**6. Results and discussion**

**Table 2** presents demographic characteristics for non-migrant and migrant households. Average long-run temperature and precipitation for the non-migrant households were 23.59°C and 879.37 mm, respectively. The long-run temperature and precipitation for the migrant households were 23.72°C and 876.38 mm. The average age of household heads was 49 years for non-migrants and 50 for migrants, showing little between-group difference. On average there were two males and two females for non-migrant households and three each for migrant households. Seventy percent of non-migrant household heads and 74% of migrant household heads were literate. Household labor supply averaged 1.5 males and 1.6 females for migrants, and 1.2 males and 1.3 females for non-migrants. The household wealth measure (TLU) varied from 3.9 for migrant households to 2.7 for non-migrants.


#### **Table 2.**

*Summary statistics for selected variables by migration status.*

Land-related analysis is also considered for both categories of households. Land ownership (tenure security) impacted a household's migration decision. On average, 11.8% of non-migrant household and 17% of migrant households have title to land, with migrant and non-migrant households owning 3.61 and 2.33 hectares of land respectively. Household information availability was important to migration decisions as 13% of migrant households received agricultural and livestock advice, but only 9% of non-migrant households were well informed.

Bio-physical variables included household plot distance to market and soil quality. Non-migrant household plots average 11.8 km from market, while migrant household plots were only 9.9 km from market. By contrast, non-migrant households typically had better quality plots (46% good soil), compared to 44% good soil for migrant households.

Next, we show spatial patterns of climatic factors as well as distance from household plots to nearest market and land-size distributions by region. **Figure 3** depicts the migration patterns by sub-village. Yellow dots depict sub-villages where non-migrants are dominant, while brown dots represent sub villages where the majority are migrants. While migrants and non-migrants are evenly distributed, there are more non-migrants in the northern part of the country as opposed to the southern. Similarly, southeastern Tanzania is non-migrant dominated compared to its northwestern section which has more migrants.

**Figures 4** and **5** show average annual temperature and precipitation by sub-village for the period 1983–2012, and indicate spatial variation in highest average annual temperatures from 26.67 (southwest) to 29.52°C (northwest). Lowest temperatures ranged from 15.18 to 18.25°C as one travels northwards in Tanzania. Average annual precipitation for 1983–2012 is shown in **Figure 4**, which ranges between 1616.23 and 2113.44 mm, which is mainly received in parts of the southern and central sections of the country.

**Figures 6** and **7** illustrate long-term temperature and precipitation conditions through Tanzanian space. The coefficient of variation is defined as the long-term deviation divided *Spatial Analysis of Climate Driver Impacts on Sub-Saharan African Migration Patterns… DOI: http://dx.doi.org/10.5772/intechopen.106067*

#### **Figure 3.**

*Tanzanian migration patterns.*

by the long-term mean for temperature and precipitation taken independently (Charles et al. 2005). A negative coefficient of variation indicates an area is semi-arid or arid, while a positive coefficient of variation indicates sub-humid and humid environments. **Figure 6** shows regions of low variability were observed in the central and eastern part of the regions for temperature. As shown in **Figure 7** high precipitation variability areas were dominantly in the northwest, central, and southern regions.

**Figure 8** shows average distance from household plots to the nearest market by sub-villages. Shortest distance ranges varied from 0.00 to 5.80 km, with the greatest variability observed in the east. On the other hand, longest observed distances ranged from 81 to 190 km along the northern, central and southern regions. **Figure 9** depicts household land size spatial distributions across the country. In general, plot sizes are smallest in the southeast, while plots are largest in the west and northeast.

In general, the foregoing spatial analyses seem to show the following patterns and correlations between the migration and climatic variables. Long-run average precipitation statistical results show that the coefficient of rainfall has a significant and positive impact on migration. Spatial results indicate south and central Tanzania mainly received the highest rainfall. Migration is significantly more likely from the southern and central sections of the country where rainfall is the highest. Spatial analyses also seem to show that distance of plot to market have a significant negative impact on migration. Moreover, spatial results indicate the shortest distance recorded ranged from 0.00 to 5.80 km, mainly observed in the eastern part of the country. Therefore, migration is significantly less likely from eastern parts of the country where average distance from plots to market is the shortest. Finally, household

#### **Figure 4.** *Long-run temperature by sub-village and region.*

land-size tends to be a significant migration determinant. Spatial results indicate that western and northeastern Tanzania have most of the largest farm plots. Thus, the impact of land-size on migration will be stronger in the west and northeast.

In **Table 3**, we show the logistic regression outputs for the determinants of migration, in which the interpretation of the results is based on the log likelihood ratio. Despite a poor fit to the data as indicated by the Hosmer-Lemeshow (HL) likelihood ratio test significance (a pseudo R2 of 3.6%), all 12 original variables but two were found to be significantly affecting migration.

The empirical analysis indicates climate expressed by long-run precipitation had a significant positive effect on migration, where a 1 millimeter rise in precipitation led to a likelihood of 0.0034 migration increase. By contrast, long-run temperature had an insignificant impact on migration. These findings have two implications. First, households respond to rainfall instead of temperature. Second, rainfall is a potential migration driver. It should be noted that the descriptive statistics discussed above show that there is a significant difference in temperature (and not precipitation) between migrant and non-migrant households' locations. However, the descriptive statistics suggest differences in magnitude and not climatic factors which may impact migration. This observation alone, however, because the t-test is indicative of stock differences between the two variables with respect to migration while the logistic regression shows the impact of each of variables on migration of a given variable when everything else is held constant. There are multiple possible explanations for this apparent paradox in study results including the potential influence of flooding on migration decisions which is beyond the scope of this study.

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