INTANGIBLE INVESTMENT AND THE INTENSITY OF ENERGY USE

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1 INTANGIBLE INVESTMENT AND THE INTENSITY OF ENERGY USE JANET X. HAO AND BART VAN ARK NEUJOBS WORKING PAPER NO. 3.4 OCTOBER 2013 Abstract Technological change is an important source of energy conservation, but the broader impact of innovation and the rise of the knowledge economy on energy intensity (which is the energy use relative to GDP), especially in advanced economies, has been widely underappreciated. This paper discusses the correlation between energy intensity and intangible investment using data on Germany, Finland, Italy, the Netherlands, Spain and the UK from 1995 to At the national level, we find that energy intensity is negatively correlated to the ratio of intangible to tangible investment. At the industry level, we also find that energy intensity is negatively related to R&D investment, software investment and ICT tangible investment, while it is positively related to non-ict tangible investment and total tangible investment. These results are consistent with the view that the rise of the knowledge economy in advanced economies (and ultimately elsewhere in the world once those economies mature) is compatible with the notion of the socio-ecological transition. NEUJOBS Working Documents are intended to give an indication of work being conducted within the NEUJOBS research project and to stimulate reactions from other experts in the field. Texts published in this series are ultimately destined for academic publishing. The views expressed in this paper are those of the author and do not necessarily represent any institution with which he is affiliated. See the back page for more information about the NEUJOBS project. Available for free downloading from the NEUJOBS website ( Janet X. Hao and Bart van Ark / The Conference Board, 2013

2 2 JANET X. HAO AND BART VAN ARK INTANGIBLE INVESTMENT AND THE INTENSITY OF ENERGY USE JANET X. HAO AND BART VAN ARK 1 NEUJOBS WORKING PAPER NO. 3.4 / OCTOBER We are grateful to Bert Colijn and Kirsten Jäger for their advice in using the EUKLEMS data for the purpose of this study.

3 GREEN JOBS IN EUROPE AND THE INCREASING DEMAND FOR TECHNICAL SKILLS 3 1. Introduction Since the start of the Industrial Revolution, the use of energy relative to the GDP has dramatically increased. The rise of manufacturing production, together with more intensive mining and transportation activities, have led to a large increase in the use of energy-intensive machinery and materials. Also, the unprecedented rise in per capita income over the past two and half century has contributed to higher demand for energy, especially because of the rise of electricity and faster transportation modes by households increasing the demand for coal, oil and other non-renewable energy sources. Between 1850 and 1973, global primary energy consumption per capita increased more than 5 times, or at 1.6 percent on annual basis, slightly faster than per capita income growth at 1.5 percent over the same period (Maddison 2007). An important concern has been the increase in carbon emissions which, on a per capita basis, increased at 2.2 percent per year between 1870 and 1973, much faster than per capita income and reflecting a shift to more polluting energy sources. The call for drastic reductions in energy use by, for example, the Club of Rome in the 1970s was based on the expectation that this trend would continue on an exponential basis. Especially the rapid increase in population in poorer countries, together with the increased demand coming from rising middle classes in emerging economies, were seen as major factors driving unsustainable energy consumption. The environmental concerns therefore led to calls for reductions in energy consumption both through regulatory mechanisms as well as technological change and innovation. Whether or not in response to those calls, the use of energy relative to GDP has slowed down quite dramatically. For example, while global energy use per capita increased by another 70 percent between 1973 and 2003, this equaled only a 0.2 percent annual increase compared to a 1.5 percent increase in per capita income for the world as a whole over this period. Carbon emissions per capita even fell at 0.1 perfect during this period (Maddison, 2007). However, there was wide variation across the globe. While mature economies, on the whole, managed to bring energy use more in line with per capita income growth, emerging economies saw rapid increases in energy use per head. While the increased demand for energy in absolute terms remains a concern from the perspective of how much carbon emissions the planet can endure in the longer term, it is important to understand what has brought about the slowing energy intensity (i.e. the drop in energy use relative to GDP) and whether those developments might help to bring about the socio-ecological transition, which is the main focus of the NEUJOBS project. It is well known that technological change has helped to reduce energy intensity. However, the broader impact of the rise of the knowledge economy, especially in advanced economies, has been much underappreciated. This link is to a large extent the result of a change in the industrial composition of the economy. For example, the shift from manufacturing to services has created significant reductions in energy use (Colijn and van Ark, 2013). Related to this change in industrial composition, is the reduced emphasis on the use of tangible capital, including machinery and equipment, which has been the key force of the Industrial Revolution and the subsequent two centuries of unprecedented growth. This has been replaced by the greater role for intangible types of inputs, including education, information, R&D and other economic competencies, as the key sources of growth. 3

