INTANGIBLE ASSETS AND HUMAN CAPITAL IN MANUFACTURING FIRMS

INTANGIBLE ASSETS AND HUMAN CAPITAL IN MANUFACTURING FIRMS DIPARTIMENTO DI ECONOMIA FACOLTÀ DI ECONOMIA UNIVERSITÀ DEGLI STUDI DI PARMA Alessandro Arrghett * Fabo Landn Andrea Lasagn * alessandro.arrghett@unpr.t landn4@uns.t andrea.lasagn@unpr.t PROVISIONAL VERSION PLEASE DO NOT QUOTE * Department of Economcs, Unversty of Parma Department of Economcs, Unversty of Sena Correspondng author

Abstract The postve mpact of ntangble assets on several measures of economc performance s well documented n the lterature. Less clear s what leads frms to nvest n ntangble assets n the frst place. The latter s partcularly mportant snce, at least for the Italan manufacturng sector, frms exhbt a very strong heterogenety n ther level of ntangble asset nvestments. In lne wth the capablty-based theory of the frm we argue that the frm s propensty to nvest n ntangble assets can be explaned by factors that are nternal and specfc to the frm. Makng use of a rch dataset we test and provde support for our hypotheses. In partcular we fnd that the propensty to nvest n ntangble assets ncreases wth the frm s sze, human captal and organzatonal complexty and wth the past level of ntangble assets. Ths ponts toward the exstence of a cumulatve dynamcs n the process of ntangble assets accumulaton that may account for most of the heterogenety observed n the data. The paper adds to the prevous lterature n two ways: frst t hghlghts the exstence of a strong ntra-ndustry heterogenety n ntangble assets nvestments; and second, t offers an explanaton for such heterogenety. Key words: ntangble assets, human captal, frms heterogenety, organzatonal complexty, complementarty, knowledge economy, organzatonal capabltes JEL Code: D22 (Frm Behavor: Emprcal Analyss); L21 (Busness Objectves of the Frm); L25 (Frm Performance: Sze, Dversfcaton, and Scope); O32 (Management of Technologcal Innovaton and R&D) 2

1. Introducton Intangble assets consst of the stock of mmateral resources that enters the producton process and are necessary to the creaton and sale of new or mproved products and processes. They nclude both nternally produced assets e.g. desgns, blueprnts, brand equty, n-house software, and constructon projects and assets acqured through external market e.g. technology lcenses, patents and copyrghts, and the economc competences acqured through purchases of management and consultng servces (Corrado et al., 2006). A large and growng body of emprcal lterature has shown ntangble assets to play a major role n the modern knowledge economy. Corrado et al. (2005), for nstance, estmates that n the early 2000s the value of US ntangble assets was already close to $3.4 trllon and suggest that n the same perod ntangble assets accounted for more than the 75% of US output growth. Smlarly, Nakamura (2003) shows that n the last 40 years ntangble assets as a proporton of US GDP have more than doubled rasng from 4.4% to 10%, and n the year 2000 almost one thrd of the value of US corporate assets were ntangbles. At the frm level, Hulten and Hao (2008) show that for US frms the value of total assets ncreases by 57% when R&D expendture and ntangble captal s consdered n addton to conventonal fnancal accounts. Smlar trends, among the other countres, have been shown to ext n Japan (Myagawa and Km, 2008), UK (Marrano and Haskel, 2006), Fnland (Jalava et al., 2007), Netherland (van Roojen-Horsten et al., 2008) and Italy (Bontemp and Maresse, 2008). In addton to the quanttatve dmenson of ntangble assets, varous works have also stressed the qualtatve lnk between ntangble assets and frm performance. Marrocu et al. (2009), Olner et al. (2007) and O Mahony and Vecch (2009), for example, fnd a postve contrbuton of ntangble assets to both frm and ndustry productvty. Hall et al. (2005), Greenhalgh and Rogers (2006) and Sandner and Block (2011) show ntangble assets to sgnfcantly contrbute to company values n fnancal market. Denekamp (1995), Braunerhjelm (1996), and Delgado-Gómez and Ramírez- Alesón (2004) provde evdence for a postve relatonshp between frms' ntangble assets and nternatonalzaton. In spte of ths large and growng lterature, however, lttle research has been so far conducted on the determnants of ntangble assets nvestments wthn frms. Although 3

t s wdely accepted that ntangble assets are becomng a crtcal source of compettve advantage (Barney, 1991), very few emprcal works have actually nvestgated the factors that may lead frms to undertake ths type of technologcal nvestment n the frst place. In the majorty of the cases, on the contrary, the level of ntangble assets has been taken as gven and treated more as an explanatory varable rather than as a varable to be explaned. From the pont of vew of both managers and polcy makers, however, the achevement of a clear understandng on what determnes the frms propensty to nvest n ntangble assets can be of crucal mportance, especally f t helps dentfyng the varables that dscrmnate between hgh and low-performance busnesses. Moreover, such a perspectve s nterestng for the academc debate too, n that t may offer a test for alternatve theores of the frm. For these reasons, ths paper wll make some frst steps n fllng such gap. When the frm level of ntangble assets s taken as the man varable to be explaned, the frst strkng evdence that comes out of the data s that, at least for the Italan manufacturng sector, ntangble assets nvestments seem to vary consderably across frms. On ths respect Panel A of Fgure 1 reports the quantle dstrbuton of ntangble assets as a proporton of total assets n 2008 for the sample of Italan manufacturng frms ncluded n our dataset. The value of both ntangble and total assets s derved from the frms dsaggregated balance sheet (see Secton 3). On average, ntangble assets account only for the 0.8% of total assets. A more detaled analyss, however, reveals that there exst hgh heterogenety n the populaton. The medan of ths rato, n fact, s barely above 0% and for more than the 75% of the frms ntangble assets count for less than 1% of total assets. At the same tme there s a top 10% of frms that massvely nvest n ntangble assets, wth fgures that range from 2% to 38% of total assets. [Fgure 1 about here] The evdence resultng from Panel A of Fgure 1 s even more nterestng f one consders that the observed heterogenety n ntangble assets nvestments remans hgh even at the ndustry level. In ths respect Panel B reports the quantle dstrbuton of the same varable reported n Panel A, after normalzng the rato by the sample (rght) and the ndustry mean (left). In partcular, the Eurostat NACE Rev. 1 classfcaton (NACE) 4

