What explains regional inequality in
Uganda?
The role of infrastructure, productive assets, and
occupation
Isis Gaddis, University of Goettingen
Welfare Congress 2011, OECD, Paris
Introduction
• While poverty has fallen in Uganda since 1992, inequality
has increased
• Analysis in World Bank (2009) show that halting the
trend in increasing inequality while sustaining growth is
important if Uganda is to reach its poverty targets
• But what explains high and rising inequality in Uganda?
• One of the simplest ways to see what factors are driving
inequality is to perform a between-within decomposition
• Bivariate decomposition (theil-t or theil-l)
• This shows that regional inequality is unusually high in
Uganda, and it has been growing over time
Introduction
Regional and International Comparison of
and Within-Group Inequality (theil-t)
Rural-urban
decomposition
Regional
Decomposition
Number of
Between
Within
groups
Between
Within
2005/06
2002
2000/01
2005/06
27
2
7
15
73
98
94
85
24
6
5
16
76
94
95
84
(8)
(20)
(4)
(4)
Other countries
Benin
2003
Brazil
2004
Vietnam
1997/98
14
5
86
9
21
8
25
79
92
75
(12)
(5)
(61)
East Africa
Kenya
Mozambique
Tanzania
Uganda
Sources: East Africa: World Bank staff estimates. Other countries: World Bank (2003); Ferreira, Leite and
Litchfield (2006); Minot, Baulch and Epprecht (2006).
Introduction
Inequality Decomposition (theil-t), 1992/93 - 2005/06
100%
80%
Within urban within regions
60%
Within rural within regions
40%
Within urban between regions
20%
Within rural between regions
Between urban/rural
0%
1992
2002
2005
Introduction
Poverty by Region, 2005/06
p0
p1
p2
National
0.311
0.087
0.035
Rural
Central
Eastern
Northern
Western
0.209
0.375
0.642
0.214
0.047
0.095
0.223
0.054
0.016
0.036
0.099
0.019
Urban
Central
Eastern
Northern
Western
0.055
0.169
0.397
0.093
0.011
0.044
0.115
0.020
0.005
0.015
0.045
0.006
Introduction
• This paper seeks to understand which factors explain
inequality between regions (Central, Northern,
Western, Eastern)
• Analyze differences between urban regions, and
between rural regions (not urban-rural differential)
• The welfare measure is consumption per adult
• We focus on the following explaining factors:
– Infrastructure (roads and electricity)
– Productive assets (education and land)
– Employment structure
Methodology
• Micro-simulation approach based on Bourguignon, Ferreira
and Lustig (2005) – adapted to consumption data
• Extension of the traditional Oaxaca-Blinder decomposition
• Typically used to explain income-distribution dynamics
• Simulates are series of counterfactual distributions to
decompose the differences between actual distributions:
–
–
–
–
Multivariate (unlike the bivariate Theil decompositions)
Distinguishes between endowment and price effects (like OB)
Can accommodate interdependencies between variables
Simulates full distributions and can thus decompose any functional
indicator (e.g. poverty and inequality indices)
Methodology
• Estimate a model of consumption (at the hh-level)
by region (r)
• XCONS,h,r includes:
– productive assets: education of all hh members and (rural)
size of land holdings
– infrastructure: electricity access and (rural) distance to a
trunk road
– employment of the head and other hh members
– demographic control variables (not used for simulation)
• αc,r are county-specific intercepts
Methodology
• Price simulations: equalize returns to (specific) household
endowments across regions (by importing the coefficient vector
from the reference region)
• Endowment simulations: use non-parametric and parametric
approaches to equalize (specific) endowments across regions
– Rank-preserving transformation for continuous or dichotomous variables
(land holding size, years of education, road distance, electricity access)
– Multinomial logit for categorical variables (occupation)
– The endowment distribution simulated by importing the coefficients
