Technical Efficiency of Some Selected Manufacturing Industries in Bangladesh: A Stochastic Frontier Analysis

Transcription

1 The Lahre Jurnal f Ecnmics 11 : 2 (Winter 2006) pp Technical Efficiency f Sme Selected Manufacturing Industries in Bangladesh: A Stchastic Frntier Analysis Md. Azizul Baten *, Masud Rana *, Sumnkanti Das * and Md. Abdul Khaleque ** Abstract This paper investigates the technical efficiency f selected manufacturing industries f Bangladesh using a stchastic frntier prductin functin apprach suggested by Battese and Celli (1992) applied t panel data. A feasible Cbb-Duglas stchastic frntier prductin functin, which has time-varying technical inefficiency effects, was estimated. Tw alternative distributins were used t mdel the randm inefficiency term: a truncated nrmal distributin and a halfnrmal distributin. The estimated average technical efficiency fr fur grups f industries f Bangladesh ver the reference perid was 40.22% f ptential utput fr the truncated nrmal distributin, whereas was 55.57% f ptential utput fr the half-nrmal distributin. Keywrds: Stchastic frntier, Prductin functin, Technical efficiency Intrductin One f the mst imprtant and fascinating aspects f ecnmic change in Bangladesh in the last three decades has been the grwth f manufacturing. There is great scpe fr the manufacturing sectr f Bangladesh t imprve s technical efficiency; whut imprving s technical efficiency, the sectr cannt play the desired rle in the prcess f ecnmic develpment f the cuntry. The manufacturing prcess may play a val rle in the develpment prcess by creating new jbs, increasing exprts, and displacing imprts. But efficiency is the first cndin that has * Department f Statistics, Shah Jalal Universy f Science and Technlgy, Sylhet- 3114, Bangladesh. ** Department f Statistics and Cmputer, Dhaka Cmmerce Cllege, Dhaka, Bangladesh.

2 24 Md. Azizul Baten, Masud Rand, Sumnkanti Das and Md. Abdul Khaleque t be achieved t be cmpetive internatinally. In rder t accelerate the develpment prcess, industries have t be cme technically efficient. Fllwing the seminal paper by Farrell (1957), frntier prductin functins were intrduced and have been widely applied by different researchers. The stchastic frntier prductin functin was independently prpsed by Aigner, Lvell and Schmidt (1977), Meeusen and van dn Breck (1977) and Battese and Crra (1977), and there have been a vast range f applicatins in the lerature. (Fr lerature surveys see Greene (1993) and Ra and Celli (1998)). The mdel was riginally defined fr the analysis f crss-sectinal data but varius mdels t accunt fr panel data have been intrduced by Pt and Lee (1981), Crnwell, Schmidt and Sickles (1990), Kumbhakar (1990), Kumbhakar, Ghsh and Mcgukin (1991). Battese, Malik and Brca (1993) and Battese. Malik and Gill (1996) studied the frntier prductin functin, cnsidering fur years f panel data fr each f fur districts f Pakistan and a mdified Cbb-Duglas prductin frntier in which the mdels fr the technical inefficiency effects were specified by Battese and Celli (1992,1995). Battese and Celli (1995) prpsed a stchastic frntier prductin frntier fr panel data, which has firm effects assumed t be distributed as truncated nrmal randm variables, which are als permted t vary systematically wh time and in which the inefficiency effects are directly influenced by the number f variables. By using the same mdel, Taymaz and Saatci (1997) estimated the stchastic prductin frntier fr Turkish textile, cement and mtr vehicle industries. A frntier prductin functin studied by Ajibefun, Battese and Kada (1996) applyied time-varying inefficiency mdel using eleven years f data n rice prductin in prefectures in Japan. They suggested that the tradinal average respnse functin, which des nt accunt fr the technical inefficiency f prductin, is nt an adequate representatin f the data. Tzuvelekas et. al. (1999) investigated the relative cntributin f technical efficiency, technlgical change and increased input use t the utput grwth f the Greek live-il sectr using a stchastic frntier prductin functin apprach applied t panel data. Jafrullah (1996) studied the technical efficiency f 19 fur-dig manufacturing industries f Bangladesh and cncluded that the manufacturing industries f Bangladesh analyzed were nt highly technical but efficient. In fact, few studies have been dne t see the technical efficiency f Bangladeshi manufacturing industries using panel data. Future, efficiency has seldm been studied fr manufacturing industries in Bangladesh using the stchastic frntier prductin functin [Jafrullah M, (1996)]. Since estimatin f the prductin functin by standard panel analysis des nt

