Estimates of Fixed Reproducible Tangible Assetsin the Republic of Korea, 1953-1996

by

Hak K. PyoSeoul National University

February, 1998

I am grateful for research collaboration by Dr. Ho-Young Kwon of Korea Broadcasting Research Instituteand able research assistance by Seung Hyun Hong and Sungbae An of Seoul National University. Theresearch was supported in part by Korea Development Institute.

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Contents

1. Introduction ....................................................................................................................... 1

2. The Core Definition of Capital Revisited ....................................................................... 3

(1) Terms and Coverage ................................................................................................ 3

(2) Gross Stocks vs. Net Stocks ................................................................................... 5

3. A Comparative Review of Alternative Estimation Methods ........................................ 6

(1) OECD .......................................................................................................................... 7

(2) BEA ............................................................................................................................ 11

(3) National Wealth Survey in Korea ........................................................................... 16

4. Estimation Method .......................................................................................................... 19

5. Estimates and Summary ..................................................................................... 31

References ............................................................................................................................. 44

Appendix Tables .................................................................................................................... 46

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1. Introduction

The debate on how to define capital and how to measure it has been described in theeconomics literature as either pandora’s box or black hole. A century has passed since B hm-Bawerk(1890) first pointed out the lack of transparent and consistent definition of capital.Schumpeter(1954) initiated the capital debate and Denison(1979) and Kendrick(1976) followed itup in decades later but it is still far from being resolved as evidenced in recent reviews byTriplett(1992) and Mamalakis(1992).

One of the most fundamental problems in defining capital is the coverage of capital, in otherwords what to include. In measurement, the core problem lies in availability, dependability andconsistency of data on benchmark stock estimates, past investments, and other informations ofasset retirement and depreciation. As a result, by and large, the data situation in the countryconditions the estimation of capital measures by its statistical authorities. The measurementproblem in developing countries is worse as underscored by Ward(1976) and Osada(1992).

The purpose of the present study is to update and improve my earlier estimates on fixedreproducible tangible assets in Korea which were published as Pyo(1988)(1992). The majordifferences in estimating methodology of the present study from my earlier ones lie in two aspects:One is the sequence of deriving estimates and the other is the method of deriving net capitalstocks prior to 1968(the first National Wealth Survey year) and after 1987 (the third and latestNational Wealth Survey year). For the sequence, I derived estimates of gross stock and netstocks independently in earlier studies using estimated retirement rates and depreciation ratesrespectively by the polynomial benchmark estimation method. But, the estimates of retirementrates in most industries were negative indicating that the data on capital formation used ingenerating gross capital stocks must have been underestimated. A careful reexamination of theBEA(1993) methodology reveals that the data on capital formations to be used in generatinggross stocks should include “transfers and net purchases of used assets”. Since the data on usedassets transactions are not available in Korea as pointed out in Hyun and Pyo(1997), I had togenerate net capital stocks first and then convert them into gross stocks by using the reciprocal ofthe residual cost ratio(net stock/gross stock) reported on National Wealth survey in 1968, 1977and 1987.

The second difference from earlier studies is the treatment of pre-1968 in all industries andpost-1987 estimates of gross and net stocks in Manufacturing. For pre-1968 estimates, I haveavoided using 1968 fixed weights by industries and have generated changing weights byregressing the post-1968 weights on a linear time trend variable over the period of 1953-1968. Onthe other hand, for post-1987 estimates, I have updated weights of Manufacturing industries byutilizing Census and Manufacturing survey data avoiding the use of fixed weights reported in 1987National Wealth Survey.

However, before updating and improving my earlier estimates, I have revisited the capitaldebate to clarify the definition of capital I have adopted in the present paper. In addition, I havereviewed alternative capital measures by OECD(1996) and BEA(1993) of the United States tohighlight the limitation of my estimates and its difference with their estimates.

In the present study, I have adopted the core definition of capital which is the narrowest oneamong alternative definitions. It includes only fixed reproducible tangible assets such asresidential and nonresidential structures and equipments and excludes such assets as inventorystocks, land and consumer durables. The reasons of going back to somewhat earlier coredefinition of capital is to minimize potential errors in imputations and to improve estimationmethods by distinguishing net stocks and gross stocks in a more consistent way.

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The paper is organized as follows. Section 2 revisits the capital debate and adopts the coredefinition as the working definition of capital following Mamalakis(1992). Section 3 reviewscomparative alternative estimation methods and treatment of national income data by OECD,BEA of the United States and National Wealth Survey in Korea. The estimation method in thepresent paper is outlined in datail in Section 4 which links benchmark estimates of NationalWealth Surveys with national income accounts data on capital formations in fixed reproducibletangible assets by industries. The resulting estimates by industries and by types of assets in bothconstant and current prices with quarterly industry-wide estimates are presented and acomparison is made with other earlier estimates in the last section.

2. The Core Definition of Capital Revisited

In order to put the capital debate in the right perspectives and to derive working definitions ofcapital stocks for the measurement purpose, we need to review various terms of definitions, theircoverage, and the distinction between gross stock and net stock.

(1) Terms and Coverage

In the literatures of capital debate and measurement, the terms “capital stock”, “assets”, and“wealth” have often been used interchangeably. For example, BEA(1993, M-2) uses the terms“fixed reproducible tangible wealth” and “capital stocks” synonymously. However, in myjudgement, the term “fixed reproducible tangible assets” seems more appropriate for the narrowerdefinition of capital because “capital” may include “human capital” as defined by Kendrick(1976),while “national wealth” often includes “consumer durables” as in BEA(1993) and net foreignliabilities as in Goldsmith(1975).

As summarized in Table 1 the alternative definitions of capital stocks and national wealth vary agreat deal in terms of coverage. It indeed ranges from the narrowest definition of capital which iscalled the core definition and covers fixed reproducible tangible assets only to the broadestdefinition of “total capital” by Kendrick(1976) which includes unreproducible fixed assets,intangible physical assets and human capital. Another wider definition was adopted byGoldsmith(1975) who used the term “gross national wealth” and included net foreign liabilities butexcluded human capital from consideration.

On the other hand, Mamalakis(1992) argues that broadening the coverage of capital stock mayentail too much imputations and measurement errors. He defines capital as those assets whichcan pass three criteria: “means-of-production”, “produced means of production”, and “durability”.The first criteria, “means-of-production”, eliminates assets held for the purpose nonmarketproduction such as consumer durables. The second criteria, “produced means of production”eliminates human capital from the core boundary of capital stock because human capital is notproduced commodity. Similarly, it excludes “social capital”, “political capital”, “institutional capital”,“technological capital” from consideration because all of these capitals are not produced means ofproduction. It also implies that neither leisure nor time can be treated as capital.

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However, not all produced means of production can become capital, because those belongingto consumers are not capital and intermediate products which are clearly produced means ofproduction but which are used up in production process cannot be regarded as capital. The lastcriteria “durability” means “durability in time” or “economic durability” rather than “materialdurability”. In other words, for c certain asset to be defined as capital, it must be used as an inputover a certain accounting periods. For example, a software can be regarded as capital because itis repeatedly used as an input over a certain period but a microchip is not capital but anintermediate product which cannot be separated from microchip functions and therefore, is usedup to make a personal computer. In this regard, public goods such as public administration,police, and national defense as well as semi-public goods such as education, health and welfareprograms cannot be defined as capital because they are either consumed (public administration,

<Table 1> Coverage and Definitions of Capital Stock

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

1. Coverage

(a) Private capital stock

(b) Government capital stock

(c) Consumer durables

(d) Inventory

(e) Land

(f) Physical intangible asset

(g) Net foreign liabilities

(h) Human capital

2. Definitions

Industrial Capital Stocks(Current Estimates) = (a) + (b)

BEA(1993)'s Total Capital = (a) + (b) + (c)

Fixed Tangible Assets1) = (a) + (b) + (c) + (d) + (e)

Kendrick(1976)'s Gross National Wealth

= (a) + (b) + (c) + (d) + (e) + (f) + (g) + (h)

Goldsmith(1975)'s Gross National Wealth

(1) = (a) + (b) + (c) + (d) + (e)

(2) = (a) + (b) + (d)

(3) = (a) + (b) + (d)

National Wealth Survey of Korea = (a) + (b) + (c) + (d)

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−1) This definition is equivalent to Goldsmith’s(1975) gross domestic wealth. He defines grossnational wealth as the sum of gross domestic wealth and net foreign liabilities.

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police, national defense, education and health) or used as intermediate inputs and used up inproduction process (expecially, R&D and medical services, etc.).

As a conclusion, any tangible or intangible asset which passes the above three criteria can bedefined as capital, therefore, for example, R&D expenditure cannot be defined as capital becauseit cannot pass, the criteria of economic durability.

(2) Gross stocks vs. Net stocks

In addition to the coverage, the distinction between gross stock and net stock has to be madeproperly so that the definition of capital can serve for the relevant purpose. According toBEA(1993, M-3), the gross stock is a measure of the cumulative value of past investment still inexistence after deduction of the cumulated value of investment that has been discarded, usingestimated average service life. The net stock is equal to the gross stock less accumulateddepreciation-that is, the decline in value due to wear and tear, obsolescence, accidental damage,and aging-on the items in the gross stock.

The net stock estimates and their associated estimates of depreciation are used in studies ofnational income, product, and wealth. On the other hand, there is a different concept of capitalstock called “productive capital stock” which measures the remaining productive servicesavailable in the stock and is used to derive estimates of capital input for productivity studies. Thenet stock and productive capital stock are compared by Triplett(1992).

In the International Sectoral Data Base(ISDB) of OECD(1996), gross capital stock data areused as measures of capital input in the production process representing the total volume of theexisting physical capital assets available in the respective countries and sectors. The gross stockmeasure is derived from a perpetual inventory model using data for past capital formationexpenditure, adjusted for scrapping assuming a delayed linear retirement pattern.

National Wealth Survey of Korea has adopted the following working definition for gross stockand net stock. The gross stock was defined as “replacement cost” and measured by multiplyingthe purchase price and price ratio. On the other hand, the net stock was defined as “adjustedreplacement cost” and measured by multiplying the gross stock by the residual cost ratio.

3. A Comparative Review of Alternative Estimating Methods

In order to generate time-series estimates of gross and net stocks, there are about threedifferent ways depending on the availability of benchmark year survey estimates. The first methodis the perpetual inventory model which uses past capital formation expenditure. As BEA(1993,M2) properly points out, capital stock estimates based on direct measurement of the stock aredifficult to be adopted because “these stock data are usually stated as book values and do notprovide the detailed information about the vintage of the assets that is necessary to derive stockestimates in the current-cost and constant-cost valuations that are useful for many kinds ofeconomic analysis”. However, the perpetual inventory model requires much longer investmentflows in both current and constant prices and additional informations on asset retirements anddepreciation. Therefore, it is mostly used for developed countries. Even OECD(1996) does notprovide us with net stock estimates because some of member countries do not have them andthere are too much difference in the assumed depreciation.

