Global COE Hi-Stat Discussion Paper Series 168

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

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

Hi-Stat Discussion Paper

Kyoji Fukao
Tomohiko Inui
Keiko Ito
YoungGak Kim
Tangjun Yuan

February 2011

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

February 2011

Kyoji Fukao(Institute of Economic Research, Hitotsubashi University)
Tomohiko Inui(Economic and Social Research Institute, Cabinet Officeand College of Economics, Nihon University)
Keiko Ito
(School of Economics, Senshu University)
YoungGak Kim(School of Economics, Senshu University)
Tangjun Yuan
(School of Economics, Fudan University)Abstract
Focusing on Japanese, Korean, Taiwanese, and Chinese firms in the manufacturing sector,
this paper examines productivity catch-up at the firm level using the distance from thetechnology frontier as a direct measure of the potential for catch-up. We also examine the role ofabsorptive capacity for technological catch-up by including variables such as R&D expenditureand foreign ownership in our empirical estimation.
We find that the national frontier has a stronger pull on domestic firms than the regional
frontier, which is in line with findings by Bartelsman, Haskel and Martin (2008). This result
indicates that policies to raise the technology level of national frontier firms are beneficial for
all firms in that country.

Keywords: productivity; catch-up; absorptive capacity
JEL classification: D24, O47, O53

Corresponding author. Economic and Social Research Institute, Cabinet Office, Government of Japan, 3-1-1,
Kasumigaseki, Chiyoda-ku, Tokyo, 100-8970, Japan. Tel:+81 3 3519 3820: fax:+81 3 3504 0883.
E-mail address: tomohiko.inui@cao.go.jp

1. Introduction
Recent empirical and theoretical studies on productivity suggest that high productivity inforeign countries tends to have a positive impact on domestic productivity and the catch-up
towards the technology frontier. For example, Cameron, Proudman and Redding (2005),
examining the role of technology transfer in productivity growth at the industry level in the
United Kingdom since 1970, found that research and development (R&D) affects productivity
growth through innovation, while international trade facilitates the transfer of technology.
Meanwhile, Griffith, Redding and Van Reenen (2003, 2004) examined both theoretically andempirically the role of R&D in innovation, absorptive capacity, and convergence. Using a panelof industries across twelve OECD countries, they found evidence of positive R&D effects on
both rates of innovation and technology transfer. Moreover, Kneller and Stevens (2006),
empirically investigating whether absorptive capacity helps to explain cross-country differencesin technical efficiency, found that absorptive capacity provides a useful explanation ofdifferences in industrial productivity among OECD countries. They argue that human capital
affects productivity both directly through improvements in efficiency and indirectly throughenhancing absorptive capacity.
More recently, utilizing micro data, the divergence or convergence of productivity amongfirms has been intensively scrutinized, providing us with insights into the mechanisms

1underlying productivity convergence or divergence across countries. The large body of literatureon micro-level productivity has shown that firms’ managerial ability, use of technology, andhuman capital, as well as competitive pressure and technology diffusion or spillovers areimportant determinants of productivity levels and productivity growth.1 On the other hand,empirical studies focusing on the connection between aggregate and micro productivity growth
have examined the contribution of resource reallocation across firms to aggregate productivity
growth, based on the idea that aggregate productivity grows faster if more inputs are allocated tofirms with high productivity.
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 productivity growth within a country or examine whether non-frontier firms within the
country are catching up with national frontier firms. Unfortunately, such studies on individual
countries remain silent on whether productivity across countries is converging, since theycannot identify the global technology frontiers that can be regarded as sources of international
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 andSchivardi (2003) and Bartelsman, Haltiwanger and Scarpetta (2004, 2005), which attempt to
explore the country-specific factors that affect aggregate patterns of productivity growth.

2

Although the coverage of the datasets of these studies differs across countries, the authors ofthese studies do manage to compile comprehensive firm-level data covering almost all firms in
manufacturing and other industries in many developed countries. Unfortunately, however, Japanand China are not analyzed in these studies. Although Korea is included in the study by
Bartelsman, Haltiwanger and Scarpetta (2004, 2005), no TFP analysis for Korea is conducted.
In this paper, focusing on Japanese, Korean, Taiwanese, and Chinese firms in the
manufacturing sector, we examine productivity catch-up at the firm level using the distancefrom the national and the regional technology frontier. The reason for using the reginaltechnology frontier is that although most previous studies regard the United States as the global
productivity leader, we do not have micro-data suitable for the measurement of the TFP of U.S.
firms.3 Hence, we assume that the average of the TFP of firms within the top hexadecimal(roughly top 6%) of the TFP distribution within the four countries by industry and by year
represents the regional frontier. 4 We also examine the role of absorptive capacity intechnological catch-up by including variables such as R&D expenditure and foreign ownershipin our empirical estimation.
Our main results can be summarized as follows. First, although Japanese firms enjoy thehighest average TFP level in many industries, their TFP growth has been relatively slow during
the past two decades. On the other hand, Taiwanese and Korean firms have achieved quite high3

TFP growth in certain industries and in one industry they have even surpassed – the TFP level of
their Japanese counterparts. However, the average TFP level of Chinese firms is still much
lower than that of Japanese, Korean and Taiwanese firms in many industries. Second, in Korea,
the TFP levels of low-performing firms are approaching those of the national frontier firms at amore rapid pace than those in other countries. In addition, once they have reached the national
frontier-level TFP, Korean firms continue catching up with the regional frontier. In contrast,
Chinese firms are very slow in catching up. Third, in all four countries, the speed ofconvergence of firms far behind the national frontier is faster than that of firms close to thefrontier.
Overall, we find that in the four countries, the national frontier has a stronger pull on
domestic firms than the regional frontier. This finding is consistent with Bartelsman, Haskel andMartin’s (2008) result on the national and global frontier for other countries.
The remainder of this paper is organized as follows. Section 2 explains our data and our
method for the international comparison of firm-level TFP in Japan, Korea, Taiwan and China.
Section 3 discusses the econometric model and estimation procedures used for our empiricalanalysis. Section 4 then reports our results, while Section 5 concludes and makes suggestions
for the future direction of international comparative studies on productivity growth andconvergence.

