Global COE Hi-Stat Discussion Paper Series 089

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

An International Comparison of the TFP Levels and theProductivity Convergence of Japanese, Korea, Taiwanese andChinese Listed Firms

Hi-Stat Discussion Paper

Kyoji Fukao
Tomohiko Inui
Keiko Ito
Young Gak Kim
Tangjun Yuan

October 2009

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

An International Comparison of the TFP Levels and the Productivity Convergence ofJapanese, Korean, Taiwanese and Chinese Listed Firms

October 2009

Kyoji Fukao(The Institute of Economic Research, Hitotsubashi University)

Tomohiko Inui
(College of Economics, Nihon University)

Keiko Ito
(Department of Economics, Senshu University)

Young Gak Kim(Hitotsubashi University)

Tangjun Yuan
(Hitotsubashi University)Abstract
In this paper, we analyzed productivity catching up at the firm level in the Japanese,
Korean, Taiwanese and Chinese manufacturing sector using the distance from the global
technological frontier as a direct measure of the potential for technological frontier. We also
examined the role of the absorption capacity for the technological catch-up by including thevariables, such as R&D expenditure and foreign ownership in our empirical estimation model.
Our main results can be summarized as follows. First, although Japanese firms enjoy thehighest average TFP level in many industries, their TFP growth rate has been relatively low
during the past two decades. Taiwanese and Korean firms have achieved considerably high TFP
growth in certain industries, and the some firms in the industries almost caught up or exceeded
the Japanese firms' TFP level. The average TFP level of Chinese firms is still much lower thanthat of Japanese, Korean and Taiwanese firms in many industries. Second, in Korea, the TFPlevels of low-performing firms are approaching those of the national frontier firms at a more
rapid pace than in other countries. In addition, Korean firms try to catch up the global frontier
once they reached to the national frontier level TFP. Chinese firms are very slow in catching up
and the only engine of the knowledge creation is firms located in the trade-oriented coast. Third,in the all four countries, the speed of the convergence of the firms far from the national frontieris faster than the firms near the frontier.

1. Introduction
In Japan, Korea, Taiwan, China and other East Asian countries, the expansion of foreign
direct investment and the growth of China's economy have created a rapid increase ofinternational trade and the division of labor. Korean firms such as Samsung Electronics andHyundai Motor are now rapidly catching up with Japanese manufacturing firms. Meanwhile,through the conclusion of negotiations on a US-Korea Free Trade Agreement (FTA), thepotential conclusion of the ongoing negotiations on a Japan-Korea FTA, and China's fulfillmentof her World Trade Organization commitments, liberalization of the Chinese and Koreanmarkets will continue. Against this background, the question of which industries and what typeof firms will be able to thrive following such liberalization is becoming a hot topic in these twocountries. Although how far Korean and Chinese firms have caught up with Japanese firms is an
important question, very little research has been done on this topic.
The recent empirical and theoretical literature emphasized that the improvement in the
productivity in the foreign countries can have positive impact on the domestic productivity andthe catch up. Cameron, Proudman and Redding (2005) evaluate the role of technologicaltransfer in explaining productivity growth at the industry-level in the United Kingdom since1970. They found that R&D affects rates of UK productivity growth through innovation, whileinternational trade facilitates the transfer of technology. Griffith, Redding and Van Reenen(2003, 2004) have examined both theoretically and empirically the role of R&D on thestimulating innovation, and the absorption capacity and convergence. They find evidence of
R&D effects on both rates of innovation and technology transfer by using a panel of industries
across twelve OECD countries. Kneller and Stevens (2006) investigate whether absorptive
capacity helps to explain cross country differences in the technical efficiency. They empiricallyfound that absorption capacity provides the useful explanation of the difference in industrial
productivity between OECD countries. They claimed that human capital affects the production
both directly and through its indirect effect through efficiency.
More recently, utilizing micro data, the divergence or convergence of productivity amongfirms has been intensively scrutinized, providing us with insights into the mechanisms
underlying productivity convergence or divergence across countries. The large body of literatureon micro-level productivity has shown that firms’ managerial ability, use of technology, humancapital, competitive pressure, and technology diffusion or spillovers are important determinantsof productivity levels and productivity growth. 1 On the other hand, empirical studies focusing
on the connection between aggregate and micro productivity growth have examined the1

For a comprehensive literature survey on this issue, see Bartelsman and Doms (2000).

1contribution of resource reallocation across firms to aggregate productivity growth, based on theidea that aggregate productivity grows faster if more inputs and output are allocated tohigh-productivity firms and less to low-productivity firms.
However, the number of micro-level productivity analyses from an international
comparative perspective is very limited. 2 Most recent micro-level studies compare productivity
levels or growth within a country or examine whether non-frontier firms within the country arecatching up with national frontier firms. Unfortunately, such studies on individual countries
remain silent on whether productivity across countries is converging, since they cannot identify
the global technology frontier that is the hypothesized source of knowledge spillovers. A small
number of pioneering works on the international comparison of productivity and firm dynamics
based on micro data do exist, such as Bartelsman, Scarpetta and Schivardi (2003) and
Bartelsman, Haltiwanger and Scarpetta (2004, 2005), which attempt to explore thecountry-specific factors that affect aggregate patterns of productivity growth. Although thecoverage of the datasets of these studies differs across countries, they do manage to compile
comprehensive firm-level data covering almost all firms in manufacturing and other industries.
Unfortunately, however, Japan and China are not analyzed in these studies. Although Korea isincluded in the study by Bartelsman, Haltiwanger and Scarpetta (2004, 2005), no TFP analysis
for Korea is conducted.
In this paper, we analyzed productivity catching up at the firm level in the Japanese,
Korean, Taiwanese and Chinese manufacturing sector using the distance from the global
technological frontier as a direct measure of the potential for technological frontier. Althoughmost of the previous studies regard the US as the global productivity leader, we do not have a
micro-data suitable for the measurement of the TFP in US firms. Hence we assumed the averageof the TFP of firms within the top-duodecimal of the TFP distribution within four countries byindustry and year as a global frontier. We also examined the role of the absorption capacity forthe technological catch-up by including the variables, such as R&D expenditure and foreign
ownership in our empirical estimation model.
Our main results can be summarized as follows. First, although Japanese firms enjoy thehighest average TFP level in many industries, their TFP growth rate has been relatively low
during the past two decades. Taiwanese and Korean firms have achieved considerably high TFP
In contrast, there have been extensive international productivity comparisons at the industry or macro level,
conducted by the EU KLEMS project (see http://www.euklems.net) and at the Groningen Growth and Development
Centre at the Economics Department of the University of Groningen (see http://www.ggdc.net). A comparative studyof East Asian countries has been conducted by the ICPA (International Comparison of Productivity Among AsianCountries) project at RIETI (Research Institute of Economy, Trade and Industry) in Japan (seehttp://www.rieti.go.jp/jp/database/data/icpa-description.pdf).

22

growth in certain industries, and the some firms in the industries almost caught up or exceeded
the Japanese firms' TFP level. The average TFP level of Chinese firms is still much lower thanthat of Japanese, Korean and Taiwanese firms in many industries. Second, in Korea, the TFPlevels of low-performing firms are approaching those of the national frontier firms at a more
rapid pace than in other countries. In addition, Korean firms try to catch up the global frontier
once they reached to the national frontier level TFP. Chinese firms are very slow in catching up
and the only engine of the knowledge creation is firms located in the trade-oriented coast. Third,in the all four countries, the speed of the convergence of the firms far from the national frontieris faster than the firms near the frontier.
The remainder of this paper is organized as follows. Section 2 explains the estimation
method used for the international comparison of firm-level TFP in Japan, Korea, Taiwan and
China. Section 3 discusses the data we use in our empirical analysis in the next section and in
Section 5 econometric results are presented and a final section concludes and makes suggestions
for the future direction of international comparative studies on productivity growth andconvergence.

