Decomposisi Tfp, China

8/16/2019 Decomposisi Tfp, China

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Journal of Productivity Analysis, 16, 269–281, 2001.

C 2001 Kluwer Academic Publishers. Manufactured in The Netherlands.

A Decomposition of Total Factor ProductivityGrowth in Korean Manufacturing Industries:A Stochastic Frontier Approach

SANGHO KIM [email protected]

Department of International Trade, College of Business, Honam University, Kwang ju, 506-714 South Korea

GWANGHO HAN [email protected] Department of Economics, Sunchon National University, Sunchon, Chonnam, 540-7 42 South Korea

Abstract

This paper applies a stochastic frontier production model to Korean manufacturing indus-

tries, to decompose the sources of total factor productivity (TFP) growth into technical

progress, changes in technical efficiency, changes in allocative efficiency, and scale effects.

Empirical results based on data from 1980–1994 show that productivity growth was driven

mainly by technical progress, that changes in technical efficiency had a significant positive

effect, and that allocative efficiency had a negative effect. This study suggests that specific

guidelines are required to promote productivity in each industry, and provides additional

insight into understanding the recent debate on TFP growth in Korean manufacturing.JEL classification: D24, C23, O47

Keywords: Korean manufacturing, total factor productivity, technical progress, technical efficiency, scale com-

ponents, allocative efficiency

1. Introduction

Following recent developments in the measurement of productivity growth, a stochastic

frontier production function is applied to decompose total factor productivity (TFP) growth

in Korean manufacturing into technical progress and changes in technical efficiency. In the

“Solow” residual approach, technical progress is usually considered to be the unique source

of TFP growth. Recent developments acknowledge that along with technical progress,changes in technical efficiency—the gap between frontier technology and a firm’s actual

production—can also contribute to productivity growth. Stochastic frontier models assume

that firms do not fully utilize existing technology because of various non-price and orga-

nizational factors that lead to inevitable technical inefficiencies in production. Under these

circumstances, TFP growth may arise from improvements in technical efficiency (TE),

without technical progress (TP).

From a policy perspective, researchers acknowledge that the decomposition of TFP

into efficiency changes and technical changes provides useful information in productivity


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270 KIM AND HAN

analysis. Policy makers can recommend policies that are more effective in improving the

productivity of firms if they understand the sources of variation in productivity growth. For

example, if low productivity growth results from slow TP, then a policy to induce techno-

logical innovation should be recommended to shift up the production frontier. If high rates

of TP coexist with deteriorating TE, resulting in slow productivity growth, then a policy to

increase the ef ficiency with which a known technology is applied is required, which might

include improvements in learning-by-doing processes and in managerial practices.

Since Nishimizu and Page (1982) first proposed the decomposition of TFP into ef ficiency

changes and technical changes, researchers have applied their approach to various datasets

in order to investigate productivity growth. Bauer (1990) estimated a translog cost frontier

using data on the U.S. airline industry to decompose TFP growth into ef ficiency, technical

progress, and scale components. Fecher and Perelman (1992) applied this method to themanufacturing industries of OECD countries. Recently, Granderson (1997) analyzed regu-

lated firms in the U.S.; Kalirajan, Obwona and Zhao (1996) analyzed Chinese provincial-

level agricultural data; and Bayarsaihan, Battese and Coelli (1997) studied Mongolian grain

farms.

This paper applies a stochastic frontier production model to decompose TFP growth in

Korean manufacturing industries from 1980–1994. This paper decomposes TFP growth into

four components according to Kumbhakar (2000): technical progress, changes in technical

ef ficiency, changes in allocative ef ficiency, and scale effects.

Despite an extensive literature on TFP growth in Korean manufacturing, no other studies

have used a stochastic frontier production model to measure and decompose TFP growth.

This research vacuum is rather surprising, considering the strong interest and prolonged

debate on TFP growth in the Korean manufacturing sector. This paper is the first attempt

to decompose TFP growth in Korean manufacturing using a stochastic frontier production

model,and provides additional insights into understanding the recent debateon TFP growth.

Previous studies on TFP growth in Korean manufacturing measured TFP as a residual

of “Solow” growth accounting using aggregate data. Thus, these studies were not able to

consider changes in technical inef ficiency, which this study estimates to have considerable

effects on TFP growth. This study also enables us to examine firms’ individual TFP perfor-

mance by using micro-level firm data that was largely ignored by previous studies, which

used aggregated data.

