Measuring the Impact of Ethiopia s New Extension Program on the Productive Efficiency of Farmers

Transcription

1 Measurng the Impact of Ethopa s New Extenson Program on the Productve Effcency of Farmers Arega D. Alene and Rashd M. Hassan Department of Agrcultural Economcs, Extenson, and Rural Development Unversty of Pretora, Pretora 0002 South Afrca Contrbuted paper selected for presentaton at the 25th Internatonal Conference of Agrcultural Economsts, August 16-22, 2003, Durban, South Afrca Copyrght 2003 by Arega D. Alene and Rashd M. Hassan. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 1. Introducton The growng gap between food demand and supply n Ethopa s manly attrbuted to the very low productvty of the agrcultural sector. Heavy relance on obsolete farmng technques, poor complementary servces such as extenson, credt, marketng, and nfrastructure, and napproprate agrcultural polces are among the major factors that have greatly retarded the development of Ethopa s agrculture. Despte ts domnant share n the country s total agrcultural output, and hence n the GDP, smallholder agrcultural producton lacked the necessary attenton n the country s agrcultural development efforts n the past. One of the major polcy shfts snce the change of government n 1992 has been the substantal emphass placed on mprovng the productvty of peasant agrculture through ncreased use of a package of mproved agrcultural technologes. As part of the agrcultural development-led ndustralzaton development strategy, the Ethopan government ntroduced the new extenson program (NEP) based on the experences of SG 2000 (SG) project whch embarked upon the popularzaton of large-scale (usually half-hectare) on-farm demonstraton plots for already avalable mproved agrcultural producton technologes. In formulatng NEP, attempts had been made to screen out and preclude the shortcomngs of past extenson systems. Frst, the extenson servce was erroneously organzed by commodty rather than by functon. Second, the extenson servce was rather prescrptve n the sense that t only transmtted nformaton wth lttle or no supply of nputs. Thrd, the extenson servce was lmted only to hgh potental areas of the country, neglectng other agro-ecologcal zones. Fourth, demonstraton stes were not wdely dstrbuted and they were rather undertaken n fences. Ffth, extenson nformaton was not effectvely communcated through dfferent methods. Sxth, budgets, manpower, means of transport, etc., were not adequately allocated for the extenson servce and there was neffcency n admnstraton and management (TGE, 1994). NEP was thus developed aganst the above backgrounds amng to mprove the productvty of smallholder farmers through better access to mproved producton technologes such as fertlzer, mproved seeds, pestcdes and better cultural practces manly for cereal crops, ncludng maze, wheat, and teff. The program provded credt, nputs and extenson 1

3 assstance to partcpants wllng to establsh half-hectare demonstraton plots on ther own land and to settle 25-50% down payments for mproved nputs. It promotes ntegrated technology packages developed for dfferent agro-ecologcal zones, ncludng the hghland mxed farmng zone, hghland degraded and low mosture zone, lowland agro-pastoralst zone, and lowland pastoralst zone. Its mplementaton was launched n 1995/96 croppng season as an expanson of SG package approach, prmarly through dssemnaton of crop technologes. In 1995/96, about 36,000 half-hectare on-farm demonstraton plots were establshed and average yelds for the major crops ncludng, maze, wheat, teff and sorghum have ncreased by 98% and the ncrement was more than double for maze and wheat (Takele, 1996). In 1996/97 and 1997/98, the number of government sponsored demonstraton plots was 650, 000 and 2.9 mllon, respectvely (Befekadu and Berhanu, 1999). The rapd expanson of NEP has taken place at a tme of major changes n markets, polces, and nsttutons affectng the agrcultural sector: a new credt system launched n 1994, gradual lberalzaton of the fertlzer market from 1991 to 1997, and government decentralzaton. Despte consderable yeld ncrements obtaned from the demonstraton plots of the SG project n the hgh-potental agrcultural areas, knowledge about the mpact of NEP on the producton effcency of farmers s very scanty. The success of NEP s beleved to depend upon how well the three functons of extenson, credt and nput delvery meet the partcular needs of smallholders, a stuaton very dfferent from that of SG project, whch was lmted to specfc hgh-potental zones wth relatvely better functonng credt and nput delvery servces. For example, NEP credt system s more complex: there are multple actors (banks provde credt, regonal governments guarantee credt, and extenson agents approve partcpants and collect payments); nterest s charged; and local admnstraton follows strct enforcement rules. Further, NEP needs to deal wth a fertlzer sector characterzed by ncreasng retal prces due to subsdy removal and supply neffcences. There are growng concerns about NEP that would serously harm ts effectveness n enhancng new technology utlzaton and n brngng about the desred mprovements n productvty. Frst, extenson agents, apart from ther own lttle techncal knowledge about new technologes, are nvolved n too many non-extenson tasks: processng credt applcatons, dealng wth nput dstrbutors, moblzng farmers for publc works, and 2

