Tadele Ferede 1 Deble Gemechu 2

Transcription

1 An econometrc analyss of the lnk between rrgaton, markets and poverty n Ethopa: The case of smallholder vegetable and Frut Producton n the North Omo Zone, SNNP Regon By Tadele Ferede 1 Deble Gemechu 2 Aprl Department of Economcs, Unversty of Antwerp, Belgum. E-mal: tadeleferede@yahoo.com 2 Department of Economcs, Adds Ababa Unversty, Ethopa. E-mal: d_guta@yahoo.com 1

2 Abstract Ths paper examnes the ant-poverty mpacts of rrgaton and markets on the welfare of rural households wthn the PRISM framework. Specfcally, the papers addresses: the magntude of ant-poverty effects of rrgaton and condtons strengthenng the poverty-reducng mpact of t; and the market constrants of frut and vegetable producers. The relatonshp between rrgaton, market-orentaton of smallholders and poverty s examned usng descrptve statstcs and multvarate analyss. In the descrptve analyss, low prces for vegetable and frut, weak demand, lack of prce nformaton, and nadequate transportaton have been dentfed as the man lmtng factor for output market. The benefcal effect of rrgaton, lteracy rate of household heads, and extra years of schoolng s readly apparent from the regresson. A smulaton approach s also used to explore the mpact of rrgaton and other factors, ndvdually and together, on poverty. The results show that although rrgaton reduces poverty, the effect s greater when combned wth mprovng the lteracy level of households. Ths evdence calls for polcy measures that focus on the concurrent nterventons n rrgaton, educaton, markets, and other supportng nputs, thereby reducng poverty n the cash growng rural areas. Key words: Ethopa, Fruts, rrgaton, markets, poverty,vegetables. 2

3 1. Introducton Ethopa, lke other sub-saharan Afrcan (SSA) countres, s an agraran economy, wth a very small ndustral sector. The agrcultural sector, on average, accounts for about 45% GDP; 90 percent of merchandse export earnngs; 80% of employment; more than 90% of the total foregn exchange earnngs; 70% of the raw materal supples for agro-ndustres, and s also a major suppler of food stuff for consumers n the country. Smallholders who produce more than 90% of the total agrcultural output and cultvate close to 95% of the total cropped land domnate the sector. Inter-regonal dfference n terms of agrcultural producton s qute notceable. For nstance, the three regons, namely Amhara, Oromya and Southern Natons, Natonaltes and Peoples (SNNP) contrbute more than three-fourth of the total agrcultural producton n the country. Agrcultural producton s hghly dependent on the vagares of nature wth sgnfcant varablty n producton and actual producton patterns. The majorty of smallholders have not practced rrgaton to mtgate the adverse effects of weather varablty and water s the man lmtng factor of agrcultural producton. As a result, the poverty stuaton of the country has not changed over tme. For nstance, the proporton of people lvng under the absolute poverty lne n 1999/00 was close to the level fve years earler (1995/96), estmated at 45% (MOFED 2002). Poverty (on the aggregate) has, at best, not decreased n spte of mproved economc performances n the 1990s. Moreover, poverty s concentrated n the rural areas where basc servces are n crtcal shortage to meet the bare mnmum demands (Mulat et al, 2005). It has been documented that low farm producton and productvty resultng from use of backward technology and other productvty-enhancng modern nputs are the major reasons for rampant poverty and food nsecurty n rural Ethopa (Workneh, 2005; Mulat et al, 2005). Gven dmnshng arable land per capta 3 and lmted off-farm actvtes, ncreasng farm producton through mproved technology-based farm ntensfcaton can be an mportant strategc element for agrcultural growth and rural development. Utlzaton of modern technologes s extremely low n Ethopa (Mulat 1999; Belay 2003; Mulat et al, 2003). For example, close to 2% of peasants use mproved seeds (Abebe and Mulat 2003). Smlarly, less than 5% of the total rrgable land s utlzed so far and use of other modern nputs s very low. Ths s extremely scary but reflects the exstence of a huge gap between the actual performance and the potental that could be attaned through mproved technologes (Mulat et al, 2003). By all counts, the country has experenced very lttle n terms of productvty-drven agrcultural growth and poverty reducton. Note that whatever growth has been regstered n the agrcultural sector, t s manly drven by area expanson wth lttle gan n productvty. For nstance, based on the past trends of agrcultural 3 As for holdng sze, about 65 % of farm households n the country hold less than a hectare (Workneh, 2005). 3

