Table 7. 4: Parameter Estimates MNL Livelihood Diversification Strategies Model Livelihood Strategy Participation in Agriculture = 1 Participant in Self Employment = 2
Coef. Std. Err P>|z| Coef. Std. Err P>|z| Gender of Employee -0.234 0.956 0.806 0.591 0.657 0.369 Age of Employee 2.500 0.750 ***0.001 0.808 1.113 0.468 Age squared -0.035 0.012 ***0.002 -0.017 0.023 0.455 Marital Status 2.529 1.063 **0.017 0.327 0.642 0.610
Head of hh 1.830 1.358 0.178 -0.036 0.762 0.961
Number of adult equiv. 0.755 0.340 ***0.026 -0.158 0.237 0.504 Service year -0.343 0.159 **0.031 -0.296 0.146 **0.042 Education Level 0.212 0.393 0.589 -0.455 0.251 **0.070 Travel Time/day 0.507 0.702 0.470 -0.579 0.438 0.187 Total Working Hour/day 0.173 0.163 0.289 -0.081 0.110 0.462
Constant -48.739 13.501 0.000 -6.436 12.840 0.616
Wage employment = 0 is the base outcome) Number of obs = 139
LR chi2(20) = 109.29 Prob > chi2 = 0.000 Pseudo R2 = 0.3920
***,**,* Significant at 1%, 5% and 10% probability level respectively
7.3.4.1. Fitness of the Model
The significant Chi-square value (109.3 with a P value <0.001) indicates the fit of the model in explaining the relationship between independent and dependent variables (Table 7.4). The Pseudo indicates that about 40.0% of the total variance in livelihood diversification was explained by the independent variables (Table 7.4). The parameter estimates of the MNL model provide only the direction of the effect of the independent variables on the dependent variable relative to the reference group; the coefficients do not represent either the actual magnitude of change nor probabilities as OLS regression does (Gujarati, 2011: p.160).
MNL was run and tested for the assumption of the Independence of Irrelevant Alternatives (IIA). There was no evidence that this assumption was violated when the Wld test and Small- Hsiao test of IIA assumption were run, justifying the applicability of the MNL specification to the data. Table A1.4 (see Appendix 1) contains results of the Wald test that all coefficients associated with an independent variable equal zero. Moreover, there are no multicolliniarty problems in the fitted model.
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7.3.4.2. Participation in Agriculture relative to Wage Employment
In line with the study expectation, age of the worker, which represents experience and family responsibility and wealth, positively and significantly affected participation in agriculture (Table 7.4). A unit increase in the age of the employee will increase the probability of participation in agriculture rather than being in wage employment only. This finding is in agreement with the comparative literatures that show a positive relation between age and wealth level, where wealth could be invested in diverse livelihood activities. Beyene (2010), reported similar findings in Tigray, Amhara, Oromia and SNNP regions of Ethiopia.
The coefficient on the number of adult individuals has a positive sign and is significant at 5 per cent probability level, showing an increase in the number of adult individuals per household would significantly increase the probability of participation in agriculture, rather than relying only on wage employment. This could be due to high labour endowment which allows households to carry out diverse livelihood activities by allowing household members to participate on various activities. Tefera (2011), has observed similar situation in the Hararge highland regions of Ethiopia.
The coefficient of the married employees has a positive sign and is significant at 1 percent probability level. Although the coefficient is low, it may imply that married employees’ are more likely to participate in agricultural activities as compared to being in wage employment only. Agriculture is usually men’s job in the region and requires more labour. Therefore, being married and the presence of male in the household may encourage the household to participate in agricultural activities than relying on low paying wage employment in the flower farm.
Service year has a negative sign and is significant at 5 percent level. It implies that, the probability of participation in agriculture decreases with an increase in service year. This coincides with age square which has a negative coefficient and significant p-value, showing participation of employees in agriculture decreases after a certain age. This could be explained by better wage income for employees who have long years of experience, and may decide not to participate in agricultural activities to support their families.
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7.3.4.3. Participation in Self Employment Relative to Wage Employment
Only two variables; education level and service years (tenure before and since employment) of flower farm employees significantly affect participation in self employment activities rather than being only in wage employment.However, education and service years of flower farm workers are inversely associated with self employment activities as indicated by their negative coefficient. The negative coefficient for education level and service year suggests that a unit decrease in education level and service year of the employee will increase the likelihood of participation in self employment as compared to the probability of being in wage employment.
Normally, better education and work experience are considered as investments that would increase earning potentials and diversification of livelihood (Reardon et al., 2007). Contrary to the study expectation, those with low education level and service year were participating more in self employment activities rather than being only in wage employment. However, this result is consistent with the descriptive findings that show most of the low paying self- income generating activities were filled by uneducated and unskilled poor workers. In agreement with the study finding, positive association of lower education level and increased participation in low paying income generating activities was reported in Nicaragua by Corral and Reardon (2001).
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7.4. Chapter Summary
This chapter analysed livelihood coping strategies of flower farm workers with low wage income. The results show that the majority of farm workers who were living below the poverty line, and adopted unpopular coping strategies and detrimental lifestyles such as skipping breakfast or lunch, and sleeping on the floor. In order to offset low wages, employees participated in different income generating activities. Those households participating in agriculture and self-employment earned relatively better incomes, although they work relatively longer hours.
Although households may have different motives in pursing different livelihood strategies, as argued by Ellis (2000) and Reardon et al. (2007), the mlogit estimates of the determinants of a employees households’ participation in agricultural activities rather than being only in wage employment was influenced by Age of employee, the number of adult individuals in the employee household, and marital status of the employee. All these factors positively influence participation of employee household in agriculture. However, Service year of employees’ negatively influence participation in to agriculture.
On the other hand, the mlogit estimates of the determinants of a households’ participation in self income generating activities indicates that low levels of education and service year of employees significantly and negatively influenced the participation of employees in self employment activities rather than being only in wage employment. Most of the low paying self-income generating activities, such as shoe cleaning, carrying items in the market, working on farm fields (tilling, harvesting, etc.) are dominated by uneducated and unskilled poor workers who earn less than a dollar a day.
The finding shows that flower farm employees’ pursue diversification to generate income more as a survival and coping strategy than as an asset accumulation venture. Therefore, contrary to the original assumption of the Ethiopian government, the flower industry has created neither decent job opportunities for local people nor has it lifted farm workers out of poverty.
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