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Paper to be presented at the DRUID Tenth Anniversary Summer Conference 2005 on

DYNAMICS OF INDUSTRY AND INNOVATION: ORGANIZATIONS, NETWORKS AND SYSTEMS Copenhagen, Denmark, June 27-29, 2005

EXPLORING THE RELATIONSHIP BETWEEN INTANGIBLE CAPITAL AND PRODUCTIVITY IN ITALIAN MANUFACTURING FIRMS Maria Elena Bontempi Jacques Mairesse Abstract The aim of this paper is to investigate the relationship between output, employment, and physical and intangible capital for an unbalanced panel of Italian manufacturing companies during the 1982-1999 period. In Italy the literature on this issue is less developed, if compared, as an example, to that for the US or for France. Hence, attention is devoted to test whether our results are analogous to those presented in the existing empirical literature. A second interest aspect, linked to the particular country analysed, is the Italian accounting normative on intangibles. Differently from the Anglo-Saxon Generally Accepted Accounting Principles (GAAP) and from International Accounting Standards (IAS), intangibles costs may be capitalized as an asset, rather than directly expensed: an example is advanced R&D, capitalised, versus basic R&D, expensed. Therefore, we try to disentangle the informative content of intangibles assets compared to the one of intangible capital stock reconstructed from direct expenses. Finally, the data-set we use reports detailed accounting information on different intangible components. This allows us to explore the contribution to productivity of intellectual capital (R&D and patents) and of customer capital (marks and advertising). The relationship between firms’ productivity and intangibles appears both comparable with that of other countries and robust to the use of alternative panel estimators. Keywords:

Very preliminary draft, please do not quote. Comments welcome. Prepared for The DRUID 10 th Anniversary Conference 2005 (Copenhagen, June 27-29)

Exploring the Relationship between Intangible Capital and Productivity in Italian Manufacturing Firms by Maria Elena Bontempi (*) and Jacques Mairesse (**)

Abstract The aim of this paper is to investigate the relationship between output, employment, and physical and intangible capital for an unbalanced panel of Italian manufacturing companies during the 1982-1999 period. In Italy the literature on this issue is less developed, if compared, as an example, to that for the US or for France. Hence, attention is devoted to test whether our results are analogous to those presented in the existing empirical literature. A second interest aspect, linked to the particular country analysed, is the Italian accounting normative on intangibles. Differently from the Anglo-Saxon Generally Accepted Accounting Principles (GAAP) and from International Accounting Standards (IAS), intangibles costs may be capitalised as an asset, rather than directly expensed: an example is advanced R&D, capitalised, versus basic R&D, expensed. Therefore, we try to disentangle the informative content of intangibles assets compared to the one of intangible capital stock reconstructed from direct expenses. Finally, the data-set we use reports detailed accounting information on different intangible components. This allows us to explore the contribution to productivity of intellectual capital (R&D and patents) and of customer capital (marks and advertising). The relationship between firms’ productivity and intangibles appears both comparable with that of other countries and robust to the use of alternative panel estimators.

This draft: June, 7th , 2005; updated versions will be downloadable at http://didattica.spbo.unibo.it/pais/golinelli/bontempi/ricerca/ricerca.htm

(*) Department of Economics, University of Ferrara; e- mail: [email protected] (**) INSEE-CREST (Paris, France) & NBER; e- mail: [email protected]

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Introduction The process of inventing new products, of improving existing products, and of refining the techniques used to produce goods and services is an important determinant of productivity growth. However, the link between innovation and productivity is a poorly understood process. This is because technological changes arrive in a stream that is neither predictable, nor steady, nor continuous. Studies trying to estimate the connection between spending in innovation and productivity growth face a lot of problems. As examples: data level (e.g. line of business, firm, industry); no available data on all the components of technical changes (human capital, R&D, patents, etc.), or, when available, difficulties in selecting the “right” measures to be used for output and input; the firm heterogeneity in innovation; the presence of simultaneity (companies undertaking innovations are motivated by the profits that are expected to flow, and these profits, in turn, provide a spur for further innovation). Hence, empirical estimates span a wide range, from no effect on productivity to a very large effect, exceeding that of other types of investment. A survey of the empirical literature and obtained results is, e.g., Mairesse and Sassenou (1991).

In this paper we run a number of experiments on the link between innovation, measured by the sum of different categories of intangibles, and productivity. An innovative aspect of the paper is the data-set, a large unbalanced panel of Italian manufacturing companies: Italy is unusual for this kind of empirical studies. Also the definition of intangible capital stock is new, if compared to what habitually done by both the Anglo-Saxon and European literature. The paper is organised as follows. In Section 1 we present a quick view of the accounting information available at the firm level for both intangible and tangibles capital stocks, along with main statistics of these variables. In Section 2 we illustrate our framework of analysis: the CobbDouglas production function with both multiplicative and additive specifications of total capital. We also adopt a variety of specifications (conventional production function, total factor productivity production function, intangible intensity formulation) and of estimation techniques (pooled OLS, within, first and long differences, IV and GMM). In Section 3 we try to disentangle the contribution to productivity of each component of total intangibles. Further details on data, estimating methods and more checks are reported in the Appendices.

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1

The Measurement of Intangible Capital: the Firm Accounting Evidence

1.1

Capitalising or expensing the different categories of intangible investments Establishing whether it is better to capitalise or to expense intangibles is one of the most

controversial issue recently emerged in the literature, as it is manifested by the debate tackled by the International Accounting Standards Committee (IASC) in developing International Financial Reporting Standards (IFRS, known until 2002 as International Accounting Standards, IAS) that should be overall adopted. Other examples are the works of Lev (e.g. Lev, 2001). Nowadays, in Italy, intangibles’ reporting is subject to a combination of national Generally Accepted Accounting Principles (GAAP, based on art. 2424 cc of the Civil Code, a el gislative decree of 1991 1 , and the principle n. 24 of the Commissione per la Statuizione dei Principi Contabili of the Consiglio Nazionale dei Dottori Commercialisti e Ragionieri) and IAS 38 plus IFRS 3 (that supersedes IAS 22). This combination implies that, notwithstanding the definition and recognition criteria for intangible assets are similar to those of IAS/IFRS, more intangible assets than the ones predicted by IAS/IFRS are allowed for. In fact, Italian GAAP identifies the following categories of intangible assets: I1.

formation-expansion expenses (IKNstart);

I2.

research, development and advertising costs (these last under the condition that are functional and essential to the start-up phases) (IKNrd );

I3.

rights for industrial patents and rights for the exploitation of intellectual properties (IKNpat);

I4.

concessions, licences, trademarks and similar rights (IKNmark);

I5.

goodwill (IKNgod );

I6.

fixed assets in course of evaluation and payments on account (a mixture of I1.-I5. categories when not yet accomplished);

I7.

others (particular expenses related to I1.-I5. items, plus amelioration’s costs on rented goods, plus financial deferred charges). In brackets we report the labels (IKNj, j=start, rd, pat, mark, god) utilised for denoting the

book-net-value of the corresponding capital stock. Since available firm- level accounting information are very detailed, we were able to identify each category listed by the Italian GAAP. Note that no label is reported for I6. and I7. items: in consideration of their particular contents, we reallocated them to the other categories, from I1. to I5.. We also reconstructed the financial deferred charges component of I7., which, of course, we exclude from the analysis of enterprises’ productivity. Bontempi (2004) illustrates the procedures adopted to create a connection between 1

That implemented the Fourth Directive approved by the European Commission, introducing a structural break since

1992.

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reporting rules, available accounting information, and empirical variables suitable for our analysis,2 along with details on both the specific content of each category, and the comparison between the Italian GAAP and IAS/IFRS. Besides the classification of intangible assets, the Italian normative requires that some specific intangibles (or previous intangibles not respecting the conditions for capitalisation) must be recognised as an expense when incurred. These intangibles may be classified as: I8. basic R&D, and applied R&D if recognition criteria are not met (DErd and IKNDE rd ); I9. advertising not related to I1., but operative and recurrent (DEadv and IKNDE adv ); I10. intellectual capital (patents and intellectual property rights, concessions, brands, software) if purchased with a limited licence to use by paying period fees, or if obtained free of charge, or if recognition criteria are not met (DEpat and IKNDE pat). Again, in brackets, we report the labels we use for denoting the corresponding expenses (DEh , h=rd, adv, pat). We also add the labels of the corresponding net capital stock (IKNDE h , h=rd, adv, pat), reconstructed according to a perpetual inventory formula. 3 As it is evident from I1.-I10. classifications, the Italian accounting normative allows to recognise as an asset deferred charges like I1. and I2.. This is contrary to what established by IAS/IFRS, justified by the uncertain and discontinuous nature of these kinds of intangibles. The amount of intangibles to be capitalised wo uld be too subjective, thus offering to managers a means to manipulate reported earnings and asset values. The prudence principle requires the expensing of such uncertain intangibles. It could be argued, however, that the expensing of intangibles also affords managers a potent manipulation tool, arguably more damaging than the manipulation-via-capitalisation. Furthermore, the level of uncertainty of specific intangibles is not notably higher than the uncertainty of other corporate investments, such as stocks or bonds. Finally, several studies report a significant statistical association between (a) current and past intangible expenditures and (b) future growth in sales, earnings, and stock prices (Lev, 2001), as expected from assets. There is also empirical evidence suggesting that, at least on average, the capitalised value of intangibles, like software development and R&D costs, provides information relevant to investors when pricing securities (see, among others, Aboody-Lev, 1998, and Lev-Sougiannis, 1996). Previous arguments are central to the present debate on capitalisation versus expensing of intangibles tackled by IASC in defining IAS/IFRS. In this paper we try to lighten what is the effect 2

The reallocation procedures also take into account the normative changes occurred since 1992, when the fourth Directive approved by the European Commission was implemented through statutory law (D.Lgs. n. 127/91). 3

IKNDEith+1 = (1 − δ ) IKNDEith + DE ith+1 , where: δ is the average rate of depreciation, supposed equal to 30%;

IKNDEih0 = DEih0 /(δ + g ) is the initial value of the stock; and g is the pre-sample growth rate, assumed equal to 3%.

