Productivity convergence in OECD manufacturing industries

Transcription

1 Eonomis Letters 66 (000) lote/ eonse Produtivity onvergene in OECD mnufturing industries,,,,d M.A. Crree *, L. Klomp, A.R. Thurik Fulty of Eonomis, Centre for Advned Smll Business Eonomis, Ersmus University Rotterdm, P.O. Box 1738, 3000 DR Rotterdm, The Netherlnds Fulty of Eonomis nd Business Administrtion, Mstriht University, Mstriht, The Netherlnds Sttistis Netherlnds, Voorurg, The Netherlnds d EIM Smll Business Reserh nd Consultny, Zoetermeer, The Netherlnds Reeived 8 Septemer 1998; epted 9 Septemer 1999 Astrt The extent of - nd s-onvergene of verge lor produtivity ross mnufturing industries in 18 OECD ountries over the period shows lrge inter-industry differenes. One reson for these differenes is knowledge nd pitl rriers preventing the ourrene of th-up. We find the level of verge lor produtivity, s proxy for these rriers, is orrelted with the extent of onvergene. 000 Elsevier Siene S.A. All rights reserved. Keywords: Convergene; Lor produtivity; Mnufturing JEL lssifition: J4; L60 1. Introdution The extent to whih eonomies onverge hs reeived undnt ttention y eonomists nd politiins. Reserh hs onentrted on the question of onvergene of GDP per pit ut muh less so on the question of onvergene of lor produtivity t the disggregted level of industries. The mehnisms ehind th-up nd onvergene of GDP per pit n never e estlished unless the developments of its lower level of ggregtion equivlent of lor produtivity re well understood. The urrent nlysis ims t estimting the extent of onvergene in lor produtivity t the industry level nd relting it to simple mesure of physil pitl nd knowledge rriers, viz. the verge level of lor produtivity. Industries with high rriers my lso e expeted to e industries with reltively low degrees of onvergene. *Corresponding uthor. Tel.: ; fx: E-mil ddress: m.rree@mw.unims.nl (M.A. Crree) / 00/ $ see front mtter 000 Elsevier Siene S.A. All rights reserved. PII: S (99)008-1

2 338 M.A. Crree et l. / Eonomis Letters 66 (000) Reently, Bernrd nd Jones hve pulished series of ppers on produtivity onvergene t the setor level (Bernrd nd Jones, 1996 ). They lim tht -onvergene t the mro level of GDP per pit hs not een used y produtivity onvergene in the mnufturing setor ut insted y onvergene in the servie setor (see lso Gouyette nd Perelmn, 1997). Arguments for th-up resulting from tehnology trnsfer in mnufturing etween ountries nd/ or gloliztion of the mnufturing setor therefore seem not to hve empiril support. However, the lk of onvergene found within the mnufturing setor does not revel the spred of the extent of onvergene ross mnufturing industries. Differenes in onvergene rtes ross industries help to understnd why produtivity gps etween ountries exist nd (dis)pper. Dollr nd Wolff (1993) py some ttention to onvergene t the industry level in their Chp. 3. The urrent pper is more forml pproh to this line of reserh. The onvergene dete hs een inresingly shifting into dete on eonometri tehniques with lims tht the rtes of onvergene hve een overestimted (Lihtenerg, 1994) or underestimted (Islm, 1995; Lee et l., 1998). A first hoie reserher is onfronted with is whether to onsider -onvergene or s-onvergene. -onvergene implies tht less developed ountries or industries perform etter (th up) on verge when ompred to more developed ountries or industries. The effet of GDP per pit or produtivity in the first period on its reltive hnge in the onseutive period should therefore e negtive. The ide ehind s-onvergene is tht the vrine of (log) GDP per pit or produtivity dereses s prodution tehniques eome more similr. It n esily e shown tht s-onvergene is suffiient ondition for -onvergene, ut not the other wy round (Lihtenerg, 1994; Quh, 1993). In this pper we onsider oth -onvergene 1 nd s-onvergene nd ompre the results when either of these two mesures is hosen. The reminder of this pper is orgnized s follows. In Setion we disuss simple model of endogenous tehnologil progress to explin differenes in speed of onvergene ross industries. In Setion 3 the two mesures of onvergene re introdued. Next, the mesurement of verge produtivity level t the industry level is disussed. In Setion 4 the results re presented for the 8 mnufturing industries in the OECD ountries. The mesures of onvergene re ompred nd relted to the initil level of lor produtivity. The lst setion is used for onluding remrks nd questions for future reserh.. Convergene of produtivity In this setion model of endogenous tehnologil progress is presented. Tehnologil progress is ssumed to e funtion of onsious tion y gents who my e involved in prodution work, R&D nd lerning from other ountries tehnologil led. Assume tht Aijt is mesure for (lor) produtivity in industry i, ountry j nd time period t. The ojetive funtion is weighted omintion of produtivity in this period nd in the next period. Employees in the industry my e engged either in produing or in improvement of tehnology. Tehnology n e improved y trnsferring tehnologies from the ountry with the tehnologil led, or y independently undertking R&D. The extent to whih imittion improves tehnology is 1 Le Pen (1997) provides n overview of the theoretil nd empiril literture on the interntionl onvergene of per pit inome distriution. See lso Bumol et l. (1989); Dollr nd Wolff (1993); Bumol et l. (1994).

