Technical efficiency of olive oil manufacturing and efficacy of modernization programme in Tunisia

Transcription

1 Techncal effcency of olve ol manufacturng and effcacy of modernzaton programme n Tunsa By KASHIWAGI, KENICHI; MTIMET, NADHEM; ZAIBET, LOKMAN; and NAGAKI, MASAKAZU Contrbuted Paper presented at the Jont 3 rd Afrcan Assocaton of Agrcultural Economsts (AAAE) and 48 th Agrcultural Economsts Assocaton of South Afrca (AEASA) Conference, Cape Town, South Afrca, September 19-3, 010.

2 Techncal effcency of olve ol manufacturng and effcacy of modernzaton programme n Tunsa KENICHI KASHIWAGI 1 The Allance for Research on North Afrca, Unversty of Tsukuba, Japan NADHEM MTIMET Ecole Supéreure d Agrculture, Mograne, Zaghouan, Tunsa LOKMAN ZAIBET Internatonal Lvestock Research Insttute, Kenya MASAKAZU NAGAKI Unversty of Tsukuba, Japan Abstract Ths study nvestgates frm level techncal effcency of producton and ts determnants n a sample of 137 olve ol manufacturng frms n Tunsa usng a stochastc fronter producton model appled to cross-secton data. Results ndcate that techncal effcency of producton n the sample of olve ol manufacturng frms nvestgated ranges from a mnmum of 47.1% to a maxmum 99.5% wth an average techncal effcency estmate of 86.5%. Ths mples olve ol manufacturng frms n Tunsa can ncrease ther producton on average by 13.5% through more effcent use of technology and producton nputs. The fact that 93 frms represented more than 64.4% of the sample ht more than 80% of techncal effcency score mples the effcacy of modernzaton programme mplemented n Tunsa. The estmated coeffcents n the techncal neffcency effects model ndcate that level of technology, frequent use of computer and nternet, the owner s age, the share of sklled labour, the employment of management staff, and the nput sourcng by the own producton have a sgnfcant and postve effect on techncal effcency. On the other hand, negatve relatonshps are found between techncal effcency and entrepreneur dummy varable, contnuous relatonshp wth the supplers n the same dstrct, and wth the prvate sector and trader as customers. These results mply that the adopton of new technology, accumulaton of skll and knowledge as well as stable nput sourcng contrbute to mprove the techncal effcency of olve ol manufacturng. Keywords: olve ol manufacturng; stochastc fronter producton functon; techncal effcency; modernzaton programme; Tunsa 1. Introducton The olve ol sector consttutes an mportant part of the Tunsan agrcultural economy. Investgaton on level of productvty and degree of effcency not only on olve growng 1 Correspondng author: kenk@sakura.cc.tsukuba.ac.jp 1

