1. Adoption and Impact of Mobile
Phone- based Money Transfer Services
in Agriculture: Case of Smallholder
Farmers in Kenyan
Kirui, Oliver, Okello J. & Nyikal R.
University of Nairobi, Kenya
3rd IAALD Africa Chapter Conference
Emperors Palace Hotel, Johannesburg,
South Africa
May 21st - 23rd, 2012 1

2. Outline
 Introduction
 Background Information
 Purpose & Objectives
 Justification
 Methodology
 Sampling Procedure
 Empirical Models
 Results and Discussion
 Conclusions and Implications
2

3. Introduction
 One of the factors limiting agric. productivity enhancement
is lack of agric. finance
 Access to financial services by smallholder farmers has the
potential to alleviate the extreme rural poverty
 Dev. of rural financial systems is hampered by the high cost of
delivering services to smallholder farmers. These farmers are:
 widely dispersed customers,
 Reside in difficult financial terrain,
 Subject to high covariant risks,
 lack of suitable collateral
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4. Introduction cont’d…
 Lack of appropriate financial services is exacerbated by
 Poor access to and the cost of rural financial services are major contributing
factors to the decline in agric. productivity & commercialization
 Rural coverage of financial services estimated at just 10%
 Financial services operated by formal financial orgs. are usually
inaccessible to farmers, particularly in the more remote areas
 Under-represented banking infrastructure and poor infrastructure
 High fixed commission costs charged
 Consequently, there have been efforts to find alternative means of
promoting farmer access to agric. finance
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5. Mobile Phone-based Money Transfer (MPMT)
 The leading mobile phone service provider (Safaricom)
introduced MPMT service to mediate money transfer among
the largely unbanked individuals in Kenya
 The service ( known as M-PESA) was officially launched in
Kenya 2007 (M=mobile Pesa=money)
 Subsequently, other mobile phone service providers have
introducing competing services. These include:
 Airtell-Money
 YU-Cash
 Orange Money
5

6. MPMT Facts and Figures
 Launched in March 2007 by Safaricom
 19,671 users in December 2007
 15 million users by April 2012 vs 28 Million Phone users (72% penetration)
 The number of authorised transaction agents
 355 in December 2007 (in some specific urban centres)
 37,000 by April 2012 – now countrywide
 Transactions
 Ksh: 10% of Kenyan GDP per month
 Ksh: 1.4 Trillion in 2011 financial year
 Amount that one can transact
 Minimum: Reduced from Ksh.100 in 2007 to Ksh.10 in 2012
 Maximum: Maximum daily value of transaction increased from Ksh.35,000 in
2007 to Ksh.140,000 in 2012
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7. Facts and Figures cont’d…
 Cost per transaction
 Free: Purchase of airtime, pay utility bills (water, electricity)
 Send money: range from Ksh.5 to max of Ksh.175
 Withdraw from an agent: range from Ksh.5 to max of Ksh.200
 MPMT is now becoming an everyday tool
 Purchase of airtime (self and other- across networks in Kenya)
 Payment of utility bills
 Payment of goods and services e.g. in supermarkets
 Flight tickets (KQ) and many more…….
 ‘Temporary’ savings – money can be transferred thru’ phone to
bank account and vice versa
 More recently: Micro-loans to SMEs and agro-enterprises by
Airtel-Money
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8. Facts and Figures cont’d…
 Mpesa agents now available in all the EAC states
 Kenya, Uganda, Tanzania and Rwanda
 Also in the UK and the USA
 Partnerships
 25 banks in the M-PESA network with a coverage of 700+ ATMs
 Further, through Western Union, money can now be received from over 70
countries worldwide via MPesa
 Recognition: Both Regional and global
 Group System for Mobile Communication Association (GSMA): Best
Mobile Transfer Service
 Africom: Innovative Technology and Life Changing Solutions
 Kenyan success now emulated globally: (Indonesia, Philippines, Afghanistan,
Tanzania)
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9. Can MPMT services offer answers to
smallholder farmers?
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10. Can MPMT Offer Answers?
