Journal of Economic Cooperation, 28, 4 (2007),

Transcription

1 Jurnal f Ecnmic Cperatin, 8, 4 (007), 8-04 PRIVATIZATION AND REGIONAL AGGLOMERATION EFFECT ON TECHNICAL EFFICIENCY OF BANGLADESH MANUFACTURING INDUSTRY Azizul Baten, Masud Rana, Sumnkanti Das & Msammet Kamrun Nesa This paper examines hw far private investment in manufacturing industries f Bangladesh, tgether wh the spatial agglmeratin f thse industries affect technical efficiency ver the perid 98-8 thrugh using a panel data. Using the Translg stchastic frntier mdel utlined by (Huang and Liu, 994:7) we fund that private and freign investment play an imprtant rle in explaining technical inefficiency, whereas impact f agglmeratin is negligible. The paper als explred whether the degree f private investment has a greater impact n technical efficiency where the dmestic industry is characterized by cmparatively high prductivy. The mean technical efficiency in the perid analyzed was estimated t be 56.8%. Intrductin In the last tw decades, many cuntries launched extensive privatizatin prgrams. Despe this grwing experience we still lack empirical knwledge f sme crical issues. Des privatizatin affect technical efficiency? Hw exactly des technlgy change as a result f Cntact authr, Ass. Prf., Department f Statistics, Shah Jalal Universy f Science & Technlgy, Sylhet-34, Bangladesh. baten_math@yah.cm Department Of Statistics, Shah Jalal Universy Of Science & Technlgy, Sylhet- 34Bangladesh

2 8 Jurnal f Ecnmic Cperatin privatizatin? D agglmeratin ecnmies matter? In this paper we address these questins as we empirically examine the effects f privatizatin n technical efficiency and technlgical change, tgether wh the nature and determinants f private investment in the regin f Bangladesh, paying particular attentin t agglmeratin factrs, wh a panel data set f Bangladesh manufacturing industries. The cntributin f the private sectr f Bangladesh is very remarkable fr ecnmic develpment in the dmestic and glbal arena. During the last 33 years the ecnmy f Bangladesh has wnessed fundamental changes in ecnmic, industrial and trade plicies. In pst-liberatin perid, the gvernment faced wh pressures n financial and management resurces, the gvernment sn iniated the prcess f privatizatin and gradual expansin f private sectr. Private investment ceiling was raised frm TK..5 millin in 973 t TK. 30 millin in 974. It was further raised t TK. 00 millin in 975 and ttally whdrawn in 978. The private sectr perfrmance is mre spectacular in freign exchange earnings frm exprt. Out f the ttal freign exchange earning f US $ 8.66 billin in , private enterprises represented mre than 95% f the ttal earning which has risen frm 74.7% in The ecnmic thery f privatizatin is a subset f the vast lerature n the ecnmics f wnership and the rle fr gvernment wnership f prductive resurces. There are tw main branches in this lerature: The Scial View (Shapir and Willig, 990)) and the Agency View (Vickers and Yarrw, 988); (Shleifer and Vishny, 994:995)). (Bhaskar and Khan, 995:67) find that privatizatin has a large and significant negative effect n whe cllar wrkers using emplyment data frm Bangladesh, fr 6 jute mills f which 3 were privatized in 98 and cntrlling fr firm fixed effects. Industry agglmeratin may play a rle in reducing technical inefficiency in the dmestic sectr as a whle, there is als the pssibily that industries that are reginally cncentrated might als benef mst frm private investment induced prductivy spillvers, wh gegraphical prximy expected t affect the degree f knwledge transmissin thrugh labr markets, buyer-supplier partnerships and general cmmunies f interest. The cncept f agglmeratin is linked wh new appraches in ecnmic gegraphy which have highlighted the

