1 University of Kabianga, Department of Agricultural

Transcription

1 Asan Journal of Economcs and Emprcal Research ISSN(E) : ISSN(P) : X Vol. 3, No. 2, , Impact of Ml Maretng Channel Choce Decson on Income, Employment and Breedng Technologes among Dary Farmer Households n Kercho County, Kenya Elah K. Ng eno 1 1 Unversty of Kabanga, Department of Agrcultural Bosystems and Economcs, Kercho, Kenya and PhD Student, Mo Unversty, Eldoret, Kenya Abstract The study examned the mpact of ml maretng channel choce decsons on dary farmer household ncome, employment and breed technologes among dary farmer households n Kercho County. Data was collected from 432 dary farmer households usng multstage cluster samplng technque. Both prmary and secondary data were used n the analyss. Processng and analyss of survey data was carred out usng STATA verson 12. Multvarate probt and propensty score matchng was used n data analyse. Propensty score matchng was also used to account for selecton bas. Matchng results show that the average effect of the farmer household that sold ml to commercal buyers had hgher probablty of obtanng Kenya shllngs per day as compared to households that dd not sell ml to commercal buyer. Whle sellng through commercal ml buyers had sgnfcant postve effect on farmer welfare, maorty of dary farmers were hestant to engage wth them. Ml buyers value securty n supply whch comes from trusted relatonshps and contracts. Establshng such relatonshps s n the long-term nterest of the dary farmer. Therefore, to mprove farmer welfare, group formaton and partnershp development should be strengthened, ml cooperatve socetes needs to be bolstered and an ncreased fnancal nvestment n lvestoc marets by natonal and county governments. Keywords: Propensty score matchng, Ml maretng channels, Dary farmer households. Contents 1. Introducton Obectves Research Methodology Emprcal Results and Dscusson References Ctaton Elah K. Ng eno (2016). Impact of Ml Maretng Channel Choce Decson on Income, Employment and Breedng Technologes among Dary Farmer Households n Kercho County, Kenya. Asan Journal of Economcs and Emprcal Research, 3(2): DOI: /ournal.501/ / ISSN(E) : ISSN(P) : X Lcensed: Ths wor s lcensed under a Creatve Commons Attrbuton 3.0 Lcense Fundng: Ths study receved no specfc fnancal support. Competng Interests: The author declares that there are no conflcts of nterests regardng the publcaton of ths paper. Transparency: The author confrms that the manuscrpt s an honest, accurate, and transparent account of the study was reported; that no vtal features of the study have been omtted; and that any dscrepances from the study as planned have been explaned. Hstory: Receved: 28 September 2016/ Revsed: 14 November 2016/ Accepted: 17 November 2016/ Publshed: 19 November 2016 Ethcal: Ths study follows all ethcal practces durng wrtng. Publsher: Asan Onlne Journal Publshng Group 145

2 Asan Journal of Economcs and Emprcal Research, 2016, 3(2): Introducton Output prces receved by farmers sgnfcantly determne ther welfare especally n rural areas where there s wea non-farm ncome whch lmts dversfcaton of agrcultural producton amongst producers. Whle there s some debate about the actual and potental mpacts of havng a wde array of commercal ml buyers on broader welfare of the rural poor, case study evdence suggests that farmers are worst placed when faced wth a prvately owned or government-controlled monopsony (Sadler, 2006; Gorton 2007). The choce of a ml maretng channel depends on a number of aspects. These nclude avalablty of the marets, ml prces offered n the marets, dstance to the ml maret and the potental of the maret to absorb the volume on sale (Paterson, 1997). However, there s relatvely lttle evdence lnng ml maretng channel choces to dary farmer household ncome and employment outcomes n the study area. Whle the theoretcal arguments n favor of maretng cooperatves are well nown, n practce ther performance n developng countres has been patchy (Glover, 1987). Erratc rent seeng government nterventon may renforce these problems. Whle case studes (Strewe, 1999; Cocs et al., 2005; Gorton et al., 2006) and aggregate maret analyss dentfy these dffcultes, there s an absence of cross-sectonal data analyss on the mpact of ml maretng channel choce decsons on ncome, employment and technology among dary farmer households n Kercho County, Kenya. Ths study therefore, attempts to fll ths gap by analyzng the mpacts of the avalable commercal ml buyng channels on dary farmer households n Kercho County, Kenya. However, certan factors are beyond the scope of the dary farmers n the study area. For example, wea rural nfrastructure, land fragmentaton and wea captal base. Therefore, most dary farmers are constraned by hgh transacton costs due to the great dstances to ml marets, lac of adequate access to fnance, and n some cases socal factors (Nos and Krsten, 1994). Also, nformaton about marets and maret prces gude the farmer n mang nformed decsons (Nos and Krsten, 1994). Mang unnformed decsons may result n the farmer accessng the maret when t s not proftable to do so. Ths s a common stuaton where lvestoc farmers approach saturated marets wth the wrong prce sgnals (Nos and Krsten, 1994). The study contrbutes to the lterature by emprcally examnng the mpacts of the ml maretng channel choce decsons on dary farmer household ncome, employment and technology. The study used propensty score matchng model to control for self-selecton snce the choce decson for a partcular ml buyng channel s not random, wth the group of dary farmers beng systematcally dfferent. By consderng the causal relatonshp between partcpaton n sellng ml to commercal ml buyers and dary farmer household welfare, ths paper see to address counterfactual queres that could be mportant n forecastng the mpacts of polcy changes. The study analyzed ndependently both farmers sellng under commercal channels and for those that sell to fnal consumers n order to assess ther mpacts on the choce decsons made by the dary farmer. 