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Paper to be presented at OECD Workshop on Productivity Analysis and Measurement, Bern, 16-18 October 2006

Estimates of Labor and Total Factor Productivity by 72 industries in Korea (1970-2003)

October 16, 2006

Hak K. Pyo, Keun Hee Rhee and Bongchan Ha*

*Seoul National University, Korea Productivity Center and Korea Institute for Industrial Economics respectively. An earlier version of this paper (data structure part) was presented at EU-KLEMS Workshop in Valencia, May 7-9, 2006. We acknowledge financial support by the Bank of Korea and Korea Institute of International Economic Policy and research assistance of Eunkyung Jeon and Sun Young Jung at Seoul National University. [email protected] for correspondence.

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Estimates of Labor and Total Factor Productivity by 72 industries in Korea (1970-2003) Hak K. Pyo, Keun Hee Rhee and Bongchan Ha*

Abstract

As Krugman (1994), Young (1994), and Lau and Kim (1994)'s studies showed, the East Asian economic miracle may be characterized as `input-led' growth. However, both the stagnation in investment and the decrease in average working hours combined with decrease in the fertility rate require a productivity surge for a renewed sustainable growth in East Asia. The purpose of our study is to identify the sources of economic growth based on a KLEMS model for the Republic of Korea which experienced a financial crisis in 1997 after joining OECD. We report estimates of KLEMS inputs and gross output in Korea based on 72-industry classification following EU KLEMS project guidelines. We also provide estimates of 72 industry －level labor productivity and total factor productivity. We have found that Korea’s catch-up process with industrial nations in its late industrialization has been predominantly input-led and manufacturing based as documented in Timmer(1999) and Pyo (2001). We have also found that TFP growth has been positively affected by the growth of labor productivity and output growth. However, since its financial crisis in December 1997, the sources of growth seem to have switched to TFP-growth based and IT-intensive Service based. But lower productivity in service industries due to regulations and lack of competition seems to work against finding renewed sustainable growth path.

JEL-Classification: O14, O47 Key-Words: Labor Productivity, Total Factor Productivity, sustainable growth

*Seoul National University, Korea Productivity Center and Korea Institute for I ndustrial Economics respectively. [email protected] for correspondence.

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1. Introduction In recent years, especially since the 1997 economic crisis in the East Asian countries including Korea, considerable changes have taken place in the Korean economy, such as investment stagnation (see e.g. Pyo(2006) Pyo and Ha (2005)), changes in production input patterns, and so on. One of the most important changes is the demand for high productivity, which would compensate the recent slowdowns of growth rates in capital and labor inputs. As Krugman (1994), Young (1994), and Lau and Kim (1994) showed, the East Asian economic miracle may be summarized as `input-led' growth. Korea was no exception in this respect of growth pattern. However, both the stagnation in investment and the decrease in average working hours require a productivity surge for long-term growth in Korea. In addition, a sharp decrease in the fertility rate in Korea necessitates productivity increase in order to improve the present income level and facilitate the support of the large elderly population by the small numbers of working age adults. For these reasons, `productivity-driven' growth is indispensable for Korea. According to Lewis (2004), the fast economic growth in Korea is the result of both large labor input and capital accumulation. He argues that the average working hours is 40% higher than that of the U.S., and almost a third of GDP has been allocated to investment, while GDP per capita in Korea is about half of the U.S. GDP per capita. The focus is changing from how much inputs are put into production to how well those are organized. The purpose of this paper is to explain the data structure of Korea for the estimation of productivities by industry in KLEMS model and present preliminary estimates of labor productivity and total factor productivity (TFP) at reasonably detailed industry level. We have used 72-sector industrial classification following the guidelines of EU KLEMS project for the future comparability with EU member countries, the United States, and Japan. Therefore, an analysis based on detailed industrial classification gives us better views on productivity and growth, which is difficult to grasp in broader industrial classifications. Industries in an economy have shown different productivity trends and growth patterns according to their characteristics of production, competition policies, and other economic and non-economic circumstances.

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KLEMS model is a kind of gross output growth accounting in which output is measured by gross output and inputs are decomposed by capital (K), labor (L), energy (E), material (M), and service (S). Since this methodology is basically based on gross output, it has the advantage of eliminating effects of intermediate inputs from other industries on productivity, therefore allowing productivities by industry to be more accurate. Moreover, the assumption on real value-added production function (separability assumption) is not usually guaranteed1, which also gives legitimacy to gross output growth accounting. However, gross output growth accounting requires more information on intermediate inputs than value-added growth accounting. Therefore, the data structure for estimating productivity has to be consistent with not only national income accounts but also input-output tables, Use and Make Matrix etc. and the estimation methodology for unavailable data should be examined more carefully. We have found that Korea’s catch-up process with industrial nations in its late industrialization has been predominantly input-led and manufacturing based. We have also found that TFP growth has been positively affected by the growth of labor productivity and output growth. However, since its financial crisis in December 1997, the sources of growth seem to have switched to TFP-growth based and IT-intensive Service based. But lower productivity in service industries due to regulations and lack of competition seems to work against finding renewed sustainable growth path. This paper is organized as follows. Section 2 examines data structure including the methodology of measuring gross output by industry from Input-Output Tables and National Accounts published by the Bank of Korea and input measurements. Section 3 presents the estimates of labor productivity and TFP by 72-industry and examines the relations between labor productivity and TFP and between output growth and TFP growth by periods. Section 4 concludes the paper.

1

See Berndt and Christensen (1973,1974), Berndt and Wood (1974), Denny and Fuss (1977), and Yuhn (1991) for the U.S., and Pyo and Ha (2006) for Korea

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2. Data Structure 2.1 Gross Output Data National Accounts by the Bank of Korea (1999, 2004) report annual series (1970-2002) of nominal gross outputs at basic prices, both nominal and real value-added at basic prices, nominal compensation of employees, and operating surplus at current prices of 21 industries including 9 manufacturing industries. Those data can be extended to the year 2005 from ECOS (Economic Statistics System) in the Bank of Korea website2. National Accounts (1987, 1994, 1999, 2004) also reports annual series (1985-2002) of both nominal and real Make Tables (V-Tables) and real Use Tables (U-Tables). In addition to nominal gross output and both nominal and real value-added, real gross output at basic prices and real intermediate inputs at purchase prices can be obtained from Use Tables. However, since Make Tables and Use Tables for the years 1970-1984 and 2003-2004 are unavailable, we have generated them through RAS method using annual data from National Accounts and InputOutput Tables, and benchmark tables of 1985 and 2000, respectively. As the published Use Tables of National Accounts in Korea present the Domestic and Import Use Tables combined, we have not been able to isolate them into two separate tables. In the case of Use Tables before 1995, all the intermediate commodity inputs by industry are measured at purchase prices. Since 1995, those inputs have been measured at incomplete basic prices in the sense that those inputs include trade and transportation margins but isolate net production tax to the last row of intermediate input matrix. Because we have no information for transformation of the Use Tables from purchase prices to basic prices before 1995 and the Use Tables after 1995 have been measured at incomplete basic prices, we have changed the Use Tables at basic price after 1995 into Use Tables at purchase price allocating net production tax to each commodity proportional to each volume.

2

http://www.bok.or.kr

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The Bank of Korea has also published Input-Output Tables (commodity-bycommodity) since 1960. Its most recent 2000 Input-Output Table is the 19th Table. The detailed description of Input-Output Tables during 1970-2000 is summarized in Table 1. Input-Output Tables of Korea have relatively detailed information, even though they are restricted to commodity-by-commodity tables. For example, the table for 2000 has 28, 77, 168, 404 commodities in large, medium, small, and basic classifications, respectively.

Table 1. Input-Output Tables in Korea

Year Basic Small Medium Large 1970

153

56

1973

153

56

164

60

164

60

1975

392

1978 1980

396

162

64

1983

396

162

64

19

1985

402

161

65

19

1986

161

65

20

1987

161

65

20

1988

161

65

20

163

75

26

163

75

26

168

77

28

168

77

28

168

77

28

1990

405

1993 1995

402

1998 2000

404

(Number of commodity classification)

(1) Estimation of Use Tables While National Accounts do not contain detailed information about industries (21 industries), our industrial classification is 72 industries according to EU KLEMS classification (See Appendix Table A-1). In order to reconcile the National Accounts data to our industrial classification, we have used other data sources, such as Mining & Manufacturing Census and Surveys, Wholesale and

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Retail Surveys, and so on. Since we do not have detailed information on intermediate input structures, we have assumed the same intermediate input structures for the industries belonging to the same category of National Accounts classification. As for Input-Output Tables, they have detailed commodity classifications enough to match the 21-commodity classification in National Accounts. However, since they are not annually published, we have used interpolation method for the missing years. We have attached reclassification of National Accounts into 72 industries in Appendix Table A-2. Since the Bank of Korea reports nominal and real Make(V) Tables and real Use(U) Tables, nominal Use Tables should be generated for obtaining nominal intermediate input shares, which are used in estimating total factor productivity. Following Timmer (2005), we have used the commodity prices to nominalize all uses of the commodities under the assumption that the same commodity has the same price whichever industry uses it.

where industry

P Xij

denotes a price index for intermediate input of commodity

j , and

P Ci

denotes a price index of commodity

i

in

i .

C The commodity prices ( P i ) we have used are the weighted averages of

domestic and imported commodity prices, since the Use Tables cannot be separated into domestic and imported Use Tables to apply separated prices. Producer's Price Index (PPI) has been used as a proxy for domestic commodity D prices ( P i ), and Imported Price Index (CIF) has been used as imported

IM commodity prices( P i ). Even though PPI is not purchaser's price for the lack

of transportation and trade margins, it is a reasonable proxy for domestic commodity price index. The above procedure can be shown as follows: D We can derive real domestic commodity inputs ( X i ) subtracting real imported

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IM commodity inputs in Input-Output Tables ( X i ) from real commodity inputs in

Use Tables ( X i ).

From Eq (2), we can calculate the weighted average commodity price index using nominal domestic and imported intermediate commodity inputs.

Using Eq (1) and (3) we have nominalized each intermediate input in Use Tables, and normalized its value in order to equalize it with the nominal intermediate inputs in National Accounts as following:

where

1Ä PX Ã ij and

PX ij

denote a first estimate and a final estimate of

intermediate input commodity

i

in industry

real intermediate input commodity

j , respectively.

in industry

i

total nominal intermediate input in industry

8

j .

j , and

X ij

denotes

PX j

denotes

(2) Estimation of Make and Use Tables for the Missing Years We have estimated the Make and Use Tables for the missing years, 1970-1984 and 2003-2004 through a biproportional adjustment methodology, RAS. For the years 1970-1984 we have used the 1985 tables as benchmark tables, and for the years 2003-2004 we have used the 2002 tables. We have annual series of each industry's gross output, value-added, intermediate input, and so on. However, because we do not have annual series of each commodity's data in Input-Output Tables, we have applied the interpolation method between existing tables and normalized them to the National Accounts data.

(3) Aggregation Issues A Make and Use Table framework gives more detailed information of transactions in an economy than aggregated National Accounts data: constant values, volume indices, and price indices of commodities. In addition to this, the ESA 95 Input-Output Manual (2002) gives more advantages: (1) the numerical consistency, reliability and plausibility, (2) different volume indices and deflators according to different level of aggregation, and (3) relationship between trade and transport margin, taxes, subsidies, and so on. We have applied a simple summation for the Make Table aggregation over commodities under the assumption of the same deflator over all commodities produced in the same industry following Timmer (2005). With regard to the aggregation in Use Tables, we have not applied any aggregation technique considering each commodity as different inputs. In order to aggregate detailed industrial data, we have used chained Laspeyres Index following Timmer (2005). While the major advantage of chained Laspeyres index is additivity, it can also give us an advantage of a better match between products in consecutive time periods than between periods that are far apart (SNA -1993). Since this index uses changing weights, the problems of rapid shifts in the composition of an economy are minimized.