3 M.A. Crree et l. / Eonomis Letters 66 (000) mx mx ijt it it ssumed proportionl to ln(a /A ) where A is the mximum of the produtivities ross the ountries in industry i. The lrger the distne to the most produtive ountry the more opportunities to lern in se pitl nd knowledge rriers re not too high. The extent to whih undertking R&D improves tehnology is ssumed to e proportionl to ln(a ijt). Countries lgging in their tehnology performne re less likely to produe innovtions thn highly produtive industries using modern tehnologies. This ide is relted to the onept of sorptive pity (Cohen nd Levinthl, 1989). The ojetive funtion is equl to: n 1n ij,t11 mx ha (1 )(1 )j A with 0, n, 1 (1) ijt mx sujet to A 5 A (1 1 u ln(a ))(1 u ln(a /A )) () ij,t11 ijt 1 ijt ijt it where is the shre of time spent on undertking R&D nd is the shre of time (R&D effort exluded) spent on imittion efforts. The growth of lor produtivity my e inresed y inresing the prmeters nd. However, this goes t the expense of produtivity in the urrent period. The optiml vlues for these two prmeters re: n n 5 1 n ]]] nd 5 1 n 1 ]]]]] mx (3) u ln(a ) u ln(a /A ) 1 ijt ijt it The optiml growth rte of lor produtivity, ln Aij,t11 ln A ijt, n e derived from Eq. () nd mx equls ln(1 n) 1 ln(1 1u1 ln(a ijt)) 1 ln(1 u ln(a ijt /A it )). Using the ft tht ln(1 1 x) x mx when x is lose to zero, the optiml growth rte n e pproximted y n 1u ln Ait 1 (u 1 u )lna ijt. As in Romer (1990) the optiml growth rte is positively ffeted y the reserh suess oeffiient nd negtively y the disount rte. The model predits onvergene in se the tehnology trnsfer suess oeffiient u is lrger thn the reserh suess oeffiient u 1. Tht is, industries fed with high knowledge rriers nd/ or with sorptive pity eing n importnt determinnt of suessful R&D efforts will tend not to hve high th-up rte. The empiril question is whether opportunities for tehnology diffusion for speifi industry re lrge enough to hve produtivities ross ountries onverging over time. An importnt soure of lk of tehnology diffusion is the prevlene of knowledge rriers, whih is relted to the omplexity of the prodution tehnology. High omplexity of the tehnology is ound to led to high knowledge rriers nd to the importne of eing le to ssimilte nd exploit informtion, i.e. hving high sorptive pity. Hene, the differene etween u1 nd u is rising funtion in the omplexity of the tehnology. 3. Testing for onvergene t the industry level There re two min pprohes to test for onvergene. The first is to mesure -onvergene nd the seond is to mesure s-onvergene. In this setion we disuss these two mesures. This is followed y short disussion of the mesurement of produtivity levels. We end the setion with rief review of the dt set of 8 mnufturing industries. We denote the logrithm of the produtivity in industry i, ountry j nd period t, yy. Denote y ijt