3 Privatizatin and Reginal Agglmeratin Effect n Technical Efficiency f Bangladesh Manufacturing Industry 83 cmpetive ptential assciated wh tight demand and supply interlinkages amng reginal clusters f allied industries see fr example, (Sctt, 988:7); (Prter, 990). Marshall at the end f nineteenth century identified three types f external ecnmies that generate agglmeratin: specialized labr, specific inputs and technlgical spillvers. The agglmeratin f industry activy may impact n prductivy grwth because f s influence n the rate f technical change (Beesn, 987:36). A key area f debate, hwever, is the distance ver which such agglmeratin benefs are significant (Krugman and Venables, 995:857); (Audretch, 998:8). Hwever, a number f authrs (Head, Ries and Swensn, 999:97) and (Guimareas, Figueired and Wdward, 000:5) cnsider this measure t be smewhat crude since the variable shuld be, at least in part, industry-specific, especially when is nly variable being used t calculate agglmeratin ecnmies. T date, few studies f lcatinal determinants have examined the variables f new ecnmic gegraphy and even fewer studies have examined the lcatinal determinants f private investment in Bangladesh at the reginal and industry level. Therefre an effrt has been made t examine the determinants f technical efficiency fcusing particularly n the rle f private manufacturing investment and spatial agglmeratin f similar industry activies using three dig industry data frm the Census f Manufacturing Industries (CMI) f Bangladesh fr the perids thrugh The study als explres hw far the impacts f private firm spillvers vary accrding t existing levels f industry prductivy and spatial agglmeratin. The methd adpted invlves the estimatin f a stchastic prductin frntier wh randm cmpnents assciated wh industry technical inefficiency and a standard errr. The cntributin then attempts t link research n the estimatin f technical efficiency, private investment and spatial agglmeratin. The paper cntinues wh the fllwing structure. The secnd sectin utlines the stchastic frntier prductin functin apprach wh the inefficiency effects mdel and the functinal frms f the frntiers. The third sectin presents the empirical results frm estimating the stchastic frntier prductin mdel. Finally, the last sectin cntains sme cnclusins.

4 84 Jurnal f Ecnmic Cperatin Stchastic Frntier Prductin Functin Let us cnsider a panel data mdel fr inefficiency effects in stchastic prductin frntiers based n the mdel prpsed by (Huang and Liu, 994:7). Efficiency is measured by separating the efficiency cmpnent frm the verall errr term. Having data fr i firms in year t fr input and utput data ( ( X, Y ) ), the stchastic frntier prductin functin mdel wh panel data is wrten as: Y = f ( X ; β t ).exp( V U )...() where Y is the firm utput at the t th bservatin ( t =,,3,... T ) fr the i th firm ( i =,,3,... n ); f (). represents the prductin technlgy; X is a vectr f input quanties f the i th firm in the th t time perid; th β t is a vectr f unknwn parameters in the t time perid; V are assumed t be independent and identically distributed randm errrs, which have nrmal distributin wh mean zer and unknwn variance σ v. U are nn-negative unbservable randm variables assciated wh the technical inefficiency in prductin, such that, fr the given technlgy and level f input, the bserved utput falls shrt f s ptential utput. Accrding t the specificatin f (Huang and Liu, 994:7), the technical inefficiency effect mdel, referred t as Nn-neutral stchastic frntier mdel, U, culd be defined as: * * U = Z δ + Z δ + W...() where Z is a vectr f explanatry variables which may influence the efficiency f the firm; δ is a vectr f unknwn parameters t be estimated; * Z is a vectr f values f apprpriate interactins between the variables in Z and X ; * δ is a vectr f unknwn parameters; W is unbservable randm variable, which are assumed t be independently distributed, btained by truncatin f the nrmal

5 Privatizatin and Reginal Agglmeratin Effect n Technical Efficiency f Bangladesh Manufacturing Industry 85 distributin wh mean zer and unknwn variance, σ u, such that, U is nn-negative (i.e. W Z δ ). The mean Z δ ; i =,,3,... n ; t =,,3,...T may be different fr different firms and time but the variances are assumed t be the same. An estimated measure f technical efficiency (TE) fr the i th firm in the th t time perid is defined as the rati f the bserved utput, Y, t the crrespnding frntier utput, Y *, cndinal n the levels f inputs used by the firm. Thus the technical efficiency f firm i at time t in the cntext f the stchastic frntier prductin functin () is as: Y TE = * Y The unbservable quanty f ( X, β ).exp( V U ) = ( U ) f ( X, β ).exp( V ) = exp. U may be btained frm s cndinal expectatin given the bservable value f ( ) U V (Jndrw et. al., 98:33); (Battese and Celli, 988:387); (Kalirajan and Flinn, 983:67). Functinal Frms There are basically tw cmmn functinal frms f prductin functin used in studying technical efficiency using stchastic frntier prductin functins, namely Cbb-Duglas and general Translg functinal frms. Since the Cbb-Duglas specificatin is nested in the Translg mdel and the frm is flexible and impses fewer restrictins n the data, we start wh the Translg specificatin in ur analysis and define as fllws: Y 4 4 = β + β j X j + βkj j= j k = X j X k + V U...(3) where Y is the lg f grss utput and fur input variables ( ) j X are the lgs f capal, manual labr, nn-manual labr and year f bservatin. In this mdel year f bservatin and s interactin wh input variables are included in a way t specify bth neutral and nn-neutral technical change respectively.

