The Effect of Structural Adjusment Programme (SAP) on Technical Efficiency on Rice Farming in Nigeria

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1 The Effect of Structural Adjusment Programme (SAP) on Techncal Effcency on Rce Farmng n Ngera SANUSI W.A AND AKINNIRAN T.N Department of Agrcultural Economcs, Ladoke Akntola Unversty of Technology, Ogbomoso, Ngera. Abstract The study analyzed techncal effcency of rce farmng n Ngera usng tme seres data obtaned from FAO STAT (FAO Data base) coverng a perod of 1960 to The man feature of the data use ncludes rce output and nput of land, fertlzer, labour, seed, tractor number and land area under rrgaton. The major tool of analyss s stochastc fronter and t-test of dfference between two ndependent mean. Results of the analyss showed that estmated producton functon for both OLS and MLE models showed that almost all explanatory varables exhbt expected sgn and magntude except for rrgaton nput n OLS model. Coeffcents of the estmated parameters were all statstcally sgnfcant n the fronter functon whle n the average functon, the coeffcent of captal (X 5 ) and rrgaton (X 6 ) were not sgnfcant. The estmated mean techncal effcences values of 94.3 percent for all perod; 91.7 percent for pre- SAP perod and 95.6 percent for post-sap perod confrm the fact that rce farmng n Ngera experence hgh techncal effcency over the perod of study. The result of the hypothess of the study also revealed that, there s no sgnfcant dfference n the mean techncal effcency recorded before SAP and post-sap perod. The study therefore suggested approprate polcy that wll encourage proper and effectve use of prmary nputs such as land, labour, seed, captal and fertlzer for effcency mprovement. Key Words:- Rce Farmng, Techncal Effcency, Structural Adjustment Programme Background to the study Rce has become a major staple n Ngera. It s relatvely easy to produce and s grown for both sale and consumpton. In some areas, there s a long tradton of growng but for others, ts cultvaton s relatvely recent.the earlest cultvaton of mproved rce varetes (O. satva L) started n about 1890 wth the ntroducton of mproved varetes to the hgh forest zone n western Ngera (Hardcastle. 1959). Consequently, by 1960, O. satva has take over from O. glaberrma, whch s now lmted to some deep- flooded plan of the sokoto rma rver basn and other solated pockets of cultvated deep swamps all over the country (Atanda 1978). There are many varetes of rce grown some of whch are consdered as tradtonal varetes, whle other have been ntroduced wthn the last twenty years from research nsttutes, or are mports from Asa. Rce s grown n paddles or on upland felds dependng on the requrement of the partcular varety; there s also lmted mangrove cultvaton.wth expanson of the land area of rce there has been a steady ncrease n rce producton and consumpton n Ngera. The producton ncrease has however not been enough to meet the consortum of the rapdly growng urban populaton, who has a great preference for parboled rce (Sngh and Jan, 1997). Ngera has experenced rapd grown n per capta rce consumpton durng the last three decades from5kg n 1960s to 25kg n the late 1990s. The successve programmes launched to ncrease rce producton have not been able to reduce the resultng rce defct. The mposton of ban on rce mportaton from 1985 to 1995 and the ensung ncrease n the relatve prce aganst other major staples, boosted rce producton manly through area ncreases. Yelds reached plateau n the 1990s and there are now some evdences that they are actually declnng. In spte of the relatve ncrease n the prce of rce, per capta consumpton has mantaned ts upward trend, showng that rce has become a structural components of Ngeran det wth a low elastcty of demand. Rce s now an ordnary good (West Afrca Rce Development Assocaton, 2003). Rce s mportant n so many ways: Rce may be grnded nto flour, whch can be used by people allergc to wheat flour. (Potter and Hotchkss 1996) Rce s a food source for starch. Ngera has