economic evaluation of the small-scale marine …

ECONOMIC EVALUATION OF THE SMALL-SCALE MARINE FISHERIES

USING THE S-C-P APPROACH AT SELECTED LANDING SITES ALONG

THE KENYAN COAST

PATRICK KIMANI MANG’URIU

A thesis submitted in partial fulfilment of the requirements for the Degree of

Doctor of Philosophy in Fisheries of Pwani University

JANUARY, 2020

ii

DECLARATION

iii

DEDICATION

This has been a long journey that robbed my family quality time. I dedicate this achievement to

the Kimanis and my parents for their support.

iv

ACKNOWLEDEGMENT

I wish to thank my Supervisors; Prof. Mlewa, Chrisestom Mwatete, Prof. Julius Manyala and

Dr. Andrew Wamukota for their dedicated guidance throughout the period of this study. Their

insight and advice have made all the difference. I also thank Dr. Bernerd Fulanda for his initial

participation in developing the study.

I would like to sincerely thank Western Indian Ocean Marine Science Association (WIOMSA)

for providing Marine Research Grant (MARG 1) to support this study in the study sites of

Shimoni and Vanga. I also wish to thank the National Commission of Science, Technology and

Innovation (NACOSTI) for providing a partial grant to undertake part of the study in Malindi

and Mayungu study sites.

Sincere gratitude goes to my field assistants; Salim Ali and Jane Mwikali for their dedication

in data collection and organizing meetings with respondents. Lastly all respondents are duly

acknowledged for providing their valuable time to answer our questions.

v

ABSTRACT

Small-scale marine fisheries in Kenya lack adequate data and information to help in decision-

making concerning required interventions. These inadequacies include data and information on

actors’ behaviour, financial and economic outcomes in context of structure, conduct and

performance (S-C-P) paradigm, constraints and opportunities and level of government support.

Earlier studies have addressed some aspects, but gaps still exist and this thesis contributes to

bridging them. This study was conducted at selected sites along Kenya’s coast, namely;

Malindi, Mayungu, Shimoni, Vanga and Mombasa. A total of 403 respondents (fishers,

middlemen and small-scale processors) were interviewed from November 2014 through

September 2016. In addition, 114 respondents participated in 12 Focus Group Discussions

(FGD) meetings. Key findings showed that structure did not significantly influence

respondents’ conduct in most cases. There was no statistical significant influence of structure

in context of percent ownership and value of equipment on conduct factors amongst fishers,

middlemen and processors. Similarly, structure and conduct did not significantly influence

profitability amongst middlemen (p=0.462) and processors (p=0.538), but had significant

influence on fishers’ profitability (p=0.008). Therefore, S-C-P factors were less important in

influencing profitability. Instead other variables were more important. Fish sale was

significantly influencing profitability amongst all actors; fishers (p=<0.005), middlemen

(p=<0.005) and processors (p=<0.005), while variable costs influenced profitability amongst

fishers (p=<0.005) and processors (p=<0.005). Financial profits were above opportunity cost of

labour for unskilled alternative employment (KES 54-7,481), indicating importance of the

fishery to livelihoods. Findings on constraints and opportunities showed that lack of access to

capital was the most severe constraint. Erratic supply of fish also affected pricing and access to

fish at different times, where prices increased during scarcity periods and declined during

oversupply periods. Results also showed that majority of respondents had negative perception

regarding performance of county and national government in promoting fish marketing, value

vi

addition and provision of equipment, but rated promotion of cooperation and support to

functioning of Beach Management Units (BMUs) positively. Demographic and socio-economic

factors had minimal effect on actors’ perception and lacked strong clear patterns. Therefore,

respondent characteristics were not reliable predictors of actors’ perception of government

performance in implementation of value chain development objectives. This study provides new

insight on marine small-scale fisheries useful for management and development of small-scale

fisheries at national, regional and global scales.

vii

TABLE OF CONTENTS PAGE

DECLARATION .................................................................................................................. ii

DEDICATION ..................................................................................................................... iii

ACKNOWLEDEGMENT .................................................................................................. iv

ABSTRACT ........................................................................................................................... v

CHAPTER 1: BACKGROUND OF THE STUDY ................................................................ 1

General introduction ..................................................................................................... 1

Statement of the problem .............................................................................................. 8

Goal of the study ............................................................................................................ 9

Specific objectives .......................................................................................................... 9

Research questions ...................................................................................................... 10

Study hypotheses ......................................................................................................... 10

Justification of the study ............................................................................................. 11

Significance of the study ............................................................................................. 11

Scope of the Study ....................................................................................................... 13

Conceptual model ....................................................................................................... 14

Kenya’s small-scale marine fisheries ........................................................................ 15

General review of literature ...................................................................................... 18

1.12.1 Description of small-scale fisheries ....................................................................... 18

1.12.2 Global overview and significance of small-scale fisheries .................................... 19

1.12.3 Overview of Kenya’s small-scale fisheries ............................................................. 21

1.12.4 Data challenges and perceived low contribution of small-scale fisheries ............. 23

1.12.5 The value chain approach in data collection ......................................................... 24

1.12.6 The Structure-Conduct-Performance approach ..................................................... 26

1.12.7 Institutional and regulatory environment ............................................................... 34

1.12.8 Constraints and opportunities ................................................................................ 38

Description of the study area ..................................................................................... 40

viii

Research design and sampling .................................................................................. 47

1.14.1 Study design ............................................................................................................ 48

1.14.2 Sampling ................................................................................................................. 49

CHAPTER 2: STRUCTURE, CONDUCT AND PERFORMANCE IN SMALL-SCALE

FISHERIES VALUE CHAIN ................................................................................................ 52

Introduction ................................................................................................................. 52

Materials and methods ................................................................................................ 53

2.2.1 Data collection on structure ..................................................................................... 53

2.2.2 Data collection on conduct ....................................................................................... 54

2.2.3 Data collection on performance ............................................................................... 55

2.2.4 Data analysis of structure based on actor capitalization ......................................... 56

2.2.5 Data analysis of structure in context of market concentration ................................ 57

2.2.6 Data analysis of conduct in context of fish grading and actor choices ................... 58

2.2.7 Data analysis on performance ................................................................................. 59

2.2.8 Statistical analysis of relationship between structure, conduct and performance ... 60

Results ........................................................................................................................... 65

2.3.1 Demographic and socio-economic characteristics of small-scale fisheries ............ 65

2.3.2 Structure in context of actor capitalization .............................................................. 66

2.3.3 Structure in context of market concentration ........................................................... 69

2.3.4 Conduct in context of fish grading, pricing and actors’ choice ............................... 69

2.3.5 Results of diagnostic tests on models for influence of structure on conduct ............ 72

2.3.6 Influence of structure on fishers’ conduct ................................................................ 73

2.3.7 Influence of structure on middlemen’s conduct ....................................................... 75

2.3.8 Influence of structure on processors’ conduct ......................................................... 81

2.3.9 Actors’ performance in context of fish quantities, purchases and sales .................. 85

2.3.10 Actors’ performance in context of costs ................................................................. 87

2.3.11 Actors’ performance in context of financial profit ................................................. 88

ix

2.3.12 Comparison of time spent, workforce, opportunity cost of labour and

profitability ........................................................................................................... 89

2.3.13 Factors influencing fishers’ financial performance ............................................... 91

2.3.14 Factor influencing middlemen’s financial performance ........................................ 94

2.3.15 Factors influencing processors’ financial performance ........................................ 97

Discussion ................................................................................................................... 100

Conclusion .................................................................................................................. 108

CHAPTER 3: ANALYSIS OF CONSTRAINTS AND OPPORTUNITIES IN SMALL-

SCALE FISHERIES VALUE CHAIN ................................................................................ 110

Introduction ............................................................................................................... 110

Materials and methods .............................................................................................. 111

3.2.1 The Analytical Hierarchical Process (AHP) .......................................................... 111

3.2.2 Data collection ....................................................................................................... 112

Data analysis .............................................................................................................. 114

Results ......................................................................................................................... 116

3.4.1 Fishers’ constraints ................................................................................................ 116

3.4.2 Fishers’ opportunities ............................................................................................ 119

3.4.3 Middlemen’s constraints ........................................................................................ 121

3.4.4 Middlemen’s opportunities ..................................................................................... 124

3.4.5 Processors’ constraints .......................................................................................... 127

3.4.6 Processors’ opportunities....................................................................................... 130

3.4.7 Actor’s loan uptake, training, ownership of equipment and support received ...... 132

Discussion ................................................................................................................... 134

Conclusion .................................................................................................................. 139

CHAPTER 4: ANALYSIS OF POLICY, REGULATORY AND INSTITUTIONAL

FRAMEWORKS IN SMALL-SCALE FISHERIES VALUE CHAIN ............................ 141

x

Introduction ............................................................................................................... 141

4.1.1 Policy and legal provisions in support of Kenya’s small-scale fisheries value chain

development ........................................................................................................ 142

Materials and methods .............................................................................................. 145

4.2.1 Data collection ....................................................................................................... 145

4.2.2 Data analysis .......................................................................................................... 146

Results ......................................................................................................................... 149

4.3.1 Actors’ perceptions of government performance ................................................... 149

4.3.2 Factors influencing actors’ perception of government performance on policy and

legal objectives ................................................................................................... 153

Discussion ................................................................................................................... 157

Conclusion .................................................................................................................. 162

CHAPTER 5: GENERAL DISCUSSION ........................................................................... 164

CHAPTER 6: GENERAL CONCLUSIONS AND RECOMMENDATIONS ................ 173

Conclusions ................................................................................................................ 173

New scholarly contributions from the study ........................................................... 174

Recommendations ..................................................................................................... 175

REFERENCES ...................................................................................................................... 177

APPENDICES ....................................................................................................................... 205

Appendix 1: Individual survey questionnaire................................................................. 205

Appendix 2: Focus group discussions questionnaire...................................................... 219

xi

LIST OF TABLES

Table 1.1. Sampling frame of respondents interviewed by actor group and site ...................... 51

Table 2.1. The description of variables used in Generalized Additive Model regression models

to analyze performance ........................................................................................ 64

Table 2.2. Summary of respondent’s demographic and socio-economic characteristics .......... 66

Table 2.3: Actor categorization based on capitalization, listed in decreasing order of value of

equipment ............................................................................................................. 68

Table 2.4. Herfindahl-Hirschman Index scores by site ............................................................. 69

Table 2.5. Percentage of fish grades targeted by respondents ................................................... 69

Table 2.6. Multinomial logistic regression results showing influence of percent ownership of

equipment on fish price determinants amongst fishers ........................................ 73

Table 2.7. Logistic regression results showing influence of percent ownership of equipment on

selling collusion amongst fishers ......................................................................... 74

Table 2.8. Logistic regression results showing influence of percent ownership of equipment on

access to selling price information amongst fishers ............................................. 75

Table 2.9. Multinomial logistic regression results showing influence of value of equipment on

fish buying price determinants amongst middlemen ............................................ 76


fish selling price determinants amongst middlemen ............................................ 77

Table 2.11. Logistic regression results showing influence of value of equipment on buying

collusion amongst middlemen .............................................................................. 78

Table 2.12. Logistic regression results showing influence of value of equipment on selling

collusion amongst middlemen .............................................................................. 78

Table 2.13. Logistic regression results showing influence of value of equipment on access to

buying price information amongst middlemen .................................................... 78

xii


selling price information amongst middlemen ..................................................... 80


choice of fish grade amongst middlemen ............................................................. 81


fish buying price determinants amongst processors ............................................. 82

Table 2.17. Logistic regression results showing influence of value of equipment on buying

collusion amongst processors ............................................................................... 83

Table 2.18. Logistic regression results showing influence of value of equipment on selling

collusion amongst processors ............................................................................... 83


buying price information amongst processors ...................................................... 84


selling price information amongst processors ...................................................... 84


choice of fish grade amongst processors .............................................................. 85

Table 2.22. Averages for variables on workforce, time spent, opportunity cost of labour and

financial profit per type of enterprise or fishing unit ........................................... 91

Table 2.23. Generalized Additive Model results of factors influencing fisher’s profitability .. 93

Table 2.24. Generalized Additive Model results of factors influencing middlemen’s financial

profitability ........................................................................................................... 95

Table 2.25. Generalized Additive Model results of factors influencing processors’ financial

profitability ........................................................................................................... 99

Table 3.1. Number of respondents involved in focus group discussions by site .................... 112

Table 4.1. Identified policy and legal objectives of Fisheries Acts and policy in support of SSFs

value chain development in Kenya .................................................................... 144

Table 4.2. Contextualization and description of statements used in Likert scale ................... 146

xiii

Table 4.3. Description of factor variables used in ordinal logistic regression models to analyze

governments’ performance of policy and legal objectives ................................. 148

Table 4.4. Actors’ perceptions of performance (in %) of national and county governments in

implementation of fisheries policy and legal objectives .................................... 150

Table 4.5. Coefficent estimates of factors influencing actors’ perceptions of performance of

national and county governments in implementation of fisheries policy and legal

objectives. Significant results are in bold ........................................................... 156

xiv

LIST OF FIGURES

Figure 1.1. Conceptual model for the study. Adopted from GTZ (2008) ................................. 15

Figure 1.2. A map of the Kenyan coastline showing study sites. Source Kimani, (2020) ........ 42

Figure 2.1. Detrended Correspondence Analysis plot showing association between fish type and

grade ..................................................................................................................... 70

Figure 2.2. Detrended Correspondence Analysis plot showing association between fish grade

and actor node ...................................................................................................... 70

Figure 2.3. Plot of median ex-vessel price of fish by grade. ..................................................... 71

Figure 2.4. (a.) Mean fish amounts that fishers, middlemen and processors deal in (b.) purchases

and sales amount per day in local currency (KES). Error bars represent mean

standard errors. All variables are expressed as per day units. .............................. 86

Figure 2.5. (a.) Mean variable and fixed costs amongst fishers and buyers, (b.) financial profit

per person. All variables are expressed as per day units. ..................................... 89

Figure 2.6. Estimated smooth terms of three variables on fishers’ profitability. Y-axes are the

dependent variable’s partial effects and the grey shadow bands show standard-

error confidence intervals. .................................................................................... 94

Figure 2.7. Estimated smooth terms of sales on middlemen’s profitability. Y-axis is the

dependent variable’s partial effects and the grey shadow bands show standard-

error confidence intervals ..................................................................................... 97

Figure 2.8. Estimated smooth terms of four variables on processors’ profitability. Y-axes are

the dependent variable’s partial effects and the grey shadow bands show standard-

error confidence intervals. .................................................................................. 100

Figure 3.1. Fishers’ constraints a) by combined sites, b) by each site. ................................... 118

Figure 3.2. Fishers’ opportunities a) by combined sites, b) by each site ................................ 121

Figure 3.3. Middlemen’s constraints a) by combined sites, b) by each site ............................ 123

Figure 3.4. Middlemen’s opportunities a) by combined sites, b) by each site ........................ 126

xv

Figure 3.5. Processors’ constraints a) by combined sites, b) by each site ............................... 129

Figure 3.6. Processors’ opportunities a) by combined sites, b) by each site ........................... 131

Figure 3.7. Percent of actors with a), access to bank loans b), ownership of equipment c),

equipment support and d) training support ......................................................... 133

1

CHAPTER 1: BACKGROUND OF THE STUDY

General introduction

Contribution of Small-Scale Fisheries (SSFs) to the economy, employment, food security and

nutrition is of significance to riparian countries and populations (Mills et al., 2011; Barnes-

Mauthe et al., 2013; Government of Kenya, 2016a). Key fisheries statistics show that 54% of

fish traded globally in 2014 originated from SSFs in developing countries (FAO, 2016). SSFs

also employ about 88% of the estimated 107 million global fish workers (Mills et al., 2011).

Fish and fisheries provide nutrition and food security to many dependent communities, with

30% of the global population drawing their daily protein intake from fish (Andrew et al., 2007;

Smith et al., 2010; McClanahan et al., 2015; FAO, 2016). Despite this contribution of fisheries

to global food security, SSFs are still not fully appreciated due to inadequacy of data and

information at national and individual scales (Pauly, 1997; FAO and World Fish Centre, 2008;

Jacquet & Pauly, 2008; Cunningham et al., 2009). Inadequacy in fine-scale resolution data has

led to perennial marginalization of SSFs in government policies and interventions, hence

leading to limited private sector investment.

There are several reasons why SSFs are not fully appreciated in their contribution to national

economies. First, much of the focus on contribution of fisheries to national economies has been

in terms of national level indicators e.g. tax revenues, GDP, employment, export volumes and

foreign exchange (FAO and World Fish Centre, 2008; Béné et al., 2016). SSFs do not often

feature much at such macro-economic levels since they are largely informal, and lack requisite

data with higher level indicators (Pauly, 1997; Jacquet et al., 2008). Secondly, Systems of

National Accounting (SNA) place fisheries data at the primary production level under

agricultural sector, while post-harvest data is captured by other government agencies or not

captured at all (UN, 2003; de Graaf & Garibaldi, 2014). Thirdly, SSFs receive less funding and

2

support for data collection since there is insufficient understanding amongst policy makers on

how they plug into national economies, and what their potential contribution is (Simon et al.,

2007; Cunningham et al., 2009). Other factors such as highly geographically dispersed landing

sites, remoteness and lack of political voice to influence policy, partly exacerbates this apathy

(Colloca et al., 2004; Jacquet et al., 2008).

Drawing of the macro-economic picture of SSFs, requires microeconomic indicators at

individual actor’s level (Rodrigo, 2012; Barnes-Mauthe et al., 2013; de Graaf et al., 2014).

However, data collection on these microeconomic indicators (such as income level and its

distribution, supply and demand, costs, capital levels and employment) in SSFs is largely

ignored in many countries (Mills et al., 2011; Barnes-Mauthe et al., 2013; Béné et al., 2016).

Besides understanding micro-economic indicators, knowledge about the operational

environment of SSFs is critical for policy formulation and fisheries management. Several key

questions arise concerning the operational environment, such as; who are the actors in the

fishery? what do they do? who controls what and how? who benefits?, and how are benefits

distributed? who wins and who loses? how are actors affected by external factors?

Consequently, there is need for a systematic and comprehensive approach in an attempt to

answer these key questions.

The operational environment of fisheries activities can be internal or external. The internal

operational environment relates to the structure of the value chain or how actors are organized.

Conceptually, structure includes the level of capital invested, barriers to access capital,

infrastructure and the resource base (Kaplinsky & Morris, 2001; Fews Net, 2008). For

example, lack of capital endowments such as physical and financial capital, acts as steep

barriers to new and less endowed market entrants (Fews Net, 2008). Actors’ positioning based

on structure, also determines their functions, where higher capital-endowed actors coordinate

3

and control operations in the value chain (Kaplinsky et al., 2001; Fews Net, 2008).

Market governance characteristics such as vertical integration are also elements of structure,

which instils disproportionate power and control to some actors and not others (Viaene &

Gellynck, 1995; Kaplinsky et al., 2001; Jordaan et al., 2014). In fisheries for example, high-

capitalized middlemen facilitate fishers and coordinate distribution and marketing of fish,

while low-capitalized small-scale middlemen and fish processors retail and process fish for

sale, respectively (Crona et al., 2010b; Matsue et al., 2014).

Structure also influences price setting mechanisms as well as products in the market, and hence

control of the market and profitability (Banson et al., 2016). It also influences distribution of

benefits due to actor’s positioning in the value chain (Kaplinsky et al., 2001). Markets with

few economically powerful actors, leads to monopolistic or oligopolistic tendencies, where

decision-making and prices are decided by a few individuals (Kaplinsky et al., 2001; Fews

Net, 2008). Impact of capital-based positioning of actors, in processes such as decision-

making, is still poorly understood in Kenya’s SSFs.

Configuration in terms of the value chain structure has significant implications on actor

behaviour termed “conduct” (Bain, 1959). These are the strategies employed by actors based

on their capital-based positioning structural positioning in order to stay competitive in the

market. For instance, more powerful actors often control pricing of goods to their advantage

(Fews Net, 2008; Crona et al., 2010b). Conceptually, conduct relates to marketing practices

such as price information flow (Banson et al., 2016), competitive strategy of firms (Harre &

Pirscher, 2009), choice of fish grades targeted (Lee, 2014; Asche et al., 2015; Sjöberg, 2015),

and price setting mechanisms such as actors colluding to set prices (Pomeroy & Trinidad, 1995;

Fews Net, 2008; Banson et al., 2016).

Extent of market collusion for example amongst powerful middlemen impacts negatively on

4

vulnerable fishers who quickly sell their catch to avoid loss due to perishability (Pomeroy et

al., 1995). Some middlemen also offer lower prices to fishers, in return for guaranteed

purchases (Kulindwa & Lokina, 2013). Information flow about prices is important in value

chains, since it either contributes to open competitiveness or collusion (Fews Net, 2008).

Conduct is considered important due to its key role in informing strategic and competitive

measures taken by actors in the market. Whereas conduct aspects in a market should be

informed by structural factors, studies of this causal-effect relationship in SSFs are scanty.

In turn both structure and conduct influence financial and economic outcomes, termed

“performance” (Bain, 1959). Performance can be described as the desirable financial and

economic outcome that meets social and business expectations of actors (Fews Net, 2008).

Key questions relating to performance include; what quantities are produced? What are the

costs involved? What are the returns on investment? What is the contribution to the economy?

Performance includes analysis of indicators such as costs levels (Pascoe et al., 2015),

profitability (Pascoe et al., 1996b; Brinson et al., 2006), income inequalities (Kulindwa et al.,

2013), contribution to the economy in terms of value added and employment (Kaplinsky et al.,

2001).

Performance indicators in SSFs are widely reported in literature e.g. on profitability of fishing

units in different contexts (Brinson et al., 2009; Maynou et al., 2013; Ba et al., 2017), and costs

by gear and vessel type (Daurès et al., 2013; Guillen & Maynou, 2016). Other authors have

reported on revenue and income under varied contexts, type of value chain and purpose

(McClanahan, 2010; Kulindwa et al., 2013; Wamukota et al., 2014), rate of return on capital

(Boncoeur et al., 2000; Garza-Gil & Amigo-Dobaño, 2008) and employment at local and

global scales (Teh & Sumaila, 2011a; Barnes-Mauthe et al., 2013).

The cause-and-effect relationship between structure, conduct and performance was formally

5

described by Bain (1959) in the Structure-Conduct-Performance (S-C-P) paradigm. The

paradigm postulated that structure influences conduct, which ultimately influences

performance. In SSFs where there is pervasive control by middlemen, the S-C-P approach has

been suggested as a suitable tool for analysis due to its ability to relate performance to conduct

and structure aspects of the market (Pomeroy et al., 1995). However, despite early advocacy

for use of the S-C-P paradigm in fisheries (Pomeroy et al., 1995), not many studies have

adequately employed this approach. This makes it difficult to holistically compare different

SSFs value chains to benchmark with established norms in S-C-P.

The external environment is the second type of operational environment under which value

chains operate. It constitutes the institutional, policy and regulatory environment (Kaplinsky

et al., 2001; Mills et al., 2011; Béné et al., 2016). Fisheries policies and regulations play a

critical role in influencing actors’ operations and performance. For example, they may limit

number of entrants into a fishery, impose minimum allowable fish sizes, impose seasonal and

permanent closures, restrict certain gears, outline fiscal measures, stipulate standards for fish

quality and set prices. However, badly designed policies and regulations can also create new

problems such as disenfranchisement of some actors or increase inequalities (Isaacs et al.,

2007). On the other hand, when policies succeed, they can lead to resource sustainability and

open new opportunities.

Institutional support from state and non-state institutions is also important in building capacity

of players through training and financing (Government of Kenya, 2016b; Melo et al., 2016).

Government financing schemes in particular can have significant implications in employment,

income generation and contribute to rapid value chain development (Loizou et al., 2014; Ngoc

et al., 2015). Although such beneficial interventions to SSFs exists in government policy

6

papers, their implementation towards value chain development remains poorly understood

(Mondaca-Schachermayer et al., 2011).

Governments, remain as chief value chain development agents due to their mandate and larger

implementation capacity, even as non-state actors complement their efforts by performing

other crucial functions (Melo et al., 2016). Collaboration between non-state actors,

government, scientists and industry, for example in studying resource use patterns, eco-

certification schemes, fair trade among other incentivized sustainability measures, can increase

resource sustainability and open new market niches (Field et al., 2013).

Evaluation of government performance in implementation of its value chain development

mandate is important in shaping required reforms (Yeeting et al., 2016). Participation of value

chain actors in such evaluations is useful in gauging their views (Turyahabwe et al., 2017).

Different actors often have varied views on performance of government, depending on their

social, demographic and economic characteristics (Muatha et al., 2017; Olorunfemi et al.,

2017; Turyahabwe et al., 2017). Such studies on complementary roles by different fisheries

management institutions in Kenya’s SSFs are rare. Furthermore, specific studies in Kenya on

impact of policies and regulations on the whole value chain are scarce.

Constraints facing the value chain also form part of the operational environment encountered

by actors. Constraints can be categorized as: i) intrinsic and operational (Olsson, 2009, 2010;

Emdad et al., 2015) and ii) extrinsic and environmental (Salmi, 2015; Pedroza-Gutiérrez &

López-Rocha, 2016). Intrinsic and operational constraints relate to economic and governance

challenges. These include weak governance, poor infrastructure, high costs of operations, poor

access to capital, scarcity of fish and low market demand or prices, economic and political

marginalization (Olsson, 2009, 2010; Andalecio, 2010; Emdad et al., 2015). Extrinsic and

environmental constraints relates to external environment and resource challenges such as

7

overfishing, habitat degradation, resource decline, illegal and destructive fishing practices,

resource use conflicts, siltation and pollution (Andalecio, 2010; Salmi, 2015; Pedroza-

Gutiérrez et al., 2016). While these constraints limit performance of SSFs, resolving them can

turn them into opportunities. There are several studies on constraints and opportunities in SSFs,

for example those by (Olsson, 2009; Emdad Haque et al., 2015 and Pedroza-Gutiérrez et al.,

2016). However, studies analyzing constraints and opportunities in the whole value chain are

scarce. Furthermore, studies that incorporate stakeholder perspectives of constraints and

opportunities in SSF value chains are equally rare.

Robust analysis tools are required to bring comprehensive understanding of SSFs under

complex, varied contexts and across the whole value chain. Value Chain Analysis (VCA) has

been advocated as one of the suitable tools for such analysis (Jacinto & Pomeroy, 2011;

Rosales et al., 2017). The value chain itself is defined as the “whole range of activities required

to bring a product or a service from conception through production to consumption and waste

disposal” (Kaplinsky et al., 2001). Thus VCA in a fisheries context, involves assessment of

economic activities taking place at different nodes such as services and input supplies, fishing,

processing, trading and consumption (Macfadyen et al., 2012). It also includes analysis of

constraints, legal, institutional and policy environment around the value chain (Gereffi &

Kaplinsky, 2001; Kaplinsky et al., 2001; GTZ, 2008).

The S-C-P paradigm approach is also considered to be suitable for analysis of SSFs in order to

bring understanding of how actors operate (Pomeroy et al., 1995). It is recommended that S-

C-P is integrated with VCA in order to systematize and comprehensively analyze value chains

(de Figueirêdo et al., 2014). This integration implies taking a cross-sectional view of value

chain activities at each node with an S-C-P lens. While integration of S-C-P with VCA is

advocated as an important approach to analyze value chains, there is no such study in Kenya,

addressing marine SSFs.

8

However, there are several VCA studies in Kenya addressing SSFs, for example CDA (2007),

USAID (2008), Wamukota et al. (2014), Manyala (2011) and Mwirigi & Theuri (2012) . These

studies are broad in their approach and focus, with some analyzing both fresh water and marine

fisheries at a broad scale but limited on details about marine SSFs, for example USAID (2008).

Others have analyzed specific species and thematic aspects but limited in coverage of the

complexity of SSF value chains for example Wamukota et al. (2014). There is only one study

by Abila (1995) addressing S-C-P in SSFs and only focused on trading nodes in freshwater

fisheries. The present study targets at filling gaps in knowledge and contributes to the growing

literature on SSFs nationally, regionally and internationally.

Statement of the problem

A clear understanding of micro-economic indicators such as revenue, costs, prices of

differentiated products and income inequality amongst others, is useful in building a

macroeconomic picture and higher-level interventions. However, SSFs lack such crucial

information at national and global scales. Kenya is not an exception to this problem despite the

several studies on marine fish value chain. To date, there is still poor understanding of key

microeconomic indicators, across all nodes of the value chain. Analysis of S-C-P in context of

the value chain has also not been adequately addressed in marine SSFs in Kenya, despite the

usefulness of this approach in linking performance to other processes of the value chain.

9

There is also inadequacy of information on the impact of institutional support, policy and

regulatory framework on actors’ performance. Information on the role of institutions

particularly concerning service provision and building capacity of actors, is also poorly

documented. Constraints affecting each node in the value chain and existing opportunities in

SSFs have also not been adequately studied.

With these gaps, it is difficult to build an overall understanding of the economy of marine SSFs

and their contribution to the national economy. This situation also hinders identification of

required interventions to improve income of actors, upgrading of required technologies,

investments in the sub-sector and support to governance. This study integrated S-C-P with

VCA to comprehensively address these information gaps and contribute ideas for improvement

of the sub-sector.

Goal of the study

The goal of the study was to characterize and analyze Kenya’s marine small-scale fisheries

along the value chain so as to generate knowledge useful for value chain improvements and

management.

Specific objectives

Specifically, the study aimed to achieve the following five objectives: -

1. To evaluate the structure of marine small-scale fisheries value chain and implications for

actors

2. To investigate how actors’ conduct is influenced by structure

3. To analyze actors’ market performance along the value chain

4. To examine constraints and opportunities along the value chain

10

5. To analyze actors’ perception of government performance in implementation of value

chain development related policy and regulatory frameworks

Research questions

The study aimed to answer five key questions: -

1. What is the structure and characteristics of small-scale marine fisheries value chain in

Kenya and implications for actors?

2. How does structure influence conduct of different actor groups and the impact to the

value chain?

3. How is market performance of different actors along the value chain?

4. What are the constraining factors and opportunities for improvement in the value chain?

5. What is actors’ perception of government performance in implementation of value chain

development related policy and regulatory frameworks?

Study hypotheses

Study hypotheses were set following objectives as stated below:

1. Objective two; Structure of the value chain based on actor-capitalization has a significant

influence on actors’ conduct

2. Objective three; Structure and conduct have a significant influence on actors’

performance in the context of profitability.

3. Objective five; Actors’ structural factors (value of equipment owned and actor category),

social factors (livelihood rank, education and experience), economic factors

(profitability) and site have a significant influence on their perception about government

performance in implementation of its policy and legal objectives.

11

Justification of the study

Kenya’s small-scale marine fisheries have faced myriad challenges that affect actors’ optimal

financial and economic performance. However, the extent of these challenges is not fully

understood empirically, due to lack of data and information. There is need to understand

constraints facing actors and also document their proposed solutions in order to come up with

the best suited interventions.

There is also need to understand the fisheries internal and external environments that influence

their operations in order to develop required interventions. Internal environment includes

actors’ competitive behavior, their characteristics and how these interact to influence their

financial and economic performance. External environment revolves around how the policy,

regulatory and institutional framework supports or impedes actors’ financial and economic

performance. Empirical understanding of how the internal and external environment impacts

on Kenya’s marine small-scale fisheries is still scanty. Therefore, the present study empirically

contributes to this understanding to inform policy, regulatory and institutional reforms and

value chain governance interventions.

Significance of the study

This study provides key value chain information addressing S-C-P, constraints and

opportunities, institutional, policy and regulatory environment of SSFs. The study sheds light

on processes and impact of actors’ conduct and performance at different nodes. Understanding

these business processes and actors’ actions in SSFs has the potential to improve the whole

12

value chain, including the identification of areas of value chain upgrading, reduction of

constraints and policy reforms.

The present study has analyzed constraints facing actors in SSFs and possible solutions from

the actors’ perspective. Information generated by this study is useful in informing donors and

the private sector regarding value chain development and aspects that need more attention.

Results from the study provide useful information for further development of SSFs value

chains at national and regional levels.

Globally, there are efforts to elevate contribution of SSFs to livelihoods and national

economies by intensifying value chain development interventions (Mills et al., 2011; FAO,

2016; Rosales et al., 2017). FAO Sub-Committee on Fish Trade in its 14th session in 2014,

highlighted the importance of SSF value chain development and proposed strategic

programmes towards achieving this objective (FAO, 2014). This milestone signifies a shift in

global focus towards interventions that support SSFs value chains. This study provides

information that may be useful to this global agenda.

In terms of scholarly purposes, the study integrates VCA and S-C-P in the same study in marine

SSFs in Kenya, for the first time. This approach does not only provide comprehensive analysis

of SSFs, but also capitalizes on integrating existing methodologies. The combination of these

two approaches advances research by looking at the contextual environment under which SSFs

operate such as policy, regulatory and institutional frameworks. The marine SSF sub-sector

value chains have received little research attention in Kenya in the past, this study helps to

close this knowledge gap.

13

Scope of the Study

The current study investigated marine SSFs with respect to S-C-P, policy and regulatory

contextual environments, constraints facing actors and existing opportunities. The study

considers the classical definition of value chains as defined by Kaplinsky & Morris (2001).

The study therefore does not address non-value chain concerns that could be pertinent to SSFs

such as resource-use conflicts, resource sustainability, resource health etc. It is confined within

the term “activities” and only focuses on direct or indirect actions that are associated with

moving the product from fishers to consumers.

Required data and information was obtained from value chain actors starting with fishers,

middlemen and small-scale fish processors. The study did not address value chain input

suppliers due to limitations of getting the few respondents from such a diverse group with

diverse services and incomparable data between themselves and other actor groups. The study

did not also address large-scale fish processors due to the differentiated nature of their business,

in terms of high capital requirements and types of constraints faced, and thus incomparable to

the other value chain actors.

The study was conducted from November to March 2014, June to September 2015, and again

in June and September 2016. Thus, the study covered both North East Monsoon (NEM) which

occur from November to April and South East Monsoon (SEM) seasons from May to October

(UNEP, 1998), where a lot of changes in the fishery occur. These include; changes in number

of actors, movement of migrant fishers, amounts of catches, gears used and fish types caught

and traded (Fulanda et al., 2009; Wanyonyi et al., 2016a).

14

Conceptual model

While the S-C-P paradigm postulates that performance is influenced by structure and conduct

of actors, the study conceptualizes that internal operational and external operational

environment also play a critical role in shaping actors’ performance (Figure 1.1). External

operational environment relates to the regulatory, policy and institutional framework in place

and constraints facing actors. Laws and regulations regulate the operational environment, while

policies set the institutional framework to guide functions as well as coordinate provision of

services. When regulatory, policy and institutional frameworks work optimally, constraints

decrease, and better performance can be realized.

The internal operational environment relates to actors’ characteristics such as demographic and

socio-economic factors. It also includes operational variables such as costs, volumes and sales

handled. Actors who increase volumes traded, while reducing costs can realize higher

performance. The study analyzes how these relationships affect financial performance. The

study tests the role of structure and conduct as well as internal operational factors in influencing

financial performance. The study also analyzes actors’ perception of government performance

concerning its mandate and functions.

15

Figure 1.1. Conceptual model for the study. Adopted from GTZ (2008)

Kenya’s small-scale marine fisheries

Fishing in Kenya’s marine waters is dominated by SSFs with approximately 13,000 fishers

operating about 3,000 vessels (Government of Kenya, 2014a, 2016c). About 73.4% of the

vessels are canoes, 22.1% large wooden boats (Mashua) and only 3.1% are reinforced plastic

boats (Government of Kenya, 2014b). The fishery is largely non-mechanized with only 20%

of vessels using engines and the rest using sails, poles and paddles. The fishery is multispecies

where most commonly used gears are basket trap, gill net, hand line, spear gun and beach seine

(Samoilys et al., 2017). Kenya’s marine fisheries production is estimated at 9,000 Mt/year

(Government of Kenya, 2012, 2014a). However, with the recent revision of the marine fishery

appraising methodology in 2016 by Kenya Fisheries Service, production is now estimated at

24,709 Mt/year, valued at KES 4.612 Billion or 10% of national total production (Government

of Kenya, 2016a). Most fishing is concentrated in the inshore waters, around creeks and the

coral reef lagoons or outer reefs, but mostly still within 12 nm, with little offshore activity

(Samoilys et al., 2017).

16

The fishery has a significant presence of seasonal local and foreign migrants from Tanzania,

who are often facilitated with operational cash and equipment by middlemen. They arrive just

before the onset of North East Monsoon (NEM) season and exit just before the onset of South

East Monsoon (SEM) (Fulanda et al., 2009; Wanyonyi et al., 2016a, 2016b). Mayungu and

Vanga are frequented by migrants (Fulanda et al., 2009) as wells as Malindi, compared to the

low frequency in Shimoni.

Trading of fresh fish is undertaken by middlemen who also act as facilitators in the value chain

by financing fishers and seeking markets (Wamukota, 2009; Crona et al., 2010b). Some

middlemen operate at the primary level where they buy fish directly from fishers, while others

buy from other middlemen at the secondary level (Wamukota, 2009). Fish processing in

Kenya’s SSFs is largely localized, with small-scale fish processors locally known as Mama

Karanga, buying fish from either fishers or middlemen and frying it for sale to consumers

(Karuga et al., 2007; Wamukota, 2009; Matsue et al., 2014). A smaller number of restaurant

operators also fry fresh fish for sale.

Most landing sites in coastal Kenya are poorly developed, lacking critical infrastructure such

as fish depots, cold rooms, jetties/pontoons, potable water facilities, electricity supply, toilet

facilities, equipment repair facilities and roads (Karuga et al., 2007; Government of Kenya,

2016c). By 2016, seven out of nine cold rooms along the whole coastline were functional. Only

11% of landing sites had electricity, 15% with potable water, 10% with engine repair facilities

and 41% with all-weather roads (Government of Kenya, 2016c).

In the study area, Malindi and Mombasa are relatively well served with facilities, though they

still have several deficiencies. For example, despite Malindi having a government-installed

cold room, it had never been operational due to management challenges. Similarly, Vanga also

has a cold room and ice making machine, but due to constant breakdowns, they are hardly in

17

operation. Shimoni and Mayungu by the time of this study had no such facilities. Mayungu

also lacked portable water, electricity, toilets and a fish depot. Mayungu, Shimoni and Vanga

by the time of this study did not have tarmacked roads, thereby posing a great challenge with

road transportation during heavy rains.

