THE EFFECT OF SHOCKS : AN EMPIRICAL ANALYSIS OF ETHIOPIA

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).


Introduction
A shock is defined as a 'sudden event that impacts on the vulnerability of a system and its components'.In case of slow onset hazards it is 'when the event passes its tipping point and becomes an extreme event' [1].Shocks are natural, social, economic, and political in nature.They can occur as slow or rapid onset shocks or longerterm stresses or trends and can be idiosyncratic or covariate in nature.Shocks can be transitory, seasonal, or structural, and their frequency, severity and duration can vary widely [2].Shock includes human health shocks, natural shocks, economic shocks, conflict, and crop and livestock health shocks [3].
Shocks are usually localized and therefore threaten the livelihoods of only parts of the population directly affected through loss of household assets, market access, and income earning opportunities, among others [4].Shocks can destroy assets directly.They can also force people to abandon their home areas and dispose off assets (such as land) prematurely as part of coping strategies [3].
The Horn of Africa is acutely vulnerable to food security crises that arise from complex causes, including swift shocks from the vagaries of climate, particularly exposure to drought and flooding, and slower moving stresses like the complex nexus of rapid population growth, land fragmentation, natural resource degradation, and conflict [5].
Due to recurrent natural and manmade hazards, degradation of natural resources, lack of land and labor fertility and other related reasons, many Ethiopian people live with food insecurity problems [6].Even though there are some variations across regions; drought, flood, erosion, frost, crop pests, livestock pests, input access, input price rise, death and illness are the most important shocks in Ethiopia [7].
The study area, Libo Kemkem, is among the food insecure areas of Amhara region.Productive Safety Net Program and other interventions have been practiced for food insecure households.However, they could not build their resilience to protect themselves from shocks and food insecurity is still the major problem of the area.The identification of shocks and their effect should be a prerequisite and an input for food security building activities.Despite this, it does not gain adequate consideration neither at country level nor at local level.In view of this, the study was done to identify the shocks in the Libo Kemkem district and to estimate their effect.independent variables, crop shocks, animal shocks, human shocks and economic shocks affect food production, income and asset of the household directly and food availability indirectly.Households affected by shocks could have low food production, income and assets possession and this affects household food availability status from production, purchase or shared out negatively.The working hypothesis of this study stated as: shocks have direct effect on food production, income and assets and indirect effect food availability of households (Figure 1).

Methodology
Study Area-This study was undertaken in Ethiopia, Amhara National Regional State of Libo Kemkem District.Based on the data from Libo Kekem District Agriculture and Rural Development Office [8], the population of the district is around 225,499.The district is adjacent to West Belesa in the South, Fogera and Ebenat in the West and East respectively and Lake Tana in the West [9].Addis Zemen is the center of the district and it is found 652 km from the country capital Addis Ababa and 80 km from the regional capital Bahir Dar.

Methods of Sampling and Data Collection
Data of this study was obtained from field survey.Using a two stage random sampling technique, 185 households were selected for the study.First, three kebeles were randomly selected as a representative of the district.Then, the sample households were selected proportionally from each kebele.A semi-structured questionnaire was used as a data collection tool.Data collectors randomly selected the households to be interviewed.

Data Analysis Technique
Data obtained was analyzed through descriptive statistical analysis, multiple regression, binary logistic regression, independent sample T-test and chi-square.Descriptive statistics were computed to describe some of the variables in the form of mean, standard deviation and percentage.Independent sample T-test was used for the purpose of comparison of mean differences between food available and food non available households with regard to continuous variables.Likewise chi-square was used for dummy variables.The data analysis was done using SPSS version 16.
The multiple regression models were used for this study to analyze the effect of shocks on intermediate variables.Multiple regression is the instrument of choice when the researcher believes that several independent variables interact to predict the value of a dependent variable and when the dependent variable is measured on continuous scale.Thus, three multiple regression models were run to observe the effect of shocks on income, food production and assets.
The general formula for multiple regression models is given as follows Where, Yi denotes the dependent variable, βo is the constant term,X 1 to X n denote the explanatory variables, βo to βn are the coefficients associated with explanatory variables and ε i is the observable random error term or disturbance.
The multiple linear regression models were estimated using the ENTER method and F-test computed to observe the significance of the models.Diagnostic tests were also carried out to check for multicollinearity of the variables included in the model.
Binary logistic regression was used to estimate the effect of intermediate variables on the dependent variable (food availability).The dependent variable is measured in terms of dummy variables; 0 for food non available and 1 for food available, based on their food availability status throughout the year.The analysis of the logistic regression model shows that changing an independent variable alters the probability that a given household becomes food available.The equation of logistic regression is Where, β 0 is an intercept, β 1 , β 2 …β n are slopes of the equation in the model, X 1 X 2 … X n are intermediate variables and U i is a disturbance term.

