Farm Interventions and Job
Creation: Evidence from Nigeria
Terfa W. Abraham, Chiedo Akalefu and Oluwasola
Omoju
Paper Presented at the 4th Annual Conference on
Regional Integration (ACRIA 4), Abidjan, Ivory Coast
Contact Email: [email protected]
Cc: [email protected]
Introduction
 The role of the agricultural sector in Nigeria’s macro-
economy cannot be over-emphasized.
 Over 60% of the population depends on this sector for
their means of survival, and hence it plays a crucial
role in poverty alleviation and job creation.
 Though various interventions and reforms have been
implemented in the sector, the sustainability of job
creations still poses a challenge.
 The transformation of sectors such as financial and
advertising through mobile telephony has raised
questions on the applicability of mobile phone as a
means of intervention in the agriculture sector.
 International organisations such as UN and FAO have
recognized and funded studies and projects on mobile
phones and performance of the agriculture sector. This
has increased inquiry into the use of mobile phone as a
farm intervention mechanism.
 The reasoning behind using mobile phone as a farm
intervention is rooted in the fact that economic
theories also recognized the role of information in
boosting market access and outcomes (Akerlof, 1970;
Spence, 1973; Stiglitz, 1985; Schnitkey et al, 1992; Ogen,
2007).
 In the spirit of this reasoning, the Federal Ministry of
Agriculture announced this programme in the 1st
quarter of 2013. It was argued that the policy would
boost farmers’ income and job creation in the sector.
Research Problem
 The literature for mobile phone as a farm intervention
is quite extensive for other countries, but limited
evidence exist for Nigeria.
 This is because the programme is relatively new, and
the debate on its feasibility and effectiveness has been
limited to the media. Thus, it is difficult to make
useful economic inference among researchers and
policy makers.
 This therefore calls for a careful empirical evaluation
of the programme.
Objectives of the Study
 Examine the debates around the mobile phone for
farmers’ intervention programme.
 Deduce trends and lessons from developing and
emerging countries that have implemented the
programme.
 Estimate the impact of farm intervention on the
growth of the agriculture sector and job creation in the
sector using a two-stage least square method.
Brief Review of Literature
 Schnitkey et al (1992) – Mobile phone intervention
improves market access.
 Bekele (2006) – Revealed preference include
interventions that improve market access, address
irrigation and resettlement issues and programmes
that address water and soil conservation.
 Sanyang et al (2009) – 92% of farmers considers
variety improvement as a priority area for intervention.
 Lam and Ostrom (2009) – Infrastructure is necessary
but not a sufficient intervention. Other factors include
rural-urban migration, climate variability, rural
poverty, poor access to credit, weak govt policies, high
interest rates, and low private sector participation.
The Case for Mobile Phone for Farmers in Nigeria
 A survey of 426,000 farmers in the 2011/2012 period
shows that 71% did not have phones. Thus, the scheme
to help poor farmers to acquire phones.
 10 million phones are to be made available, out of
which about 2 million will be given to those who do
not already have as at 2013, but are in areas with
network coverage.
 Telephone service providers will have incentive to
expand to rural areas with no coverage.
Supposed Benefits
 Provide phone subsidies to farmers
 Enhance farmers’ access to farm information
 Eliminate middlemen and enhance bargaining powers




of farmers
Easy access to extension information
Reduce farmers’ vulnerability to shocks as simple
alerts can help to prevent or minimize the effect of
catastrophe e.g floods, drought.
Aid distribution of fertilizers and seeds
Enhance access to target farmers in good time
Cross-Country Evidence
 Kenya: In 2003, launched a text-messaging platform
that provide pricing info to farmers. There was also the
SMS and voice service that allow diary farmers to track
their cow’s gestation, and also receive tips on breeding
and nutrition. Small scale farmers in Kenya use
phones for social purposes, and this facilitates its use
to enhance their market access. Constraints include
cost of recharge and lack of electricity (Okello et al,
2010)
 Tanzania: ICT has been used to reduce info asymmetry
among market player and to create linkages actors in
the agric value chain. Local farmers use phones to
investigate selling price of farm output in the open
market and report back to the villages (Mwarkaje,
2010). Phone has also helped to strengthen market
chain from producers to consumers in Tanzania (IFAD,
2007).
