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Comparative Analysis of smallholder farmers in Kenya, Zambia and Tanzania
Comparing results from FinScope Tanzania 2017 and AFA benchmark studies in Kenya and Zambia ANDREW KARLYN Director for Strategy and Learning

Executive Summary (1) Agricultural livelihoods SHFs in all three countries are poor, and though they often engage in multiple income-generating activities the majority of their income comes from agriculture. Productivity varies greatly between the study countries, with Zambia in particular lagging behind. While this productivity gap is likely driven by many factors (infrastructure/access to markets etc) there may be opportunities here for digital financial inclusion and programmes like Agrifin to reduce this gap. Gender There is a clear gender gap in terms of income across all three countries, and interestingly this is most pronounced in Kenya where farmers have the highest comparative income Although the gender gap is closing in mobile money in Kenya, it remains significant in Tanzania and Zambia. Women continue to lag in terms of their access to formal financial services (with the exception of insurance). Financial Inclusion Levels Financial exclusion is by far the highest in Zambia. Exclusion in Tanzania is much lower though SHFs tend to rely more on informal services than their counterparts in Kenya who mainly use mobile money. Kenya leads the three countries in terms of uptake of nearly all formal services (mobile/bank accounts/insurance). Mobile money uptake varies in in each country, underlining their different progress in terms of market evolution. Some clear opportunities emerge for savings and credit products considering the proportion of farmers who save at home/informally. Specifically in Tanzania there is an opportunity to further deepen mobile money usage.

Executive Summary (2) Digital preparedness and Mobile Money Readiness index Mobile phone penetration is well progressed in all three countries, but in Zambia, this has not led to an uptake of mobile money. Key barriers may lay elsewhere (penetration of agents; awareness etc). Applying a mobile money readiness index shows that readiness is low in Zambia and high in Kenya, which is reflected in uptake. In Zambia and Tanzania there seems an opportunity among SHFs that show high mobile money readiness but do not use mobile money yet. Information, advisory services and training There is not a consolidated channel through which SHFs access agricultural advisory services and many do not access any services at all. Of the services used, government officers seem to have the greatest penetration followed by peers/cooperatives. In terms of how information is accessed, this is predominantly through demonstrations and field days. Resilience and threats to livelihoods The resilience of farmers and the greatest threats to their livelihoods are not as easily comparable across the three countries. We do however find that many farmers remain vulnerable and experience shocks that have a significant impact on their household income with many lacking appropriate coping strategies. Many of these shocks are agriculture related and issues such as pests/weather and loss of harvest are important across different markets. Insurance does not provide resilience for agricultural shocks: despite a growing uptake of insurance products, this is very rarely ag-related. Mostly health and life insurance is taken-up.

Contents 1 2 3 4 5 Research framework and methodology
Comparative Analysis of smallholder farmers in Kenya, Zambia and Tanzania Contents 1 Research framework and methodology 2 Segmentation of by smallholder farmers and comparability 3 Research findings 3.1: Profile of Smallholder Farmers 3.2: Access and usage of financial services 3.3: Digital Services 3.4: Information, advisory services and training 3.5: Resilience 4 Discussion points 5 Annex

1 Research framework and methodology
This research framework is designed to generate deeper insights on smallholder farmers in Tanzania, Kenya and Zambia. For Tanzania, data from FinScope’s 2017 survey is used. For Kenya and Zambia, data from AgriFin Accelerate’s benchmark studies are used. These were administered in 2017 by Research Solutions Africa (RSA). Please see a summary of each survey below: Survey name Target population Sample size Sampling frame Sample design Outliers in Variables Tanzania (FinScope Tanzania 2017) Individuals aged 16 years and older. 9,459 2012 Tanzania Population and Housing Census Multi-stage stratified sampling approach. Nationally representative for both urban and rural areas. (i)Income (ii)Borrowing amount (iii)Savings amount Kenya (RSA benchmark study) Smallholder farmers aged 18 years and older 2,005 2009 Kenya Population and Housing Census Multistage sampling. Screening rural SHFs only. Zambia (RSA benchmark study) 1,215 2009 Zambia Population and Housing Census

RSA Benchmark Studies for Zambia and Kenya*
1 Research framework and methodology Defining Financial Inclusion across countries FinScope Tanzania* RSA Benchmark Studies for Zambia and Kenya* Banked Access to account with: Commercial banks; or Post bank Commercial banks (whoever has bank account and using banking facilities) Post office account Non-bank Formal Insurance; SACCOs; MFIs Remittances company; or Mobile Money Insurance SACCOs Government institution Remittances company Informal only Making use of: money lender; savings group; or Shops / supply chain credit Money lender Local trader (i.e. buyer of your produce crops/livestock) Agricultural input supplier Chama Millers/ processers; or Farmers’ savings and loan group Financially Excluded Individuals that save or borrow only from: Friends / family; or Save at home / in-kind. Family / neighbours/friends in rotating Save at home / in-kind * See the Annex of this presentation for an extensive list of questions used to define each category.