4 collectng loans and taxes. Second, rapd expanson of NEP to less favorable and margnal areas requred more supervson and credt, than less, due to the low lteracy rates and poor asset endowments of the farmers n these areas, aganst the background of a rather lmted number of extenson agents and dwndlng credt portfolos to regons. The overall mpact of ncreased plots per extenson agent and the extra tasks s a lower qualty extenson message. Ths opens up possbltes for farmers to experence greater producton neffcency and hence loss of potentally obtanable output from new technology due to lack of famlarty wth the new technology, market nformaton and credt. There s, however, lack of adequate emprcal evdence regardng the mpact of NEP on producton effcency n dfferent agroecologcal zones, gven a package of mproved technologes. The objectve of ths paper s, therefore, to assess the mpact of NEP on the techncal and allocatve effcency of farmers and to dentfy the underlyng factors nfluencng ther level of effcency n eastern Ethopa. It specfcally measures and compares the techncal, allocatve, and overall productve effcences of partcpant and non-partcpant farmers n NEP, relatve to ther respectve technologes. The paper s organzed as follows. The next secton presents the analytcal framework and the data and emprcal procedures are presented n the thrd secton. In the fourth secton, the results are presented and dscussed and the last secton draws concluson and polcy mplcaton. 2. Analytcal framework Producton effcency has two components: techncal and allocatve effcency. Techncal effcency s the extent to whch the maxmum possble output s acheved from a gven combnaton of nputs. On the other hand, a producer s sad to be allocatvely effcent f producton occurs n a subset of economc regon of the producton possbltes set that satsfes the producer's behavoral objectve (Ells, 1988). Farrell (1957) dstngushes between techncal and allocatve effcency n producton through the use of a 'fronter' producton functon. Techncal Effcency s the ablty to produce a gven level of output wth a mnmum quantty of nputs under certan technology. Allocatve effcency refers to the ablty of choosng optmal nput levels for gven factor prces. Overall productve effcency s the product of techncal and allocatve effcency. Thus, f a frm has acheved 3

5 both techncally effcent and allocatvely effcent levels of producton, then the frm s economcally effcent and new nvestment streams may be crtcal for any new development. Snce Farrell s (1957) semnal paper, there has been a growng nterest n methodologes and ther applcatons to effcency measurement. Whle early methodologes were based on determnstc models that attrbute all devatons from maxmum producton to neffcency, recent advances have made t possble to separately account for factors beyond and wthn the control of frms such that only the latter wll cause neffcency. Agner et al. (1977) and Meeusen and Van den Broeck (1977) ndependently proposed the stochastc fronter producton functon to account for the presence of measurement errors and other nose n the data, whch are beyond the control of frms. Stochastc fronters have two error terms. The frst accounts for the presence of techncal neffcences n producton and the second accounts for measurement errors n output, weather, etc and the combned effects of unobserved nputs n producton. The producton technology of a frm s represented by a stochastc fronter producton functon (SFPF) as ( ; β ) Y = f X + v u (1) where Y measures the quantty of agrcultural output; X s a vector of the nput quanttes; β s a vector of parameters; f ( ; ) ndependently and dentcally dstrbuted Ν ( 0, σ X β s the producton functon; v s assumed to be 2 v ) random error, ndependent of the u ; and u s a non-negatve random varable, assocated wth techncal neffcency n producton, and s assumed to be ndependently and dentcally dstrbuted as half-normal, u ~ N(0, σ 2 u). The maxmum lkelhood estmaton of equaton (1) yelds estmators for β σ u and λ where λ = σ v and σ = σ u + σ v. Jondrow et al. (1982) have shown that the assumptons made on the statstcal dstrbutons of v and u, mentoned above, make t possble to calculate the condtonal mean of u, gven ε = v u as 4

6 σσ u v φ( ελ / σ) ελ E( u / ε) = σ 1 Φ( ελ/ σ) σ (1) where Φ and φ are, respectvely, the standard dstrbuton and the standard normal densty functons, evaluated at ελ / σ. The conventonal stochastc effcency decomposton methodology uses the level of output of each frm adjusted for statstcal nose (.e., Y v ), observed nput ratos, and the estmated parameters of the SFPF to decompose overall effcency nto techncal and allocatve effcency (e.g., Bravo-Ureta and Reger, 1991; Bravo-Ureta and Evenson, 1994). However, whle the parameters of the SFPF are estmated usng an output-orentated approach, techncal effcency s derved by mposng an nput-orentated approach mpled by the smultaneous soluton of adjusted outputs and the observed nput ratos to yeld the techncally effcent nput vectors. Inconsstency arses when effcency estmates that could be obtaned drectly from the SFPF dffer from those actually derved through the decomposton, due to possble scale effects. Therefore, adoptng an nput orentaton for effcency decomposton when orgnal SFPF specfcatons have an output orentaton requres that observed output be adjusted for statstcal nose as well as scale effects. Ths s accomplshed by frst defnng a scale factor as the devaton from constant returns to scale as π = θ 1, (2) where π s the scale factor for the th frm and θ s the quas functon coeffcent of the producton technology. Imposng an nput orentaton on the orgnal output-orentated SFPF wll produce overall neffcency effect, u *, that s composed of pure techncal neffcency effect equvalent to the output-orentated techncal neffcency, u, and a scale effect, ϕ, so that we have u = u + ϕ. (3) * 5

