Access to Microfinance: Does it Matter for Profit Efficiency Among Small Scale Rice Farmers in Bangladesh?

Transcription

1 Access to Mcrofnance: Does t Matter for Proft Effcency Among Small Scale Rce Farmers n Bangladesh? John Sumelus Department of Economcs and Management, Faculty of Agrculture and Forestry P.O. Box 7, FIN-14 Unversty of Helsnk, Fnland. E-mal: john.sumelus@helsnk.f K. M. Zahdul Islam Department of Economcs and Management, Faculty of Agrculture and Forestry P.O. Box 7, FIN-14 Unversty of Helsnk, Fnland. E-mal: zahdul.slam@helsnk.f Tmo Splänen Department of Economcs and Management, Faculty of Agrculture and Forestry P.O. Box 7, FIN-14 Unversty of Helsnk, Fnland. E-mal: splan@mapp.helsnk.f Paper prepared for presentaton at the EAAE 11 Congress Change and Uncertanty Challenges for Agrculture, Food and Natural Resources August 3 to September, 11 ETH Zurch, Zurch, Swtzerland Copyrght 11 by [authors]. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 Access to Mcrofnance: Does t Matter for Proft Effcency Among Small Scale Rce Farmers n Bangladesh? Abstract: Ths paper measures proft effcency and examnes the effect of access to mcrofnance on the performance of rce frms n Bangladesh. An extended Cobb-Douglas stochastc fronter proft functon was used to assess proft effcency and proft loss of rce farmers n Bangladesh n a survey data of 36 farms throughout the 8-9 growng seasons. Model dagnostcs reveal that serous selecton bas exsts that justfes the uses of sample selecton model n stochastc fronter models. After effectvely correctng for selectvty bas, the mean proft effcency of the mcrofnance borrowers and non-borrowers were estmated at 68% and 5% respectvely, thereby suggestng that a consderable share of profts were lost due to proft neffcences n rce producton. The results from the neffcency effect model show that households age, extenson vsts, off-farm ncome, regon and the farm sze are the sgnfcant determnants of neffcency. Some ndcatve polcy recommendatons based on these fndngs have been suggested. Key Words: Stochastc fronter functon Proft effcency Selecton bas Bangladesh Mcrofnance INTRODUCTION Bangladesh s predomnately an agraran country wth over 53% of ts 14.6 mllon populaton engaged n agrculture [1]. Agrculture accounts for 1% of the gross domestc product (GDP) and 5% of overall employment []. The economc prosperty of the country also depends upon sustaned growth n agrcultural producton and productvty. Agrcultural productvty s lnked to farm proftablty. Improved productvty may provde ncreasng revenues and lower unt costs also for resource lmted rce farmers n Bangladesh [3]. Productvty and ncome mprovements are, however, dependent upon access to suffcent fnancal captal. Impovershed farmers n Bangladesh generally have to rase loans from money lenders who charge exorbtant nterest rates. There s a huge gap between the need for credt and the avalablty of affordable credt sources. Consequently, poor smallholders may be perpetually trapped n poverty due to the lack of funds needed for undertakng the purchases of varable nputs and productve nvestments n farmng [4]. For these fnancally constraned but economcally actve poor people, mcrofnance has emerged as a substtute for nformal credt [5]. Despte the fact that proftablty of agrculture s generally low and nterest rates from nformal credt sources are hgh, t s possble for mcrofnance provders to operate on a cost coverng bass and to offer fnancal servces to farmers at the reasonable nterest rates that contrbute to mprovng productvty and proft effcency. The success of mcrocredt has been well reported n several studes n broad areas such as poverty allevaton [6], group-based lendng ([7], [8]), women s empowerment ([9], [1]) and sustanablty and outreach ([11], [1]). Nevertheless, provders of mcro credts have not generally addressed the credt needs of small and margnal farmers 1 for certan perceved problems, whch nclude nter ala the rsk of nvestment n agrculture, seasonalty of agrcultural producton, poor loan repayment performance, and the techncal nature of agrcultural producton [13]. Thus, the analyss of effects of mcrofnance on effcency and productvty of agrculture s largely mssng. We expect that nadequate fundng of poor farmers has a negatve mpact on agrcultural productvty and effcency of small farms. 1 Accordng to loan provdng nsttutons n Bangladesh, margnal and small farmers operate land areas between. to1 hectares. (PKSK and IFAD, 4). 1

