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L I C O S Martha Negash and Jo Swinnen, 2012 LICOS Biofuels, poverty and food security: Micro-evidence from Ethiopia
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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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Page 1: 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

Page 2: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

Outline

LICOS

1. Introduction2. Data3. Methods4. Preliminary results5. Conclusion

Page 3: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

3

1. Introduction

LICOS

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)

Page 4: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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)

Page 5: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 6: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

Research questions

- what explains farm household's biofuel crop adoption decisions?

- how participation decision affects food security?

LICOS

Page 7: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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?

Page 8: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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)

Page 9: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 10: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

Supply chain:

-mainly feedstock export – no processing?

Page 11: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

2. Data

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

Page 12: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

Study area –food insecurity

Source: FEWS, 2010

Policy – may direct biofuel projects to dry & arid zones – to ease resource competition w/t food

Page 13: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 14: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 15: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 16: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 17: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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:

Page 18: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 19: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 20: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 21: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 22: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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***

Page 23: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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)

Page 24: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 25: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 26: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 27: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

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

Page 28: Biofuels, Poverty and Food Security: Micro-evidence from Ethiopia

THANK YOU!

LICOS