DETERMINANTS OF TECHNICAL EFFICIENCY OF RICE FARMS IN NORTH- CENTRAL AND NORTH-WESTERN REGIONS IN BANGLADESH

Transcription

1 DETERMINANTS OF TECHNICAL EFFICIENCY OF RICE FARMS IN NORTH- CENTRAL AND NORTH-WESTERN REGIONS IN BANGLADESH ABSTRACT Stefan Bäckman Unversty of Helsnk, Fnland K.M. Zahdul Islam Unversty of Helsnk, Fnland John Sumelus Unversty of Helsnk, Fnland Ths paper estmates a quadratc stochastc fronter producton functon to examne the determnants of techncal effcency n rce farmng n Bangladesh usng the computer program FRONTIER 4.1. Prmary data has been collected usng mult-stage random samplng technque from twelve vllages n north-central and north-western regons n Bangladesh. Rce cultvaton dsplayed much varablty n techncal effcency rangng from.16 to.94 wth mean techncal effcency of.83 whch suggested substantal gans n output wth avalable resources and exstng technologes. The analyss of the determnants of techncal effcency revealed that the age and educaton of the household heads, avalablty of off-farm ncomes, land fragmentaton, access to mcrofnance, extenson vsts, and regonal varaton were the major factors that caused effcency dfferentals among the farm households studed. Hence, the study proposes strateges such as provdng better extenson servces and farmer tranng programs, ensurng access to agrcultural mcrofnance, reducng land fragmentaton and rasng educatonal level of the farmers to enhance techncal effcency. JEL Classfcaton: C1, Q1, D4 Key Words: Quadratc stochastc fronter producton functon, Techncal effcency, Bangladesh, mult-stage random samplng, agrcultural mcrofnance Correspondng Author s Emal Address: zahdul.slam@helsnk.f INTRODUCTION Agrculture s the most mportant sector n the economy of Bangladesh as t contrbutes about 1% of the gross domestc product (GDP) and 5% of overall employment (Bangladesh Agrcultural Census 8). The domnant food crop of Bangladesh s rce. Rce accounts for 94% of the cereals consumed, supples 68% of the proten n the natonal det, accounts for approxmately 78% of the value of agrcultural output, and 3% of consumer spendng (Ahmed and Haggblade, ). It also accounts for 94% of the total crops produced (Bangladesh Economc Revew, 9) and 76.6% of the cropped area (BBS, 6). In Bangladesh, 88.44% of the total households are located n rural areas and they are more or less dependent on agrculture for a lvng (Bangladesh Agrcultural Census, 8). Agrculture provdes the basc food for the survval of the

2 subsstence farmers n Bangladesh. Subsstence farmers account for the greatest proporton of those engaged n farmng. Bangladesh agrculture already operates at ts land fronter and there s lttle or no scope to expand the cultvable land to meet the ncreasng demand for food requrements for ts ever-ncreasng populaton (Rahman, 3). Moreover, hgh populaton growth, frequent crop falures resultng from floodng or droughts put pressure for ntensfcaton of land use. So, ths country smply cannot afford any dmnuton n the productvty of ts lmted 8.44 mllon hectare arable lands (BBS, 6). We also need to mantan optmum productvty of our exstng cultvable lands n order to get ncreased yelds. The man agrcultural products are rce, jute, sugarcane, potatoes, vegetables, olseeds, pulses and tea. Three rce crops are grown durng the crop cycle begnnng n Aprl- the 'Aus' (sprng) crop, the 'Aman' (summer) crop, and the 'Boro' (wnter) crop. The frst two are tradtonal 1, ran-fed crops, whereas the Boro crop s the Hgh Yeld Varety (HYV ). However, there s evdence that actual farm yelds for both hgher yeldng and tradtonal varetes show consderable shortfalls n yeld from those attaned by expermental staton levels; whch gves rse to a yeld gap (De Datta et al., 1978) of approxmately 4% to 5% (BRRI, ; Sattar, ). The average rce yeld n Bangladesh s.74 tonnes/ha (BBS, 8), whch s much lower than those of other Asan countres. The potental gan from closng ths yeld gap s hgher for Bangladesh compared to other Asan countres such as Chna, Korea, Indonesa, Myanmar, Nepal and Vetnam (Pngal et al., 1997). Ths yeld gap ndcates a dfference n productvty between best practce and on other less effcent farms that operate wth comparable resource constrants under smlar crcumstances (Wadud, 1999; Vllano, 5). The dfference between the actual and techncally feasble output for most crops mples great potental for ncreasng food and agrculture producton through mprovements n productvty. For a resource scarce country such as Bangladesh where opportuntes to develop and adopt new technologes are rare, emprcal nvestgatons of techncal effcency and ts determnants n rce farmng are a dre necessty. Such studes help to determne the level at whch farmers are usng exstng technologes, and also explore the possblty of rasng the productvty by ncreasng the effcency. A great deal of emprcal studes (e.g. Sharma et al. 1999, Nyemeck et al., 3; Tzouvelekas et al.,; Vllano, 5; Kalrajan, 1984; Kumbhkar, 1987; Battese et al., 1996; Coell and Battese, 1996; Battese and Coell, 199 ; Bnam et al., 4; Bravo-Ureta and Pnhero, 1997; Wang et al., 1996b) have been conducted n other countres to measure the techncal effcency by usng producton functon, mathematcal programmng technque, panel data, as well as usng the cross-secton data. Determnants of neffcency nclude some exogenous varables that have some mpacts on effcency. Examples of such nfluences are age of the farmer, the educaton level of the farmer, the sze of the farm, access to credt, land tenure, farmers capabltes to use the nputs and so forth. The measurement of effcency entals the determnaton of factors nfluencng the overall effcency. The most common approach to do ths s the determnaton of an neffcency ndex (consdered as the dependent varable) and then regress the dependent varable aganst a set of explanatory varables consdered to affect the effcency levels. Kumbhakar et al. (1991) proposed that the determnants of neffcency should be estmated smultaneously by notng that the two-stage procedures ntroduce some bas n estmaton. In the two-stage approach, effcency scores 74