4 4 JANET X. HAO AND BART VAN ARK The rise of the knowledge economy, which makes greater use of those intangible inputs, may therefore be a key underlying force in energy reduction, as its activities are less intensive users of energy. In addition to the change in industrial composition, intangibles can also help to support further innovations in energy conservation, and it is therefore a continuous driver of socioecological transition. This paper studies the relationship between intangible investment and energy efficiency. It examines the correlation between investment and energy intensity, using data on the market sector of Germany, Finland, Italy, Spain, the Netherlands and the UK from 1995 to The data sources of intangible investment are Corrado et. al. (2012) for total investment, Eurostat for R&D, and the EU KLEMS Growth and Productivity Database for software. Our data source of energy intensity and tangible investment at the industry level is EU KLEMS updated by Colijn and van Ark (2013). We find that, averaged from 1995 to 2009, the change in energy intensity is negatively correlated with the ratio of intangible to tangible investment, indicating that if a country invests heavily in intangibles rather than tangibles, that country is likely to experience a drop in energy intensity. OLS regressions at the industry level find a negative relationship between energy intensity and software and R&D investment, implying that more investment in software and R&D is associated with less energy intermediates. We also carry out regressions at the industry level for ICT tangible investment, non-ict tangible investment and total tangible investment against energy intensity, and find that more tangible investment at industry level is associated with more energy intermediates, that ICT investment is related to low energy intensity, and that non-ict investment is related to high energy intensity. While there may be therefore be some role for changes in industry composition, the changing mix of investment within industries is likely to play an important role as well in understanding the negative relationship between intangibles and energy intensity.. It should be stressed upfront, that in this paper we do not attempt to discuss causation, mainly because of the constraints on data availability. Data on total intangible investment is unavailable at the industry level for most countries except for Japan and the UK. In addition, for analysis at the industry level, because of the reverse causality between macro variables such as investment and value added, we cannot identify a causal relationship without an instrumental variable, which is very hard to find for this type of research. Finally, our results are subject to the quality of data on energy intensity. Hence the results should be interpreted as indicative of a possible relationship between intangibles and investment which seems to hold for a variety of different regressions. The structure of the remainder of this paper is as follows. Section 2 reviews the relevant literature. Section 3 discusses data and summary statistics used for this research. Section 4 discusses the results, especially the negative correlation between energy intensity and the ratio of intangible to tangible investment. It also reports on regressions results for R&D, software, and tangible investment against energy intensity at the industry level. Finally, section 5 concludes. 2. Review of Literature on Intangible Capital and Energy Efficiency

5 GREEN JOBS IN EUROPE AND THE INCREASING DEMAND FOR TECHNICAL SKILLS Intangible Capital Since the early days of the study of modern economic growth, tangible assets, which include machines, equipment and buildings, have featured prominently. Intangible capital has received much less attention, at least as a concept of assets which also generate economic growth. Intangible assets can simply be described as capital other than tangible capital such as machinery, equipment and buildings that you can see and touch. Intangible assets have three categories (computerized information, innovative property and economic competencies) and ten detailed types of assets (software, database, R&D, mineral exploration, copyright and licenses, product development in the financial industry, new architectural and engineering design, brand equity, firm-specific human capital and organizational capital) (Corrado, Hulten and Sichel 2005). They are related to the creation and control of knowledge, pricing power, capture of market share, improvement on the efficiency of the organization capital, and so on. Appendix I provides a more detailed overview of the assets in the three main categories. These are all important assets which typically give companies their strategic advantages relative to their competitor. But intangible assets are also important for companies to innovate and therefore they help to create what we call a knowledge economy. Although intangibles have become more important as assets for business, they mostly do not make it to the balance sheet of a company or the national accounts of a country. Spending on intangibles is mostly measured as cost and therefore they are being expensed. At a national accounts level, a similar treatment of intangibles expenditures as intermediate inputs implies that it detracts from value added rather than contributes to value added if it would be measured as an investment. If one treats intangibles as investments, it turns out to be a pretty big deal for mature and emerging markets alike. Figure 1 shows that for the United States, intangible investment made up as much as 12 percent of the total output in That is in fact slightly more than our investments in all tangibles, which is roughly 9 percent. The share of intangibles in GDP is somewhat lower in the UK, at 9.5 percent, and substantially lower in the Euro Area, at 5.5 percent on average, and less than half of the share of tangibles in the Euro Area s GDP. At the macro level, intangible assets clearly contribute to economic growth. Its growth contribution is included in the Total Factor Productivity growth, which is the residual of growth accounting analysis. When intangibles are separately measured as one of the inputs to production, and its effect removed from the residual, intangibles appear to have contributed 19.9 percent of the total growth of labor productivity from 1995 to 2007 in EU countries and 33.7 percent in the United States (Corrado et. al., 2012). In addition to growth, intangible assets are related to economic activity in many other ways. Van Ark et. al. (2009) show that intangible investment is positively correlated to GDP per capita, labor productivity, the size of stock market, and the availability of venture capital. At the company level, intangibles are among the major components of company assets, and explain a large portion of the gap between market value and book value. After including intangibles into the balance sheets of 617 R&D-intensive U.S. corporations, Hulten and Hao (2008) found that the assets increased from $4707 billion to $7394 billion in 2006, and that Tobin s Q decreased from 3.22 to 1.34, meaning intangibles explained part of the gap between market value and book value. Intangibles assets, however, did not distribute evenly across firms. Hulten, Hao and Jaeger (2010) compared German firms and U.S. firms. On average, German 5

