The dynamics of
poverty in Ethiopia:
persistence, state dependence
and transitory shocks
By
Abebe Shimeles, PHD
Organization of the
presentation
1.
2.
3.
4.
Motivations (key research issues)
Methodology
Data
Major Findings
1. Motivation of the study




How persistent is extreme poverty in
Ethiopia?
What factors determine exit from poverty or
re-entry into poverty?
What are the relative roles of different
sources of poverty persistence?
Addressing these issues is very important for
policy purposes since they can provide
important insight into the poverty impacts of
key reform programs.
2. Methodology
1.
Poverty persistence has been
studied in the past using
methods of variance
components and spell’s
approach. Recently, dynamic
discrete choice models are
becoming popular to study
poverty persistence.
Methodology (contd)
2 Variance components essentially
decomposes the residual from an income
regression into three components:
unobserved heterogeneity, serial
correlation and the purely white noise.
The contribution of each of these to total
residual measures the persistence of
time-invariant household characteristics,
shocks and random processes.
Methodolgoy (contd)
3. The spell’s approach constructs a binary
variable to identify poverty states, and
models the effects of persistence through
the spell duration.
contd
4.
5.
The findings in these presentations are
based on the spell’s approach (Bigsten and
Shimeles) and the dynamic discrete choice
approach (Islam and Shimeles)
Semi-parametric and parametric methods
were used to capture the effects of spelldurations on poverty persistence.
3. Data and variables


Panel data for rural and urban areas for
the period 1994-2000.
Key variables are real consumption
expenditure on non-durables in adult
equivalent.
Data and variables (contd)

In rural areas, household and village
characteristics , and in urban areas,
demographics, occupation of the head,
unemployment rate in the household,
and other characteristics, such as
ethnicity are used to analyze poverty
exit and re-entry
Data and variables (contd)

Variations in consumption less than
20% of the poverty line are dropped to
reduce the impact of measurement
error on poverty transitions.
4. Results
4.1. Descriptive Statistics

The percentage of households who
remained poor through out the survey
period was 11% in rural and 13% in urban
areas. The percentage for the non-poor
was 16% in rural areas, while it was 32%
in urban areas (Table 1)
Table 1: Percentage of Households by
Poverty Status: 1994-2000
Poverty Status
Rural
Urban
Never poor
Once poor
16
24
32
21
Twice poor
25
18
Thrice poor
23
15
Four times poor
11
13
Descriptive continued


The overall probability of becoming poor in
rural Ethiopia was 47%, while it was 32%
in urban areas. Since this estimate is based
on poverty transition matrices over four
waves, it provides a better sense of
poverty incidence in Ethiopia (Table 2)
The probability of an initially poor
remaining poor is very high in urban areas
than rural areas, suggesting higher rate of
poverty persistence in urban areas.
Table 2: Poverty transition
probabilities
Poverty Status
Rural
Poor
Non-Poor
Total
Urban
Poor
Non-Poor
Total
Poor
Non-Poor Total
47.8
38.3
47
52.2
61.6
53
100
100
100
65
23.4
32.4
35
76.6
67.6
100
100
100
4.2. Spell’s approach

A household may be observed beginning a
spell of poverty, and thus, at risk of exiting it,
or beginning a spell out of poverty, and at
risk of entering it. Thus we have two
probabilities


The probability that a household exits poverty
after a spell of ‘d’ years or rounds in poverty (exit
rate).
The probability that a household re-enters poverty
after a spell of ‘d’ years out of poverty.
Spell’s approach (contd)

There are two approaches to capture
the exit and re-entry rates. Nonparametric joint probability function
(survival function) and parametric
hazard functions
Non-parametric survival
functions
4.2. Kaplan-Meier
Survival function
^
S (t ) 
(
j|t j t
nj  d j
nj
)
Spell’s approach (contd)

The K-M estimator cumulates the joint
probability of staying in poverty (in case
of poverty spell) or staying out of
poverty (in case of out of poverty spell)
past some period t. From this, it is
possible to find the probability of exiting
poverty after “t” periods in poverty and
so on for re-entry
Table 3a: Rural Survival Function, Poverty
Exit and Re-entry Rates Using the KaplanMeier Estimator
Rounds since start of poverty spell
1
2
3
Survivor
function
1
(.)
0.72
(0.0404)
0.33
(0.033)
Rounds since start of non-poverty spell
1
2
3
1
(.)
0.62
(0.037)
0.32
(0.03)
Exit Rates
0.28
(0.05)
0.15
(0.02)
----Re-Entry
Rates
0.38
-0.047
0.23
(0.03)
-----
Spell’s approach (contd)


From Table (3a) we see that the probability of
exiting poverty after one round since the start
of poverty spell was 28%, or the probability
of remaining in poverty three rounds after the
start of poverty spell was 33%.
The probability of exiting poverty declines to
just 15% after a spell of two rounds in
poverty.
Spell approach (contd)


