How a lobal Inte-County Input-Output Table wth Pocessng Tade Account Can be constucted fom TAP Database Manos Tsgas and Zh Wang U.S. Intenatonal Tade Commsson* Mak ehlha U.S. Depatment of Inteo* (Pelmnay daft, not fo quotaton) Abstact We developed a method to constuct a global ICIO table fom veson 8 TAP database as well as detaled tade data fom U COMTRADE, and two addtonal IO tables fo majo emegng economes whee pocessng expots ae a lage poton of the extenal tade. We ntegate the TAP database and the addtonal nfomaton wth a quadatc mathematcal pogammng model that (a) mnmzes the devaton of the esultng new data set fom the ognal TAP data, (b) ensues that supply and use balance fo each secto and evey county, and (c) keeps all sectoal blateal tade flows n the TAP database constant. Blateal and aggegate elablty ndexes ae computed fo each TAP sectos and end use categoes whch ae used to contol the elatve amount of adjustment fo each end-use categoes wthn each ognal blateal tade flows fom the TAP database. The new database coves 63 countes/egons and 41 sectos fo 2004 and 2007 two yeas. Pape fo Pesentaton at the 15 th TAP Confeence on lobal Economc Analyss Intenatonal Tade Cente, eneva June 28, 2012 *The vews expessed n ths pape ae those of the authos alone. They do not necessaly eflect the vews of the US Intenatonal Tade Commsson, o U.S. Depatment of Inteo. 1
1. Intoducton Thee s esugence n the applcatons of nput-output (I-O) tables n the economc lteatue dung ecent yeas fo both analytcal and statstcal pupose (ohko Yamano and adm Ahmad, 2006). As an analytcal data souce and accountng famewok, nput-output tables povde consstent analyss and measuement of vetcal specalzaton of ntenatonal tade (Hummels, Ish, and Y, 2001), domestc and foegn contents n a county s goss expots (Koopman, Wang and We, 2008, 2012), the development of value-chan n global poducton netwok (Wang, Powe and We, 2009), the patten of goss vesus value-added tade aound the wold (Johnson and oguea, 2009), the decomposton of goss tade to the value-added contents ( Koopma, Powes, Wang and We, 2010), and tade flows n ntemedate goods and sevces among OECD countes (Sébasten Moudot, Rane Lanz, and Alexandos Ragousss, 2010). It s also nceasngly beng used n envonmental analyss such as measung dect and ndect pollutants poduced by ndustal sectos wthn an economy and estmate consumptonbased emssons, thus accountng 'leakages' between economes (Davs and Caldea, 2010), as well as polcy debates on the ole of vetcal specalzaton n the damatc declne of wold tade dung ecent global fnancal css(bems, Johnson, and Y, 2010) and the economc and tade mpact of Japan's ecent eathquake and tsunam (Escash, Keck, ee and Teh, 2011). As a statstcal analyss tool, nput-output and the closely elated supply-use tables ae nceasngly becomng the most mpotant vehcles used to balance the ncome, expendtue and poducton estmates of DP to satsfy the Unted aton standads of System of atonal Account (SA) 1993 and 2008. Howeve, contast wth ths suged analytcal and statstcal demand, the lack of consstent global II-O data sets, especally such data wth a tme dmenson eman as a majo obstacle fo many economsts to addess the vaous ssues mentoned above at the hands. Ths s because global II-O tables ae vey ae due to the temendous amount of data equed and the dffeences n statstcal classfcatons acoss countes. Most exstng global I-O databases ae a collecton of ndvdual county tables such as vaous veson of OECD STAI I-O database 1, 1 It povdes a bulk of the equed data and s egulaly compled fo about 50 countes acoss the globe, but ntegatng them wth blateal tade statstcs nto global consstent database stll emans as a substantal challenge. 2
the few avalable II-O tables, such as the Asan ntenatonal I-O table compled by the Insttute of Development Economes (IDE) n Japan, cove only a select set of Asan economes and teat othe countes (ncludng EU) n the est of the wold as exogenous blocks. In addton, ts publcaton has a sgnfcant tme delay (the avalable most ecent table s 2000) and ts ndusty classfcaton s ad-hoc, not vey easy concod to common used ntenatonal classfcatons, so make t vey dffcult fo update usng statstcs publshed by U and othe ntenatonal agences. Pogess has been made n ecent yeas. Most developed countes, such as the 27 Euopean Unon membe states and the Unted States, now comple and publsh annual supply and use tables. Majo ntatves ae unde way to help developng countes to comply wth the 1993 System of atonal Accounts (SA), ncludng publshng supply and use tables. 2 The Euopean Commsson, has funded a consotum of eleven Euopean eseach nsttutons to develop a woldwde tme sees of natonal nput-output tables, called the Wold Input Output Database (o WIOD), that ae fully lnked though blateal tade data (27 EU membe and 13 othe majo economes), geneatng a tme sees, mult-county IO table (fo 1995-2009). WIOD contans tables n both cuent and pevous yea s pces. The data set just become publc accessble snce Apl ths yea. 3 The OECD s also constuctng an nte-county IO table fo thee benchmak yeas (1995, 2000 and 2005) by combnng the ndvdual county IO databases and STA blateal ndusty tade statstcs, coveng about 50 countes. TAP database s a publc accessble global data set to facltate contempoay appled geneal equlbum analyss of global economc ssues. It has a boade county and secto coveage than WIOD, wth 57 sectos, 109 ndvdual countes and 20 composte egons n ts most ecent veson (veson 8); It has also full global coveage and benchmaked on econcled offcal tade statstcs based on data elablty. Fo example, e-expots though Hong Kong ae systemcally adjusted to the ogn and destnaton countes. It uses entopy theoetc methods 2 ADB oganzed a poject wth patcpaton of 17 developng countes (RETA 6483) n Asa Pacfc to constuct supply and use tables fo each patcpatng county. 3 Despte many of advantages, such as mpoved allocaton of mpots by end use categoy; closely lnked wth EU KLEMS and Wold KLEMS and wth bette and detaled captal types and labo skll levels beakdown, thee ae also obvous shotcomngs n the WIOD data set need to be futhe mpoved, such as ts tade flows ae based on mpot statstcs only, and expots to the est of the wold s calculated as esduals and could become negatve fo some poducts; each county's data just smply put togethe and no econclaton pocedue based on data elablty has been used. In addton, whle the coveage of the 27 EU membe countes s detaled, less than 10 developng countes ae ncluded. Pocessng tade s also not consdeed. 3
