Productivity Growth and Competition across the Asian Financial Crisis:
Evidence from Korean Manufacturing Firms

Chulwoo Baek
Institute of Economic Research, Hitotsubashi University,
Korea Institute of Science and Technology Evaluation and Planning (KISTEP)

Younggak Kim
Institute of Economic Research, Hitotsubashi University

Hyeog Ug Kwon
Department of Economics, Nihon University, RIETI

Abstract
Using firm level data during 1985-2005, we measured the TFP of Korean manufacturingsector and identified determinants of the TFP growth rate. Given that various institutions for faircompetition were introduced in 1988, investigating the difference of the TFP trends before andafter the Asian financial crisis is meaningful. With the massive unbalanced panel data, we
examined quantitatively whether or not the comprehensive structural reforms have the effect on
the Korean economy in terms of productivity.
The data used in this research are financial statements obtained from KIS (Korea
Information Service). These cover statutory audit firms and registered firms as well as listed
firms in KOSPI and KOSDAQ from 1985 to 2005. Given that statutory audit firms andregistration firms are smaller and less competent than listed firms in KOSPI and KOSDAQ andtheir proportion in the sample amounts to 74.5%, the data used in this paper is expected tolessen the sample bias than previous literatures which only focused on listed firms. When the1coverage of the data used here is compared with the Business Survey by National Statistical
Office which covers the whole firms with fifty or more employees, the data used here accountfor more than 70% of employees, sales and tangible asset, thus the data used in this research has
the representativeness to some extent.
It is shown that the TFP growth rate in the period 1985-1992 is negligible, 0.5% per annum,
thus the TFP is not the main source of economy growth during this period. However, after steep
decline in 1998, the TFP bounced back rapidly with 3.1% growth rate per annum in the period
1999-2005, which implies that the paradigm of economy growth has changes inputs-driven into
TFP driven growth. When the TFP growth is decomposed, it is revealed that the output
reallocation effect as well as within effect are main sources of the TFP growth. With regard to
the determinants of the TFP growth rate, the reinforcement of competition after the Asianfinancial crisis contributed to the TFP growth rate, justifying introduction of various institutions
for fair competition during the crisis. When industries are classified into sub industries bytechnology intensity, it can be said that the TFP growth has been driven by high technology andmedium-high technology, and in high technology industry, the reinforcement of competition
during post-crisis period and R&D intensity affected the TFP growth rate positively andsignificantly.

2

1. Introduction

This paper investigates the impact of the dramatic structural reforms initiated from 1998 forthe purpose of the reinforcement of fair competition in terms of the productivity. Over the past
three decades East Asia countries has achieved remarkable economic growth with the almost
eight percent of real GDP growth per annum before the Asian financial crisis. It has been argued
in the literature that the Total Factor Productivity (TFP) explains one-third of East Asia’s rapid
growth which is large relative to other economies, both absolutely and as a share of output
growth (World bank, 1993), and Korea’s successful economic development before the Asianfinancial crisis has resulted from government-led industrial policy and strategic trade policy(Chang, 1994). However, Krugman (1994), Young (1995) and Rodrik (1995) raised critical viewon the economic growth of Asian countries that the rapid growth of Asian countries was
attributed to excessive usage of production input factors, not to the enhancement of productivity.
As the Asian financial crisis which originated from the sudden depreciation of the Baht inThailand swept over the East Asian countries in the end of 1977, the debates on East Asia’s
economy growth including Korea became fiercer.
The presence of economic crisis allowed radical structural reforms based on consensus thatnew reforms are essential for recovery from the economic crisis and sustainable growth of
Korean economy. As the IMF’s bailout program began in December of 1997, the IMF asked the
Korean government to undertake a series of actions to improve market environment for faircompetition cutting off the unreasonable backward management practice from the past. In detail,the financial reform was implemented to restructure the entire financial sector and to improvethe efficiency of the operations of financial institutions. As labor market reform, government
legalized the usage of temporary agency workers and dismissal of workers through a simple
procedure to reduce labor cost and to improve the flexibility of labor market. Regarding
corporate sector reform, due to the newly introduced institutions for fair competition, which are
mentioned in detail in Section 2, the firms could not rely on the privilege from government aswell as could not derive benefits by illegal trade within business group any longer. In addition,3

restrictions on inflows of FDI were abandoned.
In this study, by using firm level data before and after the Asian financial crisis, we examine
how structural reforms in post-crisis Korea affected TFP growth and the efficiency of resource
reallocation. To the best of our knowledge, there are almost no quantitative studies on whether
the comprehensive structural reforms occurred in Korea have contributed to productivity growth.
Compared with the country or industry level, the measurement of TFP at the firm level canconsider the heterogeneity among firms and offer rich information on firms’ behavior. Moreover,
we investigate whether the reinforcement of fair competition contributed to post-crisis TFP
growth or not.
We found that aggregate TFP growth is higher after the structural reforms than before. The
TFP growth until 1992 is negligible, the 0.5% growth rate per annum, which implies at least the
TFP growth is not the main engine of economic growth and supports the arguments of Krugman(1994), Young (1994, 1995) and Rodrik (1995). However, after the Asian financial crisis, weobserved the TFP bounced back rapidly with the 3.1% growth rate per annum, which means that
the TFP growth has contributed to rapid Korean economic recovery. We also found that thecompetition has a significant effect on the TFP growth in the post-crisis period.
The remainder of this paper is organized as follows. After brief consideration on the changeof competition environment after the financial crisis in section 2, section 3 reviews previous
literature. Section 4 explains data and TFP measurement used in analysis. Section 5 presents thesources of aggregate productivity growth and section 6 shows the effect of competition withstructural reform on the TFP growth. Finally section 7 summarizes our findings and offers
policy implication.

4

2. The change of competition environments after the financial crisis

In the past, under the government-driven development policies, government offered various
benefits in resource distribution to a few “chaebols” that are highly diversified,family-controlled conglomerates. Korean governments set the next technology target on thebasis of the opinion of business leader, then helped selected firms enter and prosper in targetedindustries though credit allocation at below market interest rates, research and development
assistance, and temporary protection from domestic and foreign competition. The government
tightly regulated and coordinated the investment plans of chaebols to assure that investment wasof the proper magnitude as well as allocated efficiently (Crotty and Lee, 2001). Moreover, inorder to regulate the investment of chaebols, the government controlled both the domesticbanking system and cross border capital (Cho and Kim, 1994).
The IMF asked the Korean government to undertake a series of action to improve the poor
governance and low transparency in Korean firms, in return for the financial rescue packaging(Chang et al., 2007). In this vein, Korean government introduced various institutions to improve
managerial transparency and financial soundness, and forced chaebols to concentrate on corecompetencies.
In order to improve managerial transparency, combined financial statement became
obligatory to the biggest 30 business group (Feb. 1998), the financial accounting standards wererevised to be in line with international accounting standards (Dec. 1999), and the government
forced the listed firms to assign outside directors totaling as many as over a fourth of their board
member (Feb. 1998). These institutions prevented firms from obtaining improper benefits via
releasing wrong information and made managerial decision more transparent.
Institutions for chaebols’ financial soundness also introduced simultaneously. chaebols wererequired to abolish cross-debt guarantees (Apr. 1998) and the ceiling on equity investment was
revived (Dec. 1999) to reduce improper transaction within business group. On the basis of thefaith that growth strategy depending on excessive loan and expansion was not effective any
longer, Korean government forced chaebols to lower debt-equity ratio under 200% by the end of5

1999, and this resulted in disposal of business sector and asset with low profitability.
Korean government recognized that excessive diversification of chaebols caused their
unstable financial structure and low profitability. As a consequence, government pushedbusiness exchange, so called Big Deal, for concentrating on core competencies, reducing
excessive capacity. Big Deal was focused on capital intensive industries such as chemicals,
semiconductor, motor vehicles and airplanes.
With liberalization of capital market and foreign investment regimes as well as
aforementioned institutions, the paradigm of market competition has changed from closed anddomestic competition to open and global one since 1999. After partially opening stock market toforeign investors for the first time in 1992, the Korean Stock Exchange (KSE) continually
mitigated foreign investment limit and finally opened its market completely to foreign investor
as of May, 1998 (Chang et al., 2007).
Under the changed market environments resulting from the newly introduced institutions,firms could not enjoy various privileges from government any longer and they were forced to
improve their corporate governance and transparency in line with international standards. In
addition, as trade barrier of capital market as well as product market disappeared, firms were
exposed fierce global competition. In this vein, this research aims to examine whether theenvironmental changes fair competition after the Asian financial crisis contributed to the
productivity growth or not.