3 farms but also olve ol manufacturng frms may provde valuable nsghts nto potental mprovement of productvty. Ths s partcularly mportant due to the mplementaton of free trade agreement wth EU that lead to elmnaton of tarffs and other trade barrers on agrcultural commodtes traded wth EU. The modernzaton of the olve ol manufacturng has began n the 1990s wth the natonal ndustral upgradng program (know as programme de mse à nveau). Ths program has been launched n 1996 amng to mprove the compettveness of the manufacturng sector to meet the new challenges of the accesson of Tunsa to WTO and the European partnershp. Substantal fnancal support had been granted through a dedcated fund for mprovement of ndustral compettveness. The upgradng process had two components: physcal nvestment n modernzaton and laboratory equpment; and ntangble nvestment n the form of tranng and capacty buldng manly for qualty control and adopton of ISO or European qualty schemes. The program s run n conjuncton wth the Industral Modernzaton Progamme (IMP) and the support of the European Unon (Zabet, 007). The olve ol manufacturng, beng the frst agro-food exportng sector was among the frst served by these programmes. An assessment of an mprovement n techncal effcency as a result of the above programmes would reveal useful nsghts about the effcacy of these programmes but also on future steps and programmes. It would have also useful to have baselne (at the begnnng) estmated of these effcency scores but our search show no references and the paper, to our best knowledge s the frst to assess techncal effcency n the olve ol sector n Tunsa. In the olve ol sector fewer studes are found n the lterature. In Tunsa, there are two studes on (at the farm level) techncal effcency: Zabet and Omezzne (1998) and Lachaal et al. (005). These studes pont to the relatvely low level of techncal effcency scores and ther determnants namely the small sze, the hgh number of plots by farm as well as scarce sklled labor and tranng. Olve ol beng Medterranean-ste specfc product, we could also encounter more studes n the regon, such as the work done by Tzouvelekas et al. (1999) on olve ol producton n Greece. The authors used a decomposton of output growth nto ts three components and nvestgated the relatve contrbuton of techncal effcency, technologcal change and ncreased nput use to the output growth of the Greek olve-ol sector. Fndngs show that the overall effcency of olve-growng farms n Greece remaned stable durng the study perod and that the contrbuton of conventonal nputs was the man source of that growth. Although most studes have been devoted to nvestgatng techncal effcency on olve growng farms, studes focus on the stage of olve ol manufacturng are merely absent. Ths paper nvestgates frm level techncal effcency on manufacturng of olve ol n Tunsa, usng a stochastc fronter producton model wth techncal neffcency effects appled to a sample of 137 olve ol manufacturng factores. The objectve s to dentfy the sources of techncal effcency n the stage of manufacturng of olve ol by explanng dfferences n effcency levels. Frst, we measure techncal effcency of a sample of olve ol manufacturng frms n Tunsa. Second, we analyze the determnants of techncal effcency varaton among these frms. Ths paper assumes that adopton of new technology, accumulaton of skll and knowledge as well as stable nput sourcng contrbutes to mprove techncal effcency of manufacturng as nternal factors. As those of external, resource of management wth respect to marketng would have postve effect on enhancng techncal effcency. The

4 rest of the paper s organzed as follows. In the next secton, a quck revew of the Tunsan olve ol sector and ts manufacturng frms s descrbed. In secton 3 methodologcal framework of the stochastc fronter model s descrbed. Data collecton and model specfcaton are presented n Secton 4. In Secton 5 we present the emprcal results and dscussons. Concluson s hghlghted n the Secton 6.. Olve ol sector n Tunsa Olve orchards n Tunsa occupy 1.7 mllon ha, the equvalent of 30% of the total arable land, and represent about 19% of the world olve orchards (second largest olve land after Span whch counts 3 mllon ha). Sxty-sx mllon olve trees are wdespread all-over the country: North, Centre and South. The olve ol sector contrbutes to the Tunsan socoeconomc development provdng 40 mllons workng days per year (0% of agrcultural employment) and decreasng exodus by fxng rural populaton. The olve ol sector employs drectly or ndrectly over 1 mllon persons and 69,000 farmers are dedcated to ths growng. Olve ol producton n Tunsa s hghly dependent on precptatons. Durng the last decade, the lowest producton regstered for the campagn was about 35,000 tons due to water shortage. The hghest producton was obtaned n season wth 80,000 tons. For the last three years olve ol producton was stablzed around 170,000 tons/year. Tunsan government s encouragng the use of rrgaton (ntensve or hyper-ntensve growng) to ncrease the proporton of rrgated olve orchards (% actually) n order to decrease producton fluctuaton manly due to clmatc change. Olve ol consumpton n Tunsa ranges between 35,000 to 50,000 tons per year (5% to 30% of total producton). Trend consumpton s showng a decreasng pattern durng the last decade essentally due to prce ncrement, but also to culnary and habt changes n the Tunsan populaton. Compared to other producng countres, olve ol per capta consumpton n Tunsa s very low. Greece, Span and Italy present annual per capta consumpton of 4, 14 and 13 kg respectvely, whereas Tunsan average consumpton s about 4 kg/capta/year. Olve ol consumpton s manly n bulk. Tunsan consumers are used to purchase olve ol drectly from the manufacture. Bottled olve ol purchase stll very lmted (3% of total consumpton) and concentrated n large ctes lke Tuns Captal. Olve ol exports account for 10,000 tons per year representng 70% of total producton. These exports are manly drected to the European Unon (Italy and Span) and to the USA. Tunsa occupes the fourth poston as olve ol exporter preceded by Span, Italy and Greece. Tunsan olve ol exports are manly n bulk (99%) and a large proporton of t forms part of the olve ol contngent free-trade agreement sgned between Tunsa and the E.U. Tunsan government s seekng to ncrement bottled olve ol exports to reach 10% of total exports by 010, but ths goal was not acheved. Only % to 3% are actually exported n bottle. The am of ncreasng bottled olve ol exports s to generate hgher added value and to be present n overseas markets wth Tunsan country of orgn label. Actually olve ol exports represent 10% of total exports n values and about 45% of agro-food exports. Improvng product qualty s an mportant factor to ncrease Tunsan olve ol 3

