AgriFin Accelerate Program Data Analytics: FinScope 2017

AgriFin Accelerate Program Data Analytics: FinScope 2017

Executive Summary (1) Agriculture as main income source to define smallholder farmers: Income from agricultural as a proportion of total income is a key factor in our definition of smallholder farmers. We find that while 80% of the Tanzanian population is involved in agriculture, agriculture contributes significantly (>40%) to overall income for only 30%. The analysis focuses primarily on this sub-set of SHFs. Youth The average SHF is older than the average Tanzanian. A higher percentage of SHFs (58%) are in age groups above 35 than is the case for the population as a whole. Similarly, only 42% of SHFs fall into youth age brackets (<35), less than the wider population. Gender Female SHFs earn on average significantly less than male SHFs (achieving 54% of the total income achieved by males). The gender gap remains pronounced for uptake of mobile money and banking products. Savings groups however are dominated by females. Income levels SHFs have very low incomes with 82% earning less than $2/day and 64% earning less than $1/day. SHFs are also worse off compared to the average Tanzanian with 75% in the two lowest PPI quintiles (compared to 58%Tanzanians as a whole). Livelihoods Tanzanian SHFs engage in a range of value chains and combine crops and livestock - few focus on one crop or livelihood only. 32% are involved in cash crops, and these SHFs tend to have the highest average incomes.

Executive Summary (2) Mobile money uptake and usage
Uptake of mobile money is high for SHFs and is on par with the average Tanzanian. Usage, however, remains low with 67% of SHFs using services less than once a month. Savings & borrowing behaviour Mobile money is a strong savings channel for SHFs (35% of SHFs who save do so using mobile money). Opportunity for FSPs: of those using other savings channels most already use mobile money. Despite the extensive uptake of mobile money, barely any SHFs use mobile money for credit. Mobile Money Readiness Index A subset of SHFs with high mobile money readiness have not yet taken-up mobile money. Income levels and mobile phone ownership are potential key barriers. Commercial SHFs or Lead Farmers We segment SHFs by land size, cash crops, additional labour on farm and value chain involvement. More commercial, or lead farmers, work more land, have higher incomes and a greater uptake of mobile money and banking products than those who are less ‘commercial’. Agricultural shocks and resilience About half of all SHFs experience unexpected agricultural events. For 75% of those, the event has a significant effect on household income driving farmers to use-up savings, reduce consumption or do additional work to make-up for the loss. No insurance is used and only a few SHFs access cash savings.

Contents 1 2 3 4 5 Research framework and methodology
Segmentation of FinScope 2017 data 3 Research findings 3.1: Profile of Smallholder Farmers 3.2: Financial Inclusion: uptake, usage and challenges 3.3: Digital financial services: readiness, uptake, usage and challenges 3.4: Information gaps 3.5: Growth and resilience 4 Discussion points & research outlook 5 Annex

1 Research framework and methodology
This research framework is designed to generate deeper insights on smallholder farmers in Tanzania using data mainly from the recent FinScope 2017 Survey. Building on Mercy Corps’ suggested learning questions, we structure the analysis around 5 thematic areas: 1. Profiles of SHFs RQ1 SHF’s profile in terms of demographic and socio-economic variables. 2. Uptake and usage of financial services RQ2 What is the need for financial services? RQ3 How do SHFs access financial services? RQ4 What is the current usage of financial services? RQ5 What challenges are there in the uptake of financial and information services? 3. Digital Services RQ6 What is the current level of uptake and usage of digital financial services? RQ7 What are the challenges in accessing and using digital services?

1 Research framework and methodology
4. Information, advisory services and training RQ8 How do farmers currently access information on agriculture and finance? RQ9 What are the potential information gaps? 5. Growth and resilience RQ10 What are the opportunities for increasing SHFs income through provision of information? RQ11 Segmentation of SHFs by commercialisation* index RQ12 What do farmers perceive as the largest threat or constraint to their livelihoods? How do unexpected events affect income and what are the coping strategies? * We are using the term ‘commercialised farmers’ to describe a sub-group of smallholder farmers that are more commercial in their farming activities. These farmers access and till more larger land plots, focus more on cash crops, are more downstream (not selling produce to neighbours or villagers but to wholesalers), and employ additional labour on the farm. Alternatively, these farmers could be consider as, and/or include ‘lead farmers’.

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. We develop a commercialisation index for RQ11 and a mobile money readiness index for RQ6. Summary Statistics Analysis Summary statistics are used to make comparisons in all research questions. We provide statistical measures such as: Mean and median; Maximum, and minimum; and Frequency analysis and cross-tabulation analysis to highlight importance of factors such as age and gender. 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. We use this technique for RQ11 to identify the factors driving household income such as age, gender, education, financial uptake and usage, etc.

Formal Financial Inclusion
1 Research framework and methodology Banked Access to following products and services: Commercial banks; or Post bank Informal only Making use of: money lender; savings group; or Shops / supply chain credit Usage of financial services Active users are defined as making use of a financial service in the last 90 days. This includes both registered and un-registered users (see chart below). Frequent users are defined as making use of a financial service either ‘daily’, ‘once a week’ or ‘several times per month’. Uptake of financial services Uptake of financial services is defined as either having a registered account in your name or accessing an account that is not in your name through friends or family. Non-bank formal Insurance; SACCOs; MFIs Remittances company; or Mobile Money Formal Financial Inclusion Financially excluded Individuals that save or borrow only from: Friends / family; or Save at home / in-kind. Excluded Financial Inclusion Uptake and usage Informal inclusion *Base: SHFs that use mobile money

2 Segmentation of FinScope Tanzania 2017 data
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 total income* 3) We define SHFs based on how much of their total income comes from agriculture (>40%). ~50% agriculture complements income 4) For other respondents agriculture is not the main income source. A portion of agri-produce may 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.