2. Obectves 2.1. General Obectve The general obectve of the study was to evaluate the mpact of ml maretng channel choce decson on dary farmer household s ncome, employment and technology adopton n Kercho County, Kenya Specfc Obectves The specfc obectve of the study was to estmate the mpact of ml maretng channel choce on dary farmer household s ncome, labor hours and breed technology n Kercho County Hypothess The study tested the followng hypothess:- H O1: The ml maretng channel choce has no mpact on dary farmer household ncome, labour hours and breed technology n Kercho County. 3. Research Methodology 3.1. Research Desgn Ths study used cross-sectonal and correlatonal research desgns. A cross-sectonal survey of dary farmer households was carred out n early The study estmated ncome, employment and technology functons, endogenously stratfed for each of the maretng channel used Study Area Prmary data was collect from smallholder dary farmer households n sx sub countes of Kpelon East, Kpelon West, Kercho West, Kercho East, Sgowet/Son and Buret of Kercho County as shown n table 2.1. Table-1. Smallholder Lvestoc Ml Producers and Cooperatve Socetes Sub County Number of Number of Dary Cattle Average Dary Dary Selfhelp households Dary Populaton Number of Cooperatve groups Farmers Dary Cows socetes /Farmer /companes Kpelon East , Son/Sgowet , Kercho west , Buret , Kercho East , Kpelon West , Kercho County , Source: Kercho County Development Profle,

3 Asan Journal of Economcs and Emprcal Research, 2016, 3(2): Target Populaton The prmary unt of analyss was the household wth dary cows wth ml for maretng. The target populaton was restrcted to the 94,427 smallholder lvestoc ml producers and mareters, dvded proportonately amongst the sx sub-countes of Kercho County as shown n Table 1. Gven the obectve of the study, the populaton of nterest was defned as the prmary ml producer household who sell cows ml to another supply chan actor. Therefore, farmers wthout dary cows, those who dd not sell any of the ml produced or those who processed all the ml themselves were excluded from the study. Wth the gven focus, these restrctons were ustfed, and t meant that the sample could not be drectly compared to offcal data on the structure of ml producton Samplng Procedure A multstage cluster samplng procedure was used to get the total populaton and sample sze of nterest.to acheve the study obectves, the county was clustered nto sx sub-countes, namely, Kpelon East, Kpelon West, Kercho West, Kercho East, Sgowet/Son and Buret as shown n Table 2. These sub-countes formed the sample stes for the study and the mean of the results from all these stes consttuted the results for the whole county. Table-2. Sub-County Samplng Areas Consttuency Sub-countes Dvsons Area (Km 2 ) Number of Number of Locatons Sub locatons Anamo Kercho East Anamo Belgut Kercho West Kabanga and Belgut Sgoewet/Son Sgowet Son and Sgowet Kpelon West Kpelon West Kunya, Chlchla, Kamasan and Kpelon Kpelon East Kpelon East Londan, Sorget and Chepseon Buret Buret Buret, Roret and Cheborge Source: County Commssoner s Annual Report, Kercho, 2013 To acheve representatve sample sze, the sx sub-countes formed the frst-stage cluster that had the target populaton. These sx clusters were selected based on the fact that small scale dary farmng was domnant and practced throughout the county. Furthermore, t reflected sgnfcant dfferences n structure of the dary ml maretng ndustry n the county. Further, wthn the sx sub-countes, second-stage cluster sample of wards and vllages wth hgh concentraton of small scale dary farmers was then selected for the study. Sample selecton of dary farmer households from the clustered wards was done usng random samplng and effort was made to nclude statstcally sgnfcant sub samples of dary ml producers representng dfferent ml maretng channels and szes for each of the sub countes. The sampled ml producng n th smallholder farmer household was determned by the proportonate sze samplng methodology (Anderson et al., 2007). 2 Z pq N0 2 e (1) Where N0 was the sample sze, Z s the standard normal value of 1.96 sgnfcant at 5 percent confdence level, e s themargn of error (the samplng error or desred level of precson), p s the estmated populaton proporton of smallholder dary farmers wth characterstcs of nterest assumed at 70 percent (Table 3.3). Thus tang p at 70 percent gave a representatve sze wth mnmal error mang q = 1-p,.e = 0.3, Z = 1.96, and e = 0.04 for a good precson respectvely *0.7 *0.3 N dary farmer households. Fnally, based on the above calculaton, the sample unts (number of dary farmers households) were calculated proportonately based on the number of dary farmer households n each sub county and as a proporton of the total dary farmers n the county aganst the desred sample sze of 504 as shown n Table 3. Table-3. Proportonate Dstrbutons of Dary Farmer Households Sub County Number of Number of Dary Percent Dary farmer Total proporton Households Farmers Households Kpelon East 27,791 13, Son/Sgowet 20,940 15, Kercho west 31,394 17, Buret 30,977 28, Kercho East 27,700 8, Kpelon West 14,615 11, Total Kercho County 153,417 94, Source: Author s Computaton from County Data, 2016 Therefore, a random sample of 504 dary farmer households was set for the whole county wth the ntenton of samplng representatve cross-secton of small scale dary farmer households sellng raw ml to dfferent maretng channels avalable at farm gate. After data entry and cleanng, a total of 432 households were fnally used for data analyss (Table 4). Wthn the county, samplng was weghted to the sx sub-countes that had sgnfcant dary cow producton. The results that were obtaned were assumed vald for the whole County. 147

