International Technology Diffusion - spot.colorado.eduspot.colorado.edu/~kellerw/ITD.pdf ·...

Journal of Economic LiteratureVol XLII (September 2004) pp 752ndash782

International Technology Diffusion

WOLFGANG KELLER1

752

1 University of Texas Austin NBER and CEPR I havebenefited from discussions related to this paper with BobBaldwin Elhanan Helpman Danny Quah AndresRodriguez-Clareacute Dani Rodrik Jim Tybout and StephenYeaple Oded Galor Peter Howitt Sam Kortum PeteKlenow Pierre Mohnen Jim Rauch Carol Shiue DanTrefler and seminar participants at Industry Canada threeanonymous referees and John McMillan provided helpfulcomments Support by NSF grant SES 9818902 is grate-fully acknowledged

2 In addition institutions and policy have been empha-sized as well see eg Daron Acemoglu Simon Johnsonand James Robinson (2001) Abhijit Banerjee and LakshmiIyer (2003) and Carol Shiue and Wolfgang Keller (2004)

3 The G-7 countries (the largest seven industrializedcountries) accounted for about 84 percent of the worldrsquosresearch and development (RampD) spending in 1995 forexample their share in world GDP was only 64 percentThe worldrsquos distribution of production is much less skewed

1 Introduction

Productivity differences explain a largepart of the variation in incomes across

countries and technology plays a key role indetermining productivity (William Easterlyand Ross Levine 2001 Robert Hall andCharles Jones 1999 Edward Prescott 1998)2

For most countries foreign sources of tech-nology account for 90 percent or more ofdomestic productivity growth At presentonly a handful of rich countries account formost of the worldrsquos creation of new technol-ogy3 The pattern of worldwide technicalchange is thus determined in large part byinternational technology diffusion Whatdetermines the extent of technology flowsbetween countries and the means by which

4 The models differ in that some predict global diffu-sion would lead to convergence in productivity levels whileothers predict convergence in productivity growth ratessee Philippe Aghion and Peter Howitt (1998) and CharlesJones (1995) as well as the framework in section 2

technology diffuses This paper surveys whatis known about the extent of internationaltechnology diffusion and the channelsthrough which technology is spread

International technology diffusion isimportant because it determines the pace atwhich the worldrsquos technology frontier mayexpand in the future Do countriesrsquo incomesconverge over time or not The answer turnson whether technology diffusion is global orlocal as several widely used models show(Gene Grossman and Elhanan Helpman1991 Peter Howitt 2000 Luis Rivera-Batizand Paul Romer 1991) 4 A better under-standing of technical change therefore pro-vides insights on the likelihood that certainless-developed countries will catch-up torich countries

Some of the major channels for technologydiffusion across countries may include inter-national trade and foreign direct investment(FDI) both of which are discussed in thissurvey There is evidence that imports are asignificant channel of technology diffusion Atthe same time the evidence for benefits asso-ciated with exporting is weaker The impor-tance of FDI has long been emphasized inthe case-study literature and recently that

sp04_Article 2 81604 749 AM Page 752

Keller International Technology Diffusion 753

5 See Grossman and Helpman (1995) and Aghion andHowitt (1998) for broader overviews I will also discusssome related work on learning-by-doing and human-capi-tal accumulation that falls into the broad category of mod-els of knowledge accumulation

6 Many of these ideas have been discussed in the liter-ature before important contributors include Paul DavidGiovanni Dosi Robert Evenson Jan Fagerberg RichardNelson Keith Pavitt Nathan Rosenberg Luc SoeteSidney Winter Larry Westphal and others (see Fagerberg1994 and Robert Evenson and Larry Westphal 1995 foroverviews) What distinguishes the recent work is that itincludes fully specified general equilibrium models Thismeans that these technology effects can in principle beestimated in a well-defined framework

evidence has been complemented by somemicro-econometric findings

Despite the global reach of computer pro-grams there is no indication that a global poolof technology yet exists This localized charac-ter of technology suggests that an importantcomponent of it is tacit in nature Althoughthe relative importance of international tech-nology diffusion appears to be increasingalong with higher levels of economic integra-tion international diffusion of technology isneither inevitable nor automatic Domestictechnology investments are necessary

Before discussing some of the evidence inmore detail (sections 6ndash8) section 2 pres-ents a conceptual framework that will guidethis discussion The following sectionsreview various measures of technology (sec-tion 3) and technology diffusion (section 4)that are available while section 5 discussesthe pros and cons of a number of empiricalapproaches that have been employedSection 9 summarizes the overall evidenceon international technology diffusion andsection 10 contains a synthesis and somesuggestions for future work

2 International Technology Diffusion A Framework for Analysis

Theories of endogenous technical changeof the early 1990s (Aghion and Howitt 1992Grossman and Helpman 1991 Romer 1990and Paul Segerstrom TCA Anant andElias Dinopoulos 1990) emphasize twoaspects of technology5 6

7 Other authors have coined the terms ldquoperfectly expan-siblerdquo (David 1992) and ldquoinfinitely expansiblerdquo (DannyQuah 2001ab) to positively define this characteristic

8 The focus of this work is on technical change drivenby private not public research The majority of allresearch and development (RampD) is privately fundedalthough public RampD is substantial in many countries

9 International technology diffusion in this paperrefers to both technology spillovers as well as non-spillovers As will be discussed below the two are oftendifficult to separate

1 Technology is non-rival in the sense thatthe marginal costs for an additional agentto use the technology are negligible7

2 The return to technological investmentsis partly private and partly public

Point 1 distinguishes technology fromrival factor inputs such as human and physi-cal capital the latter can only be used by onefirm at a time or put differently the margin-al costs of using the same factor somewhereelse are infinite Point 2 highlights that whileprivate returns must be strong enough tokeep innovation ongoing technologicalinvestments often create benefits to individ-uals other than the inventor8 These externaleffects are called technology or knowledgespillovers As an example the introductionof one product might speed up the inventionof a competing product because the secondinventor can learn from the first by carefullystudying the product or its product design(the ldquoblueprintrdquo)

The focus of this paper is the empirics ofinternational technology diffusion and itseffect on productivity 9 Before discussingthe empirical results I will lay out a model ofinternational technology diffusion based onJonathan Eaton and Samuel Kortum (1999)and Kortum (1997) to help clarify the issues

Consider a model with n=1hellipN coun-tries Output in n at time t Ynt is producedby combining intermediate inputs subject toa constant-returns-to-scale Cobb-Douglasproduction function

(1)

where Xnt(j) is the quantity of intermediate

ln ln ( ) ( )Y J J Z j X j djnt nt nt

J

( ) = [ ]minus int1


754 Journal of Economic Literature Vol XLII (September 2004)

10 Past RampD is discounted because the technologiesmight have been superseded by higher-quality morerecent technologies

input j at time t in country n and Znt(j) is thequality of that input This quality is increasedover time through new ideas or technolo-gies The range of inputs is the same acrosscountries and constant over time Output ishomogeneous and tradable while interme-diates are nontraded

Each input j is produced anywhere by aCobb-Douglas combination of capital K(j)and labor L(j) X(j)=K(j)φL(j)1ndashφ withφ isin[01] New technologies are the result ofresearch effort Consider country i at time twith Lit workers If a fraction sit of them areengaged in research they create new tech-nologies at rate αitsit

βLit where αit is theresearch productivity and β0 characterizesthe RampD talent distribution

Each technology has three dimensions (i)quality (ii) use and (iii) diffusion time lagFirst the quality of a technology is a randomvariable drawn from the cumulative distribu-tion F(q) F(q)=1ndashqθ θ0 This quality iscommon to all countries to which the tech-nology diffuses Second the technology canbe used only in one intermediate sectorwhich is determined randomly

Third technologies become productiveonly once they have diffused Diffusion is astochastic process with a mean diffusion lagbetween country i and country n of ni

ndash1 orni measures the speed of technology diffu-sion between the countries The rate atwhich technologies diffuse to country n attime t is given by

(2)

where A in equation (2) is the bilateral speedof diffusion and B is country irsquos cumulativeoutput of technologies up to time t10

Even though every technology will even-tually be in every country (assuming ni0)a given technology may not be employed in

˙ ndash ( )

nt ni

Ai

N t sis is is

t

B

J e s L dsni= minus=

minus

minusinfinsum int1

1

1 24444 34444

11 Eaton and Kortum (1999) show that due to imperfectcompetition TFP is not identical to Ant but it is propor-tional to it also see their paper for the exact definition of Ant

12 For simplicity we set γ to equal one The parameterγ is related to the question of whether the model hasldquoscale effectsrdquo see eg Jones (1995) and Aghion andHowitt (1998) ch 12 for more details

production because by the time it has dif-fused it is dominated by a higher qualityThe stock of technologies in country n attime t is micront=ndash

tinfinmicromiddot nsds The fraction of inter-

mediates in country n at time t that arebelow some quality threshold q is decreasingin micront because the higher is the number ofdiffused technologies the greater is the like-lihood that the quality in country nrsquos sectorsis relatively high

Let Ant be the geometric mean of qual-ities across sectors Final output (1) is maxi-mized when factors are evenly divided across all sectors and it is given byAntKnt

φ[Lnt(1ndashsnt)]1ndashφ This means that Ant isequal to output divided by a factor-shareweighted sum of inputs or total factor pro-ductivity (TFP) 11

To obtain a steady-state equilibriumwhere micront grows at the same constant rate inall countries in the long run we assume thatthe research productivity αit is given byαit=α(it t)t

t=sumiN=1it le1 and αgt0

The higher is the greater is the strength ofthe international research spillovers12

Finally steady-state relative TFP levels canbe computed as

(3)

and world TFP growth is proportional to

(4)

where (in) and the share of labor inresearch siLi are constant in steady-stateEquation (4) says that world growth is pro-portional to a weighted sum of researchefforts in all countries

n N= 1

gJ g

s Ln

n

ni

ni

i

ni i

i

N

= =+=

sum˙

εε1

AA

n Nnt

Nt

nt

Nt

=

= minus

1

1 1

θ



13 The same equations indicate that domestic RampDraises TFP as well

The model has a number of implications1) Foreign RampD raises domestic TFP An

increase in foreign research leads to agreater inflow of technologies and higherTFP This holds in both the short and thelong-runs (equations 2 3 respectively)13

2) Receiving technology from abroad Agiven research effort abroad has a greatereffect on domestic TFP the faster foreigntechnologies diffuse to the domesticeconomy (ni in equation 2)

3) Global sources of technology A countryis important in determining the worldrsquosrate of growth if it has (i) a relativelyhigh share of the worldrsquos research laborand technologies (siLi and microi in equation4) andor (ii) a relatively high rate oftechnology diffusion to other countries(the weight ni (nig) in equation 4 isincreasing in ni)

International technology diffusion in thisframework captures the notion that productquality innovations of country i becomeavailable in country n at rate ni Moreoverthis is a spillover because although researchis costly in country i (it withdraws labor fromfinal output production) conditional on thetechnology having diffused it can be used toproduce final output there without incurringadditional costs The diffusion of technologyfrom country i also raises the productivity ofresearch in country n due to the internation-al research spillover (as long as 0) Analternative partial-spillover interpretationmay be that the diffusion lag ni

ndash1 is indica-tive of the strength of costly country-n tech-nology investment to acquire the technologyfrom country i

In either case it becomes clear that ourability to give the diffusion rates ni a struc-tural interpretation that can be empiricallytested is crucial for making further progressWhich are the major channels of interna-tional technology diffusion or what isbehind the rates ni This is the question that 14 In the model above this is not explicitly modeled

but it would suggest that ni is related to intermediateinputs trade see the analysis in Eaton and Kortum (1996)

15 See Ricardo Caballero and Adam Jaffe (1993) forempirical results in a closed economy

is addressed by the major portion of the lit-erature to date and will be reviewed in sec-tions 6ndash9 A number of issues are importantwhen thinking about the strength of differ-ent diffusion channels I will briefly turn tothem now

How do technologies move from onecountry to another One possibility isthrough international trade in intermediategoods (Rivera-Batiz and Romer 1991Grossman and Helpman 1991 and Eatonand Kortum 2002)14 Employing a foreignintermediate good in final-output produc-tion involves the implicit usage of the tech-nology in embodied form There is aspillover in this process of internationaltechnology diffusion to the extent that theintermediate good costs less than its oppor-tunity costsmdashwhich include the RampD costsof product development If trade is animportant diffusion channel one shouldexpect that international technology diffu-sion is geographically localized because ofthe well-documented fact that trade fallswith geographic distance (eg EdwardLeamer and James Levinsohn 1995) In anycase this is a relatively weak form of tech-nology diffusion because the technology assuch is not available domesticallymdashonly themanufactured outcome of it is

Of course international technology diffu-sion is not limited to the channel of tradeIn principle just as researchers today ldquostandon the shouldersrdquo of researchers of the pastone might expect researchers in one coun-try to directly benefit from research con-ducted in other countries15 The modelabove captures this in that research produc-tivity it is proportional to the global stockof technologiest) which is increasing inworld RampD This is an international RampDspillover (ie no domestic opportunity



16 Recall that we assume γ is equal to 1 More general-ly there is an international RampD spillover as long as γgt0

costs)16 Moreover this seems to be astronger form of diffusion than the interme-diate goods trade discussed above if theforeign technology raises the productivity ofdomestic researchers it suggests full mas-tery of the technology as opposed to onlythe ability to use the technology that isembodied in the intermediate good

Which patterns of technology diffusionshould thus be expected The first type ofdiffusion above suggests that it should followthe pattern of intermediate goods tradeInternational RampD spillovers appear to bemore difficult to pin down They are theresult of acquiring technology that is not tiedto any particular form In addition tolibraries conferences and other sourcestechnology may be stored in as little as a cou-ple of bytes and thus sent over the internetto practically every country in the world atclose to zero marginal cost At the sametime this does not mean that this technolo-gy diffusion in fact creates equal levels oftechnological knowledge in all countries forthe following reasons

First irrespective of feasibility it is not inthe interest of the original inventormdashwhohas incurred the RampD costmdashto send it toothers at no charge On the contrary theinventor may decide to spend additionalresources to keep the technology secretSecond even if some domestic technologybecomes available abroad it may be possi-ble to preclude others from using it bypatenting the technology Third even if thetechnology is non-rival and can be movedfrom one country to another at zero mar-ginal costs operating the technology effi-ciently often requires making costlyinvestments in terms of complementaryskills (see section 8)

Another issue is that technology may infact not be transferable at essentially zerocost even if all parties involved desire thissuch as multinational parents providing

17 For transferring machinery equipment technologythis share was 36 percent (Teece 1977 p 248) see alsoEric von Hippel (1994)

18 See also Kenneth Arrow (1969) David (1992) andEvenson and Westphal (1995) and references given there

19 This follows because the costs of moving people inspace are typically increasing in geographic distance seevon Hippel (1994) and Maryann Feldman and FrankLichtenberg (1997) for empirical support

technology to their subsidiaries DavidTeece (1977) estimates that the costs ofsuch within-firm transfers were on averagealmost 20 percent of the total project costsin the cases he analyzed17 One reason forthat is that only the broad outlines of tech-nology are codifiedmdashthe remainderremains ldquotacitrdquo (Michael Polanyi 1958)18

