Follow me to the innovation frontier& Leaders, laggards ... · leader or laggard. They ﬁnd that...

Follow me to the innovation frontier? Leaders,laggards, and the differential effects of importsand exports on technological innovation

Sheryl Winston Smith

Department of Strategic Management,Fox School of Business, Temple University,Philadelphia, USA

Correspondence:S Winston Smith, Department of StrategicManagement, Fox School of Business, TempleUniversity, 540 Alter Hall, 1801 LiacourasWalk, Philadelphia, PA 19122, USA.Tel: +1 215 204 4555;Fax: +1 215 204 8029

Received: 29 August 2011Revised: 3 July 2013Accepted: 22 July 2013Online publication date: 20 February 2014

AbstractInternational trade and R&D offer significant opportunities for knowledgetransfer through exports, but simultaneously increase potential competitionthrough imports. In this paper, the author examines industry-level heterogeneityin the relationship between domestic innovation and international trade. Using amodel of innovation in the global economy and a novel measure of relativeindustry strength, the paper examines differential effects of exports and importsin high-technology industries. These relationships are tested empirically usingpanel data from four high-technology industries in the US over the period 1973–2001. In industries that are relative global leaders, the empirical evidence pointsto gains from both exporting and importing. On the other hand, in industriesthat are relative global laggards, the results are more fluid. The author finds thatexporting contributes favorably to domestic innovation in both leading andlagging industries when foreign R&D is at its maximum; at lower levels ofknowledge abroad, however, the net effect of exporting on lagging industries isnegative. Results for importing are likewise nuanced. In industries that arerelative leaders, increasingly sophisticated imports lead to greater domesticinnovation when industry structure is more concentrated, providing a competi-tive kick-start. In industries that are relative laggards, this effect is not present.Journal of International Business Studies (2014) 45, 248–274. doi:10.1057/jibs.2013.57

Keywords: innovation and R&D; international trade theory; global competition; industrydynamics; learning from exporting; import competition

INTRODUCTIONMajor changes in the landscape of high-technology industriesrequire firms to leverage diverse ideas and sources of knowledge,while simultaneously adapting to global competition. Firms thusstand to benefit – and to lose – depending upon the extent to whichboth domestic and foreign sources of knowledge can be successfullyaccessed as well as utilized. Indeed, firms in different industriesdemonstrate remarkable heterogeneity in the ability to respondsuccessfully in the face of growing internationalization (Chung &Alcacer, 2002; Feinberg & Gupta, 2004; Salomon & Jin, 2008). In thiscontext, it is surprising that research in international business andrelated fields has yet to develop theory defining the processes bywhich internationalization strategies enable firms in some industriesto increase their innovative performance while firms in other

Journal of International Business Studies (2014) 45, 248–274© 2014 Academy of International Business All rights reserved 0047-2506

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industries lag behind in such performance. In thispaper, I address one of the conundrums in interna-tional business: Why do the internationalizationstrategies that serve firms in some industries well, atsome points in time, not prove beneficial in otherindustries or in the same industry under changedcircumstances?The international economics literature explicitly

identifies both imports and exports as significantchannels for international technology diffusion(Grossman & Helpman, 1991). At the same time,the international business and strategic manage-ment literature examines industry-level heterogene-ity in knowledge spillovers and learning. Oneimportant dimension along which firms vary is inthe position of the industry in terms of relativeadvantage, or industry strength, relative to globalpeers. In this way an industry that exhibits greatercomparative advantage relative to its global peerscan be considered a “leading” industry, while anindustry exhibiting relative disadvantage is consid-ered a “lagging” industry. These distinctions begquestions about how leading or lagging industrystatus might moderate the ability of firms to positionthemselves to access, utilize, and benefit from newsources of foreign knowledge through expansioninto export markets or growth in foreign imports,particularly as the level of knowledge in thoseforeign markets increases.The distinction between leading and lagging

industries provides novel insights into mechanismsthrough which firms can most effectively leverageR&D investment abroad in knowledge-intensiveindustries. This distinction is crucial along threedimensions. First, spillovers of both codified (disem-bodied) and tacit (embodied) knowledge are widelyacknowledged drivers of innovation and growth(Grossman & Helpman, 1994). Such spillovers aremost prominent within a country, and diminishgreatly with geographic distance (Jaffe, 1993; Jaffe &Trajtenberg, 1999; Keller & Yeaple, 2013). Likewise,for firms competing in industries defined by ongoinginnovation, the potential to leverage internal R&Dresources through positive externalities from otherfirms’ R&D investments increases with the availabil-ity of knowledge spillovers from other firms engagedin closely related activities – that is, within a givenindustry (Griliches, 1992). In this manner, a firm in adomestic industry that leads the global frontier willbenefit more from spillovers from local peers thanfrom global peers.Second, beyond knowledge spillover benefits, the

international business literature also suggests that

fiercer competitive pressures within a strong domes-tic industry force firms to remain innovative andthus confer benefits abroad (Sakakibara & Porter,2001).Third, the international business literature indi-

cates that leaders and laggards benefit differentlyfrom internationalization strategies. Laggards gainfrom the opportunity to catch up to global leaders;on the other hand, firms in leading industries mayuse knowledge sourcing to obtain complementarytechnical knowledge (Cantwell & Janne, 1999;Chung & Alcacer, 2002; Salomon & Jin, 2008).1

Taken together, the above arguments providecompelling reasons to focus on differences in globalleader/laggard status to more precisely understandthe moderating role of industry context on firmstrategy in understanding the relationship betweeninternational trade, foreign R&D intensity, andinnovativeness.A rich literature in international business addresses

distinct facets through which relative industrystrength influences innovation-related strategies.Fundamentally, firms’ response to changes in theglobal competitive environment will be conditionedby both domestic and foreign relative strengths andweaknesses. For example, Cantwell and Janne (1999)find at the industry level that multinationals fromleading technological locations pursue more differ-entiated knowledge-seeking strategies abroad thanthose from technologically lagging locations. In arelated vein, Chung and Alcacer (2002), examininglocation decisions of foreign subsidiaries within theUS, find that firms from both laggard and leadingdomestic industries seek R&D spillovers as a func-tion of the relative importance of knowledge in theindustry and other industry characteristics. Examin-ing a single industry, Gu and Lu (2011) identify bothpositive spillover effects and negative competitioneffects in the global venture capital industry as afunction of whether the host country is a relativeleader or laggard. They find that the greatest spil-lover benefits are generated by venture capitalistsfrom leading nations investing in laggard nations,but that this is accompanied by greater competition.Focusing on the export dimension, Salomon and Jin(2008) find that exporting by Spanish manufactur-ing firms is associated with greater knowledgetransfer benefits for firms in industries that aretechnological laggards relative to the global frontier.Building on these earlier studies, the current paperfocuses on industry-level differences that helpexplain the differential effects of increasing tradeand the impact of trade-related innovation and

Follow me to the innovation frontier? Sheryl Winston Smith249

Journal of International Business Studies

competition in four knowledge-intensive industriesover a period of three decades.This paper further adds to the international busi-

ness literature by highlighting both exports andimports as conduits of foreign knowledge. Theinternational economics literature offers explicittheoretical grounding for both imports and exportsas key drivers for international knowledge diffusion.To date, the international business literature hasfocused on exporting as a primary mechanism forlearning and knowledge transfer; however, the rela-tionship between imports and innovation is less wellunderstood (Cassiman & Golovko, 2011; Salomon &Jin, 2008). In international economics models,knowledge spillovers associated with internationaltrade occur through reverse engineering, proof ofconcept, and contact in the normal course of busi-ness through both exports and imports (Grossman &Helpman, 1995). At the country level, numerousempirical studies link import-weighted foreigninvestment in R&D with gains in domestic innova-tion, particularly through trade between nationsat different points on the technological frontier(Bernstein & Mohnen, 1998; Coe & Helpman, 1995;Keller, 2002; Nadiri & Kim, 1996). Empirically, in apanel of French firms, MacGarvie (2006) finds thatfirms learn about foreign inventions through bothexporting and importing patterns. Specifically,import participation facilitates the inward flow ofknowledge to French firms from foreign countries, asmeasured through backward patent citations, andoutward flows of knowledge occur from French firmsto foreign countries through exporting. However, inthat study, export participation is not significantlyassociated with inward knowledge flows.In this paper, I bridge the international trade

concept of knowledge diffusion through bothimports and exports with the international businessand strategic management attention to exports as akey component of a learning-oriented internationa-lization strategy. I integrate the importance of indus-try-level heterogeneity by incorporating themoderating potential of the dynamic position ofthe domestic industry as a leader or laggard relativeto its global peers. Using an established model frominternational trade theory, I develop key insightsinto the interplay of the simultaneous flows ofknowledge and competition and their influence onlong-term industry-level capabilities. I test theserelationships in a novel empirical setting, using ahand-collected panel dataset that covers three dec-ades (1973–2001), spanning major macroeconomicshifts in internationalization patterns in four high-

technology industries in the US that exhibited adiversity of responses to increasing internationaliza-tion: computer equipment, communications equip-ment, household audio and video equipment, andscientific instruments. I disentangle the effects ofexports and imports on domestic innovation in thecontext of increasing foreign R&D expenditure. Theselection of industries and the panel setting allowme to discern the contrasting outcomes in industriesexhibiting a range of leading vs lagging status overthis span of time.The results in this paper provide strong empirical

support for the hypotheses relating to the contin-gent role of industry leadership in moderating theeffect of distinct internationalization strategies.Specifically, the results delineate a balance betweenincreased foreign knowledge accessed through bothexports and imports, and more sophisticated compe-tition both at home and abroad. In industries thatare relative global leaders, the empirical evidencepoints to gains from both exporting and importing.On the other hand, in industries that are relativeglobal laggards, the results are more fluid. I find thatexporting contributes favorably to domestic innova-tion in both leading and lagging industries whenforeign R&D is at its maximum; at lower levels ofknowledge abroad, however, the net effect of export-ing on lagging industries is negative. Results forimporting are likewise nuanced. In industries thatare relative leaders, increasingly sophisticatedimports lead to greater domestic innovation asindustry structure is more concentrated, providing acompetitive kick-start. In industries that are relativelaggards, however, I do not see this effect.This paper makes a theoretical contribution to the

international business literature in several ways.First, by highlighting the tension between theopportunities presented by greater global stocks ofknowledge that can contribute to domestic innova-tion and the challenges posed from increasinglysophisticated foreign competition, this paper con-ceptually connects the impact of knowledge flowsembodied in international trade to the relative dif-ferences in leading and lagging status of the domes-tic and foreign industries. Thus this paper suggeststhat a coherent framework for understanding inter-nationalization strategies must take into account thedifferential impacts of knowledge transfer as a func-tion of relative industry technological capabilitiesand resources.Second, this paper incorporates a detailed picture

of international trade that affects firms in a givenindustry – that is, both imports and exports. Thus it



extends the international business literature thatlooks at performance differences in leader and lag-gard industries primarily through the lens of exportrelationships (Cassiman & Golovko, 2011; Salomon& Jin, 2010). In this way, this paper expands ourunderstanding of the nuanced interaction betweenindustry strength and the knowledge flows asso-ciated with distinct components of internationaliza-tion. Taken as a whole, the model in this paperprovides novel insights into how foreign and domes-tic innovation capabilities might serve as eithercomplements or substitutes under different industryconditions over time. The flexibility of comparingacross industries – which individually and separatelyvary in leading and lagging status over three decades– allows us to isolate the impact of this relative statuson domestic innovation as both foreign innovationcapabilities and levels of international trade expand.We can thus better understand the effectiveness bywhich firms may leverage their R&D investments, ormay benefit better from importing and exporting inthe face of mutable industry capabilities.Ultimately, these insights allow us to understand

more fully the distinct conditions under whichdifferent internationalization strategies will havepositive or negative effects on firms in relativelyleading or lagging industries. The results suggest thatexporting can help firms in both leading and laggingindustries as foreign R&D increases, which is inkeeping with insights from the learning from export-ing literature. The industry-level heterogeneity inthis study allows us to identify significantly strongereffects in lagging industries as foreign R&D increases,suggesting that exporting may provide access totechnical knowledge abroad that is lacking in arelatively weak domestic industry. At the same time,when taking domestic industry concentration intoaccount, the results suggest that an internationaliza-tion strategy focused on importing might providefirms in leading industrieswith incentives to innovatein order to maintain their position at the globalfrontier. By integrating into our theoretical frame-work insights from international trade theory,industrial organization and innovation economics,and strategic management, the results in this paperlet researchers see these important industry-leveldistinctions and make sense of sometimes contrast-ing relationships between trade and innovationperformance.The rest of the paper proceeds as follows. The next

section provides a brief theoretical overview, anddevelops hypotheses based on the relevant litera-tures. It first reviews the background literature on

international trade and knowledge diffusion, andthen investigates the contingent role of industrystatus. The subsequent section describes the sample,data, and empirical methods. Results of the econo-metric analysis are discussed in the next section. Thefinal section discusses the results and gives a broadercontext for the conclusions, including limitations ofthe study and areas for future research.