6 6 JANET X. HAO AND BART VAN ARK firms are relatively R&D-intensive but not intangible-intensive compared to U.S. firms, while the top firms both in Germany and in the U.S. engage in relatively heavy investment in intangibles. 2.2 Energy Intensity Energy intensity is the ratio of energy intermediates to value added, and is the inverse of energy productivity. There are many reasons why energy intensity changes in the process of economic development. One can examine the energy intensity of a country by decomposing the change in national energy intensity into the change in energy intensity by industry and change in industry composition of the whole economy. Industry composition changes as a country moves into the knowledge economy, raising the role of services. For example, Mulder and de Groot (2011) decompose the change in energy intensity of 51 sectors in the U.S., Japan, South Korea and 16 European countries. They find that technologydriven energy efficiency, rather than industry composition of an economy, drives the change in energy intensity of those countries from 1995 to However, the United Nations Industrial Development Organization (2011) found that while technology is the primary driver of energy intensity in developing countries, structural change is the primary driver in developed countries. The difference in results may be due to varying levels of disaggregation. The recent study by Colijn and van Ark (2013) which focuses on the same six advanced European countries as the current study, shows a mixed picture, in which both industry composition effects (the shift to less energy-intensive service sectors) and technological change within industries play a role. However, in addition to technology, innovation more broadly plays a role as well to drive reduction in energy intensity. For example, energy efficiency improves with new technology and better energy management system, which can result from investment in R&D and changes in organizational structures, which are both intangible assets. 3. Data and Summary Statistics The data sources of intangible investment are Corrado et. al. (2012). It provides data on total tangible investment from 1995 to 2010 and data on 13 detailed types of intangible assets in the U.S. and 28 European countries (the EU-27 and the Norway). Corrado et. al. (2012) uses comparable data sources from national accounts and trade associations, and is the best currently available sources for international comparison of intangibles in Europe. Unfortunately, intangible investment at the industry level is unavailable, except for two components of intangible assets, R&D and software. The data source of R&D at the industry level is Eurostat, and that of software at the industry level is EU KLEMS. Data on tangible investment at the industry level is available by types of tangible assets from EU KLEMS (Table 1).

7 GREEN JOBS IN EUROPE AND THE INCREASING DEMAND FOR TECHNICAL SKILLS 7 For only six of the 28 European countries, there are consistent data on energy input which are integrated in the KLEMS growth framework and updated to Our data source of energy intensity and value added at industry level is EU KLEMS updated by Colijn and van Ark (2013) to EU KLEMS provides data on value added, capital input, labor input and three intermediate inputs including energy input, raw materials and services input. EU KLEMS derives industry-level energy intermediates from supply-and-use tables in National Accounts. Energy intermediates include all energy mining products, oil refining products and electricity and gas products, which are deflated at the relative price index of each type of energy. Colijn and van Ark (2013) update the estimates to 2009 for the market sector and by 2-digit industries for Germany, Italy, Spain, Finland, the UK and the Netherlands. Table 2 lists the average of tangible investment and intangible investment from 1995 to 2009 of 6 countries. Countries which invest heavily in tangible assets seem to invest less in intangible assets (Figure 2). As to tangible investment, Spain invested the most in tangible assets, 12.9 percent of GDP, followed by Italy, 12.5 percent of GDP. The UK invested the least in tangible assets, 9.4 percent of GDP. As to intangible investment, the UK invested the most in intangible assets, 12.6 percent of GDP, followed by the Netherlands, 10.7 percent, and Finland, 10.2 percent. Spain invested the least in intangible assets, 6.0 percent of GDP, followed by Italy, 6.6 percent. We define energy intensity as energy intermediate input divided by value added in 1995 prices. Energy intensity increased the most from 1995 to 2009 in the market sector of Spain by 4.4 percent annually on average, followed by Italy, 1.6 percent (Table 2). The relatively large role for construction, infrastructure, etc., may have contributed to those high growth rates. Energy intensity fell the most in Finland and the UK by 2.4 percent annual on average. In Finland the large declines in energy seemed to be related to by large productivity improvements in electrical and optical equipment and transport equipment sectors (Colijn and van Ark, 2013). In the United Kingdom, the relatively large size of the service sector has helped to lower energy intensity for the economy as a whole. Germany and the Netherlands, shows slower but still decent declines in energy intensity at 1.9 percent and 1.6 percent from , respectively. 4. Correlations and Regressions Between Energy Intensity and Intangibles When examining the relationship between energy intensity and the type of assets a country invests in, we first estimate the correlation between energy intensity and intangible investment at the national level, as data on intangibles is available only at the national level. Next, we estimate the correlations between energy intensity and ICT and non-ict investment at the industry level. For both the national and industry level, we carry out regressions of investment against energy intensity, but we treat the regression results as indicative of possible relationships between the two variables rather than any causation in one or the other direction. 4.1 Correlation between energy intensity and intangibles at the national level. 7