4 Asan Journal of Economcs and Emprcal Research, 2016, 3(2): Table-4. Dstrbuton of Sample Smallholder Dary Households Sub-county Respondents Percentage Anamo Buret Kercho West Kpelon East Kpelon West Son/Sgowet Total Source: Author s Computaton from Survey Data, Data Types and Sources Both prmary and secondary data were used n ths study. Prmary data were collected through a survey. A structured pre-tested questonnare was used and admnstered by traned enumerators through drect ntervews amongst selected dary farmer households. Prmary data was also collected through dscusson and observatons of the farmers dary farmng actvtes. Seasonal observatons were also used to correlate those dary farmer households left out from the networ wth the secondary ml maret n order to estmate ther supply and prce varatons. Ths nvolved observng the natural behavor (nonverbal expresson of feelngs, determne who nteracts wth whom, how dary farmers communcate wth each other, and to chec for how much tme s spent on varous actvtes) of the dary farmer households n order to descrbe exstng stuatons and to obtan nformaton that was relevant to the goals of the study. Secondary data was obtaned manly from varous sources ncludng economc surveys, economc ournals, statstcal abstracts, conference revews, boos, magaznes, offcal government of Kenya reports and documents such as statstcal abstracts and bulletns, natonal and dstrct development plans, natonal and county development and strategc plans, and Kenya Dary Board records and annual reports. Dfferent documents of lvestoc producton and maretng, regonal level reports and consultants reports as well as Natonal Bureau of Statstcs publcatons were revewed to gather more relevant nformaton. Destop lterature and nternet were also used to access credble nformaton from avalable and accessble documents, publshed and unpublshed reports, boos and agrcultural ournals. Farm records from a few dary farmer households were also used to supplement secondary data sources. Gven the mportance accorded to the nvolvement of smallholder ml producers n the varous ml maretng channels n ths study, data types encompassed representatve sample of households representng varous categores of households, types of maretng channels (commercal and non-commercal), and changng structure of dary sector was adopted. In order to analyze the response of the smallholder ml producers, the study focused manly on whether the dary farmer household sold ml at farm gate to commercal ml maretng channels (Y 1 ) and f farmer household chose to sell also to fnal consumers (non-commercal channel) (Y 0 ) or otherwse. Commercal ml maretng channels n ths study were taen to mean three maor maretng channels: organzed cooperatve socetes, organzed prvate sector ml buyers, and tradtonal/unorganzed ml buyers. For a gven vllage, there were four types of farmers: () farmers who chose to supply ml to the organzed cooperatve socetes, () farmers who chose to sell ml to the organzed prvate sector ml buyers, () farmers who chose to supply ml to the tradtonal or unorganzed ml buyers such as ml vendors, restaurants, or drectly to consumers and contractors and (v) farmers who suppled ml to multple channels le ml cooperatve socetes self-help groups, tradtonal and prvate ml buyers. The data collected ncluded dary farmers soco-economc characterstcs, actual ml producton, ml maret compettveness and other related oblgatons wth the ml buyers. The soco-economc data collected comprsed the farmer s age, educaton level, household sze, gender, and farm ownershp, off farm ncome, access to credt, access to extenson servce, membershp to ml cooperatve socety and access to other ml maretng channels. The farm producton data collected comprsed the sze of land under dary producton, average volume of ml produced per year, amount of lvestoc nputs such as feeds, breedng methods, types of labor used, captal used, cost of nputs, and farm gate prces of lvestoc outputs. Respondents were also expected to provde nformaton regardng maret compettveness and an estmated total number of potental commercal buyers for ther ml. Ths would capture the degree of swtchng power from one commercal buyer to the other that farmers have n maretng ther ml. The study also ncluded data on whether the farmer sold total ml output on contract or on sgnng agreements or on spot cash sale as an ndependent varable. Farmers may sell ther ml on sgnng agreements wth ml buyers rather than va spot cash sales. Agreements wth buyers provde a greater degree of certanty for buyers regardng the avalablty of supply, for whch a buyer may pay a premum (Gow and Swnnen, 2001). To capture the trustworthness of commercal ml buyers, a measure of trust on the commercal ml buyer by the dary ml farmer was ncluded. Ths attrbute was analyzed by a proxy that dentfed the percepton that the dary ml farmer had n relaton to ther trust n the commercal ml buyer. Fnally regardng ml maretng characterstcs, a dummy varable was ntroduced that captured whether the farmer sells va ml coolng/chllng plants, ml sheds or ml bars or not. Tme seres data on farm gate ml prces receved by farmers over a perod of three years (2013, 2014 and 2015) was also collected from the farmers. Ths entaled the use of parwse comparson of the sx sub county mean ml prces (means that were sgnfcantly dfferent from each other) for the three years usng Tuey's HSD (honest sgnfcant dfference) test Instruments of Data Collecton A structured questonnare was used as an nstrument for data collecton. The questonnare was desgned to address the obectves of the study. The questonnares were admnstered by traned enumerators. The enumerators were dentfed from among the people who were conversant wth the sub county wards and vllages n the county to 148