THEORYAND HYPOTHESIS DEVELOPMENTIn this paper, I draw upon assumptions of interna-tional trade theory, innovation theory, and indus-trial organization theory to outline the effects thatforeign R&D may have on domestic innovationthrough the channels of international trade, andhow these are moderated by relative industrystrength. Increasingly, international trade theoryrecognizes that both exporting and importing engen-der direct and indirect exchange of ideas in thecourse of doing business, thereby exposing domesticfirms to new knowledge (Grossman & Helpman,1991). On the other hand, as foreign R&D increases,the intensity of sophisticated competition fromabroad also increases, and foreign markets maybecome harder for domestic exporters to penetratesuccessfully (Syverson, 2011). These two countervail-ing effects suggest that as the level of investment inR&D by firms in the foreign industry increases,engaging in exporting and importing concomitantlyexpands the pool of knowledge upon which domes-tic firms can draw, and enlarges the field of techni-cally sophisticated competition in both domestic andforeign markets.Below, I propose that increasing R&D by foreign

firms will influence domestic industries differently,depending on their worldwide industry leading orlagging status: leaders benefit from contact with thesefirms and their knowledge, but must be preparedfor increasing competition; laggards face steeper cha-llenges in trying to benefit from such contact, but alsomay have the most to learn. The extent of contact viatrademagnifies these different effects. This propositionrests on assumptions drawn from international trade,innovation, and industrial organization theories thatare described in the remainder of this section.

Background: International Trade, KnowledgeDiffusion, and InnovationA longstanding theoretical and empirical literatureidentifies international trade as a source of knowl-edge transfer between nations (e.g., Bayoumi, Coe, &Helpman, 1999; Coe, Helpman, & Hoffmaister,1997; Grossman & Helpman, 1995). In turn, this



effect rests on the inherent properties of knowledgeitself. The key insight into why knowledge “spillsover” to multiple parties comes from Arrow (1962a):namely, that knowledge is fundamentally a “publicgood” that is characterized by being non-rival (i.e.,multiple parties can “consume” it without diminish-ing the enjoyment of others) and at least partiallynon-excludable (i.e., it is hard to prevent additionalparties from consuming it).2 Knowledge spillovers arecharacterized by benefits from investment in specificknowledge generation (e.g., R&D) accruing not just tothe investing party, but to others as well (Griliches,1979, 1992; Jaffe, 1986). The caveat, however, is thatrecipient parties must have the requisite absorptivecapacity to reap these benefits fully (Cohen &Levinthal, 1989, 1990). In thismanner, processes thatinvolve the exchange of goods or services thatembody advanced knowledge facilitate transfer ofthe knowledge itself (Eaton & Kortum, 1996).Moreover, investment in R&D by one party leads

to an expanding stock of knowledge accessible to all,which theoretically undergirds “endogenous growththeory” models in the economics literature (Aghion,Harris, Howitt, & Vickers, 2001; Romer, 1986). Inthese models of endogenous innovation, economicgrowth derives from innovation – that is, technolo-gical change – in which the ability to produce moreoutput from the same quantity of inputs is achieved.Such technological change represents an outwardshift of the production possibilities frontier, andthus the direct connection between technologicaladvance and the observable level of output producedwith a given quantity of the relevant inputs iscaptured by measuring total factor productivity(TFP) (Griliches, 1998; Grossman &Helpman, 1994).In the international arena, the potential for

knowledge transfer enables foreign R&D spending toinfluence domestic innovation performance and pro-ductivity. For example, the existence of knowledgespillovers underlies the rationale for internationali-zation strategies that involve accessing knowledgegenerated outside the home country (Branstetter,2001; Cantwell & Mudambi, 2011; Chang & Xu,2008; Feinberg & Gupta, 2004; Liu, Siler, Wang, &Wei, 2000; Wei & Liu, 2006). The Grossman andHelpman theoretical architecture, in which foreignR&D investment increases the global stock of knowl-edge available to all firms (domestic and foreign), hasbeen particularly influential in framing the under-standing of the underlying relationship betweeninternational trade and knowledge spillovers(Grossman & Helpman, 1991, 1995). In this widelyapplied model, international trade – both exports

and imports – serves as the main vehicle throughwhich these global knowledge stocks are accessed(Grossman & Helpman, 1991: 167). Over time, thisgeneral Grossman and Helpman framework hasbeen applied to various levels of analysis, includingmultiple economies and industries, and has gener-ally supported the idea that foreign R&D spills overto domestic innovation – usually measured in termsof TFP growth – through the channels of interna-tional trade.3

Domestic firms will be forced to respond to increas-ing competitive pressures from foreign rivals. Indus-trial organization theory suggests that R&D impactsrivals through competition, which can have bothpositive and negative effects (Scherer & Ross, 1990).On one hand, increasing foreign rivalry can serve as apositive spur to domestic firms. For example, consid-ering the dynamic evolution of competition in theglobal disk drive industry, Barnett and McKendrick(2004) find that strategies that isolate firms fromcompetition, either domestic or foreign, are associatedwith an inability to sustain comparative advantage inthe long run. Specifically, Barnett and McKendrickfind that increasing rivalry from foreign companieshas a stronger impact on market exit than increasingdomestic rivalry. They present evidence of a non-linear pattern, with early increases in the number offoreign rivals leading to greater stability amongdomestic disk drive manufacturers, but later hasten-ing their likelihood of exit. Similarly, Sakakibara andPorter (2001) find that sufficient exposure to domesticrivalry facilitates the ability of Japanese firms tocompete abroad across a variety of industries.On the other hand, in a world of technological

competition and R&D rivalry, foreign technologicalactivity might adversely impact domestic firms’ inno-vation by leading to a submissive R&D response. Theeconomics literature on innovation recognizes poten-tially harmful effects of one firms’ R&D activities onanother firm, for example in the context of R&Dduopoly (Scherer & Ross, 1990; Spencer & Brander,1983). The literature on technology races confirmsthis view that increasing R&D by rivals can lead to adecrease in R&D spending by the lagging firm (Lerner,1997). These two countervailing effects imply thatthe ultimate impact of increasing foreign R&D will beparticularly sensitive to both the relative preparednessand the absorptive capacity of the domestic industry.

The Contingent Role of Relative IndustryLeadershipIncreasingly, the international business literaturehas sought to unpack the differential ability of firms



in domestic industries that are relative leaders andlaggards to adapt to internationalization in the con-text of changing innovation environments at homeand abroad. Revealed compared advantage (RCA)incorporates a quantitative measure of the relativeperformance of domestic industries along twodimensions: (1) relative to the same foreign industry;and (2) relative to other domestic industries (Balassa,1979). RCA thus indicates distribution of competi-tiveness among industries. In this way, an industrythat exhibits greater comparative advantage relativeto its global peers can be considered a “leading”industry, while an industry exhibiting relative dis-advantage is considered a “lagging” industry. Differ-ent conditions will prevail across industries, shapingthe competitive environment in distinct ways acrossidiosyncratic industry settings, and influencing thebalance between opportunities for learning againstthe challenges of sophisticated competition. Forexample, industry-level differences shape the entryand exit behavior of firms in response to increasingopenness to international trade that brings bothincreased competition from imports and increasedpotential to penetrate export markets (Colantone &Sleuwaegen, 2010).A large literature posits that absorptive capacity is

an important component of R&D spillover. Spil-lovers are more likely to occur if the “receiver” isadvanced enough to find new knowledge and recog-nize its importance, and is otherwise prepared toincorporate this knowledge effectively (Cohen &Levinthal, 1989, 1990). Firms in industries that arerelative leaders can be thought of as having greaterpotential absorptive capacity, and thus are mostlikely to benefit from increasing foreign R&D. Forexample, firms that are relatively strong invest inforeign R&D to augment technological capabilities,whereas technology laggards do not benefit underthe same conditions (Berry, 2006). Likewise, success-ful innovation by multinational firm subsidiariesdepends more heavily on the ability to utilizehost-country knowledge rather than home-countryknowledge (Phene & Almeida, 2008). Because know-ledge is highly localized, firms from a leading coun-try-industry are more likely to possess the uniquecapabilities to benefit from internationalization(Chung & Alcacer, 2002).What is the relationship between increasing tech-

nological capability abroad and domestic innova-tion? When the domestic industry is a relative leader,domestic firms in this industry stand to benefit alongseveral dimensions. Geographic nearness facilitatescapabilities that build on embeddedness (Cantwell &

Mudambi, 2011; Frost, 2001) and information flows(Jaffe & Trajtenberg, 1999). Also, core strategies suchas the R&D intensity of firms are influenced byindustry-level innovation capacity (Mauri &Michaels, 1998). Firms coming from relatively strongdomestic industries have greater technological cap-abilities and enhanced absorptive capacity to learnfrom both domestic and foreign sources of knowledge(Cantwell & Janne, 1999). More broadly, asymmetryin the capabilities for knowledge combinationand integration may mean that knowledge flowsfrom foreign R&D are more likely to be captured bydomestic firms in a leading industry, since knowledgecombinations of the specialized fields of expertise offoreign and domestic firms are more likely to bedeveloped by firms in the domestic leading industrythan those in the foreign lagging industry.Overall, we expect that firms in leading industries

will be more likely to have the technological cap-abilities, absorptive capacity, and resources to utilizeglobal sources of diverse knowledge. Thus:

Hypothesis 1a: All else equal, when the domes-tic industry is a relative leader, domestic innovationshould increase as foreign R&D spending increases.

What about industry laggards? Firms in an indus-try that lags relative to global peers may be morelikely to seek knowledge abroad that they lack athome (Chung & Alcacer, 2002). In the process ofcatching up, firms in laggard industries stand tolearn from best practices among their foreign peers.Such substitution of foreign R&D spillover in placeof domestic spillover benefits might enable firms inlaggard domestic industries to eventually catch up.Also, within a given industry, firms possess differentcapabilities. Thus a leading firm in a laggard industrymay be able to utilize foreign R&D as a substitute fordomestic R&D in its relatively weak home base.4 Forexample, Bartelsman, Haskel, and Martin (2008)show for a panel of firms in the United Kingdomthat in an industry that is a relative laggard com-pared with its global peers, leading national firms(i.e., those above the industry average) convergetowards the global – rather than the national –

technological frontier.However, endogenous growth theory implies that

knowledge spillover will magnify imbalances ininnovative capabilities, particularly given the loca-lized concentration of knowledge transmission(Keller, 2002). Thus, when the domestic industry isa relative laggard, domestic firms face diminishedprospects of spillover benefits and consequent inno-vation relative to a leading industry.



