R&D and Technology Purchasing Decisions in the

Mexican Pharmaceutical Industry

Zuniga, M. P. , Guzman, A. & Brown, F.

April 30, 2004

Abstract

This paper investigates firms’ technology sourcing strategies and in-dustrial performance in a developing country context. First, we analyzethe determinants of technology investments in the Mexican Pharmaceu-tical and test for endogeneity of investment decisions both in continuousand discrete choice models. Second, complementarities of strategies areexplored in a productivity equation. Differences between domestic andmultinational firms are highlighted. R&D and technology purchasing(TP) are found to be non related decisions. Different motivation leadthe firms to conduct these strategies. Further, whilst R&D is explainedby export participation, TP is motivated by the size of domestic sales.Nevertheless, for firms conducting the two strategies, a substitutive re-lationship is found. Finally, the importance of TP as a determinant ofproductivity is confirmed, particularly for the case of indigenous firms.

Key words: technology acquisition, developing countries, R&D, Phar-maceutical Industry.

JEL : O31, O312, L65

Acknowledgement 1 We are grateful to David Margolis from TEAM-Sorbonne for useful comments. This papers is part of the ECOS NORDResearch Programme, Action No. MS031 supported by ANUIES and CONA-CYT.

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1 Introduction

It has been persistently argued in the literature that innovation in developingcountries [henceforth LDCs] is limited by a large number of factors. Amongstthem are: financial constraints, institutional obstacles, and weak technical com-petences in local industries. In addition, firms in LCDs find it difficult to appro-priate the rents from new technology [e.g. weak industrial property protection,etc.], hence devote few resources to basic and innovative research [Archibugi;2002; Braga & Willmore, 1991].

In this context of prohibited R&D investment, there has been a great dealof debate as how beneficial technology purchases are to LDCs as alternativepromoters of technological developement. Countries as India and Korea in par-ticular, has moved to restrict such agreements in the early 1980s in hopes ofstimulating indigenous R&D [Lanjouw, 1998; Jefferson et al, 2002]. Only anumber limited of LDCs has promoted technology purchasing [TP] as a comple-mentary strategy to innovation and a precondition to technological development[i.e. India, Taiwan, Katrack, 1997; Arora, 1999]. Moreover, innovation policiesare until recently being implemented in a way to push the local firms´R&Dcapacity and collaborate in technology agreement with foreign firms [Kumar,1999].

As Bassan and Fikkert [1996] have pointed, to upgrade technology, LDCscannot rely on their own R&D effort, which implies a ”reinventing the wheel”investment.To improve technical performance firms import new knowledge fromforeign countries both embodied and disembodied.1 In fact, in most LDCstechnology imports are the most important source of knowledge acquisition byenterprises. In this context, firms should decide how use their internal R&Dresources effectively. They decide whether to invest on own R&D and how much,or to acquire external knowledge which can complement or substitute internalefforts [Arora and Gambardella, 1994; Veugelers, 1997]. The understandingof the determinants of this decision taking is essential to explain the firms’technology performance in LDCs and it thus has very important implicationsin terms of industrial and innovation policies [i.e. technology transfer policies,R&D subsidies, etc.] .

Whereas a theoretical framework exploring technology sourcing choices isstill absent some insights are found in the Industrial Organization literature.Around the idea of the effects of R&D spillovers [own internal R&D affectingrivals’ R&D investment] these studies demonstrate that exernal R&D will typ-ically substitute for own R&D in the receiving firm [De Bondt, 1996; Kamienand Swartz, 1982]. The imperfect appropriability of spillovers (i.e. imitation

1Knowledge can be embodied in imports [i.e. machinery and production technologies,materials, inputs, etc.] and disembodied through arms´length and technology contractingand other technology purchases related to know-how contracting and patenting [Griliches,1992].

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hasard) as an -informal- knowledge may decrease strategic incentives to invest inR&D among competing firms [D’Aspremont and Jacquemin, 1988]. Empiricalworks suggest however that firms are likely to conduct a strategy of complemen-tarity between formal technology strategies [R&D cooperation, joint ventures,etc.]. It has been found that a positive rather than a negative relationshipmight predominate between internal and quasi-external and external sources ofknowledge, at least for the case of DCs firms [Veugelers, 1997, Cassiman andVeugelers, 2001; Kaiser, 2002].

In this context, complementarity between TP and a R&D absorptive capac-ity might be necessary in a way that facilitates technology-lagging behind firmsto discriminate among technology options, concentrate and specialize, and byassisting in production [Arora, 1999].Nevertheless, few evidence exist about acomplementary relationship and few is known about the real impact of TP oninternal R&D effort and their returns related to productivity.

In the line of the absorptive capacity and the technology sourcing litera-ture, this paper presents an exploration about the existence of complementarystrategies. For this purpose, it presents evidence on R&D and technology pur-chasing determinants as jointly determined investment decisions in the MexicanPharmaceutical Industry. Three critical questions on firm technological behav-ior and economic performance are approached: i) How firms source technologyin a-priori rather knowledge intensive industry in a developing country context,ii) What characteristics of firms determine the technology sourcing choice, iii)How technology sourcing strategies interact: Is there a complementarity be-tween R&D and technology contracting? And, iv) How much these technologysourcing strategies contribute to productivity, both separately as single strate-gies, and as complementary strategies?

We analyze micro level data in the Mexican Pharmaceutical Sector for theyears 1994 and 2000. In a first part, we analyze the determinants of technologyinvestments using a simultaneous system and a discrete choice framework. Ina second part, we explore interactions of these strategies and their returns ina productivity equation. Differences between domestic and multinational firmsare highligthed. The findings suggest that R&D and technoloy purchasing de-cisions are exogenous and non significant to each other. Technology investmentand external acquisition are then two different non related decisions. Thereforea different motivation conduct the firms to invest in internal and external tech-nology: whilst R&D is indeed explained by export participation, TP is rathermotivated by the size of domestic sales.Nevertheless, estimates in the produc-tivity equation show that for firms engaged into the two strategies (internaland external) there is a substitutive significant relationship as reflected by thecoefficient in the interaction term. The significant importance of technology pur-chases as a strong determinant of productivity levels is confirmed, particularlyfor the case of indigenous firms.

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The reminder of the paper is divided into five sections. The following sectiondiscusses results by previous studies and exposes our hypotheses. Section IIprovides a statistical profile of the Mexican Pharmaceutical firms that compriseour dataset, distinguishing technology strategies by multinationals and localfirms. Section III shows the model set up and provides the estimation equations.Section IV presents econometric methods and discusses results. The last sectionsummarizes our conclusions.

2 R&D and Technology Sourcing Strategies

Since the decade of the 80s, an important strand of the literature on LDCshas sought to give some insights about the relationship between indigenoustechnical capacity and external sources of knowledge [Katrack, 1989, Kumar,1987, Arora, 1991].2 Mostly focusing on the interplay between imports andR&D, these studies have arrived however to mixed results.Furthermore, and inspite of its importance, few studies have explored technology purchases and itsimpact on technology investment and productivity in the context of LDCs.3

Empirical studies on technological strategies in industrialized countries [DCs]has shed some light on this matter. In this context [i.e. more technology activefirms], it has been found that a complementary relationship would tend to pre-dominate between external technology and internal R&D effort. As a commonargument of departure, it has been argued that R&D plays a double criticalrole in economic performance: as an innovation determinant R&D, and as ab-sorptive capacity for external knowledge [Cohen and Levinthal, 1989, 1990].Accordingly, a positive relationship between internal and external knowledge[the ”make” and ”buy” strategies, as well as, with the cooperation strategy] hasbeen found by numerous studies : Caves and Uekusa [1976], Veugelers [1997],Arora and Gambardella [1990], Branstetter and Sakakibara [1998]; Cassimanand Veugelers [2000, 2001].

Firms that invest more in own R&D, or firms that are more innovative,are more likely to source technology externally and to conduct R&D alliancesand collaborations [Kaiser, 2002]. In addition, outward orientation and foreignownership have been found to influence positively the propensities to engageinto these complementary strategies [Cassiman and Veugelers, 2001]. Evidenceon the complementarity of spillovers and external knowledge on R&D in thePharmaceutical Industry is provided by Henderson and Cockburn [1996]. Theirresults seem to indicate that there are significant returns to size in this sector

2Moreover, differences in technology intensity (i.e. technological change) across industriesand the important disparities between LDCs and OECD firms in terms of technology perfor-mance, preempt any generalized conclusion about causality linking the technology sourcingstrategies.

3The importance of technology sourcing through imports and local R&D capacity to fosterproductivity growth has been previously stressed in the literature [Coe & Helpman, 1995;Keller, 1996, 1997; etc.]

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and particulary, that large firms seem to enjoy a primary advantage to captureand use internal and external spillovers of knowledge. Similar findings are foundby Arora and Gambardella [1990] in the biotechnology industry [see Arora etal, 2001].

