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SOUTHWESTERN UGANDA SUSTAINABLE NATURAL RESOURCES MANAGEMENT (SUNAREM) PROJECT
COMMUNITY AND HOUSEHOLD-LEVEL INCOME & ASSET STATUS BASELINE SURVEY REPORT1
Rhona Walusimbi2
Ephraim Nkonya3
April 2004
AFRICARE- Uganda International Food Policy Research Institute, Kampala Office, Uganda
1 The authors are grateful to U.S. Agency for International Development (USAID) for their financial support of this research through ECOTRUST, and AFRICARE; to the local government officials from district to the village level for their excellent facilitation of the research, AFRICARE –Uganda staff for their collaboration; to the district officials who attended a workshop in Kabale to discuss these research findings for their useful comments and advice on the results; and especially to the many farmers and community leaders who graciously and patiently participated in the study. We are grateful to Patrick Lubega, Alex Tatwangire, Moses Odeke and Michael Opige for data entry and cleaning assistance and for Edward Kato and Ronnie Babigumira for providing data analysis support. We are grateful to Dr. Simon Bolwig for supervising this research and for giving constructive comments on an earlier draft of this report. Any errors are solely the responsibility of the authors. 2 IFPRI Kampala Office 3 IFPRI Washington
TABLE OF CONTENTS
EXECUTIVE SUMMARY ...............................................................................................................v
INTRODUCTION .............................................................................................................................1
Background to SUNAREM Project...................................................................................................1
Justification and Objectives of the Baseline Survey ..........................................................................3
Expected Outputs ...............................................................................................................................3
CONCEPTUAL FRAMEWORK ......................................................................................................4
RESEARCH HYPOTHESES ............................................................................................................6
METHODOLOGY ..........................................................................................................................13
RESULTS ........................................................................................................................................16
Descriptive statistics ........................................................................................................................16
Determinants of Income, Asset, Choice of Income Strategies and Participation in Programs
and Organizations............................................................................................................................20
CONCLUSIONS AND POLICY IMPLICATIONS .......................................................................32
REFERENCES ................................................................................................................................36
APPENDIX 1: SUNAREM PROJECT OBJECTIVES, EXPECTED OUTPUTS AND
ACTIVITIES....................................................................................................................................38
APPENDIX 2: DETAILS OF WEALTH RANKING RESULTS AND PROCEDURES..............40
APPENDIX 3: APPROACHES TO DATA AND ECONOMETRIC PROBLEMS.......................42
APPENDIX 4: DETAILED TABLES OF DESCRIPTIVE ANALYSIS RESULTS....................43
APPENDIX 5: DETAILED TABLES OF ECONOMETRIC ANALYSIS RESULTS..................58
LIST OF TABLES
Table 1: Survey Enumeration Area and Sample Size......................................................................14
Table 2: Highest Education Level of Household Head in 2002 (% reporting)................................17
Table 3: Highest Education Level of Spouse in 2002 (% reporting)...............................................17
Table 4: Primary Income Sources in the Watershedsin 2002 (%hhds reporting)............................17
Table 5: Total Gross Income and Contribution of Different Income Sources in 2002....................18
Table 6: Average household Per Capita Gross Income in the Watersheds in 2002 ........................18
Table 7: Average farm size & value of livestock and equipment/durable goods owned by
households in 2002 ..........................................................................................................................19
Table 8: Determinants of Household Income and Assets................................................................22
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Table 9: Determinants of Choice of Major Source of Income of the Household Head...................25
Table 10: Determinants of Participation in Programs and Organizations (Probit models) .............29
Table A2.1 - Estimation of Sample Size for Kagyeyo Village........................................................40
Table A2.2: Characteristics of Wealth Groups in the SUNAREM Sample Villages ......................41
Table A4.1 - Access to Agricultural and Other Training and Extension ........................................43
Table A4.2 - Access to AFRICARE Training and Extension Services in 2002..............................43
Table A4.3 - Access to Credit in 2002 ............................................................................................43
Table A4.4 - Main Focus of Programs and Organizations in 2002 .................................................44
Table A4.5 - Household Membership in Programs and Organizations in 2002..............................44
Table A4.6 - Livestock Ownership in 2002 ....................................................................................44
Table A4.7 - Main Food Crops Grown by Households in 2002......................................................45
Table A4.8 – Main Cash Crops Grown by Households in 2002 .....................................................46
Table A4.10 - Average Area and Number of Commercial Woody Trees per Land Parcel
Owned by Households in 2002........................................................................................................47
Table A4.11 - Perceptions of Change in Resource Conditions Since 1997 ....................................48
Table A4.12 - Restrictions on Private Land that are Related to Land Management in 2002 .........49
Table A4.13 - Household use of Agricultural Technologies and Practices in 2002 ......................50
Table A4.14 – Household use of Soil and Water Conservation Technologies in 2002 ..................50
Table A4.15 - Level of Enforcement of Restrictions relating to Land Management on Private
Land in 2002 ....................................................................................................................................51
Table A4.16– Participation in Collective Action ............................................................................52
Table A4.17 - Proportion of the Farm Harvest Sold by Farmers’ ..................................................52
Table A4.18 - Type of Markets Mainly Utilized by Farmers to Sell their Crop Produce ..............53
Table A4.19- Average Distance to Nearest Agricultural Market (Kilometers)...............................53
Table A4.20 - Average Distance to Nearest All Weather Road in Kilometres .............................53
Table A4.21 - Major Marketing Problems ......................................................................................54
Table A4.22 - Farmer Willingness to Sell Produce as a Marketing Group ....................................55
Table: A4.23 - Desired Ways to Improve Marketing .....................................................................55
Table A4.24 - Most Effective Way of providing Price and Market Information ...........................55
Table A4.25 - Storage of Farm Harvests ......................................................................................55
Table A4.26 - Perceptions of Change in Welfare of Households Since 1997 .. ............................588
Table A5.1: Determinants of Household Income ...........................................................................58
Table A5.2: Determinants of household assets................................................................................59
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Table A5.3 - Determinants of Major source of income of the household head (Multinomial
logit model) .....................................................................................................................................62
Table A5.4: Determinants of Participation in Programs and Organizations (Probit models) .........62
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ACRONYMS AND ABBREVIATIONS
AFRENA Agro forestry Research Network for Africa
ARD Agricultural Research and Development
COBS Conserve Biodiversity for Sustainable Development Project
CBO Community Based Organization
ECOTRUST Environmental Conservation Trust of Uganda
HPI Heifer Project International
ICRAF International Center for Research in Agro forestry
IFPRI International Food Policy Research Institute
MBIFCT Mgahinga and Bwindi Impenetrable Forest Conservation Trust
NAADS National Agricultural Advisory Services
NARO National Agricultural Research Organization
NEMA National Environmental Management Authority
NGO Non Governmental Organization
NRM Natural resource management
SPEED Support for Private Expansion and Development Project
SUNAREM Southwestern Uganda Sustainable Natural Resources Management Project
UFSI Uganda Food Security Initiative
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EXECUTIVE SUMMARY
Lack of data and empirically supported policy recommendations for guiding
formulation, implementation and evaluation of rural development strategies and policies is a
big challenge that faces many project managers in Uganda. This study was undertaken to
support AFRICARE’s Southwestern Uganda Sustainable Natural Resources Management
(SUNAREM) Project. The study reports the baseline statistics of key income and wealth
indicators and other poverty measures. The research involved a survey of 15 communities
and 203 households in the year 2003.
The findings of the study support SUNAREM’s strong investment in agriculture and
natural resource management (NRM) extension and training programs as primary means of
reducing poverty in the southwestern region of Uganda since these efforts contribute to
increasing both household income and wealth. However, our results show that participation in
agricultural extension and training is less in remote areas suggesting the need to target such
programs in areas with limited access to market. In using extension and advisory services to
address poverty, it is crucial to ensure that the approach is responsive to local demands and
conditions. Similarly, efforts to improve market access significantly contribute to poverty
reduction. However, Nkonya, et al., (2004) showed that access to market also contributes to
soil nutrient depletion due to harvesting and selling agricultural products without adequate
replenishment of the exported soil nutrients. Hence, agricultural commercialization and
efforts to improve market access need to be implemented together with efforts to address land
degradation problems.
Education also increases household income and asset stocks suggesting that such
efforts are crucial in addressing poverty in the region. SUNAREM therefore needs to
consider promoting investment in education as a way of reducing poverty in the region.
v
Participation in non-farm activities increases income and asset stock. This suggests
that non-farm activities are important in addressing poverty. However, there are potential
barriers to entry into non-farm activities since a start-up capital and skills are required
(Barrett, et al., 2001). The fact that we did not find evidence that access to credit is a major
factor influencing income or participation in non-farm activities suggests that the mostly
informal, short-term and small loans that the farmers are currently getting does not appear to
overcome barrier to entry into non-farm activities. This points to the need to develop rural
finance institutions to provide credit for agricultural modernization and for addressing
poverty. Post-primary education increases the probability to participate in non-farm activities
implying that education is important not only in increasing income directly, but also
indirectly through its positive impact on non-farm activities. To increase the competitiveness
of non-farm products, farmers’ skills in making them need to be increased through training in
polytechnic and vocational schools based in rural areas.
Assets, as expected are crucial in increasing income suggesting that poor farmers with
limited access to land, livestock and agricultural equipment are likely to be caught in a
poverty trap. These results suggest the need to target small and poor farmers through non-
asset interventions that increase income. According to this study, such efforts are agricultural
extension, education, programs and organizations focusing on agriculture, NRM, and health,
improvement of access to market and promotion of non-farm activities. Supporting
community-based organizations with a focus on social/mutual support is also an important
strategy for increasing income.
The Southwestern highland region has an enormous potential for improved livestock
production. However, the region’s livestock production potential is dogged by limited market
access, and lack of livestock product processing capacity in the region. Our results show that
access to market, credit, agricultural extension and training services increase probability to
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have livestock as the primary source of income. Given that livestock products, such as milk
and meat are highly perishable, the need to have better market access and processing capacity
in the region is apparent. Extension and training services in controlling pests and diseases and
managing both local and improved livestock are also crucial in promoting profitable
production in the region.
Given that the southwestern highlands region of Uganda has abundant forest
resources, forest-based livelihoods are important options that need to be developed. Due to
their labor-intensive nature, household head being male and the size of the household
enhances forest-based livelihoods. Access to market and extension services and being in
Rukungiri rather than Kabale increase participation in forest-based livelihood. These results
imply that female-headed households are less likely to benefit from forest resources as
primary sources of income. Training women in carpentry and facilitating them to acquire
forest-harvesting and processing equipment may increase their opportunities to participate in
forest-based livelihoods. However, both the government and her development partners
recognize the current deforestation rate in the region. Thus, it is important to sensitize and
educate farmers on how to sustainably produce and harvest forest products. Presently, forest
production and utilization extension efforts are limited, pointing to the need to increase such
efforts in the region.
Age of household head, size of the household, access to market and presence of such
organizations in the community enhance participation in programs and organization that
focus on agriculture, natural resource management and health. This suggests that these
programs need to target the youth, smaller families, and farmers in remote areas.
Farmers mainly form Social/mutual support organizations as community based
organizations (CBOs). Our results show that these organizations are crucial in increasing
access to informal credit. This implies there is need to use these social/mutual CBOs to
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increase the low access to formal credit. For example, CBOs with major focus on
social/mutual support may take the role of credit intermediation between financial institutions
and its members.
viii
INTRODUCTION
Background to SUNAREM Project
AFRICARE is an international non-government organization with operations in
around 25 countries in sub Saharan Africa. Its principal intervention areas are civil society
development and governance, food security and health (HIV/AIDS). In Uganda, AFRICARE
has focused on food security. AFRICARE -Uganda is currently implementing the second
phase of its Uganda Food Security Initiative Project (UFSI). The project is based in
southwestern highlands (SWH) region of Uganda. SWH is among the richest ecological
regions in terms of biodiversity and endemism and is therefore a significant attraction to
tourists. The SWH region includes parts of the districts of Bundibugyo, Bushenyi, Kabale,
Kabarole, Kasese, Kisoro, Ntungamo, Kanungu, and Rukungiri. However, this study will be
limited to districts where AFRICARE works namely Kabale, Kanungu, Kisoro, Ntungamo,
and Rukungiri. The SWH region is characterized by high population density areas and hilly
and mountainous areas that accelerate soil erosion. Most areas in the SWH rise above 1500
meters above sea level (m.a.s.l.). The high Rwenzori Mountains and the Virunga volcanic
mountains characterize the topography of the region (Clausen, 2001). The major concerns in
the region are land degradation and threat to the biodiversity, especially with the current
emphasis on commercialization of agriculture (Clausen, 2001).
The UFSI project focuses on soil conservation, increasing agricultural productivity,
human nutrition, and improving household food access through improved road/market access
and increased disposal income. The first phase of UFSI covered Kabale District and the
second phase expanded to Kanungu, Kisoro, Rukungiri and Ntungamo Districts. The second
phase of UFSI has five components namely agriculture and post-harvest handling;
community nutrition and sanitation; community roads construction; marketing; natural
resources management; and monitoring and evaluation. The natural resources management
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component is implemented under the USAID/ECOTRUST funded 4 Southwestern Uganda
Sustainable Natural Resources Management (SUNAREM) Project. SUNAREM started in
2002 and will run for two years with a possibility of expansion, funds allowing.
