An Economic Analysis of Ugandan Agricultural Constraints

Transcription

1 An Economc Analyss of Ugandan Agrcultural Constrants by Adam. M. Komarek and Fredoun Ahmad-Esfahan Agrcultural and Resource Economcs The Unversty of Sydney, NSW, 2006 Contrbuted paper presented to the 50 th Annual Conference of the Australan Agrcultural and Resource Economcs Socety Manly Pacfc, Sydney 8-10 th February

2 An Economc Analyss of Ugandan Agrcultural Constrants Adam M. Komarek and Fredoun Z. Ahmad-Esfahan Agrcultural and Resource Economcs Unversty of Sydney, NSW, 2006 Abstract Transacton costs and poor asset endowments appear to be major mpedments to small scale agrcultural development n Uganda. Reasons for the lack of commercalsaton of agrculture, and the barrers to ncreasng the value of banana crop sales and banana producton are the focus of ths paper. Usng agrcultural household economcs theory, an emprcal analyss based on the Heckman model s undertaken. Data collected through a prmary farm survey n three dfferent regons of Uganda form the crux of the analyss. Prelmnary fndngs ndcate that transacton costs and producton constrants are hnderng development, thereby hamperng poverty allevaton efforts. Keywords; Agrcultural development, Household economcs, Ugandan survey, Market access 2

3 1. Introducton The majorty of the world s poor are engaged n small scale agrculture (Ells 1993), wth Uganda beng no excepton. Development of the small scale agrcultural sector has the greatest potental to make deep nroads nto poverty rates. In recent years ncreasng attenton has been focused on the mpact of mperfect markets on agrcultural lvelhoods, n part, stemmng from the exstence of transacton costs. Transacton costs are defned the full cost of carryng out exchange (Coase 1960). They nclude marketng costs and the non- prce costs of exchange. Uganda s a tropcal landlocked country n East Afrca, t s well endowed n natural resources relatve to other sub-saharan Afrca countres. Ugandan socal ndcators reveal ts underdevelopment on a global scale. Agrculture accounts for 85 per cent of export earnngs and over 80 per cent of natonal employment (Government of Uganda 2000). The sector s contrbuton to GDP has declned from over 50 per cent n the 1980s to 36 per cent n 2002 (World Bank 2004). In Uganda, the three most common causes of poverty are poor health, lmted access to land and a lack of market access (MFPED 2002). The man causes of poverty outlned by NAADS (2000) are poor access to agrcultural markets due to nadequate nfrastructure, the generally low level of educaton whch mpars rapd technologcal change, and the prevalent small sze of land holdngs whch tself s aggravated by nadequate access to extenson servces, fnance and tools. As ponted out by Dennger and Okd (2001), three stylsed facts characterse rural areas n Uganda. Frst, nformatonal mperfectons gve rse to hgh levels of credt ratonng. Second, transacton costs drve a wedge between buyng and sellng prces for dfferent commodtes, generatng a wde margn wthn whch t s economcally ratonal for producers to reman self-suffcent. Thrd, households' endowments of human and physcal captal are mportant not only from an effcency pont of vew, but also for ther ablty to access markets. Restrcted access to markets means a lack of ncome to purchase producton nputs, consumer goods, and asset accumulaton. Statstcs from UBOS (2002) show ths lack of market orentaton, wth over 75 percent of households sell less than 25 percent of ther output. Rural poverty, whlst on the 3

4 declne, stll accounts for 40 percent of the populaton (UBOS 2003). Low nput agrculture s common n Uganda. Statstcal nformaton s not readly avalable at the dstrct level, so a study on specfc areas could shed lght on these ssues Gven the mportance of agrculture to natonal development, the Ugandan government has ncluded agrculture as a major pllar n the quest for poverty eradcaton. The Plan for Modernsaton of Agrculture s a holstc framework developed to help eradcate poverty, and consttutes part of the Ugandan Governments broader Poverty Eradcaton Plan. The am of the plan s eradcatng poverty by transformng subsstence agrculture to commercal agrculture (MFPED 2000). An economc analyss of how transacton costs, producton shfters and household characterstcs are nfluencng Ugandan farm households has the potental to provde an enhanced understandng nto the complex web of agrcultural development. It appears that market access and producton constrants are causng great varatons n ncome. For polcy analyss, t s of nterest to focus attenton on strateges that promote the commercalsaton of agrculture, as well as focusng on ncreasng the value of crop sales. To date, studes n Uganda have not fully addressed how transacton costs, namely dfferent marketng strateges, are mpactng on the value of crop sales. Uganda makes an excellent case study, as t has enjoyed stable governance and steady economc development progress n the past 20 years (World Bank 2004). Polcy advce for Uganda may be benefcal, as t could be mplemented easly, relatve to less stable countres n sub-saharan Afrca. Econometrc modellng based on survey results s a most sutable approach to polcy analyss. Polces can be devsed based on what varables have the most explanatory power, n explanng varaton n the value of banana sales. Improvng the value of sales wll help reduce poverty, as extra ncome can be used to purchase goods and servces. The purpose of ths paper s to provde a framework for analysng households facng transacton costs, to examne a farm household survey conducted n Uganda, overvew the data collected, and report on some prelmnary results. The prelmnary results are amed at provdng some nsght nto farm household marketng and producton. After a bref lterature revew, the paper provdes an overvew of agrcultural household behavour, and the emprcal model that s used to estmate the mpacts of transacton 4

