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ECONOMICS OF MUD CRABS FARMING IN PANGANI:
Is there Significant Income Contribution to the Coastal Community?
By
Janeth Malleo
A Dissertation Report Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Arts (Economics) of the University of Dar-es-salaam
University of Dar-es-salaam September, 2011
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CERTIFICATION
The undersigned certify that he has read and hereby recommend for acceptance by the
University of Dar es salaam a dissertation entitled: Economics of mud crabs farming in
Pangani .Is there significant income contribution to the coastal community? in
fulfillment of the requirements for the degree of Masters of Arts (Economics) of the
University of Dar es salaam.
……………………………………….
Dr. R. Lokina
(Supervisor)
Date:………………………….
ii
DECLARATION
AND
COPYRIGHT
I, Janeth Amanieli Malleo, declare that this dissertation is my own original work and
that it has not been presented and will not be presented to any other University for a
similar or any degree.
Signature: ………………………..
This dissertation is copyright material protected under the Berne Convention, the
copyright Act1999 and other international and national enactment, in that behalf, on
intellectual property. It may not be reproduced by any means, in full or in part, except
for short extracts in fair dealings, for research or private study, critical scholarly review
or discourse with an acknowledgement, without the written permission of the Director of
Postgraduate Studies, on behalf of both the author and the University of Dar es salaam.
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ACKNOWLEDGEMENT
Ebenezer - thus far The Lord has brought me! I am grateful to God almighty who
sustain my life and grant me unaccountable blessings and his support in my studies.
Successful accomplishment of this dissertation report was due to valuable contributions
from several people. This work therefore is a product of many dedicated individuals,
whom it will be impossible to mention each of them by name. I therefore plead them to
accept my compliments beginning with Pangani community who during their busy work
hours received us with courtesy and gave whatever assistance they could. I especially
wish to express my sincere gratitude and appreciation to my mother Jane Mlay and
family members who supported and encouraged me a lot during my studies.
My sincere gratitude and appreciation goes to Dr. Lokina, R. my supervisor for his
guidance, support and supervision. He was firm and critical, wholehearted and patient in
our numerous and intensive discussion. I am also deeply grateful to Mr. Selejio and Mr.
Lameck Kassana for their relentless support, their insights and criticisms were useful in
improving this work. I am particularly indebted to Muumin Abdulaziz from Zanzibar
University for his support in data collection, focus group discussion and physical
observation of crabs’ projects in Pangani.
I would like to thank Swedish International Development Cooperation Agency (Sida),
University of Dar es Salaam, African Economic Research Consortium (AERC) and
Environment for Development – Tanzania for their financial support to my studies. My
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heartfelt thanks go to the academic and administrative staff of the Department of
Economics, University of Dar-es-salaam and Joint Facilities for Electives at Nairobi
who taught me during the academic year 2009/2011 for their dedicated commitment and
friendly atmosphere they shows to me.
I would like to express my gratitude to various institutions that support me in one way or
another to accomplish this report. Special thank to Mr. Kauta, and Mr. Mkapanda and all
fisheries officers of Pangani District Council at Pangani - Tanga for their administrative
and social support. Many thanks to Sea Products-Tanga for granting me opportunity to
visit their industry and the support they provide.
Going back to my roots, special appreciation to my beloved sisters Lillian, Glory, my
brother Obrey, Reagan, my uncle Willbard and my cousins Junior, Caroline, Laura and
Jerry. Many thank to Mr. and Mrs. Mushi for their love and support to be able to
accomplish my studies.
Colleagues, my fellow students and friends also deserve special mention. I acknowledge
sincerely the support of Peter Wankuru, Regina Ndakidemi, Martina John, Anita Jonas,
Lillian Maua, Karen Rono, Oscar Mkude, Caroline Israel, Peter John, Bernard Oyayo
and William Masika. While remaining grateful to all those who have helped, I assume
full responsibility for the findings, interpretations and conclusion expressed in this work.
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DEDICATION
I dedicate this dissertation to my loving mother, Jane Mlay for her love, prayers,
support, encouragement and guidance throughout my life endeavors. I always love you
mama, you’re the best mama and my inspiration. Special dedication to my niece Joan
Filbert for her warmth love and passionate, you have brought happiness in our home Jo.
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ABSTRACT
Sustainable coastal environment management is the current global arguable issue for
poverty alleviation. New opportunities for generating income have been introduced
which increase income to people while conserve environment. Crabs have been
introduced in Pangani after chain analysis proved that the project is viable. Yet the rate
of adopting it as a source of alternative income is low. A better understanding of the
possible driving forces for adoption would help design research policy and mechanisms
to facilitate beneficial outcomes from the process. Furthermore, there are concerns on
income contribution to people who have adopted crabs cage farming comparing to those
who did not adopt. One of the elements which was hypothesized to influence crabs
farming adoption is social capital which has been ignored in many projects where only
financial, physical and human capital were concerns. The objective of the study is to
find the underlying factors for crabs farming adoption and to find if there is significance
income difference between those who adopted crabs farming and those who did not. The
approach applied is the Logistic model which is more appropriate in studying crabs
farming adoption decision since the dependent variable is a binary variable. Generally
the results suggest social capital to be of concern in adopting crabs farming. Government
and other investors need to intervene in the market to improve competition and hence
this will increase price and favor farmers.
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TABLE OF CONTENTS
Certification........................................................................................................................... i
Declaration and Copyright ...................................................................................................ii
Acknowledgement...............................................................................................................iii
Dedication ............................................................................................................................ v
Abstract ...............................................................................................................................vi
Table of Contents ...............................................................................................................vii
List of Tables.......................................................................................................................xi
List of Figures ...................................................................................................................xiii
List of Abbreviations.........................................................................................................xiv
CHAPTER ONE: INTRODUCTION .............................................................................. 1
1.0 Background to the Study........................................................................................... 1
1.1 Statement of the Problem......................................................................................... 4
1.2 Objectives of the Study ............................................................................................ 5
1.3 Significance of the Study ......................................................................................... 5
1.4 Organization of the Study ........................................................................................ 6
CHAPTER TWO: AN OVERVIEW OF PANGANI ..................................................... 7
2.0 Mariculture in Pangani............................................................................................. 7
2.1 State of Tanzania Coast............................................................................................ 9
2.2 Geographical Location ........................................................................................... 11
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2.3 Climate ................................................................................................................... 12
2.4 Topography and Drainage...................................................................................... 13
2.5 Population Distribution .......................................................................................... 13
2.6 Economic Activities ............................................................................................... 16
2.6.1 Agriculture ............................................................................................................ 16
2.6.2 Fisheries ................................................................................................................ 17
2.6.3 Mud Crabs Farming .............................................................................................. 20
2.6.4 Tourism ................................................................................................................. 24
2.7 Economic Infrastructure........................................................................................ 25
2.8 Energy Infrastructure ............................................................................................ 27
CHAPTER THREE: LITERATURE REVIEW........................................................... 29
3.0 Introduction ........................................................................................................... 29
3.1 Theory of Social Capital ....................................................................................... 30
3.2 Effects of Social Capital........................................................................................ 32
3.3 Summary and Conclusion ..................................................................................... 37
CHAPTER FOUR: METHODOLOGY ........................................................................ 39
4.0 Introduction ........................................................................................................... 39
4.1 Theoretical Framework .......................................................................................... 39
4.2 The Logit Model .................................................................................................... 43
4.3 Principal Component Analysis for Wealth and Social Capital .............................. 46
4.4 Characteristics of PCA........................................................................................... 48
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4.5 Hypothesis.............................................................................................................. 49
4.6 Empirical Model Specification .............................................................................. 49
4.7 Definition of Variables........................................................................................... 50
4.7.1 Gender .................................................................................................................... 50
4.7.2 Age of the Respondent ........................................................................................... 51
4.7.3 Labour Force .......................................................................................................... 51
4.7.4 Sex of the Head of Household ............................................................................... 51
4.7.5 Marital Status ......................................................................................................... 52
4.7.6 Education Level of the Respondent ....................................................................... 52
4.7.7 Household Size....................................................................................................... 53
4.7.8 Natural Logarithm of Agriculture Income ............................................................. 53
4.7.9 Agriculture as a Source of Income........................................................................ 53
4.7.10 Role of Social Capital ........................................................................................... 53
4.7.11 Food Reserve......................................................................................................... 54
4.7.12 Poverty .................................................................................................................. 54
4.7.13 Fishing as Source of Income ................................................................................. 54
4.7.14 Natural Logarithm Income of the Individual ........................................................ 55
4.8 Approaches of Study ............................................................................................. 55
4.9 Sampling Technique.............................................................................................. 55
4.10 Sample Data .......................................................................................................... 56
4.11 Sample Size........................................................................................................... 57
4.12 Estimation Technique............................................................................................ 58
4.13 Scope and Limitation of the Study........................................................................ 58
CHAPTER FIVE: EMPIRICAL RESULTS AND THEIR INTERPRETATION.... 59
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5.0 Introduction ........................................................................................................... 59
5.1 Descriptive Analysis ............................................................................................. 59
5.2 Cost Benefit Analysis of Crabs Cage Farming ..................................................... 64
5.3 PCA on Social Capital .......................................................................................... 66
5.4 Variable of Study for PCA.................................................................................... 67
5.5 PCA on Individual Wealth .................................................................................... 69
5.6 Variables of Study for Asset Index ....................................................................... 70
5.7 Estimation ............................................................................................................. 71
5.8 Logit Regression ................................................................................................... 73
5.8.1 Diagnostic Test of Logit Regression..................................................................... 73
5.8.2 Model Specification Test ...................................................................................... 73
5.8.3 Goodness of Fit Test ............................................................................................. 75
5.8.4 Multicollinearity Test............................................................................................ 76
5.8.5 Results of Estimation and Interpretation of the Logistic Regression Results ....... 78
5.9 Conclusion............................................................................................................. 83
CHAPTER SIX: CONCLUSION AND RECCOMENDATIONS .............................. 85
6.1 Main Conclusion ................................................................................................... 85
6.2 Recommendations for Policy ................................................................................ 87
6.3 Recommendations for Further Research............................................................... 88
APPENDIX ....................................................................................................................... 97
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LIST OF TABLES
Table 2. 1 Land and Water Surface Area (km²) by District in the Region, 2006 ..............................................12
Table 2. 2 Population distribution by age groups in Pangani district compared to other districts in Tanga region...............................................................................................................................................14
Table 2. 3 Estimated Distribution of Dependency Ratios in Pangani compared to other Districts in Tanga Region 2006.....................................................................................................................................15
Table 2.4 Estimated Area (Ha) under selected Major Food Crops in Pangani District (2006) .........................16
Table 2. 5: Large scale cash crops production per Districts in Tanga region, 2006. .........................................17
Table 2. 6: Types and number of fishing vessels in Pangani ............................................................................18
Table 2. 7: Weight of Fish Catches (Tons) and Value in Pangani District 2002/03 – 2005/06.........................19
Table 2. 8: Government Revenue from Fishing Industry in Pangani 1999/00 – 2005/06 .................................20
Table 2. 9: Individual Economic Returns for Crab and Seaweed Farming in Tanga ........................................22
Table 2. 10: Crabs production data for different groups in Pangani, October to December 2009 ....................22
Table 2. 11: Roads network in Pangani district by types and class, 2006 .........................................................26
Table 2. 12: Total number of household’s main source of energy for lighting in Pangani ...............................27
Table 2. 13: Main Source of Energy for Cooking (2002) .................................................................................28
Table 4. 2: Keiser-Meyer Oklin test for Principal Component Analysis. .........................................................47
Table 4. 3: Variables definition.........................................................................................................................50
Table 4. 4: Respondent’s Sample Size. .............................................................................................................57
Table 5. 2: Mean income of the sample respondents ........................................................................................61
Table 5. 3: Two-sample t test (of the means) with equal variances. .................................................................62
Table 5. 4: Mean income from different economic activities taking place at Pangani......................................63
Table 5. 5: descriptive statistics of costs –benefit analysis of crabs farming in Pangani. .................................65
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Table 5. 6: The First Four Components of Social Capital PCA ........................................................................67
Table 5. 7: Keiser Meyer-Oklin Test for Social Capital ...................................................................................68
Table 5. 8: First Component for Asset Index Computation ..............................................................................71
Table 5. 9: Descriptive Statistics of Dependent Variables. ...............................................................................71
Table 5. 10: Model Specification Test ..............................................................................................................75
Table 5. 11: The Hosmer and Lemeshow's Goodness-of-fit test.......................................................................76
Table 5. 12: VIF for Multicollinearity test. .......................................................................................................77
Table 5. 13: Odds Ratio of Logistic Regression on Adoption of Crabs Cage Farming as an Alternative Source of Income. ....................................................................................................................78
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LIST OF FIGURES
Figure 2. 1: Total Amount and Value of Crabs Exported to Italy by Tanga Sea Products................................21
Figure 5. 1: Pie Chart of Respondents in Their Respective Villages ................................................................60
xiv
LIST OF ABBREVIATIONS
ACDI-VOCA Agricultural Cooperative Development International-Volunteers in Oversees Cooperative Assistants
SEEGAAD Smallholders Empowerment and Economic Growth through Agribusiness and Association Development
NGO’s Non Governmental Organizations.
SEMMA Sustainable Environmental Management through Mariculture Activities
MACEMP Marine and Coastal Environment Management Project
EEZ Exclusive Economic Zone
URT’s United Republic of Tanzania
WAKAPA “Wafugaji wa Kaa Pangani” Crab Producers of Pangani.
SHG’s Self Help Group
PCA Principal Component Analysis
KMO Keyser Meyer Oklin
UNEP United Nation Environmental Programme.
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CHAPTER ONE
INTRODUCTION
1.0 Background to the Study
The mud crab remains species with good potential for aquaculture due to its fast growth
and good market acceptability and price. There have been rise in demand for the live
mud crabs than the supply in the world market. Because of their delicacy and larger size,
the live mud crabs are always in greater demand and fetch a higher price (Kathirvel
1993). The high price of mud crabs provides a strong incentive for mud crabs fishing as
it can be among the major source of income for the coastal people and contribute to the
national income. At present crab has good market and in the future crab is poised to be
the next potential sea food in the world market among the edible marine crustaceans
after shrimp and lobster (Breinl and Miles 1994) With gradual increase in market
demand through the tourism industry and increasing coastal population, mud crab
culture has the potential of developing significantly as an alternative of improving
livelihood for the people (UNEP, 1998; Omodei Zarini et al., 2004).Mud crab culture
has been successfully introduced in the Philippines, to provide alternative livelihood for
fishers in the villages (Triño and Rodriquez, 1999).
Currently fisheries resources are over exploited and are deteriorating. Where alternatives
avails artisanal fishermen left fisheries to other more promising occupation. Mud crabs
farming is one of the alternatives for a reliable source of income and as a solution of
fisheries overexploitation. In Pangani, fisheries and agricultural activities are the only
2
reliable sources of income. Mud crabs farming was introduced in 2005 under
Smallholder Empowerment and Economic Growth through Agribusiness and
Association Development (SEEGAAD) project as the way of promoting economic
diversity. This project aim at reducing environmentally unsustainable practices and
alleviate poverty within rural coastal communities in Tanzania. Mud crab farming had
not been implemented in the Pangani region prior to SEEGAAD project. Market
assessments revealed that three activities, mud crab cage culture, lobster sheltering and
prawn farming in salt ponds, were potentially highly profitable ventures for smallholder
associations given the high demand both locally and also for the export market.