In this view knowledge is to some extenttacit because the person who is activelyengaged in a problem-solving activity cannot necessarily define (and hence prescribe) what exactly she is doingTechnology is only partially codifiedbecause it is impossible or at least very costly to fully codify it

What does this imply for the channels ofinternational technology diffusion Polanyi(1958 p 53) argues that tacit knowledge canbe passed on only ldquoby example from masterto apprenticerdquo A broader view is that non-codified knowledge is often transferredthrough person-to-person demonstrationsand instructions (David 1992) The mosteffective way of doing this despite tele-phone video and other remote methods isface-to-face interaction This howevermeans that technology diffusion impliesincurring the costs of moving teacher andstudent into the same geographic location

We can summarize the implications of thisfor the pattern of international technologydiffusion as follows1) The partial codified nature of technology

means that technology diffusion will beincomplete and technology stocks in dif-ferent countries will vary Diffusion willtend to be more geographically localizedthe higher is the non-codified share intotal technology19



20 This process could also in part be sequential (1)using foreign technology in embodied form and gradually(2) learning of the technology per se (3) imitation and (4)(original) innovation

21 However RampD data becomes more widely availableas countriesrsquo incomes are rising There is also increasinglyinformation on poorer countries because surveys encom-pass the RampD conducted by affiliates of multinationalcompanies located abroad see eg NSF (2004) for RampDexpenditures of US firms in China The main OECDRampD statistics are on a geographic not ownership basis

2) Because international economic activities(trade FDI etc) lead to additional contacts with foreign persons who maypossess advanced technological knowl-edge (exporter importer engineersresearchers) this may stimulate the diffu-sion of (non-codified) foreign technology

Trade and interaction with multinationalsmay thus lead not only to technology diffu-sion of the limited kind (technology embod-ied in intermediate goods) but it may alsoraise the probability of international RampDspillovers20

I now turn to how recent empirical workhas studied these issues The following twosections discuss the data that is availableon technology (section 3) and technologydiffusion (section 4)

3 Measures of Technology

Technology is an intangible that is difficultto measure directly Three widely used indi-rect approaches are to measure (1) inputs(RampD) (2) outputs (patents) and (3) theeffect of technology (higher productivity)

First internationally comparable data onRampD expenditures have been published bythe Organization of Economic Cooperationand Development (OECD) since about 1965According to the OECDrsquos definition (OECD2002) only about two dozen relatively richcountries report substantial amounts ofRampD because the definition captures prima-rily resources spent towards innovation andnot those spent on imitation and technologyadoption Technology investments of middleand poor countries can therefore typically notbe analyzed using RampD data21

22 This requires an assumption on the rate of RampDdepreciation standard values range between 0 and 10 per-cent see Zvi Griliches (1995)

23 See Griliches (1990) for more discussion24 Typically a patent contains one or more references to

other patents that indicate which earlier knowledge wasused to come up with the technology underlying the pres-ent patent application these references are called patentcitations

A drawback of RampD as a measure of tech-nology is that it ignores the stochastic natureof the process of innovation The currentflow of RampD expenditures is thus a noisymeasure of technology improvements in thatperiod Many authors construct RampD stocksfrom the flows using the perpetual inventorymethod22 Beyond year-to-year noise thereturn to RampD expenditures may vary sub-stantially across agents and over time whichlimits comparability One important aspectof this is that the return to publicly fundedRampD is lower than the return to privatelyfunded RampD (Frank Lichtenberg 1993) sothat many studies focus on business researchand development spending

Second a patent gives its holder a tempo-rary legal monopoly to use an innovation in aspecific market at the price of public disclo-sure of technical information in the patentdescription23 An innovation must be suffi-ciently important to be worthy of a patentwhich is judged by a trained official (calledpatent examiner) Relative to RampD patentshave the advantage that patent data has beencollected for a longer time (more than 150years for some countries) and also poorercountries have a substantial number ofpatents (see WIPO 2003)

There are some issues with using patentdata as well First a small number ofpatents accounts for most of the value of allpatents This means that simple patentcounts may not measure technology outputwell Recent work has addressed this issuein part by using citation-weighted patentdata (see Adam Jaffe and ManuelTrajtenberg 2002)24 Second the patentdecision is an act of choice on the part of thefirm and a large set of innovations is not



25 The fact that there is variation in the propensity topatent in several dimensionsmdashfor instance it is profitableto patent only in a large-enough marketmdashcould also pro-vide important information

26 See Charles Hulten (2000) for more details on TFP27 This could have major social welfare implications as

consumers benefit from higher productivity but not fromhigher mark-ups See also Tor Klette and Griliches (1996)as well as Eric Bartelsman and Mark Doms (2000) for arecent analysis of micro productivity data

ever patented25 And third if technology is inpart non-codifiable as argued above patentstatistics will necessarily miss that part

The third measure of technology dis-cussed here is total factor productivity(TFP) The idea well-known since the1950s is that if one subtracts from outputthe contribution of inputs such as labor andcapital the remainder is due to the factorldquotechnologyrdquo26 A simple example is the termA in the Cobb-Douglas production functionYAKL1ndash Other TFP measures are moregeneral and have certain desirable proper-ties that are important for comparability(eg the ldquosuperlativerdquo index proposed byRichard Caves Laurits Christensen andErwin Diewert 1982)

In contrast to RampD and patents TFP is aderived measure of technology as it is com-puted from data on inputs and output Thisintroduces measurement error and perhapsbiases because the appropriate data oninputs and outputs is rarely if ever availableHajime Katayama Shihua Lu and JamesTybout (2003) for example show that theuse of (1) real sales revenues (2) depreciat-ed capital spending and (3) real inputexpenditures instead of (unavailable) dataon the physical quantities (1) of output (2) ofcapital and (3) of intermediate inputs as isfrequently done will often confound higherproductivity with higher mark-ups 27 Otherfactors might thus contaminate the use ofTFP as a measure of technological efficiencywhich ultimately goes back to the concernthat TFP is constructed as a residual andmay potentially capture a host of spuriousinfluences (Moses Abramovitz 1956) 28 Sometimes firm-level datasets provide information

on the fraction of computer-controlled machinery orwhether a firm satisfies certain technological standardeither of these would also provide information about thetechnological sophistication of the firm

29 One reason is that technology is not fully codifiableso that complete contracts cannot be written Another rea-son is market failure in the market for technology due toasymmetric information the buyer does not know (theproductivity of) the technology while the seller cannotcommit to truthful claims about it This is considered to bea major reason why international technology transfers areoften internalized within firms (between multinationalparent and subsidiary) see eg William Ethier (1986)

Because of these difficulties in computingTFP researchers have pursued a number ofstrategies One is to consider changes in TFPas opposed to TFP levels This will help inidentifying technology (or rather technicalchange) if spurious factors do not change overtime or more generally if they change lessthan technology For example in KatayamaLu and Tyboutrsquos (2003) case from above if afirm faces higher adjustment costs to chang-ing its mark-up this will reduce the mark-upvariability in equilibrium A second strategyhas been to employ TFP measures in studiesof technical change together with data onRampD (eg Griliches 1984) By establishing arelationship between TFP changes and itspresumed major cause RampD spending thelikelihood of measuring changes in technologyinappropriately is substantially reduced

I now turn to measures of technology diffusion and spillovers28

4 Measures of InternationalTechnology Diffusion

The diffusion of technology involves bothmarket transactions and externalities (seeabove) and obtaining data on the former isfairly straightforward For instance firmsmake royalty payments for their use ofpatents licenses and copyrights and thisdata is available for major countries in theinternational services balance (eg OECD2003) Many economists believe thoughthat most international technology diffusionoccurs not through market transactions butinstead through externalities (spillovers)29



30 It is not a perfect indicator because sometimes cita-tions are added by the patent examiner not the applicant

31 This approach goes back to the closed-economy workby Griliches (1995) and Frederic Scherer (1984)

Naturally data on spillovers does not existMeasures that are related to it do exist buttypically they capture spillovers only partial-ly because the measures do not account forcosts of acquisition (learning) For instanceif one patent application cites an earlierpatent this generally indicates that theapplicant has benefited from the earlierpatent30 At the same time it is impossible toknow how large these benefits are net of thelearning costs that the patent applicant hadto incur

Among the different methods that try tomeasure international spillovers the largestset of papers employs international RampDspillover regressions In one set of papers ifRampD of firm j is positively correlated withTFP in firm i all else equal this is consistentwith international technology spillovers fromfirm j to firm i (eg Keller 2002a)31 A vari-ant of this approach replaces TFP by thenumber of patents (eg Lee Branstetter2001b) and Giovanni Peri (2002) presents ahybrid approach by relating patents in regioni to patents in other regions where the latteris instrumented by RampD expenditures

There are two alternatives to this basicapproach a generalization and a simplifica-tion In the former a particular channel oftechnology diffusion is added to the analysisDavid Coe and Elhanan Helpman (1995)eg analyze the relationship between pro-ductivity and foreign RampD conditional onimports from that foreign country The otheralternative to the international RampDspillover regressions is to relate productivitynot to foreign RampD but to other measuresof foreign activity Brian Aitken and AnnHarrison (1999) eg study the correlation ofinward FDI and domestic firm productivity(so-called FDI spillover regressions)

A common concern in these studies is thatfor various reasons the estimate might give

32 See section 62 for more on this particular case study

us correlations but not causal effects Theseas well as issues in other approaches such asestimates of spillovers as the parameter ni inthe model of section 2 are discussed in thefollowing section

5 Empirical Methods

51 Case Studies

Case studies can offer a rich description ofthe setting and the major factors that deter-mine international technology diffusion Forinstance the paper by Felipe Larrain LuisLopez-Calva and Andres Rodriguez-Clareacute(2000) analyzes whether Intelrsquos foreigndirect investment into Costa Rica in the1990s generated any technology spilloversThe authors have spoken to the main actorsincluding Intelrsquos top management and thegovernment of Costa Rica about their con-cerns and motivations This is informativeThe major limitation of case studies is thatone does not know how general one particu-lar case is Nevertheless there seems to be arole for case study research especially if thestudy ismdashas in the Intel casemdashbacked up byquantitative evidence32

52 Econometric Studies

521 Association Studies

In association studies authors askwhether a specific foreign activity (FA)leads to a particular domestic technologyoutcome (DTO)

DTOƒ(XFA)u (5)

In equation (5) X is a vector of control vari-ables and u is a regression error Forinstance Aitken and Harrison (1999) exam-ine the importance of foreign direct invest-ment (FDI) as a source of internationaltechnology spillovers here FA is the indus-try share of employment in foreign-ownedfirms and DTO is increases of domestic firmproductivity



33 Here using the foreign employment share as themeasure of FDI may be justified because multinationalenterprises tend to be RampD-intensive and share theirtechnology between parent and subsidiaries

34 Lael Brainard (1997) finds that FDI is relatively highin high-trade cost industries

This approach is based on economic theo-ry in the following sense Often there areseveral models that have been proposed toexplain in this case FDI spillovers andmodel-specific evidence often does not yetexist Association studies try to shed light onthe most interesting models by proposingwhat might be the common reduced-formequation of all these FDI spillover modelsIn order to accommodate several modelsthe framework cannot be very specific Thisexplains why authors of FDI spillover stud-ies employ a simple measure such as the for-eign employment share If FDI has a majoreffect on domestic firm productivity it willbe picked up by foreign employment nomatter what the particular mechanism is

The generality is attractive but it also pre-cludes a precise interpretation of the resultsMoreover by using a foreign activity (FA)instead of a foreign technology variable thisapproach is prone to estimating technologydiffusion only with some error33 This mat-ters because calculating a proper causaleffect (instead of a correlation) becomesmore difficult as the possible causes of spu-rious correlation multiply This could be dueto business cycle effects or to unobservedheterogeneity If productivity were exoge-nous and FDI would vary inversely withtrade costs across industries a positive coef-ficient of FDI on productivity would suggestspurious FDI spillovers if high-trade costindustries would exhibit on average higherproductivity for example34 Endogeneity isanother problem here it could take theform of foreign investors choosing to moveFDI into industries that have high rates ofproductivity growth (those industries aretypically also relatively profitable)

How can these issues be addressed Firstspurious business-cycle effects can be avoided

35 For instance Coe and Helpman (1995) report thatincluding a generalized time trend in their regressionreduces the international spillover coefficient by about 60percent and using conventional standard errors theparameter might be insignificantly different from zero(table B1 and table 3 coefficient on mlowastSf)

36 For instance critical values are dependent on theexact specification of the intercept and trend componentsas well as how much cross-sectional heterogeneity isallowed for see GS Maddala and In-Moo Kim 1998) foran introduction and Chris Edmonds (2000) for an appli-cation to spillover regressions

37 Time-differencing is often used for the same pur-pose see eg Keller (2000)

by including time fixed effects as long as it isthe case that the business cycle is common tothe entire sample More difficult is the ques-tion whether trend variables are appropriategiven the time-series properties of the data inparticular whether it is stationary This is animportant issue because including trend vari-ables can have a major impact on the resultsin this area 35 Panel unit root tests and coin-tegration analysis can be applied but thatapproach is still relatively new and resultsvary36 This suggests trying out alternativeapproaches if results change dramaticallydepending on the assumption on the datageneration process this warrants at leastsome discussion

Second a standard approach in the pres-ence of unobserved heterogeneity is to usefixed effects37 The consequence of course isto reduce the variation in the dependentvariable that is left to explain As such fixedeffects tend to be of little interest as theyrarely capture the economics that isinvolved Moreover the differencing that isimplied by the fixed effects can exacerbatemeasurement error problems (Zvi Grilichesand Jerry Hausman 1986) These considera-tions suggest keeping the number of fixedeffects low At the same time there is oftensome uncertainty about the lsquotruersquo modeland if a low number of fixed effects meansomitted variables bias the results can beseriously misleading In general it will beworth trying alternative specifications

Fixed effects will of course not work as acontrol for unobserved heterogeneity if the



38 See for instance Sourafel Girma and KatharinaWakelin (2001) Garrick Blalock and Paul Gertler (2002)and Wolfgang Keller and Stephen Yeaple (2003) On Olleyand Pakesrsquo (1996) method see also Zvi Griliches andJacques Mairesse (1998)

heterogeneous effects are actually not time-invariant Not much is known on the extentto which this has led to biases in the existingstudies At any rate the dynamic industryframework of Steven Olley and Ariel Pakes(1996) does not rely on this assumptionmdashitallows for time-varying heterogeneitymdashandit has been employed in a number of microstudies38

The third point endogeneity has beenrecognized in the literature but it is rarelyfully addressed For instance Keller(2002a) splits his sample into two the high-and low-RampD expenditure sectors basedon the fact that four out of his twelve indus-tries account for more than 80 percent of allRampD For a variety of reasons endogeneityproblems are much more likely to arise inthe former than in the latter sectorsKellerrsquos result for the low-RampD sampleturns out to be similar to that for the wholesample In general association studies con-duct often a wide number of robustnessanalyses These analyses are hardly defini-tive in dealing with endogeneity they aresequential in nature (looking at one sus-pected problem at a time) they proceedunder assumptions that might not hold(eg lagging a regressor while assumingthat there is no serial correlation) and forother reasons Nevertheless such robust-ness analysis can be very helpful in reveal-ing whether endogeneity might have afirst-order effect in a particular context

