L I C O S Martha Negash and Jo Swinnen, 2012 LICOS Biofuels, poverty and food security: Micro-evidence from Ethiopia
LICO
S
Martha Negash and Jo Swinnen, 2012
LICOS
Biofuels, poverty and food security:
Micro-evidence from Ethiopia
Outline
LICOS
1. Introduction2. Data3. Methods4. Preliminary results5. Conclusion
3
1. Introduction
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Biofuel development : controversial
Disadvantages:
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)
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)
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
Research questions
- what explains farm household's biofuel crop adoption decisions?
- how participation decision affects food security?
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Type of business model
No of project
Production location
Type of feedstock specialized
Total area (ha)
Total allotted(‘000 ha)
Under cultivation (‘000 ha)
Plantations 4 SNNPR, Oromia, Beneshangul
Jatropha, Pongamia, Castor
66.7 3.1
Outgrowers 1 SNNPR Castor _ _
PPP 1 Tigray Jatropha, Candlenut, Croton, Castor
15 7
Table: Inventory of biodiesel feedstock projects in Ethiopia (active in 2010)
Classification of liquid biofuels:– ethanol- biodiesel
Feedstock sources for liquid biofuel:– edible crops e.g. corn
- non-edible e.g. castor bean
Said to be less food security
threatening?
price hikes &volatility have been attributed?
Studied: castor outgrower scheme in Ethiopia
Castor 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)
The outgrower scheme
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
Supply chain:
-mainly feedstock export – no processing?
2. Data
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- 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
Study area –food insecurity
Source: FEWS, 2010
Policy – may direct biofuel projects to dry & arid zones – to ease resource competition w/t food
Village name
Participation rate(% in the population)
Distance to the nearest town (km)
Land size per capita (ha) (in sample) (ave. .14)
Fixed telephone network availability (Yes=1)
Mobile Network availability (Yes=1)
Access to Electricity
Other dominant cash source
2008 (ave.20%)
2010 (ave. 33%)
Ade Dewa Mundeja 0.11 0.37 16 0.12 ü ü û Cereal retailAnka Duguna 0.24 0.50 42 0.11 ü ü û NADegaga Lenda 0.19 0.36 12 0.12 û ü û NAFango Sore 0.52 0.54 90 0.14 û û û NoneSura Koyo 0.13 0.55 14 0.12 ü ü û Cereal retailTura Sedbo 0.19 0.63 35 0.18 û ü û None
Mundeja Sake 0.17 0.49 42 0.09 ü ü û NAOlaba 0.01 0.13 25 0.10 û û û Cereal retailMayo Kote 0.31 0.41 16 0.09 ü ü û NAHanaze 0.26 0.36 61 0.10 û ü û AvocadoTulicha 0.07 0.32 73 0.13 û ü û GingerSorto 0.14 0.30 69 0.13 û ü û NABade Weyde 0.10 0.31 70 0.11 û û û NoneBola Gofa 0.48 0.28 9 0.10 ü ü ü Less Dairy Sezga 0.08 0.28 4 0.20 û ü û PotteryUba Pizgo 0.17 0.30 17 0.18 ü ü û NoneZenga Zelgo 0.54 0.28 18 0.14 ü ü û NASuka 0.09 0.29 3 0.16 ü ü û DairyTsela Tsamba 0.05 0.12 7 0.13 û ü û DairyLotte Zadha Solle 0.17 0.33 15 0.17 ü ü û NAGurade 0.08 0.20 11 0.17 û ü ü DairyBala 0.07 0.41 65 0.22 ü ü û Live animalShalla Tsito 0.04 0.31 80 0.22 ü ü û Live animalZaba 0.17 0.35 68 0.18 ü ü û Live animal
Table : Characteristics of sampled villages & castor seed distribution
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
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***
Descriptive (explanatory variables)
* p<.1; ** p<.05; *** p<.01
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*
Descriptive – welfare indicator variables
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
* p<.1; ** p<.05; *** p<.01
3. Method
We analyzed using- Endogenous Switching Regression (ESR)