4

2. Comparing Firm-Level TFP in Japan, Korea, Taiwan and China: Methodological Issues
2.1 Estimation of Firm-Level TFP within Each Country
As a first step, we estimate each firm’s TFP level relative to the industry average TFPlevel in the country where this firm is located. We use the multilateral TFP index method
developed by Good, Nadiri and Sickles (1997). 5 This method makes possible not only
cross-sectional comparisons but also time-series comparisons of firm-level TFP. Suppose thatthe data cover a period from t=0 to T and t0 (0<t0<T) is the benchmark year. In this method, the
TFP level of firm 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 ) (1)i 1 2for t=t0,





n

t





t





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  
n





n



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


(2)



for t>t0, and



t0





t0

n





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  





1
  ln Qs , j ,m  ln Qs 1, j ,m   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
s t 1
(3)

for t<t0,
where Qf,t,j,m stands for the real output (real sales) of firm f in year t, and Xf,i,t,j,m represents the5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 in the above equation is 3 in our case.
Sf,i,t,j,m is the cost share6 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 in industry j of country m to whichfirm 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 of country 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, inyear t, of all firms in industry j of country m to which firm f belongs.
We define a hypothetical (representative) firm for each year and for each industry whose
input and output are calculated as geometric means of those for all firms in this industry. The
first two terms on the right-hand side of each of the three equations denote the cross-sectional
TFP index based on the Theil-Tornqvist specification for each firm, in each year, relative to the
hypothetical firms. Since this cross-sectional TFP index is not comparable between t and t-1, we
adjust the cross sectional TFP index with the growth rate of TFP for the hypothetical firms,
which is expressed by the third and fourth terms in equations (2) and (3). In other words, thecalculations compare the performance of a firm with that of the representative firm of theindustry in the benchmark year.7
We construct the firm-level TFP measure using annual financial data for the period61985-2005 for Japan, Taiwan, and Korea, and for the period 1999-2005 for China. Our data
cover all sectors except finance and insurance. In the case of Japan, our data cover firms listedon the Tokyo Stock Exchange (first and second sections), JASDAQ, Hercules, and Mothers. In
the case of Korea, our data cover firms listed on the Korea Exchange plus firms subject toKorea’s compulsory audit system. We obtain data for Korean firms from the KIS (Korea
Information Service) Database. In the case of China, our data cover firms listed on the Shanghai
Stock Exchange and the Shenzhen Stock Exchange. In the case of Taiwan, our data cover firms
listed on the Taiwan Stock Exchange.
Nominal output8 and intermediate input are obtained from the financial statements ofeach firm.9 The real values of output and intermediate input are obtained by deflating nominal
values using the price index for each industry10 in each country. In order to take account ofdifferent depreciation rates for different assets, we estimate three types of capital assets –structures, machinery, and vehicles – separately, using the perpetual inventory method for Japanand Korea. In the case of Taiwan and China, since such detailed information on assets is notavailable, we use total investment series for the estimation of the total capital stock of each firm.
Since financial statements only provide the number of employees, the labor input of each firm
was obtained by multiplying the number of employees by the average number of hours worked
in each industry in each country.11

7

2.2 Purchasing Power Parities (PPPs) for Output and Input
In order to compare the TFP levels of firms across countries, we need to take account ofdifferences in the price levels of output, intermediate input, and investment goods acrosscountries. In other words, we need purchasing power parity (PPP) data in order to convert firms’
output and input in the four countries into a common currency unit. In this study, we obtain PPP
data for industry output from the results of International Comparison of Productivity Among
Asian Countries (ICPA) Project conducted by the Research Institute of Economy, Trade andIndustry (RIETI).12 When comparing per capita GDP across countries, usually PPPs based on
price information for the final expenditure side are used, such as the PPPs of the International
Comparison Program (ICP). However, in order to compare TFP levels across countries, we need
PPPs for domestic output and input, which are difficult to estimate from price information for
the final expenditure side. Therefore, following the methodology of the ICOP (International
Comparison of Output and Productivity) project at Groningen University, the ICPA project
mainly uses information on the unit value of output in addition to final expenditure side priceinformation.
The unit value of product s of industry j in country m, uvs,j,m is computed by dividing thevalue of output of the product, os,j,m, by the quantity, qs,j, m, as shown below:

8uvs , j ,m 

os , j , m(4)q s , j ,mThe unit value ratio of product s for industry j between country A and country B,
UVRs,j,B,A is obtained by making an international comparison of the unit prices of similar productitems:

UVRs , j , B , A 

uvs , j , A(5)

uvs , j ,B

The UVR on an industry basis is derived from the UVR on a product basis as the weighted
average using the share of each product in the total output of a particular industry as a weight.
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

(6)

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.13

2.3 Methodology for International Comparison of Firms’ TFP Level
In this subsection, we explain our methodology for comparing firm-level TFP acrosscountries. The most straightforward way to compare the productivity of firms in the four
countries is to convert the value of output, intermediate input, and capital assets into the same9

currency unit, for example the Japanese yen, in a certain year, and to pool the data of all listed
firms in the same industry across the four countries and directly apply Good, Nadiri and Sickles’
methodology, that is, measure each firm’s TFP level by equations (1), (2) and (3). However, thistime, variables with an upper bar denote the average value of all listed firms in the same
industry across the four countries. For example, equation (2) now becomes





n

t





t

n





1
S f ,i ,t , j ,m  Si ,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    Si ,s , j  Si ,s 1, j ln X i ,s , j  ln X i ,s1, js t0 1s t0 1 i 1 2




(7)

We employ this approach and measure the internationally comparable TFP for firms in
the four countries. For this measurement, we adopt the Japanese yen to express monetary values,
converting local currency values into yen using the PPPs for year 2000. We choose the year
2000 as our benchmark year, and the PPPs for 2000 are estimated by taking account ofinter-temporal changes in industry price deflators in each country between the ICPA’s
benchmark year, 1997, and our benchmark year, 2000. For output, we use production PPPs by
industry to convert firms’ output into yen. For intermediate input, we convert the ICPA’s
producer price PPP into purchaser price PPP utilizing information on distribution margins anddifferences between the prices for domestic inputs and imported inputs in each country from the
Asian International Input-Output Table 1995 prepared by the Institute of Developing Economies(IDE-JETRO).
10

For capital input PPPs, assets are divided into structures, machinery and vehicles. For
structures, we use the production PPP for construction; for machinery, we use the simple
average of the production PPP for the general machinery, electric machinery, and precision
machinery industries; and for vehicles, we use the simple average of the output PPP for the
motor vehicle and other transportation equipment industry.14 Our estimation results of output,
capital, and intermediate PPP by industry are shown in Table A1.
As for labor input, we take account of differences in working hours across industries andacross countries but do not take account of differences in labor quality resulting fromdifferences in educational attainment. At this point, we do not have sufficient information for
estimating labor quality at the firm level in each country.

