Estimates of Labor and Total Factor Productivity
by 72 industries in Korea (1970-2004)
Hak K. Pyo and Keun Hee, Rhee*

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 theRepublic 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 preliminary dataset of 72industry 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 havealso found that TFP growth has been positively affected by the growth of labor productivity andoutput growth. However, since its financial crisis in December 1997, the sources of growth seemto have switched to TFP-growth based and IT-intensive Service based. But lower productivity inservice 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. An earlier version of this paper waspresented at OECD Workshop on Productivity Analysis and Measurement, Bern, 16-18 October
2006. We acknowledge financial support by the Bank of Korea and Korea Institute ofInternational Economic Policy and research assistance of Eunkyung Jeon and Sun Young Jungat Seoul National University. pyohk@plaza.snu.ac.kr for correspondence.

1

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 andHa (2005)), changes in production input patterns, and so on. One of the mostimportant 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 supportof 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 ofboth 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 theestimation 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 basedon detailed industrial classification gives us better views on productivity andgrowth, 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 othereconomic and non-economic circumstances.
KLEMS model is a kind of gross output growth accounting in which output ismeasured by gross output and inputs are decomposed by capital (K), labor (L),2

energy (E), material (M), and service (S). Since this methodology is basically
based on gross output, it has the advantage of eliminating effects ofintermediate inputs from other industries on productivity, therefore allowing
productivities by industry to be more accurate. Moreover, the assumption onreal 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 onintermediate 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 theestimation methodology for unavailable data should be examined more carefully.
We have found that Korea’s catch-up process with industrial nations in its lateindustrialization has been predominantly input-led and manufacturing based.
We have also found that TFP growth has been positively affected by the growthof 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 InputOutput 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.

2. Data Structure
2.1 Gross Output Data

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

3

National Accounts by the Bank of Korea (1999, 2004) report annual series(1970-2002) of nominal gross outputs at basic prices, both nominal and realvalue-added at basic prices, nominal compensation of employees, andoperating surplus at current prices of 21 industries including 9 manufacturingindustries. 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 andreal 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 andImport 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 intermediatecommodity inputs by industry are measured at purchase prices. Since 1995,
those inputs have been measured at incomplete basic prices in the sense thatthose 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.
The trend of gross output has been shown in Figure 1. There was no real
break in gross output growth in Korea’s economy-wide 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 togo through IMF-mandated adjustment and restructuring program as2

http://www.bok.or.kr

4

documented in Pyo (2004). We observe that even though economy-wide laborproductivity 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 ofService sectors except a few IT-related finance and communication sectors has
been lagging behind.
Figure 1 Trend of Real Gross Output (2000 prices)
1800
1600

Trillion Won

1400
1200
1000
800
600
400
200
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 04

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 itschanging distribution among sectors were not realized in their development
process. However, it is difficult to identify these factors empirically because thetime series data of capital stocks in fast-developing economies by both types ofassets 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 wealthsurvey four times since 1968. Korea is one of a few countries which haveconducted 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 both5

developed and developing countries, an analysis on the dynamic profile ofnational 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 wasmade 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 from1995 to 2000. Since the database of Pyo (2003) covers 10 broad categories ofindustrial 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; residentialbuilding, non-residential building, other construction, transportation vehicles,
and machinery, while excluding large animals & plants, household durables,
and inventory stocks. We have used estimated depreciation rates in Pyo as
shown in Table 1.
Table 1 Estimated Depreciation Rates of Assets (Unit:%)
1968-1977

1977-1987

1987-1997

5.1

5.7

4.6

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

Total

Source: Pyo(2003)

In order to derive capital service inputs from capital stocks, 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
assuming that the flow of capital service is proportional to the average of current
6and one-year lagged capital stocks, which means that currently installed capitalstock is available in the midpoint of the installed period. We have estimated theprice of capital service through the user cost of capital formula. Thismethodology derives the cost of capital by the equality between two alternative
investments: earning a nominal rate of return and investing in asset earning a
rental fee and selling the depreciated asset. We have used yields of corporate
bonds for nominal rates of return and Pyo's (2003) results for depreciation rates
as shown in Table 1. We did not consider tax effects in estimating cost of capitalfor the unavailability of data.
2.3 Measurement of Labor Input
In order to measure labor input for KLEMS model, we have to obtain bothquantity data of labor input such as employment by industries and hours worked
and quality factors such as sex, education and age. Both availability andreliability of labor statistics in Korea have improved since 1980. But themeasurement of labor input by industries cannot be readily made because thestatistics 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 andmanufacturing only.
Economically Active Population Yearbook by National Statistical Officereports 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 Structurefrom the Ministry of Labor and Economically Active Population Survey from
National Statistical Office for the period of 1980-2004. The data are classified by
two types of gender (Male and Female), three types of age (below 30, 30-49,
and 50 above), and three types of education (middle school and under, high7

school, college above) and, therefore, there is a total of 18 categories of laboras shown Table 2.
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 aswell 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 havetaken the method of Jorgenson, Gollop, and Fraumeni (1987)
Table 2. Classification of Labor Input
Categories
GenderAge