SUNAREM’s goal is to improve the management of natural resources for expanded
economic opportunities for southwestern Uganda. It seeks to achieve this goal through
building the capacity of local communities to plan and implement natural resources
management activities and training them in production enhancing technologies.5 Appendix
1 summarizes the objectives, expected outputs and activities of SUNAREM. The project
outputs are expected to contribute to increased access for farmers to food production
technologies; improved resource utilization in selected critical landscapes; increased market
access of rural enterprises through promotion of quality production technologies; and
increased provision of services and effective advocacy for environmental and natural
resources policies. The project is implemented at watershed level6. This unit of intervention
is more suitable for the mountainous landscape of southwestern Uganda with its small and
fragmented farms than individual farms or villages. The project plans to cover a total of
eleven watersheds with serious natural resources management and productivity problems (i.e.
three watersheds each in Kabale, Kisoro and Rukungiri districts and one each in Kanungu
and Ntungamo districts). Implementation work begun with one watershed in each district.
AFRICARE’s principal collaborators for SUNAREM are Local Government from district to
4 The Environmental Conservation Trust of Uganda (ECOTRUST) is an indigenous NGO that provides technical support and funding for biodiversity conservation and poverty alleviation initiatives in Uganda. SUNAREM is funded by a grant awarded to ECOTRUST by USAID/Uganda under the USAID/Uganda Expanded Sustainable Economic Opportunities for Rural Sector Growth Strategic Objective 7 (S07). The SO7 is one of the three strategic objectives of USAID /Uganda’s six year Integrated Strategy Plan (ISP). Implemented in 2001, the main goal of ISP is to assist Uganda to reduce mass poverty. This goal directly supports the Government of Uganda‘s Poverty Eradication Action Plan’s (PEAP) overall goal of reducing absolute poverty to less than 10 percent by 2017. 5 Innovations to increase agricultural productivity while maintaining or enhancing the state of natural resources have been developed in pilot areas in southwestern Uganda. However in order to scale up implementation, it is important to build the capacity of local communities to implement these innovations. 6 A watershed consists of all the land and waterways that drain into the same body of water
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village level, NEMA, NAADS, ICRAF-AFRENA, NARO, CARE, MBIFCT, SPEED, NPI, ARD-
COBS and HPI
Justification and Objectives of the Baseline Survey
During a progress review of SUNAREM in 2003, it was noted that the UFSI project
baseline survey did not collect information that would enable AFRICARE to assess the
livelihood impact of the project. A decision was therefore made to undertake this baseline
survey in 2003. The survey was also designed to provide information that could be used to
improve implementation of the project.
Objectives
The specific objectives of the survey were:
1) To collect and analyze data in five watersheds in southwestern Uganda that will i)
increase AFRICARE’S ability to assess the household income and asset status impact of
the SUNAREM project and ii) improve the quality and effectiveness of project
implementation through an improved understanding of the characteristics and
determinants of household income choice of livelihood activities and participation in
programs and organizations in the five watersheds.
To help build the technical capacity of AFRICARE-Uganda staff to conduct socio-economic
baseline surveys.
Expected Outputs The expected outputs of the survey were:
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1) A well documented database of community characteristics and household incomes and
asset status in five watersheds in southwestern Uganda.
2) A technical report on community characteristics, household income levels and asset status
and factors influencing household income and choice of livelihood activities (income
sources) in the selected watersheds of southwestern Uganda.
3) Increased technical capacity of AFRICARE-Uganda staff in conducting socio-economic
baseline surveys and handling and analyzing data on household incomes and asset status.
CONCEPTUAL FRAMEWORK
In the conceptual framework, we consider the theoretical determinants of household
income and assets. We also consider the possible determinants of choosing major sources of
income and participation in programs and organizations with different focus. Household
income is determined by household level factors such as households’ endowments of physical
assets (PC) (e.g., livestock and equipment), and “human capital” (HC) (assets embodied in
people’s knowledge and abilities, such as education, experience, and training). Other possible
determinants of income are household “social capital” (SC) (assets embodied in social
relationships, such as through participation in organizations or networks), “financial capital”
(FC) (access to liquid assets, including credit and savings), and natural capital (NC) (assets
embodied in natural resources, including the quantity and quality of land, trees, and access to
other resources). Choice of sources of income, hereafter referred to as income strategies (IS),
also determines income and asset.
Choice of major sources of income is determined by many village level factors,
namely, agricultural potential, access to markets, and population density (Pender, Scherr and
Duron 2001; Pender, Place and Ehui 1999). These factors largely determine the comparative
advantage of a location by determining the costs and risks of producing different
4
commodities, the costs and constraints to marketing, local commodity and factor prices, and
the opportunities and returns to alternative activities, such as farming vs. non-farm
employment, etc.7 These factors may have generalized village level effects on income
strategies, such as through their impact on village level prices of commodities or inputs, or
they may affect farm household level factors, such as average farm size.
Government policies, programs, organizations and institutions may influence income
strategies and their implications for production, resource conditions, and household income at
many levels. Policies, organizations, or programs may seek to promote particular income
strategies (e.g., non-traditional export cash crop production), or may seek to address
constraints arising within a given income strategy (e.g., startup capital for non-farm activities,
credit needs arising in cash crop production). Programs may attempt to address land
management approaches directly, for example by promoting particular soil fertility
management practices, as is the case with SUNAREM.
The key questions of interest in this study are determinants of household income and
assets, choice of income strategies, and participation in programs and organizations. Answers
to these questions will guide government and other stakeholders to design effective and
efficient policies for addressing poverty and redressing the land degradation problem.
7 Comparative advantage refers to the profitability of the economic activities (or more broadly income strategies) that a group of people may pursue, relative to other activities that could be pursued by that group (Stiglitz (1993), p. 61). Having a comparative advantage in a given activity does not imply that the group earns more profit from the activity than could other groups (that would be absolute advantage); rather it means that the group profits more by pursuing that activity than other activities, and by trading with others who have comparative advantage in pursuing other activities. Comparative advantage can be defined for groups of different sizes at different scales (e.g., nation, region, community, household, individual), though is most commonly discussed at a national scale in discussions of trade theory and policy. In this study, we focus on comparative advantage of income strategies at the household level.
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Determinants of major sources of income and asset status
Household income is the sum of income from crop production, livestock production,
income from non-farm activities (e.g., trading, selling handicrafts, beer brewing, making
bricks, selling poles or charcoal) and transfers. Decisions about allocation of labor and other
assets to these different activities determine the household’s income. As stated earlier,
income and asset stock are determined by IS, PC, HC, NC, NC, and SC; village level factors
that determine local comparative advantages, including agroecological conditions, access to
markets and infrastructure, and population density (X); household endowment of labor (L),
and random factors (uy):
1) Income or asset ),,,,,,,,( yuXSCHCFCPCNCLISy=
Land rights and tenure characteristics may influence choice of major sources of
income. However, we will not include this variable since land tenure in the study region is
uniform.
Determinants of income strategy choice and participation in programs and organizations
Choice of income strategies and participation in programs and organizations are
influenced by household endowments of labor, physical, human, natural, social and financial
capital, and the local factors that determine comparative advantage. Thus, household choice
of income strategy and participation in programs and organizations depend upon those same
factors:
2) IS or PO ),,,,,,,( yuXSCHCFCPCNCLy=
where PO is participation in programs and organizations.
RESEARCH HYPOTHESES
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We now discuss the possible relationships of several policy relevant factors on the decision
variables (income strategy, participation in program and organizations, and income and
assets). The causal factors considered include watershed, which will represent the agro-
ecological zones, access to markets and roads, and farm size (Xv and part of NCh), access to
credit (FCh), participation in technical assistance programs (part of HCh) and organizations
(SCh), education (part of HCh), and physical assets such as livestock and equipment (PCh).
Agricultural potential
Agricultural potential is an aggregation of biophysical factors namely, climate,
biological, physical and chemical characteristics of the soil, topography, altitude,
temperature, and biodiversity. In this research, we will represent agricultural potential by the
watersheds. Since farmers depend on farming, agricultural potential is likely to increase their
income if other factors are held constant. Agricultural potential is also likely to dictate the
types of income strategies that can be supported this potential. For instance, it may not be
feasible to grow perennial crops in dry areas.
Access to Markets
Market access is determined by an index of “potential market integration” based upon
estimated travel time to the nearest five markets, weighted by their population (Wood, et al.,
1999). Given the substantial transaction costs of storing, transporting and marketing
commodities, access to markets is critical for determining the comparative advantage of a
given location, given its agricultural potential. For example, a community in an area of high
agricultural potential may have an absolute advantage in producing perishable vegetables.
Even if high value crops are profitable, farmers faced with high transport costs may need to
produce low-value crops for their subsistence purposes rather than higher value cash crops
(Omamo 1998; Key, et al. 2000).
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Dairy production and other intensive livestock operations, such as intensive
production of pigs and poultry are also more likely to be found close to urban areas, due to
economies of scale in production and marketing, high transport costs, perishability of the
products (e.g., milk and eggs) and the need for markets, access to inputs such as purchased
compound feeds. Extensive production of livestock that are relatively easy to transport, such
as cattle and small ruminants, can occur in areas far from markets, and is likely to have a
comparative advantage in areas that are low in potential for crop production.
Opportunities for rural non-farm activities are also likely to be greater closer to urban
markets and roads (Haggblade, et al. 1989; Reardon 1997; Barrett, et al. 2001). This includes
activities linked to agriculture, such as processing agricultural commodities, commodity
trading, and provision of agricultural inputs, as well as other activities stimulated by higher
demand resulting from higher incomes in areas of better access. Employment opportunities
in urban industries are also likely to be greater for people who live closer to urban centers. In
general, better market access is expected to have a positive impact on income and asset, since
access increases households’ income earning opportunities, whether through increased
agricultural production or through non-farm activities. Market access is also likely to increase
participation in programs and organizations since such programs and organizations tend to
concentrate in high market access areas (Jagger, Pender, 2003).
Major sources of income
Choice of major sources of income influences household incomes and asset.
Households able to rely on high value crops, livestock or remunerative non-farm activities are
likely to earn higher incomes than those confined to subsistence food crop production (Tiffen,
et al. 1994; Barrett, et al. 2001). On the other hand, households dependent upon low wage
non-farm employment may be poorer than even subsistence farm households. Major sources
of income may also influence participation in programs and organizations. For example,
8
farmers depending on non-farm activities are less likely to participate in programs and
organizations that focus on agriculture and natural resource management (NRM).
Programs and Organizations
Since NRM technologies are knowledge-intensive (Barrett, et al., 2002), technical
assistance offered by programs and organizations is likely to be an important determinant of
their adoption. Presence of programs and organizations is likely to improve feasibility of
adopting NRM technologies (Swinkels and Franzel, (1997). However, the impacts of
participation in programs and organizations will depend upon their focus. Programs focusing
on agriculture and NRM may help to increase crop production, and consequently increase
income and asset. However, income may be negatively affected (in the short run at least) by
programs that encourage labor-intensive agricultural practices, if those practices do not
increase production significantly in the near-term, because of the opportunity costs of labor.
Program focus may also influence choice of income strategies by facilitating farmers to
pursue strategies that are supported by the program.
Credit
Access to credit may enable farmers to purchase inputs or acquire physical assets,
thus contributing to increased income. Credit may also promote increased production and
marketing of high value crops or intensification of livestock production, and a reduction of
subsistence food crop production. Credit availability may also enable households to invest in
non-farm activities. Hence, the impact of credit availability on income and asset is likely to
be positive, provided households have profitable uses for it. Impact of credit on participation
in programs and organizations is likely to be ambiguous. For example, obtaining credit for
starting up a non-farm business may lead to disinvestments in agriculture, which in turn
would make participation in programs and organizations focusing on agriculture and NRM
9
irrelevant. On the other hand, credit may help farmers to buy technologies that are promoted
by agriculture and NRM programs and organizations.
Human capital (Education, Sex and Age of household head)
Education is likely to increase households’ opportunities for salary employment off
farm, and may increase their ability to start up various non-farm activities (Barrett, et al.
2001; Deininger and Okidi 2001). Education may increase households’ access to credit as
well as their cash income, thus helping to finance purchases of assets. Education may also
promote changes in income strategies by increasing households’ access to information about
alternative market opportunities and technologies, and hence households’ ability to adapt to
new opportunities (Feder, et al. 1985). Thus, the impact of education on household income
and asset is expected to be positive. However, its impact on participation in programs and
organizations may be ambiguous since the high opportunity cost of better educated farmers
may not allow them to participate in such activities, or it may increase their need to learn
more about better agricultural production technologies.
Sex of household head is an important determinant of income since in African setting,
most decisions on investment, location of household and other major decisions are made by
men. For example, Manundu (1997) observed that women in Kenya are usually not equal partners
when communities create property rights over any resource. It is therefore likely that female-
headed households are likely to be poor, as they will have less access to natural and physical
capital. However, it is likely that female-headed households work much harder to compensate
for their disadvantaged position and save their hard-earned income instead of using it for
buying beer or other luxuries. Hence, it is likely that female-headed households may have
higher income.
Impact of gender of household head on choice of income strategies is likely to be
context specific. For example, women may not choose strategies that require prolonged
10
absence from home – such as non-farm activities that require traveling, livestock keeping that
may require taking animals in search of pasture or water. Women are also likely to grow
crops that are mainly for food. Participation in programs and organizations is also likely to be
more difficult for women since they are less likely to talk to strangers such as extension
agents.
