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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 ConferenceEmperors Palace Hotel, Johannesburg,
South Africa
May 21st - 23rd, 2012 1
Outline Introduction
Background Information Purpose & Objectives Justification
Methodology Sampling Procedure Empirical Models
Results and Discussion Conclusions and Implications
2
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
3
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
4
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
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
6
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
7
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)
8
Can MPMT services offer answers to smallholder farmers?
9
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
10
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
11
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
12
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
13
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
14
Results
15
Characteristics of RespondentsCharacteristic Users Non-Users Differen
ce t -
valuesNatural log of age in years
3.71 3.73 -0.02 -0.62
Natural log of age squared
7.43 7.47 -0.04 -0.66
Education (years) 9.78 6.99 2.78*** 7.95Years of experience in farming
16.49 20.25 -3.76*** -2.82
Household size 5.64 5.85 0.21 0.93
Gender 0.57 0.44 0.13*** 2.58
Literacy 0.85 0.33 2.71*** 2.58
Occupation 0.92 0.89 0.24 1.28
Group membership 0.69 0.34 0.14*** 2.84Awareness of MPMT services
1.00 0.92 0.08 1.28
16
Characteristics of Respondents cont’d…
NB: Significance of mean difference is at the *10%, **5% and ***1% levels
17
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*** 3.54Number 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
Awareness and Use of MPMT services
18
Awareness by Region of Survey
M-PESA = the most widely known method in all the districts Postapay (Orange-money) = largely unknown by the
respondents
19
Learning about MPMT
Majority of the respondents learnt from the radio, friends and relatives
Low usage of newspapers, TV and billboards/posters 20
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%
21
Uses of Money received via MPMT cont’d…
22
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,
23
Reverse money transfer by region
School fees is the most important reason for sending money out from agric communities
24
Determinants of Use and Intensity of Use of MPMT – The Double
Hurdle Model
25
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)
26
Determinants of Use of MPMT
Likelihood ratio shows that the model fits the data well (p-value = 0.001)
27
Dependent variable = Use of MPMT
Logit Reg.Marginal Effects
Coeffp-value Coeff
p-value
Gender (dummy) 0.54 0.041 0.12 0.036Age (years) 0.03 0.118 0.06 0.118Education (years of formal education) 0.19 0.000 0.05 0.000Distance to MPMT agent (km) -0.31 0.001 -0.09 0.001Distance to nearest bank (km) 0.51 0.009 0.02 0.005Household size -0.09 0.159 -0.02 0.149Years of experience in farming (years) -0.03 0.064 -0.01 0.064Distance to agric extension agent (km) -0.01 0.642 -0.03 0.642Group membership (dummy) 0.71 0.007 0.16 0.003Natural log of current value of assets 0.11 0.028 0.09 0.022Natural log of household income 0.24 0.005 0.06 0.002Region of Survey 1.22 0.435 1.08 0.476Constant -1.13 0.000
Determinants of intensity of use of MPMTDefinition of variables Poisson Negative
Binomial Dep. Variable: number of times of using MPMT
Coeff p-value Coeff p-value
Age 0.25 0.011 0.22 0.019Age2 -0.01 0.014 -0.01 0.024Education 0.16 0.000 0.19 0.000Gender 0.73 0.563 0.62 0.633Group membership 0.32 0.121 0.55 0.017Household size -0.13 0.134 -0.32 0.144Distance to MPMT agent -0.06 0.029 -0.04 0.016Distance to the bank -0.15 0.480 0.06 0.002Natural log of household assets
0.03 0.549 0.06 0.190
Natural log of agric income 0.06 0.886 0.08 0.017Natural log of other income 0.02 0.383 0.03 0.028Number of enterprises -0.21 0.112 -0.15 0.078Region of Survey 2.28 0.222 1.78 0.276Constant -2.71 0.041 -4.31 0.000
28
Impact of MPMT on input use, household income and smallholder household
agricultural commercialization
- Results of the PSM Model
29
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
30
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
31
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
In the sense that this parameter focuses directly on actual treatment participants
32
]1|)0([]1|)1([)1|( DYEDYEDEATT
Impact of Use of MPMT
t-values level of significance are: ***1%, **5% and *10% level. Treated=197,controls=182
Matching Algorithm Outcome Variables
Av. Treatment Effect on treated
(ATT)t-value
NearestNeighborMatching
Commercialization
Index
0.378** 2.27
HH per capita input
use
3379.69* 1.83
HH per-capita income 17,727.62*** 3.36Kernel BasedMatching
Commercialization
Index
0.377*** 2.91
HH per capita input
use
3323.11** 1.99
HH per-capita income 17,720.61*** 3.19Radius 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
33
Sensitivity analysis & test for hidden bias Matchin
g Algorith
m
Outcome
Median bias
before matchin
g
Median bias after
matching
% Bias Reductio
n
Pseudo R2
(unmatched)
Pseudo R2
(matched)
p-value of LR
(unmatched)
p-value of LR
(matched)
Critical level of hidden bias (┌)
NearestNeighborMatching
Comm Index 32.4 16.5 73.6 0.167 0.091 0.000 0.607 1.80-1.85HH per capita input use (Ksh)
27.2 15.5 35.9 0.188 0.111 0.024 0.884 1.45-1.50
HH per-capita income (Ksh)
28.5 6.5 36.2 0.171 0.124 0.000 0.636 1.30-1.35
KernelBasedMatching
Comm Index 26.3 9.8 30.8 0.108 0.015 0.000 0.343 1.75-1.85HH per capita input use (Ksh)
20.5 12.1 45.6 0.117 0.026 0.000 0.763 1.40-1.50
HH per-capita income (Ksh)
38.9 10.4 21.0 0.126 0.019 0.000 0.873 1.35-1.40
Radius Matching
Comm Index 32.4 12.8 44.8 0.203 0.122 0.000 0.440 1.60-1.75HH per capita 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
34
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
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
36
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
37
Thank you!!
Asante sana
38
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