Estimating the causal effect of improved fallows on farmer welfare using robust identification strategies in Chongwe - Zambia

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1 Estmatng the causal effect of mproved fallows on farmer welfare usng robust dentfcaton strateges n Chongwe - Zamba Elas Kuntashula Unversty of Pretora Department of Agrcultural Economcs, Extenson and Rural Development, Pretora 000, South Afrca. ekuntashula@unza.zm, Tel: Erc Mungatana Department of Agrcultural Economcs, Extenson and Rural Development, Unversty of Pretora. Pretora 000, South Afrca. erc.mungatana@up.ac.za Tel: ABSTRACT Agrcultural technologcal mprovements are crucal to ncrease on farm producton and thereby reduce poverty. However the use of mproper dentfcaton strateges on the mpacts of mproved technologes on farmer welfare could potentally pose a threat to good practce agrcultural polcy makng. In ths paper, propensty matchng strateges and endogenous swtchng regresson were used to test whether an mproved fallow, a sol fertlty mprovng technology that passed the requrements for a hgh mpact nterventon based on non randomsed mpact assessment methodologes could stll pass ths test. Usng data from 34 randomly surveyed households n Chongwe dstrct of Zamba, the rgorous econometrc methods confrmed the postve mpact of mproved fallows on household maze yelds, maze productvty, per capta maze yeld and maze ncome. Conflctng results were obtaned when a broader welfare ndcator per capta crop ncome, was consdered. Whereas the non-randomsed and kernel matchng methods showed that per capta crop ncomes were sgnfcantly hgher for the adopters than for the non adopters, the causal effect of mproved fallows on ths varable was non sgnfcant when nearest neghbour matchng strategy and the more robust endogenous swtchng regresson were used. It was concluded that the technology mproves welfare through ncreased maze and hence ncreased food securty, and through ncomes from the maze crop. The maze ncome derved from mproved fallows were however not suffcent enough to drve the general crop ncome to sgnfcantly hgher 1

2 levels. The need to dversfy the use of mproved fallows on hgh valued crops was recommended whle the mportance of usng better and more robust methodologes n evaluatng mpact of nterventons was emphassed. Key words: Confoundng factors, Identfcaton strategy, selecton bas 1. INTRODUCTION Sol fertlty problems are wdely spread throughout sub-saharan Afrca. Several studes (Sanchez and Jama, 00; Vanlauwe and Gller, 006) have noted that a fundamental mpedment to agrcultural growth and a major negatve socal externalty n sub-saharan Afrca s declnng sol fertlty and low macro-nutrent levels. In the past, the regon s small scale farmers who could not afford norganc fertlsers used tradtonal methods of farmng such as shftng cultvaton n order to sustan land productvty. However, the decrease n hgh potental land and the ncrease n human populaton have added pressure to farmng extendng nto more fragle lands, thus undermnng the sol resource captal base (Ajay et al. 007). In an effort to contrbute towards brdgng the gap posed by sol fertlty problems, lmted use of external nputs and acute poverty among small scale farmers, the mproved fallow technology was developed for use n Zamba and elsewhere n sub-saharan Afrca (Mafongoya et al. 006). The mproved fallow, an ecologcally robust approach to sol fertlty mprovement, s a product of many years of agroforestry research and development by the World Agroforestry Centre (WAC). The technology s composed of fast growng mostly ntrogen fxng trees of Fhaderba albda Sesbana sesban, Glrcda sepum, Teprosa vogel and Cajanus cajan, that ensure the shortest sol regeneraton perod of to 3 years. Farmers can grow ther crop on prevously mproved fallow plots for the next 3 to 4 years wthout applyng any external nputs. The technology also enhances envronmental qualty through the generaton of several ecosystem servces such as carbon sequestraton (Makumba et al. 007), conservaton of bodversty (Slesh et al. 007), protecton of natural forests by provdng an alternatve source of fuel wood supply, and preventon of sol eroson (Mafongoya and Kuntashula, 005). The fnancal proftablty of mproved fallows n Zamba and sub-saharan Afrca has been demonstrated by several studes ncludng those conducted by Ajay et al. (007), Ajay et al.

3 (009), Franzel (004) and Place et al. (00). These studes demonstrate that mproved fallows are more proftable than the non-use of any external nputs, a practse prevalent among resource poor farmers (Mafongoya et al. 006). Several studes (Aknnfes et al. 006; Ajay et al. 007, Phr et al. 004; Qunon et al. 010) also ndcate that farmers who take up the technology have hgher welfare, measured n terms of outcome parameters such as ncreased maze yelds, household ncomes, and assets among others. Despte all these demonstrated benefts, only a few resource constraned farmers have taken up the technology (Aknnfes et al. 006; Ajay et al. 007). A crtcal lterature revew of the methodologes used to estmate welfare mpact n the above cted studes show that they faled to move beyond estmatng ncremental maze yelds, crop ncomes and assets that adopters supposedly gan. For nstance n the study done n Zamba, Ajay et al (007) used two ndcators: farmer perceptons of yelds and number of months per year when the household had enough food to feed famly members, to measure mpact. The study s fndngs were that the technology postvely mpacts on welfare. When analysng the number of months per year when households have enough food, the study only controlled for household sze. However, ncludng the number of months the household has enough food wthout necessarly controllng for other varables may produce msleadng estmates about causalty. Both bophyscal varables as well as socoeconomc characterstcs of farmers could be mportant n so far as ncreasng the avalablty of food on-farm s concerned. Franzel, (004) and Ajay et al. (009) used enterprse budgets through farm modellng to assess the mpact of adoptng mproved fallows n Zamba. The technology was found to have a postve effect on household annual maze ncomes. These studes used net present value and cost beneft rato crtera to arrve at ths concluson. Whle these crtera are ndeed mportant and benefcal n estmatng proftablty, they fal short of measurng causalty snce covarates that equally would have led to an ncrease n maze yelds (hence maze ncome) were not controlled for. A more recent and detaled study on agroforestry and mprovement n resource poor farmers lvelhoods was conducted n Malaw by Qunon et al. (010). The study used sgn and sgned rank nonparametrc analyss to test for a change n crop yeld and asset varables between pre- and postadopton. These tests were complemented wth a test for equalty of proportons to examne the probablty of an ncrease n ncome, the number and type of ncome sources, and maze yeld as a result of adoptng agroforestry. Whle ths study analysed the effects of agroforestry on poverty 3