to econcle data fom dffeent souces and ceate a consstent database. Ths consstency s the coe advantage of the TAP data base offeed to the CE modelng communty. Howeve, benchmaked only on tade statstcs, secto level supply and demand data fo ndvdual countes may have lage dscepances wth coespondng statstcs n natonal accounts 4 ; Thee s no consstency mposed fo dffeent vesons of the data, makng t dffcult to make ove tme compasons. In addton, the II-O table undelyng TAP database s based on the so called Mult-Regon Input-Output (MRIO) table n the lteatue, thee s no dstncton between ntemedate and fnal goods and sevces tade flows n the data. Theefoe, sgnfcant tansfomaton has to be made n ode to constuct an Inte-County IO (ICIO) table fom the TAP database. Ths pape documents how an ICIO table wth sepaate pocessng tade account can be constucted fom TAP database step by step. It stats wth a specfcaton of the mathematcal elatonshp between MCIO and ICIO model, and dscuss how expots subsdes, mpots taffs, commodty taxes and ntenatonal tanspotaton magn n the TAP database should be teated n the coespondng MCIO and ICIO accountng famewok; followed by pesentaton of a quadatc pogammng model wth vaous elablty ndex n ts objectve functon to sepaate goss blateal tade flow n the TAP database nto ntemedate, consumpton and nvestment goods tade flows, thus tansfe the MRIO table emboded n the TAP database nto a ICIO table. The ntal allocaton of blateal tade flows n the TAP database nto the thee end use categoes s based on mpoved concodance between HS and U BEC (Boad Economc Categoes) and detaled tade statstcs at 6-dgt HS level fom U COMTRADE(Commodty Tade Statstcs). Fnally, a mathematcal pogammng model that ntegates pocessng tade nfomaton fom majo developng countes s ntoduced. Chna s expanded IO table wth a sepaate accounts fo pocessng expots fom Koopman, Wang and We (2012) and 2003 Mexco IO table wth sepaate domestc and Maquladoa accounts fom Mexco statstcal agency, Insttuto aconal de Estadístca, eogafía e Infomátca (IEI) ae meged wth the ICIO table constucted fom TAP database usng the model by mnmzng the devaton between the esulted new data set fom ognal TAP data. The new database coves 63 4 Fo nstance, the mpots use by secto n the efeence yea does not coespond to the benchmak yea of mpot matx nfomaton publshed by the atonal Statstcs Agences. 4
countes and 41 sectos and was used to suppot ou ntal global AE modelng of pocessng tade and global value-chan analyss effots. The pape concludes wth a dscusson on emanng ssue to be solved and dectons of futue wok to futhe mpove the data. 2. Fom Mult-county Input-Output (MCIO) Account to Inte-County Input-Output (ICIO) Account 2.1 MCIO and ICIO accounts and the mathematcal descptons Assume thee ae countes, wth ndustes n each county. The poducton n each secto n any county can potentally use ntemedate nputs fom any secto (ncludng ts own) n any county. Assumng a pedetemned locaton of poducton based on ndvdual county's I-O table that defnes the stuctue of the global poducton, the delvees of goods and sevces between countes ae detemned by mbalances between supply and demand nsde the dffeent countes. A wold MCIO table s a compehensve account of annual tansacton and payment flows wthn and between countes. Followng notaton wll be used to descbe the elements of the wold MCIO account: x = oss output of commodty n egon v = Value added by poducton of commodty n egon t = Blateal tade flows of commodty fom souce county s to destnaton county z = Domestc ntemedate demand of commodty by secto j n county y k = Domestc fnal demand of commodty by fnal demand type k n county = Demand of mpoted ntemedates of commodty by secto j n county m z m y k = Demand of fnal goods of commodty by fnal demand type k n county All vaables ae measued n annual values. The total numbe of fnal demand types, such as pvate consumpton o goss captal fomaton s K. Then the followng thee accountng denttes descbe the elatonshp among elements of each ow (, ) and column (j, s) of the global MCIO table can be specfed as: m z z v = x (1) j j =1 =1 5
z ss K ss s y t = x (2) k=1 k s K g m m z yk = t (3) k=1 s=1 Equaton (1) specfes the value of goss output of commodty j n county s attbuted to the value of all secto j domestc and mpoted ntemedate nput puchases and to the value of sevces fom secto j pmay facto nputs. Equaton (2) ndcates that total goss output of commodty n the souce county s equals the sum of ts poduct delvees to domestc and ntenatonal uses, but thee ae no dstncton about the type of end uses n the ntenatonal makets. Equaton (3) ndcates total ntemedate and fnal mpot demand fo commodty n destnaton county must be met by mpots fom all souce countes. Thus, equatons (1) (3) togethe consstently defnes an accountng famewok fo the global economy, conventonally called a MRIO table n the lteatue (Mlle and Bla, 1985, Isad, et al. 1998). Such an account guaantees that ntenatonal poducton and tade flows exactly meet all countes supply and demands, but stops shot of assgnng specfc ntemedate o fnal uses fo ntenatonal tade flows. The above accountng famewok can be extended to an ICIO account by futhe dsaggegatng goss goods and sevces tade flows uses. Defne: z t by end use categoes to secto and fnal = Intemedate tade flows of commodty poduced n souce county s fo use by secto j n destnaton county ; y k = Fnal goods and sevces tade flows of commodty poduced n souce county s fo type k fnal use n destnaton county ; mg = Magn dffeences fo a specal tansacton between the souce county s and destnaton county. Then flowng thee dentfes wll hold: s s ( 1 mg ) z z (4) k m ( 1 mg ) y y (5) m k z K k=1 y k t Inset equaton (4) nto equaton (1), (6) 6
1 1 s s s s ( 1 mg ) z v = x (7) Inset equatons (5) and (6) nto equaton (2), k j s1 (1 mg ) z s1 K k=1 (1 mg ) y k = x (8) The economc meanngs of these two equatons ae staghtfowad. Equaton (7) defnes the value of goss output fo commodty goup j n poducton county s as the sum of the values fom all of ts (domestc plus mpoted) ntemedate and pmay facto nputs. Equaton (8) states that total goss output of commodty goup n destnaton county s equal to the sum of all delvees to ntemedate and fnal uses fom all countes (ncludng tself) n the wold. The delvey of ntemedate and fnal goods n ths ICIO account should be consstent wth ntenatonal tade statstcs each yea, whch s the blateal tade flow defnton equaton (6). Because ths extended accountng famewok (Equatons (6) to (8)) s mathematcally equvalent to equatons (1) to (3), ths ICIO account s fundamentally consstent wth the MCIO account defned eale, ths s the theoetcal foundaton that a MCIO table can be used as an mpotant ntemedate step towads estmatng a full-fledged ICIO account. Howeve, because the MCIO account has a much smalle dmenson thus sgnfcant addtonal nfomaton wll be equed to empcally sepaate nte-county tade flows nto end use categoes that delvey to secto and fnal uses. 5 An ICIO account povdes the best avalable and consstent nfomaton that allow us to model the value-added geneaton pocess among elated countes at ndusty aveage level. It taces nte-county tansacton n ntemedate nputs and fnal use sepaately, matches blateal tade flow n majo end use categoes to nput-output elatons theefoe ncludes moe detaled souce/destnaton, supply/use nfomaton than a MCIO table, whch s the coe of the TAP database. In shot, an ICIO table extended fom the TAP database wll not only povde the ogn and destnaton of ntenatonal tade flows n ts coveed ndustes, but also specfes evey ntemedate and/o fnal use fo all such flows. Fo example, fom such an extended table 5 The aggegate model only has (+ 2 +5) vaables and (3++5) constants, whle the full detaled model has ( 2 + H)(+1) vaables and ( 2 +++5) constants. It s a much smalle model, havng 2 (-1) + (H-5) less vaables and (+-3) less constants. 7
