Determinants of Livelihood Diversification among Households in the Sub-Saharan Town of Merawi, Ethiopia

Abstract

Livelihood diversification could be determined by complex and diversified factors. Yet, unlike the rural areas, the situation is unexplored in the case of towns of developing economies. The objective of this study was to identify the determinants of households’ livelihood diversification in a sub-Saharan town. Data were collected from 151 households and 4 key informants. In addition, secondary data were collected to supplement the primary data. Descriptive statistics were employed to identify the households’ livelihood strategies. The level of households’ livelihood diversification was estimated by the Herfindahl–Hirschman Index, whereas multinomial logistic regression was employed to investigate the determinants of the households’ livelihood diversification. The result of the Herfindahl–Hirschman Index shows the presence of three levels of livelihood diversification among households: no diversification (11.26%), moderately diversified (26.49%), and highly diversified (62.25%). The model analysis revealed that out of eighteen predictor variables, only seven variables, namely, total cattle possession (B = 0.329, p < 0.1 ), land ownership (B = 120.572, p < 0.01 ), income from irrigation (B = 2.902, p < 0.05 ), total annual cash income (B = 0.000, p < 0.01 ), price fluctuation problem (B = 2.899, p < 0.05 ), market price fluctuation plus total cattle possession (B = 12.892, p < 0.01 ), and no price fluctuation plus total cash income (B = 0.000, p < 0.01 ) were found significantly influencing households’ livelihood diversification. Households in the study town are engaged in different livelihood diversification strategies rather than relying on farm only for improving their wellbeing, and livelihood diversification was gaining a dominant role in households’ income. Even if the Ethiopian agricultural policy gives more attention to the agriculture sector, there is evidence that households’ income is not limited to agriculture. Therefore, nonfarm livelihood diversification should be strengthened by government initiatives to sustain households’ livelihood diversification.

Livelihood diversification strategies and food security in the Weaving-based livelihood system of Gamo Zone, Southern Ethiopia

“…Qualitative data collected through key informant interviews, focus group discussions, and eld observation were qualitatively analyzed using describing and narrating. Livelihood income diversi cation was determined by using the Her ndahl-Herschman Index (HHI) [40] (Admasu et al, 2022). On the other hand, the association between socio-economic characteristics and the food security status of households was examined by adopting the bivariate Pearson correlation analysis technique.…”

“…The value of livelihood diversi cation (D=1) is equal to 1 when there is a complete specialization or no livelihood diversi cation, moderately diversi ed (1<D<2), and highly Diversi ed (D≥2) (Admasu et al, 2022) [40].…”

“…Based on the experiences of previous studies [43] (Admasu et al, 2022) [40], the households are classi ed into three classes of diversi cation levels namely; 'Low diversi ed', 'Moderately Diversi ed', and 'Highly Diversi ed'. Since, the dependent variable has more than two outcomes, and three ordered outcomes, the ordered probit regression model was the most commonly recommended technique used to examine the determinant factors affecting the probability of the household either into a higher or lower category.…”

“…Accordingly, this study attempted to determine the status of livelihood diversi cation and its association with the food security status of households. Following the experiences precedents [42,43] (Admasu et al, 2022) [40] the status of livelihood diversi cation is identi ed by using the Her ndahl-Hirschman Index (HHI).…”

Livelihood diversification strategies and determinants by smallholder farmers in the highland areas of North Shewa Ethiopia

“…Studies by Admasu et al (2022) and Muluneh (2022) indicate that different factors like the distribution of income and wealth status influence the choice of livelihood diversification alternatives. Further empirical research by Alobo and Bignebat (2017) classified the push and pull dynamics that influence livelihood diversification strategies.…”