MSc thesis presented at Gondar University

2. MSc Thesis
Andualem Tadesse
October, 2021

3. Contents
• Introduction
• Problem statement
• Objectives
• Material and methods
• Result and discussion
• Summary and recommendation

4. 1. Introduction
• Sesame is produced in about 75 countries worldwide.
• The top sesame producing countries are China, India, Myanmar,
Sudan, Tanzania, Nigeria and Ethiopia (FAO, 2019).
• For Ethiopia, sesame is strategically relevant because it is
consistently rated as a top-performing export agricultural
commodity.
• The major sesame producing regions in Ethiopia are Tigray,
Amhara, Oromia and Benshangul Gumuz

5. Intro…
• Northwest Ethiopia is the area where more than 80% of the sesame is
produced.
• West Gondar zone sesame value chain contribute very significant proportion of
nationally produced sesame
• Studies indicated that sesame value chain actors especially farmers lack the
required support services from relevant institutions
• lack of and very limited availability of formal financial services is the most
challenging problems for farmers and other value chain actors

6. 1.1. Statement of the Problem
• Despite the country’s immense potential, both the production and marketing
system of sesame is full of challenges
• Operating the value chain from production through aggregation to transporting,
quality control and exporting requires capacity, wisdom and business
intelligence.
• The challenges are multi-facet related to: production, processing and marketing
• The challenges are very much interlinked, value chain finance being at the
center.

7. Statement of…
• Other forms of agricultural value chain financing instruments hardly exist in the chain.
• Whilst a shortage of cash is the most commonly heard challenge, finance issue lies
beyond this.
• There is only one MFI (ACSI)
• Other sources of finance in the sesame value chain are cooperatives/unions,
relatives/friends and the significant one are the informal money lenders.
• The formal financial institutions in the sesame subsector has stringent lending
conditions and therefore reluctant to provide credit services to stallholder farmers.

8. Statement of…
• These limitations left value chain actors especially farmers in the grip of informal
money lenders.
• Private Banks are reluctant to finance cooperative unions and primary cooperatives
• It affected the value chain efficiency and the benefit of different actors in the chain
and the country at large.
• Evidence to support such statements was not well identified and this study will try
to get more insights on the finance dynamics

9. 1.3. Objectives of the Study
• The main purpose of this study is to assess the sesame value
chain financial service challenges and leverage points for
improvement.
Specific objectives:
– To identify determinant factors for choosing different credit sources
– To assess characteristics of sesame value chain financing instruments
– To identify challenges and options for improving the sesame value
chain finance system.

10. 1.4. Research questions
• What determined sesame value chain actors’ selection of a
specific finance source?
• What were the different value chain finance instruments
available in sesame value chain?
• What were the characteristics of the available financial
instruments?
• What were the major challenges and opportunities in the
financial sector of the sesame value chain?

11. 1.5. Scope and Limitation of the Study
• The study was restricted to the assessment of sesame value chain financing. Hence,
the generalizations might not be possible for the whole agricultural system or value
chains in the area and other regions of the country.
• Though the informal credit source significantly contributes to the credit need of
value chain actors, it is not lawful. So it was very difficult to get detailed and
accurate information about informal money lenders.

12. 1.6. Significance of the study
• Determining those factors has a paramount importance for policy makers to
formulate new policies and revise strategies, which improve access to SVF services.
• Thus the study assumed to produce very important information on locally specific
factors related to social, cultural, economic, institutional factors on sesame value
chain financing.
• The information produced from this study is expected to be of some value for
financial institutions, value chain actors, decision makers and other organization
working in agricultural value chains and the finance sector in the study area.

13. 1.7. Conceptual Framework of the Study
• The theoretical basis of this thesis is based on the principle of rational
choice theory, which notes that entrepreneurs are rational and thus are
likely to select the sources of funding that will allow them to minimize
costs and optimize returns on their business's sources of financing.
• The conceptual structure work is built on the basis of the study of (Fufa,
2016).
• The credit sources are formal credit sources, semi-formal credit sources
and non-formal credit sources.
• Non-users of credit use own source of finance under which sesame value
chain actors cover the financing they need from their own pocket.