Food availability situation
Food availability refers to the physical presence of food stocks in desired quantities [10].The result showed that of the total 185 households, 52 households (28.1%) were not able to cover their food need by their own throughout the year.The remaining 133 households (71.9%) had the ability to cover their food need all over the year.

The existence and effects of shocks
The study identified eight shocks and among these weather variability, weed, plant insect and pest infestation and soil fertility problem were crop shocks.Animal disease and epidemics was animal shock and price fluctuation problem was economic shock.In addition, human disease and epidemics and conflict were categorized under human shocks.
The effect of the shocks on food production was measured by comparing the gained production with that of expected production if that shock never happened.Since the households cultivate different types of crops, it is difficult to estimate their crop production loss in kilograms and sum.Thus, the extent of each crop loss was converted into its respective price value and summed to gain the total crop loss in Birr.

Description of and correlation between food production, income and asset
The sample households gained an annual mean income of 5,864.95Birr.The Food available households earned a mean of 6,869.29 Birr while the food non available households gained 3,296.17 Birr.Besides this, the sample households possessed a mean of 11,753.46Birr estimated assets while 6,093.21Birr and 13,966.49Birr were the estimated asset possessions of the non food available and food available households, respectively.
In addition, the households produced food which was sufficient enough for a mean of their 8.99 months food need.The food non available households produced food for a mean of 7.13 months while the food available households produced a mean of 8.99 months food.The independent sample T-test result shows that there was a significant difference in the mean of food non available and food available households at less than 1% probability level in the income, asset and food production (Table 3).
In order to identify the degree of association between intermediate variables correlation analysis was computed.All of the 3 cells in the correlation matrix were positively and significantly correlated at less than one percent probability level (2-tailed) (Table 4).
Food production had a positive relation with income of the household (r=0.359,p<0.01) and asset (r=0.395,p<0.01).In addition, assets showed a strong positive relationship with income of the household (r=0.616,p<0.01).

The effect of shocks on food production, income and assets
Multiple linear regression models were used to estimate the effect of shocks on intermediate variables.Three multiple linear regression models, for income, asset and food production, were computed (Table 5).These three models showed 22.9, 19 and 12.7% of the variation, respectively.Multicollinearity tests conducted showed that there was no strong correlation between variables.
The result of F-test revealed the significance of all of the models at less than one percent significance level.
Out of eight shock variables included in the income model, three shocks were found to have a significant effect on income of the household.Plant insect and pest infestation (β =-0.279) and soil fertility problem (β =-0.321) were found significant at less than one percent probability.In addition, weed existence (β =-0.142) was found to have a significant effect on income at less than 5% probability level.
The direction of coefficient of all of these significant variables showed a negative relation with income of the household.With constant condition of other variables, a one standard deviation unit increase in plant insect and pest infestation, soil fertility problem and weed existence resulted in the decrease of household income by 0.279, 0.321 and 0.142 standard deviation units respectively.
Likewise, the second multiple regression model, revealed three significant shocks that affect assets.Plant insect and pest infestation (β=-0.229)and soil fertility problem (β=-0.317)were found to negatively and significantly affect assets at less than 10% probability level.However, human disease and epidemics (β=0.145) was In the third model, food production was found to be significantly affected by soil fertility problem (β = -0.314)at less than 1% probability level.Income, asset, food production and food availability Binary logistic regression was used to show the effect of intermediate variables (food production, income and asset) on food availability (Table 6).The model chisquare value was 40.232 and it was significant at less than 1% probability level.It also had a prediction success of 75.7%.The result showed that asset (Exp (B) =1.000, P<0.1) and food production (Exp (B) =1.379, P<0.01) significantly affected food availability of the households.With constant condition of other things, the odd ratio in favour of food availability increased by 1.000 and 1.379 when the asset possession increased by one Birr and food production increased by one month, respectively.