Consultants volunteer from the Philippines helped to establish trials for mud crab cage
culture in three villages in Pangani. Training was provided to farmers on how to
identify a suitable site for crab cage placement and utilize locally available sources of
feed, including oysters, snails and fish offal.
The local market for the mud crabs is the tourists’ hotel. Most often live mud crabs are
sold to the tourists hotels around the coast and also they are exported to the Far East
where there is only one prominent exporter. China, United State of America, Japan,
Korea, and Thailand (Breinl and Miles 1994) ranked as the top five biggest consumers
of crabs. Frozen muds crabs are exported to Europe but are small size and hence do not
catch the best price.
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Mud crabs farming are mainly done by artisanal fishermen who use local instruments in
catching juvenile mud crabs soon after they settle at low tides which are mainly
harvested on a small scale. But there is very high mortality in early juvenile stages
(>99% per month) and if instead seed-crabs were collected before this high mortality
occurred negative impacts on local populations would be minimized. Larval hatcheries
have been suggested as a long-term solution to meet an increasing demand for seed-
crabs in Tanzania as it will help to stabilize the supply and prices of juveniles and reduce
the cost of production in grow-out farms. As pointed in Fransis (2010) there is slow
development of hatcheries especially for Scylla serrata, for example in Asia, thus it
may take time to realistically expect high technology of hatcheries which could lower
the price of seed crabs to local farmers in East Africa. Because of the high costs of deep
fishing operations and the long distances between Pangani fishing grounds and far east
where the crabs could be marketed, there is potential for developing a large-scale
fishery. It is ascertained that despite the huge demand for the mud crabs in the world
market but still in Pangani the sector has not expanded enough to capture that
opportunity. This can be due to low technology used in mud crabs farming, poor
infrastructure, inadequate capital and also possibly lack of necessary skills to run the
sector and also unreliable information about the market and the potential of the sector.
There is a need for expansion of investment in this sector so as to increase income of the
community around Pangani. Improvement of all these will enable more people to engage
in mud crabs farming as the means of generating income rather than concentrating in
few existed and highly exhausted fishing and agriculture activities.
4
1.1 Statement of the Problem
Poverty in Tanzania coastal communities is still high with majority living below national
basic needs poverty line. There are still limited options of economic activities for coastal
communities which are mainly agriculture and fishing. These sectors have been severely
affected by the unreliable rainfall, land degradation, coastal degradation and
overexploitation of the coast resources. This calls for a need of the coastal community to
adapt other alternative income generating activities to supplement agriculture and
fishing. The coastal land provides an excellent environment for the coastal communities
to diversify their means of livelihood.
Mud crab farming is one of the opportunities which have a high potential of providing
an alternative income generating source and offer employment opportunity to the people
around the coast. The high price of mud crabs provides a strong incentive for mud crabs
farming and it appears to provide potential alternative source of coastal livelihood. In
recent years there has been an effort to introduce mud crab farming in the Tanzanians
coastal communities. One of these areas is Pangani District in Tanga Region. Despite the
promising future market few people have engaged in mud crabs farming and they have
benefited from engaging in this industry. If certain groups of farmers are not adopting
the techniques or are adopting them at a lower rate than the other groups then we need to
determine why, because only by understanding the reasons we will be able to
introduce/develop technique that are appropriate for all. Therefore this study will try to
investigate what are key factors in explaining the decision of household to engage in
5
mud crab farming in Pangani District. The analysis will go further by assessing if there
are any significant income differences among the household who participates in mud
crab farming and those who are not.
1.2 Objectives of the Study
The main objective of the study is to find out if mud crabs farming adoption can increase
cash income and provide an alternative economic activity for sustainable economic
growth of the Pangani coastal community. The specific objectives are
(i) Identifying factors that influence people to engage in mud crabs farming and the
underlying determinants for the expansion of mud crabs farming in Pangani.
(ii) To examine if there is income difference between those who participate in mud
crabs farming and those who do not.
1.3 Significance of the Study
This study is significant in four broad ways. First this study will provide the clear picture
of the poverty reduction policy implication by suggesting alternative source of
employment and income generation to the people of Pangani by adopting mud crabs
farming. Secondly this study identifies alternative marine natural resource which can be
exploited to avoid ecosystem imbalances which arises due to overfishing and the
challenges of declining stock of fishes in the sea. Thirdly, pertaining to academics the
empirical findings of this study are expected to give basis for further studies and as a
reference to other academic works. And fourthly, this study will make contribution to
6
existing literature by reflecting on the potential of natural resources existing in Tanzania
and it will also add to the extension of frontier of existing stock of knowledge in
environmental and natural resources economics.
1.4 Organization of the Study
The rest of this study is structured as follows. Chapter two gives an overview of Pangani
coastal land in Tanzania. Chapter three covers theoretical and empirical literature review
on the subject matter. Chapter four describes the methodology used. Chapter five
presents the empirical analysis results and interpretations. Conclusion and policy
recommendations are provided in chapter six.
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CHAPTER TWO
AN OVERVIEW OF PANGANI
2.0 Mariculture in Pangani
Mariculture is the cultivation of fish or other marine life for food. All mariculture
initiatives in Tanga Region are small scale at the village level and communities have
engaged in the practice largely due to encouragement and support from Non
Governmental Organizations (NGO’s) driven programmes. In Pangani coastal land,
mariculture was developed as the means of empowering coastal communities to improve
livelihoods and sustainable marine ecosystem management. Mariculture project was
implemented by Sustainable Environmental Management through Mariculture Activities
(SEMMA) from December 2005 to December 2009. There after the project was
undertaken by Marine and Coastal Environment Management Project (MACEMP).
Fishing, aquaculture, salt making and harvesting coastal forests and mangroves all offer
potential sources of income, but unsustainable practices over the years have depleted
resources and increased poverty of the people who live in the coastal region of Pangani.
Aquaculture offers employment to about 18,000 (Freshwater fish farming, seaweed
farming and prawn farming) in Tanzania. Also due to growing coastal populations and
persistent foreign interests in marine fisheries are placing increasing pressures on
fisheries and the marine and coastal habitats that support them. Local fishermen and to
much larger extent foreign fleets are fishing in de facto open access conditions in most
of Tanzania’s Exclusive Economic Zone (EEZ) and territorial seas. The objectives of
8
these projects were to improve sustainable management and use of the United Republic
of Tanzania (URT’s) Exclusive Economic Zone, territorial seas, and coastal resources.
Sustainable management and use will be reflected in enhanced revenue collection,
reduced threats to the environment, improved livelihoods of participating coastal
communities and improved institutional arrangements. The project global objectives are
(i) to develop an ecologically representative and institutionally and financially
sustainable network of marine protected areas, and, (ii) to build URT’s capacity to
measure and manage trans-boundary fish stocks.
To implement this number of training of extension agents was conducted in coastal
conservation and mariculture technical skills. Also the project aim to develop four
environmentally sound mariculture protocols in simple Swahili in collaboration with the
private sector, government, and other stakeholders. In order to help coastal community
to generate income in sustainable way the project trained 400 producers in
environmentally sound mariculture protocols and also trained 200 producers in
mariculture conservation guidelines with the private sector and mariculture producers.
To facilitate awareness of environmental policies and regulation among mariculture
private sector stakeholders the project organized focus groups in ten villages and train
ten associations in conflict resolution.
9
Activities were carried out in four focus areas: business skill training, extension support
for production of select products, association building and improving the enabling
environment. Interventions were centered in three coastal districts of Muheza, Pangani
and Tanga municipality in the north, although a few other activities were carried out in
south coastal Regions of Lindi and Mtwara. The Tanga Region was the primary focus
area for the project. Among the activities that were introduced to reduce threats to
environment and as alternative source of income was mud crabs cage culture in Pangani.
This is potentially highly profitable venture for smallholder associations given the high
demand both locally and also for the export market. At the beginning 85 people were
successfully trained in mangrove crab fattening protocols. Since mangrove crab
fattening was new to most coastal producers many producers were hesitant to begin mud
crab farming until they could see success of the early adopters.
2.1 State of Tanzania Coast
Human and environmental condition of the coast of Tanzania is so critical to future
social and economic development. There are many ways in which human and
environmental dimensions of the coast are interlinked. National awareness regarding all
aspects of the coastal and marine environment has significantly improved in the past
decade. Much of the degradation of the inshore marine environment has been caused by
destructive fishing methods and overfishing. The inshore fishery of Tanzania shows
signs of overexploitation and in the vicinity of high population areas shallow reefs are
highly degraded. The demand for fishery resources has been gradually increasing with
10
the increase in population and tourism growth. This has caused an increase in fishing
pressure and the use of gear and techniques that are destructive and cause damage to
reefs. A decline in coastal ecosystem productivity has a direct negative impact on coastal
communities. In most rural coastal communities there is highly linkage between socio
economic wellbeing and the environment as they depend directly on nearby water and
land to generate income and food. Hence, protecting environmental resources that
people depend on for income generation and their livelihood is critical to the survival of
coastal families, poverty reduction and slowing down rural-urban migration. Many
marine reserves, protected areas and coastal management efforts have been established
in the last decade. National guidance for sustainable development of coastal aquaculture
has been adopted.
In order to alter the unsustainable resource use patterns that damage the coastal and
marine environment ultimately requires creating alternative livelihood opportunities,
increasing income and food security and raising education levels. Currently there are a
growing number of community organizations, village committees, and NGO’s that can
provide the foundation for resource management at a local level scale. Different
alternatives sources of income have been introduced along the coastal areas. One of
them is mud crabs farming in Pangani – Tanga region.
11
2.2 Geographical Location
Tanga region is situated at the extreme north-east corner of Tanzania between 4o and 60
degrees below the Equator and 370-39010'degrees east of the Greenwich meridian. The
region occupies an area of 27,348 sq Kms, being only 3% of total area of the country out
of which 572 km² are covered by water which is equivalent to 2.1% of the total area of
Tanga region. Tanga shares borders with Kenya to the north, Morogoro and Coast
regions to the south, Kilimanjaro and Arusha regions to the west. To the east it is
bordering the Indian Ocean. Mligaji River also forms a large part of the border in the
South. Tanga is the most northern coastal administrative region in Tanzania extending
approximately 180 km south of the Kenyan border. The region consists of eight districts
of which Pangani district is one of them, others are Muheza, Handeni, Tanga, Kilindi,
Korogwe and Lushoto. Pangani, situated 50km south of Tanga in the north-Eastern
corner of Tanga with a total of area of 1425km2 of which 406km2 is covered by water
bodies equivalents to 71 percent of the total region’s water body. Pangani can be reached
via Muheza (42km) or Tanga 47km. Table 2.1 describes the districts in the Tanga
regions and the distribution of their total area as of 2006. Though Pangani district is
having more than 70% of the total regions’ water body, her total land area is only 5
percent of the whole land area of Tanga region.
12
Table 2. 1 Land and Water Surface Area (km²) by District in Tanga Region, 2006
District Land Area Water Area Total Area Percentage Pangani 1,019 406 1,425 5.2 Muheza* 4,818 104 4,922 18.0 Tanga 474 62 536 2.0 Handeni 6,112 Negligible 6,112 22.4 Kilindi 7,091 Negligible 7,091 25.9 Korogwe *** 3,756 Negligible 3,756 13.7 Lushoto 3,500 Negligible 3,500 12.8 TANGA REGION 26,770 572 27,342 100.0
* Includes Mkinga district
*** Includes Korogwe Town Council and Korogwe District Council
Source: Tanga Regional Social Economic profile: 2008
2.3 Climate
Pangani experiences moderate temperature and rainfall climate. The warm season
normally runs from October to February. Generally, the district experiences two major
rainfall seasons, that with long rains between March and May and short rains between
October and December. Temperature in Pangani ranges from 14.5 to 31.5 (Celsius). The
zone receives moderate rains with average annual precipitation ranging from 800mm to
1,400mm.
The tropical western Indian Ocean is the major source of moisture into Pangani and
winds over Somali, coastal areas, thermocline dome and tropical southwestern Indian
Ocean (East Madagascar and Mozambican Channel) advert seasonally this moisture into
Pangani. But due to climatic changes which is a global incidence, in Pangani the
prediction shows some variation in rainfall as years goes on.
13
2.4 Topography and Drainage
The topography of Pangani is characterized by coastal lowlands with varying degrees of
soil texture and fertility. It is located between 0-150 Meters above sea level. The major
soil types that are found in this zone include sand and sandy-clay. A variety of crops are
grown in this zone. They include sisal, coconuts, cashew nuts, maize, cassava and
paddy.
2.5 Population Distribution
The indigenous people of Pangani are mainly of Bantu origin and the tribes that
dominate in Pangani district are Zigua, Makonde and Yao. Besides these there are many
people from different origins and tribes who constitute a significant section of the
population of the region. Basing on the last national census of 2002, Pangani have a
population of about 43,920 of which 22,094 are males and 21,826 are female. The
population growth rate of Pangani is about 1.1, which far below the national average of
2.9. Because of this low population growth rate, Pangani district is the least populated
district in Tanga region. The population density of 32.2 people per square Km, however,
is on the higher side for a district when you compare with other district in the region, the
density is almost close to the national average of 39 people per square Km. From the
region, Tanga district is the most populated with population density of 488.1 people per
square Km followed by Lushoto district with population density of 124.9 people per
square Km. When comparing with all districts in Tanga, Kilindi is the least populated
with population density of 23.3 people per square Km. The structure of age groups in
14
Pangani represent typical characteristics of developing country age structure where the
dominant age group is the young that is 0-4 and 5– 14 age groups. This is due to high
population growth rate and led to high dependence ratio in the society. Table 2.2
summarizes the details of population distribution in Tanga region. As can evident in the
table Pangani district has the lowest population figure when compared with other
districts in Tanga region.
Table 2.2: Population Distribution by Age Groups in Pangani District Compared to Other Districts in Tanga Region
Age groups (Years) District 0 – 4 5 – 14 15 – 44 45 - 64 65+
Pangani 5,726 11,207 19,531 4,991 2,465 Muheza* 40,220 75,240 115,274 31,285 16,386 Korogwe*** 38,966 70,790 108,260 28,499 13,723 Tanga 29,991 60,957 119,305 23,335 9,052 Handeni 44,086 71,001 101,358 22,136 10,052 Kilindi 26,852 42,097 56,401 11,678 6,764 Lushoto 69,166 134,176 155,427 40,167 19,716 Tanga region 255,007 465,468 675,556 162,091 78,158
* Contains Mkinga District
*** Includes Korogwe Town Council and Korogwe District Council
Source: Tanga Regional Social Economic profile: 2008
The estimated dependence ratio in Pangani (2006) is 83.3 (See Table 2.3) where
economically active group (15 – 44 and 45 - 64) totaled 25619 while dependents (0-14
and 65+) totaled 21331.