14. Conceptual…
Independent Variables
Financing options
Dependent variables
Creditors’ characteristics
Sex, Age, Family size
Religion
Land holdings
Educational status
Farm income
Social participation
Perception on the credit services
Financial sources characteristics
Loan acquisition procedure
Interest rate
Repayment period
Loan amount
Credit
Source
choice
Formal sources
Semi-formal Sources
Non-formal sources
Use own capital
Non-Credit
user
Credit
user
Source: Adapted from (Fufa, Fikadu Goshu, 2016)

15. 2. MATERIALS AND METHODS
2.1. Description of the Study Area
• The study area was lowland areas of Northwest Amhara region: (Metema woreda from West Gondar
and Tach Armaciho woreda from Central Gondar zone)
• These zones, with a land area of 30-35,000 km2 and limited population density (average of 25
persons/km2), account for more than 70% of the national sésame production (CSA, 2018).
• There is one MFI, one public (CBE) and around 10 private banks branches in the study area.
• There are more than 150 farmer cooperatives and three unions in Northwest lowlands of Amhara
region.
• Northwestern low land areas of Ethiopia are a plethora of informal money lenders and they are the
major source of credit to producers.

16. MATERIALS…
2.2. Research Design
– Cross-sectional survey design was used.
– Both quantitative and qualitative research method used.
2.3. Data types, Data Sources and Data Collection Methods
Data type: both quantitative and qualitative data were collected.
Data Sources: primary and secondary data sources. The primary data sources
were sesame value chain actors mainly producers, key informants, as well as
experts from proximity financial and related service providing institutions.
Secondary data were collected from institutions' documents , records and reports

17. MATERIALS…
Data collection method: interview schedule was the primary method for collecting
information.
• Key informant interview
• Focus Group Discussion
• In-depth interviews
• Some cases were identified with sesame producing farmers which show practical
experiences.
• The researchers personal observation also used as a reliable data collection method

18. MATERIALS…
2.4. Sampling Method
• A multi stage random sampling procedure used to select samples from the target population
• Two woredas from West Gondar zone and one woreda from Centeral Gondar zone were selected.
• Two kebeles per woreda, randomly selected
• 220 producers (238 selected but 220 completed the questionnaire)
• From the sampling frame respondents were selected proportionally according to the following
formula.

19. MATERIALS…
Sampling formula:
𝒏 =
𝑃 1 − 𝑃
𝐴2
𝑧2 +
𝑃 1 − 𝑃
𝑁
𝑅
– Where: n is required sample size;
N is the number of population;
P is the estimated variance in a population in terms of access to credit; as a decimal 0.2 for 80-20
A is the precision desired, expressed as decimal of 0.05 for 5 percent;
Z is the confidence level of 1.96 for 95 percent; and
R is the estimated response rate, as decimal of 0.95 for 95 percent response to be returned.
(Watson, 2001)

20. MATERIALS…
Woreda Keble Farm households
Metema Kokit 1,100
Kumer-Aftit 395
Tach Armaciho Kisha 656
Kokera 647
Total 2798
Table 1 Sampled woredas and kebeles
So, the sample size will be
𝒏 =
𝑃 1 − 𝑃
𝐴2
𝑧2 +
𝑃 1 − 𝑃
𝑁
𝑅
𝑛 =
0.2 1 − 0.2
0.052
1.962 +
0.2 1 − 0.2
2798
0.95
n= 237.89 (238 producers)

21. MATERIALS…
2.5. Data Analysis
• The qualitative data put into different categorical variables.
• Major themes were identified and analyzed in line with research objectives and
summarized for use in descriptive analysis.
• The complete interview schedules was scrutinized, verified, edited and arranged
serially for coding.
• STATA statistical software was used for analyzing the quantitative data.