The second method is the so-called modified benchmark year method which links onebenchmark year estimate with investment flows data. Ohkawa et. al. (1996) used this methodwidely to generate stock estimates in late 19th century and early 20th century for Japan. I also

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used this method in my first study (Pyo, 1988) to derive stock estimates for Korea prior to 1968,the year when the first national wealth survey was conducted. Kim and Hong(1997) also adoptedthis method by using 1987 NWS data as a single benchmark year. The third estimating method isthe use of polynomial-benchmark equation which was initially adopted by Nishimizu(1974) toestimate economic depreciation rates and derive estimates of net stocks. I applied the method inPyo(1988) to estimate retirement rates and derive estimates of gross stocks following thespecification suggested by Ward(1976). But it requires at least two years’ benchmark estimatesand investment data between two years.

(1) OECD

OECD(1996) adopts a delayed linear retirement pattern with scrapping beginning five yearsafter the capital asset has been installed. It notes that, in practise, the assumptions on theretirement pattern vary widely between different national statistical offices. A linear retirementpattern is equivalent to assuming a uniform survival function. OECD(1996) argues that it is easy touse and consistent with the assumption of an acceleration in the rate of scrapping as capitalassets approach the end of their service lives. It also comes close to the class of survival functionsused by a majority of national statistical offices in OECD.

Under the assumption of a uniform survival function, the average service life(ASL) of a certainasset is equal to the sum of the parts of the initial investment scrapped multiplied by the relevantclass average of their age left( x + 1 over 2 right)‘ over the period 0 to n, its assumed finite servicelife1 :

ASL= SUM from { {x=0}} to n-1 a‘ ‘ left( x+ { 1} over {2 } right) ‘ ‘=‘ ‘ a ‘ ‘SUM from { {x=0 }}to n-1 x ‘ ‘+‘ ‘ a‘ ‘n cdot { 1} over {2 } (1)where a‘ is the proportion of retirement and therefore, a= { 1} over {n } under a uniform densityfunction.

Since a= { 1} over {n } , the expression of ASL is further simplified as :

ASL= {1} over {n} {n(n-1)} over {2} + {1} over {n} n {1 } over {2} = {n } over { 2} (2)

or

n ‘ ‘=‘ ‘ 2‘cdot‘ASL‘ ‘ (3)

Now, consider the survival coefficient(g) which is the complement of retirement of asset aged x:

g~=~ 1‘ ‘ -‘ ‘a‘ ‘ left(‘ ‘x‘+‘ { 1} over {2 } right)~ =~ 1‘ ‘ -‘ ‘ { 1} over {2AS L } left(‘x‘ ‘+‘ ‘ { 1}

over {2 } right) ‘ ‘ (4)

1) Suppose, for example, n=5. Then, each year the retirement proportion is a=1/5. First,

consider x=0 which is the case of retirement before one year and, therefore, the relevantclass average of their age is (0+1/2). Second, consider x=1 which is the case of retirementbefore two years and, therefore, the relevant class average of their age is (1+1/2). It goes onuntil x=4 because at x=5 the asset retires. In sum, the ASL of retired assets is :sum_x=0^n-1‘ ‘a‘ left(x+ 1 over 2 right) ‘ ‘=‘ ‘ 1 over 5 ‘ ‘ left( 1 over 2 + 3 over 2 + 5 over 2 + 7 over 2 + 9 over 2 right) ‘ ‘=‘ ‘ 1 over 5 times 25 over2 ‘ ‘=‘ ‘ 2.5

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Therefore, the perpetual inventory method can now be specified for gross capital stock in constantprices (GCS) as follows:

GCS= SUM INV _{j} g _{j } (5)

where INV is gross fixed capital formation in constant prices; and j is vintage of investment.

Substituting eq. (4) into (5), we get:

GCS = SUM INV_{j} ‘ ‘ left[‘ ‘ 1‘ ‘ -‘ ‘ { 1} over {2A S L } ‘ ‘ left(x+ { 1} over {2 } right) ‘ ‘ right]‘ ‘ (6)

However, if we introduce a delayed scrapping in period s‘ , then n = m + s‘ . As a result, thefinal specification for GCS is as follows :

GCS~=~ SUM from { { i=0}} to s-1 INV _{t-i} ~+‘ ‘ SUM from { { m=0}} to n-s INV_t-m-s‘ ‘ left[‘ ‘1‘ ‘ -‘ ‘a‘ ‘ left(m+ {1 } over {2 }right)‘ ‘ right] ‘ ‘ (7)

As OECD(1996, P.14) notes, the impact of a delay in scrapping on the estimated levels of thecapital stock depends on the rate of growth of capital investment. If the level of investment is risingover time, the estimated level of the capital stock will be higher, the longer the initial delay period,conversely, its level will be smaller when investment is falling. They have considered a number ofalternative specifications but the gross capital stock estimates proved to be relatively insensitiveto the choice of mortality functions.

While the OECD method of estimating gross capital stock is relatively easy to use, it cruciallydepended on the linear retirement pattern and assumed average asset lives. But the scrappingrate assumptions used by different national authorities tend to differ widely, for reasons whichreflect the methods of estimation used rather than fundamental difference in the nature of thecapital goods or their utilization. For example, the assumed average service life of buildings inmanufacturing is 42 years in Finland, compared with 70 years in Sweden as shown in Table 2

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<Table 2> Average Service Lives of Fixed Capital Stock for OECD Countries

CanadaME BC TOT

United StatesME BC TOT

JapanME BC TOT

AustraliaME BC TOT

BelgiumME BC TOT

FinlandME BC TOT

1 10 28 15 17 38 23 6 44 18 13 43 22 15 30 20 14 50 25

2 20 27 22 13 27 17 8 35 16 16 31 21 15 30 20 20 27 22

3 22 44 29 17 29 21 11 43 21 19 39 25 15 31 20 17 42 25

3-1 28 43 33 20 29 23 11 43 21 19 43 26 15 43 23 20 42 27

3-2 21 43 28 16 29 20 10 43 20 19 43 26 15 43 23 19 42 26

3-3 26 44 31 13 29 18 10 43 20 19 43 26 15 43 23 18 42 25

3-4 26 44 31 16 29 20 12 44 22 19 44 26 15 44 24 17 42 25

3-5 19 42 26 17 29 21 9 42 19 19 42 26 15 42 23 18 42 25

3-6 26 42 31 19 29 22 9 42 19 19 42 26 15 42 23 15 42 23

3-7 22 42 28 27 29 28 13 42 22 19 42 26 20 42 27 15 42 23

3-8 24 43 30 19 29 26 11 43 21 19 43 26 15 43 23 15 42 23

3-8-1 21 43 28 24 29 26 11 43 21 19 43 26 15 43 23 15 42 23

3-8-2 21 43 28 25 29 26 12 43 21 19 43 26 15 43 23 15 42 23

3-8-3 23 43 29 19 29 22 11 43 21 19 43 26 15 43 23 15 42 23

3-8-4 22 43 28 14 29 19 10 43 20 19 43 26 15 43 23 15 42 23

3-8-5 30 43 34 15 29 19 11 43 21 19 43 26 15 43 23 15 42 23

3-9 13 43 22 15 29 19 11 43 21 19 43 26 15 43 23 20 42 27

4 35 55 41 20 28 22 15 48 25 24 48 31 20 40 26 25 40 30

5 10 25 15 12 29 17 5 43 16 13 45 23 15 30 20 10 35 18

6 20 50 29 11 35 18 10 49 22 16 51 27 15 30 20 15 40 23

6-1 20 50 29 11 35 18 10 49 22 16 51 27 15 30 21 15 40 23

6-2 20 50 29 11 48 22 17 56 29 15 51 26 17 60 30 17 55 28

7 15 50 26 18 31 22 17 45 25 20 50 29 15 30 20 17 45 25

7-1 15 50 26 18 31 22 17 45 25 20 50 29 15 30 20 17 45 25

8 15 50 26 12 36 19 15 50 25 13 58 26 15 30 20 10 40 19

8-1 15 50 26 12 36 19 15 50 25 13 58 26 15 30 20 10 40 19

8-2 .. 62 62 .. 72 72 .. 47 47 .. 62 62 .. 62 62 .. 62 62

9 20 50 29 11 48 22 17 56 29 15 51 26 17 60 30 17 55 28

10 19 44 27 19 38 25 15 49 25 16 47 25 16 36 22 16 43 24

11 20 53 35 15 50 35 .. .. 35 15 54 35 15 80 35 .. 60 35

12 20 45 28 17 40 24 17 50 27 15 40 23 19 48 28 17 45 25

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<Table 2 (Continued)>

FranceME BC TOT

GermanyME BC TOT

ItalyME BC TOT

NorwayME BC TOT

SwedenME BC TOT

U.K.ME BC TOT

AverageME BC TOT

1 10 40 19 15 69 31 18 44 26 15 44 24 17 60 30 13 40 21 14 44 23

2 18 35 23 15 41 23 17 35 23 16 35 22 30 70 42 16 20 17 17 35 23

3 18 37 23 15 41 23 17 38 23 25 43 31 23 70 37 26 60 36 19 43 26

3-1 17 35 22 15 41 23 18 40 25 25 43 30 20 70 35 26 60 36 20 43 27

3-2 21 35 25 15 41 23 18 40 25 25 43 30 20 70 35 26 60 36 19 43 26

3-3 21 35 25 12 41 21 18 37 24 25 43 30 15 70 32 23 60 34 18 43 25

3-4 21 40 27 16 41 24 16 40 23 25 44 31 30 70 42 32 60 40 20 44 27

3-5 17 35 22 17 41 24 16 35 22 25 42 30 20 70 35 25 60 36 18 42 25

3-6 17 35 22 14 41 22 16 35 22 25 42 30 33 70 44 24 60 35 19 42 26

3-7 21 35 25 17 41 24 15 35 21 25 42 30 35 70 46 26 60 36 21 42 28

3-8 17 35 22 14 41 22 17 40 24 25 43 30 22 70 36 26 60 36 19 43 26

3-8-1 17 35 22 14 41 22 20 40 26 25 43 30 25 70 39 26 60 36 19 43 26

3-8-2 17 35 22 13 41 21 16 40 23 25 43 30 25 70 39 25 60 36 19 43 26

3-8-3 17 35 22 14 41 22 16 40 23 25 43 30 22 70 36 26 60 36 18 43 26

3-8-4 17 35 22 15 41 23 16 40 23 25 43 30 25 70 39 25 60 36 18 43 26

3-8-5 17 35 22 14 41 22 16 40 23 25 43 30 15 70 32 27 60 37 18 43 26

3-9 21 40 27 16 41 24 18 36 23 25 43 30 20 70 35 24 60 35 18 43 26

4 17 38 23 17 62 31 18 35 23 25 48 32 35 75 47 27 53 35 23 48 31

5 13 30 18 10 47 21 18 40 25 12 43 21 10 75 30 26 80 42 13 43 22

6 21 30 24 12 66 28 16 49 26 15 49 25 15 75 33 30 80 45 16 49 26

6-1 21 30 24 12 66 28 16 49 26 15 49 25 15 75 33 30 80 45 16 49 26

6-2 17 30 24 13 65 29 17 56 29 20 56 31 20 75 37 17 75 35 17 56 29

7 13 40 21 13 42 22 17 45 25 17 45 25 17 70 33 20 55 31 17 45 25

7-1 13 40 21 13 42 22 17 45 25 17 45 25 17 70 33 20 55 31 17 45 25

8 17 30 21 13 68 29 15 50 25 15 50 25 20 75 37 21 80 39 15 50 25

8-1 17 30 21 13 68 29 15 50 25 15 50 25 20 75 37 21 80 39 15 50 25

8-2 .. 62 62 .. 62 62 .. 62 62 .. 62 62 .. 75 75 . 100 100 .. 62 62

9 17 30 21 13 65 29 17 56 29 20 56 31 20 75 37 17 75 35 17 56 29

10 16 34 21 14 57 27 18 39 24 18 .. .. 25 72 39 30 67 41 18 48 27

11 17 30 35 15 77 35 .. .. 35 20 .. 35 20 75 35 20 75 35 .. .. 35

12 17 35 22 17 50 27 17 45 25 19 48 28 20 65 34 30 75 44 19 28 48

Source: OECD(1996)Ex.) ME : Machinery and equipment BC : Building and construction TOT : Total capital stock