3 Technical Efficiency f Sme Selected Manufacturing Industries in Bangladesh 25 present infrmatin such as efficiency in the prductin functin, we analyze the stchastic frntier prductin functin in this study. The bjective f this study is t apply the stchastic frntier prductin functin t investigate the technical efficiencies f fur threedig level industries f Bangladesh fr panel data. This study is imprtant in predicting the technical efficiencies fr the selected grup f manufacturing industries, but als indicates the trend f efficiency ver the perid, 1981/ /2000. At the same time, is desirable t see whether technical efficiency is time varying r time invariant. The paper prceeds as fllws: the next sectin reviews the stchastic frntier prductin functin apprach t mdeling inefficiency. This includes a discussin f the determinants f inefficiency used here. The data is discussed in sectin 3, while sectin 4 prvides and discusses the results frm estimating the stchastic prductin frntier. Finally, the last sectin presents cnclusins. Stchastic Frntier Mdel wh Technical Efficiency Effects In this study we have cnsidered the Stchastic Frntier Mdel t measure the technical efficiency f selected manufacturing industries in Bangladesh. The framewrk assumes the existence f a best practice frntier crrespnding t fully efficient peratin in the industry under investigatin. This frntier defines the maximum level f utput that can be btained frm any vectr f resurce inputs in the absence f uncertainty. The stchastic cmpnent f the frntier cnsists f tw types f disturbance r errr terms. The first is a regular symmetric disturbance that represents statistical nise in a typical regressin. The secnd disturbance r errr term, which is firm specific, is a ne-sided deviatin frm this idealized frntier, and is referred t as technical inefficiency. The greater the amunt by which the realized prductin falls shrt f the stchastic frntier, the greater the level f technical inefficiency. The measurement f technical inefficiency has received renewed attentin since the late eighties frm an increasing number f researchers, as the frntier appraches t efficiency measurement have becme mre ppular. The intrductin f the frntier apprach has raised the level f analysis and bradened the range f efficiency hyptheses that can be frmulated and tested. The prductin frntier apprach t technical inefficiency measurement makes pssible t distinguish between shifts in technlgy frm mvements twards the best-practice frntier. By estimating the best-practice prductin functin (an unbservable functin) this apprach calculates technical efficiency as the distance between the frntier and the bserved utput. The advantage f frntier analysis is that

4 26 Md. Azizul Baten, Masud Rand, Sumnkanti Das and Md. Abdul Khaleque prvides an verall, bjectively determined, numerical efficiency value and ranking f individual firms that is nt therwise available. The stchastic frntier apprach allws bservatins t depart frm the frntier due t bth randm errr and inefficiency. This paper adpts the mdel specificatin f Battese and Celli (1992) wh prpsed a stchastic frntier prductin functin fr (unbalanced) panel data wh firm effects that can vary systematically ver time and are assumed t be distributed as truncated nrmal randm variables. Thus the mdel is Y = X β + ( V U ) i = 1,2,... N, t = 1,2,.... T (1). where, Y is the lgarhm f the prductin f the i-th industry in the t-th time perid. X is a vectr f input quanties f the i-th industry in the t-th time perid and β is a vectr f unknwn parameters. The errr term cmprises tw separate parts. V are randm variables assumed t be identically and independently distributed (iid) N ( 0, σ 2 v ) and independent frm U. U captures technical inefficiency in prductin. U is defined by Battese and Celli (1992) as: U = U {exp[ η ( t T )]} (2) i where U i i =1,2,.... N are assumed t be firm-specific nn-negative randm variables independently distributed as nn-negative truncatins at 2 zer f the distributin N ( μ, σ u ). η is an unknwn parameter t be estimated, which determines whether inefficiencies are time-varying r time invariant. In this mdel, the technical inefficiency effect fr the i-th industry in the t-th time perid, U is defined t be the prduct f an expnential functin f time, exp [ η ( t T )], invlving the unknwn parameter, η, and the nn-negative randm variable U i, which is the technical inefficiency effect fr the i-th industry in 1999/2000, the last year f ur data set. If η is psive, then η ( t T ) η ( T t ) is psive fr t <T and s exp[ η ( t T )] > 1, which implies that the technical inefficiencies f industries decline ver time. Hwever, if η is