1 Research framework and methodology
Our analysis is structured in the following sections and research questions: 1. Profiles of SHFs RQ1 What is the profile of an average (or median) farmer in Tanzania, Kenya and Zambia? 2. Access and usage of financial services RQ2 What is the uptake and usage of financial services by SHFs in Tanzania, Kenya and Zambia? RQ3 How do SHFs save and borrow in Tanzania, Kenya and Zambia? 3. Digital Services RQ4 How prepared are SHFs to use digital services in Tanzania, Kenya and Zambia? RQ5 What is the uptake and usage of digital financial services in Tanzania, Kenya and Zambia? 4. Information, advisory services and training RQ6 How do farmers currently access information, advisory services or trainings on agriculture or finance? 5. Resilience RQ7 What do farmers perceive as the largest threat to their livelihoods? What are the coping strategies?

1 Research framework and methodology Analytical methods applied
Segmentation and Indices Segmentation approaches identify clusters of households with similar characteristics. One way to segment the sample is to develop an index to rank households from high to low index values. In this analysis we develop a mobile money readiness index for RQ4. Summary Statistics Analysis Summary statistics are used to make comparisons in nearly all research questions. We provide statistical measures such as: Mean, median and mode; Maximum, and minimum; and Frequency analysis and cross-tabulation analysis. Graphical Analysis Graphical analysis visualises differences in data groups. All research questions make use of this method. Multiple Regression Analysis Regression analysis is used to identify any relationship between a dependant variable and one or more independent variables.

2 Segmentation of smallholder farmers and comparability
Defining Smallholder farmers across Tanzania, Kenya and Zambia Note that there are methodological differences with respect to how our data sources define SHFs in Tanzania as opposed to Kenya and Zambia. These are highlighted below: SHFs segmentation FinScope Tanzania* FinScope Tanzania is a general population survey. SHFs are therefore a sub-set of the overall sample that was collected. To define SHFs the following criteria was applied to the overall sample: Condition 1: Income from agriculture (either crops or livestock) contributes a significant amount to overall income (>40%) Condition 2: HH sells the produce that it grows [not just trading]. Condition 3: The HH cultivates less than 10 acres of land. Condition 4: Only SHFs in rural areas are considered. SHF screening questions for Zambia and Kenya RSA’s benchmark studies in Zambia and Kenya only interviewed households in rural areas that passed the following set of screening question: Screening Q1: Does your household practice any form of agriculture? Screening Q2: Has agriculture been among the major income sources for your HH in the past 12 months? Screening Q3: Will your HH continue with Agriculture? Screening Q4: Do you consider yourself as a farmer? Screening Q5: The HH cultivates less than 10 acres of land. * See the Annex of this presentation for more detail on the FinScope Tanzania segmentation.

Notes on comparability
2 Segmentation of smallholder farmers and comparability Comparability of key variables: It is important to also note differences in the way variables are defined in FinScope vis-à-vis RSA’s benchmark studies. Below we list differences in the definition of key variables. Key variable Notes on comparability Savings FinScope limits questions regarding savings to the last 12 months while RSA’s benchmark studies can relate to savings of more than 10 years. This does not apply to borrowing however. Both surveys relate loans which were taken-out in the last 12 months. Total household income FinScope probes for each income source, e.g. selling agricultural produce, earning a wage etc and the monthly or annual amount earned. This allows us to calculate the total income of the respondent (not the household). RSA benchmark studies probe for each crop, e.g. maize, tomatoes, coffee, and type of livestock the monthly income gross income, as well as for other income sources, e.g. casual labor or self-employment. Particularly non-agricultural income categories are defined differently across the two surveys. We therefore focussed our analysis on total income and income from agriculture. Accessing information or advisory services FinScope does not have extensive section on how information and financial training is accessed. Some insights can however be derived. There is very little information on agricultural advisory services. RSA’s benchmark studies have extensive sections on agricultural advisory services and financial advisory services. The response rate for some questions however seems low.

Notes on comparability
2 Segmentation of smallholder farmers and comparability Comparability of key variables (continued) Key Variable Notes on comparability Education Both FinScope and RSA’s benchmark studies use similar definitions for education attained. For the analysis we grouped SHFs in the following categories [no formal education; primary school attended or completed, secondary school attended or completed, tertiary attended or completed] Value chains FinScope categorises value chains in 9 different types, e.g. food crops, cash crops, cattle, etc. RSA’s benchmark studies list 29 different value chains, e.g. tea, maize, beans etc. For the purpose of the analysis we grouped these in similar categories as per the FinScope. Land size FinScope is asking for the approximate land size that is used for farming purposes. RSA benchmark studies ask for parcels of land used for each crop and then ask for the size of each parcel. All parcels were added-up to arrive overall land size. Mobile money readiness FinScope has information on mobile phone ownership, proof of identification, literacy and numeracy, proximity to financial access point and a set of question probing attitudes towards digital. RSA’s benchmark studies do have similar information but are lacking proof of identification and proximity to financial access points. We have defined an index based on overlapping variables between the two surveys.

