Growth Accounting, Productivity Analysis, and Purchasing Power Parity in Korea ( )*

Transcription

1 Growth Accountng, Productvty Analyss, and Purchasng Power Party n Korea ( )* June 27, 2005 Hak K. Pyo, Keun-Hee Rhee, and Bong Chan Ha** *Paper for presentaton at the 15 th nternatonal Input-Output Conference, June 27 - July 1, 2005, Beng, P.R. of Chna *Professor, Dvson of Economcs, Seoul Natonal Unversty, (E-mal) pyohk@plaza.snu.ac.kr ; Senor Researcher, Korea Productvty Center ; Researcher, Insttute of Economc Research, Seoul Natonal Unversty

2 1.Introducton In recent years, both growth accountng and productvty analyss by sector have attracted renewed attenton on a global scale. The decomposton of nputs nto captal (K), labor(l), energy(e) and ntermedate materals(m) for detaled ndustry-level analyss of productvty growth was frst appled to the post-war US Economy by Jorgenson, Gollop, and Fraumen(1987). The basc KLEM-methodology has been extended to cover eght European countres by the European KLEM proect as explaned n Tmmer(2000) and Canada-Japan-USA database(see e.g. Lee and Tang(1999)). In addton, the number of decomposed nputs has ncreased to nclude mported goods (I) and servces (S) formng the framework of KLEMS methodology as done by Forsgerau and Sorenson (1999). Earler studes along the lne of the KLEM approach n Korea nclude Pyo(1984), Kwon(1986), Kwon and Yuhn(1990), and Pyo and Kwon(1991). But ther man nterests were lmted to estmatng elastcty of substtuton and productvty growth, usng data n manufacturng sector only (Pyo(1984) and Kwon(1990), Kwon and Yuhn(1990)) or value-added accountng (Pyo and Kwon(1991)). More recent emprcal works such as Km and Hong (1997), Pyo(2000), Tmmer and van Ark(2000), Rhee(2001) also gross output accountng. The earler excepton was Km and Park (1988) but t was also lmted to manufacturng sector. Therefore, t would be desrable for us to attempt a growth accountng of gross output n a consstent framework of the KLEM approach. In addton to growth accountng, the nternatonal comparson of productvty among countres requre a consstent database for purchasng power party (PPP) at ndustral level by each country. The purpose of ths paper s to dscuss the applcaton of the KLEM-methodology to the Korean database and present the result of gross output growth accountng. After a revew of avalablty of data, I conclude that the detaled consstent output and nputs data for the Korean economy exst for the perod of except estmates of PPP. But there has been progress made n estmatng PPP by Izum(2002) and ESCAP(2002). The data n constant prces are avalable wth 1995 as the base year and the decomposton of data s as detaled as specfed by the summary of the frst ICPA proect by Kuroda (2001) and qute comparable to the Japanese database. In what follows n secton 2, a revew of KLEM framework s presented. Secton 3 examnes data on gross output and value-added from natonal accounts and Input-Output Tables, the measurement of factor nputs and purchasng power party. Secton 4 presents the result of growth accountng and productvty analyss. The last secton concludes the 2

3 paper. 2.Framework of KLEM Model Followng Jorgenson, Golop and Fraumen (1987) and Jorgenson and Stroh(1999) and Kuroda(2001), Suppose that an ndustry wth a state of technology, T(t), at the perod t s descrbed by a lnear homogeneous producton functon wth n nputs, Z = f X, X,..., X, T ( )), (1) ( 1 2 n t where the functon s twce-dfferentable, concave and monotonc. Under compettve market condton, the producer s behavor s alternatvely descrbed by a prce possblty fronter dual to (1): q = g (p 1, p2,..., p, T ( t)), (2) n where sector and respectvely. q and p and Z stand for the vector of prces and quanttes of outputs of the -th X represent the prces and quanttes of nputs of the -th sector, Assumpton of the producer s behavor n the compettve market s a sort of workng hypothess n our analyss. Changes of the relatve factor prces had an mpact on the shfts of resources among factors and the allocatons of those among sectors serously. From equatons (1) and (2), the growth rate n techncal effcency n the producton functon and the growth rate of output prce reducton derved from techncal change are defned respectvely as follows: ln Z T dt dt d = ln Z dt n = ln Z d ln X ( ) ( 1 ln X dt ), (3) and lnq T dt dt = n = 1 lnq ( ln p d ) ( ln p dt d ) lnq dt. (4) 3

4 4 Usng the Konus-Byushgen lemma under the condton of producers equlbrum n compettve market, we obtan: n n v X p X p X X Z X X Z X Z = = = = = 1 1 ) / ( ) / ( ln ln. (5) Symmetrcally applyng the Shephard s lemma to the dual prce possblty fronter functon, we obtan: n v X p X p p q = = =1 ln ln (6) Insertng (5) and (6) nto (3) and (4) respectvely we obtan: dt dt T q dt dt T Z = = ln ln. ϕ ϕ. (7) On the other hand, as a defnton of the accountng balance n the -th sector, the followng equaton can be ntroduced: X P Z q =, (8) where P and Z stand for the vectors of prces and quanttes of nputs. Dfferentatng by tme, X X P P Z Z q q + = +. (9) We can deduce the TFP growth rate of the -th sector from equaton (9) as follows:

5 ϕ ϕ = Z Z X X = P P q q, (10) where ϕ s Z / X, that s, an ndex of TFP Equaton (10) mples that the TFP growth rate as an ntegrated measure of the effcency of producton can be defned as the dfference of the growth rates between output and nputs. Under the condton of producer s equlbrum n a compettve market, t s completely correspondng to the formulaton of the rate of techncal change of (7). The necessary condtons for producer equlbrum are combned wth growth rates of nputs and output n each sector to provde the ndex of sectoral techncal change that depends only on prces and quanttes of the sector s nputs and output. Consderng data for the -th ndustral sector at any two dscrete ponts of tme, the average rate of TFP change n that sectoral can be expressed as the dfference between successve logarthms of output less a weghted average of the dfferences between successve logarthms of captal(k), Labor(L), energy(e), and ntermedate nputs (M) : ln Z ( t ) ln Z ( t 1) = { v K [ ln K ( t ) ln K ( t 1) ] + v L [ ln L ( t ) ln L ( t 1) ] + v E [ ln E ( t ) ln E ( t 1) ] + v M [ ln M ( t ) ln M ( t 1) ] + v T }, ( = 1,..., J ). (11) The weghts are gven by the average shares of captal, labor, energy, ntermedate nputs n the value of output: and: v v v v K L E M 1 = 2 1 = 2 1 = 2 1 = 2 [ v ( t) + v ( t 1) ] K [ v ( t) + v ( t 1) ] L [ v ( t) + v ( t 1) ] E [ v ( t) + v ( t 1) ] M L K E M 5