3 The man objectves of ths study are to estmate the contrbuton of mcrofnance on proft effcency and to measure the absolute proft-loss ncurred by rce farmers n Bangladesh. In the analyss, we apply a recently developed approach by Greene [14], whch provdes a general framework for testng and takng nto account the sample selecton n the stochastc (proft) fronter functon analyss. In addton, we dentfy the determnants of proft neffcency and estmated proft loss at the farm level separately for mcrofnance borrowers and non-borrowers. Ths paper contrbutes to the growng lterature on the mpact of mcrofnance by estmatng ts effect on proft effcency of rce growng farms n Bangladesh. We also suggest that agrcultural development polcy can strengthen the lnks between fnancal development, agrcultural productvty and proft effcency by focusng on agrcultural mcrofnance. The paper s organzed as follows: Secton presents the concepts of proft effcency and gves the theoretcal background of sample selecton. Secton 3 deals wth the data and the study areas. Secton 4 deals wth the emprcal model. Secton 5 dscusses the results of the study. Secton 6 concludes and provdes some recommendatons. ANALYTICAL FRAMEWORK The analyss of the effect of a specfc treatment lke the partcpaton to mcrofnance cannot be estmated drectly by comparng partcpatng and non-partcpatng groups f there s sample selectvty. The typcal approach to control and test for selecton bas s to ft the probt model for the sample selecton equaton and then usng the selected sample, ft the second step model (Ordnary least squares or Weghted Least Squares) by appendng the nverse mlls rato ( ˆ ) from the frst step as an ndependent varable to correct for selectvty bas n the second step and to test ts sgnfcance. Greene [14], however, clams that such a specfcaton s not approprate n non-lnear models. The reasons are: (a) the mpact on the condtonal mean of the model under consderaton wll not necessarly take the form of nverse mlls rato (IMR) snce ˆ arses as E[ d 1] n lnear models only, (b) the bvarate normalty assumpton needed to justfy the ncluson of IMR does not even appear n the orgnal model, and (c) the dstrbuton of the observed dependable varable condtoned on the selecton wll not be what t was wthout the selecton. Thus, one cannot just add the IMR. Greene [6], proposed the followng nternally consstent method of ncorporatng the sample selecton nto a stochastc fronter model: Sample selecton: Stochastc fronter: d y * z w, d 1, d *, w ~ N [,1] (1) x v u () (y, x) s observed only when d =1 Error structure: v ~ N [, ] u v u, u U, where U ~ N [,1 ] vv, where V ~ N [,1 ]. ( w, v ) ~bvarate normal wth [(,1),(1,, )] The Greene s model assumes that the unobserved characterstcs n the selecton model correlate wth the nose term n the stochastc fronter functon. In Equaton (1) v * d s a probt selecton equaton and y the stochastc fronter functon, specfed only for the selected group. z-varables represent characterstcs that determne the partcpaton n the selected group, w beng the error term,