Age of household head is likely to impact positively the income and asset base since
older farmers are well established and likely to have better land, more livestock and more
experienced in production and marketing strategies. However, young farmers may be more
innovative, more energetic even though constrained in asset base and less experienced.
Hence, impact of age on income and asset is ambiguous. However, age is likely to be
negatively related to participation in programs and organizations since old farmers are likely
to be set in their ways - hence more resistant to new ideas from programs and organizations.
Impact of age on income strategy is also likely to be ambiguous. Old farmers may not venture
into new non-farm activities or emerging crops such as vanilla. However, older farmers may
be wealthier, hence able to finance new non-farm activities that require substantial startup
capital.
Household Endowments
The impact of labor availability on household income and asset is expected to be
positive if the marginal product of labor is positive. Dependence ratio (number of
dependents/number of working adults) is likely to decrease income and asset due to tendency
of households with more dependents to be poorer and to lead subsistence production. The
impact of livestock and other physical assets on household income is expected to be positive,
to the extent that such assets are accumulated for purposes of increasing income. However,
there may be other reasons for accumulating assets. For example, livestock may be kept as a
store of relatively liquid wealth and as an insurance substitute, where financial and insurance
11
markets are poorly developed (Binswanger and McIntire 1987). Livestock or other assets
also may be accumulated for dowry or bequest purposes. Thus, the impacts of physical
assets on income may be limited.
If credit is constrained, farmers who own income and assets may be better able to
finance the purchase of inputs or investments, either by liquidating assets or through better
access to credit. The impacts of household endowment on choice of major source of income
are thus qualitatively similar to the impacts of access to credit discussed above, and are
ambiguous for the same reasons.
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METHODOLOGY
Data
Data was collected at community and household levels from one watershed in each of
the following districts: Kabale, Kanungu, Kisoro, Rukungiri and Ntungamo using a semi
structured questionnaire during the year 2003. A community was defined as a single village
within a watershed. Each watershed assisted by the SUNAREM project consists of a number
of villages whose boundaries are determined by the project administration. Three villages in
each watershed were surveyed. (See Table 1) These villages were the first to receive
assistance under the project. Ten to fifteen key informant village members were interviewed
in each village. The respondents included LC1 chairman or secretary, their secretaries for
production, finance, youth women, disabled, members of CBO committees and opinion
leaders.
The total size for the survey was 203 households8. The number of households
sampled per village was proportionate to (weighted against) the number of households in the
village relative to the total number of households in all 15 villages. The household level
questionnaire collected data on household composition, human and social capital, income and
expenditures, household endowment of assets, natural resources management technologies
and agricultural marketing in 2002.
A participatory wealth ranking using two wealth groups (“poor” and “better off") was
undertaken to enhance the representatives of the sampled households in terms of wealth
status. In all the watersheds, the main criteria for wealth ranking identified by the community
members were based on land and livestock ownership, quality of residential house in terms of
construction materials and food availability. The methodology and results of the wealth
ranking are reported in more detail in Appendix 2.
8 Enumerators erroneously interviewed three extra households.
13
Table 1: Survey Enumeration Area and Sample Size Watershed Name District
No. of Villages selected
No. of households in watershed
No. of sampled households
% Poor households
% Better off households
Igomanda Kabale
3 237 49 87.76 12.24
Rutenga Kanungu
3 63 12 83.33 16.67
Rukoro Kisoro
3 111 20 95.00 5.00
Nyakisa Ntungamo
3 330 62 82.26 17.74
Nyakishenyi Rukungiri
3 336 60 90.00 10.00
Total 15 1077 203
Analysis
Analysis of data was done using two stages, i.e. univariate and multivariate analysis.
The univariate analysis used simple descriptive statistics to examine income and asset levels
and the state of affairs of factors that influence livelihood choices and incomes and asset
status in the area of the study. How these issues have changed will be the basis for evaluation
of the livelihood impact of the SUNAREM project. Total gross income and per capita gross
income were used to assess household income levels in the watersheds. For products
produced by households for example crops and livestock, the income calculations considered
the value of sales and the value of home consumption. For livestock, the changes in
inventory or net capital gains (animals born, or aged into or out of a specific category, died,
slaughtered, stolen, lost, received or given away) were considered. For all selected assets
except land, household asset status was measured in terms of monetary value of the assets.
We use econometric analysis of equations 1) and 2) that are discussed under the
conceptual framework to analyze the determinants of income, asset, income strategies, and
participation in programs and organizations. We use ordinary regression to estimate the
determinants of income and assets and probit models to estimate determinants of major
14
sources of income and participation in programs and organizations, and Details on how we
dealt with the statistical and econometric problems are in Appendix 3.
The village level explanatory variables (X) include the market access; household level
factors include income strategy; ownership of natural and physical capital (area of land, value
of livestock and farm equipment); human capital (education, age and gender of household
head); family labor endowment (size of household and proportion of dependents); social
capital (participation in extension and training) and in various types of organizations); the
distance of the land parcel from the farmer’s residence.
15
RESULTS
This section is organized in two parts. In the first part we report the results of the descriptive
statistics analysis in table format and in the second part we report the results of the
econometric analysis. In both parts, the watersheds are referred to by their district names.
The descriptive analysis also generated some data that is useful for assessment of
SUNAREM performance particularly in the areas of advocacy and training in conservation
practices production enhancing technologies and market development. These results are
reported in Appendix 4.
Descriptive statistics
Household Education Levels
Tables 2 and 3 report the highest education levels for household heads and their spouses
respectively in the five watersheds. The dominant highest education level for both groups is
primary education in all the watersheds. For household heads, the Kabale watershed had the
lowest level of primary education and Kisoro had the lowest level of secondary education.
Kanungu watershed had the highest level of primary education and the Rukungiri watershed
had the highest level of post-secondary education.
Household Income Sources Crops are the dominant income source and non-farm income is the second most important
income source in all the watersheds (Table 4). Livestock, forest products and salary
employment score low as incomes sources in all the watersheds.
16
Table 2: Highest Education Level of Household Head in 2002 (% reporting) Watershed
Education level of household head
Kabale (N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=60)
None 30.61 8.33 30.00 38.71 11.67
Primary education 55.10 66.67 60.00 46.77 50.00
Secondary education
12.24 8.33 0.00 11.29 28.33
Post-secondary education
2.04 16.67 10.00 3.23 10.00
Table 3: Highest Education Level of Spouse in 2002 (% reporting) Education level of Spouse
Kabale (N=49) Kanungu (N=12)
Kisoro (N=20) Ntungamo (N=62)
Rukungiri (N=60)
None 36.96 25.00 47.37 29.03 8.62
Primary education
56.52 75.00 47.37 59.68 67.24
Secondary education
6.52 0.00 5.26 9.68 17.24
Post-secondary education
0.00 0.00 0.00 1.61 6.90
Table 5 reports the mean total gross income and the percent contribution of the different
income sources in the watersheds in 2002. Rukungiri watershed had the highest total income
(about UShs 941,000) and Kisoro watershed the lowest (about UShs 481,000).
In all the watersheds crop income contributed highest to the total income and non-
farm income come second. Kisoro watershed had the highest non-farm income contribution
followed by Rukungiri watershed. The contribution of forest products and livestock was
highest in the Kanungu watershed.
Table 4: Primary Income Sources in the Watersheds in 2002 (%hhds reporting) Watershed
17
Source of Income Kabale (N=49)
Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=60)
Ftest*
Production /sale of crops 65.30 83.33 80.00 53.22 56.67 0.993
Production/sale of livestock & livestock products
10.20 0.00 0.00 6.45 10.00 0.211
Forest products** 2.04 0.00 0.00 20.97 3.33 0.000
Non-farm Activities*** 20.41 8.33 15.00 16.33 20.00 0.705
Salary Employment 2.04 8.33 0.00 3.23 8.33 0.000
*Statistical test comparing means in the different watersheds. **Lumbering, carpentry and charcoal burning. ***Non farm activities include; service providers (motorcycle, bar hotel, photography etc),beer brewing and sale, crafts and art, shop selling mainly industrial products, remittances (relatives, friends, rental income, donation gifts), sale of land and dividends from group savings. Table 5: Total Gross Income and Contribution of Different Income Sources in 2002
Contribution to the total gross income (percent) Watershed Total Income (UShs)
Livestock income
Crop Production
Collective* action
Non farm sources
Tree products
Kabale 730,040.20 3.87 50.37 5.79 31.56 8.39
Kanungu 643,469.30 9.86 29.86 0.31 23.08 36.88
Kisoro 481,109.40 -1.44 40.19 0.00 46.01 15.24
Ntungamo 924,115.70 2.15 56.67 0.12 35.78 5.28
Rukungiri 940,989.40 1.48 53.69 0.38 39.81 4.66
Overall 893,643.90 15.92 53.89 0.24 36.17 6.89
*Includes building of construction of community fish ponds and construction of soil and water conservation structures on common land.
The overall mean household per capita income in the watersheds in 2002 was
approximately UShs 180,000. Comparing watersheds, the Ntungamo watershed had the
highest household per capita gross income followed by the Rukungiri watershed. Kisoro
watershed had the lowest (Table 7).
Table 6: Average household Per Capita Gross Income in the Watersheds in 2002 Watershed Obs Income (UShs) Std. Dev.
Kabale 48 162,494.0 124,366.3
Kanungu 10 65,996.1 41,919.7
Kisoro 19 76,038.9 59,502.7
18
Ntungamo 60 206,084.7 146,713.1
Rukungiri 58 188,733.7 156,103.3
Overall 199 179,849.6 162,408.1
Note: Per capita gross income = total gross income/household size Table 7 reports the average farm size, value of livestock and productive equipment or
durable goods owned by households in the watersheds. Farm size or total area of land parcels
owned or operated 9 by households in the watersheds is generally small. Rukungiri watershed
had the largest average farm size i.e. 1.58 hectares and the Kisoro watershed had the smallest
i.e. 0.79 hectares.
Table 7: Average Farm Size & Value of Livestock and Equipment/Durable Goods Owned By Households n 2002 Watershed Obs Mean Standard deviation
Farm size (hectares) Kabale 39 1.27 0.65 Kanungu 10 0.88 0.93 Kisoro 20 0.79 0.74 Ntungamo 61 1.26 0.73 Rukungiri 57 1.58 1.21
Total Livestock Value (Ushs) Kabale 29 328,206.9 387,128.8 Kanungu 5 150,800 180,144.9 Kisoro 9 104,111.1 103,429.3 Ntungamo 41 289,158.5 406,500.9 Rukungiri 45 346,338.9 430,917.8
Total Equipment Value (Ushs) Kabale 49 165,251.0 172,990.9 Kanungu 11 85,045 109,249.6 Kisoro 20 62,875.0 163,071.0 Ntungamo 62 192,893.4 305,788.4 Rukungiri 60 319,479.0 343,060.0 * Equipment/ durable goods include farm implements, processing equipment and transport means (motorcycle, bicycle) and radio. The main livestock types that were reported in the watersheds were cattle, livestock, goats,
pigs, sheep and chicken (see Appendix 4). The average total value of livestock and
productive equipment, which includes farm implements, processing equipment, transport,
means (motorcycle, bicycle) and radio highest in the Rukungiri watershed and lowest in the
Kisoro watershed.
9 Includes land rented in, sharecropped in, leaded in or borrowed by a household in 2002.
19
Determinants of Income, Asset, Choice of Income Strategies and Participation in
Programs and Organizations
In this section, we investigate the determinants of households’ income, asset, choice
of income strategies and participation in programs and organization with different focus. We
present the results of econometric analyses, which show partial effects of each variable on the
response or outcome of interest, controlling for other factors.
Household Income
Education level higher than primary school increases household income significantly,
as expected (Table 8). Deininger and Okidi (2001) and Appleton (2001) observed the same
results in Uganda. Surprisingly, farmers with primary education do not appear to earn higher
incomes than those with no formal education.
Participation in agricultural and NRM, health and mutual support organizations leads
to higher income. This underscores the importance of programs and organizations and is
consistent with other studies that have shown contribution of organizations in poverty
reduction (Lecomte, and Smillie, 2004). Likewise, contact with extension significantly
increases household income as expected. This positive impact may be due to effect of
extension on increasing agricultural productivity. However, long-term agricultural training
did not have a significant impact on income.
Values of agricultural equipment and livestock both have very strong positive impact
on household income, underscoring the importance of mechanization in enhancing
agricultural productivity. The agricultural equipment included are: bicycles, carts, water
pumps, plow sets, radio, grain mill, dehuskers, sprayers, hoes, motor vehicles, tractors,
motorbikes, etc. Livestock also increase income probably because it can be used to provide
20
animal power, may be liquidated to buy agricultural inputs and animal manure may be used
to improve soil fertility.
The potential market integration has a positive impact on income, as expected and as
observed by Fan, et al., (2004) and Pender, et al., (2001) who found that improvements in
access to a paved road were associated with improvements in many welfare indicators. This
underscores the need to develop access to market in remote areas in order to lift farmers in
such areas out of poverty. However, Nkonya, et al., (2004) observed that as markets access
increases soil nutrient depletion. Hence, as market access is being developed, there is need to
address its potential impact on soil nutrient depletion.
Residing in Rukungiri watershed as opposed to Kabale watershed significantly
increases the household income. Residence in Kisoro and Ntungamo watersheds did not have
significant impact on household income when compared to Kabale. However, descriptive
statistics show a significant low income of farmers in Kisoro, when compared to other
watersheds suggesting the need to have targeted development programs for Kisoro.
