Transcript The Spatial Distribution of Urban Poverty: Metropolitan Areas versus Small Towns Peter Lanjouw (World Bank) Urbanization and Poverty and Poverty Reduction: Bridging Urban and Rural Perspectives World.

The Spatial Distribution of Urban
Poverty: Metropolitan Areas versus
Small Towns
Peter Lanjouw (World Bank)
Urbanization and Poverty and Poverty
Reduction: Bridging Urban and Rural
Perspectives
World Bank, May 13-14, 2013
Unpacking urban poverty in a selection
of developing countries
•
•
•
•
Why do we know so little about spatial distribution
of urban poverty?
Small area estimation of poverty
Urban poverty across city size in 9 countries
4 Robustness checks:
–
–
–
–
•
Why do we observed a poverty-city size gradient?
–
•
Cost of living differences
Non-monetary indicators of poverty
Subjective welfare
Artifact of SAE methodology?
Limited access to infrastructure services in small towns.
Is Africa Different? Preliminary Evidence
Heterogeneity of Urban Poverty:
Why do we know so little?
• Main source of information on distributional
outcomes - household surveys - permit only
limited disaggregation.
• Very large data sources (e.g. census) typically
collect very limited information on welfare
outcomes.
• Our solution: Small Area Estimation (ELL, 2002;
2003)
– Impute a measure of welfare from household survey
into census, using statistical prediction methods.
International Experience
• Poverty Maps are completed or underway in around
50+ countries
– Including Brazil, China, India, Indonesia
• In some countries poverty maps available for
multiple periods in time
– Ecuador, Panama, Paraguay, Morocco
– RIMISP project has produced panel poverty maps across
LAC
• PovMap2 Software development by WB
– Available at: http://iresearch.worldbank.org
Small Area Estimates of Urban Poverty:
8 countries
Albania
Brazil
Kazakhstan
Kenya
Mexico
Morocco
Thailand
1.3m
125m
8.2m
5.0m
54.5m
12.7m
18.5m
Sri
Lanka
2.2m
0.58
0.83
0.57
0.19
0.60
0.51
0.31
0.12
Census Year
2001
2000
1999
1999
2000
1994
2000
2001
Survey Year
2002
2002-3
2001
1997
2000
1998
2000
2002
Survey
Name
LSMS
POF
HBS
WMS
III
ENIGH
ENNVM
SES
HIES
Survey
Sample Size
3,600
48,470
11,883
10,874
10,108
5,184
24,747
20,100
Poverty Line
ALL
4,891
BRL
100
KZT
3,157
KES
2,648
PES
768
DHS
3,400
BAH
1370
LKR
1423
Poverty Line
(2005 PPP$)
95
83
63
147
128
57
88
47
Equivalence
Scale
No
No
No
Yes
No
No
No
No
Urban
Population
Urban
Population
%
Box-Plot of Poverty Rate by City Size:
Mexico and Thailand
Box-Plot of Poverty and City Size:
Vietnam
Urban Poverty: Summary
Country
Reference Year
Incidence of Poverty (%)
Smallest town size
category
Largest city size
category
Share of small town in
urban population (%)
Share of small town
poor in urban poor (%)
Brazil
2000
30
9
33
55
Mexico
2000
31
18
10
18
2004/5
30
15
73
84
Sri Lanka
2001
12
8
33
40
Albania
2001
20
11
33
35
Kazakhstan
1999
19
3
26
35
Thailand
2000
14
2
45
76
Vietnam
2009
12
1.4
27
55
Morocco
1994
16
4
6
9
India
Robustness
• Are our findings of a poverty-city size
gradient robust?
– cost of living variation
– non-income dimensions of wellbeing
– Subjective welfare
– Statistical artifact?
Spatial Price Variation
• Household surveys rarely permit construction
of price indices that distinguish between city
size
• Evidence from Brazil indicates that adjusting
for price variation does attenuate the poverty
city size gradient
• But overall gradient remains
– Ferre et al (2012)
Non-Income Dimensions
• It is sometimes suggested that gradient
disappears for non-income dimensions such as
child-health
• Evidence from Mexico indicates presence of a
similar gradient based on SAE estimates of
child malnutrition (anthropometrics)
– Lanjouw and Rascon (2011)
Subjective welfare
• Evidence from Vietnam indicates that after
controlling for access to services, average
welfare, as well as households’ real expenditure
levels, households in large cities have lower
subjective wellbeing than households in small
town.