22. MATERIALS…
Model specification
• Multivariate Probit regression model (MVP) was used to analyses the categorical variables
• Multinomial models are appropriate when individuals can choose only one outcome from
among the set of mutually exclusive, collectively exhaustive alternatives.
• However, in this study, participants credit source choice are not mutually exclusive
• MVP was adopted for this study to estimate several correlated binary outcomes jointly
because it simultaneously capture the influence of the set of explanatory variables on each
of the different credit source choices

23. MATERIALS…
Model specification…
• The household decision of whether or not to choose is considered under the general frame work
of utility or profit maximization (Djalalou et al., 2015).
• Sesame producers’ selection of credit sources depends on the amount of utility obtained from
alternative credit sources.
• The possible outcome of credit source choice can be modeled following random utility
formulation.
• A credit source which has a greater level of expected utility as compared to other credit source is
supposed to be chosen by the farmer (Masten & Saussier, 2000).

24. MATERIALS…
Model specification…
• Consider the ith farm households (i = 1, 2 … … N), facing a decision problem on whether or not to choose
available credit sources.
• Let V0 represent the utility expected to obtain by the farmer who chooses Kth credit source and Vk represents
the actual utility of farmer to choose the Kth credit source:
– where K denotes a choice of formal credit sources (Y1), semi-formal credit sources (Y2), and non-formal credit sources (Y3) of
credit sources.
• The farmer decides to choose the Kth credit source if Y*ik = V*ik–V0 > 0. The net benefit that the farmer derives
from choosing a credit source is a latent variable determined by observed explanatory variable (Xi) and the error
term which represent an observed utility (ei):

25. MATERIALS…
Model specification…
Y∗ik=BkXik+ei (K=Y1, Y2, Y3)
• Where, Bk is the vector of parameter. K represents a different level of utility from the
different credit sources (Yi). Using the indicator function, the unobserved preferences
translate into the observed binary outcome equation for each choice as follows:
Yik={(1 if yik*>0)/(0 otherwise)┤ (K=Y1, Y2, Y3)
• where Yi1 = 1, if farmers choose formal credit source (0 otherwise), Yi2 = 1, if farmers choose
semi-formal credit source (0 otherwise), and Yi3 = 1, if farmers choose non-formal credit
source (0 otherwise).

26. MATERIALS…
Model specification…
• The Variance Inflation Factor (VIF) was used to test the existence of Multi-Colinearity problem
among explanatory variables.
• VIF is computed as follow:
• VIF= 1/1-R2
• Where VIF = Variance Inflation Factor
• R2 = adjusted R square
• If R2 is the adjusted square of the multiple correlation coefficients that result when the explanatory
variable is regressed against all other
• As a rule of thumb, a value of VIF greater than 10 is assumed often as a signal for the existence of
multi-collinearity problem in the model (Gujarati, 2004).

27. MATERIALS…
Value chain mapping
• Mapping the value chain helps to understand characteristics of the chain actors and the
relationships among them,
• This information was obtained by conducting FGD and interviews as well as by collecting
secondary data from various sources.
Stakeholder analysis
• In the broadest sense, a ‘stakeholder’ is any person, group or organization that is impacted in
some way by the action or inaction of another (Kujala et al., 2012).
• Importance/influence matrix was used to analyze stakeholders in the SVC
• Helped to identify the key stakeholders and to evaluate their knowledge, interest, positions,
alliances and importance in relation to the value chain

28. MATERIALS…
1.6. Definition of Variables and Working Definition
• Sources of credit: in this study, farmers have alternatives of three
categories of choices of source of credit.
• Described by:
– 1 if the source of credit is formal source of credit (Banks, MFIs)
– 2 if the source of credit is semi-formal and (Coop, union, RuSACCOs)
– 3 if the farmer get credit from non-formal (IML, friends, relatives)

29. MATERIALS…
Independent variables
No. Independent variable Description and Level of measurement Expected sign
1 Sex Sex of the household head +/-
2 Age Number of years one has lived +
3 Family size Number of members who are currently living within the family +
4 Religion A religious faith one is affiliated to. It is a categorical variable +/-
5 Land holdings The total size of land a household has (hectare). It is a continuous variable. +
7 Educational status Formal schooling completed by the household during the survey time
(dummy=1,only read and write; 0=illiterate)
+
8 Participation in off-farm
activities
Income gained by a household outside of its main agricultural activities +
9 Farm income The total annual earnings of the family from sale of agricultural produce +
10 Social participation Membership and leadership in community organization +/-
11 Access to Agricultural Extension
Services
Access to extension and advisory services and frequency of contact with
agricultural experts
+/-
12 Distance to the nearest finance
source
It refers to the distance (in minute) of the rural households from credit
institution.
+/-
13 Interest rate An interest rate is the amount of interest due per period, as a proportion of
the amount lent, deposited or borrowed
-
14 Repayment period It is the period of time during which the entire loan must be repaid. It is a
dummy variable, taking 0 for short and long term repayment periods (based
perception of respondents)
-

