DtI T T -Y r T'Crr A n T T AV7T TV T J TT- nD A inT~ r 1' iQ I JL kJ I- I I Lk E 3 A \1 ) W JXN18IN I AJrE Zl 0 U I ~~1 1 _*r iracle-Kelatect lecnnology L)lttuslon and the Dynamics of North-South qni Soi ith-Soiith Tntegration Maurice Schiff Yanling Wang Marcelo C)larreaga The. World Bank- fr.101 Development Research Group Trade june 2002 F POLICY RESEARCH WORKING PAPER 2861 Abstract This paper examines the impact on total factor * North-South and South-South R&D flows have a productivity of North-South and South-South trade- positive impact on roral factor productivity, thnough the related research and development (R&D) spillovers. It is former is larger. the first, as far as we know, to do so at the industry level * R&D-intensive industries benefit mainly from for developing countries. North-South and South-South North-South R&D flows while low R&D-intensive R&D flows are constructed based on industry-specific industries benefit mainly from South-South R&D flows. R&D in the North, North-South and South-South trade These results have implications for dynamic comparative patterns, and input-output relations in the South. The advantage and for the dynamics of North-South and main findings are: Sonth-South regional integration= This paper-a product of Trade, Development Researcn Group-is part of a larger efforr in rhe group To undersrand the impact of trade on technology diffusion. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Kasilag, room MC3-303, telephone 202-473-9081, fax 202-522-1159, email address mkasilag@worldbank.org. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at mschiff@worldbank.org or molarreaga@worldbank.org. June 2002. (25 pages) The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the 'World Bank, its Executive Direcrors, or the countries they represent. l Produced by the Research Advisory Staff 0 CD SD3 ; -i ~~~~~~~~~~~~ ml~~~~~~~~r 0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- In.l NON-TrFFHNrA1. RU.MMARY R%Vecent LLLIFULqG L%.,al,od1 VI eIcoUr,Lm r1VWUI LaV. lUrLLULl6&LVU UIv HUhjJUiWfl'V' VI trade as a channel of technology diffusion. Empirical studies of North-South trade-related technology diffusion and its impact on total factor productivity (TFP) have been undertaken at the aggregate level. This paper is, as far as we know, the first to examine North-South-as well as South-South--trade-related technology diffusion at the industry level. We find that North-South and South-South R&D spillovers have a positive impact inn T;PP tholugh thp formpr is lanrgr SPeprarting thp sanmnle intn hiah antd low RkTh_ intensity industries, our results indicate ihat Nort-h-So-utih R&D spil;overs raise TFP mainly in the R&D-intensive industries and South-South R&D spillovers raise TFP mainly in the low R&D-intensity industries. Thus, R&D-intensive industries learn mainly from trading with the North and low R&D-intensity industries learn mainly from trading within the South. The findings are consistent with a situation of comparative advantage by the North in R&D-intensive industries- and with the comnarative advantage in the different low .T R&D-ntnsity indtries. in +heo Sou+l *aryngn by f.t rth.. T1.0 rno .-e1+,s have lo XMAT% LULenOJLt 11UUU&LLA10. JUI UA%. UVUUUI V UL.TL 7111 LIJ. %1VU4A% 7 1 1&. .Ut. 11(1 ' implications for dynamic comparative advantage and for the dynamics of FNAs: Norti- South RIAs will tend to favor the development of R&D-intensive industries while South- South RIAs will tend to favor the development of low-R&D-intensity industries and are likely to retard the economic transformation of member countries to a high-R&D economy by reducing technology spillovers from the North. 2 TRADE-RELATED TECHNOLOGY DIFFUSION AND THE DYNAMIcs OF NORTH-SOUTH AND SOUTH-SOUTH iNTEGRATION 1. Introduction TTntil thlp miA_ QRAe nrr,zAth thfnfv aciimptA thlnt Pe-rfnnmf urnwth kanrA tPrhnirn1 change were determin-ed exogenously. According to tuus theory, policy aiiects tue rate at which economies converge to the long-term (steady-state) growth rate but not the long- term rate itself. And the gains from trade that are obtained on the basis of exogenous growth theory are typically small. Growth theory underwent a fundamental change in the mid-1 980s with the development of endogenous growth theory, which originated with the papers of Romer (1986, 1990) and Lucas (1988). These papers posit that the returns to the acrcumulation of knowledge (Romer) and human canital (Lucas) do not diminish at *tu.. a..ea.ate lavei beausen of positivea spillove;e.ecs andi +i,a+ nnlicis can havea a permanent impact on the rate of economic growth.! While Lucas and Romer dealt with closed economies, Grossman and Helpman (1991) explored endogenous growth theory in an open economy setting. The basic idea is that goods embody technological know-how and therefore countries can acquire foreign knowledge through imports.2 Coe and Helpman (1995) provide an empirical implementation of the open economy endogenous growth model. They construct an index of foreign., R&^D as thn e fade eih.tmed sinof nftr2hia pranrtsr' stnrcks nfPRrT They fir.l for a sample of developed countries tlat both domestic and foreign R&D ha-ve a 'An excellent review of the origins of endogenous growth is Romer (1994). 