 Theoretically, MPMT can resolve the constraints by reducing the
transaction costs farmers face in using banking services
 Easy, instant and cost effective way to transfer money
 The large network of MPMT agents in the rural areas - reduce the
time and cash expense in accessing the funds
 Include the hitherto excluded farmers into the banking services by
reducing the costs of accessing funds and/or depositing savings
 It attracts no ledger fees and minimum balances, very modest
withdrawal fee that is affordable to farmers
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11. Purpose and Objectives
 The purpose of the study was to assess the level of awareness,
determinants of use and intensity of use and impact of MPMT services
on smallholder agriculture in Kenya
 The specific objectives of this study were :
 To assess the level of awareness of MPMT services among
smallholder farmers in Kenya
 To examine the use of MPMT services in smallholder agriculture
 To assess the impact of MPMT services on smallholder farmers
- Use of agricultural inputs,
- Household income and
- Household agric. commercialization
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12. Justification
 Provides some baseline info on the effect of m-banking among
the farming communities in Kenya
 Contributes to the pioneering literature especially in agriculture
 Emphasizes the importance of new generation ICT tools in
revolutionizing agric. communities
 Harnessing the benefits of ICT to improved rural financial system
that is key to addressing the low equilibrium poverty trap (MDG 1)
 Findings help in guiding future efforts to out-scale the
electronic money transfer services especially amongst rural
communities
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13. Study Area and Sampling procedure
 Study carried out in 3 districts (3 provinces) of Kenya:
Kirinyaga, Bungoma and Migori:
 Kirinyaga: considered a high potential area - export oriented crops
(French beans, baby-corn and Asian vegetables)
 Bungoma: considered medium potential - maize and sugarcane
 Migori: considered low potential area - maize and tobacco
 Diverse agro-ecological zones, socio-economic environment,
cultural diversity and varying production systems and differing levels
of agric. commercialization
 All the three districts were characterized by:
 Poor access to markets
 Reliance on agriculture
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14. Sampling Procedure cont’d…
 3-stage sampling technique used:
 1st - identified and purposely selected the three districts were
 2nd – randomly selected one location > three sub-locations randomly
selected. In the selected sub-locations, lists of all households obtained
from the local admin (chiefs)
 3rd – sampling of respondents from the three lists using probability
proportionate to size sampling method
 Data then collection: personal interviews using pre-tested
questionnaire
 Entered and analysed in SPSS and STATA packages
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15. Results
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16. Characteristics of Respondents
Characteristic Users Non-Users Difference t -values
3.71 3.73 -0.02 -0.62
Natural log of age in years
7.43 7.47 -0.04 -0.66
Natural log of age squared
9.78 6.99 2.78*** 7.95
Education (years)
Years of experience in 16.49 20.25 -3.76*** -2.82
farming
5.64 5.85 0.21 0.93
Household size
0.57 0.44 0.13*** 2.58
Gender
0.85 0.33 2.71*** 2.58
Literacy
0.92 0.89 0.24 1.28
Occupation
0.69 0.34 0.14*** 2.84
Group membership
1.00 0.92 0.08 1.28
Awareness of MPMT services
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17. Characteristics of Respondents cont’d…
Characteristic Users Non-Users Difference t -values
Distance to bank (km) 8.61 11.75 -3.13*** -4.17
Distance to the nearest market (km) 6.54 5.60 0.93 1.11
Distance to agric extension agent (km) 6.66 8.59 -1.93 -1.41
Distance to MPMT agent (km) 2.17 4.29 7.31*** 3.54
Number of enterprises 6.31 3.20 3.03** 1.92
Natural log of agric. Income (KSh.) 9.09 6.56 2.53*** 6.02
Natural log of other income 9.79 9.10 0.69** 1.97
Natural log of current value of assets 10.59 9.79 0.79*** 3.04
Number of farmers 197 182
NB: Significance of mean difference is at the *10%, **5% and ***1% levels
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18. Awareness and Use of MPMT services
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19. Awareness by Region of Survey
 M-PESA = the most widely known method in all the districts
 Postapay (Orange-money) = largely unknown by the respondents
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20. Learning about MPMT
 Majority of the respondents learnt from the radio, friends and relatives
 Low usage of newspapers, TV and billboards/posters
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21. Uses of Money Received via MPMT
 Agric-related purposes (purchase of seed, fertilizer, farm equipment/
implements, leasing of farming land, paying of farm workers) = 32%
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22. Uses of Money received via MPMT cont’d…
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23. Reverse money transfer – How much is
from agric. to other uses?
