Impact of coffee certification on small holder coffee farming in Embu County, Kenya

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LUCY W. MURIITHI

A103/200072/2010

A Thesis submitted in Partial fulfillment of the requirements for

the Degree of Masters of Science in Agribusiness Management

and Trade in the School of Agriculture of

Kenyatta University

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DECLARATION

I Lucy Wanjiku Muriithi declare that this thesis is my original work and has not been presented for a degree in any other University.

Signature ……… Date ……… Lucy Wanjiku Muriithi (A103/20072/2010)

Department of Agribusiness Management and Trade

SUPERVISORS

We confirm that the work reported in this thesis was carried out by the candidate under our supervision.

Signature ……… Date……… Dr. Ibrahim Macharia

Department of Agribusiness Management and Trade Kenyatta University

Signature………. Date……… Dr. Elijah Gichuru

Coffee Research Institute

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DEDICATION

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ACKNOWLEDGMENT

I express my sincere gratitude to Coffee Research Institute through the Coffee Leaf Rust Project (CFC/ICO/40) for financing my studies, sincere gratitude go to my supervisors Dr.Ibrahim Macharia and Dr. Elijah Gichuru for their guidance and support during the course of the study. I am also grateful to my colleagues and Coffee Research Institute staff for their support. Special thanks to Mr. Kennedy Gitonga and the staff of Economics and Research Liaison Departments for their dedication in data collection and analysis.

I thank my husband Mr. Newton Mwaniki for his support and inspiration during this study, special thanks to my children Jermaine Kiama and Abigail Nkantha for their patience and love.

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TABLE OF CONTENTS

TABLE OF CONTENTS

TITLE PAGE……….i

DECLARATION... ii

DEDICATION... iii

ACKNOWLEDGMENT ... iv

TABLE OF CONTENTS ... v

LIST OF TABLES ... ix

LIST OF FIGURES ... x

OPERATIONAL DEFINITIONS OF KEY CONCEPTS AND TERMS ... xi

ABBREVIATIONS AND ACRONYMS ... xii

ABSTRACT ... xiv

CHAPTER ONE: INTRODUCTION ... 1

1.0 BACKGROUND INFORMATION ... 1

1.1. CERTIFICATION... 3

1.2 STATEMENT OF THE PROBLEM ... 4

1.3 RESEARCH OBJECTIVES ... 6

1.3.1 MAIN OBJECTIVE ... 6

1.3.2 SPECIFIC OBJECTIVES ... 6

1.4 RESEARCH HYPOTHESES ... 6

1.5 SIGNIFICANCE OF THE STUDY ... 7

1.6 LIMITATIONS AND ASSUMPTIONS OF THE STUDY ... 7

1.7 CONCEPTUAL FRAMEWORK ... 8

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2.0 INTRODUCTION... 10

2.1 APPROACHES TO IMPACT MEASUREMENT ... 10

2.1.1 EXPERIMENTAL DESIGNS ... 11

2.1.2 QUASI-EXPERIMENTAL DESIGNS ... 11

2.1.2.1 BEFORE AND AFTER APPRAISAL ... 12

2.1.2.2 WITH AND WITHOUT APPRAISAL ... 12

2.1.2.3 DIFFERENCE IN DIFFERENCE ... 12

2.1.3 ESTIMATING PROPENSITY SCORE USING BINARY RESPONSE LOGIT MODEL ... 14

2.2 CERTIFICATION STANDARDS ... 15

CHAPTER THREE: RESEARCH METHODOLOGY ... 31

3.0 INTRODUCTION... 31

3.1 STUDY AREA ... 31

3.2 STUDY DESIGN ... 33

3.3 SAMPLE AND SAMPLING DESIGN ... 33

3.4 RESEARCH INSTRUMENTS ... 34

3.5 DATA ANALYSIS ... 35

3.5.1 FACTORS THAT INFLUENCE SMALL HOLDER COFFEE FARMER‟S DECISION TO PARTICIPATE IN CERTIFICATION ... 35

3.5.2 PROPENSITY SCORE MATCHING METHOD ... 38

3.5.3 IMPACT OF CERTIFICATION ON COFFEE PRODUCTIVITY ... 40

3.5.4 IMPACT OF CERTIFICATION ON COFFEE PRICE ... 41

CHAPTER FOUR: RESEARCH FINDINGS AND DISCUSSIONS ... 42

4.1 INTRODUCTION... 42

4.2 DESCRIPTIVE STATISTICS ... 42

4.3 FACTORS THAT INFLUENCE FARMERS‟ DECISION TO PARTICIPATE IN COFFEE CERTIFICATION ... 46

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4.3.1.1. REGRESSION DIAGNOSTICS ... 46

4.3.1.1.1. NORMALITY... 46

4.3.1.1.2. MULTICOLLINEARITY... 47

4.3.1.1.3. HETEROSCEDASTICITY ... 48

4.3.2 ODDS RATIO RESULTS ... 50

4.4 PROPENSITY SCORES AND COVARIATES ... 52

4.5 ASSESSING THE IMPACT OF CERTIFICATION ON FARM LEVEL COFFEE PRODUCTIVITY ... 55

4.5.1 QUANTITY OF COFFEE PRODUCED ... 55

4.5.2 DISTANCE, FARM SIZE, ACQUISITION AND NUMBER OF COFFEE TREES ... 56

4.6 THE IMPACT OF CERTIFICATION ON COFFEE PRICES... 57

4.6.1 IMPACT PROPENSITY ESTIMATE ON COFFEE PRICES ... 57

4.6.2 IMPACT PROPENSITY ESTIMATE ON INCOME FROM COFFEE AND OTHER CROPS... 58

4.6.3 IMPACT PROPENSITY SCORE ON INCOME FROM OTHER CROPS 58 4.6.4 IMPACT PROPENSITY ESTIMATE ON INCOME FROM COFFEE ... 59

4.7 SENSITIVITY ANALYSIS ... 60

CHAPTER FIVE: DISCUSSION ... 62

5.0 INTRODUCTION... 62

5.1 FACTORS THAT INFLUENCE SMALL HOLDER COFFEE FARMER‟S DECISION TO PARTICIPATE IN CERTIFICATION ... 62

5.2 IMPACT OF CERTIFICATION ON COFFEE PRODUCTIVITY ... 63

5.3 IMPACT OF CERTIFICATION ON COFFEE PRICES ... 64

CHAPTER SIX: SUMMARY, CONCLUSION AND RECOMMENDATIONS ... 66

6.0 INTRODUCTION... 66

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6.2 RECOMMENDATIONS ... 68

REFERENCES ... 69

APPENDICES ... 75

APPENDIX 1: HISTOGRAM OF PROPENSITY SCORES ... 75

APPENDIX 2: PERFORMANCE OF DIFFERENT MATCHING ALGORITHMS (LIKELIHOOD RATIO TEST) ... 76

APPENDIX 3: VARIABLES ... 78

APPENDIX 4: EMBU COUNTY MAP ... 81

APPENDIX 5: SMALL SCALE COFFEE FARMERS QUESTIONNAIRE ... 82

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LIST OF TABLES

Table 1.1: Coffee (cherry) production and prices before and after certification ...4