21
Table 8: Determinants of Household Income and Assets Income Livestock Farm size Equipment Determinants
Direction & magnitude of impact Level of education (cf no formal education) Primary education
0
0
0
++
Post primary education ++ +++ +++ Age of household head 0 +++ +++ +++ Dependence ratio 0 - --- 0 Sex of household head 0 0 0 0 Household size 0 ++ 0 +++ Membership to organizations: Agriculture & natural resource management
0
+
0
0
Health organization + 0 0 Social/mutual support organization 0 0 0 0 Attended any agricultural training? Yes=1,No=0
0 0 0 0
Duration of extension contact (hours) +++ +++ 0 0 Square root (Farm size (acres)) 0 + 0 0 ln(Value of agricultural equipment) +++ 0 0 0 Value of livestock (‘000 Ush) +++ 0 0 0 Distance from homestead to parcel (miles) 0 ++ 0 Potential market integration (PMI) +++ -- 0 ++ Watershed (cf Kabale): Kisoro 0 - --- --- Ntungamo 0 ++ ++ 0 Rukungiri +++ +++ +++ Access to credit 0 0 ++ 0 Primary activity of household head (cf crop production): Livestock
0 +++
0
0
Forest management/harvesting 0 0 0 0 Non-farm activities + 0 0 ++ _Constant +++ 0 0 +++ Notes: +, ++, +++ mean associated variable has a positive and statistically significant impact at the 10%, 5%,
and 1% levels, respectively. -, --, --- mean the associated variable has negative and statistically significant impact at the 10%, 5%, and 1% levels, respectively.
0 means the associated variable is not statistically significant at 10% or it is significant at 10% but not robust, i.e. it is significant under only one specification among the three models estimated (see Table A5.1 and A5.2 and appendix 5 for details).
22
Access to credit does not have a significant impact on income. However, as noted
earlier, majority of farmers reported to have access to informal credit, which are always
short-term and of less amount than formal credit. Access to credit has an indirect effect via its
impact on non-farm activities, which in turn has a positive impact on income. Farmers
probably need a start-up capital to start a non-farm activity since these activities normally
pose a barrier to entry in terms of knowledge and financial capital (Barrett et al., 2001).
Consistent with other studies in Sub-Saharan Africa farmers who have non-farm activity as
their primary source of income have significantly higher income than those depending on
crop production. Contribution of non-farm activities to income appears to be more significant
in the region than the case in the rest of Uganda (Table 5; Nkonya, et al., 2004). The
importance of non-farm activities is consistent with numerous other studies of rural
livelihoods in Africa (Reardon 1997; Ellis 2000; Barrett, et al. 2001; Ellis and Bahiigwa
2003; Ellis, Kutengule and Nyasulu 2003; Ellis and Mdoe 2003). The results emphasize the
importance of non-farm activities in addressing poverty in the SWH region of Uganda.
Determinants of assets
Table 9 reports the determinants of assets. As expected, most of the variables that influence
income also have impact on level of assets. Compared to no formal education, primary and
post-primary education increases significantly the value of agricultural equipment stock and
farm size. Older household heads are also significantly wealthier than younger ones in terms
of agricultural equipment, land and livestock. Number of dependents (dependence ratio)
significantly lowers the value of livestock and size of farms, consistent with theory that the
poor have more dependents than the rich. The gender of household head has a weak impact
on value of assets and size of the farm, even though male household heads tend to be
wealthier than female household heads.
23
Household size significantly increases the asset stock of livestock and equipment, as
expected. However, per capita asset value is lower for larger households. Membership in
programs and organization does not appear to have a significant impact on household asset,
suggesting that these programs and activities do not choose participants based on their
wealth. Land shortage, as measured by distance from homestead to land parcel does not have
a significant effect on equipment and livestock value. However, it significantly increases the
size of the farm implying that farmers who venture into far away lands as a way of coping
with land shortage get larger tracks of land. Migration from overpopulated highlands in the
SWH to sparsely populated areas has created land conflicts in Kibaale, Hoima, Kabarole and
other sparsely populated areas. Hence, a guided migration of farmers from the overpopulated
to sparsely populated areas need to be explored by the government to avoid deepening the
current land crisis.
Market integration has a negative impact on livestock value implying that farmers
with larger herds live in remote areas as expected. However, value of agricultural equipment
has a positive association with market integration, perhaps due to lower cost of procuring
agricultural equipment in high market access areas. This suggests that access to market is
important in efforts to increase the level of mechanization among farmers.
Watersheds have significant differences in their asset stocks. Kisoro has significantly
lower livestock and equipment stock than Kabale. Ntungamo has significantly higher stock of
livestock and size of farm than Kabale. Rukungiri has significantly more equipment stock
than Kabale. These findings suggest that Kisoro and Kabale appear to be trailing behind in
wealth indicators.
As expected, extension and training significantly increase the stock of livestock
though both do not a have a significant impact on size of the farm. Extension increases
significantly the value of equipment. As was the case with income, these results provide
24
Table 9: Determinants of Choice of Major Source of Income of the Household Head Livestock Non-farm Forest Determinant
Direction & magnitude of impact Level of education (cf no formal education) Primary education
0
0
0
Post primary education 0 ++ 0 Age of household head -- 0 0 Dependency ratio 0 0 0 Sex of household head 0 0 +++ Household size +++ +++ +++ Membership to organizations: Agriculture & NRM
0
0
---
Health organization --- --- Social/mutual support organization 0 0 0 Square root (Farm size (acres)) 0 0 0 Value of equipment (Ush) 0 +++ -- Value of livestock (Ush) +++ 0 -- Distance from home to land parcel (miles) +++ 0 0 Potential market integration +++ 0 --- Watershed (cf Kabale)1: Kisoro +++ ++ --- Ntungamo --- 0 0 Rukungiri +++ 0 +++ Received any training (yes=1, no=0) +++ 0 ++ Number of contact hours with extension ++ 0 ++ Access to credit 0 0 -- Constant 0 --- 0 Note: (i) Control group is crop production
(ii) +, ++, +++ mean associated variable has a positive and statistically significant impact at the 10%, 5%, and 1% levels, respectively. -, --, --- mean the associated variable has negative and statistically significant impact at the 10%, 5%, and 1% levels, respectively. 0 means the associated variable is not statistically significant at 10% or it is significant at 10% but not robust, i.e. it is significant under only one specification among the three models estimated (see Table A5.3 and appendix 1 for details).
25
more evidence that extension and training efforts are important in poverty reduction efforts.
Contrary to expectations, access to credit has limited impact on value of equipment
and livestock. This may be due to the small and short-term nature of the credits given, mostly
from informal sources. Such loans may not be useful for long-term investment such as buying
livestock and equipment. However, access to credit increases marginally the size of the farm.
Primary source of income has a significant impact on stock of assets. As expected, having
livestock rather than crop production as primary source of income significantly increases the
value of livestock. Non-farm activities increase significantly the value of equipment
suggesting that farmers use earnings from non-farm activities to finance procurement of
agricultural equipment.
Determinants of participation in programs and organizations.
Level of education has limited impact on participation in programs and organizations
with focus on agriculture and NRM, health and social/mutual support (Table 10). Only post-
primary education appears to reduce probability to participate in social/mutual support
organizations. This suggests that less educated farmers seem to seek more mutual support
than more educated farmers. Older farmers are more likely to belong to agriculture and NRM
organizations than younger farmers. However, age does not have a significant impact on
participation in health and social/mutual support organizations.
Human and physical endowments also have a large influence on participation in
programs and organizations. Size of household increases probability to participate in
agriculture and NRM organizations. This may be due to the labor requirement for adopting
labor-intensive agriculture and NRM technologies promoted by these organizations such as
soil and water conservation (SWC) technologies. Farm size appears to increase probability to
participate in programs and organizations that focus on health. Value of agricultural
26
equipment reduces probability to participate in both agriculture and NRM and health
organizations. This may be due to high opportunity cost of labor of wealthy farmers.
Market integration increases probability to participate in organizations that focus on
agriculture and NRM and health but reduces probability to participate in social and mutual
support organizations. This suggests that the agriculture and NRM and health organizations
appear to be in concentrated in high market access areas. Social/mutual organizations, most
of which are CBOs, appear to be concentrated in remote areas. Being in Kisoro rather than
Kabale increases the probability to participate in agriculture, NRM, and social/mutual support
organizations. Being in Ntungamo rather than Kabale decreases the probability to participate
in agriculture, NRM and health organizations but increases the probability to participate in
social/mutual support organizations. The probability to participate in agriculture, NRM and
health organizations is higher in the Rukungiri watershed than in the Kabale watershed.
As expected, presence in the community of organizations that focus on health, and
agriculture and NRM increases the probability to participate in activities of these
organizations. However, presence of organizations that offer credit do not appear to influence
participation in programs and organizations that focus on agriculture, NRM, health but
increases probability to participate in social/mutual support organizations. The major sources
informal credits are friends, relatives and neighbors, who usually form a large social capital
through social/mutual support organizations. It appears membership to these CBOs serve as
credit intermediation for members. Primary source of income does not appear to have much
influence on participation in programs and organizations. However, only livestock appears to
reduce probability to participate in programs and organizations that focus on agriculture and
NRM.
27
Determinants of Income Strategies
The primary sources of household income reported by the sample households include
crop production; livestock production; forestry activities; non-farm activities such as brewing
beer; petty trade, masonry, butchery, etc.
In general, most households in rural Uganda have diversified income sources,
regardless of their reported primary income source. Crop production as a whole is the largest
source of income, followed by non-farm activities, which are quite important for most
households in the SWH region.
Acquiring primary education increases the probability to have livestock rather than
crop production as the primary source of income. Acquiring post-primary education has no
impact on choice between livestock and crop production. Post-primary education has a
positive and significant impact on probability to have non-farm activities as the primary
source of income. This is consistent with the findings of studies that covered the entire
country (Nkonya, et al., 2004) and much of the literature on determinants of rural non-farm
income in Africa, which note the barriers to entry (such as limited education) into higher
income occupations (e.g., Barrett, et al., 2001).
Age of household head is negatively related with the probability to choose livestock
production as primary source of income, suggesting that older farmers tend to choose crop
production. Female-headed households are less likely to choose livestock as their primary
source of income. Livestock keeping may involve a considerable amount of time away from
home as farmers graze and water their animals. This may not be attractive to women who
always want to be around the home to take care of children and cook food for the family.
Household size also has a positive impact on probability to choose livestock keeping
rather than crop production as the primary source of income. This may be due to the ability of
large households to have labor to manage cattle. For example, small children always take care
28
of small ruminants and calves (DOL, 2004). Membership to organizations focusing on
agriculture, NRM and health has a negative impact on probability to choose livestock as the
major source of income. It is likely that these organizations target crop producers.
Value of equipment and livestock has a strong positive impact on probability to
choose livestock as the main source of income. This was expected, as livestock value is likely
to increase if a farmer chooses this sector to be the primary source of income. In turn having
a large value of livestock is likely to make the farmer afford buying other agricultural
equipment. Distance from homestead to crop plot increases the probability to choose
livestock as the primary source of income. This suggests that farmers living in areas with
high land pressure are likely to engage in non-crop activities such as livestock, which can be
moved to areas with abundant land areas for grazing.
Table 10: Determinants of Participation in Programs and Organizations (Probit models)
Agriculture & NRM
Health Social/mutual support
Variables
Direction & magnitude of impact Level of education (cf no formal
education): Primary education 0
0
0
Post primary education 0 0 ---
Age of household head (years) +++ Dependence ratio 0 0 0 Sex of hhd head (Male=1, female=0) 0 0 0 Household size +++ 0 0 Square root (Farm size (acres)) 0 +++ 0 ln(Value of equipment ‘000Ush) --- --- 0 Value of livestock (‘000 Ush) 0 0 0 Distance from home to parcel (miles) 0 0 0 Potential market integration (PMI) +++ +++ -- Watershed (cf Kabale)1: Kisoro + +++ Ntungamo --- --- +++
Rukungiri ++ ++ 0 Presence of extension programs +++ +++ 0 Presence of agricultural training programs
+++ ++ 0
Access to credit? (Yes=1, No=0) --- --- +++
29
Primary activity of household head (cf crop production): Livestock
-
0 Forest management/harvesting
0 0 0
Non-farm activities 0 0 0 Constant 0 0 0 Notes: +, ++, +++ mean associated variable has a positive and statistically significant impact at the 10%, 5%,
and 1% levels, respectively. -, --, --- mean the associated variable has negative and statistically significant impact at the 10%, 5%, and 1% levels, respectively. 0 means the associated variable is not statistically significant at 10% or it is significant at 10% but not robust, i.e. it is significant under only one specification among the three models estimated (see Table A5.4 and appendix 1 for details).
Potential market integration increases the probability to choose livestock as the major
source of income. Market integration has a positive but not a significant impact on
probability to choose non-farm as source of income. Nkonya et al., (2004) also found less
impact of access to markets and roads on off-farm employment and non-farm activities.
Market integration has a positive impact on probability to choose forestry as the primary
source of income. We included carpentry, charcoal burning, firewood marketing, production
and sale of other forest products in this category. Since such products are likely to be sold in
high market access areas, these results are not surprising.