we wll not only know how many electoncs poduced n Chna was shpped nto the Unted States, but also can dstngush how many of them used as ntemedate nputs n whch patcula U.S. ndusty and how many of them used fo U.S. pvate household consumpton o captal fomaton. 2.2 MCIO account n the TAP Database About 40 aays n each veson of TAP database ae used to stoe elated data set fo each elease. Flowng 13 aays ae needed to constuct an MCIO account valued n maket pce: TVOM(TRAD_COMM,RE) sales of domestc poduct, at maket pces; VFM(EDW_COMM,PROD_COMM,RE) pmay facto puchases, by fms, at maket pces; EVFA(EDW_COMM,PROD_COMM,RE) pmay facto puchases, at agents' pces; VDFM(TRAD_COMM,PROD_COMM,RE) domestc puchases by fms at maket pces; VDFA(TRAD_COMM,PROD_COMM,RE domestc puchases, by fms, at agents' pces ; VDM(TRAD_COMM,RE) domestc puchases by govenment at maket pces; VDPM(TRAD_COMM,RE) domestc puchases by households at maket pces; VIFM(TRAD_COMM,PROD_COMM,RE) mpot puchases, by fms, at maket pces; VIFA(TRAD_COMM,PROD_COMM,RE) mpot puchases, by fms, at agents' pces; VIM(TRAD_COMM,RE) mpot puchases, by govenment, at maket pces; VIPM(TRAD_COMM,RE) mpot puchases, by households, at maket pces ; VST (MAR_COMM,RE) magn expots; VXMD (TRAD_COMM,RE,RE) non-magn expots, at maket pces. Equatons (1) - (3) that defne the MCIO account can be wtten n TAP notaton as follows: TVOM(,) = sum(j, VDFM(j,,)) + sum(j, VIFM(j,,)) + sum(f,evfm(f,,)) + sum(j, VDFA(k,,)-VDFM(j,,)) + sum(k, VIFA(j,,)-VIFM(j,,)) + sum(f, EVFA(f,,) - VFM (f,,)) + (TVOM(,) - TVOA(,)); (1) TVOM(,) = sum(k, VDFM(,j,)) + VDPM(,) + VDM(,) + VDFM(,"cgd",) + SUM(s, VXMD(,,s)) + VST(,); (2) Sum(s, VXMD(,s,)) = sum(j, VIFM(,j,)) + VIPM(,) + VIM(,) 8
+ VIFM(,"cgd",) (3) Equaton (1) specfes the column (cost of poducton) balance of the MCIO account. Whee sum(j, VDFA(j,,)-VDFM(j,,)), sum(k, VIFA(j,,)-VIFM(j,,)), and sum(f, EVFA(f,,) - VFM (f,,)) ae taxes of domestc ntemedate nputs, mpoted ntemedate nputs and poducton facto nputs, espectvely; (TVOM(,) - TVOA(,)) s tax on poducton and TVOA(,) = SUM(j,VDFA(j,,)+VIFA(j,,)) + SUM(f, EVFA(f,,)), all these taxes plus the payment to poducton factos, sum(f,evfm(f,,)), consttute total value-added n county. Equaton (2) specfes the ow (supply and demand) balance of the MCIO account. Whee sum(j, VDFM(,j,)), VDPM(,), VDM(,) and VDFM(,"cgd",) gve demand of domestc poducts fo ntemedate nputs, pvate and publc consumpton as well as nvestment espectvely; The emanng two tems ae magn and non-magn commodty expots, the extenal demand fo goods and sevces poduced n county. Equaton (3) specfes the mpot supply and demand balance condton n the MCIO account. It s the same as equaton (3), and splts mpot demand fo fnal goods nto thee end use categoes,.e. K=3 as pvate, govenment and nvestment demand espectvely. 3. A Mathematcal Pogammng Model to Sepaate oss Blateal Tade nto Tade Flows by End Use Categoes 3.1 Estmatng ICIO table fom exstng MCIO table - the optmzaton model Assume an MCIO table exsts. Ths mples that all vaables on the ght sde of equatons (4) to (8) specfed n secton 2.2 ( x, y, z m m, t ) and value-added by secto n each county ( v ) ae known and can be teated as paametes. Suppose ntenatonal tanspotaton magns and taff nfomaton ae also avalable. Then to estmate an ICIO table contanng dffeent ntemedate tade flow matx (Z s,,s), and K dffeent fnal goods flow matx matx (Y s,,s) fom the exstng MCIO table can be fomulated as an optmzaton model and specfy a coss-entopy (Hagan & Buchanan, 1984, olan et al., 1994) o a quadatc objectve penalty functon subject to equatons (4) to (8) as constants 6. 6 The quadatc functon has a numecal advantage n mplementng the model. It s ease to solve than the entopy functon n vey lage models because they can use softwae specfcally desgned fo quadatc pogammng. As showed by Cannng and Wang (2005), the quadatc functon s equvalent to the entopy functon n the neghbohood 9
Fo example, the quadatc objectve penalty functon fo such an optmzaton model can be specfed as follows: Mn S = 1 2 { s1 1 =1 ( z z wz ) 2 + s=1 1 A soluton to ths quadatc pogammng model povdes a complete set of estmates fo a full-fledged ICIO table. It s smla n many aspects wth the nteegonal accountng famewok poposed by Batten (1982) two decades ago, who used an entopy fomulaton based on an unnfomed data poolng appoach fo ntal estmates whee all weghts ae equal to one. In theoy, one can constuct ethe nfomed (e.g., suvey based) o unnfomed (e.g., data poolng) ntal estmates fo each endogenous element of the ICIO table z and wth elablty measues to weght each ntal estmate wz 1 K k1 (y and k y wy k wy k k ) 2 } (9) y k, along. The unnfomed ntal estmates ae deved n the absence of nfomaton about vaatons n ow o column stuctues n the tageted ICIO account. In such cases, one typcally adopts popotonal allocaton methods and assgns weghts n these same popotons. The nfomed ntal estmates eques usng the geatest amount of pmay nfomaton fom multple souces that collectvely povde consstent descptons of all ow o column stuctues n the tageted ICIO account. Ideally, the pmay nfomaton souces nclude statstcal measues of elablty that can be used to weght these ntal estmates. Theefoe, the key steps n mplementng ths optmzaton model wth eal data popely ae constuct these ntal estmates wth avalable nfomaton fom dffeent souces and select a full set of elablty weghts n the objectve functon n ode to obtan a meanngful soluton fom the model. We wll dscuss these mplementaton ssues n followng sub-sectons. 3.2 Constuct ntal estmates based U BEC classfcaton and detaled blateal tade statstcs To estmate detaled nte-ndusty and nte-county ntemedate and fnal tansacton flows n an ICIO table, we need addtonal nfomaton beyond a MCIO table n the TAP database to () dstngush ntemedate and fnal use of mpots fom dffeent souces n each secto, and () allocate ntemedate goods fom a patcula county souce to each secto t s of ntal estmates, unde a popely selected weghng scheme. 10
used wthn all destnaton countes. We addess the fst nfomaton ssue based on U Boad Economc Categoes (BEC) and detaled tade statstcs. Howeve, no addtonal nfomaton s avalable to popely allocate ntemedates of a patcula secto fom a specfc souce county to ts use ndustes at the destnaton economy. Thus, secto j s mpoted ntemedate nputs of a patcula poduct ae ntally allocated to each souce county by assumng they ae consstent wth the aggegate souce stuctue of that patcula poduct. 7 Although the TAP database povdes blateal tade flows, t does not dstngush whethe goods ae used as ntemedates o fnal goods. Ou ntal allocaton of blateal tade flows nto ntemedate and fnal uses s based on the U BEC appled to detaled tade statstcs at the 6-dgt HS level fom COMTRADE based on concodance used n WIOD poject 8. Ths dffes fom the appoaches n Johnson and oguea (2010) and Daudn, Rfflat, and Schwesguth (2010), whch also tansfom the MCIO table n the TAP database nto an ICIO table. Howeve, they do not use detaled tade data to dentfy ntemedate goods and fnal goods tade n each blateal flow. Instead, they apply a popotonalty method dectly to the tade data n TAP database;.e., they assume that the popoton of ntemedate to fnal goods s the same fo domestc supply and mpoted poducts. Suppose we could obtan estmates fo shae of ntemedate, consumpton and captal goods tansactons n each blateal tade flows based on U BEC classfcaton and detaled tade statstcs as sh (,s,) and fsh(,s,) espectvely, then we can ntalze these endogenous vaables z and y k n the model as follows: 1. Compute the shae of ntemedate goods dstbuted to ts use ndustes based on data avalable n the MCIO table and dstbute mpoted ntemedate goods by popoton fo s 7 Fo example, f 20% of U.S. mpoted ntemedate steel comes fom Chna, then we assume that each U.S. ndusty obtans 20% of ts mpoted steel fom Chna. Such an assumpton gnoes the heteogenety of mpoted steel n dffeent sectos. It s possble that 50% of the mpoted steel used by the U.S. constucton ndusty may come fom Chna, whle only 5% of the mpoted steel used by auto makes may be Chnese. 