4 performance (specfcally, between 1995 and 2002), about 70% of the ncrease n crop producton has been attrbuted to area expanson (Dao et al, 2005). In Ethopa, roads are extremely underdeveloped: the average road densty s 27 klometers per 1,000 km 2. Close to 70% of Ethopan farmers s reportedly more than half a day s walk away from an all-weather road. Poor market access whch entals hgh transportaton costs sgnfcantly ncreases the gap between consumer and producer prces. Ths n turn lowers the farm gate prces receved by farmers located n remote areas. It has been documented that the average gran prce gap s estmated to be n the range of 30 to 70 percent across regons. Moreover, domestc marketng costs can account for more than half of fertlzer prces pad by farmers, whch tends to reduce proftablty of modern nputs (Jayne et al, 2003). A recent study also confrms that agrcultural growth n Ethopa requres concurrent nvestments n roads and other market condtons (Dao et al, 2005). Better access to markets has reduced the cost of nputs and expanded the market for produce. Any soluton to reduce rural poverty must focus on ncreasng the producton and productvty of smallholder agrculture and speed up the process of structural transformaton. In order to overcome the challenges faced by small farm households 4, comprehensve market-based poverty reducton nterventons, whch establsh a framework to operatonalze ntegrated market systems for the rural poor, are requred. The nterventon would ensure sustanable natural resource management, reduce poverty and enhance gender equty f based on access and control of water for crop rrgaton. The framework, whch focuses on reducng rural poverty va use of rrgaton and smallholder markets, s known as Poverty Reducton through Irrgaton and Smallholder Markets (PRISM). The PRISM model places a hgh prorty on dentfyng strateges that enable smallholders to access, store and control water for crop rrgaton va low cost, household level, mcro-rrgaton systems whch maxmze water-use effcency, mnmze labourburdens and brngs hgh economc returns to the poor small farm households. The man objectve of the paper s to provde emprcal evdence regardng the mpact of rrgaton and markets on poverty n the cash growng rural area of Southern Natons, Natonaltes, and Peoples (SNNP) regon. To do so, both descrptve, econometrc and smulaton technques have been used. The rest of the paper s organzed as follows. In the second secton, we revew the lnk between rrgaton, markets and poverty. Secton III presents a model of welfare determnaton and develops a smulaton framework. Data descrpton, smulaton results and analyss also are gven n ths secton. The fnal secton, Secton four, concludes. 4 The term small farm households s synonymous wth smallholders. 4

5 2. The lnk between rrgaton, markets and poverty It has been qute a whle snce poverty has posed a serous problem n most developng countres of the world. Durng the past two to three decades, a wde array of studes and researches have been undertaken to understand the root causes of poverty n developng countres. The results of these studes ndcate that the poverty problem n developng countres s complex and multdmensonal and s a result of a myrad of nteractons between resources, technologes, nsttutons, strateges and actons and others (Hussan, 2003). It s now well understood that poverty n most developng countres s a result of lack of resources, nformaton, approprate nsttutons and napproprate domestc polces. Gven that agrculture s largely ran-fed, rrgated water has become a very crucal resource n agrcultural producton, productvty and poverty reducton. Accordng to the avalable evdence, regons such as East Asa and Pacfc and Mddle East that have succeeded n poverty reducton have the greatest proporton of rrgated land. The povertyreducng mpact of rrgaton s substantal as evdenced n many Asan countres. For nstance, about 35-40% of cropland n Asa s rrgated and poverty reducton n the 1970s was substantal (Hussan and Hanjra, 2003). It should be noted that the avalablty of rrgaton not only boosts agrcultural producton but also makes possble the adopton of modern nputs such as mproved seeds, fertlzers and pestcdes (Ray, Rao and Subbarao, 1988). Note that the transmsson mechansms through whch rrgaton may lead to poverty reducton are va ncreased yelds, ncreased croppng areas and hgher value crops. It also leads to hgher employment. The cumulatve effect of all these s that rrgaton ncreases food supples and rases calore ntakes and better nutrton levels. A number of emprcal studes confrm the above facts: rrgaton has been nstrumental n the fght aganst poverty n many countres. In Bangladesh and Nepal, rrgaton has been an effectve tool for reducng poverty, ncreasng croppng ntensty, gran producton, household ncomes, waged labour employment and lvelhood dversfcaton (Angood et al, 2003, 2002;Hussan et al, 2004; Hussan and Hanjra, 2003; Madhusudan et al, 2002). Apart from these, there are also stablty effects n agrcultural producton because of reduced relance on ranfall. In other words, rrgaton lowers the varance of yelds, output, and employment (Dao et al, 2005; Dhawan, 1988). Comparson of rrgated versus non-rrgated areas ndcates that crop productvty and output tend to be much hgher n rrgated systems than the non-rrgated and ran-feed areas (Jatleksono and Otsuka, 1993; Datt and Ravallon, 1998). Smlarly, value of crop producton, household ncome and consumpton are almost double n rrgated settngs than the non-rrgated 5