30. 3. RESULTS AND DISCUSION
3.1. The Sesame Value Chain in Northwest Amhara Region
• Getting sesame from producer to consumers involves a range of players.
• The majority of the sesame is traded through the ECX
• However, it is not the only fixed sesame market channel
• Very limited value addition practiced in the SVC

31. ACTORS ROLES
SESAME VALUE CHAIN MAIN ACTORS
Farmers and their associations
 Small scale farmers
 Investor farmers
 Primary cooperatives/unions
Traders and processors
 Spot market traders
 Exporters
 Private companies
o E.g. Ambasel Trading
 Producing sesame
 Collecting production for bulk distribution and trading
 Financial and organizational management
 Provide agricultural inputs
 Create market transparency
 Linking to the buyer
 Covering the marketing cost for sesame
 Stabilizing market prices
SESAME VALUE CHAIN ENABLERS
Public funded and governmental
 Bureau of Agriculture
 Cooperative promotion office
 Trade and market development office
 Knowledge institutes
o Gondar ARC
o University of Gondar
 District office of agriculture
 NGOs
 Creating an enabling environment
 Setting the standards for fair and competitive trade
 Broad mandates, but including community outreach
 Knowledge brokering
 Process facilitation and innovation brokering
 Supporting community based organizations
SESAME VALUE CHAIN SUPPORTERS
Financial service providers
 Governmental and private commercial banks
 Microfinance institutions
 Metema union
 Informal money lenders
Agro-input suppliers
 Traders
 Unions
 Private companies
 Providing credit
 Avail smaller loans requiring low/zero collateral
 Most practical source for credit in many cases
 Input provision
 Have as important a role to play in import of inputs as in marketing member produce

32. Support functions: finance, agro-inputs, machinery,
maintenance, information, communication, labour, transport,
advisory services (private and NFP)
Institutions: land rights, laws, regulations,
sector policies, standards, taxes and subsidies,
procedures, cultural values, corruption, …
ECX
Chain operators
Enablers
Supporters
Producers
Export
Institutions
Support
functions
T E
F P
Input providers
Banks/ MFI’s
Transporters
Seed companies
Labourers
Business support services
Ministries
Courts
Extension BoA
Research
ARARI/GARC
Local Govt. Standards
SVC Map

33. Sesame Value Chain Map
Production
Production
Production
input
Support service

34. RESULTS AND DISCUSION…
3.2. Socio-Economic Characteristics of Sample Households
• A data collected from 220 (out of 238 filled questionnaire the complete one is 220)
Variable Response
Formal N=81 Semi-formal
N=39
Non-formal
N=78
Credit non-user
N=22
Total sample
N=220 X2
N % N % N % N % N %
Gender Male 63 36.21 38 21.84 66 37.93 7 4.02 174 100
39.1898 **
Female 18 39.13 1 2.17 12 26.09 15 32.61 46 100
Marital Status
Married 72 35.12 38 18.54 73 35.61 22 10.73 205 100
5.3920
Divorced 1 50 0 0 1 50 0 0 2 100
Widowed 8 61.54 1 7.69 4 30.77 0 0.00 13 100
Education status
Read and Write 16 33.33 10 20.83 19 39.58 3 6.25 48 100
27.3542**
Elementary 48 39.02 22 17.89 45 36.59 8 6.50 123 100
Secondary 4 66.67 1 16.67 1 16.67 0 0.00 6 100
High school and
above
1 16.67 3 50.00 2 33.33 0 0.00 6 100
Illiterate 12 32.43 3 8.11 11 29.73 11 29.73 37 100
Do you get off-farm
income
Yes 20 32.26 13 20.97 13 20.97 16 25.81 62 100
27.6779**
No 61 38.61 26 16.46 65 41.14 6 3.80 158 100
Do you have your
own land
Yes 72 36.36 36 18.18 72 36.36 18 9.09 198 100
2.4398
No 9 40.91 3 13.64 6 27.27 4 18.18 22 100

