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Intangible investment in the EU and US before and since the Great Recession and its contribution to productivity growth

This paper uses a new cross-country cross-industry dataset on investment in tangible and intangible assets for 18 European countries and the US. We set out a framework for measuring intangible investment and capital stocks and their effect on output, inputs and total factor productivity. The analysis provides evidence on the diffusion of intangible investment across Europe and the US over the years 2000-2013 and offers growth accounting evidence before and after the Great Recession in 2008-2009. Our major findings are the following. First, tangible investment fell massively during the Great Recession and has hardly recovered, whereas intangible investment has been relatively resilient and recovered fast in the US but lagged behind in the EU. Second, the sources of growth analysis including only national account intangibles (software, R&D, mineral exploration and artistic originals), suggest that capital deepening is the main driver of growth, with tangibles and intangibles accounting for 80% and 20% in the EU while both account for 50% in the US, over 2000-2013. Extending the asset boundary to the intangible assets not included in the national accounts (Corrado, Hulten and Sichel (2005)) makes capital deepening increases. The contribution of tangibles is reduced both in the EU and the US (60% and 40% respectively) while intangibles account for a larger share (40% in EU and 60% in the US). Then, our analysis shows that since the Great Recession, the slowdown in labour productivity growth has been driven by a decline in TFP growth with relatively a minor role for tangible and intangible capital. Finally, we document a significant correlation between stricter employment protection rules and less government investment in R&D, and a lower ratio of intangible to tangible investment. JEL: O47, E22, E01

Intangible investment in the EU and US before and since the Great Recession and its contribution to productivity growth

Introduction
The changing nature of the global economy has placed novel attention on intangible capital as a new source of growth. Corrado, Hulten and Sichel (2005, hereafter CHS) expanded the core concept of business investment in national accounts by treating much business spending on "intangibles"computerised databases, R&D, design, brand equity, firm-specific training, and organisational efficiencyas investment 1 .
When this expanded view of investment is included in a sources-of-growth analysis, intangible capital is found to account for one-fifth to one-third of labour productivity growth in the market sector of advanced economies.
As overall business intangible investment is large and growing in advanced countries (Corrado et al., 2013) the development of harmonised methods and measures of intangible capital coherent with national accounting practices is essential for a deeper understanding of the sources of growth and for the design of macroeconomic policies aimed at stimulating sustained growth, competitiveness and sustainable development.
Although the fixed asset boundary in national accounts has been continuously expanded in recent decades to better account for the role of intangibles, official estimates treat as investment only a limited range of intangible assets: R&D, mineral exploration, computer software and databases, and entertainment, literary and artistic originals (SNA 2008/ESA 2010. Following the work of Sichel (2005, 2009) and Nakamura (1999Nakamura ( , 2001) a significant research effort has expanded the number of countries for which estimates of investment in intangible assets based on the CHS approach are available. Much work on intangibles focused on Europe and is comparative in nature. This applies to two projects funded by the European Commission (COINVEST and INNODRIVE) under the 7th Framework Programme and to work conducted by The Conference Board and published by the European Investment Bank (EIB) in December 2009. These projects generated estimates of business intangible investment and capital for the European economies. More recently, great efforts have been devoted to producing harmonised national estimates. This has led to the publication of the INTAN-Invest dataset 2 , which 1 The seminal contribution of Sichel (2005, 2009, hereafter CHS) was to use an economic view of investment to formalise the arguments for capitalising a broad range of intangibles (not just R&D and software) in company and national accounts. Such assets are created when today's resources are set aside and used to expand tomorrow's production capacity. The criterion applies equally to firms' expenditures on product, market and organisational development because firms expend resources on such activities to increase their future production capacity through "organic growth", or innovation. This view of investment is common sense, yet it is firmly grounded in economic theory via the optimal growth literature (e.g. Weitzman 1976; see also Hulten 1979). covered 27 countries of the European Union, plus Norway and the United States (Corrado et al., 2012). This paper uses a newly revised and updated release of the INTAN-Invest dataset for the market sector (INTAN-Invest 2016) of 18 European countries and the US to analyse the diffusion of intangible investment within Europe and in the US, to investigate the role of intangible capital as a source of growth and to improve our understanding of the drivers of the intangible investment gaps across countries.
The paper is structured into seven sections. Section 2 illustrates the theoretical framework and section 3 provides a data description (INTAN-Invest dataset, 2016). Section 4 illustrates the distribution and trends of intangible investment in the US and in the European economies over the years 2000 to 2013. Section 5 provides evidence about the economic relevance of intangible investment while section 6 explores the drivers of intangible capital accumulation. Section 7 concludes.