1. Agriculture, hunting, forestry and fishing2. Mining and quarrying3. Manufacturing 3-1. Food, beverages and tobacco 3-2. Textile, wearing apparel and leather industries 3-3. Wood and wood products, incl. furniture 3-4. Paper, paper products, printing, publishing 3-5. Chemicals and chemical petroleum, coal, etc. 3-6. Non-metallic mineral products, etc. 3-7. Basic metal industries 3-8. Fabricated metal products, machinery and etc. 3-8-1. Metal products except machinery and etc. 3-8-2. Agricultural and industrial machinery 3-8-3. Office and data processing machines, etc. 3-8-4. Electrical goods 3-8-5. Transport equipment 3-9. Other manufacturing industries

4. Electricity, gas and water 5. Construction 6. Wholesale and retail trade, restaurants and hotels 6-1. Wholesale and retail trade 6-2. Restaurant and hotels 7. Transport, storage and communication 7-1. Communication 8. Finance, insurance real estate and etc. 8-1. Financial institutions and insurance 8-2. Real estate and business services 9. Community, social and personal services 10. Total industries 11. Producers of government services 12. Subtotal

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. Therefore, the OECD method will be replicable by developing countries if and only if theassumption of the linear retirement pattern and the information of average asset lives are correct.

(2) BEA

Bureaus of Economic Analysis of the United States published their estimates of fixedreproducible tangible wealth in the United State(1925-89) as BEA(1993). BEA’s estimates for thegross capital stock are derived by the perpetual inventory method cumulating past investment anddeducting the cumulated value of investment that has been discarded using estimated averageservice lives and retirement patterns. Thus, the gross stock is a measure of the cumulative valueof past investment still in existence. Their estimates for the net capital stock are equal to the grossstock less accumulated depreciation-that is, the decline in value due to wear and tear,obsolescence, accidental demage, and aging. They use the straight-line depreciation formulawhich assumes equal dollar depreciation over the life of the asset. Therefore, the net stockestimates based on straight-line depreciation represents the value remaining in the capital stock ifthe value of assets declines in a straight line manner.

As BEA(1993, M-3) properly notes, there are two main limitations of the perpetual inventorymethod. One relates to the assurance of the service lives and availability of long investment flowsused to implement it. The information currently available on service lives is deficient, both in termsof asset detail and in terms of coverage over time. In the perpetual inventory calculation, separateservice lives are used for each type of asset. In principle, separate lives should be used for eachindustry in which a particular type of asset is purchased and service lives should be varied overtime to account for changes in business conditions and technology. But, because of datalimitations, BEA’s Table B lists limited type of assets in limited industries.

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For example, as shown in Table 3

<Table 3> Service Lives Used to Derive BEA Wealth Estimates (Selected)Type of Asset and Industry Life

(years)Type of Asset and Industry Life

(years)

Private nonresidential equipment Residential capital

(private and government)

Office, computing, and accounting

machinery: 1-to-4 unit structures : new 80

year before 1978 8 1-to-4 unit structures : additions 40

1978 and later years 7 5-or-more unit structures : new 65

Communication equipment: 5-or-more unit structures : additions 32

Business services 11 Other structures 40

Other industries 15 Equipment 11

Other fabricated metal products 10 Durable goods owned by consumers

Steam engines and turbines 32 Furniture 14

Trucks, buses, and truck trailers Kitchen and other appliances 11

Local and interurban passenger

transit14 Autos 10

Trucking and warehousing etc. 10 Tires, tubes, and other parts 3

Other industries 90 Government nonresidential equipment

Autos 10 Federal

Aircraft 12-27 Military

Ships and boats 27 Aircraft

Metalworking machinery 1982 and later years 20

Nonmanufacturing industries 16 Ships 30

Manufacturing industries 12-27 Nonmilitary

Private nonresidential structures Government-owned

Industrial buildings 31 Department of Energy 25

Office buildings 36 Department of Defense 19

Commercial warehouses 40 Enterprises

Educational buildings 48 U.S. Postal Services 15

Hotels and motels 32 All others 25

Telecommunications 40

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, the service life of communication equipment in business services is 11 years while that in otherindustries is 15 years. Office, computing, and accounting machinery’s service life is assumed tobe shorter(7 years) after 1978 then that before 1978(8 years). By Table 2 and Table 3, we cancompare service lives assumed by OECD and BEA.

For example, the OECD average service lives of buildings and construction in total privateindustries was 45 years(row 10 at the last column of Table 2) but BEA’s average service lives ofprivate residential structure is shorter ranging between 31 years and 48 years as shown in thelower left column of Table 3.

Regarding retirement patterns, BEA uses the Winfrey(1967) curves which were revalidated byRusso and Cowles(1980). As shown in Table 4

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<Table 4 > Modified Winfery Retirement Patterns

Percent of average servicelife

Cumulativepercent of

originalexpenditures

discarded

Percent of average servicelife

Cumulativepercent of original

expendituresdiscarded

Nonresidential S-31)

95 46.1

Less than 45 0 100 53.9 45 1.2 105 61.6 50 2.4 110 68.7 55 4.1 115 75.2 60 6.5 120 81.0 65 9.7 125 85.9 70 13.7 130 89.8 75 18.7 135 92.8 80 24.6 140 95.0 85 31.2 145 96.5 90 38.4 150 97.3 95 46.1 155 97.9 100 53.9 160 98.3 105 61.6 165 98.7 110 68.8 170 99.0 115 75.4 175 99.3 120 81.3 180 99.5 125 86.3 185 99.7 130 90.3 190 99.9 135 93.5 195 100.0 140 95.9 More than 195 100.0 145 97.6 150 98.8

Durable goods owned by consumers L-22)

155 100.0 Less than 25 0More than 155 100.0 25 1.5

35 3.6Residential structure S-3

45 7.2Less than 5 0 55 13.2 5 .1 65 21.6 10 .3 75 31.4 15 .5 85 41.6 20 .7 95 51.2 25 1.0 105 59.8 30 1.3 115 67.3 35 1.7 125 73.7 40 2.1 135 79.2 45 2.7 145 83.9 50 3.5 155 87.9 55 5.0 165 91.1 60 7.2 175 93.7 65 10.2 185 95.7 70 14.1 195 97.2 75 19.0 205 98.2 80 24.8 215 100.0

85 31.3 More than 215 100.0

90 38.4

1) Private nonresidential structures and equipment (except autos), private residential equipment, governmentnonresidential structures and equipment.

2) Except autos

Source: BEA(1993) Table C

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, the Winfrey S-3 curve is modified for fixed nonresidential private and government-owned capitalso that retirement start at 45 percent and end at 155 percent of the average asset life. Forresidential capital, the Winfrey S-3 curve is modified so that retirements start at 5 percent and endat 195 percent of the average service life; and for durable goods owned by consumers, theWinfrey L-2 curve is modified so that retirement start at 25 percent and end at 215 percent of theaverage service life. The modified S-3 curves are bell-shaped distributions centered on theaverage service life of the asset. The modified L-2 curve is an asymmetrical distribution withheavy discards before the average service life is reached and a tapering pattern thereafter.BEA(1993, M-19) notes that, this curve is used for durable goods owned by consumers because itappears that many of these goods are discarded often within a few years, while others remain inuse for beyond the average service life.

Lastly, the depreciation estimates(d) in BEA are based on the straight-line formula, whereannual depreciation for a fixed asset is equal to its gross value(GK) divided by its servicelife(ASL):

d ‘ ‘=‘ ‘ GK ‘ ‘ /‘ ‘ ASL‘ ‘ (12)

The other limitation of the perpetual inventory method adopted by BEA relates to the fact thatthe information currently available on investment flows of transfer of used assets among owners islargely limited to transfers among private business, governments, consumers, and nonresidentsbut is not available on transfers of used assets among industries or among legal forms oforganization. These two problems are compounded in case of many developing countriesbecause there are neither long series of investment data nor informations on asset lives andtransfer of used assets.

BEA(1993, M-14) estimates the value of transfers of used assets and adds it to the flows ofnew investment by industry. Data are available only to adjust for transfers among different typesof owners(private business, governments, households, and non-residents) and are based for themost part on modified NIPA(National Income and Product Account) flows for net purchases ofused assets. They note that data are not available to adjust for transfers among industries oramong legal forms of organization.

They also note that the largest transfers of used nonresidential capital assets between privatebusiness and other types of owners involve sales of used autos by private business tohouseholds, exports of used equipment, purchases of government surplus assets, andgovernment purchases of privately owned public utilities. On the other hand, the largest transfersof used residential capital among private business and other types of owners and amongindustries involve purchases of private housing by state and local governments, conversions offederal military housing to private ownership, and transfers of farm housing to nonfarm ownership.BEA obtains the estimates of transfers between private business and government from the NIPSflows and the estimates of conversions of farm housing from the census of housing. The data ontransfers of used nonresidential and residential capital assets are not readily available in manydeveloping countries because their NIPA are not detailed enough and because their markets forused assets are not well-developed.

(3) Korea’s National Wealth Survey.

There have been three National Wealth Surveys(NWS) conducted so far by the Koreangovernment in years of 1968, 1977, and 1987. The 1997 NWS is in preparation by the NationalStatistical Office. NWS has adopted a relatively simple method of evaluating tangible fixed assets.

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First the gross capital stock(GK) was evaluated by multiplying the purchase price of the fixedtangible asset by the appropriate price index by types of assets which have been compiled andprepared by the Bank of Korea dating back to the year 1910 as follows:

GK_i ~=~ P‘ ‘_i^t ~ times~ P I‘ ‘_i^t‘ ‘ (13)

where GK_i‘ is the value gross capital stock of asset i evaluated at a certain benchmark year m(m>t), P‘ ‘_i^t‘ ‘ is the purchase price of the asset i in the year t, and P I‘ ‘_i^t is the price index toreflate the value of the asset i. In other words, the gross capital stock is supposed to reflect therepurchase value or reacquisition value of the fixed tangible asset.