Fisheries management is vested in Kenya Fisheries Service under State Department of

Fisheries Aquaculture and the Blue Economy to address policy issues (Government of Kenya,

2016b). Some functions of fisheries management have also been devolved to counties as

outlined in the Kenya Constitution of 2010 (Government of Kenya, 2010). Management

structures also include community level management under Beach Management Units (BMUs)

at landing sites (Government of Kenya, 2007).

18

General review of literature

1.12.1 Description of small-scale fisheries

A significant portion of global fisheries are SSFs (FAO, 2016). While SSFs appear a lot in

literature, their definition is varied depending on the geographical context, technical

specifications, management measures and regulations (Mills et al., 2011; García-de-la-Fuente

et al., 2016). The source of variation is based on description metrics used, primarily; length,

weight capacity and engine power of vessels. There is variation even within continents, for

example in Europe, vessels in Spain falling within a length of 8.8 m - 9.5 m are classified as

SSF (Garza-Gil et al., 2008; García-de-la-Fuente et al., 2013; Maynou et al., 2013), while in

Italy SSFs vessels range from 4.5-15.8 m (Battaglia et al., 2010). In South America, for

example in Peru, vessels with a maximum length of 15m are classified as SSF (Alfaro-Shigueto

et al., 2010). SSFs are further described as fishing units that fish near coastal areas and use

diverse gear to catch multiple species (Colloca et al., 2004; García-de-la-Fuente et al., 2013).

In Kenya, according to the Kenya Fisheries Management and Development Act of 2016

(Government of Kenya, 2016b), SSFs are defined as “small-scale traditional fisheries that may

be carried out for subsistence or commercial purposes in which the owner is directly involved

in the day-to-day running of the enterprise and relatively small amounts of capital is invested

in the fishing operations”. An artisanal fishing vessel is defined as “any local fishing vessel,

canoe or undecked vessel with a length overall of not more than ten meters, which is not

motorized or motorized by an outboard or inboard engine not exceeding forty horsepower, or

powered by sails or paddles, but does not include decked or undecked semi-industrial fishing

vessels or vessels used for recreational fishing”. These definitions of both the fishery and

vessels mirror the internationally agreed definitions.

19

In terms of size of the fishing fleet, there are an estimated 46 million fishing vessels globally

(FAO, 2016). More than 85% of these are below 12 m in length, suggesting that a bulk of

fishing vessels are in SSFs and are small in size (FAO, 2016). Out of these, 6.8 million vessels

are in Africa, where over 75% are <12m in length and <65% un-motorized. In Europe, Maynou

et al (2013) indicates that 81-87% of the vessels are SSFs and range between 12-15 m in length.

While description of SSFs internationally tends to concentrate on the fishing node, other nodes

such as trading and small-scale processing are also part of SSFs. Although fish trading may

transcend beyond the small-scale level, majority of the players operate at the landing site level

thus forming an integral part of SSFs. At this level, traders handle only low volumes of fish

and mostly sell to direct consumers or other small outlets or factory agents and rarely sell

directly to processing factories or large establishments. Small-scale fish processing is also done

in low volumes through simple methods of processing such as smoking, frying and salting.

The small-scale processing node is mainly dominated by women processors (Fröcklin et al.,

2013; Matsue et al., 2014).

1.12.2 Global overview and significance of small-scale fisheries

In 2014, global total fisheries production estimates stood at 167 million tonnes. The Western

Indian Ocean contributed only about 4.7 million tonnes or 5.8% of the marine capture fisheries

production (FAO, 2016). Thirty six percent of the global total production (or 60 million tonnes)

was traded at a value of USD 148 billion, making fish the most traded food commodity globally

(FAO, 2016). From these amounts, 54% came from SSFs (FAO, 2016), thus highlighting

importance of SSFs.

SSFs also provide other benefits such as employment to millions of people dependent on them.

A global review by Mills et al. (2011), estimated that 107 million people are employed by

20

SSFs, or 88% of global fish workers. This excluded workers and fishers spending less than

30% of their time in fisheries activities and also workers in ancillary services such as engine

and boat repairers, equipment manufacturers, ice makers etc. Mills et al (2011b) also estimated

up to 33.1 million fishers in SSFs of developing countries or 97% of global fishers.

A recent estimate by FAO (2016) indicates a higher figure of 37.9 million fishers but also lacks

to give a breakdown of actors in ancillary industries. In a different study of African fisheries,

an estimated 12.3 million people are employed by fisheries, where 50% are fishers, 42.4% are

processors and 7.5% work in aquaculture (de Graaf et al., 2014). Africa scores comparatively

low in employment of women in fisheries where only 27.3% were involved in fish related

activities, compared to the global figure of 47% (Mills et al., 2011; de Graaf et al., 2014).

Fish is important as a source of food with consumption trending upwards despite economic

downturns in many countries over the years (FAO, 2016). Fish production for human

consumption has been growing at a rate of 3.2%, above that of global population growth at

1.6%. This explains the continued growth in per capita consumption to the current 20 kg

person-1 year-1. However, Africa still lags behind with countries like Kenya only recording 4.3

kg person-1 year-1 (Government of Kenya, 2014a).

Fish is today recognized and advocated as an important source of nutrition for humans

especially for children, convalescing adults and mothers (FAO, 2016; Thilsted et al., 2016). It

contains high quality proteins, essential amino acids, essential fats (e.g. long chain omega-3

fatty acids), vitamins (D, A and B) and minerals (including calcium, iodine, zinc, iron and

selenium) (Thilsted et al., 2016). Fish now contributes 15-20% of animal protein intake to at

least 30% of the global population and up to 50% in some Islands and West African states

(Smith et al., 2010; FAO, 2016). It also forms 6.7% of all consumed proteins. This success is

due to transformation of SSFs from subsistence to commercial exploitation and is attributed to

21

consistently rising global demand, liberalized trade, globalization, improved marketing,

transportation, communications and technology (FAO, 2016).

SSFs are also an important component of food security that is sometimes the only source of

protein for a majority of the world’s poor who only undertake simple processing (Andrew et

al., 2007; Smith et al., 2010; Mcclanahan et al., 2015). Small-sized fish, especially pelagics —

a common feature of SSFs, provide food and rich nutrition that benefits the poor. Thus any

negative dynamics such as price increases and scarcity in these fish may have negative

nutritional and food security consequences to the poor (FAO, 2016). However, SSFs also face

challenges of post-harvest losses which is common in developing countries (Thilsted et al.,

2016). This results to loss in nutritional value and jeopardizes food security.

Contribution of fisheries to national and regional economies in Africa is low and typically

contributes between 0.5–2.5% to GDP, with exceptions such as Senegal and Seychelles where

it is higher (FAO, 2005). A more recent study by de Graaf & Garibaldi (2014) reveals that

fisheries contributes an average of 1.26% to the overall GDP with 0.43% coming from marine

SSFs, 0.36% from marine industrial fisheries, 0.33% from inland fisheries and 0.15% from

aquaculture. Post-harvest sub-sector injects about 0.33% directly into the GDP representing

26.19% of Africa’s total GDP from fisheries. Out of this, small-scale fish processing accounts

for 52% of the post-harvest value. Notably these results are from data calibrations and

extrapolations, highlighting the lack of readily utilizable statistics amongst countries and

undervaluing of fisheries contribution to national economies.

1.12.3 Overview of Kenya’s small-scale fisheries

Kenya’s national fisheries production and trade data indicates continuing growth in value but

with recent declines in production (Government of Kenya, 2016a). National total production

22

from aquaculture and capture fisheries (freshwater and marine) dropped to 147,726 metric

tonnes in 2016 from 168,413 in 2014. The ex-vessel value of marine capture fisheries was

KES. 4.6 billion (Government of Kenya, 2016a). Inland waters accounted for 73.2% of

national production, with Lake Victoria accounting for 90.8% of inland production and 66%

of national production.

Kenya’s marine production has been increasing over past decades, with a corresponding steady

increase in value throughout this period, with exception of a few slight drops in value.

However, after revision of the data appraisal methodology in 2016 by the Kenya Fisheries

Service, marine capture production shot to 24,709 Mt from a range of 7,000-9,000 Mt in the

last decade (Government of Kenya, 2016a). Contribution of Kenya’s fisheries to the national

GDP has been small at about 0.54% (de Graaf et al., 2014). The post-harvest sub-sector

contributes only 0.1% to the national GDP with small-scale fish processing accounting for only

10% of the post-harvest value.

In terms of employment, in 2016 it was estimated that 1.2 million people earned their

livelihood from fisheries directly or indirectly (Government of Kenya, 2016a). These

livelihoods are in fishing, trading, farming, processing, direct employment, supplies and

fisheries business-related activities. However, except for fishing and aquaculture, estimates for

other nodes are unavailable. Fishers were estimated at 65,250 and farmers 59,095 (Government

of Kenya, 2016a). Of the national fishers population, marine SSFs accounted for 21% (or

13,417) fishers (Government of Kenya, 2016a, 2016c). The number of fishers in marine SSFs

increased steadily over the last ten years. Fishing vessels in marine SSFs have also been

increasing steadily in the last ten years to 2,974 in 2016.The ratio of craft to fishers has been

oscillating at one craft for every 4-5 fishers on average over the last decade.

Kenya’s marine SSFs show signs of steady mechanization, especially using outboard engines

23

over the last ten years, as use of sails and paddles declined from 2012, while engines increased

by 37% in the period 2014-2016 (Government of Kenya, 2016c). In terms of supportive

infrastructure and services, there has been a steady increase of landings sites and all-weather

roads leading there. The rate of growth of other supportive infrastructure and services such as

fish receiving sheds, cold rooms, portable water sources and toilets has either been very

minimal or stagnated.

1.12.4 Data challenges and perceived low contribution of small-scale fisheries

SSFs are often not fully appreciated despite their significant contributions to food security,

nutrition, employment and GDP to many communities and countries (Pauly, 1997; FAO and

World Fish Centre, 2008; Jacquet et al., 2008; Cunningham et al., 2009). This stems from

incomplete understanding, since many statistics on SSFs are underestimates. For example due

to difficulties of capturing data on ancillary and post-harvest activities in SSFs, the total global

employment figures are believed to be underestimated (Smith, 1979; Morand et al., 2005; Mills

et al., 2011). Lack of disaggregation of employment data in fisheries at different nodes has also

been a challenge in most SSFs and therefore difficult to know contribution of each node to

employment (FAO, 2016). Diversified product portfolio in SSFs’ post-harvest sub-sector also

contributes to problems faced in data collection due to difficulties in standardization and

generalization of data (Mills et al., 2011; de Graaf et al., 2014).

In some instances data from developing countries where a majority of fisheries are SSFs is

totally unavailable or under-reported (Salas et al., 2007; Teh et al., 2011b; de Graaf et al.,

2014). This problem is well illustrated by de Graaf & Garibaldi (2014), who extrapolated data

from 23 countries to infer for the rest of Africa due to unavailability of economic data.

Inadequacy of economic data on SSFs is persistent even in developed nations where data

collection is mandatory (Daurès et al., 2013). Even when data is available, there have been

24

reliability concerns. For example there are questions from researchers concerning reliability of

FAO bi-annual statistics (Pauly & Zeller, 2017a, 2017b) and counter-arguments from FAO

(Ye et al., 2017). This has led to calls for data reconstructions especially from data poor

regions, where most SSFs fall (Pauly et al., 2017a, 2017b).

There has also been concerns about too much focus on macro-economic level indicators e.g.

on tax revenues, GDP, employment, export volumes and foreign exchange, while ignoring

micro-economic-indicators at individual levels (FAO and World Fish Centre, 2008; Béné et

al., 2016). While the focus on macro-economic-indicators as discussed above are important in

giving the global and national overview (Mills et al., 2011; Béné et al., 2016), micro-economic

indicators at individuals’ level are also important in order to build the macro-economic picture

(Mills et al., 2011; de Graaf et al., 2014).

These problems concerning underestimations and lack of data have in effect led to economic

and political marginalization of SSFs in government policies and interventions (Pauly, 1997;

Salas et al., 2007). SSFs have also not been widely promoted amongst the private sector as

areas of investment due to their perceived meagre contribution and low returns. They have also

often been ignored in most food security and nutrition policy debates, partly due to

insufficiency of data (HLPE, 2014; Thilsted et al., 2016).

1.12.5 The value chain approach in data collection

To overcome challenges of inadequacy in data and understanding of SSFs, a value chain

approach is ideal for systematic data collection to improve information for management and

fisheries development interventions. Typically three levels of information are required in order

to attain these goals (M4P, 2008); i) micro-level information on costs of inputs, processing,

logistics and marketing, ii) meso-level information on cost of support services such as training,

25

ice provision, permits and licenses and iii) and macro-level information on analysis of existing

institutional, legal and policy frameworks relevant to SSFs.

These levels of information corresponds to the three value chain levels; value chain players

(micro level), value chain supporters (meso-level) and value chain influencers (macro level)

(Junior et al., 2016). A value chain in the context of fisheries has three main characteristics; i)

sequenced business activities that include input supplies, fishing, processing and

transformation, marketing and consumption, ii) a set of actors that performs these functions

such as fishers, processors, traders and distributors of fish products and iii) a business model

with specific technologies and coordinating mechanisms (GTZ, 2008; M4P, 2008; Macfadyen

et al., 2012).

There has been debate about value chain elements and terminology. However, there is a general

consensus amongst key value chain practitioners (Schmitz, 2005; GTZ, 2008; M4P, 2008) on

adoption of the definition by Kaplinsky and Morris (2001). Prior to Kaplinsky and Morris’

definition, there was considerable work done on value chains, albeit under different

terminologies. For instance, Porter (1985) described the value chain in terms of increasing

firms’ competitiveness through distinct functions in their niche market. The concept supposes

that, to increase a firm’s competitiveness, the firm must seek ways to improve its position by

performing distinct activities at each function and hence creating value for its customers

(Porter, 1985). Although the original conceptualization was hinged on commercial and

industrial firms, the concept is now increasingly used in other enterprises including agriculture

and fisheries.

From the foregoing, Value Chain Analysis (VCA) can therefore be defined as “the process of

documentation of a wide range of variables relating to the value chain such as; structure,

processes, performance, constraints and regulatory aspects among others” (Gereffi et al.,

26

2001; Kaplinsky et al., 2001; GTZ, 2008). In practice VCA involves mapping and analysis of

functions, analysis of the economy and constraints, as well as the regulatory environment

(GTZ, 2008). In fisheries, variables of interest may include input costs; labour and equipment,

costs of services and transformation; gutting, filleting, preservation, transportation; constraints

in accessing capital and markets and regulatory aspects; resource access and quality

maintenance among others. VCAs are also important in documenting baselines useful in

tracking down changes of value chain interventions and evaluating impacts of interventions

(Bolwig et al., 2010; Macfadyen et al., 2012).

1.12.6 The Structure-Conduct-Performance approach

The core of the S-C-P paradigm is its causal links to performance. The paradigm, posits that

structure influences conduct choices of actors, which in turn influence performance outcomes

(Bain, 1959; Fews Net, 2008). S-C-P paradigm originates from Industrial Organization (IO)

economic theory of the firm and was initially used for anti-trust purposes. Primarily it was

concerned with how conduct of firms created monopolistic and oligopolistic, anti-competitive

behaviour that negatively affected performance of smaller firms (Bain, 1959). It has since

been adopted widely in other areas of study other than competitiveness of firms and sectors

(Harre et al., 2009). Its applications today include other areas of study such as strategic network

analysis of supply chains (Klint & Sjöberg, 2003) and food industries and food security

(Viaene et al., 1995; Fews Net, 2008; Harre et al., 2009). One of the strengths of the S-C-P

paradigm is its ability to go beyond market access and price analysis to looking at structural

and actor behaviour to predict performance (Fews Net, 2008).

Whereas the S-C-P paradigm has been in use for many years and across disciplines, there is no

consensus on use of specific indicators that fit all sectors (Harre et al., 2009). Instead,

individual disciplines have highlighted common indicators applicable to them (Viaene et al.,

27

1995; Klint et al., 2003; de Figueirêdo et al., 2014). In fisheries, key elements and indicators

proposed for analysis have been suggested for example by Pomeroy and Trinidad (1995) and

are discussed below.

1.12.6.1. Structure

Structure is important in determination of actor market participation (Kaplinsky et al., 2001;

Fews Net, 2008). Structure in terms of size of the market looks at number of actors, market

share of firms or segments, number people employed per enterprise and average turnover of

firms (Harre et al., 2009). It also includes characteristics and organizational features of a

market (Viaene et al., 1995; Jordaan et al., 2014). Structure is also about market concentration

in terms of the number of players in the market, nature of goods produced and market demand

(Harre et al., 2009; Banson et al., 2016).

Various entry barriers also constitute structure and can be categorized as; endogenous (created

by actors) or exogenous (created by external forces such as policy) (Kaplinsky et al., 2001).

Common entry barriers include; high license and permits fees, lack of access to critical assets

and capital and high tax regimes. Entry barriers are also about advantages held by some firms

over others, which implies potential prohibitive costs to be borne by new entrants but not by

existing ones (Pomeroy et al., 1995). In fisheries, the most studied indicators of structure and

perhaps the most important include barriers to entry in perspective of actor capitalization and

market concentration (Pomeroy et al., 1995).

Capital is an important barrier that determines actors’ entry into value chains (Pomeroy et al.,

1995; Fews Net, 2008). It plays a key role in distribution of power, where higher capitalized

actors functionally wield more power over decisions in the value chain (Platteau, 1984). Such

steep entry barriers lock out smaller players, drives down competition and benefits only few

28

operators (Fabinyi et al., 2016). High entry barriers also disadvantage less endowed actors in

negotiations. For example fishers dependent on middlemen for equipment get

disproportionally low catch share, even when the catch is high (Fabinyi et al., 2016), and low

prices even when demand is high due to fisher-middleman tying-in arrangements (Kulindwa

et al., 2013).

1.12.6.2. Conduct

Conduct entails actors’ behaviour in the market and has a direct bearing on performance in

terms of profitability, growth in revenues, price stability and productivity (Viaene et al., 1995;

Harre et al., 2009; Banson et al., 2016). Conduct can be viewed in two broad forms; production

conduct and market conduct (Jordaan et al., 2014). Production conduct entails production

processes employed during production of goods. Product development and innovation to create

differentiated goods is an example of production conduct and can be used as a measure of

industry competitiveness (Viaene et al., 1995). It is common in high capitalized food industries

and can be observed in the differentiated processed fish products in the West (Roheim et al.,

2007; Bronnmann & Asche, 2016). The level of product development and innovation in SSFs

is low and this may have implications on value addition strategies employed. However, this

topic is not well addressed in SSFs.

Market conduct entails behaviour employed in marketing such as contracting arrangements,

marketing strategies, market information flow and pricing practices such as collusion to set

market prices (Pomeroy et al., 1995; Fews Net, 2008; Jordaan et al., 2014). Actors’ conduct

stems from market’s structural factors such as market concentration which determines

competitive pricing or collusion (Fews Net, 2008). Collusion which is associated with skewed

capitalization, is sustained under high entry barriers (Pomeroy et al., 1995). Such actor

behaviour, also commonly results to high consumer and low producer prices, especially where

29

middlemen are involved (Fews Net, 2008).

Such practices are common in SSFs where fishers receive highly disproportionate low prices

in comparison to final selling prices (Kulindwa et al., 2013). Extent of collusion practices in

SSFs is not extensively discussed. Powerful sellers also commonly practice price

discrimination depending on perceived status. Weaker producers receive a lower price, and

weaker buyers buy at a higher price (Fews Net, 2008). Small-scale fish processors in Kenya

often face this problem from middlemen. They buy fish at high prices, and are overlooked in

favour of hotels that pay higher prices (Matsue et al., 2014).

Apart from price collusion, there are other forms of conduct exhibited in SSFs such as access

to market information, choice of products dealt with and exercise of power to determine prices.

Collusion is worsened by lack of transparency in price information. Thus facilitation of access

to information limits collusion and allows price transparency (Jensen, 2007; Fews Net, 2008;

Courtois & Subervie, 2014; Ranjan, 2017). For example, Sambuo & Kirama (2018) found that

availability of market information to fishers across landing sites in Lake Victoria, enabled them

to negotiate for better fish prices. Patterns and extent of collusion, and access to market

information in capital-differentiated value chains have been scantily addressed in SSFs.

Exercise of power to determine prices is also another form of conduct, and is embedded in

level of actor-capitalization, where less endowed actors are unable to determine or negotiate

prices (Platteau, 1984; Pomeroy et al., 1995; Fews Net, 2008). For example, fishers dependent

on middlemen for facilitation are forced to sell to single buyers, under labour-tying

arrangements (Platteau, 1984; Abraham & Platteau, 1987). Such arrangements often stifle

price negotiations and fair catch shares (Mangi et al., 2007; Fabinyi et al., 2016). Poorly

capitalized middlemen and small-scale processors also face challenges to access fish and

usually buy at higher prices (Matsue et al., 2014). They also depend on sellers goodwill to

30

access fish and are sometimes by-passed in favour of high-end buyers (Matsue et al., 2014).

Such capital asymmetries and price setting mechanisms are well documented in the fishing

node (Crona & Bodin, 2010a; Ferrol-Schulte et al., 2014; Miñarro et al., 2016). However,

studies addressing processing and trading nodes are scarce. Although, capital linked disparities

are still explored poorly, empirically in East Africa, despite being a concern in SSFs (Smith,

1979; Wamukota et al., 2014; Pomeroy, 2016).

Choice of fish type purchased as a form of conduct in fisheries is linked to buyer’s

capitalization and fish grading attributes. It is also a function of types of goods in a market,

innovation and market demand. Common fish grading attributes include; species, size and

quality for fresh fish (Lee, 2014; Asche et al., 2015; Sjöberg, 2015), while species, branding,

origin, package size are considered in processed fish (Roheim et al., 2007; Bronnmann et al.,

2016). These attributes play a key role in pricing, where unclassified fresh fish, low quality

processed products and small-sized fish, attract lower prices than higher graded fish products

(Fröcklin et al., 2013; Matsue et al., 2014; Sjöberg, 2015). These products are likely to end up

at lower end markets dominated by small-scale fish processors and low-income consumers. It

has also been established that targeting of premium quality fish in some fisheries, is dependent

on the level of investment in technology to catch specific fish types (Ferse et al., 2014;

Grydehøj & Nurdin, 2016).

Most studies on fish grade attributes have been conducted in industrial and commercial

fisheries, where auction and scanner data from supermarkets is readily available (Roheim et

al., 2011; Bronnmann et al., 2016). Only a few studies in East Africa exist, for example

Thyresson et al., (2013) analyzing fish marketing in perspective of functional groups and

maturity stages of coral reef fish in Zanzibar. Mbaru and McClanahan (2013) looked at size-

dependent fish prices from gated basket traps in Kenya.

31

1.12.6.3. Performance

Analysis of financial performance has been considered critical in building overall

understanding of SSFs (Pascoe et al., 1996a; Barnes-Mauthe et al., 2013). It plays a pivotal

role in actors’ decisions-making in many ways. For example the choice of gear, fishing

grounds, species caught or bought, and markets targeted are determined by costs incurred and

the expected revenue (Pascoe, 2006). Performance outcomes are also useful in evaluating

impact of management regimes, such as gear restrictions and closed areas on fishers income

(McClanahan, 2010). It can also be used to evaluate impact of value chain development

interventions such as subsidies on incomes (Mondaca-Schachermayer et al., 2011; Ngoc et al.,

2015). Performance in S-C-P perspective is described through several indicators such as

revenue, income and inequality, costs, value added and rate of return on investment or labour.

Revenue is the product of price of a commodity and its quantity. It can also be described as the

accrued returns from sales before subtracting any costs (Brinson et al., 2006). Many actors in

fishing confuse revenue to be income, by not considering costs (Kulindwa et al., 2013). It is

possible to attain high revenue but accrue low income due to high level of costs. For example

high costs of fishing and the disproportionate catch shares between fishers and middlemen,

leaves fishers with lower income (Kulindwa et al., 2013). Indeed several studies have found

that vessel owners get >60% of the net income compared to fishers, although revenues are high

(Maynou et al., 2013; Ba et al., 2017). This problem is perverse and results from client-patron

arrangements that allow fishers to use middlemen’s equipment in return of assured fish

supplies (Kulindwa et al., 2013). These arrangements require fishers to shore up most costs,

but share income with middlemen for their gear, boat and engine separately.

Costs are an important component of performance since profits are linked to costs and reducing

them ensures firms’ competitiveness (Viaene et al., 1995; Klint et al., 2003). In fisheries costs

32

are captured as either variable (those that vary with fishing effort), or fixed (those that don’t

vary with effort) (Brinson et al., 2009; Pascoe et al., 2015). Fixed costs generally include longer

term costs incurred while operating boat, engine, fishing gear and accessories (Brinson et al.,

2006, 2009; Maynou et al., 2013). Variable costs also commonly referred to as running costs

are short term expenses such as fuel, labour, ice, bait, food, landing costs, transport,

communication, maintenance and repairs and miscellaneous costs. There is however a grey

area on cost allocation, for example whether to treat maintenance and repair costs as fixed, or

variable costs (Pascoe et al., 2015). In the present study, these costs have been captured as

variable since they occur frequently in the fisheries studied.

The cost structure is also important in identifying type of activities or segments that have the

highest cost contribution and therefore devise control strategies. The cost structure is varied in

different SSFs, with labour, repairs and maintenance accounting for the highest costs in non-

motorized fishing units (Kamphorst, 1995; Inoni & Oyaide, 2007). In motorized fishing units,

fuel costs have been found to be highest, (>50%) of variable costs (Brinson et al., 2009;

Maynou et al., 2013; Ba et al., 2017). Larger fishing vessels with higher effort and active gear

have also been found to cost more in fuel and maintenance (Daurès et al., 2013; Pascoe et al.,

2015). Generally, variable costs are usually higher than fixed costs (Kamphorst, 1995),

although considerable variations in costs by gear type have been noted elsewhere (Turay &

Verstralen, 1997; Maynou et al., 2013). There is a general lack of information on costs and

cost structure in SSFs, and where the information is available, it has focused on the fishing

node and very little on trading and processing nodes.

Income as the core element of performance is one of the most discussed indicators in fisheries

literature, yet the most confusing as a terminology. Some authors refer to it as net margins,

gross operating income, gross cash flow or financial profit (Whitmarsh et al., 2000; Ba et al.,

2017). However, most studies generally agree on calculation of income as a function of

33

turnover, less operating costs (Brinson et al., 2009; Maynou et al., 2013; Pascoe et al., 2015).

In the present study, income, financial profit or profitability are used interchangeably and are

distinguished from economic profit. Financial profit refers to returns made by an individual

after subtracting all costs and interests, while economic profit refers to returns made after

subtracting all costs, interests and opportunity costs of capital and labour (Whitmarsh et al.,

2000; Brinson et al., 2009). This important distinction implies that it is possible to accrue a

positive financial profit, but a negative economic profit (Brinson et al., 2009).

Economic analysis goes beyond profitability and considers opportunity cost of actors pursuing

one economic alternative against another (Boncoeur et al., 2000; Cinner et al., 2009).

Opportunity cost of labour is considered to be equivalent wages from manufacturing or

minimum rural wage, while opportunity cost of capital is considered as the possible rental

income from equipment, or central bank interest rate (Brinson et al., 2006; Maynou et al.,

2013). Opportunity costs in fisheries highly determine decisions such as choice to stay or exit

the fishery. For example, some studies argue that fishers are unlikely to exit from fisheries with

low opportunity costs of labour, where alternatives are low paying (Boncoeur et al., 2000;

Cinner et al., 2009). In such cases calculation of opportunity cost of labour may be irrelevant

(Boncoeur et al., 2000). This argument resonates with findings of Cinner et al., (2009), who

noted that Kenyan fishers were unlikely to exit the fishery even when incomes are projected to

fall, due to scarcity of alternative livelihoods.

Similarly, concerning opportunity cost on capital, it has been argued that some fishing units

are unlikely to exit the fishery for alternative investments, even when incomes fall due to

limited investment alternatives (Ramírez-Rodríguez, 2017). Fisheries also often face the

problem of capital malleability, where it is difficult to transform capital held in equipment to

other income activities, and thus actors are unable to exit (Duy et al., 2012; Ngoc et al., 2015).

There is only limited information concerning dynamics around opportunity cost of both labour

34

and capital in SSFs and how this compares to earned incomes in the fishery.

Numerous factors influence profitability in fisheries value chains. In fishing, profitability is

influenced by number of days fished, distance to fishing grounds, capital invested, vessel type

and crew size (Long et al., 2008; Maynou et al., 2013). In trading and processing, it is

influenced by marketing experience, level of capitalization and education (Obayelu et al.,

2016), among other factors e.g. location of the market.

Profitability is also influenced by structural factors, where for example, fishers with lower

capitalization are often disadvantaged in un-equitable catch shares due to dependence on

middlemen (Kulindwa et al., 2013; Wamukota et al., 2014). Studies addressing variation in

profitability and influencing factors in SSFs are scanty. Furthermore, studies addressing these

variations in downstream nodes are rare. There has however been recent global interest in

analysis of financial performance to address such gaps (Brinson et al., 2006; García-de-la-

Fuente et al., 2013). In Kenya, several studies have also been undertaken e.g Wamukota

(2009), Wamukota et al., (2014) and Wamukota & McClanahan (2017) addressing some

aspects of financial performance.

1.12.7 Institutional and regulatory environment

Contextual factors within and outside the fishery such as policy, institutional and regulatory

environment are important in determining outcomes in the fishery. For example, value chain

development functions such as provision of infrastructure and equipment, training of actors,

provision of extension services, organizing of actors, support to marketing and value addition,

and promotion of research and development are highly dependent on the policy, institutional

and regulatory framework (Lin et al., 2014; Donovan et al., 2015; Even & Donovan, 2017).

35

The regulatory framework also plays a critical role in determining costs of operations, licensing

regime, price of fish products and hence income. Some policy-related interventions, for

example provision of subsidies on fuel and fishing equipment can have long term positive

impacts on fishers’ income (Horemans et al., 1994; Turay et al., 1997). Fisheries development

and management are also imbedded in policy and regulatory framework, which also outlines

future strategies (Parris, 2010; Sunoko & Huang, 2014; Yeeting et al., 2016). The policy and

regulatory frameworks also shape other aspects, such as resources access, equitable benefit

sharing, level of investments and incentives for fisheries development (Sowman et al., 2014).

A robust institutional framework to implement policies, laws and regulations is required for

guidance and fostering sound management of the resource base (Dang et al., 2017). A weak

institutional framework may lead to haphazard implementation and ineffective measures that

could affect the whole value chain negatively (Pedroza-Gutiérrez et al., 2016). Institutions also

play different roles such as improving performance of the value chain and creating an enabling

policy environment (Loc et al., 2010; Lin et al., 2014; Yeeting et al., 2016).

The extent to which value chain development measures, policy, institutional and legal

provisions address needs of the value chain can be varied and requires constant evaluation.

Such evaluation is necessary in order to identify weaknesses and implement reforms that meet

needs of both actors and the resource base (Yeeting et al., 2016). The institutional framework,

which provides feedback to political and legislative levels of governance should also be

regularly reviewed to conform to policies and laws (Yeeting et al., 2016).

In fisheries, there are numerous studies analyzing policy and legal frameworks related to

management and conservation (Wever et al., 2012; Muawanah et al., 2018; Shamsuzzaman &

Islam, 2018). There is also considerable work on policy and legal frameworks in industrial

fisheries (Lin et al., 2014; Yeeting et al., 2016). There are however inadequate studies focusing

36

on analysis of value chain development related policies and laws targeting SSFs. Furthermore,

analysis has rarely been along the whole value chain and from actors’ perspectives.

Consultations with value chain actors concerning policy and legal frameworks, has added value

since it considers their views, gauges adequacy of the frameworks and determines required

reforms (Turyahabwe et al., 2017). It is also crucial to bring actors’ views in evaluations, in

order to voice concerns of the marginalized (Sowman et al., 2014).

Actors’ awareness and perception of government performance in policies and laws is

influenced by myriad factors such as education, age, livelihood options, and income.

Awareness is known to improve as level of education and income increases (Turyahabwe et

al., 2017). On the contrary, actor awareness and perception diminishes amongst actors

dependent on multiple livelihoods, due to lack of focus on any particular livelihood

(Turyahabwe et al., 2017). Other studies have found that factors such as income, education and

age influence access to services outlined in polices and laws e.g. extension services and

training (Muatha et al., 2017; Olorunfemi et al., 2017). Information on influence of such factors

on actors’ perception of government performance in value chain development policies and

laws is scanty in SSFs.

Kenya’s fisheries institutional and regulatory framework dates to the colonial era. Under the

colonial government, fisheries were managed through the fisheries protection ordinance of

1908. After independence in 1963, a host of amendments to the colonial law were undertaken,

giving rise to the Fisheries Act Cap (378) of 1989, later amended in 1991, 2007 and 2012

(Government of Kenya, 1991). More recently, the fisheries law was revised to conform to

provisions of Kenya’s new constitution of 2010, giving rise to the Fisheries Management and

Development Act (FMDA) No. 35 of 2016 (Government of Kenya, 2016b). In addition,

Kenya’s fisheries management has since 2008 been guided by the National Oceans and

Fisheries Policy (2008) (Government of Kenya, 2008).

37

Kenya’s 2010 constitution (Government of Kenya, 2010), provides for every treaty entered by

the country, to be part of its laws. Thus, international treaties, conventions and agreements

signed and assented to are deemed to be part of Kenyan fisheries management laws. The key

international instruments include; United Nations Convention on the Law of the Sea

(UNCLOS) of 1982, FAO Compliance Agreement of 1993 to Promote Compliance with

International Conservation and Management Measures by Fishing Vessels on the High Seas,

United Nations Fish Stock Agreements (UNFSA) of 1995, Code of Conduct for Responsible

Fisheries (CCRF) of 1995, International Plan of Action to Prevent, Deter and Eliminate Illegal,

Unreported and Unregulated Fishing (IPOA-IUU) of 1999 and Port State Measures Agreement

(PSMA) of 2009.

Institutional framework to manage fisheries has changed from time to time in tandem with

changes in government structure, leading to the de facto management body being a department,

ministry or parastatal. For example between 2008 and 2012, fisheries were managed by a fully-

fledged Ministry of Fisheries. From 2012 to 2015 they were managed under a department in

the Ministry of Agriculture, Livestock and Fisheries. Since 2017, as per provisions of FMDA

(2016), fisheries management is under transition to be managed by the Kenya Fisheries

Service; a parastatal with semi-autonomous status in the Ministry of Agriculture, Livestock,

Fisheries and Irrigation. Fish marketing is also under transition and will be coordinated by the

Kenya Fisheries Marketing Authority (KFMA). FMDA (2016) also establishes the Kenya

Fisheries Advisory Council to advice the government on fisheries issues and priorities.

There are other institutions such as the Kenya Wildlife Service and Kenya Maritime Authority

that have jurisdictions in some aspects of fisheries management such as managing marine parks

and reserves and regulation of water vessels respectively. Under the new constitution and

FMDA, counties, which form the second level of administration in Kenya, are also mandated

to perform some devolved functions related to fisheries management and development.

38

Kenya’s new constitution advocates for inclusiveness and thus communities, civil society and

the private sector have a recognized role in fisheries management as part of public

participation.

Despite Kenya’s historical and elaborate institutional and regulatory frameworks on fisheries,

only limited literature on their impacts in Kenya exists (Karuga et al., 2007; Kamau et al.,

2009; Manyala, 2011). These authors cite challenges facing fisheries institutional and

regulatory frameworks as; fractured institutional framework, inadequate and un-harmonized

laws, overlapping or contradicting mandates, unimplemented statutes, and unsupportive

policies. Weaknesses in organizational capacity of community groups involved in fisheries

management are still evident, and this hampers progress in SSFs development (Karuga et al.,

2007; USAID, 2008). This is despite adoption of the fisheries co-management framework

under the Beach Management Unit (BMU) gazette notice of 2007 (Government of Kenya,

2007). Specific empirical analysis of the impact of institutional, policy and regulatory

frameworks on value chain development of Kenya’s SSFs is still scanty.

Kenya’s first National Oceans Fisheries Policy (Government of Kenya, 2008) attempted to

address some of these challenges, but information on the effect of this policy to small-scale

fisheries to date is unavailable. Linkages between economic performance of SSF actors, and

institutional and regulatory framework in Kenya have also not been adequately addressed in

studies. With the reformed fisheries law, FMDA (2016), it is expected that most of these

challenges will be addressed.

1.12.8 Constraints and opportunities

SSFs face myriad constraints that hamper their full potential to attain economic goals. A vast

majority of constraints facing SSFs actors are extensively documented in literature, and mostly

39

address extrinsic environmental, resource or governance issues. These include; economic and

political marginalization, perennial underfunding, lack of political voice, poor infrastructure,

overfishing, habitat degradation, resource decline, poverty, threats from commercial vessels

fishing inshore, illegal and destructive fishing practices, resource use conflicts, siltation and

pollution and weak governance (Salas et al., 2007; Andalecio, 2010; Salmi, 2015; Pedroza-

Gutiérrez et al., 2016). SSFs also face value chain intrinsic operational constraints, such as;

lack of infrastructure and equipment, poor transportation, high costs of operations, poor access

to capital, scarcity of fish, low market demand and low prices (Olsson, 2009; Emdad et al.,

2015; Pedroza-Gutiérrez et al., 2016).

In Kenya’s marine small-scale fisheries, only few studies analyzing value chain related

constraints and opportunities exist. Karuga et al. (2007) identified constraints facing marine

fisheries value chain as; inaccessibility and high cost of inputs, low capacity to access offshore

fisheries, declining stocks and catch rates, inadequate policy support, weak actor

organizational capacity, inadequate cold chain facilities, lack of business management skills,

lack of appropriate markets, lack of market information, poor roads and landing site

infrastructure, low value addition, lack of banking services and actor’s saving culture and

inadequate training on various areas.

Wamukota (2009) identified fish marketing constraints as; lack of ownership of equipment by

fishers, lack of cold chain facilities and poor means of transport. Karuga et al. (2007) also

identified opportunities as; facilitation of training in organizational, managerial and business

skills, facilitation to acquire modern fishing equipment, improvement of the cold chain, full

implementation of the fisheries policy, promotion of aquaculture and support to formation of

producer and trading groups. Despite these analyses, there is still lack of studies that

empirically rank importance of constraints and opportunities in fisheries in Kenya. Impact of

interventions to resolve constraints has also not been adequately addressed in Kenyan SSFs.

