Analysis of the Honey Market Chain. The Case of Chena Woreda, Kaffa Zone, Southern Ethiopia

TABLE OF CONTENTS

DEDICATION

BIOGRAPHICAL SKETCH

ACKNOWLEDGEMENTS

ACRONYMS AND ABBREVIATIONS

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF TABLES IN THE APPENDIX

LIST OF FIGURES IN THE APPENDIX

ABSTRACT

1. INTRODUCTION
1.1. Background of the Study
1.2. Statement of the Problem
1.3. Research Questions
1.4. Objective of the Study
1.5. Significance of the Study
1.6. Scope and Limitations of the Study
1.7. Organization of the Thesis

2. LITRATURE REVIEW
2.1. Definition of Terms and Basic Concepts of Market Chain Analysis
2.2. Honey Production and Marketing in Ethiopia
2.3. Approaches to the Study of Agricultural Marketing
2.4. Analytical Framework for the Study
2.4.1. Structure, conduct and performance (SCP) of market
2.4.1.1. Structure of the market
2.4.1.2. Conduct of the market
2.4.1.3. Performance of the market
2.4.2. Factors affecting volume of market supply
2.4.3. Determinants of market outlet choices
2.5. Empirical Evidences
2.5.1. Empirical literature on S-C-P
2.5.2. Empirical studies on the determinants of market supply
2.5.3. Empirical studies on the determinants of market outlets choices
2.6. Conceptual Framework of the Study

3. RESEARCH METHODOLOGY
3.1. Description of the Study Area
3.3. Sampling Procedure and Sample Size
3.4. Methods of Data Analysis
3.4.1. Descriptive analysis
3.4.2. Econometric analysis
3.4.2.1. Determinants of honey market supply
3.4.2.2. Determinants of honey producers’ market outlets choice
3.5. Definition of Variables and Hypothesis
3.5.1. Dependent variables
3.5.2. Independent variables
3.5.2.1. Independent variables for volume of honey marketed
3.5.2.2. Independent variables for honey market outlets choice
3.6. Model Diagnosis

4. RESULTS AND DISCUSSION
4.1. Descriptive Statistics Results
4.1.1. Demographic and socio-economic characteristics of producers
4.1.2. Honey production characteristics of the sample households
4.1.3. Institutional services
4.1.4. Market related issues
4.1.5. Demographic and socio-economic characteristics of sampled traders
4.1.6. Demographic characteristics of sampled consumers
4.2. Honey Market Chain Actors, their Roles and Marketing Channels
4.2.1. Honey market chain actors and their roles
4.2.2. Honey marketing channels
4.3. Structure, Conduct and Performance of the Honey Market
4.3.1. Honey market structure
4.3.1.1. Degree of market concentration
4.3.1.2. Degree of market transparency and barriers to entry
4.3.2. Honey market conduct
4.3.2.1. Honey producers conduct
4.3.2.2. Conduct of honey traders
4.3.3. Marketing performance
4.3.3.1. Marketing costs
4.3.3.2. Structure of production costs and profitability of honey production
4.3.3.3. Marketing margin
4.4. Econometric Results
4.4.1. Determinants of honey market supply
4.4.2. Determinants of honey producers market outlets choices

5. SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1. Summary and Conclusion
5.2. Recommendations

6. REFERENCES

APPENDIXICS

I. Appendix Tables

DEDICATION

I dedicated the manuscript of this thesis to whole members of my family especially my mother Abebach Ersumo, and my father Tarekegn Erekalo for their encouragement, prayer, support and partnership in the success of my academic career.

BIOGRAPHICAL SKETCH

The author was born from his parents Tarekegn Erekalo and Abebach Ersumo on April 16, 1991 in Elefata kebele, in West Badewacho district of Hadiya zone Southern Ethiopia. He attended his elementary education at Elefata Primary and Junior School. The author attended his secondary and preparatory education in Durame comprehensive high school.

The author joined Hawassa University in October 2009 and graduated with Bachelor of Science Degree in Agricultural Resource Economics and Management in July 2011. Soon after his graduation, he was employed by Southern Agricultural Research Institute of Bonga Agricultural Research Center and served as socio-economics researcher until he joined Haramaya University in October 2015 for his MSc study in Agricultural Economics.

ACKNOWLEDGEMENT

Almighty God deserves all praise for this work. His providence of love, mercy, grace, forgiveness, strength, health and the gift of life provided in me tolerance and endurance throughout the years of study from course work to research. I have always been protected and safely shielded under His Mighty Name.

I express my genuine gratitude to my major advisor Dr. Jema Haji, for his consistent guidance, encouragement and critical reviews while developing the proposal, and for giving me constructive and valuable comments and suggestions that shaped this thesis. It is my great pleasure to extend my appreciation and gratefulness to my co-advisor, Dr. Bosene Tegegne, for her positive influence on my thesis research, devoting her precious time, warm welcome at her office, and pertinent comments at each level of the analysis.

I owe thanks to Southern Agricultural Research Institute (SARI) for giving me chance to pursue MSc study and financing the research project. It is also a great pleasure to extend my appreciation to staff members of Bonga Agricultural Research Center (BARC) for their facilitation of the study process and encouragement.

I am very much indebted to all enumerators who assisted me during my data collection. I also would like to extend my thanks to all staff members of Chena district Livestock and Fishery office that helped me at various levels during my research work. I also feel great to express my thanks to the respondents who participated in the study for sparing their precious time and for responding positively to the lengthy interview schedule.

Finally, I am greatly indebted to my parents, my brothers and sisters for their special love and prayer throughout my life. My sincere appreciation and thanks also go to my colleague Kibreab, Mestafe, Zelelam, Engida and Assefa for the remarkable memories and constant moral support during the study period.

My God bless you all!!