8 8 JANET X. HAO AND BART VAN ARK The use of tangible assets requires energy. Machines and equipment need electricity, gas or other fuel to operate. Buildings need energy for lighting, heating, air-conditioning, ventilating and powering equipment and other services. In contrast, intangible assets may consume much less or no energy at all. For example, when a company builds up human capital through education and training, the employees become more knowledgeable or more efficient, without using more non-renewable energy. Similarly, more efficient organizational structures (investment in organizational capital) may not cost more energy. In fact, an efficient organization may save energy, for example through a better use of office space, remote working, etc.. Also the role of the services sector in the knowledge economy is crucial, as investigated below. In the market sector of the six European economies included in this study, tangible investment as a percentage of GDP decreased in all countries from 1995 to 2009, except for Finland, while intangible investment increased in all countries. Corrado et. al. (2012) shows that intangible investment exceeded tangible investment as a percentage of GDP in Europe in By 2010, intangible investment amounted to 11.4% of GDP, while tangible investment was only 5.7 % of GDP. If we ignore intangible investment, we are missing most of the macro dynamics of investment in advanced economies and some emerging economies in the past two decades. The historical evidence and international comparisons show that ratio of tangible to intangible investment is negatively related to GDP per capita (van Ark et. al., 2009). The possible reasons for the negative correlation are that less-developed countries tend to specialize in manufacturing industries that takes advantage of low wages; lower-income countries may not be able to afford risky upfront intangible investment such as a multimillion R&D project to develop a new drug; high-income countries are likely to support intangible capital in maturing financial market; and high risk innovations and intangibles require a flexible labor market to dismiss unsuccessful investment something which less developed countries often cannot deliver. Table 2 shows that the average growth rate of energy intensity from 1995 to 2009 is negatively correlated to the ratio of intangible to tangible investment with a correlation coefficient of Separately, it is positively correlated to tangible investment (0.91), and negatively correlated to intangible investment (-0.90) (Figure 2 and Figure 3). However, the strength of the correlations is in part determined by the small number of 2 sets of 6 observations. In Table 3 we carry out regressions of the ratio of intangible to tangible investment against the growth rates of energy intensity from 1996 to 2009, with and without country dummies. The coefficients of the ratio of intangible to tangible investment are negative and significant at the 95 percent confidence level. These results imply that the choice to invest in intangible assets could ease the burden of energy input. However, the causation could also be in the opposite direction, when countries depending heavily on energy intermediates are more likely to invest in tangible assets such as machines, equipment and buildings, rather than intangible assets such as R&D, design, human capital and organizational structure. One reason for the negative relationship between energy and intangibles is outsourcing and the related specialization in the production process. Intangible investment reflects who controls the knowledge (a form of competitive advantage) across countries. The country that controls the knowledge specializes in the intangible part of the production process that requires less energy

9 GREEN JOBS IN EUROPE AND THE INCREASING DEMAND FOR TECHNICAL SKILLS 9 and generates more profit than the manufacturing (tangible) part of the production process. Multinational companies tend to concentrate on higher value design, marketing, training, organizational capital and other intangible capital in the home country, while they outsource manufacturing production activities to low cost economies. As the manufacturing of goods would use more energy than design, marketing goods or building up an efficient organizational structure, this would reduce energy intensity for the home country. For example, the well-known example of Apple shows that ipod and iphone are mostly designed and marketed in the U.S. and manufactured in Asia. The design and marketing of Apple captures market shares, builds up pricing power and creates the profits. Apple employees in the U.S. accounted for one third of the jobs and 70 percent of labor compensation related to ipod (Linden, Dedrick and Kraemer, 2009). Similarly, for a $321 iphone, the factories in China only captured 3.6 percent of that revenue (Xing and Detert, 2010). The developing countries that manufactured the Apple products bear most of the burden of the energy use and enjoyed only a small fraction of the revenues of Apple products. In addition to globalization, technology, innovation and changes in industry composition all impact energy intensity. For example, energy efficiency improves with new technology and better energy management system, which can result from investment in R&D and changes in organizational structures, which are both intangible assets. Industry composition of a country changes as a country moves into the knowledge economy, raising the role of services. 4.2 Regressions of Type of Investment against Energy Intensity at the Industry Level. In order to look deeper into the relationship between intangible investment and energy intensity, we require more detailed data, such as data at the industry level. Unfortunately, intangible investment at the industry level is unavailable, except for two components of intangible assets, R&D and software. The data source of R&D at the industry level is Eurostat, and that of software at the industry level is EU KLEMS. Data on tangible investment at the industry level is available by types of tangible assets from EU KLEMS. Because intangible investment is strongly and negatively correlated with tangible investment, we also examine the relationship between tangible investment and energy intensity. After we create a dataset on energy intensity with tangible investment and value added, we end up with 26 industries in the market sector (Table 4), spanning from agricultural, construction, utilities, manufacturing, services to public sector. Using Germany in 2009 as an example, it is clear that energy producing industries are obviously also the most energy-intensive industries, including electricity, gas and water supply (energy intensity is 40%) and mining and quarrying (18%). Other industries with high energy intensity are transport and storage (13%), and chemicals (11%). The least energy intensity industries are real estate activities (0.1%), financial intermediation (0.9%), health and social works (1.3%), sale, maintenance and repair of motor vehicles and motorcycles; retail sale of fuel (2.2%), and machinery, nec (2.4%). As to tangible investment, the industries investing the most heavily on tangible assets are agriculture, hunting, forestry and fishing (43.0% of value added), post and telecommunications (38.6%), Coke, refined petroleum and nuclear fuel (38.2%), transport and storage (34.1%), and mining and quarry (28.7%). The industries investing the least on 9

