The approach provides more details about the processes that cause the various distribution changes which may either increase or decrease the Ginico efficient . The percentile shares approach is more useful incases where time series data is used but it also compliments the analysis of income in equality using cross-sectional data. As mentioned earlier, the Gini index is a widely used and favoured measure of income inequality over other alternatives because this index can be applied to both time series and cross-sectional data simultaneously . The value of the Gini Index ranges from 0 to 1. With the value 1, the Gini coefficient represents perfect unequal distribution of income, while with the value 0, it represents perfect equality of income . Links with the Lorenz curve make the Gini coefficient an attractive statistic for the decomposition by income components, as the Lorenz curve graphically represents the Gini coefficient. The concentration coefficient of each income component with respect to total income is obtained from a concentration curve .
However, it should be noted here that, ebb and flow tray the Gini coefficient cannot be used to rank distributions if the Lorenz curves intersect. According to Litchfield , there are alternative ways to decompose the Gini, however the component terms of total inequality are not always intuitively or mathematically appealing. Notwithstanding this concern the Gini coefficient still remains a popular inequality measure of total inequality and as a decomposable measure.Using the CV approach, we decomposed the total household incomes into three major categories of income namely; income from crops, income from timber products , and income from off-farm activities . This is useful because conventionally, most studies have often attempted to evaluate the distributional impact of certain types of income by merely comparing the size of distribution of that particular income with that of the total rural income as a whole. Because it neglects the twin issues of income weights and covariance between income sources, any approach, which solely compares the size distribution of one particular income with that of total income, is likely to arrive at erroneous conclusions regarding the distributional impact of that particular income .
Corresponding to the CV, we applied the decomposition formula presented in Equation . 90% of the households had incomes of less than TZS 3,342,022 and only 10% had income higher than this. These results suggest existence of income inequality in the study area. At the 50 percentile, the mean incomes for disaggregated analysis were the highest for farmers with farmland located far from homestead , followed by those of farmers who accessed extension services during the past two years and farmers who were members of community-based financial institutions . The mean incomes were the lowest for female-headed households , ebb and flow trays followed by farmers who did not access extension services , and farmers with farmland located close to homestead .Farmlands located far from homestead were mostly found along the footsteps of the mountains or lowland areas where landholdings were relatively larger allowing for more intensification and crop revenues than the farmlands located in the upper gradients. This relationship is common in mountain areas.
Mountain areas are considered as less favoured due to difficulties caused by short growing seasons, steep slopes at lower altitudes, or by a combination of the two . Land holdings in high altitudes of mountain areas are limiting the scale of economic activities performed by farmers to increase farm income. Doucha et al. , for example showed that, farmers in in Czech less favoured areas could rarely grow permanent pasture along with extensive cattle breeding or undertake any additional non-agricultural activities on farm . In fact, Kata confirmed decreasing value of income from operational farm activity toward higher altitude. In this circumstance, farm incomes may remain insufficient for smallholder farmers to undertake a profitable agricultural production. The influence of altitudinal variation on crop production and animal husbandry is also reported by Zhang etal. who investigated the response of altitudinal vegetation belts of the Tianshan Mountains of north-western China.