Update notes Uganda
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### [ ] Canagarajah2001
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* looks at distribution of earnings in rural Uganda (& Ghana) by income type and gender
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* results:
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* non-farm earnings contribute to rising inequality
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* but lower income groups benefit through strong overall growth in non-farm earnings
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* inequality is induced through self-employment; wage employment reduces inequality
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* determinants of non-farm income: location, education, age, distance to market
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* gender:
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* self-employment increased inequality among women, wage-work reduced inequality
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* self-employment was mixed among men, wage-work increased inequality
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* may show men being employed in wider variety of not just low-income waged jobs
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### [ ] Jagger2012
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### [ ] Jagger2012
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* looks at income inequality in Uganda and how income from forests and other wild areas relates to it
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* looks at income inequality in Uganda and how income from forests and other wild areas relates to it
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* cost of user fees prohibite for some to participate
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* cost of user fees prohibite for some to participate
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* technology and ability to repair were expensive and usually far away (spare parts, resulted in delayed repairs)
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* technology and ability to repair were expensive and usually far away (spare parts, resulted in delayed repairs)
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### [ ] Cooper2016
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### [ ] Cooper2016
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* looks at vulnerability of rural farmers to climate events
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* looks at vulnerability of rural farmers to climate events
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* farmers with more land, education, access to gov extension, non-farm livelihood, larger households, older age more capacity to buffer shock (through increased assets and entitlements)
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* farmers with more land, education, access to gov extension, non-farm livelihood, larger households, older age more capacity to buffer shock (through increased assets and entitlements)
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* inequality arises due to different abilities to be resilient toward climatic shock events
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* inequality arises due to different abilities to be resilient toward climatic shock events
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### [ ] Yikii2017 - food insecurity in wetlands area
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* looks at prevalence and determining factors of food insecurity in wetland adjacent areas, (Isingiro)
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* results:
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* ~93% of HHs in wetlands area food insecure
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* primary reasons: poverty,
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* low labor productivity (/unemployment)
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* low levels of education
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* HHs with fewer adult members more food secure than with more adults
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* HHs with more educated head more food secure than less education
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* requires govt promotion of:
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* food/nutrition education
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* income generating activities
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* drought resistant crop varieties
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* water conservation
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* or wetland degradation, malnutrition and income inequality may further rise
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### [x] Mulogo2018
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### [x] Mulogo2018
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* looks at access to water, sanitation, hygiene at health care facilities
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* looks at access to water, sanitation, hygiene at health care facilities
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* Isingiro: men became more actively engaged in firewood collection (62.8%) and fetching water (45.9%)
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* Isingiro: men became more actively engaged in firewood collection (62.8%) and fetching water (45.9%)
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* women's exclusion from land ownership brings them further in state of dependence, thus more vulnerable to climate change effects
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* women's exclusion from land ownership brings them further in state of dependence, thus more vulnerable to climate change effects
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### [ ] Sempewo2021
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### [ ] Sempewo2021a
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* looks at changes in water suuply use (quantity) in Ugandan HHs (due to COVID-19)
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* looks at changes in water suuply use (quantity) in Ugandan HHs (due to COVID-19)
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* most HHs had increase in water quantity usage
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* most HHs had increase in water quantity usage
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* associated HH characteristics age, sex, education, main occupation of HH head, household size, region of residence
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* associated HH characteristics age, sex, education, main occupation of HH head, household size, region of residence
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* results can be used for equitable water supply during emergencies
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* results can be used for equitable water supply during emergencies
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### [ ] Sempewo2021 - willingness to pay for water during emergency
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* looks at willingness to pay for access to improved water during COVID-19 (lockdown)
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* results:
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* majority of households not willing to pay for water
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* sg explanatory variables: sex of HH head, region of residence, water source, number of times hands are washed, whether household already buys/pays for water
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* suggests increasing/even maintaining water revenue will be challenge in emergencies without addressing disparity in socio-economic attributes of HHs
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* INT: may also show possibility of one dimension of health inequality increase due to income inequality/poverty during emergency situations (e.g. extreme climate events)
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### [x] Atamanov2022 - see poverty for main part
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### [x] Atamanov2022 - see poverty for main part
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* water access
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* water access
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