Compounding the diminished spillover benefitsavailable to lagging industries, increasing invest-ment in innovation abroad reflects the developmentof more technically sophisticated competitors.Insights from industrial organization suggest thatincreasing investment by foreign rivals might leadto a submissive R&D response and diminished inno-vation (Scherer & Huh, 1992). This effect may beexacerbated by the relative lack of sophisticateddomestic competition (Sakakibara & Porter, 2001).On balance, facing increasingly advanced global

competition may make it harder for firms in arelatively weak industry to innovate effectively(Lawrence, 2000). Thus:

Hypothesis 1b: All else equal, when the domes-tic industry is a relative laggard, domestic innova-tion should decrease as foreign R&D spendingincreases.

A substantial literature documents how firms gainfrom exporting. At its core, exporting providesdomestic firms with access to a larger market,thereby allowing greater economies of scale andhence increased potential for learning-by-doing(Arrow, 1962a, b). A growing literature suggests thatthe pressure of entering global markets where theywill be exposed to additional competitive forcesinduces firms to develop their technological capabil-ities. For example, Argentinean exporters invested inactivities geared towards technology upgradingmore extensively in Brazilian markets that posedreduced trade barriers after the MERCOSUR agree-ment (Bustos, 2011). Likewise, Canadian firms thatbegan exporting in response to NAFTA adopted newtechnologies, and engaged in greater product marketinnovation than firms that were already exporting tothe US prior to the elimination of trade barriers(Lileeva & Trefler, 2010). Clerides, Lach, and Tybout(1998) find that more productive firms in Moroccoand Columbia self-select into exporting, and theninduce other firms in the industry or region to beginexporting. Taken together, these studies imply thatengaging in exporting provides opportunities forfirms to enhance their production processes. Impor-tantly, exporting also expands the potential fordirect and indirect knowledge spillovers (Grossman& Helpman, 1991). For example, exporting presentsdomestic firms with access to “privileged informa-tion” about foreign markets and technologies alongboth product and process dimensions (Salomon &Shaver, 2005). Overall, as foreign R&D and tech-nical capacity abroad increase, domestic firms mayglean technical insights through trade in more

sophisticated markets; however, the competitive barto succeed will also be higher.How might relative industry strength matter? In

leading industries, growing foreign R&D intensity inexport markets increases both the pressure andthe opportunity to innovate. First, increasing R&Dintensity abroad reflects increasingly advanced riv-als, driving domestic industries to innovate furtherto remain ahead. Firms in industries that are relativeleaders face strong incentives to maintain their placeat the front of the global frontier through increasedinnovation efforts, and this effect will be magnifiedas the technological frontier advances throughincreased investment in R&D by the foreign indus-try. Second, increasing R&D intensity abroad pro-vides more nuanced opportunities for knowledgespillover from technically sophisticated competitorsand customers. For example, domestic manufac-turers in the scientific instrument industry – wheresophisticated users provide important feedback –

were able to incorporate insights into their innova-tion efforts from customers and suppliers around theworld (Riggs & von Hippel, 1994). In a relativelystrong industry such as scientific instruments, firmspossessed the technological capabilities, absorptivecapacity and resources to mobilize to meet newneeds of sophisticated competitors and consumersin technologically advanced foreign markets.In lagging industries, increasing R&D intensity in

export markets provides an alternative spilloverchannel that does not exist in the domestic market.Firms in an industry that is a relative laggard gainexposure to more advanced knowledge as theyexport to countries with substantial R&D invest-ment in the industry; these industries abroad will beat or closer to the global frontier than the laggingdomestic industry. Salomon and Jin (2008) find thatfirms in industries that are relative technology lag-gards, as measured through investment in R&D,apply for more patents as a function of exporting.However, at the firm level they also find that themarginal effect of exporting on innovation is greaterfor firms in technologically more advanced indus-tries than for those in laggard industries (Salomon &Jin, 2010).5 However, firms in laggard industries areless likely than those in leading industries to possessthe necessary technological capabilities, absorptivecapacity, and resources at home to utilize spilloverbenefits from abroad fully, or to meet demand formore advanced products abroad. For example, in thehousehold electronics industry, US-based manufac-turers lacked resources and capabilities to invest inautomation while their foreign rivals moved forward



in this direction, focusing instead on exportinglow-end products and using foreign locations tosource cheap labor (MIT Commission on IndustrialProductivity, 1989).On balance, as foreign investment in R&D

increases, firms in both leading and laggard indus-tries stand to gain from the spillover of increasedknowledge abroad through their exporting activities;however, in laggard industries these gains will bemore limited by the extent to which firms have thecapabilities and resources to utilize this knowledgeto move closer to the global frontier. These effectswill be magnified as foreign R&D intensity increases.Taken together, the arguments above suggest the

following two hypotheses:

Hypothesis 2a: All else equal, when the domes-tic industry is a relative leader, domestic innovationshould increase in response to foreign R&D spendingas exports increase.

Hypothesis 2b: All else equal, when the domes-tic industry is a relative laggard, domestic innova-tion should increase in response to foreign R&Dspending as exports increase.

International trade theory provides a solid role forimporting as a transmitter of knowledge flows as wellas direct product market competition. Knowledgeembedded in imported goods can be used as inputsto innovation, for example through reverse engineer-ing or leapfrogging (Caves, 1996; Coe & Helpman,1995). For example, the Japanese electronic calculatorindustry had its roots in direct imitation and reverseengineering of calculators imported from the USin the early 1960s (Majumdar, 1980).6 Also, in thenormal course of doing business, importing providesopportunities for discourse and learning that lead toadditional knowledge flows (Grossman & Helpman,1994; Hejazi & Safarian, 1999).Within the international business literature,

greater theoretical and empirical attention has beenfocused on the effects of import competition ondomestic market structure, which can induce inno-vation to “escape” the competition. Wiersema andBowen (2008) find that foreign-based competitioninduces greater changes in corporate strategy thanincreases in domestic rivalry alone, which theyattribute to the powerful combination of firm – andcountry-specific advantages possessed by foreignentrants. Empirically, Scherer and Huh present evi-dence of increasing import competition leading tosubmissive R&D reactions by domestic firms, parti-cularly in more concentrated industries – that is,

those that were effectively isolated from domesticcompetition (Scherer & Huh, 1992). Likewise,import competition has been shown to introducegreater disruptive effects on a firm’s core businessthan strictly domestic competition (Bowen &Wiersema, 2005). Colantone and Sleuwaegen(2010) find that increasing import intensity maydiscourage entrepreneurial entry in industries char-acterized by greater import intensity in a panel ofeight European nations over the period 1997–2003.More generally, Aghion, Bloom, Blundell, Griffith,and Howitt (2005) present evidence that firms inleading industries in the UK innovate more inten-sively following increased competition: that is, theyinnovate to escape competition, whereas those inlagging industries succumb.A number of empirical studies suggest substantial

industry-level differences in the response to importcompetition. Notably, import competition fromsophisticated competitors can lead to dynamic real-location of resources oriented towards innova-tion. Lawrence (2000) finds evidence that increasedimport competition from developed nationsinduced hiring of more highly skilled workers in apanel of US industries from 1978 to 1989. Otherstudies have shown that international competitioninduces changes in the rate of technological changein an industry (Caves, 1996; Scherer & Huh, 1992),and forces greater efficiency by driving down theprofit margin for domestic firms (Chung, 2001).Why do we expect that firms in leading industries

and those in lagging industries might respond dif-ferently to increased imports from more technologi-cally sophisticated foreign rivals? The internationaleconomics literature recognizes that imports incor-porate the knowledge itself, as well as opportunitiesfor contact. As foreign R&D increases, imports willcontain increasingly sophisticated technologicalknowledge. For firms in leading industries, this mayrepresent new fodder for further innovation. At thesame time, this embedded knowledge may be tech-nologically superior to that in a lagging domesticindustry, providing the potential for knowledge spil-lover if the recipient is able to make use of it. Further,the strategy literature recognizes that import compe-tition inherently incorporates exposure to differentresources and practices in addition to distinctknowledge, which makes it distinct from simpledomestic competition. For example, foreign firmsmay possess both country- and firm-specific capabil-ities that are different from those of domestic firms(Wiersema & Bowen, 2008). Thus import competi-tion potentially introduces new and different



capabilities to domestic firms (Ghoshal, 1987). Firmsin leading industries should be better positioned tolearn from this than those in lagging industries.Combining the insights from international trade

theory and strategic management suggests thatimports from increasingly sophisticated foreignpartners – similarly to exports – opens domesticfirms to opportunities for knowledge transfer butexposes them to greater competitive pressures.Taken together, these arguments suggest the follow-ing hypotheses:

Hypothesis 3a: All else equal, when the domes-tic industry is a relative leader, domestic innovationshould increase in response to foreign R&D spendingas imports increase.

Hypothesis 3b: All else equal, when the domes-tic industry is a relative laggard, domestic innova-tion should decrease in response to foreign R&Dspending as imports increase.

DATA AND METHODS

Sample Selection and DataThe hypotheses developed above are tested using ahand-collected panel dataset on four high-technol-ogy manufacturing industries at the three-digit level:computer equipment (SIC 357), household audioand video equipment (SIC 366), communicationsequipment (SIC 365), and scientific instruments (SIC381+382). The sample is a balanced panel observedfrom 1973 to 2001 for a total of 116 industry-yearobservations. The extended time period covers aperiod of significant change in underlying funda-mental technologies, such as the transition fromanalog technology to digital technology, and as theintroduction and adoption of information technol-ogy. The time period also covers significant shifts inthe general competitiveness of US high-technologyindustries.As the first criterion, all four industries were iden-

tified as high-technology industries, measured interms of R&D intensity relative to all US manufactur-ing industries (National Science Foundation, 1972–2004). Industries were selected to allow identifica-tion along the main dimensions of interest (King,Keohane, & Verba, 1994). Thus the four specificindustries were identified to provide a range in keycharacteristics:7

(1) the nature of technological innovation;(2) the extent of internationalization; and(3) the response to international competition.

(Brief industry descriptions and relevant compara-tive historical innovation trajectories are detailed inthe Appendix.)The level of analysis chosen in this study, industry

level delineated by three-digit SIC, affords severaladvantages compared with other possible choices.Foremost, the effects being studied in this papercondition the environment in which the individualfirms that make up an industry operate (Porter,1991). It is a hallmark of the microeconomics ofinnovation that the state of technological advanceand the stock of scientific and technological advancevary significantly among industries, and that firm-level innovation depends on the larger industryenvironment (Dosi, 1988).8 In a seminal paper,Teece, Pisano, and Shuen (1997: 515) identifychanges in the global technological environment asdriving the need for dynamic capabilities to achievesustainable competitive advantage. The strategicmanagement literature provides additional evidencethat in core strategies, such as R&D intensity andadvertising, industry-level variance outweighs firm-level fixed effects. Indeed, Mauri and Michaels(1998) suggest that nearly two-thirds of the variancein R&D intensity among firms is explained byindustry effects. While firm-level data might yieldinsight into intra-industry heterogeneity, it wouldreduce the ability to distinguish intersectoral differ-ences. Following others in the literature, it is possibleto conceptualize the industry-level data to be repre-sentative of the average of all firms in that industry(Nachum & Zaheer, 2005).The final dataset is developed using multiple

sources. Measures of domestic R&D investment,foreign R&D investment, US imports and exports,and the inputs and output that allow one to calcu-late TFP are used to estimate the model. A revealedcomparative advantage index is constructed to pro-vide a quantitative measure of industry strength.Details are given in the variable descriptions below.

Dependent Variable

Total Factor Productivity (TFP)In order to best capture a direct indicator of innova-tion at the industry level, TFP is used as the depen-dent variable in this study. This follows an extensivetradition in the economics literature at the firm,industry, and aggregate level of recognizing thedirect connection between technological advanceand the observable level of output produced with agiven quantity of the relevant inputs (Griliches,1998; Grossman & Helpman, 1994).