 Some farmers now transfer the money to the input dealers who in turn
send inputs without the farmer going to the markets physically,
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24. Reverse money transfer by region
 School fees is the most important reason for sending money out
from agric communities
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25. Determinants of Use and Intensity of Use
of MPMT – The Double Hurdle Model
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26. Determinants of Use and Intensity of Use
of MPMT – The Double Hurdle Model
1st Hurdle (Use of MPMT):
Logit Regression Model
2nd Hurdle (Intensity of use of MPMT):
The Poisson Regression Models (PRM) &
The Negative Binomial Regression Models (NBRM)
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27. Determinants of Use of MPMT
Logit Reg. Marginal Effects
Dependent variable = Use of MPMT Coeff p-value Coeff p-value
Gender (dummy) 0.54 0.041 0.12 0.036
Age (years) 0.03 0.118 0.06 0.118
Education (years of formal education) 0.19 0.000 0.05 0.000
Distance to MPMT agent (km) -0.31 0.001 -0.09 0.001
Distance to nearest bank (km) 0.51 0.009 0.02 0.005
Household size -0.09 0.159 -0.02 0.149
Years of experience in farming (years) -0.03 0.064 -0.01 0.064
Distance to agric extension agent (km) -0.01 0.642 -0.03 0.642
Group membership (dummy) 0.71 0.007 0.16 0.003
Natural log of current value of assets 0.11 0.028 0.09 0.022
Natural log of household income 0.24 0.005 0.06 0.002
Region of Survey 1.22 0.435 1.08 0.476
Constant -1.13 0.000
Likelihood ratio shows that the model fits the data well (p-value = 0.001)
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28. Determinants of intensity of use of MPMT
Definition of variables Poisson Negative Binomial
Dep. Variable: number of times of Coeff p-value Coeff p-value
using MPMT
Age 0.25 0.011 0.22 0.019
Age2 -0.01 0.014 -0.01 0.024
Education 0.16 0.000 0.19 0.000
Gender 0.73 0.563 0.62 0.633
Group membership 0.32 0.121 0.55 0.017
Household size -0.13 0.134 -0.32 0.144
Distance to MPMT agent -0.06 0.029 -0.04 0.016
Distance to the bank -0.15 0.480 0.06 0.002
Natural log of household assets 0.03 0.549 0.06 0.190
Natural log of agric income 0.06 0.886 0.08 0.017
Natural log of other income 0.02 0.383 0.03 0.028
Number of enterprises -0.21 0.112 -0.15 0.078
Region of Survey 2.28 0.222 1.78 0.276
Constant -2.71 0.041 -4.31 0.000
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29. Impact of MPMT on input use, household
income and smallholder household
agricultural commercialization
- Results of the PSM Model
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30. Measuring Impact
 There are at least 3 methods of measuring impact
 Heckman method
 The instrumental variable methods
 Difference in difference methods
 However, these methods have major limitations
 The Heckman imposes a strong assumption of linearity
 The IV technique is simple to use, but its often an difficult task finding the
instrument
 The difference-in-difference method requires panel data that captures
situation before and after
 Unfortunately finding such data for most interventions such as the MPMT
services is hard
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31. Measuring impact: Propensity Score Matching
 Recent attempts in the literature to control for selection bias has
focused on the use of propensity score matching technique
 Propensity score matching is suitable for addressing the problem of
possible occurrence of selection bias
 This problem occurs when one wants to determine the difference between
the participant’s outcome with and without the program
 Unfortunately it is not possible to observe both outcomes for a given
individual simultaneously using cross-sectional data
 Propensity score matching technique allows one to match the
treatment with comparison units that are similar in terms of their
observable characteristics
 That is, it takes two individuals that are exactly similar in all characteristics
EXCEPT the treatment and computes the difference in the outcome
between them
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32. Propensity Score Matching cont’d…
