innovation and internationalization: the case of italy
TRANSCRIPT
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Innovation and internationalization: the case of Italy
Rosanna PITTIGLIO, Edgardo SICA, Stefania VILLA
R. Pittiglio and E. Sica Department of SEMS, Faculty of Economics, University of Foggia, Largo Papa Giovanni Paolo II n. 1, 71100 Foggia, ITALY. S. Villa Birkbeck College, University of London, Malet Street, WC1E 7HX, UK e-mail: [email protected] This work was jointly conceived and produced by the three authors. However, sections 3 and 5 were written by Rosanna Pittiglio, sections 4 and 6 by Edgardo Sica, and sections 1 and 2 by Stefania Villa.
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Abstract The aim of this paper is to analyze the impact of international activities on knowledge output. At this end, we employ a dataset containing exclusively qualitative information about a sample of small and medium Italian manufacturing firms. In the econometric analysis, a probit model is used. The results of the estimations highlight that firms active in international markets generate more knowledge than their counterparts which sell in the national market only. There are two possible explanations of this result. First, globally engaged firms employ more knowledge inputs, such as higher innovation expenditures. Second, international firms are more innovative because they can access to a larger flow of ideas from external sources.
Keywords: firms’ internationalization, innovation, knowledge flows. JEL classification: F14, O14, O31, O40.
1. Introduction
In the latest years the availability of longitudinal micro-level data has determined a
remarkable increase in empirical studies on innovation, with particular regard to the
analysis of factors affecting firms’ knowledge output. The firm investing in innovation
might develop and license new technologies, adopt more efficient and less costly
production techniques, introduce new product and processes, and, therefore, become
more competitive (Mansfield, 1968; Castellani and Zanfei, 2007). The firm’s incentive
to invest in innovation decreases if knowledge generated by its innovation effort can be
appropriated costless by competitors. Spence (1984) showed that the appropriability
problem leads to a reduction of firm’s incentives to invest in R&D.
In the prevailing literature (Teece, 1996, for a survey), two broad classes of variables
are found to influence firm’s decision to engage in innovative activities:
firm’s characteristics and other internal factors;
external factors.
Firm’s characteristics are, for example, the monopoly power and size. The internal
factors are inputs used in the innovation process; as standard in this literature (Griliches,
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1979, 1990; Romer, 1990), the inputs, which affect the production of innovation, are
innovation expenditure and the existing stock of knowledge available in the economy.
Teece (1996) also considered the internal formal structure and internal informal
structure as other internal factors.
In order to test the empirical validity of these factors influencing the production of
knowledge, the knowledge production function (KPF), formalized by Griliches (1990),
has been used as an analytical tool. According to this function, the production of new
knowledge depends on some observable measure of resources invested in innovative
activity, on the stock of knowledge available to the firm, and other control variables,
such as firm size. It is worth noting that not all the variables introduced in the literature
are easily measurable, such as the internal informal structure.
In the latest years a great deal of attention has been devoted to the “external” inputs
to innovation activity: firms may create knowledge not only from their own R&D
laboratories, but they can also take advantages from research activities carried out by
universities and by other firms, and, generally speaking, from collaborations with other
agents in the domestic and foreign markets. People share knowledge for different
reasons; for example, to get feedback from other people, to receive acknowledgment of
own ideas, or in team working. Once this knowledge is spread, it can be used to benefit
other people’s work and could lead to other inventions, resulting in spillovers and other
knowledge externalities. There is an associated risk of knowledge spillovers, which
arises due to the imperfect mechanism of protection of knowledge generated in an
innovating firm; firms may guard against unintended use of their innovation.
The flow of knowledge can happen within a country as well as abroad; a strand of
literature links internationalization and innovation. International research collaboration,
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international networking for innovation inputs and ideas, international recruitment of
research and managerial staff encourage exchange of ideas. According to Keeble et al.
(1998), internationalization is a very important process underpinning firms’ innovative
activities and technological dynamism. The returns to innovation can be affected by
internationalization (Grossman and Helpman, 1991; Kafouros et al., 2008, among many
others). Hitt et al. (1997) illustrated that globally engaged firms may benefit from
product and geographical diversifications through economies of scale and scope.