3. Literature review

The previous literature related to this research can be categorized into the change of Korean
economy after the Asian financial crisis, the measurement of the TFP, and the effect ofcompetition on the TFP. First, regarding the change of Korean economy after the Asian financial
crisis, previous literature offers mixed evidence on the change after the financial crisis. As
pessimistic views, Crotty and Lee (2002) claimed that post-crisis neoliberal restructuring which

6

moved Korea towards a globally open and capital market-based financial system, failed togenerate sustainable economic recovery and threatens to significantly lower Korea’s long-term
rate of capital accumulation. In the same vein, Jo (2005) offered empirical evidence that
operating profits of Chaebol in post crisis stayed at the same level as that in pre crisis in spite of
improvement of financial structure by reducing debt. In addition, Park (2001) and Chopra et al.
(2001) raised criticism on the rapid recovery from the crisis arguing that that the major factorsof Korean economic recovery are not the structural reforms, but the expansionary fiscal andmonetary policies and a more favorable external environment characterized by depreciatedKorean Won. On the other hand, as optimistic views, Kim and In (2004) also insisted averagedebt-equity ratio of the top 5th firms decreased from 355% in 1997 to 182% in 2001 and that of
the top 30th firms decreased more dramatically from 545% to 188% for the same period.
According to Chang el al. (2007), the information asymmetry of Korean firms is lower after thefinancial crisis than before, implying that corporate transparency, in effect, improve with thechange in business environment.
With respect to the measurement of the TFP of Korea, Aw et al. (2000, 2003), Hahn (2000,
2004), Ahn et al. (2004), Ahn (2006), Pyo et al. (2006), Kim (2006), Kwack (2007), Jung
(2008) and Oh et al. (2009) measured the TFP of Korean manufacturing using micro level data.
While Jung (2008) used firm level data, others used plant level data. Previous literatures offer
somewhat different TFP growth rates before and after the Asian financial crisis. For example,
Ahn (2006) showed that the annual TFP growth rate is 1.7% in the period 1990-1997 and 4.9%
in the period 1997-2003, which implies the growth rate in post crisis is higher than that in precrisis. On the other hand, Kwack (2007) showed 2.2% in the period 1988-1996 and 2.1% in the
period 2000-2004 implying that there is not significant TFP increase after the crisis.
In the relation of market competition and TFP, Nickell (1996) estimated the production
function including the markup percentage of the company level and the market competitive
indices, such as the degree of concentration to analyze the market competition and productivity.
The results presented that market competition has a positive influence on the growing rate of
TFP at the firm level. In the sequential study, Nickell et al. (1997) showed that when a firm7

received strong pressure from corporate governance, market competition has a weaker influenceon the productivity of a firm, which implies market competition encourages management effort
and improve the productivity. Besides Funakoshi et al. (2006) examined the empirical
relationship between market competition and corporate productivity using around 2,400
Japanese firm data. This showed that market competition increased the TFP of small firms morethan that of large firms and enhanced the TFP of R&D intensive firms.

4. Data and the productivity measurement

4.1. Data

Considering the difficulties in measuring inputs and outputs of service sectors and theheterogeneity among service sectors, we confine this analysis to manufacturing sectors. The
data used in this research are financial statements obtained from KIS (Korea Information
Service). These cover statutory audit firms and registered firms as well as listed firms in KOSPI
and KOSDAQ from 1985 to 20051. Given that statutory audit firms and registration firms are
smaller and less competent than listed firms in KOSPI and KOSDAQ and their proportion in thesample amounts to 74.5%, the data used in this paper is expected to lessen the sample bias thanprevious literatures which only focused on listed firms2. When the coverage of the data usedhere is compared with the Business Survey by National Statistical Office which covers thewhole firms with fifty or more employees, the data used here account for more than 70% of
employees, sales and tangible asset as shown in Table 1, thus the data used in this research has

1

Listed firms in KOSPI consist of large and well established firms and listed firms in KOSDAQ are
relatively young firms with high technology. Statutory audit firms are the firms whose total asset ismore than 7 billion Korean Won so that external audit is mandatory by laws. Registered firms are firms
which are registered in Financial Supervisory Service to transact securities.
2 As mentioned Section 3, Jung (2008) is the only previous literature which used firm level data.
However, Jung (2008) covered only listed firm in KOSPI and KOSDAQ, excluding statutory audit
firms and registration firms, thus his result has the possibility to overestimate the TFP of manufacturing
via aggregation.

8the representativeness to some extent.

[Table 1 here]

The standard of ICPA (International Comparison of Productivity among Asian Countries) is
adopted for industry classification. According to ICPA standards, the total industry can beclassified into 33 sub industries and the 6th to 25th sub industries among them are classified as
manufacturing sector. As shown in Table 2, the total number of observations is 59,002 andelectrical machinery, non-electrical machinery, chemicals and motor vehicles sectors hold17.2%, 12.1%, 10.7% and 10.1%, respectively.

[Table 2 here]

The price indexes for output and inputs and depreciation rate are calculated from EU
KLEMS Growth and Productivity Account. The information on Korea in EU KLEMS data isbased on the National Account from the Bank of Korea and several labor statistics from
National Statistical Office and Ministry of Labor. By using the nominal and real value of inputs
and output as well as depreciation and labor hour in EU KLEMS data, price indexes,
depreciation rate and average labor hour are derived.

4.2.The measurement of TFP

Following Good, Nadiri and Sickles (1996) and subsequent empirical studies including Kim(2007) and Fukao et al. (2006), firm level TFP was estimated by the chained-multilateral index
number approach. It is derived from translog production function with satisfying four desirable
properties: transitivity for cross sectional and time series comparison, superlativity for second
order local approximation, allowance for technological change over time and no assumption on

9perfect competition. TFP level for firm f in year t in a certain industry is defined in comparison
with the TFP level of a hypothetical representative firm in the base year in that industry as
follows:

n 1
ln TFPf ,t = (ln Q f ,t − ln Qt ) − ∑i =1 ( S i , f ,t + S i ,t )(ln X i , f ,t − ln X i ,t )
2
t −1
t −1
n 1
+ ∑s0=t (ln Qs − ln Qs +1 ) − ∑s0=t ∑i =1 ( S i ,s + S i ,s +1 )(ln X i ,s − ln X i ,s +1 )2

for t<t0,
n 1
ln TFPf ,t = (ln Q f ,t − ln Qt ) − ∑i =1 ( Si , f ,t + Si ,t )(ln X i , f ,t − ln X i ,t )2

(1)

for t=t0, andn 1
ln TFPf ,t = (ln Q f ,t − ln Qt ) − ∑i =1 ( S i , f ,t + S i ,t )(ln X i , f ,t − ln X i ,t )
2
t
t
n 1
+ ∑s =t +1 (ln Qs − ln Qs −1 ) − ∑s =t +1 ∑i =1 ( S i ,s + S i ,s −1 )(ln X i ,s − ln X i ,s −1 )00
2

for t>t0.