Table 2.1: Literature review………...20

Table 4.1: Descriptive statistics of coffee farming households in Embu North district Sub County ... 43

Table 4.2: Test for multicollinearity ... 47

Table 4.3: Logistic regression results for coffee farmers in Embu County ... 50

Table 4.4: Odds ratios results ... 52

Table 4.5: Performance of different matching estimators ... 53

Table 4.6: Chi-square test for significance ... 54

Table 4.7: ATT for coffee production in kilograms for farmers in Embu County between 2006 and 2007 ... 55

Table 4.8: Distance, farm size, acquisition and number of coffee trees for coffee farmers in Embu County ... 56

Table 4.9: ATT for the prices of coffee in Kenya shillings for farmers in Embu County between 2006 and 2007 ... 57

Table 4.10: ATT for combined income from coffee and other crops for farmers in Embu County between 2006 and 2007 ... 58

Table 4.11: ATT for income from other crops in Kenya shillings for coffee farmers in Embu County between 2006 and 2007 ... 59

Table 4.12: ATT for income from coffee in Kenya shillings for farmers in Embu County between 2006 and 2007 ... 60

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LIST OF FIGURES

Figure 1.1: Conceptual framework in the study………..9

Figure 3.1: Study area ... 32

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OPERATIONAL DEFINITIONS OF KEY CONCEPTS AND TERMS

Certification - Certification is the process through which an organization grants recognition to an individual, firm, process, service, or product that meets certain established criteria. Certification also means that a state of affairs has been stated to be so, by means, most commonly, of a document self-described as a certificate.

Certification and verification standards- Are used as a mean of communicating information about the quality, traceability, social, environmental and financial conditions surrounding the production of goods or provision of services.

Certified farmers- These are farmers who have undertaken certification standards through their cooperatives societies.

Farm household- Defined as a social entity that collectively makes productive and consumptive decisions and often eats from the same granary.

Small scale coffee farmers/small holder farmers- They are defined as farmers with small land unit under coffee between 0-5 acres and they deliver coffee as individuals but they pulp, mill and market coffee collectively in a cooperative.

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ABBREVIATIONS AND ACRONYMS

ATT Average treatment effect on certified group CAFÉ Coffee and Farmers equity

CBK Coffee Board of Kenya currently Coffee Directorate CIDIN Centre for International Development Issues Nijmegen COSA Committee on Sustainability Assessment

CRI Coffee Research Institute

FAO Food and Agriculture Organization of the United Nations FCS Farmers‟ Cooperative Society

FLO Fair Labeling Organization FME Free Market Environmentalism

FT Fair Trade

GAP Good Agricultural Practices GDP Gross Domestic Product

GPP Good Processing Practices

HH House Hold

ICA International Coffee Agreement ICO International Coffee Organization

IFOAM International Federation of Organic Agriculture Movements IISD International Institute for Sustainable Development

ISEAL International Social & Environmental Accreditation and Labeling Alliance

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Kgs Kilograms

Ksh Kenya shillings

MoA Ministry of Agriculture, Livestock and Fisheries NGOs Non-Governmental organizations

ORCA Organic and Resource Conserving Agriculture

Sq. Km Square Kilometers

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ABSTRACT

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CHAPTER ONE: INTRODUCTION

1.0 Background Information

Coffee is one of the most important commodities in the world today. It is the second most traded commodity after petroleum and a vital source of export earnings for any of the developing countries that grow it (Rice & Jennifer, 1999).

Coffee remains a major cash crop and top foreign exchange earner for the Kenyan economy and is ranked 5th contributor to GDP after horticulture, tourism, tea, and diaspora remittance. The industry contributes about 1% national GDP, about 8% of the total agricultural export earnings and up to 25% of the total labor force employed in agriculture (Affa, 2013).

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Kenya‟s coffee cooperative system was formed after the end of World War II and is regulated by the government under the Cooperatives Act. This act requires small holders with less than five acres of coffee to come together and form coffee cooperative societies where they pulp mill and sell their coffee collectively. The societies vary greatly in size, where merging and splitting are common. Some cooperatives have only one wet mill whilst others have more. Factories typically provide services to 300 to 800 members of a society (CIDIN, 2014).

Coffee consumers on the other hand are mostly found in the developed economies, with the Nordic countries showing some of the highest per capita consumption. Sweden, for example, is among the world‟s top coffee consuming nations, with an annual per capita consumption of around 9kg of coffee beans equivalent, or about 3.4 cups of coffee per person per day (ICO, 2015).

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1.1. Certification

Increased awareness among coffee consumers of the impact of their consumption habits on the people and environment in coffee producing countries has resulted in implementation of certification programs in the coffee sector as an assurance of good practices in production and marketing of coffee (Mercy et al., 2010). Sustainable certification initiative creates incentives for farms and firms to improve their environmental and socio-economic performance (Giovannucci & Ponte, 2005). Certification enables the consumer to differentiate among goods and services based on their environmental and social attributes. This improved information facilitates price premiums for certified products, and these premiums, in turn, create financial incentives for farms and firms to meet certification standards.

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Table 1.1: Coffee (cherry) production and prices before and after Certification

Before certification After certification

Year Production/ Kg

Price/ Kg

Year Production/ Kg

Price/ Kg

2003/04 746,888 18.20 2006/07 939,947 31.55 2004/05 405,825 21.50 2007/08 1,060,410 33.15 2005/06 489,846 26.10 2008/09 1,268,358 37.40 2009/10 1,357,392 60.15 2010/11 1,476,851 102.45

Source: (Ndumberi FCS, 2011)

However, the complexity of certification mechanisms, their reliance on cooperative formation to make them economically viable, and the tangible livelihood benefits (and disadvantages) remain poorly understood by the small scale coffee farmers in Kenya (Mercy et al., 2010).

1.2 Statement of the problem

Certification of coffee in Kenya has been suggested as an important tool that every farmer needs to embrace because of its potential benefits, such as increased market access, increased production efficiency and sustainable production (CBK, 2010).

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livelihoods, trade, or the environment? To date, the nature and distribution of these impacts remain largely unknown. Data on the impacts of different initiatives exists but it has been often piecemeal or anecdotal, leaving the major questions of overall sustainability and global effects unanswered. The absence of a more expansive and rigorous information base leaves policy makers, consumers, supply chain decision-makers and, worst of all, producers, increasingly challenged as they attempt to determine when and where investment in such initiatives is warranted (Potts & Sanctuary, 2010).

Although a fast-growing academic literature examines sustainable certification, little is known on whether it actually affects farms‟ and firms‟ environmental and socioeconomic performance. Relatively few studies specifically aim to evaluate the impacts of certification, and many of those that do rely on crude methods that do not correct for selection effects or are likely to bias results for other reasons (Blackman & Rivera, 2010).

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1.3 Research objectives

1.3.1 Main Objective

The purpose of the study is to assess the impact of coffee certification on small holder coffee farming in Embu County.

1.3.2 Specific Objectives

The specific objectives of the study were to:-

(i) To identify and analyze factors that influence small holder coffee farmers decision to participate in certification in Embu County.