Criscuolo, Haskel and Slaughter (2005), (CHS), and Wagner (2006) demonstrated
that international firms innovate more thanks to the access to a larger flow of ideas from
external sources. CHS used a knowledge production function (KPF) as an analytical
tool. They divided the existing stock of knowledge available in the economy in two
types of flow: internal and external, demonstrating that globally engaged UK firms
innovate more not only because they use more researchers, but also because they have a
better access to external stock of knowledge.
Using the same knowledge production framework and a data set of plant level data in
Germany, Wagner (2006) demonstrated that firms active on international markets
generate more knowledge than domestic firms, also because the former learn more from
external sources.
External linkages and knowledge spillovers are particularly important for small
enterprises, because they are means to supplementing and complementing limited
internal resources (Freel, 2000). Financing R&D activities can be a serious constraint on
small firms’ innovative effort. Innovation requires access to capital; alternatively,
alliances can be entered which reduce the need for new investment in complementary
assets. Adding external factors in the KPF introduces another channel through which
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small enterprises get the innovation producing inputs. This channel is particularly
important in Italy, where between 2001-2003 Italian large firms (more than 250
employees) represented only about 1.7 percent of total Italian manufacturing firms
(source: authors’ elaborations on AIDA database from Bureau van Dijk). Therefore, the
SMEs represent a qualitatively peculiar and quantitatively relevant component of the
Italian productive structure.
The above discussion suggests that three categories of variables influence innovation
output:
the variables generally included in the standard KPF;
external factors, which allow flows of ideas within a country;
external factors, which allow flows of ideas from abroad.
And the last two factors are particularly important for SMEs.
In this paper we consider what affects the propensity to innovate among the Italian
SMEs. Adopting, to the best of our knowledge, the CHS approach for the first time to
the Italian case, the present work contributes to the existing literature by including
international activities among the potential determinants of knowledge output for the
Italian SMEs. In particular, we consider two measures of knowledge output, patents and
product innovations, and two indicators of global engagement, exports and
collaboration agreements with foreign firms. The empirical approach consists in
estimating the knowledge production function, in which the regressors are not only
innovation expenditure and internal flow of knowledge, but also external flow of
knowledge and indicators of international activities.
The structure of the paper is as follows: section 2 reviews the theoretical framework;
section 3 gives some descriptive statistics; section 4 discusses the variables used;
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section 5 presents the estimation results; and the final section ends with some
concluding remarks.
2 Theoretical Framework
According to the literature, three main factors affect innovation output: 1) innovative
inputs and firm’s characteristics; 2) external factors, which allow flows of ideas within a
country; and 3) external factors, which allow flows of ideas from abroad.
Griliches (1979, 1990) and Romer (1990) assumed that devoting more capital - both
physical and human - to R&D leads to a higher production of knowledge. The
production of new knowledge, the output, depends not only on investment in the
process of knowledge creation but also on the existing stock of knowledge in the firm
available to researchers. The transfer of knowledge within the firm itself is another input
which generates innovative output. The literature proposed other (control) variables
potentially affecting the creation of new knowledge, such as firm size and monopoly
power.
External factors may also play a significant role in the innovation process.
Evolutionary models of the innovation process suggest the importance of networks as a
cause of knowledge transfer (Cagliano et Al., 2000; Love and Roper, 1999). These
external inputs basically come from three different sources. First, the firm can enhance
its innovation activity by making contracts and establishing alliances with other firms
(Santos et al., 2004). Second, firms are also the recipients of R&D spillovers from
knowledge generated in universities. Finally, the flow of ideas can also come from
sources such as customers and suppliers.
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Links with other firms results in benefits and costs. Schmookler (1966) noted that
technological progress achieved by a firm might not solely be the result of its own
research efforts, but also the consequence of other firms’ research. According to
Dodgson (1994), collaboration with other enterprises can lead to positive sum gains, as
firms can achieve mutual benefits that could not be obtained independently. Other
benefits consist in accessing new markets, complementing and supplementing internal
product development efforts (Rothwell and Dodgson, 1991), and the transfer of explicit
and tacit knowledge (Karlsson and Olsson, 1998). The concern over appropriability
increases the costs of links with other firms, which may be, at the same time,
competitor; and the other part should have no interest in giving a wrong suggestion and
should be competent in giving a good advice.