5 compettveness n local and especally n foregn markets. Extra-vrgn olve ol (hgher qualty) export proporton s ncreasng compared to ordnary vrgn olve ol. It represented 56% of total Tunsan olve ol exports n 008. To mprove product qualty, Tunsan government supported olve ol manufactures (through the fund for mprovement of ndustral compettveness) to mprove trturatng processes and capactes by renewng machnery and adoptng new technologes. The number of modern olve ol mlls (called contnuous chans) has substantally ncreased durng the last ffteen years leadng to a natonal trturatng capacty of 38,000 tons per day three tmes greater than the trturatng capacty durng the eghtes. The Tunsan olve ol manufacturng system s composed by three trturatng systems that coexst actually: the tradtonal one called classc, the Super-Press, and the modern one. The red accounts for 170 olve ol mlls (APIA, 008) decomposed as follows: 719 classc unts, 450 Super-Press, 515 contnuous chans and 18 mxed unts. Mxed unts are composed of more than one type of processng. In addton to ths processng structure, the sector counts wth 40 ndustral unts for olve ol packagng, or for pomace olve ol extracton. Actually the overall trend s to ncrease the number of modern olve ol mlls. 3. Methodologcal framework Followng the semnal paper by Farrell (1957), fronter producton functons were ntroduced. The stochastc fronter producton functon (SPF) was then ntroduced by Agner, Lovell and Schmdt (1977) and Meeusen and van don Broeck (1977). Jondrow et al. (198), who extended the SPF to allow for the estmaton of ndvdual farm effcency levels wth cross-sectonal data, ntroduced a major development n the SPF. Whereas the orgnal model by Jondrow was defned for the analyss of cross-sectonal data, extensve lterature show the use of these models to account for panel data (see Cornwell, Schmdt and Sckles, 1990; Kumbhakar, Ghosh and Mcgukn,1991; Battese, Malk and Broca,1993 and Battese, Malk and Gll, 1996). Snce then, the SPF has been wdely used n emprcal work. Applcatons nclude the estmaton of farm effcency n US dary farms (Kumbhaker et al., 1989), and techncal effcency n commercal fsheres (Krkley et al., 1995), and techncal effcency n bankng (Caudll et al., 1995). Effcency ndcators are management orented measures. Lovell (1993) consders productve effcency as success ndcators, performance measures of producton unts. Shu and Lee (003) assert that effcency measurement gves more management mplcatons; techncal effcency tells how much devaton the real producton s to the maxmum level of producton. Lovell further emphaszed the need to explore the sources of these neffcences: the dentfcaton of sources s essental to the nsttuton of publc and prvate polces desgned to mprove performance. Effcency of producton has then become an mportant ndcator of frm performance and consderable efforts have been devoted to ntroduce polces amng at ncreasng effcency to mprove economc growth. Lovell provdes a framework to the measurement of productve effcency and a dstncton s made between techncal effcency and allocatve effcency. Techncal effcency wth respect to an nput-output vector (x, u) corresponds to a frm producng to ts maxmum producton level. Allocatve effcency s defned wth respect to an 4