35. Socio-Economic Characteristics…
Variable Credit source N Mean Std. Deviation
Years of residence Formal 81 48.21 9.527
Semi-formal 39 51.10 11.729
Non-formal 78 46.08 8.960
Non-users 22 52.09 16.832
Family size Formal 81 5.78 2.049
Semi-formal 39 4.85 2.072
Non-formal 78 4.85 1.968
Non-users 22 3.45 1.101
RESULTS AND DISCUSION…

36. Membership status and access to institutional support
Variable
Formal
N=81
Semi-formal
N=39
Non-formal
N=78
Non-user
N=22
X2
N N % N % N % N %
Get extension service Yes 81 100 36 92.31 70 89.74 5 22.73
95.6477
No 0 0 3 7.69 8 10.26 17 77.27
Cooperative membership
status
Yes 67 82.72 29 74.36 62 79.49 5 22.73
34.5592
No 14 17.28 10 25.64 16 20.51 17 77.27
Do you have access to
credit information
Yes 54 66.67 30 76.92 62 79.49 5 22.73
27.1959
No 27 33.33 9 23.08 16 20.51 17 77.27
Socio-Economic Characteristics…

37. 3.3. Finance service providers

38. 0
10
20
30
40
50
60
70
80
90
100
Formal
Formal source
Bank MFI Gurantee
0
5
10
15
20
25
30
Semi-formal
Formal source
Coop RSACCOs Associations
0
1
2
3
4
5
6
Non-formal
Formal source
Friends Relatives IML
Time period between loan application and receipt
RESULTS AND DISCUSION…

39. 3.4. Finance Need and Supply for Sesame Value Chain Actors
RESULTS AND DISCUSION…
Parameter N Minimum Maximum Mean Std. Deviation
Total finance requirement for the season 213 0 78500 23062.35 16800.794
Finance used from own pocket 210 0 50000 11002.86 11201.479
Finance borrowed and used for the season 194 0 42500 14203.09 11018.035
Finance needed but missed 52 2000 20000 6865.38 4737.331
• The amount missed also range from 2,000 to 20,000 birr which depend on the respondent’s financial
planning on the amount of activities he or she needed to do or purchase inputs.

40. 3.5. Value Chain Finance Instruments in the Sesame Value Chain
RESULTS AND DISCUSION…
• According to the findings four broad category of value chain financing
instruments:
• product financing
• receivable financing
• risk mitigation product and
• financial enhancement instruments, exist with different
intensity.

41. Credit sources used by farmers
source of credit
no yes
N % N %
Commercial bank 220 100.0 0 0.0
MFIs (ACSI) 101 45.9 119 54.1
Credit from exporters 220 100.0 0 0.0
Credit from processing company 210 95.5 10 4.5
Credit (advance payment) from traders 209 95 11 5
From IML 192 87.27 28 12.73
Credit via a guarantee fund 187 85.0 33 15.0
In kind credit from a company 215 97.7 0 0.0
Credit through a warehouse receipt system 220 100.0 0 0.0
Finance from relatives/neighbor/friends 135 61.4 85 38.6
Source: own survey (2021)
Value Chain Finance Instruments…

42. 3.6. Determinants of Sesame Farmers Credit Source Choice
Interaction between credit
sources
Coef. Std. Err. z P>z
Likelihood ratio test of rho21 = rho31 = rho32 = 0:
chi2(3) = 17.8876 Prob > chi2 = 0.0005
/atrho21 -0.281 0.137 -2.050 0.040
/atrho31 0.289 0.141 2.050 0.040
/atrho32 0.334 0.137 2.440 0.015
rho21 -0.274 0.127 -2.160 0.030
rho31 0.281 0.130 2.170 0.030
rho32 0.322 0.123 2.630 0.009
• The results of the likelihood ratio test in the model (LR χ2 (3) = 17.8876, χ2
> p = 0.0000) indicates the null that the independence between credit source
selection decision (rho21= rho31 = rho32 = 0) is rejected at 1% significance
level and there are significant joint correlations for two estimated
coefficients across the equations in the models.
RESULTS AND DISCUSION…

