Poverty and gender perspectives in marine spatial planning: lessons from Kwale County in coastal Kenya Poverty and gender perspectives in marine spatial planning: lessons from Kwale County in coastal Kenya 2024-481-1 https://doi.org/10.5878/mjpj-v424 Swedish National Data Service Svensk nationell datatjänst Landing page Poverty and gender perspectives in marine spatial planning: lessons from Kwale County in coastal Kenya Poverty and gender perspectives in marine spatial planning: lessons from Kwale County in coastal Kenya 2024-481-1 https://doi.org/10.5878/mjpj-v424 Mulwa, Richard Mulwa, Richard Turpie, Jane Turpie, Jane Uku, Jacqueline Uku, Jacqueline Ndwiga, Michael Ndwiga, Michael Musembi, Elly Musembi, Elly Munyi, Fridah Munyi, Fridah Brühl, Johanna Brühl, Johanna Swedish National Data Service Svensk nationell datatjänst Environment for Development Environment for Development (EfD) is a global network of environmental economics research centers solving the world’s most pressing environmental and development challenges. We contribute to effective management of the environment in the Global South through policy-relevant research, capacity development and policy engagement. Environment for Development (EfD) är ett globalt nätverk av forskningscentra inom miljöekonomi som arbetar med att lösa världens mest angelägna miljö- och utvecklingsutmaningar. Vi bidrar till en effektiv förvaltning av miljön i det globala syd genom policyrelevant forskning, kapacitetsutveckling och policyengagemang. Landing page SEX KÖN POVERTY FATTIGDOM This dataset was used for a report that provides an overview of three pilot cases of baseline data collection to better understand local communities’ dependence on marine resources and other livelihood activities, with emphasis on understanding the role of marine spatial zonation and resource manage-ment on poverty and gender equality. Pilot studies were conducted in Kenya, Tanzania and Madagascar. This dataset only contains data from Kenya, in particular, from the Kwale county which is the southernmost coastal county. The survey employed a mixed-method crosssectional study design, collecting qualitative and quantitative data at different levels. The study adopted a multi-stage sampling procedure where three sub-counties in Kwale county that border the ocean front, Lunga Lunga, Msambweni, and Matuga were purposively selected in the first stage. In the second stage, nine locations bordering the ocean in these sub-counties were randomly selected, and thereafter villages selected randomly from the nine locations. The sampling of households in the villages was random and involved drawing transects across the villages and picking individual households randomly. The key method of primary data collection was face-to-face interviews. A survey questionnaire was developed. Quantitative data collection tools were digitized for electronic capture and transmission using Kobo Toolbox. The electronic questionnaire was uploaded to enumerators’ mobile smartphones using a unique Kobo Collect app. Data collected were submitted to a server daily. A total of 446 households were included in this dataset. This datasets is part of a wider data collection that comprises three countries: Kenya, Tanzania, and Madagascar. This dataset was used for a report that provides an overview of three pilot cases of baseline data collection to better understand local communities’ dependence on marine resources and other livelihood activities, with emphasis on understanding the role of marine spatial zonation and resource manage-ment on poverty and gender equality. Pilot studies were conducted in Kenya, Tanzania and Madagascar. This dataset only contains data from Kenya, in particular, from the Kwale county which is the southernmost coastal county. The survey employed a mixed-method crosssectional study design, collecting qualitative and quantitative data at different levels. The study adopted a multi-stage sampling procedure where three sub-counties in Kwale county that border the ocean front, Lunga Lunga, Msambweni, and Matuga were purposively selected in the first stage. In the second stage, nine locations bordering the ocean in these sub-counties were randomly selected, and thereafter villages selected randomly from the nine locations. The sampling of households in the villages was random and involved drawing transects across the villages and picking individual households randomly. The key method of primary data collection was face-to-face interviews. A survey questionnaire was developed. Quantitative data collection tools were digitized for electronic capture and transmission using Kobo Toolbox. The electronic questionnaire was uploaded to enumerators’ mobile smartphones using a unique Kobo Collect app. Data collected were submitted to a server daily. A total of 446 households were included in this dataset. This datasets is part of a wider data collection that comprises three countries: Kenya, Tanzania, and Madagascar. Kenya Kenya HouseholdHousehold HushållHushåll Households from the Kwale county which is the southernmost coastal county in Kenya. The survey employed a mixed-method crosssectional study design, collecting qualitative and quantitative data at different levels. The study adopted a multi-stage sampling procedure where three sub-counties in Kwale county that border the ocean front, Lunga Lunga, Msambweni, and Matuga were purposively selected in the first stage. In the second stage, nine locations bordering the ocean in these sub-counties were randomly selected, and thereafter villages selected randomly from the nine locations. The sampling of households in the villages was random and involved drawing transects across the villages and picking individual households randomly. Households from the Kwale county which is the southernmost coastal county in Kenya. The survey employed a mixed-method crosssectional study design, collecting qualitative and quantitative data at different levels. The study adopted a multi-stage sampling procedure where three sub-counties in Kwale county that border the ocean front, Lunga Lunga, Msambweni, and Matuga were purposively selected in the first stage. In the second stage, nine locations bordering the ocean in these sub-counties were randomly selected, and thereafter villages selected randomly from the nine locations. The sampling of households in the villages was random and involved drawing transects across the villages and picking individual households randomly. Numeric Text Cross-sectionCross-section TvärsnittTvärsnitt ProbabilityProbability SannolikhetsurvalSannolikhetsurval InterviewInterview IntervjuIntervju Access to data through SND. Access to data is restricted. Åtkomst till data via SND. Tillgång till data är begränsad. restrictedAccess UNESCO-IOC and SwAM 2024. Poverty and Gen-der Perspectives in Marine Spatial Planning: Lessons from Kwale County in Coastal Kenya. Paris. Nairobi, UNESCO. (IOC Technical Series, 179).Authors:Richard Mulwa (EfD, Kenya), Jane Turpie (EfD South Africa), Jacqueline Uku (KMFRI), Michael Ndwiga (EfD, Kenya), Elly Musembi (KMFRI), Fridah Munyi (KMFRI), Johanna Bruehl (EfD South Africa). UNESCO-IOC and SwAM 2024. Poverty and Gen-der Perspectives in Marine Spatial Planning: Lessons from Kwale County in Coastal Kenya. Paris. Nairobi, UNESCO. (IOC Technical Series, 179).Authors:Richard Mulwa (EfD, Kenya), Jane Turpie (EfD South Africa), Jacqueline Uku (KMFRI), Michael Ndwiga (EfD, Kenya), Elly Musembi (KMFRI), Fridah Munyi (KMFRI), Johanna Bruehl (EfD South Africa). 2024