thus become a major rce mporter, second only to Idonesa over the perod Beyond the large volume nvolved, the Ngera rce market s even more attractve than other West Afrcan markets because Ngera mports hgh value (parboled) rce rather than rce of lower qualty typcally mported nto the other countres of the sub-regon. One of the key components of the strategy s ncreasng effcency at producer level. Ths ncludes ncreasng productvty of the varous rce producton n Ngera farm.in 1984 nternatonal nsttute of tropcal Agrculture (IITA) yearly reports shown that rce s a preferred food n the urban centers of many countres. Based on the problem dscussed above, rce producton n Ngera s replete wth a plethora of problems. Specfcally, the problem confrontng rce cultvaton and/ or producton n some area. Local rce has not kept up 17

2 wth the domestc consumpton demand of the Ngera populace and consequently rce s stll mported (Sngh and Jan 1997). Ngera government both past and present recognzed the mportance of rce n the det of populate and hence, varous polcy have been ntroduce to mprove productvty and effcency of rce farmng n Ngera. Ths study wll provde answer to the followng research questons. 1. What s the level of techncal effcency n rce producton n Ngera? 2. Is there any dfference n Techncal effcency level of rce producton before and after SAP? Hypothess of the study There s no sgnfcant dfference n techncal effcency of rce producton n pre and post structural adjustment programme (SAP). METHODOLOGY 3.1 The study area The study area s Ngera. Ngera s stuated on the Gulf of Gunea n West Afrca. It s located between the equator and the tropc of cancer and ths makes t hot. Ngera s one thrd larger than Texas and the most populous country n Afrcan. Its Neghbors are Benn, Nger, Cameroon and Chad. The lower course of the Nger rver flows south through the western part of the country n to the Gulf of Gunea. Swamps and mangrove forest border the southern coast, wood and hardwood forest. The two man temperatures are the tropcal regon and savanna regon n north wth temperature between 60 0 F and F. Ngera comprses of thrty sx States n whch the federal captal s stuated n Abuja. It occupes a land area of about 923, 764 square klometers wth a populaton of about 140mllon (2006 census) makng t the largest county n West Afrca. There are two man seasons that occurs n Ngera and they are:. Dry season: It s called dry season because we do not get as much ran compared to the ran season and t lasts the remander of the year.. Rany season: It s called ranng season because we get a lot of ran durng ths season whch last from around may to September n the north and march to November n the south. 3.2 Method of data collecton Data used for ths study s secondary data obtaned from FAO STAT. The commodty that was studed s rce. The data used wll cover the perod between 1960 to The feature of data ncludes: Output of rce (n thousand tones) Land area planted to rce (n thousand hectares) Quantty of fertlzer used n rce producton(n thousand tones) Labour (Rural populaton) Rce land area under rrgaton (n thousand Hectares) Quantty of rce seed planted (n thousand kg) Dsaggregate number of tractors avalable for rce farms 3.3 Method of data analyss Descrptve statstcs lke frequency counts, percentage, means, medan, mode and standard devaton. Stochastc fronter analyss usng computer program fronter verson 4.1 were used T test was also used to test hypothess of the study 3.4 The stochastc fronter producton and cost functons Stochastc producton fronter model proposed by Battese and Coell (1995) was used to acheve the general objectve of the study, where the parameters of the producton functons was estmated smultaneously wth the techncal neffcency effects. The stochastc producton fronter functon model s specfed n the mplct form as follows: Y = f(x, β) + (V-U) Y = the output of the 1 st farm X = a Kx vector of nput quanttes of the t farm β = a vector of unknown parameters to be estmated 2 V = random varables whch