Poverty and Income Distribution in Ethiopia:1994-2000

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Poverty and Income Distribution in
Ethiopia1994-2000

• By
• Abebe Shimeles, PhD

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Structure of the presentation

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

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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.

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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

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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)

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4. Key Results

• Poverty trends during 1994-2000
• Poverty decreased between 1994-1997 and increased
  between 1997-2000 (Table 1)

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Table 1 evolution of poverty and inequality in
Ethiopia
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Robustness of poverty trends

• Semi-parametric kernel density estimates (Figures
  1 and 2)
• Non-parametric dominance criterion (Figures 3-6)

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Figure 1 Kernel density Estimates for Rural
Households 1994-2000
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Figure 2 Kernel Density Estimates for Urban HHs
1994-2000
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Decomposition of poverty the role of unobserved
household characteristics

• Modelling poverty

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Variables

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

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Variables (contd)

• Urban areas
• Household demographics
• Occupation
• Ethnic background
• Assets

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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.

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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.

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Table 2 observed vs predicted poverty
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Table 3 some policy simulations
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Table 4 Decomposition of inequality rural areas
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TAble 5 Decomposition of inequality urban areas
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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.
• .

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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.

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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.

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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.

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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

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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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Thank you!!