where Qf, t, Si, f, t, and Xi, f, t denote the gross output of firm f in year t, the cost share of factor ifor firm f in year t, and firm f’s input of factor i in year t, respectively. Variables with an upperbar denote the industry average of that variable. The representative firms for each industry aredefined as a hypothetical firm whose output, inputs, and cost shares of all production factors are
identical with the industry average.
The first two terms on the right hand side of equation (1) denote the gap between firm f’s
TFP level in year t and the representative firm’s TFP level in that year. The third and fourthterm denote the gap between the representative firm’s TFP level in year t and the representativefirm’s TFP level in base year t0. Therefore, lnTFPf, t in equation (1) denotes the gap between
firm f’s TFP level in year t and the representative firm’s TFP level in the base year. As in theprevious literature, constant returns to scale is assumed on the production function whose input
factors are capital input, labor input and real intermediate input.
In calculating equation (1), base year t0 is set to be 2000. As usual with micro-level data, it

10

is often pointed out that since some observation may have extraordinary values in their inputs or
output, values of inputs and output of representative firm may be affected. For this reason, we
take two-step calculation method. As the first step, the multilateral TFP is calculated in a normal
way. Then, the outliers are screened out by excluding the observation whose TFP index deviatesfrom the industry average of TFP in the year farther than three times standard deviation. As thesecond step, multilateral TFP is calculated again with recalculated industry average value. See
the Appendix for more detailed description on variables.
The result of aggregate TFP measurement is shown in Table 3 and Figure 1. The aggregate
TFP of Korean manufacturing has increased 29.5% in the period 1985-2005 with annual growthrate of 1.3%. The decline in 1998 due to the Asian financial crisis is remarkable. In 1998 the
TFP decreased to the level of 1985, but next year it quickly recovered to the level of 1997.
Remarkable result is that aggregate TFP growth rate is higher after the structural reforms than
before. The TFP growth in the period 1985-1992 is negligible, the 0.5% growth rate per annum,
which implies at least the TFP growth is not the main engine of economic growth and isconsistent to the arguments of Krugman (1994), Young (1994, 1995) and Rodrik (1995). After
small increase of annul growth rate of aggregate TFP, 1.6% in the period 1993-1997, the TFP
bounced back rapidly with the 3.1% growth rate per annum in the period 1999-2005, whichmeans that TFP growth has contributed to rapid Korean economic recovery and the paradigm ofeconomy growth has changed from inputs-driven into TFP-driven growth. Another remarkable
result is that the increasing gap between weighted and unweighted mean implies the movementof market share toward more productive firms.

[Table 3 here][Figure 1 here]

In addition, on the basis of methodological work at the OECD, manufacturing industries
were classified into four different categories of technological intensity; high, medium-high,11

medium-low and low technology industry.3 As shown in Figure 2, aggregate TFP growth of
manufacturing was driven mainly by high and medium-high technology industries while
aggregate TFP of low and medium-low technology industries have been hardly improved overtime.

[Figure 2 here]

The weighted means of lnTFP for major industries are presented in Figure 3. Electricalmachinery industry marked the highest increase of TFP, 44.8%, and then instruments industry
ranked the second highest increase, 42.7%. The TFP of motor vehicles industry during thefinancial crisis declined most seriously to the level of 1985 although all the industries
experienced commonly the TFP decline during this period. This is partly due to the sensitivityof motor vehicles to the business cycle and domestic market demand. In the case of primary
metal industry, it shows U-shaped curve distinctively because POSCO, the largest steal
company in Korea, constructed new work phase in Gwangyang to expand its production
capacities twice from 1987 to 1992, which seems to have caused the decline of productivity.

[Figure 3 here]

5. The sources of aggregate productivity growth

In order to identify the source of aggregate productivity growth, we decompose aggregate
TFP using cross-sectional decomposition methodology utilized by Olley and Pakes (1996) and
Griliches and Regev (1995). To begin with, an aggregate productivity in a given industry can be
represented by a weighted average of each individual firm’ productivity as follows;3

Refer to the research of Schaaper (2004) for more detailed standard of categorization.

12ln TFPt = ∑ f ∈F θ f ,t ln TFPf ,t(2)

where θf, t denotes firm f’s gross output share in year t in that industry. F is the set of all thefirms existed in year t in the industry. Then, as Olley and Pakes (1996) showed, we candecompose the manufacturing sector’s TFP in year t into the following two factors.

ln TFPt = ln TFPt + ∑ f (θ f ,t − θt )(ln TFPf ,t − ln TFPt )(3)

where an upper bar over a variable represents the cross-sectional unweighted mean acrossall firms in the same industry. Therefore an aggregate productivity can be decomposed into twoparts: The unweighted aggregated productivity and the total covariance between firm’s share ofindustry output and its productivity. In this decomposition, covariance term reflects gains in
productivity resulting from high productive firms’ expanding output shares or from low
productive firms’ shrinking output shares, that is output reallocation effect.
The decomposition results for major industries are presented in Table 4. In most industries,
the covariance terms have positive sign which means output reallocation effect contributed tothe aggregate productivity. Especially, major industries in Korea such as electrical machineryand motor vehicles have positive sign of covariance term over the whole period and their
proportions to the aggregate productivity are relatively high.

[Table 4 here]

The Olley and Pakes decomposition indicates whether or not more productive firms are
gaining at the expense of the less productive firms. But it does not differentiate between theindividual contribution of resource reallocation and factors that are not related to resource
reallocation on productivity growth. This can be captured by the Griliches and Regev

13decomposition (Dimova, 2008).
Griliches and Regev (1996) suggested that the change in a firm’s contribution to the total
can be decomposed as follows:θt ln TFPt − θt −i ln TFPt −i = θ ( a) d ln TFP + (dθ ) ln TFP(a )where

x(a ) = ( xt + xt −i ) / 2(4)

designates the period average for a variable anddx = xt − xt −i its change. This decomposition is meaningful for continuing firms in that it
makes possible to separate the contribution of within-firm productivity growth from thebetween-firm shifts in the relative weight of high – versus low-productivity firms.
The result of Griliches and Regev decomposition is shown is Table 5 and Figure 4. Except
apparel and transportation industries, between effects contributed to the productivity positively
during 1999-2002, and especially in fifteen industries within effect during 1999-2002 washigher than during 1993-1996. In the case of major industries in Figure 3, the between effects ofchemicals, non-electrical machinery, electrical machinery and motor vehicles were enhanced
after the financial crisis. Similarly, the within effects of primary metal, non-electrical machinery,electrical machinery and motor vehicles were also improved after the financial crisis.

[Table 5 here][Figure 4 here]

In order to identify output reallocation effect more intuitively, firms are divided into four
quartiles from the lowest quartile (Q1) to the highest quartile (Q4) according to the productivity
level by year and industry as shown in Figure 5. It is remarkable that the output share of the top
50% (Q3+Q4) tends to increase over time and exceeds 70% except chemical industry since1993. In the case of chemical industry, the output share of the top 50% is not high around 40%
compared with other industries, but since 1999 it began to increase rapidly to around 70%. This
trend of chemical industry is closely related to the trend of lnTFP in Figure 3 in that the14

productivity of chemical industry was stagnant until 1999, then, it increased rapidly.

[Figure 5 here]

The results of the decompositions by equation (3) and (4) implies commonly that the output
reallocation effect that more productive firms take up higher market share has contributed to theincrease of the TFP and Figure 5 is the ex-post evidence for this effect. Moreover, Griliches andRegev decomposition provides additional information that firms’ effort to improve theirproductivity, i.e. within effect, is another source of productivity increase.

