• No se han encontrado resultados

The influence of industry knowledge spillovers on the innovation performance of firms in a developing country - a multilevel analysis

N/A
N/A
Protected

Academic year: 2020

Share "The influence of industry knowledge spillovers on the innovation performance of firms in a developing country - a multilevel analysis"

Copied!
30
0
0

Texto completo

(1)

1

The influence of industry knowledge spillovers on the innovation

performance of firms in a developing country. A multilevel analysis

Julio Cesar Zuluaga

Tesis de grado para optar al grado de Magister en Estudios Organizacionales, Facultad de Administración, Universidad de los Andes, 2015.

Director:

Profesor Eduardo Wills

ABSTRACT

The purpose of this research is to analyze the relationship between industry knowledge spill-overs and the innovation performance of Colombian manufacturing firms. The author adopts a multilevel Poisson regression to analyze these effects, taking into account the hierarchical structure of the data. A database of 6,670 Colombian firms operating in 25 sectors is used in the analysis. By the mean of the simultaneous analysis of both firm-level and sector-level variables, the research concludes that the innovation performance of firms in this emerging country is a related with both its own R&D activities as well as those carried out by competitors, suppliers and buyers via intra and inter industry knowledge spill-overs. As a result of this research, it becomes clear that in order to improve the innovation capacity of firms in developing countries the creation and support of knowledge appropriation mechanisms is a possible target for public policies.

Key words: Industry knowledge spill-overs, innovation, multi-level analysis, developing country

(2)

2

1. INTRODUCTION

The question of what sparks innovation has moved research scholars, practitioners and policy makers for decades; Schumpeter´s (1934, 1942) groundbreaking thesis that innovation is the driver of economic growth remains valid as of today. Innovations are the essential means by which firms develop and sustain a competitive advantage (Brown and Eisenhard, 1995); it has become a sine qua non for firms. Additionally, the innovative activity of all firms is one of the important underlying factors for countries´ competitiveness (Michie, 1998). Thus, the factors which drive innovations are of increasing interest for scholars (Becheik, Landry & Amara, 2006).

The determinants of a firm´s innovation performance can be found within as well as outside the firm (Drazin and Schoonoven, 1996). The innovation success of a firm is associated with different organizational aspects in the academic literature; examples are the organizational structure (Mintzberg, 1979), the organizational culture (Kanter, 1983), the firm´s strategy (Day, 1990) as well as the organizational network (Tsai, 2009). Besides these internal factors, the innovation performance of a firm also depends upon determinants external to the firm (van Beers, Kleinknecht, Ortt & Verburg, 2008). Among these contextual factors, a significant effect of the industry on the firm´s innovation capacity and performance has widely been confirmed in empirical research (Becheik et al., 2006).

Besides factors such as the technological dynamism (e.g. Kam et al., 2003; Uzun, 2001), the demand growth of the industry (e.g. Crépon, Duguet and Mairesse, 1998) and its concentration (e.g. Smolny, 2003), Marshall (1920) already pointed to the possibility that firms may benefit from knowledge and ideas that are ‘in the air’; that is knowledge and ideas that are created outside the firm by other firms either within the same or in another industry. The approach that has explored the relationship of industry knowledge and firm’s innovation performance is the concept of industry knowledge spill-overs. Industry knowledge spill-overs refer to the quantity of knowledge generated and incorporated in the innovation of a sector j which cannot be appropriated by the firm i in sector j and that

(3)

3 affects the innovation performance of firms n≠ i in sector j (intra industry or horizontal spill-overs) or the innovation performance of firms m in sectors k ≠ j (inter industry or vertical spill-overs).

The industry knowledge spill-overs may generate competitive and diffusive externalities (Yao, 2006). The competitive externalities of spill-overs suggest that the innovation success of one firm reduces the possibilities of successful innovation for the firm´s rivals in the market due to the patent protection mechanism; that is an increase in the innovation effort of one firm increases its probability of success while at the same time decreases that of others. Crepón and Duget (1997), for example, provided empirical evidence of such negative externalities. On the other hand, the diffusive externalities of spill-overs suggest that a firm is benefited by the innovations of other firms that innovate within or outside the sector at which it competes (Yao, 2006). These externalities are positive as a firm can build upon the knowledge generated by others; own R&D investments to generate this external knowledge are not necessary (Almeida and Kogut, 1999).

While competitive spill-overs have been extensively analyzed both in the theoretical and empirical literature (e.g., Crepón and Duget, 1997), diffusion spill-overs have been much less in the focus of attention (Yao, 2006; Hongxia and Xiuli, 2007, Kugler, 2005). In order to extend the knowledge regarding diffusive knowledge spill-overs, we investigate whether the intra- and inter-industry knowledge base relates to the innovative performance of a firm by the means of these diffusive spill-overs. We hypothesize that the product innovation produced by a firm´s competitors, suppliers, and buyers creates a stock of technological knowledge which can exploited in its innovation process.

Our approach differs from previous research in two distinct ways. First, we are interested in the effect intra- and inter-industry spill-overs have on the innovation performance of a focal firm. While most previous studies analyze the effect of knowledge spill-overs on firms´, regions´ or nations´ productivity (Kugler, 2005), we investigate the effect on firms´ innovation performance; studies focusing on this relationship remain scarce (Gorg and Greenaway, 2004). As a result, the current research may provide first indications that knowledge spill-overs influence the productivity of a firm by the means of its innovation performance. Provided that a decreasing flow of innovations may lead a firm to

(4)

4 settle in an inactive state with little growth (e.g. Metcalfe, 1998), a confirmed relationship between knowledge spill-overs and innovative performance may extend the current understanding. Realizing this research in the context of a developing country only enhances its value; as innovation surveys, a commonly used database for large-scale empirical innovation research, are only available very recent, empirical research in these contexts remains scarce (Cimoli, Primi & Rovira, 2011).

Second, this research breaks with the traditional proxies for innovation based on Research and Development (R&D) which are often measured in terms of patents granted. The capacity of these measures to capture innovation results in developing countries has been questioned (Gerosky, van Reenen & Walter, 1997). In this context, innovation has been described as a process by which firms master and implement the design and production of goods and services which are new to them, irrespective of whether they are new to their competitors, clients, their countries or their world (Mytelka, 2000); putting at the heart of the innovation process minor and incremental changes (Goedhuys, 2007). Relying on the number of product imitations and innovations overcomes the limitation of patent and R&D data as a proxy for measuring innovation performance in developing countries.

