This dataset describes the households and their members surveyed as part of the Baseline Study of Food for Peace Title II Development Food Assistance Program in in Segamil and Paisano in Guatemala. It has 36 columns and 37,454 rows. In fiscal year 2012, USAID's Office of Food for Peace (FFP) awarded funding to private voluntary organizations (PVOs) to design and implement a multi-year Title II development food assistance program in Guatemala. The main purpose of the Title II program is to improve long-term food security of chronically food insecure populations in the target regions. FFP contracted a firm, ICF International, to conduct a baseline study in targeted areas of the country prior to the start of the new program. The purpose of the study was to assess the current status of key indicators, have a better understanding of prevailing conditions and perceptions of the population in the implementation areas, and serve as a point of comparison for future final evaluations. The results were also used to further refine program targeting and, where possible, to improve the understanding of the relationship between variables to inform program design. The study was conducted in 2013, while FFP expects to conduct final evaluations as close as possible to the end of the program five years later. The data asset is comprised of six datasets: Agricultural Practices, Child Health, Household Description, Maternal Health and Household Sanitation, Food Consumption and the sample weights to be applied during analysis.

Data describing the anthropometry in Segamil and Paisano in Guatemala

Data on maternal Health and household sanitation in Segamil and Paisano in Guatemala In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully.

Data on food consumption in Segamil and Paisano in Guatemala In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully.

In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully. This is the fifth spreadsheet of sixin the Guatemala Maternal health and Sanitation Data.

In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully. This is the third spreadsheet of sixin the Guatemala Maternal health and Sanitation Data.

In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully. This is the fourth spreadsheet of sixin the Guatemala Maternal health and Sanitation Data.

In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully. This is the second spreadsheet of sixin the Guatemala Maternal health and Sanitation Data.

In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully. This is the second spreadsheet of sixin the Guatemala Food Consumption Data.

In the process of migrating data to the current DDL platform, datasets with a large number of variables required splitting into multiple spreadsheets. They should be reassembled by the user to understand the data fully. This is the sixth spreadsheet of sixin the Guatemala Maternal health and Sanitation Data.