This paper is organizedas follows.Section 2 presents a decomposition of TFP andpresents

the functional form of the estimation model. Section 3 discusses the data and estimation

results. Section 4 contains the conclusions.

2. Decomposition and Functional Form

2.1. Decomposition of TFP

A stochastic frontier production function is defined by

yit = f ( x it , t ) exp(−uit ), (1)

where yit is the output of the i th firm (i = 1, . . . , N ) in the t th time period (t = 1, . . . , T );

f (·) is the production frontier; x is an input vector; t is a time trend index that serves as a


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DECOMPOSITION OF TOTAL FACTOR PRODUCTIVITY GROWTH 271

proxy for technical change; and u ≥ 0 is the output-oriented technical inef ficiency. Notice

that technical inef ficiency in equation (1) varies over time.

The production frontier, f (·), is totally differentiated with respect to time to get1

d ln f ( x , t )

dt =

∂ ln f ( x , t )

∂t +

j

∂ ln f ( x , t )

∂ x j

d x j

dt . (2)

The first and second terms on the right-hand side of equation (2) measure the change in

frontier output caused by TP and by change in input use, respectively. From the output

elasticity of input j, ε j = ∂ ln f /∂ ln x j , the second term can be expressed as

j ε j•

x j ,

where a dot over a variable indicates its rate of change. Thus, equation (2) is rewritten as

d ln f ( x , t )dt

= TP +

j

ε j• x j . (3)

Totally differentiating the logarithm of y in equation (1) with respect to time and using

equation (3), the change in production can be represented as

• y =

d ln f ( x , t )

dt −

du

dt = TP +

j

ε j•

x j −du

dt . (4)

The overall productivity change is not only affected by TP and changes in input use, but

also by the change in technical inef ficiency. TP is positive (negative) if exogenous technical

change shifts the production frontier upward (downward), for a given level of inputs. If

du/dt is negative (positive), TE improves (deteriorates) over time, and −du /dt can be

interpreted as the rate at which an inef ficient producer catches up to the production frontier.To examine the effect of TP and a change in ef ficiency on TFP growth,

•

TFP is defined as

output growth unexplained by input growth:

•

TFP =•

y −

j

S j•

x j , (5)

where S j is input j ’s share in production costs.

By substituting equation (4) into equation (5), equation (5) is rewritten as

•

TFP = TP −du

dt +

j

(ε j − S j ) • x j

= TP −du

dt

+ ( RTS − 1)

j

λ j•

x j +

j

(λ j − S j ) • x j , (6)

where RTS (=

j ε j ) denotes the measurement of returns to scale, and λ j = f j x j /l f l x l = ε j /

l εl = ε j / RTS . The last component in equation (6) measures inef ficiency in

resource allocation resulting from deviations of input prices from the value of their marginal

product. Thus, in equation (6), TFP growth can be decomposed into TP, the technical ef fi-

ciency change (−du /dt ), scale components (SC= ( RTS −1)

j λ j•

x j ), and the allocative

ef ficiency change (AE =

j (λ j − S j ) • x j ). The decomposition formula in equation (6) is

drawn from Kumbhakar (2000).2


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272 KIM AND HAN

If technical inef ficiency does not exist or is time-invariant, the above decomposition

implies that technical inef ficiency does not affect TFP growth, as in the Solow residual

approach. If technology exhibits constant returns to scale, the TFP growth formula in

equation (6) is identical to the one derived in Nishimizu and Page (1982).

2.2. Functional Form

The components of productivity change can be estimated within a stochastic production

frontier framework, and the time-varying production frontier can be specified in translog

form as

ln yit = α0 +

j

α j ln x ji t + αT t +1

2

j

l

β jl ln x li t ln x ji t

+1

2βT T t

2 +

j

βT j t ln x ji t + vit − uit , j, l = L , K , (7)

where yit is the observed output; t is the time variable; and the x variables are inputs. Sub-

scripts j and l indicate inputs ( j, l = L , K ). The ef ficiency error, u , represents production

loss due to firm-specific technical inef ficiency; thus, it is always greater than or equal to zero

(u ≥ 0), and it is assumed to be independent of the statistical error, v, which is assumed to

be independently and identically distributed as N (0, σ 2v ).