Regarding te technology sourcing strategies by firms in LDCs, more incon-clusive results remain. The opposing views seems to be here more accentuated.On the one hand, it is argued that a substitutive relationship between TP andR&D may predominate. Firms can choose to buy technology or develop them-selves. Hence LDCs firms would be likely to become more dependent of foreigntechnologies, and increasingly substitute local R&D effort for external technol-ogy. On the other hand, this relationship might be complementary becauseTP can serve as a catalyst for domestic R&D and, secondly because importedtechnology is often be adapted to local conditions [Lee, 1996]. Nonetheless,several arguments support the hypothesis that a positive relationship may existbetween external knowledge and internal effort. First, the need for adaptingforeign knowledge thorugh imports is facilitated by own R&D.

Second, if the imported knowledge or product is technologically related tofirms’ existing products, the existing in-house capabilities may complement theimported know-how in a cost-efficient division of labour [H. Katrack, 1997].Hence the imported knowledge in turn, may lead the enterprise to undertakefurther technological effort. In this line, TP may allow the firm not only to takeR&D investments decisions more efficiently but also to satisfactorily absorb newknowledge to generate new ones [Cohen & Levinthal, 1989; Glass, 1998].In theopposite, the importing of unrelated knowledge [diversification] might be morelikely to substitute R&D investment [Katrack, 1997]. And third, technologypurchasing [TP] might make LDCs domestic firms achieve faster productivitygains [Arora, 1995; Bassan & Fikkert, 1996].

Firms in LDCs may find technology sourcing highly productive and becomemore efficient with TP -technology purchasing-, which can serve them as abuilding-block for in-house capabilities In such a way, TP are meant to facilitatelearning by doing R&D process -unit cost reduction by increasing production-which will facilitate the building of internal technical competences [Johnson,2002].Likewise, firms’ outward [inward] market orientation should matter toconduct a technological active [less active] strategy, either conducting R&D andconducting a complementary strategy. to become competitive in internationalmarkets.

Contrarily to the studies on DCs’ firms, empirical studies regarding tech-nology sourcing strategies and industrial performance by firms in LDCs arriveto more mixed results. Earlier studies report report a positive, though weakrelationship between some mesure of imported technology and firms’ in houseR&D effort [Deolalikar & Evenson, 1989, Lee, 1996; Katrack, 1997; Fikkert,1994, Basant, R. and Fikkert, B., 1996].

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Further empirical evidence on a complementary correlation between tech-nology purchasing [i.e. technology licensing] and internal effort in LDCs’s firmshas been found by Braga & Willmore [1991] for Brazilian firms, Alvarez [2001]for Chilean firms, and Chang & Robin [2003] for Taiwanesse firms. These stud-ies corroborates Veugelers and Cassiman [1998] findings on the positive impactof export participation and foreign ownership on the propensities to innovate.Moreover, their findings suggest that this complementarity might predominantlyprevail in the high-tech industries, contrary to traditional industries where asubstitutive strategy seems to dominate [Bassant and Fikkert, 1996; Chang &Robin, 2003].

Alvarez [2001] finds for instance, that technical licenses show a positive im-pact on R&D and administration-related innovation. Nevertheless, he has alsofound that licensing has non significant effect on product and process innovationoutputs.4 Mixed results are found by Lee [1996] on sample of Korean manufac-turing firms. He shows that firms importing technology (TP) tend to committhemselves to R&D effort through having formal R&D institutes. Controllingfor sample selection bias [firms propensities to conduct R&D and intensities,Heckman, 1979], amongst the firms engaged into R&D, TP shows however anon-complementary relationship with R&D efforts, and it appears to be substi-tute when a interaction with international inventive activity is included.

Despite the obstacles related to technology markets in LDCs, there is a in-creasingly view suggesting that there do appear to be real opportunities forLDCs to obtain high yields to their investments in technology purchasing [TP]agreements [Arora, 1996; Bassant and Fikkert, 1996].5 For instance, in a studyon Indian-Firms technology contracting, Arora [1996] shows that the trans-ferring of know-how when is successfully bundling [patents wit know how] tocomplementary inputs can bring efficiency gains for local firms. In a recentpaper on Brazilian firms, Johnson [2002] shows evidence on a higher probabil-ity to innovate and apply for a patent, when the firm has previous experienceon technology licensing. Basant, R. and Fikkert, B. [1996] have found thatTP can play even a more significant role than R&D to explain productivity. 6

Nevertheless, while their evidence confirms the effectiveness of TP to push com-petitiveness, it also corroborates previous results by Basant [1993] and Fikkert

4Alvarez [2000] reports a negative although non significant relationship of TP with prod-uct and process innovation. He finds that exports explain mainly technological innovation inChilean firms as measured by different indicators of innovation [Direct foreign direct invest-ment and technical licenses are found to be less important to explain innovation].

5Nevertheless, obstacles to TPs contracting related to the imperfectness of technologymarkets for [moral hazard, asymmetric information and opportunistic behavior, Caves et al.,1983, Teece, 1981, Markusen, 1995] which are particularly acute in LDCs, deter knowledgecontracting with local firms.

6Surprisingly and particularly relevant for industrial policy, they found -in an all-industriesmixed estimation-, that there were high and significant private rates of return to expendi-tures on technology purchase, whereas R&D turned to be non significant in the productivityequation. In addition, they also found differences across industries on the mode of sourcingtechnology and their returns related to production.

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[1994] about the negative relationship that can emerge between internal R&Dand TP. Particularly in high technology or scientific industries, local firms inLDCs would tend to substitute R&D investment by TP in the production ofknowledge.

This study attemps to fill this gap in the literature in technology strategiesin LDCs countries by exploring the determinants of R&D and TP (technol-ogy purchasing) as external knowledge acquisition in a simultaneous frameworkwhere decisions are assumed to be jointly determined. As we just seen, theissue of simultaneity of decisions has being neglected in the empirical studies onLDCs. We attempt to evaluate the determinants of technology sourcing and theinterrelatedness of the firms’ decisions: investing in R&D and buying externalknowledge (TP).

2.1 Determinants of technology Investments

The decision to innovate and develop internally technology is determined by avariety of factors. These have been distinguished between demand or market-related factors and supply-side factors [Levin and Reiss [1986]; Dasgupta &Stiglitz, [1985]].

From the demand-size, following schumpeterien hypothesis, firms’ size andthe nature of competition matter for R&D investment and innovation: monopolypower would be translated into a larger capacity to invest [ibid, Cohen and Klep-per, 1986]. Second, larger firms have a stronger capacity to appropriate thereturns to R&D [first mover advantages, quality reputation, superior market-ing and distribution networks, etc.] and they have an easier access to financing,both in domestic and international markets. From the supply-side, technologicalopportunities and appropriability conditions [whether technology will dissipateeasily to other firms] affect firms’ innovation decisions [Cohen and Levin, 1989]:the former stimulating technology-races and stimulating R&D investment sub-sequent to the new invention options, the latter deterring or stimulating internaltechnology investment according to the nature of the R&D [an absorptive andinnovative capacity, Cohen & Levinthal [1989], Kamien & Zang [2000]].

In a larger sense, the optimal technology sourcing strategy [i.e. generationand/or acquisition] will further depend on the nature of the firm [technology lag-ging or advance firm, i.e. being a MNS or domestic firm] and upon the relativecots of technology sourcing, mainly the costs of technology transfer or absorp-tion costs [transaction costs and moral-hazard related costs, Williamson [1989],Teece [1986], Arrow [1962]]. The expected profits -discounted values- related toeach technology sourcing choice will allow firm to discriminate between the twooptions, internal and external sourcing.

Technology sourcing choice can be defined either simultaneously or sequen-tially [conditional on the decision to do or not R&D investment, see Crepon et

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al, 1998]. The firm has two broad although not necessarily substitute technol-ogy options: it can develop its own R&D [internal technology or the ”make”option] to develop product and/or process innovations and achieve productivitygains; and/or it can purchase production and product technologies from otherfirms.7 This external technology [the ”buy” option], or disembodied technologypurchasing [TP] may concern technology patent licensing [product and/or pro-cess], arms’ length and know-how contracting, etc.; that is, acquisition implyingthe transfer of tacit knowledge [Foray, 1999]. Technology purchasing then [eg.technology imports, technology licensing, etc.] are likely to affect the propensityto undertake R&D activity. In the opposite, embodied technology purchasing(ETP) concerns technology acquired through imports machinery, material andinputs. 8

We proceed now to explain the expected impact of traditional variables high-lighted in the literature to be taken into account in our technology choice modelsexplained in the following section.