Second, the net capital stock(NK) was deduced by multiplying the gross capital stock by theresidual cost ratio as follows:

NK_i ~=~ GK_i ~times ~ left(‘S ‘ /‘P‘_i^t ‘ ‘ right)^n/N ~=~ P‘ ‘_i^t ~times~ P I‘ ‘_i^t ~times~left( ‘S ‘ ‘ /‘ ‘ P‘ ‘_i^t‘ ‘ )^n/N‘ ‘ (14)

where S is the value of survived assets, N is average service life of the asset and n is the numberof years elapsed.

In other words, a proportional depreciation method is adopted. The value of survived assets isassumed to be 10 percent of the purchase value when it reaches the assumed average servicelife and 1 percent of the purchase value when it reaches double the assumed average service life.

Suppose for example, the average service life of a certain asset such as personal computer is5 years and it was purchased three years age at the price of 3,000 dollars. In addition, assumethat the inflation rate of the computer price since the purchase year is 20 percent. Then thefollowing calculations can be made:

GK ~=~ 3,000 ~times~ 1.2 ~=~ 3,600‘ ‘NK~=~ 3,000~ times~ 1.2 ~times~ left(‘ 300 over 3,000 right)~ ^{3 over 5}‘ ‘

Selected service lives used in 1987 National Wealth Survey is listed in Table 5. Only service livesof equipments and facilities are categorized by industries. The service lives are quoted from 1985Corporate Tax Law.

By comparing Table 5 with Table 2 and Table 3, we can see the difference in assumed assetlives among OECD, BEA, and Korea’s National Wealth Survey. For example, for buildings andstructures, the OECD average asset life is 48 years and BEA’s are 32-80 years for residentialcapital and 31-48 years for nonresidential structure while Korea’s residential buildings’ andnonresidential building average service lives are 23-60 years and 8-60 years respectively. Forautomobiles, Korea’s assumed average life(4-5 years) is shorter than BEA’s(10 years). In general,Korea’s assumed service lives are shorter than OECD’s and BEA’s reflecting higher rate ofdepreciation, which is typically observed in the process of late industrialization.

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While Korea’s National Wealth Surveys provide benchmark year estimates of gross and netcapital stock by types of assets, by industries and by ownership, they entail several limitations.The first limitation is that there are no time-series estimates prior to 1968, after 1987 and in theyears between successive NWS. Therefore, to generate time series estimates, it will benecessary to apply the perpetual inventory method and the polynomial benchmark method. Thesecond limitation is the fact that they did not consider the retirement pattern. In other words, theyhave assumed that all of the assets will continue to survive until the expected average service life.They considered the depreciation only not the retirement of assets. The third limitation is the lackof incorporating the value of transfers of used assets. It is probably due to the nature ofunorganized used asset markets and the lack of systematic data in Korea.

<Table 5> Service Lives Used in 1987 National Wealth SurveyType of Asset Life

(years)Type of Asset Life

(years) Buildings Equipments and Facilities

Residential Buildings By industries

Concrete and Steel Frame 50-60 11. Agriculture 5-14

Stone and Cement Block 45-50 12. Forestry 7-14

Iron rod-structured 45-50 13. Fishing 7-14

Clay Structure 25 21. Coal Mining 5

Wood Structure 23-25 22. Oil and Gas 4-12

Wood plus Mortar 25 23. Metal Mining 7

Temporary Wood Structure 2 29. Other Mining 6-7

Nonresidential Buildings 31. Food and Tobacco 3-15

Concrete and Steel Frame 25-60 32. Textile and Clothing 3-11

Stone and Cement Block 22-50 33. Wood Products 9

Iron rod-structured 20-50 34. Paper and Printing 5-11

Clay Structure 10-15 35. Chemicals and Plastics 3-11

Wood Structure 10-25 36. Nonferrous Metals 3-12

Wood plus Mortar 8-25 37. Primary Metals 7-9

Temporary Wood Structure 2 38. Fabricated Metals 5-12

Building Equipments 39. Other Manufacturing 7-13

Electrical and Lighting 6-15 41. Electricity and Gas 6-20

Water Supply and Gas 18 42. Water supplying 12

Aircondition, Heating, and Boilers 10 51. Construction 3-6

Elevators 18 61. Wholesale and Retail 9-11

Other Equipments 10-18 63. Restaurants and Hotels 5-8

Structures 71. Transportation and Storage 3-11

Transport Facilities 5-40 72. Telecommunication 5-8 Electricity Facilities 30-40 84. Business Service 5-6 Water Supply 10-40 92. Health Service 7-16 Other Structures 5-40 93. Social Service 7 Tools and Fixtures 94. Entertainment and Arts 3-8 Tools 2-6 95. Personal Service 3-7 Fixtures 2-20 Transport Equipments

Steel Ships 7-18 Trains etc. 10-15 Automobiles 4-5 Airplanes 2-10

Source: 1987 National Wealth Survey

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4. Estimation Method

In order to make best use of available NWS data and investment data from National IncomeAccounts,(NIA), I have taken the following steps to deduce time series estimates of gross and netcapital stocks for Korea during the period of 1953-1996. First, I have reconciled industrialclassifications among NWS, National Income Accounts(1984), and National Accounts(1994 and1996). Second, using implicit GNP deflator for gross capital formation in NIA, I have deflated NWSdata to be used as benchmark year estimates from current price figures to constant price figuresin 1990 prices. Then the data were linked to investment data in 1990 prices. Third, I estimatedeconomic depreciation rates in two periods, 1968-1977 and 1977-1987, by using the polynomialequation method and used them to generate net stocks ofter 1987 based on a perpetual inventorymodel. For the period prior to 1968, I simulated the perpetual invenory model in order to obtain areasonable estimate for depreciation rate of all industries and then used the rate to generate netstocks for the period of 1953-1967. Fourth, I used the reported conversion ratios i.e., netstock/gross stock, to convert the net stock estimates into gross stock estimates. For the yearsprior to 1968, the conversion ratios reported on 1968-1987 NWS were used; for the period of1968-1987, I interpolated the conversion ratios reported on 1968 NWS, 1977 NWS and 1987NWS, and finally for the period after 1987, those reported on 1987 NWS were used.

In my previous studies(Pyo(1988)(1992)), I estimated both retirement rates and depreciationrates using a proportional retirement model and a proportional depreciation method. However,there were negative solutions reported in many industries and types of assets. It must have beendue to the underestimation of investment and/or the failure of adequate inflationary accounting forthe stock measurement. As a result, when I compared the predicted stock estimates of 1987 with1987 NWS data, the gross stock was underestimated by 22.8 percent while the net stock wasunderestimated by only 0.4%. Therefore, in the present study, I have estimated the net stock firstand used gross-net stock conversion ratio to estimate the gross stock rather than estimatingretirements separately. The lack of used asset market data and data on the transfer of usedassets to adjust investment data is another reason for adopting the indirect method of estimatinggross stock.

In what follows, a more detailed explanation of each step and estimating procedure is provided.

(1) Reconciliation of Industrial Classifications

NIA(1984) provides us with national income data for the period of 1953-1983 and NationalAccounts(1994)(1996) provide us with those for the period of 1970-1996. Therefore, it isnecessary to combine these two sources of national income data to link them with benchmarkyear estimates reported on three National Wealth Surveys. Since the industrial classifications areall different among the three sources of data as shown in Table 6

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<Table 6> Comparison of Industrial Classification

National Wealth Survey National Income Accounts National Accounts

Periods 1968, 1977, 1987 1953-1983 1970-1996

Classification

Method

Industries

Types of capital

Types of capital

Industrial use

Types of capital

Kind of Economic activity

Kind of Expenditure

Criteria for

Industrial

Classification

Owner of capital(1968)

User of capital(1977,1987)User of capital Owner of capital

Classification by

Industrial Use

1. Agriculture, hunting, forestry

and fishing

2. Mining

3. Manufacturing

4. Electricity, gas and water

5. Construction

6. Wholesale and retail trade

and restaurants and hotels

7. Transport, storage and

communication

8. Financing, insurance, real

estate and business services

9. Community, social and

personal services

1. Agriculture, hunting,

forestry and fishing

2. Mining and quarrying

3. Manufacturing

4. Electricity, gas and water

5. Construction

6. Wholesale and retail trade

and restaurants and hotels

7. Transport, storage and

communication

8. Financing, insurance, real

estate and business

services

9. Community, social and

personal services

10. Public administration

11. Ownership of dwellings

Industries

1. Agriculture, hunting, forestry

and fishing

2. Mining and quarrying

3. Manufacturing

4. Electricity, gas and water

5. Construction

6. Wholesale and retail trade,

restaurants and hotels

7. Transport, storage and

communication

8. Finance, insurance, real

estate and business services

9. Community, social and

personal services

Producers of government

services

1. Public administration and

defense

2. Social, recreational, sanitary

and related community

services

3. Others

Source: National Bureau of Statistics Economic Planning Board (1972) (1980) (1989)

The Bank of Korea (1984) (1994) (1995) (1996)

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, they need to be reconciled to come up with a consistent classification.

For this purpose, we made the following adjustments:

1) The investment in dairy cattle is included in gross capital formation of national incomeaccounts but the investments in animals and plants are included in inventory investment.Therefore, we deducted stocks of animals and plants from NWS stock data.

2) Since residential investment is included in gross capital formation of national incomeaccounts, we added residential building stocks to the stocks of Finance, Insurance and RealEstate Industry.

3) These reclassified stock estimates of NWS in current prices are converted into those inconstant prices by using implicit price deflator for gross capital formation in national incomeaccounts.

4) Gross capital formation in residential structures during the period of 1953-1970 which areavailable in old National Income Accounts(1984) is reclassified into gross capital formation inIndustry 8(Fiance, Insurance, Real Estate and Business Service)

5) Some fixed proportions of gross capital formation in Agriculture, hunting, forestry and fishing,Electricity, gas, and water, and Transport, storage and communication in old National IncomeAccounts are redistributed to Government services. Unpublished data by the Bank of Korea in1984 and 1985 were used as the fixed weights for this purpose.

6) Since we have deducted the stock of dairy cattle from benchmark year estimates of NationalWealth Survey, we also have excluded gross capital formation in dairy cattle from the total grosscapital formation in Agriculture, hunting, forestry and fishing.

