Does commercialisation drive technical efficiency improvements in Ethiopian subsistence agriculture?

Transcription

1 Afrcan Journal of Agrcultural and Resource Economcs Volume 13 Number 1 pages Does commercalsaton drve techncal effcency mprovements n Ethopan subsstence agrculture? Wondmagegn Trkaso Department of Economcs, Swedsh Unversty of Agrcultural Scences (SLU), Uppsala, Sweden. E-mal: wondmagegn.tafesse@slu.se Sebastan Hess* Dary and Food Industry Economcs, Department of Agrcultural Economcs, Chrstan-Albrechts-Unversty, Kel, Germany. E-mal: shess@ae.un-kel.de * Correspondng author Abstract The condtons n whch ncreased market partcpaton leads to mproved techncal effcency are stll not adequately understood. Ths study therefore nvestgated farmers market partcpaton rates and ther predcted techncal effcency scores by performng a two-stage least squares (2SLS) regresson analyss usng household-level data obtaned from the 2009 Ethopan rural household survey. The predcted effcency score from the stochastc fronter producton functon showed that the farmers had a mean techncal effcency score of 40.2%, mplyng that ther output could be ncreased substantally f mprovements were made to exstng nput mxes. The varables related to educatonal level and rado and moble telephone access were postvely lnked to a farmer s techncal effcency. The estmated results also ndcated that farmers wth a hgher degree of commercalsaton were techncally more effcent compared to those wth lower market partcpaton. The overall results suggest the mportance of ncreasng the market partcpaton rate of smallholders n order to mprove agrcultural productvty n rural Ethopa. Key words: techncal effcency; commercalsaton; smallholders; stochastc fronter; Ethopa 1. Introducton The emprcal relatonshp between commercalsaton and techncal effcency (TE) has been at the heart of polcy debate n varous farmng regmes, ncludng commercal, sem-commercal and subsstence farmng (Bnswanger & Von Braun 1991; Pngal & Rosegrant 1995; Barrett 2008). Farmers n commercal and sem-commercal regmes tend to supply surplus produce to the market wth the objectve of maxmsng proft, subject to nput constrants. The addtonal ncome earned can have an mportant welfare effect, partcularly when mprovements n farmers productvty levels result n the producton of and access to more nutrtous, healther food (Von Braun, 1995). However, ths may not be the case wth subsstence farmng, n whch households prmarly produce for ther own consumpton but stll supply a certan proporton of ther output to the market, even when ther own food consumpton needs are not fully met (Gebre-ab, 2006). The latter fndng contradcts the noton that hgher market partcpaton contrbutes to productvty mprovements; nstead, t s possble that a hgher level of productvty s the man drver of market partcpaton and commercalsaton. Hence, t s necessary to consder the potental causalty between commercalsaton and techncal effcency to mprove the understandng of whether ncreasng commercalsaton boosts farmers productvty levels.

2 The concept of techncal effcency has been a central part of the theory of producton economcs. Farmers are supposed to optmse producton dependng on the resources avalable, but there are several reasons why a degree of effcency loss mght be experenced (Nshmzu & Page 1982; Byrnes et al. 1987; Tham et al. 2001), and ths stuaton s common n sub-saharan Afrca (Corna 1985; Coller & Dercon 2014). For nstance, Ethopa s one of the economes n sub-saharan Afrca n whch average farmers have low techncal effcency scores as low as 45% compared to the best farms n the correspondng regons (Nsrane et al. 2011; World Bank 2015). The mplcaton s that the agrcultural sector can grow by mprovng farmers productvty levels (Block 1999; Bgsten et al. 2003; Dao & Pratt 2007; Trkaso 2013). Several socoeconomc factors have been gven as the root causes of low agrcultural productvty n rural Ethopa. These nclude poor lnkages between the market and the farmng sector, backward technologcal set-ups coupled wth dmnshng cultvated land sze, poor adopton of technology, and nsttutonal falures (Croppenstedt & Muller 2000; Fafchamps et al. 2005). What remans unclear n the lterature, however, s the drecton of causalty between a farmer s techncal effcency score and commercalsaton, partcularly n relaton to the knd of subsstence agrculture found n Ethopa. Hence, the am of ths paper was to examne the possble nterconnecton between commercalsaton and techncal