2.4 Results of the International Comparison of Firms’ TFP Level
2.4.1 Comparison of Sectoral TFP Growth in Japan, Korea, Taiwan, and China15
Let us begin by looking at the results on the level and growth rate of the weighted average
TFP in each country’s manufacturing and non-manufacturing sectors (Table 1). As weights, we
use the sales share of each firm. The table shows that TFP growth in Japan’s manufacturingsector slowed down somewhat in the first half of the 1990s before accelerating again slightly inthe second half of that decade, and then further accelerating in the early 2000s. In Korea, the11

TFP growth rate in the manufacturing sector was negative in the latter half of the 1980s, turned
positive in the early 1990s, and increased even further in the latter half of the 1990s. However,
TFP growth in the 2000-2005 period then dropped below levels in the 1990s. Turning to Taiwan,
the manufacturing sector here enjoyed very high TFP growth between 1985 and 2005 with the
exception of the second half of the 1990s, when the Taiwanese economy was hit by the Asianfinancial crisis. As mentioned, data for China are available only from 1999, and we find that the
TFP growth in China’s manufacturing sector in the period 2000-2005 was very slow.
Turning to the non-manufacturing sector, in Japan, TFP growth tended to be slower thanin the manufacturing sector until 2000. In 2000-2005, however, the TFP growth rate in Japan’s
non-manufacturing sector exceeded that in the manufacturing sector. In Korea, thenon-manufacturing sector showed high TFP growth in the 1990s at rates that were substantiallyhigher than those for the non-manufacturing sector in Taiwan and Japan, although this reversedin the early 2000s. In Taiwan the non-manufacturing sector showed strong TFP growth in
1985-2005, although in most sub-periods, growth was slower than in the manufacturing sector.
Finally, in China, the non-manufacturing sector in 2000-2005 registered considerable negative
growth, mainly because of the large decline in the TFP of the oil and gas extraction sector.

Insert Table 1 about here.

122.4.2. Comparison of the TFP Level of Listed Firms in Selected Industries in Japan, Korea, Taiwan,
and China
Figures 1 through 5 show a comparison of the average TFP levels of firms in Japan,
Korea, Taiwan, and China for five principal manufacturing industries: the chemical, primary
metal, non-electrical machinery, electrical machinery, and motor vehicle industries.
In the chemical industry (including pharmaceuticals), the average TFP of Taiwanese firms
increased very rapidly in the early 2000s and in 2005 caught up with the average TFP ofJapanese firms (Figure 1). On the other hand, Korean firms’ TFP growth in this industry wasvery slow during 1985-2000 and their TFP level remains low relative to that of their Japaneseand Taiwanese counterparts. Finally, despite some growth in TFP in the early 2000s, the TFPlevel of Chinese firms in this industry remains far below that of Japanese and Taiwanese firms.

Insert Figure 1 about here.

In the primary metal industry (Figure 2), Taiwanese firms improved their TFP levels
steadily over the estimation period and by 2003 had almost caught up with Japanese firms,
although they fell back again slightly in the following years. Korean firms’ TFP in this sector

13plummeted in the late 1980s and early 1990s and continues to lag considerably behind the TFPlevels of Japanese and Taiwanese firms. Meanwhile, the TFP level of firms in China did not
improve much during the early 2000s, remaining low relative to the level in other three
countries.

Insert Figure 2 about here.

In the non-electrical machinery industry (Figure 3), Taiwanese firms’ TFP has grown
rapidly and the average TFP level exceeds that of Japanese and Korean firms since the early
2000s. The TFP levels of Chinese machinery firms remain very low relative to those of their
Japanese, Korean and Taiwanese counterparts.

Insert Figure 3 about here.

In the electrical machinery (Figure 4), all four countries experienced steady
improvements in TFP levels in 1985-2005. While the TFP levels of Korean, Chinese, andTaiwanese firms were almost level in 2000, firms from China and Taiwan subsequently
registered faster TFP growth than Korean firms, so that in 2005, the average TFP level of14

Korean firms was lower than that of firms in Japan, Taiwan, and even China.

Insert Figure 4 about here.

Finally, in the motor vehicle industry (Figure 5), the TFP level of Japanese firms
continues to be considerably higher than that of Korean, Taiwanese, and Chinese firms,
although TFP levels in the latter three countries improved greatly in the early 2000s.

Insert Figure 5 about here.

3. Examining TFP Catch-Up: Econometric Model and Estimation Procedures
3.1. Econometric ModelIn this section, we present the econometric model we use for our analysis of the catch-up
process in East Asia. In our econometric analysis of catch-up, we focus on Japanese, Korean,
Taiwanese, and Chinese firms in the manufacturing sector.
Following Bartelsman, Haskel, and Martin (2008), we estimate the speed of convergenceto the productivity frontier. Like them, we assume that the growth rate of the knowledge capitalof firm f, ΔAf, is determined by the following equation:

15

A 
A 
 ln A f  1 ln X f   2,N ln N    2,G ln G 
A 
A 
 f 
 f 

(8)

where Xf denotes this firm’s input in the process of accumulating knowledge capital. We use
firm f’s TFP level as a proxy for this firm’s knowledge capital stock level, Af. As the dependent
variable, we use the one-year (log value) growth rate of each firm’s TFP level. Since the TFPlevels of some firms are implausibly high or low, we trimmed the upper and lower 2.5% ofobservations for each country and each manufacturing industry.16
The second and third terms on the right-hand side respectively denote the spillover effects
from a country’s own national technology frontier and from the regional technology frontier.
For each year, country, and industry, we divide all firms into four groups based on their TFPlevel for each year, and use the average TFP levels of the top groups as that country’s national
frontier in that year in that industry. As for the regional frontier, for each industry, we pooled all
firms in the four countries, divided these firms into sixteen groups according to their TFP level,and defined the regional frontier as the average TFP level of the top group firms.17 This meansthat if the TFP levels of firms from, say, country C are all much higher than those of firms fromother countries and if the number of firms in each country is the same, then the top group of thesixteen groups of the pooled firms becomes identical with the top group of the four groups incountry C.
The distance of each firm to the national or regional frontier is measured as the difference
16

between the firm’s TFP level and the average TFP for the national or regional top group firms.18
AN denotes the average TFP level of the top quartile of firms in the country where firm f islocated, and AG denotes the average TFP level of the top hexadecimal firms in the four countries.
If intra-national and international technology spillover effects are larger for firms with a low
TFP level,19 α2, N and α2, G will take positive values.
In the case of the regressions using pooled data for all four countries, the only proxy forfirms’ investment in knowledge capital, Xf, we can use is firm age. Firm age is measured as thedifference between the establishment year and the current year. As for Chinese firms, since
information on the year of establishment is not available, the difference between the year oflisting and the current year is used. In the case of the country specific regressions, we can also
take account of many other firm-specific variables which might influence firms’ accumulationof knowledge capital or spillover effects from frontier firms. We will explain these additional
data later.
In addition to firm age and distance to the national and regional technology frontiers, we
include the growth potential of the industry to control for industry characteristics. The growthpotential is measured as the average growth rate of the national frontier (GNfrontier) and theregional frontier (GAfrontier). In order to take account of industry-specific factors of TFP
growth, we also add industry dummies to all our estimations.