6 86 Jurnal f Ecnmic Cperatin In this specificatin if β kj, the secnd-rder terms, are all equal t zer then the mdel reduces t standard Cbb-Duglas frm. The inclusin f year f bservatin as a variable allws fr the shifts f the frntier ver time, which is interpreted as technical change. Technical change is neutral if all β 4 j, j =,, 3 are equal t zer. Using generalized likelihd rati test we can test the significance f the neutral and nnneutral technical change in the mdel. In the secnd part f the mdel, the inefficiency effects fllw frm equatin (), prvided these effects are stchastic and nt merely a deterministic functin f the relevant explanatry variables. Thus, the mean efficiencies fr each firm, m, are explained as fllws: = δ + δ k Z k + k = k= j= m δ Z X + W kj k j...(4) where Z is the dummy fr freign investment, Z is PRIVATE and Z 3 is AGGLOM, are three explanatry variables. Here PRIVATE is the variable that shws the degree f private penetratin f the given industry sectr, AGGLOM is the reginal industry agglmeratin variable. The variable Z takes the value if the industry receives freign investment therwise takes zer. The dummy variable is included in the mdel t capture the significance f freign investment in the average efficiency levels f the industries. Our study area cvers 3-diged census industries, under registered manufacturing sectrs f Bangladesh ver the reference perid thrugh The numbers f sample industries, whse data are cnsidered in this study, were 6 fr each year. Thus, these data invlved a ttal f 46 bservatins ver the 6-year perid. Private penetratin at the industry level (PRIVATE) is measured as the share f industry grss utput that is accunted fr by private wned industry. The Census f Manufacturing Industries (CMI) reprts the industry grss utput fr each f the six administrative divisins f Bangladesh. The divisinal shares f given industry grss utput was calculated and then a lcatin qutient was derived. The lcatin qutient reveals hw specialized a divisin is in terms f a given industry. The lcatin qutient was calculated by dividing the divisinal share f grss utput f the selected industry f Bangladesh, by the same divisin s share f ttal grss utput. A lcatin qutient f greater than ne indicates that

7 Privatizatin and Reginal Agglmeratin Effect n Technical Efficiency f Bangladesh Manufacturing Industry 87 the divisin in questin has a share f selected industry grss utput greater than s size in terms f share f ttal grss utput f Bangladesh manufacturing industry wuld suggest. Fr each f the 6 defined, this gave a series f 6 lcatin qutients. The standard deviatin f these lcatin qutients were calculated and then a cefficient f variatin. The value f the cefficient f variatin is the measure f agglmeratin (AGGLOM) used here. Where shares f industry grss utput are evenly spread acrss divisins then the divisinal lcatin wuld tend twards ne and the resulting standard deviatin and cefficient f variatin wuld tend t zer. Mre spatially cncentrated industries wuld tend t have higher cefficients f variatins. Agglmeratin f industry was measured by (Driffeld and Munday, 00:39). It is imprtant t recgnize that this agglmeratin variable describes divisinal cncentratins f industry activy and is hence nly a guide t the existence f clusters f allied industry activy. Empirical Results Fllwing (Huang and Liu, 994:7), the frntier prductin functin defined by (3) and the inefficiency mdel defined by (4) are estimated simultaneusly by using maximum likelihd methd fr each industry separately. The estimatin prcedure is perfrmed using FRONTIER 4. cmputer prgram (Celli, 996), which uses Davidsn-Fletcher-Pwell Quasi-Newtn methd t btain the maximum likelihd estimates. This simultaneus estimatin is cnsidered t be superir t the tw-stage estimatin because f tw reasns. First, the tw-stage estimatin is incnsistent in s assumptin regarding the independence f the inefficiency effects in the tw estimatin stages (Celli, 996a). Secnd, the efficiency scres are bunded variables, because f the nnnrmaly and bunded range f the errr term (Lvell, 993). The σ u variance parameters are estimated in terms f γ = and σ σ = σ u + σ v. As utlined abve, the inial stage in the estimatin f the frntier is t determine the apprpriate specificatin fr the frntier mdel. This invlves several tests based n technical efficiency restrictins implied by the different errr structures (Battese and Celli, 99:53); (Kumbhakar, 993:). A number f statistical tests were carried ut t identify the apprpriate functinal frms and the presence f