6. Market competition and productivity growth

It is revealed that within effect is one of the sources of productivity growth in section 5, andKorean government introduced various institutions for fair competition after the Asian financial
crisis. In this vein, this section investigates whether or not the change of competition
environment after the Asian financial crisis contributed to firm’s productivity growth. Thus, as ameasure of market competition at the industry level, the index Aghion et al.(2002) suggested isused as follows:c jt = 1 −

1
∑ lit
N

where i indexes firms,(5)

j indexes industry, t indexes time, Njt is the number of firms in theindustry j in year t, and lit is defined as (operating profit – financial cost)/sales 4. A value of 1
indicates perfect competition, i.e. price equals marginal cost while values below 1 indicate some

4

The financial cost is the multiplication of capital cost and capital stock. The capital cost is assumed tobe the bond rate and the capital stock is measured using the perpetual inventory method. See the
Appendix for detailed information on the bond rate and the perpetual inventory method.

15

degree of market power.
To examine the effect of market competition on the productivity growth rate, pooled OLS
model is used as follows;dlnTFPijt = f(lnTFPijt-1, Xijt-1, cjt-1, crisisit, cjt-1*crisisit, yeart, industryj)

where i indexes firms,(6)

j indexes industry, t indexes time, dlnTFPijt is the TFP growth rate(=lnTFPijt - lnTFPijt-1), Xijt-1, is control variable for firm i at time t-1, cjt-1, is the competitionindex of industry j at time t-1, and crisisit is post-crisis dummy which has value 1 if year>1998,
otherwise 0. The interaction term between competition and post-crisis dummy is expected tocapture the effect of competition environment’ change after the financial crisis on the
productivity growth rate, and industry and year dummy are added to control the industry
characteristics and macro-economic effect. With regard to control variable Xijt-1, firm size(=log(total asset)), firm age, group dummy which has value 1 if a firm belongs to businessgroup, debt ratio (=debt/total asset), no R&D dummy which has value 1 if a firm do not invest
on R&D, R&D intensity (=R&D/sales) and export/output defined as the ratio of industry export
to industry output.5 Descriptive statics for variables are presented in Table 6.

[Table 6 here]

However, the presence of unmodeled heterogeneity among firms, such as manager’s
capability and the skill of the employees can make the coefficient from pooled OLS biased.
Thus the fixed effect model is also used to remove systematic heterogeneity from the error termby adding firm specific constant term as follows.

dlnTFPijt = αi + f(lnTFPijt-1, Xijt-1, cjt-1, crisisit, cjt-1*crisisit, yeart)5(7)

There is a criticism that depreciated Korean won during crisis period provided favorable environment
for export, and this might contributed to the recovery from the crisis (Park; 2001, Chopra et al.; 2001).
To control export effect, we added the ratio of export to output at industry level.

16

Estimation of fixed effect model sacrifices a lot of degree of freedom. And with so many
firms and short time period, these intercepts may be picking up on a lot of random error andthus be quite inconsistent. Therefore, we convert variables for each observation into a deviationfrom the mean in each firm. This sweeps out the unit effect and we no longer need to include anintercept term.

[Table 7 here]

The regression results of pooled OLS and fixed effect model presented in Table 7 implies asfollows. First, the reinforcement of market competition after the financial crisis stimulated forfirms to increase their productivity. Although post-crisis dummy has positive and statisticallysignificant value in model 1 and 3, it turns to negative (Model 2) or lessened (Model 4) wheninteraction term is added, and interaction term has positive and statistically significant value
both in pooled OLS and fixed effect model. Although the TFP growth due to the favorable
export environment during post-crisis period is controlled by inserting export/output ratio at theindustry level, the interaction term of (A) and (B) has significant and positive effect on the TFP
growth rate.
Second, the productivity tends to converge strongly. A firm which has higher value in the
TFP or size or age at previous year is likely to have lower TFP growth rate next year. Given that
it is general for the TFP level and size of a firm to increase gradually as time goes on, thenegative coefficients of lnTFP, log(total asset) and age support the convergence of productivity
among firms. This trend becomes stronger when the heterogeneity among firms is controlled.
Third, the firms affiliated with business groups have higher productivity. Tangible andintangible support within the same business group can contribute to enhance the productivity.
This fact is consistent with many previous studies which claimed that affiliation with a businessgroup improves firm performance (Carney et al. 2008, Ma et al., 2006, Khanna and Palepu,
2000, Blanchard et al., 2004)

17

Fourth, R&D investment contributes to the TFP growth rate. The coefficient of R&D
intensity in Pooled OLS implies that 10% increase in R&D intensity results in 1.9% increase in
the TFP growth rate. Non-R&D performer has lower TFP growth rate than R&D performer, but
this is not statistically significant.
Fifth, the TFP growth rate is affected by the export environment. When the export
proportion of certain industry to which a firm belongs increases, a firm’s TFP growth rate also
increases. This effect may be valid during post-crisis period when Korean firms had advantage
in trade due to the depreciated Korean won.
The determinants of the TFP growth rate are also considered by technology intensity
following OECD standards. Except low technology industry, the reinforcement of competition
after the Asian financial crisis contributed to TFP growth rate significantly, and its effect is thelargest in high technology industry. Moreover, the effect of R&D intensity on the TFP growthrate is positive and significant only in high technology and medium-high technology industries.
It is also remarkable that group dummy is not significant only in high technology industry,
which implies that the reinforcement of core competency by R&D is more important thantangible and intangible support within business group in high technology industry.

[Table 8 here]

7. Concluding remarks

Using firm level data during 1985-2005, we measured the TFP of Korean manufacturingsector and identified determinants of the TFP growth rate. Given that various institutions for faircompetition were introduced in 1988, investigating the difference of the TFP trends before andafter the Asian financial crisis is meaningful. With the massive unbalanced panel data, we
examined quantitatively whether or not the comprehensive structural reforms have the effect on
the Korean economy in terms of productivity.

18

It is shown that the TFP growth rate in the period 1985-1992 is negligible, 0.5% per annum,
thus the TFP is not the main source of economy growth during this period. However, after steep
decline in 1998, the TFP bounced back rapidly with 3.1% growth rate per annum in the period
1999-2005, which implies that the paradigm of economy growth has changes inputs-driven into
TFP driven growth. When the TFP growth is decomposed, it is revealed that the output
reallocation effect as well as within effect are main sources of the TFP growth. With regard to
the determinants of the TFP growth rate, the reinforcement of competition after the Asianfinancial crisis contributed to the TFP growth rate, justifying introduction of various institutions
for fair competition during the crisis. When industries are classified into sub industries bytechnology intensity, it can be said that the TFP growth has been driven by high technology andmedium-high technology, and in high technology industry, the reinforcement of competition
during post-crisis period and R&D intensity affected the TFP growth rate positively andsignificantly.
However, this study raises many issues as future research. First, the dynamic characteristics
of firms such as entry and exit should be considered. By using the information on entry and exit,
the more detailed sources of the TFP growth can be provided. Second, the usage of moreaccurate competition index is required. Competition index used in this study assumes that thedegree of competition is in the relation of reciprocal proportion with average mark-up. However,the degree of competition which firms face can be affected by market structure, importpenetration, regulation etc. More accurate competition index is expected to improve thereliability of the research.

19

Appendix. Main variables to measure TFP6

OutputFor output, gross sales after adjusting for inventory are used. Gross output is deflated by output
deflators derived from EU KLEMS data.

Capital Stock
The perpetual inventory method is used to calculate real capital stock.