7) Old National Income Accounts(1984) provide us with gross capital formation in 1975 pricesfor the period of 1953-1975 and National Accounts(1994)(1996) provide us with gross capitalformation in 1990 prices for the period of 1970-1996. Therefore, I used the year 1970 as a bridgeyear and converted gross capital formation data in 1975 prices for the period of 1953-1969 intothose in 1990 prices by using the following ratio by industries:

(gross capital formation in 1970 in 1975 prices) (gross capital formation in 1970 in 1990 prices)

(2) Net Capital Stocks

After making adjustments to capital formation data, I linked them with net capital stock data ofNational Wealth Survey(1968)(1977)(1987) deflated by implicit investment deflator in nationalaccounts converting them into those in 1990 prices. For the two subperiods, 1969-1976 and 1978-1986 between three surveys, I used the following polynomial benchmark year equation toestimate economic depreciation rates by industries:

NK‘_t^i &=& I‘_t^i ~+~ (‘ ‘1-d_i‘ )‘ I‘_t-1^i ~+~ (‘ ‘1-d_i )‘^2 I‘_t-2^i ~ +‘ ‘ cdots‘ ‘ +~ (1-d_i)^s‘ ‘ NK‘ ‘_t-s^i‘ ‘ (15)

where NK‘ ‘_j^i‘ ‘ is the net capital stock as of j th year end in industry i and { I}‘_{j } ^{i } is thegross capital formation of industry i in year j. Since we have two benchmark year estimates andinvestment data, we can solve for an implicit economic depreciation for each industry.

The resulting estimates are presented in Table 7.

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There are two negative solutions appearing in Construction for the period of 1968-1977 and inCommunity, social and personal service for the period of 1977-1987. Finding negative solutions inthe polynomial benchmark year equation is not unusual as reported in Nishimizu(1974) andPyo(1988). I conjecture that negative solutions indicate either severe asset inflation going on inthe industry or implicit deflator used to convert investment data was not adequate and probablyunderestimated price increase in the sector. There is also unusually high depreciation rateestimated in Agriculture, forestry and fishing for the period of 1968-1977 which cast doubt on thesector’s benchmark year estimates of 1968. But, since the depreciation rates serve for the

<Table 7> Estimates of Depreciation Rate1)

Unit: %−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− 1968-1977 1977-1987 −−−−−−−−−−−−−−−−−− −−−−−−−−−−−−−−−−−−− Depreciation rate Depreciation rate−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−By Industries

1. Agriculture, Forestry, 46.6 16.0 and Fishing 2. Mining and Quarrying 18.1 16.2 3. Manufacturing 2.5 6.6 4. Electricity, Gas, and Water 11.0 12.6 5. Construction N.S.2) 14.6 6. Wholesale, Retail Trade, 2.7 5.4 Restaurants and Hotels 7. Transport, Storage, 13.1 14.2 and Communication 8. Financing, Insurance, 4.6 2.1 Real Estate and Business Services 9. Community, Social 6.8 N.S. and Personal Services 10. Government Services 7.5 8.4

By Types of Assets

1. Residential Building 6.8 1.3 2. Nonresidential Building N.S. N.S. 3. Other Construction 2.9 4.5 4. Transportation Equipment 48.5 30.2 5. Machinery & Equipment 5.2 12.6−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− Total 7.6 6.6−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

1) Estimated by the polynomial benchmark method using a GAUSS program 2) Negative Solution 3) Estimated by applying the aggregate investment data and the economy-wide capital stock data

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purpose of interpolating net capital stock series during 1968-1987, I ignored both negativesolutions and the unusually high depreciation rate in a particular industry and generated the time-series estimates of net capital stock (NK‘_t^i‘ ) using equation (15).

In a similar way, we can use the same type of the polynomial equations to estimatedepreciation rates by types of assets. The estimated depreciation rates are reported at the bottomof Table 7. However, we cannot use both rates because the aggregate capital stock by industrieswill be different from that by types of assets due to the difference in implicit deflators andaggregation problem. Following the procedure adopted in Pyo(1988) (1992), I have estimated netstocks by industries first and then distributed them among different types of assets by usingweights of each asset in NWS. The opposite way which estimates net stock by types of assetsand then distribute them among nine industries could be more cumbersome because we have toassume fixed weights among different industries over time.

For the period prior to 1968, I attempted to use a perpetual inventory method: starting from1968 benchmark year estimates and deducting net investment by industries from stock estimatesusing estimated depreciation rates. However, the procedure could not work out because in manyindustries the net stock estimates themselves became negative values even before reaching theyear 1954. Kim and Hong(1997) also had to make upward adjustment to net stock figures of 1987National Wealth Surveys to avoid obtaining the negative values in the first half of the 1960s. I alsoattempted to use regression equations estimated by industries linking net stocks with GDP and atime trend variable during the period of 1968-1996 to estimate net stocks of the pre-1968 period.Again, I encountered several negative estimates of net stocks in some industries. One optionwould be to use a fixed weight reported in 1968 NWS. But, then, the rates of capital accumulationin all industries will become identical, which is hardly acceptable.

In order to circumvent these problems, I decided to simulate a perpetual inventory modellinking pre-1968 investment data in 1990 prices to 1968 NWS net stocks in 1990 prices bychanging depreciation rate by one decimal point between d=0 and d=0.076(7.6%), which weestimated from the polynomial benchmark equation for the period of 1968-1977. When thedepreciation rate becomes 3 percent(d=0.03), the earlier net stock estimates did not turn out to benegative values and their growth rate during the period of 1953-1967 was most reasonable.

After generating the total net stock of all industries in 1990 prices by assuming a 3 percentdepreciation rate. It was distributed to 10 industries by interpolating weights using industry-specific regression equations. The regression equations of share weights were specified withshare weights as dependent variable and a linear time trend variable as independent variable andestimated for the period of 1968-1996. And then, the pre-1968 weights were estimated using theabove equations. The tenth sector was used as the balancing sector to make the sum of totalweights equal to one. The estimated shares of net capital stocks is attached in Appendix as TableA-1.

For the period after 1987, a modified perpetual inventory model was used to generate time-series estimates of net capital stocks by cumulating the industries’ gross capital formation to 1987stock estimates and then deducting the estimated depreciation from the estimates of 1977-1987.For Community, social and personal services industry which had produced a negative solution ofestimated depreciation rate, a value of zero was assigned; depreciation was not deducted fromgross capital formation.

We used 1987 NWS estimates and 1994 Census estimates as two benchmark estimates andintrapolated weights of the years between 1987 and 1994. For the years of 1995 and 1996, weused 1994 Census estimates as weight for distributing net stock of total manufacturing into 28

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submanufacturing sectors.

(3) Gross Capital Stocks

Once net capital stocks are estimated, gross capital stocks are deduced in a very simplemechanical way. As noted before, in principle gross capital stock should be generatedindependently of net capital stocks by utilizing informations on asset retirements and by adjustinggross capital formation which incorporated the transfer and net exports of used assets. However,the lack of both information and data forced me to adopt rather a simplified procedure.

For the period prior to 1968, we used the gross-net ratio reported in 1968 NWS to deducegross stocks from estimated net stocks by industries as follows:

GK‘_t^i ~=~ NK‘_t^i ~times~ {GK‘_1968^i} over {NK‘_1968^i}‘ ‘ (17)

where GK‘_t^i‘ ‘ and NK‘_t^i‘ are gross and net stocks estimated for the period of 1954-1967respectively and GK‘_1968^i‘ and NK‘_1968^i‘ are gross and net stocks reported on 1968 NWS.

For the period between 1968 and 1987, we interpolated the gross-net ratios using three NWSand applied the interpolated ratios to deduce gross stocks from net stocks by industries. And then,for the period after 1987, the 1987 gross-net ratio was used to deduce gross stocks from net stockestimates.

Gross capital stocks by type of assets are generated by multiplying net stocks by type of assetsby gross-net ratio from three National Wealth Surveys. For the period prior to 1968, the 1968 ratiowas used; for the period between 1968 and 1987, the interpolated ratios were used; and for theperiod after 1987, the 1987 ratio was used.

(4) Quarterly Capital Stocks

Quarterly net and gross capital stock series are also generated from their annual estimates.Quarterly stock series both by industries and by types of assets have been generated bydistributing end-of-year annual estimates of net and gross stocks over four quarters according torelative weights of the quarter’s gross capital formation in the year’s total gross capital formation.

The quarterly investment data are available in National Accounts only from 1970 and,therefore, my quarterly stock estimates start from 1970.

(5) Gross and Net Stocks by 28 Manufacturing Subsectors.

In order to identify changing capital accumulation and industrial restructuring withinManufacturing, the Manufacturing Industry is reclassified into 28 subindustries following theClassification of 1987 NWS. The basic principle I adopted in distributing net and gross stocks oftotal Manufacturing into 28 subusectors is the use of 1968 weights for the period of 1960-1968;the use of interpolated weights for the period of 1968-1987; and the use of weights fromManufacturing Census data on reproducible tangible assets by submanufactruuing industries forthe period after 1987 to accommodate recent structural adjustments within the Manufacturingindustry.

Since the 1968 NWS has fewer sub-manufacturing classifications, we used 1968Manufacturing Census data on reproducible tangible assets to reclassify them into 28 subsectors.On the other hand the 1977 NWS has longer sub-manufacturing categories so that we had tomerge them into 28 subindustries as shown in Table 8.

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The redistribution procedure of Manufacturing weights among 28 submanufacturing industriescan now be explained in detail as follows:

- 25 -

<Table 8> Comparison of Industrial Classification in National Wealth Survey

(1) Comparison of industrial classification between 1968 and 1977 National Wealth Survey−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− 1968 National Wealth Survey 1977 National Wealth Survey−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− −−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−Code Industrial Classification Industrial Classification Code−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−1 Food manufacturing 311,312 311,312 Food manufacturing 12 Beverage industries 313 313 Beverage industries 23 Tobacco manufactures 314 314 Tobacco manufactures 34 Manufacture of textiles 321 321 Manufacture of textiles 45,7 Manufacture of Footwear 322,324 −+−− 322 Manufacture of wearing apparel, 5 and wearing apparel | except footwear +−− 324 Manufacture of footwear, except 7 vulcanized or moulded rubber or plastic footwear6 Manufacture of leather 323 323 Manufacture of leather and products 6 and leather products of leather, leather substitutes and fur, except footwear and wearing apparel8 Manufacture of wood and cork, 331 331 Manufacture of wood and 8 except furniture wood and cork products, except furniture9 Manufacture of furniture 332 332 Manufacture of furniture and 9 and fixtures fixtures, except primarily of metal10 Manufacture of paper and 341 341 Manufacture of paper and 10 paper products paper products11 Printing and publishing 342 342 Printing, publishing and 11 allied industries12,13 Manufacture of chemical 351,352 −+−− 351 Manufacture of industrial chemicals 12 and chemical products +−− 352 Manufacture of other chemical 13 products14,15 Manufacture of petroleum 353,354 −+−− 353 Petroleum refineries 14 and coal products +−− 354 Manufacture of miscellaneous 15 products of petroleum and coal16 Manufacture of rubber 355 355 Manufacture of rubber products 16 and rubber products18,19,20 Manufacture of glass, 361,362,369 −+−− 361 Manufacture of pottery, china and 18 clay and stone products | earthen-ware +−− 362 Manufacture of glass and glass 19 +−− 369 Manufacture of non-metallic mineral 20 products, except products of petroleum and coal21,22 Basic metal industries 371,372 −+−− 371 Iron and steel basic industries 21 +−− 372 Non-ferrous metal basic industries 2223 Manufacture of metal products 381 381 Manufacture of fabricated metal 23 products, except machinery and equipment24 Manufacture of machinery 382 382 Manufacture of machinery, 24 except electrical25 Manufacture of electric machinery 383 383 Manufacture of electrical machinery, 25 and appliances apparatus, appliances and supplies26 Manufacture of transport equipment 384 384 Manufacture of transport equipment 26 +−− 356 Manufacture of plastic products, N.E.C. 17 +−− 385 Manufacture of professional and 27 | scientific and measuring and | controlling equipment, N.E.C.17,27,28 Miscellaneous 390 −+−− 390 Other manufacturing industries 28−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