ACRONYMSAND ABBREVIATIONS

Abbildung in dieser Leseprobe nicht enthalten

LIST OF TABLES

1. Sample distribution of honey producers in selected kebeles

2. Demographic characteristics of sampled honey producers

3. Sources of income by sampled honey producers per year

4. Experience, number of hives owned and quantity of honey produced

5. Frequency of extension contact and amount of credit received

6. Cooperative membership and average quantity of honey supplied in Kg

7. Access to market information, price of honey and distance to market

8. Demographic characteristics of sampled traders

9. Financial capital of sampled traders

10. Sources of working capitals and loans of sampled traders

11. Demographic characteristics of consumers

12. Concentration ratio of sample traders

13. Market information, lack of capital and licensing procedure

14. Production and selling strategies of producers

15. Buying and selling behavior of traders

16. Honey average marketing costs for different marketing agents

17. Structure of honey production costs and profitability by type of beehives used

18. Honey market margin for different channels

19. OLS estimate of determinants of honey market supply

20. Multivariate probit estimations for determinants of producers outlets choice

LIST OF FIGURES

Figure 1: Conceptual framework of the study

Figure 2: Map of the study Area

Figure 3: Types of bee hives used by the sample honey producers

Figure 4: Honey marketing channels for different market actors

LIST OF TABLES IN THE APPENDIX

1. Test for multicollinearity of explanatory variables

2. Specification /omitted variable test result

3. Heteroscedasticity test result

4. Questionnaires

LIST OF FIGURES IN THE APPENDIX

1. Norma probability plot for residuals

2. Boxplot for volume of honey supplied to check outliers

Analysis of honey Market Chain: The Case of Chena Woreda, Kaffa Zone, Southern Ethiopia

ABSTRACT

This study was initiated to identify honey marketing channels, actors and their roles; to analyze the structure, conduct and performance of honey markets; to identify factors affecting volume of honey marketed and producers’ market outlets choices in Chena woreda of Kaffa zone, Southern Ethiopia. Data from a total of 154 sample honey producers from three randomly selected honey producing kebeles, 30 traders and 20 consumers were collected and analyzed. SCP model for structure conduct and performance analysis, multiple linear regression model for determinants of market supply and MVP model for outlet choice decision determinants analysis were used. The identified honey market chain actors in the study area include producers, cooperatives, collectors, retailers, wholesalers, processors and consumers. Seven honey marketing channels were identified in the study area with major share of volume of honey marketed goes to marketing through channel VII. The result from analysis of market concentration indicates that the structure of honey market in Chena district is weak oligopoly with four largest honey traders’ concentration ratio of 33.63%. In line with degree market of concentration, lacks of market information for producers, problems in licensing and unfair competition with the unlicensed traders are identified to be the major entry barriers to honey marketing; as the result honey market in the district shows some deviations from competitive market norms. Market conduct shows that price of honey was set by traders and producers are price takers. Market performance analysis based on marketing margins shows that all the actors generated positive gross profit. The result reveals that the total gross marketing margin is highest in channel V (38.55%) and lowest in channel II (22.67%). The results also showed that the maximum producers’ share from the total consumers’ price is highest in channel II (77.33%) and lowest in channel V (61.45%). Multiple linear regression model result reveals that beekeeping experience, hive types used, number of beehives owned, extension contact and cooperative membership positively and significantly affected honey market supply while distance from nearest market negatively and significantly affected it. The simulation result of MVP model shows that 69.05%, 73.4%, 61% and 46.9% probability for choice retailers, cooperatives, collectors and consumer outlets, respectively and with 10.96% probability of jointly choosing the four outlets simultaneously. The MVP result for honey producers’ market outlet choices also reveals that quantity of honey sold, frequency extension contact, beekeeping experience, distance to market, access to market information, cooperative membership and trust in buyers significantly determined honey producers market outlet choices in the study area. To enhance volume supplied with appropriate market outlet choices which in turn increase producers income generated from honey, all concerned bodies need to focus on experience sharing with experienced households, capacity building through training on improved honey production, increasing access to improved beehives, improving poor road facility, strengthen financial capacity of existing and establishment of additional beekeepers cooperatives.

Key words: Honey; Marketing margin; Multiple linear regression; Multivariate probit; Chena woreda

1. INTRODUCTION

1.1. Background of the Study

Beekeeping is one of the oldest farming practices in Ethiopia as result of its forests and woodlands contain diverse plant species that provide surplus nectar and pollen to foraging bees (Workneh, 2011). The country has comparative advantage for beekeeping due to its favorable natural resource endowment for the production of honey and wax (MoA and ILRI, 2013). Owing to its varied ecological and climatic conditions, Ethiopia is home to some of the most diverse flora and fauna with the largest honey producer in Africa (Nuru, 2007).

Ethiopia is among the major producer of honey both in Africa and in the world. For instance in 2013 the country produced about 45 thousand tones which accounted about 27% and 3% of African and World honey production respectively which makes the country the largest producers in Africa and the tenth in the world (FAOSTAT, 2015). According to CSA (2015), the total volume of honey production in Ethiopia was about 49 thousand tones.

Apiculture is a promising off-farm enterprise, which directly and indirectly contributes to smallholder’s income in particular and it accounts 1.3% of agricultural GDP of the country (Demisew, 2016). It has been reported that annually an average of 420 million Ethiopian Birr is obtained from the sale of honey (MoA and ILRI, 2013). The subsector is also creating job opportunities in both rural and urban areas through organizing jobless urban and landless rural youth and women to involve in them in bee equipment production and beekeeping activities (Chagwiza, 2014).

According to USAID (2012), about 10% of the honey produced 2011/12 in the country is consumed by beekeeping households. The remaining 90% is sold for income generation; of this amount it is estimated that about 70% is used for brewing tej and the rest is consumed as table honey. Domestic honey consumption is increasing due to highly increasing demand for tej and birzi increased consumption of processed table honey in most urban areas and increased demand for honey in the local industries (Gemechis, 2015).

Despite the long tradition of beekeeping in Ethiopia, being the leading honey producer and the availability of huge potential, the production system of the sector is traditional (Miklyaev et al., 2014). According to CSA (2014), 96% of the hives are reported to be traditional and 91% of the total honey produced comes from traditional hives. This results in low productivity, which in turn result the low contribution of the sector to agricultural GDP of the country. Proper understanding of the performance of the market system apparently required for making market orientation of product (CIAT, 2004).

