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Table 45: Socio-economic factors to the most vulnerable by location. KilosaDistrict, Tanzania, 2010Household socio-economic factors Lunenzi(25%)Nyali(25%)Masugu(45%)TotalMean age of household heads (yrs) 47 45 62 55 43Religion (Christians) (%)50 80 56 80 74Religion (Muslims) (%)50 20 44 20 26Mean household size (number)*4,83 4 2,44 3,55 4,9Mean household land (ha) 1,51 2,06 1,9 1,61 2,28Primary school (%) 67 80 22 72 72Worker/consumer ratio 0,8 0,5 0,7 0,7 0,8Female head of household (%) 17 60 13 22 12Married (%) 83 40 55 60 79Engagement in agriculture (%) 83 100 100 95 99TotalpopulationEngagement in charcoal, poles, timber 0 0 0 0 17production (%)Collection of NTFP‟s (%) 33 20 89 55 78Engagement in off-farm activities (%) 0 0 0 0 13Engagement in non-farm activities (%) 0 0 0 0 27Mean total income (USD) 110 138 96 111 1024,64N = 20, * indicates significantly difference between locations (p < 0.05)First of all, the poorest households are a lot poorer than the rest of the population.They are also older older (55 yrs). People have the same religion regardless ofincome, but the size of households seems to differ. The average size of households inthe whole population was 4,9, but among the poorest it was 3,55. This also differsbetween the villages, where poor households in Masugu had smaller households. Withsmaller households, they also have less land to work on (1,61 ha compared to themean total of 2,28 ha). Despite limited land though, all were almost entirelydependent on agriculture alone, showing that they are more vulnerable in terms ofshocks. That no charcoal, poles or timber were produced is quite remarkable.Although some NTFPs were collected by some, more households on average stillcollected more. Interesting enough though, we see that this group are more dominatedby female headed households (22% compared to 12%) and less households aremarried. This tells us that for example widowed or single women could in times ofshocks and income shortfalls be affected more than the rest.By emphasising on the vulnerability context, we have seen that livelihoods areaffected by shocks and unexpected income shortfalls. This again affects households‟strategies. In Kilosa District, the climatic conditions, and big variations along differedseasons have resulted in diversification as a risk management strategy, as well as210