Endogeneity issues can in principle befully addressed by using instrumental vari-able (IV) techniques So far these tech-niques have not been used widely in thisliterature although that seems to be chang-ing now In general the application of IVtechniques has been limited because findinggood instruments for technology variablessuch as RampD stocks is difficult These are

39 See section 8 for a broader discussion of this

estimated to begin with and moreover mostvariables that are uncorrelated with theerror term tend to be not particularly corre-lated with RampD especially at the industry-or micro-level

The lack-of-instruments problem shouldbecome smaller because some recent IVtechniques use primarily lags of right handside variables as instruments The instru-ments are valid as long as there is not toomuch persistencemdashwhich can be testedformdashand the robustness of the approachexceeds earlier techniques because it usesboth the variablesrsquo levels and theirchanges (see Richard Blundell andStephen Bond 1999)

Two papers that have used IV estimationrecently are Rachel Griffith StephenRedding and Helen Simpson (2003) andKeller and Yeaple (2003) Both of thesepapers study the importance of technologyspillovers associated with FDI39 TheGriffith et al (2003) paper studies the influ-ence of foreign-owned subsidiaries in theUnited Kingdom Their instruments theeconomic conditions in France and theUnited States are quite plausible becausethese are the countries from which many of the UK subsidiaries come Using over-identification tests the authors find thatendogeneity is not an issue in their sampleThis may be somewhat surprising at firstperhaps suggesting that the instruments arenot as good as one might hope for HoweverKeller and Yeaplersquos (2003) IV analysis doesnot suggest an endogeneity bias eitherThese authors use industry level transportcosts and tariffs as instruments for FDIindustry variation in the United StatesKeller and Yeaplersquos IV estimates suggest ahigher effect through FDI spillovers than isobtained with OLS This is the opposite ofthe suspected endogeneity bias and could bethe result from a FDI variable that does notmeasure too well the source of technologyspillovers



40 This follows Romer (1990) and Grossman andHelpman (1991) see also Keller (2000) time subscriptsare omitted when no ambiguity arises

Despite these results it is too early toargue that endogeneity does not play a majorrole in many studies More research is clear-ly needed Other approaches that should beused more broadly are difference-in-differ-ence and other control-group estimationsespecially when a clear regime shift (ldquonaturalexperimentrdquo) can be identified

I now turn to a second class of empiricalpapers that has been employed in the econo-metric literature

522 Structure Studies

These studies incorporate more of thestructure of the underlying model than asso-ciation studies I distinguish between twotypes of structure studies Generally the firstset is given by

DTOƒ(XMFT)u (6)

where the foreign technology variable FTreplaces the foreign activity variable inequation (5) and the specification adds aspecific channel or mechanism of diffusion(denoted by M)

An influential example is the study by Coeand Helpman (1995) These authors test theprediction of the trade and growth models ofGrossman and Helpman (1991) and Rivera-Batiz and Romer (1991) in which foreignRampD creates new intermediate inputs andperhaps spillovers that the home country canaccess through imports Assume that outputis produced according to

zAld1ndash 01 (7)

where A is a constant l are labor servicesand d is a CES aggregator of differentiatedintermediate inputs x of variety s

(8)

where ne is the range of intermediate goodsthat are employed in this country40 It can be

d x s dsne

=

minusminus

int ( ) 1

0

11

distinct from n the range of intermediategoods produced in the country The latter isaugmented through RampD (denoted ) ifintermediate goods do not become obsoletethe range of intermediate goods at time T isgiven by the cumulative resources devotedto RampD nTST0

T(τ)dτ The goods x(s)are best thought of as differentiated capitalgoods produced with foregone consump-tion If the x(s) are symmetric and linearlyproduced with foregone consumption onecan write the stock of capital ask0

nx(s)dsnx which can be used to obtaina reduced-form expression for output as

zA(ne)lk1ndash (9)

Defining TFP as one obtains

ln flnAlnne (10)

Equation (10) shows that TFP is positivelyrelated to the range of intermediate prod-ucts employed in this country (Ethier 1982)

In a symmetric model with C countriesand no trade barriers in equilibrium allcountries will employ all intermediates fromall countries Given that the intermediatedesigns are produced through nationalRampD the range ne will be the same in allcountries equal to the worldrsquos cumulativeRampD spending

(11)

Coe and Helpmanrsquos (1995) test of themodel recognizes that there is both countryheterogeneity as well as trade barriers byseparating domestic and foreign RampD

ln fccd lnSc f lnScfc (12)

where country crsquos so-called foreign knowl-edge stock Sc

f is defined as the bilateralimport-share weighted RampD stocks of itstrade partners

(13)

This captures the prediction of Grossmanand Helpmanrsquos (1991) model that if a country

S m Sc

f

cc cc c

=ne

sum

n n St

e

ct ctc

C

c

C

= ===

sumsum11

fz

l kequiv minus 1



41 The approach of computing a weighted sum of RampDgoes back to Griliches (1979)

imports primarily from high-RampD partnercountries it is likely to receive relativelymuch technology embodied in intermediategoods which should be reflected in a higherproductivity level and vice versa

This approach has been influentialbecause of its plausibility simplicity and ver-satility and has been widely used to examinealternative channels of international diffu-sion for instance Frank Lichtenberg andBruno van Pottelsberghe de la Potterie(2000) have examined FDI by substitutingbilateral measures of FDI instead ofimports41 The approach is more specificabout the mechanism which helps in inter-preting the results At the same time suchregressions estimate different models onlypartially what determines RampD forinstance is typically not modeled whichmeans that endogeneity could be an issueGiven the partial equilibrium nature of theestimation authors should and generally doconsider alternative specifications

The second set of studies in this sectionputs relatively less emphasis on a particularmodel of growth and diffusion and moreemphasis on econometric specification andestimation A good example is the paper bySofronis Clerides Saul Lach and JamesTybout (1998) where the authors provideevidence on learning externalities fromexporting using micro data from ColumbiaMorocco and Mexico Clerides Lach andTybout (1998) extend the sunk cost model ofexporting due to Richard Baldwin (1989)Avinash Dixit (1989) and Paul Krugman(1989) to include the possibility that export-ing experience lowers the costs of produc-tion Clerides et al take account of theimportant point that it is on average thealready-productive firms that self-select intothe export market They do this by estimat-ing simultaneously a dynamic discretechoice equation that determines exportmarket participation

42 This includes full information maximum likelihoodand generalized methods of moments the latter with ageneralized cost function

and an autoregressive cost function

(14)

(15)

where yit is the export indicator of plant i inperiod t Xit is a vector of exogenous plantcharacteristics et is the exchange rate AVCit

are average costs Kit is capital and F 0 and Fj are sunk costs terms (of export marketparticipation)

Equation (14) states that one only sees aplant exporting if the profits from doing soare greater than from not exporting (thelatent threshold is expressed in terms ofobservables) Equation (15) estimateswhether past exporting experience reducescurrent cost (captured by the parametersj

y) conditional on past costs and size (prox-ied by capital) These learning-by-exportingparameters are not constrained in a signifi-cant waymdashrecall that yitndashj are 01 indicatorvariablesmdashwhich is probably a good startingpoint given that it is unknown how learningfrom exporting works (if indeed it exists)Clerides Lach and Tybout (1998) employalternative estimation methods in theiranalysis42 Moreover the econometric evi-dence is combined with descriptive evidencein form of over-time plots of average costsfor different types of plants (exporters non-exporters etc) The emphasis throughout isto distill the results that emerge consistentlyfrom all empirical approaches (they are dis-cussed in section 612 below) I would

ln( )AVC y vj

c

it j j

y

it j itj

J

j

J

+ + +minus minus==

sumsum 11

ln( ) ln( ) ln( )AVC K eit j

k

it je

tj

J

= + +minus=

sum 01

y

if X e AVC

F F y

otherwise

it

xit

eit j

c

it jj

J

jit j it

j

J

=

le + +

+ minus +

minus=

minus=

sum

sum

1 0

0

1

0

1

ln( )

( )



43 See also Eaton and Kortum (2002) Andrew Bernardet al (2003) and the discussion in 62 below

expect more work along these lines to bevery fruitful

523 Empirical Analysis with GeneralEquilibrium Models

Eaton and Kortum (1996 1997 1999)have studied international technology diffu-sion using general equilibrium models Intheir models productivity growth is relatedto increases in the quality of intermediategoods which is key to Aghion and Howittrsquos(1992) quality ladder model of growthEaton and Kortum add a process that gov-erns technology diffusion between coun-tries (see section 2 above) They thenexplore the quantitative relationshipsbetween RampD technology diffusion anddomestic productivity

Eaton and Kortumrsquos work is importantbecause instead of focusing on the reduced-form relationship between a subset of vari-ables they use their full model for makingpredictions on all (endogenous) variablesendogeneity as in the single-equation studiesabove is therefore no longer an issue Eatonand Kortum study both the long-run equilib-rium and the modelsrsquo predictions for thetransitional dynamics (Eaton and Kortum1999 and 1997 respectively) Eaton andKortum (1996) shed additional light on therates of diffusion in their model by usingreduced-form techniques Several of theirpapers study comparative-static changes inthe long-run equilibrium which is especiallyuseful for economic policy analysis (EatonEva Gutierrez and Kortum 1998)43

There are costs to this approach as wellthough First in order to arrive at a frame-work that can be analyzed typically somestrong assumptions have to be made InEaton and Kortum (1999) for instance thequality of technology that is discovered in acountry is a random variable with a Paretodistribution while the distribution of the

44 In addition it can be difficult to find other studiesthat have estimated a particular model parameter forinstance θ which governs technology discovery in Eatonand Kortum (1999)

diffusion lag to other countries is exponen-tial Within the context of a given modelsuch assumptions are very difficult to testIt means that this empirical work does notso much test or select among several mod-els as it estimates one particular modelSecond the models are usually too compli-cated for estimation to identify all modelparameters If as is common practice anumber of parameters are fixed based onvalues from earlier studies this means thatthe results are partly simulated and notestimated44

Third it is often difficult to judge howempirically successful a model is One meas-ure of success is the difference betweenactual versus predicted values for theendogenous variables If a model predictsproductivity levels that lie within an errormargin of 15 percent of the actual levels forinstance how much of this is due to ldquofreerdquomodel parameters (ie those that are set ex-ante) With those parameters how muchworse would a different perhaps simplermodel fare And what if the set parametersare adjusted to maximize the fit in the alter-native model Peri (2002) for instance usesa partial equilibrium approach to estimatetechnology diffusion in a model similar toEaton and Kortum (1996) who work in ageneral equilibrium framework With Ncountries NN equations pin down bilater-al technology diffusion and the impliedtechnology stocks determine (N131) relativeproductivity levels as in equation (3) aboveAccording to Peri (2002) the technologystocks do not have a significant effect on pro-ductivity In Eaton and Kortum (1996) thatrelationship is one element of their modelrsquosgeneral equilibrium structure which meansthat it could be that the structural assump-tions have too strong an influence on theempirical results



45 In comparison it is straightforward to assess partial-equilibrium work For instance according to Coe andHelpman (1995) bilateral import patterns are importantfor explaining variation in productivity see equation (13)A simple test of that is to drop the import shares in (13)and see whether it leads to a major reduction in explainedvariation of productivity (Keller 1998 see section 61)Empirical work on technology diffusion in general equilib-rium has not been subjected to the same level of scrutinypresumably because the nature of the framework has moreto do with simulation and less with testing to begin with

It is important to ask which part of thevariation in the data or which assumptionsin the model are primarily responsible forthe results Despite the sensitivity analysisthat may be presented the informed readerwill typically find this question impossible toanswer and this reduces the usefulness ofthe general equilibrium results45

To summarize the different empiricalapproaches each have their advantages anddisadvantages There is a case for adopting abroad estimation framework as in associa-tion studies if there is model uncertaintyand this case is stronger if endogeneityissues can be addressed It is useful toimpose more structure as more informationbecomes available because the structureaides interpretation General equilibriummodels allow us to conduct counterfactualswhich is crucial for policy and welfare analy-sis and they will be most influential if thesources of variation that underlie the resultsare clear

The following section discusses theimportance of different potential channelsof international technology diffusion

6 Channels of International Technology Diffusion

61 Trade

611 Imports and Technology Diffusion

First I turn to the evidence on diffusionthrough intermediate input imports InEaton and Kortum (2002 2001) the authorshave combined the structure of technologydiffusion in Eaton and Kortum (1999)mdashsee

46 See also the extension by Bernard et al (2003) toplant-level analysis

47 As far as I know the literature to date lacks a modelin which imports have a direct effect on technology dif-fusion at least for the multi-country case with sectoraldifferences

section 2mdashwith the Ricardian model oftrade due to Ruumldiger Dornbusch StanleyFischer and Paul Samuelson (1977)46 InEaton and Kortumrsquos model trade augmentsa countryrsquos production possibilities for theclassic Ricardian reason trade gives accessto foreign goods or implicitly technologiesBy specializing in their respective compara-tive advantage goods countries can gainfrom trade in the sense that given a countryrsquosresources the efficient level of output withtrade is higher than without trade At thesame time there are no spillovers in thismodel in the sense that importers pay thecompetitive price and importing has noeffect on innovation47

Eaton and Kortum assume that unit trans-port costs are increasing in geographic dis-tance This implies that the price ofintermediate (or equipment) goods inremote countries is relatively high or equiv-alently that productivity in these countries isrelatively low These effects are shown to bequantitatively important as differences inthe relative price of equipment account for25 percent of the cross-country productivitydifferences in a sample of 34 countries(Eaton and Kortum 2001)

However it is not clear at this pointwhether this provides strong support forimports as a major channel for technologydiffusion This is because the equipmentgoods prices predicted by Eaton andKortumrsquos (2001) model are inversely relatedto those reported in Robert Summers andAlan Hestonrsquos International ComparisonProgram of Prices data (CIC 2003) whileaccording to Summers and Hestonrsquos datarich countries have higher equipment pricesthan poorer countries Eaton and Kortumrsquosmodel predicts the opposite (Eaton andKortum 2001 figure 7)



Summers and Hestonrsquos price data mightbe imperfect but it is unlikely that revisionsof this data will reverse how equipmentprices correlate with income At this pointEaton and Kortumrsquos model althoughappealing because it suggests plausibly largeproductivity effects come from importingforeign technologies seems to require amajor modification in order to be consistentwith international equipment goods pricedata The latter is not the only core predic-tion of the model but it appears to beimportant enough to put at least a questionmark behind the estimated quantitativeimportance of trade for technology diffusion

Second there is evidence on the impor-tance of imports that comes from interna-tional RampD spillover regressions Based onthe framework laid out in section 522above Coe and Helpman (1995) relate TFPto domestic and foreign RampD

(12rsquo)

where Sctf is defined as the bilateral import-

share weighted RampD stocks of its trade part-ners Sct

fsumcnec

mccSct A positive effect fromthe foreign RampD variable Sct

f would implythat a countryrsquos productivity is increasing inthe extent to which it imports from high- asopposed to low-RampD countries supportingthe hypothesis that imports are a channel oftechnology diffusion along the lines of themodels discussed in Grossman andHelpman (1991) In a sample with 22OECD countries Coe and Helpman (1995)estimate a positive and quantitatively largeeffect from import-weighted foreign RampDSimilar effects are found for technology dif-fusion from highly industrialized to 77 lessdeveloped countries (Coe Helpman andAlexander Hoffmaister 1997)