6 areas and labour employment and wages are much hgher n rrgated areas. In a comparatve study, Hussan et al (2004) ndcate that poverty ncdence s about 20-30% hgher n ran-fed settngs than rrgated settng. A study by Haung et al (2005), usng a plot-level data n rural Chna, ndcates that rrgaton boosts croppng ncome and reduces poverty and nequalty. In general, the results of these econometrc studes ndcate that crop output and productvty, farm ncome, consumpton, employment and rural wages tend to be much hgher n rrgated areas and rrgaton s a postve and sgnfcant determnant of ncome and consumpton and a negatve determnant of poverty 5. Note that rrgaton alone may not lead to poverty reducton. Rather, the poverty-reducng mpact of rrgaton wll be stronger f t s supported by use of other yeld-enhancng nputs. It s often argued that even though rrgaton and other modern nputs are used to enhance producton, ths may not ental the ntended result f farm households don t have access to markets for ther produce. A combnaton of rrgaton, other modern nputs and access to markets s crtcal for poverty reducton and ths wll eventually lead to accelerated agrcultural growth. For nstance, reducng marketng costs prmarly benefts smallholders va better prces for ther produce and rases farmers ncome. Moreover, there s also another effect of mprovng market condtons: t stmulates the tradng sector, whch tself can generate greater nonagrcultural ncome. 5 Hussan and Hanjra (2004) provde an extensve revew of past work on rrgaton-poverty lnkages. 6

7 3. Modellng the effect of rrgaton and markets on Poverty In ths secton, an attempt wll be made to quantfy the lnk between rrgaton, markets and household welfare, measured n terms of consumpton per capta. In the process of modellng such lnkage, smulatons wll also be carred out to examne the mpact of some polcy nterventons and other soco-economc factors on the well-beng of rural households. 3.1 Econometrc models and methods of estmaton The objectve of specfyng the model s to assess to what extent rrgaton and markets affect the well-beng of rural households. To answer questons about the effect of these varables, condtonal on the many other potental determnants of poverty, multvarate analyss s requred (Gbson and Rozelle, 2002; Ravallon, 1998). In ths regard, econometrc models of the determnants of poverty where key modern agrcultural nputs such as rrgaton and market access varables would be entered explctly as an argument n the model. The usual approach n the multvarate analyss of poverty s to classfy households as poor and non-poor based on consumpton per capta (Datt, 1998; Gbson and Rozelle, 2002; Mulat et al, 2003). Denotng the th household s per capta expendture by C, then a household s classfed as poor f the th household s C s less than the poverty lne (Z). Accordngly, a bnary varable s constructed to classfy households as poor and non-poor. Then the probt estmaton assumes the followng functonal forms: pr ( h X ) = Φ( β = 1 ) [1] X where Φ s the standard cumulatve normal dstrbuton functon, X s a matrx of explanatory varables such as agrcultural technology and market-related varables and other determnants of consumpton, and β s a vector of parameters to be estmated. However, the probt estmaton, as ndcated n equaton (1), focuses only on ncdence of poverty and gnores the poverty gap and severty. A more generalzed poverty measure for household can be specfed as (Foster, Greer and Thorbecke, 1984): P α Z C [ max( ), 0) ], 0 α, = α [2] Z Where P α, s the estmated poverty measure of household, Z refers to the poverty lne and α s a non-negatve parameter takng nteger values 0, 1 and 2. It should be noted that aggregate poverty of a gven populaton s smply the weghted mean of the above poverty measure, where the weghts are gven by household sze. When α assumes values of zero, one and two, the aggregate poverty measure corresponds to the ncdence of poverty or head-count ndex, the 7