6 v T [ v ( t) + v ( 1) ] 1 = T T t 2 Two components of ths ndex takng account of techncal change, the translog quantty ndces of change n sectoral labor nput and n sectoral captal nput, wll dscuss n detal later respectvely. Indces are based on translog aggregator functons, defnng sectoral labor nput and captal nput over each nput s ndvdual components. Lkewse, the translog specfcaton for the aggregator functon of ntermedate nput can be wrtten as: X exp J J J 1 α ln X + β h ln X ln X h, ( = 1,2..., J ) 2 (12) h = Consderng data for the -th sector at any two dscrete ponts of tme, the quantty ndex of change n sectoral energy and ntermedate nputs can be wrtten as the weghted average of the dfferences n logarthms of ndvdual nputs: [ ln E ( t) ln E ( t 1 ] ln E ( t) ln E ( t 1) = v E ) [ ln M ( t) ln M ( t 1 ] ln M ( t) ln M ( t 1) = v M ) (13) where the weghts are gven by the average value shares of the -th ndustry s total ntermedate nput outlay accrung to respectve types of ntermedate materal ( X = M ) and energy ( X = E) nputs: v X 1 = [ v X ( t) + v X ( t 1) ], 2 and where the value shares of each ntermedate nput s: v X = p X X p X X 6

7 3. Database for Korea ( ) 3.1 Gross Output Data from Natonal Accounts and Input-Output Table Natonal accounts by the Bank of Korea (1999) (2002) reports annual seres ( ) of gross output, ntermedate consumpton, GDP, ndrect taxes, consumpton of fxed captal, domestc factor ncome, compensaton of employees, and operatng surplus of 21 ndustres ncludng 9 manufacturng ndustres and 3 sub-sectors of government servces n current prces followng 1993 UN System of Natonal Accounts. The Bank of Korea has also publshed Input-Output Tables snce Its most recent 2000 Input-Output Table s the 19 th Table. The detaled descrpton of Input- Output Tables durng s summarzed n Table 1. The 1995 Table has 77 ndustral sectors and 28 larger classfcaton. Therefore, the estmaton of tme seres Input-Output Tables followng those methods descrbed n Kuroda(2001) would be requred f we have to estmate KLEM model wth more than 21 ndustral classfcatons snce Input-Output Tables are avalable only n selected years. For the present study, we have generated gross output and value-added by 33 ndustres through RAS method. The generated annual data of both gross output and value-added have been adusted to match aganst Natonal Income Accounts whch do not contan both ndrect tax and subsdy. Snce RAS method s senstve to the ntal value of the I/O coeffcents, we have used the I/O Table n the closest year as ntal value. We have used V-Table to generate commodty prces by 33 sector and then used the generated commodty prces to estmate output prces by 33 sector. The resultng estmates of output and value-added are presented n Appendx Table A-3 and Table A- 4. The respectve prce ndces of output and commodtes are presented n Table 17 and Table Measurement of Captal Stocks The success of late ndustralzaton by newly ndustralzng economes could not have been made possble f both the rapd accumulaton of captal and ts changng dstrbuton among sectors were not realzed n ther development process. However, t s dffcult to dentfy these factors emprcally because the tme seres data of captal stocks n fast-developng economes by both types of assets and by ndustres are not readly avalable. The lack of nvestment data for a suffcently long perod of tme to apply the perpetual nventory estmaton method was the man cause of the problem. 7

8 However, the Natonal Statstcal Offce of the Republc of Korea has conducted natonwde natonal wealth survey four tmes snce Korea s one of fewer countres whch have conducted economy-wde natonal wealth surveys at a regular nterval. Snce the frst Natonal Wealth Survey(NWS) was conducted n 1968, the subsequent surveys were made n every ten years n 1977, 1987, and 1997, respectvely. Snce such regular surveys wth naton-wde converge are very rare n both developed and developng countres, an analyss on the dynamc profle of natonal wealth seems warranted to examne how natonal wealth n a fast growng economy s accumulated and dstrbuted among dfferent sectors. The estmaton of natonal wealth by types of assets and by ndustres was made by Pyo (1998) and updated n Pyo(2002) by modfed perpetual nventory method and polynomal benchmark-year estmaton method usng four benchmark-year estmates. The latter study modfes and extends the earler one n two respects. Frst, the result of 1997 NWS has been released n 1999 so that we can make use of addtonal benchmark-year estmates. Second re-basng the estmates of captal stocks from 1990 prces to 1995 prces seems nevtable because Bank of Korea has re-based ther natonal accounts accordngly 1) Natonal Wealth Survey n Korea In Natonal Wealth Survey (NWS), the gross captal stock (GK) was evaluated by multplyng the purchase prce of the fxed tangble asset by the approprate prce ndex by types of assets whch have been compled and prepared by the Bank of Korea datng back to the year 1910 as follows : GK = P PI t t where GK s the value of gross captal stock of asset evaluated at a certan benchmark year m (m>t), P s the purchase prce of the asset n year t, and PI s the prce ndex to reflect the value of the asset. In other words, the gross captal stock s supposed to reflect the repurchase value or reacquston value of the fxed tangble asset. Second, the net captal stock (NK) was deduced by multplyng the gross captal stock by the resdual cost rato as follows: n N t t NK = GK S ( S ) = P PI ( ) P P t t n N 8

9 Table 1. Input-Output Tables n Korea ( ) 1. Transacton Tables at Producers Prces (number of sector classfcaton) Year basc small medum large * * * * * * * * * Extended I-O tables wth sector classfcaton of the precedng man I-O tables. 2. Lnked Input-Output Tables (number of sector classfcaton) Year small medan large Supportng Tables (1) Employment Table: * 1995* 1997* (*ncludng employment matrx) (2) Fxed Captal Formaton Table: U-Table and V-Table (22 ndustres ncludng 9 Manufacturng ndustres) Annual U-Table at 1990, 1995, or 2000 constant prces and annual V-Table n both current and constant prces for are avalable n 1994, 1999, 2004 Natonal Accounts. Sources: The Bank of Korea Input-Output Tables( ) CD-ROM and Monthly Bulletn (May 2001) 9

10 where S s the value of survved assets, N s average servce lfe of the asset and n s the number of years elapsed. In other words, a proportonal deprecaton method s adopted. The value of survved assets s assumed to be 10 percent of the purchase value when t reaches the assumed average servce lfe and 1 percent of the purchase value when t reaches double the assumed average servce lfe. Suppose for example, the average servce lfe of a certan asset such as personal computer s 5 years and t was purchased three years age at the prce of 3,000 dollars. In addton, assume that the nflaton rate of the computer prce snce the purchase year s 20 percent. Then the followng calculatons can be made: GK NK = 3, = 3,600 = 3, ( 300 3,000 ) 3 5 Korea s NWS assumes shorter average lves than OECD(1996) and BEA(1993). For example, the OECD average asset lfe for buldngs and structures n 48 years and BEA s are years for resdental structure and years for nonresdental structure whle Korea s NWS assumes average servce lfe of years for resdental buldngs and 8-60 years for nonresdental buldngs. For automobles, Korea s assumed average lfe (4-5 years) s shorter than BEA s (10 years). The shorter average lfe and, therefore, hgher deprecaton rate are typcal n the process of late ndustralzaton. 2) Estmaton of Net and Gross Captal Stock (1) Estmatng Method for In prncple the exstence of four benchmark year estmates of gross and net captal stocks makes t possble for us to apply the polynomal benchmark year estmaton method. In my earler studes (Pyo 1988, 1992, and 1997), I estmated proportonal retrement rates and deprecaton rates both by types of assets and by ndustres based on the polynomal equatons. When we appled the polynomal benchmark year equaton to estmate the proportonal retrement rates for the sub-perods of and , most of estmates became negatve ncludng the average economy-wde retrement rates (-3.0% for and 3.1% for ) except other Constructon(0.6%) and Transport Equpment(3.4%) n and Nonresdental Buldng(0.9%) n Therefore, 10