has been used for the ndustry. As t s easy to see the shape of the dstrbuton remans practcally unchanged n the two cases, wth a 10% of frms that seems to nvest n ntangble assets from 3 to 60 tmes more than the average frm of the ndustry they belong to. Such a dstrbuton reveals qute clearly that there exst a degree of heterogenety that extent well beyond what could be reasonably explaned by nterndustry structural dfferences alone. The man am of the present paper s thus to nvestgate the factors that, n addton to the ndustry, can effectvely explan ths degree of heterogenety. In lne wth the capablty-based vew of the frm we argue that the heterogenety n ntangble assets nvestments ought to be studed by focusng on frm-specfc trats, such as sze, organzatonal structure, human captal and the hstorcal evoluton of the organzaton. In ths sense we see the frm's propensty to nvest n ntangble assets more as a product of the unque bundle of resources and capabltes that the frm has evolved over tme, rather than as the consequence of exogenous technologcal contngences. Intangble assets, n fact, represent a form of technologcal nvestment that (a) needs a certan set of nternal resources to be carefully dentfed, planned, and managed and (b) s addressed at the satsfacton of needs that can be purely organzatonal n nature (e.g. to facltate the management of a complex organzaton). Where such nternal resources lack and/or the nternal structure of the frms does not requre ths type of specfc nvestments, ntangble assets are less lkely to be ncluded n the frm s busness strategy, and hence they are less lkely to be accumulated. Moreover, for a gven dstrbuton of ntangble assets n the populaton of frms, the exstence of complementartes among dfferent components of the ntangble stock may generate a process of accumulaton whose dynamcs s largely persstent, wth the consequent permanence of heterogenety over tme. Makng use of a rch dataset n terms of frm-specfc characterstcs we test and provde support for our hypotheses. Overall, the paper contrbutes to the prevous lterature on ntangble assets and ndustral dynamcs n two ways. Frst, t hghlghts the exstence of a large heterogenety n ntangble assets nvestments. Ths dmenson of the problem has so far receved lttle attenton n the lterature, and t has certanly not been documented wth respect to the Italan manufacturng sector. Second, the paper suggests a capabltybased vew explanaton for the frm s propensty to nvest n ntangble assets and provde an emprcal test of ths hypothess. In ths way the paper can make much sense 5

of the observed heterogenety and t offers some nsghts for manageral polcy desgn too. Overall, the results of the paper can open new nterestng lnes of research The structure of the paper s the followng. Secton 2 presents a bref overvew of the lterature on frms heterogenety and ntangble assets, and motvates our hypotheses. Secton 3 descrbes the dataset and the varables ncluded n our models. Secton 4 dscusses the emprcal strategy employed n the estmaton. Secton 5 presents and dscusses the results. Secton 6 lsts some robustness checks. Secton 7, fnally, concludes. 2. Lterature revew and theoretcal hypotheses Most of the authors n the busness and management lterature have focused on the relatve effect of ntangble assets on economc performance (Marrocu et al., 2009; Oler at al., 2007; Bontemp and Maresse, 2008; Corrado et al. 2006). On the contrary, contrbutons concernng the factors nfluencng the level of ntangble assets accumulated n the frm are rarer. Among organzatonal and busness scholars the factor that has receved the wdest attenton for ts effect on the frm s propensty to nvest n ntangble resources s surely the ndustry. Snce nnovaton ntensty and approprablty dffer sgnfcantly across ndustres (Cockburn and Grlches, 1988), frms face dfferent ncentves for the development and formalzaton of ntangble assets. Changng the market structure, the nature of technology and the regulatory envronment, frms may consder approprate to nvest dfferent amounts of resources n nnovaton and n ts protecton. On ths ground, ndustry-related varables are expected to be able to explan most part of the varablty of ntangble assets accumulaton (Vllalonga 2004; Daley 2001). Gu and Lev (2001), for nstance, shows that the level and growth rate of ntangble assets are dfferent across ndustres: the hghest levels are measured n nsurance, drugs, and telecommuncatons; the lowest n truckng and wholesale trade. Smlarly, Klock and Megna (2000) show that n more nnovatve ndustres the market value of the frm, capturng the mportance of ntangble assets, s markedly hgher than book value, whle n the tradtonal ones the dfference between the two varables turns out to be modest. Smlar fndngs are dscussed n Gleason and Klock (2003), Ballardn et al. (2005) and Abowd et al. (2005). In addton, Vergauwen et al. (2007) mantan that non-tradtonal 6

ndustres have more ncentve n dsclosng more nformaton about ntangbles snce nvestors expect contnuous nvestments n R&D and mmateral projects. Frms n tradtonal ndustres, on the contrary, tend to nvest less and randomly n mmateral assets and are less prone to reveal snce such expendtures may sgnal to compettors nnovatve strateges. As prevously underlned and shown n Fgure 1, however, the avalable evdence for Italy suggests that ndustres can explan only a small proporton of the dstrbuton of ntangble assets nvestments. Even at the ndustry level, n fact, manufacturng frms tend to be largely heterogeneous so far as the level of accumulated ntangble assets s concerned. In ths sense some factors other than the ndustry must necessarly play an mportant role. Our man am s thus to dentfy what these other factors actually are. A stream of lterature that has placed partcular attenton on the sources of nterfrms heterogenety s the one assocated wth the so-called capablty-based theory of the frm (Dos et al., 2000). Such an approach, whch bears large overlappng wth another well-known theory among management scholars such as the resource-based vew (Barney, 1991, 2001), defnes capabltes as frm s specfc ways of dong and stresses heterogenety as the dstnctve feature of busness organzatons (Dos et al., 2006). Although a thoughtful dscusson of ths theory goes beyond the scope of our paper (for ths we refer to Dos et al., 2000), t suffces to notce that accordng to such vew frm s decsons are determned manly by the capabltes that the frm has evolved over tme, and only margnally by exogenous technologcal contngences. On ths bass, the explanaton of a gven frm s behavoral path must necessarly rely on the nteracton between frm s specfc and system-specfc (e.g. ndustry-related) factors, and admt an explct dynamcs of capabltes acquston (Dos et al., 2006). The adopton of a capablty-based approach n the study of ntangble assets nvestments prompts us to focus on a set of frm-specfc trats that, n nteracton wth external factors, can actually explan the frm s propensty to undertake ths type of technologcal nvestment. On ths bass, we choose to focus on four varables n partcular: sze, human captal, organzatonal complexty, and the past level of the accumulated ntangble stock. The role that each of these varables play n affectng the frm s decson to nvest n ntangble assets wll be analyzed separately. 7

