export chain of french beans from kenya · the main objective of this study is to analyse the...

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Export chain of French Beans from Kenya

Tineke voor den Dag Development Economics Group Marketing and Consumer Behaviour Group

Export chain of French beans from Kenya

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Export chain of French beans from Kenya Author: Tineke voor den Dag Registration number: 810422167100 Number of credits: 21 Supervisors: Dr. Ir. W.E. Kuiper Marketing and Consumer Behaviour Group Dr. R. Ruben Development Economics Group University: Wageningen University Date: August 2003


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Acknowledgements In my opinion, you cannot do a research on your own. In the different stages it is good to have some people around you for support and advise. I would like to thank all the people that supported me in one way or another. A few of them I would like to mention by name. First of all I would like to thank my supervisors Dr. Erno Kuiper and Dr. Ruerd Ruben for guiding me through the whole process. Because they have a complete different background, it was a challenge for me to find the best way out of two different opinions. I would like to thank Dave Boselie, Tjalling Dijkstra and Maarten Siebe van Wijk for helping me with defining my subject in one way or another. Beatrice Salasya has been a great help to me in preparing my stay in Kenya. Without ever meeting me, she was willing to organise an attachment to the Kenya(n) Agricultural Research Institute (KARI). She welcomed me at my arrival in Kenya and introduced me to my supervisor at the KARI, Susan Munene. Susan has been as a mother to me. She welcomed me in her family and made me feel home in Kenya. Unfortunately I had to leave the KARI before the end of my stay in Kenya. During the time I was attached to the KARI, I stayed in an apartment in Thika. There I became close friends with Samuel Karoki and Joseph Chege. They informed me about the country and the coming elections, they welcomed me in their families and made me feel a part of their family. They took me on some trips through the country and advised me on practical issues. I would like to thank Peter for taking me upcountry to meet farmers and middlemen of French beans. If he has not been there to offer his help, it would have been difficult to execute my fieldwork. After 5 weeks I was invited by a Dutch exporter of French beans to come to Nairobi and do some research for his company. I would like to thank Chris Benard, who gave me the opportunity to take a look in the kitchen of an exporting company. It was very interesting for me to see the whole process of buying French beans till the shipment to the Netherlands. During these weeks in Nairobi, I shared an apartment with a Dutch student, Rients van der Wal. He was willing to give me a ride to Indu Farm (the exporting company) each day and invited me to join him on some trips during the weekends. Furthermore I would like to thank James, the field manager of Indu Farm, who took me to their farmers day after day. I would

like to thank Susi for using her e-mail and the nice talks during the days I was in the office. Back in the Netherlands I got a lot of support from friends. I would like to thank them all for encouraging in writing my thesis. Especially Janine, who made my daily stay at The Leeuwenborch much more fun!! My family has showed their interest during my stay in Kenya and afterwards. It was a pleasure for me to receive an e-mail each week or so. Last but not least I would like to thank my husband Egbert. He was the one that encouraged me to go to Kenya. He was also the one that supported me throughout the process.


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Summary Setting of the research It is important to link smallholder farmers in developing countries to the emerging fruit and vegetable markets, because the production and commercialisation of these high-value crops can be beneficial to small farmers in several respects. Recently more and more attention has been given to food safety. Various rules and regulations have been developed. The EUREPGAP protocol is one of them. It is especially meant for growers of fresh fruits and vegetables and has become effective in January 2003. These Good Agricultural Practices (GAP) include regulations regarding food safety, labour standards and environmental management. It would be interesting to see what new requirements like the EUREPGAP protocol do with smallholders. Are they able to maintain their position within the chain? The country Kenya has been chosen, because small-scale farmers are important actors in the export chain of vegetables and fruits within this country. This thesis deals with the crop French beans, because (1) it is one of the most important horticultural export crops, (2) a large amount is shipped to the Netherlands and (3) one of the EU retailers that set up the EUREPGAP protocol, sells these in his supermarkets. Research questions and methods used The main objective of this study is to analyse the production and marketing channel of the horticultural crop French beans, which is grown in Kenya and is exported to various countries. The research questions deal with relationships within the channel between farmers, middlemen and exporters and the implications for them on topics as price, quality, certainties and contracts. The data has been collected by means of four surveys: a survey among farmers concerning relations, a survey among farmers concerning their knowledge, a survey among middlemen concerning relations and a survey among exporters concerning relations. Econometric analyses were performed using t-tests, Logit Analyses, Cluster Analyses and Ordinary Least Squares. French bean export market French beans are a highly specific vegetable. In Kenya they are mainly grown for export. There is a large demand for French beans in both fresh and processed form, especially from Europe. Exporters require produce

that has a particular size (not too large and not too small), is not infected by insects and has a particular shape. French beans are packed in boxes in extra-fine and fine grades and shipped by air to Europe. The beans are not only picked and shipped, but also chopped, washed, combined into multi-product pack, labelled and bar-coded. There are various players active in the marketing of French beans. The most important actors within the chain are farmers, middlemen, exporters, importers and supermarkets. Results Exporters prefer farmers who have heard from the EUREPGAP protocol and who keep records of the quantities and types of pesticides and fertilisers they use. The price farmers receive depends on their storage facilities, their EUREPGAP knowledge and whether they sell their beans to an exporter directly. These aspects result in a higher price. The quantity per acre a farmer produces has a negative impact on the price. The way their buyer values the quality of the French beans depends on the operating time of the farmer, his storage facilities and the buyer of the produce. Exporters reject a higher percentage of the harvest than middlemen do. Furthermore it depends on the question whether the buyer collects the total amount harvested. Buyers who have a more stable market usually reject not more than necessary. Buyers who do not have a stable market are bowed to reject produce of a high quality. The certainties of a farmer (being paid on time and selling the total amount collected) depend on the buyer of the produce (exporter or middleman), the rejection rate, the price difference compared to the mean price of the sample and the size of the farm. The last factor has a negative impact on the certainties. Factors that determine whether a farmer has a specific agreement (verbal or written down) are the EUREPGAP knowledge, the buyer, the number of years the farmer is selling to the same buyer(s) and the number of acres under French beans. Furthermore it depends on whether the farmer keeps records, whether he owns the land he uses and the amount he produces per acre. A cluster analysis based on EUREPGAP requirements divided the middlemen into three groups, namely middlemen who could almost supply the European market, middlemen who are not qualified to supply the European market and EUREPGAP certified middlemen. These groups differ significantly on a number of aspects.


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Exporters prefer middlemen who have a large quantity to offer and whose farmers keep records. Furthermore their operating time has a negative influence on the choice of the exporter. The commission rate they receive for the produce depends on their storage facilities and whether they supply inputs to their farmers. The fact whether they are able to trace the produce has a negative influence on the commission rate. Middlemen who visit their growers value the quality produced by their farmers higher than middlemen who do not. The number of years a middleman is supplying the same buyer(s) has a positive influence on the rejection rate. There were no variables that influenced the certainties of a middleman. Middlemen can enclose agreements with their buyer(s) and with their supplier(s). Factors that influence the agreements with the supplier(s) are whether the middleman has the same agreement with his buyer, the buyer of the produce and the number of years the middleman is buying from the same farmer(s). Aspects that influence the agreements with the buyer(s) are the number of years a middleman is selling to the same buyer and the number of different products he sells. Conclusions and policy implications On the basis of the results described above, one could conclude that farmers are better off when they deal with an exporter directly. If they do so, they receive a higher price, inputs on credit, a contract, ability to negotiate and the certainty that they will be paid on time. On the other hand, exporters do have their requirements as well. They require a high quality produce, storage facilities, up to date knowledge concerning European requirements and record keeping by the farmers. The advantage exporters offer middlemen is a contract. This might be very valuable for middlemen, because their market is usually unstable. On the other hand, exporters do have a number of requirements. They want their supply from middlemen who have a high education level, supply a large quantity of French beans, do have storage facilities and supply inputs to their growers. Furthermore they want their farmers to keep records.


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Table of contents

SUMMARY ................................................................................................ 6

TABLE OF CONTENTS ............................................................................ 8

1 INTRODUCTION........................................................................... 10

1.1 INTRODUCTION TO THE TOPIC........................................................ 10 1.2 DEMARCATION OF THE SUBJECT ................................................... 10 1.3 OBJECTIVE AND RESEARCH QUESTIONS ........................................ 10 1.4 APPROACH OF THE RESEARCH...................................................... 11 1.5 STRUCTURE OF THE THESIS .......................................................... 11

2 THE FRENCH BEAN EXPORT MARKET .................................... 12

2.1 THE HORTICULTURAL EXPORT SECTOR .......................................... 12 2.2 FRENCH BEANS ........................................................................... 14 2.3 THE MARKETING CHANNEL........................................................... 15

2.3.1 Farmers ........................................................................... 15 2.3.2 Middlemen....................................................................... 18 2.3.3 Exporters......................................................................... 19 2.3.4 Importers ......................................................................... 20 2.3.5 Supermarkets.................................................................. 20

2.4 PRODUCT FLOW........................................................................... 21 2.5 INSTITUTIONS .............................................................................. 22

3 THEORETICAL FRAMEWORK.................................................... 24

3.1 INDUSTRIAL ORGANISATION THEORY ............................................. 24 3.2 MARKETING CHANNEL THEORY ..................................................... 24 3.3 INSTITUTIONAL ECONOMICS .......................................................... 26 3.4 MARKET IMPERFECTIONS AND TRANSACTION COSTS ...................... 26 3.5 IMPLICATIONS FOR SMALL FARMERS.............................................. 29 3.6 FOOD QUALITY ............................................................................ 29 3.7 SEASONALITY IN PRODUCTION AND PRICE...................................... 31

4 METHODOLOGY AND HYPOTHESES........................................ 32

4.1 INTRODUCTION ........................................................................... 32 4.2 SURVEY AMONG PRODUCERS CONCERNING RELATIONS .................. 33

4.3 SURVEY AMONG PRODUCERS CONCERNING KNOWLEDGE ................39 4.4 SURVEY AMONG MIDDLEMEN.........................................................39 4.5 SURVEY AMONG EXPORTERS.........................................................48 4.6 SECONDARY DATA .......................................................................48 4.7 THE ANALYSES USED....................................................................48

4.7.1 The Ordinary Least Squares ..........................................49 4.7.2 The Logit analysis...........................................................49 4.7.3 Cluster analysis...............................................................49

5 RESULTS......................................................................................50

5.1 FARMERS ....................................................................................50 5.1.1 Channel choice concerning farmers..............................50 5.1.2 Implications for the price, quality and certainties ........52 5.1.3 Contracts .........................................................................56

5.2 Farmers and their knowledge concerning the EUREPGAP PROTOCOL...................................................................................60 5.3 MIDDLEMEN.................................................................................61

5.3.1 Segmentation ..................................................................61 5.3.2 Channel choice concerning middlemen........................64 5.3.3 ........... Implications for the commission rate, quality and

certainties.................................................................................67 5.3.4 Contracts .........................................................................69

5.3.4.1 Agreements between the middleman and his suppliers ...................................................................................71

5.3.4.2 Agreements between the middleman and his buyer .73 6 CONCLUSIONS AND DISCUSSION.........................................76

REFERENCES.........................................................................................84

ANNEXES


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List of Tables Table 2-1 Composition of Kenya’s fruit and vegetable exports Table 2-2 Market share of horticultural export products 1991-1999 Table 2-3 Production indicators for French beans Table 2-4 Returns from French beans Table 2-5 Kenya: Enterprise budgets for contract and non-contract

French bean growers, 1991 Table 3-1 Operating conditions and appropriate institutional

arrangements Table 3-2 Dimensions of food quality Table 4-1 Research questions and estimation methods farmers Table 4-2 Research questions and estimation methods middlemen Table 5-1 Differences in farmers’ characteristics because of their

market outlet Table 5-2 Channel choice farmer (logit) Table 5-3 Price per kg (OLS) Table 5-4 The rejection rate (OLS) Table 5-5 Paid on time (logit) Table 5-6 Total amount (logit) Table 5-7 Agreement Quantity Farmers (logit) Table 5-8 Agreement Price Farmers (logit) Table 5-9 Agreement Quality Farmers (logit) Table 5-10 Agreement Pesticides Farmers (logit) Table 5-11 The clusters Table 5-12 Characteristics of the 3 segments Table 5-13 Differences in middlemen characteristics because of their

market outlet Table 5-14 Channel choice middlemen (logit) Table 5-15 Commission rate per carton (OLS) Table 5-16 The rejection rate (OLS) Table 5-17 Agreements of middlemen with their suppliers Table 5-18 The incentive instruments Table 5-19 Agreements of middlemen with their buyers Table 5-20 Agreement quantity supplier middlemen Table 5-21 Agreement pesticides supplier middlemen Table 5-22 Agreement pesticides buyer middlemen Table 5-23 Agreement fertilisers buyer middlemen Table 5-24 Seasonality in demand

List of Figures Figure 2-1 Map of Kenya Figure 2-2 The volume of fresh produce from Kenya, 1998-2001 Figure 2-3 The value of fresh produce from Kenya, 1998-2001 Figure 2-4 Export market for fresh fruits and vegetables from Kenya Figure 3-1 A hypothetical food marketing channel Figure 3-2 The different flows within the channel Figure 3-3 Different institutional agreements from low to high

integration Figure 4-1 The lay-out of the marketing channel Figure 4-2 The map of Kenya Figure 4-3 The composition of the commission rate Figure 5-1 Relationship between quantity and rejected percentage Figure 5-2 Gender of the respondents Figure 5-3 The education level of the respondents Figure 5-4 The number of acres under French beans Figure A-1 The organisation around the EUREPGAP protocol List of abbreviations and acronyms FPEAK -Fresh Produce Exporters Association of Kenya HCDA -Horticultural Crops Development Authority KARI -Kenyan Agricultural Research Institute NIE -New Institutional Economics

Chapter 1 Introduction

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1 Introduction This chapter gives an introduction to the research. In subsection 1.1 a brief explanation to the topic will be given. Subsection 1.2 describes the demarcation of the subject. In subsection 1.3 the objective and the research questions will be described. Subsection 1.4 gives the approach of the research. Finally in subsection 1.5 the structure of the thesis will be described. 1.1 Introduction to the topic Recently more and more attention has been given to food safety. Various rules and regulations have been developed.1 The EUREPGAP protocol (see Annex A) is especially meant for growers of fresh fruits and vegetables and has become effective in January 2003. A group of major European supermarkets united in the European Retail Partners (EUREP) working group took in 1997 the initiative to develop a normative strategy for product standards and handling procedures for fresh fruits and vegetables. These Good Agricultural Practices (GAP) include regulations regarding food safety, labour standards and environmental management. The EUREPGAP system is meant to demonstrate to consumers the company’s commitment and ability to produce safe and clean food under exhaustive process standards (HACCP) verified by an internationally recognised independent party. The EUREPGAP protocol for agricultural products is fast becoming an international standard for good agricultural practices based on inspection and control of production and handling practices throughout the whole chain. It is important to link smallholder farmers in developing countries to the emerging fruit and vegetable markets, because the production and commercialisation of high-value crops such as fruits and vegetables can be beneficial to small farmers in several respects. First, it increases their access to cash income to spend on items such as cloths and school fees. Second, it increases their access to inputs on credit, thereby improving their productivity. Third, the use of inputs on cash crops has spill over effects on domestic staple production because food and cash crops are 1 Examples of these rules and regulations are BRC, HACCP, ISO 9001:2000 and the EUREPGAP protocol.

often intercropped in the same field. Fourth, the household can consume whatever is not sold on the market and this improves the nutritional quality of their diet. Fifth, growing fruits and vegetables is usually more complicated than household food crops and therefore it contributes to increasing the technical and marketing management skills of the grower. Finally, promotion of smallholder farm production contributes to food security, employment and income generation in rural areas, which reinforces the overall development and poverty reduction goals of a government. Dolan et al. (2000) note that in order to secure a continuous supply of commodities meeting high quality, environmental and social standards, supermarkets and their suppliers are shying away from small exporters and smallholders and relying more on large firms and large-scale producers where control over standards is more reliable and less costly. However, the issue of the cost of meeting standards is not that simple. The trade-off is between high external monitoring and supervision costs and high internal monitoring and supervision costs. Thus, is it more costly to meet the standards through vertically co-ordinated contracts or through complete vertical integration? It would be interesting to see what new requirements like the EUREPGAP protocol do with smallholders. Are they able to maintain their position within the chain? 1.2 Demarcation of the subject Because of a lack of time it was not possible to take a closer look at the production and marketing of all horticultural export products in Kenya. This thesis will deal with the crop French beans, because (1) it is one of the most important horticultural export products, (2) a large amount is shipped to the Netherlands and (3) one of the EU retailers that set up the EUREPGAP protocol, sells these beans in his supermarkets. This thesis will only take a closer look at the Kenyan actors operating in the chain, because it would take too much time to visit also the actors in the importing countries. 1.3 Objective and research questions The main objective of this study is to analyse the production and marketing channel of the horticultural crop French beans, which is grown in Kenya and is exported to various countries.


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The research questions that are related to the objective are:

1. Which factors influence the choice of the channel? (for the farmer: exporter or middleman; for the middleman: exporter or middleman)

2. What are the implications of this choice for the received price, the rejection rate, payment system (according to contract) and certainties (what is written down in contracts)?

3. What is the impact of contracts on the received price and the delivered quality?

1.4 Approach of the research This study has been carried out in Kenya and the Netherlands. It involved both desk research and fieldwork. The fieldwork has been done in Mwea district in Kenya, while the analysing and writing part have been executed in the Netherlands. A number of different methods have been used, including t-tests, logit analyses, cluster analyses and Ordinary Least Squares. The research is based upon the theoretical concepts of the New Institutional Economics (NIE), marketing channel theory and contract farming. The NIE originates from economists criticising the traditional neo-classical approach. Institutions are seen as essential as well as market failures that occur in market exchange. Two fields within the NIE have received particular attention, namely the Economics of Information and the Transaction Cost Economics. Contract farming is seen as a possible solution for these market failures. 1.5 Structure of the thesis The remainder of this thesis is structured as follows. Chapter Two describes the French beans export market of Kenya. The Third chapter provides a theoretical framework for the research. In the Fourth chapter the methods used for the analyses are explained. In the Fifth chapter the data analysis is conducted and the results are presented. Finally, the Sixth chapter contains the conclusions, discussion and suggestions for policy implementation.

Chapter 2 The French bean export market

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2 The French bean export market This chapter provides an insight into the French bean export market of Kenya. First of all, a general overview of the horticultural sector will be given. Second, the vegetable itself will be briefly clarified. In the end, the various actors operating in the chain will be described. 2.1 The horticultural export sector The Republic of Kenya, named after the first president Kenyatta, lies across the Equator along the eastern seaboard of Africa and has a landmass of 582,646 square kilometres. Kenya shares a common border with Ethiopia, Sudan, Somalia, Uganda and Tanzania (see Figure 2-1). It has a population of approximately 30,3 million people. The capital city is Nairobi, which is also one of the most important cities of East Africa.

Figure 2-1 Map of Kenya (source: http://www.cobra-verde.de)

Kenya has different ecological zones owing to its varying altitude, which allows diverse agricultural activities, key among these being horticultural

farming. Among the factors that have supported Kenya's rise in the fresh produce exports are: • conductive Equatorial climate, which allows year-round production; • fertile soils; • a competitive labour force with good education and technical

background. The horticultural industry (including fresh-cut flowers, fruits and vegetables) is an important source of foreign exchange earnings, ranking third among agricultural export commodities after tea and coffee (Harris et al., 2001). In addition it is an important source of employment: the industry employs about 2 million people directly and another estimated 0.5 million indirectly (Harris et al., 2001). Although the volume of vegetables exported has declined considerably during the last few years (see Figure 2-2), the value has increased (see Figure 2-3).

Figure 2-2 The volume of fresh produce from Kenya, 1998-2001

(Source: HCDA internal files)

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Figure 2-3 The value of fresh produce from Kenya, 1998-2001 (Source: HCDA Internal files)

The rapid growth of the export horticulture can be attributed to several factors. First, preferential treatment under the Lomé Convention between African Caribbean Pacific (APC) countries and the EU provides concessionary access for Kenyan flowers and vegetables to the European market. Second, the sustained demand for horticultural products as a result of high -and growing- incomes in Europe provides a stable and growing market for Kenyan producers. Third, Nairobi’s location as a centre of air transport between Europe and the East and Southern Africa region, and Kenya’s role as a major tourist destination, ensure that there is sufficient northbound air cargo to transport exports. Finally, the presence of ample local and international investors, particularly in the cut-flower business, provides Kenya with an added advantage (Markandya et al., 1999). The main flowers exported from Kenya include Roses, Carnation, Statice Alstroemeria and a variety of summer flowers. Under the vegetables category, French beans, snow & snap peas, Asian vegetables (such as karella, chillies, aubergines and okra) dominate the export list. Mangoes, avocadoes and passion fruit are the most important export fruits. Kenya is

the leading supplier to the EU of French beans, snow peas, eggplant and capsicums. Table 2-1 Composition of Kenya’s fruit and vegetable exports Fruits Percent of total fruit

exports Vegetables Percent of total

vegetable exports Avocadoes 59.2 French

beans 30.9

Mangoes 25.6 Canned beans

17.3

Passion fruit 6.0 Okra 5.8Strawberries 4.8 Karella 3.8Pineapple 3.0 Snap beans 2.5Other 1.4 Frozen

beans 2.3

Eggplant 1.2 Chillies 1.1 Other beans 11.1 Other

vegetables 24.0

Total 100.0 Total 100.0Source: FPEAK Magazine (2000) The European Union (EU) is the principal importer of Kenya fresh produce. The Netherlands imports the bulk of flowers for sale through the auction system. The United Kingdom, Germany, The Netherlands and France are the major importers of fruits, flowers and vegetables (see Table 2-2). The Middle East market is an important market outlet for Kenyan fruits. The Scandinavian countries are also emerging as potentially lucrative markets. UK supermarket standards are the highest and most difficult to meet. Continental European supermarket standards are also high but less demanding in part due to different legal systems. For Kenyan producers and marketers, what is most important is maintaining appropriate records of the production, harvesting, transportation, processing and storage of their produce in ways that ensure their conformity to safety and quality standards (Harris et al., 2001).

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Table 2-2 Market share of horticultural export products 1991-1999 (%) Country 1995 1996 1997 1998 1999 UK 29.5 27.0 31.0 31.8 33.6 Netherlands 35.6 33.0 38.0 31.0 30.9 France 16.8 12.0 13.0 15.2 15.4 Germany 8.8 7.0 7.0 6.5 4.6 Belgium - 3.0 1.7 2.3 1.9 Others 9.3 18.0 9.3 13.2 13.6 Total 100.0 100.0 100.0 100.0 100.0 Source: Horticultural Crops Development authority, 2000 The horticultural sector is controlled by the private sector, incorporating large and small-scale farmers and exporters scattered across the nation. While largely controlled by private investors, who have continued to export top quality fresh produce to the markets, the government has helped in policy and regulation of the sector (Dijkstra, 1997). 2.2 French beans French beans are a highly specific vegetable. In Kenya they are mainly grown for export. There is a large demand for beans in both fresh and processed form from West European countries. On local markets, especially in supermarkets in the capital city, there is a limited but growing demand for this vegetable. The optimum altitude for French beans is between sea level and 1800 metres above sea level. They are grown where temperatures are warm, ranging between 12 and 34 degrees. The optimal temperature for French beans is 20oC. With temperatures below 12oC, French beans are likely to be destroyed by frost while in temperatures above 34oC, flowers drop at an alarming rate thus affecting the overall yield. French beans grow in a wide variety of soils ranging from light sand to heavy clays but does best in well-drained loam soils rich in organic manure. They need a well-distributed rainfall throughout the growing season of between 600 to 1500mm. Irrigation should be done where rainfall is either not enough or is unevenly distributed because they are very sensitive to the amount of water they receive, which invariably affects the yield. French beans require a seed rate of 74kgs per hectare with intra-row spacing of 30cm and inter-row spacing of 50cm. Cutworms and bean flies are common French bean pests. A common disease of the

crop is Fusarium root rot. Harvest time of beans depends on the climatic conditions in which it is grown and the bean variety but generally picking of pods begins seven to eight months after planting and may go on for two months. Harvesting should be done regularly in order to have a product with the same quality (www.kenyaweb.com). French beans are labour intensive employing 3.285 man-hours (mhrs) per hectare (ha) per year compared to hybrid maize as a cash crop, which employs 984 mhrs/ha/year. Maize and beans intercrop employs 1579 mhrs/ha/year, Irish potatoes employs 1760 mhrs/ha/year while milk production employs 380-482 mhrs/ha/year depending on the production system (Salasya, 1989). Kenya’s French beans have been more or less constant in terms of exports, but the area under production has been fluctuating (Table 2-3). Table 2-3 Production indicators for French beans Indicator 1991 1992 1993 1994 1995 Production (in tons) 24265 22265 19624 18271 17400 Area under production (ha)

5939 6190 5807 4792 4524

Exports (tons) 14852 15197 14476 10011 9449 Source: HCDA, Ministry of Agriculture The average return that could be derived from French bean production is presented in Table 2-4. Table 2-4 Return from French beans Yield/ha Total revenue/ha

(Ksh) Total cost/ha (Ksh)

Profits/ha (Ksh)

1606 kgs 96330 49533 46797 Source: Egerton and others (1995) Exporters require produce that has a special size (not too large and not too small), is not infected by insects and has a particular shape. French beans are packed in boxes in extra-fine and fine grades and shipped by air to Europe. Extra fine are the thinner ones of the two, not exceeding the nine millimetres in diameter. Both output per acre and the ratio of the two grades vary depending on the frequency of harvest. Harvesting more than


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three times a week yields a higher percentage of the extra fine beans. Exporters sometimes indicate in advance, especially to contract growers, the proportions of the two grades they want to buy. Extra fine beans are more popular in importing countries and realise a higher price (see Table 2-5). The beans are not only picked and shipped, but also chopped, washed, combined into multi-product packs, labelled and bar-coded. Kenya has a number of food safety and control laws with implications for the fruit and vegetable industry (Harris et al., 2001). Kenyan fruit and vegetable exporters must comply also with the laws and regulations of the countries importing Kenyan produce (Harris et al., 2001). 2.3 The Marketing channel There are various players active in the marketing of fresh fruits and vegetables from Kenya. The most important ones that operate in the French bean trade are described below. In Figure 2-4 the layout of the channel is presented to get a better idea of the different choices the actors have in selling their produce. 2.3.1 Farmers Kenya’s strategy of promoting smallholder commercial farming began with the Swynnerton Plan of 1954, which gave priority to helping smallholders to increase the production of traditional export crops, such as coffee and tea, that were previously grown by large-scale farmers. As a result, the smallholder share of total marketed output reached 50 percent by the late 1970s (Kimenye, 1995). Fruit and vegetable growers can be categorised by size into four groups: 1. Small-scale producers are farmers with less than 10 acres;2 2. Medium-scale producers are farmers with between 10 to 20 acres; 3. Large-scale producers are farmers with between 20 and 200 acres; 4. Plantations are farmers with more than 200 acres of production (Harris

et al., 2001). Dolan et al. (2000) reported that the overall share of small-scale production in fresh vegetable exports has dropped to less than 30 percent by the mid-

2 An acre is 0.4047 ha

1990s. Both small-scale and large-scale farmers grow French beans.3 The size of smallholder farms that produce French beans for export varies depending on the availability of land and access to irrigation water. The size can be as small as a 1/10 of an acre and as large as 5-10 acres4 (Kimenye, 1995). A small-scale farmer will typically produce 30 to 90 kilograms of French beans a week, receiving about KSh 1.000 to KSh 3.000 for this crop (Dijkstra, 1997). The French bean export sector faces a number of constraints for small-scale farmers: 1. Lack of access to high quality seed

The imported seeds are too expensive for small-scale farmers to afford. Locally produced seeds instead are of poor quality. A number of contract growers is able to obtain credit from their buyer to purchase seed. Other buyers offer seed on credit to the farmers they deal with. The majority of the farmers still uses seed saved from a previous crop (second-generation seed) (Kimenye, 1995). This has led to deterioration in the quality of exportable French beans.

2. Lack of capital and limited access to credit Small-scale farmers lack sufficient capital to produce high quality vegetables. Therefore, they use a low level of inputs, obtain low yields and produce low-quality vegetables.

3. Limited access to technical information The government extension service does not have adequate resources and personnel to meet the needs of small-scale farmers producing vegetables for export (Kimenye, 1995). Often farmers do not know which chemicals and application levels to use. Most of the growers rely on other growers, labels or chemical stockists for advise.5

3 Reliable statistics on the number of farmers in each group, how much they are producing, and for which market are not available. The Ministry of Agriculture statistics are estimations. Reports on the contribution of small-scale producers to export production vary widely. 4 The average amount of acres the interviewed farmers used for French bean production was 1.6 acres. About 35% of the farmers has less than 3 acres of land at their disposal, while only 13 percent of the questioned farmers has more than 10 acres. 5 About 40% of the interviewed farmers relied on a local store or the product labels for their advice about pesticides and fertilisers.

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Small growers Less than 10 acres

Transporter Exporter broker

Independent broker Exporter

Cold storage and shipping services

Importer

Wholesaler

Food service Retail

Consumer

Medium growers 10 to 20 acres

Large growers 20 to 200 acres

Plantation 200+ acres

Figure 2-4: Export market for fresh fruits and vegetables from Kenya Source: Harris et al., 2001


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4. Limited access to contract production and marketing arrangements The majority of small-scale farmers cultivate French beans without consulting exporters with the hope that they will be able to sell the produce when harvested. Consequently, they realise low and unstable prices and low returns.

5. Lack of access to market information Although the Horticultural Crop Development Authority receives weekly market information by fax and telex about quantity, quality and prices of French beans sold in European markets, the information is seldom disseminated to small-scale farmers in the production areas some 100 to 200 km from Nairobi. Instead, smallholders are forced to depend on the buyers (exporters or middlemen) for information, which weakens their bargaining position vis-à-vis their buyers.6 Also, without credible market information, smallholders cannot plan their production to match the demand for French beans. As a result, they frequently incur losses from unsold produce. A survey among smallholders of French beans in 1992 (Kimenye, 1995) indicates that 14 percent of the beans from non-contract farmers went unharvested because of the lack of a market. By contrast only 5 percent of the beans from contract growers went unharvested.

The relationship between exporter and small-scale producer is governed by several different arrangements. There are three types of governance structures for direct contact between small-scale producers and exporters (Kamau, 2000; Jaffee, 1995):

1. Informal market arrangements (market reciprocity agreements) are highly personalised repeat transactions in which some degree of loyalty is built up between the exporter and a certain group of growers. Produce is usually exchanged at the current market price. Although no inputs are provided, some technical advice on variety selection and harvesting and handling practices is usually transmitted to the farmers.

6 From the interviewed farmers everyone got his information about prices and the required quality from his buyer.

2. A forward market contract is a formal commitment to buy and sell specific quantities and qualities of produce at a particular time. Prices may be agreed on in advance or at the time of actual exchange and the buyer is not involved in the production process or in providing inputs to the farmers. These contracts are more common for larger-scale farms where the farmer can afford to buy his inputs and has more advanced technical and managerial skills.

3. Production contracts (interlinked factor market contracts) are formal agreements to buy and sell a specific quantity and quality of produce at a specific price. They are often referred to as ‘outgrower schemes’. This type of transaction is most common for fresh exported vegetables including French beans. In outgrower schemes, farmers are usually organised into groups of 15 to 30 members to facilitate co-ordination of activities, technical assistance, and supervision. The exporter provides a specific quantity of seeds on credit7 (to ensure quality seeds are planted), and technical advice on chemical use and agronomic practices.8 Farmers pay for the seed at the end of the season when traders deduct input costs from the total value of the produce. The buyers may have field officers who visit the farmers’ fields on a regular basis to ensure quality control and proper use of inputs. Some exporters may spray pesticides for the farmers in order to ensure meeting the Maximum Residue Levels (MRL’s) and the buyers’ requirements for due diligence in pesticide use. In some cases, farmers have to follow a specific production program set by the buyer with specified times for planting and harvesting in order to provide a sequential and continuous supply of produce. The exporter often provides transport from the farmer group storage and grading area.

7 55% of the interviewed farmers received seed from their buyer on credit. 8 Almost all interviewed middlemen visited the growers where they bought their produce from. They advised the growers about chemical use, irrigation and the best planting methods.


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According to a survey held among growers of French beans (Kimenye, 1993), contract growers receive a significant higher net income than non-contract growers do. Table 2-5 illustrates the differences in yields, costs and returns per acre of French beans produced under contract and non-contract arrangements. Besides cultivating larger bean acreage, contract growers on average achieved 37 percent higher yields and 80 percent higher net margins. Contract growers obtained higher prices per carton9 of beans than non-contracted growers. Although fresh bean prices vary markedly within the season, the contract prices remained relatively stable throughout the season.10 Most exporters rely at least in part on contract growers (Dijkstra, 1997). The export firms provide farmers with inputs on credit such as high quality seed, irrigation equipment, fertilisers and pesticides. In addition, exporters provide technical guidance to their contracted growers. The contracts are basically informal, seldom written down, and are based on mutual trust between the parties. They are often the subject of disagreement. A farmer may decide to sell his produce to another exporter who offers a slightly higher price. That is all the more tempting if the farmer has received inputs on credit. Therefore the contract system is not very popular among the exporters unless they make sure it will not be profitable for farmers anymore to sell outside. One-third of the 30 smallholders in the 1992 bean survey (Kimenye, 1995) was growing French beans under contract. 2.3.2 Middlemen The term middleman is a general definition for all actors that operate as intermediaries between the farmer and the exporter. They live in the rural area and often grow French beans themselves as well. According to Harris et al. (2001) there are two types of middlemen, namely exporter agents and independent middlemen. 9 A carton is another measure for 3kg. 10 The contract price for extra fine beans ranged between 50 and 60 shillings per carton, whereas non-contract prices fluctuate between 40 and 70 shillings (Kimenye, 1995).

Table 2-5 Kenya: Enterprise budgets for contract and non-contract French bean growers, 1991 Contract growers Non-contract growers Number of growers 11 29 Bean Area (acres) 4.98 1.53 Output (carton/acre) Extra fine 498 354 Fine 532 393 Yield (cartons/acre) 1025 747 Price (Ksh./carton) Extra fine 55.00 47.50 Fine 44.09 35.26 Revenue (Ksh./acre) 50557 30672 Operating costs (Ksh./acre) 18194 12802 Fixed costs (Ksh./acre) 2117 1165 Gross margin (Ksh./acre) 32377 17870 Net margin (Ksh./acre) 30260 16705 Opportunity cost of capital11 1909 1313 Net income (Ksh./acre) 28351 15392 Source: Kimenye, 1993 Exporter agents are paid a commission based on the volume of sales for providing certain services. These include identifying and recruiting farmers, communicating short-term information to farmers regarding exporter quantity and timing requirements, communicating short-term information to farmers regarding exporter quantity and timing requirements, communicating information about expected prices, informing the exporters about local supply and competitive conditions, distributing packaging materials to farmers, issuing payments to farmers and providing a grading shed where farmers deliver their produce and the exporter collects is. Independent middlemen buy produce from growers and then

11 Estimated return on invested capital (operating plus fixed costs) using the 9.4 percent real interest rate charged by commercial banks on loans in 1991.


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sell it to an exporter. These middlemen have limited access to market information, which they may transmit sporadically to growers. Both types of middlemen provide transportation. Some of them have their own trucks for transportation. Others hire a truck to provide transportation or ask their buyer to send one. The price paid to the growers is determined by the exporter in case of exporter agents and otherwise by the middleman himself, based on current spot market prices. Generally, middlemen pay farmers cash at the point of transaction. However, especially with independent middlemen, payment to the farmer is not made until the middleman receives payment from the exporter.12 In case of the independent middlemen, there is a clear difference between two groups. One group operates as a middleman throughout the year. They have fixed buyers for their produce, mainly exporters. The other group appears in the field in times of shortage. Because exporters need to satisfy their customers, they are willing to pay more for the beans. The middlemen take this opportunity to trade beans against a considerable commission rate. Middlemen keep in touch with their buyers by phone and mail.13 Furthermore the buyers come to visit them or they go to see the buyer. Because of the new requirements a group of European retailers has set up, exporters are forced to be able to trace their produce (EUREPGAP protocol). Therefore they also expect the middlemen to be aware where they got the produce from.14 2.3.3 Exporters There are about 200 licensed fresh produce exporters in Kenya. However, only 50 are consistently operational while the other 150 exporters exploit favourable short-term market conditions, entering and exiting the industry

12 15% of the interviewed middlemen paid their farmers while collecting the produce. 13 Source: author’s interviews 14 About 88% of the questioned middlemen were able to trace the beans back to the farmer who grew them. They mainly use tags and stickers to recognise the produce on which they write the name of the farmer and the day of the harvest.

sporadically during the October-April peak season (Dolan et al., 2000). These exporters vary much in size. Some exporters only have a pick up for buying the produce. They store and pack the beans in the buildings of the HCDA (horticultural Crop Development Authority). This kind of exporters does come and go. Other exporters have a pack house, a large number of employees, their own trucks and cooling facilities. Their success is determined by commercial skills and their willingness to take risks, although the latter are sometimes passed on to smallholders, labourers and the physical environment. The involvement of foreigners, multinationals and Kenyans of Asian origin is notably high, but the heavy investments that are required to become and remain successful suggest that most of the value added is ploughed back into the Kenyan economy (Dijkstra, 1997). Most exporters are located around the international airport of Nairobi. Some of them grow French beans themselves as well. Most exporters buy produce from farmers directly. Some have a fixed number of farmers from who they buy every season, others buy from other farmers every time. Export possibilities fluctuate weekly or even daily, notwithstanding the well-established contacts between Kenyan exporters and European importers. These fluctuations stem from the demand in Europe and competition from other countries. Exporters react to such market developments by staying at home, leaving their contract farmers with unsaleable produce that does not even have a local market. Besides supplying from farmers, exporters often also rely on middlemen to get the required amount of produce.15 Small and seasonal exporters use middlemen to buy their produce. Larger exporters favour some type of contractual arrangement but also use spot market purchases to fill gaps.

Most of the large exporters have a subsidiary firm for logistical services including cold storage, handling and shipping. Independent logistics companies provide these services for small and medium-scale exporters. 15 About 68% of the interviewed middlemen sold their produce directly to an exporter.


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Most of the medium and large-scale exporters are involved in some type of value-added activities include washing, trimming, packaging, bar-coding and labeling. The size and sophistication of these activities depends on the exporters’ ability to invest in the necessary equipment and management resources and its customer base. Export pack houses have varying degrees of quality control programs. Exporters face more and more requirements from their European buyers. The introduction of the EUREPGAP protocol may become a threat to a number of exporters who are for their produce dependent on small-scale farmers. The requirements as keeping records, trace-ability and hygiene, will cause a lot of work for exporters. The initiative of adapting to these new rules will have to come from the exporter instead of the farmer. Because farmers lack the knowledge and the capital to adjust to these requirements, the exporter has the choice between starting a farm himself or supporting the farmers in the adaptation process. In order to meet the quality standards demanded, exporters are using more production contracts and vertically integrated production arrangements. 2.3.4 Importers Exporters sell their produce usually to importers of one or two countries. The importers then sell the produce through two channels. In the first channel, the importer sells the produce to wholesalers who service the retail and food service sectors. The final product is delivered to consumers through a variety of retail outlets including supermarkets, green grocers and fruit and vegetable stands. The food service sector, which includes restaurants, school and work cafeterias, and hospitals, is a growing outlet for food expenditures. The second channel used by fruit and vegetable importers is to bypass wholesalers and sell directly to supermarkets and food service outlets. 2.3.5 Supermarkets Supermarkets play a significant important role in the marketing channel of the French beans trade. According to Gereffi (1995), international production and trade are increasingly organised by industrial and

The successful exporter: Homegrown Homegrown, Kenya’s largest horticultural exporter, began operations in the early 1980s when the chief executive financed a colleague to grow horticultural produce for third party exporters. In 1982, Homegrown began exporting their own products to UK wholesale markets. The company now employs over 6.000 Kenyans on its eight farms, and its exports have grown from 17 tonnes in 1982 to 12.500 tonnes in 1997. It is now responsible for 15% of Kenya’s total horticultural exports. Homegrown’s export activities are governed by a corporate philosophy, the Homegrown Triangle, which integrates three components: airfreight and logistics, marketing, and production. Each component is paramount to the company’s success. Homegrown strongly believes that there is little point in having high quality production without the corresponding market and airspace to ensure that product reaches supermarket shelves in optimal condition. • Airfreight. Homegrown’s difficulties in ensuring uplift at Nairobi

Airport led the company to realise the importance of airfreight to viable operations. By the late 1980s, the company enters a joint venture with MK Airlines, which provides a freighter every evening to the UK, enabling Homegrown to secure continuity of supply and stabilise costs. The company also has a fleet of refrigerated vehicles to transport product from field to centrally located cooling and packing stations.

• Production. Over 90% of Homegrown’s crops are grown on their farms using sophisticated irrigation systems and greenhouses to safeguard crops from rainfall and disease. Homegrown recently completed a factory for prepared salads, which guarantees that salads are picked, prepared, fully labelled and transported to supermarket shelves within 48 hours.

• Marketing. When Homegrown started, it exported to a multinational importer, which diversified its supply base by relying on several overseas growers. The company has developed a strong customer base with the UK supermarkets, which are favourably impressed by Homegrown’s continual investments in modern technology, innovation capabilities and compliance with environmental and social standards. Source: Dolan et al, 2000


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commercial firms involved in strategic decision-making and economic networks at global level. Supermarkets are among these commercial firms and their power increases rapidly. In the UK, the top four retailers (Tesco, Sainsbury’s, Asda and Safeway) account for nearly 75% of all food sales in the United Kingdom, including the sales of fresh vegetables (Fearne and Hughes, 1998). Retailers start to develop their own brands and logistic systems, and specialise in the organisation of the supply chain, without owning any factories. The horticultural value chain is among the sectors that have directly been affected by this process. The UK retailers for example now control 70-90% of fresh produce imports from Africa. There are signs of similar trends in the other parts of Western Europe (Dolan et al., 1999). Fresh fruits and vegetables have become a key area of competition between retailers. Supermarkets compete on attributes such as product range, quality, packaging, year-round availability, presentation and innovation. Nowadays supermarkets are under some pressure to ensure that their production systems are socially and environmentally sound. As a result most retailers have developed codes of practices, like the EUREPGAP protocol, that address procedures regarding health and safety, employment conditions and environmental management throughout the supply chain. Besides these codes of practices they also require exporters to fulfil the requirements of other quality systems, like the Hazard Analysis and Critical Control Points (HACCP). 2.4 Product flow Most farmers harvest the French beans every Monday, Wednesday and Friday (market days) during the harvesting period. They usually employ pickers to do the work for them. Their buyers come the same day to collect the beans for transport to Nairobi. The very same day the beans will be in a cold room in Nairobi. The infrastructure in the production areas is not very well.16 During the rainy seasons from March to May (long rainy season) and from October till December (short rainy season) the trucks face difficulties in reaching the different areas. This has consequences for the quality of the beans. Because they are highly perishable, they cannot 16 None of the visited farms border on a blacktop road (authors’ interviews).

be kept in good condition without cooling facilities.17 Because most farmers lack these facilities, it is very important to transport the beans to Nairobi the very same day because of the decline in quality. During this process, several levels of grading take place to separate the good produce from the waste. The first is done by the farmer himself based on past experience and managerial skills. The produce determined by the farmer to be unsuitable for the market remains on the farm for consumption by the family and for livestock feed. However, the flexibility of grades often depends on the current supply of the commodity, so the farmers are usually fairly optimistic on what may be acceptable. A second grading occurs at the collection point. This is done either by exporter employees, exporter agents, or independent middlemen, depending on the type of transaction. This process is fairly transparent for farmers because they are present when the grading occurs and can ask questions. However, there is still confusion at this level over why some produce is rejected and other produce is accepted. In a few cases this is the final grading on which the farmers’ payment is based. However, in most cases a final grading occurs at the exporters’ pack house and provides the basis for the farmers’ payment. It is not transparent because the farmer is not present and has no means to question or challenge the grading. These rejects are not returned to the farmer, resulting in a loss of income for the farmer. Some feedback is given to farmers on how to minimise rejects, like a particular pesticide or timing of harvest, but this varies greatly among exporters. The grading process is not transparent to smallholders. The rate of product rejection by buyers can be quite high. According to Harris et al. (2001) it can range between 10 to 40 percent. Jaffee (1995) estimated that in the mid-1980s, as a result of poor co-ordination between growers and down-stream buyers as well as seasonal production surplusses and transport bottlenecks, the average level of waste and post-harvest losses was approximately 25%. In Nairobi the produce will be stored in cold stores. The produce usually stays in these rooms for a few days. Before they are shipped most beans are packed into small proportions, ready to be put in a shelf of European 17 Of the 40 farmers being interviewed, 5 had a storage room at their disposal, of which 1 contained cooling facilities.


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supermarkets. A small proportion will be shipped without being packed and is mainly meant for the hotel and catering industry. 2.5 Institutions There are various organisations active in the export of fresh fruits and vegetables, from the control of imported seed till the promotion of the products abroad. The Kenyan Agricultural Research Institute (KARI) focuses on the growing process. The Horticultural Crop Development Authority is a parastatal that stimulates the export. The Export Promotion Council is an example of an organisation that promotes the products in potential importing countries. In Annex B a more detailed description of the most important organisations is given.


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Chapter 3 Theoretical Framework

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3 Theoretical Framework This chapter provides a theoretical background for this research. There are different relevant concepts for analysing a food commodity marketing system in the field of the structure and behaviour of the channel (see Figure 3-1). These concepts will be described below. In subsection 3.1 the Industrial Organisation theory will be described. Subsection 3.2 describes important aspects within the Marketing Channel theory. Subsection 3.3 deals with the theory concerning Institutional Economics. In subsection 3.4 the theory around market imperfections and transaction costs will be described. The implications for small farmers will be described in subsection 3.5. Subsection 3.6 deals with food quality. In subsection 3.7 the seasonality in production and price will be described.

Figure 3-1 A hypothetical food marketing channel

3.1 Industrial organisation theory Industrial organisation (IO) theory deals with the functioning of markets (Tirole, 1989), or with resource allocation through market systems (Scherer and Ross, 1990), or with the structure of firms and markets and their interactions (Carlton and Perloff, 1994). Carlton and Perlof (1994) distinguish two major approaches to the study of industrial organisation. The first approach is the structure-conduct-performance analysis. An industry’s performance (the success of an industry in producing benefits for consumers) depends on the conduct (policies) of firms which, in turn, depends on the market structure (factors that determine the competitiveness). The second approach (price theory) uses economic incentives to explain market phenomena. Specific applications of price theory such as transaction cost analysis and game theory are considered to be helpful in explaining the structure, conduct and performance of markets. Competition is assumed to be a main force that helps to solve economic performance problems. Several arguments in favour of perfect competition have been put forward, e.g. political arguments and efficiency arguments, but there are also qualifications and doubts (Scherer and Ross, 1990). 3.2 Marketing channel theory The marketing channel is the trade or distribution channel and is defined by Stern et al. (1996) as a set of interdependent organisations involved in the process of making a product or service available for consumption or use. The fundamental activity in marketing channels is the transaction, i.e., the act of exchange between economic agents (Achrol et al., 1983). The channel follows a vertical structure where products flow from producer to the ultimate consumer and in which actors meet each other at markets. Producers, wholesalers and retailers as well as other channel actors exist in channel arrangements to perform marketing functions (business activities) that contribute to the product flow. Actors that stand between producers and final users are known as intermediaries. Figure 3-1 shows a hypothetical food-marketing channel.