5 costs on the value of banana sales per acre. Next, the household data set s examned, ncludng survey desgn and some descrptve statstcs. Fnally, prelmnary results are presented followed by concludng comments. 2. Lterature revew Several authors have shown that hgh transacton costs reduce output market access and sgnfcantly lower sellng prces (Goetz 1992; Jayne 1994; Omamo 1998; Key et al. 2000; Heltberg and Tarp 2002). These analyses generally have reled upon household economcs and econometrc technques to show transacton costs produce a range of market partcpaton regmes, or a buyer, and have a sgnfcant mpact on volumes and value of sales. The econometrc technques are all based on household economcs usng ether a probt model, Heckman model or ordnary least squares. Takng a dfferent approach Omamo (1998) bult a lnear program that ncorporated transacton costs and a number of central features of Afrcan farmng. Results showed that under transacton costs specalsaton mght not be vable. Makhura (2001) used a probt model to determne what nfluences the probablty of sellng and then ordnary least squares to determne what factors nfluence the value of sales. Followng Key et al. (2000), Heltberg and Tarp (2002) estmated agrcultural supply response n Mozambque. Marketng behavour was analysed n two steps, usng the Heckman procedure. Reduced form equatons were estmated for market partcpaton and the value of quanttes sold. Ths method allowed dstngushng between factors that determne whether or not to sell any output to the market, and the factors that nfluence the value of sales. Vaks et al. (2002) moved beyond measurable transacton costs such as transport costs and looked at barganng and nformaton. It was found that access to prce nformaton was equvalent to a 23 percent reducton n transportaton costs. In Uganda, the semnal studes on agrcultural development have been related to natural resource management and the usage of physcal and human captal (Pender et al. 2004a; Nkonya et al. 2005). Usng the Ugandan Natonal Household Survey from 1999/2000, Pender et al. (2004a) lnk rural poverty and agrculture, especally natural resource degradaton. The value of crop producton, crop choce, labour usage, land management 5

6 and household ncome was estmated. Land management decsons, measured by the amount of non labour nputs appled per hectare, were modelled usng the Heckman sample selecton model. For each nput, the probablty of usage and amount used was estmated. It was found that access to roads, markets and nfrastructure had mxed mpacts on non-labour nput usage. Access to nformaton and servces had an mpact, but was statstcally nsgnfcant. The mpact of access to sealed roads was estmated by Pender et al. (2004b), and was surprsngly small and actually reduced the value of crop producton. Ths fndng was surprsng, as n hgh access areas t would be expected that more pershable crops are grown. The decson to sell coffee at the farm gate was analysed by Fafchamps and Vargas Hll (2005), through usng a Probt model. A non-lnear relatonshp exsted between wealth and market sales, smaller and larger coffee farmers sold at the farm gate, whle medum szed growers traveled to the market. The nteracton between wealth and quantty sold revealed that wealther farmers are less lkely to sell at the market. Perhaps the shadow value of ther tme s hgher. The welfare effects of exogenous changes n transactons costs dffer fundamentally between households already partcpatng n markets and those that are not (Renkow and Hallstrom 2000). For the former, the benefts of dmnshed transactons costs are contnuous, for the latter, there wll be no welfare gans untl prce bands reach a threshold level at whch exchange begns to take place. Ths threshold level was estmated by (Key et al. 2000). Prevous studes have not analysed crop market access n Uganda at the household level. An approach that takes nto consderaton transacton costs and producton factors could provde evdence as to what s more sgnfcant n explanng banana sales. 3. Analytcal framework As proposed by Goetz (1992) and Sadoulet and de Janvry (1995), the effect of transacton costs s to drve a wedge between sellng and buyng prces. Ths prce dfference nfluences both quantty traded, and the decson of whether to partcpate n the market. 6