(ii)Assess the impact of certification on coffee productivity in Embu County. (iii) Determine the impact of certification on coffee prices in Embu County.

1.4 Research Hypotheses

(i) There are factors that influence small holder coffee farmers‟ decision to participate in certification.

(ii)Certified farmers attain increased coffee productivity than the non-certified farmers.

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1.5 Significance of the study

Coffee certification has been a relatively new approach focusing both small holder coffee farmers as well as large and medium estates in Kenya and the practices are not yet fully understood by all the coffee stakeholders. It is therefore expected that the findings of this study will provide basic information to institutions interested in promotion of certification standards and identify areas of coffee certification that need further research so that advantages and disadvantages of certification are well understood and to make appropriate recommendations to the stakeholders.

The results will also provide useful insights to coffee certification bodies and coffee farmers in Kenya.

1.6 Limitations and assumptions of the study

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1.7 Conceptual framework

The study is conceptualized on randomization theory where all cases are chanced over a finite universe of possibilities (Oscar, 1955). It involves a comparison of certified (treatment) and non-certified farmers (intervention groups), which are alike in all important aspects except for certification, and this eliminates selection bias, balances the group with respect to many known and unknown confounding variables (Dehejia & Wahba, 2008).

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Figure 1.2: Conceptual framework modified from Tina et al., 2009 Education level

Awareness of

farmers

Age of the farmers Gender of coffee farmers

Distance to the factory

Income from coffee

Coffee productivity

Price of coffee

Farmers‟ perception

Coffee certification

Improved income

Improved coffee prices

Better farming knowledge

Improved coffee productivity in coffee and other farm enterprises Independent variable

Dependent variable

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CHAPTER TWO: LITERATURE REVIEW

2.0 Introduction

This chapter reviews literature on impact assessment studies on coffee certification. A crucial review on the impact assessment methods used and findings from these studies and identifying the research gap.

2.1 Approaches to impact measurement

If one could observe the same individual at the same point in time, with and without the intervention, this would effectively account for any observed or unobserved intervening factors and the problem of endogeneity do not arise (Ravallion, 2005). Since this is not happening in practice, something similar is done by identifying non-participating groups identical in every way to the group that receives the intervention, except that the non-participating groups do not receive the intervention.

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The hypothetical question in this impact evaluation exercise is “what would have happened to a household if the household would not have participated in any certification program?” In literature, there are several methods available to estimate the impacts or effects of interventions or development programs.

2.1.1 Experimental Designs

Experimental designs are generally considered the most robust evaluation methodologies. By randomly allocating the intervention among eligible beneficiaries, the assignment process itself creates comparable treatment and control groups that are statistically equivalent to one another, given appropriate sample sizes (Baker, 2000).

2.1.2 Quasi-Experimental Designs

Quasi-experimental (non-random) methods can be used to carry out an evaluation when it is not possible to construct treatment and comparison groups through experimental design. These techniques generate comparison groups that resemble the treatment group, at least in observed characteristics, through econometric methodologies, which include matching methods, double difference methods, instrumental variables methods and reflexive comparison. When these techniques are used, the treatment and comparison groups are usually selected after the intervention by using non-random methods (Baker, 2000).

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intervention only from that of the factors that affect individuals (Foster, 2003). There are different econometric approaches that have been used to avoid or reduce this problem.

2.1.2.1 Before and after appraisal

It addresses changes in outcomes over a specified time period. An example is

where a baseline is compared with an ex-post survey.

2.1.2.2 With and without appraisal

This is where differences are estimated between the treatment and a control

group. In this approach, the situation amongst the control group is the

counterfactual to the situation attained in the target or treatment group.

2.1.2.3 Difference in difference

A combination of the “before and after” with the “with and without” approaches

gives a difference in difference estimator. It compares the change in outcome in

the treatment group before and after the intervention to the change in the

outcomes in the control group. The change in the control group is an estimate of

the true counterfactual i.e. what would have happened to the intervention group if

the intervention had not been implemented. The “difference in difference”

estimator requires data panel which is often unavailable particularly from rural

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The absence of historical data encourages studies on impact assessment that use

cross sectional data to estimate the difference or observed changes between the

treatment and control group.

2.1.2.4 Propensity score matching

Propensity score matching technique is increasingly used to deal with the problem of unobserved differences in an evaluation. The approach solves the “selection” problem (Heckman et al., 1998; Rosenbaum and Rubin, 2002). Matching involves pairing treatment and comparison units that are similar in terms of their observable characteristics. When the relevant differences between any two units are captured in the observable (pre-treatment) covariates, which occurs when outcomes are independent of assignment to treatment conditional on pre-treatment covariates, matching methods can yield an unbiased estimate of the treatment impact (Dehejia & Wahba, 2008).

The idea behind matching is to select a group of non-beneficiaries in order to make them resemble the beneficiaries in everything, but the fact of receiving the intervention (certification). If such resemblance is satisfactory, the outcome observed for the matched group approximates the counterfactual, and the effect of the intervention is estimated as the difference between the average outcomes of the two groups (Caliendo & Kopeinig, 2008).

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difference with respect to an experiment is that in the latter, the similarity between the two groups covers all characteristics, both observable and unobservable, while even the most sophisticated matching technique must rely on observable characteristics only (Rosenbaum & Rubin, 2002). The fundamental assumption for the validity of matching is that, when observable characteristics are balanced between the two groups, the two groups are balanced with respect to all the characteristics relevant for the outcome. This study utilized the propensity score matching method.

2.1.3 Estimating propensity score using binary response Logit model

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2.2 Certification Standards

This section reviews available literature on certification which includes the type of certification standards available in Kenya, their benefits and their commonalities.

Certification has been defined as a way of communicating information about the quality, traceability, social, environmental and financial conditions surrounding the production of goods and services (Fair Trade International, 2009).

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The UTZ certificate was the first to be introduced in the Kenyan coffee industry and currently there are five other certification standards that are being implemented namely: Fair Trade, Common Code for Coffee Community (4Cs), Rain Forest Alliance, Nespresso AAA and Cafe Practices. It is expected that more certification standards will be introduced in Kenya as competition intensifies for the high quality coffees produced in the region (Giovannucci & Ponte, 2005). Good inside coffee certification (UTZ) is a worldwide certification program that sets the standard for responsible coffee production and sourcing. UTZ which means “good” inside in Maya language gives an assurance of the social and environmental quality in coffee production (Burns & Blowfield, 2000).

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Rainforest Alliance works with farmers to improve their livelihood, health and well-being of their communities. The standard is built on the three criteria of sustainability i.e. environmental protection, social equity and economic viability. These criteria are designed to protect biodiversity, deliver financial benefits to farmers, and foster a culture of respect for workers and local communities (Giovannucci & Pierrot, 2010).

The Rainforest Alliance works directly with farmers to teach and encourage good land-use practices. Specifically, the Alliance requires the maintenance or restoration of a certain percentage of natural forest cover, and no impact on natural bodies and flows of water. Certain destructive activities are prohibited. Rainforest Alliance also promotes training, safe working conditions, sanitation and health for farm workers (Giovannuccci, 2006).

