Land reform, distribution of land
and institutions in rural Ethiopia:
Analysis of inequality with dirty
data
Bereket Kebede
School of Development Studies, University of East
Anglia (UEA)
and
Centre for the Study of African Economies (CSAE),
Oxford University
12-13 October 2006
Addis Ababa
Structure of presentation
1. Research questions
2. Data and general patterns in land
distribution
3. Methodology
4. Empirical results
5. Link between current distribution and old
tenures?
6. Conclusions
1. Research questions
• Widespread acceptance that
– rural land distribution is highly equitable in
Ethiopia (for example compared to other
African countries with private land
ownership)
– no strong link between current land
distribution patterns and pre-reform
tenures – seldom are current distribution
patterns related to pre-reform distributions
1. Research questions (cont’d)
“The low of level of [income] inequality is
consistent with the overall picture of
Ethiopia as a very poor country, with a low
per capita income. In addition, the
egalitarian land holding system might have
contributed to a more equal income
distribution in rural Ethiopia.” (FDRE,
2002; italics mine)
1. Research questions (cont’d)
“This paper is an attempt to argue, in
broad terms, that the social
homogeneity in rural Ethiopia today,
which is in large measure a
consequence of the land system, is
an inhibiting factor for agrarian
development.” (D. Rahmato, 2005;
italics mine)
1. Research questions (cont’d)
• This study questions the two widely
accepted views
1. Is rural land distribution in Ethiopia
highly equitable as generally
accepted?
2. Is there a link between current land
distribution patterns and pre-land
reform tenures?
1. Research questions (cont’d)
• Main conclusions from this study:
– rural land distribution in Ethiopia is not
as equitable as generally accepted; for
example, it is as inequitable as, if not
more inequitable, as in some other
African countries with private ownership
and land markets
– post-reform land distribution is probably
partially explained by pre-reform land
tenures
2. Data and general patterns in
land distribution
• Data from Ethiopian Rural Household
Surveys (ERHS)
• Covered 15 villages/sites (1500
households) in the main settled agriculture
farming systems
• Longitudinal survey: 1989, 1994-95 (3
rounds), 1997, 1999, 2004
• This study used data from 1995-1997: 20
years after land reform
2. Data and general patterns in
land distribution (cont’d)
• Distribution of what?
–PA allocated land or cultivated
land?
–Total household or per capita or per
adult holdings of households?
• Focuses on PA allocated land and
looks at household, per capita and
per adult holdings
Summary
statistics
PA allocated land (ha.)
Total
Per capita
Per adult
Mean
1.688
0.335
0.586
Median
1.000
0.188
0.313
1%
0.000
0.000
0.000
5%
0.000
0.000
0.000
95%
5.500
1.125
2 .000
99%
9.688
2.125
4 .000
For village mean and median figures see Table 3
0
.02
.04
.06
.08
.1
.12
.14
Histogram of paland for all sites (with bin width 0.1)
0
1
2
3
4
5 6 7 8 9 10
Land through PA allocation/approval
0
.1
.2
.3
.4
Histogram of pcpaland for all sites (with bin width 0.1)
0
1
2
3
4
5
6
7
PA allocated land per capita
8
9
10
0
.1
.2
.3
.4
Histogram of papaland for all sites (with bin width 0.1)
0
1
2
3
4
5
6
7
8
9 10
PA allocated land per active adult (15-54)
geblen
dinki
debre berhan
yetmen
shumsheha
sirbana godeti
adele keke
korodegaga
turufe kechema
imdibir
aze deboa
0
.2 .4 .6 .8
0
.2 .4 .6 .8
0
.2 .4 .6 .8
haresaw
0
gara godo
domaa
0
.2 .4 .6 .8
adado
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
Land through PA allocation/approval
Graphs by site number
5
1
2
3
4
5
geblen
dinki
debre berhan
yetmen
shumsheha
sirbana godeti
adele keke
korodegaga
turufe kechema
imdibir
aze deboa
0
.2 .4 .6 .8
0
.2 .4 .6 .8
0
.2 .4 .6 .8
haresaw
0
gara godo
domaa
0
.2 .4 .6 .8
adado
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
PA allocated land per capita
Graphs by site number
4
5
1
2
3
4
5
geblen
dinki
debre berhan
yetmen
shumsheha
sirbana godeti
adele keke
korodegaga
turufe kechema
imdibir
aze deboa
0
.2 .4 .6 .8
0
.2 .4 .6 .8
0
.2 .4 .6 .8
haresaw
0
gara godo
domaa
0
.2 .4 .6 .8
adado
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
PA allocated land per active adult (15-54)
Graphs by site number
1
2
3
4
5
2. Data and general patterns in
land distribution (cont’d)
• Do PAs compensate with quality for size of
land?