Firm Size. As mentioned previously, the firm size has generally been positedas a determinant of innovative activity in the literature [Cohen and Klepper,1996].Nevertheless, less is know about the impact on the propensity to con-duct TP. At this respect, it may have an ambiguous effect : either large firms[mostly MNs] are the ones engages into more external technology [headquar-ters’ transfer], or smaller firms are the ones lacking technical skills and relyingon external assistance [see Arora, 1990, Arora et al, 2001]. Concerning R&D, afavorable impact has been confirmed in the empirical work, including the stud-ies on LDCs’ industries. First empirical explorations assumed that there wasa positive linear [continuous] relationship between the two variables [see Cohenfor survey 1995]. Further investigations have shown mixed results about thisreal linearity. Testing for non-linearity several studies have reported a U-shapedrelationship [Kumar and Squibb [1996], Katrack, 1997] indicating that above asize threshold firms invest tend to invest less and less in technology. 9

Outward orientation. The theoretical literature suggest that openess may af-fect innovative behavior [i.e. knowledge accumulation in the endogenous growthmodels, Grosman and Helpman, 1991, Romer, 1991] to the extent that it af-fects production and reallocation of ressources in the more knowledge intensive

7Other formal means of technology sourcing between firms concern sub-contracting, out-sourcing, alliances, R&D Joint Ventures, etc. Firms also acquire technology through informalmeans, which have been recognized to constitute major means to enhance technological capa-bility in LDCs. Amongst these, imitation by reverse engineering remains an important wayof technology transfer [Glass, 1998]. Others sources are informative literature and patents onforeign inventions, industrial meetings, etc.

8A third technology-sourcing option, related mostly to the imperfect appropriability ofspillovers (i.e. imitation hasard) constitutes the R&D cooperation or technology alliances[D’Aspremont and Jacquemin, 1988, see recent work by Kaiser, 2002].

9Others have also found evidence of a cubic relationship with two thresholds acting on thefirm size and R&D intensity [Scherer, 1965; see Kumar and Aggarwal for the Indian Industry,2000].

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sectors. Empirically, export status has been found to play a significant role oninnovative effort [Braga and Willmore, 1991; Alvarez, 2001]. It is assumed thatan outward oriented firm confronts a more striking competition in the globalmarkets, and consequently a higher pressure on more innovative products 10.

Consequently, indigenous firms participating in global markets should bemore likely to invest in internal R&D. To the extent that the relationship be-tween internal and external strategies are ambiguos, it can be assumed thatexporting domestic firms, would be more likely to conduct a complementarystrategy [Braga and Willmore, 1991]. Hence, in the opposite, more inward ori-ented domestic [captive markets], would be more likely to conduct a substituterelationship: a higher propensity to conduct TP may predominate, and/or ahigher elasticity of substitution between R&D and TP [see theoretical modelson product and process R&D by Cohen and Klepper, 1996].

Foreign Ownership. Important differences in terms of economic performanceand local technology behavior distinguish Multinational from Local firms. Asexplained by Kumar, N. and Agarwal, A. [2000] on the Indian Industry, thepropensities of MNs to undertake technological investments, R&D and TP differ[could be lower or stronger] from the ones by their local counterparts [holdingother factors constant]. The effects of controlling for ownership [MNs dummy]are then ambiguous on both R&D and TP investments. Adaptative R&D is nec-essary for the commercialization of some products, the largest the adaptationto local preferences the most important the presence of a local product devel-opment. In the opposite, few R&D is in fact conducted given the advantages ofcentralizing R&D labs at the headquarters.

It may be expected that they can be lower to the extent that MNs firmsmight only exploit already developed knowledge actifs created by heardquar-ters. Particularly in LDCs countries, MNs serve to get further returns to R&Dinvestment made at the home country [Markusen 1995, 1998]. Hence indigenousfirms may confront a more competitive pressure and have stronger incentives tocompete both in a more cost-efficient and technologically-advanced levels.

In this context, the relationship between R&D and TP remains largely am-biguous, both for MNs and Domestic Firms. On the one hand, since it is likelyeasier for MNs conducting R&D, to absorb imported TP, a stronger comple-mentary relationship may predominate,vis a vis the domestic firms. In partic-ular, two hypothesis are opposed at this respect. On the one hand, technologypurchasing has been reported to be important within the MNs network, sinceknowledge is commonly transmetted by the headquarters, therefore a higherpropensity to do TP is inherent to the MN firm [Zuniga and Combe, 2001]. In

10Guarantee on production quality is needed to be authorized to entry into foreign marketsaccording to the National Drug Administration Offices (i.e. the Food and Drug Adminis-tration in the United States (FDA), requires firms to summit tests on safeguardy, clinicalbioequivalency, good clinical practice, etc.).

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addition, they enjoy a larger access to international technology markets, sourc-ing more easily the newest and finest technologies [Veugelers and Cassiman,2001]. On the other hand, production orientation of the MNs may likely af-fect a firms’ technology investment. If the firm is located mainly for exportingpurposes less R&D and eventually less TP might be needed since adaptativeR&D for local market is not relevant. In conclusion, the impact of TP on R&D[and viceversa], as well as the propensity to conduct a complementary strategyremain ambiguos.11

Regarding the technological strategy of Domestic firms, two points need tobe adressed. In a market framework where local firms compete in a differentquality level vis a vis the MNs firms, it is expected that local firms will look morefor technological innovations focused on efficient process, which would explaina higher propensity to acquire TP. 12. As explained in the previous section, if apositive impact is found -complementarity-, TP are meant to facilitate learningby doing R&D process -unit cost reduction by increasing production- which willeventually facilitate the building of internal technical competences [Johnson,2002].

3 The Mexican Pharmaceutical Industry

According to United Nations [1992], the Mexican Pharmaceutical Indus-try jointly with India, China, Argentina and Brazil, have been acknowledgedas countries embedded with ”significant production and imitative capacities”within the developing world. In addition to these competencies, Mexico as wellas this group of countries, has shown an increasing export performance in globalmarkets during the last decade. An important number of factors seemed to haveinfluenced to the actual configuration of the sector, both in terms of economicand technological performance.

As most of the industries in liberalizing LDC economies in the early 1990s,the evolution of this Mexican Pharmaceutical Industry has been dramaticallymarked by the country’s macroeconomic conditions and the industrial and eco-nomic reforms. Hence whereas the economic crisis of the 1980s deterred growthin the industry, the new economic framework brought by the liberalization re-forms13 [GATT, 1987; NAFTA, 1994] seemed to have contributed to the ouput

11Firms’ age has been also be found as a determinant for technological investment [Jaffe,[1986], Henderson and Cockburn, [1998]. In the context of MNs, firms implanted long timein the country might be assumed to have developed technology infrastructure for productionsome time before, and then they might be less likely to need further TP for production.

12Indeed, since a two separated markets co-exist within the Pharmaceutical Industry [brandand generics, Combe & Zuniga, 2001, Guzman & Zuniga, 2004], the latter characterized by amore intensive price-competition exists, TP constitute a rapid technology catching up strategyto achieve more competitive prices.

13Tariffs have gradually been reduced since 1986: Average tariff rate on pharmaceuticalproducts decreased from 24.2% in 1980 to 12.7% en 1998 (CEPAL, 1999).

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expansion which almost (4.8%) doubled in the 1990s respect to the previousdecade, as well as exports [CEPAL,1999]. Likewise, labor productivity in-creased from a 2.4% annual rate in the 1980s to a 3.9% average growth ratein the 1990s. Nonetheless, imports’ liberalization seemed to have contributeto a deteriorating trade balance of the sector, particularly in the fine-chemicalsector [active and bulk ingredients] where imports annual growth rate was 11%and the trade defficit almost doubled in the period 1990-1997.

Finally, two important industrial reforms have particularly contributed toMNs activity. En 1993, a new law on Foreign Direct Investment is enacted sup-pressing any restriction on foreign ownership in the Pharmaceutical Industry. In1991, the Industrial Property Law introduce patent protection for pharmaceu-tical products according to international standards (20 years, and retroactivepipeline protection, etc.).

3.1 Sample and Data sorce

Our data set is an extraction of the National Industrial Survey conducted annu-ally by the National Institute of Statistics and Geography [INEGI]. The phar-maceutical industry concerns 95 firms for two years, 1994 and 2000. Whereasour dataset is limited in time, and the usefulness of a panel data analysis isconsequently dismissed our small database presents however some advantages.

We have a cross section of firms conformed by multinational and local firmsin a balanced way, with an appreciable set of characteristics ranging from tech-nology spending sources and their origin, for production inputs and technologysources [technology purchases, R&D expenditure, R&D focused on friendly-environmental innovations, etc.], production variables [employees, remunera-tions, working hours, production costs, capital, etc.], market sales, publicityexpenses, etc. Two years are covered which allow us to consider whether theindustry experienced a change in their technology sourcing behavior after im-portant industrial reforms were implemented in the early1990s. They are partic-ularly the strenghtening of industrial property rights [1991] by the introductionof patent protection for pharmaceutical products and trade liberalization withNAFTA [1994].

Table 1 and table 2 shows respectively summary statistics of our small sam-ple concerning the Pharmaceutical Industry and the distribution frequency oftechnology strategies. As expected, there exist important differences betweenforeign subsidiaries and domestic firms. Whereas the R&D intensity of do-mestic firms overall over the two periods concerned is less than 10% (0.08),foreign firms invest in R&D in average 13% (12.8%) of total sales. TechnologyExpenditures by indigenous firms both in machinery and equipment (embod-ied knowledge) are around 430.49 and 1293.555 thousands 1993 constant MXP,which are much lower (around three times lower) than those made by multina-tional firms (1531.92 and 5084.13). Productivity levels as measured by value

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added per employee in the average domestic firms is 113.96, whereas produc-tivity by multinational firms is around the double of this value (211.52, 1993constant MXP).

