The Role of Demand in Fostering Product vs Process Innovation: A Model and an Empirical Test

Transcription

1 Dscusson Paper Seres IZA DP No The Role of Demand n Fosterng Product vs Process Innovaton: A Model and an Emprcal Test Herbert Dawd Gabrele Pellegrno Marco Vvarell june 2017

2 Dscusson Paper Seres IZA DP No The Role of Demand n Fosterng Product vs Process Innovaton: A Model and an Emprcal Test Herbert Dawd Unversty of Belefeld Gabrele Pellegrno EPFL Marco Vvarell Unverstà Cattolca del Sacro Cuore, IZA and UNU-MERIT june 2017 Any opnons expressed n ths paper are those of the author(s) and not those of IZA. Research publshed n ths seres may nclude vews on polcy, but IZA takes no nsttutonal polcy postons. The IZA research network s commtted to the IZA Gudng Prncples of Research Integrty. The IZA Insttute of Labor Economcs s an ndependent economc research nsttute that conducts research n labor economcs and offers evdence-based polcy advce on labor market ssues. Supported by the Deutsche Post Foundaton, IZA runs the world s largest network of economsts, whose research ams to provde answers to the global labor market challenges of our tme. Our key objectve s to buld brdges between academc research, polcymakers and socety. IZA Dscusson Papers often represent prelmnary work and are crculated to encourage dscusson. Ctaton of such a paper should account for ts provsonal character. A revsed verson may be avalable drectly from the author. Schaumburg-Lppe-Straße Bonn, Germany IZA Insttute of Labor Economcs Phone: Emal: publcatons@za.org

3 IZA DP No june 2017 Abstract The Role of Demand n Fosterng Product vs Process Innovaton: A Model and an Emprcal Test Whle the extant nnovaton lterature has provded extensve evdence of the so-called demand-pull effect, the possble dverse mpact of demand evoluton on product vs process nnovaton actvtes has not been yet nvestgated. Ths paper develops a formal model predctng a larger nducng mpact of past sales n fosterng product rather than process nnovaton. Ths predcton s then tested through a dynamc mcroeconometrc model, controllng for R&D persstence, sample selecton, observed and unobservable ndvdual frm effects and tme and sectoral peculartes. Results are consstent wth the model and suggest that an expansonary economc polcy may beneft the dffuson of new products or even the emergence of entre new sectors. JEL Classfcaton: Keywords: O31 technologcal change, R&D, demand-pull nnovaton, dynamc two tobt Correspondng author: Marco Vvarell Unverstà Cattolca del Sacro Cuore Largo Gemell Mlano Italy Emal: marco.vvarell@uncatt.t

4 IZA DP No june 2017 non-techncal summary Consstently wth the most updated vew put forward by the nnovaton scholars, ths study provdes further evdence that both the technology-push and the demand-pull hypotheses play an mportant role n explanng nnovaton actvtes, here represented by the R&D expendtures. However, the extant lterature does not provde any clue about the possble dverse mpact of demand evoluton on product vs process nnovaton actvtes. Ths paper flls ths gap and proposes a formal model where past sales foster both product and process nnovaton expendtures, but wth the product elastcty systematcally larger than the process one. Ths theoretcal predcton s tested through a dynamc mcroeconometrc model controllng for R&D persstence, sample selecton, observed and unobservable ndvdual frm effects and tme and sectoral peculartes. Results are consstent wth the model and reveal a larger mpact of past sales over the product nnovatve expendtures rather than the process ones. Ths outcome has an mportant polcy mplcaton. Indeed, polcy makers should be aware that the demand-pull leverage s partcularly crucal for product nnovaton. Therefore, f the dffuson of new products or even the emergence of entre new sectors are assumed as targets, a talored expansonary polcy mght be seen as a proper and effectve strategy.

5 1 Introducton Back n the Sxtes and the Seventes, a vvd debate has occurred between the supporters of the technology-push approach and those underlnng the crucal role of demand (demandpull approach) n fosterng and shapng nnovaton. Whle the former (see Rosenberg, 1976, 1982; Freeman, 1982) focused on scentfc and technologcal opportuntes as necessary precondtons for a strongly path-dependent technologcal progress, the latter (Schmookler, 1962, 1966; Meyers and Marqus, 1969) ponted out that market condtons were at least as much as mportant n creatng the rght ncentves for nnovaton. Analytcally, the technology-push perspectve calls for dentfyng nnovaton as an autoregressve process, where the essental role of prevous knowledge s captured together wth the cumulatve, localzed and persstent nature of technology (see Atknson and Stgltz, 1969; Ruttan, 1997; Antonell, 1998) and where the specfc sectoral technologcal opportuntes are properly taken nto account (see Malerba and Orsengo, 1996; Malerba, 2005; Klepper and Thompson, 2006). Indeed, startng from the Eghtes, nnovaton scholars have agreed that the technology-push and the demand-pull perspectves should be seen as complementary, snce nnovaton s drven both by the ntrnsc nature of scence and technology and by market forces, prmarly demand evoluton (see Nelson and Wnter, 1982; Dos, 1988; Pavtt, 2005; Tosell, 2017; D Stefano et al., 2012). Ths paper wll focus on the role of demand n dfferently affectng the ncentves for product vs process nnovaton, albet the proposed emprcal test wll fully take nto account both the cumulatve and persstent nature of nnovaton (represented by the an AR(1) specfcaton of R&D nvestment, see Secton 3) and the role of sectoral peculartes (captured by sectoral dummes, see Secton 3). Indeed, there are dfferent arguments supportng the vew that rsng demand may nduce an ncrease n frms nnovaton efforts (see Schmookler, 1962, 1966): frstly, ncreasng sales allow the fnancng of expensve R&D and nnovaton actvtes (see Hall et al., 1999; O Sullvan, 2005); secondly, the ntroducton of nnovaton s strongly subject to uncertanty, whch s reduced by optmstc demand condtons (see Fontana and Guerzon, 2008); thrdly, approprablty and potental proftablty of nnovaton rse wth market sze (see Schumpeter, 1942; Kamen and Schwartz, 1982). Prevous lterature has provded evdence supportng the demand-pull hypothess both at the aggregate, sectoral and at the mcroeconomc (frm) level. The emprcal debate started wth the semnal contrbuton of Schmookler (1966), who - usng US sectoral data - showed that the more nvestment there was n a user ndustry at a gven tme, the more patented captal goods nnovaton one observed n the supplyng ndustry some tme later. Scherer (1982) confrmed Schmookler s results, after checkng for seven technology class dummes n the US; however, the consderaton of dfferences n technologcal opportuntes (a way to take nto account the 2