17

To take account of the possibility of non-linear effects of the distance to the technology
frontiers, we also estimate the following equation:2  A   A 
A 
A 
1
2
1
2
 ln A f  1 ln X f   2,N ln N    2,N  ln N     2,G ln G    2,G  ln G  
A 
A 
  Af    Af  
 f 

 f 
   

2(9)In order to take account of additional firm-specific characteristics, which might influencefirms’ knowledge capital accumulation or spillover effects from frontier firms, we include
additional variables such as firms’ R&D intensity (data are available only for Korean andJapanese firms), foreign ownership share (data are available only for Chinese, Korean, andJapanese firms), and export ratio, as well as dummies for firms belonging to a corporate group(data are available only for Korean firms) and dummies for firms located in a coastal region(data are available only for Chinese firms). Since these data are not available for all four
countries, when using these variables, we only run separate country-specific regressions for
China, Korea, and Japan. Specifically, we estimate the following equation:

A 
A 
 ln A f   1, j ln X f , j   2,N ln N    2,G ln G 
A 
A 
j
 f 
 f 


 A 
 A   3, j  ln X f , j ln N     4, j  ln X f , j ln G   A 
 A  jj f 
 f 


(10)

where (Xf, 1, Xf, 2, …., Xf, J) is a vector of the variables representing firm characteristics justmentioned. Specifically, the variables are defined and measured as follows. For Korea and Japan,18

we include R&D intensity, defined as the ratio of R&D expenditure over sales, in the estimation.
We assume that firms which do not report R&D expenditure do not conduct R&D. In theestimations for China, Korea and Japan, we include a variable measuring the foreign ownershipshare, which is the ratio of the number of shares owned by foreigners to the total number ofshares issued. This information is not available for Taiwan. Since the foreign ownership share isavailable only for listed firms, our estimation of equation (10) does not include Korean unlisted
firms. In the case of Korean firms, we also prepare two additional variables, the export ratio anda business group dummy. The export ratio is defined as the ratio of the total value of exports
over sales. We also assume that firms not reporting exports do not export.20 The business groupdummy takes a value of 1 when a firm is an affiliate of one of the top 30 business groups in
Korea and a value of 0 otherwise. Finally, in the estimation for China, we include a dummy
variable which takes a value of 1 when a firm is located in a coastal region and 0 otherwise.
As explanatory variables we also use products of the above variables with both the
distance to the national frontier and the distance to the regional frontier.

4. Estimation Results
We now turn to our estimation results for the various equations. We start with the resultsfor equation (8), which are shown in Table 3. Here, we only include firms’ age as a proxy for

19

accumulated knowledge. Column 1 shows the results for the complete sample of firms from all
four countries. In the Table, Ndist denotes distance from the national frontier, ln(AN/Af), andAdist denotes distance from the regional frontier (in our estimation, the top group firms in thefour countries), ln(AG/Af). In the case of the complete sample regression, we added country
dummies.
The results suggest that the marginal pull from the national frontier is 0.277 and that fromthe regional frontier 0.006. In order to examine whether the pull from the national and theregional frontier is different across countries, we estimate equation (8) for each country
separately. The results are shown in columns (2), (3), (4) and (5). They show that the marginal
impact of the national frontier is largest for Korean firms, followed by that for Taiwanese,
Chinese, and then Japanese firms. This result suggests that the speed of convergence to thenational frontier is weak for Chinese and Japanese firms. Looking at the marginal impact of theregional technology frontier on Korean, Chinese, and Taiwanese firms’ TFP growth (columns(2), (4), and (5)), we find for Korean firms, this is much smaller than the marginal impact of thenational frontier (0.025 and 0.294, respectively), but both impacts are statistically significant.
These results indicate that Korean firms are catching up not only to the national frontier but also
to the regional frontier. In the case of Chinese and Taiwanese firms, the marginal impact of theregional frontier is also smaller than that of the national frontiers and is statistically20insignificant.
Table 4 shows our estimation results for equation (9), which allows for non-linear effectsof the distance to the national and regional frontiers, Ndist and Adist, by adding squared termsof these distances. When we take account of the possibility of non-linear effects of the distanceto the technology frontiers, our main results, that is, that the marginal pull from the national
frontier is larger than that from the regional frontier, do not change. We also find that, as column(1) shows, the marginal effect of distance to the national frontier is an increasing function of
distance.
Table 5 shows our estimation results for equation (10) which includes a number ofvariables on firm characteristics which might influence firms’ accumulation of knowledge
capital or spillover effects from frontier firms, such as R&D intensity, export ratio, etc., as wellas cross-terms of these variables and distance to the technology frontiers to capture the effect ofabsorptive capacity on catching up to the technology frontier. The result for Chinese firms areshown in column (1) and indicate that coastal location has a significant positive impact on TFP
growth while foreign ownership does not. Both the cross-term of the coastal location dummy
and the distance to the regional technology frontier and the cross term of the foreign ownership
ratio and the distance to the regional technology frontier are not statistically significant. Finally,
firms’ age has a positive impact on the catch-up to the regional technology frontier.

21

Next, column (2) shows the results for Korean firms. Being part of a corporate group isassociated with catching up to the regional technology frontier, but not the national frontier.
Moreover, firms’ age has a positive impact on TFP growth, but a negative impact on the effectof the distance to the national technology frontier.
Turning to the estimation for Japan in column (3), none of the variables related to
distance to the regional frontier are statistically significant. The reason probably is that many of
the Japanese top firms are also top firms in the four countries as a whole. Therefore, ourmeasure of the distance to regional frontier is not appropriate for an examination of thecatching-up process of Japanese firms to the global technology frontier. To analyze the issues
addressed here for Japanese firms, we need more data for developed economies such the UnitedStates and European countries.

Insert Tables 2, 3, 4, and 5 about here.

5. Concluding Remarks and Implications for Future Research
Using firm-level data, this paper investigated the pattern of productivity convergenceamong firms from Japan, China, Taiwan, and Korea.
Our convergence analysis revealed that the pull from the national frontier is stronger in22the case of Korea than that of Taiwan, China, and Japan. Our estimates based on the data for all
four countries showed that lower-TFP firms were catching up to the national frontier at a faster
speed than higher-TFP firms, providing evidence of strong convergence toward the national
frontier.
Korean firms were also catching up toward the regional frontier. In addition, being part ofa corporate group contributes to catching up to the regional frontier, but not to the national
frontier. In China, firms located in the coastal regions tended to have higher TFP growth. We
also find that firms’ age has a positive impact on Chinese firms’ catch-up to the regional frontier.
Overall, we found that the national frontier has a stronger pull on domestic firms than theregional frontier, a finding that is in line with that obtained by Bartelsman, Haskel, and Martin(2008). These results indicate that policies designed to raise the technology level of national
frontier firms is beneficial for all domestic firms.
Because of data limitation, we were not able to examine East Asian firms’ catch-up to
frontier firms in the United States and the European Union. A comparison of the performance
and/or competition between Asian frontier firms and frontier firms in non-Asian developed
countries would be an interesting future research topic.
The mechanism of productivity convergence to frontier firms within a country and acrosscountries is an issue that deserves further attention and more rigorous empirical analysis.