2. Comparing Firm-Level TFP in Japan, Korea, Taiwan and China: Methodological Issues

2.1. Estimation of Firm-Level TFP in Japan, Korea, and China
As a first step, we estimated each firm’s TFP level relative to the industry average TFPlevel in its country. We used the Multilateral TFP Index method developed by Good, Nadiri andSickles (1997). 3 The adoption of this method makes possible not only cross-sectional
comparisons but also time-series comparisons of firm-level TFP. Suppose that the data cover a
period from t=0 to T and t=t0 (0<t0<T) is the benchmark year. In this method, the TFP level offirm f in industry j of country m in year t, TFPf,t,j,m is calculated byn1
ln TFPf ,t , j ,m  (ln Q f ,t , j ,m  ln Qt , j ,m )   ( S f ,i ,t , j ,m  S i ,t , j ,m )(ln X f ,i ,t , j ,m  ln X i ,t , j ,m )i 1 2(1)
for t=t0, and3

Good, Nadiri and Sickles (1997) use an equation that accounts for changes in the composition of items for sale due to businessdiversification, but we conducted the TFP estimation on the assumption that firms produce only manufactured goods of theindustry to which they belong.

3





1
S f ,i ,t , j ,m  Si ,t , j ,m ln X f ,i ,t , j ,m  ln X i ,t , j ,mi 1 2nnln TFPf ,t , j ,m  ln Q f ,t , j ,m  ln Qt , j ,m  



t



t





1
Si ,s , j ,m  Si ,s1, j ,m ln X i ,s , j ,m  ln X i ,s1, j ,ms t0 1 i 1 2

  ln Qs , j ,m  ln Qs1, j ,m   
s t0 1







(2)
for t>t0, and





n

t0





t0

n







1
Si ,s , j ,m  Si ,s1, j ,m ln X i ,s , j ,m  ln X i ,s1, j ,ms t 1 i 1 2

  ln Qs , j ,m  ln Qs 1, j ,m  s t 1

1
S f ,i ,t , j ,m  Si ,t , j ,m ln X f ,i ,t , j ,m  ln X i ,t , j ,mi 1 2ln TFPf ,t , j ,m  ln Q f ,t , j ,m  ln Qt , j ,m  





(3)
for t<t0, where ln Qf,t,j,m stands for the real output (real sales) of firm f in year t, and ln Xf,i,t,j,m
represents the natural logarithm of real input of production factor i of firm f in year t. Since
there are three types of production factor – capital, labor, and intermediate input – the n for thesigma notation is 3 in this case. Sf,i,t,j,m is the cost share of production factor i at firm f in year t.
ln Qt , j ,m denotes the arithmetic average of the log value of the output, in year t, of all firms inindustry j of country m to which firm f belongs, while ln X i ,t , j ,m stands for the arithmetic
average of the log value of the input of production factor i, in year t, of all firms in industry j ofcountry m to which firm f belongs. Finally, S i ,t , j ,m is the arithmetic average of the cost share of
the input of production factor i, in year t, of all firms in industry j of country m to which firm f
belongs.
The first line of equation (2) calculates the deviation of the TFP level of firm f from theaverage firm-level TFP in a given year, while the second line calculates the sum of the annualchanges of the industry average of TFP from the benchmark year. The set of these two
calculations makes it possible to conduct both a time-series and a cross-section comparison offirms’ TFP levels.
Nominal output 4 and intermediate input were obtained from the financial statements ofeach firm. The real values of output and input were obtained by deflating nominal output andintermediate input using the price index for each industry 5 in each country. In order to takeaccount of different depreciation rates for different assets, we estimated three types of capital
assets – structures, machinery, and vehicles – separately, using the perpetual inventory method
in Japan and Korea. In the case of Taiwan and China, we do not have such a detailed
45

Output is based on sales after adjusting for increases/decreases in inventories. For wholesalers and retailers,
instead of sales, the difference between sales and purchases was used as output.
Following the industry classification of the PPP data of the ICPA project, we reclassified each firm into one of 33
industries, using industry classification information of firms in the stock market where the firm is listed.

4

information on the assets, we use total investment series for the estimation of capital stock ofeach firm. Since financial statements only provide the number of employees, the labor input ofeach firm was obtained by multiplying the number of employees by the average number ofhours worked in each industry.
Firm f’s cost of capital for each type of asset is obtained by multiplying the capital stockby the capital service price. 6 The capital service prices are calculated by the following equation:c f ,l ,t , j , m 

1  z f ,l ,t ,i , m
1  ut , m

pl ,t , m { f ,t , j , m RB ,t , m  1  ut , m 1   f ,t , j , m RL ,t , m  l , m  ln  pl ,t 1,m   ln  pl ,t ,m }(4)where pl,t,m stands for the price of investment good l in year t in country m, ut,m is the effective
corporate tax rate, RB,t,m is the long-term government bond rate, RL,t m is the long-term lending
rate, λf,t,j,m is the own-capital ratio of firm f, and δl,m is the depreciation rate of asset l in country
m. Meanwhile, zf,l,t,j,m is the expected present value of tax saving due to depreciation allowanceson one unit of investment, which was obtained using the following equation:z f ,l , t , j , m 

u t , m l , m

 f ,t , j , m RB ,t ,m  1  ut , m 1   f ,t , j ,m RL ,t , m   l ,m(5)

We obtain the cost for materials and labor from the financial statements of each firm.
The cost shares of the three production factors differ substantially in the three countries.
Tables 1 to 3 show changes in the cost share of each production factor for the manufacturing
and non-manufacturing sectors in Japan, Korea, Taiwan and China. While in Japan, the costshare of each production factor remained relatively stable, in Korea, the cost share of capitaldeclined from 14 % in 1995 to 5% in 2005 in the manufacturing sector. The declines are
mirrored by a rise from 75 % to 83 % in the cost share of intermediate input in the sector, which
probably largely reflects the increasing division of labor between firms in the period. BothTaiwanese and Chinese firms are characterized by a high intermediate cost shear and a low
labor cost share compared to their Japanese and Korean counterparts. In the manufacturingsector, the intermediate cost share 87% and 81% in Taiwan and China in 2005, respectively.
The labor cost share was 8 and 9 % in China and Taiwan respectively in 2005, considerably
lower than the 16% for Japan and 12% for Korea. Taiwanese manufacturing sector increased theintermediate cost share similar to that in Korea since 1990.
6

The method of estimating the capital service price in principle is based on equation (4). However, it should be
noted that the estimation methods for Japan, Korea, Taiwan and China slightly differ because of data constraints.

5

Table 1. Cost Share of Labor （％）

Japan
Korea
Taiwan
China

1990
16
28
12
14
11
14

1995
18
30
11
13
12
12

Table 2. Cost Share of Capital

（％）

1985
4
6
8
6
16
14

1995
5
6
14
16
8
11

ManufacturingNon-manufacturing
ManufacturingNon-manufacturing
ManufacturingNon-manufacturing
ManufacturingNon-manufacturing

1985
17
28
11
16
6
13

2000
17
30
11
13
10
9
8
11

2005
16
30
12
14
9
9
8
11

Source: Authors’ calculations.

Japan
Korea
TaiwanChina

ManufacturingNon-manufacturing
ManufacturingNon-manufacturing
ManufacturingNon-manufacturing
ManufacturingNon-manufacturing

1990
5
6
13
19
11
13

2000
5
5
8
7
7
8
13
19

2005
4
4
5
8
4
8
11
18

Source: Authors’ calculation.

Table 3. Cost Share of Intermediate Input (%)

Japan
Korea
TaiwanChina

ManufacturingNon-manufacturing
ManufacturingNon-manufacturing
ManufacturingNon-manufacturing
ManufacturingNon-manufacturing

1985
79
66
81
78
78
73

Source: Authors’ calculation.