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(4) Make Tables at Purchase Prices and Use Tables at Basic Prices Purchase prices and basic prices are defined as follows:

We will explain the method of transforming Make Tables at basic prices into those at purchase prices, but the reverse procedure can be applied without significant modification. In order to transform Make Tables at basic prices into those at purchase prices, valuation matrices of the above four components ( TRij ,

TT ij ,

TV ij , and

T ij ) are needed. However, the information we have regarding those components are only the commodity vectors of TR, TT, and T 3 . The data regarding non-deductible VAT does not exist. Due to data unavailability, we have not been able to transform Make Tables at basic prices into those at purchase prices in matrix forms as follows:

3

Those vectors have been obtained from Input-Output Tables (commodity-bycommodity), so we are not able to know industry-by-commodity matrix.

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where

pur

PYij

and

PYbas ij

denote the supply of commodity

i

in industry

j. We have transformed Make Tables at purchase prices into vector forms of commodity adding up the rows over industry like the following

Also, in order to isolate imported commodities from total commodity supply, we have used the information on imported commodity vectors from Input-Output Tables to derive the following vectors:

where

PYDi

and

PYIM i

denote domestic and imported (before tariffs)

commodity supply, respectively. Trade margins and Transportation margins are normalized to equal the total supply of the respective commodity. Net taxes on products and imports are also normalized to respective National Accounts annual series. The above procedures have been applied to transform Use Tables at purchase prices into those at basic prices. Use Tables at basic prices have also been generated incompletely by subtracting the valuation commodity vectors from the commodity directions. The above procedures have been shown in Figure 1 and 2, and the trends of gross output and value-added have been shown in Figure 3. As can be seen in Figure 3, there was no real break in gross output growth in Korea’s economywide economic performance except in the year 1998 after the financial crisis in December 1997. Even during the years of first oil crisis of 1974-1975 and the second oil crisis of 1980-1981, the Korean economy’s real gross output continued to grow without major setbacks. After the economic crisis of December 1997, Korean economy had to go through IMF-mandated adjustment and restructuring program as documented in

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Pyo (2004). We observe that even though economy-wide labor productivity continues to grow, the disparity between labor productivity in Manufacturing and that in Service has been widening. As the IMF-mandated restructuring in Manufacturing sector has improved on labor productivity gain through cut-back of unnecessary manpower, the restructuring in most of Service sector except a few IT-related finance and communication sectors has been lagging behind.

Figure 1. Make Table at purchase prices commodity

industry

PYbas ij

PYD i PYIM i TRi TT i Ti pur

PYi

12

PYj

Figure 3. Trend of Real Gross Output (2000 prices)

1800 1600 1400

1000 800 600 400 200 0 19 70 19 72 19 74 19 76 19 78 19 80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04

Trillion Won

1200

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2.2 Measurement of Capital Input The success of late industrialization by newly industrializing economies could not have been made possible if both the rapid accumulation of capital and its changing distribution among sectors were not realized in their development process. However, it is difficult to identify these factors empirically because the time series data of capital stocks in fast-developing economies by both types of assets and by industries are not readily available. The lack of investment data for a sufficiently long period of time to apply the perpetual inventory estimation method was the main cause of the problem. However, the National Statistical Office of the Republic of Korea has conducted nation-wide national wealth survey four times since 1968. Korea is one of a few countries which have conducted economy-wide national wealth surveys at a regular interval. Since the first National Wealth Survey (NWS) was conducted in 1968, the subsequent surveys were made in every ten years in 1977, 1987, and 1997, respectively. Since such regular surveys with nation-wide coverage are very rare in both developed and developing countries, an analysis on the dynamic profile of national wealth seems warranted to examine how national wealth in a fast growing economy is accumulated and distributed among different sectors. The estimation of national wealth by types of assets and by industries was made by Pyo (2003) by modified perpetual inventory method and polynomial benchmark year estimation method using four benchmark-year estimates. We have extended his estimates to the year 2004, and changed the base year from 1995 to 2000.

(1) Estimation of Capital Stock4 1) Estimating Method for 1970-1997 In principle the existence of four benchmark year estimates of gross and net capital stocks makes it possible for us to apply the polynomial benchmark year estimation method. In Pyo's earlier studies (Pyo 1988, 1992, and 1998), he estimated proportional retirement rates and depreciation rates both by types of

4

This section has been quoted from Pyo, Rhee, and Ha (2004)

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assets and by industries based on the polynomial equations. When we applied the polynomial benchmark year equation to estimate the proportional retirement rates for the sub-periods of 1977-87 and 1987-97, most of estimates became negative including the average economy-wide retirement rates (-3.0% for 1977-87 and --3.1% for 1987-97) except other Construction (0.6%) and Transport Equipment (3.4%) in 1977-87 and Nonresidential Building (0.9%) in 1987-97. Therefore, following Pyo (1998), we have applied the polynomial benchmark year estimation method to estimating depreciation by types of assets only. Thus we have generated net stocks by types of assets first for the period of 1968-97 and then, distributed them over different sectors of industries by using interpolated industrial weights between the respective benchmark years. We have decided to estimate net capital stock first and then to estimate gross capital stock by using interpolated net-gross conversion ratios for the following two reasons. The basic reason is due to the fact that the margin of prediction error from the polynomial benchmark year equation turns out to be larger with gross capital stock than with net capital stock as had been observed in Pyo (1992).

2) Estimating Method after 1997 National Statistical Office of Korea has decided to terminate National Wealth Survey by 1997 and to switch from direct estimation to indirect estimation of national wealth following the method of BEA and OECD. The cost of such direct national wealth survey has increased significantly as the size of national economy has expanded considerably. In addition, some of the participating institutions such as Kookmin Bank for unincorporated business enterprises have been privatized so that National Statistical Office alone can no longer afford national wealth survey. Japan had terminated its National Wealth Census in 1970 for almost the same reasons. Therefore, for the period after 1997, which is the last year of national wealth survey, we have to estimate capital stocks by a modified perpetual inventory method using 1997 NWS as benchmark estimates. First, we estimate net stocks by type of assets in constant prices by using the depreciation rates estimated from the period of 1987-1977 and distribute them across industries using both industrial weights in 1997 NWS and those in subsequent Mining & Manufacturing Census and Surveys and Wholesale and Retail Surveys. In the

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long run, the estimated depreciation rates by type of assets may need to be updated and revised by the micro data-based studies. Second the generated net stocks by type of assets and by industries have to be converted into gross stock by using the net-gross conversion ratio of 1997 NWS for the time being. But ultimately we may need further studies on the trend of net-gross conversion ratio by type of assets and by industries and the average asset life.

3) Reconciliation with Database of Pyo (2003) Since the database of Pyo (2003) covers 10 broad categories of industrial sector together with 28 sub-sectors of Manufacturing, it has been reclassified and reconciled with 72 industry classification using other sources such as Mining & Manufacturing Census and Surveys, Wholesale and Retail Surveys, and so on. We have classified assets into five categories; residential building, non-residential building, other construction, transportation vehicles, and machinery, while excluding large animals & plants, household durables, and inventory stocks as shown in Table 2.

Table 2. Depreciation Rates of Assets (Unit: %)

before 1978 1978-1987 after 1988 Residential Building

5.5

1.2

3.3

Non-residential Building

-6.7

-1.3

3.0

Other Construction

9.7

8.4

1.0

Transportation Vehicles

49.3

28.7

16.9

Machinery

1.1

11.4

9.2

Source : Pyo (2003)

(2) Estimation of Capital Service Inputs The purpose of this subsection is to outline the estimation of capital service flows in Korea. We have followed the method of Jorgenson, Ho, and Stiroh (2005) except the adjustment for a rapid IT asset price decline. The capital service flows for each asset have been estimated from the capital stocks, and have been aggregated over all the assets.

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We have assumed that the flow of capital service is proportional to the average of current and one-year lagged capital stocks, which means that currently installed capital stock is available in the midpoint of the installed period. The k -type capital service flow in industry j at time t can be defined as in (eq10).

where

Z k , j, t

t , and

q k,t

denotes the

k -type capital service flow in industry

j

at time

denotes normalizing factor.

We have estimated the price of capital service through the user cost of capital formula. This methodology derives the cost of capital by the equality between two alternative investments: earning a nominal rate of return ( it ) and investing in asset earning a rental fee and selling the depreciated asset:

where

denotes the (expected) capital gains (

),

and

denotes the depreciation rate specific to k -type asset. We have used yields of corporate bonds for nominal rates of return ( it ) and Pyo's (2003) results for depreciation rates as shown in Table 2. We did not consider tax effects in estimating cost of capital for the unavailability of data. Using the above capital service flow and capital service price of each asset, we have derived aggregated capital service input by a Tornqvist index.

where the

v k,j

are the two-period average shares of the

k -type capital

income in total capital income. K We have also estimated implicit price index of capital inputs ( P j ) from the

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following equality:

2.3 Measurement of Labor Input (1) Data In order to measure labor input for KLEMS model, we have to obtain both quantity data of labor input such as employment by industries and hours worked and quality factors such as sex, education and age. Both availability and reliability of labor statistics in Korea have improved since 1980. But the measurement of labor input by industries cannot be readily made because the statistics of employment by industries are not detailed enough to cover 72 sectors. Therefore, we have used other sources for breaking down the labor data. More detailed classifications of employment will have to rely on Employment Table, which is published as a supporting table to Input-Output Table. But it is available only every five year when main Input-Output Tables are published. Mining and Manufacturing Census and Survey by National Statistical Office also report employment statistics but it is limited to mining and manufacturing only. Economically Active Population Yearbook by National Statistical Office reports the number of employment, unemployment, not-economically-active population and economically active population. Report on Monthly Labor Survey by Ministry of Labor publishes monthly earnings and working days of regular employees. Survey Report on Wage Structure by the same ministry reports wages. Nominal wages are also available from this survey. For the present study, we have obtained the raw data file of Survey Report on Wage Structure from the Ministry of Labor and Economically Active Population Survey from National Statistical Office for the period of 1980-2003. The data are classified by two types of gender (Male and Female), three types of age (below 30, 30-49, and 50 above), and four types of education (middle school and under,

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high school, college, and university above) and, therefore, there is a total of 24 categories of labor as shown Table 3.

Table 3. Classification of Labor Input Categories Gender Age

(1) male (2) female (1) below 30 (2) 30-49 (3) above 50

Education (1) middle school and under (2) high school (3) college (4) university or above

(2) Estimating Labor Quantity and Quality Inputs Since the raw-data file of the Survey Report on Wage Structure contains more detailed industrial classification than that of the Economically Active Population Survey, we have calculated the quantity of labor from the Economically Active Population Survey and the quality of labor from the Survey Report on Wage Structure. This enables us to include self-employed labor as well as to use more detailed data. However, since the Survey Report on Wage Structure does not include Agriculture and Government sectors, we had to use the average value of the entire economy for the quality measure of these two sectors. In order to make quality adjustments to the employment data, we have taken the following steps5: j

(1) Defining P Ll as wage rate for j industry and the share of labor income by expressed as;

l

l

type category of labor,

type category of labor in

5

Jorgenson, Gollop, and Fraumeni (1987)

19

j

industry can be

The average weight of j industry and l of ( t-1 ) and t can be generated as;

type labor income during the period

(2) In order to make a quality adjustment to labor input data, we have further decomposed labor input of j industry and l

where

denotes relative weight of working hours of

industry. In other words, industry. hours of

type as follows:

and

j

measures labor input of l

type in

j

type labor in

j

l

denote the employment and average working

industry respectively.

(3) Finally, the growth rate of j industry labor input has been computed as follows:

where the first bracket on the right-hand side measures change in employment, the second bracket measures change in average working hours, and the third bracket measures the change in quality of labor through change in weighted working hours. This method defines that total labor input growth is measured by the sum of separate growth of different categories of labor and that the quality of labor is measured by the difference between the growth of aggregated labor and the sum of the separate growth of different categories of labor.