2.1 Frm s sze Sze s a frm s trat that, ndependently of the ndustry n whch the frm operates, s lkely to have a postve mpact on the propensty to nvest n ntangble assets. In the frst place large frms are better able than small ones to explot economes of scale n ntangble assets accumulaton (Derckx and Cool, 1989). Secondly, bg frms can be more effectve n protectng ther ntangble stock than small ones, and thus have a greater ncentve to nvest. Thrdly, t may be argued that large frms are also capable of supportng a greater share of the uncertanty that s assocated wth ntangble assets nvestments as compared to small frms (Ghosal and Loungan, 2000). On ths bass, the frst hypothess that we put forward s that: Hypothess 1 The probablty that a frm nvests n ntangble assets s greater the larger the frm s sze. 2.2 Frm s human captal In addton to sze, another trat that s lkely to affect the propensty to nvest n ntangble assets s the frm s human captal. Several authors have ndeed suggested that the qualty of the human resources employed by frms s a basc condton both for generatng ntangble assets and for ther economc explotaton (Abramovtz and Davd, 2000; Galor and Moav 2004). In ths framework human captal s made up not only by the formal educaton receved by the workforce before the enrolment, but also by formal and nformal on-the-job tranng (Barney, 1991; Nerdrum Erkson 2001). It represents the collecton of sklls and abltes that are embedded n the members of the organzaton (Bonts and Ftz-enz, 2002) and can be leveraged to expand ntangble resources at the frm level. In ths sense, therefore, the more a frm s endowed wth a hghly educated workforce, the more we should expect the frm to have the manageral and nnovatve capabltes necessary to extend her ntangble stock. As notced by Abowd et al. (2005), however, human captal n the form of workforce educaton s not by tself the only drver of the accumulaton of ntangble resources. It s how human captal s organzed that explans the varance of productvty 8

and the endowment of ntangble assets. The way workers are organzed, together wth the qualty of human resources, yeld n fact complementartes that may facltate the generaton of nnovaton and create n turn ncentves for the accumulaton of the ntangble stock. An organzatonal varable that could reflect such complementartes s for nstance the amount of human resources effectvely employed n drect R&D actvtes (Lu et al. 2000). In the language of the capablty-based vew the latter s synonymous wth the adopton of routnes and procedures that facltate the explotaton of external sources of knowledge (Dos, 1988; Cohen and Levnthal, 1990) and that support, as a consequence, the process of ntangble assets accumulaton. In ths respect some supportng evdence comes from Lu et al. (2010) who fnd R&D expenses to mpact postvely on the frm s ntangble assets wth predctably postve effects on future cash flow and frm value. On the bass of the above arguments, and consderng workforce educaton and R&D actvtes as dfferent proxes of a frm s human captal, we formulate our second hypothess as follows: Hypothess 2 The probablty that a frm nvests n ntangble assets s greater the larger the frm s human captal. 2.3 Frm s organzatonal complexty Another frm-specfc trat that, smlarly to human captal and sze, can affect the process of ntangble assets accumulaton s the degree of organzatonal complexty 4. Accordng to some authors, n fact, the frm s ntangble stock ncludes assets that drectly ncrease what has come to be known as the frm s organzaton captal see Kaplan and Norton (2004), Lev and Radhakrshnan (2003) and Bonts (2001) among the others. The latter, n the semnal contrbuton by Prescott and Vsscher (1980), was defned as the set of nformaton assets that the frm uses n order to coordnate the materal factors of producton, namely physcal captal and labour. Lately, more manageral defntons have been formulated, e.g. Lev and Radhakrshnan (2003), Hsu 4 Organzatonal complexty has been defned and measured n dfferent ways. In ths paper we wll follow Damanpour (1996) and defne t as the degree of varety n the organzaton s spatal, occupatonal, herarchcal and functonal dmensons (Hall 1977, Mller and Contay 1980). 9

(2007), Atkeson and Kehoe (2005). Under all the alternatve specfcatons, a drect lnk between the complexty of the frm s nternal organzaton and the accumulaton of organzaton captal and thus of ntangble assets, seems to emerge. Pekkola (2009), for nstance, argues that globalzed frms use more organzaton captal. The hgher the number of employees n the frm that are workng abroad, the greater the amount of organzaton captal that s needed n order to montor the relatonshps between dfferent producton unts and markets. A smlar relatonshp has been dentfed by Denekamp (1995) and Braunerhjelm (1997) wth reference to the need of managng a varety of foregn drect nvestment (FDI), and by Bartel et al. (2007) and Brynjolfsson et al. (2002) wth respect to the need of complementng the enhancement of ICT wth parallel extensons of organzatonal resources. Concernng the dynamcs of vertcal ntegraton, moreover, the results obtaned by Gonzalez et al. (2000) suggests the exstence of a postve relatonshp between the degree of vertcal dsntegraton and the amount of organzatonal captal accumulated n subcontractors as a way to cope wth ncreasngly complex market transactons. From a dfferent but smlar perspectve, other authors have stressed the role that competton and the pace of technologcal progress play n shapng the frm s nternal complexty, and thus n affectng the accumulaton of ntangble resources. The sharpenng of competton n the markets, n fact, leads frms to ntegrate resources focused on protectng from mtaton and updatng the most specfc nternal assets. Ths means n turn an extenson of the manageral resources but also an ncrease n ther complexty. Petrck et al. (1999), for nstance, show that as the speed of comparable tangble asset acquston accelerates and the pace of mtaton quckens, frms resort to the less mtable ntangble assets to enhance ther dstnctve know how and product dfferentaton. Hence, the greater the complexty of the relatonshp wth the market the greater wll be the nvestment n ntangbles. From the assocaton between ntangble assets and organzaton captal, and by relyng on the above arguments on the relatonshp between complexty and organzatonal resources, we deduce that: Hypothess 3 The probablty that a frm nvests n ntangble assets s hgher the greater the frm s organzatonal complexty. 10

2.4 Cumulatve dynamcs Fnally, consderng the ssue of ntangble asset nvestments from a dynamc perspectve a crucal role may be played also by the stock of ntangble assets accumulated n the past. In ths respect, several dmensons of the process of ntangble assets accumulaton can be mportant. Frst of all ntangble assets conssts of knowledge and the latter s by ts own nature cumulatve. Wthn the context of the resource-based vew, for nstance, Derckx and Cool (1989) defnes two man features that seem to dstngush ntangble assets accumulaton from physcal captal accumulaton: asset mass effcency,.e. economes of scale n the producton of ntangble asset stock from exstng asset stock; and tme compresson dseconomes,.e. dmnshng returns to current perod nvestments n ntangble assets. As argued by Knott et al. (2003) asset mass effcency mples that the more ntangble assets a frm has, the lower the margnal cost of nvestng n further extenson of the asset stock. The reason could be the exstence of what Teece (1987) calls nterconnectedness of asset stocks, that s complementartes among the dfferent components of the knowledge stock. Tme compresson dseconomes mply nstead that ntangble assets accumulaton can't be rushed. Even f an entrant nvests n one year the total sum of the ncumbent nvestments made over several years, t won't acheve the same resource poston (Knott et al., 2003:192). The combnaton of these two factors lead to the nevtable emergence of dvergent ntangble assets accumulaton paths, where the frms who nvested more (less) n the past tend to nvest more (less) also n the future. Movng from the structural characterstcs of ntangble assets to the specfc features of frm s behavour, a smlar argument for the exstence of a cumulatve dynamcs can be formulated by relyng on the dea of organzatonal learnng (see Dos et al., 2006). In a nutshell, the dea of organzatonal learnng suggests that when a frm adjusts her nternal organzaton n order to (a) search the knowledge landscape and (b) nvest n a partcular type of asset, the frm learns a set of capabltes. Such capabltes are lkely to generate a relatve advantage n pursung nvestments n smlar and related assets compared to compettors who dd not nvest n the frst place. As a result the set of ntangble resources accumulated n the frm at any gven pont n tme largely 11