6

1990
79
66
74
67
77
73

1995
77
64
75
71
80
77

2000
78
65
81
80
82
83
79
70

2005
80
67
83
78
87
83
81
71

2.2. Purchasing Power Parities (PPPs) for Industry Output
In order to compare TFP levels of firms across countries, we need to take account of thedifference of price levels of output, intermediate input and investment goods across countries. In
other words, we need purchasing power parity (PPP) data in order to convert firms’ output andinput in the three countries into a common currency unit. In this study, as mentioned earlier, we
obtained PPP data for industry output from the results of the ICPA project. When comparing
per-capita GDP across countries, usually PPPs based on price information of the final
expenditure side are used, such as the PPPs of the International Comparison Program (ICP). But
in order to compare TFP levels across countries, we need PPPs for domestic output andintermediate input, which are difficult to estimate from price information of the final
expenditure side. Following the methodology of the ICOP project of Groningen University, theICPA project mainly used information of the unit value of output in addition to final expenditureside price information.
The unit value of product s of industry j in country m, uvs,i,m is computed by dividing theoutput of product os,j,m by its quantity qs,j, m, as shown below:

uvs , j ,m 

os , j , m(6)q s , j ,mThe unit value ratio of product s of industry j between country A and country B, UVRs,j,B,A
is obtained by making an international comparison of unit prices of similar product items:

UVRs , j , B , A 

uvs , j , A(7)

uvs , j ,B

The UVR on an industry basis is derived from the UVR on a product basis through theweighted average using the weight of each product in the total output of a particular industry as
a whole. Thus, the UVR between country A and country B in industry j is calculated as follows:
Sj

UVR j ,B , A    s , jUVRs , j ,B , A

(8)

s 1

where Sj denotes the number of products in industry j, while ωs,j denotes the production
weights of product s in industry j. Each weight is derived as the geometric average of the
production share of product s in industry i of country A and that of country B. 7

7

See Timmer and Ypma (2007) for a detailed explanation of the estimation method of PPPs in the ICPA project.

7

2.3. Methods for International Comparison of Firms’ TFP Level
In this subsection, we explain our method for comparing firm-level TFP across countries.
The most straightforward way to compare the productivity of firms in the three countries is toconvert the value of output, intermediate input and capital assets into the same currency unit, forexample, the Japanese Yen value in a certain year, and to pool the data of all listed firms in thesame industry across the three countries and directly apply Good, Nadiri and Sickles’ method,that is, measure each firm’s TFP level by equations (1), (2) and (3). But this time, the variables
with upper bars must denote the average value of all listed firms in the same industry across thethree countries. For example, equation (2) now becomes





n

t





t

n





1
S f ,i ,t , j ,m  S i ,t , j ln X f ,i ,t , j ,m  ln X i ,t , ji 1 2ln TFPf ,t , j ,m  ln Q f ,t , j ,m  ln Qt , j  





1
  ln Qs , j  ln Qs 1, j   S i ,s , j  S i ,s 1, j ln X i ,s , j  ln X i ,s 1, js 1
s 1 i 1 2




(2’)

We employed this approach and measured the international comparable TFP in firms
among the three countries. For this measurement, we adopted the Japanese Yen to expressmonetary values and converted values in Korean Won and values in Chinese Yuan into Yen
using the PPPs for year 2000. In this analysis we chose year 2000 as our benchmark year, and
the PPPs for year 2000 are estimated by taking into account of the differences of industrial pricedeflators between the countries and the PPPs estimation for year 1997 of the ICPA project,which are reported in Motohashi (2007). For output, we used production PPPs by industry toconvert firms’ output into Yen. For intermediate inputs, by utilizing the information about
distribution margins and difference between the prices for domestic inputs and imported inputs
in each country from Asian International Input-Output table 1995 prepared by Institute ofDeveloping Economies- Japan External Organization, we estimated purchaser prices instead of
producer prices and used this estimation as purchase price PPP.
For capital input PPPs, assets were divided into structures, machinery and vehicles. For
structures, we used the production PPP for construction; for machinery, we used the simple
average of the production PPP for the general machinery, electric machinery, and precision
machinery industries; and for vehicles, we used the simple average of the output PPP for the
motor vehicle and other transportation equipment industry. 8 As for labor input, work hours are
directly compared and differences of labor quality resulting from differences in educational
backgrounds are not controlled for. At this point, we do not have sufficient information for
8

Since we have no information about the capital asset composition of capital stock in Taiwanese and Chinese firms,
we applied the industrial share of each capital asset in Japan to the firms in the associated industry in each country.
This information about capital asset composition was obtained form from Japan Industry Productivity Database2008.

8

estimating labor quality at the firm level in each country. 9

Table 4. Relative Output, Capital and Intermediate Input Price (Japan=1)
Relative Output Price
China
Korea
Taiwan1 Agriculture
0.09981 0.49082 0.33419
2 Coal mining
0.08674 0.36588 0.97308
3 Metal and nonmetallic mining
0.20450 0.92902 0.83379
4 Oil and gas extraction
0.54682 0.40534 0.92432
5 Construction
0.23363 0.37675 0.26305
6 Food and kindred products
0.15904 0.50057 0.36739
7 Textile mill products
0.39278 0.63763 0.53775
8 Apparel
0.31203 0.79488 0.55977
9 Lumber and wood
0.26858 0.38444 0.33261
10 Furniture and fixtures
0.46609 0.41637 0.54229
11 Paper and allied
0.34669 0.76330 0.60562
12 Printing publishing and allied
0.33309 0.61988 0.43394
13 Chemicals
0.44223 0.59418 0.48607
14 Petroleum and coal products
0.30923 0.41876 0.32194
15 Leather
0.11292 0.43915 0.32400
16 Stone clay glass
0.45237 0.53245 0.56898
17 Primary metal
0.50520 0.81384 0.65314
18 Fabricated metal
0.35681 0.48533 0.53413
19 Machinery non-elect0.46911 0.46771 0.41184
20 Electrical machinery
0.45548 0.64864 0.68480
21 Motor Vehicles
0.66475 0.79192 1.02436
22 Transportation equipment and ordnance 0.51488 0.53374 0.78197
23 Instruments
0.47891 0.82949 0.71732
24 Rubber and misc plastics
0.24838 0.63629 0.75073
25 Misc manufacturing0.37823 0.59124 0.55437
26 Transportation
0.23682 0.43090 0.80774
27 Communication
0.47577 0.66715 0.32542
28 Electrical utilities
0.27678 0.50212 0.49637
29 Gas utilities
0.18940 1.21548 2.16558
30 Trade
0.07573 0.57793 0.44358
31 Finance Insurance and Real Estate
0.30548 0.46233 0.24143
32 Other private services
0.03232 0.21379 0.25151
33 Public service
0.12948 0.36384 0.91466

Relative Capital Price
China
Korea
Taiwan0.23006 0.41827 0.30046
0.30597 0.47703 0.38189
0.30724 0.47703 0.38189
0.30860 0.47703 0.38189
0.34583 0.49642 0.43152
0.35085 0.46906 0.38203
0.31734 0.42877 0.35528
0.33684 0.42877 0.35615
0.32952 0.47219 0.38829
0.33370 0.40566 0.38421
0.33757 0.37659 0.36826
0.38668 0.39073 0.40426
0.32183 0.45541 0.36567
0.31102 0.44563 0.35561
0.35994 0.44078 0.38581
0.30375 0.45567 0.37008
0.29080 0.46182 0.35044
0.29390 0.44401 0.34589
0.34607 0.45895 0.39961
0.34259 0.47358 0.40227
0.34581 0.40145 0.36318
0.33667 0.41681 0.38368
0.36211 0.46758 0.41429
0.34810 0.41379 0.37347
0.35718 0.42940 0.40016
0.28015 0.44474 0.35308
0.29718 0.48468 0.38030
0.26242 0.45249 0.33874
0.23437 0.39812 0.28205
0.28188 0.40398 0.34778
0.24655 0.37071 0.28599
0.31111 0.42226 0.39094
0.29486 0.42516 0.35994

Relative Intermediate Price
China
Korea
Taiwan0.21659 0.48771 0.39488
0.27975 0.06866 0.47074
0.28203 0.48875 0.44776
0.29207 0.27871 0.51645
0.31100 0.46226 0.49730
0.18484 0.60687 0.43306
0.39931 0.56574 0.51005
0.37699 0.51688 0.48647
0.25269 0.34705 0.38249
0.31354 0.48729 0.47622
0.34313 0.64434 0.59644
0.32727 0.55627 0.51651
0.34296 0.55163 0.53876
0.65655 0.69354 0.75084
0.31551 0.49635 0.47950
0.28751 0.46641 0.56231
0.42843 0.64851 0.58719
0.38462 0.58781 0.53831
0.39010 0.52691 0.53151
0.41284 0.44904 0.58745
0.50711 0.69528 0.73716
0.43683 0.53423 0.66417
0.37750 0.57174 0.55183
0.36427 0.54842 0.55224
0.30686 0.47288 0.46353
0.32397 0.50900 0.50784
0.22157 0.49584 0.36799
0.29613 0.53737 0.59002
0.30284 0.52673 0.62251
0.25113 0.35496 0.37844
0.25170 0.42165 0.30939
0.25733 0.36155 0.37025
0.28375 0.49661 0.45678

Source: Authors’ calculation.