The stochastic frontier model, as specified in equation (7), allows for non-neutral TP.

TP is input j -using (saving) if βT j is positive (negative); and TP is neutral if all βT j s

( j = L , K ) are equal to zero. If all β s are equal to zero (β L L = βK K = β L K = βT T =βT L = βT K = 0), the production function reduces to the Cobb-Douglas function with

neutral TP.

Following Battese and Coelli (1992), technical inef ficiency is assumed to be defined by

uit = u i exp(−η[t − T ]), (8)

where the distribution of u i is taken to be the non-negative truncation of the normal dis-

tribution, N (µ,σ 2u ), and η is a parameter that represents the rate of change in technical

inef ficiency. A positive value (η > 0) is associated with the improvement of firms’ techni-

cal ef ficiency over time.3

The maximum-likelihood estimates for the parameters of the stochastic frontier model,

defined by equations (7) and (8), can be obtained by using the program, FRONTIER 4.1,

in which the variance parameters are expressed in terms of γ = σ 2u /σ 2

s and σ 2

s = σ 2

u + σ 2

v

(see Coelli, 1996).

The technical ef ficiency level of firm i at time t (TE it ) is defined as the ratio of the actual

output to the potential output as

TE it = exp(−uit ). (9)

The elasticity of output with respect to the j th input is defined by

ε j = ∂ ln f ( x , t )/∂ ln x j = α j +

l= j

β jl ln x l + β j j ln x j + βT j t , j, l = L , K . (10)


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The elasticity of scale (=RTS) is defined as RTS =

j ε j , and RTS decreases, is constant

and increases if RTS < 1, RTS = 1 and RTS > 1, respectively.

The rate of TP is defined by

TP = ∂ ln f ( x , t )/∂t = αT + βT T t +

j

βT j ln x j , j = L , K . (11)

In the estimation of equations (10) and (11), output elasticity and TP are functions of

input levels and are estimated at the sample means of input levels.

3. Data and Empirical Results

3.1. Data

The data used in this paper are an unbalanced panel consisting of annual time-series for

508 Korean manufacturing firms during 1980–1994, with a total of 6,203 observations.

The sample covers all manufacturing firms whose stocks are listed on the Korean Stock

Exchange. The enlisted firms are required to report their financial status. All firms’ data are

taken from their financial reports.

The capital stock (K ) is the real amount of tangible fixed assets, labor input ( L) is proxied

by the number of workers, and real value-added (VA) is used for output. Labor costs (C L )

consist of employee remuneration, including wages, bonuses, retirement compensation,

and other welfare costs, and capital costs (C K ) are calculated as the sum of the interest

payments, rents, and depreciation costs. Total costs (C ) are calculated as the total sum of these factor costs (C = C L + C K ), and the factor share in total costs ( S L , S K ) is calculated

as the factor’s share out of the total costs (S j = C j /C , j = L , K ). Table 1 presents sample

means and standard deviations.

Table 1. Summary statistics for variables in the stochastic frontier production functions for Korean manufacturing

industries.

Total

Sample Food Textiles Paper Chemical Non-metal Basic-metal Fabrication

No. of Firms 508 49 82 28 107 26 38 163

No. of Obs. 6,203 665 981 321 1,367 341 465 1,888

Labor 6.711 7.007 7.067 5.943 6.386 6.832 6.657 6.744

(1.411) (1.037) (1.171) (0.713) (0.977) (0.843) (1.243) (1.243)

Capital 16.154 16.992 16.520 16.292 16.303 17.279 17.104 16.263

(1.530) (1.103) (1.650) (1.324) (1.468) (1.374) (1.702) (1.571)

Value Added 16.353 16.683 16.399 15.876 16.264 16.820 16.679 16.197

(1.343) (1.104) (1.362) (1.126) (1.198) (1.131) (1.509) (1.484)

Labor Share 0.583 0.538 0.591 0.488 0.583 0.536 0.514 0.627

(0.165) (0.143) (0.171) (0.148) (0.161) (0.173) (0.182) (0.149)

Notes: Standard deviations are in parentheses. Capital and Value Added are logarithmic values as used in actual

estimation.