becomes a functon of pror accumulated set of resources. Hghly persstent ntangble assets accumulaton are then the most lkely consequence. On the bass of these arguments, and ndependently of the factors that have the strongest power n explanng the cumulatve path, our fourth and fnal hypothess s thus that: Hypothess 4 The probablty that a frm nvests n ntangble assets s greater the larger the stock of ntangble assets accumulated n the past. 3. Data, varables and descrptve analyss In order to test the hypotheses defned n the prevous secton we use a jont dataset retreved from two man sources. The frst one s the 9th wave of the Survey on Manufacturng Frms collected by Captala. The tme span of data s 2001-2003. The dataset contans qualtatve and quanttatve nformaton for a large stratfed sample of Italan frms. In partcular, t gathers a wealth of nformaton on the structure of the workforce and governance aspects; nformaton on nnovaton, dstngushng whether product, process or organzatonal nnovatons; nformaton on nvestments and R&D expendtures. The second source s the database AIDA-Bureau van Dck that contans nformaton on all Italan frms dsaggregated balance sheets for the perod 2001-2008. After combnng these two sources the fnal dataset counts nearly 1,500 observatons. The representatveness of the orgnal sample s mantaned n terms of both frm s sze and ndustry of actvty. 5 In the lterature there are several possble ways of measurng ntangble assets. In partcular, two man approaches have been pursued: the frst s based on estmates derved from frm expendtures on ntangbles such as R&D, tranng and nnovaton; the second uses drect measures based on stocks orgnally reported as assets n companes balance sheets. For a dscusson on the relatve adequateness of these two approaches wthn the framework of Italan legslaton we refer to Bontemp and Maresse (2008). In our paper we choose to adopt a balance sheet-type of measure and 5 Data on sample representatvty are avalable from the authors upon request. 12

to consder, n partcular, a subset of the costs usually reported under the tem ntangble fxed assets,.e. the costs for research and advertsement, the costs of patents and the costs of lcensng. In dong so we dffer from some prevous contrbutons based on smlar data (e.g. Marrocu et al., 2009) that consdered the total tem nstead. The reason why we make ths choce s that the tem ntangble fxed assets ncludes also expendtures such as the start-up/goodwll costs whose captalzaton s hghly subject to managers dscreton and s therefore dffcult to nterpret. On the contrary, the tems that we consder n our measure are all objectve expenses ncurred by the frms. On the bass of ths measure of ntangble assets, we defne a frm s ntangble captal ntensty (ICI) as the percentage of ntangble assets over total assets. At any gven pont n tme ICI represents the amount of ntangble assets accumulated by frm, and can be consdered a proxy of s propensty to nvest n ntangble assets. ICI s the crucal varable of our analyss. In partcular, for the frms n our sample, we want to understand whch factors can explan the probablty to be an ntangble captal ntensve frm (ICIF), where the latter s defned as a frm that belongs to the 10 th decle of ICI. Followng the dscusson presented n Secton 2, we focus our attenton on two man types of explanatory varables. Frst we consder systemc-lke varables such as the ndustry n whch the frm operates. The man classfcaton that we adopt s the NACE (see Appendx). In the emprcal strategy, n order to fully capture the observed heterogenety, we choose to control for ndustry-related effects through a normalzaton of the dependent varable by the ndustry mean (see Secton 4). The second type of explanatory varables that we consder conssts of a vector of frm-specfc characterstcs, wth partcular attenton beng pad to sze, human captal, organzatonal complexty and the degree of ntangble captal accumulated n the past. To each of these dmensons corresponds a set of proxes. As a proxy of sze we consdered the total number of employees (SIZE). In partcular, n order to control for non-lneartes, we classfed frms as small (SIZE 50), medum (50 < SIZE 100) and medum-large / large (SIZE > 100) accordng to ther sze. For each of these groups of frm we then consdered an apposte set of dummy varables (D_SIZE_S, D_SIZE_M, D_SIZE_L) As a proxy of human captal we consdered a combnaton of two dstnct varables: the frst s a synthetc ndex elaborated wth a factoral analyss (FCT_EDU) usng as 13

nputs the rato between whte collars and blue collars (STAFFRATIO), the workforce s average years of educaton (AVEDU) and the percentage of employees holdng a unversty degree (UNIDEG); the second varable s nstead the percentage of employees engaged n R&D (R&D). The ratonale behnd such specfcaton s that n our vew FCT_EDU and R&D capture two dstnct effects of human captal 6 : FCT_EDU s a proxy of a frm s capablty to manage knowledge-ntensve assets, R&D captures nstead the frm s propensty to engage her human resources n ntangble stock expandng actvtes, ncludng the evaluaton, assmlaton and applcaton of new knowledge. As argued n Secton 2, each of these dmensons of human captal has been treated as a relevant component of the process of ntangble asset accumulaton n the lterature. For what concerns nstead the degree of organzatonal complexty we consdered a synthetc ndex based on a scale gong from 0 to 4 for mn and max complexty respectvely (COMPLEX). The ndex captures two dstnct features of the frm: frst, the extent to whch the frm does busness as a subcontractor; and second the degree of nternatonalzaton. The dea n ths case s that the more the frm s nternatonalzed and operates as subcontractor, the more dversfed and flexble her structure, and thus the more complex her organzaton. In partcular the ndex s constructed on the bass of four dstnct varables: the percentage of turnover due to subcontractng (SUBCT); the porton of turnover due to subcontractng that s earned abroad (SUBCT_ABD); a dummy takng value 1 f the frm faces nternatonal compettors (D_INTCOMP), and a dummy takng value 1 f the frm realzed nvestments abroad (D_FDI). A complete descrpton of how the ndex s constructed s reported n the legend of Table 1. Fnally, as a proxy of the degree of accumulated ntangble captal we consdered a dummy takng value 1 f the frm belonged to the fourth quartle of the 5-years lagged value of ICI (ICI_PAST) and zero otherwse (D_ICI_PAST). In ths case, snce we were nterested n the mpact of the past level of ntangble assets n absolute terms, the nonnormalzed value of ICI has been consdered. [Table 1 about here] 6 Our vew s confrmed also by the emprcal evdence. In fact, the factoral analyss that we conducted n order to compute FCT_EDU revealed R&D to contan a dfferent set of nformaton wth respect to STAFFRATIO, AVEDU and UNIDEG. 14

