The Economics Of Agri-SME Lending In East Africa - Agrilinks
The Economics of Agri-SMELending in East AfricaIn partnership with:FINAL REPORTDECEMBER 2018
Executive summary: Challenges and knowledge gaps in Agri-SMEfinance Small and Medium Enterprises (SMEs) are important generators of employment and GDP in emerging economies, but chroniclack of access to credit limits their growth and impact. Despite employing 50-80% of the workforce, less than half of the EastAfrican SMEs in most countries and size segments have access to formal bank finance. Lenders often find it difficult to assessthe bankability of SMEs, given their less-formal business practices and small size. Local commercial banks serve largerenterprises, and microfinance models have emerged to address “micro” and small SMEs, but mainstream models to addressthe needs of the “missing middle” – which in various sectors and economies may be from 20K or 100K up to 500K or 12m in borrowing needs – have not yet emerged. Agri-SMEs in East Africa face an acute need for finance tailored to their specific requirements. While agriculture contributes to25-30% of the GDP in the countries covered in this report (Kenya, Rwanda, Tanzania, Uganda, and Zambia), it receives only 27% of total bank credit. This is similar to the situation across Africa, where a recent Dalberg-KfW report estimated that thereis an annual 180Bn agri-SME lending gap, of which 65Bn is for medium-sized value chain businesses1 with revenue of 200k - 15m. Lenders find financing agricultural SMEs especially difficult due to external risks (such as price volatility,climate change, and government regulations), business risks (such as management capacity and inadequate financial records),product mis-alignment (caused by the seasonal nature of cashflows and lack of favoured types of collateral), and the expenseinvolved in serving businesses in rural locations. There is limited evidence available on the economics of financing SMEs – especially agricultural SMEs - making it difficult toidentify where market interventions are required and how they should be calibrated to incentivize increased lending withoutdistorting markets. Quantitative assessments of lenders’ and investors’ financial performance are challenging to conductbecause of data security and competition concerns, plus the complexity of standardizing and analysing the data. Absentinformation on financial institutions’ profitability, operating costs, and credit losses, calibrating effective support packages canbe a guessing game. This data gap is problematic, as development actors have prioritised blended finance as a tool for catalysing private investmentin developing countries and could likely mobilize significant amounts of funding to close the agri-SME finance gap if it could beproperly targeted. The count of blended finance deals has grown from 35 in 2005 to at least 314 in 2017, representing 100Bn in funding mobilized to date. With initiatives such as the EU-funded AgriFi blended finance facility and the USgovernment’s new International Development Finance Corporation, the use of public and philanthropic resources to mobiliseinvestment in emerging market businesses seems likely only to grow.1) “Value-chain business” means aggregators, traders, processors, and other non-producers.Sources: “The State of Blended Finance 2018,” Convergence; “Africa Agricultural Finance Market Landscape, 2018, Dalberg and KfW.2
Executive summary: The market structure of agricultural lending inEast Africa We have sought to close this information gap with two reports. A globally-focused report looking at social lenders (“Phase 1”),supported by USAID1 and in collaboration with CSAF2, demonstrated that social lenders have pioneered previously-overlookedagri-SME markets but faced significant economic challenges along the way. Focusing on 9 social lenders, the study showed thatloans to the “missing middle” of the SME market (defined there as financing needs between 50k and 1M) wereunprofitable in many cases for these lenders – especially in the early years of their operations in a given market. This follow-up (“Phase 2”) seeks to analyse East African lending in greater depth to understand the variety of operating modelsand lending economics seen in a given region. To do this, we reached out to 29 lenders of various types across Kenya,Rwanda, Tanzania, Uganda, and Zambia, ultimately gathering quantitative data on lending economics or qualitative data onchallenges and support needs from 17 local lenders and 2 additional global social lenders. The banks engaged represent anestimated 36% of bank agri-lending across Kenya, Tanzania, Uganda, and Zambia. We found three broad categories of actors currently serving financing needs of different agri-SME segments:1. Global social lenders, a group of impact-oriented actors that use capital from socially-minded investors to lend to agri-SMEsegments. These lenders tend to lend in hard currency to address financing needs in export oriented value