2.4. Results for International Comparison of Firms’ TFP Level

2.4.1 Comparison of TFP Growth in Japan, Korea Taiwan and China: Manufacturing and
Non-manufacturing 10
The growth rate of TFP in Japan’s manufacturing sector slowed down markedly in thefirst half of the 1990s before accelerating again in the second half of that decade and again in
the early 2000s. In Korea, the manufacturing sector TFP growth rate was negative in the latter
half of the 1980s, but was turned into positive territory in the early 1990s, and was accelerated
in the latter half of 1990s. Yet, Korea’s rate of TFP growth in 2000-05 has become lower
compared with the 1990s. Taiwanese Manufacturing sector enjoyed very high TFP growth rates
in the period between 1985 and 2005, except the latter of the 1990s due to the financial crisis.
The growth rate of TFP in Chinese Manufacturing sector in 2000-2005 was very slow and
910

For more detailed explanation of the measurement method of TFP, see Inui, Kabe and Kim (2008).
TFP growth in the manufacturing and the non-manufacturing sector is calculated as the average of firms’ TFP
growth weighted by their output share in their respective sector.

9Chinese manufacturing sector was far behind in catching up in other thee rivals in East Asia.
In the non-manufacturing sector, TFP growth tended to be low relative to the
manufacturing sector until 2000 in Japan. In 2000-2005, however, the rate of non-manufacturing
TFP growth topped 2% in Japan to exceed that for the manufacturing sector. In the 1990s,
Korean non-manufacturing sector had a high TFP growth and it was higher than those in the
sector in Taiwan and Japan during the period. Taiwanese non-manufacturing sector haveexperienced a strong TFP growth rate in the 1985-2005 period, but slower than that in the
Taiwanese manufacturing sector in the most of the periods. The TFP in China’s
non-manufacturing sector showed a large negative growth in the 2000-05 period, mainly
because of the large decline in Oil and Gas Extraction Sector's TFP in China.

Table 5. TFP Growth Rate (percent per annum)

Japan
Korea
TaiwanChina

ManufacturingNon-manufacturing
ManufacturingNon-manufacturing
ManufacturingNon-manufacturing
ManufacturingNon-manufacturing

1985-900.95%
0.50%-1.19%-2.32%
2.85%
2.80%

1990-95
0.71%-0.44%
1.34%
3.37%
3.00%
0.95%

1995-00
0.93%
0.70%
3.02%
5.01%
0.18%
1.11%

2000-05
1.76%
2.12%
1.00%
0.57%
5.01%
3.88%
0.80%
-3.34%

Source: Authors’ calculations.
2.4.2. Comparison of the TFP Level of Listed Firms in the selected industries in Japan, Korea,
Taiwan and China
Figures 1 through 5 show a comparison of the TFP levels of listed firms from Japan,
Korea, Taiwan and China in five different industries: the chemical, the primary metal, themachinery, non electric and the electric machinery, and the motor vehicle manufacturingindustry.
For the chemical industry (including pharmaceuticals, see Figure 1), average Taiwanesefirms' TFP has increased very rapidly in the early 2000s and it has caught up the average
Japanese firms in 2005. Korean firms' TFP growth was very slow between 1985 and 2000 and
the TFP level is staying low relative to those of Japanese and Taiwanese counterparts. Chinese
chemical firms are far outstripping the TFP levels of major Japanese and Taiwanese chemical
firms.
In the primary metal sector (Figure 2), Taiwanese firms has increased their TFP levels
10steadily in the estimation period and almost caught up Japanese firms' TFP in 2003. Korean
average TFP in the sector plummeted in the late 1980s and the early 1990s and it is lag far
behind the ones in Japan and Taiwan. China have not improved much, staying low relative tothose in other three countries.
In the machinery industry (Figure 3), due to high TFP growth rates in Taiwanese firms,
the levels have been higher than those of Japanese and Korean firms in the early 2000s. The
levels of TFP are almost same among Japanese and Korean firms since 1995. The TFP levels ofChinese machinery makers remain very low relative to those of their Japanese, Korean andTaiwanese rivals.
In the electrical machinery industry (Figure 4), all of the four countries have experienced
steady improvements of TFP levels in 1985-2005 period. The tempo of the improvements of
TFPs in Chinese and Taiwanese firms has been faster than that in Korean firms, the average TFPlevel of Korean firms become lower than those in Japan, Taiwan and even China in 2005.
In the Motor vehicle industry (Figure 5), the TFP levels of Japan are considerably higher
than those of their Korean, Taiwanese and Chinese counterparts, although the three countries
have experienced the large improvement of the TFP levels in the early 2000s.
2.5. International Comparison of Firms' ROA Level in Japan, Korea, and China
In this section, we compare ROA (return on assets) of firms in Japan, Korea and China inorder to compare the corporate profitability and asset efficiencies in these three countries. ROA
is determined by the three factors: (1)Capital-Labor ratio (K/L), (2) TFP, and (3) Capital
Revenue Ratio.
Among three countries, Japan firms' ROA is lower than that in Korea and China both in
the manufacturing sector and non-manufacturing sector. Japanese firms' TFP level is higher than
the two countries, low ROA in Japanese firms mainly dues to the high capital labor ratios andlow capital revenue ratios in Japanese firms. In Korea, ROA is declining very rapidly from
10.7% in 1985 to 4.9% in 2005 in the manufacturing sector, and from 7.4% in 1985 to 1.9% in2005 in the non-manufacturing sector. Korean non-manufacturing sector's ROA in 2005 is evenlower than that in the Japanese non-manufacturing sector. This decline in ROA brought by the
rapid increase in capital labor ratio and sluggish TFP improvement in the 2000-05 period.
Chinese firms enjoyed very high ROA, even they suffered from low TFP level.

11

Table 6. Firms' ROA Level in Japan, Korea, and China
ManufacturingNon-manufacturing
ManufacturingNon-manufacturing
ManufacturingNon-manufacturing

Japan
Korea
China

1985
5.30%
4.90%
10.70%
7.40%

1990
5.80%
5.90%
8.70%
6.30%

1995
3.50%
4.30%
7.80%
5.80%

2000
3.90%
4.40%
7.60%
6.20%
11.50%
8.90%

2005
4.40%
5.60%
4.90%
1.90%
12.50%
10.40%

Source: Authors’ calculations.

3. Data for the Estimation
As described in the TFP estimation method in the above, the level of TFP of firms is inlogarithmic value. The dependent variable, one-year growth rate of TFP of a firm, is defined as
the difference in the firm TFP levels between this period and the next. We defined and measured
two kinds of productivity frontier, that is, East-Asian frontier, and National frontier. First we
divided all the firms into four groups according to their TFP level of the year by country andindustry, and took the average of the TFP level of top group to define the values as national
frontiers. As for the East-Asian frontier, we assumed it to be similar to the national frontier ofJapan in all the industries in a earlier version of this paper. But since we found that in someindustries, the national frontier of Korea, China and Taiwan is higher than that of Japan, we
defined the East-Asian frontier in a different way in this version. First we divided all the firmsof China, Korea, Taiwan and Japan into sixteen groups (intuitively four groups for each country)
according to their productivity level, and defined the East-Asian frontier as the average TFPlevel of the top group firms.
The distance of each firm to the national frontier (East-Asian frontier) is measured as adifference between the firm’s TFP level and the average TFP for that national (East-Asian) top
group.
Other explanatory variables are defined and measured as follows. Firm age is measured
as the year difference between the establishment year and the current year. As for the Chinese
firms, since the information on establishment year is not available, the difference between theyear of listing and the current year is used. We include in the Chinese estimations a dummy
variable which takes the value of 1 when the firm is located in the coastal area and 0 otherwise.
The ratio of the foreign ownership is measured as the ratio of the number of the stock owned byforeigners over the total number of the stock issued. R&D intensity is measured as the ratio of

12

R&D expenditure over sales. We assumed that the firms which do not report R&D expenditure
are not R&D performers. Export ratio is defined and measured as the ratio of the total volume ofexports over sales. We also assume that the firms not reporting exports are not exporters. 11 We
also define a business group dummy variable that takes the value 1 when a firm is an affiliate ofone of the top 30 business groups and 0 otherwise. These last two variables are used only in theestimation with Korean data.
Our estimation will also include as explanatory variables the products of these variables
with the distance to the national frontier and the distance to the East-Asian frontier.
As a robustness check, we also measure R&D intensity and export ratio as theirdifference from the industry averages.
In our sample, since some firms report implausibly high or low TFP growth rates, we
trimmed the upper 2.5% and the lower 2.5% observations for every country and every
manufacturing industry.