I calculate three-digit industry-level TFP fromthe NBER Manufacturing Productivity database(Bartelsman & Gray, 1996). The underlying long-itudinal micro-level data provide measures of outputand inputs based on the US Census of Manufacturessurvey of all US manufacturing establishments andthe Annual Survey of Manufactures from annualsamples of 55,000–75,000 manufacturing establish-ments, and thus provide large-scale, time-series datafrom individual manufacturing establishments atmultiple points in time (Bartelsman & Doms, 2000).The data are aggregated to the level of 459 four-digitSIC industry codes for the years 1958–1996 in theManufacturing Productivity database. In order tomatch the level of aggregation of domestic andforeign R&D measures (discussed below), I furtheraggregate the TFP measures to the three-digit level.9

I extend the NBER data through 2001 using origi-nal source data from the Annual Survey of Manufac-tures and the 1992 benchmark input–output tablesfor the US economy. Price deflators for shipmentswere derived from published Bureau of EconomicAnalysis deflators. Annual capital stock and deflatorsare from the Federal Reserve.10 One complication inextending the TFP data beyond 1996 is the switchfrom the SIC system of industrial classification tothe NAICS classification in 1997. Thus I allocate theNAICS-based data for the years 1997–2001 to theSIC system using bridge tables provided by the USCensus Bureau.The choice of TFP as the dependent variable

invites commentary on alternative choices. Patentdata are frequently used in the management litera-ture as a measure of innovation output (Ahuja &Katila, 2001; Hitt, Ireland, & Harrison, 1991; Sampson,2005). While patents capture particular aspects ofinventions very well – specifically non-triviality andnovelty – they do not capture many types of researchoutcomes that lead to innovation, particularly thosethat can have the most spillover effects (Jaffe &Trajtenberg, 2002). Levin, Klevorick, Nelson, andWinter (1987) show that patents are an imperfectmeasure of spillovers, particularly in industries thatrely heavily on other means of protecting intellec-tual property. Moreover, an invention, as measuredby a patent, does not necessarily become an innova-tion until it is successfully introduced into themarket.11 On the other hand, the outward shift ofthe production function implied by technologicalchange captures both appropriable and spilloveraspects of innovation. Thus productivity measuresare commonly used to capture implicit spillovereffects – for example from R&D (Feinberg &

Majumdar, 2001; Hejazi & Safarian, 1999) and fromFDI (Chung, 2001; Liu et al., 2000) – and to measuretechnological change directly (Schilling & Steensma,2001). I follow this literature.

Independent Variables

Foreign R&DGlobal stocks of knowledge that both contribute todomestic innovation and create sophisticated for-eign competitors will result predominantly from thecumulative available stock of knowledge in otheradvanced economies. I use R&D data from the OECDANBERD database to measure the combined cumu-lative investment in R&D in the European Unionand Japan over the time period 1973–2001 (OECD,2006).12 The variable foreignR&D (1987=1) mea-sures foreign R&D stock, the cumulative investmentin R&D in the European Union and Japan.13

I modified the ANBERD data to match the indus-try-level data from US sources. I matched the ISIC(Revision 2) code to the US SIC code. Followingconvention, I measure the cumulative knowledgeavailable by calculating foreign R&D stock using theperpetual inventory method (Coe & Helpman, 1995;Feinberg & Majumdar, 2001; Griliches, 1979). Forthe benchmark year, 1973,

S0 ¼ R0

g + δ(1)

where S0 is benchmark R&D capital stock, R0 is R&Dexpenditure in the benchmark year, g is the averageannual logarithmic growth of R&D expendituresover the period, and δ is the depreciation rate ofR&D capital, using δ=0.11.14 R&D stock for subse-quent years is given by

St ¼ 1 - δð ÞSt -1 +Rt -1 (2)

Imports and exportsIndustry-level import and export data are calculatedfrom the Feenstra bilateral trade dataset (Feenstra,1996, 1997; Feenstra, Romalis, & Schott, 2002). Iextracted bilateral import and export data betweenthe US and Japan and the US, and then includednations in the European Union to match the foreignR&D data. For the years 1972–1988 these data are inthe 1972 revision of the SIC code. Thus data for thisperiod had to be translated from the 1972 SIC codeto the 1987 SIC code. The data were translated at thefour-digit level using the concordance in the NBERManufacturing Productivity database, and then aggre-gated to the three-digit level. Data for 1989–2001 were



in the 1987 SIC classification, which I then aggre-gated to the three-digit SIC level.In the regressions, imports and exports are normal-

ized by net sales. The variables imports and exports areintroduced with a one-year lag. Econometrically,including the lagged variables reduces our concernabout endogeneity between cotemporaneous tradeand total factor productivity.

Industry relative strengthAn important feature of this paper is the intro-duction of industry relative strength as an explana-tory variable. The four industries are characterizedby constructing an RCA index, which provides aquantitative measure of the relative performanceof domestic industries (Balassa, 1979). FollowingBalassa, revealed comparative advantage is calcu-lated as

RCAi ¼USxi

�Worldxi

USx=Worldx´100 (3)

where USxi equals US exports in industry i, andWorldxi equals combined exports from the EuropeanUnion, Japan, and the US in industry i. Likewise, USxand Worldx are, respectively, the US and combinedEuropean Union, Japan, and US exports in all goods.Revealed comparative advantage can be used as ameasure of the distribution of competitivenessamong industries. Relative industry strength is intro-duced into the regressions through the variableRCA. We use the natural logarithm of this value inour regressions.The industries exhibit a range of revealed com-

parative advantage. According to this framework,the scientific instruments and computer and officeequipment industries exhibit strong domesticadvantage over this time period. Communicationsequipment also exhibits domestic advantage, butto a lesser extent. Household audio and videoequipment exhibits relative disadvantage over thisperiod.Table 1 lists the four industries in order of domes-

tic revealed comparative advantage. A revealed com-parative advantage value above 100 means that theUS has a larger share in world exports in thisindustry than it does in total exports of all goods.Conversely, a revealed comparative advantage below100 means that the US has a smaller share in worldexports in this industry than it does in total exportsof all goods.

Control Variables

Domestic R&D stockDomestic investment in R&D has been shown tohave a significant positive contribution to TFP.Several decades of empirical work have shown agenerally positive relationship between R&D andproductivity growth. Estimates of the contributionof R&D to productivity growth are sizeable.15 Thevariable R&D is the log of domestic R&D stock(1987=1), which measures the cumulative invest-ment in R&D. The domestic R&D stock variable iscreated using the perpetual inventory method.US R&D data are assembled from the National

Science Foundation R&D in Industry series. Thisannual survey is the most comprehensive source ofUS R&D data. The data include company and otherfunding (but not federal funds) for industrial R&D,in millions of current dollars. The data are based on asurvey of a panel of R&D-performing companies thatis updated periodically. The data are available at thethree-digit SIC level, with some exceptions. Industrynet sales data are also taken from the NSF R&D inIndustry series.

Industry structureControls for differences in industry structure, con-duct and performance are included to capture factorsthat differ across the four industries. The four-firmconcentration ratio (CR4) is obtained from the USCensus Survey of Manufactures to control for marketstructure. More concentrated markets are expectedto shield incumbents from competitive pressures athome (Scherer & Ross, 1990). Malerba, Orsenigo,and Peretto (1997) find that industry technologicalperformance is strongest in the presence of a

Table 1 Industry rank based on RCA index

Industry 1973–1982 1983–1992 1993–2001

SIC 357 234 216 213SIC 381+382 217 204 157SIC 366 185 97 127SIC 365 15 17 37

Source: Author calculations from OECD, International Trade in CommodityStatistics.Following Balassa (1979), revealed comparative advantage is:

RCAi ¼USxi

.Worldxi

USx=Worldx´ 100

where USxi=US exports in industry i and Worldxi= combined exports fromthe European Union, Japan, and the US in industry i. Likewise, USx andWorldx are, respectively, the US and combined European Union, Japan,and US exports in all goods.



“competitive core of persistent innovators”, indicat-ing the nuanced relationship between industry mar-ket structure and technological innovation.

Industry conduct and performanceStandard controls from the industrial organizationliterature are used to control for additional industrycharacteristics (Bain, 1968). Inter-industry variationin conduct is captured using annual operatingexpenses, and inter-industry variation in operatingperformance is controlled for using return on assetsgleaned from RMA Annual Statement Studies (RobertMorris Associates, 1973–2001).

TimeGiven the trending nature of TFP and R&D, it iscommon in the literature on productivity to includeyear dummies or a time trend.16 I include the vari-able time as an exponential time trend to control foreffects due primarily to a common trend over timethat would otherwise result in a spurious relation-ship between the dependent variable and trendingindependent variables. In other regressions (not

shown) I include the full set of individual yeardummies instead of the time trend, which does notaffect the econometric results. Given the samplesize, I choose to include the time trend in place ofyear dummies to preserve degrees of freedom.

Specification of the ModelTo test these hypotheses about the relationshipbetween foreign R&D and domestic innovation, Iemploy a parsimonious structural model relatingdomestic innovation to domestic and foreign R&Dstocks and international trade. The model draws onthe widely used analytical framework developed byGrossman and Helpman (1991), which examinesTFP performance as a function of both domesticand foreign R&D stock, and integrates internationaltrade as a conduit for knowledge transmission.Expectations from the hypotheses are summarizedin Figure 1.The dependent variable, TFP, captures innovation

through technical change, as discussed above. Inno-vation occurs through investment in R&D, whichresults in an increase in either the number or the

Industry

Type

Effect of:

Foreign R&D As Exports Increase As Imports Increase

Leader

H1a:

Net +

Sophisticated

Markets & Users

(Spillover) (+)

H2a:

Net +

Sophisticated

Competition

(Spur) (+)

Sophisticated

Products &

Suppliers

(Spillover) (+)

H3a:

Net +

Sophisticated

Competition

(Spur) (+)

Laggard

H1b:

Net -

Sophisticated

Users&Suppliers

(Spillover) (+)

H2b:

Net +

Sophisticated

Competition

(Kick) (-)

Sophisticated

Products &

Suppliers

(Spillover) (+/-)

H3b:

Net -

Sophisticated

Competition

(Kick) (-)

Figure 1 Taxonomy of predicted effects of international trade and increasing foreign R&D on domestic innovation, by industry type.



quality of intermediate goods available for the pro-duction of final products. As discussed previously,international trade allows access to the cumulativeglobal stock of knowledge: thus the cumulative stockof knowledge increases with the cumulative volumeof international trade. The specification that will beestimated can be expressed as

TFP ¼ β0 + β1 R& D + β2 Foreign R& D + β3 Exports+ β4 Imports + β5 Exports ´ Foreign R& D

+ β6 Imports ´ Foreign R& D + β7 year + ε ð4Þ

for industry i and year t. This specification allows usto separate the effects of domestic R&D, foreignR&D, international trade and, importantly, theinteraction between them. Industry relative strengthis introduced as RCA and an interactive termbetween RCA and foreign R&D stock:

TFP ¼ β0 + β1 R& D + β2 Foreign R& D + β3 Exports+ β4 Imports + β5 Exports ´ Foreign R& D

+ β6 Imports ´ Foreign R& D + β7 RCA+ β8 RCA ´ Foreign R& D + β9 year + ε ð5Þ

The model above is estimated using panel techni-ques. I utilize the panel structure to account forunobserved heterogeneity across industries andyears. For example, differences might arise as a resultof common factors affecting R&D investment andinnovation in a given industry. The long time andrelatively narrow cross-sectional nature of the datasuggests controlling for unobserved heterogeneityusing fixed effects over random effects (Wooldridge,2002). Results of a Hausman test confirm this pre-ference. The Breusch–Pagan test for heteroskedasti-city rejects the null hypothesis of homoskedasticstandard errors. Heteroskedasticity-robust standarderrors are used in estimation of the model.Several econometric concerns might result in

biases in these results. One issue arises from thepotential correlation between R&D and the errorterm due to simultaneity between R&D spendingand TFP. In this case, as TFP increases we mightexpect investments in R&D to also increase, thusimparting an upward bias to the estimated coeffi-cient on R&D stock. Using R&D stock should alle-viate this effect to an extent, because as a cumulativeinvestment it is less co-temporaneous with TFP in agiven year. Likewise, bias potentially is introducedby endogeneity between the international tradeterms and TFP. To some extent, using lagged exportto sales and lagged import to sales variables, as isdone here, might alleviate some of the reverse

causation, as the current year productivity shouldnot drive the previous year’s exports or imports.One econometric issue that arises in using a panel

that arises with the relatively long time series andnarrow cross-section is the potential for serial corre-lation in the errors.17 Feasible generalized leastsquares (FGLS) regression can be implemented totake this into account. There are tradeoffs involvedbetween ordinary least squares regression (OLS) andFGLS. Specifically, OLS panel estimates are asympto-tically consistent, although less efficient than FGLS,under certain assumptions; however, the additionalassumptions that FGLS imposes on the model makeit more sensitive to specification (Wooldridge, 2002:Chapter 7).