4 340 M.A. Crree et l. / Eonomis Letters 66 (000) ŝit the stndrd devition of yijt ross the ountries in yer t. The mesure of -onvergene is then derived from the lest-squres regression of yijt on onstnt nd yij1 where 1 is the first period of investigtion nd T the lst period. For eh industry i we hve the following regression eqution: y 5 1 (1 )y 1 ijt i i ij1 ij (4) The estimte of i indites the rte of -onvergene, with 0 implying no onvergene nd 1 implying omplete onvergene. Whether the rte of onvergene is signifintly different from zero is derived from the t-vlue of the estimted i. It is ler tht in the urrent study i is equl to uu1 for industry i. A lrger vlue of u u1 orresponds to less omplex prodution tehnology nd, hene, more possiilities for th-up. Some uthors hve ritiized the use of the -onvergene riterion s it my indite onvergene when in ft there is regression to the men (Friedmn, 199). A solution to this prolem is to onsider the development of the stndrd devition of yijt over time. The rte of s-onvergene n e mesured y the hnge in the vlue of the stndrd devition from period 1 to period T, i.e. ˆ sit ˆ s i1. The extent to whih this onvergene mesure is signifintly different from zero is mesured y test sttisti reently introdued y Crree nd Klomp (1997). Their T3-sttisti hs stndrd norml distriution under the null hypothesis of no s-onvergene nd is omputed s follows: ˆ ] s i1 /s it 1 Œ 1T ]]]]] ˆ 1 (1 i) S 5 N œ ˆ where N is the numer of ountries. Our mesure for lor produtivity is vlue dded per employee. We deflte industry vlue dded y the purhsing power prity (US51) nd divide it y the numer of employees in the industry. The use of generl prie index insted of n industry-speifi prie index is lrgely onsequene of lk of prie dt for vlue dded ross the ountries in our dt set. Additionlly, the qulity nd omposition of the industry produt pkge hnges quite signifintly over two-dede period, whih omplites the use of prie dt. Our dt set is the OECD STAN Dtse (OECD, 1997). It provides us with dt on 8 mnufturing industries over the period t the three-digit ISIC level. The ountries for whih dt re ville re Austrli, Austri, Belgium, Cnd, Denmrk, Finlnd, Frne, (West) Germny, Greee, Itly, Jpn, the Netherlnds, New Zelnd, Norwy, Portugl, Sweden, the United Kingdom nd the United Sttes. It should e stressed tht some of the dt points in the OECD STAN Dtse re estimted y the OECD Seretrit to hieve interntionl omprility t detiled industry level. Tle 1 shows some key figures on the dt set of 8 mnufturing industries retrieved from the OECD STAN Dtse. Dt re ville for 18 ountries with the exeption of petroleum nd ol produts (ISIC 354) for whih dt re ville for 16 ountries nd other mnufturing (ISIC (5) See OECD (1996) for olletion of ppers on the mesurement of prie indies. For exmple, it hs een reported y severl uthors (e.g. Hooper, 1996) tht pries in the Jpnese food industry re muh higher thn the interntionl verge. Gersh nd Bily (1996) find tht, when using n industry PPP, the rel vlue dded per hour is muh lower in this industry in Jpn. Conversions of industry-level inditors to ommon urreny sed on PPP s should therefore e interpreted with ution (OECD, 1997). We note here tht leving out Jpn from the nlysis for the food industry rely ffeted the results for tht speifi industry. Clerly, this is n inomplete test.

5 M.A. Crree et l. / Eonomis Letters 66 (000) Tle 1 Summry sttistis of mnufturing industries ISIC Industry Employment Produtivity (199) m 1 3 Totl mnufturing / Food Beverges Too Textiles Wering pprel Lether nd produts Footwer Wood produts Furniture nd fixtures Pper nd produts Printing nd pulishing Industril hemils Other hemils Petroleum refineries Petroleum nd ol produts Ruer produts Plsti produts, n.e Pottery, hin, et Glss nd produts Non-metlli produts, n.e Iron nd steel Non-ferrous metls Metl produts Non-eletril mhinery Eletril mhinery Trnsport equipment Professionl goods Other mnufturing Employment is the verge employment (in 1000 numer engged) in the industries ross the ountries. Produtivity (199) is the vlue dded (in $1000) per employee in 199. m1 is the logrithm of the vlue dded (in $1000) per employee in 197 (Yer 1). 39) for whih they re ville for 17 ountries. The first row of the tle shows the dt for the totl mnufturing setor. The third olumn of Tle 1 shows the verge employment in 199 (in 1000 persons) per industry ross the ountries. The verge employment in the mnufturing setor per ountry is out 3.7 million persons. Industries with mny employees on verge inlude non-eletril mhinery (ISIC 38), eletril mhinery (ISIC 383), trnsport equipment (ISIC 384), food (ISIC 311/ ) nd metl produts (ISIC 381). These five industries provide on verge out hlf of totl mnufturing employment. The fourth olumn shows the verge vlue dded per employee (in $1000), i.e. our mesure of lor produtivity, lso in 199. The lst olumn of Tle 1 shows the logrithm of the lor produtivity in 197. This is highly orrelted (orreltion oeffiient of 0.98) with the sme vrile in 199. The verge lor produtivity in mnufturing in 199 ws $41 00 per employee ross the ountries. The verge lor produtivity of mny individul industries is quite lose to this verge, suh s of the five lrge industries disussed ove. Two industries hve n verge produtivity tht