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2-4 Energy, Material, and Service and Input Shares

In order to decompose intermediate inputs into energy (E), material (M), and service (S) inputs, we have identified coal and lignite, crude petroleum and natural gas, uranium and thorium ores, metal ores, coke, refined petroleum products and nuclear fuel, gas, water, and electricity commodities as energy inputs, both primary commodities and remaining manufacturing commodities as material inputs, and remaining service inputs as service inputs. Regarding shares of inputs, we have used compensation of employees as shares of labor inputs and remaining value-added as shares of capital inputs. This method may underestimate the shares of labor input by allocating the compensation of self-employed to the shares of capital input, and this gap would be especially large in primary industry. There are some adjustment processes to correct underestimation of labor share as attempted by, for example Harberger (1978), but we have not applied it in order to avoid arbitrary adjustments. This can be improved in future studies. As for energy, material, and service inputs, we have used nominal inputs for their own shares.

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3. Estimates of Labor Productivity and TFP by 72industry 3.1 Trend of Labor Productivity Level and Growth Rates by Sector (1) The Level of Labor Productivity and its Trend As shown in Figure 4, the general trend of labor productivity reveals a rising trend but with a remarkable difference between Manufacturing and Service. While the labor productivity level in Manufacturing measured as the ratio of real price output to working hours increased sharply, the level in Service increased very slowly. The role of productivity gain in Manufacturing in the catch-up process of Korea has been well-documented by Timmer (1999) and Pyo (2001). As observed in Pyo and Ha (2005), the labor productivity level was not reduced during the years (1997-1998) of the Asian Financial Crisis because of IMFmandated industrial restructuring: the reduced output was matched by reduced employment leaving labor productivity level unaffected. The relatively sluggish productivity gain in Service sector has been pointed out by IMF in their recent consultation with the Korean authorities as a bottleneck of sustainable growth for Korea. Inklaar, Timmer and van Ark (2006) also pointed out the slower productivity gain of service industries in Europe relative to those in the United States. A more detailed decomposition of labor productivity by sector and by sub-period is presented in Table 4 and 5. According to Kim(2006), while the share of Service sector in Korean economy has increased sharply reaching 56 percent level of GDP and 65 percent of total employment in 2005, the Service productivity is not only low in level terms compared to industrial nations’ levels but also lags behind in terms of growth rate. She also points out that Korea’s inter-industry linkage effect between Manufacturing and Service is also only about half the size of industrial nations.

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Figure 4 Trend of labor productivity level 80000 70000 60000 50000 40000 30000 20000 10000 0 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 Economy-wide

M anufacturing

Service

Table 4 Labor Productivity Level by industries: Sectoral labor productivity level in manufacturing by period Code

9

Industry

'72-'79

Food products and beverages

'80-'89

'90-'99

Before

After

crisis

crisis

'90-'98

'99-'03

'72-'03

36,999

39,367

55,163

54,193

90,177

50,884

36,922

40,586

90,594

89,379

195,570

77,609

6,452

14,174

22,709

22,007

38,216

18,203

12 Wearing apparel, dressing and dying of fur

12,161

9,768

13,732

13,860

19,494

13,037

13 Leather, leather products and footwear

16,679

7,605

17,031

16,130

34,307

16,444

4,834

13,773

26,831

26,364

47,559

20,359

15 Pulp, paper and paper products

11,621

13,755

39,931

38,555

71,876

29,278

16 Publishing

12,699

23,025

41,144

37,476

76,242

32,823

17* Printing and reproduction

151,732 388,216

23,194

23,570

15,967 168,374

18 Coke, refined petroleum products and nuclear fuel

274,511 339,248 482,051 468,709 1,013,717 464,860

10 Tobacco products 11 Textiles

14 Wood and products of wood and cork

19 Pharmaceuticals

6,287

20 Chemicals excluding pharmaceuticals

19,562

12,999

20,620

20,168 100,634

20,665 1,451,946 238,313 90,439

133,936

57,556

21 Rubber and plastics products

3,070

8,368

27,769

26,649

47,226

18,257

22 Other non-metallic mineral products

8,884

11,959

27,173

25,961

56,995

22,165

13,314

36,861

69,079

66,241

115,772

51,567

23 Basic metals

23

24 Fabricated metal products

9,132

35,166

38,886

38,054

62,489

33,739

25 Machinery, nec

9,241

10,268

20,074

19,024

45,098

17,916

26 Office, accounting and computing machinery

20,453

35,659

51,379

48,471

169,000

56,296

27 Insulated wire

24,917

13,769

26,158

23,513

155,335

41,416

28 Other electrical machinery and apparatus nec

2,854

7,251

27,079

26,237

52,172

18,511

29 Electronic valves and tubes

2,631

61,877

54,903

54,209

57,471

44,220

30 Telecommunication equipment

3,756

10,289

44,215

33,205

55,150

22,110

31 Radio and television receivers

4,067

6,231

23,202

18,350

39,864

14,354

32 Scientific instruments

1,959

10,277

42,768

41,220

37,830

21,205

-

-

-

-

-

-

19,467

36,677

61,206

61,482

88,334

47,422

158,390 286,959

83,660

88,164

89,862 168,109 90,103 26,004*

33 Other instruments 34 Motor vehicles, trailers and semi-trailers 35 Building and repairing of ships and boats 36 Aircraft and spacecraft

132*

858

31,281

29,831

37 Railroad equipment and transport equipment nec

2,838

1,879

14,641

14,314

7,200

6,448

38 Manufacturing nec

3,614

7,591

16,945

16,630

23,283

11,591

-

-

-

-

-

-

39 Recycling

* In case of the industrial classification code #36, the labor productivity is applied to the period of 19761979 or 1976-2003 because of the data insufficiency,

Table 5 Labor Productivity Level by industries: Sectoral labor productivity level in Service by period Code

Industry

'72-'79

'80-'89

'90-'99

90,511

Before

After

crisis

crisis

'90-'98

'99-'03

'72-'03

40 Electricity supply

15,448

52,504

86,384

179,068

72,544

41 Gas supply

14,371

51,533 153,843 130,874

225,650

91,763

42 Water supply

14,372

51,534 344,577 338,580

784,980 237,576

43 Construction

10,256

14,659

22,949

22,739

26,014

17,605

67,587

4,234

1,800

1,942

699

18,875

95,249

4,307

5,265

5,198

6,288

27,603

46 Retail trade

162,844

3,970

10,885

10,959

11,857

46,886

47 Hotels and restaurants

105,649

6,820

7,673

7,651

7,820

31,917

3,973

6,180

9,958

9,726

12,768

7,655

Sale, maintenance and repair of motor vehicles and 44 motorcycles 45 Wholesale trade and commission trade

48 Inland transport

24

49 Water transport

12,758

44,351 189,712 191,043

209,677 103,542

50 Air transport

48,873

47,013

73,068

75,421

136,955

69,521

51 Supporting and auxiliary transport activities

22,824

22,252

13,633

13,781

12,904

18,552

52 Post and telecommunications

22,668

14,465

30,773

27,838

69,494

28,875

53 Financial intermediation

4,580

9,163

23,830

23,034

39,388

16,641

54 Insurance and pension funding

6,943

20,992

33,231

35,061

29,756

22,806

- 28,342*

34,939

31,599

46,941 35,137*

56 Imputation of owner occupied rents

447,193 200,889 440,570 461,481

90,498 318,508

57 Other real estate activities

437,768 211,920

42,199 195,594

58 Renting of machinery and equipment

437,408 211,920 316,158 321,506 1,928,040 567,257

59 Computer and related activities

437,418 211,920

65,469

68,565

24,535 198,697

60 Research and development

417,881 211,920

94,137

98,492

89,770 212,423

61 Legal, technical and advertising

13,404 110,908

34,166

35,704

20,800

51,302

62 Other business activities, nec

13,075 110,945

12,612

12,089

14,119

43,545

63 Public admin and defence

13,557 251,094

2,552

2,751

1,256

82,826

64 Education

13,370 247,426

5,595

5,444

6,734

83,246

55 Activities related to financial intermediation

65 Health and social work

43,026

47,409

5,168

10,412

18,945

18,564

19,182

12,764

2,473

6,329

74,183

60,749

102,172

35,646

67 Activities of membership organizations nec

6,656

7,046

23,951

24,048

18,522

13,523

68 Media activities

5,870

7,046

27,001

29,255

30,274

16,627

151,168 319,953

85,555

93,977

17,390 166,925

70 Other service activities

55,559 114,660

73,632

81,243

9,723

74,090

71 Private households with employed persons

60,947 122,708

41,276

41,789

34,584

70,740

-

-

-

-

Sewage and refuse disposal, sanitation and similar 66 activities

69 Other recreational activities

72 Extra-territorial organizations and bodies

-

-

* In case of the industrial classification code #55, the labor productivity growth is applied to the period of 1986-89 or 1986-03 because of the data insufficiency

(2) The growth rates of labor productivity by Sector The growth rates of labor productivity as summarized in Table 4 and shown in Figure 5 confirm the remarkable difference between Manufacturing and Service sector. Throughout the entire period of 1971-2003, the economy-wide labor productivity has grown at the average rate of 5.59 percent but with the sectoral difference between Manufacturing (6.99 %) and Service (2.91 %). The

25

difference did not shrink but rather has expanded as the process of industrialization continued. For example, the difference in the 1990’s (9.55 % vs. 2.64 %) has been more than doubled since 1970’s (4.01 % vs. 2.15 %). The observed difference in both levels and growth rates of labor productivity between Manufacturing and Service can signal the difference in the degree of foreign competition, the proportion of tradable and non-tradable and the degree of domestic competition due to historically different regulatory environments. For example, the proportion of public enterprises and their subsidiaries in total output of many service industries such as utilities (electricity, water and gas), transportation and communication is a lot greater than their proportion in Manufacturing so that their productivity improvement could have been sluggish over time. In addition, many non-tradable sectors of service industries such as retail trade, real estate and financial services, hotels and restaurants etc. have been subject to all kinds of regulations such as zoning, sanitary standards and segregated financial market services etc. Table 6 Growth Rates of Labor Productivity by Sector

(%)

Period

Economy-wide

Manufacturing

Service

72-'79

4.32

4.01

2.15

80-'89

6.87

6.75

3.77

90-'99

5.54

9.55

2.64

90-'98

5.14

9.01

2.40

99-'03

5.87

8.61

3.33

5.59

6.99

2.91

72-'03

A more detailed analysis of labor productivity can be made by looking at growth rates of labor productivity by sub-sectors of both Manufacturing and Service industries. Table 7 reports our estimates of growth rates of labor productivity in sub-manufacturing industries. Within Manufacturing, we also observe a large difference in growth rates of labor productivity ranging from Aircraft and Spacecraft (27.08 %) to Tobacco Products (- 4.78 %). In general, exportoriented manufacturing industries such as Electrical Machinery and Electronic devices (14 % range) and Computing Machinery and Television and Radio 26

Receivers etc. (10 % range) have been leader industries in improving labor productivity. In Service industries, the intra-industry difference of labor productivity growth is even larger than in Manufacturing. As presented in Table 8, Water Supply (17.92 %), Electricity (14.48 %) and Gas Supply (14.08 %) have been leader industries because they have been under the public enterprises with rising demand for social overhead capital. On the other hand, Private House Hold with Employed Persons (- 26.69 %), Other Service Activities (- 19.47 %) and Public Administration and defense (- 15.84 %) have been on the lower end of the labor productivity growth.

