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Journal of Innovation Economics & Management

2016/3 (n°21)

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R&D and innovation are considered to be the real engines of economic growth in recent centuries. The endogenous growth models have shown the important role of innovation and R&D in long term economic growth. R. Nelson (1993), in an evolutionist economic framework, mentions that “in the long run, standards of living can be enhanced only by innovation”. The countries able to invent, innovate and quickly assimilate technological advances have expanded rapidly. Scientific and technological development participate in several new waves of growth (Pérez, 2002), in producing new “research fronts” (Lucio-Arias, Leydesdorff, 2009) and “search regimes” (Bonaccorsi, 2008), identified as the building bricks of dynamic growth regimes (Aghion et al., 2009). Research and innovation constitute the real wealth of Nations, as Rosenberg, Landau, and Mowery (1992) named it.


Arab countries of the South and East coasts of the Mediterranean sea, although displaying a variety of economies, have been very similar in their relation to research and innovation: they are rather low performing industries, mainly based on traditional products with low added value, such as textiles and agri-food, plagued with very basic and infrastructural problems that divert the attention of entrepreneurs, mainly focused in resolving production problems and solution rather than investing in innovative activities. A large variety of policies designed to foster research and innovation have been implemented during the past two decades (Arvanitis, M’henni, 2010), most of which are considered unsuitable to the local social and economic context (Mezouaghi, 2002) and, thus do not result in high performance nor more concrete results (M’henni, Arvanitis, 2012; Arvanitis, M’henni, 2010; Arvanitis et al., 2009). Additionally, most assessments have been based on macro-economic indicators (CMI, 2013) mainly based synthetic indicators that can only capture with difficulty the actual situation of innovation-related activities (Hanafi, Arvanitis, 2014, 2016).


Nonetheless, this situation is changing thanks to a number of innovation surveys that have been launched in a number of these countries: Tunisia, Morocco, Egypt, and recently Lebanon. These surveys have been instrumental in understanding the characteristics of innovative firms as well as the effective investment in R&D and innovation at the firm-level. Of course, a long way still needs to be made in order to cover the stretch between firm-level diagnosis and comprehensive macro-economic assessments. Nonetheless, the innovation surveys permit to detect some of the basic characteristics of the investment in R&D and other resources related to the investment in innovative activities either in products, processes or organizational innovations.


This article seeks to identify more precisely some of the main economic determinants decision to invest in innovation at the firm level. We use the empirical material gathered in the Lebanese innovation survey for that matter. Section two discusses the theoretical and empirical literature about the determinants of innovation. Section three presents the characteristics of the Lebanese sample and our econometric models. Section four discusses the results followed by a brief conclusion.

Theoretical and empirical background

Main Lessons from the Literature


The theoretical literature on determinants of innovation at firm-level, based on Schumpeter’s seminal work (Schumpeter, 1942) has been stressing two hypotheses: (1) large firms are more likely to undertake innovation than small firms, and (2) higher levels of innovative activity are more likely to be observed in concentrated industries.


A number of factors may increase the benefits of innovation for large firms in concentrated markets relative to smaller firms. First, large firms may obtain a larger total benefit from process innovations that lower production costs: a decline in costs will lead to greater cost savings when applied to a larger number of units of production. Second, larger firms will tend to diversify in a number of different products which will increase the likelihood of benefits reaped out from the introduction of new or enhanced products and new process. Third, large firms may be able to market new products more effectively, increasing the value of new product development.


In his early work on the incentives to innovate across different market structures, Arrow (1962) presented models where a monopolist’s incentive to innovate is always lower than its competitors. In Arrow’s model, although it ignores the difficulties of appropriating the information generated by the innovation, firms in a monopolist situation take into account pre-innovation profits and thus lower their production, thus earning fewer incremental profits out of process innovation. Yi (1999) extended this analysis to models under Cournot competition. He found that if process innovation has no drastic effects as, for example, lowering costs in such proportions that the firm’s monopoly prices fall below the cost of incumbent firms, the benefit of small process innovations decreases with the number of firms, under certain conditions. Intuitively, the explanation lies in the fact that process innovation is correlated with the output of the firm, which declines as the number of firms increases.


Economists have shown that competitive threat may lead to more innovation by incumbents, relative to potential entrants. For example, Gilbert and Newberry (1982) show that, under certain conditions, incumbents will have a greater marginal incentive to invest in R&D than new entrants, when entry is a serious threat. This encourages preemptive patenting to guarantee the monopolist position occupied by the same firm. Alternatively, the monopolistic firm will invest in R&D if the cost is lower than profits that could arise from preventing the entry of new competitors.


Harris and Vickers (1985) extend this model by distinguishing two types of patent race: a “standard race”, where a prize is awarded to the first player reaching the finish line; an “asymmetrical race” where not only is a prize awarded for winning the race but also players lose something of value when a rival reaches the finish line. In an asymmetrical race, the challenger is deterred from making an effort to win the race because incumbents, through strategic interactions, would always deter any reasonable effort made by the challenger.


Boone (2001) notes that the response of an individual response to competitive pressure will depend upon the relative cost for its competitors. As a result, the effects of competitive pressure on innovation will differ across firms. An increase in competitive pressure may bring some firms to innovate, but hinder others to do so. An increase in competition cannot increase the overall efficiency in the market, nor will it increase the number of new products introduced into the market.