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The marketing channel, which starts with the farmer and his production system and ends with the consumer and his consumption habits, is a two-way flow of market signals. The nature and means of farm production have a major determining effect on the organisation and operation of the marketing channel. At the same time, the dynamics of the marketing process itself has a direct influence on agricultural production (see Figure 3-2).

Figure 3-2 The different flows within the channel (Castano, 2001) Conventional marketing channel networks (CMC) are distinguished from vertical marketing systems (VMS). In conventional marketing channels (CMC), subsequent stages in the assembly and distribution of commodities are connected by market. The co-ordination among channel members is primarily achieved through bargaining and negotiation. The actors tend to be preoccupied with volume, costs and investments in a single stage of the marketing process (Stern and El-Ansary, 1992). Vertical marketing systems (VMS) are developed to achieve control over the costs and quality of the functions performed by various subsequent channel members. A VMS is a network of interconnected units or firms, in which market exchange transactions are substituted by the entrepreneur-co-ordinator (Coase, 1937). Co-ordination among channel members is achieved through the use of comprehensive plans and programs. Mechanisms to achieve co-ordination and co-operation among channel

members may be based on the use of power through rewards, coercion, expertness, identification and legitimacy (e.g. Stern and El-Ansary, 1992). Main modes of VMS are administered systems (in which co-ordination is achieved through programs developed by one or a few firms), contractual systems (co-ordination among channel members is achieved through contractual agreements) and corporate systems (all activities are done within one company). Core concepts of marketing (Kotler, 1997) regard: a managerial process guiding the supply of products or services which satisfy a need for customers who are willing to engage in exchange through a marketing network. In the course of time, different approaches were employed to study the role of marketing:

• The commodity approach focuses the analysis on the flow of products from producers to consumers in the marketing channel.

• In the functional approach, the main commercial services offered by traders and marketing institutions are analysed (Kohls and Downey, 1972): exchange functions (buying and selling), physical functions (transport, storage, processing) and facilitating functions (standardisation, financing, risk-bearing and market intelligence).

• The institutional approach analyses the structure, role and performance of marketing institutions such as marketing boards, marketing co-operatives, auctions and futures markets.

• The marketing management approach (planning, implementation and control of marketing programs) assumes that at least one party takes the initiative to achieve desired responses from other parties. The instruments are product policy, price policy, distribution policy and communications policy (Kotler, 1997). The marketing management approach is particularly relevant in markets where products can be differentiated and buying behaviour of customers can be influenced by suppliers (Meulenberg, 1986).


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3.3 Institutional economics The theory of institutional economics is focused on the question how alternative sets of social rules (institutional structure, property rights) and economic organisations affect behaviour, allocation of resources and equilibrium outcomes. Institutions are the organising mechanisms of a society and property rights describe these institutions (Campbell and Clevenger, 1978). Property rights are the rights that individuals appropriate over their own labour and the goods and services they possess (Eggertsson, 1990). Institutions can be understood in two ways. The first is sociological: any behavioural regularity is an institution. The second is economic: institutions are the rules of the game in a society, or the humanly devised constraints that shape human interaction (North, 1990). Markets are institutions because they embody rules and regulations, formal or informal, which govern their operation. Contracts are institutions in that they lay down rules, which govern activities of the contractual parties. Codes of conduct are institutions as well in so far they can constrain the relationships between different individuals and groups (Nably and Nugent, 1989). In the standard neo-classical general equilibrium model, commodities are identical, the market is concentrated in a single point in space and the exchange is instantaneous; individuals are fully informed about the commodity and the terms of trade are known for both parties. Prices are sufficient allocative device to achieve highest value uses. The theory of price has provided valuable insights into the fundamental nature of exchange and resource allocation in decentralised markets (North, 1990). Coase (1960) made clear that only in the absence of transaction costs the neo-classical paradigm yields the implied allocative results. With transaction costs, resource allocations are altered by property right structures (North, 1990). In institutional economics, commodity markets are supposed to be imperfect and characterised by transaction costs which require institutions to regulate property rights and contracts, for example, marketing organisations, standardisation in grading or in contracts. A broad and narrow definition of transaction costs is distinguished. The broad definition of transaction costs states that transaction costs originate

in imperfect information, transportation, search, negotiation, recruitment, monitoring and supervision, motivation, enforcement, co-ordination, management et cetera (Sadoulet and De Janvry, 1995), or that transaction costs not only consist of measured costs (transportation, storage) but also of unmeasured costs such as waiting, bribery, uncertainty and governance (Lutz, 1994). In the narrow definition, transaction costs are associated with the costs of both acquiring information about a potential exchange and completing the exchange. Eggertsson (1990) distinguishes: information costs (the search for information about price, quality, potential buyers and sellers), bargaining costs (the bargaining process) and enforcement costs (the making of contracts, the monitoring of contractual partners, the enforcement of the contract, the protection of property rights against third-party encroachment). 3.4 Market imperfections and transaction costs To efficiently meet their marketing requirements, private firms participating in the horticultural export sub-sector must achieve commodities that match their needs in terms of quantity, quality and timing. In developing their crop procurement strategies, such firms will normally consider the following implications of alternative institutional arrangements:

1. the levels of production and transaction costs incurred, 2. the levels and distribution of production, political and other risks

and 3. their ability to effectively control the available supplies.

Figure 3-3 Different institutional agreements from low to high integration

Spot Market Forward Interlinked Vertical Market Reciprocity Market Factor and Integration Purchase Agreement Contract Market Contract

Market Contract Hierarchy


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(Jaffee, 1992) In spot markets, growers and their buyers meet at the time of the purchase. They agree on price, and delivery is immediate. At the other extreme is vertical integration, where the grower of the crop and the user of the crop are within the same firm, and so growing of the crop is fully co-ordinated with processing and marketing. In between these extremes are various intermediate arrangements. One of them is the market reciprocity agreement. This is an informal, yet highly personalised repeat trading tie in which some degree of loyalty is built up between the buyer and a certain sub-set of growers. Still, produce is exchanged at the current market price and the buyer is not involved in the production process. Another possibility is a forward market contract. This contract involves formal commitments to buy and sell specified quantities and qualities of produce at particular times. Prices may be agreed on in advance or at the time of actual exchange. A more intensive arrangement combines these forward purchase and sale commitments with buyer promises to provide specified production inputs and technical advise on credit and farmer agreement to follow the buyer’s instructions regarding production. Such interlinked factor and market contracts are frequently referred to as contract farming. According to Bardhan (1989), different institutional arrangements are shown to have particular advantages and disadvantages, with their relative suitability depending upon the actual operating conditions surrounding the trading relationship. In defining the operating conditions, emphasis is given to two main elements, namely the extent to which the required investments in productive assets are specialised for a particular product or trading relationship and the overall degree of uncertainty surrounding the exchange relationship. For any particular production and trading operation, individuals may undertake either generalized or specialized investments. Certain types of plant, equipment, materials, and knowledge have potentially generalized use across a broad range of products or trades. Other assets are highly specialized for a particular product or trade outlet and have little or no alternative use or value outside of this product or trading area. Examples of asset-specificity in agriculture include crops with extended gestation periods or production cycles (such as fruit trees), large-scale specialized

processing and post-harvest facilities, and use of highly specialized production inputs and technical knowledge. In any particular trading context, the degree of uncertainty may vary - uncertainty regarding the availability of supplies or market outlets, the quality of the products on offer, the timing of supply and demand, the trading terms being offered, possible political interventions, and so on. Such uncertainties tend to be more pronounced in agriculture than in industry because of the important influence of changing weather conditions and the wider geographical dispersion of primary producers and intermediate users. In the theoretical literature of transaction cost economics, it is proposed that spot market exchange, long-term contracts, and vertical integration will each be efficient modes of organization, defined in terms of economizing on a combination of production and transaction costs, under different degrees of asset-specificity and uncertainty. The table below summarizes the hypothesized relationships. Table 3-1 Operating conditions and appropriate institutional arrangements

Asset specificity High Medium Low High Vertical

Integration Vertical Integration

Long-term Contract

Medium Vertical Integration

Long-term Contract

Long-term Contract or Spot Market

Uncertainty

Low Vertical Integration

Long-term Contract

Spot Market

Source: Jaffee (1992) The literature contends that under conditions of high asset-specificity, the most efficient mode of organization is the vertical integration of the two adjacent stages of production or marketing. The firm investing in specialized assets, particularly those which are durable and involve large sunk costs, will be highly vulnerable to opportunistic bargaining on the part


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of suppliers or buyers since they will know that this investor has little or no alternative use for such assets and thus must come to terms unless it is in a monopolistic or monopsonistic position. Vertical integration is also viewed as an effective means of countering high levels of operational uncertainty, since a central management gains control over the different stages and direct supervision can be introduced. On the other hand, when asset specificity is moderate to low, other institutional arrangements are viewed as more suitable than vertical integration since they tend to provide greater flexibility of action, have lower 'start-up' costs, and do not incur the heavy overhead costs associated with integrating separate operations. Long-term contracts enable buyers and sellers to counter market uncertainties by offering mutual assurances and by supplementing price signals with other informational devices related to the quantity, quality, and timing of expected deliveries and purchasing requirements. Where both asset-specificity and uncertainty are low, spot market arrangements may be most efficient as this gives the participants greatest flexibility of action and immediate signals about performance. It is generally easier (and less costly) to negotiate an adjustment in price levels than to agree upon and implement changes in trading rules or lines of command. Market imperfections and transaction costs influence the decision of firms to contract-out, vertically integrate or use spot markets to obtain raw product. A lack of access to capital, inputs and information restrict the ability of small farmers to become involved in new commercial activities that require high initial investments and/or involve specialised inputs. Contract farming could be a feasible procedure to reduce risk, both for the firm that has an assured supply of inputs and for the farmers that have an assured market outlet for their produce. Contract farming may be defined as agricultural production carried out according to an agreement between a farmer and an intermediary, which places conditions on the production and marketing of the commodity. It is an intermediate institutional arrangement that allows firms to participate in, and exert control over the production process without owning or operating the farms (Key et al., 1999). In this thesis, the definition contract will refer to any type of agreement whether it is explicit, with little or no role for implicit understandings, or entirely implicit. The term intermediary refers to first

handlers who supply products to a variety of outlets, including supermarkets, restaurants, institutions or other wholesalers. Contract farming is an institutional response to imperfections in markets for credit, insurance, information, factors of production and raw product and to transaction costs associated with search, screening, transfer of goods, bargaining and enforcement (Key and Runsten, 1999). One of the problems that occur between growers of produce and their intermediaries is the existence of information asymmetry. Asymmetric information creates an opportunity for emergence of an institution to mitigate the ‘moral hazard’ presented to farmers for taking self-interested actions that may not be in the interest of the intermediary. One area where this might have considerable consequences is quality management. According to Hueth et al. (1999) quality is important because product differentiation, value-adding strategies and better control of the character of inputs seem to have emerged as important factors in the restructuring of agrofood systems. The importance is particularly pronounced for fruits and vegetables because these commodities are among most likely to be observed and evaluated by consumers in their primary and unprocessed form. There exist instruments that might help the intermediary to influence the behaviour of the grower. Four instruments will be described below, namely: monitoring, input control, quality measurement and exposing risk to the grower (Hueth et al., 1999). 1. Monitoring The intermediary might choose to monitor the farmers’ activities directly by employing field persons to make periodic visits to each grower’s farm. These representatives might make visits to share relevant information from other growers, to talk about market news, to acquire information on expected yields and harvest dates and to advise growers regarding problems they face. 2. Input control Some inputs can be controlled or specified as a means of obtaining information on quality-relevant decisions made by the grower. By providing seed of known quality, the intermediary can control for variation in seed quality determining the reasons for low- or high quality outcome. In the same way he might provide fertilisers and pesticides to the growers. The


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intermediary might also offer to provide or pay for consulting services on management decisions such as irrigation timing or pest control. 3. Quality measurement Quality measurement provides direct evidence on realised quality outcomes. However, it is an imperfect indicator of grower effort, because it is difficult or almost impossible to measure the quality of the whole quantity. Furthermore there might be a number of important quality characteristics that do not become apparent until the commodity has travelled further downstream. Nevertheless there are a variety of ways that quality measurement might be used to provide incentives. A firm might invest in a highly sophisticated quality monitoring system or use a crude set of grades. In some cases third party grading might be available. 4. Exposing grower to risk By making the farmer’s payment contingent on prices that are downstream received by the intermediary (e.g. price paid to the intermediary by the supermarket), the farmer can be made directly responsible for poor expression of important quality attributes. 3.5 Implications for small farmers According to Glover and Kusterer (1990) agribusiness do prefer large farmers, because of the inconvenience of finding many small farmers and furnishing them with contracts, inputs, technical assistance, etc. The firms interviewed by Meissner (1969) also expected more uniform quality and fewer production problems from big farmers. Key and Runsten (1999) also note that many firms avoid smallholders, preferring to contract with larger capitalised growers. According to Harris et al. (2001), exporters would not find it profitable to contract with small farmers because of high risk and transaction costs. In cases where firms do deal with small growers (Nestle, 1975; Laramee, 1975), three factors are usually present, either singly or in combination. First, the area most suitable for production may be characterised by small farmer predominance, and the firm simply works with whatever is available. Second, the local government may encourage the firm to make use of small growers. Third, smallholders may have lower costs of production than large growers or be willing to accept lower prices or greater shares of risk.

With regard to the imperfect information problem, case studies demonstrate that larger producers are generally better educated and better informed about the latest production technologies, pesticide regulations, consumer quality preferences, et cetera (Bivings and Rusten, 1992). Larger producers have an advantage over smaller producers in that fixed costs with acquiring information can be subtracted from a larger revenue base. According to Harris et al. (2001), the transparency of European market standards, both public and private, varies among actors in the industry. The few large exporters, as well as large-scale farmers and plantations, seem to understand the issues and have access to information. However, medium to small-scale exporters and producers do not fully understand the actual content of the standards or their implementation. Because smallholders are less able to bear the risk of crop loss, they may be more likely to over-apply pesticides in an attempt to minimise pest damage. If smallholders are more likely to violate pesticide regulations, they will be less desirable growers from the firm’s perspective. With respect to the specialised inputs that might be necessary to grow a specific product, it is likely that intermediaries will prefer to begin contracting with large-scale producers if the technology they are promoting is better suited to large-scale operations. 3.6 Food quality Quality is not a single, recognisable characteristic. Although the quality of a food product (or service) is recognised as important for successful agribusiness, there is often a general lack of understanding or agreement on the meaning. Garvin (1984) proposed eight dimensions of quality, stressing that each dimension was self-contained and distinct, since a product could be ranked high in one dimension while being low in another. Table 3-1 shows a revised adoption of Garvin’s dimensions of product quality applied to fresh produce.


30

Table 3-2 Dimensions of food quality Dimension Explanation Possible application to

the food industry Performance Primary product

characteristics Texture, size, appearance, acidity, sweetness

Features ‘Bells and whistles’ Packaging, labelling, information on ‘how to use’, ‘alternative uses’

Reliability Frequency of failure Regular supply Conformance Match with specifications Minimum variability in

product quality standards Durability Product life Storage and shelf live Aesthetics ‘Fits and finishes’ Presentation, display Perceived quality Reputation and

intangibles Production system (e.g. organic versus conventional, integrated pest management (IPM) versus intensive agrochemicals, environmental impact, use of genetically modified organisms)

Serviceability Speed of repair Traceability, speed of recall or replacement

Source: Opara, 2000. The dimensions that can be influenced by growers and their buyers will be discussed below. 1. Performance The primary product characteristics are of a particular importance, especially in the case of fresh fruits and vegetables, because these commodities are among most likely to be observed and evaluated by consumers in their primary and unprocessed form (Hueth et al., 1999).

The produce that growers offer to their buyers will be evaluated on these characteristics. Growers can influence these characteristics by using high quality seed, the right quantities and types of pesticides and a sophisticated irrigation system. A part of their produce will be rejected, partly because it doesn’t meet the required standards. It appears that the rejection rate may depend on the level of market demand, the experience of the farmer in quality control, and how well the farmer understands the process – the smaller the farmer, the more likely he/she is to be unfairly treated (Harris et al., 2001). Jaffee (1995) estimated that in the mid-1980s, as a result of poor co-ordination between growers and down-stream buyers as well as seasonal production gluts and transport bottlenecks, the average level of waste and post-harvest losses was approximately 25%. 2. Reliability Suppliers are more and more valued at the fact whether they are able to supply the right amount at the right time. Castano (2001) argues that farmers participating in vertically integrated marketing systems are better able to implement sustainable resource management practices due to improved price stability and reduced input constraints. 3. Conformance With the definition conformance is meant that there is a minimum variability in product quality standards. This can be achieved by accurate grading. Furthermore long-term relationships might result in knowledge of the wishes of the customer. Contracts with regard to the quality an actor prefers might also result in homogeneity. 4. Durability Highly perishable products need to be treated carefully along the way to the final consumer. Storage facilities are very important in this process to maintain the quality. According to Dijkstra et al. (2001), better keeping quality could be achieved by improving either transport or storage methods or both. 5. Perceived quality To maintain their position, all actors within the chain will try to build up some kind of reputation. Farmers can do this for example by improving their production system, investing in specific assets like storage facilities and through keeping records. Middlemen may decide to only buy their


31

produce from suppliers who keep records and have storage facilities. Exporters could invest in cooling facilities to improve the quality. 6. Serviceability Traceability refers to the collection, documentation, maintenance and application of information related to all processes in the supply chain in a manner that provides a guarantee to the consumer on the origin and life history of a product (Opara and Mazaud, 2001). For a food product, traceability represents the ability to identify the farm where it was grown and sources of input materials, as well as the ability to track the post-harvest history and identify the specific location in the supply chain by means of records. 3.7 Seasonality in production and price Much of agricultural production is highly seasonal. Some farm products are produced more or less continuously year-round while others such as grains are harvested only once a year. Most fruits and vegetables have seasonal production and demand patterns that also influence their marketing. There are efforts to reduce the seasonality of fruit and vegetable suppliers. This often involves either a shift in production to an area of favourable climate or to greenhouse production (Kohls and Uhl, 2002).

Chapter 4 Methodology and hypotheses

32

4 Methodology and hypotheses This chapter provides an insight into the area of research, the samples, the data sets and the different analytical tools used in this research. In section 4.1 an introduction to the various surveys will be given. Section 4.2, 4.3, 4.4 and 4.5 deal each with a specific survey. For each survey the data collected, the limitations and the analytical framework will be described. Furthermore a number of hypotheses will be given. Paragraph 4.6 describes the secondary data that has been used in this research. Finally section 4.7 gives a brief explanation of the various analysing methods used. 4.1 Introduction This study is based on both primary and secondary data. All research questions are answered by means of four surveys, namely two surveys among growers, one survey among middlemen and one survey among exporters. Growers have two options for selling their produce. They can sell it either to an exporter directly or to a middleman. Middlemen do have the same choice. The layout of this channel is shown in Figure 4-1.

Research area The surveys were carried out in Mwea division of Kirinyaga district. Mwea is located about 170 km north-east of Nairobi. The farmers and middlemen were all situated around Mount Kenya (see Figure 4-2). The exporters were all located around the airport in Nairobi.

Figure 4-2 The map of Kenya (source: www.hamburg.de)

Figure 4-1 The layout of the marketing channel


33

The objective of the study, to analyse the production and marketing channel of the horticultural crop French beans, will be achieved by means of these surveys. The research questions ((1) which factors influence the choice of the channel, (2) what are the implications of this choice for the received price, the rejection rate, payment system and certainties and (3) what is the impact of contracts on the received price and the delivered quality) will be answered with the help of different analysing methods. 4.2 Survey among producers concerning relations This survey covers randomly selected growers within the main production area of French beans in Kenya. It aims to analyse the differences between two categories of farmers, namely those who do sell their produce directly to exporters and those who do sell their produce to middlemen. Furthermore the survey focuses on implications of the channel choice for the received price, the perceived quality and the certainties of growers. The factors that influence whether a grower receives a specific contract will be taken into account as well. The sample contains 40 cases. The survey was carried out during the low season, when there was little demand from the importing countries. The interviews were carried out with the help of a standardised questionnaire. De data descriptives can be found in Annex AF. The following data were collected

• Personal characteristics (gender, education level); • Farm characteristics (operating time farmer, total number of acres,

number of acres under French beans, quantity produced in a year, fixed assets);

• Average price farmers receive per kg; • Quality characteristics (storage facilities, record keeping, input

delivery by buyers, EUREPGAP knowledge); • Contractual arrangements (quantity, price, quality, amount of

pesticides to use, amount of fertilisers to use, delivering date).

Limitations of the data set The data set has a number of limitations that has to be taken in consideration. Although all interviews have been done personal, differences in interpretation of the questions and answers might still be a problem. Because this problem has taken place by all interviews, it may be neglected. The data were collected in August 2002. This is important to know, because the question regarding the average price will be influenced by the time of the year. Because the prices vary a lot during the year, it is difficult for the growers to determine their average received price. Furthermore it was difficult for the farmers to give the average amount produced annually. Analytical framework During the analysing part of this study, the research questions will be answered with the help of different analytical procedures. Table 4-1 shows the various estimation methods used for the separate research questions. Table 4-1 Research questions and estimation methods farmers Research question Dependent variable Estimation methodChannel choice farmers Dummy buyer Logit Implications price Price OLS Implications quality Ln(refused/(1-refused)) OLS Implications Paid-on-time Dummy paid-on-time Logit Implications Total-amount Dummy total-amount Logit Agreement quantity Dummy agreement

quantity Logit

Agreement price Dummy agreement price

Logit

Agreement quality Dummy agreement quality

Logit

Agreement pesticides Dummy agreement pesticides

Logit


34

The channel choice for a particular farmer Farmers are, just as they are price-takers, also channel-takers. They may have an opinion about which actor gives the highest price, which actor provides the best technical assistance etc. but in the end it will be the buyer who decides whether he wants to buy produce from a particular farmer. To get a better insight into the factors that might influence the choice of the channel, a t-test has been carried out. To determine the factors that influence the choice of the channel, logit analysis is used. There are a number of factors that might influence the exporters’ choice. First of all the education level of the farmer might influence this choice. The more educated a farmer is, the higher the chance the exporter will receive a good quality. The chance the exporter will go for someone who finished secondary school might be bigger, because the supply of farmers exceeds the demand. The second factor that could influence the choice is the number of years a person is a farmer. The more experience a farmer has, the higher the chance that he will produce a good quality. The size of the farm could also have an influence. The larger the farm, the better equipped the farmer could be to supply an exporter. Also the amount of French beans produced per acre might be a significant factor. The more kg a farmer produces per acre, the more willing the exporter is to buy from him. The less farmers supply the exporter, the less places he has to visit to collect the produce and the less farmers he has to control. The last three determinants that could have an influence have to do with the European requirements. The first one is whether the land is the property of the farmer himself. Because of new European requirements, a farmer has to implement crop rotation. When a farmer rents every year another piece of land, he cannot prove what has been grown here the last three years or so. Therefore one would expect that exporters prefer farmers who own the land. The second one is whether the farmer has heard from the EUREPGAP protocol, or in other words, whether the farmer is informed about European requirements. The third and also last one is whether the farmer keeps records. Because of European requirements, exporters are nowadays more and more forced to demonstrate where the produce is coming from and how it has been treated. Therefore they need the support of their suppliers who need to keep records of the amount and types of pesticides and fertilisers they have used. One would expect that the last

three variables will have a positive influence on the question whether an exporter wants to buy the produce of a particular farmer. The model that has been described above can be written down in the following formula:

)( 77665544332211 uf +Χ+Χ+Χ+Χ+Χ+Χ+Χ=Υ βββββββ With: 0=Υ if the farmer sells his produce to a middleman 1=Υ if the farmer sells his produce to an exporter =Χ1 Operating time of the farmer (in years)

=Χ 2 The size of the farm (number of acres)

=Χ 3 The amount of French beans harvested per acre (in kg)

=Χ 4 The education level of the farmer (6 levels)

=Χ 5 Whether the land is the property of the farmer (with 1=yes and 0=no)

=Χ6 Whether the farmer has heard from the EUREPGAP protocol (with 1=yes and 0=no)

=Χ 7 Whether the farmer keeps records of the amount and types of fertilisers and pesticides used (with 1=yes and 0=no) The second research question (what are the implications for the received price, the rejection rate, payment system and certainties) has implicitly been followed by the question: which factors influence the price, quality and certainties? For the price, the average received price has been used. Because quality is difficult to determine, the average percentage of the harvest that was rejected by the buyer has been used. For the last one, the certainties, two variables were used, namely the question whether the farmer was always paid the day the buyer promised and whether the buyer always bought the total amount the farmer had harvested.


35

The implications for the price The price a farmer receives depends on a number of determinants. One factor might be the buyer he sells his produce to: an exporter or a middleman. One would expect that the average price is higher when a farmer sells his produce to an exporter directly. In this case there would be one actor less who needs to make a living. Furthermore an exporter might have a more steady market and this might influence the price. Another determinant could be whether the farmer has storage facilities or not. If yes, the quality will remain better than without, and a higher quality will mean a higher price. The third one might be the amount of French beans per acre he grows during a typical year. Buyers usually prefer large amounts, so you would expect the more he supplies per acre, the higher the price. The number of years a farmer supplies the same person could also be of influence. One would expect that the longer they work together, the more trust etc. there is between the two parties and the higher the price. Another factor could be the knowledge a farmer has about regulations from Europe. The more he knows about such a subject, the more he is involved in the process, the higher the price. The quality produced might also have an influence on the price. The variable that reflects the quality in the questionnaire is the percentage of the harvest that is rejected by the buyer. The better the quality, the lower the rejection rate and the higher the price could be. The question whether the farmer keeps records might also influence the price. Keeping records can be compared with providing an extra service to your customer. This service might positively influence the received price. The last one could be whether the farmer has an agreement with his buyer regarding the price. If a farmer has an agreement, the average price received during a year could be positively influenced. This model could be written down in the following formula:

u+Χ+Χ+Χ+Χ+Χ+Χ+Χ−Χ=Υ 8877665544332211 ββββββββ With: =Υ The price in Kenyan Shillings per kg

=Χ1 The amount of French beans produced annually (in kg)/the acres under French beans

=Χ 2 The rejection rate (between 0-100)

=Χ 3 The number of years the farmer supplies to the same buyer

=Χ 4 The buyer (with 1=exporter and 0=middleman)

=Χ 5 Whether the farmer has heard from the EUREPGAP protocol (1=yes and 0=no)

=Χ 6 Whether the farmer has an agreement with his buyer regarding the price he will receive (with 1=yes and 0=no)

=Χ 7 Whether the farmer has storage facilities (with 1=yes and 0=no)

=Χ8 Whether the farmer keeps records (with 1=yes and 0=no)

The implications for the quality The quality a farmer delivers also depends on a number of factors. The first one is the buyer he sells his produce to: an exporter or a middleman. It would be expected that an exporter will be more severe than a middleman. A middleman usually shows up in the field when there is a lack of supply. He knows he does not have to be that strict then, because the exporter needs his produce very urgently and will be satisfied earlier. The second factor is the storage facilities a farmer has. The produce has a higher chance to maintain its quality when the farmer is able to store it right after the harvest. Another factor is the number of years the farmer has been farming. The longer he has been a farmer, the more experience he has, the better the quality will be. Whether the buyer supplies any input to the farmer could also have influence. Farmers usually lack the capital to buy high quality seed and the right chemicals and fertilisers. Buyers sometimes supply these inputs and deduct the costs after harvest. The better the quality of the inputs, the better the quality of the produce will be. Knowledge might also have an influence. This factor is better described by


36

two variables, namely whether the farmer has heard from the EUREPGAP protocol and whether the farmer has finished secondary school. These variables might have a positive influence on the quality. Another determinant might be whether the buyer buys the total amount harvested. During times of oversupply, buyers are inclined to reject more of the harvest, because they do not need the produce and refuse to take the risk. Keeping records might give the farmer a kind of prestige and this might have a negative influence on the rejection rate (the rate will decline when a farmer keeps records). An agreement with his buyer might also have a negative influence on the rejection rate. The farmer will know better what quality the buyer wants and will be more motivated to produce this quality. A factor that could also contribute to this explanation is the number of years a farmer is supplying the same buyer. The more years of co-operation, the better a farmer knows what quality his buyer wants. The last one could be the amount of pesticide and fertilisers the farmers use. Also because of a lack of capital, they often do not use the required amount. Therefore one could expect that the more they use, the better the quality will be. This could be the other way around as well. There also exist farmers that use too much pesticides. Because farmers are often not aware of the bad side effects of pesticides, some of them use too much chemicals. This could ruin the crop. So this variable could have two different influences. This question can be better described by a utility model where the option that a buyer buys the produce is 1=Υ and the option that he refuses to buy because of a lack of quality is 0=Υ . The buyer will derive some kind of benefit from his choice. This benefit will be

111 εµ +Α= when 0=Υ and

222 εµ +Α= when 1=Υ This can also be written in the following way:

212121 εεµµ −+Α−Α=−

)0()1( 21 <−Ρ==ΥΡ µµ

(( )Χ+Χ

= '

'

exp1)exp

ββ

( )( ) ( )Χ=Χ+Ρ '' expexp1 ββ

( )( ) Ρ−=−ΡΧ 1exp 'β

( )Ρ−

Ρ=Χ

1exp 'β

Χ=

Ρ−Ρ '

1ln β

Where:

Ρ−Ρ

1ln = the actual rejection rate (between 0-1)

='β The coefficients belonging to the explanatory variables =Χ i The explanatory variables, with =Χ1 The number of years a farmer has been farming

=Χ 2 The amount of fertiliser a farmer uses (in kg per kg of seed)


=Χ 4 The number of years a farmer has been selling to the same


37

buyer =Χ 5 Whether the farmer has heard from EUREPGAP (with

1=yes and 0=no) =Χ 6 Whether the farmer keeps records (with 1=yes and

0=no) =Χ 7 Whether the buyer provides inputs / seed / pesticides /

fertilisers to the farmer (with 1=yes and 0=no) =Χ8 Whether the farmer has storage facilities (with 1=yes and

0=no) =Χ 9 Whether the buyer always buys the total amount of French

beans the farmer harvested (with 1=yes and 0=no) =Χ10 Whether the farmer has finished secondary school (with

1=yes and 0=no) =Χ11 Whether the farmer has an agreement with his buyer

regarding the quality he should deliver (with 1=yes and 0=no)

The implications for the certainties of a particular farmer Certainties are a broad definition and can be explained in several ways. In this analysis it will be explained by using two different variables, namely whether the farmer is paid on time and whether the buyer collects all the produce that is harvested. The first factor that could influence the certainties is the person a farmer sells his produce to: an exporter or a middleman. It would be expected that an exporter will pay on time more often, because he is more willing to keep his clients. One would also expect that an exporter will more often buy the total quantity of French beans harvested, because exporters usually have a more steady market for their produce. The second factor could be the number of years a farmer sells his produce to the same buyer. The more years he does, the more certain he might be to be paid on time and to become rid of the whole harvest. Another factor might be a quantitative variable, namely the size of the farm, the number of acres under French

beans, the quantity of French beans produced annually or the quantity produced per acre. One of these farm characteristics might have an influence. One would expect that the larger this characteristic, the more certain the farmer will be. Buyers usually have a preference for large farms, because of the economies of scale, so one would expect the larger the farm, the more certain the farmer. The rejection rate might also influence the certainties. One might expect that the lower the rejection rate, the better the quality delivered, the more kindly the buyer will be. A farmer that produces a high quality is rare, so the buyer will try to keep the farmer satisfied. Another factor might be the difference between the average price received by a particular farmer and the average price of the sample. The higher this difference, the more likely the farmer will be paid on time and sell the whole harvest. The last factor that could be of any influence is whether a farmer has any agreement with his buyer. With the definition ‘any agreement’ is meant: whether a farmer has a verbal or written agreement regarding the quality, price or quantity. The agreement regarding the date of delivering is not included, because almost all farmers have an agreement with their buyer whether or not written down. The agreement regarding the pesticides a farmer was allowed to use is also not included, because this agreement usually consists of a list given by the buyer. The fact whether a farmer has ‘any agreement’ could positively influence the certainties. For the analysis of the total amount the agreement regarding the quantity delivered has been taken into account. One would expect that farmers who have an agreement regarding the quantity are more assured that the whole amount harvested will be collected. The model could be written down in an equation:

)( 665544332211 uf +Χ+Χ+Χ+Χ+Χ+Χ=Υ ββββββ With:

=Υ Whether the farmer is paid on time (1=yes and 0=no) or whether the buyer always buys the total amount of French beans (1=yes and


38

0=no)

=Χ1 The number of years a farmer is supplying the same buyer

=Χ 2 The size of the farm/acres under FB/amount of FB produced in a year/ amount of FB per acre



=Χ 5 The difference between the average price and the price a farmer receives (in Ksh)

=Χ 6 Whether the farmer has any agreement with his buyer (with 1=yes and 0=no)/ whether the farmer has an agreement regarding the quantity (with 1=yes and 0=no) Agreements To find out which variables influence whether a farmer has a particular agreement, the logit procedure has been used again. The different types of agreements that were asked for during the interviews were: 1. Whether a farmer has an agreement regarding the quantity he should

deliver; 2. Whether a farmer has an agreement regarding the date he should

deliver the produce; 3. Whether a farmer has an agreement regarding the price he will

receive; 4. Whether the farmer has an agreement regarding the quality he should

deliver; 5. Whether a farmer has an agreement regarding the types and

quantities of pesticides he is allowed to use. For all analyses the same independent variables will be used. First of all a quantitative variable could influence whether a farmer has a particular agreement or not. The quantitative variables ‘acres under French beans’, ‘amount of French beans produced in a year’ and ‘the amount produced per acre’ all could have a positive influence, as well as the variable ‘size of

the farm’. Because one would expect that buyers prefer large-scale farms, one would also expect that these farmers have a higher chance to receive an agreement. A farmer that owns the used land is more attractive for a buyer in the field of continuation of the co-operation. Therefore this might positively influence whether a farmer has an agreement. Other variables that might have a positive influence are whether a farmer keeps records, whether a farmer has storage facilities, whether a farmer has heard from the EUREPGAP protocol and whether the buyer supplies inputs to the farmer. Another factor might be the buyer of the produce. One would expect that an exporter will more often conclude agreements with his suppliers because he is a more formal buyer than a middleman. The number of years a farmer is supplying the same buyer could also positively influence the chance to receive an agreement. Furthermore the number of years a farmer has been farming might have a positive influence. The more years of experience, the more sought-after a farmer will be, so the more a buyer wants to keep him as one of his suppliers. The last factor that could influence a buyer’s decision with regard to agreements is the quality of produce a farmer grows. The lower the rejection rate, the more chance a farmer will have to receive an agreement. This model could be written down in the following equation:

)(

1111101099

8877665544332211

uf

+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ−Χ+Χ+Χ=Υ

βββββββββββ

With:

=Υ Whether a farmer has an agreement regarding the quantity he should deliver/ the date of delivery/ the received price/ the quality he should deliver/ the quantities and types of pesticides he is allowed to use (with 1=yes and 0=no)

=Χ1 A quantitative variable (the quantity produced in a year/the number of acres under FB/the quantity per acre)


39

=Χ 2 The size of the farm (in acres)

=Χ3 The number of years a farmer is supplying the same buyer


=Χ5 The number of years a farmer has been farming

=Χ6 Whether the land is the property of the farmer (with 1=yes and 0=no)

=Χ7 Whether the farmer has storage facilities (with 1=yes and 0=no)

=Χ8 Whether the farmer keeps records (with 1=yes and 0=no)

=Χ9 Whether the farmer has heard from EUREPGAP (with 1=yes and 0=no)

=Χ10 Whether the buyer provides inputs/seed/pesticides/fertilisers to the farmer (with 1=yes and 0=no)

=Χ11 The buyer of the produce (with 1=exporter and 0=middleman) 4.3 Survey among producers concerning knowledge This survey has been held among farmers who supply one specific exporter. These farmers form producer groups who operate as a kind of co-operative. From each group 5 farmers were randomly selected. A total of 38 farmers have been questioned. Aim of the survey is to find out more about the knowledge of these farmers concerning topics as chemical use, hygiene and trace ability in the light of the EUREPGAP protocol. De data descriptives can be found in Annex AG. The following data were collected:

• Personal characteristics (gender, age, family size, education level respondent);

• Farm characteristics (operating time farm, number of acres, number of acres under French beans, quantity produced in a year, fixed assets, turnover French beans, total turnover);

• General knowledge farming (crop rotation, soil erosion, quality French beans);

• Knowledge concerning chemicals; • Knowledge concerning hygiene; • Knowledge concerning traceability.

Because the answers of the different farmers were almost all the same, it was impossible to use the data for any analysis. Therefore a brief description of the data will be given in chapter 5. 4.4 Survey among middlemen This survey covers randomly selected middlemen who live in the same area as the farmers do. It is concerned with 40 traders and they were questioned on non-marketdays (Tuesday and Thursday). Also with this survey the purpose is to analyse the differences between two categories, namely the middlemen who sell directly to an exporter and middlemen who sell to other middlemen. Furthermore the survey aims at analysing the differences between those two types of middlemen in the field of incentive instruments like monitoring, exposing grower to risk, input control and quality measurement. The middlemen will also be grouped into clusters that differ in the field of European requirements. De data descriptives can be found in Annex AH. The following data were collected:

• Personal characteristics (gender, education level); • Business characteristics (the products traders buy and sell,

commission rate, amount of French beans they trade, percentage of income related to French beans, fixed assets, number of employees);

• Questions regarding monitoring, input control, quality measurement and exposing grower to risk;


40

• Contractual arrangements (quantity, price, quality, amount of pesticides to use, amount of chemicals to use, delivering date).

Limitations of the data set The data set has a number of limitations that has to be taken in consideration. Although all interviews have been done personal, differences in interpretation of the questions and answers might still be a problem. Because this problem has happened by all interviews, it may be neglected. The data were collected in August 2002. This is important to know, because the question regarding the price paid to farmers and the price received from their buyers will be influenced by the time of the year. Because these prices vary a lot during the year, it is difficult for the middlemen to determine these prices. Because they had less trouble with giving the commission rate they charge, this number will be used in the analyses. Analytical framework During the analysing part of this study, the research questions will be answered with the help of different analytical procedures. Table 4-2 shows the various estimation methods used for the separate research questions concerning the middlemen. Table 4-2 Research questions and estimation methods middlemen Research question Dependent variable Estimation methodSegmentation middlemen - Traceability

- Storage middleman

- Storage farmers - Records - EUREPGAP

Cluster analysis

Channel choice middleman Dummy buyer Logit Implications commission rate – supplier side

Commission rate OLS

Implications commission rate – buyer side

Commission rate OLS

Implications quality Ln(refused/(1-refused)) OLS Implications certainties (paid-on-time)

Dummy paid-on-time Logit

Implications certainties (total-amount)

Dummy total-amount Logit

Agreement quantity farmers middleman

Dummy agreement quantity

Logit

Agreement price farmers middleman

Dummy agreement price

Logit

Agreement date of delivery farmers middleman

Dummy agreement date of delivery

Logit

Agreement quality farmers middleman

Dummy agreement quality

Logit

Agreement pesticides farmers middleman

Dummy agreement pesticides

Logit

Agreement quantity buyer middleman

Dummy agreement quantity

Logit

Agreement price buyer middleman

Dummy agreement price

Logit

Agreement date of delivery buyer middleman

Dummy agreement date of delivery

Logit

Agreement quality buyer middleman

Dummy agreement quality

Logit

Agreement pesticides buyer middleman

Dummy agreement pesticides

Logit

Agreement fertilisers buyer middleman

Dummy agreement fertilisers

Logit

Segmentation To find out whether the middlemen can be divided into groups with the same aspects, a cluster analysis (see §4.7.3 for an explanation) has been performed. The objective was to group the middlemen into clusters, which


41

differ in the field of European requirements. The variables that are the input for the segmentation are: 1. Whether the middleman is able to trace the produce; 2. Whether the middleman knows about the EUREPGAP protocol; 3. Whether the middleman has storage facilities; 4. Whether the farmers the middleman buys his produce from have

storage facilities; 5. Whether the farmers the middleman buys from do keep records. The channel choice for a particular middleman The exporter could also choose to buy the produce he needs from a middleman instead of from farmers. As in the case of the farmers, the middlemen are channel-takers because also here supply exceeds demand. The determinants that could influence the choice of the exporter are closely related to the ones of the farmers. The factors education level, operating time and the amount of French beans traded could positively influence the choice. Furthermore the number of products traded might be an important factor. The more products a middleman trades, the more interested the exporter might be. Exporters often export more than one product and the less middlemen they need for supply, the better for them. Another factor that might have any influence is whether a middleman visits his suppliers regularly to control the production process. Of course exporters want the highest quality that’s possible, so one would expect that they prefer middlemen who control their farmers. Storage facilities (of the middleman himself and of his suppliers) have a positive influence on the quality, so they might also positively influence the choice of an exporter. Just like the storage facilities, delivering inputs to his suppliers could also have a positive effect for the middleman on the choice of an exporter. The factor whether his farmers keep records could also positively influence the exporters’ choice. Also the fact that a middleman knows about the European requirements could have a positive influence. Last but not least is the fact that middlemen can trace the produce. This determinant becomes more and more important because of new requirements of the importers. Exporters are more and more forced to prove that they know which farmer grew the produce they export.

Therefore exporters might prefer middlemen who have set up a system to trace the produce. This model could be written down in the following equation:

)(

1111101099

8877665544332211

uf

+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ=Υ

βββββββββββ

With: 1=Υ if the middleman sells his produce to an exporter 0=Υ if the middleman sells his produce to another

middleman

=Χ1 The amount of French beans the middleman trades annually (in kg)

=Χ 2 The operating time of the middleman

=Χ 3 The number of products the middleman trades

=Χ 4 The education level of the middleman

=Χ 5 Whether the middleman can trace the produce (with 1=yes and 0=no)

=Χ6 Whether the middleman visits his growers (with 1=yes and 0=no)

=Χ7 Whether the middleman has storage facilities (with 1=yes and 0=no)

=Χ8 Whether the middleman has heard from the EUREPGAP protocol (1=yes and 0=no)

=Χ9 Whether the farmers the middleman buys his produce from

have storage facilities (1=yes and 0=no)

=Χ10 Whether the farmers the middleman buys his produce from


42

keep records (with 1=yes and 0=no) =Χ11 Whether the middleman provides inputs to his farmers (with

1=yes and 0=no) The implications for the commission rate Because a middleman buys his produce from someone and sells it to another person, he has to deal with two parties and acts like an intermediary. Therefore he should be careful to avoid selling his produce against a lower price than the buying price with the consequence that he will loose his revenue. So the commission rate depends on two prices: the one he will offer the farmer and the one his buyer will offer him (see Figure 4-3). Because some factors that influence the price paid to a farmer could indirectly influence the price received from a buyer (e.g. if the price paid to a farmer increases because of a higher produced quality, the price received by the middleman might also increase because of this high quality), the analysis will be spilt into two analyses.

Figure 4-3 The composition of the commission rate

The first one is the analysis concerning the variables that could influence the price paid to a farmer. The variables that could influence this price will be explained now. The first variable is whether the farmers he buys from do have storage facilities. You might expect that he is willing to give farmers with storage facilities a higher price, because one would expect a higher quality from these farmers. Another factor that has to do with quality is the rejection rate. One would expect that the lower the rejection rate (which implicitly means the higher the quality), the higher the price a

middleman is willing to pay his farmers. A variable that also might have a positive influence on the price he is willing to pay the farmers is whether they keep records. Another factor is whether the price he is willing to pay the farmers depends on the price he will receive downstream. If this price does depend on the price downstream, it will be in the advantage of the middleman himself, so it will have a positive influence on the commission rate. Whether he has agreed on a price with his farmers might also influence the price he will have to pay. A constant price has a disadvantage for the middleman. In times when the market price is low, he will loose some of his revenue. Therefore this variable might have a negative influence on his revenue. The last variable that might have an influence is the number of years a middleman is supplying from the same farmers. One might expect that a long-term relationship will result in a higher price for the farmers. One aspect that should be reminded is that the variables which have a positive influence on the price paid to the farmers might have a negative impact on the commission rate of the middleman. This could be written down in the following equation:

665544332211 Χ−Χ−Χ+Χ−Χ−Χ=Υ ββββββ with: =Υ The commission rate in Ksh per carton18

=Χ1 The rejection rate (between 0-100)

=Χ 2 The number of years a middleman is buying from the same farmers

=Χ3 Whether the farmers the middleman buys his produce from keep records (with 1=yes and 0=no)

=Χ 4 Whether the price explicitly depends on the price the middleman receives downstream (with 1=yes and 0=no) 18 A carton is the same as 3 kg.


43

=Χ5 Whether the farmers the middleman buys his produce from have storage facilities (1=yes and 0=no)

=Χ6 Whether the middleman has an agreement with his suppliers regarding the price he should pay.

