Population living in slums 2006
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.
Interactive Map
Complete Data Rankings
Rank | ||
|---|---|---|
1 | South Sudan | 99.8 % |
2 | Sudan | 99.088 % |
3 | Lebanon | 92.41 % |
4 | Chad | 88.202 % |
5 | Madagascar | 84.231 % |
6 | Ethiopia | 82.886 % |
7 | Mozambique | 79.555 % |
8 | Congo | 78.458 % |
9 | Central African Republic | 77.439 % |
10 | Liberia | 74.596 % |
11 | Mauritania | 74.563 % |
12 | Uganda | 72.835 % |
13 | Congo, Democratic Republic of the | 72.375 % |
14 | Burundi | 71.681 % |
15 | Mali | 71.292 % |
16 | Malawi | 70.869 % |
17 | Benin | 70.686 % |
18 | Niger | 70.212 % |
19 | Djibouti | 69.894 % |
20 | Eritrea | 69.894 % |
21 | Mauritius | 69.894 % |
22 | Seychelles | 69.894 % |
23 | Somalia | 69.894 % |
24 | Sierra Leone | 69.81 % |
25 | Tanzania | 68.555 % |
26 | Cambodia | 67.9 % |
27 | Nicaragua | 67.812 % |
28 | Pakistan | 66.647 % |
29 | Nigeria | 66.57 % |
30 | Comoros | 66.27 % |
31 | Burkina Faso | 65.513 % |
32 | Cabo Verde | 63.83 % |
33 | Afghanistan | 63.6 % |
34 | Côte d'Ivoire | 62.455 % |
35 | Sao Tome and Principe | 61.4 % |
36 | Rwanda | 60.977 % |
37 | Togo | 59.542 % |
38 | Equatorial Guinea | 59.172 % |
39 | Zambia | 59.127 % |
40 | Kenya | 58.545 % |
41 | Ecuador | 57.8 % |
42 | Nepal | 57.634 % |
43 | Haiti | 57.366 % |
44 | Senegal | 56.49 % |
45 | Bangladesh | 56.378 % |
46 | Timor-Leste | 56.1 % |
47 | Yemen | 55.948 % |
48 | Cameroon | 55.556 % |
49 | Bhutan | 55.273 % |
50 | Iran | 55.273 % |
51 | Sri Lanka | 55.273 % |
52 | Lesotho | 54.466 % |
53 | Botswana | 52.325 % |
54 | Gabon | 51.817 % |
55 | Gambia | 51.512 % |
56 | Bolivia | 51.153 % |
57 | Ghana | 50.288 % |
58 | Guatemala | 48.786 % |
59 | Eswatini | 47.928 % |
60 | El Salvador | 46.491 % |
61 | Philippines | 45.755 % |
62 | Mongolia | 45.687 % |
63 | Laos | 44.1 % |
64 | Guinea | 43.168 % |
65 | Namibia | 42.11 % |
66 | Peru | 41.867 % |
67 | Maldives | 41.535 % |
68 | Honduras | 40.9 % |
69 | Myanmar | 39.027 % |
70 | Iraq | 38.528 % |
71 | Panama | 35.742 % |
72 | Libya | 35.168 % |
73 | Kyrgyzstan | 33.743 % |
74 | Paraguay | 33.036 % |
75 | Azerbaijan | 32.93 % |
76 | Brunei Darussalam | 32.359 % |
77 | Algeria | 30.8 % |
78 | Vietnam | 30.485 % |
79 | Syrian Arab Republic | 30.437 % |
80 | Indonesia | 30.163 % |
81 | State of Palestine | 29.071 % |
82 | Mexico | 27.018 % |
83 | South Africa | 26.467 % |
84 | Angola | 25.792 % |
85 | Zimbabwe | 25.736 % |
86 | Venezuela | 25.7 % |
87 | Egypt | 25.696 % |
88 | Brazil | 24.818 % |
89 | Morocco | 24.791 % |
90 | Guyana | 23.671 % |
91 | Dominican Republic | 23.066 % |
92 | Uzbekistan | 21.187 % |
93 | Albania | 20.5 % |
94 | Turkey | 20.399 % |
95 | Papua New Guinea | 20.1 % |
96 | Uruguay | 19.95 % |
97 | Argentina | 18.115 % |
98 | Republic of Moldova | 18 % |
99 | Montenegro | 17.3 % |
100 | Colombia | 17.144 % |
101 | Kazakhstan | 16.106 % |
102 | Belize | 15.753 % |
103 | Fiji | 14.5 % |
104 | Kiribati | 14.35 % |
105 | South Korea | 12.8 % |
106 | Georgia | 12.448 % |
107 | Hungary | 11.6 % |
108 | Armenia | 11.329 % |
109 | Tunisia | 10.4 % |
110 | Trinidad and Tobago | 10.28 % |
111 | Turkmenistan | 10.257 % |
112 | Costa Rica | 9.951 % |
113 | Suriname | 9.941 % |
114 | Solomon Islands | 8.95 % |
115 | Chile | 7.3 % |
116 | Ireland | 6.9 % |
117 | Bosnia and Herzegovina | 4.9 % |
118 | Vanuatu | 4.45 % |
119 | Austria | 4.4 % |
120 | Cayman Islands | 4.15 % |
121 | Saint Lucia | 3.9 % |
122 | Samoa | 3.9 % |
123 | Ukraine | 3.9 % |
124 | Tuvalu | 3.75 % |
125 | Romania | 3.5 % |
126 | Russia | 3.35 % |
127 | Cuba | 3.202 % |
128 | Oman | 2.9 % |
129 | Lithuania | 2.85 % |
130 | British Virgin Islands | 2.577 % |
131 | Nauru | 2.25 % |
132 | Latvia | 2.15 % |
133 | Marshall Islands | 2 % |
134 | Tonga | 1.65 % |
135 | Palau | 1.5 % |
136 | Poland | 1.25 % |
137 | North Macedonia | 1.15 % |
138 | United Arab Emirates | 0.95 % |
139 | Portugal | 0.75 % |
140 | Serbia | 0.6 % |
141 | Croatia | 0.35 % |
142 | Canada | 0.3 % |
143 | Cyprus | 0.25 % |
144 | Sweden | 0.25 % |
145 | Greece | 0.2 % |
146 | United States | 0.2 % |
147 | Australia | 0.125 % |
148 | Bermuda | 0.1 % |
149 | Bulgaria | 0.1 % |
150 | Estonia | 0.1 % |
151 | United Kingdom | 0.1 % |
152 | Czech Republic | 0.05 % |
153 | Slovakia | 0.05 % |
154 | Malta | 0.038 % |
155 | Italy | 0.02 % |
156 | Luxembourg | 0.003 % |
157 | Andorra | 0 % |
158 | Aruba | 0 % |
159 | Belgium | 0 % |
160 | Denmark | 0 % |
161 | Finland | 0 % |
162 | France | 0 % |
163 | Germany | 0 % |
164 | Iceland | 0 % |
165 | Kuwait | 0 % |
166 | Monaco | 0 % |
167 | Netherlands | 0 % |
168 | New Zealand | 0 % |
169 | Norway | 0 % |
170 | Singapore | 0 % |
171 | Switzerland | 0 % |
↑Top 10 Countries
- #1
South Sudan
- #2
Sudan
- #3
Lebanon
- #4
Chad
- #5
Madagascar
- #6
Ethiopia
- #7
Mozambique
- #8
Congo
- #9
Central African Republic