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of both capitalised and expensed intangibles on productivity of companies. In doing so, in Section 2, we consider an extended definition of intangible capital stock, in the sense that it takes into account not only the stock reconstructed by expensed intangibles -as usually done in the US literature- but also the intangible stocks originally reported in the balance sheets of Italian companies; the exclusion of these lasts, in fact, would imply biased estimation results. We try to go further, in Section 2.3, by exploring what is the informative content of the distinction, imposed by the Italian GAAP, between capitalised and expensed intangibles. We expect, for example, that R&D and intellectual property assets, if compared to the corresponding expenses, have a predominant role in determining productivity. If this is true, the capitalisation choices of managers, even if subjective and affected by uncertainty, reveal which part of intangibles (the advanced one, and not the basic one) drives the performance of companies. In the same Section 2.3 we also try to disentangle the contribution of intellectual capital from that of customer capital. In this case no distinction is operated between intangible assets and expensed intangibles; rather, we focus on R&D and patents (j=rd, pat and h=rd, pat), both included in the definition of intellectual capital, and on marks and advertising (j=mark and h=adv), both included in the definition of customer capital.

1.2

Intangible categories: relative magnitude and occurrence. We define total intangible capital as K = IKN+IKNDE, where: IKN = ∑ j IKN j , j=rd, pat,

mark; IKNDE = ∑h IKNDE h , h=rd, pat, adv. Hence, among previous different types of intangible, we focus on I2.-I4. assets, and on I8.-I10. stocks reconstructed by capitalising direct expenses. Formation-expansion asset (I1.) has a miscellaneous nature that implies some data-reconstruction problems (see Bontempi, 2004). Goodwill (I5.) is a category with a peculiar nature. Hence, we prefer to exclude both from the analysis of this paper, devoting them to future research. Keeping as separated capitalised and not capitalised intangible costs is important in order to disentangle the informative content of this distinction allowed for by the Italian GAAP. Total

intangible

capital

where: IK = ∑ j IKN j + ∑h IKNDE h ,

may both

also j

and

be h

defined =rd,

pat,

as is

K

=

intellectual

IK+IC, capital;

IC = ∑ j IKN j + ∑h IKNDE h , j=mark and h=adv , is customer capital. This is another important

distinction among intangible categories. As outlined by the literature (e. g. Hirschey, 1982 and, more recently, Lev, 2005) continuous advertising is important in avoiding that consumers forget innovations developed by a company. Similarly, marks are essential for the economic development Further details are available in Bontempi (2004).

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of businesses: they allow for the identification and the distinction of one product from other products, creating a unique image of product’s quality in the perception of the customers. Hence, brands and similar represent a key competitive factor which influences the sales of a company. Table 1 and Figure 1 present the analysis of total intangible capital and its different components. Information is at firm- level, distinguished between total and three manufacturing macro- industries: high- medium technology (HT+MHT); medium- low technology (MLT); low technology (LT). Company-data are drawn from CADS, collected since 1982 by the Centrale dei Bilanci, a company set up jointly by the Bank of Italy, the ABI-Italian Banking Association and other leading Italian banks. All the accounting variables are at constant prices, book values.4 Appendix A present details on the variables’ reconstruction and the cleaning rules applied to the original CADS data-set. The total sample we selected is an unbalanced panel of 14,254 Italian manufacturing firms over the 1982-1999 period (94,968 observations, on average 6.7 years per company).

Table 1 and Figure 1 here

Table 1 focuses on the occurrence of intangibles and present the average of the intangible over tangible ratios. As far as tangible capital stock is concerned, in our data-set we were able to disentangle the following six categories (again, in brackets, the labels denoting the book values of the corresponding net capital stock): T1.

lands and buildings (TKNbui);

T2.

plants and machinery (TKNpla );

T3.

equipment (TKNequ);

T4.

other tangibles, mainly dismissed, fully depreciated, or not-utilised assets (TKNoth );

T5.

uncompleted tangibles, mainly under construction or in course of purchasing (TKNunc);

T6.

tangible on lease (TKNlea). In this paper we define total tangible capital as C = ∑c TKN c , c=bui, pla, equ; hence, we

focus on T1.-T3.categories only. This choice is motivated by the irrelevance of the TKNunc and TKNlea components, as well as by the not-yet and no- more productive nature of the TKNunc and TKNoth categories (for details, see Bontempi, 2004). Cleaning rules illustrated in Appendix A exclude companies with C equal zero, because we do not consider reliable the corresponding accounting information. Hence, we never have zeros in the tangible capital stock.

4

When we use intangible and tangible assets at replacement values, estimations results do not change significantly.

6

On the contrary, we allow total intangible asset to be zero. However, such a percentage is not relevant (about 17%), as shown by comparing the “Full sample” and “K never zero” rows of Table 1. In the parameter estimation phase, these observations are accounted for by adding a specific dummy. Figure 1 decompose the items concurring to the intangible assets (IKN, in blue) and to the reconstructed intangible stock (IKNDE, in pink), as well as the categories constituting intellectual capital (IK, exploded light colours) and customer capital (IC, dark colours). For the “Full sample” rows of Table 1, the “K” column reports a high value of the ratio of total intangibles capital net stock (K) over total tangible capital net stock (C) in the LT industry. This result is explained by looking at the other columns on the right, as well as at Figure 1: it is evidently driven by the stock component reconstructed from intangibles expensed out and, in particular, by customer capital. Advertising and marks are also relevant in the HT+HMT industry; however, it is to be noted the substantial role of applied R&D (IKNrd ) and patents (IKNpat) assets also, as expected compared to other branches. These results are confirmed by the “K never null” rows and are emphasised by the “IKN alone”, “IKNDE alone”, “IK alone” and “IC alone” rows. Data in Figure 1 reveal that R&D is mainly recognised as an asset, while basic research stock reconstructed from expenses appears almost irrelevant. Thus, considering the stock reconstructed by R&D expenses only, i.e. adopting the usual approach of the literature without taking into account the informative content of the Italian GAAP, may lead to downwards biased estimation results. On the contrary, advertising is principally operative and recurrent, and thus expensed, as shown by the percentage of IKNDE adv, the highest in all the branches: it accounts for about one-half of the total intangibles, and its share tends to increase from high- to low-technology sectors. In general, basic R&D and patents components of IKNDE are almost irrelevant, if compared to the advertising item. Mark shares are quite constant in different branches, lower to the applied R&D shares in HT+MHT and MLT sectors only. The four intellectual capital items shares decrease, as expected, from high to low technology sectors. The “Both IKN and IKNDE never zero” and “Both IK and IC never zero” rows of Table 1 for the total sample respectively represent 35% and 64% of the observations corresponding to “K never zero”. This result confirms the rare presence of R&D and patent expenses, that has repercussions on the corresponding reconstructed stocks, despite the adoption of the perpetual inventory method. On the contrary, advertising expenses have less continuous initial zeros and originate a stock larger than the mark asset (IKNmark). The main statistics of the variables of interest are along the columns of Table 2.

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Table 2 here

As far as the growth rates are concerned (in the upper part of Table 2), per capita figures of production, value added, intermediate inputs, and, to a lesser extent, tangible stock statistics are very similar each other over the sample period. The employment growth is slightly more stable than previous productivity measures, while intangible stock statistics suggest a 30-50% higher variability than previous variables. Variability of intangibles is emphasised when disaggregated components are considered, mainly due to the larger presence of zeros, as shown by the reduced number of observations and companies involved in the computations of growth rates (NT and N columns, respectively). The variability is reduced when measured by a robust statistics. Per-employee level statistics, measured in million of Italian Lira at 1995 prices (in the lower part of Table 2), suggest considerable departures from normality: mean and standard deviation are always different to (in particular, bigger than) the corresponding median and pseudo standard deviation. Among the variables, the real intangibles stock per-employee represents the most extreme case: the mean and the standard deviation are about five times bigger than the median and the pseudo standard deviation (in particular, the mean is well over the 3rd quartile). The same distribution features are largely reproduced by the intangible over tangible ratio because of intangibles at numerator5 . These facts suggest that large intangible stocks are concentrated in relatively few companies, and that zeros are more prevailing here than in the other variables. On the other side, the distribution of both labour costs and intermediate inputs shares, whose statistics are reported in the mid-rows of Table 2, seem almost normal, with variability that is less than one-third of the averages. In other terms, shares are quite well represented by the measures of centre of the distribution; labour share on value added averages at about 55-65%, and intermediate inputs are even more stable at about 70% of production6 . Table 2 also present the total variability decomposition in between (i.e. across firms) and within (i.e. due to time). Variables measured in levels have a between- firm variability that is always bigger than 70-80% of the total variability, with the only exception of the labour cost share. Between variability greatly loses its relevance when growth rates are considered and level information is lost: sample variability due to individual effects drops to about 15-20%. The higher between- variability for the intangible stock growth rate confirms the relevance of few individual companies, as outlined above. In general, time never shows significant role in explaining

5

This confirms the necessity of using 1st Q, median and 3rd Q in computing the technical rate of substitution (TRS) in the multiplicative specification, and the elasticity of output with respect to intangible capital stock in the additive specification. See Section 2.1. 6 Confirming that median values in the total factor productivity approaches do not bias results (see Section 2.1).

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variability; this result is in line with the findings of other studies (see, among the others, Griliches and Mairesse, 1981). The main features illustrated in Table 2 for the whole sample are the same if we split the sample in the three sub-samples corresponding to the high-, medium- and low-technology sectors (results are available upon request).