In an effort to test the two Schumpeterian hypotheses, numerous statistical studies have attempted to choose among a large variety of factors affecting innovation besides firm size and market concentration. Some of the recent findings distinguish relations between firm size and innovation, and firm size and market concentration. Cohen and Klepper (1996) find that the relation between firm size and innovation is stronger for process innovations than for product innovations. They caution, however, that their findings do not indicate that large firms are the only engines of economic growth, nor that there are no disadvantages to a large size.


Most studies have focused on a linear relationship between market concentration and innovation, while Scherer (1965) found that there may be a non-linear relationship. Specifically, it is possible that innovation increases with concentration up to some point and then declines. This finding has been replicated (Levin, Reiss, 1984). Some early work suggested that innovation might have de-concentration effects. Subsequent work suggests that innovation and entry to the market are associated. Granger causality tests performed by Geroski (1990, 1991) suggest that entry may cause innovation, rather than vice-versa. Similarly, others have found that innovation may be associated with the growth of smaller firms or entry, which in turn can lead to lower concentration in innovative markets (Acs, Audretsch, 1990).


Industry concentration rates and firm size, either measured as an average or using a threshold, affect negatively innovativeness (Dolfsma, van der Panne, 2007). For example, evidence is provided to show that SMEs are important sources of employment growth, and innovation in the high-tech sectors, both through existing firms and “new technology-based firms” (see Colombo, Grilli 2007; Santarelli, Vivarelli 2007; Ben Youssef et al., 2014). Other authors have emphasized the possible positive effects of small size in generating innovation (Jeroen et al., 2006). For example, some studies associate small firms in certain sectors with the commercialization of disruptive technologies that generate discontinuous innovations (Spencer, Kirchhoff, 2006), while for others, certain types of SMEs have a greater ability to rely on external networks (Rothwell, Dodgson, 1994) and to create innovative alliances (van Dijk et al., 1997).


The discussion on the determinants of innovation (see the Oslo Manual, Chap. 2) introduces some additional variables that play a very important role in a neo-Schumpeterian perspective: R&D activities, export orientation, skills and partnerships.


In-house research and development (R&D) is largely admitted to be a crucial determinant of innovation. It allows companies to create, exploit and transform new knowledge into products and processes. R&D, beyond trouble-shooting, also permits firms to absorb new technologies appearing on the market and to attract collaborative partners (Arvanitis et al., 2014). R&D performed internally is particularly important for innovation in the context of new and emerging technologies (Bozeman et al., 2007), where new technologies are difficult to acquire either produced by competitors (Becheikh, 2006) or provided through interactions with public research organizations.


Exports and market internationalization play a positive and significant role on innovation, since firms need to constantly innovate in order to remain competitive on the international markets (Becheikh, 2006).


Studies on the complementarity between employees’ skills and firms’ innovation activities are quite recent (Söllner, 2010; Østergaard et al., 2011; Parrotta et al., 2014; Farace, Mazzotta, 2015). Leiponen (2005) argues that without sufficient skills, firms benefit less from innovation, because they do not have the necessary complementary capabilities or absorptive capacity (Cohen, Levinthal, 1990). Results from a panel of manufacturing firms provide support for the hypotheses that high technical skills are complementary with R&D collaboration and product or process innovation. Similarly, case studies in fast growing economies like China indicate that skills inside firms are the main concern of SMEs (see for instance, Arvanitis et al., 2007). Human capital should be considered an enabling factor for innovation. Policy implications suggest that investments in skills help expand the group of firms in the economy able to innovate successfully. Freel (2005) confirms this result with a sample of 1345 British SMEs, studying patterns of associations between firm-level innovativeness and a variety of indicators of skills, skill requirements and training activity. His key findings underline the importance of intermediate technical skills, rather than high level technology skills. Moreover, he observes a strong relation between the introduction of both products and process innovation and firm-level training intensity. Collaborations with external sources of technology were also confirmed in a sample of 597 SMEs manufacturing firms (Freel, 2003). Concerning the spatial distribution of firm linkages, it appears that increasing firms’ size and export propensity are positively associated with external linkages at a higher spatial level.

Innovation in Emerging Countries: Empirical Analysis


There is a large body of empirical literature available on determinants of innovation in the context of developing countries. Table 1 summarizes some of the latest results. The relationship between innovation and firm size has been investigated to determine whether the Schumpeterian hypothesized advantage of large firms exists in developing countries. Pamukçu (2003) found that the propensity to innovate in Turkey is significantly determined by firm size (measured by the number of employees). Chudnovsky et al. (2006) and Gonçalves et al. (2007) in Argentina and Brazil, Benavente (2006) and Crespi and Peirano (2007) in Chile, Srholec (2007) in Czech Republic and Jaklic et al. (2008) in Slovenia and de Mel et al. (2009) for Sri Lanka extend this research line, and include size as one of the main determinants of innovation activities of the firms. Some studies found innovation to be negatively related to firm size (Aralica et al., 2008, for Croatia and Koubaa et al., 2010, for Tunisia). Whereas, Goedhuys and Veugelers (2012), Oerlemans and Pretorius (2006), Sanchez et al. (2013) and Goedhuys (2007) using, respectively, a dataset of 1563 Brazilians, 145 South African, 4133 Colombian and 257 Tanzanians firms found no influence on innovation activities.

Table 1 - Literature review of the determinants of innovation in developing countries contextTable 1

ns: non-significant; ø: non included in the model; +: positive effect; -: negative effect


The inconclusive results justify the inclusion of many control variables in order to obtain robust results on the effect of size and competition on innovation capacity of the firms. Such, is the case of the average education level of production workers, export intensity and market structure. Goedhuys and Veugelers (2012) in Brazil and Radas and Božic (2009) in Croatia found that innovation is positively associated with the education level of workers. This result is confirmed by Rahmouni (2013) and El Elj (2012) in Tunisia using cross-sectional survey data.


