005 - kirui

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

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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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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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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

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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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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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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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Can MPMT services offer answers to smallholder farmers?

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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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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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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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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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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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Results

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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

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Characteristics of Respondents cont’d…

NB: Significance of mean difference is at the *10%, **5% and ***1% levels

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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

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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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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%

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Uses of Money received via MPMT cont’d…

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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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Reverse money transfer by region

School fees is the most important reason for sending money out from agric communities

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Determinants of Use and Intensity of Use of MPMT – The Double

Hurdle Model

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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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Determinants of Use of MPMT

Likelihood ratio shows that the model fits the data well (p-value = 0.001)

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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

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Impact of MPMT on input use, household income and smallholder household

agricultural commercialization

- Results of the PSM Model

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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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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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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

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]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

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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


20.5 12.1 45.6 0.117 0.026 0.000 0.763 1.40-1.50


38.9 10.4 21.0 0.126 0.019 0.000 0.873 1.35-1.40

Radius Matching


24.2 11.9 29.8 0.191 0.116 0.004 0.911 1.45-1.55


48.8 16.4 40.8 0.222 0.127 0.001 0.719 1.35-1.45

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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)

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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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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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Thank you!!

Asante sana

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Economy & Finance

005 - kirui