6 nput-output vector of prces (w, p) for a frm that s techncally effcent and producng to ts mnmum cost (use of nputs at optmal proportons). A frm that s both techncally effcent and allocatvely effcent s called proft effcent or also economc and cost effcent (the rato of mnmum cost to observed cost). Technques to estmate effcency scores ranges from programmng approach (non parametrc) to statstcal fronter or parametrc approaches. The programmng approach ntally proposed by Farrell (1957) has ganed from the developments made by Charnes, Cooper and Rhodes (CCR) among others, who called the technque Data Envelopment Analyss (DEA) approach (Charnes, Cooper and Rhodes, 1978). DEA s a mathematcal programmng methodology that provdes non-parametrc measures of optmal relatve effcency. The most common measures of the CCR Rato (Multpler) model are: Input-orented CCR Rato Model and Input-orented BCC Convex Model (Archmedean form). More recently, Bardhan, Cooper and Kumbhakar (1998) suggested a two-stage ordnary least squares (OLS) method whch uses DEA effcency measures as dummes to estmate the fronter of producton. The method uses an aggregate output as the response varable and predctors that nclude Techncal effcency dummy varables and other nputs. The OLS regresson estmates help to dentfy nfluental sources of effcency as well as returns to scale. The determnstc statstcal fronter approach on the other hand uses statstcal technques to estmate the producton fronter and the assocated effcency scores. The technque wth the frst developments of Rchmond (1974) and Greene (1980) has ganed from more recent mprovements made by Kumbhakar n the 1990s. Whle Rchmond and Greene proposed the estmaton of the effcency parameters by the Corrected OLS method or by Maxmum lkelhood and then estmate ther determnants separately, Kumbhakar et al (1991) used system approaches and proposed a one stage procedure to estmate effcency measures along wth ther determnants. Ths approach has snce then been wdely used and made popular due to the development of computer applcatons, namely Fronter (Coell, 1996). Gven the above, we adopt the Battese and Coell (1995) model of stochastc fronter producton functon, but n the context of a cross-secton data. y x v u, f (1) ; where y denotes gross output value for the th frm; s a vector of unknown parameters to be estmated; x s a vector of nputs of producton and other explanatory varables assocated wth the th frm; v refers to statstcal random dsturbance terms, assumed to be an ndependently and dentcally dstrbuted N(0, σ v ). u represents non-negatve random varables, assumed to be ndependently and dentcally dstrbuted N(0, σ u ) wth truncatons at zero. In ths specfcaton, (-u ) measures dstance between the realzed output and the fronter output. The Exp (-u ), vares between 0 and 1, s a measure of the techncal effcency of the th frm. Followng Battese and Coell (1995), the techncal neffcency See Zabet and Dharmapala (1999). 5

7 effect, u, n the stochastc fronter model (1) could be specfed n equaton (), u z w, () whereδ s a vector of unknown parameters to be estmated; z s a vector of explanatory varables assocated wth techncal neffcency n producton; w s a random varable wth zero mean and varance σ defned by the truncaton of the normal dstrbuton such that the pont of truncaton s -z δ,.e. w -z δ. The techncal effcency of producton of the th frm s defned by the raton of the observed output to the correspondng fronter output: TE u exp z w exp. (3) The predcton of techncal effcences of the th frm reles on the condtonal expectaton of u, gven the model assumpton. Gven the assumptons of the statstcal dstrbuton of u and v and the maxmum lkelhood (ML) estmates of producton fronter, the best predctor of u, gven v -u s obtaned as (Battese and Coell, 1993): 1 u E exp u v u exp u, (4) u where u z w v u v u and v u v u The functon Ф( ) denote the cumulatve dstrbuton functon (cdf) of the standard normal random varable evaluated at (μ /σ ). 4. Data collecton and model specfcaton 4.1. Data collecton The data used n ths study was drawn from a survey conducted n February and March 009 n Tunsa. It was drected to the owner or the drector of the olve ol manufacture. The studed olve ol mlls are located n the north, centre and south of the country. In total 137 questonnares were completed. The olve ol manufactures were randomly selected and the number of questonnares completed n each regon was 45, 43 and 49 respectvely for the north, centre and south of Tunsa. In Table 1, sample stratfcaton by locaton and type of olve ol mlls s summarzed. Table1: Olve ol mlls geographcal dstrbuton North Centre South Type Freq. % Freq. % Freq. % Classcal Super-Press Contnuous Mxed Total Note: It has more than one type of olve ol mll. 6