The theoretical framework
CHS advanced a simple three-sector model that specified production functions for consumer goods, conventional investment goods, and intangibles. The model was used to show how an economy's input and output growth changed when business investment in intangibles was capitalised, and its variables were used to identify the prices and quantities that needed to be measured in order to capitalise intangibles and study their contribution to growth.
Here we follow the same strategy but use the related model by Corrado, Goodridge and Haskel (2011) that integrates the various approaches to innovation and integrates innovation into the national accounts to make it measurable (see also Corrado et al., 2013).
The main assumptions of the model are the following. Knowledge (ideas) is an input needed to produce consumption and tangible investment goods together with labour and tangible capital. There exist two types of knowledge. One is knowledge that is generated without using factors of production and that is freely available to firms (free knowledge).
The other is knowledge that is produced using inputs and that firms must pay for to use in their production process (commercialised knowledge). Commercialised knowledge is accumulated over time, generating the stock of commercial knowledge via the standard perpetual inventory relation and with its own user cost (explicit or implicit). 3 applied and used for each country. 3 To be more precise, the model considers a simplified economy with just two industries/sectors. The innovation sector produces new finished ideas i.e. it commercialises knowledge (e.g. a way of organising production, or a The first implication of the model is a broad definition of investment, which includes expenditure to purchase both tangible goods and commercialised knowledge, and a broad definition of aggregate output, which includes not only consumption goods and tangible investment goods but also commercialised knowledge. P Q Q = P Y Y + P N N = P C C + P I I + P N N

[1]
The reason can be thought of by analogy to tangible investment. Suppose an aircraft factory buys in aluminium and produces both final output and its own machines. Then its value added should be properly treated as both the final aeroplanes and the machines, i.e. one might think of the factory as consisting of both an aircraft factory and also a machine factory. Its investment should be treated as the equal to the output of the machines. Now suppose the factory also writes its own long-lived software to run the machines. Then we should think of it as both an aircraft factory and machine factory and also a software factory and its investment should include not only the machines but also the software that is produced.
The second implication is that the expression for the sources of growth in value added output is, where sQ is the share of nominal value added accounted for by payments to the particular factor, dlnTFP is defined as the growth in Q (extended output including commercialised knowledge) over and above the growth contributions of labour, the accumulated stock of tangible capital and the accumulated stock of commercialised knowledge (which are in turn their growth rates, times their factor payment shares in total value added).
The final implication is that the model provides a measure of innovation. Equation 2 says that value added growth is due in part to growth in L and K. This formalises the idea that growth can be achieved by duplication i.e. adding more labour and tangible capital. It further says that growth can be due to the increased use of paid-for ideas, dlnR, but they working software programme adapted to the needs of the organisation, say that implements pay and pension calculations for many part-time workers), while the "production" sector uses the knowledge to produce consumption and tangible investment goods. The innovation sector can, at least for some period, appropriate returns to its knowledge, and so this model is identical to Romer (1990) (where patent-protected knowledge is sold at a monopoly price to the final output sector during the period of appropriability), while the production sector is price taker for commercialised knowledge. Both sectors are price takers for labour and tangible capital.
have to be paid for to be used, and hence make a contribution to dlnQ of sQ R dlnR. The final term, dlnTFP is the growth impact of everything else, which in this model can only be free ideas used in both sectors. Thus in this model, innovation in the sense of use of ideas is also growth net of K and L usage, i.e.
Many innovation studies have attempted to distinguish between innovation and diffusion, the latter being the spread of new ideas. If the ideas come for free, they are, in this framework, counted in TFP growth. So the part of innovation measured by sQ R dlnR is investment in commercialised new ideas and that part measured by dlnTFP might be regarded as the diffusion of free ideas.
3 Implementation: choice of assets and data sources

Choice of assets
What then are intangible assets? They are investments that enable knowledge to be commercialised. CHS group them into three categories (see Table 1 below) Let us review the assets in Table 1. "Computerised information" includes both purchased and own-account software: note that many intangibles are likely to be generated "in-house".
Databases are also included as recommended in SNA 2008.
The second and third broad groups are "innovative property" and "economic competencies".
"Innovative Property" is designed to capture a range of assets that may have intellectual property protection associated with them, e.g. R&D, design and artistic originals. Given the huge interest at the time in financial services the CHS list included a special category for them. "Economic competencies" aim at capturing a range of knowledge assets that firms invest to run their businesses, but that might have no IP: the costs of marketing and launching new products, including ongoing investments to maintain the value of a brand, and organisation and human capital management innovations (CHS, 2005(CHS, , 2009).

From investment to capital stock
For each asset j, the corresponding stock of intangible capital at time t, , is determined via the perpetual inventory relation: where the term is the asset specific rate of decay of appropriable revenues from the existing stock of commercial knowledge (assumed constant over time) and is the value of investment in volume terms in year t.
How can knowledge depreciate i.e. >0, if it does not wear out? As discussed in Pakes and Schankerman (1984) As for depreciation rates, we have used the values set out in the final column of Table   1 where the rate for mineral exploration is the US BEA rate. 5 The others are as discussed above or the same as CHS, bar R&D which is 15%. In the US, BEA, for example, places its central estimate of the depreciation rate for R&D at .15. Soloveichik (2010) produced depreciation rates for four categories of total artistic originals that also implied rather long service lives. Surveys conducted by the Israeli Statistical Bureau (Peleg 2008a(Peleg , 2008b) and by Awano et al. (2010) with the UK Office of National Statistics asked about the "life length" of investments in R&D (by detailed industry in Israel) and 4 In particular, we have used value-added deflator of industry M69-70 -"Legal and accounting activities; activities of head offices; management consultancy activities" for organizational capital; a weighted average of valueadded deflator of industry M71-" Architectural and engineering activities; technical testing and analysis" and M74-75 -"Other professional, scientific and technical activities; veterinary activities" for design; value-aded deflator of industry M73 -"Advertising and market research" for brand; value-added deflator of industry P-"Education" for training. 5 The lifetime of the knowledge created by mineral exploration is the service life of the discovery (a well or a mine). In the Australian national accounts, a service life of 34 years is used whereas the United States uses 12 for oil and gas exploration and 20 for mining. Most U.S. exploration is for oil and gas, and a 12-year life is used for the calculations reported in this paper. Note that investment is mineral exploration is negligible for most EU countries.
intangible assets (R&D plus 5 other asset types in the UK). The Israeli survey supports lengthening the service life for R&D, while the UK survey confirms fast depreciation rates for economic competencies.