7 Consstency requres that nput- and output-orentated techncal neffcency effects be equal (.e., u * = u ). To the extent that there s a non-zero scale effect, the conventonal decomposton methodology gves nconsstent effcency estmates. In the output-orentated approach, the scale effect s emboded, and hence already accounted for, n the determnstc structure of the producton fronter. From ths t follows that the observed output must be adjusted not only for statstcal nose but also for scale effects. The scale effect s a proporton of the output-orentated techncal neffcency effect so that the scale effect of the th frm can be gven by ϕ = π u. (4) From equatons (2) and (4), the nput-orentated adjusted output of the th frm can be derved usng the followng relaton Y f( X ; β ) u = Y v π u, (5) * * where * Y s the observed (or actual) output adjusted for statstcal nose captured by v and scale effects captured by ϕ. f ( ) s the determnstc fronter output, and u and v are, respectvely, the neffcency and random components of overall devatons from the fronter. Adjusted output, Y * t, s used to derve the techncally effcent nput vector, X. The techncally effcent nput vector for the th frm, t X, s derved by smultaneously solvng x1 equaton (5) and the observed nput ratos = k ( > 1) x rato of the two nputs n the producton of Y *. where k s equal to the observed Assumng that the producton functon n equaton (1) s self-dual (e.g. Cobb-Douglas), the dual cost fronter can be derved algebracally and wrtten n a general form as follows C = h W Y δ, (6) * (, ; ) 6

8 where C s the mnmum cost of the k th frm assocated wth output Y *, W s a vector of nput prces for the th frm, and δ s a vector of parameters to be estmated. The economcally effcent nput vector for the th frm, X e, s derved by applyng Shephard's Lemma and substtutng the frm s nput prces and adjusted output level nto the resultng system of nput demand equatons C W n = X W Y e * n (, ; θ ), (7) where θ s a vector of parameters, n= 1,2,..., N nputs. The observed, techncally effcent, and economcally effcent costs of producton of the th frm are equal to WX, / WX / t, WX / e, respectvely. These cost measures are used to compute techncal (TE) and overall productve or economc effcency (EE) ndces for the th frm as follows TE EE t WX =, (8) WX / / / / e WX =. (9) WX Followng Farrell (1957), the allocatve effcency (AE) ndex can be derved from equatons (8) and (9) as follows AE WX =. (10) WX / / e t Identfcaton of factors nfluencng effcency has also been an mportant exercse but debate as to whether the sngle or two-stage method s approprate s not settled. Battese and Coell (1995) and Kumbhakar (1994) challenge the two stage approach by argung that the farmspecfc factors should nstead be ncorporated drectly n the frst stage estmaton of the stochastc fronter because such factors can have a drect mpact on effcency and they 7

9 proposed a model ncorporatng these varables. Nevertheless, the two-stage method s mostly preferred due to a round-about effect of varables on effcency (Assefa, 1995; Kalrajan, 1991; Bravo-Ureta and Reger, 1991; Bravo-Ureta and Evenson, 1994; Sharma et al., 1999). The lnear regresson model has thus been a common approach to the analyss of the effects of farm-specfc factors on productve effcency. After transformng the effcency scores usng the Box-Cox procedure and takng logs (Judge et al., 1985) we get E / ln = X β + ε, (11) 1 E where E s the th frm s level of effcency, X s a vector of explanatory varables, β s a vector of parameters to be estmated, and ε s a vector of dentcally and ndependently dstrbuted random errors N(0, σ 2 ). Despte ts well known lmtatons, we use a Cobb-Douglas functonal form for the SFPF because the methodology employed n ths study requres that the producton functon be selfdual. In fact, Taylor et al. (1986) argued that as long as nterest rests on effcency measurement and not on the analyss of the general structure of the producton technology, the Cobb-Douglas producton functon provdes an adequate representaton of the producton technology. Moreover, n one of the very few studes examnng the mpact of functonal form on effcency, Kopp and Smth (1980) concluded " that functonal specfcaton has a dscernble but rather small mpact on estmated effcency" (pp. 1058). That s why the Cobb-Douglas functonal form has been wdely used n farm effcency analyses both for developng and developed countres (see Battese, 1992). 3. Data and emprcal procedures 3.1. Data The data for ths study come from two samples of farmers, one sample composed of farm households partcpatng n the extenson program and another composed of non-partcpant 8