chains andtypically target SMEs with borrowing needs over 200K. They often have substantial agricultural expertise, appropriatelending terms, and access to lower-cost, impact-focused capital, but have limited in-country presence to service loanscost-effectively.2. The agriculture, SME, or agri-SME business units of local deposit-taking banks. These business units typically provide arange of lending and other products to SMEs of various sizes, although the units in our study all focused on loans smallerthan 100K. Banks had varying levels of agricultural specialization; lenders with no agri-unit mostly considered only loansto producers as “agricultural loans” and served other types of agri-SMEs out of general SME or corporate units. Lenderswith a strategic agriculture focus typically showed a broader understanding of the sector and offered tailored products toagri-SMEs across the value chain.3. Other local non-banking financing institutions (NBFIs), a more diverse category of lenders with a local operational footprint(although international origin and funding base in all but one case in our sample) that are active in agriculture or SMEfinance. NBFIs in our study were generally smaller than banks or global social lenders, spanned the range of social andcommercial interests, and tended to focus on specific product offerings (e.g., asset leasing or short-term credit lines) or onspecific borrower segments (e.g., producer groups or certain value chains). They generally targeted borrowers with needsof between 10K and 100K, in rare cases lending up to 500K.(1) US Agency for International Development (2) Council on Smallholder Agricultural Finance3
Executive summary: Qualitative and quantitative findings Lenders providing data were able to lend below 100k and above 1.5M profitably. The units of local banks we examinedappeared to break even on loans of 40-50k and for loans of 100k earned modelled returns of 5-9%1 – although this cannot beextrapolated above 100k, and revenue estimates might be biased upwards by the small median loan size in our dataset 2. LocalNBFIs generally appeared to make a small loss on small and medium-sized loans, as high interest yields were offset by a high costof funds and sub-scale operating platforms dragged down efficiency. Global social lenders, which focused on 200k- 1M3 loansin our dataset, had a modelled breakeven of 1.2M, although some break even closer to 750k. However, the economics of commercial bank agri-loans 100k remain opaque. The bank BUs who were willing and able toprovide data only focused on sub- 100k loans, aimed at primary producers and producer organizations, and did not sharecorporate loan data. Local NBFIs also had 70% of loans falling in the 10k - 100k range. The limited quantitative sample ofNBFI loans above 100k were largely unprofitable after accounting for their high cost of funds. While we know there is bank agri-SME lending activity in the 100k segment outside our dataset, we believe it is insufficient tomeet demand and not always designed appropriately. First, a review of CSAF borrower records reveals that fully 63% ofborrowers in East Africa had no other source of finance when CSAF lenders began working with them. Second, interviews withsocial lender loan officers highlight a clear gap in bank activity in the 100k-500k segment specifically. Finally, we see a trendamong lenders without specific “agri-units” to accept a smaller range of collateral and to not offer specially-designed agriproducts. Overall, we infer from this information that bank lending to agri-SMEs requiring 100k is limited, heavilycollateralized, and not tailored to agri-SMEs’ seasonal cash flows and other needs. In aggregate, lenders reported a range of different challenges in terms of growing their agri-SME lending portfolio, overall leading toan inability to expand the frontiers of agri-SME finance and fully serve agri-SMEs with mid-range borrowing needs:– Market challenges include agriculture-specific risks such as price volatility and climate shocks; adverse government policiessuch as sudden export bans; and low borrower capacity, which makes building a bankable pipeline very expensive, especiallyfor small loans. These risks drive some lenders to tightly limit agri-exposure, and other lenders to focus only on a narrow setof value chains and markets they know well.– Strategic limitations varied by lender type, but included limited physical presence in rural areas for NBFIs, and a limiteddomestic presence for global social lenders, both of which drive up operating costs and make small borrowers difficult toserve. Banks had fewer cost challenges, but faced significant pressure to limit exposure, in the form of tight risk caps andlimited executive buy-in.– Capacity gaps included for some banks and NBFIs a lack of products with agri-specific terms and low ability to assesscreditworthiness in the sector, and for global social lenders limited comfort outside well-known VCs like coffee and cocoa.(1) A range is provided as not all banks were able to estimate operating costs with certainty. (2) The average revenue yield, including fees, was 22% for banks, but on amedian loan size of 30-35k; loans closer to 100k may thus have lower yields on a percentage basis. (3) Interquartile range was c. 180K to 850K for social lenders.4