8 88 Jurnal f Ecnmic Cperatin inefficiency and s trend. Fr this let us use the generalized likelihdrati (LR) statistic as defined belw: λ = [ L( H ) L( H)] where L ( H ) is the lg likelihd value f the restricted frntier mdel as specified by the null hypthesis H and L ( H) is the lg likelihd value f the unrestricted frntier mdel under alternative hypthesis H. This test statistic has a chi-square r a mixed chi-square distributin wh degrees f freedm equal t the difference between the parameters in the null and alternative hypthesis. Table in the appendix presents the results f these tests. The first test shws that, given the specificatin f the technical inefficiency effects mdel, the null hypthesis that the Cbb-Duglas functinal frm is preferred t the Translg is rejected by the data. This indicates that input elasticies and substutin relatinships are nt cnstant fr industries f different sizes and wh different input values in the manufacturing industries f Bangladesh. The LR test establishes that sme unknwn cmbinatin f the squared and crss-prduct terms in the Translg imprve the f f the mdels, even in cases where few r even nne f these variables are individually significant accrding t the t statistic. The secnd null hypthesis f n technlgical change at the frntier is als rejected, implying shift f the prductin frntier ver time whereas the null hypthesis f neutral technical change is accepted by the data. These tw hyptheses indicate that neutral technical change exists in the Bangladesh manufacturing industry. The null hypthesis explred in test 4 is that each firm is perating n the technically efficient frntier and that the systematic and randm technical inefficiency effects are zer. The null hypthesis that γ = δ 0 = δ =... = δ 43 = 0 is rejected, suggesting that inefficiency was present in prductin and that the average prductin functin is nt an apprpriate representatin f the data. The estimate f γ indicates that the prprtin f the ne-sided errr in the ttal variance f the cmpsed errr term is as high as 9% fr nn-neutral stchastic frntier mdel (Table 3). This in turn means that the variatin in the bserved level f utput is nt just due t randm shcks but als can be explained by the differences in the levels f technical efficiency f the industry and thus inefficiencies in prductin are the dminant surce f randm errr. Finally, given the specificatins f the nn-neutral stchastic frntier mdel, the hypthesis that the neutral mdel is an

9 Privatizatin and Reginal Agglmeratin Effect n Technical Efficiency f Bangladesh Manufacturing Industry 89 adequate representatin, H : δ ij = 0; i =,,3,4; j =,, 3, is rejected by the data. Thus, the hyptheses testing results shw that ur specificatins f equatin (3) and (4) are mre suable t the data cmpared t ther alternative specificatins. The parameter estimates f the preferred frntier prductin functin and inefficiency mdel are given in table 3 in appendix. Given the results f the tests f hypthesis, the preferred frntier mdel is that whut interactins between year f bservatins and the input variables. Amng the first rder cefficients, capal, manual and nnmanual labr turned ut t be statistically significant at 5 percent level f significance based n the asympttic t-values. The psive sign f the estimated cefficient f year f bservatin indicates that there was technical prgress in mean frntier mdel. Capal and nn-manual labr input variable shwed negative sign. The secnd rder cefficient f manual and nn-manual labr came ut t be negative and statistically significant whereas that f capal and year f bservatin are psive and insignificant. The estimates f inefficiency mdel give hw the technical inefficiency is related t variables f ur interests. The dummy variable representing the cntributin f freign investment in an industry alng wh private investment variable has a negative sign thugh is nt statistically significant. This indicates that freign and private investment in manufacturing industry f Bangladesh has a favrable effect n technical efficiency. The interactins between manual labr and AGGLOM, nn-manual labr and private have significant negative effect n technical inefficiency suggesting that their jint impact are cntributing psively n efficiency. The parameter estimates f divisinal agglmeratin turned ut t be psive and significant indicating that gegraphical cncentratin has an unfavrable effect n s dmestic industry level efficiency. This is a surprising result. Because Dhaka, capal cy f Bangladesh, cmprises n average 50.5% f grss utput f manufacturing industry f Bangladesh frm the lwest f 38.6% in t the highest f 68.5% in That f Chtagng, cmmercial capal cy f Bangladesh, is n average 30.8%. The rest fur divisins namely Barisal, Khulna, Rajshahi and Sylhet cmprise the rest f 8.7% share n ttal all tgether.

