IDENTIFYING AND ESTIMATING SOURCES OF TECHNICAL INEFFICIENCY IN KOREAN MANUFACTURING INDUSTRIES

Contemporary Economic Policy(ISSN 1074-3529)Vol. 21, No. 1, January 2003, 132–144 © Western Economic Association International

IDENTIFYING AND ESTIMATING SOURCES OF TECHNICALINEFFICIENCY IN KOREAN MANUFACTURING INDUSTRIES

SANGHO KIM*

This study estimated translog stochastic frontier production functions using anunbalanced panel of Korean manufacturing firms in the food, textile, paper, chemi-cal, basic-metal, and fabrication sectors. The sectors were estimated individually toinvestigate whether technical efficiency is systematically related to firm size, depen-dency on external funds, research and development investments, and exports. Theempirical results suggest that firm size has a positive and significant effect in everysector. The effects of the other factors are less systematic and vary across sectors.(JEL C23, D24, O47)

I. INTRODUCTION

The South Korean government has con-tinuously pursued an industrial policy thatpromotes strategic manufacturing industries,although the target of the policy shifted fromlight industries in the 1960s to heavy andchemical industries in the 1970s and 1980s.Under this policy, the government directedsubsidized credit to strategically chosen firmsand industries by operating an internal capi-tal market. The industrial policy contributedto building Korean manufacturing firms thatare owned and controlled by large businessgroups known as chaebols. Once large man-ufacturing firms that could compete in theworld market were established, the govern-ment repeatedly intervened to save them frombankruptcy. Despite the government’s grad-ual adoption of free market mechanisms inthe 1980s, the tradition of regulatory indus-trial policy remained strong. For example,from 1984 to 1988, the government rescuedmany firms in the construction, shipping, andmachinery industries when they became insol-vent. The government’s repeated bailoutsencouraged Korean manufacturing firms to

∗This work was supported by Korea Research Foun-dation Grant (KRF-2000-041-C00228). This is a revisedversion of the paper presented at the Western Eco-nomic Association International 76th annual confer-ence, San Francisco, July 5, 2001. The author thanksTatsuyoshi Miyakoshi and two anonymous referees fordetailed comments and suggestions.Kim: Associate Professor, Department of Interna-

tional Trade, Honam University, Kwangju, 506-714S. Korea. Phone 82-62-940-5394, Fax 82-62-940-5116,E-mail [email protected]

expand without proper consideration of prof-itability and of their mismanagement.1

This inherent problem in Korean man-ufacturing industries continued to developbut was hidden by economic growth for anextended period. However, from the late1980s until the early 1990s, when the Koreaneconomy was growing quickly and recordedan unprecedented surplus in the balance ofpayments, there was a heated debate aboutthe sustainability of the economic growthand the fundamental strength of the Koreanmanufacturing sector. In that debate, aca-demics widely believed that the economicboom had more to do with the temporaryeconomic environment, including such fac-tors as a strong Japanese yen and low oilprices, than with the strength of the Koreanmanufacturing industry. Thus, many Koreaneconomists insisted that the Korean man-ufacturing sector should be restructured tosustain the growth. However, restructuringhad not been fully implemented before thefinancial crisis struck the Korean economy inlate 1997. After the crisis, restructuring was

1. As a result, the average rate of return on equitywas lower than the borrowing rate for the decade beforethe crisis (1989–97), except for 1995 (Joh, 1999).

ABBREVIATIONS

R&D: Research and DevelopmentSIC: Standard Industry Classification

132

KIM: SOURCES OF TECHNICAL INEFFICIENCY 133

reemphasized and implemented as a key torevitalizing the Korean economy.2

The financial crisis resulted directly fromshort-term defaults in the Korean financialsector and was also associated with the failureof the Korean manufacturing sector, whichwas plagued by weak corporate governanceand resulting poor firm performance. Joh(1999) indicated that several factors wereresponsible for the failure of corporate gover-nance in Korea, such as the lack of a crediblethreat, disparity between control and owner-ship and the ensuing management problems,and lack of monitoring by financial institu-tions.3

First, the lack of a credible threatfor large firms in Korea—which resultedfrom frequent government bailouts, lengthybankruptcy procedures, and an underdevel-oped corporate control market—provided noincentive for firms to maximize their per-formance. Second, financial institutions hadnot developed adequate techniques to eval-uate firms and failed to monitor firms thatrelied heavily on debt for their financing.The control of nonbank financial institutionsby chaebols and poor accounting standardsin Korea aggravated the problem. Third, thecontrolling shareholders of the 30 largestchaebols exploited the interlocking owner-ship among firms and managed the groupsof firms, although their direct ownership wasless than 10%. This disparity between con-trol and ownership gave controlling share-holders an incentive to divert firms’ resourcesto their own interests. The boards of directorswere picked by the controlling shareholdersand played no internal monitoring or disci-plinary role; minority shareholders had fewlegal rights to prevent the controlling share-holders and managers from pursuing wastefulprojects.

Manufacturing inefficiency in Korea orig-inated not only from environmental fac-tors, such as a poor business environmentand a lagging financial sector as discussed,but also from firm-specific factors, suchas inadequate size, lack of research and

2. Ongoing corporate restructuring in Korea focuseson various issues, including enforcing cost-minimizingefforts, improving financial status, restructuring businessportfolios, and adopting a new corporate governance sys-tem (Yoo, 1999).