8 poverty gap and squared poverty gap (whch s senstve to nequalty amongst the poor), respectvely. Instead of usng poverty probts, the approach of ths paper s to model the determnants of consumpton per capta, and then derve from the regresson model estmates of the varous poverty measures followng smulated changes n certan varables. More specfcally, the model s of (log) nomnal consumpton expendture per adult equvalent, deflated by a poverty lne, whch gves a rato often known as the welfare rato (Gbson and Rozelle, 2002; Blackorby and Donaldson, 1984) 6 and s gven by: C Z = β + D β + H β + F β + A β + υ ln [3] Where C denotes per capta consumpton of household, D refers to demographc characterstcs, human captal varables are gven by H, F denotes farm characterstcs, A s a matrx of technology-related varables such as rrgaton, Z s the poverty lne and v s a stochastc term wth zero mean (0) and constant varance (σ 2 ). In a more compact form, equaton (3) can be expressed as: C Z ln = X β + υ (4) Where X s a matrx of explanatory varables ndcated above. Snce the consumpton model estmates are ndependent of the chosen poverty lne, t s potentally attractve to model household consumpton level, and then lnk t to household poverty level (Mulat et al, 2004). After normalzng consumpton per capta by poverty lne, t s possble to classfy households nto poor and non-poor,.e. f the logarthm of the normalzed welfare rato (ln (C /Z)) s less than zero, then a household s deemed to be poor, otherwse non-poor. The probablty of the th household beng poor can be derved from the estmated parameter ) and standard error (β ) (σ of the regresson. Formally, the probablty of the th household s logarthm of welfare rato beng less than zero s gven by: 6 Followng the standard approach, a consumpton-based measure of ndvdual welfare has been employed n ths study. Ths s due to () consumpton s regarded as a measure of welfare achevements by households (Atknson 1989); () consumpton fluctuates less than ncome (because households tend to smooth ther consumpton (Smler et al, 2004); and () households are more wllng to reveal ther consumpton behavour than ther ncome. In ad dependent economes such as Ethopa, households tend to report ther consumpton on the hgh sde. 8

9 = Φ < σ ln 0 ln Z C Z C prob (5) Equaton (5) gves the weghted average of the predcted ncdence of poverty for the th household (P 0, ) where the weghts are household samplng weghts n terms of adult equvalent household sze. Smlarly, the methodology can easly be extended to derve the smulated poverty gap denoted by (P 1, ) and poverty severty (P 2, ) as: 7 Φ = + σ σ β σ β ' 2 1, 1, 2 ' ' X o X e P P (6) Φ + Φ Φ = + + σ σ β σ σ β σ β σ β σ β ; 2 ' ' 2, X e X e X P X X (7) Equatons (5), (6) and (7) are employed to generate predctons of poverty followng varous polcy smulaton exercses. 3.3 Descrpton of explanatory varables of the model The set of varables that s hypotheszed to determne the level of consumpton, and hence poverty, may be categorzed nto:(a) household characterstcs; (b) human captal; (c) farm characterstcs; (d) access to market and modern technology. Among the set of potental determnants of poverty, an attempt s made to choose those varables that are arguably exogenous to current consumpton. (a) Household characterstcs: Ths ncludes household sze, age and sex of household head. In order to take nto account non-lneartes n the relatonshp between consumpton and household sze, a quadratc term has been ntroduced n the regresson model. (b) Human captal: Included n ths category are lteracy of household head and years of schoolng for adults. 7 For an ndepth dervaton and dscusson of the methodology, see Mulat et al (2003), Smler et al (2004) and Datt and Jollffe (1999). 9

10 (c) Farm characterstcs: nclude holdng sze and qualty ndcator of land. The number of plots (as a proxy for the degree of crop dversfcaton) has also been ncluded n the model. It should be noted that the number of plots ndcates the land covered by dfferent crops, hence serves as a proxy for crop dversfcaton. 8 (d) Access to modern technology and markets: wth regard to market access varables, dstance to the largest buyer (output market), dstance to the most mportant nput suppler (nput market), and the proporton of sales to the total output are ncluded. Smlarly, a number of varables have been dentfed that reflect use of modern agrcultural technology: Irrgaton practces and experence, the proporton of rrgated land, sol conservaton and water harvestng practces. 3.4 Estmaton of the model Descrpton of dataset The dataset used n the estmaton of the model s obtaned from a household survey n two woredas of North Omo zone: Arbamnch and Mrab Abbaya woredas n the Southern Natons, Natonaltes and Peoples (SNNP) regon. These woredas are the major producers of fruts and vegetables. 9 The woredas have been purposvely selected from the woreda Agrcultural Offce snce the focus of the study s on cash crop producers usng rrgaton. A lst of Peasant Assocatons (PAs) that manly produce frut and vegetable usng rrgaton was obtaned from the woreda Agrcultural Offce and then households have been randomly selected from those PAs. Accordngly, a total of 216 households have been ncluded n the survey. The survey provdes data on a wde spectrum of soco-economc varables ncludng household composton and structure, educaton, use of modern technology, household assets, employment and ncome, consumpton expendture (both food and non-food), health status and other welfare ndcators. More mportantly, the questonnare ncluded a module whch s desgned to capture plot-level nformaton such as whether a plot s rrgated, the area of rrgated land, type of crop grown on a plot, crop yeld, land qualty and slope of land. In addton, a market partcpaton module has been ncluded n the questonnare, whch ntends to capture key market varables. 8 In many emprcal lterature, number of plots denotes the degree of land fragmentaton, whch carres a geographcal connotaton. 9 For nstance, the two woredas accounted for about three-fourth of the total zonal frut and vegetable producton n