40

Although SSFs actors often face similar constraints, there are certain distinctions at different

value chain nodes. For example, fishers often face constraints related to lack of ownership of

equipment, low fish prices, lack of access to capital, lack of bargaining power, environmental

degradation, overfishing and lack of safety and security at sea (Emdad et al., 2015; Salmi,

2015; Pedroza-Gutiérrez et al., 2016). Some of the constraints facing fish traders have been

noted as high transportation costs, high start-up capital, fish scarcity, lack of marketing

premises and high levies (Obayelu et al., 2016). Small-scale processors face a different set of

constraints such as lack of capital, lack of access to fish supplies, price discrimination and sex

for fish transactions (Béné & Merten, 2008; Fröcklin et al., 2013; Matsue et al., 2014).

Presence of persistent constraints often masks opportunities existing in a fishery. As such,

many opportunities are not well analyzed or documented due to perceived low returns and

general marginalization of SSFs (Salas et al., 2007; Mills et al., 2011; Pedroza-Gutiérrez et al.,

2016). Therefore, analysis of constraints facing SSFs actors, can help to realize their full

potential by identifying existing opportunities. Involvement of all nodes of the value chain in

the analysis is also crucial, since it allows wider consultations and incorporation of local

concerns (Pita et al., 2010; Verweij et al., 2010). Local participation also allows incorporation

of Local Ecological Knowledge (LEK) in analyzing situations and making decisions

(Andalecio, 2010).

Description of the study area

The study was conducted along the Kenya coastline at five sites namely; Malindi-Shella and

Mayungu landing sites in the North, and Shimoni and Vanga landing sites in the South (Figure

1.2). Malindi and Shimoni landing sites are adjacent to Malindi Marine Protected Area and

Kisite Mpunguti Marine Protected Area respectively. Mayungu and Vanga landing sites are

farther from Marine Protected Areas (MPAs). Mombasa study site included fish markets at

41

Majengo, Gulshan and Tononoka. They are centrally located and represent a convergence

market for fish from other coastal locations. Their inclusion was to capture secondary

destination markets and dynamics around them.

This geographical spread represents diversity of activities and actors, which allowed

comprehensive study of value chains and actors’ competitive behaviour. Malindi and

Mombasa are urban areas, with Mombasa being the largest city along the coast and Kenya’s

second largest city. Mayungu is rural and closer to Malindi, but with relatively poor access,

while Shimoni and Vanga are rural and farther away from urbanized Mombasa.

Administratively, Malindi-Shella and Mayungu respectively fall within Shella and Watamu

sub-locations within Malindi sub-county of Kilifi County. Shimoni and Vanga respectively fall

within the Pongwe Kikoneni and Lunga lunga sub-locations, Lunga lunga sub-county of Kwale

County. The fish markets at Majengo, Gurshan and Tononoka Mombasa fall within Mvita sub-

county of Mombasa County.

Malindi has the highest population (54,556) among the study sites with 14,488 households

(Government of Kenya, 2019a). Shimoni has a population of 6,520 with 1,618 households.

Vanga has a population of 5,888 with 1,190 households. Mayungu’s landing site population is

not disaggregated, but the wider Watamu sub-location has a population of 12,286 with 3,575

households. Two major religions dominate the study sites. About 61% of the population in

Kwale county professes Islam, while 36% professes Christianity. In Kilifi county, 18% of the

population professes Islam, while 68% professes Christianity.

42

Figure 1.2. A map of the Kenyan coastline showing study sites. Source Kimani, (2020)

43

Education, which determines many aspects of the economy such as access to opportunities and

skills, is varied in the study sites. In Lunga lunga sub-county, at least 31% of the population is

illiterate, compared to the national average of 16.3% or 19.6% of national rural population and

8.8% of national urban population (Government of Kenya, 2019b). In Malindi sub-county at

least 15.6 % of the population is illiterate. However, 42.6% of the population is currently

enrolled for learning in Lunga lunga sub-county compared to 43.9% in Malindi. This suggests

that current efforts by Kwale county to promote education are narrowing the literacy gap

between itself and other coastal counties. About 26% of the total labour force in Kwale county

aged (15-64) is either unemployed or underemployed, compared to a national average of 30%,

while that of Kilifi is 32% (Government of Kenya, 2019b).

In terms of technology, mobile penetration which is useful in communication, access to

information and mobile money transfers and lending, is lower in the study sites compared to

the national average penetration of 47% (Government of Kenya, 2019b). In Lunga lunga sub-

county, mobile penetration is 27.7%, but higher in Malindi sub-county at 41.9%. Electricity,

which is important in various operations at the household level including fish preservation,

charging phones and lighting has varied penetration at the study sites. At Lunga lunga sub-

county it is about 14.9%, compared to the national average of 50.4%, while Malindi has the

same penetration as the national average (Government of Kenya, 2019b).

Economic activity within the study area is varied. Agriculture is widely practised in the study

area, where subsistence farming dominates compared to commercial farming. In Lunga lunga

sub-county, 96% of households practise subsistence farming, while only 4% practise

commercial farming (Government of Kenya, 2019b). In Malindi sub-county, 98% of

households practise subsistence farming, while only 2% practise commercial farming. Key

crops grown in the sites include; maize, sorghum, rice, beans, cassava, sweet potatoes, green

grams, bananas, tomatoes, onions, ground nuts, millet, watermelons, kales, sugarcane and

44

cotton (Government of Kenya, 2019b).

Tourism is also an important economic activity within the study sites. For example, Kwale

county has 11 tourist class hotels with a bed capacity of 3,053, and has plans for development

of the Diani resort city as a vision 2030 flagship project (County Government of Kwale, 2018).

Kilifi county has eight classified hotels with a bed capacity of 1,026 (County Government of

Kilifi, 2018). Industrial investments that contribute to Kwale county economy include; Coast

Calcium Limited mining calcium, Base Titanium mining titanium ore, Bixa Limited farming

and processing bixa and Kwale International Sugar Company farming and processing sugar.

Kilifi county has about 22 key industrial investments that include; three cement factories,

several sand and salt mining and processing industries and several Export Processing Zones

(EPZ) and other industrial parks (County Government of Kilifi, 2018).

In terms of fisheries, the study sites are dominated by SSFs, just like Kenya’s marine fisheries,

where SSFs account for 98% of landings (Government of Kenya, 2016a). Like the rest of the

coast, the fishery is multi-gear, multispecies with basket trap, gill net, hand line, spear gun and

beach seine as the major gears in use (Okemwa et al., 2017; Samoilys et al., 2017). Key fish

species landed include; snappers, rabbitfishes, emperors, sweetlips, unicornfishes,

surgeonfishes, tuna, mackerels, jacks and trevallys, molluscs, sharks, rays and crustaceans

(Government of Kenya, 2016a; Okemwa et al., 2017).

Catch rates at the study sites are varied by gear, where at Shimoni and Vanga, small-scale purse

seines land 15.1 Kg fisher-1day-1, large mesh gillnets 8.25 Kg fisher-1day-1 reef seines 4.16 Kg

fisher-1day-1, small mesh gillnets 7.23 Kg fisher-1day-1, beach seines 2.77 Kg fisher-1day-1 and

handlines 4.5 Kg fisher-1day-1 (Okemwa et al., 2017). A study by Munga et al., (2014) in

Malindi found that the highest catch rates was amongst canoe-gillnet and mashua-gillnet

combinations, and the lowest was amongst foot-handline and foot-seine net combinations.

45

There were also no significant differences observed between propulsion and gear categories.

In terms of biophysical characteristics, Malindi, Mayungu, Shimoni and Vanga fall within

Eastern African Marine Ecoregion (EAME), which is one of the ten global marine ecoregions

with high biodiversity of importance (EAME, 2004). Within the larger EAME, lies

Msambweni-Tanga seascape where Shimoni and Vanga fall and Mida Creek-Malindi

seascape, where Malindi and Mayungu fall. These seascapes possess key marine habitats that

include; coral reefs, seagrass beds, mangrove forests and open waters.

Fringing coral reefs are a common feature of the study sites, just like the rest of the Kenyan

coast. The coral reefs are dominated by coral families Portidae (Porites and Goniopora spp.)

and Faviidae (Meandrina and Favia species) (UNEP, 1998). Some of the common fish species

found in coral reefs include; parrot fishes, surgeonfishes, unicornfishes, moray eels,

damselfishes, wrasses, angelfish and scorpion fish. In Malindi around the Malindi Marine Park

(MMP), about 55 coral genera were recorded before the 1998 coral bleaching event associated

with the El Niño Southern Oscillation warming anomalies (Lambo & Ormond, 2006).

However, after the bleaching event, diversity was reduced to 23 genera with a coral cover of

5.1% within MMP and 2.1% within the Malindi marine reserve (Lambo et al., 2006). In

Southern Kenya after the 1998 bleaching, coral cover was reduced to 19.5% in protected reefs

and 14.4% in unprotected reefs (Obura et al., 2002).

Like the rest of the Kenyan coast, the study sites have seagrasses beds commonly encountered

in back-reef lagoons, where up to 12 seagrass species have been encountered in Kenya

(Ochieng & Erftemeijer, 2003). Seagrasses support up to 100 species of fish such as

parrotfishes, goatfishes and rabbitfishes. They also support the endangered green turtle

(Chelonia mydas), hawksbill turtle (Eretmochelys imbricata) and the dugong (Dugong dugon)

(UNEP, 1998; Ochieng et al., 2003). Macroalgae are also an important feature of the seagrass

habitats where up to 50 species have been recorded (Ochieng et al., 2003).

46

Mangroves are an important resource and habitat in Kenya’s coast, where nine species have

been recorded. They play a critical role as a habitat for many fish species and invertebrates that

spend up to 90% of their lifetime within mangroves e.g. prawns (Penaeus indicus, Penaeus

monodon, Penaeus semisulcatus, Metapenaeus monoceros); crabs (Scylla serrata, Uca spp.,

Sesarma spp. and Birgus latro); molluscs (oysters such as Brachydontes spp. and Crassostrea

cucullata) (UNEP, 1998). They also form an important sediment trap from inland runoff

before it flows onto seagrass and coral habitats, hence offering protection against sediment

plumes that leads to death of corals and seagrasses. In the study sites, Vanga has the highest

mangrove coverage with 4,265 ha, while Funzi Bay has 2,715 ha (UNEP, 1998).

The study sites share similar climatic characteristics as the rest of the Kenyan coast. They

experience a modified equatorial type of climate with two rainfall peaks influenced by the

Inter-Tropical Convergence Zone (ITCZ) low-pressure belt. This results to a short rainy season

between March and May and a long rainy season between October and December with an

annual rainfall range of 1000 to 1200 mm (ASCLME, 2012). However, Southern coast receives

more rainfall than the Northern coast. Mean daily temperature is approximately 27°C while

humidity averages about 80% (ASCLME, 2012).

Increasing climatic changes and variability are continually influencing precipitation and river

flow patterns along the coast. Affected rivers within the study sites include; Rivers Sabaki in

Malindi, Ramisi in Funzi Bay and Shimoni and Umba in Vanga (ASCLME, 2012). They are

usually loaded with sediments, especially during the rainy seasons which significantly impacts

on floodplains, deltas and coastal ecosystems. The sediments are rich in nutrients that

profoundly increases primary productivity. The highest phytoplankton production and

zooplankton abundance occurs between March and May, and November and December when

there is highest sediment load from heavy rains (ASCLME, 2012). The highest productivity

has been recorded around estuaries of Rivers Sabaki, Ramisi and Umba, (ASCLME, 2012;

47

Kiteresi et al., 2012). Upto 88 taxa of phytoplankton were recorded in estuaries of Rivers

Ramisi and Umba (Kiteresi et al., 2012). Fish eggs and fish fry also increase in abundance

during these periods where they feed on the abundant phytoplankton and zooplankton (Kiteresi

et al., 2012).

Although sediment load increases primary productivity around river mouths, it is also

detrimental to marine habitats. For example, River Sabaki discharges a sediment load of

between 30 tonnes day-1 during low river runoff and 133,000 tonnes day-1 during high river

runoff, and upto 2 million tonnes year-1. This has had a profound smothering effect on the

corals in Malindi Marine Park and surrounding reserve (UNEP, 1998; Kitheka, 2013; Kitheka

et al., 2014). Sediment deposition by River Sabaki around Malindi also explains the low coral

cover and patchiness of the reefs there (Obura et al., 2002).

Research design and sampling

The study employed individual surveys as the primary data collection tool to gather key value

chain related information as well as Focus Group Discussions (FGDs) to identify and rank

constraints and opportunities. Actors targeted included fishers by gear, middlemen and

processors (small-scale processors and small-scale restaurant operators). Interviews data was

collected in a period of nine months every month between November to December 2014,

January to March and June to September in 2015. Data on FGDs was collected in September

2016. Therefore, responses covered both North East Monsoon (NEM) and South East

Monsoon (SEM) seasons. The study design and sampling are described below.

48

1.14.1 Study design

The study was based on the mixed methods design. The mixed methods design is grounded on

the pragmatic paradigm that seeks a middle ground to address complex research problems

requiring both quantitative and qualitative approaches (Creswell, 2013). The pragmatic

paradigm is a compromise between the positivist and the interpretivist paradigms, also known

as relativist or constructivist paradigms (Denzin et al., 1994; Creswell, 2013).The positivist

paradigm supposes that the truth is singular and is inclined to quantitative techniques of

research, which uses measurable data to infer conclusions. Positivism is built around analyzing

problems from a cause and effect perspective (Denzin et al., 1994; Creswell, 2013). On the

other hand, interpretivist paradigm supposes that there is no one truth but rather multiple

viewpoints about phenomena. It seeks participants’ perspectives and constructs meaning out

of their experiences to infer conclusions (Creswell, 2013). It uses qualitative techniques that

employ inductive approaches to theorize and make conclusions.

In the present study, data with both quantitative and qualitative elements were collected. Thus

the mixed methods design was used as outlined below, based on the respective objectives;

1. In objectives one, two and three, quantitative elements included structure and

performance variables where data on cost of equipment, fish catches, prices and

incomes was collected. Qualitative aspects included conduct variables, where further

probing of respondents was conducted to obtain information on collusion practices,

power to set prices, access to information prior to transactions and fish grade targeted.

Data for the three objectives was obtained through field interviews using structured

questionnaires.

2. In objective four, quantitative aspects included respondents’ ranking of constraints and

opportunities using the Analytical Hierarchical Process method. Data was obtained

through focus group discussions. Data on extent of support services was obtained

49

through field interviews using structured questionnaires together with that of objectives

one, two and three. Qualitative aspects included further probing of respondents’

rankings, in order to gain more insight about their choices.

3. In objective five, quantitative aspects included respondents’ views on government

performance in implementation of value chain development objectives outlined in

Kenya’s fisheries policy and legal framework. Their responses were based on the Likert

scale. Data was obtained through field interviews using structured questionnaires

together with that of objectives one, two and three.

1.14.2 Sampling

Sample size of interview respondents was determined using the Slovin’s formula (Yamane,

1967; Tejada et al., 2012) as below;

𝑛 =𝑁

1 + 𝑁(𝑒)2

Where n is the sample size, N is the estimated population size and e is the level of precision at

95% degree of confidence.

The selection of respondents followed procedures described in Cochran (1977), Loc et al.,

(2010) and Wamukota et al., (2014, 2015). This involved systematic sampling, where every kth

respondent by actor category was interviewed. For fishers, boat captains were purposively

sampled in order to capture fishing unit variables. Respondents were interviewed by the author

and one trained data collector at landing sites and markets. The sampling frame of respondents

by actor group and site is shown below, where vessels were the target unit for fishers (Table

1.1).

50

A total of 403 respondents were interviewed comprising of 73 middlemen, 108 processors and

222 boat captains (representing fishing vessels) out of an estimated population of 601

respondents (109 middlemen, 157 processors and 335 vessels) (Table 1.1). Challenges

encountered included actors decline to be interviewed and seasonal migrations. This resulted

to a reduced total sample size of interviewed respondents.

51

Table 1.1. Sampling frame of respondents interviewed by actor group and site

Malindi Mayungu Shimoni Vanga

Mombas

a Total

Actor group Pop

ula

tion

Sam

ple

d

Pop

ula

tion

Sam

ple

d

Pop

ula

tion

Sam

ple

d

Pop

ula

tion

Sam

ple

d

Pop

ula

tion

Sam

ple

d

Pop

ula

tion

Sam

ple

d

Fishers

Basket trap 17 11 18 12 28 27 38 28 - - 101 78

Cast net 8 5 5 1 4 1 5 - - 22 7

Drifting gillnet 20 4 4 7 5 8 3 - - 39 12

Reef seine net 2 5 3 3 1 14 7 - - 24 11

set net 13 8 3 1 13 8 12 9 - - 41 26

Handline 40 34 15 10 23 20 30 24 - - 108 88

Sub-total fishers 100 62 50 27 78 62 107 71 - - 335 222

Middlemen and processors

Middlemen 27 23 19 12 17 11 31 20 15 7 109 73

Fish processors 43 22 55 47 15 12 44 27 - - 157 108

Sub-total Middlemen and

processors 70 45 74 59 32 23 75 47 15 7 266 181

Total 170

10

7 124 86 110 85 182 118 15 7 601 403

Note; The study focused on trading at the secondary level in Mombasa to follow trading of fish from the other sites. Therefore,

fishers in Mombasa are not included in the study. Processors were also not part of the study in Mombasa since they target fish

from primary middlemen and not secondary middlemen due to the high prices at this level.

51

52

CHAPTER 2: STRUCTURE, CONDUCT AND PERFORMANCE IN

SMALL-SCALE FISHERIES VALUE CHAIN

Introduction

This chapter analyzed how structure in Small-Scale Fisheries (SSFs) influenced actors’

conduct and how structure, conduct and other factors influenced their financial profitability at

selected sites in Kenya’s coast. Structure was based on level of actors’ capitalization, where

present value of equipment for middlemen and processors was used, while percent ownership

of equipment for fishers was used. This was also used in further analysis of influence on actors’

profitability. Structure was also analyzed based on actor grouping by type of fishing (offshore

or inshore) and trading (primary or secondary). It was also analyzed in context of market

concentration based on number of actors by market share of fish purchases amongst

middlemen.

Conduct was considered in context of actions taken by actors to remain competitive in the

market. Conduct variables considered included choice of products dealt with, in context of fish

grade, where grades 1+, 1, 2 and 3 in order of decreasing price were used. It also included

analysis of power held by individuals to determine buying and selling prices. It also included

actors’ tendency to jointly collude in setting buying and selling prices. Conduct also included

actors’ access to information on buying and selling market prices prior to transactions.

Influence of structure on conduct factors was tested, and was based on value of equipment

amongst middlemen and processors and percent ownership of equipment amongst fishers.

Performance was examined in perspective of financial profitability. First, important variables

used to derive profitability were analyzed before its computation. These include; fish

quantities, purchases, sales, fixed costs and variable costs. Factors influence of influencing

53

profitability were also tested and included conduct factors as well as other factors including;

age, experience, education, cost, sales, access to credit, actor category and site.

The study hypothesized that capital influenced conduct of actors in terms of fish grades

targeted, price collusion, access to price information and power to set prices. Conversely the

study also hypothesized that actor capitalization and conduct influenced profitability. The

study had the following objectives; 1) analyze actors’ performance in marine small-scale

fisheries in Kenya on key indicators; purchases, sales, costs and financial profitability, 2)

analyze factors driving financial profitability, including demographic, socio-economic,

structure and conduct factors. Results of the study are applicable in policy and value chain

development interventions.

Materials and methods

Data was collected through field interviews targeting fishers (vessel captains), middlemen and

processors as described in section 1.14, and included indicators described below.

2.2.1 Data collection on structure

Indicators used to derive structure included cost of equipment in local currency (KES), its

ownership and age. These were used to categorize actors by level of capital. Middlemen’s

source of fish was also recorded as; primary middlemen (sourcing fish directly from fishers)

or secondary middlemen (sourcing from other middlemen). Fishers’ primary gear was also

recorded. Fish sales data was also recorded for calculation of market concentration.

54

2.2.2 Data collection on conduct

Respondents were asked to state if they participated in market behaviour such as collusion and

power in setting fish prices. To infer collusion, respondents were asked if they consulted other

actors to set prices. To infer power to determine fish prices, respondents were asked if they

were involved in price-setting through dictation, negotiation or took prevailing market prices.

Respondents were also asked to indicate if they had access to fish buying or selling price

information, prior to transactions.

Choice of fish grade targeted was also recorded. It was based on a three-point local fish grading

system that is dependent on size and species, and it is used to determine prices. Each respondent

was asked to list five most targeted fish types in local nomenclature, grade and price. Fish price

was recorded as; lowest, average and highest, thus capturing lower and upper bound limits.

The four common fish grades recorded were grades 1+, 1, 2 and 3 in decreasing order of market

value. Grade 1+ consists of high-end fish products such as lobsters, prawns, kingfish, tuna and

squid, with exceptionally high prices. Grade 1 fish, locally referred to as “Laji”, “Mix A” or

“Portion fish”, consists of specific, large-sized fish such as snappers, rabbitfish, emperors and

goatfishes. Grade 2 fish, locally referred to as “Mix B” or “Whole fish” consists of specific

large or medium-sized fish such as parrotfish, groupers, barracuda and fusiliers. Grade 3 fish,

locally referred to as “Smalli or Karanga”, consists of small-sized fish from grades 1 and 2

families, or large-sized fish of low consumer preference such as surgeonfishes, unicornfishes,

Indian mackerels and sting rays. In most cases, they are not weighed, and numbers of fishes or

weight estimates are used in pricing.

55

2.2.3 Data collection on performance

Performance indicators recorded included; costs incurred, purchases and sales. Costs were

categorized as variable (those increasing with increase in activity) or fixed costs (those constant

regardless of increase in activity) (Boncoeur et al., 2000). Fishers’ variable costs included;

fuel, food on long trips, bait, hooks, weights, transport and porter costs. Fixed costs included;

equipment repair, service and maintenance, house rent and anchorage fees for migrant fishers.

Middlemen’s variable costs included; transport, ice and labour, while fixed costs included;

equipment repair, service and maintenance, premises rent, electricity, water and licenses.

Processors’ variable costs included; frying oil, energy, condiments and packaging material.

Processors’ fixed costs were only equipment repair and licenses.

Fish quantities, purchases and sales were based on most recent estimates on a typical day in

SEM and NEM seasons. Prices and fish quantities were stated as lowest, average and highest

in order to derive accurate average estimates within lower and upper bound limits. Fishers

additionally indicated their catch share formula, for individual profit calculation. Respondents’

demographic and socio-economic data was also recorded

56

2.2.4 Data analysis of structure based on actor capitalization

First, in order to obtain capitalization level of each actor, the present value of equipment was

calculated using the straight line depreciation method following Greene (1963). Useful lifetime

of equipment was assumed to be that of the oldest similar equipment from the collected data.

Since equipment was used for long before retirement, a salvage value of 50% of original

purchase price was considered reasonable and used as follows:

𝑆𝑎𝑙𝑣𝑎𝑔𝑒 𝑣𝑎𝑙𝑢𝑒 = 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡 ∗ 50%……………………………… .… .…… . . (i)

𝐴𝑛𝑛𝑢𝑎𝑙 𝑑𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛 =𝐶𝑜𝑠𝑡 𝑜𝑓 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡 − 𝑆𝑎𝑙𝑣𝑎𝑔𝑒 𝑣𝑎𝑙𝑢𝑒

𝑈𝑠𝑒𝑓𝑢𝑙 𝑙𝑖𝑓𝑒𝑡𝑖𝑚𝑒………………… . (𝑖𝑖)

𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝑑𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛 ∗ 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑦𝑒𝑎𝑟𝑠 𝑖𝑛 𝑢𝑠𝑒. (𝑖𝑖𝑖)

𝑃𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 = 𝐶𝑜𝑠𝑡 𝑜𝑓 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡 − 𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛……………… .…… (𝑖𝑣)

In the second step, capitalization level and specialization were used to categorize actors.

Fishers’ classification was primarily based on gear used. Further classification was based on

capitalization, where higher invested units fishing in offshore waters were categorized as high-

capitalized offshore fishers, and the low-capitalized units fishing in nearshore waters were

categorized as low-capitalized, inshore fishers. Middlemen, were classified using locally

applicable criteria, where large-scale middlemen (locally known as “Matajiri”), had the

following characteristics; 1) ownership of “critical equipment” such as fishing gear, boats,

engines and storage equipment, that allowed higher chances of accessing fish and storing it, 2)

ownership of at least two types of “critical equipment” (for example fishing gear and boats) in

combination, with a value of at least KES 200,000, which was the typical investment by large-

scale middlemen, 3) operated from own premises. Traders not meeting these criteria were

classified as small-scale middlemen. Middlemen were further categorized based on

specialization as primary middlemen or secondary middlemen.

57

Fishers were categorized based on their primary fishing gear. Handline fishers had large

variations in value of equipment and hence categorized further. Those using reinforced plastic

boats and wooden boats propelled by engine were categorized as high-capitalized handline

units. Un-mechanized wooden boats and canoes were categorized as low-capitalized handline

units. Small-scale fish processors and restaurant operators who primarily fried fresh fish, were

broadly categorized as processors.

2.2.5 Data analysis of structure in context of market concentration

Market concentration for each site was determined by computing Herfindahl-Hirschman Index

(HHI) based on buyers’ sales volumes following Villasante et al., (2013). This was done by

taking percentage share of sales for each buyer, then squaring and summing for all buyers as

expressed below:

………………………………………………………………. (v)

Where n is the total number of buyers per site and SS is the percentage share of sales for buyer

i. The HHI index ranges from 1-10,000 where an index of 1-1,000 indicates a lowly

concentrated, competitive market; 1,000-1,800 indicates moderate concentration, while 1800-

10,000 indicates highly concentrated, uncompetitive market (Rhoades, 1993; US Department

of Justice, 2017). Only sales by primary buyers were considered in the HHI index calculation

in order to capture competitiveness at the primary level. Thus, Mombasa which had secondary

buyers only, was omitted.

HHI = (𝑆𝑆)2

𝑛

𝑖=1

𝑖

58

2.2.6 Data analysis of conduct in context of fish grading and actor choices

Fish grading and actor choices were complex and required detailed analysis than other conduct

variables. In order to determine actors’ choice of fish grade, the 48 fish types recorded in local

Swahili nomenclature, were grouped into respective fish families using a fish guide (Anam &

Mostarda, 2012). A cumulative frequency curve using elbow method (Ketchen & Shook, 1996),

was used to identify most targeted fish families by actor group. This resulted in 19 fish types

within 16 fish families that were analyzed further (or 84% of the 48 fish types listed).

To determine an actor’s most targeted grade, a 65% cut-off was set to enable clear delineation

of grades. If an actor targeted >65% of grades 1+ and 1, that was considered as high-grade with

a high value, while grades 2 and 3 as low-grade with a low value. If an actor targeted <65% of

both high and low grades, this was considered as mixed grades. Delineation of fish types by

grades and actors was done by plotting DCA plots in CANOCO (Version 4.5), based on

respondent frequencies. Fish grades corresponding to fish types and actor were plotted closer

to each other and vice versa.

59

2.2.7 Data analysis on performance

Performance entailed analysis of fish quantities, purchases, sales, costs, financial profit and

opportunity cost of labour and capital. Average fish quantities, purchases and sales were

obtained by averaging those stated for SEM and NEM seasons by actor group. Costs were

averaged by actor group for each type of cost and a daily cost computed. Financial profit was

calculated following Pascoe et al., (1996b), Long et al., (2008) and Ngoc et al., (2015) as below.

𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑝𝑟𝑜𝑓𝑖𝑡 = 𝐺𝑅 − 𝑉𝐶 − 𝐹𝐶 − 𝐿𝐶…………………………………………(vi)

Where; GR is gross revenue, VC is variable costs, FC is fixed costs and LC is labour costs. In

actual profit calculation, all costs were amalgamated into a total daily cost. Descriptive statistics

were used to present data.

Opportunity cost of labour was considered as the gazetted hourly government minimum wage

for unskilled workers, set at KES 54.7 hr-1 for year 2015 (Government of Kenya, 2015). This

was multiplied by the total number of hours spent per boat crew or business unit per day.

Opportunity cost of capital was calculated by multiplying invested capital by average annual

interest rate on savings (7%) for year 2015, as averaged by the Central Bank of Kenya

.

60

2.2.8 Statistical analysis of relationship between structure, conduct and performance

Analysis of relationships between structure, conduct, performance and other variables was

done through statistical methods. Regressions were run to determine if structure indicators

(value of equipment and percent ownership) as the predictor variables, influenced actors’

conduct (collusion, price determinants, choice of fish grade targeted and access to market

information). Two types of regressions (binomial and multinomial) were run separately for

each predictor variable. The binomial logistic regressions were done for two levels of response,

while multinomial regressions were done for more than two levels of response.

The general logistic regression analysis following Hosmer & Lemeshow (2000) was expressed

in the following general equation:

𝐏(𝐘) =𝟏

𝟏+ⅇ−(𝛃𝟎+𝛃𝟏𝒊𝐱𝟏𝒊+⋯𝛃𝒋𝒊𝐱𝒋𝒊)

……………………………… .…………………(vii)

Where P is the probability of predicting Y dependent variable, e is the base of natural logarithm,

while Xs are the independent regressors for i respondent. The R statistical software (Version

3.5.3) (R Development Core Team 3.0.1., 2013) was used in the analysis as follows:

For binomial regression the glm function in R software was used, while for multinomial logistic

regression, the logitof function from the mlogit package in R was used. Assumption of linear

relationship between the log odds of the dependent variable and the continuous independent

variables was tested using visual plots in ggplot2 package in R and all were linearly related.

Test for goodness of fit of the models was also done using the Hosmer-Lemeshow test in

generalhoslem package in R, where models with p-value >0.05 should be rejected. The

Breusch-Pagan Test for homoscedacity was done using bptest function in R, and all models

held (p>0.05). Multicollinearity was inspected through Variance Inflation Factors (VIF) and

61

all models were satisfactory with values <10. Odds ratio which indicates how many times the

independent variable increases for every unit increase in dependent variable was also obtained

to help with interpretation of results (UCLA, n.d.). It was obtained by exponentiating

coefficients of the models. The coefficient sign was used to deduce direction of the odds ratio,

where a positive sign indicates increase in odds ratio and vice-versa for a negative sign.

The Hausman-McFadden test of Independent Irrelevant Alternatives using hmftest package in

R was done to assess if the full model was different from reduced model in multinomial logistic

regressions. Models with p-value > 0.05 indicate no differences in the two models and hence

the reduced model is better.

Financial performance tests included Kruskal Wallis test for significant differences in financial

profit between actor groups, where results with a p-value <0.05 at 95% CI were considered

significant. The kruskal.test function in R was used, while post-hoc Dunn’s tests using the

dunnTest function in FSA package in R were conducted to detect significant differences in

pairs. Similar tests were conducted to detect significant differences in fish prices by grades and

actor groups.

Key factors influencing financial profitability were tested using Generalized Additive Models

(GAM) due to non-linearity of some continuous variables where splines were used as

smoothers following Hastie et al. (2009) and Ruppert et al. (2018). These non-linear variables

included; sales, fixed and variable costs for fishers, sales for middlemen and processors’ sales,

variable costs and experience. The semi-parametric GAMs are an extension of GLMs and are

considered suitable for non-linear relationships between the response and explanatory

variables, by letting data determine this relationship instead of prescribing parametric solutions

a priori (Guisan et al., 2002). The general equation form is stated as:

62

𝑔(µ) = β𝜃 + 𝑠(β𝑗1𝑥𝑗1) + β𝑗2𝑥𝑗2 + (β𝑗3𝑥𝑗3

𝑘𝑗−1

𝑖=𝑗

………………… . . +β𝑗𝑝𝑥𝑗𝑝) + 𝜀 ……………(𝑣𝑖𝑖𝑖)

Where 𝜇 is the model predictor and s is the semi-parametric spline function for a range of X

covariates and ε is the error term.

The gam function in mgcv package was used in analysis. Scatter plots were used to determine

variables requiring use of splines. The default cubic spline basis (s) was chosen as the smooth

term for the non-parametric part of the model. Before undertaking GAM statistical analysis,

diagnostic tests were done. To assess normality, the Shapiro-Wilks test in R was run. In this

test, variables with p-values < 0.05 are considered to violate assumptions of normality. The

dependent variable (financial profit) violated this assumption and hence transformed using the

box-cox transformation.

Assumptions of homogeneity of variance, linearity and normality in the distribution of

residuals were checked using residual plots. Models with spline functions for different actor

groups are stated below:

1). 𝐹𝑖𝑠ℎ𝑒𝑟𝑠 = β𝜃 + s(β𝑗1𝑠𝑙𝑗1) + s(β𝑗2𝑓𝑐𝑗2) + s(β𝑗3𝑣𝑐𝑗3) + β𝑗4𝑤ℎ𝑗4 + β𝑗5𝑒𝑥𝑗5 + β𝑗6𝑝𝑜𝑗6 + β𝑗7𝑎𝑐𝑗7

+ (β𝑗8𝑠𝑡𝑗8 + β𝑗9𝑎𝑡𝑗9 + β𝑗10𝑠𝑑𝑗10

𝑘𝑗−1

𝑖=𝑗

+ β𝑗11𝑠𝑖𝑗11 + β𝑗12𝑒𝑑𝑗12 + β𝑗13𝑛𝑏𝑗13 + β𝑗14𝑡𝑟𝑗14

+ β𝑗15𝑠𝑐𝑗15) + 𝜀 …………………………………………………………………… . . … (𝑖𝑥)

2).𝑀𝑖𝑑𝑑𝑙𝑒𝑚𝑒𝑛 = β𝜃 + s(β𝑗1𝑠𝑙𝑗1) + β𝑗2𝑓𝑐𝑗2 + β𝑗3𝑣𝑐𝑗3 + β𝑗4𝑤ℎ𝑗4 + β𝑗5𝑒𝑥𝑗5 + β𝑗6𝑒𝑣𝑗6 + β𝑗7𝑎𝑐𝑗7

+ (β𝑗8𝑠𝑡𝑗8 + β𝑗9𝑎𝑡𝑗9 + β𝑗10𝑏𝑑𝑗10 + β𝑗11𝑠𝑑𝑗11

𝑘𝑗−1

𝑖=𝑗

+ β𝑗12𝑏𝑖𝑗12 + β𝑗13𝑠𝑖𝑗13 + β𝑗14𝑒𝑑𝑗14

+ β𝑗15𝑛𝑏𝑗15 + β𝑗16𝑡𝑟𝑗16 + β𝑗17𝑏𝑐𝑗17 + β𝑗18𝑠𝑐𝑗18 + β𝑗19𝑓𝑔𝑗19) + 𝜀 ………………… (𝑥)

3). 𝑃𝑟𝑜𝑐𝑒𝑠𝑠𝑜𝑟𝑠 = β𝜃 + s(β𝑗1𝑠𝑙𝑗1) + β𝑗2𝑓𝑐𝑗2 + 𝑠(β𝑗3𝑣𝑐𝑗3) + β𝑗4𝑤ℎ𝑗4 + s(β𝑗5𝑒𝑥𝑗5) + β𝑗6𝑒𝑣𝑗6 + β𝑗7𝑎𝑐𝑗7

+ (β𝑗8𝑠𝑡𝑗8 + β𝑗9𝑎𝑡𝑗9 + β𝑗10𝑏𝑑𝑗10 + β𝑗11𝑠𝑑𝑗11

𝑘𝑗−1

𝑖=𝑗

+ β𝑗12𝑏𝑖𝑗12 + β𝑗13𝑠𝑖𝑗13 + β𝑗14𝑒𝑑𝑗14

+ β𝑗15𝑛𝑏𝑗15 + β𝑗16𝑡𝑟𝑗16 + β𝑗17𝑏𝑐𝑗17 + β𝑗18𝑠𝑐𝑗18 + β𝑗19𝑓𝑔𝑗19) + 𝜀 ……………… (𝑥𝑖)

63

Description of variables used in the models is presented below (Table 2.1).

64

Table 2.1. The description of variables used in Generalized Additive Model regression models to analyze performance

Variable symbol Description of variable Type of variable Variable levels

Fishers Fishers’ financial profit person-1day-1 Continuous NA

Middlemen Middlemen’s Financial profit person-1day-1 Continuous NA

Processors Processors’ Financial profit person-1day-1 Continuous NA

sl Sales Continuous NA

fc Fixed costs Continuous NA

vc Variable costs Continuous NA

wh Working hours Continuous NA

ex Experience in years Continuous NA

po Percent ownership Continuous NA

ev Equipment value Continuous NA

ac Access to credit Continuous NA

st Site Categorical Malindi, Mayungu, Mombasa, Shimoni, Vanga

at Actor type – fishers by gear Categorical High capitalized handline, Reef seine net, Drifting

gillnet, Basket trap

Set net, Low capitalized handline, Cast net

at Actor type – fish buyers Categorical Company agent, Large-scale primary middlemen,

Large-scale secondary middlemen, Small-scale primary

middlemen, Small-scale secondary middlemen,

Processors

bd Buying price determinant Categorical Seller, Self, Negotiated

sd Selling price determinant Categorical Buyer, Self, Negotiated

bi Accesses buying price information Categorical Yes, No

si Accesses selling price information Categorical Yes, No

ed Education level Categorical Not schooled, Primary, Secondary, Tertiary

nb No. of buyers selling to Categorical Single, Multiple

tr Training Categorical Yes, No

bc Buying collusion Categorical Yes, No

sc Selling collusion Categorical Yes, No

fg Fish grade Categorical High grade, Mixed grade, Low grade

64

65

Results

2.3.1 Demographic and socio-economic characteristics of small-scale fisheries

Actors in the fishery had youthful demographic characteristics, with an average of 34, 39 and

43 years for processors, middlemen and fishers respectively (Table 2.2). Actors above 60 years

were below 13%, with majority being fishers. Over 60% of processors and middlemen were

new entrants with less than 10 years’ experience, while fishers were only 16%. Education level

across all groups was low with over 90% of fishers and processors and 67% of middlemen

having only attained primary education.

Fishers and processors spent more days in fish related activities in NEM at an average of 6

days week-1, compared to 5 days week-1 in SEM, while middlemen spent 6 days week-1 in both

seasons. On average, all actor groups engaged in alternative livelihoods. Over 49% of

processors and fishers had alternative livelihoods, but only 37% for middlemen. Despite

having alternatives, over 94% of actors still ranked fishery related livelihoods as most

important.