RESULTS

Econometric AnalysisThe results of the econometric analysis are summar-ized below. Summary statistics and correlation coef-ficients for each industry are given in Table 2.Regression results for the full sample analysis aresummarized in Table 3. The significance of relativeindustry strength is probed further by splitting thesample into relatively strong and relatively weakgroups based on revealed comparative advantage,with the results given in Table 4. Results from bothOLS regressions and FGLS regressions are presentedin Tables 3 and 4.

Foreign R&D and Domestic InnovationFull sample results are provided in Table 3. In thebenchmark model in column 1, I regress TFP oncumulative domestic R&D stock, on the ratio ofcumulative foreign R&D stock to domestic R&Dstock, and on lagged values of exports and importsrelative to sales. Industry dummies and a time trendare included to control for industry fixed effects andtrending over time.We expect that domestic and foreign R&D stocks

might be highly correlated, given the nature of theindustries. This multicollinearity between domesticand foreign R&D stocks makes it difficult to separatethe partial effect of domestic and foreign R&D stockvariables from each other. In order to determine theseparate effects of domestic and foreign R&D stocks,the variable foreignR&D/R&D is introduced. Thisvariable is the natural logarithm of the ratio offoreign R&D stock to domestic R&D stock.As expected, cumulative domestic R&D stock is

associated with an increase in domestic TFP. Thecoefficients on cumulative domestic R&D stock and



the ratio of cumulative foreign to domestic R&Dstocks are individually and jointly significant at the1% level. With foreign R&D stock introduced as aratio, we can determine the partial effect of bothforeign and domestic R&D stocks. We can see algeb-raically that the partial effect of domestic R&D onTFP will now be equal to the coefficient on domesticR&D stock minus the coefficient on the ratio offoreign to domestic R&D stock. We see from column1 that a 1% increase in domestic R&D is associatedwith a net 2.5% increase in TFP.The empirical results provide support for the

hypothesis that foreign R&D contributes to domes-tic TFP. As we would expect, the magnitude of thecoefficient on foreign R&D stock is smaller than thaton domestic R&D stock. In the benchmark modelin column 1, the coefficient on foreign R&D stockis significant individually and in tests of joint sig-nificance with the interactive international tradeTa

ble

2Su

mmarystatistic

san

dco

rrelationco

efficients

Mea

nSD

Min

Max

TFP

R&D

ForeignR

&D/

R&D

Expo

rtsIm

ports

RCA

CR4

Ope

rate

expe

nse

Sales/

assets

TFP

ln(TFP

)[198

7=1]

0.99

0.19

30.42

41.59

41

R&D

ln(R&Dstoc

k)[198

7=1]

0.96

50.10

40.71

71.19

90.59

51

ForeignR

&D/

R&D

ln(foreign

R&Dstoc

k)[198

7=1]

0.69

1.20

7−0.68

63.48

40.14

7−0.25

21

Expo

rts

(exp

orts/sales) t−

10.10

40.10

60.00

60.85

30.16

60.23

40.17

61

Impo

rts

(impo

rts/sales)t−1

0.28

20.38

10.00

52.45

50.20

8−0.00

30.62

30.45

91

RCA

ln(revea

ledco

mpa

rativ

ead

vantag

e)4.62

21.01

92.27

95.69

6−0.2

0.22

7−0.85

6−0.01

9−0.54

11

CR4

Con

centratio

nratio

,fou

rlargestfirms

45.671

11.271

29.8

79.4

−0.33

1−0.36

3−0.22

4−0.22

9−0.24

9−0.01

21

Operate

expense

Ope

ratin

gex

penses

31.842

4.07

123

.140

.10.01

10.60

5−0.44

90.08

5−0.24

70.46

4−0.20

71

Sales/assets

Net

sales/assets

1.79

10.22

51.4

2.4

0.54

10.33

30.51

20.43

0.49

8−0.38

5−0.40

4−0.22

11

Table 3 Full sample (dependent variable: TFP)

FE FGLS-AR1 FGLS-PSAR1

(1) (2) (3) (4)

R&D 2.743** 2.446*** 1.994*** 1.805***[2.55] [3.15] [5.12] [5.91]

ForeignR&D/R&D 0.254** −0.535*** −0.116 −0.0300[1.96] [−9.71] [−1.54] [−0.42]

Exports −7.054** −6.991** −3.983*** −3.494***[−2.19] [−2.33] [−3.74] [−3.95]

Imports 2.445* 2.535* 1.320** 1.093**[1.69] [2.00] [2.22] [2.31]

Exports × ForeignR&D 7.049** 7.059*** 4.096*** 3.580***[2.47] [2.60] [4.00] [4.18]

Imports × ForeignR&D −2.497* −2.573** −1.366** −1.140**[−1.74] [−2.00] [−2.29] [−2.39]

RCA −0.193*** −0.0510 −0.0172[−4.48] [−1.56] [−0.57]

RCA×ForeignR&D 0.159*** 0.0514*** 0.0277[10.55] [2.79] [1.57]

CR4 −0.00221 −0.00328 −0.000454 −1.02e−06[−0.63] [−1.08] [−0.45] [−0.00]

Operate expense −0.0112 −0.0114 −0.000435 −0.00102[−1.00] [−1.15] [−0.23] [−0.58]

Sales/assets 0.0649 0.0907 0.0315 0.00646[1.53] [1.25] [1.01] [0.23]

Time trend Y Y Y YFE Y Y Y YObservations 116 116 116 116R2 0.784 0.843 — —

Wald χ2 — — 94.69 111.77

FGLS estimators with heteroskedasticity-consistent standard errors (t-statistics in brackets in columns 1–3, z-statistics in brackets in columns 4and 5).*p<0.10; **p<0.05; ***p<0.01.



Table 4 Split sample

FE PANEL FGLS-AR1 FGLS-PSAR1

Strong Weak Strong Weak Strong Weak

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

R&D 4.532*** 3.234*** 1.127*** 1.130*** 2.786*** 2.371*** 1.586*** 1.598*** 2.774*** 1.988*** 1.650*** 1.672***[7.42] [20.85] [4.53] [5.23] [7.92] [9.44] [3.71] [3.74] [7.16] [6.41] [3.87] [3.91]

ForeignR&D/R&D 0.324 0.0971 −0.0656 −0.0645 0.157*** −0.0733 0.0884*** 0.0887*** 0.147** −0.0882* 0.0886*** 0.0898***[1.37] [1.22] [−1.00] [−1.03] [2.78] [−1.48] [2.80] [2.80] [2.44] [−1.94] [2.70] [2.73]

Exports −5.377*** −3.195** −14.10*** −14.48*** −4.059*** −4.049*** −11.81*** −12.21*** −4.020*** −4.157*** −14.28*** −14.74***[−19.73] [−2.08] [−25.82] [−12.08] [−4.68] [−5.78] [−4.02] [−4.08] [−4.56] [−5.86] [−4.93] [−5.01]

Imports 0.781 2.511** 2.029*** 1.931*** 1.464* 2.035*** 1.786** 1.759** 1.506* 1.923*** 2.115*** 2.082***[0.65] [8.14] [22.70] [6.36] [1.88] [3.31] [2.42] [2.32] [1.90] [3.12] [2.83] [2.70]

Exports × 6.476*** 4.233*** 13.58*** 13.93*** 4.694*** 4.977*** 11.47*** 11.84*** 4.633*** 5.038*** 13.83*** 14.26***ForeignR&D [63.32] [2.91] [28.76] [13.22] [5.05] [6.83] [4.11] [4.18] [4.89] [6.70] [5.04] [5.13]Imports × −0.851 −2.547*** −2.058*** −1.957*** −1.322* −1.967*** −1.820** −1.792** −1.363* −1.863*** −2.150*** −2.114***ForeignR&D [−0.76] [−8.69] [−23.33] [−6.37] [−1.67] [−3.17] [−2.47] [−2.35] [−1.70] [−3.03] [−2.87] [−2.74]RCA 0.276 0.154** 0.142*** 0.145*** −0.142 −0.0343 0.0495 0.0531 −0.136 0.000818 0.0492 0.0549

[1.25] [2.32] [11.47] [14.06] [−1.63] [−0.47] [1.47] [1.52] [−1.55] [0.01] [1.40] [1.48]CR4 — −0.0115*** −0.000658 −0.00932*** −0.000487 −0.00935*** −0.000666

— [−2.60] [−0.66] [−5.99] [−0.36] [−7.05] [−0.42]Time trend Y Y Y Y Y Y Y Y Y Y Y YFE Y Y Y Y Y Y Y Y Y Y Y YObservations 57 57 59 59 57 57 59 59 57 57 59 59R2 0.9593 0.9713 0.6883 0.6893 — — — — — — — —

Wald χ2 — — — — 236.11 684.04 60.67 62.20 185.93 468.31 85.98 88.74

FGLS estimators with heteroskedasticity-consistent standard errors (t-statistics in brackets in columns 1–4, z-statistics in brackets in columns 5–12).*p<0.10; **p<0.05; ***p<0.01.

Follo

wmeto

theinnovatio

nfro

ntier?

SherylWinston

Smith

262

JournalofInternationalBusinessStudies

variables. Taking into account the interactive terms,and evaluating at the sample means of laggedexports to sales and lagged imports to sales, the neteffect of a 1% increase in foreign R&D stock isassociated with a net 0.28% increase in domesticTFP.18 This effect increases as exports increase anddecreases as imports increase, as indicated by thecoefficients attracted by the respective interactiveterms.In column 2, the benchmark model is expanded to

test the hypothesis that relative industry strengthconditions the relationship between foreign R&Dand domestic innovation. As we see in column 2,the net effect of foreign R&D stock decreases slightlywhen we control for interaction with industrystrength. The coefficient on the interactive termbetween relative strength and foreign R&D stocksuggests that the combination of relative strengthand the availability of foreign R&D to tap intoconditions the relationship between foreign R&Dand domestic TFP. Evaluating at industry meansof lagged export and import variables and RCA, wefind that a 1% increase in foreign R&D stock isassociated with a net 0.21% increase in domesticTFP. Again, the effect is increasing with increasingexports to sales, and decreasing with increasingimports to sales.The strong correlation between foreignR&D/R&D

and relative industry strength (RCA) interferes withour ability to identify the effect of relative industrystrength separately. Splitting the sample into rela-tively stronger and relatively weaker industriesallows us to probe this relationship more carefully.The sample is divided by the median value of RCA.Results from estimation on the split sample aresummarized in Table 4. When we split the sampleaccording to relative strength, we find additionalsupport for the nuanced relationship between for-eign R&D and domestic innovation. The results incolumns 1–4 suggest that both domestic and foreignR&D have a stronger relationship to domestic inno-vation in relatively strong industries. The magnitudeof the coefficient on domestic R&D stock is approxi-mately four times greater in relatively strong indus-tries, with a 1% increase in domestic R&D stockassociated with a net 4.2% increase in TFP in rela-tively strong industries, compared with a net 1.2%increase in TFP in relatively weak industries.The distinct relationship between foreign R&D

and domestic TFP is delineated. Looking at column1, the coefficient on foreignR&D/R&D is positive andsignificant, individually and jointly with the inter-active variables, in relatively strong industries.

