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

1
Estimates of Labor and Total Factor
Productivity by 72 industries in Korea
(1970-2003)
OECD Bern Workshop , October 16-18, 2006

• Oct 17. 2006
• Hak K. Pyo, Keun Hee Rhee and Bongchan Ha
• Institute of Economic Research
• Seoul National University

2
Contents

• 1. Introduction
• 2. Data Structure
• 2.1 Gross Output Data
• 2.2 Measurement of Capital Input
• 2.3 Measurement of Labor Input
• 2.4 Energy, Material, and Service and Input
  Shares
• 3. Estimates of Labor Productivity and TFP by
  72-industry
• 3.1 Trend of Labor Productivity Level and
  Growth Rates by Sector
• 3.2 Gross Output Growth Accounting and TFP
  Growth
• 3.3 Cumulative Contribution of Sectors to TFP
  growth
• 3.4 Relations between Labor Productivity and
  TFP growth
• 4. Conclusion

3
1. Introduction

• The purpose
• - To explain the data structure of Korea for
  the estimation of productivities by industry in
  KLEMS model
• - To 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.
• An analysis based on detailed industrial
  classification gives us better views on
  productivity and growth, which is difficult to
  grasp in broader industrial classifications.

4
Economy growth and investment growth
5
Average economy growth and average investment
growth
6
2. Data Structure

• Gross Output Data
• (1) Estimation of Use Tables
• - 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
  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.

7
2. Data Structure

• Gross Output Data
• (1) Estimation of Use Tables
• - 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
  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.

8
2. Data Structure

• Gross Output Data
• (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

9
2. Data Structure

• Gross Output Data
• (3) Aggregation Issues
• - 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.

10
2. Data Structure

• Gross Output Data
• (3) Make Tables at Purchase Prices and Use Tables
  at Basic Prices

11
2. Data Structure

• Gross Output Data

12
2. Data Structure

• Measurement of Capital Input
• (1) Estimation of Capital Stock
• - Estimating Method for 1970-1997
• 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.
• - Estimating Method after 1997
• we have to estimate capital stocks by a
  modified perpetual inventory method using 1997
  NWS as benchmark estimates .

13
2. Data Structure

• Measurement of Capital Input
• (1) Estimation of Capital Stock
• - 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 .

14
2. Data Structure

• Measurement of Labor Input
• (1) data
• - 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, high
  school, college, and university above).

15
2. Data Structure

• Measurement of Labor Input
• (2) Estimating Labor Quantity and Quality Inputs

16
2. Data Structure

• 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.

17
3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (1) The Level of Labor Productivity and its
  Trend
• - The general trend of labor productivity
  reveals a rising trend but with a remarkable
  difference between Manufacturing and Service. 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 IMF-mandated industrial
  restructuring the reduced output was matched by
  reduced employment leaving labor productivity
  level unaffected.

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3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (1) The Level of Labor Productivity and its
  Trend

Figure 4 Trend of labor productivity level
ltunit gross output per hour(won)gt
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3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (1) The Level of Labor Productivity and its
  Trend
• - 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.

20
3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (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 difference did not shrink but rather has
  expanded as the process of industrialization
  continued. For example, the difference in the
  1990s (9.55 vs. 2.64 ) has been more than
  doubled since 1970s (4.01 vs. 2.15 ).

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3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (2) The growth rates of labor productivity by
  Sector
• - 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

22
3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (2) The growth rates of labor productivity by
  Sector

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
72-'03 5.59 6.99 2.91
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3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (2) The growth rates of labor productivity by
  Sector

Figure 5 The growth rates of labor productivity
ltunit log growth rates()gt
24
3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (2) The growth rates of labor productivity by
  Sector

Figure 5 The growth rates of labor productivity
ltunit log growth rates()gt
25
3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (2) The growth rates of labor productivity by
  Sector

Figure 6 Growth Rates of Labor Productivity in
Manufacturing (1972-03/ )
26
3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (2) The growth rates of labor productivity by
  Sector

Figure 7 Growth Rates of Labor Productivity in
Service (1972-03/ )
27
3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (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 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 1970s, 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
  1990s the negative growth of TFP has been
  resulted from the shrink of gross output rooted
  from economic crisis.

28
3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (3) Trend of TFP Growth by Sector
• - 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 RD 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.