 India: Phone reduces info search and transaction costs,
and increase efficiency in agric markets and general
farmers’ welfare (Lokanathan, 2010). This is
attributable to the high use of mobile technology to
increase market access and create jobs in all sectors of
the Indian economy.
60
60
40
40
20
20
0
0
Desktop
Mobile
Desktop
Mobile
2012-06
2012-09
2012-12
2012-09
2012-12
2012-12
2012-09
2012-06
2012-06
2011-09
2012-03
2011-09
2011-06
2011-03
2010-12
2010-09
2010-06
2010-03
2009-12
2009-09
2009-06
2012-03
80
2012-03
100
2011-12
Mobile
2011-12
120
100
80
60
40
20
0
2011-12
2011-06
Mobile
2011-09
2011-06
Desktop
2011-03
2010-12
2010-09
2010-06
2010-03
2009-12
Desktop
2011-03
2010-12
2010-09
2010-06
80
2010-03
100
2009-12
Mobile
2009-09
120
100
80
60
40
20
0
2009-09
Mobile
2009-06
0
2009-03
20
2009-06
80
2009-03
40
2008-12
80
2009-03
2012-12
2012-09
2012-06
2012-03
2011-12
2011-09
2011-06
60
2008-12
2012-12
2012-09
2012-06
2012-03
2011-12
2011-09
2011-06
2011-03
2010-12
2010-09
2010-06
2010-03
2009-12
2009-09
2009-06
2009-03
2008-12
100
2008-12
2012-12
2012-09
2012-06
2012-03
2011-12
2011-09
Desktop
2011-06
2011-03
2010-12
2010-09
2010-06
2010-03
2009-12
2009-09
2009-06
2009-03
2008-12
Desktop
2011-03
2010-12
2010-09
2010-06
2010-03
2009-12
2009-09
2009-06
2009-03
2008-12
100
60
40
20
0
70
60
50
40
30
20
10
0
Tanzania
Kenya
India
Ghana
South Africa
Nigeria
HDI
0.476
0.519
0.554
0.558
0.629
0.471
Share of Agric in GDP
27.1
24.2
17
24.6
2.4
30.9
Mobile Access
30.03
34.53
55.99
30.44
16.96
61.27
Theoretical Framework: The Rubin Causal
Model
 Farm intervention has to do with causal effects
between the intervention and the potential outcomes
 The RCM allows for the parameters of interest to be
defined and assumptions stated without reference to
particular
parametric
models
(Imbens
and
Wooldridge, 2008)
 The relationship between treatment assignment and
the potential outcomes for which independent of
covariates are associated with potential outcomes,
making it easy to obtain estimators for the average
effect of the treatment
Research Methodology
 The paper uses narrative, descriptive and econometric
methods
 Because the programme was announced recently,
randomized experiment using survey data is not currently
feasible
 Hence, following the RCM, instrumental variable was
employed to estimate the impact of the intervention on
GDP by agriculture sector and job creation in Nigeria.
 To control for the impact of mobile phone intervention, we
introduce another farm intervention – credit . This is used
to simulate for the impact of mobile phone intervention in
the two-stage least square regression framework employed.
We also control for the role of infrastructure, HDI and
population in agriculture sector growth and job creation.
Econometric Technique
 Two-stage least square method was employed
 The technique is used because of its ability to estimate
causal relationship when controlled experiments are
not feasible.
 Hsiao and Wang (2007) argue that the traditional 2SLS
models provide consistent estimates even when
variables are non-stationary and co-integrated.
Variables and Sources of Data
 Secondary data were collected from the CBN, NBS,
HDR, CIA World Factbook, and the World Bank from
1970 to 2012.
Variables
 Job Creation (AUN), GDP by agriculture sector
(AGDP), Infrastructure (INF), Human Development
Index (HDI), Population (POP), and Credit to the
Agriculture Sector (CREDIT).
Model Specification
 GDP = Xβ + ε ----- β is the vector of coefficients
 AUN = xα + ε ----- α is the vector of coefficients
 After this equations are estimated, AGDPTREATED and
AUNTREATED are generated by subtracting the
residual from the original GDP/AUN. The untreated
variables are then substituted with the treated
components in the original equations above.