23

Although the compilation of international micro data is not an easy task, the development ofinternationally comparable micro data could shed more light on the growth mechanisms
underlying the so-called “East Asian economic miracle,” as well as the determinants andconsequences of heterogeneity among firms.

Acknowledgements
We are grateful to Jinghai Zheng (University of Gothenburg), Harry Wu (Hitotsubashi
University), Kwon Heog Ug (Nihon University), and seminar participants at the 2008
International Workshop on Chinese Productivity and CEA (UK)-Zhejiang University Joint
Conference and at a workshop at the Japan Center for Economic Research (JCER) for helpfulcomments. We would also like to thank two anonymous referees for their valuable suggestions.
This study was supported by a Grant-in-Aid for Scientific Research from the Ministry of
Education, Culture, Sports, Science and Technology (No. 20243021), by the Global Centers of
Excellence (COE) program “Research Unit for Statistical and Empirical Analysis in Social
Sciences (G-COE Hi-Stat)” at the Institute of Economic Research, Hitotsubashi University, andby the JCER. The views expressed in this paper are those of the authors and should not be
attributed to any of the organization we are affiliated with.

Notes
1

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

2

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).
3

Including U.S. firms presents serious difficulties. First, labor cost data for U.S. firms are not available, so that wecannot estimate TFP indices for them. In addition, since unconsolidated financial statements are not available for U.S.
firms, we cannot compare the input and output of U.S. firms with those of firms from the four countries consideredhere. Consolidated financial statements would not be suitable for this study, because they include the activities ofoverseas subsidiaries.
4

As described below, this gives rise to the possibility of endogeneity problems, since the national frontier and theregional frontier defined in this paper is not exogenous. However, data limitations force us to use these endogenously
defined national and global frontiers.
5

It should be noted that while Good, Nadiri, and Sickles (1997) use an equation that accounts for changes in thecomposition of items for sale due to business diversification, we conducted the TFP estimation based on theassumption that firms produce only manufactured goods of the industry to which they belong.

24

6

See Fukao, Inui, Ito, Kim, and Yuan (2009) for a detailed explanation of the method of estimating the firm-level

cost share of each production factor.
7Here, we measure TFP using the index method. This method is not robust to scale economies and mark-ups. As analternative, it is also possible to estimate TFP by employing the production function method. In this case, the methods
that should be employed are those developed by Olley and Pakes (1996) or Levinsohn and Petrin (2003) to take into
account simultaneity issues. However, these methods do not take into account heterogeneity in production functions
across firms.
8

Output is based on sales after adjusting for increases/decreases in inventories. For wholesalers and retailers, insteadof sales, the difference between sales and purchases was used as output.
9

We calculate firms’ real output by deflating their nominal sales by the price index for the industry they operate inand then estimate firms’ TFP using this real output. This is called the revenue TFP (TFPR). A potential problem withthis method is that, as pointed out by Foster, Haltiwanger, and Syverson (2008: p. 400), "producers can have high
TFPR levels because they are efficient, but this can also be driven by high producer-specific demand."
10

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 on firms in the stock market where the firm is listed.
11

See Fukao, Inui, Ito, Kim, and Yuan (2008) for more details on our firm level data.

12

See Motohashi (2007) for details on the ICPA project.

13

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

14

Since we have no information on the asset composition of the capital stock of Taiwanese and Chinese firms, we

use the share of each capital asset in each industry in Japan for each corresponding industry in Taiwan or China. The
information on the capital asset composition in Japan was obtained from the Japan Industry Productivity Database
2008.
15

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.
16

Summary statistics for, and the distribution of, observations used in our regressions are shown in Tables A2 and A3,

respectively.
17

Defining the technology frontiers in this way poses various problems. One of these is the problem of endogeneity.

Strictly speaking, our national and regional frontier variables are endogenous in our regression equation. Instead, weshould estimate the global technology frontier using data on firms from the United States and European Union anduse this as an exogenous variable in our regression. However, the lack of appropriate data means that at present this is
not possible. This is an issue we would like to leave for future research.
18

An alternative approach to measuring distance from the technology frontier would be to use the productivity gapof a firm to the firm with the highest productivity (the frontier firm). However, doing so would mean that the measureis highly susceptible to temporary shocks and measurement errors with regard to the productivity of such frontierfirms. For this reason, we decided to define the technology frontier as the average of the productivity of the top
hexadecimal firms.
19

Whether low-productivity firms can benefit from the “advantages of backwardness” depends on patterns ofconsumption and on the existence of a threshold level of infrastructural development (Dowrick and Gemmell 1991,
Hall and Jones 1999, Barro and Sala-i-Martin 2004).
20

Since reporting on R&D and exports in surveys in Japan and Korea is not compulsory, these variables may include

serious measurement errors.

25

References
Barro, R. J., and X. Sala-i-Martin (2004) Economic Growth, 2nd Edition, MIT Press,.
Cambridge, MA.
Bartelsman, E., and M. Doms (2000) “Understanding Productivity: Lessons from Longitudinal
Micro-Data,” Journal of Economic Literature 38(3), pp. 569-594.
Bartelsman, E., J. Haltiwanger, and S. Scarpetta (2004) “Microeconomic Evidence of Creative
Destruction in Industrial and Developing Countries,” IZA Discussion Papers 1374,
Institute for the Study of Labor (IZA), Bonn.
Bartelsman, E., J. Haltiwanger, and S. Scarpetta (2005) “Measuring and Analyzing
Cross-country Differences in Firm Dynamics,” Paper presented at the 2nd Consortium
Meeting of EU KLEMS, June 9-11, 2005, Helsinki.
Bartelsman, E., J. Haskel, and R. Martin (2008) “Distance to Which Frontier? Evidence on
Productivity Convergence from International Firm-Level Data,” CEPR Discussion Paper
7032, Centre for Economic Policy Research, London.
Bartelsman, E., S. Scarpetta, and F. Schivardi (2003) “Comparative Analysis of Firm
Demographics and Survival: Macro-Level Evidence for the OECD Countries,” OECD
Economic Department Working Papers 348, Organization for Economic Co-operation andDevelopment, Paris.