General disclaimer: accounting for national differences
2 Segmentation of smallholder farmers and comparability Comparability of key variables (continued) General disclaimer: accounting for national differences It is important to note that variables that correlate with the national development status, such as education or income levels, are not very suitable to compare SHFs across countries. For example, if national income levels in Tanzania are lower than in Kenya, this might be reflected in the income of Tanzanian and Kenyan SHFs. While Kenyan SHFs might on average earn more than Tanzanian SHFs, the latter might still be relatively better off when compared to other groups in Tanzania. Since differences in income or education are biased by national levels, comparing SHFs across countries is not straightforward. Without accounting for national levels, it is difficult to conclude whether one group is better or worse off than the other. To compare incomes, PPI would be a good alternative to account for national income levels. See findings section for more detail.

Profile of Smallholder Farmers
Comparative Analysis of smallholder farmers in Kenya, Zambia and Tanzania 3.1 Profile of Smallholder Farmers Research Question 1: What is the profile of an average (or median) farmer in Tanzania, Kenya and Zambia?

3.1 Profile of Smallholder Farmers Research Question 1: What is the profile of an average (or median) farmer in Tanzania, Kenya and Zambia?. Demographic Profile: Kenyan SHFs are slightly older on average, followed by SHFs in Zambia and Tanzania. In Tanzania 18% of SHFs are between 16 and 24. Average age Tanzania:40 Kenya:44 Zambia:41 Median age Tanzania:38 Kenya::42 Zambia:38 Education difficult to compare.

3.1 Profile of Smallholder Farmers Research Question 1: What is the profile of an average (or median) farmer in Tanzania, Kenya and Zambia? Economic Profile (1): Absolute income is highest in Kenya - both in average and median terms. Zambian and Tanzanian SHFs have similar median incomes. It is important to note here that both for FinScope and for the benchmark studies extreme outliers are observed. Excluding these from the analysis could change the overall picture. Agriculture makes up the largest proportion of income (80%) for SHFs in Tanzania. Disclaimer: Note that comparing SHF’s income levels does not account for national differences in income levels and is therefore constraint in describing the relative wealth of SHFs.

3.1 Profile of Smallholder Farmers Research Question 1: What is the profile of an average farmer in Tanzania, Kenya and Zambia? Socio-economic profile: Kenya’s SHFs have the largest income gender gap both in total and relative terms. Zambia’s SHFs education income gap is most pronounced: the median SHF with tertiary education earns $121, while the median SHF with no formal education or primary education only earns $9 or $12 a month. *Income levels might be skewed due to a small sample size in the tertiary category.

3.1 Profile of Smallholder Farmers Research Question 1: What is the profile of an average (or median) farmer in Tanzania, Kenya and Zambia? Economic Profile (2) – income distribution of SHFs: Income distributions in all three countries point towards a large share of SHFs living below $1/day. Zambia observes the most unequal distribution: while it has overall fewer SHFs living below $2 /day than Tanzania, 48% of Zambian farmers still live below $0.5/day. 84% < $2 / day 69% < $2 / day 75% < $2 / day Disclaimer: Note that comparing SHF’s average income does not account for national differences in income levels. We can however interpret the income distribution for each country.

3.1 Profile of Smallholder Farmers Research Question 1: What is the profile of an average (or median) farmer in Tanzania, Kenya and Zambia? Land size in acres: Kenya’s SHFs are the most productive group, cultivating the smallest areas of land but generating the highest income from agriculture per month. Zambia’s SHFs cultivate the most land but earn the least, making them the least productive group in terms of income in USD per acre of land cultivated. Median monthly income from agriculture $15 / month $20 / month $9 / month

3.1 Profile of Smallholder Farmers Research Question 1: What is the profile of an average (or median) farmer in Tanzania, Kenya and Zambia? Engagement in Value Chains: Kenya: Many SHFs engage in livestock and dairy. Cultivation of cash and food crops is lower than in other countries. Tanzania: Most SHFs are involved in food crops and complement this with either cash crops or livelihoods. Zambia: Few SHFs engage in livestock. The emphasis is on food and cash crops.

Uptake and Usage of Financial Services
Comparative Analysis of smallholder farmers in Kenya, Zambia and Tanzania 3.2 Uptake and Usage of Financial Services Research Question 2: What is the uptake and usage of financial services by SHFs in Tanzania, Kenya and Zambia?