10 farm households, n two selected dstrcts, Meta and Bable, each representng dstnct agroclmatc zones n eastern Ethopa. Meta dstrct was selected to represent a typcal wet hghland zone where there s very hgh populaton pressure on land and receves relatvely better ranfall amount and dstrbuton rangng between 900 and 1200 mllmeters per annum. Meta s a hgh potental cereal producton zone where NEP s wdely mplemented to enhance the producton of food grans. The most wdely grown cereals n Meta are maze, barley and wheat. On the other hand, Bable dstrct was selected to represent a dry land zone recevng an annual ranfall between 500 and 700 mllmeters. Bable s an mportant target of NEP and NGO s actvtes n vew of wdespread food nsecurty. Dry land technologes generated by Alemaya Unversty and other research centers are manly tested and promoted n Bable. Technologes nclude short-cycle, drought tolerant, and better yeldng varetes of maze and sorghum along wth the approprate fertlzer recommendatons and agronomc practces. Sorghum, maze and groundnuts are wdely grown n Bable. The surveyed farmers were randomly selected after an ntal stratfcaton of farm households n three Peasant Assocatons nto partcpants and non-partcpants n NEP. The partcpant and non-partcpant sample farm households surveyed n Meta were, respectvely, 53 and 47, whereas 50 farm households from each group were surveyed n Bable. Data were collected through frequent vsts to the sample farm households crop felds to carry out ntervews and to take plot-level measurements and observatons throughout the 2001/2002 agrcultural year. Input data were collected on a fortnght bass by askng the farmer to recall hs/her actvtes durng the past two weeks. Data ncluded labor tme dsaggregated by source, gender, age, and feld operaton. The quanttes of oxen-tracton, seed, organc/norganc fertlzer, pestcdes, and herbcdes, and the prces of all purchased nputs were also collected durng ths tme. Output data on all the quanttes of cereals, pulses, and ol crops harvested were collected. A separate survey was conducted to collect output prce nformaton from Mut, Chelenko and Bable markets durng plantng and harvestng tmes of the major crops. A summary of the values of the varables used n the analyss s presented n Table 1. Partcpants n NEP n both dstrcts obtaned hgher average crop output value per hectare of cultvated land n vew of hgher average yelds of the major crops, ncludng maze, sorghum, groundnuts, wheat and barley. Moreover, the partcpants have hgher cultvated 9

11 Table 1 Summary statstcs of the varables used n the analyss Varable Meta Bable Partcpants Non-partcpants Partcpants Non-partcpants Mean (S.D.) Mean (S.D.) Mean (S.D.) Mean (S.D.) Value of crop output (Brr/ha) 3350 (1306) 1490 (769) 3660 (1508) 1471 (714) Hybrd maze yeld (klogram/ha) 5100 (2460) Local maze yeld (klogram/ha) 2040 (670) 2030 (1300) 1070 (740) 930 (550) Sorghum yeld (klogram/ha) (1000) 1100 (510) 980 (470) Groundnuts yeld (klogram/ha) (390) 670 (300) Wheat yeld (klogram/ha) 2000 (920) 1700 (1070) Barley yeld (klogram/ha) 1380 (970) 1420 (2150) Cultvated Land (ha) 0.73 (0.60) 0.65 (0.16) 1.70 (0.66) 1.45 (0.60) Labor (Man-days/ha) 65 (32) 68 (16) 57 (29) 53 (80) Fertlzer (klogram/ha) 69 (36) 22 (24) 42 (12) 17 (10) Age 39 (12) 41 (12) 37 (10) 38 (10) Educaton (lteracy) dummy 0.77 (0.35) 0.66 (0.31) 0.66 (0.42) 0.40 (0.26) Off-farm ncome (Brr) 209 (62) 91 (143) 327 (91) 287 (28) Extenson vst 6 (3) 0.8 (1) 8 (7) 4 (3) Man equvalent 1.58 (0.8) 1.39 (0.53) 1.57 (0.57) 1.45 (0.57) Cash Credt (Brr) 71 (96) 32 (78) 337 (421) 50 (21) Lvestock unt 2.47 (1.69) 2.04 (1.24) 5.67 (0.59) 3.90 (0.45) Maze-potato share (percent) 54 (12) 63 (33) Cereal-Pulse share (percent) 8 (2) 11 (5) 45 (10) 39 (13) Note: S.D. = Standard devaton. land, lvestock unts, off-farm ncome, cash credt, household labor and extenson vsts than the non-partcpants. Both groups of farmers n Meta have comparably hgh average percentage of cultvated area allocated to the maze-potato croppng system, whch provdes greater opportuntes for effcent use of land n the face of ncreasng land shortages n the wet hghland zone. 10