5 Asan Journal of Economcs and Emprcal Research, 2016, 3(2): ad n data collecton. The enumerators were traned for two days and the tranng culmnated n the pre-testng of the questonnare on the thrd wee of December, Pre-test of data collecton tool on the four dary farmer households was done n Kercho East and Buret sub countes respectvely. Observatons were also used to correlate those left out from the sampled populaton wth the secondary ml maretng channels n order to provde estmates of ther ml supply and ml prces receved Data Analyss and Dagnostcs STATA verson 12 was used for data analyss. The collected prmary data was collated, cleaned, coded and stored n excel worsheets and IBM- SPSS verson 21 before they were transferred to STATA for analyss. Emprcal analyss n ths study conssted of two stages. In the frst stage, multvarate probt model was used to estmate the factors whch determned the ml maretng channel choce decson equaton, specfcally whether farmers sell only to a commercal ml buyer or sell also to fnal consumers of ml. Secondly, propensty score matchng model was used to analyze the mpact of farmers maretng choces on gross dary ncome, ml yeld, labor hours, and on breed technology). Dagnostc tests were also conducted from the regresson results of STATA output. To chec on multcollnearty, the study used varance nflaton factor (VIF) and contngency coeffcent (CC) among dscrete and contnuous varables, respectvely. All assumptons were tested and corrected accordngly usng STATA Analytcal Framewors Theoretcal Framewor The farmer or producers behave le neoclasscal frms who control the transformaton of nputs nto valuable outputs n order to maxmze profts (Varan, 2000). The decson on whether or not to adopt a new technology s consdered under the general framewor of utlty or proft maxmzaton (Norrs and Sandra, 1987; Pryanshnov and Katarna, 2003). It s assumed that economc agents, ncludng smallholder subsstence farmers, use certan lvestoc ml maretng systems only when the perceved utlty or net beneft from usng such a method s sgnfcantly greater than s the case wthout t. Agan smallholder dary farmers are assumed to be ratonal and they want to derve the hghest utlty from the choces they mae; ether to maret ther produce ndependently or under a certan ml maretng channel. They mae ther choces wth respect to random utlty theory, whch states that a decson maer s guded by unobservable, observable and random characterstcs when mang a decson. Although utlty s not drectly observed, the actons of economc agents are observed through the choces they mae. Suppose that Y and Y represent a household s utlty for two ml maretng choces, whch are denoted by U and U, respectvely. The lnear random utlty model could then be specfed as: U X and U X (2) Where; U and U are perceved utltes of usng a certan ml maretng channel and, respectvely. X s the vector of explanatory varables that determnes and or nfluences the perceved desrablty of the choce of the ml maretng channel, B and B are parameters to be estmated, and ε and ε are error terms assumed to be ndependently and dentcally dstrbuted (Greene, 2003). Therefore, for the case of choce of a lvestoc ml maretng channel, f a household (dary farmer) decdes to use opton maretng channel, t follows that the perceved utlty or beneft from opton maretng channel s greater than the utlty from other optons (say ) maretng channel depcted as follows: 1 1 U ( X ) ( U ( X ), (3) The probablty that a dary farmer wll choose ml maretng channel among the set of lvestoc ml maretng channels to maret hs ml nstead of the maretng channel could then be defned as P Y 1 X ) P( U U ) (4) ( 1 1 Therefore, P( X X 0 X ) 1 1 Hence P( X X 0 X ) * * * P( X X 0 X F( X ) (5) Where; P s a probablty functon, U, U,, and X are as defned above, ε* = ε ε s a random dsturbance term, * 1 1 ( ) s a vector of unnown parameters that can be nterpreted as a net nfluence of the vector of * ndependent varables nfluencng the decson to sell a commercal ml maretng channel, and F( B ) s a cumulatve dstrbuton functon of the error terms (ε*) evaluated at B * X X. The exact dstrbuton of F depends on the dstrbuton of the random dsturbance term, ε*. Dependng on the assumed dstrbuton that the random dsturbance term follows, several qualtatve choce models can be estmated (Greene, 2003). Propensty score matchng whch s used n ths study s analyss requres no assumpton about the functonal form n specfyng the relatonshp between outcomes and predctors of outcome, unle the parametrc methods mentoned above. However, the drawbac of the approach s the Condtonal Independence Assumpton (CIA), whch states that for a gven set of covarates, partcpaton s ndependent of potental outcomes (Smth and Todd, 2005). Further, Smth and Todd (2005) note that there may be systematc dfferences between the outcomes of partcpants and non-partcpants, even after condtonng on observables. Such dfferences may arse because of selecton nto treatment based on unmeasured characterstcs. To address the selectvty bas problems assocated wth choce decson of a ml maretng channel, ths study employed the matchng technques n assessng the mpact of sellng to a commercal ml maretng channel on average dary farmer household s gross ncome, employment and dary breedng technology uptae. The propensty score matchng approach addresses the problem of the lmted dstrbutonal assumpton of the errors, and more mportantly allows for a decomposton of the treatment effect on outcomes (Hecman, 1999). Also the counterfactual framewor could detect two mportant sources of bas n the estmaton of treatment effects. 149