27

Figure 5 The growth rates of labor productivity Economy-w ide 12.00 10.00 8.00 6.00 4.00 2.00 0.00 -2.00

71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03

-4.00 -6.00

M anufacturing 20.00 15.00 10.00 5.00 0.00 -5.00

71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03

-10.00 -15.00

Service 15.00 10.00 5.00 0.00 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 -5.00 -10.00

28

Table 7 Growth Rates of Labor productivity in Manufacturing

( %) Before After

Code

Industry

'72-'79 '80-'89 '90-'99 crisis

crisis '72-'03

'90-'98 '99-'03 9


0.31

3.30

3.20

5.01

4.66

3.16

-36.96

6.28

4.62

3.62

15.92

-4.78

11 Textiles

9.92

5.39

6.41

6.43

8.39

7.36

12 Wearing apparel, dressing and dying of fur

-3.42

4.67

-0.02

-1.44

13.19

2.09

13 Leather, leather products and footwear

-29.77 -3.24

12.76 14.89

7.83

-3.85

14 Wood and products of wood and cork

20.78

9.02

6.89

9.38

7.69

12.12

15 Pulp, paper and paper products

1.08

7.14

9.92

11.27

6.95

6.58

16 Publishing

4.33

11.98

6.67

-1.60

16.64

6.90

17 Printing and reproduction

43.17 -28.17 -2.81

1.59

-15.13

1.38

18 Coke, refined petroleum products and nuclear fuel

-2.51

-0.44

4.61

4.47

8.51

1.69

19 Pharmaceuticals

-46.09

9.57

0.48

-0.22

86.74

3.41

12.46 17.43 15.67

-2.05

9.93

10 Tobacco products

20 Chemicals excluding pharmaceuticals

8.03

21 Rubber and plastics products

14.20 11.13

7.33

6.45

6.65

10.01

22 Other non-metallic mineral products

3.36

6.95

8.00

7.40

15.07

7.33

23 Basic metals

17.40 10.13

6.81

7.59

4.78

10.61

24 Fabricated metal products

7.66

9.06

4.28

4.69

7.95

7.32

25 Machinery, nec

2.22

9.11

7.18

5.60

14.70

7.12

26 Office, accounting and computing machinery

18.78

0.47

9.44

5.43

22.29 10.12

27 Insulated wire

-10.61 -2.90

13.39 12.05 26.55

28 Other electrical machinery and apparatus nec

24.09

12.22 13.82 15.00 14.29

29 Electronic valves and tubes

23.64 26.52

30 Telecommunication equipment

-0.95

16.22 21.79 18.65 -12.66

31 Radio and television receivers

11.99

6.38

32 Scientific instruments

-5.03

19.82 14.73 13.77

33 Other instruments

5.52

-2.40

20.71

-5.89

6.30

9.88

3.53

14.37 7.82

22.66 10.36 0.56

8.48

-

-

-

-

-

-

34 Motor vehicles, trailers and semi-trailers

-7.21

9.42

-1.52

-6.68

19.98

2.09

35 Building and repairing of ships and boats

5.27

8.21

-21.86 -29.82 28.56

0.12

36 Aircraft and spacecraft

28.90* 20.70 34.72 41.48 12.83 27.08*

37 Railroad equipment and transport equipment nec

-12.47 -3.28

38 Manufacturing nec 39 Recycling

29

23.10 26.82 -30.80 -1.75

3.43

13.34

5.20

6.12

3.80

7.23

-

-

-

-

-

-

A irc ra ft a n d sp a c e c ra ft

-10.00

30

* In case of EU-KLEMS Code, #33, #39, they are excluded because of data insufficiency. T o b a c c o p ro d uc ts

Le a th e r, le a th e r p ro d uc ts a n d fo o tw e a r

R a ilro a d e q uip m e n t a n d tra n sp o rt e q uip m e n t n e c

B uild in g a n d re p a irin g o f sh ip s a n d b o a ts

P rin tin g a n d re p ro d uc tio n

C o ke , re fin e d p e tro le um p ro d uc ts a n d n uc le a r fue l

W e a rin g a p p a re l, d re ssin g a n d d yin g o f fur

M o to r ve h ic le s, tra ile rs a n d se m i- tra ile rs

F o o d p ro d uc ts a n d b e ve ra g e s

P h a rm a c e utic a ls

In sula te d w ire

P ulp , p a p e r a n d p a p e r p ro d uc ts

T e le c o m m un ic a tio n e q uip m e n t

P ub lish in g

M a c h in e ry, n e c

T e xtile s

F a b ric a te d m e ta l p ro d uc ts

O th e r n o n - m e ta llic m in e ra l p ro d uc ts

M a n ufa c turin g n e c

S c ie n tific in strum e n ts

R a d io a n d te le visio n re c e ive rs

R ub b e r a n d p la stic s p ro d uc ts

C h e m ic a ls e xc lud in g p h a rm a c e utic a ls

O ffic e , a c c o un tin g a n d c o m p utin g m a c h in e ry

B a sic m e ta ls

W o o d a n d p ro d uc ts o f w o o d a n d c o rk

O th e r e le c tric a l m a c h in e ry a n d a p p a ra tus n e c

E le c tro n ic va lve s a n d tub e s

* In case of the industrial classification code #36, the labor productivity growth is applied to the period of

1977-1979 or 1977-2003 because of the data insufficiency,

Figure 6 Growth Rates of Labor Productivity in Manufacturing (1972-03/ %) 30.00

25.00

20.00

15.00

10.00

5.00

0.00

-5.00

Table 8 Growth Rates of Labor Productivity in Service Industries Code

Industry

'72-'79 '80-'89 '90-'99

(%)

Before

After

crisis

crisis

'72-'03

'90-'98 '99-'03 40 Electricity supply

26.31 15.59

7.68

8.61

5.98

14.81

41 Gas supply

23.56 16.87 17.62

13.12

-3.01

14.08

42 Water supply

25.62 16.87 18.63

17.48

11.31

18.36

43 Construction

-0.51

3.57

0.38

2.94

44 Sale, maintenance and repair of motor vehicles and motorcycles 13.79 -15.96 -23.47 -28.17

16.66

-6.86

45 Wholesale trade and commission trade

-4.76 -13.27

0.87

-1.15

5.07

-4.87

46 Retail trade

7.37 -16.64

6.21

8.79

1.00

-0.73


-4.40 -11.82 -1.48

-3.88

4.03

-5.25

48 Inland transport

18.24

4.88

4.47

5.24

8.97

49 Water transport

17.01 15.25 10.69

15.66

-1.83

13.13

50 Air transport

-39.14 9.24

0.55

3.70

8.22

-4.57


24.37

9.56

-3.03

-2.14

0.61

8.57


24.86

8.02

13.67

12.91

9.89

13.90


26.19 15.41

6.30

4.88

9.98

14.29


8.50

20.12

-7.05

0.57

6.51

9.59

-

-

7.53

-1.36

10.07

2.65

4.83


6.42

7.47

2.40


-1.02 -2.03

16.70

-53.65

-4.58


-0.64 -2.25 -37.80 -44.38

56.47

-4.52


-0.64 -2.25

-17.72

1.25


-0.64 -2.25 -14.28 -17.68 -15.33

-8.23


-0.64 -2.25 -10.49 -10.71

9.57

-2.38


-3.09

9.08

2.02

3.95

2.85

3.62


-48.15 9.49

0.16

-6.08

5.19

-9.97

63 Public admin and defense

-48.33 10.66 -32.22 -37.57

19.98

-16.20

64 Education

20.70

4.18

-3.65

-5.20

-17.05

2.36


-40.34 13.67

6.55

7.03

-2.79

-4.27

5.36

17.35

66 Sewage and refuse disposal, sanitation and similar activities

0.80

6.42

33.68

32.77

-64.78

1.30


3.48

6.82

11.15

15.97

-7.60

6.30

68 Media activities

-38.79 6.82

-1.19

-6.79

67.04

1.00


-3.66 14.34 -33.19 -39.83

33.58

-2.39


-89.32 19.18 -32.34 -31.03

29.95

-20.38


-88.61 19.22 -13.20 -16.44 -45.18 -27.83

31

W a te r sup p ly

-40.00

32

* In case of EU-KLEMS Code, #72 is excluded because of data insufficiency. A ir tra n sp o rt

H o te ls a n d re sta ura n ts

O th e r se rvic e a c tivitie s

-

P riva te h o use h o ld s w ith e m p lo ye d p e rso n s

-

P ub lic a d m in a n d d e fe n c e

O th e r b usin e ss a c tivitie s, n e c

C o m p ute r a n d re la te d a c tivitie s

-

S a le , m a in te n a n c e a n d re p a ir o f m o to r ve h ic le s a n d m o to rc yc le s

-

W h o le sa le tra d e a n d c o m m issio n tra d e

Im p uta tio n o f o w n e r o c c up ie d re n ts

-

O th e r re a l e sta te a c tivitie s

H e a lth a n d so c ia l w o rk

O th e r re c re a tio n a l a c tivite s

R e se a rc h a n d d e ve lo p m e n t

R e ta il tra d e

M e d ia a c tivitie s

R e n tin g o f m a c h in e ry a n d e q uip m e n t

S e w a g e a n d re fuse d isp o sa l, sa n ita tio n a n d sim ila r a c tivitie s

E d uc a tio n

A c tivitie s re la te d to fin a n c ia l in te rm e d ia tio n

C o n struc tio n

Le g a l, te c h n ic a l a n d a d ve rtisin g

A c tivitie s o f m e m b e rsh ip o rg a n iza tio n s n e c

S up p o rtin g a n d a uxilia ry tra n sp o rt a c tivitie s

In la n d tra n sp o rt

In sura n c e a n d p e n sio n fun d in g

W a te r tra n sp o rt

P o st a n d te le c o m m un ic a tio n s

G a s sup p ly

F in a n c ia l in te rm e d ia tio n

E le c tric ity sup p ly

72 Extra-territorial organizations and bodies -

* In case of the industrial classification code #41, the labor productivity growth is applied to the period of

1973-1979 or 1973-2003, and in case of code #55, the period is 1987-2003 because of the data

insufficiency

Figure 7 Growth Rates of Labor Productivity in Service (1972-03/ %) 30.00

20.00

10.00

0.00

-10.00

-20.00

-30.00

(3) Trend of TFP Growth by Sector The growth rates of TFP by sector are shown in Figure 8. Throughout the entire period 1972-2003, Korean economy experienced about 2 break-points: mid1970s which was the first oil shock and in 1997 which was the financial crisis. The difference between two break points can be summarized as follows. During the second half of 1970’s, the growth rate of gross output was not low, but the growth rates of inputs such as capital(4.56%), labor(1.79%), energy(0.69%), intermediate goods(3.34%) especially, were relatively higher. Therefore, the growth rates of TFP have been estimated as negative. In case of late 1990’s the negative growth of TFP has been resulted from the shrink of gross output rooted from economic crisis. In addition we observe that the estimated TFP growth rates in Manufacturing are in general greater than in Service. It maybe due to the fact that an innovation process such as product innovation or process innovation is more sensitive and stronger in manufacturing than in service. Also the R&D investment for innovation is in general more intensive in manufacturing than in service. So the growth rates of TFP in Manufacturing seem to be greater than in Service.