Southwestern part of Ethiopia has diversified types of forest vegetation suitable for beekeeping, as a result large volume of honey was produced annually. Despite the high honey production in the study area, due to poor infrastructural facility, poor market information and long market chain there is no ready market attracting beekeepers (Kassa et al., 2017). According to Kifle et al. (2015) knowledge on how marketing routes and systems could contribute to the household income and the implications of these for national and international trade in apiculture is the way to design any policy or institutional innovation to improve marketing for the benefit of the poor. Therefore, this study was conducted to analyze market chain of honey in Chena woreda.

1.2. Statement of the Problem

Southwestern part of Ethiopia has great potential for beekeeping activities; due to the presence of dense natural forest with different species of flora and fauna which are used as pollen and nectar source for bees and suitable environmental conditions for bee colony and the production of honey (Yoshimasa, 2014). Kaffa zone is highly suitable for beekeeping and large volume of honey is produced annually in Southwest part of the country (Nuru, 2007). However, sparsely populated rural areas, and poor infrastructural facility are physical barriers to accessing markets; lack of negotiating skills, lack of collective organizations and lack of market information are impediments to market access (Kassa et al., 2017).

Chena woreda is believed to have diversified types of vegetation; and cultivated crops and expected to be one of the areas that have considerable potential for beekeeping activities and honey production in Kaffa zone (Awraris et al., 2012). However, honey production is very traditional which is practiced mainly by hanging traditional hives on tall trees in the dense forest far from human settlement areas. Beekeepers produce honey using traditional methods and selling their honey products at the local market. Though the honey production is traditional, currently due some interventions by government and non-government organizations the beekeepers in the woreda are using improved beehives that boost volume of honey produced as the result the woreda is high honey producer in the zone (KZLFD, 2016).

Despite high honey production, the market supply of honey is low as compared its potentiality due to some socioeconomic, production, market and institution related factors. According to Kassa et al. (2017), honey producers in the study faced marketing problem due to remoteness of some kebeles, low farm-gate prices and long market chain which results low level market participation. Additionally, honey producers in Chena district are widely characterized by limited marketing linkage which emanates from physical barrier to accessing market, low bargaining power, faraway from weather road results inability to force local collectors and trader’s price setting and exploitation at farm get level. The market of the area is dominated by conventional system and honey producers are forced to sale directly for conventional transaction root like; collectors and unlicensed traders which they do not get premium price for their produce.

Improved information and marketing facility enables farmers to plan their production more in line with market demand, to schedule their harvest at the most profitable time, to decide which market to sell their produce to and negotiate on a more even footing with traders (CIAT, 2004). According to MoA and ILRI (2013), enhancing the ability of beekeepers to reach markets and actively engaging them is one of the most pressing development challenges. Without having convenient marketing conditions, the possible increment in output and rural incomes resulting from the introduction of improved production technologies could not be effective.

Even though honey is economically and socially important, the research on apiculture on the study area has largely focused on biophysical aspects such as yield enhancement, production system analysis. Besides, previous studies on honey in Kaffa zone have concentrated on production system (Nuru, 2007; Awraris et al., 2012; Gallmann and Thomas, 2012; Awraris et al., 2015), information on identification of honey market type and marketing systems look like; determinants of volume of supply to market and producers’ market outlets choices in the woreda is lacking where the great potential of honey production exists. Therefore, there is a need to employ a market chain approach to fully understand and make an intervention to resolve the problem of honey marketing at all stages by identifying major honey market chain actors and marketing channels; analyzing structure, conduct and performance of honey market and by identifying determinants of honey marketed surplus and producers’ market outlets choice in Chena woreda.

1.3. Research Questions

The study attempted to answer the following research questions:

1. What are the major honey marketing channels and who are the honey market chain actors and their roles in Chena woreda?
2. How is the structure-conduct-performance of honey market in the study area?
3. What factors influence the volume of market supply of honey in the study area?
4. What factors influence honey producers’ market outlets choice in Chena woreda?

1.4. Objective of the Study

The general objective of this study is to analyze honey market chain in Chena woreda.

The specific objectives of the study are:

1. To identify the major honey marketing channels, market actors and their roles;
2. To analyze the structure, conduct and performance of honey market;
3. To identify factors that determine market supply of honey; and
4. To identify determinants of honey producers’ market outlets choice in Chena woreda.

1.5. Significance of the Study

Critical analysis of marketing is very important before launching and implementing market development issues. Improved information and marketing facilities enable farmers to plan their production more in line with market demand, to decide which markets to send their produce to and negotiate on a more even footing with traders. Also it enables traders to move produce profitably from a surplus to a deficit market and to make decisions about the economics of storage, where technically possible. Therefore, beekeepers (producers), traders, cooperatives, government and non-government organizations, which have interest in improving honey marketing system are expected to benefit from the results of this study.

1.6. Scope and Limitations of the Study

The study seeks only to examine honey market chain by analyzing market margins among the actors within the marketing chains, honey market structure and conduct, and factors that determine honey marketed supply and market outlet choice using cross-sectional data which are collected from three kebeles of Chena woreda. At woreda market levels, role of actors in the channel and bargaining characteristics of producers, buying and selling strategies of producers and traders in the marketing channel were assessed. However, this study was conducted only in Chena, one of the 11 woredas of Kaffa zone due to time and budget limitations. Therefore, the result and data obtained from this study cannot be generalized to other woredas of the zone because their socio-economic conditions different.

1.7. Organization of the Thesis

The thesis has been organized under five chapters. Chapter one pinpoints background, statement of the problem, research questions, objectives, significance of the study, scope and limitations of the study and organization of the thesis. Chapter two presents review of theoretical and empirical evidences related to the study. Chapter three discusses research methodology (description of the study area, data types and sources, methods of data collection, sampling techniques and methods of data analysis) of the study. Chapter four presents’ descriptive and econometric results and discussed in detail. Chapter five summarizes the main findings of the study and draws conclusion and recommendations.

2. LITRATURE REVIEW

This chapter highlights definitions of basic related terms, basic concepts of market chain, honey production and marketing in Ethiopia, analytical framework for the study, empirical evidences on the determinants of market supply and producers’ market outlets choice and conceptual framework for the study.