Data sources
Among the intangible assets listed above, only a few are currently capitalised in national accounts industries P and Q but incorporated industry R as a whole). In the analysis reported in this paper we exclude the real estate industry (NACE section L).
As for sources and methods adopted to generate INTAN-Invest measures of intangibles see Appendix 1.

Intangible investment in the US and the European countries
In this section we provide evidence on the diffusion of business intangible investment over the period 2000-2013 in the US and in 18 EU economies (EU15 excluding Luxembourg (which will be referred to as EU14) plus the Czech Republic, Hungary, Slovakia and Slovenia (which will be referred to as the NMS)).

The overall picture
In 2000-2013, the average share of intangible investment in GDP is relatively higher in the US (4.2%) than in the EU14 (3.1%) as well as in the four new EU Member States (NMS) included in the analysis (2.2%) ( Figure 1). Moreover, national accounts data suggest that the GDP share of tangible investment in the three areas (7.7%, 9.2% and 16.0% respectively) is relatively higher than the intangible share.
But when new intangible assets are included in the analysis, the intangible investment gap between the European economies and the US broadens. New intangibles account for 4.6% of GDP in the US, and 4.1% and 4.2% in the EU14 and NMS respectively. Adding new intangibles to national account assets makes the GDP share of total intangible investment increase to 8.8% in the US, 7.2% in the EU14 and 6.4% in the NMS. Hence in the US intangibles outpaced tangible investment while in the European economies the opposite was the case 6 .
However, within the EU14 economies intangible shares of GDP vary considerably, revealing an interesting geographical pattern ( Sweden is the leading country with an intangible GDP share of 10.4%, followed by the UK (9.0%), Finland (8.8%), France (8.7%), the Netherlands and Ireland (both at 8.5%) and Belgium (8.1%) and Denmark (7.8%) lagging slightly behind.
The Mediterranean and German-speaking countries are relatively less intangible intensive economies. In Austria, the intangible investment rate (6.7%) is lower compared to the more intangible-oriented economies but still close to the average of the EU14.
Portugal (6.0%) and Germany (5.9%) are below the EU14 average intangible share of GDP followed by Italy (5.3%) and Spain (4.6%). Greece shows the lowest average share over the period (3.7%) being an outlier also in terms of the tangible GDP share of investment.
6 Although intangible intensity in the four NMS was slightly lower than in the EU14 region, the ratio of tangible investment to GDP (16%) was almost 50% higher than in the US and almost 60% higher than in the EU14 region.

Source: INTAN-Invest and authors' elaborations on national accounts
When looking at capital stocks instead of investment flows, the relative weight of tangible and intangible assets changes a lot ( Table 3). In all countries, the level of tangible capital stock is much higher than the level of intangible. This is due to the fact that depreciation rates of tangible assets are much higher than those of intangible ones and this implies, anything else equal, a higher level of capital stock. However, the countries that are more intangibles intensive in terms of investment ratio are also the more intangible intensive in terms of capital stock ratio.
The analysis of the composition of intangible investment (% GDP) reveals that in the US innovative property and economic competencies are the main drivers of intangible capital accumulation (3.5% and 3.7% respectively) while software (1.7%) plays a minor role ( Figure 2).
Economic competencies are the main driver of intangible expenditure also in the EU14 and NMS and computer software remains the smallest component. The same pattern holds within the European economies with the notable exception of the Scandinavian countries, Germany and Ireland (Table 4), where innovative property is the main intangible component (as a result of the high propensity for investing in R&D).
The asset breakdown suggests that Germany is lagging behind the more intangibleintensive EU14 countries and the US because of a lower propensity for investing in economic competencies and software, while Italy and Spain are relatively lower across all intangible asset categories.  The figures in Table 5 show that in the sample areas services invest more than the industry sector in intangible assets and that agriculture has negligible shares 7 . Services account for 64% of market sector intangible investment in the US, and for 61.4% and 57.6% in the EU14 and NMS. However, manufacturing has a higher value added share of intangibles than services in both the EU14 and NMS, suggesting that the predominant role of services in market sector's spending for intangible investment is driven by their larger share of value added and is not related to a higher propensity for investing in intangible assets (Figure 3).
In the NMS, instead, services are more intangible-intensive than manufacturing. In the US intangible intensity in both sectors (12.4% and 14%, respectively) is higher than in the two European regions. In the EU14 manufacturing intangible investment as a percentage of value added is much higher than in the NMS (11.9% vs 8.7%), while services display a comparable share in both European regions (10.3% and 10.2%). Table 5 shows that in Finland, Germany and Sweden manufacturing is more intangibleintensive than services, while Belgium, Ireland and the Netherland have similar intensities across both sectors.
The low intangible intensity of the Mediterranean countries and, to a lesser extent, Austria, is due to a relatively low investment level in both sectors (with the only exception of Portugal, where intensity in services is higher than the EU14 average). On the other hand, the relatively low level of intangible investment in Germany is mainly driven by the low investment propensity of services, while manufacturing is at the EU14 average (but lower than the US level).
The last three columns in Table 5 illustrate the intangible to tangible investment ratio across countries and industries. Services are more intangible than tangible-intensive in the US and in both EU regions. The difference between industry and services is much