33

Figure 8 The growth rates of TFP (%) Economy-w ide 3.00 2.00 1.00 0.00 -1.00

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03

-2.00 -3.00 -4.00 -5.00 -6.00

M anufacturing 5.00 4.00 3.00 2.00 1.00 0.00 -1.00

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03

-2.00 -3.00 -4.00 -5.00

Service 3.00 2.00 1.00 0.00 -1.00

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03

-2.00 -3.00 -4.00 -5.00 -6.00

34

3.2 Gross Output Growth Accounting and TFP Growth The growth rate of economy-wide TFP has been estimated as -0.59 percent. The growth rates of TFP in Manufacturing and Service are estimated as 0.48 percent and -0.92 percent respectively throughout the entire period of 19722003 as shown in Table 7-9. Also the economy-wide TFP growth rate during the pre-crisis period (1990-1998) has been estimated as -0.84 percent. And the growth rate during the post-crisis period (1999-2003) has been estimated as 0.86 percent. After the economic crisis in 1997-1998, the economy-wide growth rate of gross output has been recovered, at the same time the growth rates of input factors such as capital, labor and service have also been reduced from those during the pre-crisis period. Accordingly, the growth rate of TFP during the post-crisis period has been relatively higher than that during the pre-crisis period. Secondly the contributions of TFP to economy-wide gross output growth during the entire period of 1972-2003 are -7.5 percent, and 4.7 percent in Manufacturing, and -12.8 percent in Service. Then we can examine the relative contribution ratio of the input factors to the output growth. The relative contribution ratios to output growth during the entire period are in order of intermediate goods(52.3 percent), capital(15.6 percent), energy(11.3 percent), service(10.2 percent), labor(5.8 percent) in Manufacturing. So the innovation or the role of intermediate goods for enhancing productivity is more important in Manufacturing than in Service. And the contribution ratio of TFP to Manufacturing output growth (4.7 percent) is of rather insignificant magnitude. On the other hand, in Service the contribution are in order of capital(48.7 percent), service(23.9 percent), labor(20.1 percent), intermediate goods(15.8 percent ), energy(4.2 percent). Hence we can see the input’s role for enhancing productivity is different between Manufacturing and Service. Thirdly the total factor productivity growth in gross output growth accounting is lower than that without quality adjustment in input data. The quality of labor has affected the growth of output about 2.4 percent in Manufacturing and 3.9 percent in Service during the entire period, Also the quantity of labor has

35

affected the growth of output by about 3.4 percent in Manufacturing and about 16.3 percent in Service during the entire period, Thus the labor input in Service has influenced output growth both in quantity and quality of labor than that in Manufacturing. The quantity of labor input in Manufacturing has been reduced during the pre-crisis period. It reflects a drastic structural adjustment in Korea’s labor market after the crisis of 1997-1998. As a consequence, the contribution rate of labor to output growth has become negative in Manufacturing after the crisis.

Table 9 Gross Output Growth Accounting and TFP growth in economywide Labor input Gross

Capital

Period

Total output

Quantity

Energy

Intermediate

Service

input

input

input

TFP

Quality

input labor

labor

labor

'72-79

9.48

4.56

1.79

1.03

0.76

0.69

3.34

1.13

-2.03

'80-'89

8.36

3.05

0.62

0.28

0.34

0.45

3.18

0.98

0.08

'90-'99

6.43

2.40

0.49

0.19

0.31

0.70

1.64

1.76

-0.56

'90-'98

5.84

2.54

0.49

0.15

0.34

0.63

1.30

1.71

-0.84

'99-'03

7.61

1.11

0.48

0.33

0.14

0.75

2.78

1.62

0.86

7.81

2.98

0.85

0.44

0.41

0.61

2.63

1.32

-0.59

'72-'03

contribution to output growth '72-79

100.0

48.1

18.9

10.9

8.1

7.3

35.3

11.9

-21.5

'80-'89

100.0

36.5

7.4

3.4

4.0

5.3

38.0

11.8

0.9

'90-'99

100.0

37.3

7.7

2.9

4.8

10.9

25.6

27.3

-8.7

'90-'98 100.0

43.5

8.4

2.5

5.8

10.8

22.4

29.4

-14.4

'99-'03 100.0

14.6

6.3

4.4

1.9

9.9

36.6

21.3

11.4

38.2

10.9

5.6

5.3

7.8

33.7

17.0

-7.5

'72-'03

100.0

36

Table 10 Gross Output Growth Accounting and TFP growth in manufacturing Labor input Gross

Capital

Period

Total output

Quantity

Energy

Intermediate

Service

input

input

input

TFP

Quality

input labor

labor

labor

'72-79

15.30

2.41

1.72

1.28

0.43

1.66

8.29

1.17

0.06

'80-'89

10.27

1.68

0.59

0.40

0.19

0.88

5.83

0.80

0.49

'90-'99

6.94

1.20

-0.14

-0.34

0.20

1.19

2.94

1.17

0.58

'90-'98

5.56

1.26

-0.22

-0.44

0.22

1.08

2.17

1.04

0.23

'99-'03

10.11

0.70

0.26

0.16

0.09

1.02

5.26

1.32

1.55

10.18

1.59

0.59

0.35

0.24

1.15

5.33

1.04

0.48

'72-'03


100.0

15.8

11.2

8.4

2.8

10.8

54.2

7.6

0.4

'80-'89

100.0

16.3

5.7

3.9

1.8

8.6

56.8

7.8

4.8

'90-'99

100.0

17.3

-2.0

-4.9

2.8

17.2

42.3

16.9

8.4

'90-'98

100.0

22.6

-3.9

-7.9

4.0

19.5

39.0

18.7

4.1

'99-'03

100.0

6.9

2.5

1.6

0.9

10.1

52.1

13.0

15.3

100.0

15.6

5.8

3.4

2.4

11.3

52.3

10.2

4.7

'72-'03

37

Table 11 Gross Output Growth Accounting and TFP growth in service Labor input Gross

Capital

Period

Total output

Quantity

Energy

Intermediate

Service

input

input

input

TFP

Quality

input labor

labor

labor

'72-79

7.86

4.77

2.05

1.52

0.54

0.26

1.43

1.36

-2.01

'80-'89

7.92

3.70

1.33

1.11

0.22

0.18

1.52

1.27

-0.08

'90-'99

6.54

3.17

1.28

1.12

0.16

0.37

0.69

2.37

-1.35

'90-'98

6.61

3.37

1.39

1.22

0.17

0.34

0.69

2.40

-1.58

'99-'03

5.87

1.39

0.86

0.68

0.18

0.54

0.73

2.02

0.33

7.22

3.51

1.45

1.17

0.28

0.30

1.14

1.73

-0.92

'72-'03


100.0

60.7

26.1

19.3

6.8

3.3

18.1

17.3

-25.6

'80-'89

100.0

46.6

16.8

14.0

2.8

2.3

19.2

16.0

-0.9

'90-'99

100.0

48.5

19.6

17.2

2.5

5.7

10.6

36.3

-20.7

'90-'98 100.0

51.1

21.0

18.4

2.6

5.1

10.5

36.4

-24.0

'99-'03 100.0

23.6

14.7

11.6

3.1

9.3

12.4

34.4

5.6

48.7

20.1

16.3

3.9

4.2

15.8

23.9

-12.8

'72-'03

100.0

On the one hand the sectors which were based on IT technology such as i) Office, accounting and computing machinery (1.91 percent), ii) Other electrical machinery (2.45 percent), iii) Electronic valves and tubes(2.87 percent), iv) Telecommunication equipment(2.13%) in Manufacturing, have shown higher growth rate of TFP during the entire period(1972-2003) as shown in Table 11. But the labor intensive sectors such as i) Leather and foot wear(-1.25 percent), ii) Food products and beverages(-0.73percent), iii) Wearing apparel, dressing and dying of fur(-0.69percent), iv)Printing and reproduction(-0.61 percent) have shown negative growth rates of total factor productivity. In Service, Post and telecommunication which is related strongly with IT technology has recorded a relatively higher growth rate (4.93 percent) of TFP among service sectors as shown in Table 12. But the social and private sectors such as i) Public administration and defense (-10.36 percent), ii) Private households with employed persons (-8.95 percent), iii) Other service activities(8.74 percent) have shown negative growth rates of TFP. Therefore, we can see that the leading sectors for enhancing productivity 38

growth are related with IT sectors. Korean economy has heavily invested in IT sectors on a full scale since 1995 (Table 10) as analyzed in Ha and Pyo (2004). Table 12 The investment in IT sector Year

IT Investment (billion won)

Growth(%)

1995

15,125.7

-

1996

17,916.0

16.9

1997

19,122.0

6.5

1998

17,099.2

-11.2

1999

23,716.0

32.7

2000

32,190.9

30.6

2001

31,502.0

-2.2

2002

33,143.8

5.1

2003

31,551.8

-4.9

2004

31,391.9

-0.5

*Investment is real value in 2000 prices *Source: Bank of Korea (http://ecos.bok.or.kr