Education(1) male(2) female(1) below 30 (2) 30-49 (3) above 50(1) Low-skilled (middle school and under)(2) Middle-skilled (high school )(3) High-skilled (college above)

2.4 Energy, Material, and Service and Input Shares
In order to decompose intermediate inputs into energy (E), material (M), andservice (S) inputs, we have identified coal and lignite, crude petroleum andnatural 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 asmaterial inputs, and remaining service inputs as service inputs.
Regarding shares of inputs, we have used compensation of employees asshares of labor inputs and remaining value-added as shares of capital inputs.
This method may underestimate the shares of labor input by allocating thecompensation of self-employed to the shares of capital input, and this gap
would be especially large in primary industry. There are some adjustment8

processes to correct underestimation of labor share as attempted by, forexample 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.

3. Estimates of Labor Productivity and TFP by 72-industry
3.1 Trend of Labor Productivity Level and Growth Rates by Sector(1) The Level and Trend of Labor Productivity
As shown in Figure 2, 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.
Figure 2 Trend of Labor Productivity Level<unit: gross output per hour(won)>
7000
6000
5000
4000
3000
2000
1000
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 04
Economy-w ide

M anufacturing

Service

9The relatively sluggish productivity gain in Service sector has been pointed
out by IMF in their recent consultation with the Korean authorities as abottleneck 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 laborproductivity by sector and by sub-period is presented in Table 3. According toKim(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 in2005, the Service productivity is not only low in level terms compared toindustrial nations’ levels but also lags behind in terms of growth rate. She also
points out that Korea’s inter-industry linkage effect between Manufacturing andService is also only about half the size of industrial nations.
(2) The growth rates of labor productivity by Sector
The growth rates of labor productivity as summarized in Table 3 and shown inmore detail in Figures A-1 and A-2 in Appendix confirm the remarkabledifference between Manufacturing and Service sector. Throughout the entire
period of 1971-2004, the economy-wide labor productivity has grown at the
average rate of 5.41 percent but with the sectoral difference between
Manufacturing (6.88 %) and Service (2.63 %). The difference did not shrink but
rather has expanded as the process of industrialization continued. For example,
the difference in the 1990’s (9.86 % vs. 2.82 %) has been more than doubled
since 1970’s (4.15 % vs. 1.77 %).
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 degreeof 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 asretail trade, real estate and financial services, hotels and restaurants etc. havebeen subject to all kinds of regulations such as zoning, sanitary standards andsegregated financial market services etc.

10

Table 3 Growth Rates of Labor Productivity by Sector(%)

PeriodEconomy-wide

Manufacturing

Service

71-79

4.69

4.15

1.77

80-'89

8.77

6.67

3.72

90-'99

3.32

9.86

2.82

00-'04

4.19

6.26

1.64

90-'98

4.02

9.23

2.59

99-'04

3.01

7.80

2.18

5.41

6.88

2.63

71-'04

A more detailed analysis of labor productivity can be made by looking at
growth rates of labor productivity by sub-sectors of both Manufacturing andService industries as presented in Table A-1 and A-2 in Appendix. Within
Manufacturing, we observe a large difference in growth rates of laborproductivity ranging from Telecommunication equipment(17.22%) to Aircraft andSpacecraft (-24.58 %). In general, export-oriented manufacturing industries
such as Electrical machinery and apparatus(13 % range) and Chemicals, Basic
metals (10 % range) have been leader industries in improving labor productivity.
In Service industries, the intra-industry difference of labor productivity growthis even larger than in Manufacturing. Gas Supply (28.24 %) and Water transport(14.32 %) have been leader industries because most of them have been public
enterprises with rising demand for social overhead capital. On the other hand,
Health and social work (-5.78 %), Imputation of owner occupied rents (-5.72 %)and Other real estate activities (-4.72 %) have been on the lower end of thelabor productivity growth.
3.2 Gross Output Growth Accounting and TFP Growth
The growth rate of economy-wide TFP has been estimated as -0.52 percent.
The growth rates of TFP in Manufacturing and Service are estimated as 0.79
percent and -1.07 percent respectively throughout the entire period of 19712004 as shown in Table 4. Also the economy-wide TFP growth rate during thepre-crisis period (1990-1998) has been estimated as -0.65 percent. And thegrowth rate during the post-crisis period (1999-2004) has been estimated as
0.08 percent.