Place of residence has an impact on the probability to choose primary source of
income. Being in Kisoro and Rukungiri increases the probability of having livestock as the
major source of income than being in Kabale. Surprisingly, being in Ntungamo decrease the
probability of having livestock as the major source of income. These results were not
expected since at district level, Ntungamo has more livestock than Kabale. In this research,
we also found that value of livestock in Ntungamo was higher than that of Kabale. Probable
reason for these unexpected results is that AFRICARE chose poor watersheds in Ntungamo
since the major focus of the project is to address poverty. It is most likely that the
(Ntungamo) farmers participating in the SUNAREM project are mainly farmers.
30
Being in Kisoro rather than Kabale increases the probability to choose non-farm
activities as major source of income. Kisoro residents are less likely to choose forestry as
their major source of income than Kabale residents. However, Rukungiri residents are more
likely to choose forestry as their major source of income than Kabale residents. These results
may be explained by presence of more forestry resources in Rukungiri and less in Kisoro -
the latter due to more severe land shortage.
Access to extension and training also impacts choice of primary source of income.
Access to agricultural extension and training increases the probability of choosing livestock
and forestry as major sources of income rather than crops. These results were not expected
since major emphasis of extension and training has been on crop production. However, it is
possible the SUNAREM projects has been emphasizing agroforestry technologies that
include planting trees, growing fodder crops as grass strips, which both lead to soil
conservation and production of fodder. Access to credit increases the probability to choose
livestock, non-farm and forestry as major sources of income. These results were expected.
However, the positive impact of access to credit on probability to choose non-farm activity as
major source of income is not significant.
31
CONCLUSIONS AND POLICY IMPLICATIONS
The findings of the study support SUNAREM’s strong investment in agriculture and
natural resource management (NRM) extension and training programs as primary means of
reducing poverty in the southwestern region of Uganda since these efforts contribute to
increasing both household income and wealth. However, our results show that participation in
agricultural extension and training is less in remote areas suggesting the need to target such
programs in areas with limited access to market. In using extension and advisory services to
address poverty, it is crucial to ensure that the approach is responsive to local demands and
conditions. Similarly, efforts to improve market access significantly contribute to poverty
reduction. However, Nkonya, et al., (2004) showed that access to market also contributes to
soil nutrient depletion due to harvesting and selling agricultural products without adequate
replenishment of the exported soil nutrients. Hence, agricultural commercialization and
efforts to improve market access need to be implemented together with efforts to address land
degradation problems.
Education also increases household income and asset stocks suggesting that such
efforts are crucial in addressing poverty in the region. SUNAREM therefore needs to
consider promoting investment in education as a way of reducing poverty in the region.
Participation in non-farm activities increases income and asset stock. This suggests
that non-farm activities are important in addressing poverty. However, there is potential
barrier to entry into non-farm activities since a start-up capital and skills are required (Barrett,
et al., 2001). The fact that we did not find evidence that access to credit is a major factor
influencing income or participation in non-farm activities suggests that the mostly informal,
short-term and small loans that the farmers are currently getting does not appear to overcome
barrier to entry into non-farm activities. This points to the need to develop rural finance
institutions to provide credit for agricultural modernization and for addressing poverty. Post-
32
primary education increases the probability to participate in non-farm activities implying that
education is important not only in increasing income directly, but also indirectly through its
positive impact on non-farm activities. To increase the competitiveness of non-farm products,
farmers’ skills in making them need to be increased through training in polytechnic and
vocational schools based in rural areas.
Assets, as expected are crucial in increasing income suggesting that poor farmers with
limited access to land, livestock and agricultural equipment are likely to be caught in a
poverty trap. These results suggest the need to target small and poor farmers through non-
asset interventions that increase income. According to this study, such efforts are agricultural
extension, education, programs and organizations focusing on agriculture, NRM, and health,
improvement of access to market and promotion of non-farm activities. Supporting
community-based organizations with a focus on social/mutual support is also an important
strategy for increasing income.
The Southwestern highland region has an enormous potential for improved livestock
production. However, the region’s livestock production potential is dogged by limited market
access, and lack of livestock product processing capacity in the region. Our results show that
access to market, credit, agricultural extension and training services increase probability to
have livestock as the primary source of income. Given that livestock products, such as milk
and meat are highly perishable, the need to have better market access and processing capacity
in the region is apparent. Extension and training services in controlling pests and diseases and
managing both local and improved livestock are also crucial in promoting profitable
production in the region.
Given that the southwestern highlands region of Uganda has abundant forest
resources, forest-based livelihoods are important options that need to be developed. Due to
their labor-intensive nature, household head being male and the size of the household
33
enhances forest-based livelihoods. Access to market and extension services and being in
Rukungiri rather than Kabale increase participation in forest-based livelihood. These results
imply that female-headed households are less likely to benefit from forest resources as
primary sources of income. Training women in carpentry and facilitating them to acquire
forest-harvesting and processing equipment may increase their opportunities to participate in
forest-based livelihoods. However, both the government and her development partners
recognize the current deforestation rate in the region. Thus, it is important to sensitize and
educate farmers on how to sustainably produce and harvest forest products. Presently, forest
production and utilization extension efforts are limited, pointing to the need to increase such
efforts in the region.
Age of household head, size of the household, access to market and presence of such
organizations in the community enhance participation in programs and organization that
focus on agriculture, natural resource management and health. This suggests that these
programs need to target the youth, smaller families, and farmers in remote areas.
Farmers mainly form Social/mutual support organizations as community based
organizations (CBOs). Our results show that these organizations are crucial in increasing
access to informal credit. This implies there is need to use these social/mutual CBOs to
increase the low access to formal credit. For example, CBOs with major focus on
social/mutual support may take the role of credit intermediation between financial institutions
and its members.
Lastly, we would like to emphasize that these results form the basis upon which the
implementation and performance of the SUNAREM project will be based. Therefore repeated
data collection in the course of project implementation is important in order to capture the
trend of changes in the income and wealth indicators reported. These results should also not
be extrapolated out of the sampled watershed since the watersheds were not randomly
34
sampled. Those who wish to understand the overall income and asset levels in the region
need to consult other reports that used more representative data such as Uganda National
Household Survey and IFPRI’s household surveys.
35
REFERENCES Appleton, S. 2001. What can we expect from Universal Primary Education? In: R. Reinikka
and P. Collier (eds.), Uganda’s recovery: the role of farms, firms, and government. The World Bank, Washington, D.C.
Barrett, C.B., F. Place, A. Aboud. 2002. The challenges of stimulating adoption of improved natural resource management practices in African agriculture. In: Barrett, C.B., F. Place, A.A. Aboud (eds). Natural Resources Management in African Agriculture. ICRAF and CABI, Nairobi Kenya.
Barrett, C.B., T. Reardon, P. Webb. 2001. Non-income diversification and household livelihood strategies in rural Africa: Concepts, dynamics and policy implications. Food Policy 26(4):315-331.
Binswanger, H.P. and J. McIntire. 1987. Behavioral and material determinants of production relations in land-abundant tropical agriculture. Economic Development and Cultural Change 36(1): 73-99.
Clausen, R. 20001. A landscape Approach for Reviewing USAID Uganda Activities in the southwest. Consultant report submitted to the USAID/Africa Bureau, Washington D.C.
Deaton, A. 1997. The analysis of household surveys: A microeconometric approach to development policy. Baltimore: Johns Hopkins.
Deininger, K. and J. Okidi. 2001. Rural households: Incomes, productivity, and non-farm enterprises. In R. Reinikka and P. Collier (eds.), Uganda’s recovery: the role of farms, firms, and government. The World Bank, Washington, D.C.
DOL (U.S. Department of Labor). 2004. Child Labor in Uganda. at http://www.dol.gov/ilab/media/reports/iclp/Advancing1/html/uganda.htm. Accessed on March 5, 2004.
Ellis, F. 2000. Rural Livelihoods and Diversity in Developing Countries. Oxford University Press, Oxford.
Ellis, F., Bahiigwa, G. 2003. Livelihoods and rural poverty reduction in Uganda. World Development 31(6), 997-1013.
Ellis, F., Kutengule, M., Nyasulu, A. 2003. Livelihoods and rural poverty reduction in Malawi. World Development 31(9), 1495-1510.
Ellis, F., Mdoe, N. 2003. Livelihoods and rural poverty reduction in Tanzania. World Development 31(8), 1367-1384.
Fan, S., X. Zhang, and N. Rao, 2004. “Public Expenditure, Growth, and Poverty Reduction In Rural Uganda,” Development Strategy and Governance Division Discussion Paper No. 3, International Food Policy Research Institute.
Feder, G., R. Just and D. Zilberman. 1985. Adoption of agricultural innovations in developing countries: A survey. Economic Development and Cultural Change 33(2): 255-298.
Haggblade, S., P. Hazell, and J. Brown. 1989. Farm-non-farm linkages in rural sub-Saharan Africa. World Development 17(8): 1173-1201.
Hausman, J. 1978. Specification tests in econometrics. Econometrica 46:1251-1272. Jagger, P., Pender, J. 2003. Impacts of programs and organizations on the adoption of
sustainable land management technologies in Uganda. Environment, Production and Technology Division Discussion Paper. Washington, D.C.: International Food Policy Research Institute (IFPRI), 43 pages.
Manundu, M. 1997. “Social and gender considerations in water management.” Planning workshop proceedings on water demand management research networking in Africa
36
37
and the Middle East May 12-14, 1997, at http://web.idrc.ca/en/ev-31795-201-1-DO_TOPIC.html, accessed on March 2, 2004.
National Environment Management Authority (NEMA). 2001. State of the Environment Report for Uganda, 2000/2001. NEMA, Kampala Uganda.
NEAP (National Environmental Action Plan. 1992. Land tenure and land management in Uganda. Report of task force on land management. Ministry of Natural Resources, Kampala Uganda.
Nkonya, E.M. Pender, J.P., Sserunkuuma D., Jagger, P. 2004. Strategies for sustainable land management and poverty reduction in Uganda. Research Report #133 International Food Policy Research Institute, Washington D.C. Forthcoming.
Omamo, S.W. 1998. Transport costs and smallholder cropping choices: An application to Siaya district, Kenya. American Journal of Agricultural Economics, 80(1):116-123.
Pender, J. P. Jagger, E. Nkonya, and D. Sserunkuuma. 2001a. Development pathways and land management in Uganda: Causes and implications. EPTD Discussion Paper No. 85, International Food Policy Research Institute, Washington, D.C.
Pender, J., S. J. Scherr, and G. Durón. 2001b. Pathways of development in the hillsides of Honduras: Causes and implications for agricultural production, poverty, and sustainable resource use. In: D.R. Lee and C. B. Barrett (eds.), Tradeoffs or Synergies? Agricultural Intensification, Economic Development and the Environment, Wallingford (U.K.): CAB International.
Pender, J., F. Place, and S. Ehui. 1999. Strategies for sustainable agricultural development in the East African Highlands. In: A. Knox McCullough, S. Babu, and P. Hazell (eds.), Strategies for Poverty Alleviation and Sustainable Resource Management in the Fragile Lands of Sub-Saharan Africa. Proceedings of the International Conference held from 25-29 May, 1998 in Entebbe, Uganda. Food and Agriculture Development Centre (DSE/ZEL), Feldafing, Germany.
Reardon, T. 1997. Using evidence of household income diversification to inform study of the rural non-farm labor market in Africa. World Development 25(5), 735-748.
Stiglitz, J.E. 1993. Economics. W.W. Norton & Co., New York. Stoorvogel, J.J., Smaling E.M.A. 1990. Assessment of soil nutrient depletion in Sub-Saharan
Africa 1983-2000. Report 28 DLO Winand starring Center for integrated land, soil and water research (CSC-DLO), Wageningen Netherlands.
Swinkels R. and S. Franzel 1997. “Adoption Potential of Hedgerow Intercropping in the Maize-based Cropping Systems in the Highlands of western Kenya. Part II: Economic and farmers evaluation”. Experimental Agriculture 33:211-233.
Tiffen, M., M. Mortimore, and F. Gichuki. 1994. More people – less erosion: Environmental recovery in Kenya. London, UK: Wiley and Sons.
Project Purpose: To support sustainable productive natural resources management initiatives of local communities.
Output 1: Local capacity to plan & implement natural resources management programs strengthened. Activities 1.1 Local knowledge on environmental and natural resources and their implementation strengthened through
advocacy. Indicative activities include: exchange visits, drama, video shows, radio programs, newsletter for the communities in vernacular languages.
1.2 Local capacity for participating planning and implementation of environmentally sound uses of critical watersheds strengthened through activities including: practical training in resource mapping, watershed mapping, target setting action plans, annual project review and planning workshop
1.3 Capacity of local leaders to request for/provide services from/to NAADS and other service providers strengthened through activities including: supporting farmers groups to establish their on-farm training capacity, identify and prioritize their support needs, request for services and evaluate the quality of services provided, training farmers in skills in awarding contacts to service provide.
1.4 Skills of farmers and farmers groups in using natural resources management technologies strengthened through activities including: practical training in designing conservation structure, tree propagation, agro forestry, manure use, bee keeping, water harvesting and energy saving stoves.
1.5 Development of environmental action plans in specific operational areas (villages, parishes and sub-counties) in Ntungamo and Kabale districts supported through activities including: training of trainees to enable the communities to develop parish and sub-county Environment Action Plans with all stakeholders using planning workshops, assist the two districts in organizing and facilitating development of Environment Action Plans (parish, sub county) visioning process and resource mapping.
1.6 Experiences with the approach and technologies renewed, summarized and publicized through activities including: annual stakeholders’ workshops, a workshop at the end of the project phase to review, summarize and publicize the approach and technologies.