Links with public research organizations allow firms to contact a source of
significant innovation-generating knowledge. This contact is extremely useful for small
firms, which can alleviate the scarcity of internal resources by accessing university
resource networks (Westhead and Storey, 1995).
As Freel (2000) noted, there are at least four gains from links with customers. First,
customers may provide their technical and managerial skills to firms; second, customers
involvement may easily results in user feedback and associated product improvement
that can lengthen the product life span. Third, user involvement is the ideal way to
establish the best optimum price/quality combination; and finally, the participation of
customers in the product design and development stages is likely to reduce the post-
delivery learning required by customers to get used to the new item.
External inputs may be particularly important for SMEs. The dynamic view of
industrial organization emphasizes the role of SMEs as “agents of change through
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innovative activity” (Audretsch, 2002). Some authors (Acs et al. 1994 and Piergiovanni
et al. 1997) noted that, even if small firms are generally lacking of substantial R&D
investment, they provide a significant contributions in terms of innovative output.
Evidently they rely on other types of inputs; these authors found that spillovers from
university research laboratories are more important in producing innovative output in
small firms, rather than in large firm, where R&D expenditure play an important role in
generating innovation. As technology is far from costless (Pack and Westphal, 1986),
external transfer of knowledge and knowledge spillovers can alleviate the problem of
the lack of financial resources for new and growing SMEs. For this reason, venture
capital represents the instrument that may mitigate the high cost of R&D investment.
When the role of venture capital in financing innovative firms is marginal (as in our
sample where only 12 percent of firms use venture capital as a source of funds), not
only flows of ideas within a country but also flows from abroad may be another
important channel for the production of knowledge. According to Coe and Helpman
(1995), the advantages from foreign R&D are both direct and indirect; direct advantages
consist in learning about new technologies and materials, production processes, or
organizational methods. Indirect advantages originate from imports of goods and
services developed by trade partners. In this paper we concentrate on direct advantages
since our database does not contain any information on import.
Internationalization can influence the innovation activity in many ways. According to
Kotabe (2002) and Hitt et al. (1997), a globally engaged firm can improve its innovative
capacity by being better able to use a wider range of resources available globally, often
unavailable to domestic firms. Internationalization provides the opportunity to capture
ideas from a greater number of new and different markets, as well as from a wide range
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of cultural perspectives; therefore, globally engaged firms have more opportunities to
learn (Hitt et al. 1997). Moreover, internationalization allows firms to reach a large
number of potential buyers (Riviera-Batiz and Romer, 1991).
Some researchers have stressed the benefits which derive to firms from exporting. As
Blalock and Gertler (2004) noted, exporting firms may benefit from exposure to intense
competition of international markets. Whereas non-exporting firms may be insulated
from such competition by trade policy or geographical barriers, firms producing for
world markets likely cannot survive without adopting best practice technology. At the
firm level exporting might improve productivity via the transfer of knowledge from
overseas buyers to the exporting firms. Grossman and Helpman (1991: 166) pointed out
the foreign purchasing agents may give some suggestions to exporters on how to
improve the manufacturing process. In fact, according to Evensond and Westpahl
(1995), a good deal of information needed to augment basic capabilities comes from the
international buyers who freely provide product designs and offered technical assistance
to improve process technology in the context of their sourcing activities. The
international collaboration agreements may also contribute to this process since they
represent another form of participation in international activities (Vitali, 1990).
Internationalization, therefore, appears to be a stimulus for successful networking
and research and technology collaboration. It is also an additional channel of
international knowledge spillovers and it alleviates the internal resource limitation of
SMEs.
Our empirical equation builds on the previous literature, which distinguishes among
internal and external (domestic and foreign) factors influencing firms’ propensity to
innovate. Instead of assuming a specific functional form, we define the following KPF:
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Ki = f (Hi, Kii, Ki_i, IAi, Zi) (1)
where Ki is the innovative output in firm i, Hi represents the investment in R&D, Kii
indicates the internal transfer of knowledge, and Ki_i indicates the external transfer of
knowledge to firm i from outside. The term IAi indicates if the firms carry out
international activities; and the vector Zi indicates other (control) variables potentially
affecting the creation of new knowledge, such as firm size and market dimension.
