Population living in slums 2015
This statistic measures the percentage of a country's population living in slums, highlighting urban poverty and living conditions. Understanding slum populations is crucial for addressing housing challenges and improving the quality of life.
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Complete Data Rankings
Rank | ||
|---|---|---|
1 | South Sudan | 94.2 % |
2 | Chad | 83.129 % |
3 | Congo, Democratic Republic of the | 76.52 % |
4 | Sudan | 76.206 % |
5 | Madagascar | 73.424 % |
6 | Afghanistan | 70.908 % |
7 | Niger | 70.444 % |
8 | Guinea-Bissau | 69.834 % |
9 | Ethiopia | 68.957 % |
10 | Benin | 68.916 % |
11 | Central African Republic | 68.913 % |
12 | Comoros | 68.619 % |
13 | Liberia | 66.563 % |
14 | Mozambique | 63.743 % |
15 | Equatorial Guinea | 62.929 % |
16 | Uganda | 60.734 % |
17 | Pakistan | 59.786 % |
18 | Mauritania | 59.369 % |
19 | Djibouti | 58.186 % |
20 | Eritrea | 58.186 % |
21 | Mauritius | 58.186 % |
22 | Seychelles | 58.186 % |
23 | Somalia | 58.186 % |
24 | Ecuador | 57.8 % |
25 | Sierra Leone | 57.491 % |
26 | Côte d'Ivoire | 55.522 % |
27 | Nigeria | 55.263 % |
28 | Malawi | 55.056 % |
29 | Sao Tome and Principe | 53.613 % |
30 | Bangladesh | 53.48 % |
31 | Myanmar | 53.467 % |
32 | Angola | 53.384 % |
33 | Cabo Verde | 52.67 % |
34 | Mali | 52.416 % |
35 | Zambia | 52.14 % |
36 | Bhutan | 51.817 % |
37 | Iran | 51.817 % |
38 | Sri Lanka | 51.817 % |
39 | Kenya | 51.587 % |
40 | Haiti | 51.491 % |
41 | Congo | 50.415 % |
42 | Tanzania | 49.396 % |
43 | Burundi | 47.549 % |
44 | Guinea | 46.922 % |
45 | Bolivia | 46.626 % |
46 | Peru | 46.457 % |
47 | Iraq | 45.478 % |
48 | Rwanda | 45.312 % |
49 | Togo | 44.947 % |
50 | Yemen | 44.831 % |
51 | Nepal | 44.62 % |
52 | Gabon | 44.333 % |
53 | Gambia | 43.4 % |
54 | Botswana | 42.773 % |
55 | Cambodia | 42.55 % |
56 | Namibia | 41.439 % |
57 | Cameroon | 40.845 % |
58 | Senegal | 40.492 % |
59 | Burkina Faso | 40.466 % |
60 | Timor-Leste | 39.95 % |
61 | Philippines | 39.428 % |
62 | Guatemala | 38.721 % |
63 | Maldives | 37.075 % |
64 | Lesotho | 35.879 % |
65 | Ghana | 35.167 % |
66 | Honduras | 31.52 % |
67 | Laos | 28.7 % |
68 | Mongolia | 27.81 % |
69 | Venezuela | 25.7 % |
70 | South Africa | 24.753 % |
71 | Brunei Darussalam | 24.419 % |
72 | Syrian Arab Republic | 24.024 % |
73 | Zimbabwe | 23.057 % |
74 | Algeria | 23.029 % |
75 | Indonesia | 22.719 % |
76 | Papua New Guinea | 21.825 % |
77 | State of Palestine | 20.491 % |
78 | Azerbaijan | 20.023 % |
79 | Paraguay | 19.549 % |
80 | El Salvador | 19.479 % |
81 | Mexico | 19.3 % |
82 | Libya | 18.746 % |
83 | Jordan | 18.15 % |
84 | Panama | 17.398 % |
85 | Guyana | 16.258 % |
86 | Belize | 15.735 % |