– Lanjouw and Marra (2013)
• Consistent with concept of relative welfare, and
also with presence of externalities such as
pollution and congestion in large towns.
Does SAE drive finding?
• Evidence in India, based on NSS sample survey
data, is consistent with SAE results.
– World Bank (2010)
• Difficult to test elsewhere due to lack of
appropriate survey data.
Why a poverty gradient?
• Internal dynamism of large cities
– Agglomeration externalities
• “new” economic geography
– Scarcity of competent city planners and
administrators
• Neglect of small towns?
– Skewed distribution of infrastructure availability
• Ferre et al (2012) document lower per-capita service
availability in small towns
Service delivery and town size in India
Public spending in India is skewed against small towns:
Andhra Pradesh and Orissa
Town
class
(in
‘000s)
<50
50100
100500
>500
Total
No. of
latrines
(water
borne)
Domestic
electricity
connections
Hospital
beds
(per 1000 population)
99.5
125.2
0.88
96.0
135.9
2.40
Piped
Water
primary
drinking
water
source
(% of
towns)
48.5
76.5
Total
Revenues
Gov’t
Grants
Total
Expenditures
Public
Works
461.6
577.5
(Rs per capita)
285.2
430.2
272.0
509.0
80.0
113.6
90.5
126.4
1.78
76.5
638.1
248.4
552.4
119.4
145.0
98.1
186.0
127.6
2.04
1.25
100.0
57.7
677.2
509.7
164.5
275.8
522.2
462.6
166.6
92.3
Urban Poverty & City Size in Africa:
Some Preliminary results
Table 1: Data Sources
Survey
# households
# individuals
Poverty Headcount
(national povline)
Census
# households
# individuals
% urban (indiv.)
Administrative
Structure
(# Total/Urban/Rural)
Mali
ELIM 2009/10
9,035
86,492
43.6%
Swaziland
SHIES 2009/10
3,191
14,140
63.4%
Togo
QUIBB 2011
5,491
29,676
58.7%
Malawi
IHS (2010/11)
12,268
55,985
50.7%
2009
2,359,356
14,701,684
22.7%
Region
(9/9/8)
Cercle
(50/25/49)
Commune
(703/37/666)
2007
212,100
1,017,526
21.8%
Region
(4/4/4)
Inkhundla
(55/23/52)
Major Area
(195/27/181)
2010
1,206,957
5,847,453
37.3%
Region
(6/6/5)
Prefecture
(41/41/36)
Canton
(386/48/372)
CCQ
(513/141/372)
2010
2,857,704
12,922,799
85.1%
Region
(3/3/3)
District
(32/29/28)
TA/Ward
(353/137/216)
Survey
# households
# individuals
Poverty Headcount
(national povline)
Census
# households
# individuals
% urban (indiv.)
Administrative
Structure
(# Total/Urban/Rural)
Senegal
ESPS 2005
13,568
122,000
52.6%
Gabon
EGEP 2005
7,865
37,400
32.8%
Niger
QUIBB 2005
6,690
43,000
61.8%
Guinea
EIBEP (2002)
7,095
53,547
48.8%
2002
1,075,918
9,961,678
40.7%
Region
(11/11/11)
Department
(35/35/32)
Collectivite
locale
(426/106/320)
2003
267,085
1,183,199
%
Province
(9/9/9)
Department
(48/48/47)
Canton
(219/68/151)
2001
1,632,582
11,179,668
16.3%
Region
(8/8/7)
Department
(37/37/36)
Canton
(173/42/131)
1996
1,029,000
7,200,000
29.6%
Region
(8/8/7)
Prefecture
(34/34/33)
Commune
(340/40/331)
Survey
# households
# individuals
Poverty Headcount
(national povline)
Census
# households
# individuals
% urban (indiv.)