39

Table13 Sectoral TFP growth in manufacturing (%) Before After Code

Industry

'72-'79 '80-'89 '90-'99 crisis crisis '72-'03 '90-'98 '99-'03

9


-1.52 -0.65 -0.61 -0.59

0.12

-0.73

10

Tobacco products

-2.14

1.65

3.13

3.44

0.88

1.09

11

Textiles

0.97

0.38

0.33

0.05

0.73

0.49

12

Wearing apparel, dressing and dying of fur

-1.36 -0.04 -1.60 -2.23

1.85

-0.69

13

Leather, leather products and footwear

-5.91 -1.55

2.16

2.33

0.39

-1.25

14

Wood and products of wood and cork

3.26

0.87

0.39

0.57

0.57

1.34

15

Pulp, paper and paper products

-0.61

0.90

0.74

0.70

0.08

0.34

16

Publishing

-0.60

2.80

-0.19 -1.60

1.33

0.48

17

Printing and reproduction

6.20

-3.66 -2.05 -1.45 -3.90 -0.61

18

Coke, refined petroleum products and nuclear fuel

-0.84 -0.27 -0.84 -1.01

0.19

-0.55

19

Pharmaceuticals

-3.21

0.92

-1.32 -1.53

7.01

0.15

20

Chemicals excluding pharmaceuticals

1.48

1.29

1.23

0.98

0.57

1.14

21

Rubber and plastics products

2.13

1.97

0.32

-0.29

1.38

1.28

22

Other non-metallic mineral products

-0.17

0.29

0.59

0.24

2.38

0.49

23

Basic metals

3.24

1.49

0.87

0.93

0.21

1.57

24

Fabricated metal products

1.57

0.63

-0.16 -0.36

1.10

0.66

25

Machinery, nec

1.46

1.01

0.78

0.26

2.76

1.18

26

Office, accounting and computing machinery

3.13

-0.78

3.37

2.32

4.62

1.91

27

Insulated wire

-4.09 -0.87

2.22

1.54

3.15

-0.37

28

Other electrical machinery and apparatus nec

5.33

0.75

1.40

1.14

3.58

2.45

29

Electronic valves and tubes

5.36

2.91

1.08

0.45

3.15

2.87

30

Telecommunication equipment

-0.05

2.12

5.02

3.99

2.32

2.13

31

Radio and television receivers

2.22

0.29

0.95

-1.36

4.61

0.98

32

Scientific instruments

0.51

0.81

-0.04 -0.42

0.62

0.36

33

Other instruments

-

-

-

-

34

Motor vehicles, trailers and semi-trailers

0.00

0.74

-0.37 -1.43

2.98

0.29

35

Building and repairing of ships and boats

1.22

1.33

-2.81 -3.99

3.92

0.21

36

Aircraft and spacecraft

6.76*

5.14

4.22

5.60

0.52

4.62*

37

Railroad equipment and transport equipment nec

-2.96 -2.00

1.22

1.32

-5.06 -1.78

38

Manufacturing nec

-0.24

0.04

-0.08

0.30

40

2.18

-

-

0.65

39

Recycling

-

-

-

-

-

-

Table14 Sectoral TFP Growth in Service (%) Before After Code

'72-'79 '80-'89 '90-'99 crisis crisis 72-'03

Industry

'90-'98 '99-'03 40 Electricity supply

2.20

1.10

1.18

0.77

41 Gas supply

15.03 14.00 7.31

7.10

2.29 10.34

42 Water supply

8.67

3.38 -2.19 -2.51 -0.39 2.46

43 Construction

-0.13

2.31 -0.91 -0.36 -1.27 0.39

44 Sale, maintenance and repair of motor vehicles and motorcycles

-

0.76

-

1.24

-12.91 -14.71 2.09 -8.71*

45 Wholesale trade and commission trade

-3.04 -2.46 -1.28 -2.21

1.26 -1.95

46 Retail trade

-0.77 -3.31 -0.73 -0.71

0.41 -1.36


-4.75 -4.69 -4.59 -6.20

1.80 -4.11

48 Inland transport

4.14

3.06 -0.42 -0.72

1.90

2.09

49 Water transport

4.72

2.21

0.71

2.52

50 Air transport

1.31

1.92

-10.49 -0.79 -2.20 -1.85

2.70 -2.97


5.71

0.76 -4.52 -4.83

1.50

0.54


6.51

2.24

5.36

5.08

5.18

4.56


7.45

5.61

3.20

3.05

2.89

4.93


1.46

5.81

0.99

4.76

-0.28 3.47

-

-


-2.77 -9.28

7.03 -3.46*


-8.68 -7.03 5.12


-6.44 -3.41 -2.86 -3.70 12.35 -1.79


-1.69

3.45

-

-


4.14

7.66 -11.66 -4.03

9.44

-4.13 2.66

-7.89 -10.08 0.07 -6.45*


-0.73 -1.19 -2.51 -2.91

3.54 -0.82


-2.67 -0.04 -1.22 -1.24

0.38 -0.97


-11.74 3.97 -2.54 -4.20

2.69 -2.46

63 Public admin and defense

-29.94 4.94 -18.73 -22.09 11.51 -10.36

64 Education

16.39 2.01 -2.35 -3.77 -12.68 1.68


-32.93 9.88

66 Sewage and refuse disposal, sanitation and similar activities

-

-

1.43

1.88

-

-

-4.19 -5.27 -

-


-3.54 -0.31 1.36

68 Media activities

-16.43 -0.25 -9.24 -11.98 29.66 -2.92


-4.15

41

2.52

-2.73 -0.70

2.90 -17.29 -20.27 13.60 -3.71


-30.77 6.08 -18.93 -19.42 16.12 -8.74


-32.53 3.78

72 Extra-territorial organizations and bodies

-

-

5.72

5.77 -23.15 -8.95

-

-

-

-

3.3 Cumulative Contribution of Sectors to TFP growth Following Fukao et. al,(2006), we can examine the sectoral contribution of TFP growth and identify what are the core sectors for enhancing productivity. As shown in Figure 9, the weight of gross output of the sectors with positive Economy-wide TFP growth is about 52 % while the weight with negative TFP growth is about 48 % during the entire period of 1972-2003. We can identify sectors that have contributed to the growth of economy-wide TFP positively. Leading sectors in this group include Financial Intermediation and Post and Telecommunications in Service and Basic Metals and Electronic Valves and Tubes in Manufacturing among others. We also identify sectors with negative contribution to Economy-wide TFP growth such as Agriculture, Hotels and Restaurants, Imputation of owner-occupied housing and Media activities etc. Figure 9 Cumulative Contribution of sectors to TFP Growth in Economywide (1972-2003) O ffic e , a c c o u n tin g a n d c o m p u tin g m a c h in e ry T e le c o m m u n ic a tio n e q u ip E le c tric ity s u p p ly G a s s u p p ly In s u re n c e T e xile s R u b b e r a n d p la s tic s M a c h in e ry C o n s tru c tio n W a te r tra n s p o rt C h e m ic a ls O th e r e le c tric a l m a c h in e ry

1.20

0.70

W e a rin g a p p a re l C o m p u te r a n d re la te d a c tivitie s F o o d a n d b e ve ra g e s R e ta il tra d e

F o re s try

O ther recrea tio n a l a ctivities

In la n d tra n s p o rt P ost and te le c o m m u n ic a tio n s E le c tro n ic va lve s a n d tu b e s B a s ic m e ta ls 0.20

W h o le s a le tra d e H e a lth a n d s o c ia l w o rk O th e r s e rvic e a c tivitie s

F in a n c ia l in te rm e d ia tio n

M e d ia a c tivitie s Im p u ta tio n o f o w n e r o c c u p ie d re n ts

-0.30

H o te ls a n d re s ta u ra n ts

-0.80 a g ric u ltu re

-1.30 0.00

0.20

0.40

Axis Y: Cumulative contribution to TFP growth (%)

0.60

0.80

1.00

Axis X: Cumulative share of gross output (ratio)

* In case of EU-KLEMS code, #5, #6, #33, #39, #66, #72, they are excluded because of data insufficiency

42

As shown in Figure 10, the weight of gross output of the sectors with positive TFP growth in Manufacturing is 72.4% while the weight with negative TFP growth is 27.6% during the period of 1972-2003. The sub-sectors with positive TFP growth are basic metals, chemicals, machinery, textiles, rubber and plastic, fabricated metal, wood, other non metallic mineral, motor vehicles and trailers as non IT sectors, and electronic valves and tubes, office, accounting and computing machinery, telecommunications, radio and TV receivers as IT sectors. The sub-sectors with negative TFP growth are leather and footwear, wearing and apparel, coke and refined petroleum etc. Figure 10 Cumulative Contribution of sectors to TFP Growth in Manufacturing (1972-2003) 1.6 1.4

Other non metallic mineral

Leather and footwear Wearing apparel Coke, refines petroleum Motor vehicles , trailers Radio and TV receivers

Wood Office, accounting and computing machinery

1.2 1.0

Rubber and plas tics

Food and beverages

Fabricated metal Texiles

Machinery

0.8

Telecommunication

Other eletrical machinery

0.6

Chemicals Eletronic valves and tubes

0.4

Bas ic metals

0.2 0.0 0.0

0.1

0.2

0.3

0.4


0.5

0.6

0.7

0.8

0.9

1.0


On the other hand, we can look at Service industry separately. As shown in Figure 11, the weight of gross output of the sectors with positive TFP growth in Service is only about 40 % while the weight with negative TFP growth is 60 % during the period of 1972-2003. The group of service industries with positive TFP growth includes Financial intermediation, Post and communication, Inland Transport, Water Transport, Construction etc. The group with negative TFP growth includes Hotels and Restaurants, Imputation of owner-occupied housing, Media activities and Wholesale trade etc.

43

Figure 11 Cumulative Contribution of sectors to TFP Growth in Service (1972-2003) 0.80 G as supply Insurance and pention

0.60 0.40 Inland transport

C onstruction

Legal,technical and advertising Electricity supply

W ater transport

O ther business activities A ir transport C om puter and related activities

R etail trade

Post and telecom m unicatios

0.20

Financial interm edition

O ther recreational activities

0.00

H ealth and social w ork W holesale trade

-0.20

O ther service activities

-0.40

Im putation of ow ner occupied rents

-0.60

M edia activities

-0.80 -1.00

H otel and restaurants

-1.20 0.00

0.20

0.40


0.60

0.80

1.00


* In case of EU-KLEMS code, #66, #72, they are excluded because of data insufficiency

3.4 Relations of TFP growth with Labor Productivity and Output Growth In order to identify the relation between labor productivity growth and TFP growth, we can divide sectors into 4 groups by the average of growth rates in Manufacturing or Service as follows: In Manufacturing, the sectors that are above the average growth rates of both labor productivity and TFP are: - Electronic valves and tubes - Other electrical machinery and apparatus - Office, accounting and computing machinery - Basic metal etc The sectors in Manufacturing that are below the average growth rates of both

44

labor productivity and TFP are: - Pulp, paper and paper products - Printing and reproduction - Wearing apparel - Food and beverage - Leather, leather products and footwear etc On the other hand, in Service the sectors that are below the average growth rates of both labor productivity and TFP are: - Wholesale and retail and Hotels and Restaurants such as i) Retail trade, ii) Wholesale trade and commission trade, iii) Sale, maintenance and repair of motor vehicles, iv)Hotel and restaurants, and - Business service such as i) Other business activities, ii) Computer and related activities, and - Social and private service such as i) Private households with employed persons, ii) Public administration and defense etc. In order to examine the relation between output growth and TFP growth, we can divide sectors into 4 groups by the average of growth rates in Manufacturing or Service as follows: In Manufacturing (Table 15), the sectors that are above both the average growth rate of output and that of TFP are over the average growth rates in manufacturing in terms of output or TFP are: - IT manufacturing sectors such as i) Electronic valves and tubes, ii) Telecommunication equipment, iii) Office, accounting and computing machinery, iv) Radio and television receivers, v) Other electrical machinery and apparatus etc, and - Traditional manufacturing sectors such as i) Machinery, ii) Chemicals etc. Furthermore the sectors, labor intensive and relative advantage in Korean economy during 1970s-1980s, which the growth rates are over the average growth rates in manufacturing in terms of output or TFP simultaneously are: - Pulp, paper and paper products - Printing and reproduction - Wearing apparel - Food and beverage

45

- Leather, leather products and footwear etc On the other hand, the sectors in Service that are above the average growth rates (Table 16) of both output and TFP are: - Gas supply - Financial intermediation - Post and telecommunication The sectors in Service that are under the average growth rates of both output and TFP are: - Wholesale trade and commission trade, - Hotels and restaurants - Sale, maintenance and repair of motor vehicles etc In case of i) Gas supply, ii) Financial intermediation, iii) Post and telecommunication, they seem to have relative advantages in terms of sectoral potential growth. But in case of i) Wholesale trade and commission trade, ii) Hotels and restaurants, iii) Sales, maintenance and repair of motor vehicles, so called traditional service sectors, they seem to have relative disadvantages in terms of sectoral potential growth. The relations of TFP with labor productivity and output growth can be further examined by looking at the scatter diagrams such as Figure 12 and 13. A visual inspection tells us that TFP growth is positively correlated with both labor productivity growth and output growth and TFP-LP relation is stronger than TFP –Output relation. In Table 15, we have summarized two simple regression results where TFP Growth rate is regressed upon LP and output growth rate. We are adopting implicit hypotheses that higher LP and output growth induces TFP growth through enhanced human capital and economies of scale. In both regressions, the coefficients of LP growth and Output Growth are significant. The TFP-LP regression seems more significant than TFP-Output regression.

46

Figure 12 Plotting between TFP Productivity Growth (1972-2003, %)

Growth

and

Sectoral

Labor

15.00

10.00

TFP grow th

5.00

0.00

-5.00

-10.00

-15.00 -40.00

-30.00

-20.00

-10.00

0.00

10.00

20.00

30.00

Labor productivity grow th

* In case of EU-KLEMS code, #5,#6,#33,#39,#66#,#72 are excluded because of data insufficiency

Figure13 Plotting between TFP Growth and Sectoral Gross output Growth (1972-2003, %)

15.00

10.00

TFP grow th

5.00

0.00

-5.00

-10.00

-15.00 -10.00

-5.00

0.00

5.00

10.00

15.00

20.00

25.00

30.00

G ross output grow th

* In case of EU-KLEMS code, #5,#6,#33,#39,#66#,#72 are excluded because of data insufficiency

47

35.00

Table 15

Regression Results

_____________________________________________________________________________________

1. Relation between Labor Productivity Growth and TFP Growth Model: log (TFPt/TFPt-1)=α + β log (LPt/LPt-1)+ γ log (TFPt/TFPt-1) = Sectoral average TFP growth rate during 1972-2003, log (LPt/LPt-1) = Sectoral average labor productivity growth rate during 1972-2003 Number of sectors: 66 sectors (except #5,#6,#33,#39,#66#,#72 for data insufficiency) Dependent var. TFP Growth Rate

β

S.E

DW

adjR2

0.322***

0.031

1.967

0.613

***: Pr>t is 1%, **:Pr>t is 5%, *:Pr>t is 10% 1) Data for Sector 36 is available only during 1977-2003, and data for sectors of 44, 55, 59 are available only during 1989-2003.

2. Relation between Gross Output Growth and TFP Growth Model: log (TFPt/TFPt-1)=α + β log(GOt/GOt-1)+ γ log (TFPt/TFPt-1) = Sectoral average TFP growth during 1972-2003, log (GOt/GOt-1) = Sectoral average Gross output growth rate during 19722003 Number of sectors: 66 sectors(except #5,#6,#33,#39,#66#,#72 for data insufficiency) Dependent var.