effcency n Ethopa, focusng on producers of the country s man crops sampled from seven vllages. Frst, the farmers techncal effcency score was predcted from the estmates of a stochastc fronter producton functon. Second, the determnants of techncal effcency were estmated by ncludng the farmers commercalsaton ndex as an addtonal explanatory varable. Thrd, the factors nfluencng the level of commercalsaton for the man crops were dentfed by ncludng the techncal effcency score as one of the exogenous varables. Ths result revealed the possble drecton of causalty between commercalsaton and techncal effcency. Fnally, the paper draws conclusons about the nexus between techncal effcency and level of commercalsaton n predomnantly subsstence agrculture. 2. Level of commercalsaton and techncal effcency n subsstence agrculture The commercalsaton of agrculture broadly refers to the degree of farmers partcpaton n output markets (Leavy & Poulton 2008; Jaleta et al. 2009). It encompasses farmers proft-maxmsng behavour n relaton to decsons about product choce and nput use (Pngal 1997). In general, a hgher degree of commercalsaton s beleved to have a sgnfcant effect on farmers welfare. For nstance, t could mprove farmers ncome by creatng market lnkages for dfferent types of agrcultural products (Martey et al. 2012; Fscher & Qam 2012). Furthermore, t s thought to lead farmers towards more specalsed producton systems based on comparatve advantages n resource use, wth mproved outcomes n employment, health, nutrton, and macroeconomc and envronmental performance (Bnswanger & Braun 1991; Pngal & Rosegrant 1995; Jaleta et al. 2009). However, t has not been possble to acheve the desred effect of commercalsaton n subsstence agrculture because the farmers market partcpaton s not motvated by proft-maxmsng behavour. They are stll nvolved n local and regonal markets, but often do not have suffcent surplus producton to cover other basc expendture (Gebre-ab 2006; Barrett 2008). Ths ndcates that an examnaton of the nterconnecton between commercalsaton and techncal effcency needs to be consdered that takes nto account the specfc nature of commercalsaton n predomnantly subsstence agrculture. A framework llustratng why the relatonshp between commercalsaton and techncal effcency requres specal analyss s presented n Fgure 1. Accordngly, most of the prevous thnkng about the commercalsaton-effcency nexus commonly supports the noton that beng a techncally effcent farmer can have a postve effect on the level of commercalsaton (Bnswanger & Braun 45

3 1991; Barrett 2008; Pya et al. 2012). Ths reles on the farmers nvolved n commercalsaton spendng the ncome they generate from ther market actvty on aspects that are lkely to boost productvty, such as health and more nutrtous food. Ths study therefore tested the followng hypotheses: Hypothess 1: Low techncal effcency s explaned by a low degree of commercalsaton Accordng to ths hypothess, a low level of techncal effcency s the result of lmted access to nput and output markets. For nstance, lmted market partcpaton means lower ncome levels, whch have drect mplcatons for the farmers nutrtonal and health status. Poor dets and poor health subsequently contrbute to lower productvty. Ths argument s based on the emprcal work of Pngal and Rosegrant (1995), who suggest that ncreased household ncome generated by commercalsaton has an mplcaton for the nutrtonal status of households. Furthermore, farmers who are dsconnected from the market may not have access to market nformaton, whch s essental for mprovng farm productvty. Hypothess 2: A low degree of commercalsaton s the result of poor techncal effcency A low level of techncal effcency s not the result of lmted commercalsaton, but rather a cause of t. Farmers wth a low level of educaton or tranng, lmted resources and poor management do not manage to produce marketable output. These farmers are therefore more lkely to be dependent on a hgh proporton of subsstence producton and on average show lower levels of commercalsaton. Nutrtous food Educatonal access Better farm management Hgher ncome Techncal effcency Commercalsaton Hgher level of producton Fgure 1: Possble lnkage between commercalsaton and techncal effcency 3. Data and descrptve statstcs Ths paper used the 2009 Ethopan rural household survey data collected by the Internatonal Food Polcy Research Insttute (Hoddnott & Yohannes 2011). Ths survey comples household characterstcs, agrculture and lvestock nformaton, food consumpton, health and women s actvtes, as well as data on communty-level electrcty and water, sewage and tolet facltes, health servces, educaton, NGO actvty, mgraton, wages, and producton and marketng. The analyss n ths study was based on a total of 562 households (out of whch 540 could be used n the analyss) selected from seven vllages n the full survey. These vllages are known for the producton of major agrcultural crops, ncludng wheat, teff, sorghum, khat, coffee, barley, maze and enset. More 46