Administrative
Structure
(# Total/Urban/Rural)
Côte d’Ivoire
ENV 2002
38.5%
CAR
ECASEB 2008
6,897
32,000
61.9%
Mauritania
EPCV 2004
9,385
52,600
46.3%
Sierra Leone
SLIHS (2003)
3,720
22,970
67.0%
1998
2,700,000
15,400,000
42.2%
Region
(19/19/19)
Department
(58/58/58)
Sous-Prefecture
(254/127/253)
Secteur
(444/136/426)
2003
642,839
3,102,440
37.8%
Region
(7/7/6)
Prefecture
(17/17/16)
Sous-Prefeture
(79/44/71)
Commune
(177/46/158)
2000
352,349
2,049,594
42.2%
Wilaya
(13/13/12)
Moughata
(53/34/43)
Commune
(216/38/204)
2004
788,210
4,769,701
37.5%
Province
(4/4/4)
District
(14/14/14)
Chiefdom
(166/91/164)
Table 2: City/Town Size Distribution (in unit)
Mali
XXS (<5,000)
2
XS
(5,000 to 10,000)
6
S
(10,000 to 25,000)
8
M
(25,000 to 50,000)
5
L
(50,000 to 100,000)
5
XL
(100,000 to 1,000,000)
5
XXL (>1,000,000)
1
Total
32
Swaziland
18
2
1
2
23
Togo
6
9
11
3
6
1
1
37
Malawi
6
4
14
4
1
3
32
XXS (<5,000)
XS
(5,000 to 10,000)
S
(10,000 to 25,000)
M
(25,000 to 50,000)
L
(50,000 to 100,000)
XL
(100,000 to 1,000,000)
XXL (>1,000,000)
Total
Senegal
4
16
25
5
5
5
1
61
Côte d’Ivoire
XXS (<5,000)
XS
(5,000 to 10,000)
S
(10,000 to 25,000)
M
(25,000 to 50,000)
L
(50,000 to 100,000)
XL
(100,000 to 1,000,000)
XXL (>1,000,000)
Total
5
16
15
13
8
1
58
Gabon
30
6
6
5
2
Niger
1
6
22
5
3
3
49
40
Guinea
4
7
10
8
5
1
1
36
CAR
5
10
16
5
1
2
Mauritania
6
12
7
3
1
1
Sierra Leone
37
25
12
5
2
3
29
30
84
Urban Poverty in Africa
Country
Reference Year
Incidence of Poverty (%)
Smallest town category
Largest city size
category
Share of small town in
urban population (%)
Share of small town
poor in urban poor (%)
Kenya
1999
49
44
21
25
Mali
2009
29
9
0.2
0.3
Swaziland
2007
37
31
22
24
Togo
2010
43
29
3.3
5.1
Malawi
2010
28
17
2
3.8
Senegal
2002
51
34
3.4
4.6
Gabon
2003
43
26
6.4
9.0
Niger
2001
56
37
2.4
3.4
Guinea
1996
17
24
0.6
0.6
Cote d’Ivoire
1998
34
15
0.7
1.0
Central African
Republic
2003
58
48
1.3
1.2
Mauritania
2000
39
29
2.6
3.2
Sierra Leone
2009
71
36
5.3
7.5
Net School
enrol. Primary
Net School
enrol. Second.
Literacy Rate
Girl/Boy ratio Primary
Girl/Boy Secondary
Improved
sanitation
Improved Water
Supply
Electricity
Gas/elec/Coal as
cooking fuel
MALI
Rural
60.9
Urban
50.1
XXS
50.1
XS
56.7
S
52.8
M
47.5
L
44.8
XL
50.6
XXL
50.9
SWAZILAND
Rural
22.5
Urban
51.3
XXS
51.6
XS
60.1
M
50.7
L
50.3
Self-Employ.