Output 2: The productive potential of threatened agro-eco-systems conserved and enhanced. Activities 2.1 Environmentally sustainable and productivity enhancing farming practices promoted through activities including:
promotion of contour hedgerows, improved fallows, manure/composite use, rainwater harvesting and nursery management.
2.2 Environmentally and economically sustainable, alternative land use options promoted through activities including: promoting a diversified range of the tree species for woodlots (poles, timber) on degraded land, promote rotational woodlots on degraded land (rehabilitation and wood production), promote fodder banks, promote bee keeping.
2.3 Local tree seed and seedlings supply systems supported through activities including: training on tree seed production and handling field advice on the establishment of tree seed orchards and mother gardens, training in nursery management and day-today support of established nurseries, organizing a nursery competition, participatory identification and multiplication of local tree for local production and use.
2.4 On-farm establishment and use of medicinal tree encouraged and supported through activities including: training on propagation of medicinal trees, providing germplasm, establishment of on-farm observations (in collaboration with ICRAF/AFRENA)
Output 3: Income generation opportunities from the wise use of natural resources broadened. Activities 3.1 Establishment of marketable tree crops supported through activities including: procurement of rootstock
seedlings from ICRAF/AFRENA and distribution for demonstration purposes among the target communities as well as sale to specialized nurseries, training of farmers in fruit tree management, training of grafters, establishment of mother gardens, supporting marketing studies for fruit tree products, establish links between farmer groups/entrepreneurs and organizations specialized in marketing (of tree crops)
3.2 Management capacity of artisan natural resources user groups enhanced through activities including: training d b i d i idi li k t b i ti li ki d ith th i t i t i
APPENDIX 2: DETAILS OF WEALTH RANKING RESULTS AND PROCEDURES During the community survey group interview, the respondents were requested to
identify three main quantitative or qualitative criteria for classifying a household as poor or better off. Subsequently, in a separate meeting, three key informants from each village were asked to classify all households in the village as either poor or better off relative to the village as a whole, based on the three criteria identified in the group interview. Table A1 reports the criteria identification results for the 15 surveyed villages.
The enumerators then compared the three scoring sheets and identified the households that consistently fell in the same wealth group. For the households that were placed in different groups by the key informants, the key informants and enumerators discussed the characteristics (helped by the scored criteria) of these households and reached a consensus as to whether they should be classified as poor or better off. The total number of households in the village that fell into each wealth group should was noted, as it is used to determine how many households should be sampled from each wealth group. The number of households to be sampled in each wealth group in each village was weighted against the observed number of households in the wealth group. An example is given in Table A2.1. The households to be interviewed from each wealth group were randomly selected using a random numbers table.
Table A2.1 - Estimation of Sample Size for Kagyeyo Village Total number of households in village sample = 20
Total number of households in village = 106
Total number of households in village classified as “poor = 79 (74.5%)
Total number of households in village classified as “better off” = 27 (25.5%)
Number of “poor” households in village sample: 0.5604*20 = 15
Number of “better off” households in village sample: 0.4395*20 = 5
40
Table A2.2 - Characteristics of Wealth Groups in the SUNAREM Sample Villages Watershed Village 3 main criteria for “better-off”
ranking 3 main criteria for “poor” ranking
Kabale Kashekyera -more than 3 acres of land -3 or more cows -good house(semi-permanent/permanent)
-small grass thatched house -less than 5 plots of land of 0.5 acres each -works for food
Kabale Nyamiyaga -over 5 parcels of land of 2 acres each -3 or more cows -good house(semi-permanent/permanent)
-no livestock -less than 5 parcels of land of 2 acres each -small grass thatched house
Kabale Hamuko -more than 3 acres of land -feeds well(2 meals per day) -more than 1 wife
-poorly dressed -no land -small grass thatched house
Kanungu Byabahweza -more than 3 acres of land -more than 5 goats -more than 1 wife
-less than 3 acres of land -no livestock -small grass thatched house
Kanungu Karokarungi -good house(semi-permanent/permanent) -more than 5 goats -more than 1.5 acres of land
-subsistence production -does not earn a salary -less than 1.5 acres of land
Kanungu Biizi -more than 5 goats -more than 5 parcels of land of 0.5 acres each -salary earner
-less than 5 goats -less than 5 plots of land of 0.5 acres each -work for food
Kisoro Rukoro A -more than 5 parcels of land of 0.5 acres each -more than 3 cows -salary earner
-small grass thatched house -less than 5 parcels of land of 0.5 acres each -no livestock
Kisoro Rukoro -adequate food -educated children -more than 2 acres of land
-inadequate food -small grass thatched house -works for food
Kisoro Rukoro C -more than 3 cows -more than 5 parcels of land of 0.4 acres each -banana plantation of more than 1 acre
-less than 5 parcels of land of 0.4 acres each -less than 3 cows -works for food
Nuntungamo Mujwa -more than 3 acres of land -more than 5 goats -does not work for food
-works for food -less than 5 plots of land of 0.5 acres each -no livestock
Nuntungamo Mugwanzura -good house (semi permanent/permanent) -more than 2 acres of land -more than 5 goats, 5 cows & 5 sheep
-small grass thatched house -less than 0.5 acres of land -no livestock
Nuntungamo Nyamitaba II
-more than 3 cows -more than 2 acres of land -adequate food
-small grass thatched house -works for food -less than 0.5 acres of land
Rukungiri Nyamirama -more than 7.5 acres of land -banana plantation of more than 1 acre -more than 5 cows and 10 goats
-less than 5 plots of land of 0.5 acres each -banana plantation of less than 1 acre -less than 5 goats
Rukungiri Kagyeyo -over 5 parcels of land each of 2 acres -more than 5 goats -salary earner
-less than 5 parcels of land each of 2 acres -less than 10 cows -works for food
Rukungiri Buhumuriro -over 5 parcels of land of 2 acres each -more than 1 cow, 2 goats & 2 sheep -good house(semi-permanent/permanent)
- sells labor -small grass thatched house -less than 0.5 acres of land
41
APPENDIX 3: APPROACHES TO DATA AND ECONOMETRIC PROBLEMS
Inclusion of endogenous explanatory variables in these equations could result in biased estimates, due to correlation of the error term with the endogenous explanatory variables. In standard linear models, instrumental variables (IV) estimation can be used to address the endogeneity problem, if valid instruments are available (Deaton 1997). In limited dependent variable models, IV estimation cannot be used, but consistent estimates can be produced by a two-stage estimator substituting predicted values of the endogenous explanatory variables.
Identification of the effects of the endogenous variables in the IV models and two-stage models can be difficult unless one has instrumental variables that strongly predict the endogenous explanatory variables. In finite samples, results of estimation with weak instruments can be more biased than ordinary least squares (OLS) (Ibid.). This is a concern in the regressions in this paper. We address this concern by controlling for many exogenous explanatory factors in the regressions, which could cause endogeneity or omitted variable bias if left out, and by investigating the robustness of the regression results to estimation by OLS, IV or two-stage approach, and reduced form approach. In discussing our findings, we focus on results that are robust across at least two of these three specifications, or one specification with strong impact (at statistical significance of P>0.01). We also conduct exogeneity tests (Hausman, 1978), relevance of instruments and overidentification tests. Overidentification test was done to ensure that the instruments used are not relevant variables for the specifications. We estimated the income and asset models using robust regression (Berk 1990), as a further check on the robustness of the results. We used a linear specification for all of the limited dependent variable regressions, since the dependent variables could not be transformed using logarithms in these cases. In all models, we tested for multicollinearity, and found it not to be a serious problem (variance inflation factors < 5) for almost all explanatory variables in the OLS and reduced form regressions. Normality of the continuous variables was checked. Whenever serious skewness and/or heavy tails were observed, ladder transformation was used to address them.
42
APPENDIX 4: DETAILED TABLES OF DESCRIPTIVE ANALYSIS RESULTS Table A4.1 - Access to Agricultural and Other Training and Extension (% households reporting) Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=60)
F test*
Agricultural or other training since 1997 (% hhds** reporting)
59.18 58.33 45.00 54.84 46.67 1.00
Contact with extension agents in 2002 (% hhds reporting)
53.06 66.67 15.00 52.3 38.33 0.59
Average no. of extension visits in 2002
9.20 (8.32)
4.18 (4.49)
3.0 (1.0)
7.80 (7.17)
2.21 (0.96)
0.000
Note: Figures in parentheses are standard deviations *Statistical test comparing mean observations in the different watersheds **hhds refers to households Table A4.2 - Access to AFRICARE Training and Extension Services in 2002(% households reporting) Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=60)
F test*
Contact with extension agents in 2002
38.78 16.67 15.00 25.81 1.67 0.000
Agricultural or other training since 1997
30.61 41.67 35.00 30.65 11.67 0.031
*Statistical test comparing mean observations in the different watersheds Table A4.3 - Access to Credit in 2002 (% households reporting) Watershed Variable Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20) Ntungamo (N=62)
Rukungiri (N=60)
Availability of formal credit sources
0.00 8.33 10.00 3.22 3.45
Availability of informal credit sources
97.96 83.33 85.00 93.55 90.00
Applied for informal credit (N=111)
54.41
Applied and received informal credit (N=110)
53.92
Only 3 households applied for formal credit and all received it
43
Table A4.4 - Main Focus of Programs and Organizations in 2002 Main focus of program/ Organization (N=80) Percent Agric/Environment 37.5 Mutual support 16.25 Health 15.00 Education 11.25 Credit/ Savings 7.50 Water and Sanitation 6.25 Social assistance to the disadvantaged 2.50 Improving Welfare 2.50 Provision of commercial labour 1.25
Table A4.5 - Household Membership in Programs and Organizations in 2002 (% households reporting)
Watershed
Kabale (N=49)
Kanungu (N=12)
Kisoro (N=20) Ntungamo (N=62)
Rukungiri (N=60)
Program 6.12 33.33 0.00 0.00 23.33
NGO 4.08 58.33 5.00 25.81 15.00
CBO 95.92 83.33 90.00 93.32 96.62
Note: Programs are institutions associated with the Government of Uganda. Community based organizations (CBOs) are organizations that evolve and are administered, financed and managed at the local level. They are not registered with the government. Non Government Organizations (NGOs) include both indigenous and local organizations established to provide services to communities or districts. They are autonomous and are required to conform to the government’s regulatory requirements regarding registration and reporting. (Jagger and Pender, 2003) Table A4.6 - Livestock Ownership in 2002 (%households reporting) Livestock Type Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=60)
Cattle* 24.49 25.00 5.00 20.97 28.33 Goats 20.41 41.67 30.00 41.94 45.00 Chicken 24.49 50.00 25.00 38.70 43.33 Pigs 26.53 0.00 0.00 12.90 25.00 Rabbits 0.00 0.00 0.00 6.45 6.67 Sheep 20.41 33.33 10.00 16.13 26.67 Improved Cattle 2.04 0.00 0.00 1.94 4.95 Improved Goats 0.00 0.00 0.00 0.60 0.00 *Bulls, heifers, cows.