Evaluating at the sample means for this group, a 1%increase in foreign R&D is associated with a net0.86% increase in domestic TFP. Controlling for thedomestic industry concentration ratio (CR4) in col-umn 2 accounts for a slice of this, reducing the neteffect of a 1% increase in foreign R&D to a net 0.10%increase in domestic TFP. In relatively weak indus-tries (column 3), the coefficient on foreignR&D/R&Dattracts a negative sign but is not individually sig-nificant, although it is jointly significant with theinteractive variables at the 5% level. The negativesign attracted by foreignR&D/R&D suggests thatincreasing foreign R&D stock relative to domesticR&D stock, ceteris paribus, might present a greaterchallenge in relatively weak industries. When weconsider the net effect of foreign R&D stock byevaluating at the sample mean for relatively weakindustries, a 1% increase in foreign R&D stock isassociated with a net 0.51% increase in domesticTFP. In this case, when we control for domesticindustry concentration (column 4) the net effect ofa 1% increase in foreign R&D is a 0.59% increase indomestic TFP.Taken together, the results in Tables 3 and 4

suggest that the combination of relative strengthand a larger stock of foreign R&D to tap into condi-tion the relationship between foreign R&D anddomestic innovation. These points become clearwhen we consider the effects of exports and importsinteracted with foreign R&D stocks.

Exports and Domestic InnovationAs hypothesized, the relationship between domesticinnovation and exports depends on foreign R&D.Returning to the full sample (Table 3), the interactivevariable between lagged exports to sales and foreignR&D stock attracts a positive coefficient in thebenchmark model (column 1) and after we controlfor industry strength (column 2). In the benchmarkmodel, evaluated at the mean of foreign R&D stock,the net effect of exports after taking into account theinteractive term is negative; a 1 percentage pointincrease in exports to sales is associated with a 0.18%decrease in domestic TFP. When we control forindustry strength in column 2, we find that a 1percentage point increase in exports to sales isassociated with a 0.11% decrease in domestic TFP.Support for the hypothesis that domestic innova-

tion is positively related to exports in relativelystrong industries as a function of increasing foreignR&D is bolstered by the results of splitting thesample between relatively strong and relativelyweak industries (Table 4). When we split the sample



between relative leaders and laggards, the coefficienton the interactive variable remains positive andsignificant in both, but the magnitude is substan-tially larger in relatively strong industries. From theresults in column 1 and column 3, a 1 percentagepoint increase in exports to sales is associated with a0.78% increase in domestic TFP in relatively strongindustries, compared with a 0.55% decrease in TFPin relatively weak industries. Adjusting for domesticindustry concentration magnifies the divergence ofthese effects in strong and weak industries (columns3 and 4, respectively). Notably, the positive contri-bution of exports to domestic innovation occursthrough increasing foreign R&D stock; if we evaluateat the maximum value of foreign R&D stock, the neteffect of exports is positive in both strong and weakindustries.19

Imports and Domestic InnovationForeign R&D also conditions the relationshipbetween domestic innovation and imports. Theempirical results provide some support for thehypothesis that domestic innovation is positivelyrelated to imports in relatively strong industries as afunction of increasing foreign R&D. In the fullsample (Table 3), the interactive term betweenlagged imports to sales and foreign R&D stock isnegative in the benchmark specification (column 1)and after we control for industry strength (column2). When we evaluate at the mean of foreign R&Dstock, a 1 percentage point increase in imports tosales is associated with a 0.01% increase in domesticTFP; after we control for industry strength in column2, the magnitude of the effect increases to 0.03%.Splitting the sample into relatively strong and

weak industries provides additional insight (Table4). When we split the sample into relatively strongand relatively weak industries, the coefficient on theinteractive term between lagged imports to sales andforeign R&D stock is no longer statistically signifi-cant at the usual levels of significance, neitherindividually nor jointly with lagged imports (col-umn 1). Thus we cannot reject the hypothesis thatthe relationship between imports and domestic TFPis statistically different from zero in relatively strongindustries. This changes when we account fordomestic industry concentration in column 2, andthe coefficient on the interactive term betweenlagged imports to sales and foreign R&D stock ishighly significant. We find that a 1 percentage pointincrease in imports to sales is associated with a0.09% increase in domestic TFP in relatively strongindustries, evaluating at the mean of foreign R&D

stock. However, in relatively weak industries, we findthat the coefficient on the interactive term is nega-tive and significant, individually, and jointly signifi-cant with lagged imports to sales. Evaluated at themean value of foreign R&D stock, a 1 percentagepoint increase in imports to sales is associated with a0.02% decrease in domestic TFP in relatively weakindustries, providing support for our hypothesis thatforeign R&D contributes negatively to domestic TFPin relatively weak industries as imports increase. Thenet effect does not change when we adjust fordomestic industry concentration in relatively weakindustries (column 4).

Further Econometric SpecificationsIn Table 5 I present tests of the difference betweenleader and laggard industries. In order to comparethe regression coefficients across relatively strongand weak industries, I introduce a dummy variablefor relative strength, strength, which is equal to 1 inrelatively strong and 0 in relatively weak industries.As in the split sample, the median RCA value of 161is used to split the sample. Note that some SIC-delineated industries are classified as relativelystrong in some years and relatively weak in others.Using RCA in this manner allows us to considerrelative strength itself. The dummy variable forrelative strength is interacted with each explanatoryvariable in the regressions. The t-statistics on theinteractive terms indicate whether we can rejectthe null hypothesis that the coefficients are thesame across both groups. This approach has beenused to compare regression coefficients betweengroups of industries and firms distinguished by

Table 5 Test of difference between leader and laggard industries(dependent variable: TFP)

(1)Difference

(2)t-statistic

R&D 1.6209a 4.59***ForeignR&D/R&D 0.0960a 0.91Exports 8.0266b 1.89*Imports 0.6579 0.51Exports × ForeignR&D −6.8776b −1.73*Imports × ForeignR&D −0.4727 −0.37RCA −0.2889 −4.82***Year −0.0291 −6.23***No. of observations 116R2 0.9336aJointly significant at 1% level.bJointly significant at 5% level.*p<0.10; **p<0.05; ***p<0.01.



knowledge-intensity, size, and other relevant char-acteristics (Acs & Audretsch, 1988; Audretsch &Weigand, 2005; Nachum & Zaheer, 2005). Column1 shows the coefficient on the industry strengthinteractive term for each variable, and column 2indicates the significance. Coefficients that are sta-tistically significant can be interpreted as having adifferent relationship in relatively strong and weakindustries in the direction and magnitude indicatedby the coefficient. Results of the Wald test indicatethat we can reject the null hypothesis that the tworegressions are the same. There is a significant differ-ence between relatively strong and weak industriesfor most of the key parameters, further substantiat-ing the hypothesis that industry strength plays animportant role in modulating the relationshipbetween foreign R&D and domestic innovation,particularly with respect to the interactive terms.Furthermore, the signs of the differences are consis-tent with the differences we see in the split sampleresults in Table 4.As noted above, we might be concerned about the

potential for serial correlation in the errors in a panelthat is relatively long but narrow; given the tradeoffsinvolved in OLS relative to FGLS, I present estimatesfrom both techniques in Tables 3 and 4 (Wooldridge,2002: Chapter 7). Results for the full sample FGLSestimates are displayed in Table 3. I test the robust-ness of the results using FGLS estimation withheteroskedasticity-consistent standard errors andcorrecting for AR(1) correlation in the error term(column 3) and panel-specific AR(1) correlation (col-umn 4). Overall, the results are robust to the differ-ent assumptions about the error term. Coefficientsare smaller inmagnitude in the FGLS regression thanin the panel-fixed effects OLS regression (column 3),but the sign on the coefficients and the significancedo not change onmost of the variables. I also run theFGLS estimations on the split sample. Similarly,results are presented in Table 4 with AR(1) correla-tion for relatively strong industries (columns 5 and6) and relatively weak industries (columns 7 and 8).Similar results are obtained using panel-specific AR(1) (columns 9–12).

DISCUSSION AND CONCLUSIONTaken together, the extant research in internationalbusiness and international economics establishesthe substantial potential of increased internationali-zation to influence domestic industries throughboth learning and competitive dynamics. The litera-ture richly demonstrates deep heterogeneity in therelationship between internationalization strategies

and domestic innovation. However, we lack a coher-ent theory that explains how and under what cir-cumstances diverse internationalization strategiesallow firms in different industries to benefit fromincreasingly sophisticated global markets as theirown relative capabilities change. This paper helpsprovide an integrative scaffold on which to build inthis direction.An important contribution of this paper to the

international business literature is thus to provide amore coherent framework that integrates insightsfrom international trade and industrial organizationtheories to demonstrate substantively different – andsometimes divergent – implications for firms inrelatively strong and weak industries. By exploitingvariation in industry strength within the sameindustry, and among the different industries overthe course of three decades, this study isolates amajor dimension along which industry dynamicschange, and highlights the importance of industry-level capabilities in shaping internationalizationstrategies. The findings in this paper providedeeper insight into the impact of the growing inter-nationalization that occurs in conjunction withincreasing foreign R&D, in four high-technologyindustries, over a substantial and economicallymeaningful period of time. The extant literature hasrichly highlighted heterogeneous motivations forfirms’ foreign knowledge-seeking activities, and hascommented on the importance of the relativestrengths at home and abroad in deriving innova-tion-related benefits (Cantwell & Janne, 1999;Chung & Alcacer, 2002). The results in this paperpoint to more diffuse – but powerful – channelsthrough which knowledge spillovers occur, namelyexporting and importing.In a related vein, this paper builds a stronger

theoretical bridge between these three domains byhighlighting the intersection of domestic industrystructure and relative strength in moderating inter-nationalization strategies that involve both import-ing and exporting choices. Crucially, the resultspresented here signify an intersection of domesticmarket structure and internationalization strategies:relatively leading industries that operate with littledomestic competition seem to be harmed to agreater extent than relatively weak industries byincreasingly sophisticated foreign imports. Indeed,the results hint that relative laggards may be spurredto try to catch up to leading industries abroad. Theresults in this paper thus add further support to thegrowing stream in the international business litera-ture that highlights the importance of exporting for



learning while also incorporating similar nuancewith relation to the impact of importing (Cassiman& Golovko, 2011; Salomon & Jin, 2008). Theseresults also hint towards the “innovate to escape”effect modeled by Aghion et al. (2005): When facedwith the greatest tension between opportunities tobe pushed to innovate by sophisticated foreigncompetitors vs making a submissive retreat, domes-tic firms can escape profit-drowning foreign compe-tition by introducing new or better products. Forexample, competition from foreign manufacturerswas a factor in the drive by US cellphone manufac-turers towards smaller and more powerful cell-phones in the 1990s, which enabled firms in thedomestic industry to learn from importing (Frost &Sullivan, 2001; US Department of Commerce, 1998).Our results also suggest that strong industries thatare relatively protected from domestic competi-tion through concentrated market structure aremore disturbed by technically sophisticated foreignimports than are firms in concentrated, laggardindustries. It is plausible that, in laggard industries,more oligopolistic power might enable marshalingof resources towards a more innovation-orientedresponse. These results support other findings in thelarger industrial organization literature that point toa nuanced relationship among market structure,competitive pressure, and innovation (Aghion et al.,2001; Malerba et al., 1997, Scherer, 1984).A third contribution of this paper is to the litera-