4 reducton n far more detals than the earler ones, t specfcally notes that the methodologes used are based on analysng pre- and post-adopton only. The control of other factors n nfluencng welfare changes was not consdered. We can thus conclude from the above studes on welfare mpact estmaton of mproved fallows that they dd not follow proper dentfcaton strateges n solatng the causal effect of the technology. Several bophyscal as well as socoeconomc factors (ncludng unobservable factors) that could equally have an nfluence on farmer welfare were never controlled for. The purpose of ths study was to estmate the mpact of mproved fallows on farmer welfare usng more robust cause effects dentfcaton strateges. The above lterature revew clearly shows that the technology s not only affordable to resource constraned farmers but also mproves ther welfare, whch leads to a number of questons: why are resource constraned farmers not adoptng t n the nterest of maxmzng prvate profts as economc theory would predct? In measurng mpact, have economsts been measurng the rght construct? Assumng economsts have been measurng the rght construct, are they dong the measurement correctly? It s our contenton that when t comes to mpact evaluaton, approaches that do not encompass more robust dentfcaton strateges of the treatment technology on the outcome varables could produce msleadng causeeffect estmates. Over or under estmaton of mpact could occur f a clear dentfcaton strategy s not used. It s well recognzed that the estmate of a causal effect obtaned by comparng a treatment group wth a non-expermental group could be based because of selecton bas problems (Deheja and Wahba, 00). There could have been selecton bas n the assgnment of farmers takng up the mproved fallow technology. Over tme, selecton bas could have manfested n the dfference n average outcome or welfare between those who adopted and those who dd not adopt regardless of the effect of the technology. Angrst and Pschke (009) noted that the selecton bas could be so large n absolute terms that t completely masks a treatment effect. It follows that to attrbute a technology as causng mpact, selecton bas has to be overcome. Ths s the goal of most emprcal economc research (Angrst and Pschke 009). We used farm-level data collected n 011 from a random cross-secton sample of 34 small-scale farmers n Zamba to estmate the mpact of mproved fallows. Snce the mproved fallow s manly used to promote maze producton, the staple food n most parts of Southern Afrca, welfare ndcators used n ths study ncluded household total maze yeld, per capta maze yeld, maze 4

5 productvty and per capta ncome emanatng from the maze crop. In addton, we ncluded ncome from all crops grown on the farm to assess the technology s mpact on ths broad varable. The econometrc methods estmates confrmed the postve mpact of mproved fallows on the chosen welfare parameters. However, conflctng evdence was obtaned on whether the technology postvely affects per capta crop ncome. Our man contrbuton n ths paper s to demonstrate the lkelhood that the earler studes evaluatng the mpact of mproved fallows on farmer welfare mght not have succeeded n analyzng adopters and non-adopters that were smlar n terms of the dstrbuton of covarates. Stated otherwse, the earler studes could have analyzed observatons that were not necessarly comparable, possbly leadng to based conclusons concernng mpacts of the technology (Heckman et al. 1998). We base ths concluson on the fact that as opposed to earler studes, n ths study we controlled for selecton bas through matchng strateges, and endogenety bas that may potentally arse due to correlaton of the unobserved heterogenety and observed explanatory varables through use of endogenous swtchng regresson model. In addton, to mprove on the qualty of parameter estmates, only observatons that were matched durng the matchng analyss stage were used n the swtchng regresson model. The paper s structured as follows: theoretcal frameworks on propensty matchng and endogenous swtchng regresson mmedately follow ths ntroducton secton. Dscussons on the study area, samplng desgn, survey nstrument development and mplementaton, analyss and computatonal methods n ths order, complete the secton on methodology. Immedately after the survey mplementaton secton, the paper gves the results that are dscussed n the subsequent secton. Fnally conclusons are drawn based on the fndngs of the study.. THEORETICAL FRAMEWORKS AND METHODS.1 Framework for propensty score matchng The potental outcome framework for causal nference dscussed by Rubn (1974) estmates the Average Treatment effect on the Treated (ATT) or adopters of mproved fallows as: 5

6 E( Y1 -Y0 \ T = 1) (1) where E s the expectaton n the dfference n the outcome Y - ) between recevng treatment or ( 1 Y0 adoptng, T =1 and the counter factual outcome f treatment or the technology had not been receved T = 0. One possble dentfcaton strategy s to mpose the Condtonal Independent Assumpton (CIA) that states that, gven a set of observable covarates X, the potental outcome n case of no treatment or not adoptng s ndependent of treatment or technology assgnment: Y 0 T \ (X) () Besdes the CIA, a further requrement for dentfcaton s the common support or overlap condton, whch ensures that for each treated or adoptng unt there are control or non-adoptng unts wth the same observables (equaton 5). P r( T = 1 \ X ) < 1. (3) Wth the above two assumptons, wthn each cell defned by X, treatment or technology assgnment s random, and the outcome of control unts can be used to estmate the counter factual outcome of the treated n the case of no treatment (Nanncn, 007). Matchng on every covarate s dffcult to mplement when the set of covarates s large. To overcome the curse of dmensonalty, Rosenbaum and Rubn (1983) show that matchng on a sngle ndex, the propensty score, rather than on a multmensonal covarate vector s possble. Accordng to Heckman et al. (1998), the propensty score s defned as the condtonal probablty of recevng treatment or n ths case of adoptng the mproved fallow technology. Mathematcally, the propensty score can be expressed as: a a e( x) = Pr( W = 1\ X = x) = E[ W \ X x] = Where W =1, for treated farmers, and W = 0, for untreated farmers; a = mproved fallow technology; and X s the vector of treatment covarates. The Propensty Score s usually unknown and ths study estmated t through a probt regresson n whch the dependent varable equaled one f the household adopted mproved fallows and zero otherwse. Ths was followed by checkng the 6 (4)

