Biofuels and Food Security: Micro-evidence from Ethiopia

1. Biofuelsand Food Security: Micro-evidence from Ethiopia • Martha Negash & Johan Swinnen • Center for Economic Performance and Institutions (LICOS), KULeuven

2. 1. Motivation • 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’

3. Other concern: • 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’

4. 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

5. 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

6. Ethiopia Hunger index Energy poverty index Source: Nussbaumera et al., 2011 Source: IFPRI, 2010 • unutilized/underutilized land low potential areas • good case to study • modern energy (extremely poor) • food (alarming hunger)

7. 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

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

9. 2. Data • 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 Most biofuel projects are located in dry & low land areas of the country Source: FEWS, 2010

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

11. 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

12. Descriptive (outcome variables) (1/2) Figure : Food gap (number of months) Figure: Per capita foodconsumption % *** *** 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

13. Descriptive (explanatory variables) (2/2) * p<.1; ** p<.05; *** p<.01

14. 3. Estimation • Effect of castor contract participation on income • represent– participation as a regime indicator variable (1) Regime 1: (2) (3) If cov (ui , ℇ1i) and/or cov (ui, ℇ2i) arestatistically 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) Regime 0:

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

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

17. differentiated significance & magnitude of coefficients • e.g. family size & livestock coefficients have different signs • opposite sign of ρ – suggest rational sorting into participation Food gap estimation

18. Question 2 Treatment effect • 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%

19. 5. Policy implications (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

20. (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/

21. Thanks!