3. For a detailed discussion of the mechanics of thefailure of the Korean manufacturing sector during thefinancial crisis, see Joh (1999).

development (R&D) investment, inefficientmanagerial practices, and lack of interna-tional competition.

Since the crisis, the business environmenthas been improved with corporate gover-nance system reforms that enhanced thesense of threat, adopted monitoring of finan-cial institutions, and dealt with managementproblems. However, firm-specific factorsaffecting firms’ production efficiency have notbeen investigated, although firms have farmore control over these factors than they doover environmental factors. Therefore, firmsshould identify the factors specific to theirfirm and respond positively while reorga-nizing themselves. In this respect, this arti-cle investigates the firm-specific factors thataffect the production efficiency of Koreanmanufacturers to provide helpful insights intothe ongoing restructuring of the Korean man-ufacturing sector.

This article is organized as follows.The literature on the relationship betweenthe firm-specific variables used in theinefficiency-effect model herein and technicalefficiency is surveyed in section II. Section IIIpresents the proposed stochastic frontier andinefficiency model, along with a descriptionof the data. The empirical results are summa-rized and discussed in section IV. Some con-clusions are made in the final section.

II. THEORETICAL CONSIDERATIONS

The literature on the growth of firms sug-gests that large firms have an advantage oversmall firms because of their market powerand economies of scale. These advantageslead to the conclusion that large firms aremore efficient, which was elaborated in amodel of firm growth (Jovanovic, 1982), inwhich efficient firms grow and survive andinefficient firms stagnate or exit the indus-try as a result of a selection process. Con-sequently, a generally positive relationshipbetween firm size and production efficiencywas proposed. The relationship was based ona cost function that was assumed to be con-vex in output (or decreasing returns to scalein technology). This assumption was laterextended to include constant returns to scalewithout affecting the basic result (Hopen-hayn, 1992).

The existing literature seems to favorlarge firms in terms of efficiency, but it

134 CONTEMPORARY ECONOMIC POLICY

does not consider the role of managerialinput. Recently, a study explained the rela-tively higher profit rate of small firms in theU.S. industrial sector as compensation by theentrepreneurial managers of small firms forthe market uncertainties they face (Dhawan,1997). According to the study, small firms cansurvive market uncertainties and capital con-straints only if they are technically more effi-cient than their counterparts, because theytypically lack market power and an elaborateorganization. This view is consistent with thatof a model of hierarchical control that deter-mines the optimum firm size; this model pro-posed that economies of scale and relatedfactors cause the size of a firm to grow with-out bound, but decreasing returns to man-agerial efficiency limit the optimal firm size(Williamson, 1967).4

Empirical studies that have investigatedthe association between firm size and tech-nical efficiency seem to provide more evi-dence for the positive relation between thetwo variables.5 For example, Pitt and Lee(1981) applied a stochastic frontier pro-duction function to analyze the Indonesianweaving industry and found a positive corre-lation between firm size and technical effi-ciency; Haddad and Harrison (1993) con-firmed this for the Moroccan manufacturingsector. Mengistae (1996) and Lundvall andBattese (2000) supported the positive corre-lation for the Ethiopian and Kenyan manu-facturing industries, respectively.

Contrary to these studies, some empiricalstudies have reported either an ambiguousor a negative correlation. For example,Chen and Tang (1987) could not find anycorrelation for the Taiwanese electronicsindustry, and Brada et al. (1997) reported apositive correlation for 9 of 12 Czechoslo-vakian and Hungarian manufacturing sectors,but no correlation for the other 3 sectors.Biggs et al. (1996) suggested an invertedU-shaped relationship between firm size and

4. This theoretical view is consistent with that ofeconomists who regard the technical inefficiency esti-mated by the stochastic frontier production model asX-inefficiency (e.g., Meeusen and van den Broeck, 1977).X-inefficiency indicates production loss resulting fromfirms that could not realize an optimal production scale.In this theory, large firms are burdened by X-inefficiencyfor bureaucratic costs as firm size increases (Leibenstein,1966).

5. For a survey of the empirical studies, see Lundvalland Battese (2000).

technical efficiency, reporting that the rela-tionship is positive for small firms and nega-tive for large ones. Dhawan (1997) reportedthat small firms are technically more efficientthan large firms using U.S. industrial paneldata.

On the relative efficiency of internal ver-sus external financing, several studies haveinvestigated the costs and benefits of inter-nal versus external capital allocation. Somestudies insist that an internal capital marketenhances resource allocation because invest-ment information is produced and trans-ferred at less cost in an internal capitalmarket than in an external one. Thus, cap-ital is allocated more efficiently in an inter-nal capital market than in an external one.There is also a strong incentive for firmsto monitor the allocation of internal fundsand management practices, especially whenthe internal funds are allocated by eitherthe owner manager or by large sharehold-ers who have claims to them. Such a strongincentive is lacking for external funds thatare monitored by banks and other finan-cial institutions (Gertner et al., 1994). Inaddition, internal capital is allocated in thewinner-picking process of fierce competitionamong various projects within firms, improv-ing resource allocation in the internal capitalmarket (Stein, 1997). According to this view,internal financing is expected to improvethe production efficiency of firms because itenhances the efficiency of investments andresource allocation.