3 determned n the frst stage regresson are regressed by background and producton envronment related factors (Ptt and Lee, 1981). Ths approach contans serous problems concernng assumptons made for the non-negatve random varable, u. Moreover, the second stage specfcaton conflcts wth the assumpton that u s are ndependent and dentcally dstrbuted. Ths second stage was crtcsed by Battese and Coell (1995) and Wang and Schmdt (). Lke Kumbhakar et al. (1991), Battese and Coell (1995), Huang and Lu (1994) also proposed smlar models for ncorporatng techncal neffcency effects. There have been very few studes undertaken n Bangladesh that measured the determnants of techncal effcency. Khan et al. (1) nvestgated techncal effcency of a sample of 15 Bangladesh rce farmers. Separate Cobb-Douglas producton fronters were estmated for boro and aman rce producers. The mean techncal effcency scores reported were 95% and 91% respectvely and the result ndcated that farmers educaton had a sgnfcant nfluence on techncal effcency of boro rce producers. Rahman and Rahman (9) examned how the land fragmentaton and resource ownershp (land, anmal power and famly labor) affected productvty and techncal effcency of rce producers n Bangladesh, usng survey data from farms. They estmated the mean techncal effcency to be 91 % and the effcency dfferentals were markedly nfluenced by land fragmentaton and resource ownershp. Asadullah and Rahman (9) examned the nfluence of educaton on farm producton effcency for a large dataset obtaned from 141 vllages and ther analyses revealed that household educaton sgnfcantly reduced producton neffcences. Wadud (3) used both Data Envelopment Analyss (DEA) and Stochastc Fronter Approaches (SFA) to examne the techncal, allocatve, and economc effcency of a sample taken from 15 farm households and found hgh level of techncal effcency. The techncal effcency was explaned by land degradaton and rrgaton nfrastructure. Coell et al. () used DEA and examned techncal, allocatve, costs, and scale effcences for the modern Aman and modern Boro rce from a total of 46 sample households. They reported techncal effcency of 66% for Aman rce whereas a techncal effcency of 69% was reported for Boro rce. Sharf and Dar (1996) examned how educaton, growng experence, and farm sze nfluenced techncal effcency for HYV Boro rce usng a two step procedure, and found that educaton was postvely related to techncal effcency. However, there have been no studes on farm level techncal effcency and ts determnants focusng on fnancal factor by way of mcrofnance. Ths study ntroduced a new explanatory varable named access to agrcultural mcrofnance whch was not examned n the prevous studes as a potental determnant of effcency n rce farmng. The study thus attempts to test the hypothess that access to agrcultural mcrofnance affects rce producton effcency. Gven that lttle attenton has been devoted to quantfy and dentfy the determnants of techncal effcency, the present study ams to estmate the determnants of techncal effcency and each factor s contrbuton to neffcency. The present study chooses the approprate functonal form of the neffcency component and a sutable producton functon model that ft the data most based on several emprcal hypotheses. Another justfcaton of ths study s the ntroducton of a flexble producton functon rather than the commonly used SFA usng Cobb-Douglas and or DEA n estmatng techncal effcency. Further, proposng mcrofnance as a determnant of techncal effcency of farmers n Bangladesh s a substantally dfferent polcy varable. The 75

4 present paper contrbutes to the lterature n three ways. Frst, we ncorporated the whole farm rce producton n the analyss and n dong so we assumed that the economc stuaton of a farmer s better represented by aggregate producton of crops, second, we estmated and dentfed the determnants of the whole farm rather than for a specfc rce crop and thus gave recommendatons to the polcy makers to formulate polces that mprove farm productvty, thrd, our data renforces some theoretcal arguments that extenson vsts, educaton, access to fnance and regonal varaton may have on farm productvty and effcency. An understandng of these relatonshps can provde the polcy makers wth nformaton about the nature of the problems facng the rce farms n Bangladesh and to desgn programs that mprove effcency. The rest of the paper s organzed as follows: Secton two outlnes the theoretcal model. Secton three descrbes the methodology, study areas, survey method, and lst of varables of collected data. Secton four specfes the models and the results are dscussed n secton fve. Secton sx concludes. ANALYTICAL FRAMEWORK Accordng to Farrell s (1957) model, techncal effcency (TE) s defned as the ablty of a farm to obtan the best producton from a gven set of nputs (output-ncreasng orented), or alternatvely as the measure of the ablty to use the mnmum feasble amount of nputs to produce a certan level of output (nput-savng orented) (Greene, 198; Atknson and Cornwell, 1994). Consequently, techncal neffcency s defned as the extent to whch frms fal to reach the optmal producton. Farrell (1957) proposed to measure TE of a farm by comparng ts observed output to that output whch could be produced by a fully effcent farm, gven the same bundle of nputs. Agner et al. (1977) and Meeusen and van den Broeck (1977) ndependently proposed the stochastc fronter (SF) producton functon to account for the presence of measurement errors and other nose n the data, whch are beyond the control of managers. Farmers n general operate under uncertanty and therefore, the present study employs a stochastc producton fronter approach for measurng TE. Followng Battese and Coell (1995), the followng stochastc fronter producton functon and neffcency effects model are estmated smultaneously usng sngle stage wth the computer program, FRONTIER 4.1, developed by Coell (1996). Followng ther specfcaton, we specfy the general SF model defned as: y f ( ; ) = 1,,, N (1) x Where, y s the revenue from rce for the th farm, x s a vector of k nputs (or cost of nputs), β s a vector of unknown parameters to be estmated, f () s a sutable functonal form for the fronter (Cobb-Douglas, translog or quadratc), ε s an error term, and N s the total number of observatons. The stochastc fronter producton s also called composte error model, because t postulates that the error term ε s decomposed nto two components: a stochastc random error component (random shocks/whte nose) and a techncal neffcency component defned as follows: 76