43. Independent variables Formal (N=81) Semi-formal (N=39) Non-formal (N=78)
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
Age 0.001 0.020 0.032203* 0.018 -0.06837*** 0.020
Gender 0.269 0.593 -0.708 0.460 -0.753 0.486
Family size 0.173098** 0.081 0.041 0.080 0.042 0.074
Education status -0.111 0.124 -0.126 0.109 -0.131 0.105
Annual income 0.000 0.000 0.000 0.000 -2.04E-06** 0.000
Land ownership 0.815369** 0.406 -0.240 0.401 -0.236 0.375
Long term relationship 0.170 0.356 -0.204 0.323 0.604614** 0.322
Interest rate -0.6487398** 0.330 1.065493*** 0.296 -0.97496*** 0.305
Easy procedure to get credit -1.307557*** 0.381 0.010 0.367 -1.32342*** 0.397
Proximity to HH home -0.142 0.358 -0.58651* 0.343 1.426027*** 0.390
Loan provision time -0.261 0.307 -0.373 0.293 0.329 0.276
Requirement of collateral -1.304439*** 0.357 -1.63065*** 0.344 0.619521* 0.346
Repayment period -0.562 0.528 -0.352 0.438 1.073234** 0.445
Cooperative membership -0.564 0.497 0.837157** 0.372 -1.13163*** 0.394
Access to extension service -5.687 201.873 -5.757 191.616 -5.218 125.258
Access to credit information 1.093873** 0.471 -1.12885*** 0.435 0.224 0.430
_cons 6.174 201.878 5.401 191.621 10.250 125.266
Multivariate probit coefficient estimates of factors influencing household credit source choice

44. Age: The respondent’s age has a significant and positive (at α≤ 0.1%) effect on
farmers’ semi-formal credit source choice and negative and significant influence
on the probability farmers’ selection of non-formal credit sources (at α 0.01%).
Education level: Studies (Braide (2015), Hussein (2007) ), suggested that
education are more likely to increase the information base and decision making
abilities of farm households. However, result of this study revealed that
education status has no statistically significant influence on farmers’ credit
source choice.
Determinants…

45. Household annual income level: The coefficient of household income level is negative and
significant for non-formal credit sources (at α≤0.1).
This suggests that poorest households are more likely not to choose non-formal credit sources
compared to wealthier ones who mostly depend on formal and semi-formal credit sources.
Land ownership: The result shows a positive statistically significant relationship between land
ownership and farmers' choice of formal credit sources. However, it is negatively associated with
semi-formal and non-formal credit sources, though it is not statistically significant.
Long term relationship: It is non-significant for formal and semi-formal sources, and
significantly affect non-formal credit source choice of farmers (at α 0.05%).
Determinants…

46. Interest rate: The result showed that interest rate has an influence on respondents’ credit source choice.
Farmers’ probability to select semi-formal credit sources is positively and significantly affected by
interest rate (at α 0.01%).
Obviously, taking interest as determining factor probability of farmers selection of non-formal sources is
negative (at α 0.01% ).This result is in agreement with Khan and Hussain (2011) who also showed that
the higher cost of credit is inversely related to the demand for credit from formal sources. The negative
effect of interest
Collateral: One of the major constraints to select a credit source is the unavailability of collateral
required by lenders. This finding is in line with the conventional perception that households without
collateral have a lower chance to use formal credit and accordingly move towards the informal financial
sector.
Determinants…

47. Repayment period: loan repayment period significantly influence non-formal credit
source choice however it is non-significant for formal and semi-formal credit sources. It
implies that farmers tend to select non-formal finance sources as the number of loan
repayment period increases.
Distance to finance sources: The distance from households to the nearest finance
source is also meaningful in explaining to credit source choice. In the sesame value
chain formal financial sources are mainly concentrated in the district center.
Access to credit information: Farmers’ exposure to credit information is positively
associated with access to credit from formal credit providers. The results showed that,
access to information on credit impacted respondents’ credit source choice.
Determinants…