15
Table 2.3: Estimated Distribution of Dependency Ratios in Pangani Compared to Other Districts in Tanga Region 2006
Economically Active group District 15 – 44 45 - 64 Total
Dependants
(0 – 14 & 65+)
Dependency
ratio
Pangani 20,405 5,214 25,619 21,331 83.3
Muheza* 148,248 33,074 181,322 113,004 62.3 Korogwe*** 113,551 29,892 143,443 129,514 90.3 Tanga 128,634 25,160 153,794 107,819 70.1 Handeni 114,523 25,011 139,534 141,393 101.3 Kilindi 64,721 13,401 78,122 86,677 110.9 Lushoto 162,379 41,964 204,343 233,035 114.0 Tanga Region 752,461 173,716 926,177 832,773 89.9
* Contains Mkinga district
*** Includes Korogwe Town Council and Korogwe District Council
Source: Tanga Regional Social Economic profile 2008
From Table 2.3, the overall dependence ratio in Tanga region is 89.9 where Muheza has
the least dependence ratio of 62.3 and Lushoto has the highest dependence ratio of
114.0,
The term household refers to a group of persons who live together and share living
expenses. Usually these include husband, wife and children. In population census the
definition includes other relatives, boarders, visitors and servants as members of the
household, if they were present in the household on the census night. It reveals that the
Region’s average household size was 4.6 people per household in 2002. Pangani had the
least average household size of 3.9 with the estimated 11,765 households in the district.
16
2.6 Economic Activities
2.6.1 Agriculture
Food crops production in Pangani is still low depending only on seasonal rainfalls
without irrigation schemes. This has reduced income from agriculture as this sector has
been affected more by frequent drought mainly because of the climatic variation.
Currently paddy production has been much affected due to lack of enough rains.
Table 2.4 Estimated Area (Ha) under Selected Major Food Crops in Pangani District (2006)
Type of food crops Estimated area (Ha)
Maize 3,200
Paddy 532
Cassava 532
Sweet potatoes 25
Legumes/pulses 100
Total area 6,707
Source: Fisheries framework survey (2009)
Cash crops grown in Pangani are mainly coconut, cashewnut and sisal. Coconut and
cashewnut are cash crops grown by small holders in Pangani. While sisal is grown by
large scale producer in Sakura and Mwera plantations owned by Amboni plantation.
Pangani is major producer of sisal in Tanga region.
17
Table 2.5 : Large Scale Cash Crops Production per Districts in Tanga region, 2006 Crops Districts Area (ha)
Tea Muheza Lushoto Korogwe
2,200 442 681
Rubber Muheza 318
Sisal Pangani Lushoto Korogwe
14,286.75 910
4,959.5 Moringa Handeni 200
Coffee Lushoto 190
Source: Tanga Regional Commissioner’s Office, 2006
From table 2.5, in Tanga, major cash crops grown in large scale are Sisal, Moringa,
Coffee, Rubber and Tea. Pangani is the major producer of sisal in Tanga followed by
Korogwe. Coffee is grown in Lushoto in small amount.
2.6.2 Fisheries
Fishing is one of the major economic activities in Pangani district. It is mainly carried
out along the Indian Ocean and river Pangani. The district has a very long coastal-line
with Villages totally depend on fishing. In these villages, agriculture and others
economic activities such as livestock keeping are carried out in small scale only. Fishing
is carried out in the continental shelf which is fairly narrowed, between Tanga and
Pangani of about 3 to 5 nautical miles towards ocean from the beach. The stretch widens
in the northern part of Tanga and southern part of Pangani up to 25 nautical miles. Major
types of fish include Tuna, Kingfish, Sailfish, blue fish and other marine products in the
Region are crustaceans (Lobsters, Prawns and Crabs) and octopus. Since water bodies’
18
cover the large part of Pangani district then marine fishery is the dominant economic
activity. The fishery inshore is mainly artisanal and small-scale fisheries using relatively
small amount of capital and fishers have usually a traditional involvement with small
fishing vessels, making short fishing trips close to shore mainly for local consumption.
The artisanal fisheries are the important fisheries as it lands most of the catches (it
contributes to about 98% of the country’s total catch, (Annual Statistics report 2008).
Fishers support majority of the coastal community either as part time or fully engaged
fishers and they spread all along the shores since it is an open access resource. Types of
fishing vessels observed during 2009 fisheries survey are Dugout canoe, Ngalawa,
dhow, boat and catamaran.
Table 2. 6: Types and Number of Fishing Vessels in Pangani
Type of fishing vessel Number Boat 7 Dugout canoe 56 Rigger/ Ngalawa 149 Dhow 59 Catamaran 0 Total 271
Source: Fisheries Frame Survey (2009)
From Table 2.6, the common fishing vessel used by majority in Pangani is
Rigger/ngalawa. There are only 7 boats used in fisheries at Pangani. The total number of
registered vessels observed during the survey was 100 while the number of unregistered
vessels was 171 equivalents to 63.1 % of the total number of vessels observed during the
survey.
19
Fishing is the major source of income and employment in Pangani district. Random
sampling of age structure to few fishers in Pangani district was done and the result
shows that most fishers belong to 26 – 35 years old. From the fisheries the 2009 frame
survey a total of 930 fishers were registered, out of which 740 are fishers using vessels,
and 190 foot fishers. During the same survey 143 seaweed farmers were registered in the
district. . The catches are processed locally by smoking or sun drying. However, a
significant part of fish is sold when it is still fresh. There are two companies by now
doing processing of selected finfish for export but mainly exporting Octopus, Squids and
Cuttle fishes, Lobster and Crabs .The two companies are Tanga Sea Products (Tanpesca)
and Bahari Food.
Table 2. 7: Weight of Fish Catches (Tons) and Value in Pangani District 2002/03 – 2005/06
Years Tons Value Tsh(000) 2002/2003 59.3 17,150,774 2003/2004 42.7 16,335,639 2004/2005 57.8 29,843,289 2005/2006 38.6 22,815,197
Source: Tanga Regional Social Economic Survey, 2008
Table 2.7, shows fish catches (in Tons) and its values for four years. The table shows
that the fisheries registered fluctuating trends since 2002/2003 with the pick in
2002/2003 and the lowest catch in 2005/2006. The value of the catch however, has been
increasing probably due to inflationary pressure. Fishing also provides revenue to the
20
government through fishing licenses, registration of fishing vessels, trading licenses,
transportation permits and marketing levy.
Table 2. 8: Government Revenue from Fishing Industry in Pangani 1999/00 – 2005/06
Years Revenue
1999/00 2,228.08
2000/01 2,233.99
2001/02 2,008.93
2002/03 2,213.88
2003/04 4,095.22
2004/05 2,989.37
2005/06 3,540.05
Source: Fisheries Framework Survey (2009)
Table 2.8 shows the amount of revenue earned by the Government from fishing industry
for seven years. The highest revenues to the government were earned in 2003/2004.
2.6.3 Mud Crabs Farming
Mud crabs cage culture in Pangani district is mainly done in groups due to high
operating costs, whereby the activities are conducted jointly. At the beginning of the
project five groups were formed which are WAKAPA, KIWAVU, BWENI1, BWENI 2
and KIPUSA. Currently there are about three groups after collapsing of two other
groups. Crabs were mainly sold to Tanga Sea products where they process and then
exporting them to Italy while frozen.. The main reason for the collapse of the other two
21
groups was due to low price existing in the market while its highly costing to buy
juveniles and feed them till marketing time. Also since the nature of the crabs market in
Pangani is monopsony (single buyer – Tanga Sea Products) this has reduces competition
and led to lower price in the market.
Figure 2. 1: Total Amount and Value of Crabs Exported to Italy by Tanga Sea Products
Source: Tanga Sea Products
Figure 2.1 shows total exports of crabs done by Tanga Sea Products abroad and their
value in dollars units from 2007 to 2010. From the figure we see Tanga Sea Products
exported the highest amount of crabs in 2008 valued $72,240. But starting from 2009
there was a decrease in amount of frozen crabs exported and these can be due to decline
of production from crabs farming projects.
22
Commercial crab production demand in Tanga is relatively high due to a local private
buyer/exporter who is willing to purchase 500 crabs a week weighing between 500 gm
to1000 gm each (Sachak pers comm.). Another buyer from Dar es Salaam is now buying
large (>1000gm) crabs for export. Compared to seaweed farming, economic analysis has
shown that crab farming has higher returns than seaweed farming (See Table 2.9).
Table 2. 9: Individual Economic Returns for Crab and Seaweed Farming in Tanga
Tsh. USD No. of Units Invest. days Crabs 125,400 114 100 crabs 45 Seaweed 44,000 20 100*20m lines 30
Source: SEMMA Final Report (2009)
From Table 2.9 crabs farming project has high returns compared to seaweed where
farming 100crabs for 45days yield 114USD while farming seaweed for 30days yields
only 20USD. Therefore although both projects have positive returns but crabs earns
relatively more compared to seaweed.
To increase profits and optimize time consumption as a resource, farmers are being
encouraged to fatten crabs individually rather than in a group. But in Pangani crabs cage
farming is mostly conducted in groups than individually and this can be due to high
initial costs of crabs farming. The other reason for them to practice crabs farming in
groups was due to the financial assistance they have been given requires them to be in
groups rather than individually.
23
Table 2.10: Crabs Production Data for Different Groups in Pangani, October to
December 2009
Association name.
Village Number of members
Kg of Crabs sold
Tshs.
revenue
USD revenue
WAKAPA Pangani 9 900 5,400,000 $4,219 KIWAVU Pangani 12 138 828,000 $647 BWENI 1 Bweni 7 24 144,000 $113 BWENI2 Bweni 3 36 216,000 $169 KIPUSA Kipumbwi 15 565 3,390,000 $2,648 46 1,663 9,879,000 $7,796
$1 USD =1,280Tshs.
Source: SEMMA Final Report (2009)
From the Table 2.10, shows that WAKAPA group earn the highest revenue from crabs
sold in the months of October to December 2009. Revenue generated from all five
groups with 46 members totaled to $7,796. Hence the average revenue per individual
members is $169.5 (Tshs 216,932). This is an average of Tshs. 72,310 per month, which
is slightly less than the government minimum wage of Tshs. 80,000 in 2008/2009.
However, given the fact that mud crab farmers in most cases engages in other activities
such as farming or fishing could be relatively better off than a government minimum
wage earner who would be working for about 8 to 9 hours a day.
24
Figure 2.2: Share of crabs from Pangani exported by Tanga Sea Products to Italy
Source: Tanga Sea Products
From Figure 2.2, the share of crabs’ exports from Pangani was high in 2007 and 2008
but decrease by almost half in 2009. And this can be due to the collapse of two groups of
crabs farming in Pangani which are Bweni 1 and Bweni 2.
2.6.4 Tourism
Pangani district has potential tourism attraction as one of the historical town since 18’s
century under the control of Arabs. It is rich with historical sites and structures which
reflect the influence of Arabs, Germans, Indians and British in East Africa. Till today
there are number of old administration buildings which were used during colonial era.
Of all the ports on the north coast, Pangani has retained the most traditional Swahili
character. It's a beautiful area with the mouth of the Pangani River and an amazing
beach stretching off into the distance. Even though archaeologists have found the
remains of small 15th century villages on the low hills just north of Pangani, the modern
25
town came into existence relatively recently in the nineteenth century. The Zanzibar
sultans held power and used Pangani as a major terminus of caravan routes to the deep
interior. Pangani became an important hub for the slave trade, shipping captives (taken
in the wars at the fall of the Shambaa kingdom in the Usambara Mountains) to the
cloves plantations in Pemba and Zanzibar. After the Sultan of Zanzibar signed treaties
with Great Britain banning the shipping of slaves by sea 1873, Pangani became a center
for smuggling slaves across the Pemba channel to evade British warships. In 1888
Pangani was the center of an armed resistance to the German colonial conquest of the
entire mainland Tanzanian coast. Several historical sites in and around the town remain
as reminders of the Arabic roots and the later colonial eras in Tanganyika. The district
headquarters is the most significant building remaining from the period of Zanzibari
rule.
Pangani district has attractive white sands beaches and splendid coral reefs which harbor
a great diversity of tropical marine life. Different hotels facilities like hotels, lodges and
camp sites were well developed in Pangani along the beaches and they are all active.
Saadani National Park, Tanzania’s only beach plus wildlife park in close proximity to
Pangani. The tourism potential in Pangani is enough to turn around lives of the residents
in the districts.
2.7 Economic Infrastructure
Pangani River is an important link between the district with Saadani Game Reserve and
the road that links Tanga and Coast regions. The road has been earmarked for facelift in
26
order to make it an alternative route to Dar es Salaam - Tanga - Moshi and Arusha and
ease traffic along the Dar es Salaam-Morogoro- Arusha highway. The pontoon,
christened MV Pangani II, with a capacity to carry 100 passengers and 50 tones of cargo
is set to ease movement of goods and people. The roads are important as they link
different parts of the district and in particular help transportation of different produce to
the processing area and other economic activities around the district.
Table 2.11: Roads Network in Pangani District by Types and Class, 2006
Road type Length (km) Paved (km) Unpaved (km) Trunk 0 0 0 Regional 93 0 93 District 109.4 0 109.4 Feeder 128.4 0 128.4 Urban 14.3 0 14.3 total 345.1 0 345.1
Source: Tanga Regional Social Economic Survey, 2008
Note: Trunk and Regional roads are under the responsibility of TANROADS, while
District, Feeder and Urban are under the district Councils.
Table 2.11; show that Pangani has roads with total length of 345.1 which is all unpaved.
In Pangani only 34.7% (119.75km) of all roads are passable the whole years. The other
remaining 65.3% are not passable during rain seasons. Pangani district has an Air strip
which belongings to private individuals or institutions in the district such as
MASHADO, USHONGO, KWA JONI and SAADANI National Park.
27
2.8 Energy Infrastructure
Energy is an important economic infrastructure in any area. It is a source for industrial
development as well as domestics use. Source of energy for lighting is mostly
determined by economic power of the residents of particular area. In Pangani electricity
as a source of lightning
Table 2. 12: Total Number of Household’s Main Source of Energy for Lighting in Pangani
Source of lighting Total number households Percentage Electricity 1,323 11.57 Lamp 701 6.13 Presence lamp 144 1.26 Firewood 366 3.20 Candle 20 0.17 Wick lamp 8,870 77.58 Solar 22 0.19 Other 8 0.07 Total 11,434 100.00
Source: 2002 Houses and Population Census
Table 2.12, it shows that while 78% of the population of Pangani use wick lamp as a
source of lighting, about 12% of the population are connected with Electricity, the figure
compares well with the national average. Solar as a source of lighting is used by only 0.2
% of the households’ in Pangani. Like many other households in Tanzania, majority of
households in Pangani district use firewood as the major source of energy for cooking
followed by charcoal (See Table 2.13).The table shows that about 91% depends on
firewood for cooking. The figure is identical to the national average of 90%. With 5% of
28
the population depending on charcoal for cooking, this will mean 96% of the population
depends on forest as the source of cooking energy and this brings doubts about
environmental sustainability as there is clear evidence of clear cutting of trees and
mangrove forests for charcoal burning and firewood. This has resulted into the
continuous decrease in the amount of rainfalls and this will lead to further
desertification. Only 0.5% of the population uses electricity of cooking.