Table 1 reports some descrptve statstcs relatve to our dataset. Indces are dfferentated for ICIFs and the whole sample. Apart from ICI whch s evaluated at 2008, all the other varables are evaluated over the three-years perod 2001-2003. When lookng at the whole sample t s nterestng to notce the hgh standard devaton assocated wth varables such as SIZE, the varous proxes of human captal (UNIDEG, STAFFRATIO, AVEDU) and ntangble captal ntensty (ICI, ICI_PAST). Ths s symptom of a hgh degree of heterogenety n the populaton along several frm-specfc demographc dmensons. Regardng the sub-sample of ICIFs we can observe that on average, compared to non-icifs, they are bgger, more complex, and have a greater endowment of human captal. In addton, ICIFs have a level of past accumulated ntangble assets that s nearly eght tmes the level of non-icifs. Ths ponts towards persstence n ntangble assets nvestments. [Table 2 about here] The propensty of frms to mantan a level of ntangble assets nvestments that s closely related to the level realzed n the past s confrmed also by Table 2. In ths case we reported n Panel A the dstrbuton of frms across four classes of ICI null (= 0), low (> 0 and < 1%), medum ( 1% and <5%) and hgh (> 5%) for the year 2008 (columns) and the average 2001-2003 (rows). On ths bass we computed n Panel B the transton probabltes by smply averagng the content of each cell over the total of the row. The result, for nstance, reads that a frm that belonged to the class null n 2001-2003 had a 69% of probablty to belong to the same class also n 2008. We notce that the hghest probabltes are the ones on the man dagonal. Moreover, the average probablty below the man dagonal s greater than the average probablty above, whch n turn mples that f a process of movement across classes had exsted t tended to take place from hgh to low classes and not the reverse. Coupled wth the fact that on average the level of ntangble captal ntensty has ncreased movng from 2001-2003 to 2008 (as shown n Table 1), the latter result means that not only the populaton of frms was characterzed from the very begnnng by heterogenety, but also such heterogenety has polarzed over the last decade. The dentfcaton of the factors that could explan such an ncreased polarzaton s the man objectve of our emprcal 15

nvestgaton. 4. Emprcal strategy The model that we want to estmate takes the followng form: ICI 2 = f ( IND, XF, XC, ε ), ε ~ N (0, σ ) (1) where IND stands for the ndustry n whch frm operates; XF s a vector of frmspecfc characterstcs ncludng SIZE, FCT_EDU, R&D, COMPLEX and D_ICI_PAST ; XC s the vector of control varables; and ε s the vector of normally dstrbuted resduals. The key ssue affectng the estmaton of model (1) s the censored nature of ICI. As argued above ths occurs because a large proporton of the frms ncluded n our sample do not nvest n ntangble assets at all. In such a context the applcaton of standard OLS would generate based estmates, wth the magntude of the bas beng lnked to the proporton of non-censored observatons n the sample. In order to avod ths problem we decded to use an alternatve estmaton technques based on a Probt specfcaton. Moreover, n order to nvestgate the role that dfferent varables may play at dfferent pont of the dstrbuton of ICI, we also estmate the same model by the mean of quantle regressons (Koenker and Bassett, 1978). The combnaton of these two technques should gve us suffcent confdence n nterpretng the results. In the Probt specfcaton we follow three steps. Frst, for each frm, we normalze the value of ICI by the mean value of the ndustry n whch the frm operate. In ths way we can elmnate from the dstrbuton of ICI all sorts of ndustry-related effects. We call ths new ndustry-normalzed varable ICI. Secondly, we classfed as ICIF all the frms belongng to the 10 th decle of ICI. Fnally, we transformed our dependent varable n a dchotomc varable takng value 1 f the frm s ICIF and zero otherwse (ICIF ). All the other varables reman the same as n model (1), except that now IND wll obvously dsappear from the rght-hand sde of the equaton. The dea n ths case s to estmate the probablty for a frm to be ntangble-captal ntensve relatve to the mean 16

of her own ndustry, gven a set of frm-specfc characterstcs. The baselne equaton to be estmated thus becomes: Pr[ ICIF = 1] = Φ( XF ' β + XC ' β ) (2) F C where Φ( ) s the cumulatve dstrbuton functon for the standard normal, and β F and β C are the vectors of parameters to be estmated. The parameters of model (2) are estmated va maxmum lkelhood (ML) estmaton. We also conjecture that the dfferent varables ncluded n our baselne model may play a dfferent role as determnant of ICI dependng on the pont of the dstrbuton that we consder. For nstance, t could be the case that frms at the very top of the dstrbuton (.e. the hghly ntangble-captal ntensve frms) are followng a strategc pattern accordng to whch the stock of ntangble assets requres to be constantly renewed and updated (e.g. because ts accumulaton requred hgh specfc nvestments by the frm). On the contrary, frms at the bottom may be smply adoptng a blnknglke strategy accordng to whch ntangble assets are accumulated for lmted perod of tme and bought drectly on the market. If that were the case, we should then expect the dfferent components of human captal.e. R&D and workforce educaton, to change ther relatve mpact along the dstrbuton, wth R&D beng more relevant at the top (as a source of ntangble assets extenson) and workforce educaton at the bottom (as a source of ntangble assets admnstraton). In order to test for these dfferent types of effect we estmate a set of quantle regressons. An mportant advantage of ths method, n fact, s that t reveals dfferences n the relatonshp between the dependent and the ndependent varables at dfferent ponts of the condtonal dstrbuton of the dependent varable (see Koenker and Hallock, 2001). In ths case too we control for the ndustry through the normalzaton of ICI by the ndustry mean. The quantle regresson model that we estmate can be thus wrtten as follows (see Coad and Rao, 2008): ICI = X ' β θ+ u wth Quantθ( ICI X ) = X ' βθ (5) θ where X = XF, XC ) s the vector of regressors, β= β F, β ) s the vector of ( 17 ( C