7 A graphcal analyss on how transacton costs nfluence three dfferent households s provded for a food crop n Fgure 1. Prce demand Net purchasng (margnal cost) Self suffcent (margnal cost) P b =P m +t b p P* P s =P m -t s p Purchase prce Shadow prce Net sellng (margnal cost) Sale prce Quantty Fgure 1 Prce bands and market partcpaton Source: de Janvry and Sadoulet (2004) The effectve sales prce for the food crop s P s (the market prce P m net of transacton costs assocated wth sellng, t s p), and the effectve purchase prce s P b (the market prce P m plus any transacton costs assocated wth buyng the crop, t s p). If the households supply curve ntersects the demand curve (for smplcty, the same across each household) below the sale prce the household wll be a net seller, at ths ntersecton s the households shadow prce of the crop. Transacton costs are creatng a regon of self suffcent households where: s * b P P P (1) A reducton n transacton costs would have a two-fold consequence. Frstly, the number of market partcpants would ncrease, thus re-orentatng producers for the market. Ths would occur when equaton (1) does not hold. The second mpact s the potental to ncrease the value of crop sales through ncreasng the effectve prce receved. 7

8 Based on ths graphcal analyss, reduced form producton and consumpton equatons can be estmated. Ths theoretcal approach has been reled upon by Goetz (1992) and Heltberg and Tarp (2002), and was developed by Strauss (1984). The producton and consumpton of agrcultural crops, as shown n Heltberg and Tarp (2002), can be expressed as follows: s s s Q = Q ( p, z ) c c s c Q = Q ( p,( α + π( p, z ), z ) (2) where Q s s the quantty produced of crop by the household, and Q c s the consumpton of crop by the household, p s the prce of crop, z s are factors of producton that nfluence producton and z c are household characterstcs that relate to consumpton,α s exogenous ncome sources and π (.) are farm profts not ncludng famly labour. The marketable surplus s the dfference between consumpton and producton defned as: MS MS s c Q = Q (, α, z,z) (3) p Furthermore, the prce of agrcultural goods needs to account for transacton costs. Proportonal transacton costs mpact on the endogenous prce the household receves for ther marketable surplus. Proportonal transacton costs are broken up nto dfferent categores, relatng to transport costs, costs related to the exogenous output prce, costs relatng to tme spent on marketng, and other transacton costs. These costs are represented n equaton (4), where d s the dstance to market, τ s the tme requred to travel to the market, ϖ s the mplct wage rate, and tp Q σ s the quantty transported per trp. The transportaton costs dentty was derved by (Fafchamps and Vargas Hll 2005), and otc are other transacton costs. Many transacton costs are a functon of the crops exogenous prce, these are lke ad valorem taxes: I d ptc == tp + tc( p) + tc( Tm) + otc (4) = 1 Q σ τϖ 8

9 Thus, marketable surplus s a functon of prces, proportonal transacton costs, exogenous ncome, producton and consumpton characterstcs, and can be defned as: MS MS s c Q = Q ( p, ptc, α, z,z) = MS Q (X ) (5) Havng a larger marketable surplus leads to hgher ncomes, thus fndng ways to ncrease the level of marketable surplus, or n fact havng a marketable surplus wll be benefcal. There are reasons why households may not partcpate n crop markets and reman selfsuffcent. Households may keep food from home producton, thus avodng the wde prce margn between sellng and buyng prces. Relance on own producton for subsstence farmng s also a rsk management strategy. The probablty of partcpatng n the market s defned as: Pr(market partcpaton) =g(x, Φ ) (6) where Φ are fxed transacton costs. Based on ths framework, collectng data on varables that mpact marketable surpluses appears mportant, as t wll be possble to estmate what mpacts on partcpaton n markets and the value of crop sales. 4. Emprcal model To estmate the probablty of partcpatng n a crop market the Heckman procedure can be employed. Ths estmaton technque s based on Heckman (1979), and s outlned n Greene (2003). Usng ths swtchng regresson model, the choce of marketng regme between beng a seller and beng self suffcent can be jontly estmated wth the value of crop sales. The estmaton s a two-step procedure. In the frst step the probablty of beng a seller s estmated n a probt equaton. In the second step the value of crop sales condtonal on market partcpaton s estmated, where: 9