Trends in tangible and intangible investment over the period 2000-2013
In this section we look at the dynamics of tangible and intangible investment across 18 European economies and the US over the period 2000-2013. Figure 4 shows that the average annual rate of growth of intangible investment in volume terms is negative in Greece, Italy and, marginally, in Finland. Sweden is the sole country where intangible capital accumulation is significantly less dynamic than tangible capital accumulation. In the US the average rate of growth of intangible investment is 2.6 % per year over 2000-2013, while the rate of tangible investment is 1.0%. The European economies included in our analysis grow at a slower pace both in tangible and intangible investment. In the EU14, intangible investment increases by 2.0% per year while tangibles grow at the modest rate of 0.4% per year. In the NMS the patterns of growth of intangibles and tangibles is even more striking, with the former increasing at 1.2% per year and the latter decreasing by 0.5 % per year. In contrast, Figure 7 shows that the intangible investment rate in 2010-2013 increased compared to the pre-crisis period 2000-2007 in all countries but Germany and Italy (where it remained stable) and the UK where intangibles decreased. The UK is also the sole country where intangible intensity slowed down during the Great Recession. In Germany, Italy and Sweden intangible intensity remained stable while it increased in all the other economies.

Source: INTAN-Invest and authors' elaborations on national accounts
In the countries with a positive dynamic of intangible investment, the average annual rate of growth of intangible investment is positive in both the industry and service sectors (see Figure A3, in Appendix). Intangible capital accumulation is relatively faster in industry compared to services in Slovakia, Austria and, to a lesser extent, in the US. The negative rate of growth for Greece is entirely driven by the industry sector. Italy is the sole country showing a reduction of intangible capital accumulation both in industry and services. In the three sample areas intangible capital accumulation increased after the Great recession compared to the pre-crisis period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) in industry as well as in services (see Table A1, in Appendix).

Tangible and intangible investment during the crisis
The slowdown of gross fixed capital formation experienced by all advanced economies has been highly debated since the occurrence of the financial crisis. Figure 8, Figure 9 and Figure   10 illustrate the dynamics of tangible and intangible investment since 2000. In the US, tangibles grew strongly after 2002, fell sharply during the recession (by 24%) and then recovered slightly. Intangibles slowed down too (by 7%) but regained pre-crisis rates rapidly after the crisis. As a consequence, the ratio between intangible and tangible investment increased during the recession, then came back to its mid-2000s level (Figure 8).
In Europe the picture looks rather different (Figure 9 and  in intangible assets in volume terms increased by 16% in France, 7% in the UK, 6% in Germany, and 1% in Spain, while it declined by 12% in Italy. Finally, Figure 12 shows the intangible/tangible ratio for the five large EU economies. France and the UK record the largest ratio (with intangible investment higher than tangible over the whole period), with Italy and Germany further below. Spain shows the lowest value, but in 2013 it had almost completely converged to the German and Italian levels. In the five countries the ratio increased significantly during the Great Recession and reached higher levels in the following years. In 2013 the intangible/tangible ratio was about 20% higher than in 2000 in the UK, 25% in France, Germany and Italy and 75% in Spain. Overall the countries that were more intangible intensive before the crisis (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) were less affected by the crisis or experienced a faster recovery (in 2013) (see Figure A6, in Appendix). The main exceptions are Slovakia (among the countries with the lowest intangible intensity but the country that has showed the fastest growth since 2007) and, to a lesser extent, Finland and Germany. In this respect, the composition of total investment expenditure makes a material difference: there is a positive, although not very strong, correlation between the average ratio of intangible over tangible investment in 2000-2007 and the volume change of GDP from 2007 to 2013 (see Figure A7, in Appendix).

Sources of growth
The sources of growth exercise covers all 19 countries included in the descriptive analysis.
To the best of our knowledge, this is the first attempt to provide an analysis of the sources of labour productivity growth that explicitly accounts for the contribution of tangible capital and an exhaustive list of intangible assets for so many European countries. The

2000-2013
From 2000 to 2013, labour productivity growth was by far the highest in the four new Member States and in Ireland ( Table 6) The last three rows in Table 5 show the rate of growth for the US, EU14 and NMS (CZ-HU-SI-SK). In the US labour productivity growth is 1.8%, in the EU14 1% and in the NMS 3%. Intangible capital provided a relatively smaller contribution in the EU14 than in the US (0.3% against 0.6%) and the same holds for MFP&LQ. In the NMS intangible capital accounts for a similar contribution as in the EU14 while the contribution of tangible and MFP&LQ are significantly higher. The bottom line in Table 6 is that, although intangible capital has been an important driver of growth in the EU14 countries excluding Greece, Italy, Denmark, and, to a lesser extent, Germany, the growth contribution of intangible capital is comparatively too small to catch up with the US A deeper look at the differences between the composition of intangible contributions in the US and in the EU economies reveals that in the US the three asset categories provided a high contribution. Within innovative property, the contribution of minerals and artistic originals 8 seems particularly strong in the US, while R&D, design and new financial products accounted for a similar share in both areas. The high contribution of economic competencies in the US is driven by training, (which is falling in Europe), and brand equity. On the other hand, organisational capital accounted for a larger share in the EU14 than in the US.    Figure A8, in Appendix).

