Transcript Biodiesel feedstock, adoption, smallholder farmers

Martha Negash & Johan Swinnen
Center for Economic Performance and Institutions (LICOS), KULeuven
Impact 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 (caseMozambique) (Arndt et al, 2010)
- reduce the incidence of poverty & support food selfsufficiency goals (Huang, et al. 2012)
‘food vs fuel debate’
Other concern:
- 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’
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
Source: Nussbaumera et al., 2011

modern energy (extremely poor)

food (alarming hunger)
Source: IFPRI, 2010


unutilized/underutilized land
low potential areas
good case to study
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
Most biofuel projects are located in dry &
low land areas of the country
Sampling 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
Source: FEWS, 2010
Participant/Adopters
 a household that
allocated piece of land
for castor & entered
contractual agreement
w/t the company
Sampled villages & castor bean adoption
.6
Tura Sedbo
Sura Koyo
.5
Fango Sore
Anka Duguna
.4
Mayo Kote
Bala
Ade Dewa Mundeja
Degaga Lenda
Hanaze
Zaba
.3
Lotte Zadha Solle
.2
Uba Pizgo
Suka
Sezga
Bola Gofa
Zenga Zelgo
Tulicha
Bade
Weyde
Sorto
Gurade
.1
Tsela Tsamba
0
20
Olaba
40
60
Distance to near by town
80
100
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
Descriptive (outcome variables) (1/2)
Figure : Food gap (number of months)
Figure: Per capita food consumption
30
20
10
.6
0
0
%
Participants
.4
40
Non participants
.2
Cumilative fraction of households
50
.8
1
60
No food gap
***
Less than one
One to three
More than
month
months
three months
***
measured by number of food
shortage months – decline in
value  improvement in welfare
5
6
7
8
Log of total food consumption (kcal energy equivalent)
Participants
9
Non-participants
total consumption in energy
equivalent (kcal/person/day) – increase in
value ->improvement in welfare
Descriptive (explanatory variables) (2/2)
Participants
Non-participants
|t/chi-stat|
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
0.27
0.18
1.73***
33
24
9.0***
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
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***
Household wealth variables
Access related variables
Formal Media (TV/radio/NP) main info. source (1=yes)
Fertilizer use (kg/ha)
Borrowed cash money during the year (1=yes)
Household characteristics
* p<.1; ** p<.05; *** p<.01
Effect of castor contract participation on income
 represent– participation as a regime indicator variable
(1)
Regime 1:
(2)
Regime 0:
(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)
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
Participation decision position
Regime 1
(Participate)
(a) E(𝑦1𝑖 𝑑𝑖 , 𝑥1𝑖 = 1
𝛽1 𝑋1𝑖 + 𝐸 𝜀1𝑖 𝑑𝑖 = 1
Participant
Regime 2
(Not participate)
(c) E(𝑦2𝑖 𝑑𝑖 , 𝑥2𝑖 = 1
𝛽2 𝑋1𝑖 + 𝜀1𝑖 𝑑𝑖 = 1
=
𝛽1 𝑋1𝑖 +
=
𝛿𝜀1𝑢
𝛿𝑢2
𝜙 𝑧𝑖
Φ 𝑧𝑖
(b) E(𝑦1𝑖 𝑑𝑖 , 𝑥1𝑖 = 0
𝛽1 𝑋2𝑖 + 𝐸 𝜀2𝑖 𝑑𝑖 = 0
Non-participant
𝛽2 𝑋1𝑖 +
𝛿 𝜀1𝑢
𝛿 𝑢2
𝜙 𝑧𝑖
1−Φ 𝑧 𝑖
𝛿𝜀2𝑢
𝛿𝑢2
𝜙 𝑧𝑖
Φ 𝑧𝑖
(a)-(c) = TT
(d) E(𝑦2𝑖 𝑑𝑖 , 𝑥2𝑖 = 0
𝛽2 𝑋2𝑖 + 𝐸 𝜀2𝑖 𝑑𝑖 = 0
=
𝛽1 𝑋2𝑖 −
Treatment effect
=
𝛽2 𝑋2𝑖 −
𝛿 𝜀2𝑢
𝛿 𝑢2
𝜙 𝑧𝑖
(b)-(d)=TU
1−Φ 𝑧 𝑖
Source: Verbeek, 2012; Di Falco, et al. 2011; AJAE
‣ can substitute historical comparative data –but useful in the absence
of such data
Question 1
(non-significant)
First stage: selection to participation
Variabel
Marginal effects
Per capita owned land size (ha)
1.60***

Per capita owned land size squared
Pr of maize before planting made (in birr)
Gender of the head (1=Female)
-2.26**
-0.12**
-0.14*

Household head attended school (1=yes)
Log of number of social contact and friends
0.08
-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
Family member with non agri inc source (1=yes)
0.01
-0.05
District dummies
Other controls (age, age squared, labour size,
enset, livestock, plot distance)
N
distance from the
village center
gov. extension service
yes
yes
476


(---)
Maize price
Female
(+++)



Land
Media
Asset
Food gap estimation
Participant
Land per capita (ha)
Nonparticipants
-2.799**
-0.221
Log of agricultural income per capita
-0.063
-0.074*
Household attended schooling (yes=1)
-0.03
-0.140**
-0.053***
-0.014
-0.109*
-0.113**
0.412***
0.177
-0.092
-0.165**
0.212***
0.100*
Yes
Yes
-1.09***
-0.77***
-0.22*
0.40**
Family size
At least one member works off-farm (yes=1)
Family in polygamy (yes=1)
Own livestock (TLU) per capita
Borrowed cash during the year (yes=1)
District dummy
Other control
Sigma (δ)
ρ
N
476
2.98*
Likelihood ratio test of independent equations
2
(X )
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
Log of food gap (months)
Decisions stage
Not to
To participate participate
Treatment Effect
Households who participated
(a) 0.84
(c) 1.20
(treated) -0.37***
Households who did not participate
Log per capita annual food
consumption (kcal/capita/day)
Households who participated
(b) 1.04
(d) 0.98
(untreated) 0.06***
(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%
(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