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274 KIM AND HAN

For individual industry estimation, this study classifies sample firms into double-digit

industries according to the International Standard Industry Classification (SIC). The food

industry is represented by SIC 31 (food, beverages, and tobacco); the textiles industry by

SIC 32 (textiles, wearing apparel, and leather products); the paper industry by SIC 34

(paper and paper products); the chemical industry by SIC 35 (chemicals, petroleum, and

coal products); the non-metal industry by SIC 36 (nonmetallic mineral products); the basic-

metal industry by SIC 37 (basic-metal products); and the fabrication industry by SIC 38

(fabricated metal products, machinery and equipment).4

3.2. Empirical Results

Hypotheses Tests The maximum-likelihood estimates of the parameters in the translog

stochastic frontier production function, defined by equations (7) and (8), are obtained for

the total sample and each of the seven industries.5 All the estimates of γ are statistically

significant at least at the 5% significance level, except for the non-metal and basic-metal

industries. All the estimates of η are positive, except for the textiles industry, and all are

statistically significant, except for the food and non-metal industries. A significant γ along

with a positive and significant η implies the existence of technical inef ficiency that declines

over theyears, as is thecasefor thepaper, chemical,and fabrication industries. Theparameter

η is estimated to be negative only in the textiles industry, which implies increasing technical

inef ficiency.

Table 2 presents the test results of various null hypotheses on the total sample. The null

hypotheses are tested using likelihood ratio tests. The likelihood-ratio test statistic is λ =

−2[ L( H 0)− L( H 1)], where L ( H 0) and L ( H 1) are the values of the log-likelihood functionunder the specifications of the null and alternative hypotheses, H 0 and H 1, respectively. If

the null hypothesis is true, then λ has approximately a Chi-square (or a mixed Chi-square)

distribution with degreesof freedom equal to thenumber of restrictions. If thenull hypothesis

includes γ = 0, then the asymptotic distribution is a mixed Chi-square distribution (Coelli

and Battese, 1996).

The first null hypothesis, that there are no technical inef ficiency effects ( H 0 : γ = µ =

η = 0), is rejected at the 1% significance level for the total sample.6 If the null hypothesis is

true, there are no frontier parameters in the regression equation, and the estimation becomes

Table 2. Statistics for tests of hypothesesinvolving some coef ficients of the stochasticfrontier production function

for Korean manufacturing industry.

Log-Likelihood Test Critical

Null Hypothesis Function Statistics (λ) Value Decision

1. H0 : γ = µ = η = 0 −4393.06 1461.84 10.50∗ Reject H0

2. H0 : η = 0 −3721.33 118.38 6.63 Reject H03. H0 : αT = βTT = βTL = βTK = 0 −3752.18 180.08 13.28 Reject H04. H0 : βTL = βTK = 0 −3677.04 29.80 9.21 Reject H05. H0 : β LL = βKK = β LK = βTT = 0 −3682.33 40.39 13.28 Reject H0

*The critical value for this test involving γ = 0 is obtained from Table 1 of Kodde and Palm (1986, p. 1246).

Every null hypothesis is rejected at the 1% level of significance.


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an ordinary least square estimation. The results suggest that the average production function

is an inadequate representation of the Korean manufacturing sector and underestimates the

actual frontier because of technical inef ficiency effects.

The second null hypothesis, that technical inef ficiency is time-invariant ( H 0 : η = 0),

is also rejected at the 1% significance level for the total sample.7 This implies that tech-

nical inef ficiency in Korean manufacturing is not time-invariant, given the time-varying

specification of the stochastic frontier defined by equation (8).

The third null hypothesis, that there is no technical change ( H 0 : αT = βT T = βT L =

βT K = 0), and the fourth null hypothesis, that technical progress is neutral ( H 0 : βT L =

βT K = 0), are both rejected at the 1% significance level for the total sample.8 This implies

the existence of non-neutral technical progress in Korean manufacturing as a whole, given

the specified production model.The last null hypothesis, that the technology in Korean manufacturing is a Cobb-Douglas

( H 0 : β L L = βK K = β L K = βT T = 0), is rejected for the total sample.9 Thus, the Cobb-

Douglas production function is not an adequate specification for the Korean manufactur-

ing sector, given the assumptions of the translog stochastic frontier production function

model.