 The expected value of ATT is defined as the difference
between expected outcome values with and without treatment
for those who actually participated in treatment
τ ATT = E (τ | D = 1) = E[Y (1) | D = 1] − E[Y (0) | D = 1]
 In the sense that this parameter focuses directly on actual
treatment participants
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33. Impact of Use of MPMT
Matching Av. Treatment
Algorithm Outcome Variables Effect on treated t-value
Nearest (ATT)
Commercialization Index 0.378** 2.27
Neighbor
Matching HH per capita input use 3379.69* 1.83
HH per-capita income 17,727.62*** 3.36
Kernel Based Commercialization Index 0.377*** 2.91
Matching
HH per capita input use 3323.11** 1.99
HH per-capita income 17,720.61*** 3.19
Radius Matching Commercialization Index 0.377*** 3.24
HH per capita input use 3355.22* 1.88
HH per-capita income 17,724.21*** 3.03
t-values level of significance are: ***1%, **5% and *10% level. Treated=197,controls=182
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34. Sensitivity analysis & test for hidden bias
Median Critical
Median p-value of
Matching bias % Bias Pseudo R2 Pseudo R2 p-value of LR level of
Outcome bias after LR
Algorithm before Reduction (unmatched) (matched) (unmatched) hidden
matching (matched)
matching bias (┌ )
Comm Index 32.4 16.5 73.6 0.167 0.091 0.000 0.607 1.80-1.85
Nearest
HH per capita
Neighbor
input use (Ksh) 27.2 15.5 35.9 0.188 0.111 0.024 0.884 1.45-1.50
Matching
HH per-capita
income (Ksh) 28.5 6.5 36.2 0.171 0.124 0.000 0.636 1.30-1.35
Comm Index 26.3 9.8 30.8 0.108 0.015 0.000 0.343 1.75-1.85
Kernel
HH per capita
Based
input use (Ksh) 20.5 12.1 45.6 0.117 0.026 0.000 0.763 1.40-1.50
Matching
HH per-capita
income (Ksh) 38.9 10.4 21.0 0.126 0.019 0.000 0.873 1.35-1.40
Comm Index 32.4 12.8 44.8 0.203 0.122 0.000 0.440 1.60-1.75
Radius HH per capita
Matching input use (Ksh) 24.2 11.9 29.8 0.191 0.116 0.004 0.911 1.45-1.55
HH per-capita
income (Ksh) 48.8 16.4 40.8 0.222 0.127 0.001 0.719 1.35-1.45
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35. Conclusion
 Level awareness of MPMT is very high (96%),
 Level of adoption of MPMT is average (62 %)
 Largest proportion of money received via mobile phone (32%) is
used on agricultural related purposes
 Paying farm workers, buying agricultural inputs, leasing farm land
 Determinants of use:
 Education, distance to a commercial bank, membership to farmer organization,
distance to the MPMT agent, endowment with physical & financial assets
 Determinants of intensity of use:
 Distance to MPMT agent, age, education, social capital, experience in farming and
income endowment financial capital (income level)
35

36. Conclusion cont’d…
 Use of m-banking services has a significant effect on
 Level of household commercialization - by 37%
 Household per-capita income - by Ksh. 17,700
 Household per-capita input use - by Ksh. 3,300
 Results were consistent with the 3 matching algorithm
 Sensitivity test and test for hidden bias:
 Lowest critical value of 1.30-1.35 while highest value is 1.80-1.85
 Hence, even large amounts of unobserved heterogeneity would not
alter the inference about the estimated impact of use of MPMT
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37. Implications
 Findings imply that development strategy that embodies ICT-based
MPMT resolves farmer idiosyncratic market failure that arises from
high TCs
 Hence ICT-based innovations can to help smallholder farmers escape
the low-equilibrium poverty trap characterized by limited use of
agricultural inputs, low participation in agricultural markets, low
incomes and subsequently low input use again
 Attention should be given to constraints facing rural areas
 Infrastructural: like lack of electricity
 Human capita: Education and literacy as well as gender
 Other countries should follow the Kenyan model and provide
favourable policies that would ensure entry and survival of such
initiatives
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38. Thank you!!
Asante sana
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