11 3.4.2 Results and Dscussons Descrptve statstcs (a) Household demographcs and Farmng characterstcs Before gong drectly to the model results, t s mportant to gve some basc background nformaton regardng the sample households. Of the sample households, the majorty (90%) are male-headed wth only 10% are female-headed households. Farmng provdes the prmary source of lvelhood for the sample households. The average holdng sze s about 1.1ha for the sample households. 10 Ths means that, wth an average famly sze of sx persons, per capta holdng sze would be about 0.18ha n the study area. 48.4% of farm households have less than a hectare of land whle 18.4% have landholdng sze greater than 1.5ha (Table 3.1). As for the farm characterstcs, about 77.1% reported that ther farmland s of fertle quqlty whle medum qualty s ndcated n 19.2% of the cases. Only 3.7% reported that the land s of poor qualty. 11 It seems that, on average, the land s sutable for agrcultural producton n the study area. Note also that the majorty of households have flat farmland,.e. less steep and hence not susceptble to sol eroson. 97% of the sample households reported that sol eroson s not a problem n the vllage. Table 3.1: Dstrbuton of landholdng sze Descrpton Number of % of households households Landholdng sze < >=2 Land qualty Leum (Fertle) Leum-teuf (Medum) Teuf (Not fertle) Source: Own calculaton from survey data As for the educaton level of household heads, about 49.3% don't read and wrte whle 42.7% have some prmary educaton. Only 7.5% of the sample household heads have completed secondary educaton (Table 3.2). Almost all female-headed households are ether llterates (90%) or have some prmary educaton (10%). 10 The natonal average holdng sze s less than a hectare. 11 The average response for the land qualty queston s 1.1. Smlarly, the average response for the gradent of the farmland s

12 Table 3.2: Educaton level of household head by gender Educaton category Male Female Total Illterates Prmary educaton Secondary educaton Post-secondary educaton Total Source: Computed from survey data Wth regard to the use of modern nputs, t s ndcated that 3.6%, 81.3% and 17.6% of the sample households use chemcal fertlzers, mproved seeds and other chemcals such as pestcdes, respectvely (Table 3.3). It should be noted that about 90% of the cultvated land s rrgated. The average rrgaton experence of the sample households s 13 years, ndcatng that rrgaton has been practsed qute a long perod n the study area. More than half of the sample farm households have more than 15 years of experence n rrgaton. Rver or stream dverson s the man source of water for rrgaton, and pump rrgaton s nearly non-exstent. Table 3.3: Irrgaton experence and type of rrgaton scheme Descrpton N % Experence n years Less than 5 years Between sx and ten years Between eleven and ffteen years Greater than ffteen years Type of rrgaton scheme Rver/stream dverson Pump system Source: Own computaton from survey data (b) Market access and source of prce nformaton It has been documented that neffcent, underdeveloped and fragmented output and nput markets are the man cause for low and varable prces for vegetables and fruts (Mulat and Ferede, 2005). Unstable and low prces have an adverse mpact on the use and proftablty of new technologes for farmers. For nstance, low and unstable prces dscourage farmers from usng mproved farm technologes, and busness people may refran from nvestng n processng actvtes, and deterrng wholesalers, retalers and transporters from nvestng n mproved market and transport servces. The average dstance from the man output market n terms of hours was estmated at 7 hours whle t was about 50 mnutes from the most mportant nput suppler. Close to 69% sell ther produce to prvate traders n Adds Ababa, whle 22% sell n the regonal market, Arba Mnch. Only 8% sell to prvate traders n the local or vllage market (Table 3.4). It appears that the Adds 12