6 technology-push argument, see above) gave rse to a large ncrease n the ftness of hs regressons, compared wth the orgnal ones put forward by Schmookler. Shftng the attenton from patents to R&D nvestment (an ex-ante proxy of nnovaton, overcomng a possble objecton of endogenety 1 ) and usng data on 46 Dutch sectors, Klenknecht and Verspagen (1990) found evdence of a sgnfcant relatonshp between demand growth and R&D growth. Indeed, the endogenety and reverse causalty problems n the relatonshp between demand and nnovaton may also affect the lnk between aggregate demand evoluton and technologcal change at the macroeconomc level; however, Gerosk and Walters (1995) - usng macroeconomc tme seres for the UK - found sgnfcant evdence that output caused nnovaton and patents, but no evdence of the reverse effect. Most recent studes have focused on the level of the frm, usng mcrodata. For nstance, usng Communty Innovaton Survey (CIS) data from about 8,000 Dutch frms, Brouwer and Klenknecht (1996) found that demand growth nduces an ncrease n nnovaton output, measured both n terms of products new to the frm and products new to the sector. In a later study, the same authors (Brouwer and Klenknecht, 1999) - usng a panel of 441 Dutch frms - found a further confrmaton of the demand-pull hypothess. More recently, Pva and Vvarell (2007) - usng a longtudnal dataset of 216 Italan frms and controllng for the path-dependent nature of R&D - found a sgnfcant role of sales n fosterng R&D, although ths demand-pull effect turned out to be more or less effectve accordng to dfferent frm s characterstcs. However, prevous theoretcal and emprcal analyses faled to fully nvestgate whether the demand-pull drver s more or less effectve n nducng product vs process nnovaton. Ths s an nterestng theoretcal ssue, snce process and product nnovaton have dfferent mpacts, wth the former more lnked to productvty gans whle the latter enlargng markets or even creatng new ones. Therefore, the two knds of nnovatons nvolve dfferent macroand mcro-economc mplcatons and so t s relevant to know whch of the two s more lkely to be accelerated by an ncrease n demand. Moreover, to dsentangle the demand-pull effect between process and product nnovaton may be of some nterest for polcy makers, as well (see Nemet, 2009; Peters et al., 2012). For nstance, f demand s more mportant for product nnovaton and dffuson rather than for process nnovaton, governments may ndeed play a role n promotng an economc polcy combnng a Keynesan perspectve (ncreasng demand) wth a Schumpeteran one (promotng those strands of demand fosterng the ntroducton and dffuson of new products n emergng and hgh-tech sectors). 1 Snce there s generally a lag between nnovaton and fnal patentng, the tme span - detected by Schmookler - between nvestment (sales) n the user ndustry and patentng n the supplyng ndustry mght actually correspond to a smultaneous occurrence of nnovaton and ncreasng sales wthn the frms n the supplyng ndustry. Therefore, a key methodologcal problem may arse: t can be rghtly argued that nnovatve actvty tself ncreases demand because of the accelerator effects assocated wth decreasng prces due to process nnovaton and/or ncreasng market share due to product nnovaton. Thus, the hgh correlatons between demand and nnovatve evoluton dscovered by Schmookler mght be affected by an endogenety problem and actually pontng to a reverse causalty between nnovaton and demand. If R&D expendtures are used nstead of patents (as n the present study), ths problem does not arse, snce R&D expendtures wll gve rase to nnovaton n a later perod. 3