11(1) The Level of TFP Growth and its Trend by Sector
The growth rates of TFP by sector are shown in Figure 3. Throughout theentire period 1971-2004, 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(2.66%), labor(1.13%),energy(1.20%), intermediate goods(5.02%) especially, were relatively higher.
Therefore, the growth rates of TFP have been estimated as negative. In case oflate 1990’s the negative growth of TFP has been resulted from the shrink ofgross 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 innovation
processes such as product innovation or process innovation are more sensitiveand stronger in manufacturing than in service. Also the R&D investment for
innovation is in general more intensive in manufacturing than in service. So thegrowth rates of TFP in Manufacturing seem to be greater than in Service.
After the economic crisis in 1997-1998, the economy-wide growth rate ofgross output has been recovered, at the same time the growth rates of input
factors such as capital 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 1971-2004 are -6.7 percent, and 7.7 percent in Manufacturing, and 15.0 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 (57.2 percent),
capital (10.9 percent), energy (10.8 percent), service (8.9 percent), labor (4.5
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 (7.7 percent) isof rather insignificant magnitude. On the other hand, in Service the contribution
are in order of capital (39.5 percent), service (24.5 percent), intermediate goods(24.5 percent), labor (20.7 percent), energy (5.8 percent). Hence we can see
the input’s role for enhancing productivity is different between Manufacturing
and Service.

12

Figure 3 The growth rates of TFP (%)

Economy-w ide
6.00
4.00
2.00
0.00
-2.00
-4.00
-6.00
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 04

M anufacturing
5.00
4.00
3.00
2.00
1.00
0.00
-1.00
-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 04

Service
3.00
2.00
1.00
0.00
-1.00
-2.00
-3.00
-4.00
-5.00
-6.00
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 04

13

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 1.7 percent in Manufacturing and 3.8
percent in Service during the entire period, Also the quantity of labor has
affected the growth of output by about 2.8 percent in Manufacturing and about
16.9 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’slabor market after the crisis of 1997-1998. As a consequence, the contribution
rate of labor to output growth has become negative in Manufacturing before thecrisis. In Service, Post and telecommunication which is related strongly with IT
technology has recorded a relatively higher growth rate (6.09 percent) of TFPamong service sectors.
On the one hand the sectors which were based on IT technology such as i)
Telecommunication equipment(3.04 percent), ii) Other electrical machinery andapparatus(2.13 percent), iii) Office, accounting and computing machinery (1.76
percent) in Manufacturing, have shown higher growth rate of TFP during theentire period (1971-2004). But the labor intensive sectors such as i) Publishing(-0.09 percent), ii) Printing and reproduction (-0.37 percent), iii) Wearing apparel,
dressing and dying of fur (-0.44 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 (6.09 percent) of TFPamong service sectors. But the social and private sectors such as i) Other
service activities (-10.67 percent), ii) Health and social work (-5.49 percent), iii)
Public admin and defense (-1.70 percent), iv) Private households with
employed persons (-1.00 percent) have shown negative growth rates of TFP.
Therefore, we can see that the leading sectors for enhancing productivity
growth are related with IT sectors. Korean economy has heavily invested in IT
sectors on a full scale since 1995 as shown in Table 5 and recently analyzed in
Ha and Pyo (2004).

14

Table 4 Gross Output Growth Accounting and TFP growth
Economy-wide<unit: log growth rates (%)>