2000-2007 and 2007-2013
As expected, the slowdown is driven mainly by the negative contribution of MFP&LQ.
During the recession years, the measured contributions of capital and labour is distorted by swings in the rate of capital utilisation and effort that are not captured by the available measures of capital stocks and hours worked. Consequently MFP is to a large extent capturing the changes in labour productivity due to the fact that firms do not reduce instantaneously their inputs according to changes in output (due to, e.g., labour market regulations, labour hoarding, and irreversibility of installed fixed capital).
The contribution of capital deepening significantly slowed down in Greece, the Czech Republic, and, to a lesser extent, Hungary, Slovenia, Sweden and the US. In Greece, Slovenia and Sweden the slowdown was almost entirely driven by the tangible component, while in the Czech Republic, Hungary and the US by both components.
Finland and the UK are the only two countries where the contribution of the intangible capital component declined with respect to the previous period while that of the tangible one increased (Finland) or remained stable (UK).  Table 9 sets out growth accounting but using national accounts intangibles. Looking again at the lowest three lines, and comparing them with the lowest three lines in the equivalent table that uses all intangibles, we see that, broadly, including intangibles raises the capital contribution and lowers TFP growth, with, over this period, growth in output per hour unaffected. So the contribution of capital and TFP with intangibles capitalised in the US for example is 1.1% pa and 0.7% pa, but without is 1% pa and 0.9% pa. In the EU14 the equivalent figures are 0.7% pa and 0.3% pa and 0.6% pa and 0.4% pa. Thus the inclusion of intangibles lowers the "measure of our ignorance".

Source: Authors' elaborations on national accounts 6 Drivers of investment in intangible assets
In the previous section we showed that the propensity to invest in intangible assets is positively correlated with some measures of economic performance (GDP per head and GDP growth since the beginning of the Great Recession) and that, in a growth accounting sense, intangible investment is an important driver of labour productivity growth. At this point, it is natural to ask why some countries appear to invest more in intangible investment than others. A comprehensive discussion of all the potential determinants of intangible investment is well beyond the scope of the paper. In this section we present a very preliminary analysis of the correlation between intangible investment and two elements that could potentially affect it: firm size, and product and labour market regulation.

Intangibles and firm size
The issue of the link between firm size and investment in intangible assets is surveyed by Arrighetti et al. (2014). Firm size is likely to have a positive impact on the propensity to invest in intangible assets for three reasons. In the first place, large firms are better able than small ones to exploit economies of scale in intangible asset accumulation (Dierickx and Cool, 1989). Secondly, big firms can be more effective in protecting their intangible stock than small ones, and thus have a greater incentive to invest. Thirdly, it may be argued that large firms are also capable of supporting a greater amount of the uncertainty that is associated with intangible asset investments as compared to small firms (Ghosal and Loungani, 2000).

The (scant) empirical evidence on the link between firm size and intangible investment is
consistent with the view that the propensity to invest in intangible assets is positively correlated with firm size. Arrighetti et al. (2014) shows that in a sample of Italian manufacturing firms, size increases significantly the probability of being an intangibleintensive firm (where intangibles are measured as a subset of the costs usually reported under the item "intangible fixed assets" in firms' financial statements). The NESTA survey "Investing in innovation" for the UK (Awano et al., 2010) finds that among firms that replied to the survey, large firms are more likely to report positive spending on one or more intangible assets than smaller firms, although for all intangible spend, intensity does not vary with size, so large firms, who have a higher overall spend, do not have larger intensity. Likewise, a recent study from the European Commission (2013) shows that the smaller the company, the more likely they are to have made no investment in intangible assets (either using internal resources or external providers). For instance, 39% of companies with 1-9 employees say they invested nothing using internal resources for organisational or business process improvement in 2011, compared to 8% of those with 250 or more. If the empirical evidence on intangible investment is scant, there is a vast literature analysing the links between firm size and innovation (often measured as R&D expenditure). Here we only mention the results of the Community Innovation Survey 2008, which shows that large enterprises are more likely to introduce innovations than SMEs in almost all countries for which data are available (Eurostat, 2012).
To investigate this issue, we have calculated the cross-country correlation between intangible investment (measured both as a percentage of value added and as an intangible/tangible ratio) and the average firm size, measured as the share of persons employed in firms with more than 250 employees. Correlations are calculated by industry to control for different average firm size in various economic activities (see Table A2, in Appendix). Intangible intensity and the intangible to tangible ratio are positively correlated with the average firm size in 10 out of 11 industries, the only exception being "Water supply; sewerage, waste management and remediation activities" (where the correlation is negative but very close to zero). The correlation between intangible intensity and average firm size is higher than 0.2 in 8 out 11 industries, while the correlation between intangible to tangible ratio and average firm size is higher than 0.2 in 9 out 11 industries.