2 Segmentation of FinScope Tanzania 2017 data
Smallholder farmer definition* For the purpose of this analysis we categorise smallholder farmers based on three main criteria: Condition 1: Income from agriculture makes up a significant proportion of overall income (>40%). This definition follows that of AFA’s benchmark studies to ensure consistency with a comparative cross-country analysis being prepared at the same time. We use income from the sale of agricultural products over total income of the respondent. We have arrived at a cut-off point to define ‘significant proportion’ by analysing the income distribution suggesting that respondents for which at least 40% of total income come from agriculture should be selected in the SHF group. Condition 2: Pure traders and retailers of agri-products are excluded To define SHFs we exclude agricultural retailers to focus the analysis on farmers [we only use responses 1, 2, 3 and 4 to question D4 to define agricultural income] Condition 3: The HH does not own a large farm (land size <10 acres) This is following the definition of SHFs in AFA’s benchmark studies which we have applied for consistency with our comparative cross-country analysis. * Note that we are following the structure of Mercy Corps’ AgriFin Accelerate definition of SHFs by using land size and the relative importance of agriculture in income as identifiers (which is also similar to CGAP’s paper on it’s national surveys in Tanzania (

FinScope 2017 Tanzania sample (9,459)
Segmentation of FinScope Tanzania 2017 data Segmentation approach: Based on the criteria presented above, 3,384 respondents (or 32% of the overall sample) are identified as SHFs. This corresponds to a population of 9,025,880 Tanzanians. Next we segment the remaining sample into two further groups: Off-farm laborers and Diversified entrepreneurs and formal employees For the purpose of this analysis however, we focus only on the Smallholder Farmer. FinScope 2017 Tanzania sample (9,459) Research framework Profile of SHFs Uptake and usage of financial services Digital services Information gaps Growth and resilience

3.5 Growth and resilience Off-farm laborers (26%)
Smallholder farmers (32%) Diversified entrepreneurs and formal employees (42%) Description Live predominately in rural areas. Selling agricultural produce is not the main income source. Some produce might be sold to complement income but more often it is consumed by the HH. Often income is complemented through casual labor. Incomes are often low. Uptake of financial services is slightly higher compared to SHFs. Almost all live in rural areas. Selling agricultural produce is the main income source. Incomes are often low although there is a segment that more successful and regards farming as their business. SHFs have the lowest levels of formal financial service uptake. SHFs save more often than small scale farmers. More often live in urban or peri-urban areas. More often have a full-employment contract or are self-employed traders or service providers. About half are not involved in agriculture at all but some of those who are own large farms. Financial inclusion and income levels are high for this segment. Definition Condition 1: Household is involved in agriculture Condition 2: Agriculture does not contribute a significant amount to overall income Condition 3: The HH is not fully employed and the farm is not large Condition 1: Agriculture contributes a significant amount to overall income (>40%) Condition 2: HH sells predominantly what it grows. Condition 3: The HH does not own large farm (<10 acres) Condition 1: Does not have to be involved in agriculture Condition 2: Can be involved in regular employment Condition 3: Can own large farm

2 Segmentation of FinScope Tanzania 2017 data Smallholder farmers
Off-farm laborers Diversified entrepreneurs and formal employees % that live in urban areas Average and median monthly income** % that are formally financially included* * 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. ** 1 USD= TZS (as of December 8, 2017)

2 Methodological notes and caveats
Segmentation of FinScope Tanzania 2017 data Methodological notes and caveats Comparing smallholder farmers with the overall population Our analysis will focus on the smallholder farmer segment and, where appropriate, compare smallholder farmers to the overall population. The overall population is defined as all Tanzanians (including smallholder farmers). This means that comparing the smallholder farmer segment to the average or median Tanzanian has the benefit of using a more consistent standard. For example, it is more straightforward to compare results with other studies when using an average across the overall population as opposed to using an average across the ‘other respondents’ (those that are not SHFs). ‘Other respondents’ will by definition vary more across studies. However, as a result the overall population is skewed by the smallholder farmer segment as it is included in the total population. This should be kept in mind when reviewing findings. Segmentation into ‘Off-farm laborers’ and ‘Diversified entrepreneurs and formal employees’ Note that since the focus of the analysis lies on smallholder farmers, the segmentation between ‘Off-farm laborers’ and ‘Diversified entrepreneurs and formal employees’ can be further refined. Note regarding income: The FinScope Tanzania survey is interviewing individual respondents. Information on income refers to the income of the individual respondent, not the overall household. We will use PPI scores to proxy the poverty level of the overall household.

3.1 Profile of Smallholder Farmers
Research Question 1: SHF’s profile in terms of demographic and socio-economic variables.

Research Question 1: SHF’s profile in terms of demographic and socio-economic variables. Demographic Profile: Location Most SHFs live in rural areas (88%). Demographic Profile: Gender The survey was largely neutral with respect to gender Demographic Profile: Education SHFs are less likely to have attended secondary education.

Research Question 1: SHF’s profile in terms of demographic and socio-economic variables. Demographic Profile: Age SHFs are on average a little older than the wider population. A higher percentage of SHFs (58%) are in age groups above 35, with only 42% falling into the youth categories (below 35). The average SHF is about 40 years old

Research Question 1: SHF’s profile in terms of demographic and socio-economic variables. Economic Profile: Poverty Probability Index (PPI) SHFs are considerably poorer than the average population (75% in two lowest quintiles). Economic Profile: Income in USD per day Most SHFs earn very low incomes with 64% earning less than $1/day. 82% SHFs below $2/day 64% SHFs below $1/day Income in USD refers to the income of the individual respondent, not overall household income.