7 Ths paper wll try to fll ths gap n the extant lterature. Intutvely, process nnovaton are bascally cost-cuttng and so - although postvely affected by demand evoluton - they should be proftable n any case, whle product nnovaton should be promoted and ntroduced only when demand perspectves are partcularly promsng. The second secton of ths paper wll feature a formal model developng ths ntuton and ndeed predctng a larger nducng mpact of past sales n fosterng product rather than process nnovaton. Ths theoretcal predcton wll be tested n Secton 3, usng a unque longtudnal mcro-dataset. Secton 4 wll brefly conclude and dscuss some polcy mplcatons. 2 The model We consder an ndustry model, where n f frms compete by offerng a standard product. They can reduce ther margnal producton costs for that product by means of process nnovaton and can also nvest n product nnovaton. Upon successful product nnovaton they add a horzontally and vertcally dfferentated product to ther product range and produce ths new product n addton to the standard one. It s assumed that the new product s a (partal) substtute of the old product. Two perods, t = 1, 2 are consdered, where at t = 1 frms frst engage n Cournot competton based on ther current margnal producton costs for the standard product c s,1, = 1,.., n and then determne ther nvestment n process and product nnovaton actvtes. At t = 2 frms engage agan n Cournot competton based on ther new producton costs, and n case of a successful nnovaton at t = 1 on the extended product range. Frms choose product and process nnovaton effort n order to maxmze expected proft n perod t = 2 net of effort costs and n ther optmzaton rely on nave expectatons about the costs and product range of ther compettors. In partcular, frm assumes that none of the compettors ntroduces a new product n t = 0 and also that c s j,2 = cs j,1 for all j. The nverse demand functon for the standard product s gven by p s t = α β n qj,t, s β > 0, j=1 f no other product s offered, wth q,t s denotng the frms output quantty n perod t = 1, 2. The parameter α > 0 captures the strength of demand on the consdered market. In case a new product s ntroduced n perod t = 1, then n t = 2 the nverse demand system s p s 2 = α β n j=1 qs j,2 γqn,2 p n 2 = α + θ βq,2 n γ (1) n j=1 qs j,2, where q,2 n denotes the output quantty the nnovatng frm chooses for the new product, γ [0, β] governs the degree of horzontal dfferentaton between the standard and the new product and θ 0 determnes the degree of vertcal dfferentaton. Margnal producton costs 4

8 for the new product are denoted by c n,2. We assume that a frm s effcency n the producton process for the new product s closely related to that frm s effcency n producng the standard product, whch mples that, comparng costs across frms, c n,2 should be closely correlated to c s,1. Hence, we assume that cn,2 = ξcs,1 for some ξ > 1.2 Process nnovaton effort by frm, denoted by x,1 reduces margnal producton costs for the establshed product. In partcular, we assume that c s,2 = Max[c s,1 δx,1, 0] and the costs of process nnovaton are gven by χ(x,1 ) = η 2 x2,1. The parameter η s assumed to be suffcently large to guarantee that the optmal process nnovaton effort satsfes x,1 c,1 δ for all. The probablty for a successful product nnovaton s gven by mn[ay,1, 1] wth a > 0 and y,1 denotng the product nnovaton effort. Analogous to process nnovaton we also assume quadratc costs of product nnovaton gven by ζ(y,1 ) = κ 2 y2,1. Standard calculatons yeld that the quantty of frm n the Cournot equlbrum at t = 1 s gven by 3 where C s,1 = j cs j,1. q s,1 = α ncs,1 + Cs,1, (2) β(n + 1) Takng nto account that frm have nave expectatons about the costs of the compettors the expected quantty and payoff of frm n t = 2 n the absence of a product nnovaton read q s,ni,2 = α n(cs,1 δx,1) + C,1 s, π,2 NI = (α n(cs,1 δx,1) + C,1 s )2 β(n + 1) β(n + 1) 2. (3) Under the condton that the new product s ntroduced by frm the quanttes n the Cournot equlbrum are q s,i,2 = 2α(β2 γ 2 )+(n+1)γ(αγ β(α+θ)) (2nβ 2 (n 1)γ 2 )(c s,1 δx,1)+2(β 2 γ 2 )C,1 )+(n+1)βγξc s,1 2β(β 2 γ 2 )(n+1), q n,i,2 = β(α+θ) αγ βξcs,1 +γ(cs,1 δx,1) 2(β 2 γ 2 ). The expected proft of the frm under ths condton s gven by π I,2 = q s,i,2 (ps 2 c s,1 + δx,1 ) + q n,i,2 (pn 2 ξc s,1), (4) where p s 2, pn 2 are determned accordng to (1). Overall, frm chooses ts nnovaton actvtes 2 For reasons of smplcty t s assumed here that cost reductons due to process nnovaton for the standard product carred out n perod t = 1 do not have an nstantaneous effect on the producton costs of the smultaneously ntroduced new product. 3 In what follows we restrct attenton to cases where all frms produce postve quanttes n the Cournot equlbrum. 5

