The vegetation of the area is extremely variable ranging from drier lowland coastal forest to transitional rainforests, sub-montane, montane and upper montane forest types, as well as the afromontane grasslands on the Lukwangule plateau. All these ecosystems are rich in endemic species making them of high conservation priority. However, land degradation in the area is rampant due to existence of unsustainable anthropologic activities . The mountains also serve as a water catchment and water source for populations living downstream in Morogoro rural and Municipality as well as other residents in the Dar es Salaam City and the Ruvu/Wami River Catchments. Thus, we purposefully selected the study area not only for its importance as a water catchment, but also as an enormous biodiversity hotspot which is encountered by the challenge of increasing human activities that threaten biodiversity and environmental integrity.
The study used the multi-stage sampling procedure to select the study villages and sample households. The third stage entailed the selection of sample households from each stratum using the proportionate probability sampling procedure. The purpose of using wealth ranks, apart from understanding the perceptions of communities in the study area about poverty and wealth gained from the wealth ranking exercise, was to ensure that the sample drawn and quantitative analyses represent the full range of livelihoodcircumstances in the study area, rather than being accidentally clustered around the mode of range. The distribution of sample size by hamlets is given in Table 1. The study used both primary and secondary data. Prior to commencement of fieldwork, we hired six enumerators to assist during data collection. These were trained on how to administer questionnaires and use other research tools . They were also reminded about the research ethics they should comply with. The actual fieldwork started with a reconnaissance survey to get an overview and understanding of the study area and applicability of the questionnaire. During the reconnaissance survey the household questionnaire was pre-test to a small number of respondents before the actual fieldwork to check for their relevance to the study area and objectives. This was followed by the main survey which used different research tools and techniques, including structured questionnaires, interviews with key informants and Focus Group Discussions .
The FGDs were attended by at least 10 participants per village representing different socioeconomic groups that existed in the area, including the rich, poor, youth and women, men, abled and disabled people. In addition, direct observation served as a complementary tool. In selecting the key informants for interview the snowball technique was used. The technique is particularly suitable when the population of interest is hard to reach and compiling a list of the population poses difficulties for the researcher . It begins with a convenience number of initial subject which serves as “seeds,” through which wave 1 subject is identified; wave 1 subject, in turn, identifies wave 2 subjects; and the number of interviewees consequently expands wave by wave-like a snowball growing in size as it rolls down a hill .The results of qualitative analysis poverty using the wealth ranking results were complemented with quantitative analysis of income inequality and drivers of income inequality using the Gini coefficient and Lorenz curves, as well as, the coefficient of variation measure adapted from Adams . The percentile shares quantify the proportions of total outcome that go to different groups defined in terms of their relative ranks in the distribution . The approach provides more details about the processes that cause the various distribution changes which may either increase or decrease the Gini coefficient . The percentile shares approach is more useful in cases where time series data is used but it also compliments the analysis of income inequality using cross-sectional data. The approach addresses the interpretation limitation inherent in specific values of the Gini coefficient .
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, 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.