The importance of energy quality in energy intensive manufacturing: Evidence from panel cointegration and panel FMOLS

Transcription

1 1 The importance of energy quality in energy intensive manufacturing: Evidence from panel cointegration and panel FMOLS Brantley Liddle, Centre for Strategic Economic Studies, Victoria University Brantley Liddle Senior Research Fellow Centre for Strategic Economic Studies Victoria University Level 13, 300 Flinders Street Melbourne, VIC 8001 Australia ABSTRACT This paper expands on the panel GDP-energy cointegration modeling literature; it does so by using data disaggregated along sectoral lines and adjusting energy consumption for the quality of the energy source (e.g., electricity is of higher quality than oil, which is of higher quality than coal) in order to examine the role of energy quality in the five most energy intensive manufacturing sectors (iron and steel, non-ferrous metals, non-metallic minerals, chemicals, and pulp and paper). The database was constructed by combing energy consumption (and energy price data to construct the energy quality index) from the IEA with economic data (value added, labor employed, and physical capital) from the OECD s Structural Analysis Database. In addition to finding the variables analyzed are panel I(1) and cointegrated for each sector-based panel, the long-run elasticity estimates (from panel FMOLS) indicate the importance of energy quality primarily the shift toward the use of high quality electricity in these energy-intensive manufacturing sectors. In each case, the elasticity for energy quality is greater than that for conventionally measured energy consumption sometimes orders of magnitude greater. Indeed, the elasticity of conventionally measured energy consumption is insignificant to very small for three of the five sectors. Also, when using the energy quality measure, the importance of energy consumption relative to the other production factors stands out. Such results are useful for both energy-gdp cointegration/causality modelers and CGE modelers, who may need to estimate elasticities. Keywords: Energy intensive manufacturing; Panel cointegration and Panel FMOLS; energy quality index; disaggregated energy consumption

2 2 1. Introduction and literature review There is a substantial and growing literature employing cointegration modeling to examine the energy-gdp relationship at a national scale (see reviews by Payne 2010a and 2010b and Ozturk 2010); among the most popular models is one based on an aggregate neoclassical production function. Among the consensuses emerging from this vast literature are: (i) models should be multivariate to avoid the missing variable bias; (ii) analyses should employ panel data to improve the power of unit root and cointegration tests that otherwise can be impaired by the short time spans typically available for single countries; and (iii) investigators should determine the magnitude, sign, and significance of elasticities, rather than just performing Granger-type causality tests (see the concluding sections of the two Payne reviews). This paper borrows those three consensus ideas along with two seldom used ones: (i) to use data disaggregated along sectoral lines, and (ii) to adjust energy consumption for the quality of the energy source (e.g., electricity is of higher quality than oil, which is of higher quality than coal); those five ideas are used to examine with OECD panel data the role of energy quality in the five most energy intensive manufacturing sectors. The finding that accounting for energy quality does indeed impact the production function elasticity estimates should be of interest for both energy-gdp cointegration/causality modelers and CGE modelers (who may need to estimate elasticities too). Perhaps the first to use the aggregate production function model (GDP as a log-linear function of energy, labor, and capital) was Stern (2000); he focused on the US, found cointegration among the four variables, and tested for Granger-causality (but did not estimate elasticities). Recently, several papers have used panel data for such an aggregate model and have estimated elasticities via methods like panel Fully Modified OLS (FMOLS) and panel Dynamic OLS (DOLS). For example, Narayan and Smyth (2008) focused on G7 countries; Lee et al. (2008) focused on OECD countries; Lee and Chang (2008) on Asian countries; and three papers from Apergis and Payne (2009a, 2009b, and 2010) considered Central American, former Soviet, and South American countries, respectively. All six studies found the production variables to be cointegrated, and the long-run energy elasticity estimations (all statistically significant) ranged from 0.12 to 0.42.