4 x s a matrx of explanatory varables of the stochastc fronter functon, v s the two sded random error (statstcal dsturbance term), ndependent of the u, that permts for random varatons n output due to factors such as weather, omtted explanatory varables, measurement errors n y and other exogenous shocks, u s the one sded non-negatve error term (e.g., farm specfc proft neffcency). The estmator of the above equatons s documented n earler studes (see [14], [15]). In addton to the selecton equaton, we have to specfy the stochastc proft fronter functon. Al and Flnn [17] have stated that when farmers face dfferent prces and have dfferent factor endowments (n the short term analyss), t may not be approprate to use a producton functon to measure effcency. Ths has led to the applcaton of stochastc fronter proft functons n the estmaton of farm specfc effcency ([16], [17], [18]). The standard aganst whch the performance of a farm can be measured, s ts potental to maxmze proft,.e., to operate at a pont of the fronter where the margnal product of each nput equals prce rato of nput and output. It s gven by: py wx * * * (3) The proft effcency approach takes nto account the effect of techncal, allocatve and scale neffcences n the proft relatonshp and also any devatons from the optmal producton that would lead to lower profts for the enterprse [19]. Proft effcency s defned as the capablty to acheve optmal performance wth respect to profts for gven sets of prces and technologes (the level of fxed factors of the farm). In contrast, proft neffcency s defned as the loss of proft due to not operatng at the optmum level [17]. When some of the nputs are fxed and the producer adjusts the varable nputs and outputs to maxmze the varable proft, t s possble to defne a varable proft functon (varable proft = the total return - the varable costs) as: v ( P, Z ) P Y( X, G ) W X (4) Where Y () s the producton functon, P and W are the output prce and the varable nput prces and G s the vector of fxed nputs. Normalzaton of the varable proft and prces by one prce (n ths case output) mposes lnear homogenety of the proft functon wth respect to output and nput prces. It follows that the normalzed proft functon whch s well-behaved can be wrtten as: v (, ) (, ) PG PY X G WX WX ' Y( X, G) Y( X, G) P X (5) P P P W P, s the normalzed prce of nput X P To our knowledge, the Greene s model of Equaton s only defned for the Agner-Lovell- Schmdt [] model. Thus, the model can be used for assessng possble selecton bas, but t does not nclude the determnants of neffcency. Therefore, we compare the results of ths stochastc fronter model (jontly estmated wth probt selecton equaton) to that of stochastc proft fronter wth neffcency effects model of Battese and Coell [1]. The comparson takes place n the specfed group, not smultaneously n the whole sample. In the equaton, the stochastc varable proft fronter wth neffcency s defned as: v / P ln f( P, G ) exp( ) (6) j k The normalzed restrcted proft functon s non-ncreasng nput prces (w) and non-decreasng n G, convex and twce contnuously dfferentable n G. 3

5 where, /P s the normalzed varable proft of the th farm, computed as the gross revenue mnus the varable cost, dvded by farm specfc output prce P ; P j s the prce of j th varable nput on th farm dvded by the output prce; G k s the level of k th fxed factor for the th farm and s the random error term. The error term,,s assumed to be decomposable [17] for fronter proft functon, as presented n Equaton. In the neffcency effect model of Battese and Coell [1], t s assumed that farm-specfc determnants affect the mean of effcency, the varances beng homogenous (n the group of d = 1 (or d = ). The u s are assumed to be ndependently dstrbuted as truncatons at zero of the normal dstrbuton wth a mean M and varance u, where M d are the varables d d d representng soco-economc characterstcs of th farm to explan neffcency and, d are unknown parameters to be estmated. The proft effcency of th farm n ths context s gven by: D PE exp u exp d M d, (7) d 1 The farm-specfc proft neffcency (PI) ndex can be obtaned by the followng equaton PI 1 exp[ u ]) (8) ( Proft-loss (PL) s defned as the amount of proft that has been lost due to the neffcency gven the farm specfc prces and fxed factor endowments. Maxmum proft per hectare s calculated by dvdng the actual proft per hectare of ndvdual farms by ther respectve correspondng effcency scores. Proft-loss s calculated by multplyng maxmum proft per hectare by (1-PE ), where PE s the proft effcency score of the th farm. Sample and Data DATA AND THE STUDY AREAS Prmary data were collected through an ntensve farm-survey of rce producers from a total of 1 vllages of the north-west and north-central regons n Bangladesh between June-August 9. These regons were selected on the bass of ther hgh level of poverty and good agrcultural potental as well as the presence of IFAD funded agrcultural mcrofnance program. Data were collected from the farmers that produced Boro, Aman, and Aus rce crops. A multstage proportonal random samplng technque was used to obtan the study data from a total of 36 farm households. Half of the households were members of the mcrofnance program and the other half was non-members. In calculatng proft effcency, however, we consdered 35 sample farms that obtaned postve varable proft. The negatve values of proft cannot be transformed to logarthmc values. Varable Constructon Output was defned as the market value of the aggregated rce producton. Rce output prces were gathered from ndvdual farms. All rce (Boro, Aman, Aus) crops produced on the sample farms were aggregated nto one output value, whch was expressed n Taka 3. Land (Z L ) represented the total amount of land (own-cultvated land, sharecroppng land, and rented/leased land) used for rce producton and t was measured n hectares. Labor ncluded both famly (mputed for hred labor) and hred labor for pre and post plantng operatons and harvestng excludng threshng operatons. The prce (P W ) of labor was measured as the wage of hred labor per-day. Fertlzers ncluded all fertlzers used and were measured n klograms and the prce (P F ) of fertlzer was the weghted average of all 3 USD 1= Taka 69. 4, Euro 1=Taka (as of November 1, 1) 4

