005 - kirui

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  • Need to do also uses by district. And be prepared to asnwers the Qn “what is agricultural related? “
  • Health care is one work = healthcare
  • 005 - kirui

    1. 1. Adoption and Impact of MobilePhone- 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. 2. Outline Introduction  Background Information  Purpose & Objectives  Justification Methodology  Sampling Procedure  Empirical Models Results and Discussion Conclusions and Implications 2
    3. 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 3
    4. 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 4
    5. 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. 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 6
    7. 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 7
    8. 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) 8
    9. 9. Can MPMT services offer answers to smallholder farmers? 9
    10. 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 10
    11. 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 11
    12. 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 12
    13. 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 13
    14. 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 14
    15. 15. Results 15
    16. 16. Characteristics of RespondentsCharacteristic Users Non-Users Difference t -values 3.71 3.73 -0.02 -0.62Natural log of age in years 7.43 7.47 -0.04 -0.66Natural log of age squared 9.78 6.99 2.78*** 7.95Education (years)Years of experience in 16.49 20.25 -3.76*** -2.82farming 5.64 5.85 0.21 0.93Household size 0.57 0.44 0.13*** 2.58Gender 0.85 0.33 2.71*** 2.58Literacy 0.92 0.89 0.24 1.28Occupation 0.69 0.34 0.14*** 2.84Group membership 1.00 0.92 0.08 1.28Awareness of MPMT services 16
    17. 17. Characteristics of Respondents cont’d…Characteristic Users Non-Users Difference t -valuesDistance to bank (km) 8.61 11.75 -3.13*** -4.17Distance to the nearest market (km) 6.54 5.60 0.93 1.11Distance to agric extension agent (km) 6.66 8.59 -1.93 -1.41Distance to MPMT agent (km) 2.17 4.29 7.31*** 3.54Number of enterprises 6.31 3.20 3.03** 1.92Natural log of agric. Income (KSh.) 9.09 6.56 2.53*** 6.02Natural log of other income 9.79 9.10 0.69** 1.97Natural log of current value of assets 10.59 9.79 0.79*** 3.04Number of farmers 197 182 NB: Significance of mean difference is at the *10%, **5% and ***1% levels 17
    18. 18. Awareness and Use of MPMT services 18
    19. 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 19
    20. 20. Learning about MPMT Majority of the respondents learnt from the radio, friends and relatives Low usage of newspapers, TV and billboards/posters 20
    21. 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% 21
    22. 22. Uses of Money received via MPMT cont’d… 22
    23. 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, 23
    24. 24. Reverse money transfer by region School fees is the most important reason for sending money out from agric communities 24
    25. 25. Determinants of Use and Intensity of Use of MPMT – The Double Hurdle Model 25
    26. 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) 26
    27. 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) 27
    28. 28. Determinants of intensity of use of MPMTDefinition of variables Poisson Negative BinomialDep. Variable: number of times of Coeff p-value Coeff p-valueusing MPMTAge 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 0.190Natural 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
    29. 29. Impact of MPMT on input use, household income and smallholder household agricultural commercialization - Results of the PSM Model 29
    30. 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 30
    31. 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 31
    32. 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 32
    33. 33. Impact of Use of MPMTMatching Av. TreatmentAlgorithm Outcome Variables Effect on treated t-valueNearest (ATT) Commercialization Index 0.378** 2.27NeighborMatching HH per capita input use 3379.69* 1.83 HH per-capita income 17,727.62*** 3.36Kernel Based Commercialization Index 0.377*** 2.91Matching 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*** 3.03t-values level of significance are: ***1%, **5% and *10% level. Treated=197,controls=182 33
    34. 34. Sensitivity analysis & test for hidden bias Median Critical Median p-value ofMatching bias % Bias Pseudo R2 Pseudo R2 p-value of LR level of Outcome bias after LRAlgorithm 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 1.80-1.85Nearest HH per capitaNeighbor input use (Ksh) 27.2 15.5 35.9 0.188 0.111 0.024 0.884 1.45-1.50Matching 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 1.75-1.85Kernel HH per capitaBased input use (Ksh) 20.5 12.1 45.6 0.117 0.026 0.000 0.763 1.40-1.50Matching 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 1.60-1.75Radius HH per capitaMatching 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
    35. 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. 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 36
    37. 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 37
    38. 38. Thank you!!Asante sana 38

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