4 detaled descrptve statstcs for the varables used n estmatng the stochastc fronter producton functon and the determnants of a farmer s level of commercalsaton are llustrated n Table 1. Table 1: Descrptve statstcs (N = 540) Varable Label Mean SD Mn Max Producton functon varables Output Monetary value of total output (brr) Labour Total number of adults (aged 15-60) Farm sze Total farm sze (square metres) Fertlser Total fertlser use (klograms) Oxen Total number of oxen (number) Hoe Total number of hoes (number) Ineffcency determnant varables HCI Household commercalsaton ndex n % Off-farm ncome Total off-farm ncome (n brr) Gender Dummy for gender (1 = male, 0 = female) Age Age of the respondent (n years) Educaton Total amount of schoolng (n years) Rado Dummy for rado (1 = own, 0 = not) Moble telephone Dummy for moble telephone (1 = own, 0 = not) Credt access Dummy for credt access (1 = has, 0 = not) Market served Locatons of local markets (market served by household): 1 = for markets n local surroundngs 2 = for market n nearest vllage 3 = for market n other vllages 4 = for market n regon 5 = for market n Adds Ababa It can be observed that an average household produces brr of total output per year, yet wth a standard devaton of brr and the maxmum observed household output n the sample exceedng the mean by a factor of 15. Thus, the sample exhbts a substantal varaton n total agrcultural output across the sample. The average farm n the sample produced about 800 brr of agrcultural output per adult household member, or brr per hectare (calculated from sample means n Table 1). Interestngly, the number of hoes was only about half the number of avalable adults per household. However, average fertlser use n the sample, at 37 to 40 klograms per hectare, was close to the fgure reported by Rashd et al. (2013). Farmers who dd not use fertlser at all would be lkely to rely on tradtonal manure and compost from ther farm. The commercalsaton ndex was calculated as the share of agrcultural output that has been marketed. The survey ndcated that, on average, farmers supply 35% of ther agrcultural output to the market, whle the remanng share s used for home consumpton. Meanwhle, the maxmum commercalsaton ndex observed was 1 811%, reflectng the exstence of farmers n the sample who are nvolved n non-agrcultural (off-farm) ncome-generatng actvtes. All the households had access to at least one local market n ther surroundng area and the average household also served the market n the nearest vllage, whle a smaller number of households sold to other vllages, regonal markets or even to the captal, Adds Ababa. Heads of households had an average of four years formal schoolng. 4. The model The techncal effcency of the farms n the sample was assessed usng the method of stochastc fronter analyss, a method ntally developed by Agner et al. (1977). The general form of the stochastc fronter producton functon s gven as: v Q f X j ; exp u, 1,2,., N, v u (1) 47

5 where Q s a value of total output for the th farm household; f(xj; β) s a determnstc part of the producton functon; Xj s the vector of the th nput used by the j th farm household; β s the vector of 2 technology parameters; (v) s a statstcal nose component wth zero mean and dstrbuted N (0, ) and captures the effects of uncontrolled random factors, such as weather or other unexpected events; 2 and (u) s a non-negatve random varable dstrbuted N (, ) and assocated wth the measurement of techncal neffcency by the j th farm household. The techncal effcency level of each farm household was measured by the rato of observed or actual output to the correspondng fronter (or possble maxmum output), dependng on the level of nputs used by the respectve farm households. Hence, t s possble that the actual producton level s less than the fronter output or the determnstc part of the model, mplyng the exstence of possble neffcency. Mathematcally, the level of techncal neffcency for the th farm household s gven by: TE ; exp - ; exp Q f x v u exp-u (2) Q f x v * where Q corresponds to observed agrcultural output for the th farmer, and Q * corresponds to the fronter output level. The potental output level for each farm household can also be predcted after dstngushng the neffcency (u) and nose (v) components n Equaton (1). The error terms, (u) and (v) are assumed to be ndependent of each other, and ndependently and dentcally dstrbuted (..d.) across observatons. 