Intangibles and product and labour market regulation
The issue of the link between product market regulation (PMR) and investment and innovation is surveyed by Schiantarelli (2016) and we rely heavily on his work. Alesina et al. (2005) identify several ways in which product market regulation can affect investment. First, changes in regulation affect the markup of prices over marginal costs, because of their impact, for instance, on entry barriers and, hence, on the number of firms.
Second, regulation can influence the costs that even existing firms face when expanding their productive capacity. Third, for certain sectors, regulation imposes a ceiling on the rate of return on capital that firms are allowed to earn; this leads firms to increase the level of capital stock beyond the profit-maximizing level in order to obtain a greater total remuneration for capital. Removing the constraint on the rate of return (if binding) would, instead, reduce the desired capital stock and therefore investment. Finally, if product markets' regulatory reforms occur together with privatisation (or nationalisation) policies, changes in ownership structure can also affect investment. Public enterprises are often heavy investors, either because of political mandates or because of incentives to overexpand on firms' managers. Reduced investment by the public sector may therefore occur. Ultimately, which effect dominates is an empirical question. Alesina et al. (2005), in their empirical work, examine investment in non-manufacturing industries (e.g. energy, utilities, communication, and transport) in OECD countries that have experienced profound changes in their regulatory framework. The results suggest that reducing regulation has a significant and sizeable positive effect on the investment rate, particularly if the regulation affects barriers to entry.
Studies that focus on liberalisation episodes in specific sectors provide further evidence on the effect of product market regulation on investment. For instance, Schivardi and Viviano (2011) provide evidence on the relaxation of limits to the opening of large stores in Italy. The results suggest that reducing entry barriers stimulates investment in information and telecommunication technologies (which, in their data, also includes investment in computer software).
Contrasting forces may influence the effect of greater competition on innovation. On the one hand, innovation activity is primarily driven by the aim of achieving monopoly profits on new products or processes. If monopoly profits decrease as a result of regulatory reforms, the pace of innovation may likewise be reduced. Furthermore, monopoly profits help firms to accumulate enough funds to finance innovation. In fact, funds generated internally through retained profits are crucial given the presence of information asymmetries, which may make it costly or difficult to obtain external funds from financial markets for risky innovation activities that are difficult to evaluate. Indeed in the early quality ladder endogenous growth models of Aghion and Howitt (1992) and Grossman and Helpman (1991) and in the product variety model of Romer (1990) a reduction in rents generated by regulatory changes would adversely affect the incentive to innovate.
Nevertheless, in more recent models, incumbent firms also innovate (rather than just newcomers) (Aghion and Griffith, 2005). In these models, the difference between post and pre-innovation monopoly profits determines the incentive to innovate. Greater competition reduces both, but if the pre-innovation profits decrease more than the postinnovation profits, this fosters innovation. Essentially, competition stimulates innovation due to the threat of (or actual) entry of newcomers into a market, which provides incentives for incumbents to innovate in order to escape competition.
The issue of the effects of employment protection legislation (EPL) on productivity and investment is nicely surveyed by Bassanini, Nunziata, and Venn (2009) (Grout, 1984). If intangible capital is more sunk relative to tangible capital, then investment in intangibles will fall more. The wage effect, however, might be moderated by (perhaps centralised) unions, who might find it easier to precommit, perhaps in national wage bargains. This is the story in the Sapir report (Aghion et al., 2003), suggesting that centralised German unions were useful in the long period of post-war tangible capital accumulation by Europe, but might be much less useful now when intangible capital and experimentation are required. Finally, Bartelsman, Gautier, and de Wind (2011) suggest that experimentation with risky technologies might be lessened, so average productivity falls. The effects are likely to be analogous with product market regulation.
Finally, Ciriaci et al. (2016) show that product market regulation and employment protection legislation significantly affect the location decision of top R&D investors' subsidiaries. When taken separately, the level of PMR has the greatest negative effect on companies' location decisions, while EPL does not appear to play a significant role in such choices. When considering the interaction between PMR and EPL, results show that these two regulations exert a mutually reinforcing negative effect on the decision of top R&D investors about where to locate their subsidiaries.
The evidence from INTAN-Invest data is that countries with less stringent regulations in product and labour markets tend to have higher rates of investment in intangible assets and higher intangible to tangible investment ratios (see Figures A9 to A12, in Appendix).
The negative relation between the propensity to invest in intangible assets and the level of product market regulation holds for all three major components of intangible assets (computer software and databases, innovative property and economic competencies) and for all three high-level economy-wide indicators of product market regulation (state control, barriers to entrepreneurship and barriers to trade and investment) (see Table A3, in Appendix).