2.1. Definition of Terms and Basic Concepts of Market Chain Analysis

Beekeeping: It can be defined as the process of keeping honeybees to produce honey and bee wax for food, income generation and or medicinal purpose.

Honey producers: These are households who involved in beekeeping to produce honey for sale and consumption who owned minimum of five bee hives.

Market: It is the collection of buyers and sellers through their actual or potential interactions determine the price of a product or set of products. The concept of market is linked to the degree of communication among buyers and sellers and the degree of substitutability among goods. Market means a social institution that performs activities and provides facilities for exchanging commodities between buyers and sellers. Economically the term market refers not to a place but to commodities, buyers and sellers, hence they are freely interacted with one another (Kotler and Armstrong, 2003).

Marketing: It is the process of planning and executing as a social and managerial process by which individuals and groups obtain what they want and need through creating and exchanging products and value with others (Lunnd et al., 2004). Kohls and Uhl (1985) forwarded a broader definition that marketing is the set of economic and behavioral activities that are involved in coordination the various stages of economic activities from production to consumption pricing, promotion and distribution of idea, goods and services to create exchange that satisfy individual and organizational goals.

Marketing channels: It is the paths through which products pass from producers until it reaches on the hand of consumers (Mendoza, 1995). Collectors, wholesalers, processors, retailers and other sources used in getting the product to the hand of final consumers are in the classification of marketing channels. Marketing channel can be short or long, depending on the type and quality of the product marketed, available marketing services, and prevailing social and physical environment (Islam et al., 2001).

Chain actors: These are individuals or groups who involve directly or indirectly in the chain in production and delivering the products.

Market chain: The term used to describe the various links that connect all the actors and transactions involved in the movement of agricultural goods from the producer to the consumer (CIAT, 2004). Market chain analysis, therefore, identifies and describes all points in the chain (producers, traders, transporters, processors, consumers), prices in and out at each point, functions performed at each point/ who does what?, market demand/ rising, constant, declining, approximate total demand in the channel, market constraints and opportunities for the products.

Market supply: It refers to the amount actually taken to the markets irrespective of the need for home consumption and other requirements where as the market surplus is the residual with the producer after meeting the requirement of seed, payment in kind and consumption by producers at source. In order to describe market supply words like marketable surplus and marketed surplus are usually used (Allen et al., 2008).

Marketable and marketed surplus: Marketable surplus is the quantity of the product left out after meeting the farmers’ consumption and utilization requirements for kind payments and other obligations such as gifts, donation or charity. Thus, marketable surplus shows the quantity left out for sale in the market. The marketed surplus shows the quantity actually sold after accounting for losses and retention by the farmers, if any and adding the previous stock left out for sale. Thus, marketed surplus may be equal to marketable surplus, it may be less if the entire marketable surplus is not sold out and the farmers retain some stock and if losses are incurred at the farm level or during transit (Kohls and Uhl, 1985).

2.2. Honey Production and Marketing in Ethiopia

Honey production in Ethiopia is characterized by traditional beekeeping practice exercised for more than thousands of years (Beyene et al., 2014). Traditional beekeeping is mostly practiced with different types of traditional hives that are very much diversified in shape, volume and the materials used depending on the cultural differences and the local materials available for construction (Gemechis, 2014). The productivity of traditional hives is extremely low and the average yield is only about 5–8 kg per colony per annum (USID, 2012). However, with this existing practice the annual honey production in the country is increasing and has reached quite higher than 53 thousand tons in 2012 (CSA, 2013).

Currently, improved beehives and the locally made “chefeka” hives and frame box hives are being highly disseminated to the beekeepers by different GOs and NGOs (Gemachis, 2015). The annual average of the honey yield obtained from “chefeka” hive is about 17kg, while that of the frame hive is about 26kg (Demisew, 2016). On the other hand, in high potential areas of northern and southwestern parts of the country more than the average yield from well managed colonies is commonly reported (MoA and ILRI, 2013). In Ethiopia, there are generally two honey harvesting seasons: the major one that lasts from October to November and the secondary one from April to June. However, in addition to these major harvesting periods, there are many small harvesting periods which depend on the type of flowering plants and rainfall patterns in different agro ecologies (Nuru, 2007).

The domestic honey market starts at the smallholder beekeepers level, who mainly sell crude honey to collectors in the nearest town/village markets (Desalegn, 2011). According to MoA and ILRI (2013), beekeepers, honey collectors, retailers, tej brewers, processors and exporters are identified as the key actors in the value chain of the honey sub-sector. Three principal channels were identified in the value chain of the sub-sector in Ethiopia. (Chagwiza, 2014). These are tej brewery channels, honey processing and exporting channels and beeswax channels. These channels are complex and interconnected that implies absence of organized marketing channel and lack of formal linkages among the actors. Most of the harvested honey goes through tej brewery channels and the producers are indicated as price takers.

To strengthen the honey marketing chain, beekeepers form honey producing and marketing cooperatives to cope with the market challenge they face. The cooperatives collect crude honey from their members and sell the semi processed honey to processing companies and other intermediaries who buy in bulk and retail. However, in many cases the cooperatives lack proper collection, storage and transportation facilities and hence compromise the quality of the honey. They also have low business concept (market information gathering and analysis, pricing, promotion) to be competitive (Chagwiza, 2014).

Despite severe deforestation throughout many regions of the country, the southwest part of Ethiopia still contains many nectar and pollen producing plants suitable for beekeeping (Chala et al., 2013). According to Yoshimasa (2014), beekeeping is dominated by traditional methods and the quality of honey is remaining poor regardless of the potential in this part of the country. The beekeepers hang their hives on the top of a tree in the forest far from their living home that is made from tree barks, reeds, logs, grounds and clay pots.

Regarding to honey marketing in south-western part of the country, the major buyers are cooperatives, local brewers, collectors, retailers, honey and wax processing industries in the nearby markets (Gallmann and Thomas, 2012). The producers of southwest Ethiopia do not process honey before sale. However, about 45 % of the farmers strain the crude honey by simple drainage to remove the beeswax and any floating impurities simply using their hands (Mullubrahan, 2014).

