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 . We purposefully used CV to pinpoint the contribution of these three categories of income sources to total income inequality. 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 . Our results , indicate that 50% of the sample households in the study area had incomes of less than the mean income . About 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 , 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 et al. who investigated the response of altitudinal vegetation belts of the Tianshan Mountains of north-western China. They indicate that residents used the natural advantage of this area to develop animal husbandry. However, the changes in the montane steppe belt were seen to greatly affect the scale of animal husbandry and the income of herdsmen . To address the challenges of agricultural production in mountain areas, farmers who are relatively better-off, tend to move along an altitude gradient—to the lowlands .The results of analysis of income inequality using the Gini index and Lorenz curves for income distribution are shown in Table 4 and Figure 3 respectively. The Gini coefficient for the pooled sample was 0.97. The analysis of income data disaggregated by farmland location, gender of household head, access to extension services, and membership to community-based financial institutions, revealed that the latter had the most equalizing effect on income. The Gini coefficient for farmers who were not members of any community-based was 0.77 implying that nonmembership to these institutions had a more inequalising effect on income.
Importantly, income inequality was the highest among farmers with farmland located far from homestead . Overall, these findings support the argument that the size of households, access to extension service, credit access, and membership to social groups determine income distribution . Unexpectedly however, income inequality among farmers who accessed extension services was higher than that of their counterpart farmers who did not access the services . We attribute this to variations in personal household characteristics , and economic characteristics as indicated in our results of coefficients for the independent multiple linear regression models presented in Appendix 3.