Research Question 1: SHF’s profile in terms of demographic and socio-economic variables. Gender-Income Gap: Male SHFs earn higher incomes than females, though the effect is muted when looking at median income. For the overall population, the trend reverses when looking at median income, with women earning slightly more than men. Overall: 63% SHF: 54% ** 1 USD= TZS (as of December 8, 2017)

Research Question 1: SHF’s profile in terms of demographic and socio-economic variables. Primary Source of Income: While SHFs engage in several income generating activities, 93% of SHF’s household income comes from selling their crops or livestock products. This highlights how highly dependent SHFs in Tanzania are on agriculture. Base for chart 2: SHFs’ who save

Research Question 1: SHF’s profile in terms of demographic and socio-economic variables. Engagement in Value Chains: Among the various value chains analysed, most SHFs are involved in cultivating food crops and livestock. Given the seasonal nature of agriculture, most SHFs engage in livestock rearing throughout the year. Sample size for chart 1 Value chain Sample size Cattle Goats, sheep, pigs 3379 Other livestock Cash crops Food crops Fruits Vegetables Fishing Beekeeping Sample size for chart 2 Value chain Sample size Cattle Goats, sheep, pigs 981 Other livestock Cash crops Food crops Fruits Vegetables Fishing Beekeeping Base: SHFs who engage in farming activities.

Research Question 1: SHF’s profile in terms of demographic and socio-economic variables. Engagement in Value Chains: The SHFs involved in cash crops earn more than twice as much as the average SHF. Base: All SHFs. Note that the categories are overlapping, e.g. the 92.7% of SHFs farming food crops includes farmers engaging in other value chains, such as cattle or cash crops, as well. ** 1 USD= TZS (as of December 8, 2017)

Research Question 1: SHF’s profile in terms of demographic and socio-economic variables. Inputs: SHFs mainly finance farming inputs, such as seeds, fertilisers, pesticides or other farming equipment by selling some of their crops or livestock. Very few use separate savings or take out a loan. Base for chart 1: SHFs that are buying farming inputs, N= [2677]. Base for chart 2: All SHFs.

Research Question 4: What is the current usage of financial services?
3.2 Financial Inclusion: uptake, usage and challenges Research Question 2: What is the need for financial services? Research Question 3: What is the current level of uptake and usage of digital financial services? Research Question 4: What is the current usage of financial services? Research Question 5: What challenges are there in accessing financial and information services?

3.2 Financial Inclusion: uptake, usage and challenges
Research Question 2: What is the need for financial services? Saving and borrowing needs (1) Nearly half of all SHF HHs engage either in borrowings or savings. There are distinct groups of SHFs that either only save or only borrow. Saving and borrowing needs (2) Median savings amount are slightly lower than borrowing amounts. Median levels for borrowing - and to a lesser extent saving – are roughly in line with median monthly SHF HH income. For chart 2 N = [3391] for monthly total income N = [1439] for monthly borrowings N = [1499] for monthly savings Base: for chart 1: All SHFs Important to note that numbers refer to monetary savings or borrowings. SHFs savings in the form of crops or livelihood (as assets) are not counted and might increase figures if taken into account. This might explain how the group labelled as ‘neither’ manages risk, saves or invests. For chart 1 , N = [2235]

Research Question 2: What is the need for financial services? Median saving and borrowing amount in USD by age groups Older SHFs borrow and save higher amounts, but borrowing exceeds savings across the population. Saving and borrowing behaviour by education Saving behaviour increases with education while the proportion of borrowers peaks for secondary levels. For chart 1 N = [1439] for monthly borrowings N = [1499] for monthly savings Base: For both charts the base is SHFs’ median savings and borrowings in USD. For chart 2: N = [3391] ** 1 USD= TZS (as of December 8, 2017)

3.2 Financial Inclusion: uptake, usage and challenges Research Question 2: What is the need for financial services? Research Question 3: What is the current level of uptake and use of digital financial services? Research Question 4: What is the current usage of financial services? Research Question 5: What challenges are there in access to financial and information services?

Research Question 3: What is the current level of uptake and usage of digital financial services? Status of Financial Inclusion: 34% of the surveyed SHFs are financially excluded compared to 28% of respondents at the overall level. Amongst financially included SHFs, 57.4% rely on formal financial institutions for their needs while 8.6% rely only on informal sources of finance. Just under 9% use banks.

Research Question 3: What is the current level of uptake and usage of digital financial services? Nature of Financial Uptake: SHFs are catching-up with the wider population in terms of access to mobile money: the majority (51%) of financially included SHFs use mobile money followed by insurance products (15%). In the informal sector, SHFs largely use savings groups.

Research Question 3: What is the current level of uptake and usage of digital financial services? Gender gap: Males access mobile money, banking products and insurance products more often than females. Conversely, females are more often engaged in savings groups. *Base: Only SHFs

Research Question 3: What is the current level of uptake and usage of digital financial services? Financial uptake based on Age Group: For mobile money and savings groups the age distribution is bell-shaped, implying SHFs aged have the highest uptake while older SHFs and those below 25 have lower uptake. Insurance products are accessed more often by older SHFs (35+). *Base: Only SHFs

Research Question 3: What is the current level of uptake and usage of digital financial services? Financial uptake based on location: Living in urban areas strongly predicts access to mobile money services – also for SHFs. To achieve additionality, financial inclusion interventions should therefore be made specific to location (rural) when working with partner organisations. Base: SHFs only.

