**4. Study data**

Our study is based on high-quality household survey data called the Living Standards Measurement Survey-Integrated Survey on Agriculture (LSMS-ISA), initiated by the World Bank Development Economics Research Group, and implemented by the Tanzania Bureau of Statistics [44, 46]. National-level longitudinal data was collected between 2009 and 2013. The present study is specifically based on the 2012– 2013 data. The LSMS-ISA data are composed of information on households, agriculture, and community characteristics. Each survey household is associated with a georeferenced sub-village.

Sub-villages represent enumeration areas where households were selected for the survey. However, an enumeration area is not a community from the sociological aspect; instead they are designated for information collection about the study areas where households selected for the intended study are located. A total of 26 regions and 149 sub-villages were considered in this study. Some observations were removed due to incomplete surveys and georeferencing errors, generating a final sample size of 10,461 households. Accordingly, 3968 and 6493 observations were identified for nonmigrant and migrant households respectively. Non-migrants were identified in 62 enumeration areas whereas migrants were located in 75. The survey tracked all household members 15 years or older. We focused on these households and individual members aged 15–65 years. A key study variable was identification of migrant and non-migrant individuals. A migrant is an individual in a household who has left his or her initial residence and considers himself or herself to have settled in a new community. Despite the foregoing definition, the study follows the new economics of migration approach as indicated by previous work [47–50], where migration is a collective action made by a household. A household member migrates not only to maximize household income for economic reasons, but also to minimize risk. Therefore, this study analysis used household-level data.

*Spatial Analysis of Climate Driver Impacts on Sub-Saharan African Migration Patterns… DOI: http://dx.doi.org/10.5772/intechopen.106067*

In addition to the LSMS-ISA data, this study incorporated 0.5° gridded historical climate data from the University of East Anglia's Climate Research Unit [51] for 1983– 2012. As indicated in the Results section, the 30 year (1983–2012) gridded temperature and precipitation data were downloaded and assigned into sub-villages using *ArcGIS* and *STATA* software. Temperature and precipitation data were analyzed for anomalies as well as for long-run (30 year) temperature and precipitation means.

Variable selection was based on the following studies: tenure security, land size, distance to market [52]; age, number of male and female adults, literacy, tropical livestock unit (TLU) [53]; extension of advice, soil fertility [54]; and climatic variables [11]. Additionally, [54] used social links and irrigation potential as measures of information access and land quality respectively. Similarly, we used extension advice (or access to information) and soil fertility as proxy measures of land quality.