The fishery was largely gender imbalanced, where all fishers and 95% of middlemen were

male, while 97% of processors were female. Overall, 73% of respondents were male and 27%

female. Middlemen had the highest number of dependents (8) followed by fishers (7) and

processors with at least 4 dependents. On marital status, most actors were married (> 59%).

However, 41% of processors were unmarried.

66

Table 2.2. Summary of respondent’s demographic and socio-economic characteristics

Variable Levels

Fish

processors Fishers Middlemen

Age Average age 34 43 39

% in age group (20-40 yrs) 80 47 61

% in age group (41-60 yrs) 19 40 34

% in age group (60-80 yrs) 1 13 6

Experience % experience (1-10yrs) 79 16 60

% experience (11-20yrs) 16 36 30

% experience (21-30yrs) 6 23 5

% experience >30yrs - 24 4

Education % none 1 0 0

% with primary education 96 93 67

% with secondary education 2 6 28

% with tertiary education 1 1 5

Days spent Days worked per week in SEM 5 5 6

Days worked per week in NEM 6 6 6

Average annual days worked 288 274 314

Alternative

livelihood

% with other occupation 58 49 37

% with no other occupation 42 51 63 % ranking fishery activity as

primary 94 96 96

% ranking fishery activity as

secondary 6 4 4

Gender % Female (27% of all actors) 97 0 5

% Male (73% of all actors) 3 100 95

Dependents Number of dependents 4 7 8

Marital

status

% married 59 94 84

% divorced 20 1 7

% single 10 5 8

% widowed 11 0 1

2.3.2 Structure in context of actor capitalization

Classification of high-capitalized fishers fishing offshore included; reef seine, high-capitalized

handline and drifting net fishers. Low-capitalized fishers fishing inshore included; basket trap,

low-capitalized handline, set net and cast net fishers (Table 2.3).

Classification of middlemen by capitalization and specialization resulted in the large-scaled

(high-capitalized) category that included; company agents, large-scale primary and secondary

middlemen. Small-scaled (low-capitalized) category included; small-scale primary and

67

secondary middlemen. Small-scale processors and restaurant operators were also classified as

low-capitalized, and jointly classified as processors.

In terms of boat crew, reef seine net had the highest number of crews with an average of 18

fishers boat-1, while basket traps had the least, with an average of 2 fishers boat-1 (Table 2.3).

In terms of proportional ownership of equipment, large-scale middlemen dominated with

cumulative investments of KES 29,278,468 (44%) of total. Fishers, collectively held KES

33,918,822 (51%) of total investments, but only owned 27 -77% of it, with the rest belonging

to middlemen. Observably, two of the high-capitalized fishing units categories had the least

ownership among all fishers.

68

Table 2.3: Actor categorization based on capitalization, listed in decreasing order of value of equipment

Broad

category

Actors’ specific category by

activity/operation

Actor

abbreviation n

Average

crew size

Total value of

equipment in

KES

Average

value of

equipment

in KES.

%

equipment

ownership

Offshore

fishers

High capitalized handline HCH 38 4 15,535,795 408,837 27

Drifting gillnet DG 11 4 4,064,729 369,521 27

Reef seine net RSN 11 18 3,349,036 334,904 73

Inshore

fishers

Set net SN 26 4 2,917,125 116,685 62

Basket trap BT 78 2 6,368,584 81,649 58

Low capitalized handline LCH 50 3 1,533,839 30,677 45

Cast net CN 8 4 149,714 21,388 77

Fishers’ total 222 33,918,822

Large-scale

middlemen

Company agent CA 3 13,210,390 4,403,463 91

Large-scale secondary middlemen LSSM 4 3,426,860 856,715 100

Large-scale primary middlemen LSPM 15 12,641,218 842,748 100

Large-scale middlemen’s total 22 29,278,468

Small-scale

middlemen

Small-scale primary middlemen SSPM 34 1,478,422 80,766 98

Small-scale secondary middlemen SSSM 17 1,211,720 71,278 90

Small-scale middlemen’s total 51 2,690,142

All middlemen’s total 73 31,968,610

Processors Restaurant operators RO 7 70,782 10,112 100

Small-scale fish processors SSFP 101 248,977 2,465 100

Processors’ total 108 319,759

All actors’ total 403 66,207,191

Note: ‘n’ is the total number sampled actors by actor group

68

69

2.3.3 Structure in context of market concentration

Results of Herfindahl-Hirschman Index (HHI) showed that scores for the four sites were

below 1,800 (Table 2.4), thus indicating low market concentration and high market

competitiveness across the four sites (Rhoades, 1993; US Department of Justice, 2017).

Table 2.4. Herfindahl-Hirschman Index scores by site

Site HHI score

Mayungu 607

Shimoni 920

Vanga 988

Malindi 1,053

2.3.4 Conduct in context of fish grading, pricing and actors’ choice

There was strong association of kingfish, tuna and squid with grade 1+, while the rest

overlapped between grades (1, 2 and 3) (Figure 2.1). Actors’ choices of fish grade also

overlapped with fish grades targeted (Figure 2.2). However, some actors had strong

preference for specific grades based on their capital and consumer needs. For example,

30% of middlemen targeted high-grade fish and only 12% targeted low-grade (Table 2.5).

In contrast, only 5% of processors targeted high-grade fish and 72% targeted low-grade.

Table 2.5. Percentage of fish grades targeted by respondents

Actor category High-grade Mixed-grade Low-grade

Middlemen 30% 58% 12%

Processors 5% 23% 72%

70

Figure 2.1. Detrended Correspondence Analysis plot showing association between

fish type and grade

Figure 2.2. Detrended Correspondence Analysis plot showing association between

fish grade and actor node

Note: Abbreviations; CA=Company agent, LSSM= Large-scale secondary middlemen,

SSPM=Small-scale primary middlemen, SSSM=Small-scale secondary middlemen,

RO=Restaurant

71

Results also indicated that fish ex-vessel prices correlated with grading. There was a

negative ex-vessel price gradient from high-grade fish (1+ and 1) to low-grade fish (2 and

3) (Figure 2.3). These differences were also confirmed by Kruskal Wallis test results

showing highly significant differences of prices by grades (H=255.07, df=3, p<0.001).

Dunn’s post-hoc test for prices between grades revealed that all comparisons were highly

significant (p<0.001), except for between grades 2 and 3 (p=0.934). Analysis of fish price

by actor type also showed highly significant differences (H=52.125, df=3, p<0.001).

Dunn’s post-hoc test for prices between actors revealed significant differences for all

actors, except between company agents and small-scale primary middlemen (p<0.152)

and between large-scale primary middlemen and small-scale primary middlemen

(p<0.125).

Figure 2.3. Plot of median ex-vessel price of fish by grade.

Note: Grades are in decreasing order of prices where, grade 1+ attracts highest prices

and includes for example lobsters and prawns, grade 1 includes squid, kingfish and

tuna, grade 2 includes parrotfishes, small-sized rabbitfishes and emperors, while grade

3 includes surgeonfishes and unicornfishes.

72

2.3.5 Results of diagnostic tests on models for influence of structure on conduct

Results of the test on assumption of linear relationship between the log odds of the

dependent variable and the continuous independent variables tested using visual plots

indicated that they were all linearly related. Results of Hosmer-Lemeshow test for

goodness of fit of binomial and multinomial logistic regression models showed that all the

models fitted well, with p-values > 0.05, except for the test of middlemen's selling price

determinant that failed the test as shown on tables (Table 2.6-Table 2.21).

Results of the Hausman-McFadden test of Independent Irrelevant Alternatives showed

that all the reduced models were not different from full models and hence the reduced

models were preferred based on p-values > 0.05 as shown on tables (Table 2.6, Table 2.9,

Table 2.10, Table 2.15 and Table 2.16)

For the GAM models, assumptions of homogeneity of variance, linearity and normality

in the distribution of residuals, showed that all models fitted well based on residual plots.

Profitability (dependent variable), which violated assumptions of normality and

transformed using box-cox transformation was corrected to normality, where p-values

obtained after transformation were (0.064, 0.278 and 0.549) for fishers, middlemen and

processors respectively.

73

2.3.6 Influence of structure on fishers’ conduct

Model results on price determination revealed that, for one unit increase in fishers’ percent

ownership, the odds of dictating selling prices (versus buyers dictating), increased by a

factor of 0.998 as also shown by the positive coefficient (0.006) (Table 2.6).The odds for

negotiation (versus buyers dictating), however decreased by a factor of 1.006 as also

shown by the negative coefficient (-0.002). However, the results were not statistically

significant, with p-values > 0.05 at 95% Confidence Interval (CI). McFadden R2 was low

at 0.38% suggesting that percent ownership of equipment did not explain much variation

in fishers’ power to determine prices.

Table 2.6. Multinomial logistic regression results showing influence of percent

ownership of equipment on fish price determinants amongst fishers

Variable Odds

ratio Coefficient

Std.

Error

z-

value P-value

Negotiated:(intercept) 0.577 -0.551 0.253 -2.174 0.03*

Self:(intercept) 0.079 -2.533 0.546 -4.635 3.569e-06 ***

Self 0.998 0.006 0.007 0.852 0.394

Negotiated 1.006 -0.002 0.003 -0.651 0.515

McFadden R2:

0.0038272

Likelihood ratio test: chisq = 1.446 (p-value = 0.48542)

Hosmer and Lemeshow test of goodness of fit (multinomial model)

X-squared = 4.998, df = 6, p-value = 0.5441

Hausman-McFadden test of Independent Irrelevant Alternatives

chisq = 0.022456, df = 2, p-value = 0.9888

Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘’ 1. These codes have been

used in all models in this document. A significance level of 95% has been considered in

interpretation of results.

Model results on collusion behaviour revealed that, for one unit increase in fishers’ percent

ownership, the odds of collusion (versus non-collusion), decreased by a factor of 0.997 as

74

also shown by the negative coefficient (-0.003) (Table 2.7). However, the results were not

statistically significant, with p-value > 0.05 at 95% CI.

Table 2.7. Logistic regression results showing influence of percent ownership of

equipment on selling collusion amongst fishers

Variable Odds

ratio Coefficient

Std.

Error

z

value P-value

(Intercept) 11.025

2.400 0.424 5.66

1.51e-08

***

Colludes when selling 0.997 -0.003 0.005 -0.554 0.579

Model significance: 0.243

Hosmer and Lemeshow test of goodness of fit (binary model)


Model results on market information revealed that, for one unit increase in fishers’ percent

ownership, the odds of accessing selling price information (versus non- access), decreased

by a factor of 0.999 as also shown by the negative coefficient (-0.001) (Table 2.8).

However, the results were not statistically significant, with p-values > 0.05.

The non-significant results of influence of percent ownership on price determination,

collusion and access to market information, suggests weak influence of structure on

conduct variables amongst fishers.

75

Table 2.8. Logistic regression results showing influence of percent ownership of

equipment on access to selling price information amongst fishers

Variable Odds

ratio Coefficient

Std.

Error z value P-value

(Intercept) 0.439 -0.822 0.258 -3.190 0.001 **

Accesses selling price information 0.999 -0.001 0.003 -0.280 0.779




2.3.7 Influence of structure on middlemen’s conduct

Model results on buying price determination revealed that, for one unit increase in

middlemen’s value of equipment, their odds of dictating or sellers dictating buying prices

(versus negotiation), decreased by a factor of one as also shown by the negative

coefficients (-0.000) in both cases (Table 2.9). However, the results were not statistically

significant, with p-values > 0.05 at 95% CI. McFadden R2 was low at 1.6 %, suggesting

76

that value of equipment did not explain much variation in middlemen’s power to

determine prices.

Table 2.9. Multinomial logistic regression results showing influence of value of

equipment on fish buying price determinants amongst middlemen

Variable Odds

ratio Coefficient Std. Error z-value P-value

Self:(intercept) 0.557 -0.586 0.300 -1.955 0.051. Seller:(intercept) 0.510 -0.674 0.347 -1.942 0.052. Self 1.000 -0.000 0.000 -0.452 0.651

Seller 1.000 -0.000 0.000 -1.124 0.261

McFadden R2: 0.016

Likelihood ratio test: chisq = 2.417 (p.value = 0.299)




chisq = 0.001, df = 2, p-value = 0.999

Model results on selling price determination revealed that, for one unit increase in

middlemen’s value of equipment, the odds of middlemen or buyers dictating selling prices

(versus negotiation) increased by a factor of one as also shown by the positive coefficients

(0.000) in both cases (Table 2.10). However, the results were not statistically significant,

77

with p-values > 0.05 at 95% CI. McFadden R2 was low at 6 %, suggesting that value of

equipment did not explain much variation in middlemen’s power to determine prices.


equipment on fish selling price determinants amongst middlemen

Variable Odds


Self:(intercept) 1.6335 0.491 0.524 0.937 1.6335

Buyer:(intercept) 6.7957 1.916 0.454 4.219 6.796***

Self 1.000 0.000 0.000 0.870 1.000

Buyer 1.000 0.000 0.000 0.222 1.000

McFadden R2: 0.059



X-squared = 183.26, df = 10, p-value < 2.2e-16


chisq = 0.022, df = 2, p-value = 0.989

Model results on collusion behaviour revealed that, for one unit increase in middlemen’s

value of equipment, the odds of collusion to set buying and selling prices (versus non-

collusion), increased by a factor of one as also shown by the positive coefficients (0.000)

in both cases (Table 2.11 and Table 2.12). However, the results were not statistically

significant, with p-values > 0.05 at 95% CI.

78

Table 2.11. Logistic regression results showing influence of value of equipment on

buying collusion amongst middlemen

Variable Odds ratio

Coefficient

Std.

Error

z

value P-value

(Intercept) 4.527

1.510 0.328 4.601

4.2e-06

***

Colludes when buying 1.000 0.000 0.000 0.157 0.876





selling collusion amongst middlemen

Variable Odds

ratio Coefficient Std. Error z value P-value

(Intercept) 6.406 1.857 0.424 4.383 1.17e-05 ***

Colludes when selling 1.000 0.000 0.000 0.815 0.415




Model results on market information revealed that, for one unit increase in middlemen’s

value of equipment, the odds of access to buying and selling price information (versus

non-access), increased by a factor of one as also shown by the positive coefficients (0.000)




access to buying price information amongst middlemen

79

Variable Odds

ratio Coefficient

Std.


(Intercept) 0.931 -0.071 0.252 -0.284 0.777

Accesses buying price information 1.000 0.000 0.000 0.468 0.640




80


access to selling price information amongst middlemen

Variable Odds

ratio Coefficient

Std.

Error

z

value P-value

(Intercept) 0.397 -0.923 0.276 -3.348 0.000 ***

Accesses selling price

information 1.000

0.000 0.000 0.822 0.411




Model results on middlemen’s tendencies to target certain fish grades revealed that, for

one unit increase in value of equipment, the odds of targeting high and mixed fish grades

(versus low fish grade) increased by a factor of one as also shown by the positive

coefficients (0.000) in both cases (Table 2.15). However, the results were not statistically

significant, with p-values > 0.05 at 95% CI. McFadden R2 was low at 8 %, suggesting that

value of equipment did not explain much variation in middlemen’s targeting of fish

grades. It is possible that many middlemen lacked specialization in the fish grades they

targeted.

81


equipment on choice of fish grade amongst middlemen

Variable Odds


High:(intercept) 0.882 -0.126 0.532 -0.237 0.813

Mixed:(intercept) 2.283 0.826 0.488 1.692 0.091. High fish grade 1.000 0.000 0.000 1.412 0.158

Mixed fish grades 1.000 0.000 0.000 1.311 0.190

McFadden R^2: 0.0843





chisq = 1.560, df = 2, p-value = 0.459

The non-significant results of influence of value of equipment on price determination,

collusion, access to market information and targeting of fish grades, suggests weak

influence of structure on conduct variables amongst middlemen.

2.3.8 Influence of structure on processors’ conduct

Model results on buying price determination revealed that, for a one unit increase in

processors’ value of equipment, their odds of dictating buying prices (versus negotiation)

decreased by a factor of one as also shown by the negative coefficient (-0.000). However,

the odds increased by a factor of one for sellers dictating buying prices (versus

negotiation) as also shown by the positive coefficient (0.000) (Table 2.16). However, the

results were not statistically significant, with p-values > 0.05 at 95% CI. McFadden R2

was low at 0.2 %, suggesting that value of equipment did not explain much variation in

processors’ power to determine prices.

82


equipment on fish buying price determinants amongst processors

Variable Odds

ratio Coefficient

Std.

Error

z-

value P-value

Self:(intercept) 0.331 -1.103 0.983 -1.122 0.262

Seller:(intercept) 8.159 2.110 0.365 5.784 7.286e-09 ***

Self 1.000 -0.000 0.000 -0.241 0.810

Seller 1.000 0.000 0.000 0.189 0.850

McFadden R2:

0.0023127





chisq = 1.5396, df = 2, p-value = 0.4631

Model results on collusion behaviour revealed that, for one unit increase in processors’

value of equipment, the odds of collusion to set buying prices (versus non-collusion),

increased by a factor of one as also shown by the positive coefficient (0.000) (Table 2.17).

However, the odds of collusion to set selling prices (versus non-collusion), decreased by

a factor of 1 as also shown by the negative coefficient (-0.000) (Table 2.18). However,

the results were not statistically significant in both cases, with p-values > 0.05 at 95% CI.

83


buying collusion amongst processors

Variable

Odds


(Intercept) 6.284 1.838 0.706 2.603 0.009 **

Colludes when buying 1.000 0.000 0.000 0.783 0.434





selling collusion amongst processors

Variable

Odds


(Intercept) 12.282 2.508 0.391 6.410 1.45e-10 ***

Colludes when selling 1.000 -0.000 0.000 -0.806 0.420




Model results on market information revealed that, for one unit increase in processors’

value of equipment, the odds of access to buying and selling price information (versus

non-access), increased by a factor of one as also shown by the positive coefficients (0.000)



84


access to buying price information amongst processors

Variable Odds

ratio Coefficient

Std.

Error

z

value P-value

(Intercept) 0.371 -0.991 0.246 -4.033 5.51e-05 ***

Accesses buying price

information 1.000 0.000 0.000 0.009 0.993





access to selling price information amongst processors

Variable Odds

ratio Coefficient

Std.


(Intercept) 0.094 -2.365 0.372 -6.353 2.11e-10 ***


information 1.000

0.000 0.000 0.632 0.528




Model results on processors’ tendencies to target certain fish grades revealed that, for one

unit increase in their value of equipment, the odds of targeting low fish grades (versus

high fish grade) decreased by a factor of one as also shown by the negative coefficient (-

0.000) (Table 2.21). However, the odds of targeting mixed fish grades (versus high fish

grade) increased by a factor of 1 as also shown by the positive coefficient (0.000). In both

cases the results were not statistically significant, with p-values > 0.05 at 95% CI.

McFadden R2 was low at 19 %, suggesting that value of equipment did not explain much

variation in processors targeting of fish grades.

The non-significant results of influence of value of equipment on price determination,

85

collusion, access to market information and targeting of fish grades, suggests weak

influence of structure on conduct variables amongst processors.


equipment on choice of fish grade amongst processors

Variable

Odds


Low:(intercept) 0.003 4.469 1.404 3.182 0.001**

Mixed:(intercept) 0.026 0.614 1.491 0.412 0.680

Low fish grade 1.001 -0.001 0.001 -1.137 0.255

Mixed fish grade 1.001 0.001 0.001 0.888 0.374

McFadden R2: 0.19157

Likelihood ratio test: chisq = 24.837 (p.value = 4.0422e-06)




chisq = 5.761, df = 2, p-value = 0.056

2.3.9 Actors’ performance in context of fish quantities, purchases and sales

Fishers’ catch was varied by gear. Cast net fishers caught the lowest catch of 6 ± 1 kg

fisher-1 day-1 and high-capitalized handline the highest catch of 23 ± 3.1 kg fisher-1 day-1

(Figure 2.4a). Although reef seine boats caught the highest fish amounts (134 ± 25 kg

boat-1 day-1), individual share of catch was low (7 ± 1.4 kg fisher-1 day-1) due to the large

number of fishers, on average 18 fishers boat-1 (Table 2.3). Fish amounts purchased by

middlemen and processors also varied. Processors purchased the lowest amount (7 ± 0.6

kg day-1), while company agents purchased the highest amount (232 ± 134.2 kg day-1)

(Figure 2.4a).

86

Figure 2.4. (a.) Mean fish amounts that fishers, middlemen and processors deal in

(b.) purchases and sales amount per day in local currency (KES). Error bars

represent mean standard errors. All variables are expressed as per day units.

Note: Actors are arranged in decreasing order of capitalization as is throughout this

study; HCH to CN refers to fishers and CA to SSFP refers to middlemen and processors.

Abbreviations; HCH = High-capitalized handline, DG = Drifting gillnet, RSN = Reef-

seine net, SN = Set net, BT = Basket trap, LCH = Low-capitalized handline, CN = Cast

net, CA = Company agent, LSSM = Large-scale secondary middlemen, LSPM = Large-

scale primary middlemen, SSPM = Small-scale primary middlemen, SSSM = Small-scale

secondary middlemen and SSFP = Small-scale fish processor

87

Fishers’ sales varied by gear, with reef seine fishers attaining lowest sales revenue (KES

786 ± 136 fisher-1 day-1) and high-capitalized handline the highest (KES 3,327 ± 381

fisher-1 day-1) (Figure 2.4b). Middlemen and processors’ sales also varied, with small-

scale processors attaining lowest sales revenue (KES 2,005 ± 144 person-1 day-1) and

company agents the highest (KES 54,528 ± 11,416 person-1 day-1) (Figure 2.4b). Notably,

although small-scale secondary middlemen purchased lower fish amounts, their purchases

and sales value was higher than for small-scale primary middlemen, due to higher

secondary prices.

2.3.10 Actors’ performance in context of costs

Costs were considerably varied between and within actor groups, except amongst fishers.

There was also variation between fixed and variable costs. Amongst fishers, high-

capitalized handline fishers had highest variable costs (KES 1,158 ± 118 person-1day-1)

and cast net fishers lowest (KES 18 ± 18 person-1day-1) (Figure 2.5a). For fixed costs,

drifting gillnet fishers incurred the highest cost (KES 128 ± 37 person-1day-1), while cast

net fishers incurred the lowest cost (KES 16 ± 6 person-1day-1). Variable costs were

subtracted first, before sharing proceeds with boats owned by middlemen, based on share

arrangements.

The cost structure amongst middlemen and processors showed mixed patterns between

variable costs and fixed costs (Figure 2.5a). Generally, high-capitalized middlemen

incurred higher fixed costs than other actors, which is associated with maintenance of their

boats and equipment. For fixed costs, company agents incurred the highest cost (KES

88

2,527 ± 1,026 person-1day-1), while processors incurred the lowest (KES 7 ± 3 person-

1day-1). For variable costs, large-scale primary middlemen incurred the highest cost (KES

2,762 ± 1,320 person-1day-1), while processors incurred the lowest (KES 347 ± 22 person-

1 day-1).

2.3.11 Actors’ performance in context of financial profit

In terms of financial profitability amongst fishers, high-capitalized handline fishers earned

the highest amount (KES 1,436 ± 248 person-1 day-1), while reef seine net fishers earned

the lowest amount (KES 452 ± 114 person-1 day-1) (Figure 2.5b). However, Kruskal Wallis

test results showed no significant differences in profitability between gears (H=8.605, df=

6, p=0.233), possibly due to variations within gears.

Amongst middlemen and processors, company agents earned the highest profits (KES

7,839 ± 2,065 person-1 day-1), while processors earned the lowest profits (KES 599 ± 108

person-1 day-1) (Figure 2.5b). Kruskal Wallis test results showed significant differences in

profitability between middlemen (H=12.286, df = 4, p=0.02). However, Dunn’s post-hoc

test for profitability between different groups of middlemen revealed that none of the

comparisons was significant, possibly due to large individual variations within groups.

89

Figure 2.5. (a.) Mean variable and fixed costs amongst fishers and buyers, (b.)

financial profit per person. All variables are expressed as per day units.

Note: HCH = High-capitalized handline, DG = Drifting gillnet, RSN = Reef-seine net,

SN = Set net, BT = Basket trap, LCH = Low-capitalized handline, CN = Cast net, CA =

Company agent, LSSM = Large-scale secondary middlemen, LSPM = Large-scale

primary middlemen, SSPM = Small-scale primary middlemen, SSSM = Small-scale

secondary middlemen and SSFP = Small-scale fish processor

2.3.12 Comparison of time spent, workforce, opportunity cost of labour and profitability

Offshore fishing units (high capitalized handline, drifting gillnet and reef seine net) spent

on average of 7-13 hours fishing, compared to 5-10 hours fishing for inshore units (set

net, basket trap, low capitalized handline and cast net). The high number of hours spent

90

by high-capitalized handline fishers arise due to distances travelled during long fishing

trips lasting 1-3 days (Table 2.22). Large-scale middlemen also spent more time in the

business (10 hours) compared to small-scale middlemen who spent nine hours. Processors

spent most time (12 hours), compared other actor groups.

Offshore fishing units had the highest number of crew (4-18 fishers boat-1) compared to

(2-4 fishers boat-1) for inshore fishing units. Small-scale and large-scale middlemen

employed on average 2-3 workers, mostly on casual basis. Processors had a workforce of

up to three workers.

On average, opportunity cost of labour calculated from number of hours spent showed

that offshore fishing units ranged between KES 398 and KES 708, while inshore units

ranged between KES 246 and KES 525 (Table 2.22). Opportunity cost for large-scale

middlemen was lower at between KES 160 and 381 compared to small-scale middlemen

at between KES 363 and 423 and KES 371 for processors.

Although all actor groups performed financially better than their rate of opportunity cost,

some groups such as fish processors, reef seine and drifting gillnet fishers performed

poorly. Their income was close to opportunity cost as observed in the difference between

the two. This suggests that except for these few, majority earned higher returns than casual

employees in other sectors such as agriculture, manufacturing and commerce based on

government minimum wage for unskilled workers in 2015 that ranged between KES 228

to 527 (Government of Kenya, 2015).

91

Table 2.22. Averages for variables on workforce, time spent, opportunity cost of

labour and financial profit per type of enterprise or fishing unit

Actor type

Average

Workin

g hours

Average

Workforc

e

Opportunit

y cost of

wages

(OCL)

/person

Financial

profit

(FP)

/Person

Difference

(FP-OCL)

High capitalized handline 13 4 708 1,436 728

Drifting gillnet 9 4 511 617 107

Reef seine net 7 18 398 452 54

Set net 7 4 370 987 617

Basket trap 6 2 340 946 606

Low capitalized handline 10 3 525 1,159 634

Cast net 5 4 246 850 604

Company agent 12 3 358 7,839 7,481

Large-scale secondary

middlemen 9 3 160 3,675 3,515

Large-scale primary

middlemen 11 2 381 5,110 4,729

Small-scale primary

middlemen 8 2 363 2,362 1,999

Small-scale secondary

middlemen 10 2 423 1,754 1,330

Fish processors 12 3 371 639 268

2.3.13 Factors influencing fishers’ financial performance

The GAM model testing influence of structure and conduct on fishers’ performance,

revealed no significant effect. Similar results were obtained in middlemen and processors’

models. Therefore, apart from the S-C-P variables, additional variables were included in

the full model to investigate their effect on performance of all actor groups. These

included demographic variables (age, education), operational (cost, sales) and site

(Malindi, Mayungu, Shimoni and Vanga).

In the full model, parametric results showed that fishers’ profitability increased

92

significantly, as percent ownership of equipment increased (Table 2.23). Fishers at

Mayungu, Shimoni and Vanga attained significantly higher profitability than fishers in

Malindi. Fishers using other gears attained significantly higher profits compared to those

using reef seine net. Fishers, who also had accessed credit before, attained significantly

higher profits than those who hadn’t. All non-parametric results showed significant effect

on fishers’ profitability (Table 2.23).

Profitability increased as sales increased, with a sharp rise upto sales of KES 20,000, then

a slight deep, before rising again (Figure 2.6.a). On the converse, profitability decreased

gently as fixed and variable costs rose (Figure 2.6.b and c). All conduct variables in the

full model showed no significant influence on profitability. Variables in the full model

explained 76% of the deviance and 71% of the variation.

93

Table 2.23. Generalized Additive Model results of factors influencing fisher’s

profitability

Variable Levels Coefficient Std. Error t value P-value

(Intercept) 2.095 0.374 5.598 7.87e-08 ***

Experience 0.002 0.002 0.951 0.343

Working hours -0.010 0.012 -0.894 0.373

Percent ownership 0.002 0.001 2.664 0.008 **

Access to credit Yes 0.396 0.192 2.067 0.040 *

Site Mayungu 0.146 0.095 1.540 0.125

Shimoni 0.305 0.083 3.655 0.000 ***

Vanga 0.262 0.087 3.020 0.003 **

Fishing gear High-capitalized

handline

1.117 0.157 7.108 2.58e-11 ***

Cast net 0.726 0.196 3.702 0.000 ***

Basket trap 1.035 0.138 7.515 2.48e-12 ***

Drifting gillnet 0.652 0.171 3.822 0.002 ***

Low-capitalized

handline

1.041 0.141 7.400 4.85e-12 ***

Set net 0.755 0.143 5.262 3.98e-07 ***

Price determiner Buyer -0.084 0.057 -1.480 0.141

Self 0.156 0.098 1.593 0.113

Accesses selling

price information

Yes 0.000 0.060 0.006 0.995

Education Primary 0.262 0.345 0.760 0.448

Secondary 0.050 0.361 0.138 0.890

No. of buyers Multiple -0.071 0.066 -1.073 0.285

Trained Yes -0.013 0.066 -0.205 0.838

Practices price

collusion

Yes 0.022 0.089 0.252 0.801

edf Ref.df F P-value

s(Sales) 6.763 9 43.407 < 2e-16 ***

s(Fixed costs) 2.252 9 3.403 1.29e-07 ***

s(Variable costs) 3.938 9 8.514 < 2e-16 ***

R-sq.(adj) = 0.709 Deviance explained = 75.5%

-REML = 128.77 Scale est. = 0.113 n = 216

AIC: 179.831

94

Figure 2.6. Estimated smooth terms of three variables on fishers’ profitability. Y-axes

are the dependent variable’s partial effects and the grey shadow bands show standard-

error confidence intervals.

2.3.14 Factor influencing middlemen’s financial performance

Parametric results of the GAM model testing influence of structure, conduct, demographic,

operational and site variables on profitability showed that none of the variables significantly

influenced middlemen’s profitability (Table 2.24).

95

Table 2.24. Generalized Additive Model results of factors influencing middlemen’s financial profitability


(Intercept) 6.790 1.548 4.386 0.000 ***

Equipment value 0.000 0.000 0.745 0.462

Fixed costs 0.000 0.000 0.713 0.481

Variable costs 0.000 0.000 -1.031 0.310

Experience -0.001 0.012 -0.047 0.963

Working hours 0.027 0.040 0.692 0.494

Fish grade Mixed 0.460 0.393 1.173 0.249

High -0.071 0.491 -0.146 0.885

Owns critical equipment Yes 0.116 0.255 0.457 0.651

Buying price determinant Self 0.243 0.293 0.830 0.413

Seller -0.023 0.286 -0.080 0.937

Selling price determinant Buyer 0.213 0.427 0.499 0.621

Self -0.151 0.268 -0.566 0.575

Practices buying price collusion Yes -0.203 0.344 -0.590 0.559

Practices selling price collusion Yes 0.588 0.482 1.219 0.231

Site Mayungu -0.381 0.315 -1.211 0.234

Mombasa -0.504 0.469 -1.075 0.290

Shimoni -0.605 0.436 -1.388 0.174

Vanga -0.396 0.338 -1.172 0.249


information Yes -0.094 0.320 -0.295 0.770

Accesses buying price

information Yes -0.164 0.249 -0.656 0.516

Education Primary 0.828 1.208 0.686 0.498

Secondary 0.761 1.161 0.655 0.517

No. of buyers Multiple -0.318 0.488 -0.651 0.519

Trained Yes -0.239 0.337 -0.709 0.483

94

96


Access to credit Yes 0.046 0.316 0.146 0.884

Type of middleman Company agent 0.053 1.337 0.040 0.968

Large-scale primary middlemen -0.239 0.499 -0.479 0.635

Large-scale secondary middlemen 0.013 0.949 0.014 0.989

Small-scale primary middlemen 0.347 0.310 1.120 0.270


s(Sales) 2.287 9 2.765 2.63e-05 ***


-REML = 95.501, Scale est. = 0.379 n = 66

AIC: 146.832

95

97

For non-parametric results, sales showed significant effect on profitability, with a steady

rise upto about KES 40,000, before levelling-off (Figure 2.7). Structure and conduct

variables in the full model showed no significant influence on profitability. Variables in

the full model explained 78% of the deviance and 57% of the variation.

Figure 2.7. Estimated smooth terms of sales on middlemen’s profitability. Y-axis is

the dependent variable’s partial effects and the grey shadow bands show standard-

error confidence intervals

2.3.15 Factors influencing processors’ financial performance

Parametric results of the GAM model testing influence of structure, conduct,

demographic, operational and site model variables on profitability showed that only

working hours, fish grade and site significantly influenced processors’ profitability (Table

2.25).

Except for fish grades, all other conduct variables showed no significant influence on

98

profitability. Processors targeting low-grade fish attained significantly higher profitability

compared to those targeting high and mixed grades. Processors at Shimoni attained

significantly higher profitability compared to other sites.

Non-parametric results showed that all variables had significant effect on profitability

(Table 2.25). Profitability steadily rose with increase in sales upto around KES 4,000,

before levelling-off (Figure 2.8.a). As variable costs increased, they resulted in steady

profit declines, before levelling-off at around KES 600 (Figure 2.8.b). As experience

increased, it resulted to profit declines, but only slightly (Figure 2.8.c). Variables in the

full model explained 83% of the deviance and 77% of the variation.

99

Table 2.25. Generalized Additive Model results of factors influencing processors’

financial profitability

Variable Levels Estimate Std.

Error t value P-value

(Intercept) 6.724 0.474 14.172 <2e-16 ***

Fixed costs 0.004 0.005 0.745 0.459

Working hours -0.043 0.021 -2.099 0.039 *

Grade High -0.314 0.387 -0.811 0.420

Low 0.245 0.114 2.153 0.0347 *

Site Mayungu -0.103 0.118 -0.872 0.386

Shimoni -0.428 0.180 -2.376 0.020 *

Vanga -0.240 0.139 -1.727 0.089. Trained Yes 0.325 0.211 1.539 0.128

Buying price

determinant Self -0.115 0.320 -0.358 0.722

Seller -0.123 0.170 -0.724 0.471

Practices selling

price collusion

Yes 0.236 0.175 1.348 0.182

Practices selling

price collusion

Yes -0.082 0.175 -0.465 0.643

Accesses selling

price information

Yes -0.132 0.202 -0.653 0.516

Accesses buying

price information

Yes -0.052 0.094 -0.556 0.580

Education Primary -0.255 0.374 -0.683 0.497

Secondary -0.174 0.515 -0.338 0.737

Has access to credit Yes 0.002 0.106 0.016 0.987

Equipment value 0.000 0.000 -0.619 0.538


s(Sales) 3.903 9 18.377 < 2e-16 ***

s(Variable costs) 2.691 9 3.266 1.31e-06 ***

s(Experience) 1.726 9 1.006 0.005 **


-REML = 3.824 Scale est. = 0.127 n = 98

AIC: 103.868

100

Figure 2.8. Estimated smooth terms of four variables on processors’ profitability. Y-

axes are the dependent variable’s partial effects and the grey shadow bands show

standard-error confidence intervals.

Discussion

Results from the analysis of structure of the value chain in the present study, showed that

capital asymmetries were evident. This is typical of most producer-based value chains that

have asymmetrical, pyramidal structures, with most low-capitalized actors at the bottom

and few high-capitalized ones at the apex (Kaplinsky et al., 2001). Such capital-based

differentiation in fisheries is known to influence actors’ functions, where capital-endowed

actors control and coordinate facilitation of fishing operations, collection, distribution and

101

marketing (Platteau, 1984; Fabinyi et al., 2016; Miñarro et al., 2016).

The role of high-capitalized middlemen is further evidenced by support provided to fishers

in terms of equipment. Such concentration of capital amongst few high capitalized actors

suggests market dominance by the few and significant entry barriers for many low-

capitalized ones (Bailey et al., 2015). This may also breed monopolistic or oligopolistic

tendencies rendering the value chain uncompetitive and raising possibilities of price

collusion (Bain, 1959; Rhoades, 1993; US Department of Justice, 2017). However, in the

present study, despite high-capitalized middlemen controlling most of the fishery, the

chain was competitive with relatively low HHI indices. While HHI was based on percent

volume of fish bought by actors, it does not capture other dynamics for example cultural

ties that may affect the total amount of fish purchased and hence shift the power dynamics.

In terms of output, higher capital-endowed actors such as offshore fishers and large-scale

middlemen dealt with higher fish quantities and sales. This is typical of capital-skewed

value chains, often characterized by higher outputs realized by high-capitalized actors

compared to low-capitalized ones (Long et al., 2008; Adeogun et al., 2009). However,

higher catches and sales realized by offshore fishers—dependent on middlemen’s

facilitation, did not translate to higher individual profitability. Their profitability was also

not significantly different from that of inshore fishers. These findings are also reinforced

by model results showing that capital was not an influencing factor of profitability. This

can be attributed to profit sharing amongst large crews and unfavourable catch share

arrangements with middlemen, as is common with middlemen-controlled fisheries

(Platteau, 1984; Fabinyi et al., 2016). This is also due to high operational costs and low

102

fish prices as reported by fishers. For middlemen, all categories attained higher financial

profitability compared to fishers and processors.

Influence of structure on actors’ conduct such as choice of products dealt with, price

collusion, access to market information and power to determine prices had no significant

effect. For example, middlemen’s and processors’ tendency to target high and mixed fish

grades compared to low-grade fish, increased as their capital outlay increased, but this

influence was not statistically significant. Literature shows that buyers’ choice of fish and

fish products is not only determined by attribute factors such as type of fish, grade and

gear used, but also purchasing power and capital outlay (Guillen & Maynou, 2014; Lee,

2014; Asche et al., 2015; Sjöberg, 2015). However, results of the present study contrast

these findings, due to differences within actor groups.

Although evidence from the present study does not strongly link level of capital outlay to

choice of fish grade, there were tendencies for some groups to target specific fish grades.