- 26 -

(2) Comparison of industrial classification between 1977 and 1987 National Wealth Survey

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− 1977 National Wealth Survey 1987 National Wealth Survey−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− −−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−Code Industrial Classification Code Industrial Classification−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−1 Food manufacturing 1 Food manufacturing2 Beverage industries 2 Beverage industries3 Tobacco manufactures 3 Tobacco manufactures4 Spinning textiles, silk reeling −−−−−−+− 4 Manufacture of textiles and weaving textiles |4 Other manufacture of textiles −−−−−−+5 Manufacture of wearing apparel, 5 Manufacture of wearing apparel, except footwear except footwear6 Manufacture of leather and products 6 Manufacture of leather and products of leather, leather substitutes and of leather, leather substitutes and fur, except footwear and wearing fur, except footwear and wearing apparel apparel7 Manufacture of footwear, except 7 Manufacture of footwear, except vulcanized or moulded rubber vulcanized or moulded rubber or plastic footwear or plastic footwear8 Manufacture of wood and 8 Manufacture of wood and wood and cork products, wood and cork products, except furniture except furniture9 Manufacture of furniture and 9 Manufacture of furniture and fixtures, except primarily of metal fixtures, except primarily of metal10 Manufacture of paper and 10 Manufacture of paper and paper products paper products11 Printing, publishing and 11 Printing, publishing and allied industries allied industries12 Manufacture of industrial chemicals, −−−−−+− 12 Manufacture of industrial chemicals except fertilizers |12 Manufacture of fertilizers −−−−−+13 Manufacture of other chemical 13 Manufacture of other chemical products products14 Petroleum refineries 14 Petroleum refineries15 Manufacture of miscellaneous products 15 Manufacture of miscellaneous products of petroleum and coal of petroleum and coal16 Manufacture of rubber products 16 Manufacture of rubber products17 Manufacture of plastic products, N.E.C. 17 Manufacture of plastic products, N.E.C.18 Manufacture of pottery, china and 18 Manufacture of pottery, china and earthen-ware earthen-ware19 Manufacture of glass and glass products 19 Manufacture of glass and glass products20 Manufacture of non-metallic mineral 20 Manufacture of non-metallic mineral products, except products of petroleum products, except products of petroleum and coal and coal21 Iron and steel basic industries 21 Basic metal industries22 Non-ferrous metal basic industries 22 Non-ferrous metal industries23 Manufacture of fabricated metal 23 Manufacture of fabricated metal products, except machinery and equipment products, except machinery and equipment24 Manufacture of machinery, 24 Manufacture of machinery, except electrical except electrical25 Manufacture of electrical machinery, 25 Manufacture of electrical and electronics apparatus, appliances and supplies machinery,apparatus,appliances and supplies26 Ship building and repairing −−−−−+−− 26 Manufacture of transport equipment26 Manufacture of railroad equipment −−−−−+26 Manufacture of motor vehicles −−−−−+26 Manufacture of other vehicles −−−−−+27 Manufacture of professional and 27 Manufacture of medical, photographic and scientific and measuring and optical, professional, scientific, measuring controlling equipment, N.E.C. and controlling equipment and goods28 Other manufacturing industries 28 Other manufacturing industries−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−

- 27 -

1) We simplified five types of assets into three types of assets; by merging nonresidentialbuilding and structures into Building & Structure and by keeping Machinery & Equipment andTransportation. Then, the share of each type of assets is computed as follows:

SMAS‘ (K,M‘ ) ~=~ {SUM_L ‘SMK‘ (K,L,M‘ )} over {SUM_L ‘SMK‘ (K,L,5‘ )} ‘ ‘ (18)

where SMAS‘ (K,M‘ )‘ ‘ is the share of M-type asset in K year; SMK‘ (K,L,M‘ )‘ ‘ is the net or grossstock of submanufacturing industry L in K year; and SMK‘ (K,L,5‘ )~=~Sum_M ‘SMK‘ (K,L,M‘ )‘ ‘ ,which is the submanufacturing industry’s total net stock. K stands for three benchmark years of1968, 1977 and 1987 when NWS were taken.

2) For the period of 1960-1967, we applied the 1968 weights for each type of asset in L-thsubmanufacturing industry. However, for the period of 1988-1996, we applied the weights ofreproducible tangible assets reported in Manufacturing Census and Manufacturing Survey Reportin order to accommodate more recent trend in industrial structural adjustments. Accordingly, thenet or gross capital stock in constant prices(SMC) and current prices (SMS) are deduced asfollows:

SUM _L ‘ ‘SMC‘ (K,L,M‘ ) ~=~ SMTT‘ (K,C‘ ) ~times~SMAS‘ (K,M‘ ) ‘ ‘ (19)

SUM_L‘ ‘ SMS‘ (K,L,M‘ ) ~=~ SMTT‘ (K,S‘ ) ~times~ SMAS‘ (K,M‘ ) ‘ ‘ (20)

where SMC(K,L,M) is the M-type asset of L industry. K year in 1990 constant prices; SMS(K,L,M)is SMC evaluated in current prices; SMTT(K,C) is the total Manufacturing capital stock in 1990prices; and SMTT(K,S) is the total Manufacturing capital stock in current prices.

3) Three types of assets in total Manufacturing are then distributed over 28 subsectos by using thefollowing shares:

SMKS‘ (K,L,M‘ ) ~=~ {SMK‘ (K,L,M‘ )} over {SUM_L ‘SMK‘ (K,L,M‘ )} ‘ ‘ (21)

where SMKS(K,L,M) is the share of L-th subsector’s M-type asset in total Manufacturing’s M-typeasset in year K and SMK(K,L,M) is the L-th subsector’s stock of M-type asset in K-th NWS year.Again the shares in the years between two benchmark years have been intrapolated.

4) Finally, by combining the results from 2) and 3) we obtain 28 Manufacturing subsector’s net orgross stock as follows:

SMC‘ (K,L,M‘ ) ~=~ SUM from {L} ‘SMC‘ (K,L,M‘ ) ~times~ SMKS‘ (K,L,M‘ )‘ ‘ (22)SMS‘ (K,L,M‘ ) ~=~ SUM from {L} SMS‘ (K,L,M‘ ) ~times~ SMKS‘ (K,L,M‘ )‘ ‘ (23)

where SMC and SMS are net or gross stock in 1990 constant prices or current prices respectively.

5. Estimates and Summary

Both gross and net capital stocks in 1990 prices by ten industries are presented in Table 9.Over the entire period of estimation, 1953-1996, the Korean economy has achieved anaccelerated accumulation of capital in both gross and net terms. The gross capital stock grew atthe average annual rate of 10.8 percent and the net stock grew at the average annual rate of 10.9percent. In particular, the capital stock of Transport, Storage and Communication (Sector 7) grewat the fastest rate(19.6 percent for gross stock and 19.7 percent for net stock) followed byManufacturing(Sector 3) (14.4 percent and 14.2 percent respectively). Looking at growth rates insubperiods, it is noted that, in the period after 1962 when the First Five-Year Economic

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Development Plan was introduced, the capital stock was accumulated at much faster rate than thepre-1962 period. And capital stock was accumulated faster during the first half(1962-79) of thepost-1962 period than the latter half(1980-96). Both gross and net stocks by ten industries incurrent prices are attached in Appendix as Table A-2.

Estimates of gross and net stocks by types of assets are presented in Table 10. Over the entireperiod, Machinery & Equipment’s gross and net capital stock grew at the fastest rate, 12.5 percentand 11.9 percent respectively, Residential Building grew at the rate of 10.8 percent and 10.6percent respectively while Nonresidential Building grew at the rate of 11.3 percent and 11.6percent respectively. Estimates of capital stocks by types of assets in current prices are alsoattached in Appendix as Table A-3. Also attached are Table A-4 and A-5 in Appendix whichpresent estimates of gross and net stocks in 1990 constant and current prices by both industriesand types of assets.

Quarterly gross and net capital stocks in both by industries and by types of assets are attachedin Appendix as Table A-6 and Table A-7. In addition, gross and net capital stocks in 28submanufacturing industries are attached in Appendix as Table A-8. As summarized in Table 11,Manufacture of Electrical Machinery shows the fastest rate of growth in capital accumulationparticularly after mid-1970’s when the Korean Government started promoting heavy and chemicalindustries after the first oil shock.

Finally, a comparison is made between the present estimates and other existing estimates.First, let us compare the present estimates with my earlier estimates published as Pyo(1988) andPyo(1992). There are two major differences in the way the estimates are generated. The firstdifference is the fact that in my earlier studies, I generated gross and net stocks independently byestimating retirement rates and depreciation rates respectively. But in the present study, I haveestimated net stocks first by a similar method and then generated gross stocks by applyingintrapolated net-gross conversion ratios to net stocks, because I presumed that the estimation ofretirement rates is not reliable and, therefore, can generated a large margin of error(see Table 2 inPyo(1992)). They are frequently estimated as negative values due to the problems associatedwith deflators and inflation accounting. The second difference lies in the way pre-1968 estimatesare generated. In my ealier studies, I generated pre-1968 gross capital stocks for the entireeconomy by deducting annual data on gross capital formation from the estimates of 1968 NWS.And then, using the weights by industries on the survey, I distributed the gross capital stock into 9industries. As reported in Pyo(1988, p.22-23), when we applied this method to each industry, inother words, deducting ith industry’s capital formation from the industry’s capital stock estimate inthe 1968 National Wealth Survey, the capital stocks in some industries(for example,Manufacturing) became negative.

After estimating gross stocks, I proceeded to generate net stocks by using fixed gross-netconversion ratios from 1968 NWS. I could not extend the net stock series by applying theperpetual inventory model because the net stocks become negative values in the 1950s. But theproblem with applying fixed gross-net conversion ratios is the fact that both net and gross stocksin all industries end up with growing at the same rate during the period of 1953-1967.

In order to avoid this problem and the problem associated with estimating retirement rates, Ihave concentrated on estimating net stock first and then generated gross stock by usingintrapolated gross-net conversion ratios from three national wealth surveys. For pre-1968 netstocks, I simulated the perpetual inventory model with varying depreciation rates for all industries’total net stock and obtained 3 percent as the most reasonable estimate of depreciation rate. Thetotal net stock is distributed among ten industries by using estimated industrial weights from 10separate regressions with a linear trend variable. As summarized in Table 12, the present

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estimates are quite comparable with my ealier estimates which were reported originally in 1985prices and, therefore, were converted to those in 1990 prices by using implicit investment deflator.