Executive summary: Takeaways and next steps Overall, lenders showed a high degree of demand for new ways of supporting agri-SME lending. Interest in the study was highand a large number of lenders (9 in Phase 1 and 20 in Phase 21) participated in either a qualitative or quantitative formdespite receiving no tangible benefits other than a customized benchmarking report. Interviews revealed that in part this maybe because existing risk-sharing facilities are all similarly structured (i.e., 50% pari passu loan guarantees) and do not alwaysmeet lenders’ operational and risk management needs – so lenders welcomed the chance to share knowledge that mightbring new support mechanisms to market. A multi-faceted support model, targeted at lenders with a strategic commitment to the agriculture sector and tailored throughsenior executive engagement and light-touch calibration, may be the best way forward. When presented with a menu ofsupport options broader than the traditional partial risk-share, each option was ranked highly by at least one lender – which isnot surprising given the variety of financial and institutional challenges they face. Recommended interventions include:– Risk-sharing mechanisms that provide a first-loss cover rather than a partial pro rata share, to give lenders confidence thatthe full potential losses from entering new sectors will be covered.– Borrower capacity-building to increase the pipeline of bankable deals, thus reducing origination costs (a pain point forglobal social lenders especially) and reducing the perception of risk.– Low-cost capital, either as concessional debt to reduce the cost of funds (a major issue for local NBFIs) or as innovationgrants to help sub-scale lenders with potentially catalytic business models overcome the challenges of high operating costs.– Lender capacity-building and senior management engagement to help banks in particular tailor products to the agri-SMEmarket and overcome the perception of high risk that limits engagement. A different type of capacity-building could focuson exploring local shared service provision to reduce high costs associated with origination, due diligence2, monitoring loansand assessing collateral, and managing impaired loans. Finally, an iterative approach to support provision may be most effective at catalysing agri-lending for local banks. Despitemonths of engagement, data gaps still remain for local banks. However, while further quantitative analysis may help pin downexactly what type and degree of intervention is required to support a given type of lending, we believe the bigger obstacles toovercome are executive buy-in and agri-specific capability development. Rather than waiting for “perfect” data, we believe itis better to test and learn - piloting various forms of incentives and creating a "pull mechanism" for lenders to invest more inthe agri-SME market - in close collaboration with motivated lenders, adjusting as needed.(1) One Phase 1 lender has no East Africa activity, so the total dataset for this report is 28 lenders. (2) Keeping in mind that full outsourcing or sharing may not be possiblegiven the fiduciary responsibilities of the lenders.5
ContentsIntroductionKey findingsAnalysis of profitability driversAppendix6
In Phase 1, we found variations in the performance of social lenders’agri-SME portfolios, with location being a key determinant of successLoan economics averages for all CSAF loans analysed in Phase One 43k Findings from Phase 1 indicated that CSAFloans lost on average 18k per agri-SME loan(with the average loan size 665,000)- 24k- 21k1Transactionrevenue2- 2k However, further analysis of the datademonstrated that the performance of agriSME loans varied substantially by differentsegments; for example: 16k3Operating Credit losses Operatingcosts recoveryprofitcosts4Cost offunds- 18kNet profit– Excluding Sub-Saharan Africa, averageannualized net profit was only - 13k, whileloans in SSA lost over 35k on averageLoan economics averages for different segmentsRegionRest ofworldNet profit1(USD)SSALoan sizeTenor 500K 500k 1212months months - 6k- 8k- 13k- 35k Many CSAF members were relatively new tosub-Saharan Africa during the time periodanalysed (2010-2016), which may partiallyexplain the weaker performance of theirportfolios in the region (in addition to otherfactors described on the next page)- 25k- 47kPhase 1 demonstrated that agri-SME lending economics differs sharply by region and suggestedthat lenders’ operations may also play a significant role in performance(1) Net profit Interest Fees – credit losses – Operating costs – Recovery costs – Currency losses – Cost of FundsSource: “CSAF Financial Benchmarking: Final Learning Report,” Council on Smallholder Agriculture Finance and USAID7