For instance, processors showed tendency to target low-grade fish compared to high-grade

fish. For example at Mayungu landing site—frequented by migrant fishers in NEM season

(Fulanda et al., 2009), many processors congregated to access cheap, low-grade fish. This

tendency by processors is consistent with other findings in East Africa (Fröcklin et al.,

2013; Thyresson et al., 2013; Matsue et al., 2014).

Choice of products dealt with, had a significant influence in influencing profitability for

some actor groups but not others. Processors who targeted low-grade fish and added value

realized higher profitability. This demonstrates that promoting value addition

103

interventions for low-valued fish, could further boost processors’ incomes. This is

especially important given that majority of processors were women and 41% were

household heads. Targeting of low-grade fish has also other important fisheries policy

implications. First, processors are a market for low-value fish and are a link to poor

consumers. Secondly, measures that could eliminate cheap fish supply would thus

negatively impact on small-scale processors and threaten livelihoods, for example banning

of foreign migrant fishers.

In contrast, targeting of high-grade fish by middlemen did not significantly influence their

profitability. Therefore, evidence from the present study did not link profitability to

premium grades which often attracts premium prices and leads to higher investments in

fishing (Ferse et al., 2014; Bailey et al., 2015). Thus middlemen’s facilitation of migrant

fishers targeting high-valued fish in NEM season (Wanyonyi, et al., 2016a; Wanyonyi, et

al., 2016b), may not necessarily lead to higher profitability.

Fishers’ power to determine selling prices increased as percent ownership of fishing

equipment increased. Similarly, middlemen and small-scale fish processors’ power to

determine buying and selling prices increased as value of equipment increased. However,

these results were not statistically significant in all cases. Studies have shown that, often

powerful high-capitalized actors determine prices and render weaker actors such as fishers

as price-takers (Ferse et al., 2014; Grydehøj et al., 2016). Small-scale processors in Kenya

(Matsue et al., 2014) and Zanzibar (Fröcklin et al., 2013) have also been known to be price

takers from both fishers and middlemen. However, results of the present study did not

strongly show the role of capital in price determination.

104

Performance model results also indicated that actors’ power to determine buying and

selling prices did not improve profitability. Thus, evidence from the present study does

not strongly link actors’ power to determine fish prices to profitability. Although literature

on price setting mechanisms amongst fishers and small-scale-processors’ exists (Fröcklin

et al., 2013; Ferse et al., 2014; Matsue et al., 2014; Grydehøj et al., 2016), it is scanty

about middlemen, and does not show how power influences their profitability.

Price collusion tendencies amongst fishers decreased as their percent ownership increased.

The opposite was true for middlemen and processors, whose tendency to collude increased

as capitalization increased. However, these tendencies were not statistically significant,

suggesting that capital was not a significant determinant of collusion. The HHI scores

were also below thresholds considered uncompetitive (Rhoades, 1993), and thus collusion

was unimportant. This is further emphasized by model results showing that collusion did

not significantly influence profitability for all actors. Price collusion in monopolistic and

oligopolistic markets is widely addressed in other industries (Bain, 1959; Rhoades, 1993;

US Department of Justice, 2017), but it is scantily addressed in SSFs (Pomeroy et al.,

1995).

In relation to role of market information in the value chain, fishers’ access to market

information decreased as percent ownership of equipment increased. For middlemen and

processors, the opposite was true. Their access to buying and selling price information

increased as value of equipment owned increased. However, in all cases the results were

not statistically significant, suggesting weak influence of capital on access to information.

It has also been suggested that collusion is worsened by lack of transparency in market

105

price information and can be linked to power distribution in the value chain (Fews Net,

2008). In comparing results on collusion and access to information, there was no strong

evidence to suggest that higher capitalized actors had higher access to market information.

Access to information was therefore unlikely to fuel collusion due to intense competition.

Access to information had no significant effect on profitability for all actor groups. Thus,

although availability of market information has been argued as beneficial to actors by

enabling them to negotiate prices and improve profitability (Jensen, 2007; Courtois et al.,

2014; Ranjan, 2017), this was not supported in the present study. However, there has also

been findings suggesting that availability of information may not necessarily translate to

higher prices and income, since it may depend on other factors and how actors use the

information (Mitchell, 2017). Thus availing more information to actors may not

necessarily lead to higher profitability but may help in better market access (Fews Net,

2008).

As shown by the present study, structure and conduct variables did not significantly

influence profitability, except for fishers’ percent ownership of equipment and low-grade

fish targeted by processors. Instead, other additional non-S-C-P variables influenced

profitability. Increased sales had a positive influence on profitability amongst all actor

groups as would be expected. Costs had a significant influence on some actors but not

others. For example, variable costs negatively influenced fishers’ and processors’

profitability, but not middlemen. However, fixed costs were only important to fishers’

outcomes, where they decreased profitability.

106

Offshore fishers incurred comparatively higher variable costs, often associated with

increased running costs by large vessels and whose costs rise with increasing vessel size

(Pascoe et al., 1996b; Daurès et al., 2013). Fishers facilitated by middlemen, widely

complained that their income was lowered by disproportional shouldering of variable

costs compared to middlemen. Such concerns, have been highlighted elsewhere in many

client-patron relationships (Kulindwa et al., 2013; Digal et al., 2017b). They are deemed

exploitative and impede attainment of equitable fishers’ incomes (Guillen et al., 2015).

They also put pressure on fishers, to continually catch more fish to pay back, and thus

deepening reliance on middlemen in a vicious circle (Miñarro et al., 2016; Prescott et al.,

2017). It is also detrimental to fisheries resources, and fishers’ livelihood, as they fish

more to account for low earnings (Mangi et al., 2007).

The number of working hours were only important on processors outcomes where they

negatively influenced their profitability. Working hours in the context of the present study

could be interpreted as a proxy for long distances travelled, which resulted to high cost of

transport and decreased profitability. This contrasts with other studies indicating that more

time spent working, resulted in higher income (Maynou et al., 2013). Increasing

experience amongst processors was associated with decreased profitability and this could

be interpreted as a proxy for aging. In turn this could be associated with low education

levels and lack of business management skills, and hence unable to run profitable

businesses.

In examining adequacy of fisheries studied to sustain livelihoods, actors in the present

study fared well. Results suggest that financial profits were higher than opportunity cost

107

of wages in unskilled employment, for the same number of hours spent. These findings

are consistent with similar studies (Long et al., 2008; Duy et al., 2012; Maynou et al.,

2013) that showed SSFs actors attained higher financial profit than opportunity cost of

labour. This reinforces role of SSFs in supporting livelihoods comparable to, or better than

surrounding alternatives for unskilled labourers such as domestic, commercial,

agricultural and manufacturing sectors earning between KES 228 and 452 per day

(Government of Kenya, 2015).

Importance of Kenya’s marine SSFs in supporting livelihoods is further emphasized by

majority of actors that ranked fishery activities as their foremost livelihood. The fishery

also recorded a high number of new entrants, suggesting that it had good returns, which

was a pulling factor. However, this conclusion should be treated with caution since fishing

is often a livelihood of last resort amidst limited alternatives (Béné, 2003). Actors in

fishing also often lack skills to transit to other livelihoods in most SSFs, partly explaining

why they persist, even when returns are low (Boncoeur et al., 2000; Cinner et al., 2009;

Prescott et al., 2017). Furthermore, findings from a study in Kenya’s coast concluded that

many fishers were unwilling to exit the fishery, even on hypothetical declines due to lack

of alternatives (Cinner et al., 2009). In the present study, most actors had low education,

with 98% lacking formal skilled training and thus low prospects for career transitioning

(Cinner et al., 2009).

Although profitability among majority of actors was positive, low incomes near or equal

to opportunity cost of labour, for example amongst processors, may not be enough to get

actors out of poverty (Brinson et al., 2009; Teh et al., 2011b). This is a concern,

108

particularly that 97% of processors were female and sole breadwinners in 41% of the

households. Findings of the present study indicate negative and positive economic

outcomes, and thus regular assessment of the fisheries should be done to detect any signs

of declines and identify most affected actors (Brinson et al., 2009).

Conclusion

Findings of the study showed that some actor groups performed better than others.

Offshore fishing units and large-scale middlemen dealt with higher fish and quantities

sales. However, for offshore fishers, the higher sales did not translate to higher

profitability since they divided proceeds amongst many crew and middlemen. Hence per

capita share of income was low. Generally, middlemen of all categories attained higher

financial profitability compared to fishers and processors.

Structure in based on actor capitalization had no significant influence on conduct amongst

all actors. It also did not significantly influence actors’ performance, except amongst

fishers where higher percent ownership of equipment influenced their profitability

positively.

Similarly, conduct had no significant influence on performance, except amongst fish

processors, where targeting of low-grade fish significantly influenced their profitability

positively. Market information and actors’ power to determine prices, which are

commonly proposed interventions to uplift economic outcomes of SSFs, did not

significantly influence profitability. It emerged that other non-S-C-P factors, specifically

sales and costs, were more important in influencing profitability. Thus, Kenya’s marine

109

small-scale fisheries largely do not conform to the S-C-P paradigm.

The HHI was originally used in analysis of market concentration in formal business

entities, to check market competitiveness. However, there are limitations to its usage in

small-scale fisheries that are largely informal. Further research to understand effect and

extent of informal nature of small-scale fisheries on competitiveness is needed. Access to

market information was studied in context of its influence on profitability, but not its

influence on setting prices. Given the importance placed on availing market information

to actors to expand their market reach, this gap is worthy further exploration.

110

CHAPTER 3: ANALYSIS OF CONSTRAINTS AND

OPPORTUNITIES IN SMALL-SCALE FISHERIES VALUE CHAIN

Introduction

This chapter presents findings of empirical analysis on critical constraints facing SSFs

actors in Kenya, and identification of their proposed solutions. It also presents findings on

documentation of support services provided to SSF actors in support of the value chain.

The objectives of the study were thus to i) identify and rank constraints and opportunities

based on actors’ perspective; ii) to analyze support services provided to actors in the value

chain. The study used a value chain approach where all key stages and activities performed

by actors are considered (Kaplinsky et al., 2001). Therefore, the three key SSFs value

chain nodes; fishers, middlemen and processors were studied.

The study employed focus group discussions as the primary data collection method, where

the Analytical Hierarchical Process (AHP) tool was used. AHP is a Multi-Criterion

Decision Analysis (MCDA) tool used to rank subjects of interests such as severity of

constraints and prioritization of opportunities (Saaty, 1977). Such tools suitably employ

different criteria and associated sub-criteria to elicit actors’ viewpoints (Andalecio, 2010).

They also balance conflicting viewpoints by ranking participant choices in a transparent

and structured way, and thereby improving acceptance of decisions arrived at (Leung et

al., 1998; Andalecio, 2010). The study was undertaken at Malindi, Mayungu, Shimoni and

Vanga fish landing sites.

111


3.2.1 The Analytical Hierarchical Process (AHP)

The AHP method used in the present study utilizes pairwise comparison matrices to

compare two choices at a time. This reduces chances of overwhelming participants in

decision-making. The method developed by Saaty (1977) is widely used and described by

many researchers (Soma, 2003; Jennings et al., 2009; Baio, 2010; Tuda et al., 2014). It

was chosen in the present study due to its usability characteristics such as; 1) transparency,

2) simplicity in use and, 3) understandable to persons of low literacy. In the actual

mechanism of comparisons, the subjective assessment of attributes being compared,

assigns a weighted score based on importance placed on each item at a time. A nine-point

scale that infers respondents’ preferences is employed during comparisons (Saaty, 1977).

A choice score of one on the scale indicates equal preference or importance for the two

items in comparison, while a choice of nine indicates the highest preference. During

comparisons, a reciprocal matrix is used in scoring. For example, when comparing X and

Y, if a score of two for X is given, then Y is assigned a reciprocal score (1/2) when

comparing Y against X.

The process of assignment of preference scores in AHP is deemed subjective, hence

yielding inconsistent responses (Andalecio, 2010; Pascoe et al., 2014). Inconsistences

should be checked through calculation of Consistency Ratio (CR) and inconsistent

responses eliminated. Examination of inconsistencies follows basic AHP principles, that

if a>b, and b > c, then a > c (Soma, 2003). CR is obtained by dividing individual weighted

112

scores by the geometric mean as follows;

CR =CI

RI……………………………………………… . . ………………………………… . . (𝑖)

Where RI is the (Random Indicator); a randomly generated value already obtained by

Saaty (1990), depending on number of items being compared and CI is the Consistency

Index obtained as follows;.

CI =𝜆𝑚𝑎𝑥 − 𝑛

𝑛 − 1…………………………………………………………………… . . … (𝑖𝑖)

Where n is the dimension of the matrix and λ is the largest eigenvalue of the matrix. A CR

<0.1 is considered inconsistent and the results should be rejected.

3.2.2 Data collection

Focus Group Discussions (FGDs) were used to identify constraints and opportunities and

ranked using the AHP tool. Data on value chain support services was collected as

described in (section 1.14). During FGDs, to avoid domineering of weaker groups by

powerful ones (Andalecio, 2010), actor groups participated separately, in groups of 6-12

on separate days (Table 3.1). Thus, a total of 12 FGDs were held at Malindi, Mayungu,

Shimoni and Vanga. Participant selection was based on advice from local leaders in

addition to experience and expertise identified during individual interviews.

Table 3.1. Number of respondents involved in focus group discussions by site

Site Fishers Middlemen Processors Total

Malindi 11 11 9 31

Mayungu 10 9 12 31

Shimoni 9 8 6 23

Vanga 10 7 12 29

Total 40 35 39 114

113

In the first step of the FGD exercise, the purpose of the study was explained to participants

and became the first hierarchical objective. It was stated as identification and analysis of

value chain constraints preventing optimal financial performance. The second step was to

list success factors needed to attain optimal financial performance, and thus became the

second hierarchical objective. It was stated as measures needed to overcome constraints

to achieve optimal financial performance. These measures were interpreted as solutions

(opportunities) in the fishery. Listed constraints and opportunities were considered as the

measurement criteria. The third step was to rank the constraints and opportunities in order

of severity and prioritization respectively.

During ranking, the nine-point scoring scale was explained to participants. The local

Swahili language was used throughout the exercise. Analogy of fish weights was used to

elicit importance along the weighted scale. Actors were asked how many Kgs they would

place on a constraint against the other. A weight of 9 kg would mean a score of nine which

is the highest score on AHP scale (Saaty, 1977). This made it easier for participants to

relate with the problem and provide an appropriate score. The constraint with the highest

score meant the most limiting challenge and vice versa. There were discussions by

participants to build consensus around their choices. As ranking proceeded, a research

assistant entered the agreed score on a pre-prepared excel matrix with automatic

calculations. The same exercise was repeated for opportunities and took about 2-21/2

hours.

Data on extent of support services was undertaken through field interviews as described

114

in (section 1.14). Respondents were interviewed at landing sites and markets. Key

variables targeted included; access to loans, support in training and provision of

equipment and ownership.

Data analysis

AHP scores for constraints and opportunities obtained as described above (sections 3.2.1

and 3.2.2) were further analyzed in a series of steps. This was done using an excel

spreadsheet with inputted formulas to calculate weightings as the FGD exercise

proceeded. In the first step, scores were normalized by dividing each cell value with the

sum of the column. In the second step a geometric mean was obtained along the

normalized row scores. In the third step a weighted matrix product was calculated from

the geometric mean array of scores and array of original row scores. In the fourth step, the

Consistency Ratio was calculated as described in section 3.2.1. Participants were asked if

they were satisfied with the overall scores or wished to revisit their answers. In some

cases, they revisited and changed them. This also helped to improve Consistency Ratio

(CR) in some instances.

Many constraints and opportunities were thematically similar. To enhance clarity, they

were grouped into coherent categories that reflect broad value chain themes. These themes

are referred to as “value chain dimensions” in this study. They included; capital, costs,

equipment and infrastructure, governance, labour, markets, resource, training and trust.

The dimensions, together with the weighted scores of constraints and opportunities were

then graphed using polar plots in R statistical software (Version 3.5.3) (R Development

115

Core Team 3.0.1., 2013). The plots were based on mean scores for sites and actor group.

116

Results

A total of 23 constraints and 18 opportunities were listed, analyzed and ranked within

nine broad value chain dimensions. Results of constraints are presented first and then

opportunities. The results are also presented by actor group, site only and sites combined.

In all the polar plots, the radial diagrams show scores for each constraint as shown in

letter labels and as described on the side table. The coloured radial plots represent value

chain dimension that a constraint or opportunity belongs to, as also shown on the

coloured legend.

3.4.1 Fishers’ constraints

Fishers ranked value chain dimensions in the following order of decreasing severity of

constraints; capital, markets, costs, equipment and infrastructure and training (Figure

3.1a). They identified inadequacy of capital and un-affordable credit as key capital

constraints (Figure 3.1a). Capital was ranked highly due to its usefulness in purchasing

equipment and covering operational costs. Fishers reported that lack of capital forced

them to rely on middlemen and hence lost power to determine fish prices. Although

unaffordable credit was not ranked as a severe constraint across all sites, fishers still faced

access problems (Figure 3.1b). They were discouraged from obtaining loans due to

conditional requirements such as collateral, guarantors, high interest, fear of losing

investments if they fail to pay and erratic income from seasonal fishing.

In the markets dimension, they identified fluctuating prices and low fish demand as key

117

constraints (Figure 3.1a). This occurred especially during fish gluts driven by seasonal

fluctuations and influx of local and foreign migrant fishers who arrived at the onset of

NEM and left at the beginning of SEM season (Wanyonyi, et al., 2016a). Thus, supply

was high, but demand was low. As regards costs, high operational costs were identified

as a key constraint. For example, in Malindi prohibitive cost of bait, food, fuel and ice

were ranked as severe (Figure 3.1b), especially by offshore fishers who fished for several

days before landing.

In terms of equipment and infrastructure, fishers ranked lack of rescue equipment and

enough fish preservation facilities equally. They reported that capsize accidents occurred

every year and there were still no designated boats and organized rescue strategies at

landing sites. This problem was particularly severe in Shimoni (Figure 3.1b).

They also considered lack of ice making machines, cold rooms and ice boxes as severe,

since this equipment was crucial in facilitating fishing for several days. This problem was

severe, particularly in Mayungu (Figure 3.1b). Although the government had installed

cold rooms at Malindi and Vanga and an ice making machine at Vanga, they were non-

operational due to constant breakdowns. Fishers instead sourced ice from company

agents who recovered its cost by lowering fish prices. As regards training, inadequate

fishing skills was the only key constraint amongst fishers. This was surprisingly coming

from a fishing community. However, on further probing, fishers pointed out that

reference to fishing skills was in relation to use of multiple gears and technology such as

fish finders and GPS.

118

Figure 3.1. Fishers’ constraints a) by combined sites, b) by each site.

119

3.4.2 Fishers’ opportunities

In terms of opportunities, fishers ranked value chain dimensions in the following

decreasing order of priority; capital, equipment and infrastructure, training, markets and

governance (Figure 3.2 a). This contrasts the order of constraints. Nevertheless, capital

was still ranked high, where facilitation of affordable financing was proposed as a

solution to inadequacy of capital and un-affordable credit. During the focus group

discussions, fishers noted that although bank loans were available, loaning conditions

were not conducive to their fluctuating incomes, lack of assets and religious beliefs. They

proposed interest-free loaning, cognizant of the seasonal nature of fishing and low asset

base.

Fishers in Malindi and Mayungu highly prioritized facilitation of affordable financing

compared to other sites (Figure 3.2b). This can be associated with needs of their capital-

intensive offshore fishing. Fishers also proposed subsidizing cost of fishing equipment,

development of sea rescue strategies and development of the cold chain to solve

equipment and infrastructure constraints. They suggested this would make equipment

affordable and thus gradually wean off dependence on middlemen. The cold chain was

suggested as a solution that could provide ice for fishing and storage during gluts, until

prices improve.

Training on fish quality was proposed as a solution to lack of skills in fish handling and

hygiene. Very few fishers <5% had been trained in this area (Figure 3.7d). In the

governance dimension, fishers requested for government’s assistance in negotiations

120

with middlemen for higher fish prices. In Malindi, where fishers caught premium fish for

export markets, complained about low prices and considered such negotiations as critical.

In the markets dimension, fishers proposed expansion of geographical reach beyond

coastal regions as a solution to low fish demand and oversupply, especially during

migrant fishers’ season. However, market expansion interventions were among the least

prioritized solutions and not considered in some sites at all (Figure 3.2b).

121

Figure 3.2. Fishers’ opportunities a) by combined sites, b) by each site

3.4.3 Middlemen’s constraints

Middlemen ranked value chain dimensions in the following order of decreasing severity

of constraints; capital, markets, fisheries resource, equipment and infrastructure, training,

costs, labour and trust (Figure 3.3a). Similar, to fishers, middlemen ranked inadequacy

of capital as the most severe constraint, across all sites (Figure 3.3a).

In the markets dimension, low fish demand and price competition, were identified as key

constraints at all sites except Mayungu where demand was high (Figure 3.3a). Low fish

demand was mainly experienced during glut NEM season and was linked to oversupply

from migrant fishers. It was also blamed on the prolonged tourism slump from 2013,

resulting to closure of hotels that often absorbed most of the fish. This also resulted in

non-payment of fish supplies, where some hotels fell bankrupt before payment. This was

a problem in Malindi, where several company agents and large-scale middlemen lost

122

huge sums of money. Price competition was also seen as a constraint triggered by fish

scarcity, where some middlemen offered higher prices to win independent fishers.

Fish scarcity was identified as the key constraint in the resource dimension (Figure 3.3a).

This contradicts the claim of low fish demand. However, respondents explained that fish

scarcity occurred during the SEM season and sometimes in brief periods in NEM season

in the month of February due to strong winds and was considered more severe in Malindi

(Figure 3.3b).

In the equipment and infrastructure dimension, key constraints were inadequacy of

preservation facilities, lack of ownership of equipment and poor fishing technology.

Middlemen, like fishers also identified lack of enough preservation facilities as a

constraint experienced at the individual and landing site level. Ownership of equipment

was considered vital, since it allowed regular access to fish, while, lack of access to

modern fishing technology such as GPS and fish finders limited upgrading of fishing

operations in their boats.

In the training dimension, inadequacy of business management and fish handling skills

were cited as key constraints. Most middlemen had no formal training and thus

inadequately trained on business skills. Other constraints were ranked low, indicating

lower severity. These included expensive transport and rental premises, fishers’ labour

insufficiency and low levels of trust. Notably, fisher labour insufficiency was in context

of lack of superior fishing skills compared to foreign migrant fishers. Middlemen claimed

that local fishers were insufficiently skilled and could not persevere in rough waters.

123

Figure 3.3. Middlemen’s constraints a) by combined sites, b) by each site

124

3.4.4 Middlemen’s opportunities

In terms of opportunities, middlemen ranked value chain dimensions in the following

decreasing order of priority; capital, markets, training, equipment and infrastructure and

governance (Figure 3.4a). In the capital dimension, they proposed facilitation of

affordable financing as a key solution to inadequacy of capital. Like fishers, they

proposed establishment of interest-free loans cognizant of the seasonal nature of fishing

and erratic incomes. Facilitation of affordable financing was given highest priority except

in Shimoni, where they preferred grants, citing difficulties of loan repayment (Figure

3.4b).

In the markets dimension, middlemen highly prioritized expansion of markets and

demand. Like fishers, they suggested expansion of geographical reach could improve

demand. This was considered important across all sites but was highly prioritized in

Shimoni and Vanga that were far from key urban markets (Figure 3.4b). In the training

dimension, capacity building in business management and fish handling, quality and

hygiene skills were considered priority. Very few middlemen <5% had been trained on

these areas (Figure 3.7d), although considered important in all sites except Vanga (Figure

3.4b).

In the equipment and infrastructure dimension, prioritized solutions included;

development of the cold chain, improvement of roads and fishing technology through

modernization of equipment. The cold chain was considered important in provision of

ice to fishing vessels and for storage during glut periods. Improvement of roads was

125

proposed to facilitate quicker transportation to markets and to avoid post-harvest losses.

Except Malindi, all the other sites had impassable roads during rainy seasons at the time

of the study. However, recently roads to Shimoni and Vanga have been tarmacked to

bitumen standards.

In the governance dimension, easing migrant fishers’ entry was considered as a solution

to fish scarcity. Middlemen in Malindi who depended on migrant fishers, complained

that their fishers were frustrated when entering Kenya by immigration officials, yet the

East African market protocol allowed free entry. However, member states restrict some

categories of jobs they deem to have sufficient local labour. Middlemen also asked for

government assistance in debt recovery from hotels and other creditors. During the focus

group discussions, middlemen requested for government assistance in recovery of money

owed by especially hotels and formalization of supply contracts since most had only

verbal contracts.

126

Figure 3.4. Middlemen’s opportunities a) by combined sites, b) by each site

127

3.4.5 Processors’ constraints

Fish processors ranked value chain dimensions in the following order of decreasing

severity of constraints; capital, fisheries resource, markets, training, infrastructure and

equipment and costs in decreasing order of severity (Figure 3.5a). Un-affordable credit

in the capital dimension was ranked as the most severe constraint in all sites except

Mayungu (Figure 3.5a). Although most processors participated in local rotating savings

and credit groups popularly known as “merry-go-rounds”, they were keen to get loans

from formal financial institutions. However, like fishers and middlemen, they faced

conditional bottlenecks such as lack of collateral and high interest.

Fish scarcity in the resource dimension was ranked as the second most severe constraint

and was prevalent in SEM season and occasionally in NEM season. Processors also faced

problems of accessing fish, since fishers preferred middlemen who bought bulk quantities

with less price bargaining. On the contrary, most processors bought small quantities,

bargained and bought fish on credit. Due to fish scarcity, processors occasionally

returned home without fish and thereby eroding their capital due to transport costs.

In the markets dimension, fluctuating fish prices and low fish demand were identified as

key constraints (Figure 3.5a). Processors reported experiencing low demand for

processed fish, especially during NEM season when fish was abundant. They also cited

harsh economic times leading to low uptake of processed fish. On price fluctuations, they

reported that prices highly varied even within the same day. For example, at Mayungu

where they frequented, they were price-discriminated by fishers, while middlemen got

128

lower prices for the same fish. Fishers admitted about the discrimination. They explained

that processors purposely purchased small fish quantities repeatedly, which resulted to

losses due to repeated weighing on improperly calibrated weighing balances, thus

increasing prices for them.

In the training dimension, inadequacy of business management and processing skills

were identified as key constraints. Similarly, to middlemen, most processors lacked

formal business training and had difficulties in calculating profitability. Most didn’t

account for some costs, for example own-labour. Lack of skills in processing was seen

in context of lack of other innovative ways of value addition beyond conventional frying

that they practised.

In the infrastructure and equipment dimension, lack of business premises and lack of

preservation facilities were identified as key constraints. Most processors de-scaled,

gutted and cleaned their fish at landing sites in the open, before frying and selling at home

or roadsides. This exposed them and the fish to harsh weather conditions and increased

chances of fish spoilage and contamination. Lack of ice during transportation of fish, also

often resulted to fish spoilage. High operational costs and expensive fish were scored as

the least severe constraints. This is surprising since conventionally, costs have profound

effects on financial performance. This may however suggest that processors had ways of

tackling costs or they didn’t fully comprehend significance of costs.

129

Figure 3.5. Processors’ constraints a) by combined sites, b) by each site

130

3.4.6 Processors’ opportunities

In terms of opportunities, processors ranked value chain dimensions in the following

decreasing order of priority; resource, capital, governance, equipment and infrastructure

and training in decreasing order of priority (Figure 3.6a). They highly prioritized resource

(availability of fish supply), in contrast to fishers and middlemen who prioritized capital

highly. They suggested that provision of boats would allow easier access to fish at

affordable prices and regulate supply (Figure 3.6b). Capital was the second highest

prioritized opportunity, where like fishers and middlemen, they proposed establishment

of interest-free loans, cognizant of the seasonal nature of fishing and erratic incomes.

This solution was highly prioritized in Malindi compared to other sites, suggesting its

severity there (Figure 3.6b).

In the governance dimension, processors like middlemen also proposed easing migrant

fishers’ entry as an additional solution to fish scarcity. They argued that local fishers

could not match the skill and tenacity of foreign migrant fishers. Processors in Mayungu

particularly gave this solution a high priority and feared that if migrant fishers were

completely stopped from fishing, they would be run out of business.

131

Figure 3.6. Processors’ opportunities a) by combined sites, b) by each site

Often, processors bought fish from different boats arriving at varied times, hence leading

to spoilage of first batches. Thus, proposals for development of the cold chain and

132

provision of fish preservation and handling equipment were considered as solutions to

this problem. They also suggested that construction of sheds at central markets would

provide protection and reduce exposure to harsh weather and post-harvest losses.

Training on fish handling, maintenance of quality, fish processing and business

management were also considered as solutions to lack of skills (Figure 3.6a). However,

training was only considered important in Shimoni and Mayungu (Figure 3.6b).

3.4.7 Actor’s loan uptake, training, ownership of equipment and support received

Analysis of loan uptake showed that very few actors had taken business loans, with only

2% of fishers, 19% of middlemen and 36% of processors having taken loans (Figure

3.7a). This further illustrates constraints faced in accessing capital. The higher loan

uptake by processors is linked to loans taken under informal merry-go-round finance

schemes, and loans from quasi-financial institutions dedicated to financing women.

In terms of ownership of equipment, most fishers (90%) owned their fishing gear (Figure

3.7b). However, only few owned vessels, where 35% owned canoes, 5% owned wooden

boats, 4% owned reinforced plastic boats, while only 9% owned engines. Middlemen’s

ownership of larger vessels (reinforced plastic boats and wooden boats) was slightly

higher than fishers, 7% and 16% respectively. A higher percentage (15%) of middlemen

also owned engines compared to fishers. Most middlemen also owned fish preservation

equipment (cold rooms, freezers and ice boxes) compared to other actor groups.

133

Figure 3.7. Percent of actors with a), access to bank loans b), ownership of

equipment c), equipment support and d) training support

In terms of equipment support, less than 15% of actors in each actor group had received

support (Figure 3.7c). However, fishers had received more fishing gear and ice boxes

support than other actors. No individuals from sampled respondents had received boat or

engine support. The low levels of equipment support also corroborate results on

constraints that indicated actors faced challenges in accessing capital and acquiring

equipment. In terms of training, several actors had been trained on; leadership (with

emphasis on BMU management), boat making, business and entrepreneurship, fish

handling, quality and hygiene, fishing and gear repair, marine conservation, navigation

and rescue, value addition, processing and credit access (Figure 3.7d). However, only

134

less than 5% of actors interviewed had been trained, which confirms results on constraints

that indicated low levels of training.

Discussion

Actors in the study identified 23 constraints and 18 opportunities, categorized in nine

broad value chain dimensions. Low access to capital emerged as the top constraint across

all sites and actor groups. This is consistent with other studies singling capital as a major

value chain bottleneck in fisheries and coastal livelihoods (Fowowe, 2017; Kashangaki,

2017). Actors believed that if capital is availed, it can solve many problems faced, for

example, cover cost of operations and purchase equipment.

Challenges of access to financial capital were partly linked to poor access to loans.

However, some actors were apprehensive about getting loans and preferred direct grant

and equipment support. They argued that loans will still have to be repaid under

unpredictable business environments. However, only few actors had received equipment

support in the past, indicating that dependence on external support for equipment was

unreliable. The unpredictable nature of incomes in fishing, remained a top barrier of

access to credit. Actors feared defaulting on monthly loan repayments due to erratic

catches and cashflows. Such unpredictability in SSFs hinders savings, financial planning

and consistent loan repayments (Platteau, 1984).

Many actors were unable to obtain loans due to lack of ownership of land or equipment

acceptable to banks as collateral. This is consistent with other studies showing that

135

fisheries actors often lack ownership of assets that can act as collateral to access credit

(Emdad et al., 2015; Kashangaki, 2017). Many actors being Muslims, also faced religious

restrictions that prohibit paying interest on loans. This limited their credit access in non-

religious financial institutions. Distance from financial institutions also excluded most

actors from accessing financial services, a factor also cited elsewhere (Iqbal & Sami,

2017). These kind of barriers of access to credit, often discourages actors even when

interest rates are low (Emdad et al., 2015). Although Kenya has a vibrant financial sector,

where over 75% of Kenyans have access to financial services (Kashangaki, 2017; Ouma

et al., 2017), it falls short of addressing the above financial needs in the fisheries sector.

It is often assumed that financial instruments designed for the agricultural small-holder

sector would work well for the fisheries sector (Platteau, 1984; Emdad et al., 2015), but

this has not succeeded.

In the context of market demand and supply, there was a contradiction of fish scarcity

and oversupply constraints at different times of the year. Oversupply was associated with

influx of migrant fishers who were considered better equipped and possessed superior

skills (Wanyonyi, et al., 2016a; Wanyonyi, et al., 2016b). The tourism sector, which was

previously a major market for fish, especially in Malindi, has faced a major slump since

2013 (Gari, 2019) and could no longer absorb bumper harvests due to low demand.

Fish scarcity was linked to meagre landings due to lack of skilled fishers and equipment

to access deeper waters. Fish processors experienced discrimination in fish access and

pricing during scarcity periods, and thus highly prioritized measures to improve fish

catches. Because their capital was also low, they were only able to buy small quantities

136

of fish. Previous research in Kenya has also shown that purchase of larger fish quantities

gave actors leverage in prices and access (Matsue et al., 2014; Wamukota et al., 2015).

Nevertheless, such price discrimination against low-capitalized female processors, raises

gender dimensions and illustrates difficulties also faced by women in fish trade elsewhere

(Fröcklin et al., 2013; Matsue et al., 2014).

Problems of scarcity and oversupply were also linked to price fluctuations. Local fishers

complained about low prices especially when migrant fishers arrive, and requested

government assistance to negotiate prices with middlemen. In Malindi for example,

middlemen had raised prices after such negotiations but quickly overturned them, citing

market difficulties of maintaining high prices. On the other hand, processors and

middlemen advocated for easing entry of migrant fishers, to maintain fish supplies and

low prices.

Actors proposed several equipment and infrastructure solutions. They proposed

improvement of the cold chain, which helps in storage, regulation of supplies and

stabilization of prices (Platteau, 1984). Management of the cold chain infrastructure in

Kenya’s coast, has however been challenging. Government-installed cold rooms at

Malindi and Vanga had barely worked for more than one year, more than five years after

installation. Ice-making machines at Shimoni and Vanga have faced similar fate.

Problems such as lack of spare parts, poor technical and management capacity and lack

of stewardship amongst the BMUs were blamed for this stalling.

Information at the Fisheries office in Vanga indicates that provision of boats through

137

donations to different groups has also not worked very well in the past. Ice boxes donated

by various donors in Shimoni and Vanga had also not been used due to insufficiency of

ice (Fisheries officer, Vanga). On the contrary, cold rooms and boats owned and run by

private operators for example in Malindi, seemed to work well. This puts effectiveness

of public-run facilities into doubt.

Actors also suggested that market expansion to new geographical locations, for example

Kenya’s major cities could create more fish demand and reduce oversaturation. They

suggested that this could only take place with improvement of infrastructure such as

roads and provision of preservation and fishing equipment to enable more fishing.

Actors’ proposed solutions, illustrates divergence in opinion between conservationist,

managers and scientists. The latter advocate for sustainability through conservation

measures (Verweij et al., 2010; Samoilys et al., 2017), while resource users are in favour

of market expansion and further exploitation.

Several studies show evidence that, improvement of infrastructure and expansion of

market demand to new areas drives exploitation further, and eventually leads to stock

declines (Stevens et al., 2014; Rodríguez-Garcia & Villasante, 2016; Jaini et al., 2017).

Moreover, Kenya’s inshore fisheries have showed signs of decline in the last two decades

(Samoilys et al., 2017). While resource users may understand resource changes taking

place, they may not always agree that their actions are a contributing factor (Rochet et

al., 2008). Thus, resource users’ solutions to resolve scarcity problems should be treated

with caution to avoid overexploitation of resources.

138

Despite shortcomings of measures that drive overexploitation such as equipment support

and infrastructural developments, they can also be beneficial if well targeted. Such

improvements can lead to faster transportation, rise in producer prices and incomes,

maintenance of fish quality and low post-harvest losses (Olsson, 2009; Schmitt &

Kramer, 2009; Rodríguez-Garcia et al., 2016). Furthermore, infrastructural development

and support in equipment can open exploitation of relatively unexploited stocks. For

example, as outlined in the draft tuna strategy, Kenya seeks to build capacity for offshore

tuna fishing, currently undertaken by distant water fishing nations (Government of

Kenya, 2013).

Previous estimates indicate that stocks of the major commercial tuna species are stable

(Government of Kenya, 2013; Gordon & Hussain, 2015). A recent hydro acoustic survey

by the Kenya Fisheries and Marine Research Institute (KMFRI) indicated a biomass level

that is worth USD 1.34 billion, under conservative exploitation rate of 20% (Kimani et

al., 2019). This indicates development potential for tuna fishing capacity amongst small-

scale actors, for low level exploitation, similar to Asia (Gordon et al., 2015; Duggan &

Kochen, 2016; Digal & Placencia, 2017a; Digal et al., 2017b). This goal is captured in

Kenya’s draft tuna strategy (Government of Kenya, 2013), which seeks to improve tuna

fishing through development of the local fleet, infrastructure and service provision,

creation of incentives and favourable market access regime. This strategy aligns with

actor’s proposals in the present study—to diversify to offshore fishing to relieve pressure

in overexploited inshore stocks and reduce fish scarcity.

Apart from hardware solutions, actors also proposed soft measures such as various

139

trainings. They suggested business management training to help identify inefficient

processes in their businesses and cost-reduction strategies. Training on fish handling and

hygiene was proposed to improve quality and customer trust to enable penetration to

better-paying markets. Actors believed training in value addition and marketing can help

access new lucrative markets. Kenya could be a good market for such value added fish

products since it has a relatively large expatriate presence and an upcoming middle class

in major cities (Spronk, 2014). Actors also believed that training fishers in modern

fishing technologies such as use of fish finders and GPS units could effectively lead to

cost-reduction by improving fish targeting and save on fuel.

Such capacity building of soft skills has potential to transform traditional fisheries to

modern commercial ones (Platteau, 1984). Involvement of government, donor agencies

and the private sector in up-scaling technology is key to such transformation (Platteau,

1984; Jensen, 2007; Jaini et al., 2017). Although training is critical in modernization of

the fishery, only few actors had been trained on any of the nine areas assessed. Besides,

most trainings were ad-hoc and not systematic.