The impact of a firm’s trade orientation on innovative activities has been studied in developing countries frequently showing largely varying, unconclusive and contradictory results. On one hand, various studies showed a positive relationship between export and innovation in Argentina and Brazil (Chudnovsky et al., 2006; Gonçalves et al., 2007), in Sri Lanka (de Met et al., 2009) and Croatia (Aralica et al., 2008). It also appears that the opening to international markets (turnover from exports) apparently strengthens the development of innovations. Nonetheless, the export propensity has also shown to be unrelated to innovation in many Latin American economies (Crespi, Zuniga, 2012; this was also the argument of older studies such as Pirela et al., 1993). In some cases, Srholec (2007) and El Elj (2012) found that export intensity has a negative impact on the firm’s decision to undertake innovation activities. Finally, Goedhuys and Veugelers (2012), Koubaa et al. (2010) and Pamukçu (2003) show no relationship between export and innovation.


Some research supports the idea of a differentiation strategy that would have a significant and positive impact on innovation in firms. For Brazilian firms, Goedhuys and Veugelers (2012) have shown that competition seems to encourage firms to innovate. The same conclusion was found for the case of Sri Lanka by de Mel et al. (2009). However, El Elj (2012), for Tunisian experiences, revealed that firm competition is inversely related to innovation. This may be related to the strategy of domination by costs. Thus, such a strategy leads companies to keep their costs as low as possible, thus eliminating the often costly innovation projects.


Differences in innovation between firms can also be explained by strategic determinants such as R&D intensity, partnerships and technology transfers. In many developing countries, economic analysis if innovation shows a close connection between R&D activities and innovation (contractors cases of developing countries (Chudnovsky et al., 2006) and Gonçalves et al. (2007) in Argentina and Brazil, Crespi and Peirano (2007) in Chile, Goedhuys (2007) in Tanzania, and Pamukçu (2003) in Turkey confirm that the R&D activities seem to strongly encourage innovation).


In the same context, Radas and Božic (2009) and Aralica et al. (2008) for Croatia, Jaklic et al. (2008) for Slovenia and Koubaa et al. (2010) in Tunisia consider that R&D enables the production of knowledge and expertise, increasing the ability of the company to absorb new technologies emerging on the market and attract potential partners. All these studies, in addition to Rahmouni (2013) and El Elj (2012) for Tunisia, found that collaboration with public research organizations, higher education institution and other firms is a significant determinant of innovation activities. Moreover, Goedhuys and Veugelers (2012) for Brazil and Rahmouni (2013) and El Elj (2012) for Tunisia technology transfer (such as: technical center, license, quality certification, professional conferences and meetings, visits to plants belonging to foreign companies and competitors…) increased significantly innovation capacity.


The previous literature has identified several determinant factors of innovation. We could gather them under three types: internal characteristics of the firms, such as size, age, education level of education of the workforce (Schumpeter, 1942, 1934), openness to foreign markets and competition (demand-pull approach) such as export intensity and competition (Schmookler, 1966), and strategic determinants (technology push approach) such R&D activities, partnership and technology transfers (Rosenberg, 1974).


In what follows, we will try to test all of these variables in the context of a small open developing country; Lebanon.

Empirical study

The Lebanese context


Lebanon is a small-sized economy, with an estimated population of 4.1 million inhabitants, with a relatively high-level GDP of 41.7 billion USD and a per capita GDP of 15,900 USD in 2012, at the time of the survey. It is usually assumed that the economy is mainly driven by services such as banking and tourism representing 75.8% of GDP. Industry represents 19.7% of GDP, thus making it a rather non-negligible economic sector, in contrast with the usually belief that Lebanon has no industry. On the contrary, agriculture represents only 4.6% of GDP. The main problem of the country is the political instability both internally and externally due to the war in Syria that translates in a very high number of refugees and political insecurity concerning the future. Prior to the so-called “Arab spring” (2011), Lebanon experienced a rather strong economic growth reaching 8.5% in 2009, 7% in 2010. After the war in Syria, economic growth has substantially dropped reaching a mere 1.5% in 2011 and 2% in 2012.


Lebanon has a small but diverse and fragmented S&T community embedded in 41 universities and higher education institutions (12 of them with science and/or technology faculties) and 5 rather small public research centers (Hanafi, Arvanitis, 2016, chapter 4). All indicators (publication output, research budget, number of active researchers, etc.), show that most of the research is mainly carried out in four universities: the Lebanese University (UL), the American University of Beirut (AUB), the Université St-Joseph (USJ), The Lebanese-American University (LAU) and Balamand University (UB) and within one of the four specialized research centers of the National Council for Scientific Research (CNRS) as well as in the Lebanese Agricultural Research Institute (LARI). Moreover, research is quite active, with strong and increasing collaborations, mainly with European Union research collaborations: up to half of the scientific publications registered in the Web of Science are co-authored with foreign authors.


