Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI) Seminar Series, February 09, 2012

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  • -Arndt etal--Biofuelcancreat ‘growthlinkages’ – i.e. demandforother sectors, thuswage and incomeincrease, thusspending …spillovers throughconsumerdemand….Biofuelinvestement increases significantly domestic product (GDP) in Mozambique and Tanzania by between 0.2 and 0.4 percentage points - Lahitew,didsimilarpolicysimulationexcerciseforEthiopia …rise in oilprices …biofuel is predicted to increase in itsproduction…&alongwithreduction in processing cost –biofuel has lesstendency to crowd out agriculturalproduction in Ethiopiagiven –priceincreasewillcreat more demandforotheragriculturalproducts – and thatenhancesproductivity –sincethere is largepotentialforproductivitygrowth in Ethiopia-
  • The outgrowerscheme – we looked is similarwithcommonform of input loanfor output payarrengement. Farmers are contracted to allocate part of their land &labour, the companylendsthemfertilizer and supervision
  • - The domesticchain is short – farmers delivarunsheldcastor to village stores, companytransportsit the Zone towncollection center, companyusessimplemill to remove the sheld, and exports the seeds to china.
  • Family size, land size, and fertilizerapplication are significantlyhigherfor participant groupsParticipants are mostly men, & educatednodifference in land size per capita and suggestingthatparticipants are notassetrichon a per capita bases- Proportion of participantsowning mobile phones are lessthan NP althoughnot significant - -distance to anextenstion center are not different among the twogroups-participants shows smalldifference in farm tools, extenstion contact
  • - Cropincome is significant portion of totalincome. The remainingincome types consitutessmalldividedshareeachbut are a source of substantialvariation in the totalincome (close to a double standarddevation). - To assessbiofuelcropinduceddifference we choose to start lookingcropincomedifference, sincecastor is alsoannualcrop and cropincomeonlyconstitutesannualcrops…analysingcropincomegains/lose is important.-For thatreasonincome is best approximatedbyexpenditurewhich is less variant.-food gap
  • Dummy participationpositionvariablecontinousoutcomevariable – log of percapitacropincome, foodconsumption and expenditurefood gap is excluded in thisanalsysisbecause the outcomevariable is notcontinous –but is censerodvariable at 0, manyreported 0 food gap soi’llnot talk aboutit, estimationapproach is different forlimiteddependent variablesIn such a setting, ATE and TT are the common effect we wouldlike to quantifyATE-measure the effect of the program on a random personfrom the population, while TT quntifies the effect on a random personfrom the treatedpopulationDependingwhat effect we wouldlike to quantify and assumption we wouldlike to bearthen, we have different availableoptions of methods
  • Dummy participationpositionvariablecontinousoutcomevariable – log of percapitacropincome, foodconsumption and expenditurefood gap is excluded in thisanalsysisbecause the outcomevariable is notcontinous –but is censerodvariable at 0, manyreported 0 food gap soi’llnot talk aboutit, estimationapproach is different forlimiteddependent variablesIn such a setting, ATE and TT are the common effect we wouldlike to quantifyATE-measure the effect of the program on a random personfrom the population, while TT quntifies the effect on a random personfrom the treatedpopulationDependingwhat effect we wouldlike to quantify and assumption we wouldlike to bearthen, we have different availableoptions of methods
  • Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

    1. 1. Biofuels, poverty and food security: LICOS Micro-evidence from EthiopiaMartha Negash and Jo Swinnen, 2012 LICOS
    2. 2. LICOS1. Introduction2. Data3. Methods4. Preliminary results5. Conclusion
    3. 3. LICOSBiofuel development : controversialDisadvantages: price increase & volatility –worsens food security (IFPRI 2008; Mitchel 2008); - 10% biofuel expansion in EU and NAFETA – a reduction in GDP by 1% for most poor African countries. (FAO, 2008) weak land governance institutions may favor investors– risk to vulnerable hhs (Cotula et al 2010) 3
    4. 4. Advantages: - biofuels boost growth - (Arndt et al 2011) - using partial equilibrium analysis (Lashitew, 2011) reported ‘food and fuel’ – complement eachother in Ethiopia - clean & cheaper energy source to remote rural areas (IIDA, 2008; FAO, 2008)
    5. 5. Evidence in current literature: no consensus -largely focused on developed economies -based on aggregate economic wide simulations or qualitative studies -actual impact analysis on smallholder farmers - limited
    6. 6. LICOS- what explains farm households biofuelcrop adoption decisions?- how participation decision affects foodsecurity?