6 34 M.A. Crree et l. / Eonomis Letters 66 (000) is fr higher thn the other industries. These re too (ISIC 314) nd petroleum refineries (ISIC 353). These two industries re pitl-intensive nd provide only frtion of totl mnufturing employment. The too industry is somewht speil se s it is onfronted with reltively high txes resulting in higher vlue of sles. The orreltions omputed in Setion 4 re sed upon eh of the industries in Tle 1 with the exeption of totl mnufturing (ISIC 3) nd other mnufturing (ISIC 39). However, we will lso disuss results when leving out the two industries with the lowest verge employment, viz. too (ISIC 314) nd petroleum nd ol produts (ISIC 354). The first industry is ffeted y high sles txes while the seond industry is not oserved in two of the 18 ountries. 4. Results for 8 OECD mnufturing industries The onvergene estimtes for lor produtivity re presented in Tle. In the seond olumn of the tle the estimte of i is presented followed in the third olumn y the orresponding t-vlue. For totl mnufturing (ISIC 3) the estimted rte of -onvergene is out 0.. It is signifintly different from zero (i.e. no onvergene) only t the 10% signifine level. Ten out of 8 industries hve rte of -onvergene of lor produtivity tht is signifintly in exess of zero t the 1% signifine level. This numer inreses y six when onsidering 10% signifine level. The fourth nd fifth olumn of Tle show the vlue of the stndrd devition of the logrithm of lor produtivity in the yers 197 nd 199. A derese of this vlue indites tht produtivity differenes ross ountries hve delined over this 0-yer period. In the sixth olumn the S1T-sttisti, introdued in Eq. (5), is presented. Aording to this sttisti only seven industries show signifint F-onvergene t the 10% signifine level. The results for lor produtivity show tht s-onvergene is indeed suffiient ut not neessry ondition for -onvergene. Eh industry showing signifint positive vlue of the S1T-sttisti lso hs signifint positive vlue of i. However, there re lso industries [ industril hemils (ISIC 351) nd ruer produts (ISIC 355)] whih show signifint -onvergene ut hve n inrese in the vlue of ˆ s it. This my e interpreted s evidene for regression to the men. The orreltion etween the estimted vlues of i nd ˆ sit ˆ si1 is quite strong: When leving out the too (ISIC 314) nd petroleum nd ol produts (ISIC 354) industries, this orreltion is The results onfirm the finding y Bernrd nd Jones (1996,) nd Gouyette nd Perelmn (1997) tht lor produtivity in the mnufturing setor is only slowly or even not t ll onverging. 3 However, they lso show tht the spred of the speed of onvergene ross industries is lrge. We 3 The stndrd likelihood rtio test of the equlity of the i s ross industries hs vlue of 49.3 (lrger thn the ritil vlue of the x (7)-distriution orresponding to the 1% signifine level). This not only shows tht the speed of onvergene is not identil ross industries, ut it lso indites the presene of possile ggregtion is (see Theil, 1954, for the pioneering work on the prolems of ggregtion over miro units). The (unweighted) verge of the estimtes of i ross industries is When weighted with employment this verge dereses somewht to 0.8. Both exeed the estimted -onvergene for the entire mnufturing industry (ISIC 3). Hene, the slow rte of onvergene found for the mnufturing setor ppers to e prtly due to n ggregtion is.