Table 2 presents the distribution of firms engaged in technology sourcingactivities for the two kind of firms. Regarding domestic firms’ technology sourc-ing strategies in 1999 and 2000, the first thing to note is that more firms areinvesting in internal R&D, whatever its nature is, and also, more domestic firmsare engaged into licensing agreements although less dramatically. As exposedrecently by Guzman & Zuniga (2004), two main factors may explain a such anupward in the number of firms engaged in technological activities. More beneficmacro economic environments and institutional framework, i.e. stronger intel-lectual property protection (mainly patent and trademark protection) may havestimulated a particular kind of local firms willing to move to more innovativestages, towards R&D investment. Secondly, firms may be more confident tocommercialize technology and to develop licensing partnerships with locals, fa-cilitating thus a more active technology market (Arora and Gambardella, 2001;Arora and Fosfuri, 2002).

Contrarily to this schema, less MNS firms report technology contractingpayments as well as less machinery and equipment expenditure. In addition,the number of foreign firms’ reporting R&D activities for 2000 remains almostunaltered. Such a slow downing of technology activity or technology acquisitionby MNs could be explained by number of factors. As Bassan [1995] and Squibband Kumar [1998] have argued on the Indian case, R&D investment by multi-nationals remains concentrated at headquarters (i.e. scale and scope economiesof R&D laboratories), little R&D of adaptation nature is enterpriser. Secondly,multinational firms in this case implanted long time ago in the country, mighthave already settled their production capacity years before and consequently,few embody and disembodied production-related technology has been acquiredin the last years. Further, to the extent that less MNs firms are involved inlicensing agreements [i.e. royalties and licensing fees paid related to technol-ogy purchases] with third firms or to headquarters firms, it could mean thatno new product technologies [i.e. patent licensing of new chemical entities,processed-medicines or bulk ingredients] are introduced for production in thecountry. Indeed, as the figures on external trade in the sector suggest, an im-portant of products and ingredients are imported, which in turn are packagedfor commercialization [Zuniga and Combe, 2002; Guzman & Zuniga, 2004].

Table 3 shows Pair Wise correlations to give us a first evaluation of the signof the relationships between the different variables. A first sight on this datasuggests that there is a negative, although non significant relationship betweenfirms’ R&D intensity and technology expenditures. Likewise, acquisition ofmaterials and others inputs [significant at 1% level] show a negative relationshipwith firms’ internal technology investment. A priori, as these figures suggest,production sourcing and eternal knowledge acquisition will tend to substitute

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R&D investment. In other words, more production related inputs acquisitionand the more the firms relies on external TP, the less it invests in average oninternal technology activities.MNs and Domestic Firms

3.2 MNs and Domestic Firms

It is important to distinguish some features between MNs and Local Firms inthe way they generate and source technology. Two opposing effects interact toexplain R&D conducted by MNs firms. On the one hand, it is well known thatfew pharmaceutical R&D is conducted overseas. On the other hand, adaptativeR&D is sometimes needed to cover local consumers preferences and for theclinical tests. Hence most of the R&D made by multinational firms reduces tothe last stages of development, clinical trials, gallenic research. In addition, theR&D incentives may be further weaken in more outward-oriented multinationalswho use Mexico as an export platform (particularly to South America) giventhat few adaptative R&D for local markets is needed. Furthermore, to theextent that national authorities require pharmaceutical firms to produce locallyat least one product, in order to be allowed to commercialize drugs, MNs havefew incentives to locate R&D labs. In fact, most of products are imported oncea product or two are produced locally,a trend that seemed to be strengthened byliberalization of drugs and fine chemicals and the introduction patents [Cepal,1999]. This behaviour has been previously documented in the literature [Scherer& Weibster, 1998; Zuniga and Combe, 2001].

Regarding technological activity by local firms, the technology activity isalso rather small but it differs with that of MNs in the sense that differentincentives conduct local firms to implement technology investments. R&D bylocal firms is still extremely low [see table 1 and 2]. Excepting a small numberof firms engaged recently into R&D activity, technical activity by indigenousfirms concerns essentially the development of patent expiring molecules and costreduction processes. The promising generic market and consumers’ preferencefor low cost products contribute to preempt investment on innovation, whilstthe improvement in production process remains strategic to ensure a more cost-competitive product.14

4 Empirical Models for Technology Sourcing

Technology choices by firms can be thought of as being either or continuousvariables. On on the hand, non-convexities in the cost of R&D may emphasizethe discrete nature of that investment decision. On the other hand, technology

14The poor technology structure of local firms is reflected in fact in the outsourcing ofdevelopment activities for generics. Bioequialency tests and clinical trials are often outsourcedto third firms and to a public Research Centers. The National General Hospital is the mainentity conducting the necessary tests for generics to be introduced by local firms (Zuniga &Combe, 2001).

13

intensities with a corner solution model on a continuous variable is also ade-quate to deal with the zero-Investment flows. Likewise, the costs of licensing,i.e. transaction costs, may vary with intensity of contracting by the firm [repu-tation effects, etc.], introducing a continuous dimension to the external sourcingdecision.

Recognizing these possibilities, our model can be empirically investigatedwithin the two frameworks and extended to the case of simultaneity of decisions.As suggested by theory, there could be a simultaneous relationship between theinternal technology effort decision and the external R&D acquisition: a firm’sdecision on TP is unlikely to be made independently of its R&D investmentdecision [Colombo & Garrone [1996], Veugelers [1997]].

4.1 The Limited Dependent Variables Model (LDV)

Technology sourcing choices and their interrelationship can be analysed usingtraditional technology investment equations. A firm’s internal technology effortand external technology acquisition strategies can be determined within thefollowing system of equations:

R&D∗1,i = (α1 + δ2DOM) + β′

1Xri + γ1TP ∗2,i + ε1i (1)

TP ∗2,i = (α1 + δ2DOM) + β′

2Xti + γ2R&D∗1,i + ε2i (2)

Where Xri, Xtiare a set of covariates proxing for demand and marginalcosts shifts that affect respectively the technology choices. Following the schum-peterian tradition these variables are : firms’ sales (SIZE), outward orienta-tion (EXPI),and a variable on the the technology intensity of the firm, i.e.firms’ capital per employee intensity (KLAB), the average production worker’swage(PW ), etc. Simultaneity of investments exist to the extent that Cov(e1i,e2i)6= 0, which may imply a seemingly unrelated regression under the respectiveassumptions. DOM represents a dummy variable or intercept controlling fordomestic firms’ differences on technology resources and sourcing.15

Taking into account the corner solution of zero R&D investment, and zeroexternal technology acquisition (TP ) since certain firms will not find profitableto invest on R&D or do not have resources to invest or purchase technology, ourtechnology choices equations can be expressed as censored-dependent variablemodel or Tobit Model (i.e. LDV limited dependent variable model). Traditionalestimation by Ordinary Least Squares would give in this case resutls subject to

15Including a dummy for FOR, foreign firms raises multicolinearity between variables (SIZE,EXP, etc.), given the well defined superiority of foreign firms in economic resources (sales,production, etc.) and investment. Hence we proceed henceforth to control for DOM, domesticfirms’ particularities.

14

selectivity bias (the eror term would imply non zero expectation of technologicalinvestment). 16

Hence we assume that there exists a latent dependent variable y∗i , expressing

some decision criterion, i.e. the expected-present value of the firm i′s profitaccruing to the technology sourcing choice (R&D,TP ), which is given by :

y∗1,i = β′

1Xri + γ1y∗2,i + ε1i, where y2,i =

{y∗2,i if β′

1Xri + γ1y∗2,i + ε1i > 0

0 otherwise, n = 1, 2, ....N

}(3)

y∗2,i = β′

2Xti + γ2y∗1,i + ε2i, and y1,i =

{y∗1,i if β′

2Xti + γ2y∗1,i + ε2i > 0

0 otherwise, n = 1, 2, ....N

}(4)

It is assumed that y1,i and y2,i are observable scalar random variables whiley∗1,i ,y∗

2,i, ε1i, and ε2i are unobservable latent scalar random variables, Xri andXti are K1 and K2 are component vectors of known constants respectively, andγ1, γ2, β1, β2 are scalar unknown parameters.This model underlies the simultane-ous equation extension of Tobit Model from Amemiya [1974, 1979]. Accordingly,the recursive model raises a potential problem of simultaneity [Greene,1989]: acorrelation of residuals different than zero Cov( ε1i, ε2i)6= 0 implies that bothtechnology-choices made by firms are simultaneously co-determined [e.g. en-dogeneity of strategies]. Otherwise, Cov( ε1i, ε2i)=0, technology choices areexogenously defined in the models, and hence firms choose technology sourcingstrategy independently of the alternatives, but dependent of the other determi-nants [internal finance, size, export orientation, etc.]. Second, censored-natureof data to zero demands an special treatment to deal with propensities to engageinto an activity by the firm and further with the respective intensities.We willrewrite then in the vector notation the structural form of equations as:

y∗1 = γ1y

∗2 + β1X1 + ε1 (5)

y∗2 = γ2y

∗1 + β2X2 + ε2 (6)

From which, the reduced form of the above can be written as :

y∗1 = XΠ1 + u1 (7)

y∗2 = XΠ2 + u2 (8)

16Assuming that the disturbance term has a normal distribution, Tobit combines probabilityestimation with regression analysis by maximum likelihood estimation. The Tobit Modelrecognizes that there are two types of effects related to each independent variable: i) theeffects on the value of RD intensity for cases at the limit value (i.e. zero), and ii) the effectsfor cases above the limit (see Hayashi, 2000; Wooldridge 2002).