2 Followng Battese and Coell (1995), (μ) n the dstrbuton N (, ) of the neffcency term (u) can further be modelled such that each farm household exhbts an ndvdual (μ) subject to the followng functonal relatonshp: z (3) Here, z s a vector of the envronmental and management-related varables that affect household effcency (u) through a shft n the dstrbutonal parameter (μ), and δ s the parameter to be estmated. It should be noted that a postve parameter value for δ ndcates that the correspondng z varable ncreases the mean techncal neffcency. As the man concern of ths study was to dentfy the prevalng causalty between techncal effcency and commercalsaton, t was necessary to measure the commercalsaton ndex for each ndvdual farm household. Ths could be calculated by usng the rato of the total value of agrcultural sales n the market to the total value of agrcultural producton, expressed as a percentage (Von Braun & Kennedy 1994). Mathematcally, t s gven as: HCI TVS TVQ 100 (4) where HCI s the level of commercalsaton of the th household, TVS s the total value of agrcultural sales by the th household, and TVQ s the total value of the agrcultural product produced by the th household. 48

6 5. Estmaton strategy When econometrcally estmatng a producton fronter accordng to the general model n Equaton (1), the functonal relaton has to be specfed. Typcally, a Cobb-Douglas or translog functonal form s consdered. In addton, assumptons have to be made about the dstrbuton N + (μ, σ 2 ) of the neffcency term (u). Three addtonal dstrbutonal assumptons, such as truncated normal, exponental and gamma dstrbuton, are common n the lterature n ths respect (Stevenson 1980; Wllam 1990; Battese & Coell 1995; Wang & Schmdt 2009). After testng for the most approprate functonal form, ths study adopted the Cobb-Douglas form of stochastc fronter producton functon. It was then tested further for the most approprate dstrbutonal assumpton for the neffcency term (u), allowng ether a half-normal, truncated halfnormal, exponental or gamma dstrbuton. The estmates from the truncated model proved to be nsgnfcant and contradcted the core theoretcal justfcaton of the prevalence of techncal neffcency (Agner et al. 1977; Greene 1990; Coell et al. 2005). Consequently, Akake s nformaton crteron (AIC) was used to select the most robust estmate, whch fnally led to the selecton of the exponental model, snce t had the lowest AIC value compared to the other models. In the subsequent dscusson, the study reled on the estmates made by the exponental model. The Cobb-Douglas specfcaton of the general stochastc fronter producton functon outlned n Equaton (1) s gven n Equaton (5). Parameter estmates were obtaned for the k = 7 nput varables of labour, amount of land, amount of fertlser used, number of oxen avalable, number of hoes on the farm, plough avalablty or not, and access to extenson servces or not. 7 ln( Q ) ln( x ) ( v u ) (5) 0 k k k1 Furthermore, ths estmaton of the Cobb-Douglas producton functon usng the stochastc fronter approach dfferentated the neffcency (u) and dosyncratc error (v) components of the error term. As part of ths model, determnants of a farmer s techncal neffcency level z were specfed accordng to Equaton (3). Ths neffcency model provded parameter estmates for the determnants of techncal neffcency scores consderng a vector of varables capturng the household s socoeconomc covarates, namely age, gender, level of educaton, access to varous nformaton devces, credt access, access to varous regonal markets and the household s commercalsaton ndex (Helfand & Levne 2004; Jaleta et al. 2009; Trkaso 2013). 10 ln( z ) ln( HCI ) 0 k k k1 (6) Equaton (6) allows the testng of the statstcal effect of the household s commercalsaton ndex on the mean techncal neffcency wthn the stochastc fronter producton functon. It should be noted that the parameters n Equaton (6) are estmated jontly wth the parameters β of the producton fronter (Equaton (5)) usng maxmum lkelhood. The z varables do not affect output or techncal effcency drectly, snce they enter the estmaton equaton (Equaton (5)) through the dstrbuton of (u). However, n order to assess the role of the household s predcted techncal effcency score for the observed commercalsaton ndex, a slghtly dfferent emprcal model had to be formulated (Equaton (7)): 49