44
Table A4.7 - Main Food Crops Grown by Households in 2002 Watershed Crop Frequency* Percentage
Sweet potato 45 31.25 Beans 44 30.56 Irish potato 33 22.92 Sorghum 19 13.19
Kabale
Others (field pea, maize, yams) 3 2.07 Sweet potato 9 28.13 Irish potato 8 25.00 Beans 5 15.63 Field pea 5 15.63
Kanungu
Others (sorghum, cassava, banana) 5 15.63 Beans 18 32.14 Sweet potato 15 26.79 Sorghum 11 19.64
Kisoro
Others (banana, irish potato, field peas) 11 21.51 Banana (cooking type) 59 33.91 Beans 49 28.16 Sweet potato 29 16.67 Millet 21 12.07
Ntungamo
Others(cassava , groundnut, sorghum, tea) 16 9.19 Beans 43 25.29 Banana (cooking type) 41 24.12 Sweet potato 40 23.53
Rukungiri
Others (millet, cassava, sorghum, maize, field pea, irish potato, groundnut, yams, local vegetables (names unknown), pumpkin, avocado)
15 27.06
*Number of times each crop was mentioned as a food crop
45
Table A4.8 – Main Cash Crops Grown by Households in 2002 Watershed Crop Frequency* Percentage
Sorghum 36 32.14 Irish potato 28 25.00 Tobacco 17 15.18 Beans 12 10.71 Sweet potatoes 9 8.04
Kabale
Others(eucalyptus, cabbage, pineapple) 7 6.53 Field peas 6 30.00 Irish potato 4 20.00 Sorghum 3 15.00 Cabbage 3 15.00
Kanungu
Others (maize, wheat, banana**, eucalyptus) 4 16.00 Sorghum 7 22.58 Beans 4 12.90 Irish potato 4 12.90
Kisoro
Others (coffee, banana, field pea, tobacco, tomato, sweet potato, cocoa, cabbage)
15 51.89
Coffee 39 34.82 Banana** Beans
32 14
28.75 12.50
Ntungamo
Others (millet, sorghum, field peas, tobacco, eucalyptus, cassava, avocado, groundnut, sweet potato, papaya, passion fruit)
26 23.21
Banana** 18 21.43 Sorghum 12 14.29 Beans 8 9..52
Rukungiri
Others (avocado, millet, sweet potato, eucalyptus, coffee, maize, cassava, field pea, groundnut, cabbage, tomato)
39 56.31
*no of times each crop was mentioned as a cash crop **cooking type (matoke)
46
Table A4.9 – Commercial Woody Trees Ownership by Households in 2002 Watershed Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=60)
Households owning commercial woody trees (% households reporting
67.35 58.33 30.00 33.87 45.00
Land Parcels with Commercial Woody Trees in 2002 (Percent) Tree Type Kabale
(N=49) Kanungu (N=10)
Kisoro (N=20)
Ntungamo (N=28)
Rukungiri (N=27)
Makhamia 2.04 0.00 0.00 0.00 0.00 Albizia 0.00 0.00 0.00 3.57 3.70 Eucalyptus 81.63 70.00 95.00 78.57 88.89 Ficus 0.00 0.00 0.00 3.57 0.00 Grivelleas 0.00 0.00 5.00 0.00 0.00 Pines 2.04 20.00 0.00 3.570.00 0.00 Black wattle 10.20 10.00 0.00 3.57 3.70 Emisanvu 0.00 0.00 0.00 3.57 0.00 Cyprus 2.04 0.00 0.00 0.00 3.70 Table A4.10 - Average Area and Number of Commercial Woody Trees per Land Parcel Owned by Households in 2002 Kabale Kanungu Kisoro Ntungamo Rukungiri Tree Type Area
(acres) No. of trees
Area (acres)
No. of trees
Area (acres)
No. of trees
Area (acres)
No. of trees
Area (acres)
No. of trees
Makhamia 0.1 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) Albizia 0.0 0.0 0.0 0.0 0.0 0.0 0.5 10.0 0.5 6.0
(0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) Eucalyptus 0.52 173.2 0.38 64.29 0.37 132.5 0.40 179.90 0.60 117.20
(0.66) (251.4) (0.32) (34.57) (0.21) (133.9) (0.32) (181.2)2 (0.83) (45.45) Ficus 0.0 0.0 0.0 0.0 0.0 0.0 0.1 10.0 0.0 0.0
(0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) Grivellea 0.0 0.0 0.0 0.0 0.1 3.0 0.0 0.0 0.0 0.0
(0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) Pine 1.5 50.0 0.75 75.0 0.0 0.0 0.5 10.0 0.0 0.0
(0.0) (0.0) (0.35) (35.35) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) Black wattle
0.25 48.0 0.40 4.0 0.0 0.0 0.25 100 1.0
(0.20) (65.25) (10.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) Emisanvu 0.0 0.0 0.0 0.0 0.0 0.0 0.5 200.0 0.0 0.0
(0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) Cyprus 0.5 100 0.0 0.0 0.0 0.0 0.0 0.0 0.1 3.0
(0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (3.70) (0.0) Figures in parenthesis are standard deviations
47
Table A4.11 - Perceptions of Change in Resource Conditions Since 1997 (Village data N=15) Natural resource item Change since 1997 % villages reporting Abandoned farmland because of irreversible degradation
No change Major deterioration Minor deterioration
60.00 20.00 20.00
Availability of crop land Major deterioration Minor deterioration
53.33 46.67
Soil fertility Major deterioration Minor deterioration Minor improvement
86.67 6.67 6.67
Soil erosion No change Major deterioration Minor deterioration Minor improvement
13.33 60.00 13.33 13.33
Availability of forest/woodland No change Major deterioration Minor deterioration
26.67 53.33 20.00
Quality of forest woodland No change Major deterioration Major improvement
26.67 60.00 13.33
Availability of natural water sources No change Major deterioration Minor deterioration
86.67 6.67 6.67
Quality of natural water source No change Minor deterioration Minor improvement
53.33 40.00 6.67
Diversity of wild plant types available No change Major deterioration Minor deterioration
13.33 46.67 40.00
48
Table A4.12 - Restrictions on Private Land that are Related to Land Management in 2002 (Village Data N=15)
Authority enforcing restriction (% reporting) Type of restriction
% villages reporting restrictions Central
Government District officials
Sub county officials
Parish officials
LC1 officials
LC1 officials and village members
Others
Limit on cultivation of steep
6.67 0.00 0.00 100.00 0.00 0.00 0.00 0.00
No cultivation in wetlands
26.67 25.00* 0.00 25.00 0.00 25.00 0.00 25.00
Restrictions compelling community members to control erosion
40.00 0.00 0.00 0.00 17.15 57.14 48.75 20.00
Restrictions on brick-making
100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Restrictions on charcoal making
46.67 0.00 28.57 14.29 28.75 28.57 0.00 0.00
Restrictions on burning to clear land
80.00 0.00 0.00 27.27 18.88 55.24 0.00 0.00
Restrictions on soil burning
13.33 0.00 0.00 0.00 0.00 100.00 0.00 0.00
Restrictions on cutting trees
33.33 0.00 0.00 0.00 0.00 75.00 0.00 0.00
Restrictions on damaging SWC structures
53.33 0.00 0.00 0.00 0.00 0.00 0.00 100**
Other restrictions
6.67 0.00 0.00 25.00 0.00 0.00 0.00 0.00
Others are: landowners**, CBO official and LC1 and Parish officials * NEMA
49
Table A4.13 - Household use of Agricultural Technologies and Practices in 2002 (percent) Technology/Practice Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=60)
Rukungiri (N=62)
%Adopted New crop varieties 95.92 91.67 90.00 69.35 1.67 Inorganic Fertilizers 2.04 0.00 0.00 0.00 0.49 Organic Fertilizer 100.0 100 95.0 96.77 100 Agro forestry 48.98 100.0 100.0 38.71 91.67 Crop rotation 61.2 0.0 0.0 45.16 6.67 Intercropping 51.02 0.0 0.0 61.29 6.67 Other SWC* Practices 97.96 83.3 52.63 90.32 65.00 Pest management 36.73 25.00 42.11 43.55 10.00 *Soil and water conservation Table A4.14 – Household use of Soil and Water Conservation Technologies in 2002 (percent) Watersheds Measure Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=60)
Rukungiri (N=62)
Practice mulching 8.16 0.00 15.00 72.58 51.67 Practice contour ploughing
10.20 0.00 0.00 0.00 0.00
Use grass strips 16.33 16.67 25.00 1.61 0.00 Use trash lines 6.12 0.00 10.00 1.61 5.00 Practice bench Terracing
0.00 0.00 0.00 0.00 0.00
Use fanya juu Terraces
12.24 0.00 5.00 6.45 5.00
Use fanya chini Terraces
4.08 0.00 0.00 3.23 0.00
Use trenches 51.02 41.67 15.00 41.94 18.33 Use live barriers 16.33 0.00 10.00 6.45 11.67 Use stonewalls 0.00 0.00 30.00 0.00 0.00 Planting leguminous shrubs on boundaries
30.61 16.67 0.00 11.29 5.08
Irrigation 8.16 0.00 0.0 6.45 3.33 Crop rotation 61.2 0 0 45.16 6.67 Intercropping 51.02 0 0 61.29 6.67
50
Table A4.15 - Level of Enforcement of Restrictions relating to Land Management on Private Land in 2002
Level of Enforcement (% villages reporting) Type of restriction
Not enforced Poorly enforced Fairly well enforced Well enforced Limit on cultivation of steep
0.00 100.00 0.00 0.00
No cultivation in wetlands
0.00 50.00 50.00
Restrictions compelling community members to control erosion
0.00 14.29 61.43 31.43
Restrictions on brick-making
0.00 0.00 0.00
Restrictions on charcoal making
0.00 28.57 28.57 42.86
Restrictions on burning to clear land
9.09 9.09 63.64 18.18
Restrictions on soil burning
0.00 50.00 50.00
Restrictions on cutting trees
0.00 40.04 40.00 20.00
Restrictions on damaging SWC structures
0.00 25.00 50.00 25.00
Other restrictions 100.00 0.00 0.00 0.00
51
Table A4.16– Participation in Collective Action (% households reporting) Watershed
Type of collective Action* Kabale (N=49)
Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=60)
Rukungiri (N=62)
Construct SWC** Measures on common land
0.00 0.00 0.00 0.00 2.13
Construct SWC measures on private land
11.63 16.67 18.18 32.76 0.00
Plant trees on Common land 0.00 0.00 0.00 1.72 0.00 Protecting Forests/ woodland
0.00 0.00 0.00 0.00 2.13
Protecting Grazing land 0.00 0.00 0.00 1.72 0.00
Protecting water Sources 23.26 33.33 45.45 22.41 23.40
Road Construction 4.65 0.00 0.00 3.45 2.13
Road maintenance 32.56 0.00 45.45 39.66 31.91
Construct common Water supply System
4.65 0.00 0.00 5.17 8.51
Carrying patients to hospital 0.00 0.00 0.00 1.72 4.26
Slashing football grounds 62.79 50.00 63.64 48.28 51.06
Burial of residents 44.19 8.33 9.09 13.79 31.91
Construction of common Fishpond 0.00 16.67 0.00 0.00 2.13
Raising tree Seedlings
0.00 8.33 0.00 0.00 0.00
* Compensated and uncompensated **soil and water conservation Table A4.17 - Proportion of the Farm Harvest Sold by Farmers’ (%households reporting)
Watershed Kabale (N=49)
Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=59)
All the harvest 2.04 0.00 0.00 0.00 0.00
Three Quarters 6.12 0.00 0.00 0.00 1.69
Half of the harvest 24.49 8.33 15.00 4.84 8.47
A quarter of the harvest 34.69 33.330 30.00 27.42 22.03
Less than a quarter 20.41 33.33 20.00 51.61 38.98
None at all 12.24 25.00 35.00 16.13 28.81
52
Table A4.18 - Type of Markets Mainly Utilized by Farmers to Sell their Crop Produce (% households reporting)
Watershed Market Kabale (N=49)
Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=60)
Rukungiri (N=59)
On -farm 4.3 8.3 15.0 90.0 32.2 Road side near farm 4.1 25.0 20.0 0.0 6.8 Weekly/periodic market 77.6 66.7 25.0 8.3 37.3 Main town 2.0 0.0 40.0 1.7 22.0 Daily rural market 2.0 0.0 0.0 0.0 1.7 Table A4.19- Average Distance to Nearest Agricultural Market (Kilometers) Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=60)
Distance in 1997 4.04 (3.42)
3.43 (5.92)
15.15 (3.32)
8.48 (1.11)
8.78 (4.9)
Distance in 2002 3.26 (1.57)
3.43 (5.92)
15.15 (3.32)
8.48 (1.11)
5.33 (3.09)
t test* 0.159 - - - 0.000
Figures in parentheses are standard deviations *Statistical test comparing mean observations in the watersheds Table A4.20 - Average Distance to Nearest All Weather Road in Kilometres (Household Data)
Watershed
Kabale (N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=60)
Distance in 1997 2.11 (1.35)
0.27 (0.35)
1.06 (0.49)
0.35 (1.24)
7.47 (4.5)
Distance in 2002 2.61 (1.35)
1.16 (0.93)
1.06 (0.48)
0.35 (1.24)
7.47 (4.5)
t test* - 0.007 0.083 - -
Figures in parentheses are standard deviations *Statistical test comparing mean observations in the watersheds
53
Table A4.21 - Major Marketing Problems (% households reporting) Watershed
Problem Kabale (N=49)
Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=59)
Low prices 40.82 75.00 35.00 45.16 50.00
Inadequate market 22.45 25.00 20.00 19.35 21.67
Low volume for sale 20.41 58.33 40.00 32.26 38.33
Fast spoilage 4.08 8.33 0.00 1.61 0.00
High transport cost 24.49 16.67 50.00 19.35 33.33
54
Table A4.22 - Farmer Willingness to Sell Produce as a Marketing Group (% households reporting)
Watershed Response Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=59)
Yes 69.39 83.33 55.00 75.81 63.33 Whether a farmer likes to sell produce as a marketing group
No 30.61 16.67 45.00 24.19 36.67
Reasons for disliking selling of farm produce as a marketing group in the village( % households reporting)
Have little produce 62.50 20.00 58.33 73.33 34.78 Delayed payment 13.33 0.00 0.00 20.00 6.52 Dishonesty among members 20.00 11.11 9.09 13.33 8.70 Table: A4.23 - Desired Ways to Improve Marketing (% households reporting) Particulars Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=60)
Establish nearby market 10.20 0.00 5.00 0.00 6.67 Provide weighing equipment 0.00 0.00 5.00 0.00 3.33 Improve quality of roads 8.16 25.00 30.00 38.71 21.67 Provide transport/red 16.33 0.00 25.00 11.29 23.33 Fix produce prices 6.12 0.00 5.00 22.58 0.00 Form marketing boards 8.16 16.67 10.00 3.23 11.67 Provide marketing information 28.57 16.67 10.00 8.06 8.33 Increase productivity 12.24 16.67 0.00 9.68 11.67 Diversify produce range 2.04 8.33 0.00 1.61 6.67 Provide post harvest handling technologies 2.04 0.00 0.00 0.00 1.67 Does not know/missing value 6.12 16.67 10.00 4.84 5.00 Table A4.24 - Most Effective Way of providing Price and Market Information (% households reporting) Method of communication Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=62)
Rukungiri (N=59)
Radio
57.14 83.33 60.00 70.97 71.19
Newspaper
0.00 0.00 0.00 0.00 1.69
Telephone
2.04 0.00 0.00 0.00 0.00
Conversation
36.73 8.33 40.00 27.42 23.73
Marketing groups/associations 0.00 0.00 0.00 0.00 1.69 Farm extension Agents
0.00 8.33 0.00 0.00 0.00
From Churches
4.08 0.00 0.00 1.61 0.00
Market agents
0.00 0.00 0.00 0.00 1.69
Table A4.25 - Storage of Farm Harvests (% households reporting) Kabale
(N=49) Kanungu (N=12)
Kisoro (N=20)
Ntungamo (N=60)
Rukungiri (N=59)
55
Farmer that store any harvest 97.96 91.67 85.00 96.77 91.67
Reasons for storing harvest
(N=49) (N=12) (N=20) (N=60) (N=59)
For later sale 22.45 16.67 5.00 4.84 3.33
For seed 2.04 16.67 5.00 11.29 3.33
For home consumption later 75.51 83.33 85.00 79.03 86.67
56
Table A4.26 - Perceptions of Change in Welfare of Households Since 1997 (Village data N=15) Welfare item Change since 1997 % villages reporting Availability of adequate food Major deterioration
Minor deterioration Major improvement No change
66.67 13.33 13.33 6.67
Availability of drinking water No change
Minor improvement Major improvement
53.33 13.33 13.33
Nutrition of children Major deterioration Minor deterioration Minor improvement Major improvement
20.00 20.00 20.00 40.00
Quality of education services No change Major deterioration Minor deterioration Minor improvement
6.67 13.33 16.67 13.33
Average level of household durable goods (furniture, radio, bicycles etc.)