10 z z z * ' y * = w α + e = market partcpaton s a functon of a vector of varables (w), (7) * 0 f z 0 f there s no market partcpaton, = > = * 1 f z 0 f some of the farm output s sold, x ' * y s the value of sales and s a functon of a vector of varables (x), (8) y = y f z = 1 the value of sales s observed only f the household partcpates n the market, and y β + u s not observed f z = 0. A probt model s used to calculate the probablty of z = 1, done usng the method of maxmum lkelhood pr z = = φ w α φ ' ( 1) ( ) where s the standard normal cdf. The second step s to estmate the expected value of y, condtonal on z =1 E(y z=1, x ) = x β + Eu ( z= 1) = x β + E(u e> wα). (10) ' ' ' Usng the statstcal proof that ϑ w α ' ' ( ) E(u e > wα) = ρσeσu (11) φ ' ( w α ) where ϑ = the standard normal pdf nsertng (11) nto (10) yelds ϑ w α E(y z=1, ). (12) ' ' ( ) x = xβ + ρσeσu φ ' ( w α ) (9) A maxmum lkelhood estmator can be used to estmate the model, or the two steps can be estmated wth a probt model and ordnary least squares. The maxmum lkelhood ' ϑ( wα) estmator doesn t use the term, whch s the nverse mlls rato. ' φ( w α) The full Heckman model was not estmated, rather the second step was estmated n Masaka and Ntungamo, whch was the value of banana sales per acre. Ths was done through usng equaton (5). The prce varable was excluded, as t was expected the household s effectve sales prce had strong colnearty wth transacton costs. If both prce and transacton costs were ncluded t mght have been dffcult to unravel ther effects separately. The value of banana sales per acre was estmated usng the ordnary least squares, and s represented by equaton (14), the unts are Ugandan Schllngs. There s sound evdence that usng the yeld varable could lead to endogenety problems (Dutlly-Dane et al. 2003; Nkonya et al. 2004). To overcome ths, an addtonal model 10

11 was estmated, n whch yeld was a functon of producton related varables. Ths model s outlned n equaton (13): yeld = δ + δ as + δ ex + δ labour + δ kgman + e where: as= access to extenson servces, ex= years of farm experence, labour= hours of labour used/acre, kgman=klograms of manure appled/acre, and e= error term. (13) vs = β + β hhm + β yeld + β dtm + β gm + β bf + β p nf β wst + β nt + β nt + ε where: vs= the value of banana sales per acre, hhm= number of people n the household, yeld= banana yeld n kg/acre, dtm= dstance to town n klometres, gm= 1 f sell crops at the farm gate = 0 f travel to the market, bf= 1 f bargan prces wth buyers =0 f receve a fxed prce, pnf=1 f receve nformaton on banana prces =0 f don't, wst=1 f sell to a trader form outsde the local vllage =0 otherwse, nt= pnfo bf, nt= 1 f household s n Ntungamo =0 f n Masaka, and ε = error term. (14) To estmate the emprcal model, data on the dependant and explanatory varables were requred. 11

12 5. Ugandan farm households: the survey 5.1 Survey desgn The data for ths analyss come from a stratfed sample of 206 farm households n three dfferent regons of Uganda, surveyed n September and October The data were collected by a team from the Ugandan Bureau of Statstcs n collaboraton wth the Unversty of Sydney. Households were questoned concernng farm operatons, marketng behavour, crop market access, usage of nputs, consumpton and famly characterstcs. The questonnare was desgned to capture the households past 12 months, specfcally for the croppng seasons July 2004 to June The choce of questons was based on evdence from Ugandan government and research organsaton reports that a lack of captal and poor access to markets are major constrants to agrcultural development. The households were stratfed nto the dfferent regons based on the major crop grown, and ts mportance to Ugandan agrculture, whch ncluded bananas, maze and coffee. The banana sub-sample was n Ntungamo n south western Uganda, the maze subsample was n Iganga n eastern Uganda, and the coffee sub-sample was n Masaka n central Uganda. Ntungamo s a per-urban dstrct that serves as an excellent case study of a dstrct that s solated but stll modernsng agrculture. The stratfcaton was amed at provdng a survey sample that was representatve of the Ugandan agrculture sector, and was a means to ncrease the precson of the sample. Selectve descrptve statstcs of the data for the three dfferent regons are presented n Tables 1 and 2, allowng comparsons among regons and among natonal statstcs. 12

13 5.2 Summary of survey trends Table 1 Descrptve statstcs of household crop producton Dstrct Value of total crop Per cent of food crop sold. Cropped area Per cent self suffcent n Banana yeld Kg/acre sales (Ush) (acres) food crop Masaka Iganga N/A Ntungamo Mean values Source: Survey results Masaka and Iganga had much smaller cropped areas, these regons are located n more developed parts of Uganda closer to the captal, Kampala. By a large proporton, Ntungamo had the hghest value of crop sales, and the greatest per cent of food crop sold. Beng a cash croppng regon Masaka had the most food self-suffcent households. Banana yelds are smlar across both Masaka and Ntungamo. Smlar yelds ndcate smlar factors of producton. Statstcs n table two hghlght aspects of household marketng and producton. The growers who sold more produce at the market were n Ntungamo. Beng further away from Kampala, fewer traders come around to farms to collect goods. Maze and coffee traders travel from Kampala to Iganga and Masaka to collect produce (Collnson et al. 2002). The majorty of households sold crops at the farm gate, as they could not afford to carry the crop to the market. Ths could be related to labour shortages, an nablty to pay for truck servces, or not havng any method of reachng the market, or that the only way to sell s through a trader. The method of sellng crops was evenly dvded, wth 53 per cent barganng wth buyers and 47 per cent recevng a fxed prce. More barganng occurred n Ntungamo, wth the least, 19 per cent, occurrng n Iganga. Personally knowng the buyer was common, wth 55 per cent of the households havng ths knowledge. More households who knew the 13