3.2 Uptake and Usage of Financial Services Research Question 2: What is the uptake and usage of financial services by SHFs in Tanzania, Kenya and Zambia? Nature of Financial Uptake: Kenya’s SHFs have the highest financial inclusion levels with only 9% who are excluded. Of those who are financially included in Kenya, 33% are banked as opposed to 16% in Zambia and 7% in Tanzania. Mobile money penetration is 56% in Kenya, while it is lowest in Zambia (16%) and 41% in Tanzania. Base: All SHFs

3.2 Uptake and Usage of Financial Services Research Question 2: What is the uptake and usage of financial services by SHFs in Tanzania, Kenya and Zambia? Nature of Financial Uptake: SHFs in Tanzania make most use of informal services and are catching-up with insurance and mobile money. 91% of Kenyan SHFs are formally included. Kenya has the highest take up across most types of financial services, except informal services and MFIs. Base: All SHFs

3.2 Uptake and Usage of Financial Services Research Question 2: What is the uptake and usage of financial services by SHFs in Tanzania, Kenya and Zambia? Gender gap: For Kenyan farmers the gender gap seems to be closing with respect to mobile money but is pronounced for banking services for all countries. Mobile money uptake in Zambia and Tanzania has a larger gender bias in favour of men. Note that formal includes commercial banks, post bank, mobile money, MFIs, SACCOs. Base: All SHFs

3.2 Uptake and Usage of Financial Services Research Question 2: What is the uptake and usage of financial services by SHFs in Tanzania, Kenya and Zambia? Financial uptake based on education: Uptake of financial services is positively correlated with education for all three countries. Informal services increase significantly with education in Tanzania. For Kenyan farmers the gap is closing for mobile money while there is a larger correlation between higher education and uptake for Zambia and Tanzania. Base: All SHFs

3.2 Uptake and Usage of Financial Services Research Question 2: What is the uptake and usage of financial services by SHFs in Tanzania, Kenya and Zambia? Mobile money services uptake by age: In Kenya, mobile money uptake is similar for SHFs between 16 and 54 but decreases in groups older than this. In Tanzania, mobile money uptake is bell-shaped with low values for the youngest and oldest SHF segments, peaking for SHFs between 45 and 54. In Zambia, SHFs of all age groups have similar mobile money uptake. Base: All SHFs

3.2 Uptake and Usage of Financial Services Research Question 2: What is the uptake and usage of financial services by SHFs in Tanzania, Kenya and Zambia? Median Monthly Total Income by uptake of financial services: Users of banks and mobile money have significantly higher incomes than non-users across all three countries. Users of informal services have similar incomes to non-users (Note informal income in Kenya is driven by an outlier).

Comparative Analysis of smallholder farmers in Kenya, Zambia and Tanzania 3.2 Uptake and Usage of Financial Services Research Question 3: How do SHFs save and borrow in Tanzania, Kenya and Zambia?

3.2 Uptake and Usage of Financial Services Research Question 3: How do SHFs save and borrow in Tanzania, Kenya and Zambia? Savings and borrowing behaviour across countries: Savings behaviour is similar across all three countries, with Kenya leading in terms of the highest proportion of savers. Borrowing behaviour varies strongly. However this could be largely driven by methodological differences between FinScope and the Benchmark studies. Base: All SHFs

3.2 Uptake and Usage of Financial Services Research Question 3: How do SHFs save and borrow in Tanzania, Kenya and Zambia? Saving behaviour – Tanzania: 44% of SHFs in Tanzania save money. Most of these SHFs still keep cash at home (52%) as opposed to using mobile money (18%) despite the relatively high uptake of mobile money across the sample. Base: SHFs who save

3.2 Uptake and Usage of Financial Services Research Question 3: How do SHFs save and borrow in Tanzania, Kenya and Zambia? Borrowing behaviour - Tanzania: 45% of SHFs borrow but 73% of these do so informally by asking family or friends. 20% use savings groups and only 2% borrow from a mobile money service provider. Base: SHFs who borrow

3.2 Uptake and Usage of Financial Services Research Question 3: How do SHFs save and borrow in Tanzania, Kenya and Zambia? Saving behaviour: Kenyan SHFs who save (58%) often do so by using mobile money (41% use KCB M-Pesa, 15% use MPesa and 12% use M-shwari). Savings groups are also used frequently (33%) and most SHFs have moved away from savings at home (‘savings in a hidden place’ = 1.2%). Base: SHFs who save

3.2 Uptake and Usage of Financial Services Research Question 3: How do SHFs save and borrow in Tanzania, Kenya and Zambia? Borrowing behaviour: SHFs in Kenya borrow are less likely to borrow than those in Tanzania (23% vs 45%). Those that do have mostly moved away from borrowing from friends or neighbour (5%) and are using informal groups, such as Chama (30%) or SACCOs (14%), or mobile money services such as Mshwari (20%). Base: SHFs who borrow

3.2 Uptake and Usage of Financial Services Research Question 3: How do SHFs save and borrow in Tanzania, Kenya and Zambia? Saving behaviour: The most common way of saving for SHFs in Zambia is savings money in a hidden place, e.g. at home (40% of SHFs who save). Mobile money uptake for saving purposes is relatively high (12%), considering the overall low uptake of 26% across SHFs in Zambia. Base: SHFs who save

3.2 Uptake and Usage of Financial Services Research Question 3: How do SHFs save and borrow in Tanzania, Kenya and Zambia? Borrowing behaviour: Only a small proportion of SHFs in Zambia claim that they borrow money. Of those who do, 33% do not indicate a source and 32% borrow from family and friends. Base: SHFs who borrow