In equation (1) there are two additional terms with respect to the standard literature
on the KPF. First, adding the term Ki_i allows to distinguish information flows within
firm and flows from outside: firms intentionally accumulate their stock of knowledge,
which is a fundamental ingredient to innovate. The term Ki_i considers the additional
flow of knowledge that firms may benefit from intra-firm pool of information,
suppliers, customers and public research organizations.
Second, the term IAi identifies firms active in international markets; international
activities might enable firms to gain access to foreign knowledge sources. For example,
exporting firms are more easily aware of potential innovations taking place abroad, that
enterprises might assimilate to improve their position and to compete successfully both
in domestic and foreign markets.
3 Descriptive Statistics
In this paper we used a microeconomic dataset of Italian manufacturing small and
medium sized enterprises (SMEs). The database has been built up on the basis of
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information collected by Unioncamere-Tagliacarne through personal interviews with
firm owners or top managers relatively to years 2001-2003.
Although the starting sample included 3,409 firms, the number of firms for which we
have constructed our model is 2,603 because of missing values. The response rate of
about 76 percent is relatively high in comparison with similar researches in literature,
such as CHS whose response rate for UK enterprises is 42 percent.
A detailed analysis about the structure of the questionnaire and some characteristics
of the sample are presented in Morone and Testa (2005) who examined the determinants
of Italian SMEs’ turnover growth and in Reganati (2004) who analyzed the
determinants of internationalization of SMEs. In this study we prefer to summarize the
characteristics of the sample strictly linked to the aim of our analysis.
Tables I - IV show the main characteristics of our sample distinguishing between
firms that have undertaken product innovation and firms that have registered a patent in
the period considered.
In the specific case, about 28 percent of firms in our sample have undertaken product
innovation and 8 percent have registered at least a patent in order to protect their
innovations (Table I).
Table I: Innovation activity of the Italian manufacturing firms according to the size
Product
Innovations Patents
Total firms
Micro firms (0 - 9) 21.9 5.2 83.0 Small firms (10 - 49) 5.4 2.2 15.1 Medium firms (50 - 249) 0.7 0.5 1.9
Total firms 28.0 7.9 100.0
(Source: own elaboration on Tagliacarne data)
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These relatively low percentages of innovation activity resulting from our analysis
might reflect two main aspects of the Italian productive system characterized by a high
presence of micro firms (about 83 percent of total firms present in our sample have less
than nine employees) together with a strong specialization in traditional and specialized
suppliers sectors.
Moreover, considering both innovation activity and geographical distribution, most
of innovative firms are concentrated in North Italy while only 21 percent of innovative
firms are located in South Italy of which about 70 percent are micro firms. This picture
reveals the strong gap between North and South Italy. The situation is almost the same
if we consider patents as a proxy for innovation activity; in fact, in this case 55 percent
of firms that have registered a patent in the period 2001-2003 are located in North Italy
and only 16 percent in the South of Italy.
When we take into account two activities in serving foreign markets – exports and
foreign agreements - we find some interesting results (Table II). In the specific case
about 44 percent of firms present on foreign markets through export have realized
product innovation and 23 percent have registered a patent. While more than 50 percent
of firms that have concluded collaboration agreements with foreign firms have
undertaken at least one product innovation and about 31 percent of them have registered
a patent. Most of them are located in only three markets (UE15, United States and
Japan). These data confirm that firms entering in industrialized country’s foreign
markets through collaborations agreements might generate more innovation than their
counterparts operating exclusively on local markets.
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The percentages of domestic firms that have realized product innovations (23.6%)
and patents (6.7%) are rather low. This picture shows that firms present on foreign
markets are more innovative than their non-exporting counterparts.