Nespresso AAA has been working to protect coffee ecosystems by promoting sustainable agricultural best practices in ecosystem conservation, wildlife protection and water conservation. The Nespresso AAA Sustainable Quality Coffee Program sets out to ensure the cultivation of highest quality coffee in ways that are environmentally sustainable and beneficial to farming communities (Melo & Wolf, 2005).

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are grown, processed, and traded in an economically, socially, and environmentally responsible manner (Jaffee, 2007).

Benefits of certification

The expected benefits from such certification are: strengthening of farmer organizations in terms of good governance and increased efficiency in provision of technical as well as commercial services; greater accessibility of farmers to technical services, farm inputs, credit and hence higher productivity, higher producer prices and higher enterprise and farm incomes, higher disposable incomes, and consequently greater investments on-farm and in other areas/activities that improve the welfare of household members (Mercy et al., 2010).

Key commonalities in certification standards

All certification standards share common principles relating to traceability, social, environmental and economic aspects. Traceability tracks coffee from tree to cup and the flow of payments back to producer. It tracks source of coffee, production conditions, trade up to consumption. Coffee should be traceable from the field through processing and finally to the market. This requires, exhaustive record keeping detailing all production, processing and marketing activities.

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air, and water. All standards advocate need for pollution control (Giovannucci & Ponte, 2005).

Social principle:- Workers welfare including wages, working hours, living conditions, basic education etc. Child labor, safety at work, discrimination, gender equality, sexual harassment and worker‟s rights. Living conditions especially the housing, provision of clean portable water and sanitary facilities.

Quality of coffee:- Certification standards demand certain quality levels for coffee. Quality is determined through samples and assumption that adhering to the standards will result in an improved coffee quality (Giovannucci & Ponte, 2005).

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Authors and Year

Study title Objectives Analytical Method Key findings Gaps

Giovannuci & Potts (2008). The COSAP project: A multi-criteria cost benefit analysis of sustainable practices in coffee

To assess both direct and indirect costs and benefits of sustainability standards in all the 3 areas required (Econ-Environ-Social)

Application of analysis of

variance(ANOVA) to assess statistical relevance

Multi-criteria analysis to provide basic outcomes along core sustainability criteria

Certified farms observed to be better off than their counter parts but the gap is narrow, more than 60% of all certified farms visited perceived their

participation in a

sustainability initiative as having a positive economic impact on their farms

Large sample size are needed to extract more statistically significant results Ruerd & Guillermo (2008). How standards compete: Comparativ e impact of Coffee Certification in Northern Nicaragua

To assess the comparative performance of voluntary and private standards for the welfare of individual small holder families

Propensity score match and difference analysis with nearest neighbor and kernel techniques to identify unbiased impact effects 315 farmers in

Nicaragua that produce coffee under Fair trade, Rainforest and Cafe practices or deliver for independent traders, the effects were compared on income, production and investment.

Fair trade provides better prices compared to

independent producers but private labels outcompete fair trade in terms of yields and quality performance

While fair-trade can be helpful to support initial market

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Authors and Year

Study title Objectives Analytical Method Key findings Gaps

Bolwig et al., (2009).

The economics of small holder organic contract farming in Tropical Africa

To examine the revenue effects of certified organic contract farming for small holders and of adoption of organic agriculture farming methods in a tropical African context

A standard OLS regression. full information maximum likelihood estimate of Heckman selection method

There are positive revenue effects both from participation in the scheme and more modesty from applying organic farming

techniques. Organic certification can boost net coffee revenue by 75% on average.

The usefulness of further research on the economics of organic farming techniques in tropical Africa. Which techniques are most readily adopted and why/ which generate the highest returns and why?

The other concern is a

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Authors and Year

Study title Objectives Analytical Method Key findings Gaps Blackman & Naranjo (2010). Does Eco-certification Have Environmental Benefit Evaluation of the environmental impacts of organic coffee certification in Central Certification Propensity Sore Matching Organic certification improves coffee grower‟s environmental performance, significantly reduces

chemical input use and increases adoption of some environmentally friendly management practices

Certification standards are likely to entail significant costs for producers. Absent high price premiums or other benefits from certification and these costs will discourage

certification and this is reflected in the small number of certified organic producers in the sample Blackman & Rivera (2010). The evidence Base for Environmental and social Economic Impacts of Sustainable Certification Assess the evidence base on the environmental and social economic impacts of sustainable certification of agricultural commodities, tourism operations, fish and forest products

Identify studies of sustainable

certification, searched digital database, citations in relevant studies and library catalogues

Only six studies attempt to construct a credible

counterfactual, two of these studies find out that

certification has significant socio-economics benefits and one study has a significant environmental impact. Three studies find out that

certification has minimal socio economic benefits or generally a net cost

Although a considerable literature examines the link between coffee certification and the Socio-economic and

environmental characteristics of farm households, only six studies attempt to construct a credible counterfactual and therefore can be considered tests of certification‟s causal impact. Most farm-level coffee studies simply compare average characteristics of a sample of certified and non-certified farmers.

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Authors and Year

Study title Objectives Analytical Method Key findings Gaps

Potts & Sanctuary (2010). “Sustainable Markets are growing is Sustainability keeping Pace?” A Perspective on sustainable coffee markets To document the current state of a markets for sustainable coffee, globally and within Sweden, as well as the drivers behind such markets, highlights key opportunities for further improving the sustainability of global market

Survey and analysis of existing literature

The first study found out that Fair trade certification is positively correlated with coffee volume sold and price obtained. But less consistently correlated with indicators of educational and health status. The second study that Fair trade farmers have lower incomes and productivity than convectional farmers.

There is lack of coherence and coverage of the different assessment processes applied across the studies.

The few existing studies on the impacts of sustainability initiatives do not provide representative coverage and reach uncertain conclusions. More representative

counterfactually –based research is needed, in order to ensure maximum

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Authors and Year

Study title Objectives Analytical Method Key findings Gaps

Mercy et al., (2010). The impact of certification on small holder coffee farmers in Kenya: The case of UTZ Certification program

Estimate the impact of certification on income, wealth expenditure of farm households

Assess the

economic situation, willingness to invest, risk attitude and loyalty to their cooperatives

Propensity score marching approach

The results showed that there were some differences between the treatment and control groups that were important indications of the impact of the UTZ certification program.

Success that cut across all Household involved in the two cooperatives that had been UTZ certified was a higher price for coffee.

Households that are UTZ certified sold more coffee than non-certified counter parts had higher house hold savings and made more land investment, in Kiambu cooperatives received more credit, had more off farm income and more capital related investments.

Input costs in coffee in the treatment group were higher than control for Households in Nyeri and vice versa in Kiambu Districts.

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Authors and Year Study title Objectives Analytical Method

Key findings Gaps

Stellmacher & Grote (2011). Forest coffee Certification in Ethiopia Are there differences between the forest production and forest management in certified and non-certified cooperatives? To what extents do the forest coffee producers receive net benefits from certification? To what extent are the forest coffee producers aware of and involved in certification?

