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 non-membership to these institutions had a more inequalising effect on income.
Importantly,4×8 flood tray income inequality was the highest among farmers with farmland locatedfar 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. The coefficients for age of household, size of farmed land, and value of household assets in the model of farmers who accessed extension services statistically significantly determined household income with p-values of 0.001, 0.007,and 0.000 respectively. Before running our model to get the descriptive statistics such as mean, media,range and interquantile range, we generated the P-P and histogram plots of regression standardized residual against continuous predictor variables and used them to test the assumption of normality .
Where they vary more from the straight line,then the data could be considered to be not normally distributed, otherwise the data were considered to be normally distributed . The P-P and histogram plots add value to regression analysis as they can expose a biased model far more effectively than the numeric output by displaying problematic patterns in the residuals. If the model is biased then the results cannot be trusted. If the residual plots look good, ebb flow tray then the analyst can proceed with the assessment of model statistics such as the adjusted R-squared , which is also called the coefficient of determination, or the coefficient of multiple determination for multiple regression. It is used to measure the model’s goodness-of-fit and it shows the percentage of the dependent variable variation that a linear model explains.Graphically, it evaluates the scatter of the data points around the fitted regression line . The coefficients of our step by step multiple linear regression model for the pooled sample suggest that household assets, size of farmland, and age of household head positively influenced household income while the household size negatively influenced the household income. These results can be compared with findings of other previous studies. In Urban Ethiopia for example, Abebe employed Fields’ regression based on decomposition technique to investigate the factors influencing income inequality using cross-sectional analysis.The study found age and household size to be negatively influencing expenditure and household income contributing to widen income inequality. In Malaysia, Ayyashand Sek found sex and age of household heads to be contributing negatively to inequality and had inequality decreasing effects, with negative impact on inequality. Elsewhere, in South Korea, Shin analysed data linking survey data with administrative data shows that wealth, employment status, family size, and education were significant contributors of income inequality.