LiveLihoods strategies and househoLd resiLience to Food insecurity ...

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LIVELIHOODS STRATEGIES AND HOUSEHOLD RESILIENCE TO FOOD INSECURITY: AN EMPIRICAL ANALYSIS TO KENYA • pastoralists (797 households): households whose livelihood is mainly from livestock (on average, 89% of total income and 8.33 TLU per household); 89% of them live in rural area. Figure 2: Dendrogram of Livelihood Strategy Clusters in Kenya L2squared dissimilarity measure 0 5000 10000 15000 G1 G2 Table 2: Livelihood Strategy Groups in Kenya G3 G4 Livelihood Strategy Frequency Percent Agro-pastoralists 1,398 10.58% Small-holder farmers 3,790 28.68% Large-holder farmers 273 2.07% Entrepreneurs 2,699 20.43% Wage employees 4,255 32.21% Pastoralists 797 6.03% Total 13,212 100.00% The distribution of the livelihood group differs across the eight Kenyan provinces (Table 3). For example, large-holder farmers are mostly concentrated in Eastern province (42%), while pastoralists are mostly represented in the Rift Valley and North Eastern provinces. Entrepreneurs are the livelihood group most evenly distributed across provinces while large-holder farmers are the most concentrated ones. In terms of distribution within a given province (Table 4) the largest share of small-holders can be found in the Central province (53%), while the share of wage-employees is highest in Nairobi (67%). Coast province shows the most diversified distribution, while Nairobi shows the highest concentration on only a few livelihood groups. Table 3: Distribution of Livelihood Strategy Groups across Kenyan Provinces Livelihood Strategy Groups Nairobi Central Coast North Eastern Eastern Nyanza Rift Western Total Valley Shares per livelihood groups Agro-pastoralists Small-holder 0.06 7.28 11.33 32.05 1.69 9.70 25.68 12.21 100 farmers 0.04 19.11 5.59 18.43 0.27 17.76 23.67 15.13 100 Large-holder 0.00 5.34 10.78 41.86 0.00 3.89 24.44 13.69 100 16 G5 G6
LIVELIHOODS STRATEGIES AND HOUSEHOLD RESILIENCE TO FOOD INSECURITY: AN EMPIRICAL ANALYSIS TO KENYA farmers Entrepreneurs 13.17 8.37 13.42 11.90 0.87 18.61 16.99 16.68 100 Wage employees 22.25 10.71 10.54 6.49 5.81 11.69 27.70 4.82 100 Pastoralists 0.22 6.51 7.17 12.34 19.76 9.52 35.36 9.13 100 Table 4: Distribution of Livelihood Strategy Groups within Kenyan Provinces Livelihood Groups Strategy Nairobi Central Coast Eastern North Eastern Nyanza Rift Valley Western Shares per province Agro-pastoralists 0.1 8.1 16.6 26.6 7.4 9.2 14.4 13.7 Small-holder farmers 0.2 52.8 20.2 37.9 3.0 41.5 32.8 42.1 Large-holder farmers 0.0 1.2 3.3 7.2 0.0 0.8 2.8 3.2 Entrepreneurs 32.2 13.3 27.9 14.1 5.4 25.0 13.5 26.7 Wage employees 67.4 21.1 27.2 9.5 44.6 19.5 27.4 9.6 Pastoralists 0.2 3.4 4.8 4.7 39.7 4.2 9.1 4.7 Total 100 100 100 100 100 100 100 100 3.4 Resilience Assessment 3.4.1 Methodology In order to understand the key determinants of each livelihood strategy and to make a comparison between them, we used an updated version of the resilience framework developed by Alinovi et al., (2008) using data from Palestinian households. The resilience to food insecurity of a given household at a given point in time is assumed to depend primarily on the options available to that household to make a living, such as its access to assets, income-generating activities, basic services and social safety-nets. These options represent a pre-condition for the household response mechanisms to a given risk, that is, its ability to handle it. In the original framework, Alinovi et al., proposed to assess the resilience to food insecurity for the i-th household as a latent variable defined according to the following components: income and food access (IFA), assets (A), access to public services (APS), social safety-nets (SSN), stability (S), and adaptive capacity (AC): i ( IFA , A , APS , SSN , S AC ) R = f , i i i i i i . (2) In this study, given the importance of farming for the livelihoods of Kenyan households, we have broken down the assets into agricultural production assets (AA) and non-agricultural assets (NAA), and we have included an additional component called agricultural practice and technology (APT) to capture the different technological levels in farming activities: i ( IFA , AA , NAA , APT , ABS , SSN , S AC ) R = f , i i i i i i i i . (3) In this framework, resilience is not observable per se and is considered a latent variable depending on the terms on the right-hand side of equation (3). To estimate R, it is therefore necessary to estimate separately IFA, AA, NAA, APT, ABS, SSN, S, and AC, which are themselves latent variables because they cannot be directly observed in a given survey, although it is possible to estimate them through multivariate techniques. 29 29 As pointed out by Alinovi et al., (2008), two alternative estimation strategies could be adopted for the estimation of household resilience: structural equation modelling and multi-stage modelling. There are two main reasons for adopting the latter estimation strategy: (i) usually, the available variables are not all normally distributed, so their estimation requires the use of different multivariate techniques; and (ii) measuring the different components separately makes the model 17
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