K t = (1 − δ ) K t −1 +NOMI t
PK t(A.1)

NOMI t = KNBt − KNBt −1 + DEP(A.2)

where PKt is the price index for the capital asset, KNBt is the book value of tangible fixed asst
minus land and construction in-progress, DEPt is the accounting depreciation during the period,
NOMIt is the nominal investment, and δ is depreciation rate derived from EU KLEMS data.
The perpetual inventory method is sensitive to initial capital stock. Especially when thetangible asset data prior to starting year of analysis is not available, initial capital stock ignore
previously installed capital input, thus it may cause the underestimation of initial capital stock.
To adjust initial capital stock, we calculated∑ K ∑ KNB for KOSPI listed firms by year

and industry, then multiplied them to initial capital stock.

Intermediate inputs
Intermediate input is calculated as follows:

Intermediate input = Sales cost +Selling & general administrative expenses –Wage –Depreciation – R&D expenses
6

(A.3)

For variable selection and detailed calculation, we referred Kim(2007) and Fukao et al.(2006).
20

Intermediate inputs are deflated by intermediate input deflators derived from EU KLEMS data.

Labor input
Man-hours are used as labor input. Industry average man-hours are derived from EU KLEMS
data then man-hours are calculated by multiplying the number of employees and industry
average man-hours.

Capital Cost
Capital cost is measured as follows:ck =⎛ p ⎞⎫
1− z ⎧p ⎨λ r + (1 − u )(1 − λ )i + δ − ⎜ ⎟ ⎬1− u ⎩⎝ p ⎠⎭(A.4)

where u is the effective corporate tax rate, λ is the own-capital ratio, r is the long-termbond rate, i is the prime rate, δ is depreciation rate, p is the price index, p is the five
year moving average of price index, and z is the expected present value of tax savings due todepreciation allowances on a won of investment in capital goods. z is calculated as follows:z = (u iδ ) / ⎡⎣{λ r + (1 − u )(1 − λ )i} + δ ⎤⎦(A.5)

Capital cost is calculated by multiplying c by the capital stock.

Effective tax rate
In Korea, taxes imposed on firms consist of corporate and local tax. Corporate tax isdetermined as the multiplication of profit and tax rate. Local tax consists of the one proportional
to the amount of corporate tax and the other imposed uniformly in the same profit range.

21However, the latter is negligible compare with the former. Considering aforementioned tax
system, the effective tax rate can be determined as follows:τ t = τ tN (1 + τ tL )where τ tN and(A.6)

τ tL are tax rate of corporate tax and local tax in time t, respectively.

Long-term bond rate
The government bond more than five year has issued since mid 1990s, thus it is not
appropriate as long-term bond rate. However, Korean government has issued several bonds inthe relation with constructing house and road and so on. Among these, Housing bond has been
one of the largest bond and has been traded for long time with stability. Therefore interest rateof 5 year Housing bond is used as a long-term bond rate.

Prime rate
Although the prime rate has been used since 1988, the movement of prime rate was so
inflexible that usual interest rate was less than prime rate since 1999. Ultimately the prime rate
is not used any longer in 2000s. As an alternative, Industrial Financial Debentures issued by the
Korea Development Bank is used. It has been issued since 1954, and used as a standard for along-term fixed debt.

22

References

Aghion, P., N. Bloom, R. Blundell, R. Griffith, and P. Howitt (2002), “Competition andInnovation: An Inversed U Relationship”, NBER Working Paper Series 9269.
Ahn, S. (2006), “Internationalization of Production and Change of Productivity: Evidence fromMicro Data and International Comparison”, KDI Research Report 2006-5. (in Korean)Ahn, S., K. Fukao and H. Kwon (2004), “The Internationalization and Performance of Korean
and Japanese Firms: An Empirical Analysis Based on Micro Data.” Seoul Journal of
Economics Vol. 17, pp. 439-482.
Aw, B.Y., S. Chung and M.J. Roberts (2000), “Productivity and Turnover in the Export Market:
Micro Evidence from Taiwan and South Korea”, World Bank Economic Review Vol. 14,
pp. 65-90.
Aw, B.Y., S. Chung and M.J. Roberts (2003), “Productivity, output, and failure: A Comparisonof Taiwanese and Korean Manufacturers”, The Economic Journal Vol. 113, pp. 485-510.
Blenchard, P., J.P. Huiban and P. Sevestre (2004), “R&D and Productivity in Corporate Groups:
An empirical Investigation Using a Panel of French Firms”, Discussion Paper.
Carney, Michael, D. Shapiro and Y. Tang (2008), “Business Group Performance in China:
Ownership and Temporal Consideration”, Management and Organization Review,
forthcoming.
Chang, Hajoon (1994) The Political Economy and Industrial Policy, Macmillan, London andBasingstoke.
Chang, Jinho, Young Jun Cho, and Hyun-Han Shin (2007), “The change in corporatetransparency of Korean firms after the Asian Financial crisis: an analysis using analysts’
forecast data” Corporate Governance Vol 15, No. 6, pp.1144-1167.
Chopra, Ajai, Kenneth H. Kang, Meral Karasulu, Hong Liang, Henry P. Ma and Anthony J.
Richards (2001) “From Crisis to Recovery in Korea: Strategy, Achievements, andLessons, IMF Working Paper WP 01-154.
Cho, Yoon-je and Joon Kyung Kim (1994), “Credit Policies and the Industrialization of Korea,”23

World Bank Working Paper, No.286.
Crotty, James and Kang-Kook Lee (2001), “Economic Performance in Post-Crisis Korea: A
Critical Perspective on Neoliberal Restructuring,” Political Economy Research Instituteof University of Massachusetts Amherst, Working Paper Series no.23.
Crotty, J. and K. Lee (2002), “Is Financial Liberalization good for Developing Nations?: Thecase of South Korea in the 1990s”, Review of Radical Political Economics 34, 327–334.
Dimova, R. (2008), “The Impact of Labour Reallocation and Competitive Pressure on TFP
Growth: Firm-level Evidence from Crisis and Transition ridden Bulgaria”, International
Review of Applied Economics, Vol. 22, No. 3, pp.321-338.
Fischer, S. (1998), The Asian crisis: A view from the IMF, press release.
Fukao, K., Y. Kim, and H.U. Kwon (2006), “Plant Turnover and TFP Dynamics in Japanese
Manufacturing”, Hitotsubashi University, Hi-Stat Discussion Paper Series No.180.
Funakoshi, M., K. Motohashi and A. Tohei (2006), “Quantitative Analysis on Competition and
Productivity”, Competition Policy Research Center of Fair Trade Commission of Japan,
CPRC Discussion Paper Series CPDP-21-E.
Good, D.H., M.I. Nadiri, and R. Sickles (1996), “Index Number and Factor Demand
Approaches to the Estimation of Productivity”, NBER Working Paper 5790.
Griliches, Z. and H. Regev (1995), “Firm Productivity in Israeli Industry 1979-1988”, Journalof Econometrics Vol. 65, pp. 175-203.
Hahn, C.H. (2000), “Entry, Exit and Aggregate Productivity Growth: Micro Evidence on
Korean Manufacturing”, OECD Economics Department Working Papers 272.
Hahn, C.H. (2004), “Exporting and Performance of Plants: Evidence from Korean
Manufacturing” NBER Working Paper 10208.
Jo, S.W. (2001), Empirical Analysis on Performance of Policies on Chaebol after Financial
Crisis, KDI Policy Research 2001-15. (in Korean)Jung. M. (2008), “Productivity (TFP) and Catching Up of Korean Firms with the JapaneseFirms: SIS (Sectoral Innovation System) and Firm Level Learning”, Ph.D. Dissertation,
Seoul National University, Korea.