3 ciernfieait nmnpapt on TFPP undA that tIie la.fe: v,raavav ,x tAh th +a " -- Aanr,.e otf .,.~.a.w.. njJ. - -. .a.,-.-.--a -..-_ .a% 5W"fL.L 6 '.w openness of tie economy and with openness towards the larger R&D producing countries. Coe, Helpman and Hoffmaister (1997) examine the same issue for developing countries. They find that developing countries benefit more from foreign R&D spillovers, the more open they are and the more skilled is their labor force. These findings provide support for the hypothesis that trade is an important mechanism through which knowledge and technological progress is tran-smitted across colntrieS=3 Consequently, enluugenuUs grUw U1UUUeL rgellldat la4Lr[ Pailb IUUIL UaUV UI V&UgIIIUUL pVWLU olne. This paper contributes to the literature on trade-related technology spiliovers in several ways. First, it extends the developing country aggregate analysis of Coe et al. (1997) by examining these issues at the industry level. Keller (2002) provides an industry-level analysis for the G-7 and Sweden, but as far as we know, this paper provides the first analysis of trade-related technology spillovers at the industry level for developing countries. Sco,A pe vo uuAio iies A used T cRt&rDrc frnn *hs YP(Tm the iOw .roc,tv"it troAs.- weighted foreign R&D stocks; we refer to these as 'Nortn-Ioreign R&D stocks." in addition, we construct an R&D stock which measures the 'indirect' technology spillovers 2 lntermntinnal diffusion of foreign knowledge can in nrincinle occur through other channels than trade including FDI, licensing, scientific journals, the internet, and other sources of cross-border communication. 3 Keller (1998) constructs indices of foreign R&D with weights not related to trade, and obtains results that are as good or better than those of Coe and Helpman (1995), leading to doubts as to whether trade is in fact a main channel of technology diffusion. Lumenga-Neso, Olarreaga and Schiff (2000) use a trade-weighted index of foreign R&D by incorporating the effect of 'indirect' R&D. Simply put, it implies that countries learn not from the produced R&D stock of their trading partners but from their larger available R&D 4 arising from trade among developing conntri-es. This is re-ferrd tn as the "RSouthforein P1J stoc.k (du la UVis UJ iLV IJr, SueuU 2). T-hird, using industry-level data enables us to examine the impact of sectoral characteristics on intemational technology diffusion and TFP. One characteristic that is examined and which turns out to be important is R&D intensity. The main findings for TFP in the South are: 1. TFP rises with North-foreign R&D (and thus with openness to the North). 2. TFP rises with South-foreign R&D (and thus with onenness to the South)- but 3. Tne elasticity ofI rP witn respect to Norti-foreign R&D is at least twice as large for R&D-intensive industries than for low R&D-intensity industries. 4. The elasticity of TFP with respect to South-foreign R&D is positive for low R&D-intensity industries but is not significantly different from zero for R&D- intensive industries Findings 3 and 4 imply that R&D-intensive industries learn mainly from the North and tow R&D-intensity indflitriez Ienrn mninlv frm the South. Ths rpailto have implicatiors for dynalmic comparairUvU auvztan'ie and tuie dyUnaiucs of regional integration. The remainder of the paper is organized as follows. Section 2 sets forth the empirical implementation, Section 3 describes the data, and Section 4 presents the results. Section 5 concludes. stocks. They obtain significantly better results than Coe and Helpman (1995) and marginally better ones 5 Tne empiricai analysis in Coe and Heipman (1995) Duiids on Grossman and Helpman's (1991) theoretical work on endogenous growth in the open economy. The estimated equation they derive is: logTFPc, =a, +a, +d logRDd, +?f logRDLs, + e;j8d6fJ >O, (1) Z2ee¢(,v ) is a ,o.rt vh-, (t.iij..m. f;.xe ai4fw.ts sP idI ( pnf, ) i$ t Ae o.eti .; ei DQrfl YVI4% " ' * ic ciS b*SLf%j 3 +m- 4%..A M~A%^+ At..ld \ AnA.