Other studies argue a negative role forinternal financing by suggesting that the mar-ket causes an agency problem for managers.This implies that managers can easily mobi-lize internal funds to maximize their ownobjectives (such as in sales, employment,compensation, and market share) rather thanmaximize the stockholders’ interests, becauseof a lack of external monitoring. Especiallyin underdeveloped countries, where firms’managerial rights are not fully developedand their information is not fully publi-cized, managers try to maximize their benefitsrather than the firm’s value. For this reason,managers have a strong incentive to abuseinternal funds (Jensen, 1986). As anotherpossibility, multilateral firms tend to overin-vest in some business fields through cross-subsidization, and underinvest in other fields.


Consequently, internal funds aggravate capi-tal allocation within a firm (Scharfstein andStein, 1997). These factors are expected to bedetrimental to the allocation of internal fundsand negatively affect production efficiency.

A theoretical review of the relative effi-ciency of the internal versus external capitalmarkets suggests that the share of externalcapital in total capital can either improve afirm’s technical efficiency or cause it to dete-riorate. Empirically, Gokcekus (1995) applieda stochastic frontier production model to theTurkish rubber industry to analyze the rela-tionship between the ratio of external tointernal funds and technical inefficiency andfound the variable had no significant effectson technical efficiency.

Economists recognize that R&D is cru-cial for productivity growth, and they havetried to find a link between R&D outlaysand productivity growth, which is consideredto be one of the most difficult factors tomeasure (e.g., Griliches, 1979; Griliches andLichtenberg, 1984). This difficulty, originat-ing from the complexity of the relationshipbetween R&D and productivity, is associatedwith various issues, ranging from measuringR&D capital stock to estimating externalitiesresulting from the diffusion process of R&Dinvestments.

Recently, a stochastic frontier productionmodel was applied to decompose total factorproductivity into technological progress andefficiency change to specify the mechanismby which R&D investment affects productiv-ity (Perelman, 1995). According to the study,a firm’s capacity to produce from given inputsincreases with R&D investment because itraises the production frontier. If the produc-tion frontier shifts upward and a firm cannotapply newly developed technologies to actualproduction, then the gap between the pro-duction frontier and actual production, thatis, technical inefficiency, increases.

Large-scale R&D investment, which oftenleads to technological innovation, seems to fitthis explanation of the relationship betweenR&D investment and technical efficiency.However, it is questionable whether the sameexplanation can be applied to small-scaleR&D investment, which is not sufficient toraise the frontier. Furthermore, it is wellknown that only a small fraction of scien-tific and industrial research projects lead to

technical innovations in production. There-fore, small-scale R&D investments intendedto improve actual production may help firmscatch up to the frontier, thus improving tech-nical efficiency.

Empirically, Perelman (1995) applied thestochastic frontier production model to inves-tigate the effect of R&D spending ontechnical efficiency and found a negative rela-tionship between the two variables.

There is a large body of literature thatrelates exports to production efficiency. Tra-ditional neoclassical trade theory argues thatfirms exposed to international competition inthe global market experience increased pro-duction efficiency because exports producean efficient allocation of resources, greatercapital utilization, the best use of economiesof scale, and the benefits of improving tech-nology (Balassa, 1978; Feder, 1982; Ram,1985; Bodman, 1996).

Despite many theoretical studies of thecorrelation between the two variables, it ishard to find empirical studies that adopt thestochastic frontier production model to inves-tigate the relationship directly.

III. ECONOMETRIC METHODOLOGY

This study used the stochastic frontier pro-duction function model of Battese and Coelli(1993, 1995) to investigate the relationshipsbetween firm size, external capital, R&D,exports, and technical efficiency. The techni-cal inefficiency effects are specified as a func-tion of other variables, and the parameters ofan inefficiency model are simultaneously esti-mated with those of the frontier productionfunction.

The stochastic frontier production func-tion in a translog functional form is definedas:

lnyit = �0 +∑

j

�j lnxjit +�T t(1)

+ �1/2�∑

j

∑

l

�jl lnxlit lnxjit

+ �1/2��TT t2 +∑

j

�Tj t lnxjit

+vit −uit� j� l = L�K�

where yit is the observed output for theith firm in year t; t is the time variable;


and the x variables are inputs. Subscriptsj and l indicate inputs (j� l = L�K). Thevit are assumed to be independently andidentically distributed random errors, hav-ing a N�0��2

v �-distribution. uit is a nonneg-ative random variable representing technicalinefficiency effects related to the technicalinefficiency of the production of the firmsinvolved. uit is assumed to be independentlyand identically distributed and truncated atzero of the normal distribution with mean �it

and variance �2, where �it is defined by:

�it = �0 +�1z1it +�′1z21it(2)

+�2z2it +�3z3it +�4z4it�

where z1, z2, z3, and z4 represent the firm-specific factors firm size, ratio of exter-nal capital, R&D, and exports, respectively.Notice that a square term of firm size (z2

1it� isincluded to incorporate the possible nonlin-ear relationship between firm size and tech-nical inefficiency.6

The � and � coefficients are estimatedtogether with variance parameters expressedin terms of

�2s = �2

v +�2 and � = �2/�2s �(3)

where parameter � has a value between zeroand one.