Although the size of most manufacturing companies in Lebanon is rather small, the country benefits form a surprisingly active private sector in R&D (Atweh, Arvanitis, 2014, CNRS, internal survey report). The National innovation survey showed that the main difficulty concerning R&D is rather its lack of formal contacts between the academic scientific community and the business R&D units. Interestingly, anecdotal evidence shows an intense personnel interaction between the business sector and universities, both with the public Lebanese University (UL) and the private universities (AUB, USJ, LAU, etc.) (Hanafi, Arvanitis, 2016). Hopefully, initiatives such as the creation of the Lebanese Industrial Research Association (LIRA) in 1997, which has been re-instituted in 2015 after some five years of inactivity, the success of the business incubator Berytech, and the promotion of joint industry-university research projects has permitted an increase of private sector contributions and participation. There is also an increasing number of small private research institutes, often NGOs, that carry out studies, mainly socio-economic studies and policy-oriented analysis, opinion polls, market studies, and studies for international organizations. They very frequently use the services of university staff (Hanafi, 2011).


Lebanon is a very active entrepreneurial country to all experts interviewed, and, although this entrepreneurship is usually not automatically considered innovative, all field interviews show a definite orientation toward rather audacious entrepreneurial activities, with strong foreign experiences. Innovation has been a central debate concerning the private sector after 2000, triggering many studies on the subject.


Stakeholders in various studies have identified considerable and persistent obstacles to innovation, most notably weakly enforced intellectual property; a limited market; meager training on innovation; little research and development; and poor infrastructure (Doumit, Chaaban 2012). Nonetheless, the innovation survey has been providing surprising results concerning these obstacles, and to a great extent do not confirm the view of a passive entrepreneurs, limited to well-known markets (Chakour, 2011; Ahmed, Julian, 2012; Mezher et al., 2008). These studies tend to defend the idea that the business practices, mainly because most firms are SMEs, family owned and managed by the family members are opposed to innovation. Nonetheless, the few empirical studies of entrepreneurship on this particular topic point rather to a quite active attitude toward innovation even in SMEs (for a review, see Stel, 2012). What is absolutely certain is a lack of institutional innovation (banks and other financing institutions are especially seen to be conservative) (Chakour, 2001), lack of institutional support and infrastructures by the state (Ahmed, Julian, 2012), and a very unstable and rapidly changing market to which firms need to adapt (Mezher et al., 2008). These obstacles will be addressed in part 3 of this article.


The Lebanese research and innovation has been described with a lot of detail (Hanafi, Arvanitis, 2016). Lebanon has been the country of numerous experiences to support an ‘entrepreneurship ecosystem’, in particular because of post-civil war and persistent political difficulties that translate in high emigration and instability in the region. Among the various initiatives the most remarkable has been the foundation of Berytech in 2001, which is probably the most successful business incubator in the MENA region. To date, Berytech has housed more than 170 entities, assisted more than 2,000 entrepreneurs in several outreach programs, disbursed more than US$ 350,000 in grants to start-ups, and invested more than US$ 5 million in Lebanese technology companies. It was among the first of such institutions in the region to receive accreditation from the EU as a Business Innovation Center, opening access to international networks for its companies and affiliates. In 2012, and with the support of the EU, Berytech launched the Beirut Creative Cluster, grouping more than 30 leading companies in the multimedia industry, and was the European Bronze Label for Cluster Management Excellence. Other business incubators of smaller size and less successful have been created in the North of Lebanon (BIAT in Tripoli), in the South (Southbic in Saida). Finally, Berytech has been housing the first venture capital fund of Lebanon for an amount of US$6 million, for Lebanese technology start-ups.


Another important initiative has been Kafalat, which provides funding, subsidized and guaranteed loans and guarantees for SMEs. Kafalat has maintained its action steadily and created also an innovative business fund. Its blend of both risky business funding and mainstream support to entrepreneurial activities has been instrumental in periods of low investment from the banking sector. It set-up a collaboration with the World Bank addressed to support small and micro-enterprises for more than US$ 30 million.


Public policy has not been particularly efficient in supporting business investments. In order to provide some support, and also trying to attract some foreign investment, the Lebanese government created an Investment Development Authority Lebanon (IDAL) seeking to enhance entrepreneurship through tax incentives, administrative reforms and the support of the incubators. A variety of international organizations are very present in Lebanon such as the World Bank, many United Nations organizations (ESCWA, UNDP, UNIDO, UNRWA) as well as a variety of foreign foundations. The European Union has had a quite permanent presence in the country, although the policy is limited by the absence of a bilateral agreement in science and technology contrary to many other Mediterranean Partner countries of the EU (Arvanitis et al., 2013).


It’s interesting to note that all the initiatives in favor of the business sector have been supported by private institutions and mostly a variety of academic non-for profit institutions, which is a Lebanese asset. Such is the case of the Université Saint-Joseph (USJ) that housed and supported the creation of Berytech, the American University of Beirut (AUB), that more recently created an entrepreneurship center. Some smaller initiatives are known from a wide range of private sector and civil society organizations supporting entrepreneurs (e.g. entrepreneurship education, funding for mature entrepreneurs and microfinance institutions). The public sector in favor of research and innovation, apart from the decisive action of the National research council, has been very timidly developed; one can mention the existence of the Industrial Research Institute (IRI) which is an organ of the Ministry of industry, acting for mainly certification and testing activities; the activities of the Lebanese industrial association (ALI) that supports industrial activities and has been involved in supporting technology in industry quite actively; LIRA, a scheme to enhance industry-research activities, launched in 1997 by ALI in collaboration with the Lebanon’s National Council for Scientific Research (CNRS) and the Ministry of Industry.


Nonetheless, although experts praise the survival and expansion of businesses in Lebanon after the hard times of the civil war (Ahmed, Julian, 2012) they also signal the absence of a coherent industrial policy, and the difficulty in defining a strong policy. In 2012, the government launched a series of policy measures in support of the country’s industrial sector, proposing fiscal measures, subsidies, import protection, and support to specific sub-sectors such as the aluminum industry and other energy intensive industries.