Similarly, the probability of falling back into
poverty after a spell of one round out of
poverty is 38% and declines to 23% two
rounds after the spell of out of poverty.
We also note that the probability of staying or
surviving as non-poor after a spell of nonpoverty over three rounds is 32%
Table 3b: Urban Survivor Function,
Poverty Exit and Re-entry Rates Using the
Kaplan-Meier Estimator
Rounds since start of poverty spell
1
2
3
Rounds since start of non-poverty spell
1
2
3
Survivor’s function
1.000
(.)
.78
(.06)
0.39
(.04)
1.000
(.)
0.68
(.05)
0.37
(.03)
Exit Rates
.22
(.05)
.11
(.03)
……
Re-Entry Rates
0.32
(0.05)
0.14
(.02)
--------
Spell’s approach (contd)

In urban areas, the picture is indicative
of strong duration dependence of
poverty persistence:

Low-exit and re-entry rate as the duration
in poverty or out of poverty increases.
Spell’s approach

In summary, the non-parametric
method showed that in Ethiopia, the
probability of exiting poverty after a
spell of one period in poverty is very
low (in developed countries this
probability is about 50%). Similarly, the
probability of surviving as non-poor was
low.
Parametric approach



A logistic random-effects model and proportional
hazard models with and without controlling for
unobserved household heterogeneity has been
estimated separately for exit and re-entry
probabilities for both rural and urban areas (see
text).
Results show that there is strong indication that spell
duration affects poverty persistence.
The model also captured the variables that could
increase or decrease probabilities of exiting or reentering poverty.
Dynamic discrete choice model to
capture poverty persistence



The parametric approach discussed above
has two important limitations in capturing
persistence of poverty.
The first is that it does not address the issue
of initial conditions. That is, it assumes that
the probability of a household to be poor or
non-poor at the start of the survey does not
contribute to poverty persistence.
Second, the model does not distinguish the
spurious from the true state dependence.
Dynamic discrete choice
model (contd)

Spurious persistence can be caused by
unobserved household and community
characteristics, such as disabilities,
surviving in hardship areas, etc., or
time-varying effects of unobserved
variables, such as national or local
shocks (drought, price instability, etc..),
or other factors.
Dynamic discrete choice
model (contd)