The data used in this article are an unbal-anced panel consisting of the annual timeseries of 453 Korean manufacturing firmsfrom 1980–93, with a total of 3,360 obser-vations. The sample covers all of the man-ufacturing firms listed on the Korean StockExchange, and all the firms’ data are takenfrom their financial reports.

The value added of the firms was deflatedby the wholesale price index of each indus-try, obtained from the Monthly Bulletin pub-lished by the Bank of Korea, with 1990 asthe base year. Labor was measured usingthe number of employed workers, and capi-tal stock was given by the amount of tangible

6. Allocative inefficiency cannot be included in thismodel, and perfect competition is assumed in this study.The market concentration ratios derived from the sam-ple show that the market share of the eight largest firmsis less than 10% for every industry, except for the firsttwo years (1980–81) in the paper industry. Therefore,imperfect competition and the resulting allocative inef-ficiency is ignored in this study. However, other causesof allocative inefficiency may exist, such as governmentregulations.

fixed assets. Because the firms’ reported cap-ital stock figures were already deflated buthad varying base-year prices, they were madecomparable with a common base year of 1990by using the gross domestic fixed capital for-mation deflator obtained from the NationalAccounts published by the Bank of Korea.The share of an individual firm’s output inthe total industry output was used for firmsize. The ratio of total interest payments forborrowed capital to total capital was used forthe ratio of external funds to total capital.The ratio of R&D investments to total pro-duction costs was used for R&D, and theratio of exports to total sales was used forexports. Table 1 presents sample means andstandard deviations.

For individual industry estimation, thisstudy classifies sample firms into double-digit industries using the International Stan-dard Industry Classification (SIC). The foodindustry is SIC 31 (food, beverages, andtobacco); the textile industry is SIC 32 (tex-tiles, wearing apparel, and leather products);the paper industry is SIC 34 (paper and paperproducts); the chemical industry is SIC 35(chemicals, petroleum, and coal products);the basic-metal industry is SIC 37 (basic-metal products); and the fabrication industryis SIC 38 (fabricated metal products, machin-ery, and equipment).7

IV. EMPIRICAL RESULTS

A. Hypothesis Tests

The maximum-likelihood estimates of theparameters of the frontier model, definedby equations (1) and (2), were obtained foreach of the five sectors using the computerprogram FRONTIER 4.1 (Coelli, 1996). Theparameter estimates are presented in Table 2.Most of the � estimates are statistically signif-icant, and formal hypotheses are constructedto test the adequacy of the translog pro-duction function specified as equation (1)representing the Korean manufacturing data.In addition, most of the estimates of the� parameters are statistically significant, andthe economic interpretation will be discussed

7. SIC 33 (wood and wood products) and SIC 39(furniture and other manufactured products) do nothave sufficient degrees of freedom to produce significantresults, as they include only 4 and 11 firms, respectively.For SIC 36 (nonmetallic mineral products), the estima-tion failed to produce significant estimates.


TABLE 1Summary Statistics for Variables in the Stochastic Frontier Production Functions

Variable Food Textiles Paper Chemical Basic-Metal Fabrication

No. of firms 48 79 27 104 37 158No. of observations 373 575 203 794 273 1147Value added 12409 12018 11449 11991 12516 11881

�1005� �1288� �1253� �1060� �1379� �1357�Labor 7059 7025 5973 6479 6829 6806

�1048� �1141� �0724� �0924� �1218� �1226�Capital 8123 7589 7368 7547 8283 7308

�1022� �1486� �1150� �1314� �1570� �1462�Firm size 0024 0015 0044 0011 0032 0007

�0026� �0019� �0041� �0030� �0075� �0023�External capital 0536 0374 0304 0228 0199 0207

�3455� �3132� �0558� �0883� �0423� �0233�R&D 00037 00028 00014 00168 00018 00125

�00002� �00002� �00002� �0001� �00002� �00006�Exports 00784 06289 01279 02121 02332 04385

�00081� �00116� �00107� �00085� �00104� �00098�

Notes: Standard deviations are in parentheses. Capital and value added are logarithmic values as used in actualestimation. Capital and value added are in 100 million Korean won in 1990 constant prices. Firm size = firm’ssales/industry’s sales total, external capital = interest payment/total capital, R&D = R&D spending/total productioncosts, and exports = exports/total sales.

later. Estimates of the � parameter rangedfrom 0.617 to 0.993, implying that a sub-stantial proportion of the total variabilityis associated with technical inefficiency ofproduction (formal tests of hypotheses thattested the significance of the � parameter areshown in Table 3). Although this parametershould not be considered as the proportionof the variance of the inefficiency effects rel-ative to the sum of the variances of the inef-ficiency effects and the random variation, itcontributes significantly to the random com-ponent of the inefficiency effects in the anal-ysis of Korean manufacturing production.

