Poverty and Income Distribution in Ethiopia:1994-2000

1. Poverty and Income Distribution in Ethiopia:1994-2000 By Abebe Shimeles, PhD

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

3. I. Objective of the Paper • Analyze the state of poverty and income distribution during a period of peace, intense reform, good peace and recovery (1994-1997) and major drought, external war, terms of trade deterioration (1997-2000) based on a panel data set from rural and urban areas. • Simulate the effects of potential policy interventions on poverty.

4. 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

5. 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)

6. 4. Key Results • Poverty trends during 1994-2000 • Poverty decreased between 1994-1997 and increased between 1997-2000 (Table 1)

7. Table 1: evolution of poverty and inequality in Ethiopia

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

9. Figure 1: Kernel density Estimates for Rural Households: 1994-2000

10. Figure 2: Kernel Density Estimates for Urban HHs: 1994-2000

15. Decomposition of poverty : the role of unobserved household characteristics • Modelling poverty

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

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

18. 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.

19. 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.

20. Table 2: observed vs predicted poverty

21. Table 3: some policy simulations

22. Table 4: Decomposition of inequality: rural areas

23. TAble 5: Decomposition of inequality: urban areas

24. Summary and conclusions • This paper analysed the state of poverty and income distribution in rural and urban Ethiopia during 1994-2000. Poverty declined from 1994 to 1997, and then increased in 2000. • This finding is consistent with major events that took place in the country: peace and stability, reform and economic recovery during 1994-1997, then, drought, war with Eritrea and political instability during 1997-2000. • 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. .

25. 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.

26. 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.

27. 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.

28. 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

29. 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

30. Thank you!!