15

where X is a TXK matrix consistinf of distinct column vectors in (X1,X2)and Π1,Π2, u1, u2 are defined in the obvious way. Variances and the covariancesof (u1, u2) are defined by σ2

1 , σ22 ,and σ1,2 and repeatedly use the equality, u1 =

σ1,2σ22u2 + e, where u2 and e are independent because of the joint normality of

(u1, u2) [Amemiya, 1979]. Smith and Blundell (1988) present a test for effectsof endogeneity for probit and tobit models, specifying that the exogeneity of oneor more explanatory variables is under suspicion. The S-B Test of exogeneity inthe tobit-model under the null hypothesis -exogeneity of suspicious variables-,is distributed as F (m,N − k), where m is the number of explanatory variablesspecified as endogenous in the model. A rejection indicates that the standardtobit estimator should not be then employed. 17

Following this procedure, we will first evaluate the potential endogeneitybetween the technology choices and then proceed for a simultaneous-Tobit orseparated estimation of technology investments, according to the S-B test.

4.2 The Discrete-Choice Analysis

Technology choices and their interactions can also be explored within the discrete-variable choice analysis. As we previously mentioned, technology choices mightimply simultaneity of decisions or not.18 Bivariate-probit models consideringthe two technology choices simultaneously defined can be used for our researchquestions.

The relevance of using such a model is particularly appealing for our data.When exploring determinants of technology investments, we are likely to expectan important number of indigenous firms which are not engaged in technologicalactivities. Hence it is important to know the determinants of the propensities toconduct R&D and TP, along the factors explaining the variation in the amountinvested on R&D and TP .

Lets define the following dichotomous variables, R&Di and TPi:

R&Di = 1 if the firm does R&D, 0 otherwiseTPi = 1 if the firm purchases disembodied technology, 0 otherwise

As previously stated for the tobit model, firms will invest in R&D and TPif there is positive profit stemming from. Two latent variables reflecting such

17Under the null hypothesis, the models are appropriately specified with all explanatoryvariables as exogenous. Under the alternative hypothesis, the suspected endogenous variablesare expressed as linear projections of a set of instruments, and the residuals from those first-stage regression are added to the model. Under the null hypothesis, these residuals shouldhave no explanatory power.

18It is not implied that time actually passes by between the individual decisions since nemust distinguish between hierarchical behavior and hierarchical structure for the mathematicalforms of the choice probabilities” (Pudney, 1989, Kaiser, 2002). If time actually passed bybetween the decision stages, a sequential model would be more appropriate.

16

profit are defined for each strategy.

R&Di = 1 if y∗2 > 0, and 0 otherwise

TP = 1 if y∗2 > 0, and 0 otherwise

Which lead us to the following bivariate probit specification:

y∗1 = X1β1 + ui1

y∗2 = X2β2 + ui2

where X is a vector of explanatory variables and β the vectorr of pararmetersto estimate.The errors terms are assumed to follow a bivariate probit normaldistribution (ui1, ui2 )˜ (BVN) [0, 1].The two equations can be estimated consis-tently by individual single equation probit methods. However, this is inefficientin that it ignores the correlation between the disturbances. The correlationcoefficient of the error terms is denoted by ρ, which stems from the possiblepresence of omitted variables in the determinants of the firms’ choice of innova-tion strategy.

4.3 Technology Strategies and Productivity: the directapproach

Following the ”direct approach” we proceed now to specify and estimate a pro-ductivity equation in order to identify how strategies interact to explain indus-trial performance and evaluate the returns related to these technology strategies.

Lets define a traditional Cobb Douglas production function as follows:

Yit = AitCαitL

βit (9)

where α and β are output elasticites of capital and labour, A is the total fac-tor productivity parameter, which is driven by own R&D, OK (stock of knowl-edge), external disembodied knowledge EK acquired by technology purchasing(i.e. technology licensing, arms’ length contracting, know how contracts,etc),industry R&D spillovers (IK), and firm ownership characteristics. Productivityevolution is then defined as:

Ait = ef(EK,OK,IK)it + λt + θD (10)

where λt indicates a wide economy technology shock and D(F ) a dummyvariable on domestic firms (foreign firms). Since there is no theoretical frame-work well defined about the functional form f(.), we assume for our purposesa relatively flexible specification that includes the log of the three sources ofknowledge, and their parwise interactive terms. Since our data concerns crosssectional data over only two years, few information could be find on a industrialtechnology trend. Substituting previous equation in the production functionand taking logarithms and considering only internal and external knowledgesources, we obtain the following value added production function:

17

yit = α0 + α1Cit + α2lit + β1EK + β2OK + β3EK.OK + θD + εit(11)where εit = $i + λt + uit

where lower case letter denote logs, the error εit is composed by a firm timeinvariant variable $i, a dummy variable indicating the year λt and uit misiden-tification error. This equation allow us to identify the returns to R&D(OK)investment and to Technology purchasing(EK), and to ascertain the relation-ship between the two strategies through the interaction terms. A positive andsignificant sign would mean that these strategies complement each other to im-prove firms’ performance and contrarily, a negative sign may imply that firmstend to substitute internal R&D investments when acquiring external technol-ogy. In order to control for the outward orientation of the firm and its incidenceon technological activities, we include a dummy variable for exporting firms(FIREX).

When estimating productivity equations, we are confronted to some econo-metric problems concerning the endogeneity of inputs to production. As ex-plained by Blundell & Bond [2002], a likely potential correlation arises betweenindependent variables, i.e. material inputs, capital expenditure, technology in-vestments (disembodied, embodied) and firm specific unobserved characteristicsmeasured by the error term, uit [i.e. managerial practices, financial state, etc.].Consequently, ordinary least squares estimates are subject to omitted variablemisspecification and bias giving inconsistent estimates.

Given that we only have data for two years, panel estimation with fixed ef-fects or first differences alleviating for the problem of potential endogeneity is notpossible and it would undermine the importance of our cross sectional data.19

We use the Instrumental Variables approach by two least squares estimation todeal with endogeneity related with right side variables [notably Materials, R&Dand TPL decisions], and implement the Huber-White sandwhich estimator ofthe variance to obtain robust standard errors to the presence of arbitrary het-eroskedasticity.20 We include in the IV equation a variable identifying the firmand a dummy controlling for the year 2000. Exogeneity tests on individual righthand variables are conducted according to the test proposed by Durbin [1954],Wu [1973] and Hausman [1978]. A rejection of the null hypothesis [OLS esti-mation is correct] indicates that endogenous regressors’ effects on the estimatesare meaningful, and instrumental variables techniques are required. For thispurposes, correct instruments fitted should be strictly orthogonal to error term

19Studies on production funtion estimation, i.e. Griliches (1984) and Mairesse and Hall(1995), usually find significant returns to R&D in the cross section dimension, whereas intime series they have been found to be less significant and robust.

20We acknowledge that a longer series [three years] would allow us to control more ade-quately for the potential endogeneity of right hand side variables. Different methods to dealwith endogeneity and autocorrelation issuees have been proposed by Olley and Pakes [1995]and Levinsohn and Petrin [2003].

18

[firms’ specific intercepts] and correlated with firms’ input choises [Blundell andBond, 2002].

5 Results

Our analysis on the empirical resuls are discussed in the order explained inthe previous section. First, interpretation of the LDV Tobit and the Probitmodels on the two techology investment equations are discussed as well as itsrelationship.Second, we focus on the productivity model and the importance oftangible and intangible investments in industrial performance. Finally, estimateson the interaction term when simultaneity of strategies is implemented by twokind of firms (locals and MNs) are discussed.

5.1 The LDV Models and Bivariate Probit Estimation

Table 4 displays our results obtained on the Tobit (LDV) Models and simul-taneity tests on R&D and TP equations. Three main results emerge from theanalysis according to our cross sectional data.