6 Asan Journal of Economcs and Emprcal Research, 2016, 3(2): These nclude the ntal dfferences between the group sellng to commercal ml maret channels and those sellng to fnal consumers n the absence of treatment, and the dfference between the two groups n the potental effect of the treatment Emprcal Modelng of Effects of Maret Channel Choces on Income, Labor Hours and Technology Farmer s ml maretng channel choce decsons n ths study were hypotheszed to have not sgnfcant mpact on varous technologcal and economc parameters, such as ncome, productvty; employment and technology (breed composton). Here, the study estmated ncome, employment and technology functons, agan endogenously stratfyng for each of the maretng channel used. Snce the separaton of producers by maret channel ntroduces a bas derved from an endogenous stratfcaton of maret channels, ths bas needed to be corrected. The regresson equatons were estmated for the group sellng to commercal ml maret channels and those who sell to fnal consumers. Therefore, the structural model that was adopted for the analyss was the propensty score matchng model (PSM) as shown n equaton 2.7 and as adopted from Hecman (1999). Y X K u (6) Where; Y s household ncome, employment or technology; X s a vector of the explanatory varables, representng personal and household characterstcs and assets, and dstance; K s the dummy varable representng one, f a dary farmer sells to a commercal ml buyer and 0 for those sellng also to fnal consumers; are the coeffcents and u s the error term. The specfcaton above n equaton treats ml maret choce decson by the dary farmer household as an exogenous varable on the premse that households opts for a ml buyer to ncrease ther ncome, employment of resources or to mprove on ther technologcal status. Nonetheless, ths need not be the case, snce better-off dary farmer households may be better predsposed to several commercal ml marets as compared to the poor dary farmer households. Furthermore, the decsons to choose or not to choose a partcular ml maretng channel choce may be dependent on the benefts from the choce of the ml maretng channel. Thus, the choce of the ml maretng channel s not random, wth the group of dary farmers beng systematcally dfferent. However, selecton bas occurs f unobservable factors nfluence both the error term ( u ) of the choce equaton, and the error term ( ) of the ncome equaton, thus resultng n correlaton of the error terms. To evaluate the mpact of varous ml maretng channel choce decsons on smallholder dary farmer s ncome, both farmers sellng under commercal channels and for those not were expected to show the same observable characterstcs. The study assumed that those sellng through commercal ml maretng channels were taen as treatment and those not were taen as control. The average treatment effect (ATE) of nvolvng commercal buyers s the dfference between the actual ncome and the ncome for nvolvng the commercal buyers n ml maretng, whch s expressed as; ATE E Y Y / K 1 (7) ( 1 0 Where; Y 1 s the ncome when th farmer sells to commercal buyer, Y 0 s the ncome when the th farmer marets ndependently to fnal consumers and K s a dummy varable denotng the nvolvement of the commercal buyer, 1 = sellng to commercal ml buyers, 0 = otherwse. The mean dfference (D) between observable and control can be wrtten as n equaton 8 below. D E( Y1 / K 1) E( Y0 / K 0) ATE (8) Where; s the bas. The estmated model used for the fourth hypothess related to the mpact on farmers ml maretng choces, Y 1, (a bnary varable whch taes the value one f the farmer sells to commercal ml buyers only and zero f the farmer decdes to sell also to fnal consumers), and ther mpacts on farmers ncome, employment, and technology (Z ), s as specfed n the equaton below: Z 0 1, AGE 2EDC 3ROAD 4PART 5PR 6VETFDS 7HERD 8EXP 9LANDSIZE 9FAMILYSIZE 10IMR u (9) Z s a set of varables that were hypotheszed to affect the farmer s maretng channel choces (Y 1 ). These were the gross dary ncome, ml yeld, employment, and share of crossbred anmals as dependent varables. Ideally, the dependent varable was the net dary ncome. Regrettably, t was qute dffcult to obtan accurate data on the value of some of dary nputs. Ths was manly true of dary nputs for whch lvestoc marets were not well developed, such as labor, home grown feeds and fodder, home-made feed ratos and n some cases costs data were mssng completely. A maor reason why propensty score matchng was employed was to address potental unobserved heterogenety. As observed by Huer et al. (2004) a possble hdden bas mght occur f there are unobserved varables that tend to nfluence smultaneously commercal ml maretng channel choce decson and dary farmer household ncome, employment and breed technology. Gven that t s not possble to estmate the magntude of selecton bas wth non-expermental data. Rosenbaum and Donald (1985) suggested the use of the boundng approach to examne the nfluence of unmeasured varables on the selecton process. As a consequence, the study used gross dary ncome per anmal per household as the dependent varable n the second stage of the Hecman model. The Inverse Mlls Rato was also be used to correct the error terms n the mpact equatons to acheve consstent and unbased estmates Dagnostc Tests for Multnomal Logt The study used varance nflaton factor (VIF) and contngency coeffcent (CC) among dscrete and contnuous varables, respectvely. Potental multcollnearty among explanatory varables was tested and t was found not to 150