14 buyer were n Iganga and Masaka, ndcatng a regular pattern of traders comng to collect crops. Table 2 Descrptve statstcs Varable Total sample Number of people n household 6.3 (2.1) Masaka Iganga Ntungamo (1.9) (2.2) (2.1) Educaton hgher than prmary (1/0) 0.34 (0.5) 0.48 (0.5) 0.22 (0.4) 0.3 (0.5) Per cent of ncome generated from farm 64.6 (21.7) 54.8 (21.1) 80.4 (21) 58 (12.1) Transport crops to market (1/0) 0.3 (0.46) 0.15 (0.36) 0.04 (0.2) 0.71 (0.45) Dstance from household to town (km) 14.6 (9.7) 7.8 (5.7) 10.8 (3) 24 (9.4) Bargan prces (1/0) Receve nformaton through word of mouth (1/0) Member of a producer group (1/0) Receve extenson servces (1/0) Input expendture (Ush) (115959) (37511) Use chemcal nputs (1/0) Use non chemcal nputs (1/0) Mean values, wth standard devaton n brackets. Source: Survey results Informaton on crop prces was avalable to 89 per cent of households. The majorty of ths nformaton, 88 per cent, was by word of mouth. In Masaka, 35 per cent of 14

15 households receved prces on the rado. In Iganga, households only had prce nformaton at the tme of maze harvest. Access to extenson servces has long been vewed as a source of mproved crop productvty, 55 per cent of households receved on average 2.17 vsts per year. Servces were manly related to crop producton and land conservaton. These extenson servces were provded by the government, namely the Natonal Agrcultural Advsor Department. The descrptve statstcs underscore the low-external nput usage agrculture practsed n Uganda. Only about 10 per cent of households use mproved seed varetes. The use of organc fertlsers such as mulch and manure are common wth 40 per cent of households usng both. The use of non-organc nputs, for example, chemcal fertlser was on average 5 per cent. Pestcde usage was hghest n Masaka wth 31 per cent of households usng them, compared to 10 per cent for all dstrcts. Overall Iganga had the lowest applcaton of nputs. Iganga had 22 per cent of households usng machnery, manly for the purposes of dggng. Other dstrcts used no machnery. Overall, durng 2004/2005 the average amount of money spent on non labour nputs was 37660Ush. Thrty seven per cent of households hred labour to assst wth farm operatons, ndcatng that an actve labour market does exst. Value of crop sales per acre vs. dstance to town value of crop sales per acre (Ush) dstance to town (km) Fgure 2 Value of crop sales per acre vs. dstance to market Source: survey results 15

16 The fndng n fgure two s counter-ntutve, one would expect that beng closer to town would result n hgher value of sales per acre as there would be lower transacton costs and better access to nputs. The correlaton between the value of crops sales per acre and dstance to town s Possbly households further from town are on more productve land, or are larger thus beneft from economes of scale n producton. Table 3 Correlaton between where sell crop and value of sales Regon Masaka Iganga Ntungamo Total Correlaton Source: Survey results Households who sell ther crops at the farm gate n Masaka and Iganga have a lower value of sales compared to those who travel to the market, the opposte s true n Ntungamo. The correlaton between barganng and recevng a fxed prce to the value of crop sales per acre s Households who bargan are postvely correlated to value of crop sales per acre.it s apparent from the survey that there s a wde varety of households, usng dfferent marketng and producton methods. To estmate how dfferent market access and producton factors are mpactng the value of banana sales per acre the emprcal model n secton four was estmated. 6. Prelmnary estmaton results The prelmnary results presented n table 4 are for the banana growng Masaka and Ntungamo sub-samples. Equaton (13) uses yeld as an explanatory varable for sales value, ths could lead to nconsstent estmates f yeld s endogenous. To test whether or not yeld was endogenous the Hausman test, equaton (15), was conducted. H 0 1 :cov( yeld, ε ) = 0 H :cov( yeld, ε ) 0 (15) 16