parameters to be estmated, and u s a vector of resduals. Quantθ( ) denotes the θ th condtonal quantle of ICI gven X. The θ th regresson quantle, 0 < θ< 1, solves the followng problem: 1 mn β n θ ICI : ICI X ' β X ' β + : ICI< X ' β (1 θ ) ICI X ' β (6) n 1 = mn ρθ( u β n = 1 θ ) where ρ θ( ), whch s known as the check functon, s defned as: θuθ f uθ 0 ρ ϑ( uθ) = (7) ( θ 1) θuθ f uθ < 0 The soluton of ths mnmzaton problem gves OLS estmates that approxmates the θ th condtonal quantle of the dependent varable, rather than the condtonal mean. The model coeffcents are therefore allowed to vary across quantles of the condtonal dstrbuton of ICI, gvng us the possblty to test the performance of our explanatory varables at dfferent ponts of ICI. In each of these estmaton methods, there are two addtonal ssues we need to tackle. The frst s multcollnearty, whle the second s endogenety. For what concerns the former t s nterestng to notce that n spte of what one could reasonably expect, the degree of correlatons among most of our regressors s relatvely low. Table 3 reports the correlaton matrx for all the varables ncluded n XF and for some of the controls. Wth the excluson of the three varables that we used n order to compute the synthetc ndex for human captal (UNIDEG, STAFFRATIO, AVEDU), all other sgnfcant correlatons have a coeffcent smaller than 0.30. Interestngly SIZE seems to be correlated only wth AGE and wth a very low coeffcent (0.06). On ths bass we can conclude that although some degree of multcollnearty exst, the latter does not represent a severe ssue n our estmates. [Table 3 about here] 18

Wth respect to endogenety thngs are trcker. Gven our specfcaton of XF, n fact, a certan degree of correlaton between some of the regressors and the error term could emerge for two man reasons: smultanety, e.g. the amount of human captal avalable n the frm s jontly determned wth the nvestments n ntangble assets; and omtted varables, e.g. human captal affects nvestments n ntangble assets through some unobservable effect that we have not ncluded n the model. Both reasons are qute strngent and need to be dealt wth drectly. The frst opton that we could explot s to rely on an nstrumental varables approach. In our case, however, ths approach s dffcult to mplement because of the correlatons that exst among many of the regressors that are most lkely to be effectvely endogenous wth respect to ICI, e.g. FCT_EDU and R&D. Gven the lack of a clear understandng of how decsons are taken nsde each ndvdual frm, n fact, the lkelhood of fndng a vable nstrument s low. As an alternatve soluton, and ths s the key methodologcal feature of our emprcal strategy, we choose to explot what s lkely to be the man strength of our dataset, that s the avalablty of a detaled set of nformaton for a relatvely large number of frms and years. In partcular, we adopt two strateges. Frst, n order to deal wth smultanety, we evaluate the dependent varable of each model at 2008 and the ndependent varable at 2003 (for some of the latter we also consder the average for the three-years perod 2001-2003) so that there exst a lag of at least fve years between them and the regressors. Such a perod of tme makes t dffcult (at least n prncple) for the dependent varables and the regressors to be smultaneously determned, and thus reduces the rsk of nconsstent estmates. For what concerns nstead the ssue of omtted varable, the second strategy that we adopt s to saturate the thrd group of regressors (XC ) wth as many varables as we can n order to control for any knd of frm-specfc fxed effects. Snce our concern s especally related to FCT_EDU and R&D, we focused our attenton on varables that could be correlated wth human captal and the propensty to nvest n R&D, ncludng: age (AGE), nvestments n ICT (ICT_INV), and labor productvty measured n terms of added value per employee (LAB_PRDTY). In addton we ncluded a seres of frmspecfc accountng ndexes such as the ACID-test (short-term lqudtes / total assets) and the proftablty or gross earnng over total turnover (PROFIT). Fnally we 19

controlled also for geography-related effects wth the ntroducton of regonal dummes. Ths soluton, together wth the rather detaled specfcaton of vector XF, should reduce the lkelhood of omtted varable bas even though, especally for the proxes of human captal, some care must be taken n nterpretng the coeffcents. 5. Results Table 4 reports the Probt estmates for our model, translated nto margnal and mpact effects for the contnuous and dummy varables respectvely. We added the regressors ncluded n XF one at the tme, so that we estmated a total of 4 models (Probt_1 Probt_4). [Table 4 about here] The frst nterestng result n the Probt estmates concerns the two dummes for frm s sze (D_SIZE_M and D_SIZE_L). To be a medum or large frm postvely and sgnfcantly contrbutes to the probablty of beng ICIF n the frst model, when the two dummes are consdered alone (.e. Probt_1). As soon as the dummy assocated wth the past level of ntangble captal ntensty s ncluded (D_ICI_PAST), however, only the dummy for medum szed frms contnues beng postve and sgnfcant. Ths result seems to suggest that, although on average sze matters, beyond a certan threshold of frm dmenson the postve effect of beng large s overwhelmed by the effect assocated wth a cumulatve dynamcs n ntangble assets nvestment. From the theoretcal pont of vew ths s an nterestng fndng, n that t suggests that for large frms the exstence of complementartes and/or learnng dynamcs n the process of ntangble assets accumulaton plays a more powerful role than sze alone n explanng the propensty to nvest. For what concerns human captal the dmenson that seems to be the best predctor of the probablty to by ICIF s the amount of human resources employed n drect R&D actvtes (R&D). The effect of R&D nvestments, n fact, s postve and sgnfcant n all the models n whch the varable has been ncluded (Probt_3 Probt_4). On the contrary, no sgnfcant effect seems to be assocated wth the level of workforce educaton (FCT_EDU). As prevously argued, however, ths effect may depend on the partcular segment of the overall dstrbuton of ICI that the Probt approxmates (.e. top 20