Research Question 4: What is the current usage of financial services? Saving behaviour: Not much variation is observed across SHFs and the overall sample with respect to saving and borrowing behaviour. Saving through mobile money has surpassed the use of banks. There are however still large segments of SHFs who prefer informal savings methods or saving at home, despite high mobile money penetration. Base for chart 2: SHFs’ who save, N = [1562]

Research Question 4: What is the current usage of financial services? Borrowing behaviour: Here again, despite high mobile money penetration borrowing from family and friends or savings groups remain the primary method for accessing loans. Base for chart 2: SHFs’ who borrow, N = [1470]

Research Question 4: What is the current usage of financial services? Usage of Banking Services: Relative to the overall sample, SHFs show a lower usage of banking services. Only 4% SHFs are active bank users, while only 2% use banking services frequently. % that take-up banking services % that has a registered bank account % of active bank users % of frequent bank users Smallholder farmers Overall population

Research Question 4: What is the current usage of financial services? Frequency of usage of financial services: 67% of SHFs who take-up mobile money state that they use services less than once a month. No SHF uses mobile money daily. T The overall population shows slightly more frequent usage however the overall picture is similar: most respondents use mobile money less than once a month. Base: SHF’s accessing financial services

Research Question 5: What challenges are there in access to financial and information services? Challenges to uptake of Financial Products: Distance does not seem to be the main barrier. Of the 12% financially excluded who claim a financial access point is too far away, 53% live within 5 KM of one. 1) Financially excluded SHFs (34% of SHF sample) Apart from family and friends, why don’t you use any institution or organization to help you manage your money? 2) Un-banked (91.2% of SHF sample) What is the main reason why you do not use a bank? ‘They are too far away’ 47% or 70 SHFs are actually NOT close to a FAP. 53% or 79 SHFs are ARE close to a FAP. Base for chart 1: SHFs that are financially excluded, N = [1296] Base for chart 2: SHFs that are unbanked, N = [3222]

Research Question 5: What challenges are there in access to financial and information services? Challenges to uptake and usage of Financial Products: We are looking at challenges to accessing and using different financial services for different sub-groups of SHFs. 3) Insurance non-users (85% of SHF sample) What is the main reason you don’t have health insurance? 2) Mobile money non-users (49% of SHF sample) What is the main reason why you do not use mobile money? Base: For chart 1, base comprises SHF’s who do not have health insurance, N = [2865] For chart 2, base comprises SHF’s who do not use mobile money, N = [1763]

Research Question 5: What challenges are there in access to financial and information services? Finding 1: Most SHFs who borrow use savings groups or family and friends to do so. Mobile money is barely used (1%), although 57% of SHFs borrowing from family and friends and 75% of SHFs borrowing from SGs have access to a mobile money account. % that use mobile money Base: For chart 1, base comprises SHF’s who borrow. For chart 2, base comprises SHF’s accessing mobile money by borrowing channel used. For chart 3, base comprises SHF’s income by borrowing channel used. ** 1 USD= TZS (as of December 8, 2017)

Research Question 5: What challenges are there in access to financial and information services? Finding 2: There is considerable uptake of using mobile money for savings amongst SHF. Most do however save by keeping their cash at home, through a family or a friend, or through money guards although between 53-64% of these SHFs do have access to mobile money. % of SHFs accessing mobile money by financial service used for saving Base: For chart 1, base comprises SHF’s who save. For chart 2, base comprises SHF’s accessing mobile money by saving channel used.

Research Question 5: What challenges are there in access to financial and information services? Finding 2 – continued: SHFs prefer saving cash at home or through savings groups albeit the uptake of mobile money. Mobile money savings products have however build more traction in the overall population. For Smallholder population : N = [1562]

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 6: What is the current level of uptake and use of digital financial services? Research Question 7: What are the challenges in accessing and using digital services?

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 6: What is the current level of uptake and use of digital financial services? Access to Technology: Most respondents have access to a mobile phone however only 56% do actually own one. More than 1 in 10 SHFs has access to the internet (15%) Base: SHFs accessing technology.

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 6: What is the current level of uptake and use of digital financial services? Mobile money uptake and usage: There is a gap of roughly 21% of SHFs who live within 5KM of a financial access point but still do not take-up mobile money. % that lives within 5KM of a financial access point % that accesses mobile money services % that has a registered mobile money account % of active mobile money users % of respondents who borrow using mobile money SHFs Overall population

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 6: What is the current level of uptake and use of digital financial services? Usage of Mobile Money: Mobile money is the most commonly used financial channel. Its usage is most common among SHFs in the age group of 35 to 54 years. Mobile money uptake is observed to increase with higher education. Base: For both charts, base comprises SHFs who use mobile money.

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 6: What is the current level of uptake and use of digital financial services? Usage of Mobile Money: Activity levels do not vary much across gender, education, or age. Active users do however have considerably higher incomes. Base: SHFs only ** 1 USD= TZS (as of December 8, 2017)

Smallholder farmer sample (3,384)
3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 6: What is the current level of uptake and use of digital financial services? 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) Proof of identification 3) Literacy (can read and write Kiswahili) 4) Numeracy (can solve algebra) 5) Proximity to a financial access point 6) Attitudes towards digital Smallholder farmer sample (3,384) **Ward’s Linkage clustering is commonly used to define cluster of quantitative variables. For each cluster, the sum of squares is calculated and the two clusters with the smallest increase in the overall sum of squares within cluster distances are combined. As a result, the Ward’s Linkage methods yields clusters that include observations that are ‘most alike’. See also The book “Marketing Research: An Applied Orientation, 6/E By Malhotra Naresh K.” Page number The book can be found here *See the Annex of this presentation for more detail on the scoring methodology.