In addition to the theoretical advances, our research uses a new methodological approach to analyze the innovation phenomena. Departing from the recognition that innovation is a social process and as such does not take place in isolation (Blomqvist and Levy, 2006) Gupta, Tesluk, and Taylor (2007) stated that “it should be obvious that all innovation is, at the very least, a two-level phenomenon that involves first an actor (e.g., an individual, a team, an organization) and second the broader environment within which the actor is embedded” (p. 885). However, so far, very few innovation research have included more than one level (Gupta et al., 2007). The multi-level Poisson regression model we chose in this research is a relatively new approximation, taking into account that the innovation process involves actors at different levels of analysis, such as the firm and the industry sectors (Gupta et al., 2007). As a result, at this moment little is known about how variables at one level of analysis (e.g. sector) influence the innovation performance at a different level (e.g. individual firms) (Gupta et al., 2007). With the application of this technique we overcome limitations and biases of traditional regression analysis that mix

(5)

5 variables of several levels, and our conclusions are specific on factors at different levels of analysis involved in the innovation performance of the firms.

In light of the ever-increasing interest in determinants of the innovation performance of firms, this paper helps to discover whether and to which extent intra- and inter-industry diffusive knowledge spill-over relates to a firm´s innovation performance in a developing country. As such, it has major implications for policy makers regarding the decision as to whether the industry or firm level should be their target.

The remainder of this article is structured as follows: the second part we review the literature about the relationship between the industry features and firms’ innovation performance. In the third part we discuss our focus on multi-level analysis, describe the data, the variables and the model specifications. We present the results of our analysis during the fourth section. In the fifth part we present our discussion of the results obtained and the final part contains limitations, recommendations for public policies and potential lines for future research.

2. INDUSTRY KNOWLEDGE SPILL-OVERS AND THE INNOVATION PERFORMANCE OF FIRMS

Since the seminal work of Jaffe (1989), who used a knowledge production function to find an effect of local pooling of R&D on the patent productivity of a firm, knowledge spill-overs have been recognized as one of the central constructs in the economics of innovation (Knott, Posen and Wu, 2009). Economic growth theories as well as economic geography and management studies have revealed that public, locally concentrated knowledge diffusion explains the high concentration of innovation activities and its effect on economic growth (Baldwin and Martin, 2005; Acs, 1992; Acs, Audretsch and Feldman, 1994).

In the innovation literature different definitions of knowledge spill-overs can be found. De Bont (1996), for example, defines them as the involuntary outflow or exchange of useful technological information. For Monjon and Waelbroeck (2003) knowledge spill-overs are the not appropriable quantity of knowledge which is created by innovative firms of the sector j but can be used by other firms of the sector j or firms of the sectors k ≠ j in their innovation processes without having to pay for them. One widely used definition is that of Grossman and Helpman (1992) who characterize technological spill-overs as a

(6)

6 process where “(1) firms can acquire information created by others without paying for that information in a market transaction, and (2) the creators (or current owners) of the information have no effective recourse, under prevailing laws, if other firms utilize information so acquired.” (p.16).

Beyond previous theoretical and empirical research, the cornerstone of our argument is that knowledge which firm m of sector j and firm n of sectors k create and incorporate in their product innovations potentially affects the innovative performance of firm i –which pertains to sector j –via knowledge spill-overs. Focusing on the diffusive externalities, the main reason for the existence of these spill-overs is the fact that the knowledge incorporated in product innovations diffuses via public sources, such as internet usage, information sharing or knowledge learning (Yao, 2006). Empirical studies in advanced economies have shown that knowledge may spread through different channels: labor mobility (e.g. Agrawal, Cockburn, & McHale, 2007; Foster & Pöschl, 2009), firm spin-offs (e.g. Moon, 2002), research collaboration (e.g. Hoekmann, Frenken, & van Ort, 2008) as well as informal interactions between people employed in the affected firms (e.g. Fershtman & Gandal, 2011).

The knowledge exchange can occur both involuntarily through labor mobility, reverse engineering or informal networking (De Bont, 1996) and as a result of voluntary agreements of collaboration or the acquisition of intermediated goods (Coe and Helpman, 1995). It can either occur through the direct interaction between individuals of different firms or rely upon the learning capacities of individuals who are exposed to or study innovations or new technologies (Fershtman & Gandal, 2011). Additionally, knowledge diffuses among firms of the same sector as well as of different sectors. While we focus on indirect intra- and inter-industry knowledge spill-overs – that is the knowledge which spills over through learning - we include in our analysis the direct ties with competitors, suppliers and buyers in order evaluate the differentiated impact of both.

Intra-industry spill-overs represent the benefit that a firm i of sector j obtains from the knowledge produced by other firms of its own sector j (Yao, 2006). In this case, the knowledge created and incorporated in the innovation processes by the firms of sector j can be used as an innovation source by other firms of the same sector; i.e. their competitors. Due to the public character of knowledge, the existence of technologically related research

(7)

7 efforts of competitors allow a firm to build upon the innovation results of others; as a result less R&D effort than otherwise might be employed (Jaffe, 1988).

Besides intra-industry spill-overs, we also analyze the role of inter-industry knowledge spill-overs on firms’ innovation outcomes. Inter-industry spill-overs refer to the benefit that a firm i of sector j obtains from the technological knowledge created and incorporated in the product innovations by firms n of sectors k ≠ j. The analysis of inter-industry diffusion spill-overs has largely been focused on the effect it has on firm´s productivity (e.g. Hongxia and Xiuli, 2007) or spill-overs generated through Foreign Direct Investment (FDI) (Kugler, 2005). The research of Hongxia and Xiuli (2007) used a flow matrix of investments in R&D between 40 industry sectors in China and was able to trace positive inter-industry spill-overs. The results reveal that the firms in one industry sector combine the knowledge produced internally with the knowledge produced in other sectors in order to generate products. Even though this investigation confirms a positive effect of inter-industry spill-overs, it did not conclude anything with regard to the relationship between these inter-industry spill-overs and the innovation performance of firms.

Based on data derived from the Annual Colombian Manufacturing Survey (Encuesta Annual Manufacturera de Colombia), Kugler (2005) studied the existence and the effect of inter-industry knowledge spill-overs as a result of the entrance of multinational enterprises (MNE) in Colombia. The results of this study show that the knowledge incorporated in one sector generated an increase in the production of other sectors upon the entrance of an MNE, especially for those firms which were integrated in the production chain of the firm that entered the Colombian market. Despite analyzing the positive effect of the inter-industry spill-overs this author did not take into consideration the effect of the spill-overs on the innovation performance of firms pertaining to other sectors.

We therefore posit that the intensity at which competitors of a firm i create and incorporate new knowledge in their products is positively related to the possibility of i to take advantage of this technological knowledge; more knowledge available for firm i

increase its likelihood of creating new products the knowledge generated by its competitors as innovation input. Because the level of new knowledge available for a firm is reflected by the number of innovations of firms pertaining to its own sector (Kaiser, 2002) we hypothesize that:

(8)

8

H1: The innovation performance of a firm is positively related to the innovation performance of all other firms in the same sector.