3.3 Digital Services Research Question 4: How prepared are SHFs to use digital services in Tanzania, Kenya and Zambia?

3.3 Digital Services Research Question 4: How prepared are SHFs to use digital services in Tanzania, Kenya and Zambia? Access to digital technologies (1): Kenyan SHF’s are leading in terms of access to digital technologies. However Zambia’s SHFs are more likely to own a mobile phone or a smart phone than Tanzanian SHFs. This offers an interesting opportunity for mobile money uptake. Base: All SHFs

3.3 Digital Services Research Question 4: How prepared are SHFs to use digital services in Tanzania, Kenya and Zambia? Access to digital technologies – gender gap: While the gender gap in mobile phone access is closing in Kenya, it is still more pronounced in Zambia and Tanzania. Base: All SHFs

3.3 Digital Services Research Question 4: How prepared are SHFs to use digital services in Tanzania, Kenya and Zambia? Access to digital technologies – age: In Kenya, all age groups apart from those above 65 have similarly high mobile phone uptake. In Tanzania and Zambia, the distribution is more bell-shaped with younger and older SHFs being less likely to own a mobile phone. Base: All SHFs

3.3 Digital Services Research Question 4: How prepared are SHFs to use digital services in Tanzania, Kenya and Zambia? Mobile Money Readiness Index We develop a mobile money readiness index that scores each SHF HH between [1,10]* along the following criteria: Next we apply Ward’s Linkage clustering method to segment the sample into two groups: 1) Mobile phone ownership 2) Literacy 3) Numeracy 4) Smart or featured phone 5) Access to internet *See the Annex of this presentation for more detail on the scoring methodology. We find that despite higher mobile phone ownership likelihood, Zambia’s SHFs score lowest in terms of the mobile money readiness index.

3.3 Digital Services Research Question 4: How prepared are SHFs to use digital services in Tanzania, Kenya and Zambia? Mobile money readiness index by gender age: In Kenya, we find that despite the closing gender gap in mobile phone ownership, the percentage of females with high mobile money readiness scores is still 10% below males The gender gap is most pronounced in Tanzania, where only 39% of females score highly compared to 65% males. Base: All SHFs

3.3 Digital Services Research Question 5: What is the uptake and usage of digital financial services in Tanzania, Kenya and Zambia?

3.3 Digital Services Research Question 5: What is the uptake and usage of digital financial services in Tanzania, Kenya and Zambia? Mobile money uptake: Mobile money uptake varies in in each country, underlining their different progress in terms of market evolution. Although the gender gap is closing in mobile money in Kenya, it remains significant in Tanzania and Zambia.

3.3 Digital Services Research Question 5: What is the uptake and usage of digital financial services in Tanzania, Kenya and Zambia? Percentage of SHFs that take-up mobile money by mobile money readiness index (high/low)* Group 1: SHFs with high index values that do not access mobile money What are the barriers that prevent SHFs from using mobile money where technology access should not be an issue? Group 2 SHFs with low index values that do not access mobile money Which interventions could support SHFs to move from a ‘low readiness index’ to accessing and using mobile money? *Note that proximity to financial access point is not included in the index hence this could be a potential barriers

3.3 Digital Services Research Question 5: What is the uptake and usage of digital financial services in Tanzania, Kenya and Zambia? Percentage of SHFs that take-up mobile money by mobile money readiness index (high/low)* Population size of SHFs with high index values 2,347,790 Population size of SHFs with high values that do not take-up MM 1,408,674 Population size of SHFs with high index values 18,481,711 Population size of SHFs with high values that do not take-up MM 1,108,903 Population size of SHFs with high index values 4,214,895 Population size of SHFs with high values that do not take-up MM 1,393,377

3.3 Digital Services Research Question 5: What is the uptake and usage of digital financial services in Tanzania, Kenya and Zambia? Group 1: SHFs with high index values that do not access mobile money What are the barriers that prevent SHFs from using mobile money where technology access should not be an issue? Segmentation by gender Females with high index values take-up MM less frequently than males. This reflects earlier findings re MM uptake (see slide 46) Segmentation by age The average age of those that take-up MM is not significantly different from those that don’t in the high index value segment.

3.3 Digital Services Research Question 5: What is the uptake and usage of digital financial services in Tanzania, Kenya and Zambia? Group 1: SHFs with high index values that do not access mobile money What are the barriers that prevent SHFs from using mobile money where technology access should not be an issue? Segmentation by median income SHFs that take-up mobile money have higher median incomes. This holds for all three countries. Segmentation by average income The difference between SHFs that take-up MM and those that don’t is even more pronounced when considering average income.