12 In vew of the ncreasng pressure on land n the wet hghland zone, both partcpant and nonpartcpant farmers n Meta have less average cultvated land and lvestock than farmers n the dry land zone, Bable. Both groups of farmers n Bable have comparably hgh average percentage of cultvated area allocated to the cereal-pulse croppng system, whch offers opportuntes for crop dversfcaton to cope wth the rsk of crop falure due to drought as well as for mprovng yeld through sol fertlty mprovement and better control of pests and dseases (Bezabh, 2000) Emprcal models For the nvestgaton of the techncal, allocatve and economc effcences of partcpant and non-partcpant farmers, separate stochastc fronter producton functons, of the followng form, are estmated for partcpant and non-partcpant farmers lny = β + β ln land + β ln labor β ln fertlzer + β ln materals + ( v u ), 3 4 (12) where ln denotes the natural logarthm (base, e); Y denotes the gross value of crop output of the th farmer, the weghts beng the shares n total revenue; land denotes the total land cultvated n hectares; labor denotes the total of famly labor, exchange labor, and hred labor used n man-days; materals denotes the mplct quantty ndex of seeds and chemcals (pestcdes, nsectcdes, herbcdes, and fungcdes) estmated as the value of all seeds and chemcals deflated by a weghted prce ndex of the nputs, the weghts beng the share of each nput n total cost. The soluton to the cost mnmzaton problem n equaton (13) s the bass for dervng the dual cost fronter, gven the nput prces ( w ), parameter estmates of the stochastc fronter n producton functon ( ˆβ * ) n equaton (12), and the nput-orentated adjusted output level Y n equaton (5) Mn C = wnxn (13) x n 11

13 Subject to ˆ * ˆ n n n Y = A X β. Substtuton of the cost mnmzng nput quanttes nto equaton (13) yelds the followng dual cost functon CY w =ΚY w α, (14) ( * *, ) n n n where α n = ψβˆ, ψ = ( β ) 1, n n ˆn ψ 1 βˆ n Aˆ ˆ Κ= βn ψ, A ˆ = exp( βˆ 0 ). n The nvestgaton of factors nfluencng techncal and allocatve effcences of partcpant and non-partcpant farmers s carred out by estmatng the followng model E = +, (15) ln β0 βx 1-E where E s effcency (.e., techncal or allocatve), β s a vector of unknown coeffcents of the neffcency varables, X, to be estmated. The varables that are hypotheszed to nfluence farm level producton effcency n the Ethopan context (Assefa, 1995; Getachew, 1995) are: AGE (the age of the household head); RWEDUC (dummy for lteracy of the household head n terms of readng and wrtng); PREDUC (dummy for attendance of prmary educaton); CASHCR (amount of cash credt obtaned); EXTNSN (the number of vsts to a farmer by an extenson agent durng the croppng season); PARTCPN (the number of years the farmer partcpated n extenson programs); LSTKUNT (lvestock unt); OFINCM (amount of off-farm ncome obtaned by the household); CERPULS (percentage of cultvated area allocated to the cereal-pulse croppng system) for Bable; MZPOT (percentage of cultvated area allocated to the maze-potato croppng system) for Meta; and MKTDIST (dstance to the dstrct market n walkng mnutes). 12

14 4. Emprcal results The maxmum-lkelhood (ML) estmates of the parameters of the stochastc fronter producton functon are presented n Table 2. The ordnary least squares (OLS) estmates of the average producton functons are also presented for comparson. A common stochastc fronter model for all farmers n each of the dstrcts, rrespectve of whether they partcpated n NEP, was estmated to see f the two samples of farmers actually used dfferent technologes. Usng the generalzed lkelhood rato (LR) test (Coell and Battese, 1996), the aggregate model for Bable could not be rejected whle the correspondng model for Meta was strongly rejected 1. Ths ndcates that whle the partcpant and non-partcpant farmers actually used dfferent producton technologes n the wet hghland zone, those n the dry land zone used homogenous technologes. Ths confrms the serous shortage of mproved technologes for Bable, as s the case wth other mosture-stressed agro-clmatc zones (Bezabh, 2000). Therefore, the aggregate model for Bable was chosen as the preferred model to predct the effcency ndces for both groups of farmers. As expected, the output elastctes of all varables are postve n all SFPF specfcatons. For partcpants n Meta, all nput varables are postve and hghly sgnfcant n determnng crop producton. For non-partcpants n Meta, who have no access to nput credt and can nether afford to buy adequate amounts fertlzer and chemcals, these varables are not statstcally sgnfcant. The estmate of the varance parameter, λ, s sgnfcant n the SFPF of both partcpant and non-partcpant farmers n both dstrcts, mplyng that the neffcency effects are sgnfcant n determnng the level and varablty of crop producton n the study areas. The dual fronter cost functon for partcpants n Meta, derved analytcally from the stochastc producton fronter shown n Table 2, s gven as 1 The LR test-statstc for the null hypothess of aggregate functon s equal to 8 for Bable and 12 for Meta compared to 9.5, the 95 percent χ 2 crtcal value wth 4 degrees of freedom. 13