11 followng Pyo(1997), we have appled the polynomal benchmark year estmaton method to estmatng deprecaton by types of assets only. Thus we have generated net stocks by types of assets frst for the perod of and then, dstrbuted them over dfferent sectors of ndustres by usng nterpolated ndustral weghs between the respectve benchmark years. We have decded to estmate net captal stock frst and then to estmate gross captal stock by usng nterpolated net-gross converson ratos for the followng two reasons. The frst reason n due to the fact that the margn of predcton error from the polynomal benchmark year equaton turns out to the larger wth gross captal stock that wth net captal stock as had been observed n Pyo(1992). (2) Estmatng Method for Snce we have decded to use estmates of 1968 NWS as the frst benchmark year estmates, we have to estmate captal stocks for the perod of usng the perpetual nventory equaton backward. We frst deflated the net stock data of 1968 NWS n current prces nto those n 1995 prces usng mplct GDP deflator of captal formaton. Then, we have estmated captal-output coeffcents by ndustres by regressng net captal stock n 1995 constant prces on real GDP by ndustres and a lnear tme trend varable durng the perod of n order to generate estmate of net stocks by ndustres durng n 1995 prces. We have also used the 1968 NWS weghts of dfferent types of assets for the perod of In order to estmate net stocks by ndustres for the perod of , we have used both 1968 NWS and 1977 NWS to estmate nterpolated ndustral weghts by each type of assets. Then, for the perod of , we have used both cumulated weghts of captal formaton by ndustres from old Natonal Income Accountng by the Bank of Korea (1984 ) and ndustral weghts of 1968 NWS. (3) Estmatng Method after 1997 Natonal Statstcal Offce of Korea has decded to termnate Natonal Wealth Survey by 1997 and to swtch from drect estmaton to ndrect estmaton of natonal wealth followng the method of BEA and OECD. The cost of such drect natonal wealth survey has ncreased sgnfcantly as the sze of natonal economy has expanded consderably. In addton, some of the partcpatng nsttutons such as Ctzens Bank for unncorporated busness enterprses have been prvatzed so that Natonal Statstcal Offce alone can no longer afford natonal wealth survey. Japan had termnated ts 11

12 Natonal Wealth Census n 1977 for almost the same reasons. Therefore, for the perod after 1997 whch s the last natonal wealth survey, we have to estmate captal stocks by a modfed perpetual nventory method usng 1997 NWS as benchmark estmates. Frst, we estmate net stocks by type of assets n constant prces by usng the deprecaton rates estmated from the perod of and dstrbute them across ndustres usng both ndustral weghts n 1997 NWS and those n subsequent Mnng & Manufacturng Census and Surveys and Wholesale and Retal Surveys. In the long run, the estmated deprecaton rates by type of assets may need to the updated and revsed by the mcro data-based studes. Second the generated net stocks by type of assets and by ndustres have to the converted nto gross stock by usng the net-gross converson rato of 1997 NWS for the tme beng. But ultmately we may need further studes on the trend of net-gross converson rato by type of assets and by ndustres and the average asset lfe. 3) Reconclaton wth Database of Pyo(2002) Snce the database of Pyo (2002) covers 10 broad categores of ndustral sector together wth 28 sub-sectors of Manufacturng, t can be reclassfed and reconcled wth 33-sector classfcaton for the ICPA proect. Assumng that the flow of captal servce s proportonal to captal stock, we used the average captal stock of two years as the captal servce 1. We have attached the captal servce nput n 33 sectors n Appendx Table A-5. In order to make qualty adustments to the captal nput data, we have taken the followng steps : (1) Followng Kuroda(2001, we defne the captal servce of asset n ndustry as K ( t) = b ( t) A ( t) = 1,.., n; = 1,... m 1 = b ( t) [ A ( t) + A ( t 1)] 2 where b (t) denote the proporton of the -th asset type on the -th sector s total captal stock A (t) whch s the average of unweghted sum over all assets 1 We could not use the formula of Kuroda and Nomura(2000) because nvestment data classfed by both asset type and ndustry was unavalable. 12

13 durng the t-th and (t-1)th perod. (2) The growth rate of captal servce nput s defned as ln K ( t) ln K ( t 1) = + [ ] v ln b ( t) ln b ( t 1) [ ln A ( t) ln A ( t 1) ] l = 1,2,..J where v s the average share of an ndvdual component n the value of property compensaton. The frst term on the rght sde s the change of the quantty of captal servce and the second term s the change of the qualty of the captal servce. The growth rate of the qualty of captal s so neglgble, n that t s so small n comparson to the growth rate of the quantty of captal. The average growth rate of the qualty of captal s % whch s % of the total growth rate of captal. There was no substantal change n the structure of captal n Korea durng We have attached the growth rate of the qualty of captal n 33 sectors n Appendx Table A-20. 4) Measurement of Captal Input Prce Followng Jorgenson, et.al (1987) and Tmmer(2001), the aggregate ndex of captal servces over the dfferent types of assets n -sector( (t) ) can be assumed as a translog functon of the servces of ndvdual assets ( (t) ) as follows: A K [ ln A ] ( t) ln A ( t 1 ln K ( t) ln K ( t 1) = v ) where weghts are gven by the average shares of each type of captal n the value of 1 property compensaton, v = [ v ( t) + v ( t 1) ] and 2 p ( t) K ( t) v ( t) = wth p ( t) K ( t) P (t) the rental prce of captal servces from asset type. In order to apply the above aggregaton formula, t s necessary to mpute the rental prces of captal servces. In the absence of taxaton, Hall and Jorgenson(1967), Jorgenson, Golop and Fraumen(1987) and Jorgenson and Yun(1991) have derved the followng formula for mputng the rental prce of captal servces from asset type. P ( t) = { r( t) + Π ( t)]} q ( t 1) δ 13