First, the Smith Blundell test on exogeneity of TP when considering a test ofcorrelation of residuals for both equations suggest that TPL investments are ex-ogenous to R&D (as reported by the non significativity of the F-Test, column 3),and alternatively, R&D appears to be exogenous to TPL tobit model (column6).21 Hence at least for this industry in a LDC context, R&D decisions by firmsare taken independently of the decisions on acquiring external -disembodied-knowledge. Furthermore, TP investment has no explanatory power in the R&Dequations, and viceversa. Hence Mexican Pharmaceutical firms conducting TPagreements (for production, know-how, arms’ length contracting) and those en-gaged into innovative investments are conducted by different motives to invest inany of the two strategies. TP could be conceived for production-efficiency mat-ters whereas R&D investment (decision and intensity) is lead for new-productor development product related technologies, which would explain exogeneitybetween them (Arora, 1999). Wald Test [χ2] reported at the bottom justifiesfor the adoption of the Tobit Model in the different specifications.An importantpart of the variation of the endogenous variables are correctly explained by ourgroup of left hand side variables.22

21We are conscious about the limited explanation power of the Endogeneity Test (Smith &Blundell) given the very large number of degrees of freedom used related to the cross sectionalnature of our data. Undoubtedly, a real panel data with more years available will offer richertests and more confidence on the real exogeneity on the variables of interest.

22A model correcting for a potential sample selection bias for firms conducting R&D andthose which are not reported or report zero values has been also explored [Heckman, 1979].Coefficient on the Mills ratio have been found non significant, indicating that there is noevidence for a sample selection bias [see Katrack, 1997].

19

Second, we corroborate previous results on technological activity by firms onLDCs’ [Fikkert, 1994, Basant, R. and Fikkert, B., 1996] about the significantimpact of firms’ size as measured by total sales, as main explanatory variablein the two investment equations. Although, the elasticity of TP related to salesis much larger than the one by R&D when including more control variables.Hence, an increase in sales may conduct a larger increase in TP investmentthan R&D. Firms might found more profitable to invest in TP [i.e.expandingproduction] in the grounds of improving production costs, rather than investingin innovation. As Cohen and Klepper [1996] and Young [1993] explain, firmsconfront a trade off between process and product innovation, which might differaccording to firms’ size and.the magnitude of invention costs relative to marketsize.23

A point to note however, is that the magnitude of coefficients is rathersmall and less than unity, meaning that all increase in sales is followed bya less than proportional increases in technology investments. Firms’ capitalintensity (LKL) seems not to affect R&D investment whereas it does explainpositively TPL expenditures. That would mean that more capital intensivefirms (i.e. more productive) firms would tend to source external technology,independently of its investment on internal R&D. In addition, the acquisition ofembodied technology as evidenced by a large machinery and equipment capitalby employee is complemented with knowledge assistance, know-how, etc, whichwould explain such a positive relationship.

Third, as expected firms’ participation in foreign markets has a prominentrole to explain engagement in R&D investment, as shown by the highly signif-icant coefficient on the dummy variable (FIREX). Contrarily, participationin exporting (not the intensity of exporting) turns out to be not relevant fortechnology purchasing. Column 7 and 8 displays firms’ profits by distinguishingexports and national sales. Both are positive and significant to explain R&Dand TP, nonetheless the two coefficients are stronger for TP and particularly,the coefficient on domestic sales is the largest to explain TP. Hence the domesticmarket seems to stimulate more strongly external technology acquisition ratherthan investment in innovation.

Table 5 displaying the results of the Bivariate Probit model confirms previousfindings of the LDV Models (column 1-4). Again, there is no correlation ofresiduals when the two probit equations are jointly estimated as reported bythe χ2 Test on ρ. Size affects positively both investment propensities, whereascapital to labor ratio affects only the decision to acquire external technology.

23The more important the invention costs are relative to market size the lower the profabilityof invention, and the lesser the incentives to withdraw resources from current production foruse in costly research. In contrast, when the cost of invention is small relative to market size,invention is profitable and tends to run ahead of the society’s cumulated learning experience.The incentives to produce different types of goods [patters of consumer demand] become thenthe relevant constraint on the growth process [Young, 1993].

20

Being a exporting firm affects the probability of doing R&D, whereas it has noimpact on TP. National sales again determines strongly the firms’ decision toacquire disembodied knowledge externally, which may reflect the importance oflocal market on firms’ goals to have a more cost-efficient production.

Column 5-8 present probit estimates for Multinational and Domestic Firms.Important differences on explaining technology behavior emerge at this respect.First, regarding the impact of size it appears that large Multinational Firmsare more incited to conduct R&D and TP. Nevertheless, capital intensive firmswithin the multinational firms, are less likely to invest in R&D. Further, beingan exporter does have a role to explain only MNs firms’ propensities to investin a kind of R&D activities (clinical trial and testing, adaptation R&D, etc.).Firms’ size as well as capital intensity contribute to explain domestic firms’propensity to acquire technology licensing and know how contracting whereasthey are meaningless to explain the decision to conduct R&D.

Non linearities of firms’ size on propensities and intensities to conduct tech-nology investments are explored in table 6. As Squibb and Kumar have previ-ously reported, a U-shaped relationship between firms’ size, as measured hereby sales, and R&D seems to exist whereas size in TP equations (both tobit andprobit) show an increasing non linear relationship. Incentives to engage intoR&D seems to be important for medium firms whereas for medium large, suchan effect is weaken to a some threshold, where finally large firms seems indeedto be the most likely to engage into technological activity.

5.2 The Productivity Equation

We proceed now to analyze our results on the productivity equation. As wementioned earlier, estimation of productivity is confronted to a potential endo-geneity of right hand variables, both production inputs as well as technologyinvestments. We implement a Durbin Wu Hausman Test to test the exogeneityof these variables, and it is found that R&D could raise endogeneity problems,which is not the case for TP. Column 1 and 2 presents estimates by OLS re-gression and IV-2SLS estimation. Correction of endogeneity by IV as it can benoted, is reflected in the higher and more significant value of return to R&D in-vestment. Nevertheless, TP becomes now non significant. Column 3 show thatthere is no dramatic variation in the estimates when including the interceptterm for the year 2000 and a variable (folio) denoting firms’ code.

When including the interaction term denoting the firms conducting R&Dand TPL (TLR), estimation is better performed. Hansen J Statistic Test on thevalidity of instruments is better fitted (Hypothesis null of Overidentification).Coefficient on TPL becomes significant, explaining positively firms’ industrialperformance although in a less proportion than R&D. The interaction term in

21

the three specifications (column 4, 6 and 7) is found to be negative which sug-gests that firms tend to substitute R&D when they acquire external technology.Furthermore, this substitutive effects seems to be larger for domestic firms thanfor multinationals as evidenced by the size of coefficients. R&D returns howeverappear to be larger for indigenous firm than for foreign subsidiaires.Our resultscorroborate previous findings on a substitute relationship by Fikkert [1993],Bassant and Fikkert [1995] on Indian firms, and Lee [1996] on Korean firms.

Nevertheless, when distinguishing the technology status of the firms by in-cluding a group of dummies variables for the strategies previously cited (1-4), itis found that technology-active firms as evidenced by the conduction of any ofthe three strategies (firms conducting or not the strategy) show positive effectson productivity. Acquisition and generation of technology although in differ-ent magnitudes contribute to improve firms’ industrial performance. Althoughwhereas the interactive term suggests the predominance of a substitutive rela-tionship between strategies (internal and external), firms’ conducting only TPinvestments seems to enjoy a superior industrial productivity (Column (5)).

We confirms thus previous findings on Indian firms by Bassant and Fikkert[1995] about the stronger returns related to technology purchasing that canemerge in LDCs industries relative to R&D. Surprisingly, this effect seems tobe quiet important for domestic firms (column 8), while technology status formultinational firms are found to be not relevant to explain difference in industrialperformance within this group.

6 Conclusions

This paper has attempted to explore empirically the determinants of technologyinvestments both internal an external knowledge sourcing in the Mexican Phar-maceutical Industry and the impact of technology sourcing on firms’ industrialperformance. One of the main motivation of this research has been to shed somelight on the relationship between internal and external knowledge acquisition:are these strategies complementarities or substitutes ?

In order to build robust empirical findings, continuous (LDV) and discretemodels have been explored in a simultaneous context, testing the interrelated-ness of firms’ decisions : to invest in R&D and to acquire external knowledge(TP). While our database is rather small, our estimations on the technologyinvestments models allowed us to explain some of the aspects of technologychoices in this Industry. Three main findings stem from our analysis.

First, our empirical findings suggest that in average, Mexican pharmaceuti-cal firms’ investment and propensity to conduct R&D are independent from thecurrent technology purchasing. R&D and technology purchasing decisions areexogenous and non significant to each other. Technology investment and exter-nal acquisition are then two different non related decisions. Therefore different

22

motivation conduct the firms to invest in internal and external technology:whilst R&D is indeed explained by export participation, TP is rather motivatedby the size of domestic sales.