4. Model and Estimation Procedure
In this section, following the methodology employed by Bartelsman, Haskel and Martin(2006), we estimate the speed of convergence to the productivity frontier. Like Bartelsman,
Haskel and Martin (2006), we assume that changes in the knowledge capital of firm f, ΔAf,
originate from changes in the knowledge stock within the firm itself and from outside the firm,
because knowledge inputs are potentially transferable and non-rival within and across firms.
Therefore, we may write:A f  f X f , A f , A_ f (4)where Xf are the physical inputs into the idea process. Log linearizing this yields:

A  ln Af  1 ln X f   2   3 ln Af   3 ln _ f 
 A 
 f 

(5)

where it is usual to impose α2=α3, so the overall growth of A only depends on the relative levelsof A_f and Af. As in Bartelsman, Haskel, and Martin (2006) and other studies in the convergence
literature, we identify A_f as the productivity level of the leading firm in a nation or the four
countries. We refer the average productivity level of the top-quartile firms as the national
frontier, AN. The term ln(AN/Af) indicates the productivity gap between the national frontier and11

Since report on R&D and export was not compulsory in Japan and Korea, those variables may include serious measurement error.

13

firm f. We refer to the average TFP of firms within the top-duodecimal of the TFP distribution
across the three countries by industry and year as the global frontier, AG.
Using firm-level TFP as a proxy for firms’ knowledge capital, we can estimate the versionof (5) given by:

 AN 
 AG    2G ln 
 Ai 
 Ai 

 ln Ai  1 ln X i   2 N ln (6)where α2 measures the pull from the frontier. If the marginal effect of technology spillovers or
diffusion is larger for firms with a low TFP level, 12 the value ofα2N andα2G will be positive andwe will see a catching-up of low-productivity firms both to the national and global frontiers. We
include a proxy for investment in knowledge creation such as R&D intensity (data is available
for Korea and Japan), firm age, export ratio (data is available only for Korea) and a dummies forthe firm within corporate group in Korea and for the firm located in the coastal region in China.
In addition, we include the growth potential of the industry to control for industry characteristics.
The growth potential is measured as the average growth rate of both global and national
frontiers. We also consider the possibility that 2N and 2G vary linearly with the (log) distanceand estimate the equation below.
2 ln Ai  1 ln X i  

1
2N

A 
A 
A 
A 
ln  N    22N ln  N    21G ln  G    22G ln  G 
 Ai 
 Ai 
 Ai 
 Ai 

2(7)

A second approach is to let 2N and 2G be functions not of distance but of absorption
capacity proxies at the firm level such as R&D, exports,

 2 N   0  1 ln(age, MNE status and coastal location.

RDiEX)   2 ln( i )  3 Agei   4 MNE _ dummyi  5Coast _ dummyi
Yi
Yi

(8)

RDi
EX
)   2 ( i )  3 Agei   4 MNE _ dummyi  5Coast _ dummyi
Yi
Yi

(9)

 2G  0  1 ln(

5. Estimation Results
The estimation results of equation (6) are shown in Table 11. Here we only include the12

Whether low-productivity firms can benefit from the “advantages of backwardness” depends on patterns of consumption and on
the existence of a threshold level of infrastructural development (Dowrick and Gemmell 1991, Hall and Jones 1999, Barro andSala-i-Martin 2004).

14

firm's age as a proxy for investment in knowledge creation. Column 1 shows the results for thecomplete sample of firms (in the three countries). The marginal pull from the national frontier is0.277 and from the global frontier is 0.006, respectively. In order to examine whether the pullfrom the national frontier and global frontier is different among countries, we estimated theequation (6) for each country (China, Japan, Korea, and Taiwan) separately. The result is shownin column 2, 3, 4 and 5 and indicates that the marginal impact of the national frontier is largest
for Korean firms, followed by that for Taiwan, China and then Japanese firms. This result
suggests that the convergence speed to the national frontier is the weakest for Chinese firms.
Looking at the convergence speed to the global frontier for Korean, Chinese and Taiwanesefirms (columns 2, 4 and 5), we find that the marginal impact of the global frontier on Korean
TFP growth is much smaller than that of the Korean national frontier (0.025 and 0.294
respectively), but it is statistically significant. However, in the case of Chinese and Taiwanesefirms, the marginal impact of the global frontier is smaller than that of the Chinese andTaiwanese national frontiers and statistically insignificant. This results indicate only Korean
firms are continuing to try to catch up to the global frontier once they reached to the national
frontier.
Table 12 shows the estimation results of equation (7). We allow the marginal impact of
distance to frontier (DTF) to vary by simply allowing the marginal impact to vary linearly with
DTF, which implies entering a linear and squared term. As column 1 shows, the effect of
National DTF is increasing with distance, with both positive in linear and squared terms.
We substitute equation (8) and (9) into equation (6) and estimate the obtained equation inorder to capture the effect of absorption capacity on the catching up to the frontier. Here we
include R&D intensity (data is available for Korea and Japan), firm age, export ratio (data isavailable only for Korea), dummy for the firms in the corporate group in Korea and dummy forcoastal location in China as proxies for investment in knowledge creation and the absorption
capacity. As in Table 13, coastal location plays a significant role for the creation but not theforeign-owned multinational status in China. The firm's age has a positive impact on thecatching-up to the global frontier. Colum 2 in Table 13 shows the results for Korean firms. The
participation in corporate group contributes to the increase in the absorption capacity when thefirms are trying to catch up to the global frontier, but it is not the case for catching up to thenational frontier. Export activity contributes negatively to the catching up to the global frontierin Korea. In addition, firm's age has a positive impact on the creation of the knowledge, but anegative impact on the catching up.
In order to take into account the possible multicollinearity problem between the15

explanatory variables, we estimated the catch-up model with absorption capacity while dropping
the various explanatory variables one at a time (results are in Table 14). We obtained virtuallyunchanged results in Table 13. In case of Japan, R&D intensity has a positive impact on theknowledge creation and the foreign participation contributes positively to the catching up to theglobal frontier. As a robustness check, we define R&D intensity and export ratio as difference tothe industry averages. The results are reported in Table 15 and Table 16 and again we get similar
results to those in Table 13 and Table 14.

16

.4

Figure 1 Comparison of Chemicals Industry TFP in Japan, Korea, Taiwan and China

0

.2

Japan
Taiwan

Japan

Korea

lnTFP

Taiwan

-.2

Korea

China

-.6

-.4

China

1985

1990
China

1995
Year
Japan

(Source) Authors' calculation

2000
Korea

2005
TaiwanChemicals.4

Figure 2 Comparison of Primary Metal Industry TFP in Japan, Korea, Taiwan and China

Japan

Taiwan

Korea
China

China

Korea

Taiwan

-.6

-.4

-.2

lnTFP

0

.2

Japan

1985

1990
China

(Source) Authors' calculation

1995
Year
Japan
Primary metal

17

2000
Korea

2005
TaiwanFigure 3 Comparison of Machinery, non-electrical Industry TFP in Japan, Korea,.5

Taiwan and China

Taiwan

lnTFP
0

Korea
Japan
Japan
Taiwan

Korea

-.5

China

China

1985

1990
China

1995
Year

2000

Japan

(Source) Authors' calculation

Korea

2005
TaiwanMachinery, non-electricalFigure 4 Comparison of Electrical Machinery Industry TFP in Japan, Korea,
Taiwan and China

.5

Japan

Taiwan

0

China
Japan

lnTFP
-.5

Korea

China

Korea

-1

Taiwan

1985

1990
China

1995
Year
Japan

(Source) Authors' calculation

Electrical machinery

18

2000
Korea

2005
Taiwan.4

Figure 5 Comparison of Motor Vehicle Industry TFP in Japan, Korea, Taiwan and China