14 where r (t) s the rate of return, q (t) s the acquston prce of nvestment good [ q ( t) q ( t 1)] wth Π ( t) = whch s the rate of nflaton n the prce of q ( t 1) nvestment good. The nomnal rate of return after tax s usually assumed the same for all assets n an ndustry so that r (t) does not have subscrpt. The applcaton of the above formula requres data on the rate of return, the acquston prce of nvestment good, and the rate of deprecaton of asset n each ndustry. Even though we may assume that the nomnal rate of return after tax s the same for all assets n each ndustry, we do not have data for the nomnal rate of return for each ndustry. Therefore, we estmated the rate of return for each ndustry, r (t), by usng the equalty between the nomnal captal share and p ( t) K( t). In addton, the acquston prces of each asset n dfferent ndustres are not usually avalable and, therefore, nvestment deflators are frequently used as substtutes for the acquston prces. But nvestment deflators n Natonal Accounts are avalable ether by types of assets or by ndustres not by both. Estmates of deprecaton rates n Pyo(2002) are also avalable ether by types of assets or by ndustres not by both. As a consequence, the lkely outcome of the applcaton of the formula could be the same estmates of rental prce of captal for each type of asset across all ndustres. In case of Korea, there have been studes by Yun and Km(1997) and Won and Hyun(2000) on the estmaton of effectve margnal tax rates followng methods developed by Kng and Fullerton(1984) and Jorgenson and Landau(1993). Snce they have used the above formula for cost of captal, they must have generated mputed cost of captal. But ther estmates are based on all ndustres not for each ndustry and even those estmates are not avalable. Faced wth lack of data and consstent estmates for the varables to mpute rental prce of captal n each ndustry, we have adopted the followng approach. The followng approach can be defned as an ndrect method of mputng rental prce of captal n -sector from the annualzed I/O Table. Snce we have the data of total compensaton to captal n -sector n current prces( P K K ) from the annualzed I/O Table, we mpute the mplct prce of captal n -sector( P K ) by dvdng the total compensaton to captal by our estmates of captal stock n -sector n constant prces( K ): 14

15 q K = qk K / K The resultng estmates by 33 ndustres are presented n Table A Measurement of Labor Input In order to measure labor nput for KLEM model, we have to obtan both quantty data of labor nput such as employment by ndustres and hours worked and qualty factors such as sex, educaton and age. Both avalablty and relablty of labor statstcs n Korea have mproved snce But the measurement of labor nput by ndustres cannot be readly made because the statstcs of employment by ndustres are not detaled enough to cover 33 sectors. Followng the characterstcs of labor nput descrbed n Kuroda (2001), the sources of labor statstcs are presented n Table 2. Economcally Actve Populaton Yearbook by Natonal Statstcal Offce reports number of employed, unemployed, noteconomcally-actve populaton and economcally actve populaton by 10 categores of age group (15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, and 60- over 60). Employment by ndustres s avalable n 7 broad categores of ndustres:(1) Agrculture, Forestry and Fshng (2) Mnng (3) Manufacturng (4) Constructon (5) Wholesale, Retal, Restaurants and Hotel (6) Electrcty and Gas, and (7) Transportaton, Storage and Fnance. More detaled classfcatons of employment wll have to rely on Employment Table, whch s publshed as a supportng table to Input-Output Table. But t s avalable only every fve year when man Input-Output Tables are publshed. Mnng and Manufacturng Census and Survey by Natonal Statstcal Offce also report employment statstcs but t s lmted to mnng and manufacturng only. Unemployed persons by gender and educatonal attanment are also avalable from the same source. Report on Monthly Labor Survey by Mnstry of Labor publshes monthly earnngs and workng days of regular employees by 12 broad categores of ndustres. Survey Report on Wage Structure by the same mnstry reports wages by 6 categores of occupatonal classfcaton n old seres ( ) and now reports 9 new categores n new seres ( ): (1) Senor Offcals and Managers (2) Professonals (3) Techncans (4) Clerks (5) Servce and Sales Workers (6) Sklled Agrculture and Fshery Workers (7) Craftmen and Assembler (8) Plant and Machne Operator and (9) Other Laborer. Nomnal and real wage ndex are also avalable from Report on Monthly 15

16 Labor Survey by Mnstry of Labor. For the present study, we have obtaned the raw data fle of Survey Report on Wage Structure from the Mnstry of Labor and Economcally Actve Populaton Survey from Natonal Statstcal Offce for the perod of The data are classfed by two types of gender (Male and Female), three types of age(16-34old, 35-54old, and 55 above old), and three types of educaton(mddle school, hgh school, and college school) and, therefore, there s a total of 18 categores of labor. Snce the raw-data fle of the Survey Report on Wage Structure contans more detaled ndustral classfcaton (3-dgts) than that of the Economcally Actve Populaton Survey (2-dgts), we calculated the quantty of labor from the Economcally Actve Populaton Survey and the qualty of labor from the Survey Report on Wage Structure. Ths enables us to nclude self-employed labor as well as to use more detaled data. However, snce the Survey Report on Wage Structure does not nclude the Agrculture and Government sectors, we had to use the Economcally Actve Populaton Statstcs for these two sectors. In order to make qualty adustments to the employment data, we have taken the follow steps : 1) Defnng P Ll as wage rate for -sector and l-type category of labor, the share of labor ncome by l-type category of labor n -sector can be expressed as; v Ll p Ll l = pllll L The average weght of -sector and l-type labor ncome durng the perod of (t-1) and t can be generated as; v Ll [ v ( t) + v ( 1) ] 1 = Ll Ll t 2 2) In order to make a qualty adustment to labor nput data, we have further decomposed labor nput of -sector and l-type as follows : L ( t) d ( t) M ( t) H ( t) l = l where d l denotes relatve weght of workng hours by. In other words, L l (t) measures labor nput of l-type labor n -sector. 16

17 3) Fnally, the growth rate of -sector labor nput has been computed as follows: ln L ( t) ln L ( t 1) = [ ln M ( t) ln M ( t 1) ] + [ ln H ( t) ln H ( t 1) ] + v Ll [ d ( t) ln d ( t 1) ] l ln = 1,2,..J where the frst bracket on the rght hand sde measures change n employment, the second bracket measures change n average workng hours, and the thrd bracket measures the change n qualty of labor through change n weghted workng hours. The average growth rate of the qualty of labor s 1.47% and t s about 35% of the growth rate of labor. It s a relatvely hgh proporton n comparson to the proporton of the qualty of captal. We have attached the growth rate of the qualty of labor n 33 sectors n Appendx Table A-20. For the present study, we have obtaned from the Mnstry of Labor the raw data fle of the Survey Report on Wage Structure for the perod of We have reclassfed the survey fle nto 33 ndustral sectors. Snce the Survey Report dd not nclude the agrculture and publc sector, we have used the data of the Economcally Actve Populaton Survey(NSO) for those sectors. 3.4 Measurement of Energy Input and Materal Input In order to separate energy nput from materal nput, we have to decompose ntermedate nputs nto these two nput categores. For ths purpose, we have used IO Tables and dentfed 5 sectors (sector 2, 4, 14, 28 and 29) as energy nput sector and the remanng 28 sectors as materal nput sector. For the years when IO Tables are not avalable, we have used the straght-lne nterpolaton method to measure energy nputs and materal nputs nto 33 sectors. 3.5 Deflators for Gross Output and Inputs The 21-sector gross output data by Bank of Korea s natonal accounts are avalable only n current prces. For the perod after 1985, we have used V Table n both constant and current prces to generate mplct gross output deflators by sector. For the perod before 1985, we have used Lnked IO Table n constant prces to generate mplct gross output deflators by sector for 1985 and nterpolated the data for For the deflators of energy nput and materal nput, we have used the same sources of data; V Table for the perod after 1985 and Lnked IO Table before The basc characterstcs of 17