8 4.. Model specfcaton In ths study, the followng translog functonal form was used for the estmaton of the stochastc fronter producton functon: L L L 1 ln y 0 ln x ln x ln x DN DS v u, (5) j j jm jm k M jk j where the subscrpt refer to the th frm; j and k represents nputs appled to olve ol producton ( j, k = M, F, E, W, K, L); y denotes gross output value for the th frm measured n Tunsan Dnar; x M s volume of ntermedate nputs utlzed n producton n Tunsan Dnar; x F, x E, x W denote cost expenses of fuel, electrcty, water, respectvely; x K s the captal stock of the th frm n Tunsan Dnar; x L s total cost of labour devoted to olve ol producton by the th frm; DN, DS are dummy varables represent north, south regon, respectvely; v s d random dsturbance term, and u refers to d non-negatve truncatons of the normal dstrbuton. Summary statstcs of the varables are presented n Table. Table : Summary statstcs of the varables Varables Gross Output Values Intermedate Inputs Cost of Fuel 7 k N Cost of Electrcty S Cost of Water Captal Stock Cost of Labor Mean Values Standard Devaton Maxmum Mnmum Note: All varables are n thousand Tunsan Dnar (1TND ). The techncal neffcency effects to be estmated s defned as follows: u 8 0 1TEC INT 3AGE 4 ENT 5 OTH 6 SKL 7 MAS SOW SMA SFA SDT CHH CPR CTR w, where TEC s dscrete varables that represent level of technology on a scale of 1 to3 (1: tradtonal, : modern, 3: up-dated); INT shows frequency of use of computer and nternet on a scale from 1: no use at all to 5: frequent use; AGE denotes the frm owner's age (years); OTH s the dummy varable that equals to 1 f the owner engaged n other actvtes, zero otherwse; ENT denotes the entrepreneur dummy varable that equals 1 f the current owner establshed the frm, zero otherwse; SKL s the share of sklled labour to total employee; ASM s the management dummy varable that equals to 1 f the frm employed management staff for accountng, supplyng, or marketng, zero otherwse; SOW s the suppler dummy varable that equals to 1 f the man suppler s own producton, zero otherwse; MAS s the suppler dummy varable that equals to 1 f the man suppler s olve seeds market, zero otherwse; SFA s the suppler dummy varable that equals to 1 f the man suppler s olve farmers, zero otherwse; SDT s the suppler dummy varable that equals to 1 f the relaton wth suppler n same dstrct s contnuous, zero otherwse; CHH s the customer dummy varable that equals to 1 f the relaton wth households as customer s contnuous, zero otherwse; CPR s the customer (6)