There are some reasons to remain skepti-cal here as well First the analysis of Keller(1998) has shown that the import shares inthe construction of the foreign RampD variableSct

f are not in fact essential to obtain Coeand Helpmanrsquos (1995) results SpecificallyKeller (1998) uses randomly created shares

ln ln lnf S Sct cd

ctf

ct

f

ct= + + + ε

48 Alternatively Keller (1998) sets all microcc equal to 1which produces similar results This confirms that theimport shares are inessential for the results whether or notthey are truly random (see Keller 1997 2000 and Coe andHoffmaister 1999)

49 Kellerrsquos (1998) analysis has the same implication forCoe Helpman and Hoffmaisterrsquos (1997) paper

50 Keller (2000) uses specialized machinery importsdata at the industry level in a related analysis His resultsshow that technology diffusion through imports does affectproductivity growth for high skewed trade patterns (egfor Canada which imports to about 70 percent from theUnited States) suggesting that the effect is nonlinear

denoted by cc in place of the actual bilater-al import shares to create the counterfactualforeign knowledge stock Sct

fsumcnecccSctUsing this alternative foreign RampD variableyields similarly high coefficients and levels ofexplained variation as the regressions usingSct

f (that is imports data)48 Given thatimport shares are not essential for Coe andHelpmanrsquos (1995) results their analysis doesnot allow us to draw strong conclusionsregarding the importance of imports as avehicle for diffusion49

A number of authors have made progressby examining the international RampDspillover regressions further Bin Xu andJianmao Wang (1999) emphasize that tech-nology diffusion in recent trade and growthmodels is associated specifically with differ-entiated capital goods trade This is in con-trast to the trade data Coe and Helpman(1995) use to construct their import shares(from overall trade) Xu and Wang (1999)show that this distinction matters the capitalgoods-foreign RampD variable accounts forabout 10 percent more of the variation inproductivity than does Coe and Helpmanrsquosanalysis and it also performs better thanKellerrsquos (1998) counterfactual variable 50

It has also been noted that the foreignRampD variable Sct

f captures only current-period bilateral trade it is clearly possiblethough that country A benefits from countryCrsquos technology without importing from thissource if country C exports to country Bwhich in turn exports to A OlivierLumenga-Neso Marcelo Olarreaga andMaurice Schiff (2001) use a specification



that captures such indirect RampD spilloversand show that it performs better than Coeand Helpmanrsquos (1995) and Kellerrsquos (1998)models These results are consistent withthe importance of dynamic effects fromimports but more research in an explicitlydynamic framework is needed to learn moreabout this

The importance of imports for technologydiffusion has also been assessed with patentcitation data Frederic Sjoumlholm (1996) stud-ies citations in patent applications ofSwedish firms to patents owned by foreigninventors Controlling for a number of othercorrelates and also conducting an extreme-bounds analysis Sjoumlholm finds a positivecorrelation between Swedish patent cita-tions and bilateral imports This is consistentwith imports contributing to internationalknowledge spillovers

Overall the evidence points to a significantrole for important in international technolo-gy diffusion However the various strands ofthe literature leave still some questions openand we do not have yet a firm estimate ofthe quantitative importance of imports forinternational technology diffusion

612 Exports and Technology Diffusion Is There Learning-By-Exporting

A major question is whether firms learnabout foreign technology through exportingexperience There is anecdotal evidenceclaiming that firms do benefit from interact-ing with foreign customer for instancebecause the latter impose higher productquality standards than domestic customerwhile at the same time providing informa-tion on how to meet the higher standardsCase studies of the export success of a num-ber of East Asian countries starting in the1960s are particularly strong in theiremphasis on learning-by-exporting effects(Yung-Whee Rhee Bruce Ross-Larson andGarry Pursell 1984) The question iswhether this evidence can be supportedwith econometric evidence

51 Note that the evidence on learning-by-exportingeffects discussed in the following comes from micro stud-ies while the evidence on diffusion associated with importsdiscussed above is based on more aggregate studies

There is abundant evidence that in a givencross-section exporters are on average moreproductive than nonexporters (eg Bernardand Jensen 1999 Clerides Lach and Tybout1998 Mary Hallward-Driemeier GiuseppeIarossi and Kenneth Sokoloff 2002)51 Thatdoes not settle the issue of causality howev-er are exporting firms more productivebecause of learning effects associated withexporting or is it rather the case that firmsthat are more productive to begin with startexporting The conventional wisdom todayis that learning-by-exporting effects are non-existent This position is consistent with cur-rent evidence but at the same time there area number of issues which in my view areworth further research before the questionshould be considered settled

An important paper is by Clerides Lachand Tybout (1998) These authors studymanufacturing plants in ColumbiaMorocco and Mexico during the 1980s tofind evidence on learning-by-exportingeffects Their framework is based on a modelwith sunk costs of (export) market entryleading to a dynamic discrete choice equa-tion for participation and an autoregressivecost function as a performance measure (seesection 5 above)

Clerides Lach and Tybout show resultsfor each country separately and also bymajor industry In general they tend toshow no significant effects from past export-ing experience on current performance(this is parameter j

y in equation 15)Clearly this does not lend support for theexistence of strong positive learning-by-exporting effects In fact to the extent thatClerides et alrsquos estimates are significantthey go into the wrong direction (exportingraising costs) It would be surprising ifindeed there would be negative learningeffects and the authors give a number of



52 Four types of firms can be distinguished exportersnonexporters starters (plants that start exporting) andquitters (plants that stop exporting)

53 Bernard and Jensenrsquos estimates using TFP instead oflabor productivity are lower but the labor productivity fig-ures are preferred in this case The TFP measure is a sim-ple regression residual that is fallible to a number ofproblems (eg Griliches and Mairesse 1998)

plausible reasons of why this finding mayhave to be discounted Another interpreta-tion of the generally insignificant estimatesmay be that the estimation framework isdemanding too much of the data HoweverClerides et alrsquos plots of average cost beforeand after export market entry seem to sup-port their main estimation result of no evidence for learning-by-exporting effects

While learning-by-exporting has beenemphasized to be important primarily forlow- and middle-income countriesrsquo firmsthere is in principle no reason why it is lim-ited to these countries especially given thefirmsrsquo heterogeneity in terms of productiv-ity in any given country Bernard andJensen (1999) study the learning-by-exporting question using data on USfirms This has the advantage that the sam-ple is relatively large and there is compara-tively much experience with data collectionand preparation which may result in lowermeasurement error

Unlike Clerides Lach and Tybout (1998)Bernard and Jensen (1999) do not modelexport market participation explicitlyInstead they study the performance of thedifferent sets of firms separately52 Bernardand Jensen estimate that labor productivitygrowth is about 08 percent higher amongexporters than nonexporters 53 This esti-mate is fairly small and it becomes evensmaller (and insignificant) for longer timehorizons

The estimate of 08 percent howeverappears to be a downward-biased estimateof the learning-by-exporting effect becauseit comes from an analysis conditional onplant survival In fact Bernard and Jensenshow that conditional on size exporters are10 percent more likely to survive than non-

54 Size is the main predictor of survival in recent indus-try-equilibrium models (eg Olley and Pakes 1996)because a small firm might have to exit after only one badshock (or a small number of successive bad shocks)whereas a large firm has substance enough to weather alonger succession of bad shocks

55 This point is related to the fact that none of the esti-mates I discuss are claimed to be spillover effects ratherthey are ldquolearningrdquo effects which could be costly toacquire Clerides Lach and Tybout (1998) do estimatespillovers those that might accrue to other plants thisevidence is mixed

exporters54 It is plausible that this 10 per-cent survival probability difference is indica-tive of higher productivity growth forexporters than nonexporters because plantstend to fail because their productivitygrowth is low This suggests that the overalldifference in productivity growth betweenexporters and nonexporters may be largerthan 08 percent although at this point it isnot clear how much larger

Some of these estimates come from a rel-atively small number of years and theremay be an argument that this time horizonis too short to see major learning-by-export-ing effects Alternatively Hallward-Driemeier Iarossi and Sokoloff (2002)focus on the time before entering the exportmarket These authors use data from fiveSoutheast Asian countries to show that firmswhich eventually become exporters makemore investments to raise productivity andthe quality of their goods than firms thatplan to stay out of the export market This isplausible but if these investmentsrequiremdashwhich is likelymdashreal resourcesthose need to be subtracted from any learn-ing effects the firms receive after they haveentered the export market55 Moreovergiven that the productivity increases pre-date the firmrsquos entry into the export marketat best these are indirect learning-by-exporting effects

The analysis has shown that there is noeconometric evidence for a strong learning-from-exporting effect At the same timethere are a number of issues that still need tobe addressed First it is puzzling that the



56 Industry heterogeneity has recently been empha-sized in the literature on FDI (see 62) Clerides Lachand Tybout (1998) however do generally not estimatemajor differences across industries

57 See Magnus Blomstroumlm and Ari Kokko (1998) andKamal Saggi (2000) for a more detailed discussion of pos-sible spillover channels associated with FDI

econometric evidence is so strongly at oddswith the case-study evidence Second therecould be heterogeneity across industriesmasking strong but industry-specific learningeffects especially with respect to high-tech-nology products56 Third the analysis couldbe improved if we knew more on both theexport destination and the exporter insteadof simply an indicator variable (exportingyesno) For instance to which firms inwhich countries do the exports go What arethe characteristics of these foreign firmsmdashare they engaged in RampD for example

62 Foreign Direct Investment

Foreign direct investment (FDI) has longbeen considered as an important channel fortechnology diffusion It is a plausible chan-nel from a theoretical standpoint because aninfluential theory says that firm-specifictechnology is transferred across internation-al borders by sharing technology amongmultinational parents and subsidiaries (seeeg James Markusen 2002) There are anumber of models showing how multination-al enterprises (MNEs) might generate tech-nological learning externalities for domesticfirms for instance through labor training andturnover (Andrea Fosfuri Massimo Mottaand Thomas Roslashnde 2001) or through theprovision of high-quality intermediate inputs(Rodriguez-Clareacute 1996)57

Whether FDI indeed generates substan-tial technological externalities for domesticfirms is also a major policy issue becausegovernments all over the world spend largeamounts of resources to attract subsidiariesof MNEs to their jurisdiction For instancein 1994 the US state of Alabama spent$230 million or $150000 per newly createdjob to attract a new plant of Mercedes-Benz

58 In the words of another author ldquotodayrsquos policy litera-ture is filled with extravagant claims about positivespillovers from FDI [but] the hard evidence is soberingrdquo(Dani Rodrik 1999 p 37)

59 It is an open question whether or to what extentthese are spillover effects see also the discussion at theend of this section

(see Jonathan Haskel Sonia Pereira andMatthew Slaughter 2001)

What is the evidence on FDI spillovers Anumber of surveys have recently concludedthat there is no evidence for substantial FDIspillovers (Gordon Hanson 2001 HolgerGoumlrg and David Greenaway 2002)58

However this view based primarily on anumber of micro-level productivity studiesseems to be overly pessimistic First of allsome more recent evidence for the microproductivity approach suggests that on thecontrary FDI spillovers are large and eco-nomically important Second there aresome results outside the micro productivityliterature that do provide evidence for FDIspillovers

A case study of Intelrsquos FDI into CostaRica for example already mentioned insection 5 above provides interesting infor-mation on how widespread the changes canbe that FDI by a major high-technologycompany can trigger in a relatively smallcountry (Larrain Lopez-Calva andRodriguez-Clareacute 2000)59

Other authors have provided econometricevidence on whether multinational enter-prises (MNEs) raise the rate of internationaltechnology transfer as measured by patentcitations (Steven Globermann Kokko andSjohoumllm 2000 Branstetter 2001a and JasjitSingh 2003) Here the results are less clearFirst of all MNE subsidiaries could eitherdisseminate technology to domestic firms oftheir host country (inward FDI technologytransfer) or MNE subsidiaries might pick upnew technologies from the firms in the hostcountry (outward FDI technology sourcing)It turns out that the relative strength of theseeffects is estimated differently although thetechnology sourcing effect appears to be



60 Branstetter (2001a) finds evidence for spillovers ofboth types for Japanese FDI in the US Singh (2003)finds the sourcing effect to be stronger than the transfereffect in a sample of 10 OECD countries and GlobermanKokko and Sjoumlholm (2000) find only evidence for the outward FDI effect

stronger60 This resultmdashMNE subsidiarieslearn more from the firms in their host coun-try than vice versamdashmight be indicative of anumber of problems however

The first is firm heterogeneity MNE sub-sidiaries are larger and more technologicallyintensive than the average firm in the hostcountry and this might be the reason whythey are good at sourcing technology Thisinterpretation seems to be confirmed bySinghrsquos (2003) finding that patent citationsbetween two MNE subsidiaries are strongerthan either from MNE subsidiary to adomestic firm or the reverse The secondissue is endogeneity It could be that onefinds MNE subsidiaries to be sourcing moretechnology than they provide because theMNE parent set up the subsidiary with theexplicit goal of technology sourcing whilethe average host-country firm in contrasthas not made a comparable location deci-sion This suggests that the estimates are notfully comparable and future research isneeded to settle this issue

The value of a patent is difficult to esti-mate (see eg Pakes 1986) An importantissue in the patent citation studies is there-fore how the economic significance of tech-nology diffusion measured in this way shouldbe assessed A large literature has thus triedto estimate directly the extent to which FDIleads to productivity increases for domesticfirms Xu (2000) for instance uses the USBureau of Economic Analysisrsquo comparabledata on US outward FDI into forty coun-tries over almost thirty years (between 1966and 1994) He finds generally a positive rela-tion between FDI and productivity growthwhich is stronger in the richer than in thepoorer countries

Xursquos (2000) analysis is at the manufactur-ing level which may cause aggregation bias

61 Goumlrg and Greenaway (2002) is a recent survey62 Another possibility is endogeneity FDI into

Venezuela targets sectors in which Venezuelan firms are rel-atively weak Aitken and Harrison do not treat endogeneityexplicitly

because of heterogeneity across sectors andacross firms For this reason the literatureon FDI spillovers has moved towards usingmicro (firm or plant) level data (eg Aitkenand Harrison 1999 Girma and Wakelin2001 Haskel Pereira and Slaughter 2001Griffith Redding and Simpson 2003 Kellerand Yeaple 2003)61 These papers presentregressions of productivity on FDI and anumber of control variables (they are associ-ation studies in the sense of section 52above) If productivity growth of domesticfirms is systematically higher in industrieswith more FDI than in industries with lessFDImdashand endogeneity and other problemscan be ruled out see section 51 abovemdashthisprovides support for the FDI spillovershypothesis Irrespective of the precisespillover mechanism a greater presence offoreign-owned subsidiaries in a particularindustry is plausibly conducive to domesticfirmsrsquo technological learning if they belongto this industry

What is the evidence on this In Aitkenand Harrison (1999) the authors estimate anegative relationship between FDI and pro-ductivity for a sample of Venezuelan plantsThe finding of a negative relationship pointsto a specification error while FDI spilloversmay or may not exist there is no reason tobelieve that they would be negative Onepossibility raised by Aitken and Harrison(1999) is that their results capture theincreased competition through FDI in addi-tion to spillovers62 Haskel Pereira andSlaughter (2001) among others control forsuch competition effects These authors aswell as Girma and Wakelin (2001) andGriffith Redding and Simpson (2003) studyinward FDI for the United Kingdom WhileGirma and Wakelinrsquos results are somewhatmixed Haskel Pereira and Slaughter(2001) and Griffith Redding and Simpson