~ ;a +k . PAL. \rTIflfl stock, E is an error term, and c (t) denotes country (year).4 Due to lack of data for developing countries--and as in Coe et al. (1997)--the equations estimated in this paper do not include domestic R&D. This is unlikely to have a significant impact on our results because most of the world's R&D is performed in developed countries.5 We estimate TFP eauations both for each industry separately and with pooled Ao+o UWn ;imt-hAa +,t2rt tvmie of fi%roinn ownT emAnore Wr%rf1_fr%arvnn VArT ainA Qmitk- foreign R&D. Nortih-foreign R&D in industry 1 ot developing country c, NKL)cI, is defined as: k VAcj r than Keller (1998). 4 The derivation is also provided in Keller (1998). 5 In 1990, 96% of the world's R&D expenditures took place in industrial countries (Coe et al., 1997). Moreover, recent empirical work has shown that much of the technical change in OECD countries is based on the international diffusion of technology among OECD countries (Eaton and Kortum, 1999). For instance, Eaton and Kortum (1999) estimate that 87% of French growth is based on foreign R&D. Since developing countries invest much fewer resources in R&D than OECD countries, foreign R&D must be even more important for developing countries as a source oI growth. 6 where c (k) idexes developing (OECD) coun;es, 3 mndexes du;s M~ (nIA\ /DTh denotes imports (value added) (R&D), and acy is the import input-output coefficient (which measures for country c the share of imports of industry j that is sold to industry i). The first part of equation (2) says that, in developing country c, North-foreign R&D in industry i, NRD¢,, is the sum, over all industries j, of RD,,, the industry-j foreign R&D obtained through imports, multiplied by aCU, the share of imports of industry j that is sold to industry i. Because data on import input-output flows are not available, they are proxied by domestic input-output flows in the estimation. The second part of equation (2) says that RD4, is the sum, over OECD countries k, of Mk IVAd, the inaUpols OJ LUUUSLL-j FLUUSLO IrLJ.uL %fUrUo.. A, jkVL ULL UL oI UiI.UUY-J VaLUe adUeUd (i.e., the bilateral openness share), multiplied by RDjk, the stock of industry-j R&D in OECD country k. Note that this specification enables us to separate imports of intermediate and capital goods from imports of final consumer goods. Equation (2) includes the sales of imports to all the manufacturing sector indus.tries hut nnt sales% of imports for final consumption. in other words, in equation (2), a. < 1. Since we have no data on domestic R&D in developing countries, and since most R&D in developing countries is imported from the develoned countries, we constrnct a m.easure oL iLn-LU c'L SoUtLh-fLorei I%X.LJ. ILLLU conLep.tJLa Uis U Vb I Uon LUVia UIaL developing countries obtain knowledge from the North, absorb and assimilate it, transform it to fit their own needs, and incorporate it into their production process, and 7 R&D, SRDC,, captures this 'indirect' learning effect. Tnat effect is given by Cl ' ' [.VMcgJ- 1n (Vc where Mc>n are industry-j imports by developing country c from developing country n.6 lTt_ 1A *P_ L... 4u A nr _3 opr%)1 nS Clo ..TL:-%94)D WVu soeaMI 1LLv UW ULv lIulpJJOA4 UL PIu aL.LU oLJ vay WILL UUoULLv) NA.U. intensity. T he sixteen industries are clustered into two R&D-intensity groups, withi a large gap between the high and low groups and relatively small differences within each group. We use a dummy variable to capture the differential effects associated with the two R&D-intensity groups. The two groups-and their R&D intensity--are shown in Section 3. Finally, as in Coe et al. (1997), we also include an education variable. The estimated equation is: 1°'TFPcDf =Fo . * (0 1nD\I.- ATDr's +(0 *+ rIDN 1_ CID n l 0 ElE +SA.D. +SB;D; +SB6,D, + , !flBS'iE >,0, (4) . ~ ~c I where E denotes education and DW(DJ)(D1) represents time (countryj (industry) dummies, and DR = 1 (0) for high (low) R&D-intensity industries. Equation (4) was also estimated for each industry individually, in which case industry and R&D dummies are not included. 6 On the concept of 'indirect' trade-related R&D spillovers, see footnote 3. 8 3 Definition of Variables and Data Sources 0J ur sample corsUIstLs UL o L 16LULa"j4VLUL, UIUU0UdLb Lu £i J U2VV1UjJiULg %;UUU;eV over the period 1976-98& ' he data series are briefly described here, with data provided in the appendix for some of the variables.8 The R&D intensity of the 16 industries is based on US data. An industry's R&D intensity was calculated as R&D expenditures divided by the value added of that industry. Figure 1 shows the R&D intensity of the 16 industries. As is immediately apparent, industries are clustered into two groups according to their R&D intensitv. The ave rage ID R&D-intensityr nf thp "hinh" courpn ;a 1 1A 0 h%le talst n+f tlh "Inix," nvomp ;Q I 40/A (me respectuve staiidard deviauuns aru 3.6%7 ana .9%7). lts, tns e ua nimgn ciuster is on average more than 8 times more R&D intensive than the "low" cluster. 