7 Asan Journal of Economcs and Emprcal Research, 2016, 3(2): have any potental nfluence on estmates from the model. The hghest par-wse correlaton was 0.4 whereas multcollnearty s a serous problem f par-wse correlaton among regressors s n excess of 0.5 (Guarat, 2004). An analyss of varance nflaton factor dd not show any problem snce none of the VIF of a varable exceeded 8 (Greene, 2003). 4. Emprcal Results and Dscusson Probt model was used to estmate the propensty scores. The scores were only used to balance the observed dstrbuton of covarates across the treated group (farmers sellng to commercal buyers) and the untreated group (farmers not sellng to commercal buyers). The ndependent varables used n the probt regresson model to predct the propensty scores were based on past research on determnants of partcpaton n nonfarm employment (Barrett et al., 2001) n Owusu et al. (2014). From Table 5 results, most of the varables ncluded n the estmaton have the expected sgn. In partcular, partcpaton n ml maretng and household sze were found to be postvely and sgnfcantly related to ml maretng. The coeffcent for dary farmer household partcpaton n ml maretng was postve and sgnfcant. The presence of a ml buyer enhanced the probablty of partcpaton n the ml maret. Accordng to Kousar and Abdula (2015) endowments wth valuable household assets represents household s wealth and the presence of a development proect n an area enhance the probablty of partcpaton for both male and female n non-farm earnng actvtes. The coeffcent of household sze was postve and sgnfcant balancng factor for propensty score matchng. Ths suggests that the presence of labor avalablty n the household tend to ncrease the ml output levels whch wll then ncrease the probablty of dary farmer households sellng ther ml to commercal ml buyers. These results are n contrast to the study of Barrett et al. (2001) and n lne wth the study of Kousar and Abdula (2015) n the case of male labor supply. The dstrbuton of propensty of propensty scores before and after matchng clearly ndcate that estmatng the p-score appears to balance the treated and untreated groups extremely well than wthout the p-score, a result whch underscored the sgnfcance of the propensty score matchng approach for ths study. Table-5. Probt Estmates of the Propensty Score for Dary Farmer Household s Ml Maretng Involvement Maor farm gate ml buyers (Commercal or Coeffcent Standard Error Z P> z Fnal consumers) Age of household head Educaton level Dstance to Ml Maret ** Ml maretng partcpaton ** Prce rs * Veternary feeds Farmng experence ** Total farm sze * Total household sze * Gross Income ** Net Income ** Employment hour * Technology (Breedng) ** Constant Key: Calper = Nearest Neghbour = 1 Number of observatons = 432 LR ch 2 (13) = Prob > ch 2 = Log lelhood = Pseudo R 2 = * Sgnfcant at 1 percent; ** sgnfcant at 5 percent; *** sgnfcant at 10 percent Source: Author s Computaton from Survey Data, 2016 Pseudo-R 2 from probt estmaton ndcated the goodness of ft of the model or how well the regressors explaned the probablty to sell to commercal buyers. From the table of results, after matchng, pseudo- R 2 was farly low (0.0436), whch showed that the matchng procedure balanced the determnng factors (covarates) very well for ths study The Mean Dfferences n Outcome Varables n the Matchng Analyses Table 6 compares the mean dfferences n the outcome varables and other household and farm-level varables between dary farmer households sellng ml to commercal buyers and dary farmer group not sellng ml to commercal buyers. Gven that the mean dfference comparsons do not account for the effect of other characterstcs of farm households, they confound the mpact on household gross ncome, employment and breed technology adopton (dary breedng) status wth the nfluence of other characterstcs. The sgnfcance levels suggest that there are some dfferences between those who sell to commercal ml buyers and those not wth respect to household and farm-level characterstcs. Wth regard to the outcome varables, there were statstcally sgnfcant dfferences n household ncome and employment hours between the two categores of dary farmers. Therefore, we agan reected the null hypothess that the ml maretng channel choce has no mpact on dary farmer household ncome, labour hours and breed technology n Kercho County. 151

8 Asan Journal of Economcs and Emprcal Research, 2016, 3(2): Table-6. Propensty Score (Pscore) Matchng Analyss Varable Sample Treated Controls Dfference S.E. T-stat Net Income Unmatched ATT ATU ATE Gross Income Unmatched ATT ATU ATE Employment hours Unmatched ATT ATU ATE Technology Unmatched ATT ATU ATE Note: Standard error does not tae nto account that the propensty score s estmated. Source: Author s Computaton from Survey Data, 2016 Results n Table 6 further shows that the means of the treated group (farmers sellng to commercal buyers) and the control group (farmers not sellng to commercal buyers) are dfferent. The magntudes of the coeffcents of the treatment effects ndcate that the average treatment effects for the treated (ATT) are hgher than the average treatment effects for the entre sample (ATE) and the average treatment effects for the untreated (ATU) except for technology outcome varable. The matchng estmates generally ndcated that sellng to commercal ml buyers exerted postve, sgnfcant and unbased mpacts on household gross ncome and hence net ncome (Table 6). These ATT effects demonstrate that dary farmer households who sell ml through these commercal ml buyers ncrease ther gross ncome thereby mprovng ther welfare over and above those that are less motvated to commercal ml buyng channels. After matchng, dary farmer households sellng to commercal ml buyers rased ther daly net ncome n the household by Kenya shllngs on average per dary cow. Accordng to Owusu and A. (2009) households that have a hgher probablty of partcpatng n non-farm wor are able to obtan hgher ncomes and mprove ther food securty status over and above those that are less nclned to partcpate n non-farm wor. Ths s n convergence wth the current study fndngs. The matchng estmates of the ATU effect ndcates that f farmer households