β

S.E

DW

adjR2

TFP Growth rate

0.306***

0.066

1.479

0.235

***: Pr>t is 1%, **:Pr>t is 5%, *:Pr>t is 10% 1) Data for Sector 36 is available only during 1977-2003, and data for sectors of 44, 55, 59 are available only during 1989-2003

_______________________________________________________________

48

Table 16 Relation between Labor Productivity and TFP in Manufacturing (1972-2003) EU-KLEMS Industry

Period

Labor productivity

TFP

72-'03

6.99

0.48

Code Manufacturing High Labor productivity/High TFP 36


77-'03

27.08

4.62

29


72-'03

14.33

2.87

28


72-'03

14.24

2.45

26


72-'03

9.88

1.91

23

Basic metals

72-'03

10.42

1.57

14


72-'03

12.11

1.34

21


72-'03

9.66

1.28

20


72-'03

9.73

1.14

31


72-'03

9.31

0.98

24


72-'03

7.08

0.66

38

Manufacturing nec

72-'03

7.67

0.65

22


72-'03

7.26

0.49

72-'03

8.52

0.36

High Labor productivity/Low TFP 32

Scientific instruments Low Labor productivity/High TFP

30


72-'03

6.70

2.13

25

Machinery, nec

72-'03

6.79

1.18

10

Tobacco products

72-'03

-5.55

1.09

11

Textiles

72-'03

6.98

0.49

16

Publishing

72-'03

6.73

0.48

Low Labor productivity/Low TFP 15


72-'03

6.41

0.34

34


72-'03

2.78

0.29

35


72-'03

-0.07

0.21

19

Pharmaceuticals

72-'03

3.27

0.15

27

Insulated wire

72-'03

3.51

-0.37

18


72-'03

1.50

-0.55

17


72-'03

1.28

-0.61

12

Wearing Apparel, Dressing And Dying Of Fur

72-'03

1.58

-0.69

49

9


72-'03

3.19

-0.73

13


72-'03

-4.89

-1.25

37


72-'03

-1.94

-1.78

* In case of EU-KLEMS Code, #33, #39, they are excluded because of data insufficiency.

Table 17 Relations between Labor Productivity and TFP in Service (19722003) EU-KLEMS Industry

Period

Labor productivity

TFP

72-03

2.91

-0.92

Code Service High Labor productivity/High TFP 41

Gas supply

73-03

14.08

10.34

53

Financial intermediation

72-03

14.29

4.93

52

Post and telecommunications

72-03

13.90

4.56

54

Insurance and pension funding

72-03

9.59

3.47

49

Water transport

72-03

13.13

2.52

42

Water supply

72-03

18.36

2.46

48

Inland transport

72-03

8.97

2.09

40

Electricity supply

72-03

14.81

1.24

51

Supporting and auxiliary transport activities

72-03

8.57

0.54

43

Construction

72-03

2.94

0.39

67

Activities of membership organizations nec

72-03

6.30

-0.70

72-03

3.62

-0.97

High Labor productivity/Low TFP 61

Legal, technical and advertising Low Labor productivity/High TFP

58

Renting of machinery and equipment

72-03

1.25

2.66

64

Education

72-03

2.36

1.68

60

Research and development

72-03

-2.38

-0.82

Low Labor productivity/Low TFP 46

Retail trade

72-03

-0.73

-1.36

57

Other real estate activities

72-03

-4.52

-1.79

45

Wholesale trade and commission trade

72-03

-4.87

-1.95

62

Other business activities, nec

72-03

-9.97

-2.46

68

Media activities

72-03

1.00

-2.92

50

Air transport

72-03

-4.57

-2.97

50

55

Activities related to financial intermediation

90-03

2.72

-3.46

69

Other recreational activities

72-03

-2.39

-3.71

56

Imputation of owner occupied rents

72-03

-4.58

-4.03

47

Hotels and restaurants

72-03

-5.25

-4.11

65

Health and social work

72-03

-4.27

-5.27

59

Computer and related activities

90-03

-16.84

-6.45

44

Sale, maintenance and repair of motor vehicles

90-03

-12.16

-8.71

70

Other service activities

72-03

-20.38

-8.74

71

Private households with employed persons

72-03

-27.83

-8.95

63

Public admin and defense

72-03

-16.20

-10.36


Table 18 Output and TFP Growth in Manufacturing (1972-2003) EU-KLEMS

Output Industry

Period

Code

TFP growth

Manufacturing

72-'03

10.18

0.48

High output growth/High TFP 36


77-'03

21.25

4.62

29


72-'03

22.74

2.87

28


72-'03

17.22

2.45

30


72-'03

22.94

2.13

26


72-'03

20.46

1.91

23

Basic metals

72-'03

13.65

1.57

21


72-'03

12.05

1.28

25

Machinery, nec

72-'03

15.29

1.18

20


72-'03

13.98

1.14

31


72-'03

13.65

0.98

24


72-'03

12.81

0.66

High output growth/Low TFP 32

Scientific instruments

72-'03

14.93

0.36

34


72-'03

16.48

0.29

35


72-'03

13.43

0.21

Low output growth/High TFP 14


72-'03

4.91

1.34

10

Tobacco products

72-'03

4.05

1.09

51

38

Manufacturing nec

72-'03

8.57

0.65

22


72-'03

9.89

0.49

11

Textiles

72-'03

6.34

0.49

16

Publishing

72-'03

7.72

0.48

Low output growth/Low TFP 15


72-'03

9.79

0.34

19

Pharmaceuticals

72-'03

9.89

0.15

27

Insulated wire

72-'03

8.23

-0.37

18


72-'03

9.10

-0.55

17


72-'03

6.80

-0.61

12

Wearing Apparel, Dressing And Dying Of Fur

72-'03

4.14

-0.69

9


72-'03

5.01

-0.73

13


72-'03

7.64

-1.25

37


72-'03

5.93

-1.78


Table 19 Output and TFP Growth in Service(1972-2003) EU-KLEMS

Output Industry

Period

Code

TFP growth

Service

72-'03

7.22

-0.92

High output growth/High TFP 41

Gas supply

72-'03

28.94

10.34

53

Financial intermediation

72-'03

12.89

4.93

52

Post and telecommunications

72-'03

17.36

4.56

54

Insurance and pension funding

72-'03

13.49

3.47

58

Renting of machinery and equipment

72-'03

15.88

2.66

49

Water transport

72-'03

10.01

2.52

42

Water supply

72-'03

13.37

2.46

40

Electricity supply

72-'03

10.28

1.24

51

Supporting and auxiliary transport activities

72-'03

10.22

0.54

43

Construction

72-'03

8.11

0.39

67

Activities of membership organizations nec

72-'03

10.86

-0.70

60

Research and development

72-'03

10.33

-0.82

72-'03

11.69

-0.97

High output growth/Low TFP 61

Legal, technical and advertising

52

46

Retail trade

72-'03

7.56

-1.36

57

Other real estate activities

72-'03

8.43

-1.79

62

Other business activities, nec

72-'03

14.09

-2.46

50

Air transport

72-'03

8.00

-2.97

55

Activities related to financial intermediation

90-'03

11.07

-3.46

65

Health and social work

72-'03

8.60

-5.27

59

Computer and related activities

90-'03

15.28

-6.45

63

Public admin and defense

72-'03

11.54

-10.36

Low output growth/High TFP 48

Inland transport

72-'03

7.00

2.09

64

Education

72-'03

6.97

1.68

Low output growth/Low TFP 45

Wholesale trade and commission trade

72-'03

5.90

-1.95

68

Media activities

72-'03

4.24

-2.92

69

Other recreational activities

72-'03

3.82

-3.71

56

Imputation of owner occupied rents

72-'03

5.33

-4.03

47

Hotels and restaurants

72-'03

4.58

-4.11

44

Sale, maintenance and repair of motor vehicles

90-'03

2.73

-8.71

70

Other service activities

72-'03

5.58

-8.74

71

Private households with employed persons

72-'03

3.01

-8.95

In case of EU-KLEMS Code, #66, #72, they are excluded because of data insufficiency.

In addition to regression analysis, we have used the Wilcoxon Rank-Sum (Mann-Whitney) test for two Independent samples following Bailey Hulten and Campbell (1992). This test is used in place of a two sample t test when the populations being compared are not normal. It requires independent random samples of sizes

and

. The test is very simple and consists of combining the two samples

, sorting the result, assigning ranks to the sorted into one sample of size values (giving the average rank to any `tied' observations), and then letting T be the sum of the ranks for the observations in the first sample. If the two populations have the same distribution then the sum of the ranks of the first sample and those in the second sample should be close to the same value. Stataquest returns a p value for the null hypothesis that the two distributions are the same.

53

Table 20 Wilcoxon Rank-Sum (Mann-Whitney) Test results: 1. TFP-LP test n1

n2

U

66

66

2178

normal approx z=0

P (two-tailed)

P (one-tailed)

1*

0.500766*

1*

0.5*

*These values are approximate. The two samples are not significantly different (P >= 0.05, two-tailed test). 2.TFP-Gross Output growth test n1

n2

U

66

66

2178

normal approx z=0

P (two-tailed)

P (one-tailed)

1*

0.500777*

1*

0.5*

*These values are approximate. The two samples are not significantly different (P >= 0.05, two-tailed test).

4. Conclusion The purpose of this paper is to explain the methodologies of how the database of Korea has been constructed for estimating productivities by industry in KLEMS model. For this purpose we have employed several conceptual tool and data generation system as follows. (1) Gross Output and Intermediate Inputs We have used Make Tables and Use Tables as well as Input-Output Tables (commodity-by-commodity) for measuring gross output and intermediate input (energy, material, and service). In order to generate tables in missing years, we have applied RAS method. The weighted average of Producer's Price Index and Imported Price Index has been applied for input prices in Use Tables.

54

(2) Capital Input Pyo (2003)'s estimates have been reconciled to generate capital stock series, and the user costs of capital has been generated for estimating capital service inputs.

(3) Labor input Total labor input is decomposed into labor quantity (men-hour) and labor quality. The former is generated form the Economically Active Population Yearbook by NSO, and the latter from the Survey Report on Wage Structure by Ministry of Labor while following the methodology of Jorgenson, Gollop, and Fraumeni (1987). Based on data structure we have generated, we have estimated 72-industry level labor productivity and TFP. We have also conducted a gross output growth accounting. Throughout the entire period of 1971-2003, the economy-wide labor productivity has grown at the average rate of 5.59 percent but with the sectoral difference between Manufacturing (6.99 %) and Service (2.91 %). The difference did not shrink but rather has expanded as the process of industrialization continued. For example, the difference in the 1990’s (9.55 % vs. 2.64 %) has been more than doubled since 1970’s (4.01 % vs. 2.15 %). The observed difference in both levels and growth rates of labor productivity between Manufacturing and Service can signal the difference in the degree of foreign competition, the proportion of tradable and non-tradable and the degree of domestic competition due to historically different regulatory environments. The growth rate of economy-wide TFP has been estimated as -0.59 percent. The growth rates of TFP in Manufacturing and Service are estimated as 0.48 percent and -0.92 percent respectively throughout the entire period of 19722003. Throughout the entire period 1972-2003, Korean economy experienced about 2 break-points: mid-1970s which was the first oil shock and in 1997 which was the financial crisis. The difference between two break points can be summarized as follows. During the second half of 1970’s, the growth rate of gross output was not low, but the growth rates of inputs such as capital(4.56%), labor(1.79%), energy(0.69%), intermediate goods(3.34%) especially, were relatively higher. Therefore, the growth rates of TFP have been estimated as