3 3 Very few energy-gdp cointegration studies, however, have used disaggregated data and the production function model (perhaps, only one). Furthermore, to my knowledge, no studies using disaggregated data and the production function model have (i) used data disaggregated at a greater level than manufacturing (i.e., ISIC two-digit or higher resolution) or (ii) estimated elasticities. Soytas and Sari (2007) considered manufacturing in Turkey, and determined that electricity consumption, capital, labor, and value added are integrated order one and cointegrated. (They performed Grangercausality tests but did not estimate elasticities.) Neither Sari et al. (2008), who focused on industrial output in the US, nor Ziramba (2009), who focused on manufacturing in South Africa, considered capital, but both did estimate elasticities via the autoregressive distributed lag approach. Sari et al., in addition to focusing on industrial production, appeared to use aggregate employment and coal consumption (as opposed to industrial employment and consumption). They found those three variables to be cointegrated, but their estimated long-run coefficients for employment and coal to be negative, significant, and large, and their estimated short-run coefficients for both to be insignificant. Ziramba (2009) analyzed manufacturing production and manufacturing employment, and separately, both electricity and oil consumption (again, it is not clear whether that consumption was economy-wide or industrial consumption). Ziramba found both sets of variables to be cointegrated (production, employment, and electricity; and production, employment, and oil); but for both models, employment s elasticity is highly insignificant (p-value 0.7 or higher), as is oil s elasticity. Electricity s elasticity is significant and equal to The Stern (2000) paper used a quality weighted index of energy, an inclusion apparently motivated by the results of a still earlier paper, Stern (1993). Stern (1993) considered both a conventional measure of energy consumption and a quality weighted index of energy (that paper, also based on the US, used an aggregate production function model, but did not test for cointegration). Consideration of a quality weighted index is important because some forms of energy can produce 1 Bowden and Payne (2009), employing US data, used the Toda-Yamamoto causality tests, and thus, did not test for cointegration (they did not estimate elasticities either). In addition, they appeared to analyze aggregate real GDP, gross fixed capital formation, and civilian employment with industrial primary energy consumption. Thus, as with Sari et al. (2008) and Ziramba (2009), they were not technically using a sectoral-level production function model.

4 4 more work than others: a unit of electricity is of higher quality than a unit of oil, which itself is of higher quality than a unit of coal. Also, arguably reflecting these differences in productivity, electricity tends to be the most expensive energy source, followed by oil, and coal tends to be among the least expensive energy sources. Berndt (1978) describes this situation as follows: the different prices of energy forms per Btu illustrate the fact that end-users of energy are concerned not only with the Btu heat content of the various energy types, but also with other attributes. Or according to Stern (1993), quality weighted final energy use is likely to be a superior measure of the energy input to economic activity as it will reflect better the productivity of the uses to which energy is put. Indeed, Stern (1993) found energy consumption to be endogenous when measured conventionally, but found that energy quality weighted consumption Granger-caused GDP. Despite the results of Stern (1993 and 2000) and the well known energy ladder phenomenon (i.e., the shift away from biomass and coal and toward oil and electricity as countries develop), very few energy-gdp cointegration/causality studies have considered quality adjusted energy consumption since then. 2 Oh and Lee (2004) applied Stern s methods to Korea and arrived at similar conclusions. Similarly, Warr and Ayres (2010) replicated Stern s model for the US using their own measure of exergy and useful work in place of Stern s energy quality index, and found both short- and long-run causality running from either of their energy quality measures to GDP but not vice versa. 2. Data, models, and methods The database used in this paper was constructed by combing energy consumption data (and energy price data to construct the energy quality index) from the IEA s Energy Balances with economic data (value added, labor employed, and physical capital) from the Structural Analysis Database (STAN) published by the OECD. Table 1 shows, for each manufacturing sector, the sector designation used for the remainder of the paper, the sector classification (based on the ISIC Rev. 3) used to assemble the database, and the average energy intensity (in thousand tons of oil equivalent, ktoe, per million yr-2005 US$) for OECD countries in 2005 for that sector. This analysis focuses on 2 There is a separate strand of the literature that focuses on electricity consumption; again, see the survey by Payne (2010b).