10% of frms). In addton to sze and R&D actvtes, the probablty of beng ICIF n 2008 s also postvely assocated wth the presence of hgh ntangble captal ntensty n 2001-2003 (D_ICI_PAST). The coeffcent n ths case s postve and sgnfcant n all the models. Qute nterestngly, the sze of the coeffcent n the most complete model (Probt_4) s even greater than the one assocated wth R&D nvestments. Overall, ths effect seems to confrm the evdence reported n Table 2 on the persstence of ntangble assets nvestments. Fnally, n the last model (Probt_4), we also fnd a postve and sgnfcant effect assocated wth organzatonal complexty (COMPLEX). Although n terms of both sgnfcance level (10% confdence ) and margnal mpact such an effect does not seem to be partcularly strong, t s mportant to notce that the effect holds after controllng for all the other ndependent varables ncluded n our baselne model, such as human captal, sze and past level of ICI. In ths sense, such a results lend credblty to our hypothess accordng to whch organzatonal complexty plays an ndependent role as a determnant of a frm s ntangble captal ntensty. [Table 5 about here] Smlar results are obtaned from the estmates of the quantle regressons. For the sake of smplcty we report n Table 5 only the results for the 25 th, 75 th and 95 th percentle, together wth the medan (50 th percentle). In ths case the mpact of each varable can be nvestgated n more detal. Frst of all we fnd that, even after controllng for the past level of ntangble assets (D_ICI_PAST), the two dummes for medum and large szed frms (D_SIZE_S and D_SIZE_M ) are postve and sgnfcant for most part of the dstrbuton of ICI, except for the 95 th percentle. Ths result provdes further evdence n support of the dea that sze actually matters. However, t also confrm the evdence from the Probt that for frms wth very hgh nvestments n ntangble assets the scale effects assocated wth sze are of farly low mportance. Secondly we obtan that, n lne wth sze, also the effect of human captal changes accordng to the quantle consdered. In partcular, whle the coeffcent of the R&D 21

component (R&D) s postve and sgnfcant n the central part and rght tal of the dstrbuton (medan, 75 th and 95 th percentle), the educatonal component (FCT_EDU) s postve and sgnfcant n the central part and left tal (medan, 25 th and 75 th percentle). If compared to the Probt, ths result s nterestng n that t confrms our hypothess accordng to whch the two components of human captal can play a dfferent role for frms wth a dfferent level of ntangble captal ntensty. The thrd result that we obtan from quantle regressons s a farly strong effect of the dummy for the past level of ntangble captal ntensty (D_ICI_PAST). The coeffcent assocated wth the latter, n fact, s always postve and sgnfcant, ndependently of the quantle that we approxmate. Ths suggests, once agan, that there exst a path dependency n the process of ntangble assets accumulaton. If we consder also the outcomes of the Probt estmates, ths s defntely the most robust effect that we observe. Fnally, we nterestngly fnd that for all proxes of sze, human captal and past level of ntangble captal ntensty the margnal effects tend to ncrease the more we move up n the condtonal dstrbuton of ICI. Ths seems to suggest the exstence of ncreasng return to scale n ntangble assets nvestments. Moreover, t supports the dea that, overall, there s a tendency toward an ncreased polarzaton n so far as the level of ntangble captal ntensty s concerned. For the sake of completeness t s also mportant to notce that n the quantle regresson we do not fnd any sgnfcant effect assocated wth organzatonal complexty (COMPLEX). Brngng together the results obtaned from the Probt and quantle regressons we derve a farly encouragng pcture concernng the test of our theoretcal hypotheses. Frst of all, n accordance wth HP1, we fnd sze to be a sgnfcant predctor of the frm s level of ntangble captal ntensty. The dummy for medum szed frms s postve and sgnfcant n all the estmated models wth the only excepton of the quantle regresson approxmatng the 95 th percentle. The dummy for large szed frms, on the contrary, s sgnfcant n the quantle regressons but only lmtedly n the Probt. Overall ths result s the symptom of the fact that, whle for the large majorty of frms n our sample n the process of ntangble assets accumulaton scale effects are mportant, there exst a set of hghly ntangble captal ntensve frms for whch sze matters lttle. The latter represents a group of frms that, ndependently of the sze and 22

the ndustry n whch they operate, have delberately adopted a busness strategy that entals a substantal nvestment n ntangble assets. Wth reference to HP2,.e. the role of human captal, the man fndng that we obtan s that the dfferent dmensons of human captal.e. workforce educaton and R&D affect the propensty to nvest n ntangble assets n dfferent ways dependng on the pont that we consder n the condtonal dstrbuton of ICI. Ths result could reflect the fact that frms wth dfferent levels of ntangble captal ntensty tend to accumulate dfferent types of ntangble assets, and thus rely on dfferent components of human captal to procure them. In relatve terms, both the Probt and the quantle regresson estmates suggest that the strongest effect s the one assocated wth the amount of human resources nvolved n actve R&D. Such a fndng supports the dea that t s not only the qualty of human resources that matter n the process of ntangble assets accumulaton, but also the ways n whch the latter are organzed. It s mportant to notce that n all the estmated models the dfferent proxes of human captal are postve and sgnfcant controllng for both ndustry and frm s sze. Such fndng confrms our vew accordng to whch human captal explans by tself part of the propensty to nvest n ntangble assets. In partcular, the role of human captal seems to be partcularly robust exactly for the group of frms for whch sze has no explanatory power,.e. the sub-sample of frms that belongs to the 95 th percentle of ICI. For the latter, n fact, the amount of human resources nvolved n R&D s the regressor wth the strongest margnal mpact on the level of ntangble captal ntensty. A relatvely weaker emprcal evdence s nstead found n support of the thrd explanatory varable that we consdered n our hypotheses, that s organzatonal complexty (HP3). Our proxy of organzatonal complexty, n fact, turns out to be only weakly sgnfcant n the Probt, and not at all sgnfcant n the quantle regressons estmates. One of the reasons for ths result could be that wth our ndex of complexty we are capturng only two dmensons of the frm s organzatonal structure, that s the role as subcontractor and the degree of nternatonalzaton. The latter dmensons are lkely to requre relatvely specfc nvestments n organzatonal captal, and are therefore unable to offer a complete proxy of the frm s manageral needs. Overall, however, especally n consderaton of the fact that n spte of such specfcty our ndex turns out to be sgnfcant n some of the estmates, we do not reject HP3. Further analyss s anyway requred on ths ssue. 23

Fnally, for what concerns HP4, we fnd a clear supportng evdence n favor of the exstence of path dependency n the dynamcs of ntangble assets accumulaton. The most robust fndng that we obtan across all estmates, n fact, s that the coeffcent assocated wth the past level of ntangble assets s postve and sgnfcant. Ths mples that frms havng hgh ntangble captal ntensty at a gven pont n tme tend to reman on the same technologcal trajectory, whle the others tend to dverge towards less knowledge-ntensve types of productons. The sze of the coeffcent, moreover, suggests that such dvergent dynamcs n technologcal trajectores s farly strong, and becomes even stronger the more we move up n the condtonal dstrbuton of ICI. The latter result confrms the exstence of an ncreased polarzaton n level of ntangble captal ntensty. As argued n Secton 2 the causal mechansms underlyng ths dynamcs could be related to: (a) the exstence of complementartes among knowledge bts; and (b) a process of organzatonal learnng. Although both factors could theoretcally play a role, we are unable to dstngush between them on the bass of our data. 6. Robustness checks In order to ncrease the relablty of our results we conducted a seres of robustness checks 7. Frst of all, n normalzng the dependent varable, we tred out alternatve taxonomes for the ndustres, ncludng the OECD (2009) and Pavtt (1984) classfcatons. In both cases the results do not change. Independently of the taxonomy that we adopt, n fact, frms reman very heterogeneous so far as ther level of ntangble assets nvestments s concerned. Moreover, for all ndustry classfcatons, the proxes of sze, human captal, organzatonal complexty and past levels of ntangble captal ntensty turn out to be the most sgnfcant regressors. Secondly, as a further test on the role of the ndustry, we also tred a dfferent emprcal strategy n whch, nstead of normalzng, we control for ndustry-related effects by addng an apposte set of dummy varables. In ths case too the results are n lne wth our hypotheses. In all the estmated models, n fact, the type of ndustry 7 The results are avalable from the authors upon request. 24