ture on international innovation. Through this lensof relative industry strength, it is possible to discernlarger patterns that help explain when and howfirms in heterogeneous industries may benefit (or beharmed) as relative innovation capabilities change.In keeping with the hypotheses developed in thispaper, the main empirical results strongly suggestthat foreign R&D impacts relative leaders and lag-gards differently through channels of exporting andimporting. By peering deeper into these industries,some bigger picture insights emerge into why thefour industries in this study had different responsesover the course of this time period. First, path-dependency helped shape the response to increasinginternationalization in each of these industries.In the computer equipment industry, for example,export channels and international innovationoccurred from the beginning of the industry(Chandler, 1997). The results in this paper also pointto the importance of considering learning and com-petition effects together in technologically basedindustries. For example, MacGarvie (2006) presentsevidence that French firms engaging in exporting

and those engaging in importing learn to innovatebetter both by “analyzing competing products” andthrough contact with foreign buyers and suppliers.Finally, the experiences of these industries pointtowards the interplay between major technologicaldiscontinuities – such as the switch from analogtransmission to digital, the introduction of fiberoptics, the development of cellular radio, and so on– and growing internationalization (Yoffie, 1994). Inthis way, industries that are more strongly con-nected to a robust science and engineering frame-work may be magnified through channels ofinternational trade that facilitate the exchange ofgoods and ideas.This research has important implications for prac-

titioners and policymakers as well. The empiricalresults in this paper suggest that domestic policiescan complement andmoderate the effect of differentinternationalization strategies. Many of the insightsfrom this paper may pertain in particular to “on-the-verge” industries in which competition can go eitherway. For example, the communications equipmentindustry was characterized by extensive technologi-cal changes accompanied by frequent switches inrelative strength among the domestic and foreignindustry for much of the period (Scherer & Huh,1992; Yoffie, 1994). Interestingly, mobile phonemanufacturers in Japan, long insulated from compe-tition at home through technical standard barriers,are currently reeling from “miss[ing] the [smart-phone] trend: Too much focus on the domesticmarket; too slow and inflexible to adapt to dynamicconditions; a misread of consumer preferences and adose of arrogance about hardware superiority”(Wakabayashi, 2012). Fundamentally, understand-ing global industry dynamics is critical from both ananalytic and a policy perspective.While this paper contributes a detailed and inte-

grated picture of the response of these four industriesover a critical period in time, it of course has limita-tions and shortcomings. To start with, while theindustries studied in this paper represent a diversityof experience and trajectories, future research mightexpand this substrate. We would like to understandthe generalizability of these results to other indus-tries in which innovation is important. Thus theseresults likely generalize more to other knowledge –

and skill-intensive industries – such as biotechnol-ogy or medical devices – and less so to industries inwhich absorptive capacity might not be as impor-tant. This research suggests, in the broader contextof industry competitive analysis, that we mightplace the industry on the spectrum of relative



strength compared with these industries, and thenassess other dimensions such as whether the indus-try is at or near the technological forefront, theimportance of knowledge flows, and the nature offoreign competition to help predict the impact ofinternational trade and foreign R&D on innovationin that industry. Future research can help move us inthis direction. This study also highlighted theheterogeneous impact of different internationaliza-tion strategies in different industries as foreignR&D increases in other advanced industrial econo-mies. A fruitful and intriguing area for futureresearch might be to explore how these relationshipschange or remain similar in the context of increasinginternationalization and R&D in emerging-marketeconomies.Finally, this study faces limitations in its level of

analysis. While individual firms within an industryexhibit heterogeneity, this is analyzed at the indus-try level. In a sense, this analysis might be thought ofcapturing a distribution of firms of varying capabil-ities, where we capture the average strength – butnot the variance – around that average. For example,intra-industry strategic groups may be leaders in anotherwise laggard industry; likewise, other intra-industry firms that may be laggards in an otherwiseleader industry. These differences will be more gran-ular than the average industry-wide trends docu-mented in this study, and would be a fruitfulsubject for further study.20

In sum, the current study provides insights intorichly heterogeneous responses by relatively leadingand relatively lagging industries in the face of differ-ent internationalization strategies in a subset of fourindustries. This is both a limitation of the currentwork and a call that whets the appetite of scholars tofurther understand these dynamics in a larger rangeof industry and country settings. At the same time,the findings in this study provide guidance forscholars, policymakers, and practitioners in under-standing the sometimes contradictory implicationsof distinct internationalization strategies.

ACKNOWLEDGEMENTSThe author is grateful for comments received fromthree anonymous reviewers and editors Paul Almeidaand John Cantwell. Earlier versions received valuablecomments from Lewis Branscomb, Robert Lawrence,Josh Lerner, Suzanne Cooper, F. M. Scherer, PaulVaaler, Robert Kudrle, Masaaki Kotabe, and RamMudambi. She also thanks seminar participants atHarvard University, the University of Minnesota, Temple

University, the Academy of Management AnnualMeeting, and the Midwest International Trade Meetingfor their comments.

NOTES1Within any given industry there will be a distribution

of firms of leading and lagging firms. Individual firm-level heterogeneity means that lagging firms exist inleading industries, and leading firms exist in laggingindustries. On balance, we can think of industry-levelanalysis as the average of all of the firms in that industry.Econometrically, the logic behind this has beenformalized in Melitz (2003) and subsequent papers.

2A classic example of a good that is both rival andexcludable is a chocolate bar: it cannot be sharedwithout one party having less than if one consumes italone, and it is easy to prevent someone else fromconsuming it. Likewise, a class example of a good that isboth non-rival and non-excludable is a public park,where multiple parties can enjoy it simultaneously, andno one can be excluded.

3Empirically, at the level of national economies ofindustrialized nations, various empirical studies haveshown that there are spillovers from foreign R&D todomestic TFP (Bernstein & Mohnen, 1998; Coe &Helpman, 1995; Keller, 2002; Keller & Yeaple, 2009;Nadiri & Kim, 1996). However, in these studies there isgreat variation among countries, with the US the greatestsource of spillovers to other nations and the smallestreceiver of productivity benefits that can be attributed toforeign R&D. In some studies carried out at the countrylevel, the effect of foreign R&D on US domestic TFP isnegative (Engelbrecht, 1997; Park, 1995). Empiricalstudies at the broader sectoral level have also shownmixed results. In several studies, Keller (2000) findsevidence of import-weighted foreign R&D contributingto domestic TFP in 13 pooled manufacturing sectorsamong major OECD economies, including the US. Keller(2002) also finds that the effect of foreign R&Don domestic R&D is inversely related to geographicdistance. Evenson (1997) finds weak evidence of import-weighted foreign R&D contributing positively to domes-tic TFP growth in 11 pooled manufacturing sectorsamong major OECD economies.

4To be clear, industry-level analysis captures theaverage of capabilities, knowledge stock, andabsorptive capacity within the industry as a whole,relative to other national and global industry peers,but cannot capture distinctions among individual firms,such as laggard firms in leading industries or leadingfirms in relatively laggard industries. In other words, wecapture the average, but not the variance, of thedistribution of firms in the industry.



5To be clear, the argumentation above goes beyondthe seminal studies by Salomon. This study highlightsthe contingent effect of leading or lagging industrystatus on the relationship between exporting andintensifying foreign R&D. The argumentation in thispaper addresses firms in industries that are both leadingand laggard industries relative to the global frontier(and, indeed, can be leaders in one time period yetlaggards in another). In contrast, in Salomon and Jin(2010) individual firms may be leaders or laggardsrelative to firms in the industry, but the Spanishindustries lag the global frontier (p 1097). In Salomonand Jin (2008), the focus is on industry relative distancefrom the global frontier, where industries may lag or beat parity with the global frontier (but are not the globalleaders). More broadly, the argumentation in this studyposits the influences that industry-leading or -laggingstatus may have that go beyond the learning-by-exporting relationship. These include competitivepressures to innovate deriving from the industrialorganization perspective for firms in leading industries,and the potential for firms in lagging industries to learnfrom leaders abroad and thereby substitute foreignR&D spillovers in place of diminished potential fordomestic R&D spillovers. (While this study cannotempirically differentiate these channels, themechanisms outlined above could potentially betested with other data.)

6Majumdar (1980: 104) notes: “It must be pointedout that the Japanese imitation of the electroniccalculator technology in 1964 occurred without anyassistance from the original producers in the US. Therewere no licensing or joint venture agreements. TheJapanese producers rather copied the product througha process of so-called ‘reverse engineering’.”

7The industries were selected for this study based onthe following criteria. While all four industries areconsidered high technology relative to all ofmanufacturing, they differ along key dimensions thatwe wish to exploit. First, these four industries provide arange in technological maturity. The computerequipment industry (SIC 357) has been characterizedby rapid technological advance. Communicationsequipment (SIC 366) is also a technology-drivenindustry. In contrast, the household audio andvideo equipment (SIC 365) industry involves lower-margin mass production. Finally, scientific instruments(SIC 381+382) generally involves incremental tech-nological advance, with input from varied scientificdisciplines. Second, these four industries have hadmarkedly different experiences in the global tradingarena. For example, during the period from 1973 to1996, the average share in world exports for all US

manufacturing industries was 11.5%. However, theaverage was as low as 2.1% in the household audioand video industry (SIC 365) and as high as 24.4%in computer and office equipment (SIC 357) and24.7% in scientific instruments (SIC 381+382) overthat period.

8For example, Scherer (1984: Chapter 9) identifiesdifferences in technological opportunity amongindustries as being responsible for nearly half of firm-level differences in innovative output.

9Aggregation to the three-digit level is accomplishedthrough adding the four-digit totals for real inputs(capital, labor, energy, and non-energy materials) andsumming to the three-digit totals. Inputs are firstdeflated at the four-digit level, using the input-specific,four-digit price deflator. The resulting real four-digitinputs are then aggregated to the three-digit level.Factor shares are calculated using the three-digitinputs and three-digit output – that is, aggregatedfirst.

10Capital stock data were provided by NormanMorin at the Federal Reserve.

11Many patents are neither licensed nor producerevenue, and thus have little direct connection toinnovation at the macroeconomic level. Moreover,while some industries are very dependent on patentprotection, such as biotechnology, others – such ascomputer equipment – rely heavily on trade secrets.

12These data include all R&D performed in industry,regardless of source of funding. Thus government-funded R&D is included. However, this should notpose a problem, as it is reasonable to assume that theoverall stock of R&D abroad, including government-funded, constitutes the available pool of scientific andtechnological knowledge upon which both domesticand foreign firms will draw.

13The specific countries in the sample are: the UnitedKingdom, Germany, France, Denmark, Finland, Ireland,Italy, the Netherlands, Spain, Sweden, and Japan. Thesecountries are included on the basis of continuous dataavailability over the period, and they represent asubstantial portion of R&D activity in the industriesincluded in the panel.

14Estimates of the rate of depreciation of R&D stockrange from 0.05 to 0.15: see Griliches (1998). Coe andHelpman (1995), for example, assume δ=0.05, Keller(2002) assumes δ=0.10, and Griffith et al. (2004)assumes δ=0.15. Sensitivity tests in these studies, andin my regressions, do not find that the results arechanged significantly with varying values of δ. Ipresent results based on δ=0.11, based on the valueused in a comprehensive US Department of Labor study(US Department of Labor, 1989).



15For example, Bureau of Labor Statistics estimatea contribution of domestic R&D to TFP growth of0.49% for the years 1973–1987 for all manufacturing.Griliches (1994) estimates 0.36% for the years 1973–1989.

16See, for example, Griliches (1979, 1998), Coe andHelpman (1995), and Keller (2000).

17The literature provides mixed guidance as to thebest methodology to approach this. In the seminalwork on international R&D spillovers, Coe andHelpman (1995) used panel co-integration techniques,while later papers have questioned this (Kao, Chiang, &Chen, 1999; Luintel & Khan, 2004).

18The net effect is determined by evaluating at aparticular point. In this case, the calculation is at themeans of exports to sales and imports to sales.

19Evaluated at the maximum instead of the meanvalue of foreign R&D stock, a 1 percentage pointincrease in exports to sales is associated with a 1.77%increase in domestic TFP in relatively strong industries

and a 1.87% increase in TFP in relatively weakindustries.