The second analysis focuses on the factors that could influence the price received by a middleman. The first one is the buyer he sells his produce to. One would expect that selling to an exporter directly would be more profitable, because you exclude an actor (a middleman) whom otherwise also needs to make a living. The second factor that might have any influence is the number of years the middleman is selling to the same buyer. The longer the relationship, the more trust etc. you would expect and this might result in a higher price. The amount of French beans a middleman trades annually could also have a significant influence. The more he can offer his buyer, the higher the price would be because of economies of scale for both the buyer and the middleman himself. Another determinant could be whether the middleman has storage facilities himself to preserve the quality. It would be expected that middlemen who do have these facilities would receive a higher price. Whether the middleman is able to trace the produce might also be important. Exporters prefer middlemen who have set up a system to trace the produce and they might be willing to reward this with a higher price. A factor that might influence the quality and implicitly the price is whether the middleman supplies inputs to his farmers. High quality inputs will result in a higher quality, which is usually rewarded with a higher price. Another factor that might influence the quality is whether the middleman visits his suppliers to control the production process. Those visits might positively influence the quality and implicitly also the price. Middlemen who have an agreement with their buyer have more chance to receive a higher price, because this price will be more steady than the market price. Whether the buyer of the produce supplies seed to the middleman could also have a significant influence. If the buyer does, the produce could have a higher quality because of the seed. Therefore the price might be higher. Two other factors might be whether the farmers the middleman buys his produce from have storage facilities and keep records. These two factors might

have a positive influence on the quality and therefore they might result in a higher commission rate. Whether the middleman has an agreement with his buyer regarding the price could also influence the commission rate. If a middleman does have such an agreement, the average commission rate might be higher than when he doesn’t have such an agreement. The last factor that might influence the price the buyer is willing to pay, is the knowledge a middleman has concerning rules from Europe. The more knowledge he has, the more preferred he is as a business partner and this might result in a higher price. This could be written down in the following equation:

u+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ=Υ

1414131312121111101099

8877665544332211

ββββββββββββββ

With: =Υ The commission rate in Ksh per carton =Χ1 The number of years the middleman supplies the same

buyer =Χ 2 The amount of French beans a middleman trades

annually (in kg) =Χ3 Whether the buyer supplies seed to the middleman (with

1=yes and 0=no) =Χ 4 The buyer of the produce (with 1=exporter and

0=middleman) =Χ5 Whether the middleman has storage facilities (with

1=yes and 0=no) =Χ6 Whether the middleman has heard from EUREPGAP (with

1=yes and 0=no) =Χ7 Whether the middleman can trace the produce (with

1=yes and 0=no


44

=Χ8 Whether the middleman has an agreement with his buyer regarding the price he will receive (with 1=yes and 0=no)

=Χ9 Whether the farmers the middleman buys his produce from have storage facilities (with 1=yes and 0=no)

=Χ10 Whether the middleman provides inputs to his farmers (with 1=yes and 0=no)

=Χ11 Whether the farmers the middleman buys his produce from keep records (with 1=yes and 0=no)

=Χ12 Whether the middleman visits his suppliers (with 1=yes and 0=no)

The implications for the rejection rate the middleman handles The middleman usually rejects a percentage of the harvest a farmer offers. But why does he refuse a certain amount? There are a number of factors that could influence his decision. The first one is the person to whom he sells the produce. One would expect that an exporter is stricter in the selection of the produce. So the fact that a middleman supplies an exporter is supposed to result in a higher rejection rate. The second determinant is the number of years the middleman operates as a middleman. The more years he has been a middleman, the better he knows the demands from buyers, the more experience he has with grading, the stricter he will be, the higher the rejection rate. Another factor could be whether the farmer has storage facilities. These facilities will keep the produce in a better quality, so the rejection rate will be lower. Whether he has made an agreement with his buyer regarding the quality he should deliver, could be of influence. If so, he is expected to be stricter on the quality he supplies, so the rejection rate will be higher. When a middleman visits his growers to advise them about the produce, the quality would be expected to be higher. Also the fact if the middleman supplies inputs/seed to the farmers could influence the quality, because farmers often lack the capital to buy high quality inputs themselves. The requirement for farmers to keep records becomes more and more important. For middlemen it is easier to control farmers who keep records. Farmers who do keep records,

could build a reputation with their buyer, which could result in a lower rejection rate. Whether a middleman has an agreement with his suppliers could have a negative influence on the rejection rate. The farmers would know better what quality the middleman wants and this could influence the quality of the produce they offer the middleman. Whether the buyer of the produce buys the total amount the middleman has collected, could also negatively influence the rejection rate. When a buyer does not buy everything, the loss is for the middleman himself. Therefore he could reject more than necessary when he knows that his buyer has his limits. So this variable might have a negative influence on the rejection rate. Middlemen who have heard from the EUREPGAP protocol might be stricter in the grading process and might therefore reject a higher percentage than middlemen who do not. The more years a middleman is selling to the same buyer, the better he will be able to know what quality his buyer wants. This might have a negative influence on the rejection rate. Also the number of years a middleman is buying from the same farmers might have a negative influence on the rejection rate. The more years they are collaborating, the more the farmers will know what quality the middleman wants and the lower the rejection rate. The last variable that might influence the rejection rate is whether the buyer provides seed to the middleman. This variable could have a negative influence on the rejection rate for the same reason as mentioned above for the variable ‘input delivery by the middleman’. This model can be written down in the following formula:

u+Χ−Χ−Χ+Χ+Χ−Χ−Χ−Χ−Χ−Χ+Χ−Χ+Χ=Υ

131312121111101099

8877665544332211

βββββββββββββ

With:

Α−Α

=Υ1

with A = the rejection rate (between 0 and 1)


45

=Χ1 Operating time of the middleman (in years)

=Χ 2 The number of years a middleman supplies the same buyer

=Χ 3 The number of years a middleman buys from the same farmers

=Χ 4 Whether the middleman has an agreement with his buyer regarding quality he should deliver (with 1=yes and 0=no)

=Χ 5 Whether the middleman visits the farmer (1=yes and 0=no)

=Χ 6 Whether the middleman provides inputs/seed (1=yes and 0=no)

=Χ 7 Whether the farmer keeps records (1=yes and 0=no)

=Χ8 Whether the middleman has an agreement with his farmers regarding the quality they should deliver (1=yes and 0=no)

=Χ 9 Whether the buyer buys the total amount the middleman has in stock (1=yes and 0=no)

=Χ10 Whether the middleman has heard from the EUREPGAP protocol (1=yes and 0=no)

=Χ11 The buyer of the produce (1=exporter and 0=middleman)

=Χ12 Whether the farmer has storage facilities (with 1=yes and 0=no)

=Χ13 Whether the buyer provides seed to the middleman (1=yes and 0=no) The implications for the certainties of a particular middleman The middlemen have been asked the same questions as the farmers with regard to the certainties they have. They were also asked the questions whether they were always paid on time and whether their buyer always bought the total amount they had in stock. When they are not paid on time, they will face difficulties in paying their farmers. Furthermore they might face difficulties in buying new produce for trade. They will also loose a lot of money when their buyer does not buy the total amount they have in stock.

There are a number of factors that might influence these variables. The first two are the same as in the case of the farmers, namely the buyer they sell their produce to and the number of years they sell their produce to the same buyer. Furthermore the amount of French beans they trade might have any influence. The more French beans they trade, the more important they will be for their buyer, and so the higher the chance that he will pay on time or buy the whole stock. Another factor that might have an influence is the number of products the middleman trades. The more products he trades, the more a buyer can rely on him, the higher the chance the buyer will threat him well. Whether a middleman has an agreement with his buyer could also influence his certainties. With the definition ‘any agreement’ is meant: whether a middleman has a verbal or written agreement regarding the quality, price or quantity. The agreement regarding the date of delivering is not included, because almost all middlemen have an agreement with their buyer whether or not written down. The agreement regarding the pesticides and fertilisers a middleman was allowed to use is also not included, because this agreement usually consists of a list given by the buyer. For the question whether the buyer collects the whole stock, the variable ‘agreement quantity’ has been used. Middlemen who have such an agreement know the quantity they should deliver and the chance is higher that their buyer will take the whole amount. The last variable that could influence the certainties of a middleman, is the difference between the commission rate a particular middleman receives and the average commission rate of the interviewed middlemen. The higher this difference, the more satisfied a buyer is, and so the higher the chance that the buyer will buy the total amount and pay on time. The model will be:

)( 665544332211 uf +Χ+Χ+Χ+Χ+Χ+Χ=Υ ββββββ With:

=Υ Whether the middleman is paid on time/whether the buyer buys all


46

the produce (1=yes and 0=no)

=Χ1 The number of years the middleman supplies the same buyer

=Χ 2 The amount of produce a middleman trades annually (in kg)

=Χ 3 The number of products a middleman trades

=Χ 4 The buyer of the produce (1=exporter and 0=middleman)

=Χ 5 Whether the middleman has any agreement with his buyer (1=yes and 0=no) / whether the middleman has an agreement regarding the quantity that he should deliver

=Χ 6 The difference between the received commission rate and the average commission rate (in Ksh) Agreements Middlemen are the second actor in the chain and act like an intermediary between farmers and exporters or other middlemen. They can have agreements with their suppliers and/or with their buyers. Therefore the analysis that could influence whether they have a particular agreement is divided into two analyses. One analysis is focused on the supplier side of the chain, the other on the buyer side. The supplier side In the questionnaire attention has been given to agreements in the field of: 1. The quantity the farmers should supply; 2. The price they will receive; 3. The date the middleman will come to collect the produce; 4. The quality the farmers should deliver; 5. The types and quantities of pesticides the farmers are allowed to use. The first variable that could influence whether a middleman has such an agreement is the number of years a middleman is buying from the same farmers. The more years of co-operation, the higher the chance that a

farmer might receive an agreement. The second variable could be the rejection rate the middleman handles. The lower this rate, the higher the perceived quality and the higher the chance for a farmer to receive an agreement. The third factor could be whether the farmer has storage facilities. One would expect that middlemen prefer farmers who do have storage facilities and that this influences whether a middleman is willing to conclude an agreement. Another factor that could influence the behaviour of the middleman is whether the farmer keeps records. Farmers who do are preferred by middlemen and have a higher chance to receive an agreement. The buyer of the produce could also have some influence. Middlemen who supply exporters might have a more steady market and could therefore be more willing to give their farmers an agreement. Whether the middleman himself has the same type of agreement with his buyer, could also influence his decision. It would be expected that middlemen are more willing to give their suppliers a specific agreement if they have one themselves too. Whether the middleman visits his growers could also be of significant influence. Middlemen who do visit their farmers are better able to control the production process. Therefore they might be more willing to offer their farmers an agreement then middlemen who do not visit their suppliers. The last variable that could have any influence is whether the middleman supplies inputs to his farmers. Inputs are very expensive. Middlemen who supply inputs to their farmers might be more willing to conclude agreements to be assured that they will receive the harvested produce. The model that has been described above can be written down in the following formula:

)( 8877665544332211 Χ+Χ+Χ+Χ+Χ+Χ+Χ−Χ=Υ ββββββββf With:

1=Υ if a middleman has a particular agreement with his farmers 0=Υ if a middleman does not have a particular agreement with his

farmers


47

=Χ1 The number of years a middleman is buying from the same

farmers =Χ 2 The rejection rate a middleman handles

=Χ3 Whether a farmer has storage facilities (with 1=yes and 0=no)

=Χ 4 Whether a farmer keeps records (with 1=yes and 0=no)

=Χ5 The buyer of the produce (with 1=exporter and 0=middleman)

=Χ6 Whether the middleman has the same agreement with his buyer (with 1=yes and 0=no)

=Χ7 Whether the middleman visits his growers (with 1=yes and 0=no)

=Χ8 Whether the middleman supplies inputs to his growers (with 1=yes and 0=no) The buyer side In the questionnaire attention has also been given to agreements of middlemen with their buyers. The focus was on agreements regarding: 1. The quantity a middleman should deliver; 2. The price he will receive; 3. The date his buyer will come to collect the produce; 4. The quality he should deliver; 5. The types and quantities of pesticides his farmers are allowed to use; 6. The types and quantities of fertilisers his farmers are allowed to use. The first variable that could influence whether a middleman has a particular agreement with his buyer is the number of products a middleman trades. The more products he trades, the more preferred he might be and so the higher the chance he has to receive an agreement. The second factor is the average quantity a middleman trades annually. One would expect that buyers prefer large suppliers, so the quantity could have a positive influence on the question whether a middleman has a

specific agreement. The third variable that could have an influence is whether the middleman has storage facilities. Middlemen who do are expected to be preferred above middlemen who do not and do have a higher chance to receive an agreement. Another factor is whether the middleman has heard from the EUREPGAP protocol. This might have a positive influence for the same reason as the storage variable. The buyer of the produce could also have influence. One would expect that exporters are enclosing more often agreements with their suppliers than middlemen do. The number of years a middleman is supplying the same buyer could also positively influence the question whether he has a specific agreement or not. The more years of co-operation, the higher the chance a middleman will receive an agreement. Another factor that might have an influence is the operating time of the middleman. The more years he is in business, the more valuable he is for buyers, so the higher the chance that he will receive an agreement. A variable that can connect the buyer and the middleman is the question whether the buyer supplies seed to the middleman. Seed is a very expensive input. Buyers who supply seed to their suppliers might be more willing to conclude agreements to be assured that they will receive the output. Factors that have to do with the growers are whether the farmers keep records and whether they have storage facilities. These factors might have a positive influence on the number of agreements a middleman receives. Two variables that might influence the quality of the produce and implicit the performance of a middleman are the question whether the middleman supplies inputs to his farmers and whether the middleman visits his growers. A variable that reflects the produced quality is the rejection rate the middleman handles. The lower this rejection rate, the better the quality, so the higher the chance to receive an agreement. Traceability becomes more and more important for the export of vegetables. Therefore middlemen who are able to trace their produce might have a higher chance to receive an agreement. The model that has been described above can be written down in the following formula:


48

)(

15151414131312121111101099

8877665544332211

Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ+Χ−Χ+Χ+Χ=Υ

βββββββββββββββf

With:

1=Υ if a middleman has a particular agreement with his buyer(s) 0=Υ if a middleman does not have a particular agreement with his

buyer(s)

=Χ1 The number of products a middleman trades

=Χ 2 The average quantity of French beans a middleman trades annually (in kg)

=Χ3 The number of years a middleman is selling his produce to the same buyer(s)

=Χ 4 The rejection rate a middleman handles (0-100)

=Χ5 The operating time of the middleman (in years)

=Χ6 Whether the middleman has storage facilities (with 1=yes and 0=no)

=Χ7 Whether the middleman has heard from the EUREPGAP protocol (with 1=yes and 0=no)

=Χ8 The buyer of the produce (with 1=exporter and 0=middleman)

=Χ9 Whether the buyer supplies seed to the middleman (with 1=yes and 0=no)

=Χ10 Whether his farmers keep records (with 1=yes and 0=no)

=Χ11 Whether his farmers have storage facilities (with 1=yes and 0=no)

=Χ12 Whether the middleman is able to trace the produce (with 1=yes and 0=no)

=Χ13 Whether the middleman supplies inputs to his farmers (with 1=yes and 0=no)

=Χ14 Whether the middleman visits his farmers (with 1=yes and 0=no) 4.5 Survey among exporters Because of a lack of time and logistic problems only 5 exporters were questioned. These questionnaires were supposed to be used for a comparison with the ones of the middlemen in the field of quality perception and incentive instruments. Because of the low number of questionnaires they will be used for writing a case-study about the way the exporters operate and the problems they face (see Annex G). The following data were collected:

• Company characteristics (operating time, products for trade, number of employees, amount of French beans exported);

• Marketing channel characteristics (from who do they buy, to whom do they sell);

• Contractual arrangements; • Problems they face.

4.6 Secondary data Secondary data are used to analyse seasonality in the volumes exported during the last 7 years. These data were obtained from the Horticultural Crop Development Authority of Kenya (HCDA). 4.7 The analyses used In this paragraph the different methods to perform the analyses in this research are mentioned and explained in short.


49

4.7.1 The Ordinary Least Squares The most frequently used estimation procedure in econometrics is the method of Least Squares, which is more commonly known as Ordinary Least Squares (OLS). This method requires one to choose α̂ and β̂ as estimates of α and β , so that

2

1)ˆˆ( i

n

ii xyQ ∑

=

−−= βα

is a minimum. Q is also the sum of squares of the (within-sample) prediction errors when one predict iy given ix and the estimated regression equation (Maddala, 1992). The OLS procedure finds the straight line ‘closest’ to the data (Ramanathan, 1998). 4.7.2 The Logit analysis If the dependent variable in a model is a binary variable, one of the models that can be used is the logit model. This model uses the standard normal distribution to reflect the probability that a certain choice takes place (Greene, 1997). The assumption underlying logit analysis is that there is a response function of the form ttt u+Χ+=Υ βα* where tΧ is

observable but where *tΥ is an unobservable variable. What we observe

is a dummy variable iy defined by

>

=otherwiseyif

yi 001 *

(Maddala, 1992) The criterion that is often used to determine the quality of the model is the McFadden’s R-squared. This criterion takes a closer look at the

contribution of the explaining variables in the model, by looking at the value of the Log-likelihood:

−

−

l

l1 where

−

l is the restricted log likelihood.

4.7.3 Cluster analysis For segmentation various methods exist. A-priori and post-hoc methods have been distinguished. When segments are made without using the data, segmentation is a-priori. When using post-hoc methods, segments will be formulated on the basis of the results of data analysis. In addition, descriptive and predictive methods can be distinguished. Predictive analysis will predict one variable with the help of a set of other variables, while the descriptive method analyses relations between cases or variables. This research is descriptive, because it will describe relations between the cases of respondents and post-hoc, because the segments will be formulated on the basis of the data. In this situation cluster analysis can be used to find segments on the basis of the data. Cluster analysis is a multivariate procedure for detecting groupings in the data. Cluster analysis begins with no knowledge of group membership. For segmentation analysis first the number of clusters have to be found. This can be realised by doing hierarchical clustering using Ward’s method. Ward’s method is an often-used method for segmentation. Compared to other segmentation methods Ward’s method is more accurate. Ward’s technique consistently outperforms other methods (Kuiper & Fisher, 1975; Mojena, 1977). In the hierarchical clustering method, clustering begins by finding the closest pair of cases according to a distance measure and combines them to form a cluster. The algorithm continues one step at one time, joining pairs of cases, pairs of clusters, or a case with a cluster, until all the data are in one cluster. The clustering steps are displayed in a dendogram. The method is hierarchical because once two clusters are joined, they remain together until the final step.

Chapter 5 Results

50

5 Results The results of the executed analyses will be presented in this chapter. Section 5.1 describes all analyses related to the farmers. In section 5.2 a brief description will be given of the results of the survey among a producer group. The results that deal with the middlemen are published in section 5.3. In section 5.4 the seasonality analysis will be described. 5.1 Farmers This section deals with the channel choice of exporters concerning the farmers they prefer, the variables that influence the average price farmers receive, the variables that influence the quality of the produce and the certainties a farmer has. In the end the variables that influence the different types of contractual arrangements will be analysed. 5.1.1 Channel choice concerning farmers First of all a t-test has been carried out to examine the determinants that influence the choice of exporters concerning the farmers they prefer as their suppliers (see Table 5-1). One of the most important determinants is the significant difference in the price the farmers receive for their produce. Farmers who sell their French beans directly to an exporter receive a significant higher price than farmers who do not. The average amount produced is also significantly higher. Exporters provide more often inputs to their farmers than middlemen do. They have also significantly more often a verbal or oral agreement with their farmers with regard to the quantity the farmers should deliver, the price they are willing to pay, the quality the farmers should deliver and the amount and types of pesticides farmers are allowed to use. Farmers who supply an exporter are more often able to negotiate about the price. They also receive their revenue more often on the day the buyer promised them. On the other hand, they receive their payment usually not in cash on the delivering day. Finally farmers who supply an exporter are significantly more informed about

European requirements and do more often keep records of the amount and types of chemicals used. To find out more about the farmer-characteristics an exporter prefers, a logit model has been carried out. The variables that were entered into the model were: 1. The operating time of the farmer (YEARS); 2. The size of the farm (SIZE); 3. The amount of French beans produced per acre (VOLUME); 4. The education level of the farmer (EDU); 5. The dummy whether the farmer has heard from the EUREPGAP

protocol (EUREP); 6. The dummy whether the farmer keeps records (RECORDS); 7. The dummy whether the land is the property of the farmer

(PROPERTY). The dependent variable is a dummy for the buyer of the produce that takes the value one if a farmer sells his produce to an exporter and zero if he sells it to a middleman. The method used for including variables into the model is the Forward method. The variables have been entered one by one, to find out which variable had the highest significance. This variable was put in the model first. The method has been repeated, until there was no variable left that was significant. Table 5-2 shows the results obtained in this analysis (see Annex I for the complete output). When assessing the normality of the residuals using the Jarque-Bera statistic, it appears that the residuals are not normally distributed. To correct this, the variables’ logarithms should be used. This has not been done in this research, but it should be considered for further research. Signs of relationships will not change because of this and therefore the results will be interpreted as they are.

Chapter 5 Results

51

Table 5-1 Differences in farmers’ characteristics because of their market outlet Exporter

N=17Middleman

N=23Sign.

Education level (level 1-6) 3.88 4.22 Operating time (in years) 20.235 18.783 Size farm (in acres) 6.421 5.043 Acres under French beans 1.926 1.424 Average price per kg (in KsH) 27.8235 21.5800 ** The average amount of FB produced annually (in kg) 9798.2353 4046.3478 * The percentage of the farmers’ income that arises of French bean growing (%) 48.24 44.35 The acres belong to the farmer himself (yes=1, no=0) 0.88 0.78 The percentage of the harvest that is refused by the buyer because a lack of quality (%) 17.706 14.065 Does the buyer provide inputs? (yes=1, no=0) 0.82 0.35 *** For how many years have you been selling to this buyer? (number of years) 2.82 2.39 Farmer able to choose between different buyers (yes=1, no=0) 0.88 1.00 Farmer has an agreement regarding the quantity he should deliver (yes=1, no=0) 0.65 0.00 *** Farmer has an agreement regarding price he will receive (yes=1, no=0) 0.59 8.70E-02 *** Farmer could negotiate about the price (yes=1, no=0) 0.57 0.26 * Farmer has an agreement regarding the date he should deliver the produce (yes=1, no=0) 1.00 0.96 Farmer has an agreement regarding the quality he should deliver (yes=1, no=0) 0.88 0.13 *** Farmer has an agreement regarding the amount of pesticides he is allowed to use (yes=1, no=0)

0.76 0.43 **

Farmer receives the money with delivering (yes=1, no=0) 0.00 0.13 * Farmer always receives the money the day the buyer promised (yes=1, no=0) 0.76 0.33 *** Buyer always buys the total amount (yes=1, no=0) 0.71 0.74 Farmer has storage facilities (yes=1, no=0) 0.24 4.35E-02 * Farmer has heard from the EUREPGAP protocol (yes=1, no=0) 0.82 0.35 *** Farmer keeps records of amount of chemicals used (yes=1, no=0) 0.82 0.52 ** How many kg of fertiliser do you use per kg of seed? (in kg) 22.86 28.19 *Significant at the 0.10 level. **Significant at the 0.05 level. ***Significant at the 0.01 level.

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Table 5-2 Channel choice farmer (logit)

Variable Coefficient Std. Error z-Statistic Prob. C -3.234867 1.054922 -3.066453 0.0022

EUREP 2.600823 0.882666 2.946554 0.0032RECORDS 2.032547 0.914156 2.223414 0.0262

Log likelihood -19.58366 Avg. log likelihood -0.489592Restr. log likelihood -27.27418 McFadden R-squared 0.281971LR statistic (2 df) 15.38105 Probability(LR stat) 0.000457Jarque bera statistic 97.62065

The logit model indicates that being informed about new rules and regulations concerning European requirements has a positive influence on being chosen by an exporter. The requirements from European customers become more and more stringent throughout the chain. Also growers have to comply with more standards before they are allowed to supply the European market. One requirement of the EUREPGAP protocol is that growers keep records of the types and quantities of chemicals they use. Some European retailers have adapted to the EUREPGAP protocol as from the 1st of January 2003. Exporters who supply these retailers therefore need their suppliers to fulfil these rules. Therefore the fact whether a farmer keeps records has a positive influence on the choice of the exporter for such a farmer. 5.1.2 Implications for the price, quality and certainties Price To provide their family with food and clothing, to educate their children and to improve their farm, farmers need money. Therefore the height of the price they receive is very important. To find out which factors determine this price, the Ordinary Least Squares method was used. The following variables were put into the model: 1. To whom the farmer did sell his produce (1=exporter or 0=middleman)

(BUYER); 2. For how many years he has been selling to this buyer (SAME);

3. What percentage of the harvest was rejected by his buyer (RATE); 4. The average amount of French beans per acre produced in a year

(VOLUME); 5. Whether he has heard from EUREPGAP (EUREP); 6. Whether he has storage facilities (STORAGE); 7. Whether he keeps records (RECORDS); 8. Whether he has an agreement with his buyer regarding the price he

would receive (AGR_PRICE). The dependent variable is the average price the farmers received during the last 12 months. To get the best-fitted model, the most significant variable was entered first. After this one, the other variables were entered in turn, to see which variable had the highest significance. This method was used for entering each variable. The Jarque-Bera statistic indicates that the residuals are normally distributed (the Jarque-Bera statistic should be smaller than 5.99 to reject the hypothesis of the data differing significantly from a normal distribution). The results are shown in Table 5-3 (see Annex J for the complete output). Table 5-3 Price per kg (OLS)

Variable Coefficient Std. Error t-Statistic Prob. C 20.97402 0.798457 26.26820 0.0000

STORAGE 14.90056 1.642089 9.074151 0.0000VOLUME -0.000272 0.000105 -2.593360 0.0159EUREP 2.704128 0.941639 2.871724 0.0084BUYER 1.910113 1.031264 1.852207 0.0763

R-squared 0.823122 F-statistic 27.92175Adjusted R-squared 0.793643 Prob(F-statistic) 0.000000Log likelihood -62.60191 Jarque-Bera statistic 0.079169Durbin-Watson stat 1.851055

One of the significant variables that influences the price is whether the farmer sells his produce to an exporter directly or not. Farmers who sell their produce to an exporter directly will receive a significantly higher price

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than farmers who do not. Farmers who supply an exporter directly have significantly more often an agreement with their buyer regarding the price they will receive (see Table 5-1). Therefore their price is more or less steady throughout the year. Farmers who do not have an agreement face a higher fluctuation of the price they receive. This is according to the theory. Because there is one channel actor less involved in the marketing process, the profit per actor will be higher. Furthermore the price is influenced by the quality a farmer delivers: whether a farmer has storage facilities does influence the quality he delivers. Because French beans are a highly perishable vegetable, storage facilities are important to maintain the quality. Therefore the fact that a farmer has storage facilities, does have a positive influence on the price. This is in line with the theory. Durability is one of the eight dimensions that Garvin (1984) has set up. One of the possibilities to influence the durability of products is by using storage facilities. Because these facilities improve the shelf life, they will result in a higher price. Another factor that influences the price is the average amount of French beans produced per acre in a year (see the variable Volume). This variable has a negative influence on the price. The higher this volume, the more chance that the price will be lower. As can be seen in Figure 5-1, the larger the quantity produced, the lower the quality. According to theory, there is an optimal number of acres a grower could farm on his own. If he goes above this number, the quality will decrease because of a lack of time. The farmer could solve this problem by hiring employees. This could also cause some problems, because employees are on the whole less motivated than a farmer himself. This could also decrease the quality and result in a lower price. The last factor that has any influence is the knowledge that farmers have concerning European requirements. The better they are informed, the higher the price they will receive. The problem with bad informed farmers is that they might over-apply pesticides (see §3.5). This might harm the crop on the one hand and result in a refusal of the produce by the importer. Therefore buyers (exporters and middlemen) prefer farmers that are well informed, which results in a higher price.

Figure 5-1 Relationship between quantity and rejected percentage Quality The quality a farmer produces depends on a number of aspects. The variables that were put into the model were: 1. The operating time of the farmer (YEARS); 2. The buyer of his produce (1=exporter and 0=middleman) (BUYER); 3. The number of years the farmer supplies this buyer (SAME); 4. Whether the farmer has storage facilities (STORAGE); 5. The amount of fertilisers the farmer used (FERTILISER); 6. Whether the farmer has heard from EUREPGAP (EUREP); 7. Whether the farmer keeps records (RECORDS); 8. Whether his buyer supplies inputs to the farmer (INPUTS); 9. Whether he finished his secondary school (SEC_SCHOOL);

The percentage that is rejected by the buyer

6050403020100-10

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10. Whether his buyer buys the total amount he has harvested (TOTAL_AMOUNT);

11. Whether the farmer has an agreement with his buyer regarding the quality he should deliver (AGR_QUALITY).

The quantity of pesticides used has not been taken into account, because the answers varied too much to be converted into one uniform variable. The dependent variable is ln(rejection rate/1-rejection rate). To find out which factors determine this price, the Ordinary Least Squares method was used. Because of the small number of cases (see Annex K for the complete output), it would not be advisable to put all the eleven variables into the model. Therefore an alternative method was used. The most significant variable was entered first. After this one, the other variables were entered in turn, to see which variable had the highest significance. This method was repeated until the fifth variable was entered. The residuals are normally distributed (see Annex K for the complete output). The results are shown in Table 5-4. Table 5-4 The rejection rate (OLS)

Variable Coefficient Std. Error t-Statistic Prob. C -0.917687 0.329477 -2.785280 0.0089

YEARS -0.027367 0.008971 -3.050510 0.0046TOTAL_AMOUNT -0.766873 0.301049 -2.547333 0.0159

STORAGE -0.965444 0.428238 -2.254455 0.0311BUYER 0.632882 0.290408 2.179282 0.0368

R-squared 0.398179 F-statistic 5.292991Adjusted R-squared 0.322951 Prob(F-statistic) 0.002183Log likelihood -43.03363 Jarque-Bera statistic 5.062669

The number of years a grower has been farming, does have a negative influence on the rejection rate. The more years of experience a farmer has, the lower the rejection rate, or, in other words, the higher the presented quality. The experience also involves that a farmer knows better what quality his buyer wants. This brings about the fact that the farmer will

only offer the produce that comes close to the wishes of the buyer. Therefore this outcome could mean two things: the farmer has learned from past experience and produces a better quality or the farmer adapts the offered produce better to the wishes of his buyer. During times of oversupply, buyers often do not buy the total quantity a farmer has harvested. They have their limits and place the loss on the farmers. They grade stricter and accept only the highest quality, while during times of shortage they are satisfied with almost everything they receive. The variable total amount indicates whether the farmer sells his produce to a buyer who has a stable market for his produce or not. The model indicates that this variable has a significant negative influence on the rejection rate. The fact that a buyer has a more or less constant market, will reflect in a lower rejection rate. This could mean that the quality produced is higher or that the buyer has a more or less stable market. The buyer of the produce has a significant positive influence on the rejection rate. Farmers who supply an exporter directly face a higher rejection rate than farmers who do not, or in other words, exporters are stricter in the grading process. The last significant variable is whether the farmer has storage facilities. This variable has a negative influence on the rejection rate, or, in other words, a positive influence on the offered quality. Farmers who do have storage facilities are better able to maintain the quality. This is according to the theory, which states that storage facilities have a positive influence on the shelf life of fresh produce (see § 3.6). Certainties Paid on time Growing French beans needs a number of inputs like seed, fertilisers, pesticides and labour. Because small-scale farmers often lack the capital for these inputs, they buy them on credit. Because they know the dates of the harvest about, they know when they will receive the money for paying their creditors. Therefore it is very important for them to be paid on time. A logit model has been used to see which variables have a significant

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influence on the on-time-payment of the farmers. The independent variables that were put in the model are: 1. A quantitative variable (size of the farm, number of acres under French

beans, quantity of French beans produced annually) (SIZE/ACRES/QUANTITY);

2. The rejection rate (RATE); 3. The number of years a farmer has been supplying the same buyer

(SAME); 4. The buyer of the produce (1=exporter and 0=middleman) (BUYER); 5. The difference between the price received by a particular farmer and

the average price of the sample (PRICE_DIF); 6. The question whether the farmer has any agreement with his buyer

(ANY_AGR). The dependent variable is whether the farmer is paid on time (1=yes and 0=no). The variables were put in the model one by one, with the most significant one first. The model is reported in Table 5-5. The residuals are normally distributed, according to the Jarque-Bera statistic (the complete output is shown in Annex L). Table 5-5 Paid on time (logit)


BUYER 1.503904 0.777423 1.934475 0.0531PRICE_DIF 0.124167 0.072861 1.704166 0.0884

Log likelihood -20.67889 Avg. log likelihood -0.544181Restr. log likelihood -26.28694 McFadden R-squared 0.213340LR statistic (2 df) 11.21610 Probability(LR stat) 0.003668Jarque-Bera statistic 1.168448

The variable ‘buyer’ has a positive influence on the question whether a farmer is paid on time. Farmers who sell their produce to an exporter directly do have a higher chance to be paid on time. As has been noticed before, exporters have more often a stable market throughout the year.

Therefore they have less problems with paying their suppliers. The other significant variable is the price difference. The higher this difference, or in other words, the higher the price a farmer receives, the more chance a farmer has to be paid on time. As is shown in Table 5-3, there are a number of factors that influence the price a farmer will receive. One of these factors is the question whether a farmer has storage facilities. These facilities improve the quality and therefore raise the price. So farmers who receive a high price produce high quality French beans. These farmers are very valuable for their buyers and therefore the buyers will make sure that the farmers will receive their payment on time. Total amount Farmers always harvest the French beans that have a particular size and shape that are favoured by their buyers. If they do not harvest the beans that are ready, they will not be able to sell them anymore. Therefore they harvest all the saleable produce hoping that a potential buyer will buy the whole amount. A logit model has been used to determine which variables influence whether the buyer takes the whole amount or not. The independent variables that were put into the model are the same as the ones that could influence whether the farmer is paid on time. The dependent variable takes the value one when a buyer buys the total amount and zero when a buyer does not. The variables were put in the model one by one, with the most significant one first. The results are shown in Table 5-6. The residuals are not normally distributed, but the results will be interpreted as they are (the complete output is published in Annex M).

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Table 5-6 Total amount (logit)

Variable Coefficient Std. Error z-Statistic Prob. C 3.882022 1.282824 3.026153 0.0025

RATE -0.105775 0.043648 -2.423397 0.0154SIZE -0.269842 0.159227 -1.694695 0.0901


The percentage of the harvest that is refused by the buyer because of a lack of quality has a negative influence on the question whether the buyer buys the total amount. The higher the rejection rate, or, in other words, the lower the quality, the higher the chance that the buyer will not take the whole amount harvested. Buyers prefer of course high quality produce. The second variable that influences the model is the size of the farm. This variable also has a negative influence on the dependent variable. The larger the farm, the higher the chance that the buyer will not buy the whole quantity. 5.1.3 Contracts Farmers have all types of agreements with their buyers. They were asked whether they had an agreement regarding the quantity they should deliver, the price they would receive, the date the buyer would collect the produce, the quality they should deliver and the amount and types of pesticides they were allowed to use. One of them, a farmer who supplied the exporter Homegrown (see Box 1), had a formal contract in which all the elements mentioned above were written down. In this subsection the variables that influence whether a farmer has a particular agreement are analysed. The agreement regarding the date of delivery has not been taken into account, because the unequal distribution between the farmers with and the farmers without any agreement. 97.5% of the farmers actually has such an

agreement (see Annex H). The other agreements all have been analysed. The variables that could have any influence are: 1. The size of the farm (SIZE); 2. A quantitative variable (the quantity of French beans harvested

annually, the number of acres under French beans and the quantity harvested per acre) (QUANTITY/ACRES/VOLUME);

3. Whether the land is the property of the farmer (PROPERTY); 4. Whether the farmer has storage facilities (STORAGE); 5. Whether the farmer keeps records (RECORDS); 6. Whether the farmer has heard from the EUREPGAP protocol

(EUREP); 7. To whom the farmer sells his produce (exporter or middleman)

(BUYER); 8. For how many years the farmer has been selling to the same buyer

(SAME); 9. The percentage of the harvest that has been rejected by the buyer

(RATE); 10. The number of years a farmer has been farming (YEARS); 11. Whether the buyer supplies inputs to the farmer (INPUTS). 5.1.3.1 Agreement regarding the quantity a farmer should deliver 27.5% of the interviewed farmers has an agreement with his buyer regarding the quantity he should deliver. Exporters and middlemen face the risk that farmers sell their produce outside when there is a shortage of supply. Therefore some of them make arrangements with their suppliers regarding the quantity that they should deliver to them. On the other hand arrangements could be negative for them as well. Suppose there is a lack of demand from importing countries. Then the exporters and middlemen will be better of without any arrangements that force them to buy a specific quantity. A logit analysis has been used to see which factors influence whether a farmer has such an agreement. The results are shown in Table 5-7. The model explains 27% and the residuals are not normally distributed, still the results will be interpreted as they are (see Annex N for the complete output).

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Table 5-7 Agreement Quantity Farmers (logit)


VOLUME 0.000273 0.000122 2.244466 0.0248EUREP 2.320631 1.135554 2.043611 0.0410

Log likelihood -17.08968 Avg. log likelihood -0.427242Restr. log likelihood -23.52675 McFadden R-squared 0.273606LR statistic (2 df) 12.87414 Probability(LR stat) 0.001601Jarque-Bera statistic 31.23867 The more French beans per acre a farmer has to offer, the higher the chance that he will receive an agreement. According to a number of authors mentioned in paragraph 3.5, exporters prefer large farmers because of economies of scale, more uniform quality and fewer production problems. They are therefore more willing to contract farmers that have a high productivity. The other variable that has a positive influence on this chance is whether a farmer has heard from the EUREPGAP protocol. In other words, the more knowledge a farmer has concerning new regulations from importing countries, the higher the chance that he will receive a contract. 5.1.3.2 Agreement regarding the price a farmer will receive The price a farmer will receive is very important for him. In Table 5-1 is shown that exporters pay a significantly higher price to their suppliers and these farmers also have significantly more often an agreement with their buyer. Therefore it is interesting to see whether there are other factors that influence whether a farmer has such an agreement. A logit analysis has been carried out. The results are shown in Table 5-8. The model explains 66% and the residuals are not normally distributed. Still the results will be interpreted as they are (see Annex O for the complete output).

Table 5-8 Agreement Price Farmers (logit)


BUYER 10.52001 5.215581 2.017035 0.0437SAME 1.142525 0.593585 1.924786 0.0543

VOLUME 0.000403 0.000207 1.942837 0.0520PROPERTY 9.682366 5.308424 1.823963 0.0682

Log likelihood -8.253702 Avg. log likelihood -0.206343Restr. log likelihood -24.43457 McFadden R-squared 0.662212LR statistic (4 df) 32.36174 Probability(LR stat) 1.61E-06Jarque-Bera statistic 271.099

The buyer of the produce has a significant influence on the question whether a farmer has an agreement regarding the price. Farmers who sell their produce to an exporter directly have a higher chance to receive an agreement. Exporters have more often a stable market for their produce. Therefore they are more willing to agree on a fixed price during the year. Middlemen on the other hand usually show up during times of shortage of supply. They are able to offer very high prices. Because they do not have a market for the produce during times of surplus of supply, they are not able to agree on one price with their suppliers. Furthermore the number of years a farmer is supplying the same buyer has a positive influence. The more years a farmer is supplying the same buyer, the higher the chance that he will receive an agreement. During the years of co-operation, the buyer will find out whether the farmer produces a high quality or not. He will continue doing business with the farmers that produce a high quality. To keep this farmers as his suppliers, he will be willing to offer them a contract. Because he first needs to find out more about the skills of the farmer, it takes some years before a contract will be concluded. The quantity of French beans harvested per acre also has a positive influence. The higher this variable, the higher the chance that a farmer will receive a contract. For the theoretical explanation I would refer to the

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analysis regarding the agreement related to the quantity a farmer has to deliver. The last variable that has an influence is whether the land is the property of the farmer. Buyers favour farmers that own the land they grow French beans on. Because of new European requirements, a farmer has to implement crop rotation. When a farmer rents another piece of land every year, he cannot prove what has been grown here the last three years or so. Furthermore a farmer that owns his land might be more attractive in the field of continuation of the co-operation. 5.1.3.3 Agreement regarding the quality a farmer should deliver For export high quality produce is required. Therefore exporters and middlemen could enclose agreements with their suppliers to make sure that the farmers will do the best they can to guarantee a high quality output. To find out which factors determine whether a farmer has an agreement regarding the quality, a logit analysis has been used. The results are shown in Table 5-9. The residuals are not normally distributed, but still the results will be interpreted as they are (see Annex P for the complete output). Table 5-9 Agreement Quality Farmers (logit)


BUYER 4.217842 1.164931 3.620679 0.0003ACRES 1.081764 0.653543 1.655231 0.0979


The first variable that has a significant influence is the buyer of the produce. Farmers who sell their produce to an exporter directly have a higher chance to receive an agreement regarding the quality they should

deliver. As mentioned before, exporters usually have a more stable market than middlemen. Therefore they need to be sure to receive the right quality to prevent loosing their contacts with the importing countries. The second variable is the number of acres a farmer uses for the production of French beans. The higher this number the more chance a farmer has to receive an agreement. Because of economies of scale, buyers prefer farmers who have a large number of acres to produce French beans (see § 3.5). 5.1.3.4 Agreement regarding the pesticides a farmer is allowed to

use Because of requirements from importing countries, farmers are only allowed to use specific types of pesticides in restricted quantities. Exporters and middlemen usually lack the facilities to control all the inputs used by their suppliers. They could solve this problem by providing the farmers with a list of allowed and banned pesticides or by fixing a formal agreement. The question whether a farmer receives such an agreement or not could be influenced by several factors. A logit model has been used to find out which factors influence the exporters’ decision. The results are shown in Table 5-10. The model explains 18%. From the outcome of the Jarque-Bera statistic can be concluded that the residuals are normally distributed (see Annex Q). Table 5-10 Agreement Pesticides Farmers (logit)


EUREP 1.729097 0.772678 2.237798 0.0252RECORDS 1.717599 0.800615 2.145350 0.0319


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Farmers who have heard from the EUREPGAP protocol have a higher chance to receive an agreement from their buyer. Furthermore this chance is higher for farmers who keep records. These farmers write down which pesticides and fertilisers they use at which dates and in which quantities. Exporters and middlemen prefer these farmers because of new European requirements and so they have a higher chance to receive an agreement.

Summary Results Farmers Exporters prefer farmers who are up to date concerning European requirements and do keep records. The price farmers receive depends on whether they have storage facilities and whether they are up to date concerning European requirements . The more kg per acre a farmer produces, the lower the price he will receive. Exporters pay a significantly higher average price per year than middlemen do. The quality farmers produce depends on the number of years they are farming and whether they have storage facilities. Furthermore it depends on the stability of the market of their buyer(s). The more stable this market, the lower the percentage of the harvest that is rejected. Exporters do reject a significant higher percentage of the harvest than middlemen do. Farmers who supply an exporter directly face a higher chance to be paid on time. Another factor that has a significant influence is the price difference compared to the average price of the sample. The higher this difference, or in other words, the higher the price a farmer receives, the higher the chance a farmer will be paid on time. The rejection rate has a significant influence on the question whether the buyer collects the total amount harvested. The higher the rejection rate, or in other words, the lower the quality, the lower the chance that a buyer will collect the total amount. The size of the farm has a negative influence as well. Whether a farmer has a specific agreement, depends on a number of factors. The number of kg produced per acre has a positive influence. Exporters provide farmers more often with a contract than middlemen do. Farmers who have an up to date knowledge concerning European requirements, do keep records and own the acres they use for French bean production have a higher chance to receive an agreement. The number of years a farmer is selling to the same buyer(s) has also a positive influence. The last factor that has a significant influence is the number of acres a farmer uses for French bean production. The more acres he uses, the higher the chance that he will receive an agreement.

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5.2 Farmers and their knowledge concerning the EUREPGAP protocol

A survey has been held among 38 farmers who all supply the same exporter. The farmers form producer groups, which operate as a kind of co-operative. The exporter supplies the Dutch market. The aim of the survey was to find out more about the knowledge of these farmers concerning topics as chemical use, hygiene and traceability in the light of the EUREPGAP protocol. Because the answers of the farmers were almost all the same, it was impossible to use the data for any analysis. Therefore a brief description of the data will be given here. The group with respondents was made up of male and female farmers. Figure 5-2 shows the distribution between these two groups. The average age of the respondents was 40. The youngest respondent had the age of 20 and the eldest respondent had the age of 67. The average family consisted of 5 family members.

Figure 5-2 Gender of the respondents

The education level of the respondents differs. In Figure 5-3 can be seen that the majority of the respondents has finished secondary school. Almost the same number has only finished primary school. About 11% has had no education at all. The average operating time of the respondents is 13 years. The maximum operating time is 57 years, the minimum is 1 year.

Figure 5-3 The education level of the respondents

The average size in acres amounts to 3,2 acres. The maximum number of acres amounts to 15,0 and the minimum to 1,0. About 58% of the respondents grew French beans on less than 1 acre as can be seen in Figure 5-4. Only 18% of the respondents used 2 acres or more. Apparently this exporter deals with small-scale farmers. About 82% owned these acres. The average turnover the respondents got from the French bean production amounted to 55.000 Ksh per year. Their average total turnover amounted to 98.000 Ksh per year. The respondents were asked whether they were satisfied with their exporter. About 68% claimed to be satisfied. 11% of the respondents said to be very satisfied. The reasons they gave for their satisfaction were that they can earn a living, that the exporter is honest and that the exporter collects the produce from January till December. ‘Not very satisfied’ was the answer of about 21% of the respondents. They complained about a variable payment, low prices and a high percentage of rejects. The most important aspect the respondents wanted to change was the price they receive for their produce. About 74% of the respondents would like to receive a higher price for their produce.

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Figure 5-4 The number of acres under French beans

The other questions were related to the subjects chemical spraying, hygiene and traceability. The respondents were asked whether they thought that several aspects were important or not. According to their answer they were asked for the reason why they thought it was important or not. From the results can be concluded that the respondents are well informed about the reasons of European policies. Because the results were very uniform, they cannot be used for any analysis. 5.3 Middlemen The segments in which the middlemen can be divided will be described in this section first. Then the channel choice of exporters concerning the middlemen they prefer to operate with will be described. Furthermore the variables that determine the commission rate of the middlemen, the rejection rate they handle and the certainties they have will be observed. In the end the different types of contractual arrangements between the middlemen and their suppliers and buyers will be described and the

factors that determine whether an actor has a specific agreement will be analysed. 5.3.1 Segmentation To find out whether the interviewed middlemen can be divided into groups on the basis of EUREPGAP requirements, a cluster analysis has been carried out. The variables that are related to the EUREPGAP protocol are: 1. Whether the middleman has heard from the EUREPGAP protocol; 2. Whether the middleman has storage facilities; 3. Whether the farmers the middleman buys from have storage facilities; 4. Whether the farmers the middleman buys from keep records; 5. Whether the middleman is able to trace the produce. These variables have been used to see whether the middlemen can be divided into segments. Number of clusters The agglomeration schedule shows which cases are combined at each step, while the dendrogram helps to follow the junction of clusters. If there is a sudden jump in the size of the difference, this might consider that a solution is reached. At stage 38, the coefficient reaches a level of 3.051; while at stage 39 it jumps to 5,083. The classification of the clusters show that the 3-cluster solution generates distinguished clusters. One cluster groups middlemen that meet most of the requirements, one contains middlemen that meet about half of the requirements and there is one cluster containing middlemen who meet almost all requirements completely. Description of the clusters The clusters have been classified using the 5 aspects mentioned above. The result of this analysis is presented in Table 5-11.