2

Assessing the productivity of intangible capital.

2.1

Models specification and estimation. We assume a Cobb-Douglas productio n function with two specifications of total capital. The

first is a multiplicative specification of total capital, TCit-1 = (C αit-1 Kit-1 γ)1/(α+γ), that leads to the following production function: (1)

Qit=Ai Bt Cαit-1 Lβ it Kγit-1 eεit ,

where C and K are tangible and intangible capital stocks, respectively; parameters α and γ are the elasticities of the output with respect to each capital stock. Q indicates the value added. Terms Ai and Bt respectively capture not measurable firm-specific advantages (like ma nagerial ability), and macroeconomic events (like technology shocks) that affect, in the same manner, all the companies. L is the labour input and the associated parameter, β, is the elasticity of output with respect to the labour input. Finally, ε is the disturbance term capturing omitted variables, measurement errors, and any other error committed in specifying the production function (e.g. not-appropriateness of the Cobb-Douglas functional form, or non validity of the assumption of parameters’ homogeneity). The second specification of total capital is an additive one, TC*it-1 = (Cit-1 + ζ*Kit-1 ), and it implies the following Cobb-Douglas production function: (1’)

Qit=Ai Bt (C it-1 + ζ*Kit-1 )α* Lβ it eεit ,

where ζ* is the unknown technical rate of substitution (TRS), i.e. the marginal productivity of intangible capital stock over the marginal productivity of tangible capital stock. Note that, in both cases above, we suppose one-period gestation lag before intangible and tangible capital stocks become fully productive. This assumption may also be viewed as a way to avoid the correlation between capital inputs and ε, due to the simultaneous choice of capital stocks and output level by company. 7 Taking logarithms of (1) and defining all the variables per-employee, the multiplicative specification becomes (lower-case letters denote logarithms): (2)

(q-l)it=ai + bt + α(cit-1 -lit) + γ(k it-1 -lit) + (µ -1)lit +ε it

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We also tried estimates without this assumptions: results are qualitative the same. All the not reported results of the paper are available upon request.

9

where µ = (α+β+γ). The additive specification (1’) in logarithms and per-employee is: (2’)

(q-l)it= ai + bt + α*(tc0 it-1 -lit) + α*log[1+(ζ*-ζ0 )pK0 it-1] + (µ* -1)lit +ε it

where µ*

=

(α*+β) and we express the TC*it-1 term as TC0 it-1 (1+(ζ*-ζ0 )pK0 it-1 ), with TC0 it-1 =(C it-1 +

ζ0 Kit-1 ), pK0it-1 =Kit-1 /TC0 it-1 , and ζ0 any assumed value for TRS. In estimating (2) and (2’) parameters we undertake a number of alternative options about: (a) the specification of the individual and temporal heterogeneity (ai and bt), and of the error term (ε it); (b) the approximation of the α*log[1+(ζ*-ζ0 )pK0 it-1] r.h.s. term in (2’) that is non- linear in the α* and ζ* unknown parameters; (c) the endogeneity issue ; (d) the estimation of the TRS in equation (2), and of the elasticity of output with respect to intangible capital stock in equation (2’). Point (a) concerns: not measurable firm-specific advantages (like manager ability); macro influences (like business cycle, macroeconomics shocks, and changes over time in the rates of productivity growth); the assumption of parameters homogeneity (while companies may have different production functions and utilisation rates of the various input categories). We assume four alternative specifications for the ai and bt terms: Ø absence of individual effects (pooled OLS estimation), but presence of per- industry and temporal heterogeneity, tackled by adding temporal and industry dummies to the model; Ø two-ways, both individual and temporal, fixed effects (within estimation); Ø estimates of growth rates (first-differences OLS); Ø estimates of rates of growth over 5- years (non-overlapping long-differences). In addition, we also assume that the error term, ε it, is zero- mean and has variance varying by firm i and time t; hence, we estimated all the models with the Eicker-Huber-White sandwich estimator, robust to the presence of generic heteroschedasticity. 8 To tackle point (b), we adopt either a linear approximation, α*log[1+(ζ*-ζ0 )pK0 it-1]≅ α*(ζ*ζ0 ) pK0 it-1 , or a non-linear approximation until the quadratic power α*log[1+(ζ*-ζ0 )pK0 it-1 ]≅ α*(ζ*ζ0 ) pK0 it-1 - [½α*(ζ*- ζ0 )2 (pK0 )2 it-1], where (pK0 )2 it-1 is measured by the squared deviation of pK0 it-1 from its sample median. The non- linear estimation of α* and ζ* parameters is obtained by iterative procedures, setting as the starting value for ζ0 the one found by a grid search. Details about the gridsearch and the iterative procedures are described in the Appendix B. The endogeneity issue outlined in point (c) derives from: the simultaneous choice of inputs and output ; the efficiency levels, known by companies but unknown by the researcher, may originate correlation between firm-effects and explanatory variables; the labour and capital intensity 8

All the estimates are based on the STATA command developed in Baum, Schaffer and Stillman (2003), which constitutes a very flexible tool.

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of utilisation variables (such as hours of work per employees and hours of operation per machine) are omitted; measurement errors deriving from accounting normative changes, lack of information on economic depreciation rates and prices at the firm- level. This issue may be tackled by IV and GMM estimation methods, that we explore in Section 2.4. Another way is that of restricting some parameters of the general equations (2) and (2’) to specific values, in order to assess the estimation results when particular theory-driven assumptions are imposed. In particular, the constant returns to scale hypothesis implies that µ =µ*=1 in equation (2) and (2’) above; hence, the corresponding restricted models are obtained by deleting the l regressor. Few additional assumptions, permitting to move some inputs to the left hand side, may represent another way to tackle simultaneity. In particular, under the assumption of perfect competition (price-taking firms in both labour and output markets), the β parameter is not estimated but set equal to slmed, the sample median of the share of labour cost in value added (sl). 9 In this case, still assuming constant return to scale, we can obtain conventional measures of the total factor productivity, and use such measures to estimate the intangible parameters in equations: (3)

tfpcit = at + γ(k-c)it-1 +ε it

(3’)

tfpc*it = at +α*(ζ*-ζ0 )pK0 it-1 - [(1/2)α*(ζ*- ζ0 )2 pK02 it-1] +ε it

where tfpcit = qit –slmed lit -(1-slmed)cit-1 and tfpc*it = qit –slmed lit -(1-slmed)tc0 it-1 are the two definitions to obtain measures for the total factor productivity under previous theoretical assumptions. Finally, point (d) above considers how to summarise measures of interest when they also depend on the level of some variables. On the one hand, equation (1) does not assume that different types of capital are fully substitutable. In fact, the technical rate of substitution (TRS, defined as the marginal productivity of intangible capital stock over the marginal productivity of tangible capital stock) changes according to the ratio C/K. Hence, it may be estimated as: (4)

ζ =−

∂Q / ∂K γˆQ / K γˆ C =− =− , ∂Q / ∂C αˆQ / C αˆ K

where we adopt three measures of the tangible over intangible capital stock: the median, the 1st and the 3rd quartiles of the C/K ratio distribution. On the other hand, in equation (1’) the marginal productivity of intangible capital relative to that of tangible capital (ζ*) is constant, and the assumption of a unit elasticity of substitution (often not supported by the data) is relaxed. However, the elasticity of output with respect to intangible capital stock changes according to the ratio K/TC*:

9

We also tried different measure of the share of labour cost in value added: the by-industry median, the by-company median, and the Törnqvist 1/2∆slit . Results are robust.

11

(4’)

γ* =

∂Q / ∂K αˆ * Q ˆ K K = ζ * = αˆ * ζˆ * . Q/ K Q C + ζˆ * K TCˆ *

In this case, we estimate three γ*, corresponding to the median, the 1st and the 3rd quartiles of distribution of the ratio between intangible capital stock and estimated total capital stock.

2.2

Results: overall intangible capital stock In Tables 3 and 4 we report estimation results for the multiplicative and additive

specifications of the Cobb-Douglas production function (equations (2) and (2’), respectively). In particular, in Table 4, equation (2’) parameters are estimated adopting the linear approximation mentioned above. Results about the non- linear approximation until the quadratic power are not reported because they are not significantly different from the linear approximation ones.

Tables 3 and 4 here Both tables 3 and 4 have the same structure: moving to the right along the columns, we impose a growing number of restrictions on the model parameters (see section 2.1). The estimation of equation (2) and (2’) requires prederminedness of the inputs with respect to output. By using beginning of period capital measures, we hope to minimise the effects of simultaneity between capital inputs and output (the input factors are under the control of the firms which may choose them simultaneously with the output level acting on information unknown to the econometrician). Simultaneity problem for labour should be less relevant because we measured it with the average number of employees. Another way to tackle the simultaneity, that may still affect the model, involves attempting to measure total factor productivity, as we made in the latter three columns of tables 3 and 4. To implement this method, we proceed in three steps. Firstly, we assume perfect competition (price taking in both labour and output markets), which implies that the elasticity of labour can be estimated by the share of labour cost in value added. Secondly, we calculate total factor productivity, implicitly imposing constant returns to scale to the traditional factors (labour and tangible capital stock). Thirdly, we regress the total factor productivity against the intangible capital stock. This way to account for simultaneity it is easy to be implemented and interpreted. Going down along the rows of tables 3 and 4, less restrictive hypotheses on the error term are assumed. In particular, “pooled” uses the OLS estimator (no individual effects are allowed, but sectoral and time dummies are added to the model specification). The error term in equations (2) and (2’) includes specification errors arising because firms may have different production functions and a different rate of utilisation of the input categories. These firm-effects in the disturbance term, possibly correlated with the regressors, induce an omitted-variable bias in the pooled estimates; in 12