who dd not sell ther ml to commercal ml buyers had actually sold the ml (counterfactual condton), then ther household ncome and employment hours would be on average hgher than that of those who dd sell ther ml to commercal ml buyers.owusu and A. (2009) further notes that the mplcaton of such an outcome s that ncome and food securty gans from partcpaton n non-farm employment are slghtly hgher for households wth a hgher probablty of partcpatng than households wth slghtly lower chances of partcpatng n non-farm employment. Results further revealed that the matchng estmates for gross ncome per anmal on the treatment group, whle balancng for the orgnal level of gross ncome before and after treatment, also ncreased by Kenya shllngs (the nearest neghbour estmate of the average gan) after matchng. Smlarly, employment hours ncreased on average by 4 hour 46 mnutes for dary farmer households n the treatment group (those sellng to commercal buyers) after matchng results of the unobserved. Ths confrm earler fndngs by Heshmat (2007) that the underlyng technologes employed defnes a producton functon to estmate the mean output rather than the maxmum output. After matchng, the percentage of cows bred usng modern breedng technologes for example AI or sexed semen decreased for the dary farmer households n the treatment group (dary farmer households sellng ml to commercal buyers) by about 69 percent. Ths can be attrbuted to the hgh cost of dary cow breedng n the study area that has been brought about by asymmetrc nformaton flow. Of partcular concern have been breedng (A.I) prces that do not fully reflect qualty because dary farmers and breeders do not have the same nformaton. Ths result s n convergence and n conformty wth earler studes by Eggertson (1990) who argues that before mang a decson about how to maret a product and to whom to sell t, producers must determne the prce that they expect to receve. Further, Eggertson (1990) argues that transacton costs arse when maret nformaton s asymmetrc as ths nduces actvtes such as nformaton searchers, barganng, maret contractng, montorng, enforcement and protecton of property rghts, whch are, by nature costly. The magntude of the coeffcents of the treatment effects ndcated that the average treatment effects for the treated (ATT) were hgher than the average treatment effects for the entre sample (ATE) and the average treatment effects for the untreated (ATU) for the outcome varables except for technology (Table 6). These results ndcated that farmers who sold ml to commercal buyers had a hgher probablty of obtanng hgher gross ncome per anmal and had a hgher probablty of mprovng ther welfare over and above those farmers who dd not. The average effect of the treatment (ATE) for a dary farmer household drawn from the overall populaton at random was Kenya shllngs hgher because of sellng to commercal ml buyers. Ths was because a postve effect was estmated for the dary farmer households not sellng to commercal buyers (ATU). The ATU effect by calper matchng estmates ndcated that f farmer households that dd not sell ml to commercal ml buyers had actually sold the ml, a counterfactual condton, then ther household ncome and employment hours would be on average lower than that of those who dd sell ther ml to commercal ml buyers. The mplcaton here s that the net ncome and employment hour gans from sellng to commercal ml buyers are slghtly hgher for dary farmer 152

9 Asan Journal of Economcs and Emprcal Research, 2016, 3(2): households wth a hgher probablty of sellng to commercal ml buyers than to dary farmer households wth slghtly lower chances of sellng to commercal ml maretng channel. In performng PSM usng the common opton mposes a common support by droppng treatment observatons whose pscore s hgher than the maxmum or less than the mnmum pscore of the controls (Table 7). Accordng to the results, fve observatons were dropped from the entre observatons. Three from the treated group (farmers sellng to commercal buyers) and two from the untreated group (farmers not sellng to commercal buyers), respectvely. Table-7. Becer and Ichno (Psmatch2) PSM Estmaton psmatch2 psmatch2 Treatment assgnment Common support Off support On support Total Untreated Treated Total Source: Author s Computaton from Survey Data, Matchng Success for Impact/Outcome Factors Table 8 gve result of t-test on the hypothess that the mean value of each of the outcome varables; namely age, educaton level, dstance to the ml maret, ml maret partcpaton, prce rs, lvestoc feeds, farmng experence, total farm sze, household sze, gross and net household ncome, employment hours and breed technology adopton was the same for the treatment group (farmers sellng to commercal buyers) and non-treatment group (farmers not sellng to commercal buyers). It was done both before and after matchng. Pstest was used to chec for the success of the matchng for the outcome varables. Further, a bas before and after matchng was calculated for each of the varables and the change n the bas stated. Table-8. Indcators of Matchng Qualty before and after Matchng (PSM Results) Maor farm gate ml buyers Commercal or Fnal Unmatched Mean %reducton t-test V(T)/V(C) Outcome Varables Matched Treated Control %bas bas T p> t Age of household head U M * 0.89 Educaton level U M Dstance to Ml Maret U Ml maretng partcpaton M U M Prce rs U M Veternary feeds U M Farmng experence U M * 0.99 Total farm sze U * M * Total household sze U M * Gross Income U * M * 1.87* Net Income U * M * 1.68* Employment hours U * M * 5.00* Technology (Breedng) U M Source: Author s Computaton from Survey Data, 2016 The bas before and after matchng was calculated for each varable. Ths bas was the dfference between the mean values of the treatment group and the control group, dvded by the square root of the average sample varance n the treatment group and the not matched control group. Table 8 shows the dfferences n the values of the exogenous varables between the two groups before and after matchng. For example, 46.61% and 35.2% of the treatment and control group respectvely partcpated n ml maretng. Ths means that the results have sgnfcant nfluence on the treatment probablty. The ndcators of matchng qualty presented n Table 9 show substantal reducton n absolute bas for all the outcome varables for both the treated and non-treated. As ndcated n the table (column 5), the mean bas n the covarates Z after matchng les below the 20 % level of bas reducton suggested by Rosenbaum and Donald (1985). Ths ndcates that the covarates were sgnfcantly balanced as a result of the propensty score procedure. 153