vector of the discrete choice models from the reference region
• Reference: Central Uganda (keeps urban-rural differences)
Methodology
Results: price simulations (p0)
Base region:
Eastern Northern Western Eastern Northern Western
rural
urban
Observed
Base region
Central region
0.372
Δ%
Price simulations
electricity
Δ%
rural roads
Δ%
education
Δ%
rural land
Δ%
infrastructure
(electricity & rural roads)
productive assets
(education & rural land)
occupation
Δ%
Δ%
Δ%
0.214
0.173
-44%
0.641
0.21
-67%
-2%
-72%
0.371
0%
0.389
5%
0.275
-26%
0.383
3%
0.389
5%
0.294
-21%
0.404
9%
0.641
0%
0.648
1%
0.54
-16%
0.646
1%
0.647
1%
0.545
-15%
0.673
5%
0.214
0%
0.228
7%
0.186
-13%
0.219
2%
0.228
7%
0.194
-9%
0.225
5%
0.405
0.048
-88%
0.095
-49%
(see infrastructure below)
(not applicable)
(see productive assets below)
(not applicable)
0.187
0.405
0.097
8%
0%
2%
0.189
0.406
0.102
9%
0%
7%
0.262
0.472
0.086
51%
17%
-9%
Results: returns to education
Rural Uganda
1.00
0.80
0.60
Central
0.40
Eastern
0.20
Northern
0.00
Western
no education
some primary
completed
primary
some secondary
completed
secondary
Urban Uganda
1.00
0.80
0.60
Central
0.40
Eastern
0.20
Northern
0.00
Western
no education
some primary
completed
primary
some secondary
completed
secondary
Results: endowment simulations (p0)
Base region:
Eastern Northern Western
rural
Observed
Base region
Central region
0.372
Δ%
Endowment simulations
electricity
Δ%
rural roads
Δ%
education
Δ%
rural land
Δ%
infrastructure
(electricity & rural roads)
productive assets
(education & rural land)
occupation
Δ%
Δ%
Δ%
Eastern Northern Western
urban
0.214
0.173
-44%
0.641
0.21
-67%
-2%
-72%
0.353
-5%
0.372
0%
0.345
-7%
0.387
4%
0.353
-5%
0.362
-3%
0.384
3%
0.623
-3%
0.638
0%
0.592
-8%
0.646
1%
0.62
-3%
0.598
-7%
0.64
0%
0.204
-5%
0.213
0%
0.198
-7%
0.224
5%
0.203
-5%
0.206
-4%
0.216
1%
0.405
0.048
-88%
0.095
-49%
(see infrastructure below)
(not applicable)
(see productive assets below)
(not applicable)
0.115
0.247
0.066
-34%
-39%
-31%
0.136
0.315
0.069
-21%
-22%
-27%
0.149
0.396
0.103
-14%
-2%
8%
Results: combined simulations
Combined simulations Northern rural
Combined simulations Western rural
40
percent
20
100
20
40
60
80
100
0
20
percentile
40
60
80
100
0
20
percentile
difference to Central rural
infr., assets, occup.
percent
all
Combined simulations Western urban
0
20
40
60
80
100
percentile
difference to Central urban
infr., assets, occup.
-20
-50
-20
0
0
0
50
percent
100
40
Combined simulations Northern urban
100
60
40
20
80
difference to Central rural
infr., assets, occup.
all
150
80
Combined simulations Eastern urban
60
percentile
difference to Central rural
infr., assets, occup.
all
40
20
0
0
0
0
50
20
40
percent
150
60
200
60
250
Combined simulations Eastern rural
0
20
40
60
80
100
percentile
all
difference to Central urban
infr., assets, occup.
0
20
40
60
80
100
percentile
all
difference to Central urban
infr., assets, occup.
all
Some caveats
•
•
•
•
•
•
•
No a causal model, no clear identification of effects
Potential endogeneity problems (esp. for electricity access)
Accounting exercise
No general equilibrium effects
No standard errors/confidence intervals
County-effects (unobservables) play a huge role
Not all simulations have a clear policy implication (e.g.
equalizing land holding sizes)
• Simulations do not necessarily reduce total regional inequality
(because the urban-rural gap may even get larger)
Conclusion
• The simulations show that the following factors come out as
determinants of regional inequality in Uganda
–
–
–
–
Educational attainment (urban and rural)
Access to electricity (urban and rural)
Returns to education (rural)
Returns to non-agricultural activities (urban and rural)
• This suggests policies to invest in education and electricity
and increase profitability of non-agricultural employment in
lagging areas
• However, inequality considerations need to be balanced with
overall growth considerations
Thank you!