17 To test the hypothess n equaton (15) t was necessary to estmate equaton (14), wth the estmated values of e from equaton (13) ncluded. The coeffcent of e was not sgnfcantly dfferent from zero, t-statstc of Consequently the null hypothess, whch states there s no endogenety, was not rejected at a 5 per cent level of sgnfcance. Therefore the ntal model seemed theoretcally sound. The estmaton results of equatons (13) and (14) are presented n Table 4. 17

18 Table 4 Factors affectng the value of banana sales per acre and the klograms of bananas produced per acre. Independent varable Ordnary least squares Bananas produced Kg/acre Value of banana sales/acre In Ugandan Schllngs Number of household 4742 members (12458) Dstance to town 105 (2624) Sell at farm gate * (58999) Bargan prces ** (140996) Sell to trader from out local dstrct ** (58872) Know exogenous prce (66218) Household n Ntungamo *** (109534) Yeld 95*** (19) Interacton * (107508) Access to extenson servces 895*** (372) Years of farm experence -17 (17) Hours of labour used/acre 0.23 (0.26) Kg manure/acre (0.06) Number of observatons F R Coeffcents values reported wth standard errors n brackets. * ** ***,, means the assocated coeffcent s sgnfcant at 10, 5, and 1 per cent respectvely 18

19 Yelds were exogenous to the household, hghlghtng the low nput usage nature of agrculture n the sample. Households have relatvely smlar means of producton, and possbly wthn a certan area exogenous factors determne yeld lke ranfall, sol type, f government extenson offcers vst farms, or f an nput suppler s located nearby. The only varable to have explanatory power n determnng banana yelds was access to extenson servces. Havng ths servce ncreased yelds by, on average, 895kg/acre. There s a need for extenson servces to target the poorest households, and not just the households whch wll gve the best offcal results. Nkonya et al. (2005) also found access to extenson servces sgnfcantly ncreased the value of sales per acre. Labour, years of experence and amount of manure appled all had neglgble affects. Wth an R 2 of 0.1, there appear to be other varables that explan the varaton n crop yelds. Larger households had hgher banana sales per acre, whch appears counter-ntutve. It would be expected that larger famles have to provde more food for household consumpton and less can be marketed. Larger famles mght have more labour whch can be used to mprove crop yelds and help market crops. Ths could ncrease sales per acre. However, the varable s statstcally nsgnfcant. Goetz (1992) found the amount sold declned when famly szes ncreased, the fndng wasn t statstcally sgnfcant. Compared to households that receved a fxed prce for ther bananas, households who bargan when sellng had statstcally sgnfcant hgher per acre sales. Those who barganed receved, on average, an extra Ush per acre compared to those who dd not bargan. Results suggest those who are offered a fxed prce do not have many optons to sell ther bananas, and the prce s dctated by the buyer. Sellng crops at the farm gate s postvely assocated wth banana sales per acre, compared to those who travelled to the market. One would sell at the farm gate f prces were better, other reasons for sellng at the farm gate nclude not beng able to reach the market due to poor nfrastructure, poor nformaton about when the market s on, and the opportunty cost of tme spent accessng markets. Farm gate sales may be prevalent also because quanttes sold are small thus the cost of accessng the market s hgh per kg/banana. The average quantty sold was four banana bunches at a tme. Ownng bcycles and carts may make access to markets easer. To mnmse transacton costs, 19

20 when larger quanttes are sold at the farm gate, buyers mght be wllng to pay more f larger volumes can be purchased at once. Households further from the market often had hgher value of sales per acre, possbly as one travels nto more solated areas the land s of better qualty thus yelds are hgher. However, one would expect that n solated areas the cost of accessng markets would be hgher. Although hghly statstcally nsgnfcant, the fndng s somewhat surprsng. In Uganda, (Nkonya et al. 2005) has a smlar result, as the dstance to an all weather road ncreased so dd the value of sales per acre, agan the varable was nsgnfcant. In Mozambque, (Heltberg and Tarp 2002) found as dstances to the provnce captal ncreased so dd the quantty of crops sold. The varable was not statstcally sgnfcant, but t was sgnfcant n determnng market partcpaton. Dstance to market was also postvely and nsgnfcantly assocated wth the quantty sold to the market Key et al. (2000). The nsgnfcance of dstance s mportant. Wthn the dstrct dstance to town s not mportant. Havng nformaton avalable on the exogenous prce of bananas postvely affected banana sales per acre, although the varable wasn t sgnfcant. An nteracton term was ncluded n equaton (7). Ths varable captured the effect of recevng prce nformaton for those households who barganed the prce they receved. The varable had a negatve sgn, ndcatng that sellers who bargan and have pror nformaton on prces, receved a lower value of sales per acre, compared to barganers wthout prce nformaton. Ths fndng s also unusual, t would be expected that people wth prce nformaton could command a better prce as there s less chance of beng underprced as the seller has more nformaton about market prces. Banana farmers sold ther crops to a varety of sources, ncludng local market places and to traders. Those who sold to traders from places out of the local area receved, on average, Ush less per acre than those who sold to other sources. Often households sold to traders from Kampala, these traders have ther own transacton costs to account for, lke searchng for a buyer, and transportng bananas back to Kampala. Provdng more sellng optons appears to be a good polcy to mprove sales. Thus, facltatng trade through mproved nfrastructure and marketng channels appear potentally mportant. The varable also shows that there s sgnfcant demand for bananas wthn the dstrct, as 20