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Table 5-11 The clusters

Middle-man has heard from EUREP-GAP

Middle-man has storage facilities

Farmers have storage facilities

Farmers keep records

Middle-man can trace produce

Clus ter

Num-ber of res-pon- dents

yes no yes no yes no yes no yes no 1 23 23 0 0 23 1 22 21 2 23 0 2 10 4 6 0 10 0 10 9 1 5 5 3 7 7 0 7 0 0 7 6 1 7 0 Total 40 34 6 7 33 1 39 36 4 35 5 The first cluster is constituted of 23 middlemen who meet most of the EUREPGAP requirements. This cluster will get the name ‘exporter agents’. These middlemen all have heard from the EUREPGAP protocol and are able to trace the produce. Almost all middlemen receive their produce from farmers who keep records. None of the middlemen has storage facilities themselves and only one middleman gets his produce from farmers who have storage facilities. The second cluster exists of 10 middlemen who meet some of the EUREPGAP requirements. This cluster will get the name ‘independent middlemen operating during times of shortage’. Within this group half of the middlemen meet the requirements with regard to traceability and knowledge concerning the EUREPGAP protocol. None of these middlemen has storage facilities, while almost all middlemen get their produce from farmers who keep records. The third cluster exists of 7 middlemen who meet almost all EUREPGAP requirements. This cluster will get the name ‘independent middlemen operating throughout the year’. These middlemen meet all requirements except the one with regard to storage facilities of the farmers. This aspect

is the least important one, because the produce is usually brought right to the middleman. Characteristics of the segments In Table 5-12 a description of some characteristics of the segments can be seen. Table 5-12 Characteristics of the 3 segments 1 2 3 F-value Sign. Number of products 2.30a 1.90a 1.57a 1.339 0.275 Seed supplied by buyer 0.96b 0.60a 0.71ab 3.827 0.031 Heard from Eurepgap 1.00b 0.40a 1.00b 20.813 0.000 Able to trace produce 1.00b 0.50a 1.00b 13.875 0.000 Delivers inputs to farmers 0.91b 0.50a 0.86b 4.343 0.020 Supplies buyer for … years 5.13b 1.50a 3.33ab 4.952 0.014 Operating time middleman 9.26b 7.00ab 4.04a 3.069 0.058 Whether buyer buys total amount

0.35ab 0.60b 0.14a 1.966 0.154

Commission rate 13.30a 17.10a 19.29a 2.040 0.144 Buyer of the produce 0.61a 0.70a 0.86a 0.745 0.482 Farmers keep records 0,87a 0,90a 0,86a 0,162 0.851 Middleman has storage facilities

0.00a 0.00a 1.00b . .

Farmers that supply middleman have storage facilities

4.35E-02a

0.00a 0.00a 0.357 0.702

Education level middleman 4,09a 4,20a 4,57a 0.595 0.557 Rejection rate 13,61a 17,40a 14,86a 0.793 0.460 Middleman is paid on time 0,87a 0,80a 0,86a 0.125 0.883 Quantity traded in a year (in kg)

93148a 70350a 108829a 0.456 0.637

Gender 0.00a 0.00a 0.29b 2.544 0.092 Visit growers 0.96a 0.80a 1.00a 1.581 0.219 Buys from farmers for … years

0.14a 0.90a 1.43a 4.326 0.021

Number of employees 4.26a 5.30a 11.29b 3.358 0.046 Whether any part of the 0.17a 0.50a 0.57a 3.091 0.057

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price is adjusted to the produced quality Middleman is able to negotiate with buyer

0.70a 0.60a 0.57a 0.239 0.788

Middleman has more than one buyer

0.35a 0.40a 0.43a 0.086 0.918

Middleman has an agreement with his buyer regarding the quantity he should deliver19

0.48ab 0.10b 0.57a 2.761 0.076

Middleman has an agreement with his farmers regarding the price he should give them

0.00a 0.00a 0.14b 2.544 0.092

Description of the segments Exporter agents This cluster does consist of middlemen who are all familiar with European requirements and who are all able to trace their produce. Most of them deliver inputs to their growers. They have on average long-term relationships with their buyer(s) and are traders for a considerable number of years. They often receive seed from their buyer, who could be another middleman or an exporter as well. Compared to the other segments they receive the lowest commission rate. Furthermore their buyer usually does not buy the total amount of French beans they have collected. They have the shortest business relation with their farmers. All respondents were male. The price they give their suppliers is not dependent on the quality they deliver. They have a significant lower number of employees than the middlemen of the third segment and they do not have any storage facilities at all. With regard to agreements they have with their buyers and suppliers, about half of the group has an agreement regarding the quantity they should deliver. On the basis of these characteristics this group 19 The agreements all have been compared between the segments. Because the other agreements did not result in a significant difference, they have not been displayed in this table.

conforms to the description of exporter agents described in subsection 2.3.2. Especially the fact that they receive a low commission rate and have long-term relationships with their buyer(s) points to this description. Independent middlemen operating during times of shortage This cluster is made up of middlemen who have little knowledge about the European requirements and who are often not able to trace their produce. They do not have long-term relationships with their buyers and usually do not receive any seed from them. They also do not supply inputs to their farmers as well. Compared to the other segments they have an average operating time and receive an average commission rate for their produce. They buy their produce from the same farmers for almost a year. Their buyer(s) usually buy(s) the total amount they have collected. This group consists of only male middlemen. The price they pay to the farmers depends on the quality they deliver. They have a significant lower number of employees than the middlemen of the third segment. According to the number of years they supply the same buyer, the fact that their buyer usually buys the total amount, their insignificant knowledge about the European requirements, their lack of storage facilities, their inability to trace the produce, the lack of agreements and the average commission rate one would expect that these middlemen operate only in times of shortage (see § 2.3.2 for a description of this type of middlemen). Independent middlemen operating throughout the year This cluster is constituted of middlemen that are all familiar with European requirements, have storage facilities and are all able to trace their produce. Furthermore they receive the highest commission rate per kg and they have the highest number of employees. The number of years they supply the same buyer lies between the one of segments 1 and 2. They have the shortest operating time, but they have the longest relationship with their farmers. The group consists mainly of male middlemen, but there are a few exceptions. They receive quite often seed from their buyer(s) and they usually supply inputs to the farmers as well. However, their buyers usually do not buy the total amount they have collected. The price they give their farmers does depend on the delivered quality. According to these characteristics this group conforms to the description of independent

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middlemen operating throughout the year (see § 2.3.2). Especially the fact that they have the longest relationship with their suppliers and that they receive the highest commission rate point to this description. 5.3.2 Channel choice concerning middlemen First of all a t-test has been carried out to examine the determinants that influence the choice of exporters concerning the middlemen they prefer as their suppliers (see Table 5-13). Middlemen who supply exporters are more educated and deal with larger amounts of beans. On the other hand the percentage of income that arises from French bean trade is significantly lower. This group of middlemen also has a smaller number of buyers, is less able to negotiate about the price and has more often an agreement regarding the amount and types of fertilisers the farmers are allowed to use. To find out more about the middleman-characteristics an exporter prefers, a logit model has been carried out. The variables that were entered into the model were: 1. The amount of French beans a middleman trades annually

(QUANTITY); 2. The operating time of the middleman (YEARS); 3. The education level of the middleman (EDU); 4. Whether the middleman is able to trace the produce (TRACEABILITY); 5. The number of products the middleman trades (PRODUCT); 6. Whether the middleman visits growers (VISIT); 7. Whether the middleman provides inputs (INPUTS); 8. Whether his farmers keep records (RECORDS); 9. Whether the middleman has storage facilities (STORAGE); 10. Whether the middleman has heard from the EUREPGAP protocol

(EUREP); 11. Whether his farmers have storage facilities (STORAGE_FARM). The dependent variable is a dummy for the buyer of the produce that takes the value one if a middleman sells his produce to an exporter and zero if he sells it to another middleman. Because there were a lot of variables that

could have an influence on the exporters’ choice, the most significant variable has been entered in this model first. After that, the other variables were put in the model one by one to see which variable contributed the most to the model. In the end, five variables were entered. The model is significant (0.000) and the variables explain 46% (see Annex S for the complete output). The residuals are normally distributed. Table 5-14 shows the results obtained in this analysis. Table 5-14 Channel choice middlemen (logit)


YEARS -0.423057 0.147991 -2.858672 0.0043RECORDS 5.405987 3.004838 1.799094 0.0720QUANTITY 2.49E-05 1.44E-05 1.722116 0.0850


The first significant variable is the operating time of the middleman. The more years the middleman is operating in this field, the less eager the exporter is to buy from him. This is not what would be expected. An explanation could be that exporters prefer middlemen that have a limited knowledge of the business, which could favour the exporter. The second variable is the question whether the suppliers of the middleman keep records. If they do so, an exporter is more willing to buy from such a middleman. This has to do with the new European requirements that have been set up. Buyers who supply the European market have to adjust to these requirements. The last significant variable is the quantity of French beans traded by a middleman. The more kg he has to offer, the more eager an exporter is to buy from him. This can be explained by the theoretical concept of the term economies of scale.

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Table 5-13 Differences in middlemen’ characteristics because of their market outlet Exporter Middleman Sign. N=40 Education level (level 1-6) 4.37 3.85 ** Operating time (in years) 6.122 11.231 Number of products the middleman trades 2.22 1.77 The received commission rate per carton (in Kenyan Shillings) 15.59 14.69 The average amount of FB traded annually (in kg) 103733.33 62069.23 * The percentage of the middleman’s’ income that arises of French bean trade (%) 44.26 56.92 ** Does the middleman have any fixed assets? (yes=1 and no=0) 0.93 1.00 The middleman has any employees (yes=1 and no=0) 0.96 0.92 If yes, how many employees do you have? (number) 5.69 6.83 The percentage of the harvest that is refused by the middleman because a lack of quality (%) 0.22 0.38 The middleman provides inputs to the farmers (yes=1and no=0) 0.85 0.77 The middleman visits growers in the field (yes=1 and no=0) 0.96 0.85 Is any part of the price you’re willing to pay the farmers adjusted to the quality they deliver? (yes=1 and no=0)

0.22 0.46

Does the payment you make to the farmers explicitly depend on the price you receive for the sale of the produce downstream? (yes=1 and no=0)

0.93 1.00

Does your buyer provide seed to you? (yes=1 and no=0) 0.85 0.77 Do you have more than one buyer for your produce? (yes=1 and no=0) 0.26 0.62 ** For how many years have you been selling to the same buyer(s)? (number of years) 3.34 1.69 Are you able to choose between more than one potential buyer? (yes=1 and no=0) 0.96 1.00 Agreement regarding quantity (yes=1 and no=0) 0.37 0.46 Agreement regarding price (yes=1 and no=0) 7.41E-02 0.00 Are you able to negotiate with your buyer? (yes=1 and no=0) 0.59 0.85 * Agreement regarding delivering date (yes=1 and no=0) 0.93 0.85 ***Significant at the 0.01 level. **Significant at the 0.05 level. *Significant at the 0.10 level.

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Continuation of Table 5-13 Differences in middlemen’ characteristics because of their market outlet Agreement regarding quality (yes=1 and no=0) 0.78 0.85 Agreement regarding the amount of pesticides used (yes=1 and no=0) 0.81 0.54 Agreement regarding the amount of fertiliser used (yes=1 and no=0) 0.44 0.15 ** Do you receive your money the same day? (yes=1 and no=0) 3.70E-02 0.15 Are you always paid on the day the buyer promised you to pay? (yes=1 and no=0) 0.85 0.83 Does your buyer always buy the total amount? (yes=1 and no=0) 0.41 0.31 Do you have a fixed number of farmers? (yes=1 and no=0) 0.22 0.23 For how many years do you buy from the same farmers? (number of years) 0.63 0.92 Agreement regarding quantity (yes=1 and no=0) 0.26 0.54 Agreement regarding price (yes=1 and no=0) 3.70E-02 0.00 The farmers are able to negotiate with me about the price (yes=1 and no=0) 0.78 0.69 Agreement regarding delivering date (yes=1 and no=0) 1.00 0.92 Agreement regarding quality (yes=1 and no=0) 0.70 0.85 Agreement regarding the amount of pesticides used (yes=1 and no=0) 0.85 0.77 Farmer has storage facilities (yes=1 and no=0) 3.70E-02 0.00 Do you have storage facilities? (yes=1 and no=0) 0.22 7.69E-02 Middleman has heard from the EUREPGAP protocol (yes=1 and no=0) 0.85 0.85 Middleman is able to trace the beans back to the farmer who grew them (yes=1 and no=0) 0.89 0.85 Farmer keeps records of amount of pesticides and fertilisers used (yes=1 and no=0) 0.96 0.69 * ***Significant at the 0.01 level. **Significant at the 0.05 level. *Significant at the 0.10 level.

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Exporters prefer large suppliers because this results in a fewer number of places to visit to collect the produce and a fewer number of middlemen to control. The reasons that have been given in paragraph 3.5 count for the middlemen as well. 5.3.3 Implications for the commission rate, quality and certainties Commission rate The commission rate a middleman has left after the trade with the different parties, could depend on a large number of factors. On the one side he has to deal with the farmers he buys his produce from. The factors that might influence the price paid to the farmers are: 1. Whether the price he pays the farmers depends on the price he

receives downstream (PRICE_DOWNSTREAM); 2. Whether the farmers keep records (RECORDS); 3. The percentage of the harvest that is rejected by the middleman

(RATE); 4. Whether the middleman has an agreement with his suppliers

regarding the price he should pay (AGR_PRICE_FARMER); 5. Whether the farmer has storage facilities (STORAGE_FARM); 6. The number of years a middleman is supplying from the same farmers

(SAME_FARMER). The dependent variable is the commission rate. The result of this analysis shows that none of the variables had a significant influence on the commission rate (the complete output is shown in Annex T). However, the commission rate depends also on the price he receives from his buyer. This price also depends on a number of factors, namely: 1. The buyer of the produce (1=exporter and 0=middleman) (BUYER); 2. The number of years the middleman supplies the same buyer (SAME); 3. The quantity of French beans a middleman trades annually

(QUANTITY); 4. Whether the middleman has storage facilities (STORAGE); 5. Whether the middleman has heard from EUREPGAP (EUREP); 6. Whether the middleman is able to trace the produce (TRACEABILITY);

7. Whether the middleman visits his suppliers (VISIT); 8. Whether the middleman has an agreement with his buyer regarding

the price (AGR_PRICE_BUYER); 9. Whether the farmers the middleman buys his produce from have

storage facilities (STORAGE_FARM); 10. Whether the farmers the middleman buys his produce from keep

records (RECORDS); 11. Whether the buyer provides seed to the middleman (SEED_BUYER); 12. Whether the middleman supplies inputs to his growers (INPUTS). The dependent variable is the commission rate a middleman receives in Kenyan Shillings. The model is shown in Table 5-15. The residuals are normally distributed (see Annex U for the complete output). Table 5-15 Commission rate per carton (OLS)


TRACEABILITY -14.92940 3.894582 -3.833375 0.0005INPUTS 8.372323 3.177752 2.634668 0.0123

STORAGE 6.513297 2.798426 2.327486 0.0257R-squared 0.334500 F-statistic 6.031553Adjusted R-squared 0.279042 Prob(F-statistic) 0.001946Log likelihood -130.2350 Jarque-Bera statistic 4.042017

Middlemen who have storage facilities receive a significant higher commission rate than middlemen who do not. Because French beans are a highly perishable vegetable, storage facilities are important to maintain the quality. Therefore the fact that a middleman has storage facilities, does have a positive influence on the price. This is in line with the theory. Durability is one of the eight dimensions that Garvin (1984) has set up. One of the possibilities to influence the durability of products is by using storage facilities. Because these facilities improve the shelf life, they will result in a higher price. Furthermore does the fact whether a middleman supplies inputs to his growers have a positive effect on the received

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commission rate. The imported seeds are too expensive for small-scale farmers to afford. Locally produced seeds instead are of poor quality. The majority of the farmers still uses seed saved from a previous crop (second-generation seed) (Kimenye, 1995). This has led to deterioration in the quality of exportable French beans. Therefore the quality of the produce is expected to be higher when middlemen provide inputs. This results in a higher price. The last significant variable is whether the middleman is able to trace the beans. This determinant has a negative influence on the commission rate. This was not what would be expected. An explanation could be that the commission rate will be lower because of a number of extra costs for supplying this service to a buyer. Quality The middleman has to grade the produce that farmers offer. The OLS procedure has been used to find out which variables determine the percentage of the harvest that he will reject. The independent variables are: 1. The operating time of the middleman (YEARS); 2. The buyer of the middleman (BUYER); 3. Whether the farmer has storage facilities (STORAGE_FARM); 4. Whether the farmer keeps records (RECORDS); 5. Whether the middleman has an agreement with his buyer regarding

the quality (AGR_QUAL_BUYER); 6. Whether the middleman has an agreement with his farmers regarding

the quality they should deliver (AGR_QUAL_FARMER); 7. Whether the middleman visits his growers (VISIT); 8. Whether the middleman supplies inputs/seed to the farmers (INPUTS); 9. Whether the buyer buys the total amount the middleman has collected

(TOTAL_AMOUNT); 10. Whether the middleman has heard from EUREPGAP (EUREP); 11. The number of years the middleman is supplying the same buyer

(SAME); 12. The number of years the middleman is buying from the same farmers

(SAME_FARMER); 13. Whether the buyer provides seed to the middleman (SEED_BUYER).

The dependent variable is ln(rejection rate/1-rejection rate). Because the number of variables that could have any influence is much larger than the number that is considered to be put into the model, the following method has been used. To get the best fitted model, the most significant variable was entered first. After this one, the other variables were entered in turn, to see which significant variable contributed the most to the model. The results are shown in Table 5-16 (see Annex V for the complete output). The residuals are normally distributed. Table 5-16 The rejection rate (OLS)


VISIT -0.818806 0.326678 -2.506463 0.0167SAME 0.049405 0.027333 1.807535 0.0788

R-squared 0.183117 F-statistic 4.147064Adjusted R-squared 0.138961 Prob(F-statistic) 0.023711Log likelihood -30.32278 Jarque-Bera statistic 1.001932

Middlemen who visit their growers, value the quality produced as higher than middlemen who do not. Because they are able to keep an eye on the production process, the rejection rate is lower. The other significant variable is the number of years a middleman is supplying the same buyer. This variable has a positive influence on the rejection rate, or, in other words, a negative influence on the quality produced by the farmers. This is not what would be expected. An explanation could be that during the years of co-operation the middleman has learned about the wishes and demands of his buyer(s). This could result in more severity towards the grading process. Certainties Just like farmers, middlemen also have bills that need to be paid. Because they usually do not have enough cash to pay their suppliers immediately, they depend on their buyers for paying their accounts. Therefore it is very important that they can rely on their buyers in the field of paying on time

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and buying the whole amount collected. The result of these analyses show that there are no variables that influence whether a middleman is paid on time and whether a buyer collects the whole amount or not. Annex W and X show the results of these analyses. 5.3.4 Contracts Because of the fact that middlemen are the second group of actors in the chain, they can have agreements with their suppliers and/or their customers. First, the agreements with their growers will be described. In the questionnaire attention has been given to agreements in the field of: 1. The quantity the farmers should supply; 2. The price they will receive; 3. The date the middleman will come to collect the produce; 4. The quality the farmers should deliver; 5. The types and quantities of pesticides the farmers are allowed to use. As can be seen in Table 5-17, the number of written agreements is very low. Only in the case of the pesticides, half of the sample does have such an agreement. An explanation might be that exporters often provide a list of recommended and banned chemicals to their suppliers, among which middlemen, who distribute them to their growers. They value such a list as a written agreement. Only one middleman has made agreements with his suppliers regarding the price he is willing to give. Most of the agreements are informal. Almost all middlemen have informed their suppliers about the collecting days, which are the same for the whole sector, namely Monday, Wednesday and Friday. Only produce that is meant for canning might be harvested on Tuesday and Thursday. On the whole middlemen who supply an exporter directly have more often an agreement with their supplier. Table 5-17 Agreements of middlemen with their suppliers

BUYER Exporter Middleman

Yes, written down

0 3

Yes, verbal 7 4

Agreement regarding

No 20 6 Yes, written down

1 0

Yes, verbal 0 0

Agreement regarding the price?


0 2

Yes, verbal 27 10

Agreement regarding the delivering date?


2 4

Yes, verbal 17 7

Agreement regarding the quality?


14 6

Yes, verbal 9 4

Agreement regarding the amount of pesticides? No 4 3 Middlemen face some problems regarding their suppliers, especially in the quality management of the production process. Because they contract the growing process out, they are not sure that the final quality will be the way they want it. There are a number of ways to influence the farmers’ behaviour. In the questionnaire attention has been given to four possible instruments. The first one is the ability to monitor farmers directly. By visiting the farmers, one could keep an eye on the production process himself. Almost all middlemen interviewed visited their suppliers. The number of visits during a year varied a lot, from twice a season till twice a week. Almost all middlemen had at least one employee to assist them. The middlemen advised their farmers on chemical and fertiliser use, irrigation and planting methods. Furthermore they checked how the farmers handled the produce during the growing season. The second

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instrument that middlemen could use to influence the produced quality is the provision of inputs to the farmers. By providing seed, pesticides and/or fertilisers, the middlemen are at least certain of the quality of the inputs. Because small-scale farmers often lack the capital to purchase these inputs, the quality of the final product is expected to be lower. Middlemen who supply inputs to their growers usually deduct the costs after the harvest. The third instrument that could be useful is to adjust a part of the payment to the measured quality. By making the farmers responsible for the quality they produce, it would be expected that they will care more about it. The majority of the interviewed middlemen did not use this instrument. An explanation could be that they think it would take too much time to do so. Table 5-18 The incentive instruments

THE MIDDLEMAN’S BUYER:

Exporter MiddlemanYes 26 11 Visit growers No 1 2 Yes 23 10 Provide

inputs No 4 3 Yes 6 6 Any part of

payment adjusted to measured quality

No 21 7

Yes 25 13 Does payment depend on price downstream

No 2 0

Farmers could also influence this process by putting the best quality on top, in the hope that the middleman will take a sample from the top. The

last possible instrument that could be used is transferring a part of the middleman’s’ risk to the farmer. This could be done for example by letting the price of the farmer depend on the price the middleman receives downstream. By doing so, the farmer will be responsible for the production of low quality. This instrument would be impossible when the middleman pays the farmer with collecting, without knowing the price he will receive. From the interviewed middlemen, about 15% paid their suppliers with the collection of the produce. Almost all middlemen let the price depend on the price they received downstream. The results of these instruments are shown in Table 5-18. In the questionnaire attention has also been given to agreements of middlemen with their buyers. The focus was on agreements regarding: 1. The quantity a middleman should deliver; 2. The price he will receive; 3. The date his buyer will come to collect the produce; 4. The quality he should deliver; 5. The types and quantities of pesticides his farmers are allowed to use; 6. The types and quantities of fertilisers his farmers are allowed to use. As can be seen in Table 5-19 the number of written agreements is very low. Only in the case of pesticides and fertilisers, a significant number of middlemen has such an agreement. The same explanation as for the farmers holds.

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Table 5-19 Agreements of middlemen with their buyers

BUYER Exporter Middleman

Yes, written down

3 4

Yes, verbal 7 2

Agreement regarding the quantity?


1 0

Yes, verbal 1 0

Agreement regarding the price?


0 1

Yes, verbal 25 10

Agreement regarding the delivering date?


4 6

Yes, verbal 17 5

Agreement regarding the quality?


18 6

Yes, verbal 4 1

Agreement regarding the amount of pesticides? No 5 1

Yes, written down

11 2

Yes, verbal 1 0

Agreement regarding the amount of fertilisers? No 15 11 5.3.4.1 Agreements between the middleman and his suppliers To find out which factors influence whether a middleman has an agreement with his suppliers, a number of logit analyses have been

carried out. The agreements regarding the price and the delivering date have been left out because of an unequal distribution between having an agreement and not having an agreement (see Annex H). For all analyses the same independent variables have been used, namely: 1. Whether the farmer keeps records (RECORDS); 2. Whether the farmer has storage facilities (STORAGE_FARM); 3. The number of years a middleman is supplying from the same farmers

(SAME_FARMER); 4. The rejection rate (RATE); 5. The buyer of the produce (BUYER); 6. Whether the middleman has the same specific agreement with his

buyer (AGR_…_BUYER); 7. Whether the middleman supplies inputs to his farmers (INPUTS); 8. Whether the middleman visits his farmers (VISIT). The agreement regarding the quality a farmer should deliver has not been included. The result of this analysis is that there are no variables that have a significant influence. The results are shown in Annex Y. Agreement regarding the quantity a farmer should deliver to a middleman A middleman needs the guarantee that his suppliers will deliver the amount of French beans he needs. Therefore he could conclude an agreement with his suppliers in which the right amount needed is stated. The results of this analysis are shown in Table 5-20. The residuals are not normally distributed, but the results will be interpreted as they are (see Annex Z for the complete output).

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Table 5-20 Agreement quantity supplier middlemen


AGR_QUAN_BUYER 3.278907 1.347740 2.432892 0.0150SAME_FARMER 1.674490 0.604764 2.768835 0.0056

BUYER 2.401282 1.281345 1.874033 0.0609Log likelihood -13.24052 Avg. log likelihood -0.331013Restr. log likelihood -25.89787 McFadden R-squared 0.488741LR statistic (3 df) 25.31470 Probability(LR stat) 1.33E-05Jarque-Bera statistic 6.664729

The first variable that has a significant influence is whether the middleman has the same agreement with his buyer. This factor has a positive influence on the chance for a supplier to receive an agreement. Middlemen who have an agreement regarding the quantity they should deliver are more willing to conclude the same agreement with their suppliers. They will receive an agreement themselves if their buyer has a stable market. If they receive such an agreement, they will have a stable market as well. Then they will be willing to conclude the same agreement with their suppliers. Another factor is the number of years a middleman is buying produce from the same farmers. The more years he is doing business with the same farmers, the higher the chance that they will receive an agreement. During the years of co-operation, the buyer will find out whether the farmer produces a high quality or not. He will continue doing business with the farmers that produce a high quality. To keep this farmers as his suppliers, he will be willing to offer them a contract. Because he first needs to find out more about the skills of the farmer, it takes some years before a contract will be concluded. The last significant variable is the buyer of the produce. Farmers who supply a middleman who is dealing directly with an exporter have a higher chance to receive an agreement. Exporters have more often a stable market for their produce. Therefore they are more willing to agree on a fixed quantity during the year. Middlemen on the other hand usually show up during times of

shortage of supply. Because they often do not have a stable market, they are not able to agree on a fixed quantity. Agreement regarding the pesticides a farmer is allowed to use according to the middleman Channel members who buy their produce from a middleman have little or no control over the production process. Because of the rise in the number of European requirements, actors have to demonstrate that they are able to find out what happened at which stage of the production and marketing process. One of the most important topics is the spraying of pesticides. Because it is not possible to control all the pesticides sprayed, actors have to think about another solution. A possibility is to force their suppliers to sign a contract in which is stated which chemicals the farmers are allowed to spray and which chemicals not. The residuals are not normally distributed, but the results will be interpreted as they are (see Annex AA for the complete output). The results are shown in Table 5-21. Table 5-21 Agreement pesticides supplier middlemen


AGR_PEST_BUYER 3.514526 1.184220 2.967798 0.0030Log likelihood -11.92895 Avg. log likelihood -0.298224Restr. log likelihood -18.54906 McFadden R-squared 0.356897LR statistic (1 df) 13.24021 Probability(LR stat) 0.000274Jarque-Bera statistic 563.9492

The only significant variable is whether the middleman has an agreement with his buyer as well in the field of pesticide use. Middlemen who do so are more willing to conclude the same agreement with their suppliers. Here the same holds as for the agreement regarding the quantity a farmer should deliver.

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5.3.4.2 Agreements between the middleman and his buyer To find out which factors influence whether a middleman has an agreement with his buyer, a number of logit analyses have been carried out. The agreements regarding the price and the delivering date have been left out because of an unequal distribution between having an agreement and not having an agreement (see Annex H). For all analyses the same independent variables have been used, namely: 1. The number of products a middleman trades (PRODUCT; 2. The average quantity of French beans a middleman trades annually

(QUANTITY); 3. Whether the middleman has storage facilities (STORAGE); 4. Whether the middleman has heard from the EUREPGAP protocol

(EUREP); 5. The buyer of the produce (with 1=exporter and 0=middleman)

(BUYER); 6. The number of years a middleman is selling to the same buyer

(SAME); 7. The operating time of a middleman (YEARS); 8. Whether his buyer supplies seed to him (SEED_BUYER); 9. Whether his farmers keep records (RECORDS); 10. Whether his farmers have storage facilities (STORAGE_FARM); 11. Whether he is able to trace the produce (TRACEABILITY); 12. Whether he supplies inputs to his farmers (INPUTS); 13. Whether he visits his growers (VISIT); 14. The average quantity he rejects from his farmers (RATE). The dependent variables differ for each model and are dummies, which take the value 1 if the middleman has a specific agreement and the value 0 if he has no agreement. The agreements regarding the quantity and quality a middleman should deliver are not included. The results of these analyses are that none of the variables had a significant influence. The results are shown in Annex AB and Annex AC.

Agreement regarding the pesticide use of the suppliers of the middlemen from the buyer’s side Here the same holds as for the agreement between a middleman and his suppliers. The results of this analysis are shown in Table 5-22. The residuals are not normally distributed, but the results will be interpreted as they are (see Annex AD for the complete output). Table 5-22 Agreement pesticides buyer middlemen


SAME 0.629530 0.284447 2.213173 0.0269Log likelihood -18.82051 Avg. log likelihood -0.470513Restr. log likelihood -23.52675 McFadden R-squared 0.200038LR statistic (1 df) 9.412474 Probability(LR stat) 0.002155Jarque-Bera statistic 347.8513

The number of years a middleman is supplying the same buyer(s) has a positive influence on the question whether he has an agreement regarding the pesticides he is allowed to spray. The more years he is supplying the same buyer(s), the higher the chance for a middleman that he will receive an agreement. Agreement regarding the fertiliser use of the suppliers of the middlemen from the buyer’s side Another process that buyers have no control over is the use of fertilisers. The same holds as for the pesticides. Buyers could force their suppliers to sign a contract in which the permitted and banned fertilisers are listed. The results of this analysis are shown in Table 5-23. The residuals are not normally distributed, but the results will be interpreted as they are (see Annex AE for the complete output).

Chapter 5 Results

74

Table 5-23 Agreement fertilisers buyer middlemen


SAME 0.390904 0.172997 2.259596 0.0238PRODUCT 0.904000 0.392236 2.304735 0.0212


The first significant variable is the number of years a middleman is supplying the same exporter(s). The more years of co-operation, the higher the chance an exporter will conclude an agreement with the middleman. The second variable is the number of products a middleman trades. The more products he trades, the higher the chance that he will receive an agreement.

Summary results Middlemen Exporters prefer middlemen who buy their produce from farmers who keep records and who are able to supply large quantities. Furthermore they prefer middlemen who have little experience. The commission rate middlemen receive, depends on whether they have storage facilities and whether they supply inputs to their growers. These factors have a positive influence on their commission rate. A factor that has a negative influence is whether they are able to trace the produce. Middlemen who can, receive a lower commission rate. Middlemen who visit their growers regularly value the quality produced higher. Middlemen who have a long-term relationship with their buyer(s) reject a higher percentage of the farmers’ harvest. None of the variables had a significant influence on the questions whether a middleman was paid on time and whether his buyer collected the total quantity of French beans. Middlemen can have agreements with both their suppliers and their buyer(s). Factors that influence whether the middleman is willing to conclude an agreement with his suppliers are whether he has the same agreement with his buyer(s) and the number of years he is supplying from the same farmers. These factors have a positive influence. Middlemen who supply to an exporter directly are more willing to enclose agreements with their suppliers. Whether a middleman has an agreement with his buyer depends on the number of years he is supplying to the same buyer and the number of products he trades. Both factors have a positive influence.

Chapter 5 Results

75

5.4 Seasonality in demand With regard to the seasonality, an OLS procedure has been carried out to see whether there was any seasonal influence in the volume exported each month. As can be seen in Table 5-24 there is a clear difference in the volume exported each month. The month December has been taken as starting-point. In January, February, March, July, August and September the volume of French beans exported was significantly lower than the volume exported in December for the years 1995 till 2002. In April, May, June, October and November the volume exported did not differ significantly from the volume exported in December. So during the first and the third quarter, the export was significantly lower than the second and the fourth quarter. The interviews among farmers, middlemen and exporters have been held in August. All farmers complained about low prices and a low demand from the exporters. The results of this analysis show the same situation. During the European summer, Kenyan exporters face competition from European countries. These countries have the advantage of a smaller distance to the consumer market.

Table 5-24 Seasonality in demand Dependent Variable: LOG(VOLUME) Method: Least Squares Date: 02/24/03 Sample: 1995:01 2002:07 Included observations: 91


January -0.501695 0.142552 -3.519383 0.0007February -0.486216 0.142514 -3.411711 0.0010

March -0.363145 0.142484 -2.548674 0.0128April -0.213030 0.142463 -1.495336 0.1389May -0.288302 0.142450 -2.023884 0.0464June -0.138426 0.142446 -0.971778 0.3342July -0.425207 0.142450 -2.984958 0.0038

August -0.686847 0.147183 -4.666615 0.0000September -0.310362 0.147154 -2.109092 0.0381

October -0.263681 0.147134 -1.792116 0.0770November -0.176475 0.147121 -1.199522 0.2340

@TREND(94.12) 0.006087 0.001101 5.528778 0.0000R-squared 0.474222 Mean dependent var 13.82913Adjusted R-squared 0.393333 S.D. dependent var 0.353364S.E. of regression 0.275231 Akaike info criterion 0.389153Sum squared resid 5.908670 Schwarz criterion 0.747847Log likelihood -4.706442 F-statistic 5.862623Durbin-Watson stat 1.697625 Prob (F-statistic) 0.000000

Chapter 6 Conclusions and discussion

76

6 Conclusions and discussion In paragraph 6.1 the conclusions with regard to the research questions will be given. Paragraph 6.2 gives recommendations for farmers, middlemen and exporters of French beans. 6.1 Conclusions and discussion In this paragraph the research findings will be summarised and compared to the hypotheses made. The objective of this study was to analyse the production and marketing channel of the horticultural crop French beans, which is grown in Kenya and is exported to various countries. Figure 6-1 shows the different flows within the production and marketing channel. Within this research three channel actors have been distinguished, namely farmers, middlemen and exporters. This figure will be used to describe the channel choice of farmers and middlemen.

Figure 6-1 The different flows within the channel Farmers Farmers can choose to sell their French beans to an exporter directly or to a middleman. Which factors influence this choice was the first research

question. Their choice will bring about consequences for the price they will receive, the quality they will have to produce and the certainties their buyer will give them. These aspects refer to the second research question. The third research question is the following: ‘What is the impact of contracts on the received price and the delivered quality?’ According to Figure 6-2 channel actors send signals to the farmer. These signals can be divided into two groups. The first group contains signals concerning the benefits a channel actor will offer the farmer. The second group holds signals with regard to the conditions a channel actor claims from his potential suppliers. Besides the channel actors the channel environment also sends some signals to the farmer. The farmer will use all signals to make decisions regarding the production and marketing of his French beans. These decisions will result in the choice of a channel actor. The analyses in chapter 5 will be used to describe the different signals. First of all the offers of an exporter will be described. As can be seen in Figure 6-2 the exporters provide inputs to the farmers on credit. Secondly, they offer a higher average price than middlemen do. They also pay the farmers on time. Last but not least they offer the farmers a contract. Besides these offers they also have their demands. They require a high quality produce, their suppliers should have storage facilities and the farmers should be up to date concerning European requirements. Furthermore the farmers should keep records of the quantities and types of pesticides and fertilisers they use. With this information in mind, farmers have to make decisions regarding the production and marketing of their French beans. These decisions contain aspects of the eight dimensions of quality, which are described in paragraph 3.6. Farmers can for example decide to build storage facilities to improve the shelf live of their produce. Or they can start keeping records to maintain their position within the chain. These decisions have consequences for the channel actor they will deal with. For farmers a high price is the most important requirement they have (see § 5.2). Therefore they will continuously look for the channel actor that offers the highest price. According to the results of the t-test described in paragraph 5.1.1, exporters offer a slightly higher average price than middlemen do. However, in times when there is a shortage in supply, middlemen offer incredibly high prices.


77

Exporter

Channel Environment

- Inputs on credit - High average price per

year - Receiving payment on time- A contract - Ability to negotiate

- High quality - EUREPGAP knowledge - Record keeping - Storage facilities

offers

claims

Farmer

Production:- Storage facilities - Record keeping - EUREPGAP

knowledge - Quality

Marketing:- Exporter /

middleman - One buyer /

Several buyers

Decisions

Decisions

Yes/No

Figure 6-2 The channel choice of a farmer

- EUREPGAP protocol- HACCP - BRC - ISO 9001:2000


78

Exporters on the other hand offer the same price during the year, which is not dependable on the extent of supply. Therefore farmers are willing to change from channel actor during times of shortage in supply. For them it is easy to change from partner, because they do not invest in their relationship. The exporters on the other hand do invest in their relationship with the farmers by providing expensive seed on credit, assisting them on farming techniques and European requirements and enclosing contracts. These investments can be seen as asset specificities. The more asset specificities a channel actor provides, the more difficult it is to change from partner. According to theory, asset specificity might bring about consequences for the institutional arrangement between two parties. In this case exporters might be willing to enclose contracts with their farmers to ensure that they will not seek for a buyer who offers a higher price. In practice it appears that farmers are not loyal to their buyer when they find another buyer who offers a slightly higher price (see Annex G). Contracts seem to have no influence on the received price and the produced quality. Middlemen Middlemen can also choose between selling their produce to an exporter directly or to another middleman. Which factors influence this choice was the first research question. Their choice will bring about consequences for the price they will receive, the way they value the quality produced by their farmers and the certainties their buyer will give them. These aspects refer to the second research question. The third research question is the following: ‘What is the impact of contracts on the received price and the middlemen’s’ valuation of the quality?’ The channel actors do send signals to middlemen as well. On the basis of these signals and those of the channel environment, middlemen decide to whom they will sell their French beans. According to Harris et al. (2001) there are two types of middlemen, namely exporter agents and independent middlemen. Exporter agents are paid a commission based on the volume of sales for providing certain services. Independent middlemen buy produce from growers and then sell it to an exporter. In case of the independent middlemen, there is a clear difference between two groups. One group operates as a middleman throughout the year. They have fixed buyers for their produce, mainly exporters. The other group appears in the

field in times of shortage. Because exporters need to satisfy their customers, they are willing to pay more for the beans. The middlemen take this opportunity to trade beans against a considerable commission rate. With the help of a cluster analysis, three groups of middlemen can be distinguished (see § 5.3.1), namely exporting agents, independent middlemen operating during times of shortage and independent middlemen operating throughout the year. The analyses in chapter 5 will be used to describe the different signals. First of all the advantages an exporter has to offer will be described. From the results of chapter 5 appeared that exporters have one advantage for middlemen, namely the provision of a contract. This could be an important reason for middlemen to sell directly to an exporter. A contract might give them certainty. On the other hand, the exporters have also their demands. The first one is the education level of the middleman. Exporters prefer highly educated middlemen. The second one is the quantity of French beans a middleman is able to supply. Exporters prefer large quantities. This is in line with the theory about economies of scale. Buyers prefer large suppliers because of lower transportation costs and fewer production problems. Furthermore exporters require that the growers a middleman buys from do keep records. This has all to do with the new European requirements in which is stated that farmers should keep records of the quantities and types of pesticides and fertilisers used. One of the new European requirements is that each actor must be able to trace the produce back to the farmer who grew it. This variable appeared to be not significant in the channel choice of middlemen. However, traceability has a significant negative influence on the commission rate middlemen receive. This was not what would be expected. Because traceability is an extra provided service by middlemen, it would be expected to be rewarded. An explanation for the negative relationship could be that the traceability process costs a lot of money, which lowers the commission rate. Two factors that have a positive influence on the price are storage facilities and the supply of inputs to farmers. Storage facilities will improve the shelf life of the produce and therefore result in a higher price. Farmers usually lack the possibility to buy high quality inputs (see § 2.3.1).


79

Exporter

Channel Environment

- Contract

- High education level - Large quantity of French

beans - Record keeping by farmers - Storage facilities will

improve the price - Supply of inputs to the

farmers will result in a higher price

offers

claims

Middle-men

Origin, support of growers and preservation: - Storage facilities - Record keeping

by farmers - EUREPGAP

knowledge - Quality - Visit growers - Provision of inputs

Marketing:- Exporter / another

middleman - One buyer /

Several buyers - Long-term

relationship with buyer

- Long-term relationship with suppliers

Decisions

Decisions

Figure 6-3 The channel choice of middlemen

- EUREPGAP protocol- HACCP - BRC - ISO 9001:2000

Yes/No


80

This often results in low quality produce. Middlemen who supply inputs to their growers avoid this problem and this results in a higher price. Middlemen face decisions with regard to the origin of the produce, support of growers, preservation and marketing. Middlemen might decide to supply inputs and visit growers to stimulate a high quality produce. Furthermore they will have to choose between farmers who do keep records and farmers who do not. They can improve the quality themselves by building storage facilities. It might also be important to be up to date with European requirements. Middlemen also have to choose between selling to an exporter directly or selling to another middleman. This decision might depend on whether a middleman is an exporter agent or an independent middleman. 6.2 Recommendations Recommendations will be given for farmers, middlemen and exporters separately. Farmers If small-scale farmers want to stay in business, some things have to be improved. Exporters can choose between producing the French beans themselves on big farms or buying the produce from small-scale farmers. Both options do have their advantages and disadvantages. However, if farmers cannot adjust to new rules and regulations from the importing countries, they will be out of business. The recommendations given below could help farmers to stay competitive. Exclude middlemen According to the results of chapter 5, farmers will be better of when they sell to an exporter directly. Although middlemen might offer higher prices during times of shortage of supply, still selling to an exporter directly seems to be more profitable.

Market information To produce the right varieties, smallholders need to have access to market information about demand developments in the international market. All interviewed farmers received market information from their buyer(s) only. It is recommended that a group of extension workers be trained to deal specifically with horticultural export crops. They should also teach the farmers to practice some kind of market oriented production planning. Farmers should be advised about planting and harvesting dates, based on seasonal price developments per commodity. European requirements should also be made known, so that farmers could adjust and do not risk being bypassed. Price information The bargaining power of horticultural farmers towards middlemen and exporters depends not only on supply and demand conditions, but also on knowledge among the farmers about these conditions. The Kenyan government has tried to improve the farmers’ knowledge by regularly broadcasting prevailing prices in selected urban markets. The effect has been minimal, however, since very few horticultural farmers listen to the broadcasts (Dijkstra and Magori, 1995). All interviewed farmers received their price information from their buyer(s) only. Price information should be broadcasted on the regional radio stations that use local languages. Extension workers should sensitise the farmers to the price broadcasts on the radio. A daily routine would be the best, as prices of vegetables and fruits fluctuate substantially from one day to another. Farmer groups Middlemen play a crucial role at present when it comes to marketing horticultural commodities and most farmers cannot do without them. However, if farmers organised themselves into farmer groups (like the farmers from the sample concerning knowledge) and went into trade themselves, they would receive the trade profits which are now being made by the middlemen.


81

Chemical residues Chemical residues are a threat to the Kenyan consumers and to Kenya’s horticultural exports. Horticultural farmers spray their crops until the last moment, and sometimes even after harvesting because they think it will increase the shelf life or makes them look nice. Crops that are meant for export stand an increasing chance of being rejected or destroyed on arrival at their destination. Farmers have to be taught when and what to spray in order to remain within a minimum required level of chemical residues at the time of selling. Fertiliser application Fertiliser application by horticultural farmers is usually below recommended levels or even complete absent. This affects yields and revenues in the short run, while being a threat to the soil fertility in the long run. Farmers either lack knowledge about fertiliser use or do not have the money to buy the inputs. Extension officers need to educate the farmers on the use of fertilisers. If farm manure is available, farmers have to be explained how to use it in agriculture. Credit is needed for those horticultural farmers who cannot afford to buy fertilisers. Middlemen Provision of inputs to farmers Farmers often lack the credit to buy high quality seed, pesticides and fertilisers. Therefore middlemen could fulfil this role. By investing in their growers, middlemen might be more willing to keep their promises and pay on time. Technical assistance Middlemen could also play a significant role in the technical assistance. They could advise farmers on a number of topics. It is also in their own interest because one might expect the quality to improve and this might increase their commission rate. Middlemen who act as a fixed intermediary between an exporter and his growers (exporter agents) might also fulfil the role of transmitting European rules and regulations to the growers. Especially small exporters who often rely on middlemen could benefit,

because they do not need their own people to go up and down to the production areas. Storage facilities It would be advisable for middlemen to invest in storage facilities. Especially during the rainy seasons, trucks face difficulties reaching the production areas. Storage facilities might improve the shelf life of French beans. Traceability For an accurate traceability system, the less channel actors are needed, the better. Because traceability has become very important, some exporters think of bypassing middlemen to ensure that they will always be able to know the farmer who grew the produce. If the middlemen want to stay in business, they should set up an accurate traceability system. Exporters Air freight charges The tax on jet fuel used to be much higher in Kenya than in neighbouring countries. Although it has been reduced, a further reduction may have to be considered. Adjusting to legal matters in importing countries The interviewed exporters mentioned the adjustment to legal matters in importing countries as a problem. Importers are not always as clear as they should be about new rules and regulations, which results in a slow adoption of the exporters themselves. It should be recommended that the Fresh Produce Exporters Association of Kenya and the Horticultural Crops Development Authority have to play a significant role in the contact with the importing countries. Loyalty of the farmers One of the biggest problems exporters who buy from small-scale farmers cope with is the lack of loyalty of these farmers. As soon as the prices rise, farmers start to sell their produce to an alternative buyer. Exporters often


82

put a lot of effort and money into their growers. They assist them on planting methods and production problems, they supply expensive inputs on credit and they guarantee a stable market throughout the year. Unfaithful farmers cost a lot of money. Therefore exporters start to think about getting their supply from large farms or producing the French beans themselves. To keep the farmers loyal, exporters should be able to punish unfaithful farmers with a penalty.