References
• Bourguignon, François, Francisco H. G. Ferreira and Phillippe G. Leite
(2008). “Beyond Oaxaca-Blinder: Accounting for Differences in Household
Income Distributions.” Journal of Economic Inequality Vol. 6: 117-148.
• Bourguignon, François, Francisco H. G. Ferreira and Nora Lustig (eds.)
(2005). The Microeconomics of Income Distribution Dynamics in East Asia
and Latin America. Washington DC: World Bank and Oxford University
Press.
• Ferreira, Francisco H. G. (2010). “Distributions in Motion: Economic
Growth, Inequality and Poverty Dynamics.” World Bank Policy Research
Working Paper No. 5424, Washington DC: World Bank.
• Leite, Phillippe G., Alan Sanchez and Caterina R. Laderchi (2009). “The
Evolution of Urban Inequality in Ethiopia.” Draft version March 2009,
World Bank HDNSP and AFTP2.
Results: simulated education
Rural
Urban
Central Eastern Northern Western Central Eastern Northern Western
Actual educational attainment (%)
no formal educ.
12.2
17.1
24
22.7
3.6
9.8
14.9
9.4
some primary
46.2
49.5
53.1
47.3
24.2
33.5
41.1
29.2
compl. primary
16.2
14.8
12.5
15.1
18.7
18.5
16.9
19.9
some secondary
15.8
12.2
6.6
9.3
24.3
20.2
14.5
16.5
compl. secondary
9.5
6.4
3.8
5.6
29.2
18
12.8
25
Total
100
100
100
100
100
100
100
100
Average years
5.7
5
4.2
4.5
8.0
6.7
5.7
7.1
Simulated educational attainment (%, rank-preserving transformation)
no formal educ.
12.2
12.1
12.1
12.1
3.6
3.6
3.7
3.6
some primary
46.2
46.3
46.4
46.4
24.2
24.2
24.3
24.2
compl. primary
16.2
16.2
16.2
16.2
18.7
18.8
18.7
18.9
some secondary
15.8
15.8
15.8
15.8
24.3
24.3
24.3
24.2
compl. secondary
9.5
9.5
9.5
9.5
29.2
29.1
29.1
29.1
Total
100
100
100
100
100
100
100
100
Average years
5.7
5.7
5.7
5.7
8.0
8.0
8.0
8.0
Results: simulated electricity
Rural
Central Eastern Northern Western
Actual electricity access
percent
10.8
2.7
0.2
2.0
Urban
Central Eastern Northern Western
55.1
28.2
9.2
26.8
Simulated electricity access (rank-preserving transformation)
percent
10.8
11.2
11.4
11.3
55.1
58.0
56.7
56.7
(I)
Actual 2005/06
Price simulations:
infrastructure
productive assets
occupation
Endowment simulations:
infrastructure
productive assets
occupation
Combined simulations:
infrastructure, productive
assets and occupation
all (incl. county FE and
demographic prices)
(II)
between
urban
theil-t and rural
0.321
15%
(III)
(IV)
(V)
Share of inequality …
within
urban
within
regions
25%
within
urban
between
regions
4%
within
rural
within
regions
48%
(VI)
(VII)
total
inequality
within
between
rural
regions
between =(II)+(IV)+(VI
regions
)
8%
27%
0.322
0.301
0.342
15%
14%
16%
24%
26%
25%
4%
4%
5%
49%
51%
46%
8%
6%
8%
27%
24%
28%
0.320
0.320
0.322
18%
16%
16%
26%
25%
25%
2%
3%
4%
47%
50%
47%
7%
6%
8%
27%
24%
28%
0.302
15%
25%
3%
53%
5%
22%
0.272
14%
27%
0%
59%
0%
14%