Innovation Strategies and Firm Growth: New Longitudinal Evidence from Spanish Firms

Transcription

1 Innovation Strategies and Firm Growth: New Longitudinal Evidence from Spanish Firms Stefano Bianchini 1 2, Gabriele Pellegrino 3 4, Federico Tamagni 1 1 Scuola Superiore San'Anna, Pisa, Italy. 2 BETA, University of Strasbourg, France 3 WIPO, Genève, Switzerland 4 EPFL, Lausanne, Switzerland Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 0/ 18

2 Background and motivation Is innovation a crucial key to determine growth? Theoretical models acknowledge the positive effect of innovation on growth (see Aghion and Howitt, 1992; Aghion et al., 2005) Empirical evidence is mixed: both positive (Mansfield, 1962; Storey, 1994; Stam and Wennberg, 2009) and negative (see Bottazzi et al., 2001; Geroski and Mazzucato, 2001)effects Recent emphasis (motivated by fat-tails) on asymmetric effects across the growth distribution quantiles, with innovation more effective for high-growth firms (Coad and Rao, 2007; Hölzl, 2009; Segarra and Teruel, 2014) Far from having a complete understanding... The complexity and the uncertainty of R&D activities, together with the diversity of innovation strategies and the multiplicity of growth modes, requires a multidimensional approach to examine the contribution of innovation on firm growth (Audretsch et al., 2014) Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 1/ 18

3 Contribution: innovation and sales growth Firstly, we pursue a multidimensional account of firms innovative activity (going beyond R&D) Correlate sales growth with a broad set of innovation activities: one-by-one analysis Systematic control for unobserved firm heterogeneity, both in standard regression analysis and quantile regression framework Secondly, we increase the complexity of innovation modes: does it pay off to undertake multiple innovation activities at the same time (complementarity vs. substitutability)? If so, are there specific combinations that fosters growth? Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 2/ 18

4 The data Spanish Technological Innovation Panel (PITEC), CIS-type data: few firm characteristics, but large set of innovation indicators Unlike other CIS-type datasets: longitudinal nature, allowing for panel techniques Focus on sales growth (no M&A): where G it = s it s i,t 1, s it = log(s it ) 1 log(s it ). N Only manufacturing: open panel of 5,064 firms, of which 3,144 observed over the entire sample period i Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 3/ 18

5 Descriptives 1: the sample Time obs. #Firms % %Cum #Obs , , , ,864 Total 5, ,386 Note: Time obs. indicate the minimum number of years over which firms are observed: T=3 refers to firms that are observed for at least three periods: T=4 refers to firms that are observed for at least four periods, and so on. Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 4/ 18

6 Innovation variables Internal R&D (intensity): Intramural R&D expenditures, normalized by total turnover. External R&D (intensity): Extramural R&D expenditures, normalized by total turnover. Product Innov. New-to-MKT: Share in firm s total sales of new or significantly improved products, new to both the firm and the market. Product Innovation New-to-firm: Share of firm s total sales of new or significantly improved products, new only for the firm. Process Innovation: 0-1 dummy for firms that have introduced new or significantly improved processes. Embodied technical change (intensity): Investments in innovative machinery and equipment, normalized by total turnover. Disembodied technical change (intensity): Acquisition of external knowledge (patents, know-how, and other types of knowledge from other enterprises or organizations), normalized by total turnover. Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 5/ 18

7 Main analysis: empirical framework The baseline regression model is where G i,t = α INNOV i,t 1 +β Z i,t 1 +u i +ǫ i,t, (1) INNOV stands alternatively for one of the different innovation variables, Z is a set of firm-level control variables, u i is a firm fixed-effect, and ǫ i,t a standard error term. Controls include: Gt 1, lnempl, lnage, and three dummy variables (Export, PubFund, Group). Two sets of estimates: panel (FE and GMM) and FE-quantile regressions Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 6/ 18

8 Main analysis 1: GMM estimates Internal R&D External R&D New-to-Firm New-to-Mkt Process Embod.TC Disemb.TC INNOV t *** (0.063) (0.511) (0.048) (0.030) (0.162) (0.002) (1.139) G t ** (0.178) (0.155) (0.156) (0.157) (0.199) (0.050) (0.092) lnempl t ** * (0.229) (0.199) (0.211) (0.199) (0.235) (0.204) (0.295) lnage t ** ** *** *** *** *** ** (0.038) (0.097) (0.082) (0.074) (0.085) (0.068) (0.078) Export t ** ** *** ** ** ** ** (0.038) (0.038) (0.039) (0.038) (0.038) (0.038) (0.100) PubFund t (0.021) (0.021) (0.019) (0.019) (0.038) (0.019) (0.059) Group t (0.030) (0.034) (0.029) (0.032) (0.033) (0.033) (0.034) Obs 21,291 21,291 21,291 21,291 21,291 21,291 21,291 AR(1) AR(2) Sargan Hansen Notes: GMM-DIFF estimates. Robust standard errors in parenthesis. Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 7/ 18