Labor input
Gross

CapitalPeriodoutput

input

Total

Quantity

Quality

labor

labor

labor

EnergyIntermediate

Service

input

input

input

TFP

71-'79

9.44

2.65

1.15

0.70

0.45

1.12

4.90

1.02

-1.39

80-'89

8.36

2.52

-0.03

-0.06

0.02

0.69

4.28

0.89

0.01

90-'99

6.53

2.22

0.93

0.67

0.27

0.43

1.97

1.65

-0.67

00-'04

6.46

1.22

0.65

0.45

0.20

0.33

2.60

1.40

0.26

90-'98

5.95

2.33

0.72

0.42

0.30

0.38

1.57

1.59

-0.65

99-'04

7.35

1.22

1.02

0.86

0.16

0.41

3.10

1.52

0.08

7.83

2.28

0.66

0.43

0.23

0.67

3.51

1.22

-0.52

71-'04Contribution to output growth71-'79

100.0

28.1

12.2

7.4

4.7

11.9

51.9

10.8

-14.8

80-'89

100.0

30.2

-0.4

-0.7

0.3

8.3

51.2

10.7

0.1

90-'99

100.0

34.0

14.3

10.2

4.1

6.6

30.1

25.2

-10.2

00-'04

100.0

18.9

10.1

6.9

3.2

5.1

40.3

21.6

4.0

90-'98

100.0

39.3

12.1

7.0

5.1

6.5

26.4

26.8

-10.9

99-'04

100.0

16.6

13.9

11.7

2.2

5.6

42.1

20.7

1.0

100.0

29.1

8.5

5.5

3.0

8.6

44.9

15.6

-6.7

EnergyIntermediate

Service

input

input

input

71-'04

Manufacturing
Labor input
Gross

CapitalPeriodoutput

input

Total

Quantity

Quality

labor

labor

labor

TFP

71-'79

15.14

1.42

1.11

0.94

0.16

2.00

8.78

0.94

0.89

80-'89

10.27

1.29

0.54

0.39

0.14

1.03

6.44

0.64

0.34

90-'99

7.02

1.00

-0.12

-0.37

0.25

0.76

3.35

1.07

0.97

00-'04

8.16

0.53

0.32

0.21

0.11

0.40

4.66

1.12

1.15

90-'98

5.65

1.08

-0.18

-0.46

0.28

0.70

2.49

0.93

0.64

99-'04

10.03

0.48

0.33

0.24

0.09

0.54

5.73

1.32

1.62

10.29

1.13

0.46

0.29

0.18

1.11

5.89

0.92

0.79

71-'04Contribution to output growth71-'79

100.0

9.4

7.3

6.2

1.1

13.2

58.0

6.2

5.9

80-'89

100.0

12.5

5.2

3.8

1.4

10.1

62.7

6.2

3.3

90-'99

100.0

14.2

-1.7

-5.3

3.6

10.8

47.7

15.2

13.8

15

00-'04

100.0

6.5

3.9

2.5

1.4

4.8

57.0

13.7

14.0

90-'98

100.0

19.1

-3.1

-8.2

5.0

12.3

44.0

16.4

11.3

99-'04

100.0

4.8

3.3

2.4

0.9

5.4

57.1

13.2

16.2

100.0

10.9

4.5

2.8

1.7

10.8

57.2

8.9

7.7

EnergyIntermediate

Service

input

input

input

71-'04ServiceLabor input
Gross

CapitalPeriodoutput

input

Total

Quantity

Quality

labor

labor

labor

TFP

71-'79

7.98

2.39

1.89

1.48

0.41

0.73

2.65

1.55

-1.22

80-'89

7.92

3.38

1.20

1.10

0.09

0.44

2.31

1.36

-0.77

90-'99

6.66

3.18

1.46

1.17

0.29

0.17

0.84

2.34

-1.33

00-'04

5.17

1.85

1.35

1.00

0.35

0.29

0.86

1.73

-0.89

90-'98

6.74

3.29

1.60

1.27

0.33

0.14

0.82

2.36

-1.48

99-'04

5.30

1.90

1.16

0.88

0.28

0.31

0.88

1.80

-0.75

7.16

2.83

1.48

1.21

0.27

0.42

1.75

1.75

-1.07

71-'04Contribution to output growth71-'79

100.0

29.9

23.7

18.5

5.1

9.1

33.2

19.4

-15.3

80-'89

100.0

42.7

15.1

13.9

1.2

5.5

29.2

17.2

-9.7

90-'99

100.0

47.7

22.0

17.6

4.3

2.6

12.6

35.1

-20.0

00-'04

100.0

35.8

26.0

19.2

6.8

5.5

16.6

33.4

-17.3

90-'98

100.0

48.8

23.8

18.9

4.9

2.1

12.2

35.0

-22.0

99-'04

100.0

35.9

21.8

16.6

5.2

5.9

16.6

33.9

-14.1

100.0

39.5

20.7

16.9

3.8

5.8

24.5

24.5

-15.0

71-'04

Table 5 The Investment in IT sector(in 2000 constant prices)Year

IT Investment(billion won)Growth (%)

1995

15,125.7-

1996

17,916.0

18.4

1997

19,122.0

6.7

1998

17,099.2

-10.6

1999

23,716.0

38.7

2000

32,190.9

35.7

2001

31,502.0

-2.1

16

2002

33,143.8

5.2

2003

31,551.8

-4.8

2004

31,794.8

0.8

Source: Bank of Korea (http://ecos.bok.or.kr)

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 4, the weight of gross output of the sectors with positive
Economy-wide TFP growth is about 57 % while the weight with negative TFP
growth is about 43 % during the entire period of 1971-2004.
We can identify sectors that have contributed to the growth of economy-wide
TFP positively. Leading sectors in this group include Electrical supply and Post
and Telecommunications, and Financial intermediation in Service andChemicals and Basic metals 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 andOther service activities etc.
As shown in Figure 4, the weight of gross output of the sectors with positive
TFP growth in Manufacturing is 90.7% while the weight with negative TFP
growth is 9.3% during the period of 1971-2004. 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 andcomputing machinery, telecommunications, radio and TV receivers as IT
sectors. The sub-sectors with negative TFP growth are Publishing, Printing andreproduction, Wearing apparel, Aircraft and spacecraft etc.
On the other hand, we can look at Service industry separately. As shown in
Figure 4, the weight of gross output of the sectors with positive TFP growth inService is only about 30 % while the weight with negative TFP growth is 70 %during the period of 1971-2004. The group of service industries with positive
TFP growth includes Financial intermediation, Post and communication, Inland
Transport, Insurance and pension, Water Transport etc. The group with negative
TFP growth includes Hotels and Restaurants, Imputation of owner-occupied
housing, Other service activities and Wholesale trade etc.