are assumed to be normally dstrbuted. N (O, σ V) and ndependent of the V. It s assumed / 0 account for measurement error and other factors not under the control of the farmer. U = Non-negatve random varables, called techncal neffcency effects, whch are assumed to account 2 techncal neffcency n producton and are often assumed to be half normally dstrbuted N ((O, σ V) (Algner et al., 1977). A Cobb-Douglas functonal form was be employ n ths study because, accordng to Kopp and Smth (1980) functonal form has a lmted effect on emprcal effcency measurement. Moreover the Cobb- Douglas form has been used n many emprcal studes, partcularly those relatng to developng country 18

3 agrculture. The fronter producton functon model s estmated usng maxmum lkelhood procedures. Ths s because t s consdered to be asymptotcally more effcent than the corrected ordnary least square estmators (Coell, 1995). The model n ts explct form s as follows, Y = β 0 + β 1 X 1 + β 3 X 3 + β 4 X 4 + β 5 + β 5 + β 6 X 6 + V 1 -U 1 (v) Accordng to Coell (1995), to estmate a Cobb-Douglas producton functon, nput and output data must be logged before analyss. The equaton thus become. In Y = β 0 + β 1 Inx 1 + β 2 Inx 2 + β 3 Inx 3 + β 4 Inx 4 + β 5 Inx 5 + β 6 Inx 6 + ( V 1 -U 1 ) (v) Where: Y = Yeld (output of Rce) tones X1 = Land (Ha) X2 = Seed (Ha) X3 = Fertlzer (tones) X4 = Labour (Rural populaton) X5 = Captal (Number of tractors avalable) X6 = Land area under rrgaton 3.5 Techncal Effcency The techncal effcency of producton of the th farmer n the approprate data set, gven the levels of hs nputs, s defned by: exp( X 1β + V U TE= = exp(-v).(v) exp( Xβ + V) From equaton above, the two components V and U are assumed to be ndependent of each other, where V s the one-sded, normally dstrbuted random error (V N(0, σ v 2 ), and U s the one-sded effcency component wth a half normal dstrbuton (U /N(O,σ u 2 ). Y, ad X are as defned earler. The β s are unknown parameters to be estmated together wth the varance parameters. The varance of the parameters, V and U are σ 2 2 u and σ u respectvely and the overall model varance gven as related thus: σ 2 = σ 2 v + σ 2 u..(v) The measures of total varaton of output from the fronter, whch can be attrbuted to techncal effcency, are lambda (λ ) and gamma (γ) (Battese and Cora 1977) whle the varablty measures derved by joadrow et al (1982) are presented by equatons (x) and (x). σ λ = and λ = σ n v 2 σ 2u σ (x)..(x) On the assumpton that V, and U, are ndependent and normally dstrbuted, the parameters β, σ 2, σ u 2, σ v 2, λ and γ were estmated by method of Maxmum Lkelhood Estmated 9MLE), usng the computer program FRONTIER Verson 4.1 (Coell, 1996). Ths computer program also computed estmates of techncal and allocatve effcences. Followng Olowofeso and Ajbefn (1999), a three-step procedure n estmatng the MLE estmates of the parameters of the stochastc fronter producton was then used. The steps are: Step I: The OLS estmates of the producton functon were obtaned. Step II: A two-phase grd search for the gamma (γ) and men (u) parameters were conducted wth the β 0 and σ 2 parameters were sutably adjusted. Step III: The values selected n the grd search were used as startng values to obtan the maxmum lkelhood estmates. The farm specfc techncal effcency (TE) of the farmer was estmated usng the expectaton of U condtonal on the random varable (ε) as shown by Battese and Coell (1988). The TE of an ndvdual farmer s defned n terms of the rato of the observed output to the correspondng fronter output gven the avalable technology, that 19

