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, 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. 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 as well as farmers with farmland located close to homestead. 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. Unexpectedly however, 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. Our results also suggest that, gender disparity remains one of the key issues to be addressed, and it should be taken into account in formulating future policies, especially those aiming to reduce inequality among populations in mountain areas and thus, improving living standards and well-being of smallholder farmers in these areas. In addition to promoting livelihood diversification, we therefore recommend tailor-made training and farm financing mechanism to help the less resource endowed farmers, including the female-headed households in mountain areas to raise their economic portfolios and social status.This paper is based on a postgraduate research conducted under the Department of Forest and Environmental Economics of the Sokoine University of Agriculture in Tanzania.
The author would therefore wish to extend his sincere gratitude to Ms. Willickister R. Kadigi for allowing her raw data to be used for analysis in this paper, to the former and current heads of the Department of Forest and Environmental Economics at SUA, Prof. Jumanne Abdallah and Dr. Greyson Z. Nyamoga respectively, as well as the other academic staff in the Department for their enormous academic support. My sincere acknowledgements are also due to Mr. Raymond R. Kilenga, the Programme Officer of the Eastern Arc Mountains Conservation Endowment Fund; Ms. Bernadetha Chille, the Principle Forest Officer of the Uluguru Forest Nature Reserve; the respondents and village/hamlet leaders in the study area for their hands of support as well as excellent cooperation and inputs during data collection.