5 5 the five most energy intensive manufacturing sectors: iron and steel, non-ferrous metals, non-metallic minerals, chemicals, and pulp and paper. Table 1 Despite the increased importance of the service sector, seen both in an increasing share of GDP and share of trade flows and in the phenomenon of deindustrialization, energy intensive manufacturing is not unimportant in OECD countries. Table 2 shows the share of manufacturing value added attributable to each of the five most energy intensive sectors for individual OECD countries in On average, these five sectors account for nearly one-quarter of OECD manufacturing value added. The non-ferrous metal sector (e.g., aluminium smelting) tends to be concentrated in areas with inexpensive energy. Australia, Canada, Norway, and the US produced 70% of the OECD s primary aluminium in Although more evenly distributed across the OECD than smelting activity, iron and steel is a sector in which several OECD countries are substantial net importers (e.g., Belgium, Finland, and Japan); whereas, other countries are substantial net exporters (e.g., Canada, Poland, US). By contrast, chemicals, non-metallic minerals (e.g., ceramics, cement, and glass), and pulp and paper are sectors in which value-added has increased more or less steadily and uniformly in all OECD countries. 2.1 Models Table 2 3 I take a constant return to scale Cobb-Douglas production function used elsewhere in the energy-gdp literature (e.g., Lee et al. 2008); depending on whether energy consumption or the energy quality index is used, there are two equations for each sector: β α γ ( K E L ) VA, i, t = i, t i, t i t (1) β α γ ( K EQ L ) VA, i, t = i, t i, t i t (2) 3 The translog production function is a highly flexible form (e.g., unlike the Cobb-Douglas function, it does not assume an elasticity of substitution among inputs to be equal to unity) that is often used in empirical analyses (e.g., Smyth et al. 2011). However, because the variables analyzed here will be shown to be I(1) and nonlinear transformations of such variables introduce statistical problems, a translog estimation would require the firstdifferencing of the variables. When a first-difference, translog model was estimated, the hypothesis of the Cobb- Douglas form could be rejected only for pulp and paper; and even for that panel, all of the cross-product terms were insignificant.

6 6 Where VA i,t is value added for country i in time period t, K i,t is capital stock, E i,t is energy consumption, L i,t is labor, and EQ i,t is energy quality (defined in Equation 5 below). Taking natural logs forms the following two models, which are estimated for each of five energy intensive manufacturing sectors: ln VA i, t ai + bt + β ln Ki, t + α ln Ei, t + γ ln Li, t + ε i, t = (3) ln VA i, t ai + bt + β ln K i, t + α ln EQi, t + γ ln Li, t + ε i, t = (4) Where the constants a and b are the country and time fixed effects, respectively, and ε is the error term. The time span of the data 4 and the countries included depend on data availability and are described for each sector in Appendix Table A-1.Value added and gross fixed capital formation are in real yr-2000 US$, and labor is the number of persons engaged (from STAN). Energy consumption is in thousand tons of oil equivalent (ktoe), and the energy quality index is described below. All variables are in natural log form. Berndt (1978) proposed a Divisia index in which the consumption of the individual fuel types is weighted by their expenditure shares, i.e., the differences in prices reflect the differences in energy quality or productivity. Both Stern (1993 and 2000) used this approach in his analysis of energy and economic growth in the US. Borrowing from Berndt and Stern, I use the following formula to calculate quality weighted final energy use, EQ i,t : ln, ln. ln ln (5) Where P are the prices of the fuels i, and E are the quantities consumed (in ktoe) for each fuel in final energy use. The (n = 5) fuel types are electricity, oil, natural gas, coal, and combustible renewables and waste. Like Stern (1993), I assume the price of combustible renewables and waste is equal to 60% of the coal price. The energy mix for industry as a whole in the OECD changed dramatically over The share of oil and coal consumption fell from 32% to 15% and from 19% to 13%, respectively, while the share of electricity rose from 17% to 31% to become the main source of energy in industry. 4 The IEA price series begins in 1978.