Despite all the benefits that honey can bring to the beekeepers in south western Ethiopia, the producers are tackling with a number of challenges and constraints that can potentially hamper the honey production and the economic contribution it brings to their livelihoods. Low price of honey, lack of access to credit, lack of support, private trader cheats on price and weight, lack of capital for an organization to buy all our honey, transport problem, fewer buyers and lack of access to timely information are some of the challenges that producers faces in southwest Ethiopia (Kassa et al., 2017).

2.3. Approaches to the Study of Agricultural Marketing

Different circumstances involved in the demand and supply of agricultural products, and the unique product characteristics, require a different approach for analyzing agricultural marketing problems (Kohls and Uhl, 1985). The major and most commonly used approaches are functional, institutional, commodity behavioral and SCP.

Functional approach: In this approach we took all the basic marketing activities (functions) that have to be performed in the agricultural commodities and at the marketing of inputs in to agricultural production. Functional approach studies marketing in terms of the various activities that are performed in getting farm product from the producer to the consumer. These activities are called functions (Crammer et al., 1997).

Institutional approach: This approach focuses on the description and analysis of different organizations engaged in marketing (producers, wholesalers and retailers) and pays special attention to the operations and problems of each type of marketing institution. The institutional analysis is based on the identification of the major marketing channels and it considers the analysis of marketing costs and margins (Mendoza, 1995). An institutional approach for the marketing of agricultural product should be instrumental in solving the three basic marketing problems, namely consumers’ demand for agricultural products, the price system that reflects these demands back to producers and the methods or practices used in exchanging title and getting the physical product from producers to consumers in the form they require, at the time and place desired (Kotler and Armstrong, 2003).

Commodity approach: In this approach a specific commodity or groups of commodities are taken and the functions and institutions involved in the marketing process are analyzed. This approach focuses on what is being done to the product after its transfer from its original production place to the consumer (CIAT, 2004). It helps to pinpoint the specific marketing problems of each commodity as well as improvement measures. The approach follows the commodity along the path between producer and consumer and is concerned with describing what is done and how the commodity could be handled more efficiently.

Behavioral approach: In this approach either a particular marketing firm or an organization of firms, such as the marketing channel, can be viewed as a system of behavior. Each is composed of people who are making decisions in an attempt to solve particular problems. If these problems and their behavioral systems for solving them can be classified and a greater understanding of changes that may be forthcoming can be obtained. In either the firm or the organization of firms’ four major types of problems with their associated behavioral systems can be identified: input-output system, power system, communication system, and adaptive behavior system (Crammer et al., 1997).

Structure, conduct and performance (S-C-P) paradigm: The basic view of this approach is that, given certain basic conditions, the structure of an industry or market determines conduct of buyers and sellers which influence its performance. The basic conditions refer to characteristics which are exogenous to the market, for example infrastructure, legal and policy environment and available technology. Efficiency factors can be evaluated by examining marketing enterprises for structure, conduct and performance (Abbott and Makeham, 1981). Both the functional and institutional approaches are useful in analyzing the existing marketing activities. However, the marketing process is continually changing in its organization and functional combinations are a major problem to understand and predict change. SCP approach is one of the most common methods to study marketing systems which analyzes the relationship between functionally similar firms and their market behavior as a group. It is mainly based on the nature of various sets of market attributes and relations between them and their performance (Scarborough and Kydd, 1992). Thus, SCP approach has been used for this study as a guideline to study honey marketing system in Chena district.

2.4. Analytical Framework for the Study

2.4.1. Structure, conduct and performance (SCP) of market

The Structure-conduct-performance approach was served as a tool to evaluate the performance of the marketing system. The approach distinguishes between three related levels; the structure, conduct and performance of the market (Cramer et al., 1997).

2.4.1.1. Structure of the market

Market structure is defined as characteristics of the organization of a market which seems to influence strategically the nature of competition by pricing behavior within the market (Kohls and Uhl, 1985). Structural characteristics may be used as a basis for classifying markets. The four salient aspects of market structures include the degree of seller concentration, the degree of buyer concentration, the degree of product differentiation, and the condition of entry (Kotler and Armstrong, 2003). Based on these aspects markets may be perfectly competitive, monopolistic, oligopolistic or monopoly.

Market concentration: It shows the number and relative size distribution of buyers/sellers in a market (Abbott and Makeham, 1981). It is generally believed that higher market concentration implies non-competitive behavior and thus inefficiency. The common measures are Concentration Ratio, Gini-coefficient and Hirshman Herfindahl Index.

Concentration Ratio (CR): It indicates the relative size of k-large firms in relation to their industry as a whole. It shows whether an industry is dominated by a few large firms or many small firms. CRk is also used as an indicator of the relative size of firms in relation to the industry as a whole (Kohls and Uhl, 1985). The problem associated with this index, there is no justification for focusing on the market shares of the top four firms is somewhat arbitrary.

Gini-Coefficient (GC): It is an alternative concentration measure that has some similarities to the concentration ratio. It is done based on Lorenz curve and the line at 45 degree thus represents perfect equality of market shares. Gini-coefficient ranges from zero (perfect equality) to one (perfect inequality). Gini-coefficients provide useful information based on Lorenz curve shapes, a problem arises when Lorenz curves cross. It is problematic whether we can in this special case claim that a higher coefficient means a more unequal distribution. The other problem associated with Gini-coefficients is that it favors equality of market shares without regard to the number of equalized firms.

Hirschman Herfindahl Index (HHI): The index considers the number and size distribution of all firms and also it takes into account all points on concentration curve. A very small index indicates the presences of many firms of comparable size, one or near one, suggests that the number is small and/ or that they have unequal shares in the market (Scarborough and Kydd, 1992). HHI includes all firms in the calculation. On the other hand, this needs more data to be collected. The problem of HHI is lack of information about small firms. Thus, it is difficult to obtain the market share of every firm that operates in a single market.

Unlike HHI and GC, CR4 ratio is measures relative market concentration for top four traders that influence the holy market of commodity (Abdulrazak et al., 2013). Thus, concentration ratio was used in the analysis to measure market concentration of honey market in the district.