13 Ababa central market s the man out let for smallholders. A remark s n order as farmers don t sell ther produce drectly n the Adds Ababa central marrket nor traders at Adds Ababa buy from farmers drectly. The marketng channel can be descrbed as follows: Farmers Vllage brokers Adds Ababa Central market. In other words, vllage brokers, who serve as an agent for the central traders, determne both the qualty and prce of vegetables and fruts at the farm. The central traders nform vllage brokers how much to buy and at what prce to buy. However, borkers fx another prce, whch s usually lower than that determned by the central trader. Farmers can t bargan as they don t have prce nformaton. Hence, output prces are depressed at two levels: traders at the central fx a lower prce than what s prevaled n the market and brokers also set another prce whch s lower than that determned by the central trader. In the absence of prce nformaton, farmers fnd themselves n a weak barganng poston and lose a substantal amount of revenue. As for the prce nformaton, the majorty of sample households get nformaton regardng the prces of vegetables and fruts from prvate traders (or brokers). Ths s not relable prce nformaton as traders or brokers usually understate the prevalng market prces of vegetables and fruts. Accordng to the avalable evdence, vegetable and frut producers n Meke and Zway areas n the SNNP regon lose a sgnfcant amount of revenue as a result of naccurate prce nformaton obtaned from prvate traders or brokers (Mulat et al, 2004). Table 3.4: Dstrbuton of vegetable and frut buyers Buyer N % Peasant assocaton/cooperatves/unons Prvate trader n local market/vllage Prvate trader n regonal market /Arba-Mnch or Awassa Prvate traders n Adds Ababa Indvdual consumers Total Source: Computed from survey data There are a number of problems wth regard to undermnng the output market of stallholders n the study area. The sample households were asked to rank the problems of the output market on a four-pont scale: (1) no obstacle, (2) mnor obstacle, (3) moderate obstacle and (4) very severe obstacle. It turns out that low prces for vegetable and frut, weak demand, lack of prce nformaton, and nadequate transportaton have been dentfed as the man lmtng factors for the output market (Table 3.5). 13

14 Note also that access to credt s another problem for sample households. Access to credt s usually low, as about 62% of the respondents don t acqure any loans. Only 38% have acqured loans manly from frends and relatves. Table 3.5: Extent of vegetable and frut output marketng problems (n %) Type of problem No obstacle Mnor Moderate Very severe obstacle Low vegetable and frut prces Unstable prces Inadequate transportaton Hgh tax rates Lack of prce nformaton Lack of standards or gradng Too many local brokers or dealers Lmted access to credt Crme, theft, dsorder and lack of trust Ant-compettve practces (e.g. monopoly) Weak demand for vegetables and fruts Inadequate access to market nformaton Other Source: Computed from survey data Total (c) Changes n welfare: Household perceptons about welfare trends Many of the poor, who depend largely on ran-fed agrculture for ther lvelhood, are located n rural Ethopa. At the natonal level, much of the ncrease n agrcultural producton has come from expanson of cultvated area wth lmted yeld ncrease (Mulat et al, 2005). However, farm households depend on rrgated agrculture for ther survval n the study area. Moreover, they produce manly vegetables and fruts: the major vegetable beng banana. Note that the poverty stuaton n the country s closely lnked to the performance of the agrcultural sector snce off-farm employment s lmted. To examne the evoluton of household welfare, households were asked about welfare status and changes n ther lvng condtons over tme. The majorty (about 65%) of sample households declared that they classfy themselves n the mddle (.e. average) compared to other households n the same vllage, whle only 2.8% classfed themselves as the rchest. About 16.7% classfed themselves as poor relatve to other households n the same vllage (Table 3.6). 14

15 Table 3.6: Relatve self-declared status of households n the vllage Welfare status N % Rchest Rcher than most households About average A lttle poorer than most households Poorest Total Source: Computed from survey data A look at the evoluton of household welfare reveals that the proporton of self-reported very rch households declned from 1.4% some ten years ago to 0.5% three years ago. Whle 51% of households classfed themselves as poor ten years ago, the proporton declned three years ago: only 13.5% reported as poor. On the other hand, households that declared themselves as medum or average ncreased from 40% ten years ago to 71% three years ago. Close to 51%, 18% and 13.5% classfed themselves as poor ten, fve and three years ago, respectvely. Ths ndcates that the welfare stuaton of most households tend to mprove and concentrate at the margn (Table 3.7). 12 Table 3.7: Dynamcs of self-declared welfare status Descrpton Ten years ago Fve years ago Three years ago N % N % N % Very rch Rch Average Poor Source: Computed from survey data It appears that rrgaton, mproved agrcultural technology and access to markets s very crucal for sustanable agrcultural producton, therefore for the allevaton of poverty n the country. Although there s huge potental for rrgaton n the area, t has not been fully utlzed as smallholders face both producton and marketng constrants. Ths reflects the exstence of a a huge gap between the actual performance of the agrcultural sector and the potental that could be attaned va mproved technologes, ncludng rrgaton. As mentoned above, despte avalablty and use of rrgated agrculture for crop producton, many households have felt that ther lvng standard mproved n recent years. Accordng to the self-declared welfare status, the 12 Ths s consstent wth the poverty statstcs reported by the Mnstry of Fnance and Economc Development (MOFED). In the report, t s ndcated that the ncdence of poverty n the SNNP regon declned from 55.8% n 1995/6 to 50.9% n 1999/00, representng a 8.78% declne (MOFED, 2002). Note also that the ncdence of poverty n the North and South Omo Derashe and Konso zone s 66.1% n 1999/00, whch s hgher than the regonal ndex. However, f the regonal poverty lne (brr ) s used, then the zonal ncdence of poverty would be 48% durng the same perod. 15