26

Cameron, G., J. Proudman, and S. Redding (2005) “Technological Convergence, R&D, Tradeand Productivity Growth,” European Economic Review 49(3), pp. 775-807.
Dowrick, S., and N. Gemmell (1991) “Industrialisation, Catching Up and Economic Growth: A
Comparative Study Across the World’s Capitalist Economies,” The Economic Journal101(405), pp. 263-275.
Foster, L., J. Haltiwanger and C. Syverson (2008) “Reallocation, Firm Turnover, andEfficiency: Selection on Productivity or Profitability?” American Economic Review 98(1),pp. 394-425.
Fukao, K., T. Inui, K. Ito, Y.G. Kim, and T. Yuan (2009) “An International Comparison of the
TFP Levels and the Productivity Convergence of Japanese, Korean, Taiwanese andChinese Listed Firms,” Global COE Hi-Stat Discussion Paper Series 89, Institute of
Economic Research, Hitotsubashi University, Tokyo.
Good, D.H., M.I. Nadiri, and R.C. Sickles (1997) “Index Number and Factor Demand
Approaches to the Estimation of Productivity,” in: M. Hashem Pesaran and Peter Schmidt(eds.), Handbook of Applied Econometrics: Microeconomics, vol. II, pp. 14-80, Blackwell,
Oxford.
Griffith, R., S. Redding, and H. Simpson (2002) “Productivity Convergence and Foreign
Ownership at the Establishment Level,” CEPR Discussion Paper 3765, Centre for27

Economic Policy Research, London.
Griffith, R., S. Redding, and J. Van Reenen (2003) “R&D and Absorptive Capacity: Theory
and Empirical Evidence,” Scandinavian Journal of Economics 105(1), pp. 99-118.
Griffith, R., S. Redding, and J. Van Reenen (2004) “Mapping the Two Faces of R&D:
Productivity Growth in a Panel of OECD Industries,” Review of Economics and Statistics
86(4), pp. 883-895.
Hall, R.E., and C.I. Jones (1999) “Why Do Some Countries Produce So Much More Output
per Worker than Others?” Quarterly Journal of Economics 114(1), pp. 83-116.
Kneller, R., and P. H. Stevens (2006), “Frontier Technology and Absorptive Capacity:
Evidence from OECD Manufacturing Industries,” Oxford Bulletin of Economics andStatistics 68, pp. 1-21.
Levinsohn, J., and A. Petrin (2003), “Estimating Production Functions Using Inputs to Control
for Unobservables,” Review of Economic Studies 70, pp. 317-341.
Motohashi, K. (2007), “Assessing Japan’s International Competitiveness by International
Productivity Level: Comparison with China, Korea, Taiwan and the United States,” in D.
Jorgenson, M. Kuroda, and K. Motohashi (eds.), Productivity in Asia: Economic Growthand Competitiveness, Edward Elgar Publishing, Cheltenham, pp. 215-238.
Olley, S.G., and A. Pakes (1996) “The Dynamics of Productivity in the Telecommunications

28

Equipment Industry,” Econometrica 64, pp. 1263-1297.
Timmer, M.P., and G.. Ypma (2007), “Purchasing Power Parities for International Comparisonof Output and Productivity in Japan, South Korea, Taiwan and the United States,” in D.
Jorgenson, M. Kuroda and K. Motohashi (eds.), Productivity in Asia: Economic Growthand Competitiveness, Edward Elgar Publishing, Cheltenham, pp. 185-214.

29

Table 1. TFP Growth Rate (percent per annum)

ManufacturingNon-manufacturing
Manufacturing
Korea
Non-manufacturing
Manufacturing
TaiwanNon-manufacturing
Manufacturing
China
Non-manufacturing
Source: Authors’ calculations.
Japan

1985-90
0.95
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
0.08
-3.34

Table 2. Definitions of Variables
Variable

Definition

g1lnTFP

One-year growth rate of TFP from the current period to the next period

Ndist

Log distance of the TFP level from the national frontier

Adist

Log distance of the TFP level from the regional frontier

GNfrontier

One-year growth rate of the national frontier

GAfrontier

One-year growth rate of the regional frontier

Foreign ownership Ratio of shares owned by foreigners
DcoastExport ratio

Dummy variable denoting whether the firm is located in one of China's coastal regions
Ratio of exports to gross sales

R&Di

R&D-sales ratio

DgroupDummy variable denoting whether the firm is an affiliate of one of the top 30 business groups in Korea

Age

Firm age30

Table 3. Estimation Result 1

Pooled

China

Japan

Korea

Taiwan

(1)

(2)

(3)

(4)

(5)

Ndist

0.277 ***
0.203 ***
0.132 ***
0.294 ***
(0.005)(0.076)(0.010)(0.011)Adist
0.006 **
0.010
-0.015 *0.025 ***
(0.002)(0.074)(0.008)(0.009)GNfrontier
0.83700 *** 0.45100 *** 0.49800 *** 0.75400 ***(0.011)(0.072)(0.022)(0.019)******GAfrontier-0.170-0.021-0.056-0.150 ***(0.014)(0.122)(0.011)(0.020)***Age-0.00020.00090.0000-0.0003 ***(0.000)(0.001)(0.000)(0.000)
D_China-0.039 ***(0.002)
D_Korea-0.015 ***(0.001)
D_Taiwan-0.019 ***(0.001)R-squared
0.314
0.143
0.273
0.372
Observations
78,406
3,462
22,98142,846Note 1. ***p<0.01, **p<0.05, *p<0.1.
Note 2. Industry dummies and year dummies are included, but not reported.

31

0.243 ***
(0.025)0.037(0.023)0.54700 ***(0.035)0.081(0.053)-0.0013 ***(0.000)

0.263
9,117

Table 4. Estimation Result 2
Pooled
China
Japan
Korea
(1)
(2)
(3)
(4)
Ndist
0.202 ***
0.098
0.076 ***
0.244 ***
(0.010)(0.090)(0.023)(0.016)20.171 ***
0.175 *
0.313 ***
0.127 ***
Ndist
(0.029)(0.102)(0.095)(0.035)Adist-0.034 ***-0.015-0.035 **0.007(0.007)(0.093)(0.014)(0.017)2
0.043 ***
0.026
0.044 *
0.013
Adist
(0.008)(0.055)(0.024)(0.014)
GNfrontier
0.833 ***
0.445 ***
0.515 ***
0.757 ***
(0.011)(0.072)(0.022)(0.019)GAfrontier-0.169 ***-0.021-0.056 ***-0.152 ***(0.014)(0.118)(0.011)(0.020)Age-0.001 ***
0.003
0.000
-0.001 ***(0.000)(0.002)(0.000)(0.000)2******0.000004-0.000256-0.000001
0.000026
Age(0.000)(0.000)(0.000)(0.000)
D_China-0.042 ***(0.003)
D_Korea-0.012 ***(0.001)
D_Taiwan-0.019 ***(0.002)R-squared
0.320
0.150
0.281
0.375
Observations
78,406
3,462
22,98142,846Note 1. ***p<0.01, **p<0.05, *p<0.1.
Note 2. Industry dummies and year dummies are included, but not reported.