Finally, it is necessary to remind that the National Council for Scientific Research (CNRS) has drafted a comprehensive science, technology and innovation policy (CNRS, 2006) after a consultation of 30 experts. An action plan had been approved by the government in 2002, but its implementation was interrupted by the assassination of Prime Minister Rafic Hariri in February 2005. Since then there has been no explicit innovation or science and technology policy, but at the same the CNRS has been promoting actively research, international collaborations, and supported research-production activities (See Hanafi and Arvanitis, 2016, p. 191).

Sample and Data description


This study uses the data gathered from the first national innovation survey in Lebanon, which was conducted in the fall of 2012 by the Lebanese National Council for Scientific Research (CNRS) with the support of the World Bank. The questionnaire was designed by an author of this article on behalf of the CNRS and the survey was conducted by INFOPRO, a private enterprise in Lebanon under CNRS guidance. Survey results were handed over to CNRS early in 2013.


A total of 478 firms operating in the industrial sector have been surveyed. The sample was composed of manufacturing firms (441 enterprises), micro-firms (110 enterprises) and firms in the information and communication technologies (ICT) (37 enterprises). All industrial sectors have been included but activities in agriculture, mining, pharmaceutical and services (except ICTs) have been left out of the realm of the survey. The survey sample was designed also to be as close as possible to the distribution of the industry in Lebanon. An effort was made to cover more appropriately the very small firms (1 to 5 employees) and also a bias toward medium to large firms that are known to be more inclined to R&D and technological development.


Employment represents around 20,000 persons in the Innovation Survey, when the 2007 Industrial Survey of the Ministry of Industry counted 75000 employees in manufacturing industry. The Innovation survey thus represents around 10% of enterprises of the industrial survey and 25% of employment.


The ICT sector comprised of 37 firms in the sample represents about 2700 persons, while 110 micro companies represent around 1000 persons. The bulk of the sample (which is called the “industrial segment” in the survey report) contains 441 companies (85.5% of the sample). Most firms are rather young: 59.8% in the survey were created in the last twenty years. The survey shows no statistical relation between the age of the firms and their innovation capabilities, which is first indication that that their accumulated experience along the lifetime though substantial is not the only determinant of the introduction of innovation. As we will see, it rather depends upon strategic decisions.


60% of the firms do not have engineers among their employees and 25% of firms do not have technicians among their employees. 42% of firm owners have a B.S. or License degree. Only a total of 23% of firm owners have higher levels of education such as a Master’s or engineering degree. Less than 7% of firms with market shares between 50% and 80% have more than 20% of employees with science oriented majors.


The most common legal types of firms found in the survey were Anonymous Capital firms (S.A.R.L) or Family Owned, and they rely mainly on private and national capital. Foreign capital represents a negligible source of investment. Firms mostly target the domestic market, whereas 24.9% export more than half their sales and 12.7% of firms are strongly export oriented. The sampled firms are estimated to represent around 10% of industrial exports of the country. Only 56.9% of the survey sample is exporting.


The survey was asking firms to declare what was their share of the market. 167 firms (34.9% of the whole sample and 34.5% of the industry segment of the sample) did not answer to this question; indicating in the survey, that they did not know these figures. Nonetheless, 81% of firms know both the number and the names of their competitors and 12% mention they know their competitors without mentioning a name. In other words, the firms believe they know quite well their market. Not surprisingly, smaller firms know less well their competitors; nonetheless, the figures statistically do not show a relation between the size of the firm and the degree of knowledge of the competition. Similarly, more firms that export seem to know better the market competition, but again there is no statistically significant relation between export-orientation and knowledge of the competition.


The survey undergoes in a detailed description of R&D, showing firms seem to prefer to engage in R&D mainly for trouble-shooting and product development. The majority of firms have quite bad capacities of identifying future technologies – with the notable exception of ICT companies. Although only 23% of firms (110 firms) report having an R&D department, some 185 more firms (38%) report developing R&D activities even without having an R&D department (“Informal R&D”). Thus, 62% of firms realize some R&D activity and 38% have no R&D activity whatsoever. Traditional sectors, with the exception of Food products, have lower intensity in R&D than sectors oriented towards industrial clients (mechanics, machinery, equipment, chemistry…). For all these sectors, internal R&D is among the main channels of innovation.


Few firms accepted to report on expenditures in R&D (18%). 89 firms responded for this figure in 2010, reporting a total of US$ 4,992,500 and 87 firms for 2011, reporting a total of US$ 5,757,000. Average figures are an average of US$ 56,000 (2010) and US$ 66,000 (2011) per firm. Despite a bad economic prospect, average R&D expenditures for those firms reporting these figures, increased by 17% between 2010 and 2011. If all firms had answered, the survey sample would have estimated overall R&D expenditures between 9.8 and 11 million USD. By extrapolating the survey data, taking into account size and sector distribution, and by excluding the very atypical case of ICT firms and micro-firms, the survey estimated that the R&D expenses of the Lebanese industry to be around US$ 120 million for 2011, which represents around 0.3% of the GDP. This is a very large and until this survey unknown figure, showing the importance of private R&D.