Table II: Innovation activity of the Italian manufacturing firms according to the internationalization activities
Product Innovations Patents Total firms Export 44.4 23.0 20.4 Foreign Agreements 52.5 30.9 5.7
UE15 55.3 36.8 3.9 Japan 62.7 39.0 0.4
United States 59.0 49.3 0.9 Domestic firms 23.6 6.7 78.2
(Source: own elaboration on Tagliacarne data)
When we consider the sectoral distribution of innovative firms, Table III shows that
most of innovative firms are concentrated in the sector of Electronics and transport
equipment (4.8%), followed by Food products, beverages and tobacco (4.7%) and
Wood and products of wood (3.7%). Considering patents as an indicator of innovation
activity, the same table shows that firms which have undertaken a patent are
concentrated in Food products, beverages and tobacco, followed by Machinery and
equipment and Electronics and transport equipment with the percentages rather low.
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Table III: Sectoral distribution of Innovation activity of the Italian manufacturing firms
Product
Innovations Patents Total firms
Food products, beverages and tobacco 4.7 1.4 12.4 Textiles and wearing apparel 3.1 0.5 13.6 Leather, leather products and footwear 0.7 0.4 4.0 Wood and products of wood (furniture inclusive) 3.7 0.7 15.0 Chemicals, chemical products, rubber and plastics products 1.1 0.4 3.5 Non-metallic mineral products 0.7 0.0 4.9 Basic metals and fabricated metal products 3.4 0.8 18.8 Machinery and equipment 3.0 1.3 7.8 Electronics and transport equipment 4.8 1.3 11.0 Others 2.8 1.1 9.1
(Source: own elaboration on Tagliacarne data)
Finally, Table IV shows summary statistics on the three knowledge inputs:
innovation expenditure, internal transfers of knowledge and external transfers of
knowledge. Globally engaged firms (i.e. firms active on international markets through
both exports and foreign collaborations) use more inputs for making new ideas; this is
true not only for the first two inputs, but also for the external flows of knowledge. In
particular, a significantly higher fraction of globally engaged firms (29.9%) make use of
the latter input compared to domestic firms, whose fraction is only 19.2 percent.
Table IV: Knowledge inputs in the globally engaged and domestic firms
Globally engaged
firms Domestic firms Total firms
Innovation expenditure 69.0 44.3 49.7 Internal flows 40.3 23.5 27.2 External flows 29.9 19.2 21.6
(Source: own elaboration on Tagliacarne data)
From tables II and IV we conclude that globally engaged and domestic firms differ
along three dimensions of the knowledge production function: knowledge outputs,
knowledge investment and access to internal and external stock of knowledge. The last
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dimension captures the idea that the firms active on international markets benefit from
many channels for technology diffusion.
4 Variables used
The present section analyses the variables used to estimate equation (1), starting from
the measurement of the knowledge in the firm i (Ki). There is an ongoing debate in
economic literature about how best to conceptualize and operationalize this variable. In
general, innovation can be interpreted following both an object and a subject approach.
In the first, the unit of observation is the individual innovation; in the second, the unit of
observation is the product/process developed or improved at the firm level. Since in the
object approach individual innovations are selected on the perceptions of experts, with
the consequence that comparisons between studies and countries are difficult (Amara
and Landry, 2005), in this study we followed the subject approach: innovative firms are
determined according to the kind of innovation they have adopted during the previous
two years. Obviously, in case a firm has not adopted any type of innovation it is
classified as ‘not-innovative’. We suppose that, to compete in international markets,
firms need to develop different products both in terms of quality and features, i.e.
product innovation (Herguera and Lutz, 2003). As a consequence, we proxy the
knowledge in a firm through a dummy variable assuming a value equal to one if the
enterprise has adopted at least one product innovation and zero otherwise.
Following most of the studies on this topic, in order to test the robustness of our
model we use patent data as another measure of innovation output.
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However, this proxy generally exhibits a number of weaknesses. In particular, we
employ a dummy variable assuming a value equal to one if a firm has registered some
patents in the previous two years, and zero otherwise.
To measure the current flow of resources devoted to the generation of innovation
(Hi), we use as a proxy the amount of innovation expenditure. More specifically, we
employ a dummy variable assuming a value equal to one if the firm has invested in
innovation and zero otherwise. We should expect a positive relationship between the
amount of innovation expenditure and innovativeness. However, as observed by some
authors (Cabral, 1998; Cohen and Levin, 1989), this proxy is valid under the
assumption that innovation is a ‘linear’ consequence of innovation expenditure.
Alternatively, we could employ the percentage of people working in R&D, but our
dataset do not contain information about it.