10 90 Jurnal f Ecnmic Cperatin In rder t further examine the relatinship between technical efficiency, private investment and agglmeratin, equatin (3) and (4) are reestimated using a series f sub-samples f the data. The sample was spl accrding t bserved levels f industry labr prductivy and agglmeratin. This is dne in the fllwing ways: labr prductivy is calculated by dividing grss utput by ttal labr input i.e. sum f manual and nn-manual labr fr each industry, average labr prductivy is calculated and the sample is spl accrding t grand average f labr prductivy. This gives 6 industries as lw prductive and the rest is treated as high prductive. The tw sub-samples are then re-estimated. This prcedure enables us tw cnsistent sub-samples fr estimatin. The results f the re-estimatin f the tw sub-samples are given in Table 4 and 5. The results demnstrate that is imprtant t spl the sample in rder t explain the variatins in ttal factr prductivy. Fr example, spillvers frm private investment are nly negative and significant (Table 4) in industries f abve average prductivy whereas that is psive and insignificant (Table 5) in industries f belw average prductivy. This suggests that a crical level f prductivy is a necessary cndin fr spillvers frm private investment t ccur. The results als suggest that the cefficient f inefficiency effect parameter is que different fr the tw sub-samples. It is almst uny in lw prductive industries. Again, the cefficient reginal agglmeratin parameter turned ut t be psive and significant fr bth samples. This means, fr example, that the effect f private investment in a given industry sectr culd vary accrding t whether the industry is characterized by cmparatively high r lw prductivy. Therefre the equatin must be estimated separately fr these sub grups. Technical efficiencies fr the 6 manufacturing industry f Bangladesh ver the reference perid t are estimated fr each year. The mean technical efficiency is estimated t be 56.8%. That is, ver the perid analyzed, average industry prduced nly abut 57% f maximum attainable utput. Mean efficiency by year increased frm the lwest level (0.370) in t the highest level (0.85) in This means that, accrding t the stchastic prductin frntier, the cntributin f the efficiency change t ttal factr prductivy after was an increment in prductivy grwth.

11 Privatizatin and Reginal Agglmeratin Effect n Technical Efficiency f Bangladesh Manufacturing Industry 9 Cnclusin This study prvides inefficiency estimatin and variatins in inefficiency between industries thrugh decisins cncerning wnership factrs alng wh spatial agglmeratin ver the perid thrugh in a panel f manufacturing industries f Bangladesh. A Translg stchastic frntier prductin functin wh inefficiency effects mdel, utlined by Huang and Liu (994), is applied. The results indicate that inefficiency was present in prductin and that the tradinal average respnse functins and Cbb-Duglas functinal frm wh neutral stchastic frntier mdel are nt an apprpriate representatin f the data. Our analysis shws that the chice f efficiency estimatin methd can make a significant difference in relatin t average efficiencies. The results reveal smething f the dynamic benefs f private investment in Bangladesh. The extent f private investment in a dmestic industry is a determinant f technical efficiency. Such imprvements in technical efficiency are expected t feed thrugh int the internatinal cmpetiveness fr manufacturing industry f Bangladesh. These findings are imprtant in the cntext f cncerns ver the cntributin f private investment t natinal and reginal develpment prcess. The result suggests that spillvers are mre prnunced in industries that are relatively prductive. At the same time, we fund that freign investment plays an imprtant rle in explaining technical efficiency levels in manufacturing industry f Bangladesh. The mean technical efficiency is estimated t nly 56.8% accrding t the nn-neutral stchastic prductin frntier. Cnsiderable technical inefficiencies exist in manufacturing industry f Bangladesh and the results shwed that mean technical efficiencies had highly increased in tw manufacturing industries, namely Drugs and pharmaceutical prducts and Beverage industry whereas Manufacture f Textiles and Fabricated metal prducts had experienced a decline in the mean technical efficiencies ver the perid. The industries perate 43.% belw the ptential frntier prductin level wh the given inputs and prductin technlgy. Thus the industries are nt in a psin t tap the benefs f the develpment f prductin technlgy. Since the