.2

Japan

lnTFP
0

Japan

Korea
Taiwan

-.2

China
Korea
China

-.4

Taiwan

1985

1990
China

1995
Year
Japan

(Source) Authors' calculation

2000
Korea

2005
TaiwanMotor vehicles

Table 7 Definitions
Variables
g1lnTFP
GAfrontier
GNfrontier
Ndist
Gdist
ratio_foreign
Dcoastexportratio
dexportratio
rndintdrndint
DgroupDefinition
1 year growth rate of TFP from the current period to the next period
1 year growth rate of East Asian frontier
1 year growth rate of National frontier
log distance of the TFP level from the National frontier
log distance of the TFP level from the Global frontier
the ratio of the stock owned by foreigners
Dummy variable denoting whether the firm is located on a trade-oriented coast
the ratio of export over the gross sales=export ratio - industry average of export ratio
R&D intensity= R&D intensity - industry average of R&D intensity
Dummy variable denoting whether the firm is a affiliate of the top-30 business group in Korea

19

Table 8 Summary Statistic 1
variable

N

mean

sd

min

p25

p50

p75

max

Whole sample
g1lnTFP
Ndist
Adist
GAfrontier
GNfrontierage

91,148105,888
105,846
97,25097,290101,125

0.016
0.144
0.386
0.013
0.014
23.016

0.115
0.144
0.251
0.033
0.039
22.078-1.995-0.924-0.689-0.295-0.312-16-0.0250.067
0.191-0.005-0.005
6

0.012
0.127
0.359
0.010
0.013
14

0.052
0.196
0.544
0.031
0.032
37

1.286
1.585
1.947
0.339
0.366
125

China
g1lnTFP
Ndist
Adist
GNfrontierage
Dcoastratio_fore~n

3,692
4,565
4,577
3,807
4,577
3,141
3,098

0.005
0.195
0.607
0.010
4.835
0.509
0.012

0.142
0.188
0.280
0.044
3.136
0.500
0.053

-0.959-0.599-0.281-0.21800
0.000-0.0450.081
0.417-0.017
2
0
0.000

0.010
0.171
0.611
0.011
5
1
0.000

0.059
0.271
0.796
0.035
7
1
0.000

1.057
1.415
1.804
0.216
15
1
0.558

age
Dgroupexportratio
rndint47,76558,49258,39553,41758,492
2,847
58,49258,49257,806

0.020
0.157
0.487
0.013
14.031
0.079
0.022
0.168
0.008

0.136
0.154
0.228
0.042
11.320
0.128
0.145
4.607
0.025

-1.163-0.924-0.689-0.295-16.000
0.00010
0.000-0.038-0.0370.073
0.332-0.0096.000
0.0022
0
0.000
0.000

0.015
0.139
0.468
0.012
11.0000.0192
0
0.000
0.000

0.070
1.286
0.214
1.505
0.622
1.947
0.033
0.366
20.000 109.000
0.1
0.93350
10.092 734.263
0.005
1.184

Japan
g1lnTFP
Ndist
Adist
GNfrontierage
ratio_fore~nrndint29,924
31,861
31,83130,22827,013
31,91531,915

0.007
0.104
0.155
0.011
52.614
0.054
0.020

0.056
0.090
0.107
0.026
17.5530.092
0.038-1.995-0.590-0.606-0.187
0
0.000
0.000

-0.0130.056
0.089-0.003
41
0.003
0.000

0.007
0.104
0.151
0.010
51
0.018
0.007

0.029
0.152
0.216
0.023
62
0.065
0.026

1.106
1.585
1.544
0.160
125
0.968
2.085

Taiwang1lnTFP
Ndist
Adist
GNfrontier
age

9,767
10,97011,043
9,838
11,043

0.033
0.167
0.426
0.030
5.741

0.125
0.170
0.189
0.046
4.609

-0.842-0.751-0.517-0.3120.000-0.019
0.073
0.315
0.013
2.000

0.030
0.146
0.408
0.042
5.000

0.079
0.229
0.510
0.057
8.000

1.081
1.492
1.773
0.338
20.000Korea
g1lnTFP
Ndist
Adist
GNfrontier

20

Table 9. Summary Statistic 2
After Data Cleaning
Whole sample
g1lnTFP
Ndist
Adist
GAfrontier
GNfrontierage

78,41278,41278,41278,41278,40678,412

0.016
0.139
0.383
0.013
0.014
23.793

0.089
0.112
0.232
0.034
0.039
21.931-0.959-0.458-0.290-0.295-0.3120-0.0220.069
0.194-0.005-0.005
6

0.013
0.126
0.363
0.010
0.013
15

0.052
0.191
0.540
0.032
0.032
39

0.965
1.150
1.658
0.339
0.366
117

China
g1lnTFP
Ndist
Adist
GNfrontierage
Dcoastratio_fore~n

3,468
3,468
3,468
3,462
3,468
2,866
2,828

0.005
0.187
0.593
0.010
4.352
0.506
0.013

0.116
0.155
0.262
0.045
2.872
0.500
0.055

-0.959-0.376-0.194-0.21800
0.000-0.0420.083
0.410-0.017
2
0
0.000

0.009
0.170
0.597
0.011
4
1
0.000

0.056
0.263
0.783
0.035
6
1
0.000

0.812
1.150
1.658
0.216
12
1
0.558

age
Dgroupexportratio
rndint42,84642,84642,84642,84642,846
1,601
42,84642,84642,846

0.018
0.151
0.481
0.012
13.938
0.081
0.023
0.180
0.006

0.103
0.117
0.204
0.042
10.125
0.129
0.150
4.469
0.019

-0.763-0.458-0.107-0.2950.000
0.00010
0.000-0.019-0.0330.076
0.338-0.0106.000
0.0023
0
0.000
0.000

0.015
0.138
0.469
0.012
12.0000.0202
0
0.000
0.000

0.065
0.208
0.611
0.030
20.0000.1072
0
0.125
0.004

0.806
0.968
1.603
0.366
73.0000.93351734.263
0.571

Japan
g1lnTFP
Ndist
Adist
GNfrontierage
ratio_fore~nrndint22,981
22,981
22,981
22,981
22,981
22,981
22,981