Wieser (2005) claims that intra-industry spill-overs seem to be more significant than inter-industry; however, his conclusions are drawn relying on a summary of heterogeneous contributions where comparability is doubtful. The general argument, though, that firms which produce similar products using comparable technologies of production may appropriate rapidly part of the knowledge created by its competitors (De Bont, 1996; p.4) has been confirmed by Malerba, Mancusi, and Montobbio (2007) who use patent applications for six industrialized countries for their analysis. Based on a similar knowledge base, the transfer of knowledge, according to these authors, is less costly and difficult. On the other hand, Choo and Lee (2012) concluded that no clear picture as to the dominance of intra- versus inter-industry knowledge spill-overs can be drawn.

Provided that, as shown above, the discussion regarding the dominance of either type knowledge spill-overs remains open, we posit that when firms n of sectors k ≠ j

generate and incorporate high levels of technological knowledge in their product innovations, the firm i has high levels of knowledge available for its own innovation activities, increasing its likelihood to innovation. Following the argumentation of Kaiser (2002), we hypothesize that:

H2a: The innovation performance of a firm is positively related to the innovation performance of all firms in the sector to which its main suppliers pertain.

H2b: The innovation performance of a firm is positively related to the innovation performance of all firms in the sector to which its main clients pertain.

3. METHODOLOGY 3.1. DATA

In order to test the proposed hypothesis, we relied on the data from the Second Development and Innovation Survey (Segunda Encuesta de Desarrollo e Innovación Tecnológica - EDIT-II), carried out in 20051

1

In Colombia, as in many Latin-Americans countries, there is a delay (around 4-5 years) in the access of the information provided by the innovation surveys carried out by national governments. For this reason, the data is restricted to these years.

by the National Statistical Office (Departamento Administrativo Nacional de Estadistica, DANE) in Colombia. The survey, following the indications of the Bogotá Manual (Manual Bogotá) (RICYT, OEA, CYTED,

(9)

9 COLCIENCIAS, 2001), and inspired by Oslo Manual (OECD, 1997), is a self-reported which seeks to characterize the dynamics of the technological development in Colombia in 2003-2004. The focus lies on the intensity of innovation activities and the innovation trajectory of manufacturing firms (Dane, 2005). Data was generated for a census of 6,670 small, medium and large firms.

For the analysis, we constructed two independent variable vectors: one vector contains the data at the firm level; whereas the second vector represents the variables at the industry level. A detailed description of the dependent variable is followed by a description of each variable of these two vectors.

3.1.1 Dependent variable.

The number of product innovations generated by each firm between 2003 and 2004 (measured in the survey as “weighted innovation objectives”) was used as the dependent variable. In the context of developing countries as it is the case of this research, innovation has been described as a process by which firms master and implements the design and production of goods and services which are new to them, irrespective of whether they are new to their market, their countries or their world (Mytelka, 2000). In this context, minor and incremental changes are put at the heart of the innovation process (Goedhuys, 2007). Thus, we included in the number of product innovations both invention and imitation based innovations.

We used this count variable as an independent variable in the current research, following the argumentation of Geroski (1994). This author states that the number of innovation allows the direct analysis of antecedents and consequences of the innovation activity of firms in developing countries. This form of measuring the innovation performance overcomes the potential problem of over-estimating the innovation capacity inherent in the use of R&D investments for measuring innovation performance Geroski (1994). The relationship between patents and innovation is relatively weak especially in developing countries (Roper & Hewitt-Dundas, 2008). We thus did not rely on patents or R&D investments as a measure of innovation performance and instead opted for the number of product innovations.

(10)

10 As we have mentioned above, two vectors at different levels of analysis represent the independent variables. The vector at the firm level includes traditional control variables that have been ascribed to the innovation performance (Laursen & Masciarelli, 2007); the selection of these variables has been based on the previous research focusing on Colombia by Forero-Pineda, Corredor y Forero (2009). We included as control variables of this study the number of employees with a technological degree (Personel_Tech) and employees working in Research & Development (Personel_R+D), the investment in R&D (Invest_R+D) and the firm size (Size).

As an additional variable we included in this analysis the number of relationships a firm reported to have established with competitors, suppliers and clients with the specific objective to drive innovation performance for the variable (Relation_CSC). As we mentioned above, such linkages can be interpreted as direct knowledge spill-overs or network spill-overs (Choo and Lee, 2012) which differ substantially from the spill-overs we concentrate upon in this research. However, the inclusion of the network spill-overs as a control variable allows us to make a comparison as to the extent the two different types of knowledge spill-overs have on the innovation performance of a firm.

The second vector includes variables at the industry level. For this objective, we grouped the data of the 6,607 manufacturing enterprises into 25 industry sectors (measured at the CIUU level with three digits). Two groups of variables at the firm level were included: the first group consists of control variables generally taking into consideration when the relationship between the industry context and the innovation performance of firms is analyzed (Srholec, 2010; 2011). The second group contains the variables related to the knowledge spill-overs (Jaffe, 1989; Jaffe, Trajtenberg, and Henderson, 1993; Acs, 1992; Acs, Audretsch and Feldman, 1994; Feldman, 1994). The industry concentration (Industry _Concentration) was calculated based on the Herfindahl Index. We used the data base of the Regulatory Authority for Societies (Superintendencia de Sociedades) of 2004 for this calculation, which includes the reported financial statements of the firms of each sector. The R&D investment (Invest_R+D Sector) of each sector was calculated by adding the individual firm-level data.

We based the calculation of inter-industry knowledge spill-overs on the supply matrix elaborated by the DANE, thus allowing us to the identify the sectors with which the

(11)

11 firms of one sector had more intense exchange relationships; that is the highest amount of intermediate goods transfer. Adopting this form of measuring inter-industry knowledge spill-overs, we assumed based on Coe and Helpman (1995) that the flow of products and services between two sectors are a proxy for the volume of knowledge bought and sold by the firms of sector j to the firms of other sectors k ≠ j. Additionally, we assumed that the aggregated transaction performance reflects the sum of the individual firm´s performance pertaining to this sector.

The inter-industry knowledge spill-overs were then characterized through two variables: (a) the number of product innovations in sector kS which supplied the highest

amount of knowledge to sector j between 2003 and 2004 (Innov_Prov) and (b) the number of product innovations of sector kB which bought the highest amount of knowledge from

sector j in the same time period (Innov_Buy).We obtained these measures by summing up the total number of product innovations of each sector. Intra-industry knowledge spill-overs (Innov_Comp) were constructed in a similar form; we measured the number of product innovations of all firms n ≠i of sector j to which firm i belongs. We standardized all variables used in the analysis.

3.2. MULTI-LEVEL POISSON REGRESSION

The process to innovate does not occur in a vacuum; it is rather a multilevel phenomenon that involves actors (individuals, teams, units, organizations) and contexts within which actors are involved (Gupta et al., 2007). Empirical research that deals with and explicitly combines different levels of analysis are scarce, and as a result, studies of innovation have provided little information about how the variables of one level affect innovation at other levels (Gupta et al., 2007, 885).