7 balancng propertes of the propensty scores. The balancng procedure tests whether or not adopter and non-adopter observatons have the same dstrbuton of propensty scores. Varous specfcatons of the probt model were attempted untl the most complete and robust specfcaton that satsfed the balancng tests and establshment of the common support regon was obtaned. Matchng was mplemented usng nearest neghbour wth replacement and Epanechnkov kernel (bandwdth 0.06) matchng technques. For both technques, the sample was bootstrapped 100 tmes. Wth nearest neghbour matchng, the ndvdual from the comparson group s chosen as a matchng partner for a treated ndvdual that s closest n terms of propensty score. Wth replacement meant that an untreated ndvdual could be used more than once as a match. Matchng wth replacement ncreases the average qualty of matchng and decreases bas (Calendo and Kopeng, 005). Unlke the nearest neghbour matchng algorthm that ensures only a few observatons from the comparson group are used to construct the counterfactual outcome of a treated ndvdual, Kernel matchng (KM) s a non-parametrc matchng estmator that uses weghted averages of all ndvduals n the control group to construct the counterfactual outcome. KM s therefore assocated wth lower varance because more nformaton s used. One drawback of ths approach s the possblty of usng bad matches. It s for ths reason that the proper mposton of the common support condton s of major mportance for KM (Calendo and Kopeng, 005).. Endogenous swtchng model Matchng strateges only control for heterogenety effects due to observable covarates. To account for endogenety bas and the effects of unobservable covarates, the study employed endogenous swtchng regresson technques. The study specfed the model for technology adopton followng Loksn and Sajaa (004). Ths model s comprsed of the selecton equaton or the crteron functon and two contnuous regressons that descrbes the behavour of the farmer as he faces the two regmes of adoptng the mproved fallows or not. The selecton equaton s defned as; I = a + m * Z wth I * ì 1 f I > 1 = í î0 otherwse (5) 7

8 where I * s the unobservable varable for technology adopton and I s ts observable counterpart whch s the dependent varable (adopton of mproved fallow) whch equals one, f the farmer has adopted and zero otherwse. a s a vector of parameters whle Z are non-stochastc vectors of observed farm and non-farm characterstcs determnng adopton and dsturbances assocated wth the adopton of mproved fallows. m s random The two welfare regresson equatons where farmers face the regmes of adoptng or not to adopt mproved fallows are defned as follows: Regme 1: Regme : y1 b X 1 + e1 = f I = 1 (6) y b X + e = f I = 0 (7) where Y j are the dependent varables or outcome varables (such as maze yeld, crop ncome etc) n the contnuous equatons; X 1 and X are vectors of exogenous varables; β1 and β are vectors of parameters; and e 1 and e are random dsturbance terms. The error terms are assumed to have a trvarate normal dstrbuton wth mean vector zero and covarance matrx: æ ç W = çs ç ès s m 1 31 s. 1.. ö. s ø (8) where s m s a varance of the error term n the selecton equaton, and the error terms n the contnuous equatons. s 1 s a covarance of m and m and e. Snce Y 1 and s 1 and e 1. s 31 Y are never observed smultaneously the covarance between s are varances of s a covarance of e 1 and s not defned. Accordng to Asfaw (010), an mportant mplcaton of the error structure s that because the error term of the selecton equaton m s correlated wth the error terms of the welfare e 8

9 outcome functons are nonzero: e 1 and e, the expected values of e 1 and e condtonal on the sample selecton [ e I 1] ( az ) ( az ) [ e / I = 0] ( az) s e m ( az ) f f = (9) E 1 / = s e 1m = s e1m l1 and E = -s e m = l F 1- F Where f(.) s the standard normal probablty densty functon, F(.) ( ) ( ) f az functon, l1 =, and F az f l = - 1 ( az) ( az ) the standard normal cumulatve. If the estmated covarances e m -F s 1 and s e m are statstcally sgnfcant, then the decson to adopt and the welfare outcome varables are correlated, that s we fnd evdence of endogenous swtchng and reject the null hypothess of absence of sample selectvty bas. Accordng to Maddala and Nelson (1975), ths model s defned as swtchng regresson model. There are several ways n whch ths model can be estmated. Maddala (1983) proposes a two step procedure that however requres some adjustments to derve consstent standard errors and accordng to Hartman (1991) and Nawata (1994) quoted n Asfaw (010), ths procedure shows poor performance n case of hgh multcollnearty between the covarates of the selecton equaton and the covarates of the welfare outcome equatons. The endogenous swtchng regresson models can effcently be estmated usng the full nformaton maxmum lkelhood (FIML) estmaton (Lokshn and Sajaa, 004). The FIML method smultaneously estmates the probt crteron or selecton equaton and the regresson equatons to yeld consstent standard errors. The model s dentfed by constructon through non-lneartes. Gven the assumpton of trvarate normal dstrbuton for the error terms, the logarthmc lkelhood functon for the system of equatons 5, 6, and 7 can be gven as follows: InL = å{ I w[ In( F( 1 ) + In( f( e1 / s 1 )/ s 1 ) + ( 1- I ) w [ In( 1- F( h )) + In( f / e / s )/ s ])] } = 1 h (10) 9

10 where w s an optonal weght for observaton and h j ( az + r e / s ) j j = j = 1, where 1 - r j j s 1 r1 = and s s m 1 r s 31 = are the coeffcents of correlaton between e and m.. To make sure that s s m the estmated r, r are bounded between -1 and 1 and estmated s,s 1 1 maxmum lkelhood drectly estmates In s, Ins 1 and a tanh r : are always postve, the a tanh r = 1 æ1+ r j Inç è1- r j ö ø The FIML estmates of the parameters of the endogenous swtchng regresson model can be obtaned usng the STATA command movestay proposed by Lokshn and Sajaa (004)...1 Condtonal expectatons, treatment and heterogenety effects After estmatng the model s parameters the followng condtonal expectatons can be used to compare the varous expected outcomes of the farm households: (a) that adopted the mproved fallows ( y1 / I = 1, x1 ) = x1 b1 + s 1 l E e m 1 (11a (b), that dd not adopt the mproved fallows ( y / I = 0, x ) = x b + s e m l E (11b) (c) that the adopted farm households dd not adopt, and ( y / I = 1, x ) = xb1 + s l E e m 1 (11c) (d) that the non-adopters farm households adopted. ( y1 / I = 0, x1 ) = x1 b + s e1m l E (11d) 10