6 fertlzers purchased (n Taka/kg). Seeds ncluded all seeds used and the prce represented the average prce (P S ) of seed (Taka/kg) used for rce cultvaton. Irrgaton covered the total area of rrgated land under rce. The prce of rrgaton (P I ) ncluded the cost of rrgaton per hectare of land. Captal was not ncluded n the proft functon snce the captal lacked any sgnfcance when t was ncluded n the models. Probably ths s because of dffcultes n the relable measurement of captal and the fact that on many farms captal nput was very low. EMPIRICAL MODELS The stochastc proft functon model wth correcton for sample selecton s estmated. Therefore, the decson to partcpate n the mcrofnance program has been modeled. The decson of the th farm to partcpate n mcrofnance program s a functon of farmers soco-economc characterstcs as well as some crtera set by the MFIs to select borrowers. The decson of the th farm to partcpate n the mcrofnance program s descrbed by an unobservable selecton crteron functon, H *. The model s specfed as follows: H Z w, (9) H 1 ff H Z w, H, otherwse Where, Z s a vector of varables explanng the partcpaton n the mcrofnance program, s a vector of parameters to be estmated, w s the error term dstrbuted as N (,1). However, we do not observe the selecton crteron functon, but a dummy varable, H, s observed. The dummy varable, H, takes a value of 1 f the farm partcpates n the mcrofnance program and otherwse. As a second step, the fronter proft functon for mcrofnance partcpants s estmated. The functonal form of the stochastc fronter proft functon was determned by testng the adequacy of the more restrctve functonal forms aganst the full transcendental logarthmc (translog) functon. The model was chosen based on the lkelhood rato 4 (LR) test. The full translog proft fronter functon s defned as: j lnpj 1/ jklnpj lnpk jllnpj lng llng ll(lng l) vu, l j 1 j1k1 j1l1 ln ff H=1 (1) Where s the restrcted normalzed proft (total revenue mnus total cost of varable nputs) of the th farm normalzed by the rce output prce (P y ); ln s the natural logarthm, P s the prce of the j th nput (P) normalzed by the rce output prce (P y ); j 1 s the labor wage (total expendture of hred labor dvded by the output prce); j s the seed prce; j 3 s the fertlzer prce; j 4 s the rrgaton prce; G l s the quantty of fxed nputs; l 1 s the areas under rce producton; v s the two sded random error and u s the one sded non-negatve error term. In the stochastc fronter model, takng the sample selecton nto account, t s assumed that the error term (w) of equaton (9) s correlated wth the error term (v) of equaton (1), and therefore, ( v, w) ~bvarate normal wth [(,1),(1, v, v ]. In the stochastc fronter wth neffcency effect model (equaton 7), M d ncludes the varables representng soco-economc characterstcs of the farm to explan neffcency: d 1 s the age of the head of the farm household; d s the educaton of the farm household head; d 3 s the famly sze; d 4 s the off-farm ncome share out of total farm ncome; d 5 s the extenson vsts (no. of contacts); d 6 s the regon (dummy varable to account for the varatons at nter-regonal level wth ' j 4 The lkelhood-rato test statstc, LR = -{ln[lkelhood (H )-ln[lkelhood(h 1 )]}, has approxmately v dstrbuton wth v = number of parameters assumed to be zero n the respectve null hypotheses, (H ). To conduct tests nvolvng parameter, the crtcal value of the s taken from Kodde and Palm [] 5