Major deterioration Minor improvement Major improvement
6.67 60.00 33.33
Availability of energy sources for heating and cooking (fuel wood, etc.)
No change Major deterioration Major improvement
26.67 60.00 13.33
Availability of energy sources for lighting No change Major deterioration Minor improvement Major improvement
40.00 6.67 26.67 26.67
Access to health services No change Major deterioration Minor deterioration Minor improvement
6.67 66.67 20.00 6.67
Houses without latrines No change Major decrease Minor decrease Major increase
13.33 16.67 6.67 13.33
Houses with mud floor No change Major decrease Minor decrease Major increase
Children of primary school age in school Major decrease Minor increase
6.67 93.33
Children of secondary school age in school No change Major decrease Minor decrease Minor increase Major increase
13.33 6.67 26.67 40.00 13.33
57
APPENDIX 5: DETAILED TABLES OF ECONOMETRIC ANALYSIS RESULTS Table A5.1 - Determinants of Household Income (Ordinary regression) Determinants OLS
CoefficientReduced
OLS Coefficients
2SLS Coefficients
Level of education (cf no formal education) Primary education
-0.0191 -0.0210 0.0840
Post primary education 0.1416 0.2204 0.3467**Age of household head 0.0029 0.0007 0.0039Dependence ratio 0.0004 0.0002 0.0017Sex of household head 0.2310 0.2174 0.3600Household size -0.0022 0.0079 0.0053Membership to organizations: Agriculture & natural resource management
0.0484 0.2341*
Health organization 0.3017* 0.2304* Social/mutual support organization 0.1333 0.3091*Attended any agricultural training? Yes=1,No=0 -0.1033 -0.1778Duration of extension contact (hours) 0.0084*** -0.0048Square root (Farm size (acres)) 0.0812 0.1160 0.0650ln(Value of agricultural equipment) 0.2815*** 0.3045*** 0.2710***Value of livestock (‘000 Ush) 0.0003*** 0.0003*** 0.0004**Average distance from homestead to parcel (miles) 0.0001 0.0001 0.0001Potential market integration (PMI) 0.0055*** 0.0044*** 0.0053***Watershed (cf Kabale): Kisoro 0.1664 0.1632 0.1016 Ntungamo -0.04104 0.1382 0.0256 Rukungiri 0.5273** 0.5030** 0.4511**Amount of loan received (‘000 Ush) -0.0007 -0.0023Primary activity of household head (cf crop production): Livestock
0.0217 0.0687
Forest management/harvesting 0.3297 0.3014* Non-farm activities 0.2702* 0.1369*_Constant 7.9640*** 7.7355*** 7.717***R2 0.56 0.53 Wu-Hausman test of exogeneity of extension, training, loan variables
p=0.0001***
Extension p=0.004***
Training p=0.000***Relevance tests of excluded instruments Loan p=0.242 Hansen’s J test of over-identifying restrictions p=0.3096 a Instrumental variables used to predict participation in extension, training and use of a loan include all of the other explanatory variables, plus several community level variables representing focus of organizations and programs operating in community, village tax revenue, and availability of informal credit. *, **, *** mean statistical significance at the 10%, 5%, and 1% levels, respectively. Coefficients and standard errors adjusted for sampling weights and robust to heteroskedasticity. Reduced form OLS coefficients are computed after excluding the potentially endogenous variables (participation in extension, programs and organizations, primary source of income for household head, and amount of loan used).
58
Table A5.2 - Determinants of household assets Variables Livestock2 (‘000
Ush) Equipment (‘000 Ush)
Farm size
(acres) Level of education (cf no formal education) Primary education
-19.7102 0.4017**R 0.1959+
Post primary education 203.6764R 1.1438***+++R 0.6128***+++R
Age of household head (years) 12.2101***+++R 0.0242***++R 0.2355***+++R
Dependence ratio -3.8417*R -0.0037- -0.0050***--R
Sex of hhd head (Male=1, female=0) 113.6318 0.3290 0.0724Household size 44.8184**+ 0.0814***++ 0.0067+Membership to organizations: Agriculture & NRM
216.5320*-- 0.1754 0.1798+
Health organization 257.9317 0.2243 0.3663 Social/mutual support organization 95.119 0.2649 0.1492Distance from home to parcel (miles) -0.0444 0.0005 0.0002**++Potential market integration (PMI) -4.0685** 0.0053**+ 0.0005Watershed (cf Kabale)1: Kisoro -376.3505*+++R -1.2237***---R -0.6276***---R
Ntungamo 412.5522**R -0.0575 0.3204** Rukungiri -200.0739+++ 0.9829***+++R -0.1438Duration of extension contact (hours) 13.0004***+++ 0.1323* 0.0097*Attended any agricultural training? 184.3363*+++ 0.2387 -0.1049Access to credit? (yes=1, No=0) 280.4211 -0.2339 0.3701**+++Primary activity of household head (cf crop production): Livestock
647.4191***+++
-0.0387 0.2264
Forest management/harvesting
-52.47300 0.1259 0.1537
Non-farm activities 51.4144 0.4902** 0.1556Constant -501.2626 7.7593***+++R 0.0252Heteroscedasticity (White test) NS NS NSMaximum VIF 4.97 4.97 4.97Wu-Hausman test of exogeneity (P> χ 2) 1.00 1.00Hansen-Jensen Statistic (over identification)
0.035 0.346
*, **, *** mean associated variable is statistically significant at the 10%, 5%, and 1% levels, respectively for the OLS regression. Coefficients and standard errors adjusted for sampling weights and robust to heteroskedasticity +,++,+++ mean associated variable has positive impact, which is statistically significant at the 10%, 5%, and 1% levels, respectively for the IV regression. -,--,--- mean associated variable has negative impact, which is statistically significant at the 10%, 5%, and 1% levels, respectively for the IV regression. R mean associated variable has same sign in the full and reduced form specifications and that the reduced model coefficient is significant at P>0.05. 1 Kanungu was consistently dropped due to multicollinearity with the constant since its sample is small. 2 Estimated using Tobit model since over 50% of households did not have livestock. The rest of the models were OLS.
59
Table A5.3 - Determinants of Major source of income of the household head (Multinomial logit model)
Livestock MLE 2 Stage MLE Level of education (cf no formal education) Primary education 0.2971 3.4698* Post primary education -4.8732 -1.3439Age of household head -0.1510 -0.1786**Dependency ratio 0.1025 0.0063Sex of household head 26.7023 16.4671*Household size 0.9350* 0.6292***Membership to organizations: Agriculture & NRM -3.6378* 3.7269 Health organization -81.5576*** -74.5402 Social/mutual support organization -6.0277 -0.0019Square root (Farm size (acres)) 3.5742 -1.8274Value of equipment (Ush) -2.0483 3.2143*Value of livestock (Ush) 0.0131* 0.0135***Distance from home to land parcel (miles) -0.0019 0.0036***Potential market integration 2.4326*** 0.4052***Watershed (cf Kabale)1: Kisoro 101.4438*** 7.8615 Ntungamo -68.083*** -13.6041*** Rukungiri 296.584*** 40.9817***Received any training (yes=1, no=0) 10.4777** -1.1018Number of contact hours with extension 0.2431** -8.5799Access to credit 12.8675* 0.3052Constant -673.418 -167.426
Non-farm activities
Level of education (cf no formal education) Primary education 1.0375 1.3096 Post primary education 2.4062** 2.4506**Age of household head -0.0313 -0.0514Dependency ratio 0.0050 0.0250*Sex of household head 0.8522 0.8805Household size 0.2525*** 0.3128***Membership to organizations: Agriculture & NRM 0.4653 2.2184 Health organization 0.6122 1.8698 Social/mutual support organization 0.6051 1.3358Square root (Farm size (acres)) -0.0156 0.1515Value of equipment (Ush) 0.8726*** 1.5872**Value of livestock (Ush) -0.0002 0.0001Distance from home to land parcel (miles) 0.0003 0.0007*Potential market integration 0.0054 0.0057Watershed (cf Kabale)1: Kisoro 1.6364** 0.5441 Ntungamo 1.2994 2.0429 Rukungiri 0.9670 0.2355Received any training (yes=1, no=0) 0.6054 -0.1009Number of contact hours with extension -0.05031 -2.4039Access to credit 1.1744 0.0896Constant -18.4916*** -29.2115***
60
Livestock MLE 2 Stage MLE
Forest Level of education (cf no formal education) Primary education 1.0413 0.1375 Post primary education 1.7178 -0.0359Age of household head -0.0292 0.1611Dependency ratio -0.0006 -0.0617Sex of household head 19.5143*** 20.8773Household size 0.4634*** 0.1603Membership to organizations: Agriculture & NRM 0.1866 -13.0052*** Health organization -0.3806 -11.9592*** Social/mutual support organization 0.8577 -1.8408Square root (Farm size (acres)) 0.6606 -0.0408Value of equipment (Ush) -0.0139 -4.8825**Value of livestock (Ush) -0.0003** -0.0085**Distance from home to land parcel (miles) 0.0002 -0.0010Potential market integration -0.0117 0.7421***Watershed (cf Kabale)1: Kisoro -25.9671*** 15.9416 Ntungamo 23.6814 -1.5123 Rukungiri 18.7742*** 118.7374***Received any training (yes=1, no=0) -0.2014 2.0059**Number of contact hours with extension -0.0357 18.8236**Access to credit -2.3374* -0.8646**Constant -41.2044 -173.221Note: (i) Control group is crop production (ii) Coefficients robust to heteroscedasticity (iii) *, **, *** mean associated variable is statistically significant at the 10%, 5%, and 1% levels,
respectively.
61
Table A5.4 - Determinants of Participation in Programs and Organizations (Probit models)
Variables Agriculture & Natural resource
management
Health Social/mutual support
Level of education (cf no formal education) Primary education
0.2246
-0.4541
-0.31036R
Post primary education -0.1977 -0.3667 -0.9620**--R
Age of household head (years) 0.0444***+++ 0.0018 0.00127Dependence ratio 0.0032 -0.0088 0.0036Sex of hhd head (Male=1, female=0) -0.4021 -0.1188 0.2314Household size 0.1673***+++R -0.0426 -0.0132Square root (Farm size (acres)) 0.2290 0.6793***+++ -0.1821ln(Value of equipment ‘000Ush) -0.8939***--- -0.5643***--- 0.2662Value of livestock (‘000 Ush) -0.0005 -0.0001 -0.0004Distance from home to parcel (miles) 0.0001 -0.0005 0.0002Potential market integration (PMI) 0.0304***++ 0.0190***++ -0.0114**--Watershed (cf Kabale)1: Kisoro 1.6166*+ - 2.1800***+++ Ntungamo -2.1658***--- -3.1226***--- 2.3476***+++R
Rukungiri 3.6685**++ 2.6407**++ 0.6587R
Presence of extension programs 2.0826***+++ 0.8448***+++ 0.1760Presence of agricultural training programs 1.2105***+++ 0.3040***++ 0.1200Access to credit? (yes=1, No=0) -0.1402***--- -0.1864***--- 0.1971***+++Primary activity of household head (cf crop production): Livestock
-0.8641*-
- -0.3370R
Forest management/harvesting 0.1521 0.5081 0.4565 Non-farm activities -0.0828 0.4662 0.4212Constant -0.3560 2.4649 -2.1300Smith-Blundel Test of exogeneity (P> χ 2) 0.7285 0.641 0.1808 *, **, *** mean statistical significance at the 10%, 5%, and 1% levels, respectively. Coefficients and standard errors adjusted for sampling weights and robust to heteroskedasticity +,++,+++ mean associated variable has positive impact, which is statistically significant at the 10%, 5%, and 1% levels, respectively for the IV regression. -,--,--- mean associated variable has negative impact, which is statistically significant at the 10%, 5%, and 1% levels, respectively for the IV regression. R mean associated variable has same sign in the full and reduced form specifications and that the reduced model coefficient is significant at P>0.05. 1 Kanungu was consistently dropped due to multicollinearity with the constant since its sample is small. 2 Estimated using Tobit model since over 50% of households did not have livestock. The rest of the models were
OLS.
62