12 9 Jurnal f Ecnmic Cperatin verall technical efficiency level is just mre than half, there is n justificatin at present t further develp the technlgy. Table : Data and Variable All mnetary variables were put int real terms (98-98 prices). Industry data fr 6 three dig industry (BSIC) derived frm the Census f Manufacturing Industries (CMI)f Bangladesh. Data available fr thrugh Dependent Variable: Y: Grss utput: Grss utput is the value f prducts and by-prducts, plus receipts fr wrk dne and fr services t thers, plus net change in wrk-in-prgress. Prducts and by-prducts are valued at the ex-factry prices, including excise duty, sales tax and ther indirect taxes. Independent Variables: X : Ttal fixed assets: Ttal fixed assets mean all assets, whether btained frm ther enterprises r prduced by the establishment ut f s resurces fr s wn use, which are expected t have a prductive life f mre than ne year. It cnsists f land, buildings, ther cnstructin, machinery tls and equipment, transprt etc. X : Manual labr: Manual labr includes all classes f permanent and salaried emplyees f the establishment such as managers, clerks, typists and ther administrative wrkers. X 3 : Nn-manual labr: Nn-manual labr means thse wh are engaged directly in the prductin prcess and includes thse engaged in manufacturing, assembling, packing, repairing etc. Wrking supervisrs and persns engaged fr repair and maintenance are als included. X 4 : Year: Year is the year f bservatin where X 4 =,, 3,,3, 5, 7, 9 fr the years 98-98, , ,.., , , and respectively. Explanatry Variables: Z : Z is the dummy variable fr freign investment in manufacturing industry. It takes the value if the industry receives freign investment, therwise zer. PRIVATE ( Z ): Percentage share f industry grss utput that is accunted fr by private wned manufacturing industry. AGGLOM ( 3 Z ): Industry agglmeratin measured as the cefficient f variatin fr industry level lcatin qutients acrss the 6 administrative divisins f Bangladesh.

13 Privatizatin and Reginal Agglmeratin Effect n Technical Efficiency f Bangladesh Manufacturing Industry 93 Table : Generalized Likelihd-Rati Tests f Hyptheses fr Parameters f the Stchastic Frntier Prductin Functin fr Manufacturing Industries in Bangladesh Null Hypthesis Likelihd Functin Test Statistic λ Crical Value Decisin Nn-neutral Stchastic Frntier Cbb-Duglas Prductin Functin H H H H : = 0, i j =,,3,4 β ij N Technical Change : i4 β 4 = β = 0, i =,,3,4 Neutral Technical Change H : 4 = 0, i =,,3 β i N Technical Inefficiency : γ = δ = δ =... = δ 43 = Neutral Stchastic Frntier : = 0, i =,,3,4; j =,,3 δ ij Reject H Reject H Reject H * 6.59 Reject H Reject H Surce: Authr s cmputatin Ntes: All crical values are at 5% level f significance. * The crical value is btained frm table f (Kdde and Palm, 986:43). The null hypthesis which includes the restrictin that γ is zer des nt have a chi-square distributin, because the restrictin defines a pint n the bundary f the parameter space.

14 94 Jurnal f Ecnmic Cperatin Table 3: Maximum-Likelihd Estimates fr Parameters f the Nn- Neutral Stchastic Frntier Invlving Firm-Specific Variables and Year Variable Parameter Nn-neutral Stchastic Frntier Cefficient t-rati Cnstant β Capal β * Manual Labr β.7548 * Nn-manual Labr β * Year β (Capal) β (Manual Labr) β * (Nn-manual Labr) β * (Year) β Capal * Manual Labr β Capal * Nn-manual Labr β Manual Labr * Nn-manual Labr β *.8567 Cnstant δ.445 * Dummy δ PRIVATE δ AGGLOM δ *.5978 Capal * Dummy δ Capal * PRIVATE δ Capal * AGGLOM δ Manual Labr * Dummy δ Manual Labr * PRIVATE δ * 3.68 Manual Labr * AGGLOM δ * Nn-manual Labr * Dummy δ Nn-manual Labr * PRIVATE δ * Nn-manual Labr * AGGLOM δ * (Cntinued)

15 Privatizatin and Reginal Agglmeratin Effect n Technical Efficiency f Bangladesh Manufacturing Industry 95 Table 3 (Cntinued) Variable Parameter Nn-neutral Stchastic Frntier Cefficient t-rati Year * Dummy δ Year * PRIVATE δ Year * AGGLOM δ Variance σ 0.33 *.479 Parameters γ *.6703 Lg likelihd Functin * means significant at 5% Surce: Authr s cmputatin