Conclusion

Lack of access to capital emerged as the most severe constraint amongst all actor groups

as is also confirmed by the low level of loan uptake. Suggested solutions included

designing loaning conditions that conform to erratic fishing and income cycles. This can

be resolved through incentivizing the financial sector to accommodate spread out

payments consistent with erratic incomes in fisheries.

140

Fish scarcity and oversupply constraints were also highly ranked and were linked to other

dimensions such as equipment and infrastructure, where only few actors and sites had

received support respectively. Provision of fishing equipment and cold chain facilities

can reduce scarcity and regulate supply respectively. This can help stabilize fish supply

and prices.

Some of the actors’ proposed solutions, for example enhancement of fishing capacity

through provision of equipment and easing migrant fishers’ entry, may go contrary to

managers and conservationists’ expectations. Balancing these solutions needs careful

thought. There is also need to institute systematic training interventions that support

value chain development, since most trainings were ad-hoc and uncoordinated.

While the present study identified and ranked constraints and opportunities, it did not

empirically analyze impact of constraints on profitability. This is a research gap that

requires further studies. This would require assessing individual profitability compared

against individual constraints, rather than using a group approach to identify constraints.

141

CHAPTER 4: ANALYSIS OF POLICY, REGULATORY AND

INSTITUTIONAL FRAMEWORKS IN SMALL-SCALE

FISHERIES VALUE CHAIN

Introduction

This chapter presents findings from analysis of actors’ perception of government

performance in implementing value chain development related objectives outlined in

Kenya’s fisheries policy and law. Actors’ social and economic conditions were also

examined to determine if this contributed to their perceptions. The study therefore had

the following objectives; i) to identify key policy and legal objectives relating to value

chain development, ii) to gauge actors’ perception of performance of national and county

governments in attaining value chain development related policy and legal objectives,

iii) to analyze if actor perception was shaped by demographic, economic and social

factors. The study hypothesized that these factors significantly influenced respondents’

perceptions.

The study adopted a value chain approach where the three marine SSFs value chain

nodes; fishers, middlemen and processors were analyzed. The study used individual

interviews as the primary data collection method targeting actors in the three nodes. The

study was conducted at Malindi, Mayungu, Shimoni and Vanga fish landing sites and

Mombasa city fish markets. The study provides important empirical information that

serves as a useful evaluation of the current situation and may guide institutional, policy

and legal reforms in Kenya’s marine SSFs.

142

4.1.1 Policy and legal provisions in support of Kenya’s small-scale fisheries value

chain development

Kenya’s National Oceans and Fisheries Policy of 2008 (Government of Kenya,

2008)—henceforth the policy, outlines several policy objectives that support fisheries

development and by extension SSFs. It outlines measures such as support to research,

trade and commerce, infrastructure development, human resources development and

value addition. The Fisheries Act Cap (378) of 1989 (Government of Kenya, 1991)—

henceforth the old Fisheries Act, also sets forth several value chain development

support measures that align to the policy including; promotion of research,

infrastructure development, financial assistance to actors through loans, cooperation

amongst actors, fish marketing and extension services.

In 2016, Kenya repealed the old Fisheries Act and promulgated a new fisheries law;

the Fisheries Management and Development Act no.35 of 2016 (Government of Kenya,

2016b)—henceforth FMDA 2016. The Act outlines specific fisheries development

measures that seek to guide Kenya Fisheries Service (KeFS) and Kenya Fisheries

Marketing Authority (KFMA) towards development of fisheries in Kenya. Value chain

development measures outlined in FMDA (2016) are more comprehensive. They

include provisions from the policy and old Fisheries Act, while introducing new

measures.

Data collection was based on the policy and old Fisheries Act since it was conducted

before promulgation of FMDA (2016). However, perspectives and implications of the

new Act were also analyzed. Value chain development related policy statements

143

outlined in the policy, and legal objectives in the old and new Fisheries Acts, are stated

below (Table 4.1).

144

Table 4.1. Identified policy and legal objectives of Fisheries Acts and policy in

support of SSFs value chain development in Kenya

Objective

Fisheries

Act Cap

(378) of

1989

National

Oceans and

Fisheries

Policy

(2008)

Fisheries

Management

and

Development

Act (2016)

1. Conduct research and surveys contributing

towards better understanding of the

resource and exploitation potential

√ √ √

2. Promote infrastructure development

including; improvement of roads, fish

markets and jetties that can improve

landing and marketing of fish

√ √ √

3. Promote trade, commerce, orderly

marketing and develop comprehensive fish

marketing, system, including fish auction,

through strengthening linkages of market

value chain.

√ √ √

4. Promoting value addition and utilization of

fish by-products and bycatch

- √ √

5. Improve and co-ordinate fish quality

assurance and operations

- √ √

6. Promote co-operation among fishers. √ - √

7. Provide a national framework of extension

and training services

√ - √

8. Promote human resources development - √ -

9. Provide for the establishment of accredited

fish safety and quality control laboratories

- √ -

10. Promote adoption of alternative livelihood

amongst fishers

- - √

11. Provide for financial assistance by way of

loans to fishermen

√ - -

12. Encourage persons in the private sector to

organize into associations and form a

national coordinating mechanism to ensure

efficient marketing systems

- - √

13. Provide for the establishment of investor

friendly licensing and approval systems

- - √

14. Facilitate participation in national,

regional and international trade

negotiations and meetings

- - √

15. Promote development of other sustainable

methods of in-situ and ex-situ fishing

- - √

145


4.2.1 Data collection

Data was collected as described in (section 1.14). A structured questionnaire with a

five-point Likert scale (strongly disagree to strongly agree) was used to record

respondents’ perception of statements posed. The statements were drawn from policy

statements of the Kenya’s National Oceans and Fisheries Policy (2008) and objectives

of the Fisheries Act Cap (378) of 1989 listed above (Table 4.1).

Statement questions are listed and contextualized as shown below (Table 4.2). A brief

explanation of each statement was given to respondents before the question was posed.

The questions focused on weighing performance of national and county governments.

The questionnaire used is shown in appendices.

146

Table 4.2. Contextualization and description of statements used in Likert scale

Statement questions Description of contextualized statements

1. Government promotes

cooperation

This referred to governments’ active engagement of

actors and their leadership, consideration of their

views, facilitation to establish new linkages and

enhance internal understanding

2. Government supports

Beach Management Units

(BMU) functioning

This referred to regular funding of BMU operations,

equipment support and training of its leaders

3. Government promotes fish

marketing

This referred to governments’ active involvement in

identification and facilitation to access lucrative fish

markets outside actors’ localities to attain better

prices


value addition

This referred to facilitation towards value addition

activities such as icing, packaging and processing

(filleting, frying, salting and drying)

5. Government provides

equipment

This referred to support in acquiring equipment such

as boats, engines, fishing gear, ice boxes and freezers

6. Government promotes fish

handling

This referred to governments’ support in conducting

trainings on fish handling, hygiene and quality and

also facilitating development of the cold chain


extension services

This referred to regular visits by government

officials to discuss or advice actors concerning

fishing, maintenance of fish quality, marketing and

business management

8. Government conducts

impactful research

This referred to benefits that had been accrued from

either participating in research or positive

contribution to actors’ profitability or well-being

4.2.2 Data analysis

In descriptive analysis, percentages of raw Likert scores of actors’ perceptions were

computed for each of level of the five-point Likert scale. Further statistical analysis

147

involved first condensing the five-point scale to a three-point scale for better clarity on

actors’ agreement (either agreeing or disagreeing). Thus, strongly disagree and disagree

were lumped together as disagree. Similarly, strongly agree and agree were lumped as

agree. Undecided scores were unaffected. Test of differences of Likert score responses

within objectives was done using chi-test goodness of fit in R statistical software

(Version 3.5.3) (R Development Core Team 3.0.1., 2013). Test of differences in Likert

scores between objectives was done using one-way ordinal permutation test in coin

package in R (Mangiafico, 2016). Statistical test of factors influencing actors’

perception was conducted using the new three-point scale as the dependent variables.

The test was done using ordered logistic regressions following Hosmer et al., (2000) as

expressed below:

𝑃(𝑌 ≤ 𝑗) =exp[𝛼𝑗 − (𝐵1𝑋1 +⋯+ 𝛽𝑘𝑋𝑘)]

1 + exp[𝛼�̇� − (𝐵1𝑋1 +⋯+𝛽𝑘𝑋𝑘)]………………………… . . (𝑖)

Where P is the probability of predicting Y dependent variable with j ordered levels, X

are the independent influencing covariates and factors described below (Table 4.3). The

dependent variable levels were specified as ordered; agree, undecided and disagree.

Independent variables included; demographic factors (education), structural factors

(value of equipment owned and actor category), social factors (livelihood rank and

experience), economic factors (profitability) and site. Diagnostic tests were done to

check assumptions of ordered logistic regressions. Multi-collinearity was checked by

examining Variance Inflation Factors (VIF) of independent variables using the VIF

function in R statistical software (R Development Core Team 3.0.1., 2013), where

values were below 6 and thus indicating non-multi-collinearity (O’Brien, 2007). Non-

148

conforming variables were omitted from models. Assumption of proportional odds was

also tested and upheld in all models using Brants test function in R software (Benjamin

Schlegel, 2018).

Table 4.3. Description of factor variables used in ordinal logistic regression

models to analyze governments’ performance of policy and legal objectives

Variable

symbol Description of variable Type of variable Variable levels

fpp Financial profit person-1day-1 Continuous NA

ex Experience in years Continuous NA

ev Equipment value Continuous NA

as Agreement scale (for each of

the eight statement objectives

tested)

Ordinal - Agree

- Undecided

- Disagree

lr Livelihood rank Categorical - Primary

- Secondary

st Site Categorical - Malindi

- Mayungu

- Mombasa

- Shimoni

- Vanga

ac Actor category Categorical - Fishers

- Middlemen

- Processors

ed Education level Categorical - Not schooled

- Primary

- Secondary

- Tertiary

149

Results

4.3.1 Actors’ perceptions of government performance

Results of Chi-test showed significant differences (p<0.005) in respondents’

perceptions about performance of national and county governments within each value

chain objective tested. The one-way ordinal permutation tests for respondents’

perceptions across objectives also showed significant differences (p<0.005). Generally,

most respondents avoided extreme choices; either strongly agreeing or disagreeing,

while only few were undecided. Majority of the respondents (60%) were in agreement

that the national government had promoted cooperation, while only 37% agreed the

same about county governments (Table 4.4). None of the respondents strongly agreed

that the national and county governments, had promoted cooperation, while 5% and

9% disagreed respectively. Generally, the national government was rated highly in

promotion of cooperation, compared to county governments.

About 51% of the respondents agreed that the national government had provided

support to functioning of BMUs, while a lower percentage (29%) agreed the same

about county governments (Table 4.4). Twenty-six percent of respondents were

undecided if the national government had supported BMUs and about 40% about the

same for county governments. The relatively higher percentage of the undecided was

due to large numbers of processors who reported engaging rarely on BMU matters, and

thus likely unfamiliar with government interventions.

150

Table 4.4. Actors’ perceptions of performance (in %) of national and county governments in implementation of fisheries

policy and legal objectives

Indicator Strongly disagree Disagree Undecided Agree Strongly agree

National County National County National County National County National County

Promotes

cooperation 5 9 26 28 9 26 60 37 0 0

Supports BMU

functioning 1 1 18 23 26 40 51 29 4 7

Promotes fish

marketing 8 12 65 61 14 18 13 10 0 0

Promotes value

addition 9 12 69 66 11 15 11 6 0 0

Provides

equipment 17 17 76 72 1 2 6 8 0 0

Promotes fish

handling 10 12 44 46 13 21 32 20 0 0

Promotes

extension

services

5 11 46 49 4 20 44 19 0 0

Conducts impact

research 4 - 47 - 44 - 5 - 0 -

149

151

Patterns in perceptions concerning promotion of cooperation and support to functioning

of BMUs showed similarities. One-way ordinal permutation post-hoc tests for respondent

choices between the two VCD objectives showed no statistical differences (p=0.058) for

national government, and (p=0.736) for county governments. These results suggest that

the two objectives were not rated differently.

About 65% of the respondents disagreed that the national government had promoted fish

marketing, while 61% disagreed the same about county governments (Table 4.4). Only

13% and 10% of respondents agreed that the national and county governments

respectively had promoted fish marketing, while none strongly agreed. Percentage of the

undecided was also comparatively low.

About 69% of the respondents disagreed that the national government had promoted value

addition, while 66% disagreed the same about county governments (Table 4.4). Only 11%

and 6% of respondents agreed that the national and county governments respectively, had

promoted value addition, while none strongly agreed. Percentage of the undecided was

also low. One-way ordinal permutation post-hoc tests showed no statistical differences

between actor choices concerning promotion of fish marketing and value addition

(p=0.155) for national government, and (p=0.072) for county governments. These results

suggest that the two objectives were not rated differently.

About 76% of the respondents disagreed that the national government had provided

equipment such as boats, engines, fishing gear, ice boxes, cold rooms and freezers, while

72% disagreed the same about county governments (Table 4.4). Only 6% and 8% of

respondents agreed that the national and county governments respectively had provided

152

equipment, while none strongly agreed. Percentage of the undecided was also

comparatively low. Although majority of respondents had not received any equipment

support, a few fishers in Shimoni and Vanga had received fishing nets from the county

government and ice boxes from other donors. The BMU in Shimoni had also received two

boats with engines from the county government. While Malindi, Mombasa and Vanga

landing sites had cold rooms installed by the national government, only the one in

Mombasa was functional.

About 44% of the respondents disagreed that the national government had promoted fish

handling, while 46% disagreed the same about county governments (Table 4.4). Only 32%

and 20% of respondents agreed that the national and county governments respectively,

had promoted fish handling, while none strongly agreed. Percentage of the undecided was

13% and 21% for national and county governments respectively. Notably, several fish

handling, hygiene and quality trainings had been conducted at Shimoni and Vanga by a

donor funded project. However, none were recorded in Malindi, Mayungu and Mombasa.

About 46% of the respondents disagreed that the national government had promoted

extension services, while 49% disagreed the same about county governments (Table 4.4).

Only 44% and 19% of respondents agreed that the national and county governments

respectively had promoted extension services, while none strongly agreed. Percentage of

the undecided was 4% and 20% for national and county governments respectively.

Results on perception concerning conduct of impactful research were in context of

national institutions only. In Kenya, the nationally mandated research institutions on

fisheries are mainly Kenya Marine and Fisheries Research Institute (KMFRI) and

153

Universities. Private entities and NGOs also conduct research. About 47% of respondents

disagreed that they had benefitted from research, while 4% strongly disagreed. Only 5%

of actors agreed that they had benefitted, with none strongly agreeing. About 44% of

actors were undecided. This is partly because most respondents did not know of any

research being conducted and by whom. This suggests that up to 95% of actors could not

pinpoint benefits from research. They reported that even when aware about research

studies, many were not involved.

4.3.2 Factors influencing actors’ perception of government performance on policy and

legal objectives

Results for factors influencing actor perceptions were mixed and without strong patterns

across the tested objectives (Table 4.5). Only statistically significant results are described.

Influence of structural factors (value of equipment owned and actor category) on actors’

perception was minimal across several objectives. Value of equipment owned, only

significantly influenced perception about impact of research. As value of equipment

owned increased, respondents tended to agree that research conducted was impactful.

Actor category, influenced respondents’ perception about promotion of cooperation,

extension services, impactful research, fish handling and marketing (Table 4.5). Fishers

and middlemen tended to disagree that county governments had promoted cooperation

and that the national government had conducted impactful research. They however agreed

that the national government had promoted extension services. Middlemen disagreed that

both national and county governments had promoted fish marketing but agreed that the

national government had promoted fish handling.

154

Social factors (alternative livelihoods rank and experience) and demographic factors

(education) also had minimal significant influence on respondents’ perception about

performance of governments on various objectives. Respondents undertaking fisheries

activities as a secondary livelihood, perceived the national government as having

conducted impactful research, but disagreed on its performance in promoting fish

handling. Education only significantly influenced respondents’ perception towards

promotion of fish handling. Respondents with secondary education, tended to agree that

the national government had promoted fish handling. However, influence of tertiary

education showed no statistical significance on any objective, perhaps due to

comparatively low numbers of respondents with tertiary education.

Experience had significant influence on respondents’ perception concerning promotion of

value addition, equipment support and impactful research. Respondents with longer

experience tended to agree that the national government had promoted value addition and

that both national and county governments had provided equipment. However, they

disagreed that the national government had conducted impactful research.

Economic factors (financial profitability) only significantly influenced respondents’

perception concerning promotion of value addition and extension services. As

respondents’ profitability increased, they tended to disagree that national and county

governments had promoted value addition. They also tended to disagree that the national

government had promoted extension services, as profitability increased. Financial

profitability did not significantly influence any other objective.

Site showed more profound effect on respondents’ perception than other variables.

155

However, it was omitted in several models due to multicollinearity. Respondents in

Malindi and Mombasa tended to disagree that both national and county governments had

promoted cooperation, while the national government had supported BMUs. Similarly,

actors in Mayungu disagreed that the national and county governments had supported

BMUs. In Malindi and Mayungu respondents also disagreed that both national and county

governments had promoted fish marketing, fish handling and extension services.

However, in Vanga, respondents agreed that both national and county governments had

promoted fish handling. This is not surprising since trainings on fish handling had taken

place in Shimoni and Vanga.

156

Table 4.5. Coefficent estimates of factors influencing actors’ perceptions of performance of national and county

governments in implementation of fisheries policy and legal objectives. Significant results are in bold

Indicator

Ty

pe

of

go

ver

nm

ent

Structural Factors Demographic

Factors Social Factors

Economic

Factors Site

Va

lue

of

equ

ipm

ent

ow

ned

Fis

her

s

Mid

dle

men

Sec

on

da

ry

edu

cati

on

Ter

tia

ry

edu

cati

on

Sec

on

da

ry

liv

elih

oo

d r

an

k

Ex

per

ien

ce

Fin

an

cia

l p

rofi

t

Ma

lin

di

Ma

yu

ng

u

Mo

mb

asa

Va

ng

a

Promotes

cooperation National 0.10 0.05 0.44 -0.32 -0.44 -0.56 -0.01 0.03 -0.72* 0.02 -2.05* 0.59

County 0.17 -1.02** -0.84* 0.41 0.13 -0.78 0.00 0.1 -1.47** -0.57 -2.05* 0.26

Supports BMU

functioning National -0.10 0.73* -0.17 -0.39 -0.73 -0.66 0.01 0.06 -1.20** -2.21**

-

2.33** -0.14

County -0.19 0.53 0.00 0.21 -1.52 -0.78 0.00 -0.15 -0.29 -1.13** -1.07 0.48

Promotes fish

marketing

National 0.17 -0.16 -1.00* 0.65 -0.27 -0.15 -0.01 -0.14 -1.10** -1.04* 0.02 0.41

County 0.13 -0.2 -0.92* 0.40 -0.23 -0.16 -0.02 0.00 -1.27** -1.02* -0.02 0.32

Promotes value

addition

National -0.19 0.00 -0.04 0.04 1.83 -0.29 0.01** -0.44** - - - -

County -0.12 0.03 0.00 -0.17 0.53 -0.51 0.00 -0.42* - - - -

Provides equipment National 0.02 0.50 0.37 -0.63 2.25 - 0.05** -0.06 - - - -

County 0.03 0.72 0.68 0.74 2.11 -0.57 0.07** -0.14 - - - -

Promotes fish

handling National -0.04 -0.58 -1.35* 0.87* 1.61

-

1.33* 0.00 -0.06 -0.73* -1.24** -0.12 3.30**

County -0.10 -0.45 -0.6 -0.23 -0.01 -0.79 0.00 0.04 -0.50 -0.82* 0.71 2.74**

Promotes extension

services

National -0.10 1.10** 1.26** 0.07 -1.15 -0.60 0.01 -0.29* -0.77* 0.21 0.36 0.39

County 0.05 -0.65* -0.28 - - 0.22 0.00 0.03 -0.97** -0.80* 0.90 0.47

Conducts impact

research

National

research

institutions 0.29* -0.69* -1.69** -0.59 -0.27 0.99* -0.02* -0.08 -0.18 -0.3 1.05 -0.1

155

157

Discussion

The present study shows that respondents seemed to agree that the national government

had performed relatively well in promotion of cooperation and support to BMU

functioning, while county governments performed relatively poorly on the two objectives.

National and county governments’ ratings by actors were not statistically different for the

two objectives, hence suggesting similarities. However, a sizeable percentage of

respondents was undecided due to lack of information. Notably, county governments were

still rolling out their functions after coming into place in 2013, and hence most respondents

were unsure about their performance.

Respondents also felt that both national and county governments performed dismally in

promoting fish marketing, value addition and provision of equipment. National and county

governments’ ratings by actors were not statistically different for the two objectives, hence

suggesting similarities. By the time of the present study, Kenya did not have an established

programme for promoting marketing and value addition, either by government, private

sector or civil society. However, there have been efforts by NGOs and the Kenya Marine

and Fisheries Research Institute (KMFRI) to promote value addition through provision of

equipment and training.

Provision of fishing and preservation equipment by national and county governments,

private sector and donors, had also been undertaken, but was minimal and ad-hoc since

there was no systematic programme. For example, Kilifi and Kwale county governments

had provided a few boats, engines, fishing gear and ice boxes to a few landing sites. This

equipment was insufficient, considering the large number of landing sites and actors that

158

needed support. Thus, while programmed government support in equipment and

infrastructure is vital in promoting value chain development (Webber & Labaste, 2010; Lin

et al., 2014; Chu et al., 2017), national and county governments performed sub-optimally.

National and county governments also performed poorly in provision of extension services

and capacity building in fish handling. Extension services are crucial in building actors’

skills in fish handling, marketing, business management and value addition (Olorunfemi et

al., 2017). Although Kenya has a long history of promoting extension services in

agriculture (Muatha et al., 2017), only minimal effort seems to have gone into fisheries.

This is corroborated by results of the frame survey of 2016, where out of 197 landing sites,

only 10% were served daily by extension officers, 34% on weekly basis, 26% on monthly

basis and 18% on quarterly basis (Government of Kenya, 2016c). Therefore, poor delivery

of extension services is limiting upgrading of skills in many aspects of value chain

development as corroborated by the low level of training in fish handling.

Most respondents could not enumerate benefits of research, either due to lack of perceived

benefits or lack of information concerning ongoing research. Most respondents felt

alienated from research and commonly repeated this statement; “we just see people getting

into the water and do not know what they go to do”. Such lack of involvement of resource

users in scientific studies is likely to lead to apprehension, disputation or disregard of study

recommendations (Daw & Gray, 2005). However, when research results are well

communicated, they can have immediate use in local resource management and

development of policies to improve resource management and the value chain (Daw et al.,

2005; Field et al., 2013; Lin et al., 2014).

159

Generally, factors influencing respondents’ perceptions lacked clear patterns and only few

had significant influence. Structural factors only influenced respondents’ perception

concerning promotion of extension services and impact of research. Better equipped

respondents had negative perceptions over government performance towards provision of

extension services and impactful research. This may suggest that they had less contact with

extension officials and researchers.

Social factors were only influential on a few objectives. Respondents with alternative

livelihoods agreed that the national government had conducted impactful research. This

suggests they had more access to information on research. They however disagreed that

the national government had promoted fish handling. This suggests that they may have

missed out on trainings on fish handling. This is not surprising since actors with

multiplicity of livelihoods have less focus on primary livelihoods and are often left out of

capacity building interventions targeting mainstream livelihoods (Turyahabwe et al.,

2017).

Level of education also showed no obvious patterns. However, respondents with

secondary education agreed that the national government had promoted fish handling,

suggesting that trainings may have mostly reached educated actors. This is consistent with

other findings in East Africa suggesting that, a higher level of education enables actors to

engage in actions targeting them (Muatha et al., 2017; Turyahabwe et al., 2017).

However, most respondents had low levels of education with majority only attained

primary education. This may have implications on fish quality of the whole value chain,

since majority of respondents had lower education and may not have been targeted in fish

160

handling trainings. Respondents with longer experience tended to agree that the national

and county governments had provided equipment. This may suggest that the less

experienced persons forming more that 60% of respondents with less than ten years’

experience, had neither received assistance nor aware about such assistance.

Economic factor (financial profitability), was only significant in influencing respondents’

perception about extension services and value addition. As profitability increased,

respondents tended to disagree that the national and county governments had promoted

extension services and value addition. This contrasts other findings in Kenya by Muatha et

al., (2017), that suggested that farmers with higher incomes had more access to extension

services. However, this may be largely due to absence of extension services in capture

fisheries in Kenya (Government of Kenya, 2016c). In terms of geographical influence,

most respondents at all sites had negative perception concerning BMU functioning, fish

marketing, fish handling and extension services. However, perceptions were positive about

fish handling support at Shimoni and Vanga, where donor projects had been collaboratively

undertaken with government.

Findings from the study suggests that most objectives relating to support of value chain

development in the policy and old Fisheries Act had not been fully realized due to lack of

implementation or ineffectiveness. Kenya for a long time lacked a long-term systematic

programme of action to implement such objectives. A longer-term dedicated value chain

development programme can be effective in upgrading SSFs, as has been shown in East

Asia, for example in Indonesia and Taiwan (Lin et al., 2014; Sunoko et al., 2014). Longer-

term measures such as training, fishing fleet development, financing, research and

development, policy and legal reforms, and infrastructural development in the two

161

countries, contributed to their ranking as globally significant fisheries producers (Lin et al.,

2014; Sunoko et al., 2014; FAO, 2016). Kenya’s new fisheries legal reforms captured in

FMDA (2016) contain similar interventions.

The new law addresses fisheries development and management mandates separately. For

example, it establishes the Kenya Fisheries Marketing Board charged with addressing

marketing of fish, separately from management functions undertaken by Kenya Fisheries

Service. The Act also establishes the Fisheries Advisory Council to guide fisheries

management, research and development. It also establishes the Fisheries Research and

Development Fund for supplementary funding of fisheries research, and the Fish Levy

Trust Fund to finance development, management and capacity building.

There have also been other reforms targeted at transforming fisheries. In 2016, the

Government of Kenya through the Executive Order No. 1/2016 (Government of Kenya,

2016d), outlined its commitment towards the blue economy including fisheries

development. This created the State Department for Fisheries, Aquaculture and the Blue

Economy and later the Presidential Blue Economy Standing Committee, consisting of

relevant blue economy related agencies. Kenya in 2018 also hosted the first global blue

economy conference, cementing its commitment to the blue economy agenda.

All these initiatives, legal provisions and institutions will be instrumental in playing a key

role in value chain development functions such as provision of infrastructure and

equipment, training of actors, provision of extension services, organizing of actors, support

to marketing and value addition, and promotion of research. These reforms seem to address

gaps highlighted in the present study.

162

However, even with the legal and institutional reforms in place, there is danger of lack of

adherence to the principle of subsidiarity between the county and national government.

There has often been confusion on roles between national and county government, for

example on licensing and enforcement functions. Lack of clarity on such issues may lead

to inaction and lack of support to value chain actors (Dang et al., 2017). Implementation

also requires measures in place, to forestall the perennial problem of insufficient funding

for SSFs, as is common in most developing countries (Mills et al., 2011).

Conclusion

Findings showed that government rating at both national and county levels was varied and

without clear-cut patterns. However, actors’ perceptions showed low rating on most

objectives concerning government performance. Generally, county governments’

performance was rated lower than national government. This rating could be expected,

given that county governments were still at their infancy.

Actor characteristics (structural, social, economic, demographic and geographical factors),

largely did not influence respondents’ perceptions. Based on respondents’ perceptions,

most objectives of the policy and old Fisheries Act were ineffectively achieved. However,

the new law is more comprehensive in addressing SSFs value chain development needs

and may cure previous shortcomings and perceptions. Regular evaluation of actor

perception concerning implementation of the new law should be done to gauge government

performance.

163

The present study only looked at respondents’ perceptions concerning performance of

government in implementation of policy and legal objectives. However, it did not look at

how the different ratings of government performance amongst individuals and different

sites influenced profitability. This can be useful in formulating policy and legal reforms

that directly impact on actors’ incomes.

164

CHAPTER 5: GENERAL DISCUSSION

This chapter provides a general summary discussion of key findings of the present study.

The study used the S-C-P paradigm to analyze the structure and conduct of actors in the

marine SSFs value chain and their implications to performance in context of profitability.

The study also analyzed constraints in marine SSFs in Kenya, as well as the corresponding

opportunities from actors’ perspective. Actor perception of government performance in

implementing value chain development related objectives as outlined in fisheries policy,

institutional and regulatory instruments was also analyzed. These included; Kenya’s

National Oceans and Fisheries Policy of 2008 and the Fisheries Act Cap (378) of 1989.

Perspectives were also drawn from the new fisheries law; Fisheries Management and

Development Act of 2016. The goal of the study was to characterize and analyze Kenya’s

marine small-scale fisheries along the value chain, to generate knowledge useful for value

chain improvements and fisheries management.

In addressing structure, findings showed that, although high-capitalized actors dominated

functions in the value chain, the fisheries studied were competitive with relatively low HHI

indices. Thus, expected monopolistic or oligopolistic tendencies common with capital

skewed value chains, were not present in the fisheries studied. This is can be attributed to

intense competition amongst high-capitalized actors. This is a significant finding

suggesting that skewed capital in fisheries value chains, does not always lead to

monopolistic or oligopolistic tendencies. Therefore, creation of competitive market

conditions for middlemen with similar market power, may level-off the playing field.

However, the levelled playing field did not seem to favour all actors. For example, fishers

165

did not receive equitable profitability compared to middlemen, despite existence of a

competitive market. This was especially profound among categories that shouldered most

of the variable costs such as the high-capitalized handline and drifting gillnet. This further

suggests that equitable returns cannot be achieved by enhancing market competition alone.

However, fishers’ increasing percent ownership of fishing equipment significantly

improved their profitability. This suggests that improving fishers’ ownership of equipment

is beneficial and resonates with their proposed solution to assist them acquire own

equipment. Although government policy and regulatory framework seeks to assist actors

to acquire equipment, findings showed that only few had benefitted.

Findings of the study also showed that structure had limited role in influencing actors’

conduct within the context of S-C-P paradigm. Choice of fish grade was not significantly

influenced by level of capitalization within individual nodes. However, high-capitalized

middlemen tended to target high-priced grades, while processors targeted low-priced

grades. Such targeting of high-priced grades fuels more capitalization by middlemen who

also support migrant fishers with equipment in order to fish for them (Ferrol-Schulte et al.,

2014; Bailey et al., 2015). However, in the present study, targeting of high-priced grades

did not lead to significantly higher profitability.

Fish grading also has implications to processors who primarily target low-grade fish for

further value addition (Matsue et al., 2014). Targeting of low-grade fish by processors

increased their profitability significantly. They also provide a ready market for fish that

would otherwise be discarded, particularly for small pelagics and other low-grade fish.

Processors also form a complex market relationship with migrant fishers who provide

reliable fish supplies to them. Thus policy recommendations restricting migrant fishers’

166

movement would negatively affect processors’ fish supplies and affordability.

Collusion to set prices, access to market information and actors’ power to determine fish

prices, which are important in markets with skewed capitalization, were not significantly

influenced by capitalization across all nodes. Similarly, these conduct variables had no

significant influence on actors’ profitability. Lack of influence of collusion on profitability

corroborates findings on market concentration showing low HHI indices. Therefore,

collusion and un-competitiveness were not serious problems in Kenya’s marine SSFs.

Improving market information empowers actors’ to negotiate prices, and is commonly

proposed to improve economic outcomes of small-scale holders (Courtois et al., 2014;

Ranjan, 2017). However, access to information did not significantly influence profitability.

Therefore, measures to improve access to market information may not yield benefits in

terms of improved profitability. However, market information may help actors to access

supplies and new markets (Fews Net, 2008).

As shown in the study, S-C-P factors showed only minimal significant influence on

profitability. However other factors such as variable costs and sales emerged as the most

important across all actor groups. Profitability increased as sales increased, but declined as

variable costs increased. Although sales significantly influenced profitability, this did not

translate to higher profitability for offshore fishers who landed more fish. Their

profitability was not significantly different from inshore fishers who had lower catches, but

higher ownership of equipment. This can be attributed to unequal shouldering of variable

costs by offshore fishers mostly dependent on middlemen. Such unequitable share of

variable costs is detrimental to their economic welfare and pressurizes them to continually

catch more fish to pay back debts, while deepening reliance on middlemen (Miñarro et al.,

167

2016; Prescott et al., 2017). This is also detrimental to resource sustainability (Mangi et

al., 2007).

Therefore, strategies that can reduce reliance on middlemen, reduce costs and increase

sales are most likely to improve profitability amongst fishers (Rodrigues et al., 2019).

Improvements in the cold chain and fish landing markets can also stimulate fish trade and

boost sales (Schmitt et al., 2009), while eliminating fishers’ desperate need for quick sales

at low prices (Jimenez et al., 2020). Reduction of license fees and permits through review

of the fish trade policy environment, can help reduce costs and improve sales amongst

middlemen and processors.

Modernizing the fishery for example use of Global Positioning Systems (GPS), fish finders

and nearshore Fish Aggregating Devices (FADs) can help fishers locate fish efficiently and

reduce fuel costs to improve profitability (Sharp, 2011; Olivier et al., 2013). This can be

done through support to acquire equipment, from favourable government loaning schemes

as suggested and promoted elsewhere (Emdad et al., 2015; Jueseah et al., 2020; Kimani et

al., 2020). This kind of support is crucial for fishers, since higher percent ownership of

equipment was linked to higher profitability. However, modernizing the fishery should

incorporate sustainability measures to prevent stock collapse from higher fishing effort

(Stevens et al., 2014; Jaini et al., 2017). Other measures that can improve actors’

profitability include formation and revival of collapsed cooperatives for aggregation of

produce to improve prices and marketing.

The fishery was viable and attractive, based on the positive financial profitability for

majority of actors and the high number of new entrants, as is common with well

performing fisheries (Prescott et al., 2017). Financial profits were also higher than

168

opportunity cost of wages in unskilled employment, which suggests better returns than

surrounding opportunities in commercial, domestic and agricultural sectors (Government

of Kenya, 2015). However, despite the overall positive performance among most

respondents, some attained low incomes which may not be sufficient. Nevertheless, many

Kenyan fishers are unwilling to exit the fishery even with low incomes, due to lack of

alternatives (Cinner et al., 2009). Thus, conclusions about viability of the fishery should be

treated with caution since fishing is often the livelihood of last resort amidst limited

alternatives (Béné, 2003).

Analysis of constraints and opportunities facing Kenya’s marine SSF actors, showed that

they face a large number of constraints within the value chain. However, others were

ranked as more severe than others. Lack of access to capital, which is common in coastal

enterprises (Fowowe, 2017; Kashangaki, 2017), emerged as the most severe constraint

amongst all actor groups. This is also corroborated by the study’s findings, indicating low

uptake of loans and is associated with several access challenges. Most actors feared taking

loans due to their erratic incomes, which reflects their proposed solution for development

of flexible government loaning schemes.

Other reasons leading to poor access to loans, and commonly cited in other small-holder

enterprises (Emdad et al., 2015; Iqbal et al., 2017; Kashangaki, 2017) included; lack of

collateral, religious restrictions amongst Muslims and distance from financial institutions.

Majority of respondents opted to borrowing from relatives and friends, which is a common

adaptation strategy in SSFs (Emdad et al., 2015). Although Kenya’s financial sector is

advanced (Kashangaki, 2017; Ouma et al., 2017), it fails to adequately meet needs of SSFs,

which hampers individual longer-term investments in SSFs.

169

Other key constraints faced by actors included market challenges revolving around fish

supply and demand. A once vibrant, but poorly performing tourism industry was blamed

for low fish demand. Oversupply occurred in the NEM season and was associated with

influx of migrant fishers who land more fish (Wanyonyi, et al., 2016a; Wanyonyi, et al.,

2016b). However, scarcity was experienced in the months of May to August in SEM

season. Middlemen and processors blamed the scarcity on lack of skilled fishers,

equipment and training in use of technology for offshore fishing. This is also corroborated

by the low level of equipment support and training to fishers. Findings also showed that

only a few fishers had access to equipment such as GPS and eco-sounders. Thus, provision

of equipment should be prioritized, while improving ownership, particularly amongst

fishers. Furthermore, study findings also showed that percent ownership of equipment

amongst fishers improved their profitability. Improved technology such as GPS and eco-

sounders can reduce fishers’ variable costs and improve on profitability.

Fish scarcity was also noted as a severe problem in the SEM season when fishing is limited

due to bad weather. This was a big concern especially amongst processors. This is because

of discrimination faced in accessing fish due to their low purchasing power and over-

pricing during such periods (Matsue et al., 2014; Wamukota et al., 2015). This highlights

the gendered problems faced by women in fish trade especially during scarcity periods

(Fröcklin et al., 2013; Matsue et al., 2014). They therefore highly prioritized measures to

improve fish catches. This included proposals to ease entry of migrant fishers. However,

local fishers are opposed to this and blame migrant fishers for triggering low fish prices.

Respondents proposed improvement of the cold chain for fish storage, regulation of supply

and stabilization of prices, which are commonly suggested interventions to oversupply

170

(Platteau, 1984). However, Kenya’s coast has historically faced challenges of managing

cold chain infrastructure with examples of stalled projects in Malindi, Lamu, Shimoni and

Vanga. On the contrary privately-owned facilities and equipment, for example in Malindi,

seemed to work well and lessons can be learnt by public managed facilities. In addition,

respondents also proposed improvement of roads and expansion of markets to new

geographical locations to overcome oversupply. Road improvement as suggested by actors

can also reduce cost of doing business. Furthermore, study findings also showed that high

variable costs were a critical determinant of profitability amongst all actor groups.

Findings concerning respondent’s perceptions of the national and county governments in

implementing value chain development objectives were varied. Respondents seemed to

agree that the national government had performed relatively well in promotion of

cooperation amongst actors and support to BMU functioning, compared to county

governments. They were also in consensus that both national and county governments

performed poorly in promoting fish marketing, value addition and provision of equipment.

These findings also corroborate those on analysis of constraints, which indicated that

respondents lacked adequate and appropriate equipment, as well as skills in fish handling

and value addition. They also faced many challenges in fish marketing.