Discussion
Three of the intermediate variables were found with highly significant relationship.Food production had showed positive correlation with income of the household and asset.This is due to the possibility of converting food production in to income.Some of the produced food which is beyond the household need will be taken to the local market and sold.In addition, assets are used as an input for food production activities.Human labour, livestock and farming equipments can be used as an input.Also, fertilizers, pesticides and herbicides are purchased for crop production.
The possible reason for the strong positive relationship of assets with income of the household is the ability of assets to be used as a generation of income source for the household.Livelihoods of the households depend on their asset possession and these livelihoods create income for the household.Also income could be used as a source of asset.The income could be vested for the creation or development of assets.
The first multiple regression model result showed that income significantly affected by plant insect and pest infestation, soil fertility problem and weed existence.
There are some possible reasons for this.First the household incur some cost for the management of these shocks.Insecticide, pesticide, herbicides and fertilizer invested to manage these shocks.Also employed human power could be used for weeding.These are additional costs which has effect on net income of the households.
In addition, agricultural land with such shocks creates low income in rent out.
Among the three shocks affecting asset, plant insect and pest infestation (β=-0.229)and soil fertility problem (β=-0.317)were with negatively effect.This is due to the damage created by the pests and insects on stored agricultural output.Also some pests and insects reduce the quality of agricultural land.In addition, soil fertility problem reduces the quality of agricultural land which is natural asset.
In contrast to expected, human disease and epidemics was found having a positively effect on asset.The possible reason for this is the frequent occurrence of disease and epidemic on the non productive segment of the households.It was recorded that most of the victims were children and elders who have less role in asset creation and protection.
Soil fertility problem was the only shock significantly affecting food production.Soil fertility is one of the essential requirements of crop production.Due to overploughing of the land, it has lost its fertility.Losing this quality of soil creates a huge reduction on the production.Some of the households produced less than the mean expected production as a result of this.
The result showed that asset (Exp (B) =1.000, P<0.1) and food production (Exp (B) =1.379, P<0.01) significantly affected food availability of the households.With constant condition of other things, the odd ratio in favour of food availability increased by 1.000 and 1.379 when the asset possession increased by one Birr and food production increased by one month, respectively.
The binary logistic regression model showed that asset and food production positively and significantly affect the food availability situation of the household.The rural households spent most of their food production to cover the need of the family.Thus it is not surprising to see a strong effect of food production on availability.In addition, assets had positive significant effect on food availability.The reason for this is the ability to found food from shared out.Households shared their land and/or livestock and they got some percent of the crop production for their consumption.

Conclusions
The study was employed to identify shocks and their effect based on the conceptual framework developed in Ethiopia, Amhara National Regional State of Libo Kemkem District.Through semi structured interview eight shocks were identified as affecting households in the study area.Among these weather variability, weed, plant insect and pest infestation and soil fertility problem were crop shocks.Animal disease and epidemics was an animal shock and price fluctuation problem was a socio- The results of the study show that 28.1% of the households were not able to cover their food need while 71.9% had the ability to cover their food need all over the year.Among the shocks only soil fertility problem had a statistically significant relationship (p<0.01) with food availability. The

Acknowledgement
My first acknowledgement goes to my family, Addisu Damtie, Melkam Addisu, brothers and sister, for their support and encouragement.Also, I would like to thank Dr. Temsegen Tilahun for his valuable comments.My special thanks go to my lovely friends for their quick and kind response when I needed help.Your technical and moral support makes this paper real.Finally, I would like to thank Libo Kemkem District Agriculture and Rural Development Office staff members and respondent households for their cooperation.Thank you all for your help.

Figure 2 :
Figure 2: the existence of the shocks in sample households in percent (N=185)

: 2319-5614 Volume 4, No.7, July 2015
More than three-fourths (83.78%) of the sample households were affected by weather variability.The sample households lost a mean of 3,821.38Birr estimated production damage due to early emergence, delay, excessiveness of rain (flood) or any other form of weather variability.
© The Author © Blue Ocean Research Journals www.borjournals.comOpen Access Journals Blue Ocean Research Journals 508 Figure 2 below shows the existence of the shocks in households.Price fluctuation problem was the primary shock existing in 88.11% of households.Weather variability and soil fertility problem were the second and third shocks in occurrence, respectively.In contrast, conflict was recorded as the least existing shock occurring only in 14.59% of sample households.

Table 2
depicts the mean score of the sample households with regard to the effect of the shocks aggregated by food availability.Independent sample T-test and chisquare were employed to show the relationship between the food availability and the shocks.The results indicated that only soil fertility problem had statistically significant relationship with food availability at less than 1% probability level.

borjournals.com Open Access Journals Blue Ocean Research Journals 510 economic
shock.Human disease and epidemics and conflict were categorized under human shock.

is significant at 1% probability level Table 4: Correlation between assets, income and food production (N=185)
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