7 7 As an initial indication of the importance of energy quality, the quality index was calculated for each of the five energy intensive manufacturing sectors using data from the OECD as a whole. Figure 1 displays the ratio of energy quality to conventionally measured energy consumption for each of those five sectors; thus, a value greater than unity means that energy quality is larger than energy consumption (as was the case in Stern s analysis of the US see Figure 2 in Stern, 1993). Figure 1 shows that energy quality was greater than energy consumption (at least from the 1990s onward) for all sectors but non-metallic minerals, and energy quality was mostly increasing faster than energy consumption for all five sectors from the mid-1980s onward. Figure Methods The first step is to determine whether all the variables are integrated of the same order. A number of panel unit root tests have been developed to determine the order of integration of panel variables. These tests typically extend to a panel model the Augmented Dickey-Fuller (ADF) unit root framework, where the first difference of a series is regressed on the one-period lag of that series and on a selected number of additional lagged first difference terms to control for autocorrelation. Im, Pesaran and Shin (2003) developed a test (IPS) that allows for a heterogeneous autoregressive unit root process across cross-sections by testing a statistic that is the average of the individual (i.e., for each cross-sectional unit) ADF statistics. Maddala and Wu (1999) proposed a panel unit root test based on Fisher (1932) that, like Im et al., allows for individual unit roots, but improves upon Im et al. by being more general and more appropriate for unbalanced panels. Maddala and Wu s test (ADF-Fisher) is based on combining the p-values of the test statistic for a unit root in each cross-sectional unit, is non-parametric, and has a chi-square distribution. Both of the above tests assume that the cross-sections are independent; yet, for variables like GPD per capita, cross-sectional dependence is possible to likely for panels of similar countries because of, for example, regional and macroeconomic linkages. Pesaran (2007) determined that when cross-sectional dependence is high, the first-generation tests tend to over-reject the null. More recently, panel unit root tests so-called second-generation tests have been developed that relax this independence assumption. Among the most commonly used so far is that of Pesaran (2007), which is

8 8 based on the IPS test, allows for cross-sectional dependence to be caused by a single (unobserved) common factor, and is valid for both unbalanced panels and panels in which the cross-section and time dimensions are of the same order of magnitude. All three tests assume the null hypothesis of nonstationarity. If all the variables are integrated of the same order, the next step is to test for cointegration. Engle and Granger (1987) pointed out that a linear combination of two or more nonstationary series may be stationary. If such a stationary linear combination exists, the nonstationary series are said to be cointegrated. The stationary linear combination is called the cointegrating equation and may be interpreted as a long-run equilibrium relationship among the variables. The Pedroni (1999, 2004) heterogeneous panel cointegration test is an extension to panel data of the Engle-Granger framework. The test involves regressing the variables along with cross-section specific intercepts, and examining whether the residuals are integrated order one (i.e., not cointegrated). Pedroni proposes two sets of test statistics: (i) a panel test based on the within dimension approach (panel cointegration statistics), of which four statistics are calculated: the panel v-, rho-, PP-, and ADF-statistic; and (ii) a group test based on the between dimension approach (group mean panel cointegration statistics), of which three statistics are calculated: the group rho-, PP-, and ADF-statistic. Pedroni (1999) further showed that the panel ADF and group ADF statistics have the best small-sample properties of the seven test statistics, and thus, provide the strongest single evidence of cointegration. Pedroni (1999, 2004) recommends subtracting out common time effects to capture a limited form of cross-sectional dependency. If the variables are shown to be cointegrated, then the FMOLS estimator from Pedroni (2000) produces asymptotically unbiased estimates of the long-run elasticities and efficient, normally distributed standard errors. In addition, the FMOLS uses a semi-parametric correction for endogeneity and residual autocorrelation, and the FMOLS estimator is a group mean or between-group estimator that allows for a high degree of heterogeneity in the panel. 3. Results and discussion Table 3 shows the results of the panel unit root tests. For no variable is a panel unit root in levels rejected by both tests; in addition, for all the variables, panel unit roots in first differences are

9 9 rejected by both tests. 5 Thus, there is no evidence that any variable is I(2); at the same time I(1) integration cannot be ruled out for any variable. Table 3 Table 4 displays the panel cointegration test results for both models (using energy and energy quality) for each of the five sectors. For all variants the two ADF-test statistics reject the no cointegration null. 6 Thus, given the several previous findings of cointegration of the production variables discussed above (using both aggregate and more disaggregated data), the results in Table 4 provide no plausible reason to reject cointegration for the panels used here. Table 4 Table 5 reports the long-run elasticity estimates from FMOLS for the five sectors/panels. The table demonstrates the importance of accounting for energy quality in the analysis of these energy intensive sectors, and, by extension, the importance for production/value added of the technological switch to higher quality energy (i.e., electricity). For each of the sectors the elasticity for energy when conventionally measured is either small (less than 0.1) or highly insignificant. Indeed, the elasticity of conventionally measured energy is insignificant for iron and steel and non-ferrous metals and very small for pulp and paper. However, in each case when energy quality is measured, the elasticity for energy is considerably larger (never less than 0.19) than that for conventionally measured energy, and for iron and steel, non-ferrous metals, and pulp and paper, the elasticity for energy quality is larger than the elasticity for either physical capital or labor. Table 5 Figure 2 displays the average productivity/efficiency of energy quality (value added/energy quality) for each country and for each sector. For the three energy intensive sectors in which all OECD countries are active (chemicals, non-metallic minerals, and pulp and paper), there is not much 5 Several additional first-generation unit root tests were applied as well. None of those results conflict with the ones displayed. 6 The second-generation Westerlund (2007) cointegration test was applied as well. The Westerlund test s results are particularly sensitive to the setting of lag and lead lengths when the time dimension is short (Persyn and Westerlund 2008) as is the case with this data. Thus, the failure of that test to reject the null of no cointegration for some of the panels seems likely to be the result of the small panel size rather than the presence of cross-sectional dependence. Indeed, the motivation for developing the test was to avoid an incorrect failure to reject, rather than to avoid an incorrect rejection (Westerlund 2007).