beng t a macro category as n OECD (2009) and Pavtt (1984) or a mcro sector as n NACE does not sgnfcantly predct the frms ntangble captal ntensty. On the contrary, sze, human captal, organzatonal complexty and past levels of ntangble captal ntensty reman hghly sgnfcant. Snce several contrbutons found a relatvely strong effect of ntangble assets on productvty (e.g. Marrocu et al., 2009; Oler at al., 2007), we also tred dfferent measures of productvty n our vector of control varables. In partcular, apart from the value added per employee whch we used n the estmates reported n Tables 4 and 5, we estmated total factor productvty usng the method developed by Levnshon and Petrn (2003). Also n ths case, however, results do not change. Gven the relevance that scale effects play n the lterature on ndustral dynamcs, we also tested how our results react to a dfferent specfcaton of sze. In partcular we estmated both the Probt and the quantle regressons by substtutng the dummy varables for medum and large szed frms wth the contnuous measure nstead. The results are n lne wth the ones reported n Table 4 and 5 wth the effect of sze that, n the Probt, tends to dsappear as soon as the proxy for the past level of ntangble assets s ntroduced. Ths test lend further credblty to our nterpretaton. A fnal robustness check s related to a possble sample selecton bas. As dscussed n Secton 3, our sample of manufacturng frms s based on the orgnal dataset comng from the 9 th Captala s survey (about 4,500 observatons). However, t was possble to obtan data on ntangble assets only for about 1,500 frms havng a detaled balance sheet report. In fact, n ths case the tem ntangble fxed assets s actually detaled n sub-tems such as costs for research and advertsement, the costs of patents, costs of lcensng, etc. ect. We tred to control for ths possble selecton bas by applyng a two-step Heckman procedure. Frst, a Probt estmate of selecton from the whole sample (all frms n the orgnal Captala s dataset) s made; second, a Probt estmate (n the case of Probt_4 specfcaton) for the selected sample of frms usng the Inverse Mll s rato obtaned from the frst step s used as a correcton factor (Heckman 1976). We dd not fnd evdence of selecton bas n our results 8. 8 The tables are not reported, but are avalable from the authors upon request. 25

7. Conclusons The results of our estmates tend to confrm our ntal hypotheses. In partcular we fnd that sze, human captal and the past level of ntangble captal ntensty sgnfcantly ncrease the probablty of beng ICIF. A smlar outcome s obtaned for the proxy of organzatonal complexty, although the evdence concernng the latter s weaker. All these results are obtaned after controllng for ndustry-related effects. Overall these results suggest three man conclusons. Frst of all, t seems clear that frm-specfc trats are mportant determnants of ntangble assets nvestments. In our sample, n fact, what makes the dfference between ICIFs and the other frms s the presence of a composte mx of nternal resources (e.g. hgh human captal and accumulated ntangble stock) that enable a frm to effectvely absorb, manage and reproduce ntangble resources. Secondly, there seems to exst a rather specfc although weak relatonshp between the dynamcs of ntangble assets nvestment and the need to manage complex market transactons. In ths sense, and as a way to complement the most standard nterpretaton whch s focused on the lnk wth nnovatons, the choce of nvestng n ntangble assets seems to have also an organzatonal connotaton. Fnally, there s evdence of a strong cumulatve effect among the determnants of ntangble captal accumulaton. Such an effect can be held responsble to generate a dvergent dynamcs n ntangble assets nvestments, and may thus explan a relevant part of the observed heterogenety. These conclusons, accordng to us, open new nterestng research questons Frst of all t would be nterestng to nvestgate how the propensty to nvest n ntangble assets evolves over tme. In ths respect, we beleve that some of the varable ncluded n our baselne model may stll play an mportant role. Secondly, t would be of value an analyss amed at testng how frms wth dfferent degree of ntangble captal ntensty react to exogenous shocks. In ths sense, the recent economc depresson may represent an nterestng applcaton. 26

Appendx Correspondence between aggregate and 2-dgts Eurostat NACE Rev.1 classfcaton codes: S01 NACE code 15 (food products and beverages); S02 NACE code 17 (textles), 18 (wearng apparel; dressng and dyeng of fur) and 19 (Tannng and dressng of leather; luggage, handbags, saddlery, harness and footwear); S03 NACE code 20 (wood and of products of wood and cork, except furnture; artcles of straw and platng materals), 21 (pulp, paper and paper products) and 22 (Publshng, prntng and reproducton of recorded meda); S04 NACE code 23 (coke, refned petroleum products and nuclear fuel), 24 (chemcals and chemcal products) and 25 (rubber and plastc products); S05 NACE code 26 (other non-metallc mneral products); S06 NACE code 27 (basc metals) and 28 (fabrcated metal products, except machnery and equpment); S07 NACE code 29 (machnery and equpment n.e.c.); S08 NACE code 30 (offce machnery and computers) and 31 (electrcal machnery and apparatus n.e.c.); S09 NACE code 32 (rado, televson and communcaton equpment and apparatus) and 33 (medcal, precson and optcal nstruments, watches and clocks); S10 NACE code 34 (motor vehcles, tralers and sem-tralers) and 35 (other transport equpment); S11 NACE code 36 (furnture; manufacturng n.e.c.). 27

Fgures and Tables Fgure 1 Quantle dstrbuton of the rato ntangble assets over total assets before (Panel A) and after the normalzaton by the sample and ndustry mean (Panel B). Panel A Panel B Legend: Panel A reports the quantle dstrbuton of the rato ntangble assets over total assets for the sample of frms ncluded n our dataset. Panel B reports the quantle dstrbuton of the same varable after normalzng the rato by the sample (rght) and the ndustry (left) mean. As t s easy to see the shape of the dstrbuton does not change sgnfcantly after the normalzaton. Ths suggests the exstence of hgh heterogenety at the ndustry level. 28