The structure of the variables in our model is hierarchical: the individual level data is embedded in the regional variables. Thus, applying a traditional regression model based on OLS the assumption of independent observations would be violated (Snijders & Bosker, 1999). In the present case, the firms are embedded in the industry variables; thus firms belonging to the same sector are not independent. OLS regression models would treat industry variables as if they were firm-level variables in a single line of regression generating a correlated error term for the firms that operate in the same sector (Austin et al.,

(12)

12 2001). Consequently, inefficient regression coefficients and biased standard errors are produced (Bryk and Raudenbush, 1992).

Applying a multi-level method overcomes this problem by explicitly taking into account the social context, thus differentiating between the two levels proposed for this research (Snijders & Bosker, 1999). This introduces a degree of realism frequently absent from single-level models (Austin et al., 2001, 151), such as OLS regression. Multilevel regression offers unbiased standard errors (Austin et al., 2001, 151), minimizing the probability of committing the error of rejecting the null hypothesis when it is correct (Type I Error), at the same time as estimating the contextual variability (between sectors) of the regression coefficients (Austin et al., 2001, 151-152).

Multilevel models offer advantages compared to traditional regression models when it comes to proving hypotheses concerning relationships between variables of different levels (Snijders & Bosker, 1999; Austin et al., 2001; Rabe-Hesketh & Skrondal, 2008). One advantage is that it reduces conceptual problems related to the level of analysis at which the results and conclusions are deduced. Among the best known of these (Snijders & Bosker, 1999, 13) is the problem of the ecological fallacy (Robinson, 1950; Alker, 1969), which consists of concluding something at the individual level while using aggregate data derived from a general level. Research into the relation between contexts and firm innovation outputs suggests that this is one of the most common errors (Gordon & McCann, 2005; Beugelsdijk, 2007).

While the multilevel methodology provides the possibility to understand how phenomena and processes at one level of analysis are related to or nested with those of other levels (Klein, Dansereau and Hall, 1994; Rousseau 1985), empirical testing of hypotheses that seek to approach these problems are only just beginning (Buesa, Heijs and Baumert, 2010; Srholec, 2010; 2011). Assuming this challenge, this research adopts a multilevel approach taking into account the hierarchical structure of the data. We estimate a multilevel model of innovation at firm level, where variables of both the firm and industry levels intervene.

A part from the decision to choose a multi-level approach, we also need to take into account the characteristics of the dependent variable in order to choose an adequate statistical method. The dependent variable used in this study is a count variable with

(13)

non-13 negative integers. Often, count variables are treated as if they were continuous, thus applying a linear regression. This, however, can generate problems in the estimation of parameters and give unfortunate results regarding the efficiency, consistency and bias of the estimators (Scott, 1997, 217). Poisson models were designed to be applied to count variables and prove a useful way to respond to the structure of the dependent variable. The number of product innovations weighted by the objectives in EDIT II is a count variable; it is discrete and possesses non-negative values with its distribution adjusting to the characteristics of a Poisson distribution.

4. RESULTS.

In a first step, we carried out the variance inflation factor (VIF) test in order to discard the possible presence of multicollinearity between two or more of the independent variables. The results, as reported in Table 1, clearly show that for none of the variables the critical value of 10 was reached (Kennedy, 2003); we thus included all the variables mentioned above in the data analysis.

Table 1.

Variable VIF 1/VIF

Size 1.28 0.783983

Invest_R+D 1.28 0.778963

Personel_Tech 1.02 0.982042

Personel_R+D 1.02 0.983002

Relation_CSC 1.01 0.987762

Industry _Concentration 1.38 0.724375

Invest_R+D Sector 1.42 0.704614

Innov_Comp 3.80 0.263196

Innov_Prov 6.88 0.145446

Innov_Buy 3.66 0.273126

Mean VIF 2.27

In Table 2 the results of the multi-level Poisson regression analysis are presented. The value of the adjusted McFadden´s R square, calculated based on the likelihood ratio, is 0,014. While we cannot interpret this as the amount of variance explained as in the R square of OLS regressions, the value clearly indicates that the full model is a clear improvement over the intercept model only.

(14)

14 The five control variables at the firm level all show a positive and significant relationship at the 0.01 level with the number of product innovations of a firm. While size and the firm´s R&D investments do not exercise a strong effect on the number of product innovations, human capital and the control variable for diffusive knowledge spill-overs via direct interaction among members of different companies matter strongly.

The sector’s concentration, one of the control variables at the sector level, has a negative and significant (at the 0.05 level) relationship with the number of product innovations of a firm while the amount invested in R&D by the industry sector to which a firm belongs does not significantly influence a firm’s innovation performance.

Table 2.

Dependent variable: Number of product innovations

Variables eq1 lns1_1_1

Individual

Level

Size 0.000284***

(7.94e-06)

Invest_R+D 4.07e-09***

(2.78e-10)

Personel_Tech 0.349***

(0.0205)

Personel_R+D 0.204***

(0.0540)

Relation_CSC 0.265***

(0.00318)

Industry Level Industry _Concentration -3.69e-05**

(1.46e-05)

Invest_R+D Sector 4.96e-10

(6.36e-11)

Innov_Comp 0.0359***

(0.00734)

Innov_Prov 0.0179**

(0.00823)

Innov_Buy 0.0177***

(0.00648)

Constant 1.353*** -2.984***

(15)

15

Observations 4,823 4,823

Number of groups 25 25

Adj R-squared/McFadden's Adj R2 0,14121435

Log likelihood Null mode -76.019

Log Likelihodd Full mode -65.284

Robust standard errors in brackets

*** p<0.01, ** p<0.05, * p<0.1

The results confirm our first hypothesis, suggesting that the firm’s product innovation performance is positively related to innovation performance of the firm’s sector measured in numbers of total product innovations; that is the innovation performance of the firm’s competitors (exp(Innov_Comp)=1.0366) This result provides evidence for the existence of diffusive intra-industry knowledge spill-overs and its positive effect on the product innovation performance of a firm.

A positive and significant (at the 0.05 and the 0.01 level respectively) relationship of the innovation performance of the sector a main firm´s buyers and suppliers belong to (exp(Innov_Prov)=1.0181 and exp(Innov_Buy)=1.0179) confirms our second hypothesis; the product innovation performance of firm’s principal buyers and suppliers positively influence its innovation performance. We therefore provide empirical evidence for the existence of inter-industry knowledge spill-overs. However, when we compare the results presented, it becomes clear that the intra-industry knowledge spill-overs have a stronger positive influence on a firm´s innovation performance than the inter-industry knowledge spill-overs. It is important to note as well that the direct knowledge spill-overs through direct interactions with competitors, buyers and suppliers exercise a greater positive influence on the product innovation performance of a firm than the knowledge spill-overs which formed the focus of our analysis (exp(Relation_CSC)=1.3034).