3.3 Digital Services Research Question 5: What is the uptake and usage of digital financial services in Tanzania, Kenya and Zambia? Group 1: SHFs with high index values that do not access mobile money What are the barriers that prevent SHFs from using mobile money where technology access should not be an issue? Segmentation by education In Kenya, the education gap of MM users is closing. In Tanzania and Zambia this gap is still strongly pronounced: with SHFs with no mobile money uptake have on average attended less schooling than those that take-up mobile money

Information, advisory services and training
Comparative Analysis of smallholder farmers in Kenya, Zambia and Tanzania 3.4 Information, advisory services and training Research Question 6: How do farmers currently access information, advisory services or trainings on agriculture or finance?

3.4 Information, advisory services and training Research Question 6: How do farmers currently access information, advisory services or trainings on agriculture or finance? SHFs accessing agricultural advisory services in Kenya and Zambia* Overall agricultural advisory services uptake is low. 40-41% of SHFs in Kenya and Zambia claim they have not accessed agricultural services. In Zambia, government officers (24%) and co-operatives (14%) seem to play the major role. In Kenya, more often others farmers are asked for advice. *Note that these questions where only available in AFA’s benchmark studies however not in the FinScope Tanzania. We therefore only display findings for Kenya and Zambia here.

3.4 Information, advisory services and training Research Question 6: How do farmers currently access information, advisory services or trainings on agriculture or finance? SHFs accessing agricultural advisory services in Kenya and Zambia: Government officers are the most trusted source of advice for SHFs in Kenya and Zambia. Advice focusses on fertilizers, seeds, pest control and technical products. Fewer advice is recieved on input prices, market prices and weather information.

3.4 Information, advisory services and training Research Question 6: How do farmers currently access information, advisory services or trainings on agriculture or finance? SHFs receiving farmers training in Kenya and Zambia Between 16-19% of SHFs in Kenya and Zambia have attended a farmers training. Only 10% of the farmers who attended a training did pay for the services. The most common training received are plot demonstrations and field days. MNOs and input suppliers play a larger role in Kenya. Base: All SHFs Base: SHFs that attended training Base: SHFs that attend farmers training

3.4 Information, advisory services and training Research Question 6: How do farmers currently access information on finance in Tanzania? Tanzania: What is the main reason you belong to a savings group? In Tanzania, only few farmers join savings groups for financial advice Tanzania: Do you sometimes ask somebody for advice regarding money matters? While 73% of SHFs in Tanzania do seek advice on money matters, most do so within the family. Yes – I do Who do you ask? * Others includes (Bank, Microfinance institution, Savings and credit cooperative, Financial advisor, Farmers association, Business association, Savings group, Moneylender in community, Government official, Village elder and Other specify)

3.5 Growth and Resilience Research Question 7: What do farmers perceive as the most serious threat to their livelihoods? What are the coping strategies?

3.5 Growth and Resilience Research Question 7: What do farmers perceive as the most serious threat to their livelihoods? What are the coping strategies? Agricultural Events - Tanzania About half of all SHFs have experienced unexpected agricultural events in the past 12 months. For 75% of those the event has a significant effect on household income, urging to either use-up savings, reduce consumption or do additional work to make-up for the loss. No insurance is used and only few SHFs uptake cash savings. Percentage of SHFs that suffer from agricultural shocks (1) Coping mechanisms for SHFs that experience crop failure (2) Base for chart 1 is all SHFs in Tanzania. For chart 2, it is SHFs who experience crop failures.

3.5 Growth and Resilience Research Question 7: What do farmers perceive as the largest threat to their livelihoods? What are the coping strategies? Agricultural Events – Kenya The main threats to agriculture are pests and diseases and weather-related events. Other reasons are very scattered and most are only mentioned by 2% or less of SHFs. *Others includes: Appropriate Inputs Not Affordable, Appropriate Inputs Not Available, Shortage or Poor Quality of Seeds, Low Access to Credit Facilities, Poor Access to Markets; No, Buyers, Theft, Others, Inability to Pay for Wedding Expenses, Inability to Pay School Fees, Lack of Knowledge/Skills/Information, Shortage of Pasture or Feed, Unusually High Prices for Food, High Variability of Prices in The Market, Theft of Productive Resources, Death of The Head of The Household, Loss or Reduced Employment/Income for a Household Member, Storage, Difficulties in Accessing Veterinary Care, Fires, Insecurity/Violence, Inability to Pay for Medical Fees, Death of Other Household Member, Serious Illness or Accident of Household Member, Access to reliable of fresh water for livestock , Unusually high level of human disease/epidemic Base: All SHFs in Kenya. **Others includes: Go to friends/family, Look for money, Spent savings, Reduce expenses, Borrowed money, Look for more and different food, Reduce household food consumption, Sold productive assets, Increase work, Sold household assets, Go to Community Savings Group Base: All SHFs in Kenya. *Others includes: Appropriate Inputs Not Affordable, Appropriate Inputs Not Available, Shortage or Poor Quality of Seeds, Low Access to Credit Facilities, Poor Access to Markets; No, Buyers, Theft, Others, Inability to Pay for Wedding Expenses, Inability to Pay School Fees, Lack of Knowledge/Skills/Information, Shortage of Pasture or Feed, Unusually High Prices for Food, High Variability of Prices in The Market, ETC