7 12 ln( HCI ) ln( z ) ln( TE ) (7) 0 k k k1 In ths regresson, HCI s the th farmer commercalsaton ndex, Z are the nstrumental varables representng (fully exogenous) contnuous and dummy varables respectvely, and v s the stochastc error term. Furthermore, the predcted techncal effcency score from the stochastc fronter model was ncluded as an explanatory varable. However, ths could result n endogenety bas affectng the estmated parameter,, because of a potental reverse causalty between the level of commercalsaton and a household s estmated techncal neffcency score. Such an endogenety bas may result n a non-zero covarance between Z and v, whch leaves the OLS estmator based and nconsstent (Wooldrdge 2010). Hence, applyng the OLS estmator to the model n Equaton (7) would not be nformatve about the actual causal relatonshp between a household s commercalsaton and techncal neffcency score, because the estmated coeffcent of the effect of techncal effcency on commercalsaton cannot be trusted. A two-stage least squares (2SLS) estmator was therefore employed. Ths estmator uses the nstrumental varable (IV) technque to correct for the bas of the estmated coeffcent from the endogenous regressor. The IV approach uses a frst-stage regresson n order to predct the ln(te), usng nstrumental varables that have to be dfferent from the set of explanatory varables already ncluded n Equaton (7). Generally, such nstruments are requred to meet the exogenety and rank condton, mplyng that they should be uncorrelated wth the error term v and correlated wth the endogenous varable n the structural model. The explanatory varables from the neffcency model n Equaton (6) would be natural canddates, snce they could potentally explan ln(te). However, a vald nstrumental varable has to fulfl addtonal statstcal condtons, whch requres checkng the exogenety and valdty of the nstruments n a frst-stage auxlary regresson model (Sargan 1958; Hausman 1978; Wooldrdge 1995; Klebergen 2007; Wooldrdge 2010). It was possble to test the exogenety of the regressors n queston n Equaton (7) usng the Hausman test. Furthermore, the valdty of the nstruments could be assessed through the Klebergen-Paap LM test for under-dentfyng restrctons. In addton, based on the F-statstcs n the frst-stage regresson model, an assessment was undertaken of whether the selected nstruments were potentally only weakly correlated wth the endogenous varable. In the case of such a weak nstrument, the 2SLS estmator could be even more nconsstent than the orgnal OLS estmator (Bound et al. 1995; Angrst & Pschke 2009; Sanderson & Wndmejer 2016). As a result of ths testng procedure, the educatonal level of the household head was selected as an nstrument. The ntuton behnd usng ths nstrument s related to the evdence that a hgher educatonal level may mprove a farm s techncal effcency score, snce t s expected to ncrease human captal and may contrbute to changes n farmers atttudes towards modern technology (Tchale 2009; Nsrane et al. 2011; Dhehb et al. 2012). 6. Results and dscusson The calculaton of consstent and unbased maxmum lkelhood estmates of a stochastc fronter producton functon begns wth verfcaton of the skewedness of ordnary least squares (OLS) resduals (Olson et al. 1980; Waldman 1982). If the thrd moment of a resdual s postve, then t wll always be the case that all the least squares estmates represent a local maxmum of the lkelhood 50

8 functon. The estmated kernel densty plot for the predcted OLS resduals showed a postve skewedness, confrmng the unqueness and consstency of the maxmum lkelhood estmator. Table 2 presents the maxmum lkelhood estmates of a stochastc fronter producton functon consderng the exponental dstrbuton of the error terms. Frst, a one-step estmaton of the stochastc fronter producton functon was performed, whch showed that farm sze, fertlser use, oxen and hoe were statstcally sgnfcant. Importantly, σu became statstcally sgnfcant at the 1% level, confrmng the exstence of techncal neffcency n the sample. A two-step model was therefore estmated that consdered the covarates expected to affect the techncal neffcency level n the fronter model. The