In this paper, the statistical cost function approach was used to model the drying and milling cost

for government paddy/rice processing complexes in Malaysia. The quadratic and inverse cost [unctions

were estimated. The results suggest that there exists cost economies at relatively high output levels for

both the drying and milling operations. Examination of output data indicates that a large number of

government paddy mills operate at low outputs, with cost economies unexploited

Chew , T. A

Ghaffar, Roslan A.

Hassan, Azman

Universiti Putra Malaysia Institutional Repository

Pertanika 11(2), 283-288 (1988)Drying and Milling Cost Functions of Paddy: Empirical Estimatesfor Government Processing Complexes in MalaysiaROSLAN A. GHAFFAR, T.A. CHEW and AZMAN HASSANFaculty ofEconomics and ManagementUniversiti Pertanian Malaysia43400 Serdang, Selangor, MalaysiaKeywords: Paddy/rice processing; government mills; drying cost functions; milling cost functions; costeconomies.ABSTRAKKajian ini menggunakan kaedah fungsi kos statistik untuk model kos pengeringan dan pengilanganbagi kilang-kilang padi kerajaan di Malaysia. Fungsi-fungsi kuadratik dan songsang dianggarkan. Hasildaripada kajian ini menunjukkan bahawa skala ekonomi wujud dalam pengeringan dan pengilangan.Kos ekonomi dicapai pada paras output yang tinggi. Seterusnya kajian yang lebih terperinci terhadap datakos dan output menunjukkan bahawa kebanyakan kilang padi kerajaan beroperasi pada tingkat kuantitiyang kebanyakan kos ekonomi tidak dipergunakan.ABSTRACTIn this paper, the statistical cost function approach was used to model the drying and milling costfor government paddy/rice processing complexes in Malaysia. The quadratic and inverse cost [unctionswere estimated. The results suggest that there exists cost economies at relatively high output levels forboth the drying and milling operations. Examination of output data indicates that a large number ofgovernment paddy mills operate at low outputs, with cost economies unexploited.INTRODUCTIONOver the years, paddy processing in Malaysia hasbeen increasingly taken over by the government.In 1983, capacity utilization by private millerswas at 33 percent whereas the governn:tent millsutilized more than 80 percent of their availablecapacity (National Paddy and Rice Board, 1984).The increasing role of the government complexescan be attributed to the present paddy pricingsystem and the closure of many private processingplants in recent years. During the 1981-83, 25private rice mills were reported to have closeddown (National Paddy and Rice Board, 1984)because of low profit margins.The shutdown of many private mills issymptomatic of adjustments within the paddypostharvest industry. Rising input cost and stag-nating output price have placed a squeeze onprivates millers' margin. Various governmentprogrammes, notably the price control on rice,has adversly affected millers' activities. At thesame time, the high capital investment requiredto enter the postharvest industry effectivelydiscourages new entrants.If the present trend continues, with nosignificant changes to the paddy and rice pricingsystem, it is likely that there will be a furtherincrease in the role to be played by governmentcomplexes in paddy processing. Already calls toincrease the number of government mills andcomplaints concerning the inadequacy of thepresent government paddy procurement systemare being raisell by various quarters. The irnpli-ROSLAN A. GHAFFAR, T.A. CHEW AND AZMAN HASSANcations of setting up more government mills aretremendous in terms of cost. 1This paper reports a study on the costefficiency of government rice processing mills.While industrial organization theory suggeststhat private firms are either cost minimizersor profit maximizers, the objective of govern-ment statutory bodies may be quite different.With regard to government mills, there is nowritten information available as to how theyshould be operated. It is, however, generallyunderstood that complex managers are expectedto minimise monetary losses. Given the currentgovernment paddy purchasing policies and riceprice, it would not be possible for these complexesto generate any profit. Unfortunately, publishedwork in the area of cost efficiency of governmentmills are scanty. Runte and Ali (1973) providedan economic evaluation of drying units in thecountry, while Fredericks and Wells (1983)considered the technical and economic aspectsof rice processing in Malaysia. In both studies,cost analysis was given scanty treatment. Anearlier paper on the same topic (Ghaffar andHassan, 1985), provided some preliminary esti-mates of costs associated with rice drying andmilling in Peninsular Malaysia. Although thisparticular study suffers from several drawbacks,notably the small number of observations usedfor the computation of costs, it neverthelessprovides a general idea of cost patterns withingovernment rice mills. Items such as fuel, elec-tricity and labour make up a good portion of thetotal operation cost. Administrative overheadcontributes the largest portion to total operationcost.MATERIALS AND MEmODSModelling, Drying and Milling Cost RelationshipsThe