0.008
0.102
0.154
0.011
52.379
0.050
0.018

0.040
0.072
0.094
0.027
15.8880.079
0.028-0.740-0.379-0.290-0.187
0
0.000
0.000

-0.0120.057
0.091-0.002
42
0.004
0.000

0.008
0.102
0.150
0.010
51
0.018
0.007

0.028
0.146
0.212
0.023
62
0.063
0.025

0.515
0.686
0.850
0.160
117
0.782
0.831

Taiwang1lnTFP
Ndist
Adist
GNfrontier
age

9,117
9,117
9,117
9,117
9,117

0.031
0.157
0.421
0.029
5.451

0.096
0.128
0.156
0.047
4.366

-0.688-0.365-0.230-0.3120.000-0.016
0.073
0.319
0.013
2.000

0.030
0.144
0.408
0.042
5.000

0.076
0.221
0.505
0.056
8.000

0.965
0.965
1.215
0.338
19.000Korea
g1lnTFP
Ndist
Adist
GNfrontier21

Industry
1 Agriculture
2 Coal mining
3 Metal and nonmetallic mining
4 Oil and gas extraction
5 Construction
6 Food and kindred products
7 Textile mill products
8 Apparel
9 Lumber and wood
10 Furniture and fixtures
11 Paper and allied
12 Printing publishing and allied
13 Chemicals
14 Petroleum and coal products
15 Leather
16 Stone clay glass
17 Primary metal
18 Fabricated metal
19 Machinery non-elect20 Electrical machinery
21 Motor Vehicles
22 Transportation equipment and ordnance23 Instruments
24 Rubber and misc plastics
25 Misc manufacturing
26 Transportation
27 Communication
28 Electrical utilities
29 Gas utilities
30 Trade
31 Finance Insurance and Real Estate
32 Other private services
33 Public service
Total
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Total
13 14 17 19 19 21 22 23 23 19 22 27 24 27 46 56 50 53 55 60 57
667
6
6
6
6
6
6
7
7
7
7
8
8
8
9 14 15 17 18 19 22 22
224
4
5
5
5
5
5
6
7
7
9
9 15 18 25 24 25 28 27 28 30 29
316
3
3
3
3
3
3
3
3
3
3
3
3
3
3
6
6
7
9
9
9
8
96
259 285 307 311 353 392 477 514 530 557 620 759 846 925 919 945 990 1073 1188 1260 1210 14720
197 206 226 239 253 263 289 300 312 311 314 346 349 377 409 417 410 440 449 479 462
7048
79 89 99 111 125 131 154 157 157 148 155 166 180 192 197 206 214 232 243 237 2263498
60 70 74 79 92 93 110 118 125 130 140 150 153 161 155 144 149 157 172 175 167
2674
10 10 11 12 12 13 16 17 17 19 19 22 25 32 41 39 37 38 38 44 45
517
14 15 16 17 19 21 22 24 24 25 27 32 40 39 42 49 51 56 58 61 61
713
71 81 86 89 92 99 109 114 110 118 127 130 128 132 138 150 152 166 181 176 158
2607
35 35 44 52 59 71 88 93 92 102 111 137 143 169 167 154 200 257 265 269 254
2797
313 332 358 392 428 441 487 511 510 520 555 589 603 647 752 774 836 913 925 958 914 12758
20 20 23 21 22 23 25 25 23 24 24 27 28 31 51 47 47 53 54 52 48
688
11 11 16 18 22 23 27 27 27 26 26 28 27 30 30 29 25 25 33 38 32
531
129 144 155 169 175 183 203 212 210 213 220 249 252 275 287 296 313 336 361 374 355
5111
166 174 183 200 227 244 277 285 288 303 328 356 392 420 470 471 505 587 580 601 572
7629
98 106 117 144 163 164 181 196 186 201 221 269 295 332 355 380 417 462 472 483 469
5711
221 230 269 292 317 338 373 387 376 397 439 592 665 762 829 860 952 1043 1084 1143 1071 12640
265 296 352 384 437 469 554 591 616 676 759 925 1088 1284 1566 1709 1946 2181 2293 2380 2311 23082
135 160 187 205 230 241 281 287 292 322 357 383 438 469 583 610 647 736 760 771 737
8831
36 34 39 43 44 44 46 51 53 53 59 77 99 108 128 137 139 159 162 167 164
1842
48 50 59 65 67 72 81 82 83 90 99 132 144 167 195 193 217 242 256 266 253
2861
62 79 96 114 123 128 152 161 161 171 188 218 256 296 312 325 348 399 428 446 405
4868
25 26 38 42 47 49 52 58 60 61 64 77 86 98 109 115 115 122 122 126 119
1611
153 168 180 193 215 213 234 241 247 259 263 273 282 289 308 324 328 338 342 363 360
5573
3
5
7
6
9
9
9 11 14 14 16 17 16 26 64 66 66 76 84 88 89
695
9 10 10 12 12 12 12 13 13 13 13 14 14 14 47 50 57 68 65 72 75
605
23 25 27 26 26 28 32 35 37 36 37 39 41 40 29 33 33 34 36 35 34
686
289 301 346 391 433 474 505 537 585 647 689 732 759 794 885 910 931 959 986 998 1015 14166
72 78 95 115 124 127 153 163 153 144 140 152 166 173 188 203 219 237 280 309 317
3608
171 199 234 276 317 344 384 411 436 480 541 597 643 715 797 868 968 1041 1100 1187 1232 12941
1
2
3
4
2
6
7 10 14 12 19 22 30 34 42 42 52 54 55 54465
3000 3268 3687 4054 4480 4746 5377 5668 5787 6112 6605 7560 8233 9091 10177 10648 11456 12589 13182 13734 13325 162779

Table 10. Distribution of Sample

22

Table 11. Estimation Result 1

Ndist
Adist
GNfrontier
GAfrontierage
D_China
D_Korea
D_TaiwanR-squared
Observation

Ndist
Ndist2Adist
Adist2
GNfrontier
GAfrontierage
age2
D_China
D_Korea
D_TaiwanR-squared
Observation

Globalb/se
0.277 ***
(0.005)0.006 **
(0.002)0.83700 ***(0.011)-0.170 ***(0.014)0.000 ***
(0.000)-0.039 ***(0.002)-0.015 ***(0.001)-0.019 ***(0.001)
0.314
78,406Chinab/se
0.203 ***
(0.076)0.010(0.074)0.45100 ***(0.072)-0.021(0.122)0.001(0.001)0.000(0.000)0.000(0.000)0.000(0.000)
0.143
3,462

Japan
b/se
0.132 ***
(0.010)-0.015 *(0.008)0.49800 ***(0.022)-0.056 ***(0.011)0.000(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.273
22,981Korea
b/se
0.294 ***
(0.011)0.025 ***
(0.009)0.75400 ***(0.019)-0.150 ***(0.020)0.000 ***
(0.000)0.000(0.000)0.000(0.000)0.000(0.000)0.372
42,846

Taiwanb/se0.243 ***
(0.025)0.037(0.023)0.54700 ***(0.035)0.081(0.053)-0.001 ***(0.000)0.000(0.000)0.000(0.000)0.000(0.000)
0.263
9,117

Globalb/se
0.202 ***
(0.010)0.171 ***
(0.029)-0.03410 ***(0.007)0.04300 ***(0.008)0.83300 ***(0.011)-0.169 ***(0.014)-0.001 ***(0.000)0.000 ***
(0.000)-0.042 ***(0.003)-0.012 ***(0.001)-0.019 ***(0.002)0.320
78,406

Table 12. Estimation Result 2
China
Japan
Korea
b/se
b/se
b/se
0.098
0.076 ***
0.244 ***
(0.090)(0.023)(0.016)0.175 *
0.313 ***
0.127 ***
(0.102)(0.095)(0.035)-0.01510-0.03490 **0.00684(0.093)(0.014)(0.017)0.025900.04420 *0.01310(0.055)(0.024)(0.014)0.44500 ***0.51500 ***0.75700 ***(0.072)(0.022)(0.019)-0.021-0.056 ***-0.152 ***(0.118)(0.011)(0.020)0.003
0.000-0.001 ***(0.002)(0.000)(0.000)0.000
0.000
0.000 ***
(0.000)(0.000)(0.000)
0.000
0.000
0.000
(0.000)(0.000)(0.000)
0.000
0.000
0.000
(0.000)(0.000)(0.000)
0.000
0.000
0.000
(0.000)(0.000)(0.000)
0.150
0.281
0.375
3,462
22,98142,846

Taiwanb/se
0.126 ***
(0.037)0.250 ***
(0.080)0.06740(0.052)-0.02530(0.055)0.53500 ***(0.035)0.123 **
(0.053)-0.005 ***(0.001)0.000 ***
(0.000)0.000(0.000)0.000(0.000)0.000(0.000)
0.272
9,117

23

Table 13. Estimation Result 3

Ndist
Adist
GNfrontier
GAfrontierage
age2
DcoastRatio_foreign

China
b/se
0.266 ***
(0.092)-0.053(0.083)0.48200 ***(0.083)-0.01140(0.138)0.001(0.004)0.000(0.000)0.029 **
(0.012)-0.051(0.077)

RNDi
exportratio
DgroupNdist_age
Ndist_coast
Ndist_foreign-0.007(0.012)-0.030(0.055)0.097(0.307)

Ndist_rnd

Ndist_group

Adist_coast
Adist_foreign

0.009 *
(0.005)-0.010(0.021)
0.065(0.124)

Adist_rnd

0.003(0.014)0.201(0.547)0.029(0.025)0.008(0.016)-0.004 *(0.003)0.015(0.010)-0.048(0.054)-0.663(1.158)-0.101 *(0.060)0.061 *
(0.037)
0.228
1,601

Adist_export
Adist_groupR-squared
Observation

Japan
b/se
0.146 ***
(0.033)-0.029(0.021)0.49700 ***(0.022)-0.05620 ***(0.011)0.000(0.000)0.000(0.000)0.343(0.944)0.114(0.106)-0.190 *(0.115)0.000(0.001)

Ndist_export

Adist_age

Korea
b/se
0.283 *
(0.152)0.139(0.145)0.81000 ***(0.131)-0.20200(0.153)0.002 **
(0.001)0.000(0.000)