    7. 7. price hikes Classification of liquid biofuels: &volatility have – ethanol been attributed? - biodiesel Said to be less Feedstock sources for liquid biofuel: food security threatening? – edible crops e.g. corn - non-edible e.g. castor beanTable: Inventory of biodiesel feedstock projects in Ethiopia (active in 2010) Type of business No of Production Type of Total area (ha) model project location feedstock specialized Total Under allotted cultivation (‘000 ha) (‘000 ha) Plantations 4 SNNPR, Jatropha, 66.7 3.1 Oromia, Pongamia, Beneshangul Castor Outgrowers 1 SNNPR Castor _ _ PPP 1 Tigray Jatropha, 15 7 Candlenut, Croton, Castor
    8. 8. Studied: castor outgrower scheme in EthiopiaCastor  45% oil bearing seed  grows best in arid zones 1100~1600 m.a.s.l  poisnous – non food, for chemical & biofuel industry 4-5 months maturity (shifting is possible based on market conditions)  good for soil fertility but bad to biodiversity (invasive species)
    9. 9.  foreign company contracting farmers to grow castor common form of ‘input loan’ for ‘pay in output’ arrangement allocate a maximum of ¼ but keep traditional crops on the side – food security reasons Castor has no other use in the area - default is minimal once farmers make decision to grow the remaining default is often – redirecting inputs to other crops thus contract farmers may in general record higher productivity
    10. 10. Supply chain:-mainly feedstock export – no processing?
    11. 11. LICOS- 4 districts –that represent castor growing zones in southern region- all villages in altitude range of 1100– 2000 m.a.s.l. covered by the program – included in our sampling frame- 24 villages randomly selected- 18-21 households per village- total of 478 household- 30% participants (who received seeds &other inputs)- participation – allocated piece of land for castor & entered contractual agreement w/t company
    12. 12. Source: FEWS, 2010Policy – may direct biofuel projects to dry & aridzones – to ease resource competition w/t food
    13. 13. Table : Characteristics of sampled villages & castor seed distribution Land size Fixed Participation rate per capita telephone Mobile (% in the population) Distance to (ha) (in network Network Other 2008 2010 the nearest sample) availability availability Access to dominant cash Village name (ave.20%) (ave. 33%) town (km) (ave. .14) (Yes=1) (Yes=1) Electricity source Ade Dewa Mundeja 0.11 0.37 16 0.12    Cereal retail Anka Duguna 0.24 0.50 42 0.11    NA Degaga Lenda 0.19 0.36 12 0.12    NA Fango Sore 0.52 0.54 90 0.14    None Sura Koyo 0.13 0.55 14 0.12    Cereal retail Tura Sedbo 0.19 0.63 35 0.18    None Mundeja Sake 0.17 0.49 42 0.09    NA Olaba 0.01 0.13 25 0.10    Cereal retail Mayo Kote 0.31 0.41 16 0.09    NA Hanaze 0.26 0.36 61 0.10    Avocado Tulicha 0.07 0.32 73 0.13    Ginger Sorto 0.14 0.30 69 0.13    NA Bade Weyde 0.10 0.31 70 0.11    None Bola Gofa 0.48 0.28 9 0.10    Less Dairy Sezga 0.08 0.28 4 0.20    Pottery Uba Pizgo 0.17 0.30 17 0.18    None Zenga Zelgo 0.54 0.28 18 0.14    NA Suka 0.09 0.29 3 0.16    Dairy Tsela Tsamba 0.05 0.12 7 0.13    Dairy Lotte Zadha Solle 0.17 0.33 15 0.17    NA Gurade 0.08 0.20 11 0.17    Dairy Bala 0.07 0.41 65 0.22    Live animal Shalla Tsito 0.04 0.31 80 0.22    Live animal Zaba 0.17 0.35 68 0.18    Live animal