9 The R&D flow data are taken from the ANBERD 2000 (OECD) database (DSTI/EAS Division). The database covers 15 OECD countries from 1973 to 1998 at either the two-, three- or four-digit level.'0 From this, we construct R&D flow data for 16 ' The 25 developing countries are: Bangladesh, Bolivia, Chile, Cameroon, Colombia, Cyprus, Ecuador, Egypt Arab Rep., Gutemala, Hong Kong- Chinqa Indonesia, Tndia, Tran Islamic Rep., Jordan, Korea Rep., Kuwait, Mexico, Malawi, Malaysia, Pakistan, Panama, Philippines, Poland, Trinidad and Tobago, Venemela_ 8 The 16 industries consist of two groups of high and low R&D-intensity industries. The ten low R&D- intensity industries are: 31-Food, Beverage & Tobacco; 32-Textiles, Apparel & Leather; 33-Wood Products & Furniture; 34-Paper, Paper Products & Printing; 355/6-Rubber & Plastic Products; 36-Non-Metallic Mineral Products; 371-Iron & Steel; 372-Non-Ferrous Metals; 381-Metal Products; and 39-Other Manufacturing. The six R&D-intensive industries are: 351/2-Chemicals, Drugs & Medicines; 353/4- Petroleum Refineries & Products; 382-Non-Electrical Machiriery, Office & Computing Machinery; 383- Electrical Machinery and Communication Equipment; 384-Transportation Equipment; and 385- Professional Goods. 9 Note that for the "high" group, the average R&D-intensity minus two standard deviations is 3.8%, which is more than the average plus two standard deviations of the "low" group or 3.1%. Assuming a normal distribution, the hypothesis that any of tne industries in the "nignh" R&D intensity cluster belongs to the "low" cluster is rejected at the 1% significance level.. ir 1I.;5 OECD cou,,u-ies cue. Ausrualia, Quiadu, 1JemItURL, FiULaU,U, Uri i, r.a..W[y, u iaud, Itly, Japan, Netherlands, Norway, Spain, Sweden, United Kingdom, and United States. 9 mannfactnring industries at the two- or three-diigt level (ancording to the United Nations I11MML-lUE1iHU SOulumu L-ala.d I-.1'4Cla111sUto (LISI) ReXV1isU11 2). RX&.DJ LJLWa coUVeL aIL intramural business enterprise expenditures. R&D flows mi domestic currency were deflated by respective GDP deflators (with 1990 GDP deflator = 100) and were converted in US dollars using 1990 nominal exchange rates. Cumulative R&D stocks are derived from these R&D flows using the perpetual inventory method with a 10% depreciation rate. The input-outvut matrices for the twentv five developinz countries are derived 4w,so rzTAP (198OO2. 1latj.rnal meimn.ss shares farom ths- w,Jorld B>nit database "Trade and Produciuon i97I6-i998" iNicita ana O;arreaga, 2001). ror eacn country, industry and year, the shares are measured as the ratio of industry imports over value added. Trade data were collected at the 4-digit level and input-output data at the 3-digit level for the period 1976-98, and both were aggregated to 2- and 3-digit levels for consistency with the R&D data (16 industries). Average bilateral openness shares with the North are provided in Table A.2 and with the South in Table A.3. The matrix of bilateral trade shares is of dimension (25*16*23*(25+15))*16. IL rv P I .ndex is c,aLu'cLaLau i lVog as Uhe U1IIifrLe. Ul-,LWen VJULjJUI. andU Ia%LV use, with the inputs weighted by their income shares, i.e., log TFP = logY - a log L - (1-a) log K, with a equal to labor's share. The capital stocks are derived from investment series using the perpetual inventory model with a 5% depreciation rate. The labor share is equal to the wage bill divided by the value of output. TFP data are derived from Nicita and Olarreaga (2001) and are in current dollars. These 10 were deAlated by +te Us GDPD A-I-+-. (1990 = 100)A T'h TFP v&D -- oe &1 industries are provided in Table A. i. The measure of education used is the share of the population aged twenty five and above that completed secondary education. This is taken from Barro-Lee (2000) which provides five-year averages for 1960-2000. These data were annualized by interpolation and are shown in Table A.4.11 This measure was preferred to enrollment variables because we are interested in stock rather than flow variables. The values for 1998 are cshnlm in T2hlp. A ; 4. Estimation Results 4.1. Pooled Data Table 1 reports the estimation results with the pooled data for four alternative specifications. The explanatory variables are foreign R&D, education and governance. North-foreign R&D (NRD) is used in specification (i)-which is equation (4) with R_ = V5 = O - NRD and itq intermtinn with the hiah-R&D intensitv rilimmv m R) are used in specification (ii-i.e., equation (4) with f3s = Ys = 0. NRp and SRD (South- foreign R&D) are used in specification (iii), i.e., equation (4) with YN = YS = 0. Finally, NRD, SRD and their interaction with DR are used in specification (iv). The education 2n.d gavPm at vrhlbe are iiused in the fonir snenifinhtnnq The t- tictipS qre oiven in This seems reasonable since the annual voiatiiity of the share of the population that completed secondary education is very small. 