18 KLEM database n Korea( ) n 1995 prces s presented n Table 3. Durng the perod of , Korea s gross output and GDP have grown at the average annual rate of 7.95 percent and 7.56 percent respectvely. Four nputs have grown at the rate of 9.36%(K), 3.15%(L), 5.28%(E), and 8.47%(M) respectvely. Table 2. Characterstcs of Korean Database for KLEM Model Gross output Natonal Accounts Input-Output Table (1980,1985,1990,1995,2000) U-Table and V-Table Annual tables ( ) Captal stock (captal servces) Gross and Net Stock data: Natonal Wealth Survey (1987)(1997) Captal Formaton data: Natonal Accounts(1999, 2004) Annual Gross and Net Stock by Industres and by Types of Assets and Estmated Deprecaton Rates are avalable n Pyo (2001) Net stock and Investment data: Mnng and Manufacturng Census Mnng and Manufacturng Survey - Types of assets: resdental buldng, nonresdental buldng, other constructon, transport equpment, machnery equpment Labor nput (hours worked) Survey Report on Wage Structure (Mnstry of Labor) Economcally Actve Populaton Survey (NSO) -sex (male/female) -educatonal attanment (mddle school graduates, hgh school graduates and college above) -age classes (16~34, 35~54, and 55 above) Number of Sectors or types of assets and 10 large ndustres wth 28 submanufacturng ndustres 18 types 2 types 3 types 3 types 18

19 Intermedate nputs Natonal Accounts Input-Output Table(1980, 1985, 1990, 1995,2000) U-Table and V-Table Annual Tables( ) Energy Input; sectors 2, 4, 14, 28, and 29 PPP -prces of outputs of each ndustry n both Korea and the reference economy and exchange rate 21 sector by V- Table 3.6 Input Shares Regardng shares of nputs, we have used Compensaton of Employees n Gross Domestc Product and Factor Income by knd of Economc Actvty n natonal accounts and Operatng Surplus to generate relatve share of labor nput and captal nput respectvely n total value-added and then adusted them nto shares n total gross output. We have dvded the amount of energy nput and materal nput by gross output to generate shares of energy nput and materal nput respectvely. Estmated shares of nputs are attached n Appendx as Table A-9 to A

20 Table 3. Characterstcs of KLEM Database n Korea( ) (n 1995 prces) year Gross Output bllon Won K bllonwon L hour E bllon Won M bllon Won Average growth rate Measurement of Purchasng Power Party So far there was no systematc efforts to measure PPP by sector n Korea except the aggregate estmates beng used n the estmaton of real nterest rates by Lee(1997), ESCAP, OECD(2002), and Izum(2002) whch estmated PPP between Japan and Korea. The estmates by Izum(2002) are shown n Table 4. But the Bank of Korea has ust started a proect for estmatng PPP wth OECD. Therefore, we may be able to use ther estmates at the later stage of ICPA proect. 20

21 OECD(2002) reports estmates of PPP for 1999 benchmark year as shown n Table 5 for selected countres. The unsutablty of usng ndces of nomnal GDP to make nternatonal volume comparsons s even more apparent when the per capta ndces of nomnal and real GDP are consdered. For example, when ranked by ther ndces of nomnal and real GDP per head, Japan s 3, the Unted States s 5, Sweden s 8 and Canada s 17. However, when the prce level effect s removed and countres are ranked by ther ndces of real GDP per head, the Unted States s 2, Canada s 8, Japan s 11 and Sweden s 16. Generally ndces of real GDP per head narrow the gap between hgh ncome countres and low ncome countres. The Bank of Korea(2000) reports the estmates of Purchasng Power Party (PPP) exchange rates among 14 member countres by UN ESCAP. UN had ntated Internatonal Comparson Program (ICP) snce 1968 and ts sxth program was conducted durng (1993 as baseyear) wth the partcpaton of 123countres (14 countes n ESCAP regon). Under the sxth ICP ESCAP had adopted 1993 as baseyear and Hongkong dollar as benchmark currency for the ESCAP regon. The program proceeded by the followng three steps; (1) GDP (expendture sde) data from each country was readusted followng ICP classfcaton (a total of 142 tems ncludng 120 tems of household consumpton expendture, 3 tems of pubc consumpton expendture, and 19 tems of captal formaton). Then prce data of ICP core commodty lst were collected and readusted accordngly. (2) Prce ndces by tems were computed by makng use of prce matrx table. (3) To make nternatonal comparson possble, prce ndces by tems were averaged and converted nto HongKong dollar.. As shown n Table 6, the estmated PPP exchange rates n 1993 n the ESCAP regon except Japan were lower than the market exchange rates ndcatng that the purchasng power of ESCAP member countres currences except Yen were undervalued. Yen was overvalued by 71.8 percent whle Korean Won, for example, was undervalued by 20.1 percent. Whle Japan s PPP-adusted per capta GDP n 1993 was 154,663 HK$, ts per capta GDP evaluated by market average exchange rate was 265,638 HK$, Korea s correspondng fgures were 73,235 HK$ and 58,474HK$ respectvely. Accordng to the Bank of Korea(2000), the gap between PPP-adusted per capta GDP n US$ and per capta GDP evaluated by market average exchange rate n US$ had peaked n 1975 by 21

22 the rato of 2.55 to 1 but has declned over tme to reach to the rato of 1.31 to 1 n The ESCAP report also presents an nterestng relatve prce ndces by maor expendture categores of member countres. In general, countres wth hgher per capta ncome have mantaned relatvely hgher prces of government consumpton goods and relatvely lower prces of nvestment goods. For example, compared wth 1993 GDP deflator ndex of 100, Japan s relatve prce ndces of government consumpton goods and nvestment goods were 150 and 92 respectvely. Korea s correspondng ndces were estmated to be 158 and 78 respectvely. The relatve prce structure by commodty groups reveals that the relatve prce of prced-servces and non-tradable goods tend to be hgher as per capta ncome grows. Ths pattern of relatve prce structure ndcates that hgher-ncome economes tend to have hgher productvty n producng commodtes and lower productvty n producng servces due to relatvely hgher wage costs n servce sector. In case of Japan., the relatve prce ndces of prced servces and all servces were and respectvely and those of tradable goods and non-tradable goods were and respectvely. In case of Korea, the correspondng prce ndces of servces were and respectvely, whle the correspondng prce ndces of tradable goods and non-tradable goods were and respectvely. In order to prepare for the measurement of purchasng power party for the ICPA proect by 33 sectors, we have generated mplct gross output deflators by 33 sectors as attached n Appendx Table A-17. The only avalable data source for both gross output and GDP deflators for the perod of was V-Table (Make Matrx) publshed by Bank of Korea(1994)(1999) as attached Appendx Table A-18. Snce t covers only 21 sectors, we have assumed that the mplct deflators would be equal among subsectors. For the year 1984, we have used lnked IO Tables of 1980 and 1985 n both current and constant prces and nterpolated the mplct deflators. Durng the perod of , the gross output deflator of total ndustres has grown at an average annual rate of 3.70 percent. Lastly, the relatve prce structure of gross output deflators could be generated and attached n Appendx Table A