fact, these last do not take into account the heterogeneity across companies in the technologies and types of output utilised. On the contrary, the “within” and “first differences” estimates allow for additive firm-effects in two alternative ways: the first uses demeaned data (by firm); the second estimates growth rates. Finally, “long differences”, that we computed over five years, is another usual route for estimating equations with individual effects. The advantage of long differences with respect to within and first differences transformations is that of preserving the cross-sectional dimension of variability. In panel data with large N compared to T this implies that more variance between companies of the explanatory variables is used to identify the relevant coefficients, thus avoiding other misspecifications obscure the remaining signal in the data (Griliches and Mairesse, 1995). Another component of the error term collects any incorrectness in the use of price deflators common across firms, other macro influences (such as the business cycle), and changes over time in the rate of productivity growth (“disembodied technical change”). Instead of capture these effects by a deterministic or stochastic trend assumed to be the same for all the firms, we prefer to use time dummies. Except for simultaneity and measurement error bias in the right hand side variables (see Section 2.4), within and first differences estimation methods should give the same results. The two capital coefficient estimates appear to be similar, while labour coefficient, lower in the growth rates estimates, suggests the presence of simultaneit y and/or measurement errors. The hypothesis of constant return to scale, when imposed, does not rise significantly the standard error of estimates. The intangible capital is more correlated than tangible capital with the overall firm-effects. This is seen in the decline of the former coefficient relative to the latter when moving from total to within and in-difference estimates in the specification with no constant returns to scale imposed. The most important finding is that intangible capital coefficients remain significant even when we control for firm-effects, and are particularly high in the total factor productivity specification (the median value of the share of intangibles in value added is 2.4%). Equation (3) estimates by long differences gives, in our opinion, reliable results when we do not adopt estimation methods that account for simultaneity bias. In general, the magnitude of our capital estimates are quite comparable with those of Hall and Mairesse (1996) for US (6,521 observations for the 19811989 period) 10 , and of Hall and Mairesse (1992) for France (2,670 observations for the 1980-1987 period) 11 . 10

For tangibles: 0.289 (total), 0.126 (within), 0.148 (first differences). For intangibles: 0.035 (total), 0.041 (within), 0.010 (first differences). In the same paper, estimates for France are: 0.295 (total), -0.046 (within) and -0.001 not significant (first differences) for tangibles; 0.90 (total), 0.08 and -0.003 not significant (within and first differences, respectively) for intangibles. 11 For tangibles: 0.167 (total), 0.183 (within), 0.225 (first differences), 0.113 not significant (long differences). For

13

Tables 5 and 6 allow the comparison between multiplicative and additive specifications, by reporting the elasticity of output with respect to intangible capital stock directly estimated by the multiplicative specification and the one computed from estimates of the additive specification; similarly for the marginal productivity of intangibles relative to that of tangibles. The elasticity directly estimated by the multiplicative specification is close to the one computed from the additive specification in correspondence of the third quartile of the distribution of intangible capital stock over estimated total capital stock. Symmetrically, the technical rate of substitution directly estimated by the additive specification resembles the one obtained by the estimates of the multiplicative specification in correspondence of the first quartile of the distribution of tangible over intangible capital stocks. These facts may reflect the pattern of the intangible capital stock over total tangible capital stock ratios. The distribution of intangibles over tangibles does more conspicuously appear positively skewed; and intangibles seem to be concentrated in few Italian companies.

Tables 5 and 6 here

2.3

Results: different components of intangible capital stock By following the same theoretical framework illustrated in Section 2.1 for the multiplicative

specification of total capital, we try to disentangle the different contrib ution to productivity of intangible assets ( IKN = ∑ j IKN j , j=rd, pat, mark) and of intangible stocks reconstructed from direct expenses ( IKNDE = ∑h IKNDE h , h=rd, pat, adv ). The same is done for intellectual capital ( IK = ∑ j IKN j + ∑h IKNDE h ,

both

j

and

h

=rd,

pat)

versus

customer

capital

( IC = ∑ j IKN j + ∑h IKNDE h , j=mark and h=adv). In order to tackle, in a simple way, the endogeneity issue, we restrict the analysis to the total factor productivity model. Results are reported in Tables 7 and 8, respectively.

Tables 7 and 8

In the last columns of the Tables the estimated marginal productivity of different intangible components over the marginal productivity of tangible capital stock are reported. As an example, in the IKN case, we have :

intangibles: 0.198 (total), 0.070 (within), 0.067 and 0.077 not significant (first and long differences, respectively).

14

ζ

IKN

∂Q / ∂IKN γˆ IKN (Q / IKN ) γˆ IKN C = = , ∂Q / ∂C αˆ (Q / C ) αˆ IKN

=

where the C/IKN ratio is taken at the median, the 1st and the 3rd quartiles of its sample distribution. In all the estimation methods the differences in coefficients between IKN and IKNDE, and between IK and IC show statis tical significance. The IKN coefficients do not statistically differ from the IK coefficients. On the contrary, this is no more true comparing IKNDE and IC, this last showing a lower elasticity. ……… In general, results in the sub-samples of “Both IKN and IKNDE never zero” and “Both IK and IC never zero”, reported in Appendix C, show robustness. The differences between within and short-term growth rates estimates are reduced for the IKN component and emphasised for the IKNDE and IC components……….

2.4

Results: another look Along with the simultaneity issues outlined above, the estimation of equations (2) and (2’)

may be affected by measurement-bias problems. For example, labour input is a generic measure that not distinguishes between blue and white collars because of a lack of data for the whole period. As far as capital measure is concerned, it is worth remembering that they were partially reconstructed,12 and that balance-sheet data may not represent capital actually in use. The changes in the parameter estimates when first differences are used - the method most affected by measurement errors and simultaneity problems - make preferable an instrumental variables approach. Hence, in Table 9, we present both IV for the level- model and GMM-sys (Blundell and Bond, 1998) estimates. The IV approach in levels uses moment conditions which are appropriate if the firm- level effects are not correlated with the right hand side variables. GMM-sys, on the contrary, add to the equation in levels also the equation in first differences, thus allowing for individual effects potentially correlated with the explanatory variables. The IV estimates reported in Table 9 are very close to GMM-sys estimates based only on the moment conditions corresponding to the stacked model (we create one instrument for each variable and lag distance, rather than one for each time period, variable, and lag distance), results not reported. GMM-sys estimates reported in Table 9, conversely, impose all the available moment conditions for each year of data separately; thus, it should be more efficient than the IV approach, unless the excess of over- identifying restrictions emphasizes the problem of weak instruments (Ziliak, 1997), see below. The advantage of IV on levels and GMM-sys approaches, if compared to IV on first differences and GMM-dif, is 12

A possible measurement problem arises from the accounting normative changes occurred since 1992 (EC fourth

15

that of avoiding the poor performance of first differences transformation, in which measurement and timing errors predominate, while the remaining information content in the instruments (in levels) is too small to allow the extraction whatever signal still left in the variables (Griliches and Mairesse, 1995). Another cause of invalid instruments in levels for first differenced equations is the almost random walk statistical behaviors of the variables (about this topic, see e.g. Bond, 2002). In order to tackle both simultaneity and possible measurement problems, we instrumented all the inputs, checking for alternative instrument sets. 13 The choice of relevant (correlated with endogenous explanatory variables) instruments is a difficult task that impacts on both the bias of parameters estimation and the distribution to depart from asymptotic normal in finite-samples. Further, the “weak instruments” related problems apply even in samples of considerable size (see Bound, Jaeger and Baker, 1995). Usually, in production function estimates, lags 3 and higher are used as a caution. The tests for the admissibility of more recent lags as instruments in our data show that this is not necessary, and that lag 2 and 1 are valid. In the first five rows of Table 9, estimation results are obtained by using “external instruments”, i.e. variables that do not belong to the regressors’ list of each equation. In the latter row, instead, we adopted the “standard” GMM instrumenting technique: lags of the right hand side variables. Due to the large number of observations, in GMM-sys we use as instruments only the lags from 2 to 3 of the variables. 14 We prefer the external instrument approach because lags of the explanatory variables may be affected by the same measurement error (possibly correlated over time) that we try to tackle on. Among the external instruments, we prefer gross investments (information taken from Nota Integrativa). First differences of the net capital stock could be a good alternative that takes into account for disinvestment also. However, they may be more pronouncedly affected by measurement errors than the levels; in particular, by occasional de- and re-valuations. Moreover, they imply the larger loss of observations.

Table 9 here Results in Table 9 are very close to the total factor productivity specification estimated by long-differences in Table 3, confirming its effectiveness in tackling simultaneity problems in a very simple way. Compared to instrumented outcomes for France and US reported in Hall-Mairesse (1996), the instrumental approaches applied to Italy show more robustness. Directive). However, if we limit our sample to the 1994-1999 sub-period, estimation results do not change significantly. 13 Estimates instrumenting labour input only do not produce significantly different estimates for both the capital stocks. Thus, apparently, measurement errors seem to affect more pronouncedly labour. 14 A very computing intensive check by using lag 2 and higher show that adding longer lags as instruments when available produce no efficiency gain and not significant changes: these additional lags are highly correlated with the instruments already present.

16

3

Conclusion In this paper we were able to try some experiments on the relationship between productivity

and intangibles for the Italian country. Our definition of intangibles in an enlarged one, that, according to the Italian GAAP, takes into account intangible expenses (in line with the empirical literature), as well as intangible assets. Moreover, many categories are separately considered: R&D, patents, marks, advertising. The model specifications assumed and estimation panel techniques used tackle a number of problems, like simultaneity, measurement errors, unknown ind ividual and temporal effects. Measurement errors seem to particularly affect labour input; consequently, the total factor productivity model seems to be the specification less biased. The general predominance of between firms variability over temporal variability leads to a preference for the estimates in long-term growth rates. In fact, we found that: in the cross sectional dimension of the data there is a not negligible relationship between firm productivity and intangibles; in the time dimension, using deviations from firm means or short-term growth rates as observations and unconstrained estimation, this relations hip becomes weaker. Overall, estimates are quite robust and comparable with those obtained for other countries, such as the US. Various checks of the model (like alternative measures of both tangible and intangibles capital stocks, alternative measures of the dependent variable, alternative sub-sample) show robustness of our results (many checks are not reported but available upon request).