5 Technical Efficiency f Sme Selected Manufacturing Industries in Bangladesh 27 negative, then η ( t T ) < 0 ver time. and thus technical inefficiencies increase The primary advantage f a stchastic frntier prductin is that enables ne t estimate U i and therefre t estimate industry specific technical efficiencies. The measure f technical efficiency is equivalent t the rati f the prductin f the i-th industry in the t-th time perid t the crrespnding prductin value if the industry effect is zer. Given the specificatins f the stchastic frntier prductin functin, defined by equatin (1), the technical efficiency f the i-th industry in the t-th time perid is defined by: TE = ( X β U ) / ( X β) (3) U i where U and equatin (1). β X are defined by the specificatins f the mdel in The technical efficiencies are predicted using the cndinal expectatin f the functin U given the cmpsed errr term f the stchastic frntier (c.f. Battese and Celli (1995)). On the basis f panel data, if the prductin frntier being estimated is Cbb-Duglas, like equatin (1), can be expressed in the fllwing frm: Y β L β K V U = A L K e e (4) 2 where V fllws N (0, σ v ) and U fllws a half r truncated nrmal distributin at zer. Taking natural lg n bth sides f equatin (4), the fllwing equatin is btained: lny = ln A + β ln L + β ln K + ( V U ) (5) L K the subscripts, i and t represents i-th industry (i = 1, 2, 3, 4) and t-th year f bservatin (t = 1, 2, 3,..16), respectively. The ne-sided distributin f guarantees inefficiency t be psive nly. U Given the specificatins f the stchastic frntier prductin functin, defined by equatin (1), the null hypthesis, that technical inefficiency is nt present in the mdel, is expressed by H : γ = 0, where γ is the variance rati, explaining the ttal variatin in utput frm the frntier level f utput attributed t technical efficiencies and defined by

6 28 Md. Azizul Baten, Masud Rand, Sumnkanti Das and Md. Abdul Khaleque 2 σ u 2 γ =. This is dne wh the calculatin f the maximum 2 σ v + σ u likelihd estimates fr the parameters f the stchastic frntier mdel by using the cmputer prgram FRONTIER Versin 4.1 (Celli 1996). The parameter γ must lie between 0 and 1. If the null hypthesis is accepted, 2 this wuld indicate that σ u is zer and hence that the U term shuld be remved frm the mdel, leaving a specificatin wh parameters that can be cnsistently estimated using rdinary least squares. Further, the null hypthesis that the technical inefficiency effects are time invariant and that they have half-nrmal distributin are defined by H : η = 0 and H : μ = 0 respectively. These hyptheses are tested using the generalized likelihd rati test and the generalized likelihd rati statistic, λ is defined by λ = 2 ln [ L ( H ) / L ( H 1 ) ], where H and H 1 are the null and alternative hyptheses invlved. If the null hypthesis, H, is true, then λ is asympttically distributed as a Chi-square (r mixed Chisquare) randm variable. If the null hypthesis invlvesγ = 0, then λ has mixed Chi-square distributin (see Celli, 1995, 1996) because γ = 0 is a value n the bundary f the parameter space frγ. Data surces and variables cnstructin Data descriptin Data fr the selected grup f industries have been drawn frm the Census f Manufacturing Industries (CMI), cnducted by the Bangladesh Bureau f Statistics (BBS) every year. Our area study cvers selected 3-dig census factries, under the registered manufacturing sectrs f Bangladesh ver the reference perid 1981/1982 t 1999/2000. As data fr three years, viz., 1994/1995, 1996/1997, and 1998/1999 were nt published, data fr the remaining 16 years have been cnsidered fr ur present study. The estimates at cnstant prices (1981/1982=100) are derived. The study fcuses n a selected grup f industries f the Bangladesh registered manufacturing sectr. The selected grup f industries are fd manufacturing industries, beverage industries and tbacc industries under grup ne; textile manufacturing industries and apparel under grup tw; leather and s prducts, ftwear and rubber prducts under grup three; and nn-metallic mineral prducts, fabricated metal