7 respect to physcal and envronmental factors on proft effcency. The value s 1 f the farmer was located n the north-central regon and otherwse); d 7 s the farm sze; d 8 s the dstance of home to market and s the two sded random error term. RESULTS AND DISCUSSION Table 1 presents the summary statstcs (mean and standard devaton) of the output and nputs used n the analyss and other, relevant to the neffcency effect model, varables. Household characterstcs are broken down by the mcrofnance borrowng status. The fgures show that, there are no sgnfcant dfferences between the two groups n terms of output and the level of nput use. Non-borrowers have larger land holdngs (1.35 hectares) than the borrowers (1.11 hectares). However, among the socoeconomc and nsttutonal factors, sgnfcant dfferences exst between the two groups. Table 1: Summary statstcs of the varables used n the analyss Mcrofnance borrowers (N= 176) Non-borrowers of mcrofnance (N= 174) Varables Mean SD Mean SD t-rato Output (kg) Proft (taka) Rce prce (taka/kg) Land cultvated (ha) Labor wage (Taka/day) Seed prce (taka/kg) Fertlzer prce (Taka/kg) Irrgaton prce (Taka/ha) Farm-specfc varables Age (years) ** Famly Sze (no.) Educaton (Years) Extenson (%) *** Off-farm ncome share (%) Experence (years) ** Note: SD, standard devaton The results of the probt selecton equaton are presented n Table. Frst, to model the selecton equaton and to obtan the results wth whch the outcome equaton (equaton 1) was compared, bnary probt regresson was used wth partcpaton n mcrofnance program as the dependent varable and fve ndependent varables entered nto to the regresson equaton. The ch-squared test statstc n the probt selecton equaton s statstcally sgnfcant at 1% level that confrms the jont sgnfcance of the relatonshp between the explanatory varables and partcpatng n mcrofnance program. Seventy-two percent of the observatons were predcted accurately. 6

8 Table : Parameter estmates of probt selecton equaton: probablty of beng n the mcrofnance program 1 (Partcpaton n mcrofnance program) Varables Coeffcent t-rato Constant *** Age of head of household (years) *** Farm sze * Wealth (Taka) ** Household savngs (Taka) *** Dstance to credt faclty (km.) *** Model dagnostcs Log lkelhood McFadden R-squared.86 Ch-squared Degrees of freedom 5 Accuracy of predcton (%) Number of total observatons 35 *** Sgnfcant at 1% level (P<.1); * * Sgnfcant at 5% level (P<.5); * Sgnfcant at 1% level (P<.1). The functon 1 s an ndcator functon equal to one f the condton s true and zero otherwse. Table 3 presents the results of the stochastc proft functon corrected for selecton bas (columns 3 and 4). The coeffcent of the selectvty varable ( ), s sgnfcantly dfferent from zero at the 1% rsk level, whch confrms that a serous selecton bas exts. Ths fndng justfes the use of sampleselecton framework. In other words, ths result ndcates that the estmaton of proft fronter usng only the mcrofnance partcpants provdes based estmates of productvty, whch wll then be passed on to the based proft effcency scores afterward. w, v Table 3: MLE of stochastc proft fronter model for mcrofnance partcpants Stochastc proft fronter model (jontly estmated wth probt selecton equaton) Conventonal stochastc proft fronter wth neffcency effects model Varables Parameters Coeffcent t-rato Parameters Coeffcents t-rato Constant *** *** ln P W w w ln P S S S ln P F F F *** ln P I *** I I *** 1/ (ln P W ln P W ).1.39 WW WW / (ln P S ln P S ).4.6 SS SS / (ln P F ln P F ) FF FF *** / ln( P lnp ) I I II II 7