Second, a comparison is made between the present estimates and those by Kim andHong(1997). The major difference between the two estimates lies in whether 1968 NWS and 1977NWS are used as benchmark year estimates in addition to 1987 NWS. Kim and Hong(1997, p.50)regard 1987 NWS as more reliable than 1967 NWS and 1977 NWS. But they found the net stockfor 1987 NWS data unreasonably low(47.4 percent of the gross stock) and had to make upwardadjustment accommodating the two previous surveys(56-60 percent of the gross stock). Acomparative table of estimated net stocks/gross stocks ratio is attached in Appendix as Table A-9.Therefore, there is no reason to argue that 1987 NWS is more reliable than 1968 NWS and 1977NWS.

Kim and Hong(1997, p. 51) estimated the unadjusted net stock of structures and equipment bylinking the net fixed investment(gross fixed investment less the consumption of fixed capital) withthe net stock value of 1987 NWS. The unadjusted gross stock was estimated by linking grossfixed investment with the gross stock value of 1987 NWS and then deducted capital retirementsand construction-work in-progress from unadjusted net and gross stock to generate adjustedstock. They assumed about 70 percent of the “extraordinary losses” recorded in the FinancialStatement Analysis by the Bank of Korea represent the capital retirements. In other words, theyavoided estimating economic depreciation rates and using asset retirement informations. Actuallythe consumption of fixed capital in national income accounts reflects tax amount of depreciationrather than economic depreciation. In addition, “extraordinary losses” in the Financial StatementAnalysis are far from being asset retirements because it could be purely financial losses.

The resulting estimates of Kim and Hong(1997) are quite close to my estimates in years around1987 as they should be. But their estimates in pre-1987 period are much larger than my estimatesas Table 12 indicates. As a result, the growth rates of their net and gross stock estimates areabout half the size of the growth rates of my estimates. Therefore, how we could reconcile thewidening gap in two estimates in earlier years of pre-1987 period remains as an open question.

The drastic difference between the two estimates particularly in earlier years is also reflected inestimated capital-output(GDP) ratios. Kim and Hong(1997, p.53) observes a trend of gradualdecline in gross fixed capital-output ratio for the nonresidential business sector from nearly 6.0 to2.8 between 1963 and 1976 to be followed by a slow upward trend to reach 3.6 by 1993.However, my estimates of both gross and net capital-output ratios show a secular upwardtrend(see Table 13). The gross capital stock-output ratio increased from 1.3 level in 1953 to 5.05level in 1996 while the net capital-output ratio increased from 0.65 to 2.58. It might have been dueto the underestimation error in 1968 NWS. However, we should note the historical fact that, by1953, there were very few physical structures and equipments left as a result of massivedestruction during the Korean War. Therefore, my conjecture is that the income(output)generating capacity of the Korean economy in the 1950s and early 1960s mainly came fromhuman capital and accelerated labor input. It is consistent with my earlier finding(Pyo, 1995) thathuman capital played a significant role in Korea's rapid growth.

It also supports the proposition I advanced in the paper that human capital serves as aproductive input at the earlier stage of economic development and it starts to provide economy-wide externality only after it reaches a certain threshold point. As the Korean economy meets andpasses the Lewisian turning point in early 1970s labor became scarce and the rates of return onhuman capital started to fall. As a result, the process of a switch from labor-intensive production tocapital-intensive production began to emerge and the rate of return on human capital declined.Therefore, the capital deepening outpaced the income(output) growth revealing a secular upward

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trend in capital-output ratio. If we interpret capital-output ratio as a reciprocal of capital productivityor efficiency, it implies that the Korean economy still has been engaged in expanding productivecapacity rather than promoting its efficiency. At some point in the future, the economy will have tomeet a turning point in which the capital-output ratio declines; the output growth must outpace thecapacity expansion.

- 31 -

References

B hm-Bawerk, E. v., Capital and Interest: A Critical History and Economical Theory, Translatedwith Preface and Analysis by William Smart, Macmillan and Co., London, 1890.

Bureau of Economic Analysis(BEA), U.S. Department of Commerce, Fixed Reproducible TangibleWealth in the United States, 1925-89, Washington D.C: U.S. Government Printing Office,January 1993.

Denison, Edward F., Accounting for Slower Economic Growth: The United States in the 1970’s,Washington D.C.: The Brookings Institution, 1979

Hyun, Jin-Kwon and Hak K. Pyo, “The estimation of Retirement and Economic Depreciation ofFixed Tangible Assets: Comparison between Capital Stock Approach and Micro-Data BasedApproach”, Journal of Korean Economic Analysis, Panel for Korean Economic Analysis andKorea Institute of Finance, June 1997, Seoul(in Korean).

Kendrick, John W., The Formation and Stocks of Total Capital, New York, NY: ColumbiaUniversity Press for National Bureau of Economic Research, 1976.

Kim, Kwang Suk and Sung Duk Hong, Accounting for Rapid Economic Growth in Korea, 1963-1995, Korea Development Institute, KDI Press, Seoul, 1997.

Nishimizu, M., Total Factor Productivity Analysis: A Disaggreagated Study of the Post-WarJapanese Economy, Doctoral Dissertation, The Johns Hopkins University, 1974.

Ohkawa, K., S. Ishiwata, S. Yamada, and H. Ish, Capital Stock, Estimates of Long-term EconomicStatistics of Japan since 1896, K. Ohkawa et. al. (eds) vol. 3. Toyo Keizai Shinposa, Tokyo,1966. (in Japanese)

Organization for Economic Cooperation and Development(OECD), International Sectoral DataBase, 1960-1995, Paris, 1996.

Osada, Hiroshi, "A Review of the Estimation of Capital Stock for Developing Countries," inProjections for Asian Industializing Region, edited by H. Osada and D. Hiratsuka, Institute ofDeveloping Economies, Tokyo, 1992.

Pyo, Hak K., Estimates of Capital Stock and Capital/Output Coefficients by Industries for theRepublic of Korea(1953-1986), KDI Working Paper No. 8810, Korea Development Institute,Seoul, 1988.

, A Synthetic Estimate of the National Wealth of Korea, 1953-1990, KDI Working Paper No.9212, Korean Development Institute, Seoul, 1992.

__________, “A Time-Series Test of the Endogenous Growth Model with Human Capital”, inGrowth Theories in Light of the East Asian Experience, edited by Takatoshi Ito and Anne O.Krueger, The University of Chicago Press, Chicago, 1995.

Russo, J.G. and H.A. Cowles, "Revalidation of the Iowa Type Survivor Curves," The EngineeringEconomist 26, Fall 1980; 1-16.

Triplett, Jack E., "Measuring the Capital Stock: A Review of Concepts and Data Needs", Paperpresented at the Workshop on the Measurement of Depreciation and Capital Stock,Conference on Research in Income and Wealth, National Bureau of Economic Research,Washington D.C. June 5, 1992.

Winfrey, Robley, Statistical Analysis of Industrial Property Retirements, Bulletin 125 Revised,Ames, IA: Engineering Research Institute, Iowa State University, April 1967.

<Table 9> Gross and Net Capital Stocks byIndustires

(1) Gross Capital Stocks by Industries in1990 Constant Prices

in billion won

Year Total 1 2 3 4 5 6 7 8 9 10

1953 16 704.40 705.60 128.04 1 279.72 691.15 169.93 644.15 66.82 4 428.71 1 937.06 6 653.23

1954 17 421.03 736.85 132.97 1 429.44 725.55 184.80 681.12 69.68 4 644.22 1 977.35 6 839.04

1955 18 324.96 776.11 139.27 1 603.35 768.20 202.36 726.29 73.30 4 912.03 2 034.93 7 089.13

1956 19 354.63 820.81 146.46 1 798.78 816.65 222.14 777.48 77.42 5 216.38 2 101.71 7 376.79

1957 20 580.20 873.95 155.07 2 024.70 873.98 245.16 837.75 82.32 5 576.83 2 184.23 7 726.23

1958 21 668.32 921.38 162.56 2 249.68 926.10 267.55 893.66 86.67 5 903.42 2 246.48 8 010.84

1959 22 780.69 969.96 170.16 2 489.16 979.86 291.19 951.75 91.12 6 239.83 2 305.83 8 291.83

1960 23 983.78 1 022.53 178.36 2 751.16 1 038.15 317.00 1 014.88 95.94 6 604.49 2 368.67 8 592.60

1961 25 217.21 1 076.54 186.71 3 029.90 1 098.42 344.27 1 080.59 113.85 6 981.07 2 428.53 8 877.33

1962 26 974.57 1 153.08 198.85 3 387.86 1 182.33 379.99 1 170.36 210.37 7 507.08 2 531.49 9 253.15

1963 29 366.25 1 256.97 215.52 3 848.08 1 295.18 426.45 1 289.88 325.48 8 215.69 2 683.79 9 809.22

1964 31 388.40 1 345.30 229.33 4 283.90 1 392.93 469.47 1 395.53 450.98 8 827.38 2 791.47 10 202.12

1965 34 138.00 1 465.06 248.31 4 844.97 1 524.26 525.44 1 536.08 602.60 9 650.65 2 952.12 10 788.50

1966 39 003.13 1 676.05 282.43 5 747.73 1 752.14 617.29 1 775.91 816.58 11 083.11 3 277.00 11 974.90

1967 45 055.79 1 938.69 324.78 6 884.91 2 036.34 732.68 2 075.66 1 091.28 12 869.01 3 674.83 13 427.61

1968 53 627.54 2 011.54 420.69 6 621.04 1 229.48 456.46 1 912.84 1 089.06 21 678.77 3 955.82 14 251.85

1969 60 041.50 2 709.10 451.14 7 845.86 1 445.92 575.19 2 364.02 1 308.41 24 584.91 4 056.75 14 700.21

1970 66 818.90 3 335.33 482.78 9 180.75 1 665.23 710.91 2 863.87 1 552.28 27 689.55 4 162.80 15 175.40

- 2 -

1971 73 932.04 3 785.28 540.09 10 843.50 2 026.53 877.05 3 403.70 1 936.91 30 614.48 4 253.92 15 650.58

1972 81 330.66 4 222.06 594.24 12 359.23 2 444.94 1 085.00 4 085.53 2 551.00 33 681.77 4 378.55 15 928.35

1973 90 809.64 4 780.50 665.37 14 673.12 2 864.18 1 297.52 4 834.73 3 595.71 37 433.89 4 523.07 16 141.55

1974 101 896.19 5 795.41 759.68 17 313.76 3 204.41 1 556.26 5 611.06 4 981.38 41 774.50 4 657.08 16 242.65

1975 114 103.08 6 034.94 843.99 20 679.67 3 631.32 1 937.72 6 540.59 6 302.49 46 557.94 4 853.04 16 721.39