In Phase 2, we expanded our focus to global and local lender types,while narrowing our geographic focus to East Africa In Phase 1, social lenders’ agri-SME loans performed below their global average in Sub-Saharan Africa by 25k per annum Loans in Sub-Saharan Africa faired poorly relative to the rest of the world on three of the four profitability drivers (cost offunds was assumed to be equal across regions)1123Profitability driverIncome: loan sizeIncome: currency lossOperating costsCredit lossesSSA performance compared to rest of world43% lower12% higher22% higher205% higher This phase (Phase 2) continues the pioneering work of estimating loan-level profitability, with a focus on local lenders in EastAfrica (plus the East Africa loans from CSAF members collected in Phase 1), while also seeking to understand themacroeconomic, strategic, and operational challenges lenders face. Key areas of investigation include: Sizes: What ticket sizes do local lenders cover, and does this fill the gap in the ‘missing middle’? Borrower characteristics: Which value chains and borrower types do various lenders focus on, and is there a gap? Types of financing: Are the specific financing needs of agri-SMEs well-served by local lenders?Loan products Terms: Are screening criteria, collateral requirements, and repayment terms of local lenders tailored toagribusiness needs? If not, how can this be improved?Loaneconomics Income, Cost to Serve, and Risk: Which economic factors make lending difficult for various lenders, and which agriSME segments are most affected by these factors? Does the variety of lender business models found in themarket allow all segments to be served effectively, or are there cross-cutting gaps due to lending economics? Blended finance instruments: Which lender and borrower support options could result in increased lending to agriSMEs in the ‘missing middle’?Loan segmentsSolutionsSource: “CSAF Financial Benchmarking: Final Learning Report,” Council on Smallholder Agriculture Finance and USAID8
180B in agricultural finance demand goes unmet in SSA annually;around 80B of this is for small/medium value chain businessesEst. annual gap in agricultural finance,Sub-Saharan Africa (USD, 2017) 240BSmall and MediumV.C. businesses 60B 180B 81B 99BAnnualfinancingdemandAnnual supplyAnnualfinancing gapProducers Dalberg’s analysis, supported by KfW,expanded the scope of previous studies toassess financing gaps across a greaterrange of the market in Sub-Saharan Africa– Prior research focussed on smallholderfinance in three regions (Sub-SaharanAfrica, Latin America, and South &Southeast Asia) excluded agribusinesses andemerging commercial farmers. This research has estimated a gap inagriculture financing of 180B annually inSub-Saharan Africa. 81B81%– Working capital needs represented 66% ofthe shortfall for VC businessesValue chainbusinesses– In addition to the 81B gap for small andmedium VC businesses, the types of smalland medium farmers (above subsistencelevel) supplying these agri-SME borrowersalso face an estimated gap of 25B per yearNote: “Small” and “Medium” value chain businesses are most similar to the target financing market of this report“Small” enterprises in value-chain businesses (i.e. traders, processors, and other non-producers) were defined by financing needs of 10k- 100k andhad a gap of 15B; “Medium” enterprises by financing needs of 250k- 5m and revenues of 200k - 15m, and had a 66B gap.Note: This analysis excludes the financing needs of large-scale agribusinessesSource: Dalberg and KFW, “Africa Agricultural Finance Market Landscape”9
In East Africa, agriculture and SMEs make vital contributions to theeconomy and are a major source of employmentRole of agriculture in selectEast African countries - 201732% 38%31%69%67%67%30%The effect of agricultural SMEs on low incomeworkers is likely even larger than suggested by theirGDP and employment contributions53%25%7%KenyaRwandaContribution to GDPTanzaniaUgandaZambiaPercent workforceRole of SMEs in selectEast African countries1 - 201788%80%55%45%70%70%27%KenyaRwandaContribution to GDPTanzania33%45%UgandaPercent workforceZambia Because three-quarters of the developingworld lives in rural areas, agricultural growthcan lead to a four fold-reduction in poverty,according to some studies SMEs are not only a source of existing jobs butare more likely to create jobs – 75% of all newjobs were created by SMEs in a sample of 85countries with net job creation Members of low-income households are morelikely to obtain employment from SMEs thanfrom large enterprises because SMEs generallyhave lower skill requirements and are morelabour intensive Agri-SMEs, in particular, can play an outsizedrole in poverty reduction by frequently servingas a source of off-farm labour in poor ruralareas and helping smallholder farmers to obtainmodern inputs and find markets for theirproduce(1) includes all funding regardless of SME sectorSource: African Review, “SMEs are Growing Kenya’s Economy”; AGRA, “African Agriculture Status Report: 2017”; “Tanzania Small and Medium Enterprises,”(https://tanzaniainvest.com/sme); The World Bank, World Development Indicators (https://data.worldbank.org/indicator); The World Bank, “Small vs. Young Firms across theWorld”; The World Bank, “World Development Report 2008”; Uganda Investment Authority, y/); “Zambia to SetEntrepreneurial Fund for SMEs,” al-scheme-smes)10