87 | Brazil | 14.897 % |
88 | Argentina | 14.85 % |
89 | Eswatini | 14.52 % |
90 | Turkey | 14.475 % |
91 | Suriname | 13.708 % |
92 | Kyrgyzstan | 13.619 % |
93 | Dominican Republic | 12.431 % |
94 | Morocco | 12.245 % |
95 | Montenegro | 11.8 % |
96 | Colombia | 11.094 % |
97 | Fiji | 9.9 % |
98 | Turkmenistan | 9.132 % |
99 | Armenia | 9.128 % |
100 | Albania | 9.05 % |
101 | Georgia | 8.996 % |
102 | Tunisia | 8.894 % |
103 | Kiribati | 8.725 % |
104 | Uzbekistan | 8.672 % |
105 | Vietnam | 8.238 % |
106 | Ireland | 8.15 % |
107 | Cuba | 7.921 % |
108 | Trinidad and Tobago | 7.5 % |
109 | Chile | 7.312 % |
110 | Hungary | 7.2 % |
111 | Republic of Moldova | 6.5 % |
112 | Nicaragua | 5.953 % |
113 | Costa Rica | 5.833 % |
114 | South Korea | 5.25 % |
115 | Lebanon | 4.5 % |
116 | Cayman Islands | 4.15 % |
117 | Vanuatu | 4.05 % |
118 | Bosnia and Herzegovina | 4 % |
119 | Kazakhstan | 3.578 % |
120 | Uruguay | 3.174 % |
121 | Russia | 2.95 % |
122 | Antigua and Barbuda | 2.646 % |
123 | Solomon Islands | 2.575 % |
124 | Egypt | 2.277 % |
125 | Poland | 2.275 % |
126 | Romania | 2.25 % |
127 | Austria | 1.951 % |
128 | British Virgin Islands | 1.639 % |
129 | Saint Lucia | 1.325 % |
130 | Tuvalu | 1.153 % |
131 | Ukraine | 1.1 % |
132 | Latvia | 0.925 % |
133 | Lithuania | 0.875 % |
134 | Tonga | 0.85 % |
135 | Samoa | 0.825 % |
136 | Canada | 0.675 % |
137 | Marshall Islands | 0.6 % |
138 | Nauru | 0.55 % |
139 | Palau | 0.525 % |
140 | Serbia | 0.45 % |
141 | North Macedonia | 0.4 % |
142 | Oman | 0.4 % |
143 | United Arab Emirates | 0.325 % |
144 | Croatia | 0.275 % |
145 | Cyprus | 0.275 % |
146 | Bulgaria | 0.2 % |
147 | United States | 0.2 % |
148 | Portugal | 0.125 % |
149 | Sweden | 0.125 % |
150 | Bermuda | 0.1 % |
151 | Estonia | 0.1 % |
152 | United Kingdom | 0.1 % |
153 | Czech Republic | 0.05 % |
154 | Slovakia | 0.05 % |
155 | Spain | 0.05 % |
156 | Australia | 0.04 % |
157 | Malta | 0.038 % |
158 | Italy | 0.02 % |
159 | Luxembourg | 0.015 % |
160 | Belarus | 0.004 % |
161 | Andorra | 0 % |
162 | Aruba | 0 % |
163 | Belgium | 0 % |
164 | Denmark | 0 % |
165 | Finland | 0 % |
166 | France | 0 % |
167 | Germany | 0 % |
168 | Iceland | 0 % |
169 | Kuwait | 0 % |
170 | Monaco | 0 % |
171 | Netherlands | 0 % |
172 | New Zealand | 0 % |
173 | Norway | 0 % |
174 | Qatar | 0 % |
175 | Singapore | 0 % |
176 | Switzerland | 0 % |
177 | Greece | -0.025 % |
- #1
South Sudan
- #2
Chad
- #3
Congo, Democratic Republic of the
- #4
Sudan
- #5
Madagascar
- #6
Afghanistan
- #7
Niger
- #8
Guinea-Bissau
- #9
Ethiopia
- #10
Benin
Analysis: These countries represent the highest values in this dataset, showcasing significant scale and impact on global statistics.