24

Khanna, T. and K. Palepu (2000), “Is Group Affiliation Profitable in Emerging Markets? An
Analysis of Diversified Indian Business Groups”, The Journal of

Finance, Vol. 55, No.

2, pp. 867-891.
Kim, C (2006), “Firm Dynamics and Productivity in Korean Manufacturing”, Ph. D.
Dissertation, Seoul National University, Korea. (in Korean).
Kim, J.K., and J.I. In (2004), Performance Evaluation of Restructuring after Financial Crisis:
Profitability and Financial Soundness, KDI Policy Forum 168, 2004-01. (in Korean)Kim, Younggak (2007), “Estimating Production Functions with R&D Investment andEndogeneity”, Hitotsubashi University, Hi-Stat Discussion Paper Series No.229.
Krugman, Paul (1994), “The Myth of Asia’s Miracle,” Foreign Affairs, Vol. 73, No.6, pp.62-78.
Krugman, Paul, (1998), What Happened to Asia, Conference Paper in Japan.
Kwack, S (2007), “Knowledge Capital and Export Growth in Korean Manufacturing Industries”,
KIET Korea Institute for Industrial Economics & Trade) Occasional Paper No. 65.
Ma, X., X. Yao and Y. Xi (2006), “Business Group Affiliation and Firm Performance in a
Transition Economy: A Focus on Ownership Voids”, Asia Pacific Journal of Management
Vol. 23, pp. 467-483
Nickell, S. (1996), “Competition and Corporate Performance”, Journal of Political Economy Vol.
104, pp. 724-746.
Nickell, S., D. Nicolitasas and N. Dryden (1997), “What Makes Firms Perform Well?”,
European Economic Review, Vol. 41, pp. 783-796.
Oh, I., A. Heshmati, C. Baek. and J. Lee, (2009), "Comparative Analysis of Plant Dynamics bySize: Korean Manufacturing" Japanese Economic Review (forthcoming).
“Comparative Analysis of Firm Dynamics by Size: Korean Manufacturing.” Ratio Working
Papers 94.
Olley, G.S., and A. Pakes (1996), “The Dynamics of Productivity in the Telecommunications
equipments Industry”, Econometrica, Vol. 64(6), pp. 1263-1297.
Park, Yung Chul (2001) “The East Asian Dilemma: Restructuring Out or Growing Out?”Princeton Essays in International Economics no.223.
25

Pyo, H, K. Rhee and B. Ha (2006), “Growth Accounting and Productivity Analysis by 33
Industrial Sectors in Korea (1984-2002)”, Hi-Stat Discussion Paper Series of Institute of
Economic Research, Hitotsubashi University.
Radelet, S., and S. Jeffrey, 1998, “The Onset of the East Asian Crisis”, NBER Working Paper.
Rodrik, D. (1995), “Getting Interventions Right: How South Korea and Taiwan Grew Rich.”
Economic Policy Vol. 10, pp. 54–107.
Schaaper, M. (2004), “An emerging knowledge-based economy in China? Indicators from
OECD databases”, OECD STI working paper 2004/4.
Young, A. (1994), “Lessons from the East Asian NICs: A Contrarian View.” European
Economic Review Vol. 38, pp. 964–973.
Young, A. (1995). “The Tyranny of Numbers: Confronting the Statistical Realities of the East
Asian Growth Experience”, Quarterly Journal of Economics Vol. 110, pp. 641–680.
World Bank (1993), “The East Asian Miracle: Economic Growth and Public Policy,” Oxford
University Press, New York, NY.

26

Table 1. The coverage of data used, as of 2005
sales

tangible asset(bil. Won)(bil. Won)

1,539,465

712,316

259,873

5,141

1,131,349

537,519

196,900

0.84

0.73

0.75

0.76

no of firms

employees

6,144

The whole firms with 50 or more
employees and with 300 million
Won of capital stocks (A)Data usedin this research (B)(B)/(A)(A) is referred from the Business Survey by National Statistical Office.