10 10 JANET X. HAO AND BART VAN ARK tangible assets are construction (3.3%), real estate activities (4.8%), hotels and restaurants (6.2%), Sale, maintenance and repair of motor vehicles and motorcycles; retail sale of fuel (6.6%), and Retail trade, except of motor vehicles and motorcycles; repair of household goods (6.6%). We then break down tangible investment into ICT and non-ict investment. ICT investment is usually more high-tech than non-ict investment. Moreover, ICT investment typically requires co-investment in intangibles such as training and organizational capital. Table 4 shows that in Germany in 2009, the industry investing most heavily on ICT assets are post and telecommunications (6.8% of value added), financial intermediation (1.4%), Wholesale trade and commission trade, except of motor vehicles and motorcycles (1.1%), transport and storage (1.1%), and Coke, refined petroleum and nuclear fuel (1.1%). R&D, software, and energy intensity Table 5 shows the results of OLS regressions of R&D spending (investment) and software investment on energy intensity. The measure of investment is expressed as a percentage of value added. Energy intensity is energy intermediate divided by value added in 1995 prices. The data source of R&D is Eurostat. Eurostat uses a different version of NACE industry codes from EU KLEMS energy data, so after we combine R&D data with energy data, we end with 19 industries from 1995 to We expect a negative relationship between R&D spending and energy intensity, because energy innovation would typically result from R&D spending. Table 5 shows the regression results of R&D against energy intensity at the industry level. The coefficient of R&D investment on energy intensity is negative (-0.17) and significant at the 95 percent confidence level. The estimated coefficient remains negative and significant after we add year dummies or country dummies to the regression. Similar to the results on R&D, the coefficient of software investment on energy intensity is also negative (-0.45) and significant. The regressions on R&D and software indicate that energy intensity is also related to knowledge-based investment in nonenergy industries. Tangible investment and energy intensity. Table 6 shows the results of OLS regressions of tangible investment on energy intensity. The coefficient for total tangible investment (as a percentage of value added) on energy intensity is positive and significant at the 95% confidence level. The coefficient and its significance do not change much if we add countries dummies, or year dummies or both country and year dummies to the regression. We also carry out regressions on ICT (Information and Communications Technology) and non- ICT tangible investment on energy intensity. Tangible assets can be classified into ICT and non- ICT assets. Non-ICT tangible assets include transportation equipment, other machines and equipment, non-residential buildings and other tangible assets. ICT includes ICT equipment and

11 GREEN JOBS IN EUROPE AND THE INCREASING DEMAND FOR TECHNICAL SKILLS 11 hardware which are big energy users. Hypothetically this should lead to a positive relationship between ICT investment and energy intensity. But the relationship could also be negative correlation because ICT assets, as well as R&D, are among the main inputs into innovation and productivity (Hall, Lotti, and Mairesse, 2012). Investment in ICT assets frequently happen during process innovation and investment in organizational capital (a type of intangible assets) (Greenan et al., 2001). ICT investment interacts with human capital and organizational innovation. Investment in ICT assets also need firms to re-organize around the new technology and build up related human capital, and organizational capital and human capital are intangibles (Black and Lynch, 2001, Bresnahan, 2002). Brynjolfsson et al. (2002) found that the effect of IT acquisition partly proxies the costs of organizational change in response to IT acquisition. Human capital of a firm also helps to make ICT investment effective (Greenan et al. 2001, Bugamelli and Pagano, 2004). The adjustment costs of adopting new energy inputs, however, could be significant (Ketteni, Manumeas and Pashardes, 2013). Table 6 lists the estimated coefficients of ICT and non-ict investment. The coefficient of non- ICT investment is positive and significant at the 95% confidence level. The coefficient (4.16) is larger than the coefficient of total tangible investment (3.68). When using year dummies, country dummies, or both year and country dummies, the coefficient remains significant and the magnitude increases to about 4.4. In contrast, the estimated coefficient of ICT Investment on energy intensity is and significant at the 95% confidence level, implying that ICT Investment may help to ease the burden of energy use. The coefficient stays similar and significant if we use year dummies, or country dummies, or both year and country dummies. Although we cannot directly observe intangible investment at the industry level, the estimated coefficient of ICT investment compared with that of non-ict investment implies that technology and intangible assets might help to decrease the burden of energy intermediate use. But if the causation goes the opposite direction, industries depending heavily on energy intermediates tend to invest less in ICT capital. Finally, table 7 drops four industries ( Coke, refined petroleum and nuclear fuel, Chemicals, Rubber and plastics and Electricity, gas and water supply ) from the regression, because they use energy as feedstock instead of power sources. In this case the coefficients on total tangible investment and non-ict investment all stay significant, but the magnitude decreases. The coefficient of tangible investment drops from 3.68 to 0.17, and that of non-ict investment decreases from 4.16 to The coefficients on ICT investment are still negative but insignificant. 5. Closing remarks Previous research has documented the trends and the decomposition of energy intensity, but so far we have not seen an in-depth analysis of the relationship between energy intensity and the rise of the knowledge economy, measured through investment in intangible capital. The latter not only refers to technological change, in particular R&D but also to the larger role of innovation in the economy, the increase in ICT investment, the fragmentation of the global value chain according to advantages in production versus services activities, and the greater importance of 11