SMEs in East Africa report facing major constraints in access toadequate financing Percent of firms identifying access to financeas a major constraint37%15% 20%Kenya31%RwandaSmall (5-19 employees)43% 47%19% 15%TanzaniaUganda30%24%Zambia30%Kenya– Policies, laws, and support functions: Contractsare difficult to enforce and little creditinformation is available53%39%30%26%RwandaSmall (5-19 employees)– Demand: SMEs are often informal, poorlymanaged, operate in risky environments, andlack access to collateral– Supply: Financial sectors in developing countriesare small and banks have limited SME oragriculture experienceMedium (20-99 employees)Percent of firms with a bank loan/line of credit42% Globally, constraints exist across SME financingecosystems, such as:13%9% 9%TanzaniaUgandaMedium (20-99 employees)24%Zambia Strict collateral requirements for all SMEssurveyed also prevented them from accessingthe required finance – while collateralrequirements were not correlated exactly withaccess, SMEs in some countries in East Africareported requirements in excess of 200%, witha country average of 216%.Note: Staff sizes for small and medium enterprises based on World Bank classifications; agri-SMEs are often on thesmaller end of the spectrum if measured by employeesSource: Enterprise Surveys (http://www.enterprisesurveys.org), “The Elephant in the Room,” Innovations; The World Bank; Kenya BankersAssociation, “Realisation of Full Potential of the Agriculture Sector”11
. which are typically even more pronounced for agri-SMEs, givencommercial banks’ low share of lending to agricultureAgriculture’s economic role vs. share of bank lending (2017)67%67%69%53%Agri-SMEs face major constraints as lendersfind serving agricultural SMEs even moredifficult than SMEs in other sectors, due tosector-specific factors that acutely impactagribusinesses, including: Unpredictable external risk factors such asweather shocks and crop disease38%32%31%4%Kenya30%2%RwandaContribution to GDPPercent workforce% of country bank credit25%7%Tanzania20%5%Uganda7%Zambia High cost to serve in low population densityareas with poor infrastructure Irregular cash flow cycles due to cropseasonality Low understanding of agricultural enterprisesand risks Weak enabling environment with inadequateinstitutional coverage of property rightsLittle research has been conducted to assess theeconomics of lending to agri-SMEs prior to thePhase 1 CSAF financial benchmarking reportand this follow-up reportThis study builds on the prior analysis to determine economics of lending to agri-SMEs in EastAfrica by surveying a spectrum of local and global lendersCEIC (https://www.ceicdata.com/en/Zambia) Kenya Bankers Association, “Realisation of Full Potential of the Agriculture Sector”; The World Bank;Country central bank reports; USAID, “Lending to the Agriculture Sector”’ World Development Indicators (https://data.worldbank.org/indicator)12
ContentsIntroductionKey findingsMarket structureLoan characteristics and profitability based on our data setPotential lending gapsLender needs and implicationsAnalysis of profitability driversAppendix13
Three types of lenders with distinct characteristics emerged from ourlandscape of East African agri-SME lendingGlobal social lendersLenderoverviewLocal banks Internationally-based lendersthat are impact-oriented Locally-based commercial,deposit-taking lenders Our dataset consists of: Our dataset consists of:– Council of Smallholder AgricultureFinancing (CSAF) members (10)– Other global social lender (1)– Tier-1 large-sized banks (2)– Tier-2 mid-sized banks (1)– Small-sized banks (1)Local non-banking financialinstitutions (NBFIs) Locally-based lenders that arenot deposit-taking (often withinternational parent / affiliate /investors); most in operatio lessthan five years Our dataset consists of:– Commercial lenders (2)– Impact-oriented lenders (2)– Development finance institution (1) Typically loan origination andmonitoring in country with backField presenceoffice operations in Europe orNorth AmericaProducttypes2Borrowertypes2 Large local operations through abranch network system Scale of domestic operations isin between that of social lendersand local banks Asset finance: 18% Asset finance1: 38% Asset finance: 38% Working capital 82% Working capital1: 62% Working capital: 62% Mostly short term ( 9 to 18months) Mostly medium to long term( 12 to 36 months) Mostly long term( 24 and 36 months) Almost three-quarters primaryproduction (e.g., aggregatedsmallholders) and processors More commonly in tight valuechains Banks do not typically gather data Evenly split between primaryon borrower type or role in theproduction and processing, withVC, leading to information gapsless than a tenth trading Predominantly loose value chains More commonly loose valuechainsNote: definitions of all categorisations (product and borrower types) can be found in the Appendix(1) Data is for local banks’ agri or SME units; corporate business units may offer other products and are not included in our data set (2) Figures are based onthe average of the totals for each lender in the lender type, not an average of all the loans across lenders in the lender typeSource: Lender 2017 Annual Reports, Lender interviews and survey responses; Dalberg analysis14