16 sze of poor households has declned over tme. Ths rases the queston: what are the causal factors condtonng household welfare n the study area? To assess the relatve and combned effects of rrgaton and marketng on the welfare of smallholders, an econometrc model s requred where welfare depends on a set of demographc, farm, and envronmental characterstcs. 16

17 3.4.3 Determnants of consumpton and poverty The regresson results whch nclude the parameter estmates, t-ratons and 95% confdence nterval for the determnants of welfare are presented n Table 3.8. The measure of the goodness of ft of the model, R 2, s on the hgh sde (0.52) for models based on a cross-secton data. Although the statstcal sgnfcance of the dfferent varables of nterest vares markedly, the sgns of key varables are as expected. 13 As can be gleaned from the estmated model, whle household sze tends to reduce welfare, the estmated coeffcent of square household sze s found to be postve and statstcally sgnfcant, suggestng a U-shaped relatonshp between welfare and household sze. Age and gender of household head don t seem to be assocated wth welfare as both are statstcally nsgnfcant. As expected, rrgated land, extra years of schoolng and lteracy of household head affect welfare postvely. Smlarly, welfare ncreases wth holdng sze, nvestment n sols, and water harvestng. Contrary to the expectaton, the sgn on the coeffcent measurng the degree of market-orentaton of farm households s found be negatve. But the effect of the varable when nteractng wth educaton s found be welfare mprovng and also statstcally sgnfcant. Ths suggests that educaton ncreases the barganng poston of households n the process of buyng and sellng acts Noted that the dependent varable of the model s the natural logarthm of welfare rato. The estmated regresson coeffcents measure the percentage change n real consumpton per capta for a unt change n the ndependent varables. 14 It should be noted that nteracton terms have been ncluded to account for the dfferental effects of demographc, farm and envronmental factors on household welfare (Datt and Jollffe, 1999). Accordngly, an nteracton term manly between schoolng and degree of market-orentaton has been ncluded n the model. 17

18 Table 3.8: Determnants of rural poverty n Ethopa Logarthm of welfare rato (Dep. Varable) Coef. Robust Std. err t P> t [95% Conf. Interval] Demographcs Age of household head 0,002 0,014 0, ,025 0,030 Age of household head squared 0,000 0,000-0, ,000 0,000 Sex of head of household -0,187 0,152-1, ,486 0,112 Household sze -0,304 0,080-3, ,462-0,146 Household sze squared 0,014 0,007 2, ,001 0,027 Educaton Average years of schoolng of adults 0,051 0,022 2, ,007 0,096 Educaton of household head 0,871 0,295 2, ,288 1,454 Holdng sze and Farm characterstcs Landholdng sze 0,170 0,060 2, ,052 0,288 Land qualty 0,316 0,078 4, ,163 0,470 Number of plot 0,030 0,023 1, ,016 0,076 Access to markets and modern technology Percentage of land that s rrgated 0,403 0,164 2, ,079 0,727 Dummy for sol conservaton 0,416 0,096 4, ,227 0,605 Dummy for water harvestng 0,721 0,114 6, ,497 0,945 Partcpate n the extenson programme -0,260 0,132-1, ,520 0,000 Commercalzaton -0,436 0,285-1, ,998 0,126 Commercalzaton*educaton of household head 0,957 0,327 2, ,311 0,160 Constant term -2,430 0,540-4, ,496-1,365 Regresson wth robust standard errors Number of obs =205 F (16, 188)=13,19 R-squared=0,5236 Root MSE=0,43252 After estmatng the welfare model and usng the smulaton framework developed earler, an attempt has been made to generate the mpact of rrgaton and other factors on poverty. Table 3.9 provdes the results of the varous poverty smulatons performed wth the model. In the frst smulaton, we examne the effect of convertng all non-rrgated nto rrgated land and that t appears that the three poverty would fall by 1.2, 3.5 and 5.0%, respectvely. The effects of ncreasng the educatonal levels of household heads and adults on poverty are presented n smulatons 2 and 3, respectvely. It appears that the poverty reducng mpact of educaton s found to be dramatc, compared to rrgaton. For nstance, the headcount ndex would fall by a sgnfcant magntude (25.5 %) f all household heads could be made lterate, regardless the characterstc and gender of the head (sm2). Smlarly, the ncdence of poverty falls by about 1.6% f the average schoolng years per adult ncreases by one year (sm3). It should be noted that what s common n both smulaton experments s that the depth and severty of poverty ndces would fall by a greater percentage than the ncdence of poverty as those n greatest poverty currently have the least access to educaton. It mples that provdng educaton servces 18