63 Haskel Pereira and Slaughter (2001) present a cal-culation of costs and benefits

64 The two types of sectors are distinguished by RampDintensity see Keller and Yeaple (2003)

(2003) present evidence in support of a sig-nificant positive FDI spillover effect Theimplied economic magnitudes are fairlysmall however certainly relative to the sub-sidies that have been paid to attract FDI63

In contrast Keller and Yeaplersquos (2003)study of recent FDI activity in the UnitedStates suggests that FDI spillovers are pos-itive and large They estimate that FDI inthe United States accounts for about 11percent of US manufacturing productivitygrowth in that period This estimate ismuch larger than Haskel Pereira andSlaughterrsquos (2001) as well as other esti-mates and the question is why There seemto be two primary reasons

The first reason is that FDI spilloversappear to be much stronger in relativelyhigh-technology than in relatively low-tech-nology sectors64 This explains in partKeller and Yeaplersquos larger FDI spilloverestimate because in their Compustat sam-ple the share of high-technology firms ishigher than in the broader sample ofHaskel Pereira and Slaughter (2001) forexample The strong difference betweensectors which is implicitly present also inGriffith Redding and Simpson (2003)table 5mdashsuggests that FDI might be animportant conduit for international technol-ogy diffusion but only some forms of FDIthat occur in a limited set of sectors OtherFDI for instance that primarily seekinglower factor costs is not likely to generatemajor spillovers Moreover the practice ofpooled FDI spillover estimationmdashacross allmanufacturing industriesmdashis likely to leadto misleading results

The second finding of Keller and Yeapleunderscores the importance of good meas-ures of foreign economic activity They usedetailed US statistics to allocate theemployees of a given MNE subsidiary topotentially different industries In contrast 65 As noted in section 5 instrumental variable estima-

tions are presented in Griffith Redding and Simpson(2003) and Keller and Yeaple (2003) In neither case arethe least-squares estimated FDI spillovers larger than thecorresponding IV-estimates

66 See also Beata Smarzynska Javorcik (2004)

in most previous studies all employees areallocated to one the so-called major indus-try To the extent that MNE subsidiaries arediversified and their industry mix changesover time the major-industry method ofmeasuring FDI is prone to year-to-yearvolatility that is essentially noise Keller andYeaple (2003) show that for their samplethe preferred FDI data yields estimates thatare seven times as large as those with theinferior by major-industry FDI data

We can summarize the literature on FDIspillovers as follows In contrast to the earli-er literature recent micro productivity stud-ies tend to estimate positive and in somecases also economically large spillovers asso-ciated with FDI This difference does notappear to be primarily due to endogeneity(or other problems)65 Moreover althoughthe current evidence from micro productivi-ty studies comes from the United Kingdomand the United States there are reasons tobelieve that the findings might apply inother countries as well If these micro pro-ductivity FDI spillovers hold up in thefuture it would also provide support for theFDI learning effects that are found in someof the case studies

There are other important questions Firstare there effects of FDI outside the industryin which MNE subsidiaries are active It isplausible to assume that to the extent thatMNE subsidiaries buy local intermediateinputs they have an incentive to providetechnological knowledge to their suppliers astronger incentive at any rate than to localcompetitors that produce the same productMaurice Kugler (2002) as well as Blalock andGertler (2002) have found some evidence forsuch vertical FDI spillovers66

Second could there be more generalexternalities Larrain Lopez-Calva and



Rodriguez-Clareacute (2000) argue that amongthe most important consequences of thechipmaker Intelrsquos recent FDI into CostaRica was that (1) Intel funded schools thattaught local workers certain skills (but byno means Intel specific skills) and (2)Intelrsquos FDI served as a signal for otherpotential foreign investors that it might betime to invest into Costa Rica nowEstimating such signaling effects (if theyexist ) is a challenge because the morediffuse and indirect the alleged external-ities are the more difficult it becomes toidentify clean causal effects

7 Geographic Effects on InternationalTechnology Diffusion

Global technology spillovers favor incomeconvergence and local spillovers tend tolead to divergence no matter throughwhich channel technology diffuses In addi-tion to research on channels of diffusionanother strand of the literature has exam-ined international technology diffusion in itsgeographic dimension (Jaffe Trajtenbergand Rebecca Henderson 1993 DouglasIrwin and Peter Klenow 1994 Eaton andKortum 1999 Branstetter 2001b LauraBottazzi and Peri 2000 and Keller 2002a)An advantage of this is that geography isarguably exogenous in this process

One question is whether technology diffu-sion within countries is stronger than acrosscountries The evidence generally supportsthis hypothesis although there are excep-tions In particular Jaffe Trajtenberg andHenderson (1993) compare the geographiclocation of patent citations with that of thecited patents in the United States They findthat US patents are significantly more oftencited by other US patents than they arecited by foreign patents This is confirmedby Branstetter (2001b) who uses RampD andpatenting data on US and Japanese firms tocompute weighted RampD spillover stocksanalogous to Coe and Helpmanrsquos (1995)

67 Patenting data by technological field allowsBranstetter to compute weights that are increasing in thesimilarity of two firmsrsquo patenting activities this capturesthe idea that RampD expenditures of another firm are morelikely to generate spillovers the closer the two firms are intechnology space

68 The average international diffusion rate ni for nneiin Eaton and Kortum (1999) is about 0088 while thedomestic rate of diffusion (ni for n= i) is fixed at 177 seetheir table 2

69 Irwin and Klenow (1994) estimate learning-by-doingspillovers which are more closely related to human-capitalmodels (eg Robert Lucas 1988) than to models of techno-logical change They might be different in that they meas-ure improvements in the efficiency of an already adoptedtechnology whereas international technology spilloversmay capture to a greater extent learning about new andnot-yet-adopted technologies Empirical analysis has notbeen able to clearly separate one from the other so far

import share weights (see section 5)67 Hefinds that within-country spillovers are muchstronger than between-country spilloversMore evidence for stronger diffusion withinthan across countries comes from Eaton andKortumrsquos (1999) study these authors esti-mate that for the G-5 countries the rate ofdomestic technology diffusion is about 200times the size of the average rate of interna-tional technology diffusion between the G-5countries68

In contrast Irwin and Klenow (1994) donot find stronger within country spillovercompared to across country spillovers Irwinand Klenow estimate that for eight vintagesof semiconductors introduced between 1974and 1992 the spillovers from one US firmto another US firm are not significantlystronger than those between an US firmand a foreign firm The different resultsmight be obtained because Irwin andKlenowrsquos spillovers which are identifiedfrom the effects of cumulative production onmarket shares are different from knowledgespillovers as measured in the other studies69

It could also have to do with the particularsof the semiconductor industry at the timeMost of the relatively small number of firmswere located in the United States and inJapan which means that the scope for iden-tifying the within versus between countrydifference is limited



The analysis has therefore been extendedbeyond the national versus internationaldistinction by estimating spillovers condi-tional on geographic distance and the coun-triesrsquo locations relative to each other(Keller 2001 2002a) Keller (2002a) usesindustry-level productivity in nine mostlysmaller OECD countries to RampD in the G-5 countries (France Germany Japan theUnited Kingdom and the United States)using a simple exponential decay functionin distance

(16)

Here Dcc is the geographic distancebetween countries crsquo and c and X is a vectorof control variables If is estimated to begreater than zero variation in productivity isbest accounted for by giving a lower weightto RampD conducted in countries that arelocated relatively far away whereas if 0geographic distance and relative location donot matter Keller (2002a) finds that is pos-itive and moreover the decay of technologydiffusion implied by the estimate is substan-tial with every additional 1200 kilometersdistance there is a 50-percent drop in tech-nology diffusion Applying this estimate toAustralia for example with its remote geo-graphic location relative to the G-5 countrieswould suggest that Australia benefitsextremely little from technology created inthe G-5 countries Along the same linesBottazzi and Peri (2003) find a strong geo-graphic decay in their analysis of technologydiffusion between European regions using asimilar framework These studies suggestthat technology is highly geographicallylocalized in particular regions and countries

A related question is whether the degreeof localization has fallen in recent yearsThis may be expected as a consequence oftransport cost improvements informationand communication technology innova-tions increased multinational activity and

X cit+ prime + ε

ln lnTFP S S ecit cit citD

c G

cc= + times

minus

primeisinsum

5

70 See Jaffe Trajtenberg and Henderson (1993) egwho adopt a control group approach they examine citedand not cited patents that are matched in terms of other-wise similar characteristics See also Kellerrsquos (2002a) dis-cussion of whether his results may be explained bygeographic distance being correlated with technologicaldistance (ie that geographically proximate countries pro-duce relatively similar products)

other changes Keller (2002a) examines thisby estimating different decay parameters (δin equation 16) for the late 1970s and theearly 1990s The estimates indicate that δhas shrunk substantially over time inabsolute value suggesting that the degreeof localization has become smaller This isconsistent with the changes mentionedabove leading to a greater international diffusion of technology

A major concern is that the estimatedgeography effect may be spurious perhapsdue to unobserved heterogeneity acrosslocations This issue is addressed in a num-ber of studies and while the proposed solu-tions are clearly imperfect overall theresults suggest that geography in fact is animportant determinant of technology diffu-sion70 Another concern seems to be moreimportant This is that we still need to findout exactly what the geography effect meansin terms of economic analysis does this cap-ture trade costs for instance We know thattrade volumes are strongly declining withdistance (Leamer and Levinsohn 1995) sothis seems clearly a possibility Keller (2001)is a first attempt to explain the geographiceffects in international technology diffusionin terms of imports foreign direct invest-ment patterns and person-to-person com-munication but more work is needed thatreveals what geography stands for in termsof economic models and behavior

8 Human Capital and RampD asDeterminants of International

Technology Diffusion

As international technology diffusionbecomes stronger over time this favorsincome convergence and vice versa weaker



71 Others distinguish specific and currently embodiedfrom more general skills and argue that technology diffu-sion destroys the former skills in the short-run while in thelong-run there is a complementary relationship betweentechnical change and skills (Oded Galor and DanielTsiddon 1997)

diffusion makes divergence more likelyHowever this result applies only if the trendtowards (or away from) greater diffusion isglobal affecting all countries to the sameextent In fact there seem to be big differ-ences in how effective countries are in adopt-ing foreign technology Given that the bulk ofnew technology is created in a handful of theworldrsquos richest countries greater technologydiffusion could in fact lead to divergence inthe world income distribution this will hap-pen if todayrsquos richer countries are on averagebetter at adopting foreign technology thantodayrsquos poorer countries Alternatively poorercountries could gain more from better tech-nology diffusion than richer countries Afterall poorer countries have more to gainfrom it or in the words of AlexanderGerschenkron (1962) latecomers may benefitfrom their relative backwardness

This underlines the importance of identi-fying the major determinants of successfultechnology diffusion Among the many thathave been proposed two stand out humancapital and RampD expenditures Both areassociated with the notion of absorptivecapacity the idea that a firm or countryneeds to have a certain type of skill in orderto be able to successfully adopt foreigntechnology see Keller (1996) for a formal-ization of this idea71 These skills can comefirst of all in the form of human capital(Richard Nelson and Edmund Phelps1966) They can also be in the form of RampDspending a notion that was first emphasizedby Wesley Cohen and Daniel Levinthal(1989) These authors argue that RampDinvestments are necessary for a firm toacquire outside technology This is becauseRampD is critical to enabling the firm tounderstand and evaluate new technologicaltrends and innovations

There is evidence that human capital facil-itates the adoption of new technology in aclosed economy setting (eg Ann Bartel andLichtenberg 1987) Does the same also holdtrue for international technology adoptionThe evidence presented in a number ofpapers suggests the affirmative Eaton andKortum (1996) find that inward technologydiffusion is increasing in the level of a coun-tryrsquos human capital In Francesco Caselli andWilbur Colemanrsquos (2001) study inward tech-nology diffusion is captured by imports ofoffice computing and accounting machin-ery on the grounds that many countries donot have a domestic computer industry sothat computer technology comes necessarilyfrom abroad They find that computerimports are positively correlated with humancapital The results presented by Xu (2000)are consistent with the importance of humancapital for technology diffusion as well Hefinds that the reason relatively rich countriesbenefit from hosting US multinational sub-sidiaries while poorer countries do not asmuch has to do with a threshold level ofhuman capital in the host country

Does absorptive capacity in the form ofRampD have a similar effect on technologydiffusion To date there exist only a fewstudies In one of them Griffith Reddingand John Van Reneen (2000) use industry-level data from twelve OECD countries forthe years 1974 to 1990 to study the maindeterminants of productivity dynamicsThey show that conditional on a certain pro-ductivity gap to the leader country subse-quent productivity growth in an industry ishigher the higher are its RampD expendituresThis is consistent with RampD playing a rolesimilar to that of human capital in providingthe necessary skills for technology adoption

The second question is whether condi-tional on human capital and RampD speedingup the diffusion of technology there is morelikely to be convergence or divergence Asdiscussed above this will depend on the dis-tribution of human capital and RampD acrosscountries and the evidence on this is mixed



so far First the human-capital thresholdidentified by Xu (2000) might not have beensurpassed by poor countries which suggeststhat differences in human capital could leadto divergence Second a number of FDIspillover studies have found that it is the lessproductive firms that benefit most from for-eign technology (Haskel Pereira andSlaughter 2001 Griffith Redding andSimpson 2003 and Keller and Yeaple 2003)and that favors convergence

One difference between Xu (2000) on theone side and the FDI studies on the otherside is that the latter do not specificallyfocus on the role that a firmrsquos own RampD (orskills) might have for convergence A possi-ble explanation for these seemingly contra-dictory results could be that while thedistribution of RampD and skills in favor of therelatively rich is as such a force towardsdivergence this is more than outweighed bythe stronger benefits that the relatively poorderive from foreign technology that are notconditional on skills and RampD It is of coursealso possible that convergence of countriesand convergence of firms in a particularcountry are not comparable in this wayMore work in this area is clearly needed

9 Quantifying Domestic and ForeignSources of Productivity Growth

Building on the analysis in section 5 thefollowing equation shows the relationbetween a countryrsquos productivity (fc) anddomestic (Sc) as well as foreign RampD (Sc

f)

(12rsquorsquo)

Two hypotheses regarding extreme cases ofinternational technology diffusion can bestated easily If f is equal to zero there is nointernational technology diffusion at allwhereas if d is equal to zero then there isperfect international technology diffusionor a global pool of technological knowledgeThe discussion so far has indicated that nei-ther of these two cases finds empirical sup-portmdash f is clearly greater than zero but at

ln ln lnf S Sc cd

cf

c

f

c= + + + ε

the same time geographic localization meansthat d is not equal to zero either Goingbeyond these results this section asks whatwe know about the relative magnitude ofdomestic and foreign technology sources