- #177
Greece
- #176
Switzerland
- #175
Singapore
- #174
Qatar
- #173
Norway
- #172
New Zealand
- #171
Netherlands
- #170
Monaco
- #169
Kuwait
- #168
Iceland
Context: These countries or territories have the lowest values, often due to geographic size, administrative status, or specific characteristics.
Analysis & Context
In 2015, South Sudan recorded the highest percentage of its population living in slums at 94.2%, while countries like Greece, Switzerland, and Netherlands reported close to zero or even negative values. The global range for the "Population living in slums" statistic spans from -0.02% to 94.20%. The global average percentage of populations living in slums was 24.48%, highlighting significant disparity in urban living conditions across the globe.
Drivers of High Slum Populations
The prevalence of slum populations in countries such as South Sudan (94.2%), Chad (83.13%), and the Democratic Republic of the Congo (76.52%) can be attributed to several factors. These nations often experience rapid urbanization without the corresponding development of infrastructure and housing. Political instability and conflict, particularly in South Sudan and the Democratic Republic of the Congo, exacerbate these issues by displacing populations and limiting government capacity to provide adequate housing.
Additionally, economic constraints play a significant role. Countries like Madagascar (73.42%) and Niger (70.44%) face high poverty rates and limited economic growth, which hinder investment in urban development. As a result, urban populations often resort to informal settlements due to the lack of affordable housing options.
Low Slum Populations and Policy Success
On the other end of the spectrum, countries such as Switzerland, Netherlands, and Belgium report zero populations living in slums. This can largely be attributed to effective urban planning and strong policy frameworks that prioritize affordable housing and infrastructure development. For instance, stringent building regulations and comprehensive urban policies in these countries ensure that housing is both available and accessible to all socioeconomic groups.
The presence of robust social safety nets and economic stability further contributes to the low slum populations in these countries. Wealthier nations are better equipped to invest in public housing projects and maintain a high standard of living, reducing the necessity for informal settlements.
Trends in Slum Populations: Movers and Shakers
Between 2014 and 2015, some countries experienced notable changes in their slum populations. Jordan saw the largest increase of 5.25%, possibly due to the influx of refugees and economic migrants. Similarly, Angola saw a 3.07% increase, reflecting its ongoing urban challenges despite economic growth driven by oil revenues.
Conversely, Eswatini experienced the most significant decrease, with a 3.71% reduction in slum populations. This decrease may indicate successful government interventions aimed at improving urban housing and living conditions. Other countries like Congo and El Salvador also noted decreases, suggesting that targeted urban development policies and international aid efforts might be yielding positive outcomes.
Implications of Slum Populations on Development
The percentage of populations living in slums is a critical indicator of urban poverty and inequality. High slum populations, as seen in countries like Afghanistan (70.91%) and Guinea-Bissau (69.83%), highlight the urgent need for comprehensive urban planning and policy interventions. Addressing this issue is not only a matter of improving living conditions but also of fostering sustainable economic growth and social stability.
For nations with lower slum populations, the challenge lies in maintaining these levels amidst changing economic and demographic conditions. Continued investment in infrastructure, housing, and social services is essential to prevent the emergence of new slum areas as urban populations grow.
Overall, the data from 2015 underscores the importance of integrated approaches to urban development that consider economic, social, and environmental factors. By understanding the dynamics of slum populations, policymakers can better address the root causes of urban poverty and work towards more equitable and sustainable cities.
Data Source
UN Habitat
The Data and Analytics Section (DAS) is the specialized statistics unit within UN-Habitat. The data section is responsible for overall data oversight across all urban monitoring domains within UN-Habitat, methodological developments, supporting member states in their monitoring efforts around global agenda such as the SDGs, implementing direct data collection and compilation, providing data to UN-Habitat global reports, and publicly and openly disseminating urban data through its urban indicators programme.
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