Table 2. 13: Main Source of Energy for Cooking (2002)
Main source of energy for cooking No. of households Percentage
Electricity 57 0.50 Paraffin 150 1.31 Gas 8 0.07 Firewood 10,411 91.05 Charcoal 631 5.52 Others 79 0.69 Not applicable 96 0.84 Total 11,434 100.00
Source: 2002 Population and Housing Census, Regional Profile
29
CHAPTER THREE
LITERATURE REVIEW
3.0 Introduction
This chapter focuses on studies that have been done on economics of mud crabs in
different countries. It highlights the methodology and variables that have been used, and
how each study is related to the problem under study. Some critiques that have been
raised against some of the methodologies used and gaps are also addressed. Mud crabs
of genus Scylla, also known as green crabs or mangrove crabs constitute an important
secondary crop in the traditional prawn or fish culture systems. They are distributed in
the tropical and subtropical regions of the indo-pacific. Large market for mud crabs are
found in Hong Kong, China, Singapore, Japan and Malaysia (ACDI-VOCA 2005). The
importance of live mud crabs as an export commodity has opened up great opportunities
for coastal communities in crab farming.
As literature suggests the adoption phases are classified in five groups; (1) awareness,
understanding about something new, (2) interests, being interested in something
new/being active to search for information, (3) evaluation, evaluating and measuring
distributed innovation, (4) trial, trying phase to get new innovation, and (5) adoption,
receiving/applying/implementing of innovation based on a trial in small scale (Rogers
(1983). It is shown that people who adopt innovation at the beginning phase on diffusion
process possess some characteristics. They, generally tend to have higher in educational
background, they manage agricultural units in larger scale (they are owner), and the
30
management of agricultural crops commonly are more specific compared to agricultural
crops managed by other farmers. The study concerning the adoption of rice technology
conducted by Suhendar (1997) tells us that the factors that influence the adoption level
of rice technology are land–owning, farmer’s age, and farmer’s economic motivation.
Before making a final decision about economic activities on coastal land, farmers
consider a number of the things. The main consideration in the adoption process is the
price of product (70%) and the cost of production materials (85%). It is indicated that
most of farmers take into account the input and output of agricultural crops cultivated.
Most farmers (62.5 %) still have the authority to make the final decision in the adoption
process; family decision (32%), group decision (27 %) and other factors influence other
decisions. The main motives in adopting the economic activity on coastal land are to
increase farmers income (80%) and to follow the farmers companions who live in the
same or neighborhood villages 72.5 %(Adrianzén 2009).
3.1 Theory of Social Capital
Social capital has been argued in literature to have an important role in adoption
decision at the household level (see for example, Nyangena, 2005). The main argument
put on this is the fact that social capital has the potential of supplementing existing
extension services, thus people are considered as the principal agents of change in
provision of extension services. Social capital is also thought to reach and include the
majority of poor farmers, thus increasing adoption of innovations. Robert M. Solow
(1997) define social capital as willingness and capacity to cooperate and coordinate the
31
habit of contributing to a common effort even if no one is watching where payoffs are in
terms of aggregate productivity. Social capital is made up of ‘the norms and networks
that enable people to act collectively’ (Woolcock and Narayan, 2000, p. 226). As
Ostrom (1997, 2000) has pointed out, ‘social capital is not as easy to find, see and
measure as is physical capital. Social capital consist of two elements in commons which
are, they all consist of some aspects of social structures and they facilitate certain actions
of actors within the structure, also social capital is productive making possible the
achievement of certain ends that in its absence would not be possible.
All social relations and social structures facilitate some forms of social capital. Certain
kinds of social structure however are especially important in facilitating some forms of
social capital like closure of social networks and voluntary social organizations that are
brought into being to aid some purpose of those who initiate them. Also there is social
capital in the family for child’s development and also social capital outside the family
(Nyangena 2005). In explicating the concepts of social capital three forms were
identified, obligations and expectations which depend on trustworthiness of social
environment, information flow capability of the social structure and norms accompanied
by sanctions (Putnam 1993). Various farmers’ ages, educational backgrounds, assets,
farmer's mobility and social status have caused the time period of adoption process to
each person to be different. Young people who have high educational backgrounds,
many assets, high mobility, high social status will have a faster tendency (time period of
adoption shorter) to adopt the process of new innovation (Rogers, 1993). The adoption
process for the innovation is influenced by internal factors and external factors. The
32
external factors include farmer’s access to information sources (awareness) and the role
of the leader's opinion in their community.
3.2 Effects of Social Capital
There is growing evidence that social capital can have an impact on development
outcomes including growth, equity and poverty alleviations (Grootaert 1996).
Associations and institutions provide an informal framework for sharing information,
coordinating activities and making collective decisions. Bardhan (1995) argues that what
makes this informal model work is peer monitoring, a common set of norms and
sanctions at the local level.
Formal and informal institutions can help avert market failures related to inadequate or
inaccurate information. Economic agents often make inefficient decisions because they
lack needed information or because one agent benefits from relying in incorrect
information to another. Incorrect decision led to un-optimal decisions due to uncertainty.
Social capital helps in coordinating activities. This is because mostly uncoordinated or
opportunistic behavior by economic agents can also led to market failure due to lack of
formal or informal means of imposing equitable agreement of sharing projects (Putnam
1993). Dasgupta (2002) argues that associations reduce opportunistic behavior by
creating a framework within which individuals interact repeatedly and enhancing trust
among members. Also social capital helps in making collective decision necessarily for
the provision of public goods and management of market externalities.
33
Adrianzén (2009) studied The Role of Social Capital in the Adoption of Firewood
Efficient Stoves in the Northern Peruvian Andes and the results in this paper indicate
that the effect of village adoption patterns on the household’s likelihood of adoption is
significantly higher in villages with stronger social capital, and that the marginal impact
of social capital may be negative if village success in adoption is relatively low.
Sabatini (2005) studied the role of social capital in economic development where the
paper carries out an empirical assessment of the causal nexus connecting social capital’s
diverse aspects to the “quality” of economic development in Italy. The analysis accounts
for three main social capital dimensions (i.e. bonding, bridging and linking social
capital) and measures them through synthetic indicators built by means of principal
component analyses performed on a dataset including multiple variables. The causal
relationship between social capital’s and developments different dimensions is then
assessed through structural equations models (SEMs). The analysis accounts for three
main social capital dimensions: strong family ties, or the so-called bonding social
capital, weak ties connecting friends and acquaintances (i.e. bridging social capital) and
more formal ties linking members of voluntary organizations (i.e. linking social capital).
The main findings of the paper can be summarized as follows: strong family ties exert a
negative influence on human development and the economic performance. On the
contrary, weak ties may act as bridges across different communities, fostering
knowledge sharing and the diffusion of trust, and therefore benefiting the process of
economic development. However, there are different kinds of weak ties. Bridging ties
connecting friends and acquaintances are proved to negatively affect income and
34
development, while the linking social capital connecting members of voluntary
organizations exerts a positive influence on income and development.
Sesabo and Tol (2005) studied the Factors affecting Income Strategies among
households in Tanzanian Coastal Villages. In the study Tobit models was used to
investigate factors that explain households’ decision-making on whether or not to
participate in various activities, using household data collected from two Tanzanian
coastal villages (Mlingotini and Nyamanzi). The results indicate that factor shaping
activity participation differ across board of activities and households’ decision to
participate in various activities is significantly influenced by asset endowments,
households’ structure, local institutions, and location- specific characteristics of both
villages. In addition, these results reveal that fishing assets entitlements and access are
the main determinants for variation in total household’s income. The study found that
majority (97%) of households participates in other activities (this include self-
employment and wage employment), where agricultural activities account for 82 % of
all households, followed by fishing activities with 57.1 %. However, very few
households participate in seaweed-farming (37.7 %) activities. The study noted that
contribution of agriculture activity to household income is only 14% while income form
fisheries account for 52 % of total income for all households.
35
Ferdoushi (2010) studied mud crabs marketing system in Bangladesh to create a better
understanding of the current marketing flow and trading practices for mud crab. A
survey was conducted from December to August 2009 using a semi-structured
questionnaire through interviews among a cross section of people including the mud
crab fatteners, crab catchers, depot owners and exporters in the southwest part of
Bangladesh. Descriptive method of analysis was used to describe the survey results
using means and percentage. In this study, factors like Price fluctuation, lack of buyers
and market information, credit problems, high mortality and poor transportation systems
in the marketing of crab have been reported to have negative effects on the competitive
efficiency in both the domestic and international markets and therefore negatively
affecting the adoption of the farming practice. Domestic demand needs to increase
through increasing social awareness and promoting awareness of the nutritive value of
this export oriented species.
Patterson and Samuel (2003) studied the Participatory Approach of Fisherwomen in
Crab Fattening for Alternate Income Generation in Tuticorin, Southeast Coast of India
and prove that there is great success not only in terms of generating extra income to the
family through the 12 women self help groups (SHG’s) but also in creating awareness
among fisher folk about the value of marine resources and the need for conservation and
sustainable utilization. Women are successful in crab fattening and creating alternate
income through this project. From the study the findings observe that active
participation, training programs, infrastructure such as fattening shed and settlement
36
tanks, support from the district administration and technical back up are the main
determinants for the successful women mud crabs fattening project.
Jarungrattanapong and Manasboonphempool (2008) studied the Adaptation Strategies
for Coastal Erosion/Flooding: A Case Study of Two Communities in Bang Khun Thian
District, Bangkok. The objective of the study is to determine the adaptation strategies of
households and communities with regard to coastal erosion/flooding. The major
economic activity in this area is coastal aquaculture, with the raising of shrimp and
blood cockles being the main occupation. All the shrimp farmers in the study area use
extensive farming techniques, requiring little management and investment. Based on this
approach, the farmers, or more accurately aqua culturists, impound wild larvae from the
sea and then grow them to market size. Owing to the decreasing yields from shrimp
farming caused by water pollution and a decrease in the number of larval shrimp in
nature, the farmers have been turning to raising blood cockles along with farming
shrimp in order to maintain their earnings. From the study they found out that farmers
adopt different strategies to protect their shrimp ponds for more than 30 years. The
choices of household adaptation may be classified into three types of autonomous
adaptation as follows :(1) Protection: some households have applied hard structures in
parallel with the coast in order to protect their aquaculture ponds. (2) Retreat: some
farmers needed to retreat or move their ponds inland; thus, they had to build new water-
gates and reconstruct the dikes. (3) Accommodation: some households had to
rebuild/renovate their houses due to flooding.
37
Agbayani (2001) studied the production economics and marketing of mud crabs in the
Philippines. This paper discusses the economic viability of four grow-out culture
methods for mud crabs namely; pond monoculture, polyculture with milkfish, culture in
mangroves, and fattening in ponds. The marketing system of mud crabs covers product
development, pricing, distribution channels, and promotion activities. Standard
production economics in computing cost and returns and discounted cash flows were
performed. Sensitivity analysis was also done to determine the levels of risk caused by a
20% decrease in market prices of mud crabs and a 30% decrease in farm production.
From the analysis he found out that the mangrove system had the highest working
capital because of longer culture periods and higher feed costs while the fattening
method had the lowest costs because of a shorter culture period. Thus, total investment
was highest in the mud crab culture in mangrove and lowest in the fattening method
based on 1999 prices. In the comparative costs and returns of the four mud crab culture
methods, the monoculture system registered the highest revenue per year due to the
higher production and the crab fattening method registered the lowest revenue of only
because of the low stocking rates. Variable cost per crop was highest in the mangrove
method and lowest in the fattening method.
3.3 Summary and Conclusion
A general overview of the studies reviewed above shows that many studies done on the
aquaculture potential to the coastal communities. It can be observed that there are many
factors for successfully mud crabs farming. In the literature reviewed the studies
38
concentrated on the potential of mud crabs farming due to its high price in the
international market as an alternative source of income, employment and its contribution
to the national income. From the literature less attention has been given to the individual
factors that may influence adoption of mud crabs farming. This study therefore will fill
the gap by accessing the determinant of individual decision to adopt mud crabs farming
around Pangani coastal community.
39
CHAPTER FOUR
METHODOLOGY
4.0 Introduction
This chapter present the methodology used to analyze factors that will influence coastal
people to adopt crabs farming as an alternative source of income. First, it describes the
data type and source, followed by the theoretical framework adopted in this study based
on the literature reviewed. Lastly the model for analysis, justification for the choice of
variables and the tested hypothesis. Due to categorical nature of the dependent variable
then Logit model was adopted.
4.1 Theoretical Framework
Farm-household models integrate in a single institution the decisions regarding
production, consumption and reproduction over time (Sadoulet and de Janvry, 1995). In
economic theory, the problems of production decisions, consumption decisions and
labour supply are usually analyzed separately through the behavior of three classes of
agents: (1) producers, who maximize net revenues with respect to levels of products and
factors, subject to constraints determined by market prices, fixed factors and technology;
(2) consumers, who maximize utility with respect to the quantities of goods consumed,
subject to constraints determined by market prices, disposable income, household
characteristics and tastes; (3) workers, who maximize utility with respect to income and
home time, subject to constraints determined by the market wage, total time available
and worker characteristics.
40
In the case of the household, the decision-maker is engaged simultaneously in making
decisions about production, consumption and work. Following Sadoulet and de Janvry
(1995), we can integrate the three problems into the single household problem as
follows:
Max U (Ca, Cb, Cl, Zh) Utility function …………………………………………….4.1
Subject to: g (qa, x, t; zh) =0 Production Function…………………………………..4.2
PxX+PmCm=Pa(qa-ca) +w (ls-l) Cash constraint………………………...4.3
Cl-ls=E Time constraint ………………………………………………..4.4
Where u is a utility function to be maximized; Ca is an agricultural good; Cb is a
manufactured good; Cl is home time; qa is the quantity produced of good a; g is the
function symbol; and pa is the price of product a. Similarly, pm is the price of product m;
ls is time worked; and E is total time endowment. Z represents the household
characteristics; h is the household number; t is the production technology; x is the vector
input; l is labour; and w is the wage rate.
It is obvious that in the context of developing economies, characterized by market
failures and credit constraints, this typical household model doesn't work. According to
Sadoulet and de Janvry (1995), the household's problem is to maximize u (c, zh), subject
to cash and credit constraints, production technology, exogenous effective market prices
for tradable and equilibrium conditions for non-tradable.
41
In decision-making, farmers have to make a choice between many risky alternatives.
Although the decisions they make are complex, they are typically modeled as a binary
choice. They accept or reject a given technology or policy according to their own
perceptions of the expected benefits and costs of the technology and the expected risks.
The rationale for their decision is based on a myriad of factors and some complex social
relationships, which can condition benefits and costs. For a poor household in rural
coastal areas income maximization, for instance, is not a goal per se; income smoothing,
or reducing variability in income, may be more relevant here, for example, and this may
be particularly true of food-insecure households.
The adoption of any technology can be modeled as an economic decision based on
expected marginal benefits and costs. Most empirical specifications deal with a variety
of models of farmer or household optimization: maximizing profits, expected utility of
profits or expected utility of consumption and leisure subject to production function and
time. The economic analysis of the behavior of individual decision-makers often leads to
models of a limited dependent variable or qualitative variable nature. The decision will
depend on each farmer's own characteristics, beliefs and objectives. The dichotomous
nature of the response in that framework will require a specific econometric approach,
namely, the use of qualitative response models.