Hypotheses tests associated with both thespecified stochastic frontier production func-tion and the inefficiency effects are summa-rized in Table 3. The null hypotheses aretested using likelihood-ratio tests.8 The nullhypothesis that the technology in Korean

8. The test statistic is =−2�L�H0�−L�H1��, whereL�H0� and L�H1� are the values of the log-likelihoodfunction under the specifications of the null and alter-native hypotheses, H0 and H1, respectively. If thenull hypothesis is true, then has an approximatelychi-squared (or a mixed chi-squared) distribution withdegrees of freedom equal to the number of restrictions.If the null hypothesis includes = 0, then the asymptoticdistribution is a mixed chi-squared distribution (Coelli,1995).

manufacturing is a Cobb-Douglas productionfunction (H0 �LL = �KK = �LK = �TT = 0) isrejected for every industry at the 1% sig-nificance level. Thus, the Cobb-Douglasproduction function is not an adequate speci-fication for the Korean manufacturing sectorgiven the assumptions of the translog stochas-tic frontier production function model.

The null hypothesis that there is no techni-cal change (H0 �T = �TT = �TL = �TK = 0� isrejected at the 1% significance level for everyindustry and the null hypothesis that techni-cal progress is neutral (H0 �TL = �TK = 0�is rejected at the 1% significance level forevery industry except the food industry. Thisimplies the existence of nonneutral techni-cal progress in Korean manufacturing for thetextile, paper, chemical, basic-metal, and fab-rication industries given the specified produc-tion model. For all of these industries, �TL

and �TK are estimated as significantly positiveand negative, respectively, implying the exis-tence of labor-saving and capital-using tech-nical progress.9

9. Rapid growth of capital, induced by government-directed low-priced loans and various investment credits,might explain this capital-using technical progress (seeKang and Kwon, 1993).


TABLE 2Maximum-Likelihood Estimates for Parameters of the Stochastic Frontier

and Inefficiency Models


Stochastic frontier

Constant 6601 4445 −0837∗ 5661 6771 5117�9996� �9172� �0863� �1458� �8891� �1669�

log�L� 0098 1009 3911 0324 0158∗ 0231�2412� �5220� �6684� �2148� �0541� �2801�

log�K� 0591 0115 −0963 0461 0429 0683�2582� �2201� �2561� �4642� �2195� �6855�

t 0087∗ 0030 0342 0080 0024∗ 00005∗

�1592� �2322� �3749� �2208� �0333� �0018��log�L��2 −0008 0026 −0277 0059 −0178 0029

�2121� �2106� �2331� �2591� �3366� �1983��log�K��2 −0023 0083 0109 0044 −0108 −0003∗

�2104� �6135� �3137� �4035� �3155� �0193�log�L�∗ log�K� 0041∗ −0127 −0010∗ −0094 0295 −0026∗

�0698� �3536� �0081� �3304� �3559� �0761�t2 −0003∗ 0004∗ −0014 0000∗ −0001∗ 0001∗

�1480� �1353� �3240� �0159� �0257� �0773�log�L�∗ t 0018 0031 0062 0023 0068 0031

�2092� �3268� �3016� �3207� �4094� �4396�log�K�∗ t −0007∗ −0025 −0062 −0018 −0046 −0015

�0869� �3506� �3016� �3589� �3706� �2573�

Inefficiency model

Constant −2260 −3079 −8915 −0499 0328 −0344�1173� �7116� �5396� �3601� �2591� �2098�

Firm size −3790 −5559 −1812 −9105 −6562 −1235�2021� �3901� �3758� �2413� �2135� �2273�

(Firm size)2 2199 2954 2062 0872∗ 1020∗ 0544∗

�2146� �3367� �2802� �0844� �1038� �0725�External capital −0032 0056 −1343 0016∗ 0135 0354

�1151� �5520� �1987� �0662� �2163� �4849�R&D 1037∗ −2062 −1627∗ −6029 0016∗ 1004

�1399� �3549� �1340� �3889� �1048� �4040�Exports −1583 1849 −9353 0849 −0318∗ 0806

�2001� �5659� �1503� �4867� �0799� �7467�

Variance parameters

�2s 0893 0755 5259 0259 0165 0136

�2206� �9257� �7087� �9965� �5056� �9365�� 0973 0919 0993 0797 0759 0617

�2074� �7184� �5479� �2492� �1332� �1793�

Log-likelihood function

−3488 −17107 −9649 −16068 −6269 −23617

Note: Absolute values of t-statistics are in parentheses.∗Indicates that the coefficients are statistically insignificant at the 5% level.

The null hypotheses that there is no tech-nical inefficiency (H0 � = �0 = �1 = �′1 =�2 = �3 = �4 = 0) and that technical inef-ficiency is not stochastic (H0 � = 0) areboth rejected for all sectors. Thus the aver-age response function, in which all firms areassumed to be fully technically efficient, is

not an adequate representation of Koreanmanufacturing production, given the specifi-cation of the translog stochastic frontier andinefficiency model, defined by equations (1)and (2).