 E-Views software was used to estimate the equation.
Results and Discussion
 Dependent Variable: AGDP
Variables
Coefficient
Prob.
C
-0.53882
0.6997
AUNTREATED
-0.474022
0.0126
POP
0.140316
0.6231
INF
0.164082
0.4734
HDI
1.066749
0.0561
CREDIT
0.055375
0.0504
MOBILE
-0.050784
0.0521
Adj. R-squared 0.9541
DW 1.6312
F-statistic 115.4623
Dependent Variable: AUN
Variables
Coefficient
Prob.
C
6.056796
0.0001
AGDPTREATED
0.285259
0.3022
INF
-0.137223
0.6667
HDI
0.899131
0.2536
POP
-1.540017
0.0028
CREDIT
0.039998
0.0977
MOBILE
-0.085080
0.4310
AR(1)
-0.502031
0.0098
Adj R-Squared 0.9953
DW 1.60
F-statistic 859.3684
Dropping Mobile Phones from the
Models
D(AGDP)
D(AUN)
Variables
Coefficient
Prob.
Variables
Coefficien Prob.
t
C
1.2083
0.2985
C
5.9015
0.0001
AUNTREATED
-0.4194
0.0312
AGDPTREATR
ED
0.076348
0.2546
POP
-0.2117
0.3748
INF
-0.2590
0.3470
INF
0.0445
0.8476
HDI
1.2743
0.0430
HDI
0.1673
0.6001
POP
-1.2673
0.0003
CREDIT
0.0304
0.2402
CREDIT
0.0393
0.1001
AR (1)
-0.4968
0.0099
Adj. R-square 0.949 DW 1.60 F-statistic
123.8
Adj. R-square 0.995 DW 1.67 Fstatistic 1018.8
Dropping Credit from the Models
AGDP
AUN
Variables
Coefficie Prob.
nt
Variables
Coefficie
nt
Prob.
C
-0.9783
0.5028
C
7.0087
0.0000
AUNTREATE
D
-0.2507
0.1009
AGDPTREAT
ED
0.3241
0.2625
POP
0.2358
0.4289
INF
-0.0541
0.8684
INF
0.0498
0.8306
HDI
0.4652
0.5454
HDI
0.3658
0.4056
POP
-1.7502
0.0010
MOBILE
-0.0275
0.2508
MOBILE
-0.0777
0.4929
AR(1)
-0.4745
0.0130
Adj R-square 0.949 DW 1.6 F-statistics
123.6
Adj R-squared 0.99 DW 1.61
statistics 919.4
F-
Interference for Partial Treatment Effect
S/N Estim AGDP
ates
-0.8118
SIC = 0.382 3.161
(0.0006) DW = 0.879 (0.002)
Adj-R2 =
0.327
0.2285
0.1931
(0.0008)
(0.0102)
3.161
(0.002)
SIC = 0.451
DW = 0.958
R2 = 0.064
2.445
(0.0029
)
-0.8118
(0.0006
)
(b)
β
-0.0525
(0.014)
2(a) α
-0.0179
(0.7557)
(b)
-0.0329
(0.1071)
Mobile
Model
Criteria
Credit
α
Credit
-0.8027
(0.0007
)
-0.709
(0.0045
)
3(a) α
-0.6171
(0.0071)
(b)
0.2031
β
AUN
Mobile
1(a)
β
Model
Criteria
SIC = 0.476
DW = 0.737
R2 = 0.229
0.3755
(0.0689
)
0.1188
SIC = 2.887
DW = 1.172
Adj. R2 =
0.336
SIC = 2.99
DW =
0.9499
R2 = 0.2258
SIC = 2.955
DW = 1.108
R2 = 0.067
Conclusion and Policy Issues
 Credit to the agricultural sector contributes to the
share of agriculture in GDP and job creation in the
sector while mobile phone does not.
 Credit to farmers as a farm intervention has been
implemented under the various development plans.
This has manifested in the establishment of CACS and
NACRDB.
 Mobile phone is a recent phenomenon in Nigeria, and
its use is mostly limited to the urban cities. It could
however play its role in increasing market access and
farmers’ social network.
 This however depends on the availability of adequate
infrastructure.
Thank You!
Comments and Questions