5 77 ε v u Where v s a symmetrcal two sded normally dstrbuted random error that captures the stochastc effects outsde the farmers control (for example, weather, natural dsasters, omtted varables, luck, exogenous shocks, measurement errors, and other statstcal nose). It s dentcally, ndependently and normally dstrbuted v ~d N (, ), ndependent of the u s. Thus, v, allows the producton fronter to vary across farms, v or over tme for the same farms and therefore, the producton fronter s stochastc n nature. The term u (asymmetrc non-negatve error term) s a one sded (u ) effcency component that captures the techncal neffcency of the th farm. Ths may follow a halfnormal, exponental, truncated-normal or gamma dstrbuton (Stevenson, 198; Agner et al., 1977; 199; Meeusen and Broeck, 1977). In ths study we assumed that u follows the exponental dstrbuton as was done n varous publshed studes n appled stochastc fronter lterature. It s obtaned by the truncaton at zero of the normal dstrbuton wth u mean μ, and varance ( ). If μ s pre-assgned to be zero, then the dstrbuton s halfnormal. The varance parameters of the model are parameterzed as:, So that γ 1 (3) s v u The parameter γ must le between and 1. Here, u s () s denotes the total varaton n the dependent varable due to techncal neffcency ( ) and random shocks ( ) together. The gamma ( ) parameter explans the mpact of neffcency on output. The maxmum lkelhood estmaton (MLE) of equaton (1) provdes consstent estmators for β, γ, and parameters. Agner et al. (1977) expressed the lkelhood functon n terms s of the two varance parameters, (1977) suggested that the parameter, u s u v and u / v / s u v. Battese and Corra, be used because t has a value between zero and 1 and ths property permts to obtan a sutable startng value for an teratve maxmzaton process, whereas the -parameter could be any non-negatve value. A value of closer to zero mples that much of the varaton of the observed output from fronter output s due to random stochastc effects, whereas a value of closer to one mples proporton of the random varaton n output explaned by neffcency effects or dfferences n techncal effcency. SURVEY DATA The Study Areas and Samplng Methods Data were collected from twelve vllages n north-west and north-central regons n Bangladesh through a survey conducted n June-August 9. These regons were

6 selected due to ther hgh levels of poverty and good agrcultural potental. For mcrofnance users, data were collected wth the help of Mcrofnance Insttutons (MFIs) clents lsts. Personal ntervews were conducted for both the users and non users of mcrofnance to collect the data. We ntervewed 18 agrcultural mcrofnance borrowers and 18 non-borrowers (the control group) of agrcultural mcrofnance who operated farm land between. to 1 hectare. Ths land holdng crtera was set by the mcrofnance nsttutons whle grantng agrcultural mcrofnance loan. Non-borrowers are selected based on smlar land holdngs and soco-economc background to provde a control group for comparson wth borrowers. As the entre sample s used n explanng effcency among the sampled farms, there are no sample-selecton ssues as well. Farmers havng land more than 1 hectare but takng mcrofnance exclusvely for agrculture were also consdered. Data were collected from the farmers producng Boro, Aman, and Aus rce crop from the selected areas. As most farmers n Bangladesh are llterate, most of them, wth some exceptons, do not keep any vouchers or wrtten record of nput prces as well as do not mantan any wrtten documents about nput- output data. Wth a vew to mnmzng errors stemmng from relance on farmers memory, data were collected mmedately after the harvest from the three growng seasons. In conductng the research, multstage samplng technque was used. The frst stage was the purposve selecton of two dstrcts (Mymensngh and Sherpur) form northcentral and four dstrcts (Rajshah, Naogoan, Dnajpur, and Gabandha) from the northwest regon n Bangladesh. The second stage nvolved the dentfcaton of those farmers who had taken mcrofnance specally allocated for agrcultural producton. Fnally, a mult-stage proportonal random samplng method was used to select 6 households (3 from mcrofnance borrowers and 3 from non-borrowers of mcrofnance) from each dstrct, thus a total of 36 households were surveyed. Descrpton of the Data Output s defned as the market value of the aggregated rce producton n the survey perod. Rce output prces were gathered from ndvdual farms. All rce (Boro, Aman, Aus) produced on the sample farms were aggregated nto one output value (Taka 3 ). Land represents the total amount of land (own-cultvated land, sharecroppng land, and rented/leased land) used for rce producton and was measured n hectare. Labor ncludes both famly (mputed for hred labor) and hred labor utlzed for pre and post plantng operatons and harvestng excludng threshng. It was measured n annual labor-days used for rce producton. Fertlzers nclude all sorts of organc and norganc fertlzers used by the farm households for rce producton. It represents the total cost of fertlzer measured by market prces. Seeds ncluded all seeds used n rce producton and was measured n Taka. If seedlngs were purchased, t was converted nto equvalent amount of seeds to compute the seed prce. 78

7 TABLE 1. DESCRIPTIVE STATISTICS Varables Unt Mean Std. Dev. Mnmum Maxmum Output Taka Land Hectare Seeds Taka Fertlzers Taka Labor Days Irrgaton Taka Pestcdes Taka Other costs varable Tractor & anmal power Taka Taka Captal Taka Age of farmer Years Educaton Years Extenson No Off-farm ncome Taka Experence Years Numbers of plots No Source: Computed by the authors. Irrgaton represents the total rrgaton costs for rce producton. Ths cost s estmated from total rce land rrgated and t was measured n Taka. Tllng ncludes the total land tlled wth tractor and or bullocks. It represents the total cost of tllng measured n Taka. Other costs nclude pestcde, seed bed preparaton, and crop transportaton costs and t was measured n Taka. Captal s the sum of farm tools, machneres and anmal power used n rce producton and was measured n Taka. A large set of data were also collected about the farmers soco-economc characterstcs and other aspects such as the farmer s age, years of schoolng, access to credt, numbers of contacts wth extenson agents, wealth, nvestment, nsttutonal constrants to get loan, land ownershp etc. Some basc characterstcs of the sample farms are presented n Table 1. It s evdent that farms were small n terms of output and total area farmed. On average each farm produces rce worth Taka 8836 and t s hghly varable rangng from Taka 4 to Taka 795. Farm operators averaged 4 years old and t ranged from 17 years to 85 years. Approxmately 98% of the farm households were adult. Ther experence n rce farmng was vast and t ranged from 1 year to 7 years whle ther educaton level was moderate. 79