5. DISCUSSION OF RESULTS

This research empirically analyzes intra- and inter-industry knowledge spill-overs concentrating on the diffusive externalities generated. Results show that the innovation performance of the sector a firm competes in as well as those sectors with main buyers and suppliers positively influence the product innovation performance of the firm. At the same

(16)

16 time, the analysis also revealed that the direct interaction with competitors, buyers and suppliers exercises an even stronger effect. Human capital in form of technical personnel exercises the strongest positive influence on the product innovation performance of Colombian manufacturing firms while employees working in the R&D area additionally positively influence their innovation output.

These findings reinforce and extend previous empirical studies; Kugler (2001) found widespread positive inter-industry spill-overs with horizontal spill-overs being important only for the machinery industry in Colombia. Harris and Robinson (2002) provided a similar result for the UK. However, both authors´ analyses did not evaluate the impact of spill-overs on the innovation production of a firm. The present research evidenced stronger positive effects of intra- than inter-industry knowledge spill-overs on a firm´s product innovation performance. This might imply that the vertical knowledge spill-overs, though less present as evidenced by Kruger (2005), are more effective in improving the product innovation performance of manufacturing firms. Provided that competitors likely dispose of a similar knowledge base (Malerba et al., 2007), it seems intuitive that knowledge diffused from competitors can be appropriated and absorbed easier than that coming from buyer- or supplier-industries. Quella (2007) hints at such a possible relationship: the rise of inter-industry at the expense of intra-industry knowledge spill-overs is associated with a productivity slowdown in the US economy of the early seventies. However, this proposition needs to be further evaluated in future research as the data used for both analyses is not easily comparable.

As we have mentioned above, previous research on intra- and inter-industry knowledge spill-overs concentrated on their productivity impact. The present study focuses on the relationship between knowledge spill-overs and innovation performance, an under-researched link. Hall (2011) concluded based on a survey of empirical evidence that innovations exert a substantial positive impact on firm´s productivity. This research shows that both inter- as well as intra-industry knowledge spill-overs positively influence the innovation performance of Colombian manufacturing firms provide; it thus suggests a mediation of the relationship between knowledge spill-overs and productivity through the innovation performance. Due to data restrictions, we were not able to carry out such an analysis; a gap that should certainly be filled in future research.

(17)

17 Following the argumentation of Fershtman and Gandal (2011), direct knowledge spill-overs may be defined as those taking place between two firms that are directly connected. Indirect knowledge spill-overs are then those that take place between two firms that are not directly connected. The first type of spill-overs, often considered in network and collaboration studies, constantly influences positively the innovation performance of firms in empirical analysis (Becheik et al., 2006). Our analysis for Colombian manufacturing firms confirms these findings – direct interactions with competitors, suppliers and buyers exerts the second strongest positive impact on the product innovation performance. Knowledge spill-overs are impossible to measure, as Krugman (1999) already warned, because “knowledge flows are invisible, they leave no paper trail by which they may be measured and tracked” (p. 53). Indirect knowledge spill-overs are even harder to measure. Patent citations, the direct measure mostly employed (Scherer, 1982), have strong limitations in the context of a developing country (Forero-Pineda et al., 2009). We used the sum of product innovations in the different sectors as a proxy with obvious limitations. Even though we cannot assert whether all of the effects measured really represent knowledge spill-overs nor can we differentiate between the different mechanisms through which knowledge spill-overs take place, it allowed us to evaluate the influence of indirect knowledge spill-overs. The results clearly show that even though the direct knowledge spill-overs are much stronger in their effect on the innovation performance of a firm, indirect knowledge spill-overs positively influence the innovation output.

Placing this research in the context of a developing country where studies on knowledge spill-overs are scarce (Kesidou and Romijn, 2008), this research deepens current knowledge of how innovation success can be achieved in the rather hostile environment of a developing country. The mentioned knowledge spill-overs may present a possible way to overcome to some extent the innovation barriers firms encounter in developing countries; profound institutional, financial and knowledge restrictions (Schmitz, 1982) and limitations regarding their infrastructure (McCormick and Atieno (2000). Despite the fact that the current research did not further analyze the reasons why or mechanisms of how intra- and inter-industry spill-overs emerge, the mentioned possibility adjusts to externalities based on the competitive intensity as suggested by Porter (1990).

(18)

18 According to this author´s argumentation, in sectors with a high degree of rivalry and a strong, demanding supplier and buyer base innovation is stimulated (Geroski, 1990).

Cohen and Levinthal (1990) introduced the now widely used concept of absorptive capacity: the ability of firms to identify value, assimilate and exploit external information. The debate of how to measure absorptive capacity is ongoing (Upadhyayula & Kumar (2004); the number of doctorates in a R&D department or a fully staffed R&D department are just two of the suggest proxies (Veugelers, 1997). We included the number of technical personnel as well as the number of personnel in the R&D department. Interestingly, the number of technical personnel exercises the strongest effect of all variables included with R&D personnel also influencing positively. This result may hint at the importance of qualified technical personnel for reverse engineering and learning from copying, a common practice in developing countries (e.g. Unesco, 2010) over a formalized R&D department. Future investigations should take into consideration how the absorptive capacity of which kind of personnel of firms in developing countries can enhance the capacity to use existent knowledge spill-overs.

6. CONCLUSIONS AND LIMITATIONS

The presented research extents the current knowledge in several aspects by overcoming limitations of earlier studies. First, it provides empirical evidence of knowledge spill-overs in the context of a developing country that goes beyond the consideration of international spill-overs which are usually at the heart of such research (Kesidou & Romijn, 2008). The current study relies on a measure for innovation which is appropriate for the context – the number of product innovations – instead of the innovation input R&D investment or patents. The use of a multi-level regression analysis allows overcoming limitations of previous studies which included variables at two distinct levels but did not take into account the hierarchical structure of data when choosing the statistical method. Again, we overcome problems of previous research as mentioned above.

Different than in most previous studies, the present research focuses on the relationship between knowledge spill-overs and product innovation output of a firm. This focus is important as it may, like we mention above, provide first empirical evidence for a possible mediation between knowledge spill-overs and productivity. Our analysis provides empirical evidence for the existence of both direct – through interaction among different

(19)

19 firms – and indirect knowledge spill-overs, where no interaction takes place. We also show that both intra- and inter-industry knowledge spill-overs matter with regard to the product innovation output of a firm.

7. Further research and limitations

The present research has some limitations. The knowledge spill-overs were analyzed statically (cross-sectional). Future research should include a longitudinal approach, as the effect of knowledge spill-overs on the innovation performance of firms is likely to take time. This an important step in order to obtain more robust analysis and inferences about causal relationships and in order to address reverse causality and endogeneity problems. Additionally, a comparative research including different countries with different institutional settings should be considered in the future in order to verify whether industry knowledge spill-overs are contingent upon the context of a country and whether differences exist between developing and developed countries. While analyzing the impact of knowledge spill-overs on the innovation performance is important, future research should extent this research analyzing the possible mediation at which we hinted.