11 Cases (a) and (b) represent the actual expectatons observed n the sample whle cases (c) and (d) represent the counterfactual expected outcomes. The effect of the treatment on the treated (TT) (effect of mproved fallows on the adopters) s the dfference between (a) and (c) whle the effect of the treatment on the untreated (TU) for the farm households that actually dd not adopt mproved fallows s the dfference between (d) and (b). Accordng to Asfaw (010), heterogenety effects due to unobservable factors such as management sklls can also be estmated. These nclude; the dfference n the expected outcomes of the adopters of mproved fallows (a) and that of the non-adopters had they adopted (d). Smlarly for the group of farm households that decded not to adopt, ths s the dfference between (c) that the adopters dd not adopt and (b) the non-adopters. Fnally, the dfference between TT and TU can be estmated. Ths effect called transtonal heterogenety (TH), estmates whether the mpact of adoptng mproved fallows s larger or smaller for the farm households that actually adopted the technologes or for the farm household that actually dd not adopt n the counterfactual case that they dd adopt..3 Study area The study was conducted n Chongwe dstrct of Lusaka provnce of Zamba n November and December 011. Agroforestry research and development n Zamba has manly been conducted n the Eastern provnce wth Chpata dstrct beng the man hub and n Lusaka provnce, wth Chongwe dstrct housng the Kass Agrcultural Tranng Centre (KATC) that promotes agroforestry among ts other actvtes. Snce the scalng down of agroforestry actvtes by WAC n eastern Zamba n late 000, farmer enthusasm towards the agroforestry n Eastern Provnce has been on the declne. Chongwe dstrct was purposvely chosen for ths case study snce KATC s stll very actve n the area. Informal ntervews specfcally desgned to plan for the study and to dentfy areas where agroforestry s most concentrated n the dstrct were held wth extenson offcers from KATC. Three agrcultural (out of 8) camps namely Nyangwena, Chnkul and Katoba were dentfed as the man catchment areas wth farmers practsng mproved fallows. These camps were targeted for the study. The farmers n the study area are mostly subsstence who grow manly the staple maze crop for food and the surplus for sale. The common cash crops grown n the area nclude groundnuts, cotton, beans and garden vegetables such as rape, cabbage, tomato and onon. The most common anmals reared nclude cattle, chckens and goats. 11

12 .4 Samplng The study used agrcultural camp lsts compled n consultaton wth Mnstry of Agrculture camp extenson offcers to devse a samplng frame. To ensure a complete lstng of the households n the study area, agrcultural camp extenson offcers who stay wth the local communtes were ntally requested to thoroughly go through exstng lsts and update accordngly f there were any households that they had omtted wthn ther catchment areas. The resultng lsts from the three camps were then consoldated nto one samplng frame, whch was then stratfed nto adopters and non-adopters of mproved fallows. The samplng frame had a total of 7,081 households of whch approxmately 0 percent were adopters. Due to lmted logstcs, the study amed at ntervewng around 5 percent (335 households) from ths samplng frame. Snce matchng strateges requre treatment unts to have a larger pool of control unts from whch matches can be obtaned (Calendo and Kopeng, 005), the sample was stratfed nto :3 ratos for the adopters and non-adopters respectvely. Therefore from a stratum of 1,416 lsted mproved fallow adoptng households, 134 were selected randomly usng stata (Stata verson 11., 009). Smlarly, from 5,665 lsted households, 01 non-adopters of mproved fallows were randomly selected usng stata. Eventually, due to non-responses, 130 adopters and 194 non-adopters respectvely were fnally ntervewed. Ths study defned an adopter of mproved fallows as one who has been usng the technology for at least the last sx years (snce 006 and before) and has been growng at least a quarter of a hectare usng ths technology. The mnmum sx year perod of use crteron was meant to exclude farmers who just tested the technology wth the nfluence of KATC but decded to abandon t after the frst cycle or before they could even experence a post fallow crop. We noted n the ntroducton that t takes -3 years for mproved fallows to mature. Ths s followed by up to 3 rounds of post fallow croppng before the cycle starts agan. It follows that t takes a mnmum of 5-6 years for a farmer to reap maxmum benefts from plantng mproved fallows. Key nformant ntervews wth KATC offcers revealed that farmers who do not adopt after testng the technology would have started usng other forms of external nputs on former mproved fallow plots before ths fve to sx year full cycle s completed. Although some farmers would plant the subsequent mproved fallow before the resdual effect from the precedng fallow s completely exhausted, the sx year mnmum perod would ensure that they had beneftted n terms of post fallow crop producton even after the ntal testng of the technology. Ths condton manly knocked out the households who had mproved 1

13 fallows at the tme of the study but had not experenced a post fallow crop (17 farmers). The crteron on area was meant to exclude households who had planted just a few mproved fallow trees for ornamental purposes. Only two farmers who had just planted a few scattered mproved fallow trees were affected by ths condton. Therefore n total, 19 households dropped out from the adopton category. These were added to the non-adopters at the results analytcal stage on grounds that whatever fallows they may have had on ther farms had no mpact on post-fallow crop producton. As a result the fnal sample used n analyss was composed of 111 adopters and 13 non-adopters of mproved fallows..5 Survey nstrument development and pre-testng Consderable tme and effort was expended n desgnng the survey nstrument. The frst author nformally ntervewed offcers at KATC, agrcultural camp extenson offcers and some lead farmers (defned as farmers who are the entry ponts to vllages and work closely wth agrcultural extenson offcers n ther areas) n the catchment areas. The nformal ntervews covered a wde range of ssues ncludng the general agrcultural practces and agroforestry actvtes n the area. Factors affectng the farmers up take of the mproved fallows were also dscussed. Usng fndngs from these dscussons and a revew of lterature, a structured formal questonnare was drafted. The questonnare went through several refnements followng the nteractons between the authors. The fnal verson of the questonnare partcularly useful for ths specfc study covered three man sectons. The frst secton covered the basc households demographc and socoeconomc characterstcs. The second secton explored the wealth status of households and use of mproved fallows. The fnal secton assessed the general agrcultural practces such as agrcultural related challenges; type and amounts of nputs used and crop producton levels for the dfferent nputs ncludng mproved fallows. We also ncluded questons on whether the current demographc and socoeconomc characterstcs, and agrcultural related challenges where the same at technology adopton (for adopters) or sx years before the survey perod (for non adopters). Ths was mportant for assessng mpact of the technology usng pre-adopton covarates. 13