7 Technical Efficiency f Sme Selected Manufacturing Industries in Bangladesh 29 prducts, electrical and nn-electrical machinery under grup fur. The industries are gruped based n their nature. The fd manufacturing sectr, presented in grup ne, plays an imprtant rle in the ecnmy being necessary gds that are needed fr daily life. Grup tw cnsists f textile manufacturing industries and apparel frm which Bangladesh earns a majr part f s freign currency. Abut 75% f the ttal exprts f Bangladesh came frm this grup. Abut 10 millin peple depend n the textile industries directly and indirectly fr earning their livelihd. Every year Bangladesh earns abut 6,500 millin US dllars by exprting textile prducts. Leather and s prducts are anther imprtant sectr fr earning freign currency. Variable cnstructin Value added (Y): Grss value added figures are used in this study t represent value added and is equal t grss utput minus industrial cst. Industrial csts include the cst f raw materials, fuel and electricy. We use value added instead f net value added t avid the arbrariness invlved in depreciatin estimates. T btain the grss value added series in cnstant prices, the yearly current values were deflated by the industry price index f the relevant year. Capal (K): Capal is ne f the essential inputs in measuring prductivy. Grss fixed assets are used in this study as capal inputs and these are the bk values f land, buildings, machinery, tls, transprt and ffice equipment, etc. The grss values f fixed assets have been weighted by the base year rates f return t get the measure f capal input. The rate f return is the rati f nn-wage value added t fixed assets as used here. The weighted capal input was then deflated by the capal gds price index that stands as a prxy f the whle machinery price index. Labr (L): The number f emplyees directly r indirectly in prductin is used in this study as a labr input. It cvers all wrkers including administrative, technical, clerical, sales and purchase staff. Thus all prductin and nn-prductin wrkers except temprary daily casuals and unpaid wrkers are included in the analysis. In brief, they include prductin wrkers, salaried emplyees, and wrking prprietrs. The best measure f labr input is the number f hurs wrked. As n such data are available fr any industry, emplyment figures were taken as the secnd measure and were weighted by the base year wage rates t btain measures f labr input.

8 30 Md. Azizul Baten, Masud Rand, Sumnkanti Das and Md. Abdul Khaleque Empirical Results Estimatin The maximum-likelihd estimates f the parameters in the Cbb- Duglas stchastic frntier prductin functin were btained by using the FRONTIER 4.1 prgram (Celli, 1996). Tables 1 and 2 shw the estimatin results f the Cbb-Duglas prductin functin n the basis f the stchastic frntier mdel by the methd f maximum-likelihd estimatin. The rdinary least square estimates f the parameters are used as inial values (t estimate) fr the maximum-likelihd estimates f the parameters. The adjusted R-squared fr the rdinary least square estimates is 0.78, which indicates that 78 percent ttal variatin f the utput is explained by the input variables. The maximum-likelihd estimate f the parameter wh time-varying inefficiency effects fr labr input is and fr the truncated nrmal distributin and half-nrmal distributin respectively presented in Table-1, which indicates that they are insignificant. Bangladesh is ne f the mst densely ppulated cuntries and has a labr surplus ecnmy and s labr has a lw utput elasticy (see Celli et. al., 2003). The parameter estimate fr capal input is significantly different frm zer at the 1 percent level f significance fr bth the distributins. Again the elasticies f labr ( β L ) and capal ( β K ) respectively, indicate the values f and Like the previus results in panel analysis, the stchastic frntier prductin functin als shws greater elasticy fr capal than fr labr. Hwever, ecnmies f scale shw variable returns t scale as in the stchastic frntier prductin functin. Here is nt imprtant t shw increasing returns t scale r decreasing returns t scale, because we d nt have an inference n the estimatin f efficiency in the prductin functin by the stchastic frntier mdel. In addin, inefficiency f the prductin functin is calculated by the errr term. In the truncated and half-nrmal distributins, the rati f industry specific variabily t ttal variabily,γ, is psive and significant at the 1 percent level, implying that industry specific technical efficiency is imprtant in explaining the ttal variabily f utput prduced. Hwever, the γ -estimate assciated wh the variance f the technical inefficiency effects is relatively small. The estimates fr the parameters fr the time-varying inefficiency mdel (1), presented in Table-1, indicate that the technical efficiency effects tend t decline ver time since the estimate fr the η parameter is psive (i.e. ) η =0.0255). Als the parameter μ is psive indicating that the distributin f the inefficiency effects is nt mre cncentrated abut zer than that f the half-nrmal distributin.