Another important limitations are related with some control variables that are missing in our study. Specially, the age of the firms and the internalization process that could affect the ability of the firms to absorb and benefit form knowledge spillovers.

As we mentioned above, we consider it important include a broad definition of product innovation in the presented context, including imitations. However, to widening the understanding of knowledge flows a differentiation between imitations and innovations might shed further light on analyzed relationship. Also, including different kinds of innovations as organizational, process or marketing could bring different and interesting results. Although investigating exactly how the different types of knowledge spill-overs take place and through which mechanisms they influence the product innovation performance of manufacturing firms is important, this topic is left for future research. Our data is limited in that we not analyze different mechanisms of knowledge diffusion. But at the same time we are able to evaluate the geographical dimension of the analyzed knowledge spill-overs. Provided that knowledge spill-overs generally are thought to be

(20)

20 local (Kesidou & Romijn, 2011), such a spatial component needs to be included in future research.

One interesting line of research also could be taking into account the problem related with the level of integration of the firms. It is possible studying how the degree of vertical and horizontal integration can affect the ability of firms to absorb and benefit from knowledge spillovers.

The results of the current research have some implications for policy makers. The innovation performance of firms in developing countries can be incentivized through public policies by the means of strengthening the collective learning for innovation (Doner, Hicken & Ritchie, 2008) between all the actors of a value chain. Provided the positive influence industry knowledge spill-overs have on the innovation performance of a firm, it is necessary not only to strengthen the innovation capabilities of singular firms in emerging countries but rather take into account the entire industry and even further the sectors of entire value chains. Porter´s cluster (1998) seems to be a relevant level for public policies. Another area where public policy can intervene is the strengthening of mechanisms which allow the appropriation of knowledge generated by others. The absorptive capacity of manufacturing firms clearly stand out in that it is this capability that allow firms to exploit the existence of knowledge spill-overs. Additionally, creating an institutional setting which strengthens the interaction among the different firms of a value chain is another task that emerges from the present analysis.

7. REFERENCES

Acs, Z. (1994). R&D spillovers and recipient firm size. Review of economics and statistics, 76 (2), 336.

Acs, Z., Audrestsch, D., & Feldman, M.P. (1992). Real effects of academic research: comment. American Economic Review, 82(1), 363-367.

Almeida, P. & Kogut, B. (1999). Localization of knowledge and the mobility of engineers in regional networks. Management Science, 45(7), 905-917.

Alker, H.R. (1969). A typology of ecological fallacies. In: Dogan, M. and Rokkan, S. (Eds.). Quantitative Ecological Analysis in Social Sciences, Cambridge, M1T Press, pp. 69-86.

(21)

21 Agrawal, A., Cockburn, I., & McHale, J. (2006). Gone but not forgotten: knowledge flows, labor mobility, and enduring social relationships. Journal of Economic Geography, 6 (5), 571-591.

Austin, P., Goel, V., & Walraven, C. (2001). An introduction to multilevel regression models. Canadian Journal of Public Health, 92(2), 150-154.

Baldwin, R. & Martin, P. (2005). Agglomeration and Regional Growth. In: V. Henderson & J.-F. Thisse (Eds.). Handbook of Urban and Regional Economics: Cities and geography, North Holland

Becheikh, N., Landry, R., & Amara, N. (2006). Lessons from innovation empirical studies in the manufacturing sector: A systematic review of the literature from 1993–2003.

Technovation, 26(5-6), 644–664.

Beugelsdijk, S. (2007). The regional environment and a firm's innovative performance. A plea for a multilevel interactionist approach, Economic Geography, 83(2), 181-199. Blomqvist, K., & Levy, J. (2006). Collaboration capability–a focal concept in knowledge

creation and collaborative innovation in networks. International Journal of Management Concepts and Philosophy. 2(1), 31–48.

Blundell, R., Griffith, R. & van Reenen, J. (1999). Market share, market value and innovation in a panel of British manufacturing firms. Review of Economic Studies,

66(3), 529-54.

Breschi, S., Malerba, F., & Orsenigo, L. (2000). Technological regimes and Schumpeterian patterns of innovation. Economic Journal - Royal Economic Society, 110(463), 388-410.

Brown, S. L., & Eisenhardt, K. M. (1995). Product development: Past research, present findings, and future directions. Academy of Management Review, 20(2), 343–378. Bryk, A.S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications and

data analysis methods. Newbury Park, CA, Sage Publications.

Buesa, M., Heijs, J., & Baumert, T. (2010). The determinants of regional innovation in Europe: A combined factorial and regression knowledge production function approach. Research Policy, 39(6), 722-735.

Cimoli, M., Primi, A., & Rovira, S. (2011). National innovation surveys in Latin America : empirical evidence and policy implications (pp. 1–149). Santiago de Chile: ECLAC.

(22)

22 Choo, K. & Lee, K. (2012). Comparing productivity impacts of knowledge spillovers from network and arm´s length industries: The case of business groups in Korea.

th 2012.

Coe, D., & Helpman, E. (1995). International R&D spillovers. European Economic Review, 39, 859–887.

Cohen, W.M., Levinthal, D.A., 1990. Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly, 35 (1), 128–152.

Crepón, B. & Duguet, E. (1997). Estimating the innovation function from the patent numbers: GMM on count panel data. Journal of Applied Econometrics, 12(3), 243-264.

Crépon, B., Duguet, E., & Mairesse, J. (1998). Research, innovation and productivity: an econometric analysis at the firm level. Economics of Innovation and New Technology. 7, 115–158.

Day, G. (1990). Market driven strategy: Processes for creating value, New York: The Free Press.

De Bondt, R. (1997). Spillovers and innovative activities. International Journal of Industry Organization, 15(1), 1-28.

Doner, R. F., Hicken, A., & Ritchie, B. K. (2009). Political challenges of innovation in the developing world. Review of Policy Research, 26(1–2), 151-171.

Drazin R., & Schoonoven C.B. (1996). Community, population, and organization effects on innovation: A multilevel perspective. Academy of Management Journal, 39(5), 1065-1083.

Feldman, M. P. (1994). The Geography of Innovation, Kluwer Academic Publishers, Boston.

Fershtman, Ch. & Gandal, N. (2011). Direct and indirect knowledge spillovers: the “social network” of open-source projects. RAND Journal of Economics. 42 (1), 70–91

Fillipetti, A. (2011). Innovation modes and design as a source of innovation: a firm-level analysis. European Journal of Innovation Management, 14(1), 5-26.

Forero-Pineda, C., Corredor, S. & Forero, N. (2009). Business networks and innovation in SMEs of a developing country. Working Paper, Atiner.

(23)

23 Foster, N. & Pöschl, J. (2009). The importance of labour mobility for spillovers across

industries, wiiw Working Papers 58, The Vienna Institute for International Economic Studies.

Forny, M. & Paba, S. (2002). Spillovers and the growth of local industries. The Journal of Industry Economics, 50(2), 151-171.