Descriptive analysis

Empirical data shows that farmers undertake considerable interventions in the forest ecosystem in order to

increase their coffee yields e.g. by cutting trees which promotes

degradation of the forest ecosystem and biodiversity and occurs irrespective of certification

Empirical data also illustrate practical difficulties of certification for the season for some cooperatives did not pay significantly higher producer prices than non- certified groups Certification is not actively promoted nor understood by those who are certified. None of the interviewed members could answer to the question what certification actually mean.

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Authors and Year

Study title Objectives Analytical Method Key findings Gaps

International Trade Centre (ITC) (2011). Impact of private standards on producers in developing countries A systematic literature review to assess the resources tackling the socioeconomic and environmental impacts of private standards at the producer level in developing countries

Descriptive analysis of the research including the type and timing of

publications, the topics and geographies covered, methodologies applied and

Analyzing the literature using a systematic review approach

Direct impact of participating in private standards in terms of price and profits received by producers tended to be positive though it was not a uniform conclusion some studies found a negative impact on net income for producers while the increased earnings did not

compensate for the additional costs and increased labour involved in complying with standards requisites

Knowledge base that exists today in certification is very thin, sparse and fragile in terms of scope method and depth of coverage.

Many of the studies in the field lack a

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Authors and Year

Study title Objectives Analytical Method Key findings Gaps Cohn & O‟Rourke (2011). Agricultural Certification as a Conservation Tool in Latin America

Impact of

certification of small holder coffee farmers in Western Elsavador

A case study using Semi-structured interviews were conducted with coffee extension agents, development project coordinators, representatives from cooperative societies in Elsavador

Certification poses a greater challenge than other voluntary schemes however because it is often marketed on the basis of the very conservation goals it proposes to achieve. Consumers and other supply chain actors care very little about

conservation outcomes. Thus eco-certification schemes are unlikely to do much but stamp a green seal of approval on business as usual

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Authors and Year

Study title Objectives Analytical Method Key findings Gaps

Bennett & Franzel (2013). Can Organic and Resource-conserving Agriculture improve Livelihoods? Assess the capacity of organic and resource-conserving agriculture to improve the livelihoods of poor small holders in Africa

Reviews of studies done on ORCA. The methodology for this report was to identify using internet search engines including web of science, Google and goggle scholar, following leads from other sources studies about the

livelihood effects of ORCA systems on small-holders in developing countries, analysis is done to understand the factors contributing to the likelihood that small holder farmers adopting ORCA systems could sustainably improve their livelihoods.

Results show that ORCA often outperformed conventional agriculture with respect to yield, net income and food security. Yield improved upon conversion to ORCA in 16 out the 25 cases that reported on it and net income improved in 19 out of 23 such cases

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Authors and Year

Study title Objectives Analytical Method Key findings Gaps

Centre for Internation al Developme nt Issues Nijmegen, (2014).

The impact of coffee certification on small holder farmers in Kenya, Uganda and Ethiopia

Several surveys were done in 2009 and 2013; case studies were also done through focus group discussions. The study combined with and without assessment of certification by

comparing Fair Trade, Utz and non-certified cooperatives, and before and after analysis of certification by

comparing baseline with ex-post survey

The results found in the quantitative data are ambiguous. Involvement in Fair trade certification does not influence production volumes in one case (Kiambaa versus Mecari) and in the other case (Rugi versus Kiama) negatively influences coffee production volumes, compared to non-certification. Utz certified farmers showed higher

production at baseline (2009) compared to NC farmers, but at end line (2013) these effects disappear

In prices Fair Trade farmers(Kiambaa)received higher prices compared to Non Certified farmers in both years and the difference between the two grew significantly over the years

Other Fair-trade certified farmers (Rugi) received lower prices over time.

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CHAPTER THREE: RESEARCH METHODOLOGY

3.0 Introduction

This chapter outlines the theoretical background of the analytical procedures used. The selection of the study area, the sampling design, the research instruments as well as the tools used in the research and analysis are presented.

3.1 Study area

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3.2 Study design

This study adopted the descriptive research design to yield both qualitative and quantitative data in order to interpret effects of coffee certification on household characteristics. Descriptive surveys was used when collecting information about households‟ attitude, opinions, habits or any of the variety of social and education factors (Kombo & Tromp,2009).

This study was structured to provide results that objectively demonstrate certification effects on coffee productivity and prices.

3.3 Sample and sampling design

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n

=

Where

n= required sample size

z = table value from the normal table p = probability of success.

q = (1-p) probability of failure.

e = desired precision 5% (standard value of 0.05).

n

=

= 384

Because of estimating the propensity score matching the sample was increased to 480farmers; Muramuki (82), Rianjagi (71), Kamurai (85), Ivinge (80), Kithungururu (80) and Kirindiri (82).

Data was collected through single farm visit interviews using structured questionnaires administered to respondents by enumerators in November 2012. Respondents were identified with the assistance of the cooperative society‟s staff.

3.4 Research instruments

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certification licenses and audit reports. Quantities of cherry deliveries by the members were obtained from monthly and annual entries of the societies and clean coffee records were obtained from the society and the coffee millers. The researcher personally visited the selected societies, factories and members and administered the questionnaire to obtain primary data.

3.5 Data analysis

The data collected was examined, coded and categorized using Stata software version 12. Descriptive statistics was used to explore the underlying features in the data on coffee certification and its impact on small scale farmers of coffee. Descriptive statistics was used to assess the households‟ characteristics in order to determine the general performance between the non- certified and the certified group. Further, the t test was used to determine if the differences between the two groups were statistically significant at the 1% - 10% significance levels.

3.5.1 Factors that influence small holder coffee farmer’s decision to participate in certification

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propensity scores. In estimating the Logit model (Gujarati, 2004) the dependent variable was certification, which took the value of one if a household was certified and zero otherwise. The mathematical formulation of Logit model was as follows:

(1)

Where, pi was the probability of participation for the ith household and it ranges

from 0-1

Z is a function of N-explanatory variables which is also expressed as:

+ + (2)

Where, į= 1, 2, 3… n

= intercept

= regression coefficients to be estimated or Logit parameter = a disturbance term, and

= certification characteristics which were specified as:- x1 – Gender of household head

x2– Age of household head

x3 – Education level of household head

x4– Distance from the coffee factory (km)

x5– Farmers perception

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x7 – Income from coffee (Kshs)

x8 – Awareness level

Hypothesis one which predicts there are factors that influence small holder coffee farmer‟s decision to participate in certification, would be rejected in the event that the coefficients above did not significantly influence the decision of the farmer to participate in certification.

The probability that a household is none certified is

1 - pi

(3)

The odd ratio can be written as:

-

=

=

(4)

- is the odds ratio in favor of farmers participating in the certification

program. This is defined as the ratio of the probability that a household/family will participate in certification to the probability that a household will not participate in certification.

Lastly, by taking the natural log of equation above the log of odds ratio can be written as:

= ln(

- ) =

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The results were presented as descriptive statistics of dependent and independent variables as used in the study as well as empirical results of logistics regression analysis.