8 We thank D. Robet Stehe at WIIW kndly povdes the concodance. Both the zeo/one and a weghtng scheme ae be used n WIOD concodance to allocate blateal tade flows at 6-dgt HS level nto the thee majo U BEC end use categoes, thee ae 703 (among 5718 n total) 6 dgt HS code ae dentfed as dual used poducts that wee splt nto two o moe end use categoes n the WIOD concodance. Ths s bette than the zeo/one classfcaton fom USD we used n an eale veson of the pape. Shaes based on county-specfc nfomaton could be appled as weghts to futhe mpove the allocaton. These ae aeas fo futue eseach. 11
z m z z sh m f (10) 9 j 1 2. Compute the shae of fnal goods dstbuted to ts fnal uses based on data avalable n the MCIO table and dstbute mpoted fnal goods by popoton fo s y k K y m k K 1 y m k fsh f 3. Keep domestc ntemedate nputs and fnal goods use as what n the MCIO table (11) z z y k y k 4. Compute magns between the souce county s expots and destnaton county s mpots, ths could nclude expots tax o subsdes n the souce countes and mpot dutes n the destnaton countes as well as ntenatonal tanspotaton cost fo each blateal oute. The use of end-use categoes to dstngush mpots by the fnal uses s becomng moe wdespead n the lteatue and avods some noted defcences of the popotonalty method. 10 Feensta and Jensen (2009) use a smla appoach to sepaate fnal goods fom ntemedate nputs n U.S. mpots n the ecent e-estmaton of the Feensta-Hanson measue of mateal off shong. Dean, Fung, and Wang (2011) show that the popotonalty assumpton undeestmates the shae of mpoted goods used as ntemedate nputs n Chna s pocessng tade. odas (2005) states that the lage ndustal countes have a hghe shae of ntemedates n the expots than n the mpots, whle the opposte s tue fo lage developng countes. These esults mply that the ntemedate content of mpots dffes systematcally fom the ntemedate content n domestc supply. The less dstoted ntemedate tade shae estmates fom end use classfcaton povdes a bette ntal ow sum fo each block matx of Z n the ICIO flow matx Z, thus gvng a bette ow total contol of the most mpotant paametes (the IO coeffcents) n an ICIO model. 9 m z, z and x y, y, the ntemedate and fnal goods tade flows computed based on the shae m c x c epoted by mpotes and expotes ae used as up and low bound to constant fo model solutons. 10 The lteatue notes that the U BEC classfcaton has shotcomngs of ts own howeve, patculaly ts nablty to popely dentfy dual-use poducts such as fuels, automobles, and some food and agcultual poducts. 12
Howeve, t stll does not popely allocate patcula ntemedate goods mpoted fom a specfc souce county to each usng ndusty (the ICIO flows n each cell of a patcula ow n each block matx Z stll have to be estmated by popotonalty assumpton). Ths allocaton s especally mpotant to pecsely estmate value-added by souces fo a patcula ndusty, although t s less ctcal fo the county aggegates because total mpots of ntemedates fom a patcula souce county ae fxed by obseved data, so msallocatons wll lkely cancel out. 3.3 Addtonal ssues of model ntalzaton n the TAP database The ntenatonal tanspotaton cost often vay fo ntemedate, captal and consumpton goods n each blateal oute and dffeent county may mpose dffeent taff ate fo ntemedate and fnal goods. Howeve, the ntenatonal tanspotaton magn and taff data n cuent TAP database cannot make such dstnctons, we have to assume ntenatonal tanspotaton magns ae the same fo ntemedate and fnal goods "" n the same blateal tade oute and splt VTWR (ts,,s,), the magns commodty aay n TAP database, accodng to the popoton of each end use categoy n the blateal tade flows, and teat them as ntemedate nputs fom the ntenatonal tanspotaton magn supply ndustes (a, wate and othe tanspotaton sectos) at the souce county to the use ndustes n the destnaton countes. We also have to assume expots subsdes/mpot taffs have the same ate between ntemedate and fnal goods n the same souce/destnaton countes and teat them as pat the value-added ceated by the souce/destnaton countes. Among the 129 county/egon n veson 8 TAP database, 20 of them ae composte egons. The new ICIO database we constucted fom the TAP database coves 63 countes/egons, 17 of them consttuted by moe than one county. Chna and Mexco have nomal and pocessng tade egons. (see Appendx A fo county aggegaton of the new database fom V8 TAP classfcatons) 11. The blateal tade flows wthn these composte egons ae emoved and teated as the composte egons' domestc supply and demand. The detals of these specal teatments can be found n the AMS code n Appendx. 3.4 Selecton of elablty ndexes n the objectve functon 11 The new database has smla secto classfcaton, except most pmay sectos. It aggegates the 12 pmay agcultual secto nto two sectos, ol and gas nto one secto, and the 8 food pocessng secto nto 3 sectos. 13
As ponted by Wang et al (2010), one of the most desable analytcal and empcal popetes of ths class of data econclaton models such as the one we specfed by equatons (4) (9 ) s t uses elablty weghts n the objectve functon to contol how much an ntal estmate may be adjusted. If the selected weghts popely eflect the elatve elablty of the assocated ntal estmates, the model wll adjust those elatvely unelably epoted data moe than those elatvely elably epoted data n the econclaton pocess. In othe wods, ntal estmates wth a hghe elablty wll be adjusted less than ntal estmates wth a lowe elablty, thus the best avalable nfomaton can always be used to nsue that statstcs epoted by elable tade outes o epotes ae not petubed by the econclaton pocess as much as statstcs epoted by unelable tade outes o epotes. Fom statstcal pont of vew, the best way to systematcally assgn elablty weghts n the objectve functon s to obtan estmates of the vaance-covaance matx of the ntal estmates. Then the nveted vaance-covaance matx can be justfed as the best ndex of the elablty of ntal estmates. The lage the vaance, the smalle the assocated tem ( z z wz ) 2 o (y y k wy k k ) 2 contbutes to the objectve functon, and hence the lesse the penalty fo the assocated vaables to move away fom the ntal value (only the elatve, not the absolute sze of the vaance affects the soluton). A small vaance of the ntal estmates ndcates, othe thngs beng equal, that t s moe elably epoted data and thus should not be equed to change by as much. In contast, a lage vaance of the ntate estmates ndcates unelably epoted data that may be adjusted consdeably. Howeve, the lack of consstent hstocal data often makes the estmaton of the vaance-covaance matx assocated wth the ntal estmates vey dffcult to mplement. Fo example, the common pactce n SAM balancng execses s assgn dffeng degees of subjectve elabltes to the ntal entes of the matx follow the method poposed by Stone (1984), 12 almost no attempt to date has been made to statstcally estmate data elablty such as eo vaance of the ntal estmates fom hstocal data, except Weale (1989), who developed a statstcal method that uses tme sees nfomaton on accountng dscepances to nfe data elablty n a system of natonal accounts. Theoetcally speakng, a smla statstcal method can be appled to the hstocally epoted dscepances of blateal tade data to deve those vaances assocated wth ntenatonal tade statstcs. In pactce, 12 Stone poposed to estmate the vaance of x 0 as va(x 0 ) = (θ x 0 ) 2, whee θ s a subjectve detemned elablty atng, expessng the pecentage ato of the standad eo to the ntal estmates of x 0. 14