9 n perod t = 1 such that the followng expected proft functon s maxmzed: π (x,1, y,1 ) = (1 mn[ay,1, 1])π NI,2 + mn[ay,1, 1]π I,2 χ(x,1 ) ζ(y,1 ) For the extreme cases where the effectveness of ether process nnovaton or product nnovaton actvtes are zero the optmal nnovaton profles can be characterzed analytcally. Proposton 1 If no product nnovaton s possble,.e. a = 0, then the optmal profle of nnovaton actvtes s gven by x,1 = 2δn(n + 1) βη(n + 1) 2 2δ 2 n 2 qs,1, y,1 = 0. If no process nnovaton s possble,.e. δ = 0, then the optmal profle of nnovaton actvtes s gven by x,1 = 0, y,1 = a(β2 γ 2 ) (q n,i,2 κβ )2. The Proposton (the proof of whch s provded n Appendx A3) shows that n ndustres domnated by process nnovaton we should expect a lnear relatonshp between past sales and nnovaton expendtures, whereas for product nnovators ncentves for engagng n such actvtes are postvely related to the frm s expected sales of the new product. Snce the strength of demand affects sales of the standard as well as the new product, ths relatonshp suggests a postve relatonshp also between past sales and product nnovaton expendtures. To obtan a testable outcome n terms of a possble dversfed effect of the demand-pull over the ncentve to spend n nnovaton actvtes separately for product and process nnovaton (see next secton), n what follows we consder the elastcty of nnovaton expendtures wth respect to past sales f the dynamcs of sales s trggered by a varaton the strength of demand α. 4 More formally, we defne and ɛ proc = x,1 q,1 s q,1 s x,1 ɛ prod = y,1 q,1 s q,1 s y,1 = x,1 α = y,1 α / q s,1 α / q s,1 α From Proposton 1 we obtan mmedately that the elastcty of process nnovaton expendtures for the specal case wthout product nnovaton s gven by ɛ proc = 1, whereas n the 4 Alternatvely, one could consder the elastcty of nnovaton expendtures wth respect to past sales f the dynamcs of sales of frm s trggered by a varaton of ts compettveness, expressed by the margnal costs c s,1. We have carred out the entre analyss also for elastctes based on a varaton of the parameter c s,1 and there are no qualtatve dfferences between the results obtaned n that case and the ones reported below. q s,1 x,1 q s,1 y,1 6

10 absence of process nnovaton the elastcty of product nnovaton expendtures can be calculated as ( ) 2 α nc s ɛ prod,1 + Cs,1 = α + β β γ θ ξβ γ. (5) β γ cs,1 Clearly, also ths elastcty s postve and whether t s smaller or larger than the elastcty of process nnovaton n prncple depends on the characterstcs of the consdered market and of frm. However, several general observatons can be made. Takng nto account that ξ > 1, t follows that n a market n whch (frst perod) producton costs for the standard product are symmetrc across frms,.e. C,1 s = (n 1)cs,1, the elastcty of product nnovaton wth respect to past sales s larger than one, and therefore larger than the elastcty of process nnovaton, f the vertcal dfferentaton of the new product s small,.e. f θ s close to zero. The elastcty of product nnovaton of frm decreases for an ncreasng degree of vertcal dfferentaton of the new product θ. Also an ncrease of the margnal producton costs of the frm, c s,1, nduces a decrease of the elastcty of product nnovaton wth respect to past sales as long as degree of horzontal dfferentaton between the standard and the new product s suffcently hgh,.e. β γ s not too small. The ntuton for these observatons follows from the expressons for optmal product and process nnovaton efforts gven n Proposton 1. Snce process nnovaton reduces unt costs for the standard product, the ncentves to nvest are proportonal to past sales, whch are the estmator for future sales. Expected profts resultng from the ntroducton of a new product are convex n the market sze, such that sze of the product nnovaton expendtures, whch ncrease the probablty to be able to ntroduce the new product, s convex n the strength of demand. Hence, the elastcty of nvestment wth respect to past sales tends to be larger for product than for process nnovaton. To get more detaled nsghts nto the factors that determne the szes of these two elastctes and to deal also wth the the general case, n whch frm engages n both product and process nnovaton we rely on a numercal analyss. 5 In Fgure 1 we show the two elastctes for varyng strength of demand α. 6 It can be clearly seen that not only both elastctes are postve, but also the elastcty of product nnovaton expendtures wth respect to past sales s larger than that of process nnovaton actvtes. The gap between the elastctes becomes larger as the strength of demand ncreases. Hence, ths fgure confrms our conclusons based on the extreme cases covered n Proposton 1 and suggests 5 Indeed, for the case wth both types of nnovatve actvtes an analytcal characterzaton of the relatonshp between past sales and nnovaton expendtures s no longer possble. 6 The parameter settng used n ths llustraton s n = 5, β = 0.2, γ = 0.1, θ = 0.15, δ = 0.1, a = 0.7, η = 25, κ = 40, C s,1 = 0.8, c s,1 = 0.2, ξ = 1.5. Ths settng has been chosen to generate equlbrum outcomes that are compatble wth emprcally plausble stylzed facts. In partcular, under ths parameter settng the Lerner Index for the standard product n equlbrum s (p s 1 c s,1)/p s 1 = 0.4 and the R&D ntensty s about 11%, where R&D actvtes are approxmately evenly dstrbuted between product and process nnovaton. Hence, we consder a rather nnovatve olgopolstc ndustry, n whch frms have substantal market power. 7