    14. 14. Overall observation- dissemination of the castor crop into inaccessible & remote places- widespread adoption rate (20-33%) in three years of promotion -unlike low rate of other technology adoptions in developing countries- vast diversification (7 crops types on 0.81ha (3.2 timad) - adoption may interact with performance of other crops
    15. 15. Descriptive (explanatory variables) Participants Non-participants |t/chi-stat| Household wealth variables Owned land size (in ha) 0.93 0.72 3.54*** Own land per capita 0.15 0.13 1.00 Farm tools count (Number) 4.20 3.84 1.48 Proportion of active labour 0.49 0.51 0.99 Access related variables Formal Media (TV/radio/NP) main info. source (1=yes) 0.27 0.18 1.73*** Fertilizer use(kg/ha) 33 24 9.0*** Borrowed cash money during the year (1=yes) 0.42 0.36 1.14 Distance from extension center (Minutes) 27.53 27.80 0.10 Contact with govt. extension agent (Number of visits) 12.63 11.08 0.98 Household characteristics Gender of the HH head (1=female) 0.06 0.14 2.95*** HH head attended school (1=yes) 0.60 0.50 1.67* Family size 6.87 6.10 2.98*** * p<.1; ** p<.05; *** p<.01
    16. 16. Descriptive – welfare indicator variables Outcome Variables Participants Non-participants Diff Crop income (000 Birr) 5.141 4.491 769 ** Per capita crop income 824 770 54* Food gap (months) in 2010 1.02 1.58 - 0.56*** Food consumption per capita (birr) 534 458 75*** Total expenditure (‘000 Birr) 7.144 6.292 852* Per capita expenditure 1130 1062 67* * p<.1; ** p<.05; *** p<.01 Definition: Food gap months - hh short of own stock & cash to buy food (lean seasons) - easily memorable esp. for long periods of scarcity - a decrease in value – improvement in food security
    17. 17. 3. MethodWe analyzed using - Endogenous Switching Regression (ESR) & - Two Step Heckman selection (TEM) Selection (di) – to participate or not participate in castor  Potential correlation (ui & ℇji) Selection: (1) Participants: (2) Non-particips: (3)
    18. 18. Identification assumption to estimate using Heckman & ESR  the error terms in (1) , (2) and (3) are jointly normally distributed (assumption specification varies slightly b/n the two models)  Adding exclusion restriction –makes estimates more robustExcluded variables  village level past adoption rate X eligibility criteria  past asset indicator (livestock holding in TLU)  pcorr significant for participation but not to income
    19. 19. Table: Estimation of treatment effects under ESR model  Using the info. contained in the distribution of the error terms  The model allow us to get predictions of the counterfactuals Selection decision Treatment Participation Non-participation effect Participant (a) E(𝑦1𝑖 𝑑 𝑖 , 𝑥 𝑖 = 1 (c) E(𝑦1𝑖 𝑑 𝑖 , 𝑥 𝑖 = 0 = = households 𝛿 𝜀1𝑢 𝜙 𝑧𝑖 (a)-(c) = TT 𝛽1 𝑋1𝑖 + 𝛿2 Φ 𝑧𝑖 𝑢 𝛿 𝜀2𝑢 𝜙 𝑧𝑖 𝛽2 𝑋1𝑖 + 𝛿2 𝑢 Φ 𝑧𝑖 Non-participant (b) E(𝑦2𝑖 𝑑 𝑖 , 𝑥 𝑖 = 1 (d) E(𝑦2𝑖 𝑑 𝑖 , 𝑥 𝑖 = 0 (b)-(d) = TU = = households 𝛿 𝜀1𝑢 𝜙 𝑧𝑖 𝛿 𝜀2𝑢 𝜙 𝑧𝑖 𝛽1 𝑋2𝑖 − 𝛽2 𝑋2𝑖 − 𝛿2 𝑢 1−Φ 𝑧 𝑖 𝛿2 𝑢 1−Φ 𝑧 𝑖 Source: Verbeek, 2009 where Φ = is the standard normal cumulative function of the selection equation distribution and ϕ = - standard normal probability density function of the distribution