15 ln C = ln w ln w ln w A L F + + * ln wm ln Y. (16) Table 2 OLS and ML estmates of the average and stochastc fronter producton functons a, b Meta Bable Partcpants Non-partcpants Aggregate Varable OLS estmates ML estmates OLS estmates ML estmates OLS estmates ML estmates Intercept 6.174*** (19.414) 6.632*** (19.785) 5.624*** (10.968) 6.013*** (12.146) 6.069*** (19.053) 6.615*** (24.374) ln (Land) 0.262*** (3.372) 0.330*** (3.774) 0.884** (2.500) 0.747** (2.095) 0.415*** (3.477) 0.433*** (3.631) ln (Labor) 0.179*** (2.669) 0.171** (2.011) 0.309** (2.183) 0.256* (1.787) 0.145*** (2.990) 0.183*** (3.812) ln (Fertlzer) 0.140** (2.152) 0.118** (2.105) (1.168) (0.971) 0.141*** (3.991) 0.098** (1.936) ln (Materals) 0.111*** 0.092** (2.454) (1.208) (0.796) (3.028) (0.738) (1.031) R λ 4.146* (1.715) 2.332* (1.624) 2.729*** (2.513) 2 σ u σ v Loglkelhood a ***, sgnfcant at 0.01 level; **, sgnfcant at 0.05 level; *, sgnfcant at 0.1 level. b Fgures n parentheses represent asymptotc t-ratos. The dual cost fronter for non-partcpants n Meta s gven as ln C = ln w ln w ln w A L F + + * ln wm ln Y. (17) The dual cost fronter for all sample farmers n Bable s gven as ln C = ln w ln w ln w A L F * ln wm ln Y. + + (18) 14

16 where C s the mnmum cost of producton of the th farmer; Y * s the ndex of output adjusted for any statstcal nose and scale effects as specfed n equaton (5); seasonal rent of a hectare of land n Brr, fertlzer n Brr per klogram; and w L s the daly wages n Brr; w M s the prce ndex of seeds and chemcals. w A s the w F s the prce of 4.1. Effcency estmates Usng the cost fronters, average nput prces and equatons (8), (9), and (10), the techncal, allocatve, and economc effcency ndces are computed for each producer. The frequency dstrbutons and summary statstcs of these ndces for partcpant and non-partcpant farmers n NEP are presented n Tables 3 and 4. For partcpant farmers n Meta, the estmated mean techncal, allocatve, and economc effcency ndces are 79%, 80%, and 65%, respectvely, whereas the correspondng results for non-partcpants are 72%, 85%, and 63%. Table 3 Frequency dstrbuton and summary statstcs of effcency estmates for partcpant and non-partcpant farmers n Meta dstrct, eastern Ethopa Level TE Number (percent farms) AE Number (percent farms) EE Number (percent farms) (percent) Partcpants Non-partcpants Partcpants Non-partcpants Partcpants Non-partcpants <50-5(11) - 4(9) 6(11) 13(28) (13) 5(11) - 2(4) 11(21) 8(17) (15) 7(15) 5(10) 1(2) 19(36) 11(23) (17) 12(25) 16(30) 4(9) 12(22) 10(21) (30) 16(34) 23(44) 12(25) 5 (10) 5(11) (25) 2(4) 9(17) 24(51) - - Mean 79 a 72 a 80 b 85 b Mnmum Maxmum a, b Sgnfcant mean dfference at 0.05 level. 15

17 The results for Meta ndcate that both partcpant and non-partcpant farmers n NEP exhbt equally hgh overall (or productve) neffcences due to ther low techncal and allocatve effcences of producton. Relatve to ther respectve technologes, the partcpants have sgnfcantly hgher techncal but lower allocatve effcences than the non-partcpant farmers wth the result that both groups exhbt smlar overall (productve) effcences. The partcpants and non-partcpants can gan, respectvely, an average crop output growth of 35% and 37% through full mprovements n techncal and allocatve effcences. The results suggest that although NEP mproved the techncal effcency of partcpant farmers n Meta, gven ther mproved technology, t agan nduced greater allocatve neffcences and hence ddn t mpact on overall productve effcences. For partcpant farmers n Bable, the results n Table 4 show that the mean techncal, allocatve, and productve effcency ndces are 68%, 81%, and 54%, respectvely, whereas the correspondng results for non-partcpants are 66%, 84%, and 57%, ndcatng substantal productve neffcences among both groups of farmers. The partcpants and nonpartcpants can gan, respectvely, an average crop output growth of 46% and 43% through full mprovements n techncal and allocatve effcency. Apart from usng homogenous technologes, the two groups do not have sgnfcantly dfferent techncal, allocatve, and overall productve effcences. The results n both agro-clmatc zones confrm the falure of NEP n enhancng the productve effcences of farmers. NEP has had no postve mpact on the productve effcences of farmers. The emprcal evdence regardng the nfluence of new technologcal nterventons on techncal effcency s mxed. The postve mpact of NEP on techncal effcency especally n the wet hghland zone s n agreement wth Seyoum et al. (1998) who found consderably hgher techncal effcency of maze producton among partcpants n the SG project compared wth the non-partcpants n eastern Ethopa. Taylor et al. (1986) also obtaned a postve nfluence, though nsgnfcant, of an agrcultural credt program on techncal effcency of farmers n Brazl. On the contrary, Xu and Jeffrey (1998) obtaned sgnfcantly lower techncal effcency for hybrd rce producton n Chna as compared wth conventonal rce producton whle Sngh et al. (2000) obtaned lower techncal effcency for 16