Respondents also reported that both national and county governments had inadequately

provided extension services, despite Kenya’s long history of promoting agricultural

extension services (Muatha et al., 2017). Fish handling trainings which are typically

conducted through extension services were also poorly rated, and explains the low numbers

of respondents trained. These findings also corroborate those of analysis of constraints,

where lack of training was listed as a constraint. Many respondents also reported not having

171

received any benefits from research, or lacked information about it. Such negative actor

perceptions are likely to lead to disputation or disregard of study recommendations (Daw

et al., 2005; Field et al., 2013; Lin et al., 2014). Therefore, research needs to be

participatory and findings shared widely for community level actors to find them useful.

Findings concerning influence of respondents’ characteristics on rating of national and

county governments were weak and lacked clear patterns. Therefore, respondents’

characteristics were not a good predictor of their perception of government performance.

Nevertheless, as respondents’ capitalization increased, their rating of national and county

government performance declined, suggesting that well-endowed respondents had less

contact with extension services and research. Similarly, as respondents’ financial

profitability increased, they tended to disagree that national and county governments had

promoted extension services and value addition. This suggests that respondents with higher

capital and earnings, were either independent or had higher expectations from government.

Respondents with alternative livelihoods agreed that the national government had

conducted impactful research but disagreed that it had promoted fish handling. Level of

education, which was low, also showed no obvious patterns, but actors with secondary

education tended to agree that the national government had promoted fish handling.

Respondents with longer experience also tended to agree that the national and county

governments had provided support in equipment. In terms of geographical characteristics,

respondents across all sites had negative perceptions of national and county governments

in performance of most policy and legal objectives. However, respondents at Shimoni and

Vanga study sites had positive perception about training in fish handling, where it had been

collaboratively undertaken with donor projects.

172

Findings from the study suggest that most policy and legal objectives in support of value

chain development had not been fully realized due to poor delivery or ineffectiveness. This

seems to correlate with constraints listed by respondents, and perhaps fueled their severity.

Literature shows that a well-designed and implemented fisheries policy, institutional and

regulatory framework can overcome constraints and transform fisheries, as happened in

Indonesia and Taiwan (Lin et al., 2014; Sunoko et al., 2014). These countries put long term

measures in training, fishing fleet development, financing, research and development,

policy and legal reforms and infrastructural development. This possibly contributed to their

global positioning as significant fish producers (Lin et al., 2014; Sunoko et al., 2014; FAO,

2016).

Kenya has taken steps towards fisheries development similar to the East Asian countries,

through legal reforms contained in FMDA (2016). It takes a new approach to fisheries

management and development, by establishing the Kenya Fisheries Service to manage

fisheries and Kenya Fisheries Marketing Board to address fish marketing. The Act also

establishes the Fisheries Advisory Council to advice on fisheries management and

development. It also establishes the Fish Levy Trust Fund to provide supplementary

funding for management, development and capacity building in fisheries. These provisions

and institutions will be instrumental in playing a key role in roll out of value chain

development functions. Thus, the new legal and institutional reforms may answer to

previous shortcomings. However, funding for fisheries has to be secured in government

annual fiscal planning and budgeting. This will forestall the perennial insufficient funding,

common in developing countries (Mills et al., 2011).

173

CHAPTER 6: GENERAL CONCLUSIONS AND

RECOMMENDATIONS

Conclusions

Findings from objective 1 to 3 (in context of S-C-P) showed that:

1. The marine small-scale fisheries value chain in Kenya was competitive as revealed by

the low HHI indices, despite being dominated by high capitalized actors. Therefore,

there was no evidence of monopolistic or oligopolistic tendencies expected in high-

capitalized value chains.

2. The market performance was not influenced by structure and conduct of actors in the

value chain. However, non-S-C-P factors, specifically sales and costs were important

in influencing profitability.

Findings from objective 4 on analysis of constraints and opportunities showed that:

3. Constraints related to capital, resource and markets were most severe in the marine

small-scale fisheries value chain in Kenya.

4. To mitigate capital constraints, actors proposed design of loaning schemes that

conform to their unpredictable income flows. For constraints related to resource

dimension, they proposed easing entry of migrant fishers and provision of equipment.

For market constraints they proposed expansion of markets to other geographical areas

Findings from objective 5 on analysis of institutional, policy and regulatory frameworks

in the value chain, showed that:

5. Based on rating by actors in the marine small-scale fisheries in Kenya, the national

174

government performed better in promotion of cooperation and support to BMU

functioning, while county governments performed poorly on the two objectives.

However, both national and county governments performed poorly in promoting fish

marketing, value addition, promotion of extension services, fish handling and

provision of equipment.

6. Actors perceptions concerning performance of government on policy and legal

objectives was not significantly influenced by structural, social, economic,

demographic and geographical factors.

New scholarly contributions from the study

1. The present study is the first empirical study on marine SSFs in Kenya that has used

the S-C-P paradigm to explain structure of the value chain, actor behavior and how

both impact on performance in terms of profitability

2. Combination of the value chain approach with the S-C-P paradigm has added value to

scholarship by addressing varied actor groups in the same study

3. This is the first study to empirically rank value chain constraints and opportunities in

marine SSFs in Kenya

4. The present study is the first empirical study on marine SSFs in Kenya to analyze

actors’ views of government performance in implementing policy and legal objectives

that support value chain development. It is also the first study that draws perspectives

of the old and the new fisheries Acts in context of value chain development

175

Recommendations

Considering findings from the present study the following recommendations to improve

marine small-scale-fisheries value chain development in Kenya are proposed;

a. Data collection on fisheries by national and county governments should comprise

fisheries indicators such as fish grading and economic indicators such as pricing,

costs and income among others

b. Government should provide support to fishers to acquire fishing and preservation

equipment to address constraints experienced, given that higher ownership of

equipment influenced profitability positively

c. Government should enhance infrastructure development to mitigate market

constraints and improve actor performance. Improved infrastructure also helps in

cost-reduction which is a significant contributor to actors’ profitability

d. Training in business management, value addition, fish handling among other relevant

areas should be organized in a systematic way and inclusive of all actors. This will

mitigate against the current trainings organized on ad-hoc basis. It will also mitigate

against post-harvest losses

e. Government should enhance service delivery to actors in marine small-scale fisheries

in Kenya to improve their business environment and perceptions.

f. Actors should form cooperatives to aggregate their produce to enable negotiations for

prices and seek new markets

g. Actors should develop mechanisms through cooperatives to pool financial resources

for inward lending to members at affordable rates, with appropriate repayment

periods cognizant of their erratic incomes

176

h. Actors should consider improving fish handling and value addition practices in order

to open new market niches and attract better prices

177

REFERENCES

Abila, R. O. (1995). Structure, Conduct and Performance of Kisumu fish marketing

system. University of Nairobi.

Abraham, A., Platteau, J. P. (1987). An inquiry into quasi-credit contracts: the role of

reciprocal credit and interlinked deals in small-scale fishing communities. The

Journal of Development Studies, 23(4), 461–490.

https://doi.org/10.1080/00220388708422044

Adeogun, O. A., Abohweyere, P. O., Ogunbadejo, H. K., Tanko, A., Jim-Saiki, L.

(2009). Economic viability of small-scale marine capture fisheries in the Bonny

area, Rivers State, Nigeria. Marine Resource Economics, 24(2), 195–203.

Alfaro-Shigueto, J., Mangel, J. C., Pajuelo, M., Dutton, P. H., Seminoff, J. A., Godley,

B. J. (2010). Where small can have a large impact: Structure and characterization of

small-scale fisheries in Peru. Fisheries Research, 106(1), 8–17.

https://doi.org/10.1016/j.fishres.2010.06.004

Anam, R., Mostarda, E. (2012). Field identification guide to the living marine resources

of Kenya. FAO species identification guide for fishery purposes. Rome: FAO.

Andalecio, M. (2010). Multi-criteria decision models for management of tropical coastal

fisheries. A review. Agronomy for Sustainable Development, Springer Verlag/EDP

Sciences/INRA, 30(2), 557–580. https://doi.org/10.1051/agro/2009051

Andrew, N. L., Bene, C., Hall, S. J., Allison, E. H., Heck, S., Ratner, B. D. (2007).

Diagnosis and management of small-scale fisheries in developing countries. Fish

and Fisheries, 8, 227–240. Retrieved from http://www.blackwell-

synergy.com/doi/abs/10.1111/j.1467-2679.2007.00252.x

Asche, F., Chen, Y., Smith, M. D. (2015). Economic incentives to target species and

178

fish size: Prices and fine-scale product attributes in Norwegian fisheries. ICES

Journal of Marine Science, 72(3), 733–740. https://doi.org/10.1093/icesjms/fsu208

ASCLME. (2012). National marine ecosystem diagnostic analysis, Kenya. Contribution

to the Agulhas and Somali Current Large Marine Ecosystems Project (supported

by UNDP with GEF grant financing).

Ba, A., Schmidt, J., Dème, M., Lancker, K., Chaboud, C., Cury, P., … Brehmer, P.

(2017). Profitability and economic drivers of small pelagic fisheries in West Africa:

A twenty year perspective. Marine Policy, 76, 152–158.

https://doi.org/10.1016/j.marpol.2016.11.008

Bailey, M., Bush, S., Oosterveer, P., Larastiti, L. (2015). Fishers, fair trade, and finding

middle ground. Fisheries Research, 182, 59–68.

Bain, J. S. (1959). Industrial Organization. New York: John Wiley and Sons.

Baio, A. (2010). Show me the way: Inclination towards governance attributes in the

artisanal fisheries of Sierra Leone. Fisheries Research, 102, 311–322.


Banson, K. E., Nguyen, N. C., Bosch, O. J. H. (2016). A Systems thinking approach to

the structure, conduct and performance of the agricultural sector in Ghana. Systems

Research and Behavioral Science. https://doi.org/10.1002/sres.2437

Barnes-Mauthe, M., Oleson, K. L. L., Zafindrasilivonona, B. (2013). The total

economic value of small-scale fisheries with a characterization of post-landing

trends: An application in Madagascar with global relevance. Fisheries Research,

147, 175–185. https://doi.org/10.1016/j.fishres.2013.05.011

Battaglia, P., Romeo, T., Consoli, P., Scotti, G., Andaloro, F. (2010). Characterization

of the artisanal fishery and its socio-economic aspects in the central Mediterranean

Sea (Aeolian Islands , Italy). Fisheries Research, 102(1–2), 87–97.

179


Béné, C. (2003). When fishery rhymes with poverty: A first step beyond the old

paradigm on poverty. World Development, 31(6), 949–975.

https://doi.org/10.1016/S0305-750X(03)00045-7

Béné, C., Arthur, R., Norbury, H., Allison, E. H., Beveridge, M., Bush, S., … Williams,

M. (2016). Contribution of fisheries and aquaculture to food security and poverty

reduction: assessing the current evidence. World Development, 79, 177–196.

https://doi.org/https://doi.org/10.1016/j.worlddev.2015.11.007

Béné, C., Merten, S. (2008). Women and fish-for-sex: transactional sex, HIV/AIDS and

gender in African fisheries. World Development, 36(5), 875–899.

https://doi.org/10.1016/j.worlddev.2007.05.010

Benjamin Schlegel. (2018). CRAN - Package brant. Retrieved July 11, 2018, from

https://cran.r-project.org/web/packages/brant/index.html

Bolwig, S., Ponte, S., Toit, A., Halberg, N. (2010). Integrating poverty and

environmental concerns into value-chain analysis: A conceptual framework.

Development Policy Review, 28(2), 173–194.

Boncoeur, J., Coglan, L., Gallic, B. Le, Pascoe, S. (2000). On the (ir)relevance of rates

of return measures of economic performance to small boats. Fisheries Research,

49(2), 105–115. https://doi.org/10.1016/S0165-7836(00)00209-5

Brinson, A. A., Alcalá, A., Die, D. J., Shivlani, M. (2006). Contrasting socioeconomic

indicators for two fisheries that target Atlantic billfish: Southeast Florida

recreational charter boats and Venezuelan artisanal gill-netters. Bulletin of Marine

Science, 79(3), 635–645. Retrieved from

https://www.ingentaconnect.com/content/umrsmas/bullmar/2006/00000079/000000

03/art00016

180

Brinson, A. A., Die, D. J., Bannerman, P. O., Diatta, Y. (2009). Socioeconomic

performance of West African fleets that target Atlantic billfish. Fisheries Research,


Bronnmann, J., Asche, F. (2016). The value of product attributes, brands and private

labels: An analysis of frozen seafood in Germany. Journal of Agricultural

Economics, 67(1), 231–244.

Chu, J., Garlock, T. M., Sayon, P., Asche, F., Anderson, J. L. (2017). Impact evaluation

of a fisheries development project. Marine Policy, 85, 141–149.


Cinner, J. E., Daw, T., McClanahan, T. R. (2009). Socioeconomic factors that affect

artisanal fishers’ readiness to exit a declining fishery. Conservation Biology, 23(1),

124–130. https://doi.org/10.1111/j.1523-1739.2008.01041.x

Cochran, G. W. (1977). Sampling Techniques (Third ed). New York: John Wiley and

Sons, Ltd.

Colloca, F., Crespi, V., Cerasi, S., Coppola, S. R. (2004). Structure and evolution of the

artisanal fishery in a southern Italian coastal area. Fisheries Research, 69(3), 359–

369. https://doi.org/10.1016/j.fishres.2004.06.014

County Government of Kilifi. (2018). Kilifi county integrated development plan, 2018-

2022 (Vol. I).

County Government of Kwale. (2018). Kwale county integrated development plan,

2018-2022. Kwale.

Courtois, P., Subervie, J. (2014). Farmer bargaining power and market information

services. American Journal of Agricultural Economics, 97(3), 953–977.

https://doi.org/10.1093/ajae/aau051

Creswell, J. W. (2013). Research design : qualitative, quantitative, and mixed methods

181

approache. SAGE (4th ed.). Thousand Oaks, California: SAGE Publications.

Crona, B., Bodin, Ö. (2010a). Power asymmetries in small-scale fisheries: A barrier to

governance transformability? Ecology and Society, 15(4). https://doi.org/32

Crona, B., Nystrom, M., Folke, C., Jiddawi, N. (2010b). Middlemen, a critical social-

ecological link in coastal communities of Kenya and Zanzibar. Marine Policy,

34(4), 761–771.

Cunningham, S., Arthur, E. N., Michael, A., Tim, B., Neiland, A. E., Arbuckle, M., …

Tim, B. (2009). Wealth-based fisheries management: Using fisheries wealth to

orchestrate sound fisheries policy in practice. Marine Resource Economics, 24(3),

271–287. https://doi.org/10.5950/0738-1360-24.3.271

Dang, N. B., Momtaz, S., Zimmerman, K., Thi, P., Nhung, H. (2017). Effectiveness of

formal institutions in managing marine fisheries for sustainable fisheries

development: A case study of a coastal commune in Vietnam. Ocean and Coastal

Management, 137, 175–184. https://doi.org/10.1016/j.ocecoaman.2016.12.021

Daurès, F., Trenkel, V. M., Guyader, O. (2013). Modelling the fishing costs of French

commercial vessels in the Bay of Biscay. Fisheries Research, 146, 74–85.


Daw, T., Gray, T. (2005). Fisheries science and sustainability in international policy: a

study of failure in the European Union’s Common Fisheries Policy. Marine Policy,

29, 189–197. https://doi.org/10.1016/j.marpol.2004.03.003

de Figueirêdo, J. H. S., Meuwissen, M. P. M., Oude Lansink, A. G. J. M. (2014).

Integrating structure, conduct and performance into value chain analysis. Journal

on Chain and Network Science, 14(1), 21–30.

de Graaf, G., Garibaldi, L. (2014). The value of african fisheries (Vol. 1093). Rome:

FAO.

182

Denzin, N. K., Lincoln, Y. S., Guba, E. G. (1994). Paradigmatic controversies,

contradictions, and emerging confluences. Handbook of Qualitative Research, 643.

https://doi.org/10.1111/j.1365-2648.2005.03538_2.x

Digal, L. N., Placencia, S. G. P. (2017a). Factors affecting the adoption of sustainable

tuna fishing practices: The case of municipal fishers in Maasim, Sarangani

Province, Region 12, Philippines. Marine Policy, 77, 30–36.


Digal, L. N., Placencia, S. G. P., Balgos, C. Q. (2017b). Market assessment on the

incentives and disincentives for the adoption of sustainable practices along the tuna

value chain in Region 12, Philippines. Marine Policy, 86, 39–46.


Donovan, J., Franzel, S., Cunha, M., Gyau, A., Mithöfer, D. (2015). Guides for value

chain development: a comparative review. Journal of Agribusiness in Developing

and Emerging Economies, 5(1), 2–23. https://doi.org/10.1108/JADEE-07-2013-

0025

Duggan, D. E., Kochen, M. (2016). Small in scale but big in potential: Opportunities

and challenges for fisheries certification of Indonesian small-scale tuna fisheries.

Marine Policy, 67, 30–39. https://doi.org/10.1016/j.marpol.2016.01.008

Duy, N. N., Flaaten, O., Anh, N. T. K., Ngoc, Q. T. K. (2012). Open-access fishing rent

and efficiency — The case of gillnet vessels in Nha Trang, Vietnam. Fisheries

Research, 127–128, 98–108. https://doi.org/10.1016/j.fishres.2012.04.008

EAME. (2004). The Eastern African Marine Ecoregion Biodiversity Conservation

Strategic Framework 2005 - 2025. Dar es Salaam.

Emdad, H., Julián Idrobo, Berkes, F., Giesbrecht, D. (2015). Small-scale fishers’

adaptations to change: The role of formal and informal credit in Paraty, Brazil.

183

Marine Policy, 51, 401–407. https://doi.org/10.1016/j.marpol.2014.10.002

Even, B., Donovan, J. (2017). Value chain development in Vietnam: a look at

approaches used and options for improved impact. Enterprise Development and

Microfinance, 28(1–2), 28–44. https://doi.org/10.3362/1755-1986.16-00034

Fabinyi, M., Dressler, W., Pido, M. (2016). Do fish scales matter? Diversification and

differentiation in seafood commodity chains. Ocean and Coastal Management,

134, 103–111.

FAO. (2005). Increasing the contribution of small-scale fisheries to poverty alleviation

and food security. FAO Technical Guidelines for Responsible Fisheries. No. 10.

Rome, FAO. 79pp. https://doi.org/10.1017/CBO9781107415324.004

FAO. (2014). The small-scale fisheries sector in relation to international trade and

sustainable livelihoods. COFI:FT/XIV/2014/9. Rome.

FAO. (2016). The State of World Fisheries and Aquaculture 2016. Contributing to food

security and nutrition for all. Rome.

FAO and World Fish Centre. (2008). Small-scale capture fisheries: A global overview

with emphasis on developing countries. Washington D.C.

Ferrol-Schulte, D., Ferse, S. C. A., Glaser, M. (2014). Patron-client relationships,

livelihoods and natural resource management in tropical coastal communities.

Ocean and Coastal Management, 100, 63–73.

Ferse, S. C. A., Glaser, M., Neil, M., Schwerdtner Máñez, K. (2014). To cope or to

sustain? Eroding long-term sustainability in an Indonesian coral reef fishery.

Regional Environmental Change, 14(6), 2053–2065.

Fews Net. (2008). Structure-Conduct-Performance and food security. Fews Net Market

Guidance, (2), 18.

Field, J. G., Attwood, C. G., Jarre, A., Sink, K., Atkinson, L. J. (2013). Cooperation

184

between scientists, NGOs and industry in support of sustainable fisheries: the South

African hake Merluccius spp. trawl fishery experience. Fish Biology, 1–16.

https://doi.org/10.1111/jfb.12118

Fowowe, B. (2017). Access to finance and firm performance: Evidence from African

countries. Review of Development Finance, 7(1), 6–17.

https://doi.org/10.1016/j.rdf.2017.01.006

Fröcklin, S., De La Torre-Castro, M., Lindström, L., Jiddawi, N. S. (2013). Fish traders

as key actors in fisheries: Gender and adaptive management. Ambio, 42(8), 951–

962. https://doi.org/10.1007/s13280-013-0451-1

Fulanda, B., Munga, C., Ohtomi, J., Osore, M., Mugo, R., Hossain, M. Y. (2009). The

structure and evolution of the coastal migrant fishery of Kenya. Ocean and Coastal

Management, 52(9), 459–466. https://doi.org/10.1016/j.ocecoaman.2009.07.001

García-de-la-Fuente, L., Fernández-Vázquez, E., Ramos-Carvajal, C. (2016). A

methodology for analyzing the impact of the artisanal fishing fleets on regional

economies: An application for the case of Asturias (Spain). Marine Policy, 74(26),

165–176. https://doi.org/10.1016/j.marpol.2016.09.002

García-de-la-Fuente, L., González-Álvarez, J., García-Flórez, L., Fernández-Rueda, P.,

Alcázar-Álvarez, J. (2013). Relevance of socioeconomic information for the

sustainable management of artisanal fisheries in South Europe. A characterization

study of the Asturian artisanal fleet (northern Spain). Ocean and Coastal


Gari, A. (2019, May 18). Future of Malindi tourism at stake as investors flee over

massive losses. Star Newspaper. Retrieved from https://www.the-

star.co.ke/counties/coast/2019-05-18-future-of-malindi-tourism-at-stake-as-

investors-flee-over-massive-losses/

185

Garza-Gil, M. D., Amigo-Dobaño, L. (2008). The profitability of the artisanal galician

fleet. Marine Policy, 32(1), 74–78. https://doi.org/10.1016/j.marpol.2007.04.008

Gereffi, G., Kaplinsky, R. (2001). Introduction: Globalisation, value chains and

development. IDS Bulletin, 32(3). https://doi.org/10.1111/j.1759-

5436.2001.mp32003001.x

Gordon, D. V., Hussain, S. (2015). Price determination and demand flexibilities in the

ex-vessel market for tuna in the republic of Maldives. Aquaculture Economics and

Management, 19(1), 8–28. https://doi.org/10.1080/13657305.2015.994234

Government of Kenya. (1991). Kenya Fisheries Act Cap 378. Nairobi.

Government of Kenya. Fisheries (Beach Management Unit) Regulations (2007).

Nairobi, Kenya.

Government of Kenya. (2008). Kenya National Oceans and Fisheries Policy. Nairobi.

Government of Kenya. (2010). Constitution of Kenya. Kenya Law Reports. Nairobi,

Kenya.

Government of Kenya. (2012). Kenya fisheries annual statistical bulletin. Nairobi.

Government of Kenya. (2013). Kenya tuna fisheries development and management

strategy. Nairobi.

Government of Kenya. (2014a). Kenya fisheries annual statistical bulletin. Nairobi.

Government of Kenya. (2014b). Marine artisanal fisheries frame survey report. Nairobi.

Government of Kenya. Kenya Gazette Supplement No.91, Pub. L. No. 116, 587 (2015).

Kenya. Retrieved from

http://kenyalaw.org/kl/fileadmin/pdfdownloads/LegalNotices/LN_No.116__117_of

_2015_1_.pdf

Government of Kenya. (2016a). Fisheries annual statistical bulletin. Nairobi.

Government of Kenya. (2016b). Fisheries Management and Development Act No. 35.

186

Nairobi.

Government of Kenya. (2016c). Marine artisanal fisheries frame survey report. Nairobi.

Government of Kenya. (2016d). Organization of the government of the Republic of

Kenya. Nairobi.

Government of Kenya. (2019a). Kenya Population and Housing Census: Volume II:

Distribution of population by administrative units (Vol. II). Nairobi.

Government of Kenya. (2019b). Kenya Population and Housing Census: Volume IV:

Distribution of population by socio-economic characteristics. Nairobi.

Greene, E. D. (1963). Changing from declining balance to straight-line depreciation.

Source: The Accounting Review, 38(2), 355–362. Retrieved from

http://www.jstor.org/stable/242926%5Cnhttp://www.jstor.org/stable/242926

Grydehøj, A., Nurdin, N. (2016). Politics of technology in the informal governance of

destructive fishing in Spermonde, Indonesia. GeoJournal, 81(2), 281–292.

GTZ. (2008). ValueLinks Manual: The Methodology of Value Chain Promotion.

Eschborn: GTZ.

Guillen, J., Macher, C., Merzéréaud, M., Boncoeur, J., Guyader, O. (2015). Effects of

the share remuneration system on fisheries management targets and rent

distribution. Marine Resource Economics, 30(2), 123–138.

https://doi.org/10.1086/679970

Guillen, J., Maynou, F. (2014). Importance of temporal and spatial factors in the ex-

vessel price formation for red shrimp and management implications. Marine Policy,

47, 66–70.

Guillen, J., Maynou, F. (2016). Increasing fuel prices, decreasing fish prices and low

productivity lead to poor economic performance and capacity reduction in the

fishing sector: evidence from the Spanish Mediterranean. Turkish Journal of

187

Fisheries and Aquatic Sciences, 16, 659–668.

Guisan, A., Edwards, T., Hastie, T. (2002). Generalized linear and generalized additive

models in studies of species distributions: setting the scene. Ecological Modelling,

157, 89–100.

Harre, H., Pirscher, F. (2009). The food industry in the new EU member states; A

comparative view on structure , conduct and performance. Outlook on Agriculture,

38(1), 23–29.

Hastie, T., Tibshirani, R., Friedman, J. (2009). The elements of statistical learning:

Data mining, inference, and prediction (Second Edi). Carlifornia: Springer.

https://doi.org/10.1007/978-0-387-84858-7

HLPE. (2014). Sustainable fisheries and aquaculture for food security and nutrition: A

report by the High Level Panel of Experts on Food Security and Nutrition of the

Committee on World Food Security. Rome.

Horemans, B., Kébé, M., Odoi-Akersie, W. (1994). Working group on capital needs

and availability in artisanal fisheries: Methodology and lessons learned from case

studies. IDAF Project, IDAF/WP/65. Cotonou.

Hosmer, D. W., Lemeshow, S. (2000). Applied logistic regression. (A. C. N. Cressie, N.

I. Fisher, I. M. Johnstone, J. B. Kadane, D. W. Scott, B. W. Silverman, … D. G.

Kendall, Eds.) (Second Edi). New York: John Wiley and Sons, Ltd.

Inoni, O. E., Oyaide, W. J. (2007). Socio-economic analysis of artisanal fishing in the

South agro-ecological zone of Delta State, Nigeria. Agricultura Tropica et

Subtropica, 40(4), 135–149.

Iqbal, B. A., Sami, S. (2017). Role of banks in financial inclusion in India. Contaduria y

Administracion, 62, 644–656. https://doi.org/10.1016/j.cya.2017.01.007

Isaacs, M., Hara, M., Raakjær, J. (2007). Has reforming South African fisheries

188

contributed to wealth redistribution and poverty alleviation? Ocean and Coastal

Management, 50(5–6), 301–313. https://doi.org/10.1016/j.ocecoaman.2006.11.002

Jacinto, E. R., Pomeroy, R. S. (2011). Developing markets for small-scale fisheries:

utilizing the value chain approach. In R. S. Pomeroy N. L. Andrew (Eds.), Small-

scale fisheries management: frameworks and approaches for the developing world

(pp. 160–177). Cambridge, UK: CAB International.

https://doi.org/10.1079/9781845936075.0160

Jacquet, J., Pauly, D. (2008). Funding priorities: Big barriers to small-scale fisheries.

Conservation Biology, 22(4), 832–835. https://doi.org/10.1111/j.1523-

1739.2008.00978.x

Jaini, M., AdvaniI, S., Shanker, K., Oommen, M., Namboothri, N. (2017). History,

culture, infrastructure and export markets shape fisheries and reef accessibility in

India’s contrasting oceanic islands. Environmental Conservation, 1–8.

https://doi.org/10.1017/S037689291700042X

Jennings, S., Pascoe, S., Norman-lopez, A., Bouhellec, B. Le, Hall-Aspland, S.,

Sullivan, A., Pecl, G. (2009). Identifying management objectives hierarchies and

weightings for four key fisheries in South Eastern Australia. FRDC Project No

2009/073.

Jensen, R. (2007). The digital provide: information (technology), market performance,

and welfare in the South Indian fisheries sector. Quarterly Journal of Economics,

CXXII(3). https://doi.org/10.1093/qje/qjt005.Advance

Jimenez, É. A., Amaral, M. T., Souza, P. L. de, Ferreira Costa, M. de N., Lira, A. S.,

Frédou, F. L. (2020). Value chain dynamics and the socioeconomic drivers of

small-scale fisheries on the amazon coast: A case study in the state of Amapá,

Brazil. Marine Policy, 115(February 2018).

189

https://doi.org/10.1016/j.marpol.2020.103856

Jordaan, H., Grové, B., Gerhard, R. B., Backeberg, G. R. (2014). Conceptual

framework for value chain analysis for poverty alleviation among smallholder

farmers. Agrekon: Agriculture Economics Research, Policy and Practice in

Southern Africa, 53(1), 1–25.

Jueseah, A. S., Knutsson, O., Kristofersson, D. M., Tomasson, T. (2020). Seasonal

flows of economic benefits in small-scale fisheries in Liberia : A value chain

analysis. Marine Policy, 119. https://doi.org/10.1016/j.marpol.2020.104042

Junior, H. S. de F., Meuwissen, M. P. M., Filho, J. do A., Lansink, A. G. J. M. O.

(2016). Evaluating strategies for honey value chains in brazil using a value chain

structure-conduct-performance (SCP) framework. International Food and

Agribusiness Management Review, 19(3), 225–250.

Kamau, E. C., Wamukota, A., Muthiga, N. A. (2009). Promotion and management of

marine fisheries in Kenya. In W. Gerd (Ed.), Towards sustainable fisheries law. A

comparative analysis (p. 340). Gland, Switzerland: IUCN.

Kamphorst, B. (1995). A cost and earnings study at Cotonou harbour, Benin, for

Cotonou, Programme for the Integrated Development of Artisanal Fisheries in

West Africa. idaf programme. Cotonou.

Kaplinsky, R., Morris, M. (2001). A Handbook for value chain research. Report

prepared for IDRC. IDRC, Canada: IDRC-International Development Research

Center.

Karuga, S., Abila, R. (2007). Value chain market assessment for marine fish sub-sector

in Kenya’s coast region. Mombasa, Kenya.

Kashangaki, J. (2017). Options for enhancing access to credit for micro & small

enterprises in coastal Kenya. Mombasa.

190

Ketchen, D., Shook, C. (1996). The application of cluster analysis in strategic

management research: An analysis and critique. Strategic Management Journal,

17(6), 441–458.

Kimani, E., Okemwa, G., Abubakar, A., Ontomwa, M., Omukoto, J. (2019). An

overview of the industrial tuna fishery in Kenya. Mombasa, Kenya.

Kimani, P., Wamukota, A., Manyala, J. O., Mlewa, C. M. (2020). Analysis of

constraints and opportunities in marine small-scale fisheries value chain: A multi-

criteria decision approach. Ocean and Coastal Management, 189.

https://doi.org/10.1016/j.ocecoaman.2020.105151

Kiteresi, L. I., Okuku, E. O., Mwangi, S. N., Ohowa, B., Wanjeri, V. O., Okumu, S.,

Mkono, M. (2012). The influence of land based activities on the phytoplankton

communities of Shimoni-Vanga system, Kenya. Int. J. Environ. Res, 6(1), 151–162.

Kitheka, J. U. (2013). River sediment supply, sedimentation and transport of the highly

turbid sediment plume in Malindi Bay, Kenya. Journal of Geographical Sciences,

23(3), 465–489. https://doi.org/10.1007/s11442-013-1022-x

Kitheka, J. U., Mavuti, K. M., Nthenge, P., Obiero, M. (2014). The dynamics of the

turbidity maximum zone in a tropical Sabaki estuary in Kenya. Journal of

Environmental Science and Water Resources, 3(4), 86–103.

Klint, M. B., Sjöberg, U. (2003). Towards a comprehensive SCP-model for analysing

strategic networks / alliances. International Journal of Physical Distribution &

Logistics Management, 33(5), 408–426.

https://doi.org/10.1108/09600030310481988

Kulindwa, K., Lokina, R. (2013). The winners and losers in finfish trade in Mafia

island: A value chain analysis. Western Indian Ocean Journal of Marine Science,

12(2), 151–168. Retrieved from

191

https://www.ajol.info/index.php/wiojms/article/view/78595

Lambo, A. L., Ormond, R. F. G. (2006). Continued post-bleaching decline and changed

benthic community of a Kenyan coral reef. Marine Pollution Bulletin, 52, 1617–

1624. https://doi.org/10.1016/j.marpolbul.2006.05.028

Lee, M. (2014). Hedonic pricing of Atlantic cod: Effects of size, freshness and gear.

Marine Resource Economics, 29(3), 259–277.

Leung, P., Muraoka, J., Nakamoto, S. T., Pooley, S. (1998). Evaluating fisheries

management options in Hawaii using analytic hierarchy process (AHP). Fisheries

Research, 36(2–3), 171–183. https://doi.org/10.1016/S0165-7836(98)00097-6

Lin, K. L., Jhan, H. T., Ting, K. H., Lin, C. L., Liu, W. H. (2014). Using indicators to

evaluate the Taiwanese distant-water fishery-policy performance. Ocean and

Coastal Management, 96, 29–41. https://doi.org/10.1016/j.ocecoaman.2014.04.028

Loc, V. T. T., Bush, S. R., Sinh, L. X., Khiem, N. T. (2010). High and low value fish

chains in the Mekong Delta: Challenges for livelihoods and governance.

Environment, Development and Sustainability, 12(6), 889–908.

https://doi.org/10.1007/s10668-010-9230-3

Loizou, E., Chatzitheodoridis, F., Polymeros, K. (2014). Sustainable development of

rural coastal areas: Impacts of a new fisheries policy. Land Use Policy, 38, 41–47.

https://doi.org/10.1016/j.landusepol.2013.10.017

Long, L. K., Flaaten, O., Thi, N., Anh, K. (2008). Economic performance of open-

access offshore fisheries — The case of Vietnamese longliners in the South China

Sea. Fisheries Research, 93, 296–304. https://doi.org/10.1016/j.fishres.2008.05.013

M4P. (2008). Making value chains work better for the poor: A tool book for

practitioners of Value Chain Analysis, Version 3. UK Department for International

Development (DFID), Agricultural Development Internationa:Phnom Penh,

192

Cambodia. https://doi.org/10.1684/agr.2014.0708

Macfadyen, G., Nasr-Alla, A. M., Al-Kenawy, D., Fathi, M., Hebicha, H., Diab, A. M.,

… El-Naggar, G. (2012). Value-chain analysis - An assessment methodology to

estimate Egyptian aquaculture sector performance. Aquaculture, 362–363, 18–27.

https://doi.org/10.1016/j.aquaculture.2012.05.042

Mangi, S. C., Roberts, C. M., Rodwell, L. D. (2007). Financial comparisons of fishing

gear used in Kenya’s coral reef lagoons. Source: AMBIO: A Journal of the Human

Environment, 36(8), 671–676. https://doi.org/10.1579/0044-

7447(2007)36[671:FCOFGU]2.0.CO;2

Mangiafico, S. S. (2016). An R Companion for the Handbook of Biological Statistics.

Retrieved May 20, 2020, from https://rcompanion.org/handbook/F_02.html

Manyala, J. O. (2011). Fishery value chain analysis: background report – Kenya.

FAO,Rome. Retrieved from http://www.fao.org/search/en/

Matsue, N., Daw, T., Garrett, L. (2014). Women fish traders on the Kenyan coast:

Livelihoods, bargaining power, and participation in management. Coastal


Maynou, F., Morales-Nin, B., Cabanellas-Reboredo, M., Palmer, M., García, E., Grau,

A. M. (2013). Small-scale fishery in the Balearic Islands (W Mediterranean): A

socio-economic approach. Fisheries Research, 139, 11–17.


Mbaru, E. K., McClanahan, T. R. (2013). Escape gaps in African basket traps reduce

bycatch while increasing body sizes and incomes in a heavily fished reef lagoon.

Fisheries Research, 148, 90–99.

Mcclanahan, T., Allison, E. H., Cinner, J. E. (2015). Managing fisheries for human and

food security. Fish and Fisheries, 16(1), 78–103. https://doi.org/10.1111/faf.12045

193

McClanahan, T. R. (2010). Effects of fisheries closures and gear restrictions on fishing

income in a Kenyan Coral Reef. Conservation Biology, 24(6), 1519–1528.

https://doi.org/10.1111/j.1523-1739.2010.01530.x

Melo, L. De, Damasio, A., Fabiana, P., Lopes, M., Pennino, M. G., Carvalho, R.,

Sumaila, U. R. (2016). Size matters: fishing less and yielding more in smaller-scale

fisheries. ICES Journal of Marine Science, 2–9.

Mills, D., Westlund, L., De Graaf, G., Kura, Y., Willman, R., Kelleher, K. (2011).

Under-reported and undervalued: Small-scale fisheries in the developing world. In

R. S. Pomeroy N. Andrew (Eds.), Small-scale fisheries management: Frameworks

and approaches for the developing world (pp. 1–15). Cambridge, UK: CAB

International. https://doi.org/10.1079/9781845936075.0001

Miñarro, S., Navarrete Forero, G., Reuter, H., van Putten, I. E. (2016). The role of

patron-client relations on the fishing behaviour of artisanal fishermen in the

Spermonde Archipelago (Indonesia). Marine Policy, 69, 73–83.

Mitchell, T. (2017). Is knowledge power? information and switching costs in

agricultural markets. American Journal of Agricultural Economics, 99(5), 1307–

1326. https://doi.org/10.1093/ajae/aax035

Mondaca-Schachermayer, C. I., Aburto, J., Cundill, G., Lancellotti, D., Tapia, C., Stotz,

W. (2011). An empirical analysis of the social and ecological outcomes of state

subsidies for small-scale fisheries: A case study from chile. Ecology and Society,

16(3), 14. https://doi.org/10.5751/ES-04239-160317

Morand, P., Sy, O. I., Breuil, C. (2005). Fishing livelihoods: Successful diversification,

or sinking into poverty? In B. Wisner, T. Camilla, R. Chitiga (Eds.), Towards a

New Map of Africa (pp. 71–96). London: Earthscan.

https://doi.org/10.4324/9781849773393

194

Muatha, I. T., Otieno, D. J., Nyikal, R. A. (2017). Determinants of smallholder farmers

awareness of agricultural extension devolution in Kenya. African Journal of

Agricultural Research, 12(51), 3549–3555.

https://doi.org/10.5897/AJAR2017.12603

Muawanah, U., Yusuf, G., Adrianto, L., Kalther, J., Pomeroy, R., Abdullah, H.,

Ruchimat, T. (2018). Review of national laws and regulation in Indonesia in

relation to an ecosystem approach to fisheries management. Marine Policy, 91,


Munga, C. N., Omukoto, J. O., Kimani, E. N., Vanreusel, A., Bay, M. (2014).