howeve, the hstocal data and knowledge of the changes n elated county s tade epotng system ae too demandng and make such a statstc method less attactable n lage empcal applcatons. Theefoe, hee we suggest a pactcal altenatve appoach to estmate the elablty weghts, whch s constucted by epote elatve elablty ndexes fo both expotes and mpotes. 3.4.1 Repote elablty ndexes Tade data epoted by each county and ts patnes ae often used n the ntenatonal economc lteatue to check the qualty of tade statstcs. An appoxmate match of mo statstcs suggests that tade data epoted va that oute ae elable. Howeve, such weghts teat the epoted tade statstcs fom both epotes equally and do not dstngush whch epote s moe elable. In the case thee s a vey unelable epote n the pa, t may adjust the elable data epoted by the patne too much thus loss ognal accuate nfomaton fom the elable patne. Ths s undesable. To coect ths poblem, a epote s elatve elablty ndex needs to be developed. Such an ndex should be able to deal wth thee ctcal ssues. The fst ssue s elated to the dffeence of epotng countes n the ablty to epot blateal commodty tade by end use categoes. Vaablty n epotng qualty acoss countes s hghly elevant nfomaton fo the poblem we ty to solve n ou poposed data econclaton appoach. As dscussed eale, the adjustment pocess hnges heavly on the elatve elablty of the each epotng countes. An ndcato of epote elablty s a measue of how consstency a county epots ts tade n each end use categoes elatve to all ts tadng patnes. Howeve, judgng a county s tade data based on a sngle blateal flow alone s a poo efeence, because a patne can mepesent ts tade theeby potentally dscedtng a elable epote. Theefoe, a good epote elablty measue should take all epotng countes n the wold nto account n assessng a county s epotng elablty. The second ssue s what exactly should be captued by the elablty measue. The sze of dscepances could be ncopoated nto a measue of elablty. Howeve, placng emphass on the magntude of dscepances only may ove-penalze the elablty of a legtmate epote. A poo epote that makes an eo fo a gven tade flow usually makes a smla eo wth othe patnes. Fo example a epote that has mstaken the dentty of one of ts patnes has 15
mplctly made a mstake fo othes. It bngs a systemc bas fo that epote. Ths type of poblem should be detected and eflected n the epote elablty measue wthout penalzng the elable epote. The thd ssue s the capablty of the measue to eflect both end-use-categoes by secto- and county-specfc elablty nfomaton fo each county as an expote and as an mpote. Countes typcally have commodty by end use categoy specfc stength and weaknesses. Fo example one expotng county may have an excellent epotng ecod on steel used as ntemedate goods but at the same tme s hghly nconsstent n ts epotng pactce fo oganc chemcal n fnal goods tade. All thee ssues dscussed above ae effectvely dealt wth n the elablty ndex developed by ehlha (1996) whee epote elablty ndces wee used to make a dsceet choce whethe to degad o accept epoted tade flows. The ndex s calculated as the shae of accuately epoted tansactons of a epote s total tade fo a patcula end use categoy n a secto usng a theshold level. It assesses epote elablty fom a complete set of global epotng patnes, captues the epote s ablty to accuately epot wthout ntefeences fom goss dscepances n epotng, and contans expote and mpote-secto and end use categoy specfc elablty nfomaton. Specfcally, the mpote-secto and end use categoy specfc and expote-secto and end use categoy specfc elablty ndexes n the objectve functon (equaton (9)) ae defned as: RIM c MA s M c c whee MA c M c sal c 0. 20 AL c M s c M E s c c (12) RIX s c XA s c c E whee XA s c E c sal c 0. 20 AL c M s c M E s c c (13) Unde such defned epote elablty ndexes, the sze of the dscepances becomes mmateal because naccuate tansactons ae teated the same egadless of the magntude of the naccuacy. The ndexes have the flexblty of beng mplemented at the detaled 6-dgt HS level and can be aggegated to any secto level. We computed such epote elablty measues 16
fo each TAP county/egon fo the 3 end use categoes at the TAP secto level. Majo data ae fom U COMTRADE wth supplements fom county souces. 3.4.2 Relablty weghts used n objectve functon Afte obtanng RIM and RIX, thee s an addtonal ssue need to be solved befoe we can empcally compute the elablty weghts n the objectve functon (equaton (9)) of the data econclaton model. Thee s only one numbe fo tade flow n each oute at the secto level n the TAP database, whch s a combnaton of both epote and patne epoted tade statstcs based on epote s elablty. Theefoe, the popoton of such composton fo each tade outne at TAP secto level ae used as weghts to compute a weghted aveage of RIM and RIX as the fnal epote elablty ndex and the weghts n the objectve functon ae assgned by multplyng one mnus these weghted aveage epote ndexes wth the coespondng ntal values fo each endogenous vaable n the model. The complete set of weghts n equaton (9) s defned as follows: wz ( 1 RIM ) zm (1 RIX ) zx (14) wy ( 1 RIM ) ym (1 RIX ) yx (15) c c c Whee z m, z x and y m c, yx c ae the ntemedate and fnal goods tade flows computed based on the shae epoted by mpotes and expotes espectvely (shaes multple blateal tade flows n TAP database). Wth such a weghtng scheme, we acheve ou goal to encouage the model to change those unelable ntal data moe than those elable ones n the econclaton pocess. It means the econcled soluton fom the model not only adjust less to the elable outes than the unelable ones, but also adjust moe to the elatve unelable epote than the elatve elable epote n each tade oute, although n a ough manne. 4. Include Pocessng Tade Infomaton fom Majo Developng Countes 17 t, the The Wold Tade Oganzaton has dentfed moe than 130 countes that use some fom pocessng expots (WTO and IDE JETRO, 2011) and epots that about 20% of developng county expots come fom Expot Pocessng Zones (EPZs). Such pocessng egmes povde ncentves to use mpoted ntemedate nputs, povded that the esultng fnal goods ae entely expoted. Pocessng tade can thus damatcally ncease the mpoted content of expots elatve
