The role of financial institutions in value chain finance in the global south

Submitted at Freising, September 30, 2021

Technical University of Munich

The Role of Financial Institutions in

Value Chain Finance in the Global

South

Scientific work to obtain the degree

M.Sc. Agrarmanagement

At the Chair Group Agricultural Production and Resource Economics of the TUM School of

Life Sciences.

Supervisors M.Sc. Roberto Villalba Camacho

Chair Group Agricultural Production and Resource Economics M.Sc.Terese Venus

Chair Group Agricultural Production and Resource Economics

Examiner Prof. Dr. agr. Johannes Sauer

Chair Group Agricultural Production and Resource Economics

Submitted by Moahmed Ali Trabelsi

Matrikelnummer: 03703889

Giggenhausser str. 2985354, Freising [phone]

Declaration of authorship

I, Mohamed Ali Trabelsi, born in Tunis, Tunisia, with matriculation number 03703889; declare that this thesis and the work presented in it are my own. It has been generated as the result of my own original research on the subject «The Role of Financial Institutions in Value Chain Finance in the Global South»

I confirm that:

ü This work was done wholly in candidature for MSc degree thesis fulfillment at the Technical University of Munich.

ü Where I have consulted published work of others, that was always clearly credited.

ü Where I have taken some ideas from other sources, I have mentioned the sources and except these kinds of quotes, the entire work is mine.

Date 30.09.2021 Signature:

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Table of Contents

Table of Contents ii

List of Figures v

List of Tables vi

List of Abbreviations vii

Acknowledgment viii

Abstract ix

1. Introduction 1

1.1. Problem statement 1

1.2. Objectives 2

1.3. Research questions and hypothesis 2

1.4. Expected results from the research 3

1.5. Organization of the thesis 3

2. Literature Review and Theoretical Background 4

2.1. Agriculture finance 4

2.1.1. Agricultural credit 4

2.1.2. Financial institutions (FIs) in the Agriculture Sector 6

2.1.3. Microfinance 7

2.2. AVCF definition 9

2.3. AVCF Challenges 11

2.4. Competitiveness of agricultural finance 14

2.5. Determinants of agricultural credit 17

2.6. Literature on AVCF 19

2.6.1. Gap of the Literature on AVCF 19

2.6.2. Available literature on AVCF 20

3. Methodology and Data 22

3.1. Description of the study 22

3.2. Building the database 23

3.3. Statistical Analyses 25

3.3.1. Qualitative Analyses: 25

3.3.2. Quantitative Analyses 25

3.3.3. Cluster Analysis 26

3.3.4. Data Types and Variables 27

3.3.5. Other components of the database 30

3.4. Structure of survey 30

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3.5. Sample Design 31

3.5.1. Sampling frame 31

3.5.2. Sampling techniques 32

3.5.3. FIs Listing: 33

4. Survey Design and Conceptual Framework 34

4.1. Exploring other surveys 34

4.1.1. Survey with FIs 34

4.1.2. Survey with farmers (World Bank & CGAP) 35

4.1.3. Integrated Financing for Value Chains (WOCCU) 37

4.1.4. Survey on national development bank (World Bank Group) 37

4.2. Credit Scoring for Agricultural Loans 38

4.3. Financial instruments employed by FIs 40

4.4. Survey design for FIs officials 41

4.4.1. General Information 42

4.4.2. Economic information 42

4.4.3. Credit screening, scoring, and monitoring for Agricultural loans 42

4.4.4. Agricultural finance within value chains 43

4.4.5. Financial product & Instrument employed 43

4.5. Overview of the online questionnaire 43

5. Analysis and Results 45

5.1. Descriptive Analysis: 45

5.1.1. Geographic distribution: 45

5.1.2. Distribution by institutional type: 45

5.1.3. Foundation Year: 46

5.1.4. Number of Branches 47

5.1.5. Agricultural loans 47

5.1.6. Gender Equality 48

5.1.7. Digital Solutions 49

5.2. Cluster Analysis 50

5.2.1. Confirm Data: 50

5.2.2. Scale the data 51

5.2.3. Select segmentation variables 51

5.2.4. Define similarity measure: 51

5.2.5. Number of clusters 51

5.2.6. K-means Clustering Method 52

5.2.7. Hierarchical Clustering Method 54

5.2.8. Selected method and number of clusters 55

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5.2.9. Extracting Results 57

6. Discussion 62

6.1. An information provider database 62

6.2. Analysis of the clustering analysis 63

6.3. Limitations and further research needs 64

7. Conclusion and Recommendations 67

7.1. Conclusion 67

7.2. Recommendations 68

References 70

Annex A I

Annex B: Database II

Annex C: Survey for FIs Officials IX

Annex D: R Script XVI

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List of Figures

Figure 1: Sources of agriculture credit 5

Figure 2: Components of direct and indirect agriculture credit 6

Figure 3: Development Process through Micro-finance 7

Figure 4: Overview of value chain finance Triangle 10

Figure 5: Considered factors to reduce TC and risk management in agricultural finance 15