14 .6 Survey mplementaton Before the formal survey a pre-test study comprsng 16 households was carred out n the study area. The pre-test survey served two purposes; frst, the study wanted to ensure that the questonnare had questons that were well understood by the farmers and were flowng n a logcal way. Secondly, the pre-testng provded the opportunty to practcally tran the research assstants (who have had a day of theoretcal tranng) on the survey mplementaton. Only a few modfcatons were made on the questonnare after the pre-testng. The fnalsed questonnare was used to ntervew the 34 households selected for ths study. The frst author, the three camp extenson offcers from the catchment areas and an offcer from KATC were nvolved n both the pre-testng and fnal mplementaton of the survey..7 Analyss and computatonal methods We used Stata verson 11., 009 to randomly select the households dscussed n secton.4 and to perform several analytcal procedures n estmatng the mpact of mproved fallows. Frst, we analysed means and proportons for the whole sample and then compared the characterstcs between adopters and non-adopters of mproved fallows usng the t-dstrbuton (contnuous varables) and ch-square dstrbuton (dscrete varables) at P = 0.05 sgnfcance level. These characterstcs (and other varables) were later used as explanatory varables n the estmaton of the propensty score (appendx 1), and treatment and outcome models that are presented under the matchng and endogenous swtchng regresson models. A combnaton of mproved fallow adopton lterature, economc theory and the outcome of nformal meetngs wth KATC staff and lead farmers were helpful n selectng the explanatory varables used. To estmate the propensty score (PS), we used probt regresson n whch the dependent varable equalled 1 f the household had adopted the mproved fallow technology and zero otherwse. Varous specfcatons of the probt model were attempted untl the most complete and robust specfcaton that satsfed the balancng tests was obtaned. Usng the estmated propensty score, the estmaton of the Average Treatment effect on the Treated (ATT) on several outcome varables was mplemented. As s common practse, we weghted the non- adopters propensty scores by the 14

15 propensty score dvded by one mnus the propensty score (PS/(1-PS)). Durng matchng we bootstrapped the sample 100 tmes to obtan standard errors. We then used the nearest neghbour matchng (ATTn) and kernel matchng (ATTk) stata commands (Stata verson 11., 009) to estmate the average treatment effect of the mproved fallows on welfare. To test for matchng results robustness and account for unobservable selecton bas, the welfare outcome varables were subjected to endogenous swtchng regresson analyses. Swtchng regresson was used to predct and compute welfare outcomes n the mean dfferences between a) adopters havng adopted and had they not adopted, and b) non-adopters havng not adopted and had they adopted. The dfferences n (a) and (b) gave the treatment effect on the treated (TT) and the treatment effect on the untreated (TU); the dfferences n outcome varables between the adopters and the non-adopters called base heterogenety (BH), and the dfference n TT and TU called transtonal heterogenety (TH). The computatons were performed usng the movestay command n stata (Stata verson 11., 009). 3. RESULTS 3.1 Descrptve statstcs The frst secton of results provdes a descrpton of the socoeconomc characterstcs of the sample households wth a specal focus on the comparson between the adopters and non-adopters of mproved fallows. A descrpton of socoeconomc characterstcs of the households heads n the surveyed area s shown n Table 1. The table only shows the characterstcs whose dfferences between the adopters and non adopters were sgnfcant. There was no sgnfcant dfference n the average age of the adopters and non-adopters. Overall, the average age of the surveyed household heads was about 46.7 years. The average actve famly labor force was 4.6 persons for adopters and 3.8 for non-adopters and the dfference was statstcally sgnfcant supportng the mportance of effectve famly labor for adopton of mproved fallows. Both farm sze and cropped land n 010/011 season were statstcally hgher for the adopters than the non-adopters of mproved fallows. 15

16 The sample was domnated by male headed households wth no dstngushable dfferences n gender between the adopters and non-adopters. More adopters of mproved fallows were educated compared to non-adopters. About 40% of the adopters had been to secondary school compared to about 30% of the non-adopters. No sgnfcant dfference was observable n the martal status of household heads. For both categores more than 80% of households were from marred homesteads. Adopters had large farm szes, cropped land as well as land put to maze producton n 010/011 season (Table 1). Table 1: Households socoeconomc characterstcs of sample farmers n Chongwe dstrct, Zamba 1 Adopters (N = 111) Non-adopters (N = 13) Over all (N = 34) Household sze (MEU) 4.6 (0.181) 3.8 (0.14)*** 4.1 (0.104) Farmland sze (ha) 5. (0.79) 3.5 (0.133)*** 3.90 (0.139) Cropped land(ha) 3.4 (0.175). (0.089)***.6 (0.089) Cropped maze area (ha).3 (0.13) 1.4 (0.071)*** 1.7 (0.069) Improved fallow area (ha) 0.86 (0.049) 0.04 (0.01)*** 0.9 (0.08) Educaton (% households heads) Never been to school ** 8 Attended prmary ** 31.5 Completed secondary *** 6. Martal status (% households) Dvorced (= 1, otherwse = 0) 0 3.8*.8 Farmng group membershp (% households) (Yes = 1, otherwse =0) *** 76.6 *, **, *** sgnfcant dfference between adopters and non-adopters means at 90%, 95% and 99% confdence levels. Fgures n parentheses are standard errors of the mean 1 Varables showng non-sgnfcant dfferences between adopters and non-adopters are not ncluded n the Table. Man equvalent unts (meu) were calculated followng Runge-Metzger (1988) as: < 9years = 0; 9 to 15 and over 49 years = 0.7; 16 to 49 = 1. Usng meu s mportant snce not all household members would provde farmng labour. 16