to domestc use. Falue to account fo pocessng tade can damatcally ovestate the domestc content of expots (Koopman, Wang, and We, 2008). To eflect the ealty and mpotance of pocessng tade and Expot Pocessng Zones (EPZs) n emegng economes and the ole n global value-added tade and poducton netwok, we extend Koopman, Wang, and We (2008, 2012) to a mult-county global settng that sepaates standad nput-output tables of a subset countes n ou database nto nomal and pocessng tade accounts. In what follows we fst specfy a mathematcal pogammng model that s able to splt a standad ICIO tables nto nomal and pocessng tade accounts fo a subset countes, then befly dscuss the data souces and majo mplementaton ssues. 4.1 Mathematcal pogammng model to sepaate pocessng tade account fo a subset of developng countes The objectve of ths second stage optmzaton model s to splt the economes wth pocessng tade nfomaton n the ICIO table estmated fom the fst stage optmzaton model nto sepaate nomal and pocessng accounts, each wth the own nput-output stuctue..e futhe splt zp, z, ynk and yk and yp, vn and k s j s v j n the ICIO account specfed n equatons (6) to (8) nto zn and s vp j fo a subset countes espectvely. The addtonal lette "n" and "p" n the elated vaables epesent nomal and pocessng economy espectvely. The basc dea s to use nfomaton fom the ICIO table to detemne secto-level blateal mpots/expots, and addtonal nfomaton of pocessng expots/mpots fom tade statstcs n a subset countes to detemne the elatve popoton of pocessng and nomal tade flows wthn each secto, thus use up all avalable data to splt the subset economes nto pocessng and nonpocessng blocks, each wth ts own IO stuctue. The fst step (usng tade data fom the ICIO table to detemne secto-level total mpots/expots) helps to ensue that the balance condtons n the ICIO account ae always satsfed, and that the sepaate pocessng and non-pocessng accounts n the subset economes ae consstent wth the ICIO table. The second step (usng data fom tade statstcs to detemne the elatve popoton of pocessng and nomal flows wthn each secto level blateal tade oute) helps to ensue that the estmated new ICIO table wth pocessng tade account fo subset countes s consstent wth the tade stuctues mpled by offcal tade statstcs obtaned fom these economes,.e t tn tp always hold. 18
Assume thee ae P countes n the county wold engage n pocessng tade wth >P. Assume all output fom the P economes wth pocessng tade s expoted to the ntenatonal maket, then output of the nomal economes n each of the P county can be obtaned by subtactng pocessng expots to all destnaton fom the souce county's secto level total output. The ICIO table wth pocessng tade account can be specfed as follows: Column balance of these economes wth pocessng tade account 1 1 1 1 s s s s ( 1 mg ) zn vn = x zp yp (16) k j s1 1 s1 K k=1 s s s ( 1 mg ) zp vp = zp yp (17) k s1 1 s1 Column balance of these economes wth pocessng tade account s1 (1 mg ) zn s1 K k=1 (1 mg ) yn k K k=1 = x k k zp s1 1 s1 Tade flow balance fo mpots fom and expots to all othe -P countes wthout pocessng tade account: K k=1 yp k (18) K zn yn k=1 k tn (19) K zp yp k=1 k tp Tade flow among all the P countes wth pocessng tade account: (20) znn K k=1 ynn k zpn K k=1 ypn k tn (21) znp K k=1 ynp k zpp K k=1 ypp k tp (22) Addng up condtons vn s j vp s j v s j (23) zn zp z (24) 19
yn tn k yp tp k y t k (25) (26) The basc balance condton of ICIO table, Equatons (6)-(8) contnue to hold fo the -P economes wthout pocessng tade account. The second stage optmzaton model can be constucted wth followng quadatc penalty functon as objectve functon and equatons (6) to (8) as well as equatons (16) to (26) as constants. + Mn S = 1 2 K s=1 1 1 k1 2 2 ( zn zn ) ( zp zp ) s1 1 =1 wzn wzp s s (yn ) 2 ) 2 k yn k (yn yn ( vnj vn j ) k k s wyn wyn s1 j1 wvn k k j { 2 s ( vpj vp s wvp j s j ) 2 } (27) 4.2 Data souce and majo mplementaton ssues Due to data lmtaton, only two countes, Chna and Mexco, ae selected nto the subset economes to empcally mplement the model descbed n last subsecton. We use an expanded Chnese IO table wth sepaate accounts fo pocessng expots and a 2003 Mexcan IO table wth sepaate domestc and Maquladoa accounts, 13 to ntalze elated vaables n the model. Chna and Mexco ae the two lagest uses of expot pocessng egmes n the developng wold, and togethe account fo about 85% of woldwde pocessng expots. Dung 2000-2008, Chna alone accounted fo about 67% of all epoted pocessng expots n the wold whle Mexco epesents anothe 18% (Maue and Degan, 2010). 14 Theefoe, usng pocessng tade nfomaton fom these two countes that nvolve majo pocessng tade actvtes n the wold, the constucted database should get the lage pctue ght fo the wold poducton and tade pattens. When smla nfomaton fom othe developng county becomes accessble, the model can be extended to cove moe developng countes easly. 13 The Mexcan table s fom the Mexcan statstcal agency Insttuto aconal de Estadístca, eogafía e Infomátca (IEI). 14 Smlaly, based on IMF BOP statstcs povded by Andeas Maue, we estmate that Chna and Mexco togethe accounted fo about 80% of goods fo pocessng n the wold n 2005 and 2007. 20
5 Mean absolute pecentage adjustment fo majo vaables n the TAP database Among the 13 data aay used to constuct MCIO account fom TAP database, blateal tade flows (VXMD), Total goss output (TVOM), pmay facto demand (VFM) and supply of ntenatonal tanspotaton magn (VST) ae fxed as constant n the optmzaton model 15, but allow domestc and mpoted puchase goods and sevces to adjust to ft the balance condton n the ICIO table n constucton. The data econclaton pocedue poduces a dffeent set of estmates fo those domestc and mpoted puchases than what gave n the TAP database, t s desable to know how much each set of estmates dffes fom the ognal TAP data. Howeve, t s dffcult to use a sngle measue to compae the ognal and adjusted data, snce thee ae so many dmensons n the data. It s meanngful to use seveal measues to gan moe nsght on the model pefomance. eneally speakng, t s the popotonate devaton and not the absolute devaton that mattes; theefoe, we compute the "Mean Absolute Pecentage Adjustment" wth espect to the ognal TAP data fo dffeent county and secto aggegatons. Consde the followng aggegate ndex measue fo county and commodty goup total adjustment fo both ntemedate and fnal demand. Domestc ntemedate demand: MAPADI MAPADI = = 100 100 =1 =1 1 z 1 VDFM VDFM z VDFM VDFM (28) (29) Impoted ntemedate demand MAPAII = 100 z =1 s =1 VIFM VIFM (30) 15 Anothe thee aays EVFA, VDFA and VIFA ae used to compute taxes. 21
MAPAII = 100 1 s 1 z VIFM VIFM (31) Domestc fnal demand 100 yk VDPM VDM VDFM,'cgd', =1 k MAPADF = (32) ( VDPM VDM VDFM ) =1,'cgd', 100 yk VDPM VDM VDFM,'cgd', =1 k MAPADF = (33) ( VDPM VDM VDFM ) =1,'cgd', Impoted fnal demand 100 yk VIPM VIM VIFM,'cgd', =1 s k MAPAIF = (34) ( VIPM VIM VIFM ) =1,'cgd', 100 yk VIPM VIM VIFM,'cgd', =1 s k MAPAIF = (35) ( VIPM VIM VIFM ) =1,'cgd', The numecal esults fo the 8 aggegate ndexes defned above ae epoted n tables 1 and 2 fo the yea 2007, and tables 3 and 4 fo the yea of 2004 espectvely. We focus on esults fo county total adjustments to llustate some key chaactestcs of the adjustment pocess. Each county s elablty as an expote and mpote s a key facto that govens the magntude of adjustment of ts expots and mpots. eneally speakng, thee ae thee notceable featues of the adjustment made n the data econclaton pocess. Fst, the adjustment made fo developed countes s smalle than the adjustment made fo developng 22