The last null hypothesis specifies that allthe coefficients of the explanatory variables


TABLE 3Statistics for Hypotheses Tests of the Stochastic Frontier and Inefficiency Models

Null Hypothesis Food Textiles Paper Chemical Basic-Metal Fabrication Critical Value

Cobb-Douglas 1637 2392 8745 1898 1582 2812 1327��LL = �KK = �LK = �TT = 0�

No technical progress 29899 17394 8873 41255 14049 70250 1327��T = �TT = �TL = �TK = 0�

Neutral technical change 418∗ 1238 1816 1453 1901 2504 921��TL = �TK = 0�

No inefficiency 17995 13319 26383 9496 4343 20604 1775�� = �0 = · · · = �5 = 0�

Nonstochastic inefficiency 8704 9846 10329 5321 4317 13451 541�� = 0�

No inefficiency effects 2026 4023 2296 3649 2232 13459 1508��1 = · · · = �5 = 0�

Notes: Every null hypothesis is rejected at 1% level of significance, except those indicated by ∗. Critical values of thetest statistic are presented at the 1% level of significance and derived from �2 distribution, except the null hypothesesthat involve � = 0, which have a mixture of �2 distribution (see Kodd and Palm, 1986).

in the inefficiency model are equal to zero(H0 �1 = �′1 = �2 = �3 = �4 = 0). If the nullhypothesis is true, the technical inefficiencyeffects have the same truncated-normal dis-tribution. The null hypothesis is rejected atthe 1% significance level for every sector.Thus, given the specification of the stochas-tic frontier and inefficiency model, defined byequations (1) and (2), the inefficiency effectsof the Korean manufacturing sectors cannotbe regarded as independently and identicallydistributed random variables that arise fromthe truncation of a normal distribution withzero mean.

B. Technical Inefficiency Effects

The estimates of the coefficients of firmsize in the inefficiency effect model are bothsignificant and negative, showing that there isa positive relationship between technical effi-ciency and firm size in every industry. How-ever, the coefficients of the square term offirm size are positive and significant for thefood, textile, and paper industries, implyingan inverted U-shaped relationship betweentechnical efficiency and firm size. Thus, forthese industries, technical efficiency will dete-riorate with an increase in firm size after acertain level.

The effect of firm size on technical effi-ciency is important for policy purposes. Forexample, if an industry consists of small firmsthat are technically inefficient in produc-tion, industrial policy should be directed at

enhancing the efficient use of existing tech-nology to catch up to the production fron-tier. To accomplish this, improvements inlearning-by-doing processes and managerialpractices are necessary. The empirical resultsuggests that such policy needs to be pursuedfor the Korean manufacturing industry. Theresults also suggest that mergers and acqui-sitions by more efficient firms can improvetechnical efficiency; this explains the preva-lence of mergers and acquisitions in Koreanmanufacturing industries after the financialcrisis.

The estimates of the coefficient of theratio of external funds to total capital, asmeasured by the ratio of interest paymentsto total capital, are negative and significantfor the food and paper industries but positiveand significant for the textile, basic-metal,and fabrication industries. An increase inexternal funds improves technical efficiencyin the former industries, but decreases it inthe latter industries.

It is worth examining this differentialresponse of production efficiency to externalfunds across industries for the Korean man-ufacturing sector. The industries (food andpaper) in which increased external financ-ing brought about technical efficiency con-sist of a large number of small firms thatface fierce competition in the market. Suchfirms generally lack internal funds and mustcompete for external funds, which will ulti-mately be loaned to the firms that are tech-nically efficient. The financial market seems


to screen efficient firms according to marketconditions. Meanwhile, the industries (tex-tile, basic-metal, and fabrication) in whichan increase in external financing broughtabout technical inefficiency consist of largefirms that have easy access to bank creditand other external funds. In addition, thesesectors represent major exporting industriesin the Korean manufacturing sector andwere the main target of the Korean gov-ernment’s promotion drive during the 1970sand 1980s. Thus, in these industries, exter-nal funds were abundant and more easilyavailable to less efficient firms than in otherindustries not promoted by the government.The empirical results suggest that the exter-nal funds the Korean government uses topromote certain manufacturing industries arean inefficient way to ration credit. It alsoindicates that resource allocation for Koreanmanufacturing sectors could be improved byimplementing a screening mechanism for thefinancial sector.

The estimates of the coefficients of R&D,represented by the ratio of R&D spending tototal output, are negative and significant forthe textile and chemical industries but posi-tive and significant for the fabrication indus-try. For the latter, R&D brought about a pos-itive shift in the frontier; this upward move-ment of the frontier caused a loss in efficiencyfor firms that could not utilize the frontiertechnology in actual production. This expla-nation from Perelman (1995), who founda negative relationship between R&D andtechnical efficiency, seems to explain R&Dinvestment in the fabrication sector of theKorean manufacturing industry. This sectorproduces many products, such as electron-ics and computers, and requires large R&Dinvestment to develop new products. R&Dconsistently raises the frontier in this sector,leaving behind other firms that cannot followthis path.10

For the other industries (textile and chem-ical) that showed a positive relationshipbetween R&D and technical efficiency, R&Dhelped the firms’ production catch up tothe frontier, instead of shifting the frontier.This seemingly contradictory movement waspossible if R&D was largely associated withimproving production practices and apply-ing existing advanced technology, rather than

10. Lags were introduced in R&D variables, but theestimates did not change in significance or sign.

with innovation, as observed in the fabrica-tion industry. Thus, R&D in these industriesresults in catching up with the frontier ratherthan raising the frontier. Because the textileand chemical industries are mature industriesthat are less innovative, this explanation fits.

