Poverty and Income Distribution in Ethiopia: By Abebe Shimeles, PhD

Structure of the presentation 1.Objectives of the study 2. Methodlogical Issues 3. Data 4. Key Results

I. Objective of the Paper Analyze the state of poverty and income distribution during a period of peace, intense reform, good peace and recovery ( ) and major drought, external war, terms of trade deterioration ( ) based on a panel data set from rural and urban areas. Simulate the effects of potential policy interventions on poverty.

2. Methodlogical Issues 2.1.Poverty measurement: identificaiton and aggregation issues Setting the poverty line Aggregating poverty among the poor population 2.2.Robustness of poverty estimates Semi-parametric Kernel Densities Non-parmetric dominance criterion 2.3. Poverty and Inequality Decompositions The roles of observed and unobserved household characteristics and the residual (including measurement errors) A model of poverty Regression based inequality decompositions

3. Data Panel data for urban and rural areas Not nationally representative, but represents major agro-climatic conditions and major urban centers Sampling and non-sampling errors Attrition Selecitivity bias (demographic and other time-varying household characteristics)

4. Key Results Poverty trends during Poverty decreased between and increased between (Table 1)

Table 1: evolution of poverty and inequality in Ethiopia

Robustness of poverty trends Semi-parametric kernel density estimates (Figures 1 and 2) Non-parametric dominance criterion (Figures 3-6)

Figure 1: Kernel density Estimates for Rural Households:

Figure 2: Kernel Density Estimates for Urban HHs:

Decomposition of poverty : the role of unobserved household characteristics Modelling poverty

Variables Rural areas Household demographics Farming systems Access to market Size of land Rainfall Major crops produced Off-farm activity, etc.

Variables (contd) Urban areas Household demographics Occupation Ethnic background Assets

Dealing with endogenity of regressors Random-effects is preferred to fixed- effects if regressors are strictly exogenous. Hausman-specification test can be used to test if the two are equivalent. If not, Instrumental variable methods(Hausman-Taylor random- effects model) is recommended to deal with endogenity.

Contd. In our case, the random-effects specification was rejected for rural as well as urban regressors. The HT method was employed to address endogenity. Results showed that the HT and Fixed effects specification are equivalent. So, HT is the preferred model of consumption.

Table 2: observed vs predicted poverty

Table 3: some policy simulations

Table 4: Decomposition of inequality: rural areas

TAble 5: Decomposition of inequality: urban areas

Summary and conclusions This paper analysed the state of poverty and income distribution in rural and urban Ethiopia during Poverty declined from 1994 to 1997, and then increased in This finding is consistent with major events that took place in the country: peace and stability, reform and economic recovery during , then, drought, war with Eritrea and political instability during To examine the robustness of these results, we used stochastic dominance criteria and model based decompositions of poverty and inequality. Poverty trends were unchanged regardless of where one sets the poverty line..

contd In addition, the paper attempted to look at the relative contributions of observed and unobserved household characteristics, and the residual, which includes random shocks and measurement error to observed poverty.

contd This decomposition is useful to get a sense of how much of the observed poverty is due to persistent differences in household characteristics, and random transitory shocks that includes simple measurement errors. From our results, we found that the contribution of the residual in observed poverty is in the range of 4%-27% in rural areas and 3%-18% in urban areas, which is reasonably low given the commonly held assumption that transitory factors account for much of observed poverty than persistent household characteristics.

Contd.. Part of the reason is that most of the omitted variables that could affect permanent attributes of a household are captured through the household-specific error term. In addition, attempt was also made to control for the effects of these error terms on observed regressors by using valid instruments in estimation. Perhaps this feature makes this paper interesting as it made an attempt to grapple with the often-ignored aspects of poverty measurement.

contd The rest of the paper reported simulation results as well as inequality decompositions using standard methods The results revealed that in rural areas, poverty responds quite strongly to improvements in infrastructure and increased size of land or its productivity, while in urban areas educational expansion could reduce poverty significantly

contd Decomposition of inequality revealed that in rural areas 65% of overall inequality was due to location differences, access to market, size of land, dependency ratio in the household, and age of the household. In urban areas, 49% of inequality was attributed to differences in education, occupational categories, and household durables. The results therefore imply that inequality is caused mainly by structural factors with the possibility that it may persist over time before significant decline can be observed

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