Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=18,300, vars=17) contains variables from sub-Module E2, Non-Food Expenditures over Past 7 Days. Each household with data for non-food expenditurs over the past week has multiple records (for the 10 non-food items in sub-Module E2) (18,300 records divided by 10 non-food items=1,830 Module E households with sub-Module E2 data.)

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This baseline survey seeks to capture data on women’s empowerment in agriculture, household food security, consumption, nutrition, and wellbeing of households in the geographic areas targeted by Feed the Future interventions, known as Feed the Future Zones of Influence (ZOI). The ZOI in northern Kenya comprises nine counties and approximately two-thirds of Kenya’s total land area. This dataset is a household-level file with records for each sampled household with a completed interview (n=1837, variables=102).

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=27,804, vars=8) contains data from sub-Module F2 Repeat: Sources of Income. Each household with data has multiple records (for each source of income the household collects from).

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This baseline survey seeks to capture data on women’s empowerment in agriculture, household food security, consumption, nutrition, and wellbeing of households in the geographic areas targeted by Feed the Future interventions, known as Feed the Future Zones of Influence (ZOI). The ZOI in northern Kenya comprises nine counties and approximately two-thirds of Kenya’s total land area. This dataset is the household roster file with one record per household member captured in Module C of the questionnaire (n=8,555, vars=35).

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=69,540, vars=23) contains data from sub-Module E7: Durable Goods Expenditures. Each household with data for durable goods expenditures has multiple records (for the 38 durable goods in sub-Module E7). (69,540 records divided by 38 non-food items = 1,830 Module E households with sub-Module E7 data.)

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This baseline survey seeks to capture data on women’s empowerment in agriculture, household food security, consumption, nutrition, and wellbeing of households in the geographic areas targeted by Feed the Future interventions, known as Feed the Future Zones of Influence (ZOI). The ZOI in northern Kenya comprises nine counties and approximately two-thirds of Kenya’s total land area. 13 datasets were submitted including one household dataset comprising of 1,900 records and a roster dataset with 9,809 records. Other datasets were a Women dataset (N=1,382), a children dataset (N=1,346), and 9 modules-based or indicators-based datasets.

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=3,662, vars=15) contain data from sub-Module E5 regarding non-food items that may or may not have been purchased (e.g., construction items such as wood poles and thatching grass). Each household with data for these construction items over the past 12 months has multiple records (for the two construction items in sub-Module E5). (3,662 records divided by 2 construction items = 1,831 Module E households with sub-Module E5 construction data.)

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=3,662, vars=15) contain data from sub-Module E5 regarding non-food items that may or may not have been purchased (e.g., construction items such as wood poles and thatching grass). Each household with data for these construction items over the past 12 months has multiple records (for the two construction items in sub-Module E5). (3,662 records divided by 2 construction items = 1,831 Module E households with sub-Module E5 construction data.)

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=1,772, vars=18) contains data from sub-Module F2: Income Sources During Periods of Stress. Each household with data has a single record = 1,772 Module F households with sub-Module F2 data.

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset contains records for all children under 5 years of age in the sampled households (n=1,435, vars=217).