Appendix A: Data description and cleaning rules The source of data is CADS, a large database with detailed accounting information of above 50,000 Italian companies from all industries for the 1982-1999 period. CADS is well representative of the population of Italian companies, covering over 50% of the value added produced by the firms included in the Census of the Italian Central Statistical Office. More details on the data-set are in Bontempi (2004). We selected manufacturing limited liabilities companies (about 35% of the total CADS dataset) respecting basic accounting criteria and having information on the variables of interest (206,538 observations for 22,387 companies). Then, we defined our clean sample (94,968 observations for 14,254 firms) according to the following criteria (similar to the ones of Hall and Mairesse, 1992). 15 15

The following cleaning rules are basically the same we used to obtain the data-set of section 1.2, where larger

17

1. Observations for which value added, labour cost, production, and intermediate costs are zero or negative were removed (1.0% of initial sample), because they create obvious problems for logarithmic transformations. Also observations with tangible net capital stock, according to our restricted definition (TKNbui+TKN pla +TKNequ), equal to zero were removed (4.7% of observations), because we do not consider reliable data on production in firms lacking tangible capital. 2. Usually people work with companies with a minimum of 20, or even 50, employees. In Italy small (less than 20 workers) firms are predominant. Hence, in order to preserve the representativeness of our sample while maintaining meaningfulness of accounting data, we removed observations of firms with a number of employees less than 5 in the first year of the sample (0.3% of observations). 3. Observations for which value added per worker, tangible capital stock per worker, or intangible capital stock per worker is outside the range median ± three times the inter-quartile range were removed (5.6% of observations). These outliers could affect the distribution of the variables: in a Gaussian variable they in fact should represent 0.0002% of observations (see HoaglinIglewicz-Tukey, 1986). 4. Observations for which the growth rate of value added is outside the [–90%, +300%] range, or for which the growth rates of employees, tangible capital and intangible capital are outside the [–50%, +200%] range were removed (16.5% of observations). 5. We removed observations for which the mean of the labour cost share over production in t and t+1 and the mean of the intermediate costs share over production in t and t+1 are lower than the 1st Q of the corresponding per-industry Tonquist, or greater than 1 (1.5% of observations). 6. Previous selection criteria add further gaps in the temporal per- firm data to the ones originally present in the sample (for a discussion, see Bontempi, 2004). Hence, we selected only those companies whose data are available for 4 consecutive years at least, choosing the longest or most recent sub-period if an interruption in the temporal pattern is present (37.1% of observations). Overall, 46% of total observations were dropped; this number is lower than the one involved by selection rules from 1. through 6. (66.6%) because some observations have wrong data according to several criteria simultaneously.

Table A1 illustrates the composition of our cleaned sample according to by-industry and bysize classifications. Manufacturing industries are classified according to their global technological definitions of tangible and intangible capital stocks are considered.

18

intensity (ISIC Revision 2, see Hatzichronoglou, 1997) at the 4-digit level. Given the low dimension of the HT macro-industry if compared to the others, all the paper is based on the aggregation of HT with MHT, as illustrated in Table A2.

Tab. A1 here

Reflecting the Italian industrial structure in the 1982-1999 period, our data mostly cover non- listed companies: only 0.51% of firms are listed on the stock exchange (compared to the 0.13% of Italian manufacturing companies listed on the Stock Exchange in 1995); 22.23% of companies belong to a business group (mainly pyramidal). As shown in Table A2, another aspect of our data-set is the inclusion of a high number of small and medium firms. These lasts are predominant in Italy: on average, Italian manufacturing limited liability companies have 44 employees. The average number of employees in our sample is 132, and 47.28% of our companies have less than 50 employees.

Tab. A2 here

In what follow we explain how we measured the variables of interest in equations (1), (1’), (2), (2’) of the paper. In symbols: l is the logarithm of the number of employees (L) in period t (this is mainly reported by the firms at the close of the fiscal year -64% of total observations, when the accounting scheme before the IV European Directive is adopted; it is reported as the average of workers during the fiscal year when the IV European Directive accounting scheme is adopted -24% of total observations); the two definitions coincide in the 12% of observations ); q and p are the logarithms of value added (Q) and of production (P), respectively, all in period t; m is the logarithm of the intermediate costs (M) in period t; c is the logarithm of the total tangible capital stock (C), computed in period t- 1 at the net-book value. The paper utilises total tangibles defined as the sum of buildings, plants, and equipment, that is C= TKNbui+TKNpla +TKN equ. Estimates reported in Appendix C employs a larger definition of total

tangibles,

that

adds

to

C

the

other,

uncompleted,

and

leased

tangibles

(TKNoth +TKN unc+TKNlea). k is the logarithm of the total intangible capital stock (K), computed in period t-1 at the net-book value. In Section 2 of the paper, intangibles are given by the sum of applied R&D asset, basic R&D reconstructed asset, advertising reconstructed asset, patents and intellectual property rights asset,

19

licences and concessions and marks asset, intellectual capital reconstructed asset, that is: K=IKNrd+IKNDE rd +IKNDE adv+IKNpat+IKNmark+IKNDE pat. Estimates reported in Appendix C employs an extended definition of K, that adds formation-expansion asset, IKNstart. Section 3 of the paper tries to distinguish between the effect of advertising and trademarks (IKNDE adv and IKNmark) and the effect of R&D and intellectual capital stocks, both capitalised by the firm (IKNrd +IKNpat) and reconstructed by us (IKNDE rd +IKNDE pat). Thus, we define customer capital as CK=IKNDE adv+IKNmark, an extended version of intellectual capital asset as IK=IKNrd +IKNpat, and an extended version of intellectual capital reconstructed stock as IKDE=IKNDE rd +IKNDE pat. All the nominal variables are transformed in real term. As deflators, we use: value added deflator for value added and labour cost; production deflator for production; the investment in buildings deflator for TKNbui; the investment in machinery, transport equipment and other tangibles deflator for TKNpla , TKNequ , TKN oth , TKNunc, TKNlea ; the GDP deflator for all intangible stocks. 16

Appendix B: The grid search and the iterated procedures The initial value of the ζ0 parameter used by the iterative procedure in estimating unconstrained equation (2’) is obtained from the per-employee equation (1’) expressed in logarithms: (A1’) (q-l)it= ai + bt + α*(tc* it-1 - lit) + (µ* -1)lit +ε it , where µ*

=

(α*+β) and tc*it-1 = log(C it-1 + ζ*Kit-1 ).

To do so, we use a grid-search of the equation (A1’) that sets ζ* equal to all the values in the range 0-2 with step 0.01, and that looks for the value of ζ* that minimises the residual sum of squares (min- RSS ). Setting this min-RSS as the initial value, the iterative procedure estimates µ*, α* and ζ*(n+1) parameters from the linear approximation of equation (2’) (A2’) (q-l)it= ai + bt + α*(tc(n) it-1 -lit) + α* (ζ*(n+1) - ζ* (n)) pK(n)it-1 + (µ* -1)lit +ε it , where: the exponent (n) is for the nth iteration; µ*

=

(α*+β); ζ* (n)=ζ0 and tc(n)it-1 = log[Cit-1 + ζ0

Kit -1 ) for n=0; tc(n)it-1 = tc(n-1)it-1 + log[1+p(ζ*(n) - ζ*(n-1)) pK(n-1)it-1] for n>0, where p is a smoothing parameter 17 ; finally, pK(n)it-1 = Kit-1 / [exp(tc(n)it-1 )].

ζ *( n +1) −ζ *( n ) The iterative procedure stops when either < 0.0001, or the estimated parameter ζ * ( n) associated to p K(n)it-1 is not significantly different from zero (because in this case ζ*(n+1) is not

16

All the deflators (base 1995=1) are from the National Accounts of the Italian Statistical Office, ISTAT. Apart from GDP deflator, they are disaggregated at 2-digit industry level. 17 In the estimates reported in Table 4 of the main text, we set p = 1. Alternatively, we also set p=0.8 and p=2 without significant changes in the convergence es timates.

20

significantly different to ζ*(n)).18 Of course, this procedure to estimate the non- linear equation (2’) converges in one iteration if the final estimate of the ζ* parameter is in the 0-2 range. In this case, in fact, the grid-search step delivers an initial value ζ0 very close to the final estimate of ζ*, and the linear approximation is very precise, since (ζ* - ζ0 ) pK0 it-1 ≅ 0 in equation (2’). Similar procedures, modified in order to take into account for constraints, are used for estimating the constant-return-to-scale version of (2’), as well as equation (3’). Another (quadratic) approximation can be assumed for the non- linear term of equation (2’), as suggested in the main text of the paper. In this context, the initial grid-search step is the same as above, and the iterated estimation procedure is applied to the equation: (A2”) (q-l)it= ai + bt + α*(tc(n) it-1 -lit) + + α* (ζ*(n+1) - ζ* (n)) pK(n)it-1 - [½ α*(ζ*(n+1) - ζ(n))2 (pK(n))2 it-1] + (µ* -1)lit +ε it If the estimated parameters associated to pK(n) and (pK(n))2 regressors are both not significantly different from zero, the ζ*(n+1) and ζ(n) estimates are not statistically different, and correspond to the unknown ζ* estimate.