42
In this study, of investigating the decision of households to adopt mud crab farming,
logistic regression model is used due to the categorical nature of the dependent variable,
Green (2005) when the decisions are the qualitative choices we equate “no” with 0 and
“yes” with 1. In most applications of binary response models, the primary goal is to
explain the effects of the independent variable on the response probability P(y = 1|x).
Estimation of these dummy dependent variables can be done basically in the general
framework of probability models namely the linear probability model (LPM), the
logistic regression model (LOGIT) and the Probit model.
The linear probability model is the easiest of all with (Y=1) represent respondent
participate and (Y=0) is for respondent who do not participate, and set of factors
gathered in vector X explaining the decision so that
Prob (Y=1|X) = F(x, β)………………………………………………4.5
Prob (Y=0|X) = 1-F(x, β)…………………………………………….4.6
And the regression model will be
Y=E [Y|X] + (Y-E [Y|X] ) = X’β +Ɛi ……………………………….4.7
Where β’s parameters reflects the impact of changes in X on the probability. But the
LPM has a shortcoming because ε is heteroscedastic in a way that depends on β. Since
x_β +ε must equal 0 or 1, ε equals either −x_β or 1−x_β, with probabilities 1−F and F,
43
respectively. Since we cannot constrain x_β to the 0–1 interval, such a model produces
both nonsense probabilities and negative variances (Green 2002). Also the LPM
assumes that the conditional probabilities increase linearly with the values of the
explanatory variables. Hence, what is generally needed is the probability model that has
an S-shaped feature of the cumulative distribution function. In practice the logistic and
the normal cumulative distribution functions are chosen, the former giving rise to the
Logit and the latter to the Probit.
4.2 The Logit model
Let Pi represent the probability of a person to adopt mud crabs farming and1-Pi is the
probability of the person not to adopt mud crabs farming. And we have the outcome 1 if
the person engages in mud crabs farming and 0 if he does not, then we have the
following.
………………………………………………………………………4.8
…………………………………………………………………...4.9
The probability of a person to adopt mud crabs farming is given as
…………………………………………………4.10
Where, X is a vector of independent variables and β is a vector of their respective
coefficients.
44
For ease of expression and understanding, equation (4.10) is thus simplified
as =٨۸(x,β) ……………………………………4.11
Notation ٨۸(x, β) indicate the logistic cumulative distribution function.
The probability therefore of a person not to engage in mud crabs farming can thus be
given as
=٨۸(x,β) ………………………….4.12
The logistic distribution (Logit) always gives larger probabilities to Y=0 when x β is
extremely small and smaller probabilities to Y=0when β x is very large. This is fairly
different to the normal distribution (Probit).
It is noted from equations 4.8 and 4.9 that ranges from 0 to 1 and is non-linearly
related not only to the regressors but also to the parameters thereby causing some
estimation problems in as far as ordinary least squares (OLS) estimation technique is
concerned. Due to non linearity which will lead to estimation problem then we can
reformulate these equations in terms of the odds ratio of the probability of the person to
adopt mud crabs farming to the probability of the person not to adopt mud crabs
farming. And this equation will be as follows:
…………………………………………………………4.13
45
Where is simply the odds ratio in favor of a person who adopt mud crabs
farming and can thus be simplified as follows:
………………………………………………………………4.14
Then we take the natural logarithms of the equation to get the Logit model and it can be
observed that the log of the odds ratio, L , is not only linear in X ,but also in the
parameters β;
……………………………………………………4.15
The interpretation of Logit as odds-ratio is an attractive feature of Logit model. Since
Logit gives log of the odds and that is the reason for the Logit estimates sometimes to be
referred as log-odds estimates. Therefore odds ratio can be calculated simple by
exponentiate the Logit estimates. Interpretation of odds ratio depends on whether the
coefficients are greater, less or equal to 1. Expressed in this way, it is a little easier to see
what is going on with the odds ratio. When the probability of a one (“success”) is less
than the probability of a 0 (“failure), then the odds ratio will be less than 1. When the
probability of a one is greater than the probability of a 0, the odds ratio will be greater
than 1. When the odds ratio is exactly 1, this says the odds of success and failure is
even. Therefore when interpreting an odds ratio, if the value is greater than 1 then any
46
change in variable will favor success and when the coefficient is less than 1 then any
change in variable will favor failure. Also in interpreting odds ratio it is often helpful to
look at how much it deviate from 1.
4.3 Principal Component Analysis for Wealth and Social Capital
Principal Component Analysis (PCA), is a data reduction technique, which creates
orthogonal linear combinations from a set of variables, and orders them according to
their contribution to the overall variability of the variables analyzed (Filmer & Pritchett,
2001). It is a statistical component that derives summary measures (principal
components) from a set of indicators. The leading eigenvectors from the Eigen
decomposition of the correlation or covariance matrix of the variables describe a series
of uncorrelated linear combinations of the variables that contain most of the variance. In
addition to data reduction, the eigenvectors from a PCA are often inspected to learn
more about the underlying structure of the data. From the data collected at Pangani,
there is large set of variables explaining individual wealth and social capital.
Aggregating and interpreting wealth information is difficult. Since these many variables
are highly collinear, so PCA was conducted in this report to overcome those hardships
by aggregate the asset variables into single asset index which is uncorrelated to the
linear combination of the original variables and capture most of their information. Like
wise to social capital variables, in this study PCA was conducted to reduce
dimensionality of the variables from twenty two highly correlated variables to four
uncorrelated variables.
47
In order to conduct PCA for the set of variables we have to test for the sampling
adequacy of the variables set. To test for the sampling adequacy we use Kaiser-Meyer-
Oklin (KMO) test which takes the values between zero and one with small values
indicating that overall the variables have too little in common to warrant PCA analysis.
The minimum required KMO for the PCA to be done for the set of variables is 0.50.
Small values of MSA indicate that the correlations between and the other variables
are unique, that is, not related to the remaining variables outside each simple correlation
(See Table 4.2).
Table 4. 1: Keiser-Meyer Oklin Test for Principal Component Analysis
KMO values of MSA Labels
0.00 to 0.49 Unacceptable
0.50 to 0.59 Miserable
0.60 to 0.69 Mediocre
0.70 to 0.79 Middling
0.80 to 0.89 Meritorious
0.90 to 1.00 Marvelous
Source: Authors Computation
To retain Principal Components we use Kaiser Criterion on which we retain only values
with Eigen values greater than 1. In fact, it turns out that the eigenvector with the
highest eigenvalue is the principle component of the data set. This means that unless a
factor extracts at least as much as the equivalent of one original variable, it is dropped.
48
4.4 Characteristics of PCA
The first component extracted in a principal component analysis accounts for a maximal
amount of total variance in the observed variables. Under typical conditions, this means
that the first component will be correlated with at least some of the observed variables. It
may be correlated with many. The second component extracted will have two important
characteristics. First, this component will account for a maximal amount of variance in
the data set that was not accounted for by the first component. Again under typical
conditions, this means that the second component will be correlated with some of the
observed variables that did not display strong correlations with component 1.
The second characteristic of the second component is that it will be uncorrelated with
the first component. Literally, if you were to compute the correlation between
components 1 and 2, that correlation would be zero. The remaining components that are
extracted in the analysis display the same two characteristics: each component accounts
for a maximal amount of variance in the observed variables that was not accounted for
by the preceding components, and is uncorrelated with all of the preceding components.
A principal component analysis proceeds in this fashion, with each new component
accounting for progressively smaller and smaller amounts of variance (this is why only
the first few components are usually retained and interpreted). When the analysis is
complete, the resulting components will display varying degrees of correlation with the
observed variables, but are completely uncorrelated with one another. (Morris, Carletto
et al., 2000)
49
4.5 Hypothesis
It was hypothesized that:
(a) Households whose primary occupation is agriculture are likely to adopt mud crab
farming as a food security and income stability strategy due to the seasoned
nature of the activity. Low-income households are more likely than wealthier
households to invest in mud crab farming so as to reduce poverty incidence to
coastal community.
(b) People with an ethnic background in fisheries are more likely to adopt mud crab
farming.
4.6 Empirical Model Specification
From the previous studies both the theoretical and the empirical literature reviewed as
well as the availability of data, this study will estimate the following econometric model
Li =β0+β1GENDER+ β2AGE + β3HEADSEX + β4LABFORCE + β5NHHM +
β6LNAGRIC + β7SINGLEPARENTS + β8 MARRIED+ β9SECONDARY +
β10PRIMARY + β11PRIMAGRIC +β12SECAGRIC + β13LNINCOME + β14TRUST
+β15COMMITMENT+β16COHESION+ β17FOODRESERVE +β18POOR +
β19PRIMFISHING +β20TERTFISHING + Ɛi
50
Table 4. 2: Variables definition
Variable name Variable definition GENDER Sex of respondent. AGE Age of respondent. HEADSEX Sex of the household head. LABFORCE Working group in the household. NHHM Number of household members. LNAGRIC Natural logarithm of agricultural income SINGLEPARENTS Marital status of the respondent MARRIED Marital status of the respondent SECONDARY Education level attained by the respondent PRIMARY Education level attained by the respondent PRIMAGRIC If agriculture is the primary source of income SECAGRIC If agriculture is the secondary source of income of the respondent LNINCOME Natural logarithm of income TRUST Measure of social capital in terms of trust between people COMMITMENT Measure of social capital in terms of commitment people have in
their association. COHESION Measure of social capital in terms of unit people have in their
society FOODRESERVE If the respondent have reserve of food from one harvest period to
next harvest period POOR Level of an individual PRIMFISHING If fishing is the primary source of income ofthe respondent. SECFISHING If fishing is the secondary source of income of the respondent
4.7 Definition of Variables
4.7.1 Gender
This variable refers to if the respondent is male or female. The probability for the female
to adopt mud crabs farming is high than male in Pangani. This is due the high income
men gets from fishing while women do not engage in fishing but in agriculture and other
small business. And as we see from the data fishing activities provide the highest mean
51
income than any other economic activities in Pangani. This will influence women to
adopt faster crabs farming to increase their income levels than men.
4.7.2 Age of the Respondent
The age of the farmer identify one of the factors influencing adoption of new
technology. Young people at working age are more likely to adopt new innovation or
technology as an alternative source of income than the aged people.
4.7.3 Labour Force
Labour force refers to the working group available in the household. If members are
mostly at the working age group then the probability of adopting crabs farming as a
source of alternatives income is high compared to the household’s whose members are
in dependant age group.
4.7.4 Sex of the Head of Household
The sex of the head of household also influences the adoption of crabs farming. Most of
household’s controlled by male have many members than those which are controlled by
female and therefore they have high expenditures. Being many members in the single
household will influence members to adopt alternatives sources of income to be able to
cover their expenditures in the household. But most female headed small households
whose expenditures incurred are small. Therefore the probabilities of the members of the
households which are headed by male to adopt crabs farming is high than members of
52
households which are headed by female. This variable is measured by the dummy where
1is for male head and 0 is for female head.
4.7.5 Marital Status
Marital status influence more people to adopt other source of generating income. In our
model marital status is measured for dummy variables for married, single and single
parents. There is high probability of the married men to adopt new innovation to
generate more income to cover the cost of taking care of the family as large percent of
married women in coastal areas depend on their husband to take care of the family. Also
the probability of adopting crabs farming is high for divorced (single parents) as they
have to take care of the family alone. Married and single parents dummy are included in
the model while single is left as a reference category.
4.7.6 Education Level of the Respondent
Education level of the respondent determines the rate of the adoption of the new
technology. Roger (1983) argues that people with higher educational background tend to
adopt new innovation earlier. This variable will be captured as a dummy variable.
Primary will be for the farmer with primary education and Secondary will be for the
farmer with secondary education. The base category will be the farmer with no
education.
53
4.7.7 Household Size
The household size influences the members or head or members of household to adopt
crabs farming as a source of alternative income. Since with large household members
expenditures are high then this drives them to diversify their source of income to
increase their cash income.
4.7.8 Natural Logarithm of Agriculture Income
Agriculture income can influence farmers to adopt new opportunities to supplement their
income level. As revenue of agriculture decrease this influence people to adopt crabs
farming at Pangani to stabilize their income level.
4.7.9 Agriculture as a Source of Income
As it was hypothesized; that people who engage in agriculture have high probability of
adopting crabs farming due to the persistence declining of the yields and income from
agriculture. This variable is represented by dummy variable of agriculture as the primary
source of income (primagric) and variables of agriculture as the secondary source of
income (secagric) and agriculture as a tertiary source of income (tertagric) were left as a
base categories.
4.7.10 Role of Social Capital
Social capital has a big role in influencing a person to adopt new innovation. In our
study social capital is represented by three variables which are trust people have to each
54
other, commitment to their association and cohesion measuring the unity people have in
their community.
4.7.11 Food Reserve
The households are said to have secure food if they can get meals three times per day
and have a reserve food. Households without food reserve are easily to adopt mud crabs
farming so as to supplement their income in order to buy food for consumption. This
reserve is measured by dummy variable where 1 is if individual have food reserve and is
food secured from one harvest season to the next harvest season. While 0 stands for
individual who have no food reserve and is not food secured.
4.7.12 Poverty
This refers to the wealth of the respondent. Amount of the wealth owned by individual
determine the rate of the person to adopt new income opportunity. This variable will be
included in the model by computing the index of the poverty by principal component
analysis.
4.7.13 Fishing as Source of Income.
Since the major economic activity at Pangani is fishing, then this variable fishing as a
source of income will measure the influence of fishing activity in adopting crabs
farming. In our model it was presented by dummy variables PRIMFISHING and
SECFISHING for those who engage in fishing as a primary source of income and as a
secondary source of income.
55
4.7.14 Natural Logarithm Income of the Individual
The income of the individual has a great role in influencing the person to engage in
crabs farming. Since the activity was introduced to supplement individuals’ low income
then people with low income have the high probability of adopting crabs farming as the
alternative source of income. From our data we use the natural logarithm of previous
year’s income to measure the income of the individual.
4.8 Approaches of Study
The basic method used in this study is the logit regression. The study was conducted in
coastal land in the Tanga region –Pangani district. The purposive method was used to
select the district since is the only district in Tanga region where the techniques of mud
crab fattening was introduced. Thus two steps have been taken to select the location. The
first step was to identify the district, and the villages. Second, was to randomly select the
households from the identified villages where the techniques of mud crab fattening was
introduced. Thus the sampling method used in this study is random for the
households/farmers. The data collecting technique is direct interview, direct observation
and questionnaires. The first phase was a survey using individual interviews, designed to
gain an overview of development activity and statistical information on participants’
experiences.
4.9 Sampling Technique
The main sampling technique is purposive sampling where the people who have adopted
mud crabs cage farming as an alternative source of income were all captured. The study
56
analyzes the factors for people to adopt or not to adopt crabs farming. Also the study
goes further to investigate other households who engage in other economic activities but
they have not adopted mud crabs farming to supplement their income in order to see if
there is significant income contribution of crabs farming to the people who have
adopted. But the major problem of purposive sampling is that the type of people who
available for the study may be different from those in the population and this might
introduce the source of bias.