The variable Kii was proxied through a dummy variable indicating whether or not
suggestions/ideas for innovations come from: technical workforce or R&D area or
parent company. It is worth noting that since we assume that innovations may be
developed, independently of R&D, from an interactive process characterized by
technological interrelations between various subsystems (Caloghirou, 2004; Teece,
1996), the restrictive hypothesis of a linear relationship between innovation and amount
of innovation expenditure is overcome.
The flow of ideas from outside (Ki_i) was assessed through the introduction of a
dummy variable, indicating whether or not innovation originates from: R&D activities
with Universities, Research Organizations, or Scientific and Technological Parks or
suggestions from suppliers, customers, technical consultants and other upstream and
downstream firms in the same supply chain. Furthermore, we consider the possibility
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that the ideas to develop a new product come from a process of imitation of other firms.
It is widely recognized, in fact, that innovation and imitation interact with each other:
successful innovations encourage imitations as well as imitations lead to a larger
diffusion of innovation (Zeng, 2001). On the whole, we expect that the coefficient of the
dummy variable Ki_i is positive.
The variable IAi was measured through two different dummies. The first assumes a
value equal to one if a firm supplies its product(s) mainly to international markets. The
second assumes a value equal to one if a firm has entered into some collaboration
agreement with foreign companies located in industrialized countries. Actually the main
forms of international penetration are exports and Foreign Direct Investments (FDI)
(Basile et al., 2002), but due to the lack of data on FDI, we have included
“Collaboration agreements” as an additional measure of firms’ internationalization. In
fact, being the latter “the process of increasing involvement in international operations”
(Welch and Luostarinen, 1988), collaboration agreements with foreign enterprises may
represent a good alternative way to measure the level of companies’
internationalization. For the reasons illustrated in the section 2, we expect a positive
coefficient for both these variables, meaning that the global engaged companies are
more innovative.
Finally, the vector of other variables which might favor the creation of new
knowledge (Zi) includes indicators concerning firm’s and market’s size.
Firm size was measured in terms of total number of employees, using a dummy
variable assuming the value equal to one whether the firm is considered ‘larger’ (i.e. if
the total number of employees is included between 50 and 249 units) and zero
otherwise. The question of whether a large firm is more innovative compared to a small
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one or vice versa has been subject of a great deal of controversy and research (Rogers,
2004; Stock et al., 2002; Symeonidis, 1996; Cohen and Klepper, 1994; Rothwell and
Dodgson, 1994; Acs and Audretsch, 1991). From a Schumpeterian perspective, there
exists a positive relationship between market power and innovation, mainly due to the
possibility to reach economies of scale in R&D activities. When a firm dominates a
market it is able to finance investment in innovation and, since it typically produces
different types of products, needs to develop new technologies (Marlin and Scott, 2000).
Thus, larger enterprises should be more innovative compared to the small ones. On the
opposite, part of literature argues that a smaller firm might be more innovative given its
flexibility and its major willingness to new challenges and changes. As a consequence,
the expected sign of this variable is uncertain: it could be both positive and negative.
The market’s size was proxied through a control variable assuming a value equal to
one if a firm supplies its product(s) mainly in a niche market, and zero otherwise. The
reason is that focusing on a niche, a firm addresses a need for a product not addressed
by the mainstream suppliers: in this way it obtains the great advantage of being alone in
that subset of market. Obviously, this asks for a deep specialization in a specific
product/service. According to the characteristics of the product supplied, some firms
will need to innovate incessantly to preserve their position in the niche, while others
could not have this necessity. Being this only an hypothesis, the expect sign of this
variable is uncertain.
5 Methods of estimation and results
Before conducting a complete discussion about the results of econometric
estimations, we provide some explanations about the choice of the model.
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In implementing our estimations, we face two difficulties. First of all, we can build
only binary variables since the Tagliacarne Institute supplies exclusively qualitative
information (such as export/not export or invest/not invest and so on). Secondly, our
database does not contain the fiscal code or VAT number of any single firm. Therefore,
we cannot match this database with others in order to include other firms’ specific
variables that might be relevant for our analysis.