– If size and quality of land are strongly
negatively related, use of land holdings for
analysing distribution would be problematic
– Quality of land indicators are not significantly
correlated to size of land after controlling for
village level fixed effects (see Table 1)
3. Methodology
• If data are completely unreliable, of course don’t
use them
– the survey data compares well with others including
nationally representative samples
• But inequality measures can be sensitive even
for minor contamination
• Dirty data may arise due to
– errors in collection, coding, transcribing, etc., of data
– under (over) reporting by respondents
– but also from true outliers: influential observations
(observations with high leverage)
3. Methodology (cont’d)
Does G Lorenz dominates F?
●μ(Fε(z))
G
Fε(z)
(Cowell _& _ Victoria  Feser ,2002)
Elementary _ distributi on : H ( z ) ( x)  ( x  z )
  indicator _ function :
( D)  {1 _ if _ D _ is _ true,0 _ otherwise)
Observed _ distributi on :
F
(z)
( x)  [1   ]F ( x)  H ( z ) ( x)
Influence _ function :

(z)
IF ( z; T , F ) :
T ( F )

 0
3. Methodology (cont’d)
• Sensitivity of inequality measures to data
contamination at high and low incomes
vary
– Generalised entropy index with α>1: very
sensitive to high incomes
– Generalised entropy index with α<0 and
Atkinson index with ε>1:very sensitive to small
incomes
– Middle level values have small influence
Values of IF for inequality measures for 10 highest,
middle & smallest observations (Cowell & Flachaire,
2002)
3. Methodology (cont’d)
• Re-estimating inequality measures using
trimmed distributions to see how robust
are results to outliers
• In our case trimming from above is the
more appropriate procedure because
– zeros represent important phenomenon –
landlessness (not the same as with zero
incomes)
– generally influence of lower values smaller
than higher values
4. Empirical results
• A cursory look at
– Box and Whiskers graphs
– Cumulative distributions
– Non-parametric kernel densities
makes one suspect that there could be
some influential observations significantly
affecting inequality measures
0
5
10
15
20
Box and whiskers graph for total PA
land
0
2
4
6
8
Box and whiskers graph for PA land
per capita
0
5
10
15
Box and whiskers graph for PA land
per adult
0
5
10
15
20
Box and whiskers graphs for total
PA land by village
haresaw
geblendinki
debre berhan
yetmen
shumsheha
sirbanaadele
godeti
korodegaga
keke
turufe kechema
imdibir
aze deboa
adado
gara godo
domaa
0
5
10
15
20
Pen's parade of dwarfs and few giants for paland
0
.2
.4
.6
cupaland
.8
1
0
2
4
6
8
Pen's parade of dwarfs and few giants for pcpaland
0
.2
.4
.6
cupcpaland
.8
1
0
5
10
15
Pen's parade of dwarfs and few giants for papaland
0
.2
.4
.6
cupapaland
.8
1
4. Empirical results (cont’d)
• Even after treating influential observations
inequality measures are high (Table 4)
– Gini coefficients range from
• 42%-56% for total
• 44%-59% for per capita
• 44%-60% for per adult
• These are within the same range or higher
than estimates for other African countries
(see Table 5)
4. Empirical results (cont’d)
• As expected, inequality within villages
more sensitive to treatment (Table 6)
– partly due to lower number of observations
– but still significant levels of within village
inequality
• with 5% trimming from top, Gini ranges from 23%62%
• Lower but still significant intra-village
inequality
4. Empirical results (cont’d)
• Looking at whole distributions for villages
after treatment
– Lorenz (relative) curves of some villages
dominate others - first order stochastic
dominance
– Generalised Lorenz curves of some villages
dominate others - second order stochastic
dominance
• There are significant regional inequalities
in size as well as distribution of land
0
.2 .4 .6 .8
1
Lorenz curve for paland5a (5% trimmed from top)