17 3. Adopton of mproved fallows and dstrbuton of wealth assets Among the mproved fallow technologes, pgeon pea (Cajanus cajan) was found to be the most popular n the study area. Seventy eght percent of the adopters had pgeon pea growng n ther felds at the tme of the survey. The average area under pgeon pea was 0.56ha. Thrty percent of the adopters had Fhaderba albda coverng an area of 0.89 ha on average whle 18.9% of the adopters had Tephrosa vogel on an area of = 0.48ha. Some nsgnfcant number of adopters (0.05%) had Sesbana sesban growng n ther feld and one household had Glrcda sepum. The adopters of mproved fallows had more cattle, goats, poultry and bcycles than the non-adopters (Table ). However, the average number of oxen, pgs, donkeys, oxen mplements, sprayers, rados, televson sets and ron roofed houses were not statstcally dfferent between the adopters and nonadopters of mproved fallows. Table : Proportons of households ownng varous levels of assets n Chongwe dstrct, Zamba 1 Adopters (N =111) Non-adopters (N = 13) Over all (N = 34) % households Mean (std. error) % households Mean (std. error) % households Mean (std. Cattle (0.99) (0.904)*** (0.673) Goat (0.973) (0.654)* (0.547) Poultry (1.049) (0.95)* (0.706) Bcycles (0.078) (0.044)** (0.041) *, **, *** sgnfcant dfference between adopters and non-adopters means at 90%, 95% and 99% confdence levels 1 Varables showng non-sgnfcant dfferences between adopters and non-adopters are not ncluded n the Table. error) The adopters of mproved fallows were well off n most of the outcome or welfare varables (Table 3). They had sgnfcantly hgher ncome from crop sales and ncome from the staple maze crop. The adopters of mproved fallows also had sgnfcantly hgher maze yelds than the non-adopters. The adopters also recorded a hgh number of months per year when they had ther own home grown food. The non-adopters had sgnfcantly hgher off farm ncome than the adopters (Table 3). 17

18 Table 3: Average dfferences n several outcome varables between adopters and non-adopters of mproved fallows n Chongwe dstrct, Zamba Adopters (N = 111) Non-adopters (N = 13) Mean dfference t stat 1 Crop Income per MEU (ZK, 000) 888 (99) 366 (51) 5 (11) Maze Income per MEU (ZK, 000) 811 (96) 79 (44) 53 (105) Off farm Income 3 per MEU (ZK,000) 47 (43) 470 (49) -3 (65) Total Maze yeld (ton) 4.61 (0.30).10 (0.150).5 (0.337) Maze yeld (ton/ha).1 (0.119) 1.50 (0.070) 0.7 (0.138) Months per year wth enough grown food 10.9 (0.145) 9.8 (0.136) 1.10 (0.199) Equal varance not assumed, fgures n parentheses are standard errors of the means Man Equvalent Unts (MEU) were calculated followng Runge-Metzger (1988) as: < 9years = 0; 9 to 15 and over 49 years = 0.7; 16 to 49 = 1. 3 Off farm actvtes ncluded remttances, sale of charcoal and petty tradng. 3.3 Estmatng the causal mpact of mproved fallows usng matchng approaches The results of the propensty score used n estmatng the matchng algorthms are shown n appendx I. The explanatory varables used n estmatng the propensty score are shown and descrbed n Table 4 that only nclude varables showng sgnfcant dfferences between adopters and non adopters. The other varables that dd not show any sgnfcant dfferences between the adopters and non adopters ncluded age of household age, whether marred, sngle, wdowed or not, whether households experenced sol fertlser challenges or not, and the camp area dummes (Nyagwena, Katoba and Chanda). 18

19 Varable Table 4: Descrptve statstcs of sgnfcant varables used n estmatng the propensty score and outcome models Defnton Adopters (N = 111) Non-adopters (N = 13) Over all (N = 34) Educaton Years of formal educaton of household head 3.5 (0.103)***.75 (0.075).95 (0.06) Martal status 1 f dvorced, 0 otherwse 0.01 (0.009)* 0.04 (0.013) 0.03 (0.009) Totfertuse Total Fertlser Use (tons) 0.44 (0.039)* 0.31 (0.034) 0.35 (0.06) SandySol 1 f farm has sandy sols, 0 otherwse 0.3 (0.045)*** 0.15 (0.05) 0.3 (0.045) Farms Sze of farm n hectares 5.16 (0.79)*** 3.5 (0.133) 3.90 (0.139) AreaFa Sze of fallowed land n hectares 1.78 (0.199)*** 1.0 (0.094) 1.8 (0.094) HszeE Number of MEU n a household 4.55 (0.181)*** 3.81 (0.14) 4.06 (0.104) Group 1 f household belongs to agrcultural group, 0 otherwse 0.96 (0.018)*** 0.66(0.033) 0.77 (0.04) Wndex 3 Household wealth ndex 0.605*** *, **, *** sgnfcant dfference between adopters and non-adopters means at 90%, 95% and 99% confdence levels, 1 see Table 1 for the defnton of categores. Man Equvalent Unts (MEU) calculated followng Runge-Metzger (1988) as: < 9years = 0; 9 to 15 and over 49 years = 0.7; 16 to 49 = 1 were used to MEU n households. 3 computed for household assets usng prncpal component analyss followng Langyntuo (008) Matchng results are reported n Table 5 for the nearest neghbour method and Table 6 for the kernel matchng approach. The nearest neghbour strategy used 43 households among the control unts to match aganst 110 adoptng households. Usng the nearest neghbor matchng strategy, the mproved fallow technology showed postve mpact n some but not all of the welfare ndcators consdered. For the 010/011 season, the technology had a sgnfcant mpact on per capta maze ncome, total maze yelds, per capta maze yelds, maze yelds per hectare and the number of months n a year the household had enough own grown food for consumpton. The technology dd not have a sgnfcant mpact on per capta crop ncome (Table 5). 19