29
3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (3) Trend of TFP Growth by Sector

Figure 8 The growth rates of TFP ()
30
3. Estimates of Labor Productivity and TFP by
72-industry

• Trend of Labor Productivity Level and Growth
  Rates by Sector
• (3) Trend of TFP Growth by Sector

Figure 8 The growth rates of TFP ()
31
3. Estimates of Labor Productivity and TFP by
72-industry

• Gross Output Growth Accounting and TFP Growth

Table 9 Gross Output Growth Accounting and TFP
growth in economy-wide ltunit log growth rates()gt
Period Gross output Capital input Labor input Labor input Labor input Energy input Intermediate input Service input TFP
Period Gross output Capital input Total labor Quantity labor Quality labor Energy input Intermediate input Service input TFP
'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
'72-'03 7.81 2.98 0.85 0.44 0.41 0.61 2.63 1.32 -0.59
  contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth 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
'72-'03 100.0 38.2 10.9 5.6 5.3 7.8 33.7 17.0 -7.5
32
3. Estimates of Labor Productivity and TFP by
72-industry

• Gross Output Growth Accounting and TFP Growth

Table 10 Gross Output Growth Accounting and TFP
growth in manufacturing ltunit log growth
rates()gt
Period Gross output Capital input Labor input Labor input Labor input Energy input Intermediate input Service input TFP
Period Gross output Capital input Total labor Quantity labor Quality labor Energy input Intermediate input Service input TFP
'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
'72-'03 10.18 1.59 0.59 0.35 0.24 1.15 5.33 1.04 0.48
  contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth
'72-79 100.0 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
'72-'03 100.0 15.6 5.8 3.4 2.4 11.3 52.3 10.2 4.7
33
3. Estimates of Labor Productivity and TFP by
72-industry

• Gross Output Growth Accounting and TFP Growth

Table 11 Gross Output Growth Accounting and TFP
growth in service ltunit log growth rates()gt
Period Gross output Capital input Labor input Labor input Labor input Energy input Intermediate input Service input TFP
Period Gross output Capital input Total labor Quantity labor Quality labor Energy input Intermediate input Service input TFP
'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
'72-'03 7.22 3.51 1.45 1.17 0.28 0.30 1.14 1.73 -0.92
  contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth contribution to output growth
'72-79 100.0 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
'72-'03 100.0 48.7 20.1 16.3 3.9 4.2 15.8 23.9 -12.8
34
3. Estimates of Labor Productivity and TFP by
72-industry

• Gross Output Growth Accounting and TFP Growth

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
35
3. Estimates of Labor Productivity and TFP by
72-industry

• Cumulative Contribution of Sectors to TFP growth
• - Following Fukao et. al, (2006) we can
  examine the sectoral contribution of TFP growth
  in Manufacturing and we can identify what are
  core sectors for enhancing productivity. As
  shown in Figure 9, the weight of gross output of
  the sectors with positive TFP growth is 72.4
  while the weight with negative TFP growth is
  27.6 during 1972-2003.
• - The former 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 latter are leather and footwear,
  wearing and apparel, coke and refined petroleum
  etc.

36
3. Estimates of Labor Productivity and TFP by
72-industry

• Cumulative Contribution of Sectors to TFP growth

Figure 9 Cumulative Contribution of sectors to
TFP Growth in Economy-wide (1972-2003)
37
3. Estimates of Labor Productivity and TFP by
72-industry

• Cumulative Contribution of Sectors to TFP growth

Figure 10 Cumulative Contribution of sectors to
TFP Growth in Manufacturing (1972-2003)
38
3. Estimates of Labor Productivity and TFP by
72-industry

• Cumulative Contribution of Sectors to TFP growth

Figure 11 Cumulative Contribution of sectors to
TFP Growth in Service (1972-2003)
39
3. Estimates of Labor Productivity and TFP by
72-industry

• Relations between Labor Productivity and TFP
  growth

Figure 12 Plotting between Sectoral Labor
Productivity  Growth and TFP Growth (1972-2003,
)
In case of EU-KLEMS code, 5,6,33,39,66,72
are excluded because of data insufficiency
40
3. Estimates of Labor Productivity and TFP by
72-industry

• Relations between Gross Output Growth and TFP
  growth

Figure13 Plotting between Sectoral Gross output
Growth and TFP Growth (1972-2003, )
In case of EU-KLEMS code, 5,6,33,39,66,72
are excluded because of data insufficiency
41
3. Estimates of Labor Productivity and TFP by
72-industry

• Relations between Labor Productivity and TFP
  growth

42
3. Estimates of Labor Productivity and TFP by
72-industry

• Relations between Gross Output Growth and TFP
  growth

43
Wilcoxon Rank-Sum(Mann-Whitney) Test
This test is used in place of a two sample t test
when the populations being compared are not
normal.Null hypothesis two distributions are
the same.
44
(1)TFP-LP Rank Test
n1 n2 u P(two-tailed) P(one-tailed)
66 66 2178 1 0.500766
1 0.5
These values are approximate The two
samples are not significantly different(Pgt0.05,
two-tailed test)
45
(2)TFP-Gross output Rank Test
n1 n2 u P(two-tailed) P(one-tailed)
66 66 2178 1 0.500777
1 0.5
These values are approximate The two
samples are not significantly different(Pgt0.05,
two-tailed test)
46
4. Conclusion

• 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.

47
4. Conclusion

• 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 1972-2003.

48
4. Conclusion

• 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 sectors
  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.

49
4. Conclusion

• 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.