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Martha Negash and Jo Swinnen, 2012

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Biofuels, poverty and food security:

Micro-evidence from Ethiopia

Outline

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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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