1976 129 071.35 6 326.37 991.23 25 058.17 4 055.44 2 532.54 7 683.60 8 450.01 51 511.39 5 051.71 17 410.90

1977 148 391.97 6 977.93 ######## 30 995.93 4 956.38 3 391.36 8 957.94 10 538.41 57 401.13 5 390.93 18 595.63

1978 173 961.13 9 263.58 ######## 38 693.76 6 218.43 3 964.07 ######### 12 511.03 64 463.77 6 441.76 20 779.61

1979 202 422.19 11 723.23 ######## 47 351.18 8 000.79 4 609.80 ######### 14 844.87 71 799.47 7 623.12 23 382.38

1980 229 123.25 13 952.77 ######## 54 698.83 9 671.52 5 222.64 ######### 17 594.02 78 825.80 8 884.00 25 772.06

1981 255 768.44 16 107.44 ######## 62 131.72 ######### 5 842.30 ######### 20 662.95 84 953.98 ######### 28 135.64

1982 285 402.84 18 289.44 ######## 70 163.04 ######### 6 532.10 ######### 23 577.43 92 353.34 ######### 30 955.89

1983 319 824.53 20 868.46 ######## 78 795.72 ######### 7 157.26 ######### 27 845.63 ######### ######### 34 219.76

1984 357 851.37 23 699.78 ######## 89 555.19 ######### 7 952.49 ######### 31 931.34 ######### ######### 37 932.73

1985 398 297.90 26 737.25 ######## ######### ######### 8 713.85 ######### 36 210.85 ######### ######### 41 960.38

1986 443 278.65 30 297.48 ######## ######### ######### 9 374.97 ######### 40 479.00 ######### ######### 45 773.16

1987 495 607.96 34 679.51 ######## ######### ######### ######### ######### 44 730.85 ######### ######### 49 878.18

1988 552 989.56 38 009.11 ######## ######### ######### ######### ######### 47 878.76 ######### ######### 54 449.96

1989 622 107.76 45 109.88 ######## ######### ######### ######### ######### 51 462.71 ######### ######### 59 159.10

1990 712 641.59 50 764.91 ######## ######### ######### ######### ######### 57 175.05 ######### ######### 64 737.63

1991 813 199.48 56 724.07 ######## ######### ######### ######### ######### 62 603.10 ######### ######### 72 183.96

1992 904 800.96 61 486.13 ######## ######### ######### ######### ######### 68 292.63 ######### ######### 80 426.09

1993 997 331.55 65 145.90 ######## ######### ######### ######### ######### 73 055.13 ######### ######### 87 890.98

1994 ########## 68 835.61 ######## ######### ######### ######### ######### 80 729.29 ######### ######### 95 893.66

1995 ########## 72 905.77 ######## ######### ######### ######### ######### 92 741.51 ######### ######### #########

- 3 -

1996 ########## 77 701.32 ######## ######### ######### ######### ######### ######### ######### ######### #########

Average GrowthRate(%)53-61 5.3 5.4 4.8 11.4 6.0 9.2 6.7 7.0 5.9 2.9 3.7

53-67 7.4 7.5 6.9 12.8 8.1 11.0 8.8 24.0 8.0 4.7 5.2

62-79 12.3 14.5 13.1 16.7 12.8 16.8 14.2 32.1 14.4 6.7 5.6

80-96 11.9 11.8 4.8 13.5 11.7 11.2 10.7 12.3 11.6 12.3 9.7

62-96 12.1 13.2 9.1 15.1 12.3 14.0 12.5 22.5 13.0 9.4 7.6

53-96 10.8 11.8 8.3 14.4 11.1 13.1 11.4 19.6 11.7 8.2 6.8

(2) Net Capital Stocks by Industries in 1990Constant Prices

in billion won

Year Total 1 2 3 4 5 6 7 8 9 10

1953 8 278.18 349.67 63.45 634.19 342.51 84.21 319.22 33.11 2 194.73 959.95 3 297.13

1954 8 633.31 365.16 65.89 708.38 359.56 91.58 337.54 34.53 2 301.53 979.91 3 389.22

1955 9 081.28 384.62 69.02 794.57 380.70 100.28 359.93 36.33 2 434.25 1 008.45 3 513.15

1956 9 591.55 406.77 72.58 891.42 404.71 110.09 385.29 38.37 2 585.07 1 041.54 3 655.71

1957 10 198.90 433.10 76.85 1 003.38 433.12 121.49 415.16 40.80 2 763.70 1 082.44 3 828.88

1958 10 738.14 456.60 80.56 1 114.87 458.95 132.59 442.87 42.95 2 925.55 1 113.28 3 969.92

1959 11 289.40 480.68 84.33 1 233.55 485.59 144.30 471.66 45.16 3 092.27 1 142.70 4 109.17

1960 11 885.61 506.74 88.39 1 363.39 514.48 157.09 502.94 47.54 3 272.98 1 173.84 4 258.22

1961 12 496.86 533.50 92.53 1 501.52 544.34 170.61 535.51 56.42 3 459.60 1 203.50 4 399.33

1962 13 367.75 571.43 98.54 1 678.92 585.93 188.31 579.99 104.26 3 720.27 1 254.53 4 585.57

1963 14 552.99 622.92 106.80 1 906.99 641.85 211.34 639.22 161.30 4 071.44 1 330.00 4 861.14

1964 15 555.11 666.69 113.65 2 122.97 690.29 232.66 691.58 223.49 4 374.57 1 383.36 5 055.85

1965 16 917.72 726.04 123.05 2 401.01 755.38 260.39 761.23 298.63 4 782.56 1 462.98 5 346.44

1966 19 328.73 830.60 139.96 2 848.39 868.30 305.91 880.08 404.67 5 492.44 1 623.98 5 934.38

1967 22 328.24 960.75 160.95 3 411.95 1 009.14 363.09 1 028.63 540.80 6 377.48 1 821.13 6 654.30

1968 26 576.13 1 241.46 189.01 3 592.38 1 228.57 281.54 1 133.58 549.22 7 316.71 2 582.61 8 461.05

1969 30 638.78 1 688.16 187.10 4 313.22 1 355.40 356.60 1 399.49 600.00 9 041.68 2 568.02 9 129.10

1970 34 576.13 1 969.00 186.87 5 049.50 1 479.12 437.35 1 670.43 649.19 10 772.88 2 561.08 9 800.72

1971 38 404.01 2 044.68 212.48 6 011.72 1 739.29 535.42 1 933.61 811.04 12 124.05 2 541.32 10 450.39

1972 42 091.99 2 145.20 230.57 6 698.86 2 041.28 659.23 2 288.05 1 147.02 13 441.62 2 557.80 10 882.37

- 2 -

1973 47 373.55 2 391.68 262.58 8 072.18 2 326.30 767.52 2 645.17 1 815.14 15 264.02 2 594.96 11 234.00

1974 53 522.95 3 070.72 312.30 9 593.15 2 516.43 900.76 2 959.21 2 644.03 17 445.14 2 621.29 11 459.92

1975 59 813.05 2 823.28 343.43 11 636.21 2 784.84 1 130.87 3 355.22 3 176.87 19 799.93 2 709.82 12 052.57

1976 68 115.38 2 782.02 432.24 14 430.25 3 036.66 1 537.64 3 878.90 4 349.08 22 030.36 2 798.02 12 840.21

1977 79 643.54 3 167.59 553.49 18 439.83 3 753.22 2 153.13 4 427.36 5 118.12 24 901.70 3 023.80 14 105.29

1978 96 523.77 4 667.06 711.34 23 116.95 4 663.38 2 454.32 5 164.45 6 328.06 30 016.96 3 651.92 15 749.33

1979 114 383.05 6 009.32 859.30 27 997.06 6 009.98 2 791.79 5 792.42 7 720.63 35 122.50 4 330.27 17 749.78

1980 128 419.51 6 808.35 947.26 30 741.43 7 118.80 3 055.24 6 340.56 9 322.77 39 629.35 4 996.35 19 459.40

1981 140 698.30 7 315.31 ######## 32 961.81 8 302.18 3 294.76 7 020.78 11 007.76 42 968.98 5 687.32 21 082.14

1982 154 353.69 7 681.99 ######## 35 200.80 9 327.19 3 577.26 8 092.05 12 288.52 47 357.56 6 554.30 23 107.20

1983 171 111.62 8 299.38 ######## 37 425.17 ######### 3 762.56 8 767.84 14 731.25 53 225.23 7 451.80 25 503.54

1984 189 437.09 8 966.77 ######## 41 126.18 ######### 4 098.73 9 718.91 16 629.99 59 043.95 8 583.39 28 260.87

1985 207 928.40 9 619.89 ######## 45 778.38 ######### 4 361.98 ######### 18 439.00 64 282.87 9 689.39 31 230.02

1986 228 559.21 10 569.32 ######## 51 856.61 ######### 4 496.13 ######### 19 965.25 70 567.58 ######### 33 874.55

1987 253 843.61 12 045.79 ######## 61 096.14 ######### 4 927.62 ######### 21 244.05 77 220.58 ######### 36 721.36

1988 282 450.79 13 202.32 ######## 71 502.19 ######### 5 444.68 ######### 22 739.09 85 554.53 ######### 40 087.20

1989 316 461.12 15 668.74 ######## 83 123.76 ######### 6 070.94 ######### 24 441.22 95 786.96 ######### 43 554.17

1990 361 964.09 17 632.99 ######## 96 597.24 ######### 7 182.76 ######### 27 154.19 ######### ######### 47 661.20

1991 413 201.93 19 702.89 ######## ######### ######### 8 960.04 ######### 29 732.13 ######### ######### 53 143.35

1992 460 758.96 21 356.97 ######## ######### ######### ######### ######### 32 434.27 ######### ######### 59 211.37

1993 509 333.26 22 628.17 ######## ######### ######### ######### ######### 34 696.12 ######### ######### 64 707.18

1994 564 503.05 23 909.78 ######## ######### ######### ######### ######### 38 340.82 ######### ######### 70 598.93

1995 626 777.38 25 323.53 ######## ######### ######### ######### ######### 44 045.79 ######### ######### 76 255.02

1996 694 458.61 26 989.25 ######## ######### ######### ######### ######### 49 980.45 ######### ######### 82 598.22

- 3 -

Average GrowthRate(%)53-61 5.3 5.4 4.8 11.4 6.0 9.2 6.7 7.0 5.9 2.9 3.7

53-67 7.4 7.5 6.9 12.8 8.1 11.0 8.8 24.0 8.0 4.7 5.2

62-79 13.2 15.2 13.5 17.8 14.4 17.6 14.2 32.8 13.8 7.8 8.2

80-96 11.2 9.3 3.6 11.7 10.1 9.6 10.6 11.7 12.3 11.6 9.5

62-96 12.2 12.3 8.7 14.8 12.3 13.7 12.5 22.6 13.1 9.6 8.8

53-96 10.9 11.0 8.0 14.2 11.2 12.9 11.4 19.7 11.7 8.4 7.9