16 96 Jurnal f Ecnmic Cperatin Table 4: Maximum-Likelihd Estimates fr Parameters f the Nn-Neutral Stchastic Frntier f high prductive industry Variable Cnstant Nn-neutral Stchastic Frntier Parameter Cefficient t-rati β * Capal β Manual Labr β Nn-manual Labr β Year β (Capal) β (Manual Labr) β * (Nn-manual Labr) β * (Year) β Capal * Manual Labr β Capal * Nn-manual Labr β Manual Labr * Nn-manual Labr β * 6.59 Cnstant δ Dummy δ PRIVATE δ * AGGLOM δ * Capal * Dummy δ Capal * PRIVATE δ Capal * AGGLOM δ Manual Labr * Dummy δ Manual Labr * PRIVATE δ * Manual Labr * AGGLOM δ * Nn-manual Labr * Dummy δ Nn-manual Labr * PRIVATE δ * Nn-manual Labr * AGGLOM δ * (Cntinued)

17 Privatizatin and Reginal Agglmeratin Effect n Technical Efficiency f Bangladesh Manufacturing Industry 97 Table 4 (Cntinued) Variable Parameter Nn-neutral Stchastic Frntier Cefficient t-rati Year * Dummy δ Year * PRIVATE δ Year * AGGLOM δ Variance Parameters σ * γ * Lg likelihd Functin * means significant at 5% Surce: Authr s cmputatin

18 98 Jurnal f Ecnmic Cperatin Table 5: Maximum-Likelihd Estimates fr Parameters f the Nn-Neutral Stchastic Frntier f lw prductive industry Variable Cnstant Nn-neutral Stchastic Frntier Parameter Cefficient t-rati β Capal β Manual Labr β.696 * Nn-manual Labr β * Year β * (Capal) β (Manual Labr) β * (Nn-manual Labr) β * (Year) β Capal * Manual Labr β Capal * Nn-manual Labr β Manual Labr * Nn-manual Labr β * 7.96 Cnstant δ Dummy δ PRIVATE δ AGGLOM δ * 3.8 Capal * Dummy δ Capal * PRIVATE δ Capal * AGGLOM δ Manual Labr * Dummy δ Manual Labr * PRIVATE δ Manual Labr * AGGLOM δ Nn-manual Labr * Dummy δ Nn-manual Labr * PRIVATE δ Nn-manual Labr * AGGLOM δ (Cntinued)

19 Privatizatin and Reginal Agglmeratin Effect n Technical Efficiency f Bangladesh Manufacturing Industry 99 Table 5 (Cntinued) Nn-neutral Stchastic Frntier Variable Parameter Cefficient t-rati Year * Dummy δ Year * PRIVATE δ Year * AGGLOM δ Variance Parameters σ * γ * Lg likelihd Functin * means significant at 5% Surce: Authr s cmputatin

20 00 Jurnal f Ecnmic Cperatin Table 6: Mean Technical Efficiency f Different Manufacturing Industry f Bangladesh Industry Technical Efficiency Fd Manufacturing (3-3) Beverage Industry (33) Tbacc Manufacturing (34) 0.7 Manufacture f Textiles (3-3) 0.83 Wearing Apparel Expt. Ftwear (33) Leather and s Prducts (34) Ft Wear Expt. Vulcanize/Mld (35) 0.58 Ginning, Press & Baling f FIB. (36) Wd & Wd Crk Prducts (33) Furnure & Fixtures Mfg. (33) Mfg. Paper & s Prducts (34) Printing & Publishing (34) 0.55 Drugs & Pharmaceutical Prducts (35) 0.88 Industrial Chemicals (35) 0.6 Other Chemical Prducts (353) Mfg. Rubber Prducts (356) 0.50 Mfg. Plastic Prducts (357) Pttery, China &Earthenware (36) Mfg. Glass & s Prducts (36) Nn-metallic Mineral Prducts (369) 0.55 Irn & Steel Basic Inds. (37) 0.67 Fabricated Metal Prducts (38-38) 0.40 Nn-electrical Machinery (383) 0.45 Electrical Machinery (384) 0.65 Mfg. Transprt Equipment (385) 0.6 Phtgraphic, & Optical Gds (387) Mean Nte: Numbers in parentheses are industrial cdes accrding t the Bangladesh Standard Industrial Classificatin (BSIC). Surce: Authr s cmputatin

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