Technical Efficiency and Returns to Scale Table 3 represents the average technical ef-

ficiency (TE) and returns to scale (RTS) for some selected time periods. Estimates of TE

Table 3 . Average technical ef ficiency (TE), and return to scale (RTS) for Korean manufacturing industries.

Total Non- Basic-

Sample Food Textiles Paper Chemical m etal metal Fabrication

TE 1980–82 0.448 0.790 0.652 0.605 0.439 0.843 0.619 0.480

(0.028) (−0.00 7) (−0.03 6) (0.046 ) (0.076 ) (0.01 3) (0.031 ) (0.023 )

1983–85 0.485 0.770 0.602 0.683 0.497 0.795 0.666 0.524

(0.027) (−0.00 6) (−0.02 3) (0.020 ) (0.029 ) (−0.023) (0.025) (0.039)

1986–88 0.533 0.758 0.554 0.747 0.550 0.815 0.702 0.602

(0.037) (−0.00 5) (−0.02 6) (0.057 ) (0.033 ) (0.01 2) (0.018 ) (0.051 )

1989–91 0.597 0.768 0.517 0.829 0.611 0.842 0.767 0.686

(0.039) (0.009) (−0.02 2) (0.030 ) (0.035 ) (0.01 0) (0.027 ) (0.042 )

1992–94 0.662 0.786 0.486 0.893 0.677 0.869 0.810 0.761

(0.032) (0.005) (−0.01 8) (0.022 ) (0.034 ) (0.01 1) (0.018 ) (0.031 )

1980–94 0.545 0.775 0.562 0.751 0.555 0.833 0.713 0.611

(0.033) (0.000) (−0.024) (0.034) (0.039) (0.004) (0.023) (0.038)

RTS 1980–82 0.890 0.886 0.944 0.633 0.872 0.960 0.967 0.978

1983–85 0.900 0.863 0.940 0.900 0.865 0.965 0.985 0.980

1986–88 0.918 0.862 0.944 0.915 0.878 1.006 0.961 0.984

1989–91 0.942 0.872 0.945 1.093 0.893 1.044 0.989 0.990

1992–94 0.962 0.871 0.945 1.216 0.902 1.113 1.004 0.996

1980–94 0.917 0.876 0.935 1.018 0.877 1.021 0.986 0.986

(0.01 1) (0.03 1) (0.02 3) (0.090 ) (0.027 ) (0.03 4) (0.041 ) (0.017 )

Notes: Average annual growth rates and asymptotic standard errors are in parentheses below TE and RTS,

respectively.


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276 KIM AND HAN

vary considerably, both across manufacturing industries and across time periods. The av-

erage TE is 0.545 for the total sample. The non-metal and food industries have the highest

and second highest estimates, 0.833 and 0.775, respectively, and the chemical and textiles

industries have the lowest and second lowest estimates, 0.555 and 0.562, respectively. The

other industries—paper, basic metal, and fabrication—have estimates that range from 0.611

to 0.751.

The average TE for all industries improves throughout the sample period, and this trend

of steady improvement is also observed in the paper, chemical, basic-metal, and fabrication

industries. The average TE deteriorates through the years in the textiles industry. The annual

growth rate of TE is estimated to be almost zero in the food industry and positive, but small,

in the non-metal industry. The average TE decreases in the early sample period(s), but

improves later in the food and non-metal industries.10

For the total sample, the average estimate of RTS is 0.917. RTS continuously increases

and approaches one during the sample period. The null hypothesis that RTS is one is

tested using the T -test and is rejected at the 1% significance level. Thus, the alternative

hypothesis, that production technology is not subject to constant returns to scale (CRS),

is accepted for the Korean manufacturing dataset, given the specified translog production

model.

For themanufacturing sector, estimatesrangefrom 0.876 to 1.021.The null hypothesis that

an industry has CRS technology is tested using the T -test against the alternative hypothesis

that technology is notCRS.The null hypothesis is rejected forthe food, textiles, andchemical

industries, butcan’t be rejected for the otherindustries. Thus,production technologyexhibits

decreasing returns to scale in the food, textiles, and chemical industries, and exhibits CRS

for all other industries.