Most of the estimates we have come fromanalyzing samples of relatively rich coun-tries In poor countries there is very littlecomparable data on domestic technologyinvestments The data on the most importantforeign technology sources that is availablemdashRampD in the G-7 countriesmdashby itself is thusnot sufficient to assess the relative foreigncontribution for poorer countries In order toget some idea nevertheless I will rely on thedifferences in relative contribution of foreigntechnology among OECD countries

There are several different ways of assess-ing the relative importance of foreign tech-nology diffusion roughly corresponding tothe different empirical methods described insection 5 One approach is to compare theTFP elasticities of domestic and foreignRampD Coe and Helpman (1995) estimate adomestic RampD elasticity of about 23 percentfor the relatively large G-7 countries where-as the corresponding foreign RampD elasticityis only about 6 percent (these are approxi-mately equal to d and f in equation 12rsquorsquo)Thus for the G-7 countries the relative con-tribution from foreign RampD is about 21 per-cent (006 divided by 006 plus 023) This isalmost the same as in Kellerrsquos (2002b) analy-sis of the G-7 countries plus Sweden usingdata at the industry level where he arrives ata share of about 20 percent for foreign RampDin the total effect on productivity

For the smaller OECD countries Coeand Helpman estimate a domestic RampDelasticity of about 8 percent and a foreignRampD elasticity of about 12 percent Thusthe ratio of foreign to domestic effect is esti-mated to be 32 for smaller and 14 for larg-er OECD countries What lies behind theresult that foreign RampD is relatively muchmore important for smaller countries Herethis follows because smaller countries aremore open than larger countriesmdashrecall



72 These figures are based on results in Eaton andKortum (1999) and Keller (2002a) GDP data from HestonSummers and Bettina Aten (2002) In the year 1990 theUnited States contributed about 48 percent to the com-bined size of the G-5 countries plus Sweden Denmark andthe Netherlands while the Netherlands accounted for 22percent Eaton and Kortum estimate that about 60 percentof US productivity growth was due to US RampD whileKeller finds that about 47 percent of Dutch productivitygrowth can be attributed to Dutch RampD

from section 61 that the foreign RampD effectis proportional to a countryrsquos overall importshare in this specification

This openness effect points to country sizeas an important determinant of the relativeimportance of foreign versus domestic tech-nology sources Is the relative contribution offoreign RampD indeed so much larger for smallcompared with large countries A first lookat other evidence seems to suggest yes Forinstance Eaton and Kortum (1999) estimatethat the part of productivity growth that isdue to domestic as opposed to foreign RampDis between 11 percent and 16 percent inGermany France and the United Kingdomaround 35 percent for the larger Japan andabout 60 percent for the United StatesKeller (2002a) computes the domestic andforeign share from his distance-adjusted esti-mate of effective RampD (equation 16) Fornine countries that are smaller than theUnited Kingdom Keller estimates that thedomestic source share is about 10 percenteven smaller than Eaton and Kortumrsquos esti-mate of 11 percent for the United Kingdom

That size matters in some way is clearhowever because in the limit as a countryrsquosshare in the world approaches one the sharedue to foreign technology goes to zero Tocontrol for size differences one possibility isto use GDP data Doing this gives for theUnited States a ratio of domestic technologyshare to GDP share of 60 percent to 48 per-cent or 124 whereas the correspondingvalue for the Netherlands is 21872 This sug-gests that relative to GDP productivitygrowth in the United States relies less ondomestic sources of technology than in theNetherlands

This may suggest that more generally thesmaller a country the more intensively itrelies on domestic technology sources If soit would be interesting to find out whetherthis is only a country size or also a distance-to-the-frontier phenomenon The latterwould mean that even though poor countriesreceive almost all of their technology fromabroadmdashthere is a vast pool of technologyout theremdashit is domestic technology effortsthat matter most for them This would beconsistent with theories where technologythat is invented in frontier countries is lessappropriate for poorer countries (egSusanto Basu and David Weil 1998)

10 Concluding Discussion

What have we learned from this literatureso far and what do we still need to know Iwill begin by giving a snapshot summary ofthe evidence in some major areas Theempirical literature is still quite fragmentedbut there are signs of consolidation which Iwill highlight I will turn then to an outlookon what research might have a particularlyhigh payoff in the future

For most countries foreign sources oftechnology are of dominant importance (90percent or more) for productivity growth(section 9) This fact underlines the signifi-cance of international technology diffusionand the importance of finding out whichactivities determine its success and whethersubstantial technology spillovers are associat-ed with them Foreign sources of technologyare more important for small (and relativelypoor) than for bigger countries which iswhat one expects given the size difference indomestic RampD investments

There is no indication however that inter-national learning is inevitable or automaticNeither does it seem to be easier for coun-tries that are relatively backward asGerschenkron (1962) has called it It couldwell be that controlling for size the poorer acountry is the greater is the importance ofdomestic technology investments Instead of



simply being far from the frontier the suc-cess of countries is in part explained by howtheir firms engage in international economicactivities What is the evidence on this so far

Early on international trade has been sug-gested as a major channel for technology dif-fusion With regard to imports I think thatoverall the evidence now supports the notionthat importing is associated with technologyspillovers At the same time there is still anabundance of disparate results that need tobe brought together and quantifiedMoreover we do not know yet how strongdiffusion is through embodied technology in intermediate goods versus other tech-nology diffusion associated with importsLearning effects from exporting have beenfound in the case study literature whereasauthors of econometric studies take a muchmore skeptical view While the econometricevidence on learning-by-exporting effects isnot as clear cut and negative as it is oftenstated right now I do not see evidence infavor of strong important learning-by-exporting effects either

What about learning effects for domesticfirms from FDI The FDI literature seemsto be closer to a consensus than the tradeliterature is right now For instance bothmicro-econometric studies and case studiespoint in the same direction The evidencesuggests that there can be FDI spilloversbut they do not occur everywhere to thesame degree The remaining questionsinclude the following how large are verticalcompared to horizontal spillovers andwhich firmsmdashstronger or weakermdashbenefitmost from spillovers In addition we needto know whether FDI spillovers in richerand poorer countries are equally strongand last not least whether they are quantita-tively large enough to justify the large sub-sidies that governments provide to attractmultinationals

There is a thin line between recognizingthe importance of international technologydiffusion and getting carried away in thebelief that in todayrsquos world technology is

global In fact technology is not global todayas the studies on the localization of technolo-gy diffusion have shown Yes we can zaptechnology in the form of computer pro-grams or designs easily around the globe butno this does not mean that there is effective-ly a global pool of technology Perhaps it isbecause technology is in part tacit requiringthe sender at times to actually go there andimplement the technology that there is still ageographic pattern to technology diffusionmdashalthough the grip of geography has becomeweaker recently We still need to know moreabout why geography mattersmdashexplainingchanges in the geographic pattern of technol-ogy diffusion will tell us much I think aboutthe key mechanisms especially in analysesthat include both rich and poor countries

In order to decide what kind of researchwould be most useful at this point it isworth recalling what the options areBroadly speaking those are case studieseconometric analyses and simulation stud-ies As long as their respective strengths andweaknesses are recognized all three canpotentially prove to be very useful Of greatimportance is whether the data that isemployed is closely related to issues of tech-nology and technology diffusion Forinstance FDI spillovers estimated fromdata on foreign subsidiariesrsquo (and their par-entsrsquo) RampD should tell us much more ontechnology transfer than a variable like theforeign share of employment The generalprinciple is if you know something aboutactual technological activity try to use it

An important question is the appropriatelevel of data aggregation This issue wasmoot for a long time in international eco-nomics because there was very little microdata Recently however micro data sets havestarted to become available It is thus impor-tant to know the differences between microand more aggregate data for studying inter-national technology diffusion and which ofthe two if any is preferred Not only canone use micro data today but moreover itseems to matter a great deal for the results



generally the higher is the level of aggrega-tion the stronger tends to be the evidencefor externalities and learning effects Whatcan explain this difference between microand more aggregate evidence

Micro data can capture heterogeneityacross firms something that features promi-nently even in narrowly defined industrieswhereas industry- and aggregate-level stud-ies cannot control for this and may sufferfrom composition and aggregation biasesthat tend to lead to inflated spillover esti-mates I think that this is indeed sometimesthe case At the same time firm heterogene-ity is less important if one is primarily inter-ested in industry-level outcomes Forexample it may not matter too much foreconomic policy whether industry produc-tivity rises because all firms benefit fromspillovers or because only the more produc-tive firms benefit and become larger whilethe least productive firms exit

Simultaneity and endogeneity seem to bemore important issues than aggregation andin this respect there is little differencebetween micro and more aggregate studiesfor instance interpreting a cross-sectionalcorrelation of foreign ownership and pro-ductivity as evidence for FDI spilloverswould be just as inappropriate at the firmlevel as it is at the aggregate level Insteadthe work in question is more or less impor-tant depending on how thorough the authorsare in trying to identify a truly causal effect

Most strategies for doing that rely on com-paring sets of firms This comparison or tak-ing differences may allow us to see whetherthe treatment (say inward FDI) has an effectThere is no doubt that differencing is crucialfor capturing a causal effect At the sametime the assumptions on the nature of thedifferences are often quite simple (eg firm-specific time-invariant heterogeneity) and itis not clear whether the results would remainthe same if firms are actually allowed to reactto a major change in their environment

More generally I expect the biggest con-tribution of micro data sets to come from a

better estimation of micro behavior as thedata is recorded right at the decision-takinglevel And to reach their full potentialempirical micro studies of technology diffu-sion will probably involve modeling microstructure as well because it is the structurethat shapes behavior As of now there is lit-tle of that For instance currently empiricalstudies of technology diffusion are not basedon models whose structure explicitly allowsfor learning and spillover externalitiesbetween firms That is so far the micro evi-dence is based on ldquono-externalityrdquo modelsThis suggests that the micro evidence maybecome stronger once externalities areincluded in the models as a matter of theory(some work in this direction is CleridesKeller and Tybout 2003)

If the idea of differencing is to control forthe averse effects of heterogeneity (acrossfirms or over time) where does one stop Ofcourse solving the heterogeneity issue fun-damentally means conducting a case studywith a sample of one observationmdashbut it isnot necessarily advisable to go that far atleast not as the sole empirical methodologyMore generally can there be a problem ofoverdifferencing An important issue is thattechnology data tends to be not of very highquality and this limits the amount of differ-encing that one can do Once the estimatedcoefficients look like a collection of randomnumbers scattered around zero with typical-ly large standard errors the researcher hasprobably asked too much of the dataSimilarly is it possible to probe so deeplyinto the structure of micro behavior that onefails to notice more diffuse more aggregateand slower-acting effects As noted aboveone benefit of attracting a major foreigninvestor according to Larrain Lopez-Calvaand Rodriguez-Clareacute (2000) is that it sendsa signal to other potential foreign investorsIt is difficult to see how a deeper analysis ofmicro structure would help to detect thesetypes of effects

Summarizing I think that we can expectto obtain additional major results on interna-



tional technology diffusion in the future andthis will be in part because of the increasedusage of micro data and estimation At thesame time it will be important to keep anopen mind and not expect too much fromany one empirical methodology

What are the policy implications of this lit-erature The evidence is not strong enoughyet to provide support for specific policymeasures such as a particular subsidy to amultinational enterprise for locating in acountry This would require more agreementon quantitative effects than there is right nowAt this point the results suggest that theinternational dimension of technologicalchange is of key importance for most coun-tries In this situation a closed-off internation-al economic regime must have detrimentalwelfare consequences for a country

There is also evidence that the relativeimportance of international technology diffu-sion has been increasing with the level of eco-nomic integration in the world This suggeststhat the performance advantage of outward-oriented economies over inward-orientedeconomies will be higher in the future than itis today What if future research were toestablish that international technology diffu-sion raises productivity more in advancedthan in less-developed countries The impli-cation for less-developed countries would notbe that a turn towards autarky is beneficialmdashon the contrary given the evidence on posi-tive productivity effects from internationaltechnology diffusion Rather if the benefitsfrom operating in an international economicenvironment differ across countries this sug-gests we should investigate further the majorreasons for thismdashwhat works and why

While there is no consensus yet on theexact magnitude of spillover benefits it isclear that well-functioning markets and anundistorted trade and foreign-investmentregime are conducive to these learningeffects However the evidence suggests thatthe latter are quite difficult to pin down Forone it does not seem to be primarily a mat-ter of simply specializing in the production

of high-tech goods Rather the goods char-acteristics are only part of what is importantIndia for example aimed at producing rela-tively advanced products during its era ofimport-substitution policy but their qualitywas low compared to international stan-dards In contrast Southeast Asian countriessuch as Hong Kong and South Korea wereinitially specializing in relatively low-techproducts and moved slowly but successfullyinto the range of higher-tech products

Instead technological knowledgespillovers appear to be resulting from adeliberate commitment to learning andmatching international performance stan-dards through ongoing interaction with for-eigners Local efforts are clearly necessaryfor successful technology adoption At thesame time the ongoing interaction with for-eign firms and consumers seems to be aprocess of knowledge discovery for firmsthat cannot be had from interacting onlywith other domestic firms To model as wellas empirically capture these in a convincingway continues to be a challenge

REFERENCES

Abramovitz Moses 1956 ldquoResource and OutputTrends in the United States since 1870rdquo Amer EconRev 462 pp 5ndash23

Acemoglu Daron Simon Johnson and JamesRobinson 2001 ldquoThe Colonial Origins ofComparative Developmentrdquo Amer Econ Rev 915pp 1369ndash401

Aghion Philippe and Peter Howitt 1992 ldquoA Model ofGrowth through Creative DestructionrdquoEconometrica 60 pp 323ndash51

mdashmdashmdash 1998 Endogenous Growth TheoryCambridge MA MIT Press

Aitken Brian and Ann Harrison 1999 ldquoDo DomesticFirms Benefit from Foreign Direct InvestmentEvidence from Venezuelardquo Amer Econ Rev 893pp 605ndash18

Arrow Kenneth 1969 ldquoClassificatory Notes on theProduction and Transmission of TechnologicalKnowledgerdquo Amer Econ Rev Pap Proceed 592pp 29ndash35

Baldwin Richard 1989 ldquoSunk Costs HysteresisrdquoNBER work pap 2911 Cambridge MA

Banerjee Abhijit and Lakshmi Iyer 2003 ldquoHistoryInstitutions and Economic Performance TheLegacy of Colonial Land Tenure Systems in IndiardquoBREAD work pap

Bartel Ann and Frank Lichtenberg 1987 ldquoTheComparative Advantage of Educated Workers in



Implementing Technologyrdquo Rev Econ Statist 691pp 1ndash11

Bartelsman Eric and Mark Doms 2000ldquoUnderstanding Productivity Lessons fromLongitudinal Microdatardquo J Econ Lit 383 pp569ndash94

Basu Susanto and David Weil 1998 ldquoAppropriateTechnology and Growthrdquo Quart J Econ 1134 pp1025ndash54

Bernard Andrew and Bradford Jensen 1999ldquoExceptional Exporter Performance Cause Effector Bothrdquo J Int Econ 471 pp 1ndash25

Bernard Andrew Jonathan Eaton Bradford Jensenand Samuel Kortum 2003 ldquoPlants and Productivityin International Traderdquo Amer Econ Rev 934 pp1268ndash90

Blalock Garrick and Paul Gertler 2002 ldquoTechnologyDiffusion from Foreign Direct Investment throughSupply Chainsrdquo work pap Haas School Bus UCBerkeley