Propulsion-gear-based characterisation of artisanal fisheries in the Malindi-

Ungwana Bay, Kenya and its use for fisheries management. Ocean and Coastal


Mwirigi, F. M., Theuri, F. S. (2012). The challenge of value addition in the seafood

value chain along the Kenyan North coast. International Journal of Business and

Public Management, 2(2), 51–56.

Ngoc, N., Flaaten, O., Kim, L. (2015). Government support and profitability effects –

Vietnamese offshore fisheries. Marine Policy, 61, 77–86.


O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors.

Quality and Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6

Obayelu, A. E., Arowolo, A. O., Ibrahim, S. B., Oderinde, C. O. (2016). Socioeconomic

determinants of profitability of fresh fish marketing in Ogun State, Nigeria.

International Journal of Social Economics, 43(8), 871–883.

https://doi.org/10.1108/IJSE-07-2014-0147

Obura, D., Celliers, L., Machano, H., Mangubhai, S., Mohammed, M. S., Motta, H., …

195

Schleyer, M. (2002). Status of coral reefs in Eastern Africa: Kenya, Tanzania,

Mozambique and South Africa. In Status of Coral Reefs of the World.

Ochieng, C. A., Erftemeijer, P. (2003). The Seagrasses of Kenya and Tanzania. In T.

Green, E. P., Short, F. T., Frederick (Ed.), World Atlas of Seagrasses (pp. 82–92).

University of California Press.

Okemwa, G. M., Maina, G. W., Munga, C. N., Mueni, E., Barabara, M. S., Ndegwa, S.,

… Ntheketha, N. (2017). Managing coastal pelagic fisheries: A case study of the

small-scale purse seine fishery in Kenya. Ocean and Coastal Management, 144,

31–39. https://doi.org/10.1016/j.ocecoaman.2017.04.013

Olivier, G., Manuel, B., Lionel, R., Sébastien, D. (2013). Fishing strategies, economic

performance and management of moored fishing aggregating devices in

Guadeloupe. Aquatic Living Resources, 26, 97–105.

Olorunfemi, O. D., Adekunle, O. A., Oladipo, F. O., Oladele, O. I. (2017). Training

needs of fish farmers on value addition initiatives in Kwara State, Nigeria. Sarhad

Journal of Agriculture, 33(1), 14–21.

Olsson, J. (2009). Improved road accessibility and indirect development effects:

evidence from rural Philippines. Journal of Transport Geography, 17(6), 476–483.

https://doi.org/10.1016/j.jtrangeo.2008.09.001

Olsson, J. (2010). Road investment as enabling local economic development? Evidence

from a rural Philippine fishing village. Singapore Journal of Tropical Geography,

31(3), 343–356. https://doi.org/10.1111/j.1467-9493.2010.00407.x

Ouma, S. A., Odongo, T. M., Were, M. (2017). Mobile financial services and financial

inclusion: Is it a boon for savings mobilization? Review of Development Finance,

7(1), 29–35. https://doi.org/10.1016/j.rdf.2017.01.001

Parris, H. (2010). Tuna dreams and tuna realities: Defining the term “maximising

196

economic returns from the tuna fisheries” in six Pacific Island states. Marine

Policy, 34(1), 105–113. https://doi.org/10.1016/j.marpol.2009.04.022

Pascoe, S. (2006). Economics, fisheries, and the marine environment. ICES Journal of

Marine Science, 63(1), 1–3. https://doi.org/10.1016/j.icesjms.2005.11.001

Pascoe, S., Brooks, K., Cannard, T., Dichmont, C. M., Jebreen, E., Schirmer, J.,

Triantafillos, L. (2014). Social objectives of fisheries management: What are

managers’ priorities? Ocean and Coastal Management, 98, 1–10.

https://doi.org/10.1016/j.ocecoaman.2014.05.014

Pascoe, S., Robinson, C., Coglan, L. (1996a). Economic and financial performance of

the UK. CEMARE Research Report No. 44, CEMARE, University of Portsmouth,

UK.

Pascoe, S., Robinson, C., Coglan, L. (1996b). Economic and financial performance of

the UK English Channel fleet.

Pascoe, S., Vieira, S., Thebaud, O. (2015). Allocating repairs and maintenance costs to

fixed or variable costs in fisheries bioeconomic models. Applied Economics Letters,

22(2), 127–131. https://doi.org/10.1080/13504851.2014.929619

Pauly, D. (1997). Small-scale fisheries in the tropics: Marginality, marginalization, and

some implications for fisheries management. Global Trends: Fisheries

Management, 20, 40–49.

Pauly, D., Zeller, D. (2017a). Comments on FAOs State of World Fisheries and

Aquaculture (SOFIA 2016). Marine Policy, 77, 176–181.


Pauly, D., Zeller, D. (2017b). The best catch data that can possibly be? Rejoinder to Ye

et al. “FAO’s statistic data and sustainability of fisheries and aquaculture.” Marine

Policy, 81, 406–410. https://doi.org/10.1016/j.marpol.2017.03.013

197

Pedroza-Gutiérrez, C., López-Rocha, J. A. (2016). Key constraints and problems

affecting the inland fishery value chain in central Mexico. Lake and Reservoir


Pita, C., Pierce, G. J., Theodossiou, I. (2010). Stakeholders’ participation in the

fisheries management decision-making process: Fishers’ perceptions of

participation. Marine Policy, 34(5), 1093–1102.


Platteau, J. (1984). The drive towards mechanization of small-scale fisheries in Kerala:

A study of the transformation process of traditional village societies. Development

and Change, 15(1), 65–103. https://doi.org/https://doi.org/10.1111/j.1467-

7660.1984.tb00174.x

Pomeroy, R. (2016). A research framework for traditional fisheries: Revisited. Marine

Policy, 70, 153–163.

Pomeroy, R., Trinidad, A. C. (1995). Industrial organization and market analysis: Fish

marketing. In G. J. Scott (Ed.), Prices, products and people: analysing agricultural

markets in developing countries. (pp. 217-238.). Boulder: Lynne Rienner.

Porter, M. (1985). Competitive advantage: Creating and sustaining superior

perfomance. New york: The Free Press.

Prescott, J., Riwu, J., Prasetyo, A. P., Stacey, N. (2017). The money side of livelihoods:

Economics of an unregulated small-scale Indonesian sea cucumber fishery in the

Timor Sea. Marine Policy, 82, 197–205.


R Development Core Team 3.0.1. (2013). A language and environment for statistical

computing. Vienna: R Foundation for Statistical Computing. Retrieved from

http://www.r-project.org

198

Ramírez-Rodríguez, M. (2017). A profitability analysis of catch quotas for the Pacific

hake fishery in the Gulf of California. North American Journal of Fisheries


Ranjan, R. (2017). Challenges to farm produce marketing: A model of bargaining

between farmers and middlemen under risk. Journal of Agricultural and Resource

Economics, 42(3), 386–405. https://doi.org/10.22004/ag.econ.264068

Rhoades, S. A. (1993). The Herfindahl-Hirschman Index. Federal Reserve Bulletin, 79,

188–189. https://doi.org/http://www.federalreserve.gov/pubs/bulletin/default.htm

Rochet, M. J., Prigent, M., Bertrand, J. A., Carpentier, A., Coppin, F., Delpech, J. P., …

Trenkel, V. M. (2008). Ecosystem trends: Evidence for agreement between fishers’

perceptions and scientific information. ICES Journal of Marine Science, 65(6),

1057–1068. https://doi.org/10.1093/icesjms/fsn062

Rodrigo, G. C. (2012). Micro and macro: The economic divide. Retrieved May 15,

2017, from http://www.imf.org/external/pubs/ft/fandd/basics/bigsmall.htm

Rodrigues, A. R., Raggi Abdallah, P., Gasalla, M. A. (2019). Cost structure and

financial performance of marine commercial fisheries in the South Brazil Bight.

Fisheries Research, 210(October 2018), 162–174.


Rodríguez-Garcia, J., Villasante, S. (2016). Disentagling seafood value chains: Tourism

and the local market driving small-scale fisheries. Marine Policy, 74, 33–42.

https://doi.org/10.1016/J.MARPOL.2016.09.006

Roheim, C. a., Asche, F., Santos, J. I. (2011). The elusive price premium for ecolabelled

products: Evidence from seafood in the UK market. Journal of Agricultural

Economics, 62(3), 655–668.

Roheim, C. a, Gardiner, L., Asche, F. (2007). Value of brands and other attributes:

199

Hedonic analysis of retail frozen fish in the UK. Marine Resource …, 22, 1–32.

Retrieved from http://ageconsearch.umn.edu/bitstream/47053/2/02-Roheim-06-

44.pdf

Rosales, R. M., Pomeroy, R., Calabio, I. J., Batong, M., Cedo, K., Escara, N., …

Sobrevega, M. A. (2017). Value chain analysis and small-scale fisheries

management. Marine Policy, 83, 11–21.


Ruppert, D., Harezlak, J., Wand, M. (2018). Semiparametric Regression with R. (R.

Gentleman, K. Hornik, G. Parmigiani, Eds.). Springer.

Saaty, T. (1990). How to make a decision: The analytic hierarchy process. European

Journal of Operational Research, 48(1), 9–26. https://doi.org/10.1016/0377-

2217(90)90057-i

Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of

Mathematical Psychology, 15(3), 234–281. https://doi.org/10.1016/0022-

2496(77)90033-5

Salas, S., Chuenpagdee, R., Carlos, J., Charles, A., Seijo, J. C., Charles, A. (2007).

Challenges in the assessment and management of small-scale fisheries in Latin

America and the Caribbean. Fisheries Research, 87(1), 5–16.


Salmi, P. (2015). Constraints and opportunities for small-scale fishing livelihoods in a

post-productivist coastal setting. Sociologia Ruralis, 55(3), 258–274.

https://doi.org/10.1111/soru.12095

Sambuo, D., Kirama, S. (2018). Fish price determination around Lake Victoria,

Tanzania: Analysis of factors affecting fish landing price. Global Business Review,

1–16. https://doi.org/10.1177/0972150917811509

200

Samoilys, M. A., Osuka, K., Maina, G. W., Obura, D. O. (2017). Artisanal fisheries on

Kenya’s coral reefs: Decadal trends reveal management needs. Fisheries Research,


Schmitt, K. M., Kramer, D. B. (2009). Road development and market access on

Nicaragua’s Atlantic coast: Implications for household fishing and farming

practices. Environmental Conservation, 36(4), 289–300.

https://doi.org/10.1017/S0376892910000159

Schmitz, H. (2005). Value chain analysis for policy-makers and practitioners. ILO.

Shamsuzzaman, M. M., Islam, M. M. (2018). Analysing the legal framework of marine

living resources management in Bangladesh: Towards achieving Sustainable

Development Goal 14. Marine Policy, 87, 255–262.


Sharp, M. (2011). The benefits of fish aggregating devices in the Pacific. SPC Fisheries

Newsletter, 135, 28–36.

Simon, H., Christophe, B., Roberto, R.-G. (2007). Investing in African fisheries:

building links to the Millennium Development Goals. Fish and Fisheries, 8(3),

211–226. Retrieved from http://dx.doi.org/10.1111/j.1467-2679.2007.00251.x

Sjöberg, E. (2015). Pricing on the fish market — does size matter? Marine Resource

Economics, 30(3), 277–296.

Smith, I. R. (1979). A research framework for traditional fisheries. ICLARM Studies and

Reviews No. 2. International Center for Living Aquatic Resources Management,

Manila., (2), 45.

Smith, M. D., Roheim, C. A., Crowder, L. B., Halpern, B. S., Turnipseed, M., Anderson,

J. L., … Selkoe, K. A. (2010). Sustainability and global seafood. Science.

https://doi.org/10.1126/science.1185345

201

Soma, K. (2003). How to involve stakeholders in fisheries management — A country

case study in Trinidad and Tobago. Marine Policy, 27(1), 47–58.

https://doi.org/10.1016/S0308-597X(02)00050-7

Sowman, M., Sunde, J., Raemaekers, S., Schultz, O. (2014). Fishing for equality: Policy

for poverty alleviation for South Africa’s small-scale fisheries. Marine Policy, 46,


Spronk, R. (2014). Exploring the middle classes in Nairobi: From modes of production

to modes of sophistication. African Studies Review, 57(1), 93–114.

https://doi.org/10.1017/asr.2014.7

Stevens, K., Irwin, B., Kramer, D., Urquhart, G. (2014). Impact of increasing market

access on a tropical small-scale fishery. Marine Policy, 50, 46–52.


Sunoko, R., Huang, H. W. (2014). Indonesia tuna fisheries development and future

strategy. Marine Policy, 43, 174–183. https://doi.org/10.1016/j.marpol.2013.05.011

Teh, L. C. L., Sumaila, U. R. (2011a). Contribution of marine fisheries to worldwide

employment. Fish and Fisheries, 14(1), 77–88. https://doi.org/10.1111/j.1467-

2979.2011.00450.x

Teh, L. S. L., Teh, L. C. L., Sumaila, U. R. (2011b). Quantifying the overlooked socio-

economic contribution of small-scale fisheries in Sabah, Malaysia. Fisheries

Research, 110(3), 450–458. https://doi.org/10.1016/j.fishres.2011.06.001

Tejada, J. J., Raymond, J., Punzalan, B. (2012). On the misuse of Slovin’s formula. The

Philippine Statistician, 61(1), 8.

Thilsted, H. S., Thorne-lyman, A., Webb, P., Bogard, R. J., Subasinghe, R., John, M.,

Hugh, E. (2016). Sustaining healthy diets: The role of capture fisheries and

aquaculture for improving nutrition in the post-2015 era. Food Policy, 61, 126–131.

202

https://doi.org/10.1016/j.foodpol.2016.02.005

Thyresson, M., Crona, B., Nystrom, M., de la Torre-Castro, M., Jiddawi, N. (2013).

Tracing value chains to understand effects of trade on coral reef fish in Zanzibar,

Tanzania. Marine Policy, 38, 246–256.

Tuda, A. O., Stevens, T. F., Rodwell, L. D. (2014). Resolving coastal conflicts using

marine spatial planning. Journal of Environmental Management, 133, 59–68.

https://doi.org/10.1016/j.jenvman.2013.10.029

Turay, F., Verstralen, K. (1997). Costs and earnings in artisanal fisheries: Methodology

and lessons learned from case studies, Programme for the Integrated Development

of Artisanal Fisheries in West Africa. IDAF/WP/100. Cotonou.

Turyahabwe, N., Tumusiime, D. M., Yikii, F., Kakuru, W., Barugahare, V. (2017).

Awareness, perceptions and implementation of policy and legal provisions on

wetlands in Uganda. African Journal of Rural Development, 2(May 2016), 161–

174.

UCLA. (n.d.). Logit regression, R data analysis examples. Retrieved October 10, 2018,

from https://stats.idre.ucla.edu/r/dae/logit-regression/

UN. (2003). Handbook of national accounting. National accounts: A practical

introduction. New York.

UNEP. (1998). Eastern Africa atlas of coastal resources, Kenya. United Nations

Environment Programme. https://doi.org/10.1007/s13398-014-0173-7.2

US Department of Justice. (2017). Concentration and market shares. Retrieved July 3,

2017, from https://www.justice.gov/atr/15-concentration-and-market-shares

USAID. (2008). The Kenya capture fisheries value chain: an AMAP-FSKG value chain

finance case study.

Verweij, M. C., van Densen, W. L. T., Mol, A. J. P. (2010). The tower of Babel:

203

Different perceptions and controversies on change and status of North Sea fish

stocks in multi-stakeholder settings. Marine Policy, 34(3), 522–533.


Viaene, J., Gellynck, X. (1995). Structure, conduct and performance of the European

food sector. European Review of Agricultural Economics, 22(3), 282–295.

https://doi.org/10.1093/erae/22.3.282

Villasante, S., Rodrıguez-Gonzalez, D., Antelo, M., Susana Rivero-Rodrıguez, Joseba,

L.-N. (2013). Why are prices in wild catch and aquaculture industries so different?

Ambio, 42, 937–950.

Wamukota, A. (2009). The structure of marine fish marketing in Kenya: The case of

Malindi and Kilifi districts. Western Indian Ocean J. Mar. Sci, 8(2), 215–224.

Wamukota, A., Brewer, T. D., Crona, B. (2014). Market integration and its relation to

income distribution and inequality among fishers and traders: The case of two

small-scale Kenyan reef fisheries. Marine Policy, 48, 93–101.


Wamukota, A. W., Crona, B., Osuka, K., Daw, T. M. (2015). The importance of

selected individual characteristics in determining market prices for fishers and

traders in Kenyan small-scale fisheries. Society and Natural Resources, 28(9), 959–

974. https://doi.org/10.1080/08941920.2015.1014600

Wamukota, A. W., McClanahan, T. R. (2017). Global fish trade, prices, and food

security in an African coral reef fishery. Coastal Management, 45(2), 143–160.

https://doi.org/10.1080/08920753.2017.1278146

Wanyonyi, I. N., Wamukota, A., Mesaki, S., Guissamulo, A. T., Ochiewo, J. (2016a).

Artisanal fisher migration patterns in coastal East Africa. Ocean and Coastal

Management, 119, 93–108.

204

https://doi.org/https://doi.org/10.1016/j.ocecoaman.2015.09.006

Wanyonyi, I. N., Wamukota, A., Tuda, P., Mwakha, V. A., Nguti, L. M. (2016b).

Migrant fishers of Pemba: Drivers, impacts and mediating factors. Marine Policy,

71, 242–255. https://doi.org/https://doi.org/10.1016/j.marpol.2016.06.009

Webber, C. M., Labaste, P. (2010). Building competitiveness in Africa’s agriculture: A

guide to value chain concepts and applications. Washington: The International

Bank for Reconstruction and Development / The World Bank.

Wever, L., Glaser, M., Gorris, P., Ferrol-Schulte, D. (2012). Decentralization and

participation in integrated coastal management: Policy lessons from Brazil and

Indonesia. Ocean and Coastal Management, 66, 63–72.

https://doi.org/10.1016/j.ocecoaman.2012.05.001

Whitmarsh, D., James, C., Pickering, H., Neiland, A. (2000). The profitability of marine

commercial fisheries: a review of economic information needs with particular

reference to the UK. Marine Policy, 24, 257–263.

Wind, Y., Saaty, T. L. (1980). Marketing applications of the Analytic Hierarchy

Process. Management Science. https://doi.org/10.1287/mnsc.26.7.641

Yamane, T. (1967). Elementary sampling theory. New Jersey: Prentice-Hall, Inc.

Ye, Y., Barange, M., Beveridge, M., Garibaldi, L., Gutierrez, N., Anganuzzi, A.,

Taconet, M. (2017). FAO’s statistical databases and the sustainability of fisheries

and aquaculture: Comments to Pauly and Zeller 2017. Marine Policy, 81, 401–405.


Yeeting, A. D., Bush, S. R., Ram-Bidesi, V., Bailey, M. (2016). Implications of new

economic policy instruments for tuna management in the Western and Central

Pacific. Marine Policy, 63, 45–52. https://doi.org/10.1016/j.marpol.2015.10.003

205

APPENDICES

Appendix 1: Individual survey questionnaire

This study on value chains of small-scale fisheries is part of PhD work at Pwani

University, Kilifi. You have been randomly nominated as a respondent to answer some

questions. Kindly assist me by answering questions to the best of your understanding. I

assure that confidentiality will be observed. You may interject at any point for clarification

or questions you are uncomfortable with. Thank you in advance for your cooperation.

Data Collector Initials: ……………... Date: ………….....Study site………………

Main Occupation…………….. Main gear………………………………

Section A: Demographic data.

1. Respondent’s name: …………….….(For fishers, target Captain of the vessel only)

Name of boat/business………….……………Tel No: ……………………….

2. Sex: …………………Number of years in school ………………………………

3. Age: …………………..Marital status………………………

4. No. of dependents below (Watu wangapi wanakutegemea)

Below 18 years Above 18 years

5. How long have you been in fish related activities? (Umekua katika shughuli za

samaki hapa kwa muda gani?)

6. How long in this area? (Muda gani katika sehemu hii?)

7. Where is your home origin? (Nyumbani ulikotoka ni wapi?)

206

8. If from elsewhere, when and why did you relocate here? (Ni lini na mbona ukahamia

hapa?)

Section B: Socio-economic data.

9. List your occupations? rank the occupations in importance of income generation

(from most important) (Taja shughuli zako muhimu za kuleta mapato?

Orodhesha shughuli hizi kuanzia iliyo muhimu zaidi kimapato?)

List occupations Rank in importance of income

1.

Section C: Inputs and costs

a) Investment costs

10. List equipment you use in your business? What is its ownership and costs in Kshs?

(Note: This refers to equipment only and not materials). (Taja umiliki wa vyombo na

vifaa vya kufanyia kazi, gharama ya ununuzi na mwaka wa kuvinunua?)

List Equipment

e.g. (Gear, boat,

engine, sail,

transport means,

ice box, freezer)

Chombo au kifaa

Ownership

Umiliki

If yes;

cost of

purchase

Bei

Age of

equipment/

year of

purchase

Mwaka wa

kununua

If not

owned;

the cost

of rent/

day

Sources of

inputs

(location)

Yes/ No

Have the costs been given to you by the owner of the gears ……………….

b) Operational costs (Fixed and variable)

11. What is the cost of equipment repair per week? Who bears it? Indicate share

arrangement of costs. (Inakugharimu pesa ngapi kurekebisha vyombo na vifaa

vinapoharibika kwa kila wiki?Na inachukua muda gani kurekebisha kila wiki?)

207

Costs borne by; Mwenye

gharama

Type of

equipment

(Chombo

au kifaa)

Repair costs/

week in Kshs.

(Gharama ya

kurekebisha)

Hours taken to

repair/ week?

(Muda wa

kurekebisha

kila wiki)

Myself/

crew

only

Jointly

with

owner

Owner

only

12. If a fisher using engine boat, what is the fuel consumption and it’s cost per day and

who bears it? Indicate share arrangement of costs (Unatumia mafuta kiasi gani kwa

chombo, gharama yake ni ngapi, na nani anaisimamamia?)

Costs borne by; (Mwenye gharama)

Fuel amount in Litres if

applicable

(Unatumia mafuta kiasi gani?)

Myself /

crew only

Jointly with

owner of fish

Owner of fish

only

Kusi

Kaskazi

13. What type of transport do you use to reach the market or arrive at work, it’s cost

per day and who bears it? Indicate share arrangement of costs (Unatumia usafiri wa

aina gani kusafirisha samaki sokoni na gharama ni ya nani?)


Means of transport e.g boat,

mkokoteni labour, canoe,

bicycle, motorcycle, public

transport, truck. (Aina ya

usafiri)

Cost of

transport

Myself /

crew

only

Jointly

with

owner of

fish

Owner of

fish only

14. What are other associated costs per day? Who bears them? Indicate share

arrangement of costs (Taja gharama nyingine za kila siku? Mfano; chambo, leseni,

mawasiliano, malazi, chakula wakati wa kazi)

208


Type of cost e.g

(bait, license,

BMU registration

fee, cess,

anchorage,

communication,

accommodation

and food during

work only) (Aina

ya gharama)

Kusi-

cost per

day

(Gharam

a ya kila

siku-

Kusi)

Kaskazi-

cost per

day

(Gharam

a ya kila

siku-

Kaskazi)

Mysel

f /

crew

only

Jointly

with

owner

of fish

Owner

of fish

only

Sources of

inputs

(location)

b) Fish handling, value addition and associated costs

15. In what condition/rule e.g. (size, composition and freshness) does the buyer require

the fish to be in? What are the consequences of not meeting the requirements e.g.

(lowered price, rejected)? (Wanunuzi wanahitaji samaki wako wawe katika hali

/masharti gani? Mfano; ubora, ukubwa, aina, jeraha? Je kutotimiza haya masharti

kuna adhari gani?)

Condition /rule

(Masharti)

Type of buyer

(Aina ya mnunuzi)

Consequences of not meeting

requirement (Athari za kutotimiza )

16. How do you process/preserve your fish e.g. (ice-chilled, frozen, salted, sun dried,

smoked, gutted, filleted, frying, salting or no preservation)? Who bears the cost? If

shared state the share arrangement (Unatayarisha samaki wako namna gani?Mfano;

kutia chumvi na kukausha, kukaanga, kutia barafu. Gharama yake ni pesa ngapi kila

siku, na ni nani anayesimamia gharama hii?)


Type of preservation/

processing

Cost per

day

Myself /

crew only

Jointly with

owner of fish

Owner of

fish only

17. What quantity of fish gets spoilt in Kg per month during? (Ni kilo ngapi za samaki

wako zinaharibika kila mwezi?)

209

a). Kusi (SEM)…………………………. b). Kaskazi (NEM)…………………….

Section D: Fishery products, marketing and income

Fish buying

18. *If a trader or processor; what are the five most common fish types you buy and

their prices by commonest grades. Where do you source the fish? (Taja aina tano za

samaki unazonunua kwa kawaida. Kisha elezea kuhusu bei ya kununua)

Fish type

traded/

processed

(Aina ya

samaki

unazonunua)

Commonest

Grade you buy

Gredi Large

(A) or (1) Mix-

B (B)or (2)

Smalli (C)

or(kitoweo)

Lowest

buying

price/Kg

(Bei ya

chini zaidi

kwa Kilo)

Highest

buying

price/Kg

(Bei ya

juu zaidi

kwa Kilo)

Average

buying

price/Kg

(Bei ya

kawaida

kwa Kilo)

Source of

fish;

(Unanunu

a wapi?)

Fish selling

19. What is the selling price of the five most common fish types you deal with, their

commonest grades and destination? (Taja aina tano za samaki unazouza kwa

kawaida. Kisha elezea kuhusu bei ya kuuza)

Fish type

caught/

traded/

processed

(Aina ya

samaki

unazouza)

Commonest

Grade you

sell

(Gredi

Large (A)

or (1) Mix-

B (B)or (2)

Smalli (C)

Kusi Kaskazi Lowest

selling

price/Kg

(Bei ya

chini

zaidi ya

kuuza

kwa

Kilo)

Highest

selling

price/

Kg

(Bei ya

juu

zaidi ya

kuuza

kwa

Kilo)

Average

selling

price/Kg

(Bei ya

kawaida

ya kuuza

kwa

Kilo)

Customer

market;

(Unauzia

nani?)

20. If lowest and highest prices are different, what causes the variations? (Kwa nini bei

ya samaki ni tofauti katika nyakati tofauti?)

210

21. If a trader or processor; from how many boats do you buy from?…………

22. If a trader or processor; do you have arrangements with these boats?

Yes…………….No…………

23. What quantity of fish in Kgs do you catch/trade/process per day and what is your

daily income variation in Kshs? (These refers to the fishing unit/boat) (Kwa kila

siku ni kiasi gani cha samaki unanunua/ unashika na mapato yako ni ngapi?)

Quantity

Lowest quantity

your boat catches

or sold (Kiasi cha

chini zaidi)

Highest quantity your

boat catches or sold

(Kiasi cha juu zaidi)

Average quantity

your boat catches or

sold

(Kiasi cha kawaida/

wastani)

Kusi

Kaskazi

Lowest buying

amount & revenue

from fish sales before

costs (Ununuzi na

mauzo ya chini zaidi

kabla ya kutoa

gharama)

Highest buying

amount & revenue

from fish sales before

costs (Ununuzi na

Mauzo ya juu zaidi

kabla ya kutoa

gharama)

Average buying

amount & revenue

from fish sales

before costs

(Ununuzi na mauzo

ya kawaida/

wastani kabla ya

kutoa gharama)

Kusi Buying

Selling

Kaskazi Buying

Selling

24. On average, what amount of fish in (Kg) goes to personal consumption or gifts/fees

(Kwa kadri ni kiasi gani cha samaki wewe/nyinyi hutenga kwa kitoweo, zawadi ama

ada kila siku?)

Home consumption Gifts

211

25. What amounts of fish on a typical day do you commonly sell to the following

destinations per day? (Unauza kiasi gani cha samaki kwa wateja wafuatao kila

siku?)

Market destination

Local

consumer

(Walaji

wa hapa)

Small Scale Trader/

Restaurant/ Mama

karanga (Matajiri

wadogo wadogo na

vibanda hapa)

Fish Shop

Dealer /

Middlemen

(matajiri

wakubwa)

Beach

Hotels

(Mahoteli )

Amount in

Kgs

Place

26. If trader/processor; do you buy fish usually from only one specific supplier or

multiple suppliers every day? (Je unanunua samaki kutoka wauzaji/wavuvi maalum

kila siku na kwa nini? Ni nani anayeweka bei?)

Tick appropriately

Suppliers / fishers

(Wauzaji/wavuvi)

Single

Multiple

27. Who determines the price?

28. If price is not determined by self, why?

29. Do you sell your fish usually to only one specific or multiple trader/s every day? (Je

unauza samaki kwa wauzaji maalum pekee kila siku?)

Tick appropriately

Trader (Mnunuzi) Single

Multiple

30. Who determines the price? (Nani anayeweka bei?)

31. If price is not determined by self, why? (Kama sio wewe, kwa nini?)

32. Do you usually know at what price colleagues are buying or selling fish?

(Je unajua ni bei gani wavuvi, mama karanga na matajiri wanauza samaki?)

212

Yes No

Buying

Selling

33. If yes, how do you usually know? (Hua unajua bei ya wengine namna gani?)

34. If knows the price of others, do you discuss to set the price? (Huwa mnajadiliana na

kuweka bei ya kunua au kuuza?)

Yes No

Buying

Selling

35. On average how many fulltime crews/assistants do you engage in your fish business/

fishing unit per day? (Ni kadri mabaharia / watu wangapi unafanya nao kazi ya

samaki kila siku) (This targets crew or employees if applicable)

36. If a fisher; how do you share the catch amongst crew, gears and boat owner?

(Je mnagawana mapato ya samaki kwa mafungu mangapi?)

Share for Crew Share gear Share for Boat Share Engine

37. If fisher, is catch share arrangement done after off-setting operational costs?

(Je mnagawana kwa mafungu badaa ya kutoa gharama?)

Yes No

38. On average how much time do you spend in your fish related activity per day? (Kwa

kadri, ni muda gani inakuchukua kufanya shughuli za samaki kutoka kuanza hadi

kukamilisha?) (This refers to total time taken from home to completion of business).

Start time End time Total time taken

39. How many days per week do you fish/trade/ process fish during? (Unafanya

shughuli za samaki kwa siku ngapi kwa wiki katika majira yafuatayo?)

a). Days/week: Days/Kusi (SEM)………. b). Days/Kaskazi (NEM)…………

213

c). Months/year: Months/Kusi (SEM)…… ….d). Months/Kaskazi (NEM)………

Section F: Support services and regulatory framework

a) Access to capital/credit

40. Do you operate a bank account? (Je una njia ya kuweka pesa zako kwenye benki

ama mashirika mengine ya kifedha?)

Yes No

Comments

41. Have you ever got a bank loan? (Umewahi pata mkopo kutoka kwa benki? Kama

hujawai pata mkopo ni kwa nini?)

Yes No

42. *If not? State reasons.

43. Do you save your money through Merry-go-rounds or chama?

Yes No

44. Do you use any of the mobile savings schemes? (e.g. M-shwari and M-Benki (KCB)

etc.)? (Je unatumia njia ya kuweka pesa kwenye benki kwa kutumia simu? Mfano;

M-swari au M-Benki?)

Yes No

45. *If uses mobile savings system, why do you prefer this mode? (Je ni kwanini

unapendelea kuweka pesa kwa kupitia simu?)

46. Do you use mobile phone money transfer services in your fish business/activity?

(e.g. M-pesa and airtel money etc.). (Je unatumia simu ya rununu kutuma au

kupokea pesa? Mfano; M-pesa, Airtel money).

214

If uses mobile phone transfer system, what do you use it for?

(Je unaitumia kufanyia nini?)

47. Who finances your daily operational costs? (Je unadhaminiwa na nani katika

shughuli zako za samaki kila siku na ni kwa masharti gani?)

Source (e.g.

bank, grant,

micro-finance,

middlemen,

Mobile money)

(Mtaji. Mfano;

Benki, mtajiri)

Amount Interest

rate

(Riba)

Repayment

period in

months

(Muda wa

kulipa)

Condition (e.g. need for

collateral, staggered monthly

premium payments, must

sell fish to middleman etc.)

(Masharti ya kulipa. Mfano;

title ya shamba, lazima

kuuzia tajiri, lazima tajiri

kuweka bei)

48. What was the source of your start-up capital/credit (to purchase your equipment and

cater for operational costs) and what were the amounts? (Je ulipata mtaji wako wa

kwanza wa kuanzia shughuli zako za samaki wapi?)

Source (e.g.

bank, grant,

micro-finance,

middlemen,

Mobile money)

Mtaji. Mfano;

Benki, mtajiri,

Amount Interest

rate

Riba

Repayment

period in

months

Muda wa

kulipa

Condition (e.g. need for

collateral, staggered monthly

premium payments, must sell

fish to middleman etc.)

Masharti ya kulipa. Mfano;

title ya shamba, lazima

kuuzia tajiri, lazima tajiri

kuweka bei

49. What would be the start-up capital today for the same business? (Inagaharimu kiasi

gani cha fedha kuanzisha shughuli hii leo?)

Yes No

215

b) Institutional support

50. Have you received institutional support on the following? If yes name institutions

(Je umewahi pata aina ya misaada ifuatayo? Ni nani aliyekufadhili?)

Tick

Yes No Type No. Institution/s

Equipment supply (Vyombo na vifaa vya

kazi)

Training (Mafunzo)

Assistance to access credit (Msaada

kupata mkopo)

Assistance to access markets

(Msaada kupata soko)

Others:

51. What other training do you require? (Je ni mafunzo mengine yapi unahitaji?)

c) Regulatory aspects in Kenya (Likert scale)

In the following statements, I request that you agree or disagree with them. (Kwa

maoni yafuatayo nakuomba ukubali ama ukatae)

52. Government has promoted cooperation between fisheries operators (Serikali

imedumisha ushirikiano baina ya wenye shughuli za samaki?)

Strongly

disagree

Sikubali

kabisa

Disagree

Sikubali

Undecided

Sina

uhakika/

sijui

Agree

Nakubali

Strongly

agree

Nakubali

kabisa

Central Government

County Government

Comments:

53. Government has supported your BMU? (Serikali imeboresha BMU yenu?)

216

Strongly

disagree

Sikubali

kabisa

Disagree

Sikubali

Undecided

Sina

uhakika/

sijui

Agree

Nakubali

Strongly

agree

Nakubali

kabisa

Central Government

County Government

Comments:

54. Government promoted fish marketing (Serikali imeboresha uuzaji wa samaki)

Strongly

disagree

Sikubali

kabisa

Disagree

Sikubali

Undecided Sina

uhakika/ sijui

Agree

Nakubali

Strongly

agree

Nakubali

kabisa

Central

Government

County

Government

Comments:

55. Government has helped you in value addition? (Serikali imekusaidia katika

kuongeza dhamani ya samaki; mfano kutayarisha samaki ?)

Strongly

disagree

Sikubali

kabisa

Disagree

Sikubali

Undecided Sina

uhakika/ sijui

Agree

Nakubali

Strongly

agree

Nakubali

kabisa

Central

Government

County

Government

Comments:

56. Government has helped you in provision of equipment? (Serikali imekusaidia kwa

vifaa)

Strongly

disagree

Sikubali

kabisa

Disagree

Sikubali

Undecided Sina

uhakika/ sijui

Agree

Nakubali

Strongly

agree

Nakubali

kabisa

Central

Government

County

Government

Comments:

217

57. Government has helped you to promote fish handling, hygiene and quality? (Serikali

imekusaidia kudumisha ubora wa samaki; mfano kutoa mitambo ya barafuu na

mafunzo?)

Strongly

disagree

Sikubali

kabisa

Disagree

Sikubali

Undecided

Sina uhakika/

sijui

Agree

Nakubali

Strongly

agree

Nakubali

kabisa

Central

Government

County

Government

Comments:

58. Government Officials provide extension services (Maafisa wa Serikali huwa

wanakutembelea na kukupa mawaidha kuhusu shughuli za samaki?)

Strongly

disagree

Sikubali

kabisa

Disagree

Sikubali

Undecided Sina

uhakika/ sijui

Agree

Nakubali

Strongly

agree

Nakubali

kabisa

Central

Government

County

Government

Comments:

59. Has there been any research conducted in this area that you know of and by whom?

(Kuna utafiti gani uliofanywa hapa kwenu kuhusu samaki nan i nani aliyefanya?)

60. *If yes The research results have been beneficial to me/us (Matokeo ya utafiti

yametufaidi)

Strongly

disagree

Sikubali

kabisa

Disagree

Sikubali

Undecided Sina

uhakika/ sijui

Agree

Nakubali

Strongly

agree

Nakubali

kabisa

Central

Government

County

Government

Comments:

218

Certificate of consent

I have understood the foregoing information. I have had the opportunity to ask questions

about it and all my questions have been answered to my satisfaction. I therefore give my

consent to voluntarily participate as a respondent in this research.

Name of participant:

Signature of participant:

Date

Day/Month/Year

Statement by the researcher/person taking consent

219

Appendix 2: Focus group discussions questionnaire

This part of the study addressed the following:

1. Objective 4: analyze constraints and opportunities existing in the value chain

2. Key Research Question 4: What are the constraints facing actors and

opportunities for improvement of the value chain?

Based on researcher’s field experience during field interviews, respondents were selected

based on experience, reliability and ability to analytically discuss issues at hand. About 6-

12 respondents amongst fishers, middlemen and processors participated from each site.

Questions addressed

1. List most important success factors in advancement of your business and

profitability (training, costs, credit, fish supply, markets etc.)

2. Which of these factors is a challenge/constraint to attain? Score severity of each

constraint using the Analytical Hierarchical Process (AHP) ranking method by

applying the pairwise comparison (Saaty, 1977; Wind & Saaty, 1980).

Constraints

Score of importance (Equal importance=1, Moderate

importance=3, Strong importance=5, Very strong importance=7,

Extreme importance=9)

3. What do you think can be done in order to attain success (opportunities)? Score

priority for each opportunity using the AHP ranking method

Opportunities

Score of importance (Equal importance=1, Moderate

importance=3, Strong importance=5, Very strong importance=7,

Extreme importance=9)

economic evaluation of the small-scale marine …

Documents