Technical Progress, Scale Components, Allocative Ef fi ciency, and Total Factor Produc-

tivity Table 4 presents the averages of the rates of technical progress (TP), the scale com-

ponents (SC), the changes in allocative ef ficiency (AE), and the total factor productivity

growth (•

TFP) for selected time periods.11

The average rate of TP was estimated at 0.047 and declined continuously in the total

sample during the sampling period. For industry-level estimation, TP was highest in the

basic-metal and textiles industries with estimates greater than 0.1, and it was lowest in

the paper and chemical industries with estimates of about 0.04. The rate of TP declined

continuously over time in the chemical, non-metal, basic-metal, and fabrication industries.

This decline was most apparent in the basic-metal and non-metal industries, where initially

the TP was the fastest growing, then slowed continuously, and finally lagged behind other

industries. Meanwhile, in the textiles and paper industries, the rate of TP increased untilthe first half of the 1980s then decreased thereafter. TP was confined within a small range

in the food and textiles industries, contrasting with the wide changes in the non-metal and

basic-metal industries.

Scale components, which measure the effects of input changes on output growth, are zero

if RTS is constant, or are greater (less) than zero if RTS is increasing (decreasing), assuming

positive input growth. Average scale components are −0.002 for the whole manufacturing

sector, negative but small in the food and chemical industries, and close to zero in the other

five industries.


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Table 4. Technical progress (TP), scale components (SC), allocative ef ficiency change (AE) and total factor

productivity growth (•

TFP) of Korean manufacturing industries.

Total Non- Basic-

Sample Food Textiles Paper Chemical metal metal Fabrication

TP 1980–82 0.066 0.078 0.110 0.068 0.067 0.115 0.141 0.089

1983–85 0.056 0.079 0.111 0.069 0.057 0.089 0.120 0.071

1986–88 0.047 0.082 0.112 0.080 0.048 0.068 0.076 0.056

1989–91 0.035 0.080 0.107 0.055 0.038 0.047 0.060 0.047

1992–94 0.024 0.081 0.104 0.042 0.028 0.029 0.039 0.038

1980 – 94 0.047 0.075 0.107 0.047 0.049 0.078 0.112 0.069

SC 1980–82 0.009 0.002 0.004 −0.005 0.018 0.001 0.005 0.004

1983–85 0.005 0.003 0.009 0.009 −0.010 0.002 0.005 0.0021986–88 −0.007 −0.012 −0.003 −0.007 −0.010 0.001 −0.001 −0.002

1989–91 −0.005 −0.009 −0.004 0.002 −0.010 0.002 −0.001 −0.001

1992–94 −0.001 −0.003 0.001 0.001 −0.006 0.004 0.000 0.000

1980 – 94 −0.002 −0.004 0.001 0.000 −0.006 0.000 0.000 0.000

AE 1980–82 −0.008 −0.003 −0.016 0.018 −0.040 −0.055 −0.077 −0.011

1983–85 −0.002 0.000 −0.001 −0.004 −0.025 −0.006 −0.076 −0.004

1986–88 −0.002 −0.006 −0.003 −0.038 −0.019 −0.009 −0.008 0.005

1989–91 0.000 −0.009 −0.009 −0.081 −0.010 −0.007 −0.051 0.003

1992–94 −0.007 −0.006 −0.019 −0.091 −0.010 −0.038 −0.044 −0.008

1980 – 94 −0.001 0.000 −0.005 −0.021 −0.015 −0.019 −0.061 −0.007

•

TFP 1980–82 0.094 0.072 0.062 0.128 0.120 0.070 0.097 0.102

1983–85 0.087 0.077 0.096 0.094 0.052 0.062 0.074 0.108

1986–88 0.075 0.059 0.081 0.092 0.052 0.072 0.086 0.111

1989–91 0.069 0.071 0.072 0.006 0.053 0.052 0.035 0.0911992–94 0.048 0.077 0.068 −0.026 0.046 0.006 0.013 0.060

1980 – 94 0.073 0.071 0.077 0.054 0.061 0.051 0.058 0.094

The Korean government pursued an industrial policy to promote the heavy and chem-

ical manufacturing sectors during the 1970s. This policy tried to direct limited national

resources into strategically chosen industries (mostly in chemical, basic-metal, and fabri-

cation). One of the policy objectives was to enable firms to grow large enough to utilize

scale economies and to compete in foreign markets. Estimated scale components in TFP

growth for the heavy industries (chemical, non-metal, basic-metal, and fabrication) are very

small or negative, implying that firms in these industries had already reached a certain size

where scale economies no longer existed. For example, scale economies in the chemicalindustry vanished in the early 1980s, as we can see from the change in SC from 0.018 in

the first period to −0.010 in the second period, and those in the basic-metal and fabrication

industries disappeared by the third period (1986–1988). Thus, this study suggests that the

prior industrial policy of exploiting economies of scale is no longer effective in promoting

productivity in the heavy manufacturing sector.