83

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Annexes

88

ANNEXES ANNEX A EUREPGAP ANNEX B The Institutional Framework ANNEX C Questionnaire farmers ANNEX D Questionnaire farmers concerning knowledge ANNEX E Questionnaire middlemen ANNEX F Questionnaire exporters ANNEX G Case study of exporters of French beans ANNEX H Important frequencies ANNEX I Output Farmers Channel-choice ANNEX J Output OLS Price Farmers ANNEX K Output OLS Quality Farmers ANNEX L Output Farmers Paid-on-time ANNEX M Output Farmers Total-amount ANNEX N Output Agreement Quantity Farmers ANNEX O Output Agreement Price Farmers ANNEX P Output Agreement Quality Farmers ANNEX Q Output Agreement Pesticides Farmers ANNEX R Correlation matrix Agreements Farmers ANNEX S Output Middlemen Channel-choice ANNEX T Output Commission rate Middlemen - supplier side ANNEX U Output Commission rate Middlemen - buyer side ANNEX V Output OLS Quality Middlemen ANNEX W Output Middlemen Paid-on-time ANNEX X Output Middlemen Total-amount ANNEX Y Agreement Quality Supplier Middlemen ANNEX Z Agreement Quantity Supplier Middlemen ANNEX AA Agreement Pesticide Use Supplier Middlemen ANNEX AB Agreement Quantity Buyer Middlemen ANNEX AC Agreement Quality Buyer Middlemen ANNEX AD Agreement Pesticide Use Buyer Middlemen ANNEX AE Agreement Fertiliser Use Buyer Middlemen ANNEX AF Data descriptives farmers

ANNEX AG Data descriptives farmers (questionnaire concerning knowledge)

ANNEX AH Data descriptives middlemen

Annexes

89

ANNEX A EUREPGAP A group of major European supermarkets united in the European Retail Partners (EUREP) working group took in 1997 the initiative to develop a normative strategy for product standards and handling procedures for fresh fruits and vegetables. The current version of the EUREPGAP document and procedures has been agreed among partners from the entire food chain for fruits and vegetables after a wide consultation phase over three-years. Formerly speaking EUREPGAP is a set of normative documents suitable to be accredited to international certification laws. Representatives from around the globe and all stages of the food chain have been involved in the development of these documents and it has produced a very robust and challenging protocol, which focuses the producer on the key issues that need to be addressed during the pre-farm gate stage. EUREPGAP members include retailers, suppliers/growers and associate members from the input and service side of agriculture. The associate members have a responsibility to primary producers and agriculture and they contribute quite significantly to the development of better processes, but they are not part of the EUREPGAP decision-making process. Decisions are made by the EUREPGAP Steering Committee, which is chaired by an independent Chairperson. A Technical and Standards Committee approves the standard documents and certification system. Both committees have 50% retailer and 50% grower representation (see figure A-1). The organisation is no longer driven by the retailers and has developed into a much more democratic organisation that makes decisions that are in the best interest of the entire supply chain and ultimately the consumer. EUREPGAP was driven by the desire to reassure consumers, principally on issues of food safety following food safety scares with BSE (mad cow’s disease) and the rapid introduction of GM (genetic modified) foods. Consumers generally throughout the whole of Europe and other parts of the world are asking how food is produced; they do not really understand all the techniques employed in modern agriculture and need assurance that it is safe. Food safety is a global issue; it does not respect international boundaries. A lot of the members are global players in the retail industry and cannot

afford to operate double standards for produce bought from different parts of the world. Therefore the need for a common internationally recognised standard exists.

Figure A-1 The organisation around the EUREPGAP protocol (www.eurep.org)

Another key driver is the triple bottom line – people, planet and profit. Major corporations and multinational supply bases increasingly have to impress their investors. If a particular business is buying product in a way that is perhaps degrading the environment or does not pay respect to occupational health and safety issues, then there is increased risk and less likelihood of attracting investors. The third key driver is that they are focused on good agricultural practice. EUREPGAP is based on HACCP principles, and although its scope is limited to pre-farm gate, codes of practice which deal with the interface areas of packaging on the farm and

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transport from the farm to the processor ensure that the protocol can provide a whole of chain assurance. The EUREPGAP goals are essentially to reduce the risks of food safety lapses in agricultural production and to objectively verify best practice with some reference points so that it is done systematically and consistently throughout the world. This will be achieved through the protocol and compliance criteria.

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ANNEX B The Institutional Framework There are several governmental and private organisations involved in the export of fresh fruits and vegetables, among which French beans. These organisations play different roles in the process of growing, marketing, monitoring and promoting the exports of fruits and vegetables. The organisations that were directly or indirectly involved in this research are mentioned below:

1. The Kenya Agricultural Research Institute (KARI) is a National Agricultural Organisation with the responsibility for carrying out research on all matters relating to agriculture with the exception of coffee, tea and pyrethrum. The most important research areas of the institute are the improvement of horticultural and other export crops, promotion of underdeveloped food crops and animal resources, development and promotion of industrial crops for local manufacturing industries and the conservation of the national resource base (www.kari.org).

2. The Ministry of Agriculture, Livestock Development and Marketing (MOALD&M) has the overall mandate of all agricultural extension activities in Kenya. The Ministry has different divisions charged with the extension activities in special groups of crops. The Horticultural Division is in charge of extension in the areas of production, post-harvest handling, and marketing of fruits, vegetables and flowers.

3. The Horticultural Crops Development Authority (HCDA) was established in 1967. The Authority remained actively involved in the export of commodities until 1986, when the government decided to leave all export trade to the private sector. The HCDA had become too much a competitor to private exporters rather than a provider (Dijkstra, 1997). From then on, the HCDA was empowered to undertake the following key activities:

• Dissemination of marketing information and export statistics to investors, exporters and producers for planning purposes;

• Organising groups of small scale growers for production and marketing purposes of export crops;

• Advising growers, exporters and processors to plan production in relation to market demand;

• Advising growers on the use of certified planning materials and assisting them to identify both local and export marketing outlets for their produce;

• Monitoring prices and foreign exchange remittances into Kenya in collaboration with the central Bank of Kenya;

• Training farmers on the proper use of inputs, particularly pesticides so that farmers adhere to the MRL’s;

• Advising producers and exporters on appropriate post-harvest handling techniques.

The HCDA receives no subsidies from the Kenyan government, but finances its operating costs with export levies on fresh and produced produce and fees for export licensing. The Authority also collects a levy on behalf of the Fresh Produce Exporters Association of Kenya (FPEAK), to which all exporters of fresh produce belong by virtue of paying the levy.

4. The Export Promotion Council is a government body charged with the responsibility of promoting the export of all commodities from Kenya including fruits and vegetables.

5. The Kenya Plant Health Inspectorate Service (KEPHIS) is a government body formed in 1997 that is charged with ensuring plant health in Kenya, including not only the imports of all plant materials but also the export of plant materials. They control for example all the French bean seed that is imported.

6. The Fresh Produce Exporters Association of Kenya (FPEAK) was founded in 1975 to cater for the needs of Kenyan exporters of horticulture. It has become Kenya's major private organisation representing exporters of fresh horticultural produce to world markets. Ordinary membership is open to growers and exporters of fresh cutflowers, fruits or vegetables, while affiliate membership is for organisations that offer support services and inputs to the industry. The Administrative body of the Association is formed by

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the secretariat, which is responsible for providing services to the members. The Board of Directors consisting of nine ordinary members actively engaged in exporting fresh produce governs the activities of FPEAK. FPEAK provides market intelligence, export founded promotion facilitation, technical support and training services for exporters and their growers. The Association also negotiates favourable terms for provision of goods and services to the industry. The Association provides contacts of potential importers, organises and participates in local and international trade events on behalf of members (www.fpeak.org). To ensure that Kenya's export horticulture meets accepted international standards of responsible production, environmental protection and social accountability, FPEAK has developed an internationally recognised Code of Practice. Implementation of the Code of Practice is one of the Association' s main activities.

7. SGS International is an international certification company. It is represented in about 140 countries and has about 840 offices around the world. SGS operates a certification division under International Certification Services (SGS-ICS) whose role is to carry out audits for certification under ISO 900X series, ISO 14000, HACCP and codes of practices (like the EUREPGAP protocol). Due to the paucity of experts in quality management and/or quality assurance, they also carry out training in these areas. SGS is one of the few certification bodies that are EUREPGAP approved (www.sgs.com, www.eurep.org).

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ANNEX C Questionnaire farmers A. General information about the farm 1. Name of the respondent 1a. Gender

Male Female

2. Education level of the respondent

No education (NE) Primary school not finished (PNF) Primary school finished (PF) Secondary school not finished (SNF) Secondary school finished (SF) University (U)

3. Position respondent

Owner of the farm Son of the owner

4. Operating time farm 5. Size of the farm in acres 6. Number of acres under French beans 7. Average price per kg 8. Average amount produced annually 9. The percentage of the income that arises from French bean

production 10. Are the acres the property of the farmer?

11. Do you have any other fixed assets?

Yes No

11a. If yes, do you have a house?

Yes No

11b. If yes, do you have cows?

Yes No

11c. If yes, do you have a bicycle?

Yes No

11d. If yes, do you have a water pump?

Yes No

11e. If yes, do you have a car?

Yes No

11f. If yes, do you have any business?

Yes No

11g. If yes, do you have a pack house?

Yes No

11h. If yes, do you have another fixed asset? 12. What percentage of the harvest is meant for export?

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13. What is the main destination for your French beans? 14. What percentage of the harvest does your buyer refuse to buy

because of lack of quality? 15. Does your buyer provide any inputs?

Yes No

15a. If yes, does he provide seed to you?

Yes No

15b. If yes, does he provide fertilisers to you?

Yes No

15c. If yes, does he provide chemicals to you?

Yes No

15d. If yes, does he provide transport to you?

Yes No

16. From who do you get information about the prices?

From the exporter From the middleman

17. From who do you get information about the required quality?

From the exporter From the middleman

B. Information about contracting

1. To who do you sell your French beans? To an exporter To a middleman

1a. If to an exporter, to which one?

Homegrown Indu Farm East African Growers Greenland Avenue Vegepro

2. Do you have one or several buyers?

I have one buyer I have two buyers I have several buyers (>2)

3. If one buyer, for how many years have you been selling to him? 4. If several buyers, do you sell to them every year, or do you have

different buyers every year? I have different buyers every year They are the same for … year

5. Are you able to choose between more than one potential buyer?

Yes No

6. Do you have made agreements regarding the quantity that you should

deliver? Yes No

7. If yes, are these agreements written down?

Yes No

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8. Do you have made agreements regarding the quality that you should

deliver? Yes No


Yes No

10. If yes, what price do you receive? 11. Could you discuss the price or did your buyer set it without

negotiation? The buyer sets the price We negotiate We negotiate sometimes

12. Do you have made agreements regarding the delivering date?

Yes No


Yes No

14. Do you have made agreements regarding the quality?

Yes No


Yes No

16. Do you have made agreements regarding the amount of pesticides

used?

Yes No


Yes No

18. When do you receive your revenue (money): when you deliver the

beans or at a later moment? With delivering Later Depends on the arrangement

19. If at a later moment, when exactly?

After 1 day After 1 week After 2 weeks After 3 weeks After a month After a season I’m never sure when I’ll get paid

20. Do you always receive the money at the day the buyer promised to

pay you? Yes No

21. Does your buyer always buy the total amount of beans or just a part?

He always buys the total amount He has his limits during low season

C. Facilities 1. Do you have storage facilities where you can store the French beans?

Yes No

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1a. If yes, do you have cooling facilities?

Yes No

1b. If no, does your buyer always come at the day that you harvest the

beans? Yes No

2. Do you have packing facilities where you can pack the beans in small

proportions? Yes No

3. Do you know in advance the date that the buyer will visit you?

Yes No

4. How many days before the buyer will come will you harvest the

beans? Same day Day before Two days before

D. EUREPGAP requirements 1. Have you heard from the Good Agricultural Practice Protocol that the

supermarkets in Europe have set up? Yes No

2. Do you keep records of the amount of pesticides and fertilisers that

you use? Yes No

2a. If yes, for how long do you save these records? 3. What types of fertiliser do you use? 4. How many fertiliser of each type do you use per kg of seed? 5. What types of pesticides do you use? 6. How many pesticides of each type do you use per kg of seed? 7. What types of fungicides do you use? 8. How many fungicides of each type do you use per kg of seed? 9. Where does the water come from that you use for irrigation?

From the river From the dam From an other source, namely…

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ANNEX D Questionnaire farmers concerning knowledge

General information about the farm Name of farm owner: Group: Gender: Age: Family size: Education level: How many years has the farm been operating: What is the size of the farm (number of acres): Acres under French beans: Average amount of French beans produced annually (in kg’s): Turnover that arises from French beans: Total annual turnover: What fixed assets do you own (for example house, acres, equipment, bicycle, water pump etc.):

General knowledge about farming

1. Do you think crop rotation(grow other crops on the same field every season) is important? i) If yes, why? o Soil fertility (because the same crop every season will exhaust

the soil; every crop uses other elements from the soil) o The yield will be higher than when you grow the same crop for

years. o Integrated pest management (prevents pests, diseases etc.) o Other reason, namely

ii) If no, why not?

2. Do you think it is important to use cultivation techniques (like preparation, weeding etc.) that minimise soil erosion? i) If yes, why? o Because soil erosion will ruin my soil (the top of the soil will be

swept away) o Because soil erosion is dangerous for the environment o It prevents the soil from a flood; improve soil fertility o To get more yield o It makes the field look nicer o Other reason, namely ii) If no, why not?

3. What do you think is most important for the quality of French beans?

(=> For this question give the answers in advance) o Quality of the seed o Amount of pesticides being sprayed o Dimensions of the French beans (length, diameter) o Colour of the French beans (defines whether bean is infected by

rust) o Insect dimension (if beans are infected) o Dirt on bean o Climate o Management o Irrigation o Other, namely

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4. Do you think the quality of the produce is related to the seed that

you use? Yes/No If no, do you think you’ll get the same quality from producing the seed yourself as you’ll get from seed you buy?

Chemical spraying

1. Why do you think it is important for the exporter to know which chemicals you spray and the amount being sprayed?

o To check if I do not spray less than is needed o To check if I do not spray more than is needed o When there is a problem with the produce, they can find out if the

chemicals being used are the reason for the problem. o To check if I do not use chemicals that are not allowed in Europe o Consumers’ health (Because people can become ill if I spray the

wrong chemicals) o To create better markets for his produce o It will improve the relationship between the exporter and the

farmer o Other reason, namely 2. What is the best way to distribute the chemicals? What do you

prefer, chemicals supplied by the exporter against cost price or buying the chemicals in a store? Exporter / Store If you prefer to buy chemicals in a store, why?

3. How do you want the spraying to be done in the future o by people from the exporter o by a few people of my own group who will receive training o I want to spray myself after being trained 4. Why

5. Do you think it is important to be trained before you are allowed to spray? i) If yes, why? o Because it is important to know exactly the amount that I’m

allowed to spray o Because it is important to know how to use the spray

equipment o Because it is important to know the way I have to spray o Because the chemical may be harmful for my health o Other reason, namely ii) If no, why not?

6. Do you think it might be important to wear special clothes while you’re spraying? i) If yes, why? o Because chemicals might me dangerous for my health o Because the chemicals might ruin my clothes o Other reason, namely ii) If no, why not?

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7. Do you think it is important to check the spray equipment every year? i) If yes, why? o To be sure whether I’m still allowed to use it (regulations etc.) o To be sure that it is still working properly o Other reason, namely ii) If no, why not?

8. Why do you think it is important that you do not spray too much chemical?

o Because chemicals are very costly o Because to export to Europe there are limits on chemical spraying o Because it will ruin the crop o Because it might be harmful for the consumers’ health o After sending better quality, the exporter might increase the price o The relationship with the exporter will improve o Other reason, namely

9. Why do you think it is important to know exactly what chemical

you sprayed, on which day, the amount etc.? o To follow up my records to see if I do not spray more/less than I

should (to avoid overdose) o To trace problems regarding pests, bad quality, etc. o It makes me more responsible of my work o To avoid picking the product when it is not already allowed o Other reason, namely

10. Where do you get the information from regarding the amount and type of chemicals you are allowed to spray?

o The exporter o Other farmers o From the store I buy the chemicals o Other source, namely 11. Do you feel informed enough? Do you think you get enough

information regarding chemical use from the exporter? Yes/No If no, what kind of information do you miss?

Hygiene

1. Do you think it is important for the pickers to wash their hands before they start picking? i) If yes, why? o Because it will influence the quality of the beans, which is

important for the exporter o To improve hygiene o Because the consumers can become ill; if the hands of the

pickers are not clean, it will infect the beans. o Because the beans must be clean o Other reason, … ii) If no, why not? o It involves too much work o Other reason, namely

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2. Do you feel responsible for the cleanliness of your pickers? Yes/No

3. What do the pickers do to promote the cleanliness of the produce o They wash their hands before they start picking o They wear caps o They avoid smoking in the field during harvest o They wash their hands after visiting the toilet o They put on clean clothes before they start picking o They wash themselves o They do not use any make-up or perfume when picking o They wash the beans after picking o Other, namely o They do not do anything at all to promote the cleanliness of the

produce

4. Do you think the company gives you enough information regarding hygiene? Yes/No If no, what kind of information do you miss?

Trace ability

1. Why do you think trace ability is important for the supermarkets in Europe?

o They like to know more about the personal situation of each farmer (from which piece of land the beans come from etc.)

o They want to know which farmer grew which French beans in case of problems concerning the consumers’ health

o To avoid farmers to use other seed like Samantha o Other reason, namely

2. Why do you think trace ability is important for the exporter? o To control the farmers to see which farmers produce low quality o To check problems quicker o To get rid of dishonest farmers o To make sure the farmer will get the revenue he deserves (right

percentage of reject) o To create a better market for the beans o Other reason, namely

3. Do you think trace ability is important for you? i) If yes, why? o It makes sure that other farmers are not able to lower my revenue

(by grading badly) o I can improve my quality control; to avoid making the same

mistake twice. o Now I know exactly which percentage of my harvest is rejected

and the reason why. o Other reason, namely ii) If no, why not?

Relationship with exporter

1. Do you sell your French beans to an exporter directly or to brokers?

2. If you sell to an exporter directly, to whom do you sell?

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3. For how many years have you been selling to this exporter?

4. How satisfied are you with your exporter? o Very satisfied o Satisfied o Not very satisfied o Totally not satisfied at all

5. Why? 6. What do you think should be improved in the relationship with your

exporter? o Higher prices o Faster payment o More information about chemical spraying o More contact with field manager o Something else, namely

7. Do you think your exporter gives you enough information

regarding o reasons for reject

Yes/No,

o information regarding rules from Europe Yes/No,

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ANNEX E Questionnaire middlemen A. General information 1. Name and address of the respondent 2. For how many years have you been a middleman for French beans

already? 3. What is your education level?

No education (NE) Primary school not finished (PNF) Primary school finished (PF) Secondary school not finished (SNF) Secondary school finished (SF) University (U)

4. Which products do you buy and sell?

French beans Snow peas Passion fruit Cereals Baby corn Cabbages Avocado Tomato Mango Sugar snaps Anything else, namely…

5. What is the commission rate that you charge per carton (3 kg)?

6. What is the average amount of French beans that you buy and sell

annually in kg? 7. What percentage of your income does arise from French bean trade?

8. Do you own any fixed assets?

Yes No

8a. If yes, which ones?

House Cows Bicycle Water pump Car Plot Any acres A shop/business

9. What percentage of the French beans is meant for export? 10. What is the main destination for export?

UK France The Netherlands Germany The Middle East South Africa Belgium Unknown Another country, namely…

11. Do you hire any employees to assist you?

Yes No

12. If yes, how many? 13. What percentage of the harvest do you refuse to buy from farmers

because it doesn’t meet the quality requirements?

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B. Input control 1. Do you provide or specify any of the following inputs that are needed

to grow French beans? (seeds, fertiliser, pesticides, labour, equipment, credit, other input)

Yes No

2. If yes, which inputs? 3. Do the farmers have to pay for these inputs?

Yes No

4. If yes, how?

I reduce the payment to the farmers after harvest They have to pay in advance Other, namely….

C. Monitoring 1. Do you visit growers in the field?

Yes No

2. If yes, on average, how many times do you or your representatives

visit each grower during a typical year? Once a week Twice a week Three times a week Once every two weeks Once a month Other, namely…

3. What kind of thinks do you check during such a visit?

I advise about chemical use I advise about fertiliser use I advise about irrigation I advise about the planting method I check how the farmer threats the French beans

D. Quality Measurement 1. Is any part of your growers’ final payment adjusted on the basis of

measured quality for the French beans? Yes No

2. How do you measure the quality? 3. Which product attributes are important to you (for example colour,

length, diameter, amount of pesticides used, amount of fertilisers used)?

The amount of chemicals being sprayed The physical quality The pest infection The size of the beans The cleanliness of the produce Anything else, namely…

E. Exposing grower to risk 1. Does the payment you make to your growers of French beans depend

explicitly on the price you receive for the sale of the product downstream?

Yes No

F. Information flow

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1. From whom do you get the information about prices and the required quality?

From the exporter From a middleman From someone else, namely…

2. How do you receive the information about the prices?

By phone By fax By mail The buyer visits me regularly I visit the buyer regularly Other, namely….

3. Does your buyer provide seed to you?

Yes No

4. When do you get an order from your buyer?

The same day as that he needs the produce One day before Three days before A week before Other, namely…

5. How do you make sure that you’ll have enough beans when the

exporter needs them very urgently? By visiting farmers who usually do not supply me By visiting other middlemen I just give what I have I tell my farmers to pick more Anything else, namely…

G. Information contracting-exporter

1. Do you sell your beans to an exporter directly or to another middleman?

I sell my French beans to an exporter directly I sell my French beans to a middleman

2. If to an exporter directly, to which one(s)?

Homegrown Indu Farm East African Growers Greenland Avenue Vegepro Sunripe Wilham Fian Green Sun Fresh Wamu Everest Sunfresh Freshpak Topsamrek Kenya Horticultural Exports

3. Do you have one buyer for your beans every year or do you have

several buyers? I have one buyer I have two buyers I have several buyers (>2)

4. If one buyer, for how many years have you been selling your beans to

this buyer? 5. If several buyers, do you sell to them every year or do you have other

buyers every year? The buyers differ every year I have the same buyers for 1 year

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I have the same buyers for 2 years I have the same buyers for 3 years I have the same buyers for more 3 years

6. Are you able to choose between more than one potential buyer or is

there just one person you could sell your beans to? I am able to choose There’s only one person I could sell my French beans to

7. Do you have made agreements with your buyer regarding the quantity

that should be delivered? Yes No


Yes No

9. Do you have made agreements regarding the price?

Yes No


Yes No

11. If yes, what price do you receive? 12. Could you negotiate with your buyer?

Yes No

13. Do you have made agreements regarding the delivering date? 14. If yes, are these agreements written down?

Yes

No 15. Do you have made agreements regarding the quality? 16. If yes, are these agreements written down?

Yes No

17. Do you have made agreements regarding the amount of pesticides

used? 18. If yes, are these agreements written down?

Yes No

19. Do you have made agreements regarding the amount of fertilizer

used? 20. If yes, are these agreements written down?

Yes No

21. When do you receive your money?

The day I deliver the beans Later

22. If later, when exactly?

After a day After one week After two weeks After three weeks After a month After a season Differs with the buyer Other, namely…

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23. Do you always receive your money the day the buyer promised you? Yes No

24. Does your buyer always buys the total amount of beans you have, or

just a part? Yes No, he has limits during low season

H. Information about contracting-farmer 1. Do you have a fixed number of farmers who deliver the French beans

to you after every harvest? Yes No

2. If yes, for how many years have you been buying from them? 3. Do you have made agreements with the farmer regarding the quantity

you are willing to buy from them? Yes No


Yes No

5. Do you have made agreements regarding the price?

Yes No


Yes No

7. If yes, what price do they get?

8. Can they negotiate with you about the price?

Yes No

9. On what factor(s) does the price you are willing to pay the farmers

depend? On the price I receive from my buyer On the quality the farmer delivers On the costs I have to make Something else, namely…

10. When do you pay the farmers?

When I collect the produce After receiving the money from my buyer After two weeks After a month Other, namely…

11. Do you have made agreements regarding the delivering date?

Yes No


Yes No

13. Do you have made agreements regarding the quality?

Yes No


Yes No

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15. Do you have made agreements regarding the amount of pesticides the farmers should use?

Yes No

16. Do the farmers have storage facilities where they can store the

produce? Yes No

17. If yes, do they have cooling facilities?

Yes No

I. Shelf live of the French beans 1. Do you collect the produce at the same day as the farmers harvest it?

Yes No

2. Do you have storage facilities?

Yes No

3. If yes, do you have cooling facilities?

Yes No

4. Does your buyer come to you or do you bring the produce to the

buyer? The buyer comes to me I go to see the buyer This differs

J. EUREPGAP requirements

1. Have you heard from the EUREPGAP protocol? Yes No

2. Are you able to trace the beans back to the farmer who grew them?

Yes Sometimes No

3. If yes, how do you do this?

I use stickers I use tags Other, namely…

4. How do you make sure the farmers do not use more pesticides than is

allowed? I visit the farmers regularly I trust the farmers I buy the produce from I train the farmers I buy the produce from Something else, namely…

5. How do you make sure the farmers do not use more fertiliser than is

allowed? I visit the farmers regularly I trust the farmers I buy the produce from I train the farmers I buy the produce from Something else, namely…

6. Do you know if the farmers you buy from keep records of the

pesticides and fertilisers they used the last two years? Yes No Some of them

7. If yes, for how long do they keep those records?

For one season

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For a year For two years Other, namely… I do not know

8. Where does the water come from that the farmers use for irrigation?

River Dam Other, namely…

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ANNEX F Questionnaire exporters A. General information about the company 1. Name of the company and the respondent and address of the

company 2. Position of the respondent within the company 3. How many years have you already been working in this company? 4. How many years does this company exist? 5. Which products does the company trade for export?

French beans Snow peas Passion fruit Cereals Baby corn Cabbages Avocado Tomato Mango Sugar snaps

6. How many people do work for the company?

7. What is the average amount of French beans exported annually? 8. What is the percentage of the turnover that arises from French

beans? 9. What is the company’s total turnover? 10. What percentage of the French bean trade is for export?

11. What is the main destination for export? UK France The Netherlands Germany Middle east South Africa Another country, namely…

12. Do you pack the beans into small proportions before you ship

them? Yes No

B. The buying process of the French beans 1. Do you buy the French beans from farmers directly or from

middlemen? From farmers directly From middlemen From both farmers and middlemen

2. If you buy from farmers, do you have a fixed number of farmers from

who you buy after every harvest? Yes No

3. If yes, for how many years have you been buying from these farmers?

Yes No

4. If you buy from middlemen, do you have a fixed number of middlemen

from who you buy after every harvest? Yes No

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5. If yes, for how many years have you been buying from these middlemen?

6. Do you make agreements with the farmers/middlemen regarding the

quantity that you’re willing to buy from them? Yes No


Yes No

8. Do you make agreements with the farmers/middlemen regarding the

price you’re willing to pay? Yes No

9. Do you set a price?

The company sets the price We negotiate

10. If yes, is this agreement written down or do you provide seed?

The agreement is written down We provide seed It’s not written down and we do not provide seed

11. On what factors does the price depend that you’re willing to pay the

farmers/middlemen? On the costs we make On the quality of the French beans On the price we receive from the importer

12. When will you receive money from the importer?

Before delivery of the French beans After delivery of the French beans

13. If after delivery, how many days exactly after delivery? 14. When will you pay the farmers/middlemen?

When we receive the beans After we receive the money from the importer Other option, namely….

15. Do the farmers/middlemen know in advance when you’ll come?

Yes No

16. If yes, how do they know? 17. If no, are the beans always harvested when you come to collect them? 18. Do the farmers/middlemen have storage facilities where they can store

the French beans before you’ll take them? Yes No

19. If yes, do they have cooling facilities?

Yes No

20. Do you always buy the total amount the farmers/middlemen harvest

(except the reject) or do you sometimes leave a part? Yes No, I have my limits during low season

C. Input control 1. Do you provide or specify any of the following inputs used by your

growers of French beans? (seeds, fertilisers, pesticides, plants, labour, equipment, other)

Yes No

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2. If yes, which inputs? 3. Do the growers have to pay for these inputs?

Yes No

4. If yes, how?

I reduce the payment to the farmers after harvest They have to pay in advance Other option, namely….

D. Monitoring 1. Do you or representatives of your firm visit growers in the field?

Yes No

2. If so, on average, how many times do representatives visit each

grower during a typical year? Once a week Twice a week Three times a week Once every two weeks Once a month Other, namely…

3. What kind of things do you check during such a visit

I advise about chemical use I advise about fertiliser use I advise about irrigation I advise about the planting method I check how the farmer threats the French beans

E. Residual Claimancy

1. Does the payment you make to your growers of French beans depend explicitly on the price you receive for the sale of the product downstream?

Yes No

F. Quality measurement 1. Is any part of your growers’ final payment adjusted on the basis of

measured quality for the French beans? Yes No

2. How do you measure the quality? 3. Which product attributes are important to you (for example colour,

length, diameter, amount of pesticides used, amount of fertiliser used)?

The amount of chemicals being sprayed The physical quality The pest infection The size of the beans The cleanliness of the produce Anything else, namely…

G. EUREPGAP requirements 1. Have you heard from the Good Agricultural Practices protocol that the

supermarkets in Europe have set up? Yes No

2. Are you able to trace the French beans back to the farmer who grew

them? Do you know from which farm the beans come that you export? Yes No

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3. If yes, how do you do this? If no, are you thinking about possible

solutions? 4. How do you make sure that the farmers do not use more pesticides

than is allowed? By providing the pesticides to the farmers By visiting the farmers to check whether they do not use too much

pesticides By trusting them Other option, namely…

5. How do you make sure that the farmers do not use more fertilisers

than is allowed? By providing the pesticides to the farmers By visiting the farmers to check whether they do not use too much

pesticides By trusting them Other option, namely…

H. Contact with importers 1. To how many importers do you sell your beans? 2. For how many years have you been selling to this importer(s)? 3. Do you receive information from your importer(s) about the quality they

are willing to receive? Yes No

4. If yes, how do you receive this information

5. If no, how do you know where they are interesting in? 6. How many times a year do you export French beans?

7. When do you get the order from your importer(s)

Less than one week before you have to deliver the beans? If yes, how many days before delivering?

One week before you have to deliver the beans? More than one week before you have to deliver the beans? If yes, how

many days before delivering? 8. How do you make sure that you’ll have enough beans when an

importer needs them very urgently? This never happens I buy from brokers I buy from other farmers directly (farmers who didn’t supply you

before) Other option, namely…

9. How long are you able to store the French beans before they will be

exported? 10. Do you have cooling facilities where you can store the beans?

Yes No

11. When the French beans are transported by aeroplane, are there

cooling facilities on the aeroplane as well? Yes No

12. Does the importer set the price or do you negotiate about the price?

The importer sets the price We negotiate This depends Other option, namely

13. When do you know what price you’ll receive from the importer?

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Before delivery of the French beans? If yes, how many days before delivery?

After delivery of the French beans? If yes, how many days after delivery?

14. How do you communicate with your importer?

By telephone By fax By computer Other option, namely…

15. Is there some person working for the importer located in Nairobi?

Yes No

16. When does the importer inspect the French beans?

The beans are inspected before delivery by an employee of the importer, who is situated in Nairobi

The beans are inspected after their arrival in the importing country Other option, namely…

17. Do you have stable orders during the year or do they differ very much?

I have stable orders They vary very much during the year

18. How did you get in touch with the importer(s) you work with?

You contacted them They contacted me Other option, namely…

I. Most important costs 1. What does it cost to transport 1000 kg of French beans by aeroplane

(is this different for the various importing countries?)

J. Major problems exporter is facing 1. What are the major problems you face in exporting French beans?

Competition from other Kenyan exporters Lack of demand for French beans in importing countries Competition from other countries exporting French beans Problems in fulfilling demand from importing countries Problems in sustaining the quality during transport Problems regarding the loyalty of farmers Problems with adjusting to legal matters of importing countries Other problem, namely…

2. Do you have any solutions in mind for these problems? 3. From which countries do you face most competition in exporting

French beans? 4. What do you think is the advantage that these countries have

compared to you? General remarks:

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ANNEX G Case study of exporters of French beans During the period of fieldwork, a small number of exporters has been interviewed as well. Because these interviews cannot be used for analytical purposes, a short description will be given in this Annex. Contact with the farmers All but one of the exporters does exist already for more than 10 years. They mainly export French beans, Snow peas and Sugar snaps, but some of them export flowers and fruits as well. They all rely for their produce on farmers and some of them admitted they buy from middlemen as well. Some of the exporters grow French beans themselves as well too. One exporter was very dissatisfied with the farmers, because they do not keep their promises with regard to the quantity they should deliver. When the market price increases, the farmers are willing to sell their produce outside. The other exporters all have agreements with their growers regarding the quantity they should deliver. They also provide their growers with seed, pesticides, fertilisers and credit. Furthermore they send their employees to the farmers at least once a week to check the hygiene, the pesticides and fertilisers the farmers use, the stage of growth of the crop and the update of the records farmers have to keep. They also advise the farmers on technical issues and offer help in case of problems. According to the exporters, the most important product attributes are the colour, length, pest damage indicators and freshness of the produce. For some of them the price paid to farmers depends explicitly on the price received downstream. Other exporters also take the quality and the costs they have to make into account. All exporters said that they have heard from the EUREPGAP protocol and are able to trace the produce back to the farmer who grew it. Contact with the importer(s) The number of shipments varies from once a week till every day during the year. Also the number of importers varies from one to ten. Most of the exporters have a long-term relationship with their buyer(s). They communicate by fax, phone and e-mail. The number and size of the orders vary by season. Only one exporter has stable orders throughout the year.

The moment they receive the order from the importer differs. Some of them receive it a day before the shipment, others a week before and one received it a month before the actual delivering. Some of the exporters are able to negotiate about the price; other exporters just have to accept the price offered by their buyer(s). When they are not able to fulfil the demand from their importer(s), they buy produce from unknown farmers and middlemen to close the gap. Problems and competition The problems the exporters face are: 1. Competition from other Kenyan exporters; 2. Competition from other countries exporting French beans; 3. Problems in sustaining the quality during transport; 4. Problems regarding the loyalty of farmers; 5. Problems with adjusting to legal matters of importing countries; 6. Very high freight charges; 7. Occasionally a lack of demand for French beans from importing

countries. They face most competition from Egypt, Burkina Faso, Morocco, Tunisia and most of the West African countries. The advantages that these countries have compared to Kenyan exporters are mainly that: 1. They face lower air freight charges; 2. They have subsidised or low costs of inputs (fertilisers, pesticides,

seeds and fuel for irrigation); 3. They can rely on cheap finance made available by foreign exchange

earners; 4. They’re located closer to Europe.

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ANNEX H Important frequencies Important frequencies farmers: Variable Yes (in %) No (in %) Agreement delivering date 97.5 2.5Agreement price 30.0 70.0Agreement quantity 27.5 72.5Agreement quality 45.0 55.0Agreement pesticide use 42.5 57.5 Important frequencies middlemen: Variable Yes (in %) No (in %) Agreement delivering date with buyer(s) 90.0 10.0Agreement price with buyer(s) 5.0 95.0Agreement quantity with buyer(s) 40.0 60.0Agreement quality with buyer(s) 80.0 20.0Agreement pesticide use with buyer(s) 72.5 27.5Agreement fertiliser use with buyer(s) 35.0 65.0Agreement delivering date farmer(s) 97.5 2.5Agreement price with farmer(s) 2.5 97.5Agreement quantity with farmer(s) 35.0 65.0Agreement quality with farmer(s) 75.0 25.0Agreement pesticide use with farmer(s) 82.5 17.5

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ANNEX I Output Farmers Channel-choice Model information:

Dependent Variable: BUYER Method: ML - Binary Logit Date: 02/24/03 Time: 08:27 Sample: 1 40 Included observations: 40 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives


EUREP 2.600823 0.882666 2.946554 0.0032RECORDS 2.032547 0.914156 2.223414 0.0262

Mean dependent var 0.425000 S.D. dependent var 0.500641S.E. of regression 0.402051 Akaike info criterion 1.129183Sum squared resid 5.980869 Schwarz criterion 1.255849Log likelihood -19.58366 Hannan-Quinn criter. 1.174981Restr. log likelihood -27.27418 Avg. log likelihood -0.489592LR statistic (2 df) 15.38105 McFadden R-squared 0.281971Probability(LR stat) 0.000457 Jarque bera statistic 97.62065 Obs with Dep=0 23 Total obs 40Obs with Dep=1 17

The process of including variables into the model: Step 1: Variable Coefficient Std. Error z-Statistic Prob.

C -0.424159 0.520766 -0.814491 0.41541. YEARS 0.006250 0.020960 0.298185 0.7656

C -0.573643 0.612240 -0.936957 0.34882. SIZE 0.045738 0.122104 0.374580 0.7080

C -0.692522 0.447763 -1.546628 0.12203. VOLUME 9.43E-05 7.56E-05 1.246919 0.2124

C 0.654297 1.141791 0.573045 0.56664. EDU -0.235784 0.270670 -0.371114 0.3837

C -1.299283 0.651339 -1.994788 0.04615. RECORDS 1.453434 0.760923 1.910092 0.0561

C -0.916291 0.836660 -1.095177 0.27346. PROPERTY 0.733969 0.906765 0.809437 0.4183

C -1.609438 0.632456 -2.544745 0.01097. EUREP 2.169054 0.772288 2.308608 0.0050

After the first step, the variable EUREP has been entered into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C -2.072328 0.879886 -2.355222 0.0185EUREP 2.297466 0.806658 2.848129 0.0044

1.

YEARS 0.020092 0.025026 0.802821 0.4221C -1.687456 0.844644 -1.997832 0.0457

EUREP 1.940069 0.783709 2.475496 0.01332.

SIZE 0.036131 0.133965 0.269708 0.7874C -2.259869 0.839928 -2.690550 0.0071

EUREP 2.345856 0.832677 2.817246 0.00483.

VOLUME 0.000135 9.75E-05 1.384281 0.1663C -1.961549 1.569740 -1.249601 0.2114

EUREP 2.238999 0.827102 2.707041 0.00684.

EDU 0.076995 0.312946 0.246033 0.8057C -3.234867 1.054922 -3.066453 0.0022

EUREP 2.600823 0.882666 2.946554 0.00325.

RECORDS 2.2032547 0.914156 2.223414 0.0262C -2.472976 1.118565 -2.210848 0.0270

EUREP 2.238585 0.788452 2.839215 0.00456.

PROPERTY 0.986459 1.007844 0.978781 0.3277 After the second step, the variable RECORDS has been included in the model. This variable now has the smallest probability and does not strongly affect the probability of the variable EUREP.

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Step 3: Variable Coefficient Std. Error z-Statistic Prob.

C -3.705240 1.246323 -2.972938 0.0029EUREP 2.708063 0.908831 2.979721 0.0029

RECORDS 2.028457 0.914685 2.217656 0.0266

1.

YEARS 0.022707 0.028703 0.791101 0.4289C -3.322499 1.218784 -2.726076 0.0064

EUREP 2.288627 0.882987 2.591916 0.0095RECORDS 1.936145 0.913634 2.119168 0.0341

2.

SIZE 0.083422 0.160841 0.518662 0.6040C -3.478576 1.132959 -3.070345 0.0021

EUREP 1.825152 0.912754 2.928086 0.0034RECORDS 8.46E-05 0.939964 1.941726 0.0522

3.

VOLUME -3.163823 0.000103 0.818528 0.4131C 2.586723 1.855959 -1.704683 0.0883

EUREP 2.037513 0.932497 2.773974 0.0055RECORDS -0.016307 0.920868 2.212600 0.0269

4.

EDU -3.980409 0.351529 -0.046389 0.9630C 2.656360 1.461383 -2.723726 0.0065

EUREP 2.646360 0.895603 2.954837 0.0031RECORDS 2.001422 0.922354 2.169907 0.0300

5.

PROPERTY 0.891527 1.096287 0.813224 0.4161 There has been no variable included after the third step.

Correlation matrix: Years Size Volume Edu EUR Record Prop. Years 1.000 .632**

(.000) -.342** (.044)

-.225 (.163)

-.141 (.384)

-.025 (.876)

.405** (.009)

Size .632** (.000)

1.000 -.095 (.597)

.223 (.184)

.049 (.773)

-.208 (.216)

.277 (.097)

Volume -.342** (.044)

-.095 (.597)

1.000 .235 (.174)

-.222 (.199)

.138 (.429)

-.328 (.054)

Edu -.225 (.163)

.223 (.184)

.235 (.174)

1.000 -.365* (.021)

.134 (.409)

.084 (.605)

EU REP

-.141 (.384)

.049 (.773)

-.222 (.199)

-.365* (.021)

1.000 -.032 (.846)

-.020 (.903)

Re cords

-.025 (.876)

-.208 (.216)

.138 (.429)

.134 (.409)

-.032 (.846)

1.000 .076 (.642)

Prop. .405** (.009)

.277 (.097)

-.328 (.054)

.084 (.605)

-.020 (.903)

.076 (.642)

1.000

* Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

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Expectation-Prediction table: Dependent Variable: BUYER Method: ML - Binary Logit Date: 02/28/03 Time: 08:38 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)

Estimated Equation Constant Probability Dep=0 Dep=1 Total Dep=0 Dep=1 Total

P(Dep=1)<=C 21 5 26 23 17 40P(Dep=1)>C 2 12 14 0 0 0

Total 23 17 40 23 17 40Correct 21 12 33 23 0 23

% Correct 91.30 70.59 82.50 100.00 0.00 57.50% Incorrect 8.70 29.41 17.50 0.00 100.00 42.50Total Gain* -8.70 70.59 25.00

Percent Gain** NA 70.59 58.82 Estimated Equation Constant Probability Dep=0 Dep=1 Total Dep=0 Dep=1 Total

E(# of Dep=0) 16.82 6.18 23.00 13.22 9.78 23.00E(# of Dep=1) 6.18 10.82 17.00 9.78 7.22 17.00

Total 23.00 17.00 40.00 23.00 17.00 40.00Correct 16.82 10.82 27.63 13.22 7.22 20.45

% Correct 73.12 63.63 69.08 57.50 42.50 51.12% Incorrect 26.88 36.37 30.92 42.50 57.50 48.88Total Gain* 15.62 21.13 17.96

Percent Gain** 36.74 36.74 36.74 *Change in "% Correct" from default (constant probability) specification

**Percent of incorrect (default) prediction corrected by equation

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ANNEX J Output OLS Price Farmers Model information: Dependent Variable: PRICE Method: Least Squares Date: 03/07/03 Time: 16:39 Sample: 1 40 Included observations: 29 Excluded observations: 11


STORAGE 14.90056 1.642089 9.074151 0.0000VOLUME -0.000272 0.000105 -2.593360 0.0159EUREP 2.704128 0.941639 2.871724 0.0084BUYER 1.910113 1.031264 1.852207 0.0763

R-squared 0.823122 Mean dependent var 22.36793Adjusted R-squared 0.793643 S.D. dependent var 5.070514S.E. of regression 2.303360 Akaike info criterion 4.662201Sum squared resid 127.3312 Schwarz criterion 4.897941Log likelihood -62.60191 F-statistic 27.92175Durbin-Watson stat 1.851055 Prob(F-statistic) 0.000000Jarque-Bera statistic 0.079169

The process of including variables into the model: Step 1: Model Variable Coefficient Prob. R-squared

C 20.97938 0.00001. BUYER 3.097548 0.1027

0.095596

C 21.19843 0.00002. SAME 0.413605 0.1673

0.069422

C 23.59468 0.00003. RATE -0.078447 0.3097

0.038190

C 21.07505 0.00004. VOLUME 0.000271 0.1531

0.074117

C 20.38462 0.00005. EUREP 3.594760 0.0559

0.128749

C 20.70680 0.00006. STORAGE 12.04320 0.0000

0.694742

C 22.07952 0.00007. AGR_ PRICE

1.045476 0.62850.008796

C 21.30583 0.00008. RECORDS 1.811814 0.3526

0.03207

After the first step, the variable STORAGE has been entered into the model, because this variable has the smallest probability. Step 2: Model Variable Coefficient Prob. R-squared

C 20.97938 0.0000STORAGE 12.52778 0.0000

1.

BUYER 0.757153 0.5343

0.699329

C 20.22581 0.0000STORAGE 11.73795 0.0000

2.

SAME 0.184997 0.2838

0.708185

C 20.91888 0.0000STORAGE 11.95235 0.0000

3.

RATE -0.012760 0.7756

0.695713

C 21.80330 0.0000STORAGE 14.55365 0.0000

4.

VOLUME -0.000302 0.0160

0.756854

C 19.49930 0.0000STORAGE 11.50911 0.0000

5.

EUREP 2.322117 0.0285

0.747101

C 20.35366 0.0000STORAGE 12.08104 0.0000

6.

AGR_ PRICE

1.261209 0.2960

0.707536

C 21.30583 0.0000STORAGE 12.59615 0.0000

7.

RECORDS -1.151987 0.3223

0.706246

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After the second step, the variable VOLUME has been included in the model. This variable now has the smallest probability and does not strongly affect the probability of the variable STORAGE. Step 3: Model Variable Coefficient Prob. R-squared

C 22.10992 0.0000STORAGE 15.10301 0.0000VOLUME -0.000305 0.0163

1.

BUYER 0.828657 0.4544

0.762344

C 21.63944 0.0000STORAGE 14.37377 0.0000VOLUME -0.000290 0.0330

2.

SAME 0.045388 0.7903

0.757554

C 21.92269 0.0000STORAGE 14.49113 0.0000VOLUME -0.000301 0.0187

3.

RATE -0.007435 0.8555

0.757182

C 20.62902 0.0000STORAGE 13.85080 0.0000VOLUME -0.000275 0.0191

4.

EUREP 2.067229 0.0334

0.797839

C 21.71203 0.0000STORAGE 14.48590 0.0000VOLUME -0.000294 0.0328

5.

AGR_ PRICE

0.210420 0.8614

0.757156

C 21.96940 0.0000STORAGE 14.64014 0.0000VOLUME -0.000289 0.0289

6.

RECORDS -0.413476 0.7110

0.758212

After the third step, the variable EUREP has been put in the model. This variable now has the smallest probability and does not strongly affect the probability of the variables STORAGE and VOLUME.

Step 4: Model Variable Coefficient Prob. R-squared

C 20.97402 0.0000STORAGE 14.90056 0.0000VOLUME -0.000272 0.0159EUREP 2.704128 0.0084

1.

BUYER 1.910113 0.0763

0.823122


2.

SAME 0.039952 0.8015

0.798381


3.

RATE -0.034136 0.3862

0.804195


4.

AGR_ PRICE

-0.371182 0.7474

0.798729


5.

RECORDS -0.216831 0.8355

0.798209

After the fourth step, the variable BUYER has been entered into the model, because this variable now has the smallest probability and does not strongly affect the probability of the variables STORAGE, VOLUME and EUREP.

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Step 5: Model Variable Coefficient Prob. R-squared

C 20.94103 0.0000STORAGE 14.86160 0.0000VOLUME -0.000269 0.0281EUREP 2.700917 0.0101BUYER 1.902981 0.0858

1.

SAME 0.008913 0.9536

0.823149

C 21.35464 0.0000STORAGE 14.53729 0.0000VOLUME -0.000264 0.0202EUREP 2.925466 0.0066BUYER -1.883415 0.0824

2.

RATE -0.032149 0.3934

0.828755

C 20.67647 0.0000STORAGE 14.99943 0.0000VOLUME -0.000216 0.0819EUREP 2.711840 0.0084BUYER -2.732042 0.0485

3.

AGR_ PRICE

1.377129 0.3209

0.830698

C 20.84433 20.84433STORAGE 14.87874 14.87874VOLUME -0.000282 -0.000282EUREP 2.764809 2.764809BUYER -2.013016 -2.013016

4.

RECORDS 0.331827 0.331827

0.823917

There has no variable been included into the model after the fifth step.