23 4. Growth Accountng and Productvty Analyss We have conducted a prelmnary growth accountng and productvty analyss for Korea ( ). We have qualty-adusted the nput data. In other words, we have used educaton, sex and age data for labor nput and ndvdual asset data for captal nput to consder the changes of the nput structures. 4.1 Gross Output Growth Accountng In Table 8, we present average growth rates of gross output and four nputs n 1995 constant won wth nput shares durng the perod of n Appendx as Table A- 9, A-10, A-11 and A-12. The gross output (YO) of all ndustres has grown at the average annual rate of 7.95 percent whle captal (K), labor (L), energy (E), and materal nput (M) have grown at the rate of 9.36 percent, 3.15 percent, 5.28 percent, and 8.47 percent respectvely durng the perod. The average estmated shares of four nputs were 0.20( V K ), 0.20 ( V L ), 0.08 ( V E ) and 0.52 ( V M ) respectvely. The economy-wde growth rate of total factor productvty(tfp) s estmated to be 0.57 percent. Therefore, the relatve contrbuton of TFP to output growth s estmated to be 7.35 percent, whch s of rather sgnfcant magntude. The total factor productvty growth n gross output growth s lower than the one wthout qualty adustment n nput data. However, t s stll qute sgnfcant magntude reectng the Krugman s(1994) proposton and earler emprcal fndngs by Young(1994) and Lau and Km(1994). 4.2 Productvty Trend and Captal-Output Coeffcents We have generated a seres of labor productvty; gross output per employee( YO / L ). Durng the perod of , the growth rates of economy-wde output labor productvtes were 6.16 percent. The trends n labor productvty are shown n Fgure 1. We have also generated a seres of captal-output coeffcents; captal-gross output coeffcents ( K /Y0 ) as shown n Table 9. The economy-wde captal-output coeffcent has grown at average rate of 1.42 percent. The economy-wde captal-labor rato has grown at 7.66 percent. 23

24 Table 4 Korea /Japan Purchasng Power Partes 1990 offcal Rate : 4.89 won per yen 1995 offcal Rate : 8.20 won per yen Industral Classfcaton Korea Japan Korea Japan Geometrc average Geometrc average Weght Weght Weght Weght 001 Paddy Other agrcultural Products Lvestock Forestory Fshery Crude petroleum and natural gas* Other mnng Food, beverage and tobacco Textle, leather, and the products thereof Tmber and wooden products Pulp, paper and prntng** Chemcal products Petroleum and petro products Rubber products Non-metallc mneral products Metal products Machnery Transport equpment Other manufacturng products** Electrcty, gas, and water supply Constructon Trade and transport Servces Publc admnstraton All Industres average Sources : Izum (2002) 24

25 Table 5. Exchange rates, PPPs, comparatve prce levels and ndces of nomnal and real fnal expendture on GDP, 1999 n Selected Countres Country Exchange Rates PPPs Comparatve prce levels Nomnal GDP Real GDP Nomnal GDP per head Real GDP per head France Germany Italy Unted Kngdom Japan Korea Mexco Unted States Euro Eu OECD Sources : OECD(2002) Table J 25

26 Table 6 Internatonal Comparson of Purchasng Power Party n Selected Countes (1993) Hong Kong Japan Korea 1. PPP adusted Exchange Rate and Market Exchange Rate PPP-adusted Exchange Rate (A) Market Exchange Rate (B) A/B(%) Comparson of GDP and Per Capta GDP n the ESCAP Regon GDP PPP Exchange rate(a) 894,734 19,332,901 3,226,430 (HK$ Market Average Exchange 894,734 33,204,751 2,576,143 Mllon) rate(b) A/B(%) Per PPP Exchange rate(a) 151, ,663 73,235 Capta Market Average Exchange 151, ,638 58,474 GDP (HK $) rate(b) A/B(%) Composton of Per Capta Expendture (ESCAP AVERAGE = 100) GDP Consumer Expendture Government Expendture Captal Formaton Relatve Prce by the types of Goods Prced Servces All Servces Commodtes Traded Goods Non-Traded Goods Source: Bank of Korea (2000) 26

27 Table 7 Relatve Prce Structure n Selected ESCAP Countres(1993) Per Capta Expendture (ESCAP Average = 100) Relatve Prce (GDP = 100) Composton of Per Capta Expendture(GDP = 100) HK Japan Korea HK Japan Korea HK Japan Korea Consumer Expendture Food, Tobacco Beverage, Bakery, cereal Meat Fshery Dary Products Fruts, Vegetables Beverage Tobacco Clothng, Footwear Housng, water, electrcty, gas Furnshngs, Household Equpment Health Transport Educaton, Recreaton Recreaton Educaton Others Government Expendture Captal Formulaton Equpment Constructon GDP

28 Table 8 Gross Output Growth Accountng for Korea ( ) Growth output Captal Input Labor Input Energy Input Materal Input TFP 1 Agrculture coal mnng Metal non-metal Ol and gas Constructon Food Textle Apparels lumber and wood Furnture paper alled prntng,publshng,alled Chemcals petroleum products Leather stone,clay,glass prmary metal fabrcated metal Machnery electrcal machnery Motor transportaton equp Instrument Rubber msc.manufacturng transportaton communcaton electrc utlty gas utlty Trade Fnance and real estate other prvate servce Publc servce Total

29 Table 9. Trends n Productvty and Captal-Output Coeffcents n Korea( ) Year YO/L K /YO K/L Average Growth Rate(%)

30 Fgure 1. Labor Productvty YO/L Year Fgure 2. Captal-Output Coeffcent / Captal-Labor Rato

31 5. Concluson In the present paper, we have constructed a Korean database of gross output, GDP, and four nput seres for the perod of n the framework of KLEM Model. Accordng to a gross-output growth accountng, the Korean economy has recorded a sgnfcant growth of gross output at the average rate of 7.95 percent durng the perod of On the other hand, captal(k), labor(l), energy(e), and materal nput(m) have grown at the rate of 9.36 percent, 3.15 percent, 5.28 percent, and 8.47 percent respectvely durng the perod. The average estmated shares of four nputs were 0.20( V K ), 0.20 ( V L ), 0.08 ( V E ) and 0.52 ( V M ) respectvely. As a consequence, the total factor nput and total factor productvty has ncreased at the average annual rate of 7.38 percent and 0.57 percent respectvely. Therefore, the relatve contrbuton of total factor productvty to gross output growth s estmated to be only 7.35 percent whch s of rather nsgnfcant magntude. We thnk that the qualty adustment n labor nput has played a role n estmatng lower growth rate of total factor productvty. We are reconfrmng both Krugman s(1994) proposton and emprcal fndngs by Young(1994) and Lau and Km(1994). We also have noted that there was a dscernable structural turnng pont after the 1997 economc crss n Korea: both captal-gross output coeffcent and captal-value-added coeffcent started to fall after