Appendix C: Robustness of models estimates in sub-samples Tables A3 and A4 here

18

We also try another approximation: tc(n)it-1 = tc( n-1)it-1 + p(ζ(n) -ζ( n-1)) DEVp K(n-1)it-1 , where DEVp K( n-1)it-1 is the deviation of from its sample median. Results do not change significantly.

p K( n-1)it-1

21

References Aboody D. and Lev B. (1998) “The value-relevance of Intangibles: the Case of Software Capitalisation” Journal of Accounting Research, Supplement, pp. 161-191. Baum C. F., Schaffer M.E. and Stillman S. (2003) “Instrumental Variables and GMM: Estimation and Testing”, The Stata Journal, v. 3, n. 1, pp. 1-31. Bond S. (2002), “Dynamic panel data models: a guide to micro data methods and practice”, Cemmap working Paper, No CWP09/02, IFS, Dept. of Economics, UCL, London Bound J., D. A. Jaeger and R. M. Baker (1995), “Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variables is weak”, Journal of the American Statistical Association, Vol. 90, No. 430, pp. 443-450 Blundell R. and Bond S. (1998) “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models”, Journal of Econometrics, 89, pp. 115-43. Bontempi M. E. (2004) “The Measurement of Intangible and Tangible Assets in a Panel of Italian Manufacturing Firms: from the Reporting Rules to the Empirical Variables”, working paper, Dipartimento di Economia, Università di Ferrara. Griliches Z. and Mairesse J. (1981) “Productivity and R&D at the Firm Level”, NBER WP. N. 826, December, Cambridge, Mass. Griliches Z. and Mairesse J. (1995) “Production Functions: the Search for Identification”, NBER WP. N. 5067, March, Cambridge, Mass. Hall B. H. and Mairesse J. (1992) “Exploring the Relationship between R&D and Productivity in French Manufacturing Firms”, NBER WP. N. 3956, January, Cambridge, Mass. Hall B. H. and Mairesse J. (1996) “Estimating the Productivity of Research and Development: an Exploration of GMM Methods Using Data on French and United States Manufacturing Firms”, NBER WP. N. 5501, March, Cambridge, Mass. Hansen L. (1982) “Large Sample Properties of Generalised Method of Moments Estimators”, Econometrica 50(3), pp. 1029-1054. Hatzichronoglou T. (1997) “Revision of the High-Technology Sector and Product Classification”, STI WP. N. 1997/2, OCDE, Paris. Hirschey M. (1982) “Intangible Capital Aspects of Advertising and R&D Expenditures”; Journal of Industrial Economics, v. 30, n. 4, pp. 375-390. Hoaglin D.C., Iglewicz B. and Tukey J.W. (1986) “Performance of Some Resistant Rules for Outlier Labelling”; Journal of the American Statistical Association, v. 81, n. 396, pp. 991999. Lev B. (2001) “Intangibles: Management, Measurement, and Reporting”, Washington D.C.: Brookings Institution Press. Lev B. and Sougiannis T. (1996) “The Capitalisation, Amortisation, and Value-Relevance of R&D” Journal of Accounting and Economics, feb., pp. 107-138. Mairesse J. and Sassenou M. (1991) “R&D and Productivity: a Survey of Econometric Studies at the Firm Level” NBER WP. N. 3666, March, Cambridge, Mass. Ziliak J. P. (1997), “Efficient estimation with panel data when instruments are predetermined: an empirical comparison of moment-condition estimators”, Journal of Business and Economic Statistics, Vol. 15, pp. 419-431 22

Table 1- Occurrence of intangible capital stocks and ratios over total tangible capital stock

Full sample Total HT+MHT MLT LT K never zero Total HT+MHT MLT LT Both IKN and IKNDE never zero Total HT+MHT MLT LT Both IK and IC never zero Total HT+MHT MLT LT IKN alone Total HT+MHT MLT LT IKNDE alone Total HT+MHT MLT LT IK alone Total HT+MHT MLT LT IC alone Total HT+MHT MLT LT

Averages of intangible over tangible ratios (% values) K IKN IKNDE IK IC

NT

N

94968 28196 28543 38229

14254 4327 4408 5778

6.66 6.52 6.48 6.62

32.63 38.91 13.46 42.31

7.78 12.24 3.25 7.87

24.85 26.66 10.21 34.44

10.75 14.35 3.9 13.2

21.88 24.55 9.56 29.11

78481 23929 23378 31174

11528 3608 3528 4606

6.81 6.63 6.63 6.77

38.92 45.01 16.22 51.27

9.11 13.89 3.86 9.39

29.81 31.12 12.36 41.88

12.73 16.41 4.67 15.96

26.19 28.6 11.55 35.32

27483 9074 7295 11114

4028 1354 1109 1624

6.82 6.70 6.58 6.84

63.87 69.21 27.46 83.41

14.15 16.68 5.56 17.72

49.72 52.53 21.9 65.69

19.4 22.32 8 24.5

44.47 46.89 19.47 58.91

50317 16028 14495 19794

7646 2461 2267 3003

6.58 6.51 6.39 6.59

46.69 53.63 17.95 62.12

11.71 17.14 4.77 12.39

34.98 36.48 13.19 49.73

17.11 20.41 6.19 22.43

29.58 33.22 11.76 39.69

21656 7007 6782 7867

3759 1232 1199 1377

5.76 5.69 5.66 5.71

10.65 18.88 5.09 8.1

10.65 18.88 5.09 8.1

0 0 0 0

7.85 15.2 3.88 4.72

2.8 3.69 1.2 3.38

3211 752 1019 1440

564 136 174 261

5.69 5.53 5.86 5.52

25.44 27.53 12.79 33.3

0 0 0 0

25.44 27.53 12.79 33.3

2.87 8.22 1.11 1.32

22.57 19.31 11.68 31.97

1446 493 495 458

299 104 104 99

4.84 4.74 4.76 4.63

11.73 23.17 5.64 6.01

8.99 17.05 5.55 4.04

2.74 6.11 0.09 1.97

11.73 23.17 5.64 6.01

0 0 0 0

3573 829 1055 1689

643 152 188 311

5.56 5.45 5.61 5.43

23.4 20.83 13.78 30.66

2.83 3.87 0.81 3.59

20.56 16.96 12.97 27.07

0 0 0 0

23.4 20.83 13.78 30.66

T

Notes: NT = total number of observations; N = total number of firms; T = average number of years per-firm. Intangible and tangible capital stocks are net of depreciation fund, at book values, and in million of ITL at 1995 prices. Manufacturing firms are classified according to their global technological intensity at the 4-digit level: HT+MHT = high and medium-high intensity; MLT = medium-low intensity; LT = low intensity. K = IKN+IKNDE = IK+IC is the total intangible capital stock; IKN = IKNrd + IKNpat + IKNmark is the intangible capital stock capitalised by firms; IKNDE = IKNDErd + IKNDEpat + IKNDEadv is the intangible capital stock reconstructed by us from direct expenses; IK = IKNrd + IKNpat + IKNDEpat is the intellectual capital; IC = IKNmark + IKNDEadv is the customer capital.

23

Figure 1

Intangible capital stock composition IKN: assets at book values (in blue); IKNDE: capitalised expenses (in pink). IK: intellectual capital (exploded light colours); IC: customer capital (dark colours). HT+MHT

MLT 3% 3% 21%

8% 25%

8%

50%

42% 10% 9%

LT

12% 11%

Total 8% 5%

8% 8% 4% 4%

17%

7% 13% 50%

17% 60%

IKNrd: R&D advanced

IKNpat: Patents

IKNmark: Marks

IKNDEadv: Advertising

IKNDErd: R&D basic

IKNDEpat: Pat royalties

Total and by sectors aggregated according to technological intensity. Capital stocks are at constant prices.

24

Table 2– Statistics on the variables of interest; period 1982-1999. 1st Q

Mean Median

3rd Q

sd

robust sd

% variability

NT

N

T

Between Within

Growth rates (%) P / L [a] Q / L [a] L [b] M / L [a] C / L [a] K / L [a] Labour cost / Q share [c] M / P share [c] K / C ratio [a] IKN / C ratio [a] IKNDE / C ratio [a] IK / C ratio [a] IC / C ratio [a] Levels P / L [a] Q /L [a] L [b] M / L [a] C / L [a] K / L [a] Labour cost / Q share [c] M / P share [c] K / C ratio [a] IKN / C ratio [a] IKNDE / C ratio [a] IK / C ratio [a] IC / C ratio [a]

-7.1 -8.2 -4.0 -8.7 -13.6 -30.5 -7.7 -3.4 -29.4 -36.3 -32.7 -35.6 -31.5 167.6 53.0 29.0 107.1 22.8 0.3 51.3% 61.5% 0.7% 0.0% 0.0% 0.0% 0.1%

5.1 5.3 2.2 6.4 4.0 0.6 3.7 0.6 1.7 65.6 0.5 91.33 30.2

3.1 2.8 0.0 3.3 -3.2 -10.4 0.8 0.2 -10.1 -11.1 -20.6 -13.1 -14.5

14.3 23.9 14.7 26.7 6.3 15.8 16.6 31.8 12.8 31.6 15.9 44.4 10.3 28.9 4.0 8.9 19.3 47.9 28.6 1142.4 11.1 258.9 25.3 3795.5 16.8 1952.0

15.9 17.0 7.6 18.7 19.6 34.5 13.3 5.5 36.1 48.1 32.5 45.2 35.8

17.7% 13.7% 23.0% 20.5% 18.4% 29.3% 22.3% 12.8% 28.6% 26.3% 22.0% 33.3% 9.9%

1.5% 0.8% 0.5% 1.0% 1.8% 0.6% 0.6% 1.2% 0.8% 0.0% 0.1% 0.0% 0.1%

80,714 80,714 80,714 80,714 80,714 70,567 80,714 80,714 70,567 60,201 48,399 60,535 67,446