3.5.2 Propensity score matching method

One of the critical problems in non-experimental methods is the presence of selection bias which could arise mainly from the non-random selection of participant households that make evaluation problematic (Heckman et al., 1998). An important problem of causal inference is how to estimate treatment effects in observational studies, situations (like an experiment) in which a group of units is exposed to a well-defined treatment, but (unlike an experiment) no systematic methods of experimental design are used to maintain a control group. It is well recognized that the estimate of a causal effect obtained by comparing a treatment group with a non-experimental comparison group could be biased because of problems such as self-selection or some systematic judgment by the researcher in selecting units to be assigned to the treatment (Dehejia & Wahba, 2002).

Propensity scores are an increasingly common tool for estimating the effects of interventions in non-experimental settings. The approach solves the “selection” problem (Rosenbaum & Rubin, 1983) by identifying from among the non-target

group, households with similar pre-treatment characteristics X as those of the

target group. Any differences in outcomes in the target and control groups are

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In order to achieve objectives 2 and 3, propensity score matching was employed to know the impact of certification on different outcome variables. It is chosen among other non-experimental methods because the treatment assignment is not random and considered as second-best alternative to experimental design in minimizing selection biases (Baker, 2000).

Propensity score matching entails forming matched sets of treated (certified) and untreated (non-certified) subjects who share a similar value of the propensity score (Rosenbaum & Rubin, 1985). The most common implementation of propensity score matching is one-to-one or pair matching, in which pairs of treated and untreated subjects are formed, such that matched subjects have similar values of the propensity score. The matching of households in treatment and control groups is based on a balancing score b(x) which is a function of the

covariates X. The balancing score used is based on the likelihood of participation

in a development program given the observed characteristics X (Mercy et al.,

2010).

Propensity scores are estimated from the initial conditions using a logit model specified as follows:

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Where:

wc = Dichotomous variable taking a value of one if household belongs to a

treatment group and zero otherwise

q = Represents a normal cumulative distribution function b &d = Parameters to be estimated

X = Household characteristics that are hypothesized to influence households

belonging to a treatment group e is an error term

3.5.3 Impact of certification on coffee productivity

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3.5.4 Impact of certification on coffee price

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CHAPTER FOUR: RESEARCH FINDINGS AND DISCUSSIONS

4.1 Introduction

This section presents the statistical analysis of the study results. It consists of four subsections. The first subsection presents the results of descriptive statistics of the different variables considered under the study. The second subsection gives the propensity score matching and the effect of treatment on coffee production and prices. The third subsection is the logistic regression results that utilize the Logit model to assess the factors that significantly influence participation in coffee certification. In the fourth subsection, the results of sensitivity analysis are presented.

4.2 Descriptive Statistics

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Table 4.1: Descriptive statistics of coffee farming households in Embu North district Sub County

Total Certified Non-certified

Variable Mean n Mean n Mean n t-Stat

Household characteristics

Gender of respondent (1 = Male, 0 = Female) 0.61 480 0.59 238 0.63 242 0.64 a

Head of household (1 = Husband, 0 = Wife) 0.79 478 0.80 237 0.79 241 0.53a Marital status of farmer (1 = Married, 0 = Single) 0.94 479 0.97 237 0.91 242 -1.38a * Age of household head (1 = 18-30 years, 2=31-40 years,

3 = 42-50 years, 4 = 51-60 years, 5 = Over 60 years) 3.59 477 3.32 235 3.85 242 -4.7 a

*** Education of household head (1 = None, 2 = Primary,

3 = Secondary, 5 = Tertiary) 2.31 479 2.35 237 2.28 242 1.01

a

Distance to the factory (km) 2.01 478 1.88 236 2.15 242 -1.26 *

Land and Acreage

Size of coffee farm (acres) 1.99 480 1.95 238 2.03 242 -0.80

Acquired land (acres) 1.12 480 1.10 238 1.14 242 -1.29*

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Total Certified Non-certified

Variable Mean n Mean n Mean n t-Stat

Number of mature trees planted in 2012 279.80 480 276.80 238 282.74 242 1.96

Coffee area in acres in 2007 0.63 313 0.59 71 0.64 242 -2.25**

Coffee area in acres in 2012 0.54 313 0.58 71 0.52 242 -1.36*

Planted coffee in last 5 years 1.65 480 1.64 238 1.65 242 -0.15

Number of trees planted 35.55 480 44.63 238 26.84 242 2.39*

Coffee production (cherry) in Kilograms

Cherry produced in 2006/2007 469.41 480 398.11 238 539.53 242 -2.54**

Cherry produced in 2007/2008 344.59 480 401.32 238 288.80 242 2.06**

Cherry produced in 2008/2009 486.59 480 482.32 238 490.79 242 -1.13

Cherry produced in 2009/2010 534.44 480 574.97 238 494.59 242 1.20

Cherry produced in 2010/2011 288.68 480 348.60 238 229.75 242 2.51**

Coffee prices (cherry) in Kenya shillings

Price per kg in 2006/2007 25.46 480 20.97 238 19.85 242 6.3***

Price per kg in 2007/2008 29.14 480 30.86 238 27.46 242 7.1***

Price per kg in 2008/2009 31.78 480 31.42 238 32.14 242 -2.2**

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Total Certified Non-certified

Variable Mean n Mean n Mean n t-Stat

Price per kg in 2010/2011 72.28 480 72.28 238 64.19 242 9.95***

Coffee income in 2006/2007 14905 480 18807 238 11067 242 -1.40

Coffee income in 2007/2008 10342 480 12256 238 8459 242 2.63*

Coffee income in 2008/2009 15603 480 15177 238 15022 242 0.34

Coffee income in 2009/2010 28358 480 30919 238 25840 242 -1.84*

Coffee income in 2010/2011 21685 480 28613 238 14871 242 3.86**

Other crops 2006/2007 26093 313 41846 71 21471 242 2.90**

Other crops 2007/2008 32632 480 43313 238 22127 242 1.83*

Other crops 2008/2009 34531 480 47112 238 22158 242 2.53**

Other crops 2009/2010 37721 480 52656 238 23034 242 2.34**

Other crops 2010/2011 34586 480 45759 238 23898 242 3.00**

Certification

Heard of coffee certification (1 = Yes, 0 = No) 0.61 480 0.99 238 0.33 242 -17a *** Where heard of certification (1 = Management,

2 = Neighbour, 3 = Media, 4 = Government officer 1.22 286 1.07 214 1.67 72 -6.7 a

***

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4.3 Factors that influence farmers’ decision to participate in coffee certification

The research design sought to establish the factors that influenced farmers‟ participation in coffee certification in Embu County.

4.3.1 Logistic Regression

A multiple logistic regression was performed to estimate the relationship between participation in coffee certification and the independent variables under study namely; household head, age of household head, education level, distance from the factory, farmers‟ perception, coffee income and awareness level.

4.3.1.1. Regression diagnostics

Before running the regression model, the following regression diagnostics were carried out in order to ensure that the requirements for regression analysis were met. This included testing for normality, multi-collinearity and Hetero-scedasticity as discussed below.

4.3.1.1.1. Normality

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4.3.1.1.2. Multicollinearity

Multicollinearity means that some of the explanatory variables are not independent but are correlated. When multicollinearity is present it becomes difficult to assign the change in the dependent variable precisely to one or the other of the explanatory variables (Gujarati, 2004).