result ndcated that the educatonal level of the household s head, moble telephone access and level of commercalsaton were statstcally sgnfcant. Meanwhle, the two models were compared usng the lkelhood rato test. Ths favoured the two-step estmate (as ndcated by the lkelhood rate and AIC statstcs). Overall, the fndngs were robust wth respect to the assumed dstrbuton of the error term. All statstcally sgnfcant factors of producton had theoretcally consstent sgns, mplyng the postve effect of regressors on output level. However, labour was statstcally not sgnfcant. The estmated result representng the varance components of the two error terms, σu and σv, was also statstcally sgnfcant. Table 2: Estmates of the parameters n the Cobb-Douglas fronter producton functon Dependent varable: ln(output) parameters One-step estmate Two-step estmate Coeffcent SE Coeffcent SE Producton functon varables ln(labour) ln(farm sze) 0.046** * ln(fertlser) 0.113*** *** ln(oxen) 0.441*** *** ln(hoe) 0.135* Plough (dummy) Extenson access (dummy) Ineffcency determnant varables ln(age) ln(educaton) ** Gender (dummy) Rado access (dummy) Moble phone access (dummy) * HCI (%) *** Market_2 (nearby vllage) Market_3 (dstant vllage) Market_4 (regonal centres) Market_5 (Adds Ababa) Credt access (dummy) *** u v 0.603*** *** Log lkelhood AIC Mean effcency Observatons *** p < 0.01, ** p < 0.05 and * p < 0.1; SE represents standard error Note: λ = σ u / σ v shows whether there s techncal neffcency by comparng the rato of two sgmas concernng the extent to whch total output vares due to the degree of nose or neffcency u u v γ σ σ σ s the share of neffcency n the total varaton of the error term. 51

9 Fgure 2 llustrates the kernel densty estmate plot for the predcted techncal neffcency score. The plot shows a smoothly ftted normal probablty dstrbuton, supportng the robustness of the estmate. Densty Techncal effcency va exp[-e(u e)] Fgure 2: Kernel densty estmate plot of the predcted techncal neffcency score 6.1 Estmates of the techncal effcency score The examnaton of the parameters of the error components, such as σu, σv, λ and γ s a crucal step n measurng the effcency level. In ths respect, the varance parameter of the neffcency component σu for the exponental dstrbuton had statstcally sgnfcant varance parameters. Furthermore, t was vtal to examne the statstcal propertes of lambda (λ), whch confrms the exstence of effcency loss, and gamma (γ), showng the percentage of total varaton n output that was lost due to the exstence of techncal neffcency or other, uncontrolled factors n these models (see Agner et al. 1976). Accordngly, the estmated λ confrmed the presence of effcency loss n the exponental model at a 99% confdence nterval. Moreover, the estmate of γ ndcated that the 73% varaton n total output was due to the presence of techncal neffcency among the farmers. Accordngly, the farmers had a mean effcency score of 51% under the selected model. The predcted mean effcency score s summarsed n Table 3. Table 3: Summary of techncal effcency score Techncal effcency (N = 540) Mean Standard devaton Mnmum Maxmum Implcatons of factor elastcty estmates kernel = epanechnkov, bandwdth = The factor nput elastcty estmates, representng a proportonate change n total output nduced by a gven proportonate change n nput level, are gven n Table 2. Accordngly, all varable nputs n the 52

10 producton functon had theoretcally consstent sgns: farm sze, fertlser use and number of oxen were sgnfcant at a confdence nterval of 90%, 99% and 99% respectvely. Ths ndcated that, allowng farmers to have 10% more agrcultural land n hectares would lead about to a 0.4% ncrease n total output, all other factors beng constant. Smlarly, a 10% greater use of fertlser n klograms would result n an ncrease of about 1.1% n total output, assumng other factors are constant. Importantly, the effect of oxen was consderable n comparson: f an ndvdual farmer owned 10% more oxen, ths would result n an ncrease of about 4% n total output, holdng other factors constant. The overall mplcatons behnd the elastcty values were that total output was hghly responsve to a small change n the percentage of farm sze, fertlser use and number of oxen, whch s n lne wth a study by Nsrane et al. (2011). In partcular, fertlser utlsaton at the sample mean amounted to 132 klograms per hectare, 1 whch demonstrates that, n prncple, fertlser s avalable n the survey regon, but stll used by many households at a