9 Technical Efficiency f Sme Selected Manufacturing Industries in Bangladesh 31 Table-1: Maximum-Likelihd Estimates f the Cbb-Duglas Stchastic Frntier Prductin Functin wh Time-Varying Inefficiency Effects fr the Selected Manufacturing Industries in Bangladesh Cnstant Truncated Nrmal Half-Nrmal Variable Parameter Cefficient S.E. t-statistics Cefficient S.E. t-statistics Labr input Capal input β *** *** β L β *** *** K Variance parameter Sigma-Squared 2 σ *** *** Gamma γ *** *** Mu μ ** Eta η ** *** Likelihd Functin S.E. = Standard errr Nte: *** Significant at 1 per cent level (p<0.01) ** Significant at 5 percent level (p<0.05)

10 32 Md. Azizul Baten, Masud Rand, Sumnkanti Das and Md. Abdul Khaleque Table-2: Maximum-Likelihd Estimates f the Cbb-Duglas Stchastic Prductin Frntier wh Time-Invarying Inefficiency Effects fr the Selected Manufacturing Industries in Bangladesh Truncated Nrmal Half-Nrmal Variable Parameter Cefficient S.E. t-statistics Cefficient S.E. t-statistics Cnstant Labr input Capal input β *** *** β L β *** *** K Variance parameter Sigma-Squared σ *** *** Gamma γ *** *** Mu μ ** Eta η Mean efficiency Likelihd Functin S.E. = Standard errr Nte: *** Significant at 1 per cent level (p<0.01) ** Significant at 5 percent level (p<0.05)

11 Technical Efficiency f Sme Selected Manufacturing Industries in Bangladesh 33 On the ther hand, in Table-2, the maximum-likelihd estimates f the parameter (wh time-invarying inefficiency effects) fr the labr input, are negative and insignificant fr bth the truncated and the halfnrmal distributins, while the cefficient f capal input values are psive and highly significant. In the case f bth the truncated and halfnrmal distributins, the values f γ are psive and are highly significant demnstratin that technical inefficiency exists in the selected manufacturing industries f Bangladesh. Hwever, the γ -estimate assciated wh the variance f the technical inefficiency effects is relatively small. The η parameter is restricted t zer in the mdel wh time-invarying inefficiency effects. Tests The results f frmal tests f varius null hyptheses were btained using the likelihd rati (L-R) statistic and are presented in Table-3. These are btained by using the values f lg-likelihd functins fr the selected manufacturing industries and the stchastic frntier prductin functin. The first null hypthesis H : γ = 0, which specifies that there are n technical inefficiency effects in the mdel, is rejected by the data. S the average respnse functin is nt an adequate representatin f the data. This implies that the technical inefficiency effects assciated wh manufacturing industries in Bangladesh are significant. The technical inefficiency effects having a half-nrmal distributin, is tested by the null hyptheses H : μ = 0. In ur study this hypthesis is accepted which indicates that the half nrmal distributin is preferable t the truncated nrmal (at zer) distributin fr the technical inefficiency effect. The hypthesis H : η = 0 is rejected, indicating that the technical inefficiency effect varies significantly ver time.