27

Table 2. Firm distribution by industry and year
Industry

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Total

Food and kindred products

96

100

109

113

121

125

145

149

152

146

147

175

175

195

186

183

176

206

208

231

216

3,354

Textile mill products

46

52

60

67

80

84

101

105

104

94

99

106

120

132

115

117

123

142

152

140

129

2,168

Apparel

24

28

30

35

44

46

57

63

65

64

71

77

80

87

75

60

63

70

88

93

85

1,305

Lumber and wood

4

4

5

6

6

5

8

9

8

8

8

11

14

21

30

28

27

28

29

34

32

325

Furniture and fixtures

7

7

7

7

9

10

11

13

13

13

15

20

27

26

28

35

37

42

44

46

46

463

Paper and allied

36

42

47

50

54

58

67

70

68

74

83

86

85

88

83

91

91

109

122

115

97

1,616

Printing, publishing and allied

25

25

31

35

40

50

66

71

68

77

84

103

107

130

123

110

146

205

214

219

202

2,131

Chemicals

152

167

181

201

229

237

275

292

288

285

310

332

341

365

334

319

355

420

421

435

391

6,330

Petroleum and coal products

10

10

13

11

12

13

15

15

13

14

14

16

17

20

27

23

23

27

28

28

24

373

Leather

10

10

14

16

19

20

24

24

24

23

23

25

24

27

26

25

21

21

28

32

26

462

Stone, clay, glass

61

72

78

85

89

96

114

123

118

117

121

148

152

174

151

157

172

189

213

227

208

2,865

Primary metal

68

75

78

90

109

123

154

159

154

167

188

210

244

272

287

284

316

395

387

408

374

4,542

Fabricated metal

41

49

55

74

87

85

100

111

99

106

116

160

186

221

233

258

295

340

350

360

348

3,674

Machinery, non-elec

40

47

72

82

96

105

139

150

139

156

188

336

407

502

511

534

624

714

758

810

734

7,144

Electrical machinery

105

121

150

158

182

196

236

251

252

280

313

404

489

570

623

679

834

1,003

1,074

1,125

1,024

10,069Motor vehicles

51

73

91

104

127

133

171

176

177

199

228

245

291

323

401

434

468

559

581

589

559

5,980

Transportation equipment & ordnance

9

7

10

11

13

13

15

20

22

22

28

47

69

78

83

90

91

111

115

120

117

1,091

Instruments

16

17

21

25

26

29

38

38

38

40

48

77

88

111

131

122

143

167

181

190

179

1,725

Rubber and misc plastics

22

29

37

46

55

54

74

78

73

76

89

116

149

186

187

197

217

268

299

312

273

2,837

Misc. manufacturing

9

10

13

13

17

16

19

21

19

18

17

27

31

42

40

39

36

41

42

43

35

548

Total

832

945

1,102

1,229

1,415

1,498

1,829

1,938

1,894

1,979

2,190

2,721

3,096

3,570

3,674

3,785

4,258

5,057

5,334

5,557

5,099

59,00228

Table 3. The result of lnTFP measurement

weighted

unweighted

mean

mean

821

-0.016

1986

929

1987

year

no of obs

min

max

std dev

1985-0.040-0.569

0.489

0.119

-0.016-0.027-0.649

0.414

0.116

1,079

-0.002-0.006-0.704

0.398

0.119

1988

1,216

-0.007-0.001-0.551

0.630

0.122

1989

1,386

0.014

0.005

-1.076

0.545

0.142

1990

1,470

0.022

0.024

-0.924

0.857

0.153

1991

1,804

0.029

0.026

-1.393

0.810

0.183

1992

1,902

0.019

0.016

-1.069

0.580

0.174

1993

1,860

0.023

0.012

-1.381

0.600

0.171

1994

1,939

0.053

0.021

-0.667

0.623

0.160

1995

2,158

0.076

0.031

-0.781

1.013

0.166

1996

2,677

0.077

0.051

-0.720

0.727

0.168

1997

3,055

0.087

0.060

-0.929

0.971

0.181

1998

3,519

-0.0140.001-1.019

0.803

0.214

1999

3,625

0.091

0.073

-1.199

1.103

0.181

2000

3,745

0.173

0.103

-0.929

0.880

0.171

2001

4,222

0.167

0.118

-0.719

0.934

0.173

2002

5,011

0.223

0.127

-0.918

0.924

0.187

2003

5,274

0.247

0.140

-0.780

0.868

0.193

2004

5,137

0.274

0.144

-0.794

1.046

0.197

2005

4,728

0.279

0.164

-0.697

1.098

0.197

`

Table 4. The result of Olley and Pakes decomposition
Industryyear

Food and1985

aggregate unweighted
covarianceproductivity
mean-0.026-0.040Industry

year

0.014

Textile mil

1985

products

aggregate unweighted
covarianceproductivity
mean-0.030-0.035

0.005

1990

0.016

-0.001

0.017

1995

-0.054-0.093

0.039

0.062

2000

-0.062-0.039-0.023

0.091

2005

-0.025-0.0420.016

kindred1990-0.026-0.0370.011

products

1995-0.014-0.070

0.056

2000

0.011

-0.050

2005

0.052

-0.03829

1985-0.008-0.0270.019

Lumber and

1985

0.001

0.019301-0.018

1990

0.109

0.081

0.028

wood

1990

0.000

0.017894-0.018

1995

0.097

0.023

0.074

1995

-0.121-0.09541-0.026

2000

0.119

0.071

0.048

2000

-0.082-0.03731-0.045

2005

0.154

0.139

0.015

2005

-0.0150.007072-0.022

Furniture and

1985

0.057

-0.015

0.072

Paper and

1985

0.024

-0.023

0.047

fixtures

1990

0.237

0.272

-0.035

allied

1990

0.050

0.037

0.013

1995

0.187

0.152

0.035

1995

0.078

0.046

0.033

2000

0.153

0.104

0.049

2000

0.048

0.010

0.038

Apparel

2005

0.180

0.146

0.034

Printing and

1985

0.056

-0.0370.093

publishing and

1990

0.150

0.006

allied

1995

0.146

2000

0.199

2005
Petroleum andcoal products

Stone, clay,
glass

2005

0.032

-0.018

0.051

1985

-0.024-0.054

0.030

0.144

1990

-0.0130.021-0.034

0.055

0.090

1995

-0.045-0.031-0.014

0.125

0.074

2000

0.086

0.083

0.004

0.063

0.026

0.037

2005

0.144

0.099

0.044

1985

-0.017-0.031

0.014

1985

-0.029-0.016-0.0141990-0.0100.009-0.019

1990

0.030

0.032

-0.002

1995-0.009-0.028

0.019

1995

0.055

0.006

0.049

2000

-0.042-0.069

0.027

2000

0.009

0.004

0.004

Chemicals

Leather

2005

0.035

-0.041

0.075

2005

0.033

0.046

-0.013

1985-0.035-0.061

0.026

Primary1985-0.019-0.037

0.017

metal

1990

-0.017-0.005-0.0121990-0.162-0.024-0.137

1995

0.009

0.028

-0.0191995-0.0660.005-0.071

2000

0.062

0.028

0.034

2000

0.013

0.036

-0.022

2005

0.090

0.072

0.017

2005

0.144

0.038

0.106

Fabricated1985-0.124-0.043-0.080Machinery,

1985-0.035-0.027-0.008

metal

1990

0.034

0.017

0.017

non-elec

1990

0.153

0.115

0.038

1995

-0.001-0.065

0.064

1995

0.218

0.164

0.054

2000

-0.047-0.087

0.040

2000

0.204

0.175

0.029

2005

-0.033-0.085

0.052

2005

0.296

0.266

0.030

Electrical

1985-0.007-0.042

0.035

Motor

1985

0.002

-0.0280.030

machinery

1990

0.063

0.018

0.045

vehicles

1990

0.088

0.025

0.064

1995

0.254

0.064

0.190

1995

0.182

0.094

0.088

2000

0.380

0.224

0.155

2000

0.218

0.142

0.075

2005

0.441

0.350

0.090

2005

0.233

0.201

0.032

Transportation

1985

0.043

-0.035

0.079

1985

-0.021-0.0520.031

equipment &

1990

0.214

0.171

0.042

1990

0.158

0.161

-0.003

ordinance

1995

0.214

0.176

0.038

1995

0.268

0.259

0.009

2000

0.264

0.180

0.084

2000

0.394

0.293

0.101

2005

0.290

0.273

0.017

2005

0.407

0.339

0.067

Rubber and

1985

0.007

-0.017

0.024

Misc.

1985

0.044

-0.0340.078

misc plastics

1990

0.058

-0.0210.078

manufacturing

1990

0.183

0.144

0.039

1995

0.010

-0.032

0.042

1995

0.133

0.133

0.000

2000

-0.001-0.018

0.018

2000

0.209

0.183

0.027

2005

0.050

0.003

0.047

2005

0.138

0.154

-0.01530

Instruments

Table 5. The result of Griliches and Regev decomposition
Industry

1993-1996

1999-2002

2002-2005

withinbetweenTotal

withinbetweenTotal

withinbetween

Total

Food and kindred products

0.019

0.010

0.029

-0.008

0.008

0.000

0.021

0.001

0.022

Textile mill products-0.003

0.004

0.001

0.046

0.018

0.064

-0.012

0.028

0.016

Apparel-0.0220.016-0.0060.054-0.020

0.034

0.033

0.011

0.044

Lumber and wood

-0.004-0.011-0.015

0.080

0.001

0.081

-0.008

0.011

0.003

Furniture and fixtures

0.008

0.004

0.012

0.013

0.017

0.031

0.008

-0.0050.004

Paper and allied-0.030-0.004-0.034

0.060

0.000

0.060

0.001

-0.0010.000

Printing, publishing and allied

0.012

0.017

0.030

-0.0590.006-0.053

0.048

0.025

0.073

Chemicals

0.036

0.001

0.038

0.017

0.013

0.030

0.008

0.009

0.017

Petroleum and coal products

0.024-0.009

0.015

0.001

0.000

0.001

0.001

0.003

0.004

Leather

0.001

0.009

0.011

0.004

0.019

0.023

0.033

0.014

0.047

Stone, clay, glass-0.004

0.005

0.001

0.047

0.007

0.053

-0.004

0.006

0.003

Primary metal

0.028

0.009

0.037

0.042

0.005

0.047

0.010

0.008

0.017

Fabricated metal-0.0210.014-0.007

0.008

0.001

0.009

0.003

-0.004-0.001

Machinery, non-elec

0.008

0.007

0.015

0.017

0.019

0.036

0.011

0.019

0.030

Electrical machinery-0.0370.006-0.031

0.107

0.036

0.143

-0.0140.009-0.005Motor vehicles

0.030-0.005

0.025

0.058

0.010

0.067

-0.018-0.003-0.020Transportation equipment & ordnance-0.007-0.002-0.0090.031-0.003

0.028

0.024

-0.0030.021

Instruments

0.028

0.015

0.043

0.005

0.009

0.014

0.010

0.018

0.027

Rubber and misc plastics-0.0150.007-0.009

0.008

0.004

0.012

0.015

0.005

0.020

Misc. manufacturing-0.0180.014-0.005-0.0160.010-0.006-0.010

0.031

0.022

31

Table 6. Descriptive statistics
level

mean

medianminmax

std. dev.