2.4.1.2. Conduct of the market

Conduct of the market refers to the strategies that firms pursue with regard to price, product and promotions, and the linkages/relationships between and among firms. The use of regular partners, long-term relations with clients and suppliers, the use of intermediaries and trade within personalized networks are common the strategies (Abah et al., 2015).

According to Kohls and Uhl (1985), conduct is pattern of behavior which enterprises follow in adopting or adjusting to the market in which they sell or buy, in other words the strategies of the actors operating in the market. Market conduct deals with the behavior of firms that are price-searchers are expected to act differently than those in a price-taker type of industry (Cramer and Jensen, 1982).

However, there are no agreed upon procedures for analyzing the elements of market conduct. More specifically, it cover the following topics: the existence of formal and informal marketing groups that perpetuate such practice; formal and informal producer groups that affect bargaining power; the availability of price information and its impact on prevailing price; the distance from the major market and its impact on price; and the feasibility of utilizing alternative market outlets (Hugo et al., 2016). In this study, the availability of information about purchasing and selling strategies for producers and traders, adjusting to the market in which they sell or buy and practice of storage and processing were used to measure conduct of honey market.

2.4.1.3. Performance of the market

Market performance refers to economic results: product suitability in relation to consumer preferences (effectiveness); rate of profits in relation to marketing costs and margins; price seasonality and price integration between markets (Kohls and Uhl, 1985). One can imagine causal relations starting from the structure, which determine the conduct, which together determine the performance (Cramer et al., 1997). The performance of market for a particular commodity can be evaluated by analyzing costs and margins of marketing agents in different channels.

Marketing costs

Marketing costs are the expenditure incurred by various market intermediaries from the time when commodity leaves the farm until it reaches the consumers (Wisdom et al., 2014). It is cost of performing various marketing functions, which are required to transfer a commodity from the place of production to the ultimate consumers. Such costs are necessarily incurred to create form, time, place, and possession utilities in the products to make them marketable. To determine whether the marketing margins (amount received by the different marketing agencies for providing their services) were reasonable, it was essential to calculate the 'costs' of these agencies. The costs incurred by the producers and other marketing intermediaries have impact on prices as well as on the margins of the market intermediaries (Wandschneider and Yen, 2006).

Marketing margins

A marketing margin is the percentage of the final weighted average selling price taken by each stage of the marketing chain (Mendoza, 1995). The total marketing margin is the difference between what the consumer pays and what the producer/farmer receives for his product. In other words it is the difference between retail price and farm gate price (Cramer et al., 1997).

The marketing margin in an imperfect market is likely to be higher than that in a competitive market because of the expected abnormal profit. A wide margin means usually high prices to consumers and low prices to producers. But marketing margins can also be high, even in competitive market due to high real market cost (Wolday, 1994). Marketing margin can be a useful descriptive statistics if it is used to show how consumers’ expenditure is divided among market participants at different levels of the marketing systems (Jema, 2008). It is commonly used measure for a performance of marketing system.

2.4.2. Factors affecting volume of market supply

Different models can be employed to analyze the determinants market supply. The commonly used ones are the well-known multiple linear regression, Tobit and Heckman’s sample selection models. If some households may not prefer to participate in a particular market in favor of another, while others may be excluded by market conditions Tobit or Heckman models were used to analyze market supply. By using Tobit model, the volume of market supply can be analyzed by clustering the respondents’ in to supplier and non-suppliers. If censored regression is applied, the model estimates are biased because of there is no clustering honey producers as all of households supply there product to market (Wooldridge, 2010).

Like Tobit model, sample selection model (Heckman) was used in some cases when sample selection biased occurred in addition to clustering of respondents. The first stage of the Heckman model a ‘participation equation’, used to construct a selectivity term known as the ‘inverse Mills ratio’ which is added to the second stage ‘outcome’ equation that explains factors affecting volume of product marketed and estimated by using ordinary least square (Wooldridge, 2010). However, in the study area all honey producers participate in the market by supplying their produce and therefore there no clustering of the honey producers in honey market participant and non-participant. Thus, for this study multiple linear regression model was used to identify determinants of honey marketed supply.

2.4.3. Determinants of market outlet choices

A farmer’s decision to select a given market or not is made by evaluating the return in expected utility, taking into account the related investment and transaction costs (Urquieta, 2009). Thus, farmers will select the market outlets that show the most positive profit.

Econometric models such as multivariate probit/logit, multinomial probit/logit, conditional or mixed or nested logit are useful models for analysis of categorical choice dependent variables. The choice decision over the different groups of market outlet can be modeled in two ways; by either multinomial or multivariate regression analysis. One of the underlying assumptions of multinomial models is the independence of irrelevant alternatives that is error terms of the choice equations are mutually exclusive (Greene, 2012). Thus, multinomial models are appropriate when individuals can choose only one outcome from among the set of mutually exclusive, collectively exhaustive alternatives.

However, the choices among the market outlet are not mutually exclusive as honey producers are selling honey at more than one market outlets at the same time and therefore the random error components of the market outlets may be correlated (Arinloye et al., 2014). Therefore, the researcher considers using a multivariate probit model which allows for the possible contemporaneous correlation in the choice to access the four different market outlets simultaneously. Hence, multivariate probit model (mvprobit) was applied to identify determinants of honey producers’ market outlets choice.

2.5. Empirical Evidences

2.5.1. Empirical literature on S-C-P

Assefa (2009) conducted study on honey market chain analysis at Atsbi Wemberta District in Tigray region, Ethiopia. The study identified producers, honey collectors, retailers, processors and final consumers of the product as honey marketing participants in the study area. In addition, by using marketing margin analysis he found that reveals that 17% of total gross marketing margin was added to honey price when it reaches the final consumer. The study result of sample market honey traders’ concentration ratio CR4 shows that 35.82 percent of honey was controlled by top four honey traders in the study area.