Thus, given these limitations, we believe that the best econometric specification to
investigate the relationship between innovative activity and firm’s characteristics is a
probit model which is well-suited to analyze qualitative data when we have two
alternatives. As we have seen in section 4, we used two different measures of
innovation. We start considering as innovative a firm that has implemented at least one
product innovation in the period under analysis. Subsequently, we test for the
robustness adopting an alternative model specification where we define as innovative a
firm that has registered at least a patent.
For expositional simplicity we consider a linearized version of the KPF of equation
(1), specified in the following way:
iiiii XY
with E[ε] = 0 and V[ε]=σε2
To determine the influence of the international activity on a firm’s innovativeness we
implement two main estimates according to the definition of innovation used (Table V
and Table VI). For any estimation we report, respectively, the estimated coefficients,
their standard errors and their marginal effects. Following CHS we estimate equation
(1) by adding different regressors in the course of analysis; this method allows us to
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have more precise information about the impact of global engagement variables.
Estimations have been implemented using “STATA” econometric software.
Table V reports estimation results when product innovation is used as a dependent
variable. Column 1 summarizes the results when only global-engagement indicators
(Export and Industrialized) are used. Both the variables are statistically significant.
Precisely, the coefficients on Export and Industrialized indicate that the firms of our
sample are, respectively, 23 percentage points and 18 percentage points more likely to
be innovative than their counterparts which are active only on the national market. In
the specific case, the variable Industrialized confirms the relevance of collaboration
with firms located in industrialized countries in order to develop innovation activity.
Adding to the estimation in column 1 the variable Innovation expenditure, we obtain
column 2, where all the coefficients are statistically significant and have the expected
sign; the addition of this variable reduces only slightly the coefficients of global-
engagement variables. These results are consistent with those obtained by CHS.
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The results change when we add information flows from existing knowledge inside
and outside the firm (Table V column 3). In this case, the coefficients of global
engagement have the expected signs, but the variable Industrialized becomes significant
at 5 percent level. Many considerations could be argued. Firstly, the addition of these
measures of information flows reduces the marginal impact of both our global
engagement variables (by almost 33 percent for export and 30 percent for
industrialized). As CHS noted, this is an important finding of our analysis: almost 30
percent of innovative activity of globally engaged firms is explained by their superior
access to information from existing knowledge, both internal and external.
Distinguishing between information flows, external flows have a slightly higher
marginal effect than internal flows: a firm who learns a great deal from external sources
Table V: Probit estimations for 'Product Innovation'
coeff.marginal effects
coeff.marginal effects
coeff.marginal effects
coeff.marginal effects
-0.751 -0.965 -1.411 -1,418(-23.41)*** (-26.40)*** (-33.43)*** (-33.16)
0.637 0.231 0.531 0.189 0.403 0.136 0,413 0.139(10.75)*** (10.49)*** (8.65)*** (8.38)*** (6.03)*** (5.80)*** (6.05)*** (5.81)***
0.479 0.179 0.373 0.136 0.296 0.102 0,284 0.097(4.57)*** (4.34)*** (3.41)*** (3.22)*** (2.70)*** (2.54)** (2.54)** (2.40)**
0.845 0.307 0.436 0.148 0,427 0.144(14.16)*** (13.91)*** (6.10)*** (5.78)*** (5.96)*** (5.65)***
0.911 0.314 0,916 0.315(13.62)*** (13.15)*** (13.67)*** (13.2)***
0.986 0.352 0,984 0.351(13.93)*** (13.39)*** (13.87)*** (13.31)***
-0,067 -0.021(-0.74) (-0.76)
0,419 0.149(2.51)*** (2.32)***
N. of Obs. 2603 2603 2603 2603
Pseudo R2 0.0565 0.1204 0.2560 0.2584*, **, *** statistically significant at the 10, 5 and 1 percent level, respectively
probit probit probit(1) (2) (3) (4)
Const
probitIndipendent variables
Dependent variable: PRODUCT INNOVATION
Export
Internal Flows
Industrialized
Innovation Expenditure
Niche
External Flows
Size
22
(competitors, customers, suppliers) for innovation would be 35 percentage points more
likely to be innovative. The coefficient on internal flows indicates that a firm benefiting
from intra-firm knowledge flows is 31 percentage points more likely to innovate.
