The Effect of Shocks: An Empirical Analysis of Ethiopia

Abstract

Besides striving for the increase of production and development, it is also necessary to reduce the losses created by the shocks. The people of Ethiopia are exposed to the impact of both natural and man-made shocks. Following this, policy makers, governmental and non-governmental organizations need to identify the important shocks and their effect and use as an input. This study was conducted to identify the food insecurity shocks and to estimate their effect based on the conceptual framework developed in Ethiopia, Amhara National Regional State of Libo Kemkem District. Descriptive statistical analysis, multiple regression, binary logistic regression,  2 and independent sample t-test were used as a data analysis technique. The results showed eight shocks affecting households which were weather variability, weed, plant insect and pest infestation, soil fertility problem, animal disease and epidemics, human disease and epidemics, price fluctuation problem and conflict. Weather variability, plant insect and pest infestation, weed, animal disease and epidemics created a mean loss of 3,821.38, 886.06, 508.04 and 1,418.32 Birr, respectively. In addition, human disease and epidemics, price fluctuation problem and conflict affected 68.11%, 88.11% and 14.59% of households, respectively. Among the sample households 28,1 % were not able to meet their food need throughout the year while 71,9 % could. The result of the multiple regression models revealed that weed existence (β = -0,142, p < 0,05), plant insect and pest infestation (β = -0,279, p < 0,01) and soil fertility problem (β = -0,321, p < 0,01) had significant effect on income. Asset was found significantly affected by plant insect and pest infestation (β = -0,229, p < 0,01), human disease and epidemics (β = 0,145, p < 0,05), and soil fertility problem (β = -0,317, p < 0,01) while food production was affected by soil fertility problem (β = -0,314, p < 0,01). Binary logistic regression model revealed that food availability of the households was highly affected by the asset (Exp(B) = 1,00, p < 0,1), and food production (Exp(B) = 1,379, p < 0,01).

Fields of Action for Designing Measures to Avoid Food Losses in Logistics Networks

“…Nearly all of the articles mentioned the use of technology for realizing transparency. However, some researchers also explained more profound interrelationships and described the added value of transparency for the networks [18,19]. Sarpong (2014), for example, examined the influence of the horse meat scandal on the European market and its effects on discussions of transparency [20].…”