11 prod proc α Fgure 1: Elastcty of product nnovaton (sold lne) and process nnovaton (dashed) expendtures wth respect to frst perod sales for alpha [0.5, 1.5]. that the elastcty of product nnovaton s ndeed larger than that of process nnovaton also for frms that smultaneously engage n both type of nnovaton actvtes. To check the robustness of these fndngs, n Fgure 2 we explore how the elastctes of the two types of nnovaton actvtes depend on dfferent key model parameters. Wth respect to the strength of demand we now always assume that the reservaton prce parameter s gven by α = 1. In the two panels n the frst row the effect of changes n the effectveness of product and process nnovaton s consdered, n the two panels n the second row the parameters for horzontal and vertcal product dfferentaton between the new and the standard product are vared and n the thrd row we explore the effect of changng the slope of the nverse demand functon and the producton costs of frm. It can be clearly seen that the effectveness of product and process nnovaton has almost no nfluence on the elastcty of product and process nnovaton actvtes wth respect to past sales. The degree of horzontal and vertcal dfferentaton of the new from the standard product has some nfluence. In partcular, as predcted also n our analytcal consderatons above, the elastcty of product nnovaton actvtes becomes smaller the more strongly vertcally dfferentated the new product s. Intutvely, such an ncrease n dfferentaton ncreases the product nnovaton expendtures of the frm wthout ncreasng the sales or profts of the frm n perod t = 1. In such a scenaro, where the fracton of perod 1 profts spent for nnovaton s partcularly hgh, the change of nnovaton expendtures nduced by an ncrease of α s relatvely small compared to the total nnovaton expendtures. Therefore the elastcty of expendtures wth respect to past sales s relatvely small. Whether an ncrease n the degree of horzontal dfferentaton,.e. a decrease of γ, ncreases or decreases the expected proft of product nnovaton depends on the degree of vertcal dfferentaton. For small values of θ ths expected proft goes up, whereas for large values of θ the expected proft from a new product 8

12 prod prod proc proc a δ (a) (b) prod prod 1.0 proc 1.0 proc (c) γ (d) θ proc prod prod 1.0 proc β (e) (f) s c, 1 Fgure 2: Elastcty of product nnovaton (sold lne) and process nnovaton (dashed) expendtures for a varaton of the effectveness of product nnovaton (a), effectveness of process nnovaton (b), degree of horzontal dfferentaton (c), vertcal dfferentaton (d), slope of the nverse demand (e) and margnal producton costs of the consdered frm (f). 9

13 ntroducton becomes larger the lower the degree of horzontal dfferentaton of the new product s. Hence, t depends on the sze of θ whether the elastcty of product nnovaton expendtures ncreases or decreases wth γ. For the default parameter settng depcted n Fgure 2, the degree of vertcal dfferentaton s suffcently strong such that the elastcty decreases wth γ. We have verfed numercally that ths relatonshp ndeed turns around f θ s smaller. However, n any case the elastcty of product nnovaton s larger than that of process nnovaton. Due to essentally the same arguments just dscussed wth respect to changes of γ aslo the monotoncty of the elastcty wth respect to β depends on the sze of θ. For the default scenaro the elastcty s ncreasng n β, whereas t s decreasng for small values of θ. If we vary producton costs of the consdered frm, c s,1, then ths has the strongest effect on the elastctes of nnovaton expendtures among all parameter varatons. The elastcty of product nnovaton decreases substantally as c s,1 ncreases, but for the elastcty of process nnovaton for frm to be larger than that of product nnovaton the frm s producton costs have to be close to c s,1 = 0.3, whch s 50% hgher than the average costs n the ndustry. For such a large cost value the frm s proft on the establshed market are so low that the rato of R&D expendtures to proft would be close to 0.3, whch seems to be a rather extreme value. Hence, also n ths respect the observaton that product nnovaton nvestments react more senstvely to demand varatons than process nnovaton nvestments s confrmed for the emprcally relevant parameter range. Comparng the results from our numercal analyss for frms engagng n product and process nnovaton wth our dscusson of the analytcal expresson (5) shows that the nsghts obtaned for frms engagng ether only n product or only n process nnovaton are qualtatvely dentcal to those for frms smultaneously nvestng n both actvtes. The nterplay between these actvtes does hardly affect the elastcty of product respectvely process nnovaton wth respect to past sales. On the whole, key predctons of our theoretcal model are that the elastctes of both types of nnovaton actvtes wth respect to past sales are postve and that, wth the excepton of frms characterzed by partcularly hgh R&D ntenstes, we expect that the elastcty of product nnovaton expendtures s larger than that of process nnovaton expendtures. Recallng the dscusson n Secton 1, our model predcts a postve and sgnfcant mpact of the demand-pull over the expendtures addressed to both product and process nnovaton. However, ths effect s expected as sgnfcantly larger n the case of product rather than process nnovaton. 3 The emprcal evdence The unque database used n ths study s based on the Encuesta Sobre Estrategas Empresarales (ESEE), a survey on busness strateges whch has been run yearly snce 1990 by the SEPI foundaton, on behalf of the Spansh Mnstry of Industry. Ths survey comprses extensve nformaton on about 2,000 companes, wth a focus on nnovaton actvty. Based on 10