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 6: What is the current level of uptake and use of digital financial services? Mobile Money Readiness Index (continued): SHFs with low index values: Which interventions could support SHFs to move from a ‘low readiness index’ to accessing and using mobile money? SHFs with high index values that do not take-up mobile money: What are the barriers that prevent SHFs from using mobile money where proximity and technology should not be an issue? Percentage of SHFs accessing mobile money by mobile money readiness index Most SHFs with a low readiness index have low mobile money uptake. About one third of SHF with a high readiness index do not take-up mobile money. Base: All SHFs

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 6: What is the current level of uptake and use of digital financial services? Mobile Money Readiness Index – focus on SHFs with high index values: We have further drilled-down on the group of SHFs with high index values and compared income, savings and borrowings for those who do and do not access mobile money. Base: SHFs with high Mobile Money Readiness Index values

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 6: What is the current level of uptake and use of digital financial services? Mobile Money Readiness Index (continued): With a weaker economic profile comes lower phone ownership. This seems to be a driving factor in mobile money uptake since 49% of SHFs with high index values that do not access mobile money do not own a mobile phone. Base: SHFs with high Mobile Money Readiness Index values

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 6: What is the current level of uptake and use of digital financial services? Research Question 7: What are the challenges in accessing and using digital services?

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 7: What are the challenges in accessing and using digital services? Challenges faced by SHFs in using Digital Financial Services: The analysis looks at 1) challenges of individuals that are accessing mobile money; 2) individuals who have access to MM but are inactive users; and 3) individuals who have access but do not have a registered account in their name. 1) Individuals with mobile money uptake (51% of SHF sample) What do you dislike most about mobile money services? 2) Inactive mobile money users (~25% of those accessing MM) Why are you not using mobile money more often? Base: Chart 1 represents SHFs most disliked reason for mobile money services. N = [1628] Chart 2 represents SHFs reason for not using mobile money. N = [442]

3.3 Digital financial services: readiness, uptake, usage and challenges Research Question 7: What are the challenges in accessing and using digital services? 3) Individuals who do not have a registered account (~20% of those accessing MM) What is the main reason for not having a registered account? Base: SHFs main reason for not having registered account N = [315]

3.4 Information gaps Research Question 8: How do farmers currently uptake information on agriculture and finance? Research Question 9: What are the potential information gaps?

3.4 Information gaps Research Question 8: How do farmers currently access information on agriculture and finance? What is the main reason you belong to a savings group? Due to how the question is phrased it is not possible to understand how many SHFs are accessing financial advice or other information from SGs. Only for some SHFs this is the main reason. Do you sometimes ask somebody for advice regarding money matters? While 73% of SHFs do seek advice on money matters, nearly all of them 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) Base: For chart 1, base comprises SHFs who save from different savings groups. N = [555] For chart 2, the base comprises SHFs who take advice from others for money matters. N = [2413]. For chart 3 N = [3391]

3.4 Information gaps Research Question 8: How do farmers currently access information on agriculture and finance? Research Question 9: What are the potential information gaps?

3.4 Information gaps Research Question 9: What are the potential information gaps? Note that there is little information on advisory services or trainings in the FinScope We focus here on what we can infer from our analysis regarding information gaps. Most SHFs ask household members for financial advice: Only 1% refer to banks, Microfinance institutions, Savings and credit cooperatives, Financial advisors or other. Mobile money usage remains low albeit high penetration rates: Only few SHFs accessing mobile money use loan products. Most SHFs use mobile money less than once a month. This could be either due to barriers of usage such as transaction costs, relevance of the financial services currently offered, or due to lack of information and understanding of available services 43% of inactive MM users say they don’t need the service: Here it should be investigated in how far this relates to barriers of usage or the relevance of the current product portfolio. Information might play a role here too. Most SHFs do not have a good understanding of insurance products Mobile money readiness index: There is a subset of SHFs with high index values that does not access mobile money. Here information gaps or relevance of the financial products might be issues and should be further investigated. Understanding mobile money: 23% of SHFs that do not access mobile money believe that a (smart) phone is necessary to use the service.

3.5 Growth and Resilience Research Question 10: What are the opportunities for increasing SHFs income through provision of information? Research Question 11: Segmentation of SHFs by commercialisation index Research Question 12: What do farmers perceive as the largest threat or constraint to their livelihoods? How do unexpected events affect income and what are the coping strategies?

3.5 Growth and resilience Research Question 10: What are the opportunities for increasing SHFs income through provision of information? Financial uptake and Monthly Income: Respondents that access finance have higher monthly incomes (median) compared to respondents that do not access finance. Respondents having access to banks have the highest monthly incomes. ** 1 USD= TZS (as of December 8, 2017)

3.5 Growth and resilience Research Question 10: What are the opportunities for increasing SHFs income through provision of information? Demographic Characteristics and Monthly Income: Monthly median income varies with location, gender, education, age and marital status. ** 1 USD= TZS (as of December 8, 2017)

3.5 Growth and resilience Research Question 10: What are the opportunities for increasing SHFs income through provision of information? Demographic Characteristics and Monthly Income (ctd.): In order to determine the impact of these factors on income, we next use regression analysis.