- #10
Liberia
Analysis: These countries represent the highest values in this dataset, showcasing significant scale and impact on global statistics.
↓Bottom 10 Countries
- #171
Switzerland
- #170
Singapore
- #169
Norway
- #168
New Zealand
- #167
Netherlands
- #166
Monaco
- #165
Kuwait
- #164
Iceland
- #163
Germany
- #162
France
Context: These countries or territories have the lowest values, often due to geographic size, administrative status, or specific characteristics.
Analysis & Context
In 2006, the global issue of urban poverty was starkly highlighted by the statistic measuring the percentage of the population living in slums. This metric provides a critical lens through which to assess living conditions, economic disparities, and environmental impacts on urban communities worldwide. By analyzing this data, stakeholders can better understand the challenges and opportunities for improving housing policies and enhancing the quality of life for vulnerable populations.
The Urban Poverty Landscape in 2006
In 2006, the prevalence of slum living was a pressing concern, with an average of 31.55% of the population across 171 countries residing in such conditions. This situation is emblematic of the broader issues associated with urbanization, such as inadequate infrastructure, economic inequality, and insufficient public services. South Sudan stood at the forefront with a staggering 99.8% of its population living in slums, followed closely by Sudan at 99.0883%. These figures underscore the severe housing deficits and the urgent need for comprehensive urban planning and resource allocation to uplift these communities.
Regional Disparities and Influencing Factors
The data from 2006 reveals significant regional disparities in slum populations. African nations overwhelmingly dominated the top of the list, as evidenced by countries like Chad (88.2019%), Madagascar (84.231%), and Ethiopia (82.8858%). In contrast, many European and some Middle Eastern countries, including Iceland, Kuwait, and Monaco, reported a 0% slum population, showcasing the stark contrast between different parts of the world. These regional variations can be attributed to a range of factors, including the historical development of cities, governance structures, and economic policies that either mitigate or exacerbate urban poverty.
Economic Implications of Slum Populations
The economic ramifications of high slum populations are profound. Countries with substantial slum populations often grapple with high rates of unemployment and underemployment, which further perpetuates the cycle of poverty. In 2006, nations like Mozambique (79.5545%) and Congo (78.4582%) faced significant economic challenges, limiting their capacity to invest in necessary infrastructure and public services. The presence of slums often indicates a mismatch between rapid urbanization and economic growth, necessitating targeted policies to stimulate job creation and economic development while improving living conditions within these urban areas.
Policy Responses and Success Stories
Several countries have made strides in addressing the issue of slums through innovative policy responses. Successful interventions often include integrated approaches that combine upgrading informal settlements with providing access to essential services like clean water, sanitation, and education. In 2006, countries such as Brazil and South Africa were noted for implementing policies that aimed to reduce slums by investing in infrastructure and promoting inclusive urban development. These efforts demonstrate the potential for transformative change when governments prioritize housing and quality of life improvements for their most vulnerable citizens.
Future Directions for Global Slum Reduction
Moving forward from 2006, the goal of reducing slum populations remains a critical target for international development agendas. The data signifies the necessity for global cooperation and sustained investment in urban planning and development. Addressing the root causes of slum growth, such as economic disparities and rapid urbanization, is imperative. As nations strive to meet the Sustainable Development Goals set by the United Nations, prioritizing sustainable urbanization and improving living conditions in slums will be vital to fostering equitable and resilient cities worldwide.
In conclusion, the 2006 statistics on populations living in slums underscore a significant global challenge that affects various aspects of society, from economic stability to environmental sustainability. By understanding these patterns and implementing comprehensive policies, the global community can work towards reducing slum populations and enhancing the quality of life for millions living in urban poverty.
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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