relatvely low ntensty. It s therefore crucal to mprove farmers nput mxes, gven the ndvdual effects of greater farm sze, greater fertlser use and more oxen. The estmated coeffcent on labour was not sgnfcant, possbly due to the fact that ths varable could only be approxmated by the number of adults n the household. Regardng the determnants of techncal neffcency, prevous studes on the sources of farmers techncal effcency ndcate that socoeconomc, demographc and nsttutonal characterstcs are the man determnants of the techncal neffcency score (Kebede 2001; Nsrane et al. 2011; Trkaso 2013). These nclude the farmer s age, educatonal level, gender, access to ICT servces, level of commercalsaton and dstance to markets. Consderng these fndngs from the lterature, the maxmum lkelhood estmate for the determnants of techncal neffcency ndcated that all the explanatory varables had the expected sgn. The lower part of the two-step estmate n Table 2 llustrates the maxmum lkelhood estmates for the determnants of the techncal neffcency score. The estmated result essentally ndcates that a farmer s level of educaton, access to moble telephones and level of commercalsaton are statstcally sgnfcant at the 95%, 90% and 99% level respectvely. Ths denotes the mportance of mprovng a farmer s educatonal level n order to acheve a hgher level of techncal effcency. The effect of moble telephone access was also mportant n reducng techncal neffcency n the household. Smlarly, an ncrease n the level of household commercalsaton led to a reducton n techncal neffcency, other factors remanng constant. The mplcaton s that a hgher degree of market partcpaton has a sgnfcant effect n reducng techncal neffcency among farmers. However, ths requres scrutny of the potental causalty between techncal effcency and the level of commercalsaton, whch wll be dscussed below. The varables representng the types of market served by the household were not statstcally sgnfcant. 6.3 Causalty between techncal effcency and level of commercalsaton Ths secton provdes estmates of the dentfcaton of the causal relatonshp between a farmer s level of commercalsaton and techncal effcency, takng the smultanety bas problem explaned n Secton 5 nto consderaton. The specfcaton gven n Equaton (7) suggests the exstence of a potental endogenety problem due to smultanety bas between commercalsaton and techncal effcency, whch the Hausman test faled to reject. Table 4 presents estmates from the frst-stage auxlary model n whch techncal effcency estmates were regressed upon the nstrumental varable; these estmates were useful n determnng the applcablty of the 2SLS estmaton technque. In partcular, they showed the relevance and valdty of the household head s level of educaton. The correspondng result n Table 4 ndcates that educatonal level of the household head was postve and statstcally sgnfcant. In ths case, an 1 Ths fgure s calculated by dvdng total fertlser use n all vllages by total farm sze n square klometres. 53

11 ncrease n educatonal level of the household head by 10% could ncrease the techncal effcency level by about 1.2%, assumng that other factors are fxed. The Klebergen-Paap LM (KPLM) statstc rejected the null hypothess of the under-dentfcaton test at 10%, mplyng that the nstrument s rank condton was satsfed. Two addtonal and mportant robustness tests were conducted n order to valdate the relevance and exogenety of the nstruments used n the model (Stock & Watson 2003). Accordngly, the jont sgnfcance test for nstruments rejected the null hypothess at the 1% sgnfcance level (7, , wth a 0.000), mplyng that the estmated coeffcents were both dfferent from zero (also called the weak nstruments test). Moreover, the Hausman F- statstcs-based test for endogenety rejected the null hypothess at the 5% level. Table 4: Frst-stage regresson on a household s techncal effcency level (TE) Dependent varable s lnte Educatonal level of household head (years of schoolng) 0.119*** (0.046) Constant *** (0.167) F-statstcs 6.84*** KPLM statstcs 6.89*** Observatons 538 Robust standard errors n parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 Hence, the 2SLS estmator was mplemented based on the varable educaton as a vald nstrument. The potental relatonshp between the level of the farmers commercalsaton and ther correspondng techncal effcency score thus can be dentfed by the correspondng 2SLS estmaton technque. Table 5: 2SLS estmate for the determnants of household commercalsaton, HCI Varables