4.10 Sample Data
The study has used primary and secondary data for description and analysis. Primary
data was collected in Pangani in selected five wards of Pangani East, Pangani West,
Bweni, Mwera and Kipumbwi. From these wards seven villages were selected which are
along the seashore of Indian Ocean since these are the place where crabs cage farming
can be done in mangroves. Structured questionnaires and field observation was done as
the source of primary data. Structured questionnaire were used to collect information,
which was relevant for the study like age of the respondent, education level, source of
income of the individual, and information about the adoption of crabs farming. The
questionnaire comprised both closed ended and open ended questions with six parts
which are individual characteristics, information about mud crabs farming, information
about wealth of individuals, food security material life style and information about social
capital. Field observation include visiting the place to verify the information given,
mostly it is physical observation. It was observed that environment which is suitable for
57
crabs farming are along the seashore with mangroves and mud. These crabs were placed
in cages in which it include many rooms with bucket where they kept one crab in each
room. And during rain seasons is not the suitable time for crabs farming as salinity of
water decrease. Secondary data was obtained from various sources such as Pangani
district council and Tanga regional office.
4.11 Sample Size
The study covers 198 respondents drawn from seven villages. Out of 198 respondents
124 were males and 74 were females. Three samples were classified so as to be able to
analyze the study. The first sample was people who are aware of mud crabs cage
farming as a source of income and they have adopted crabs farming. The second group
is the one who were aware of mud crabs farming as a source of income but they did not
adopt the activity. The third group is of the people who are not aware of the crabs
farming as the source of income.
Table 4. 3: Respondent’s Sample Size
males Females Aware and adopt crabs farming. 38 54 Aware but did not adopt crabs farming
42 12
Not aware of crabs farming 44 8 Total 124 74
Source: Author’s Computation from the Survey Data
58
4.12 Estimation Technique
This study will use the Logit model as outlined above. The rationale for this is the fact
that, the functional relationship being investigated is binary in nature since the
regressands can only assume values between 0 and 1. In this case making use of
standard linear regression is felt inappropriate and hence ruled out. The standard Logit
model therefore, employing maximum likelihood method is thus the obvious choice
because the functional form of our model is non-linear. The study is going to use Stata
package to estimate the model.
4.13 Scope and Limitation of the Study
This study will use primary data collected from selected two villages in Pangani. The
main source of data will be through direct interview and questionnaires which will be
filled by people who engage in agriculture and fisheries. The limitation will be the
willingness of the interviewer to provide the information and to give out the correct
information. The reporting bias particularly of the age, income and other information
will be lead to unreliable estimate.
59
CHAPTER FIVE
EMPIRICAL RESULTS AND THEIR INTERPRETATION
5.0 Introduction
This chapter presents empirical analysis results for the study of economics of mud crabs
farming in Pangani. The chapter is divided into various sections. Section one presents
some descriptive statistics for both the dependent and the independent variables,
followed by regression results of the logistic regression and lastly compares the results
of this study with those of other similar studies done elsewhere.
5.1 Descriptive Analysis
In this section data are described to see their behavior before undertaking regression
analysis. This will help to understand the nature of the sample data collected. Data was
collected in seven villages in Pangani which are along the shore line. A total sample of
198 randomly selected households from villages was interviewed. From the sample
data collected 58% of the adopter respondents are females. This can be due to the fact
that most of the males in the shoreline of the Pangani district engage in fisheries and
have higher income than females. Also due to demanding nature of crabs farming where
farmer need to cook food for crabs and feed them at least twice daily then most women
can do this than men. The average household size in Pangani district is 4.8 which is
relatively less than the national average of 6 household members and the dependence
ratio in Pangani district is 48.8%.
60
Figure 5.1, shows sample distribution of the respondents where data was collected in
seven villages.
Figure 5. 1: Pie Chart of Respondents in their Respective Villages
Source: Author’s Computation from Survey Data
The sample respondents are divided into three groups depending on their awareness of
mud crabs farming. The first group is the one who are aware and engage in crabs
farming, second group is for those who were aware but did not engage in crabs farming
and lastly sample is for those who were not even aware of mud crabs farming.
61
Table 5. 1: Mean Income of the Sample Respondents
INCOME Variable Mean Std.Dev Min. Max. Aware but did not engage in mad crab farming
2,179,844 3,083,678 10,000 24,800,000
Not aware of the mud crab farming 1,353,379 1,293,591 20,000 6,606,000 Aware and participant in mud crab farming
1,880,012 2,012,593 50,000 7,052,000
Source: Author’s Computation from the Survey Data
From table 5.1, we see the mean income of the respondent who are aware but did not
adopt crabs farming is Tsh. 2,179,844, while those who are aware and participate in
crabs farming is Tsh. 1,880,012 and for those who are not aware of crabs farming is Tsh.
1,353,379. The higher mean earnings of those who are aware but did not adopt, will
suggests that might have compared their current occupation and the mud crab farming.
So their decision of not adopting mud crab farming could be based in earning potentials
from their next best alternative. Next we conducted a test to whether the difference in
mean income of the three categories of the household is significantly different. Table 5.2
provides the test results.
62
Table 5. 2: Two-sample t test (of the means) with Equal Variances
Difference = mean (yes) - mean (no) t = 1.9677
Group Obs Mean Std.Err
Std.Dev 95% conf. interval
yes 132 2110908 252135.5 2896816 1612123 2609692 no 66 1373136 162320.7 1318699 1048960 1697313 combined 198 1864984 178029.4 2505096 1513896 2216072 diff 737771.2 374934.3 -1652.16 1477195
Ha: diff < 0 Ha: diff! = 0 Ha: diff > 0
Pr(T < t) = 0.9747 Pr(|T| > |t|) = 0.0505 Pr(T > t) = 0.0253
Source: Author’s Computation from the Survey Data
From the test results in Table 5.2, the difference of the mean income of the respondent
who are aware of crabs farming to the mean income of the respondent who are not aware
of crabs farming is statistical significant at 10%. Therefore generally from the data,
people who are aware of crabs farming whether they adopted it or not have significantly
high income compared to those who were not aware of crabs farming.
From the sample data 61.5% of the respondent who adopted crabs farming as a source of
alternative income are farmers, this is consistence with our hypothesis that people who
engage in agricultural activities are likely to engage in crabs farming. And when we
compare income from different economic activities we can see that the mean income
from agriculture is small compared to mean income of fishing, livestock keeping and
other economic activities. Thus mud crab farming is taking as an alternative source of
income to majority of the farmers. Low mean income from agriculture activities can be
63
due to dependence on rainfall for cultivation that in recent years it has been highly
unreliable and also the low prices of farm yields. It was for this unreliability of rainfall
and hence food insecurity which encouraged people to engage in mud crab farming as an
alternative source of household income. Also the other reason can be the season nature
of agriculture activities where the activity takes place during rainy seasons and people
remain idle during the dry season, as there are very limited irrigation activities in the
shoreline villages. And since crabs farming is mostly done during drought season where
salinity of seawater is higher, then farmers get enough time to engage in crabs farming.
Table 5.3: Mean Income from Different Economic Activities taking place at Pangani
Variable Mean Std.Dev Min Max Income from agriculture for last year. 177,495
448,996 0
4,200,000
Income from livestock for last year. 1,484,934
3,674,778
6,000
17,500,000
Income from fishing for last year. 1,496,780
1,317,593
3,000
5,760,000
Income from wages for last year. 1,249,188
1,344,494
40,000
6,000,000
Income from trading or shop keeping for last year. 1,942,284
2,347,506
20,000
10,000,000
Income from crabs farming for last year. 211,440
271,895
10,000
1,100,000
Income from small business for last year. 1,298,042
168,902
13,440
7,520,000
Income from relatives or husband for last year. 234,000
379,352
10,000
672,000
Source: Author’s Computation from the Survey Data
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Table 5.3, shows respondent’s income from participating in different economic activities
in previous year (2010). Trading and shop keeping provide the highest income for last
year and lowest income for last year was from agricultural activities.
5.2 Cost Benefit Analysis of Crabs Cage Farming
From this part we describe crabs farming in Pangani to see if it is profitable venture.
Data has been collected for 92 crab farmers in Pangani coastal villages. Data shows
that the initial costs of establishing the project is relatively high for the average
individual in the rural village to afford. The cost incurred to have crab farming include
to building cages, buying buckets, buying juveniles, fatten them till marketing time and
transportation costs to the market place. Since mortality rate of crabs is high, then they
have to be kept in the cages with separate partitions as they eat each other when they get
in contact. The average cost of establishing single cage with average of 10 to 15
partition is Tsh. 400,000/=. This is the initial cost of developing the crabs farming
project. When comparing this amount with a rural village economic status of
respondents then this is very high cost.
Farmer also has to incur other variables costs to run the project. These include buying
juveniles from fishermen, fattening them to get the weight that is reasonable to market
them and the transportation costs. Sometimes farmers incur costs of security if the place
is not safe. Farmers incur variable costs during each new stocks of crabs and the average
variable costs of the project is Tsh. 4,987/= to fatten single crab to reach a marketable
weight. The average hours of labour required for crabs is 4 hours per day which include
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cooking crabs food, feeding them and cleaning cages to ensure good environment for
them to grow. Considering the fact that majority of the people engaged in mud crab
farming primary school leavers without any other training, their average wage could be
Government minimum wage. Hence assuming that these people their best alternative
employment could earn them Government minimum wage of Tsh. 150,000 per month,
this would translate into Tsh. 4,355/= per day Since the aim of the project is to reduce
poverty level of the people around the Coast by using the available marine resources
then this costs will be difficult for people to incur especially initial costs and variable
costs. This could explain why the mud crab farming started as a project supported by
international NGOs and later on by MACEMP –a World Bank funded project, and that
farmers were advised to work in a group.
Table 5. 4: Descriptive Statistics of Costs –Benefit Analysis of Crabs Farming in Pangani (Units in Tsh)
Variable Mean Std. dev Minimum Maximum
Initial costs 2,078,269 1,739,733 160,000 6,000,000
Variables costs 1,022,344 1,261,731 136,800 6,441,480
Labour costs 251,260 452,328.5 15,540 2,188,800
Total costs 3,100,613 2,284,687 316,600 7,939,000
Revenue 1,074,769 1,259,336 120,000 4,200,000
Profit 52,425.38 1,239,679 -2,591,480 2,767,600
Source: Author’s Computation from the Survey Data
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Table 5.4, gives the summary statistics for the costs and benefit analysis of crabs
farming in Pangani. The average revenue obtained from crabs farming is Tsh
1,074,769/=. The average total costs for crabs farming is Tsh 3,100,613/= which include
initial costs (sunk costs), variable costs and labour costs which was calculated basing on
minimum wage rate in Tanzania. From the table mean profit is positive showing that the
crabs’ cage farming is a viable business that can generate income. Thus assuming that on
average each round of harvest mud crab farmer will make a profit of Tsh. 52,000, and
with three rounds of harvest per year this will mean that in a year the profit earned will
be Tshs. 200,000. Thus with average investment of Tshs. 3 million, it will take about 14
years to recover his investment.
5.3 PCA on Social Capital
Whereas physical capital refers to physical objects and human capital refers to the
properties of individuals, social capital refers to connections among individuals – social
networks and the norms of reciprocity and trustworthiness that arise from them. In that
sense social capital is closely related to what some have called “civic virtue.” The
difference is that “social capital” calls attention to the fact that civic virtue is most
powerful when embedded in a sense network of reciprocal social relations. A society of
many virtuous but isolated individuals is not necessarily rich in social capital (Putnam,
1995). From the data collected from Pangani, information about social capital has been
captured by 22 set of variables. PCA is conducted to reduce the dimensionality of this
data set and generate four latent variables that are not correlated but represent the same
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variability of the original data set. A small set of uncorrelated variables is easier to use
than a large set of correlated variables (Nyangena, 2006).
5.4 Variable of Study for PCA
Out of 15 variables capturing information on social capital, PCA was used and four
principal components were selected based on Kaiser Criterion where it needs Eigen
values to be greater than one. From the loadings of the four components selected we
choose factors with Eigen vectors at least greater than 0.3 to explain the components.
The first Principal Component captured trust, the second principal component reflected
membership, the third principle component captures commitment and finally the fourth
principal component captured neighborhood cohesion(See Table 5.5).
Table 5. 5: The First Four Components of Social Capital PCA
Component Eigen value Difference Proportion Cumulative
Comp1 2.87186 0.627651 0.1915 0.1915
Comp2 2.24421 0.533879 0.1496 0.3411
Comp3 1.71033 0.108667 0.1140 0.4551
Comp4 1.60166 0.262919 0.1068 0.5619
Source: Author’s Computation from the Survey Data
From Table 5.5, the first component explains about 19.15% of all the variation of the
original variables. And by choosing the first four components then they explain about
56.2% of all the variation of the original variables. Hence we have narrowed down the
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data set from 15 original variables to 4 new variables. From the PCA post estimation,
Keiser- Mayer – Oklin test was conducted and the overall Keiser - Measure of Sample
Adequacy value is 0.53.
Table 5. 6: Keiser Meyer-Oklin Test for Social Capital
VARIABLE KMO
Number of close friends 0.5506 People you could turn to for help 0.5852 People you have helped 0.5893 Amount of money you have helped 0.5649 Membership in groups 0.4322 Farmers group 0.4530 Traders and business association 0.1910 Political group 0.2692 Fishermen group 0.2248 Participation in groups 0.7319 Money contributed 0.5018 Time spent 0.4918 Willingness to spend time 0.7294 Willingness to spend goods 0.6490 Willingness to spend time 0.7224 Overall average 0.5348
Source: Author’s Computation from the Survey Data
Table 5.6 shows the Keiser-Meyer-Oklin test where an overall average is 0.53 for
15variables. And as we saw before the minimum required KMO for the PCA to be
computed from the given variables must be 5.0. Therefore we can calculate PCA of
social capital from our chosen set of variables.
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Trust explains the belief people have for each other. This can be explained by the way
people treat each other in friendship, lending money to each other during economic
hardships, all these express the trust they have to each other. Memberships explain the
associations or groups in which people joined to share ideas either about business,
agriculture, sports and many other. Commitment refers to the obligation people have
from these association while neighborhood cohesion measure social capital in terms of
unity people do have in their community and this can be explained in terms of how
people are willing to spend time and resources in community projects. Now the four new
variables obtained from Principal Component analysis can be used in the regression
model to see the influence of social capital in adoption of crabs farming at Pangani.
5.5 PCA on Individual Wealth
A measure of socioeconomic status of a household is an important element in most
economic and demographic analyses. The measure is useful in estimating the poverty
and income inequality and it can also be used in as a control variable in finding the
effects of other variables associated with wealth (Filmer and Pritchett, 2001). It is very
difficult in survey data to collect income and wealth information especially in
developing countries which are used widely as a measure of socioeconomic status. The
asset-based approach to determine socioeconomic status has been widely used in
previous studies as an appropriate measure of household wealth (Montgomery et al,
2000; Sahn and Stiefel, 2000 and 2003; McKenzie, 2005). Calculation of the asset index
is performed through Principal Component Analysis (PCA), a data reduction technique,
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which creates orthogonal linear combinations from a set of variables, and orders them
according to their contribution to the overall variability of the variables analyzed. In
order to apply PCA to survey data, all variables are transformed into a dichotomous
version, including the categorical variables housing characteristics (e.g. material of walls
or floor) or access to utilities (e.g. type of water or sewage service). In the process of
producing the asset index, other methodological alternatives for the weighting procedure
will be explored. Even though the general recommendation has been to use the most
variables available, as long as those are related to unobserved wealth (Rutstein and
Johnson, 2004; McKenzie, 2005), it remains unclear which types of assets have larger
contributions to the constructed measure and what the minimum number of necessary
variables is. Based on the asset index, we produce wealth quintiles which reflect the
resulting rankings of population by socioeconomic status.