20 Table 5: ATT estmaton of varous outcome varables usng Nearest Neghbour Method Average Treatment on Treated(ATT) Standard Error t value Crop Income per MEU (ZK, 000) Maze Income per MEU (ZK, 000) Total Maze yeld (tons) Maze yeld per MEU (tons) Maze yeld (ton/ha) Months per year wth enough grown food Number of treated unts used =110 and number of control unts used = 43 1 Man Equvalent Unts (MEU) were calculated followng Runge-Metzger (1988) as: < 9years = 0; 9 to 15 and over 49 years = 0.7; 16 to 49 = 1. The kernel matchng strategy used more control unts (19) to match aganst the 110 adoptng households. Unlke the nearest neghbour approach, the kernel matchng strategy results showed that the technology had postve and sgnfcant mpacts on all the welfare varables consdered. It had a postve mpact on per capta maze ncome, total maze yeld, per capta maze yeld, maze productvty and months per year a household has enough food. In addton the technology had postve and sgnfcant effect on per capta crop ncome (Table 6). Table 6: ATT estmaton of varous outcome varables usng Kernel Matchng Average Treatment on Treated (ATT) Standard Error t value Crop Income per MEU (ZK, 000) Maze Income per MEU (ZK, 000) Total Maze yeld (tons) Maze yeld per MEU (tons) Maze yeld (ton/ha) Months per year wth enough grown food Number of treated unts used = 110 and number control unts used = Man Equvalent Unts (MEU) were calculated followng Runge-Metzger (1988) as: < 9years = 0; 9 to 15 and over 49 years = 0.7; 16 to 49 = 1. 0

21 3.4 Estmatng the causal mpact of mproved fallows usng endogenous swtchng regresson models The full nformaton maxmum lkelhood estmates of the endogenous swtchng regresson model are shown n Tables IIa to IIe n the appendx. The frst and second columns n these tables present the welfare functons for households that dd and dd not adopt the mproved fallow technology whle the last column represent the selecton equaton on adoptng mproved fallows or not. The correlaton coeffcent (rho) between the adopter s regme and the selecton equaton n the total maze yelds model s negatve and sgnfcantly dfferent from zero. Ths suggests that farmers who adopted mproved fallows get hgher maze yelds than a random farmer from the sample would have obtaned. There exst both observed and unobserved factors nfluencng the decson to adopt mproved fallows and ths welfare outcome gven the adopton decson. The swtchng regresson model s results on the expected welfare outcomes under actual and counter factual condtons are shown n Table 7. The results stll ndcates that the technology has a postve mpact on maze ncome per capta, total maze yelds, maze yeld per capta and maze yeld per hectare. The mean values of these outcome varables were sgnfcantly hgher for adopters than had they not adopted. The gap n the mean crop ncome value was however not sgnfcant (Table 7). The swtchng regresson model also predcted a postve and sgnfcant effect of the technology on all the welfare varables on the non-adopters had they adopted. In fact the effect of the technology on the non-adopters could have been much hgher than on the adopters n all outcome varables except maze yeld per hectare. The treatment effects on the adopters from swtchng regresson were generally lower than those from the matchng strateges. For nstance whle the per capta maze ncome ATT was estmated at ZK509, 000 and ZK 487, 000 usng the nearest neghbour and kernel matchng strateges, the swtchng regresson model gave an estmate of about ZK300,

22 Table 7: Endogenous swtchng regresson model results Decson stage Treatment effect Adopted Not to adopt Dfference (TT or TU) a) Crop ncome per meu (ZK) Adopters 1,160,054 (57580) 1,087,408 (56176) 7,645 (64445) Non-adopters 1,484,130 (5711) 648,600 (34577) 835,530(63611)*** Heterogenety effects BH 1 = -34,076 BH = 438,808 TH = -76,885 b) Maze ncome per meu (ZK) Adopters 81,108 (48408) 511,903 (35473) 300,05 (41500)*** Non-adopters 90,81 (40306) 304,951 (0519) 597,871 (3714)*** Heterogenety effects BH 1 = -90,713 BH = 06,95 TH = -97,665 c) Maze yeld (ton) Adopters 5.94 (0.81) 4.6 (0.30) 1.3 (0.170)*** Non-adopters 6.59 (0.146).1 (0.104) 4.46 (0.11)*** Heterogenety effects BH 1 =-0.65 BH =.5 TH = d) Maze yeld per meu (ton) Adopters 1.4 (0.065) 1.16 (0.054) (0.051)* Non-adopters 1.7 (0.045) 0.63 (0.031) (0.038)*** Heterogenety effects BH 1 = BH = 0.53 TH = e) Maze yeld per hectare (ton) Adopters.14 (0.049) 1.40 (0.033) 0.81 (0.053)*** Non-adopters.09 (0.043) 1.46 (0.03) (0.050)*** Heterogenety effects BH 1 = BH = TH = TT = treatment effect on the treated (adoptng had not adopted), TU = treatment effect on the untreated (had they adopted not adopted), BH = Base heterogenety (adopted had they adopted), TH = Transtory heterogenety (TT TU) 4. DISCUSSION The evaluaton of mpact of adopton of a technology requres meanngful estmaton so that over or under estmaton s avoded. Ths study was concerned wth the estmaton of the mpact of mproved fallows on farmer welfare. The study used data from 34 households surveyed n Chongwe dstrct of Zamba to demonstrate the causal effect of the mproved fallow technology by