3.5 Growth and resilience Research Question 10: What are the opportunities for increasing SHFs income through provision of information? Hypothesis to be tested: Monthly income is determined by the SHFs’ demographics and financial access Potential model to test the hypothesis: Monthly Household Income = f(Formal Financial service uptake, Gender, Marital Status, Education, Age, Location (Urban / Rural)) Results: Financial uptake Factors: Respondents that take-up formal financial services are likely to have higher monthly incomes. Impact of uptake of formal financial services on income is relatively strong at the overall level.* Demographic Factors: 1. Relative to male respondents, females are likely to have lower monthly incomes 2. Married respondents at the overall level are more likely to have higher incomes. This does not hold true in case of SHFs 3. In case of SHFs, education and age do not have a significant impact on monthly income. 4. As expected, relative to rural areas, households in urban areas are likely to have higher monthly incomes. * While Formal Financial uptake has an impact on Monthly Income, the latter also has an impact on a household’s Formal Financial uptake. To account for this reverse causality, we modelled a system of simultaneous equations as shown in Annex.

3.5 Growth and resilience Research Question 11: Segmentation of SHFs by commercialisation index Agricultural Commercialisation Index Jaleta et al (2009) define a smallholder household as more commercialised if: “[…] it is producing a significant amount of cash commodities, allocating a proportion of its resources to marketable commodities, or selling a considerable proportion of its agricultural outputs.” We have defined SHFs as earning most of their income from selling their agricultural produce. This encompasses a diverse group of smallholders of which some have more commercialized farms than others. We define an index of commercialisation defined by the following identifiers:* Land size SHF’s focus on cash crops; labour employed on the farm (e.g. using family labour versus hiring additional labour); and position in the value chain (e.g. selling in the village versus selling to wholesalers). Smallholder farmer sample (3,384) * See the Annex of this presentation for a more detailed methodology.

3.5 Growth and resilience Research Question 11: Segmentation of SHFs by commercialisation index Less Commercialised SHFs Sample size: 2,515 or 74% More Commercialised SHFs Sample size: 869 or 26% % that employ additional labor on the farm (outside of the family) % that produces cash crops Average and median monthly income Base: For SHFs more and less commercialisation ** 1 USD= TZS (as of December 8, 2017)

3.5 More commercialised: 51% > 4 acres
Growth and resilience Research Question 11: Segmentation of SHFs by commercialisation index Agricultural Practices: Land Size in Acres More commercialised SHFs use more land compared to less commercialised SHFs and employ additional labor on the farm (non-family). More commercialised: 51% > 4 acres Less commercialized: 29% > 4 acres Base: For SHFs more and less commercialisation.

3.5 Growth and resilience Research Question 11: Segmentation of SHFs by commercialisation index Financial Inclusion of More and Less Commercialised SHFs Less commercial SHFs have lower levels of financial inclusion: uptake of formal banking and formal non-banking services is significantly lower for less commercialized SHFs. Base: All SHFs

3.5 Growth and resilience Research Question 11: Segmentation of SHFs by commercialisation index Uptake of Informal Services More commercialized SHFs access informal financial services, such as savings groups, more often. Borrowing and Saving Behaviour A higher proportion of more commercialised SHFs saves. Base: For chart 1, base comprises SHFs’ who access informal financial services. For chart 2, base comprises SHFs’ who save and borrow by commercialisation index

3.5 Growth and resilience Research Question 11: Segmentation of SHFs by commercialisation index Mobile Money Readiness: Most Small scale farmers and commercial SHFs are comparable in terms of mobile phone and ID ownership. Commercial SHFs are however the most remotely living sub-group with the lowest percentage of respondents living within 5KM of a financial access point. Base: For chart 1, base comprises SHFs who access mobile phones and own identification documents. For chart 2, base comprises SHFs’ who have taken-up financial services.

3.5 Growth and resilience Research Question 12: What do farmers perceive as the largest threat or constraint to their livelihoods? How do unexpected events affect income and what are the coping strategies? Major expected events, unexpected household events and unexpected agricultural events The analysis for this question will focus on expected and unexpected events and how SHFs cope when such events occur. 1) Major expected events Which are the most costly major events (by education-level of respondent)? Most respondents, irrespective of gender, education-level and location (rural/urban) perceive that children’s education is the most costly major event. We do find a significant positive relationship between the education-level of the respondent and children’s education being valued as most costly event. Base: SHFs’ perception of major expected events. N =[3391]

3.5 Growth and resilience Research Question 12: What do farmers perceive as the largest threat or constraint to their livelihoods? How do unexpected events affect income and what are the coping strategies? 1) Major Expected Events How do you cope when these events occur? SHFs in rural areas more often have to cut back on expenses, rely on family or friends for support or sell HH assets to finance expected events such as education or child birth. SHFs in urban areas more often make use of loans or savings. Major events are felt Base: SHFs’ who use coping strategies to overcome major expected events. N= [3340]

3.5 Growth and resilience Research Question 12: What do farmers perceive as the largest threat or constraint to their livelihoods? How do unexpected events affect income and what are the coping strategies? 2) Unexpected Household Events (such as sudden medical expenses) More often SHFs in rural areas struggle with paying for unexpected events than SHFs in urban areas. No significant differences between males and females were found. The analysis showed that older SHFs struggle significantly more with paying for unexpected expenses than the young do. How often do you struggle with paying unexpected events by location (rural/urban)? How often do you struggle with paying unexpected events by location (gender)? Percentage of SHFs that struggle very often with paying unexpected expenses Base: For chart 1, base comprises SHFs’ who struggle with unexpected events. For chart 2, base comprises SHFs’ who struggle very often to pay unexpected expenses. For all charts, N = [3391]