32 References Bank of Korea, The Report on the Sxth Internatonal Comparson Program by ESCAP, Aprl 2000 (In Korean) Bank of Korea, Natonal Accounts, (1994, 1999) Bank of Korea, Input-Output Tables ( ) CD-ROM Forsgerau, M.and A. Sorenson, Deflaton and Decomposton of Dansh Value-added growth usng the KLEMS-methodology, mmeograph, December 7, Centre for Economc and Busness research, Mnstry of Trade and Industry, Copenhagen, 1999 Hall and Jorgenson, Tax Polcy and Investment Behavor, Amercan Economc Revew, 57:3, June, pp , 1967 Izum and Lee, Innovaton and I-O Technque, Pan-pacfc Assocaton of Input-Output Studes, No.4. volume 8, 1999 (n Japanese) Izum, Hrosh Korea s PPP, Paper presented at thrd ICPA Workshop November 29-30,2002 Tokyo, Japan Jorgenson, D.W., F. M. Gollop and B.M.Fraumen, Productvty and US Economc Growth, Cambrdge MA: Harvard Unversty Press, 1987 Jorgenson, D.W. and K. Stroh, Informaton Technology and Growth, Amercan Economc Revew, Papers and Proceedng, 89(2), pp , 1999 Jorgenson, D.W., Captal Theory and Investment Behavor, Amercan Economc Revew, May, 1963 Jorgenson, D.W. and Landau, Ralph, Tax reform and the Cost of Captal, The Brookngs Insttuton, Washngton, D.C., 1993 Kng, Mervyn and Don Fullerton, The Taxaton of Income from Captal, Unversty of Chcago Press, 1984 Km, Kwang Suk and Sung Duk Hong, Accountng for Rapd Economc Growth n Korea, , Korea Development Insttute, KDI Press, Seoul,

33 Km and Park, Sources of Economc Growth n Korea, , Seoul: KDI Press, 1985 Krugman, Paul, The Myth of Asa s Mracle. Foregn affars, November/December 1994 Mythng My Pont, Letters to the Edtor, Foregn affars, March/Aprl 1995, La Crox Sumner J. and Chung H.Lee, Letters to the Edtor, March/Aprl Kwon, J.K, Captal Utlzaton, Economes of Scale and Techncal Change n the Growth of Total factor Productvty, Journal of Development Economcs, vol. 24, No. 2, November 1986, pp Kwon, J.K, Kyhyang Yuhn, Analyss of Factor Substtuton and Productvty Growth n Korean Manufacturng , Korean Economc Development, Greenwood Press, Westport, CT, pp , Kuroda, M., The Internatonal Comparson of the Productvty among Pan-pacfc Countres, mmeo, 2001 Lau, Lawrence J. and Jong-Il Km, The Sources of Growth of East Asan Newly Industralzed Countres, Journal of Japanese and Internatonal Economes, Lee and Km, Estmates of Human Captal Stock n Korea, , Korean Socal Scence Journal, 23, PP , 1997 Lee, F.C. and J. Tang, Methodologcal Issues n Comparng Productvty Levels and Internatonal Compettveness n Canada and the Untes States, paper presented at AEA meetng, 7-9 January 2000, Boston Pyo, Hak K., Estmates of Captal Stock and Captal/Output Coeffcents by Industres for the Republc of Korea, , KDI Workng Paper No. 8810, Korea Development Insttute, Seoul, 1988a. Pyo, Hak K., Estmates of Captal Stock and Captal/Output Coeffcents by Industres: Korea, , Internatonal Economc Journal, summer 1988b. Pyo, Hak. K., A Synthetc Estmate of Natonal Wealth of Korea, , KDI Workng Paper No.9212, Korea Development Insttute, Seoul, 1992 Pyo. Hak. K, Estmates of Fxed Reproducble Tangble Assets n the Republc of Korea, , KDI Workng Paper No.9810, Korea Development Insttute, Seoul,

34 Pyo, Hak K., Estmates of Captal Stocks by Industres and Types of Assets n Korea ( ), Mmeograph, Insttute of Economc Research, Seoul Natonal Unversty, 2002 Rhee, Keun Hee, Total Factor Productvty Analyss n Korean Manufacturng, Korea Productvty Center, 2001(In Korean) Tmmer, M.P Towards European Productvty Comparsons usng the KLEMS Approach- An Overvew of Sources and Methods,, Gronngen Growth and Development Centre and The Conference Board, 2000 Tmmer, M.P and van Ark, Captal Formaton and Productvty Growth n South Korea and Tawan: Realsng the Catch-up Potental n a World of Dmnshng Returns, Gronngen Growth and Development Centre Unversty of Gronngen, 2000 Young, Alwyn, Lessons from the East Asan NICs : A Contraran Vew, European Economc Revew, papers and proceedngs, 1994 Yun and Km, Margnal Effectve Tax Rate of Corporate Investment n Korea, Publc Economcs, 1997 (n Korean) 34

35 Appendx Table A-1. Reclassfcaton of IO Tables nto 33 sectors Table A-2. Reclassfcaton of Natonal Accounts nto 33 Sectors Table A-3. Gross Output n 33 Sectors Table A-4. GDP n 33 Sectors Table A-5. Captal Stocks n 33 Sectors Table A-6. Labor Input n 33 Sectors Table A-7. Energy Input n 33 Sectors Table A-8. Materal Input n 33 Sectors Table A-9. Average Shares of Captal Inputs Table A-10. Average Shares of Labor Inputs Table A-11. Average Shares of Energy Inputs Table A-12. Shares of Intermedate Materal Inputs Table A-13. Captal Prce Index Table A-14. Captal Input Prce Table A-15. Energy Prce Index Table A-16. Materal Prce Index Table A-17. Prce Index of Commodtes from V-Table Table A-18. Prce Index of Gross Output Deflators by 33 Industres Table A-19. Growth rate of the qualty of captal nput Table A-20. Growth rate of the qualty of labor nput 35