Determinant of the intangible to tangible investment ratio
In this section we attempt to explore econometrically why some countries appear to invest more in intangible investment than others, allowing for more factors than just the regulatory factors set out above. The following points are worth noting.
First, there may be some "structural" reasons for this. For example, countries with more services might be more intangible-intensive. Or countries with more ICT intensity.
Second, public sector R&D might be complementary to private sector intangible investment, and hence it might be that countries with more government-funded R&D are investing more. Third, the neo-classical explanation is that relative prices will determine relative investment, with relative prices particularly affected by the tax treatment of intangibles and tangibles.
Fourth, econometric estimation of investment equations has not often found it easy to find plausible price elasticities and discover the effects of e.g. liquidity constraints and the like.
Part of this is that investment seems to be cyclical in ways that prices and adjustment costs have problems describing them, perhaps due to animal spirits and other unmeasureables.
This suggests that we might proceed by exploring intangible investment relative to tangible investment, thereby sweeping out any common effects affecting investment "sentiment" that seem so hard to model. Thus we ran the following regression where the dependent variable is the log of relative intangible to tangible real investment , 1 , 2 , 3 , Where the terms on the right are, respectively, relative investment price, the OECD index of employment strictness, the ratio of ICT capital rental payments to total tangible rental capital payments, the share of employment in manufacturing and the ratio of governmentfunded R&D to GDP. Each variable is at the country-year dimension, where for convenience the variables are all averages over the following four periods : 1997-1999, 2000-2003, 2004-2008 and 2011-2013. The equation also includes a constant and three time dummies and estimation is by random effects (we could not reject the hypothesis that the fixed effects were jointly zero). For this exercise we have data on 12 countries. The relative investment, prices and ICT intensity data are all for the private sector.
Column 1 in Table 10 shows the results. The relative price term is correctly signed and significant, showing a strong relative price effect. It would be preferable to incorporate tax adjustment factors for intangibles and tangibles, but at the time of writing we do not have them. Turning to the second and third rows, countries with higher ICT intensity and lower manufacturing shares are associated with higher relative intangible investment, in line with the view that intangibles are complementary to ICT and that the intangible to tangible ratio is higher in the service sector. The OECD strictness index is strongly negatively correlated with relative intangible investment, in line with the graphs in the Appendix. Finally, countries with more government R&D have high relative intangible investment, in line with the view that such public investment is complementary to private intangible investment.
The rest of Table 10 explores robustness. Column 2 replaces employment strictness with product market regulation and finds, again, a negative and statistically significant association. Column 3 puts them together, but they would seem too collinear (i.e. countries that tend to have a lower level of product market regulation also tend to have a lower level of employment protection and vice versa) to get a strong relation with both.

Conclusions and policy implications
Summing up from the descriptive analysis reported in section 4 we can identify the following stylised facts. First, from 2000 to 2013 average intangible intensity (% GDP) in the US (8.8%) was higher than in the EU14 (7.2%) and in the four new Member States included in our analysis (6.4%). In the US investment in intangible assets outpaced tangible capital accumulation, while in the EU regions it is the opposite. Within the EU14 countries the propensity for investing in intangibles varies considerably with Scandinavian, Northern Europe (Denmark, Finland, Ireland, Sweden and the UK) and non-German-speaking continental European countries (France, the Netherlands and Belgium) characterised by relatively high intangible shares of GDP. On the other hand, the Mediterranean and German-speaking countries are relatively more tangible-intensive economies.
In all the sample economies, intangible investments are more dynamic than tangibles.
Greece, Italy and marginally Finland are an exception because they experienced a slowdown of intangible capital accumulation (even if less pronounced than the downturn of tangible capital accumulation). The Great Recession had a differentiated effect on tangible and intangible investment: tangibles fell massively during the crisis and have hardly recovered, whereas intangible investment has been relatively resilient and recovered fast in the US but lagged behind in the EU.
In the previous sections we have shown that intangible intensity and the intangible to Our preliminary analysis of the drivers of investment in intangible assets shows that countries with higher average firm size and less stringent regulations in product and labour markets have a higher intangible investment rate and higher intangible to tangible investment ratio. The econometric analysis on a subset of countries reveals a significant correlation between having stricter employment protection rules and less government investment in R&D, such as in the Mediterranean countries, and a lower ratio of intangible to tangible investment (controlling for other factors).
Our findings suggest that intangible investment is a key policy variable. A relevant characteristic of intangible capital is that it is growth-promoting  thus potentially contributing to reducing the growth gap between the EU and the US. Therefore policies designed to foster innovation and to make the economic environment more conducive to investment in intangible assets should adopt a view of innovation that is broader than R&D. In fact, our growth accounting results show that the investment gap between the EU14 and the US is more related to the lower contributions of computer software and databases, artistic originals, mineral exploration, brand and training than to the contribution of R&D.
Finally, the very preliminary evidence presented in this paper on the drivers of intangible investment is consistent with the view that economic policies should target SMEs, focus on maintaining well-functioning product and labour markets and guarantee an appropriate level of government investment in R&D. Additional research is needed to validate our preliminary findings. The next steps will be to refine our econometric analysis extending the number of countries, including additional explanatory variables and exploiting the industry dimension of INTAN-Invest 2016.
Weitzman, Martin L., "On the Welfare Significance of National Product in a Dynamic Economy," Quarterly Journal of Economics, 90, 156-62, 1976.