8 8 MODEL SPECIFICATION Stochastc Fronter Producton (SFP) We specfy a log-quadratc producton functon as ntroduced by Chu, Agner and Frankel (197) to estmate the stochastc fronter producton functon. We used a less restrctve log-quadratc specfcaton that takes nto account both the Cobb-Douglas specfcaton and translog second order (excludng the cross-term) log-lnear form. The followng quadratc model was specfed n ths study: ln y 8 16 j ln xj j (log xj) j1 j9 v u (4) Where y represents the value of rce output of th farm and j s the j th nput used n producton. ln = natural logarthms, X 1 = Total Land used for rce producton; X = Farm Captal ; X 3 = Total labor days used ; X 4 = Costs of Fertlzers; X 5 = Irrgaton costs ; X 6 = Seeds costs ; X 7 = Tractor and anmal power costs, and X 8 = other varable costs. Ineffcency Model for the Cross Secton Data The techncal neffcency (u ) could be estmated by subtractng TE from unty. The functon determnng the techncal neffcency effect s defned n ts general form as a lnear functon of soco-economc and management factors. It can be defned n the followng equaton: u 8 (5) k1 k k Where, u s the techncal neffcency effect, δ k s the coeffcent of explanatory varables. The Z varables represent the soco-economc characterstcs of the farm explanng neffcency and may not be functons of y. We proposed that the techncal neffcency could be explaned by the followng determnants: Z 1 = Age of the household head (years); Z = Educaton (number of years of schoolng of the farmer); Z 3 = Experence (years); Z 4 = Off-farm ncome (n Taka); Z 5 = Land fragmentaton (t ncludes the total number of plots operated); Z 6 = Extenson vsts (number); Z 7 = Access to mcrofnance (A dummy varable to measure the nfluence of mcrofnance on effcency. Value s f the farmer had cash credt n the last 1 months pror to the survey from mcrofnance nsttutons exclusvely for agrculture, otherwse 1) and Z 8 = Regon (A dummy varable. It takes a value of 1 f the regon s northwest and otherwse).

9 81 Hypotheses Tests The followng tests have been carred out for testng the functonal forms, neffcency effects, and determnants of coeffcents for rce farmers n the study areas: (1) Fronter model specfcaton for the data s Cobb-Douglas producton functon. That s H : C-D ( H : ) s an adequate representaton of the producton Functon. () Fronter model specfcaton for the data s a Quadratc producton functon. That s H... s an adequate representaton of the producton : 9 16 functon. Here represent the quadratc terms. (3) Fronter model specfcaton for the data s a Translog producton functon. That s H... s an adequate representaton of the producton : 9 44 Functon. Here represent the quadratc terms and also the cross terms. (4) There s no neffcency effect that s H... : 1 8 (5) The coeffcents of determnants of neffcency model equals zero that s H All the above hypotheses were tested usng the generalzed Lkelhood Rato (LR) whch s defned as: -[L(H )-L(H 1 )], where L(H ) and L(H 1 ) are the values of the lkelhood functon of the fronter model under null hypothess and alternatve. If the null hypothess respectvely. The null hypothess was rejected when L. R C hypothess was true, the test statstc had approxmately a -dstrbuton or mxed - dstrbuton wth degrees of freedom equal to the dfference between the number of parameters specfed n the null hypothess and alternatve hypothess. If there s no neffcency effect, H..., then the test statstc s dstrbuted : 1 8 lke a mxed -dstrbuton wth degrees of freedom equal to 9. All the hypotheses are conducted assumng. 5. Thus, f the statstc exceeds the 95% pont for the approprate -dstrbuton, the null hypothess would be rejected. The crtcal value for the Lkelhood rato for was obtaned from Table 1 of Kodde and Palm (1986). Output Elastcty ( ) j The rce output elastcty for land, labour, fertlzers, rrgaton, seeds, tractor hours, captal, and other varable costs are ncluded n the regresson of nterest. The output elastcty ( ) wth respect to nputs were computed for the quadratc model as follows: j

10 f (ˆ, x ) x x j j j ) (6) j y For quadratc terms, we may represent the above elastcty n the followng equaton: = j nputs, ˆ xj j ˆ (8 j xj) (7) y ( ) Where, xj s the mean of 8 th j nput, y s the mean producton estmated at mean ˆ j s the estmated coeffcent of the X term and (8 j) coeffcent of the X term. Returns to Scale (RTS) ˆ s the estmated Returns to scale s equal to the sum of margnal producton elastcses of each nput. It s defned n the followng equaton: 8 RTS = j (8) j 1 RESULTS AND DISCUSSIONS The MLE of the parameters of the Cobb-Douglas stochastc fronter producton functon, the quadratc producton functon, and the translog model were obtaned usng computer program NLOGIT 4. (Greene, 7). The results are presented n Table. To select the most sutable model (Cobb-Douglas, quadratc, or translog) we tred all models wth dfferent dstrbuton assumptons of the error component (u ) and tested all models wth the results of Log lkelhood at the predetermned crtcal value ( (1),.95.71) to reject or accept one model over another. Frst, we tested the Cobb-Douglas wth the translog to determne whether Cobb-Douglas ftted the data by usng the lkelhood rato test 4. We rejected the null hypothess and excluded ths model from further consderaton. Fnally, we compared the translog model wth the quadratc functon and found that lnearquadratc model ftted the data well wth the expected sgns for producton coeffcents and wth the results of hypothess, whch assumed the error term to be exponental. The estmates of the quadratc stochastc fronter producton are presented n Table. The result revealed that wth the excepton of fertlzer all the explanatory varables conform to pror expectaton of sgns of the coeffcents for the quadratc producton functon wth nne coeffcents sgnfcant at dfferent sgnfcance levels and suggestng that model fts the data well.

11 TABLE. MAXIMUM LIKELIHOOD ESTIMATES OF COBB-DOUGLAS AND QUADRATIC STOCHASTIC FRONTIER PRODUCTION FUNCTION Varables Parameters Cobb-Douglas ML Estmaste Quadratc ML Estmates Intercept *** (1.59) 1.39 (1.13) Ln (Land).56 *** (14.1).74 *** (3.4) 1 Ln (Captal) -.16 ** (-.).75 (1.14) Ln (Labor).16 *** (4.1) 1.79 *** (3.7) 3 Ln (Fertlzer).1 (1.) -.85 ** (.36) 4 Ln (Irrgaton).3 *** (.73).17 (1.6) 5 Ln (Seed).13 *** (3.96).198(.188) 6 Ln (Tractor and bullock).1 *** (3.5).631 *** (.67) 7 Ln (Other varable costs).1 *** (4.86).11(.31) 8 [Ln (Land)] -.3(-1.) 9 [Ln (Captal)] -.5 (.4) 1 [Ln (Labor)] -.68 *** (-.7) 11 [Ln (Fertlzer)].1 *** (3.33) 1 [Ln (Irrgaton)].37 * (1.95) 13 [Ln (Seed)] -.8(.66) 14 [Ln (Tractor)] -.34 ** (-.7) 15 [Ln (Other varable costs)].8 *** (.67) 16 Varance parameters u /( u v)..119 s ( u v ) Log Lkelhood v.31 u Ineffcency effects Constant (-.54) Age.3 (.17) 1 Educaton.17 (1.13) Experence -.57 *** (-.71) 3 Off-farm Income.1 (1.) 4 83