32

Taiwan(5)0.126 ***
(0.037)0.250 ***
(0.080)0.067(0.052)-0.025(0.055)0.535 ***
(0.035)0.123 **
(0.053)-0.005 ***(0.001)0.000287 ***(0.000)

0.272
9,117

Table 5. Estimation Result 3

Ndist
Adist
GNfrontier
GAfrontier
Age
Age

2

DcoastForeign ownership

China

Korea

Japan

(1)

(2)

(3)

0.266 ***
(0.092)-0.053(0.083)0.4820 ***(0.083)-0.0114(0.138)0.0007(0.004)-0.000469(0.000)0.029 **
(0.012)-0.051(0.077)0.283 *
(0.152)0.139(0.145)0.8100 ***(0.131)-0.2020(0.153)0.0016 **(0.001)-0.000016(0.000)0.146 ***
(0.033)-0.029(0.021)0.4970 ***(0.022)-0.0562 ***(0.011)0.0001(0.000)-0.000001(0.000)0.003(0.014)0.201(0.547)0.029(0.025)0.008(0.016)-0.0042 *(0.003)0.015(0.010)-0.048(0.054)

R&Di
Export ratio
DgroupNdist×Age
Ndist×Dcoast
Ndist×Foreign-0.0069(0.012)-0.030(0.055)0.097(0.307)

Ndist×R&Di
Ndist×Export
Ndist×Group
Adist×Age
Adist×Dcoast
Adist×Foreign
Adist×R&Di
Adist×Export
Adist×Dgroup0.0095 *(0.005)-0.010(0.021)
0.065(0.124)0.343(0.944)0.114(0.106)-0.190 *(0.115)-0.0003(0.001)-0.663(1.158)-0.101 *(0.060)0.061 *
(0.037)
0.228
1,601

-0.0005(0.001)-0.164 **(0.077)0.725(0.482)0.0002(0.000)0.038(0.030)0.170(0.323)R-squared
0.150
0.278
Observations
2,816
22,981
Note 1. ***p<0.01, **p<0.05, *p<0.1.
Note 2. Industry dummies and year dummies are included, but not reported.

33

Appendix
Table A1. Relative Output, Capital, and Intermediate Input Prices (2000, Japan=1)
1 Agriculture
2 Coal mining
3 Metal and non-metallic mining
4 Oil and gas extraction
5 Construction
6 Food and kindred products
7 Textile mill products
8 Apparel
9 Lumber and wood products
10 Furniture and fixtures
11 Paper and allied products
12 Printing, publishing, and allied products
13 Chemicals
14 Petroleum and coal products
15 Leather
16 Stone, clay, and glass products
17 Primary metal
18 Fabricated metal
19 Non-electrical machinery
20 Electrical and electronic machinery
21 Motor vehicles
22 Transportation equipment and ordnance23 Instruments
24 Rubber and miscellaneous plastics
25 Miscellaneous manufacturing
26 Transportation
27 Communications
28 Electricity utilities
29 Gas utilities
30 Trade
31 Finance, insurance, and real estate
32 Other private services
33 Public services

Relative Output Price
China
Korea
Taiwan
0.10
0.49
0.33
0.09
0.37
0.97
0.20
0.93
0.83
0.55
0.41
0.92
0.23
0.38
0.26
0.16
0.50
0.37
0.39
0.64
0.54
0.31
0.79
0.56
0.27
0.38
0.33
0.47
0.42
0.54
0.35
0.76
0.61
0.33
0.62
0.43
0.44
0.59
0.49
0.31
0.42
0.32
0.11
0.44
0.32
0.45
0.53
0.57
0.51
0.81
0.65
0.36
0.49
0.53
0.47
0.47
0.41
0.46
0.65
0.68
0.66
0.79
1.02
0.51
0.53
0.78
0.48
0.83
0.72
0.25
0.64
0.75
0.38
0.59
0.55
0.24
0.43
0.81
0.48
0.67
0.33
0.28
0.50
0.50
0.19
1.22
2.17
0.08
0.58
0.44
0.31
0.46
0.24
0.03
0.21
0.25
0.13
0.36
0.91

Relative Capital Price
China
Korea
Taiwan
0.23
0.42
0.30
0.31
0.48
0.38
0.31
0.48
0.38
0.31
0.48
0.38
0.35
0.50
0.43
0.35
0.47
0.38
0.32
0.43
0.36
0.34
0.43
0.36
0.33
0.47
0.39
0.33
0.41
0.38
0.34
0.38
0.37
0.39
0.39
0.40
0.32
0.46
0.37
0.31
0.45
0.36
0.36
0.44
0.39
0.30
0.46
0.37
0.29
0.46
0.35
0.29
0.44
0.35
0.35
0.46
0.40
0.34
0.47
0.40
0.35
0.40
0.36
0.34
0.42
0.38
0.36
0.47
0.41
0.35
0.41
0.37
0.36
0.43
0.40
0.28
0.44
0.35
0.30
0.48
0.38
0.26
0.45
0.34
0.23
0.40
0.28
0.28
0.40
0.35
0.25
0.37
0.29
0.31
0.42
0.39
0.29
0.43
0.36

Source: Authors’ calculations.

34

Relative Intermediate Price
China
Korea
Taiwan
0.22
0.49
0.39
0.28
0.07
0.47
0.28
0.49
0.45
0.29
0.28
0.52
0.31
0.46
0.50
0.18
0.61
0.43
0.40
0.57
0.51
0.38
0.52
0.49
0.25
0.35
0.38
0.31
0.49
0.48
0.34
0.64
0.60
0.33
0.56
0.52
0.34
0.55
0.54
0.66
0.69
0.75
0.32
0.50
0.48
0.29
0.47
0.56
0.43
0.65
0.59
0.38
0.59
0.54
0.39
0.53
0.53
0.41
0.45
0.59
0.51
0.70
0.74
0.44
0.53
0.66
0.38
0.57
0.55
0.36
0.55
0.55
0.31
0.47
0.46
0.32
0.51
0.51
0.22
0.50
0.37
0.30
0.54
0.59
0.30
0.53
0.62
0.25
0.35
0.38
0.25
0.42
0.31
0.26
0.36
0.37
0.28
0.50
0.46

Table A2. Summary Statistics
Variable

N

mean

sd

min

p25

p50

p75

max

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
DcoastForeign ownership

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

Korea
g1lnTFP
Ndist
Adist
GNfrontier
Age
Foreign ownership
Dgroup
Export ratio
R&Di

42,84642,84642,84642,84642,846
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

-0.763-0.458-0.107-0.29500.00010
0.000-0.019-0.0330.076
0.338-0.0106
0.0023
0
0.000
0.000

0.015
0.138
0.469
0.012
12
0.0202
0
0.000
0.000

0.065
0.806
0.208
0.968
0.611
1.603
0.030
0.366
20
73
0.10720.9335010.125 734.263
0.004
0.571