Blomstroumlm Magnus and Ari Kokko 1998ldquoMultinational Corporations and Spilloversrdquo J EconSurveys 123 pp 247ndash77

Blundell Richard and Stephen Bond 1999 ldquoGMMEstimation with Persistent Panel Data AnApplication to Production Functionsrdquo work pap994 Inst Fiscal Studies London

Bottazzi Laura and Giovanni Peri 2003 ldquoInnovationDemand and Knowledge Spillovers Evidence fromEuropean Patent Datardquo Europ Econ Rev 47 pp687ndash710

Brainard Lael 1997 ldquoAn Empirical Assessment of theProximity-Concentration Trade-off betweenMultinational Sales and Traderdquo Amer Econ Rev874 pp 520ndash44

Branstetter Lee 2001a ldquoIs Foreign DirectInvestment a Channel of Knowledge SpilloversEvidence from Japanrsquos FDI in the United StatesrdquoNBER work pap 8015

mdashmdashmdash 2001b ldquoAre Knowledge SpilloversInternational or Intranational in ScopeMicroeconometric Evidence from the US andJapanrdquo J Int Econ 53 pp 53ndash79

Caballero Ricardo and Adam Jaffe 1993 ldquoHow HighAre the Giantrsquos Shoulders An Empirical Assessmentof Knowledge Spillovers and Creative Destruction ina Model of Growthrdquo NBER MacroeconomicAnnual vol 8 Olivier Blanchard and StanleyFischer eds

Caselli Francesco and Wilbur Coleman II 2001ldquoCross-Country Technology Diffusion The Case ofComputersrdquo Amer Econ Rev Pap Proceed 912pp 328ndash35

Caves Richard Laurits Christensen and ErwinDiewert 1982 ldquoMultilateral Comparisons ofOutput Input and Productivity Using SuperlativeIndex Numbersrdquo Econ J 92365 pp 73ndash86

CIC 2003 The International Comparison of PricesProgram Center Int Comparisons U Penn

Clerides Sofronis Wolfgang Keller and James Tybout2003 ldquoForeign Direct Investment Exports andSpilloversrdquo in progress Brown U

Clerides Sofronis Saul Lach and James Tybout 1998

ldquoIs Learning by Exporting Important Micro-dynamic Evidence from Colombia Mexico andMoroccordquo Quart J Econ 113 pp 903ndash48

Coe David and Elhanan Helpman 1995ldquoInternational RampD Spilloversrdquo Europ Econ Rev395 pp 859ndash87

Coe David Elhanan Helpman and AlexanderHoffmaister 1997 ldquoNorth-South Spilloversrdquo EconJ 107 pp 134ndash49

Coe David and Alexander Hoffmaister 1999 ldquoAreThere International RampD Spillovers amongRandomly Matched Trade Partners A Response toKellerrdquo IMF work pap 9918

Cohen Wesley and Daniel Levinthal 1989ldquoInnovation and Learning The Two Faces of RampDrdquoEcon J 99397 pp 569ndash96

David Paul 1992 ldquoKnowledge Property and theSystems Dynamics of Technological Changerdquo inProceedings of the World Bank Annual Conferenceon Development Economics 1992 Larry Summersand Anwar Shah eds pp 215ndash48

Dixit Avinash 1989 ldquoEntry and Exit Decisions underUncertaintyrdquo J Polit Econ 973 pp 620ndash38

Dornbusch Ruumldiger Stanley Fischer and PaulSamuelson 1977 ldquoComparative Advantage Tradeand Payments in a Ricardian Model with aContinuum of Goodsrdquo Amer Econ Rev 675 pp823ndash39

Easterly William and Ross Levine 2001 ldquoItrsquos NotFactor Accumulation Stylized Facts and GrowthModelsrdquo World Bank Econ Rev 152 pp 177ndash219

Eaton Jonathan Eva Gutierrez and Samuel Kortum1998 ldquoEuropean Technology Policyrdquo Econ Pol 27pp 405ndash38

Eaton Jonathan and Samuel Kortum 1996 ldquoTrade inIdeas Patenting and Productivity in the OECDrdquo JInt Econ 403-4 pp 251ndash78

mdashmdashmdash 1997 ldquoEngines of Growth Domestic andForeign Sources of Innovationrdquo Japan World Econ9 pp 235ndash59

mdashmdashmdash 1999 ldquoInternational Patenting andTechnology Diffusion Theory and MeasurementrdquoInt Econ Rev 40 pp 537ndash70

mdashmdashmdash 2001 ldquoTrade in Capital Goodsrdquo Europ EconRev 457 pp 1195ndash235

mdashmdashmdash 2002 ldquoTechnology Geography and TraderdquoEconometrica 705 pp 1741ndash79

Edmonds Chris 2001 ldquoSome Panel CointegrationModels of International RampD Spilloversrdquo JMacroecon 23 pp 241ndash60

Ethier William 1986 ldquoThe Multinational FirmrdquoQuart J Econ 101 pp 805ndash33

mdashmdashmdash 1982 ldquoNational and International Returns toScale in the Modern Theory of International TraderdquoAmer Econ Rev 72 pp 389ndash405

Evenson Robert and Larry Westphal 1995ldquoTechnology Change and Technology Strategyrdquo inHandbook of Development Economics vol 3A JereBehrman and TN Srinivasan eds North-Holland

Fagerberg Jan 1994 ldquoTechnology and InternationalDifferences in Growth Ratesrdquo J Econ Lit 323 pp1147ndash75

Feldman Maryann and Frank Lichtenberg 1997 ldquoThe



Impact and Organization of Publicly-FundedResearch and Development in the EuropeanCommunityrdquo NBER work pap 6040

Fosfuri Andrea Massimo Motta and Thomas Roslashnde2001 ldquoForeign Direct Investment and Spilloversthrough Workersrsquo Mobilityrdquo J Int Econ 53 pp205ndash22

Galor Oded and Daniel Tsiddon 1997 ldquoTechnologicalProgress Mobility and Economic Growthrdquo AmerEcon Rev 87 pp 363ndash82

Gerschenkron Alexander 1962 EconomicBackwardness in Historical Perspective CambridgeMA Harvard U Press

Girma Sourafel and Katharina Wakelin 2001ldquoRegional Underdevelopment Is FDI the SolutionA Semi-Parametric Analysisrdquo GEP Research Pap200111 U Nottingham

Goumlrg Holger and David Greenaway 2002 ldquoMuch AdoAbout Nothing Do Domestic Firms Really Benefitfrom Foreign Direct Investmentrdquo work pap UNottingham

Globerman Steven Ari Kokko and Frederic Sjoumlholm2000 ldquoInternational Technology Diffusion Evidencefrom Swedish Patent Datardquo Kyklos 53 pp 17ndash38

Griffith Rachel Stephen Redding and Helen Simpson2003 ldquoProductivity Convergence and ForeignOwnership at the Establishment Levelrdquo CEPRwork pap 3765

Griffith Rachel Stephen Redding and John VanReenen 2000 ldquoMapping Two Faces of RampDProductivity Growth in a Panel of OECDIndustriesrdquo forthcoming Rev Econ Statist

Griliches Zvi 1979 ldquoIssues in Assessing theContribution of Research and Development toProductivity Growthrdquo Bell J Econ 10 pp 92ndash116

mdashmdashmdash ed 1984 RampD Patents and Productivity UChicago for NBER

mdashmdashmdash 1990 ldquoPatent Statistics as EconomicIndicators A Surveyrdquo J Econ Lit 283 pp 1661ndash707

mdashmdashmdash 1995 ldquoRampD and Productivity EconometricResults and Measurement Issuesrdquo in Handbook ofthe Economics of Innovation and TechnologicalChange Paul Stoneman ed Oxford Blackwell pp52ndash89

Griliches Zvi and Jerry Hausman 1986 ldquoErrors inVariables in Panel Datardquo J Econometrics 31 pp93ndash118

Griliches Zvi and Jacques Mairesse 1998ldquoProduction Functions The Search forIdentificationrdquo in Econometrics and EconomicTheory in the 20th Century Ragnar FrischCentennial Symposium Steinar Stroslashm edCambridge Cambridge U Press pp 169ndash203

Grossman Gene and Elhanan Helpman 1991Innovation and Growth in the World EconomyCambridge MA MIT Press

mdashmdashmdash 1995 ldquoTechnology and Traderdquo in Handbookof International Economics vol3 Gene Grossmanand Kenneth Rogoff eds North-Holland

Hall Robert and Charles Jones 1999 ldquoWhy Do SomeCountries Produce So Much More Output PerWorker Than Othersrdquo Quart J Econ 1141 pp83-116

Hallward-Driemeier Mary Giuseppe Iarossi andKenneth Sokoloff 2002 ldquoExports and ManufacturingProductivity in East Asia A Comparative Analysiswith Firm-Level Datardquo work pap UCLA

Hanson Gordon 2001 ldquoShould Countries PromoteForeign Direct Investmentrdquo G-24 UN Discus PapSeries NY and Geneva

Haskel Jonathan Sonia Pereira and MatthewSlaughter 2001 ldquoDoes Inward Foreign DirectInvestment Boost the Productivity of DomesticFirmsrdquo pap for NBER Summer Institute

Heston Alan Robert Summers and Bettina Aten2002 Penn World Tables Version 61 Center IntComparisons U Penn

Howitt Peter 2000 ldquoEndogenous Growth and Cross-Country Income Differencesrdquo Amer Econ Rev904 pp 829ndash46

Hulten Charles 2000 ldquoTotal Factor Productivity AShort Biographyrdquo NBER work pap 7471

Irwin Douglas and Peter Klenow 1994 ldquoLearningSpillovers in the Semi-Conductor Industryrdquo J PolitEcon 102 pp 1200ndash27

Jaffe Adam and Manuel Trajtenberg 2002 PatentsCitations and Innovations A Window on theKnowledge Economy Cambridge MIT Press

Jaffe Adam Manuel Trajtenberg and RebeccaHenderson 1993 ldquoGeographic Localization ofKnowledge Spillovers as Evidenced by PatentCitationsrdquo Quart J Econ 1083 pp 577ndash98

Jones Charles 1995 ldquoRampD-Based Models ofGrowthrdquo J Polit Econ 1034 pp 759ndash84

Katayama Hajime Shihua Lu and James Tybout 2003ldquoWhy Plant-Level Productivity Studies Are OftenMisleading and an Alternative Approach toInferencerdquo work pap Penn State U

Keller Wolfgang 1996 ldquoAbsorptive Capacity On theCreation and Acquisition of Technology inDevelopmentrdquo J Devel Econ 49 pp 199ndash227

mdashmdashmdash 1997 ldquoHow Trade Patterns and TechnologyFlows Affect Productivity Growthrdquo World BankPolicy Research work pap 1831

mdashmdashmdash 1998 ldquoAre International RampD SpilloversTrade Related Analyzing Spillovers amongRandomly Matched Trade Partnersrdquo Europ EconRev 42 pp 1469ndash81

mdashmdashmdash 2000 ldquoDo Trade Patterns and TechnologyFlows Affect Productivity Growthrdquo World BankEcon Rev 14 pp 17ndash47

mdashmdashmdash 2001 ldquoKnowledge Spillovers at the WorldrsquosTechnology Frontierrdquo CEPR work pap 2815

mdashmdashmdash 2002a ldquoGeographic Localization ofInternational Technology Diffusionrdquo Amer EconRev 92 pp 120ndash42

mdashmdashmdash 2002b ldquoTrade and the Transmission ofTechnologyrdquo J Econ Growth 7 pp 5ndash24

Keller Wolfgang and Stephen Yeaple 2003ldquoMultinational Enterprises International Trade andProductivity Growth Firm Level Evidence from theUnited Statesrdquo IMF work pap 248

Klette Tor and Zvi Griliches 1996 ldquoThe Inconsistencyof Common Scale Estimators When Output PricesAre Unobserved and Endogenousrdquo J ApplEconometrics 11 pp 343ndash61



Krugman Paul 1989 Exchange Rate InstabilityCambridge MA MIT Press

Kugler Maurice 2002 ldquoThe Diffusion of Externalitiesfrom Foreign Direct Investment Theory Ahead ofMeasurementrdquo work pap U Southhampton

Larrain B Felipe Luis Lopez-Calva and AndresRodriguez-Clareacute 2000 ldquoIntel A Case Study ofForeign Direct Investment in Central Americardquowork pap 58 Center Int Develop Harvard U

Leamer Edward and James Levinsohn 1995ldquoInternational Trade theory The Evidencerdquo inHandbook of International Economics vol 3 GGrossman and K Rogoff eds Elsevier Amsterdam

Lichtenberg Frank 1993 ldquoRampD Investment andInternational Productivity Differencesrdquo in EconomicGrowth in the World Economy Symposium 1992Horst Siebert ed Tuumlbingen Mohr

Lichtenberg Frank and Bruno van Pottelsberghe de laPotterie 2000 ldquoDoes Foreign Direct InvestmentTransfer Technology Across Bordersrdquo forthcomingRev Econ Statist

Lucas Robert 1988 ldquoOn the Mechanics of EconomicDevelopmentrdquo J Monet Econ 22 pp 3ndash42

Lumenga-Neso Olivier Marcelo Olarreaga andMaurice Schiff 2001 ldquoOn lsquoIndirectrsquo Trade-RelatedRampD spilloversrdquo mimeo World Bank

Maddala GS and In-Moo Kim 1998 Unit RootsCointegration and Structural Change CambridgeCambridge U Press

Markusen James 2002 Multinational Firms and theTheory of International Trade Cambridge MITPress

Nelson Richard and Edmunds Phelps 1966ldquoInvestment in Humans Technological Diffusionand Economic Growthrdquo Amer Econ Rev 562 pp69ndash75

NSF 2004 ldquoUSndashChina RampD Linkages DirectInvestment and Industrial Alliances in the 1990srdquoNSF Info Brief NSF 04-236

OECD 2002 Frascati Manual 2002 ProposedStandard Practice for Surveys on Research andDevelopment Paris OECD

mdashmdashmdash 2003 Main Science and TechnologyIndicators Paris OECD

Olley Steven and Ariel Pakes 1996 ldquoThe Dynamicsof Productivity in the TelecommunicationsEquipment Industryrdquo Econometrica 646 pp1263ndash97

Pakes Ariel 1986 ldquoPatents as Options SomeEstimates of the Value of Holding European PatentStocksrdquo Econometrica 544 pp 755ndash84

Peri Giovanni 2002 ldquoKnowledge Flows andInnovationrdquo work pap UC Davis

Polanyi Michael 1958 Personal Knowledge Towards aPost-Critical Philosophy Chicago U Chicago Press

Prescott Edward 1998 ldquoNeeded A Theory of TotalFactor Productivityrdquo Int Econ Rev 393 pp 525ndash51

Quah Danny 2001a ldquoTechnology Dissemination andEconomic Growthrdquo work pap London School Econ

mdashmdashmdash 2001b ldquoDemand-Driven Knowledge Clustersin a Weightless Economyrdquo work pap LondonSchool Econ

Rhee Yong-Whee Bruce Ross-Larson and GarryPursell 1984 Korearsquos Competitive Edge ManagingEntry into World Markets Baltimore Johns HopkinsU Press for World Bank