5.6 Variables of Study for Asset Index
From the data dummy variables for asset have been generated where by they will be
used to calculate asset index. In our Principal Component analysis, 18 dummy variables
of the asset owned by household were used. There were six components with Eigen
value greater than 1 and the first component has 3.5 eigenvalue and explain about 19.5%
of the variation of original variables.
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Table 5. 7: First Component for Asset Index Computation
Component Eigen Value Difference Proportion Cumulative Compo 1 3.50399 1.83048 0.1947 0.1947
Source: Author’s Computation from the Survey Data
Table 5.7, shows the first component of PCA which explain about 19.5% of total
variation of asset index. The overall Measure of the Sampling Adequacy (MSA) of the
model by Keiser- Meyer- Oklin test is 72.5 which according to KMO categorization are
middling showing the model fit PCA criteria. From the loadings of the first component,
there are five eigenvectors with the value greater than 0.3 as required and hence the first
component is used to calculate asset index. Two quintiles were formed from the asset
index which is poor and non poor categories.
5.7 Estimation
Estimation of the likelihood of a person to adopt crabs farming as the alternative source
of generating income to the coastal community. From Table 5.8, the household
characteristics are indicated by age, gender, education level, labour force, number of
household members, marital status and food security. On average, respondent were 39
years of age suggesting that majority can be head of their household and participate in
decision making at the household level. Also on average each household has got about
4.8 members and where 2.1 is the labour force of the household and 2.7 is dependants
(members below 18 and above 61years of age).
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Table 5. 8: Descriptive Statistics of Dependent Variables.
Variable Obs Mean Std. Dev.
Min
Max
household characteristics age 198 39.54545 13.47927 19 86 gender 198 0.6262626 0.4850215 0 1 headsex 198 0.8585859 0.3493315 0 1 q6 198 4.873737 2.806667 1 20 married 198 0.6515152 0.4776983 0 1 singleparents 198 0.1767677 0.3824389 0 1 secondary 198 0.1464646 0.3544677 0 1 primary 198 0.6767677 0.4688962 0 1 labforce 198 2.136364 1.224368 0 9 foodreserve 198 0.459596 0.4996281 0 1 dependant 198 2.777778 2.228362 0 11
agriculture as a source of income
primagric 198 0.1868687
0.3907942 0 1 secagric 198 0.1868687 0.3907942 0 1
income of previous year lnagric 198 5.201217 6.132814 0 15.2506 lnincome 198 13.7338 1.340036 9.21034 17.02716
social capital trust 197 1.75E-09 1.265567 -2.625276 4.336307 commitment 197 -7.02E-10 1.498067 -1.67081 10.65369 cohesion 197 3.19E-10 1.694655 -9.31382 5.255464
asset index poor 197 0.5 0.5012937 0 1
Source: Author’s Computation from the Survey Data
Social capital was presented in terms of commitment, membership, cohesion and trust
calculated by PCA from the set variables explaining role of social capital. Asset index
was presented by a dummy variable poor which was calculated by Principal Component
Analysis.
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5.8 Logit Regression
5.8.1 Diagnostic test of Logit Regression
In order for our analysis to be valid, our model has to satisfy the assumptions of logistic
regression. When the assumptions of logistic regression analysis are not met, we may
have problems, such as biased coefficient estimates or very large standard errors for the
logistic regression coefficients, and these problems may lead to invalid statistical
inferences. Therefore, before we can use our model to make any statistical inference, we
need to check that our model fits sufficiently well and check for influential observations
that have impact on the estimates of the coefficients. In this section, we are going to
check on how to assess model fit, how to diagnose potential problems in our model and
how to identify observations that have significant impact on model fit or parameter
estimates.
In order to check on the reliability of the estimates then diagnostic test of the Logit
regression was conducted. Model specification test was done to check if we have put all
relevant variables in our regression.
5.8.2 Model Specification Test
When we build a logistic regression model, we assume that the Logit of the outcome
variable is a linear combination of the independent variables. This involves two aspects,
as we are dealing with the two sides of our logistic regression equation. First, consider
the link function of the outcome variable on the left hand side of the equation. We
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assume that the Logit function (in logistic regression) is the correct function to use.
Secondly, on the right hand side of the equation, we assume that we have included all
the relevant variables, and we have not included any variables that should not be in the
model, and the Logit function is a linear combination of the predictors. It could happen
that the Logit function as the link function is not the correct choice or the relationship
between the Logit of outcome variable and the independent variables is not linear. In
either case, we have a specification error. The misspecification of the link function is
usually not too severe compared with using other alternative link function choices such
as Probit (based on the normal distribution). In practice, we are more concerned with
whether our model has all the relevant predictors and if the linear combination of them is
sufficient. To detect specification error, stata command linktest was used as a post-
estimation command after the logistic command. The linktest specify that there is no any
other additional predictors which are statistically significant. Linktest uses the linear
predicted value (_hat) and linear predicted value squared (_hatsq) as the predictors to
rebuild the model. The variable _hat should be a statistically significant predictor, since
it is the predicted value from the model. This will be the case unless the model is
completely mis-specified. On the other hand, if our model is properly specified, variable
_hatsq shouldn't have much predictive power except by chance. Therefore, if _hatsq is
significant, then the linktest is significant. This usually means that either we have
omitted relevant variable(s) or our link function is not correctly specified.
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Table 5.9: Model Specification Test
Adoption coefficient Std. Err. Z P>|z|
_hat 1.00476 .2859894 3.51 0.000
_hatsq .0020583 .0771979 0.03 0.979
_cons -.0034182 .4036729 -0.01 0.993 Number of obs.
= 198
Prob > chi = 0.0000
Pseudo R2=0.6743 Log likelihood = -24.843209
Source: Author’s Computation from Survey Data
The variable _hatsq has a probability value of 0.979, implying that it is statistically
insignificant at the conventional level. Hence the result indicate that majority of the
relevant variable(s) has been included therefore the function is correct specified.
5.8.3 Goodness of Fit Test
The commonly used test of model fit is the Hosmer and Lemeshow's goodness-of-fit
test. The idea behind the Hosmer and Lemeshow's goodness-of-fit test is that the
predicted frequency and observed frequency should match closely, and that the more
closely they match, the better the fit. The Hosmer-Lemeshow goodness-of-fit statistic is
computed as the Pearson chi-square from the contingency table of observed frequencies
and expected frequencies. For the model to fits the data well then the p-value suppose to
be large when measured by Hosmer and Lemeshow's test.
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Table 5. 10: The Hosmer and Lemeshow's Goodness-of-fit test
Logistic model for adoption, goodness-of-fit test
Number of observation. 198
Number of groups. 10
Hosmer-Lemeshow chi2(8) 3.3
Prob > chi2 0.9144
Source: Author’s Computation from the Survey Data
From table 5.10, we see that the p-value is 0.91 showing that our model fits well our
data.
5.8.4 Multicollinearity Test
Multicollinearity (or collinearity for short) occurs when two or more independent
variables in the model are approximately determined by a linear combination of other
independent variables in the model. The degree of multicollinearity can vary and can
have different effects on the model. When severe multicollinearity occurs, the standard
errors for the coefficients tend to be very large (inflated), and sometimes the estimated
logistic regression coefficients can be highly unreliable. When perfect collinearity
occurs, that is, when one independent variable is a perfect linear combination of the
others, it is impossible to obtain a unique estimate of regression coefficients with all the
independent variables in the model. Also multicollinearity can increase the significance
level of the variables in the estimated model and even causing variables which were not
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significant to be significant due to the presence of multicollinearity. To test for
multicollinearity in our model we use variance inflation factor (VIF) which is an
indicator of how much of the inflation of the standard error could be caused by
collinearity. The average VIF which is recommended for estimation is below 5.
Table 5. 11: VIF for Multicollinearity test.
Variable VIF 1/VIF
lnagric 2.85 0.351126 Married 2.66 0.375971 headsex 2.66 0.376224 singleparents 2.61 0.382837 Number of household members. 2.52 0.396925 Primagric 2.43 0.412365 Secagric 2.33 0.428735 Gender 2.33 0.429615 Labforce 2.32 0.430412 Secondary 2.13 0.468747 Poor 1.96 0.511160 Primary 1.95 0.513428 Lnincome 1.68 0.595105 Age 1.60 0.625744 Cohesion 1.53 0.653929 Foodreserve 1.43 0.698258 Trust 1.41 0.707297 Primfishing 1.31 0.761142 Commitment 1.14 0.876768 Tertfishing 1.09 0.919152
Source: Author’s Computation from the Survey Data
From Table 5.11, the mean VIF is 2 which show that variables are not facing
multcollinearity and hence estimation can give reliable results.
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5.8.5 Results of Estimation and Interpretation of the Logistic Regression Results
This section represents the estimation of the entire sample by logistic estimations.
Table 5. 12: Odds Ratio of Logistic Regression on Adoption of Crabs Cage Farming as an Alternative Source of Income
Variable name Odds ratio Std. Z P>|z| Sign.level Socio-Demographic Variables
Age 0.987 0.389 -0.33 0.743 Gender 0.012 0.019 -2.87 0.004 *** Headsex 95.392 236.733 1.84 0.066 * Hholdmembers 1.510 0.358 1.74 0.082 * Lnagric 1.366 0.187 2.28 0.022 ** Married 5.613 12.318 0.79 0.432 Singleparents 0.541 1.050 -0.32 0.752 Secondary 0.098 0.238 -0.96 0.338 Primary 0.887 1.059 -0.1 0.92
Socio-Economic variable Labforce 0.855 0.354 -0.38 0.706 Primagric 0.060 0.103 -1.64 0.1 Secagric 0.035 0.064 -1.84 0.065 * Lnincome 1.966 0.878 1.51 0.13 Foodreserve 0.665 0.720 -0.38 0.706 Poor 11.751 13.944 2.08 0.038 ** primfishing 0.035 0.058 -2.03 0.043 ** tertfishing 0.025 0.232 -0.39 0.694
Socio-Capital Variables Trust 0.407 0.165 -2.21 0.027 ** Commitment 10.197 6.788 3.49 0 *** Cohesion 0.179 0.088 -3.51 0 ***
Note: ***, ** and * signals significance at respectively 1% 5% and 10% levels.
Observation: 198. Pseudo R2 = 0.6743
Source: Author’s Computation from the Survey Data
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Results from estimation (Table 5.12) indicate that gender is very important in
influencing adoption of crabs farming as an alternative source of income. Most of
variables in this study are dummies, to enhance interpretation of the results which are
given in comparison to the selected reference categories.
Results show that the odds of crabs farming adoption will increase by factor 0.012 for a
unit increase of female member of Pangani community. And this is represented by
gender coefficient which is less than 1 and significant at one percent level of confidence.
It means females can adopt faster crabs farming as the source of income than males.
This can be due to limited sources of income to females as the dominant source of
income in Pangani is fishing and females do not participate in fishing activities. So it
could be easier for them to adopt other new economic activities to supplement their
income. More specifically, it indicates that in Pangani the chance of female member of
community to adopt crabs farming as an alternative source of income is 1.2% of the
odds of male member to adopt.
Sex of the household head also influences adoption of crabs farming. For the
household’s who’s their head are males are likely to adopt crabs farming than
households with female head. The main reason for this can be that most families headed
by males are large (many household members). This means their daily expenditures are
large, therefore in order to balance with income then either head or wife or other
members have to engage in other economic activities to supplement income. From the
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result table it shows that probability of adopting crabs farming increase by the factor
95.4 for a unit increase of household’s headed by male.
Similarly number of household members also influences adoption of crabs farming. For
the household’s with many members the chances of adopting crabs farming is high
compared with household with few members. This could be due to large expenditures
the big household incurs than small household hence to match with income they are
likely to engage in new opportunities to create more income. From the result it shows
the chances for the individual to adopt crabs farming increase by the factor 1.5 for a unit
increase in household members. Therefore in household level, the sex of the head of
household and number of household members influence the probability of adopting
crabs cage farming as an alternative source of income.
The effect of agriculture income on log value of agriculture income is significant but
inconsistent with the observation and descriptive analysis. Descriptive analysis showed
that agricultural income significantly influenced the adaptation decision of the
household. And the reason for this could be the low income obtained from agriculture
which was mainly due to climatic variability which is affecting agricultural output.
Therefore in order for the farmers to supplement their income they adopt new
opportunity to create income. But results show the probability of an individual to adopt
crabs farming increase by the factor 1.4 for a unit increase in log of agriculture income.
But this inconsistence can be due to large initial costs of establishing crabs farming
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project. Therefore for the farmer to be able to adopt crabs farming needs enough capital
which he/she can raise from increasing agriculture income.
Individuals who engage in agriculture as their primary source of income have the low
chances to adopt crabs farming as their alternative source of income. The result shows
the odds ratio of an individual who engage in agriculture as the secondary source of
income not to adopt crabs farming is 3.5% of the odds of individual who engage in
agriculture as the tertiary source of income. Hence the probability of an individual not to
adopt crabs farming will increase by the factor 0.035 for a unit increase people who
engage in agriculture as the secondary source of income. For the fishing as the main
economic activity at Pangani the result shows the chances of the person who engage in
fishing as the primary source of income to not to adopt crabs farming as the alternative
source of income 3.5% of the odds of members who engage in fishing as the secondary
source of income.
Turning to social capital variables we find a number of interesting results. First of all,
the chances of an individual who have trust to his/ her neighbor to decide not to adopt
crabs farming is only about 40.7% of the odds of member without trust not to adopt. So
let us exponentiate the coefficient of trust (-0.8988) to get the probability of not to adopt
crabs farming as a function of trust variable. We get the probability of failure to adopt is
0.29. From the laws of probability says that when adding probability of adopting and
probability of not adopting you get 1. Now given the probability of failure to adopt, then
the probability of adopting crabs farming as a function of trust is 0.71. For the
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commitment, estimated results show that; chances for the individual to adopt crabs
farming increase by the factor 10.2 for a unit increase in commitment of individuals in
their respective groups/ association. This can be due to nature of crabs farming activity
which is mainly done in groups rather than individually at Pangani. Therefore if
members are committed they will be able to engage in new activities together so as to
improve their standard of living. The last variable measuring social capital in our model
is cohesion (unity). Estimated results shows that the probability of individual who have
unity in his/her society, not to adopt crabs farming is very low about 18% of the odds of
individual without unit to fail to adopt crabs farming. Calculating the probability of an
individual to adopt crabs farming as a function of cohesion is 0.85, this explain that
when people are ready to act together for their development and protecting their
environment then most of them will be ready to adopt crabs farming as a source of
alternatives income and as a means of conserving environment. Therefore the results
confirmed the importance of social capital in adopting new technology in any
community as suggested in many literature (Nyangena 2006, Putnam 1993).