14,254 14,254 14,254 14,254 14,254 12,748 14,254 14,254 12,748 11,919 8,034 11,775 12,232

5.7 5.7 5.7 5.7 5.7 5.5 5.7 5.7 5.5 5.1 6.0 5.1 5.5

350.9 256.3 415.6 326.8 183.9 79.1 70.2 94.3 41.1 30.6 131.8 52.0 105.0 737.7 56.3 273.3 181.6 323.9 305.4 160.7 62.7 42.8 76.4 72.8 39.8 7.9 1.7 6.0 28.7 4.2 64.5% 63.5% 75.0% 26.6% 17.6% 69.6% 70.4% 78.8% 12.6% 12.9% 32.6% 3.8% 16.5% 266.5% 11.8% 7.8% 0.7% 3.4% 151.0% 2.5% 24.9% 0.7% 9.4% 176.0% 6.9% 10.8% 0.6% 3.5% 122.2% 2.6% 21.9% 1.4% 9.3% 183.0% 6.8%

90.1% 76.9% 99.0% 90.5% 88.5% 88.5% 52.4% 86.0% 88.7% 93.6% 80.0% 89.2% 83.4%

0.3% 0.8% 0.0% 0.2% 0.1% 0.1% 0.4% 0.3% 0.0% 0.0% 0.0% 0.0% 0.0%

94,968 94,968 94,968 94,968 94,968 94,968 94,968 94,968 94,968 94,968 94,968 94,968 94,968

14,254 14,254 14,254 14,254 14,254 14,254 14,254 14,254 14,254 14,254 14,254 14,254 14,254

6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7

Notes: Robust sd is the interquartile range divided by 1.349, where 1.349=2*0.674 is the interval containing 50% of the cases in a normal distribution. NT = total number of observations; N = total number of firms; T = average number of years per-firm. Decomposition of variability in between and within is obtained by a two-ways fixed effects model. P = production; Q = value added; L = number of employees; M = cost of intermediate goods; C = TKNbui + TKNpla + TKNequ is the total tangible capital stock; K = IKNrd + IKNpat + IKNmark + IKNDErd + IKNDEpat + IKNDEadv is the total intangible capital stock; IKN = IKNrd + IKNpat + IKNmark is the intangible capital stock capitalised by firms; IKNDE = IKNDErd + IKNDEpat + IKNDEadv is the intangible capital stock reconstructed by us from direct expenses; IK = IKNrd + IKNpat + IKNDEpat is the intellectual capital; IC = IKNmark + IKNDEadv is the customer capital. Intangible and tangible capital stocks are net of depreciation fund, at book values. [a] In million of ITL at 1995 prices. [b] The number of employees is mainly reported by the firms at the close of the fiscal year (64% of total observations, when the accounting scheme before the IV European Directive is adopted); it is reported as the average of workers during the fiscal year when the IV European Directive accounting scheme is adopted (24% of total observations); the two definitions coincide in the 12% of observations. [c] Ratios are computed on the variables at current prices.

25

Table 3- Production Function Estimates with multiplicative intangible capital Equation (2) estimates

Type of estimates

γ (k-l)

α (c-l)

µ-1 (l)

β

Constant returns to scale imposed to equation (2) (µ=1) MSE

γ (k-l)

Equation (3) (µ=1 and β=slmed(1))

α (c-l)

β

MSE

γ (k-l)

α

MSE

Pooled (NT=80714)

0.025 0.132 -0.013 0.829 0.3693 0.026 (0.001) (0.002) (0.001) (0.002) (0.001)

0.133 (0.002)

0.841 (0.002)

0.3695

0.070 (0.001)

0.295 0.4044 (0.001)

Within (NT=80714)

0.006 0.090 -0.224 0.680 0.1853 0.018 (0.002) (0.003) (0.006) (0.006) (0.002)

0.129 (0.003)

0.853 (0.003)

0. 1880 0.059 (0.001)

0.306 0.1957 (0.001)

First differences (NT=66460)

0.008 0.070 -0.515 0.407 0.2223 0.038 (0.002) (0.004) (0.009) (0.008) (0.002)

0.186 (0.004)

0.776 (0.005)

0. 2309 0.041 (0.002)

0.324 0.2346 (0.002)

Five -years long 0.016 0.091 -0.140 0.753 0.3446 0.023 differences (0.004) (0.009) (0.016) (0.017) (0.004) (NT=6432)

0.113 (0.009)

0.864 (0.009)

0. 3472 0.041 (0.003)

0.324 0.3685 (0.003)

(1 ) slmed=0.635 is the sample median of the share of labour cost in value added.

Table 4- Production function estimates with additive intangible capital (linear approximation) Equation (2’) estimates

Type of estimates

ζ (pK)

α* (tc0-l)

µ-1 (l)

β

Constant returns to scale imposed to equation (2’) (µ=1) MSE

ζ (pK)

α* (tc0-l)

β

MSE

Equation (3’) (µ=1 and β=slmed(1)) ζ (pK)

α

MSE

Pooled (NT=80714)

1.391 0.161 -0.013 0.827 0.3670 (0.002) (0.002) (0.001) (0.002)

1.424 0.162 0.838 0.3673 (0.063) (0.002) (0.002)

1.380 (0.030)

0.365 (-)

0.4034

Within (NT=80714)

0.397 0.098 -0.222 0.680 0.1853 (0.084) (0.003) (0.006) (0.006)

0.610 0.144 0.856 0.1880 (0.074) (0.003) (0.003)

0.728 (0.036)

0.365 (-)

0.1964

First differences (NT=66460)

0.417 0.076 -0.516 0.408 0.2223 (0.165) (0.004) (0.009) (0.008)

1.159 0.216 0.784 0.2310 (0.118) (0.004) (0.004)

1.399 (0.090)

0.365 (-)

0.2341

Five -years long differences (NT=6432)

0.692 0.103 -0.142 0.755 0.3446 (0.293) (0.009) (0.016) (0.017)

0.824 0.130 0.870 0.3473 (0.264) (0.009) (0.009)

0.638 (0.089)

0.365 (-)

0.3689

(1 ) slmed=0.635 is the sample median of the share of labour cost in value added.

26

Table 5- γ and ζ estimates with multiplicative and additive intangible capital (no constant return to scale imposed) Elasticity of output with respect to intangible capital stock (no constant return to scale imposed) Type of estimates

γ eq. (2)

γ(med) eq. (4’)

γ(Q1) eq. (4’)

γ(Q3) eq. (4’)

Marginal productivity of intangible capital relative to that of tangible capital stock (no constant return to scale imposed) ζ eq. (2’)

ζ(med) eq. (4)

ζ(Q1) eq. (4)

ζ(Q3) eq. (4)

Pooled (NT=80714)

0.025 (0.001)

0.009 (0.000)

0.002 (0.000)

0.031 (0.001)

1.391 (0.002)

3.258 (0.120

0.899 (0.033)

12.346 (0.454)

Within (NT=80714)

0.006 (0.002)

0.002 (0.000)

0.0003 (0.000)

0.006 (0.001)

0.397 (0.084)

1.102 (0.293)

0.304 (0.081)

4.175 (1.111)

First differences (NT=66460)

0.008 (0.002)

0.001 (0.0005)

0.0002 (0.000)

0.005 (0.002)

0.417 (0.165)

1.898 (0.525)

0.524 (0.145)

7.193 (1.990)

Five -years long differences (NT=6432)

0.016 (0.004)

0.003 (0.001)

0.0005 (0.000)

0.011 (0.005)

0.692 (0.293)

2.968 (0.811)

0.819 (0.224)

11.247 (3.073)

Table 6- γ and ζ estimates with multiplicative and additive intangible capital (total factor productivity) Elasticity of output with respect to intangible capital stock (total factor productivity) Type of estimates

γ eq. (2)

γ(med) eq. (4’)

γ(Q1) eq. (4’)

γ(Q3) eq. (4’)

Marginal productivity of intangible capital relative to that of tangible capital stock (total factor productivity) ζ eq. (2’)

ζ(med) eq. (4)

ζ(Q1) eq. (4)

ζ(Q3) eq. (4)

Pooled (NT=80714)

0.070 (0.001)

0.019 (0.000)

0.003 (0.000)

0.070 (0.002)

1.380 (0.030)

4.029 (0.060)

1.112 (0.017)

15.267 (0.229)

Within (NT=80714)

0.059 (0.001)

0.011 (0.001)

0.002 (0.000)

0.041 (0.002)

0.728 (0.036)

3.300 (0.099)

0.910 (0.027)

12.505 (0.375)

First differences (NT=66460)

0.041 (0.002)

0.020 (0.001)

0.004 (0.000)

0.071 (0.005)

1.399 (0.090)

2.189 (0.112)

0.604 (0.031)

8.296 (0.425)

Five -years long differences (NT=6432)

0.041 (0.003)

0.009 (0.001)

0.002 (0.000)

0.036 (0.005)

0.638 (0.089)

2.173 (0.206)

0.600 (0.057)

8.236 (0.782)

27

Table 7- γ and ζ estimates for intangible assets (IKNrd +IKNpat +IKNmark ) and intangible stock constructed

from

direct

expenses

(IKNDE rd +IKNDE adv +IKNDEpat). Multiplicative

specification, total factor productivity (1 ). Elasticity of output with respect to the two types of intangible capital stock Type estimates

of

γ IKN

γ IKNDE

MSE

Marginal productivity of the two types of intangible capital stock relative to that of tangible capital stock

ζ IKN (med)

ζ IKNDE (med)

ζ IKN (Q1)

ζ IKNDE (Q1)

ζ IKN (Q3)

ζ IKNDE (Q3)

Pooled (NT=80714)

0.041 (0.001)

0.066 (0.001)

0.3944

10.404 (0.262)

3.677 (0.060)

3.045 (0.077)

1.018 (0.017)

36.786 (0.927)

14.708 (0.240)

Within (NT=80714)

0.021 (0.001)

0.058 (0.002)

0.1948

4.834 (0.244)

2.963 (0.093)

1.415 (0.072)

0.821 (0.026)

17.092 (0.864)

11.852 (0.370)

First differences (NT=66460)

0.015 (0.001)

0.044 (0.002)

0.2339

3.300 (0.221)

2.101 (0.116)

0.966 (0.065)

0.582 (0.032)

11.668 (0.781)

8.406 (0.464)

Long differences (NT=6432)

0.018 (0.003)

0.038 (0.003)

0.3669

3.854 (0.578)

1.803 (0.183)

1.128 (0.169)

0.499 (0.051)

13.625 (2.045)

7.213 (0.733)

(1) slmed=0.635 is the sample median of the share of labour cost in value added.