Rivera-Batiz Luis and Paul Romer 1991 ldquoEconomicIntegration and Endogenous Growthrdquo Quart JEcon 1062 pp 531ndash55

Rodriguez-Clareacute Andres 1996 ldquoMultinationalsLinkages and Economic Developmentrdquo AmerEcon Rev 86 pp 852ndash73

Rodrik Dani 1999 ldquoThe New Global Economy andDeveloping Countries Making Openness WorkrdquoPolicy Essay 24 Overseas Develop CouncilWashington DC

Romer Paul 1990 ldquoEndogenous TechnologicalChangerdquo J Polit Econ 985 pp S71ndashS102

Saggi Kamal 2000 ldquoTrade Foreign DirectInvestment and International Technology TransferA Surveyrdquo forthcoming in World Bank ResObserver

Scherer Frederic 1984 ldquoUsing Linked Patent andRampD Data to Measure Interindustry TechnologyFlowsrdquo in RampD Patents and Productivity ZviGriliches ed U Chicago for NBER pp 417ndash61

Segerstrom Paul TCA Anant and Elias Dinopoulos1990 ldquoA Schumpeterian Model of the Product LifeCyclerdquo Amer Econ Rev 804 pp 1077ndash92

Shiue Carol H and Wolfgang Keller 2004 ldquoMarketsin China and Europe on the Eve of the IndustrialRevolutionrdquo CEPR discus pap 4420

Singh Jasjit 2003 ldquoKnowledge Diffusion andMultinational Firms Evidence using Patent CitationDatardquo work pap Grad School Bus Admin andEcon Dept Harvard U

Sjoumlholm Frederic 1996 ldquoInternational Transfer ofKnowledge The Role of International Trade andGeographic Proximityrdquo Weltwirtschaftliches Archiv132 pp 97ndash115

Teece David 1977 ldquoTechnology Transfer byMultinational Firms The Resource Cost ofTransferring Technological Know-Howrdquo Econ J 87pp 242ndash61

von Hippel Eric 1994 ldquolsquoSticky Informationrsquo and theLocus of Problem Solving Implications forInnovationrdquo Manage Sci 404 pp 429ndash39

Xu Bin 2000 ldquoMultinational Enterprises TechnologyDiffusion and Host Country Productivity GrowthrdquoJ Devel Econ 622 pp 477ndash93

Xu Bin and Jianmao Wang 1999 ldquoCapital GoodsTrade and RampD Spillovers in the OECDrdquo Can JEcon 325 pp 1258ndash74

WIPO 2003 World Intellectual PropertyOrganization wwwwipoorg

World Bank 1999 Knowledge for Development WorldDevelopment Report 199899 Washington DCWorld Bank



5 See Grossman and Helpman (1995) and Aghion andHowitt (1998) for broader overviews I will also discusssome related work on learning-by-doing and human-capi-tal accumulation that falls into the broad category of mod-els of knowledge accumulation

6 Many of these ideas have been discussed in the liter-ature before important contributors include Paul DavidGiovanni Dosi Robert Evenson Jan Fagerberg RichardNelson Keith Pavitt Nathan Rosenberg Luc SoeteSidney Winter Larry Westphal and others (see Fagerberg1994 and Robert Evenson and Larry Westphal 1995 foroverviews) What distinguishes the recent work is that itincludes fully specified general equilibrium models Thismeans that these technology effects can in principle beestimated in a well-defined framework

evidence has been complemented by somemicro-econometric findings

Despite the global reach of computer pro-grams there is no indication that a global poolof technology yet exists This localized charac-ter of technology suggests that an importantcomponent of it is tacit in nature Althoughthe relative importance of international tech-nology diffusion appears to be increasingalong with higher levels of economic integra-tion international diffusion of technology isneither inevitable nor automatic Domestictechnology investments are necessary

Before discussing some of the evidence inmore detail (sections 6ndash8) section 2 pres-ents a conceptual framework that will guidethis discussion The following sectionsreview various measures of technology (sec-tion 3) and technology diffusion (section 4)that are available while section 5 discussesthe pros and cons of a number of empiricalapproaches that have been employedSection 9 summarizes the overall evidenceon international technology diffusion andsection 10 contains a synthesis and somesuggestions for future work

2 International Technology Diffusion A Framework for Analysis

Theories of endogenous technical changeof the early 1990s (Aghion and Howitt 1992Grossman and Helpman 1991 Romer 1990and Paul Segerstrom TCA Anant andElias Dinopoulos 1990) emphasize twoaspects of technology5 6

7 Other authors have coined the terms ldquoperfectly expan-siblerdquo (David 1992) and ldquoinfinitely expansiblerdquo (DannyQuah 2001ab) to positively define this characteristic

8 The focus of this work is on technical change drivenby private not public research The majority of allresearch and development (RampD) is privately fundedalthough public RampD is substantial in many countries

9 International technology diffusion in this paperrefers to both technology spillovers as well as non-spillovers As will be discussed below the two are oftendifficult to separate

1 Technology is non-rival in the sense thatthe marginal costs for an additional agentto use the technology are negligible7

2 The return to technological investmentsis partly private and partly public

Point 1 distinguishes technology fromrival factor inputs such as human and physi-cal capital the latter can only be used by onefirm at a time or put differently the margin-al costs of using the same factor somewhereelse are infinite Point 2 highlights that whileprivate returns must be strong enough tokeep innovation ongoing technologicalinvestments often create benefits to individ-uals other than the inventor8 These externaleffects are called technology or knowledgespillovers As an example the introductionof one product might speed up the inventionof a competing product because the secondinventor can learn from the first by carefullystudying the product or its product design(the ldquoblueprintrdquo)

The focus of this paper is the empirics ofinternational technology diffusion and itseffect on productivity 9 Before discussingthe empirical results I will lay out a model ofinternational technology diffusion based onJonathan Eaton and Samuel Kortum (1999)and Kortum (1997) to help clarify the issues

Consider a model with n=1hellipN coun-tries Output in n at time t Ynt is producedby combining intermediate inputs subject toa constant-returns-to-scale Cobb-Douglasproduction function

(1)

where Xnt(j) is the quantity of intermediate

ln ln ( ) ( )Y J J Z j X j djnt nt nt

J

( ) = [ ]minus int1










(2)



˙ ndash ( )

nt ni

Ai

N t sis is is

t

B


minus

minusinfinsum int1

1

1 24444 34444












(3)


(4)


n N= 1

gJ g

s Ln

n

ni

ni

i

ni i

i

N

= =+=

sum˙

εε1

AA

n Nnt

Nt

nt

Nt

=

= minus

1

1 1

θ







































































5 Empirical Methods

51 Case Studies





DTOƒ(XFA)u (5)
































DTOƒ(XMFT)u (6)



zAld1ndash 01 (7)


(8)


d x s dsne

=

minusminus

int ( ) 1

0

11




zA(ne)lk1ndash (9)


ln flnAlnne (10)



(11)





(13)


S m Sc

f

cc cc c

=ne

sum

n n St

e

ct ctc

C

c

C

= ===

sumsum11

fz

l kequiv minus 1









(14)

(15)





ln( )AVC y vj

c

it j j

y

it j itj

J

j

J

+ + +minus minus==

sumsum 11


k

it je

tj

J

= + +minus=

sum 01

y

if X e AVC

F F y

otherwise

it

xit

eit j

c

it jj

J

jit j it

j

J

=

le + +

+ minus +

minus=

minus=

sum

sum

1 0

0

1

0

1

ln( )

( )



















61 Trade












(12rsquo)



fsumcnec





ln ln lnf S Sct cd

ctf

ct

f

ct= + + + ε


























































































(16)



X cit+ prime + ε


c G

cc= + times

minus

primeisinsum

5





















f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES












































































































































(2)



˙ ndash ( )

nt ni

Ai

N t sis is is

t

B


minus

minusinfinsum int1

1

1 24444 34444












(3)


(4)


n N= 1

gJ g

s Ln

n

ni

ni

i

ni i

i

N

= =+=

sum˙

εε1

AA

n Nnt

Nt

nt

Nt

=

= minus

1

1 1

θ







































































5 Empirical Methods

51 Case Studies





DTOƒ(XFA)u (5)
































DTOƒ(XMFT)u (6)



zAld1ndash 01 (7)


(8)


d x s dsne

=

minusminus

int ( ) 1

0

11




zA(ne)lk1ndash (9)


ln flnAlnne (10)



(11)





(13)


S m Sc

f

cc cc c

=ne

sum

n n St

e

ct ctc

C

c

C

= ===

sumsum11

fz

l kequiv minus 1









(14)

(15)





ln( )AVC y vj

c

it j j

y

it j itj

J

j

J

+ + +minus minus==

sumsum 11


k

it je

tj

J

= + +minus=

sum 01

y

if X e AVC

F F y

otherwise

it

xit

eit j

c

it jj

J

jit j it

j

J

=

le + +

+ minus +

minus=

minus=

sum

sum

1 0

0

1

0

1

ln( )

( )



















61 Trade












(12rsquo)



fsumcnec





ln ln lnf S Sct cd

ctf

ct

f

ct= + + + ε


























































































(16)



X cit+ prime + ε


c G

cc= + times

minus

primeisinsum

5





















f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES









































































































































































































5 Empirical Methods

51 Case Studies





DTOƒ(XFA)u (5)
































DTOƒ(XMFT)u (6)



zAld1ndash 01 (7)


(8)


d x s dsne

=

minusminus

int ( ) 1

0

11




zA(ne)lk1ndash (9)


ln flnAlnne (10)



(11)





(13)


S m Sc

f

cc cc c

=ne

sum

n n St

e

ct ctc

C

c

C

= ===

sumsum11

fz

l kequiv minus 1









(14)

(15)





ln( )AVC y vj

c

it j j

y

it j itj

J

j

J

+ + +minus minus==

sumsum 11


k

it je

tj

J

= + +minus=

sum 01

y

if X e AVC

F F y

otherwise

it

xit

eit j

c

it jj

J

jit j it

j

J

=

le + +

+ minus +

minus=

minus=

sum

sum

1 0

0

1

0

1

ln( )

( )



















61 Trade












(12rsquo)



fsumcnec





ln ln lnf S Sct cd

ctf

ct

f

ct= + + + ε


























































































(16)



X cit+ prime + ε


c G

cc= + times

minus

primeisinsum

5





















f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES

















































































































































sp04_Article 2 81604 749 AM 58










5 Empirical Methods

51 Case Studies





DTOƒ(XFA)u (5)
































DTOƒ(XMFT)u (6)



zAld1ndash 01 (7)


(8)


d x s dsne

=

minusminus

int ( ) 1

0

11




zA(ne)lk1ndash (9)


ln flnAlnne (10)



(11)





(13)


S m Sc

f

cc cc c

=ne

sum

n n St

e

ct ctc

C

c

C

= ===

sumsum11

fz

l kequiv minus 1









(14)

(15)





ln( )AVC y vj

c

it j j

y

it j itj

J

j

J

+ + +minus minus==

sumsum 11


k

it je

tj

J

= + +minus=

sum 01

y

if X e AVC

F F y

otherwise

it

xit

eit j

c

it jj

J

jit j it

j

J

=

le + +

+ minus +

minus=

minus=

sum

sum

1 0

0

1

0

1

ln( )

( )



















61 Trade












(12rsquo)



fsumcnec





ln ln lnf S Sct cd

ctf

ct

f

ct= + + + ε


























































































(16)



X cit+ prime + ε


c G

cc= + times

minus

primeisinsum

5





















f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES













































































































































5 Empirical Methods

51 Case Studies





DTOƒ(XFA)u (5)
































DTOƒ(XMFT)u (6)



zAld1ndash 01 (7)


(8)


d x s dsne

=

minusminus

int ( ) 1

0

11




zA(ne)lk1ndash (9)


ln flnAlnne (10)



(11)





(13)


S m Sc

f

cc cc c

=ne

sum

n n St

e

ct ctc

C

c

C

= ===

sumsum11

fz

l kequiv minus 1









(14)

(15)





ln( )AVC y vj

c

it j j

y

it j itj

J

j

J

+ + +minus minus==

sumsum 11


k

it je

tj

J

= + +minus=

sum 01

y

if X e AVC

F F y

otherwise

it

xit

eit j

c

it jj

J

jit j it

j

J

=

le + +

+ minus +

minus=

minus=

sum

sum

1 0

0

1

0

1

ln( )

( )



















61 Trade












(12rsquo)



fsumcnec





ln ln lnf S Sct cd

ctf

ct

f

ct= + + + ε


























































































(16)



X cit+ prime + ε


c G

cc= + times

minus

primeisinsum

5





















f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES

































































































































































DTOƒ(XMFT)u (6)



zAld1ndash 01 (7)


(8)


d x s dsne

=

minusminus

int ( ) 1

0

11




zA(ne)lk1ndash (9)


ln flnAlnne (10)



(11)





(13)


S m Sc

f

cc cc c

=ne

sum

n n St

e

ct ctc

C

c

C

= ===

sumsum11

fz

l kequiv minus 1









(14)

(15)





ln( )AVC y vj

c

it j j

y

it j itj

J

j

J

+ + +minus minus==

sumsum 11


k

it je

tj

J

= + +minus=

sum 01

y

if X e AVC

F F y

otherwise

it

xit

eit j

c

it jj

J

jit j it

j

J

=

le + +

+ minus +

minus=

minus=

sum

sum

1 0

0

1

0

1

ln( )

( )



















61 Trade












(12rsquo)



fsumcnec





ln ln lnf S Sct cd

ctf

ct

f

ct= + + + ε


























































































(16)



X cit+ prime + ε


c G

cc= + times

minus

primeisinsum

5





















f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES











































































































































(14)

(15)





ln( )AVC y vj

c

it j j

y

it j itj

J

j

J

+ + +minus minus==

sumsum 11


k

it je

tj

J

= + +minus=

sum 01

y

if X e AVC

F F y

otherwise

it

xit

eit j

c

it jj

J

jit j it

j

J

=

le + +

+ minus +

minus=

minus=

sum

sum

1 0

0

1

0

1

ln( )

( )



















61 Trade












(12rsquo)



fsumcnec





ln ln lnf S Sct cd

ctf

ct

f

ct= + + + ε


























































































(16)



X cit+ prime + ε


c G

cc= + times

minus

primeisinsum

5





















f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES





















































































































































61 Trade












(12rsquo)



fsumcnec





ln ln lnf S Sct cd

ctf

ct

f

ct= + + + ε


























































































(16)



X cit+ prime + ε


c G

cc= + times

minus

primeisinsum

5





















f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES







































































































































(12rsquo)



fsumcnec





ln ln lnf S Sct cd

ctf

ct

f

ct= + + + ε


























































































(16)



X cit+ prime + ε


c G

cc= + times

minus

primeisinsum

5





















f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES



















































































































































































































(16)



X cit+ prime + ε


c G

cc= + times

minus

primeisinsum

5





















f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES

















































































































































f)

(12rsquorsquo)


ln ln lnf S Sc cd

cf

c

f

c= + + + ε











































REFERENCES









































































































































































REFERENCES




































































































































International Technology Diffusion - spot.colorado.eduspot.colorado.edu/~kellerw/ITD.pdf ·...

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Transcript of International Technology Diffusion - spot.colorado.eduspot.colorado.edu/~kellerw/ITD.pdf ·...