12 12 JANET X. HAO AND BART VAN ARK human capital, design, brand and organizational capital. It is hard to measure all of these aspects of the knowledge economy, but discussing energy intensity in the context of the increased importance of intangible assets, is an effective way to approach this issue. Intangible investment reflects many new trends in the knowledge economy which combined them in one indicator. The current measure of intangible assets effectively shows the shift from a traditional economy to a knowledge-based economy and from a brick-and-mortar economy to an intangible economy. We find that energy intensity is negatively correlated to intangible investment, is positively correlated to tangible investment, and is negatively correlated to the ratio of intangible to tangible investment at the national level. At the industry level, we find that energy intensity is negatively related to R&D investment, software investment and ICT tangible investment. Moreover, energy intensity is positively related to non-ict tangible investment and total tangible investment. As service industries use more intangibles, they typically are less energy intensive, although there are differences between countries (Colijn and van Ark, 2013). This suggests that industry composition is playing some role in explaining the negative relationship between intangible investment and energy intensity. However, even at industry level we find negative relationships for R&D as well as ICT and software, suggesting that technological change and innovation go hand in hand with declining energy intensity. The results from this paper should be interpreted as relationships, not causations, between energy intensity and intangible investment. Future research on intangible assets and energy intensity would require estimates of intangibles at the industry-level and estimates of energy intensity of more countries. One key question that remains is the broader impact of the rise of knowledge economies in mature economies on the global economy. Some of the channels which relate energy intensity to the intensity of intangible assets also point at the role of the outsourcing of energy-intensive production activities. Multinational companies tend to outsource manufacturing production activities to low cost economies, while they concentrate on higher value design, marketing, training, organizational capital and other intangible capital in the home country. As the manufacturing of goods would use more energy than design, or marketing goods, or building up an efficient organizational structure, this would reduce energy intensity for the home country, but it may increase energy intensity in emerging markets. Indeed sparse measures show that the share on intangible investment in GDP is considerably lower in emerging market economies, even though there is much variation among countries. The extent to which higher energy intensity in emerging markets offsets the gains in mature markets is a topic for further research.

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14 14 JANET X. HAO AND BART VAN ARK Appendix Categories of Intangible Capital Intangible assets have three categories (computerized information, innovative property and economic competencies) and ten detailed types of assets (software, database, R&D, mineral exploration, copyright and licenses, product development in the financial industry, new architectural and engineering design, brand equity, firm-specific human capital and organizational capital) (Corrado, Hulten, and Sichel, 2005). Because energy efficiency relates to knowledge based capital in many intricate ways, here we only try to list some of the relationships between energy efficiency with each type of asset. The list is far from comprehensive. 1. R&D. Energy efficiency to a certain extent is a result of R&D on energy. Between 1850 and 1973, global primary energy consumption per capita increased more than 5 times, or at 1.6 percent on annual basis, slightly faster than per capita income growth at 1.5 percent over the same period (Maddison 2007). For a sustainable increase in income per capita, countries look for innovative solutions to minimize energy use. A group of selected advanced countries spent about 17 percent of public R&D (15.8 billion Euros) on energy in 2008 (UNIDO, 2011). As a component of energy R&D, R&D on energy efficiency accounted for 2.3 billion Euros. Spending on energy efficiency pays off in the form of cost saving. For example, Nelson (1989) and Nelson and Rosenberg (1993) examine 575 energy-efficiency projects costing less than $0.2 million in Dow Chemicals over the period of 1981 to 1993, and find that the average payback time was less than one year and that the average return on investment was 204 percent. Worrell et al. (2009) uses data on 70 industrial firms in six OECD countries, and estimates an average payback time of 4.2 years. 2. Computerized information. There are many ways computer science helps to save energy. For example, an important tool of energy research is computer software and hardware, as many researchers use computers and share information on Internet. In addition, computer software of energy management can help companies optimize energy use. Finally, energy companies list programs of energy saving on their websites. On the other hand, data centers, personal computers and communication networks are heavy users of energy. They used 4.7 percent of world electricity in Data centers accounted for about 1.4 percentage points, personal computers 1.6 percentage points, and communication networks 1.8 percentage points. Personal computers include desktops, laptops, LCDs and CRTs. Communication networks include customer premises equipment, office networks and telecom operate networks (Lannoo et. al., 2013). 3. Mineral exploration and evaluation.

15 GREEN JOBS IN EUROPE AND THE INCREASING DEMAND FOR TECHNICAL SKILLS 15 Mineral exploration finds new reserves of oil or gas. The UK spent 400 million pounds on mineral exploration and evaluation in 2004 (Marrano and Haskel, 2006). The Netherlands spent 100 million Euros in 2005 (van Rooijen-Horsten, et. al., 2008). The recent discovery of the large amount of shale gas is a result of mineral exploration and technology innovation. 4. Product development in the financial industry, Implementing new technology at a large scale needs support from the financial industry. New financial products designed to facilitate energy efficiency could accelerate the process of saving energy. International Energy Agency (IEA) reviewed many recent studies on the barriers to implement energy-efficient technology in large scale in advanced and developing countries. One of the four major barriers is financial barriers. The other three major barriers are policy and regulatory barriers, barriers related to energy end users, and barriers related to providers of energy-using equipment and energy services. IEA presented new financial services such as dedicated credit lines or risk-sharing facilities to finance energy efficiency projects (IEA, 2011). 5. New architectural and engineering design. It takes architects and engineers to convert new technology into energy-efficient buildings and machines and equipment. The construction of energy efficient buildings needs new architectural and engineering designs. Similarly, to create energy efficient machines and equipment, engineers are indispensable in turning the ideas into real machines. 6. Brand equity, Energy efficiency could enhance corporate brands. Economic development has environmental costs. The topic of energy saving is at the center of public media. A company that works for the good of the public such as saving energy would be more likely to have a positive image. A recent report of Deloitte (2012) finds that 81% of sampled companies use energy efficiency to stay competitive of corporate image. 7. Training and organizational capital Just like new energy-efficient machines have to be designed by engineers, any program of energy efficiency would have to be implemented by people technology is a necessary condition for energy saving, not a sufficient condition. In Deloitte (2012), 61% of firms report that technology available today is inadequate to be very helpful in managing our electricity cost. 23% of firms identify bureaucracy as barriers to achieving goals of energy efficiency, and 23% of firms list lack of dedicated staff to accomplish the goal as a barrier. 15