14 longtudnal data, the survey s characterzed by the systematc trackng of changes n frms characterstcs (such as changes of legal status, mergers, splttng, acqustons, etc.), n order to check the nformaton provded by the frms and to preserve ther relablty and consstency over tme. The adopted samplng procedure n desgnng the ESEE ensures representatveness for each two-dgt NACE-CLIO manufacturng sector, followng both exhaustve (frms wth more than 200 employees, equal to 715 n 1990) and random samplng crtera (specfcally, n 1990 a sample of 1,473 frms employng between 10 and 200 employees was bult, usng a stratfed, proportonal, restrcted and systematc samplng method wth a random start). Furthermore - n order to guarantee a persstent level of representatveness and to preserve the nference propertes - start-up companes have been ncorporated n the survey year by year, accordng to the same random samplng crtera. 7 In ths study, we consder ESEE data for the perod 1991 to The orgnal longtudnal dataset - once taken nto account mssng nformaton and the occurrence of mergers and acqustons - comprsed 36,032 observatons. Then, gven the purpose of ths study, we restrcted our attenton to the frms engaged n process and/or product nnovaton, endng up wth an unbalanced panel of 13,815 observatons. The proposed specfcaton tests the demand-pull hypothess through the lnk between current R&D expendtures and our key regressor (sales) lagged one perod. As far as the technologypush hypothess s concerned (see Secton 1), the role of frm s knowledge stock and the persstent nature of technologcal change are taken nto account by the ncluson of the lagged dependent varable. Controls nclude: 1) frm s sze (measured through employment) - snce larger frms are more lkely to have ther own R&D department performng formalzed R&D actvtes and should be less constraned n fnancng costly and uncertan R&D nvestments (see Cohen and Levn, 1989; Cohen, 2010; Cohen and Klepper, 1996; Olvar, 2016); 2) frm s age, snce more experenced ncumbents are more lkely to massvely nvest n R&D (see Huergo and Jaumandreu, 2004; Artés, 2009); 3) company s belongng to a busness group (dummy varable), snce frms takng part to a busness group have more opportuntes to share the uncertanty mpled by nnovaton actvtes (see Flatotchev et al., 2003); 4) year and sectoral dummes, the latter takng nto account sector-specfc technologcal opportuntes (see Secton 1). Therefore, our econometrc test wll be based on the followng specfcaton: y,t = c + β 1 lnr&d,t 1 + β 2 lnsales,t 1 + β 3 lnemp,t + β 4 lnage,t + β 5 Group,t + (δ + ɛ,t ). (6) where δ s the tme-nvarant unobserved ndvdual effect and ɛ,t s the dosyncratc error term. However, as common n nnovaton studes, the explanaton of R&D expendtures has to 7 Many studes have used ESEE as a relable data source and provde evdence of ts representatveness (see, for nstance, González et al., 2005; López, 2008). 11

15 take nto account both the persstent (dynamc) nature of such expendtures (see Secton 1) through the ncluson of the lagged dependent varable and the occurrence of sample selecton n between those frms engagng n R&D and those that are nactve (see Crepon et al., 1998; Mohnen and Hall, 2013). Therefore, eq.1 should be splt nto a bnary selecton equaton - where the choce to engage n R&D s nvestgated - and a man equaton where the ntensty of R&D nvestment s explaned. The resultng smultaneous two-equaton model has been tested through a dynamc type-2 tobt estmator, recently proposed by Raymond et al. (2010); formal detals about ths estmator are dscussed n the Appendx A1. 8 Takng nto account what dscussed n Secton 1 and what put forward through the model llustrated n the prevous secton, we expect: 1) consstently wth the technology-push approach, overall persstence n R&D expendtures (that s a postve and sgnfcant coeffcent n all the estmates); 2) consstently wth the demand-pull approach, an overall support of demand as a drver of nnovaton (that s a postve and sgnfcant coeffcent n all the estmates); 3) however, consstently wth our model, the magntude of ths latter effect s expected to be larger for the product-only nnovators rather than for the process-only nnovators (that s a coeffcent larger and possbly more sgnfcant n the case of companes exclusvely devoted to product nnovaton); 4) consstently wth the extant lterature (see above) a confrmaton of the postve lnks between company s sze, age and group belongng on the one sde and R&D nvestment on the other sde (that s postve and sgnfcant coeffcents: β 3, β 4 and β 5 ). Table 1 reports the econometrc results for the whole sample and separately for those frms only engaged n process nnovaton and those only engaged n product nnovaton. As can be seen, the lagged dependent varable s postve and hghly sgnfcant (99% level of confdence) all over the dfferent estmates and both n the selecton and n the man equaton; ths s a further proof of the path-dependent and auto-regressve nature of the R&D nvestment and t s fully consstent wth the technology-push hypothess. Also consstent wth the extant lterature are the outcomes concernng the role of frm s sze, age and group belongng n spurrng nnovaton, at least as far as the man equaton and the entre sample are concerned. Turnng our attenton to the man focus of ths work and lookng at the entre sample, the demand-pull hypothess appears to be supported at least as the man equaton s concerned: whle past sales postvely (but not sgnfcantly) affect the decson to nvest n R&D, they sgnfcantly (at 99% of statstcal confdence) ncrease the amount spent n R&D expendtures. However, consstently wth the predcton of our model (see prevous secton), ths latter effect s obvously larger n the case of the frms only engaged n product nnovaton (elastcty equal to 0.277), rather than n ther counterparts only engaged n process nnovaton (elastcty equal to 0.129). Moreover, the coeffcent s sgnfcant at the 99% level of confdence n the product-only case and only at the 95% level n the process-only one. Fnally, the statstcal sgnfcance of 8 Descrptve statstcs are also provded n the Appendx A2 (Table A1). 12

