Biofuels and Food Security: Micro-

evidence from Ethiopia

Martha Negash & Johan Swinnen

Center for Economic Performance and Institutions (LICOS),

KULeuven

1. MotivationImpact 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’

- 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’

Other concern:

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

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

Hunger index

Ethiopia

modern energy (extremely poor)

food (alarming hunger)

unutilized/underutilized land low potential areas

good case to study

Energy poverty index

Source: IFPRI, 2010Source: Nussbaumera et al., 2011

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

Supply chain

Raw seed export

Company -> via supervisors -> input loan & seed -> farmers

Farmers-> village centers-> via supervisors -> company -> export-> China processors

2. DataSampling 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

Source: FEWS, 2010

Most biofuel projects are located in dry & low land areas of the country

Ade Dewa Mundeja

Anka Duguna

Degaga Lenda

Fango SoreSura Koyo

Tura Sedbo

Olaba

Mayo Kote

Hanaze

TulichaSortoBade Weyde

Bola GofaSezgaUba PizgoZenga ZelgoSuka

Tsela Tsamba

Lotte Zadha Solle

Gurade

Bala

Zaba

.1.2

.3.4

.5.6

Ad

op

tion

ra

te

0 20 40 60 80 100Distance to near by town

-better access

-better infras

-dairy supply to

town

- poor acce

ss;

- poor infra

s (tel.,

electric)

- no alternativ

e cash

crop

Sampled villages & castor bean adoption

-distant villa

ges

-alternative cash crop

– fruits

& ginger

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

No food gap Less than one month

One to three months

More than three months

0

10

20

30

40

50

60

Non participants

Participants

0.2

.4.6

.81

Cum

ilativ

e fr

act

ion

of h

ous

ehol

ds

5 6 7 8 9 10Log of total food consumption (kcal energy equivalent)

Participants Non-participants

Figure : Food gap (number of months)

******

Figure: Per capita food consumption

Descriptive (outcome variables) (1/2)

%

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

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 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) (2/2)

* p<.1; ** p<.05; *** p<.01

3. Estimation

Effect of castor contract participation on income

- represent– participation as a regime indicator variable

(1)

Regime 1:

Regime 0:

(2)

(3)

If cov (ui , ℇ1i ) and/or cov (ui , ℇ2i ) are statistically 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)

Participation decision position

Treatment effect Regime 1

(Participate) Regime 2

(Not participate) Participant (a) E(𝑦1𝑖ȁ�𝑑𝑖,𝑥1𝑖 = 1ሻ 𝛽1𝑋1𝑖 + 𝐸ሺ𝜀1𝑖ȁ�𝑑𝑖 = 1ሻ

=

𝛽1𝑋1𝑖 +ቆ𝛿𝜀1𝑢𝛿𝑢2 ቇቆ

𝜙ሺ𝑧Ƹ𝑖ሻΦሺ𝑧Ƹ𝑖ሻቇ

(c) E(𝑦2𝑖ȁ�𝑑𝑖,𝑥2𝑖 = 1ሻ 𝛽2𝑋1𝑖+ ሺ𝜀1𝑖ȁ�𝑑𝑖 = 1ሻ =

𝛽2𝑋1𝑖 +ቆ𝛿𝜀2𝑢𝛿𝑢2 ቇቆ

𝜙ሺ𝑧Ƹ𝑖ሻΦሺ𝑧Ƹ𝑖ሻቇ

(a)-(c) = TT

Non-participant (b) E(𝑦1𝑖ȁ�𝑑𝑖,𝑥1𝑖 = 0ሻ 𝛽1𝑋2𝑖 + 𝐸ሺ𝜀2𝑖ȁ�𝑑𝑖 = 0ሻ

=

𝛽1𝑋2𝑖 −൬𝛿𝜀1𝑢𝛿𝑢2 ൰ቀ 𝜙ሺ𝑧Ƹ𝑖ሻ1−Φሺ𝑧Ƹ𝑖ሻቁ

(d) E(𝑦2𝑖ȁ�𝑑𝑖,𝑥2𝑖 = 0ሻ 𝛽2𝑋2𝑖 + 𝐸ሺ𝜀2𝑖ȁ�𝑑𝑖 = 0ሻ =

𝛽2𝑋2𝑖 − ൬𝛿𝜀2𝑢𝛿𝑢2 ൰ቀ 𝜙ሺ𝑧Ƹ𝑖ሻ1−Φሺ𝑧Ƹ𝑖ሻቁ

(b)-(d)=TU

‣ can substitute historical comparative data –but useful in the absence of such data

Source: Verbeek, 2012; Di Falco, et al. 2011; AJAE

Endogenous Switching Regression Model

allows estimation of heterogeneous effect of covariates

using the information contained in the distribution functions of the error terms & their covariance, allows predicting counterfactual effects

4. Results Question 1

First stage: selection to participationVariabel Marginal effects

Per capita owned land size (ha) 1.60*** Per capita owned land size squared -2.26** Pr of maize before planting made (in birr) -0.12** Gender of the head (1=Female) -0.14* Household head attended school (1=yes) 0.08 Log of number of social contact and friends -0.05** Media (1= main info source) 0.10** Pre program asset indicator 0.09** Farmers choice indicator (eligibility*past adoption) 0.05* Log of distance from extension center -0.02 Log of number of gov. extension visits 0.01 Family member with non agri inc source (1=yes) -0.05 District dummies yes Other controls (age, age squared, labour size,

enset, livestock, plot distance) yes N 476

distance from the village center

gov. extension service

(---) Maize price Female

(+++) Land Media Asset

(non-significant)

Food gap estimation  Participant Non-

participants

Land per capita (ha) -2.799** -0.221

Log of agricultural income per capita -0.063 -0.074*

Household attended schooling (yes=1) -0.03 -0.140**

Family size -0.053*** -0.014

At least one member works off-farm (yes=1) -0.109* -0.113**

Family in polygamy (yes=1) 0.412*** 0.177

Own livestock (TLU) per capita -0.092 -0.165**

Borrowed cash during the year (yes=1) 0.212*** 0.100*

District dummy Yes Yes

Other control

Sigma (δ)-1.09*** -0.77***

ρ-0.22* 0.40**

N476

Likelihood ratio test of independent equations ( X2)

    2.98*

differentiated significance & magnitude of coefficients

e.g. family size & livestock coefficients have different signs

opposite sign of ρ – suggest rational sorting into participation

Question 2

Treatment effect

Sub-sample

Decisions stage Treatment Effect

To participateNot to

participateLog of food gap (months)

Households who participated (a) 0.84 (c) 1.20 (treated) -0.37***

Households who did not participate (b) 1.04 (d) 0.98 (untreated) 0.06***

Log per capita annual food consumption (kcal/capita/day)

Households who participated (a) 7.86 (c) 7.59 (treated) 0.27***

Households who did not participate (b) 7.23 (d) 7.41 (untreated) -0.18***

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%

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

(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/

Thanks!