Tests to determine if the data met the assumption of collinearity were carried out using the Variance Inflation Factor (VIF) and these indicated that multicollinearity was not a concern (1.02 ≤ VIF ≤ 1.3). There were therefore no two variables that were highly correlated and the logistic regression analysis was carried out without further analysis on individual variables (Belsley et al., 1980).

Table4.2: Test for multicollinearity

Coffee certification (0 = non certified, 1 = Certified) VIF 1/VIF

Average rate paid per kg of coffee 1.36 0.74

Age of the household head (1 = Husband, 0 = Wife) 1.23 0.81

Distance to the coffee factory (km) 1.21 0.82

Education (1 = None, 2 = Primary, 3 = Secondary, 4 = Tertiary) 1.20 0.83 Have you ever heard of coffee certification? (1 = Yes, 0 = No) 1.17 0.86 Gender of household head (1 = Male, 0 = Female) 1.12 0.80

Average income coffee in (Kshs) 1.04 0.96

Consider other crops profitable than coffee? (1 = Yes, 0 = No) 1.02 0.98

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4.3.1.1.3. Heteroscedasticity

Heteroscedasticity means that the variance of the residuals is non-constant. A test of homoscedasticity of error terms was carried out to determine whether the logistic regression model's ability to predict the response variable (certification) was consistent across all values of the explanatory variables. This was performed using the Breusch-Pagan/Cook - Weisberg results which failed to support the presence of heteroscedasticity ( = 35.42, p = 0.0000).

The logistic results presented in Table 4.3 below indicate that the estimate logistic regression model is good fit for the propensity matching score (Pseudo R2 = 0.50). From these results, there was statistically significant evidence that participation in the coffee certification program is positively influenced by the following explanatory variables: gender of household head, distance to the factory (both significant at 10% level of significance), price of coffee and farmers‟ awareness level (both significant at 1% level of significance). The study thus finds that households headed by wives, furthest from the factory and who were aware of the existence of coffee certification had a higher likelihood of participating in the certification program. Further, the results indicate that certified farmers received higher prices for each kilogram of cherry delivered to the factory.

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therefore more likely to participate in certification than their older counterparts. This result was statistically significant at 5% level of significance.

The regression parameters as presented in Table 4.3 when inserted into the regression model leads to the predicted logistic regression equation below.

ln ( ) = -3.85 + 0.72x1 – 0.31x2 – 0.23x3 + 0.22x4 + 0.10x5 + 0.22x6 + 0.10x7–

4.23x8

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Table 4.3: Logistic regression results for coffee farmers in Embu County

Covariates Coefficients Std. Error Z P>|z|

Gender of household head 0.72 0.39 1.83 0.07*

Age of the household head -0.31 0.12 -2.51 0.01** Education level of household

head -0.23 0.20 -1.13 0.26

Distance to the coffee factory

(km) 0.22 0.11 1.88 0.06*

Farmers‟ perception 0.10 0.29 0.34 0.74

Price of coffee (Kshs/kg) 0.22 0.04 5.82 0.00***

Income from coffee (Kshs) 0.01 0.01 1.33 0.18

Farmers‟ awareness level 4.23 0.42 -10.05 0.00***

Cons -3.85 1.91 -2.02 0.04

N 472.00

LR (8) 323.96

Prob > 0.00 ***

Pseudo R2 0.50

Log Likelihood -165.15

* Significant at 10% ** Significant at 5% *** Significant at 1%

4.3.2 Odds ratio results

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implied that the families headed by wives had higher odds of participating in the coffee certification.

The odds ratio associated with a one-year increase in the age of a household head was 0.73 (p = 0.01), younger farmers had higher odds of participating in the coffee certification as compared to their older counterparts.

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Table 4.4: Odds ratios results

Covariates Odds Ratio Std. Error Z P>|z|

Gender of household head 2.06 0.81 1.83 0.07*

Age of household head 0.73 0.09 -2.51 0.01**

Education level of household head 0.80 0.16 -1.13 0.23 Distance to the coffee factory 1.24 0.14 1.88 0.06*

Farmers perception 1.10 0.32 0.34 0.74

Coffee price 1.25 0.05 5.82 0.00***

Income from coffee 1.00 0.00 1.33 0.18

Awareness level 0.01 0.01 -10.05 0.00***

Cons 0.02 0.04 -2.02 0.04**

N 472.00

LR (8) 323.96

Prob > 0.00 ***

Pseudo R2 0.50

Log Likelihood -165.15

* Significant at 10% ** Significant at 5% *** Significant at 1%

4.4 Propensity Scores and Covariates

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Different algorithms were employed in matching the certification and control groups; from which the final matching procedure was selected using the equal mean tests criterion (Dehajia & Wahba, 2002) and the pseudo-R2.

Table 4.5: Performance of different matching estimators Performance criteria

Sample size Balancing test Pseudo-R2

Nearest Neighbour

NN(1) 13 0.15 334

NN(2) 18 0.08 343

NN(3) 13 0.40 320

Kernel matching

Band width 0.01 08 0.13 244

Band width 0.25 14 0.24 289

Band width 0.50 18 0.09 256

Radius caliper

Radius 0.01 12 0.21 278

Radius 0.25 10 0.25 242

Radius 0.50 14 0.07 263

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neighbour, Kernel and Radius caliper matching. Following the above criteria, the nearest neighbour algorithm was found to be the best estimator and thus was used to check the balance of propensity scores and covariates, the t-test was used to test the equality of means while the chi-square test in Table 4.6 of the same analysis was used to test the joint significance of the variables that were used in the analysis.

Table 4.6: Chi-square test for significance

Sample Pseudo R2 LR P > Mean Bias Med Bias

Raw 0.499 326.80 0.000 47.2 17.3

Matched 0.217 1270000.00 0.000 39.4 11.8

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4.5 Assessing the impact of certification on farm level coffee productivity The second objective was to assess the impact of certification on coffee productivity per household. This was done by computing the ATT for coffee production.

4.5.1 Quantity of coffee produced

Table 4.7 presents the results of ATT for coffee production from which it was found that at the 10% level of significance, the certified farmers produced significantly more coffee than the non-certified farmers in the year 2008/2009. However, in the year 2010/2011, non-certified farmers produced significantly more coffee than the certified farmers.

Table 4.7: ATT for coffee production in kilograms for farmers in Embu County between 2006 and 2007

Year Quantity of coffee cherry produced in kilograms Certified Non-certified Difference SE t-stat

2006/2007 419.53 352.86 66.67 86.57 0.77

2007/2008 363.27 264.71 98.55 161.75 0.61

2008/2009 534.96 417.98 116.98 114.99 1.02*

2009/2010 439.69 463.27 -23.57 114.19 -0.21

2010/2011 195.16 263.51 -68.35 72.44 -0.94*

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4.5.2 Distance, farm size, acquisition and number of coffee trees

The study considered distance, farm size, acquisition and number of mature coffee trees owned by farmers as factors that contribute to the quantity of coffee produced. Table 4.8 presents the results of each of these estimates. It was found that at the 10% level of significance, the variables size of farm, coffee area in 2012 and number of coffee trees were statistically significantly different between the certified and non-certified farmers. In particular, certified farmers had large farms, larger acreage under coffee and more coffee trees than the non-certified farmers.