16 16 JANET X. HAO AND BART VAN ARK References: Black, S. E., and L. M. Lynch (2001). How to Compete: The Impact of Workplace Practices and Information Technology on Productivity, Review of Economics and Statistics 83(3), Bresnahan, T. F., E. Brynjolfsson, and L. M. Hitt (2002). Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence, Quarterly Journal of Economics 117(1), Brynjolfsson, E., L. M. Hitt, and S. Yang (2002). Intangible Assets: Computers and Organizational Capital. Brookings Papers on Economic Activity 1, Bugamelli, M. and P. Pagano (2004), Barriers to Investment in ICT, Applied Economics, 36(20), pp Colijn, Bert and Bart van Ark (2013), Energy Productivity and Economic Growth in Six European Countries, NEUJOBS Working Paper, forthcoming. Corrado, Carol, Charles Hulten, and Daniel Sichel, Measuring Capital and Technology: An Expanded Framework. In Measuring Capital in the New Economy, C. Corrado, J. Haltiwanger, and D. Sichel, eds., Studies in Income and Wealth, Vol. 65. Chicago: The University of Chicago Press, pp Corrado, Carol, Jonathan Haskel, Cecilia Jona-Lasinio and Massimiliano Iommi, Intangible Capital and Growth in Advanced Economies: Measurement Methods and Comparative Results. Working Paper, June, available at Deloitte, Deloitte resources 2012 Study Insights into Corporate Energy Management Trends. Available at SouthAfrica/Local%20Assets/Documents/Deloitte%20reSources%202012%20Study.pdf Dutz, Mark, Sérgio Kannebley Jr., Maira Scarpelli, and Siddharth Sharma, Measuring Intangible Assets in an Emerging Market Economy: An Application to Brazil. World Bank Policy Research Working Paper Available at Greenan N., A. Topiol-Bensaid and J. Mairesse, Information Technology and Research and Development Impacts on Productivity and Skills: Looking for Correlations on French Firm Level Data, in Information Technology, Productivity and Economic Growth, M. Pohjola ed., Oxford University Press, Hall, Bronwyn H., Francesca Lotti, Jacques Mairesse, Evidence on the Impact of R&D and ICT Investment on Innovation and Productivity in Italian Firms. NBER Working Paper Available at Charles Hulten and Janet Hao, The Role of Intangible Capital in the Transformation and Growth of the Chinese Economy, NBER Working Paper 18405, September. Hulten, Charles, Janet Hao and Kirsten Jäger, The Measurement of India s Intangible Capital, Deliverable to World Input Output Database Project (to be published).

17 GREEN JOBS IN EUROPE AND THE INCREASING DEMAND FOR TECHNICAL SKILLS 17 Hulten, Charles and Janet Hao, What is a Company Really Worth? Intangible Capital and the "Market to Book Value" Puzzle. NBER WP Available at Hulten, Charles, Janet Hao, and Kirsten Jäger, Macro versus Micro Comparisons of Intangible Capital: The Case of Germany and the U.S.. Available at International Energy Agency, Joint Public-Private Approaches for Energy Efficiency Finance Policies to Scale-up Private Sector Investment. Available at Ketteni, Elena, Theofanis Manumeas and Panos Pashardes, ICT and Energy Use: Patterns of Substitutability and Complementarity in Production, NEUJOB S Working Paper, forthcoming. Lannoo, Bart, et. al., Overview of ICT energy consumption. Deliverable D8.1 to Network of Excellence in Internet Science. Available at Linden, Greg, Jason Dedrick, and Kenneth L. Kraemer, Innovation and Job Creation in a Global Economy: The Case of Apple s ipod, Working Paper, Personal Computing Industry Center, UC Irvine. Maddison, Angus, The Contours of the World Economy, AD, Oxford University Press. Marrano, Mauro Giorgio and Jonathan Haskel, How much does the UK invest in intangible assets? Department of Economics, Queen Mary University of London, Working paper No Mulder, Peter and Henri L.F. de Groot, Energy Intensity across Sectors and Countries: Empirical Evidence CPB Discussion paper N Nelson, K.E., Are There Any Energy Savings Left? Chemical Processing, January. Nelson, R.R., and Rosenberg, N., National Innovation Systems: A Comparative Analysis. New York: Oxford University Press. United Nations Industrial Development Organization, Industrial Development Report Industrial energy efficiency for sustainable wealth creation capturing environmental, economic and social dividends. Available at EBOOK.pdf van Ark, Bart, Janet X. Hao, Carol Corrado, and Charles Hulten, Measuring Intangible Capital and Its Contribution to Economic Growth in Europe. EIB papers, Vol. 14 (1), pp van Rooijen-Horsten, Myriam, Dirk van den Bergen, Mark de Haan, Angelique Klinkers and Murat Tanriseven, Intangible capital in the Netherlands: Measurement and contribution to economic growth. Statistics Netherlands, Discussion Paper No Worrell, E., Bernstein, L., Roy, J., Price, L., and Harnisch, J., Industrial Energy Efficiency and Climate Change Mitigation. Energy Efficiency, 2(2), pp