3.5 Growth and resilience Research Question 12: What do farmers perceive as the largest threat or constraint to their livelihoods? How do unexpected events affect income and what are the coping strategies? 2) Unexpected Household Events (such as sudden medical expenses) SHFs in urban areas more often use savings or loans than those in rural areas. Education-level and coping mechanisms strongly correlate. SHFs with primary education or no schooling more often need to borrow from friends or sell livestock or crops. Only SHFs with tertiary education use insurance. Coping mechanisms for unexpected household events by education-level of respondent* * Note that most SHFs take-up health insurance only. No crop-insurance or other agriculture-related insurance has been registered. N = [3391]

3.5 Growth and resilience Research Question 12: What do farmers perceive as the largest threat or constraint to their livelihoods? How do unexpected events affect income and what are the coping strategies? 2) Unexpected Agricultural Events About half of all SHFs experience unexpected agricultural events. For 75% 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 use cash savings. Coping mechanisms for SHFs that experience crop failure Percentage of SHFs that suffer from agricultural shocks For chart 1, Harvest/ crop failure, N = [3388] Loss of income as result of drop in price, N = [3391] For chart 1, base comprises SHFs’ who experience unexpected agricultural events. For chart 2, base comprises SHFs’ who experience crop failures. N = [1489]

4 Discussion points (1) Input finance:
Albeit larger penetration of mobile money, only very few HHs use formal financial services to finance farming inputs. Neither informal methods nor cash savings are used. Instead, SHFs mostly liquidate savings in the form of crops or livestock to finance seeds or fertilizers. This bears an opportunity to innovate products to finance agricultural inputs – especially for those SHFs that already use mobile money. Location has a high impact on mobile money uptake of the SHF population: Living in urban areas* strongly predicts accessing mobile money services. This does not only hold for the average population but also for SHFs. This is in line with the idea that mobile money eco-systems roll out from urban areas – spilling over to rural areas as they gain more traction in the population. This should be taken into account when designing interventions and defining beneficiaries. Mobile money usage remains low albeit penetration: Most SHFs use mobile money less than once a month. No SHFs are transacting daily using mobile money. Most SHFs that use mobile money still prefer to save and borrow using informal financial services, such as savings groups, or family and friends. Current usage seems light touch for most SHFs accessing mobile money. Barriers to deepening mobile money should be further explored. *Note that urban areas includes provincial capitals and hubs with “urban settings” that supply water, electricity, have well connected roads and other infrastructure that allows for an active economic atmosphere.

4 Discussion points (2) Income is a potential barrier to deepening mobile money usage: Mobile money activity does not seem to be affected by gender, age, or education. Median and average income are however considerably higher for active mobile money users compared to inactive mobile money users. SHFs that access mobile money and have lower incomes are more likely to borrow or save through family or friends than to use mobile money. Both points raise questions around the costs of using mobile money more frequently and whether having lower incomes is a barrier to using certain mobile money services. Insurance products do not provide resilience for struggling SHFs: 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 HH do therefore not benefit from insurance cover to provide resilience. Most SHFs take-up health insurance. No crop-insurance or other agriculture-related insurance has been registered. Mobile money readiness parameters predict mobile money uptake well: About 55% of SHFs score highly on mobile money readiness. Here an intervention should focus on deepening usage. 45% do not score highly – for most of these SHFs mobile money uptake is low. Readiness parameters, such as proximity, financial literacy, and mobile phone ownership play a role and should be addressed to include these SHFs.

4 Research Outlook (1) Financial behaviour of SHFs
Cross-frequencies of using different financial services: Additional analysis could shed light on the portfolio of financial services used by SHFs. The analysis could explore cross-frequencies of financial product use cases, for example how many of those SHFs who save at home do also use mobile money? Are there combinations of use cases that suggest that certain products are complementary? Do SHFs who are financially included use a portfolio of services or do they focus on one specific service to cater their financial needs? Cross-frequencies of savings and borrowing behaviour: Our analysis has shown that savings and borrowing behaviour is mixed and there are large groups of SHFs that either only save or only borrow while only about 25% engage both in savings and borrowings. Profiling these groups could help to understand whether the farmers who save and / or borrow are similar or whether borrowers are struggling farmers who cannot save and are those who save and borrow more financially evolved and a different portfolio of financial services.

4 Research Outlook (2) Income-generation of SHFs
How does off-farm labour complement SHF’s overall HH income? Additional analysis could further look into the importance of off-farm labour for SHF households. Alternative data sources, such as CGAP’s Financial Diaries, could be used to produce detailed analysis of how off-farm labour complements agricultural income through-out the year. Segmenting SHFs by urban and rural could show differences between reliance on off-farm labour which might have implications on financial needs. What are the specific financial needs of “commercialising SHFs” (or lead farmers)? Our analysis has made a start on segmenting SHFs by their commercial activity defined by focus on cash crops, labour employed on the farm and position in the value chain. We are finding that more commercialised SHFs have higher incomes and are more likely to be financially included. Further analysis could drill-down on the financial needs of these more commercialised farmers as they are a specifically interesting group for FSPs. Cross-frequencies of value chains: Additional analysis could shed light on how SHFs combine different agricultural activities, different crops and livestock, and show which combinations have shown most successful in terms of revenue. The complement the analysis, other data, such as financial diaries, and additional qualitative research should be implemented.

4 Research Outlook (3) Mobile money preparedness, uptake and usage
Income as barrier to Mobile Money usage: Our analysis is pointing in the direction that income is a main driver for deepening mobile money usage – specifically when comparing active and in-active mobile money users. Further analysis could further drill-down on the costs of using mobile money more frequently and whether specific services are either nor affordable for poorer SHFs or are not serving their financial needs. Events and resilience of SHFs How do SHFs cope with expected and unexpected events? Further analysis could be done to look at which financial services are used to cope with expected and unexpected events. Our analysis has shown a clear gap in financing unexpected events. Using other data, such as CGAP’s financial diaries, could allow for a more detailed analysis of the types and impact of unexpected events on SHF’s lives to estimate the financing gap that financial products could offer to close.