21 households sellng to buyers wthn the dstrct have hgher sales than those who sell to outsde traders. Intutvely one would expect hgher yelds to sgnfcantly rase banana sales, ths was ndeed true. For every extra klogram of bananas produced per acre the household generated, on average, an extra 95Ush. Whle ths varable s hghly sgnfcant, compared to other transacton cost varables t has a smaller mpact on banana sales. The yeld varable had a small range (Table 1) mplyng that producton s exogenous. The most sgnfcant varable was household locaton. Households n Ntungamo earn, on average, Ush per acre more than those located n Masaka. Ntungamo may have more fertle sols or better ranfall. Alternatvely because Ntungamo farmers are larger they could beneft from economes of scale when marketng bananas. Also n Masaka coffee producton s more prevalent, and bananas may be seen as only a food crop not a cash crop. 7. Concludng comments and further work In ths paper, t has been shown that transacton costs and yelds nfluence the value of banana sales per acre. The paper has ntroduced explanatory varables that haven t been used before n the lterature to test what mpacts the value of banana sales per acre. Whle the survey s a small sze, and results are only prelmnary, polcy optons that focus on mprovng farm yelds and lowerng transacton costs appear the best development strategy. Transacton costs have a larger nfluence on value of sales than producton, as the magntude of ther coeffcents was much larger. Some households may produce better qualty produce ths wll lead to hgher sales values, f proper gradng of bananas s done. Results n ths study and n most prevous studes show that dstance to market s not mportant n determnng the amount of a crop sold, although t can be a barrer to crop market entry. Before any detaled polcy recommendatons can be gven full results and a feld trp have to be completed, ths wll ensure polcy advce s well nformed. Prelmnary results show much of the marketng of bananas may be out of the hands of households. Creatng more marketng optons appears benefcal. Provdng an envronment that s conducve for trade should be a prorty. Ths could be done through 21

22 mprovng nfrastructure such as roads and market facltes, by provdng households wth nformaton about market tmes and locatons. Collectve acton by households to sell bananas could also boost ncomes by lowerng transport costs. Ths opton has barrers n tself as farmers are often untrustng of others handlng ther goods. It appears that there could be monopsony power for banana buyers, and f households have very lmted optons to sell bananas, the buyers may be ganng rent from ths lack of competton. Large marketng margns are evdent. At the tme of the survey bananas n Kampala sold for 300Ush/kg whle surveyed households receved 153Ush/kg, a 50 percent margn appears to be gong to mddlemen. Bananas do not undergo any processng, ths margn must account for transport costs and profts to mddlemen. Reducng ths apparent market power on purchases wll assst n enhancng competton. Gven bananas are the world s most mportant traded frut, further developng the export trade of the apple banana could provde farmers wth a worthy dversfcaton opportunty. The apple banana s ganng prce premums n Unted Kngdom supermarkets. Reducng the cost of ar freght and mprovng the packagng of apple bananas would be benefcal. Whle a lot of bananas are sold to traders from Kampala to meet urban demand, households receve lower prces when sellng to these traders. Ths suggests that local and regonal demand for bananas s also strong, and that collectve acton by producers to market larger quanttes may lower the cost of exchange. Formaton of producer groups to sell bananas and buy trucks appears a worthwhle nvestment strategy. Such questons as, why households who bargan have hgher sales? Why households who sell to traders from outsde the local dstrct have lower sales? and why so many households sell at the farm gate? requre further attenton. Further work ncludes, collectng more data on crop producton and marketng channels, and mplementng the Heckman model for market partcpaton. Work n progress also ncludes an adapted household model whch ncorporates transacton costs and mssng markets, from ths a non-lnear model wll be bult usng survey data. 22