TFP growth rate

firm

0.016

0.013

-1.380

1.527

0.124

lnTFP

firm

0.079

0.076

-1.393

1.103

0.189

log(total asset)

firm

16.51916.2989.832

24.646

1.435

age

firm

13.991

11.0000.000

109.000

11.343group dummy

firm

0.571

1.000

0.000

1.000

0.495

debt ratio

firm

0.669

0.691

0.000

15.412

0.263

no R&D dummy

firm

0.435

0.000

0.000

1.000

0.496

R&D intensity

firm

0.008

0.000

-0.035

1.056

0.024

export/output

industry

0.450

0.389

0.011

2.918

0.448

crisis

firm

0.555

1.000

0.000

1.000

0.497

competitionindustry

0.997

0.989

0.937

1.255

0.036

32

Table 7. The determinants of the TFP growth
Pooled OLS
Model 1constant-0.0375*(-1.70)lnTFP(t-1)-0.4023***(-117.48)
log(total asset)(t-1)-0.0015-0.0003******debt ratio(t-1)

no R&D dummy(t-1)

R&D intensity(t-1)

0.0054-0.4026***-0.0015-0.0003

0.0054-0.6174***(-143.21)***-0.0115-0.0097-0.618***-0.0115***(-4.33)-0.0099***(-0.39)(-0.42)(6.43)(6.36)-0.001-0.0011-0.001-0.0011(-0.90)(-0.91)(-0.65)(-0.68)0.00890.04150.0367(1.77)(A)*(B)

0.1931***(7.65)**

0.0095*-0.0752

0.0781*****(2.02)**(2.50)***

0.00780.27690.0764*0.0079***0.1258(2.21)

0.006

0.0055-0.0354(0.26)(0.27)(-1.55)0.157(2.87)
Year dummy

Included

IncludedIndustry dummy

Included

Included

Adj. R-squared

0.280

no of obs

45,929***(1.93)(6.80)******(1.97)(1.91)*

0.0204(-1.84)0.1186***(-4.39)0.0206******(-9.25)-0.0009

0.1939***(-143.29)(-9.26)******(7.94)-0.0009(10.83)competition(t-1) (B)(7.05)

0.2416(4.74)(2.35)
post-crisis dummy (A)(-0.29)***Model 4(4.75)(7.68)
export/output(t-1)

0.2006(-6.06)***

Model 3-0.0071(-3.15)(-6.08)group dummy(t-1)

Model 2(-117.53)(-3.14)age(t-1)

Fixed effect model

**

***

(3.82)
Included

Included

0.28

0.291

0.291

45,92945,92945,929The values in the parenthesis are t-values. *** , ** and * indicate significance at the 1, 5, 10 percent
levels, respectively. Dependent variable is the growth rate of TFP (=lnTFP(t)-lnTFP(t-1)), and postfix(t-1) means the one-year lags.

33

Table 8. Determinants of TFP growth rate by technology intensity

samplehigh tech.

constant

0.4979***(5.59)
lnTFP(t-1)-0.4465***(-50.66)
log(total asset)(t-1)-0.0035**(-2.52)age(t-1)-0.0003*(-1.91)group dummy(t-1)
debt ratio(t-1)
no R&D dummy(t-1)
R&D intensity(t-1)

post-crisis dummy (A)0.1517(0.85)(3.62)-0.4631-0.002(1.12)(-1.39)(-1.75)-0.0006(-0.47)(-3.92)0.0035-0.0036(0.94)(-2.02)

***

0.212

-0.0005
0.0099

***

**

***

-0.0102-0.0015(-0.17)(-0.61)-0.0861

0.0339-0.0251(-1.83)-0.0699-0.2493(-1.19)(-1.91)

0.0612-0.1197(1.45)(-3.11)0.1543***0.2902*****0.0058-0.0003(-3.59)*****(1.33)(-0.34)0.60230.0077(-2.48)-0.0197*(2.95)(-0.84)***-0.0002***(-2.27)***(4.95)-0.0031-0.389***(-4.62)***(5.05)***-0.3595-0.0012-0.015******0.0008-0.0029-0.4798(0.24)(-47.10)(3.59)***-0.4137

0.0164(-61.93)(-0.66)

0.1248******low tech.

(-80.93)

0.0061(-4.19)(A)*(B)

0.0358-0.0022(-2.81)competition(t-1) (B)medium-low(-6.52)(2.78)
export/output(t-1)

medium-high(0.48)*

0.0219(1.40)*

0.3809***(3.21)***

0.0057(0.09)**-0.367(3.42)(2.66)(2.28)(-3.12)

Year dummy

Included

Included

Included

IncludedIndustry dummy

Included

Included

Included

Included

Adj. R-squared

0.288

0.336

0.309

0.224

no of obs

7,966

17,448

11,0659,450***

The values in the parenthesis are t-values. *** , ** and * indicate significance at the 1, 5, 10 percent
levels, respectively. Dependent variable is the growth rate of TFP(=lnTFP(t)-lnTFP(t-1))

34

Figure 1. The mean of lnTFP

0.30

weightedmean

unweighted
mean

0.20

0.10

0.00
1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

-0.10

Figure 2. The mean of lnTFP by technology intensity
0.5

0.4

0.3

lnTFP

0.2

0.1

0
1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

-0.1

-0.2

high tech

med-high tech35

med-low tech

low tech

2005

Figure 3. Gross output weighted mean of lnTFP by major industry
Chemicals

Primary metal
0.200

0.200

0.100
0.100

0.000
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

0.000
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

-0.100-0.200-0.100Machinery, non-elec

Electrical machinery

0.400

0.500
0.400

0.300

0.300

0.200
0.200

0.100
0.100

0.000
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

0.000
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

-0.100-0.100Motor vehicles

Instruments
0.500

0.300

0.400

0.200
0.300

0.100

0.200
0.100

0.000
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

0.000
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

-0.100-0.10036

Figure 4. Griliches and Regev decomposition by major industry
Chemicals

Primary metal

0.040

0.050

between0.030

between
0.040

withinwithin

0.030
0.020
0.020
0.010

0.010

0.000

0.000

1993-1996

1999-2002

2002-2005

1993-1996

Machinery, non-elec

1999-2002

Electrical machienry
0.200

0.040

betweenbetween0.030

2002-2005

0.150

withinwithin

0.100

0.020

0.050
0.010
0.000

1993-1996

0.000

1993-1996

1999-2002

2002-2005

1999-2002

2002-2005-0.050Motor vehicles

Instruments

0.100

0.050

betweenbetween
0.040

within0.050

within

0.030
0.020

0.000

1993-1996

1999-2002

0.010

2002-2005

0.000

1993-1996-0.05037

1999-2002

2002-2005

Figure 5. Output share by quartile of lnTFP : major industries
El e c t r i c al m ac h i n e r y

C h e m i c al s
100%

100%

80%

80%

Q4
Q3
Q2
Q1

M ac h i n e r y, n o n - e l e c

20
05

20
03

20
01

19
99

19
97

19
95

M o t o r ve h i c l e s

100%

100%

80%

80%

Q4
Q3
Q2
Q1

38

20
05

20
03

20
01

19
99

19
97

19
95

19
93

19
85

20
05

20
03

20
01

19
99

19
97

19
95

19
93

0%
19
91

0%
19
89

20%

19
87

20%

19
91

40%

19
89

40%

Q4
Q3
Q2
Q1

60%

19
87

60%

19
85

19
93

19
85

20
05

20
03

20
01

19
99

19
97

19
95

19
93

19
91

0%

19
89

0%
19
87

20%

19
85

20%

19
91

40%

19
89

40%

Q4
Q3
Q2
Q1

60%

19
87

60%