The study conducted by Betsalot (2012) in Ada’a woreda of East Shoa zone of Oromia region on honey value chain analysis used marketing margin analyze to evaluate honey market performance. The result reveals that producer’s marketing margin constitute about 82.35% of the final consumer’s price and 12.5% share of the marketing margin goes to the processors, who collect the honey for their own processing. The study also identified actors participating in the honey value chain such as beekeepers, local honey collectors, cooperatives, local brewery (tej) houses, wholesalers, honey processors, beeswax processors, retailers, input suppliers and exporters.

Samuel (2014) conducted a study using marketing margin analysis on performance of honey marketing system in southern Ethiopia with special emphasis on Sodo Zuria woreda and found that showed that, the total gross marketing margin was highest in longest channel which was 83.6% of the consumers’ price and from all honey traders, processors have got the highest gross marketing margin which accounted for 50%. Regarding honey market structure at the district, the CR4 measures of showed that the top four were controlled 58.84% of the honey market which implies strong oligopoly.

The study by Atsbaha (2015) on value chain analysis of honey in Ahferom Woreda, Central Zone of Tigray Regional state shows that the honey traders’ concentration ratio was found to be 76.86 percent which indicates the presence of strong oligopoly market structure in the study area. This implies that the market is controlled by few traders. Further the study reveals that without considering producers to consumers’ channel, producers share was highest in channel V(beekeeping cooperatives- enticho retailers-consumers) and lowest in channel III(Individual-beekeepers-local collectors-enticho –retailers-consumers) at the percent of 87.5 and 67.1, respectively.

2.5.2. Empirical studies on the determinants of market supply

A number of studies were conducted on the factors affecting supply of agricultural commodities to the market. For instance, Assefa (2009) employed multiple linear regression model to identify factors affecting market supply of honey in Atsbi Wemberta district Tigray National Regional State. Accordingly, the study found that education level of household, experience in beekeeping, extension access, quantity honey of produce, price of honey, access to credit and distance to the nearest market significantly affect market supply of honey.

A study conducted by Getachew (2009) used Heckman two stage model to identify determinants of honey market supply in Burie woreda of West Gojjam zone, Ahmara National Regional State. The model result shows that income from farm and non-farm activities, beekeeping experience, beekeeping training, apiary visit, and access to improved beekeeping equipment’s are the major determinants of market supply of honey at household level significantly and positively. Similarly, Betselot (2012) employed Heckman maximum likelihood method to identify factors affecting volume of honey sale in Ada’a woreda, East Shoa zone of Oromia National Regional State. The result reveals that sex of the household, number of beehives owned, type of beehive used, and credit access positively and significantly influence volume of honey marketed in the study area.

Pandey et al. (2013) conducted study on an economic study of marketed surplus of chickpea in Rewa District of Madhya Pradesh using cross sectional data by adopted multiple linear regression. The study came up with the finding that yield/ha, size of family, production of chickpea, size of holding and income from other sources variables are significantly affected on marketed surplus.

Samuel (2014) used multiple linear regression model to identify factors that determine volume of honey marketed by the sample households in Sodo Zuria district, Southern Ethiopia . He found that age of household, previous year price, family size, beekeeping training, agro-ecology, literacy status of household, size of livestock holding and total numbers of modern hives used in production by household heads significantly determine volume of honey marketed.

A study by Nega et al. (2015) employed multiple linear regression model to identify factors that determine banana, mango and avocado market supply of the producers in Tembaro district of Kembata Tembaro zone, South Ethiopia. The model result indicates that price, access to extension service, distance to the market, access to market information and quantity produced affected mango and avocado market supply whereas active family size, distance to the market, quantity produced, access to market information, and price for banana affected banana supply significantly.

Bizualem et al. (2015) used multiple linear regressions to identify determinants of marketed surplus of coffee by smallholder farmers in Jimma zone, Ethiopia. The result of OLS regression shows that sex, coffee farming experience, access to credit, adequacy of extension services, attractiveness of coffee price, cooperative membership and non and/or off farm income are significant positive factors affecting marketed surplus of coffee.

2.5.3. Empirical studies on the determinants of market outlets choices

A number of studies have been done that have revealed factors influencing marketing channel choice decisions. Anteneh et al. (2011) employed Tobit model and identified factors affecting marketing channels choice of coffee farmers in Sidama zone. The finding of their study revealed that younger coffee farmers, with better education, higher proportion of off-farm income to total income, and higher proportion of land allocated to coffee tend to diversify their market choices by selling to traders. Farmer delivering exclusively to the cooperatives seems to be the older ones, with a relative lower individual performance. Among non-members however, younger farmers with lower proportion of off-farm income are ones using the cooperative outlet channel through their relatives.

Kadigi (2013) employed multinomial logistic regression to identify factors influencing choice of milk outlets in Iringa and Tanga city, Tanzania. The result revealed that access to credit decreases the choice of neighbor milk market outlet. The probability of choosing to sell to milk vendors is positively influenced by the price per liter and gender. Milk vendors who offer better price are likely to increase dairy farmers’ willingness to market their milk produce through the milk vendor market outlet, which are more rewarding than milk collection centers.

Geoffrey (2015) conducted a study on factors affecting the choice of marketing outlets among small-scale pineapple farmers in Kericho country. The result of multinomial logistic regression revealed that gender, group marketing, pineapple produce, price information and vehicle ownership significantly influenced the choice of pineapple marketing outlets. The result confirmed that price information had a positive influence on the choice of local market outlet while vehicle ownership positively and significantly influenced the choice of both local and urban market outlets.

A study by Atsbaha (2015) used multinomial logit model in an attempt to determine factors affecting honey marketing channels in Ahferom woreda of Central zone, Tigray region. The model result indicated that the probability to choose the collector outlet was significantly affected by average monthly income, previous agreement with buyers and market information. Similarly variables such as age, beekeeping experience, market information and distance to nearest market affected the choice of retailers channel compared to the consumers’ channel.

Solomon et al. (2016) used multinomial logit model to analyze factors affecting farmers’ coffee market outlet preference in coffee potential districts of Jimma zone, South-western Ethiopia The model result revealed that age of the household has negative and significant effect on the preference of farmers for formal markets and brokers and farm experience of the household has positive and significant effect on the preference of the farmer for formal market and brokers as compared to informal markets. Distance to formal coffee market has positive and significant effect on the preference of the farmer to cooperatives and brokers and it has negative and significant effect on formal markets preference.