approach used in this study is the statisticalcost function approach. One generally accepteddrawback of this approach was outlined by Fried-man (1955) and is now commonly known as the"~egression fallacy". However, several researchersare of the opinion that regression fallacy is not aserious problem on pragmatic grounds and also,if the objective of estimating the cost functionis to estimate expected cost functions instead ofcertain cost functions from perfect technologicalfrontiers (Heady, 1956; and Walters, 1960, Wal-ters, 1963, Bhati, 1974).Statistical cost analysis essentially involvesthe examination of the relationship between thecost of producing some output and the size orlevel of the output produced. Economic theorysuggests that for any firm of a given size, its costper unit of output (AC) curve in the short run willbe U-shaped. As the firm increases its capacity orsize in the long run, there will be short-run averagecost curves (SACs) for each size (Figure 1). Acurve enveloping the SACs from below in themanner shown in Figure 1, is the long-run averagecost curve (LAC). LAC therefore represents thecost-size relationship. LAC initially declines dueto internal physical and pecuniary economies ofscale occurring as the fum size increases. The fumwhose SAC's lowest point coincides with thelowest point on the LAC operates at the optimumsize according to the minimum per unit cost ofproduction criterion. When the size of firmsincreases beyond the optimum size, internal dis-economies set in and as a result the LAC takes anupward turn. Thus in short, the internal economiesand diseconomies give rise to the U-shape of LAC.A vcrage COStper unit ofoutputSAC,LACOutputFig. 1.' Hypothetical shortrun and longrun AverageCost Curves1A study conducted by LPN indicates that if the trend in shut down of private rice processing plantscontinues, the government would have to build at least 66 more processing plants which would cost the govern-ment over 1 billion ringgits (NPRB, 1984).284 PERTANlKA VOL. 11 NO.2, 1988DRYING AND MILLING COST FUNCTIONS OF PADDYIn many studies on average cost relationship,the following functional forms have been usedwidely (e.g. Barthwal and Nair, 1979; Bhati, 1974;and McLemore et al. 1983).The linear version of relation (1) is expected togive a negative sign for the coefficient of Q and apositive sign for Q2. This relationship generallyleads to an average cost curve that has the U-shape.Relation (2) is expected to give a positivie sign forthe coefficient of (1/Q) and a negative sign for(1/Q)a. This relationship implies that average costis inversely related to output and that the curvedeclines continously. Log linear version of equa-tion (1), without the Q2 variable, also leads toa declining average cost. Such an application inexisting literature is, however, quite limited.Data and Data CollectionCross-sectional data on the costs of various outputand activities of government rice mills were collec-ted by means of specially designed questionnairesto cover the four processing activities namely:buying, drying, milling and administration. Thequestionnaires have been designed to match themonthly reports submitted by each complex toLPN Headquarters. Detailed data from all the 31government rice mills for 1984 and 1985 wereobtained and the costs were grouped into fourcomponents, namely labour, energy, repairs andmaintenance, and other costs. Both drying andmilling operating costs were categorized in thesame manner. These components were thenaggregated to get estimates of total operating costfor drying and total operating cost for milling.As a measure of the throughput, the quan-tity of continuously dried paddy was used. Thischoice was made as all paddy had to go throughthe continuous drying unit during processingAC = a + {3 Q + [j Q2 + UAC =a + (3(1/Q) + [j (1/Q)2. + VRESULTS AND DISCUSSIONFor the drying cost function estimation, 48 datapoints were used while for the milling cost func-tion estimation, 40 data points were used.As the data used were collected over twoseparate years, it is possible that there is the yearto year effect i.e. the coefficients obtained forone particular year may differ substantially fromthe ones estimated for the other year. The Chowtest (Chow, 1960) was used to detennine if thecoefficients of the two regression models for thetwo separate years were significantly differentfrom each other. We found no evidence to sU8llestthat the parameters of the cost functions for thetwo separate years were significantly different(Ghaffar and Hassan, 1985). For milling output,the quantity of rice produced was used.As the processing complexes were built indifferent years, they were stratified into fourphases according to the year the complexes werecommissioned. These time periods were defmedby LPN to reflect technological change. Phase Iis for mills commissioned in the pre-war periodup to 1971. Phase II covers 1972 to 1973 whilePhase III is for the period from 1974 to 1982.Phase IV is for complexes which started operatingfrom 1983 onwards.. Drying facilities in many ofthe complexes in the earlier phases had also beenupgraded. This was mainly in the fonn