Figure 6: Geographical location of the financial institutions 22

Figure 7: Geographical distribution of financial institutions 32

Figure 8: Listing criteria of financial institutions in the final database 33

Figure 9: Financial institutions Questionnaire components 41

Figure 10: List of instruments enquired during the survey 43

Figure 11: Questionnaire Framework 44

Figure 12: Determination of the optimum number of clusters 52

Figure 13: Grouping Data scaled in different clusters 53

Figure 14: Clusters Visualization 53

Figure 15: Hierarchical Clustering 54

Figure 16: Validation of the number of clusters 57

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List of Tables

Table 1: Distribution of MFIs by institutional type 8

Table 2: The challenges of the agriculture finance according to the literature review 12

Table 3: Determinants of credit Access 19

Table 4: Number of FIs mentioned in the literature review 21

Table 5: Composition of database 24

Table 6: Most popular Qualitative Analysis method 25

Table 7: Quantitative statistic types 26

Table 8: Data types 27

Table 9: Continent Attributes 27

Table 10: Institutional Type Attributes 27

Table 11: Agricultural loans Attributes 28

Table 12: Gender Attributes 28

Table 13: Digital Solutions Attributes 29

Table 14: financial Institutions General Information 34

Table 15: Specific loan features 35

Table 16: focal points of the World Bank Survey 36

Table 17: Lending decision variables 39

Table 18: Loan accreditation Characteristics 40

Table 19: AVCF Instrument 40

Table 20: Classification of financial institutions by continent 45

Table 21: Classification of financial institutions by institutional type 46

Table 22: classification of financial institutions by foundation year 47

Table 23: Classification of financial institutions according to the number of branches 47

Table 24: Percentage of credit offered by type of financial institution 48

Table 25: Percentage of gender equality program offered by type of financial institution 49

Table 26: Percentage of digital solutions offered by type of financial institution 49

Table 27: Descriptive statistics of the dataset 50

Table 28: Cluster membership IDs using K means method 54

Table 29: Cluster membership IDs using Hierarchical method 55

Table 30: Nomination of the FIs groups 57

Table 31: Cluster's characteristics 60

List of Abbreviations

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ADB Asian Development Bank

AFD Agence française de développement

AfDB African development Bank

AFRACA African Rural and Agricultural Credit Association

AL1 Farmer credit

AL2 Agribusiness Credit

AOI Agriculture orientation index

AVCF Agricultural value chain finance

CB commercial banks

CGAP Consultative Group to Assist the Poor

DBs Development Banks

DS1 Online Banking

DS2 E-products Email and SMS Banking

DS3 Online loan application

FI Financial institution

G1 Credit facility for women

G2 Career development opportunities to female staff

G3 Gender Programmes

GS Global South

IDFC International Development Finance Club

IFAD International Fund for Agricultural Development

IFC International Finance Corporation

IFI International financial institutions

IIRR International Institute of Rural Reconstruction

INSE Institute of New Structural Economics at Peking University

ISF advisory group

KIT Royal Tropical Institute

MBFI membership-based financial institutions

MFCs microfinance Companies

TC transaction costs

VC value chain

VCF value chain finance

WFDFI World Federation of Development Financing Institutions

WBG World Bank Group

WOCCU World council of credit unions

Acknowledgment

VIII

The first thing I want to say is how grateful I am to God and my father for the opportunity to study at the Technical University of Munich. I would also like to extend my deepest gratitude to my sister Emna for her support throughout the entire thesis process. Also, I want to thank my girlfriend Myriam, as well as my friends Anas, Christian, Cyrine, Dali, Mourad, Rached, Ramzi, Sabrina, Safa, Youssef, Wajih, Werner, and Zeineb for their continued encouragement.

My thanks go out to Villalba Camacho Roberto and Venus Terese for their helpful guidance and valuable comments and corrections during this work. This work cannot be done without them. My strong gratitude to Susanne Minges and Papaja-Hülsbergen Susanne for their continuous support during my studies at TUM.

The internship opportunity with Agribusiness Facility for Africa (ABF) and Green Innovation Centres for the Agriculture and Food Sector (GIC) at the GIZ was a big milestone in my career development. It was a great chance for learning and applying my knowledge and fresh skills in a real working setting. I will strive to use everything I learned in the best possible way. Through this internship, I met wonderful people and professionals who helped me develop my experience. I would like to express my deepest thanks to Dr. Annemarie Mathess, Carsten Schüttel, Wahid Marouani, and Melanie Hinderer for their guidance and allowing me to participate in their projects which helped me expand our knowledge on various topics.