16 Selecton Equaton Table 1: Results from the dynamc type 2 tobt estmates Total Only Process Only Product R&D dummy t *** (0.066) 2.038*** (0.082) 2.312*** (0.143) Ln Sales t (0.046) (0.058) (0.093) Ln emp t *** (0.057) 0.285*** (0.071) (0.110) Ln Age (0.044) (0.052) 0.177** (0.082) Group (0.079) (0.095) (0.152) Constant *** (0.324) *** (0.371) *** (0.633) N of Obs 13,875 7,226 2,383 Man Equaton ln(r&d exp.) t *** (0.014) 0.314*** (0.021) 0.320*** (0.024) Ln Sales t *** (0.039) 0.129** (0.053) 0.277*** (0.066) Ln emp t *** (0.047) 0.446*** (0.064) 0.233*** (0.080) Ln Age 0.075** (0.037) (0.048) (0.058) Group 0.197*** (0.055) (0.080) 0.217** (0.089) Constant *** (0.267) *** (0.357) *** (0.407) N of Obs 7,853 3,037 1,508 Extra Parameters In.con. (R&D dummy) 0.567*** (0.090) 0.507*** (0.107) 0.366** (0.160) In.con. (Ln R&D) 0.098*** (0.012) 0.091*** (0.016) 0.053*** (0.019) ρ u1u ** (0.071) 0.149* (0.081) (0.135) ρ ɛ1ɛ *** (0.052) 0.575*** (0.072) 0.676*** (0.118) σ u *** (0.105) *** (0.151) ** (0.347) σ u *** (0.043) *** (0.073) *** (0.084) σ ɛ *** (0.016) (0.025) *** (0.032) Notes: Standard errors n brackets. ***, ** and * ndcate sgnfcance at 1%, 5% and 10% level, respectvely. All regressons nclude tme and ndustres dummes (results avalable upon request). the dfference between the two estmated coeffcents account at 90% (t-statstcs equal to 1.75). Taken together, these outcomes offer a consderable support to the predcton of our model: ndeed, the demand-pull effect s overall mportant, but stronger when product nnovaton s nvolved. 13

17 4 Concluson Consstently wth the most updated vew put forward by nnovaton scholars, ths study provdes further evdence that both the technology-push and the demand-pull hypotheses play an mportant role n explanng nnovaton actvtes, here represented by the R&D expendtures. However, the extant lterature does not provde any clue about the possble dverse mpact of demand evoluton on product vs process nnovaton actvtes. Ths paper flls ths gap and proposes a formal model where past sales foster both product and process nnovaton expendtures, but wth the product elastcty systematcally larger than the process one. Ths theoretcal predcton s tested through a dynamc mcroeconometrc model controllng for R&D persstence, sample selecton, observed and unobservable ndvdual frm effects and tme and sectoral peculartes. Results are consstent wth the model and reveal a larger mpact of past sales over the product nnovatve expendtures rather than the process ones. Ths outcome has an mportant polcy mplcaton. Indeed, polcy makers should be aware that the demand-pull leverage s partcularly crucal for product nnovaton. Therefore, f the dffuson of new products or even the emergence of entre new sectors are assumed as targets, a talored expansonary polcy mght be seen as a proper and effectve strategy. 14

18 Appendx A1) The econometrc methodology The econometrc test put forward n ths study s based on a new estmator proposed by (Raymond et al., 2010). 9 In partcular, we test the role of past sales on both the condtonal frms bnary choce whether to engage n R&D actvty or not, and the subsequent decson concernng how much to nvest n R&D. Usng a notaton smlar to Raymond et al. (2010, p. 499), we thus have: d,t = 1[ρd t 1 + δ Z,t + α 1 + ɛ 1,t > 0], (7) δy,t + β X,t + α 2 + ɛ 2,t f d t = 1 y,t = 0, f d t = 0, (8) Equaton 7 s the selecton equaton and t models the dscrete strategc decson of frm to nvest n R&D actvtes (or not) as a functon of ts past R&D decson (d t 1 ), a battery of exogenous explanatory varables (Z,t ), tme-nvarant unobserved ndvdual effects (α 1 ) and an dosyncratc error term (ɛ 1,t ). The man equaton 7 represents the subsequent decson of the nnovatve frm (condtonal on: d t = 1) on how much to nvest n R&D as a functon of ts past R&D expendtures (y,t ), ts characterstcs (X,t ), tme-nvarant unobserved ndvdual fxed effects (α 2 ) and an dosyncratc error term (ɛ 2,t ) ndependent of X,t. The smultaneous estmaton of the two dynamc eqs. 7 and 8 has to take nto account three key methodologcal problems: frstly, the occurrence of sample selecton; secondly, the presence of unobserved frm s specfc ndvdual effects; thrdly, the possble correlaton between the ntal condtons and the ndvdual effects, snce the frst observaton referrng to a dynamc varable s also determned by the same data generaton process. Indeed, (Raymond et al., 2010) propose an estmator that jontly solves these problems; n partcular, the ndvdual error terms, (α 1 ) and α 2, are assumed to have a jont dstrbuton and a random-effects approach s put forward. Moreover, the problem assocated wth the ntal condtons s taken nto account assumng that the unobserved frm-specfc effects depend on the ntal condtons and on the exogenous varables 10 : a 1, = b b 1 1d 0 + b 2 1 Z + u 1, (9) a 2, = b b 1 2γ 0 + b 2 2 X + u 2, (10) 9 The focus of (Raymond et al., 2010) s the analyss of persstence both n the bnary decson to engage n R&D actvty and n the subsequent decson about how much to spend n R&D. 10 As qualfed by (Raymond et al., 2010, p. 500), ths soluton - dealng wth ths specfc aspect of the adopted model - was orgnally put forward by (Wooldrdge, 2005). 15