of installa-tion of batch dryers to cater for the peak harves-ting period. Only 25 of these LPN mills wereintegrated mills Le. having both drying and millingfacilities.Data from all the 31 government mills werescrutinized to ensure that the data used weresufficiently accurate. For estimating the millingcost function, data· from eleven mills were exclu-ded. This is because four of these mills did nothave milling activities while seven others wereeliminated because many of their figures forvarious items were not reported. Seven mills werenot included in the estimation of the drying costrelationship. One mill was eliminated because ofvarious inconsistencies in coSt and output figures.The other six were dropped as many items r~latedto cost and output were not available.(1)(2)AC =average costQ =level of output or throughputu, v = random error termsWherePERTANIKA VOL. II NO.2, 1988 285ROSLAN A. GHAFFAR, T.A. CHEW AND AZMAN HASSANTABLE 1Coefficient Estimates of Average Drying Cost Modelafrom each other~ Also no attempt was made toestimate separate cost functions for differentphases, as intercept dummies used to differen-tiate phases have coefficients not significantlydifferent from zero.The functional forms for the drying relationshipestimated were:whereDAC = drying average cost in units of M$l,OOODQ drying output, in units of 1,000 metrictonsDQ2 drying output squaredThe functional forms estimated for the millingrelationship were:MAc=a+{3MQ+8MQ2 (5)MAC = a + {3 (l/MQ) + 8 {l/MQ)2 (6)Coefficient Estimate t-valuea 3.275 0.859(3 275.403 4.7998 -507.290 -3.782R2 0.372F 13.601~he model used was DAC = a + (30/DQ) + 80/DQ)2whereMAC = milling average cost in units of M$l ,000MQ = milling output, in units of 1,000 metrictonsMQ2 = milling output squaredTo each of the drying and milling average costmodels, a stochastic error term was included addi-tively_Initial attempts at estimating the averagedrying cost function included estimating the loglinear version of equations (3) and (4) withoutthe variables DQ2 and {l/DQ)2 respectively. How-ever, none of these variables yielded coefficientssignificantly different from zero. The 'best'equation for the average drying cost model is asshown in Table 1. All the estimated coefficientsexcept the intercept term are significant at theone percent level. The positive sign for the coeffi-cient of (l/DQ) is consistent with the a prioriexpectation that average cost declines as outputincreases, hence suggesting the presence of sizeeconomies. As for the variable {l/DQ)2, anydefinitive interpretation is not possible. Howeverit shOUld have the right sign such that the appro-priate curvature of the estimated function can beobtained. The emphirical result indicates thatthe curve of the estimated regression is· conti-nuously declining (Figure 2).As in the average drying cost model, variousforms of the average milling cost model were alsoestimated. The best regression is as shown in Table2. The results indicate that in general, there issupport for the negative relationship betweenaverage milling cost with output. The estimatedvalue of {3 is consistent with a priori expectation.(3)(4)DAC = a + {3DQ + 8DQ2DAC = a + (3{l/DQ) + 8 {l/DQ)22Test of equality of coefficient for different reg-ression equations was done using the F-statistic whichwas calculated using the formula2(RSS - L RSS)/ki = 1Residual sum of squares of the regressionfitted to the entire period.n = Number of observations.RSS 1 = Residual sum of squares of the regressionfor the first n/2 observations.= Number of estimated parameters.kThe F values obtained were 2.593 and 0.263 for dryingand milling cost models respectively, both not significantat 5% level.RSS 2 = Residual sum of squares of the secondn/2 observations.; i = 1,22L RSS )/( n - 2k)i =1F(k, n-k) =where RSS286 PERTANIKA VOL. II NO.2, 1988DRYING AND MILLING COST FUNCTIONS OF PADDYMSI = USSO.38 (July 1986)Fig. 2: Estimated drying Average Cost Curveso ~Output (thousand tons)milling was computed as -0.649 and -1.054respectively. 3 These elasticity 'values imply thata one percent change in output will lead to areduction of 0.65 percent and slightly more thanone percent reduction in drying and milling costrespectively. These figures indicate that if outputwas to be expanded, cost savings can be achievedby using existing facilities, as a substantial portionof present capacity is not utilized.4 Examinationof the cost output data further reveals that theminimum cost outputs were about 15,730 1:onsfor drying and 8,000 tons for milling, respectively.The results indicate that currently, only about 45percent of optimum drying output, and 52 percentof optimum milling output, were produced.