Martha Negash & Johan Swinnen Center for Economic Performance and Institutions (LICOS), KULeuven

Impact of biofuel expansion views: - worsen food insecurity (von Braun, 2008; Mitchel, 2008) on the contrary: - high food prices - not always bad - biofuels stimulates economic growth & reduce poverty (case- Mozambique) (Arndt et al, 2010) - reduce the incidence of poverty & support food self- sufficiency goals (Huang, et al. 2012) ‘food vs fuel debate’

- weak land governance & property rights – risk to vulnerable hhs (Cotula et al 2010) “Fueling exclusion” -> conflict Foreign land investment:  investment brings inefficiently utilized/under-utilized land  emp’t & income effect  cheaper energy source to remote rural areas (quite an issue ‘energy poor countries’) ‘land grab vs land investment’ Other concern:

Evidence in current literature: - based on aggregate economic wide simulations or qualitative studies - largely focused on developed economies - impact analysis on poor smallholder context - limited

Research questions: 1- identify factors associated with biofuel crop adoption decisions? 2- how participation decision influences food security status? Survey– privately organized castor (biofuel feedstock crop) outgrowers in Ethiopia

 modern energy (extremely poor)  food (alarming hunger)  unutilized/underutilized land low potential areas  good case to study Source: IFPRI, 2010Source: Nussbaumera et al., 2011

Castor outgrower scheme in Ethiopia Advantages -can be preserved on the field relatively for longer periods - allows piecemeal collection of seeds -good for soil fertility -contract farmers may record higher productivity in food crops through – higher input use - spillover effects - crop management practices Disadv. - Invasive species - castor has no other use in the area – (bargaining power of farmers ??) - default is mainly from redirecting input use for other crops

Supply chain Raw seed export Company -> via supervisors -> input loan & seed -> farmers Farmers-> village centers-> via supervisors -> company -> export-> China processors

Sampling frame  all villages in range of 1100– 2000 m.a.s.l. covered by the program included in our sampling frame Sample size -24 villages randomly selected -total of 478 household -30% participants Participant/Adopters  a household that allocated piece of land for castor & entered contractual agreement w/t the company Source: FEWS, 2010 Most biofuel projects are located in dry & low land areas of the country

-better access -better infras -dairy supply to town - poor access; - poor infras (tel., electric) - no alternative cash crop Sampled villages & castor bean adoption -distant villages -alternative cash crop – fruits & ginger

Village level observation - dissemination of the castor crop into inaccessible & remote places - widespread adoption rate (20-33%) in three years of promotion -unlike low rate of new crop or fertilizer adoption rates in developing countries - villages with limited alternative cash crop markets show higher adoption incidence

Figure : Food gap (number of months) *** Figure: Per capita food consumption Descriptive (outcome variables) (1/2) % measured by number of food shortage months – decline in value  improvement in welfare total consumption in energy equivalent (kcal/person/day) – increase in value ->improvement in welfare

ParticipantsNon-participants | t/chi-stat| Household wealth variables Owned land size (in ha) *** Own land per capita Farm tools count (Number) Proportion of active labour Access related variables Formal Media (TV/radio/NP) main info. source (1=yes) *** Fertilizer use (kg/ha) *** Borrowed cash money during the year (1=yes) Distance from extension center (Minutes) Contact with extension agent (Number of visits) Household characteristics Gender of the HH head (1=female) *** HH head attended school (1=yes) * Family size *** Descriptive (explanatory variables) (2/2) * p<.1; ** p<.05; *** p<.01

Effect of castor contract participation on income  represent– participation as a regime indicator variable (1) Regime 1: Regime 0: (2) (3) If cov (u i, ℇ 1i ) and/or cov (u i, ℇ 2i ) are statistically significant, switching is endogenous, self-selection - on obs. or unobser. or both). Identification – assume error terms are jointly distributed IV –improves identification – eligibility & past adoption history (farmers choice)

‣ can substitute historical comparative data –but useful in the absence of such data Source: Verbeek, 2012; Di Falco, et al. 2011; AJAE Endogenous Switching Regression Model  allows estimation of heterogeneous effect of covariates  using the information contained in the distribution functions of the error terms & their covariance, allows predicting counterfactual effects

Question 1 First stage: selection to participation  distance from the village center  gov. extension service (---)  Maize price  Female (+++)  Land  Media  Asset (non-significant)

Food gap estimation ParticipantNon- participants Land per capita (ha)-2.799** Log of agricultural income per capita * Household attended schooling (yes=1) ** Family size-0.053*** At least one member works off-farm (yes=1)-0.109*-0.113** Family in polygamy (yes=1)0.412***0.177 Own livestock (TLU) per capita ** Borrowed cash during the year (yes=1)0.212***0.100* District dummyYes Other control Sigma (δ) -1.09***-0.77*** ρ -0.22*0.40** N 476 Likelihood ratio test of independent equations ( X 2 ) 2.98*  differentiated significance & magnitude of coefficients  e.g. family size & livestock coefficients have different signs  opposite sign of ρ – suggest rational sorting into participation

Question 2 Treatment effect Sub-sample Decisions stageTreatment Effect To participate Not to participate Log of food gap (months) Households who participated(a) 0.84(c) 1.20 (treated) -0.37*** Households who did not participate(b) 1.04(d) 0.98 (untreated) 0.06*** Log per capita annual food consumption (kcal/capita/day) Households who participated(a) 7.86(c) 7.59 (treated) 0.27*** Households who did not participate(b) 7.23(d) 7.41 (untreated) -0.18*** Participants  reduction in food gap, 37%, (-11 days)  increase in consumption, 27% Non-participants  do not benefit, rather food gap would increase, 6% (+2 days)  reduction in consumption, 18%

(Question 1) Determinants of adoption:  assets are key factors for adoption  adoption of biofuel declines with price of food crop  physical distance showed no significance unlike most studies Policy implication:  privately organized technology transfer –may efficiently surpass physical barriers

(Question 2) Effect of participation:  impact is heterogeneous  participants are better-off producing castor than if they had not  non-participants would have been worse-off if they had participated Policy implication:  grant farmers more choice  explore castor’s potential contribution to narrow food gap /smooth consumption/