Estmate SE P > t ln(techncal effcency) ln(labour) ln(farm sze) ln(fertlser) ln(oxen) ln(hoe) ln(plough) Credt access (dummy) Assocaton membershp (dummy) Market_2 (Nearby vllage) Market_3 (Dstant vllage) Market_4 (Regonal centres) Market_5 (Adds Ababa) Constant F-statstcs Hausman test statstcs Observatons 538 Robust standard errors n parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 The estmaton results n Table 5 show that farmers can partcpate n up to fve dfferent types of markets (Table 1) categorsed as local, nearby vllage, dstant vllage, regonal centres and Adds Ababa. The effects of these markets were controlled gven that they reflect the market sze from vllage level to large ctes. It was expected that those farmers nvolved n tradng actvtes n large ctes such as Adds Ababa would also be more lkely to exhbt a hgher level of commercalsaton compared to those framers partcpatng n local markets. Accordng to the estmaton results, all types 54

12 of markets were postve and statstcally sgnfcant at the 1% level of sgnfcance. The magntude of the estmated coeffcents suggested that, on average, those farmers partcpatng only n local vllage markets had a lower commercalsaton level than those farmers also partcpatng n markets beyond ther nearest vllage. However, the 2SLS estmates reported n Table 5 show that the techncal effcency score became statstcally nsgnfcant. Moreover, wth the excepton of varables representng market sze, the man explanatory varables were not statstcally sgnfcant n determnng a farm household s level of commercalsaton. The estmates support the clam that techncal effcency s endogenous and does not cause commercalsaton. Ths concdes wth the argument by Gebre-ab (2006) that surplus producton, or beng productve, s not a man drver of market partcpaton n largely subsstence agrculture, snce smallholder farmers can stll supply a certan proporton of ther produce to the market wth the objectve of coverng other household requrements (or basc needs, such as medcne). Thus, for the households n the dataset, ths study dentfed low techncal effcency to be the result of a low level of commercalsaton, rather than ts cause. 7. Conclusons Ths study analysed the prevalence of the potental lnk between smallholder commercalsaton and techncal effcency and found that the former played a sgnfcant role n mprovng the latter. Specfcally, the stochastc fronter estmates ndcated that ncreasng the level of market partcpaton could enhance a farmer s level of techncal effcency, supportng the argument that commercalsaton mproves smallholders productvty by ncreasng ther ncome and thereby mprovng access to healthy and nutrtous food (Pngal & Rosegrant 1995). Ths mples that any polcy effort amed at creatng an effcent te between the farm sector and the market wll mprove the performance of agrcultural producton. Thus polcy measures drected at ncreasng the market partcpaton rate of farmers by provdng an mproved level of educaton, suffcent access to ICT tools such as rados and moble telephones, an mproved transport nfrastructure and access to transportaton servces wll sgnfcantly contrbute to mprovements n agrcultural productvty. References Agner D, Lovell C & Schmdt P, Formulaton and estmaton of stochastc fronter producton functon models. Journal of Econometrcs 6: Angrst JD & Pschke JS, Mostly harmless econometrcs: An emprcst's companon. Prnceton NJ: Prnceton Unversty Press. Barrett CB, Smallholder market partcpaton: Concepts and evdence from eastern and southern Afrca. Food Polcy 33: Battese GE & Coell TJ, A model for techncal neffcency effects n a stochastc fronter producton functon for panel data. Emprcal Economcs 20: Bgsten A, Kebede B, Shmeles A & Taddesse M, Growth and poverty reducton n Ethopa: Evdence from household panel surveys. World Development 31: Bnswanger HP & Von Braun J, Technologcal change and commercalzaton n agrculture: The effect on the poor. The World Bank Research Observer 6: Block SA, Agrculture and economc growth n Ethopa: Growth multplers from a four-sector smulaton model. Agrcultural Economcs 20: Bound J, Jaeger DA & Baker RM, Problems wth nstrumental varables estmaton when the correlaton between the nstruments and the endogenous explanatory varable s weak. Journal of the Amercan Statstcal Assocaton 90: Byrnes P, Färe R, Grosskopf S & Kraft S, Techncal effcency and sze: The case of Illnos gran farms. European Revew of Agrcultural Economcs 14:

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