Second, our results highlights the differences in technology behavior by Do-mestic and MNs Firms. It is found that indigenous firms for which their mainmarket is the local market have less incentives to innovate but they show astronger reliance on technology purchasing as a way to improve cost-efficiencyproduction and become more price competitive. Given the nature of the Mexi-can Pharmaceutical Market, local firms’ compete in a different quality level tothe extent that majority are generic producers and consequently compete in amore price-sensitive market where the demand for innovation is rather negli-gible. Therefore, whereas outward orientation indeed explain R&D in averageand by MNs as well as ”complementarity” between these two strategies, moredomestic oriented firms [captive markets, Guzman & Zuniga, 2004] Likewise,differences in firms’ size and their impact on technology reliance are identified.A U-shaped relationship between firms’ size and R&D seems to exist whereassize in TP equations (both tobit and probit) show an increasing non linearrelationship

Third, estimates in the productivity equation show that for firms engagedinto the two strategies (internal and external) there is a substitutive significantrelationship as reflected by the coefficient in the interaction term. We corrob-orate previous findings by Lee [1996] on Korean Firms and by Fikkert [1993]on a negative relationship in a framework that treat decisions as jointly deter-mined. Nonetheless, the significant importance of technology purchases as astrong determinant of productivity levels is confirmed, particularly for the caseof indigenous firms.R&D investment as shown in our productivity measure doesexplain industrial performance by indigenous firms, and it exhibits even a largerreturn than Multinational Firms.Nevertheless, for those local firms already en-gaged into the two strategies, our evidence suggest that these firms would tendto substitute TP for R&D eventually, rather than complement it.

In terms of public policy, these results suggest important implications First,innovation policies must be designed taking into account the nature of the firm[outward/inward orientation, size, etc.].To the extent that a substitutive rela-tionship may emerge between R&D and TP, for local firms engaged into thetwo strategies, policymakers must be careful when designing the correct policiesto stimulate R&D and technology collaborations.Technology transfer policiesfacilitating access to superior and new technologies should not undermine theimportance of policies stimulating R&D by the firm. In other words, TPagreements should be accompanied by programs to enhance internal R&D ca-pabilities and to ensure a continuous technological engagement by indigenousfirms.

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25

Mansfield E., Schwartz, M. and S. Wagner [1968] Imitation Costs and Patents:an Empirical Study. Economic Journal 91, 907-918.

Mansfield, E. [1994]. Intellectual Property Protection, Foreign Direct In-vestment, and Technology Transfer,

International Finance Discussion Paper, n.19.Markusen, J. R. [1998] Contracts, Intellectual Property Rights, and multi-

national investment in developing countries, NBER working paper 7164.Markusen, J. R. [1995]. The Boundaries of Multinational Enterprises and

the Theory of International Trade, Journal of EconomicPerspectives.9[2]: 169-189.Prakash, P., J. [2003] Liberalization, Firm Size and R&D Performance: A

firm level Study of Indian Pharmaceutical Industy. Research andInformation System for Developing Countries (RIS) Working Paper.Saggi, K., Glass ; A. [1999], FDI and the nature of R&D , Canadian Journal

of Economics, 32, 92-117.Saphiro, C. [1985] Patent licensing and R&D rivalry, American Economic

Review, 75, pp. 25-30.Veugelers, R. [1997] Internal R&D expenditures and external technology

sourcing, Research Policy, 26, 303-316.Veugelers, R. and Cassiman, B. [2001] Foreign Subsidiaries as a channel of

international technology diffusion: Some direct firm level evidencefrom Belgium. UCL Louvain la Neuve, mimeo.Veugelers, R. and Cassiman, B. [2002], Complementarity in the innovation

strategy: Internal R&D, External Technology Acquisition, and cooperationin R&D, working paper.Vishwasrao, S. [1994] Intellectual Property Rights and the mode of techno-

logical Transfer. Journal of Development Economics 44, 381-402.Young, A. [1993] Invention and Bounded Learing by Doing, The Journal of

Political Economy, Vol. 101 No. 3, pp. 443-472.

26

Table 1: SUMMARY STATISTICSVariable Mean Std. Dev. Min MaxDOMESTIC FIRMSR&D Intensity .0053571 .014722 0 .0841333Productivity 113.9659 109.2057 0 629.2775Total Exports 1819.949 5998.87 0 31363.49Production 90144.21 135497.5 0 762992.7National Sales 221075.8 421438.1 0 2543799Patent Lic. & royalties 1293.555 4722.445 0 35850.29Labor Expenditures 42.37925 24.64744 6.298213 113.9873Machinery and Equipment 430.4953 1373.01 0 7953.138National inputs 5953.04 7921.672 0 43951.23Imported inputs (i.e.) 17890.62 35363.13 0 179863.1MULTINATIONAL FIRMSR&D Intensity .0068887 .0184066 0 .1281848Productivity 212.5178 169.226 37.30409 977.8268Total Exports 15521.5 48054.11 0 392977.9Production 200018 192749 20327.88 1020896National Sales 448026.3 619323.6 0 3354559Patent Lic.& royalties 5084.135 10392.87 0 49130.9Labor Expenditures 65.75673 24.67875 18.64849 121.5554Machinery and Equipment 1531.924 3587.143 0 24788.35National inputs 17614.32 26009.65 0 171656.2Imported inputs 41585.27 61024.09 0 434187**1993 Mexican Pesos.

27

Tab

le2:

TEC

HN

OLO

GY

AC

QU

ISIT

ION

ST

RA

TEG

IES

1994

2000

Fir

ms

conduct

ing

R&

DY

ES

NO

Tot

alY

ES

NO

Tot

alD

omes

tic

Fir

ms

1123

,91%

3546

2350

,00%

2346

Mul

tina

tion

alFir

ms

2141

,18%

3051

2242

,31%

3052

Tot

al32

32,9

9%65

9745

45,9

2%53

98Fir

ms

wit

hTec

h.

lice

nsi

ng

Dom

esti

cFir

ms

1328

,26%

3346

1634

,78%

3046

Mul

tina

tion

alFir

ms

3364

,71%

1851

2446

,15%

2852

Tot

al46

47,4

2%51

9740

40,8

2%58

98Fir

ms

wit

hM

ach.

Eq.

Ac.

Dom

esti

cFir

ms

1532

,61%

3146

1634

,78%

3046

Mul

tina

tion

alFir

ms

3364

,71%

1851

2242

,31%

3052

Tot

al48

49,4

8%49

9738

38,7

8%60

98

28

Tab

le3:

PA

IRW

AIS

EC

OR

RELA

TIO

NS

LR

DI

LTP

LI

LSA

LLM

PT

LE

XP

OLE

MP

LA

BW

STR

A1

STR

A2

STR

A3

STR

A4

DO

MLR

DI

-1.

000

LTP

LI

-0.0

381.

000

LSA

LE

S-0

.165

*0.

440*

1.00

0LM

PT

-0.1

95*

0.43

4*0.

853*

1.00

00LE

XP

O-0

.251

*0.

306*

0.46

2*0.

473*

1.00

0LE

MP

-0.2

08*

0.35

1*0.

651*

0.63

3*0.

476*

1.00

0LA

BW

0.20

0*0.

023

-0.2

72*

-0.2

77*

-0.2

07*

-0.6

78*

1.00

0ST

RA

10.

533*

-0.6

40*

-0.3

71*

-0.4

08*

-0.3

31*

-0.3

22*

0.09

31.

000

STR

A2

-0.5

62*

-0.4

08*

-0.0

07-0

.026

0.00

3-0

.057

-0.1

00-0

.360

*1.

000

STR

A3

0.41

8*0.

648*

0.22

1*0.

232*

0.09

0.11

00.

114

0.13

8-0

.467

*1.

000

STR

A4

-0.5

94*

0.45

3*0.

220*

0.27

2*0.

29*

0.33

2*-0

.069

-0.1

75*

-0.3

60*

-0.2

82*

1.00

0D

OM

0.04

4-0

.249

*-0

.244

*-0

.303

*-0

.592

*-0

.323

*0.

087

0.22

0*0.

007

-0.1

57*

-0.1

031.

000

*si

gnifi

cant

at10

%;**

sign

ifica

ntat

5%;**

*si

gnifi

cant

at1%

29

Tab

le4:

R&

DA

ND

TEC

HN

OLO

GY

PU

RC

HA

SIN

GIN

VEST

MEN

TEQ

UA

TIO

NS

-1-2

-3-4

-5-6

-7-8

OLS

R&

DR

&D

Tob

itR

&D

Tob

itO

LS

TP

TP

Tob

itT

PTob

itR

&D

Tob

itT

PL

Tob

itLSA

LE

S0.

290.

390.

190.

350.

330.

44(0

.11)

***

(0.0

9)**

*(0

.10)

**(0

.14)

**(0

.12)

***

(0.1

6)**

*LK

L-0

.36

-0.3

7-0

.40

0.54

0.56

0.73

-0.4

30.

76(0

.47)

(0.2

8)(0

.28)

(0.3

0)*

(0.2

8)**

(0.3

0)**

(0.2

8)(0

.30)

**FIR

EX

P1.

590.

97(0

.62)

**(0

.73)

LTP

L0.

060.

01(0

.07)

(0.0

7)LE

XP

O0.

150.