12 84 Number of plots.4 ** (.) 5 Extenson Vsts -.4 (-.88) 6 Access to mcrofnance.13 (.6) 7 Regon.154 (.38) 8 Source: Computed by the authors. Notes: t-statstcs are n parentheses; *, **, *** ndcate sgnfcance at 1%, 5% and 1% level, respectvely, Log Lkelhood under OLS estmates s The sgnfcant postve coeffcents of land, labour, and tractor mply that as each of these varables s ncreased, rce producton also ncreases. One explanaton of negatve coeffcent of fertlzer may be due to the wrong applcaton leadng to excessve use of urea as source of N fertlzer snce t s relatvely cheap and use very lttle of expensve fertlzers lke P and K. Lack of farmers knowledge about the need for balancng the applcaton of fertlzer s another plausble reason of ths negatve sgn. Government subsdy for fertlzer n Bangladesh may also encourage the farmers to use too much urea and t may have long term damagng effects on the long-term productvty of sol. Snce the fertlzer dealers are more responsve than the government to local fertlzer requrements and preferences, government may encourage the dealers to gude and motvate the farmers n mantanng an optmum nutrent balance on the farms whle sellng fertlzers to the farmers. Other ndependent varables such as seeds, rrgaton, and other varables costs have postve coeffcents but are nsgnfcant under quadratc producton functon. For labor, the poor performance s attrbuted to hgh average man days of labour (199). Ths s an ndcaton of over-utlzaton of labor as s typcal of developng countres. Analyss of Productve Effcency The result of the TE estmates s presented n Table 3. The TE analyss revealed that techncal effcency score of sample farms vared from 16.% to 94.47%, wth the mean effcency level beng 83%. The mean techncal effcency mples that the average farm produces 83% of the maxmum attanable wth gven nput levels. Ths varaton s also confrmed by the value of gamma ( ) that s.5. The gamma value of.5 suggests that 5% varaton n output s due to the dfferences n techncal effcences of farm household n Bangladesh. Ths fndng establshes the fact that neffcences exst n the sampled farmers. Moreover, the correspondng varance-rato parameter 5 *,, mples that 11% dfferences between observed and maxmum fronter output for rce farmng s due to the exstng dfferences n effcency among the sample farms.

13 85 TABLE 3. SUMMARY OF TECHNICAL EFFICIENCY OF THE RICE FARMERS Effcency level Frequency Percentage Total farms 36 1 Mean 8.65 Standard Devaton 9.84 Mn. 16. Max Source: Computed by the authors. The ndces of TE ndcates that f the average farmer of the sample could acheve the TE level of ts most effcent counterpart, then average farmers could ncrease ther output by 1% approxmately [that s, 1-(83/94)]. Smlarly the most techncally neffcent farmer could ncrease the producton by 83% approxmately [that s, 1-(16/94)] f he/she could ncrease the level of TE to hs/her most effcent counterpart. Snce the mean TE s 83%, t can be deduced that 17% of the output s lost due to the neffcency n rce producng system or n the neffcency among the sampled farmers or both combned. It also ndcates that small farms n the study area, on average, can gan output growth at least by 1% through the mprovements n the techncal effcency. For a land scarce country lke Bangladesh ths gan n growth wll help much to ensure food securty n the country. These fndngs may nvte attenton of the polcy makers to mprove the effcency of the farmers through adopton of rght polces. Results of the Hypotheses Test The formulaton and results of dfferent hypotheses (model selecton, neffcency effect, determnants of coeffcents) are presented n Table 4. All the hypotheses are tested by usng generalzed lkelhood-rato (LR). The frst hypothess relates to the approprateness of the Cobb-Douglas functonal form n preference to translog model. The computed LR statstc exceeded the tabulated value of at 5% sgnfcance level. So, we rejected the null hypothess by ndcatng that the translog functonal form s a better representaton of the data.

14 86 TABLE 4. SUMMARY OF HYPOTHESES FOR PARAMETERS OF STOCHASTIC FRONTIER AND INEFFICIENCY EFFECTS MODELS Null Hypotheses L(H L(H 1 ) LR crtcal value Decson 1.Producton Functon s Reject H Cobb-Douglas ( H : ). Producton Functon s Reject H Cobb-Douglas ( H... ) : Producton functon s Quadratc H : There s no neffcency effect (H : = ) Accept H Reject H 5. The coeffcents of determnants of neffcency model equals zero H Source: Computed by the authors Reject H The second hypothess relates to the approprateness of the Cobb-Douglas n preference to the quadratc functonal form. Ths hypothess was also rejected at 5% level of sgnfcance and ndcated that quadratc functonal form s a better formulaton than the Cobb-Douglas functonal form. The thrd hypothess relates to the approprateness of the quadratc functonal form n preference to the translog functonal form. The computed LR statstc fell below the tabulated value of at 5 % sgnfcance level and we faled to reject the null hypothess ndcatng that the quadratc functonal form was the best ft for the data. Therefore, we selected ths functonal form n our analyss. The fourth hypothess stated that, s rejected at the 5% level of sgnfcance confrmng that neffcences exst and are ndeed stochastc (LR statstc 3.89>. 71). The ffth hypothess that... d d 1,.95, whch means that the techncal neffcency effects were not related to the varables specfed n the neffcency effect model, s also rejected at the 5% level of sgnfcance (LR statstc 19.1> ). Thus the 9,.95 observed neffcency among the rce farmers n Bangladesh can be attrbuted to the varables specfed n the model and the varables play a sgnfcant role n explanng the observed neffcency.