19 to the poorest of the poor would reduce poverty snce the depth and severty ndces fall faster than the headcount ndex. Smulaton 4 s concerned wth an ncrease n the holdng sze per household by 50% for all holders. Note that the average holdng sze s 1.1 ha for the sample populaton., and a 50% ncrease n holdng means that, on average, each household would have a holdng sze of about 1.65ha.Ths knd of nterventon leads to a fall n the ncdence, depth and severty of poverty by 3.3, 8.4, and 11.6%, respectvely. It should be noted that smulatons 1-4 are carred out ndependently, one after the other. Smulaton 5 presents the results for the combned smulated effects of convertng all non-rrgated land nto rrgated and makng all household heads lterate. It appears that the poverty-reducng effects of such smultaneous nterventon s hghly sgnfcant compared to ndvdual effects. The ncdence of poverty, for nstance, falls by a large magntude (27.3%), whch s hgher than n any of the ndvdual nterventons ndcated above. Ths supports that argument that a holstc approach, nstead of a one sded nterventon, s requred to reduce poverty wthn a reasonable perod. Table 3.9: Smulaton results of certan changes of explanatory varables on rural poverty Headcount ndex Poverty gap Poverty Severty Percentage change from the baselne predcted value Sm1: If all non-rrgated land s converted nto rrgated -1,20-3,50-5,02 Sm2: Increase the lteracy rate of household heads to 100% -25,47-31,17-33,25 Sm3: Increase household average school years per adult by one year -1,65-4,36-6,13 Sm4: Increase landholdng sze of each household by 50% -3,32-8,41-11,60 Sm5: Sm1 and Sm2 smultaneously -27,28-34,17-37,06 Source: Model smulaton 19

20 4. Concluson The man objectve of ths study was to examne the quanttatve relatonshp between rrgaton, market access and poverty n the Southern part of the country. Specfcally, the study assessed the effects of rrgaton, farm characterstcs and communty factors on the welfare of the sample farm households. There s a negatve relatonshp between household sze and household welfare. In other words, households wth larger famly sze are more lkely to fall nto poverty than those households wth smaller famly sze. A quanttatve analyss undertaken n ths study uncovers the fact that rrgaton has a postve mpact on welfare. Welfare ncreases wth holdng sze, nvestment n sols, and water harvestng. Smlarly, extra years of schoolng and mprovng lteracy of household head enhance welfare. The effect of the degree of market-orentaton (commercalzaton) when nteractng wth educaton s found be welfare mprovng. The analyss also ponts to the mportance of educaton n terms of realzng the benefts of rrgaton. In other words, the poverty reducng mpact of rrgaton s stronger when households are lterate. It should be noted that better access to markets tends to reduce the cost of nputs and to expand the market for produce. Gven the severty of poverty and from the pont of vew of reducng such rampant poverty, ether of these nterventons s nadequate,.e. one nterventon could not be seen as an alternatve strategy to the other. Ths renforces the argument that smultaneous nterventon n rrgaton, educaton and other market condtons s mportant for reducng rural poverty. Ths study suggests that poverty and food nsecurty can be reduced through a coordnated applcaton of a set of complementary nterventons such as rrgaton, educaton, markets, and other supportng nputs. Hence, promotng small scale, low cost and labour-ntensve rrgaton projects and buldng the capacty of farmers are very mportant for reducng poverty n the cash growng rural areas of Ethopa. 20

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23 sustanable development: a publc dalog, co-hosted by Forum of for Socal Studes (FSS), the Ethopan Economc Assocaton (EEA) and the Agrcultural Economcs Socety of Ethopa (AESE), held on August 5, 2005, Hlton Hotel, Adds Ababa. 23