20I am thankful to an anonymous referee forhighlighting this distinction.

21AT&T held the patents for the technology ofsending voice signals through copper wire. After thepatents expired in the 1890s, AT&T was still able toretain control over the connections among exchanges(Crandall, 1991: Chapter 2). The manufacture ofequipment was controlled largely through WesternElectric, which was the “manufacturing and supply unitof the Bell System” from 1881 until the breakup of AT&Tin 1984, when the breakup of AT&T in 1984 separatedWestern Electric from the Bell companies.

22RCA, GE, and AT&T were key players in developingthe early technology. In the UK, Electric and MusicalIndustries Ltd (EMI) formed a partnership with Marconito develop early television (Inglis, 1990). In the US, RCAand GE were the main producers of transmitters andantennas.

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APPENDIX

Selection of Industries and Industry Background

Computer equipment (SIC 357)The US computer equipment industry is innovation-driven, with strong ties to science and engineering.Innovation in the computer equipment industry hasbeen characterized by rapid technological advance,with complex, distinct technologies, and numerousbreakthrough advances. The industry has gonethrough several periods of discontinuity, character-ized by more frequent entry and exit of smallerstartup firms (and the growth of some of thesestartups into formidable companies), followed byconsolidation around several large companies con-ducting R&D in centralized research facilities (Yoffie,1997). From the beginnings of the computer indus-try, changes in market structure evolved concomi-tantly with technological advance (Bresnahan,1999; Bresnahan & Malerba, 1999). While technolo-gical advance propelled the growth of the US com-puter industry, market demand that sustained theprice elasticity of demand was critical. The existenceof a substantial base of scientists and engineers, theimportance of small business, and perhaps to a lesserextent the vast potential market of home usersplayed an important role in the rapid expansion ofthe computer industry in the US. As early as 1977more than 70 manufacturers were making personalcomputers, primarily for scientists and hobbyists,with small businesses beginning to use these as well;by 1978, 15,000 personal computers were in house-holds (US Department of Commerce, 1978).From the origins in the tabulating machines of

Hollerith (an antecedent of IBM) and of Powers(ultimately Remington Rand and its successor com-panies), international distribution was an essentialcomponent, establishing the broad global channelsthat remained important as the computer industryevolved. Prior to the First World War, these USinventors took their tabulating machines to Englandand to Germany, and then through the rest ofEurope, developing the initial market for whatwould eventually be computer equipment. Theseinitial partnerships also became the precursors foreventual British and European competitors. Like-wise, even before the establishment of researchfacilities abroad, these international channels alsoformed the basis for global sources of incrementalimprovements to the products (Connolly, 1967;Flamm, 1988). At the same time as there was aconcerted effort in the US to develop powerful

computing machines, no comparable industry wasdeveloping in Europe, at least not on the scale of thatin the US (Connolly, 1967; Flamm, 1988). In Japan,the government protected the domestic infantindustry from foreign competition while encoura-ging access to foreign technology through licensingagreements; the combination of protection andaccess to state-of-the-art technology proved success-ful in the development of a globally competi-tive Japanese computer industry by the 1980s(e.g., Toshiba, Sharp, and others) (Bresnahan, 1999;Chandler, 1997; Flamm, 1988).

Household audio and video equipment (SIC 365)Commercial success in the household audio andvideo equipment industry has been heavily depen-dent on the introduction of new products at increas-ingly rapid intervals (McKinsey Global Institute,1993; MIT Commission on Industrial Productivity,1989; Rosenbloom & Abernathy, 1982). One of themajor technological transitions in the home audioand video equipment industry, as in other indus-tries, was the transition to solid-state circuitry in theearly 1970s (US Department of Commerce, 1973).The use of integrated circuits and single-boarddesigns was another important transition in theindustry, which allowed decreases in size and costalong with improved technology (MIT Commissionon Industrial Productivity, 1989). Japanese compa-nies were early innovators in applying solid-statetechnology to consumer electronics, starting withportable radios in the 1960s and continuingwith television, including an all-solid-state color TVintroduced by Hitachi in 1969 (Rosenbloom &Abernathy, 1982). Likewise, Japanese companieswere similarly quick to use integrated circuits inconsumer electronics.International trade in the US household audio and

video equipment industry has been consistentlydominated by imports since the 1960s (Marcus,Pettingill, & Stearns, 1975; US Department ofCommerce, 1998). US exports in this industry wererelatively small throughout the period, whileimports continued to rise. While foreign manufac-turers were actively pursuing segments of the USindustry, US radio and TV manufacturers did notactively seek overseas markets in the 1960s and1970s, concentrating on meeting domestic demand(Rosenbloom& Abernathy, 1982). US manufacturersresponded to lower-priced imports in part by seekingout lower-cost offshore manufacturing opportu-nities (MIT Commission on Industrial Productivity,1989; US Department of Commerce, 1972). While



this strategy lowered costs in the short term, it mighthave contributed to longer-term problems such asthe failure to invest in automation. Further, USconsumer electronics manufacturers responded toincreased import pressure by seeking trade restric-tions and protection. In 1971 Japanese firms werefound guilty of dumping with little penalty in a USCustoms case initiated in 1968. The antidumpingduty law of 1979 provided some more stringentpenalties. Several lawsuits against Japanese firms inthe 1970s were initiated by Zenith and other com-panies (US Congress, 1990).

Communications equipment (SIC 366)Engineers, entrepreneurs, and inventors developedtelegraph, telephone, radio, and TV technologiesin the late nineteenth and twentieth centuriesusing common roots in physics, much of whichwas understood by the early nineteenth century(Brinkman & Lang, 1999). The early history oftechnology in the telephone industry in the US wasdominated by AT&T (Crandall, 1991: Chapters 2 and4).21 In radio, a mix of foreign and domestic compa-nies influenced the early development of the indus-try. After developing the wireless telegraph in Italy,with the first transatlantic communication in 1901,Marconi remained the dominant international tele-graph company until the end of the First WorldWar.In the US, RCA was formed after the First World WarfromMarconi’s US assets, including wireless patents.The technology for television broadcasting equip-ment evolved from roots in radio, and was devel-oped in the US and abroad (Inglis, 1990).22 Mobiletelephony also developed internationally from earlyroots in radio technology. AT&T demonstratedthe first mobile telephone service in 1946. In 1950,the first cellphone prototype, developed by Ericssonand Swedish Telecom, was demonstrated in Sweden.The Nordic Mobile Telephone Group was estab-lished in 1969 as a joint project to put in place amobile telephone network in Sweden, Finland, Nor-way, and Denmark. The first commercial mobilephone network started in Tokyo in 1979, based onthe analog Advanced Mobile Phone System (AMPS),soon followed by the Nordic group in 1981. Cabletelevision first started in the 1940s in the US ascommunity antenna television systems in which asingle large or well-placed antenna captured weaktelevision broadcast signals and redistributed themvia cable to a group of homes. Cable further evolvedin the 1950s into distant station importation usingmicrowave signals to transmit signals from distant

stations to a central cable network. In the 1970ssatellite distribution of programming became a thirdmode for cable systems to receive programming. Theearly history of satellite communication started inthe SecondWorld War, when UK engineer Arthur C.Clarke, a British RAF officer working with engineersat MIT, developed the concept of satellite commu-nication. The development of such a system wasaccelerated in the1960s by the space program in theUS (Inglis, 1990). Satellites remained a strong USdomestic industry through the 1990s (Knott, Bryce,& Posen, 2003).New technologies continued to provide competi-

tion as the industry evolved. Major technologicaldevelopments include the switch from analog trans-mission to digital, the introduction of fiber optics,and the development of cellular radio (Yoffie, 1994).A consistent theme in the telecommunications seg-ment is that new technologies, frequently coupledto changes in the regulatory framework, have dis-placed older technologies and the companies thathold onto them. Another significant technologicalevolution has been the feasibility of and demand forintegration of data transmission capabilities withexisting audio and video transmission technologies.This has been accelerated by the transfer fromanalog to digital transmission. Early analog advancesin 1970s included the introduction of coaxialcable with increasing capacity to carry simultaneousphone and data calls, and the development of adomestic satellite system capable of high-speed datatransmission, as well as delivering phone, fax, andTV services (Marcus et al., 1975, US Department ofCommerce, 1974). The US HDTV standards also takeinto account the possibility of including other typesof data in the television transmission (Farrell &Shapiro, 1992).The communications equipment industry was

shaped extensively by the regulatory framework thatgoverned the separate sectors. In 1978, the FederalCommunications Commission extended the inter-connect market to include telephone sets (USDepartment of Commerce, 1979). The 1982 consentdecree and subsequent divestiture of AT&T in 1984into seven regional Bell companies had importantconsequences for further opening up communica-tions equipment to a variety of manufacturers,domestic and foreign. The Telecommunications Actof 1996 had important consequences for commu-nications equipment, including telephone, TV, andcable. Most of the telephone companies operating inEurope and Japan historically were state-ownedmonopolies. State monopoly of services generally



engendered preferential relationships with equip-ment manufacturers as well.

Scientific instruments (SIC 381+382)Innovation in the scientific and industrial instru-ment industry depends on close ties to science andengineering, with feedback from users an importantaspect (Klevorick, Levin, Nelson, & Winter, 1995;von Hippel, 1976). Innovation in this industry hastended to involve incremental improvements ofexisting product designs. Sometimes, technologicaladvances allowed the transformation of existingtechnology, often requiring the development of all-new products. This, for example, was often the casewith the transformation from analog to digital tech-nology during this period (US Department ofCommerce, 1973, 1979). Similarly, the incorpora-tion of microprocessors generated greater demandfor precision testing equipment in other industriesdependent upon microelectronics, including semi-conductor manufacturing, communications equip-ment, and the computer hardware industry (USDepartment of Commerce, 1973, 1976).The scientific and industrial instruments industry

differs notably from the other industries under con-sideration, in that few of the products of this indus-try have consumer markets. This industry is veryR&D intensive, and the users typically demand atechnically advanced product without being veryprice sensitive. The industry is closely tied to broadertrends in the national and international economicand policy sphere. For example, the growth ofenvironmental rules in the 1970s, and the subse-quent need for compliance with environmental andhealth regulations, spurred demand for monitoringand controlling instruments (US Department ofCommerce, 1973, 1979). Energy conservation mea-sures, a focus on improving labor productivity, andthe need for quality control also increased demand

for more sophisticated instruments in the 1970s (USDepartment of Commerce, 1976).US manufacturers actively sought to sell to mar-

kets overseas. In the early 1970s US manufacturers ofscientific and industrial instruments establishedoverseas subsidiaries and joint ventures. They alsoparticipated in international industrial expositions(US Department of Commerce, 1973). Frequently,US affiliates abroad worked with the corporate engi-neers within the parent company to design newproducts (US Department of Commerce, 1976).Growing investment in R&D in Europe and Japanled directly to demand for the scientific instrumentsand laboratory equipment, as the US industrywas a leader in many sectors (US Department ofCommerce, 1979, 1986). Also, as R&D-intensiveindustries abroad expanded, such as communica-tions equipment, computer equipment, and otherindustries, demand for scientific and testing equip-ment increased concomitantly (US Department ofCommerce, 1986). On the other hand, a counter-vailing relationship between growing foreign R&Dabroad and domestic innovation was the increasingtechnological competitiveness of the scientific andindustrial instruments industry abroad. Thus in the1970s and 1980s the US industry faced increasingcompetition from more sophisticated imports (USDepartment of Commerce, 1979, 1986).

ABOUT THE AUTHORSheryl Winston Smith is Assistant Professor ofStrategic Management and Entrepreneurship at theFox School of Business at Temple University. Shereceived her PhD from Harvard University and herBS from Yale University. Her research interests focuson innovation strategy, global knowledge search,and entrepreneurship.

Accepted by John Cantwell, Editor-in-Chief, and Paul Almeida, Area Editor, 22 July 2013. This paper has been with the author for two revisions.



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