15 87 Elastctes and Returns to Scale Table 5 reports output elastcty estmates wth respect to eght producton nputs used and were evaluated at the sample means. Furthermore, the last column of the same table gves the scale elastctes for combned nputs. Scale elastcty exceeds unty thus leadng to the concluson that rce producers operate n the regon of ncreasng returns to scale. The sample mean of RTS, 1.4, ndcates that the farmers could be made scale effcent by provdng more nput to produce more output wth the excepton of captal (tractors, buldngs, machneres). TABLE 5. ELASTICITIES AND RETURN TO SCALE OF THE QUADRATIC FRONTIER PRODUCTION FUNCTION Independent Varable Mean Value Elastctes RTS Land 1.5 ha Labor 199 labor days.16 Fertlzer Taka Irrgaton Taka Seeds Taka Tractors and anmal power Taka 59.7 Captal Taka Other varable costs Taka Source: Computed by the authors. The negatve sgn of captal mples low margnal ncrements to total output f more captal s provded. One explanaton may be that farmers n Bangladesh are mostly subsstence farmers and operate very small sze of land (Table 1). Ths leads to ncreasng the opportunty cost of captal tems lke tractor and other expensve cultvatng and harvestng machneres. The elastcty of output wth respect to land s the hghest among all the nputs, whch demonstrates the mportance of scarce land n boostng rce producton n Bangladesh. It s concluded that land had the major effect on the total value of rce producton. The polcy mplcaton of ths fndng s that government could gve ncentves and encouragement to the farmers to keep ther exstng arable land and brng the remanng fallow land under cultvaton, f any. Elastcty of labor s the thrd hghest but excess use of the labor exerts negatve mpacts on output as s observed from the second order of labor. Both fertlzers and rrgaton should be utlzed effcently to ensure optmum growng condtons of land snce ther napproprate utlzaton may have far reachng mpacts through degradaton of land and ts sol qualty.

16 88 Factors Explanng Ineffcency The parameters of the explanatory varables n the neffcency model were smultaneously estmated n a sngle stage usng computer program, FRONTIER 4.1. The dependent varable of the model was neffcency and the negatve sgns mply that an ncrease n the explanatory varable would decrease the correspondng level of neffcency. Lower part of Table shows the coeffcents of explanatory varables n the neffcency model. The results show that most of the sgns related to neffcency determnants were as expected. The parameter estmates showed that factors such as age, educaton, number of plots, regon (dummy varable), access to mcrofnance (dummy varable), and off-farm ncome were postvely related wth neffcency whle extenson vsts and experence were negatvely related to neffcency. The age coeffcent s postve but nsgnfcant, whch ndcates that younger farmers are more effcent than older farmers. Ths conforms to the results obtaned by Coell and Battese (1996) and Battese and Coell (1995). A possble explanaton mght be that the adopton of new technology and manageral capablty to carry out farmng actvtes decreases wth age. However, ts ncluson n the neffcency model mproved the model s explanatory power. 6 The analyss revealed that educaton, measured n terms of years of schoolng, had a statstcally nsgnfcant effect on techncal neffcency. Ths result conforms to those obtaned by Wadud (3) and Coell and Battese (1996). It can be deduced that fve or more years of formal educaton are requred before ncreases n effcency can be observed. The off-farm ncome varable s postvely and nsgnfcantly related wth techncal neffcency. Ths ndcates that hgher off-farm ncome ncreases the techncal neffcency of rce farmers. It also mples that the more off-farm hours a producer works, the less tme s devoted to farmng, thus resultng n hgher techncal neffcency. Ths result s consstent wth Abdula and Eberln (1) and Coell et al. (). The estmated coeffcent of farmng experence had a sgnfcant negatve mpact on techncal neffcency, whch mples that rce farmers expertse asssts them n ensurng the optmal tmng and use of nputs and thereby reduces ther techncal neffcency. Another probable reason for the sgnfcant negatve contrbuton of experence on techncal neffcency could be that farmers wth more years of experence tend to gan more profcency through learnng-by-dong n uncertan producton envronment. Several other emprcal studes have also reported smlar results (Bozoglu and Chehan, 7; Huffman 1; Kalrajan and Flnn, 1983). The estmated co-effcent of the number of plots operated by the farm household s postve and sgnfcant. It mples that the more the lands are fragmented, the more the techncal neffcency ncreases. That s farmers wth less fragmented land wll operate at hgher techncal effcency levels. Ths result s also consstent wth those of Wadud (3), Wadud and Whte () and Coell and Battese (1996). Hgher techncal effcency assocated wth less fragmented land can be attrbuted to adoptng modern technologes and better farm practces such as the use of rrgaton (Wadud, 1999). Extenson vsts were negatvely related wth techncal effcency. Although not sgnfcant, however, the extenson vsts may be an mportant polcy nstrument by whch the government could rase agrcultural productvty snce the agrcultural extenson vsts enable the farmers to learn better farm management methods and more effcent uses of lmted resources. The polcy mplcaton of ths fndng s that the

17 government could support further the agrcultural extenson network n order to make the nteractons between the farm and extenson agent more partcpatve and feld orented through practcal demonstratons rather than just conveyng some recommendatons. The coeffcent of the access to mcrofnance (dummy varable) was postvely related to techncal neffcency. It ndcates that those farmers who dd not have agrcultural mcrofnance, tended to have hgher techncal neffcency levels than ther peers. It mples that access to mcrofnance reduces the techncal neffcency of the sample farms as the estmated average techncal effcency of mcrofnance borrowers was 84% and for the non-borrowers the average techncal effcency was 81%. The dfference n mean techncal effcency s also sgnfcant at 5% sgnfcance level. 7 The surveyed farms n the study areas faced acute shortage of workng captal for farmng. Average loan obtaned by the mcrofnance borrowers for agrculture was Taka whle the average demand was Taka The shortage of workng captal due to the ncreasng prce of nputs as well as the low returns to farm produce resulted n hgh level of techncal neffcency. Most rce farms faced negatve cash flow durng the plantng and growng perod due to the tme lag of purchasng the nputs and recevng the returns long after the crops are harvested. Credt thereby helps to mtgate the fnancal constrant and to reduce neffcency. Ths fndng also conforms to the results of Bnam et al. (4). Credt also helps the farmers to ncrease farm revenue whle lack of credt decreases the effcency of the farmers by lmtng ther adopton of hgh yeldng varetes and acqurng of nformaton for ncreased productvty, a vew supported by Woznak (1993). Thus, mproved access to agrcultural mcrofnance remans an mportant ssue for mprovng the rural farm producton effcency n Bangladesh. Another mplcaton of ths fndng s that farmers who are ndebted need to meet ther repayment oblgatons and ths puts more pressure on the farmers to produce more output to repay the loan by generatng more cash. For mcrofnance borrowers, the future possblty of gettng loan depends on current repayment behavour and ths mplct pressure to repay the loan acts as a catalyst to optmze the resources to produce more. The dummy varable regon s postvely related wth techncal neffcency. Thus farmers operatng n the north western regon perform less effcently compared to those of the north central regon. Ths fndng renforces the argument that regonal concentraton s a vtal polcy nstrument that should be addressed n formulatng agrcultural polcy n Bangladesh. CONCLUSION Ths study uses a quadratc stochastc fronter producton functon on survey data (9) to determne the techncal effcency and ts determnants n rce producton n northcentral and north-western regon of Bangladesh. Frst, we draw concluson on the methodology choce of producton technology. Ths was based on some selected hypotheses and we concluded that tradtonal producton functon model was not adequate for farm level analyss. Consequently, we proposed the quadratc stochastc producton functon. Second, the results revealed that mean techncal effcency of farms was.83, ndcatng that there are opportuntes to gan substantal addtonal output or decreases the nputs, gven the exstng technology and resource endowments of rce farmers n the study areas. 89