17Figure 4 Cumulative Contribution of sectors to TFP Growth (1971-2004)
Economy-wide

C um ulative contribution to TFP grow th(% )2.50
Food products &beverages
Radio & TV receivers

2.00

Legal, technical &advertis ing
Cons truction
W holes ale trade
Public admin. & defence
Other real es tate

Motor vehicles
Electrnoicvalves
TextilesElectrical machinery Telecommucation equip
Machinery
Inland trans port
Education
Bas ic metals
Financial intermediation
Chemicals

1.50
1.00
0.50

Health &s ocial work
Other s ervice

Post & Telecommunications

Imputation of ow ner
occupied rents

Electrical s upply

0.00

Hotels & res taurants

-0.50
-1.00

Agriculture

-1.50
0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Cumulative share of gross output(ratio)

Manufacturing

C u m ulative c ontribution to TFP frow th (% )1.20
Scientific ins truments Pharmaceuticals
Fabricated metal
W earing & apparel
Food & beverages
W ood
Paper
Rubber and plas tics Office, acu't & computer Aircraft & s pacecraftRadio & TV receivers
Motor vehicles
Other non-metallic

1.00

0.80

Coke, refines petroleum
Texiles
Telecommunication eqip
Eletronic values

0.60

Other electrical mach.

0.40

Machinery
Bas ic metals

0.20

Chemicals

0.00
0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Cumulative share of gross output(ratio)

18Service

Cumulative contribution to TFP growth(%)

1.00

Computer related activities

Retail trade
Legal, technical & advertising
W ater trans port Gas s upply
Ins urance and pens ion
Education
Cons truction
Inland trans port
W holes ale trade
Financial intermediation
Public admin & defence
Post & telecommunications

0.50

Other real es tate

Electricity s upply

0.00
Health and s ocial w ork
Other s ervice-0.50

Imputation of owneroccupied rents-1.00

Hotels and res taurants

-1.50
0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Cumulative share of gross output(ratio)

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 growth rates in
Manufacturing and Service. The relations of TFP with labor productivity andoutput growth can be further examined by looking at the scatter diagrams suchas Figure 5 and 6. 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 6, 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 growththrough 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.

19

Figure 5 Plotting between TFP
Productivity Growth (1971-2004, %)GrowthandSectoral

20.00

30.00

Labor

15.00

TFP growth(%)

10.00

5.00

0.00

-5.00

-10.00-30.00-20.00-10.00

0.00

10.00

40.00

Labor productivity grow th(%)

Figure 6 Plotting between TFP Growth and Sectoral Gross output Growth(1971-2004, %)15.00

TFP grow th(%)

10.00
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

35.00

Gross output grow th(%)

20

Table 6 Regression Results of TFP Growth_______________________________________________________________
1. Regression of TFP Growth on Labor Productivity Growth
Model: log (TFPt/TFPt-1)=α + β log (LPt/LPt-1)+ γ
log (TFPt/TFPt-1) = Sectoral average TFP growth rate during 1971-2004,
log (LPt/LPt-1)= Sectoral average labor productivity growth rate during
1971-2004
Number of sectors: 49 sectors (except some sectors for data insufficiency)
Dependent var.

Β

S.E

DW

adjR2

TFP Growth Rate

0.345***

0.034

1.711

0.677

***: Pr>t is 1%, **:Pr>t is 5%, *:Pr>t is 10%

2. Regression of TFP Growth on Gross Output Growth
Model: log (TFPt/TFPt-1)=α + β log(GOt/GOt-1)+ γ
log (TFPt/TFPt-1) = Sectoral average TFP growth during 1971-2004,
log (GOt/GOt-1) = Sectoral average Gross output growth rate during 19712004
Number of sectors: 66 sectors(except some sectors for data insufficiency)
Dependent var.

Β

S.E

DW

adjR2

TFP Growth rate

0.326***

0.060

1.473

0.301

***: Pr>t is 1%, **:Pr>t is 5%, *:Pr>t is 10%_______________________________________________________________

21

3. A Linear Rank Test of Independence
In addition to regression analysis, we have used a type of distribution-freelinear rank statistic, a generalization of the Mann-Whitney-Wilcoxon statistic for
two Independent samples following Baily, Hulten and Campbell (1992) and
Hogg and Craig (1978) and Choi (2003) and Neter et al.(1996).
Let ( X 1 , Y1 ), ( X 2 , Y2 ),..., ( X n , Yn ) be a random sample from a bivariate
distribution of the continuous type. Let Ri be the rank of X i among
X 1 , X 2 ,..., X n and Qi be the rank of Yi among Y1 , Y2 ,..., Yn . If X and Y have alarge positive correlation coefficient, we would anticipate that Ri and Qi would
tend to be large or small together. In particular, the correlation coefficient of( R1 , Q1 ), ( R2 , Q2 ),..., ( Rn , Qn ) , namely the Spearman rank correlation coefficient :nrs =∑ ( R − R )( Q − Q)
i

i =1

n

i

n

∑ ( R − R ) ∑ ( Q − Q)
2

i

i =1

(18)
2

i

i =1

would tend to be large. Since R1 , R2 ,..., Rn and Q1 , Q2 ,..., Qn are permutations of
1,2,…,n, this correlation coefficient can be shown to equal :nrs = 1 −

6∑ ( Ri − Qi ) 2
i =1

(19)

n(n 2 − 1)

The mean and the variance of rs under H 0 is derived as:

μ s = 0,σ s2 =

1
n −1

(20)

As shown in Table 7, the computed linear rank statistics reject the null
hypotheses that TFP growth is stochastically independent of LP growth and that
TFP growth is stochastically independent of output growth at the 1 %significance level.