    20. 20. 4. Preliminary results 4.1A. Crop income determinants – endogenous switching regression Table 1– Switching regression estimation (Joint participation selection & crop income determinants) Selection Non- Variable (Jointly estimated Probit) Participants participants Per capita owned land size (ha) 6.91** 7.76*** 4.60*** Per capita owned land size squared -10.37* -7.63* -2.78** Pr of maize before planting made (in birr) -0.42** 0.31 0.01 Media (1= main info source) 0.31** 0.24 0.07 Family member with non agri inc source (1=yes) -0.14 0.13 0.17* Log of number of govt. extension visits 0.02 0.12** 0.14*** Log of number of social contact and freinds -0.17** 0.06 0.00 Log of distance from extension center -0.05 0.10 0.10 Proportion of labour force -0.53 -0.10 0.38** Gender of the head (1=Female) -0.44* 0.15 -0.4 Household head attended school (1=yes) 0.25 0.02 0.05 Log of number of enset trees 0.02 -0.03 -0.13 Age of the head (years) 0.04 -0.02 -0.00 Age squared 0.00 0.00 0.00 EligabilityXintensity indicator 0.12 Pre program asset indicator 0.67** District dummies yes yes yes _cons -0.74 3.75*** 4.20*** prob >F/ chi2 0.000 Pseudo R2 0.11 ρ 0.13* -0.50*** LR test of independent equations (prob>chi2) 0.05 N 467 * p<.1; ** p<.05; *** p<.01
    21. 21. Table 4.1B : Average expected crop income for castor adopters and non-adopters Decisions stage Treatment Effect (TT/TU) Sub-sample To participate Not to participate Log per capita annual crop income (birr) Households who participated (a) 6.37 (c) 5.93 (treated) 0.44*** Households who did not participate (b) 6.06 (d) 6.16 (untreated) -0.10*** • Average crop income gain of participants (TT)is 44% • Non-participants would have lost 10% had they enter into contract • Suggests households selected into where they be better off
    22. 22. 4.1C. Crop income determinants – standard Heckman two steps Heckman two step Probability to (treatement effect Dependent var. log per capita crop income participation model) Participation 0.57*** Per capita owned land size (ha) 8.12*** 4.61*** Per capita owned land size squared -12.66** -2.65 Pr of maize before planting made (in birr) -0.32* 0.06 Media (1= main info source) 0.27* 0.03 Family member with non agri inc source (1=yes) -0.22 0.19** Log of number of govt. extension visits -0.08 0.08* Log of number of social contact and freinds -0.18** 0.05 Log of distance from extension center 0.00 0.09** Proportion of labour force -0.4 0.21 Gender of the head (1=Female) -0.42* 0.03 Household head attended school (1=yes) 0.24 -0.11 Log of number of enset trees 0.02 -0.02 Age of the head (years) 0.03 -0.01 Age squared 0.00 0.00 EligabilityXpast part.rate indicator 0.09** Pre program asset indicator 0.54 _cons -0.22 4.59*** District dummies yes yes ρ -0.61***
    23. 23. 4.2 Food gap months Table 2A– Switching regression estimation (dependent=Food gap months in last 12 months) Variable Participants Non-participants Log of asset value per capita -0.47** -0.55** Owned land size per capita (ha) -4.88** -4.67** Polygamy family (1=yes) 0.43* 1.12* HH head attended school (1=yes) -0.46 -1.46*** Family member with non agri inc source (1=yes) -0.78 -0.80* Log of number of social contact and friends 0.52 0.67** District dummy yes yes _cons 1.82 2.04** Table 2B: Average expected food gap months for castor adopters and non-adopters Decisions stage Treatment Effect Not to (TT/TU) Sub-sample To participate participate Log per capita annual food gap (no. of months) Households who participated (a) 1.84 (c) 2.42 (treated) -0.58*** (-16 days) Households who did not participate (b) 3.05 (d) 2.31 (untreated) 0.24*** (22 days)
