Vulnerability Analysis

4.4.4.1. Sources of Vulnerability and Coping Strategies

The study assumes that involuntary dispossession will make the dispossessed households more vulnerable to poverty. Therefore, in Chapter Eight, following Hoddinott (2003a), a stepwise retrospective approach to vulnerability analysis was employed to answer the following two questions (1) what are the sources of vulnerability? And (2) How do households cope with vulnerability? The information was analyzed using descriptive statistics. The major types of shocks experienced by households and their coping strategies (how households coped with the problems) are identified.

4.4.4.2. Determinants of Livelihood Diversification Strategies

The premature dispossessed households needed to adopt a range of coping strategies to sustain livelihoods and adjust to a new reality. To investigate the influence of shocks, socio- economic and demographic factors in livelihood diversification and coping strategies, the study employed a Multinomial Logistic regression (MNL) model, which is similar to the model described under section 4.4.3.1.

The livelihood strategies of these households were disaggregated based on share of income. Thus, the dependent variable livelihood coping strategies (income source) which could be agriculture, farm wage employment, nonfarm wage employment, nonfarm self-employment, and transfer has more than two discrete alternatives. This model was selected because households were typically participating in more than two livelihood activities. It is employed in chapter eight to examine the association between explanatory variables and livelihood strategies (Hoddinott and Quisumbing, 2003b; Koop, 2008).

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4.4.4.3. Model Specification

The Multinomial Logistic Regression (MNL) model has been widely used to analyse polytomous response categories in different areas of socio economic studies. Similar to the model described under the previous sub heading, in this section, it is used to explain factors that determine the probabilities of household participation in alternative activities as a coping strategy to generate income. The data set for this analysis contains 168 observations, 86 households from the displaced group and 82 from the control group.

The dependent variable is choice of livelihood strategy. Exposure to shocks, household demographic characteristics and asset holding were taken as explanatory variables. One coping strategy is selected as the ‘base’ alternative, and then each other possible choice is compared to this base alternative with a logit equation to examine the association between explanatory variables and coping strategies (Hoddinott and Quisumbing, 2003b; Gujarati, 2011).

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P

P

ln

P1i = the probability of the ith person choosing the first coping strategy and

Pbi = the probability of the ith person choosing the base alternative.

4.4.4.4. Description of Dependent Variables

The study examines changes in households’ livelihood activities as a coping mechanism in response to the introduction of the flower farms to the Ethiopian Rift Valley region in the last decade. Therefore, following Reardon et al. (2007), the livelihood activities were aggregated into three major activities: self employment, wage employment and agriculture. Depending on exposure to shocks, household demographic characteristics and asset holding, the dependent variable, livelihood activity, can be one of three general categories: Pure Agriculture (1), Agriculture and Self employment (2), and Agriculture and Wage employment (3).

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Wage employment in the rural areas is considered as a temporary employment contract and it does not require as much capital as self employment; the main requirement may be only one’s own labour (Reardon et al., 2007). On the other hand, off-farm self employment involves ownership of an enterprise that produces goods and services, and consumers (ibid). Because of shocks that they encounter, the study assumes the displaced households participate more in these livelihood activities as a coping mechanism rather than staying in pure agriculture. Therefore, “1” is assigned to individual households if they participate only in pure agriculture (crop and livestock production), “2” is assigned to individual households if they participate in self employment and agriculture, and ‘3” is assigned to individual households if they participate in wage employment and agriculture.

4.4.4.5. Description of Explanatory Variables

The mechanism by which some of the explanatory variables affect livelihood activities is provided in the previous section (see 4.4.3.2). For those explanatory variables only the hypothesis on how it affects participation in agriculture and other rural nonfarm activities is given, to avoid redundancy. Definition of additional key explanatory variables that determine participation in alternative livelihood activities as a coping mechanism is presented in this section.

Age refers to age of household, and it is measured in years. Here it is hypothesized that

participation in wage employment decreases with age. Total number of adult individuals refers to individuals of working age, defined here as those between the ages of 10 and 65. The study expected that households with a larger pool of labour are more likely to participate in alternative livelihood activities as a coping mechanism. Education level is a discrete variable: 0 for no education and 5 for high school. Higher level of education is believed to be associated with more diversified livelihood, therefore, this study hypothesized that farmers with higher levels of education are more likely to diversify by participating in self and wage employment.

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a) Gender

As already stated, because of the social constraints and requirements for females to stay at home to manage the household activities, it is argued that female-headed households are less likely to participate in labour markets than male-headed households (Davis et al., 2009). Although the majority of females stay at home, it is also argued that they are engaged in a wide range of self employment activities (Beyene, 2010). Therefore, the study hypothesized that to cope with shocks, male headed households participate in wage employment and female headed households participate in self employment activities.

b) Land Holding

There is mixed feeling about the effect of land holding on participation in and earning income from rural nonfarm activities. On the one hand, land holding as compared to landlessness may allow individuals to access group membership, working capital and or social capital, thus land can be a determinant of a diverse livelihood strategy (Reardon et al., 2007). On the other hand, land has historically been viewed as a key asset for rural households because of the link between land and agriculture. Accordingly, a negative relationship between land holding and participation in labour markets is reported (Davis et al., 2010). This study hypothesized that displaced households, due to loss of farm land or reduced farm size, participate more in rural non-farm income generating activities.

c) Livestock Ownership

Livestock represent wealth. They play a very important role by serving as a store of assets. Oxen provide traction and manure required for soil fertility maintenance and fuel (Berhanu, 2007). Thus, it is frequently reported that there is a negative relationship between size of livestock owned by the household and participation in other rural non-farm income generating activities (Berhanu, 2007; Tefera, 2011). Thus, for this study, participation in rural non-farm activity is hypothesized to increase with a decrease in livestock ownership.

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d) Shocks

Households hit by shocks are more often poor than families that are not. Diversification into the labour market or low paying self employment, to minimize risks and make ends meet, has been reported as a survival strategy for vulnerable households and individuals who are pushed out of their traditional occupations (Dercon and Krishnan, 2000; Satterthwaite and Tacoli, 2003; Reardon et al., 2007). As the study mainly focuses on the impact of displacement, here it is hypothesized that loss of farm land leads to a decline in income from agriculture, which makes non-farm employment even more important. The information on shocks experienced by the household is from the household survey. The shock variables are included as dummies which take the value one if the household experienced a particular shock. The major shocks that the study includes are an increased price of land rent, a reduced number of livestock, high prices of commodities in the market, loss of farm land and flooding.

e) Distance to the Town

This is distance of farm households to the town measured in kilometres. Distance to the town largely determines the participation of households or individuals in self employment activities. This is because people must be able to sell their produce (Barrett et al., 2001; Reardon et al., 2007) and as distance decreases, people get better access to labour employment in the town (Woldehanna, 2001). Thus, for this study, participation in self and wage employment is hypothesized to increase with decrease in distance to the town.

f) Access to credit

The availability of a credit service allows households to participate in self employment activities (Woldehanna and Oskam, 2001; Barrett et al., 2001). This study therefore hypothesized a positive correlation between the availability of a credit service and participation in self employment.

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