3.5 Growth and Resilience Research Question 7: What do farmers perceive as the most serious threat to their livelihoods? What are the coping strategies? Agricultural Events – Zambia The main threats to agriculture are pests and diseases and weather-related events. Other reasons are very scattered and most are only mentioned by 2% or less of SHFs. An interesting finding compared to Kenya is that SHFs in Zambia more often rely on national government agencies for coping strategies. *Others includes: Appropriate Inputs Not Affordable, Appropriate Inputs Not Available, Shortage or Poor Quality of Seeds, Low Access to Credit Facilities, Poor Access to Markets; No, Buyers, Theft, Others, Inability to Pay for Wedding Expenses, Inability to Pay School Fees, Lack of Knowledge/Skills/Information, Shortage of Pasture or Feed, Unusually High Prices for Food, High Variability of Prices in The Market, Theft of Productive Resources, Death of The Head of The Household, Loss or Reduced Employment/Income for a Household Member, Storage, Difficulties in Accessing Veterinary Care, Fires, Insecurity/Violence, Inability to Pay for Medical Fees, Death of Other Household Member, Serious Illness or Accident of Household Member, Access to reliable of fresh water for livestock , Unusually high level of human disease/epidemic Base: All SHFs in Zambia. *Others includes: Appropriate Inputs Not Affordable, Appropriate Inputs Not Available, Shortage or Poor Quality of Seeds, Low Access to Credit Facilities, Poor Access to Markets; No, Buyers, Theft, Others, Inability to Pay for Wedding Expenses, Inability to Pay School Fees, Lack of Knowledge/Skills/Information, Shortage of Pasture or Feed, Unusually High Prices for Food, High Variability of Prices in The Market, ETC Base: All SHFs in Zambia. **Others includes: Go to friends/family, Look for money, Spent savings, Reduce expenses, Borrowed money, Look for more and different food, Reduce household food consumption, Sold productive assets, Increase work, Sold household assets, Go to Community Savings Group

3.5 Growth and Resilience Research Question 7: What do farmers perceive as the most serious threat to their livelihoods? What are the coping strategies? Insurance in agriculture: Insurance products used across Kenya, Zambia and Tanzania mainly focus on health insurance or life insurance. Little to no crop insurance is taken-up.

4 Discussion Points (1) SHF sub-groups for further analysis:
Of the three countries, Zambia shows most pronounced signs of inequality in terms of land size, income and financial service uptake. There seems to be a relatively wealthy sub-set of SHFs in Zambia with larger incomes and, potentially, larger land size that use formal financial services. The larger share of the SHF population however seems to be very marginalised with very low incomes and no access to formal financial services. Further analysis of the sub-segments in Zambia could shed more light on the different profiles. Agricultural productivity: Kenyan SHFs seem to be the most productive in terms of income from agriculture in USD over land size cultivated. Zambian SHFs are the least productive when applying this metric. Disclaimer: the analysis does not account for national income differences. More analysis should be done to confirm these findings.

4 Discussion Points (2) Financial inclusion:
SHFs in Zambia have the highest probability of being financially excluded. This is driven by a low mobile money uptake. Kenyan SHFs have the lowest levels of financial exclusion. This is driven by both formal banking services and mobile money. The mobile money gender gap has nearly closed for SHFs in Kenya and is more pronounced in Tanzania and Zambia. Savings behaviour: The high mobile money uptake in Kenya shows deepened usage with most SHFs using mobile money to save. In Tanzania, saving at home is still the main savings channel, despite the relatively high mobile money uptake. Here we find opportunities for deepening usage. In Zambia, while mobile money uptake is low, 12% of SHFs that save do so using mobile money. This makes a promising case to further the roll-out of mobile money. Borrowing behaviour: SHFs in Kenya (23%) and Zambia (13%) do not borrow often compared to Tanzanian SHFs (45%). While Zambian and Tanzanian SHFs still mainly borrow through family or friends, Kenyan SHFs engage much more through informal groups, such as Chama or SACCOs, or mobile money (M-shwari). In Tanzania, despite the increased mobile money uptake, only very few SHFs use mobile money to borrow.

4 Discussion Points (3) The mobile money readiness index predicts mobile money uptake well: Interventions should distinguish between the following two groups: SHFs with high index values, i.e. high MM readiness, that do not access mobile money. SHFs with low index values, i.e. low MM readiness, that do not access mobile money. Further analysis should focus on these groups and explore their demographic and socio-economic profiles. Agricultural advisory services in Zambia and Kenya Government officers are the most accessed and trusted source of advice. These mainly consult on fertilizers, seeds, pest control and technical production. Between 16% and 19% of SHFs have attended farmers training. 90% of the training sessions were free of costs. Insurance products do not provide resilience for struggling SHFs: Most SHFs that use insurance take-up health insurance. Very little to no SHFs have crop-insurance or other agriculture-related insurance across the three countries. In Tanzania, only SHFs that have tertiary education access insurance products. SHFs with lower education levels, such as secondary education or primary education, do not access insurance. The more vulnerable SHFs do therefore not benefit from insurance cover to provide resilience.