Geroski, P.A. (1990). Innovation Technological Opportunity and Market Structure. Oxford Economic Papers, 42, 586-602.

Geroski, P. A. (1994). Market Structure, Corporate Performance and Innovative Activity. Oxford University Press, New York.

Geroski, P. A. Van Reenen, L. & Walters, C. F. (1997). How persistently do firms innovate? Research Policy, 26, 33-48.

Goedhuys, M. (2007). The impact of innovation activities on productivity and firm growth: evidence from Brazil. UNU-MERIT working paper 002.

Gordon, I. R., & McCann, P. (2005). Innovation, agglomeration, and regional development.

Journal of Economic Geography, 5(5), 523-543.

Gorg, H. & Greenaway, D. (2004). Much ado about nothing? Do domestic firms really benefit from foreign direct investment? World Bank Research Observer, 19(2), 171-197.

Grossman, G. and Helpman, E. (1992). Innovation and growth in the global economy. MIT University Press.

Gupta, A., Tesluk, P., & Taylor, M. (2007). Innovation at and across multiple levels of analysis. Organization Science, 18(6), 885-897.

Hall, B. (2011). Innovation and Productivity. NBER Working Paper 17178.

Harris, Richard & Robinson, C. (2002). Productivity Spillovers to Domestic Plants from

Foreign Direct Investment: Evidence from UK Manufacturing, 1974-1995. Royal

Economic Society Annual Conference 2002 96, Royal Economic Society.

Hoekmann, J., Frenken, K. & van Ort, F. (2008). Collaboration networks as carriers of knowledge spillovers: Evidence from EU27 regions. CRESPI Working Paper 222. Hongxia, Z. & Xiuli, L. (2007). The R&D Inter-industry Spillover Change in China: on the

analysis of 1997 and 2002 IO tables of China. Recuperado el 11 de septiembre de 2010 de www.iioa.org/pdf/16th%20Conf/Papers/ZhangHongxia.pdf

(24)

24 Jaffe, A. (1989). The real effects of academic research. American Economic Review. 79,

957-70.

Jaffe, A., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidence by patent citations. Quarterly Journal of Economics, 108(3), 577-98.

Kaiser, U. (2002). Measuring knowledge spillovers in manufacturing and services: An empirical assessment of alternative approaches. Research Policy, 31, 125–144.

Kam, W.P., Kiese, M., Singh, A., & Wong, F. (2003). The pattern of innovation in Singapore’s manufacturing sector. Singapore Management Review. 25 (1), 1–34. Kanter, R.M. (1983). The Change Masters. New York: Simon & Schuster.

Kennedy, P. (2003). A Guide to Econometrics, 5th edition, MIT Press and WileyBlackwell. Kesidou, E. & Romijn, H. (2008). Do Local Knowledge Spillovers Matter for

Development? An Empirical Study of Uruguay’s Software Cluster. World Development, 36 (10), 2004–2028

Klein, K. J., Dansereau, F., & Hall, R. J. (1994). Levels issues in theory development, data collection, and analysis. Academy Management Review. 19, 195–229.

Knott, A. M., Posen, H.& Wu, B. (2009). Spillover asymmetry and why it matters.

Management Science. 55(3), 373-388.

Krugman P. (1991) Economic Geography and Trade. Cambridge, MA: MIT Press. Kugler, M. (2005). Spillovers from foreign direct investment: Within or between

industries? Journal of Development Economics, 80, 444– 477.

Los, B. & Verspagen, B. (2000). R&D spillovers and productivity: Evidence from U.S. manufacturing microdata. Empirical Economics, 25, 27-48.

Malerba, F. (2005). Sectorial systems of innovation: A framework for linking innovation to the knowledge base, structure and dynamics of sectors. Economics of Innovation and New Technology, 14(1-2), 63-82.

Malerba, F. (2004). Sectorial systems of innovation: Concepts, issues and analyses of six majorsectors in Europe, Cambridge University Press, 519.

Malerba, Mancusi, M.L., & Montobbio, F. (2007). Innovation, international R&D spillovers and the industry heterogeneity of knowledge flows, KITeS Working Papers 204

(25)

25 Masciarelli, F., Reichstein, T., & Laursen, K. (2010). A matter of location: the role of

regional social capital in overcoming the liability of newness, in R&D acquisition strategies. Paper presented at the Summer Conference Opening up Innovation: Strategy, Organization and Technology, Imperial College London Business School, June 2010.

McCormick, D., & Atieno, R. (2002). Linkages between small and large firms in the Kenyan food processing sector. En M.P. van Dijk, & H. Sandee (Eds.). Innovation and Small Enterprises in the Third World (pp. 223-248). Northampton, MA: Edward Elgar.

Metcalfe, J. S. (1998). Evolutionary Economics and Creative Destruction, London: Routledge

Michie, J. (1998). Introduction. The internationalization of the innovation process.

International Journal of the Economics of Business, 5(3), 261-277.

Mintzberg, H. (1979). The Structuring of Organization. Englewood Cliffs, N.J.: Prentice Hall.

Monjon, S. & Waelbroeck, P. (2003). Assessing spillovers from universities to firms: Evidence from French firm-level data. International Journal of Industry Organization. 21, 1255–1270.

Moon, J. (2002). Spin-offs and spillovers: Tracing knowledge by following employees across firms. Discussion Paper 2002/5.

Mytelka, L. (2000). Local systems of innovation in a globalized world economy. Industry and Innovation. 7(1), 15–32.

OECD (1997). Proposed Guidelines for Collecting and Interpreting Technological Innovation Data – Oslo Manual, Paris, OECD.

Porter, M. (1990). The Competitive Advantage of Nations. New York: Free Press.

Quella, N. (2007). Intra- and inter-industry knowledge spillovers and TFP growth rates.

MPRA Working Paper, 2853.

Porter, M.E. 1990. The Competitive Advantage of Nations. Free Press: New York.

Rabe-Hesketh, & Skrondal, (2008). Multilevel and longitudinal modeling using Stata. Texas: Second edition, Stata Press.

(26)

26 RICYT, OEA, CYTED, COLCIENCIAS (2001). Normalización de Indicadores de

Innovación Tecnológica en América Latina y el Caribe - Manual de Bogotá. Colombia.

Robinson, W.S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review. 15, 351-357.

Roper, S., & Hewitt-Dundas, N. (2008). Innovation persistence: Survey and case-study evidence. Research Policy, 37, 149-162.

Rousseau, D. M. (1985). Issues of level in organizational research: Multi-level and cross-level perspectives. In: L. L. Cummings & B. Staw (Eds.). Research in Organizational Behavior, Vol. 7, JAI Press, Greenwich, CT. pp. 1–38.

Scherer, F.M. (1982). Inter-Industry Technology Flows and Productivity Measurement.

Review of Economics and Statistics, 64, 627-634.

Schmitz, H. (1982). Growth constraints on small-scale manufacturing in developing countries: A critical review. World Development, 10(6): 429-450.