4 Y TE = Y exp( X β + V U ) exp( X β + V = exp (---U)..(x) so that O < Te < 1 The techncal effcency of an ndvdual farm defned n terms of the rato of the observed output (Y) to the correspondng fronter output (Y*), gven the avalable technology, condtonal on the level of nput used by the farm, hence the techncal effcency of farm I assumng Cobb-Douglas producton functon s express as; Techncal effcency = Y /Y* = f(x, β) exp (V, - U )/ f (X, β) exp (V ) Ths s obtanable from the result of the FRONTIER 4.1 (Coell, 1995). Based on the ndvdual farm s techncal effcency, the mean techncal effcency for the sample wll be obtaned (Yao and Lu, 1998). RESULT AND DISCUSSION 4.1 ESTIMATED PRODUCTION FUNCTION The ordnary least square (OLS) and the maxmum lkelhood (ML) estmate of the producton functon parameters of rce farmng n Ngera are presented n table 1.The OLS functon provdes estmates of the average producton functon whle the ML model yeld estmates of the stochastc producton functon. A comparson of the functons shows that the stochastc producton functon has a hgher ntercept term than the average producton functon. Besde the slope parameter was dfferent n both functons. Ths suggest that the stochastc fronter functon represents a non-neutral upward shft of the average producton functon. Ths result were consstent wth those of Shenggen, Wles and Young (1997) for rce n Egypt, and contrast from the neutral upward. Shft obtaned by Bravo Ureta and Evenson (1994) n Eastern Paraguay and Bravo Ureta and Pnhero (1997) n Domncan Republc. The coeffcent of the estmated parameters had the desred sgns n both functons except for rrgaton n OLS model, whch had unexpected negatve sgn. However, they coeffcents of the estmated parameters were all statstcally sgnfcant n the fronter functon whle n the average functon, the coeffcents of captal (X 5 ) and rrgaton (X 6 ) were not sgnfcant. The rato of the standard error of u to that of V and called lamb s estmated as 2.84 n the fronter functon and was statcally sgnfcant Gamma was estmated as Ths mples that 99.91% of varaton n rce output n Ngera was due to techncal neffcency. The estmated coeffcent for land was statstcally sgnfcant and postve, ths mples that the employment of more land resources would lead to greater output n rce farmng. Ths conform to a pror expectaton. Labour s one of the most mportant resources next to land n tradtonal agrculture. The coeffcent of labour was postve and statstcally sgnfcant sgnfcant n all the estmated models and ths conforms to a pror expectaton because farm operatons n Ngera had remaned labour ntensve.plantng materals consstng of seeds and seedlngs and fertlzer were also hghly sgnfcant and postve n both models, ths mply that as more and more seeds and fertlzer were used output of rce n Ngera ncreases. Captal nput (Number of Tractors) and rrgaton were sgnfcant n MLE model but not sgnfcant n OLS model, but were postvely sgn n ch b n whoth model. It cam therefore be deduced that usng more and more of these nput wll lead to an ncrease n rce nput n Ngera and ths s n contrast wth the fndngs of Nwaru et al, (2006) n whch captal was negatvely sgn. 20

5 Table 1: Estmated Producton Functon for Rce Farmng n Ngera. Varables Parameter Average OLS Functon Fronter MLE Functon Constant term b o (3.91) xxx (9.85) xxx Land (X 1 ) b (3.83) xxx (2.27) xx Seed (X 2 ) b (3.22) xxx (4.31) xxx Fertlzer (X 2 ) b (3.37) xxx (4.97) xxx Labour (X 4 ) b (2.70) xxx (8.27) xxx Tractor (X 5 ) b (0.28) (5.25) xxx Irrgaton (X 6 ) b (0.23) (2.32) xxx R F-rato xxx Log lkelhood functon Sgma square (11.97) xxx Gamma 2.84 Lambda (2.012) xx Extracted from computer prntout 2010 Fgure n parenthess are t rato, ( xxx ) mples sgnfcant at 5% and 1% respectvely. 4. Techncal effcency estmate for rce farmng Ngera. The techncal effcency of rce farmng n Ngera were summarzed and n table 2,3 and 4. The range of techncal effcences vared from to wth a mean of t can be seen from the table that over the study perod, the mean techncal effcency of Ngera rce farmng was about 94.3 percent. Ths s also mpled that techncal effcency n Ngera rce farmng can stll be mproved by about 5.7 percent. In order to compare techncal effcency of rce farmng before structural adjustment programme (Pre- SAP) and post structural adjustment Programme (post SAP) a t test of dfference between Pre SAP ( ) and post SAP ( ) means was carred out. The result n table 5, showed that the mean techncal effcency