23 usng well establshed dentfcaton strateges. Our fndngs showed that wthout randomzaton there s a tendency to over estmate the mpact of mproved fallows on farmer welfare varables. By smply usng the conventonal t test approaches n analyzng the dfferences n varous outcome varables, adopters were found to be well off than the non-adopters. The adopters had sgnfcantly hgher levels of per capta ncomes, crop ncomes and ncomes from maze. In addton, the maze yelds and maze productvty were hgher than those of non-adopters. The adopters also had more months n whch they were suffcent n home grown food and were wealther n terms of assets than the non-adopters. On the other hand the non-adopters had more off farm ncomes than the adopters. Wthout rgorous analyses, the mean dfferences n the outcome varables consdered were so sgnfcantly hgh that an attempt to nfer to mproved fallows as the cause of these dfferences cannot be ruled out. Evaluatng mpact of mproved fallows usng more rgorous econometrc analytcal tools confrmed the postve mpact of mproved fallows on per capta ncome, maze ncome, maze yeld, maze yeld per hectare and number of months per year the household has enough home grown food. Estmatons from both the matchng strateges (nearest neghbour and kernel) and endogenous swtchng regresson model ndcated that the technology has a postve and sgnfcant mpact on the welfare varables noted above. Notably, the technology s postve mpacts appear to be more pronounced wth outcome varables that are closely related wth the maze crop. Ths s not surprsng snce the most common crop grown after the mproved fallows s maze (Slesh et al. 008). Maze beng the staple food n Zamba and most parts of sub Saharan Afrca, the contrbuton of the mproved fallows n ensurng food securty and hence allevatng food poverty cannot be over emphaszed. There however was a contrast n the fndngs from kernel and nearest neghbour matchng strateges on the mpact of the technology on crop ncome per capta. The former method showed a postve and sgnfcant mpact of the technology on crop ncome whle the latter method showed that the effect was nsgnfcant. The nsgnfcance of the technology to nfluence crop ncomes was also confrmed by the more robust endogenous swtchng regresson whch accounted for the unobserved bas. There are two explanatons that ths fndng seems to suggest. Frst, t mght be that other sol mprovement optons are the ones drvng the ncreases n crop ncome. A closer scrutny of our data showed that 89.% of the adopters of mproved fallows were also usng 3

24 norganc fertlsers. The mpact of fertlser on crop ncome and other welfare ndcators may need to be nvestgated further. Second, ths fndng could reflect the fact that the mproved fallow technology s not necessarly beng used on hgh value crops such as cotton and some hortcultural products that are common n the study area. Most farmers n the study area are aware that the technology mproves sol fertlty. However there s lttle evdence to suggest that farmers are aware that any crop can farly do well on the sols mproved by the technology other than maze. Maze was the only crop planted after mproved fallows by all the adopters. There mght be need to senstze farmers on the need to grow hgh value crops on mproved fallow plots as well. Results from swtchng regresson also showed that they would have been a sgnfcant postve treatment effect f the non-adopters had adopted the technology. Although a detaled adopton study would provde nsghts nto the factors constranng adopton of the technology, more than 80% of the non adopters cted the long watng perod (for accrual of benefts) as the man reason for not takng up the technology. Research at KATC s actvely pursung the ssue of short duraton mproved fallows. Key nformant ntervews revealed that t s the more reason why pgeon pea s the most common mproved fallow spece n the study area. Compared to others such as Sesbana, Tephrosa and Glrcda mproved fallows that requre at least years to reach fallow maturty, some pgeon peas speces have been known to reach maturty after only 1 year. There s need therefore to promote such speces among the small scale farmers n a much more vgorous way. The matchng technques and the swtchng regresson model accounted for observables and unobservable factors such as dfferences n management sklls between the adopters and nonadopters. In essence we created a quas expermental desgn n estmatng the mpact of the mproved fallow technology. We therefore expected the causal effect of the technology to approxmate the productvty yelds from randomsed expermental trals. The causal effect of the mproved fallows on maze productvty was estmated at about 800kg per hectare. Mafongoya et al (006) showed that mproved fallows on randomsed expermental plots n eastern part of Zamba can gve up to 3,000 4,000 kg of maze per hectare n the frst year of fallow termnaton. In subsequent years, the yelds declne up to around 1,500 kg after 3 years or so. The 800kg of maze per hectare estmated s far from these fgures. Ths gves some evdence that the farmers sklls n the management of the mproved fallows and probably the maze crop as well, may not be very 4

25 good. For the farmers to get optmum yelds there s need to contnuously tran them n management of new mproved agrcultural technologes such as mproved fallows. By analyzng crop ncome estmates from the robust econometrc methods, one could easly dsmss the postve mpact of mproved fallows. Conversely, assessng the outcome varables that are closely assocated wth mproved fallows such as maze yeld, one could quckly conclude that mproved fallows have a postve effect on household welfare. Ths suggests that the measurement of welfare needs to be contextualzed. Household welfare may have dfferent meanng to dfferent stakeholders. Ths study delberately used a broad lst of these welfare varables so that an assessment of the stage at whch mproved fallows cease to have mpact on the household well beng may be establshed. Ths s mportant so that polcy makers know exactly were to target n the promoton of technologes. For nstance, general agrcultural developmental support mght not necessarly boost the uptake of mproved fallows. Ths s because some technologes that are used on hgh value crops such as norganc fertlzer mght have a sgnfcant nfluence on broader varables such as crop ncome. In ths case prce support polces that drectly mpact on maze producton and exchange could be more meanngful n havng a pull effect on the adopton of mproved fallows. 5. CONCLUSION We estmated the causal effect of mproved fallows on several outcome varables among resource poor small scale farmers n Chongwe dstrct of Zamba. We used propensty score matchng technques complemented wth endogenous swtchng regresson models to ensure results robustness. The estmates from these methodologes show that there s a causal effect of the technology on maze producton, productvty, per capta maze yeld and maze ncome. Maze productvty from these quas-expermental desgns was lower than that from randomzed expermental trals suggestng the need for contnuous tranng of famers n management of mproved fallows. The maze ncome from the technology was also not observed to have had a sgnfcant nfluence on overall crop ncome. Ths hghlghts the mportance of dversfyng the use of the technology on other hgh valued crops. Estmates from the econometrc methods were generally lower than those from the conventonal evaluaton wthout randomzaton suggestng the 5