    24. 24. 4.3 Consumption and expenditure effects Table 4.3A: Average expected consumption for castor adopters and non-adopters Decisions stage Treatment Effect Sub-sample To participate Not to participate (TT/TU) Log per capita annual food consumption (birr) Households who participated (a) 6.06 (c) 5.46 (treated) 0.39*** Households who did not participate (b) 5.42 (d) 5.84 (untreated) -0.35*** Table 4.3B: Average expected expenditure for castor adopters and non-adopters Decisions stage Treatment Effect Sub-sample To participate Not to participate (TT/TU) Log per capita annual exp. (birr) Households who participated (a) 6.60 (c) 6.58 (treated) 0.02 Households who did not participate (b) 6.30 (d) 6.52 (untreated) -0.22***
    25. 25. 4.5. Factors that explain participation Table 4.5. : Selection into participation ESR Probit Tobit Jointly estimated (Liklihood to Probit (Liklihood of Selection equation adopt castor) dy/dx allocating an Variable (Probit) extra ha of land) Per capita owned land size (ha) 6.91** 5.25*** 1.60*** 6.31*** Per capita owned land size squared -10.37* -7.40** -2.26** -8.94* Pr of maize before planting made (in birr) -0.42** -0.39** -0.12** -0.40** Gender of the head (1=Female) -0.44* -0.45* -0.14* -0.48* Household head attended school (1=yes) 0.25 0.25 0.08 0.27* Log of number of social contact and freinds -0.17** -0.17** -0.05** -0.18** Media (1= main info source) 0.31** 0.32** 0.10** 0.31* Pre program asset indicator (livestock in TLU) 0.67** 0.71** 0.09** 0.40* Farmers choice indicator 0.12 0.15* 0.05* 0.13* Log of distance from extension center -0.05 -0.06 -0.02 -0.07 Log of number of extension visits 0.02 0.02 0.01 0.02 Log of number of enset trees 0.02 0.03 0.01 0.03 Family member with non agri inc source (1=yes) -0.14 -0.15 -0.05 -0.15 Age of the head (years) 0.04 0.04 0.01 0.05 Age squared 0.00 0.00 0.00 0.00 Proportion of labour force -0.53 -0.46 -0.14 -0.5 District dummies yes yes yes _cons -0.74 -0.1 -0.13 prob >F/ chi2 0.000 0.000 Pseudo R2 0.10 0.11 N 467 * p<.1; ** p<.05; *** p<.01
    26. 26. Factors positively associated with adoption  Assets – land & livestock  Formal media (+10%)Negatively associated with adoption  Land squared  Price of maize (main food crop) (-12%)  Female  More social contactNon-associated  Government extension visit  Distance from village center
    27. 27. Effect of participation:  Impact is heterogeneous - implying presence of rational sorting  Castor growers gain from participating which they would not otherwisePolicy implication : grant farmers more choice : as farmers with comparative adv. will engage in biofuel supply chainDeterminant of adoption:  HH assets are key factors for adoption  Adoption of biofuel declines with price of food crop  Physical accessibility showed no significance unlike most studiesPolicy implication: privately organized techn. transfer –may efficiently surpass physical barriers
    28. 28. LICOS

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