The estimates of the coefficients ofexports, represented by the ratio of exportsto total sales, were negative and significantfor the food and paper industries but posi-tive and significant for the textile, chemical,and fabrication industries. Increased exportsled to improvement in technical efficiency forthe former industries and to deteriorationfor the latter industries. The latter industriesare major exporting industries in the Koreanmanufacturing sector. This result contradictsthe general presumption that relatively effi-cient firms will export more than inefficientones.11

This empirical result can be explainedin the following ways. First, this could justreflect the fact that for Korean manufactur-ing industries, a firm’s technical efficiencyis not necessarily correlated with exports.If exports are more closely associated witha firm’s excess production capacity thanwith beating international competition, thenexporting firms might be less efficient thanother firms that concentrate their sales inthe domestic market. This coincides with theobservation that many exporting firms in theKorean manufacturing industry have excesscapacity as a result of the heavy invest-ment the Korean government made duringthe chemical and heavy industry drive duringthe 1970s and early 1980s. This explanationseems appropriate for the chemical and fab-rication industries.

Second, many exporting firms in theKorean manufacturing industry target low-priced products and don’t necessarily pro-duce their products at the frontier of technol-ogy. For these firms, price matters so muchthat they cannot utilize the factor intensityor production methods implied by the fron-tier; instead they try to produce at low cost

11. This study constructed an export variable in twoways, to discover whether it makes any difference to theinefficiency effect. First, exports themselves were used,instead of the ratio of exports to total sales; the estimatesof the coefficients did not change in either significanceor sign. Second, a dummy was constructed to representexporting firms that export more than 50% of their prod-ucts in the foreign market; however, the dummy variablewas estimated as insignificant in most of the sectors.


TABLE 4Average Elasticities of Mean Technical Inefficiency �� with regard to Firm-Specific Factors


Firm size −1144 −1036 −1085 −0122 −0288 −0112External capital −0022 0026 −0561 0004 0037 0095R&D 0005 −0007 −0003 −0123 0000 0016Exports −0156 1447 −1644 0219 −0103 0458

Note: Elasticities are evaluated at the average values of firm-specific factors, representing the relative percentagechange of mean technical inefficiency �� to firm-specific factors.

by sticking to traditional production methods,rather than taking on the burden of invest-ing in new technologies with resulting highercapital costs. This interpretation applies tothe export mix of Korean manufacturers thatusually target low-priced goods, rather thanthose that require a high standard of efficientproduction. This mix of export goods explainsthe general features of Korean manufactur-ing as a whole, because more firms are in thiscategory than are not. Thus, exporting firmsmay end up with inefficient production.

Third, exports may have a negativeeffect on technical efficiency if technologicalspillover resulting from the exports doesn’tpermeate other firms in the same industry.In this case, exports widen the gap betweenthe production frontier of firms engagingin exports and the actual production ofother firms. This begs the question: Whydo exports have a positive effect on techni-cal efficiency in some sectors, such as thefood and paper industries? This might bebecause these industries consist mainly ofdomestic firms, and technological spilloversfrom exports were kept to a small numberof exporting firms, who could easily keepthis knowledge to themselves because of thelimited number of firms aware of frontiertechnology. In summary, exporting does notnecessarily improve technical efficiency insome industries, and the export promotionpolicy of the Korean government should becomplemented with an industrial policy thatenhances the technical efficiency of exportingfirms to increase the international competi-tiveness of Korean manufacturers.

To provide a numerical estimate of tech-nical inefficiency effects, the elasticities ofmean technical inefficiency (�) with respectto firm-specific variables (such as firm size,external capital, R&D, and exports) were

estimated at the average values of the firm-specific variables, based on the parameterestimates.12 The elasticities, presented inTable 4, represent the percentage change ofmean technical inefficiency (�) for a 1%change of the firm-specific variables.

Firm size’s effect on technical inefficiencyis always negative, unlike the other factorsthat flip signs, and tends to be large in magni-tude compared to the nonexport factors, withelasticities ranging from −0112 to −1444.However, the absolute value of exports’ effecton technical inefficiency is larger in four ofthe six industries than that of firm size’seffect, with estimates ranging from –1.644to 1.447. R&D tends to have the smallestimpact on technical inefficiency, with elastic-ities ranging from −0123 to 0.016.

C. Robustness of the Findings

This study employed an estimation tech-nique developed by Battese and Coelli (1993)to show how technical efficiency is related tofirm size, ratio of external capital, R&D, andexports. This technique allows these variablesto affect the mean of the normal distribu-tion that is truncated at zero and representstechnical inefficiency. Shifting the mean ofthe underlying normal distribution will affectthe mean and variance of technical ineffi-ciency, but in a very nonlinear fashion.13 Thusit is natural to question the robustness of theempirical findings in alternative model spec-ifications. This section summarizes some ofthe corroborating evidence that supports the

12. From equation (2), the elasticity for factors z1

and zi �i = 2�3�4� is estimated as ��̂1 + 2�̂′1 · z̄1� · �z̄1/��and �̂i�z̄i/��, respectively, where the caret and bar overthe characters represent parameter estimates and meanvalues, respectively.