Appendix 1. Measuring Intangible Investments: the INTAN-Invest database
The INTAN-Invest estimates reported in this paper (INTAN-Invest 2016)  for almost all countries also data on Soft and R&D by 21 industries are available. For these countries, we estimate overall GFCF in Mineral Explorations and Originals ("MinArt") by 21 industries as a residual. Instead, for countries where only total IPP by industry is available, we have adopted the following approach. First, we have produced preliminary estimates of the industry distribution of GFCF in Soft, R&D, and MinArt using the available indicators. Then we have rescaled preliminary estimates to make them consistent with total GFCF in IPP by industry and with aggregate GFCF in Soft, R&D and MinArt (using an iterative bi-proportional fitting procedure). The preliminary estimates have been derived from ESA95 national accounts data on GFCF by industry or from capital stocks estimates 9 , depending on data availability. 9 The country coverage of capital stocks data on Soft and R&D by industry is larger than the country coverage of GFCF data. Then, there are several countries for which capital stocks data by industry are available and GFCF is not. In this case, we have used capital stocks data as follows. Starting from capital stock estimates (chained values) for year t and t-1 (Kt and Kt-1) and making an assumption on the value of the depreciation rate (delta) we have computed the implied value of chained investment for year t (It), as It=Kt -Kt-1 + Kt-1*delta. If net capital stocks were estimated with the geometric model and if we knew the actual depreciation rate used to compute capital stocks the above calculation would provide the correct value for It. In the EU, national Once we have obtained total investment in the three asset types by industry, we have obtained the split between the market and the non-market component for each asset in each industry simply deducting from total GFCF by industry the estimates for the non- The general approach is quite similar for all three assets. The first step is to make the initial data a better proxy of expenditure in the corresponding asset. We deem that in the case of Advertising and Market Research Services (CPA M73) and Architectural and engineering services, technical testing and analysis services (CPA M71) the products identified in the USE Table are good proxies of the corresponding assets and no further statistical institutes usually do not use the geometric method (with the exception of R&D), then the result of the calculation above can provide only an approximation of the real value of It. We use these approximated estimates as a preliminary estimate of investment by industry (i.e., as seeds for the iterative bi-proportional fitting). On the other hand, it is likely that the bias is quite similar across industries and therefore it should decrease when the initial estimates are re-scaled to make them consistent with total GFCF in IPP by industry and with aggregate GFCF in Soft, R&D and MinArt. 10 Old INTAN-Invest estimates by industry were obtained as follows. We first produced a detailed benchmark estimate of intangible investment in 2008 based on the USE table and then we built time series for the period 1995 to 2007 applying the rate of change of gross output (National Accounts) by industry to the level of the estimated intangible gross fixed capital formation in 2008. Finally, since our benchmark was the INTAN-invest market sector estimate of intangibles, we rescaled the estimated value for each industry, in each country, for every year, to the total provided by INTAN-invest (see   Once expenditure for each asset is identified, the second step is to split total expenditure in each industry between the component due to the market sector and the component due to the non-market sector. This adjustment is applied only to the SPINTAN mixed industries (M72, P, Q and R90-92), while for all other industries we deem that the expenditure is entirely made up either by the non-market sector (industry O) or by the market sector (all remaining industries). The split is based on the share of non-market output over total market and non-market output in each industry.
Finally, in each industry the capitalization factor is applied to total expenditure by market producers to obtain the value of total expenditure that we deem should be treated as The estimates based on data available from the USE Tables guarantee the exhaustiveness of purchased GFCF in Brand (based on product CPA M73) and Organisational capital (based on product CPA M6970), but not that of Design (based on product CPA M71). In fact, in the CPA classification, part of design activity is also classified in the CPA M741 "Specialised design activities". The USE Tables currently available from Eurostat do not allow identifying expenditure in CPA M741 because they only report data for the CPA M74_75 ("Other professional, scientific and technical services and veterinary services").
Instead, Structural Business Statistics report data on turnover of NACE M741. Then we have taken the turnover of NACE M741 as a proxy of total expenditure in CPA M741, we have assumed that only the market sector purchases "Specialised design activities" and, finally, we have obtained GFCF estimate applying the same capitalisation factor than CPA M71.
As for the own account component, its estimate requires detailed employment data by type of occupation and by industry (e.g., from the Structure of Earning survey or the Labour Force survey) or a special survey. Eurostat available occupational data allow identifying only those occupations related with organizational capital. This is why, at this stage, we measure only the own account component of Organizational capital, while for Design and Brand we only estimate the purchased component.
In order to estimate organisational capital produced on own account we need to estimate total compensation of managers and then apply a capitalisation factor. The estimate of total compensation of managers requires data on the number of managers and their average compensation. The main data sources for these variables is the Structure of We have then estimated total expenditure for management compensation consistent with national accounts data by applying the share of gross earnings of managers to national accounts measures of total compensation of employees in each industry. Finally, we have estimated the value of own-account investment in organisational capital by applying the capitalisation factor to the total managers' compensation.
As for Firm specific Human Capital, our estimates for the market sector are based on data from the Continuing Vocational Training Survey (CVTS) and Labour Cost Survey, that allow to produce industry level estimates of expenditure in training that include both the purchased and the own account component. For this asset the main improvement with respect to the old estimates is due to the availability of the CVTS for 2010 (while old estimates only used the 1999 and the 2005 survey). For training, we assume that all expenditures increase the value of the stock of FSHC and therefore should be considered as GFCF (i.e. we assume a capitalisation factor equal to one).