7 M.A. Crree et l. / Eonomis Letters 66 (000) Tle The rte of onvergene of produtivity in mnufturing industries ISIC t s1 st S1T / N.A For the industries orresponding to the ISIC odes, see Tle 1. The supersripts, nd men signifint onvergene t the 10%, 5% nd 1% signifine levels. N.A. mens not ville. lim tht one of the resons for this spred is the vriety in the omplexity of prodution tehnologies, or the existene of knowledge nd pitl rriers. A possile proxy for the omplexity of prodution tehnologies is the level of lor produtivity. Industries with high levels of lor produtivity use, on verge, tehnologies of higher omplexity nd pitl intensity thn industries with low level of lor produtivity. The reltionship etween the rtes of -onvergene nd s-onvergene on the one side nd the logrithm of lor produtivity in 197, m 1i, on the other side is omputed y the orreltion oeffiients of ˆ with m nd of ˆ s ˆ s with m. We lso employ ˆ i 1i it i1 1i i weighted with the reiprol of its stndrd error, i.e. the t-vlue t. The orreltion etween the estimted vlues of i for the 7 i industries nd their m1i is 0.7. The orreltion etween the t-vlues of i nd m1i is somewht stronger: For the vrile ˆ sit ˆ si1 we find orreltion oeffiient of These three orreltion oeffiients hnge somewht to 0.18, 0.8 nd 10.4, respetively, when the

8 344 M.A. Crree et l. / Eonomis Letters 66 (000) too (ISIC 314) nd petroleum nd ol produts (ISIC 354) industries re exluded. Tht is, we find evidene for industries with reltively high lor produtivity hving low rte of 5 (espeilly s-)onvergene of produtivity. This is in line with high knowledge or pitl rriers preventing quik th-up. 5. Conlusion We investigte onvergene of verge lor produtivity ross mnufturing industries in 18 OECD-ountries over the period For eh industry we determine the extent of - onvergene nd s-onvergene nd their sttistil signifine. The results show lrge inter-industry differenes in the extent of onvergene, prt of whih n e explined from differenes in the level of verge lor produtivity. This level my funtion s proxy for knowledge or pitl rriers preventing th-up ourring. Low knowledge rriers whih llow for imittion of new tehnologies nd whih limit the degree of sorptive pity neessry for tehnologil progress my filitte onvergene. High knowledge rriers whih mke imittion diffiult nd whih n generte tehnologil gps into lsting ones my hmper onvergene. This my e reson for the lk of onvergene of GDP per pit found in mny lrge dt sets inluding developing ountries while there is mple evidene of onvergene hving tken ple for developed ountries (Crree nd Klomp, 1997). The urrent nlysis leves some questions unresolved. First, the use of the level of lor produtivity s mesure for pitl nd knowledge rriers is n indiret pproh. Do more diret mesures give the sme onlusion s our indiret mesure? Seond, wht is the effet of industries eing more or less interntionlly oriented? Gersh nd Bily (1996), for exmple, lim tht n industry in whih there is only lol ompetition (suh s the Jpnese food industry) rnk quite low on the produtivity sle. Resolving these nd other questions will llow us to etter understnd when onvergene tkes ple nd when it does not. 4 We tested whether these orreltion oeffiients re sensitive to outliers. For the smple of 5 industries ( too nd petroleum nd ol produts industries exluded) we find two outliers. Both re pitl-intensive industries in Belgium tht hve inreses of lor produtivity whih re muh lrger thn for ny other oservtion. These two industries re the petroleum refineries (ISIC 353) nd the iron nd steel (ISIC 371) industries. The rte of onvergene for those two industries dereses sustntilly when Belgium is exluded from the smple. The estimtes of, t, s1 nd st for the petroleum refineries (ISIC 353) industry hnge to 0.079, 0.38, 0.68 nd 0.843, respetively, when exluding the Belgin refineries from the smple. Similrly, the estimtes of, t, s1 nd st for the iron nd steel (ISIC 371) industry hnge to 0.39,.14, nd 0.315, respetively. The orreltion etween onvergene nd produtivity eomes stronger s result of exluding the two Belgin industries. The orreltion oeffiients etween the estimtes of, t nd Ds nd the logrithm of lor produtivity in 197 hnge to 0.40, 0.39 nd 10.6, respetively. Tht is, the orreltion oeffiients re somewht sensitive to outliers ut when removing these outliers the results re reinfored. 5 The reltion etween onvergene nd level of produtivity n lso e seen from the five industries with the highest (exluding too nd petroleum nd ol produts ) nd the lowest m. Of the five industries with highest lor 1i produtivity in 197 only one shows -onvergene t the 10% signifine level while none shows s-onvergene. Out of five industries with the lowest lor produtivity four show -onvergene nd three show s-onvergene.

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