Correlation matrix: Vo-

lume Rate Buyer Same Agr_

price Sto-rage

Eurep Re-cords

Vo-lume

1.000 -.079 (.629)

-.205 (.205)

-.139 (.393)

-.267 (.096)

.51** (.001)

-.007 (.967)

.305 (.056)

Rate -.079 (.629)

1.000 -.157 (.335)

-.168 (.300)

-.021 (.899)

-.132 (.417)

.317* (.046)

-.054 (.739)

Buyer -.205 (.205)

-.157 (.335)

1.000 -0.74 (.651)

-.54** (.000)

-2.87 (.073)

-.47** (.002)

-.313* (.049)

Same -.139 (.393)

-.168 (.300)

-0.74 (.651)

1.000 .303 (.057)

.160 (.325)

.093 (.569)

-.108 (.509)

Agr_ Price

-.267 (.096)

-.021 (.899)

-.54** (.000)

.303 (.057)

1.000 .082 (.613)

.373* (.018)

.137 (.398)

Sto-rage

.51** (.001)

-.132 (.417)

-2.87 (.073)

.160 (.325)

.082 (.613)

1.000 .190 (.240)

.277 (.083)

Eurep -.007 (.967)

.317* (.046)

-.47** (.002)

.093 (.569)

.373* (.018)

.190 (.240)

1.000 -.032 (.846)

Re-cords

.305 (.056)

-.054 (.739)

-.313* (.049)

-.108 (.509)

.137 (.398)

.277 (.083)

-.032 (.846)

1.000

* Correlation is significant at the 0.05 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed)

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ANNEX K Output OLS Quality Farmers Model information: Dependent Variable: QUALITY Method: Least Squares Date: 03/07/03 Time: 16:54 Sample: 1 40 Included observations: 37 Excluded observations: 3


YEARS -0.027367 0.008971 -3.050510 0.0046TOTAL_AMOUNT -0.766873 0.301049 -2.547333 0.0159

STORAGE -0.965444 0.428238 -2.254455 0.0311BUYER 0.632882 0.290408 2.179282 0.0368

R-squared 0.398179 Mean dependent var -1.864405Adjusted R-squared 0.322951 S.D. dependent var 1.011799S.E. of regression 0.832538 Akaike info criterion 2.596413Sum squared resid 22.17982 Schwarz criterion 2.814104Log likelihood -43.03363 F-statistic 5.292991Durbin-Watson stat 1.912718 Prob(F-statistic) 0.002183Jarque-Bera statistic 5.062669


C -1.375026 0.00001. YEARS -0.024305 0.0204

0.144229

C -1.313091 0.00012. TOTAL_ AMOUNT

-0.784563 0.02900.129101

C -1.812250 0.00003. STORAGE -0.385950 0.4354

0.017478

C -2.047048 0.00004. BUYER 0.422360 0.2130

0.043955

C -1.572155 0.00155. FERTI-LISER

-0.011720 0.46640.017818

C -1.724647 0.00006. SAME -0.055603 0.3606

0.023929

C -2.239400 0.00007. EUREP 0.630673 0.0616

0.096256

C -1.950357 0.00008. RECORDS 0.138270 0.6928

0.004515

C -1.697706 0.00009. INPUTS -0.308394 0.3628

0.023714

C -1.783000 0.000010. SEC_

SCHOOL -0.143429 0.6754

0.005069

C -1.888550 0.000011. AGR_

QUALITY 0.052550 0.8775

0.000689

After the first step, the variable YEARS has been entered into the model, because this variable has the smallest probability. Step 2: Model Variable Coefficient Prob. R-squared

C -0.888758 0.0094YEARS -0.022881 0.0214

1.

TOTAL_ AMOUNT

-0.732801 0.0301

0.256362

C -1.247525 0.0001YEARS -0.026614 0.0123

2.

STORAGE -0.599365 0.2005

0.185078

C -1.559659 0.0000YEARS -0.024903 0.0163

3.

BUYER 0.454823 0.1518

0.195114

C -1.094815 0.0238YEARS -0.028954 0.0189

4.

FERTI- LISER

-0.007403 0.6200

0.190438

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C -1.341258 0.0000YEARS -0.023309 0.0332

5.

SAME -0.021410 0.7188

0.147535

C -1.731950 0.0000YEARS -0.021085 0.0431

6.

EUREP 0.491255 0.1326

0.200101

C -1.465061 0.0001YEARS -0.024352 0.0217

7.

RECORDS 0.146362 0.6558

0.149287

C -1.337448 0.0000YEARS -0.023376 0.0366

8.

INPUTS -0.104133 0.7546

0.146722

C -1.231827 0.0006YEARS -0.025053 0.0186

9.

SEC_ SCHOOL

-0.225747 0.4840

0.156649

C -1.429441 0.0000YEARS -0.024817 0.0203

10.

AGR_ QAULITY

0.140868 0.6615

0.149113

After the second step, the variable TOTAL_AMOUNT has been included in the model. This variable now has the smallest probability and does mot strongly effect the probability of the variable YEARS. Step 3: Model Variable Coefficient Prob. R-squared

C -0.717548 0.0391YEARS -0.025430 0.0109TOTAL_ AMOUNT

-0.772313 0.0206

1.

STORAGE -0.681582 0.0253

0.308860


-0.717795 0.0309

2.

BUYER 0.433742 0.1486

0.302592


-0.845539 0.0463

3.

FERTI- LISER

-0.000423 0.9768

0.299209


-0.728896 0.0334

4.

SAME -0.016376 0.7713

0.258293


-0.712077 0.0318

5.

EUREP 0.462475 0.1348

0.305790


-0.756452 0.0274

6.

RECORDS 0.218708 0.4829

0.267539


-0.736143 0.0316

7.

INPUTS -0.122388 0.6978

0.259803


-0.724917 0.0334

8.

SEC_ SCHOOL

-0.201436 0.5097

0.266238


-0.761637 0.0266

9.

AGR_ QUALITY

0.224041 0.4638

0.268542

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124

After the third step, the variable STORAGE has been put in the model. This variable now has the smallest probability and does not strongly affect the probability of the variables YEARS and TOTAL_AMOUNT. Step 4 : Model Variable Coefficient Prob. R-squared


-0.766873 0.0159

STORAGE -0.965444 0.0311

1.

BUYER 0.632882 0.0368

0.398179


-0.823376 0.0479

STORAGE -0.754013 0.1467

2.

FERTI-LISER -0.006292 0.6702

0.352741


-0.774021 0.0226

STORAGE -0.691082 0.1352

3.

SAME 0.004854 0.9321

0.309019


-0.754179 0.0197

STORAGE -0.781569 0.0718

4.

EUREP 0.534032 0.0783

0.534032


-0.828795 0.0135

STORAGE -0.877167 0.0601

5.

RECORDS 0.417459 0.1917

0.345259


-0.773255 0.0224

STORAGE -0.748162 0.1330

6.

INPUTS 0.106874 0.7555

0.310983


-0.765024 0.0222

STORAGE -0.744251 0.0978

7.

SEC_ SCHOOL

-0.279040 0.3551

0.327368


-0.835326 0.0130

STORAGE -0.865736 0.0619

8.

AGR_ QUALITY

0.406634 0.1924

0.345151

After the fourth step, the variable BUYER has been included into the model. This variable now has the smallest probability and does not strongly affect the probability of the variables YEARS, TOTAL_AMOUNT and STORAGE. Step 5: Model Variable Coefficient Prob. R-squared


-0.952341 0.0216

STORAGE -0.947864 0.0684BUYER 0.589821 0.0909

1.

FERTI-LISER -0.001106 0.9393

0.421349

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125


-0.766215 0.0179

STORAGE -0.962082 0.0388BUYER 0.633478 0.0400

2.

SAME -0.001854 0.9727

0.398202


-0.758270 0.0176

STORAGE -0.953187 0.0342BUYER 0.484074 0.1623

3.

EUREP 0.291018 0.3917

0.412477


-0.802088 0.0134

STORAGE -1.054561 0.0240BUYER 0.564257 0.0728

4.

RECORDS 0.255909 0.4212

0.410807


-0.764312 0.0174

STORAGE -0.868524 0.0691BUYER 0.712547 0.0342

5.

INPUTS -0.212936 0.5499

0.405192


-0.762751 0.0176

STORAGE -0.988488 0.0300BUYER 0.599799 0.0536

6.

SEC_ SCHOOL

-0.168678 0.5643

0.404698

C -0.921067 0.00987. YEARS -0.027092 0.0060

0.399146

TOTAL_ AMOUNT

-0.751683 0.0225

STORAGE -0.952550 0.0376BUYER 0.699390 0.1052

AGR_ QUALITY

-0.094339 0.8247

There has been no variable included after the fifth step.

Annexes

126

Correlation matrix:

Input Seed Buyer Same_ buyer

Agr_ quality

EUREP Fertiliser Storage Edu Records Years

Input 1.000 1.000** (.000)

-.47** (.002) .405** (.010)

.414** (.008)

.091 (.577)

.096 (.582)

.342* (.031) -.168 (.301) .179 (.269)

.255 (.112)

Seed 1.000** (.000)

1.000 -.47** (.002) .405** (.010)

.414** (.008)

.091 (.577)

.096 (.582)

.342* (.031) -.168 (.301) .179 (.269)

.255 (.112)

Buyer -.47** (.002) -.47** (.002) 1.000 -.074 (.651) -.75** (.000) -.473* (.002)

.232 (.180)

-.287 (.073) .182 (.262)

-.31* (.049) -.047 (.772)

Same_ buyer

.405** (.010)

.405** (.010)

-.074 (.651) 1.000 .081 (.621)

.093 (.569)

-.014 (.937) .160 (.325)

-.144 (.377) -.108 (.509) .228 (.157)

Agr_ quality

.414** (.008)

.414** (.008)

-.75** (.000) .081 (.621)

1.000 .313* (.049) -.035 (.841) .266 (.097)

.066 (.685)

.348* (.028) .118 (.468)

EUREP .091 (.577)

.091 (.577)

-473* (.002) .093 (.569)

.313* (.049) 1.000 -.39* (.020) .190 (.240)

-.37* (.018) -.032 (.846) -.141 (.384)

Fertiliser .096 (.582)

.096 (.582)

.232 (.180)

-.014 (.937) -.035 (.841) -.39* (.020) 1.000 -.281 (.102) .117 (.503)

-.143 (.412) .105 (.548)

Storage .342* (.031) .342* (.031) -.287 (.073) .160 (.325)

.266 (.097)

.190 (.240)

-.281 (.102) 1.000 -.134 (.410) .277 (.083)

-.154 (.343)

Edu -.168 (.301) -.168 (.301) .182 (.262)

-.144 (.377) .066 (.685)

-.371* (.018)

.117 (.503)

-.134 (.410) 1.000 .111 (.494)

-.110 (.498)

Records .179 (.269)

.179 (.269)

-.31* (.049) -.108 (.509) .348* (.028) -.032 (.846) -.143 (.412) .277 (.083)

.111 (.494)

1.000 -.025 (.876)

Years

.255 (.112)

.255 (.112)

-.047 (.772)

.228 (.157)

.118 (.468)

-.141 (.384)

.105 (.548)

-.154 (.343)

-.110 (.498)

-.025 (.876)

1.000


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127

ANNEX L Output Farmers Paid-on-time Model information: Dependent Variable: PAID_ON_TIME Method: ML - Binary Logit Date: 02/24/03 Time: 14:54 Sample: 1 40 Included observations: 38 Excluded observations: 2 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives


BUYER 1.503904 0.777423 1.934475 0.0531PRICE_DIF 0.124167 0.072861 1.704166 0.0884

Mean dependent var 0.526316 S.D. dependent var 0.506009S.E. of regression 0.451065 Akaike info criterion 1.246257Sum squared resid 7.121086 Schwarz criterion 1.375540Log likelihood -20.67889 Hannan-Quinn criter. 1.292255Restr. log likelihood -26.28694 Avg. log likelihood -0.544181LR statistic (2 df) 11.21610 McFadden R-squared 0.213340Probability(LR stat) 0.003668 Jarque-Bera statistic 1.168448 Obs with Dep=0 18 Total obs 38Obs with Dep=1 20


C -0.020123 0.609177 -0.033033 0.97361. SIZE 0.004793 0.121444 0.039456 0.9685

C -0.592112 0.604627 -0.979301 0.32742. ACRES 0.435249 0.324682 1.340541 0.1801

C -0.100661 0.401078 -0.250977 0.80183. QUANTITY 3.32E-05 3.97E-05 0.836823 0.4027

C 0.053982 0.437531 0.123379 0.90184. VOLUME 1.25E-05 7.16E-05 0.174931 0.8611

C 0.356887 0.565224 0.631407 0.52785. RATE -0.015524 0.028474 -0.545198 0.5856

C -0.127465 0.446732 -0.285328 0.77546. SAME BUYER 0.087121 0.116138 0.750150 0.4532

C -0.693147 0.462910 -1.497369 0.13437. BUYER 1.871802 0.735669 2.544355 0.0109

C 0.183075 0.369040 0.496084 0.61988. PRICE_DIF 0.140381 0.064183 2.187213 0.0287

C -0.875469 0.532290 -1.644720 0.10009. ANY AGR 1.791759 0.718795 2.492727 0.0127

After the first step, the variable BUYER has been entered into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C -0.654193 0.710896 -0.920237 0.3574BUYER 1.707394 0.746274 2.287890 0.0221

1.

SIZE -0.009539 0.132479 -0.072002 0.9426C -1.156577 0.715994 -1.615343 0.1062

BUYER 1.760082 0.746570 2.357559 0.01842.

ACRES 0.322228 0.368203 0.875138 0.3815C -0.699270 0.490301 -1.426206 0.1538

BUYER 1.862151 0.777437 2.395242 0.01663.

QUANTITY 1.62E-06 4.28E-05 0.037916 0.9698C -0.577194 0.529486 -1.090102 0.2757

BUYER 1.955307 0.769572 2.540771 0.01114.

VOLUME -3.63E-05 8.17E-05 -0.444345 0.6568C -0.243558 0.647854 -0.375946 0.7070

BUYER 2.016709 0.774167 2.605004 0.00925.

RATE -0.031873 0.033661 -0.946877 0.3437C -0.935390 0.584383 -1.600644 0.1095

BUYER 1.877209 0.740994 2.533367 0.01136.

SAME BUYER 0.087898 0.122173 0.719454 0.4719

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128

C -0.435165 0.497347 -0.874973 0.3816BUYER 1.503904 0.777423 1.934475 0.0531

7.

PRICE_DIF 0.124167 0.072861 1.704166 0.0884C -0.957183 0.542407 -1.764697 0.0776

BUYER 1.202079 0.958029 1.254741 0.20968.

ANY AGR 1.005929 0.942704 1.067067 0.2859 After the second step the variable PRICE_DIF has been included in the model. This variable has the smallest probability and does not strongly affect the probability of the variable BUYER. Step 3: Variable Coefficient Std. Error z-Statistic Prob.

C -0.341760 0.791614 -0.431726 0.6659 BUYER 1.292434 0.795021 1.625659 0.1040

PRICE_DIF 0.136243 0.077858 1.749897 0.0801

1.

SIZE -0.018106 0.154178 -0.117437 0.9065 C -0.964917 0.757998 -1.272980 0.2030

BUYER 1.346843 0.797333 1.689186 0.0912 PRICE DIF 0.126930 0.072949 1.739995 0.0819

2.

ACRES 0.370431 0.389271 0.951602 0.3413 C -0.459379 0.519636 -0.884041 0.3767

BUYER 1.462274 0.816769 1.790317 0.0734 PRICE_DIF 0.124590 0.072968 1.707464 0.0877

3.

QUANTITY 6.72E-06 4.17E-05 0.161231 0.8719 C -0.286729 0.576144 -0.497670 0.6187

BUYER 1.599616 0.809852 1.975195 0.0482 PRICE_DIF 0.127421 0.073913 1.723933 0.0847

4.

VOLUME -4.57E-05 8.89E-05 -0.51339 0.6077 C -0.003073 0.686584 -0.004476 0.9964

BUYER 1.628277 0.812188 2.004802 0.0450 PRICE_DIF 0.118597 0.070882 1.673152 0.0943

5.

RATE -0.030903 0.035131 -0.879662 0.3790 C -0.666598 0.629234 -1.059380 0.2894

BUYER 1.540938 0.783990 1.965508 0.0494 PRICE_DIF 0.125404 0.074875 1.674841 0.0940

6.

SAME BUYER 0.080990 0.130945 0.618504 0.5362

C -0.751735 0.596509 -1.260224 0.2076 BUYER 0.735037 1.068424 0.687964 0.4915

PRICE_DIF 0.117988 0.069079 1.708013 0.0876

7.

ANY AGR 1.096911 1.057076 1.037685 0.2994 There has been no variable included after the third step. Correlation matrix: Size Quanti

ty Volume

Acres FB

Price diff.

Rejec-ted

Buyer Same buyer

Any_ agr

Size 1.000

.41** (.008)

.160 (.325)

.409* (.012)

.028 (.861)

-.086 (.598)

-.115 (.482)

.168 (.301)

.042 (.798)

Quantity

.41** (.008)

1.000 .81** (.000)

.47** (.004)

.036 (.823)

.085 (.603)

-.309 (.053)

.006 (.971)

.253 (.115)

Volume

.160 (.325)

.81** (.000)

1.000 .124 (.466)

.055 (.736)

-.079 (.629)

-.205 (.205)

-.139 (.393)

.129 (.426)

Acres FB

.409* (.012)

.47** (.004)

.124 (.466)

1.000 -.041 (.809)

.001 (.994)

.008 (.963)

.205 (.224)

.182 (.281)

Price diff.

.028 (.861)

.036 (.823)

.055 (.736)

-.041 (.809)

1.000 .085 (.603)

-.399* (.011)

.166 (.305)

.264 (.100)

Rejec-ted

-.086 (.598)

.085 (.603)

-.079 (.629)

.001 (.994)

.085 (.603)

1.000 -.157 (.335)

-.168 (.300)

.035 (.828)

Buyer -.115 (.482)

-.309 (.053)

-.205 (.205)

.008 (.963)

-.399* (.011)

-.157 (.335)

1.000 -.074 (.651)

-.72** (.000)

Same buyer

.168 (.301)

.006 (.971)

-.139 (.393)

.205 (.224)

.166 (.305)

-.168 (.300)

-.074 (.651)

1.000 .206 (.202)

Any_ agr

.042 (.798)

.253 (.115)

.129 (.426)

.182 (.281)

.264 (.100)

.035 (.828)

-.72** (.000)

.206 (.202)

1.000


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129

Expectation-Prediction table: Dependent Variable: PAID_ON_TIME Method: ML - Binary Logit Date: 02/24/03 Time: 14:54 Sample: 1 40 Included observations: 38 Excluded observations: 2 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 13 6 19 0 0 0P(Dep=1)>C 5 14 19 18 20 38

Total 18 20 38 18 20 38Correct 13 14 27 0 20 20

% Correct 72.22 70.00 71.05 0.00 100.00 52.63% Incorrect 27.78 30.00 28.95 100.00 0.00 47.37Total Gain* 72.22 -30.00 18.42

Percent Gain** 72.22 NA 38.89 Estimated Equation Constant Probability Dep=0 Dep=1 Total Dep=0 Dep=1 Total

E(# of Dep=0) 10.93 7.07 18.00 8.53 9.47 18.00E(# of Dep=1) 7.07 12.93 20.00 9.47 10.53 20.00

Total 18.00 20.00 38.00 18.00 20.00 38.00Correct 10.93 12.93 23.87 8.53 10.53 19.05




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130

ANNEX M Output Farmers Total-amount Model information: Dependent Variable: TOTAL_AMOUNT Method: ML - Binary Logit Date: 02/28/03 Time: 08:51 Sample: 1 40 Included observations: 37 Excluded observations: 3 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives


RATE -0.105775 0.043648 -2.423397 0.0154SIZE -0.269842 0.159227 -1.694695 0.0901

Mean dependent var 0.702703 S.D. dependent var 0.463373S.E. of regression 0.408658 Akaike info criterion 1.098863Sum squared resid 5.678060 Schwarz criterion 1.229478Log likelihood -17.32896 Hannan-Quinn criter. 1.144911Restr. log likelihood -22.51660 Avg. log likelihood -0.468350LR statistic (2 df) 10.37528 McFadden R-squared 0.230392Probability(LR stat) 0.005585 Jarque bera statistic 11.37042Obs with Dep=0 11 Total obs 37Obs with Dep=1 26


C 1.673907 0.713164 2.347155 0.01891. SIZE -0.185931 0.133651 -1.391170 0.1642

C 1.299993 0.651719 1.994715 0.04612. ACRES -0.195336 0.313926 -0.622236 0.5338

C 1.045988 0.434184 2.409090 0.01603. QUANTITY -1.14E-05 3.64E-05 -0.314296 0.7533

C 1.095687 0.485609 2.256315 0.02414. VOLUME -2.98E-05 7.60E-05 -0.391714 0.6953

C 2.509633 0.7799672 3.138329 0.00175. RATE -0.087793 0.037128 -2.364565 0.0181

C 0.840066 0.468689 1.792372 0.07316. SAME BUYER 0.052275 0.129096 0.404930 0.6855

C 1.041454 0.474858 2.193190 0.02837. BUYER -0.165985 0.713319 -0.232694 0.8160

C 0.994645 0.362027 2,747432 0.00608. PRICE_DIF -0.045648 0.043974 -1.038073 0.2992

C 0.965081 0.415474 2.322840 0.02029. AGR_

QUANTITY 0.015748 0.794325 0.019826 0.9842

After the first step, the variable RATE has been included into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C 3.882022 1.282824 3.026153 0.0025RATE -0.105775 0.043648 -2.423397 0.0154

1.

SIZE -0.269842 0.159227 -1.694695 0.0901C 2.803788 1.019587 2.749926 0.0060

RATE -0.087727 0.037623 -2.331742 0.01972.

ACRES -0.180838 0.361220 -0.500632 0.6166C 2.532460 0.828948 3.055030 0.0023

RATE -0.087530 0.037183 -2.354040 0.01863.

QUANTITY -4.09E-06 3.85E-05 -0.106200 0.9154C 2.796839 0.936619 2.986102 0.0028

RATE -0.090597 0.037819 -2.395510 0.01664.

VOLUME -5.57E-05 8.38E-05 -0.664454 0.5064C 2.523871 0.940884 2.682446 0.0073

RATE -0.087941 0.037477 -2.346567 0.01895.

SAME BUYER -0.004537 0.156869 -0.028923 0.9769C 2.465840 0.833247 2.959313 0.0031

RATE -0.088954 0.0037877 -2.348499 0.01886.

BUYER 0.143299 0.800116 0.179098 0.8579

Annexes

131

C 2.541592 0.815355 3.117159 0.0018RATE -0.087457 0.037279 -2.346044 0.0190

7.

PRICE_DIF -0.045319 0.049356 -0.918223 0.3585C 2.459167 0.810262 3.035027 0.0024

RATE -0.091028 0.038480 -2.365600 0.01808.

AGR_ QUANTITY

0.389802 0.902891 0.431727 0.6659

After the second step, the variable SIZE has been entered into the model. This variable now has the smallest probability and does not strongly affect the probability of the variable RATE. Step 3: Variable Coefficient Std. Error z-Statistic Prob.

C 4.051704 1.365830 2.966478 0.0030RATE -0.104556 0.044340 -2.358089 0.0184SIZE -0.247617 0.169248 -1.463047 0.1435

1.

ACRES -0.191021 0.466124 -0.409807 0.6819C 4.406699 1.418885 3.105747 0.0019

RATE -0.115175 0.047488 -2.425380 0.0153SIZE -0.258258 0.164051 -1.574250 0.1154

2.

QUANTITY -6.97E-05 5.35E-05 -1.303257 0.1925C 4.616705 1.466096 3.148979 0.0016

RATE -0.119421 0.047423 -2.518197 0.0118SIZE -0.269172 0.163463 -1.646686 0.0996

3.

VOLUME -0.000124 9.61E-05 -1.292009 0.1964C 3.851471 1.330165 2.895484 0.0038

RATE -0.105549 0.043819 -2.408756 0.0160SIZE -0.272107 0.161380 -1.686127 0.0918

4.

SAME BUYER 0.015047 0.179513 0.083822 0.9332C 3.855536 1.296805 2.973103 0.0029

RATE -0.106798 0.044691 -2.389704 0.0169SIZE -0.270643 0.158702 -1.705353 0.0881

5.

BUYER 0.107633 0.867835 0.124025 0.9013C 3.8143689 1.291613 2.953182 0.0031

RATE -0.100788 0.042921 -2.348250 0.0189SIZE -0.269062 0.166376 -1.617195 0.1058

6.

PRICE_DIF -0.032869 0.056907 -0.577587 0.5635

C 3.919752 1.320762 2.967795 0.0030RATE -0.105310 0.043760 -2.406535 0.0161SIZE -0.272390 0.160575 -1.696338 0.0898

7.

AGR_ QUANTITY

-0.127197 0.960425 -0.132438 0.8946

There has no variable been included after the third step. Correlation matrix: Size Quanti

ty Volume

Acres FB

Price diff.

Rejec-ted

Buyer Same buyer

Agr_ quan

Size 1.000

.41** (.008)

.160 (.325)

.409* (.012)

.028 (.861)

-.086 (.598)

-.115 (.482)

.168 (.301)

.144 (.374)

Quantity

.41** (.008)

1.000 .81** (.000)

.47** (.004)

.036 (.823)

.085 (.603)

-.309 (.053)

.006 (.971)

.49** (.001)

Volume

.160 (.325)

.81** (.000)

1.000 .124 (.466)

.055 (.736)

-.079 (.629)

-.205 (.205)

-.139 (.393)

.43** (.005)

Acres FB

.409* (.012)

.47** (.004)

.124 (.466)

1.000 -.041 (.809)

.001 (.994)

.008 (.963)

.205 (.224)

-.002 (.993)

Price diff.

.028 (.861)

.036 (.823)

.055 (.736)

-.041 (.809)

1.000 .085 (.603)

-.399* (.011)

.166 (.305)

.264 (.100)

Rejec-ted

-.086 (.598)

.085 (.603)

-.079 (.629)

.001 (.994)

.085 (.603)

1.000 -.157 (.335)

-.168 (.300)

.128 (.431)

Buyer -.115 (.482)

-.309 (.053)

-.205 (.205)

.008 (.963)

-.399* (.011)

-.157 (.335)

1.000 -.074 (.651)

-.72** (.000)

Same buyer

.168 (.301)

.006 (.971)

-.139 (.393)

.205 (.224)

.166 (.305)

-.168 (.300)

-.074 (.651)

1.000 .032 (.843)

Agr_ quan

.144 (.374)

.49** (.001)

.43** (.005)

-.002 (.993)

.264 (.100)

.128 (.431)

-.72** (.000)

.032 (.843)

1.000


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132

Expectation-Prediction table: Dependent Variable: TOTAL_AMOUNT Method: ML - Binary Logit Date: 02/28/03 Time: 08:51 Sample: 1 40 Included observations: 37 Excluded observations: 3 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 5 1 6 0 0 0P(Dep=1)>C 6 25 31 11 26 37

Total 11 26 37 11 26 37Correct 5 25 30 0 26 26



E(# of Dep=0) 5.36 5.64 11.00 3.27 7.73 11.00E(# of Dep=1) 5.64 20.36 26.00 7.73 18.27 26.00

Total 11.00 26.00 37.00 11.00 26.00 37.00Correct 5.36 20.36 25.73 3.27 18.27 21.54




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133

ANNEX N Output Agreement Quantity Farmers Model information: Dependent Variable: AGR_QUANTITY Method: ML - Binary Logit Date: 03/13/03 Time: 08:48 Sample: 1 40 Included observations: 40 Convergence achieved after 8 iterations Covariance matrix computed using second derivatives


VOLUME 0.000273 0.000122 2.244466 0.0248EUREP 2.320631 1.135554 2.043611 0.0410

Mean dependent var 0.275000 S.D. dependent var 0.452203S.E. of regression 0.391584 Akaike info criterion 1.004484Sum squared resid 5.673504 Schwarz criterion 1.131150Log likelihood -17.08968 Hannan-Quinn criter. 1.050282Restr. log likelihood -23.52675 Avg. log likelihood -0.427242LR statistic (2 df) 12.87414 McFadden R-squared 0.273606Probability(LR stat) 0.001601 Jarque-Bera statistic 31.23867Obs with Dep=0 29 Total obs 40Obs with Dep=1 11 The process of including variables into the model: Step 1: Variable Coefficient Std. Error z-Statistic Prob.

C -0.800416 0.691873 -1.156883 0.24731. SIZE -0.084109 0.150606 -0.558470 0.5765

C -2.092728 0.621657 -3.366369 0.00082. QUANTITY 0.000175 8.49E-05 2.058205 0.0396

C -1.909116 0.557303 -3.425634 0.00063. VOLUME 0.000205 8.76E-05 2.341917 0.0192

C -1.615391 0.675849 -2.390164 0.01684. ACRES 0.373065 0.316415 1.179036 0.2384

C -0.916291 0.836660 -1.095177 0.27345. PROPERTY -0.064539 0.923460 -0.069888 0.9443

C -1.386294 0.422577 -3.280571 0.00106. STORAGE 2.772589 1.195229 2.319714 0.0204

C -2.564949 1.037749 -2.471647 0.01347. RECORDS 2.094946 1.113294 1.881755 0.0599

C -2.079442 0.749998 -2.772595 0.00568. EUREP 1.711717 0.866332 1.975820 0.0482

C 9. BUYER

C -1.033687 0.477188 -2.166205 0.030310. SAME 0.024531 0.119928 0.204548 0.8379

C -1.363306 0.623410 -2.186852 0.028811. RATE 0.024174 0.030188 0.800785 0.4233

C -0.529322 0.559362 -0.946296 0.344012. YEARS -0.024311 0.025580 -0.950371 0.3419

C -1.609438 0.632456 -2.544745 0.010913. INPUTS 1.049822 0.772288 1.359366 0.1740

The analysis with the variable BUYER gave an error (near singular matrix), so this variable will be excluded from the continuation of this analysis. After the first step, the variable VOLUME has been included into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C -1.615336 0.841956 -1.918552 0.0550VOLUME 0.000204 9.49E-05 2.150561 0.0315

1.

SIZE -0.111273 0.172804 -0.643928 0.5196C -1.678655 0.906160 -1.852492 0.0640

VOLUME 0.000208 8.77E-05 2.367898 0.01792.

PROPERTY -0.303721 0.958406 -0.316902 0.7513C -1.952255 0.584179 -3.341877 0.0008

VOLUME 0.000154 9.82E-05 1.563075 0.11803.

STORAGE 2.085904 1.302834 1.601051 0.1094

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C -2.972445 1.066424 -2.787300 0.0053VOLUME 0.000167 8.96E-05 1.869679 0.0615

4.

RECORDS 1.625167 1.149424 1.413897 0.1574C -3.684506 1.245034 -2.959361 0.0031

VOLUME 0.000273 0.000122 2.244466 0.02485.

EUREP 2.320631 1.135554 2.043611 0.0410C -2.156000 0.710353 -3.035111 0.0024

VOLUME 0.000214 9.03E-05 2.368273 0.01796.

SAME 0.080110 0.131096 0.611080 0.5411C -2.596705 0.883179 -2.940179 0.0033

VOLUME 0.000221 9.16E-05 2.411584 0.01597.

RATE 0.037333 0.033552 1.112700 0.2658C -1.477951 0.700101 -2.111055 0.0348

VOLUME 0.000211 9.10E-05 2.319104 0.02048.

YEARS -0.025702 0.028171 -0.912357 0.3616C -2.384060 0.767527 -3.106159 0.0019

VOLUME 0.000197 9.00E-05 2.188910 0.02869.

INPUTS 0.867760 0.831341 1.043808 0.2966 After the second step, the variable EUREP has been included into the model. This variable now has the smallest significance and does not strongly affect the probability of the variable VOLUME. Step 3: Variable Coefficient Std. Error z-Statistic Prob.

C -3.179022 1.390823 -2.285712 0.0223VOLUME 0.000289 0.000136 2.123716 0.0337EUREP 2.199129 1.206623 1.822548 0.0684

1.

SIZE -0.153161 0.179311 -0.854166 0.3930C -3.424197 1.438299 -2.380726 0.0173

VOLUME 0.000277 0.000122 2.261930 0.0237EUREP 2.325486 1.134314 2.050125 0.0404

2.

PROPERTY -0.354084 1.024285 -0.345689 0.7296C -3.612221 1.273628 -2.836166 0.0046

VOLUME 0.000226 0.000128 1.765766 0.0774EUREP 2.173156 1.170210 1.857065 0.0633

3.

STORAGE 1.572609 1.334552 1.178380 0.2386

C -5.051499 1.640754 -3.078767 0.0021VOLUME 0.000234 0.000125 1.866416 0.0620EUREP 2.503470 1.171519 2.136944 0.0326

4.

RECORDS 1.893139 1.203324 1.573258 0.1157C -3.909798 1.371859 -2.849999 0.0044

VOLUME 0.000284 0.000127 2.235189 0.0254EUREP 2.318016 1.150346 2.015059 0.0439

5.

SAME 0.069457 0.146991 0.472525 0.6366C -4.039477 1.467463 -2.752694 0.0059

VOLUME 0.000287 0.000129 2.228740 0.0258EUREP 2.206358 1.153644 1.912513 0.0558

6.

RATE 0.022417 0.040605 0.552061 0.5809C -3.301432 1.321092 -2.499018 0.0125

VOLUME 0.000283 0.000126 2.247776 0.0246EUREP 2.298892 1.142196 2.012695 0.0441

7.

YEARS -0.023676 0.031052 -0.762480 0.4458C -4.271946 1.486553 -2.873727 0.0041

VOLUME 0.000284 0.000134 2.128722 0.0333EUREP 2.387653 1.195357 1.997438 0.0458

8.

INPUTS 0.890276 0.890776 0.999439 0.3176 No variable has been included after the third step.

Annexes

135

Expectation-Prediction table: Dependent Variable: AGR_QUANTITY Method: ML - Binary Logit Date: 03/13/03 Time: 08:48 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 28 7 35 29 11 40P(Dep=1)>C 1 4 5 0 0 0

Total 29 11 40 29 11 40Correct 28 4 32 29 0 29



E(# of Dep=0) 23.40 5.60 29.00 21.02 7.97 29.00E(# of Dep=1) 5.60 5.40 11.00 7.98 3.03 11.00

Total 29.00 11.00 40.00 29.00 11.00 40.00Correct 23.40 5.40 28.79 21.02 3.03 24.05


Percent Gain** 29.73 29.73 29.73 *Change in "% Correct" from default (constant probability) specification **Percent of incorrect (default) prediction corrected by equation

Annexes

136

ANNEX O Output Agreement Price Farmers Model information: Dependent Variable: AGR_PRICE Method: ML - Binary Logit Date: 03/13/03 Time: 10:43 Sample: 1 40 Included observations: 40 Convergence achieved after 10 iterations Covariance matrix computed using second derivatives


BUYER 10.52001 5.215581 2.017035 0.0437SAME 1.142525 0.593585 1.924786 0.0543

VOLUME 0.000403 0.000207 1.942837 0.0520PROPERTY 9.682366 5.308424 1.823963 0.0682

Mean dependent var 0.300000 S.D. dependent var 0.464095S.E. of regression 0.273845 Akaike info criterion 0.662685Sum squared resid 2.624696 Schwarz criterion 0.873795Log likelihood -8.253702 Hannan-Quinn criter. 0.739016Restr. log likelihood -24.43457 Avg. log likelihood -0.206343LR statistic (4 df) 32.36174 McFadden R-squared 0.662212Probability(LR stat) 1.61E-06 Jarque-Bera statistic 271.0199Obs with Dep=0 28 Total obs 40Obs with Dep=1 12


C -0.621246 0.650998 -0.954297 0.33991. SIZE -0.059033 0.137046 -0.430757 0.6666

C -0.430016 0.469583 -0.915740 0.35982. QUANTITY -8.10E-05 7.48E-05 -1.083201 0.2787

C -0.098259 0.547735 -0.179392 0.85763. VOLUME 0.000234 0.000163 1.433548 0.1517

C -1.353321 0.646185 -2.094324 0.03624. ACRES 0.296668 0.308982 0.960148 0.3370

C -0.287682 0.763763 -0.376664 0.70645. PROPERTY 0.693147 0.857969 0.807893 0.4192

C -0.916291 0.374166 -2.448890 0.01436. STORAGE 0.510826 0.986577 0.517776 0.6046

C -1.299283 0.651339 -1.994788 0.04617. RECORDS 0.663294 0.770829 0.860495 0.3895

C -2.079442 0.749999 -2.772594 0.00568. EUREP 1.897120 0.863615 2.196719 0.0280

C -2.351375 0.740013 -3.177479 0.00159. BUYER 2.708050 0.889087 3.045877 0.0023

C -1.470870 0.513508 -2.864355 0.004210. SAME 0.219719 0.122336 1.796032 0.0725

C -0.786006 0.580029 -1.355116 0.175411. RATE -0.003952 0.030269 -0.130575 0.8961

C -0.639693 0.551161 -1.160628 0.245812. YEARS -0.010997 0.023383 -0.470282 0.6382

C -1.609438 0.632456 -2.544745 0.010913. INPUTS 1.241713 0.766834 1.619273 0.1054

After the first step, the variable BUYER has been included into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C -1.859085 0.926488 -2.006594 0.0448BUYER 2.796993 0.933096 2.997540 0.0027

1.

SIZE -0.120634 0.163489 -0.737872 0.4606C -1.867154 0.785008 -2.378517 0.0174

BUYER 3.359858 0.982004 3.421429 0.00062.

QUANTITY -0.000135 7.92E-05 -1.704023 0.0884C -1.615537 0.812186 -1.989121 0.0467

BUYER 3.276730 0.982654 3.334572 0.00093.

VOLUME 0.000272 0.000140 1.946244 0.0516

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137

C -2.464820 0.902273 -2.731788 0.0063BUYER 2.671877 0.901956 2.962313 0.0031

4.

ACRES 0.078060 0.349548 0.223317 0.8233C -1.139904 0.969720 -1.175498 0.2398

BUYER 3.273809 1.129671 2.898020 0.00385.

PROPERTY -1.943548 1.297460 -1.497963 0.1341C -2.332365 0.740898 -3.148026 0.0016

BUYER 2.819788 0.923766 3.052490 0.00236.

STORAGE -0.540926 1.116099 -0.484657 0.6279C -2.219317 0.871846 -2.545539 0.0109

BUYER 2.797112 0.959970 2.913750 0.00367.

RECORDS -0.267231 0.986084 -0.271002 0.7864C -2.808822 0.924606 -3.037860 0.0024

BUYER 2.329534 0.940093 2.477982 0.01328.

EUREP 1.021551 0.988137 1.033815 0.3012C -3.673520 1.251921 -2.934306 0.0033

BUYER 3.178359 1.111136 2.860460 0.00429.

SAME 0.328419 0.167690 1.958488 0.0502C -1.945705 0.861261 -2.259136 0.0239

BUYER 2.892871 0.943637 3.065662 0.002210.

RATE -0.032813 0.040117 -0.817932 0.4134C -2.035612 0.851614 -2.390299 0.0168

BUYER 2.764670 0.901755 3.065877 0.002211.

YEARS -0.018054 0.025771 -0.700561 0.4836C -2.375396 0.821401 -2.891883 0.0038

BUYER 2.676280 0.999206 2.678406 0.007412.

INPUTS 0.067814 0.988365 0.068613 0.9453 After the second step, the variable SAME has been included into the model. This variable now has the smallest significance and does not strongly affect the probability of the variable BUYER. Step 3: Variable Coefficient Std. Error z-Statistic Prob.

C -3.405833 1.527970 -2.228993 0.0258BUYER 3.894636 1.433775 2.716352 0.0066SAME 0.424634 0.210080 2.021292 0.0432

1.

SIZE -0.229429 0.186249 -1.231841 0.2180

C -3.492745 1.439235 -2.426807 0.0152BUYER 4.098811 1.335238 3.069724 0.0021SAME 0.368633 0.187054 1.970735 0.0488

2.

QUANTITY -0.000143 8.57E-05 -1.670380 0.0948C -3.213406 1.457720 -2.204405 0.0275

BUYER 4.014470 1.346300 2.981853 0.0029SAME 0.345592 0.191285 1.806688 0.0708

3.

VOLUME 0.000262 0.000131 2.002581 0.0452C -3.484221 1.293433 -2.693778 0.0071

BUYER 3.368711 1.172813 2.872334 0.0041SAME 0.374440 0.185840 2.014848 0.0439

4.

ACRES -0.281024 0.428133 -0.656395 0.5116C -2.242834 1.295130 -1.731745 0.0833

BUYER 5.675875 2.405107 2.359926 0.0183SAME 0.675241 0.310955 2.171505 0.0299

5.

PROPERTY 5.116194 2.780923 1.839747 0.0658C -3.731210 1.272043 -2.933241 0.0034

BUYER 3.431519 1.178165 2.912595 0.0036SAME 0.341859 0.166244 2.056369 0.0397

6.

STORAGE -1.063709 1.274570 -0.834563 0.4040C -3.705336 1.413522 -2.621349 0.0088

BUYER 3.163585 1.149813 2.751390 0.0059SAME 0.330016 0.171057 1.929281 0.0537

7.

RECORDS 0.052107 1.059814 0.049167 0.9608C -3.996992 1.325783 -3.014815 0.0026

BUYER 2.793546 1.162357 2.403346 0.0162SAME 0.321282 0.169113 1.899814 0.0575

8.

EUREP 0.897932 1.047080 0.857559 0.3911C -3.419270 1.417718 -2.411812 0.0159

BUYER 3.240368 1.131082 2.864839 0.0042SAME 0.313717 0.171192 1.832551 0.0669

9.

RATE -0.015407 0.043504 -0.354142 0.7232C -3.363427 1.341090 -2.507980 0.0121

BUYER 3.446545 1.210407 2.847427 0.0044SAME 0.417434 0.195167 2.138859 0.0324

10.

YEARS -0.037986 0.028837 -1.317274 0.1877C -3.471252 1.237796 -2.804382 0.005011.

BUYER 3.507052 1.234287 2.841359 0.0045

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138

SAME 0.372783 0.182937 2.037762 0.0416 INPUTS -0.775135 1.166633 -0.664421 0.5064

After the third step, the variable VOLUME has been put in the model. This variable now has the smallest probability and does not strongly affect the probability of the variables BUYER and SAME. Step 4: Variable Coefficient Std. Error z-Statistic Prob.

C -2.873649 1.674118 -1.716516 0.0861BUYER 4.549674 1.677727 2.711808 0.0067SAME 0.423386 0.228258 1.854855 0.0636

VOLUME 0.000229 0.000137 1.666622 0.0956

1.

SIZE -0.242155 0.202492 -1.195877 0.2317C -1.479341 1.595716 -0.927070 0.3539

BUYER 10.52001 5.215581 2.017035 0.0437SAME 1.142525 0.593585 1.924786 0.0543

VOLUME 0.000403 0.000207 1.942837 0.0520

2.

PROPERTY 9.682366 5.308424 1.823963 0.0682C -3.682956 1.833049 -2.009197 0.0445

BUYER 4.871913 1.821201 2.675110 0.0075SAME 0.426264 0.239703 1.778305 0.0754

VOLUME 0.000531 0.000233 2.273683 0.0230

3.

STORAGE 4.125475 2.476895 1.665584 0.0958C -3.974774 1.857379 -2.139991 0.0324

BUYER 3.959297 1.407852 2.812295 0.0049SAME 0.394801 0.212099 1.861404 0.0627

VOLUME 0.000295 0.000142 2.078028 0.0377

4.

RECORDS 1.069046 1.240575 0.861735 0.3888C -3.468268 1.501145 -2.310416 0.0209

BUYER 3.683379 1.395258 2.639926 0.0083SAME 0.337743 0.191253 1.765948 0.0774

VOLUME 0.000254 0.000128 1.985431 0.0471

5.

EUREP 0.741492 1.137256 0.652001 0.5144C -2.509219 1.656221 -1.515027 0.1298

BUYER 4.281642 1.426239 3.002050 0.0027SAME 0.305746 0.193542 1.579743 0.1142

6.

VOLUME 0.000285 0.000144 1.978773 0.0478

RATE -0.041001 0.051902 -0.789959 0.4296C -2.699016 1.617721 -1.668406 0.0952

BUYER 4.529475 1.579868 2.866996 0.0041SAME 0.471365 0.229095 2.057514 0.0396

VOLUME 0.000296 0.000151 1.955572 0.0505

7.

YEARS -0.052792 0.036737 -1.437032 0.1507C -3.153390 1.470136 -2.144965 0.0320

BUYER 4.103432 1.434073 2.861383 0.0042SAME 0.358800 0.203875 1.759896 0.0784

VOLUME 0.000258 0.000132 1.958715 0.0501

8.

INPUTS -0.240141 1.262597 -0.190197 0.8492 After the fourth step, the variable PROPERTY has been included into the model. This variable now has the smallest probability and does not strongly affect the probability of the variables BUYER, SAME and VOLUME. Step 5: Variable Coefficient Std. Error z-Statistic Prob.

C -1.133722 1.912852 -0.592687 0.5534BUYER 15.33933 7.293360 2.103191 0.0354SAME 1.707469 0.847745 2.014131 0.0440

VOLUME 0.000356 0.000205 1.730881 0.0835PROPERTY 13.33636 6.728528 1.982062 0.0475

1.

SIZE -0.520350 0.374797 -1.388352 0.1650C -2.103142 2.050704 -1.025571 0.3051

BUYER 10.44008 5.409974 1.929784 0.0536SAME 1.139756 0.622871 1.829842 0.0673

VOLUME 0.000499 0.000239 2.088708 0.0367PROPERTY 8.894669 5.552633 1.601883 0.1092

2.

STORAGE 2.340705 2.514807 0.930769 0.3520C -2.541469 2.159051 -1.177123 0.2391

BUYER 11.57833 5.854645 1.977632 0.0480SAME 1.371058 0.717184 1.911723 0.0559

VOLUME 0.000469 0.000221 2.120066 0.0340PROPERTY 11.10273 5.954410 1.864622 0.0622

3.

RECORDS 1.720567 1.612191 1.067223 0.2859C -2.131072 1.856409 -1.147954 0.2510

BUYER 9.995541 5.334157 1.873875 0.06094.

SAME 1.125011 0.594072 1.893728 0.0583

Annexes

139

VOLUME 0.000379 0.000198 1.912509 0.0558PROPERTY 9.329240 5.419852 1.721309 0.0852

EUREP 0.970440 1.467392 0.661337 0.5084C -0.370832 2.161648 -0.171551 0.8638

BUYER 11.07614 5.516402 2.007856 0.0447SAME 1.120856 0.612734 1.829270 0.0674

VOLUME 0.000407 0.000206 1.978622 0.0479PROPERTY 10.27614 5.646814 1.819812 0.0688

5.

RATE -0.046547 0.069006 -0.674526 0.5000C -1.412281 1.766305 -0.799568 0.4240

BUYER 12.44062 6.276815 1.981995 0.0475SAME 1.416975 0.732209 1.935206 0.0530

VOLUME 0.000448 0.000241 1.861550 0.0627PROPERTY 10.40969 5.689780 1.829542 0.0673

6.

YEARS -0.060626 0.055669 -1.089041 0.2761C -1.316669 1.927951 -0.682937 0.4946

BUYER 10.49438 5.191678 2.021385 0.0432SAME 1.144334 0.590739 1.937121 0.0527

VOLUME 0.000400 0.000208 1.921401 0.0547PROPERTY 9.624112 5.312827 1.811486 0.0701

7.