0
.2
.4
.6
Cumulative population proportion
paland5a[1]
paland5a[3]
paland5a[5]
paland5a[7]
paland5a[9]
paland5a[11]
paland5a[13]
paland5a[15]
.8
paland5a[2]
paland5a[4]
paland5a[6]
paland5a[8]
paland5a[10]
paland5a[12]
paland5a[14]
1
0
1
2
3
4
Generalised lorenz curve for paland5a (5% trimmed from top)
0
.2
.4
.6
Cumulative population proportion
paland5a[1]
paland5a[3]
paland5a[5]
paland5a[7]
paland5a[9]
paland5a[11]
paland5a[13]
paland5a[15]
.8
paland5a[2]
paland5a[4]
paland5a[6]
paland5a[8]
paland5a[10]
paland5a[12]
paland5a[14]
1
5. Link between current distribution
and old tenures?
• Why should we expect a link between pre- and
post-reform distributions?
1. Significant amounts of land held by households
in 1995 were acquired from pre-reform periods
– on the average, 36.6% of household land holdings in
1995 were acquired through pre-land reform
inheritance and purchases!
– for some villages pre-land reform inheritance and
purchases accounted for the majority of land holding
in 1995
•
•
•
•
Adele Keke & Adado:>90%
Imdibir & Aze Deboa:>80%
Gara Godo: 70%
Haresaw: 41%
5. Link between current distribution
and old tenures? (cont’d)
2. No strict national level guideline/rule for
allocation of land by PAs
• Family size considered as rough guide but
can be interpreted in different ways
• If PAs were left to their own device,
wouldn’t the different land tenures within
which farmers previously lived in influence
the way they allocate land after reform?
5. Link between current distribution
and old tenures? (cont’d)
• For a proper analysis an economic history
approach is required
– detailed information on land tenure in each
village before the land reform
– how post-reform distribution was done over
time in villages
– examine if the pattern of distribution after
reform systematically varied with pre-reform
tenures
5. Link between current distribution
and old tenures? (cont’d)
• If the current distribution is at least partly
influenced by pre-reform tenures, the current
distribution may contain some information on
previous tenures
• Classified the villages into four
– villages where the rist tenure (inheritance through
ambilineal/cognatic descent) was dominant (group 1)
– Non-rist tenures but with significant changes in
traditional tenures (commercialisation, settlement,
etc.) (group 2)
– Non-rist tenures with traditional systems preserved
(group 3)
– a resettlement village established after the land
reform (group 4)
5. Link between current distribution
and old tenures? (cont’d)
• Gini coefficients for the four groups (Table
7)
– as expected the lowest Gini is in re-settlement
village
– villages located in previous more traditional
tenures (both rist and non-rist) have higher
inequality
• If these results capture features of
distributions before the reform → more
individualised (privatised) tenures were
more egalitarian
5. Link between current distribution
and old tenures? (cont’d)
• The Lorenz curve for group 4 (resettlement village) dominates all as
expected
• The Lorenz curve for group 2 (nontraditional) dominates that for group 1
(rist) & 3 (non-rist) – higher equality in
those villages that have passed
through more individualised tenures
0
.2
.4
.6
.8
1
Lorenz curve for paland5t (by old land tenure)
0
.2
.4
.6
Cumulative population proportion
paland5t[1]
paland5t[3]
.8
paland5t[2]
paland5t[4]
1
6. Conclusions
• The land reform didn’t seem to have significantly
solved the problem of inequality as usually
assumed
– Distribution of land is part of a wider dynamic
distribution of social, economic and political power
• PA allocation influenced by livestock ownership,
demographics, etc.
– Land reform created an institutional barrier to ruralrural migration and hence to regional inequality
– The reform couldn’t avoid the development of
significant landlessness