Secondly, adding the set of control regressors – Size and Niche - the impacts of
global engagement and ‘information-source’ regressors do not change to a large extent
(column 4). The coefficient of Niche is positive and highly statistically significant,
whereas the coefficient on Size is negative but not significant.
Table VI replicates the regression results when Patents is used as a dependent
variable. In this case, the pattern of findings is broadly similar. All the variables
considered are highly significant at 1 percent level (except for Niche in the column 4
significant at 10 percent level). Also in this case, the impact of global engagement
variables decreases once the internal and external flows variables are included (columns
2-4). However, the magnitude of the marginal effect of export variable is now slightly
lower while that of Industrialized is higher.
23
6 Summary and concluding remarks
This study has explored the differences in innovation output across the Italian
manufacturing firms with a different degree of international commitment.
We have adopted the knowledge production function’s approach, which consists in
exploring the differences in the production of new knowledge across firms. Therefore,
we have examined the innovation output in a firm as depending on a set of variables,
specifically the level of international engagement, innovation expenditure, and the
amount of internal and external flows of knowledge transfer. In particular, we have
assumed that a firm’s international activity is carried out through both exports and/or
collaboration agreements with foreign firms.
Table VI: Probit estimations for 'Patents'
coeff.marginal effects
coeff.marginal effects
coeff.marginal effects
coeff.marginal effects
-1.439 -1.579 -1.942 -1.979(-33.77)*** (-32.45)*** (-33.90)*** (-34.01)***
0.768 0.185 0.693 0.159 0.598 0.118 0.531 0.103(11.22)*** (9.93)*** (9.97)*** (8.81)*** (8.19)*** (7.19)*** (7.04)*** (6.21)***
0.743 0.204 0.682 0.179 0.599 0.136 0.554 0.123(6.88)*** (5.49)*** (6.29)*** (5.01)*** (5.36)*** (4.20)*** (4.92)*** (3.90)***
0.518 0.115 0.216 0.038 0.218 0.039(7.56)*** (6.76)*** (2.68)*** (2.49)*** (2.69)*** (2.49)**
0.856 0.176 0.838 0.171(11.28)*** (9.64)*** (10.98)*** (9.39)***
0.423 0.082 0.426 0.082(5.45)*** (4.74)*** (5.44)*** (4.71)***
0.344 0.067(3.88)*** (3.38)***
0.319 0.064(1.82)* (1.54)
N. of Obs. 2603 2603 2603 2603
Pseudo R2 0.1142 0.1405 0.2220 0.2302*, **, *** statistically significant at the 10, 5 and 1 percent level, respectively
(4)probitprobit
Dependent variable: PATENTS(2) (3)(1)
Const
probit probitIndipendent variables
Export
Internal Flows
Industrialized
Innovation Expenditure
Niche
External Flows
Size
24
In order to test for estimations’ robustness, we have employed alternative measures
of innovation (product innovations and ‘patents’).
Our findings - broadly in line with the results of the recent studies on UK and
German firm level data - can be summarized as follows.
First, firms active in international markets generate more knowledge than their
counterparts which sell in the national market only; this is true for alternative measures
of innovation.
Second, the previous result may be partly explained by the fact that globally engaged
firms employ more knowledge inputs, such as higher innovation expenditures.
Third, international firms innovate more thanks to the access to a larger flow of ideas
from external sources. In other words, they have many more opportunities of learning
from external flows of knowledge with respect to ‘domestic-market-oriented’
companies. These flows of ideas can originate from several sources, such as suggestions
and proposals from people external to the firm but someway involved in the production
process (customers, suppliers etc.), and inter-firm structural linkages, i.e. cooperative
relationships which take place between a firm and its competitors or between a firm and
external organizations (Universities, Research Organizations, Scientific and
Technological Parks etc.). This suggests the importance of foreign sources of
technology as a strategic key of firms to generate more knoweledge output.
Consequently, we conclude that, in order to generate innovation, the investment
intensity on internal R&D should be supported by international engagement, networking
capabilities, and by the use of external sources of knowledge and information.
25
As a future line of research, therefore, it would be interesting to analyze the effects
on firms’ knowledge production of the policy measures adopted with the purpose of
promoting firms’ internationalization.
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