• • DAC :,. + Hob! + '(..l...Y• DQ DQ'"•Average costcost per unitof oUlpUIThe negative sign obtained for {3 suggeststhe presence of cost economies. That is, there isa reduction in the average cost as milling outputis increased. The nature of size economies maybe seen further by drawing the actual and esti-mated cost curves. The actual cost curve appearsto take the typical V-shape (Figure 3).Since both the underlying cost structuresare characterized by size economies, it is ofinterest to consider the extent to which costcould be saved if output was to be increased.This may be achieved by computing the costelasticity with respect to output. The elasticityof cost with respect to output for drying andTABLE 2Coefficient Estimates of Average Milling Cost ModelaCoefficient Estimate t-valuea 94.198 8.032{3 -14.907 -4.1126 0.719 2.906R2 0.536F 21.378~he model used was MAC = a + {3MQ + 6MQ2MSI = USSO.38 (July 1986)Fig. 3: Estimated Milling Average Cost CurvesOL_:--~'=::::=:;:::::~--15OutpUI (chousand tons)CONCLUSIONThis paper has presented estimates of average costfunctions for both drying and milling of paddy,using data from government owned processingplants. Although both average and total costrepresentations of cost-output behaviour aretheoretically acceptable, empiricial work inthe estimation of cost functions do not seemto indicate any definite preference for eitherone type over the other. In practice, as indicatedby existing literature, there is a fair mix of thetwo approaches. In some cases the choice ofMAC ~ • +~Q t.MlY...'.100Averagecost per unitof output3These elasticities were computed at the mean.4Drying and milling capacities vary among the processing complexes. A study reported by Ghaffar and Hassan(1985) indicates that drying and milling capacities were 20,000 and 16,000 metric ton per year respectively.Capacity here was measured with the assumption that each complex runs for 16 hours/day, 200 days a year.PERTANIKA VOL. 11 NO.2, 1988 287ROSLAN A. GHAFFAR, T.A. CHEW AND AZMAN HASSANwhether to use average or total cost may notbe obvious. Researchers are often constrainedby data availability or other estimation problems.The results obtained here indicate, however, thatthe use of average cost is appropriate for paddyprocessing.To the extent that the results obtained inthis study provide a fair representation of cost-output relationship, some tentative conclusionscan be drawn. For both drying and milling opera-tions, cost economies were present. Cost econo-mies were available at relatively high. output levels.However, examination of the cost output dataindicated that for both years, large numbers ofthe government paddy processing plants operatedI at outputs that leave substantial cost economiesunexploited. It is possible that because of theseasonal nature of the paddy crop, with peakharvesting occuring in a short space of time andthe need to process the paddy within a shorttime period, such cost economies are at besttheoretical and cannot be exploited. However,this can only be true for drying and not formilling because the latter can surely be staggeredout over the year. Further investigation 'as towhy government mills operate at nonoptimaloutput levels is ongoing.REFERENCESBARTHWAL, B.R. and K.N.S. NAIR, (1978): The coststructure of Indian industries: Some empiricalevidences, The Indian Economic Journal, 27(2):35-47.BHATI, U.N., (1974): Cost functions for Selangor padifarm, Kajian Ekonomi Malaygia, II: 1-13.CHOW G.C., (1960): Tests of equality between two setsof coefficients in two linear regressions, Econo-metrica, 28: 591-605.FREDERICKS, L.J. and R.J.G. WELLS (1983): RiceProcessing in Malaysia: An Economic and Tech-nical Analysis. Oxford University Press, KualaLumpur.FRIEDMAN, M., (1955): Comments In Conference onBUsiness Concentration and Price Policy. Princetoncited in Walters (1963).GHAFFAR, R.A. and AZMAN HASSAN, (1985): APreliminary investigation of the costs associatedwith rice drying and milling in Peninsular Malaysia,In Research and Development Systems and Lin-kages for a Viable Grain Post-Harvest Industry inthe Humid Tropics: Proceedingg of the EightASEAN Technical Seminar on Grain Pogt-HarvegtTechnology, eds., R.L. Semple and A.S. Frio:424pp.HEADY, E.O. (1956): Budgeting and linear programmingin. estimating resource productivity and cost rela-tionships, In Heady, E.O. et at, Resource Produc-tivity, Return to Scale and Farm Size. Ames. Iowa.MC LEMORE, D.L., G. WHIPPLE and K. SPIERLMAN(1983): OLS and frontier function estimates oflong-run average cost for Tennessee LivestockAuction Market, Southern Journal of AgriculturalEconomicg, 15(2): 79-83.NATIONAL PADI AND RICE BOARD (NPRB) (1984):The Status of the Rice/padi Processing industry;Problems and Suggestions, A paper presented atSeminar Penilaion ke atas Kecekapan dati seg! Kosdalam Pemprosesan PadilBeras, LPN, Kuala Lum-pur 1984 (in Bahasa Malaysia).RUNTE, R. and A. ALI, (1973): Evaluation of existingartificial units in Malaysia, Alor Setaro (Unpub-lished)WALTERS, A.A., (1960): Expectations and the regres-sion fallacy in estimating cost function, ReviewofEconomicg and StatlstiCB, 42(2): 210-215.WALTERS,.A.A., (1963): Production and cost functions:An econometric survey, Econometrica. 31(1):1-66.(Received 5 March, 1987)288 PERTANIKA VOL. II NO.2, 1988

Drying and Milling Cost Functions of Paddy: Empirical Estimates for Government Processing Complexes in Malaysia

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Drying and Milling Cost Functions of Paddy: Empirical Estimates for Government Processing Complexes in Malaysia

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