countes n aveage, eflectng the facts that the data qualty s bette n developed countes than that n most developed countes n the TAP database. Second, the adjustment made fo domestc demand s smalle than the adjustment made fo mpoted demand, eflectng that the nfomaton on how and whee mpoted commodty wee souced and used ae geneally poo than nfomaton on how and whee domestc poducts wee used n the TAP database. Fnally the adjustment fo domestc ntemedate nputs puchase s geneally lage than the adjustment made fo domestc fnal demand, but t s n the opposte fo the adjustment n mpoted demand, ndcatng the shae of mpoted fnal good usage n the extended database s qute dffeent fom the ognal TAP database, whethe ths caused by the naccuacy fnal demand nfomaton n the TAP database o due to ou BEC to HS concodance needs futhe nvestgaton. Lookng nto the adjustment at secto level, seems these sectos have lage poton of the poducts could be used as both ntemedate and fnal goods often assocated wth lage adjustments. 6. Concludng Remaks Ths pape descbes how a lobal Inte-County Input-Output Table wth pocessng tade account can be constucted fom TAP database. It fst povdes a theoetcal foundaton that explans how the MRIO table emboded n TAP database could be consstent wth an ICIO table and what addtonal nfomaton s needed fo the tansfomaton. Usng a quadatc pogamng model wth elablty weghts n ts objectve functon, we constucted two pelmnay ICIO tables fo the yea 2004 and 2007 fom veson 8 TAP database, coveng 63 countes and 41 sectos. Addtonal wok s needed to futhe mpove the HS to U BEC concodance and extend t to sevces tade. Bette methods also need to be developed to popely dstbute mpots to domestc uses ethe based on secto specfc nfomaton, o coss county statstcal suveys of the domestc dstbuton of mpots o lnked fm level and Customs tansacton-level tade data. Ths wll need jont effots by statstcal agences and academc communtes acoss the wold. 23
Refeences Booke, Kendck, Meeaus, and Raman, 2005, AMS -- Use's ude AMS Development Coopeaton, Washngton, DC. Byon, Ray P. 1978. "The Estmaton of Lage Socal Account Matx," Jounal of Royal Statstcal Socety, A, 141 (Pat 3), 359-367. Patck Cannng and Zh Wang A Flexble Mathematcal Pogammng Model to Estmate Inteegonal Input-Output Accounts. Jounal of Regonal Scences 45(3):539-563, August 2005. Feantno Mchael and Zh Wang, Accountng fo Dscepances n Blateal Tade: The Case of Chna, Hong Kong, and the Unted States Chna Economc Revew, 19(4): 502-520, Octobe 2008. ehlha, Mak, 1996, Reconclng Blateal Tade Data fo Use n TAP, TAP Techncal Pape no 10, Pudue Unvesty. Hagan, J. Fank 1990, "The Reconclaton of Inconsstent Economc Data: the Infomaton an," Economc System Reseach, Vol.2, o.1, pp. 17-25 Ploeg, van de F, 1984, "eneal Least Squaes Methods fo Balancng Lage Systems and Tables of atonal Accounts," Revew of Publc Data Use, 12, 17-33 Hummels, D., J. Ish, and K. Y, 2001, The atue and owth of Vetcal Specalzaton n Wold Tade, Jounal of Intenatonal Economcs 54:75 96. Johnson, Robet, and ullemo oguea, 2009, Accountng fo Intemedates: Poducton Shang and Tade n Value-added, Mmeo, Pnceton Unvesty, June. Koopman, Robet, Zh Wang and Shang-jn We, 2008, How much Chnese expots s eally made n Chna Assessng foegn and domestc value-added n goss expots, BER Wokng Pape 14109, June. Robet Koopman, Zh Wang and Shang-jn We Estmatng domestc content n expots when pocessng tade s pevasve." Jounal of Development Economcs 99(2012):178-189. Robnson, Sheman, Andea Cattaneo and Moataz El-Sad. Updatng and Estmatng a Socal Accountng Matx Usng Coss Entopy Methods Economc System Reseach, 13(1), Mach 2001, p. 47-64. Sébasten Moudot, Rane Lanz and Alexandos Ragousss, 2010 TRADE I ITERMEDIATE OODS AD SERVICES OECD Tade Polcy Wokng Pape o. 93, Januay. Stone, Rchad, Davd. Champenowne and James E. Meade, 1942, "The Pecson of atonal Income Estmates," Revew of Economc Studes, 9(2), 110-125. 24
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Table 1 Mean Absolute Pecentage Adjustment fom TAP Database (V8) by Regon, 2007 Intemedate demand Fnal demand Intemedate demand Fnal demand Countes Domestc Impots Domestc Impots Countes Domestc Impots Domestc Impots Austala 10.3 61.8 6.1 96.0 Fance 8.3 37.8 2.4 92.3 ew Zealand 14.1 77.6 3.7 94.6 emany 9.7 33.6 3.9 89.4 Chna 30.2 65.6 13.0 90.2 eece 19.9 43.3 3.8 97.0 Hong Kong 12.3 43.1 5.8 97.3 Hungay 22.4 40.6 7.7 96.4 Japan 5.3 44.7 1.3 95.2 Ieland 19.9 31.1 9.0 91.0 Koea 12.5 44.8 4.2 91.1 Italy 8.9 46.1 2.3 92.1 Tawan 15.7 33.9 4.1 94.8 ethelands 13.6 41.6 4.1 97.7 Indonesa 10.4 54.4 3.9 97.9 Poland 13.7 45.4 3.7 93.6 Malaysa 13.3 33.3 6.3 100.6 Potugal 13.9 48.1 4.1 94.5 Phlppnes 19.1 38.0 4.8 93.3 Rest of EEU 24.2 39.9 13.3 97.3 Sngapoe 30.0 25.0 8.7 107.9 Span 11.2 41.9 2.9 91.6 Thaland 13.8 31.0 5.2 95.7 Sweden 15.9 50.4 4.9 91.3 Vet am 25.5 36.1 9.2 100.6 Unted Kngdom 10.1 49.4 2.8 91.3 Rest of East Asa 20.1 58.5 9.4 94.1 EFTA 15.2 37.7 3.7 97.3 Inda 9.4 48.2 3.1 94.9 Bulgaa 24.3 52.0 7.7 100.3 est of south Asa 23.3 44.2 15.8 95.4 Romana 18.3 53.8 4.1 95.2 Canada 8.7 34.8 1.7 98.5 Russan Fedeaton 7.4 69.7 3.9 94.7 Unted States 5.0 39.6 1.3 93.6 Rest of East Euope 19.0 38.6 12.0 97.5 Mexco 64.0 123.4 18.2 96.5 Rest of Fome SU 16.9 54.2 11.9 97.7 Agentna 12.8 71.7 2.7 97.8 Tukey 14.2 53.8 3.5 93.2 Bazl 6.4 65.7 1.4 96.0 Saud Aaba 17.5 47.3 6.4 78.5 Rest of Mecosu 26.3 78.5 8.2 104.0 Rest of Westen Asa 19.5 43.4 12.7 95.9 Chle 14.1 56.1 4.2 105.4 Egypt 25.3 64.3 7.1 96.2 Peu 12.4 137.3 8.2 96.1 Moocco 18.0 69.7 9.1 89.0 CAFTA 21.9 46.0 14.5 95.8 Rest of oth Afca 19.0 51.4 11.4 98.4 Colomba 11.8 72.3 2.0 99.0 West Afca 32.5 55.2 20.2 94.7 Rest of Ameca 16.1 50.4 5.8 99.0 Cental Afca 23.9 49.8 14.3 92.5 Austa 20.3 42.3 4.7 101.8 East Afca 24.5 52.2 18.0 93.0 Belgum and Lux 18.7 20.6 5.4 106.2 South Afca 9.9 56.7 4.0 91.5 Czech and SVK Republc 17.4 37.1 8.1 91.6 Rest of South Afcan Customs Unon 38.8 82.7 15.7 108.6 Denmak 23.6 50.0 6.6 96.1 Rest of Wold 23.3 54.7 9.9 95.3 Fnland 19.3 58.1 5.9 96.0 Wold Total 12.9 45.7 4.0 93.9 26
Table 2 Mean Absolute Pecentage Adjustment fom TAP Database (V8) by Secto, 2007 Intemedate demand Fnal Demand TAP secto Domestc Impots Domestc Impots agp Cop poducton 11.6 55.6 15.5 88.3 an Anmal husbandy 20.1 74.7 7.6 97.6 fs Foesty 39.7 91.4 42.7 98.0 fsh Fshng 80.4 69.8 13.1 91.8 coa Coal 74.1 33.8 308.1 118.2 ol Ol and gas 8.1 8.8 142.9 1050.5 omm Mneals nec 25.4 43.0 33.3 102.3 met Meat and Day poducts 23.0 66.2 6.2 89.3 ofd Food poducts nec 16.7 61.6 8.0 84.9 b_t Beveages and tobacco poducts 24.9 79.1 7.5 86.9 tex Textles 18.5 50.5 24.8 68.7 wap Weang appael 48.0 73.3 5.7 59.3 lea Leathe poducts 88.4 65.7 15.2 103.4 lum Wood poducts 35.2 49.7 13.7 78.3 ppp Pape poducts publshng 15.2 69.0 12.1 89.0 p_c Petoleum coal poducts 19.8 63.5 19.8 99.0 cp Chemcal ubbe plastc poducts 18.0 32.9 21.9 51.1 nmm Mneal poducts nec 13.7 73.1 45.0 64.1 _s Feous metals 14.2 38.9 95.3 101.0 nfm Metals nec 23.3 29.6 143.9 148.4 fmp Metal poducts 14.0 60.7 24.0 59.2 mvh Moto vehcles and pats 18.8 31.4 9.1 62.4 otn Tanspot equpment nec 34.3 38.0 11.3 72.6 ele Electonc equpment 22.4 26.6 22.9 69.5 ome Machney and equpment nec 20.8 35.5 13.8 56.3 omf Manufactues nec 26.3 77.2 12.4 71.5 ely Electcty 10.1 94.0 3.4 97.5 gdt as manufactue and dstbuton 10.9 106.9 17.3 98.0 wt Wate 8.1 760.0 6.0 99.7 cns Constucton 3.5 134.2 0.4 99.5 td Tade 3.9 87.5 1.4 98.2 otp Othe tanspotaton 8.5 99.5 3.8 95.9 wtp Wate tanspotaton 21.4 50.8 37.9 87.7 atp A tanspotaton 23.9 66.7 32.6 59.9 cmn Communcaton 7.6 115.8 4.7 98.8 of fnancal sevces nec 5.7 78.2 3.4 97.7 ns Insuance 12.7 89.2 5.1 97.4 obs busness sevces nec 6.5 80.8 4.9 97.1 os eceatonal and othe sevces 10.5 144.6 2.6 98.1 osg publc admn and defence educaton health 7.0 96.6 0.4 99.3 dwe Dwellngs 5.6 100.0 0.1 100.0 Tot Total 12.9 45.7 4.0 93.9 27