10 Asan Journal of Economcs and Emprcal Research, 2016, 3(2): Table-9. Indcators of Matchng Qualty before Matchng and After Matchng Sample Ps R 2 LR Ch 2 p>ch 2 Mean Bas Med Bas B R % Var Unmatched * Matched * * If varance rato outsde [0.77; 1.29] for Unmatched and [0.77; 1.29] for Matched Ps R 2 Pseudo R 2, LR Ch 2 Ch square lelhood rato Note:* p-value of Lelhood Rato Test (Pr > 2 ) Note: Pseudo-R 2 from probt estmaton ndcates the goodness of ft or how well the regressors explan the probablty to partcpate n an employment actvty. Source: Author s Computaton from Survey Data, 2016 The pseudo-r 2 from the propensty score estmaton and from re-estmaton of the propensty score matchng on the matched samples for both the groups was and 0.032, respectvely (table 3.5). However, f p>0.05, the null hypothess could not be reected on the 5% sgnfcance level. Therefore, the ont sgnfcance of the regressors on the treatment status could not be reected after matchng. It was, however, not reected before matchng ether. The null hypothess that the mean values of the two groups do not dffer after matchng cannot be reected for the varables except for age, farmng experence, and gross ncome. Therefore, t s possble to generate a control group, whch s smlar enough to the treatment group to be used for the ATT estmaton. The relatvely low pseudo- R 2 after matchng and the p-values of the lelhood-rato test of the ont sgnfcance of the regressors mply that there s no systematc dfference n the dstrbuton of covarates between treatment and non-treatment groups after matchng, suggestng that the overall results from the matchng procedure are satsfactory n balancng the covarates between the treatment and non-treatment (Calendo et al., 2005) Dstrbuton of the Propensty Scores for Treated and Untreated Groups Fgure 1 gves the hstograms of estmated propensty scores for the treated and non-treated groups of dary farmer households. It shows the dstrbuton and overlap (regons of common support) condtons of the propensty scores after matchng for the two groups of dary farmers (nearest neghbours).vsual nspecton of the results reveals that the denstes of the mean propensty scores are smlar after matchng and there s a clear overlap of the dstrbutons. Propensty scores before and after matchng as shown clearly ndcate that estmatng the p-score appears to balance the treated and untreated groups extremely well than wthout the p-score, a result whch underscored the sgnfcance of the propensty score matchng approach for ths study. There s a clear balance between the two coordnates as they moved towards a central and a common area. Fgure-1. Densty Dstrbuton (Covarate Balance) of the Estmated Propensty Scores Source: Author s Computaton from Survey Data, Summary and Conclusons Ths study examned the mpacts of ml maretng channel choce decsons on dary farmer household ncome, employment and breed technologes, usng a cross-sectonal sample of 432 dary farmer households from sx sub countes n Kercho County, Kenya. A propensty score matchng model was employed to account for selecton bas 154

11 Asan Journal of Economcs and Emprcal Research, 2016, 3(2): that normally occurs when unobservable factors nfluence both partcpaton and non-partcpaton n dary ml maretng on the outcomes. The paper addressed dary farmer heterogenety by explctly provdng separate estmates for treated group (farmers sellng to commercal buyers) and the untreated group (farmers not sellng to commercal buyers). Results of the propensty score matchng showed that the magntudes of the coeffcents of the average treatment effects for the treated (ATT) were hgher than the average treatment effects for the entre sample (ATE), and the average treatment effects for the untreated (ATU) for the outcome varables except for breedng technology. The results ndcated that farmers that sold ml to commercal ml buyers had a hgher probablty of obtanng hgher gross ncome per anmal per day and could mprove ther welfare over and above those farmers who dd not. The average effect of the treatment (ATE) for a dary farmer household drawn from the overall populaton at random was Kenya shllngs hgher because of sellng to commercal ml buyers. The ATU effect by calper matchng estmates revealed that f a farmer household who dd not sell ml to commercal ml buyers had actually sold the ml, ther household ncome and employment hours would be on average lower than that of those who dd sell ther ml to commercal ml buyers. The mplcaton here s that the net ncome and employment hour gans from sellng to commercal ml buyers are slghtly hgher for dary farmer households wth a hgher probablty of sellng to commercal ml buyers than to dary farmer households wth slghtly lower chances of sellng to commercal ml maretng channel. The growng nterest of polcy maers n promotng dary ml producton, partcularly n the rural areas of Kenya s n the rght drecton as revealed by the results of ths study. 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