0.150
2,816
24

0.000
(0.001)-0.164 **(0.077)0.725(0.482)0.000(0.000)0.038(0.030)0.170(0.323)0.278
22,981

Table 14. Estimation Result 4

Ndist
Adist
GNfrontier
GAfrontierage
age2
DcoastRatio_foreign

China1b/se
0.266 ***
(0.092)-0.053(0.083)0.48400 ***(0.084)-0.01170(0.138)0.001(0.004)0.000(0.000)0.028 **
(0.012)-0.007(0.034)China2b/se
0.258 ***
(0.092)-0.059(0.083)0.48400 ***(0.083)-0.01140(0.138)0.001(0.004)0.000(0.000)0.017 ***
(0.005)-0.032(0.077)-0.007(0.012)-0.028(0.055)-0.008(0.012)

RNDi
exportratio
DgroupNdist_age
Ndist_coast
Ndist_foreign

Korea1b/se
0.335 **
(0.147)0.091(0.139)0.83700 ***(0.131)-0.21800(0.153)0.002 **
(0.001)0.000(0.000)

Korea2b/se
0.293 *
(0.150)0.113(0.143)0.81400 ***(0.131)-0.20500(0.153)0.001 *
(0.001)0.000(0.000)Japan1b/se
0.138 ***
(0.033)-0.026(0.021)0.49700 ***(0.022)-0.05590 ***(0.011)0.000(0.000)0.000(0.000)Japan2b/se
0.160 ***
(0.037)-0.030(0.019)0.49600 ***(0.022)-0.05610 ***(0.011)0.000(0.000)0.000(0.000)0.005(0.014)0.164(0.551)-0.008(0.009)0.013(0.015)-0.004(0.003)0.003(0.014)-0.157(0.117)0.027(0.025)0.006(0.016)-0.004 *(0.003)0.005(0.004)-0.041(0.051)

0.004(0.008)0.054 ***
(0.019)-0.001(0.001)0.000(0.001)

0.047(0.308)

Ndist_rnd-0.084(0.067)0.244(0.962)

Ndist_export
Ndist_group
Adist_age
Adist_coast

0.009 *
(0.005)-0.009(0.021)Adist_foreign

0.009 *
(0.005)-0.176(0.116)-0.001(0.001)

0.109(0.107)-0.188(0.115)0.000(0.001)0.000(0.000)0.047(0.122)Adist_rnd-0.567(1.172)Adist_group
0.150
2,816

0.149
2,816

0.000
(0.000)0.050 *
(0.030)Adist_export

R-squared
Observation

0.641(0.464)0.051(0.037)
0.227
1,601

25

0.184
(0.323)-0.098(0.060)0.063 *
(0.037)
0.228
1,601

0.277
22,9810.275
22,981

Table 15. Estimation Result 5

Ndist
Adist
GNfrontier
GAfrontierage
age2
DcoastRatio_foreign

China
b/se
0.266 ***
(0.092)-0.053(0.083)0.48200 ***(0.083)-0.01140(0.138)0.001(0.004)0.000(0.000)0.029 **
(0.012)-0.051(0.077)dRNDi
dexportratio
DgroupNdist_age
Ndist_coast
Ndist_foreign-0.007(0.012)-0.030(0.055)0.097(0.307)

Ndist_drnd

Ndist_group

Adist_coast
Adist_foreign

0.009 *
(0.005)-0.010(0.021)
0.065(0.124)

Adist_drnd

0.003(0.014)0.149(0.555)0.023(0.025)0.008(0.016)-0.004(0.003)0.013(0.009)-0.071(0.066)-0.526(1.179)-0.092(0.060)0.061(0.037)
0.228
1,601

Adist_dexport
Adist_groupR-squared
Observation

Japan
b/se
0.162 ***
(0.034)-0.033 *(0.019)0.49700 ***(0.022)-0.05680 ***(0.011)0.000(0.000)0.000(0.000)0.218(0.965)0.124(0.115)-0.194 *(0.115)0.000(0.001)

Ndist_dexport

Adist_age

Korea
b/se
0.312 **
(0.147)0.101(0.139)0.82300 ***(0.130)-0.21200(0.152)0.002 **
(0.001)0.000(0.000)

0.150
2,816
26

0.000
(0.001)-0.139 *(0.074)0.688(0.556)0.000(0.000)0.040(0.030)0.271(0.359)0.277
22,981

Table 16. Estimation Result 6

Ndist
Adist
GNfrontier
GAfrontierage
age2
DcoastRatio_foreign

China1b/se
0.266 ***
(0.092)-0.053(0.083)0.48400 ***(0.084)-0.01170(0.138)0.001(0.004)0.000(0.000)0.028 **
(0.012)-0.007(0.034)China2b/se
0.258 ***
(0.092)-0.059(0.083)0.48400 ***(0.083)-0.01140(0.138)0.001(0.004)0.000(0.000)0.017 ***
(0.005)-0.032(0.077)-0.007(0.012)-0.028(0.055)-0.008(0.012)dRNDi
dexportratio
DgroupNdist_age
Ndist_coast
Ndist_foreign

Korea1b/se
0.343 **
(0.145)0.076(0.137)0.83900 ***(0.130)-0.21800(0.153)0.002 **
(0.001)0.000(0.000)

Korea2b/se
0.311 **
(0.146)0.093(0.139)0.82300 ***(0.131)-0.21500(0.152)0.001 *
(0.001)0.000(0.000)Japan1b/se
0.154 ***
(0.032)-0.029(0.019)0.49700 ***(0.022)-0.05640 ***(0.011)0.000(0.000)0.000(0.000)Japan2b/se
0.164 ***
(0.036)-0.034 *(0.019)0.49800 ***(0.022)-0.05680 ***(0.011)0.000(0.000)0.000(0.000)0.005(0.014)0.113(0.557)-0.008(0.009)0.012(0.015)-0.004(0.003)0.004(0.014)-0.154(0.118)0.021(0.025)0.007(0.016)-0.004(0.003)0.005(0.004)-0.066(0.063)0.005(0.008)0.045 **
(0.019)0.000(0.001)0.000(0.001)

0.047(0.308)

Ndist_drnd-0.082(0.067)0.108(0.980)Ndist_dexport
Ndist_group
Adist_age
Adist_coast

0.009 *
(0.005)-0.009(0.021)Adist_foreign

0.009 *
(0.005)-0.177(0.116)-0.001(0.001)

0.124(0.115)-0.192 *(0.115)0.000(0.001)0.000(0.000)0.047(0.122)Adist_drnd-0.427(1.188)Adist_group
0.150
2,816

0.149
2,816

0.000
(0.000)0.048(0.030)Adist_dexport

R-squared
Observation

0.628(0.541)

0.052(0.037)
0.227
1,601

0.283
(0.359)-0.090(0.060)0.062 *
(0.037)
0.227
1,601

0.277
22,9810.275
22,981

6. Concluding Remarks and Implications for Future Research
Using firm-level data, this paper investigates the productivity convergence pattern forJapan, China, Taiwan and Korea. The mechanism of productivity convergence to frontier firms
within a country and across countries is an issue that deserves further attention and more
rigorous empirical analysis. Although the compilation of international micro data for East Asian
countries is not an easy task, the development of internationally comparable measures based on
micro data could shed more light on the growth mechanisms underlying the so-called “East27

Asian economic miracle,” as well as the determinants and consequence of the heterogeneity offirms.
Our convergence analysis revealed that the pull from the national frontier was stronger inthe case of Korea than that of Taiwan, China and Japan. In every country, lower-performing
firms have been catching up to the national frontier at a faster speed than higher-performing
firms, which provides evidence of strong convergence toward the national frontier. The Korean
firms once reached to their national frontier, they continue to try to catch up toward the global
frontier. In addition, the participation in corporate group contributes to the increase in theabsorption capacity when the firms are trying to catch up to the global frontier, but it is not thecase for catching up to the national frontier. In China, the engine of the knowledge creation isfirms located in the trade-oriented coast and the firm's age has a positive impact on thecatching-up to the global frontier.
In this study, we were not able to analyze the productivity of global frontier firms because
comprehensive firm-level data were not available for the United States and for European
countries. A comparison of the performance and/or competition between Asian frontier firms
and frontier firms in developed countries from other regions would be another interesting
research topic which deserves further investigation.

28

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30