18 The emprcal results revealed that neffcency exsts n the rce producton systems and we found farmers experence and extenson vsts negatvely affected techncal neffcency whereas factors such as access to mcrofnance, regonal dummy, off-farm ncome, age, educaton, and land fragmentaton postvely affected techncal neffcency. In partcular polces leadng to grantng access to mcrofnance, rasng the educatonal level of farmers, ensurng land preservaton for agrcultural purposes, and ensurng suffcent returns to the farmers could be benefcal for reducng neffcency n rce producton n Bangladesh. The fndngs of the relatonshp between mcrofnance and techncal effcency suggest that mprovng greater access of farmers to agrcultural mcrofnance wll mprove producton effcency. Consequently, streamlnng the mcrofnance to the credt constrants farmers would be vtal factor n ncreasng farm techncal effcency and revenue. However, ths s a mult-dscplnary work that needs to be addressed out more rgorously by the government polcy makers n collaboraton wth Non Government Organzatons (NGOs) and the donor agences. To mprove farmers access to mcrofnance, at frst polces geared towards addressng the features of agrcultural mcrofnance products are vtal. These polces should nclude substantal modfcatons to conventonal operatonal methodologes of agrcultural mcrofnance and should take nto account the seasonalty of crops and farm ncomes. Such modfcatons should match the heterogeneous households demands and enable farmers to afford credt by devsng flexble repayment schedules. Second, effectve lnkages between the rural MFIs wth lqudty constrants and manstream banks wth excess lqudty may mnmze the demand-supply gap and ensure greater access to mcrofnance for those farmers that are largely excluded or untapped by the MFIs. Thrd, the establshment of poor-frendly mcrofnance banks to mprove the access of farmers to fnance wthout collateral and at reasonable cost s suggested. The delvery of such talor-made agrcultural mcrofnance that s backed up by drect support from the government through regulatory framework and nsttutonal nnovatons, would mprove farmers access to mcrofnance. Ths n turn, may lead to more effcent allocaton of resources and ncreased producton through mproved effcency. Polces leadng to the mprovement of farm educaton and land holdng wll be favorable for mprovng the techncal effcency of farmers. More nvestments n educaton n rural areas through prvate and publc partnershps, ntatng programs to encourage those at school-gong age and food for educaton programs may be harnessed as a central ngredent n the development strateges. Moreover, the farmer feld schools (FFS) program, promoted by dfferent development agences ncludng the World Bank, may be rgorously mplemented and practced. Ths would help farmers develop ther learnng by dong practces and mprove ther analytcal and decson makng sklls that contrbute to adaptng to mproved farmng technologes. Intatng well-desgned adult lteracy programs that have drect mpact on household producton could also contrbute to ensurng basc lteracy and numeracy sklls for the farmers. The land fragmentaton problems n Bangladesh should be drected through addressng the law of nhertance of parental property, developng the land market and tracng the causes of such fragmentaton. The broad polcy and legal measures that may be devsed should nclude, nter ala, revsng the laws of nhertance and land tenancy 9

19 and motvatng the small and margnal farmers to consoldate ther lands through creatng vable farms. Enlargement of farms through formng cooperatves, encouragng voluntary exchange of plots to form larger untary plots, motvatng farmers to buy and enlarge contguous plots by sellng dscrete dstant plots, passng legslaton that supports such consoldaton and formulatng natonal land use polcy wll restrct land fragmentaton and are recommended. These measures, f addressed n natonal agrcultural polcy formulaton, may drect the farmers producton fronter upward n the long run, whch may n turn, reduce techncal neffcency on the one hand and lead to food securty through ncreased producton on the other hand. ENDNOTES 1 Aus and Aman are local breed crops and typcally known as tradtonal rce crop. Boro s the rrgated rce crop and typcally known as Hgh Yeldng Varety rce. 3 USD 1= Taka approxmately; Euro 1=Taka 93.5 (as of Aprl 13, 1). 4 The lkelhood-rato test statstc, 91 -{ln[lkelhood (H )]-ln[lkelhood(h 1 )]}, has approxmately ch-square dstrbuton wth parameter equal to the number of parameters assumed to zero n the null hypothess, (H ), provded. 5 s not equal to the rato of varance of neffcency to total resdual varance. Ths s because u the varance of u ( ) s equal to [ ( ) / ] not. The relatve contrbuton of the * neffcency effect of the total varance term /[ (1 ) / )] (Coell et al., 1998). 6 Age and experence are generally nterrelated but ther mpacts on techncal neffcency are not necessarly dentcal. In ths analyss, the coeffcent of age s postve whle that of experence s negatve. Ths fndng s n lne wth Coell et al. () and Bozoglu and Chehan (7), who found experence to be a better predctor of techncal neffcency than age for farm household producton effcency. 7 The crtcal of t 358 (.5) s 1.96 and the t-test statstc s 3.56 and thereby suggestng sgnfcant dfferences n averages of techncal effcency between mcrofnance borrowers and nonborrowers of mcrofnance. 8 The results are not reported here but avalable on request from the authors.