18(0

.06)

**(0

.08)

**LN

AT

SALE

S0.

190.

38(0

.09)

**(0

.15)

**LR

D0.

020.

01(0

.09)

(0.0

9)ln

(sig

ma)

1.14

1.21

1.26

1.25

1.12

1.24

(0.0

3)**

*(0

.03)

***

(0.0

3)**

*(0

.03)

***

(0.0

3)**

*(0

.03)

***

Con

stan

t-1

.13

-1.1

3-0

.77

0.24

0.29

-3.4

1-0

.34

-3.0

1(1

.58)

(1.0

3)(0

.78)

(1.7

2)(1

.43)

(1.5

8)**

(0.7

6)(1

.48)

**O

bser

vati

ons

190

190

190

190

190

190

190

190

R-s

quar

ed0.

070.

26Sm

ith

Blu

ndel

lTes

t6.

547.

54Log

PLH

-919

.49

-915

.27

-113

0.30

-113

5.79

-915

.97

-113

4.33

Wal

dC

hi2

34.1

8***

45.2

6***

55.7

6***

48.6

9***

40.4

8***

51,2

7***

Stan

dard

erro

rsin

pare

nthe

ses,

Hub

er/W

hite

/san

dwic

hes

tim

ator

ofva

rian

ceis

used

inst

ead

ofM

LE

vari

ance

.*

sign

ifica

ntat

10%

;**

sign

ifica

ntat

5%;**

*si

gnifi

cant

at1%

The

Smit

hB

lund

ellTes

tco

nsid

ers

2SLS-

TO

BIT

esti

mat

ion,

30

Tab

le5:

BIV

AR

IAT

EP

RO

BIT

MO

DELS

ON

R&

DA

ND

TEC

HN

OLO

GY

PU

RC

HA

SIN

G-1

-2-3

-4-5

-6-7

-8T

OTA

LT

OTA

LM

NS

DO

MR

D=

1T

PL=

1R

D=

1T

PL=

1R

D=

1T

PL=

1R

D=

1T

PL=

1LSA

LE

S0.

120.

410.

150.

560.

070.

41(0

.05)

**(0

.12)

***

(0.0

7)**

(0.1

8)**

*(0

.10)

(0.1

7)**

LK

L-0

.189

100.

19-0

.21

0.30

-0.3

90.

21-0

.17

0.37

(0.1

1)*

(0.1

3)(0

.12)

*(0

.13)

**(0

.16)

**(0

.21)

(0.1

8)(0

.20)

*FIR

EX

P0.

550.

240.

900.

510.

340.

06(0

.21)

***

(0.2

3)(0

.53)

*(0

.45)

(0.4

4)(0

.43)

LN

AT

SALE

S0.

080.

40(0

.05)

(0.1

1)**

*LE

XP

O0.

060.

053

(0.0

3)**

(0.0

3)*

LM

AQ

K0.

05-0

.05

-0.0

6-0

.08

0.14

-0.0

4(0

.04)

(0.0

4)(0

.06)

(0.0

6)(0

.05)

***

(0.0

6)D

OM

0.17

0.17

(0.2

5)(0

.26)

YE

AR

2000

0.21

-0.3

90.

14-0

.48

0.09

-0.6

30.

48-0

.05

(0.1

9)(0

.18)

**(0

.20)

(0.2

1)**

(0.2

9)(0

.27)

**(0

.35)

(0.2

8)C

onst

ant

-1.5

9-5

.23

-1.4

3-5

.15

-1.3

3-6

.61

-1.7

7-5

.48

(0.4

6)**

*(1

.25)

***

(0.4

9)**

*(1

.13)

***

(0.6

1)**

(2.2

1)**

*(0

.87)

**(1

.68)

***

Log

-PLH

-220

.31

-218

.32

-120

.25

-91.

37W

ald

Chi

249

.46*

**59

.83*

**31

.59*

**41

.54*

**W

ald

Tes

trh

o=0

0.09

570.

2276

0.02

710.

1955

Obs

.19

019

019

019

010

210

288

88St

anda

rder

rors

inpa

rent

hese

s,H

uber

/Whi

te/s

andw

ich

esti

mat

orof

vari

ance

isus

edin

stea

dof

MLE

vari

ance

.*

sign

ifica

ntat

10%

;**

sign

ifica

ntat

5%;**

*si

gnifi

cant

at1%

31

Table 6: TESTING SIZE EFFECTS ON R&D AND TP DECISIONSPROBIT PROBIT TOBIT TOBITR&D=1 TPL=1 R&D TP

LKL -0.15 0.25 -0.29 0.61(0.12) (0.12)** (0.28) (0.25)**

FIREXP 0.59 0.30(0.21)*** (0.23)

MEDIUM 0.89 0.73 2.20 1.33(0.28)*** (0.34)** (0.59)*** (0.68)**

MLARGE 0.37 1.17 1.35 3.41(0.38) (0.40)*** (0.78)* (0.80)***

LARGE 0.79 1.51 2.72 4.72(0.36)** (0.49)*** (0.82)*** (1.13)***

LTPL 0.03(0.07)

LRD 0.03(0.09)

DOM -0.44 -0.34(0.47) (0.63)

YEAR2000 0.05 -0.80 -0.35 -2.24(0.22) (0.26)*** (0.52) (0.52)***

Ln(sigma) 1.25 1.22(0.03)*** (0.04)***

Constant -0.88 -1.46 1.74 0.57(0.31)*** (0.38)*** (0.76)** (0.81)

Log-PLH 217,89 -916,72 -1129,64Wald Chi2 48,30*** 31,72*** 62,40***Wald rho=0 0,0259Obs. 190 190 190 190Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%Huber/White/sandwich estimator of variance is used in place of the conventional

32

Tab

le7:

PR

OD

UC

TIV

ITY

AN

DT

EC

HN

OLO

GY

ST

RA

TEG

IES(I

V2S

LS

EST

IMA

TIO

N)

-1-2

-3-4

-5-6

-7-8

-9O

LS

2SLS

IVW

itho

utIn

t.In

tera

ctio

nC

urre

ntT

CD

OM

MN

SD

OM

MN

SLK

L0.

330.

320.

310.

300.

300.

060.

640.

100.

55(0

.04)

***

(0.0

1)**

*(0

.13)

**(0

.17)

*(0

.08)

***

(0.2

5)(0

.18)

***

(0.0

5)*

(0.0

9)**

*LT

PL

0.05

0.01

0.00

0.30

0.35

0.25

(0.0

2)**

*(0

.04)

(0.0

5)(0

.08)

***

(0.1

0)**

(0.0

8)**

*LR

D0.

030.

590.

601.

010.

980.

81(0

.02)

*(0

.16)

***

(0.1

8)**

*(0

.29)

***

(0.3

1)**

*(0

.26)

***

LTR

-0.1

3-0

.12

-0.1

0(0

.04)

***

(0.0

4)**

*(0

.03)

***

TC

20.

420.

460.

38(0

.20)

**(0

.28)

(0.2

9)T

C3

0.56

0.72

0.28

(0.1

5)**

*(0

.27)

**(0

.21)

TC

40.

340.

510.

14(0

.15)

**(0

.23)

**(0

.22)

YE

AR

2000

-0.1

10.

020.

19-0

.54

0.51

0.20

0.14

(0.2

9)(0

.32)

(0.1

3)(0

.48)

(0.3

5)(0

.21)

(0.1

4)FO

LIO

-0.0

0-0

.00

-0.0

0-0

.02

-0.0

0-0

.01

-0.0

02(0

.00)

(0.0

1)(0

.00)

**(0

.01)

*(0

.01)

(0.0

0)**

(0.0

0)C

onst

ant

3.64

2.55

2.74

2.06

3.87

4.30

0.99

4.40

3.33

(0.1

3)**

*(0

.42)

***

(0.5

8)**

*(0

.66)

***

(0.2

9)**

*(1

.04)

***

(1.0

5)(0

.34)

***

(0.3

4)**

*O

bs.

190

190

190

190

190

8810

288

102

No.

Fir

ms

9595

R-s

quar

ed0.

370.

320.

48H

anse

nSt

at.

9.27

24.

324

0.74

52.

278

Chi

-sq(

2)0.

0097

00.

1151

20.

6890

90.

3201

9R

obus

tst

anda

rder

rors

inpa

rent

hese

s,H

uber

/Whi

te/s

andw

ich

esti

mat

orof

vari

ance

isus

ed,as

wel

las

clus

teri

ngon

firm

s.H

anse

nJ.

(Ove

ride

ntifi

cati

onte

stof

allin

stru

men

ts)

isSa

rgan

Stat

isti

cco

rrec

ted

bya

robu

stco

vari

ance

mat

rix.

*si

gnifi

cant

at10

%;**

sign

ifica

ntat

5%;**

*si

gnifi

cant

at1%

Inst

rum

ente

dV

aria

ble

LR

D,In

stru

men

ts:

LSA

LE

S,LP

UB

I,LE

XP

I,FO

LIO

and

YE

AR

2000

.

33