19 where b 0 1 and b0 2 are constants, d 0 and γ 0 are the ntal values of the dependent varables and Z and X are (Mundlak, 1978) wthn-means of Z t and X t. Moreover, the vectors (ɛ 1,t, ɛ 2,t ) and (u 1 and u 2 ) are assumed to be ndependently and dentcally (both over tme and across ndvduals) normally dstrbuted wth means 0 and covarance matrces, equal to: ( ) 1 ρ ɛ1,ɛ2 σ ɛ2 Ω ɛ1,ɛ2 = ρ ɛ1,ɛ2 σ ɛ2 σ ɛ2 ( ) 1 ρ u1,u2 σ u2 and Ω u1,u2 = ρ u1,u2 σ u2 σ 2 u2 Therefore, the resultng lkelhood functon of a gven frm, startng from t = 1 and condtonal on the covarates and the ntal condtons, can be wrtten as: L = + + t=1 T L t (d t, γ t d 0, d,t 1, Z, γ 0, γ,t 1, X, u 1, u 2 )g(u 1, u 2 ) du 1 du 2 (11) where T t=1 L t(d t, γ t d 0, d,t 1, Z, γ,t 1, X, u 1, u 2 ) s the lkelhood functon, once the ndvdual frm-specfc effects have been ntegrated out and can be treated as fxed; whle g(u 1, u 2 ) s the bvarate normal densty functon of (u 1, u 2 ). Furthermore, to properly take nto account sample selecton, equatons 7 and 8 are jontly estmated through a condtonal maxmum lkelhood estmator and are correlated through the ndvdual effects (ρ u1,u2 0) and the dosyncratc error terms (ρ ɛ1,ɛ2 0). In partcular, the overall correlaton between the two equatons turns out to be: ρ tot = ρ u1,u2σ u1 σ u2 + ρ ɛ1,ɛ2 σ ɛ2 (σ 2 u1 + 1)(σu2 2 + σ2 ɛ2 ) (12) 16

20 A2) Descrptve statstcs Table A1: Descrptve statstcs: mean and standard devaton (n brackets) Total Sample Only Proc. Only Prod. R&D dummy (0.50) (0.49) (0.48) ln(r&d exp.) (3.01) (2.78) (2.86) R&D dummy t (0.50) (0.49) (0.49) ln(r&d exp.) t (3.02) (2.80) (2.86) Ln Sales t (1.94) (1.89) (1.88) Ln emp t (1.48) (1.42) (1.42) Ln Age (0.83) (0.84) (0.80) Group (0.49) (0.48) (0.48) N 13,815 7,226 2,383 A3) Proof of Proposton 1 Assume frst that a = 0. Under ths assumpton the frm s objectve functon reduces to π (x,1, y,1 ) = π NI,2 ξ(x,1 ) ζ(y,1 ) and therefore obvously y,1 = 0 s optmal. Maxmzng wth respect to x,1 we obtan the frst order condton π x,1 = 2nδ ( q,1 s + n + 1 ) δn β(n + 1) x,1 ηx,1 = 0 Solvng for x,1 yelds the expresson for x,1 n the frst part of the Proposton. Consderng the case where δ = 0, t follows drectly that x,1 = 0. The frst order condton wth respect to y,1 s gven by π y,1 = a π,2 κy,1 = 0, where π,2 = π I,2 πni,2. Usng (2),(3) and (4) t s easy to check that for x,1 = 0 we have q s,ni,2 = q s,1 = q s,i,2 + γ β qn,i,2. (13) 17

21 Consderng the best reply quanttes of the compettors of frm, t s easy to see that the above equalty mples that the compettors output s dentcal n the cases wth and wthout product nnovaton by frm,.e. q s,i 2,j = q s,ni 2,j = (n 1)α + (n 1)cs,1 2Cs,1. β(n + 1) j j Together, these observatons drectly mply that p s 1 = ps,ni 2 = p s,i 2. Therefore, the dfference n proft for frm between the scenaros wthout and wth product nnovaton can be rewrtten as π,2 = (q s,i,2 qs,ni,2 )(p s 1 cs,1 ) + qn,i,2 (pn,i 2 c n,2 ) ( ) = q n,i,2 γ β (ps 1 cs,1 ) + (pn,i 2 ξc s,1 ) = (β2 γ 2 ) β (q n,i,2 )2. The last equalty above follows from the frst order condtons of the frm wth respect to q,1 s and q n,i,2 whch mply (ps 1 cs,1 ) = βqs,1 and pn,i 2 ξc s,1 = γq,2 + βq n,i,2 and (13). 18

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