R&D expenditures are highly concentrated and larger firms tend to spend proportionally more on R&D than middle-sized firms. The sectors where higher spending firms are located are ICTs and software, food and beverage, machinery. Sectors concentrating more R&D expenses are ICT, furniture and consumer goods, food and beverages, machinery and equipment. The share of R&D personnel in the workforce of a firm correlates loosely with the amounts dedicated to R&D in total sales, and R&D expenditures do not correlate to sales in significant levels. This is common to all innovation surveys as R&D has no direct effects on sales and profits but is the guarantee of a sustainable presence in markets. Moreover large firms do not necessarily spend more on R&D, but when they do, they spend proportionally much more than medium-sized firms. In other words, spending in R&D is related to a strategy that involves R&D and innovation.


Finally, the innovation survey indicates a close relation between the R&D activities and innovation, and all firms that innovate with product, services or process, show important R&D expenditures.


It’s necessary to remind that patenting and intellectual property management in Lebanon is very low. Less than 4% of the firms report registering a patent claim either in Lebanon or in a foreign patent system. Trademarks are deposited in one quarter of the cases. In 2010-2011 63% of firms acquired an internationally recognized quality certificate while only 28% of firms sought a quality certification. Quality is one of the main motives of technical interest of firms. Table 2 present some statistical details related to the sample.

Table 2 - Summary statistics of the variablesTable 2

The Models


In order to study the factors influencing firm’s innovation, we use a probit econometric model to determine the basic determinants of a firm’s decision to innovate. We use variables such as the age, size, competition, etc. Probit models are regression-based models used to analyze binomial variables.


In our case, the innovation decision is measured by a binary dependent variable, which is equal to 1, if the firm is innovating; or equal to 0, if the firm does not innovate. Probit models measure the probability of a variable influencing this innovation results. These models are generally used to model binary data.


Considering the innovation response as the dependent variable, let p(innovation) be the probability of the firm to innovate, p(innovation) = p[innovation = 1].


We define a latent variable y*i given by the following relation:


y*i is observed only if the firm i innovated


Where Xi is the is a vector representing the variables that summarize the characteristics of the firm i, βi a vector of unknown parameters associated with the vectors Xi et εi the term of error, and we suppose εi follows a normal law N(0;σ2).


The observed dichotomy variable yi is related to the latent variable y*i by the relation as follows:


yi = 1 if y*i > 0 where yi = 1 if firm innovate yi = 0 if y*i ≤ 0 where yi = 0 if firm did not innovate


Taking into account the presence of a problem endogeneity, the basic model specification estimated for the innovation decision yi is as follows:

Instrumental variable models

Model 1


The objective of this model is to estimate, for the totality of the sample, the impact of each explanatory variable on the probability of innovation in the Lebanon’s firm.


Model 2 & Model 3


In model 2 and 3, we used two different subpopulations. Model 2 is estimated just for firms belonging to the manufacturing sector excluding those belonging ICT sector. Model 3 is estimated just for a random sample of 85%. Thus it is estimated only 406 firms

Model 4


innovation = f(Age; Size; export; competition; partnership; technology trasfer; R&D * Skill)

Model 5


innovation = f(Age; Size; export; competition; partnership; technology trasfer; R&D * Skill; R&D * partnership)

Model 6


innovation = f(Age; Size; export; competition; technology trasfer; ForeignCapital; R&D * Skill; R&D * partnership; R&D * ForeignCapital)


In models 4, 5 and 6, we propose to estimate the impact of R&D activities on innovation while controlling this impact using specific variables such as Skill, partnership and foreign capital share.

Endogeneity model


The unobservable random shocks affecting the firm’s decision to innovate could affect his ability to export. Indeed, export intensity variable is a dichotomous endogenous. It’s instrumented using other variables in the dataset as follows:


export = f(Age; Size; Sector; Skill; Group’Membership; Foreign Capital Share; Firms’location; R&D; competition; partnership; technology transfer)


To rule out the possibility that our results are driven by our choice of instruments, the three selected instruments (Sector, Group membership, Firms’ location) are introduced into the model: each separately, in pairs and three at the same time. After accounting for this endogeneity, our results suggest that by introducing the three instruments at the same time gives the most robust results. Results of these regressions are available from the authors upon simple request.

The Variables


Dependent variable: The firm is considered innovative if it has introduced at least one innovation in process or product or organizational or market.

Explanatory variables


Firm Age: New firms tend to present the highest probability of innovation. The oldest firms tend to present a somewhat lower probability, although some firms from intermediate to old ages present a high probability, which may be attributed to selection.


Firm Size: Schumpeter’s hypothesis claims that innovative activity increases more proportionately than the firm size.


Skill: A greater proportion of highly qualified workers in the firm would positively affect the firm’s innovation performance.


R&D: R&D is positively (and more) correlated to innovation (than any other determinant). (Mairesse, Mohnen, 2005).


Competition: Competition measured by the number of competitors in a sector would have a positive impact on the probability of innovation (Arrow, 1962).


Technology Transfer: Most of the empirical studies illustrate the importance of TT of various types in the innovation process.


Export intensity: The export intensity is regarded as a potentially endogenous explanatory variable. Thus, it is analysed based on the following instrumental variables.

Instrumental variables


Sector: This variable represents the firm’s technological intensity. Based on the OCDE classification, we consider four ordinal classes: Low Technological intensity (LT), Middle-Low Technological intensity (MLT), Middle-High Technological intensity (MHT) and finally High Technological intensity sectors (HT).


Group’ Membership: firms belonging to a local group or to a foreign group would be more innovative.


Foreign Capital Share: Foreign shareholding would be correlated to substantial innovation capabilities.