Segmentation of FinScope 2017 data 3 Research findings 3.1: Profile of Smallholder Farmers 3.2: Financial Inclusion: uptake, usage and challenges 3.3: Digital financial services: readiness, uptake, usage and challenges 3.4: Information gaps 3.5: Growth and resilience 4 Discussion points 5 Annex

Annex 5

5.1 Additional findings for Research Question 1
Purchasing and Sale Assessment: In terms of financial inputs, most SHFs rely on sale of their crops to obtain funding for their farming activities. For manpower needs, 22 % SHFs employ non-family members to assist in farming activities. In terms of the sale of output, retailers are the most common buyers for SHFs to sell their produce followed by neighbours. Only 1 % SHFs report to be selling their output to the government. Base: For all charts, base comprises SHFs who engage in farming activities.

5.2 Additional findings for Research Question 4
Activeness in Using Bank Services: 63% SHFs report to be using bank services actively, in contrast to 37% who report to being inactive bank users. Relative to females, male SHFs report to being more active in using bank services. As expected, SHFs in urban areas are relatively more active bank users compared to SHFs in rural areas. Base for chart 2: Bank using SHFs

5.3 Additional analysis on segmentation of FinScope 2017 data for Research Question 11 Main Income Source: Commercial smallholder farmers mainly earn their income from agricultural, i.e. from selling their crops or livestock produce. Small scale farmers earn some of their income from agriculture but consume most of their harvest and complement household income with casual labour.

5.3 Additional analysis on segmentation of FinScope 2017 data for Research Question 11 Financial Inclusion: Commercial SHFs access and use financial services the least often amongst the three groups. They do, however, engage in savings as often as diversifying households do and are the group that is most likely to take out a loan.

5.3 Additional analysis on insurance products
Insurance policy types: Most SHFs that access insurance use health insurance. No agricultural insurance, such as crop insurance, has been registered. Base: SHF’s who access various insurance policies. N = [3391]

5.3 Additional analysis on segmentation of FinScope 2017 data for Research Question 11 Agricultural Commercialisation Index Each household is scored according to the following criteria: Commercialisation scores Strong Medium Weak 2 points 1 point 0 points Land size High low Type of value chain (C17.1) [17.1.4] Cash Crops [other categories] cattle, goats, pigs, other livestock, fruit, vegetables, fishing or aquaculture, beekeeping [17.1.5] Food crops D6_3: Who do you mainly sell your goods to? Wholesaler Government, Co-operative, processor, other retailers, middlemen Neighbors/public, other How many people other than yourself do you employ on your farm? C20_2: Do you employ others who are not family members to work for you? Yes - No

5.3 Additional analysis on segmentation of FinScope 2017 data for Research Question 11 Agricultural Commercialisation Index (continued) The sum of scores equals the commercialisation index. Each household gets an index value assigned which allows to rank HHs according to the index: Next we apply Ward’s Clustering method* to segment the sample in more commercialised and less commercialised SHFs: Smallholder farmer sample (3,384) * Please see the Annex of this presentation for more detail on Ward’s Clustering method

5.4 Additional analysis for Research Question 6
Mobile Money Readiness Index: We define a mobile money readiness index by scoring each SHF household along the following variables: Mobile money readiness scoring Strong Medium Weak 2 points 1 point 0 points 1) Mobile phone ownership - Yes No 2) Has necessary proof of identification Has proof of identification Does not have proof of identification 3) Literacy (can read and write Kiswahili) 4) Numeracy (can solve algebra) More than one question is answered correctly At least one question is answered correctly None of the questions are answered correctly 5) Is within 5KM of a financial access point 6) Has positive attitude towards digital 3 or 4 questions indicate POSITIVE attitudes* 2 questions indicate POSITIVE attitudes* 1 or 0 out of 4 questions indicate POSITIVE attitudes*

5.4 Additional analysis for Research Question 6 Mobile Money Readiness Index The sum of scores for each question equals the mobile money readiness index. Next, SHF households are ranked according to the index. Ward’s Linkage Clustering method* is applied to segment the sample in households with high and low index values: Smallholder farmer sample (3,384) * Please see the Annex of this presentation for more detail on Ward’s Clustering method

5.5 Regression analysis for Research Question 10
Hypothesis to be tested: Monthly income is determined by the SHFs’ demographic and financial access factors. In other words, Monthly Income of Respondents= f (Financial uptake, Gender, Marital Status, Education, Age, Location) Type of Model: While monthly income is determined by a respondent’s uptake of formal financial institutions, it can be said that formal financial uptake is also dependent on the respondent’s monthly income making it endogenous. Because of this endogeneity, ordinary least square regression generates biased estimates. Hence, we use the following 2SLS regression procedure. First stage : Probit of endogenous binary variable - Formal Financial uptake on other dependent variables and instrumental variables having an impact on formal financial uptake but not necessarily on monthly income. Formal financial uptake = f( Gender, Marital status, Education, Age, Location, Owning a Mobile Phone, Having Registration Documents and Proximity to a Mobile Money Agent) Second stage : Instrumental variable regression of monthly income on all dependent variables, replacing formal financial uptake with the values predicted in stage 1 Monthly Income = f { Gender, Marital status, Education, Age, Location, ( Formal financial uptake = Predicted values from Stage 1) Financial uptake Factor Demographic Factors

5.5 Regression analysis for Research Question 10 Regression results:

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AgriFin Accelerate Program Data Analytics: FinScope 2017

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