11 parenthesis. We first describe the results dealing with foreign R&D, and then describe those for education and governance. Collumn (i) showq an elasticity of TFP with respect to North-foreign R&D of about V .19, significant at the 10 leve'. Thus, a 10% increase in openness-say, from 20% to 22%, distributed uniformly across all trading partners in the North, raises TFP by 1.9%. Thus, simply opening up the economy to the North leads to a higher TFP and a higher income. In column (ii), we examine whether there is an added effect for R&D-intensive industries. We find that the elasticity of TFP with respect to low R&D-intensity industries is .138 and that it is twiee 'as large (.138 + .141 = .279) for RD-intensive inrus;es, wit1 both S.mCcant at +eL 1,S leve-d A lAiY case . ini openness to the North distributed uniformly across all industries raises TFP of low R&D intensity industries by about 1.4% and TFP in high R&D-intensive industries by almost 2.8%. One interpretation of this result is that the technology gap between the North and the South is larger for R&D-intensive industries, and that--by trading with the North--the South experiences a greater "catch-up" effect in those industries. In column (iii). we add the effect of South-foreign R&D to the vaniables in column (i). The elasticity ts lso positive (.068) a"d -iaifc.ut at +he 5°,S level, but it iS sigrnificantly smaller than the elasticity or .125 with respect to North-foreign R&D.'2 Thus, the South learns more from trading with the North than from trading with the South. 12 In column (iv), the interaction effects with the dummy variable for R&D-intensive industries are included. We find that the elasticity of TFP with respect to North-foreign R&D is positive (.063) for low R&D-intensity industries but not significant, and that the elastici^ty;a nipotiue (t.)73 anti -irnifiant at the 1% lAvA1 fnr PArTh..intPnqivue indi,qtripq interestingly, the opposite holds for South-foreign R&D: the effect is positUive (.084) aid significant for low R&D-intensity industries, and it is positive, small (.01) and not significantly different from zero for R&D-intensive industries. These results indicate that, in developing countries, R&D-intensive industries learn essentially from trading with the North and low R&D-intensity industries learn essentially from trading within the South, with the former about three times as large as thet latter. These results can be exnlained with the theory of comnarative advantage, and a-v-V i1lVaLIV1i0 for uyuuialvi. %c%J1.LpaULIvV adv Gr.Lg aUiL Zvi uLL, %d.uY 1u- f .i integration agreements (RiAs). Given that the North has a comparative advantage in R&D-intensive industries-- and the South has a comparative advantage in low R&D-intensity industries--the South would be expected to absorb more knowledge in the R&D-intensive industries from its trade with the North. Within the South, industries with comparative advantage among low R&D-intensive industries differ by country. For instance, the industry with the higheI. UJ fl,_ a veage) ."- V IUA iS A raxf !es, Appare! "A TLat. ir. Bangladesh, Lndia and Mexico; Wood Products and Furniture in iran and Trinidad and Tobago; Non- Metallic Mineral Products in Jordan; Iron & Steel in Egypt; Non-Ferrous Metals in 12 Tp ting fnr enualitv of coefficients. the F-value obtained is 20.81 while the critical value at i%lis F- - 13 Pakistan; and Metal Products in Colombia, Ecuador, Indonesia and Kuwait. Thus, countries in the South are likely to learn mainly from trading with each other in low R&D-intensive products. JTII.heI &.%FLJAJAI.LA anLP.d LJ'J o% UJre ULLogIyLL AiIU lValL ngIVproces Will affect productivity and the degree of comparative advantage over time. An early model of trade, technical change and dynamic comparative advantage is Krugman (1987). The issue has also been examined by Grossman and Helpman (1990, 1991). Redding (1999) argues that a trade-off exists for developing countries between low-technology sectors with existing comparative advantage and high-technology sectors where a comparative advantage may be acquired as a result of productivity growth based on learning by doing. Tn thiS paper, produ ctiv.ty chnnge is baed on int.ernationnal teo-ihnnIvgy dif.f%sion anu can be aLvucted by irade policy. A umiorm IVLrLN ianif reaucuon raises irr in aii industries, though more so in the R&;D-intensive ones. A North-South RIA increases trade flows among member countries and reduces it with excluded countries. This raises TFP in R&D-intensive industries and reduces it in low R&D-intensity industries, with the former being larger than the latter. On the other hand, a South-South RIA raises TFP in low R&D-intensity industries and lowers it in R&D-intensive industries. Thus, South- South RlAs are likely to retard the transformation of the economy of memher colintrite to a ,Ll-IwcI, X n5i-Lte1 1 er.oJlm bLULLy U' IeUUcLI UnLge L oL4UIUy sp1.LoVVe'rs iLn Luuo sec'u.rs from the North. 