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Abstract

Small-scale farmers and agribusinesses in the Global South still face many barriers to access credit, despite the efforts of development agencies, facilitator, and even financial institutions. An agricultural gap persists that limits sector potential. The current master's thesis examines the ways in which financial institutions make credit easier to obtain for smallholder farmers and value chain players. The study uses a unique database of 347 financial institutions in 106 countries from Africa, Asia, South America, and Oceania as well as international institutions. It primarily contains cooperatives, commercial banks, NGOs, microfinance institutions, and agricultural banks. The database is constructed through a snowball effect process using literature sources, online search, and open-source bank platforms. This database contains several details about these institutions including their institutional type, agricultural loans, gender equality, and digital solutions.

The number of financial institutions was reduced to 144 for the statistical analysis due to the lack of available data for several financial institutions. Then an analysis of clusters was conducted to answer the research question and determine patterns, similarities, and differences among the selected financial institutions. Five clusters were identified. It emerges from the study that financial institutions deliver customized and enhanced rural financial services in high demand and in line with gender issues, as proved in clusters 1, 2, and 3. Moreover, the youngest group of institutions is cluster 4, which has the most digital solutions to offer. Cluster five, which contains individuals primarily using traditional banking methods, has the lowest level of financial services. This study has addressed the research question in terms of credit provision, gender promotion, and digital solutions, as well as identified the kind of similarities and differences between financial institutions. Several recommendations are made in this study, including the need to encourage women and provide digital solutions to ease the lending process for small-scale farmers and value chain actors.

Keywords: Agricultural Value Chain Finance, financial institutions, cluster analysis, financial products, Global South

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Zusammenfassung

Kleinbauern und Agrarunternehmen im globalen Süden sehen sich trotz der Bemühungen von Entwicklungsorganisationen, Vermittlern, Maklern und sogar Finanzinstituten immer noch vielen Hindernissen beim Zugang zu Krediten gegenüber. Es besteht weiterhin eine Lücke in der Landwirtschaft, die das Potenzial des Sektors einschränkt. In der vorliegenden Masterarbeit wird untersucht, wie Finanzinstitute Kleinbauern und Akteuren der Wertschöpfungskette den Zugang zu Krediten erleichtern können. Die Studie stützt sich auf eine einzigartige Datenbank von Finanzinstituten in 106 Ländern Afrikas, Asiens, Südamerikas und Ozeaniens sowie von internationalen Institutionen. Sie enthält vor allem Verbände, Geschäftsbanken, NGOs, Mikrofinanzinstitute und Landwirtschaftsbanken. Die Datenbank wurde in einem Schneeballeffekt-Verfahren unter Verwendung von Literaturquellen, Internetrecherchen und Open-Source-Bankenplattformen erstellt. Die Datenbank enthält verschiedene Details über diese Institutionen, darunter ihre institutionelle Einrichtung, Agrarkreditangebote, Frauenförderung und digitale Lösungen.

Die Zahl der Finanzinstitute wurde für die statistische Analyse auf 144 reduziert, da für mehrere Finanzinstitute keine Daten vorlagen. Anschließend wurde eine Clusteranalyse durchgeführt, um die Forschungsfrage zu beantworten und Muster, Ähnlichkeiten und Unterschiede zwischen den ausgewählten Finanzinstituten zu ermitteln. Es wurden fünf Cluster identifiziert. Aus der Studie geht hervor, dass die Finanzinstitute maßgeschneiderte und verbesserte Finanzdienstleistungen für den ländlichen Raum anbieten, welche auf geschlechtsspezifische Aspekte Wert legen, wie in den Clustern 1, 2 und 3 nachgewiesen wurde. Darüber hinaus ist die jüngste Gruppe von Instituten in Cluster 4 zu finden, die die meisten digitalen Lösungen zu bieten hat. In Cluster 5, in dem sich Finanzinstitute befinden, die hauptsächlich traditionelle Bankmethoden nutzen, ist das Angebot an Dienstleistungen am geringsten. In dieser Studie wurde die Forschungsfrage in Bezug auf die Kreditvergabe, die Förderung der Geschlechtergleichstellung und digitale Lösungen beantwortet und die Gemeinsamkeiten und Unterschiede zwischen den Finanzinstituten ermittelt. In dieser Masterarbeit werden mehrere Empfehlungen ausgesprochen, darunter die Notwendigkeit, Frauen zu fördern und digitale Lösungen anzubieten, um die Kreditvergabe für Kleinbauern und Akteure der Wertschöpfungskette zu erleichtern.

Schlüsselwörter: Finanzierung der landwirtschaftlichen Wertschöpfungskette, Finanzinstitute, Clusteranalyse, Finanzprodukte, Globaler Süden.

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