36 Table A-1. Reclassfcaton of IO Tables nto 33 sectors (basc classfcaton) 33 Sectors Classfcaton n IO Table(1983) Classfcaton n IO Table(1985) 1. Agrculture 1~38 1~37 2. Coal Mnng 39 38~39 3. Metal and Mon-metal 40~54, 56~58 40~44, 46~51 4. Ol and Gas Constructon 313~ ~ Food 59~98 52~91 7. Textle 99~118, 121, 123~125 92~112, 117~ Apparels 119~120, 122, ~116, 123, Lumber and Wood 131~133, 135~ ~131, 133~ Furnture 134, 237, , 238, Paper alled 139~ ~ Prntng, Publshng, alled 149~ ~ Chemcals 152~172, 174~ ~ Petroleum products 186~ ~ Leather 126~ ~128, Stone, Clay, Glass 199~ ~ Prmary metal 214~217, 228~ ~218, 229~ Fabrcated machnery 218~227, 233~236, 238~242, 244~ ~228, 234~237, 239~243, 245~ Machnery 248~ ~ Electrcal machnery 262~ ~ Motor 292~ ~ Transportaton equp. 287~291, 296~ ~295, 300~ Instrument 300~ ~ Rubber and Msc. Plastc 173, 195, 197~ ~185, 196, 198~ Msc. manufacturng 304~ ~314, Transportaton 344~ ~ Communcaton 357~ ~ Electrc utlty 334~ ~ Gas and water utlty 338~ ~ Trade 341~ ~ Fnance 360~ ~ Other Prvate servce 368~ ~375, 378~ Publc servce ~377 36

37 33 Sectors Classfcaton n IO Table(1990) Classfcaton n IO Table(1995/1998) 1. Agrculture 1~34 1~30 2. Coal Mnng 35~36 31~32 3. Metal and Mon-metal 39~50 35~45 4. Ol and Gas 37~38 33~34 5. Constructon 325~ ~ Food 51~93 46~88 7. Textle 94~109, 113~115 89~104, 111~ Apparels 110~112, 116, ~108, Lumber and Wood 125~ ~ Furnture 131, 238, ~ Paper alled 132~ ~ Prntng, Publshng, alled 143~ ~ Chemcals 146~ ~ Petroleum products 177~ ~ Leather 117~121, 123~ ~110, 114~117, Stone, Clay, Glass 194~ ~ Prmary metal 210~212, 223~ ~198, 209~ Fabrcated machnery 213~222, 228~237, 289, 241~ ~208, 214~ Machnery 246~ ~ Electrcal machnery 265~ ~ Motor 298~ ~ Transportaton equp. 303~ ~ Instrument 294~ ~ Rubber and Msc. Plastc 188~ ~ Msc. manufacturng 312~ ~ Transportaton 346~ ~ Communcaton 359~ ~ Electrc utlty 318~ ~ Gas and water utlty 322~ ~ Trade 342~ ~ Fnance 361~ ~ Other Prvate servce 369~368, 378~ ~351, 360~369, 372~ Publc servce 376~ ~371 37

38 33 Sectors Classfcaton n IO Table(2000) 1. Agrculture 1~30 2. Coal Mnng 31~32 3. Metal and Mon-metal 35~45 4. Ol and Gas 33~34 5. Constructon 312~ Food 46~86 7. Textle 87~102, 109~ Apparels 103~106, Lumber and Wood 118~ Furnture 295~ Paper alled 124~ Prntng, Publshng, alled 133~ Chemcals 148~ Petroleum products 137~ Leather 107~108, 112~115, Stone, Clay, Glass 178~ Prmary metal 194~196, 207~ Fabrcated machnery 197~206, 212~ Machnery 226~ Electrcal machnery 246~ Motor 281~ Transportaton equp. 288~ Instrument 275~ Rubber and Msc. Plastc 172~ Msc. manufacturng 298~ Transportaton 333~ Communcaton 346~ Electrc utlty 305~ Gas and water utlty 309~ Trade 329~ Fnance 352~ Other Prvate servce 350~351, 361~371, 374~ Publc servce 372~373 38

39 Table A-2. Reclassfcaton of Natonal Account nto 33 Sectors Classfcaton n Natonal Account Reclassfcaton nto 33 Sectors 1. Agrculture, Forestry and fshng 1.Agrculture 2. Mnng and Quarryng 2. Coal Mnng 3. Food, Beverage and Tobacco 6. Food 4. Textle and Leather 7. Textle 3. Metal and Mon-metal 4. Ol and Gas 8. Apparels 15. Leather 5. Wood, Paper, Publshng and Prntng 9. Lumber and Wood 6. Petroleum, Coal, and Chemcals 13. Chemcals 11. Paper alled 12. Prntng, Publshng, alled 14. Petroleum products 24. Rubber and Msc. Plastc 7. Non-Metallc Mneral Products except Petroleum and Coal 16. Stone, Clay, Glass 8. Metal, Fabrcated Metal Products 17. Prmary metal 18. Fabrcated machnery 9; Machnery and Equpment 19. Machnery 10. Transport Equpment 21. Motor 20. Electrcal machnery 23. Instrument 22. Transportaton equp 11. Furnture and Other Manufacturng Industres 10. Furnture 25. Msc. manufacturng 12. Electrcty, Gas and Water 28. Electrc utlty 29. Gas and water utlty 13. Constructon 5. Constructon 14. Wholesale and Retal Trade, Restaurants and Hotels 30. Trade 15. Transport, Storage and Communcaton 26. Transportaton 27. Communcaton 16. Fnance, Insurance, Real Estate and Busness Servces 31. Fnance 17. Communty, Socal and Personal Servces 32. Other Prvate servce 18. Producers of Government Servces 33.Publc servce 39

40 Table A-3. Gross Output n 33 Sectors (Unt : mllon won) (1995 constant prce)

41 Table A-4. GDP n 33 Sectors (Unt : mllon won) (1995 constant prce) 41

42 Table A-5. Captal Input n 33 Sectors (Unt : bllon won) (1995 constant prce) 42

43 Table A-6. Labor Input n 33 Sectors (Unt : Hour) *The sum of the labor nput of ndustres 26 and 27 durng the perod of 1984~1992 s allocated n proporton to the year

44 Table A-7. Energy Input n 33 Sectors (Unt : mllon won) (1995 constant prce) 44

45 Table A-8. Materal Input n 33 Sectors (Unt : mllon won) (1995 constant prce) 45

46 Table A-9. Average Shares of Captal Inputs (Unt : %) 46

47 Table A-10. Average Shares of Labor Inputs (Unt : %) 47

48 Table A-11. Average Shares of Energy Inputs (Unt : %) 48

49 Table A-12. Average Shares of Intermedate Materal Inputs (Unt : %) 49

50 Table A-13. Captal Prce Index 50

51 Table A Captal Input Prce (Buldng and Structure) 51

52 Table A Captal Input Prce (Transportaton Equpment) 52

53 Table A Captal Input Prce (Machnery) 53

54 Table A Captal Input Prce (Total Asset) 54

55 Table A-15. Energy Prce Index 55

56 Table A-16. Intermedate Materal Prce Index 56

57 Table A-17. Prce Index of Commodtes from V-Table 57

58 Table A-18. Prce Index of Gross Output Deflators by 33 Industres 58

59 Table A-19. Growth rate of the qualty of captal nput (Unt : %) 59

60 Table A-20. Growth rate of the qualty of labor nput (Unt : %) 60