22

Table 7 Test Statistics for Testing the Stochastic Independence
rs (TFP − LP ) = 0.8864
rs (TFP − GO ) = 0.6150
H0 : ρ = 0

H1 : ρ ≠ 0

PrH 0 ( rs ) ≥ rs ( n; α ) = αr − E H 0 ( rs )VarH 0 ( rs )= rs n − 1 → N (0,1)

Z (TFP − LP ) = 0.8864 × (49 − 1) = 6.141
Z (TFP − GO ) = 0.6150 × (49 − 1) = 4.261

4. Conclusion
The purpose of this paper is to explain how the database of Korea has been
constructed for estimating productivities by industry in KLEMS model and how
we have estimated 72-industry level labor productivity and TFP. We have also
conducted a gross output growth accounting. Throughout the entire period of
1971-2004, the economy-wide labor productivity has grown at the average rateof 5.41 percent but with the sectoral difference between Manufacturing (6.88 %)and Service (2.63 %). The difference did not shrink but rather has expanded as
the process of industrialization of the Korean economy continued. For example,
the difference in the 1990’s (9.86 % vs. 2.82 %) has been more than doubled
since 1970’s (4.15 % vs. 1.77 %). The observed difference in both levels andgrowth rates of labor productivity between Manufacturing and Service cansignal the difference in the degree of foreign competition, the proportion of
tradable goods and non-tradable goods and services and the degree ofdomestic competition due to historically different regulatory environments.
The growth rate of economy-wide TFP has been estimated as -0.52 percent.
The growth rates of TFP in Manufacturing and Service are estimated as 0.79
percent and -1.07 percent respectively throughout the entire period of 19712004. Korean economy experienced two major break-points: in 1974 which was23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(2.66%), labor(1.13%), energy(1.20%), intermediate
goods(5.02%) 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 shrinkage 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 moresensitive and stronger in Manufacturing than in Service. Also the R&D
investment for innovation is in general more intensive in Manufacturing than inService. So the growth rates of TFP in Manufacturing seem to be greater than inService.
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 Electrical supply, Post and Telecommunications
and Financial Intermediation in Service and Chemicals and Basic Metals 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 Other service 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 visualinspection tells us that TFP growth is positively correlated with both laborproductivity growth and output growth and TFP-LP relation is stronger than TFP–Output relation. We have adopted an implicit hypothesis that higher LP and
output growth induces TFP growth through enhanced human capital andeconomies of scale. In both regressions, the coefficients of LP growth andOutput Growth are significant. The TFP-LP regression seems more significant
than TFP-Output regression.
Productivities in an economy are not identical across industries, andproductivity differences are also observed when compared with othereconomies. For example, most industries in Japan exhibit higher productivity in
Manufacturing such as Electrical machinery, Motor and other transport vehicles,24and Instruments industries resulting in higher productivity in the entire economy.
However, total factor productivities of Korea in Construction, Petroleum
products, Fabricated machinery, and Finance industries are higher than thoseof Japan. International comparison of productivity among industries will
demonstrate a relative productivity of each industry, illustrating whether the way
goods and services are produced is relatively efficient or not and referring to theappropriate 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 better understanding of economic growth.

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 Substitutionof 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 forEnergy," Review of Economics and Statistics, Vol.57, pp.259-268, 1975
[5] Byoung Seon Choi, SungBaek Lee, Modern Statistics Using SAS, Sekyung
Sa, 2003, in Korean[6] 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
[7] 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
[8] Hogg and Craig, Introduction to Mathematical Statistics, Fourth edition,
Collier Macmillan International Editions, 197825

[9] Inklaar, Robert, Marcel P. Timmer and Bart van Art (2006), “Mind the Gap!:
International Comparisons of Productivity in Services and Goods
Production,” unpublished manuscript[10] Jorgenson, D.W., F. M. Gollop and B.M.Fraumeni, Productivity and US
Economic Growth, Cambridge MA: Harvard University Press, 1987
[11] 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[12] Kim Hyunjeong, “The Shift to the Service Economy; Causes and Effects”,
BOK Institute, Working Paper 254, May 2006
[13]Krugman, Paul, "The Myth of Asia's Miracle," Foreign affairs,
November/December, 1994
[14] Kyoji Fukao, “Industry and firm level total factor productivity and economic
growth in Japan”, RIETI Workshop, July 2006,[15] 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[16] Lau, Lawrence J. and Jong-Il Kim, "The Sources of Growth of East Asian
Newly Industrialized Countries," Journal of Japanese and International
Economies, 1994.
[17] Lewis, W. William, The Power of Productivity: Wealth, Poverty, and theThreat to Global Stability, University of Chicago Press, 2004[18] Neter John, Michael H.Kutner, Christopher J.Nachtsheim, and William
Wasserman, “Applied Linear Statistical Models”, Mc Graw Hill, 1996[19] Pyo, Hak K., "Estimates of Capital Stock and Capital / Output Coefficientsby Industries: Korea, 1953-1986", International Economic Journal, summer
1988[20] Pyo, Hak K., "A Synthetic Estimate of National Wealth of Korea", 19531990, KDI Working Paper No.9212, Korea Development Institute, Seoul,
1992[21] 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, 199826