5 Annex

5 Annex We have used the following variables to define financial inclusion: Tanzania Section F: Savings 1. Please tell me how you saved/where you kept the money you put away in the past 12 months? Section G: Borrowings 1. Please tell me where/from whom you borrowed money in the past 12 months? Did you borrow money from …..-? Kenya and Zambia Savings and Borrowings Module 10: Financial Instruments Saving Products Do you use the following savings method? Loan/ Credit Products Did you use any of the following Loan/ credit products in the last 12 months? Financial Services Mobile Money What is the main mode of payment used for payment of the harvest sold? 7.3.8 How were you paid? How was payment made? (i). Self-employment, ii). Regular jobs (specify), iii). Seasonal Jobs 7.5.3 Channel used to send the money? ? 7.5.8 What was the channel used to send the money? 7.6.6 How did you pay for the input? 9.5.8 Do you use internet for the following activities? How do you usually or are more likely to access your savings account/ product? Method used to disburse the loan/credit? What is the repayment method for this loan/credit?

5 Annex We have used the following variables to define financial inclusion: Kenya and Zambia Financial Services SACCO 7.6.5 How did you acquire the inputs bought in the past 12 months? READ Do you use the following savings method? 10.2.4i What was the source of the outstanding loan? Did you use any of the following Loan/ credit products in the last 12 months? Banked What is the main mode of payment used for payment of the harvest sold? READ OUT OPTION 7.3.8 How were you paid? How are payments made? For Self Employment,Regular Job and Seasonal Job 7.5.3 Channel used to send the money? (Sending) 7.5.8 What was the channel used to send the money? (Receiving) 7.6.6 How did you pay for the input? MULTIPLE RESPONSES ALLOWED 7.6.9 How do you usually pay for your transport services? How was payment made? How do you usually or are more likely to access your savings account/ product? Did you use any of the following Loan/ credit products in the last 12 months? READ OUT OPTIONS Method used to disburse the loan/credit

5 Annex We have used the following variables to define financial inclusion: Kenya and Zambia Financial Services Insurance Have you considered using insurance as a coping strategy for this risk? Do you or any of your HH members have any type of insurance that is currently active? Which type of insurance do they currently have? MFI 7.6.5 How did you acquire the inputs bought in the past 12 months? READ OUT OPTIONS; MULTIPLE RESPONSES ALLOWED Do you use the following savings method? Did you use any of the following Loan/ credit products in the last 12 months? READ OUT OPTIONS 10.2.4i What was the source of the outstanding loan? 8.2. What type of agricultural advisory services have you received in the last 12 months from your primary source? CHAMA Savings Group Do you use the following savings method? (Farmers’ savings and loan group ) ROSCA Do you use the following savings method? (ROSCA) Money Lender, Government institution , Employer , Local trader and Agricultural Input supplier

5 Annex Defining the target segment for analysis
2) Our analysis focusses on smallholder farmers (SHFs). 1) Finscope 2017 for Tanzania contains nationally representative data. Total sample size: 9,459 ~80% involved in agriculture ~30% agriculture significantly contributes to HH income* 3) We define SHFs based on their share of agricultural income to total income (>40%). ~50% agriculture complements income 4) For other HHs agriculture is not the main income source. Some agri-produce might be sold but more often it is consumed. ~20% not involved in agriculture * These are households for which income from selling their agricultural produce (crops or livestock) contributes more than 40% to overall household income.

FinScope 2017 Tanzania sample (9,459)
Annex Smallholder farmer definition Condition 1: Income from agriculture contributes a significant amount to overall income (>40%) Condition 2: HH sells the produce that it grows [not just trading]. Condition 3: The HH does not own a large farm (land size <10 acres) FinScope 2017 Tanzania sample (9,459) Research framework Profile of SHFs Access and usage of financial services Digital services Information gaps Growth and resilience

5 Annex Small scale farmers Smallholder farmers
Diversifying households % that are formally financially included* % that live in urban areas Average and median monthly income * Formal financial inclusion is defined as having access to an account with a commercial bank, a post bank, or with an insurance provider; a SACCO; MFI; Remittances company; or a Mobile Money provider.

Research Question 1: What is the profile of an average (or median) farmer in Tanzania, Kenya and Zambia?. 5 Annex Demographic Profile: A very high proportion of SHFs (74%) in Tanzania have only primary education. Further, 19% of the SHFs have no formal education, and only 7% have secondary education. Education difficult to compare. Disclaimer: Note that comparing SHFs by level of education does not account for national differences.

5 Annex Formal financial service uptake by age: TBD

5 Annex Bank service uptake by age: TBD

5 Annex Financial uptake based on education:

5 Annex Informal financial service uptake by age: TBD

5 Annex

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Director for Strategy and Learning
Andrew karlyn Director for Strategy and Learning

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