3.0, and P=0. 14 Turning to education, its effect on TFP is positive and significant at the 1% level, and the coefficient is robust across the four specifications in Table 1 (about 6.8). The on.-ffripnt imnl,ip th:t if thp ehArp nf thP nnniflAtinn nf nP ,), nnti %ahnrvP thst rnmnleted _v_~~~~~~- - - - -r - - - s - - - r-r -- *__ _1L _ -u1 On/~~~ a mgn-scnoooi ed-ucation increases by I percentage point, iTrr will nrs Dy aboUL 0.070. Education levels for 1998 are shown in Table A.5. The cross-country average education level is 13.3%. Thus, a 1 percentage point increase in education is equal to an average increase of about 7.6%, implying an average elasticity of about .9. 4.2. Estimation Results by Industrv The resuilts of estimating equation (4) are shown in Tables 2 and 3= In Table 2, O A A~ -L re_ -eC'T--I T' - ¶ we assume Ps = 0. Table 2 snOws tat Une effect 0f Nortn-foreign R&D is positive in I- of 16 industries. The effect is significant in 5 of the 6 R&D-intensive industries (83%) and in 4 of the 10 low R&D-intensity industries (40%). The stronger result for R&D- intensive industries confirms the result obtained with pooled data. The effect of education on TFP is positive for all industries and significantly so in 10 of them. Table 3 includes Soiith-foreiorn R&D. Its effect is positive in 10 industries. It is sizntificantlv nositive in 2 oten Th x ~/ C 4C IA I~. o-ut o0 Ao4-irtIILLesl-VV 111ULUuebs k3JJ 70) adII ln oUL VI LV low R&DIL-Lnteltj lIULsUUrUL (50%). This also seems to confirm the result of the pooled regression of a greater effect of South-foreign R&D on TFP for low R&D-intensity industries. 15 Recent uieoretical models of economic growti have higighted the importance oI trade as a channel of technology diffusion. Empirical studies of North-South trade-related technology diffusion and its impact on total factor productivity (TFP) have been undertaken at the aggregate level. This paper is, as far as we know, the first to examine North-South-as well as South-South--trade-related technology diffusion at the industry level. It also examines the impact of education and governance on TFP. We find that North-sonth and Soith-Souith R&T) snillovers hav.ea positive impact on irr, ULUUIL UgLV, LUrLIeI l airgL. SOUVpdL4L11g Ule s4llplV UILU hilU anu iw R&D(X.'- intensity industries, we find that North-South R&D spiliovers raise TFP mainly in the R&D-intensive industries and South-South R&D spillovers raise TFP mainly in the low R&D-intensity industries. Thus, R&D-intensive industries learn mainly from trading with the North and low R&D-intensity industries learn mainly from trading within the South. The findings with respect to R&D are consistent with a situation of comparative advantage by the North in R&D-intensive industries, and with the comparative advantage .n the d;fferent ino ,thJn.tpnsi, iindustAc in tl6oe Rith v,rxAngby hu 'mirntru, Thesep results nave implications ior dynarmic comparatve auvantuge ana for the aynamics oI RIAs: North-South RIAs will tend to favor the development of R&D-intensive industries while South-South RIAs will tend to favor the development of low-R&D-intensity industries and are likely to retard the economic transformation of member countries to a high-R&D economy by reducing technology spillovers from the North. 16 Fiure 1. R&D Int'ensitv Across Industries 0.25 0.2 0.15 C, 0~~~~~~~~~~~~~~~~~~~~~~~~~04 84 1985 1986 1987 1988 1989 1.990 11991 11992 :1993 1994 1995 1996 1997 1998. Bangladestl 3.61 3.5i6 3.58 3.651 3.43 3.08 2.63 2.57 2.56 21.46 2. 44 i!.l8 ;?.81 .2.69 .2.88 2.97 Bolivia 3.210 2.413 2.78 2.:76 2 .........22 1.29 3.02 4.87 0.71 1.24 i!.35 1I.10 0.93 0.93 10.96 0. 98 1.08 1.16 1.01 1.05 1.12 1.14 Chile 4 .........22 3.64 3.06 2.86 2.52 2.50 i!.51 i!.53 ;?55 .2.68 .2.76 2.83 2.80 2.93 2.92 2.91 2.92 2.80 Cameroon 4.80 4.619 4.73 4.95 4.6S4 4.-45 4.20 4147 3.95 6.25 5.24 7.20 7.02 Colomnbia 3.08 2. 92 2.80 2.6#8 2.52 2.,46 2.30 2.03 1.78 i'.78 1.62 .1.58 .1.44 1.38 1.70 1.98 2.06 2.08 2.12 2.27 2.20 Cyprus 3.36 3.50 3. 59 3.88 4.170 4 ..34 4.51 4.51 4.46 4141 4. 54 4.56 4.50 *1.39 4.66 .4.64 4.84 4.85 5.04 5.86 5.02 4.84 4.90 Ecuador 4.19 4.45 4.91 J.57 3.66 3.62 3. 1 7 i!87 ?.34 ;?53 .2.77 .Z.51 3.05 2.93 1.02 1.10 0.88 0.47 Egypt 5 34 16.12 5.01 6.88 7.95 5:.61 5. 15 3. 75 3. 73 Guatemala 2. 92 3.06 2.92 2.95 2.93 2.32 i1.90 i!.21 Hong Kong, 4.81 4.75 4.44 5.25 5.40 5il19 5.00 4.95 *1.86 4.95 4.91 4.52 4.92 5.02 4.81 4.88 4.85 4.88 Indoniesia 2.25 2.09 1.85 1.55 1.38 1.19 0l.91 0.95 0.85 0.32 09.41 0.56 0.64 0.66 1.37 1.28 0.36 India 4. 