18 newly establshed Indan dary processng plants after lberalzaton of the dary ndustry compared to the old plants. Table 4 Frequency dstrbuton and summary statstcs of effcency estmates for partcpant and nonpartcpant farmers n Bable dstrct, eastern Ethopa Level TE Number (percent farms) AE Number (percent farms) EE Number (percent farms) (percent) Partcpants Non-partcpants Partcpants Non-partcpants Partcpants Non-partcpants <50 5(10) 8(16) 7(14) 1(2) 14(28) 16(32) (8) 7(14) 1(2) - 24(48) 15(31) (32) 12(25) 3(6) 3(6) 12(24) 3(6) (32) 9(18) 9(18) 6(12) - 14(29) (18) 12(25) 23(46) 30(61) - 1(2) (2) 7(14) 9(19) - - Mean Mnmum Maxmum The negatve nfluence of NEP on allocatve effcency n both areas s actually consstent wth all the above studes. For example, Taylor et al. (1986) obtaned a sgnfcant negatve mpact of an agrcultural credt program n Brazl on allocatve effcency of partcpant farmers. Xu and Jeffrey (1998) also obtaned sgnfcantly lower allocatve effcency for hybrd rce producton n Chna as compared wth conventonal rce producton across all the three regons studed. Sngh et al. (2000) also obtaned lower allocatve effcency for newly establshed Indan dary processng plants after lberalzaton of the dary ndustry compared to the old plants as they needed tme to reach full operaton, the rght choce of products and other manageral sklls requred for hgher performance Factors nfluencng effcency The parameter estmates of the OLS regressons employed to dentfy the factors nfluencng partcpant and non-partcpant farmers levels of techncal and allocatve effcences n the 17

19 respectve dstrcts are presented n Tables 5 and 6. For partcpant farmers n Meta, the results show that techncal effcency of partcpants s postvely and sgnfcantly nfluenced by educaton, credt, prevous partcpaton n extenson programs, and the share of the mazepotato croppng system whle ther allocatve effcency s postvely nfluenced by educaton, credt, and prevous partcpaton n extenson programs. Table 5 Factors nfluencng the effcency of partcpant and non-partcpant farmers n Meta dstrct, eastern Ethopa a, b Varable Partcpants Non-partcpants TE AE TE AE Constant 1.211** (1.982) 0.231* (1.611) (1.125) (0.658) AGE (-0.369) (-1.478) (1.150) 0.063* (1.854) EXTNSN (1.021) (0.055) (0.667) (0.656) RWEDUC 0.183** (1.986) 0.088* (1.705) 0.058* (1.670) 0.063* (1.667) PREDUC (1.063) (1.535) (1.023) (1.002) FARMSZ (-1.012) (0.002) (-0.101) (1.023) CREDIT 0.117** (2.116) 0.205** (2.189) 0.082* (1.635) 0.102* (1.852) PARTCPN 0.201** (2.354) 0.091* (1.820) (1.221) (1.153) LSTKUNT 0.01(0.985) (1.033) (0.036) (0.786) OFINCM (1.01) (0.963) (1.185) (1.001) HHLABR (0.687) (0.990) (0.855) (0.881) MZPOT ** (2.132) (1.021) * (1.812) (1.020) MKTDIST (1.212) (-1.425) (1.127) (-1.188) R F 5*** 4*** 6*** 3*** a *** sgnfcant at 0.01 level; ** sgnfcant at 0.05 level; * sgnfcant at 0.10 level. b Fgures n parentheses are t-ratos. The role of credt and educaton cannot be overemphaszed n the effectve functonng of NEP. The serous shortage of cash facng the farmers due to deteroratng product prces and the demands of new nputs for adequate knowledge of proper utlzaton have undesrable mpact on tmely farmng operatons and optmal nput applcatons, thereby nfluencng farmers levels of techncal and allocatve effcences (Al and Byerlee, 1991; Assefa, 1995). Further, the postve and sgnfcant mpact of prevous partcpaton n extenson programs 18

20 on techncal and allocatve effcency confrms the mportant role of greater experence wth new technques of producton n promotng farmers techncal and allocatve effcency under mproved technology. Ths also mples that NEP s lkely to enhance the techncal and allocatve effcency of farmers n the long run as farmers fully respond to the new demands of the technologes and the program also begns to have better credt and nput supply systems. For non-partcpant farmers n Meta, the results show that techncal effcency s postvely and sgnfcantly nfluenced by educaton, credt, and the share of the maze-potato system whle ther allocatve effcency s postvely and sgnfcantly nfluenced by age, educaton, and credt, ndcatng that tradtonal farmers make better techncal and allocatve decsons f they acqure basc educaton, have greater experence wth tradtonal technology, and have better access to credt. However, unlke n the case of the partcpants, prevous partcpaton n extenson programs doesn t sgnfcantly nfluence the techncal effcency of nonpartcpant farmers. Ths s perhaps because these farmers have rarely benefted from extenson programs n vew of ther poor access to suffcent amount of land to allocate for the applcaton of new technology, poor awareness of the benefts of new technology, serous cash constrants to settle down payments for nput credt, and ther hghly rsk averse behavor (Assefa, 1995). Furthermore, even when farmers happen to partcpate n prevous programs, they do not seem to apply new methods and cultural practces they acqured through programs and projects to ther own tradtonal crops n the subsequent years after graduaton. For nstance, farmers destroyed sol conservaton structures followng the phasng out of projects and also contnued plantng tradtonal maze by broadcastng nstead of plantng n rows, whch they practced whle growng mproved maze. They are generally lttle prepared to take advantage of new technques learnt to mprove ther effcency n tradtonal crops producton, and nether could they contnue usng mproved technology to mprove ther effcency n food producton, due to the serous supply constrants especally of mproved seeds whch are only ratoned through NEP (Mulat, 1999). 19