Addisu (2016) applied multivariate probit model to investigate factors influencing market outlets choice of vegetable producers in Ejere district West Shoa zone, Oromia National Regional state of Ethiopia. The result indicates that the correlations between the potato producers choice of wholesaler and consumer outlet was negative and statistically significant, and correlation between retailer and rural collector outlet was also negative and significant. The study also shows that the potato producers in the study area have made their choice of market outlets for their produce based on quantity of potato sold, education level of households, sex of the household head, family size, farmers’ experience, distance to nearest market, current farm gate price, access of off/non-farm income, trust in traders, ownership of motor pump and area of land allocated for potato.

Kifle et al. (2015) employed multinomial logit regression model to analyze determinants of the choice of marketing channels among small-scale honey producers in Tigray region of Ethiopia. The study revealed that beekeeping experience distance from market, access to market information, grading and access to credit significantly affects choice of local market channel; while household head age, volume of honey sold, average price and access to market information significantly influence trader channel choice.

Finally, Shewaye (2016) employed multivariate probit model to analyze factors affecting haricot bean market outlets choice. The model result indicated that the outlet choice of rural assemblers was negatively influenced by number of equine owned and use of credit and positively influenced by distance to the nearest district market and distance to all weather road. Whereas consumers outlet was positively and significantly affected by number of equine owned. Finally, urban traders market outlet was positively and significantly affected by number of equine owned, membership in cooperative, access to price information and use of credit, and also negatively affected by distance to the nearest market. Therefore, based on the empirical studies reviewed multiple linear regression and multivariate probit models were adopted for this study to identify factors that affect household level of honey market supply and producers market outlet choice decision, respectively.

2.6. Conceptual Framework of the Study

This study built on the assumption that market supply and market outlet choices are made in sequence where producers initially decide how much to sell and then for whom to sell. Quantity of honey supplied to market was affected by numerous factors. They are divided in to socio-economic factors like (education level, gender, household income and ownership of resources), institutional factors like (cooperative membership, credit availability, extension service and road infrastructure), production factors like (year of experience, size and types of production inputs) and market factors like (prices of output, market information and distance to the market) (Bestalot, 2012; Samuel, 2014; Jinanus and Tamiru, 2016). These factors could have positive or negative effects, which could either improve or cause a decline in the welfare of the farmers. The main approach is that greater market supply of farmers results in more commodities being traded and this may lead to more return being obtained by the farmers. This becomes an incentive to increase production and hence a positive supply response is achieved (Sigie et al., 2013).

Farmers’ decision to sell in a given markets derives from the maximization of expected utility from these markets (Djalalou et al., 2015). As this study focused on analyzing factors affecting selection of market outlets in addition to market supply, assuming that the selection of different honey marketing outlets, as well as their simultaneous choose which is led by producers’ willingness to maximize their profit and conditional to a socioeconomic, institutional, production and market related factors (Jari and Fraser, 2012; Arinloye et al., 2014; Addisu, 2016; Shewaye, 2016). Following the literature, the researcher concluded that a honey producers’ decision to sell in two or more markets derives from the maximization of profit he or she expects to gain from this markets.

The conceptual framework in Figure 1 illustrates the interrelationships in the study, the key variables involved and how they are interrelated that is influence on honey market supply and market outlets choice decision. The increase in volume of honey supplied to market leads to producers’ to select alternative market outlets to sell their supply that maximizes their profit which in turn results in increased household income.

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Figure 1: Conceptual framework of the study

Source: Own sketch 2016.

3. RESEARCH METHODOLOGY

In this chapter, description of the study area, data types, sources and methods of data collection, sampling technique and methods of data analysis are presented.

3.1. Description of the Study Area

The study was conducted at Chena woreda, Kaffa zone of Southern Nations, Nationalities and Peoples Region of Ethiopia. The woreda was purposely chosen out of 11 woredas in the zone because of it is high honey production potential, which accommodates about 24% of the total honey production in Kaffa zone (KZLFD, 2016).

The woreda is found within the southwestern plateau of Ethiopia which is 510km and 785km far from Addis Ababa and Hawassa, respectively. The area is located at 07º18’48’’N Latitude and 036º16’25’’ E Longitude and at altitude of 2020 m.a.s.l. The district is bordered on the south by the Bench Majji zone, on the west by Bita, on the north by Gewata, on the northeast by Gimbo and on the east by Decha woredas (Belachewu et al., 2015). According to CWFEDO (2015), Chena woreda comprises of 42 of this 39 are rural kebles and with a total population of 158,449, of whom 78,150 are men and 80,299 women; 11,629 or 7.34% of its population are urban dwellers. The total area of Chena woreda is estimated to be 901.92 km2 that endowed with natural tropical rain forests with suitable climates that favour high honeybee population density and forest beekeeping is widely practiced (Nuru, 2007).

According to Chernet (2008), the woreda experiences long rainy season, lasting from March to October. The mean annual rainfall ranges from 1710 mm to 2000 mm. Over 85 % of the total annual rainfall, with mean monthly values in the range of 125 to 250 mm occurs in the 8 months long rainy season. The mean temperature ranges from 18.1ºC to 21.4ºC. Environmentally, it belongs to the sub-agro ecology tepid to midland and comprising of mixed arable farming and woodland, including much relict primary tropical forest. The topography is characterized by slopping and rugged areas with very little plain land (Tilahun and Kifle, 2015).

According to CWLFO (2016), the total households found in the woreda are 21685 of this households 7752 are honey producers. Total number beehives owned at district level from 2006E.C to 2008E.C in 2006 E.C about 40010 traditional, 4876 improved with the total of 44886; 43730 traditional, 6322 improved and a total of 50052 in 2007 E.C, whereas in 2008 E.C about 46140 traditional, 7932 improved with the total of 8118 beehives are owned by producers. In this woreda, there are two honey harvesting periods, April to June and September to October, of which the former is the major harvesting period contributing 95 % of the annual honey production.

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