14 3.6S4 3.82 3.38 3.42 3.30 3.41 3.30 i!.93 ;?.68 2.50 .2.19 .2.31 1.8; 1.80 1.84 1.83 1.89 1.92 Iran 6.35 6.00 5.90 5.63 5.12 5.36 41.62 4.27 4.01 Jordain 2 30 2.28 244 2.90 3.04 2.55 i!.34 i!.03 1.38 0.56 1.02 1.22 1.21 1.23 1.56 2.42 1.78 Korea, Rep). 3.01l 3.G11 3.09 3.2!6 2.!70 2.65 2.60 2.45 2.48 2.44 2'.33 i!.51 i!.74 31.02 2.74 .2.52 2.45 2.47 2.38 2.37 2.53 2.19 Kuwiait 4 34 5.07 5. 18 5.78 '5.63 4'.94 4.92 4f.85 *1.55 .5.13 4.73 4.31 4.00 3.84 3.76 4.07 3.49 Mexico 3.6f7 3.32 2..09 2.89 2.72 2.36 i!.07 i!.24 ;?.41 .2.54 42. 72 2.96 2.97 2.82 1.96 2.10 2.31 2.35 Malawi 4. 13 4.025 4.47 4.07 3.07 2.84 3.13 2.86 i!38 LB76 ;?58 .3.34 .2.83 1.69 Malaysia 3.46 3.2!5 3.;?9 3 .......34 3.45 3.20 3.20 3.19 2.94 i!.85 i!.60 ;?.34 42.32 .2.24 2.34 2.32 2.30 2.43 2.34 2.33 1.99 Pakistan 2.19 2.09 1.97 1.99 2.36 i!.35 i!.l16 ;?08 .2.13 .2.18 2.13 Panama 3'.71 3.48 4.70 4.13 4.54 4.76 Philippines; 3. 76 3.32 2.5i5 2.41 2.til 3.11 2.14 1.75 2.51 2'.72 i!32 !.4 ;?.48 2.40 .2.32 ~2.43 1.96 2.14 2.08 2.14 2.06 IPolnand 2.00 4. 13 1.51 1.82 1.63 1.42 1.34 0.95 1.12 1.02 0.57 1.72 2.16 2.23 2.39 2.67 2.84 2.96 3.07 Trinidad 5.40 5.61 5..64 6.26 5.97 5.40 5. 18 6i.09 31.58 .3.61 .3.51 4.00 2.92 3.09 2.50 Venezuela 4 36 4.13 4.10 3.29 3.46 3. 35 i!.68 2. 74 ;?.1 IO2.03 .2.03 2.01 2.26 1.47 1.63 0.87 Figures are avierages for eatch year of tlie available industries in each icountry. 22 Table A.2: 3ilateral Opemnness Shares witht the North', by InLdustry (average over OECD countries and lime) Country\ISIC 31 32 3.3 34 351)2 353/4 355/6i 316 371 372 381 38;!2 383 384 385 39 Bangladesh 0.29 0.09 0. 12 0.40 0.59 1.04 2.06 0.26 1.77 29.69 2.66 10.72 1.85 3.68 157.95 0.13 Boliivia 0.14 0.21 0.13 0.79 2.2.3 5.46 0.93 0.12 25.26 0.08 2.36 123.72 21.48 63.02 13.48 4.16 Chile 0.03 0.16 0.04 0.09 0.56 0.08 0.36 0.11 0.25 0.01 0.48 4.77 2.89 2.87 9.31 2.00 CaDmerooin 0.18 0.32 0.11 1.88 2.92 0.00 0.63 0.88 0.96 0.24 3.63 3.82 7.84 86.9.2 0.00 0.40 COIlDmbiai 0.05 0.06 0.09 0.27 0.67 0.18 0.14 0.07 0.78 0.72 0.39 4.80 1.66 1.82 2.37 0.18 CyFprus 1.08 0.82 0.27 1.35 4.9.3 5.63 1.38 0.57 0.00 0.00 1.44 7.84 13.94 28.56 78.98 1.63 Ecuador 0.09 0.16 0. 05 0.64 2.0.3 0.28 0.35 0.17 2.07 1.88 0.73 52.14 3.24 7.08 17.11 1.47 Egypt 0.60 0.08 3.03 0.59 0.79 0k.20 0.66 0.19 2.95 0.35 0.90 3.910 1.32 1.44' 7.10 1.83 Guaitemalla 0.07 0.11 0.09 0.22 0.54 1.90 0.10 0.05 0.72 3.59 0.44 7.5.5 0.94 6.02' 4.99 0.35 Horig Konmg 2. i62 2.80 8.71 0.95 10.49 1.31 8.30 5.75 21.91 12.10 2.39 7.30 8.02 7.491 7.85 9.95 Indonesia 0.08 0.16 0.011 10.83 1.465 4 .21 0.16 0.28 0.27 0.52 1.25 5.5.1 3.17 1.98' 5.69 0.67 Indlia 0.11 0.03 0.015 0.28 0.35 6.07 0.07 0.05 0.27 0.58 0.69 0.69 0.31 0.27' 1.42 1.32 Irank 0.16 0.04 0.066 i0.04 0.47 1.73 0.14 0.02 0.56 0.26 0.23 1.02 0.52 0.72 2.32 0.12 Jorian 0.57 0.84 0.79 0.83 1.39) 008 1.33 0.15 1.90 0.94 1.64 17.43 19.30 164.28 12.05 5.90 Korea,Rep. 0.7 0.20 0.09 0.28 0.85 0.20 0.07 0.13 0.49 1.04 0.71 1.883 0.67 0.54 2.07 0.25 Kuvait 1.37 1.60 1.12 10.74 1.79 0.02 1.37 0.47 11.60 0.00 1.45 16.20 8.01 52.86 5.85 2.48 Mexcico 0.25 1.68 2.86 1.33 0.81r 4' 00 1.63 0.20 0.49 0.61 1.75 7.L?2 3.99 0.93 9.01 2.85 Malawi 0.12 0.27 0.07 0.37 1.10 0!00 0.40 0.44 0.00 0.00 0.80 2.51 4.91 11.12 0.00 0.00 Malaysia 0.40 0.38 0.04 0.77 1.74 0.20 0.18 0.33 3.28 4.33 1.50 7.90 2.22 4.65 5.48 1.21 Pakistan 0.11 0.03 0.06 0.43 0.57 0.20 0.26 0.05 0.39 21.84 1.54 3.85 0.99 2.26 4.55 0.27 Panama 0.16 0.49 0.58 0.83 1.56i 0.96 0.56 0.15 1.05 1.50 1.52 27.50 2.83 3.63 1.07 0.00 Philippines 0.5 0.17 0.05 0.56 1.00 011 0.30 0.20 1.50 2. 14 1.42 10.76 1.76 2.88 8.66 0.50 Poland 0.8 0.27 0.06 0.46 0.58 0.07 0.31 0.15 0.20 0.36 0.25 0.57 0.49 0.31 1.01 0.45 TriWidad ,& T. 0.59 1.29 1.00 0.96 2.80 o.00 1.76 0.51 0.28 0.0 .3.26 138.43 7.55 8.40 0.00 0.47 Venezuelah 0 0.15 0.08 0.31 0.53 0.03 0.15 0.09 0.27 0.16 0.43 4.59 1.57 1.38 3.02 0.39 Share of total imzportsL from the North over value adided. 23 Table A.3: B3ilatieral 'Openiness Shares with the Soulhl, by Industry (average over OECD countries anlid time) Couintry\lSIC 31 32 33 34 351/2 353/4 355/6 36 371 372 3811 382 383 384 385 39 Bangladesh 0.15 0.21 0.07 0.13 0.19 1.34 1.67 1.36 0.41 :30.93 0.55 2.68 0.44 0.71 16.80 0.39 Boliivia 0.05 0.22 0.04 0.41 0.71 3.28 0.77 0.05 5.11 0.07 0.61 8.78 3.56 6.54 1.14 2.09 Chile 0.01 0.14 0.01 0.01 0.11 0.06 0.05 0.02 0.07 0.00 0.06 0.16 0.77 0.159 0.66 0.38 Carneroou 0.01 iD.10 0.00 0.02 0.04 0.00 0.03 0.07 0.02 0.00 0.10 0.02 0.25 3.23 0.00 0.04 Colombia 0.04 0.03 0.05 0.06 0.14 0.42 0.05 0.02 0.30 1.50 0.08 0.25 0.19 0.40 0.16 0.06 Cyprus 0.05 'D.33 0.02 0.03 0.12 0.17 0.16 0.01 0.00 0.00 0.05 0.23 1.51 0.50 8.96 0.25 Ecuador 0.06 '0.10 0.02 028 0.67 0.20 0.19 0.13 1.34 1.08 0.19 3.53 0.72 1.21 3.37 0.63 Egypt 0.10 D.04 0.86 0.06 0.05 0.06 0.18 0.01 0.41 0.03 0.06 0.14 0.18 0.11 0.78 1.011 Guatemala 0.01 'D.05 