Japan
g1lnTFP
Ndist
Adist
GNfrontier
Age
Foreign ownership
R&Di

22,981
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
19.00035

Table A3. Distribution of Sample
Industry

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1 Agriculture
2 Coal mining
3 Metal and non-metallic mining
4 Oil and gas extraction
5 Construction
6 Food and kindred products
7 Textile mill products
8 Apparel
9 Lumber and wood products
10 Furniture and fixtures
11 Paper and allied products
12 Printing, publishing, and allied products
13 Chemicals
14 Petroleum and coal products
15 Leather
16 Stone, clay, and glass products
17 Primary metal
18 Fabricated metal
19 Non-electrical machinery
20 Electrical and electronic machinery
21 Motor vehicles
22 Transportation equipment and ordnance23 Instruments
24 Rubber and miscellaneous plastics
25 Miscellaneous manufacturing
26 Transportation
27 Communications
28 Electricity utilities
29 Gas utilities
30 Trade
31 Finance, insurance, and real estate
32 Other private services
33 Public services
Total

13
6
4
3
259
197
79
60
10
14
71
35
313
20
11
129
166
98
221
265
135
36
48
62
25
153
3
9
23
289
72
171

14
6
5
3
285
206
89
70
10
15
81
35
332
20
11
144
174
106
230
296
160
34
50
79
26
168
5
10
25
301
78
199
1
3268

17
6
5
3
307
226
99
74
11
16
86
44
358
23
16
155
183
117
269
352
187
39
59
96
38
180
7
10
27
346
95
234
2
3687

19
6
5
3
311
239
111
79
12
17
89
52
392
21
18
169
200
144
292
384
205
43
65
114
42
193
6
12
26
391
115
276
3
4054

19
6
5
3
353
253
125
92
12
19
92
59
428
22
22
175
227
163
317
437
230
44
67
123
47
215
9
12
26
433
124
317
4
4480

21
6
5
3
392
263
131
93
13
21
99
71
441
23
23
183
244
164
338
469
241
44
72
128
49
213
9
12
28
474
127
344
2
4746

22
7
6
3
477
289
154
110
16
22
109
88
487
25
27
203
277
181
373
554
281
46
81
152
52
234
9
12
32
505
153
384
6
5377

23
7
7
3
514
300
157
118
17
24
114
93
511
25
27
212
285
196
387
591
287
51
82
161
58
241
11
13
35
537
163
411
7
5668

23
7
7
3
530
312
157
125
17
24
110
92
510
23
27
210
288
186
376
616
292
53
83
161
60
247
14
13
37
585
153
436
10
5787

19
7
9
3
557
311
148
130
19
25
118
102
520
24
26
213
303
201
397
676
322
53
90
171
61
259
14
13
36
647
144
480
14
6112

22
8
9
3
620
314
155
140
19
27
127
111
555
24
26
220
328
221
439
759
357
59
99
188
64
263
16
13
37
689
140
541
12
6605

27
8
15
3
759
346
166
150
22
32
130
137
589
27
28
249
356
269
592
925
383
77
132
218
77
273
17
14
39
732
152
597
19
7560

24
8
18
3
846
349
180
153
25
40
128
143
603
28
27
252
392
295
665
1088
438
99
144
256
86
282
16
14
41
759
166
643
22
8233

27
46
56
50
53
55
60
57
9
14
15
17
18
19
22
22
25
24
25
28
27
28
30
29
3
6
6
7
9
9
9
8
925
919
945
990 1073 1188 1260 1210
377
409
417
410
440
449
479
462
192
197
206
214
232
243
237
226
161
155
144
149
157
172
175
167
32
41
39
37
38
38
44
45
39
42
49
51
56
58
61
61
132
138
150
152
166
181
176
158
169
167
154
200
257
265
269
254
647
752
774
836
913
925
958
914
31
51
47
47
53
54
52
48
30
30
29
25
25
33
38
32
275
287
296
313
336
361
374
355
420
470
471
505
587
580
601
572
332
355
380
417
462
472
483
469
762
829
860
952 1043 1084 1143 1071
1284 1566 1709 1946 2181 2293 2380 2311
469
583
610
647
736
760
771
737
108
128
137
139
159
162
167
164
167
195
193
217
242
256
266
253
296
312
325
348
399
428
446
405
98
109
115
115
122
122
126
119
289
308
324
328
338
342
363
360
26
64
66
66
76
84
88
89
14
47
50
57
68
65
72
75
40
29
33
33
34
36
35
34
794
885
910
931
959
986
998 1015
173
188
203
219
237
280
309
317
715
797
868
968 1041 1100 1187 1232
30
34
42
42
52
54
55
54
9091 10177 10648 11456 12589 13182 13734 13325

3000

36

1999

2000

2001

2002

2003

2004

2005

Total
667
224
316
96
14720
7048
3498
2674
517
713
2607
2797
12758
688
531
5111
7629
5711
12640
23082
8831
1842
2861
4868
1611
5573
695
605
686
14166
3608
12941
465
162779.4

Figure 1. TFP in Japan, Korea, Taiwan, and China: Chemical Industry

-.2

lnTFP
0

.2

Japan
Taiwan

Japan

Korea

Taiwan

Korea

-.4

China
China

1985

1990
China

1995
Year
Japan

2000
Korea

2005
Taiwan(Source) Authors' calculation

Source: Authors’ calculations.

.4

Figure 2. TFP in Japan, Korea, Taiwan, and China: Primary Metal Industry

Japan

Taiwan

Korea

lnTFP

0

.2

Japan

China

Korea
Taiwan

-.6

-.4

-.2

China

1985

1990
China

1995
Year
Japan

(Source) Authors' calculation

Source: Authors’ calculations.

37

2000
Korea

2005
Taiwan.5

Figure 3. TFP in Japan, Korea, Taiwan, and China: Non-electrical Machinery Industry
Taiwan

lnTFP
0

Korea
Japan

Japan
Taiwan
Korea

-.5

China

China

1985

1990
China

1995
Year
Japan

2000
Korea

2005
Taiwan(Source) Authors' calculation

Source: Authors’ calculations.

Figure 4. TFP in Japan, Korea, Taiwan, and China: Electrical Machinery Industry
.5

Japan

Taiwan

0

China
Japan

Korea

-.5

lnTFP

China

Korea

-1

Taiwan

1985

1990
China

1995
Year
Japan

(Source) Authors' calculation

Source: Authors’ calculations.

38

2000
Korea

2005
Taiwan.4

Figure 5. TFP in Japan, Korea, Taiwan, and China: Motor Vehicle Industry

.2

Japan

lnTFP
0

Japan

Korea
Taiwan

-.2

China

Korea
Taiwan

-.4

China

1985

1990
China

1995
Year
Japan

(Source) Authors' calculation

Source: Authors’ calculations.

39

2000
Korea

2005
Taiwan