9 Main analysis 2: FE-quantile regressions Canay (2011) method: transform the response variable to wash out firm FE Given our panel setting: Y i,t = X i,t β +u i +ǫ i,t with X = {INNOV,Z} 1. Estimate the individual fixed effect as û i = E T[Y i,t X i,tˆβ], where E T(.) = T 1 T t=1 (.) and ˆβ is the standard panel within estimator of β; 2. Build a transformed response variable Ŷ i,t = Y i,t û i and then perform usual quantile estimation as in Koenker (1978) 3. Use bootstrapped standard errors Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 8/ 18

10 FE-qreg 1: R&D variables Internal R&D (t 1) External R&D (t 1) Quantile Quantile Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 9/ 18

11 FE-qreg 2: Product Innovation Prod.New to firm (t 1) Quantile Prod.New to MKT (t 1) Quantile Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 10/ 18

12 FE-qreg 3: Embodied vs. Disembodied TC, and Proc. Innov. Embod.Tech.Change (t 1) Quantile Proc. Innov. (t 1) Quantile Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 11/ 18

13 Main analysis: sum up Heterogeneous ability of different innovation activities to support sales growth, once we look at within-firm over-time variation (FE). Internal R&D confirmed as a key determinant: positive effect on average growth, and particularly strong impact on high-growth (top deciles of the growth rates distribution) All other variables not significant in GMM: difficult to single out effect of innovation on average growth, as previous evidence suggests. We recover a role for some activities when looking at the effect along the growth distribution: External R&D, both Product Innovation indicators (New-to-Mkt in particular) and Embodied Technical Change have positive association with high-growth. No role of Process Innov. and Disembodied Technical Change. Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 12/ 18

14 Increasing complexity of innovation modes Recent innovation studies (Mohnen and Roller, 2005; Catozzella and Vivarelli, 2014) provide evidence that the innovative inputs-output relationships enjoy supermodular properties, implying that some inputs (and or output) are complement, i.e. simultaneous adoption of inputs should be more valuable than adopting each of them separately Growth could enjoy the same properties hence coming from combinations of various innovation strategies, rather than from each single activity. Increasing complexity of innovation modes: does it pay off to undertake multiple innovation activities at the same time? If so, are there specific combinations that fosters growth? A first attempt by Goedhuys and Veugelers (2012) on Brazilian firms: combination of product and process innovations significantly improves firm sales growth Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 13/ 18

15 Supermodularity We study pairwise complementarity between 4 basic (0-1) actions: INT (Internal innovation) = 1 if the firm performs intra-mural R&D, 0 otherwise. EXT (External innovation) = 1 if the firm performs extra-mural R&D or acquires embodied or disembodied knowledge, 0 otherwise. NEWP (Product innovation) = 1 if the firm introduces new products. PROC (Process innovation) = 1 if the firm introduces new or significantly improved processes. Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 14/ 18

16 Supermodularity They give raise to 16 mutually exclusive strategies: Strategy INT EXT NEWP PROC Combination STR No inno STR PROC STR NEWP STR NEWP&PROC STR EXT STR EXT&PROC STR EXT&NEWP STR EXT&NEWP&PROC STR INT STR INT&PROC STR INT&NEWP STR INT&NEWP&PROC STR INT&EXT STR INT&EXT&PROC STR INT&EXT&NEWP STR INT&EXT&NEWP&PROC Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 15/ 18

17 Supermodularity The objective function is the growth function G(S,Z) = b 0 S 0 +b 1 S 1 +b 2 S b 15 S 15 +β Z+u i +ǫ where: Z is the set of (lagged) controls we still control for firm fixed-effects ui the Sj are dummies identifying all possible strategies, i.e. all possible combinations of the 4 basic actions Pairwise complementarity implies joint validity of a set of inequality constraints on the coefficients: INT-EXT: b 8+s +b 4+s b 0+s +b 12+s with s = 0,1,2,3 INT-NEWP: b 8+s +b 2+s b 0+s +b 10+s with s = 0,1,4,5 INT-PROC: b 8+s +b 1+s b 0+s +b 9+s with s = 0,2,4,6 EXT-NEWP: b 4+s +b 2+s b 0+s +b 6+s with s = 0,1,8,9 EXT-PROC: b 4+s +b 1+s b 0+s +b 5+s with s = 0,2,8,10 NEWP-PROC: b 2+s +b 1+s b 0+s +b 3+s with s = 0,4,8,12 Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 16/ 18

18 Results Pair Wald statistic INT-EXT INT-NEWP INT-PRO EXT-NEWP EXT-PRO NEWP-PRO In bold: complementarity accepted at 10% confidence level (lower bound=1.642, upper bound=7.094). Confirm internal R&D is important, but it pays even more off if coupled with Product innovation (new-to-mkt) Indeed, Product innovation (new-to-mkt) crucial if coupled with R&D or Process innovation Vindicate the marginal role of Process innovation from panel and qreg analysis Confirm the lacking contribution of external sources of new knowledge Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 17/ 18

19 THANK YOU! Bianchini S., Pellegrino G., Tamagni F. CONCORDi (Seville) 18/ 18