Schumpeter, J. A. (1934). The Theory of economic development. Cambridge: Harvard Economic Studies

Schumpeter, J.A. (1942). Capitalism, socialism, and democracy. New York: Harper & Brothers.

Scott, J. L. (1997). Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences), Sage Publications.

Smolny, W. (1999). International sectorial spillovers: An empirical analysis for German and U.S. industries. Journal of Macroeconomics, 21(1), 135-154.

Smolny, W. (2003). Determinants of innovation behaviour and investment estimates for West-German manufacturing firms. Economics of Innovation and New Technology. 12 (5), 449–463.

Srholec, M. (2011). A Multilevel Analysis of Innovation in Developing Countries. Industry and Corporate Change, 20, in print.

Srholec, M. (2010). A Multilevel Approach to Geography of Innovation. Regional Studies,

44, 1207-1220.

Unesco (2010). Measuring R&D: Challenges faced by developing countries. Technical Paper No. 5, Unesco Institute for Statistics.

(27)

27 Upadhyayula, R. S., & Kumar, R. (2004). Social Capital as an Antecedent of Absorptive

Capacity of Firms. Paper presented at the DRUID Summer Conference, Elsinore, Denmark.

Uzun, A. (2001). Technological innovation activities in Turkey: the case of manufacturing industry, 1995–1997. Technovation. 21, 189–196.

van Beers, C., Kleinknecht, A., Ortt, R., & Verburg, R. (2008). Determinants of innovative behaviour: A firm´s internal practices and its external environment (First., p. 307). New York: Palgrave Macmillan.

Veugelers, R., 1997. Internal RD expenditures and external technology sourcing. Research Policy, 263, 303–315.

Winkelmann, R. (2008). Econometric Analysis of Count Data, Fifth edition, Springer Press. Tsai, K.-H. (2009). Collaborative networks and product innovation performance: Toward a

contingency perspective. Research Policy, 38, 765–778.

Yao, V. (2006). Intra-industry spillovers and innovation: An econometric analysis at the

firm level.(2), 119–135.

APPENDIX

Table 1. Description of matching industry, number of firms per industry and total number of innovations reported

Code

industry Code CIIU (Rev. 3.1)

Number of firms

by industry No. Innovaciones totales reportadas en EDIT 2.

1 151 141 2.613

2 152 88 1.767

3 153 91 2.236

4 154, 155 469 5.348

5 156 70 1.156

6 157 23 277

7 158 106 2.227

8 159 77 1.294

9 160 4 16

10 171, 172, 173, 174, 175 239 3.914

11 181 573 6.181

(28)

28 13 201, 202, 203, 204, 209 72 808

14 210 152 2.549

15 221, 222, 223 351 4.828

16 231, 232 22 340

17 241, 242, 243 434 7.965

18 251, 252 418 6.516

19 261, 269 241 4.442

20 271, 272, 273, 281, 289 412 4.844

21 291, 292, 293 290 4.203

22

311, 312, 313, 314, 315, 319, 321,

322, 323, 331, 332, 333 179 3.506 23 341, 342, 343, 351, 352, 353, 359 161 2.950

24 361 243 3.176

25 369 113 1.942

Tabla 2. Descripción de las variables usadas en el modelo

Variable Indicator EDIT II

Control variables Firm Level

Size Número de empleados reportados

Inversión en I+D (Invest_R+D) Logaritmo natural del gasto en I+D reportado por las empresas.

Personal técnico ocupado (Personel_Tech) Proporción de empleados que tienen formación técnica.

Personal I+D (Personel_R+D) Proporción de empleados ocupados en actividades de I+D

Vínculos con clientes, proveedores y competidores (Relation_CSC)

Sumatoria de vínculos para la innovación con clientes, proveedores y competidores reportados por las firmas.

Control Industry Level

Concentración industrial (Industry _Concentration)

Calculado con Herfindal Index. Inversión del sector en I+D (Invest_R+D

Sector)

Logaritmo natural de la sumatoria en gastos de I+D reportados por las firmas de cada sector.

(29)

29 Promedio de innovación del sector

proveedor (Innov_Prov)

Número de innovaciones totales del principal sector proveedor (según la matriz de oferta de 2004) dividido entre el número de firmas.

Promedio de innovación del sector cliente (Innov_Buy)

Número de innovaciones totales del principal sector cliente (según la matriz de oferta de 2004) dividido entre el número de firmas.

Promedio de innovación del sector competidor (Innov_Comp)

Número de innovaciones totales del sector al que pertenece la firma dividido entre el número de firmas.

Dependent variable: Number of product innovations (measured as Innovation

objectives)

Variables eq1 lns1_1_1

Individual

Level

Size 0.000284***

(7.94e-06)

Invest_R+D 4.07e-09***

(2.78e-10)

Personel_Tech 0.349***

(0.0205)

Personel_R+D 0.204***

(0.0540)

Relation_CSC 0.265***

(0.00318)

Industry Level Industry _Concentration -3.69e-05**

(1.46e-05)

Invest_R+D Sector

4.96e-10

(6.36e-11)

Innov_Comp 0.0359***

(0.00734)

Innov_Prov 0.0179**

(0.00823)

Innov_Buy 0.0177***

(30)

30 Variable Obs Mean Std. Dev. Min Max

Size 6670 90.97138 255.8753 1 6902

Innov_Prov 6670 1897.826 897.7508 152 3965

Innov_Buy 6665 1865.65 939.9107 113 3965

Innov_Comp 6670 2107.752 878.8378 10 3965

Relation_CSC 6670 .541979 .9202529 0 3

Invest_R+D Sector 4934 11.10692 2.370385 2.484907 20.61241

Invest_R+D 6221 926135.7 1.28e+07 0 8.95e+08

Referencias

Documento similar

In the preparation of this report, the Venice Commission has relied on the comments of its rapporteurs; its recently adopted Report on Respect for Democracy, Human Rights and the Rule

The fulfillment of the hypothesis, that internationalization reduces firm’s risk, would signify that the effect, the crisis had on performance of exporting firms,

According to the IUS, the enablers ‘capture the main drivers of innovation perform- ance external to the firm’ (European Union 2014: 4) and cover three innovation dimensions:

In the “big picture” perspective of the recent years that we have described in Brazil, Spain, Portugal and Puerto Rico there are some similarities and important differences,

(1999) “The impact of stocks and flows of organizational knowledge on firm performance: An empirical investigation of the biotechnology industry” Strategic Management Journal,

For the Agri-food Sector Innovation Management Network (INNOVAGRO), as a platform that promotes innovation in the agri-food ecosystems of the rural economies in the 15

In the first model (the Theoretical Model), the impact of the existence of innovation on financial performance is potentially mitigated by the extent to which the economic and social

First, it studies the impact of the entrepreneur's capacity (training, experience and confidence) on business performance, and investigates the mediation of other factors such as