0.01, 0.,03 0.12 0.29 0.03 0.02 0.22 2.32 0.12 0.57 0.14 0.62 0.54 0.14 Hoiig Koing 0.56 1.00 4.21 0.23 3.26 0.46 0.80 0.95 5.28 3.84 0.33 0.95 2.07 0.151 0.97 2.01 Indonesiai 0.08 0.13 0.00 0.06 0.22 2.37 0.04 0.06 0.09 0.23 0.13 0.51 0.26 0.04 0.23 0.29 India 0.14 0.02 0.04 0.03 0.09 0.14 0.01 0.02 0.04 0.23 0.0:2 0.04 0.04 0.01 0.09 0.22 Iran 0.01 0.06 0.00 0.00 0.03 0.16 0.01 0.00 0.02 0.04 0.04 0.06 0.02 0.06 0.07 0.02 Jordan 0.22 '0.75 0.40 0.13 0.18 0.08 0.41 0.02 0.30 0.59 0.25 0.81 2.06 7.90 1.09 1.2;2 Korea, Rep. 0.03 0.02 0.14 0.02 0.02 0.07 0.00 0.00 0.0 1 0.24 0.00 0.01 0.03 0.09 0.03 0.0;2 Kuwait 0.32 1.16 0.58 0.12 0.11 0.00 0.35 0.09 1.45 0.00 0.18 0.36 0.84 3.16 0.46 1.12! Mexico 0.02 'D.23 0.19 0.04 0.02 0.29 0.06 0.01 0.03 0.08 0.03 0.12 0.24 0.02 0.31 0.231 Mallawi 0.02 'D.32 0.01 0.00 0.05 0.00 0.14 0.09 0.D00 0.00 0.11 0.05 0.13 0.22 0.00 0.00 Mallaysia 0.]12 'D.46 0.03 0.14 0.20 0.12 (1.04 0.05 0.61 0.83 0.22 0.51 0.31 0.21 0.50 0.42! Pakistan 0.17 0.02 0.15 0.07 0.11 1.63 0.12 0.02 0.102 4.15 0.1:3 0.15 0.18 0.05 0.71 0.1'1 Panama 0.05 0.41 0.35 0.16 0.73 0.52 0.33 0.11 1.71 0.82 0.6:3 2.34 0.64 1.1)4 0.10 0.00 Philippines 0.03 0.25 0.08 0.08 0.23 0.17 0.11 0.05 0.35 0.37 0.24 0.81 0.23 0.110 0.76 0.25 Poland 0.02 'D.03 0.00 0.01 0.01 0.00 0.01 0.00 0.00 0.01 0.00 0.02 0.05 0.03 0.03 0.06 Trinidad & T. 0.02 0.42 0.05 0.12 0.10 0.00 0.24 0.17 0.05 0.00 0.15 4.03 0.29 0.54 0.00 0.07 Venezuela 0.02 0.22 0.06 0.08 0.08 0.00 0.04 0.02 0.04 0.03 0.04 0.16 0.19 0.06 0.30 0.16 'Sh,are of'total imports fromi the South over vailue added. 24 Table A.4: Secondary School Completion Ratio of the Population Aged 25+ by Country and by Year' Country\Wear 1976 19771978197919801981 19821983 19B41983519136198171918 19E89199101991 1992199.31994199!i51991i19971998i Bangladesh 0.03 0.03 0.04 0.04 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 Bolivia 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.06, 0.06 0.06 Chiile 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.13 0.13 0.14 0.14 0.14 0.15 0.15 0. 15 0.15 0.15 0.15 0.15 Cameroon 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 Colombia 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.08 0.08 0.08 0.083 0.08 0.081 0.08 0.08 0.08 0.08 Cyprus 0.13 0.13 0.13 0.13 0.13 0.14 0.15 0.16 0.16 0.117 0.17 0.17 0.17 0.17 0.17 0.17 0.18 0.1 0.18 0.19 0.19 0.20 0.20 Ecnador 0.06 0.07 0.07 0.07 0.08 0.07 0.07 0.07 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.07 0.07 0.07 0.07 0.08 0.08 0.08 Egypt 0.03 0.03 0.04 0.04 0.04 0.05 0.06 0.06 0.07 0.07 0.07 0.08 0.08 0.08 0.08 0.09 0.09 0.09 0.10 0.10 0.10 0.11 0.12 Guatemala 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.012 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 HongKong 0.16 0.16 0.17 0.17 0.17 0.18 0.19 0.20 0.20 0.21 0.22 0.23 0.24 0.25 0.26 0.26 0.27 0.28 0.28 0.29 0.29 0.29 0.29 Indonesia 0.03 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.06 0.06 0.07 0.018 0.08 0.09 0.09 0.09 0.10 0.10 0.10 0.10 0.11 0.11 India 0.03 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.0'5 0.05 0.05 0.05 0.065 0.06 0.06 0.06i 0.06 0.06 0.06 Iran 0.05 0.05 0.05 0.06 0.06 0.07 0.07 0.08 0.08 0.09 0.09 0.10 0.10 0.11 0.11 0.11 0.12 0.1!2 0.12 0.12 0.12 0.13 0.13 Jordan 0.07 0.07 0.06 0.06 0.06 0.06 0.07 0.07 0.07 0.07 0.08 0.09 0.09 0.10 0.10 0.11 0.12 0.11 0.13 0.14 0.15 0.16 0.16 Korea, Rep. 0.15 0.16 0.17 0.18 0.19 0.20 0.22 0.23 0.24 0.26 0.28 0.30 0.32 0.33 0.35 0.35 0.3(5 0.36 0.3fi 0.36 0.36 0.35 0.35 Kuwait 0.11 0.13 0.13 0.14 0.15 0.16 0.17 0.18 0.118 0.19 0.20 0.20 0.21 0.21 0.22 0.22 0.2:i 0.24 0.24 0.25 0.25 0.25 0.25 Mexico 0.04 0.04 0.05 0.05 0.05 0.05 0.06 0.06 0.06 0.06 0.07 0.08 0.09 0.10 0.11 0.11 0.11 0.12 0.1 2 0.12 0.12 0.13 0.13 Malawi 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.011 0.01 0.01 0.01 0.01 0.01 Malaysia 0.07 0.08 0.09 0.09 0.10 0.10 0.10 0.11 0.11 0.11 0.12 0.13 0.13 0.13 0.14 0.16 0.19 0.20 0.22 0.24 0.24 0.24 0.24 Pakistan 0.03 0.03 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.05 0.06 0.07 0.07 0.08 0.08 0.08 0.018 0.08 0.08 0.08 0.08 0.08 Panama 0.10 0.10 0.11 0.11 0.12 0.12 0.12 0.12 0.112 0.113 0.14 0.14 0.15 0.16 0.16 0.115 0.115 0.16 0.16 0.16 0.16 0.16 0.16 Philippines 0.10 0.10 0.1 0.11 0.11 0.11 0.11 0.11 0.111 O 0.11 0.12 0.13 0.14 0.14 0.15 0.15 0.16 0.16 0.16 0.16 0.16 0.17 0.17 Poland 0.11 0.12 0.12 0.13 0.13 0.14 0.14 0.14 0.114 0.15 0.16 0.17 0.17 0.18 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 Trinidad 0.04 0.04 0.04 0.04 0.04 0.05 0.05 0.06 0.06 0.07 0.07 0.018 0.09 0.09 0.10 0.14) 0.111 0.1I1 0.11 0.11 0.12 0.12 0.12 Venezuela 0.06 0.07 0.08 0.08 0.09 0.09 0.09 0.09 0.09 0.09 0.08 0.07 0.06 0.06 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.04 1. 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