20 9 REFERENCES Abdula, A. and Eberln, R., Techncal Effcency Durng Economc Reform n Ncaragua: Evdence From Farm Household Survey Data, Econom. Syst., 1, Vol. 5, No., pp Agner, D., Lovell, K.C.A. and Schmdt, P., Formulaton and Estmaton of Stochastc Fronter Producton Functon Models, Journal of Econometrcs, 1977,Vol.6, No.1, pp Ahmed, R., Haggblade, S. and Chowdhury, T.E., In Out of the Shadow of Famne: Evolvng Food Markets and Food Polcy n Bangladesh,, pp. 1, Johns Hopkns Unversty Press. Asadullah, M.N. and Rahman, S., Farm Productvty and Effcency n Rural Bangladesh: The Role of Educaton Revsted, Appled Economcs, 9, Vol. 41, No.1, pp Atknson, S. E. and Cornwell, C., Estmaton of Output and Input Techncal Effcency Usng a Flexble Functonal Form and Panel Data, Internatonal Economc Revew, 1994, Vol.35, No.1, pp Bangladesh Agrcultural Census, 8. Dhaka: BBS Bangladesh Bureau of Statstcs, (6), The Yearbook of Statstcs, 6. Dhaka: BBS. Bangladesh Bureau of Statstcs, (8), The Yearbook of Statstcs, 8. Dhaka: BBS. Bangladesh Economc Revew, (9), Mnstry of Fnance and Plannng, The Government of Bangladesh. Bangladesh Rce Research Insttute, (), Annual Report. Gazupur: BRRI. Battese, G.E. and Corra, G.S., Estmaton of a Producton Fronter Model: Wth Applcaton to the Pastoral Zone of eastern Australa, Australan Journal of Agrcultural Economcs, 1977, Vol.1, No.3, pp Battese, G.E. and Coell, T.J., Fronter Producton Functons, Techncal Effcency and Panel Data wth Applcaton to Rce Farmers n Inda, The Journal of Productvty Analyss, 199, Vol. 3, Nos.1-, pp Battese, G.E. and Coell T.J., A Model for Techncal Ineffcency Effects n Stochastc Fronter Producton Functon for Panel Data, Emprcal Economcs, 1995, Vol., No., pp Battese, G.E., Malk, S.J. and Gll, M.A., An Investgaton of Techncal Ineffcences of Producton of Wheat Farmers n Four Dstrcts of Pakstan, Journal of Agrcultural Economcs, 1996, Vol. 47, No.1-4, pp Bnam, J.N., Toyne, J., Wandj, N., Nyamb, G. and Akoa, M., Factors Affectng the Techncal Effcency Among Smallholder Farmers n the Slah and Burn agrcultural Zone of Cameroon, Food Polcy, 4,Vol. 9, No.5,pp Bozoglu, M. and Ceyhan, V., Measurng the Techncal Effcency and Explorng the Ineffcency Determnants of Vegetable Farms n Samsun Provnce, Turkey, Agrcultural Systems, 7, Vol.94, No.3, pp Bravo-Ureta, B.E. and Pnhero, A.E., Techncal, Economc and Allocatve Effcency n Peasant Farmng: Evdence from the Domncan republc, The Developng Economes, 1997, Vol.35, No.1, pp Chu, S.F., Agner, D.J. and Frankel, M., On the Quadratc Law of Producton, Southern Econ, 197,Vol.37, No.1, pp.3-39.

21 Coell, T.J., A gude to Fronter Verson 4.1: A computer Program for Fronter Producton Functon Estmaton, CEPA Workng Papers, 1996, No. 7/96, ISBN , Department of Econometrcs, Unversty of New England, Armdale, pp.33. Coell, T.J. and Battese, G.E., Identfcaton of Factors whch Influence the Techncal Effcency of Indan Farmers, Australan Journal of Agrcultural Economcs, 1996, Vol. 4, No., pp Coell, T. J., Prasada, R.D.S. and Battese, G.E., Introducton to Effcency and Productvty Analyss,1998, Kluver Academc Publsher. Coell, T.J., Rahman, S. and Thrtle, C., Techncal, Allocatve, Cost and Scale Effcences n Bangladesh Rce Cultvaton: A Non-parametrc Approach, Journal of Agrcultural Economcs,,Vol.53, No.3, pp De Datta, S.K., Gomez, K., Herdt, R.W. and Barker, R., A Handbook on the Methodology for an Integrated Experment on Rce Yeld Constrants, 1978, Los Ban os. Farrell, M.J., The measurement of Producton Effcency, Journal of Royal Statstcal Socety, 1957 Seres A,Vol.1, No.3, pp Greene, W., NLOGIT Verson 4., Economc Software Inc, 7. Greene, W.H., On the Estmaton of a Flexble Fronter Producton Model, Journal of Econometrcs, 198, Vol.13, No.1, pp Huang, CJ. and Lu JT., Estmaton of a Non-Neutral Stochastc Fronter Producton Functon, Journal of productvty Analyss, 1994, Vol.5, No., pp Huffman, W.E., Human Captal: Educaton and Agrculture. In Gardener, G.L., Rausser, G.C., (Eds.), Handbook of Agrcultural Economcs, 1, Vol. A., New York, NY: North Holland, pp Kalrajan, K. and Flnn, J.C., The Measurement of Farm-Specfc Techncal Effcency, Pakstan Journal of Appled Economcs, 1983, Vol., No., pp Kalrajan, K., Farm Specfc Techncal Effcences and Development Polces, Journal of Economc Studes, 1984, Vol.11, No.3, pp Khan, A., Huda, F.A. and Alam, A.., Farm Household Techncal Effcency: A Study on Rce Producers n Selected Areas of Jamalpur Dstrct n Bangladesh, European Journal of Socal Scences, 1, Vol.14 (), pp Kodde, D.A. and Palm, F.C., Wald Crtera for Jontly Testng Equalty and Inequalty Restrctons, Econometrca, 1986, Vol.54, No.5, pp Kumbhakar, S.C., The Specfcatons of Techncal and Allocatve Ineffcency n Stochastc Producton and Proft Functon, Journal of Econometrcs, 1987, Vol.34, No.3, pp Kumbhakar, S.C., Ghosh, S. and McGuckn, J.T., A Generalzed Producton Fronter Approach for Estmatng Determnants of Ineffcency n U.S Dary Farms, Journal of Busness and Economc Statstcs, 1991, Vol.9, No. 3, pp Meeusen, W. and van den Broeck, J., Effcency Estmaton from Cobb- Douglas Producton wth Composed Error, Internatonal Economc Revew, 1977, Vol.18, No., pp Nyemeck, B.J., Sylla, K., Darra, I. and Nyamb, G., Factors Affectng Techncal Effcency Among Coffee Farmers n Coˆ te d_ivore: An Evdence of Centre West Regon, Afrcan Development Revew, 3, Vol.15, No.1, pp