Table 4.8: Distance, farm size, acquisition and number of coffee trees for coffee farmers in Embu County

Variable Certified Non-certified Difference SE t-stat

Distance to factory 1.80 1.77 0.03 0.22 0.12

Size of farm 2.02 1.80 0.23 0.18 1.27*

Land acquisition 1.14 1.14 0.00 0.08 0.00

Coffee area in 2007 (acres)

0.584 0.495 0.089 0.106 0.85

Coffee area in 2012 (acres)

0.563 0.404 0.160 0.091 1.77*

Number of coffee trees

55.87 33.65 22.21 14.66 1.52*

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4.6 The impact of certification on coffee prices

The third objective was to evaluate the effect of certification on coffee prices. This was done by computing the ATT for average income from coffee and other crops per household.

4.6.1 Impact propensity estimate on coffee prices

Table4.9: ATT for the prices of coffee in Kenya shillings for farmers in Embu County between 2006 and 2007

Year Price per kg (Kenya shillings)

Certified Non-certified Difference SE t-stat

2006/2007 18.00 19.47 -1.47 0.25 -5.94**

2007/2008 33.00 29.87 3.14 0.95 3.30**

2008/2009 33.00 33.34 -0.34 0.49 -0.70

2009/2010 50.00 54.56 -4.56 1.52 -3.00**

2010/2011 90.55 62.94 27.61 1.86 14.85**

** Significant at 5%

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4.6.2 Impact propensity estimate on income from coffee and other crops

Table 4.10 shows that the certified farmers earned 68.82% more income from other crops than the non-certified farmers. This result was found to be statistically significant. Certified farmers also earned 5.69% more income from coffee than the non-certified farmers. This result is however not statistically significant.

Table 4.10: ATT for combined income from coffee and other crops for farmers in Embu County between 2006 and 2007

Year Average income (Kenya shillings)

Certified Non-certified Difference SE t-stat 2006/2011 50,228.57 15,663.10 34,565.47 11,633.13 2.97** 2007/2011 15,369.94 14,495.20 874.74 3,580.06 0.24 ** Significant at 5%

4.6.3 Impact propensity score on income from other crops

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Table 4.11: ATT for income from other crops in Kenya shillings for coffee farmers in Embu County between 2006 and 2007

Year Income (Kenya shillings)

Certified Non-certified Difference SE t-stat 2006/2007 47,413.88 14,527.76 32,886.12 11,528.50 2.85** 2007/2008 49,403.67 14,891.84 34,511.84 11,708.67 2.95** 2008/2009 49,958.78 15,464.27 34,494.49 11,667.82 2.96** 2009/2010 50,875.10 16,159.18 34,715.92 11,716.12 2.96** 2010/2011 53,491.43 17,272.45 36,218.98 11,741.13 3.08** ** Significant at 5%

4.6.4 Impact propensity estimate on income from coffee

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Table 4.12: ATT for income from coffee in Kenya shillings for farmers in Embu County between 2006 and 2007

Year Income from coffee (Kenya shillings)

Certified Non-certified Difference SE t-stat 2006/2007 7,551.55 7,207.340 344.16 1709.06 0.20 2007/2008 11,987.76 8,353.96 3,633.80 5347.04 0.68 2008/2009 17,653.65 14,108.17 3,545.49 3864.85 0.92 2009/2010 21,984.69 26,794.62 -4809.93 6684.59 -0.72 2010/2011 17,672.03 16,011.86 1,660.17 4810.94 0.35 4.7 Sensitivity analysis

Sensitivity analysis was performed using the Rosenbaum bounding approach (Rosenbaum & Rubin, 2002) as a validity check for unobserved selection bias. All the variables that influence participation in coffee certification and the outcome variables were observed simultaneously.

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Therefore, it can be concluded that the average treatment on the certification (ATTs) were not affected by unobserved selection bias.

Table4.13: Sensitivity analysis using the Rosenbaum bounding approach to check for selection bias

Variable ey = 1.00 ey = 1.25 ey = 1.50 ey = 1.75 ey = 2.00

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CHAPTER FIVE: DISCUSSION

5.0 Introduction

5.1 Factors that influence small holder coffee farmer’s decision to participate in certification

Logistic regression analysis was done to estimate the relationship between participation in certification program and the independent variables under study namely: gender of the household head, age of the household head, education of the household head, distance from the coffee factory, farmer‟s perception, price of coffee, income from coffee and awareness level.

Gender of the household head and distance from the factory (Table 4.3), were found to positively influence decision to participate in certification. Household that were headed by women were more likely to participate in certification. While women do most of the work in the coffee farms, face unequal treatment in leadership and discrimination, yet men receive all the coffee payments. Certification programs such as Fair Trade strive to help women realize their full potential through empowerment trainings which promotes female cooperative membership (Fair trade, 2010).

Figure

Table 1.1: Coffee (cherry) production and prices before and after Certification
Table 1 1 Coffee cherry production and prices before and after Certification . View in document p.18
Figure 1.2: Conceptual framework modified from Tina et al., 2009
Figure 1 2 Conceptual framework modified from Tina et al 2009 . View in document p.23
Table 2.1: Literature review analysis
Table 2 1 Literature review analysis . View in document p.34
Figure 3.1: Study area
Figure 3 1 Study area . View in document p.46
Table 4.1: Descriptive statistics of coffee farming households in Embu North district Sub County
Table 4 1 Descriptive statistics of coffee farming households in Embu North district Sub County . View in document p.57
Table 4.3: Logistic regression results for coffee farmers in Embu County
Table 4 3 Logistic regression results for coffee farmers in Embu County . View in document p.64
Table 4.4: Odds ratios results
Table 4 4 Odds ratios results . View in document p.66
Table 4.5: Performance of different matching estimators
Table 4 5 Performance of different matching estimators . View in document p.67
Table 4.6: Chi-square test for significance
Table 4 6 Chi square test for significance . View in document p.68
Table 4.7: ATT for coffee production in kilograms for farmers in Embu
Table 4 7 ATT for coffee production in kilograms for farmers in Embu . View in document p.69
Table 4.8: Distance, farm size, acquisition and number of coffee trees for coffee farmers in Embu County
Table 4 8 Distance farm size acquisition and number of coffee trees for coffee farmers in Embu County . View in document p.70
Table 4.10: ATT for combined income from coffee and other crops for
Table 4 10 ATT for combined income from coffee and other crops for . View in document p.72
Table 4.11: ATT for income from other crops in Kenya shillings for coffee
Table 4 11 ATT for income from other crops in Kenya shillings for coffee . View in document p.73
Table 4.12: ATT for income from coffee in Kenya shillings for farmers in
Table 4 12 ATT for income from coffee in Kenya shillings for farmers in . View in document p.74
Figure 4.1: Histogram of propensity scores
Figure 4 1 Histogram of propensity scores . View in document p.89

References

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