Quantitative participantsQuantitative analysis in this report is based on data provided by 20institutions; it covered only a portion of local banks’ agri-portfoliosGlobal social lendersLocal bankLocal NBFIsTotal of 11 lenders includes all agriloans made by the organisationTotal of 4 lenders includes loansclassified as agri-SME by banks’internal classificationsTotal of 5 lenders includes all agriloans made by the organisationTier-1 East Africanbank (anonymous)Data shared did not includecorporate loans or SME loans notclassified as ‘agri’ by banksQualitativeparticipantsTotal of 7 interviewsTotal of 1 interviewTier-2 EastAfrican bank(anonymous)2Over the course of Phases 1 and 2, we collected data on 3,959 loans and a loan volume of 2.7Bglobally; in East Africa, we collected data on 876 loans and a loan volume of 327MNote: (1) Figures for banks’ overall and agricultural loans and advances were calculated based on financial statements, where possible; otherwise, figures were calculatedbased on numbers provided in interviews or based on analysis of data provided by the bank. Numbers for banks not engaged calculated through central bank numbers.15
The economics of bank lending above 100K remain unclear; we havedeveloped a better understanding of activity in other segmentsLevel of lending activity in East Africa and our understanding of the economics, by size and lender typeLoan sizeGlobal social lendersLocal banksNBFIsActivity: HighActivity: Suspected ModerateActivity: Not a focusEconomics: Good sampleEconomics: Not knownEconomics: N/AActivity: HighActivity: Suspected LowActivity: Suspected LowEconomics: Good sampleEconomics: Not knownEconomics: Limited SampleActivity: LowActivity: HighActivity: HighEconomics: Good sampleEconomics: Limited sampleEconomics: Good sample 500k 100-500k 100kSource: Lender 2017 Annual Reports, Lender interviews and survey responses; Dalberg analysis16
ContentsIntroductionKey findingsMarket structureLoan characteristics and profitability based on our data setPotential lending gapsLender needs and implicationsAnalysis of profitability driversAppendix17
Local banks shared their “Agri” portfolios, which were mainly small-ticket,non-corporate loans; NBFIs and global lenders shared full portfoliosDistribution of loan sizes by lenderLoan size (USD, log scale) 10k4.00 4.20 25k4.40Global 1Global 2Global 3Global 4Global 5Global 6Global 7Global 8Global 9Global 10Bank 1Bank 2Bank 3Bank 41NBFI 1NBFI 2NBFI 3NBFI 4NBFI 54.60 100k4.805.005.205.40 500k5.80 1M5.606.006.206.40 Local banks in our data setshared information on loans6.60 5M6.80with ticket sizes typically of 250k and below (though onebank extended loans of 5Mor above) Global social lenders and localNBFIs in our data set sharedthe full range of theirportfolios, which showedconcentration in the 250k 3M and 30k- 200k loan sizeranges, respectivelyNote: bank loans to agri-SMEs notclassified by the bank as “agri”were not included in our data set;it is likely that banks made loansat higher ticket sizes through theirSME or commercial units but didnot tag them as agriBold colours representinterquartile range of loansFurther research is required to understand the economics of larger agri-loans made by non-agrior SME units in local banks(1) Bank 4 provided estimates and averages on its lending portfolio, rather than a loan by loan breakdown. As a result, the interquartile range of loans is not availableNote: To preserve anonymity, the number used for each lender varies from page to pageSource: Lender 2017 Annual Reports, Lender interviews and survey responses; Dalberg analysis18
Based on the data shared, the lenders focused mainly on workingcapital loans to SMEs in primary production and processingPortfolio characteristicsProduct typeValue chain typeGlobal sociallenders41%(10 CSAFmembers; 1 nonCSAF member)LooseAsset FinanceTightWorking Capital18%82%59%38%Local banks (2TZ; 1 KE; 1 ZB)87%13%62%77%Loc
provide data only focused on sub- 100k loans, aimed at primary producers and producer organizations, and did not share corporate loan data. Local NBFIs also had 70% of loans falling in the 10k - 100k range. The limited quantitative sample of NBFI loans above 100k were largely unprofitable after accounting for their high cost of funds.
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