&- Two Step Heckman selection (TEM)
Selection (di) – to participate or not participate in castor
Potential correlation (ui & ℇji)
(1)
Participants: (2)
Non-particips: (3)
Selection:
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 robust
Excluded variables
village level past adoption rate X eligibility criteria
past asset indicator (livestock holding in TLU)
pcorr significant for participation but not to income
Selection decision Treatment
effect Participation Non-participation
Participant
households
(a) E(𝑦1𝑖ȁ�𝑑𝑖,𝑥𝑖 = 1ሻ =
𝛽1𝑋1𝑖 +ቀ𝛿𝜀1𝑢𝛿𝑢2 ቁቀ
𝜙ሺ𝑧Ƹ𝑖ሻΦሺ𝑧Ƹ𝑖ሻቁ
(c) E(𝑦1𝑖ȁ�𝑑𝑖,𝑥𝑖 = 0ሻ =
𝛽2𝑋1𝑖 +ቀ𝛿𝜀2𝑢𝛿𝑢2 ቁቀ
𝜙ሺ𝑧Ƹ𝑖ሻΦሺ𝑧Ƹ𝑖ሻቁ
(a)-(c) = TT
Non-participant
households
(b) E(𝑦2𝑖ȁ�𝑑𝑖,𝑥𝑖 = 1ሻ =
𝛽1𝑋2𝑖 −ቀ𝛿𝜀1𝑢𝛿𝑢2 ቁቀ
𝜙ሺ𝑧Ƹ𝑖ሻ1−Φሺ𝑧Ƹ𝑖ሻቁ
(d) E(𝑦2𝑖ȁ�𝑑𝑖,𝑥𝑖 = 0ሻ =
𝛽2𝑋2𝑖 − ቀ𝛿𝜀2𝑢𝛿𝑢2 ቁቀ𝜙ሺ𝑧Ƹ𝑖ሻ1−Φሺ𝑧Ƹ𝑖ሻቁ
(b)-(d) = TU
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
where Φ = is the standard normal cumulative function of the selection equation distribution
and ϕ = - standard normal probability density function of the distribution
Source: Verbeek, 2009
Variable
Selection (Jointly estimated Probit) Participants
Non-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
Table 1– Switching regression estimation (Joint participation selection & crop income determinants)
4.1A. Crop income determinants – endogenous switching regression4. Preliminary results
Sub-sample
Decisions stage Treatment
Effect
To participate Not to participate
(TT/TU)
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***
Table 4.1B : Average expected crop income for castor adopters and non-adopters
• 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
4.1C. Crop income determinants – standard Heckman two steps
Dependent var. log per capita crop income Probability to participation
Heckman two step (treatement effect 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***
4.2 Food gap months
Variable Participants Non-participantsLog 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**
Sub-sample
Decisions stage Treatment
Effect
To participate
Not to
participate
(TT/TU)
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)
Table 2B: Average expected food gap months for castor adopters and non-adopters
Table 2A– Switching regression estimation (dependent=Food gap months in last 12 months)
4.3 Consumption and expenditure effects
Sub-sample
Decisions stage Treatment
Effect
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***
Table 4.3B: Average expected expenditure for castor adopters and non-adopters
Sub-sample
Decisions stage Treatment
Effect
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 4.3A: Average expected consumption for castor adopters and non-adopters
Variable
ESR Jointly estimated
Selection equation (Probit)
Probit (Liklihood to adopt castor)
Probit dy/dx
Tobit (Liklihood of allocating an
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
Table 4.5. : Selection into participation
4.5. Factors that explain participation
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 contact
Non-associated Government extension visit Distance from village center
Effect of participation:
Impact is heterogeneous - implying presence of rational sorting Castor growers gain from participating which they would not otherwise
Policy implication : grant farmers more choice
: as farmers with comparative adv. will engage in biofuel supply chain
Determinant 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 studies
Policy implication: privately organized techn. transfer –may efficiently surpass physical barriers
5. Preliminary conclusions
THANK YOU!
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