True poverty persistence captures the
effects of past history of poverty on
current poverty.
The distinction is important. If poverty
persistence is truly state dependent, it
means policies that attempt to reduce
poverty in the short-term will have longterm impacts also.
Table 4: Results for rural areas (latent probit model)
Simple Probit
Const
Hhsize
Teff
Coffee
Chat
Landsize
Oxen
Off-farm
Market
Grozone
Wifeprim
Meanage
Agehhh
Meanage2
Agehhh2
Assetval
Land*Hhsize
LagP
AR(1)
Type 1
Type 2
Pr Type 1
Pr Type 2
Log Likelihood
Latent Class Probit
Coeff
1.044
0.088
0.011
-0.13
-0.647
-0.105
-0.016
0.166
-0.004
-0.412
-0.396
-0.018
0.005
0.011
0.001
-0.064
-0.003
-
t-ratio
12.23
16.33
0.87
-5.85
-10.12
-8.44
-1.99
9.87
-7.42
-10.26
-5.18
-2.68
1.69
1.33
0.25
-13.65
-1.308
-
Coeff
0.092
-0.002
-0.171
-0.692
-0.124
-0.013
0.184
-0.005
-0.464
-0.392
-0.023
0.006
0.018
0.001
-0.064
-0.002
1.807
0.968
0.35
0.65
t-ratio
9.94
-0.08
-3.51
-7.58
-5.16
-0.76
3.95
-6.12
-7.58
-2.61
-3.48
0.89
2.14
0.16
-8.25
-0.77
9.5
5.03
-
2956.59
-
2933.88
-
Latent Class
Dynamic SD(1)
Probit
t-ratio
Coeff
13.11
0.1
-0.58
-0.012
-0.45
-0.012
-4.48
-0.387
-4.47
-0.068
-0.21
-0.005
3.21
0.151
-3.11
-0.002
-1.24
-0.512
-1.49
-0.211
-1.61
-0.01
-0.61
-0.003
0.97
0.007
1.51
0.008
-5.26
-0.058
-2.65
-0.006
8.54
0.331
7.74
1.149
6.39
0.858
0.26
0.74
2826.82
-
Latent Class
Dynamic
SD(1)+AR(1)
CoeffProbitt-ratio
10.5
0.099
-0.06
-0.003
0.1
0.007
-4.17
-0.323
-2.14
-0.063
-0.27
-0.005
3.18
0.129
-2.81
-0.002
-7.97
-0.463
-1.3
-0.176
-0.76
-0.006
-0.69
-0.005
0.45
0.005
1.23
0.008
-7.32
-0.057
-1.69
-0.006
6.64
0.598
3.55
-0.188
2.74
0.788
2.34
0.596
0.26
0.74
2822.59
-
The latent dynamic probit model of poverty
persistence
Pi 0  10 X i 0  ui 0  0
Pit  1 Pit 1   X it  uit  0
(3)
(i  1,..., N ; t  1,.....T )
uit   i   it
 it  it 1  vit ,
vit ~ N (0, v2 ) orthogonal to i. Corr  ui 0,uit   t t=1, 2,…, T
(4)
Table 5: Results for urban areas (latent probit model)
Simple Probit
Constant
Hhsize
Hhhfem
Addis
Awasa
Bahadar
Dessie
Iredawa
Jimma
Amhara
Oromo
Tigrawi
Gurage
Wifeprime
Unemp
Fedn
Ffarmer
Fgempl
Fsempl
Meanage
Meanage2
Agehhh
Agehhh2
Avalue
LagP
AR(1)
Type 1
Type 2
Pr Type 1
Pr Type 2
Log Likelihood
Coeff
-0.33
0.113
0.169
0.143
-0.019
-0.408
0.192
-0.101
0.14
-0.141
-0.139
-0.626
-0.066
-0.465
0.522
-0.22
0.072
-0.53
-0.427
-0.036
0.034
0.003
0.004
-0.005
1828.77
t-ratio
-1.13
10.73
3.1
0.89
-0.09
-1.5
0.94
-0.55
0.79
-1.54
-1.42
-4.16
-0.63
-6.84
4.72
-1.65
0.95
-4.04
-3.87
-3.56
2.54
0.4
0.5
-11.15
-
Latent Class Probit
Coeff
0.143
0.26
0.114
-0.088
-0.551
0.093
-0.209
0.127
-0.202
-0.231
-0.88
-0.112
-0.516
0.609
-0.215
0.116
-0.667
-0.465
-0.029
0.024
0.009
0.001
-0.004
0.053
-1.37
0.38
0.62
1739.42
t-ratio
9.86
3.2
0.39
-0.26
-1.08
0.25
-0.65
0.41
-1.37
-1.48
-3.29
-0.7
-5.41
4.23
-0.96
0.92
-3.18
-2.55
-1.82
1.11
0.77
0.02
-23.87
0.11
-2.78
-
Latent Class Dynamic Latent Class Dynamic
SD(1) Probit
SD(1)+AR(1) Probit
Coeff
0.139
0.171
0.144
0.038
-0.051
0.416
0.167
0.267
-0.136
-0.132
-0.529
-0.113
-0.388
0.489
-0.112
0.089
-0.486
-0.319
-0.019
0.019
-0.003
0.007
-0.003
0.543
-0.47
-1.329
0.35
0.65
1693.56
t-ratio
14.04
6.29
4.54
0.7
-0.67
3.89
2.59
3.67
-3.72
-4.21
-6.95
-2.49
-7.31
4.37
-2.48
3.6
-3.96
-4.38
-2.86
2.18
-0.7
1.48
-7.17
10.77
-11.57
-5.11
-
Coeff -4 t-ratio
12.17
0.113
3.45
0.099
3.29
0.123
1.59
0.096
0.88
0.113
3.79
0.447
3.99
0.294
4.78
0.352
-2.12
-0.07
-2.46
-0.098
-3.46
-0.273
-2.24
-0.122
-5.07
-0.265
3.54
0.323
-1.59
-0.088
0.15
0.005
-3.35
-0.364
-3.37
-0.233
-1.37
-0.011
1.28
0.015
-1.46
-0.009
1.44
0.009
-6.28
-0.003
18.76
1.49
-12.39
-0.452
-6.08
-1.192
-9.39
-1.923
0.04
0.96
1662.76
Summary of results

The discrete choice model provides the
following picture of poverty persistence in
Ethiopia:


Current poverty is strongly influenced by past
history in poverty, particularly in urban areas.
Transitory shocks seem to have been favourable to
the poor during this period as captured by the
increase in the coefficient of the lagged dependent
variable when serial correlation is controlled for.
Summary of results (contd)

The structure of household heterogenity as
captured by two support points in the
dynamic probit model indicate that intrinsic
vulnerability to poverty is quite high (74%
in rural areas and 65% in urban areas).
Policy implications

Combined with previous results, it can
be said that poverty propagates itself by
influencing behaviour of households.
This implies that current efforts to
reduce poverty in Ethiopia will have
lasting effects on poverty.
Policy implications (contd)

There is strong tendency for persistence
of poverty due to unobserved
household and community
characteristics. This implies that
improving individual motivation to fight
poverty through mass education,
provision of health and other means can
be effective.