21 Table 6 Factors nfluencng the effcency of partcpant and non-partcpant farmers n Bable dstrct, eastern Ethopa a, b Varable Partcpants Non-partcpants TE AE TE AE Constant 2.195***(3.698) 2.103*** (5.223) 3.101*** (3.589) 1.523** (2.325) AGE (1.135) 0.071* (1.655) (1.201) 0.102* (1.944) EXTNSN (1.178) (1.02) (1.457) (1.052) RWEDUC 0.095* (1.825) 0.108** (2.078) 0.067* (1.626) 0.121** (2.005) PREDUC (1.452) (1.077) (1.142) (1.014) FARMSZ (1.014) (1.025) (-0.101) (1.110) CREDIT (0.116) (1.350) (0.833) (1.425) PARTCPN (1.256) (1.057) (1.921) (0.981) LSTKUNT (-1.211) (1.422) (-0.036) (0.861) OFINCM 0.128* (1.950) 0.092* (1.735) 0.260** (2.268) 0.092* (1.967) HHLABR (1.015) (1.273) (1.481) (0.699) CERPULS 0.119**(2.070) (0.989) 0.233*** (3.568) (1.089) MKTDIST (-1.058) (-1.512) (-1.201) (-1.114) R F 10*** 4.5** 7*** 3.6** a *** sgnfcant at 0.01 level; ** sgnfcant at 0.05 level; * sgnfcant at 0.10 level. b Fgures n parentheses are t -ratos. For both partcpant and non-partcpant farmers n Bable, the results n Table 6 show that ther techncal effcency s postvely and sgnfcantly nfluenced by educaton, the share of the cereal-pulse system, and off-farm ncome, whereas ther allocatve effcency s postvely and sgnfcantly nfluenced by age, educaton and off-farm ncome. Although not sgnfcant, whle prevous partcpaton n extenson programs, credt, extenson vsts, and household labor have a postve nfluence, market dstance has a negatve nfluence on the techncal and allocatve effcency of farmers n Bable. Although nsgnfcant, lvestock ownershp negatvely nfluences techncal effcency but has a postve mpact on allocatve effcency. The negatve nfluence on techncal effcency may be due to the compettve nature of crop and lvestock producton under condtons of serous feed shortages where farmers have to feed lvestock through heavy thnnng and defolaton (Storck et al., 1997), or have to travel long dstances n search of feed thereby delayng crtcal farmng operatons. 20

22 5. Conclusons and polcy mplcatons Ths paper employed a robust stochastc effcency decomposton technque that accounts for scale effects to derve the techncal, allocatve, and overall productve effcency of two samples of farmers, partcpants and non-partcpants n Ethopa s New Extenson Program, n two agro-clmatc zones n eastern Ethopa. The results ndcate that both groups of farmers have consderable overall productve neffcences suggestng the exstence of mmense potentals for enhancng producton through mprovements n effcency wth avalable technology and resources. In the wet hghland zone, the partcpants n the program used a superor technology and have hgher techncal but lower allocatve effcences than the non-partcpant farmers, relatve to ther respectve technologes, wth the result that both groups experenced greater and comparable overall productve neffcences. Therefore, the results show no evdence of mpact of NEP on producton effcency n the wet hghland zone. In the dry land zone, on the other hand, apart from usng homogeneous technologes, the two groups do not have sgnfcantly dfferent techncal and allocatve effcences and, therefore, NEP has had no postve mpact on overall productve effcency of farmers n the dry land zone. An nvestgaton of the nfluence of several soco-economc and nsttutonal factors on effcency revealed that educaton, credt, prevous partcpaton n extenson programs, and the share of the maze-potatoes croppng system postvely nfluence producton effcency n the wet hghland zone. In the dry land zone, on the other hand, educaton, off-farm ncome, and the share of the cereal-pulse croppng system have a postve mpact on effcency. The results suggest the need for provdng farmers wth greater access to educaton and credt for mproved nputs to rase ther productve effcency. Strengthenng exstng off-farm employment opportuntes wll also greatly help releve farmers' lqudty constrants at tmes of crtcal farmng operatons. Promotng local nnovatve croppng systems through research and extenson wthn the complex farmng systems that have evolved n response to a wde range of constrants and opportuntes wll have greater mpact on crop productvty. 21

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