Allocative inef ficiency results when factor prices are not equal to their marginal prod-

uct. Almost every estimate of AE has a negative value, implying the existence of alloca-

tive inef ficiency. For the total sample, AE was a modest −0.001, implying the existence


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278 KIM AND HAN

of a slightly inef ficient allocation of inputs in production with a resulting decline of

TFP. For specific manufacturing industries, AE was lowest in the basic-metal industry,

with an average value of −0.061, followed by the paper, non-metal, and chemical in-

dustries. In the other three industries, AE was estimated to be much larger, with esti-

mates of 0.000, −0.005, and −0.007, respectively, for the food, textiles, and fabrication

industries.

This discrepancy in AE among industries indicates that the degree of market distortion

varied across these industries. The resulting inef ficiency costs were generally greater in

the heavy and chemical manufacturing industries (chemical, non-metal, and basic-metal),

which the Korean government developed on a massive scale, than in other light manufac-

turing industries (food, and textiles). The level of government intervention was especially

high throughout the 1970s. Allocative inef ficiencies in the chemical, non-metal, and basic-metal industries—the heavy and chemical industries promoted by the government—were

estimated to be much larger during the early 1980s (italics in Table 4) as compared to other

periods and to other industries. This implies that government intervention led to severe

market distortions in these industries.

TFP growth is calculated as the sum of technical progress, as measured by a shift in

the production frontier, changes in technical ef ficiency, changes in allocative ef ficiency,

and changes in scale components. In Korean manufacturing industries, TP has been a key

contributorto TFPgrowth,and improvementsin TE made a considerablecontribution to TFP

growth, except in the textiles, food, and non-metal industries. AE exerted a negative effect

on TFP growth, although its magnitude wassmaller than that of TE. In some industries, such

as the non-metal and basic-metal industries, allocative ef ficiency losses even outweighed

technical ef ficiency gains.

Total TFP in the manufacturing sector has grown at an annual rate of 0.073, although

the rate of growth decreased continuously during the sample period. For industry estimates

during the sample period, TFP grew fastest in the fabrication industry, with an annual

average growth rate of 9.4%, followed by the textiles industry with a rate of 7.7%, and the

food industry with a rate of 7.1%. The remaining industries have grown by about 5 –6%

per annum. During the early 1990s (from 1989–1991 to 1992–1994), a large downturn in

TFP was observed in every industry except the food industry. This downturn coincided

with an economic slowdown in the Korean economy during the same period, supporting the

presumption that lagging productivity was a major reason for the depression of the Korean

economy during the early 1990s.

There are considerable deviations in TFP estimates for the Korean manufacturing sector.12

Pyo et al. (1992) reported the growth rate of TFP as 1.1% during 1970 –1990; Moon

et al. (1991) reported that it was 3.7% during 1971–1989; Dollar and Sokoloff (1990)showed that it was 6.1% during 1963–1979; and Young (1995) reported that it was 3.0%

during 1966–1990. In these Solow residual studies, there are two key reasons for differ-

ences in TFP estimates: (1) differing primary data sources and (2) assessment methods

of factor inputs. Pyo et al. (1992) and Dollar and Sokoloff (1990) used the Mining and

Manufacturing Survey, and Young (1995) and Moon et al. (1991) used the National In-

come Accounts. Differing growth rates both for inputs and output in the two datasets

partially explain the variance in the TFP estimates. The estimates also largely depend on

the way factors are measured in each study. By including quality changes into inputs,recent


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studies explain a greater part of output growth by input growth, and report lower TFP

estimates.

