Shin indicated further that income derived from economic activity and loans based on the leverage in the financial market exacerbated wealth inequality as higher income groups tended to utilize more loans in the monetized economy, widening the gap between the rich and the poor.As such, the mainstream literature on female-headed households and poverty indicates that female-headed households constitute the “poorest of the poor”, and several other studies have shown a link between female-headed households with poverty and low socioeconomic status. In 2019,Lebni et al. conducted a qualitative study among female-headed households in Kermanshah, West of Iran. They collected data through semi-structured interviews with female-headed households who were selected purposefully. They analysed their data using conventional qualitative content analysis and they found that female-headed households faced many challenges that could become a big threat or an opportunity.
A study was conducted to investigate the nature and determinants of income inequality in mountain areas using the case of Uluguru Mountains in Tanzania.Specifically, flood and drain tray the study used the cross-sectional research design, income percentile shares, Gini coefficient and Lorenz curves, as well as, the coefficient of variation, to pinpoint the nature of income inequality in the study area using both pooled and disaggregated data. The determinants of income inequality were investigated using the step by step multiple linear regression model. The results of analysis of income-inequality revealed existence of income inequality. At the50 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 as well as farmers with farmland located close to home stead.
The 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. Membership to community-based financial institutions had the most equalizing effect on income. Income inequality was the highest among farmers with farmland located far from homestead. Unexpectedly however, nft hydroponic income inequality amongst 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 .Overall, crop production was the main source of income in the agroforestry systems of the study area, followed by timber products. The contribution of income from non-farm income generating activities was the lowest but these sources constituted a major income-inequality increasing component in the pooled sample.
However, the results of disaggregated analysis showed that “non-farm sources” were decreasing income-inequality for farmers with farmlands located close to homestead, for female-headed households, for farmers who did not access extension services, and for farmers who were members of community-based financial institutions. This implies that diversification of income sources is an important strategy for reducing income inequality in mountain areas.Accordingly, policies and initiatives that aim to promote diversification of livelihoods are more likely to reduce income inequality in these areas and are therefore recommended.The values of coefficients in our step by step multiple linear regression model suggested that household assets, size of farmland, and age of household head positively influenced household income and household size negatively influenced household income.