9 dummy varable that equals to 1 f the relaton wth prvate sector as customer s contnuous, zero otherwse; CTR s the customer dummy varable that equals to 1 f the relaton wth trader as customer s contnuous, zero otherwse; w refers to random term. The parameters of the stochastc fronter producton functon n (5) and the model for techncal neffcency effects n (6) may smultaneously be estmated by the maxmum lkelhood model (Renfschneder and Stevenson, 1991; Huang and Lu, 1994). 5. Emprcal results and dscussons Maxmum lkelhood estmates of the parameters of the models of translog stochastc fronter producton and the techncal neffcency effects are obtaned usng the computer program FRONTIER 4.1 (Coell, 1996). Parameters estmates and t-values of the ML estmators are gven n Table 3. The sngs of the estmated parameters of the translog stochastc fronter producton model are as expected, except for ntermedate nputs, and costs of fuel and water. As expected, estmated coeffcents of cost of electrcty, captal stock and cost labour are postve and statstcally sgnfcant. These results ndcate postve relatonshp between nput of captal, labor and electrcty and producton of olve ol. The negatve coeffcents of the ntermedate nput and expense of water are nsgnfcant. The coeffcent sgn of cost of fuel s negatve and statstcally sgnfcant. The reason for ths unexpected sgn may be due to the world cost of fuel ncrease durng the survey year. The regonal dummy varables are both postve and statstcally sgnfcant. The estmated parameter of north regon s hgher than that of south. These results show the north regon of Tunsa enjoyed hgher level of producton compared wth south and central regon. The estmated coeffcents n the techncal neffcent model are also as expected. The estmated coeffcents of the level of technology (TEC) and frequency of use of computer and nternet (INT) are negatve and statstcally sgnfcant, whch confrms ther postve effect on techncal effcency. The owner s age varable (AGE) also has a sgnfcant and postve effect on techncal effcency. On the contrary, the estmated coeffcent of the entrepreneur dummy varable (ENT) s postve and statstcally sgnfcant at 5% level. These results suggest that havng more experence have postve effect on ncrease n techncal effcency. The varable of the share of sklled labour (SKL) s negatve and statstcally sgnfcant at 1% level. Ths ndcates that an ncrease n the share of sklled labour contrbutes to hgher techncal effcency levels for olve ol manufacturng. Also, the negatve sgn of estmated coeffcent of the employment of management staff for accountng, supplyng, or marketng (MAS) ndcates that knowledge of management also contrbutes to ncrease the level of techncal effcency. Regardng suppler of olve ol producton, the estmated coeffcents of own producton (SOW) s negatve and statstcally sgnfcant at 1% level. The postve sgn of the (SDT) ndcates techncal effcency declnes wth the contnuous relatonshp wth the supplers n the same dstrct. As for customers, the varable of prvate sector (CPR) and that of trader (CTR) are found to be postve and statstcally sgnfcant. These results suggest the contnuous relatonshp wth prvate sector and trader as customers does not contrbute to mprove techncal effcency. 8

10 Table 3: Parameter estmates and t-values of the neffcency fronter model of a sample of olve ol manufacturng frms n Tunsa Varables Estmates t-values Stochastc fronter model Intercept lnx M lnx F lnx E lnx W lnx K lnx L lnx M lnx F lnx E lnx W lnx K lnx L lnx M lnx F lnx M lnx F lnx M lnx W lnx M lnx K lnx M lnx L lnx F lnx E lnx F lnx W lnx F lnx K lnx F lnx L lnx E lnx W lnx M lnx F lnx E lnx L lnx W lnx K lnx W lnx L lnx K lnx L DN DS Ineffcency effects model Intercept TEC INT AGE ENT OTH SKL MAS SOW SMA SFA SDT CHH CPR CTR Varance parameters σ γ Log-lkelhood Note:,, ndcate sgnfcant at the 10% level, 5% level, 1% level, respectvely. 9