Based on thelower TFPestimates of recentstudies,Krugman (1994) argued that economic

growth in East Asia, including Korea, will not be sustainable in the long run, because it has

mainly been input-driven and not technology based. Chen (1997) insisted that East Asia’s

growth was mainly driven by factor embodied technical change, which is included in factor

input growth as a whole and has been ignored in growth accounting that estimates TFP

only as disembodied technical change. In this debate, it is clear that TFP estimates can vary

widely depending on the measurement of inputs because TFP is measured as the portion of

output growth that is unexplained by input growth.13

Compared to the literature, this study suggests the following. First, previous studies used

aggregate data and measured TFP as a residual of the “Solow” growth accounting. Thus,they cannot examine changes in technical ef ficiency, which this study estimates to have

had considerable effects on TFP growth. Second, this study uses micro-level firm data to

estimate TFP growth rate at 7.3%, which is much higher than in studies that use aggregate

data. TheKorean manufacturers in this samplehaveshown faster TFP growththan suggested

by previous productivity studies. Third, this study implies that part of the increase in TFP

is due to an improvement in TE. Thus, attributing all changes in TFP to technical progress,

as in previous growth accounting studies, is misleading, and overestimates actual technical

progress.

4. Conclusions

The empirical results of this study show that although productivity growth was driven

mainly by technical progress, changes in technical ef ficiency had a significant positive

effect and allocative ef ficiency had a significant negative effect on productivity growth.

The after-effects of the government’s industrial policy to promote the heavy and chemical

industries were identified in prevalent allocative inef ficiency and vanished economies of

scale across these industries. Thus, the results suggest that the promotion of a freer market

will considerably enhance productivity growth in the Korean manufacturing sector.

Policy implications derivable from this study suggest that specific guidelines are required

to promote productivity in each industry. Industries with slow TP (paper and chemical)

require the introduction of new frontier technology. Government policy should encourage

investments that can introduce newly developed production technology. In the paper, chem-

ical, non-metal, and basic-metal industries, where allocative inef ficiency is considerable,

a policy to enhance TFP by improving resource allocation should be pursued, which canbe done by promoting free markets and lessening government intervention. Meanwhile, in

industries where TE is small (food, textiles, and non-metal), a policy to enhance the ef ficient

use of existing technology is recommended to catch up to frontier technology.

This study provides additional insight into the recent debate on TFP growth in Korean

manufacturing by applying a stochastic frontier production approach to analyzing produc-

tivity. Thus, this study shows that the stochastic frontier production function model could

be a complementary and alternative model to growth accounting methods for measuring

and explaining productivity growth.


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280 KIM AND HAN

Acknowledgments

We thank Subal C. Kumbhakar and two anonymous referees for their valuable suggestions

and comments. We are responsible for any errors.

Notes

1. For simplicity, the ‘it ’ subscripts are omitted from now on.

2. For a survey of the literature on estimation and decomposition of TFP (see Kumbhakar and Lovell, 2000;

Kumbhakar, Heshmati and Hjalmarsson, 1999).

3. Thus, this specification assumes a particularparameterizationof the distribution of technical inef ficiency across

firms. In this parameterization, the ordering of firms according to the size of the technical inef ficiency effects

is the same for all years involved.

4. SIC 33 (wood and wood products) and SIC 39 (furniture and other manufactured products) don’t have a suf fi-

cient degree of freedom to produce significant results, as they include only four and eleven firms, respectively.

5. The parameter estimates are available from the authors on request.

6. Despite the fact that some parameters, γ , µ and η, are insignificant for some industries, the null hypothesis of

γ = µ = η = 0 was rejected at the 1% significance level for every industry.

7. For industry-level estimation, the null hypothesis is rejected for every industry, except the food and non-metal

industries.

8. Thethird nullhypothesisof no technicalprogresswas rejected for every industry, except the fabricationindustry,

and the fourth null hypothesis was rejected for the food, paper, and basic-metal industries, but couldn ’t be

rejected for the other industries.

9. This hypothesis was rejected for every industry, except the textiles industry.

10. The dataset is an unbalanced panel, where some firms enter and others exit during the sampling period. Thus,

trends in TE can be different from those implied by the estimated sign of η. This is the case for the food and

non-metal industries.

11. Thedecomposition results by year are omitted here to save space, butare available fromthe authors on request.

12. For a summary of TFP estimates for Korean manufacturing industry (see Young, 1995).

13. For a critical assessment of the recent debate on TFP in East Asia (see Chen, 1997; Felipe 1999).

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