55

negative. In case of late 1990’s the negative growth of TFP has been resulted from the shrink of gross output rooted from economic crisis. In addition we observe that the estimated TFP growth rates in Manufacturing are in general greater than in Service. It maybe due to the fact that an innovation process such as product innovation or process innovation is more sensitive and stronger in manufacturing than in service. Also the R&D investment for innovation is in general more intensive in manufacturing than in service. So the growth rates of TFP in Manufacturing seem to be greater than in Service. We can identify sectors that have contributed to the growth of economy-wide TFP positively by decomposing relative contribution of each sector to total TFP growth (Y-axis) with each sector’s relative weight of output (X-axis). Leading sectors in this group include Financial Intermediation and Post and Telecommunications in Service and Basic Metals and Electronic Valves and Tubes in Manufacturing among others. We also identify sectors with negative contribution to Economy-wide TFP growth such as Agriculture, Hotels and Restaurants, Imputation of owner-occupied housing and Media activities etc. The relations of TFP with labor productivity and output growth can be examined by looking at the scatter diagrams and a regression analysis. A visual inspection tells us that TFP growth is positively correlated with both labor productivity growth and output growth and TFP-LP relation is stronger than TFP –Output relation. We have adopted an implicit hypotheses that higher LP and output growth induces TFP growth through enhanced human capital and economies of scale. In both regressions, the coefficients of LP growth and Output Growth are significant. The TFP-LP regression seems more significant than TFP-Output regression. Productivities in an economy are not identical across industries, and productivity differences are also observed when compared with the same industry in other economies. For example, most industries in Japan exhibit higher productivity in Manufacturing such as electrical machinery, motor, other transport vehicles, and instruments industries resulting in higher productivity in the entire economy. However, total factor productivities of Korea in construction, petroleum products,

56

fabricated machinery, and finance industries are higher than those of Japan. International comparison of productivity among industries will demonstrate a relative productivity of each industry, illustrating whether the way the goods or services are produced is relatively efficient or not and referring to the appropriate policies for improvement such as competition, restriction, R&D policies, and so on. Establishment of dataset with the same standards for productivity measurement will facilitate these inter-industry and international comparisons, and contribute to a better understanding of economic growth.

57

References [1] Baily, Martin Neil, Charles Hulten, and David Campbell “ Productivity Dynamics in Manufacturing Plants,” Brookings Papers on Economic Activity:Microeconomics, pp.187-249,1992 [2] Berndt, E. and L. Christensen, "The Translog Function and the Substitution of Equipment, Structures, and Labor in US Manufacturing, 1929-1968," Journal of Econometrics, Vol.1, pp.81-114, 1973 [3] Berndt, E. and L. Christensen, "Testing for the Existence of a Consistent Aggregate Index of Labor Input," American Economic Review, Vol.3, pp.391404, 1974 [4] Berndt, E. and D. Wood, "Technology, Prices, and the Derived Demand for Energy," Review of Economics and Statistics, Vol.57, pp.259-268, 1975 [5] Denny, M. and M. Fuss, "The Use of Approximation Analysis to Test for Separability and the Existence of Consistent Aggregates," American Economic Revew, vol.67, pp.404-418, 1977 [6] Harberger, Arnold C., "Perspectives on Capital and Technology in LessDeveloped Countries," in Contemporary Economic Analysis: Papers presented at the conference of the Association of University Teachers of Economics," Edited by Artis, Michael J. and Nobay, A. R., Croom Helm London, 1987 [7] Inklaar, Robert, Marcel P. Timmer and Bart van Art (2006), “Mind the Gap!: International Comparisons of Productivity in Services and Goods Production,” unpublished manuscript [8] Jorgenson, D.W., F. M. Gollop and B.M.Fraumeni, Productivity and US Economic Growth, Cambridge MA: Harvard University Press, 1987 [9] Jorgenson, D.W., Mun S. Ho, and Kevin J. Stiroh, "Growth of U.S. Industries and Investments in Information Technology and Higher Education," in Measuring Capital in the New Economy, edited by Carol Corrado, John Haltiwanger, and Daniel Sichel, Studies in Income and Wealth Vol. 65, National Bureau of Economic Research, 2005 [10] Kim Hyunjeong, “The Shift to the Service Economy; Causes and Effects”, BOK Institute, Working Paper 254, May 2006 [11]Krugman, Paul, "The Myth of Asia's Miracle," Foreign affairs, November/December, 1994 [12] Kyoji Fukao, “Industry and firm level total factor productivity and economic

58

growth in Japan”, RIETI Workshop, July 2006, [13] Kyoji Fukao, et al., “Estimation Procedures and TFP Analysis of the JIP Analysis of the JIP Database 2006 Provisional Version”, Paper presented at RIETI Conference on Determinants of Total Factor Productivity and Japan’s Potential Growth: An International Perspective, July 25, 2006 [14] Lau, Lawrence J. and Jong-Il Kim, "The Sources of Growth of East Asian Newly Industrialized Countries," Journal of Japanese and International Economies, 1994. [15] Lewis, W. William, The Power of Productivity: Wealth, Poverty, and the Threat to Global Stability, University of Chicago Press, 2004 [16] Pyo, Hak K., "Estimates of Capital Stock and Capital / Output Coefficients by Industries: Korea, 1953-1986", International Economic Journal, summer 1988 [17] Pyo, Hak K., "A Synthetic Estimate of National Wealth of Korea", 19531990, KDI Working Paper No.9212, Korea Development Institute, Seoul, 1992 [18] Pyo. Hak K, "Estimates of Fixed Reproducible Tangible Assets in the Republic of Korea, 1953-1996", KDI Working Paper No.9810, Korea Development Institute, Seoul, 1998 [19] Pyo, Hak K., "Estimates of Capital Stocks by Industries and Types of Assets in Korea (1953-2000)", Journal of Korean Economic Analysis, Panel for Korean Economic Analysis and Korea Institute of Finance, Seoul 2003 [20] Pyo, Hak K., “Interdependecy in East Asia and the Post-Crisis Macroeconomic Adjustment in Korea”, Seoul Journal of Economics, Volume 17 Number 1, Spring 2004 [21] Pyo Hak K. and Bongchan Ha, "A Test of Separability and Random Effects in Production Function with Decomposed IT Capital," Forthcoming, Hitotsubashi Journal of Economics, 2006 [22] Pyo Hak K., Keun-Hee Rhee, and Bongchan Ha , "Growth Accounting, Productivity Analysis, and Purchasing Power Parity in Korea(1984-2000)", presented at the Fifth Workshop on the International Comparison of Productivity among Asian Countries, October, 2004, Tokyo, Japan, 2004 [23] Pyo Hak K. and Bongchan Ha, "Productivity Convergence and Investment Stagnation in East Asia," presented at CIRJE seminar, University of Tokyo, Japan, July 21, 2005 [24] Pyo Hak K., Keun-Hee Rhee, and Bongchan Ha , “Productivity Analysis by

59

Industry in Korea and International Comparison through EU KLEMS Database:Data Structure”, Paper presented at EU-KLEMS Workshop, May 7-9, 2006, Valencia [25] Pyo Hak K., Investment Stagnation in East Asia and Policy Implications for Sustainable Growth, Working Paper 06-01,Korea Institute for International Economic Policy, Seoul [26] Timmer, Marcel, “The Dynamics of Asian Manufacturing: A Comparative Perspective, 1963-1993”, Eindhoven Centre for Innovation Studies, Dissertation Series, 1999 [27] Timmer, Marcel, "EUKLMES Road map WP1," EU KLEMS webpage, October, 2005 [28] Young, Alwyn, "Lessons from the East Asian NICs : A Contrarian View," European Economic Review papers and proceedings, 1994 [29] Yuhn Ky-Hyang, "Functional Separability and the Existence of Consistent Aggregates in U.S. Manufacturing," International Economic Review, Vol.32, No.1, pp.229-250, 1991

60

Appendix Table A-1. EU KLEMS Industrial Classification Table A-2. Reclassification of National Accounts into 72 Industries Table A-3. Nominal Gross Output Table A-4. Nominal Value-added Table A-5. Nominal Share of Labor Table A-6. Nominal Share of Capital Table A-7. Nominal Value of Energy Table A-8. Nominal Value of Material Table A-9. Nominal Value of Service Table A-10. Real Gross Output Table A-11. Real Value-added Table A-12. Real Share of Labor Table A-13. Real Share of Capital Table A-14. Real Value of Energy Table A-15. Real Value of Material Table A-16. Real Value of Service Table A-17. Real Capital Stock by Asset in 2000 Prices(Unit: Billion Won) Table A-18. Real Capital Stock by Industry in 2000 Prices(Unit: Billion Won) Table A-19 Employed Table A-20 Aggregate Working Hour 61

Table A-21 Average Working Hour Table A-22 Labor Quantity Table A-23 Labor Quality Table A-24 Labor Growth Rate

62

Table A-1. EUKLEMS Industrial Classifications

63

Table A-1. EUKLEMS Industrial Classifications (Continued)

64

Table A-2. Reclassification of National Accounts into 72 Industries

65

Table A-3. Nominal Gross Output

66

Table A-3. Nominal Gross Output(Continued)

67

Table A-3. Nominal Gross Output(Continued)

68

Table A-4. Nominal Value-added

69

Table A-4. Nominal Value-added(Continued)

70

Table A-4. Nominal Value-added(Continued)

71

Table A-5. Nominal Share of Labor

72

Table A-5. Nominal Share of Labor(Continued)

73

Table A-5. Nominal Share of Labor(Continued)

74

Table A-6. Nominal Share of Capital

75

Table A-6. Nominal Share of Capital(Continued)

76

Table A-6. Nominal Share of Capital(Continued)

77

Table A-7. Nominal Value of Energy

78

Table A-7. Nominal Value of Energy(Continued)

79

Table A-7. Nominal Value of Energy(Continued)

80

Table A-8. Nominal Value of Material

81

Table A-8. Nominal Value of Material(Continued)

82

Table A-8. Nominal Value of Material(Continued)

83

Table A-9. Nominal Value of Service

84

Table A-9. Nominal Value of Service(Continued)

85

Table A-9. Nominal Value of Service(Continued)

86

Table A-10. Real Gross Output

87

Table A-10. Real Gross Output(Continued)

88

Table A-10. Real Gross Output(Continued)

89

Table A-11. Real Value-added

90

Table A-11. Real Value-added(Continued)

91

Table A-11. Real Value-added(Continued)

92

Table A-12. Real Share of Labor

93

Table A-12. Real Share of Labor(Continued)

94

Table A-12. Real Share of Labor(Continued)

95

Table A-13. Real Share of Capital

96

Table A-13. Real Share of Capital(Continued)

97

Table A-13. Real Share of Capital(Continued)

98

Table A-14. Real Value of Energy

99

Table A-14. Real Value of Energy(Continued)

100

Table A-14. Real Value of Energy(Continued)

101

Table A-15. Real Value of Material

102

Table A-15. Real Value of Material(Continued)

103

Table A-15. Real Value of Material(Continued)

104

Table A-16. Real Value of Service

105

Table A-16. Real Value of Service(Continued)

106

Table A-16. Real Value of Service(Continued)

107

Table A-17. Real Capital Stock by Asset in 2000 Prices(Unit: Billion Won)

108

Table A-18. Real Capital Stock by Industry in 2000 Prices(Unit: Billion Won)

109

Table A-18. Real Capital Stock by Industry in 2000 Prices(Unit: Billion Won)(Continued)

110

Table A-18. Real Capital Stock by Industry in 2000 Prices(Unit: Billion Won)(Continued)

111

Table A-19. Employed

112

Table A-19. Employed(Continued)

113


114


115

Table A-20 Aggregate Working Hour

116

Table A-20 Aggregate Working Hour(Continued)

117


118


119


120


121

Table A-21 Average Working Hour

122

Table A-21 Average Working Hour(Continued)

123


124


125

Table A-22 Labor Quantity

126

127

128

129

Table A-23 Labor Quality

130

Table A-23 Labor Quality (Continued)

131


132


133

Table A-24. Labor Growth Rate

134

Table A-24. Labor Growth Rate(Continued)

135


136


137

138

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