for pre SAP perod (0.917) was not sgnfcantly lower than the mean techncal effcency for post SAP perod (0.956). Ths mples that the SAP polces had not sgnfcantly nfluence on the performance of Ngera rce farmng n terms of techncal effcency. Table 2:- Frequency dstrbuton of techncal effcency n Ngera rce producton ( ) Frequency Percentage Total Mean Techncal Effcency Mnmum Techncal Effcency Maxmum Techncal Effcency Source:- Extracted from computer prntout Table 3 frequency dstrbuton of techncal effcency n Ngera rce farmng (Pre SAP = ) Frequency Percentage Total Mean Techncal Effcency Mnmum Techncal Effcency Maxmum Techncal Effcency Source:- Extracted from computer prnt out

6 Table 4: Frequency dstrbuton of techncal effcency n Ngera rce famng post SAP ( ) perod. Frequency Percentage Total Mean Techncal Effcency Mnmum Techncal Effcency Maxmum Techncal Effcency Source:- Extracted from computer prntout 2010 Table 5: t test (pared sample test) Pared samples Mean t-value d.f p-value Effcency Pre SAP versus Post- SAP perod Source:- Extracted from computer prntout 2010 CONCLUSION The result of the study show that although rce farmng n Ngera over the study perod recorded hgh techncal effcency but there s stll room for mprovement n techncal effcency snce 100 percent level of techncal effcency have not been acheved. Further analyss also revealed that polcy n structural adjustment programme dd not have sgnfcant effect on rce techncal effcency performance. REFERENCES Atanda; O.A. Internatonal Rce Commsson News letter FAO Corporate document Repostory 198 p1. Battese, G.E and G.S cora (1977); Estmaton of producton from the Domncan republc: The development economcs vol. 35 (1) Pp Bravo Ureta B.E and R.E Evenson (1994); Effcency n Agrcultural Producton. Thecase of peasant farmers n Eastern Praguay Agrcultural economcs. Vol 10 (1), Pp Bravo- Ureta, and A.E Pnhero (1997); Techncal, Economc and effcency and Agrcultural producton. The case of peasant farmer n Eastern Paraguay Agrcultural economcs. Vol 12 (2), Pp Coell, T.J and Buttese, G.E (1999); A model for Techncal Ineffcency effect n a stochastc fronter producton functon. Emprcal Economcs, Farell M (1957); The measurement of productve effcency, journal of Royal Stastcan Socety seres A General 120: Hardcastle, J.E.Y (1959); The development of Rce producton and research n the federal republc of Ngera Tropcal Agrculture (Trndad) pg 36, Jondreow, J.C., M. and Schmdt (1982); On the Estmaton of Techncal n effcency n fronter stochastc producton Journal of Econometrcs 19: Julano, B.O. (1994); Rce n human nutrton FAO food and nutrton seres to determne the role of boscence/botechnology n the study of agrcultural as perceved by vocatonal agrcultural nstructors. Onyenweaku C.E and J.C. Nwaru (2005); Applcaton of a stochastc fronter producton to the measurement of techncal effcency n food crop producton n Imo state Ngera. Ngera agrcultural journal. Vol. 36 Pp 1-12 Polter N.N and Hotchkss J.N. (1996); Food scence ffth Edton New York. Schultz A (1994): Producton functon. Analyss as a gude to polcy n low ncome farm areas Canadan journal of agrcultural economcs. Shapro KH (1983); Effcency dfferentals n peasant agrculture and ther mplcatons for development polces journal of development studes 19(2): Shenggen, F, E.J Wayss and K.B Young (1997); Polcy reform, and technologcal change n Egyptan rce producton. A fronter producton functon approach journal of Afrcan economc. Vol. 6 (2) Pp Sghn and Jan (1997); Internatonal Rce Commsson News Letter FAO Corporaton Documents Repostory 1985 p. Taylor, G.T. and Shronkwle, J.S. (1986); Alternatve Stochastc specfcaton of the fronter producton functon n the analyss or agrcultural credt program and techncal effcency journal of development economcs 24, WARDA (2003); Strategy for rce sector revtalzaton n Ngera. 22

7 Yao, and Lu Z. (1998); Determnants of Gran producton and Techncal Effcency n chna, journal of Agrcultural Economcs 46(2):

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