18 18 JANET X. HAO AND BART VAN ARK Xing, Yuqing, and Neal Detert, How the iphone Widens the United States Trade Deficit with the People s Republic of China, ADBI Working Paper Series No. 257, Asian Development Bank Institute December.

19 GREEN JOBS IN EUROPE AND THE INCREASING DEMAND FOR TECHNICAL SKILLS 19 Table 1: Data sources Type of Investment Level of Aggregate Data Source Intangible investment Market sector total Corrado et. al. (2012) Tangible investment Market sector total Corrado et. al. (2012) R&D spending (BERD) By Industry Eurostat Software investment By Industry EU KLEMS updated by Colijn et. al. (2013). Tangible investment By Industry and by type of assets EU KLEMS updated by Colijn et. al. (2013). Value added By industry EU KLEMS updated by Colijn Energy intensity and energy volume index By industry et. al. (2013). EU KLEMS updated by Colijn et. al. (2013). Table 2: Tangible Investment, Intangible Investment and Growth Rates of Energy Intensity, Average Countries Intangible investment (% GDP) Tangible Investment (% GDP) Ratio of intangible/tangible Average growth rates of energy intensity (%) German 9.4% 9.9% Spain 6.0% 12.9% Finland 10.2% 10.8% Italy 6.6% 12.5% the Netherlands 10.7% 9.8% the UK 12.6% 9.4% Source: The data source of tangible and intangible investment is Corrado et. al. (2012). The data source of energy intensity is EU KLEMS updated by Colijn and van Ark (2013). 19

20 20 JANET X. HAO AND BART VAN ARK Table 3: The Relationship between the Ratio of Intangible to Tangible investment and the Annual Growth Rates of Energy Intensity ( ) (1) (2) Ratio of Intangible to Tangible Investment Country dummies (-4.30**) (-3.15**) Y constant 3.53 (3.71**) 2.00 (1.14) #obs R-squared Note: Explanatory variable is the annual growth rates of energy intensity. Robust standard errors. Sources: Source: Data source of intangible investment is Corrado et. al. (2012). Data source of tangible investment and energy intensity is EU KLEMS updated by Colijn et. al. (2013).

21 GREEN JOBS IN EUROPE AND THE INCREASING DEMAND FOR TECHNICAL SKILLS 21 Table 4: Summary Statistics of Investment Shares in Value Added by Industry, using Germany 2009 as an Example. Industry Name Ind Code Energy intensity Tangible inv/ VA ICT inv/va Non- ICT/VA Agriculture, hunting, forestry and fishing AtB 9.1% 43.0% 0.4% 42.6% Mining and quarrying C 17.8% 28.7% 0.9% 27.8% Food, beverages and tobacco 15t % 13.2% 0.2% 13.0% Textiles, leather and footwear 17t19 7.7% 7.5% 0.2% 7.3% Wood and cork, pulp, paper, printing and 20to22 7.4% 11.2% 0.6% 10.6% publishing Coke, refined petroleum and nuclear fuel % 38.2% 1.1% 37.2% Chemicals and chemical % 13.7% 0.3% 13.4% Rubber and plastics, other non-metallic 25t26 8.9% 13.2% 0.3% 12.9% mineral Basic metals and fabricated metal 27t % 11.9% 0.2% 11.6% Machinery, nec % 8.9% 0.3% 8.6% Electrical and optical equipment 30t33 1.5% 8.9% 0.4% 8.5% Transport equipment 34t35 4.0% 17.9% 0.6% 17.3% Manufacturing nec; recycling 36t37 8.8% 8.9% 0.3% 8.6% Electricity, gas and water supply E 40.1% 23.7% 0.9% 22.7% Construction F 3.2% 3.3% 0.1% 3.1% Sale, maintenance and repair of motor % 6.6% 0.2% 6.3% vehicles and motorcycles; retail sale of fuel Wholesale trade and commission trade, % 6.9% 1.1% 5.7% except of motor vehicles and motorcycles Retail trade, except of motor vehicles and % 6.7% 0.7% 5.9% motorcycles; repair of household goods Hotels and restaurants H 3.8% 6.2% 0.3% 5.9% Transport and storage 60t % 34.1% 1.1% 33.0% Post and telecommunications % 38.6% 6.8% 31.8% Financial intermediation J 0.9% 6.9% 1.4% 5.5% Real estate activities % 4.8% 0.1% 4.7% Public administration and defense; L 2.4% 17.5% 0.3% 17.2% compulsory social security Education M 2.8% 8.4% 0.4% 8.0% Health and social work N 1.3% 12.4% 0.5% 11.9% Source: EU KLEMS updated by Colijn et. al. (2013). 21