Table 8- γ and ζ estimates for intellectual capital (IK=IKNrd +IKNDErd +IKNpat+IKNDEpat) and customer

capital

(IC=IKNDEadv+IKNmark ).

Multiplicative

specification,

total

factor

productivity (1 ). Elasticity of output with respect to the two types of intangible capital stock

Marginal productivity of the two types of intangible capital stock relative to that of tangible capital stock

ζ IK (med)

ζ IC (med)

ζ IK (Q1)

ζ IC (Q1)

ζ IK (Q3)

ζ IC (Q3)

γ IK

γ IC

Pooled (NT=80714)

0.044 (0.001)

0.036 (0.001)

0.4029

10.180 (0.229)

4.270 (0.092)

2.753 (0.062)

0.892 (0.019)

39.257 (0.885)

25.800 (0.559)

Within (NT=80714)

0.020 (0.001)

0.036 (0.001)

0.1958

4.297 (0.226)

4.011 (0.149)

1.162 (0.061)

0.838 (0.031)

16.572 (0.870)

24.236 (0.903)

First differences (NT=66460)

0.016 (0.001)

0.031 (0.002)

0.2344

3.272 (0.229)

3.311 (0.199)

0.885 (0.062)

0.691 (0.042)

12.620 (0.884)

20.006 (1.204)

Long differences (NT=6432)

0.017 (0.003)

0.033 (0.003)

0.3676

3.493 (0.596)

3.565 (0.372)

0.945 (0.161)

0.745 (0.078)

13.471 (2.298)

21.545 (2.246)

Type estimates

of

MSE

(1) slmed=0.635 is the sample median of the share of labour cost in value added.

28

Table 9- Production Function Estimates (multiplicative specification) with IV and GMM Equation (2) estimates γ (k-l)

Type of estimates and instrument list (1)

α (c-l)

µ-1 (l)

MSE

χ2 (d.f.)

IV - lags t-2 and t-3 of first differences of net tangible and intangible capital stocks. (NT=37952)

0.052 0.175 0.215 0.4129 (0.003) (0.007) (0.021)

61.7 (3)

IV - lags t-2 and t-3 of gross investments in tangible and intangible capital stocks. (NT=52206)

0.033 0.278 0.043 0.3892 (0.002) (0.004) (0.017)

147.5 (3)

IV - lags t-1 of first differences of net tangible and intangible capital stocks at disaggregated level. (NT=66460)

0.043 0.179 0.029 0.3736 (0.002) (0.004) (0.006)

1019.9 (15)

IV - lags t-1 of gross investments in tangible and intangible capital stocks at disaggregated level. (NT=80714)

0.034 0.233 -0.000 0.3804 (0.001) (0.004) (0.004)

2879.6 (15)

GMM – lags t-2 and t-3 of gross investments in tangible and intangible capital stocks. (NT=80714)

0.045 0.374 0.146 (0.006) (0.033) (0.015)

117.0 (93)

GMM – lags t-2 and t-3 of the explanatory variables. (NT=80714)

0.024 0.129 0.131 (0.004) (0.015) (0.014)

514.8 (132)

(1 ) All the instruments are per employee logarithms. In the GMM rows one-step is reported, because tests are more reliable; two -step estimates are not significantly different. In the chi-2 (d.f.) column, the Hansen (1982) J statistic on the overidentifying restrictions is reported; caution should be adopted in considering these statistics, especially in the IV cases, because these can be biased by the huge number of observations towards the rejection of the null.

29

Tab. A1- Industry classification. HT Aerospace Computer Electronics Pharmaceutical Scientific instruments Motor vehicles Electric machinery Chemicals Other transport eq Non-electric machinery Rubber-plastic Shipbuilding Other manufacturing Non-ferrous metal Non-metallic mineral Fabricated metal Petroleum Ferrous metal Paper-printing Textile-clothing Food-tobacco Wood

146 240 884 1,379

Total

2,649 (2.79%)

MHT

MLT

LT

(0.15%) (0.25%) (0.93%) (1.45%) 1,590 2,124 4,603 4,174 72 12,984

(1.67%) (2.24%) (4.85%) (4.40%) (0.08%) (13.67%) 5,512 328 359 777 7,352 13,173 319 723

(5.80%) (0.35%) (0.38%) (0.82%) (7.74%) (13.87%) (0.34%) (0.76%) 4,815 17,275 9,455 6,684

25,547 (26.9%)

28,543 (30.06%)

(5.07%) (18.19%) (9.96%) (7.04%)

38,229 (40.25%)

94,968

Note: Manufacturing firms are classified according to their global technological intensity at the 4-digit level: HT+MHT = high and medium-high intensity; MLT = medium-low intensity; LT = low intensity.

Tab. A2- Size and industry classification. HT+MHT

MLT

1-19 20-49 50-249 >=250

2,511 8,641 13,376 3,668

(2.64%) (9.10%) (14.08%) (3.86%)

Total

28,196 (29.69%)

3,600 10,635 12,537 1,771

LT (3.79%) (11.20%) (13.20%) (1.86%)

28,543 (30.06%)

6,130 13,384 16,464 2,251

Total (6.45%) (14.09%) (17.34%) (2.37%)

12,241 32,660 42,377 7,690

38,229 (40.25%)

94,968

(12.89%) (34.39%) (44.62%) (8.10%)

Note: Manufacturing firms are classified according to their global technological intensity at the 4-digit level: HT+MHT = high and medium-high intensity; MLT = medium-low intensity; LT = low intensity. The number of employees is reported by the firms at the close of the fiscal year in the accounting scheme before the IV European Directive (64% of total observations), and it is reported as the average of workers during the year in the IV European Directive accounting scheme (24% of total observations); the two definitions coincide in the 12% of observations.

30

Tab. A3- γ and ζ estimates for intangible assets (IKNrd +IKNpat +IKNmark ) and intangible stock constructed

from

direct

expenses

(IKNDE rd +IKNDE adv +IKNDEpat). Multiplicative

specification, total factor productivity (1 ), never-zero sample.

Elasticity of output with respect to the two types of intangible capital stock Type estimates

of

γ IKN

γ IKNDE

MSE

Marginal productivity of the two types of intangible capital stock

ζ IKN (med)

ζ IKNDE (med)

ζ IKN (Q1)

ζ IKNDE (Q1)

ζ IKN (Q3)

ζ IKNDE (Q3)

Pooled (NT=23455)

0.042 (0.002)

0.066 (0.001)

0.3887

7.902 (0.344)

2.955 (0.078)

2.277 (0.099)

0.791 (0.021)

26.880 (1.171)

12.018 (0.319)

Within (NT=23455)

0.020 (0.002)

0.060 (0.003)

0.1972

3.341 (0.311)

2.417 (0.125)

0.962 (0.090)

0.647 (0.033)

11.364 (1.058)

9.828 (0.507)

First differences (NT=19427)

0.019 (0.002)

0.086 (0.004)

0.2324

3.460 (0.393)

3.773 (0.252)

0.997 (0.113)

1.010 (0.067)

11.768 (1.336)

15.345 (1.025)

Long differences (NT=1958)

0.024 (0.005)

0.054 (0.006)

0.3654

4.126 (0.997)

2.162 (0.307)

1.189 (0.287)

0.579 (0.082)

14.036 (3.390)

8.793 (1.250)

(1) slmed=0.633 is the sample median of the share of labour cost in value added.

Tab. A4- γ and ζ estimates for intellectual capital (IK=IKNrd +IKNDErd +IKNpat+IKNDE pat) and customer

capital

(IC=IKNDEadv+IKNmark ).

Multiplicative

specification,

total

factor

productivity (1 ), never-zero sample. Elasticity of output with respect to the two types of intangible capital stock

Marginal productivity of the two types of intangible capital stock

ζ IK (med)

ζ IC (med)

ζ IK (Q1)

ζ IC (Q1)

ζ IK (Q3)

ζ IC (Q3)

γ IK

γ IC

Pooled (NT=42671)

0.045 (0.001)

0.030 (0.001)

0.4045

7.907 (0.242)

3.805 (0.125)

2.274 (0.070)

0.712 (0.023)

28.594 (0.874)

24.188 (0.796)

Within (NT=42671)

0.021 (0.001)

0.045 (0.002)

0.1945

3.556 (0.266)

5.509 (0.251)

1.022 (0.076)

1.031 (0.047)

12.858 (0.961)

35.020 (1.597)

First differences (NT=35025)

0.025 (0.002)

0.052 (0.003)

0.2327

4.488 (0.356)

6.671 (0.396)

1.291 (0.102)

1.248 (0.074)

16.229 (1.288)

42.405 (2.519)

Long differences (NT=3356)

0.018 (0.004)

0.048 (0.005)

0.3636

3.183 (0.782)

6.021 (0.682)

0.915 (0.225)

1.126 (0.128)

11.509 (2.829)

38.270 (4.338)

Type estimates

of

MSE

(1) slmed=0.637 is the sample median of the share of labour cost in value added.

31