11 The estmate of varance parameters s postve and statstcally sgnfcant at 1% level, mplyng neffcency effects are sgnfcant n determnng the level and the varablty of the olve ol manufacturng frms (Table 3). Thus, the stochastc fronter neffcency model s emprcally justfed. Further, several hypotheses for the parameters of the model are examned n Table 4 usng lkelhood test 3. Frst, the emprcal valdty of the translog specfcaton over the Cobb-Douglas form, the null hypothess thatβ jk = 0 ( j, k = M, F, E, W, K, L) = 0, s rejected. It s suggested translog specfcaton s a better representaton of the producton of olve ol manufacturng frms n Tunsa than the Cobb-Douglas one. Second, null hypothess of no neffcency effects s also rejected. Thrd, we rejected the null hypothess that no frm specfc factor makes sgnfcant contrbuton to the explanaton of the neffcency effects. Table 4: Tests of hypotheses for the parameters of the stochastc fronter neffcency model of a sample of olve ol manufacturng frms n Tunsa Null Hypotheses Log-lkelhood rato d.f. Crtcal Value at 5% Decson Cobb-Douglas Reject H 0 β jk = 0 ( j, k = M, F, E, W, K, L) No neffcency effects Reject H 0 γ = δ = 0 ( = 0, 1,,...14) No frm specfc effects Reject H 0 δ = 0 ( = 1,, 3,...14) Note: The value of the log-lkelhood functon under the specfcaton of alternatve hypothess (.e. unrestrcted model) s The estmatons of frequency dstrbuton of techncal effcency are gven n Table 5. Estmated effcency scores ndcate that there exsts techncal neffcency frms whle more than half shows relatvely techncal effcent. The average level of techncal effcency s 86.5% rangng from a mnmum of 47.1% to a maxmum 99.5%. It s suggested that frms n ths sample are producng on average at 86.5% of ther potental wth the gven present state of technology and nput levels. 80 frms represented 58.39% n a sample are relatvely more effcent than of whch an effcent score greater than 90%; however 8.% of the sample shows relatvely neffcent wth ther score rangng from 60% to 80%. These results mply the possblty of these frms that can ncrease ther producton by 13.5% gven the present state of technology and nputs level. Compared wth regons, frms locate n central regon of Tunsa hts relatvely hgh effcency score at 96.1% whle those n north reman 69.4%. 93 frms represented more than 64.4% of the sample shows relatvely techncal effcent, whch ht more than 80% of techncal effcency score. Ths result provdes an emprcal justfcaton of the effcacy of modernzaton programme for up gradng compettve of manufacturng sector. 3 The null hypothess can be tested usng the generalzed lkelhood-rato statstc,, gven by L ( H 0 ) L( H1). L ( H 0 ), L ( H1) denote the values of lkelhood functon under the null H 0, the alternatve hypothess ( H 1), respectvely. If the gven null hypothess s true, has approxmately Ch-square dstrbuton or mxed Ch-square dstrbuton. 10

12 Table 5: Frequency dstrbuton of techncal effcency for a sample of olve ol manufacturng frms n Tunsa Techncal effcency (%) Olve Ol Manufacturng frms Percentage 40 < TE < TE < TE < TE < TE < TE North 69.4 Centre 96.1 South 93.6 Mean effcency 86.5 Mn. effcency 47.1 Max. effcency Concluson In ths paper, we have nvestgated frm level techncal effcency of producton and ts determnants of olve ol manufacturng frms n Tunsa usng a stochastc fronter producton model. The data used n ths study was a sample of 137 olve ol manufacturng frms locate n north, centre and south regon of Tunsa, whch was collected through the survey mplemented durng February and March 009. Ths study revealed that the translog specfcaton s a better representaton of the technology used n olve ol manufacturng n Tunsa. The estmated coeffcent of electrcty, captal and labour are postve and statstcally sgnfcant. Results of estmaton of the techncal neffcency effects model ndcate that level of technology (TEC), frequency of use of computer and nternet (INT), the owner s age (AGE), the share of sklled labour (SKL), the employment of management staff (MAS) have a sgnfcant and postve effect on techncal effcency. The postve and sgnfcant effect was also found n nput sourcng by the own producton (SOW). A negatve relatonshp wth techncal effcency wth entrepreneur dummy varable (ENT), contnuous relatonshp wth the supplers n the same dstrct (SDT), contnuous relatonshp wth prvate sector (CPR) and trader (CTR) as customers are found. Emprcal fndngs n the estmated effcency of olve ol manufacturng frms ndcate that there exsts techncal neffcent frms whle more than half shows relatvely techncal effcent. Estmated effcency scores vary rangng from a mnmum of 47.1% to a maxmum 99.5% wth a mean value of 86.5%. Ths result mples olve ol manufacturng frms n Tunsa that can ncrease ther producton by 13.5% through more effcent use of technology and producton nputs. The fact that 93 frms represented more than 64.4% of the sample ht more than 80% of techncal effcency score mples the effcacy of modernzaton programme mplemented n Tunsa. 11

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