9 LnG L 1/(ln Lln L) Varance parameters u *** L *** L G G ** LL LL.58.7 ** - - v *** - - Selectvty bas *** - - ) ( w, v /( ) - - u /( u v ) *** u u v Log lkelhood Ineffcency Functon Constant Age ** Educaton Famly sze Off-farm ncome *** Extenson vsts *** Regon *** Farm sze *** Dstance to market Number of selected observatons Notes: Fgures n parentheses are asymptotc t-ratos. W, labor; F, fertlzer; S, seed; I, rrgaton, L, Land. *, **, *** Sgnfcant at 1% (P<.1), 5 % (P<.5), and 1% (P<.1) level. Thus, the confdence n the estmates s mproved wth the sample selecton model. For comparson purposes the same table presents the stochastc fronter proft functon wth neffcency effects (columns 6 and 7), whch s estmated drectly n a sngle stage wth the computer program FRONTIER 4.1 by Coell [3]. Nlogt 4. [4], s appled n the estmaton of the selectvty bas correcton models. For statstcal justfcaton we used a set of hypotheses for the model selecton, neffcency specfcaton and neffcency effects on the bass of Battese-Coell model [1]. All the hypotheses were tested usng the LR test statstcs. The null hypothess that extended Cobb-Douglas (.e., ncludng only addtonal quadratc terms) proft functon s an adequate representaton of rce producton was rejected at the 5% rsk level (LR statstc 18.8> 1,. 95 = 18.31). However, the more detaled analyss showed that n the case of full translog almost all the coeffcents became nsgnfcant. In addton, more complcated functonal forms were to some extent unstable n the estmatons. Therefore, we preferred extended Cobb-Douglas n our more detaled analyss. The null hypotheses that proft neffcency does not exst as well as well as neffcency effects are not present were also rejected at 5% rsk levels. The dstrbuton of proft effcency estmates of the mcrofnance partcpants and non partcpants corrected for selectvty bas as well as for the conventonal stochastc proft fronter wth neffcency effects, are presented n Table 4. The results ndcate that mcrofnance partcpants, 8

10 under the selectvty model, had sgnfcantly hgher proft effcency compared to ther nonpartcpatng counterparts and access to mcrofnance had a sgnfcant mpact on the proft effcences of these farms. Table 4: Frequency dstrbuton of farm-specfc proft effcency estmates Mcrofnance Partcpants Non-partcpants Proft Effcency estmates Stochastc proft fronter (Corrected for Stochastc proft fronter wth neffcency effects Stochastc proft fronter (Corrected for selectvty bas) Stochastc proft fronter wth neffcency effects (conventonal model) selectvty bas) (conventonal model) Mean Std dev Mnmum Maxmum Mean dfference t-rato for mean *** -6.4 *** dfference ( selectvty model vs. conventonal model) t-rato for mean 9.5 *** dfference between the two groups (bas corrected) Number of observatons *** Sgnfcant at 1% rsk level (P<.1) The smallholder farmers n both groups exhbted a wde range of proft neffcency rangng from 7% to 76% n the sample of mcrofnance partcpants whle for the non-partcpants the neffcency ranged from 11% to 94%. The mean proft effcency of mcrofnance partcpants, corrected for selectvty bas, s estmated to be 68%, whle for the non-partcpants the bas corrected mean proft effcency score s 5%. The results show that mcrofnance partcpants exhbted 16% hgher proft effcency compared to ther non-partcpant counterparts. The sgnfcant t-statstc on the rho coeffcent (Table 3) also ndcated that after controllng for all other observed characterstcs, the farmers who chose to partcpate n mcrofnance program had hgher proft effcency than ndvduals wth smlar characterstcs drawn randomly from the populaton. The dstrbuton of the 9