[22] Pyo, Hak K., "Estimates of Capital Stocks by Industries and Types ofAssets in Korea (1953-2000)", Journal of Korean Economic Analysis, Panelfor Korean Economic Analysis and Korea Institute of Finance, Seoul 2003[23] 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[24] Pyo Hak K. and Bongchan Ha, "A Test of Separability and Random Effects
in Production Function with Decomposed IT Capital," Hitotsubashi Journal of
Economics 48, 2007 (forthcoming)[25] 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[26] 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[27] Pyo Hak K., Keun-Hee Rhee, and Bongchan Ha , “Productivity Analysis by
Industry in Korea and International Comparison through EU KLEMS
Database:Data Structure”, Paper presented at EU-KLEMS Workshop, May
7-9, 2006, Valencia[28] 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[29] Timmer, Marcel, “The Dynamics of Asian Manufacturing: A Comparative
Perspective, 1963-1993”, Eindhoven Centre for Innovation Studies,
Dissertation Series, 1999
[30] Timmer, Marcel, "EU KLEMS Road map WP1," EU KLEMS webpage,
October, 2005[31] Young, Alwyn, "Lessons from the East Asian NICs : A Contrarian View,"
European Economic Review papers and proceedings, 1994
[32] 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

27

Health and social work

Imputation of owner occupied rents

Other real estate activities

Wholesale trade and commission trade

Air transport

Hotels and restaurants

Retail trade, except of motor vehicles andmotorcycles

Machinery, nec

Office, accounting and computing machinery

Fabricated metal products

Pulp, paper and paper products

Other non-metallic mineral products

TextilesLeather, leather products and footwear

Rubber and plastics products

Scientific instruments

Radio and television receivers

Aircraft and spacecraft

Pharmaceuticals

Electronic valves and tubes

Printing and reproduction

Insulated wire

Publishing

Wearing Apparel

Food products and beverages

Railroad equipment and transport equipment nec

Building and repairing of ships and boats

Motor vehicles, trailers and semi-trailers

C oke, refined petroleum products and nuclear fuel-25.00

Sewage and refuse disposal, sanitation andsimilar activities

Legal, technical and advertising

Supporting and auxiliary transport activities

Construction

Sale, maintenance and repair of motor vehicles
and motorcycles

Inland transport

Financial intermediation

Activities related to financial intermediation

C hemicals excluding pharmaceuticals

Basic metals

Wood and products of wood and cork

Other electrical machinery and apparatus nec-20.00

Water transport-15.00Insurance and pension funding-10.00

Telecommunication equipment-5.00

Gas supply

Appendix

Figure A-1 Growth Rates of Labor Productivity in Manufacturing (197104, %)
20.00

15.00

10.00

5.00

0.00

-30.00Figure A-2 Growth Rates of Labor Productivity in Service (1971-04, %)