INPUTS -0.245012 1.797666 -0.136295 0.8916 No variable has been included after the fifth step.

Expectation-Prediction table: Dependent Variable: AGR_PRICE Method: ML - Binary Logit Date: 03/13/03 Time: 10:43 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 27 3 30 28 12 40P(Dep=1)>C 1 9 10 0 0 0

Total 28 12 40 28 12 40Correct 27 9 36 28 0 28



E(# of Dep=0) 25.37 2.63 28.00 19.60 8.40 28.00E(# of Dep=1) 2.63 9.37 12.00 8.40 3.60 12.00

Total 28.00 12.00 40.00 28.00 12.00 40.00Correct 25.37 9.37 34.75 19.60 3.60 23.20



Annexes

140

ANNEX P Output Agreement Quality Farmers Model information: Dependent Variable: AGR_QUALITY Method: ML - Binary Logit Date: 03/13/03 Time: 10:04 Sample: 1 40 Included observations: 40 Convergence achieved after 5 iterations Covariance matrix computed using second derivatives


BUYER 4.217842 1.164931 3.620679 0.0003ACRES 1.081764 0.653543 1.655231 0.0979

Mean dependent var 0.450000 S.D. dependent var 0.503831S.E. of regression 0.327337 Akaike info criterion 0.813837Sum squared resid 3.964539 Schwarz criterion 0.940503Log likelihood -13.27673 Hannan-Quinn criter. 0.859635Restr. log likelihood -27.52555 Avg. log likelihood -0.331918LR statistic (2 df) 28.49764 McFadden R-squared 0.517658Probability(LR stat) 6.48E-07 Jarque-Bera statistic 81.53535Obs with Dep=0 22 Total obs 40Obs with Dep=1 18


C -0.749363 0.620301 -1.208064 0.22701. SIZE 0.114315 0.124363 0.919209 0.3580

C -0.804985 0.457655 -1.758935 0.07862. QUANTITY 0.000105 6.51E-05 1.620544 0.1051

C -0.585195 0.444140 -1.317589 0.18763. VOLUME 9.40E-05 7.65E-05 1.227447 0.2197

C -1.368331 0.660109 -2.072886 0.03824. ACRES 0.714657 0.355900 2.008028 0.0446

C -0.916291 0.836660 -1.095177 0.27345. PROPERTY 0.855666 0.906269 0.944164 0.3451

C -0.405465 0.345033 -1.175150 0.23996. STORAGE 1.791759 1.170063 1.531336 0.1257

C -1.299283 0.651339 -1.994788 0.04617. RECORDS 1.609438 0.762770 2.109991 0.0349

C -0.955511 0.526235 -1.815751 0.06948. EUREP 1.323236 0.681878 1.940577 0.0523

C -1.897120 0.619139 -3.064125 0.00229. BUYER 3.912023 0.974679 4.013651 0.0001

C -0.345339 0.428085 -0.806706 0.419810. SAME 0.055853 0.109988 0.507811 0.6116

C -0.140625 0.535872 -0.262422 0.793011. RATE -0.003853 0.027729 -0.138938 0.8895

C -0.505990 0.521797 -0.969706 0.332212. YEARS 0.015632 0.021010 0.744012 0.4569

C -1.252763 0.566947 -2.209666 0.027113. INPUTS 1.812379 0.719623 2.518512 0.0118

After the first step, the variable BUYER has been entered into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C -2.611062 1.088761 -2.398197 0.0165BUYER 3.815281 1.026430 3.717039 0.0002

1.

SIZE 0.172032 0.181344 0.948655 0.3428C -2.391427 0.880932 -2.714656 0.0066

BUYER 3.789523 1.008105 3.759057 0.00022.

QUANTITY 0.000112 0.000129 0.863657 0.3878C -2.098960 0.764238 -2.746475 0.0060

BUYER 3.855984 0.978103 3.942309 0.00013.

VOLUME 5.74E-05 0.000121 0.476513 0.6337C -3.744782 1.440096 -2.600370 0.0093

BUYER 4.217842 1.164931 3.620679 0.00034.

ACRES 1.081764 0.653543 1.655231 0.0979

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C -2.471778 1.302139 -1.898244 0.0577BUYER 3.891844 0.979721 3.972402 0.0001

5.

PROPERTY 0.697723 1.328444 0.525218 0.5994C -1.945910 0.631986 -3.079038 0.0021

BUYER 3.806662 0.983603 3.870123 0.00016.

STORAGE 0.847298 1.628851 0.520181 0.6029C -2.638295 1.002096 -2.632776 0.0085

BUYER 3.765818 0.992868 3.792868 0.00017.

RECORDS 1.180765 1.070148 1.103366 0.2699C -1.740664 0.697646 -2.495054 0.0126

BUYER 4.190150 1.219103 3.437075 0.00068.

EUREP -0.511111 1.196863 -0.427042 0.6693C -1.995457 0.754916 -2.643283 0.0082

BUYER 3.904293 0.975312 4.003121 0.00019.

SAME 0.038665 0.159752 0.242029 0.8088C -1.123609 0.785698 -1.430078 0.1527

BUYER 4.582118 1.274898 3.594105 0.000310.

RATE -0.069623 0.053081 -1.311642 0.1896C -2.441586 0.980047 -2.491295 0.0127

BUYER 3.996255 1.015556 3.935042 0.000111.

YEARS 0.026458 0.033738 0.784212 0.4329C -2.162525 0.778966 -2.776148 0.0055

BUYER 3.662030 1.020727 3.587667 0.000312.

INPUTS 0.656730 1.013582 0.647929 0.5170 After the second step, the variable ACRES has been included into the model. This variable now has the smallest significance and does not strongly affect the probability of the variable BUYER. Step 3: Variable Coefficient Std. Error z-Statistic Prob.

C -3.819026 1.547604 -2.467702 0.0136BUYER 4.119507 1.175567 3.504274 0.0005ACRES 0.959467 0.691010 1.388498 0.1650

1.

SIZE 0.082457 0.226762 0.363628 0.7161

C -3.813304 1.626112 -2.345045 0.0190BUYER 4.208821 1.167013 3.606489 0.0003ACRES 1.066967 0.670838 1.590499 0.1117

2.

PROPERTY 0.119387 1.309666 0.091158 0.9274C -3.787217 1.445771 -2.619513 0.0088

BUYER 4.140209 1.174116 3.526235 0.0004ACRES 1.084907 0.656593 1.652329 0.0985

3.

STORAGE 0.941366 2.030856 0.463532 0.6430C -4.539868 1.665777 -2.725376 0.0064

BUYER 4.070948 1.203787 3.381785 0.0007ACRES 1.115368 0.674136 1.654514 0.0980

4.

RECORDS 1.263401 1.098922 1.149673 0.2503C -3.581591 1.486876 -2.408802 0.0160

BUYER 4.447823 1.357466 3.276563 0.0011ACRES 1.063909 0.652084 1.631551 0.1028

5.

EUREP -0.460768 1.233798 -0.373455 0.7088C -3.755957 1.484588 -2.529966 0.0114

BUYER 4.278122 1.201253 3.561382 0.0004ACRES 1.189304 0.726109 1.637914 0.1014

6.

SAME -0.074391 0.179721 -0.413923 0.6789C -2.638678 1.409479 -1.872095 0.0612

BUYER 4.836667 1.446125 3.344571 0.0008ACRES 0.936711 0.572286 1.636790 0.1017

7.

RATE -0.077697 0.063432 -1.224890 0.2206C -3.739053 1.448500 -2.581328 0.0098

BUYER 4.222929 1.172202 3.602561 0.0003ACRES 1.100994 0.763990 1.441111 0.1496

8.

YEARS -0.001873 0.037839 -0.049506 0.9605C -3.755584 1.455385 -2.580475 0.0099

BUYER 4.273263 1.293693 3.303150 0.0010ACRES 1.111651 0.720739 1.542377 0.1230

9.

INPUTS -0.116704 1.141033 -0.102279 0.9185

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Expectation-Prediction table: Dependent Variable: AGR_QUALITY Method: ML - Binary Logit Date: 03/13/03 Time: 10:04 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 20 3 23 22 18 40P(Dep=1)>C 2 15 17 0 0 0

Total 22 18 40 22 18 40Correct 20 15 35 22 0 22



E(# of Dep=0) 18.03 3.97 22.00 12.10 9.90 22.00E(# of Dep=1) 3.97 14.03 18.00 9.90 8.10 18.00

Total 22.00 18.00 40.00 22.00 18.00 40.00Correct 18.03 14.03 32.07 12.10 8.10 20.20



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143

ANNEX Q Output Agreement Pesticides Farmers Model information: Dependent Variable: AGR_PEST Method: ML - Binary Logit Date: 03/13/03 Time: 10:19 Sample: 1 40 Included observations: 40 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives


EUREP 1.729097 0.772678 2.237798 0.0252RECORDS 1.717599 0.800615 2.145350 0.0319

Mean dependent var 0.575000 S.D. dependent var 0.500641S.E. of regression 0.451298 Akaike info criterion 1.265712Sum squared resid 7.535778 Schwarz criterion 1.392378Log likelihood -22.31425 Hannan-Quinn criter. 1.311511Restr. log likelihood -27.27418 Avg. log likelihood -0.557856LR statistic (2 df) 9.919871 McFadden R-squared 0.181855Probability(LR stat) 0.007013 Jarque-Bera statistic 4.694307Obs with Dep=0 17 Total obs 40Obs with Dep=1 23


C 0.943968 0.633426 1.490258 0.13621. SIZE -0.133376 0.125849 -1.059815 0.2892

C 0.318988 0.391285 0.815231 0.41492. QUANTITY -2.57E-06 3.46E-05 -0.074292 0.9408

C 0.272938 0.435170 0.627198 0.53053. VOLUME 7.15E-06 7.21E-05 0.099167 0.9210

C 0.510601 0.581449 0.878153 0.37994. ACRES -0.126105 0.292297 -0.431430 0.6662

C 0.287682 0.763763 0.376664 0.70645. PROPERTY 0.017700 0.841067 0.021044 0.9832

C 0.057158 0.338200 0.169008 0.86586. STORAGE 38.56289 1.09E+08 3.54E-07 1.0000

C -0.587787 0.557773 -1.053809 0.29207. RECORDS 1.398717 0.701189 1.994778 0.0461

C -0.451985 0.483494 -0.934831 0.34998. EUREP 1.432814 0.680391 2.105868 0.0352

C -0.262364 0.420622 -0.623753 0.53289. BUYER 1.441019 0.709821 2.030117 0.0423

C 0.183106 0.427181 0.428638 0.668210. SAME 0.046915 0.112927 0.415448 0.6778

C 0.400174 0.541429 0.739107 0.459811. RATE -0.006247 0.027772 -0.224937 0.8220

C 0.730582 0.532279 1.372555 0.169912. YEARS -0.021757 0.021238 -1.024471 0.3056

C 0.223144 0.474342 0.470428 0.638013. INPUTS 0.144581 0.642677 0.224967 0.8220

After the first step, the variable EUREP has been entered into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C 0.302874 0.718530 0.421519 0.6734EUREP 1.577542 0.746791 2.112427 0.0346

1.

SIZE -0.170362 0.137622 -1.237894 0.2158C -0.394025 0.523923 -0.752067 0.4520

EUREP 1.453011 0.685977 2.118162 0.03422.

QUANTITY -1.04E-05 3.65E-05 -0.285390 0.7753C -0.491571 0.582919 -0.843292 0.3991

EUREP 1.434026 0.680686 2.106735 0.03513.

VOLUME 9.51E-06 7.79E-05 0.122038 0.9029C -0.085993 0.660057 -0.130281 0.8963

EUREP 1.539806 0.704900 2.184431 0.02894.

ACRES -0.252924 0.312987 -0.808096 0.4190

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C -0.502706 0.890217 -0.564700 0.5723EUREP 1.433918 0.680695 2.106551 0.0352

5.

PROPERTY 0.060794 0.894856 0.067938 0.9458C -0.606136 0.507519 -1.194311 0.2324

EUREP 1.299283 0.712444 1.823700 0.06826.

STORAGE 35.25815 23143107 1.52E-06 1.0000C -1.694607 0.816360 -2.075807 0.0379

EUREP 1.729097 0.772678 2.237798 0.02527.

RECORDS 1.717599 0.800615 2.145350 0.0319C -0.627240 0.510997 -1.227483 0.2196

EUREP 1.021234 0.750402 1.360916 0.17358.

BUYER 1.002173 0.783392 1.279274 0.2008C -0.514896 0.556669 -0.924960 0.3550

EUREP 1.420080 0.682446 2.080866 0.03749.

SAME 0.027322 0.118354 0.230849 0.8174C -0.086693 0.598255 -0.144909 0.8848

EUREP 1.727943 0.773232 2.234704 0.025410.

RATE -0.033728 0.034172 -0.986997 0.3236C -0.076671 0.676364 -0.113358 0.9097

EUREP 1.380848 0.686682 2.010898 0.044311.

YEARS -0.017541 0.022365 -0.784321 0.4329C -0.462692 0.593228 -0.779956 0.4354

EUREP 1.430908 0.683033 2.094931 0.036212.

INPUTS 0.021388 0.685821 0.031187 0.9751 After the second step, the variable RECORDS has been included into the model. This variable now has the smallest significance and does not strongly affect the probability of the variable EUREP. Step 3: Variable Coefficient Std. Error z-Statistic Prob.

C -1.355391 1.078787 -1.256403 0.2090EUREP 2.029978 0.908039 2.235562 0.0254

RECORDS 2.026925 0.911600 2.223480 0.0262

1.

SIZE -0.126978 0.154788 -0.820335 0.4120

C -1.700163 0.851641 -1.996337 0.0459EUREP 1.886295 0.820347 2.299387 0.0215

RECORDS 2.021678 0.892781 2.264473 0.0235

2.

QUANTITY -4.21E-05 4.00E-05 -1.054276 0.2918C -1.591531 0.832927 -1.910769 0.0560

EUREP 1.759181 0.782984 2.246766 0.0247RECORDS 1.908148 0.868372 2.197386 0.0280

3.

VOLUME -5.75E-05 8.65E-05 -0.664667 0.5063C -1.311556 0.909384 -1.442247 0.1492

EUREP 1.889956 0.812963 2.324775 0.0201RECORDS 1.817780 0.834127 2.179259 0.0293

4.

ACRES -0.331011 0.336768 -0.982905 0.3257C -1.616858 1.097846 -1.472755 0.1408

EUREP 1.728315 0.772849 2.236291 0.0253RECORDS 1.724475 0.803967 2.144958 0.0320

5.

PROPERTY -0.099943 0.947837 -0.105443 0.9160C -1.651373 0.805313 -2.050598 0.0403

EUREP 1.518820 0.860842 1.764343 0.0777RECORDS 1.563273 0.841792 1.857077 0.0633

6.

BUYER 0.457943 0.872884 0.524633 0.5998C -1.878236 0.905813 -2.073536 0.0381

EUREP 1.707118 0.775004 2.202722 0.0276RECORDS 1.769598 0.812659 2.177541 0.0294

7.

SAME 0.064172 0.128082 0.501024 0.6164C -1.337527 0.891779 -1.499842 0.1337

EUREP 2.010594 0.858791 2.341193 0.0192RECORDS 1.715184 0.808796 2.120662 0.0340

8.

RATE -0.032530 0.035036 -0.928473 0.3532C -1.317554 0.938174 -1.404381 0.1602

EUREP 1.686947 0.781183 2.159478 0.0308RECORDS 1.751312 0.819411 2.137282 0.0326

9.

YEARS -0.019862 0.024970 -0.795418 0.4264C -1.614681 0.850623 -1.898234 0.0577

EUREP 1.768745 0.784931 2.253376 0.0242RECORDS 1.793421 0.834567 2.148924 0.0316

10.

INPUTS -0.303709 0.755619 -0.401935 0.6877

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Expectation-Prediction table: Dependent Variable: AGR_PEST Method: ML - Binary Logit Date: 03/13/03 Time: 10:19 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 6 0 6 0 0 0P(Dep=1)>C 11 23 34 17 23 40

Total 17 23 40 17 23 40Correct 6 23 29 0 23 23



E(# of Dep=0) 9.46 7.54 17.00 7.23 9.78 17.00E(# of Dep=1) 7.54 15.46 23.00 9.77 13.22 23.00

Total 17.00 23.00 40.00 17.00 23.00 40.00Correct 9.46 15.46 24.92 7.23 13.22 20.45



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146

ANNEX R Correlation matrix Agreements Farmers

Size Quan-

tity Vo-lume

Acres Pro-perty

Sto-rage

Re-cords

Eurep Buyer Same Rate Years Inputs

Size 1.000 .414** (.008)

.160 (.325)

.460** (.003)

.237 (.141)

.074 (.648)

.094 (.564)

.046 (.779)

-.115 (.482)

.168 (.301)

-.086 (.598)

.490** (.001)

.309 (.052)

Quan-tity

.414** (.008)

1.000 .809** (.000)

.294 (.066)

.100 (.540)

.450** (.004)

.268 (.095)

.087 (.593)

-.309 (.053)

.006 (.971)

.085 (.603)

.178 (.272)

.296 (.063)

Vo-lume

.160 (.325)

.809** (.000)

1.000 -.051 (.756)

.055 (.737)

.506** (.001)

.305 (.056)

-.007 (.967)

-.205 (.205)

-.139 (.393)

-.079 (.629)

-.078 (.634)

.149 (.360)

Acres .460** (.003)

.294 (.066)

-.051 (.756)

1.000 .207 (.199)

.194 (.231)

.044 (.787)

.148 (.364)

-.226 (.160)

.335* (.035)

.058 (.720)

.283 (.077)

.376* (.017)

Pro- perty

.237 (.141)

.100 (.540)

.055 (.737)

.207 (.199)

1.000 -.025 (.879)

.076 (.642)

-.020 (.903)

-.130 (.425)

.228 (.158)

-.233 (.148)

.405** (.009)

.112 (.490)

Sto- rage

.074 (.648)

.450** (.004)

.506** (.001)

.194 (.231)

-.025 (.879)

1.000 .277 (.083)

.190 (.240)

-.287 (.073)

.160 (.325)

-.132 (.417)

-.154 (.343)

.342* (.031)

Re-cords

.094 (.564)

.268 (.095)

.305 (.056)

.044 (.787)

.076 (.642)

.277 (.083)

1.000 -.032 (.846)

-.313* (.049)

-.108 (.509)

-.054 (.739)

-.025 (.876)

.179 (.269)

Eurep .046 (.779)

.087 (.593)

-.007 (.967)

.148 (.364)

-.020 (.903)

.190 (.240)

-.032 (.846)

1.000 -.473** (.002)

.093 (.569)

.317* (.046)

-.141 (.384)

.091 (.577)

Buyer -.115 (.482)

-.309 (.053)

-.205 (.205)

-.226 (.160)

-.130 (.425)

-.287 (.073)

-.313* (.049)

-.473** (.002)

1.000 -.074 (.651)

-.157 (.335)

-.047 (.772)

-.473** (.002)

Same

.168 (.301)

.006 (.971)

-.139 (.393)

.335* (.035)

.228 (.158)

.160 (.325)

-.108 (.509)

.093 (.569)

-.074 (.651)

1.000 -.168 (.300)

.228 (.157)

.405** (.010)

Rate -.086 (.598)

.085 (.603)

-.079 (.629)

.058 (.720)

-.233 (.148)

-.132 (.417)

-.054 (.739)

.317* (.046)

-.157 (.335)

-.168 (.300)

1.000 -.220 (.173)

.140 (.389)

Years .490** (.001)

.178 (.272)

-.078 (.634)

.283 (.077)

.405** (.009)

-.154 (.343)

-.025 (.876)

-.141 (.384)

-.047 (.772)

.228 (.157)

-.220 (.173)

1.000 .255 (.112)

Inputs .309 (.052)

.296 (.063)

.149 (.360)

.376* (.017)

.112 (.490)

.342* (.031)

.179 (.269)

.091 (.577)

-.473** (.002)

.405** (.010)

.140 (.389)

.255 (.112)

1.000

*Correlation is significant at the 0.05 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed)

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147

ANNEX S Output Middlemen Channel-choice Model information: Dependent Variable: BUYER Method: ML - Binary Logit Date: 02/28/03 Time: 10:36 Sample: 1 40 Included observations: 40 Convergence achieved after 12 iterations Covariance matrix computed using second derivatives


YEARS -0.423057 0.147991 -2.858672 0.0043RECORDS 5.405987 3.004838 1.799094 0.0720QUANTITY 2.49E-05 1.44E-05 1.722116 0.0850

Mean dependent var 0.675000 S.D. dependent var 0.474342S.E. of regression 0.358849 Akaike info criterion 0.879611Sum squared resid 4.635808 Schwarz criterion 1.048499Log likelihood -13.59222 Hannan-Quinn criter. 0.940675Restr. log likelihood -25.22324 Avg. log likelihood -0.339805LR statistic (3 df) 23.26205 McFadden R-squared 0.461123Probability(LR stat) 3.56E-05 Jarque Bera statistic 3.320761Obs with Dep=0 13 Total obs 40Obs with Dep=1 27


C 2.787737 0.952533 2.926656 0.00341. YEARS -0.251285 0.104396 -2.407032 0.0161

C -0.693147 1.224745 -0.565952 0.57142. VISIT 1.553348 1.276468 1.216911 0.2236

C -0.098914 0.616940 -0.160330 0.87263. QUANTITY 1.07E-05 7.52E-06 1.425891 0.1539

C -0.078757 0.732214 -0.107560 0.91434. PRODUCT 0.408449 0.342511 1.192511 0.2331

C 0.559616 0.361873 1.546441 0.12205. STORAGE 1.232144 1.139130 1.081653 0.2794

C -1.376414 1.437585 -0.957449 0.33836. EDU 0.510554 0.342426 1.490991 0.1360

C 0.405465 0.912871 0.444165 0.65697. TRACE-ABILITY

0.374693 0.982807 0.381248 0.7030

C 0.510826 0.730297 0.699477 0.48438. INPUTS 0.277632 0.2823886 0.336978 0.7361

C -1.386294 1.118034 -1.239939 0.21509. RECORDS 2.447166 1.183035 2.068549 0.0386

C 0.693147 0.339683 2.040570 0.041310. STORAGE_F

ARM 36.13905 99542722 3.63E-07 1.0000

C 0.693147 0.866025 0.800378 0.423511. EUREP 0.044452 0.940418 0.047268 0.9623

After the first step, the variable YEARS has been entered into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C 0.773633 1.362598 0.567763 0.5702YEARS -0.370503 0.145797 -2.541235 0.0110

1.

VISIT 3.346843 1.525852 2.193425 0.0283C 1.955370 1.048348 1.865192 0.0622

YEARS -0.314301 0.118160 -2.659953 0.00782.

QUANTITY 1.81E-05 1.10E-05 1.640417 0.1009C 1.809599 1.144442 1.581207 0.1138

YEARS -0.282894 0.117761 -2.402275 0.01633.

PRODUCT 0.639090 0.426140 1.499720 0.1337C 2.716190 1.060567 2.561074 0.0104

YEARS -0.245464 0.110750 -2.216386 0.02674.

STORAGE 0.181825 1.251921 0.145237 0.8845

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148

C 0.786477 1.876837 0.419044 0.6752YEARS -0.245306 0.103626 -2.367224 0.0179

5.

EDU 0.467913 0.401449 1.165559 0.2438C 1.974483 1.141032 1.730437 0.0836

YEARS -0.298337 0.122636 -2.432712 0.01506.

TRACE-ABILITY

1.410712 1.129607 1.248852 0.2117

C 2.261022 1.052103 2.149051 0.0316YEARS -0.321815 0.133562 -2.409476 0.0160

7.

INPUTS 1.438031 1.038869 1.384227 0.1663C 0.347565 1.365141 0.254600 0.7990

YEARS -0.370635 0.146098 -2.536895 0.01128.

RECORDS 3.865326 1.649762 2.342959 0.0191C 2.791512 0.955710 2.920878 0.0035

YEARS -0.259633 0.106157 -2.445740 0.01459.

STORAGE_FARM

34.63341 36553786 9.47E-07 1.0000

C 3.029102 1.384525 2.187827 0.0287YEARS -0.255175 0.106492 -2.396183 0.0166

10.

EUREP -0.252762 1.032831 -0.244728 0.8067 After the second step, the variable RECORDS has been included into the model. This variable now has the smallest significance and does not strongly affect the probability of the variable YEARS. Step 3: Variable Coefficient Std. Error z-Statistic Prob.

C -0.51130 1.939316 -0.263562 0.7921YEARS -0.378007 0.143971 -2.625577 0.0087

RECORDS 3.032215 1.966096 1.542252 0.1230

1.

VISIT 1.792946 2.035356 0.880900 0.3784C -2.461055 3.032799 -0.811480 0.4171

YEARS -0.423057 0.147991 -2.858672 0.0043RECORDS 5.405987 3.004838 1.799094 0.0720

2.

QUANTITY 2.49E-05 1.44E-05 1.722116 0.0850

C 0.014473 1.427239 0.010140 0.9919YEARS -0.401511 0.163000 -2.463263 0.0138

RECORDS 3.605023 1.722830 2.092501 0.0364

3.

PRODUCT 0.422439 0.461244 0.915868 0.3597C 0.347434 1.590770 0.218406 0.8271

YEARS -0.370624 0.162709 -2.277835 0.0227RECORDS 3.865328 1.659830 2.342865 0.0191

4.

STORAGE 0.000236 1.464352 0.000161 0.9999C -1.627799 2.412276 -0.674798 0.4998

YEARS -0.360277 0.144024 -2.501512 0.0124RECORDS 3.895681 1.702268 2.288523 0.0221

5.

EDU 0.442119 0.447018 0.989040 0.3226C -0.733061 1.808430 -0.405358 0.6852

YEARS -0.419619 0.166091 -2.526440 0.0115RECORDS 4.084180 1.858570 2.197485 0.0280

6.

TRACE-ABILITY

1.474833 1.374323 1.073134 0.2832

C 0.020513 1.552528 0.013213 0.9895YEARS -0.396585 0.154202 -2.571859 0.0101

RECORDS 3.694129 1.760494 2.098348 0.0359

7.

INPUTS 0.868429 1.153471 0.752884 0.4515C 0.368351 1.368493 0.269166 0.7878

YEARS -0.377741 0.146554 -2.577481 0.0100RECORDS 3.828893 1.649058 2.321866 0.0202

8.

STORAGE_FARM

34.40998 36576175 9.41E-07 1.0000

C 0.283409 1.805380 0.156980 0.8753YEARS -0.369482 0.147380 -2.507012 0.0122

RECORDS 3.870888 1.651798 2.343440 0.0191

9.

EUREP 0.060771 1.121384 0.054193 0.9568 After the third step, the variable QUANTITY has been put in the model. This variable now has the smallest probability and does not strongly affect the probability of the variables YEARS and RECORDS. Step 4: Variable Coefficient Std. Error z-Statistic Prob.1. C -2.931609 4.029997 -0.727447 0.4670

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YEARS -0.422101 0.147303 -2.865531 0.0042 RECORDS 4.935847 3.693740 1.336274 0.1815 QUANTITY 2.42E-05 1.47E-05 1.649729 0.0990

VISIT 0.977524 3.769996 0.259291 0.7954 C -4.542229 5.035261 -0.902084 0.3670 YEARS -0.566063 0.222132 -2.548317 0.0108 RECORDS 5.933622 4.877322 1.216574 0.2238 QUANTITY 3.60E-05 2.02E-05 1.787011 0.0739

2.

PRODUCT 1.082570 0.633621 1.708544 0.0875 C -2.293814 3.000674 -0.764433 0.4446 YEARS -0.476477 0.175547 -2.714243 0.0066 RECORDS 5.623891 2.991522 1.879943 0.0601 QUANTITY 2.92E-05 1.70E-05 1.714608 0.0864

3.

STORAGE -1.139474 1.552633 -0.733898 0.4630 C -4.434223 3.921106 -1.130860 0.2581 YEARS -0.429445 0.149782 -2.867139 0.0041 RECORDS 5.453143 3.194141 1.707233 0.0878 QUANTITY 2.37E-05 1.43E-05 1.655505 0.0978

4.

EDU 0.481431 0.534784 0.900234 0.3680 C -2.954363 3.323401 -0.888958 0.3740 YEARS -0.445123 0.158908 -2.801131 0.0051 RECORDS 5.416395 3.178428 1.704111 0.0884 QUANTITY 2.40E-05 1.47E-05 1.638324 0.1014

5.

TRACE-ABILITY

0.847449 1.433514 0.591169 0.5544

C -3.078848 3.953526 -0.778760 0.4361 YEARS -0.469667 0.172358 -2.724950 0.0064 RECORDS 5.385442 3.824932 1.407984 0.1591 QUANTITY 2.56E-05 1.46E-05 1.746785 0.0807

6.

INPUTS 1.187024 1.415785 0.838421 0.4018 C -2.296095 2.926254 -0.784653 0.4327 YEARS -0.425825 0.148119 -2.874883 0.0040 RECORDS 5.249395 2.888015 1.817648 0.0691 QUANTITY 2.36E-05 1.39E-05 1.703074 0.0886

7.

STORAGE_FARM

37.21844 1.58E+08 2.67E-07 1.0000

C -2.178043 3.291228 -0.661772 0.5081YEARS -0.429544 0.152137 -2.823402 0.0048

8.

RECORDS 5.391263 3.028870 1.779959 0.0751

QUANTITY 2.50E-05 1.45E-05 1.727138 0.0841 EUREP -0.281242 1.202880 -0.233808 0.8151

There has been no variable included after the fourth step. Expectation-Prediction table: Dependent Variable: BUYER Method: ML - Binary Logit Date: 02/28/03 Time: 10:36 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 9 3 12 0 0 0P(Dep=1)>C 4 24 28 13 27 40

Total 13 27 40 13 27 40Correct 9 24 33 0 27 27



E(# of Dep=0) 8.53 4.47 13.00 4.22 8.77 13.00E(# of Dep=1) 4.47 22.53 27.00 8.78 18.23 27.00

Total 13.00 27.00 40.00 13.00 27.00 40.00Correct 8.53 22.53 31.06 4.22 18.23 22.45




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150

Correlation matrix :

Years Inputs Trace-ability

Visit growers

Records Products Quantity Education Storage farm

Storage Eurep

Years 1.000 .231 (.151)

.187 (.247)

.207 (.200)

-.151 (.353)

.036 (.824)

.059 (.716)

-.153 (.347)

-.331* (.037)

.068 (.676)

-.071 (.662)

Inputs .231 (.151)

1.000 .567** (.000)

.569** (.000)

.189 (.243)

.317* (.046)

.029 (.858)

-.087 (.593)

.066 (.687)

.080 (.623)

-.035 (.830)

Trace-ability

.187 (.247)

.567** (.000)

1.000 .466** (.002)

.086 (.599)

.163 (.316)

.157 (.332)

-.075 (.645)

.174 (.283)

.061 (.711)

.053 (.746)

Visit growers

.207 (.200)

.569** (.000)

.086 (.599)

1.000 .466** (.002)

.277 (.083)

.099 (.541)

-.038 (.817)

.131 (.420)

.046 (.780)

-.120 (.462)

Records -.151 (.353)

.189 (.243)

.163 (

.316) .466** (.002)

1.000 .231 (151)

.004 (.981)

.150 (.354)

-.025 (.879)

.061 (.711)

-.159 (.328)

Products .036 (.824)

.317* (.046)

.163 (.316)

.277 (.083)

.231 (151)

1.000 -.230 (.153)

.099 (.543)

-.210 (.193)

.134 (.409)

-.035 (.831)

Quantity .059 (.716)

.029 (.858)

.157 (.332)

.099 (.541)

.004 (.981)

-.230 (.153)

1.000

.014 (.933)

.104 (.523)

-.024 (.885)

.061 (.710)

Education -.153 (.347)

-.087 (.593)

-.075 (.645)

-.038 (.817)

.150 (.354)

.099 (.543)

.014 (.933)

1.000

.170 (.294)

.127 (.433)

.084 (.608)

Storage farm

-.331* (.037)

.066 (.687)

.174 (.283)

.131 (.420)

-.025 (.879)

-.210 (.193)

.104 (.523)

.170 (.294)

1.000

-.074 (.651)

.193 (.232)

Storage .068 (.676)

.080 (.623)

.061 (.711)

.046 (.780)

.061 (.711)

.134 (.409)

-.024 (.885)

.127 (.433)

-.074 (.651)

1.000

.067 (.680)

Eurep -.071 (.662)

-.035 (.830)

.053 (.746)

-.120 (.462)

-.159 (.328)

-.035 (.831)

.061 (.710)

.084 (.608)

.193 (.232)

.067 (.680)

1.000

* Correlation at the 0.05 level (2-tailed) ** Correlation at the 0.01 level (2-tailed)

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ANNEX T Output Commission rate Middlemen - supplier side Model information: The process of including variables into the model: There is no model, because none of the variables has a probability smaller than 0.10. Step 1: Model Variable Coefficient Prob. R-squared

C 12.50000 0.03051. PRICE_ DOWN-

STREAM

2.947368 0.60860.006969

C 16.00000 0.00012. RECORDS -.0800000 0.8332

0.001182

C 12.07297 0.00003. RATE 0.218411 0.1693

0.049125

C 15.30769 0.00004. AGR_

PRICE_ FARMER

-0.307692 0.96950.000039

C 15.17949 0.00005. STORAGE_

FARM 4.820513 0.5482

0.009566

C 15.39026 0.00006. SAME_

FARMER -0.124501 0.8793

0.000615

Because there were no variables with a probability smaller than 0.10, this analysis gave no results.

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152

ANNEX U Output Commission rate Middlemen - buyer side Model information: Dependent Variable: COM_RATE Method: Least Squares Date: 03/05/03 Time: 10:14 Sample: 1 40 Included observations: 40


TRACEABILITY -14.92940 3.894582 -3.833375 0.0005INPUTS 8.372323 3.177752 2.634668 0.0123

STORAGE 6.513297 2.798426 2.327486 0.0257R-squared 0.334500 Mean dependent var 15.30000Adjusted R-squared 0.279042 S.D. dependent var 7.792830S.E. of regression 6.616835 Akaike info criterion 6.711751Sum squared resid 1576.170 Schwarz criterion 6.880639Log likelihood -130.2350 F-statistic 6.031553Durbin-Watson stat 2.324297 Prob(F-statistic) 0.001946Jarque-Bera statistic 4.042017


C 22.20000 0.00001. TRACE-ABILITY

-7.885714 0.03240.114870

C 13.87500 0.00002. INPUTS 1.781250 0.5698

0.008574

C 14.45455 0.00003. STORAGE 4.831169 0.1382

0.056912

C 14.12462 0.00004. SAME 0.419778 0.2893

0.029493

C 14.28571 0.00005. SEED_ BUYER

1.229437 0.70980.003686

C 16.66667 0.00006. EUREP -1.607843 0.6473

0.005567

C 15.44737 0.00007. AGR_

PRICE_ BUYER

-2.947368 0.60860.006969

C 13.66667 0.00478. VISIT 1.765766 0.7110

0.003653

C 14.69231 0.00009. BUYER 0.900285 0.7370

0.003003

C 15.59631 0.000010. QUANTITY -3.29E-06 0.8289

0.001244

C 16.00000 0.000111. RECORDS -0.800000 0.8332

0.001182

C 15.17949 0.000012. STORAGE_

FARM 4.820513 0.5482

0.009566

After the first step, the variable TRACEABILITY has been entered into the model, because this variable has the smallest probability. Step 2: Model Variable Coefficient Prob. R-squared

C 20.58553 0.0000TRACE-ABILITY

-13.42105 0.00211.

INPUTS 8.072368 0.0214

0.234357


-9.128571 0.01212.

STORAGE 6.214286 0.0462

0.206179

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153


-8.346515 0.02343.

SAME 0.504001 0.1822

0.156992


-8.634375 0.02344.

SEED_ BUYER

2.911458 0.3655

0.134503


-7.815730 0.03635.

EUREP -1.224719 0.7153

0.118090


-7.775758 0.03766.

AGR_ PRICE_ BUYER

-1.924242 0.7271

0.117818


-10.91579 0.00857.

VISIT 8.157895 0.1064

0.175886


-7.992157 0.03238.

BUYER 1.241830 0.6275

0.120562


-7.953520 0.03559.

QUANTITY 1.73E-06 0.9065

0.115204


-7.875000 3.61189810.

RECORDS -0.125000 3.611898

0.114898

C 22.20000 0.000011. TRACE-ABILITY

-8.052941 0.03040.128920

STORAGE_ FARM

5.852941 0.4447

After the second step, the variable INPUTS has been included into the model. This variable now has the smallest probability and does not strongly affect the probability of the variable TRACEABILITY. Step 3: Model Variable Coefficient Prob. R-squared


-14.92940 0.0005

INPUTS 8.372323 0.0123

1.

STORAGE 6.513297 0.0257

0.334500


-13.31525 0.0023

INPUTS 7.439471 0.0362

2.

SAME 0.358957 0.3247

0.254989


-13.41295 0.0025

INPUTS 7.964286 0.0368

3.

SEED_ BUYER

0.256696 0.9378

0.234488


-13.34705 0.0026

INPUTS 8.017704 0.0245

4.

EUREP -0.639156 0.8406

0.235229


-13.35955 0.0025

INPUTS 8.226124 0.0208

5.

AGR_ PRICE_ BUYER

-2.921348 0.5749

0.241109

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-14.11095 0.0018

INPUTS 6.859662 0.0734

6.

VISIT 4.096253 0.4412

0.247044


-13.49119 0.0023

INPUTS 8.035838 0.0235

7.

BUYER 1.110526 0.6455

0.238907


-13.63597 0.0024

INPUTS 8.148001 0.0223

8.

QUANTITY 4.15E-06 0.7650

0.236281


-12.25935 0.0060

INPUTS 8.673935 0.0151

11.

RATE 0.167469 0.2913

0.257997


-13.41848 0.0025

INPUTS 8.078804 0.0233

12.

AGR_ PRICE_

FARMER

-0.244565 0.9731

0.234382

After the third step, the variable STORAGE has been put in the model. This variable now has the smallest probability and does not strongly affect the probability of the variables TRACEABILITY and INPUTS. Step 4: Model Variable Coefficient Prob. R-squared


-14.82406 0.00051.

INPUTS 7.738130 0.0220

0.355226

STORAGE 6.516345 0.0255 SAME 0.359771 0.2961


-14.93938 0.0006

INPUTS 7.722474 0.0322STORAGE 6.770772 0.0242

2.

SEED_ BUYER

1.571546 0.6187

0.339261


-14.78124 0.0006

INPUTS 8.219527 0.0150STORAGE 6.860222 0.0224

3.

EUREP -1.973337 0.5176

0.342525


-14.95155 0.0005

INPUTS 8.673873 0.0100STORAGE 7.084397 0.0176

4.

AGR_ PRICE_ BUYER

-5.229747 0.2944

0.355367


-15.43001 0.0005

INPUTS 7.425642 0.0429STORAGE 6.366102 0.0310

5.

VISIT 3.174782 0.5298

0.342070


-14.93226 0.0006

INPUTS 8.363000 0.0138STORAGE 6.466326 0.0312

6.

BUYER 0.217645 0.9253

0.334669


-15.01988 0.00067.

INPUTS 8.405271 0.0135

0.334895

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STORAGE 6.482123 0.0289 QUANTITY 1.89E-06 0.8861

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156

Correlation matrix:

Inputs Agr_

price_ buyer

Storage Storage farmer

Records Trace-ability

Eurep Visit Buyer Same Quantity Seed_ Buyer

Inputs 1.0000 .115 (.481)

.066 (.687)

.080 (.623)

.189 (.243)

.567** (.000)

-.035 (.830)

.569** (.000)

.053 (.744)

.208 (.198)

.029 (.858)

.428** (.006)

Agr_ price_ buyer

.115 (.481)

1.0000 .196 (.225)

-.037 (.822)

.087 (.595)

.087 (.595)

.096 (.554)

.065 (.689)

.159 (.327)

-.059 (.718)

-.124 (.446)

.106 (.516)

Storage .066 (.687)

.196 (.225)

1.0000 -.074 (.651)

-.025 (.879)

.174 (.283)

.193 (.232)

.131 (.420)

.179 (.269)

.007 (.964)

.104 (.523)

-.134 (.409)

Storage farmer

.080 (.623)

-.037 (.822)

-.074 (.651)

1.0000 .061 (.711)

.061 (.711)

.067 (.680)

.046 (.780)

.111 (.495)

.367* (.020)

-.024 (.885)

.074 (.651)

Records .189 (.243)

.087 (.595)

-.025 (.879)

.061 (.711)

1.0000 .086 (.599)

-.159 (.328)

.466** (.002)

.383* (.015)

.001 (.996)

.004 (.981)

.423** (.007)

Trace-ability

.567** (.000)

.087 (.595)

.174 (.283)

.061 (.711)

.086 (.599)

1.0000 .053 (.746)

.466** (.002)

.061 (.711)

.097 (.551)

.157 (.332)

.224 (.165)

Eurep -.035 (.830)

.096 (.554)

.193 (.232)

.067 (.680)

-.159 (.328)

.053 (.746)

1.0000 -.120 (.462)

.007 (.963)

.242 (.133)

.061 (.710)

.175 (.280)

Visit .569** (.000)

.065 (.689)

.131 (.420)

.046 (.780)

.466** (.002)

.466** (.002)

-.120 (.462)

1.0000 .208 (.198)

.164 (.312)

.099 (.541)

.368* (.019)

Buyer .053 (.744)

.159 (.327)

.179 (.269)

.111 (.495)

.383* (.015)

.061 (.711)

.007 (.963)

.208 (.198)

1.0000 .246 (.125)

.236 (.142)

.102 (.532)

Same .208 (.198)

-.059 (.718)

.007 (.964)

.367* (.020)

.001 (.996)

.097 (.551)

.242 (.133)

.164 (.312)

.246 (.125)

1.0000 -.144 (.375)

.202 (.211)

Quantity .029 (.858)

-.124 (.446)

.104 (.523)

-.024 (.885)

.004 (.981)

.157 (.332)

.061 (.710)

.099 (.541)

.236 (.142)

-.144 (.375)

1.0000 .083 (.609)

Seed_ buyer

.428** (.006)

.106 (.516)

-.134 (.409)

.074 (.651)

.423** (.007)

.224 (.165)

.175 (.280)

.368* (.019)

.102 (.532)

.202 (.211)

.083 (.609)

1.0000


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157

ANNEX V Output OLS Quality Middlemen Model information: Dependent Variable: QUALITY Method: Least Squares Date: 03/05/03 Time: 10:38 Sample: 1 40 Included observations: 40


VISIT -0.818806 0.326678 -2.506463 0.0167SAME 0.049405 0.027333 1.807535 0.0788

R-squared 0.183117 Mean dependent var -1.862800Adjusted R-squared 0.138961 S.D. dependent var 0.578637S.E. of regression 0.536930 Akaike info criterion 1.666139Sum squared resid 10.66686 Schwarz criterion 1.792805Log likelihood -30.32278 F-statistic 4.147064Durbin-Watson stat 1.596179 Prob(F-statistic) 0.023711Jarque-Bera statistic 1.001932


C -1.194333 0.00061. VISIT -0.722667 0.0357

0.110985

C -1.969902 0.00002. SAME 0.038251 0.1918

0.044416

C -1.484125 0.00003. INPUTS -0.473344 0.0367

0.109814

C -1.710400 0.00004. RECORDS -0.174171 0.5359

0.010164

C -1.755000 0.00005. BUYER -0.159704 0.4207

0.017140

6. C -1.922625 0.0000 0.002741

AGR_ QUAL_ BUYER

0.074781 0.7483

C -1.764017 0.00007. YEARS -0.012693 0.4772

0.013379

C -1.850040 0.00008. TOTAL_ AMOUNT

-0.034027 0.85980.000831

C -1.875026 0.00009. STORAGE_

FARM 0.489026 0.4111

0.017856

C -1.971000 0.000010. AGR_

QUAL_ FARMER

0.144267 0.50180.011954

C -1.969833 0.000011. EUREP 0.125922 0.6293

0.006193

C -1.841239 0.000012. SAME_

FARMER -0.029740 0.6246

0.006365

C -1.891286 0.000013. SEED_ BUYER

0.034528 0.88820.000527

After the first step, the variable VISIT has been entered into the model, because this variable has the smallest probability. Step 2: Model Variable Coefficient Prob. R-squared

C -1.243738 0.0003VISIT -0.818806 0.0167

1.

SAME 0.049405 0.0788

0.183117

C -1.194333 0.0006VISIT -0.463667 0.2563

2.

INPUTS -0.299469 0.2654

0.140684

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158

C -1.234482 0.0008VISIT -0.793197 0.0434

3.

RECORDS 0.120445 0.6924

0.114788

C -1.168105 0.0011VISIT -0.693603 0.0504

4.

BUYER -0.078685 0.6857

0.114966

C -1.275026 0.0009VISIT -0.740114 0.0341

5.

AGR_ QUAL_ BUYER

0.121038 0.5881

0.118100

C -1.172893 0.0011VISIT -0.700748 0.0484

6.

YEARS -0.005360 0.7593

0.113268

C -1.124632 0.0024VISIT -0.755634 0.0324

7.

TOTAL_ AMOUNT

-0.104552 0.5752

0.118602

C -1.194333 0.0006VISIT -0.737417 0.0327

8.

STORAGE_ FARM

0.545750 0.3367

0.133177

C -1.306908 0.0006VISIT -0.737880 0.0333

9.

AGR_ QUAL_

FARMER

0.168863 0.4107

0.127313

C -1.257392 0.0039VISIT -0.712441 0.0421

10.

EUREP 0.063059 0.8020

0.112515

C -1.194333 0.0007VISIT -0.712278 0.0426

11.

SAME_ FARMER

-0.013255 0.8213

0.112226

C -1.278841 0.000412. VISIT -0.857422 0.0213

0.135551

SEED_ BUYER

0.253523 0.3118

After the second step, the variable SAME has been included into the model. This variable now has the smallest significance and does not strongly affect the probability of the variable VISIT. Step 3: Model Variable Coefficient Prob. R-squared

C -1.249266 0.0002VISIT -0.507612 0.1980SAME 0.054932 0.0510

1.

INPUTS -0.372254 0.1561

0.228105

C -1.300892 0.0004VISIT -0.919470 0.0189SAME 0.050776 0.0750

2.

RECORDS 0.167346 0.5727

0.190404

C -1.195777 0.0006VISIT -0.769949 0.0267SAME 0.054524 0.0604

3.

BUYER -0.159242 0.4091

0.198648

C -1.390202 0.0003VISIT -0.860298 0.0131SAME 0.055085 0.0567

4.

AGR_ QUAL_ BUYER

0.211177 0.3397

0.203824

C -1.231470 0.0005VISIT -0.805905 0.0229SAME 0.049015 0.0862

5.

YEARS -0.002970 0.8618

0.183814

C -1.178373 0.0013VISIT -0.848929 0.0156SAME 0.049077 0.0839

6.