Table 3 Mean Absolute Pecentage Adjustment fom TAP Database (V8) by Regon, 2004 Intemedate demand Fnal Demand Intemedate demand Fnal Demand Countes Domestc Impots Domestc Impots Countes Domestc Impots Domestc Impots Austala 20.3 129.4 8.3 97.8 Fance 16.1 98.2 4.6 98.3 ew Zealand 53.9 218.2 17.0 97.4 emany 18.4 98.2 5.4 96.9 Chna 12.7 84.3 5.6 102.0 eece 59.0 133.0 7.3 101.3 Hong Kong 28.3 107.2 15.0 106.9 Hungay 56.7 101.5 16.6 98.6 Japan 10.4 133.0 2.6 100.1 Ieland 46.4 85.6 21.8 94.2 Koea 30.9 114.2 11.0 103.6 Italy 15.7 118.2 4.8 100.8 Tawan 45.1 97.4 15.1 109.1 ethelands 24.8 103.3 6.6 102.8 Indonesa 44.1 137.9 9.5 101.8 Poland 31.8 109.5 7.2 98.2 Malaysa 42.7 82.9 29.4 112.8 Potugal 48.1 134.3 9.1 100.0 Phlppnes 73.2 101.1 18.4 102.5 Rest of EEU 51.5 123.9 19.0 105.0 Sngapoe 67.9 72.8 21.1 139.4 Span 20.8 107.3 3.5 98.8 Thaland 66.0 105.8 28.7 116.4 Sweden 30.8 117.6 7.2 97.5 Vet am 79.9 133.9 26.2 106.8 Unted Kngdom 16.5 104.4 5.6 96.8 Rest of East Asa 59.9 187.8 39.2 97.2 EFTA 28.9 101.6 5.6 101.3 Inda 25.4 125.7 6.7 103.8 Bulgaa 80.1 193.3 47.0 117.6 est of south Asa 58.0 157.9 17.6 99.0 Romana 64.6 148.3 14.1 99.9 Canada 19.3 72.5 3.3 102.3 Russan Fedeaton 23.1 157.4 10.0 97.0 Unted States 11.3 112.3 2.5 98.8 Rest of East Euope 57.5 130.9 12.6 117.8 Mexco 22.3 82.4 3.9 100.0 Rest of Fome SU 48.9 157.3 31.1 101.1 Agentna 56.2 187.8 12.7 96.8 Tukey 42.0 148.3 5.7 99.3 Bazl 25.0 143.9 4.4 98.9 Saud Aaba 38.3 126.4 11.3 85.4 Rest of Mecosu 68.9 240.4 49.5 110.7 Rest of Westen Asa 36.1 103.1 12.4 102.1 Chle 61.9 185.0 14.2 106.7 Egypt 70.5 138.1 17.3 96.6 Peu 65.1 441.8 25.8 97.7 Moocco 72.0 202.8 19.9 97.1 CAFTA 67.2 167.6 35.0 96.2 Rest of oth Afca 57.7 161.1 18.8 101.8 Colomba 60.2 210.0 14.8 97.7 West Afca 61.5 154.2 27.7 97.8 Rest of Ameca 40.4 139.1 9.0 101.0 Cental Afca 86.8 179.0 35.6 99.0 Austa 39.6 102.7 10.1 102.8 East Afca 63.1 160.6 25.7 96.3 Belgum and Lux 33.2 66.6 6.6 112.5 South Afca 34.7 152.0 9.1 99.7 Czech and SVK Republc 37.8 91.3 10.0 100.0 Rest of South Afcan Customs Unon 80.8 197.0 53.6 131.5 Denmak 39.0 124.5 13.4 97.7 Rest of Wold 61.1 175.1 23.9 97.2 Fnland 41.2 120.2 9.4 101.8 Wold Total 20.4 109.4 5.6 99.7 28
Table 4 Mean Absolute Pecentage Adjustment fom TAP Database (V8) by Secto, 2004 Intemedate demand Fnal Demand TAP secto Domestc Impots Domestc Impots agp Cop poducton 24 133 16 92 an Anmal husbandy 23 236 21 93 fs Foesty 71 221 65 88 fsh Fshng 64 412 46 92 coa Coal 29 100 132 1605 ol Ol and gas 25 100 203 20659 omm Mneals nec 43 76 68 435 met Meat and Day poducts 35 242 9 95 ofd Food poducts nec 28 168 10 94 b_t Beveages and tobacco poducts 40 385 11 96 tex Textles 35 129 24 79 wap Weang appael 80 542 25 66 lea Leathe poducts 58 349 38 93 lum Wood poducts 36 124 17 84 ppp Pape poducts publshng 20 126 14 91 p_c Petoleum coal poducts 28 111 21 82 cp Chemcal ubbe plastc poducts 27 65 28 77 nmm Mneal poducts nec 22 142 45 81 _s Feous metals 21 100 74 3506 nfm Metals nec 36 100 115 4083 fmp Metal poducts 19 112 27 83 mvh Moto vehcles and pats 25 83 13 65 otn Tanspot equpment nec 43 142 18 84 ele Electonc equpment 35 63 19 79 ome Machney and equpment nec 33 79 20 85 omf Manufactues nec 43 270 20 85 ely Electcty 22 99 8 95 gdt as manufactue and dstbuton 36 171 23 99 wt Wate 38 210 18 100 cns Constucton 12 458 1 100 td Tade 11 332 3 99 otp Othe tanspotaton 15 163 8 98 wtp Wate tanspotaton 48 131 50 89 atp A tanspotaton 42 156 40 82 cmn Communcaton 15 142 5 99 of fnancal sevces nec 11 121 5 99 ns Insuance 24 138 4 99 obs busness sevces nec 9 115 6 98 os eceatonal and othe sevces 19 211 2 99 osg publc admn and defence educaton health 17 238 1 100 dwe Dwellngs 56 100 1 100 Tot Total 20.4 109.4 5.6 99.7 29
Appendx A ew database county/egon TAP # TAP Reg County ame AUS Austala 1 AUS Austala ZL ew Zealand 2 ZL ew Zealand CH Chna 4 CH Chna HK Hong Kong 5 HK Hong Kong JP Japan 6 JP Japan KOR Koea 7 KOR Koea TW Tawan 9 TW Tawan ID Indonesa 12 ID Indonesa MYS Malaysa 14 MYS Malaysa PHL Phlppnes 15 PHL Phlppnes SP Sngapoe 16 SP Sngapoe THA Thaland 17 THA Thaland VM Vet am 18 VM Vet am ID Inda 21 ID Inda CA Canada 26 CA Canada USA Unted States of Ameca 27 USA Unted States of Ameca MEX Mexco 28 MEX Mexco AR Agentna 30 AR Agentna BRA Bazl 32 BRA Bazl CHL Chle 33 CHL Chle COL Colomba 34 COL Colomba PER Peu 37 PER Peu AUT Austa 49 AUT Austa BEL Belgum and Lux 50 BEL Belgum 64 LUX Luxemboug CEZ Czech and SVK Republc 52 CZE Czech Republc DK Denmak 53 DK Denmak FI Fnland 55 FI Fnland FRA Fance 56 FRA Fance DEU emany 57 DEU emany RC eece 58 RC eece HU Hungay 59 HU Hungay IRL Ieland 60 IRL Ieland ITA Italy 61 ITA Italy LD ethelands 66 LD ethelands POL Poland 67 POL Poland PRT Potugal 68 PRT Potugal ESP Span 71 ESP Span SWE Sweden 72 SWE Sweden BR Unted Kngdom 73 BR Unted Kngdom BR Bulgaa 78 BR Bulgaa ROU Romana 81 ROU Romana RUS Russan Fedeaton 82 RUS Russan Fedeaton SAU Sad Aba 98 SAU Saud Aaba TUR Tukey 99 TUR Tukey EY Egypt 102 EY Egypt MAR Moocco 103 MAR Moocco ZAF South Afca 127 ZAF South Afca EFTA EFTA 74 CHE Swtzeland 30
75 OR oway 76 XEF Rest of EFTA XEA Rest of East Asa 3 XOC Rest of Oceana 8 M Mongola 10 XEA Rest of East Asa 11 KHM Camboda 13 LAO Lao People's Democatc Republc 19 XSE Rest of Southeast Asa XSA est of south Asa 20 BD Bangladesh 22 PL epal 23 PAK Pakstan 24 LKA S Lanka 25 XSA Rest of South Asa XMC Rest of Mecosu 31 BOL Bolva 36 PRY Paaguay 38 URY Uuguay ROA Rest of Ameca 29 XA Rest of oth Ameca 35 ECU Ecuado 40 XSM Rest of South Ameca 45 PA Panama 47 XCA Rest of Cental Ameca 39 VE Venezuela 48 XCB Cabbean CFT CAFTA 41 CRI Costa Rca 42 TM uatemala 43 HD Honduas 44 IC caagua 46 SLV El Salvado XE12 Rest of EEU 51 CYP Cypus 54 EST Estona 62 LVA Latva 63 LTU Lthuana 65 MLT Malta 69 SVK Slovaka 70 SV Slovena XEEU Rest of East euope 77 ALB Albana 79 BLR Belaus 80 HRV Coata 83 UKR Ukane 84 XEE Rest of Easten Euope 85 XER Rest of Euope XSU Rest of Fome SU 86 KAZ Kazakhstan 87 KZ Kygyztan 88 XSU Rest of Fome Sovet Unon 89 ARM Amena 90 AZE Azebaan 91 EO eoga XWS Rest of Westen Asa 92 BHR Bahan 93 IR Ian Islamc Republc of 94 ISR Iael 95 KWT Kuwat 96 OM Oman 31
97 QAT Qata 100 ARE Unted Aab Emates 101 XWS Rest of Westen Asa XF Rest of oth Afca 104 TU Tunsa 105 XF Rest of oth Afca XWF West Afca 106 CMR Cameoon 107 CIV Cote d'ivoe 108 HA hana 109 A gea 110 SE Senegal 111 XWF Rest of Westen Afca XCF Cental Afca 112 XCF Cental Afca 113 XAC South Cental Afca XEC East Afca 114 ETH Ethopa 115 KE Kenya 116 MD Madagasca 117 MWI Malaw 118 MUS Mautus 119 MOZ Mozambque 120 TZA Tanzana 121 UA Uganda 122 ZMB Zamba 123 ZWE Zmbabwe 124 XEC Rest of Easten Afca XSC Rest of South Afcan Customs Unon 125 BWA Botswana 126 AM amba 128 XSC Rest of South Afcan Customs Unon ROW Rest of Wold 129 XTW Rest of the Wold 130 RT on-repotes 32