Firms’ location: We introduce this variable as an important determinant of exportation intensity. Indeed, firms with a significant rate of export as well as a high share of the domestic market were found to be those located in Mount Lebanon and Beirut.


The variables are summarized in table 3, as follows:

Table 3 - Variables and their descriptions*Table 3Table 3

*: Process, product, organizational and market


Table 4 below presents the correlation values for all variables, which are relatively low. From this table, we see that the largest correlation coefficient is that of R&D and the size of the firm and that is positive. This confirms the hypothesis that the large-size enterprises which have more R&D activities. We also note that the export intensity is positively correlated with all variables except Skill and firm age. This confirms that correlation between the instrument and the instrumented variable is justified. This pushes us to instrumented dichotomous endogenous variables.

Table 4 - Bivariate correlation matrixTable 4

*: Significant at 10%, **: Significant at 5%, ***: Significant at 1%



Table 5 presents the empirical results of the determinants of innovation decision by Lebanese firms using instrumental variables techniques [2][2] The model is estimated using STATA version 13.0.

Table 5 - Econometric resultsTable 5Table 5

*: Significant at 10%, **: Significant at 5%, ***: Significant at 1%


Table 5 presents the estimation results of the Probit model in two stages (Newey, West, 1987). Our econometric specifications have predictive power that exceeds 67.23% for all models. These rates are considered acceptable and confirm the robustness of our results. This is confirmed by the fact that the Chi-2 values corresponding to ours models (Wald Chi2 (14) and LR Chi2 (14)) are significant. Indeed, the results of our regressions are statistically significant, which allows us to accept that our dependent variable is very associated to the explanatory variables.


Wald test does not allow us to reject the hypothesis of exogeneity of the variable Export for all models except 2 and 3. This result is also confirmed by the exogeneity test of Smith & Blundell (1986) with Chi2(1) = 4.79 and a significant critical probability to reject the null hypothesis of exogeneity for model 2 (p-value = 0.0285) and Chi2(1) = 3.54 for model 3, with a critical probability (p-value = 0.0401). Indeed, for both models 2 and 3, the hypothesis of variable’s endogeneity problem is rejected. So, we take simple probit models to explain the determinant of innovation.


The main determinants of innovation activities in the Lebanese firms are: firm size, export orientation, R&D activity, partnerships and technology transfer activities.


These determinants can be grouped as follow:


Age: Firm’s age has no effect on the innovation decision


Econometric results show no statistical relation between the age of the firms and their innovation capabilities, which is first indication that that their accumulated experience along the lifetime though substantial is not the only determinant of the introduction of innovation. Indeed, it rather depends upon strategic decisions.


« Schumpeterian » hypothesis: Size determines innovation.


Table 5 shows that the propensity to innovate increases with firm size. On the other hand, we note that the probability to innovate increases with the size of the firm. The coefficient of variable size is significantly positive for all models. Small size firms are less tempted by innovation than big size firms.


Our results show that the Schumpeterian hypothesis that large firms are more innovative than small ones is confirmed for all models. Thus, the larger the company is, the more likely it innovates to take advantage of scale economies and to maintain its market share.


Openness: Exports play an important role.


The non-significant relationship between exports and innovation behavior in Lebanon is shown by models with instrumental variables (1, 4, 5 and 6). However, results of the two models 2 and 3 show a positive correlation between exporting and innovation decision. The positive sign of the coefficient for the variable representing part of exports in the turnover indicates that firms that produce for domestic market tend to be less innovative than those producing for export markets. Thus, high degrees of openness to foreign markets increase a firm’s probability of innovation.


Table 5 shows that the coefficients of competition pressure are statistically no-significant. Indeed, this pressure, summarized firm’s market share, cannot force the Lebanese firms to facilitate opportunities for innovation.


Strategic determinants: R&D, partnership and technology transfer all depend upon adopting a strategic view on innovation.


Our results reveal that innovation decisions are positively influenced by R&D activities, partnership and technology transfer. Thus, having R&D activities, cooperate with other companies or institutions (universities, research centers, public authorities…) and using transfer technology channels increase innovation probability for Lebanese firms.


Regression 4, 5 and 6 take into account the interaction effects between, respectively, R&D activities and Skill, R&D activities and partnership and R&D activities and foreign capital share on innovation decision. The models with interactive terms (model 4 and model 6) show that the estimated coefficient of the interactive variable R&D*Skill and R&D*ForeignCapital are positive and significant. This confirms the hypothesis that one hand does having a skilled workforce in the presence of an R&D department and also to have a foreign equity in the presence of an R&D Department increase chance to innovate in Lebanese firms.


Our results are very close to findings in other developing countries in the same region, in Latin America or even in Europe. In a similar study, El Elj and Abassi (2014) analysed the determinants of innovation in four southern Mediterranean countries, Egypt, Jordan, Syria and Turkey, using firm-level data from the World Bank Investment Climate Survey. They investigated various internal and external factors influencing the decision to innovate. Despite some differences between the four countries, they found several common characteristics like the importance of knowledge creation and learning as instruments to improve the absorptive capacity and subsequently to enhance the propensity of firms to innovate. Empirical works on the Tunisian case (Koubaa et al., 2010; Rahmouni, 2013) find similar results. R&D activities, cooperation, skills and openness were found to be the most important determinants of innovation activities.


This fundamental result is not limited to the case of South Mediterranean countries. Similar results are to be found in small Latin American countries like Chili, Uruguay or Colombia as well as in large economies such as Brazil and Argentina (see Table 1). The same can be said about some small European countries like Croatia, Slovenia and Cyprus.