TOTAL_ AMOUNT

-0.097555 0.5907

0.189746

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159

C -1.239786 0.0004VISIT -0.816959 0.0183SAME 0.045453 0.1346

7.

STORAGE_ FARM

0.216219 0.7168

0.186139

C -1.318199 0.0004VISIT -0.824254 0.0171SAME 0.046857 0.1022

8.

AGR_ QUAL_

FARMER

0.115514 0.5685

0.190566

C -1.189826 0.0051VISIT -0.831095 0.0181SAME 0.051087 0.0837

9.

EUREP -0.055595 0.8264

0.184223

C -1.243758 0.0004VISIT -0.808218 0.0207SAME 0.049425 0.0827

10.

SAME_ FARMER

-0.013559 0.8121

0.184416

C -1.304203 0.0003VISIT -0.914050 0.0131SAME 0.046067 0.1063

11.

SEED_ BUYER

0.191408 0.4388

0.196791

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Correlation matrix: Visit Same Inputs Records Buyer Agr_

quality_ buyer

Years Total_ amount

Storage_farm

Agr_ quality_ farmer

Eurep Same_ farmer

Seed_ buyer

Visit

1.000 .164 (.312)

.569** (.000)

.466** (.002)

.208 (.198)

.095 (.560)

.207 (.200)

-.172 (.290)

.046 (.780)

.055 (.737)

-.120 (.462)

.135 (.407)

.368* (.019)

Same

.164 (.312)

1.000 .208 (.198)

.001 (.996)

.246 (.125)

-.190 (.241)

-.043 (.794)

-.051 (.754)

.367* (.020)

.161 (.320)

.242 (.133)

.024 (.884)

.202 (.211)

Inputs

.569** (.000)

.208 (.198)

1.000 .189 (.243)

.053 (744)

-.094 (.565)

.231 (.151)

.000 (1.000)

.080 (.623)

.000 (1.000)

-.035 (.830)

.237 (.142)

.428** (.006)

Records

.466** (.002)

.001 (.996)

.189 (.243)

1.000 .383* (.015)

.000 (1.000)

-.151 (.353)

.137 (.401)

.061 (.711)

.131 (.421)

-.159 (.328)

-.166 (.305)

.423** (.007)

Buyer

.208 (.198)

.246 (.125)

.053 (744)

.383* (.015)

1.000 -.080 (.623)

-.460** (.003)

.096 (.554)

.111 (.495)

-.154 (.342)

.007 (.963)

-.090 (.582)

.102 (.532)

Agr_ quality_ buyer

.095 (.560)

-.190 (.241)

-.094 (.565)

.000 (1.000)

-.080 (.623)

1.000 -.117 (.472)

.000 (1.000)

.080 (.623)

.144 (.374)

-.035 (.830)

.073 (.653)

-.066 (.687)

Years

.207 (.200)

-.043 (.794)

.231 (.151)

-.151 (.353)

-.460** (.003)

-.117 (.472)

1.000 -.057 (.727)

.068 (.676)

-.013 (.936)

-.071 (.662)

.051 (.754)

.154 (.343)

Total_ amount

-.172 (.290)

-.051 (.754)

.000 (1.000)

.137 (.401)

.096 (.554)

.000 (1.000)

-.057 (.727)

1.000 -.124 (.446)

.209 (.196)

-.253 (.115)

.072 (.661)

-.051 (.755)

Storage_ farm

.046 (.780)

.367* (.020)

.080 (.623)

.061 (.711)

.111 (.495)

.080 (.623)

.068 (.676)

-.124 (.446)

1.000 .092 (.570)

.067 (.680)

-.076 (.642)

.074 (.651)

Agr_ quality_ farmer

.055 (.737)

.161 (.320)

.000 (1.000)

.131 (.421)

-.154 (.342)

.144 (.374)

-.013 (.936)

.209 (.196)

.092 (.570)

1.000 -.243 (.132)

.235 (.144)

-.114 (.484)

Eurep

-.120 (.462)

.242 (.133)

-.035 (.830)

-.159 (.328)

.007 (.963)

-.035 (.830)

-.071 (.662)

-.253 (.115)

.067 (.680)

-.243 (.132)

1.000 -.212 (.188)

.175 (.280)

Same_ farmer

.135 (.407)

.024 (.884)

.237 (.142)

-.166 (.305)

-.090 (.582)

.073 (.653)

.051 (.754)

.072 (.661)

-.076 (.642)

.235 (.144)

-.212 (.188)

1.000 -.040 (.808)

Seed_ buyer

.368* (.019)

.202 (.211)

.428** (.006)

.423** (.007)

.102 (.532)

-.066 (.687)

.154 (.343)

-.051 (.755)

.074 (.651)

-.114 (.484)

.175 (.280)

-.040 (.808)

1.000


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ANNEX W Output Middlemen Paid-on-time Model information: Dependent Variable: PAID_ON_TIME Method: ML - Binary Logit Date: 03/07/03 Time: 17:00 Sample: 1 40 Included observations: 40 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives


Mean dependent var 0.850000 S.D. dependent var 0.361620S.E. of regression 0.361620 Akaike info criterion 0.895418Sum squared resid 5.100000 Schwarz criterion 0.937640Log likelihood -16.90836 Hannan-Quinn criter. 0.910684Avg. log likelihood -0.422709 Jarque-Bera statistic 31.28284Obs with Dep=0 6 Total obs 40Obs with Dep=1 34


C 1.964160 0.616418 3.186412 0.00141. SAME -0.074869 0.127971 -0.585049 0.5585

C 0.338116 0.970157 0.348517 0.72752. QUANTITY 2.18E-05 1.66E-05 1.313279 0.1891

C 1.234550 0.934689 1.320813 0.18663. PRODUCT 0.253829 0.439838 0.577098 0.5639

C 1.704748 0.768706 2.217685 0.02664. BUYER 0.044452 0.940419 0.047268 0.9623

C 1.945910 1.069045 1.820232 0.06875. ANY_AGR -0.259511 1.174689 -0.220919 0.8252

C 1.747433 0.448285 3.898039 0.00016. COM_DIF 0.025470 0.065481 0.388977 0.6973

Correlation matrix: Same Quantity Product Buyer Any_

Agr Com_ Dif

Same 1.000 -.144 (.375)

.042 (.795)

.246 (.125)

-.190 (.241)

.170 (.293)

Quantity -.144 (.375)

1.000 -.230 (.153)

.236 (.142)

-.166 (.307)

-.035 (829)

Product .042 (.795)

-.230 (.153)

1.000 .192 (.235)

.204 (.207)

.036 (.827)

Buyer .246 (.125)

.236 (.142)

.192 (.235)

1.000 -.080 (.623)

.055 (.737)

Any_ Agr

-.190 (.241)

-.166 (.307)

.204 (.207)

-.080 (.623)

1.000 .101 (.536)

Com_ Dif

.170 (.293)

-.035 (829)

.036 (.827)

.055 (.737)

.101 (.536)

1.000


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Estimation Prediction table: Dependent Variable: PAID_ON_TIME Method: ML - Binary Logit Date: 03/07/03 Time: 17:00 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 0 0 0 0 0 0P(Dep=1)>C 6 34 40 6 34 40

Total 6 34 40 6 34 40Correct 0 34 34 0 34 34



E(# of Dep=0) 0.90 5.10 6.00 0.90 5.10 6.00E(# of Dep=1) 5.10 28.90 34.00 5.10 28.90 34.00

Total 6.00 34.00 40.00 6.00 34.00 40.00Correct 0.90 28.90 29.80 0.90 28.90 29.80




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ANNEX X Output Middlemen Total-amount Model information: Method: ML - Binary Logit Date: 03/07/03 Time: 17:03 Sample: 1 40 Included observations: 40 Convergence achieved after 3 iterations Covariance matrix computed using second derivatives


Mean dependent var 0.375000 S.D. dependent var 0.490290S.E. of regression 0.490290 Akaike info criterion 1.373126Sum squared resid 9.375000 Schwarz criterion 1.415348Log likelihood -26.46253 Hannan-Quinn criter. 1.388393Avg. log likelihood -0.661563 Jarque-Bera statistic 6.785185Obs with Dep=0 25 Total obs 40Obs with Dep=1 15


C -0.419829 0.435719 -0.963531 0.33531. SAME -0.033017 0.106290 -0.310633 0.7561

C -0.544387 0.484679 -1.123190 0.26142. QUANTITY 1.18E-07 3.95E-06 0.029851 0.9762

C -0.669507 0.698058 -0.959099 0.33753. PRODUCT 0.076063 0.294139 0.258594 0.7959

C -0.810930 0.600925 -1.349470 0.17724. BUYER 0.436237 0.717301 0.608165 0.5431

C -0.510826 0.421637 -1.211529 0.22575. AGR_

QUANTITY -2.89E-11 0.666667 -4.34E-11 1.0000

C -0.529904 0.333219 -1.590258 0.11186. COM_DIF -0.045755 0.049056 -0.932715 0.3510

Correlation matrix: Same Quantity Product Buyer Agr_

Quantity Com_ Dif

Same 1.000 -.144 (.375)

.042 (.795)

.246 (.125)

.034 (.836)

.170 (.293)

Quantity -.144 (.375)

1.000 -.230 (.153)

.236 (.142)

.031 (.851)

-.035 (829)

Product .042 (.795)

-.230 (.153)

1.000 .192 (.235)

.037 (.821)

.036 (.827)

Buyer .246 (.125)

.236 (.142)

.192 (.235)

1.000 -.087 (.593)

.055 (.737)

Agr_ Quantity

.034 (.836)

.031 (.851)

.037 (.821)

-.087 (.593)

1.000 .008 (.961)

Com_ Dif

.170 (.293)

-.035 (829)

.036 (.827)

.055 (.737)

.008 (.961)

1.000


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Expectation Prediction table: Dependent Variable: TOTAL_AMOUNT Method: ML - Binary Logit Date: 03/07/03 Time: 17:03 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 25 15 40 25 15 40P(Dep=1)>C 0 0 0 0 0 0

Total 25 15 40 25 15 40Correct 25 0 25 25 0 25



E(# of Dep=0) 15.62 9.37 25.00 15.62 9.38 25.00E(# of Dep=1) 9.38 5.63 15.00 9.38 5.62 15.00

Total 25.00 15.00 40.00 25.00 15.00 40.00Correct 15.62 5.63 21.25 15.62 5.62 21.25




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ANNEX Y Agreement Quality Supplier Middlemen Model information: There is no model, because none of the variables has a probability smaller than 0.10. The process of including variables into the model: Step 1: Variable Coefficient Std. Error z-Statistic Prob.

C 0.405465 0.912871 0.444165 0.65691. RECORDS 0.810930 0.997683 0.812814 0.4163

C 1.064711 0.366719 2.903343 0.00372. STORAGE_

FARM 53.96229 8.89E+11 6.07E-11 1.0000

C 0.785033 0.390624 2.009692 0.04453. SAME_

FARMER 0.916500 0.746044 1.228479 0.2193

C 0.407826 0.883639 0.461530 0.64444. RATE 0.049154 0.059899 0.820606 0.4119

C 1.704748 0.768706 2.217685 0.02665. BUYER -0.839751 0.876665 -0.957893 0.3381

C 0.510826 0.730297 0.699477 0.48436. AGR_QUAL_

BUYER 0.762140 0.846280 0.900576 0.3678

C 1.098612 0.816497 1.345520 0.17857. INPUTS 2.54E-12 0.912871 2.78E-12 1.0000

C 0.693147 1.224745 0.565952 0.57148. VISIT 0.441833 1.283287 0.344298 0.7306

Because there were no variables with a probability smaller than 0.10, this analysis gave no results.

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ANNEX Z Agreement Quantity Supplier Middlemen Model information: Dependent Variable: AGR_QUAN_FARMER Method: ML - Binary Logit Date: 04/14/03 Time: 18:13 Sample: 1 40 Included observations: 40 Convergence achieved after 6 iterations Covariance matrix computed using second derivatives


AGR_QUAN_BUYER 3.278907 1.347740 2.432892 0.0150SAME_FARMER 1.674490 0.604764 2.768835 0.0056

BUYER 2.401282 1.281345 1.874033 0.0609Mean dependent var 0.350000 S.D. dependent var 0.483046S.E. of regression 0.350446 Akaike info criterion 0.862026Sum squared resid 4.421239 Schwarz criterion 1.030914Log likelihood -13.24052 Hannan-Quinn criter. 0.923090Restr. log likelihood -25.89787 Avg. log likelihood -0.331013LR statistic (3 df) 25.31470 McFadden R-squared 0.488741Probability(LR stat) 1.33E-05 Jarque-Bera statistic 6.664729Obs with Dep=0 26 Total obs 40Obs with Dep=1 14


C -1.386294 1.118034 -1.239939 0.21501. RECORDS 0.860201 1.171485 0.734283 0.4628

C -0.693147 0.339683 -2.040570 0.04132. STORAGE_

FARM 54.46395 4.74E+11 1.15E-10 1.0000

3. C -1.315364 0.437333 -3.007696 0.0026

SAME_ FARMER

1.105553 0.444691 2.486113 0.0129

C -0.402185 0.726995 -0.553216 0.58014. RATE -0.014824 0.044690 -0.331719 0.7401

C 0.154151 0.556349 0.277076 0.78175. BUYER 1.203973 0.708788 1.698635 0.0894

C -1.609438 0.547723 -2.938418 0.00336. AGR_QUAN_

BUYER 2.120264 0.752773 2.816605 0.0049

C -1.945910 1.069044 -1.820234 0.06877. INPUTS 1.566421 1.128012 1.388656 0.1649

C 8. VISIT

The analysis with the variable VISIT gave an error (near singular matrix), so this variable will be excluded from the continuation of this analysis. After the first step, the variable AGR_QUAN_BUYER has been entered into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C -1.996496 1.253266 -1.593035 0.1112AGR_QUAN_

BUYER 2.083410 0.757913 2.748879 0.0060

1.

RECORDS 0.453401 1.290346 0.351379 0.7253C -1.609438 0.547723 -2.938418 0.0033

AGR_QUAN_BUYER

2.014903 0.760117 2.650780 0.00802.

STORAGE_ FARM

32.36532 13064312 2.48E-06 1.0000

C -2.661179 0.846823 -3.142545 0.0017AGR_QUAN_

BUYER 2.560656 0.997436 2.567239 0.0103

3.

SAME_ FARMER

1.313357 0.499912 2.627177 0.0086

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167

C -1.220673 0.951230 -1.283258 0.1994AGR_QUAN_

BUYER 2.152815 0.763030 2.821402 0.0048

4.

RATE -0.027861 0.058178 -0.478902 0.6320C -0.791615 0.709725 -1.115383 0.2647

AGR_QUAN_BUYER

2.195972 0.799260 2.747506 0.00605.

BUYER 1.331223 0.825333 1.612953 0.1068C -2.367124 1.150195 -2.058019 0.0396

AGR_QUAN_BUYER

1.961659 0.769452 2.549424 0.01086.

INPUTS 0.980829 1.211857 0.809361 0.4183 After the second step, the variable SAME_FARMER has been included in the model. This variable now has the smallest probability and does not strongly affect the probability of the variable AGR_QUAN_BUYER. Step 3: Variable Coefficient Std. Error z-Statistic Prob.

C -6.294937 8.484169 -0.741963 0.4581AGR_QUAN_

BUYER 2.379228 0.998783 2.382126 0.0172

SAME_ FARMER

1.259828 0.489133 2.575638 0.0100

1.

RECORDS 3.825549 8.392861 0.455810 0.6485C -2.681432 0.850039 -3.154481 0.0016

AGR_QUAN_BUYER

2.382416 1.010582 2.357469 0.0184

SAME_ FARMER

1.335158 0.498698 2.677289 0.0074

2.

STORAGE_FARM

36.06986 58551867 6.16E-07 1.0000

C -2.859927 1.316852 -2.171791 0.0299AGR_QUAN_

BUYER 2.570772 1.002787 2.563628 0.0104

SAME_ FARMER

1.339806 0.520472 2.574213 0.0100

3.

RATE 0.012591 0.061945 0.203266 0.8389

C -1.627933 0.937481 -1.736497 0.0825AGR_QUAN_

BUYER 3.278907 1.347740 2.432892 0.0150

SAME_ FARMER

1.674490 0.604764 2.768835 0.0056

4.

BUYER 2.401282 1.281345 1.874033 0.0609C -2.573718 1.242169 -2.071954 0.0383

AGR_QUAN_BUYER

2.589943 1.048963 2.469050 0.0135

SAME_ FARMER

1.327898 0.525073 2.528979 0.0114

5.

INPUTS -0.129801 1.373811 -0.094483 0.9247 After the third step, the variable BUYER has been put in the model. This variable now has the smallest probability and does not strongly affect the probability of the variables AGR_QUAN_BUYER and SAME_FARMER. Step 4: Variable Coefficient Std. Error z-Statistic Prob.

C -7.371529 16.29072 -0.452499 0.6509AGR_QUAN_

BUYER 2.872477 1.316389 2.182088 0.0291

SAME_ FARMER

1.535279 0.579509 2.649275 0.0081

BUYER 2.631773 1.282848 2.051508 0.0402

1.

RECORDS 6.351627 16.25802 0.390676 0.6960C -1.594778 0.937462 -1.701165 0.0889

AGR_QUAN_BUYER

3.107183 1.378389 2.254214 0.0242

SAME_ FARMER

1.764461 0.620992 2.841357 0.0045

BUYER 2.694751 1.329062 2.027558 0.0426

2.

STORAGE_FARM

38.95286 1.59E+08 2.45E-07 1.0000

C -1.344581 1.554198 -0.865128 0.3870AGR_QUAN_

BUYER 3.318563 1.372677 2.417584 0.0156

3.

SAME_ FARMER

1.658984 0.609922 2.719993 0.0065

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BUYER 2.469200 1.333578 1.851560 0.0641 RATE -0.018642 0.083236 -0.223971 0.8228

C -1.487844 1.234804 -1.204923 0.2282AGR_QUAN_

BUYER 3.356407 1.439184 2.332160 0.0197

SAME_ FARMER

1.712071 0.652029 2.625757 0.0086

BUYER 2.410384 1.285914 1.874452 0.0609

4.

INPUTS -0.236785 1.409451 -0.167998 0.8666 No variable has been included after the fourth step.

Expectation-Prediction table: Dependent Variable: AGR_QUAN_FARMER Method: ML - Binary Logit Date: 04/14/03 Time: 18:13 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 24 4 28 26 14 40P(Dep=1)>C 2 10 12 0 0 0

Total 26 14 40 26 14 40Correct 24 10 34 26 0 26



E(# of Dep=0) 21.69 4.31 26.00 16.90 9.10 26.00E(# of Dep=1) 4.31 9.69 14.00 9.10 4.90 14.00

Total 26.00 14.00 40.00 26.00 14.00 40.00Correct 21.69 9.69 31.39 16.90 4.90 21.80



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169

ANNEX AA Agreement Pesticide Use Supplier Middlemen Model information: Dependent Variable: AGR_PEST_FARMER Method: ML - Binary Logit Date: 04/14/03 Time: 18:18 Sample: 1 40 Included observations: 40 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives


AGR_PEST_BUYER 3.514526 1.184220 2.967798 0.0030Mean dependent var 0.825000 S.D. dependent var 0.384808S.E. of regression 0.311735 Akaike info criterion 0.696448Sum squared resid 3.692790 Schwarz criterion 0.780892Log likelihood -11.92895 Hannan-Quinn criter. 0.726980Restr. log likelihood -18.54906 Avg. log likelihood -0.298224LR statistic (1 df) 13.24021 McFadden R-squared 0.356897Probability(LR stat) 0.000274 Jarque-Bera statistic 563.9492Obs with Dep=0 7 Total obs 40Obs with Dep=1 33


C 1.386294 1.118034 1.239939 0.21501. RECORDS 0.189242 1.204637 0.157095 0.8752

C 1.519826 0.417261 3.642382 0.00032. STORAGE_F

ARM 31.75618 16818683 1.89E-06 1.0000

C 1.370463 0.445352 3.077257 0.00213. SAME_

FARMER 0.363302 0.461672 0.786926 0.4313

C 0.981076 0.988132 0.992860 0.32084. RATE 0.040565 0.066874 0.606582 0.5441

C 1.203973 0.658281 1.828966 0.06745. BUYER 0.545227 0.852532 0.639538 0.5225

C -0.182322 0.605530 -0.301094 0.76336. AGR_PEST_

BUYER 3.514526 1.184220 2.967798 0.0030

C 0.510826 0.730297 0.699477 0.48437. INPUTS 1.435085 0.905012 1.585707 0.1128

C 0.693147 1.224745 0.565952 0.57148. VISIT 0.949081 1.303428 0.728142 0.4665

After the first step, the variable AGR_PEST_BUYER has been entered into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C 0.145277 1.335048 0.108818 0.9133AGR_PEST_

BUYER 3.552882 1.196115 2.970352 0.0030

1.

RECORDS -0.401113 1.456033 -0.275484 0.7829C -0.182322 0.605530 -0.301094 0.7633

AGR_PEST_ BUYER

3.478158 1.184780 2.935700 0.00332.

STORAGE_FARM

31.98112 45739844 6.99E-07 1.0000

C -0.354786 0.662080 -0.535866 0.5921AGR_PEST_

BUYER 3.499346 1.194634 2.929220 0.0034

3.

SAME_ FARMER

0.382898 0.569151 0.672753 0.5011

C 0.447840 0.970916 0.461255 0.6446AGR_PEST_

BUYER 3.948883 1.407353 2.805895 0.0050

4.

RATE -0.057143 0.068950 -0.828765 0.4072

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C 0.098340 0.788750 0.124679 0.9008AGR_PEST_

BUYER 3.740951 1.277325 2.928739 0.0034

5.

BUYER -0.628320 1.133666 -0.554237 0.5794C -1.540989 1.245399 -1.237346 0.2160

AGR_PEST_ BUYER

3.675000 1.290011 2.848812 0.00446.

INPUTS 1.777499 1.301219 1.366025 0.1719C 0.052634 1.381031 0.038112 0.9696

AGR_PEST_ BUYER

3.558466 1.208599 2.944289 0.00327.

VISIT -0.287626 1.520097 -0.189216 0.8499 No variable has been included after the second step.

Expectation-Prediction table: Dependent Variable: AGR_PEST_FARMER Method: ML - Binary Logit Date: 04/14/03 Time: 18:18 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 6 5 11 0 0 0P(Dep=1)>C 1 28 29 7 33 40

Total 7 33 40 7 33 40Correct 6 28 34 0 33 33



E(# of Dep=0) 3.31 3.69 7.00 1.23 5.78 7.00E(# of Dep=1) 3.69 29.31 33.00 5.77 27.22 33.00

Total 7.00 33.00 40.00 7.00 33.00 40.00Correct 3.31 29.31 32.61 1.23 27.22 28.45



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ANNEX AB Agreement Quantity Buyer Middlemen Model information: There is no model, because none of the variables has a probability smaller than 0.10. The process of including variables into the model: Step 1: Variable Coefficient Std. Error z-Statistic Prob.

C -0.547197 0.689107 -0.794066 0.42721. PRODUCT 0.068044 0.291185 0.233678 0.8152

C -0.525773 0.478932 -1.097801 0.27232. QUANTITY 1.06E-06 3.86E-06 0.274474 0.7837

C -0.559616 0.361873 -1.546441 0.12203. STORAGE 0.847298 0.845154 1.002536 0.3161

C -1.609438 1.095445 -1.469209 0.14184. EUREP 1.373049 1.148607 1.195404 0.2319

C -0.154151 0.556349 -0.277076 0.78175. BUYER -0.376478 0.684359 -0.550117 0.5822

C -0.470157 0.433414 -1.084776 0.27806. SAME 0.022933 0.101774 0.225330 0.8217

C -0.568635 0.582409 -0.976349 0.32897. YEARS 0.020835 0.061543 0.338548 0.7350

C 8. SEED_ BUYER

C -1.386294 1.118034 -1.239939 0.21509. RECORDS 1.098612 1.169045 0.939752 0.3473

C -0.470004 0.329140 -1.427974 0.153310. STORAGE_

FARM 60.13745 9.05E+12 6.65E-12 1.0000

C 11. TRACE_ ABILITY

C -1.945910 1.069044 -1.820233 0.068712. INPUTS 1.820747 1.126209 1.616705 0.1059

C 13. VISIT

C -0.495779 0.689144 -0.719413 0.471914. RATE 0.006098 0.041026 0.148648 0.8818

The analyses with the variables SEED_BUYER, TRACEABILITY and VISIT gave an error (near singular matrix), so these variables will be excluded from the continuation of this analysis. Because there were no variables with a probability smaller than 0.10, this analysis gave no results.

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ANNEX AC Agreement Quality Buyer Middlemen Model information: There is no model, because none of the variables has a probability smaller than 0.10. The process of including variables into the model: Step 1: Variable Coefficient Std. Error z-Statistic Prob.

C 0.337352 0.861670 0.391510 0.69541. PRODUCT 0.557533 0.446550 1.248532 0.2118

C 1.981462 0.617336 3.209699 0.00132. QUANTITY -5.13E-06 4.29E-06 -1.195800 0.2318

C 1.504077 0.451335 3.332504 0.00093. STORAGE -0.587787 0.950633 -0.618311 0.5364

C 1.609438 1.095445 1.469209 0.14184. EUREP -0.259511 1.174689 -0.220919 0.8252

C 1.704748 0.768706 2.217685 0.02665. BUYER -0.451985 0.897326 -0.503702 0.6145

C 1.811423 0.570468 3.175326 0.00156. SAME -0.133631 0.114559 -1.166482 0.2434

C 1.802118 0.718777 2.507202 0.01227. YEARS -0.050806 0.069920 -0.726632 0.4675

C 1.791759 1.080123 1.658847 0.09718. SEED_ BUYER

-0.479573 1.161028 -0.413059 0.6796

C 1.386294 1.118034 1.239939 0.21509. RECORDS 1.83E-11 1.195229 1.53E-11 1.0000

C 1.354546 0.396558 3.415759 0.000610. STORAGE_

FARM 53.83011 9.62E+11 5.60E-11 1.0000

C 11. TRACE_ ABILITY

C 1.945910 1.069045 1.820232 0.068712. INPUTS -0.672944 1.151397 -0.584459 0.5589

C 0.693147 1.224745 0.565952 0.571413. VISIT 0.762140 1.294678 0.588672 0.5561

C 0.897518 0.923712 0.971643 0.331214. RATE 0.034475 0.061199 0.563332 0.5732

The analyses with the variable TRACEABILITY gave an error (near singular matrix), so this variable will be excluded from the continuation of this analysis. Because there were no variables with a probability smaller than 0.10, this analysis gave no results.

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ANNEX AD Agreement Pesticide Use Buyer Middlemen Model information: Dependent Variable: AGR_PEST_BUYER Method: ML - Binary Logit Date: 04/14/03 Time: 18:23 Sample: 1 40 Included observations: 40 Convergence achieved after 5 iterations Covariance matrix computed using second derivatives


SAME 0.629530 0.284447 2.213173 0.0269Mean dependent var 0.725000 S.D. dependent var 0.452203S.E. of regression 0.392636 Akaike info criterion 1.041026Sum squared resid 5.858209 Schwarz criterion 1.125470Log likelihood -18.82051 Hannan-Quinn criter. 1.071558Restr. log likelihood -23.52675 Avg. log likelihood -0.470513LR statistic (1 df) 9.412474 McFadden R-squared 0.200038Probability(LR stat) 0.002155 Jarque-Bera statistic 347.8513Obs with Dep=0 11 Total obs 40Obs with Dep=1 29


C 0.572817 0.751704 0.762025 0.44601. PRODUCT 0.196302 0.337011 0.582478 0.5602

C 0.716495 0.549883 1.302995 0.19262. QUANTITY 2.96E-06 5.18E-06 0.570752 0.5682

C 0.832909 0.378785 2.198898 0.02793. STORAGE 0.958850 1.144612 0.837708 0.4022

C 0.693147 0.866025 0.800377 0.42354. EUREP 0.328504 0.949269 0.346060 0.7293

C 0.154151 0.556349 0.277076 0.78175. BUYER 1.327454 0.744969 1.781892 0.0748

C -0.125066 0.509104 -0.245659 0.80596. SAME 0.629530 0.284447 2.213173 0.0269

C 1.229365 0.639970 1.920974 0.05477. YEARS -0.032618 0.065375 -0.498934 0.6178

C -0.287682 0.763763 -0.376664 0.70648. SEED_ BUYER

1.599868 0.874444 1.829584 0.0673

C 0.405465 0.912871 0.444165 0.65699. RECORDS 0.655407 0.991416 0.661081 0.5086

C 0.934309 0.355842 2.625631 0.008610. STORAGE_

FARM 32.02321 14342783 2.23E-06 1.0000

C 1.386294 1.118034 1.239939 0.215011. TRACE_ ABILITY

-0.470004 1.178983 -0.398652 0.6901

C 0.510826 0.730297 0.699477 0.484312. INPUTS 0.587787 0.836660 0.702539 0.4823

C -0.693147 1.224745 -0.565952 0.571413. VISIT 1.828127 1.283287 1.424566 0.1543

C -0.951424 0.991987 -0.959109 0.337514. RATE 0.145853 0.076274 1.912220 0.0558

After the first step, the variable SAME has been entered into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C -0.668356 0.930602 -0.718198 0.4726SAME 0.636383 0.281770 2.258525 0.0239

1.

PRODUCT 0.259580 0.371904 0.697976 0.4852C -0.599837 0.738060 -0.812721 0.4164

SAME 0.644408 0.285061 2.260599 0.02382.

QUANTITY 5.07E-06 5.98E-06 0.848146 0.3964

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C -0.197371 0.523069 -0.377332 0.7059SAME 0.603130 0.282087 2.138103 0.0325

3.

STORAGE 0.725228 1.229074 0.590060 0.5552C 0.127861 0.921381 0.138771 0.8896

SAME 0.641313 0.286281 2.240151 0.02514.

EUREP -0.334207 1.007834 -0.331610 0.7402C -0.520629 0.661986 -0.786464 0.4316

SAME 0.539950 0.273241 1.976097 0.04815.

BUYER 0.831366 0.851579 0.976264 0.3289C -0.333430 0.880399 -0.378726 0.7049

SAME 0.662199 0.313047 2.115336 0.03446.

YEARS 0.021068 0.072431 0.290873 0.7711C -0.960820 0.893187 -1.075722 0.2821

SAME 0.561525 0.278645 2.015199 0.04397.

SEED_ BUYER

1.181928 0.990707 1.193015 0.2329

C -0.614306 1.174253 -0.523147 0.6009SAME 0.601938 0.278297 2.162935 0.0305

8.

RECORDS 0.591725 1.258613 0.470141 0.6383C -0.123697 0.509848 -0.242614 0.8083

SAME 0.628013 0.285898 2.196635 0.02809.

STORAGE_ FARM

30.80363 1.06E+08 2.90E-07 1.0000

C 0.366984 1.236889 0.296699 0.7667SAME 0.619975 0.281711 2.200752 0.0278

10.

TRACE-ABILITY

-0.550573 1.254855 -0.438754 0.6608

C -0.288337 0.855372 -0.337089 0.7360SAME 0.625822 0.284763 2.197695 0.0280

11.

INPUTS 0.220596 0.924910 0.238506 0.8115C -1.407441 1.393610 -1.009925 0.3125

SAME 0.613668 0.288719 2.125483 0.033512.

VISIT 1.440918 1.409009 1.022646 0.3065C -1.587390 1.173735 -1.352427 0.1762

SAME 0.568058 0.292539 1.941819 0.052213.

RATE 0.121613 0.087594 1.388365 0.1650 No variable has been included into the model after the second step.

Expectation-Prediction table: Dependent Variable: AGR_PEST_BUYER Method: ML - Binary Logit Date: 04/14/03 Time: 18:23 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 8 4 12 0 0 0P(Dep=1)>C 3 25 28 11 29 40

Total 11 29 40 11 29 40Correct 8 25 33 0 29 29



E(# of Dep=0) 4.90 6.10 11.00 3.03 7.98 11.00E(# of Dep=1) 6.10 22.90 29.00 7.97 21.02 29.00

Total 11.00 29.00 40.00 11.00 29.00 40.00Correct 4.90 22.90 27.79 3.03 21.02 24.05



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ANNEX AE Agreement Fertiliser Use Buyer Middlemen Model information: Dependent Variable: AGR_FERT_BUYER Method: ML - Binary Logit Date: 04/04/03 Time: 14:56 Sample: 1 40 Included observations: 40 Convergence achieved after 4 iterations Covariance matrix computed using second derivatives


SAME 0.390904 0.172997 2.259596 0.0238PRODUCT 0.904000 0.392236 2.304735 0.0212

Mean dependent var 0.350000 S.D. dependent var 0.483046S.E. of regression 0.418045 Akaike info criterion 1.105778Sum squared resid 6.466176 Schwarz criterion 1.232444Log likelihood -19.11555 Hannan-Quinn criter. 1.151576Restr. log likelihood -25.89787 Avg. log likelihood -0.477889LR statistic (2 df) 13.56463 McFadden R-squared 0.261887Probability(LR stat) 0.001134 Jarque-Bera statistic 7.023371Obs with Dep=0 26 Total obs 40Obs with Dep=1 14


C -2.254952 0.833700 -2.704753 0.00681. PRODUCT 0.753246 0.337523 2.231688 0.0256

C 0.243266 0.609700 0.398993 0.68992. QUANTITY -1.11E-05 7.38E-06 -1.502379 0.1330

C -0.559616 0.361873 -1.546441 0.12203. STORAGE -0.356675 0.911566 -0.391277 0.6956

C -1.609438 1.095445 -1.469209 0.14184. EUREP 1.129865 1.150887 0.981734 0.3262

C -1.704748 0.768706 -2.217685 0.02665. BUYER 1.481605 0.860761 1.721273 0.0852

C -1.513907 0.527520 -2.869857 0.00416. SAME 0.302300 0.131395 2.300700 0.0214

C -0.289189 0.610512 -0.473683 0.63577. YEARS -0.043471 0.069293 -0.627352 0.5304

C -0.916291 0.836660 -1.095177 0.27348. SEED_ BUYER

0.356675 0.911566 0.391277 0.6956

C 9. RECORDS

C -0.693147 0.339683 -2.040570 0.041310. STORAGE_

FARM 36.46395 58550466 6.23E-07 1.0000

C -1.386294 1.118034 -1.239939 0.215011. TRACE_ ABILITY

0.860201 1.171485 0.734283 0.4628

C -1.945910 1.069044 -1.820234 0.068712. INPUTS 1.566421 1.128012 1.388656 0.1649

C 13. VISIT

C -0.313523 0.737699 -0.425001 0.670814. RATE -0.020979 0.045934 -0.456731 0.6479

The analyses with the variables RECORDS and VISIT gave an error (near singular matrix), so these variables will be excluded from the continuation of this analysis. After the first step, the variable SAME has been entered into the model, because this variable has the smallest probability. Step 2: Variable Coefficient Std. Error z-Statistic Prob.

C -3.710796 1.215599 -3.052648 0.0023SAME 0.390904 0.172997 2.259596 0.0238

1.

PRODUCT 0.904000 0.392236 2.304735 0.0212C -0.680594 0.752801 -0.904083 0.36602.

SAME 0.294669 0.138229 2.131738 0.0330

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QUANTITY -1.02E-05 7.60E-06 -1.339838 0.1803C -1.445968 0.545556 -2.650449 0.0080

SAME 0.305561 0.133272 2.292765 0.02193.

STORAGE -0.419800 0.965679 -0.434720 0.6638C -1.925158 1.114445 -1.727459 0.0841

SAME 0.287776 0.133696 2.152469 0.03144.

EUREP 0.518262 1.197491 0.432790 0.6652C -2.387155 0.915652 -2.607055 0.0091

SAME 0.287298 0.139345 2.061781 0.03925.

BUYER 1.237725 0.913995 1.354193 0.1757C -1.127002 0.782717 -1.439859 0.1499

SAME 0.300619 0.129360 2.323897 0.02016.

YEARS -0.054258 0.087082 -0.623069 0.5332C -1.424123 0.925846 -1.538186 0.1240

SAME 0.305071 0.133820 2.279719 0.02267.

SEED_ BUYER

-0.116775 0.998550 -0.116945 0.9069

C -1.468669 0.527142 -2.786099 0.0053SAME 0.277275 0.135812 2.041618 0.0412

8.

STORAGE_ FARM

33.46592 35498641 9.43E-07 1.0000

C -2.032068 1.175029 -1.729377 0.0837SAME 0.296746 0.132025 2.247641 0.0246

9.

TRACE-ABILITY

0.606301 1.199924 0.505283 0.6134

C -2.437942 1.113953 -2.188550 0.0286SAME 0.279064 0.133127 2.096225 0.0361

10.

INPUTS 1.177889 1.157732 1.017411 0.3090C -0.796285 0.884853 -0.899906 0.3682

SAME 0.337425 0.141786 2.379813 0.017311.

RATE -0.056015 0.058906 -0.950927 0.3416 After the second step, the variable PRODUCT has been included in the model. This variable now has the smallest probability and does not strongly affect the probability of the variable SAME. Step 3: Variable Coefficient Std. Error z-Statistic Prob.

C -2.951428 1.392171 -2.120018 0.0340SAME 0.382379 0.178012 2.148057 0.0317

PRODUCT 0.842313 0.402713 2.091599 0.0365

1.

QUANTITY -7.53E-06 7.61E-06 -0.989240 0.3225C -3.749954 1.271774 -2.948601 0.0032

SAME 0.390258 0.172918 2.256899 0.0240PRODUCT 0.912709 0.400985 2.276169 0.0228

2.

STORAGE 0.112155 1.035803 0.108278 0.9138C -4.122698 1.574016 -2.619223 0.0088

SAME 0.370238 0.176050 2.103027 0.0355PRODUCT 0.903113 0.392275 2.302245 0.0213

3.

EUREP 0.547027 1.265285 0.432335 0.6655C -4.447542 1.515630 -2.934450 0.0033

SAME 0.396657 0.189506 2.093109 0.0363PRODUCT 0.875406 0.401133 2.182332 0.0291

4.

BUYER 1.061765 0.975392 1.088552 0.2764C -3.304859 1.344911 -2.457307 0.0140

SAME 0.376220 0.169668 2.217388 0.0266PRODUCT 0.910334 0.395821 2.299863 0.0215

5.

YEARS -0.054091 0.088476 -0.611363 0.5410C -3.267963 1.363690 -2.396413 0.0166

SAME 0.406185 0.175755 2.311087 0.0208PRODUCT 0.939622 0.396139 2.371948 0.0177

6.

SEED_ BUYER

-0.671219 1.097800 -0.611423 0.5409

C -3.643585 1.226811 -2.969964 0.0030SAME 0.377524 0.178584 2.113986 0.0345

PRODUCT 0.886419 0.393870 2.250536 0.0244

7.

STORAGE_ FARM

34.98002 1.59E+08 2.20E-07 1.0000

C -3.780076 1.510601 -2.502366 0.0123SAME 0.389319 0.173860 2.239262 0.0251

PRODUCT 0.898160 0.398544 2.253603 0.0242

8.

TRACE-ABILITY

0.098441 1.262956 0.077945 0.9379

C -4.028420 1.464889 -2.749982 0.0060SAME 0.379117 0.174084 2.177787 0.0294

9.

PRODUCT 0.859299 0.402822 2.133199 0.0329

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INPUTS 0.525202 1.232190 0.426235 0.6699C -3.444329 1.616328 -2.130959 0.0331

SAME 0.398173 0.176141 2.260532 0.0238PRODUCT 0.872952 0.409297 2.132807 0.0329

10.

RATE -0.014845 0.061971 -0.239553 0.8107 No variable has been included after the third step.

Expectation-Prediction table: Dependent Variable: AGR_FERT_BUYER Method: ML - Binary Logit Date: 04/04/03 Time: 14:56 Sample: 1 40 Included observations: 40 Prediction Evaluation (success cutoff C = 0.5)


P(Dep=1)<=C 24 8 32 26 14 40P(Dep=1)>C 2 6 8 0 0 0

Total 26 14 40 26 14 40Correct 24 6 30 26 0 26



E(# of Dep=0) 19.63 6.37 26.00 16.90 9.10 26.00E(# of Dep=1) 6.37 7.63 14.00 9.10 4.90 14.00

Total 26.00 14.00 40.00 26.00 14.00 40.00Correct 19.63 7.63 27.26 16.90 4.90 21.80



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ANNEX AF Data descriptives farmers Variable N Minimum Maximum Mean Std. deviation EDU – last level of education of respondent 40 1 5 4.08 1.21 YEARS – operating time of the farmer 40 1.0 52.0 19.400 15.414 SIZE – the size of the farm in acres 40 0.3 28.0 5.629 6.018 ACRES – the acres under French beans 40 0.3 5.0 1.638 1.112 PRICE – the average price received annually 40 10.00 45.00 24.2335 7.8284 PRICE_DIF – the price difference compared to the mean price 40 -14.2335 20.7665 8.05E-16 7.828411 AMOUNT – the average amount of French beans grown during a year 40 216.00 46800.00 6490.900 9331.1935 AMOUNT_ACRE – the amount of French beans grown per acre 40 225.00 16200.00 4115.392 4515.3477 PROPERTY – whether the land belongs to the farmer himself 40 0 1 0.83 0.38 REJECTED – the percentage of the harvest that is rejected by the buyer because of a lack of quality

40 0.0 50.0 15.613 11.638

LN(ref_per/(1-ref_per)) 37 -5.293 0.000 -1.86457 1.01186 INPUTS – whether the buyer provides inputs to the farmer 40 0 1 0.55 0.50 BUYER – the buyer of the produce 40 0 1 0.43 0.50 SAME_BUYER – the number of years a farmer is supplying the same buyer 40 0 10 2.57 2.93 AGR_QUAN – whether the farmer has an agreement with his buyer regarding the quantity he should deliver

40 0 1 0.28 0.45

AGR_PRICE – whether the farmer has an agreement with his buyer regarding the price he will receive

40 0 1 0.30 0.46

AGR_QUAL – whether the farmer has an agreement with his buyer regarding the quality he should deliver

40 0 1 0.45 0.50

AGR_PEST – whether the farmer has an agreement with his buyer regarding the amount and types of pesticides he is allowed to use

40 0 1 0.58 0.50

ANY_AGR – whether the farmer has an agreement with his buyer regarding the quality he should deliver, the quantity he should deliver and/or the price he would receive

40 0 1 0.52 0.51

PAID_ON_TIME – whether the farmer is paid on the day the buyer promised 38 0 1 0.53 0.51 TOTAL_AMOUNT – whether the buyer takes the whole amount harvested (except the percentage that is rejected because of quality requirements)

40 0 1 0.73 0.45

STORAGE – whether the farmer has storage facilities 40 0 1 0.13 0.33 EUREPGAP – whether the farmer has heard from the EUREPGAP protocol 40 0 1 0.55 0.50 RECORDS – whether the farmer keeps records 40 0 1 0.65 0.48 FERTILISER – the quantity of fertiliser that the farmers use per kg of seed (in kg) 35 4 60 26.06 11.44

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ANNEX AG Data descriptives farmers (questionnaire concerning knowledge) Variable N Minimum Maximum Mean Std. deviation Gender (0 = female and 1 = male) 38 0 1 0.61 0.495 Age (in years) 38 20 67 40.21 11.245 Family size 38 1 14 5.13 2.970 Education level (1 = no education, 2 = primary school not finished, 3 = primary school finished, 4 = secondary school not finished, 5 = secondary school finished)

38 1 5 3.37 1.403

Operating time (in years) 37 1 57 13.16 12.986 Size (in acres) 38 1.0 15.0 3.263 2.8799 Acres under French beans 38 0.25 4.00 0.9539 .88504 Turnover French beans (in Ksh) 37 0 180000 54986.49 44885.423 Total Turnover (in Ksh) 32 15000 300000 98250.00 74526.029 Acres belong to the farmer (0=no and 1= yes) 38 0 1 0.82 0.393

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ANNEX AH Data descriptives middlemen Variable N Minimum Maximum Mean Std. deviation EDU – last level of education of respondent 40 1 5 4.20 1.02 YEARS – operating time of the middleman 40 0.3 28.0 7.783 5.273 PRODUCTS – number of products a middleman trades 40 1 5 2.07 1.12 COM_RATE – the commission rate a middleman receives per kg 40 10 40 15.30 7.79 COM_DIFF – the commission rate difference compared to the mean commission rate 40 -5.3 24.7 -7.63E-16 7.793 AMOUNT – the average amount of French beans traded by the middleman in kg 40 15600 470000 90192.50 83661.72 REJECTED – the percentage of the harvest that is rejected by the middleman because of a lack of quality

40 5 50 14.78 7.91

Ln(refused/(1-refused)) 40 -2.944 0.000 -1.86292 0.57863 EMPLOYEE – whether the middleman does hire any employees to assist him 40 0 1 0.95 0.22 VISIT – whether the middleman visits his growers 40 0 1 0.92 0.27 ADJUSTED – whether any part of the price is adjusted to the quality the farmers produce

40 0 1 0.32 0.47

INPUTS – whether the middleman supplies inputs to his farmers SEED – whether the buyer provides seed to the middleman 40 0 1 0.83 0.38 DOWNSTREAM – whether any part of the price paid to the farmers depend on the price received by the middleman downstream

40 0 1 0.95 0.22

BUYER – the buyer of the produce 40 0 1 0.68 0.47 SAME_BUYER – the number of years a middleman is supplying the same buyer 40 0 13 2.81 3.18 AGR_QUAN – whether the middleman has an agreement with his buyer regarding the quantity he should deliver

AGR_PRICE – whether the middleman has an agreement with his buyer regarding the price he will receive

AGR_QUAL – whether the middleman has an agreement with his buyer regarding the quality he should deliver

40 0 1 0.80 0.41

AGR_PEST – whether the middleman has an agreement with his buyer regarding the amount and types of pesticides his suppliers are allowed to use

40 0 1 0.73 0.45

ANY_AGR – whether the middleman has any agreement with his buyer regarding the quality he should deliver, the quantity he should deliver and/or the price he would receive

40 0 1 0.80 0.41

PAID_ON_TIME - whether the middleman is paid on the day the buyer promised 40 0 1 0.85 0.36 TOTAL_AMOUNT - whether the buyer takes the whole amount collected (except the 40 0 1 0.37 0.49

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percentage that is rejected because of quality requirements) SAME_FARM – the number of years a middleman is supplying from the same farmers 40 0 7 0.72 1.55 TRACEABILITY – whether the middleman is able to trace the produce 40 0 1 0.88 0.33 RECORDS – whether the farmers that supply the middleman keep records 40 0 1 0.88 0.33 EUREPGAP – whether the middleman has heard from the EUREPGAP protocol STORAGE – whether the middleman has storage facilities 40 0 1 0.18 0.38 STORE_FARM – whether the farmers who supply the middleman have storage facilities

40 0 1 2.50E-02 0.16

export chain of french beans from kenya · the main objective of this study is to analyse the...

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