30.00

25.00

20.00

15.00

10.00

5.00

-5.00
0.00

-10.0028

Table A-1 Sectoral TFP growth in manufacturing (%)
Code

9Industry

Food products and beverages

71-'7980-'8990-'99BeforeAftercrisiscrisis90-'9899-'0471-'04

0.44

0.14

-0.12

-0.10

-0.08

0.12

10 Tobacco products

1.39

1.23

1.05

1.35

-1.94

0.75

11 Textiles

1.84

0.23

0.43

0.09

0.33

0.64

12 Wearing Apparel

-0.31

-0.57

-0.98

-1.62

1.34

-0.44

13 Leather, leather products and footwear

1.09

-0.35

1.93

1.99

-0.27

0.66

14 Wood and products of wood and cork

3.20

0.37

1.05

1.23

0.31

1.34

15 Pulp, paper and paper products

0.75

1.24

0.91

0.82

-0.41

0.71

16 Publishing

-0.31

2.20

-0.20

-1.62

-1.24

-0.09

17 Printing and reproduction

1.21

-1.30

-0.08

0.62

-2.66

-0.37

18 Coke, refined petroleum products and nuclear fuel

0.51

0.41

1.12

1.02

1.00

0.70

19 Pharmaceuticals

0.71

0.50

-3.44

-3.97

3.00

-0.19

20 Chemicals excluding pharmaceuticals

2.97

0.76

2.07

2.05

1.12

1.75

21 Rubber and plastics products

1.55

1.56

-0.33

-0.87

0.91

0.80

22 Other non-metallic mineral products

1.80

0.64

0.27

-0.23

1.78

0.92

23 Basic metals

1.33

1.29

0.93

0.82

0.54

1.05

24 Fabricated metal products

1.26

0.74

-0.55

-0.75

0.66

0.47

25 Machinery, nec

2.77

0.18

1.79

1.35

2.62

1.61

26 Office, accounting and computing machinery

-0.61

3.01

3.24

2.42

2.26

1.76

27 Insulated wire

-3.02

-1.05

2.62

2.03

1.14

-0.37

28 Other electrical machinery and apparatus nec

3.55

2.17

0.89

0.23

2.79

2.13

29 Electronic valves and tubes

1.06

-1.83

4.32

4.30

3.05

1.42

30 Telecommunication equipment

5.96

-0.76

4.53

3.78

3.87

3.04

31 Radio and television receivers

-0.27

2.11

2.47

0.43

6.45

1.80

32 Scientific instruments

1.71

0.21

1.88

2.00

1.45

1.30

-

-

-

-

-

-

34 Motor vehicles, trailers and semi-trailers

-1.60

1.56

1.47

0.58

2.36

0.61

35 Building and repairing of ships and boats

-3.59

1.12

2.53

1.91

1.43

0.14

36 Aircraft and spacecraft

-1.86

2.56

-16.72 -16.29

-1.46

-4.83

37 Railroad equipment and transport equipment nec -1.82

-0.10

0.66

0.00

0.68

-0.39

38 Manufacturing nec

0.90

1.97

-0.38

-0.81

0.68

0.72

-

-

-

-

-

-

33 Other instruments

39 Recycling

29

Table A-2 Sectoral TFP Growth in Service (%)
CodeIndustry

71-'7980-'8990-'99BeforeAftercrisiscrisis90-'9899-'0471-'0440 Electricity supply

4.06

2.48

1.14

0.94

15.18

4.73

41 Gas supply

18.85

16.06

9.71

9.82

1.23

12.14

42 Water supply

9.08

3.67

-5.72

-5.92

-1.50

1.65

43 Construction

-0.56

-0.06

-1.49

-1.50

-0.80

-0.70

1.35

-2.53

1.07

0.92

4.10

0.58

45 Wholesale trade and commission trade

-3.34

2.00

-3.61

-3.88

-0.57

-1.42

46 Retail trade

1.82

-4.74

0.32

1.35

-0.75

-0.69

47 Hotels and restaurants-12.18

-2.09

-3.18

-3.69

-1.62

-5.10

48 Inland transport

3.16

1.59

1.34

0.68

2.97

2.01

49 Water transport

0.03

3.60

14.48

16.15

0.25

5.39

50 Air transport

-7.48

1.95

2.09

2.57

4.07

-0.01

51 Supporting and auxiliary transport activities

8.39

-1.09

-5.81

-6.79

2.97

0.62

52 Post and telecommunications

6.98

3.58

8.17

7.74

6.47

6.09

4.81

5.22

-2.12

-2.83

2.83

2.56

11.75

3.47

5.45

9.77

0.61

5.77

-

17.89

5.41

-0.25

5.30

4.63

56 Imputation of owner occupied rents-10.86

-6.74

-8.88

-6.80

-11.19-8.63

57 Other real estate activities-14.85

-0.47

-7.81

-7.37

-7.79

-7.39

58 Renting of machinery and equipment

-2.23

10.38

5.50

9.32

-8.10

3.50

59 Computer and related activities

-5.28

6.80

5.11

4.85

-1.71

1.59

60 Research and development

-9.61

6.17

-1.36

-0.75

1.38

-0.68

61 Legal, technical and advertising

-3.56

2.24

-6.35

-7.14

-0.30

-2.23

62 Other business activities, nec

6.13

0.56

2.61

2.85

-31.19-2.96

63 Public admin and defense; compulsory social security

-4.84

-2.10

0.58

0.50

0.37

-1.70

64 Education

6.52

1.08

-0.74

-0.47

-1.37

1.68

65 Health and social work

-27.15

2.70

4.29

4.66

-1.89

-5.49

-3.96

-1.00

3.16

1.59

1.47

-0.66

6.05

-2.44

-3.66

-2.69

-3.24

-0.40

Sale, maintenance and repair of motor vehicles and44
motorcycles

Financial intermediation, except insurance and pension
53
funding
Insurance and pension funding, except compulsory
54
social security
55 Activities related to financial intermediation

Sewage and refuse disposal, sanitation and similar
66
activities
67 Activities of membership organizations nec

30

68 Media activities

7.50

-2.07

-0.37

-2.19

0.17

0.83

69 Other recreational activities

-4.55

1.35

7.85

1.38

1.12

-0.25

70 Other service activities-23.56

-0.66

-4.61

-1.87

-21.19-10.6771 Private households with employed persons

1.38

-5.12

1.08

0.83

-0.47

-1.00

72 Extra-territorial organizations and bodies

-

-

-

-

-

-

31