When speaking of poverty and means of alleviating poverty along coastal community
the estimated result of the poverty index is as expected. From the result it shows that the
chances for individual to adopt crabs farming as an alternative source of income increase
by a factor 11.75 for a unit increase in poverty in the community. Since crabs farming
was introduced as a means of providing income and reducing the growing poverty
incidence in coastal community, then it shows that for the growing of poverty level the
probability of individual to adopt crabs farming to fight on poverty is higher.
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5.9 Conclusion
This chapter presents the data and interpretation of the estimation concerning crabs
farming adoption as alternative source of income in Pangani shoreline villages. Logistic
regression analysis was employed in this study. Results from the estimation suggest
number of variables which influence people to adopt crabs farming.
The evaluation of cost benefit analysis suggests that the project of crabs farming is
viable as it yield positive profit to farmers. Although the initial costs of establishing the
project and the labour costs (which was calculated basing on the minimum wage of
Tanzania) required is high but due to high prices existing in the world market then crabs
farming yield positive profit.
The estimation results suggest that due to poverty nature across the coast zone of
Tanzania and which face intensively women then female are likely to adopt crabs
farming than male. Education in this study was unable to explain crabs farming adoption
decision, this could possibly be due to the fact that there is no much variation is
education as majority of the shoreline villagers have attended up to primary schools,
thus can read and write. Social capital shows big influence on adoption of new
opportunity as suggested by literatures. Trust between each other, unity and commitment
lead to flow of information of new opportunities and rise awareness to people on
existing new opportunities. Increasing of poverty level seems to be another reason
influencing adoption of crabs farming. Since agriculture had been much affected by
climatic variability around the coast and most of the coastal communities rely on marine
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resources then farmers have been worse affected by climatic variability and the resulting
rainfall unreliability than fishermen. Hence as an adoption measure to climatic
variability farmers seem to be engaging in mud crab farming to supplement their
dwindling income.
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CHAPTER SIX
CONCLUSION AND RECCOMENDATIONS
6.0 Introduction
This chapter provides concluding remarks based on the field survey and from the
analysis. Based on the findings, some policy implications to influence new technologies
and opportunities which are more supportive to our environment were formulated. We
conclude by suggesting areas for further studies that were identified but could not be
explored.
6.1 Main conclusion
Environmental conservation and climatic changes adoption in Africa is of major concern
currently. Therefore in order for people to cope with climatic changes for their survival,
then adoption of new technologies which are more appropriately is important for income
stabilization and improvement. Mangrove crabs farming is among new opportunity arise
to coastal people. From the chain value analysis mangrove crabs farming conducted by
SEEGAAD project found that the project is viable, showing a promising future due to
high price existing in the world market and can improve income of people around the
coast. The project was established and in 2009 there were about five groups engage in
crabs farming generating total revenue of $7,796 per year. However, by February 2011
only about three groups were remaining after the other failed to continue with mud crab
farming. Collapse in groups can be due to lack of commitment and unity in the groups
and therefore for any community project social capital is important for the project to
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succeed. This is not as expected in the value chain analysis. Since crabs farming is
profitable, and then this study analyzes factors that will influence people to adopt crabs
farming as an alternative source of income. It was found that most of people who adopt
crabs farming are those who engage in agricultural activities and this is due to declining
agricultural income. Also data shows that there is significant income difference between
those who are aware of crabs farming and those who are not aware of crabs farming.
Therefore crabs farming improve income level of individuals who adopted it as it was
expected.
In adoption, social capital play a greater role in influencing crabs farming activity than
education and age as was suggested by literatures. Previously most of literatures suggest
education as the main influential factor of adoption but in areas where there is no much
variation in education level attained by people then education does not play a big role.
Therefore this has raised the importance of social capital which previously was ignored
and more emphasis was on human and physical capital. From the results it was found
that trust between neighbors, unity and commitment will influence people to engage in
crabs farming and benefitted from it individually and communally.
From analysis it shows there is a significant income differences between those who have
adopted crabs farming to those who have not adopted crabs farming. The mean income
of the people who have adopted crabs farming is high compared to those who were even
not aware of crabs farming. Therefore crabs farming have significant income
contribution to the adopter and can help in reducing poverty level along the coast.
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6.2 Recommendations for policy
Mariculture is among coastal income strategy promoted for sustainable use and
management of natural resources. This includes seaweed farming, mangrove crabs,
prawn and milkfish farming. All these activities aim at alleviating poverty among coast
community especially to women. And these can contribute to more effective policy
responses to alleviate poverty among people and sustainably manage marine
environment and biodiversity balance.
For the MDGs to be achieved by the year 2015, restructuring of income strategy which
fit the current global climatic changes and which are environmentally considerate must
remain a top priority. Among the main goal of MDGs to reduce poverty level and from
our study we found out crabs reduce poverty incidence around the coast by sustainable
use of the marine resources.
Another priority is for increasing employment level and reduces unemployment and
underemployment level in Tanzania. There is a need for greater emphasis on crabs
farming to employ more people. This will increase income and improve standard of
living for the farmers and hence facilitate economic growth and development.
Since crabs farming is accepted mostly by women, this sector needs support from
government so as to empower women in coastal communities. Culturally, coastal
women’s poverty is in critical situation because most of them are housewives and
depend on their husbands for provision. This increase in dependence ratio in the
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household led to increase poverty level among women. Therefore there is need of
support from government in crabs farming so as to empower women who are among
major participants. Due to high initial costs to run the projects, government and other
financial sector need to provide loans to people who are ready to adopt crabs farming.
Crabs market in Tanzania is not well competitive since there is only single buyer and
hence reduce competition and lower the market price. There is need for more market
liberalization within the country rather than existing monopsony system and other policy
support to boost crabs market. Therefore the country needs to attract more exporters to
export crabs outside the country and also government to be among exporter. This will
raise competition among buyers and hence price will go up.
6.3 Recommendations for Further Research
The study assesses the factors for adoption of crabs farming as an alternative source of
income. This study discovers the concern of social capital apart from financial capital
and human capital in innovation and development. There are new opportunities which
have been introduced in mariculture and financial support and training have been done
to support them. But still there are need to study them to find out influential factors apart
from financial capital and human capital for them to achieve intended goals. This will
enable proper decision making.
89
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APPENDIX
SAMPLE QUESTIONNAIRE
District Pangani Ward_____________Village____________________Subvillage/hamlet_________________
Interviewer _________________________________________ Code __________ Checked by _______________
Start time: _____:_____ Date: ____/____/____
Good morning (afternoon) my name is .......and I am from the University of Dar es Salaam. We are conducting a survey to better understand how community are engaged in Mud Crab farming. We would like to administer this survey to an adult in your household who participates in the household’s decision-making. Are you someone with this role in your household? [IF ANSWER IS NO, ASK FOR SOMEONE WHO DOES PARTICIPATE IN THE HOUSEHOLD’S DECISION MAKING] Thank you
Part 1: Demographics, Education and Work
1. Is the respondent male or female (do not ask just tick) Male[1] Female[2]
2. Age of respondent _______.
3. Marital status
Single [1] Married [2] Divorced [3] Widowed [4]
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4.What is the highest level of education achieved by the individual?
None [1] Primary education [2] Secondary education [3] college education [4]
5.Are you the head of the household? Yes [1] No [2]
6. Do you participate in decision making for the household? Yes [1] No [2]
7.How many members of the household? Specify by age and sex?
Age group Female (tick) Male(tick) 0 - 6 6 – 18 18 - 45 45 - 60
60 >
8 (a).How long has this household lived in this village? ______ Years (If more than 20 years go to part 2 )
8(b). (For those households who arrived in the last 20 years) where did you move from? (Circle one) (Note that we need to be explicit here that reference is only to head of the household at hand, in case when spouses moved in different years:
99
1) Other village in this ward (specify)_____________________
2) Other ward in Pangani District (specify) __________________
3) Other District in Tanga region (specify) ___________________
4) Other Region (specify)________________________
PART 2: INFORMATION ABOUT MUD CRABS FARMING
9. Are you aware of mud crabs farming YES [1] NO [2]
(If no go to part 3........)
10. How did you get the information about mud crabs farming?
Local government administration [1] Neighbourhood [2] Non government organisation [3] Other sources (specify) [4] _____________
11. Do you participate in mud crabs farming? YES [1] NO[2] (If no go to part 3 ……………)
12. How long did it take before you decide to engage in mud crabs farming since you heard about it.
Less than six months [1] One Year [2] More than one year [3] Other/specify[4]__________
13. Is it an individually owned project or a communal project?_____________,
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14. (a) Did you get any support to start your project YES [1] NO [2]
14 (b). If yes can you specify what type of support did you get? Financial [1] training [2] Others (specify)_______ [3] 15. Do you operate saving or current account? YES [1] NO [2] 16. (a) Did you take a loan during the last 12months? YES [1] NO [2] 16 (b) If yes can you identify the source of loan, if no go to question 19. Bank [1] informal savings and credit groups [2] Other district or central Govt loan scheme [3] relative [4] Others/specify__________.
17. What was the primary use of the loan taken by the first member:
(a) Mud crabs farming (b) general living expenses (food) (c) medical expenses (d) school fees/ education expenses (e) Funeral expenses (f) house construction (g) purchase fishing gears or boat (h) purchase farming inputs (pembejeo) (i) other small business (j) other (specify)________.
18. What was the total value of loans for the HH in past 12 months
(a) less than 50,000 TSh [1] (b) 51,000-100,000 [2] (c) 101,000-250,000 [3] (d) 251,000-500,000 [4] (e) 500,000 – 1million [5] (f) 1 million to 5 million [6] (g) above 5 million [7]
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19. How did you get the shore land to establish your pond for mud crabs farming? Bought it [1] Rent it [2] Inherited [3] Other, specify _________ [4]
20 (a) Do you get any help to transport your product? YES [1] NO [2] 20 (b) If yes specify kind of support
Storage facilities [1] Other/specify_________ [2]
21. How much did you incur as a cost of production for establishing the cage and feeding juvenile till the time of marketing them? Activity Cost Total cost The farming shore (rented/bought)
Establishing the cage Feeding the juvenile Transporting the product to the market.
Time involved per day. Any other cost, specify
____________.
GRAND TOTAL
22. How long does it take to harvest your product? (days/month?)_____________
23. How many harvest per year?______________________________
24. On average, how many Kilograms’ you get in each harvest? _______________. 25 How much sold out of total harvest____________________________
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26. How much do you sell your product per Kgs to the middlemen?_____________ 27. What are the sources of mud crabs market information (e.g. price of the product,
market allocation) Groups/association [1] relatives, friends, neighbours [2] government agents [3]
Community leaders [4] others/specify__________________.
PART 3: INFORMATION ABOUT WEALTH
28. Please indicate kind of property you own Property tick quantity Value of
property A Block house Motor bike Agricultural land business electricity Boat without engine
Boat with engine Ngalawa/canoe Jahazi Fishing nets Bicycle Radio Panga Jembe Other/ specify
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29. Can you specify other sources of income for the previous year.
Harvest Amount Value per each Amount sold Amount consumed
Maize Rice Millet Cassava Livestock Fishing Wages Trading or shop keeping.
Crab farming. Other small business eg.fundi.
Other/specify.
PART 4: INFORMATION ABOUT FOOD SECURITY
30 (a) How many meals do you usually get per day?__________.
30 (b) In the past 30 days have you ever had fewer meals than this usual number?
YES [1] NO [2]
If yes how many days? _________.
31. Do you have food reserve from one harvest season to another harvest season?
YES [1] NO [2]
32. In the past week how many days did the household consume the following?
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Number of days
Meat
Fish
Eggs
Milk/Dairy products
Beans/Legume types
33. How often in the last year did you have problems of satisfying the food needs of the household?
Never…………………………… = 1
Seldom…………………………. = 2
Sometimes……………………… = 3
Often…………………………… = 4
Always…………………………. = 5
34. How do you compare the overall economic situation of the HOUSEHOLD with one year ago?
Much worse now………………. = 1
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A little worse now……………… = 2
Same……………………………. = 3
A little better now………………. = 4
Much better now…………….… = 5
Don’t know………………….… = 6
35. How do you compare the overall economic situation of the COMMUNITY with one year ago?
Much worse now……………….. = 1
A little worse now………………. = 2
Same……………………………. = 3
A little better now………….……. = 4
Much better now………………… = 5
Don’t know……………………… = 6
36. How does this household compare with the others in this COMMUNITY?
Much worse now………………. = 1
A little worse now……………… = 2
Same……………………………. = 3
A little better now………………. = 4
Much better now………………. = 5
Don’t know………………….… = 6
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37. Please indicate the main sources (ie. including both cash income and produce etc.)
of support to the livelihood of the household and the approximate proportion contributed
by each source:
Primary Secondary Tertiary Crab farming % % % Fishing (other - octopus, shellfish etc.) % % % Agriculture % % % Livestock % % % Mariculture (seaweed, milkfish, crab etc) % % % Trading or shop-keeping % % % Other small business/ eg. fundi % % % Salary from employment by District/Govt % % % Salary from employment in tourism % % % Salary from employment in fish processing % % % Other (specify) % % %
PART 5: MATERIAL LIFE STYLE OF THE HOUSEHOLD
38. Does the head of this household run any other house outside of this compound in which he keeps a spouse in a polygamous marriage?
Yes = 1
No = 2
39 (a) If the answer to question 46 above is yes, how many households does the head keeps at the moment?
40 (b) Is this household owned by the head of the household?
Yes = 1
No = 2
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41. How many rooms are in the main house of this household?
42. What is the type of wall that this house is made of?
(a) grass and sticks (b) Mud, un-burnt mud bricks and sticks (c) Bricks, burnt mud bricks, stones, coral and similar material (d) Other, specify……………………………………….
43. What are the materials used for roofing in the house?
(a) grass, leaves, bamboo (b) mud and grass (c) concrete, cement (d) metal sheets (GCI) (e) asbestos sheets (f) Tiles (g) other (specify)………………………..
44. What is the floor of this house made off?
(a) Earth (b) concrete, cement, tiles, timber (c) other (specify)………………………………………..
PART 6: Survey questions used to extract social capital information.
45. About how many close friends do you have these days? (these are the people you can talk to about private matters, or call for help)___________________.
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46. Suppose you suffered a serious economic setback, how many people could you turn to for help in this situation beyond your immediate family?
No one [1] one to two [2] three to four [4] five or more people [5]
47. (a) In the past one year, how many people with a personal problem have turned to you for assistance?____________. 3.(b).If so, please state the value/amount ______________.
48. Do you or any member of the household belong to any group or association?
YES [1] NO [2]
Read the possible types of the lists (farmers group, traders and business association, church,
Soccer club, agricultural club, credit/finance group, village committee, political group, cultural
NGO or any other/specify_______________)
49. How many times in an average month do you participate in each of these groups’ activities, e.g. by attending meeting and group work?____________.
50. How much money, time or goods did you contribute to the last year? (a) Money(amount Tsh) (b) time (hours) (c) goods(value
Tsh)
_____________ _____________ _________________.
51. If a community project does not directly benefit you, but has benefit to many other in the community, would you contribute time, goods or money to it?
Time YES [1] NO [2] Goods YES [1] NO [2] Money YES [1] NO [2]
END OF THE QUESTIONNAIRE.