11 loss n proft s shown n Table 5. The estmaton of proft-loss per hectare, gven the technology, prces and fxed factor endowments revealed that mcrofnance partcpants ncurred sgnfcantly less proft-loss per hectare and operate at sgnfcantly hgher level of proft effcency. However, there s no sgnfcant dfference n terms of earnng actual proft per hectare between the two groups. Table 5: Frequency dstrbuton of proft loss among mcrofnance partcpants and non partcpants n the selectvty model Range of proft loss (Taka/ha) Mcrofnance partcpants (N=176) Average Proft Number actual effcency of farms proft per score ha Non-partcpants of mcrofnance (N=174) Average actual Proft Number of proft per ha effcency farms score -1, , , , , , , , , Mean Std dev Mnmum Maxmum t-rato for mean dfference (Actual proft per hectare) t-rato for mean dfference (Average proftloss per hectare) *** The results suggest that clear opportuntes exst to ncrease the proft effcency of rce farms for both groups by elmnatng ther techncal and allocatve neffcences. The mprovement potental wth respect to the proft-loss was even greater for the non-partcpants than for the mcrofnance partcpants. Determnants of Proft Ineffcency: The results ndcated that farmers n both groups exhbted a wde range of proft neffcency. It s, therefore, mportant to examne more n detal whether farm specfc soco-economc factors nfluence proft neffcency n rce farmng. The lower part of Table presents the determnants of neffcency 5 for the group of mcrofnance partcpants only based on the conventonal stochastc proft fronter. The results show that age, off-farm ncome, extenson 5 Only the factors explanng effcency s shown here for the mcrofnance borrowers. The counterpart s the non-borrowers of mcrofnance. The results of the non-borrowers are not presented here to save some space but are avalable on request from the authors. 1

12 vsts, regon and farms sze are the sgnfcant determnants explanng effcency dfferental among the farms. The coeffcents on the famly sze and dstance of home to market, however, are not sgnfcantly dfferent from zero. CONCLUSION Ths study appled a sample selecton framework n stochastc proft fronter models to analyze the contrbuton of mcrofnance on proft effcency and proft-loss of rce farms n north-central and north-western regons of Bangladesh usng survey data obtaned over 8-9 growng seasons. The model dagnostc ndcated that serous selecton bas exsts that justfes the use of sample selecton framework. Results of the proft effcency ndcated that, after correctng for selectvty bas, mcrofnance partcpants exhbted sgnfcantly hgher proft effcency and ncurred sgnfcantly less proft-loss per hectare than the non-partcpants. The mean levels of proft effcency of mcrofnance partcpants and non-partcpants are estmated at 68% and 5% respectvely and thereby suggestng that substantal amounts of the potental profts are lost due to techncal, allocatve and scale neffcency. Thus, ths purely observatonal study has documented a postve relatonshp between access to mcrofnance and farms proft effcency. The results of neffcency analyss suggested that farmers wth more experence n farmng, located n north-central regon, and havng more nteractons wth extenson agents tended to be more proft effcent. On the other hand, ncreasng off-farm ncome share and farm sze tended to lower proft effcency. Gven the varaton n actual proft, proft effcency and proft-loss, there appears to be substantal potental for both groups to mprove proft effcency and to mnmze proft-losses wth greater scope especally for the non-borrowers. For polcy mplcatons, greater government support to strengthenng the extenson servces as well as more focused concentraton on reducng the shortfalls of north-western regon are recommended as prorty objectves to ncrease proft effcency and to reduce proft-loss. The fndngs of the relatonshp between mcrofnance and proft effcency suggest that gettng more access to agrcultural mcrofnance for farmers wll mprove producton, proft effcency and reduce the proft-losses. Consequently, streamlnng the mcrofnance to small scale farmers by all ters of the government would be a vtal factor to ncreasng farm performance. However, ths requres a mult-dscplnary approach that needs to be addressed more rgorously by the government agrcultural polcy makers n collaboraton wth NGOs and the donor agences. REFERENCES 1. BBS, 8. Statstcal Yearbook of Bangladesh, 8. Bangladesh Bureau of Statstcs, Dhaka.. Bangladesh Agrcultural Census, 8. BBS, Dhaka. 3. Kebede, E. and J.B. Gan, The Economc Potental of Vegetable Producton for Lmted Resource Farmers n South Central Alabama. Journal of Agrbusness, 17 (1):

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