13. The author is indebted to an anonymous refereefor this reasoning.


TABLE 5Maximum Likelihood Tobit Estimates of Technical Inefficiency from the Stochastic Frontier

with Time Varying Effects (Dependent Variable: uit)


Constant 0561 0515 0269 0563 0382 0282�1909� �1851� �2882� �3660� �1714� �2024�

Firm size −1521 −7387 −1103 −4054 −1412 −3308�1006� �1123� �2757� �1112� �4724� �9558�

(Firm size)2 10157 7308 1446∗ 4159∗ 1638 3032�8278� �1112� �0950� �1374� �5560� �8764�

External capital −0009 0005 −0029 0013∗ 0025 0050�3298� �2480� �2342� �1417� �2874� �3736�

R&D 0002∗ −0004 −0021∗ −0003 0014∗ 0184�0858� �2707� �0963� �4948� �0095� �2425�

Exports −0275 0071 −0287 0112 −0263∗ 0391�3124� �2896� �3908� �3055� �1447� �1911�

� 0264 0252 0687 0245 0203 0230�2731� �3391� �2014� �3985� �2336� �4789�

Log-likelihood −3357 −2435 −21205 −3138 −4687 −5683

Note: Absolute values of t-statistics are in parentheses.∗Indicates that the coefficients are statistically insignificant at the 1% level.

empirical findings on the effects of technicalinefficiency.

First, the average production function wasestimated by including firm-specific techni-cal inefficient variables as regressors. Theestimated sign and significance of the firm-specific variables did not have any noticeableimpact on these findings.14 Thus the esti-mation showed that variables that improved(worsened) technical efficiency also increased(decreased) average production. This estima-tion of average production cannot be com-pared directly with the technical inefficiencyestimate, although it is useful for checkingthe robustness of empirical results.

As a more direct check on robustness,technical inefficiency was estimated from thefrontier production function (1), and thenthe relationship between firm-specific vari-ables and technical inefficiency was estimatedin post estimation. Thus the empirical appli-cation is divided into two steps. In thefirst step, the technical inefficiency level isderived from stochastic frontier estimation.The estimated technical inefficiency level isregressed on firm-specific variables in thesecond step to see their impact on techni-cal inefficiency. Following Battese and Coelli

14. These results are not reported to save space, butthey are available on request.

(1992), technical inefficiency is assumed to bedefined by

uit = ui exp�−��t−T ��(4)

where the distribution of ui is taken to be thenonnegative truncation of the normal distri-bution, N��2

u�, and � is a parameter thatrepresents the rate of change in technicalinefficiency.

The results of the post estimation arereported in Table 5.15 These results confirmthe empirical findings without any discernibledifference, except the change in the sign ofthe square term of firm-size effects in thechemical industry.

This relation between technical ineffi-ciency and firm-specific variables in this two-step estimation is straightforward in the sensethat technical inefficiency itself is explainedby the other variables instead of the mean oftechnical inefficiency. However, Kumbhakaret al. (1991) argued that there is a seri-ous problem with the two-step method whilesuggesting a general specification of techni-cal inefficiency using the one-step estimationmethod. They stated that estimates of theproduction function become inconsistent if

15. The post regression was estimated by themaximum likelihood Tobit model because technical inef-ficiency was truncated at zero.


technical inefficiency can be correlated withthe input.

V. CONCLUSIONS

This study estimated translog stochasticfrontier production functions using an unbal-anced panel of Korean manufacturing firmsin the food, textile, paper, chemical, basic-metal, and fabrication sectors. The sectorswere estimated individually to investigatewhether technical efficiency is systematicallyrelated to firm size, dependency on exter-nal funds, R&D investments, and exports.The empirical results suggest that firm sizehas a significant positive effect on techni-cal efficiency in every sector. The effects ofthe other factors are less systematic and varyacross sectors.

This study implies that industry-specificpolicy guidelines are required to promotetechnical efficiency in Korean manufacturingsectors. First, more external funding shouldbe directed to the food and paper indus-tries rather than to the textile, basic-metal,and fabrication industries, in which exter-nal funds lead to inefficient production. Thissuggests that if the government eliminatedcredit rationing it might enhance productionefficiency for Korean manufacturing sectors.Second, each industry may require increasedR&D spending, but they should have differ-ent objectives. For example, R&D must betargeted to catching up with the frontier inthe fabrication sector, whereas it should betargeted to raising the frontier in the textileand chemical industries. Third, the Koreangovernment’s export promotion policy maynot necessarily lead to improvements in tech-nical efficiency in the textile, chemical, andfabrication industries, in which exports areestimated to have a negative effect on tech-nical efficiency.

The empirical results suggest that increas-ing firm size should be promoted to improvetechnical efficiency in manufacturing indus-tries in developing countries, especially forindustries that consist of small firms. Theresults also suggest that R&D investmentshould be tailored to meet the specific needsof each industry, that is, some industriesrequire catching up but others need tomove up. R&D investment should enhancemanagerial practices and the application ofadvanced technology to actual production

in the former industries and technologicalinnovation in the latter industries. Finally,the results suggest that the export-promotionpolicies of governments in developing coun-tries should be complemented with industrialpolicies that enhance the technical efficiencyof exporting firms to increase the interna-tional competitiveness of the countries.

Further study should consider the specificcomposition of R&D in each industry to findout how it relates to technical efficiency. Thiswill provide the more concrete informationthat is needed for policy makers to promoteR&D tailored to each industry.

The firm-specific factors considered herearen’t exhaustive in explaining technical effi-ciency. There may well be other firm-specificfactors that affect efficiency, but the availabil-ity of data limited the scope of this analysis.

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Documents

IDENTIFYING AND ESTIMATING SOURCES OF TECHNICAL INEFFICIENCY IN KOREAN MANUFACTURING INDUSTRIES