12 34 Md. Azizul Baten, Masud Rand, Sumnkanti Das and Md. Abdul Khaleque Table-3: Generalized Likelihd-Rati Tests f Hyptheses fr Parameters f the Stchastic Frntier Prductin Functin fr the selected Manufacturing Industries in Bangladesh Null Hypthesis : γ =0 Lg-Likelihd Functin Test Statistic λ Crical Value Decisin H Reject H H : η = μ = Accept H H : μ = Accept H H : η = Reject H Technical Efficiency The estimates f technical efficiency fr the different grups f industries, btained by using the FRONTIER 4.1 prgram (Celli, 1996), are presented in Table-4. The mean efficiency fr the truncated nrmal distributin is fund t be and the range is t whereas fr the half-nrmal distributin, mean efficiency is ranging frm t This implies that 40.22% and 55.57% f ptential utput is being realized in the selected manufacturing industries f Bangladesh accrding t the truncated (at zer) nrmal distributin and half-nrmal distributin respectively. There is a wide variatin in the technical efficiencies f selected manufacturing industries. The mean technical efficiency f bth distributinal frms implies that the selected manufacturing industries are nt achieving 100 percent f ptential utput. The hypthesis test cnfirmed the existence f inefficiency. The estimated industry-specific technical efficiency measures fr each year are presented in Table-4 while Figure 1 shws the relevant prbabily histgram. The mean efficiency fr the truncated nrmal distributin indicates the range f values between and while fr the half-nrmal distributin the mean efficiency varies frm t The technical efficiency measures increased in bth distributins in each grup f industries. In ther wrds, the verall average levels f efficiency have increased ver the perid 1981/ /2000. Nevertheless, individual technical efficiency estimates exhib cnsiderable variatin. The half-nrmal distributin gives higher technical efficiency estimates than the truncated nrmal distributin. Grup ne was the mst efficient grup relatively whereas grup three is the least efficient. Althugh the grwth rate f technical efficiency fr grup three is fund t be the greatest, s technical efficiency remains the lwest.

13 Technical Efficiency f Sme Selected Manufacturing Industries in Bangladesh 35 Table-4: Estimated Technical Efficiencies f Grup f Manufacturing Industries in Bangladesh by Tw Distributin Year Grup ne Efficiency fr Truncated Nrmal Efficiency fr Half Nrmal Grup Tw Grup Three Grup Fur Mean Grup One Grup Tw Grup Three Grup Fur Mean Average

14 36 Md. Azizul Baten, Masud Rand, Sumnkanti Das and Md. Abdul Khaleque Cnclusin In this study, we have analyzed the stchastic frntier prductin functin using panel data in selected manufacturing industries in Bangladesh. We have bserved that the estimated values f the time-varying inefficiency parameter, η, are psive fr bth the truncated and the half nrmal distributin. These indicate that technical inefficiency has declined ver the reference perid. Tests fr different null hyptheses invlved in the stchastic frntier prductin functin shwed that the technical inefficiency effects fr the selected manufacturing industries in Bangladesh are significant. It has been fund that the mean efficiencies accrding t the truncated and the half nrmal distributins are and respectively. Here shuld be nted that althugh the grwth in technical efficiency was statistically significant ver time as tested by the null hypthesis, the rate f increase in technical efficiency has been very slw ver time in Bangladesh.

15 Technical Efficiency f Sme Selected Manufacturing Industries in Bangladesh 37 Figure 1: Trend f technical efficiency by grup f manufacturing industries (Truncated Nrmal) Technical Efficiency Year Grup1 Grup2 Grup3 Grup4 Figure 2: Trend f technical efficiency by grup f manufacturing industry (Half Nrmal) Technical Efficiency Year Grup 1 Grup 2 Grup 3 Grup 4

16 38 Md. Azizul Baten, Masud Rand, Sumnkanti Das and Md. Abdul Khaleque Figure 3: Average Technical Efficiency by Distributin Technical Efficiency Year Truncated Nrmal Half Nrmal

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