Datasets

To enhance access to the Feed the Future Population-Based Surveys and their interoperability with other databases, we re-compile the datasets, applying standard processing methods from original (raw) datafiles to final output ready to be displayed and reproduced. Indeed, for reproducibility and diffusion, the output of this process is documented and presented and made available during the FtF Datathon events. The objective is to support researchers and policy makers in exploring different linkages and providing answers to lingering questions, ranging from theoretical to more daily policy-making-driven. Upon completion of the process, georeferenced information on households will allow spatial visualization and matching of socio-economic data with infrastructure, market access, or overall agricultural potential. We will be able to answer important policy questions, such as whether infrastructure and market access affect women’s and men’s time burdens, group membership, and control over income. The first case study of this process was Bangladesh (using the Bangladesh Integrated Household Survey 2011-2 and 2015) and will be extended to other Feed the Future countries. Beyond visualization, the team will also propose a combined spatial and econometric analysis that goes beyond traditional mapping applications, to test for the effect of biophysical variables such as soils or weather on socio-economic characteristics of the households with a focus on climate-gender-nutrition relationships. This novel use of spatial analysis techniques can suggest policy advice tailored to different agro-ecologies and household types.

Zambia RALS Harmonized Dataset

The team processed the Feed the Future baseline and interim datasets for Zambia, applying standard processing methods to enhance their accessibility, interoperability, and comparison with other FTF datasets. This work entailed standardization of variable names and labels, the creation of derived socio-economic indicators such as dietary diversity scores, household dependency ratios, and household age and gender composition variables. Moreover, the provision of household GIS coordinates (offset for confidentiality purposes) would allow users to match data at different levels. The team used and processed the Rural Agricultural Livelihood Survey (RALS) 2012 and 2015, although the dataset at the unit-level cannot be shared as of yet pending confirmation of the data sharing agreements with the data provider.

BIHS Harmonized Dataset

To facilitate the use of Feed-the-Future Open Agriculture and Nutrition Datasets in agricultural research and development, the IFPRI's Gender, Climate Change and Nutrition Integration (GCAN) team harmonized and standardized the Bangladesh Integrated Household Survey (BIHS) (2011 and 2015, available on IFPRI Dataverse) across four key food security-relevant domains (climate, agriculture, nutrition, and gender), with the objective to make household-level data more accessible and interoperable with other databases, and in particular with spatially-explicit, biophysical data layers. The harmonization and standardization will allow users to work with the BIHS and other Feed-the-Future survey data across countries, with the same variable definitions, labels, and contents.