10 10 variation among the countries in energy quality productivity. However, for the two sectors in which the geographic distribution of production is more concentrated (iron and steel and non-ferrous metals), Figure 2 indicates a much greater degree of energy quality productivity variation. (By contrast, labor productivity levels are extremely uniform among the countries for all five sectors.) Figure 2 4. Conclusions This paper applied panel cointegration and panel FMOLS estimations to OECD panel data that was disaggregated along the lines of the five most energy intensive manufacturing sectors (iron and steel, non-ferrous metals, non-metallic minerals, chemicals, and pulp and paper) using the Cobb- Douglas production model framework. In addition, the energy consumption data was adjusted for the quality of the energy source (e.g., electricity is of higher quality than oil, which is of higher quality than coal). The results indicate the importance of energy quality primarily the shift toward the use of high quality electricity in these energy-intensive manufacturing sectors. In each case the elasticity for energy quality is greater than that for conventionally measured energy consumption sometimes orders of magnitude greater. Also, when using the energy quality measure, the importance of energy consumption relative to the other production factors (capital and labor) stands out. Such results are useful for both energy-gdp cointegration/causality modelers and CGE modelers, who may need to estimate elasticities. The importance of electricity to productivity in these energy intensive sectors has implications for the impacts of carbon policy on the geographic locations of those sectors. For countries in which electricity generation is not carbon intensive (i.e., countries that rely on renewable sources), a carbon tax would not necessarily be damaging to even the most energy intensive industries. However, a carbon tax could lead to the loss of energy intensive manufacturing in countries with carbon intensive electricity generation. An obvious next step in the analysis would be to use a more flexible production model like the translog one. However, such a model s estimations would need to be in first differences since the variables are panel I(1), and most likely additional time observations would be needed to estimate

11 11 statistically significant second order and cross-product terms, and thus, to demonstrate the inadequacy of the Cobb-Douglas framework.

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14 14 Table 1. Sector classification and average OECD energy intensity Sector ISIC Classification 2005 Energy intensity Iron and Steel 271 & Non-ferrous metals 272 & Non-metallic minerals Chemical and chemical products Paper, pulp, and printing 21 & Wood and wood products Food and tobacco 15 & Textile and leather 17, 18, & Transport equipment 34 & Fabricated metal products including machinery 28, 29, 30, 31, & Construction Note: Energy intensity is in ktoe per million yr-2005 US$. Table 2. Manufacturing value added share for each of five energy intensive sectors. Data from 2005 Chemicals Iron & Non ferrous Non metallic Pulp & Total for 5 steel metals minerals paper Australia 3.9% 2.2% 3.4% 2.6% 7.0% 19.1% Austria 6.4% 4.8% 1.8% 5.2% 7.0% 25.3% Belgium 17.6% 5.3% 1.4% 4.5% 6.6% 35.4% Canada 4.3% 1.7% 8.8% 14.8% Denmark 9.9% 0.5% 0.3% 3.0% 6.5% 20.3% Finland 5.6% 3.9% 1.1% 3.1% 15.4% 29.2% France 9.1% 2.5% 0.9% 4.4% 7.1% 24.0% Germany 9.4% 2.8% 1.3% 2.7% 6.3% 22.5% Greece 4.7% 5.8% 5.3% 15.8% Hungary 7.9% 1.3% 1.3% 3.5% 4.4% 18.3% Ireland 30.0% 2.7% 12.5% 45.2% Italy 6.1% 2.3% 0.8% 5.2% 5.4% 19.9% Japan 6.8% 5.8% 1.7% 2.6% 6.7% 23.5% Korea 8.0% 8.3% 1.5% 3.2% 3.8% 24.7% Netherlands 12.3% 2.4% 8.5% 23.2% Norway 0.4% 1.4% 1.0% 3.2% 6.1% Poland 5.4% 2.0% 0.5% 4.7% 5.7% 18.3% Portugal 4.6% 1.5% 0.7% 9.4% 8.0% 24.1% Spain 8.1% 3.7% 1.4% 7.2% 8.0% 28.5% Sweden 10.7% 3.7% 1.1% 1.8% 10.0% 27.4% Switzerland 17.0% 2.3% 6.8% 26.1% UK 8.7% 1.0% 0.7% 2.8% 10.1% 23.3% USA 9.7% 1.7% 1.0% 2.5% 11.5% 26.4% OECD average 9.4% 3.0% 1.2% 3.7% 7.6% 23.5%