48. 3.7. Major Credit Service Challenges
Non-formal credit providers (primarily informal money lenders)
are more convenient in terms of loan availability, travel distance,
time delay, and the lack of strict guarantees needed to provide
credit.
Semi-formal credit sources (cooperatives, SACCOs) are more
convenient because they charge low interest rate and only require
membership, regular savings and participation of members.
Formal credit sources are concerned they are convenient in terms
of low interest rate, and longer payback period.
RESULTS AND DISCUSION…

49. Informal money lenders charge very high interest on credit and provide shorter
payback period. Because of this farmers face challenges in two ways, one they pay extra
high interest rate, secondly they can’t sell produce preferred market times because they have
to pay back their loan the agreed date.
Formal credit sources (solely ACSI) has many weaknesses which include complicated
loan approval procedure, more time delay, high demand of group collateral and for some
farmers longer distance to travel.
Semi-formal: loan size they provide is very limited
Major Credit Service Challenges…
RESULTS AND DISCUSION…

50. 4. SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
• The study identified three primary sources of credit to sesame value chain actors;
the formal, semi-formal and non-formal credit sources.
• Amhara Credit and Saving Institute (ACSI) is the main source of credit to sesame
value chain actors with about 56.4%
• It was found that 25.5% and 12.7% of sesame value chain actors obtained credit
from cooperatives and informal money lenders respectively.
• more male than female actors accessed credit during the production years involved
4.1. SUMMARY and CONCLUSIONS

51. • The presence of both direct and indirect value chain financing at various segment of the
sesame value chain is confirmed
• four diverse categories of value chain financing instruments occur with varying degrees of
intensity: product financing, receivable financing, risk mitigation products, and financial
enhancement value chain financing instruments are proved to be available.
• The impact of different socioeconomic and institutional variables shows that credit strategies
are diversified.
• Farmers contact different institutions, including formal (ACSI), semi-formal (cooperatives,
SACCOs) and non-formal (informal money lenders, relatives and friends)
SUMMARY and CONCLUSIONS…

52. • MVP analysis findings reveal that family size, education status, annual income, interest rate,
requirement of collateral and loan repayment period among others were positively and significantly
associated with sesame value chain actors probability to select a specific credit source.
• The qualitative data showed that variables that are found to be statistically significant represent the
reality on the ground
• Non-formal credit sources provide credit relatively with easy procedure, they are close to farmers,
time delay, and the lack of explicit guarantees needed to provide credit.
• Semi-formal credit outlets, which are more convenient, include mostly membership, a low interest
rate, and occasional savings, as well as member attendance.
• Formal credit channels, they are advantageous in terms of low interest rates and longer payback
periods.
SUMMARY and CONCLUSIONS…

53. 4.2. RECOMENDATION
• Attention should be given for indirect value chain financing along the chain especially at
nodes where direct value chain financing is assumed to be costly.
• Formal finance institutions need to revisit their loan policy and loan portfolio. So, they need
to improve their policies and portfolio to meet sesame value chain actors’ financial service
demands.
• Financial institutions should have a targeting system in place to assist farm households with
limited collateral in diversifying their sources of income while preserving their agricultural
production capacity.
SUMMARY, CONCLUSIONS…

54. RECOMMENDATION…
• Longer repayment period: It is suggested that farmers
should be given loans with longer payback periods to
enable them to invest in farm activities that generate
sustainable incomes.
• Promoting RSACCOs: effort should be exerted to establish
and strengthen more and more RSACOs..
SUMMARY, CONCLUSIONS…

55. I would like to express my gratitude to my advisors Marelign
Adugna (PhD) and Asefa Tilahun (PhD) for their assistance, advice
and guidance until the completion of this project.
I also extend my deepest gratitude to all the individuals, who
have contributed to this study either directly or indirectly.
Thank you!
ACKNOWLEDGEMENTS