

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multi-factor productivity growth is measured by the difference between the gross value added output volume growth and the weighted capital and labour input growth.

In theory value added output volume should be derived using the double deflation method by separately deflating the gross output and intermediate inputs. However, value added output volume indices in EU KLEMS are based on the national accounts methodology of that particular country (Timmer et alt 2007: pp. 21), so the method used varies country by country.

For example, ONS (1998) explains that in the method used in the UK, double deflation is only used in agriculture and electricity because of the unavailability of timely information, particularly the deflators of the inputs, in other sectors. In other sectors, ONS assumes the value added output volume is proportional to the gross output volume in the short run. Every five years, ONS adjusts the ratio when ONS rebases the output measure.

For all countries and sets of countries, with the exception of Belgium, table 6.1 provides strong evidence that average labour productivity growth of the manufacturing sectors across the sample countries is higher than the average labour productivity growth of the whole economies, which in turn is higher than the labour productivity growth of the construction sectors. The result is not surprising because manufacturing is a sector subject to substantial mechanisation and automation in the last few decades. On the other hand, the construction sector is widely criticised as a sector of low labour productivity growth. For example, Jorgenson and Stiroh (2000) report that construction had the lowest growth in average labour productivity of any sector in the US between 1958 and 1996.

This pattern by and large repeats in the multi-factor productivity growth as shown in table 6.1, with the list of exceptions expanding to include the construction sectors in the UK, Belgium, Denmark and Spain. Although total factor productivity growth is a more appealing concept in principle to measure productivity growth (Crawford and

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Vogl 2006), this chapter focuses on the labour productivity measures for the following reasons:

 EU KLEMS data provides sectoral multi-factor productivity growth on value added output instead of sectoral total factor productivity growth on gross output⁵⁵, so the multi-factor productivity growth figures would include any embedded improvement in the intermediate inputs over time. Figures 6.1 and 6.2 show that the intermediate inputs in the form of materials and business services accounted for more than 50% of the gross output of the UK construction sector, whereas capital compensation accounted for less than 10%

most of the time. Therefore using the multi-factor productivity figures from EU KLEMS would continue to leave out direct measurement of the contribution of the intermediate inputs to productivity growth while inducing measurement errors of the capital service;

 Estimation of multi-factor productivity growth requires measures of capital services which involve estimation of the capital stock by the perpetual inventory method and various rental prices of the assets. These estimates would be less accurate than the estimate of labour input.

 Since the estimation methodology assumes the capital service is proportional to the capital stock in each capital stock category, therefore, from an industry’s perspective, the estimate of the capital service cannot be reduced in the downturn of the economy other than via depreciation at an assumed constant rate. In other words, the multi-factor productivity measures would pick up the variations in capital utilisation rates at various times of the economic cycle.

 The capital stock should measure the amount used rather than owned by an industry. However, Timmer et al (2007a: pp. 42) states that the figures

55 Earlier versions of EU KLEMS, released in March 2008 or before, reported total factor productivity indices for some industries. However, according to Timmer et al (2010: pp 89-90), the total factor productivity indices on gross output are based on the multi-factor productivity indices on value added.

Under the restrictive assumption of separable production function, the growth of multi-factor productivity of value added output (∆MFP) is proportional to the growth of total factor productivity of gross output (∆TFP):

∆𝑀𝐹𝑃 = 𝑉𝑎𝑙𝑢𝑒 𝐴𝑑𝑑𝑒𝑑 𝑖𝑛 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝑀𝑎𝑟𝑘𝑒𝑡 𝑃𝑟𝑖𝑐𝑒

𝐺𝑟𝑜𝑠𝑠 𝑂𝑢𝑡𝑝𝑢𝑡 𝑖𝑛 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝑀𝑎𝑟𝑘𝑒𝑡 𝑃𝑟𝑖𝑐𝑒× ∆𝑇𝐹𝑃

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reported in EU KLEMS are in accordance with ownership. This is particularly problematic in construction as the bulk of its capital is transport equipment and other machinery and equipment. If this equipment is owned by the construction companies or hired (with operators) from plant hire firms themselves classed to the construction industry, they are counted as capital of the construction sector. However, if this equipment is leased or hired without operators from asset leasing companies not belonging to the construction industry, then the equipment itself is not counted as part of the industry’s capital stock, but user charges are counted as intermediate inputs (Crawford and Vogl 2006: p212 and footnote 10). Given leasing of capital equipment is popular in construction, the capital stock and the capital service statistics are very unlikely to be representative.

 As Ive et al (2004) point out, multi-factor productivity measurement requires a series of assumptions about the production function, growth theory and income distribution theory, such as constant returns to scale, profit maximising behaviour, separable production function and competitive markets, that cannot be easily tested or verified. If these assumptions do not hold for the data, the multi-factor productivity measures would be distorted.

 Abdel-Wahab and Vogl (2011), which analysed the EU KLEMS database, Tan (2000) and Mao et al (2003) all reported negative multi-factor productivity growth of construction sectors of some countries for a more than a decade.

This result is counter intuitive and may be taken as a sign of measurement errors.

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Figure 6.1: Composition of Gross Output of UK Construction in Current Market Prices (£ million), 1970 to 2005

Source: EU KLEMS March 2008 Release

Figure 6.2: Composition of Gross Output of UK Construction in Percentage Shares, 1970 to 2005

Source: EU KLEMS March 2008 Release

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