An important common aspect between all these countries, in addition to their small size, is their weak industrial development, and its relatively low contribution to economic growth. Added to the predominance of SMEs, partnerships and transfers of technologies are paramount to the development of innovative activities in the firms of these countries.



Innovation is a complex process, which is propelled by numerous factors. In addition to the Schumpeterian explanatory variables, our results show that the relationship linking several other variables like R&D, exportation, partnerships and technology transfer with innovation are often marginalized even if they are very important determinants.


For policy makers, one of the most important decisions in fostering innovation would be to encourage large firms in the various economic sectors by encouraging strategies developed by firms for becoming larger to benefit from economies of scale as a whole and particularly economies of scale of innovation activities. It is also recommended to set up institutions to help these firms.


At this level, the role of intermediaries’ institutions of technology transfer appears to be decisive in the context of a country where very few large firms exist and where SMEs need, more than in other countries “help” for their innovation activities. One way to sustain such approach is to reinforce the role to be played by academy-industry partnership. Because Lebanon has a relatively efficient university system whose innovation potential does not seem all exhausted.


Our results confirm the point of view that the so-called “Schumpeterian conjecture” suits small and open developing countries better than it does for developed countries: size matters more in innovation decisions for smaller economies. We also show that joint research and the use of known technology transfer mechanisms are as important as the size. Additional econometric investigation is needed to find out if there is substitutability or complementarities between these two major determinants of innovation.


A number of issues deserve further research. First, considering the important contribution of the service sector in the Lebanese economy, it is crucial to focus attention on the firms in this sector to at least understand its particularities. The same is true of the agricultural sector which still counts for an important portion of the economy. The results of the Lebanese Innovation Survey concerning ICTs indicate a very dynamic portion of the entrepreneurial activities, with a strong export orientation, including Gulf countries, and a rather young personnel. A second limitation concerns with the type of SMEs. The taxonomy of Keith Pavitt recently used by De Jong and Marsili (2006) identifies four categories of small innovative firms: science-based, specialized suppliers, supplier-dominated and resource-intensive. It could be interesting to see if the determinants remain unchanged in the different categories of firms. It should also be necessary to examine the predominance of different types of innovations (product, process, marketing or organizational). Innovation in the developing countries seems to be taking the form of organizational and marketing rather than product and process innovations. This needs to be understood, since R&D in SMEs is mainly oriented toward better products and more competitive processing.


Finally, it should be noted that future research could also be oriented to make in depth comparison between the case of Lebanon and similar countries in the MENA region like Jordan, Morocco, Syria and Tunisia.


In terms of policy recommendations these results confirm the necessity to have a differentiated policy, with on one side: specific measures aiming toward larger firms to boost innovation, support internal creation of technology and R&D, and linkages with local providers; and, on the other side, specific incentives to SMEs that aim, apart from the very fundamental need to access to credits and venture finance, promote partnerships with universities, research centers and other firms.


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Acknowledgments: We wish to thank particularly the National Council for Scientific Research (CNRS) of Lebanon that authorized us to use the National innovation survey (anonymous survey data). The Innovation survey has been performed in 2011 under the supervision of Dr. Mouïn Hamzé, Secretary General of CNRS, Dr. Rigas Arvanitis, scientific coordinator, and Mrs. Rula Atweh, European and external projects manager at CNRS. Funding was provided by the World Bank. The Survey was performed by INFOPRO and analysis was published and released in February 2014 in a report by Rula Atweh and Rigas Arvanitis. The opinions expressed in this article do not necessarily reflect the views of the National Council for Scientific Research (CNRS) of Lebanon.


The model is estimated using STATA version 13.0



The question of the determinants of innovation activities in the business sector is one of the issues that take the most attention in the theoretical and empirical researches in the economics of innovation field. This paper is probably the first study that develops an econometric approach to analyze firms’ decisions to innovate in Lebanon. More precisely, we analyze the impact of firm characteristics, competition environment, human capital, R&D activities, partnership and technological transfer on good, process, organizational or marketing innovation. We use a survey of 478 industrial enterprises conducted by the local National Council for Scientific Research (CNRS) with support of the World Bank during 2011-2012. Using a probit model, the results confirm the Schumpeterian hypothesis stipulating that larger firms present a greater capability to innovate. At the same time, the market share has a positive but not significant effect on innovation. We also find significant statistical relationship between R&D activities, partnerships and technological transfers with innovation decision of Lebanese firms. In terms of policy recommendations, these results confirm the necessity to give incentives to rather large firms in order to boost innovation in the country and at the same time to implement new incentives to SMEs for the promotion of partnerships with universities, research centers and other firms, including firms in the same sector. Finally, our results confirm the point of view that the Schumpeterian hypothesis seems to suite better for small and open developing countries than developed ones.
JEL Codes: O12, O3, C25


  • innovation
  • Schumpeter’s hypothesis
  • R&D
  • probit regression
  • lebanese firms

Plan de l'article

  1. Theoretical and empirical background
    1. Main Lessons from the Literature
    2. Innovation in Emerging Countries: Empirical Analysis
  2. Empirical study
    1. The Lebanese context
    2. Sample and Data description
    3. The Models
      1. Instrumental variable models
        1. Model 1
        2. Model 2 & Model 3
        3. Model 4
        4. Model 5
        5. Model 6
        6. Endogeneity model
    4. The Variables
      1. Explanatory variables
      2. Instrumental variables
  3. Results
  4. Conclusion

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