15 15 Table 3. Panel unit root tests. Levels First differences Panel Variables ADF-Fisher Pesaran ADF-Fisher Pesaran Chemicals VA K L E EQ Iron & steel VA K L E EQ Non-ferrous metals VA K L E EQ Non-metallic minerals VA K L E EQ Pulp & paper VA K L E EQ Notes: The values in the table are the probabilities of rejecting the null of no unit root. The ADF-Fisher test assumes cross-sectional independence; whereas, the Pesaran test allows for cross-sectional dependence. The Akaike information criterion was used to select the lag lengths. Table 4. Pedroni panel cointegration test results. Panels Within dimension (Panel) ADF-Stat Between dimension (Group) ADF-Stat VA, E, K, L Chemicals -1.74** -3.55**** Iron & steel -3.03*** -2.35*** Non-ferrous metals -1.51* -1.98** Non-metallic minerals -1.81** -2.20** Pulp & paper -2.20** -2.16** VA, EQ, K, L Chemicals -2.14** -2.79*** Iron & steel -2.85*** -3.35**** Non-ferrous metals -2.37*** -1.86** Non-metallic minerals -2.31** -2.69*** Pulp & paper -1.67** -2.18** Note: Statistical significance is indicated by: **** p <0.001, *** p <0.01, ** p<0.05, and * p<0.10.

16 16 Table 5. Long run elasticity estimates from panel FMOLS. Panel Variables Coefficient T-statistic Variables Coefficient T-statistic Chemicals E EQ K K L L Iron & steel E EQ K K L L Non-ferrous metals E EQ K K L L Non-metallic minerals E EQ K K L L Pulp & paper E EQ K K L L Note: T-critical of 3.29, 2.58, 1.96, and 1.64 correspond to the following levels of statistical significance: p <0.001, p <0.01, p<0.05, and p<0.10, respectively.

17 Iron & steel Pulp & paper Non ferrous metals Chemicals Non metallic minerals Figure 1. The ratio of an energy quality index to energy consumption for the five most energy intensive manufacturing sectors using data from the OECD as a whole and spanning

18 18 Pulp & Paper Iron & Steel LN (VA/EQ) LN(VA/EQ) DNK ESP ITA BEL NLD FRA USA AUT NOR SWE FIN CAN 10 USA ITA AUT BEL SWE FIN NOR Chemicals Nonferrous metals LN(VA/EQ) LN(VA/EQ) IRE DNK AUT ESP ITA GBR SWE BEL CAN FIN USA NLD 12 USA AUT ITA BEL NOR SWE FIN Nonmetallic minerals LN(VA/EQ) AUT NOR ESP ITA FRA CAN GBR SWE NLD USA FIN DNK BEL Figure 2. Average energy quality productivity (value added/energy quality) variation across countries for five energy intensive manufacturing sectors.

19 19 Appendix Table A-1. Time span and countries for each panel. Chemicals Iron & steel Non ferrous metals Non metallic minerals Pulp & paper AUT AUT AUT AUT AUT BEL BEL BEL BEL BEL CAN FIN FIN CAN CAN DNK ITA ITA DNK DNK ESP NOR NOR FIN ESP GBR SWE USA FRA FIN IRE USA GBR FRA ITA ITA ITA NLD NLD NLD SWE NOR NOR USA SWE SWE USA