A unique environmental augmented household-level livelihood dataset from Nepal
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A person’s livelihood consists of her/his abilities, assets and activities required for a means of living. A gender analysis in the context of rural livelihood enables us to identify the different activities that men and women do. This paper intends to examine the role of gender in determining livelihood aspects like occupation structure and migration. It also investigates the role played by gender in determining employment, family income, and income distribution of individuals. The study is based on a census of 143 households of a village from the Bhadrak district of Odisha. The study observes significant gender gap in occupation structure, and income distribution across gender. If women are employed, household income increases significantly.
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Family income
Household income
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This paper explores the impact of large-scale agricultural investment on household livelihood outcomes among smallholder farmers in Kilombero Valley, Tanzania. The study used qualitative and quantitative data from a sample of 376 households. Quantitative data analysis employed independent samples t-test and multiple linear regressions. There were associations in livelihood outcomes and household headship (p < 0.05). The age of household head, years of schooling, household size, land size, group membership, household participation in the out-grower scheme, and livelihood strategies had influence on household livelihood outcomes (p < 0.05). Household livelihood outcomes depend on household socio-economic characteristics and household participation in LSAI. The study recommends Local Government Authorities, non-governmental organization to help farmers to diversify livelihood strategies, group strengthening, promoting gender dialogues in the community to transform gender norms that discriminate against women and female headed households, increase representation of out-growers in every decision making process that affects their livelihood outcomes.
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The paper empirically examines the household-level importance of various income sources and the factors associated with the non-farm work participation of community forest user households in rural Nepal. Data for the study was collected using structured survey of 275 randomly selected households. The income data are presented in absolute and relative terms by different household categories. Determinants of non-farm work participation is tested using probit regression model. Results show that non-farm income constitute an average of 55.5% of the total household income. Non-farm income such as remittances (migration from work abroad) and pensions constitute the most important source of income for all categories of household. Larger sized non-dalits households, which hold relatively larger value of implements and smaller land area show significant association with non-farm work participation. The varying share of the non-farm income to the total household income resulting by the combination of household’s individual and socio-economic characteristics are discussed.The study suggests that typical policies such as improvement in human capital , e.g. improved literacy and skills, and rural infrastructures will remain important for promoting and making the poor benefit from the income opportunities through various non-farm sectors.
Household income
Probit
Survey data collection
Net income
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The purpose of this study was to explore the determinants of rural livelihood diversification activities of the rural household in the Lemmo District, Hadiyya Zone of Southern Ethiopia. Data was gathered by household survey from 131 sample households of 4 randomly selected rural kebeles of the District through structural questionnaires. The alternative livelihood strategies that were used by the study households were agriculture only, agriculture plus off- farm, agriculture plus non- farm, and agriculture plus off- farm plus non- farm activities. Multinomial Logit model was employed in identifying the determinants of rural livelihood diversification strategies. From 12 hypothesized explanatory variables, seven variables were found to have significant effect in determining diversification of household livelihood sources. Accordingly, total family size, household head education, frequency of development agents’ visit, access to credit service and remittance receiving have positive and significantly effect on diversification livelihood activities. However, total land holding and dependent family size have negative and significant correlation with diversification livelihood sources. Therefore, the findings of this imply that rural households’ development policies should consider off-farm and non-farm livelihood activities in addition to agriculture . Keywords : Rural Livelihood, Diversification, Determinants, Multinomial Logit Model, Southern Ethiopia
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Remittance
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The study examines the impact of Micro Hydropower (MHP) projects on households’ income, consumption and diversification of livelihood strategies in District Hattian Bala, Azad Jammu and Kashmir. A multinomial logistic model is used to investigate the possible role of MHP and other control variables on households’ adoption of livelihood strategies. The Results show that MHP-micro hydropower has a positive significant effect on household’s adoption of non-farm and diversified livelihood strategies. These findings suggest that MHP projects in Northern areas of Pakistan could help in improving household’s income and consumption through adoption of high income livelihood strategies. Keywords: Micro Hydropower (MHP), Livelihood Strategies, Income and Expenditures, Poverty Alleviation, Multinomial Logistic Model
Consumption
Household income
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Purpose: This study aimed to assess the livelihood diversification and food security among peri urban households the case of Horo districts. Methodology: Multi stage sampling techniques was used to selected sample respondents. Four peri urban kebele were randomly selected based on 10 km radius of the shambu and sekela towns out of the existing 22 kebeles. Based on proportional sampling size 146 respondents were selected. Data was collected using key informant interview, focus group discussion and interview schedule. Both descriptive and econometric data analysis techniques were applied.Results: The descriptive statistical revealed that human capital variables: family size, educational level, age, sex, and land size. Binary logistic regression model result indicates that educational level, land size livelihood diversification have significantly and positive influencing household food security. Whereas household age, sex, frequency of extension agents contact, use of agricultural technologies and market distance has significant negative influence of household food security in the study area at different significant levels. The man identified constraints these households encounter with various livelihood to meet household needs lack of assets, loss of farm lands for housing purposes and unfavorable climate conditions. The result also indicates that 24.65% of households interviewed have food insecure.Unique contribution to theory, practice and policy: Food security situation with in the study area there is not chronic problems as historical but there are bad situations. There is towards their assertion is that they focused only on food availability without touching to access and use. They have implemented policies including food security demonstration to help recover the situation.
Stratified Sampling
Multistage sampling
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Cross-sectional data
Survey data collection
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The purpose of this study is to map the estimated impacts of livelihood asset indices on the livelihood status of rural households in five States of Southeastern Nigeria.In each of the five States, 120 first-order migrant-sending households were used for this study totaling 600 households for the entire study area.Questionnaire survey was used to elicit data on the livelihood status of the sampled households.The study utilized the asset indices analytical techniques to estimate the livelihood status of the households.In addition, hierarchical cluster analysis was used to categorize the States according to the impact of the asset indices on their livelihood status before depicting the categories in maps.The results show that the financial index has the greatest contribution to livelihood status in the study area.Also, Imo State has the highest aggregate livelihood status value of 34.33 while Abia State has the least livelihood status value of 23.99.Based on the findings, it is recommended that agricultural extension services be introduced, and improved upon in the study area.Furthermore, small and medium scale industries should be established in the study area to create jobs for the population.
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Household income
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