23 References Coase, R.H. (1960). The problem of socal cost, Journal of Law and Economcs 3, Collnson, C.D., Kleth, U., Burnett, D.G., Ferrs, R.S.B., Jagwe, J.N. and Muganga, A.K. (2002) Transacton cost analyss, IITA, Kampala. de Janvry, A. and Sadoulet, E. (2004), Progress n the modelng of rural households' behavour under market falures, n Alan de Janvry and Rav Kanbur (Ed), Essays n Honor of Erk Thorbecke, Kluwer publshng. Dennger, K. and Okd, J. (2001), Rural Households: Incomes, Productvty, and Nonfarm Enterprse', n Renkka, R. and Coller, P. (ed), Uganda's Recovery The role of Farms, Frms, and Government, World Bank, Washngton. Dutlly-Dane, C., Sadoulet, E. and de Janvry, A. (2003), Household Behavour under Market Falures: How Natural Resource Management n Agrculture Promotes Lvestock Producton n the Sahel, Journal of Afrcan Economes 12, Ells, F. (1993), Peasant economcs: farm households and agraran development, 2nd edn. Cambrdge Unversty Press, New York. Fafchamps, M. and Vargas Hll, R. (2005), Sellng at the Farmgate or Travelng to Market, Amercan Journal of Agrcultural Economcs 87, Goetz, S.J. (1992), A Selectvty Model of Household Food Marketng Behavor n Sub- Saharan Afrca, Amercan Journal of Agrcultural Economcs 74, Government of Uganda 2000, Unted Natons Conference on the Least Developed Countres, Brussels, May 2001: country presentaton, Unted Natons, Geneva. Greene, W.H. (2003), Econometrc analyss, 5th edn, Prentce Hall, Upper Saddle Rver, New.Jersey. Heckman, J.J. (1979), Sample Selecton Bas as a Specfcaton Error, Econometrca 47, Heltberg, R. and Tarp, F. (2002), Agrcultural Supply Response and Poverty n Mozambque, Food Polcy 27, Jayne, T.S. (1994), Do Hgh Food Marketng Costs Constran Cash Crop Producton? Evdence from Zmbabwe, Economc Development and Cultural Change 42,

24 Key, N., Sadoulet, E. and de Janvry, A. (2000), Transactons Costs and Agrcultural Household Supply Response, Amercan Journal of Agrcultural Economcs 82, MFPED (2000), Plan for modernsaton of agrculture: eradcatng poverty n Uganda, Mnstry of fnance plannng and economc development, Kampala. MFPED (2002), Uganda partcpatory poverty assessment process Second Partcpatory Poverty Assessment Report Deepenng the understandng of poverty, Mnstry of fnance plannng and economc development, Kampala. NAADS (2000), Master document of the NAADS taskforce and jont donor group, Natonal agrcultural advsor servces, Entebbe. Nkonya, E., Pender, J., Kazz, C., Edward, K. and Mugarura, S. (2005), Polcy optons for ncreasng crop productvty and reducng sol nutrent depleton and poverty n Uganda, Internatonal Food Polcy Research Insttute, Washngton. Nkonya, E., Pender, J., Jagger, P., Sserunkuuma, D., Kazz, C. and Ssal, H. (2004), Strateges for Sustanable Land Management and Poverty Reducton n Uganda Research Report 133, Internatonal Food Polcy Research Insttute, Washngton. Omamo, S.W. (1998), Farm-to-Market Transacton Costs and Specalsaton n Small- Scale Agrculture: Exploratons wth a Non-separable Household Model, Journal of Development Studes. 35, Pender, J., Ssewanyana, S., Edward, K. and Nkonya, E. (2004a), Lnkages between poverty and land management n rural Uganda: evdence from the Ugandan natonal household survey, 1999/00, Internatonal Food Polcy Research Insttute, Washngton. Pender, J., Nkonya, E., Jagger, P., Sserunkuma, D. and Ssal, H. (2004b), Strateges to Increase Agrcultural Productvty and Reduce Land Degradaton: Evdence from Uganda, Agrcultural Economcs 31, Renkow, M. and Hallstrom, D.G. (2000), Infrastructure Investment, Transactons Costs, and Rural Poverty Allevaton, Proceedngs of the 24th Internatonal Assocaton of Agrcultural Economsts meetng; Aug 2000, Berln, Germany. Sadoulet, E. and de Janvry, A. (1995), Quanttatve development polcy analyss, John Hopkns Unversty Press, Baltmore. Strauss, J. (1984), Marketed Surpluses of Agrcultural Households n Serra Leone, Amercan Journal of Agrcultural Economcs 66,

25 UBOS (2002), Uganda Natonal Household Survey 1999/2000 Report on the Crop Survey Module, Ugandan Bureau of Statstcs, Entebbe. UBOS (2003), Uganda Natonal Household Survey ; Report on the Socoeconomc survey, Ugandan Bureau of Statstcs, Entebbe. Vaks, R., Sadoulet, E. and De Janvry, A. (2002), Transacton costs and the role of barganng and nformaton: evdence from Peru, Unversty of Calforna, Berkeley. WorldBank (2004), World development ndcators CD-ROM, IBRD World Bank, Washngton. 25