Rate
Employment
Rate
Table 4: Non-monetary Indicators for different town/city sizes for four African Countries
33.5
44.4
28.6
34.1
41.8
45.5
41.5
44.3
45.3
41.0
75.0
58.4
50.8
52.1
68.5
72.8
74.7
78.4
15.4
42.1
12.9
14.6
28.0
42.1
43.8
42.6
43.3
28.0
63.0
31.0
27.2
45.4
56.1
66.6
62.6
65.4
0.77
0.95
0.47
0.59
0.87
0.93
0.93
0.93
0.98
0.38
0.62
0.09
0.29
0.46
0.54
0.63
0.56
0.68
75.2
97.0
96.3
94.2
86.9
89.7
92.8
97.7
99.0
24.5
63.9
52.8
51.9
55.3
69.4
61.6
50.5
70.3
8.8
65.8
26.9
13.9
37.6
49.5
50.9
52.8
79.1
11.9
43.7
4.8
3.9
20.7
21.7
24.5
15.9
63.8
27.4
20.2
13.6
11.2
22.7
23.2
77.9
66.9
67.7
64.7
58.3
70.1
32.9
40.8
41.6
41.5
33.6
43.3
94.9
97.7
96.8
95.8
98.2
98.1
0.93
1.03
1.03
1.05
1.03
1.02
1.04
1.14
1.11
1.03
1.19
1.15
74.3
88.5
79.9
86.1
91.3
91.3
49.6
86.9
85.5
93.1
86.2
87.1
21.2
64.9
71.9
58.6
62.2
63.9
15.3
87.1
74.8
88.6
93.1
89.6
Self-Employ.
Rate
Net School
enrol. Primary
Net School
enrol. Second.
Literacy Rate
Girl/Boy ratio Primary
Girl/Boy Secondary
Improved
sanitation
Improved Water
Supply
Electricity
Gas/elec/Coal as
cooking fuel
TOGO
Rural
Urban
XXS
XS
S
M
L
XL
XXL
MALAWI
Rural
Urban
XXS
XS
S
M
L
XL
Employment
Rate
Table 4: Non-monetary Indicators for different town/city sizes for four African Countries
72.7
62.5
59.7
59.5
59.4
63.0
57.3
66.1
62.7
91.1
69.5
74.8
77.2
75.9
76.5
70.6
68.3
67.6
76.9
80.6
83.8
82.6
83.5
80.3
83.1
80.0
79.4
32.6
57.5
60.5
59.3
56.8
46.3
62.9
54.9
58.1
64.7
86.3
87.4
82.1
87.5
71.7
89.6
84.9
87.4
0.86
1.07
1.01
0.96
0.99
0.98
1.06
1.10
1.07
0.45
0.81
0.55
0.52
0.57
0.57
0.73
0.90
0.87
20.9
83.6
58.6
40.4
54.1
46.6
72.4
96.7
91.0
28.3
78.2
39.6
33.7
66.2
51.0
65.6
90.4
82.7
8.5
75.6
45.6
46.5
67.1
58.1
72.1
85.4
75.6
10.6
86.9
34.7
36.0
54.5
71.7
78.8
97.3
94.7
58.9
54.4
51.6
52.8
53.7
53.3
52.7
54.7
84.6
47.3
42.9
51.2
60.6
63.2
39.2
43.9
72.1
80.1
85.1
82.3
77.9
77.8
80.6
80.6
7.3
30.7
33.4
30.0
27.7
24.1
34.8
31.6
82.2
96.1
96.4
94.1
94.5
93.8
96.8
96.6
1.02
1.03
1.02
1.01
1.03
1.03
1.05
1.03
0.87
1.01
0.98
1.09
0.99
1.02
1.00
1.02
2.1
20.5
19.7
19.1
16.6
18.3
37.4
20.5
70.0
94.0
95.1
97.2
96.0
95.7
97.5
93.2
1.9
37.8
38.4
34.7
29.0
27.0
49.5
39.7
4.4
58.2
24.7
29.2
34.0
32.5
50.9
66.1
Should policymakers focus on small
towns?
• How best to intervene?
– Target delivery of improved services?
– But
• possibly higher per capita cost of providing access
• no guarantee that improved service delivery will result
in economic growth of towns.
• One additional argument:
– Small town development may stimulate rural nonfarm development